From 87273e8524bc99deb5b699fdd7bcc0e59427bb3c Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Thu, 8 Feb 2024 12:36:54 +0100 Subject: [PATCH 01/20] update gitignore --- .gitignore | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index 00fba43c..f157d5e4 100644 --- a/.gitignore +++ b/.gitignore @@ -103,7 +103,7 @@ celerybeat.pid # Environments .env -.venv +.venv* env/ venv/ ENV/ From 753987a01e514574691812e43739c2163033bd75 Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Thu, 8 Feb 2024 12:37:05 +0100 Subject: [PATCH 02/20] update showcase --- stack-showcase/Dockerfile | 26 - stack-showcase/_assets/cloud_mcp.png | Bin 0 -> 237746 bytes .../_assets/cloud_mcp_predictions.png | Bin 0 -> 226866 bytes .../_assets/cloud_mcp_screenshot.png | Bin 0 -> 220224 bytes stack-showcase/app.py | 56 - stack-showcase/configs/deployment.yaml | 13 - .../configs/feature_engineering.yaml | 10 +- stack-showcase/configs/inference.yaml | 9 +- stack-showcase/configs/training_rf.yaml | 18 + ...aining.yaml => training_rf_sagemaker.yaml} | 12 +- stack-showcase/configs/training_sgd.yaml | 18 + .../configs/training_sgd_sagemaker.yaml | 26 + stack-showcase/pipelines/__init__.py | 20 +- stack-showcase/pipelines/deployment.py | 38 - .../pipelines/feature_engineering.py | 31 +- stack-showcase/pipelines/inference.py | 60 +- stack-showcase/pipelines/training.py | 67 +- stack-showcase/requirements.txt | 7 +- stack-showcase/run.ipynb | 3338 ++++++++++++++--- stack-showcase/run.py | 149 +- stack-showcase/run_deploy.ipynb | 175 - stack-showcase/run_stack_showcase.ipynb | 347 -- stack-showcase/steps/__init__.py | 3 - stack-showcase/steps/data_loader.py | 32 +- stack-showcase/steps/data_preprocessor.py | 83 +- stack-showcase/steps/data_splitter.py | 20 +- stack-showcase/steps/deploy_to_huggingface.py | 69 - stack-showcase/steps/inference_predict.py | 18 +- .../steps/inference_preprocessor.py | 6 +- stack-showcase/steps/model_evaluator.py | 77 +- stack-showcase/steps/model_promoter.py | 73 +- stack-showcase/steps/model_trainer.py | 56 +- stack-showcase/utils/__init__.py | 16 + stack-showcase/utils/preprocess.py | 56 + 34 files changed, 3370 insertions(+), 1559 deletions(-) delete mode 100644 stack-showcase/Dockerfile create mode 100644 stack-showcase/_assets/cloud_mcp.png create mode 100644 stack-showcase/_assets/cloud_mcp_predictions.png create mode 100644 stack-showcase/_assets/cloud_mcp_screenshot.png delete mode 100644 stack-showcase/app.py delete mode 100644 stack-showcase/configs/deployment.yaml create mode 100644 stack-showcase/configs/training_rf.yaml rename stack-showcase/configs/{training.yaml => training_rf_sagemaker.yaml} (57%) create mode 100644 stack-showcase/configs/training_sgd.yaml create mode 100644 stack-showcase/configs/training_sgd_sagemaker.yaml delete mode 100644 stack-showcase/pipelines/deployment.py delete mode 100644 stack-showcase/run_deploy.ipynb delete mode 100644 stack-showcase/run_stack_showcase.ipynb delete mode 100644 stack-showcase/steps/deploy_to_huggingface.py create mode 100644 stack-showcase/utils/__init__.py create mode 100644 stack-showcase/utils/preprocess.py diff --git a/stack-showcase/Dockerfile b/stack-showcase/Dockerfile deleted file mode 100644 index 0f09cfbc..00000000 --- a/stack-showcase/Dockerfile +++ /dev/null @@ -1,26 +0,0 @@ -# read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker -# you will also find guides on how best to write your Dockerfile - -FROM python:3.9 - -WORKDIR /code - -COPY ./requirements.txt /code/requirements.txt - -RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt - -# Set up a new user named "user" with user ID 1000 -RUN useradd -m -u 1000 user -# Switch to the "user" user -USER user -# Set home to the user's home directory -ENV HOME=/home/user \ - PATH=/home/user/.local/bin:$PATH - -# Set the working directory to the 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85a6c449..cca63345 100644 --- a/stack-showcase/run.ipynb +++ b/stack-showcase/run.ipynb @@ -2,7 +2,89 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# pre-demo recommendations\n", + "! zenml connect --url https://1cf18d95-zenml.cloudinfra.zenml.io \n", + "! zenml delete model breast_cancer_classifier -y" + ] + }, + { + "cell_type": "markdown", + "id": "63ab391a", + "metadata": {}, + "source": [ + "# Intro to MLOps using ZenML\n", + "\n", + "## 🌍 Overview\n", + "\n", + "This repository is a minimalistic MLOps project intended as a starting point to learn how to put ML workflows in production. It features: \n", + "\n", + "- A feature engineering pipeline that loads data and prepares it for training.\n", + "- A training pipeline that loads the preprocessed dataset and trains a model.\n", + "- A batch inference pipeline that runs predictions on the trained model with new data.\n", + "\n", + "Follow along this notebook to understand how you can use ZenML to productionalize your ML workflows!\n", + "\n", + "\"Pipelines" + ] + }, + { + "cell_type": "markdown", + "id": "66b2977c", + "metadata": {}, + "source": [ + "# 👶 Step 0. Install Requirements\n", + "\n", + "Let's install ZenML to get started. First we'll install the latest version of\n", + "ZenML as well as the `sklearn` integration of ZenML:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f76f562e", + "metadata": {}, + "outputs": [], + "source": [ + "!zenml integration install sklearn -y\n", + "\n", + "import IPython\n", + "IPython.Application.instance().kernel.do_shutdown(restart=True)" + ] + }, + { + "cell_type": "markdown", + "id": "3b044374", + "metadata": {}, + "source": [ + "Please wait for the installation to complete before running subsequent cells. At\n", + "the end of the installation, the notebook kernel will automatically restart." + ] + }, + { + "cell_type": "markdown", + "id": "e3955ff1", + "metadata": {}, + "source": [ + "Optional: If you are using [ZenML Cloud](https://zenml.io/cloud), execute the following cell with your tenant URL. Otherwise ignore." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e2587315", + "metadata": {}, + "outputs": [], + "source": [ + "!zenml connect --url https://1cf18d95-zenml.cloudinfra.zenml.io" + ] + }, + { + "cell_type": "code", + "execution_count": 70, "id": "081d5616", "metadata": {}, "outputs": [ @@ -10,50 +92,48 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1;35mNumExpr defaulting to 8 threads.\u001b[0m\n", + "\u001b[31mThe ZenML global configuration version (0.55.2) is higher than the version of ZenML currently being used (0.55.1). Read more about this issue and how to solve it here: \u001b[0m\u001b[1;36mhttps://docs.zenml.io/user-guide/advanced-guide/environment-management/global-settings-of-zenml#version-mismatch-downgrading\u001b[31m\u001b[0m\n", "\u001b[?25l\u001b[2;36mFound existing ZenML repository at path \u001b[0m\n", - "\u001b[2;32m'/home/apenner/PycharmProjects/template-starter/template'\u001b[0m\u001b[2;36m.\u001b[0m\n", + "\u001b[2;32m'/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/stack-showcase'\u001b[0m\u001b[2;36m.\u001b[0m\n", "\u001b[2;32m⠋\u001b[0m\u001b[2;36m Initializing ZenML repository at \u001b[0m\n", - "\u001b[2;36m/home/apenner/PycharmProjects/template-starter/template.\u001b[0m\n", + "\u001b[2;36m/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/stack-showcase.\u001b[0m\n", "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Initializing ZenML repository at \n", - "/home/apenner/PycharmProjects/template-starter/template.\n", + "/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/stack-showcase.\n", "\n", - "\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1;35mNumExpr defaulting to 8 threads.\u001b[0m\n", - "\u001b[2K\u001b[2;36mActive repository stack set to: \u001b[0m\u001b[2;32m'default'\u001b[0m.\n", - "\u001b[2K\u001b[32m⠙\u001b[0m Setting the repository active stack to 'default'...t'...\u001b[0m\n", - "\u001b[1A\u001b[2K" + "\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[31mThe ZenML global configuration version (0.55.2) is higher than the version of ZenML currently being used (0.55.1). Read more about this issue and how to solve it here: \u001b[0m\u001b[1;36mhttps://docs.zenml.io/user-guide/advanced-guide/environment-management/global-settings-of-zenml#version-mismatch-downgrading\u001b[31m\u001b[0m\n", + "\u001b[?25l\u001b[32m⠋\u001b[0m Setting the repository active stack to \n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[2;36mActive repository stack set to: \u001b[0m\u001b[2;32m'local-sagemaker-step-operator-wandb'\u001b[0m\n", + "\u001b[2;32m⠋\u001b[0m\u001b[2;36m Setting the repository active stack to \u001b[0m\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Setting the repository active stack to \n", + "'local-sagemaker-step-operator-wandb'...\n", + "\u001b[1A\u001b[2K\u001b[1A\u001b[2K" ] } ], "source": [ + "# Initialize ZenML and set the default stack\n", "!zenml init\n", - "!zenml stack set default" + "\n", + "!zenml stack set local-sagemaker-step-operator-wandb" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 71, "id": "79f775f2", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;35mNumExpr defaulting to 8 threads.\u001b[0m\n" - ] - } - ], + "outputs": [], "source": [ "# Do the imports at the top\n", + "from typing_extensions import Annotated\n", + "from sklearn.datasets import load_breast_cancer\n", "\n", - "import random\n", - "from zenml import ExternalArtifact, pipeline, Model\n", + "import pandas as pd\n", + "from zenml import step, pipeline, Model, get_step_context\n", "from zenml.client import Client\n", "from zenml.logger import get_logger\n", "from uuid import UUID\n", "\n", - "import os\n", "from typing import Optional, List\n", "\n", "from zenml import pipeline\n", @@ -63,48 +143,362 @@ " data_preprocessor,\n", " data_splitter,\n", " model_evaluator,\n", - " model_trainer,\n", - " inference_predict,\n", " inference_preprocessor\n", ")\n", "\n", + "from zenml.logger import get_logger\n", + "\n", "logger = get_logger(__name__)\n", "\n", + "# Initialize the ZenML client to fetch objects from the ZenML Server\n", "client = Client()" ] }, + { + "cell_type": "markdown", + "id": "35e48460", + "metadata": {}, + "source": [ + "## 🥇 Step 1: Load your data and execute feature engineering\n", + "\n", + "We'll start off by importing our data. In this quickstart we'll be working with\n", + "[the Breast Cancer](https://archive.ics.uci.edu/dataset/17/breast+cancer+wisconsin+diagnostic) dataset\n", + "which is publicly available on the UCI Machine Learning Repository. The task is a classification\n", + "problem, to predict whether a patient is diagnosed with breast cancer or not.\n", + "\n", + "When you're getting started with a machine learning problem you'll want to do\n", + "something similar to this: import your data and get it in the right shape for\n", + "your training. ZenML mostly gets out of your way when you're writing your Python\n", + "code, as you'll see from the following cell.\n", + "\n", + "\"Feature" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "3cd974d1", + "metadata": {}, + "outputs": [], + "source": [ + "@step\n", + "def data_loader_simplified(\n", + " random_state: int, is_inference: bool = False, target: str = \"target\"\n", + ") -> Annotated[pd.DataFrame, \"dataset\"]: # We name the dataset \n", + " \"\"\"Dataset reader step.\"\"\"\n", + " dataset = load_breast_cancer(as_frame=True)\n", + " inference_size = int(len(dataset.target) * 0.05)\n", + " dataset: pd.DataFrame = dataset.frame\n", + " inference_subset = dataset.sample(inference_size, random_state=random_state)\n", + " if is_inference:\n", + " dataset = inference_subset\n", + " dataset.drop(columns=target, inplace=True)\n", + " else:\n", + " dataset.drop(inference_subset.index, inplace=True)\n", + " dataset.reset_index(drop=True, inplace=True)\n", + " logger.info(f\"Dataset with {len(dataset)} records loaded!\")\n", + " return dataset\n" + ] + }, + { + "cell_type": "markdown", + "id": "1e8ba4c6", + "metadata": {}, + "source": [ + "The whole function is decorated with the `@step` decorator, which\n", + "tells ZenML to track this function as a step in the pipeline. This means that\n", + "ZenML will automatically version, track, and cache the data that is produced by\n", + "this function as an `artifact`. This is a very powerful feature, as it means that you can\n", + "reproduce your data at any point in the future, even if the original data source\n", + "changes or disappears. \n", + "\n", + "Note the use of the `typing` module's `Annotated` type hint in the output of the\n", + "step. We're using this to give a name to the output of the step, which will make\n", + "it possible to access it via a keyword later on.\n", + "\n", + "You'll also notice that we have included type hints for the outputs\n", + "to the function. These are not only useful for anyone reading your code, but\n", + "help ZenML process your data in a way appropriate to the specific data types." + ] + }, + { + "cell_type": "markdown", + "id": "b6286b67", + "metadata": {}, + "source": [ + "ZenML is built in a way that allows you to experiment with your data and build\n", + "your pipelines as you work, so if you want to call this function to see how it\n", + "works, you can just call it directly. Here we take a look at the first few rows\n", + "of your training dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "d838e2ea", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;35mDataset with 541 records loaded!\u001b[0m\n", + "\u001b[1;35mDataset with 541 records loaded!\u001b[0m\n", + "\u001b[1;35mDataset with 541 records loaded!\u001b[0m\n" + ] + }, + { + "data": { + "text/html": [ + "

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mean radiusmean texturemean perimetermean areamean smoothnessmean compactnessmean concavitymean concave pointsmean symmetrymean fractal dimension...worst textureworst perimeterworst areaworst smoothnessworst compactnessworst concavityworst concave pointsworst symmetryworst fractal dimensiontarget
017.9910.38122.801001.00.118400.277600.30010.147100.24190.07871...17.33184.602019.00.16220.66560.71190.26540.46010.118900
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" + ], + "text/plain": [ + " mean radius mean texture mean perimeter mean area mean smoothness \\\n", + "0 17.99 10.38 122.80 1001.0 0.11840 \n", + "1 20.57 17.77 132.90 1326.0 0.08474 \n", + "2 19.69 21.25 130.00 1203.0 0.10960 \n", + "3 11.42 20.38 77.58 386.1 0.14250 \n", + "4 20.29 14.34 135.10 1297.0 0.10030 \n", + "\n", + " mean compactness mean concavity mean concave points mean symmetry \\\n", + "0 0.27760 0.3001 0.14710 0.2419 \n", + "1 0.07864 0.0869 0.07017 0.1812 \n", + "2 0.15990 0.1974 0.12790 0.2069 \n", + "3 0.28390 0.2414 0.10520 0.2597 \n", + "4 0.13280 0.1980 0.10430 0.1809 \n", + "\n", + " mean fractal dimension ... worst texture worst perimeter worst area \\\n", + "0 0.07871 ... 17.33 184.60 2019.0 \n", + "1 0.05667 ... 23.41 158.80 1956.0 \n", + "2 0.05999 ... 25.53 152.50 1709.0 \n", + "3 0.09744 ... 26.50 98.87 567.7 \n", + "4 0.05883 ... 16.67 152.20 1575.0 \n", + "\n", + " worst smoothness worst compactness worst concavity worst concave points \\\n", + "0 0.1622 0.6656 0.7119 0.2654 \n", + "1 0.1238 0.1866 0.2416 0.1860 \n", + "2 0.1444 0.4245 0.4504 0.2430 \n", + "3 0.2098 0.8663 0.6869 0.2575 \n", + "4 0.1374 0.2050 0.4000 0.1625 \n", + "\n", + " worst symmetry worst fractal dimension target \n", + "0 0.4601 0.11890 0 \n", + "1 0.2750 0.08902 0 \n", + "2 0.3613 0.08758 0 \n", + "3 0.6638 0.17300 0 \n", + "4 0.2364 0.07678 0 \n", + "\n", + "[5 rows x 31 columns]" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = data_loader_simplified(random_state=42)\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "28c05291", + "metadata": {}, + "source": [ + "Everything looks as we'd expect and the values are all in the right format 🥳.\n", + "\n", + "We're now at the point where can bring this step (and some others) together into a single\n", + "pipeline, the top-level organising entity for code in ZenML. Creating such a pipeline is\n", + "as simple as adding a `@pipeline` decorator to a function. This specific\n", + "pipeline doesn't return a value, but that option is available to you if you need." + ] + }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 91, "id": "b50a9537", "metadata": {}, "outputs": [], "source": [ "@pipeline\n", "def feature_engineering(\n", - " test_size: float = 0.2,\n", + " test_size: float = 0.3,\n", " drop_na: Optional[bool] = None,\n", " normalize: Optional[bool] = None,\n", " drop_columns: Optional[List[str]] = None,\n", " target: Optional[str] = \"target\",\n", + " random_state: int = 17\n", "):\n", - " \"\"\"\n", - " Feature engineering pipeline.\n", - "\n", - " This is a pipeline that loads the data, processes it and splits\n", - " it into train and test sets.\n", - "\n", - " Args:\n", - " test_size: Size of holdout set for training 0.0..1.0\n", - " drop_na: If `True` NA values will be removed from dataset\n", - " normalize: If `True` dataset will be normalized with MinMaxScaler\n", - " drop_columns: List of columns to drop from dataset\n", - " target: Name of target column in dataset\n", - " \"\"\"\n", - " ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ###\n", + " \"\"\"Feature engineering pipeline.\"\"\"\n", " # Link all the steps together by calling them and passing the output\n", " # of one step as the input of the next step.\n", - " raw_data = data_loader(random_state=random.randint(0, 100), target=target)\n", + " raw_data = data_loader(random_state=random_state, target=target)\n", " dataset_trn, dataset_tst = data_splitter(\n", " dataset=raw_data,\n", " test_size=test_size,\n", @@ -116,27 +510,25 @@ " normalize=normalize,\n", " drop_columns=drop_columns,\n", " target=target,\n", + " random_state=random_state,\n", " )\n", - " \n", + "\n", " return dataset_trn, dataset_tst" ] }, { - "cell_type": "code", - "execution_count": 4, - "id": "bc5feef4-7016-420e-9af9-2e87ff666f74", + "cell_type": "markdown", + "id": "7cd73c23", "metadata": {}, - "outputs": [], "source": [ - "pipeline_args = {}\n", - "pipeline_args[\"config_path\"] = os.path.join(\"configs\", \"feature_engineering.yaml\")\n", - "fe_p_configured = feature_engineering.with_options(**pipeline_args)" + "We're ready to run the pipeline now, which we can do just as with the step - by calling the\n", + "pipeline function itself:" ] }, { "cell_type": "code", - "execution_count": 5, - "id": "75cf3740-b2d8-4c4b-b91b-dc1637000880", + "execution_count": 77, + "id": "1e0aa9af", "metadata": {}, "outputs": [ { @@ -144,802 +536,1899 @@ "output_type": "stream", "text": [ "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mfeature_engineering\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mReusing registered version: \u001b[0m\u001b[1;36m(version: 1)\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mNew model version \u001b[0m\u001b[1;36m34\u001b[1;35m was created.\u001b[0m\n", + "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 32)\u001b[1;35m.\u001b[0m\n", "\u001b[1;35mExecuting a new run.\u001b[0m\n", - "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36malexej@zenml.io\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mandrei@zenml.io\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mlocal-mlflow-stack\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m model_registry: \u001b[0m\u001b[1;36mmlflow\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m model_deployer: \u001b[0m\u001b[1;36mmlflow\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m experiment_tracker: \u001b[0m\u001b[1;36mmlflow\u001b[1;35m\u001b[0m\n", "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has started.\u001b[0m\n", "\u001b[1;35mDataset with 541 records loaded!\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has finished in \u001b[0m\u001b[1;36m6.777s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has finished in \u001b[0m\u001b[1;36m1.014s\u001b[1;35m.\u001b[0m\n", "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has finished in \u001b[0m\u001b[1;36m11.345s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has finished in \u001b[0m\u001b[1;36m1.782s\u001b[1;35m.\u001b[0m\n", "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has finished in \u001b[0m\u001b[1;36m14.866s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mRun \u001b[0m\u001b[1;36mfeature_engineering-2023_12_06-09_08_46_821042\u001b[1;35m has finished in \u001b[0m\u001b[1;36m36.198s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/52874ade-f314-45ab-b9bf-e95fb29290b8/runs/9d9e49b1-d78f-478b-991e-da87b0560512/dag\u001b[0m\n" + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has finished in \u001b[0m\u001b[1;36m2.333s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mPipeline run has finished in \u001b[0m\u001b[1;36m6.991s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/4f30b522-a840-4763-ba5f-9bb1f64d7bd5/runs/5da24a7d-26c0-4e21-ad6d-80f83df4c2fe/dag\u001b[0m\n" ] + }, + { + "data": { + "text/plain": [ + "PipelineRunResponse(id=UUID('5da24a7d-26c0-4e21-ad6d-80f83df4c2fe'), permission_denied=False, body=PipelineRunResponseBody(created=datetime.datetime(2024, 2, 8, 11, 8, 49), updated=datetime.datetime(2024, 2, 8, 11, 8, 49), user=UserResponse(id=UUID('c915043d-706c-4885-9263-783cae8dad20'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 11, 16, 16, 5, 33), updated=datetime.datetime(2024, 2, 8, 10, 24, 32), active=True, activation_token=None, full_name='Andrei', email_opted_in=True, is_service_account=False), metadata=None, name='andrei@zenml.io'), status=, stack=StackResponse(id=UUID('57f7dcf2-4bd5-439b-b454-da4000dd3bc8'), permission_denied=False, body=StackResponseBody(created=datetime.datetime(2023, 12, 11, 15, 24, 51), updated=datetime.datetime(2023, 12, 21, 1, 15, 28), user=UserResponse(id=UUID('ac044541-bcb1-4f33-b41e-5c60a3f39485'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 10, 23, 15, 36, 12), updated=datetime.datetime(2024, 2, 7, 9, 57, 48), active=True, activation_token=None, full_name='Alexej Penner', email_opted_in=True, is_service_account=False), metadata=None, name='alexej@zenml.io')), metadata=None, name='local-mlflow-stack'), pipeline=PipelineResponse(id=UUID('4f30b522-a840-4763-ba5f-9bb1f64d7bd5'), 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config=PipelineConfiguration(enable_cache=None, enable_artifact_metadata=None, enable_artifact_visualization=None, enable_step_logs=None, settings={'experiment_tracker.mlflow': BaseSettings()}, extra={}, failure_hook_source=None, success_hook_source=None, model=None, parameters=None, name='feature_engineering'), start_time=datetime.datetime(2024, 2, 8, 11, 8, 49), end_time=None, client_environment={'environment': 'notebook', 'os': 'mac', 'mac_version': '14.0', 'python_version': '3.11.6'}, orchestrator_environment={}, orchestrator_run_id=None), name='feature_engineering-2024_02_08-11_08_49_078982')" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "latest_run = fe_p_configured()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "69ade540", - "metadata": {}, - "outputs": [], - "source": [ - "@pipeline\n", - "def training(\n", - " train_dataset_id: Optional[UUID] = None,\n", - " test_dataset_id: Optional[UUID] = None,\n", - " min_train_accuracy: float = 0.0,\n", - " min_test_accuracy: float = 0.0,\n", - "):\n", - " \"\"\"\n", - " Model training pipeline.\n", - "\n", - " This is a pipeline that loads the data, processes it and splits\n", - " it into train and test sets, then search for best hyperparameters,\n", - " trains and evaluates a model.\n", - "\n", - " Args:\n", - " test_size: Size of holdout set for training 0.0..1.0\n", - " drop_na: If `True` NA values will be removed from dataset\n", - " normalize: If `True` dataset will be normalized with MinMaxScaler\n", - " drop_columns: List of columns to drop from dataset\n", - " \"\"\"\n", - " ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ###\n", - " # Link all the steps together by calling them and passing the output\n", - " # of one step as the input of the next step.\n", - " \n", - " # Execute Feature Engineering Pipeline\n", - " if train_dataset_id is None or test_dataset_id is None:\n", - " dataset_trn, dataset_tst = feature_engineering()\n", - " else:\n", - " dataset_trn = ExternalArtifact(id=train_dataset_id)\n", - " dataset_tst = ExternalArtifact(id=test_dataset_id)\n", - " \n", - " model = model_trainer(\n", - " dataset_trn=dataset_trn,\n", - " )\n", - "\n", - " model_evaluator(\n", - " model=model,\n", - " dataset_trn=dataset_trn,\n", - " dataset_tst=dataset_tst,\n", - " min_train_accuracy=min_train_accuracy,\n", - " min_test_accuracy=min_test_accuracy,\n", - " )\n" + "feature_engineering(random_state=42)" ] }, { - "cell_type": "code", - "execution_count": 7, - "id": "5b1f78df", + "cell_type": "markdown", + "id": "1785c303", "metadata": {}, - "outputs": [], "source": [ - "pipeline_args = {}\n", - "pipeline_args[\"config_path\"] = os.path.join(\"configs\", \"training.yaml\")\n", - "fe_t_configured = training.with_options(**pipeline_args)" + "Let's run this again with a slightly different test size, to create more datasets:" ] }, { "cell_type": "code", - "execution_count": 8, - "id": "acf306a5", + "execution_count": 78, + "id": "658c0570-2607-4b97-a72d-d45c92633e48", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 2)\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mNew model version \u001b[0m\u001b[1;36m35\u001b[1;35m was created.\u001b[0m\n", + "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mfeature_engineering\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 33)\u001b[1;35m.\u001b[0m\n", "\u001b[1;35mExecuting a new run.\u001b[0m\n", - "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36malexej@zenml.io\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mandrei@zenml.io\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mlocal-mlflow-stack\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m model_registry: \u001b[0m\u001b[1;36mmlflow\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m model_deployer: \u001b[0m\u001b[1;36mmlflow\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m experiment_tracker: \u001b[0m\u001b[1;36mmlflow\u001b[1;35m\u001b[0m\n", "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m.\u001b[0m\n", "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mDataset with 541 records loaded!\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has finished in \u001b[0m\u001b[1;36m7.368s\u001b[1;35m.\u001b[0m\n", "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has started.\u001b[0m\n", - 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body=UserResponseBody(created=datetime.datetime(2023, 11, 16, 16, 5, 33), updated=datetime.datetime(2024, 2, 8, 10, 24, 32), active=True, activation_token=None, full_name='Andrei', email_opted_in=True, is_service_account=False), metadata=None, name='andrei@zenml.io'), status=[], version='33'), metadata=None, name='feature_engineering'), build=None, schedule=None, code_reference=None), metadata=PipelineRunResponseMetadata(workspace=WorkspaceResponse(id=UUID('f3a544f2-afb5-4672-934a-7a465c66201c'), permission_denied=False, body=WorkspaceResponseBody(created=datetime.datetime(2023, 10, 23, 15, 34, 47), updated=datetime.datetime(2023, 10, 23, 15, 34, 47)), metadata=None, name='default'), run_metadata={}, steps={}, config=PipelineConfiguration(enable_cache=None, enable_artifact_metadata=None, enable_artifact_visualization=None, enable_step_logs=None, settings={'experiment_tracker.mlflow': BaseSettings()}, extra={}, failure_hook_source=None, success_hook_source=None, model=None, parameters=None, name='feature_engineering'), start_time=datetime.datetime(2024, 2, 8, 11, 9, 1), end_time=None, client_environment={'environment': 'notebook', 'os': 'mac', 'mac_version': '14.0', 'python_version': '3.11.6'}, orchestrator_environment={}, orchestrator_run_id=None), name='feature_engineering-2024_02_08-11_09_01_191710')" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "fe_t_configured()" + "feature_engineering(random_state=42,test_size=0.25)" ] }, { - "cell_type": "code", - "execution_count": 9, - "id": "ad6aa280", + "cell_type": "markdown", + "id": "64bb7206", "metadata": {}, - "outputs": [], "source": [ - "from typing import Optional\n", - "\n", - "import pandas as pd\n", - "from typing_extensions import Annotated\n", - "\n", - "from zenml import get_step_context, step\n", - "from zenml.logger import get_logger\n", - "\n", - "logger = get_logger(__name__)\n", - "\n", - "\n", - "@step\n", - "def inference_predict(\n", - " dataset_inf: pd.DataFrame,\n", - ") -> Annotated[pd.Series, \"predictions\"]:\n", - " \"\"\"Predictions step.\n", - "\n", - " This is an example of a predictions step that takes the data in and returns\n", - " predicted values.\n", - "\n", - " This step is parameterized, which allows you to configure the step\n", - " independently of the step code, before running it in a pipeline.\n", - " In this example, the step can be configured to use different input data.\n", - " See the documentation for more information:\n", - "\n", - " https://docs.zenml.io/user-guide/advanced-guide/configure-steps-pipelines\n", - "\n", - " Args:\n", - " dataset_inf: The inference dataset.\n", - "\n", - " Returns:\n", - " The predictions as pandas series\n", - " \"\"\"\n", - " ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ###\n", - " zenml_model = get_step_context().model\n", - "\n", - " print(zenml_model)\n", - "\n", - " # run prediction from memory\n", - " predictor = zenml_model.load_artifact(\"model\")\n", - " predictions = predictor.predict(dataset_inf)\n", - "\n", - " print(predictions)\n", - " predictions = pd.Series(predictions, name=\"predicted\")\n", - " ### YOUR CODE ENDS HERE ###\n", - "\n", - " return predictions\n" + "Notice the second time around, the data loader step was **cached**, while the rest of the pipeline was rerun. \n", + "This is because ZenML automatically determined that nothing had changed in the data loader step, \n", + "so it didn't need to rerun it." ] }, { - "cell_type": "code", - "execution_count": 10, - "id": "517ad39d", + "cell_type": "markdown", + "id": "5bc6849d-31ac-4c08-9ca2-cf7f5f35ccbf", "metadata": {}, - "outputs": [], "source": [ - "@pipeline\n", - "def batch_inference():\n", - " \"\"\"\n", - " Model batch inference pipeline.\n", - "\n", - " This is a pipeline that loads the inference data, processes\n", - " it, analyze for data drift and run inference.\n", - " \"\"\"\n", - " ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ###\n", - " # Link all the steps together by calling them and passing the output\n", - " # of one step as the input of the next step.\n", - " ########## ETL stage ##########\n", - " random_state = client.get_artifact(\"dataset\").run_metadata[\"random_state\"].value\n", - " target = client.get_artifact(\"dataset_trn\").run_metadata['target'].value\n", - " df_inference = data_loader(\n", - " random_state=random_state, is_inference=True\n", - " )\n", - " df_inference = inference_preprocessor(\n", - " dataset_inf=df_inference,\n", - " preprocess_pipeline=ExternalArtifact(name=\"preprocess_pipeline\"),\n", - " target=target,\n", - " )\n", - " inference_predict(\n", - " dataset_inf=df_inference,\n", - " )\n" + "Let's run this again with a slightly different test size and random state, to disable the cache and to create more datasets:" ] }, { "cell_type": "code", - "execution_count": 11, - "id": "f0d9ebb6", + "execution_count": 79, + "id": "1e1d8546", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1;35m\u001b[0m\u001b[1;36mversion\u001b[1;35m \u001b[0m\u001b[1;36mproduction\u001b[1;35m matches one of the possible \u001b[0m\u001b[1;36mModelStages\u001b[1;35m and will be fetched using stage.\u001b[0m\n" + "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mfeature_engineering\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 34)\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mExecuting a new run.\u001b[0m\n", + "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mandrei@zenml.io\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mlocal-mlflow-stack\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m model_registry: \u001b[0m\u001b[1;36mmlflow\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m model_deployer: \u001b[0m\u001b[1;36mmlflow\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m experiment_tracker: \u001b[0m\u001b[1;36mmlflow\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mDataset with 541 records loaded!\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has finished in \u001b[0m\u001b[1;36m1.051s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has finished in \u001b[0m\u001b[1;36m1.780s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has finished in \u001b[0m\u001b[1;36m2.473s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mPipeline run has finished in \u001b[0m\u001b[1;36m7.075s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/3678fc6f-919a-4564-a38d-8dea60a425ce/runs/6f8066a8-ca40-4481-96a3-d8ada6e6cf1e/dag\u001b[0m\n" ] + }, + { + "data": { + "text/plain": [ + "PipelineRunResponse(id=UUID('6f8066a8-ca40-4481-96a3-d8ada6e6cf1e'), permission_denied=False, body=PipelineRunResponseBody(created=datetime.datetime(2024, 2, 8, 11, 9, 17), updated=datetime.datetime(2024, 2, 8, 11, 9, 17), user=UserResponse(id=UUID('c915043d-706c-4885-9263-783cae8dad20'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 11, 16, 16, 5, 33), updated=datetime.datetime(2024, 2, 8, 10, 24, 32), active=True, activation_token=None, full_name='Andrei', email_opted_in=True, is_service_account=False), metadata=None, name='andrei@zenml.io'), status=, stack=StackResponse(id=UUID('57f7dcf2-4bd5-439b-b454-da4000dd3bc8'), permission_denied=False, body=StackResponseBody(created=datetime.datetime(2023, 12, 11, 15, 24, 51), updated=datetime.datetime(2023, 12, 21, 1, 15, 28), user=UserResponse(id=UUID('ac044541-bcb1-4f33-b41e-5c60a3f39485'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 10, 23, 15, 36, 12), updated=datetime.datetime(2024, 2, 7, 9, 57, 48), active=True, activation_token=None, full_name='Alexej Penner', email_opted_in=True, is_service_account=False), metadata=None, name='alexej@zenml.io')), metadata=None, name='local-mlflow-stack'), pipeline=PipelineResponse(id=UUID('3678fc6f-919a-4564-a38d-8dea60a425ce'), permission_denied=False, body=PipelineResponseBody(created=datetime.datetime(2024, 2, 8, 11, 9, 16), updated=datetime.datetime(2024, 2, 8, 11, 9, 16), user=UserResponse(id=UUID('c915043d-706c-4885-9263-783cae8dad20'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 11, 16, 16, 5, 33), updated=datetime.datetime(2024, 2, 8, 10, 24, 32), active=True, activation_token=None, full_name='Andrei', email_opted_in=True, is_service_account=False), metadata=None, name='andrei@zenml.io'), status=[], version='34'), metadata=None, name='feature_engineering'), build=None, schedule=None, code_reference=None), metadata=PipelineRunResponseMetadata(workspace=WorkspaceResponse(id=UUID('f3a544f2-afb5-4672-934a-7a465c66201c'), permission_denied=False, body=WorkspaceResponseBody(created=datetime.datetime(2023, 10, 23, 15, 34, 47), updated=datetime.datetime(2023, 10, 23, 15, 34, 47)), metadata=None, name='default'), run_metadata={}, steps={}, config=PipelineConfiguration(enable_cache=None, enable_artifact_metadata=None, enable_artifact_visualization=None, enable_step_logs=None, settings={'experiment_tracker.mlflow': BaseSettings()}, extra={}, failure_hook_source=None, success_hook_source=None, model=None, parameters=None, name='feature_engineering'), start_time=datetime.datetime(2024, 2, 8, 11, 9, 17), end_time=None, client_environment={'environment': 'notebook', 'os': 'mac', 'mac_version': '14.0', 'python_version': '3.11.6'}, orchestrator_environment={}, orchestrator_run_id=None), name='feature_engineering-2024_02_08-11_09_17_420170')" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "pipeline_args = {}\n", - "pipeline_args[\"config_path\"] = os.path.join(\"configs\", \"inference.yaml\")\n", - "fe_b_configured = batch_inference.with_options(**pipeline_args)" + "feature_engineering(test_size=0.25, random_state=104)" + ] + }, + { + "cell_type": "markdown", + "id": "e8471f93", + "metadata": {}, + "source": [ + "We can also fetch the pipeline from the server and view the results directly in the notebook:" ] }, { "cell_type": "code", - "execution_count": 13, - "id": "9901c6d0", + "execution_count": 80, + "id": "f208b200", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[33mUsing an external artifact as step input currently invalidates caching for the step and all downstream steps. Future releases will introduce hashing of artifacts which will improve this behavior.\u001b[0m\n", - "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mbatch_inference\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mReusing registered version: \u001b[0m\u001b[1;36m(version: 1)\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mExecuting a new run.\u001b[0m\n", - "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36malexej@zenml.io\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36minference_preprocessor\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36minference_preprocessor\u001b[1;35m has finished in \u001b[0m\u001b[1;36m8.661s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36minference_predict\u001b[1;35m has started.\u001b[0m\n", - "name='breast_cancer_classifier' license='Apache 2.0' description='Classification of Breast Cancer Dataset.' audience=None use_cases=None limitations=None trade_offs=None ethics=None tags=['classification', 'sklearn'] version='production' save_models_to_registry=True suppress_class_validation_warnings=True was_created_in_this_run=False\n", - "\u001b[33mYou specified both an ID as well as a version of the artifacts. Ignoring the version and fetching the artifacts by ID.\u001b[0m\n", - "\u001b[33mYour artifact was materialized under Python version 'unknown' but you are currently using '3.9.13'. This might cause unexpected behavior since pickle is not reproducible across Python versions. Attempting to load anyway...\u001b[0m\n", - "\u001b[33mCould not import Azure service connector: No module named 'azure.identity'.\u001b[0m\n", - "[1 0 0 1 1 0 0 0 0 1 1 0 1 0 1 0 1 1 1 0 0 1 0 1 1 1 1 1]\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36minference_predict\u001b[1;35m has finished in \u001b[0m\u001b[1;36m18.218s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mRun \u001b[0m\u001b[1;36mbatch_inference-2023_12_06-09_11_29_924914\u001b[1;35m has finished in \u001b[0m\u001b[1;36m32.726s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/2979acb2-c862-480a-8f50-a2be4c76a8a2/runs/7886e370-b05a-4205-931e-e4994fabd897/dag\u001b[0m\n" + "feature_engineering-2024_02_08-11_09_17_420170\n" ] } ], "source": [ - "fe_b_configured()" + "client = Client()\n", + "run = client.get_pipeline(\"feature_engineering\").last_run\n", + "print(run.name)" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "98d39df8", - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", - "id": "51690802-31a7-4e6d-9f88-e6457c6c4a96", + "id": "a037f09d", "metadata": {}, "source": [ - "# Huggingface Model to Sagemaker Endpoint: Automating MLOps with ZenML\n", - "Deploying Huggingface models to AWS Sagemaker endpoints typically only requires a few lines of code. However, there's a growing demand to not just deploy, but to seamlessly automate the entire flow from training to production with comprehensive lineage tracking. ZenML adeptly fills this niche, providing an end-to-end MLOps solution for Huggingface users wishing to deploy to Sagemaker. Below, we’ll walk through the architecture that ZenML employs to bring a Huggingface model into production with AWS Sagemaker. Of course all of this can be adapted to not just Sagemaker, but any other model deployment service like GCP Vertex or Azure ML Platform.\n", - "\n", - "This blog post showcases one way of using ZenML pipelines to achieve this:\n", - "\n", - "- Create and version a dataset in a feature_engineering_pipeline.\n", - "- Train/Finetune a BERT-based Sentiment Analysis NLP model and push to Huggingface Hub in a training_pipeline.\n", - "- Promote this model to Production by comparing to previous models in a promotion_pipeline.\n", - "- Deploy the model at the Production Stage to a AWS Sagemaker endpoint with a deployment_pipeline.\n", - "\n", - "\"Pipelines" + "We can also see the data artifacts that were produced by the last step of the pipeline:" ] }, { "cell_type": "code", - "execution_count": null, - "id": "500e3c24-b105-4a69-b2fc-e0ce1f1c1d46", + "execution_count": 81, + "id": "34283e89", "metadata": {}, - "outputs": [], - "source": [ - "# Do the imports at the top\n", - "\n", - "import numpy as np\n", - "from datasets import DatasetDict, load_dataset\n", - "from typing_extensions import Annotated\n", - "from zenml import step\n", - "from zenml.logger import get_logger\n", - "\n", - "import os\n", - "from typing import Optional\n", - "from datetime import datetime as dt\n", - "\n", - "from zenml import pipeline\n", - "from zenml import Model\n", - "\n", - "from steps import (\n", - " data_loader,\n", - " notify_on_failure,\n", - " tokenization_step,\n", - " tokenizer_loader,\n", - " generate_reference_and_comparison_datasets,\n", - ")\n", - "from zenml.integrations.evidently.metrics import EvidentlyMetricConfig\n", - "from zenml.integrations.evidently.steps import (\n", - " EvidentlyColumnMapping,\n", - " evidently_report_step,\n", - ")\n", - "\n", - "from pipelines import (\n", - " sentinment_analysis_deploy_pipeline,\n", - " sentinment_analysis_promote_pipeline,\n", - " sentinment_analysis_training_pipeline,\n", - ")\n", - "\n", - "logger = get_logger(__name__)" + "outputs": [ + { + "data": { + "text/plain": [ + "{'dataset_trn': ArtifactVersionResponse(id=UUID('5aee13f2-3a73-4f6e-8daa-b41e95c6078a'), permission_denied=False, body=ArtifactVersionResponseBody(created=datetime.datetime(2024, 2, 8, 11, 9, 23), updated=datetime.datetime(2024, 2, 8, 11, 9, 23), user=UserResponse(id=UUID('c915043d-706c-4885-9263-783cae8dad20'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 11, 16, 16, 5, 33), updated=datetime.datetime(2024, 2, 8, 10, 24, 32), active=True, activation_token=None, full_name='Andrei', email_opted_in=True, is_service_account=False), metadata=None, name='andrei@zenml.io'), artifact=ArtifactResponse(id=UUID('2f71c5a3-333a-4aa0-b5b4-341c26ee7f77'), permission_denied=False, body=ArtifactResponseBody(created=datetime.datetime(2023, 12, 12, 10, 29, 43), updated=datetime.datetime(2023, 12, 12, 10, 29, 43), tags=[]), metadata=None, name='dataset_trn'), version='261', uri='/Users/andrei.vishniakov/Library/Application 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mean radiusmean texturemean perimetermean areamean smoothnessmean compactnessmean concavitymean concave pointsmean symmetrymean fractal dimension...worst textureworst perimeterworst areaworst smoothnessworst compactnessworst concavityworst concave pointsworst symmetryworst fractal dimensiontarget
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405 rows × 31 columns

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" + ], + "text/plain": [ + " mean radius mean texture mean perimeter mean area mean smoothness \\\n", + "444 12.040 28.14 76.85 449.9 0.08752 \n", + "478 9.676 13.14 64.12 272.5 0.12550 \n", + "210 10.440 15.46 66.62 329.6 0.10530 \n", + "299 12.430 17.00 78.60 477.3 0.07557 \n", + "513 14.470 24.99 95.81 656.4 0.08837 \n", + ".. ... ... ... ... ... \n", + "71 16.070 19.65 104.10 817.7 0.09168 \n", + "106 10.510 20.19 68.64 334.2 0.11220 \n", + "270 14.410 19.73 96.03 651.0 0.08757 \n", + "435 27.420 26.27 186.90 2501.0 0.10840 \n", + "102 22.270 19.67 152.80 1509.0 0.13260 \n", + "\n", + " mean compactness mean concavity mean concave points mean symmetry \\\n", + "444 0.06000 0.023670 0.02377 0.1854 \n", + "478 0.22040 0.118800 0.07038 0.2057 \n", + "210 0.07722 0.006643 0.01216 0.1788 \n", + "299 0.03454 0.013420 0.01699 0.1472 \n", + "513 0.12300 0.100900 0.03890 0.1872 \n", + ".. ... ... ... ... \n", + "71 0.08424 0.097690 0.06638 0.1798 \n", + "106 0.13030 0.064760 0.03068 0.1922 \n", + "270 0.16760 0.136200 0.06602 0.1714 \n", + "435 0.19880 0.363500 0.16890 0.2061 \n", + "102 0.27680 0.426400 0.18230 0.2556 \n", + "\n", + " mean fractal dimension ... worst texture worst perimeter worst area \\\n", + "444 0.05698 ... 33.33 87.24 567.6 \n", + "478 0.09575 ... 18.04 69.47 328.1 \n", + "210 0.06450 ... 19.80 73.47 395.4 \n", + "299 0.05561 ... 20.21 81.76 515.9 \n", + "513 0.06341 ... 31.73 113.50 808.9 \n", + ".. ... ... ... ... ... \n", + "71 0.05391 ... 24.56 128.80 1223.0 \n", + "106 0.07782 ... 22.75 72.62 374.4 \n", + "270 0.07192 ... 22.13 101.70 767.3 \n", + "435 0.05623 ... 31.37 251.20 4254.0 \n", + "102 0.07039 ... 28.01 206.80 2360.0 \n", + "\n", + " worst smoothness worst compactness worst concavity \\\n", + "444 0.10410 0.09726 0.05524 \n", + "478 0.20060 0.36630 0.29130 \n", + "210 0.13410 0.11530 0.02639 \n", + "299 0.08409 0.04712 0.02237 \n", + "513 0.13400 0.42020 0.40400 \n", + ".. ... ... ... \n", + "71 0.15000 0.20450 0.28290 \n", + "106 0.13000 0.20490 0.12950 \n", + "270 0.09983 0.24720 0.22200 \n", + "435 0.13570 0.42560 0.68330 \n", + "102 0.17010 0.69970 0.96080 \n", + "\n", + " worst concave points worst symmetry worst fractal dimension target \n", + "444 0.05547 0.2404 0.06639 1 \n", + "478 0.10750 0.2848 0.13640 1 \n", + "210 0.04464 0.2615 0.08269 1 \n", + "299 0.02832 0.1901 0.05932 1 \n", + "513 0.12050 0.3187 0.10230 1 \n", + ".. ... ... ... ... \n", + "71 0.15200 0.2650 0.06387 0 \n", + "106 0.06136 0.2383 0.09026 1 \n", + "270 0.10210 0.2272 0.08799 1 \n", + "435 0.26250 0.2641 0.07427 0 \n", + "102 0.29100 0.4055 0.09789 0 \n", + "\n", + "[405 rows x 31 columns]" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Read one of the datasets. This is the one with a 0.25 test split\n", + "run.steps[\"data_preprocessor\"].outputs[\"dataset_trn\"].load()" ] }, { "cell_type": "markdown", - "id": "fc77b660-e206-46b1-a924-407e797a8f47", + "id": "26d26436", "metadata": {}, "source": [ - "# 🍳Breaking it down\n", - "\n", + "We can also get the artifacts directly. Each time you create a new pipeline run, a new `artifact version` is created.\n", "\n", - "\n" + "You can fetch these artifact and their versions using the `client`: " ] }, { - "cell_type": "markdown", - "id": "31edaf46-6981-42be-99b7-9bdd91c160d5", + "cell_type": "code", + "execution_count": 83, + "id": "c8f90647", "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "## 👶 Step 1: Start with feature engineering\n", + "# Get artifact version from our run\n", + "dataset_trn_artifact_version_via_run = run.steps[\"data_preprocessor\"].outputs[\"dataset_trn\"] \n", "\n", - "Automated feature engineering forms the foundation of this MLOps workflow. Thats why the first pipeline is the feature engineering pipeline. This pipeline loads some data from Huggingface and uses a base tokenizer to create a tokenized dataset. The data loader step is a simple Python function that returns a Huggingface dataloader object:" + "# Get latest version from client directly\n", + "dataset_trn_artifact_version = client.get_artifact_version(\"dataset_trn\")\n", + "\n", + "# This should be true if our run is the latest run and no artifact has been produced\n", + "# in the intervening time\n", + "dataset_trn_artifact_version_via_run.id == dataset_trn_artifact_version.id" ] }, { "cell_type": "code", - "execution_count": null, - "id": "35de0e4c-b6f8-4b68-927a-f40e4130dc93", + "execution_count": 84, + "id": "3f9d3dfd", "metadata": {}, "outputs": [], "source": [ - "@step\n", - "def data_loader() -> Annotated[DatasetDict, \"dataset\"]:\n", - " logger.info(f\"Loading dataset airline_reviews... \")\n", - " hf_dataset = load_dataset(\"Shayanvsf/US_Airline_Sentiment\")\n", - " hf_dataset = hf_dataset.rename_column(\"airline_sentiment\", \"label\")\n", - " hf_dataset = hf_dataset.remove_columns(\n", - " [\"airline_sentiment_confidence\", \"negativereason_confidence\"]\n", - " )\n", - " return hf_dataset" + "# Fetch the rest of the artifacts\n", + "dataset_tst_artifact_version = client.get_artifact_version(\"dataset_tst\")\n", + "preprocessing_pipeline_artifact_version = client.get_artifact_version(\"preprocess_pipeline\")" ] }, { "cell_type": "markdown", - "id": "49e4462c-1e64-48d3-bae7-76696a958646", + "id": "7a7d1b04", "metadata": {}, "source": [ - "Notice that you can give each dataset a name with Python’s Annotated object. The DatasetDict is a native Huggingface dataset which ZenML knows how to persist through steps. This flow ensures reproducibility and version control for every dataset iteration.\n", - "\n", - "Also notice this is a simple Python function, that can be called with the `entrypoint` wrapper:" + "If you started with a fresh install, then you would have two versions corresponding\n", + "to the two pipelines that we ran above. We can even load a artifact version in memory: " ] }, { "cell_type": "code", - "execution_count": null, - "id": "18144a6b-c266-453d-82c8-b5d6aa1be0aa", + "execution_count": 85, + "id": "c82aca75", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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mean radiusmean texturemean perimetermean areamean smoothnessmean compactnessmean concavitymean concave pointsmean symmetrymean fractal dimension...worst textureworst perimeterworst areaworst smoothnessworst compactnessworst concavityworst concave pointsworst symmetryworst fractal dimensiontarget
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29912.43017.0078.60477.30.075570.034540.0134200.016990.14720.05561...20.2181.76515.90.084090.047120.022370.028320.19010.059321
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405 rows × 31 columns

