Merge branch 'main' into 2023.11.24

.github/workflows/ci.yml

@@ -39,4 +39,3 @@ jobs:
           stack-name: ${{ matrix.stack-name }}
           python-version: ${{ matrix.python-version }}
           ref-zenml: feature/OSS-2529-passing-pipeline-parameters-as-yaml-and-document
-

README.md

@@ -182,13 +182,15 @@ To make the most of the Model Control Plane we additionally annotate the output
   <summary>Code snippet 💻</summary>
 
 ```python
-from zenml.model import ModelArtifactConfig
+from zenml import ArtifactConfig
 
 experiment_tracker = Client().active_stack.experiment_tracker
 @step(experiment_tracker=experiment_tracker.name)
 def model_trainer(
     ...
-) -> Annotated[ClassifierMixin, "model", ModelArtifactConfig()]:
+) -> Annotated[
+    ClassifierMixin, ArtifactConfig(name="model", is_model_artifact=True)
+]:
   ...
 ```
 </details>
@@ -308,7 +310,7 @@ You can follow [Data Validators docs](https://docs.zenml.io/stacks-and-component
 
 As a last step concluding all work done so far, we will calculate predictions on the inference dataset and persist them in [Artifact Store](https://docs.zenml.io/stacks-and-components/component-guide/artifact-stores) attached to the current inference model version of the Model Control Plane for reuse and observability.
 
-We will leverage a prepared predictions service called `mlflow_deployment` linked to the inference model version of the Model Control Plane to run `.predict()` and to put predictions as an output of the predictions step, so it is automatically stored in the [Artifact Store](https://docs.zenml.io/stacks-and-components/component-guide/artifact-stores) and linked to the Model Control Plane model version as a versioned artifact link with zero effort. This is achieved because we additionally annotated the `predictions` output with `DataArtifactConfig(overwrite=False)`. This is required to deliver a comprehensive history to stakeholders since Batch Inference can be executed using the same Model Control Plane version multiple times.
+We will leverage a prepared predictions service called `mlflow_deployment` linked to the inference model version of the Model Control Plane to run `.predict()` and to put predictions as an output of the predictions step, so it is automatically stored in the [Artifact Store](https://docs.zenml.io/stacks-and-components/component-guide/artifact-stores) and linked to the Model Control Plane model version as a versioned artifact link with zero effort.
 
 ```
 NOTE: On non-local orchestrators a `model` artifact will be loaded into memory to run predictions directly. You can adapt this part to your needs.
@@ -318,12 +320,10 @@ NOTE: On non-local orchestrators a `model` artifact will be loaded into memory t
   <summary>Code snippet 💻</summary>
 
 ```python
-from zenml.model import DataArtifactConfig
-
 @step
 def inference_predict(
     dataset_inf: pd.DataFrame,
-) -> Annotated[pd.Series, "predictions", DataArtifactConfig(overwrite=False)]:
+) -> Annotated[pd.Series, "predictions"]:
     model_version = get_step_context().model_version
 
     # get predictor

template/pipelines/batch_inference.py

@@ -10,8 +10,7 @@
     notify_on_failure,
     notify_on_success,
 )
-from zenml import pipeline
-from zenml.artifacts.external_artifact import ExternalArtifact
+from zenml import ExternalArtifact, pipeline
 from zenml.integrations.evidently.metrics import EvidentlyMetricConfig
 from zenml.integrations.evidently.steps import evidently_report_step
 from zenml.logger import get_logger
@@ -32,26 +31,19 @@ def {{product_name}}_batch_inference():
     # of one step as the input of the next step.
     ########## ETL stage  ##########
     df_inference, target, _ = data_loader(
-        random_state=ExternalArtifact(
-            model_artifact_pipeline_name="{{product_name}}_training",
-            model_artifact_name="random_state",
-        ),
+        random_state=ExternalArtifact(name="random_state"),
         is_inference=True
     )
     df_inference = inference_data_preprocessor(
         dataset_inf=df_inference,
-        preprocess_pipeline=ExternalArtifact(
-            model_artifact_name="preprocess_pipeline",
-        ),
+        preprocess_pipeline=ExternalArtifact(name="preprocess_pipeline"),
         target=target,
     )
 
