diff --git a/install-db-requirements.sh b/install-db-requirements.sh index e12d0e0..65640d9 100644 --- a/install-db-requirements.sh +++ b/install-db-requirements.sh @@ -15,10 +15,9 @@ cat >"$TEMP_REQUIREMENTS" <<'EOF' appnope==0.1.4 ; python_version >= "3.10" and python_version < "3.11" and (platform_system == "Darwin" or sys_platform == "darwin") colorama==0.4.6 ; python_version >= "3.10" and python_version < "3.11" and (platform_system == "Windows" or sys_platform == "win32") cython==0.29.32 ; python_version >= "3.10" and python_version < "3.11" -databricks-connect==13.1.0 ; python_version >= "3.10" and python_version < "3.11" distro==1.9.0 ; python_version >= "3.10" and python_version < "3.11" -dmlbootstrap==0.1.3 ; python_version >= "3.10" and python_version < "3.11" -dmlutils==0.7.0 ; python_version >= "3.10" and python_version < "3.11" +dmlbootstrap==0.2.0 ; python_version >= "3.10" and python_version < "3.11" +dmlutils==0.8.0 ; python_version >= "3.10" and python_version < "3.11" duckdb==1.1.1 ; python_version >= "3.10" and python_version < "3.11" et-xmlfile==1.1.0 ; python_version >= "3.10" and python_version < "3.11" flask==2.2.5 ; python_version >= "3.10" and python_version < "3.11" diff --git a/requirements-db-14.3.txt b/requirements-db-14.3.txt index cd88fbb..60c903a 100644 --- a/requirements-db-14.3.txt +++ b/requirements-db-14.3.txt @@ -3,10 +3,9 @@ appnope==0.1.4 ; python_version >= "3.10" and python_version < "3.11" and (platform_system == "Darwin" or sys_platform == "darwin") colorama==0.4.6 ; python_version >= "3.10" and python_version < "3.11" and (platform_system == "Windows" or sys_platform == "win32") cython==0.29.32 ; python_version >= "3.10" and python_version < "3.11" -databricks-connect==13.1.0 ; python_version >= "3.10" and python_version < "3.11" distro==1.9.0 ; python_version >= "3.10" and python_version < "3.11" -dmlbootstrap==0.1.3 ; python_version >= "3.10" and python_version < "3.11" -dmlutils==0.7.0 ; python_version >= "3.10" and python_version < "3.11" +dmlbootstrap==0.2.0 ; python_version >= "3.10" and python_version < "3.11" +dmlutils==0.8.0 ; python_version >= "3.10" and python_version < "3.11" duckdb==1.1.1 ; python_version >= "3.10" and python_version < "3.11" et-xmlfile==1.1.0 ; python_version >= "3.10" and python_version < "3.11" flask==2.2.5 ; python_version >= "3.10" and python_version < "3.11" diff --git a/requirements-test-14.3.txt b/requirements-test-14.3.txt index 0c93412..3521001 100644 --- a/requirements-test-14.3.txt +++ b/requirements-test-14.3.txt @@ -52,7 +52,6 @@ cython==0.29.32 ; python_version >= "3.10" and python_version < "3.11" dacite==1.8.1 ; python_version >= "3.10" and python_version < "3.11" databricks-automl-runtime==0.2.20 ; python_version >= "3.10" and python_version < "3.11" databricks-cli==0.18.0 ; python_version >= "3.10" and python_version < "3.11" -databricks-connect==13.1.0 ; python_version >= "3.10" and python_version < "3.11" databricks-feature-engineering==0.2.0 ; python_version >= "3.10" and python_version < "3.11" databricks-sdk==0.1.6 ; python_version >= "3.10" and python_version < "3.11" dataclasses-json==0.6.3 ; python_version >= "3.10" and python_version < "3.11" @@ -66,8 +65,8 @@ dill==0.3.6 ; python_version >= "3.10" and python_version < "3.11" diskcache==5.6.3 ; python_version >= "3.10" and python_version < "3.11" distlib==0.3.7 ; python_version >= "3.10" and python_version < "3.11" distro==1.9.0 ; python_version >= "3.10" and python_version < "3.11" -dmlbootstrap==0.1.3 ; python_version >= "3.10" and python_version < "3.11" -dmlutils==0.7.0 ; python_version >= "3.10" and python_version < "3.11" +dmlbootstrap==0.2.0 ; python_version >= "3.10" and python_version < "3.11" +dmlutils==0.8.0 ; python_version >= "3.10" and python_version < "3.11" docstring-to-markdown==0.11 ; python_version >= "3.10" and python_version < "3.11" duckdb==1.1.1 ; python_version >= "3.10" and python_version < "3.11" entrypoints==0.4 ; python_version >= "3.10" and python_version < "3.11" diff --git a/scripts/model_evaluation.py b/scripts/model_evaluation.py index fc748fd..8df678f 100644 --- a/scripts/model_evaluation.py +++ b/scripts/model_evaluation.py @@ -3,7 +3,7 @@ # COMMAND ---------- -# MAGIC %pip install seaborn==v0.13.0 +# MAGIC %pip install mlflow==2.13.0 seaborn==v0.13.0 # MAGIC dbutils.library.restartPython() # COMMAND ---------- diff --git a/scripts/model_training.py b/scripts/model_training.py index 47f944e..722d0c1 100644 --- a/scripts/model_training.py +++ b/scripts/model_training.py @@ -32,16 +32,21 @@ # MAGIC # MAGIC --- # MAGIC #### Cluster/ User Requirements -# MAGIC - Access Mode: Single User or Shared (Not No Isolation Shared) -# MAGIC - Runtime: >= Databricks Runtime 14.3 ML (or >= Databricks Runtime 14.3 + `%pip install databricks-feature-engineering`) -# MAGIC - Node type: Single Node. Because of [this issue](https://kb.databricks.com/en_US/libraries/apache-spark-jobs-fail-with-environment-directory-not-found-error), worker nodes cannot access the directory needed to run inference on a keras trained model, meaning that the `score_batch()` function throws and OSError. # MAGIC - Can be run on CPU or GPU, with 2x speedup on GPU -# MAGIC - Cluster-level packages: `gcsfs==2023.5.0`, `mlflow==2.13.0` (newer than default, which is required to pass a `code_paths` in logging) # MAGIC - `USE CATALOG`, `CREATE SCHEMA` privleges on the `ml` Unity Catalog (Ask Miki if for access) # MAGIC # COMMAND ---------- +# we need a newer version of MLFlow in order to use a custom loss +%pip install mlflow==2.13.0 + +# COMMAND ---------- + +dbutils.library.restartPython() + +# COMMAND ---------- + # DBTITLE 1,Set debug mode # this controls the training parameters, with test mode on a much smaller training set for fewer epochs dbutils.widgets.dropdown("mode", "test", ["test", "production"]) @@ -208,8 +213,7 @@ def convert_feature_dataframe_to_dict( for weather features contain len 8760 arrays. Returns: - - The preprocessed feature data in format {feature_name (str) : - np.array of shape [N] for building model features and shape [N,8760] for weather features} + - The preprocessed feature data in format {feature_name (str) : np.array of shape [N] """ return { col: np.array(feature_df[col]) @@ -283,6 +287,9 @@ def convert_feature_dataframe_to_dict( # skip registering model for now.. # mlflow.register_model(f"runs:/{run_id}/{sm.artifact_path}", str(sm)) + # serialize the keras model and save to GCP + sm.save_keras_model(run_id = run_id) + # COMMAND ---------- # MAGIC %md ## Evaluate Model diff --git a/src/datagen.py b/src/datagen.py index bf2f296..8e37ff0 100644 --- a/src/datagen.py +++ b/src/datagen.py @@ -352,9 +352,7 @@ def convert_dataframe_to_dict(self, feature_df: pd.DataFrame) -> Dict[str, np.nd Returns ------- - Dict[str,np.ndarray]: The preprocessed feature data in format {feature_name (str): - np.array of shape [len(feature_df)] for building model features - and shape [len(feature_df), 8760] for weather features} + Dict[str,np.ndarray]: preprocessed feature data in format {feature_name (str): np.array of shape [N]} """ return {col: np.array(feature_df[col]) for col in self.building_features + ["weather_file_city_index"]} @@ -378,8 +376,7 @@ def __getitem__(self, index: int) -> Tuple[Dict[str, np.ndarray], Dict[str, np.n Returns ------- - - X (dict): features for batch in format {feature_name (str): - np.array of shape [batch_size] for building model features and shape [batch_size, 8760] for weather features} + - X (dict): features for batch in format {feature_name (str): np.array of shape [batch_size]} - y (dict) : targets for the batch in format {target_name (str): np.array of shape [batch_size]} """ # subset rows of targets and building features to batch diff --git a/src/surrogate_model.py b/src/surrogate_model.py index 9864055..970e07a 100644 --- a/src/surrogate_model.py +++ b/src/surrogate_model.py @@ -1,5 +1,6 @@ import numpy as np -from typing import Any, Dict, List, Tuple +import os +from typing import Any, Dict, List, Tuple, Optional import mlflow import pyspark.sql.functions as F @@ -9,6 +10,7 @@ from pyspark.sql.types import ArrayType, DoubleType from tensorflow import keras from tensorflow.keras import layers, models +from tensorflow.python.lib.io import file_io from src.datagen import DataGenerator @@ -279,6 +281,31 @@ def get_model_uri(self, run_id: str = None, version: int = None, verbose: bool = else: return f"runs:/{run_id}/{self.artifact_path}" + def save_keras_model(self, run_id): + """ + Saves the keras model for the given run ID to Google Cloud Storage. + + Parameters: + - run_id (str): The unique identifier for the MLflow run associated with the model to be saved. + + """ + fname = f"sumo_{self.name}_{run_id}.keras" + gcp_model_dir = "gs://the-cube/export/surrogate_model/" + + # load mlflow model + mlflow_model = mlflow.pyfunc.load_model(model_uri=self.get_model_uri(run_id=run_id)) + # extract keras model + keras_model = mlflow_model.unwrap_python_model().model + + # save locally + keras_model.save(fname) + # then copy to gcp + with file_io.FileIO(fname, mode="rb") as f_local: + with file_io.FileIO(os.path.join(gcp_model_dir, fname), mode="wb+") as f_gcp: + f_gcp.write(f_local.read()) + # delete local file + os.remove(fname) + def score_batch( self, test_data: DataFrame,