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" + ], + "text/plain": [ + " mean radius mean texture mean perimeter mean area mean smoothness \\\n", + "444 12.040 28.14 76.85 449.9 0.08752 \n", + "478 9.676 13.14 64.12 272.5 0.12550 \n", + "210 10.440 15.46 66.62 329.6 0.10530 \n", + "299 12.430 17.00 78.60 477.3 0.07557 \n", + "513 14.470 24.99 95.81 656.4 0.08837 \n", + ".. ... ... ... ... ... \n", + "71 16.070 19.65 104.10 817.7 0.09168 \n", + "106 10.510 20.19 68.64 334.2 0.11220 \n", + "270 14.410 19.73 96.03 651.0 0.08757 \n", + "435 27.420 26.27 186.90 2501.0 0.10840 \n", + "102 22.270 19.67 152.80 1509.0 0.13260 \n", + "\n", + " mean compactness mean concavity mean concave points mean symmetry \\\n", + "444 0.06000 0.023670 0.02377 0.1854 \n", + "478 0.22040 0.118800 0.07038 0.2057 \n", + "210 0.07722 0.006643 0.01216 0.1788 \n", + "299 0.03454 0.013420 0.01699 0.1472 \n", + "513 0.12300 0.100900 0.03890 0.1872 \n", + ".. ... ... ... ... \n", + "71 0.08424 0.097690 0.06638 0.1798 \n", + "106 0.13030 0.064760 0.03068 0.1922 \n", + "270 0.16760 0.136200 0.06602 0.1714 \n", + "435 0.19880 0.363500 0.16890 0.2061 \n", + "102 0.27680 0.426400 0.18230 0.2556 \n", + "\n", + " mean fractal dimension ... worst texture worst perimeter worst area \\\n", + "444 0.05698 ... 33.33 87.24 567.6 \n", + "478 0.09575 ... 18.04 69.47 328.1 \n", + "210 0.06450 ... 19.80 73.47 395.4 \n", + "299 0.05561 ... 20.21 81.76 515.9 \n", + "513 0.06341 ... 31.73 113.50 808.9 \n", + ".. ... ... ... ... ... \n", + "71 0.05391 ... 24.56 128.80 1223.0 \n", + "106 0.07782 ... 22.75 72.62 374.4 \n", + "270 0.07192 ... 22.13 101.70 767.3 \n", + "435 0.05623 ... 31.37 251.20 4254.0 \n", + "102 0.07039 ... 28.01 206.80 2360.0 \n", + "\n", + " worst smoothness worst compactness worst concavity \\\n", + "444 0.10410 0.09726 0.05524 \n", + "478 0.20060 0.36630 0.29130 \n", + "210 0.13410 0.11530 0.02639 \n", + "299 0.08409 0.04712 0.02237 \n", + "513 0.13400 0.42020 0.40400 \n", + ".. ... ... ... \n", + "71 0.15000 0.20450 0.28290 \n", + "106 0.13000 0.20490 0.12950 \n", + "270 0.09983 0.24720 0.22200 \n", + "435 0.13570 0.42560 0.68330 \n", + "102 0.17010 0.69970 0.96080 \n", + "\n", + " worst concave points worst symmetry worst fractal dimension target \n", + "444 0.05547 0.2404 0.06639 1 \n", + "478 0.10750 0.2848 0.13640 1 \n", + "210 0.04464 0.2615 0.08269 1 \n", + "299 0.02832 0.1901 0.05932 1 \n", + "513 0.12050 0.3187 0.10230 1 \n", + ".. ... ... ... ... \n", + "71 0.15200 0.2650 0.06387 0 \n", + "106 0.06136 0.2383 0.09026 1 \n", + "270 0.10210 0.2272 0.08799 1 \n", + "435 0.26250 0.2641 0.07427 0 \n", + "102 0.29100 0.4055 0.09789 0 \n", + "\n", + "[405 rows x 31 columns]" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "hf_dataset = data_loader.entrypoint()\n", - "print(hf_dataset)" + "# Load an artifact to verify you can fetch it\n", + "dataset_trn_artifact_version.load()" ] }, { "cell_type": "markdown", - "id": "31330d3c-044f-4912-8d36-74146f48cecf", + "id": "5963509e", "metadata": {}, "source": [ - "Now we put this a full feature engineering pipeline. Each run of the feature engineering pipeline produces a new dataset to use for the training pipeline. ZenML versions this data as it flows through the pipeline.\n", - "\n", - "\"Pipelines" + "We'll use these artifacts from above in our next pipeline" ] }, { "cell_type": "markdown", - "id": "9511bd84-1e97-42db-9b75-06285cc6904c", + "id": "8c28b474", "metadata": {}, "source": [ - "### Set your stack" + "# ⌚ Step 2: Training pipeline" ] }, { - "cell_type": "code", - "execution_count": null, - "id": "76f3a7e7-0d85-43b3-9e9f-4c7f20ea65e6", + "cell_type": "markdown", + "id": "87909827", "metadata": {}, - "outputs": [], "source": [ - "!zenml stack describe hf-sagemaker-local" + "Now that we have our data it makes sense to train some models to get a sense of\n", + "how difficult the task is. The Breast Cancer dataset is sufficiently large and complex \n", + "that it's unlikely we'll be able to train a model that behaves perfectly since the problem \n", + "is inherently complex, but we can get a sense of what a reasonable baseline looks like.\n", + "\n", + "We'll start with two simple models, a SGD Classifier and a Random Forest\n", + "Classifier, both batteries-included from `sklearn`. We'll train them both on the\n", + "same data and then compare their performance.\n", + "\n", + "\"Training" ] }, { "cell_type": "code", - "execution_count": null, - "id": "04b0bf69-70c6-4408-b18c-95df9e030c0c", + "execution_count": 86, + "id": "fccf1bd9", "metadata": {}, "outputs": [], "source": [ - "!zenml stack set hf-sagemaker-local" + "import pandas as pd\n", + "from sklearn.base import ClassifierMixin\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.linear_model import SGDClassifier\n", + "from typing_extensions import Annotated\n", + "from zenml import ArtifactConfig, step\n", + "from zenml.logger import get_logger\n", + "\n", + "logger = get_logger(__name__)\n", + "\n", + "\n", + "@step\n", + "def model_trainer(\n", + " dataset_trn: pd.DataFrame,\n", + " model_type: str = \"sgd\",\n", + ") -> Annotated[\n", + " ClassifierMixin,\n", + " ArtifactConfig(name=\"sklearn_classifier\", is_model_artifact=True),\n", + "]:\n", + " \"\"\"Configure and train a model on the training dataset.\"\"\"\n", + " target = \"target\"\n", + " if model_type == \"sgd\":\n", + " model = SGDClassifier()\n", + " elif model_type == \"rf\":\n", + " model = RandomForestClassifier()\n", + " else:\n", + " raise ValueError(f\"Unknown model type {model_type}\")\n", + "\n", + " logger.info(f\"Training model {model}...\")\n", + "\n", + " model.fit(\n", + " dataset_trn.drop(columns=[target]),\n", + " dataset_trn[target],\n", + " )\n", + " return model" ] }, { - "cell_type": "code", - "execution_count": null, - "id": "de5398a4-a9ec-42d6-bbd6-390244c52d13", + "cell_type": "markdown", + "id": "73a00008", "metadata": {}, - "outputs": [], "source": [ - "!zenml stack get" + "Our two training steps both return different kinds of `sklearn` classifier\n", + "models, so we use the generic `ClassifierMixin` type hint for the return type." ] }, { "cell_type": "markdown", - "id": "152f718d-70c2-4a29-a73e-37db85675cb8", + "id": "a5f22174", "metadata": {}, "source": [ - "### Run the pipeline" + "ZenML allows you to load any version of any dataset that is tracked by the framework\n", + "directly into a pipeline using the `Client().get_artifact_version` interface. This is very convenient\n", + "in this case, as we'd like to send our preprocessed dataset from the older pipeline directly\n", + "into the training pipeline." ] }, { "cell_type": "code", - "execution_count": null, - "id": "7ca6c41e-e4b3-46d2-8264-9a453ac9aa3c", - "metadata": { - "scrolled": true - }, + "execution_count": 101, + "id": "1aa98f2f", + "metadata": {}, "outputs": [], "source": [ - "@pipeline(on_failure=notify_on_failure)\n", - "def sentinment_analysis_feature_engineering_pipeline(\n", - " lower_case: Optional[bool] = True,\n", - " padding: Optional[str] = \"max_length\",\n", - " max_seq_length: Optional[int] = 128,\n", - " text_column: Optional[str] = \"text\",\n", - " label_column: Optional[str] = \"label\",\n", - "):\n", - " # Link all the steps together by calling them and passing the output\n", - " # of one step as the input of the next step.\n", + "from zenml import ExternalArtifact\n", "\n", - " ########## Load Dataset stage ##########\n", - " dataset = data_loader()\n", + "@pipeline\n", + "def training(\n", + " train_dataset_id: Optional[UUID] = None,\n", + " test_dataset_id: Optional[UUID] = None,\n", + " model_type: str = \"sgd\",\n", + " min_train_accuracy: float = 0.0,\n", + " min_test_accuracy: float = 0.0,\n", + "):\n", + " \"\"\"Model training pipeline.\"\"\" \n", + " if train_dataset_id is None or test_dataset_id is None:\n", + " # If we dont pass the IDs, this will run the feature engineering pipeline \n", + " dataset_trn, dataset_tst = feature_engineering()\n", + " else:\n", + " # Load the datasets from an older pipeline\n", + " dataset_trn = ExternalArtifact(id=train_dataset_id)\n", + " dataset_tst = ExternalArtifact(id=test_dataset_id)\n", + " # dataset_trn = client.get_artifact_version(name_id_or_prefix=train_dataset_id)\n", + " # dataset_tst = client.get_artifact_version(name_id_or_prefix=test_dataset_id) \n", "\n", - " ########## Data Quality stage ##########\n", - " reference_dataset, comparison_dataset = generate_reference_and_comparison_datasets(\n", - " dataset\n", - " )\n", - " text_data_report = evidently_report_step.with_options(\n", - " parameters=dict(\n", - " column_mapping=EvidentlyColumnMapping(\n", - " target=\"label\",\n", - " text_features=[\"text\"],\n", - " ),\n", - " metrics=[\n", - " EvidentlyMetricConfig.metric(\"DataQualityPreset\"),\n", - " EvidentlyMetricConfig.metric(\n", - " \"TextOverviewPreset\", column_name=\"text\"\n", - " ),\n", - " ],\n", - " # We need to download the NLTK data for the TextOverviewPreset\n", - " download_nltk_data=True,\n", - " ),\n", - " )\n", - " text_data_report(reference_dataset, comparison_dataset)\n", - "\n", - " ########## Tokenization stage ##########\n", - " tokenizer = tokenizer_loader(lower_case=lower_case)\n", - " tokenized_data = tokenization_step(\n", - " dataset=dataset,\n", - " tokenizer=tokenizer,\n", - " padding=padding,\n", - " max_seq_length=max_seq_length,\n", - " text_column=text_column,\n", - " label_column=label_column,\n", + " trained_model = model_trainer(\n", + " dataset_trn=dataset_trn,\n", + " model_type=model_type,\n", " )\n", - " return tokenizer, tokenized_data" + "\n", + " model_evaluator(\n", + " model=trained_model,\n", + " dataset_trn=dataset_trn,\n", + " dataset_tst=dataset_tst,\n", + " min_train_accuracy=min_train_accuracy,\n", + " min_test_accuracy=min_test_accuracy,\n", + " )" ] }, { - "cell_type": "code", - "execution_count": null, - "id": "3c8a5be7-ebaa-41c4-ac23-4afc6e7e06aa", + "cell_type": "markdown", + "id": "88b70fd3", "metadata": {}, - "outputs": [], "source": [ - "# Run a pipeline with the required parameters. \n", - "no_cache: bool = True\n", - "zenml_model_name: str = \"distil_bert_sentiment_analysis\"\n", - "max_seq_length = 512\n", - "\n", - "# This executes all steps in the pipeline in the correct order using the orchestrator\n", - "# stack component that is configured in your active ZenML stack.\n", - "zenml_model = Model(\n", - " name=zenml_model_name,\n", - " license=\"Apache 2.0\",\n", - " description=\"Show case Model Control Plane.\",\n", - " tags=[\"sentiment_analysis\", \"huggingface\"],\n", - ")\n", - "\n", - "pipeline_args = {}\n", - "\n", - "if no_cache:\n", - " pipeline_args[\"enable_cache\"] = False\n", - "\n", - "# Execute Feature Engineering Pipeline\n", - "pipeline_args[\"model\"] = zenml_model\n", - "pipeline_args[\"config_path\"] = os.path.join(\"configs\", \"feature_engineering_config.yaml\")\n", - "run_args_feature = {\n", - " \"max_seq_length\": max_seq_length,\n", - "}\n", - "pipeline_args[\n", - " \"run_name\"\n", - "] = f\"sentinment_analysis_feature_engineering_pipeline_run_{dt.now().strftime('%Y_%m_%d_%H_%M_%S')}\"\n", - "p = sentinment_analysis_feature_engineering_pipeline.with_options(**pipeline_args)\n", - "p(**run_args_feature)" + "The end goal of this quick baseline evaluation is to understand which of the two\n", + "models performs better. We'll use the `evaluator` step to compare the two\n", + "models. This step takes in the model from the trainer step, and computes its score\n", + "over the testing set." ] }, { "cell_type": "code", - "execution_count": null, - "id": "0e7c1ea2-64fe-478a-9963-17c7b7f62110", + "execution_count": 102, + "id": "c64885ac", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 94)\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mExecuting a new run.\u001b[0m\n", + "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mandrei@zenml.io\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mlocal-mlflow-stack\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m model_registry: \u001b[0m\u001b[1;36mmlflow\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m model_deployer: \u001b[0m\u001b[1;36mmlflow\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m experiment_tracker: \u001b[0m\u001b[1;36mmlflow\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mTraining model RandomForestClassifier()...\u001b[0m\n", + "\u001b[1;35mTraining model RandomForestClassifier()...\u001b[0m\n", + "\u001b[1;35mTraining model RandomForestClassifier()...\u001b[0m\n", + "\u001b[1;35mTraining model RandomForestClassifier()...\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.993s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mTrain accuracy=100.00%\u001b[0m\n", + "\u001b[1;35mTest accuracy=90.44%\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has finished in \u001b[0m\u001b[1;36m1.154s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mPipeline run has finished in \u001b[0m\u001b[1;36m4.104s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/d7de5d44-e92c-4da8-b716-3ee8e1851698/runs/a039c329-b39f-44fc-98bc-58510f82c09d/dag\u001b[0m\n" + ] + } + ], "source": [ - "from zenml.client import Client\n", - "from IPython.display import display, HTML\n", + "# Use a random forest model with the chosen datasets.\n", + "# We need to pass the ID's of the datasets into the function\n", + "training(\n", + " model_type=\"rf\",\n", + " train_dataset_id=dataset_trn_artifact_version.id,\n", + " test_dataset_id=dataset_tst_artifact_version.id\n", + ")\n", "\n", - "client = Client()\n", - "# CHANGE THIS TO THE LATEST RUN ID\n", - "latest_run = client.get_pipeline_run(\"sentinment_analysis_feature_engineering_pipeline_run_2023_11_21_10_55_56\")\n", - "html = latest_run.steps[\"evidently_report_step\"].outputs['report_html'].load()\n", - "display(HTML(html))" + "rf_run = client.get_pipeline(\"training\").last_run" ] }, { - "cell_type": "markdown", - "id": "78ab8771-4421-4975-a3d5-12892a56b805", + "cell_type": "code", + "execution_count": 103, + "id": "4300c82f", "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 95)\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mExecuting a new run.\u001b[0m\n", + "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mandrei@zenml.io\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mlocal-mlflow-stack\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m model_registry: \u001b[0m\u001b[1;36mmlflow\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m model_deployer: \u001b[0m\u001b[1;36mmlflow\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m experiment_tracker: \u001b[0m\u001b[1;36mmlflow\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mTraining model SGDClassifier()...\u001b[0m\n", + "\u001b[1;35mTraining model SGDClassifier()...\u001b[0m\n", + "\u001b[1;35mTraining model SGDClassifier()...\u001b[0m\n", + "\u001b[1;35mTraining model SGDClassifier()...\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.880s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mTrain accuracy=91.60%\u001b[0m\n", + "\u001b[1;35mTest accuracy=90.44%\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has finished in \u001b[0m\u001b[1;36m1.159s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mPipeline run has finished in \u001b[0m\u001b[1;36m3.931s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/9d131977-eb26-44c5-90d6-bb6a5f0b9f43/runs/d3f30c80-3813-498b-ba6f-d5df041cfa8f/dag\u001b[0m\n" + ] + } + ], "source": [ - "## 💪 Step 2: Train the model with Huggingface Hub as the model registry\n", - " " + "# Use a SGD classifier\n", + "sgd_run = training(\n", + " model_type=\"sgd\",\n", + " train_dataset_id=dataset_trn_artifact_version.id,\n", + " test_dataset_id=dataset_tst_artifact_version.id\n", + ")\n", + "\n", + "sgd_run = client.get_pipeline(\"training\").last_run" ] }, { "cell_type": "markdown", - "id": "2843efa8-32b6-4b13-ac85-33c99cc94e3e", + "id": "43f1a68a", "metadata": {}, "source": [ - "Once the feature engineering pipeline has run a few times, we have many datasets to choose from. We can feed our desired one into a function that trains the model on the data. Thanks to the ZenML Huggingface integration, this data is loaded directly from the ZenML artifact store.\n", - "\n", - "\"Pipelines\n", - "\n", - "On the left side, we see our local MLOps stack, which defines our infrastructure and tooling we are using for this particular pipeline. ZenML makes it easy to run on a local stack on your development machine, or switch out the stack to run on a AWS Kubeflow-based stack (if you want to scale up).\n", - "\n", - "On the right side is the new kid on the block - the ZenML Model Control Plane. The Model Control Plane is a new feature in ZenML that allows users to have a complete overview of their machine learning models. It allows teams to consolidate all artifacts related to their ML models into one place, and manage its lifecycle easily as you can see from this view from the ZenML Cloud:" + "You can see from the logs already how our model training went: the\n", + "`RandomForestClassifier` performed considerably better than the `SGDClassifier`.\n", + "We can use the ZenML `Client` to verify this:" ] }, { "cell_type": "code", - "execution_count": null, - "id": "4c99b20f-8e3b-4119-86e9-33dd1395470a", + "execution_count": 105, + "id": "d95810b1", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 105, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "pipeline_args[\"config_path\"] = os.path.join(\"configs\", \"trainer_config.yaml\")\n", - "\n", - "pipeline_args[\"enable_cache\"] = True\n", - "\n", - "run_args_train = {\n", - " \"num_epochs\": 1,\n", - " \"train_batch_size\": 64,\n", - " \"eval_batch_size\": 64,\n", - " \"learning_rate\": 2e-4,\n", - " \"weight_decay\": 0.01,\n", - " \"max_seq_length\": 512,\n", - "}\n", - "\n", - "# Use versioned artifacts from the last step\n", - "# run_args_train[\"dataset_artifact_id\"] = latest_run.steps['tokenization_step'].output.id\n", - "# run_args_train[\"tokenizer_artifact_id\"] = latest_run.steps['tokenizer_loader'].output.id\n", - "\n", - "# Configure the model\n", - "pipeline_args[\"model\"] = zenml_model\n", - "\n", - "pipeline_args[\n", - " \"run_name\"\n", - "] = f\"sentinment_analysis_training_run_{dt.now().strftime('%Y_%m_%d_%H_%M_%S')}\"" + "# The evaluator returns a float value with the accuracy\n", + "rf_run.steps[\"model_evaluator\"].output.load() >= sgd_run.steps[\"model_evaluator\"].output.load()" ] }, { - "cell_type": "code", - "execution_count": null, - "id": "96592299-0090-4d2a-962e-6ca232c1fb75", + "cell_type": "markdown", + "id": "e256d145", "metadata": {}, - "outputs": [], "source": [ - "sentinment_analysis_training_pipeline.with_options(**pipeline_args)(\n", - " **run_args_train\n", - ")" + "# 💯 Step 3: Associating a model with your pipeline" ] }, { - "cell_type": "code", - "execution_count": null, - "id": "e24e29de-6d1b-41da-9ab2-ca2b32f1f540", + "cell_type": "markdown", + "id": "927978f3", "metadata": {}, - "outputs": [], "source": [ - "### Check out a new stack\n", - "!zenml stack describe hf-sagemaker-airflow" + "You can see it is relatively easy to train ML models using ZenML pipelines. But it can be somewhat clunky to track\n", + "all the models produced as you develop your experiments and use-cases. Luckily, ZenML offers a *Model Control Plane*,\n", + "which is a central register of all your ML models.\n", + "\n", + "You can easily create a ZenML Model and associate it with your pipelines using the `Model` object:" ] }, { "cell_type": "code", - "execution_count": null, - "id": "7c9a5bee-8465-4d41-888a-093f1f6a2ef1", + "execution_count": 110, + "id": "99ca00c0", "metadata": {}, "outputs": [], "source": [ - "### Change the stack\n", - "!zenml stack set hf-sagemaker-airflow" + "pipeline_settings = {}\n", + "\n", + "# Lets add some metadata to the model to make it identifiable\n", + "pipeline_settings[\"model\"] = Model(\n", + " name=\"breast_cancer_classifier\",\n", + " license=\"Apache 2.0\",\n", + " description=\"A breast cancer classifier\",\n", + ")" ] }, { "cell_type": "code", - "execution_count": null, - "id": "d3772c50-1c90-4ffc-8394-c9cfca16cc53", + "execution_count": 111, + "id": "0e78a520", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mReusing registered pipeline version: \u001b[0m\u001b[1;36m(version: 95)\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mNew model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[1;35m was created implicitly.\u001b[0m\n", + "\u001b[1;35mNew model version \u001b[0m\u001b[1;36m1\u001b[1;35m was created.\u001b[0m\n", + "\u001b[1;35mExecuting a new run.\u001b[0m\n", + "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mandrei@zenml.io\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mlocal-mlflow-stack\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m model_registry: \u001b[0m\u001b[1;36mmlflow\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m model_deployer: \u001b[0m\u001b[1;36mmlflow\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m experiment_tracker: \u001b[0m\u001b[1;36mmlflow\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mPipeline run has finished in \u001b[0m\u001b[1;36m3.267s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/9d131977-eb26-44c5-90d6-bb6a5f0b9f43/runs/a93c0c85-ef7e-4f11-8726-6b66bebe56d2/dag\u001b[0m\n" + ] + }, + { + "data": { + "text/plain": [ + "PipelineRunResponse(id=UUID('a93c0c85-ef7e-4f11-8726-6b66bebe56d2'), permission_denied=False, body=PipelineRunResponseBody(created=datetime.datetime(2024, 2, 8, 11, 24, 53), updated=datetime.datetime(2024, 2, 8, 11, 24, 53), user=UserResponse(id=UUID('c915043d-706c-4885-9263-783cae8dad20'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 11, 16, 16, 5, 33), updated=datetime.datetime(2024, 2, 8, 10, 24, 32), active=True, activation_token=None, full_name='Andrei', email_opted_in=True, is_service_account=False), metadata=None, name='andrei@zenml.io'), status=, stack=StackResponse(id=UUID('57f7dcf2-4bd5-439b-b454-da4000dd3bc8'), permission_denied=False, body=StackResponseBody(created=datetime.datetime(2023, 12, 11, 15, 24, 51), updated=datetime.datetime(2023, 12, 21, 1, 15, 28), user=UserResponse(id=UUID('ac044541-bcb1-4f33-b41e-5c60a3f39485'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 10, 23, 15, 36, 12), updated=datetime.datetime(2024, 2, 7, 9, 57, 48), active=True, activation_token=None, full_name='Alexej Penner', email_opted_in=True, is_service_account=False), metadata=None, name='alexej@zenml.io')), metadata=None, name='local-mlflow-stack'), pipeline=PipelineResponse(id=UUID('9d131977-eb26-44c5-90d6-bb6a5f0b9f43'), permission_denied=False, body=PipelineResponseBody(created=datetime.datetime(2024, 2, 8, 11, 21, 10), updated=datetime.datetime(2024, 2, 8, 11, 21, 10), user=UserResponse(id=UUID('c915043d-706c-4885-9263-783cae8dad20'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 11, 16, 16, 5, 33), updated=datetime.datetime(2024, 2, 8, 10, 24, 32), active=True, activation_token=None, full_name='Andrei', email_opted_in=True, is_service_account=False), metadata=None, name='andrei@zenml.io'), status=[, , ], version='95'), metadata=None, name='training'), build=None, schedule=None, code_reference=None), metadata=PipelineRunResponseMetadata(workspace=WorkspaceResponse(id=UUID('f3a544f2-afb5-4672-934a-7a465c66201c'), permission_denied=False, body=WorkspaceResponseBody(created=datetime.datetime(2023, 10, 23, 15, 34, 47), updated=datetime.datetime(2023, 10, 23, 15, 34, 47)), metadata=None, name='default'), run_metadata={}, steps={}, config=PipelineConfiguration(enable_cache=None, enable_artifact_metadata=None, enable_artifact_visualization=None, enable_step_logs=None, settings={'experiment_tracker.mlflow': BaseSettings()}, extra={}, failure_hook_source=None, success_hook_source=None, model=Model(name='breast_cancer_classifier', license='Apache 2.0', description='A breast cancer classifier', audience=None, use_cases=None, limitations=None, trade_offs=None, ethics=None, tags=['classifier', 'breast_cancer', 'sgd'], version='1', save_models_to_registry=True, suppress_class_validation_warnings=True, was_created_in_this_run=True), parameters=None, name='training'), start_time=datetime.datetime(2024, 2, 8, 11, 24, 53), end_time=None, client_environment={'environment': 'notebook', 'os': 'mac', 'mac_version': '14.0', 'python_version': '3.11.6'}, orchestrator_environment={}, orchestrator_run_id=None), name='training-2024_02_08-11_24_52_676745')" + ] + }, + "execution_count": 111, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "sentinment_analysis_training_pipeline.with_options(**pipeline_args)(\n", - " **run_args_train\n", + "# Let's train the SGD model and tag the version name with \"sgd\"\n", + "pipeline_settings[\"model\"].tags = [\"breast_cancer\", \"classifier\", \"sgd\"]\n", + "\n", + "# the `with_options` method allows us to pass in pipeline settings\n", + "# and returns a configured pipeline\n", + "training_configured = training.with_options(**pipeline_settings)\n", + "\n", + "# We can now run this as usual\n", + "training_configured(\n", + " model_type=\"sgd\",\n", + " train_dataset_id=dataset_trn_artifact_version.id,\n", + " test_dataset_id=dataset_tst_artifact_version.id\n", ")" ] }, { - "cell_type": "markdown", - "id": "be79f454-a45d-4f5f-aa93-330d52069124", + "cell_type": "code", + "execution_count": 112, + "id": "9b8e0002", "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mReusing registered pipeline version: \u001b[0m\u001b[1;36m(version: 94)\u001b[1;35m.\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[1;35mNew model version \u001b[0m\u001b[1;36m2\u001b[1;35m was created.\u001b[0m\n", + "\u001b[1;35mExecuting a new run.\u001b[0m\n", + "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mandrei@zenml.io\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mlocal-mlflow-stack\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m model_registry: \u001b[0m\u001b[1;36mmlflow\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m model_deployer: \u001b[0m\u001b[1;36mmlflow\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m experiment_tracker: \u001b[0m\u001b[1;36mmlflow\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m.\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m.\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mPipeline run has finished in \u001b[0m\u001b[1;36m3.737s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/d7de5d44-e92c-4da8-b716-3ee8e1851698/runs/4adb298a-d725-4a8b-a79f-fe89b96898d9/dag\u001b[0m\n" + ] + }, + { + "data": { + "text/plain": [ + "PipelineRunResponse(id=UUID('4adb298a-d725-4a8b-a79f-fe89b96898d9'), permission_denied=False, body=PipelineRunResponseBody(created=datetime.datetime(2024, 2, 8, 11, 24, 59), updated=datetime.datetime(2024, 2, 8, 11, 24, 59), user=UserResponse(id=UUID('c915043d-706c-4885-9263-783cae8dad20'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 11, 16, 16, 5, 33), updated=datetime.datetime(2024, 2, 8, 10, 24, 32), active=True, activation_token=None, full_name='Andrei', email_opted_in=True, is_service_account=False), metadata=None, name='andrei@zenml.io'), status=, stack=StackResponse(id=UUID('57f7dcf2-4bd5-439b-b454-da4000dd3bc8'), permission_denied=False, body=StackResponseBody(created=datetime.datetime(2023, 12, 11, 15, 24, 51), updated=datetime.datetime(2023, 12, 21, 1, 15, 28), user=UserResponse(id=UUID('ac044541-bcb1-4f33-b41e-5c60a3f39485'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 10, 23, 15, 36, 12), updated=datetime.datetime(2024, 2, 7, 9, 57, 48), active=True, activation_token=None, full_name='Alexej Penner', email_opted_in=True, is_service_account=False), metadata=None, name='alexej@zenml.io')), metadata=None, name='local-mlflow-stack'), pipeline=PipelineResponse(id=UUID('d7de5d44-e92c-4da8-b716-3ee8e1851698'), permission_denied=False, body=PipelineResponseBody(created=datetime.datetime(2024, 2, 8, 11, 21), updated=datetime.datetime(2024, 2, 8, 11, 21), user=UserResponse(id=UUID('c915043d-706c-4885-9263-783cae8dad20'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 11, 16, 16, 5, 33), updated=datetime.datetime(2024, 2, 8, 10, 24, 32), active=True, activation_token=None, full_name='Andrei', email_opted_in=True, is_service_account=False), metadata=None, name='andrei@zenml.io'), status=[, , ], version='94'), metadata=None, name='training'), build=None, schedule=None, code_reference=None), metadata=PipelineRunResponseMetadata(workspace=WorkspaceResponse(id=UUID('f3a544f2-afb5-4672-934a-7a465c66201c'), permission_denied=False, body=WorkspaceResponseBody(created=datetime.datetime(2023, 10, 23, 15, 34, 47), updated=datetime.datetime(2023, 10, 23, 15, 34, 47)), metadata=None, name='default'), run_metadata={}, steps={}, config=PipelineConfiguration(enable_cache=None, enable_artifact_metadata=None, enable_artifact_visualization=None, enable_step_logs=None, settings={'experiment_tracker.mlflow': BaseSettings()}, extra={}, failure_hook_source=None, success_hook_source=None, model=Model(name='breast_cancer_classifier', license='Apache 2.0', description='A breast cancer classifier', audience=None, use_cases=None, limitations=None, trade_offs=None, ethics=None, tags=['rf', 'classifier', 'breast_cancer'], version='2', save_models_to_registry=True, suppress_class_validation_warnings=True, was_created_in_this_run=True), parameters=None, name='training'), start_time=datetime.datetime(2024, 2, 8, 11, 24, 59), end_time=None, client_environment={'environment': 'notebook', 'os': 'mac', 'mac_version': '14.0', 'python_version': '3.11.6'}, orchestrator_environment={}, orchestrator_run_id=None), name='training-2024_02_08-11_24_58_707852')" + ] + }, + "execution_count": 112, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "## 🫅 Step 3: Promote the model to production\n" + "# Let's train the RF model and tag the version name with \"rf\"\n", + "pipeline_settings[\"model\"].tags = [\"breast_cancer\", \"classifier\", \"rf\"]\n", + "\n", + "# the `with_options` method allows us to pass in pipeline settings\n", + "# and returns a configured pipeline\n", + "training_configured = training.with_options(**pipeline_settings)\n", + "\n", + "# Let's run it again to make sure we have two versions\n", + "training_configured(\n", + " model_type=\"rf\",\n", + " train_dataset_id=dataset_trn_artifact_version.id,\n", + " test_dataset_id=dataset_tst_artifact_version.id\n", + ")" ] }, { "cell_type": "markdown", - "id": "5a09b432-7a66-473e-bdb6-ffdca730498b", + "id": "09597223", "metadata": {}, "source": [ - "Following training, the automated promotion pipeline evaluates models against predefined metrics, identifying and marking the most performant one as 'Production ready'. This is another common use case for the Model Control Plane; we store the relevant metrics there to access them easily later.\n", - "\n", - "\"Pipelines" + "This time, running both pipelines has created two associated **model versions**.\n", + "You can list your ZenML model and their versions as follows:" ] }, { "cell_type": "code", - "execution_count": null, - "id": "5bac7ae5-70d0-449c-929c-e175c3062f2d", + "execution_count": 113, + "id": "fbb25913", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "id=UUID('cb1ac539-bac3-45ca-8837-c6bc0942e4e8') permission_denied=False body=ModelResponseBody(created=datetime.datetime(2024, 2, 8, 11, 24, 51), updated=datetime.datetime(2024, 2, 8, 11, 24, 51), user=UserResponse(id=UUID('c915043d-706c-4885-9263-783cae8dad20'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 11, 16, 16, 5, 33), updated=datetime.datetime(2024, 2, 8, 10, 24, 32), active=True, activation_token=None, full_name='Andrei', email_opted_in=True, is_service_account=False), metadata=None, name='andrei@zenml.io'), tags=[TagResponse(id=UUID('dad60bad-aeda-42fc-8108-a49e0b451b34'), permission_denied=False, body=TagResponseBody(created=datetime.datetime(2023, 12, 12, 15, 8, 29), updated=datetime.datetime(2023, 12, 12, 15, 8, 29), color=, tagged_count=3), metadata=None, name='classifier'), TagResponse(id=UUID('12c4c193-5bc8-4ef7-bedc-0b4e2732bc54'), permission_denied=False, body=TagResponseBody(created=datetime.datetime(2024, 2, 8, 11, 21, 57), updated=datetime.datetime(2024, 2, 8, 11, 21, 57), color=, tagged_count=2), metadata=None, name='sgd'), TagResponse(id=UUID('469b1284-df0a-4fee-b247-d80915501fb8'), permission_denied=False, body=TagResponseBody(created=datetime.datetime(2023, 12, 12, 15, 8, 29), updated=datetime.datetime(2023, 12, 12, 15, 8, 29), color=, tagged_count=3), metadata=None, name='breast_cancer')], latest_version_name='2', latest_version_id=UUID('67259714-9ff5-4047-906a-a5bbe8f497a1'), workspace={'id': 'f3a544f2-afb5-4672-934a-7a465c66201c', 'permission_denied': False, 'body': {'created': '2023-10-23T15:34:47', 'updated': '2023-10-23T15:34:47'}, 'metadata': None, 'name': 'default'}) metadata=ModelResponseMetadata(workspace=WorkspaceResponse(id=UUID('f3a544f2-afb5-4672-934a-7a465c66201c'), permission_denied=False, body=WorkspaceResponseBody(created=datetime.datetime(2023, 10, 23, 15, 34, 47), updated=datetime.datetime(2023, 10, 23, 15, 34, 47)), metadata=None, name='default'), license='Apache 2.0', description='A breast cancer classifier', audience=None, use_cases=None, limitations=None, trade_offs=None, ethics=None, save_models_to_registry=True) name='breast_cancer_classifier'\n", + "Model breast_cancer_classifier has 2 versions\n" + ] + }, + { + "data": { + "text/plain": [ + "('1', '2')" + ] + }, + "execution_count": 113, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "!zenml stack set hf-sagemaker-local" + "zenml_model = client.get_model(\"breast_cancer_classifier\")\n", + "print(zenml_model)\n", + "\n", + "versions = zenml_model.versions\n", + "\n", + "print(f\"Model {zenml_model.name} has {len(versions)} versions\")\n", + "\n", + "versions[-2].version, versions[-1].version" ] }, { - "cell_type": "code", - "execution_count": null, - "id": "170c9ef6-4e6f-4e50-ac37-e05bef8570ea", + "cell_type": "markdown", + "id": "e82cfac2", "metadata": {}, - "outputs": [], "source": [ - "run_args_promoting = {}\n", - "zenml_model = Model(name=zenml_model_name)\n", - "pipeline_args[\"config_path\"] = os.path.join(\"configs\", \"promoting_config.yaml\")\n", - "\n", - "pipeline_args[\"model\"] = zenml_model\n", - "\n", - "pipeline_args[\n", - " \"run_name\"\n", - "] = f\"sentinment_analysis_promoting_pipeline_run_{dt.now().strftime('%Y_%m_%d_%H_%M_%S')}\"" + "The interesting part is that ZenML went ahead and linked all artifacts produced by the\n", + "pipelines to that model version, including the two pickle files that represent our\n", + "SGD and RandomForest classifier. We can see all artifacts directly from the model\n", + "version object:" ] }, { "cell_type": "code", - "execution_count": null, - "id": "e6df11e2-4591-4186-a8f8-243f9c4d1e3d", + "execution_count": 114, + "id": "31211413", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
RandomForestClassifier()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "RandomForestClassifier()" + ] + }, + "execution_count": 114, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "sentinment_analysis_promote_pipeline.with_options(**pipeline_args)(\n", - " **run_args_promoting\n", - ")" + "# Let's load the RF version\n", + "rf_zenml_model_version = client.list_model_versions(\"breast_cancer_classifier\", tag=\"rf\")[-1]\n", + "\n", + "# We can now load our classifier directly as well\n", + "random_forest_classifier = rf_zenml_model_version.get_artifact(\"sklearn_classifier\").load()\n", + "\n", + "random_forest_classifier" ] }, { "cell_type": "markdown", - "id": "6efc4968-35fd-42e3-ba62-d8e1557aa0d6", + "id": "53517a9a", "metadata": {}, "source": [ - "## 💯 Step 4: Deploy the model to AWS Sagemaker Endpoints\n" + "If you are a [ZenML Cloud](https://zenml.io/cloud) user, you can see all of this visualized in the dashboard:\n", + "\n", + "\"Model" ] }, { "cell_type": "markdown", - "id": "577aff86-bde9-48d4-9b52-209cfed9fd4e", + "id": "eb645dde", "metadata": {}, "source": [ - "This is the final step to automate the deployment of the slated production model to a Sagemaker endpoint. The deployment pipelines handles the complexities of AWS interactions and ensures that the model, along with its full history and context, is transitioned into a live environment ready for use. Here again we use the Model Control Plane interface to query the Huggingface revision and use that information to push to Huggingface Hub.\n", + "There is a lot more you can do with ZenML models, including the ability to\n", + "track metrics by adding metadata to it, or having them persist in a model\n", + "registry. However, these topics can be explored more in the\n", + "[ZenML docs](https://docs.zenml.io).\n", "\n", - "\"Pipelines\n" + "For now, we will use the ZenML model control plane to promote our best\n", + "model to `production`. You can do this by simply setting the `stage` of\n", + "your chosen model version to the `production` tag." ] }, { "cell_type": "code", - "execution_count": null, - "id": "1513ab5f-de05-4344-9d2c-fedbfbd21ef0", + "execution_count": 115, + "id": "26b718f8", "metadata": {}, "outputs": [], "source": [ - "!zenml stack set hf-sagemaker-local" + "# Set our best classifier to production\n", + "rf_zenml_model_version.set_stage(\"production\", force=True)" ] }, { - "cell_type": "code", - "execution_count": null, - "id": "606fdb3c-4eca-4d32-bccb-280743d15528", + "cell_type": "markdown", + "id": "9fddf3d0", "metadata": {}, - "outputs": [], "source": [ - "pipeline_args[\"config_path\"] = os.path.join(\"configs\", \"deploying_config.yaml\")\n", - "\n", - "# Deploying pipeline has new ZenML model config\n", - "zenml_model = Model(\n", - " name=zenml_model_name,\n", - " version=\"production\",\n", - ")\n", - "pipeline_args[\"model\"] = zenml_model\n", - "pipeline_args[\"enable_cache\"] = False\n", - "run_args_deploying = {}\n", - "pipeline_args[\n", - " \"run_name\"\n", - "] = f\"sentinment_analysis_deploy_pipeline_run_{dt.now().strftime('%Y_%m_%d_%H_%M_%S')}\"" + "Of course, normally one would only promote the model by comparing to all other model\n", + "versions and doing some other tests. But that's a bit more advanced use-case. See the\n", + "[e2e_batch example](https://github.com/zenml-io/zenml/tree/main/examples/e2e) to get\n", + "more insight into that sort of flow!" ] }, { - "cell_type": "code", - "execution_count": null, - "id": "87f1f982-ab96-4207-8e7e-e318473587e9", + "cell_type": "markdown", + "id": "2ecbc8cf", "metadata": {}, - "outputs": [], "source": [ - "sentinment_analysis_deploy_pipeline.with_options(**pipeline_args)(\n", - " **run_args_deploying\n", - ")" + "\"Model" + ] + }, + { + "cell_type": "markdown", + "id": "8f1146db", + "metadata": {}, + "source": [ + "Once the model is promoted, we can now consume the right model version in our\n", + "batch inference pipeline directly. Let's see how that works." + ] + }, + { + "cell_type": "markdown", + "id": "d6306f14", + "metadata": {}, + "source": [ + "# 🫅 Step 4: Consuming the model in production" + ] + }, + { + "cell_type": "markdown", + "id": "b51f3108", + "metadata": {}, + "source": [ + "The batch inference pipeline simply takes the model marked as `production` and runs inference on it\n", + "with `live data`. The critical step here is the `inference_predict` step, where we load the model in memory\n", + "and generate predictions:\n", + "\n", + "\"Inference" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "id": "92c4c7dc", + "metadata": {}, + "outputs": [], + "source": [ + "@step\n", + "def inference_predict(dataset_inf: pd.DataFrame) -> Annotated[pd.Series, \"predictions\"]:\n", + " \"\"\"Predictions step\"\"\"\n", + " # Get the model\n", + " model = get_step_context().model\n", + "\n", + " # run prediction from memory\n", + " predictor = model.load_artifact(\"sklearn_classifier\")\n", + " predictions = predictor.predict(dataset_inf)\n", + "\n", + " predictions = pd.Series(predictions, name=\"predicted\")\n", + "\n", + " return predictions\n" + ] + }, + { + "cell_type": "markdown", + "id": "3aeb227b", + "metadata": {}, + "source": [ + "Apart from the loading the model, we must also load the preprocessing pipeline that we ran in feature engineering,\n", + "so that we can do the exact steps that we did on training time, in inference time. Let's bring it all together:" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "id": "37c409bd", + "metadata": {}, + "outputs": [], + "source": [ + "@pipeline\n", + "def inference(preprocess_pipeline_id: UUID):\n", + " \"\"\"Model batch inference pipeline\"\"\"\n", + " # random_state = client.get_artifact_version(name_id_or_prefix=preprocess_pipeline_id).metadata[\"random_state\"].value\n", + " # target = client.get_artifact_version(name_id_or_prefix=preprocess_pipeline_id).run_metadata['target'].value\n", + " random_state = 42\n", + " target = \"target\"\n", + "\n", + " df_inference = data_loader(\n", + " random_state=random_state, is_inference=True\n", + " )\n", + " df_inference = inference_preprocessor(\n", + " dataset_inf=df_inference,\n", + " # We use the preprocess pipeline from the feature engineering pipeline\n", + " preprocess_pipeline=ExternalArtifact(id=preprocess_pipeline_id),\n", + " target=target,\n", + " )\n", + " inference_predict(\n", + " dataset_inf=df_inference,\n", + " )\n" + ] + }, + { + "cell_type": "markdown", + "id": "c7afe7be", + "metadata": {}, + "source": [ + "The way to load the right model is to pass in the `production` stage into the `Model` config this time.\n", + "This will ensure to always load the production model, decoupled from all other pipelines:" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "id": "61bf5939", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;35m\u001b[0m\u001b[1;36mversion\u001b[1;35m \u001b[0m\u001b[1;36mproduction\u001b[1;35m matches one of the possible \u001b[0m\u001b[1;36mModelStages\u001b[1;35m and will be fetched using stage.\u001b[0m\n" + ] + } + ], + "source": [ + "pipeline_settings = {\"enable_cache\": False}\n", + "\n", + "# Lets add some metadata to the model to make it identifiable\n", + "pipeline_settings[\"model\"] = Model(\n", + " name=\"breast_cancer_classifier\",\n", + " version=\"production\", # We can pass in the stage name here!\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "id": "ff3402f1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36minference\u001b[1;35m.\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 16)\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mExecuting a new run.\u001b[0m\n", + "\u001b[1;35mCaching is disabled by default for \u001b[0m\u001b[1;36minference\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mandrei@zenml.io\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mlocal-mlflow-stack\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m model_registry: \u001b[0m\u001b[1;36mmlflow\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m model_deployer: \u001b[0m\u001b[1;36mmlflow\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m experiment_tracker: \u001b[0m\u001b[1;36mmlflow\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mDataset with 28 records loaded!\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has finished in \u001b[0m\u001b[1;36m4.069s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36minference_preprocessor\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36minference_preprocessor\u001b[1;35m has finished in \u001b[0m\u001b[1;36m4.373s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36minference_predict\u001b[1;35m has started.\u001b[0m\n", + "\u001b[33mYou specified both an ID as well as a version of the artifact_versions. Ignoring the version and fetching the artifact_versions by ID.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36minference_predict\u001b[1;35m has finished in \u001b[0m\u001b[1;36m5.387s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mPipeline run has finished in \u001b[0m\u001b[1;36m15.779s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/9711266c-1ab0-411e-8ccc-18760cc5aed1/runs/c89eb7d0-e53a-48cc-92fb-ffe2a5287db4/dag\u001b[0m\n" + ] + }, + { + "data": { + "text/plain": [ + "PipelineRunResponse(id=UUID('c89eb7d0-e53a-48cc-92fb-ffe2a5287db4'), permission_denied=False, body=PipelineRunResponseBody(created=datetime.datetime(2024, 2, 8, 11, 27, 40), updated=datetime.datetime(2024, 2, 8, 11, 27, 40), user=UserResponse(id=UUID('c915043d-706c-4885-9263-783cae8dad20'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 11, 16, 16, 5, 33), updated=datetime.datetime(2024, 2, 8, 10, 24, 32), active=True, activation_token=None, full_name='Andrei', email_opted_in=True, is_service_account=False), metadata=None, name='andrei@zenml.io'), status=, stack=StackResponse(id=UUID('57f7dcf2-4bd5-439b-b454-da4000dd3bc8'), permission_denied=False, body=StackResponseBody(created=datetime.datetime(2023, 12, 11, 15, 24, 51), updated=datetime.datetime(2023, 12, 21, 1, 15, 28), user=UserResponse(id=UUID('ac044541-bcb1-4f33-b41e-5c60a3f39485'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 10, 23, 15, 36, 12), updated=datetime.datetime(2024, 2, 7, 9, 57, 48), active=True, activation_token=None, full_name='Alexej Penner', email_opted_in=True, is_service_account=False), metadata=None, name='alexej@zenml.io')), metadata=None, name='local-mlflow-stack'), pipeline=PipelineResponse(id=UUID('9711266c-1ab0-411e-8ccc-18760cc5aed1'), permission_denied=False, body=PipelineResponseBody(created=datetime.datetime(2024, 2, 8, 11, 27, 38), updated=datetime.datetime(2024, 2, 8, 11, 27, 38), user=UserResponse(id=UUID('c915043d-706c-4885-9263-783cae8dad20'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 11, 16, 16, 5, 33), updated=datetime.datetime(2024, 2, 8, 10, 24, 32), active=True, activation_token=None, full_name='Andrei', email_opted_in=True, is_service_account=False), metadata=None, name='andrei@zenml.io'), status=[], version='16'), metadata=None, name='inference'), build=None, schedule=None, code_reference=None), metadata=PipelineRunResponseMetadata(workspace=WorkspaceResponse(id=UUID('f3a544f2-afb5-4672-934a-7a465c66201c'), permission_denied=False, body=WorkspaceResponseBody(created=datetime.datetime(2023, 10, 23, 15, 34, 47), updated=datetime.datetime(2023, 10, 23, 15, 34, 47)), metadata=None, name='default'), run_metadata={}, steps={}, config=PipelineConfiguration(enable_cache=False, enable_artifact_metadata=None, enable_artifact_visualization=None, enable_step_logs=None, settings={'experiment_tracker.mlflow': BaseSettings()}, extra={}, failure_hook_source=None, success_hook_source=None, model=Model(name='breast_cancer_classifier', license=None, description=None, audience=None, use_cases=None, limitations=None, trade_offs=None, ethics=None, tags=None, version='production', save_models_to_registry=True, suppress_class_validation_warnings=True, was_created_in_this_run=False), parameters=None, name='inference'), start_time=datetime.datetime(2024, 2, 8, 11, 27, 40), end_time=None, client_environment={'environment': 'notebook', 'os': 'mac', 'mac_version': '14.0', 'python_version': '3.11.6'}, orchestrator_environment={}, orchestrator_run_id=None), name='inference-2024_02_08-11_27_39_992578')" + ] + }, + "execution_count": 122, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# the `with_options` method allows us to pass in pipeline settings\n", + "# and returns a configured pipeline\n", + "inference_configured = inference.with_options(**pipeline_settings)\n", + "\n", + "# Let's run it again to make sure we have two versions\n", + "# We need to pass in the ID of the preprocessing done in the feature engineering pipeline\n", + "# in order to avoid training-serving skew\n", + "inference_configured(\n", + " preprocess_pipeline_id=preprocessing_pipeline_artifact_version.id\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "2935d1fa", + "metadata": {}, + "source": [ + "ZenML automatically links all artifacts to the `production` model version as well, including the predictions\n", + "that were returned in the pipeline. This completes the MLOps loop of training to inference:" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "id": "e191d019", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 1\n", + "1 0\n", + "2 0\n", + "3 1\n", + "4 1\n", + "5 0\n", + "6 0\n", + "7 0\n", + "8 0\n", + "9 1\n", + "10 1\n", + "11 0\n", + "12 1\n", + "13 0\n", + "14 1\n", + "15 0\n", + "16 1\n", + "17 1\n", + "18 1\n", + "19 0\n", + "20 1\n", + "21 1\n", + "22 0\n", + "23 1\n", + "24 1\n", + "25 1\n", + "26 1\n", + "27 1\n", + "Name: series, dtype: int64" + ] + }, + "execution_count": 123, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Fetch production model\n", + "production_model_version = client.get_model_version(\"breast_cancer_classifier\", \"production\")\n", + "\n", + "# Get the predictions artifact\n", + "production_model_version.get_artifact(\"predictions\").load()" + ] + }, + { + "cell_type": "markdown", + "id": "b0a73cdf", + "metadata": {}, + "source": [ + "You can also see all predictions ever created as a complete history in the dashboard:\n", + "\n", + "\"Model" ] }, { @@ -947,11 +2436,700 @@ "id": "594ee4fc-f102-4b99-bdc3-2f1670c87679", "metadata": {}, "source": [ - "ZenML builds upon the straightforward deployment capability of Huggingface models to AWS Sagemaker, and transforms it into a sophisticated, repeatable, and transparent MLOps workflow. It takes charge of the intricate steps necessary for modern ML systems, ensuring that software engineering leads can focus on iteration and innovation rather than operational intricacies.\n", + "# 🙏 Step 5: Bringing it all together\n", + "\n", + "Let's run all the moving pieces we navigated in the previous steps using production ready python script `run.py`" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[31mThe ZenML global configuration version (0.55.2) is higher than the version of ZenML currently being used (0.55.1). Read more about this issue and how to solve it here: \u001b[0m\u001b[1;36mhttps://docs.zenml.io/user-guide/advanced-guide/environment-management/global-settings-of-zenml#version-mismatch-downgrading\u001b[31m\u001b[0m\n", + "\u001b[2K\u001b[2;36mActive repository stack set to: \u001b[0m\u001b[2;32m'local-wandb'\u001b[0m.\n", + "\u001b[2K\u001b[32m⠋\u001b[0m Setting the repository active stack to 'local-wandb'...b'...\u001b[0m\n", + "\u001b[1A\u001b[2K\u001b[31mThe ZenML global configuration version (0.55.2) is higher than the version of ZenML currently being used (0.55.1). Read more about this issue and how to solve it here: \u001b[0m\u001b[1;36mhttps://docs.zenml.io/user-guide/advanced-guide/environment-management/global-settings-of-zenml#version-mismatch-downgrading\u001b[31m\u001b[0m\n", + "\u001b[?25l\u001b[32m⠋\u001b[0m Describing the stack...\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[3m Stack Configuration \u001b[0m\n", + "┏━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mCOMPONENT_TYPE \u001b[0m\u001b[1m \u001b[0m│\u001b[1m \u001b[0m\u001b[1mCOMPONENT_NAME\u001b[0m\u001b[1m \u001b[0m┃\n", + "┠────────────────────┼────────────────┨\n", + "┃ ARTIFACT_STORE │ default ┃\n", + "┠────────────────────┼────────────────┨\n", + "┃ EXPERIMENT_TRACKER │ zenml_wandb ┃\n", + "┠────────────────────┼────────────────┨\n", + "┃ ORCHESTRATOR │ default ┃\n", + "┗━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┛\n", + "\u001b[2;3m 'local-wandb' stack (ACTIVE) \u001b[0m\n", + "\u001b[32m⠋\u001b[0m Describing the stack...\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[2;36mStack \u001b[0m\u001b[2;32m'local-wandb'\u001b[0m\u001b[2;36m with id \u001b[0m\u001b[2;32m'0b187411-e02c-4856-a7bf-779154797044'\u001b[0m\u001b[2;36m is owned by \u001b[0m\n", + "\u001b[2;36muser hamza@zenml.io.\u001b[0m\n", + "\u001b[2;32m⠋\u001b[0m\u001b[2;36m Describing the stack...\u001b[0m\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Describing the stack...\n", + "\n", + "\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[2;36mDashboard URL: \u001b[0m\n", + "\u001b[2;4;94mhttps://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/stacks/0b187411-e0\u001b[0m\n", + "\u001b[2;4;94m2c-4856-a7bf-779154797044/configuration\u001b[0m\n" + ] + } + ], + "source": [ + "!zenml stack set local-wandb\n", + "!zenml stack describe local-wandb" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[31mThe ZenML global configuration version (0.55.2) is higher than the version of ZenML currently being used (0.55.1). Read more about this issue and how to solve it here: \u001b[0m\u001b[1;36mhttps://docs.zenml.io/user-guide/advanced-guide/environment-management/global-settings-of-zenml#version-mismatch-downgrading\u001b[31m\u001b[0m\n", + "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 98)\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mNew model version \u001b[0m\u001b[1;36m5\u001b[1;35m was created.\u001b[0m\n", + "\u001b[1;35mExecuting a new run.\u001b[0m\n", + "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mandrei@zenml.io\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mlocal-wandb\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m experiment_tracker: \u001b[0m\u001b[1;36mzenml_wandb\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mDataset with 541 records loaded!\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has finished in \u001b[0m\u001b[1;36m3.972s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has finished in \u001b[0m\u001b[1;36m5.907s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has finished in \u001b[0m\u001b[1;36m7.590s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mTraining model SGDClassifier()...\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has finished in \u001b[0m\u001b[1;36m3.851s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mInitializing wandb with entity None, project name: None, run_name: training-2024_02_08-11_31_01_169272_model_evaluator.\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mzenmlcodemonkey\u001b[0m (\u001b[33mzenmlcode\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.16.3\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/stack-showcase/wandb/run-20240208_123128-u36gintc\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33mtraining-2024_02_08-11_31_01_169272_model_evaluator\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/zenmlcode/zenml-projects-stack-showcase\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/zenmlcode/zenml-projects-stack-showcase/runs/u36gintc\u001b[0m\n", + "\u001b[1;35mTrain accuracy=81.02%\u001b[0m\n", + "\u001b[1;35mTest accuracy=79.82%\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: \n", + "\u001b[34m\u001b[1mwandb\u001b[0m: \n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run history:\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: test_accuracy ▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: train_accuracy ▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: \n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run summary:\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: test_accuracy 0.79817\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: train_accuracy 0.81019\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: \n", + "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run \u001b[33mtraining-2024_02_08-11_31_01_169272_model_evaluator\u001b[0m at: \u001b[34m\u001b[4mhttps://wandb.ai/zenmlcode/zenml-projects-stack-showcase/runs/u36gintc\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: ️⚡ View job at \u001b[34m\u001b[4mhttps://wandb.ai/zenmlcode/zenml-projects-stack-showcase/jobs/QXJ0aWZhY3RDb2xsZWN0aW9uOjEzODE0MTY0OA==/version_details/v4\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Synced 5 W&B file(s), 1 media file(s), 1 artifact file(s) and 0 other file(s)\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Find logs at: \u001b[35m\u001b[1m./wandb/run-20240208_123128-u36gintc/logs\u001b[0m\n", + "\u001b[1;35mImplicitly linking artifact \u001b[0m\u001b[1;36moutput\u001b[1;35m to model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[1;35m version \u001b[0m\u001b[1;36m5\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has finished in \u001b[0m\u001b[1;36m18.209s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_promoter\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mModel accuracy 79.82% is below 80% ! Not promoting model.\u001b[0m\n", + "\u001b[1;35mImplicitly linking artifact \u001b[0m\u001b[1;36moutput\u001b[1;35m to model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[1;35m version \u001b[0m\u001b[1;36m5\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_promoter\u001b[1;35m has finished in \u001b[0m\u001b[1;36m3.817s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mPipeline run has finished in \u001b[0m\u001b[1;36m48.207s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/cca9f21f-69ad-4593-aa70-f962367a84cf/runs/1853b693-96b3-4131-b9e9-737249251d6b/dag\u001b[0m\n", + "\u001b[1;35mTraining pipeline with SGD finished successfully!\n", + "\n", + "\u001b[0m\n", + "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 99)\u001b[1;35m.\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[1;35mNew model version \u001b[0m\u001b[1;36m6\u001b[1;35m was created.\u001b[0m\n", + "\u001b[1;35mExecuting a new run.\u001b[0m\n", + "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mandrei@zenml.io\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mlocal-wandb\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m experiment_tracker: \u001b[0m\u001b[1;36mzenml_wandb\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m.\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m.\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m.\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has started.\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[1;35mTraining model RandomForestClassifier()...\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has finished in \u001b[0m\u001b[1;36m4.359s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mInitializing wandb with entity None, project name: None, run_name: training-2024_02_08-11_31_52_057837_model_evaluator.\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.16.3\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/stack-showcase/wandb/run-20240208_123204-71w79kyo\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33mtraining-2024_02_08-11_31_52_057837_model_evaluator\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/zenmlcode/zenml-projects-stack-showcase\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/zenmlcode/zenml-projects-stack-showcase/runs/71w79kyo\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[1;35mTrain accuracy=100.00%\u001b[0m\n", + "\u001b[1;35mTest accuracy=98.17%\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: \n", + "\u001b[34m\u001b[1mwandb\u001b[0m: \n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run history:\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: test_accuracy ▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: train_accuracy ▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: \n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run summary:\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: test_accuracy 0.98165\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: train_accuracy 1.0\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: \n", + "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run \u001b[33mtraining-2024_02_08-11_31_52_057837_model_evaluator\u001b[0m at: \u001b[34m\u001b[4mhttps://wandb.ai/zenmlcode/zenml-projects-stack-showcase/runs/71w79kyo\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: ️⚡ View job at \u001b[34m\u001b[4mhttps://wandb.ai/zenmlcode/zenml-projects-stack-showcase/jobs/QXJ0aWZhY3RDb2xsZWN0aW9uOjEzODE0MTY0OA==/version_details/v4\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Synced 5 W&B file(s), 1 media file(s), 1 artifact file(s) and 0 other file(s)\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Find logs at: \u001b[35m\u001b[1m./wandb/run-20240208_123204-71w79kyo/logs\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[1;35mImplicitly linking artifact \u001b[0m\u001b[1;36moutput\u001b[1;35m to model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[1;35m version \u001b[0m\u001b[1;36m6\u001b[1;35m.\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has finished in \u001b[0m\u001b[1;36m12.132s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_promoter\u001b[1;35m has started.\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[1;35mModel promoted to production!\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[1;35mImplicitly linking artifact \u001b[0m\u001b[1;36moutput\u001b[1;35m to model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[1;35m version \u001b[0m\u001b[1;36m6\u001b[1;35m.\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_promoter\u001b[1;35m has finished in \u001b[0m\u001b[1;36m6.097s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mPipeline run has finished in \u001b[0m\u001b[1;36m31.591s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/df7f887a-f644-4c4b-b07f-35697df9a670/runs/f86c03b5-63bf-4b07-9f03-47cbbd44981d/dag\u001b[0m\n", + "\u001b[1;35mTraining pipeline with RF finished successfully!\n", + "\n", + "\u001b[0m\n", + "\u001b[1;35m\u001b[0m\u001b[1;36mversion\u001b[1;35m \u001b[0m\u001b[1;36mproduction\u001b[1;35m matches one of the possible \u001b[0m\u001b[1;36mModelStages\u001b[1;35m and will be fetched using stage.\u001b[0m\n", + "\u001b[33mUsing an external artifact as step input currently invalidates caching for the step and all downstream steps. Future releases will introduce hashing of artifacts which will improve this behavior.\u001b[0m\n", + "\u001b[33mUsing an external artifact as step input currently invalidates caching for the step and all downstream steps. Future releases will introduce hashing of artifacts which will improve this behavior.\u001b[0m\n", + "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36minference\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 17)\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mExecuting a new run.\u001b[0m\n", + "\u001b[1;35mCaching is disabled by default for \u001b[0m\u001b[1;36minference\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mandrei@zenml.io\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mlocal-wandb\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m experiment_tracker: \u001b[0m\u001b[1;36mzenml_wandb\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mDataset with 28 records loaded!\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has finished in \u001b[0m\u001b[1;36m4.301s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36minference_preprocessor\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36minference_preprocessor\u001b[1;35m has finished in \u001b[0m\u001b[1;36m5.624s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36minference_predict\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36minference_predict\u001b[1;35m has finished in \u001b[0m\u001b[1;36m4.672s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mPipeline run has finished in \u001b[0m\u001b[1;36m17.363s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/e7a3f6f0-4479-431d-8689-bf9ff4d4a29b/runs/44751d4e-31fe-4c90-94d2-af12e24cf02a/dag\u001b[0m\n", + "\u001b[1;35mInference pipeline finished successfully!\u001b[0m\n" + ] + } + ], + "source": [ + "!python3 run.py --training-pipeline --inference-pipeline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now full run executed on local stack and experiment is tracked using Model Control Plane and Weights&Biases.\n", "\n", - "To delve deeper into each stage, refer to the comprehensive guide on GitHub[: zenml-io/zenml-huggingface-sagemak](https://github.com/zenml-io/zenml-huggingface-sagemaker)er. Additionally[, this YouTube playli](https://www.youtube.com/watch?v=Q1EH2H8Akgo&list=PLhNrLW_IWplw6dBbmGcL828-atJMu3CwF)st provides a detailed visual walkthrough of the entire pipeline: Huggingface to Sagemaker ZenML tutorial.\n", + "Let's move some heavy lifting steps to the Sagemaker keeping light ones on local, so we can reduce costs. This can be achieved using step operators and step configurations.\n", "\n", - "Interested in standardizing your MLOps workflows? ZenML Cloud is now available to all - get a managed ZenML server with important features such as RBAC and pipeline trigge[rs. Book a ](https://zenml.io/book-a-demo)demo with us now to learn how you can create your own MLOps pipelines today." + "To make this happen we will use extended configuration files having following step level settings:\n", + "```yaml\n", + "steps:\n", + " model_trainer:\n", + " step_operator: sagemaker-eu # instruct to run step on sagemaker\n", + " settings:\n", + " step_operator:\n", + " sagemaker:\n", + " estimator_args: \n", + " instance_type : ml.m5.large # select instance type\n", + "```\n", + "\n", + "\"Sagemaker" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[31mThe ZenML global configuration version (0.55.2) is higher than the version of ZenML currently being used (0.55.1). Read more about this issue and how to solve it here: \u001b[0m\u001b[1;36mhttps://docs.zenml.io/user-guide/advanced-guide/environment-management/global-settings-of-zenml#version-mismatch-downgrading\u001b[31m\u001b[0m\n", + "\u001b[?25l\u001b[32m⠋\u001b[0m Setting the repository active stack to \n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[2;36mActive repository stack set to: \u001b[0m\u001b[2;32m'local-sagemaker-step-operator-wandb'\u001b[0m\n", + "\u001b[2;32m⠋\u001b[0m\u001b[2;36m Setting the repository active stack to \u001b[0m\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Setting the repository active stack to \n", + "'local-sagemaker-step-operator-wandb'...\n", + "\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[31mThe ZenML global configuration version (0.55.2) is higher than the version of ZenML currently being used (0.55.1). Read more about this issue and how to solve it here: \u001b[0m\u001b[1;36mhttps://docs.zenml.io/user-guide/advanced-guide/environment-management/global-settings-of-zenml#version-mismatch-downgrading\u001b[31m\u001b[0m\n", + "\u001b[?25l\u001b[32m⠋\u001b[0m Describing the stack...\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[3m Stack Configuration \u001b[0m\n", + "┏━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mCOMPONENT_TYPE \u001b[0m\u001b[1m \u001b[0m│\u001b[1m \u001b[0m\u001b[1mCOMPONENT_NAME\u001b[0m\u001b[1m \u001b[0m┃\n", + "┠────────────────────┼────────────────┨\n", + "┃ STEP_OPERATOR │ sagemaker-eu ┃\n", + "┠────────────────────┼────────────────┨\n", + "┃ CONTAINER_REGISTRY │ aws-eu ┃\n", + "┠────────────────────┼────────────────┨\n", + "┃ EXPERIMENT_TRACKER │ zenml_wandb ┃\n", + "┠────────────────────┼────────────────┨\n", + "┃ ORCHESTRATOR │ default ┃\n", + "┠────────────────────┼────────────────┨\n", + "┃ ARTIFACT_STORE │ s3-zenfiles ┃\n", + "┗━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┛\n", + "\u001b[2;3m 'local-sagemaker-step-operator-wandb' \u001b[0m\n", + "\u001b[2;3m stack (ACTIVE) \u001b[0m\n", + "\u001b[32m⠋\u001b[0m Describing the stack...\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[2;36mStack \u001b[0m\u001b[2;32m'local-sagemaker-step-operator-wandb'\u001b[0m\u001b[2;36m with id \u001b[0m\n", + "\u001b[2;32m'5712467b-5ab0-4549-8bc9-2ea7714c8ed8'\u001b[0m\u001b[2;36m is owned by user hamza@zenml.io.\u001b[0m\n", + "\u001b[2;32m⠋\u001b[0m\u001b[2;36m Describing the stack...\u001b[0m\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Describing the stack...\n", + "\n", + "\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[2;36mDashboard URL: \u001b[0m\n", + "\u001b[2;4;94mhttps://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/stacks/5712467b-5a\u001b[0m\n", + "\u001b[2;4;94mb0-4549-8bc9-2ea7714c8ed8/configuration\u001b[0m\n" + ] + } + ], + "source": [ + "!zenml stack set local-sagemaker-step-operator-wandb\n", + "!zenml stack describe local-sagemaker-step-operator-wandb" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[31mThe ZenML global configuration version (0.55.2) is higher than the version of ZenML currently being used (0.55.1). Read more about this issue and how to solve it here: \u001b[0m\u001b[1;36mhttps://docs.zenml.io/user-guide/advanced-guide/environment-management/global-settings-of-zenml#version-mismatch-downgrading\u001b[31m\u001b[0m\n", + "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 84)\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mBuilding Docker image(s) for pipeline \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mBuilding Docker image \u001b[0m\u001b[1;36m715803424590.dkr.ecr.eu-central-1.amazonaws.com/zenml:training-model_trainer-sagemaker_step_operator\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35m- Including user-defined requirements: \u001b[0m\u001b[1;36mpyarrow\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m- Including integration requirements: \u001b[0m\u001b[1;36mPillow>=9.1.0\u001b[1;35m, \u001b[0m\u001b[1;36maws-profile-manager\u001b[1;35m, \u001b[0m\u001b[1;36mboto3<=1.26.76\u001b[1;35m, \u001b[0m\u001b[1;36mkubernetes\u001b[1;35m, \u001b[0m\u001b[1;36ms3fs>2022.3.0,<=2023.4.0\u001b[1;35m, \u001b[0m\u001b[1;36msagemaker==2.117.0\u001b[1;35m, \u001b[0m\u001b[1;36mscikit-learn\u001b[1;35m, \u001b[0m\u001b[1;36mwandb>=0.12.12\u001b[1;35m\u001b[0m\n", + "\u001b[33mCould not import GCP service connector: No module named 'google.api_core'.\u001b[0m\n", + "\u001b[33mCould not import Azure service connector: No module named 'azure.identity'.\u001b[0m\n", + "\u001b[1;35mStep 1/11 : FROM zenmldocker/zenml:0.55.1-py3.11\u001b[0m\n", + "\u001b[1;35mStep 2/11 : WORKDIR /app\u001b[0m\n", + "\u001b[1;35mStep 3/11 : COPY .zenml_user_requirements .\u001b[0m\n", + "\u001b[1;35mStep 4/11 : RUN pip install --default-timeout=60 --no-cache-dir -r .zenml_user_requirements\u001b[0m\n", + "\u001b[1;35mStep 5/11 : COPY .zenml_integration_requirements .\u001b[0m\n", + "\u001b[1;35mStep 6/11 : RUN pip install --default-timeout=60 --no-cache-dir -r .zenml_integration_requirements\u001b[0m\n", + "\u001b[1;35mStep 7/11 : ENV ZENML_ENABLE_REPO_INIT_WARNINGS=False\u001b[0m\n", + "\u001b[1;35mStep 8/11 : ENV ZENML_CONFIG_PATH=/app/.zenconfig\u001b[0m\n", + "\u001b[1;35mStep 9/11 : COPY . .\u001b[0m\n", + "\u001b[1;35mStep 10/11 : RUN chmod -R a+rw .\u001b[0m\n", + "\u001b[1;35mStep 11/11 : ENTRYPOINT $__ZENML_ENTRYPOINT\u001b[0m\n", + "\u001b[1;35mFound credentials in shared credentials file: ~/.aws/credentials\u001b[0m\n", + "\u001b[1;35mPushing Docker image \u001b[0m\u001b[1;36m715803424590.dkr.ecr.eu-central-1.amazonaws.com/zenml:training-model_trainer-sagemaker_step_operator\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mFinished pushing Docker image.