 {%- if data_quality_checks %}
     ########## DataQuality stage  ##########
     report, _ = evidently_report_step(
-        reference_dataset=ExternalArtifact(
-            model_artifact_name="dataset_trn",
-        ),
+        reference_dataset=ExternalArtifact(name="dataset_trn"),
         comparison_dataset=df_inference,
         ignored_cols=["target"],
         metrics=[

template/run.py

@@ -5,7 +5,7 @@
 import os
 from typing import Optional
 
-from zenml.artifacts.external_artifact import ExternalArtifact
+from zenml.client import Client
 from zenml.logger import get_logger
 
 from pipelines import {{product_name}}_batch_inference, {{product_name}}_training, {{product_name}}_deployment
@@ -192,17 +192,6 @@ def main(
     ] = f"{{product_name}}_batch_inference_run_{dt.now().strftime('%Y_%m_%d_%H_%M_%S')}"
     {{product_name}}_batch_inference.with_options(**pipeline_args)(**run_args_inference)
 
-    artifact = ExternalArtifact(
-        model_artifact_name="predictions",
-        model_name="{{ product_name }}",
-        model_version="{{ target_environment }}", 
-        model_artifact_version=None, # can be skipped - using latest artifact link
-    )
-    logger.info(
-        "Batch inference pipeline finished successfully! "
-        "You can find predictions in Artifact Store using ID: "
-        f"`{str(artifact.get_artifact_id())}`."
-    )
 
 
 if __name__ == "__main__":

template/steps/deployment/deployment_deploy.py

@@ -4,14 +4,13 @@
 from typing import Optional
 
 from typing_extensions import Annotated
-from zenml import get_step_context, step
+from zenml import ArtifactConfig, get_step_context, step
 from zenml.client import Client
 from zenml.integrations.mlflow.services.mlflow_deployment import MLFlowDeploymentService
 from zenml.integrations.mlflow.steps.mlflow_deployer import (
     mlflow_model_registry_deployer_step,
 )
 from zenml.logger import get_logger
-from zenml.model import EndpointArtifactConfig
 
 logger = get_logger(__name__)
 
@@ -20,8 +19,7 @@
 def deployment_deploy() -> (
     Annotated[
         Optional[MLFlowDeploymentService],
-        "mlflow_deployment",
-        EndpointArtifactConfig(),
+        ArtifactConfig(name="mlflow_deployment", is_endpoint_artifact=True),
     ]
 ):
     """Predictions step.

template/steps/etl/inference_data_preprocessor.py

@@ -6,19 +6,14 @@
 import pandas as pd
 from sklearn.pipeline import Pipeline
 from zenml import step
-from zenml.model import DataArtifactConfig
 
 
 @step
 def inference_data_preprocessor(
     dataset_inf: pd.DataFrame,
     preprocess_pipeline: Pipeline,
     target: str,
-) -> Annotated[
-    pd.DataFrame,
-    "dataset_inf",
-    DataArtifactConfig(overwrite=False, artifact_name="inference_dataset"),
-]:
+) -> Annotated[pd.DataFrame, "inference_dataset"]:
     """Data preprocessor step.
 
     This is an example of a data processor step that prepares the data so that

template/steps/inference/inference_predict.py

@@ -8,15 +8,14 @@
 from zenml import get_step_context, step
 from zenml.integrations.mlflow.services.mlflow_deployment import MLFlowDeploymentService
 from zenml.logger import get_logger
-from zenml.model import DataArtifactConfig
 
 logger = get_logger(__name__)
 
 
 @step
 def inference_predict(
     dataset_inf: pd.DataFrame,
-) -> Annotated[pd.Series, "predictions", DataArtifactConfig(overwrite=False)]:
+) -> Annotated[pd.Series, "predictions"]:
     """Predictions step.
 
     This is an example of a predictions step that takes the data in and returns
@@ -39,9 +38,9 @@ def inference_predict(
     model_version = get_step_context().model_version
 
     # get predictor
-    predictor_service: Optional[MLFlowDeploymentService] = model_version.get_endpoint_artifact(
-        "mlflow_deployment"
-    ).load()
+    predictor_service: Optional[
+        MLFlowDeploymentService
+    ] = model_version.load_artifact("mlflow_deployment")
     if predictor_service is not None:
         # run prediction from service
         predictions = predictor_service.predict(request=dataset_inf)
@@ -51,7 +50,7 @@ def inference_predict(
             "as the orchestrator is not local."
         )
         # run prediction from memory
-        predictor = model_version.get_model_artifact("model").load()
+        predictor = model_version.load_artifact("model")
         predictions = predictor.predict(dataset_inf)
 
     predictions = pd.Series(predictions, name="predicted")

template/steps/promotion/{% if metric_compare_promotion %}compute_performance_metrics_on_current_data.py{% endif %}