\u001b[0m\n", + "\u001b[1;35mFinished building Docker image(s).\u001b[0m\n", + "\u001b[1;35mExecuting a new run.\u001b[0m\n", + "\u001b[1;35mUsing a build:\u001b[0m\n", + "\u001b[1;35m Image(s): 715803424590.dkr.ecr.eu-central-1.amazonaws.com/zenml@sha256:9471e6bda044e266f246f1fadb3b90bfcac7a8df3e37d6f4a3fbca61be22c9a2\u001b[0m\n", + "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mandrei@zenml.io\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mlocal-sagemaker-step-operator-wandb\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m step_operator: \u001b[0m\u001b[1;36msagemaker-eu\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m container_registry: \u001b[0m\u001b[1;36maws-eu\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m experiment_tracker: \u001b[0m\u001b[1;36mzenml_wandb\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m image_builder: \u001b[0m\u001b[1;36mlocal\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36ms3-zenfiles\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mDataset with 541 records loaded!\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has finished in \u001b[0m\u001b[1;36m10.037s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has finished in \u001b[0m\u001b[1;36m14.984s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has finished in \u001b[0m\u001b[1;36m18.481s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mUsing step operator \u001b[0m\u001b[1;36msagemaker-eu\u001b[1;35m to run step \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mDefaulting to the only supported framework/algorithm version: latest.\u001b[0m\n", + "\u001b[1;35mIgnoring unnecessary instance type: None.\u001b[0m\n", + "\u001b[1;35mCreating training-job with name: training-model-trainer-Rigp\u001b[0m\n", + "2024-02-08 10:15:47 Starting - Starting the training job...\n", + "2024-02-08 10:16:15 Starting - Preparing the instances for trainingProfilerReport-1707387345: InProgress\n", + "......\n", + "2024-02-08 10:17:17 Downloading - Downloading input data...\n", + "2024-02-08 10:17:56 Training - Training image download completed. Training in progress.\u001b[34m#033[31mThe ZenML global configuration version (0.55.2) is higher than the version of ZenML currently being used (0.55.1). Read more about this issue and how to solve it here: #033[0m#033[1;36mhttps://docs.zenml.io/user-guide/advanced-guide/environment-management/global-settings-of-zenml#version-mismatch-downgrading#033[31m#033[0m\u001b[0m\n", + "\u001b[34m#033[33mCould not import GCP service connector: No module named 'google.api_core'.#033[0m\u001b[0m\n", + "\u001b[34m#033[33mCould not import Azure service connector: No module named 'azure.identity'.#033[0m\u001b[0m\n", + "\u001b[34m#033[1;35mTraining model SGDClassifier()...#033[0m\u001b[0m\n", + "\n", + "2024-02-08 10:18:28 Uploading - Uploading generated training model\n", + "2024-02-08 10:18:28 Completed - Training job completed\n", + "Training seconds: 72\n", + "Billable seconds: 72\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has finished in \u001b[0m\u001b[1;36m3m2s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mInitializing wandb with entity None, project name: None, run_name: training-2024_02_08-10_14_35_085276_model_evaluator.\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mzenmlcodemonkey\u001b[0m (\u001b[33mzenmlcode\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.16.3\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/stack-showcase/wandb/run-20240208_111852-cy888oje\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33mtraining-2024_02_08-10_14_35_085276_model_evaluator\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/zenmlcode/zenml-projects-stack-showcase\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/zenmlcode/zenml-projects-stack-showcase/runs/cy888oje\u001b[0m\n", + "\u001b[33mYour artifact was materialized under Python version 'unknown' but you are currently using '3.11.6'. This might cause unexpected behavior since pickle is not reproducible across Python versions. Attempting to load anyway...\u001b[0m\n", + "/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/.venv_showcase/lib/python3.11/site-packages/sklearn/base.py:318: UserWarning: Trying to unpickle estimator SGDClassifier from version 1.4.0 when using version 1.2.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n", + "https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n", + " warnings.warn(\n", + "\u001b[1;35mTrain accuracy=89.35%\u001b[0m\n", + "\u001b[1;35mTest accuracy=87.16%\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: \n", + "\u001b[34m\u001b[1mwandb\u001b[0m: \n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run history:\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: test_accuracy ▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: train_accuracy ▁\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: \n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Run summary:\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: test_accuracy 0.87156\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: train_accuracy 0.89352\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: \n", + "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run \u001b[33mtraining-2024_02_08-10_14_35_085276_model_evaluator\u001b[0m at: \u001b[34m\u001b[4mhttps://wandb.ai/zenmlcode/zenml-projects-stack-showcase/runs/cy888oje\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: ️⚡ View job at \u001b[34m\u001b[4mhttps://wandb.ai/zenmlcode/zenml-projects-stack-showcase/jobs/QXJ0aWZhY3RDb2xsZWN0aW9uOjEzODE0MTY0OA==/version_details/v4\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Synced 5 W&B file(s), 1 media file(s), 1 artifact file(s) and 0 other file(s)\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Find logs at: \u001b[35m\u001b[1m./wandb/run-20240208_111852-cy888oje/logs\u001b[0m\n", + "\u001b[1;35mImplicitly linking artifact \u001b[0m\u001b[1;36moutput\u001b[1;35m to model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[1;35m version \u001b[0m\u001b[1;36msgd\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has finished in \u001b[0m\u001b[1;36m29.290s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_promoter\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mModel promoted to production!\u001b[0m\n", + "\u001b[33mYour artifact was materialized under Python version 'unknown' but you are currently using '3.11.6'. This might cause unexpected behavior since pickle is not reproducible across Python versions. Attempting to load anyway...\u001b[0m\n", + "\u001b[1;35mImplicitly linking artifact \u001b[0m\u001b[1;36moutput\u001b[1;35m to model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[1;35m version \u001b[0m\u001b[1;36msgd\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_promoter\u001b[1;35m has finished in \u001b[0m\u001b[1;36m21.640s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mPipeline run has finished in \u001b[0m\u001b[1;36m5m10s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/6c04c6c4-9e7b-41c5-b215-3510e294c293/runs/6a8510f0-6753-4cb4-b111-832bf7315863/dag\u001b[0m\n", + "\u001b[1;35mTraining pipeline with SGD finished successfully!\n", + "\n", + "\u001b[0m\n", + "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 85)\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mBuilding Docker image(s) for pipeline \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mBuilding Docker image \u001b[0m\u001b[1;36m715803424590.dkr.ecr.eu-central-1.amazonaws.com/zenml:training-model_trainer-sagemaker_step_operator\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35m- Including user-defined requirements: \u001b[0m\u001b[1;36mpyarrow\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m- Including integration requirements: \u001b[0m\u001b[1;36mPillow>=9.1.0\u001b[1;35m, \u001b[0m\u001b[1;36maws-profile-manager\u001b[1;35m, \u001b[0m\u001b[1;36mboto3<=1.26.76\u001b[1;35m, \u001b[0m\u001b[1;36mkubernetes\u001b[1;35m, \u001b[0m\u001b[1;36ms3fs>2022.3.0,<=2023.4.0\u001b[1;35m, \u001b[0m\u001b[1;36msagemaker==2.117.0\u001b[1;35m, \u001b[0m\u001b[1;36mscikit-learn\u001b[1;35m, \u001b[0m\u001b[1;36mwandb>=0.12.12\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mStep 1/11 : FROM zenmldocker/zenml:0.55.1-py3.11\u001b[0m\n", + "\u001b[1;35mStep 2/11 : WORKDIR /app\u001b[0m\n", + "\u001b[1;35mStep 3/11 : COPY .zenml_user_requirements .\u001b[0m\n", + "\u001b[1;35mStep 4/11 : RUN pip install --default-timeout=60 --no-cache-dir -r .zenml_user_requirements\u001b[0m\n", + "\u001b[1;35mStep 5/11 : COPY .zenml_integration_requirements .\u001b[0m\n", + "\u001b[1;35mStep 6/11 : RUN pip install --default-timeout=60 --no-cache-dir -r .zenml_integration_requirements\u001b[0m\n", + "\u001b[1;35mStep 7/11 : ENV ZENML_ENABLE_REPO_INIT_WARNINGS=False\u001b[0m\n", + "\u001b[1;35mStep 8/11 : ENV ZENML_CONFIG_PATH=/app/.zenconfig\u001b[0m\n", + "\u001b[1;35mStep 9/11 : COPY . .\u001b[0m\n", + "\u001b[1;35mStep 10/11 : RUN chmod -R a+rw .\u001b[0m\n", + "\u001b[1;35mStep 11/11 : ENTRYPOINT $__ZENML_ENTRYPOINT\u001b[0m\n", + "\u001b[1;35mPushing Docker image \u001b[0m\u001b[1;36m715803424590.dkr.ecr.eu-central-1.amazonaws.com/zenml:training-model_trainer-sagemaker_step_operator\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mFinished pushing Docker image.\u001b[0m\n", + "\u001b[1;35mFinished building Docker image(s).\u001b[0m\n", + "\u001b[1;35mExecuting a new run.\u001b[0m\n", + "\u001b[1;35mUsing a build:\u001b[0m\n", + "\u001b[1;35m Image(s): 715803424590.dkr.ecr.eu-central-1.amazonaws.com/zenml@sha256:775a7f09dace326a3cdbc83ff2f21cbebc2e8bb6ec3d1a2ad113344b5c257223\u001b[0m\n", + "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mandrei@zenml.io\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mlocal-sagemaker-step-operator-wandb\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m step_operator: \u001b[0m\u001b[1;36msagemaker-eu\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m container_registry: \u001b[0m\u001b[1;36maws-eu\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m experiment_tracker: \u001b[0m\u001b[1;36mzenml_wandb\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m image_builder: \u001b[0m\u001b[1;36mlocal\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36ms3-zenfiles\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m.\u001b[0m\n", + "^C\n", + "\u001b[31mFailed preparing run step \u001b[0m\u001b[1;36mdata_splitter\u001b[31m.\u001b[0m\n", + "Traceback (most recent call last):\n", + " File \"/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/.venv_showcase/lib/python3.11/site-packages/zenml/orchestrators/step_launcher.py\", line 212, in launch\n", + " execution_needed, step_run = self._prepare(\n", + " ^^^^^^^^^^^^^^\n", + " File \"/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/.venv_showcase/lib/python3.11/site-packages/zenml/orchestrators/step_launcher.py\", line 363, in _prepare\n", + " self._link_cached_artifacts_to_model(\n", + " File \"/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/.venv_showcase/lib/python3.11/site-packages/zenml/orchestrators/step_launcher.py\", line 399, in _link_cached_artifacts_to_model\n", + " link_artifact_config_to_model(\n", + " File \"/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/.venv_showcase/lib/python3.11/site-packages/zenml/model/utils.py\", line 101, in link_artifact_config_to_model\n", + " model._get_or_create_model_version()\n", + " File \"/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/.venv_showcase/lib/python3.11/site-packages/zenml/model/model.py\", line 699, in _get_or_create_model_version\n", + " model_version = self._get_model_version()\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/.venv_showcase/lib/python3.11/site-packages/zenml/model/model.py\", line 597, in _get_model_version\n", + " mv = zenml_client.get_model_version(\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/.venv_showcase/lib/python3.11/site-packages/zenml/client.py\", line 4948, in get_model_version\n", + " model_versions = self.zen_store.list_model_versions(\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/.venv_showcase/lib/python3.11/site-packages/zenml/zen_stores/rest_zen_store.py\", line 2893, in list_model_versions\n", + " return self._list_paginated_resources(\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/.venv_showcase/lib/python3.11/site-packages/zenml/zen_stores/rest_zen_store.py\", line 3742, in _list_paginated_resources\n", + " body = self.get(f\"{route}\", params=params)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/.venv_showcase/lib/python3.11/site-packages/zenml/zen_stores/rest_zen_store.py\", line 3492, in get\n", + " return self._request(\n", + " ^^^^^^^^^^^^^^\n", + " File \"/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/.venv_showcase/lib/python3.11/site-packages/zenml/zen_stores/rest_zen_store.py\", line 3445, in _request\n", + " self.session.request(\n", + " File \"/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/.venv_showcase/lib/python3.11/site-packages/requests/sessions.py\", line 589, in request\n", + " resp = self.send(prep, **send_kwargs)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/.venv_showcase/lib/python3.11/site-packages/requests/sessions.py\", line 703, in send\n", + " r = adapter.send(request, **kwargs)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/.venv_showcase/lib/python3.11/site-packages/requests/adapters.py\", line 486, in send\n", + " resp = conn.urlopen(\n", + " ^^^^^^^^^^^^^\n", + " File \"/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/.venv_showcase/lib/python3.11/site-packages/urllib3/connectionpool.py\", line 715, in urlopen\n", + " httplib_response = self._make_request(\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " File \"/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/.venv_showcase/lib/python3.11/site-packages/urllib3/connectionpool.py\", line 467, in _make_request\n", + " six.raise_from(e, None)\n", + " File \"\", line 3, in raise_from\n", + " File \"/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/.venv_showcase/lib/python3.11/site-packages/urllib3/connectionpool.py\", line 462, in _make_request\n", + " httplib_response = conn.getresponse()\n", + " ^^^^^^^^^^^^^^^^^^\n", + " File \"/opt/homebrew/Cellar/python@3.11/3.11.6_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/http/client.py\", line 1378, in getresponse\n", + " response.begin()\n", + " File \"/opt/homebrew/Cellar/python@3.11/3.11.6_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/http/client.py\", line 318, in begin\n", + " version, status, reason = self._read_status()\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " File \"/opt/homebrew/Cellar/python@3.11/3.11.6_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/http/client.py\", line 279, in _read_status\n", + " line = str(self.fp.readline(_MAXLINE + 1), \"iso-8859-1\")\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/opt/homebrew/Cellar/python@3.11/3.11.6_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/socket.py\", line 706, in readinto\n", + " return self._sock.recv_into(b)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/opt/homebrew/Cellar/python@3.11/3.11.6_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/ssl.py\", line 1311, in recv_into\n", + " return self.read(nbytes, buffer)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/opt/homebrew/Cellar/python@3.11/3.11.6_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/ssl.py\", line 1167, in read\n", + " return self._sslobj.read(len, buffer)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + "KeyboardInterrupt\n" + ] + } + ], + "source": [ + "!python3 run.py --training-pipeline --inference-pipeline --custom-training-suffix _sagemaker" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 🐙 Step 6: Analyzing results" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": {}, + "outputs": [], + "source": [ + "from zenml.client import Client\n", + "client = Client()" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SGD version is staged as `None`\n", + "RF version is staged as `production`\n" + ] + } + ], + "source": [ + "sgd_model_version = client.list_model_versions(\"breast_cancer_classifier\",tag=\"sgd\")[-1]\n", + "rf_model_version = client.list_model_versions(\"breast_cancer_classifier\",tag=\"rf\")[-1]\n", + "print(f\"SGD version is staged as `{sgd_model_version.stage}`\")\n", + "print(f\"RF version is staged as `{rf_model_version.stage}`\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "At first, let's pull some meta information collected during models evaluation stage. To recall we used this step as evaluator:\n", + "```python\n", + "@step\n", + "def model_evaluator(\n", + " model: ClassifierMixin,\n", + " dataset_trn: pd.DataFrame,\n", + " dataset_tst: pd.DataFrame,\n", + " min_train_accuracy: float = 0.0,\n", + " min_test_accuracy: float = 0.0,\n", + " target: Optional[str] = \"target\",\n", + ") -> float:\n", + " # Calculate the model accuracy on the train and test set\n", + " trn_acc = model.score(...)\n", + " tst_acc = model.score(...)\n", + "\n", + " ...\n", + " \n", + " predictions = model.predict(dataset_tst.drop(columns=[target]))\n", + " metadata = {\n", + " \"train_accuracy\": float(trn_acc),\n", + " \"test_accuracy\": float(tst_acc),\n", + " \"confusion_matrix\": confusion_matrix(dataset_tst[target], predictions)\n", + " .ravel()\n", + " .tolist(),\n", + " }\n", + " log_model_metadata(metadata={\"wandb_url\": wandb.run.url})\n", + " log_artifact_metadata(\n", + " metadata=metadata,\n", + " artifact_name=\"sklearn_classifier\",\n", + " )\n", + "\n", + " wandb.log({\"train_accuracy\": metadata[\"train_accuracy\"]})\n", + " wandb.log({\"test_accuracy\": metadata[\"test_accuracy\"]})\n", + " wandb.log(\n", + " {\n", + " \"confusion_matrix\": wandb.sklearn.plot_confusion_matrix(\n", + " dataset_tst[target], predictions, [\"No Cancer\", \"Cancer\"]\n", + " )\n", + " }\n", + " )\n", + " return float(tst_acc)\n", + "```\n", + "First we pull Accuracy metrics out of both model version for comparison:" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SGD metrics: train=81.02% test=79.82%\n", + "RF (production) metrics: train=100.00% test=98.17%\n" + ] + } + ], + "source": [ + "sgd_clf_metadata = sgd_model_version.get_artifact(\"sklearn_classifier\").run_metadata\n", + "rf_clf_metadata = rf_model_version.get_artifact(\"sklearn_classifier\").run_metadata\n", + "print(f\"SGD{' (production)' if sgd_model_version.stage == 'production' else ''} metrics: train={sgd_clf_metadata['train_accuracy'].value*100:.2f}% test={sgd_clf_metadata['test_accuracy'].value*100:.2f}%\")\n", + "print(f\"RF{' (production)' if rf_model_version.stage == 'production' else ''} metrics: train={rf_clf_metadata['train_accuracy'].value*100:.2f}% test={rf_clf_metadata['test_accuracy'].value*100:.2f}%\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now lets' plot collected Confusion Matrixes:" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn as sns\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "def plot_confusion_matrix(metadata_pointer, tp: str,ax):\n", + " confusion_matrix = np.array(metadata_pointer[\"confusion_matrix\"].value, dtype=float).reshape((2,2))\n", + " confusion_matrix /= np.sum(confusion_matrix)\n", + " sns.heatmap(confusion_matrix, annot=True,fmt='.2%',cmap=\"coolwarm\",ax=ax)\n", + " ax.set_title(f\"{tp} confusion matrix\")\n", + " ax.set_ylabel(\"Ground Label\")\n", + " ax.set_xlabel(\"Predicted Label\")\n", + "\n", + "fig, ax = plt.subplots(1,2,figsize=(15,4))\n", + "plot_confusion_matrix(sgd_clf_metadata, \"SGD\",ax[0])\n", + "plot_confusion_matrix(rf_clf_metadata, \"RF\",ax[1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So far we were able to collect all the information we tracked using Model Control Plane, but we also had Weights&Biases tracking enabled - let's dive into.\n", + "\n", + "Thanks to Model Control Plane metadata we establish a nice connection between those 2 entities:" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SGD version: https://wandb.ai/zenmlcode/zenml-projects-stack-showcase/runs/u36gintc\n", + "RF version: https://wandb.ai/zenmlcode/zenml-projects-stack-showcase/runs/71w79kyo\n" + ] + } + ], + "source": [ + "print(f'SGD version: {sgd_model_version.run_metadata[\"wandb_url\"].value}')\n", + "print(f'RF version: {rf_model_version.run_metadata[\"wandb_url\"].value}')" ] } ], @@ -971,7 +3149,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" + "version": "3.8.10" } }, "nbformat": 4, diff --git a/stack-showcase/run.py b/stack-showcase/run.py index f8f2a21a..7055049a 100644 --- a/stack-showcase/run.py +++ b/stack-showcase/run.py @@ -1,15 +1,32 @@ -# {% include 'templates/license_header' %} +# Apache Software License 2.0 +# +# Copyright (c) ZenML GmbH 2024. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# import os +import random from typing import Optional import click +import yaml from pipelines import ( feature_engineering, inference, - breast_cancer_training, - breast_cancer_deployment_pipeline + training, ) + from zenml.client import Client from zenml.logger import get_logger @@ -18,7 +35,7 @@ @click.command( help=""" -ZenML Starter project CLI v0.0.1. +ZenML Starter project. Run the ZenML starter project with basic options. @@ -68,12 +85,6 @@ help="Version of the test dataset produced by feature engineering. " "If not specified, a new version will be created.", ) -@click.option( - "--config", - default=None, - type=click.STRING, - help="The name of the config", -) @click.option( "--feature-pipeline", is_flag=True, @@ -93,21 +104,27 @@ help="Whether to run the pipeline that performs inference.", ) @click.option( - "--deployment-pipeline", + "--custom-training-suffix", + default="", + type=click.STRING, + help="Suffix to append to the training pipeline name.", +) +@click.option( + "--no-cache", is_flag=True, default=False, - help="Whether to run the pipeline that deploys the model.", + help="Disable caching for the pipeline run.", ) def main( train_dataset_name: str = "dataset_trn", train_dataset_version_name: Optional[str] = None, test_dataset_name: str = "dataset_tst", test_dataset_version_name: Optional[str] = None, - config: Optional[str] = None, feature_pipeline: bool = False, training_pipeline: bool = False, inference_pipeline: bool = False, - deployment_pipeline: bool = False, + custom_training_suffix: str = "", + no_cache: bool = False, ): """Main entry point for the pipeline execution. @@ -117,22 +134,39 @@ def main( (some of which may come from command line arguments, but most of which comes from the YAML config files) * launching the pipeline + + Args: + train_dataset_name: The name of the train dataset produced by feature engineering. + train_dataset_version_name: Version of the train dataset produced by feature engineering. + If not specified, a new version will be created. + test_dataset_name: The name of the test dataset produced by feature engineering. + test_dataset_version_name: Version of the test dataset produced by feature engineering. + If not specified, a new version will be created. + feature_pipeline: Whether to run the pipeline that creates the dataset. + training_pipeline: Whether to run the pipeline that trains the model. + inference_pipeline: Whether to run the pipeline that performs inference. + custom_training_suffix: The suffix to append to the pipeline configuration file when training. + no_cache: If `True` cache will be disabled. """ + client = Client() + config_folder = os.path.join( os.path.dirname(os.path.realpath(__file__)), "configs", ) - client = Client() # Execute Feature Engineering Pipeline if feature_pipeline: pipeline_args = {} + if no_cache: + pipeline_args["enable_cache"] = False pipeline_args["config_path"] = os.path.join( config_folder, "feature_engineering.yaml" ) run_args_feature = {} feature_engineering.with_options(**pipeline_args)(**run_args_feature) - logger.info("Feature Engineering pipeline finished successfully!") + logger.info("Feature Engineering pipeline finished successfully!\n") + train_dataset_artifact = client.get_artifact_version( train_dataset_name ) @@ -143,14 +177,9 @@ def main( f"Version Name: {train_dataset_artifact.version} \n2. Test Dataset: " f"Name: {test_dataset_name}, Version Name: {test_dataset_artifact.version}" ) - + # Execute Training Pipeline if training_pipeline: - pipeline_args = {} - if config is None: - pipeline_args["config_path"] = os.path.join(config_folder, "training.yaml") - else: - pipeline_args["config_path"] = os.path.join(config_folder, config) run_args_train = {} # If train_dataset_version_name is specified, use versioned artifacts @@ -160,36 +189,76 @@ def main( train_dataset_version_name is not None and test_dataset_version_name is not None ) - train_dataset_artifact = client.get_artifact_version( + train_dataset_artifact_version = client.get_artifact_version( train_dataset_name, train_dataset_version_name ) # If train dataset is specified, test dataset must be specified - test_dataset_artifact = client.get_artifact_version( + test_dataset_artifact_version = client.get_artifact_version( test_dataset_name, test_dataset_version_name ) # Use versioned artifacts - run_args_train["train_dataset_id"] = train_dataset_artifact.id - run_args_train["test_dataset_id"] = test_dataset_artifact.id + run_args_train[ + "train_dataset_id" + ] = train_dataset_artifact_version.id + run_args_train[ + "test_dataset_id" + ] = test_dataset_artifact_version.id - breast_cancer_training.with_options(**pipeline_args)(**run_args_train) - logger.info("Training pipeline finished successfully!") + run_args_train["random_state"] = random.randint(0,1000) - if inference_pipeline: + # Run the SGD pipeline pipeline_args = {} - if config is None: - pipeline_args["config_path"] = os.path.join(config_folder, "inference.yaml") - else: - pipeline_args["config_path"] = os.path.join(config_folder, config) - run_args_inference = {} - inference.with_options(**pipeline_args)(**run_args_inference) - logger.info("Inference pipeline finished successfully!") + if no_cache: + pipeline_args["enable_cache"] = False + pipeline_args["config_path"] = os.path.join( + config_folder, f"training_sgd{custom_training_suffix}.yaml" + ) + training.with_options(**pipeline_args)(**run_args_train) + logger.info("Training pipeline with SGD finished successfully!\n\n") - if deployment_pipeline: + # Run the RF pipeline pipeline_args = {} - pipeline_args["config_path"] = os.path.join(config_folder, "deployment.yaml") + if no_cache: + pipeline_args["enable_cache"] = False + pipeline_args["config_path"] = os.path.join( + config_folder, f"training_rf{custom_training_suffix}.yaml" + ) + training.with_options(**pipeline_args)(**run_args_train) + logger.info("Training pipeline with RF finished successfully!\n\n") + + if inference_pipeline: run_args_inference = {} - breast_cancer_deployment_pipeline.with_options(**pipeline_args)(**run_args_inference) - logger.info("Deployment pipeline finished successfully!") + pipeline_args = {"enable_cache": False} + pipeline_args["config_path"] = os.path.join( + config_folder, "inference.yaml" + ) + + # Configure the pipeline + inference_configured = inference.with_options(**pipeline_args) + + # Fetch the production model + with open(pipeline_args["config_path"], "r") as f: + config = yaml.load(f, Loader=yaml.SafeLoader) + zenml_model = client.get_model_version( + config["model"]["name"], config["model"]["version"] + ) + preprocess_pipeline_artifact = zenml_model.get_artifact( + "preprocess_pipeline" + ) + + # Use the metadata of feature engineering pipeline artifact + # to get the random state and target column + random_state = preprocess_pipeline_artifact.run_metadata[ + "random_state" + ].value + target = preprocess_pipeline_artifact.run_metadata["target"].value + run_args_inference["random_state"] = random_state + run_args_inference["target"] = target + + # Run the pipeline + inference_configured(**run_args_inference) + logger.info("Inference pipeline finished successfully!") + if __name__ == "__main__": main() diff --git a/stack-showcase/run_deploy.ipynb b/stack-showcase/run_deploy.ipynb deleted file mode 100644 index 281f7507..00000000 --- a/stack-showcase/run_deploy.ipynb +++ /dev/null @@ -1,175 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "63ab391a", - "metadata": {}, - "source": [ - "# Intro to MLOps using ZenML\n", - "\n", - "## 🌍 Overview\n", - "\n", - "This repository is a minimalistic MLOps project intended as a starting point to learn how to put ML workflows in production. It features: \n", - "\n", - "Follow along this notebook to understand how you can use ZenML to productionalize your ML workflows!\n", - "\n", - "\"Pipelines" - ] - }, - { - "cell_type": "markdown", - "id": "8c28b474", - "metadata": {}, - "source": [ - "# ⌚ Step 1: (Feature engineering) + Training pipeline" - ] - }, - { - "cell_type": "markdown", - "id": "8e5a76e6-8655-47d5-ab61-015b2d69d720", - "metadata": {}, - "source": [ - "Lets run the feature engineering pipeline\n", - "\n", - "\"Training" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "942a20f9-244b-4761-933e-55989a7377d6", - "metadata": {}, - "outputs": [], - "source": [ - "!python run.py --feature-pipeline" - ] - }, - { - "cell_type": "markdown", - "id": "87909827", - "metadata": {}, - "source": [ - "Lets run the training pipeline\n", - "\n", - "\"Training" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fccf1bd9", - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "!python run.py --training-pipeline" - ] - }, - { - "cell_type": "markdown", - "id": "d6306f14", - "metadata": {}, - "source": [ - "# 🫅 Step 2: The inference pipeline" - ] - }, - { - "cell_type": "markdown", - "id": "b51f3108", - "metadata": {}, - "source": [ - "The batch inference pipeline simply takes the model marked as `production` and runs inference on it\n", - "with `live data`. The critical step here is the `inference_predict` step, where we load the model in memory\n", - "and generate predictions:\n", - "\n", - "\"Inference" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9918a8a1-c569-494f-aa40-cb7bd3aaea07", - "metadata": {}, - "outputs": [], - "source": [ - "!python run.py --inference-pipeline" - ] - }, - { - "cell_type": "markdown", - "id": "36140d24-a280-48eb-bb03-5e03280e128c", - "metadata": {}, - "source": [ - "## Step 3: Deploying the pipeline to Huggingface" - ] - }, - { - "cell_type": "markdown", - "id": "13bd8087-2ab0-4f9d-8bff-6266a05eb6e7", - "metadata": {}, - "source": [ - "\"Deployment" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8000849c-1ce8-4900-846e-3ef1873561f8", - "metadata": {}, - "outputs": [], - "source": [ - "!python run.py --deployment-pipeline" - ] - }, - { - "cell_type": "markdown", - "id": "594ee4fc-f102-4b99-bdc3-2f1670c87679", - "metadata": {}, - "source": [ - "## Congratulations!\n", - "\n", - "You're a legit MLOps engineer now! You trained two models, evaluated them against\n", - "a test set, registered the best one with the ZenML model control plane,\n", - "and served some predictions. You also learned how to iterate on your models and\n", - "data by using some of the ZenML utility abstractions. You saw how to view your\n", - "artifacts and models via the client as well as the ZenML Dashboard.\n", - "\n", - "## Further exploration\n", - "\n", - "This was just the tip of the iceberg of what ZenML can do; check out the [**docs**](https://docs.zenml.io/) to learn more\n", - "about the capabilities of ZenML. For example, you might want to:\n", - "\n", - "- [Deploy ZenML](https://docs.zenml.io/user-guide/production-guide/deploying-zenml#connecting-to-a-deployed-zenml) to collaborate with your colleagues.\n", - "- Run the same pipeline on a [cloud MLOps stack in production](https://docs.zenml.io/user-guide/production-guide/cloud-stack).\n", - "- Track your metrics in an experiment tracker like [MLflow](https://docs.zenml.io/stacks-and-components/component-guide/experiment-trackers/mlflow).\n", - "\n", - "## What next?\n", - "\n", - "* If you have questions or feedback... join our [**Slack Community**](https://zenml.io/slack) and become part of the ZenML family!\n", - "* If you want to quickly get started with ZenML, check out the [ZenML Cloud](https://zenml.io/cloud)." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/stack-showcase/run_stack_showcase.ipynb b/stack-showcase/run_stack_showcase.ipynb deleted file mode 100644 index 92e6dd7e..00000000 --- a/stack-showcase/run_stack_showcase.ipynb +++ /dev/null @@ -1,347 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "b567a1d3-f625-4b98-9852-fcc3f3fe9609", - "metadata": {}, - "outputs": [], - "source": [ - "# To start with, we use the default stack\n", - "#!zenml init\n", - "\n", - "# We also need to connect to a remote ZenML Instance\n", - "#!zenml connect --url https://1cf18d95-zenml.cloudinfra.zenml.io" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c53367f1-3951-48c7-9540-21daf818fa5d", - "metadata": {}, - "outputs": [], - "source": [ - "# Do the imports at the top\n", - "\n", - "import random\n", - "from zenml import ExternalArtifact, pipeline \n", - "from zenml.client import Client\n", - "from zenml.logger import get_logger\n", - "from uuid import UUID\n", - "\n", - "import os\n", - "from typing import Optional, List\n", - "\n", - "from zenml import pipeline\n", - "from zenml import Model\n", - "\n", - "from pipelines import feature_engineering\n", - "\n", - "from steps import (\n", - " data_loader,\n", - " data_preprocessor,\n", - " data_splitter,\n", - " model_evaluator,\n", - " model_trainer,\n", - " inference_predict,\n", - " inference_preprocessor\n", - ")\n", - "\n", - "logger = get_logger(__name__)\n", - "\n", - "client = Client()\n", - "client.activate_stack(\"local-mlflow-stack\")" - ] - }, - { - "cell_type": "markdown", - "id": "ab87746e-b804-4fab-88f6-d4967048cb45", - "metadata": {}, - "source": [ - "# Start local with a simple training pipeline\n", - "\n", - "Below you can see what the pipeline looks like. We will start by running this locally on the default-stack. This means the data between the steps is stored locally and the compute is also local." - ] - }, - { - "cell_type": "markdown", - "id": "33872b19-7329-4f5e-9a1e-cfc1fe9d560d", - "metadata": { - "jp-MarkdownHeadingCollapsed": true - }, - "source": [ - "\"Drawing\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "06625571-b281-4820-a7eb-3a085ba2e572", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "from sklearn.datasets import load_breast_cancer\n", - "from zenml import step\n", - "from zenml.logger import get_logger\n", - "\n", - "logger = get_logger(__name__)\n", - "\n", - "# Here is what one of the steps in the pipeline looks like. Simple python function that just needs the `@step` decorator.\n", - "\n", - "@step\n", - "def data_loader() -> pd.DataFrame:\n", - " \"\"\"Dataset reader step.\"\"\"\n", - " dataset = load_breast_cancer(as_frame=True)\n", - " inference_size = int(len(dataset.target) * 0.05)\n", - " dataset: pd.DataFrame = dataset.frame\n", - " dataset.reset_index(drop=True, inplace=True)\n", - " logger.info(f\"Dataset with {len(dataset)} records loaded!\")\n", - "\n", - " return dataset\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "754a3069-9d13-4869-be64-a641071800cc", - "metadata": {}, - "outputs": [], - "source": [ - "# Here's an example of what this function returns\n", - "\n", - "data_loader()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8aa300f1-48df-4e62-87eb-0e2fc5735da8", - "metadata": {}, - "outputs": [], - "source": [ - "from zenml import pipeline\n", - "\n", - "@pipeline\n", - "def breast_cancer_training(\n", - " train_dataset_id: Optional[UUID] = None,\n", - " test_dataset_id: Optional[UUID] = None,\n", - " min_train_accuracy: float = 0.0,\n", - " min_test_accuracy: float = 0.0,\n", - "):\n", - " \"\"\"Model training pipeline.\"\"\"\n", - " # Execute Feature Engineering Pipeline\n", - " dataset_trn, dataset_tst = feature_engineering()\n", - "\n", - " model = model_trainer(\n", - " dataset_trn=dataset_trn,\n", - " )\n", - "\n", - " model_evaluator(\n", - " model=model,\n", - " dataset_trn=dataset_trn,\n", - " dataset_tst=dataset_tst,\n", - " min_train_accuracy=min_train_accuracy,\n", - " min_test_accuracy=min_test_accuracy,\n", - " )\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d55342bf-33c5-4646-b1ce-e599a99cf568", - "metadata": {}, - "outputs": [], - "source": [ - "zenml_model = Model(\n", - " name=\"breast_cancer_classifier_model\",\n", - " description=\"Classification of Breast Cancer Dataset.\",\n", - " delete_new_version_on_failure=True,\n", - " tags=[\"classification\", \"sklearn\"],\n", - ")\n", - "\n", - "pipeline_args = {\n", - " \"enable_cache\": True, \n", - " \"model\": zenml_model\n", - "}\n", - "\n", - "# Model Version config\n", - "fe_t_configured = breast_cancer_training.with_options(**pipeline_args)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f5f4aed8-7d87-4e07-a25c-345d327ad636", - "metadata": {}, - "outputs": [], - "source": [ - "fe_t_configured()" - ] - }, - { - "cell_type": "markdown", - "id": "c3e6dc42-21b8-4b3c-90ec-d6e6d541907f", - "metadata": {}, - "source": [ - "# Let's outsource some compute to Sagemaker!" - ] - }, - { - "cell_type": "markdown", - "id": "14a840b1-288d-4713-98f4-bbe8d6e06140", - "metadata": {}, - "source": [ - "Let's farm some compute to AWS with a training job with a certain number of CPUs and Memory. This can easily be done without and changes to the actual implementation of the pipeline. " - ] - }, - { - "cell_type": "markdown", - "id": "fa9308fb-3556-472c-8fc7-7f2f88d1c455", - "metadata": {}, - "source": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\"Drawing\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "48be8f60-9fbe-4d19-92e4-d9cd8289dbf7", - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# This pip installs the requirements locally\n", - "!zenml integration install aws s3 mlflow -y" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4cb26018-aa7d-497d-a0e2-855d3becb70d", - "metadata": {}, - "outputs": [], - "source": [ - "client.activate_stack(\"local-sagemaker-step-operator-stack\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5683a1c9-f5c1-4ba1-ad7c-1e427fd265df", - "metadata": {}, - "outputs": [], - "source": [ - "from zenml.config import DockerSettings\n", - "\n", - "# The actual code will stay the same, all that needs to be done is some configuration\n", - "step_args = {}\n", - "\n", - "# We configure which step operator should be used\n", - "step_args[\"step_operator\"] = \"sagemaker-eu\"\n", - "\n", - "# M5 Large is what we need for this big data!\n", - "step_args[\"settings\"] = {\"step_operator.sagemaker\": {\"estimator_args\": {\"instance_type\" : \"ml.m5.large\"}}}\n", - "\n", - "# Update the step. We could also do this in YAML\n", - "model_trainer = model_trainer.with_options(**step_args)\n", - "\n", - "docker_settings = DockerSettings(\n", - " requirements=[\n", - " \"pyarrow\",\n", - " \"scikit-learn==1.1.1\"\n", - " ],\n", - ")\n", - "\n", - "pipeline_args = {\n", - " \"enable_cache\": True, \n", - " \"model\": zenml_model,\n", - " \"settings\": {\"docker\": docker_settings}\n", - "}\n", - "\n", - "fe_t_configured = breast_cancer_training.with_options(**pipeline_args)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "85179f52-68f0-4c8d-9808-6b080bec72c3", - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# Lets run the pipeline\n", - "fe_t_configured()" - ] - }, - { - "cell_type": "markdown", - "id": "0841f93b-9eb5-4af6-bba7-cec167024ccf", - "metadata": {}, - "source": [ - "# Switch to full Sagemaker Stack\n", - "\n", - "Just one command will allow you to switch the full code execution over to sagemaker. No Sagemaker domain knowledge necessary. No setup of VMs or Kubernetes clusters necessary. No maintenance of any infrastructure either.\n", - "\n", - "![Sagemaker local stack](_assets/sagemaker_stack.png)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d8e33484-3377-4f0e-83fa-87d7c0ca4d72", - "metadata": {}, - "outputs": [], - "source": [ - "# Finally, this is all that needs to be done to fully switch the code to be run fully on sagemaker\n", - "client.activate_stack(\"sagemaker-stack\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a03c95e9-df2e-446c-8d61-9cc37ad8a46a", - "metadata": {}, - "outputs": [], - "source": [ - "fe_t_configured()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/stack-showcase/steps/__init__.py b/stack-showcase/steps/__init__.py index 4db083c1..2f856719 100644 --- a/stack-showcase/steps/__init__.py +++ b/stack-showcase/steps/__init__.py @@ -23,7 +23,4 @@ ) from .model_promoter import ( model_promoter, -) -from .deploy_to_huggingface import ( - deploy_to_huggingface, ) \ No newline at end of file diff --git a/stack-showcase/steps/data_loader.py b/stack-showcase/steps/data_loader.py index cc6df580..1934baa8 100644 --- a/stack-showcase/steps/data_loader.py +++ b/stack-showcase/steps/data_loader.py @@ -1,9 +1,25 @@ -# {% include 'template/license_header' %} +# Apache Software License 2.0 +# +# Copyright (c) ZenML GmbH 2024. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# import pandas as pd from sklearn.datasets import load_breast_cancer from typing_extensions import Annotated -from zenml import log_artifact_metadata, step + +from zenml import step from zenml.logger import get_logger logger = get_logger(__name__) @@ -25,19 +41,20 @@ def data_loader( https://docs.zenml.io/user-guide/advanced-guide/configure-steps-pipelines Args: + random_state: Random state for sampling is_inference: If `True` subset will be returned and target column will be removed from dataset. - random_state: Random state for sampling target: Name of target columns in dataset. Returns: The dataset artifact as Pandas DataFrame and name of target column. """ - ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### dataset = load_breast_cancer(as_frame=True) inference_size = int(len(dataset.target) * 0.05) dataset: pd.DataFrame = dataset.frame - inference_subset = dataset.sample(inference_size, random_state=random_state) + inference_subset = dataset.sample( + inference_size, random_state=random_state + ) if is_inference: dataset = inference_subset dataset.drop(columns=target, inplace=True) @@ -45,9 +62,4 @@ def data_loader( dataset.drop(inference_subset.index, inplace=True) dataset.reset_index(drop=True, inplace=True) logger.info(f"Dataset with {len(dataset)} records loaded!") - - # Recording metadata for this dataset - log_artifact_metadata(metadata={"random_state": random_state, target: target}) - - ### YOUR CODE ENDS HERE ### return dataset diff --git a/stack-showcase/steps/data_preprocessor.py b/stack-showcase/steps/data_preprocessor.py index 961f1468..1bf99025 100644 --- a/stack-showcase/steps/data_preprocessor.py +++ b/stack-showcase/steps/data_preprocessor.py @@ -1,54 +1,34 @@ -# {% include 'template/license_header' %} +# Apache Software License 2.0 +# +# Copyright (c) ZenML GmbH 2024. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# -from typing import Union -import pandas as pd from typing import List, Optional, Tuple import pandas as pd from sklearn.pipeline import Pipeline from sklearn.preprocessing import MinMaxScaler from typing_extensions import Annotated -from zenml import log_artifact_metadata, step - - -class NADropper: - """Support class to drop NA values in sklearn Pipeline.""" - - def fit(self, *args, **kwargs): - return self - - def transform(self, X: Union[pd.DataFrame, pd.Series]): - return X.dropna() - - -class ColumnsDropper: - """Support class to drop specific columns in sklearn Pipeline.""" - - def __init__(self, columns): - self.columns = columns - - def fit(self, *args, **kwargs): - return self +from utils.preprocess import ColumnsDropper, DataFrameCaster, NADropper - def transform(self, X: Union[pd.DataFrame, pd.Series]): - return X.drop(columns=self.columns) - - -class DataFrameCaster: - """Support class to cast type back to pd.DataFrame in sklearn Pipeline.""" - - def __init__(self, columns): - self.columns = columns - - def fit(self, *args, **kwargs): - return self - - def transform(self, X): - return pd.DataFrame(X, columns=self.columns) +from zenml import log_artifact_metadata, step @step def data_preprocessor( + random_state: int, dataset_trn: pd.DataFrame, dataset_tst: pd.DataFrame, drop_na: Optional[bool] = None, @@ -66,7 +46,7 @@ def data_preprocessor( it is suitable for model training. It takes in a dataset as an input step artifact and performs any necessary preprocessing steps like cleaning, feature engineering, feature selection, etc. It then returns the processed - dataset as an step output artifact. + dataset as a step output artifact. This step is parameterized, which allows you to configure the step independently of the step code, before running it in a pipeline. @@ -77,39 +57,38 @@ def data_preprocessor( https://docs.zenml.io/user-guide/advanced-guide/configure-steps-pipelines Args: + random_state: Random state for sampling. dataset_trn: The train dataset. dataset_tst: The test dataset. drop_na: If `True` all NA rows will be dropped. normalize: If `True` all numeric fields will be normalized. drop_columns: List of column names to drop. + target: Name of target column in dataset. Returns: The processed datasets (dataset_trn, dataset_tst) and fitted `Pipeline` object. """ - ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### # We use the sklearn pipeline to chain together multiple preprocessing steps preprocess_pipeline = Pipeline([("passthrough", "passthrough")]) if drop_na: preprocess_pipeline.steps.append(("drop_na", NADropper())) if drop_columns: # Drop columns - preprocess_pipeline.steps.append(("drop_columns", ColumnsDropper(drop_columns))) + preprocess_pipeline.steps.append( + ("drop_columns", ColumnsDropper(drop_columns)) + ) if normalize: # Normalize the data preprocess_pipeline.steps.append(("normalize", MinMaxScaler())) - preprocess_pipeline.steps.append(("cast", DataFrameCaster(dataset_trn.columns))) + preprocess_pipeline.steps.append( + ("cast", DataFrameCaster(dataset_trn.columns)) + ) dataset_trn = preprocess_pipeline.fit_transform(dataset_trn) dataset_tst = preprocess_pipeline.transform(dataset_tst) - # Log metadata of target to both datasets + # Log metadata so we can load it in the inference pipeline log_artifact_metadata( - artifact_name="dataset_trn", - metadata={"target": target}, + artifact_name="preprocess_pipeline", + metadata={"random_state": random_state, "target": target}, ) - log_artifact_metadata( - artifact_name="dataset_tst", - metadata={"target": target}, - ) - - ### YOUR CODE ENDS HERE ### return dataset_trn, dataset_tst, preprocess_pipeline diff --git a/stack-showcase/steps/data_splitter.py b/stack-showcase/steps/data_splitter.py index dbab8157..bb0e9bd2 100644 --- a/stack-showcase/steps/data_splitter.py +++ b/stack-showcase/steps/data_splitter.py @@ -1,10 +1,26 @@ -# {% include 'template/license_header' %} +# Apache Software License 2.0 +# +# Copyright (c) ZenML GmbH 2024. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# from typing import Tuple import pandas as pd from sklearn.model_selection import train_test_split from typing_extensions import Annotated + from zenml import step @@ -34,7 +50,6 @@ def data_splitter( Returns: The split dataset: dataset_trn, dataset_tst. """ - ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### dataset_trn, dataset_tst = train_test_split( dataset, test_size=test_size, @@ -43,5 +58,4 @@ def data_splitter( ) dataset_trn = pd.DataFrame(dataset_trn, columns=dataset.columns) dataset_tst = pd.DataFrame(dataset_tst, columns=dataset.columns) - ### YOUR CODE ENDS HERE ### return dataset_trn, dataset_tst diff --git a/stack-showcase/steps/deploy_to_huggingface.py b/stack-showcase/steps/deploy_to_huggingface.py deleted file mode 100644 index 888bd397..00000000 --- a/stack-showcase/steps/deploy_to_huggingface.py +++ /dev/null @@ -1,69 +0,0 @@ -# Apache Software License 2.0 -# -# Copyright (c) ZenML GmbH 2023. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -from typing_extensions import Annotated -from huggingface_hub import create_branch, login, HfApi - -from zenml import step, log_artifact_metadata -from zenml.client import Client -from zenml.logger import get_logger - -# Initialize logger -logger = get_logger(__name__) - - -@step(enable_cache=False) -def deploy_to_huggingface( - repo_name: str, -) -> Annotated[str, "huggingface_url"]: - """ - This step deploy the model to huggingface. - - Args: - repo_name: The name of the repo to create/use on huggingface. - """ - ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### - secret = Client().get_secret("huggingface_creds") - assert secret, "No secret found with name 'huggingface_creds'. Please create one that includes your `username` and `token`." - token = secret.secret_values["token"] - api = HfApi(token=token) - hf_repo = api.create_repo(repo_id=repo_name, repo_type="space", space_sdk="gradio", exist_ok=True) - zenml_repo_root = Client().root - if not zenml_repo_root: - logger.warning( - "You're running the `deploy_to_huggingface` step outside of a ZenML repo. " - "Since the deployment step to huggingface is all about pushing the repo to huggingface, " - "this step will not work outside of a ZenML repo where the gradio folder is present." - ) - raise - url = api.upload_folder( - folder_path=zenml_repo_root, repo_id=hf_repo.repo_id, repo_type="space", - ) - repo_commits = api.list_repo_commits( - repo_id=hf_repo.repo_id, - repo_type="space", - ) - log_artifact_metadata( - artifact_name="huggingface_url", - metadata={ - "repo_id": hf_repo.repo_id, - "revision": repo_commits[0].commit_id, - }, - ) - logger.info(f"Model updated: {url}") - ### YOUR CODE ENDS HERE ### - return url diff --git a/stack-showcase/steps/inference_predict.py b/stack-showcase/steps/inference_predict.py index 8d74cc80..cd1d2921 100644 --- a/stack-showcase/steps/inference_predict.py +++ b/stack-showcase/steps/inference_predict.py @@ -15,10 +15,12 @@ # limitations under the License. # +from typing import Any import pandas as pd from typing_extensions import Annotated -from zenml import get_step_context, step + +from zenml import step from zenml.logger import get_logger logger = get_logger(__name__) @@ -26,12 +28,13 @@ @step def inference_predict( + model: Any, dataset_inf: pd.DataFrame, ) -> Annotated[pd.Series, "predictions"]: """Predictions step. - This is an example of a predictions step that takes the data in and returns - predicted values. + This is an example of a predictions step that takes the data and model in + and returns predicted values. This step is parameterized, which allows you to configure the step independently of the step code, before running it in a pipeline. @@ -41,19 +44,14 @@ def inference_predict( https://docs.zenml.io/user-guide/advanced-guide/configure-steps-pipelines Args: + model: Trained model. dataset_inf: The inference dataset. Returns: The predictions as pandas series """ - ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### - zenml_model = get_step_context().model - # run prediction from memory - predictor = zenml_model.load_artifact("model") - predictions = predictor.predict(dataset_inf) + predictions = model.predict(dataset_inf) predictions = pd.Series(predictions, name="predicted") - ### YOUR CODE ENDS HERE ### - return predictions diff --git a/stack-showcase/steps/inference_preprocessor.py b/stack-showcase/steps/inference_preprocessor.py index c7a5ae1c..d484433e 100644 --- a/stack-showcase/steps/inference_preprocessor.py +++ b/stack-showcase/steps/inference_preprocessor.py @@ -18,6 +18,7 @@ import pandas as pd from sklearn.pipeline import Pipeline from typing_extensions import Annotated + from zenml import step @@ -42,11 +43,8 @@ def inference_preprocessor( Returns: The processed dataframe: dataset_inf. """ - ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### # artificially adding `target` column to avoid Pipeline issues dataset_inf[target] = pd.Series([1] * dataset_inf.shape[0]) dataset_inf = preprocess_pipeline.transform(dataset_inf) - dataset_inf.drop(columns=["target"], inplace=True) - ### YOUR CODE ENDS HERE ### - + dataset_inf.drop(columns=[target], inplace=True) return dataset_inf diff --git a/stack-showcase/steps/model_evaluator.py b/stack-showcase/steps/model_evaluator.py index f9e72fc3..36a4bf3b 100644 --- a/stack-showcase/steps/model_evaluator.py +++ b/stack-showcase/steps/model_evaluator.py @@ -1,24 +1,45 @@ -# {% include 'template/license_header' %} +# Apache Software License 2.0 +# +# Copyright (c) ZenML GmbH 2024. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from typing import Optional import pandas as pd -import mlflow from sklearn.base import ClassifierMixin -from zenml import step, log_artifact_metadata -from zenml.client import Client +from sklearn.metrics import confusion_matrix + +from zenml import log_artifact_metadata, step, log_model_metadata from zenml.logger import get_logger -from zenml import get_step_context +import wandb +from zenml.client import Client + logger = get_logger(__name__) -experiment_tracker = Client().active_stack.experiment_tracker +et = Client().active_stack.experiment_tracker + -@step(enable_cache=False, experiment_tracker="mlflow") +@step(experiment_tracker=et.name) def model_evaluator( model: ClassifierMixin, dataset_trn: pd.DataFrame, dataset_tst: pd.DataFrame, min_train_accuracy: float = 0.0, min_test_accuracy: float = 0.0, + target: Optional[str] = "target", ) -> float: """Evaluate a trained model. @@ -50,29 +71,21 @@ def model_evaluator( dataset_tst: The test dataset. min_train_accuracy: Minimal acceptable training accuracy value. min_test_accuracy: Minimal acceptable testing accuracy value. - fail_on_accuracy_quality_gates: If `True` a `RuntimeException` is raised - upon not meeting one of the minimal accuracy thresholds. + target: Name of target column in dataset. Returns: The model accuracy on the test set. - - Raises: - RuntimeError: if any of accuracies is lower than respective threshold """ - # context = get_step_context() - # target = context.inputs["dataset_trn"].run_metadata['target'].value - target = "target" - # Calculate the model accuracy on the train and test set trn_acc = model.score( dataset_trn.drop(columns=[target]), dataset_trn[target], ) - logger.info(f"Train accuracy={trn_acc*100:.2f}%") tst_acc = model.score( dataset_tst.drop(columns=[target]), dataset_tst[target], ) + logger.info(f"Train accuracy={trn_acc*100:.2f}%") logger.info(f"Test accuracy={tst_acc*100:.2f}%") messages = [] @@ -88,15 +101,27 @@ def model_evaluator( for message in messages: logger.warning(message) - artifact = get_step_context().model.get_artifact("model") - + predictions = model.predict(dataset_tst.drop(columns=[target])) + metadata = { + "train_accuracy": float(trn_acc), + "test_accuracy": float(tst_acc), + "confusion_matrix": confusion_matrix(dataset_tst[target], predictions) + .ravel() + .tolist(), + } + log_model_metadata(metadata={"wandb_url": wandb.run.url}) log_artifact_metadata( - metadata={"train_accuracy": float(trn_acc), "test_accuracy": float(tst_acc)}, - artifact_name=artifact.name, - artifact_version=artifact.version, + metadata=metadata, + artifact_name="sklearn_classifier", ) - mlflow.log_metric("train_accuracy", float(trn_acc)) - mlflow.log_metric("test_accuracy", float(tst_acc)) - - return float(trn_acc) + wandb.log({"train_accuracy": metadata["train_accuracy"]}) + wandb.log({"test_accuracy": metadata["test_accuracy"]}) + wandb.log( + { + "confusion_matrix": wandb.sklearn.plot_confusion_matrix( + dataset_tst[target], predictions, ["No Cancer", "Cancer"] + ) + } + ) + return float(tst_acc) diff --git a/stack-showcase/steps/model_promoter.py b/stack-showcase/steps/model_promoter.py index 04b9ce2a..152ca11d 100644 --- a/stack-showcase/steps/model_promoter.py +++ b/stack-showcase/steps/model_promoter.py @@ -1,23 +1,38 @@ -# {% include 'template/license_header' %} +# Apache Software License 2.0 +# +# Copyright (c) ZenML GmbH 2024. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# from zenml import get_step_context, step +from zenml.client import Client from zenml.logger import get_logger +from sklearn.metrics import accuracy_score logger = get_logger(__name__) @step def model_promoter(accuracy: float, stage: str = "production") -> bool: - """Dataset reader step. + """Model promoter step. - This is an example of a dataset reader step that load Breast Cancer dataset. - - This step is parameterized, which allows you to configure the step - independently of the step code, before running it in a pipeline. - In this example, the step can be configured with number of rows and logic - to drop target column or not. See the documentation for more information: - - https://docs.zenml.io/user-guide/advanced-guide/configure-steps-pipelines + This is an example of a step that conditionally promotes a model. It takes + in the accuracy of the model and the stage to promote the model to. If the + accuracy is below 80%, the model is not promoted. If it is above 80%, the + model is promoted to the stage indicated in the parameters. If there is + already a model in the indicated stage, the model with the higher accuracy + is promoted. Args: accuracy: Accuracy of the model. @@ -26,17 +41,45 @@ def model_promoter(accuracy: float, stage: str = "production") -> bool: Returns: Whether the model was promoted or not. """ - ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### + is_promoted = False + if accuracy < 0.8: logger.info( f"Model accuracy {accuracy*100:.2f}% is below 80% ! Not promoting model." ) - is_promoted = False else: logger.info(f"Model promoted to {stage}!") is_promoted = True - zenml_model = get_step_context().model - zenml_model.set_stage(stage, force=True) - ### YOUR CODE ENDS HERE ### + # Get the model in the current context + current_model = get_step_context().model + + # Get the model that is in the production stage + client = Client() + try: + stage_model = client.get_model_version(current_model.name, stage) + # We compare their metrics + prod_classifier = stage_model.get_artifact("sklearn_classifier") + if prod_classifier: + # and recompute metrics for current prod model using current test set + prod_classifier = prod_classifier.load() + current_dataset = current_model.get_artifact( + "dataset_tst" + ).load() + prod_accuracy = accuracy_score( + current_dataset["target"], + prod_classifier.predict( + current_dataset.drop(columns="target"), + ), + ) + else: + prod_accuracy = 0 + if float(accuracy) > float(prod_accuracy): + # If current model has better metrics, we promote it + is_promoted = True + current_model.set_stage(stage, force=True) + except KeyError: + # If no such model exists, current one is promoted + is_promoted = True + current_model.set_stage(stage, force=True) return is_promoted diff --git a/stack-showcase/steps/model_trainer.py b/stack-showcase/steps/model_trainer.py index e278ad61..8fa063e7 100644 --- a/stack-showcase/steps/model_trainer.py +++ b/stack-showcase/steps/model_trainer.py @@ -1,22 +1,42 @@ -# {% include 'template/license_header' %} +# Apache Software License 2.0 +# +# Copyright (c) ZenML GmbH 2024. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from typing import Optional -import mlflow import pandas as pd from sklearn.base import ClassifierMixin -from sklearn.tree import DecisionTreeClassifier +from sklearn.ensemble import RandomForestClassifier +from sklearn.linear_model import SGDClassifier from typing_extensions import Annotated + from zenml import ArtifactConfig, step -from zenml.client import Client from zenml.logger import get_logger logger = get_logger(__name__) -experiment_tracker = Client().active_stack.experiment_tracker - -@step(enable_cache=False, experiment_tracker="mlflow", step_operator="sagemaker-eu") +@step def model_trainer( dataset_trn: pd.DataFrame, -) -> Annotated[ClassifierMixin, ArtifactConfig(name="model", is_model_artifact=True)]: + model_type: str = "sgd", + target: Optional[str] = "target", +) -> Annotated[ + ClassifierMixin, + ArtifactConfig(name="sklearn_classifier", is_model_artifact=True), +]: """Configure and train a model on the training dataset. This is an example of a model training step that takes in a dataset artifact @@ -26,27 +46,27 @@ def model_trainer( Args: dataset_trn: The preprocessed train dataset. + model_type: The type of model to train. target: The name of the target column in the dataset. Returns: The trained model artifact. - """ - # Use the dataset to fetch the target - # context = get_step_context() - # target = context.inputs["dataset_trn"].run_metadata['target'].value - target = "target" + Raises: + ValueError: If the model type is not supported. + """ # Initialize the model with the hyperparameters indicated in the step # parameters and train it on the training set. - model = DecisionTreeClassifier() + if model_type == "sgd": + model = SGDClassifier() + elif model_type == "rf": + model = RandomForestClassifier() + else: + raise ValueError(f"Unknown model type {model_type}") logger.info(f"Training model {model}...") model.fit( dataset_trn.drop(columns=[target]), dataset_trn[target], ) - - mlflow.sklearn.log_model(model, "breast_cancer_classifier_model") - mlflow.sklearn.autolog() - return model diff --git a/stack-showcase/utils/__init__.py b/stack-showcase/utils/__init__.py new file mode 100644 index 00000000..8d4e9614 --- /dev/null +++ b/stack-showcase/utils/__init__.py @@ -0,0 +1,16 @@ +# Apache Software License 2.0 +# +# Copyright (c) ZenML GmbH 2024. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# diff --git a/stack-showcase/utils/preprocess.py b/stack-showcase/utils/preprocess.py new file mode 100644 index 00000000..df60bce3 --- /dev/null +++ b/stack-showcase/utils/preprocess.py @@ -0,0 +1,56 @@ +# Apache Software License 2.0 +# +# Copyright (c) ZenML GmbH 2024. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from typing import Union + +import pandas as pd + + +class NADropper: + """Support class to drop NA values in sklearn Pipeline.""" + + def fit(self, *args, **kwargs): + return self + + def transform(self, X: Union[pd.DataFrame, pd.Series]): + return X.dropna() + + +class ColumnsDropper: + """Support class to drop specific columns in sklearn Pipeline.""" + + def __init__(self, columns): + self.columns = columns + + def fit(self, *args, **kwargs): + return self + + def transform(self, X: Union[pd.DataFrame, pd.Series]): + return X.drop(columns=self.columns) + + +class DataFrameCaster: + """Support class to cast type back to pd.DataFrame in sklearn Pipeline.""" + + def __init__(self, columns): + self.columns = columns + + def fit(self, *args, **kwargs): + return self + + def transform(self, X): + return pd.DataFrame(X, columns=self.columns) From 39d7da74b08c86f822e3204deb58ef65514039f6 Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Thu, 8 Feb 2024 12:39:55 +0100 Subject: [PATCH 03/20] clean-up a bit --- stack-showcase/run.ipynb | 2068 ++------------------------------------ 1 file changed, 75 insertions(+), 1993 deletions(-) diff --git a/stack-showcase/run.ipynb b/stack-showcase/run.ipynb index cca63345..31f8ed29 100644 --- a/stack-showcase/run.ipynb +++ b/stack-showcase/run.ipynb @@ -84,32 +84,10 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": null, "id": "081d5616", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[31mThe ZenML global configuration version (0.55.2) is higher than the version of ZenML currently being used (0.55.1). Read more about this issue and how to solve it here: \u001b[0m\u001b[1;36mhttps://docs.zenml.io/user-guide/advanced-guide/environment-management/global-settings-of-zenml#version-mismatch-downgrading\u001b[31m\u001b[0m\n", - "\u001b[?25l\u001b[2;36mFound existing ZenML repository at path \u001b[0m\n", - "\u001b[2;32m'/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/stack-showcase'\u001b[0m\u001b[2;36m.\u001b[0m\n", - "\u001b[2;32m⠋\u001b[0m\u001b[2;36m Initializing ZenML repository at \u001b[0m\n", - "\u001b[2;36m/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/stack-showcase.\u001b[0m\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Initializing ZenML repository at \n", - "/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/stack-showcase.\n", - "\n", - "\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[31mThe ZenML global configuration version (0.55.2) is higher than the version of ZenML currently being used (0.55.1). Read more about this issue and how to solve it here: \u001b[0m\u001b[1;36mhttps://docs.zenml.io/user-guide/advanced-guide/environment-management/global-settings-of-zenml#version-mismatch-downgrading\u001b[31m\u001b[0m\n", - "\u001b[?25l\u001b[32m⠋\u001b[0m Setting the repository active stack to \n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[2;36mActive repository stack set to: \u001b[0m\u001b[2;32m'local-sagemaker-step-operator-wandb'\u001b[0m\n", - "\u001b[2;32m⠋\u001b[0m\u001b[2;36m Setting the repository active stack to \u001b[0m\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Setting the repository active stack to \n", - "'local-sagemaker-step-operator-wandb'...\n", - "\u001b[1A\u001b[2K\u001b[1A\u001b[2K" - ] - } - ], + "outputs": [], "source": [ "# Initialize ZenML and set the default stack\n", "!zenml init\n", @@ -119,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": null, "id": "79f775f2", "metadata": {}, "outputs": [], @@ -176,7 +154,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": null, "id": "3cd974d1", "metadata": {}, "outputs": [], @@ -234,233 +212,10 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": null, "id": "d838e2ea", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;35mDataset with 541 records loaded!\u001b[0m\n", - "\u001b[1;35mDataset with 541 records loaded!\u001b[0m\n", - "\u001b[1;35mDataset with 541 records loaded!\u001b[0m\n" - ] - }, - { - "data": { - "text/html": [ - "
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mean radiusmean texturemean perimetermean areamean smoothnessmean compactnessmean concavitymean concave pointsmean symmetrymean fractal dimension...worst textureworst perimeterworst areaworst smoothnessworst compactnessworst concavityworst concave pointsworst symmetryworst fractal dimensiontarget
017.9910.38122.801001.00.118400.277600.30010.147100.24190.07871...17.33184.602019.00.16220.66560.71190.26540.46010.118900
120.5717.77132.901326.00.084740.078640.08690.070170.18120.05667...23.41158.801956.00.12380.18660.24160.18600.27500.089020
219.6921.25130.001203.00.109600.159900.19740.127900.20690.05999...25.53152.501709.00.14440.42450.45040.24300.36130.087580
311.4220.3877.58386.10.142500.283900.24140.105200.25970.09744...26.5098.87567.70.20980.86630.68690.25750.66380.173000
420.2914.34135.101297.00.100300.132800.19800.104300.18090.05883...16.67152.201575.00.13740.20500.40000.16250.23640.076780
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" - ], - "text/plain": [ - " mean radius mean texture mean perimeter mean area mean smoothness \\\n", - "0 17.99 10.38 122.80 1001.0 0.11840 \n", - "1 20.57 17.77 132.90 1326.0 0.08474 \n", - "2 19.69 21.25 130.00 1203.0 0.10960 \n", - "3 11.42 20.38 77.58 386.1 0.14250 \n", - "4 20.29 14.34 135.10 1297.0 0.10030 \n", - "\n", - " mean compactness mean concavity mean concave points mean symmetry \\\n", - "0 0.27760 0.3001 0.14710 0.2419 \n", - "1 0.07864 0.0869 0.07017 0.1812 \n", - "2 0.15990 0.1974 0.12790 0.2069 \n", - "3 0.28390 0.2414 0.10520 0.2597 \n", - "4 0.13280 0.1980 0.10430 0.1809 \n", - "\n", - " mean fractal dimension ... worst texture worst perimeter worst area \\\n", - "0 0.07871 ... 17.33 184.60 2019.0 \n", - "1 0.05667 ... 23.41 158.80 1956.0 \n", - "2 0.05999 ... 25.53 152.50 1709.0 \n", - "3 0.09744 ... 26.50 98.87 567.7 \n", - "4 0.05883 ... 16.67 152.20 1575.0 \n", - "\n", - " worst smoothness worst compactness worst concavity worst concave points \\\n", - "0 0.1622 0.6656 0.7119 0.2654 \n", - "1 0.1238 0.1866 0.2416 0.1860 \n", - "2 0.1444 0.4245 0.4504 0.2430 \n", - "3 0.2098 0.8663 0.6869 0.2575 \n", - "4 0.1374 0.2050 0.4000 0.1625 \n", - "\n", - " worst symmetry worst fractal dimension target \n", - "0 0.4601 0.11890 0 \n", - "1 0.2750 0.08902 0 \n", - "2 0.3613 0.08758 0 \n", - "3 0.6638 0.17300 0 \n", - "4 0.2364 0.07678 0 \n", - "\n", - "[5 rows x 31 columns]" - ] - }, - "execution_count": 73, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df = data_loader_simplified(random_state=42)\n", "df.head()" @@ -481,7 +236,7 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": null, "id": "b50a9537", "metadata": {}, "outputs": [], @@ -527,46 +282,10 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": null, "id": "1e0aa9af", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mfeature_engineering\u001b[1;35m.\u001b[0m\n", - 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] - }, - "execution_count": 79, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "feature_engineering(test_size=0.25, random_state=104)" ] @@ -698,18 +346,10 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": null, "id": "f208b200", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "feature_engineering-2024_02_08-11_09_17_420170\n" - ] - } - ], + "outputs": [], "source": [ "client = Client()\n", "run = client.get_pipeline(\"feature_engineering\").last_run\n", @@ -726,421 +366,20 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": null, "id": "34283e89", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'dataset_trn': ArtifactVersionResponse(id=UUID('5aee13f2-3a73-4f6e-8daa-b41e95c6078a'), permission_denied=False, body=ArtifactVersionResponseBody(created=datetime.datetime(2024, 2, 8, 11, 9, 23), updated=datetime.datetime(2024, 2, 8, 11, 9, 23), user=UserResponse(id=UUID('c915043d-706c-4885-9263-783cae8dad20'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 11, 16, 16, 5, 33), updated=datetime.datetime(2024, 2, 8, 10, 24, 32), active=True, activation_token=None, full_name='Andrei', email_opted_in=True, is_service_account=False), metadata=None, name='andrei@zenml.io'), artifact=ArtifactResponse(id=UUID('2f71c5a3-333a-4aa0-b5b4-341c26ee7f77'), permission_denied=False, body=ArtifactResponseBody(created=datetime.datetime(2023, 12, 12, 10, 29, 43), updated=datetime.datetime(2023, 12, 12, 10, 29, 43), tags=[]), metadata=None, name='dataset_trn'), version='261', uri='/Users/andrei.vishniakov/Library/Application Support/zenml/local_stores/466b79ce-3df9-4549-a50b-67ed433461f3/data_preprocessor/dataset_trn/89be426e-1def-480c-843d-89dc7c2fcf0a', type=, materializer=Source(module='zenml.materializers.pandas_materializer', attribute='PandasMaterializer', type=), data_type=Source(module='pandas.core.frame', attribute='DataFrame', type=, version='2.2.0', package_name='pandas'), tags=[]), metadata=None),\n", - 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405 rows × 31 columns

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" - ], - "text/plain": [ - " mean radius mean texture mean perimeter mean area mean smoothness \\\n", - "444 12.040 28.14 76.85 449.9 0.08752 \n", - "478 9.676 13.14 64.12 272.5 0.12550 \n", - "210 10.440 15.46 66.62 329.6 0.10530 \n", - "299 12.430 17.00 78.60 477.3 0.07557 \n", - "513 14.470 24.99 95.81 656.4 0.08837 \n", - ".. ... ... ... ... ... \n", - "71 16.070 19.65 104.10 817.7 0.09168 \n", - "106 10.510 20.19 68.64 334.2 0.11220 \n", - "270 14.410 19.73 96.03 651.0 0.08757 \n", - "435 27.420 26.27 186.90 2501.0 0.10840 \n", - "102 22.270 19.67 152.80 1509.0 0.13260 \n", - "\n", - " mean compactness mean concavity mean concave points mean symmetry \\\n", - "444 0.06000 0.023670 0.02377 0.1854 \n", - "478 0.22040 0.118800 0.07038 0.2057 \n", - "210 0.07722 0.006643 0.01216 0.1788 \n", - "299 0.03454 0.013420 0.01699 0.1472 \n", - "513 0.12300 0.100900 0.03890 0.1872 \n", - ".. ... ... ... ... \n", - "71 0.08424 0.097690 0.06638 0.1798 \n", - "106 0.13030 0.064760 0.03068 0.1922 \n", - "270 0.16760 0.136200 0.06602 0.1714 \n", - "435 0.19880 0.363500 0.16890 0.2061 \n", - "102 0.27680 0.426400 0.18230 0.2556 \n", - "\n", - " mean fractal dimension ... worst texture worst perimeter worst area \\\n", - "444 0.05698 ... 33.33 87.24 567.6 \n", - "478 0.09575 ... 18.04 69.47 328.1 \n", - "210 0.06450 ... 19.80 73.47 395.4 \n", - "299 0.05561 ... 20.21 81.76 515.9 \n", - "513 0.06341 ... 31.73 113.50 808.9 \n", - ".. ... ... ... ... ... \n", - "71 0.05391 ... 24.56 128.80 1223.0 \n", - "106 0.07782 ... 22.75 72.62 374.4 \n", - "270 0.07192 ... 22.13 101.70 767.3 \n", - "435 0.05623 ... 31.37 251.20 4254.0 \n", - "102 0.07039 ... 28.01 206.80 2360.0 \n", - "\n", - " worst smoothness worst compactness worst concavity \\\n", - "444 0.10410 0.09726 0.05524 \n", - "478 0.20060 0.36630 0.29130 \n", - "210 0.13410 0.11530 0.02639 \n", - "299 0.08409 0.04712 0.02237 \n", - "513 0.13400 0.42020 0.40400 \n", - ".. ... ... ... \n", - "71 0.15000 0.20450 0.28290 \n", - "106 0.13000 0.20490 0.12950 \n", - "270 0.09983 0.24720 0.22200 \n", - "435 0.13570 0.42560 0.68330 \n", - "102 0.17010 0.69970 0.96080 \n", - "\n", - " worst concave points worst symmetry worst fractal dimension target \n", - "444 0.05547 0.2404 0.06639 1 \n", - "478 0.10750 0.2848 0.13640 1 \n", - "210 0.04464 0.2615 0.08269 1 \n", - "299 0.02832 0.1901 0.05932 1 \n", - "513 0.12050 0.3187 0.10230 1 \n", - ".. ... ... ... ... \n", - "71 0.15200 0.2650 0.06387 0 \n", - "106 0.06136 0.2383 0.09026 1 \n", - "270 0.10210 0.2272 0.08799 1 \n", - "435 0.26250 0.2641 0.07427 0 \n", - "102 0.29100 0.4055 0.09789 0 \n", - "\n", - "[405 rows x 31 columns]" - ] - }, - "execution_count": 82, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Read one of the datasets. This is the one with a 0.25 test split\n", "run.steps[\"data_preprocessor\"].outputs[\"dataset_trn\"].load()" @@ -1158,21 +397,10 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": null, "id": "c8f90647", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 83, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Get artifact version from our run\n", "dataset_trn_artifact_version_via_run = run.steps[\"data_preprocessor\"].outputs[\"dataset_trn\"] \n", @@ -1187,7 +415,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": null, "id": "3f9d3dfd", "metadata": {}, "outputs": [], @@ -1208,398 +436,10 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": null, "id": "c82aca75", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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mean radiusmean texturemean perimetermean areamean smoothnessmean compactnessmean concavitymean concave pointsmean symmetrymean fractal dimension...worst textureworst perimeterworst areaworst smoothnessworst compactnessworst concavityworst concave pointsworst symmetryworst fractal dimensiontarget
44412.04028.1476.85449.90.087520.060000.0236700.023770.18540.05698...33.3387.24567.60.104100.097260.055240.055470.24040.066391
4789.67613.1464.12272.50.125500.220400.1188000.070380.20570.09575...18.0469.47328.10.200600.366300.291300.107500.28480.136401
21010.44015.4666.62329.60.105300.077220.0066430.012160.17880.06450...19.8073.47395.40.134100.115300.026390.044640.26150.082691
29912.43017.0078.60477.30.075570.034540.0134200.016990.14720.05561...20.2181.76515.90.084090.047120.022370.028320.19010.059321
51314.47024.9995.81656.40.088370.123000.1009000.038900.18720.06341...31.73113.50808.90.134000.420200.404000.120500.31870.102301
..................................................................
7116.07019.65104.10817.70.091680.084240.0976900.066380.17980.05391...24.56128.801223.00.150000.204500.282900.152000.26500.063870
10610.51020.1968.64334.20.112200.130300.0647600.030680.19220.07782...22.7572.62374.40.130000.204900.129500.061360.23830.090261
27014.41019.7396.03651.00.087570.167600.1362000.066020.17140.07192...22.13101.70767.30.099830.247200.222000.102100.22720.087991
43527.42026.27186.902501.00.108400.198800.3635000.168900.20610.05623...31.37251.204254.00.135700.425600.683300.262500.26410.074270
10222.27019.67152.801509.00.132600.276800.4264000.182300.25560.07039...28.01206.802360.00.170100.699700.960800.291000.40550.097890
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405 rows × 31 columns