@@ -6,7 +6,7 @@ from typing_extensions import Annotated
 import pandas as pd
 from sklearn.metrics import accuracy_score
 from zenml import step, get_step_context
-from zenml.model import ModelVersion
+from zenml.model.model_version import ModelVersion
 from zenml.logger import get_logger
 
 logger = get_logger(__name__)
@@ -54,8 +54,8 @@ def compute_performance_metrics_on_current_data(
     else:
         # Get predictors
         predictors = {
-            latest_version_number: latest_version.get_model_artifact("model").load(),
-            current_version_number: current_version.get_model_artifact("model").load(),
+            latest_version_number: latest_version.load_artifact("model"),
+            current_version_number: current_version.load_artifact("model"),
         }
 
         metrics = {}

template/steps/promotion/{% if metric_compare_promotion %}promote_with_metric_compare.py{% endif %}

@@ -1,7 +1,7 @@
 # {% include 'template/license_header' %}
 
 from zenml import get_step_context, step
-from zenml.model import ModelVersion
+from zenml.model.model_version import ModelVersion
 from zenml.logger import get_logger
 
 from utils import promote_in_model_registry

template/steps/promotion/{% if not metric_compare_promotion %}promote_latest_version.py{% endif %}

@@ -1,7 +1,7 @@
 # {% include 'template/license_header' %}
 
 from zenml import get_step_context, step
-from zenml.model import ModelVersion
+from zenml.model.model_version import ModelVersion
 from zenml.logger import get_logger
 
 from utils import promote_in_model_registry

template/steps/training/model_trainer.py

@@ -5,12 +5,11 @@
 import mlflow
 import pandas as pd
 from sklearn.base import ClassifierMixin
-from zenml import log_artifact_metadata, step
+from zenml import ArtifactConfig, log_artifact_metadata, step
 from zenml.client import Client
 from zenml.integrations.mlflow.experiment_trackers import MLFlowExperimentTracker
 from zenml.integrations.mlflow.steps.mlflow_registry import mlflow_register_model_step
 from zenml.logger import get_logger
-from zenml.model import ModelArtifactConfig
 
 logger = get_logger(__name__)
 
@@ -31,7 +30,9 @@ def model_trainer(
     model: ClassifierMixin,
     target: str,
     name: str,
-) -> Annotated[ClassifierMixin, "model", ModelArtifactConfig()]:
+) -> Annotated[
+    ClassifierMixin, ArtifactConfig(name="model", 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
@@ -79,12 +80,14 @@ def model_trainer(
         name=name,
     )
     # keep track of mlflow version for future use
-    log_artifact_metadata(
-        output_name="model",
-        model_registry_version=Client()
-        .active_stack.model_registry.list_model_versions(name=name)[-1]
-        .version,
-    )
+    model_registry = Client().active_stack.model_registry
+    if model_registry:
+        versions = model_registry.list_model_versions(name=name)
+        if versions:
+            log_artifact_metadata(
+                metadata={"model_registry_version": versions[-1].version},
+                artifact_name="model",
+            )
     ### YOUR CODE ENDS HERE ###
 
     return model

template/steps/{% if hyperparameters_tuning %}hp_tuning{% endif %}/hp_tuning_select_best_model.py

@@ -31,9 +31,7 @@ def hp_tuning_select_best_model(
     best_metric = -1
     # consume artifacts attached to current model version in Model Control Plane
     for step_name in step_names:
-        hp_output = model_version.get_data_artifact(
-            step_name=step_name, name="hp_result"
-        )
+        hp_output = model_version.get_data_artifact("hp_result")
         model: ClassifierMixin = hp_output.load()
         # fetch metadata we attached earlier
         metric = float(hp_output.run_metadata["metric"].value)

template/steps/{% if hyperparameters_tuning %}hp_tuning{% endif %}/hp_tuning_single_search.py

@@ -80,8 +80,8 @@ def hp_tuning_single_search(
     score = accuracy_score(y_tst, y_pred)
     # log score along with output artifact as metadata
     log_artifact_metadata(
-        output_name="hp_result",
-        metric=float(score),
+        metadata={"metric": float(score)},
+        artifact_name="hp_result",
     )
     ### YOUR CODE ENDS HERE ###
     return cv.best_estimator_