\n", - "
" - ], - "text/plain": [ - " mean radius mean texture mean perimeter mean area mean smoothness \\\n", - "444 12.040 28.14 76.85 449.9 0.08752 \n", - "478 9.676 13.14 64.12 272.5 0.12550 \n", - "210 10.440 15.46 66.62 329.6 0.10530 \n", - "299 12.430 17.00 78.60 477.3 0.07557 \n", - "513 14.470 24.99 95.81 656.4 0.08837 \n", - ".. ... ... ... ... ... \n", - "71 16.070 19.65 104.10 817.7 0.09168 \n", - "106 10.510 20.19 68.64 334.2 0.11220 \n", - "270 14.410 19.73 96.03 651.0 0.08757 \n", - "435 27.420 26.27 186.90 2501.0 0.10840 \n", - "102 22.270 19.67 152.80 1509.0 0.13260 \n", - "\n", - " mean compactness mean concavity mean concave points mean symmetry \\\n", - "444 0.06000 0.023670 0.02377 0.1854 \n", - "478 0.22040 0.118800 0.07038 0.2057 \n", - "210 0.07722 0.006643 0.01216 0.1788 \n", - "299 0.03454 0.013420 0.01699 0.1472 \n", - "513 0.12300 0.100900 0.03890 0.1872 \n", - ".. ... ... ... ... \n", - "71 0.08424 0.097690 0.06638 0.1798 \n", - "106 0.13030 0.064760 0.03068 0.1922 \n", - "270 0.16760 0.136200 0.06602 0.1714 \n", - "435 0.19880 0.363500 0.16890 0.2061 \n", - "102 0.27680 0.426400 0.18230 0.2556 \n", - "\n", - " mean fractal dimension ... worst texture worst perimeter worst area \\\n", - "444 0.05698 ... 33.33 87.24 567.6 \n", - "478 0.09575 ... 18.04 69.47 328.1 \n", - "210 0.06450 ... 19.80 73.47 395.4 \n", - "299 0.05561 ... 20.21 81.76 515.9 \n", - "513 0.06341 ... 31.73 113.50 808.9 \n", - ".. ... ... ... ... ... \n", - "71 0.05391 ... 24.56 128.80 1223.0 \n", - "106 0.07782 ... 22.75 72.62 374.4 \n", - "270 0.07192 ... 22.13 101.70 767.3 \n", - "435 0.05623 ... 31.37 251.20 4254.0 \n", - "102 0.07039 ... 28.01 206.80 2360.0 \n", - "\n", - " worst smoothness worst compactness worst concavity \\\n", - "444 0.10410 0.09726 0.05524 \n", - "478 0.20060 0.36630 0.29130 \n", - "210 0.13410 0.11530 0.02639 \n", - "299 0.08409 0.04712 0.02237 \n", - "513 0.13400 0.42020 0.40400 \n", - ".. ... ... ... \n", - "71 0.15000 0.20450 0.28290 \n", - "106 0.13000 0.20490 0.12950 \n", - "270 0.09983 0.24720 0.22200 \n", - "435 0.13570 0.42560 0.68330 \n", - "102 0.17010 0.69970 0.96080 \n", - "\n", - " worst concave points worst symmetry worst fractal dimension target \n", - "444 0.05547 0.2404 0.06639 1 \n", - "478 0.10750 0.2848 0.13640 1 \n", - "210 0.04464 0.2615 0.08269 1 \n", - "299 0.02832 0.1901 0.05932 1 \n", - "513 0.12050 0.3187 0.10230 1 \n", - ".. ... ... ... ... \n", - "71 0.15200 0.2650 0.06387 0 \n", - "106 0.06136 0.2383 0.09026 1 \n", - "270 0.10210 0.2272 0.08799 1 \n", - "435 0.26250 0.2641 0.07427 0 \n", - "102 0.29100 0.4055 0.09789 0 \n", - "\n", - "[405 rows x 31 columns]" - ] - }, - "execution_count": 85, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Load an artifact to verify you can fetch it\n", "dataset_trn_artifact_version.load()" @@ -1640,7 +480,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": null, "id": "fccf1bd9", "metadata": {}, "outputs": [], @@ -1704,7 +544,7 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": null, "id": "1aa98f2f", "metadata": {}, "outputs": [], @@ -1727,8 +567,6 @@ " # Load the datasets from an older pipeline\n", " dataset_trn = ExternalArtifact(id=train_dataset_id)\n", " dataset_tst = ExternalArtifact(id=test_dataset_id)\n", - " # dataset_trn = client.get_artifact_version(name_id_or_prefix=train_dataset_id)\n", - " # dataset_tst = client.get_artifact_version(name_id_or_prefix=test_dataset_id) \n", "\n", " trained_model = model_trainer(\n", " dataset_trn=dataset_trn,\n", @@ -1757,39 +595,10 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": null, "id": "c64885ac", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 94)\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mExecuting a new run.\u001b[0m\n", - "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mandrei@zenml.io\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mlocal-mlflow-stack\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m model_registry: \u001b[0m\u001b[1;36mmlflow\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m model_deployer: \u001b[0m\u001b[1;36mmlflow\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m experiment_tracker: \u001b[0m\u001b[1;36mmlflow\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mTraining model RandomForestClassifier()...\u001b[0m\n", - "\u001b[1;35mTraining model RandomForestClassifier()...\u001b[0m\n", - "\u001b[1;35mTraining model RandomForestClassifier()...\u001b[0m\n", - "\u001b[1;35mTraining model RandomForestClassifier()...\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.993s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mTrain accuracy=100.00%\u001b[0m\n", - "\u001b[1;35mTest accuracy=90.44%\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has finished in \u001b[0m\u001b[1;36m1.154s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mPipeline run has finished in \u001b[0m\u001b[1;36m4.104s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/d7de5d44-e92c-4da8-b716-3ee8e1851698/runs/a039c329-b39f-44fc-98bc-58510f82c09d/dag\u001b[0m\n" - ] - } - ], + "outputs": [], "source": [ "# Use a random forest model with the chosen datasets.\n", "# We need to pass the ID's of the datasets into the function\n", @@ -1804,39 +613,10 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": null, "id": "4300c82f", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 95)\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mExecuting a new run.\u001b[0m\n", - "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mandrei@zenml.io\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mlocal-mlflow-stack\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m model_registry: \u001b[0m\u001b[1;36mmlflow\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m model_deployer: \u001b[0m\u001b[1;36mmlflow\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m experiment_tracker: \u001b[0m\u001b[1;36mmlflow\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mTraining model SGDClassifier()...\u001b[0m\n", - "\u001b[1;35mTraining model SGDClassifier()...\u001b[0m\n", - "\u001b[1;35mTraining model SGDClassifier()...\u001b[0m\n", - "\u001b[1;35mTraining model SGDClassifier()...\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.880s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mTrain accuracy=91.60%\u001b[0m\n", - "\u001b[1;35mTest accuracy=90.44%\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has finished in \u001b[0m\u001b[1;36m1.159s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mPipeline run has finished in \u001b[0m\u001b[1;36m3.931s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/9d131977-eb26-44c5-90d6-bb6a5f0b9f43/runs/d3f30c80-3813-498b-ba6f-d5df041cfa8f/dag\u001b[0m\n" - ] - } - ], + "outputs": [], "source": [ "# Use a SGD classifier\n", "sgd_run = training(\n", @@ -1860,21 +640,10 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": null, "id": "d95810b1", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 105, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# The evaluator returns a float value with the accuracy\n", "rf_run.steps[\"model_evaluator\"].output.load() >= sgd_run.steps[\"model_evaluator\"].output.load()" @@ -1902,7 +671,7 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": null, "id": "99ca00c0", "metadata": {}, "outputs": [], @@ -1919,45 +688,10 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": null, "id": "0e78a520", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mReusing registered pipeline version: \u001b[0m\u001b[1;36m(version: 95)\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mNew model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[1;35m was created implicitly.\u001b[0m\n", - "\u001b[1;35mNew model version \u001b[0m\u001b[1;36m1\u001b[1;35m was created.\u001b[0m\n", - "\u001b[1;35mExecuting a new run.\u001b[0m\n", - "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mandrei@zenml.io\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mlocal-mlflow-stack\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m model_registry: \u001b[0m\u001b[1;36mmlflow\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m model_deployer: \u001b[0m\u001b[1;36mmlflow\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m experiment_tracker: \u001b[0m\u001b[1;36mmlflow\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mPipeline run has finished in \u001b[0m\u001b[1;36m3.267s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/9d131977-eb26-44c5-90d6-bb6a5f0b9f43/runs/a93c0c85-ef7e-4f11-8726-6b66bebe56d2/dag\u001b[0m\n" - ] - }, - { - "data": { - "text/plain": [ - "PipelineRunResponse(id=UUID('a93c0c85-ef7e-4f11-8726-6b66bebe56d2'), permission_denied=False, body=PipelineRunResponseBody(created=datetime.datetime(2024, 2, 8, 11, 24, 53), updated=datetime.datetime(2024, 2, 8, 11, 24, 53), user=UserResponse(id=UUID('c915043d-706c-4885-9263-783cae8dad20'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 11, 16, 16, 5, 33), updated=datetime.datetime(2024, 2, 8, 10, 24, 32), active=True, activation_token=None, full_name='Andrei', email_opted_in=True, is_service_account=False), metadata=None, name='andrei@zenml.io'), status=, stack=StackResponse(id=UUID('57f7dcf2-4bd5-439b-b454-da4000dd3bc8'), permission_denied=False, body=StackResponseBody(created=datetime.datetime(2023, 12, 11, 15, 24, 51), updated=datetime.datetime(2023, 12, 21, 1, 15, 28), user=UserResponse(id=UUID('ac044541-bcb1-4f33-b41e-5c60a3f39485'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 10, 23, 15, 36, 12), updated=datetime.datetime(2024, 2, 7, 9, 57, 48), active=True, activation_token=None, full_name='Alexej Penner', email_opted_in=True, is_service_account=False), metadata=None, name='alexej@zenml.io')), metadata=None, name='local-mlflow-stack'), pipeline=PipelineResponse(id=UUID('9d131977-eb26-44c5-90d6-bb6a5f0b9f43'), permission_denied=False, body=PipelineResponseBody(created=datetime.datetime(2024, 2, 8, 11, 21, 10), updated=datetime.datetime(2024, 2, 8, 11, 21, 10), user=UserResponse(id=UUID('c915043d-706c-4885-9263-783cae8dad20'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 11, 16, 16, 5, 33), updated=datetime.datetime(2024, 2, 8, 10, 24, 32), active=True, activation_token=None, full_name='Andrei', email_opted_in=True, is_service_account=False), metadata=None, name='andrei@zenml.io'), status=[, , ], version='95'), metadata=None, name='training'), build=None, schedule=None, code_reference=None), metadata=PipelineRunResponseMetadata(workspace=WorkspaceResponse(id=UUID('f3a544f2-afb5-4672-934a-7a465c66201c'), permission_denied=False, body=WorkspaceResponseBody(created=datetime.datetime(2023, 10, 23, 15, 34, 47), updated=datetime.datetime(2023, 10, 23, 15, 34, 47)), metadata=None, name='default'), run_metadata={}, steps={}, config=PipelineConfiguration(enable_cache=None, enable_artifact_metadata=None, enable_artifact_visualization=None, enable_step_logs=None, settings={'experiment_tracker.mlflow': BaseSettings()}, extra={}, failure_hook_source=None, success_hook_source=None, model=Model(name='breast_cancer_classifier', license='Apache 2.0', description='A breast cancer classifier', audience=None, use_cases=None, limitations=None, trade_offs=None, ethics=None, tags=['classifier', 'breast_cancer', 'sgd'], version='1', save_models_to_registry=True, suppress_class_validation_warnings=True, was_created_in_this_run=True), parameters=None, name='training'), start_time=datetime.datetime(2024, 2, 8, 11, 24, 53), end_time=None, client_environment={'environment': 'notebook', 'os': 'mac', 'mac_version': '14.0', 'python_version': '3.11.6'}, orchestrator_environment={}, orchestrator_run_id=None), name='training-2024_02_08-11_24_52_676745')" - ] - }, - "execution_count": 111, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Let's train the SGD model and tag the version name with \"sgd\"\n", "pipeline_settings[\"model\"].tags = [\"breast_cancer\", \"classifier\", \"sgd\"]\n", @@ -1976,47 +710,10 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": null, "id": "9b8e0002", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mReusing registered pipeline version: \u001b[0m\u001b[1;36m(version: 94)\u001b[1;35m.\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[1;35mNew model version \u001b[0m\u001b[1;36m2\u001b[1;35m was created.\u001b[0m\n", - "\u001b[1;35mExecuting a new run.\u001b[0m\n", - "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mandrei@zenml.io\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mlocal-mlflow-stack\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m model_registry: \u001b[0m\u001b[1;36mmlflow\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m model_deployer: \u001b[0m\u001b[1;36mmlflow\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m experiment_tracker: \u001b[0m\u001b[1;36mmlflow\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m.\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m.\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mPipeline run has finished in \u001b[0m\u001b[1;36m3.737s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/d7de5d44-e92c-4da8-b716-3ee8e1851698/runs/4adb298a-d725-4a8b-a79f-fe89b96898d9/dag\u001b[0m\n" - ] - }, - { - "data": { - "text/plain": [ - "PipelineRunResponse(id=UUID('4adb298a-d725-4a8b-a79f-fe89b96898d9'), permission_denied=False, body=PipelineRunResponseBody(created=datetime.datetime(2024, 2, 8, 11, 24, 59), updated=datetime.datetime(2024, 2, 8, 11, 24, 59), user=UserResponse(id=UUID('c915043d-706c-4885-9263-783cae8dad20'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 11, 16, 16, 5, 33), updated=datetime.datetime(2024, 2, 8, 10, 24, 32), active=True, activation_token=None, full_name='Andrei', email_opted_in=True, is_service_account=False), metadata=None, name='andrei@zenml.io'), status=, stack=StackResponse(id=UUID('57f7dcf2-4bd5-439b-b454-da4000dd3bc8'), permission_denied=False, body=StackResponseBody(created=datetime.datetime(2023, 12, 11, 15, 24, 51), updated=datetime.datetime(2023, 12, 21, 1, 15, 28), user=UserResponse(id=UUID('ac044541-bcb1-4f33-b41e-5c60a3f39485'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 10, 23, 15, 36, 12), updated=datetime.datetime(2024, 2, 7, 9, 57, 48), active=True, activation_token=None, full_name='Alexej Penner', email_opted_in=True, is_service_account=False), metadata=None, name='alexej@zenml.io')), metadata=None, name='local-mlflow-stack'), pipeline=PipelineResponse(id=UUID('d7de5d44-e92c-4da8-b716-3ee8e1851698'), permission_denied=False, body=PipelineResponseBody(created=datetime.datetime(2024, 2, 8, 11, 21), updated=datetime.datetime(2024, 2, 8, 11, 21), user=UserResponse(id=UUID('c915043d-706c-4885-9263-783cae8dad20'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 11, 16, 16, 5, 33), updated=datetime.datetime(2024, 2, 8, 10, 24, 32), active=True, activation_token=None, full_name='Andrei', email_opted_in=True, is_service_account=False), metadata=None, name='andrei@zenml.io'), status=[, , ], version='94'), metadata=None, name='training'), build=None, schedule=None, code_reference=None), metadata=PipelineRunResponseMetadata(workspace=WorkspaceResponse(id=UUID('f3a544f2-afb5-4672-934a-7a465c66201c'), permission_denied=False, body=WorkspaceResponseBody(created=datetime.datetime(2023, 10, 23, 15, 34, 47), updated=datetime.datetime(2023, 10, 23, 15, 34, 47)), metadata=None, name='default'), run_metadata={}, steps={}, config=PipelineConfiguration(enable_cache=None, enable_artifact_metadata=None, enable_artifact_visualization=None, enable_step_logs=None, settings={'experiment_tracker.mlflow': BaseSettings()}, extra={}, failure_hook_source=None, success_hook_source=None, model=Model(name='breast_cancer_classifier', license='Apache 2.0', description='A breast cancer classifier', audience=None, use_cases=None, limitations=None, trade_offs=None, ethics=None, tags=['rf', 'classifier', 'breast_cancer'], version='2', save_models_to_registry=True, suppress_class_validation_warnings=True, was_created_in_this_run=True), parameters=None, name='training'), start_time=datetime.datetime(2024, 2, 8, 11, 24, 59), end_time=None, client_environment={'environment': 'notebook', 'os': 'mac', 'mac_version': '14.0', 'python_version': '3.11.6'}, orchestrator_environment={}, orchestrator_run_id=None), name='training-2024_02_08-11_24_58_707852')" - ] - }, - "execution_count": 112, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Let's train the RF model and tag the version name with \"rf\"\n", "pipeline_settings[\"model\"].tags = [\"breast_cancer\", \"classifier\", \"rf\"]\n", @@ -2044,29 +741,10 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": null, "id": "fbb25913", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "id=UUID('cb1ac539-bac3-45ca-8837-c6bc0942e4e8') permission_denied=False body=ModelResponseBody(created=datetime.datetime(2024, 2, 8, 11, 24, 51), updated=datetime.datetime(2024, 2, 8, 11, 24, 51), user=UserResponse(id=UUID('c915043d-706c-4885-9263-783cae8dad20'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 11, 16, 16, 5, 33), updated=datetime.datetime(2024, 2, 8, 10, 24, 32), active=True, activation_token=None, full_name='Andrei', email_opted_in=True, is_service_account=False), metadata=None, name='andrei@zenml.io'), tags=[TagResponse(id=UUID('dad60bad-aeda-42fc-8108-a49e0b451b34'), permission_denied=False, body=TagResponseBody(created=datetime.datetime(2023, 12, 12, 15, 8, 29), updated=datetime.datetime(2023, 12, 12, 15, 8, 29), color=, tagged_count=3), metadata=None, name='classifier'), TagResponse(id=UUID('12c4c193-5bc8-4ef7-bedc-0b4e2732bc54'), permission_denied=False, body=TagResponseBody(created=datetime.datetime(2024, 2, 8, 11, 21, 57), updated=datetime.datetime(2024, 2, 8, 11, 21, 57), color=, tagged_count=2), metadata=None, name='sgd'), TagResponse(id=UUID('469b1284-df0a-4fee-b247-d80915501fb8'), permission_denied=False, body=TagResponseBody(created=datetime.datetime(2023, 12, 12, 15, 8, 29), updated=datetime.datetime(2023, 12, 12, 15, 8, 29), color=, tagged_count=3), metadata=None, name='breast_cancer')], latest_version_name='2', latest_version_id=UUID('67259714-9ff5-4047-906a-a5bbe8f497a1'), workspace={'id': 'f3a544f2-afb5-4672-934a-7a465c66201c', 'permission_denied': False, 'body': {'created': '2023-10-23T15:34:47', 'updated': '2023-10-23T15:34:47'}, 'metadata': None, 'name': 'default'}) metadata=ModelResponseMetadata(workspace=WorkspaceResponse(id=UUID('f3a544f2-afb5-4672-934a-7a465c66201c'), permission_denied=False, body=WorkspaceResponseBody(created=datetime.datetime(2023, 10, 23, 15, 34, 47), updated=datetime.datetime(2023, 10, 23, 15, 34, 47)), metadata=None, name='default'), license='Apache 2.0', description='A breast cancer classifier', audience=None, use_cases=None, limitations=None, trade_offs=None, ethics=None, save_models_to_registry=True) name='breast_cancer_classifier'\n", - "Model breast_cancer_classifier has 2 versions\n" - ] - }, - { - "data": { - "text/plain": [ - "('1', '2')" - ] - }, - "execution_count": 113, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "zenml_model = client.get_model(\"breast_cancer_classifier\")\n", "print(zenml_model)\n", @@ -2091,24 +769,10 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": null, "id": "31211413", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
RandomForestClassifier()
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" - ], - "text/plain": [ - "RandomForestClassifier()" - ] - }, - "execution_count": 114, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Let's load the RF version\n", "rf_zenml_model_version = client.list_model_versions(\"breast_cancer_classifier\", tag=\"rf\")[-1]\n", @@ -2146,7 +810,7 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": null, "id": "26b718f8", "metadata": {}, "outputs": [], @@ -2205,7 +869,7 @@ }, { "cell_type": "code", - "execution_count": 116, + "execution_count": null, "id": "92c4c7dc", "metadata": {}, "outputs": [], @@ -2236,7 +900,7 @@ }, { "cell_type": "code", - "execution_count": 120, + "execution_count": null, "id": "37c409bd", "metadata": {}, "outputs": [], @@ -2274,18 +938,10 @@ }, { "cell_type": "code", - "execution_count": 121, + "execution_count": null, "id": "61bf5939", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;35m\u001b[0m\u001b[1;36mversion\u001b[1;35m \u001b[0m\u001b[1;36mproduction\u001b[1;35m matches one of the possible \u001b[0m\u001b[1;36mModelStages\u001b[1;35m and will be fetched using stage.\u001b[0m\n" - ] - } - ], + "outputs": [], "source": [ "pipeline_settings = {\"enable_cache\": False}\n", "\n", @@ -2298,54 +954,10 @@ }, { "cell_type": "code", - "execution_count": 122, + "execution_count": null, "id": "ff3402f1", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36minference\u001b[1;35m.\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 16)\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mExecuting a new run.\u001b[0m\n", - "\u001b[1;35mCaching is disabled by default for \u001b[0m\u001b[1;36minference\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mandrei@zenml.io\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mlocal-mlflow-stack\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m model_registry: \u001b[0m\u001b[1;36mmlflow\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m model_deployer: \u001b[0m\u001b[1;36mmlflow\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m experiment_tracker: \u001b[0m\u001b[1;36mmlflow\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mDataset with 28 records loaded!\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has finished in \u001b[0m\u001b[1;36m4.069s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36minference_preprocessor\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36minference_preprocessor\u001b[1;35m has finished in \u001b[0m\u001b[1;36m4.373s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36minference_predict\u001b[1;35m has started.\u001b[0m\n", - "\u001b[33mYou specified both an ID as well as a version of the artifact_versions. Ignoring the version and fetching the artifact_versions by ID.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36minference_predict\u001b[1;35m has finished in \u001b[0m\u001b[1;36m5.387s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mPipeline run has finished in \u001b[0m\u001b[1;36m15.779s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/9711266c-1ab0-411e-8ccc-18760cc5aed1/runs/c89eb7d0-e53a-48cc-92fb-ffe2a5287db4/dag\u001b[0m\n" - ] - }, - { - "data": { - "text/plain": [ - "PipelineRunResponse(id=UUID('c89eb7d0-e53a-48cc-92fb-ffe2a5287db4'), permission_denied=False, body=PipelineRunResponseBody(created=datetime.datetime(2024, 2, 8, 11, 27, 40), updated=datetime.datetime(2024, 2, 8, 11, 27, 40), user=UserResponse(id=UUID('c915043d-706c-4885-9263-783cae8dad20'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 11, 16, 16, 5, 33), updated=datetime.datetime(2024, 2, 8, 10, 24, 32), active=True, activation_token=None, full_name='Andrei', email_opted_in=True, is_service_account=False), metadata=None, name='andrei@zenml.io'), status=, stack=StackResponse(id=UUID('57f7dcf2-4bd5-439b-b454-da4000dd3bc8'), permission_denied=False, body=StackResponseBody(created=datetime.datetime(2023, 12, 11, 15, 24, 51), updated=datetime.datetime(2023, 12, 21, 1, 15, 28), user=UserResponse(id=UUID('ac044541-bcb1-4f33-b41e-5c60a3f39485'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 10, 23, 15, 36, 12), updated=datetime.datetime(2024, 2, 7, 9, 57, 48), active=True, activation_token=None, full_name='Alexej Penner', email_opted_in=True, is_service_account=False), metadata=None, name='alexej@zenml.io')), metadata=None, name='local-mlflow-stack'), pipeline=PipelineResponse(id=UUID('9711266c-1ab0-411e-8ccc-18760cc5aed1'), permission_denied=False, body=PipelineResponseBody(created=datetime.datetime(2024, 2, 8, 11, 27, 38), updated=datetime.datetime(2024, 2, 8, 11, 27, 38), user=UserResponse(id=UUID('c915043d-706c-4885-9263-783cae8dad20'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 11, 16, 16, 5, 33), updated=datetime.datetime(2024, 2, 8, 10, 24, 32), active=True, activation_token=None, full_name='Andrei', email_opted_in=True, is_service_account=False), metadata=None, name='andrei@zenml.io'), status=[], version='16'), metadata=None, name='inference'), build=None, schedule=None, code_reference=None), metadata=PipelineRunResponseMetadata(workspace=WorkspaceResponse(id=UUID('f3a544f2-afb5-4672-934a-7a465c66201c'), permission_denied=False, body=WorkspaceResponseBody(created=datetime.datetime(2023, 10, 23, 15, 34, 47), updated=datetime.datetime(2023, 10, 23, 15, 34, 47)), metadata=None, name='default'), run_metadata={}, steps={}, config=PipelineConfiguration(enable_cache=False, enable_artifact_metadata=None, enable_artifact_visualization=None, enable_step_logs=None, settings={'experiment_tracker.mlflow': BaseSettings()}, extra={}, failure_hook_source=None, success_hook_source=None, model=Model(name='breast_cancer_classifier', license=None, description=None, audience=None, use_cases=None, limitations=None, trade_offs=None, ethics=None, tags=None, version='production', save_models_to_registry=True, suppress_class_validation_warnings=True, was_created_in_this_run=False), parameters=None, name='inference'), start_time=datetime.datetime(2024, 2, 8, 11, 27, 40), end_time=None, client_environment={'environment': 'notebook', 'os': 'mac', 'mac_version': '14.0', 'python_version': '3.11.6'}, orchestrator_environment={}, orchestrator_run_id=None), name='inference-2024_02_08-11_27_39_992578')" - ] - }, - "execution_count": 122, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# the `with_options` method allows us to pass in pipeline settings\n", "# and returns a configured pipeline\n", @@ -2370,49 +982,10 @@ }, { "cell_type": "code", - "execution_count": 123, + "execution_count": null, "id": "e191d019", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 1\n", - "1 0\n", - "2 0\n", - "3 1\n", - "4 1\n", - "5 0\n", - "6 0\n", - "7 0\n", - "8 0\n", - "9 1\n", - "10 1\n", - "11 0\n", - "12 1\n", - "13 0\n", - "14 1\n", - "15 0\n", - "16 1\n", - "17 1\n", - "18 1\n", - "19 0\n", - "20 1\n", - "21 1\n", - "22 0\n", - "23 1\n", - "24 1\n", - "25 1\n", - "26 1\n", - "27 1\n", - "Name: series, dtype: int64" - ] - }, - "execution_count": 123, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Fetch production model\n", "production_model_version = client.get_model_version(\"breast_cancer_classifier\", \"production\")\n", @@ -2443,41 +1016,9 @@ }, { "cell_type": "code", - "execution_count": 124, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[31mThe ZenML global configuration version (0.55.2) is higher than the version of ZenML currently being used (0.55.1). Read more about this issue and how to solve it here: \u001b[0m\u001b[1;36mhttps://docs.zenml.io/user-guide/advanced-guide/environment-management/global-settings-of-zenml#version-mismatch-downgrading\u001b[31m\u001b[0m\n", - "\u001b[2K\u001b[2;36mActive repository stack set to: \u001b[0m\u001b[2;32m'local-wandb'\u001b[0m.\n", - "\u001b[2K\u001b[32m⠋\u001b[0m Setting the repository active stack to 'local-wandb'...b'...\u001b[0m\n", - "\u001b[1A\u001b[2K\u001b[31mThe ZenML global configuration version (0.55.2) is higher than the version of ZenML currently being used (0.55.1). Read more about this issue and how to solve it here: \u001b[0m\u001b[1;36mhttps://docs.zenml.io/user-guide/advanced-guide/environment-management/global-settings-of-zenml#version-mismatch-downgrading\u001b[31m\u001b[0m\n", - "\u001b[?25l\u001b[32m⠋\u001b[0m Describing the stack...\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[3m Stack Configuration \u001b[0m\n", - "┏━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┓\n", - "┃\u001b[1m \u001b[0m\u001b[1mCOMPONENT_TYPE \u001b[0m\u001b[1m \u001b[0m│\u001b[1m \u001b[0m\u001b[1mCOMPONENT_NAME\u001b[0m\u001b[1m \u001b[0m┃\n", - "┠────────────────────┼────────────────┨\n", - "┃ ARTIFACT_STORE │ default ┃\n", - "┠────────────────────┼────────────────┨\n", - "┃ EXPERIMENT_TRACKER │ zenml_wandb ┃\n", - "┠────────────────────┼────────────────┨\n", - "┃ ORCHESTRATOR │ default ┃\n", - "┗━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┛\n", - "\u001b[2;3m 'local-wandb' stack (ACTIVE) \u001b[0m\n", - "\u001b[32m⠋\u001b[0m Describing the stack...\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[2;36mStack \u001b[0m\u001b[2;32m'local-wandb'\u001b[0m\u001b[2;36m with id \u001b[0m\u001b[2;32m'0b187411-e02c-4856-a7bf-779154797044'\u001b[0m\u001b[2;36m is owned by \u001b[0m\n", - "\u001b[2;36muser hamza@zenml.io.\u001b[0m\n", - "\u001b[2;32m⠋\u001b[0m\u001b[2;36m Describing the stack...\u001b[0m\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Describing the stack...\n", - "\n", - "\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[2;36mDashboard URL: \u001b[0m\n", - "\u001b[2;4;94mhttps://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/stacks/0b187411-e0\u001b[0m\n", - "\u001b[2;4;94m2c-4856-a7bf-779154797044/configuration\u001b[0m\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "!zenml stack set local-wandb\n", "!zenml stack describe local-wandb" @@ -2485,179 +1026,9 @@ }, { "cell_type": "code", - "execution_count": 126, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[31mThe ZenML global configuration version (0.55.2) is higher than the version of ZenML currently being used (0.55.1). Read more about this issue and how to solve it here: \u001b[0m\u001b[1;36mhttps://docs.zenml.io/user-guide/advanced-guide/environment-management/global-settings-of-zenml#version-mismatch-downgrading\u001b[31m\u001b[0m\n", - "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 98)\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mNew model version \u001b[0m\u001b[1;36m5\u001b[1;35m was created.\u001b[0m\n", - "\u001b[1;35mExecuting a new run.\u001b[0m\n", - "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mandrei@zenml.io\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mlocal-wandb\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m experiment_tracker: \u001b[0m\u001b[1;36mzenml_wandb\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mDataset with 541 records loaded!\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has finished in \u001b[0m\u001b[1;36m3.972s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has finished in \u001b[0m\u001b[1;36m5.907s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has finished in \u001b[0m\u001b[1;36m7.590s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mTraining model SGDClassifier()...\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has finished in \u001b[0m\u001b[1;36m3.851s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mInitializing wandb with entity None, project name: None, run_name: training-2024_02_08-11_31_01_169272_model_evaluator.\u001b[0m\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mzenmlcodemonkey\u001b[0m (\u001b[33mzenmlcode\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.16.3\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/stack-showcase/wandb/run-20240208_123128-u36gintc\u001b[0m\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33mtraining-2024_02_08-11_31_01_169272_model_evaluator\u001b[0m\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/zenmlcode/zenml-projects-stack-showcase\u001b[0m\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/zenmlcode/zenml-projects-stack-showcase/runs/u36gintc\u001b[0m\n", - "\u001b[1;35mTrain accuracy=81.02%\u001b[0m\n", - "\u001b[1;35mTest accuracy=79.82%\u001b[0m\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: \n", - "\u001b[34m\u001b[1mwandb\u001b[0m: \n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Run history:\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: test_accuracy ▁\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: train_accuracy ▁\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: \n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Run summary:\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: test_accuracy 0.79817\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: train_accuracy 0.81019\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: \n", - "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run \u001b[33mtraining-2024_02_08-11_31_01_169272_model_evaluator\u001b[0m at: \u001b[34m\u001b[4mhttps://wandb.ai/zenmlcode/zenml-projects-stack-showcase/runs/u36gintc\u001b[0m\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: ️⚡ View job at \u001b[34m\u001b[4mhttps://wandb.ai/zenmlcode/zenml-projects-stack-showcase/jobs/QXJ0aWZhY3RDb2xsZWN0aW9uOjEzODE0MTY0OA==/version_details/v4\u001b[0m\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Synced 5 W&B file(s), 1 media file(s), 1 artifact file(s) and 0 other file(s)\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Find logs at: \u001b[35m\u001b[1m./wandb/run-20240208_123128-u36gintc/logs\u001b[0m\n", - "\u001b[1;35mImplicitly linking artifact \u001b[0m\u001b[1;36moutput\u001b[1;35m to model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[1;35m version \u001b[0m\u001b[1;36m5\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has finished in \u001b[0m\u001b[1;36m18.209s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_promoter\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mModel accuracy 79.82% is below 80% ! Not promoting model.\u001b[0m\n", - "\u001b[1;35mImplicitly linking artifact \u001b[0m\u001b[1;36moutput\u001b[1;35m to model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[1;35m version \u001b[0m\u001b[1;36m5\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_promoter\u001b[1;35m has finished in \u001b[0m\u001b[1;36m3.817s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mPipeline run has finished in \u001b[0m\u001b[1;36m48.207s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/cca9f21f-69ad-4593-aa70-f962367a84cf/runs/1853b693-96b3-4131-b9e9-737249251d6b/dag\u001b[0m\n", - "\u001b[1;35mTraining pipeline with SGD finished successfully!\n", - "\n", - "\u001b[0m\n", - "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 99)\u001b[1;35m.\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[1;35mNew model version \u001b[0m\u001b[1;36m6\u001b[1;35m was created.\u001b[0m\n", - "\u001b[1;35mExecuting a new run.\u001b[0m\n", - "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mandrei@zenml.io\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mlocal-wandb\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m experiment_tracker: \u001b[0m\u001b[1;36mzenml_wandb\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m.\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m.\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m.\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has started.\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[1;35mTraining model RandomForestClassifier()...\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has finished in \u001b[0m\u001b[1;36m4.359s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mInitializing wandb with entity None, project name: None, run_name: training-2024_02_08-11_31_52_057837_model_evaluator.\u001b[0m\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.16.3\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/stack-showcase/wandb/run-20240208_123204-71w79kyo\u001b[0m\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33mtraining-2024_02_08-11_31_52_057837_model_evaluator\u001b[0m\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/zenmlcode/zenml-projects-stack-showcase\u001b[0m\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/zenmlcode/zenml-projects-stack-showcase/runs/71w79kyo\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[1;35mTrain accuracy=100.00%\u001b[0m\n", - "\u001b[1;35mTest accuracy=98.17%\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: \n", - "\u001b[34m\u001b[1mwandb\u001b[0m: \n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Run history:\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: test_accuracy ▁\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: train_accuracy ▁\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: \n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Run summary:\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: test_accuracy 0.98165\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: train_accuracy 1.0\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: \n", - "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run \u001b[33mtraining-2024_02_08-11_31_52_057837_model_evaluator\u001b[0m at: \u001b[34m\u001b[4mhttps://wandb.ai/zenmlcode/zenml-projects-stack-showcase/runs/71w79kyo\u001b[0m\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: ️⚡ View job at \u001b[34m\u001b[4mhttps://wandb.ai/zenmlcode/zenml-projects-stack-showcase/jobs/QXJ0aWZhY3RDb2xsZWN0aW9uOjEzODE0MTY0OA==/version_details/v4\u001b[0m\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Synced 5 W&B file(s), 1 media file(s), 1 artifact file(s) and 0 other file(s)\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Find logs at: \u001b[35m\u001b[1m./wandb/run-20240208_123204-71w79kyo/logs\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[1;35mImplicitly linking artifact \u001b[0m\u001b[1;36moutput\u001b[1;35m to model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[1;35m version \u001b[0m\u001b[1;36m6\u001b[1;35m.\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has finished in \u001b[0m\u001b[1;36m12.132s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_promoter\u001b[1;35m has started.\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[1;35mModel promoted to production!\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[1;35mImplicitly linking artifact \u001b[0m\u001b[1;36moutput\u001b[1;35m to model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[1;35m version \u001b[0m\u001b[1;36m6\u001b[1;35m.\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['rf'], 'tags removed': ['sgd']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_promoter\u001b[1;35m has finished in \u001b[0m\u001b[1;36m6.097s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mPipeline run has finished in \u001b[0m\u001b[1;36m31.591s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/df7f887a-f644-4c4b-b07f-35697df9a670/runs/f86c03b5-63bf-4b07-9f03-47cbbd44981d/dag\u001b[0m\n", - "\u001b[1;35mTraining pipeline with RF finished successfully!\n", - "\n", - "\u001b[0m\n", - "\u001b[1;35m\u001b[0m\u001b[1;36mversion\u001b[1;35m \u001b[0m\u001b[1;36mproduction\u001b[1;35m matches one of the possible \u001b[0m\u001b[1;36mModelStages\u001b[1;35m and will be fetched using stage.\u001b[0m\n", - "\u001b[33mUsing an external artifact as step input currently invalidates caching for the step and all downstream steps. Future releases will introduce hashing of artifacts which will improve this behavior.\u001b[0m\n", - "\u001b[33mUsing an external artifact as step input currently invalidates caching for the step and all downstream steps. Future releases will introduce hashing of artifacts which will improve this behavior.\u001b[0m\n", - "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36minference\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 17)\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mExecuting a new run.\u001b[0m\n", - "\u001b[1;35mCaching is disabled by default for \u001b[0m\u001b[1;36minference\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mandrei@zenml.io\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mlocal-wandb\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m experiment_tracker: \u001b[0m\u001b[1;36mzenml_wandb\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mDataset with 28 records loaded!\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has finished in \u001b[0m\u001b[1;36m4.301s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36minference_preprocessor\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36minference_preprocessor\u001b[1;35m has finished in \u001b[0m\u001b[1;36m5.624s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36minference_predict\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36minference_predict\u001b[1;35m has finished in \u001b[0m\u001b[1;36m4.672s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mPipeline run has finished in \u001b[0m\u001b[1;36m17.363s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/e7a3f6f0-4479-431d-8689-bf9ff4d4a29b/runs/44751d4e-31fe-4c90-94d2-af12e24cf02a/dag\u001b[0m\n", - "\u001b[1;35mInference pipeline finished successfully!\u001b[0m\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "!python3 run.py --training-pipeline --inference-pipeline" ] @@ -2687,49 +1058,9 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[31mThe ZenML global configuration version (0.55.2) is higher than the version of ZenML currently being used (0.55.1). Read more about this issue and how to solve it here: \u001b[0m\u001b[1;36mhttps://docs.zenml.io/user-guide/advanced-guide/environment-management/global-settings-of-zenml#version-mismatch-downgrading\u001b[31m\u001b[0m\n", - "\u001b[?25l\u001b[32m⠋\u001b[0m Setting the repository active stack to \n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[2;36mActive repository stack set to: \u001b[0m\u001b[2;32m'local-sagemaker-step-operator-wandb'\u001b[0m\n", - "\u001b[2;32m⠋\u001b[0m\u001b[2;36m Setting the repository active stack to \u001b[0m\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Setting the repository active stack to \n", - "'local-sagemaker-step-operator-wandb'...\n", - "\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[31mThe ZenML global configuration version (0.55.2) is higher than the version of ZenML currently being used (0.55.1). Read more about this issue and how to solve it here: \u001b[0m\u001b[1;36mhttps://docs.zenml.io/user-guide/advanced-guide/environment-management/global-settings-of-zenml#version-mismatch-downgrading\u001b[31m\u001b[0m\n", - "\u001b[?25l\u001b[32m⠋\u001b[0m Describing the stack...\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[3m Stack Configuration \u001b[0m\n", - "┏━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┓\n", - "┃\u001b[1m \u001b[0m\u001b[1mCOMPONENT_TYPE \u001b[0m\u001b[1m \u001b[0m│\u001b[1m \u001b[0m\u001b[1mCOMPONENT_NAME\u001b[0m\u001b[1m \u001b[0m┃\n", - "┠────────────────────┼────────────────┨\n", - "┃ STEP_OPERATOR │ sagemaker-eu ┃\n", - "┠────────────────────┼────────────────┨\n", - "┃ CONTAINER_REGISTRY │ aws-eu ┃\n", - "┠────────────────────┼────────────────┨\n", - "┃ EXPERIMENT_TRACKER │ zenml_wandb ┃\n", - "┠────────────────────┼────────────────┨\n", - "┃ ORCHESTRATOR │ default ┃\n", - "┠────────────────────┼────────────────┨\n", - "┃ ARTIFACT_STORE │ s3-zenfiles ┃\n", - "┗━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┛\n", - "\u001b[2;3m 'local-sagemaker-step-operator-wandb' \u001b[0m\n", - "\u001b[2;3m stack (ACTIVE) \u001b[0m\n", - "\u001b[32m⠋\u001b[0m Describing the stack...\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[2;36mStack \u001b[0m\u001b[2;32m'local-sagemaker-step-operator-wandb'\u001b[0m\u001b[2;36m with id \u001b[0m\n", - "\u001b[2;32m'5712467b-5ab0-4549-8bc9-2ea7714c8ed8'\u001b[0m\u001b[2;36m is owned by user hamza@zenml.io.\u001b[0m\n", - "\u001b[2;32m⠋\u001b[0m\u001b[2;36m Describing the stack...\u001b[0m\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Describing the stack...\n", - "\n", - "\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[2;36mDashboard URL: \u001b[0m\n", - "\u001b[2;4;94mhttps://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/stacks/5712467b-5a\u001b[0m\n", - "\u001b[2;4;94mb0-4549-8bc9-2ea7714c8ed8/configuration\u001b[0m\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "!zenml stack set local-sagemaker-step-operator-wandb\n", "!zenml stack describe local-sagemaker-step-operator-wandb" @@ -2737,220 +1068,9 @@ }, { "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[31mThe ZenML global configuration version (0.55.2) is higher than the version of ZenML currently being used (0.55.1). Read more about this issue and how to solve it here: \u001b[0m\u001b[1;36mhttps://docs.zenml.io/user-guide/advanced-guide/environment-management/global-settings-of-zenml#version-mismatch-downgrading\u001b[31m\u001b[0m\n", - "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 84)\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mBuilding Docker image(s) for pipeline \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mBuilding Docker image \u001b[0m\u001b[1;36m715803424590.dkr.ecr.eu-central-1.amazonaws.com/zenml:training-model_trainer-sagemaker_step_operator\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35m- Including user-defined requirements: \u001b[0m\u001b[1;36mpyarrow\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m- Including integration requirements: \u001b[0m\u001b[1;36mPillow>=9.1.0\u001b[1;35m, \u001b[0m\u001b[1;36maws-profile-manager\u001b[1;35m, \u001b[0m\u001b[1;36mboto3<=1.26.76\u001b[1;35m, \u001b[0m\u001b[1;36mkubernetes\u001b[1;35m, \u001b[0m\u001b[1;36ms3fs>2022.3.0,<=2023.4.0\u001b[1;35m, \u001b[0m\u001b[1;36msagemaker==2.117.0\u001b[1;35m, \u001b[0m\u001b[1;36mscikit-learn\u001b[1;35m, \u001b[0m\u001b[1;36mwandb>=0.12.12\u001b[1;35m\u001b[0m\n", - "\u001b[33mCould not import GCP service connector: No module named 'google.api_core'.\u001b[0m\n", - "\u001b[33mCould not import Azure service connector: No module named 'azure.identity'.\u001b[0m\n", - "\u001b[1;35mStep 1/11 : FROM zenmldocker/zenml:0.55.1-py3.11\u001b[0m\n", - "\u001b[1;35mStep 2/11 : WORKDIR /app\u001b[0m\n", - "\u001b[1;35mStep 3/11 : COPY .zenml_user_requirements .\u001b[0m\n", - "\u001b[1;35mStep 4/11 : RUN pip install --default-timeout=60 --no-cache-dir -r .zenml_user_requirements\u001b[0m\n", - "\u001b[1;35mStep 5/11 : COPY .zenml_integration_requirements .\u001b[0m\n", - "\u001b[1;35mStep 6/11 : RUN pip install --default-timeout=60 --no-cache-dir -r .zenml_integration_requirements\u001b[0m\n", - "\u001b[1;35mStep 7/11 : ENV ZENML_ENABLE_REPO_INIT_WARNINGS=False\u001b[0m\n", - "\u001b[1;35mStep 8/11 : ENV ZENML_CONFIG_PATH=/app/.zenconfig\u001b[0m\n", - "\u001b[1;35mStep 9/11 : COPY . .\u001b[0m\n", - "\u001b[1;35mStep 10/11 : RUN chmod -R a+rw .\u001b[0m\n", - "\u001b[1;35mStep 11/11 : ENTRYPOINT $__ZENML_ENTRYPOINT\u001b[0m\n", - "\u001b[1;35mFound credentials in shared credentials file: ~/.aws/credentials\u001b[0m\n", - "\u001b[1;35mPushing Docker image \u001b[0m\u001b[1;36m715803424590.dkr.ecr.eu-central-1.amazonaws.com/zenml:training-model_trainer-sagemaker_step_operator\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mFinished pushing Docker image.\u001b[0m\n", - "\u001b[1;35mFinished building Docker image(s).\u001b[0m\n", - "\u001b[1;35mExecuting a new run.\u001b[0m\n", - "\u001b[1;35mUsing a build:\u001b[0m\n", - "\u001b[1;35m Image(s): 715803424590.dkr.ecr.eu-central-1.amazonaws.com/zenml@sha256:9471e6bda044e266f246f1fadb3b90bfcac7a8df3e37d6f4a3fbca61be22c9a2\u001b[0m\n", - "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mandrei@zenml.io\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mlocal-sagemaker-step-operator-wandb\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m step_operator: \u001b[0m\u001b[1;36msagemaker-eu\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m container_registry: \u001b[0m\u001b[1;36maws-eu\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m experiment_tracker: \u001b[0m\u001b[1;36mzenml_wandb\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m image_builder: \u001b[0m\u001b[1;36mlocal\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36ms3-zenfiles\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mDataset with 541 records loaded!\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has finished in \u001b[0m\u001b[1;36m10.037s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has finished in \u001b[0m\u001b[1;36m14.984s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has finished in \u001b[0m\u001b[1;36m18.481s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mUsing step operator \u001b[0m\u001b[1;36msagemaker-eu\u001b[1;35m to run step \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mDefaulting to the only supported framework/algorithm version: latest.\u001b[0m\n", - "\u001b[1;35mIgnoring unnecessary instance type: None.\u001b[0m\n", - "\u001b[1;35mCreating training-job with name: training-model-trainer-Rigp\u001b[0m\n", - "2024-02-08 10:15:47 Starting - Starting the training job...\n", - "2024-02-08 10:16:15 Starting - Preparing the instances for trainingProfilerReport-1707387345: InProgress\n", - "......\n", - "2024-02-08 10:17:17 Downloading - Downloading input data...\n", - "2024-02-08 10:17:56 Training - Training image download completed. Training in progress.\u001b[34m#033[31mThe ZenML global configuration version (0.55.2) is higher than the version of ZenML currently being used (0.55.1). Read more about this issue and how to solve it here: #033[0m#033[1;36mhttps://docs.zenml.io/user-guide/advanced-guide/environment-management/global-settings-of-zenml#version-mismatch-downgrading#033[31m#033[0m\u001b[0m\n", - "\u001b[34m#033[33mCould not import GCP service connector: No module named 'google.api_core'.#033[0m\u001b[0m\n", - "\u001b[34m#033[33mCould not import Azure service connector: No module named 'azure.identity'.#033[0m\u001b[0m\n", - "\u001b[34m#033[1;35mTraining model SGDClassifier()...#033[0m\u001b[0m\n", - "\n", - "2024-02-08 10:18:28 Uploading - Uploading generated training model\n", - "2024-02-08 10:18:28 Completed - Training job completed\n", - "Training seconds: 72\n", - "Billable seconds: 72\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has finished in \u001b[0m\u001b[1;36m3m2s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mInitializing wandb with entity None, project name: None, run_name: training-2024_02_08-10_14_35_085276_model_evaluator.\u001b[0m\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mzenmlcodemonkey\u001b[0m (\u001b[33mzenmlcode\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.16.3\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/stack-showcase/wandb/run-20240208_111852-cy888oje\u001b[0m\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33mtraining-2024_02_08-10_14_35_085276_model_evaluator\u001b[0m\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/zenmlcode/zenml-projects-stack-showcase\u001b[0m\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/zenmlcode/zenml-projects-stack-showcase/runs/cy888oje\u001b[0m\n", - "\u001b[33mYour artifact was materialized under Python version 'unknown' but you are currently using '3.11.6'. This might cause unexpected behavior since pickle is not reproducible across Python versions. Attempting to load anyway...\u001b[0m\n", - "/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/.venv_showcase/lib/python3.11/site-packages/sklearn/base.py:318: UserWarning: Trying to unpickle estimator SGDClassifier from version 1.4.0 when using version 1.2.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n", - "https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n", - " warnings.warn(\n", - "\u001b[1;35mTrain accuracy=89.35%\u001b[0m\n", - "\u001b[1;35mTest accuracy=87.16%\u001b[0m\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: \n", - "\u001b[34m\u001b[1mwandb\u001b[0m: \n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Run history:\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: test_accuracy ▁\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: train_accuracy ▁\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: \n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Run summary:\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: test_accuracy 0.87156\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: train_accuracy 0.89352\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: \n", - "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run \u001b[33mtraining-2024_02_08-10_14_35_085276_model_evaluator\u001b[0m at: \u001b[34m\u001b[4mhttps://wandb.ai/zenmlcode/zenml-projects-stack-showcase/runs/cy888oje\u001b[0m\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: ️⚡ View job at \u001b[34m\u001b[4mhttps://wandb.ai/zenmlcode/zenml-projects-stack-showcase/jobs/QXJ0aWZhY3RDb2xsZWN0aW9uOjEzODE0MTY0OA==/version_details/v4\u001b[0m\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Synced 5 W&B file(s), 1 media file(s), 1 artifact file(s) and 0 other file(s)\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Find logs at: \u001b[35m\u001b[1m./wandb/run-20240208_111852-cy888oje/logs\u001b[0m\n", - "\u001b[1;35mImplicitly linking artifact \u001b[0m\u001b[1;36moutput\u001b[1;35m to model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[1;35m version \u001b[0m\u001b[1;36msgd\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has finished in \u001b[0m\u001b[1;36m29.290s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_promoter\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mModel promoted to production!\u001b[0m\n", - "\u001b[33mYour artifact was materialized under Python version 'unknown' but you are currently using '3.11.6'. This might cause unexpected behavior since pickle is not reproducible across Python versions. Attempting to load anyway...\u001b[0m\n", - "\u001b[1;35mImplicitly linking artifact \u001b[0m\u001b[1;36moutput\u001b[1;35m to model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[1;35m version \u001b[0m\u001b[1;36msgd\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_promoter\u001b[1;35m has finished in \u001b[0m\u001b[1;36m21.640s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mPipeline run has finished in \u001b[0m\u001b[1;36m5m10s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/6c04c6c4-9e7b-41c5-b215-3510e294c293/runs/6a8510f0-6753-4cb4-b111-832bf7315863/dag\u001b[0m\n", - "\u001b[1;35mTraining pipeline with SGD finished successfully!\n", - "\n", - "\u001b[0m\n", - "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 85)\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mBuilding Docker image(s) for pipeline \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mBuilding Docker image \u001b[0m\u001b[1;36m715803424590.dkr.ecr.eu-central-1.amazonaws.com/zenml:training-model_trainer-sagemaker_step_operator\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35m- Including user-defined requirements: \u001b[0m\u001b[1;36mpyarrow\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m- Including integration requirements: \u001b[0m\u001b[1;36mPillow>=9.1.0\u001b[1;35m, \u001b[0m\u001b[1;36maws-profile-manager\u001b[1;35m, \u001b[0m\u001b[1;36mboto3<=1.26.76\u001b[1;35m, \u001b[0m\u001b[1;36mkubernetes\u001b[1;35m, \u001b[0m\u001b[1;36ms3fs>2022.3.0,<=2023.4.0\u001b[1;35m, \u001b[0m\u001b[1;36msagemaker==2.117.0\u001b[1;35m, \u001b[0m\u001b[1;36mscikit-learn\u001b[1;35m, \u001b[0m\u001b[1;36mwandb>=0.12.12\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mStep 1/11 : FROM zenmldocker/zenml:0.55.1-py3.11\u001b[0m\n", - "\u001b[1;35mStep 2/11 : WORKDIR /app\u001b[0m\n", - "\u001b[1;35mStep 3/11 : COPY .zenml_user_requirements .\u001b[0m\n", - "\u001b[1;35mStep 4/11 : RUN pip install --default-timeout=60 --no-cache-dir -r .zenml_user_requirements\u001b[0m\n", - "\u001b[1;35mStep 5/11 : COPY .zenml_integration_requirements .\u001b[0m\n", - "\u001b[1;35mStep 6/11 : RUN pip install --default-timeout=60 --no-cache-dir -r .zenml_integration_requirements\u001b[0m\n", - "\u001b[1;35mStep 7/11 : ENV ZENML_ENABLE_REPO_INIT_WARNINGS=False\u001b[0m\n", - "\u001b[1;35mStep 8/11 : ENV ZENML_CONFIG_PATH=/app/.zenconfig\u001b[0m\n", - "\u001b[1;35mStep 9/11 : COPY . .\u001b[0m\n", - "\u001b[1;35mStep 10/11 : RUN chmod -R a+rw .\u001b[0m\n", - "\u001b[1;35mStep 11/11 : ENTRYPOINT $__ZENML_ENTRYPOINT\u001b[0m\n", - "\u001b[1;35mPushing Docker image \u001b[0m\u001b[1;36m715803424590.dkr.ecr.eu-central-1.amazonaws.com/zenml:training-model_trainer-sagemaker_step_operator\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mFinished pushing Docker image.\u001b[0m\n", - "\u001b[1;35mFinished building Docker image(s).\u001b[0m\n", - "\u001b[1;35mExecuting a new run.\u001b[0m\n", - "\u001b[1;35mUsing a build:\u001b[0m\n", - "\u001b[1;35m Image(s): 715803424590.dkr.ecr.eu-central-1.amazonaws.com/zenml@sha256:775a7f09dace326a3cdbc83ff2f21cbebc2e8bb6ec3d1a2ad113344b5c257223\u001b[0m\n", - "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mandrei@zenml.io\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mlocal-sagemaker-step-operator-wandb\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m step_operator: \u001b[0m\u001b[1;36msagemaker-eu\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m container_registry: \u001b[0m\u001b[1;36maws-eu\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m experiment_tracker: \u001b[0m\u001b[1;36mzenml_wandb\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m image_builder: \u001b[0m\u001b[1;36mlocal\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36ms3-zenfiles\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m.\u001b[0m\n", - "^C\n", - "\u001b[31mFailed preparing run step \u001b[0m\u001b[1;36mdata_splitter\u001b[31m.\u001b[0m\n", - "Traceback (most recent call last):\n", - " File \"/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/.venv_showcase/lib/python3.11/site-packages/zenml/orchestrators/step_launcher.py\", line 212, in launch\n", - " execution_needed, step_run = self._prepare(\n", - " ^^^^^^^^^^^^^^\n", - " File \"/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/.venv_showcase/lib/python3.11/site-packages/zenml/orchestrators/step_launcher.py\", line 363, in _prepare\n", - " self._link_cached_artifacts_to_model(\n", - " File \"/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/.venv_showcase/lib/python3.11/site-packages/zenml/orchestrators/step_launcher.py\", line 399, in _link_cached_artifacts_to_model\n", - " link_artifact_config_to_model(\n", - " File \"/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/.venv_showcase/lib/python3.11/site-packages/zenml/model/utils.py\", line 101, in link_artifact_config_to_model\n", - " model._get_or_create_model_version()\n", - " File \"/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/.venv_showcase/lib/python3.11/site-packages/zenml/model/model.py\", line 699, in _get_or_create_model_version\n", - " model_version = self._get_model_version()\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/.venv_showcase/lib/python3.11/site-packages/zenml/model/model.py\", line 597, in _get_model_version\n", - " mv = zenml_client.get_model_version(\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/.venv_showcase/lib/python3.11/site-packages/zenml/client.py\", line 4948, in get_model_version\n", - " model_versions = self.zen_store.list_model_versions(\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/.venv_showcase/lib/python3.11/site-packages/zenml/zen_stores/rest_zen_store.py\", line 2893, in list_model_versions\n", - " return self._list_paginated_resources(\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/.venv_showcase/lib/python3.11/site-packages/zenml/zen_stores/rest_zen_store.py\", line 3742, in _list_paginated_resources\n", - " body = self.get(f\"{route}\", params=params)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/.venv_showcase/lib/python3.11/site-packages/zenml/zen_stores/rest_zen_store.py\", line 3492, in get\n", - " return self._request(\n", - " ^^^^^^^^^^^^^^\n", - " File \"/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/.venv_showcase/lib/python3.11/site-packages/zenml/zen_stores/rest_zen_store.py\", line 3445, in _request\n", - " self.session.request(\n", - " File \"/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/.venv_showcase/lib/python3.11/site-packages/requests/sessions.py\", line 589, in request\n", - " resp = self.send(prep, **send_kwargs)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/.venv_showcase/lib/python3.11/site-packages/requests/sessions.py\", line 703, in send\n", - " r = adapter.send(request, **kwargs)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/.venv_showcase/lib/python3.11/site-packages/requests/adapters.py\", line 486, in send\n", - " resp = conn.urlopen(\n", - " ^^^^^^^^^^^^^\n", - " File \"/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/.venv_showcase/lib/python3.11/site-packages/urllib3/connectionpool.py\", line 715, in urlopen\n", - " httplib_response = self._make_request(\n", - " ^^^^^^^^^^^^^^^^^^^\n", - " File \"/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/.venv_showcase/lib/python3.11/site-packages/urllib3/connectionpool.py\", line 467, in _make_request\n", - " six.raise_from(e, None)\n", - " File \"\", line 3, in raise_from\n", - " File \"/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/.venv_showcase/lib/python3.11/site-packages/urllib3/connectionpool.py\", line 462, in _make_request\n", - " httplib_response = conn.getresponse()\n", - " ^^^^^^^^^^^^^^^^^^\n", - " File \"/opt/homebrew/Cellar/python@3.11/3.11.6_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/http/client.py\", line 1378, in getresponse\n", - " response.begin()\n", - " File \"/opt/homebrew/Cellar/python@3.11/3.11.6_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/http/client.py\", line 318, in begin\n", - " version, status, reason = self._read_status()\n", - " ^^^^^^^^^^^^^^^^^^^\n", - " File \"/opt/homebrew/Cellar/python@3.11/3.11.6_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/http/client.py\", line 279, in _read_status\n", - " line = str(self.fp.readline(_MAXLINE + 1), \"iso-8859-1\")\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/opt/homebrew/Cellar/python@3.11/3.11.6_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/socket.py\", line 706, in readinto\n", - " return self._sock.recv_into(b)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/opt/homebrew/Cellar/python@3.11/3.11.6_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/ssl.py\", line 1311, in recv_into\n", - " return self.read(nbytes, buffer)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/opt/homebrew/Cellar/python@3.11/3.11.6_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/ssl.py\", line 1167, in read\n", - " return self._sslobj.read(len, buffer)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "KeyboardInterrupt\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "!python3 run.py --training-pipeline --inference-pipeline --custom-training-suffix _sagemaker" ] @@ -2964,7 +1084,7 @@ }, { "cell_type": "code", - "execution_count": 127, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2974,18 +1094,9 @@ }, { "cell_type": "code", - "execution_count": 128, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SGD version is staged as `None`\n", - "RF version is staged as `production`\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "sgd_model_version = client.list_model_versions(\"breast_cancer_classifier\",tag=\"sgd\")[-1]\n", "rf_model_version = client.list_model_versions(\"breast_cancer_classifier\",tag=\"rf\")[-1]\n", @@ -3044,18 +1155,9 @@ }, { "cell_type": "code", - "execution_count": 129, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SGD metrics: train=81.02% test=79.82%\n", - "RF (production) metrics: train=100.00% test=98.17%\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "sgd_clf_metadata = sgd_model_version.get_artifact(\"sklearn_classifier\").run_metadata\n", "rf_clf_metadata = rf_model_version.get_artifact(\"sklearn_classifier\").run_metadata\n", @@ -3072,20 +1174,9 @@ }, { "cell_type": "code", - "execution_count": 130, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "import seaborn as sns\n", "import numpy as np\n", @@ -3115,18 +1206,9 @@ }, { "cell_type": "code", - "execution_count": 131, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SGD version: https://wandb.ai/zenmlcode/zenml-projects-stack-showcase/runs/u36gintc\n", - "RF version: https://wandb.ai/zenmlcode/zenml-projects-stack-showcase/runs/71w79kyo\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "print(f'SGD version: {sgd_model_version.run_metadata[\"wandb_url\"].value}')\n", "print(f'RF version: {rf_model_version.run_metadata[\"wandb_url\"].value}')" From f51345ca6da295fc6eee8dc2a5758d31f45a8f95 Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Thu, 8 Feb 2024 14:52:50 +0100 Subject: [PATCH 04/20] 0.55.2 update --- stack-showcase/pipelines/inference.py | 8 +++-- stack-showcase/requirements.txt | 2 +- stack-showcase/run.ipynb | 40 +++++++++++++++++++++---- stack-showcase/steps/model_evaluator.py | 11 +++++-- 4 files changed, 49 insertions(+), 12 deletions(-) diff --git a/stack-showcase/pipelines/inference.py b/stack-showcase/pipelines/inference.py index 1ef7e44d..f82a80b7 100644 --- a/stack-showcase/pipelines/inference.py +++ b/stack-showcase/pipelines/inference.py @@ -21,7 +21,7 @@ inference_preprocessor, ) -from zenml import get_pipeline_context, pipeline, ExternalArtifact +from zenml import get_pipeline_context, pipeline from zenml.logger import get_logger logger = get_logger(__name__) @@ -41,10 +41,12 @@ def inference(random_state: str, target: str): target: Name of target column in dataset. """ # Get the production model artifact - model = ExternalArtifact(name="sklearn_classifier") + model = get_pipeline_context().model.get_artifact("sklearn_classifier") # Get the preprocess pipeline artifact associated with this version - preprocess_pipeline = ExternalArtifact(name="preprocess_pipeline") + preprocess_pipeline = get_pipeline_context().model.get_artifact( + "preprocess_pipeline" + ) # Link all the steps together by calling them and passing the output # of one step as the input of the next step. diff --git a/stack-showcase/requirements.txt b/stack-showcase/requirements.txt index 83280eca..92512b9d 100644 --- a/stack-showcase/requirements.txt +++ b/stack-showcase/requirements.txt @@ -1,4 +1,4 @@ -zenml[server]>=0.50.0 +zenml[server]>=0.55.2 notebook scikit-learn<1.3 s3fs>2022.3.0,<=2023.4.0 diff --git a/stack-showcase/run.ipynb b/stack-showcase/run.ipynb index 31f8ed29..81e942ec 100644 --- a/stack-showcase/run.ipynb +++ b/stack-showcase/run.ipynb @@ -8,7 +8,7 @@ "source": [ "# pre-demo recommendations\n", "! zenml connect --url https://1cf18d95-zenml.cloudinfra.zenml.io \n", - "! zenml delete model breast_cancer_classifier -y" + "! zenml model delete breast_cancer_classifier -y" ] }, { @@ -549,8 +549,6 @@ "metadata": {}, "outputs": [], "source": [ - "from zenml import ExternalArtifact\n", - "\n", "@pipeline\n", "def training(\n", " train_dataset_id: Optional[UUID] = None,\n", @@ -565,8 +563,8 @@ " dataset_trn, dataset_tst = feature_engineering()\n", " else:\n", " # Load the datasets from an older pipeline\n", - " dataset_trn = ExternalArtifact(id=train_dataset_id)\n", - " dataset_tst = ExternalArtifact(id=test_dataset_id)\n", + " dataset_trn = client.get_artifact_version(name_id_or_prefix=train_dataset_id)\n", + " dataset_tst = client.get_artifact_version(name_id_or_prefix=test_dataset_id)\n", "\n", " trained_model = model_trainer(\n", " dataset_trn=dataset_trn,\n", @@ -919,7 +917,7 @@ " df_inference = inference_preprocessor(\n", " dataset_inf=df_inference,\n", " # We use the preprocess pipeline from the feature engineering pipeline\n", - " preprocess_pipeline=ExternalArtifact(id=preprocess_pipeline_id),\n", + " preprocess_pipeline=client.get_artifact_version(name_id_or_prefix=preprocess_pipeline_id),\n", " target=target,\n", " )\n", " inference_predict(\n", @@ -1213,6 +1211,36 @@ "print(f'SGD version: {sgd_model_version.run_metadata[\"wandb_url\"].value}')\n", "print(f'RF version: {rf_model_version.run_metadata[\"wandb_url\"].value}')" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With Model Control Plane we can also easily track lineage of artifacts and pipeline runs:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for artifact_name, versions in sgd_model_version.data_artifacts.items():\n", + " if versions:\n", + " print(f\"Existing version of `{artifact_name}`:\")\n", + " for version_name, artifact_ in versions.items():\n", + " print(version_name, artifact_.data_type.attribute)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for run_name, run_ in sgd_model_version.pipeline_runs.items():\n", + " print(run_name, run_.id)" + ] } ], "metadata": { diff --git a/stack-showcase/steps/model_evaluator.py b/stack-showcase/steps/model_evaluator.py index 36a4bf3b..4ea2ba81 100644 --- a/stack-showcase/steps/model_evaluator.py +++ b/stack-showcase/steps/model_evaluator.py @@ -21,10 +21,11 @@ from sklearn.base import ClassifierMixin from sklearn.metrics import confusion_matrix -from zenml import log_artifact_metadata, step, log_model_metadata +from zenml import log_artifact_metadata, step, log_model_metadata, get_step_context from zenml.logger import get_logger import wandb from zenml.client import Client +from zenml.exceptions import StepContextError logger = get_logger(__name__) @@ -109,7 +110,13 @@ def model_evaluator( .ravel() .tolist(), } - log_model_metadata(metadata={"wandb_url": wandb.run.url}) + try: + if get_step_context().model: + log_model_metadata(metadata={"wandb_url": wandb.run.url}) + except StepContextError: + # if model not configured not able to log metadata + pass + log_artifact_metadata( metadata=metadata, artifact_name="sklearn_classifier", From 7869be47047d1e52c043595b119b328aee0d5cd8 Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Fri, 9 Feb 2024 12:40:24 +0100 Subject: [PATCH 05/20] add xgboost --- .../.dockerignore | 0 {stack-showcase => classifier-e2e}/README.md | 0 .../_assets/airflow_stack.png | Bin .../_assets/cloud_mcp.png | Bin .../_assets/cloud_mcp_predictions.png | Bin .../_assets/cloud_mcp_screenshot.png | Bin .../_assets/default_stack.png | Bin .../_assets/deployment_pipeline.png | Bin .../_assets/feature_engineering_pipeline.png | Bin .../_assets/inference_pipeline.png | Bin .../inference_pipeline.png:Zone.Identifier | 0 .../_assets/local_sagmaker_so_stack.png | Bin .../_assets/pipeline_overview.png | Bin .../_assets/sagemaker_stack.png | Bin .../_assets/training_pipeline.png | Bin .../configs/feature_engineering.yaml | 0 .../configs/inference.yaml | 0 .../configs/training_sgd.yaml | 9 +- .../configs/training_sgd_sagemaker.yaml | 0 .../configs/training_xgboost.yaml | 12 ++- .../configs/training_xgboost_sagemaker.yaml | 7 +- .../pipelines/__init__.py | 0 .../pipelines/feature_engineering.py | 0 .../pipelines/inference.py | 2 +- .../pipelines/training.py | 0 .../requirements.txt | 3 +- {stack-showcase => classifier-e2e}/run.ipynb | 91 +++++++++++------- {stack-showcase => classifier-e2e}/run.py | 4 +- .../steps/__init__.py | 0 .../steps/data_loader.py | 0 .../steps/data_preprocessor.py | 0 .../steps/data_splitter.py | 0 .../steps/inference_predict.py | 0 .../steps/inference_preprocessor.py | 0 .../steps/model_evaluator.py | 14 ++- .../steps/model_promoter.py | 4 +- .../steps/model_trainer.py | 9 +- 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b/classifier-e2e/configs/feature_engineering.yaml similarity index 100% rename from stack-showcase/configs/feature_engineering.yaml rename to classifier-e2e/configs/feature_engineering.yaml diff --git a/stack-showcase/configs/inference.yaml b/classifier-e2e/configs/inference.yaml similarity index 100% rename from stack-showcase/configs/inference.yaml rename to classifier-e2e/configs/inference.yaml diff --git a/stack-showcase/configs/training_sgd.yaml b/classifier-e2e/configs/training_sgd.yaml similarity index 66% rename from stack-showcase/configs/training_sgd.yaml rename to classifier-e2e/configs/training_sgd.yaml index 2134fea4..d070395e 100644 --- a/stack-showcase/configs/training_sgd.yaml +++ b/classifier-e2e/configs/training_sgd.yaml @@ -15,4 +15,11 @@ model: # Configure the pipeline parameters: - model_type: "sgd" # Choose between rf/sgd \ No newline at end of file + model_type: "sgd" # Choose between xgboost/sgd + +steps: + model_trainer: + settings: + step_operator.sagemaker: + estimator_args: + instance_type : ml.m5.large \ No newline at end of file diff --git a/stack-showcase/configs/training_sgd_sagemaker.yaml b/classifier-e2e/configs/training_sgd_sagemaker.yaml similarity index 100% rename from stack-showcase/configs/training_sgd_sagemaker.yaml rename to classifier-e2e/configs/training_sgd_sagemaker.yaml diff --git a/stack-showcase/configs/training_rf.yaml b/classifier-e2e/configs/training_xgboost.yaml similarity index 55% rename from stack-showcase/configs/training_rf.yaml rename to classifier-e2e/configs/training_xgboost.yaml index 3060b360..6796ea97 100644 --- a/stack-showcase/configs/training_rf.yaml +++ b/classifier-e2e/configs/training_xgboost.yaml @@ -3,6 +3,7 @@ settings: docker: required_integrations: - sklearn + - xgboost requirements: - pyarrow @@ -11,8 +12,15 @@ model: name: breast_cancer_classifier license: Apache 2.0 description: A breast cancer classifier - tags: ["breast_cancer", "classifier","rf"] + tags: ["breast_cancer", "classifier","xgboost"] # Configure the pipeline parameters: - model_type: "rf" # Choose between rf/sgd + model_type: "xgboost" # Choose between sgd/xgboost + +steps: + model_trainer: + settings: + step_operator.sagemaker: + estimator_args: + instance_type : ml.m5.large \ No newline at end of file diff --git a/stack-showcase/configs/training_rf_sagemaker.yaml b/classifier-e2e/configs/training_xgboost_sagemaker.yaml similarity index 73% rename from stack-showcase/configs/training_rf_sagemaker.yaml rename to classifier-e2e/configs/training_xgboost_sagemaker.yaml index 43678338..96e3d991 100644 --- a/stack-showcase/configs/training_rf_sagemaker.yaml +++ b/classifier-e2e/configs/training_xgboost_sagemaker.yaml @@ -3,6 +3,7 @@ settings: docker: required_integrations: - sklearn + - xgboost requirements: - pyarrow @@ -11,11 +12,11 @@ model: name: breast_cancer_classifier license: Apache 2.0 description: A breast cancer classifier - tags: ["breast_cancer", "classifier","rf"] + tags: ["breast_cancer", "classifier","xgboost"] # Configure the pipeline parameters: - model_type: "rf" # Choose between rf/sgd + model_type: "xgboost" # Choose between sgd/xgboost steps: model_trainer: @@ -23,4 +24,4 @@ steps: settings: step_operator.sagemaker: estimator_args: - instance_type : ml.m5.large + instance_type : ml.m5.large \ No newline at end of file diff --git a/stack-showcase/pipelines/__init__.py b/classifier-e2e/pipelines/__init__.py similarity index 100% rename from stack-showcase/pipelines/__init__.py rename to classifier-e2e/pipelines/__init__.py diff --git a/stack-showcase/pipelines/feature_engineering.py b/classifier-e2e/pipelines/feature_engineering.py similarity index 100% rename from stack-showcase/pipelines/feature_engineering.py rename to classifier-e2e/pipelines/feature_engineering.py diff --git a/stack-showcase/pipelines/inference.py b/classifier-e2e/pipelines/inference.py similarity index 95% rename from stack-showcase/pipelines/inference.py rename to classifier-e2e/pipelines/inference.py index f82a80b7..46620c80 100644 --- a/stack-showcase/pipelines/inference.py +++ b/classifier-e2e/pipelines/inference.py @@ -41,7 +41,7 @@ def inference(random_state: str, target: str): target: Name of target column in dataset. """ # Get the production model artifact - model = get_pipeline_context().model.get_artifact("sklearn_classifier") + model = get_pipeline_context().model.get_artifact("breast_cancer_classifier") # Get the preprocess pipeline artifact associated with this version preprocess_pipeline = get_pipeline_context().model.get_artifact( diff --git a/stack-showcase/pipelines/training.py b/classifier-e2e/pipelines/training.py similarity index 100% rename from stack-showcase/pipelines/training.py rename to classifier-e2e/pipelines/training.py diff --git a/stack-showcase/requirements.txt b/classifier-e2e/requirements.txt similarity index 90% rename from stack-showcase/requirements.txt rename to classifier-e2e/requirements.txt index 92512b9d..c3fd2c34 100644 --- a/stack-showcase/requirements.txt +++ b/classifier-e2e/requirements.txt @@ -7,4 +7,5 @@ aws-profile-manager sagemaker==2.117.0 pyarrow wandb -seaborn \ No newline at end of file +seaborn +xgboost \ No newline at end of file diff --git a/stack-showcase/run.ipynb b/classifier-e2e/run.ipynb similarity index 93% rename from stack-showcase/run.ipynb rename to classifier-e2e/run.ipynb index 81e942ec..0bc616a4 100644 --- a/stack-showcase/run.ipynb +++ b/classifier-e2e/run.ipynb @@ -11,6 +11,17 @@ "! zenml model delete breast_cancer_classifier -y" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: remove me\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, { "cell_type": "markdown", "id": "63ab391a", @@ -39,7 +50,7 @@ "# 👶 Step 0. Install Requirements\n", "\n", "Let's install ZenML to get started. First we'll install the latest version of\n", - "ZenML as well as the `sklearn` integration of ZenML:" + "ZenML as well as the `sklearn` and `xgboost` integration of ZenML:" ] }, { @@ -49,7 +60,7 @@ "metadata": {}, "outputs": [], "source": [ - "!zenml integration install sklearn -y\n", + "!zenml integration install sklearn xgboost -y\n", "\n", "import IPython\n", "IPython.Application.instance().kernel.do_shutdown(restart=True)" @@ -487,7 +498,6 @@ "source": [ "import pandas as pd\n", "from sklearn.base import ClassifierMixin\n", - "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.linear_model import SGDClassifier\n", "from typing_extensions import Annotated\n", "from zenml import ArtifactConfig, step\n", @@ -502,14 +512,16 @@ " model_type: str = \"sgd\",\n", ") -> Annotated[\n", " ClassifierMixin,\n", - " ArtifactConfig(name=\"sklearn_classifier\", is_model_artifact=True),\n", + " ArtifactConfig(name=\"breast_cancer_classifier\", is_model_artifact=True),\n", "]:\n", " \"\"\"Configure and train a model on the training dataset.\"\"\"\n", " target = \"target\"\n", " if model_type == \"sgd\":\n", " model = SGDClassifier()\n", - " elif model_type == \"rf\":\n", - " model = RandomForestClassifier()\n", + " elif model_type == \"xgboost\":\n", + " from xgboost import XGBClassifier\n", + "\n", + " model = XGBClassifier()\n", " else:\n", " raise ValueError(f\"Unknown model type {model_type}\")\n", "\n", @@ -527,7 +539,7 @@ "id": "73a00008", "metadata": {}, "source": [ - "Our two training steps both return different kinds of `sklearn` classifier\n", + "Our two training steps both return different kinds of classifier\n", "models, so we use the generic `ClassifierMixin` type hint for the return type." ] }, @@ -600,13 +612,13 @@ "source": [ "# Use a random forest model with the chosen datasets.\n", "# We need to pass the ID's of the datasets into the function\n", - "training(\n", - " model_type=\"rf\",\n", + "training.with_options(enable_cache=False)(\n", + " model_type=\"xgboost\",\n", " train_dataset_id=dataset_trn_artifact_version.id,\n", " test_dataset_id=dataset_tst_artifact_version.id\n", ")\n", "\n", - "rf_run = client.get_pipeline(\"training\").last_run" + "xgboost_run = client.get_pipeline(\"training\").last_run" ] }, { @@ -617,7 +629,7 @@ "outputs": [], "source": [ "# Use a SGD classifier\n", - "sgd_run = training(\n", + "sgd_run = training.with_options(enable_cache=False)(\n", " model_type=\"sgd\",\n", " train_dataset_id=dataset_trn_artifact_version.id,\n", " test_dataset_id=dataset_tst_artifact_version.id\n", @@ -632,7 +644,7 @@ "metadata": {}, "source": [ "You can see from the logs already how our model training went: the\n", - "`RandomForestClassifier` performed considerably better than the `SGDClassifier`.\n", + "`XGBClassifier` performed considerably better than the `SGDClassifier`.\n", "We can use the ZenML `Client` to verify this:" ] }, @@ -644,7 +656,7 @@ "outputs": [], "source": [ "# The evaluator returns a float value with the accuracy\n", - "rf_run.steps[\"model_evaluator\"].output.load() >= sgd_run.steps[\"model_evaluator\"].output.load()" + "xgboost_run.steps[\"model_evaluator\"].output.load() >= sgd_run.steps[\"model_evaluator\"].output.load()" ] }, { @@ -713,8 +725,8 @@ "metadata": {}, "outputs": [], "source": [ - "# Let's train the RF model and tag the version name with \"rf\"\n", - "pipeline_settings[\"model\"].tags = [\"breast_cancer\", \"classifier\", \"rf\"]\n", + "# Let's train the RF model and tag the version name with \"xgboost\"\n", + "pipeline_settings[\"model\"].tags = [\"breast_cancer\", \"classifier\", \"xgboost\"]\n", "\n", "# the `with_options` method allows us to pass in pipeline settings\n", "# and returns a configured pipeline\n", @@ -722,7 +734,7 @@ "\n", "# Let's run it again to make sure we have two versions\n", "training_configured(\n", - " model_type=\"rf\",\n", + " model_type=\"xgboost\",\n", " train_dataset_id=dataset_trn_artifact_version.id,\n", " test_dataset_id=dataset_tst_artifact_version.id\n", ")" @@ -772,13 +784,13 @@ "metadata": {}, "outputs": [], "source": [ - "# Let's load the RF version\n", - "rf_zenml_model_version = client.list_model_versions(\"breast_cancer_classifier\", tag=\"rf\")[-1]\n", + "# Let's load the XGBoost version\n", + "xgboost_zenml_model_version = client.list_model_versions(\"breast_cancer_classifier\", tag=\"xgboost\")[-1]\n", "\n", "# We can now load our classifier directly as well\n", - "random_forest_classifier = rf_zenml_model_version.get_artifact(\"sklearn_classifier\").load()\n", + "xgboost_classifier = xgboost_zenml_model_version.get_artifact(\"breast_cancer_classifier\").load()\n", "\n", - "random_forest_classifier" + "xgboost_classifier" ] }, { @@ -814,7 +826,7 @@ "outputs": [], "source": [ "# Set our best classifier to production\n", - "rf_zenml_model_version.set_stage(\"production\", force=True)" + "xgboost_zenml_model_version.set_stage(\"production\", force=True)" ] }, { @@ -879,7 +891,7 @@ " model = get_step_context().model\n", "\n", " # run prediction from memory\n", - " predictor = model.load_artifact(\"sklearn_classifier\")\n", + " predictor = model.load_artifact(\"breast_cancer_classifier\")\n", " predictions = predictor.predict(dataset_inf)\n", "\n", " predictions = pd.Series(predictions, name=\"predicted\")\n", @@ -1012,6 +1024,17 @@ "Let's run all the moving pieces we navigated in the previous steps using production ready python script `run.py`" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "fa40175d", + "metadata": {}, + "outputs": [], + "source": [ + "# let's clean up previous partial runs first\n", + "! zenml model delete breast_cancer_classifier -y" + ] + }, { "cell_type": "code", "execution_count": null, @@ -1039,13 +1062,13 @@ "\n", "Let's move some heavy lifting steps to the Sagemaker keeping light ones on local, so we can reduce costs. This can be achieved using step operators and step configurations.\n", "\n", - "To make this happen we will use extended configuration files having following step level settings:\n", + "To make this happen we will use following configuration option for step level settings:\n", "```yaml\n", "steps:\n", " model_trainer:\n", - " step_operator: sagemaker-eu # instruct to run step on sagemaker\n", + " \n", " settings:\n", - " step_operator:\n", + " step_operator: sagemaker-eu\n", " sagemaker:\n", " estimator_args: \n", " instance_type : ml.m5.large # select instance type\n", @@ -1097,9 +1120,9 @@ "outputs": [], "source": [ "sgd_model_version = client.list_model_versions(\"breast_cancer_classifier\",tag=\"sgd\")[-1]\n", - "rf_model_version = client.list_model_versions(\"breast_cancer_classifier\",tag=\"rf\")[-1]\n", + "xgboost_model_version = client.list_model_versions(\"breast_cancer_classifier\",tag=\"xgboost\")[-1]\n", "print(f\"SGD version is staged as `{sgd_model_version.stage}`\")\n", - "print(f\"RF version is staged as `{rf_model_version.stage}`\")" + "print(f\"XGBoost version is staged as `{xgboost_model_version.stage}`\")" ] }, { @@ -1134,7 +1157,7 @@ " log_model_metadata(metadata={\"wandb_url\": wandb.run.url})\n", " log_artifact_metadata(\n", " metadata=metadata,\n", - " artifact_name=\"sklearn_classifier\",\n", + " artifact_name=\"breast_cancer_classifier\",\n", " )\n", "\n", " wandb.log({\"train_accuracy\": metadata[\"train_accuracy\"]})\n", @@ -1157,10 +1180,10 @@ "metadata": {}, "outputs": [], "source": [ - "sgd_clf_metadata = sgd_model_version.get_artifact(\"sklearn_classifier\").run_metadata\n", - "rf_clf_metadata = rf_model_version.get_artifact(\"sklearn_classifier\").run_metadata\n", + "sgd_clf_metadata = sgd_model_version.get_artifact(\"breast_cancer_classifier\").run_metadata\n", + "xgboost_clf_metadata = xgboost_model_version.get_artifact(\"breast_cancer_classifier\").run_metadata\n", "print(f\"SGD{' (production)' if sgd_model_version.stage == 'production' else ''} metrics: train={sgd_clf_metadata['train_accuracy'].value*100:.2f}% test={sgd_clf_metadata['test_accuracy'].value*100:.2f}%\")\n", - "print(f\"RF{' (production)' if rf_model_version.stage == 'production' else ''} metrics: train={rf_clf_metadata['train_accuracy'].value*100:.2f}% test={rf_clf_metadata['test_accuracy'].value*100:.2f}%\")" + "print(f\"XGBoost{' (production)' if xgboost_model_version.stage == 'production' else ''} metrics: train={xgboost_clf_metadata['train_accuracy'].value*100:.2f}% test={xgboost_clf_metadata['test_accuracy'].value*100:.2f}%\")" ] }, { @@ -1190,7 +1213,7 @@ "\n", "fig, ax = plt.subplots(1,2,figsize=(15,4))\n", "plot_confusion_matrix(sgd_clf_metadata, \"SGD\",ax[0])\n", - "plot_confusion_matrix(rf_clf_metadata, \"RF\",ax[1])" + "plot_confusion_matrix(xgboost_clf_metadata, \"RF\",ax[1])" ] }, { @@ -1209,7 +1232,7 @@ "outputs": [], "source": [ "print(f'SGD version: {sgd_model_version.run_metadata[\"wandb_url\"].value}')\n", - "print(f'RF version: {rf_model_version.run_metadata[\"wandb_url\"].value}')" + "print(f'RF version: {xgboost_model_version.run_metadata[\"wandb_url\"].value}')" ] }, { @@ -1259,7 +1282,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.11.6" } }, "nbformat": 4, diff --git a/stack-showcase/run.py b/classifier-e2e/run.py similarity index 98% rename from stack-showcase/run.py rename to classifier-e2e/run.py index 7055049a..68033d98 100644 --- a/stack-showcase/run.py +++ b/classifier-e2e/run.py @@ -221,10 +221,10 @@ def main( if no_cache: pipeline_args["enable_cache"] = False pipeline_args["config_path"] = os.path.join( - config_folder, f"training_rf{custom_training_suffix}.yaml" + config_folder, f"training_xgboost{custom_training_suffix}.yaml" ) training.with_options(**pipeline_args)(**run_args_train) - logger.info("Training pipeline with RF finished successfully!\n\n") + logger.info("Training pipeline with XGBoost finished successfully!\n\n") if inference_pipeline: run_args_inference = {} diff --git a/stack-showcase/steps/__init__.py b/classifier-e2e/steps/__init__.py similarity index 100% rename from stack-showcase/steps/__init__.py rename to classifier-e2e/steps/__init__.py diff --git a/stack-showcase/steps/data_loader.py b/classifier-e2e/steps/data_loader.py similarity index 100% rename from stack-showcase/steps/data_loader.py rename to classifier-e2e/steps/data_loader.py diff --git a/stack-showcase/steps/data_preprocessor.py b/classifier-e2e/steps/data_preprocessor.py similarity index 100% rename from stack-showcase/steps/data_preprocessor.py rename to classifier-e2e/steps/data_preprocessor.py diff --git a/stack-showcase/steps/data_splitter.py b/classifier-e2e/steps/data_splitter.py similarity index 100% rename from stack-showcase/steps/data_splitter.py rename to classifier-e2e/steps/data_splitter.py diff --git a/stack-showcase/steps/inference_predict.py b/classifier-e2e/steps/inference_predict.py similarity index 100% rename from stack-showcase/steps/inference_predict.py rename to classifier-e2e/steps/inference_predict.py diff --git a/stack-showcase/steps/inference_preprocessor.py b/classifier-e2e/steps/inference_preprocessor.py similarity index 100% rename from stack-showcase/steps/inference_preprocessor.py rename to classifier-e2e/steps/inference_preprocessor.py diff --git a/stack-showcase/steps/model_evaluator.py b/classifier-e2e/steps/model_evaluator.py similarity index 96% rename from stack-showcase/steps/model_evaluator.py rename to classifier-e2e/steps/model_evaluator.py index 4ea2ba81..db15b689 100644 --- a/stack-showcase/steps/model_evaluator.py +++ b/classifier-e2e/steps/model_evaluator.py @@ -21,7 +21,12 @@ from sklearn.base import ClassifierMixin from sklearn.metrics import confusion_matrix -from zenml import log_artifact_metadata, step, log_model_metadata, get_step_context +from zenml import ( + log_artifact_metadata, + step, + log_model_metadata, + get_step_context, +) from zenml.logger import get_logger import wandb from zenml.client import Client @@ -119,11 +124,9 @@ def model_evaluator( log_artifact_metadata( metadata=metadata, - artifact_name="sklearn_classifier", + artifact_name="breast_cancer_classifier", ) - wandb.log({"train_accuracy": metadata["train_accuracy"]}) - wandb.log({"test_accuracy": metadata["test_accuracy"]}) wandb.log( { "confusion_matrix": wandb.sklearn.plot_confusion_matrix( @@ -131,4 +134,7 @@ def model_evaluator( ) } ) + wandb.log({"train_accuracy": metadata["train_accuracy"]}) + wandb.log({"test_accuracy": metadata["test_accuracy"]}) + return float(tst_acc) diff --git a/stack-showcase/steps/model_promoter.py b/classifier-e2e/steps/model_promoter.py similarity index 95% rename from stack-showcase/steps/model_promoter.py rename to classifier-e2e/steps/model_promoter.py index 152ca11d..aac7083f 100644 --- a/stack-showcase/steps/model_promoter.py +++ b/classifier-e2e/steps/model_promoter.py @@ -48,7 +48,6 @@ def model_promoter(accuracy: float, stage: str = "production") -> bool: f"Model accuracy {accuracy*100:.2f}% is below 80% ! Not promoting model." ) else: - logger.info(f"Model promoted to {stage}!") is_promoted = True # Get the model in the current context @@ -59,7 +58,7 @@ def model_promoter(accuracy: float, stage: str = "production") -> bool: try: stage_model = client.get_model_version(current_model.name, stage) # We compare their metrics - prod_classifier = stage_model.get_artifact("sklearn_classifier") + prod_classifier = stage_model.get_artifact("breast_cancer_classifier") if prod_classifier: # and recompute metrics for current prod model using current test set prod_classifier = prod_classifier.load() @@ -76,6 +75,7 @@ def model_promoter(accuracy: float, stage: str = "production") -> bool: prod_accuracy = 0 if float(accuracy) > float(prod_accuracy): # If current model has better metrics, we promote it + logger.info(f"Model promoted to {stage}!") is_promoted = True current_model.set_stage(stage, force=True) except KeyError: diff --git a/stack-showcase/steps/model_trainer.py b/classifier-e2e/steps/model_trainer.py similarity index 91% rename from stack-showcase/steps/model_trainer.py rename to classifier-e2e/steps/model_trainer.py index 8fa063e7..79242ef6 100644 --- a/stack-showcase/steps/model_trainer.py +++ b/classifier-e2e/steps/model_trainer.py @@ -19,7 +19,6 @@ import pandas as pd from sklearn.base import ClassifierMixin -from sklearn.ensemble import RandomForestClassifier from sklearn.linear_model import SGDClassifier from typing_extensions import Annotated @@ -35,7 +34,7 @@ def model_trainer( target: Optional[str] = "target", ) -> Annotated[ ClassifierMixin, - ArtifactConfig(name="sklearn_classifier", is_model_artifact=True), + ArtifactConfig(name="breast_cancer_classifier", is_model_artifact=True), ]: """Configure and train a model on the training dataset. @@ -59,8 +58,10 @@ def model_trainer( # parameters and train it on the training set. if model_type == "sgd": model = SGDClassifier() - elif model_type == "rf": - model = RandomForestClassifier() + elif model_type == "xgboost": + from xgboost import XGBClassifier + + model = XGBClassifier() else: raise ValueError(f"Unknown model type {model_type}") logger.info(f"Training model {model}...") diff --git a/stack-showcase/utils/__init__.py b/classifier-e2e/utils/__init__.py similarity index 100% rename from stack-showcase/utils/__init__.py rename to classifier-e2e/utils/__init__.py diff --git a/stack-showcase/utils/preprocess.py b/classifier-e2e/utils/preprocess.py similarity index 100% rename from stack-showcase/utils/preprocess.py rename to classifier-e2e/utils/preprocess.py diff --git a/stack-showcase/flagged/log.csv b/stack-showcase/flagged/log.csv deleted file mode 100644 index 95b5cb76..00000000 --- a/stack-showcase/flagged/log.csv +++ /dev/null @@ -1,2 +0,0 @@ -mean radius,mean texture,mean perimeter,mean area,mean smoothness,mean compactness,mean concavity,mean concave points,mean symmetry,mean fractal dimension,radius error,texture error,perimeter error,area error,smoothness error,compactness error,concavity error,concave points error,symmetry error,fractal dimension error,worst radius,worst texture,worst perimeter,worst area,worst smoothness,worst compactness,worst concavity,worst concave points,worst symmetry,worst fractal dimension,output,flag,username,timestamp -2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,/home/htahir1/workspace/zenml_io/zenml-projects/stack-showcase/flagged/output/tmpjy2eamkw.json,,,2024-01-04 14:08:33.097778 diff --git a/stack-showcase/flagged/output/tmpjy2eamkw.json b/stack-showcase/flagged/output/tmpjy2eamkw.json deleted file mode 100644 index 9e26dfee..00000000 --- a/stack-showcase/flagged/output/tmpjy2eamkw.json +++ /dev/null @@ -1 +0,0 @@ -{} \ No newline at end of file From 229b742eb271b4df2aac22a3b14ab6696de91f05 Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Fri, 9 Feb 2024 15:37:53 +0100 Subject: [PATCH 06/20] clean up --- classifier-e2e/run.ipynb | 14 ++------------ 1 file changed, 2 insertions(+), 12 deletions(-) diff --git a/classifier-e2e/run.ipynb b/classifier-e2e/run.ipynb index 0bc616a4..31d9292b 100644 --- a/classifier-e2e/run.ipynb +++ b/classifier-e2e/run.ipynb @@ -11,17 +11,6 @@ "! zenml model delete breast_cancer_classifier -y" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO: remove me\n", - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, { "cell_type": "markdown", "id": "63ab391a", @@ -1116,6 +1105,7 @@ { "cell_type": "code", "execution_count": null, + "id": "2728fa4d", "metadata": {}, "outputs": [], "source": [ @@ -1282,7 +1272,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.6" + "version": "3.9.18" } }, "nbformat": 4, From e4a251477d2ecc017fc8df0259a1668d06ccbfd9 Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Fri, 9 Feb 2024 15:51:40 +0100 Subject: [PATCH 07/20] skip basics notebook --- classifier-e2e/run_skip_basics.ipynb | 792 +++++++++++++++++++++++++++ 1 file changed, 792 insertions(+) create mode 100644 classifier-e2e/run_skip_basics.ipynb diff --git a/classifier-e2e/run_skip_basics.ipynb b/classifier-e2e/run_skip_basics.ipynb new file mode 100644 index 00000000..3a51023b --- /dev/null +++ b/classifier-e2e/run_skip_basics.ipynb @@ -0,0 +1,792 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "63ab391a", + "metadata": {}, + "source": [ + "# 🌍 Overview\n", + "\n", + "This demo is a minimalistic MLOps project intended to showcase how to put ML workflows in production. It features: \n", + "\n", + "- A feature engineering pipeline that loads data and prepares it for training.\n", + "- A training pipeline that loads the preprocessed dataset and trains a model.\n", + "- A batch inference pipeline that runs predictions on the trained model with new data.\n", + "- A stack switching and leveraging of Sagemaker step operator to outsource training to Cloud\n", + "- An analysis of training artifacts and their lineage (including connection with W&B)\n", + "\n", + "\"Pipelines" + ] + }, + { + "cell_type": "markdown", + "id": "66b2977c", + "metadata": {}, + "source": [ + "# 👶 Step 0. Install Requirements\n", + "\n", + "Let's install ZenML to get started. First we'll install the latest version of\n", + "ZenML as well as the `sklearn` and `xgboost` integration of ZenML:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f76f562e", + "metadata": {}, + "outputs": [], + "source": [ + "!zenml integration install sklearn xgboost -y\n", + "! zenml connect --url https://1cf18d95-zenml.cloudinfra.zenml.io \n", + "! zenml model delete breast_cancer_classifier -y\n", + "\n", + "import IPython\n", + "IPython.Application.instance().kernel.do_shutdown(restart=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "081d5616", + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize ZenML and set the default stack\n", + "!zenml init\n", + "\n", + "!zenml stack set local-sagemaker-step-operator-wandb" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "79f775f2", + "metadata": {}, + "outputs": [], + "source": [ + "# Do the imports at the top\n", + "from zenml import Model\n", + "from zenml.client import Client\n", + "from zenml.logger import get_logger\n", + "\n", + "from pipelines import training, inference\n", + "\n", + "logger = get_logger(__name__)\n", + "\n", + "# Initialize the ZenML client to fetch objects from the ZenML Server\n", + "client = Client()" + ] + }, + { + "cell_type": "markdown", + "id": "8c28b474", + "metadata": {}, + "source": [ + "# ⌚ Step 1: Training pipeline" + ] + }, + { + "cell_type": "markdown", + "id": "87909827", + "metadata": {}, + "source": [ + "Now that we have our data it makes sense to train some models to get a sense of\n", + "how difficult the task is. The Breast Cancer dataset is sufficiently large and complex \n", + "that it's unlikely we'll be able to train a model that behaves perfectly since the problem \n", + "is inherently complex, but we can get a sense of what a reasonable baseline looks like.\n", + "\n", + "We'll start with two simple models, a SGD Classifier and a Random Forest\n", + "Classifier, both batteries-included from `sklearn`. We'll train them both on the\n", + "same data and then compare their performance.\n", + "\n", + "\"Training" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dc08aecc", + "metadata": {}, + "outputs": [], + "source": [ + "# let's have a look at model training step\n", + "%pycat steps/model_trainer.py" + ] + }, + { + "cell_type": "markdown", + "id": "73a00008", + "metadata": {}, + "source": [ + "Our two training steps both return different kinds of classifier\n", + "models, so we use the generic `ClassifierMixin` type hint for the return type." + ] + }, + { + "cell_type": "markdown", + "id": "a5f22174", + "metadata": {}, + "source": [ + "ZenML allows you to load any version of any dataset that is tracked by the framework\n", + "directly into a pipeline using the `Client().get_artifact_version` interface. This is very convenient\n", + "in this case, as we'd like to send our preprocessed dataset from the older pipeline directly\n", + "into the training pipeline." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "01162d23", + "metadata": {}, + "outputs": [], + "source": [ + "# let's have a look at training pipeline\n", + "%pycat pipelines/training.py" + ] + }, + { + "cell_type": "markdown", + "id": "88b70fd3", + "metadata": {}, + "source": [ + "The end goal of this quick baseline evaluation is to understand which of the two\n", + "models performs better. We'll use the `evaluator` step to compare the two\n", + "models. This step takes in the model from the trainer step, and computes its score\n", + "over the testing set." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c64885ac", + "metadata": {}, + "outputs": [], + "source": [ + "# Use an XGBoost model with fixed seed.\n", + "training.with_options(enable_cache=False)(\n", + " model_type=\"xgboost\",\n", + " random_state=42\n", + ")\n", + "\n", + "xgboost_run = client.get_pipeline(\"training\").last_run" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4300c82f", + "metadata": {}, + "outputs": [], + "source": [ + "# Use a SGD classifier\n", + "sgd_run = training.with_options(enable_cache=False)(\n", + " model_type=\"sgd\",\n", + " random_state=42\n", + ")\n", + "\n", + "sgd_run = client.get_pipeline(\"training\").last_run" + ] + }, + { + "cell_type": "markdown", + "id": "43f1a68a", + "metadata": {}, + "source": [ + "You can see from the logs already how our model training went: the\n", + "`XGBClassifier` performed considerably better than the `SGDClassifier`.\n", + "We can use the ZenML `Client` to verify this:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d95810b1", + "metadata": {}, + "outputs": [], + "source": [ + "# The evaluator returns a float value with the accuracy\n", + "xgboost_run.steps[\"model_evaluator\"].output.load() >= sgd_run.steps[\"model_evaluator\"].output.load()" + ] + }, + { + "cell_type": "markdown", + "id": "e256d145", + "metadata": {}, + "source": [ + "# 💯 Step 2: Associating a model with your pipeline" + ] + }, + { + "cell_type": "markdown", + "id": "927978f3", + "metadata": {}, + "source": [ + "You can see it is relatively easy to train ML models using ZenML pipelines. But it can be somewhat clunky to track\n", + "all the models produced as you develop your experiments and use-cases. Luckily, ZenML offers a *Model Control Plane*,\n", + "which is a central register of all your ML models.\n", + "\n", + "You can easily create a ZenML Model and associate it with your pipelines using the `Model` object:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "99ca00c0", + "metadata": {}, + "outputs": [], + "source": [ + "pipeline_settings = {}\n", + "\n", + "# Lets add some metadata to the model to make it identifiable\n", + "pipeline_settings[\"model\"] = Model(\n", + " name=\"breast_cancer_classifier\",\n", + " license=\"Apache 2.0\",\n", + " description=\"A breast cancer classifier\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0e78a520", + "metadata": {}, + "outputs": [], + "source": [ + "# Let's train the SGD model and tag the version name with \"sgd\"\n", + "pipeline_settings[\"model\"].tags = [\"breast_cancer\", \"classifier\", \"sgd\"]\n", + "\n", + "# the `with_options` method allows us to pass in pipeline settings\n", + "# and returns a configured pipeline\n", + "training_configured = training.with_options(**pipeline_settings)\n", + "\n", + "# We can now run this as usual\n", + "training_configured(\n", + " model_type=\"sgd\",\n", + " random_state=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9b8e0002", + "metadata": {}, + "outputs": [], + "source": [ + "# Let's train the RF model and tag the version name with \"xgboost\"\n", + "pipeline_settings[\"model\"].tags = [\"breast_cancer\", \"classifier\", \"xgboost\"]\n", + "\n", + "# the `with_options` method allows us to pass in pipeline settings\n", + "# and returns a configured pipeline\n", + "training_configured = training.with_options(**pipeline_settings)\n", + "\n", + "# Let's run it again to make sure we have two versions\n", + "training_configured(\n", + " model_type=\"xgboost\",\n", + " random_state=42\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "09597223", + "metadata": {}, + "source": [ + "This time, running both pipelines has created two associated **model versions**.\n", + "You can list your ZenML model and their versions as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fbb25913", + "metadata": {}, + "outputs": [], + "source": [ + "zenml_model = client.get_model(\"breast_cancer_classifier\")\n", + "print(zenml_model)\n", + "\n", + "versions = zenml_model.versions\n", + "\n", + "print(f\"Model {zenml_model.name} has {len(versions)} versions\")\n", + "\n", + "versions[-2].version, versions[-1].version" + ] + }, + { + "cell_type": "markdown", + "id": "e82cfac2", + "metadata": {}, + "source": [ + "The interesting part is that ZenML went ahead and linked all artifacts produced by the\n", + "pipelines to that model version, including the two pickle files that represent our\n", + "SGD and RandomForest classifier. We can see all artifacts directly from the model\n", + "version object:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "31211413", + "metadata": {}, + "outputs": [], + "source": [ + "# Let's load the XGBoost version\n", + "xgboost_zenml_model_version = client.list_model_versions(\"breast_cancer_classifier\", tag=\"xgboost\")[-1]\n", + "\n", + "# We can now load our classifier directly as well\n", + "xgboost_classifier = xgboost_zenml_model_version.get_artifact(\"breast_cancer_classifier\").load()\n", + "\n", + "xgboost_classifier" + ] + }, + { + "cell_type": "markdown", + "id": "53517a9a", + "metadata": {}, + "source": [ + "If you are a [ZenML Cloud](https://zenml.io/cloud) user, you can see all of this visualized in the dashboard:\n", + "\n", + "\"Model" + ] + }, + { + "cell_type": "markdown", + "id": "eb645dde", + "metadata": {}, + "source": [ + "There is a lot more you can do with ZenML models, including the ability to\n", + "track metrics by adding metadata to it, or having them persist in a model\n", + "registry. However, these topics can be explored more in the\n", + "[ZenML docs](https://docs.zenml.io).\n", + "\n", + "For now, we will use the ZenML model control plane to promote our best\n", + "model to `production`. You can do this by simply setting the `stage` of\n", + "your chosen model version to the `production` tag." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "26b718f8", + "metadata": {}, + "outputs": [], + "source": [ + "# Set our best classifier to production\n", + "xgboost_zenml_model_version.set_stage(\"production\", force=True)" + ] + }, + { + "cell_type": "markdown", + "id": "9fddf3d0", + "metadata": {}, + "source": [ + "Of course, normally one would only promote the model by comparing to all other model\n", + "versions and doing some other tests. But that's a bit more advanced use-case. See the\n", + "[e2e_batch example](https://github.com/zenml-io/zenml/tree/main/examples/e2e) to get\n", + "more insight into that sort of flow!" + ] + }, + { + "cell_type": "markdown", + "id": "2ecbc8cf", + "metadata": {}, + "source": [ + "\"Model" + ] + }, + { + "cell_type": "markdown", + "id": "8f1146db", + "metadata": {}, + "source": [ + "Once the model is promoted, we can now consume the right model version in our\n", + "batch inference pipeline directly. Let's see how that works." + ] + }, + { + "cell_type": "markdown", + "id": "d6306f14", + "metadata": {}, + "source": [ + "# 🫅 Step 3: Consuming the model in production" + ] + }, + { + "cell_type": "markdown", + "id": "b51f3108", + "metadata": {}, + "source": [ + "The batch inference pipeline simply takes the model marked as `production` and runs inference on it\n", + "with `live data`. The critical step here is the `inference_predict` step, where we load the model in memory\n", + "and generate predictions:\n", + "\n", + "\"Inference" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "92c4c7dc", + "metadata": {}, + "outputs": [], + "source": [ + "# let's have a look at training pipeline\n", + "%pycat steps/inference_predict.py\n" + ] + }, + { + "cell_type": "markdown", + "id": "3aeb227b", + "metadata": {}, + "source": [ + "Apart from the loading the model, we must also load the preprocessing pipeline that we ran in feature engineering,\n", + "so that we can do the exact steps that we did on training time, in inference time. Let's bring it all together:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "37c409bd", + "metadata": {}, + "outputs": [], + "source": [ + "# let's have a look at training pipeline\n", + "%pycat pipelines/inference.py" + ] + }, + { + "cell_type": "markdown", + "id": "c7afe7be", + "metadata": {}, + "source": [ + "The way to load the right model is to pass in the `production` stage into the `Model` config this time.\n", + "This will ensure to always load the production model, decoupled from all other pipelines:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "61bf5939", + "metadata": {}, + "outputs": [], + "source": [ + "pipeline_settings = {\"enable_cache\": False}\n", + "\n", + "# Lets add some metadata to the model to make it identifiable\n", + "pipeline_settings[\"model\"] = Model(\n", + " name=\"breast_cancer_classifier\",\n", + " version=\"production\", # We can pass in the stage name here!\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ff3402f1", + "metadata": {}, + "outputs": [], + "source": [ + "# the `with_options` method allows us to pass in pipeline settings\n", + "# and returns a configured pipeline\n", + "inference.with_options(**pipeline_settings)()" + ] + }, + { + "cell_type": "markdown", + "id": "2935d1fa", + "metadata": {}, + "source": [ + "ZenML automatically links all artifacts to the `production` model version as well, including the predictions\n", + "that were returned in the pipeline. This completes the MLOps loop of training to inference:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e191d019", + "metadata": {}, + "outputs": [], + "source": [ + "# Fetch production model\n", + "production_model_version = client.get_model_version(\"breast_cancer_classifier\", \"production\")\n", + "\n", + "# Get the predictions artifact\n", + "production_model_version.get_artifact(\"predictions\").load()" + ] + }, + { + "cell_type": "markdown", + "id": "b0a73cdf", + "metadata": {}, + "source": [ + "You can also see all predictions ever created as a complete history in the dashboard:\n", + "\n", + "\"Model" + ] + }, + { + "cell_type": "markdown", + "id": "594ee4fc-f102-4b99-bdc3-2f1670c87679", + "metadata": {}, + "source": [ + "# 🙏 Step 4: Bringing it all together\n", + "\n", + "Let's run all the moving pieces we navigated in the previous steps using production ready python script `run.py`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fa40175d", + "metadata": {}, + "outputs": [], + "source": [ + "# let's clean up previous partial runs first\n", + "! zenml model delete breast_cancer_classifier -y" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!zenml stack set local-wandb\n", + "!zenml stack describe local-wandb" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!python3 run.py --training-pipeline --inference-pipeline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now full run executed on local stack and experiment is tracked using Model Control Plane and Weights&Biases.\n", + "\n", + "Let's move some heavy lifting steps to the Sagemaker keeping light ones on local, so we can reduce costs. This can be achieved using step operators and step configurations.\n", + "\n", + "To make this happen we will use following configuration option for step level settings:\n", + "```yaml\n", + "steps:\n", + " model_trainer:\n", + " \n", + " settings:\n", + " step_operator: sagemaker-eu\n", + " sagemaker:\n", + " estimator_args: \n", + " instance_type : ml.m5.large # select instance type\n", + "```\n", + "\n", + "\"Sagemaker" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!zenml stack set local-sagemaker-step-operator-wandb\n", + "!zenml stack describe local-sagemaker-step-operator-wandb" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!python3 run.py --training-pipeline --inference-pipeline --custom-training-suffix _sagemaker" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 🐙 Step 6: Analyzing results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2728fa4d", + "metadata": {}, + "outputs": [], + "source": [ + "sgd_model_version = client.list_model_versions(\"breast_cancer_classifier\",tag=\"sgd\")[-1]\n", + "xgboost_model_version = client.list_model_versions(\"breast_cancer_classifier\",tag=\"xgboost\")[-1]\n", + "print(f\"SGD version is staged as `{sgd_model_version.stage}`\")\n", + "print(f\"XGBoost version is staged as `{xgboost_model_version.stage}`\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "At first, let's pull some meta information collected during models evaluation stage. To recall we used this step as evaluator:\n", + "```python\n", + "@step\n", + "def model_evaluator(\n", + " model: ClassifierMixin,\n", + " dataset_trn: pd.DataFrame,\n", + " dataset_tst: pd.DataFrame,\n", + " min_train_accuracy: float = 0.0,\n", + " min_test_accuracy: float = 0.0,\n", + " target: Optional[str] = \"target\",\n", + ") -> float:\n", + " # Calculate the model accuracy on the train and test set\n", + " trn_acc = model.score(...)\n", + " tst_acc = model.score(...)\n", + "\n", + " ...\n", + " \n", + " predictions = model.predict(dataset_tst.drop(columns=[target]))\n", + " metadata = {\n", + " \"train_accuracy\": float(trn_acc),\n", + " \"test_accuracy\": float(tst_acc),\n", + " \"confusion_matrix\": confusion_matrix(dataset_tst[target], predictions)\n", + " .ravel()\n", + " .tolist(),\n", + " }\n", + " log_model_metadata(metadata={\"wandb_url\": wandb.run.url})\n", + " log_artifact_metadata(\n", + " metadata=metadata,\n", + " artifact_name=\"breast_cancer_classifier\",\n", + " )\n", + "\n", + " wandb.log({\"train_accuracy\": metadata[\"train_accuracy\"]})\n", + " wandb.log({\"test_accuracy\": metadata[\"test_accuracy\"]})\n", + " wandb.log(\n", + " {\n", + " \"confusion_matrix\": wandb.sklearn.plot_confusion_matrix(\n", + " dataset_tst[target], predictions, [\"No Cancer\", \"Cancer\"]\n", + " )\n", + " }\n", + " )\n", + " return float(tst_acc)\n", + "```\n", + "First we pull Accuracy metrics out of both model version for comparison:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sgd_clf_metadata = sgd_model_version.get_artifact(\"breast_cancer_classifier\").run_metadata\n", + "xgboost_clf_metadata = xgboost_model_version.get_artifact(\"breast_cancer_classifier\").run_metadata\n", + "print(f\"SGD{' (production)' if sgd_model_version.stage == 'production' else ''} metrics: train={sgd_clf_metadata['train_accuracy'].value*100:.2f}% test={sgd_clf_metadata['test_accuracy'].value*100:.2f}%\")\n", + "print(f\"XGBoost{' (production)' if xgboost_model_version.stage == 'production' else ''} metrics: train={xgboost_clf_metadata['train_accuracy'].value*100:.2f}% test={xgboost_clf_metadata['test_accuracy'].value*100:.2f}%\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now lets' plot collected Confusion Matrixes:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import seaborn as sns\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "def plot_confusion_matrix(metadata_pointer, tp: str,ax):\n", + " confusion_matrix = np.array(metadata_pointer[\"confusion_matrix\"].value, dtype=float).reshape((2,2))\n", + " confusion_matrix /= np.sum(confusion_matrix)\n", + " sns.heatmap(confusion_matrix, annot=True,fmt='.2%',cmap=\"coolwarm\",ax=ax)\n", + " ax.set_title(f\"{tp} confusion matrix\")\n", + " ax.set_ylabel(\"Ground Label\")\n", + " ax.set_xlabel(\"Predicted Label\")\n", + "\n", + "fig, ax = plt.subplots(1,2,figsize=(15,4))\n", + "plot_confusion_matrix(sgd_clf_metadata, \"SGD\",ax[0])\n", + "plot_confusion_matrix(xgboost_clf_metadata, \"RF\",ax[1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So far we were able to collect all the information we tracked using Model Control Plane, but we also had Weights&Biases tracking enabled - let's dive into.\n", + "\n", + "Thanks to Model Control Plane metadata we establish a nice connection between those 2 entities:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(f'SGD version: {sgd_model_version.run_metadata[\"wandb_url\"].value}')\n", + "print(f'RF version: {xgboost_model_version.run_metadata[\"wandb_url\"].value}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With Model Control Plane we can also easily track lineage of artifacts and pipeline runs:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for artifact_name, versions in sgd_model_version.data_artifacts.items():\n", + " if versions:\n", + " print(f\"Existing version of `{artifact_name}`:\")\n", + " for version_name, artifact_ in versions.items():\n", + " print(version_name, artifact_.data_type.attribute)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for run_name, run_ in sgd_model_version.pipeline_runs.items():\n", + " print(run_name, run_.id)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 86da31c7a072bf5ef60f0750030c9156c046cf48 Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Fri, 9 Feb 2024 15:52:50 +0100 Subject: [PATCH 08/20] chapters numbering --- classifier-e2e/run_skip_basics.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/classifier-e2e/run_skip_basics.ipynb b/classifier-e2e/run_skip_basics.ipynb index 3a51023b..a57c6505 100644 --- a/classifier-e2e/run_skip_basics.ipynb +++ b/classifier-e2e/run_skip_basics.ipynb @@ -611,7 +611,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# 🐙 Step 6: Analyzing results" + "# 🐙 Step 5: Analyzing results" ] }, { From 019e4d8bfd0162344690d369dd6eb900e68ad39e Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Fri, 9 Feb 2024 16:16:31 +0100 Subject: [PATCH 09/20] update notebook --- classifier-e2e/run_skip_basics.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/classifier-e2e/run_skip_basics.ipynb b/classifier-e2e/run_skip_basics.ipynb index a57c6505..1d580fc4 100644 --- a/classifier-e2e/run_skip_basics.ipynb +++ b/classifier-e2e/run_skip_basics.ipynb @@ -36,7 +36,8 @@ "metadata": {}, "outputs": [], "source": [ - "!zenml integration install sklearn xgboost -y\n", + "! pip3 install -r requirements.txt\n", + "! zenml integration install sklearn xgboost -y\n", "! zenml connect --url https://1cf18d95-zenml.cloudinfra.zenml.io \n", "! zenml model delete breast_cancer_classifier -y\n", "\n", @@ -53,7 +54,6 @@ "source": [ "# Initialize ZenML and set the default stack\n", "!zenml init\n", - "\n", "!zenml stack set local-sagemaker-step-operator-wandb" ] }, From 596e696571231b50f1326681c075cee94da88386 Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Fri, 9 Feb 2024 16:16:41 +0100 Subject: [PATCH 10/20] rename run.ipynb --- classifier-e2e/{run.ipynb => run_full.ipynb} | 245 ++++++++----------- 1 file changed, 103 insertions(+), 142 deletions(-) rename classifier-e2e/{run.ipynb => run_full.ipynb} (76%) diff --git a/classifier-e2e/run.ipynb b/classifier-e2e/run_full.ipynb similarity index 76% rename from classifier-e2e/run.ipynb rename to classifier-e2e/run_full.ipynb index 31d9292b..1edcd747 100644 --- a/classifier-e2e/run.ipynb +++ b/classifier-e2e/run_full.ipynb @@ -1,32 +1,19 @@ { "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# pre-demo recommendations\n", - "! zenml connect --url https://1cf18d95-zenml.cloudinfra.zenml.io \n", - "! zenml model delete breast_cancer_classifier -y" - ] - }, { "cell_type": "markdown", - "id": "63ab391a", + "id": "63f7ab34", "metadata": {}, "source": [ - "# Intro to MLOps using ZenML\n", + "# 🌍 Overview\n", "\n", - "## 🌍 Overview\n", - "\n", - "This repository is a minimalistic MLOps project intended as a starting point to learn how to put ML workflows in production. It features: \n", + "This demo is a minimalistic MLOps project intended to showcase how to put ML workflows in production. It features: \n", "\n", "- A feature engineering pipeline that loads data and prepares it for training.\n", "- A training pipeline that loads the preprocessed dataset and trains a model.\n", "- A batch inference pipeline that runs predictions on the trained model with new data.\n", - "\n", - "Follow along this notebook to understand how you can use ZenML to productionalize your ML workflows!\n", + "- A stack switching and leveraging of Sagemaker step operator to outsource training to Cloud\n", + "- An analysis of training artifacts and their lineage (including connection with W&B)\n", "\n", "\"Pipelines" ] @@ -49,7 +36,10 @@ "metadata": {}, "outputs": [], "source": [ - "!zenml integration install sklearn xgboost -y\n", + "! pip3 install -r requirements.txt\n", + "! zenml integration install sklearn xgboost -y\n", + "! zenml connect --url https://1cf18d95-zenml.cloudinfra.zenml.io \n", + "! zenml model delete breast_cancer_classifier -y\n", "\n", "import IPython\n", "IPython.Application.instance().kernel.do_shutdown(restart=True)" @@ -64,24 +54,6 @@ "the end of the installation, the notebook kernel will automatically restart." ] }, - { - "cell_type": "markdown", - "id": "e3955ff1", - "metadata": {}, - "source": [ - "Optional: If you are using [ZenML Cloud](https://zenml.io/cloud), execute the following cell with your tenant URL. Otherwise ignore." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e2587315", - "metadata": {}, - "outputs": [], - "source": [ - "!zenml connect --url https://1cf18d95-zenml.cloudinfra.zenml.io" - ] - }, { "cell_type": "code", "execution_count": null, @@ -91,7 +63,6 @@ "source": [ "# Initialize ZenML and set the default stack\n", "!zenml init\n", - "\n", "!zenml stack set local-sagemaker-step-operator-wandb" ] }, @@ -112,19 +83,13 @@ "from zenml.logger import get_logger\n", "from uuid import UUID\n", "\n", - "from typing import Optional, List\n", - "\n", "from zenml import pipeline\n", "\n", "from steps import (\n", " data_loader,\n", - " data_preprocessor,\n", - " data_splitter,\n", - " model_evaluator,\n", " inference_preprocessor\n", ")\n", - "\n", - "from zenml.logger import get_logger\n", + "from pipelines import feature_engineering, training\n", "\n", "logger = get_logger(__name__)\n", "\n", @@ -236,39 +201,97 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "b50a9537", "metadata": {}, - "outputs": [], - "source": [ - "@pipeline\n", - "def feature_engineering(\n", - " test_size: float = 0.3,\n", - " drop_na: Optional[bool] = None,\n", - " normalize: Optional[bool] = None,\n", - " drop_columns: Optional[List[str]] = None,\n", - " target: Optional[str] = \"target\",\n", - " random_state: int = 17\n", - "):\n", - " \"\"\"Feature engineering pipeline.\"\"\"\n", - " # Link all the steps together by calling them and passing the output\n", - " # of one step as the input of the next step.\n", - " raw_data = data_loader(random_state=random_state, target=target)\n", - " dataset_trn, dataset_tst = data_splitter(\n", - " dataset=raw_data,\n", - " test_size=test_size,\n", - " )\n", - " dataset_trn, dataset_tst, _ = data_preprocessor(\n", - " dataset_trn=dataset_trn,\n", - " dataset_tst=dataset_tst,\n", - " drop_na=drop_na,\n", - " normalize=normalize,\n", - " drop_columns=drop_columns,\n", - " target=target,\n", - " random_state=random_state,\n", - " )\n", - "\n", - " return dataset_trn, dataset_tst" + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0;31m# Apache Software License 2.0\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# Copyright (c) ZenML GmbH 2024. All rights reserved.\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# Licensed under the Apache License, Version 2.0 (the \"License\");\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# you may not use this file except in compliance with the License.\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# You may obtain a copy of the License at\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# http://www.apache.org/licenses/LICENSE-2.0\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# Unless required by applicable law or agreed to in writing, software\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# distributed under the License is distributed on an \"AS IS\" BASIS,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# See the License for the specific language governing permissions and\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# limitations under the License.\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0mtyping\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;32mimport\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0msteps\u001b[0m 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"\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Feature engineering pipeline.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m This is a pipeline that loads the data, processes it and splits\u001b[0m\n", + "\u001b[0;34m it into train and test sets.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Args:\u001b[0m\n", + "\u001b[0;34m test_size: Size of holdout set for training 0.0..1.0\u001b[0m\n", + "\u001b[0;34m drop_na: If `True` NA values will be removed from dataset\u001b[0m\n", + "\u001b[0;34m normalize: If `True` dataset will be normalized with MinMaxScaler\u001b[0m\n", + "\u001b[0;34m drop_columns: List of columns to drop from dataset\u001b[0m\n", + "\u001b[0;34m target: Name of target column in dataset\u001b[0m\n", + "\u001b[0;34m random_state: Random state to configure the data loader\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns:\u001b[0m\n", + "\u001b[0;34m The processed datasets (dataset_trn, dataset_tst).\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Link all the steps together by calling them and passing the output\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# of one step as the input of the next step.\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mrandom_state\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mrandom_state\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mraw_data\u001b[0m \u001b[0;34m=\u001b[0m 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\u001b[0mtarget\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdataset_trn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataset_tst\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n" + ] + } + ], + "source": [ + "# let's see how feature engineering pipeline is implemented\n", + "%pycat pipelines/feature_engineering.py" ] }, { @@ -351,7 +374,6 @@ "metadata": {}, "outputs": [], "source": [ - "client = Client()\n", "run = client.get_pipeline(\"feature_engineering\").last_run\n", "print(run.name)" ] @@ -485,42 +507,8 @@ "metadata": {}, "outputs": [], "source": [ - "import pandas as pd\n", - "from sklearn.base import ClassifierMixin\n", - "from sklearn.linear_model import SGDClassifier\n", - "from typing_extensions import Annotated\n", - "from zenml import ArtifactConfig, step\n", - "from zenml.logger import get_logger\n", - "\n", - "logger = get_logger(__name__)\n", - "\n", - "\n", - "@step\n", - "def model_trainer(\n", - " dataset_trn: pd.DataFrame,\n", - " model_type: str = \"sgd\",\n", - ") -> Annotated[\n", - " ClassifierMixin,\n", - " ArtifactConfig(name=\"breast_cancer_classifier\", is_model_artifact=True),\n", - "]:\n", - " \"\"\"Configure and train a model on the training dataset.\"\"\"\n", - " target = \"target\"\n", - " if model_type == \"sgd\":\n", - " model = SGDClassifier()\n", - " elif model_type == \"xgboost\":\n", - " from xgboost import XGBClassifier\n", - "\n", - " model = XGBClassifier()\n", - " else:\n", - " raise ValueError(f\"Unknown model type {model_type}\")\n", - "\n", - " logger.info(f\"Training model {model}...\")\n", - "\n", - " model.fit(\n", - " dataset_trn.drop(columns=[target]),\n", - " dataset_trn[target],\n", - " )\n", - " return model" + "# let's have a look at training pipeline\n", + "%pycat steps/inference_predict.py" ] }, { @@ -550,35 +538,8 @@ "metadata": {}, "outputs": [], "source": [ - "@pipeline\n", - "def training(\n", - " train_dataset_id: Optional[UUID] = None,\n", - " test_dataset_id: Optional[UUID] = None,\n", - " model_type: str = \"sgd\",\n", - " min_train_accuracy: float = 0.0,\n", - " min_test_accuracy: float = 0.0,\n", - "):\n", - " \"\"\"Model training pipeline.\"\"\" \n", - " if train_dataset_id is None or test_dataset_id is None:\n", - " # If we dont pass the IDs, this will run the feature engineering pipeline \n", - " dataset_trn, dataset_tst = feature_engineering()\n", - " else:\n", - " # Load the datasets from an older pipeline\n", - " dataset_trn = client.get_artifact_version(name_id_or_prefix=train_dataset_id)\n", - " dataset_tst = client.get_artifact_version(name_id_or_prefix=test_dataset_id)\n", - "\n", - " trained_model = model_trainer(\n", - " dataset_trn=dataset_trn,\n", - " model_type=model_type,\n", - " )\n", - "\n", - " model_evaluator(\n", - " model=trained_model,\n", - " dataset_trn=dataset_trn,\n", - " dataset_tst=dataset_tst,\n", - " min_train_accuracy=min_train_accuracy,\n", - " min_test_accuracy=min_test_accuracy,\n", - " )" + "# let's have a look at training pipeline\n", + "%pycat pipelines/inference.py" ] }, { From 5e5034efb443e56d02931d3469f472711d64cfd9 Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Fri, 9 Feb 2024 16:27:31 +0100 Subject: [PATCH 11/20] ET for train + merge --- classifier-e2e/run_skip_basics.ipynb | 130 +++++++++----------------- classifier-e2e/steps/model_trainer.py | 6 +- 2 files changed, 51 insertions(+), 85 deletions(-) diff --git a/classifier-e2e/run_skip_basics.ipynb b/classifier-e2e/run_skip_basics.ipynb index 1d580fc4..e6bcc2fd 100644 --- a/classifier-e2e/run_skip_basics.ipynb +++ b/classifier-e2e/run_skip_basics.ipynb @@ -152,7 +152,42 @@ "The end goal of this quick baseline evaluation is to understand which of the two\n", "models performs better. We'll use the `evaluator` step to compare the two\n", "models. This step takes in the model from the trainer step, and computes its score\n", - "over the testing set." + "over the testing set.\n", + "\n", + "Soon you will see that it is relatively easy to train ML models using ZenML pipelines. But it can be somewhat clunky to track\n", + "all the models produced as you develop your experiments and use-cases. Luckily, ZenML offers a *Model Control Plane*,\n", + "which is a central register of all your ML models.\n", + "\n", + "You can easily create a ZenML Model and associate it with your pipelines using the `Model` object:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "19fe53b2", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'Model' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/classifier-e2e/run_skip_basics.ipynb Cell 13\u001b[0m line \u001b[0;36m4\n\u001b[1;32m 1\u001b[0m pipeline_settings \u001b[39m=\u001b[39m {}\n\u001b[1;32m 3\u001b[0m \u001b[39m# Lets add some metadata to the model to make it identifiable\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m pipeline_settings[\u001b[39m\"\u001b[39m\u001b[39mmodel\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m Model(\n\u001b[1;32m 5\u001b[0m name\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mbreast_cancer_classifier\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m 6\u001b[0m license\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mApache 2.0\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m 7\u001b[0m description\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mA breast cancer classifier\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m 8\u001b[0m )\n", + "\u001b[0;31mNameError\u001b[0m: name 'Model' is not defined" + ] + } + ], + "source": [ + "pipeline_settings = {}\n", + "\n", + "# Lets add some metadata to the model to make it identifiable\n", + "pipeline_settings[\"model\"] = Model(\n", + " name=\"breast_cancer_classifier\",\n", + " license=\"Apache 2.0\",\n", + " description=\"A breast cancer classifier\",\n", + ")" ] }, { @@ -162,6 +197,9 @@ "metadata": {}, "outputs": [], "source": [ + "# Let's train the XGBoost model and tag the version name with \"xgboost\"\n", + "pipeline_settings[\"model\"].tags = [\"breast_cancer\", \"classifier\", \"xgboost\"]\n", + "\n", "# Use an XGBoost model with fixed seed.\n", "training.with_options(enable_cache=False)(\n", " model_type=\"xgboost\",\n", @@ -178,6 +216,9 @@ "metadata": {}, "outputs": [], "source": [ + "# Let's train the SGD model and tag the version name with \"sgd\"\n", + "pipeline_settings[\"model\"].tags = [\"breast_cancer\", \"classifier\", \"sgd\"]\n", + "\n", "# Use a SGD classifier\n", "sgd_run = training.with_options(enable_cache=False)(\n", " model_type=\"sgd\",\n", @@ -208,91 +249,12 @@ "xgboost_run.steps[\"model_evaluator\"].output.load() >= sgd_run.steps[\"model_evaluator\"].output.load()" ] }, - { - "cell_type": "markdown", - "id": "e256d145", - "metadata": {}, - "source": [ - "# 💯 Step 2: Associating a model with your pipeline" - ] - }, - { - "cell_type": "markdown", - "id": "927978f3", - "metadata": {}, - "source": [ - "You can see it is relatively easy to train ML models using ZenML pipelines. But it can be somewhat clunky to track\n", - "all the models produced as you develop your experiments and use-cases. Luckily, ZenML offers a *Model Control Plane*,\n", - "which is a central register of all your ML models.\n", - "\n", - "You can easily create a ZenML Model and associate it with your pipelines using the `Model` object:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "99ca00c0", - "metadata": {}, - "outputs": [], - "source": [ - "pipeline_settings = {}\n", - "\n", - "# Lets add some metadata to the model to make it identifiable\n", - "pipeline_settings[\"model\"] = Model(\n", - " name=\"breast_cancer_classifier\",\n", - " license=\"Apache 2.0\",\n", - " description=\"A breast cancer classifier\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0e78a520", - "metadata": {}, - "outputs": [], - "source": [ - "# Let's train the SGD model and tag the version name with \"sgd\"\n", - "pipeline_settings[\"model\"].tags = [\"breast_cancer\", \"classifier\", \"sgd\"]\n", - "\n", - "# the `with_options` method allows us to pass in pipeline settings\n", - "# and returns a configured pipeline\n", - "training_configured = training.with_options(**pipeline_settings)\n", - "\n", - "# We can now run this as usual\n", - "training_configured(\n", - " model_type=\"sgd\",\n", - " random_state=42\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9b8e0002", - "metadata": {}, - "outputs": [], - "source": [ - "# Let's train the RF model and tag the version name with \"xgboost\"\n", - "pipeline_settings[\"model\"].tags = [\"breast_cancer\", \"classifier\", \"xgboost\"]\n", - "\n", - "# the `with_options` method allows us to pass in pipeline settings\n", - "# and returns a configured pipeline\n", - "training_configured = training.with_options(**pipeline_settings)\n", - "\n", - "# Let's run it again to make sure we have two versions\n", - "training_configured(\n", - " model_type=\"xgboost\",\n", - " random_state=42\n", - ")" - ] - }, { "cell_type": "markdown", "id": "09597223", "metadata": {}, "source": [ - "This time, running both pipelines has created two associated **model versions**.\n", + "Running both pipelines has created two associated **model versions**.\n", "You can list your ZenML model and their versions as follows:" ] }, @@ -409,7 +371,7 @@ "id": "d6306f14", "metadata": {}, "source": [ - "# 🫅 Step 3: Consuming the model in production" + "# 🫅 Step 2: Consuming the model in production" ] }, { @@ -530,7 +492,7 @@ "id": "594ee4fc-f102-4b99-bdc3-2f1670c87679", "metadata": {}, "source": [ - "# 🙏 Step 4: Bringing it all together\n", + "# 🙏 Step 3: Bringing it all together\n", "\n", "Let's run all the moving pieces we navigated in the previous steps using production ready python script `run.py`" ] @@ -611,7 +573,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# 🐙 Step 5: Analyzing results" + "# 🐙 Step 4: Analyzing results" ] }, { diff --git a/classifier-e2e/steps/model_trainer.py b/classifier-e2e/steps/model_trainer.py index 79242ef6..7b9c26d0 100644 --- a/classifier-e2e/steps/model_trainer.py +++ b/classifier-e2e/steps/model_trainer.py @@ -24,10 +24,14 @@ from zenml import ArtifactConfig, step from zenml.logger import get_logger +from zenml.client import Client logger = get_logger(__name__) -@step +et = Client().active_stack.experiment_tracker + + +@step(experiment_tracker=et.name) def model_trainer( dataset_trn: pd.DataFrame, model_type: str = "sgd", From 45d0d9e9f38e6c7d956df05624fd5d18e752739f Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Fri, 9 Feb 2024 16:32:59 +0100 Subject: [PATCH 12/20] use model configs --- classifier-e2e/run_skip_basics.ipynb | 20 ++++---------------- 1 file changed, 4 insertions(+), 16 deletions(-) diff --git a/classifier-e2e/run_skip_basics.ipynb b/classifier-e2e/run_skip_basics.ipynb index e6bcc2fd..58c399fc 100644 --- a/classifier-e2e/run_skip_basics.ipynb +++ b/classifier-e2e/run_skip_basics.ipynb @@ -163,22 +163,10 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "19fe53b2", "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'Model' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m/Users/andrei.vishniakov/Documents/GitHub/zenml-projects/classifier-e2e/run_skip_basics.ipynb Cell 13\u001b[0m line \u001b[0;36m4\n\u001b[1;32m 1\u001b[0m pipeline_settings \u001b[39m=\u001b[39m {}\n\u001b[1;32m 3\u001b[0m \u001b[39m# Lets add some metadata to the model to make it identifiable\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m pipeline_settings[\u001b[39m\"\u001b[39m\u001b[39mmodel\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m Model(\n\u001b[1;32m 5\u001b[0m name\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mbreast_cancer_classifier\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m 6\u001b[0m license\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mApache 2.0\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m 7\u001b[0m description\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mA breast cancer classifier\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m 8\u001b[0m )\n", - "\u001b[0;31mNameError\u001b[0m: name 'Model' is not defined" - ] - } - ], + "outputs": [], "source": [ "pipeline_settings = {}\n", "\n", @@ -201,7 +189,7 @@ "pipeline_settings[\"model\"].tags = [\"breast_cancer\", \"classifier\", \"xgboost\"]\n", "\n", "# Use an XGBoost model with fixed seed.\n", - "training.with_options(enable_cache=False)(\n", + "training.with_options(enable_cache=False,**pipeline_settings)(\n", " model_type=\"xgboost\",\n", " random_state=42\n", ")\n", @@ -220,7 +208,7 @@ "pipeline_settings[\"model\"].tags = [\"breast_cancer\", \"classifier\", \"sgd\"]\n", "\n", "# Use a SGD classifier\n", - "sgd_run = training.with_options(enable_cache=False)(\n", + "sgd_run = training.with_options(enable_cache=False,**pipeline_settings)(\n", " model_type=\"sgd\",\n", " random_state=42\n", ")\n", From fa389b4af8f247c1f3f5dde4336e9fe920227605 Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Fri, 9 Feb 2024 16:43:47 +0100 Subject: [PATCH 13/20] Took out exp tracker from trianer --- classifier-e2e/run_skip_basics.ipynb | 866 +++++++++++++++++++++++++- classifier-e2e/steps/model_trainer.py | 6 +- 2 files changed, 850 insertions(+), 22 deletions(-) diff --git a/classifier-e2e/run_skip_basics.ipynb b/classifier-e2e/run_skip_basics.ipynb index 58c399fc..c712a57a 100644 --- a/classifier-e2e/run_skip_basics.ipynb +++ b/classifier-e2e/run_skip_basics.ipynb @@ -47,10 +47,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "081d5616", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[?25l\u001b[2;36mFound existing ZenML repository at path \u001b[0m\n", + "\u001b[2;32m'/home/htahir1/workspace/zenml_io/zenml-projects/classifier-e2e'\u001b[0m\u001b[2;36m.\u001b[0m\n", + "\u001b[2;32m⠋\u001b[0m\u001b[2;36m Initializing ZenML repository at \u001b[0m\n", + "\u001b[2;36m/home/htahir1/workspace/zenml_io/zenml-projects/classifier-e2e.\u001b[0m\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Initializing ZenML repository at \n", + "/home/htahir1/workspace/zenml_io/zenml-projects/classifier-e2e.\n", + "\n", + "\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[?25l\u001b[32m⠋\u001b[0m Setting the repository active stack to \n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[2;36mActive repository stack set to: \u001b[0m\u001b[2;32m'local-sagemaker-step-operator-wandb'\u001b[0m\n", + "\u001b[2;32m⠋\u001b[0m\u001b[2;36m Setting the repository active stack to \u001b[0m\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Setting the repository active stack to \n", + "'local-sagemaker-step-operator-wandb'...\n", + "\u001b[1A\u001b[2K\u001b[1A\u001b[2K" + ] + } + ], "source": [ "# Initialize ZenML and set the default stack\n", "!zenml init\n", @@ -59,7 +79,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "79f775f2", "metadata": {}, "outputs": [], @@ -99,15 +119,101 @@ "Classifier, both batteries-included from `sklearn`. We'll train them both on the\n", "same data and then compare their performance.\n", "\n", - "\"Training" + "\"Model" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "dc08aecc", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "\u001b[0;31m# Apache Software License 2.0\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# Copyright (c) ZenML GmbH 2024. All rights reserved.\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# Licensed under the Apache License, Version 2.0 (the \"License\");\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# you may not use this file except in compliance with the License.\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# You may obtain a copy of the License at\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# http://www.apache.org/licenses/LICENSE-2.0\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# Unless required by applicable law or agreed to in writing, software\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# distributed under the License is distributed on an \"AS IS\" BASIS,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# See the License for the specific language governing permissions and\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# limitations under the License.\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0mtyping\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m 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"\u001b[0;34m\u001b[0m\u001b[0;34m@\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexperiment_tracker\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0met\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;32mdef\u001b[0m \u001b[0mmodel_trainer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdataset_trn\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmodel_type\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"sgd\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"target\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mAnnotated\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mClassifierMixin\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mArtifactConfig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"breast_cancer_classifier\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_model_artifact\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"Configure and train a model on the training dataset.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m This is an example of a model training step that takes in a dataset artifact\u001b[0m\n", + "\u001b[0;34m previously loaded and pre-processed by other steps in your pipeline, then\u001b[0m\n", + "\u001b[0;34m configures and trains a model on it. The model is then returned as a step\u001b[0m\n", + "\u001b[0;34m output artifact.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Args:\u001b[0m\n", + "\u001b[0;34m dataset_trn: The preprocessed train dataset.\u001b[0m\n", + "\u001b[0;34m model_type: The type of model to train.\u001b[0m\n", + "\u001b[0;34m target: The name of the target column in the dataset.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns:\u001b[0m\n", + "\u001b[0;34m The trained model artifact.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Raises:\u001b[0m\n", + "\u001b[0;34m ValueError: If the model type is not supported.\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Initialize the model with the hyperparameters indicated in the step\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# parameters and train it on the training set.\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmodel_type\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"sgd\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSGDClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mmodel_type\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"xgboost\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mxgboost\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mXGBClassifier\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mXGBClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Unknown model type {model_type}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Training model {model}...\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdataset_trn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdataset_trn\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# let's have a look at model training step\n", "%pycat steps/model_trainer.py" @@ -135,10 +241,101 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "01162d23", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "\u001b[0;31m# Apache Software License 2.0\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# Copyright (c) ZenML GmbH 2024. All rights reserved.\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# Licensed under the Apache License, Version 2.0 (the \"License\");\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# you may not use this file except in compliance with the License.\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# You may obtain a copy of the License at\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# http://www.apache.org/licenses/LICENSE-2.0\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# Unless required by applicable law or agreed to in writing, software\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# distributed under the License is distributed on an \"AS IS\" BASIS,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# See the License for the specific language governing permissions and\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# limitations under the License.\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0mtyping\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0muuid\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mUUID\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0msteps\u001b[0m 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\u001b[0mzenml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogger\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mget_logger\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0mlogger\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_logger\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m@\u001b[0m\u001b[0mpipeline\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;32mdef\u001b[0m \u001b[0mtraining\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mtrain_dataset_id\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mUUID\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mtest_dataset_id\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mUUID\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"target\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmodel_type\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"sgd\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Model training pipeline.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m This is a pipeline that loads the data from a preprocessing pipeline,\u001b[0m\n", + "\u001b[0;34m trains a model on it and evaluates the model. If it is the first model\u001b[0m\n", + "\u001b[0;34m to be trained, it will be promoted to production. If not, it will be\u001b[0m\n", + "\u001b[0;34m promoted only if it has a higher accuracy than the current production\u001b[0m\n", + "\u001b[0;34m model version.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Args:\u001b[0m\n", + "\u001b[0;34m train_dataset_id: ID of the train dataset produced by feature engineering.\u001b[0m\n", + "\u001b[0;34m test_dataset_id: ID of the test dataset produced by feature engineering.\u001b[0m\n", + "\u001b[0;34m target: Name of target column in dataset.\u001b[0m\n", + "\u001b[0;34m model_type: The type of model to train.\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Link all the steps together by calling them and passing the output\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# of one step as the input of the next step.\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Execute Feature Engineering Pipeline\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtrain_dataset_id\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mtest_dataset_id\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdataset_trn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataset_tst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfeature_engineering\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mclient\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mClient\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdataset_trn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_artifact_version\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mname_id_or_prefix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain_dataset_id\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdataset_tst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_artifact_version\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mname_id_or_prefix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtest_dataset_id\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_trainer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdataset_trn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdataset_trn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel_type\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0macc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_evaluator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdataset_trn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdataset_trn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdataset_tst\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdataset_tst\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmodel_promoter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maccuracy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0macc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# let's have a look at training pipeline\n", "%pycat pipelines/training.py" @@ -163,7 +360,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "19fe53b2", "metadata": {}, "outputs": [], @@ -180,10 +377,330 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "c64885ac", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mReusing registered pipeline version: \u001b[0m\u001b[1;36m(version: 146)\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mNew model version \u001b[0m\u001b[1;36m2\u001b[1;35m was created.\u001b[0m\n", + "\u001b[1;35mExecuting a new run.\u001b[0m\n", + "\u001b[1;35mCaching is disabled by default for \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mhamza@zenml.io\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mlocal-sagemaker-step-operator-wandb\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m step_operator: \u001b[0m\u001b[1;36msagemaker-eu\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m container_registry: \u001b[0m\u001b[1;36maws-eu\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m experiment_tracker: \u001b[0m\u001b[1;36mzenml_wandb\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m image_builder: \u001b[0m\u001b[1;36mlocal\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36ms3-zenfiles\u001b[1;35m\u001b[0m\n", + "\u001b[33mCould not import GCP service connector: No module named 'google.api_core'.\u001b[0m\n", + "\u001b[33mCould not import Azure service connector: No module named 'azure.identity'.\u001b[0m\n", + "\u001b[33mCould not import Kubernetes service connector: No module named 'kubernetes'.\u001b[0m\n", + "\u001b[33mCould not import HyperAI service connector: No module named 'paramiko'.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mDataset with 541 records loaded!\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has finished in \u001b[0m\u001b[1;36m6.566s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has finished in \u001b[0m\u001b[1;36m9.929s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has finished in \u001b[0m\u001b[1;36m13.618s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mInitializing wandb with entity None, project name: None, run_name: training-2024_02_09-15_38_23_251071_model_trainer.\u001b[0m\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mzenmlcodemonkey\u001b[0m (\u001b[33mzenmlcode\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n" + ] + }, + { + "data": { + "text/html": [ + "Tracking run with wandb version 0.16.3" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Run data is saved locally in /home/htahir1/workspace/zenml_io/zenml-projects/classifier-e2e/wandb/run-20240209_163917-2xjpgcem" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Syncing run training-2024_02_09-15_38_23_251071_model_trainer to Weights & Biases (docs)
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + " View project at https://wandb.ai/zenmlcode/zenml-projects-classifier-e2e" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + " View run at https://wandb.ai/zenmlcode/zenml-projects-classifier-e2e/runs/2xjpgcem" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;35mTraining model XGBClassifier(base_score=None, booster=None, callbacks=None,\n", + " colsample_bylevel=None, colsample_bynode=None,\n", + " colsample_bytree=None, device=None, early_stopping_rounds=None,\n", + " enable_categorical=False, eval_metric=None, feature_types=None,\n", + " gamma=None, grow_policy=None, importance_type=None,\n", + " interaction_constraints=None, learning_rate=None, max_bin=None,\n", + " max_cat_threshold=None, max_cat_to_onehot=None,\n", + " max_delta_step=None, max_depth=None, max_leaves=None,\n", + " min_child_weight=None, missing=nan, monotone_constraints=None,\n", + " multi_strategy=None, n_estimators=None, n_jobs=None,\n", + " num_parallel_tree=None, random_state=None, ...)...\u001b[0m\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(Label(value='0.004 MB of 0.004 MB uploaded\\r'), FloatProgress(value=1.0, max=1.0)))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "W&B sync reduced upload amount by 28.1% " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + " View run training-2024_02_09-15_38_23_251071_model_trainer at: https://wandb.ai/zenmlcode/zenml-projects-classifier-e2e/runs/2xjpgcem
View job at https://wandb.ai/zenmlcode/zenml-projects-classifier-e2e/jobs/QXJ0aWZhY3RDb2xsZWN0aW9uOjEzODQ5NDk3OQ==/version_details/v1
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test_accuracy
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Run summary:


test_accuracy0.97248
train_accuracy1.0

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View job at https://wandb.ai/zenmlcode/zenml-projects-classifier-e2e/jobs/QXJ0aWZhY3RDb2xsZWN0aW9uOjEzODQ5NDk3OQ==/version_details/v2
Synced 5 W&B file(s), 1 media file(s), 3 artifact file(s) and 0 other file(s)" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Find logs at: ./wandb/run-20240209_163944-oerkzyw8/logs" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;35mImplicitly linking artifact \u001b[0m\u001b[1;36moutput\u001b[1;35m to model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[1;35m version \u001b[0m\u001b[1;36m2\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has finished in \u001b[0m\u001b[1;36m18.366s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_promoter\u001b[1;35m has started.\u001b[0m\n", + "\u001b[33mYour artifact was materialized under Python version 'unknown' but you are currently using '3.8.10'. This might cause unexpected behavior since pickle is not reproducible across Python versions. Attempting to load anyway...\u001b[0m\n", + "\u001b[1;35mImplicitly linking artifact \u001b[0m\u001b[1;36moutput\u001b[1;35m to model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[1;35m version \u001b[0m\u001b[1;36m2\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_promoter\u001b[1;35m has finished in \u001b[0m\u001b[1;36m17.530s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mPipeline run has finished in \u001b[0m\u001b[1;36m2m0s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/7b61c1dd-cc67-4d51-b5e1-f609cf7794c3/runs/28a6e92a-5c69-4397-830f-afca1d44e535/dag\u001b[0m\n" + ] + } + ], "source": [ "# Let's train the XGBoost model and tag the version name with \"xgboost\"\n", "pipeline_settings[\"model\"].tags = [\"breast_cancer\", \"classifier\", \"xgboost\"]\n", @@ -202,13 +719,315 @@ "execution_count": null, "id": "4300c82f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mReusing registered pipeline version: \u001b[0m\u001b[1;36m(version: 147)\u001b[1;35m.\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['sgd'], 'tags removed': ['xgboost']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[1;35mExecuting a new run.\u001b[0m\n", + "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mhamza@zenml.io\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mlocal-sagemaker-step-operator-wandb\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m step_operator: \u001b[0m\u001b[1;36msagemaker-eu\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m container_registry: \u001b[0m\u001b[1;36maws-eu\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m experiment_tracker: \u001b[0m\u001b[1;36mzenml_wandb\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m image_builder: \u001b[0m\u001b[1;36mlocal\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36ms3-zenfiles\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m.\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['sgd'], 'tags removed': ['xgboost']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m.\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['sgd'], 'tags removed': ['xgboost']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['sgd'], 'tags removed': ['xgboost']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m.\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['sgd'], 'tags removed': ['xgboost']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['sgd'], 'tags removed': ['xgboost']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['sgd'], 'tags removed': ['xgboost']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mInitializing wandb with entity None, project name: None, run_name: training-2024_02_09-15_40_46_832350_model_trainer.\u001b[0m\n" + ] + }, + { + "data": { + "text/html": [ + "Tracking run with wandb version 0.16.3" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Run data is saved locally in /home/htahir1/workspace/zenml_io/zenml-projects/classifier-e2e/wandb/run-20240209_164109-jyjdalxg" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Syncing run training-2024_02_09-15_40_46_832350_model_trainer to Weights & Biases (docs)
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View job at https://wandb.ai/zenmlcode/zenml-projects-classifier-e2e/jobs/QXJ0aWZhY3RDb2xsZWN0aW9uOjEzODQ5NDk3OQ==/version_details/v3
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" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + " View project at https://wandb.ai/zenmlcode/zenml-projects-classifier-e2e" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + " View run at https://wandb.ai/zenmlcode/zenml-projects-classifier-e2e/runs/txtu0wof" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['sgd'], 'tags removed': ['xgboost']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['sgd'], 'tags removed': ['xgboost']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[33mYour artifact was materialized under Python version 'unknown' but you are currently using '3.8.10'. This might cause unexpected behavior since pickle is not reproducible across Python versions. Attempting to load anyway...\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['sgd'], 'tags removed': ['xgboost']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['sgd'], 'tags removed': ['xgboost']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[1;35mTrain accuracy=66.20%\u001b[0m\n", + "\u001b[1;35mTest accuracy=74.31%\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['sgd'], 'tags removed': ['xgboost']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['sgd'], 'tags removed': ['xgboost']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['sgd'], 'tags removed': ['xgboost']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(Label(value='0.004 MB of 0.004 MB uploaded\\r'), FloatProgress(value=1.0, max=1.0)))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "

Run history:


test_accuracy
train_accuracy

Run summary:


test_accuracy0.74312
train_accuracy0.66204

" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + " View run training-2024_02_09-15_40_46_832350_model_evaluator at: https://wandb.ai/zenmlcode/zenml-projects-classifier-e2e/runs/txtu0wof
View job at https://wandb.ai/zenmlcode/zenml-projects-classifier-e2e/jobs/QXJ0aWZhY3RDb2xsZWN0aW9uOjEzODQ5NDk3OQ==/version_details/v2
Synced 5 W&B file(s), 1 media file(s), 1 artifact file(s) and 0 other file(s)" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Find logs at: ./wandb/run-20240209_164131-txtu0wof/logs" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['sgd'], 'tags removed': ['xgboost']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[1;35mImplicitly linking artifact \u001b[0m\u001b[1;36moutput\u001b[1;35m to model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[1;35m version \u001b[0m\u001b[1;36m3\u001b[1;35m.\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['sgd'], 'tags removed': ['xgboost']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has finished in \u001b[0m\u001b[1;36m21.367s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_promoter\u001b[1;35m has started.\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['sgd'], 'tags removed': ['xgboost']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['sgd'], 'tags removed': ['xgboost']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['sgd'], 'tags removed': ['xgboost']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['sgd'], 'tags removed': ['xgboost']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", + "\u001b[1;35mModel accuracy 74.31% is below 80% ! Not promoting model.\u001b[0m\n" + ] + } + ], "source": [ "# Let's train the SGD model and tag the version name with \"sgd\"\n", "pipeline_settings[\"model\"].tags = [\"breast_cancer\", \"classifier\", \"sgd\"]\n", "\n", "# Use a SGD classifier\n", - "sgd_run = training.with_options(enable_cache=False,**pipeline_settings)(\n", + "sgd_run = training.with_options(enable_cache=True,**pipeline_settings)(\n", " model_type=\"sgd\",\n", " random_state=42\n", ")\n", @@ -341,9 +1160,7 @@ "cell_type": "markdown", "id": "2ecbc8cf", "metadata": {}, - "source": [ - "\"Model" - ] + "source": [] }, { "cell_type": "markdown", @@ -499,6 +1316,7 @@ { "cell_type": "code", "execution_count": null, + "id": "eea84c1e", "metadata": {}, "outputs": [], "source": [ @@ -509,6 +1327,7 @@ { "cell_type": "code", "execution_count": null, + "id": "3ee94bdf", "metadata": {}, "outputs": [], "source": [ @@ -517,6 +1336,7 @@ }, { "cell_type": "markdown", + "id": "f1778dbd", "metadata": {}, "source": [ "Now full run executed on local stack and experiment is tracked using Model Control Plane and Weights&Biases.\n", @@ -541,6 +1361,7 @@ { "cell_type": "code", "execution_count": null, + "id": "a878bd92", "metadata": {}, "outputs": [], "source": [ @@ -551,6 +1372,7 @@ { "cell_type": "code", "execution_count": null, + "id": "6363010c", "metadata": {}, "outputs": [], "source": [ @@ -559,6 +1381,7 @@ }, { "cell_type": "markdown", + "id": "df443f25", "metadata": {}, "source": [ "# 🐙 Step 4: Analyzing results" @@ -579,6 +1402,7 @@ }, { "cell_type": "markdown", + "id": "08a6cab7", "metadata": {}, "source": [ "At first, let's pull some meta information collected during models evaluation stage. To recall we used this step as evaluator:\n", @@ -629,6 +1453,7 @@ { "cell_type": "code", "execution_count": null, + "id": "2ee60c94", "metadata": {}, "outputs": [], "source": [ @@ -640,6 +1465,7 @@ }, { "cell_type": "markdown", + "id": "93baffa9", "metadata": {}, "source": [ "Now lets' plot collected Confusion Matrixes:" @@ -648,6 +1474,7 @@ { "cell_type": "code", "execution_count": null, + "id": "e2a17048", "metadata": {}, "outputs": [], "source": [ @@ -670,6 +1497,7 @@ }, { "cell_type": "markdown", + "id": "d3841d9d", "metadata": {}, "source": [ "So far we were able to collect all the information we tracked using Model Control Plane, but we also had Weights&Biases tracking enabled - let's dive into.\n", @@ -680,6 +1508,7 @@ { "cell_type": "code", "execution_count": null, + "id": "d6832660", "metadata": {}, "outputs": [], "source": [ @@ -689,6 +1518,7 @@ }, { "cell_type": "markdown", + "id": "e7b46393", "metadata": {}, "source": [ "With Model Control Plane we can also easily track lineage of artifacts and pipeline runs:" @@ -697,6 +1527,7 @@ { "cell_type": "code", "execution_count": null, + "id": "88973e3a", "metadata": {}, "outputs": [], "source": [ @@ -710,6 +1541,7 @@ { "cell_type": "code", "execution_count": null, + "id": "7b10b01b", "metadata": {}, "outputs": [], "source": [ @@ -734,7 +1566,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.8.10" } }, "nbformat": 4, diff --git a/classifier-e2e/steps/model_trainer.py b/classifier-e2e/steps/model_trainer.py index 7b9c26d0..79242ef6 100644 --- a/classifier-e2e/steps/model_trainer.py +++ b/classifier-e2e/steps/model_trainer.py @@ -24,14 +24,10 @@ from zenml import ArtifactConfig, step from zenml.logger import get_logger -from zenml.client import Client logger = get_logger(__name__) -et = Client().active_stack.experiment_tracker - - -@step(experiment_tracker=et.name) +@step def model_trainer( dataset_trn: pd.DataFrame, model_type: str = "sgd", From 82b9e9341b25248209d19cf3ec20edf6d290af01 Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Fri, 9 Feb 2024 18:09:41 +0100 Subject: [PATCH 14/20] Basics fixed --- classifier-e2e/run_skip_basics.ipynb | 1000 ++------------------------ 1 file changed, 70 insertions(+), 930 deletions(-) diff --git a/classifier-e2e/run_skip_basics.ipynb b/classifier-e2e/run_skip_basics.ipynb index c712a57a..2179af73 100644 --- a/classifier-e2e/run_skip_basics.ipynb +++ b/classifier-e2e/run_skip_basics.ipynb @@ -47,30 +47,10 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "081d5616", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[?25l\u001b[2;36mFound existing ZenML repository at path \u001b[0m\n", - "\u001b[2;32m'/home/htahir1/workspace/zenml_io/zenml-projects/classifier-e2e'\u001b[0m\u001b[2;36m.\u001b[0m\n", - "\u001b[2;32m⠋\u001b[0m\u001b[2;36m Initializing ZenML repository at \u001b[0m\n", - "\u001b[2;36m/home/htahir1/workspace/zenml_io/zenml-projects/classifier-e2e.\u001b[0m\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Initializing ZenML repository at \n", - "/home/htahir1/workspace/zenml_io/zenml-projects/classifier-e2e.\n", - "\n", - "\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[?25l\u001b[32m⠋\u001b[0m Setting the repository active stack to \n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[2;36mActive repository stack set to: \u001b[0m\u001b[2;32m'local-sagemaker-step-operator-wandb'\u001b[0m\n", - "\u001b[2;32m⠋\u001b[0m\u001b[2;36m Setting the repository active stack to \u001b[0m\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Setting the repository active stack to \n", - "'local-sagemaker-step-operator-wandb'...\n", - "\u001b[1A\u001b[2K\u001b[1A\u001b[2K" - ] - } - ], + "outputs": [], "source": [ "# Initialize ZenML and set the default stack\n", "!zenml init\n", @@ -79,7 +59,17 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, + "id": "f4876831-679a-49b1-865c-db56f3b343f2", + "metadata": {}, + "outputs": [], + "source": [ + "!zenml stack describe local-sagemaker-step-operator-wandb" + ] + }, + { + "cell_type": "code", + "execution_count": null, "id": "79f775f2", "metadata": {}, "outputs": [], @@ -124,99 +114,13 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "dc08aecc", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\u001b[0;31m# Apache Software License 2.0\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;31m# Copyright (c) ZenML GmbH 2024. All rights reserved.\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;31m# Licensed under the Apache License, Version 2.0 (the \"License\");\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;31m# you may not use this file except in compliance with the License.\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;31m# You may obtain a copy of the License at\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;31m# http://www.apache.org/licenses/LICENSE-2.0\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;31m# Unless required by applicable law or agreed to in writing, software\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;31m# distributed under the License is distributed on an \"AS IS\" BASIS,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;31m# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;31m# See the License for the specific language governing permissions and\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;31m# limitations under the License.\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0mtyping\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbase\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mClassifierMixin\u001b[0m\u001b[0;34m\u001b[0m\n", - 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"\u001b[0;34m\u001b[0m \u001b[0mdataset_trn\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mmodel_type\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"sgd\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"target\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mAnnotated\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mClassifierMixin\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mArtifactConfig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"breast_cancer_classifier\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_model_artifact\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"Configure and train a model on the training dataset.\u001b[0m\n", - "\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m This is an example of a model training step that takes in a dataset artifact\u001b[0m\n", - "\u001b[0;34m previously loaded and pre-processed by other steps in your pipeline, then\u001b[0m\n", - "\u001b[0;34m configures and trains a model on it. The model is then returned as a step\u001b[0m\n", - "\u001b[0;34m output artifact.\u001b[0m\n", - "\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m Args:\u001b[0m\n", - "\u001b[0;34m dataset_trn: The preprocessed train dataset.\u001b[0m\n", - "\u001b[0;34m model_type: The type of model to train.\u001b[0m\n", - "\u001b[0;34m target: The name of the target column in the dataset.\u001b[0m\n", - "\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m Returns:\u001b[0m\n", - "\u001b[0;34m The trained model artifact.\u001b[0m\n", - "\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m Raises:\u001b[0m\n", - "\u001b[0;34m ValueError: If the model type is not supported.\u001b[0m\n", - "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;31m# Initialize the model with the hyperparameters indicated in the step\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;31m# parameters and train it on the training set.\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmodel_type\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"sgd\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSGDClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mmodel_type\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"xgboost\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mxgboost\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mXGBClassifier\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mXGBClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Unknown model type {model_type}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Training model {model}...\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mdataset_trn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mdataset_trn\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# let's have a look at model training step\n", - "%pycat steps/model_trainer.py" + "%pycat steps/model_evaluator.py" ] }, { @@ -241,101 +145,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "01162d23", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\u001b[0;31m# Apache Software License 2.0\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;31m# Copyright (c) ZenML GmbH 2024. All rights reserved.\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;31m# Licensed under the Apache License, Version 2.0 (the \"License\");\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;31m# you may not use this file except in compliance with the License.\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;31m# You may obtain a copy of the License at\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;31m# http://www.apache.org/licenses/LICENSE-2.0\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;31m# Unless required by applicable law or agreed to in writing, software\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;31m# distributed under the License is distributed on an \"AS IS\" BASIS,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;31m# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;31m# See the License for the specific language governing permissions and\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;31m# limitations under the License.\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0mtyping\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0muuid\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mUUID\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0msteps\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmodel_evaluator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel_promoter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel_trainer\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0mpipelines\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mfeature_engineering\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0mzenml\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpipeline\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0mzenml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclient\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mClient\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0mzenml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogger\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mget_logger\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0mlogger\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_logger\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;34m@\u001b[0m\u001b[0mpipeline\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;32mdef\u001b[0m \u001b[0mtraining\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mtrain_dataset_id\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mUUID\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mtest_dataset_id\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mUUID\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"target\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mmodel_type\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"sgd\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", - "\u001b[0;34m Model training pipeline.\u001b[0m\n", - "\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m This is a pipeline that loads the data from a preprocessing pipeline,\u001b[0m\n", - "\u001b[0;34m trains a model on it and evaluates the model. If it is the first model\u001b[0m\n", - "\u001b[0;34m to be trained, it will be promoted to production. If not, it will be\u001b[0m\n", - "\u001b[0;34m promoted only if it has a higher accuracy than the current production\u001b[0m\n", - "\u001b[0;34m model version.\u001b[0m\n", - "\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m Args:\u001b[0m\n", - "\u001b[0;34m train_dataset_id: ID of the train dataset produced by feature engineering.\u001b[0m\n", - "\u001b[0;34m test_dataset_id: ID of the test dataset produced by feature engineering.\u001b[0m\n", - "\u001b[0;34m target: Name of target column in dataset.\u001b[0m\n", - "\u001b[0;34m model_type: The type of model to train.\u001b[0m\n", - "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;31m# Link all the steps together by calling them and passing the output\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;31m# of one step as the input of the next step.\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;31m# Execute Feature Engineering Pipeline\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtrain_dataset_id\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mtest_dataset_id\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mdataset_trn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataset_tst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfeature_engineering\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mclient\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mClient\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mdataset_trn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_artifact_version\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", - 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] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# let's have a look at training pipeline\n", "%pycat pipelines/training.py" @@ -360,7 +173,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "19fe53b2", "metadata": {}, "outputs": [], @@ -377,330 +190,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "c64885ac", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mReusing registered pipeline version: \u001b[0m\u001b[1;36m(version: 146)\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mNew model version \u001b[0m\u001b[1;36m2\u001b[1;35m was created.\u001b[0m\n", - "\u001b[1;35mExecuting a new run.\u001b[0m\n", - "\u001b[1;35mCaching is disabled by default for \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", - 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test_accuracy
train_accuracy

Run summary:


test_accuracy0.97248
train_accuracy1.0

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This might cause unexpected behavior since pickle is not reproducible across Python versions. Attempting to load anyway...\u001b[0m\n", - "\u001b[1;35mImplicitly linking artifact \u001b[0m\u001b[1;36moutput\u001b[1;35m to model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[1;35m version \u001b[0m\u001b[1;36m2\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_promoter\u001b[1;35m has finished in \u001b[0m\u001b[1;36m17.530s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mPipeline run has finished in \u001b[0m\u001b[1;36m2m0s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/7b61c1dd-cc67-4d51-b5e1-f609cf7794c3/runs/28a6e92a-5c69-4397-830f-afca1d44e535/dag\u001b[0m\n" - ] - } - ], + "outputs": [], "source": [ "# Let's train the XGBoost model and tag the version name with \"xgboost\"\n", "pipeline_settings[\"model\"].tags = [\"breast_cancer\", \"classifier\", \"xgboost\"]\n", @@ -718,310 +211,10 @@ "cell_type": "code", "execution_count": null, "id": "4300c82f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mReusing registered pipeline version: \u001b[0m\u001b[1;36m(version: 147)\u001b[1;35m.\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['sgd'], 'tags removed': ['xgboost']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[1;35mExecuting a new run.\u001b[0m\n", - "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mhamza@zenml.io\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mlocal-sagemaker-step-operator-wandb\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m step_operator: \u001b[0m\u001b[1;36msagemaker-eu\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m container_registry: \u001b[0m\u001b[1;36maws-eu\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m experiment_tracker: \u001b[0m\u001b[1;36mzenml_wandb\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m image_builder: \u001b[0m\u001b[1;36mlocal\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36ms3-zenfiles\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m.\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['sgd'], 'tags removed': ['xgboost']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m.\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['sgd'], 'tags removed': ['xgboost']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['sgd'], 'tags removed': ['xgboost']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m.\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['sgd'], 'tags removed': ['xgboost']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['sgd'], 'tags removed': ['xgboost']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['sgd'], 'tags removed': ['xgboost']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mInitializing wandb with entity None, project name: None, run_name: training-2024_02_09-15_40_46_832350_model_trainer.\u001b[0m\n" - ] - }, - { - "data": { - "text/html": [ - "Tracking run with wandb version 0.16.3" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Run data is saved locally in /home/htahir1/workspace/zenml_io/zenml-projects/classifier-e2e/wandb/run-20240209_164109-jyjdalxg" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Syncing run training-2024_02_09-15_40_46_832350_model_trainer to Weights & Biases (docs)
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View job at https://wandb.ai/zenmlcode/zenml-projects-classifier-e2e/jobs/QXJ0aWZhY3RDb2xsZWN0aW9uOjEzODQ5NDk3OQ==/version_details/v3
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" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - " View project at https://wandb.ai/zenmlcode/zenml-projects-classifier-e2e" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - " View run at https://wandb.ai/zenmlcode/zenml-projects-classifier-e2e/runs/txtu0wof" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['sgd'], 'tags removed': ['xgboost']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['sgd'], 'tags removed': ['xgboost']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[33mYour artifact was materialized under Python version 'unknown' but you are currently using '3.8.10'. This might cause unexpected behavior since pickle is not reproducible across Python versions. Attempting to load anyway...\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['sgd'], 'tags removed': ['xgboost']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['sgd'], 'tags removed': ['xgboost']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[1;35mTrain accuracy=66.20%\u001b[0m\n", - "\u001b[1;35mTest accuracy=74.31%\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['sgd'], 'tags removed': ['xgboost']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['sgd'], 'tags removed': ['xgboost']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['sgd'], 'tags removed': ['xgboost']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "VBox(children=(Label(value='0.004 MB of 0.004 MB uploaded\\r'), FloatProgress(value=1.0, max=1.0)))" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "

Run history:


test_accuracy
train_accuracy

Run summary:


test_accuracy0.74312
train_accuracy0.66204

" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - " View run training-2024_02_09-15_40_46_832350_model_evaluator at: https://wandb.ai/zenmlcode/zenml-projects-classifier-e2e/runs/txtu0wof
View job at https://wandb.ai/zenmlcode/zenml-projects-classifier-e2e/jobs/QXJ0aWZhY3RDb2xsZWN0aW9uOjEzODQ5NDk3OQ==/version_details/v2
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If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[1;35mImplicitly linking artifact \u001b[0m\u001b[1;36moutput\u001b[1;35m to model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[1;35m version \u001b[0m\u001b[1;36m3\u001b[1;35m.\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['sgd'], 'tags removed': ['xgboost']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has finished in \u001b[0m\u001b[1;36m21.367s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_promoter\u001b[1;35m has started.\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['sgd'], 'tags removed': ['xgboost']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['sgd'], 'tags removed': ['xgboost']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['sgd'], 'tags removed': ['xgboost']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[33mProvided model configuration does not match existing model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[33m with the following changes: {'tags added': ['sgd'], 'tags removed': ['xgboost']}. If you want to update the model configuration, please use the \u001b[0m\u001b[1;36mzenml model update\u001b[33m command.\u001b[0m\n", - "\u001b[1;35mModel accuracy 74.31% is below 80% ! Not promoting model.\u001b[0m\n" - ] - } - ], + "metadata": { + "scrolled": true + }, + "outputs": [], "source": [ "# Let's train the SGD model and tag the version name with \"sgd\"\n", "pipeline_settings[\"model\"].tags = [\"breast_cancer\", \"classifier\", \"sgd\"]\n", @@ -1073,13 +266,12 @@ "outputs": [], "source": [ "zenml_model = client.get_model(\"breast_cancer_classifier\")\n", - "print(zenml_model)\n", "\n", "versions = zenml_model.versions\n", "\n", "print(f\"Model {zenml_model.name} has {len(versions)} versions\")\n", "\n", - "versions[-2].version, versions[-1].version" + "print(f\"The latest two versions are: {versions[-2].version, versions[-1].version}\")" ] }, { @@ -1156,12 +348,6 @@ "more insight into that sort of flow!" ] }, - { - "cell_type": "markdown", - "id": "2ecbc8cf", - "metadata": {}, - "source": [] - }, { "cell_type": "markdown", "id": "8f1146db", @@ -1250,13 +436,13 @@ { "cell_type": "code", "execution_count": null, - "id": "ff3402f1", + "id": "24c6881c-eea4-49fc-88b6-6f725e11749d", "metadata": {}, "outputs": [], "source": [ "# the `with_options` method allows us to pass in pipeline settings\n", "# and returns a configured pipeline\n", - "inference.with_options(**pipeline_settings)()" + "inference.with_options(**pipeline_settings)(random_state=42, target=\"target\")" ] }, { @@ -1292,99 +478,12 @@ "\"Model" ] }, - { - "cell_type": "markdown", - "id": "594ee4fc-f102-4b99-bdc3-2f1670c87679", - "metadata": {}, - "source": [ - "# 🙏 Step 3: Bringing it all together\n", - "\n", - "Let's run all the moving pieces we navigated in the previous steps using production ready python script `run.py`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fa40175d", - "metadata": {}, - "outputs": [], - "source": [ - "# let's clean up previous partial runs first\n", - "! zenml model delete breast_cancer_classifier -y" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "eea84c1e", - "metadata": {}, - "outputs": [], - "source": [ - "!zenml stack set local-wandb\n", - "!zenml stack describe local-wandb" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3ee94bdf", - "metadata": {}, - "outputs": [], - "source": [ - "!python3 run.py --training-pipeline --inference-pipeline" - ] - }, - { - "cell_type": "markdown", - "id": "f1778dbd", - "metadata": {}, - "source": [ - "Now full run executed on local stack and experiment is tracked using Model Control Plane and Weights&Biases.\n", - "\n", - "Let's move some heavy lifting steps to the Sagemaker keeping light ones on local, so we can reduce costs. This can be achieved using step operators and step configurations.\n", - "\n", - "To make this happen we will use following configuration option for step level settings:\n", - "```yaml\n", - "steps:\n", - " model_trainer:\n", - " \n", - " settings:\n", - " step_operator: sagemaker-eu\n", - " sagemaker:\n", - " estimator_args: \n", - " instance_type : ml.m5.large # select instance type\n", - "```\n", - "\n", - "\"Sagemaker" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a878bd92", - "metadata": {}, - "outputs": [], - "source": [ - "!zenml stack set local-sagemaker-step-operator-wandb\n", - "!zenml stack describe local-sagemaker-step-operator-wandb" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6363010c", - "metadata": {}, - "outputs": [], - "source": [ - "!python3 run.py --training-pipeline --inference-pipeline --custom-training-suffix _sagemaker" - ] - }, { "cell_type": "markdown", "id": "df443f25", "metadata": {}, "source": [ - "# 🐙 Step 4: Analyzing results" + "# 🐙 Step 3: Analyzing results" ] }, { @@ -1548,6 +647,47 @@ "for run_name, run_ in sgd_model_version.pipeline_runs.items():\n", " print(run_name, run_.id)" ] + }, + { + "cell_type": "markdown", + "id": "594ee4fc-f102-4b99-bdc3-2f1670c87679", + "metadata": {}, + "source": [ + "# 🙏 Step 4: Switching to production\n", + "\n", + "Let's run all the moving pieces we navigated in the previous steps using production ready python script `run.py`" + ] + }, + { + "cell_type": "markdown", + "id": "f1778dbd", + "metadata": {}, + "source": [ + "The next thing we want to do is to switch to production on the cloud\n", + "\n", + "\"Sagemaker" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a878bd92", + "metadata": {}, + "outputs": [], + "source": [ + "!zenml stack set sagemaker-pipelines-wandb\n", + "!zenml stack describe sagemaker-pipelines-wandb" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6363010c", + "metadata": {}, + "outputs": [], + "source": [ + "!python3 run.py --training-pipeline" + ] } ], "metadata": { From f8ab6cff65132a88e7225ea901dde5ce194b4df7 Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Mon, 12 Feb 2024 12:38:21 +0100 Subject: [PATCH 15/20] refactor --- classifier-e2e/_assets/airflow_stack.png | Bin 246500 -> 0 bytes classifier-e2e/_assets/default_stack.png | Bin 185133 -> 0 bytes .../_assets/deployment_pipeline.png | Bin 708935 -> 0 bytes .../inference_pipeline.png:Zone.Identifier | 0 .../_assets/local_sagmaker_so_stack.png | Bin 250021 -> 0 bytes classifier-e2e/run_full.ipynb | 243 ++++++------------ classifier-e2e/run_skip_basics.ipynb | 90 ++++--- 7 files changed, 122 insertions(+), 211 deletions(-) delete mode 100644 classifier-e2e/_assets/airflow_stack.png delete mode 100644 classifier-e2e/_assets/default_stack.png delete mode 100644 classifier-e2e/_assets/deployment_pipeline.png delete mode 100644 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"source": [ - "# 🐙 Step 6: Analyzing results" + "# 🐙 Step 5: Analyzing results" ] }, { "cell_type": "code", "execution_count": null, + "id": "4d127837", "metadata": {}, "outputs": [], "source": [ @@ -1078,6 +913,7 @@ }, { "cell_type": "markdown", + "id": "fc9574f9", "metadata": {}, "source": [ "At first, let's pull some meta information collected during models evaluation stage. To recall we used this step as evaluator:\n", @@ -1128,6 +964,7 @@ { "cell_type": "code", "execution_count": null, + "id": "795e5b2d", "metadata": {}, "outputs": [], "source": [ @@ -1139,6 +976,7 @@ }, { "cell_type": "markdown", + "id": "0d5f9c87", "metadata": {}, "source": [ "Now lets' plot collected Confusion Matrixes:" @@ -1147,6 +985,7 @@ { "cell_type": "code", "execution_count": null, + "id": "5f4c1e8a", "metadata": {}, "outputs": [], "source": [ @@ -1169,6 +1008,7 @@ }, { "cell_type": "markdown", + "id": "5d37260e", "metadata": {}, "source": [ "So far we were able to collect all the information we tracked using Model Control Plane, but we also had Weights&Biases tracking enabled - let's dive into.\n", @@ -1179,6 +1019,7 @@ { "cell_type": "code", "execution_count": null, + "id": "9288deaf", "metadata": {}, "outputs": [], "source": [ @@ -1188,6 +1029,7 @@ }, { "cell_type": "markdown", + "id": "9743aff9", "metadata": {}, "source": [ "With Model Control Plane we can also easily track lineage of artifacts and pipeline runs:" @@ -1196,6 +1038,7 @@ { "cell_type": "code", "execution_count": null, + "id": "4904337b", "metadata": {}, "outputs": [], "source": [ @@ -1209,12 +1052,72 @@ { "cell_type": "code", "execution_count": null, + "id": "b1b036db", "metadata": {}, "outputs": [], "source": [ "for run_name, run_ in sgd_model_version.pipeline_runs.items():\n", " print(run_name, run_.id)" ] + }, + { + "cell_type": "markdown", + "id": "594ee4fc-f102-4b99-bdc3-2f1670c87679", + "metadata": {}, + "source": [ + "# 🙏 Step 5: Moving to production\n", + "\n", + "Let's run all the moving pieces we navigated in the previous steps using production ready python script `run.py`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!zenml stack set local-wandb\n", + "!zenml stack describe local-wandb" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!python3 run.py --training-pipeline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now full run executed on local stack and experiment is tracked using Model Control Plane and Weights&Biases.\n", + "\n", + "Let's move some heavy lifting to the Sagemaker. This can be achieved using Sagemaker orchestrator.\n", + "\n", + "\"Sagemaker" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!zenml stack set sagemaker-pipelines-wandb\n", + "!zenml stack describe sagemaker-pipelines-wandb" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!python3 run.py --training-pipeline" + ] } ], "metadata": { diff --git a/classifier-e2e/run_skip_basics.ipynb b/classifier-e2e/run_skip_basics.ipynb index 2179af73..29a07015 100644 --- a/classifier-e2e/run_skip_basics.ipynb +++ b/classifier-e2e/run_skip_basics.ipynb @@ -39,7 +39,6 @@ "! pip3 install -r requirements.txt\n", "! zenml integration install sklearn xgboost -y\n", "! zenml connect --url https://1cf18d95-zenml.cloudinfra.zenml.io \n", - "! zenml model delete breast_cancer_classifier -y\n", "\n", "import IPython\n", "IPython.Application.instance().kernel.do_shutdown(restart=True)" @@ -57,16 +56,6 @@ "!zenml stack set local-sagemaker-step-operator-wandb" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "f4876831-679a-49b1-865c-db56f3b343f2", - "metadata": {}, - "outputs": [], - "source": [ - "!zenml stack describe local-sagemaker-step-operator-wandb" - ] - }, { "cell_type": "code", "execution_count": null, @@ -109,7 +98,7 @@ "Classifier, both batteries-included from `sklearn`. We'll train them both on the\n", "same data and then compare their performance.\n", "\n", - "\"Model" + "\"Training" ] }, { @@ -120,7 +109,7 @@ "outputs": [], "source": [ "# let's have a look at model training step\n", - "%pycat steps/model_evaluator.py" + "%pycat steps/model_trainer.py" ] }, { @@ -211,16 +200,14 @@ "cell_type": "code", "execution_count": null, "id": "4300c82f", - "metadata": { - "scrolled": true - }, + "metadata": {}, "outputs": [], "source": [ "# Let's train the SGD model and tag the version name with \"sgd\"\n", "pipeline_settings[\"model\"].tags = [\"breast_cancer\", \"classifier\", \"sgd\"]\n", "\n", "# Use a SGD classifier\n", - "sgd_run = training.with_options(enable_cache=True,**pipeline_settings)(\n", + "sgd_run = training.with_options(enable_cache=False,**pipeline_settings)(\n", " model_type=\"sgd\",\n", " random_state=42\n", ")\n", @@ -266,12 +253,13 @@ "outputs": [], "source": [ "zenml_model = client.get_model(\"breast_cancer_classifier\")\n", + "print(zenml_model)\n", "\n", "versions = zenml_model.versions\n", "\n", "print(f\"Model {zenml_model.name} has {len(versions)} versions\")\n", "\n", - "print(f\"The latest two versions are: {versions[-2].version, versions[-1].version}\")" + "versions[-2].version, versions[-1].version" ] }, { @@ -348,6 +336,14 @@ "more insight into that sort of flow!" ] }, + { + "cell_type": "markdown", + "id": "2ecbc8cf", + "metadata": {}, + "source": [ + "\"Model" + ] + }, { "cell_type": "markdown", "id": "8f1146db", @@ -436,13 +432,13 @@ { "cell_type": "code", "execution_count": null, - "id": "24c6881c-eea4-49fc-88b6-6f725e11749d", + "id": "ff3402f1", "metadata": {}, "outputs": [], "source": [ "# the `with_options` method allows us to pass in pipeline settings\n", "# and returns a configured pipeline\n", - "inference.with_options(**pipeline_settings)(random_state=42, target=\"target\")" + "inference.with_options(**pipeline_settings)()" ] }, { @@ -480,7 +476,7 @@ }, { "cell_type": "markdown", - "id": "df443f25", + "id": "cdf22981", "metadata": {}, "source": [ "# 🐙 Step 3: Analyzing results" @@ -501,7 +497,7 @@ }, { "cell_type": "markdown", - "id": "08a6cab7", + "id": "f5d36b23", "metadata": {}, "source": [ "At first, let's pull some meta information collected during models evaluation stage. To recall we used this step as evaluator:\n", @@ -552,7 +548,7 @@ { "cell_type": "code", "execution_count": null, - "id": "2ee60c94", + "id": "ce698de6", "metadata": {}, "outputs": [], "source": [ @@ -564,7 +560,7 @@ }, { "cell_type": "markdown", - "id": "93baffa9", + "id": "1ceb5256", "metadata": {}, "source": [ "Now lets' plot collected Confusion Matrixes:" @@ -573,7 +569,7 @@ { "cell_type": "code", "execution_count": null, - "id": "e2a17048", + "id": "d488910e", "metadata": {}, "outputs": [], "source": [ @@ -596,7 +592,7 @@ }, { "cell_type": "markdown", - "id": "d3841d9d", + "id": "fcb63aee", "metadata": {}, "source": [ "So far we were able to collect all the information we tracked using Model Control Plane, but we also had Weights&Biases tracking enabled - let's dive into.\n", @@ -607,17 +603,17 @@ { "cell_type": "code", "execution_count": null, - "id": "d6832660", + "id": "f7097d4f", "metadata": {}, "outputs": [], "source": [ "print(f'SGD version: {sgd_model_version.run_metadata[\"wandb_url\"].value}')\n", - "print(f'RF version: {xgboost_model_version.run_metadata[\"wandb_url\"].value}')" + "print(f'XGBoost version: {xgboost_model_version.run_metadata[\"wandb_url\"].value}')" ] }, { "cell_type": "markdown", - "id": "e7b46393", + "id": "5de78e7b", "metadata": {}, "source": [ "With Model Control Plane we can also easily track lineage of artifacts and pipeline runs:" @@ -626,7 +622,7 @@ { "cell_type": "code", "execution_count": null, - "id": "88973e3a", + "id": "cf8891b6", "metadata": {}, "outputs": [], "source": [ @@ -640,7 +636,7 @@ { "cell_type": "code", "execution_count": null, - "id": "7b10b01b", + "id": "76d386dc", "metadata": {}, "outputs": [], "source": [ @@ -653,25 +649,23 @@ "id": "594ee4fc-f102-4b99-bdc3-2f1670c87679", "metadata": {}, "source": [ - "# 🙏 Step 4: Switching to production\n", - "\n", - "Let's run all the moving pieces we navigated in the previous steps using production ready python script `run.py`" + "# 🙏 Step 4: Moving to production" ] }, { "cell_type": "markdown", - "id": "f1778dbd", + "id": "dfe22780", "metadata": {}, "source": [ - "The next thing we want to do is to switch to production on the cloud\n", + "Let's move some heavy lifting to the Sagemaker. This can be achieved using Sagemaker orchestrator.\n", "\n", - "\"Sagemaker" + "\"Sagemaker" ] }, { "cell_type": "code", "execution_count": null, - "id": "a878bd92", + "id": "d2774cbc", "metadata": {}, "outputs": [], "source": [ @@ -682,11 +676,25 @@ { "cell_type": "code", "execution_count": null, - "id": "6363010c", + "id": "a3e37542", "metadata": {}, "outputs": [], "source": [ - "!python3 run.py --training-pipeline" + "client.activate_stack(\"sagemaker-pipelines-wandb\")\n", + "\n", + "pipeline_settings = {}\n", + "\n", + "pipeline_settings[\"model\"] = Model(\n", + " name=\"breast_cancer_classifier\",\n", + " license=\"Apache 2.0\",\n", + " description=\"A breast cancer classifier\",\n", + " tags = [\"breast_cancer\", \"classifier\", \"xgboost\"]\n", + ")\n", + "\n", + "training.with_options(**pipeline_settings)(\n", + " model_type=\"xgboost\",\n", + " random_state=42\n", + ")" ] } ], @@ -706,7 +714,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.9.18" } }, "nbformat": 4, From 728ede64fc6afd11bcf5c3f2bc47764b052f332e Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Mon, 12 Feb 2024 18:10:00 +0100 Subject: [PATCH 16/20] WIP: deployers --- .gitignore | 2 + classifier-e2e/run_skip_basics.ipynb | 212 ++++++++++++++++++ classifier-e2e/steps/model_trainer.py | 4 +- .../utils/sagemaker_materializer.py | 72 ++++++ classifier-e2e/utils/sklearn_inference.py | 41 ++++ 5 files changed, 330 insertions(+), 1 deletion(-) create mode 100644 classifier-e2e/utils/sagemaker_materializer.py create mode 100644 classifier-e2e/utils/sklearn_inference.py diff --git a/.gitignore b/.gitignore index f157d5e4..ab6d468c 100644 --- a/.gitignore +++ b/.gitignore @@ -142,3 +142,5 @@ mlruns/ zencoder/cloned_public_repos *wandb* + +.DS_Store \ No newline at end of file diff --git a/classifier-e2e/run_skip_basics.ipynb b/classifier-e2e/run_skip_basics.ipynb index 29a07015..c8c6136d 100644 --- a/classifier-e2e/run_skip_basics.ipynb +++ b/classifier-e2e/run_skip_basics.ipynb @@ -696,6 +696,218 @@ " random_state=42\n", ")" ] + }, + { + "cell_type": "markdown", + "id": "d950f97e", + "metadata": {}, + "source": [ + "# Step 100: deploy" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "id": "9dfb5642", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from typing import Tuple, Any\n", + "from typing_extensions import Annotated\n", + "\n", + "import boto3\n", + "import sagemaker\n", + "\n", + "from zenml import pipeline, step, get_pipeline_context, get_step_context\n", + "from sagemaker.image_uris import retrieve\n", + "from datetime import datetime\n", + "from sagemaker.predictor import Predictor\n", + "import pandas as pd\n", + "from steps import data_loader, inference_preprocessor\n", + "import random\n", + "\n", + "client.activate_stack(\"local-sagemaker-step-operator-wandb\")\n", + "\n", + "\n", + "def get_aws_config() -> Tuple[Any, sagemaker.Session, str]:\n", + " REGION_NAME = os.getenv(\"AWS_REGION\", \"us-east-1\")\n", + " ROLE_NAME = os.getenv(\"AWS_ROLE_NAME\", \"hamza_connector\")\n", + " os.environ[\"AWS_DEFAULT_REGION\"] = REGION_NAME\n", + "\n", + " auth_arguments = {\n", + " \"aws_access_key_id\": os.getenv(\"AWS_ACCESS_KEY_ID\", None),\n", + " \"aws_secret_access_key\": os.getenv(\"AWS_SECRET_ACCESS_KEY\", None),\n", + " \"aws_session_token\": os.getenv(\"AWS_SESSION_TOKEN\", None),\n", + " \"region_name\": REGION_NAME,\n", + " }\n", + " iam = boto3.client(\"iam\", **auth_arguments)\n", + " role = iam.get_role(RoleName=ROLE_NAME)[\"Role\"][\"Arn\"]\n", + " session = sagemaker.Session(boto3.Session(**auth_arguments))\n", + "\n", + " return role, session, REGION_NAME\n", + "\n", + "\n", + "@step(enable_cache=False)\n", + "def deploy_endpoint() -> Annotated[str, \"sagemaker_endpoint_name\"]:\n", + " role, session, region = get_aws_config()\n", + "\n", + " model = get_step_context().model._get_model_version()\n", + " if \"sgd\" in {t.name for t in model.tags}:\n", + " image_uri = retrieve(\n", + " region=region, framework=\"sklearn\", version=\"1.0-1\"\n", + " )\n", + " entry_point = \"utils/sklearn_inference.py\"\n", + " else:\n", + " image_uri = retrieve(\n", + " region=region, framework=\"xgboost\", version=\"1.5-1\"\n", + " )\n", + " entry_point = None\n", + "\n", + " model_data = (\n", + " f'{model.get_artifact(\"breast_cancer_classifier\").uri}/model.tar.gz'\n", + " )\n", + "\n", + " endpoint_name = f'breast-cancer-classifier-{datetime.now().strftime(\"%Y-%m-%d-%H-%M-%S-%f\")}'\n", + " sagemaker.Model(\n", + " image_uri=image_uri,\n", + " model_data=model_data,\n", + " sagemaker_session=session,\n", + " role=role,\n", + " entry_point=entry_point,\n", + " ).deploy(\n", + " initial_instance_count=1,\n", + " instance_type=\"ml.m5.large\",\n", + " endpoint_name=endpoint_name,\n", + " )\n", + " return endpoint_name\n", + "\n", + "\n", + "@step\n", + "def predict_on_endpoint(\n", + " endpoint_name: str, dataset: pd.DataFrame\n", + ") -> Annotated[pd.Series, \"real_time_predictions\"]:\n", + " predictor = Predictor(endpoint_name=endpoint_name)\n", + " predictions = predictor.predict(\n", + " data=dataset.to_csv(header=False, index=False),\n", + " initial_args={\"ContentType\": \"text/csv\"},\n", + " )\n", + " return pd.Series(\n", + " [float(l) for l in predictions.decode().split(\"\\n\") if l],\n", + " name=\"predictions\",\n", + " )\n", + "\n", + "\n", + "@step\n", + "def shutdown_endpoint(endpoint_name: str):\n", + " predictor = Predictor(endpoint_name=endpoint_name)\n", + " predictor.delete_endpoint()\n", + "\n", + "\n", + "@pipeline\n", + "def deploy(shutdown_endpoint_after_predicting: bool = True):\n", + " # Get the preprocess pipeline artifact associated with this version\n", + " preprocess_pipeline = get_pipeline_context().model.get_artifact(\n", + " \"preprocess_pipeline\"\n", + " )\n", + "\n", + " df_inference = data_loader(\n", + " random_state=random.randint(0, 1000), is_inference=True\n", + " )\n", + " df_inference = inference_preprocessor(\n", + " dataset_inf=df_inference,\n", + " preprocess_pipeline=preprocess_pipeline,\n", + " target=\"target\",\n", + " )\n", + " endpoint_name = deploy_endpoint()\n", + " predict_on_endpoint(endpoint_name, df_inference)\n", + " if shutdown_endpoint_after_predicting:\n", + " shutdown_endpoint(endpoint_name, after=[\"predict_on_endpoint\"])\n", + "\n", + "\n", + "# deploy.with_options(\n", + "# model=Model(name=\"breast_cancer_classifier\", version=\"latest\")\n", + "# )()" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "id": "aff4c929", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;35m\u001b[0m\u001b[1;36mversion\u001b[1;35m \u001b[0m\u001b[1;36m19\u001b[1;35m is numeric and will be fetched using version number.\u001b[0m\n", + "\u001b[1;35m\u001b[0m\u001b[1;36mversion\u001b[1;35m \u001b[0m\u001b[1;36m19\u001b[1;35m is numeric and will be fetched using version number.\u001b[0m\n", + "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mdeploy\u001b[1;35m.\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;35mReusing registered pipeline version: \u001b[0m\u001b[1;36m(version: 5)\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mExecuting a new run.\u001b[0m\n", + "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mandrei@zenml.io\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mlocal-sagemaker-step-operator-wandb\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m step_operator: \u001b[0m\u001b[1;36msagemaker-eu\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m container_registry: \u001b[0m\u001b[1;36maws-eu\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m experiment_tracker: \u001b[0m\u001b[1;36mzenml_wandb\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m image_builder: \u001b[0m\u001b[1;36mlocal\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36ms3-zenfiles\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mDataset with 28 records loaded!\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has finished in \u001b[0m\u001b[1;36m8.308s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mCaching \u001b[0m\u001b[1;36mdisabled\u001b[1;35m explicitly for \u001b[0m\u001b[1;36mdeploy_endpoint\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdeploy_endpoint\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mFound credentials in shared credentials file: ~/.aws/credentials\u001b[0m\n", + "\u001b[1;35mDefaulting to only available Python version: py3\u001b[0m\n", + "\u001b[1;35mDefaulting to only supported image scope: cpu.\u001b[0m\n", + "\u001b[1;35mCreating model with name: sagemaker-scikit-learn-2024-02-12-17-02-10-887\u001b[0m\n", + "\u001b[1;35mCreating endpoint-config with name breast-cancer-classifier-2024-02-12-18-02-08-073179\u001b[0m\n", + "\u001b[1;35mCreating endpoint with name breast-cancer-classifier-2024-02-12-18-02-08-073179\u001b[0m\n", + "------!\u001b[1;35mStep \u001b[0m\u001b[1;36mdeploy_endpoint\u001b[1;35m has finished in \u001b[0m\u001b[1;36m3m30s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36minference_preprocessor\u001b[1;35m has started.\u001b[0m\n", + "\u001b[33mYour artifact was materialized under Python version 'unknown' but you are currently using '3.9.18'. This might cause unexpected behavior since pickle is not reproducible across Python versions. Attempting to load anyway...\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36minference_preprocessor\u001b[1;35m has finished in \u001b[0m\u001b[1;36m8.924s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mpredict_on_endpoint\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mpredict_on_endpoint\u001b[1;35m has finished in \u001b[0m\u001b[1;36m8.131s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mshutdown_endpoint\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mDeleting endpoint configuration with name: breast-cancer-classifier-2024-02-12-18-02-08-073179\u001b[0m\n", + "\u001b[1;35mDeleting endpoint with name: breast-cancer-classifier-2024-02-12-18-02-08-073179\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mshutdown_endpoint\u001b[1;35m has finished in \u001b[0m\u001b[1;36m4.997s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mPipeline run has finished in \u001b[0m\u001b[1;36m4m22s\u001b[1;35m.\u001b[0m\n", + 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version='19', save_models_to_registry=True, suppress_class_validation_warnings=True, was_created_in_this_run=False), parameters=None, name='deploy'), start_time=datetime.datetime(2024, 2, 12, 17, 1, 45), end_time=None, client_environment={'environment': 'notebook', 'os': 'mac', 'mac_version': '14.0', 'python_version': '3.9.18'}, orchestrator_environment={}, orchestrator_run_id=None), name='deploy-2024_02_12-17_01_45_119323')" + ] + }, + "execution_count": 112, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "deploy.with_options(\n", + " model=Model(name=\"breast_cancer_classifier\", version=\"19\")\n", + ")()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c9e5fc22", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/classifier-e2e/steps/model_trainer.py b/classifier-e2e/steps/model_trainer.py index 79242ef6..3b84362a 100644 --- a/classifier-e2e/steps/model_trainer.py +++ b/classifier-e2e/steps/model_trainer.py @@ -25,9 +25,11 @@ from zenml import ArtifactConfig, step from zenml.logger import get_logger +from utils.sagemaker_materializer import SagemakerMaterializer + logger = get_logger(__name__) -@step +@step(output_materializers=[SagemakerMaterializer,]) def model_trainer( dataset_trn: pd.DataFrame, model_type: str = "sgd", diff --git a/classifier-e2e/utils/sagemaker_materializer.py b/classifier-e2e/utils/sagemaker_materializer.py new file mode 100644 index 00000000..33fdf353 --- /dev/null +++ b/classifier-e2e/utils/sagemaker_materializer.py @@ -0,0 +1,72 @@ +import os +from typing import Type, Union + +from zenml.enums import ArtifactType +from zenml.io import fileio +from zenml.materializers.base_materializer import BaseMaterializer +from sklearn.linear_model import SGDClassifier +from xgboost import XGBClassifier +import tarfile +import tempfile +import joblib +from sklearn.base import ClassifierMixin + + +class SagemakerMaterializer(BaseMaterializer): + ASSOCIATED_TYPES = (ClassifierMixin,) + ASSOCIATED_ARTIFACT_TYPE = ArtifactType.DATA + + def load( + self, data_type: Type[ClassifierMixin] + ) -> Union[SGDClassifier, XGBClassifier]: + """Read from artifact store.""" + fileio.copy( + os.path.join(self.uri, "model.tar.gz"), + os.path.join(tempfile.gettempdir(), "model.tar.gz"), + overwrite=True, + ) + est = None + with tarfile.open( + os.path.join(tempfile.gettempdir(), "model.tar.gz"), "r:gz" + ) as tar: + for member in tar.getmembers(): + tar.extract(member.name, tempfile.gettempdir()) + if member.name == "sklearn-model": + est = joblib.load( + os.path.join(tempfile.gettempdir(), "sklearn-model"), + ) + if member.name == "xgboost-model": + est = XGBClassifier() + est.load_model( + os.path.join(tempfile.gettempdir(), "xgboost-model") + ) + fileio.remove(os.path.join(tempfile.gettempdir(), member.name)) + if est: + break + if est is None: + raise RuntimeError( + "Failed to load estimator via SagemakerMaterializer..." + ) + return est + + def save(self, my_obj: ClassifierMixin) -> None: + """Write to artifact store.""" + with tarfile.open( + os.path.join(tempfile.gettempdir(), "model.tar.gz"), "w:gz" + ) as tar: + is_xgboost = isinstance(my_obj, XGBClassifier) + file_name = ("xgboost" if is_xgboost else "sklearn") + "-model" + tmp_ = os.path.join(tempfile.gettempdir(), file_name) + if is_xgboost: + # if model supports saving - use it over joblib + my_obj.save_model(tmp_) + else: + joblib.dump(my_obj, tmp_) + tar.add(tmp_, arcname=file_name) + fileio.remove(tmp_) + fileio.copy( + os.path.join(tempfile.gettempdir(), "model.tar.gz"), + os.path.join(self.uri, "model.tar.gz"), + overwrite=True, + ) + fileio.remove(os.path.join(tempfile.gettempdir(), "model.tar.gz")) diff --git a/classifier-e2e/utils/sklearn_inference.py b/classifier-e2e/utils/sklearn_inference.py new file mode 100644 index 00000000..bf7a8d2c --- /dev/null +++ b/classifier-e2e/utils/sklearn_inference.py @@ -0,0 +1,41 @@ +import joblib +import os +import pandas as pd +from io import StringIO + +""" +Deserialize fitted model +""" +def model_fn(model_dir): + model = joblib.load(os.path.join(model_dir, "sklearn-model")) + return model + +""" +input_fn + request_body: The body of the request sent to the model. + request_content_type: (string) specifies the format/variable type of the request +""" +def input_fn(request_body, request_content_type): + if request_content_type == 'text/csv': + request = pd.read_csv(StringIO(request_body)) + return request + else: + raise ValueError("This model only supports text/csv input") + +""" +predict_fn + input_data: returned array from input_fn above + model (sklearn model) returned model loaded from model_fn above +""" +def predict_fn(input_data, model): + return model.predict(input_data) + +""" +output_fn + prediction: the returned value from predict_fn above + content_type: the content type the endpoint expects to be returned. Ex: JSON, string + +""" + +def output_fn(prediction, content_type): + return pd.Series(prediction).to_csv(index=False,header=False) \ No newline at end of file From a274c7255d646de5188c17a9f02b974a17e96ccb Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Thu, 15 Feb 2024 12:50:48 +0100 Subject: [PATCH 17/20] pics+deployment --- classifier-e2e/_assets/cloud_mcp.png | Bin 237746 -> 242158 bytes .../_assets/deployment_pipeline.png | Bin 0 -> 93860 bytes .../_assets/feature_engineering_pipeline.png | Bin 112307 -> 118324 bytes classifier-e2e/_assets/inference_pipeline.png | Bin 257666 -> 262073 bytes classifier-e2e/_assets/pipeline_overview.png | Bin 407607 -> 341231 bytes classifier-e2e/_assets/sagemaker_stack.png | Bin 241733 -> 217747 bytes classifier-e2e/_assets/training_pipeline.png | Bin 64785 -> 129448 bytes classifier-e2e/pipelines/__init__.py | 1 + classifier-e2e/pipelines/deploy.py | 25 + classifier-e2e/run_skip_basics.ipynb | 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z|MOa?S^>6X=g~*s`vOsWQS)Q>cQi{t11K2_*JG9T0usG{#fX7OQSKaq9Yr8>Pzi^5 zFr3doLP7_Au+OTvR9FgyJ%SS^FdUh`DF+EMI_<3sQr_2nk*#_5mH&sQ-TB$Gz>>x zVEkuRTdl|>FUxHNu9gfR`Mk?PLMa91EXXeZ>l3T6G614dxBn{)@t^k;wQoQ`ybnxy#yj|aPW4*B(R@HtG9}B_VO>T}l)GyGqy4Rct zhI(T-1%bZkamF^d4Qy|uQS#foiNH<1oHJ{e@__o9|MXKzS}3}4=mg8cu#4rj$eCM8 zhEOQEaMa(n!YaKvydr-&y|+BcbX9A8kB{`5t`qne$iSEtch>+nwHFPYZ1PW0z\u001b[0m \u001b[0mAnnotated\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mClassifierMixin\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mArtifactConfig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"breast_cancer_classifier\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_model_artifact\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"Configure and train a model on the training dataset.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m This is an example of a model training step that takes in a dataset artifact\u001b[0m\n", + "\u001b[0;34m previously loaded and pre-processed by other steps in your pipeline, then\u001b[0m\n", + "\u001b[0;34m configures and trains a model on it. The model is then returned as a step\u001b[0m\n", + "\u001b[0;34m output artifact.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Args:\u001b[0m\n", + "\u001b[0;34m dataset_trn: The preprocessed train dataset.\u001b[0m\n", + "\u001b[0;34m model_type: The type of model to train.\u001b[0m\n", + "\u001b[0;34m target: The name of the target column in the dataset.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns:\u001b[0m\n", + "\u001b[0;34m The trained model artifact.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Raises:\u001b[0m\n", + "\u001b[0;34m ValueError: If the model type is not supported.\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Initialize the model with the hyperparameters indicated in the step\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# parameters and train it on the training set.\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmodel_type\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"sgd\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSGDClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mmodel_type\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"xgboost\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mxgboost\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mXGBClassifier\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mXGBClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Unknown model type {model_type}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Training model {model}...\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdataset_trn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdataset_trn\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n" + ] + } + ], "source": [ "# let's have a look at model training step\n", "%pycat steps/model_trainer.py" @@ -134,10 +216,99 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 118, "id": "01162d23", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0;31m# Apache Software License 2.0\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# Copyright (c) ZenML GmbH 2024. All rights reserved.\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# Licensed under the Apache License, Version 2.0 (the \"License\");\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# you may not use this file except in compliance with the License.\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# You may obtain a copy of the License at\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# http://www.apache.org/licenses/LICENSE-2.0\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# Unless required by applicable law or agreed to in writing, software\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# distributed under the License is distributed on an \"AS IS\" BASIS,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# See the License for the specific language governing permissions and\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# limitations under the License.\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0mtyping\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0muuid\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mUUID\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0msteps\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmodel_evaluator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel_promoter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel_trainer\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0mpipelines\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mfeature_engineering\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0mzenml\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpipeline\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0mzenml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclient\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mClient\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0mzenml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogger\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mget_logger\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0mlogger\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_logger\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m@\u001b[0m\u001b[0mpipeline\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;32mdef\u001b[0m \u001b[0mtraining\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mtrain_dataset_id\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mUUID\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mtest_dataset_id\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mUUID\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"target\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmodel_type\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"sgd\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Model training pipeline.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m This is a pipeline that loads the data from a preprocessing pipeline,\u001b[0m\n", + "\u001b[0;34m trains a model on it and evaluates the model. If it is the first model\u001b[0m\n", + "\u001b[0;34m to be trained, it will be promoted to production. If not, it will be\u001b[0m\n", + "\u001b[0;34m promoted only if it has a higher accuracy than the current production\u001b[0m\n", + "\u001b[0;34m model version.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Args:\u001b[0m\n", + "\u001b[0;34m train_dataset_id: ID of the train dataset produced by feature engineering.\u001b[0m\n", + "\u001b[0;34m test_dataset_id: ID of the test dataset produced by feature engineering.\u001b[0m\n", + "\u001b[0;34m target: Name of target column in dataset.\u001b[0m\n", + "\u001b[0;34m model_type: The type of model to train.\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Link all the steps together by calling them and passing the output\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# of one step as the input of the next step.\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Execute Feature Engineering Pipeline\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtrain_dataset_id\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mtest_dataset_id\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdataset_trn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataset_tst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfeature_engineering\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mclient\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mClient\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdataset_trn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_artifact_version\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mname_id_or_prefix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain_dataset_id\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdataset_tst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_artifact_version\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mname_id_or_prefix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtest_dataset_id\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_trainer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdataset_trn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdataset_trn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel_type\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0macc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_evaluator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdataset_trn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdataset_trn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdataset_tst\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdataset_tst\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmodel_promoter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maccuracy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0macc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n" + ] + } + ], "source": [ "# let's have a look at training pipeline\n", "%pycat pipelines/training.py" @@ -375,13 +546,77 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 119, "id": "92c4c7dc", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0;31m# Apache Software License 2.0\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# Copyright (c) ZenML GmbH 2023. All rights reserved.\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# Licensed under the Apache License, Version 2.0 (the \"License\");\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# you may not use this file except in compliance with the License.\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# You may obtain a copy of the License at\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# http://www.apache.org/licenses/LICENSE-2.0\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# Unless required by applicable law or agreed to in writing, software\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# distributed under the License is distributed on an \"AS IS\" BASIS,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# See the License for the specific language governing permissions and\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# limitations under the License.\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0mtyping\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0mtyping_extensions\u001b[0m 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\u001b[0minference_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdataset_inf\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mAnnotated\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"predictions\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"Predictions step.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m This is an example of a predictions step that takes the data and model in\u001b[0m\n", + "\u001b[0;34m and returns predicted values.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m This step is parameterized, which allows you to configure the step\u001b[0m\n", + "\u001b[0;34m independently of the step code, before running it in a pipeline.\u001b[0m\n", + "\u001b[0;34m In this example, the step can be configured to use different input data.\u001b[0m\n", + "\u001b[0;34m See the documentation for more information:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m https://docs.zenml.io/user-guide/advanced-guide/configure-steps-pipelines\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Args:\u001b[0m\n", + "\u001b[0;34m model: Trained model.\u001b[0m\n", + "\u001b[0;34m dataset_inf: The inference dataset.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns:\u001b[0m\n", + "\u001b[0;34m The predictions as pandas series\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# run prediction from memory\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset_inf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredictions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"predicted\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mpredictions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n" + ] + } + ], "source": [ "# let's have a look at training pipeline\n", - "%pycat steps/inference_predict.py\n" + "%pycat steps/inference_predict.py" ] }, { @@ -395,10 +630,79 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 120, "id": "37c409bd", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0;31m# Apache Software License 2.0\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# Copyright (c) ZenML GmbH 2024. All rights reserved.\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# Licensed under the Apache License, Version 2.0 (the \"License\");\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# you may not use this file except in compliance with the License.\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# You may obtain a copy of the License at\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# http://www.apache.org/licenses/LICENSE-2.0\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# Unless required by applicable law or agreed to in writing, software\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# distributed under the License is distributed on an \"AS IS\" BASIS,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# See the License for the specific language governing permissions and\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m# limitations under the License.\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0msteps\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdata_loader\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0minference_predict\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m 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"\u001b[0;34m\u001b[0m\u001b[0;34m@\u001b[0m\u001b[0mpipeline\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;32mdef\u001b[0m \u001b[0minference\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Model inference pipeline.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m This is a pipeline that loads the inference data, processes it with\u001b[0m\n", + "\u001b[0;34m the same preprocessing pipeline used in training, and runs inference\u001b[0m\n", + "\u001b[0;34m with the trained model.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Args:\u001b[0m\n", + "\u001b[0;34m random_state: Random state for reproducibility.\u001b[0m\n", + "\u001b[0;34m target: Name of target column in dataset.\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Get the production model artifact\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_pipeline_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_artifact\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"breast_cancer_classifier\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Get the preprocess pipeline artifact associated with this version\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mpreprocess_pipeline\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_pipeline_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_artifact\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"preprocess_pipeline\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Link all the steps together by calling them and passing the output\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# of one step as the input of the next step.\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdf_inference\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata_loader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdataset_inf\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf_inference\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n" + ] + } + ], "source": [ "# let's have a look at training pipeline\n", "%pycat pipelines/inference.py" @@ -702,203 +1006,151 @@ "id": "d950f97e", "metadata": {}, "source": [ - "# Step 100: deploy" - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "id": "9dfb5642", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "from typing import Tuple, Any\n", - "from typing_extensions import Annotated\n", - "\n", - "import boto3\n", - "import sagemaker\n", + "# 🧙‍♂️ Step 5: Deploy Sagemaker endpoint\n", "\n", - "from zenml import pipeline, step, get_pipeline_context, get_step_context\n", - "from sagemaker.image_uris import retrieve\n", - "from datetime import datetime\n", - "from sagemaker.predictor import Predictor\n", - "import pandas as pd\n", - "from steps import data_loader, inference_preprocessor\n", - "import random\n", - "\n", - "client.activate_stack(\"local-sagemaker-step-operator-wandb\")\n", + "After training and promoting production-ready model version to `production` we can proceed deploying it as a Sagemaker Endpoint.\n", + "The deployment pipeline is following:\n", "\n", + "\"Deployment\n", "\n", - "def get_aws_config() -> Tuple[Any, sagemaker.Session, str]:\n", - " REGION_NAME = os.getenv(\"AWS_REGION\", \"us-east-1\")\n", - " ROLE_NAME = os.getenv(\"AWS_ROLE_NAME\", \"hamza_connector\")\n", - " os.environ[\"AWS_DEFAULT_REGION\"] = REGION_NAME\n", - "\n", - " auth_arguments = {\n", - " \"aws_access_key_id\": os.getenv(\"AWS_ACCESS_KEY_ID\", None),\n", - " \"aws_secret_access_key\": os.getenv(\"AWS_SECRET_ACCESS_KEY\", None),\n", - " \"aws_session_token\": os.getenv(\"AWS_SESSION_TOKEN\", None),\n", - " \"region_name\": REGION_NAME,\n", - " }\n", - " iam = boto3.client(\"iam\", **auth_arguments)\n", - " role = iam.get_role(RoleName=ROLE_NAME)[\"Role\"][\"Arn\"]\n", - " session = sagemaker.Session(boto3.Session(**auth_arguments))\n", - "\n", - " return role, session, REGION_NAME\n", - "\n", - "\n", - "@step(enable_cache=False)\n", - "def deploy_endpoint() -> Annotated[str, \"sagemaker_endpoint_name\"]:\n", - " role, session, region = get_aws_config()\n", - "\n", - " model = get_step_context().model._get_model_version()\n", - " if \"sgd\" in {t.name for t in model.tags}:\n", - " image_uri = retrieve(\n", - " region=region, framework=\"sklearn\", version=\"1.0-1\"\n", - " )\n", - " entry_point = \"utils/sklearn_inference.py\"\n", - " else:\n", - " image_uri = retrieve(\n", - " region=region, framework=\"xgboost\", version=\"1.5-1\"\n", - " )\n", - " entry_point = None\n", - "\n", - " model_data = (\n", - " f'{model.get_artifact(\"breast_cancer_classifier\").uri}/model.tar.gz'\n", - " )\n", - "\n", - " endpoint_name = f'breast-cancer-classifier-{datetime.now().strftime(\"%Y-%m-%d-%H-%M-%S-%f\")}'\n", - " sagemaker.Model(\n", - " image_uri=image_uri,\n", - " model_data=model_data,\n", - " sagemaker_session=session,\n", - " role=role,\n", - " entry_point=entry_point,\n", - " ).deploy(\n", - " initial_instance_count=1,\n", - " instance_type=\"ml.m5.large\",\n", - " endpoint_name=endpoint_name,\n", - " )\n", - " return endpoint_name\n", - "\n", - "\n", - "@step\n", - "def predict_on_endpoint(\n", - " endpoint_name: str, dataset: pd.DataFrame\n", - ") -> Annotated[pd.Series, \"real_time_predictions\"]:\n", - " predictor = Predictor(endpoint_name=endpoint_name)\n", - " predictions = predictor.predict(\n", - " data=dataset.to_csv(header=False, index=False),\n", - " initial_args={\"ContentType\": \"text/csv\"},\n", - " )\n", - " return pd.Series(\n", - " [float(l) for l in predictions.decode().split(\"\\n\") if l],\n", - " name=\"predictions\",\n", - " )\n", - "\n", - "\n", - "@step\n", - "def shutdown_endpoint(endpoint_name: str):\n", - " predictor = Predictor(endpoint_name=endpoint_name)\n", - " predictor.delete_endpoint()\n", - "\n", - "\n", - "@pipeline\n", - "def deploy(shutdown_endpoint_after_predicting: bool = True):\n", - " # Get the preprocess pipeline artifact associated with this version\n", - " preprocess_pipeline = get_pipeline_context().model.get_artifact(\n", - " \"preprocess_pipeline\"\n", - " )\n", - "\n", - " df_inference = data_loader(\n", - " random_state=random.randint(0, 1000), is_inference=True\n", - " )\n", - " df_inference = inference_preprocessor(\n", - " dataset_inf=df_inference,\n", - " preprocess_pipeline=preprocess_pipeline,\n", - " target=\"target\",\n", - " )\n", - " endpoint_name = deploy_endpoint()\n", - " predict_on_endpoint(endpoint_name, df_inference)\n", - " if shutdown_endpoint_after_predicting:\n", - " shutdown_endpoint(endpoint_name, after=[\"predict_on_endpoint\"])\n", - "\n", - "\n", - "# deploy.with_options(\n", - "# model=Model(name=\"breast_cancer_classifier\", version=\"latest\")\n", - "# )()" + "First we will explore how deployment step is designed. Based on the origin of current model in `production` we need to adapt deployment action a bit. For `XGBoost` we can use Sagemaker standard image and model saved via standard XGBoost `save_model` model, but for `sklearn` this approach will not work properly, since there is no standard way of saving Sklearn models besides pickling them via various libraries, so we need a special instruction script describing how to load, predict and so on on our model." ] }, { "cell_type": "code", - "execution_count": 112, - "id": "aff4c929", + "execution_count": 121, + "id": "496adffa", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1;35m\u001b[0m\u001b[1;36mversion\u001b[1;35m \u001b[0m\u001b[1;36m19\u001b[1;35m is numeric and will be fetched using version number.\u001b[0m\n", - "\u001b[1;35m\u001b[0m\u001b[1;36mversion\u001b[1;35m \u001b[0m\u001b[1;36m19\u001b[1;35m is numeric and will be fetched using version number.\u001b[0m\n", - "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mdeploy\u001b[1;35m.\u001b[0m\n" + "\u001b[0;32mfrom\u001b[0m \u001b[0mtyping_extensions\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mAnnotated\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;32mimport\u001b[0m \u001b[0msagemaker\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0msagemaker\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimage_uris\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mretrieve\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0mzenml\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mget_step_context\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0mdatetime\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maws\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mget_aws_config\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m@\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0menable_cache\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;32mdef\u001b[0m \u001b[0mdeploy_endpoint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mAnnotated\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"sagemaker_endpoint_name\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mrole\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0;34mf'breast-cancer-classifier-{datetime.now().strftime(\"%Y-%m-%d-%H-%M-%S-%f\")}'\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0msagemaker\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mModel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mimage_uri\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mimage_uri\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmodel_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel_data\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0msagemaker_session\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mrole\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrole\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m 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steps/deploy_endpoint.py" + ] + }, + { + "cell_type": "markdown", + "id": "3c60b684", + "metadata": {}, + "source": [ + "The full deployment pipeline code is the following.\n", + "\n", + "NOTE: we deprovision model at the end of the pipeline to save cost, but this step will not be there in production setting." + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "id": "9dfb5642", + "metadata": {}, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1;35mReusing registered pipeline version: \u001b[0m\u001b[1;36m(version: 5)\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mExecuting a new run.\u001b[0m\n", - "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mandrei@zenml.io\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mlocal-sagemaker-step-operator-wandb\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m step_operator: \u001b[0m\u001b[1;36msagemaker-eu\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m container_registry: \u001b[0m\u001b[1;36maws-eu\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m experiment_tracker: \u001b[0m\u001b[1;36mzenml_wandb\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m image_builder: \u001b[0m\u001b[1;36mlocal\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36ms3-zenfiles\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mDataset with 28 records loaded!\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has finished in \u001b[0m\u001b[1;36m8.308s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mCaching \u001b[0m\u001b[1;36mdisabled\u001b[1;35m explicitly for \u001b[0m\u001b[1;36mdeploy_endpoint\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdeploy_endpoint\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mFound credentials in shared credentials file: ~/.aws/credentials\u001b[0m\n", - "\u001b[1;35mDefaulting to only available Python version: py3\u001b[0m\n", - "\u001b[1;35mDefaulting to only supported image scope: cpu.\u001b[0m\n", - "\u001b[1;35mCreating model with name: sagemaker-scikit-learn-2024-02-12-17-02-10-887\u001b[0m\n", - "\u001b[1;35mCreating endpoint-config with name breast-cancer-classifier-2024-02-12-18-02-08-073179\u001b[0m\n", - "\u001b[1;35mCreating endpoint with name breast-cancer-classifier-2024-02-12-18-02-08-073179\u001b[0m\n", - "------!\u001b[1;35mStep \u001b[0m\u001b[1;36mdeploy_endpoint\u001b[1;35m has finished in \u001b[0m\u001b[1;36m3m30s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36minference_preprocessor\u001b[1;35m has started.\u001b[0m\n", - "\u001b[33mYour artifact was materialized under Python version 'unknown' but you are currently using '3.9.18'. This might cause unexpected behavior since pickle is not reproducible across Python versions. Attempting to load anyway...\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36minference_preprocessor\u001b[1;35m has finished in \u001b[0m\u001b[1;36m8.924s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mpredict_on_endpoint\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mpredict_on_endpoint\u001b[1;35m has finished in \u001b[0m\u001b[1;36m8.131s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mshutdown_endpoint\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mDeleting endpoint configuration with name: breast-cancer-classifier-2024-02-12-18-02-08-073179\u001b[0m\n", - "\u001b[1;35mDeleting endpoint with name: breast-cancer-classifier-2024-02-12-18-02-08-073179\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mshutdown_endpoint\u001b[1;35m has finished in \u001b[0m\u001b[1;36m4.997s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mPipeline run has finished in \u001b[0m\u001b[1;36m4m22s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/78123cfe-960e-4f87-a971-3487aca76aea/runs/0db5aae1-8461-4b21-8ed0-8e8e5ae241d7/dag\u001b[0m\n" + "\u001b[0;32mfrom\u001b[0m \u001b[0mzenml\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpipeline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mget_pipeline_context\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0msteps\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdata_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minference_preprocessor\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;32mimport\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0msteps\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdeploy_endpoint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredict_on_endpoint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshutdown_endpoint\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m@\u001b[0m\u001b[0mpipeline\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;32mdef\u001b[0m \u001b[0mdeploy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshutdown_endpoint_after_predicting\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Get the preprocess pipeline artifact associated with this version\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mpreprocess_pipeline\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_pipeline_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_artifact\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m 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"\u001b[0;34m\u001b[0m \u001b[0mdataset_inf\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf_inference\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mpreprocess_pipeline\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpreprocess_pipeline\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"target\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mendpoint_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeploy_endpoint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mpredict_on_endpoint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mendpoint_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf_inference\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m 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email_opted_in=True, is_service_account=False), metadata=None, name='andrei@zenml.io'), status=, stack=StackResponse(id=UUID('5712467b-5ab0-4549-8bc9-2ea7714c8ed8'), permission_denied=False, body=StackResponseBody(created=datetime.datetime(2024, 1, 13, 12, 42, 57), updated=datetime.datetime(2024, 2, 8, 9, 59, 9), user=UserResponse(id=UUID('c6fcdcc8-69e1-4ff5-9eb2-6a53aa81a08b'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 10, 24, 7, 36, 26), updated=datetime.datetime(2024, 2, 12, 12, 34, 58), active=True, activation_token=None, full_name='Hamza Tahir', email_opted_in=True, is_service_account=False), metadata=None, name='hamza@zenml.io')), metadata=None, name='local-sagemaker-step-operator-wandb'), pipeline=PipelineResponse(id=UUID('78123cfe-960e-4f87-a971-3487aca76aea'), permission_denied=False, body=PipelineResponseBody(created=datetime.datetime(2024, 2, 12, 16, 49, 51), updated=datetime.datetime(2024, 2, 12, 16, 49, 51), user=UserResponse(id=UUID('c915043d-706c-4885-9263-783cae8dad20'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 11, 16, 16, 5, 33), updated=datetime.datetime(2024, 2, 12, 16, 13, 15), active=True, activation_token=None, full_name='Andrei', email_opted_in=True, is_service_account=False), metadata=None, name='andrei@zenml.io'), status=[, , ], version='5'), metadata=None, name='deploy'), build=None, schedule=None, code_reference=None), metadata=PipelineRunResponseMetadata(workspace=WorkspaceResponse(id=UUID('f3a544f2-afb5-4672-934a-7a465c66201c'), permission_denied=False, body=WorkspaceResponseBody(created=datetime.datetime(2023, 10, 23, 15, 34, 47), updated=datetime.datetime(2023, 10, 23, 15, 34, 47)), metadata=None, name='default'), run_metadata={}, steps={}, config=PipelineConfiguration(enable_cache=None, enable_artifact_metadata=None, enable_artifact_visualization=None, enable_step_logs=None, settings={'step_operator.sagemaker': BaseSettings(), 'experiment_tracker.wandb': BaseSettings()}, extra={}, failure_hook_source=None, success_hook_source=None, model=Model(name='breast_cancer_classifier', license=None, description=None, audience=None, use_cases=None, limitations=None, trade_offs=None, ethics=None, tags=None, version='19', save_models_to_registry=True, suppress_class_validation_warnings=True, was_created_in_this_run=False), parameters=None, name='deploy'), start_time=datetime.datetime(2024, 2, 12, 17, 1, 45), end_time=None, client_environment={'environment': 'notebook', 'os': 'mac', 'mac_version': '14.0', 'python_version': '3.9.18'}, orchestrator_environment={}, orchestrator_run_id=None), name='deploy-2024_02_12-17_01_45_119323')" - ] - }, - "execution_count": 112, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ + "%pycat pipelines/deploy.py" + ] + }, + { + "cell_type": "markdown", + "id": "5ba94464", + "metadata": {}, + "source": [ + "Ok, now we can deploy our model and explore predictions!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aff4c929", + "metadata": {}, + "outputs": [], + "source": [ + "from pipelines import deploy\n", + "\n", + "client.activate_stack(\"local-sagemaker-step-operator-wandb\")\n", + "\n", "deploy.with_options(\n", - " model=Model(name=\"breast_cancer_classifier\", version=\"19\")\n", - ")()" + " model=Model(name=\"breast_cancer_classifier\", version=\"production\")\n", + ")(shutdown_endpoint_after_predicting=True)" ] }, { @@ -907,7 +1159,10 @@ "id": "c9e5fc22", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "# explore real time predictions\n", + "client.get_model_version(\"breast_cancer_classifier\", \"production\").get_artifact(\"real_time_predictions\").load()" + ] } ], "metadata": { diff --git a/classifier-e2e/steps/__init__.py b/classifier-e2e/steps/__init__.py index 2f856719..b17987b9 100644 --- a/classifier-e2e/steps/__init__.py +++ b/classifier-e2e/steps/__init__.py @@ -23,4 +23,6 @@ ) from .model_promoter import ( model_promoter, -) \ No newline at end of file +) +from .deploy_endpoint import deploy_endpoint +from .misc_endpoint import predict_on_endpoint, shutdown_endpoint \ No newline at end of file diff --git a/classifier-e2e/steps/deploy_endpoint.py b/classifier-e2e/steps/deploy_endpoint.py new file mode 100644 index 00000000..3e549926 --- /dev/null +++ b/classifier-e2e/steps/deploy_endpoint.py @@ -0,0 +1,44 @@ +from typing_extensions import Annotated + +import sagemaker +from sagemaker.image_uris import retrieve + +from zenml import step, get_step_context +from datetime import datetime + +from utils.aws import get_aws_config + + +@step(enable_cache=False) +def deploy_endpoint() -> Annotated[str, "sagemaker_endpoint_name"]: + role, session, region = get_aws_config() + + model = get_step_context().model._get_model_version() + if "sgd" in {t.name for t in model.tags}: + image_uri = retrieve( + region=region, framework="sklearn", version="1.0-1" + ) + entry_point = "utils/sklearn_inference.py" + else: + image_uri = retrieve( + region=region, framework="xgboost", version="1.5-1" + ) + entry_point = None + + model_data = ( + f'{model.get_artifact("breast_cancer_classifier").uri}/model.tar.gz' + ) + + endpoint_name = f'breast-cancer-classifier-{datetime.now().strftime("%Y-%m-%d-%H-%M-%S-%f")}' + sagemaker.Model( + image_uri=image_uri, + model_data=model_data, + sagemaker_session=session, + role=role, + entry_point=entry_point, + ).deploy( + initial_instance_count=1, + instance_type="ml.m5.large", + endpoint_name=endpoint_name, + ) + return endpoint_name diff --git a/classifier-e2e/steps/misc_endpoint.py b/classifier-e2e/steps/misc_endpoint.py new file mode 100644 index 00000000..6bbd0198 --- /dev/null +++ b/classifier-e2e/steps/misc_endpoint.py @@ -0,0 +1,27 @@ +from typing_extensions import Annotated + + +from zenml import step +from sagemaker.predictor import Predictor +import pandas as pd + + +@step +def predict_on_endpoint( + endpoint_name: str, dataset: pd.DataFrame +) -> Annotated[pd.Series, "real_time_predictions"]: + predictor = Predictor(endpoint_name=endpoint_name) + predictions = predictor.predict( + data=dataset.to_csv(header=False, index=False), + initial_args={"ContentType": "text/csv"}, + ) + return pd.Series( + [float(l) for l in predictions.decode().split("\n") if l], + name="predictions", + ) + + +@step +def shutdown_endpoint(endpoint_name: str): + predictor = Predictor(endpoint_name=endpoint_name) + predictor.delete_endpoint() diff --git a/classifier-e2e/utils/aws.py b/classifier-e2e/utils/aws.py new file mode 100644 index 00000000..c74bb410 --- /dev/null +++ b/classifier-e2e/utils/aws.py @@ -0,0 +1,23 @@ +import os +from typing import Tuple, Any + +import boto3 +import sagemaker + + +def get_aws_config() -> Tuple[Any, sagemaker.Session, str]: + REGION_NAME = os.getenv("AWS_REGION", "us-east-1") + ROLE_NAME = os.getenv("AWS_ROLE_NAME", "hamza_connector") + os.environ["AWS_DEFAULT_REGION"] = REGION_NAME + + auth_arguments = { + "aws_access_key_id": os.getenv("AWS_ACCESS_KEY_ID", None), + "aws_secret_access_key": os.getenv("AWS_SECRET_ACCESS_KEY", None), + "aws_session_token": os.getenv("AWS_SESSION_TOKEN", None), + "region_name": REGION_NAME, + } + iam = boto3.client("iam", **auth_arguments) + role = iam.get_role(RoleName=ROLE_NAME)["Role"]["Arn"] + session = sagemaker.Session(boto3.Session(**auth_arguments)) + + return role, session, REGION_NAME From ef190c9917cf38a84b121092631356e8fdb844be Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Thu, 15 Feb 2024 14:55:59 +0100 Subject: [PATCH 18/20] deployment artifact type --- classifier-e2e/pipelines/deploy.py | 6 +-- classifier-e2e/run_skip_basics.ipynb | 44 +++++++++++++++---- classifier-e2e/steps/deploy_endpoint.py | 25 +++++++++-- classifier-e2e/steps/misc_endpoint.py | 6 +-- .../utils/sagemaker_materializer.py | 18 +++++++- 5 files changed, 79 insertions(+), 20 deletions(-) diff --git a/classifier-e2e/pipelines/deploy.py b/classifier-e2e/pipelines/deploy.py index 8b67dad0..f9b77fc1 100644 --- a/classifier-e2e/pipelines/deploy.py +++ b/classifier-e2e/pipelines/deploy.py @@ -19,7 +19,7 @@ def deploy(shutdown_endpoint_after_predicting: bool = True): preprocess_pipeline=preprocess_pipeline, target="target", ) - endpoint_name = deploy_endpoint() - predict_on_endpoint(endpoint_name, df_inference) + predictor = deploy_endpoint() + predict_on_endpoint(predictor, df_inference) if shutdown_endpoint_after_predicting: - shutdown_endpoint(endpoint_name, after=["predict_on_endpoint"]) + shutdown_endpoint(predictor, after=["predict_on_endpoint"]) diff --git a/classifier-e2e/run_skip_basics.ipynb b/classifier-e2e/run_skip_basics.ipynb index 7c4b6e9b..95d969f7 100644 --- a/classifier-e2e/run_skip_basics.ipynb +++ b/classifier-e2e/run_skip_basics.ipynb @@ -1018,7 +1018,7 @@ }, { "cell_type": "code", - "execution_count": 121, + "execution_count": 123, "id": "496adffa", "metadata": {}, "outputs": [ @@ -1030,15 +1030,21 @@ "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m\u001b[0;32mimport\u001b[0m \u001b[0msagemaker\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0msagemaker\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimage_uris\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mretrieve\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0msagemaker\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mPredictor\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0mzenml\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mget_step_context\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0mzenml\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mget_step_context\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mArtifactConfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlog_artifact_metadata\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0mdatetime\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maws\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mget_aws_config\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m\u001b[0;34m@\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0menable_cache\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;32mdef\u001b[0m \u001b[0mdeploy_endpoint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mAnnotated\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"sagemaker_endpoint_name\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;32mdef\u001b[0m \u001b[0mdeploy_endpoint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mAnnotated\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mPredictor\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mArtifactConfig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"sagemaker_endpoint_name\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_deployment_artifact\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mrole\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mregion\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_aws_config\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_step_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_model_version\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", @@ -1069,7 +1075,16 @@ "\u001b[0;34m\u001b[0m \u001b[0minstance_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"ml.m5.large\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mendpoint_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mendpoint_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mendpoint_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n" + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mlog_artifact_metadata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"endpoint_name\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mendpoint_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"image_uri\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mimage_uri\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"role_arn\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mrole\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mPredictor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mendpoint_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mendpoint_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n" ] } ], @@ -1089,7 +1104,7 @@ }, { "cell_type": "code", - "execution_count": 122, + "execution_count": 124, "id": "9dfb5642", "metadata": {}, "outputs": [ @@ -1118,10 +1133,10 @@ "\u001b[0;34m\u001b[0m \u001b[0mpreprocess_pipeline\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpreprocess_pipeline\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"target\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mendpoint_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeploy_endpoint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mpredict_on_endpoint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mendpoint_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf_inference\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mpredictor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeploy_endpoint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mpredict_on_endpoint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredictor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf_inference\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mshutdown_endpoint_after_predicting\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mshutdown_endpoint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mendpoint_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mafter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"predict_on_endpoint\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n" + "\u001b[0;34m\u001b[0m \u001b[0mshutdown_endpoint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredictor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mafter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"predict_on_endpoint\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n" ] } ], @@ -1153,6 +1168,19 @@ ")(shutdown_endpoint_after_predicting=True)" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "41cec4dc", + "metadata": {}, + "outputs": [], + "source": [ + "# explore created endpoint\n", + "run_metadata = client.get_model_version(\"breast_cancer_classifier\", \"production\").get_artifact(\"sagemaker_endpoint\").run_metadata\n", + "for k,v in run_metadata.items():\n", + " print(k, v.value)" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/classifier-e2e/steps/deploy_endpoint.py b/classifier-e2e/steps/deploy_endpoint.py index 3e549926..7f5ad977 100644 --- a/classifier-e2e/steps/deploy_endpoint.py +++ b/classifier-e2e/steps/deploy_endpoint.py @@ -2,15 +2,23 @@ import sagemaker from sagemaker.image_uris import retrieve +from sagemaker import Predictor -from zenml import step, get_step_context +from zenml import step, get_step_context, ArtifactConfig, log_artifact_metadata from datetime import datetime from utils.aws import get_aws_config +from utils.sagemaker_materializer import SagemakerPredictorMaterializer -@step(enable_cache=False) -def deploy_endpoint() -> Annotated[str, "sagemaker_endpoint_name"]: +@step( + enable_cache=False, + output_materializers=[SagemakerPredictorMaterializer], +) +def deploy_endpoint() -> Annotated[ + Predictor, + ArtifactConfig(name="sagemaker_endpoint", is_deployment_artifact=True), +]: role, session, region = get_aws_config() model = get_step_context().model._get_model_version() @@ -41,4 +49,13 @@ def deploy_endpoint() -> Annotated[str, "sagemaker_endpoint_name"]: instance_type="ml.m5.large", endpoint_name=endpoint_name, ) - return endpoint_name + + log_artifact_metadata( + { + "endpoint_name": endpoint_name, + "image_uri": image_uri, + "role_arn": role, + } + ) + + return Predictor(endpoint_name=endpoint_name) diff --git a/classifier-e2e/steps/misc_endpoint.py b/classifier-e2e/steps/misc_endpoint.py index 6bbd0198..aa6c7c8d 100644 --- a/classifier-e2e/steps/misc_endpoint.py +++ b/classifier-e2e/steps/misc_endpoint.py @@ -8,9 +8,8 @@ @step def predict_on_endpoint( - endpoint_name: str, dataset: pd.DataFrame + predictor: Predictor, dataset: pd.DataFrame ) -> Annotated[pd.Series, "real_time_predictions"]: - predictor = Predictor(endpoint_name=endpoint_name) predictions = predictor.predict( data=dataset.to_csv(header=False, index=False), initial_args={"ContentType": "text/csv"}, @@ -22,6 +21,5 @@ def predict_on_endpoint( @step -def shutdown_endpoint(endpoint_name: str): - predictor = Predictor(endpoint_name=endpoint_name) +def shutdown_endpoint(predictor: Predictor): predictor.delete_endpoint() diff --git a/classifier-e2e/utils/sagemaker_materializer.py b/classifier-e2e/utils/sagemaker_materializer.py index 33fdf353..aa886af9 100644 --- a/classifier-e2e/utils/sagemaker_materializer.py +++ b/classifier-e2e/utils/sagemaker_materializer.py @@ -4,13 +4,14 @@ from zenml.enums import ArtifactType from zenml.io import fileio from zenml.materializers.base_materializer import BaseMaterializer +from zenml.materializers.built_in_materializer import BuiltInMaterializer from sklearn.linear_model import SGDClassifier from xgboost import XGBClassifier import tarfile import tempfile import joblib from sklearn.base import ClassifierMixin - +from sagemaker import Predictor class SagemakerMaterializer(BaseMaterializer): ASSOCIATED_TYPES = (ClassifierMixin,) @@ -70,3 +71,18 @@ def save(self, my_obj: ClassifierMixin) -> None: overwrite=True, ) fileio.remove(os.path.join(tempfile.gettempdir(), "model.tar.gz")) + + +class SagemakerPredictorMaterializer(BaseMaterializer): + ASSOCIATED_TYPES = (Predictor,) + ASSOCIATED_ARTIFACT_TYPE = ArtifactType.SERVICE + + def load( + self, data_type: Type[Predictor] + ) -> Predictor: + """Read from artifact store.""" + return Predictor(endpoint_name=BuiltInMaterializer(self.uri).load(str)) + + def save(self, my_obj: Predictor) -> None: + """Write to artifact store.""" + BuiltInMaterializer(self.uri).save(my_obj.endpoint_name) From 8a46c1644e4291fd1857d3211c71bde4ef851789 Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Thu, 15 Feb 2024 15:11:04 +0100 Subject: [PATCH 19/20] bring config back --- classifier-e2e/run_skip_basics.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/classifier-e2e/run_skip_basics.ipynb b/classifier-e2e/run_skip_basics.ipynb index 95d969f7..a41577c2 100644 --- a/classifier-e2e/run_skip_basics.ipynb +++ b/classifier-e2e/run_skip_basics.ipynb @@ -995,7 +995,7 @@ " tags = [\"breast_cancer\", \"classifier\", \"xgboost\"]\n", ")\n", "\n", - "training.with_options(**pipeline_settings)(\n", + "training.with_options(config_path=\"configs/training_xgboost.yaml\",**pipeline_settings)(\n", " model_type=\"xgboost\",\n", " random_state=42\n", ")" From 78428ae4bfb2a8aff42c3b495f506bb816780522 Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Thu, 15 Feb 2024 20:54:05 +0100 Subject: [PATCH 20/20] bring config back --- classifier-e2e/run_skip_basics.ipynb | 22 ++++++++++++---------- 1 file changed, 12 insertions(+), 10 deletions(-) diff --git a/classifier-e2e/run_skip_basics.ipynb b/classifier-e2e/run_skip_basics.ipynb index a41577c2..7426d91e 100644 --- a/classifier-e2e/run_skip_basics.ipynb +++ b/classifier-e2e/run_skip_basics.ipynb @@ -103,7 +103,7 @@ }, { "cell_type": "code", - "execution_count": 117, + "execution_count": 8, "id": "dc08aecc", "metadata": {}, "outputs": [ @@ -216,7 +216,7 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 9, "id": "01162d23", "metadata": {}, "outputs": [ @@ -546,7 +546,7 @@ }, { "cell_type": "code", - "execution_count": 119, + "execution_count": 10, "id": "92c4c7dc", "metadata": {}, "outputs": [ @@ -630,7 +630,7 @@ }, { "cell_type": "code", - "execution_count": 120, + "execution_count": 11, "id": "37c409bd", "metadata": {}, "outputs": [ @@ -1018,7 +1018,7 @@ }, { "cell_type": "code", - "execution_count": 123, + "execution_count": 12, "id": "496adffa", "metadata": {}, "outputs": [ @@ -1036,14 +1036,16 @@ "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0mdatetime\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maws\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mget_aws_config\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msagemaker_materializer\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSagemakerPredictorMaterializer\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;34m@\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0menable_cache\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m@\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0menable_cache\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0moutput_materializers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mSagemakerPredictorMaterializer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m\u001b[0;32mdef\u001b[0m \u001b[0mdeploy_endpoint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mAnnotated\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mPredictor\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mArtifactConfig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"sagemaker_endpoint_name\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_deployment_artifact\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mArtifactConfig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"sagemaker_endpoint\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_deployment_artifact\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mrole\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mregion\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_aws_config\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", @@ -1104,7 +1106,7 @@ }, { "cell_type": "code", - "execution_count": 124, + "execution_count": 13, "id": "9dfb5642", "metadata": {}, "outputs": [

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