From 9f63906238ca4e70d757103b82de19112eaa8381 Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Fri, 3 Nov 2023 14:04:05 +0100 Subject: [PATCH 01/28] hide mlflow complexity and show MCP more --- ...ance_metrics_on_current_data.py{% endif %} | 69 +++++++++++++++ ...}promote_with_metric_compare.py{% endif %} | 85 +++++++++++++++++++ ...e_promotion %}promote_latest.py{% endif %} | 43 ++++++++++ template/utils/__init__.py | 6 ++ template/utils/get_model_versions.py | 22 +++++ template/utils/promote_in_model_registry.py | 29 +++++++ 6 files changed, 254 insertions(+) create mode 100644 template/steps/promotion/{% if metric_compare_promotion %}compute_performance_metrics_on_current_data.py{% endif %} create mode 100644 template/steps/promotion/{% if metric_compare_promotion %}promote_with_metric_compare.py{% endif %} create mode 100644 template/steps/promotion/{% if not metric_compare_promotion %}promote_latest.py{% endif %} create mode 100644 template/utils/__init__.py create mode 100644 template/utils/get_model_versions.py create mode 100644 template/utils/promote_in_model_registry.py diff --git a/template/steps/promotion/{% if metric_compare_promotion %}compute_performance_metrics_on_current_data.py{% endif %} b/template/steps/promotion/{% if metric_compare_promotion %}compute_performance_metrics_on_current_data.py{% endif %} new file mode 100644 index 0000000..06017ad --- /dev/null +++ b/template/steps/promotion/{% if metric_compare_promotion %}compute_performance_metrics_on_current_data.py{% endif %} @@ -0,0 +1,69 @@ +# {% include 'template/license_header' %} + +from typing import Tuple +from typing_extensions import Annotated + +import pandas as pd +from sklearn.metrics import accuracy_score +from zenml import step, get_step_context +from zenml.client import Client +from zenml.integrations.mlflow.steps.mlflow_deployer import ( + mlflow_model_registry_deployer_step, +) +from zenml.logger import get_logger + +from utils import get_model_versions + +logger = get_logger(__name__) + +@step +def compute_performance_metrics_on_current_data( + dataset_tst: pd.DataFrame, +) -> Tuple[Annotated[float, "latest_metric"],Annotated[float, "current_metric"]]: + """Get metrics for comparison during promotion on fresh dataset. + + This is an example of a metrics calculation step. It computes metric + on recent test 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 to use different input data. + See the documentation for more information: + + https://docs.zenml.io/user-guide/advanced-guide/configure-steps-pipelines + + Args: + dataset_tst: The test dataset. + + Returns: + Latest version and current version metric values on a test set. + """ + + ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### + pipeline_extra = get_step_context().pipeline_run.config.extra + target_env = pipeline_extra["target_env"].lower() + model_version = get_step_context().model_config._get_model_version() + + X = dataset_tst.drop(columns=["target"]) + y = dataset_tst["target"].to_numpy() + logger.info("Evaluating model metrics...") + + # Get model version numbers from Model Control Plane + latest_version, current_version = get_model_versions(model_version, target_env) + + if latest_version != current_version: + metrics = {} + for version in [latest_version,current_version]: + deployment_service = mlflow_model_registry_deployer_step.entrypoint( + registry_model_name=pipeline_extra["mlflow_model_name"], + registry_model_version=version, + replace_existing=True, + ) + + predictions = deployment_service.predict(request=X) + metrics[version]=accuracy_score(y, predictions) + deployment_service.deprovision(force=True) + else: + metrics = {latest_version:1.0,current_version:0.0} + ### YOUR CODE ENDS HERE ### + return metrics[latest_version],metrics[current_version] diff --git a/template/steps/promotion/{% if metric_compare_promotion %}promote_with_metric_compare.py{% endif %} b/template/steps/promotion/{% if metric_compare_promotion %}promote_with_metric_compare.py{% endif %} new file mode 100644 index 0000000..b44c58b --- /dev/null +++ b/template/steps/promotion/{% if metric_compare_promotion %}promote_with_metric_compare.py{% endif %} @@ -0,0 +1,85 @@ +# {% include 'template/license_header' %} + +from typing_extensions import Annotated, Tuple +from zenml import get_step_context, step +from zenml.client import Client +from zenml.logger import get_logger + +from utils import get_model_versions, promote_in_model_registry + +logger = get_logger(__name__) + + +@step +def promote_with_metric_compare( + latest_metric: float, + current_metric: float, +)->None: + """Try to promote trained model. + + This is an example of a model promotion step. It gets precomputed + metrics for 2 model version: latest and currently promoted to target environment + (Production, Staging, etc) and compare than in order to define + if newly trained model is performing better or not. If new model + version is better by metric - it will get relevant + tag, otherwise previously promoted model version will remain. + + If the latest version is the only one - it will get promoted automatically. + + 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 to use different input data. + See the documentation for more information: + + https://docs.zenml.io/user-guide/advanced-guide/configure-steps-pipelines + + Args: + latest_metric: Recently trained model metric results. + current_metric: Previously promoted model metric results. + """ + + ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### + pipeline_extra = get_step_context().pipeline_run.config.extra + target_env = pipeline_extra["target_env"].lower() + model_version = get_step_context().model_config._get_model_version() + should_promote = True + + # Get model version numbers from Model Control Plane + latest_version, current_version = get_model_versions(model_version, target_env) + + if latest_version == current_version: + logger.info("No current model version found - promoting latest") + else: + logger.info( + f"Latest model metric={latest_metric:.6f}\n" + f"Current model metric={current_metric:.6f}" + ) + if latest_metric >= current_metric: + logger.info( + "Latest model versions outperformed current versions - promoting latest" + ) + else: + logger.info( + "Current model versions outperformed latest versions - keeping current" + ) + should_promote = False + + promoted_version = current_version + if should_promote: + # Promote in Model Control Plane + model_version.set_stage(stage=target_env, force=True) + logger.info(f"Current model version was promoted to '{target_env}'.") + + # Promote in Model Registry + promote_in_model_registry( + latest_version=latest_version, + current_version=current_version, + model_name=pipeline_extra["mlflow_model_name"], + target_env=pipeline_extra["target_env"] + ) + promoted_version = latest_version + + logger.info( + f"Current model version in `{pipeline_extra['target_env']}` is `{promoted_version}`" + ) + ### YOUR CODE ENDS HERE ### diff --git a/template/steps/promotion/{% if not metric_compare_promotion %}promote_latest.py{% endif %} b/template/steps/promotion/{% if not metric_compare_promotion %}promote_latest.py{% endif %} new file mode 100644 index 0000000..7b583e4 --- /dev/null +++ b/template/steps/promotion/{% if not metric_compare_promotion %}promote_latest.py{% endif %} @@ -0,0 +1,43 @@ +# {% include 'template/license_header' %} + +from typing_extensions import Annotated +from zenml import get_step_context, step +from zenml.client import Client +from zenml.enums import ModelStages +from zenml.logger import get_logger +from zenml.model import ModelConfig + +from utils import get_model_versions, promote_in_model_registry + +logger = get_logger(__name__) + + +@step +def promote_latest_version() -> None: + """Promote latest trained model. + + This is an example of a model promotion step, which promotes the + latest trained model to the current version. + """ + + ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### + pipeline_extra = get_step_context().pipeline_run.config.extra + target_env = pipeline_extra["target_env"].lower() + model_version = get_step_context().model_config._get_model_version() + + # Get model version numbers from Model Control Plane + latest_version, current_version = get_model_versions(model_version, target_env) + logger.info(f"Promoting latest model version `{latest_version}`") + + # Promote in Model Control Plane + model_version.set_stage(stage=target_env, force=True) + logger.info(f"Current model version was promoted to '{target_env}'.") + + # Promote in Model Registry + promote_in_model_registry( + latest_version=latest_version, + current_version=current_version, + model_name=pipeline_extra["mlflow_model_name"], + target_env=pipeline_extra["target_env"] + ) + ### YOUR CODE ENDS HERE ### diff --git a/template/utils/__init__.py b/template/utils/__init__.py new file mode 100644 index 0000000..abfeb63 --- /dev/null +++ b/template/utils/__init__.py @@ -0,0 +1,6 @@ +# {% include 'template/license_header' %} + + +from .get_model_from_config import get_model_from_config +from .get_model_versions import get_model_versions +from .promote_in_model_registry import promote_in_model_registry \ No newline at end of file diff --git a/template/utils/get_model_versions.py b/template/utils/get_model_versions.py new file mode 100644 index 0000000..dff9025 --- /dev/null +++ b/template/utils/get_model_versions.py @@ -0,0 +1,22 @@ +# {% include 'template/license_header' %} + +from typing import Tuple +from typing_extensions import Annotated +from zenml.model import ModelConfig +from zenml.models.model_models import ModelVersionResponseModel + +def _get_mr_version(model_version: ModelVersionResponseModel) -> str: + return model_version.get_model_object("model").metadata["model_registry_version"].value + +def get_model_versions( + model_version: ModelVersionResponseModel, + target_env: str +)->Tuple[Annotated[str,"latest_version"],Annotated[str,"current_version"]]: + latest_version = _get_mr_version(model_version) + try: + current_model_version = ModelConfig(name=model_version.model.name,version=target_env)._get_model_version() + current_version = _get_mr_version(current_model_version) + except KeyError: + current_version = latest_version + + return latest_version,current_version \ No newline at end of file diff --git a/template/utils/promote_in_model_registry.py b/template/utils/promote_in_model_registry.py new file mode 100644 index 0000000..88b2a01 --- /dev/null +++ b/template/utils/promote_in_model_registry.py @@ -0,0 +1,29 @@ +# {% include 'template/license_header' %} + + +from zenml.client import Client +from zenml.logger import get_logger +from zenml.model_registries.base_model_registry import ModelVersionStage + +logger = get_logger(__name__) + +def promote_in_model_registry( + latest_version:str, + current_version:str, + model_name:str, + target_env: str +): + model_registry = Client().active_stack.model_registry + if latest_version != current_version: + model_registry.update_model_version( + name=model_name, + version=current_version, + stage=ModelVersionStage(ModelVersionStage.ARCHIVED), + metadata={}, + ) + model_registry.update_model_version( + name=model_name, + version=latest_version, + stage=ModelVersionStage(target_env), + metadata={}, + ) \ No newline at end of file From 5a0f0b4eccdd991d5ad7dd07eba47b8c963e7f05 Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Fri, 3 Nov 2023 14:04:13 +0100 Subject: [PATCH 02/28] hide mlflow complexity and show MCP more --- README.md | 4 +- template/pipelines/batch_inference.py | 21 ++--- template/pipelines/training.py | 77 +++++----------- template/steps/__init__.py | 8 +- template/steps/etl/data_loader.py | 13 ++- template/steps/inference/inference_predict.py | 22 +++-- template/steps/promotion/__init__.py | 8 +- .../steps/promotion/promote_get_versions.py | 53 ----------- ...te_model_version_in_model_control_plane.py | 26 ------ ...omotion %}promote_get_metric.py{% endif %} | 53 ----------- ...e_promoter_in_model_registry.py{% endif %} | 87 ------------------- ...}promote_with_metric_compare.py{% endif %} | 4 +- ...ote_latest_in_model_registry.py{% endif %} | 52 ----------- template/steps/training/model_trainer.py | 14 ++- .../hp_tuning_single_search.py | 2 +- template/utils/misc.py | 33 ------- 16 files changed, 74 insertions(+), 403 deletions(-) delete mode 100644 template/steps/promotion/promote_get_versions.py delete mode 100644 template/steps/promotion/promote_model_version_in_model_control_plane.py delete mode 100644 template/steps/promotion/{% if metric_compare_promotion %}promote_get_metric.py{% endif %} delete mode 100644 template/steps/promotion/{% if metric_compare_promotion %}promote_metric_compare_promoter_in_model_registry.py{% endif %} delete mode 100644 template/steps/promotion/{% if not metric_compare_promotion %}promote_latest_in_model_registry.py{% endif %} delete mode 100644 template/utils/misc.py diff --git a/README.md b/README.md index 95fd681..1fff7e4 100644 --- a/README.md +++ b/README.md @@ -168,8 +168,8 @@ def notify_on_success() -> None: @pipeline(on_failure=notify_on_failure) def e2e_example_training(...): ... - promote_metric_compare_promoter_in_model_registry(...) - notify_on_success(after=["promote_metric_compare_promoter_in_model_registry"]) + promote_with_metric_compare(...) + notify_on_success(after=["promote_with_metric_compare"]) ``` diff --git a/template/pipelines/batch_inference.py b/template/pipelines/batch_inference.py index eb6f231..393f499 100644 --- a/template/pipelines/batch_inference.py +++ b/template/pipelines/batch_inference.py @@ -1,6 +1,5 @@ # {% include 'template/license_header' %} - from steps import ( data_loader, {%- if data_quality_checks %} @@ -12,12 +11,9 @@ notify_on_failure, notify_on_success, ) -from zenml import get_pipeline_context, pipeline +from zenml import pipeline from zenml.integrations.evidently.metrics import EvidentlyMetricConfig from zenml.integrations.evidently.steps import evidently_report_step -from zenml.integrations.mlflow.steps.mlflow_deployer import ( - mlflow_model_registry_deployer_step, -) from zenml.logger import get_logger from zenml.artifacts.external_artifact import ExternalArtifact @@ -36,7 +32,12 @@ def {{product_name}}_batch_inference(): # Link all the steps together by calling them and passing the output # of one step as the input of the next step. ########## ETL stage ########## - df_inference, target = data_loader(is_inference=True) + df_inference, target, _ = data_loader( + random_state=ExternalArtifact( + model_artifact_name="random_state", + ), + is_inference=True + ) df_inference = inference_data_preprocessor( dataset_inf=df_inference, preprocess_pipeline=ExternalArtifact( @@ -60,15 +61,7 @@ def {{product_name}}_batch_inference(): drift_quality_gate(report) {%- endif %} ########## Inference stage ########## - deployment_service = mlflow_model_registry_deployer_step( - registry_model_name=get_pipeline_context().extra["mlflow_model_name"], - registry_model_version=ExternalArtifact( - model_artifact_name="promoted_version", - ), - replace_existing=True, - ) inference_predict( - deployment_service=deployment_service, dataset_inf=df_inference, {%- if data_quality_checks %} after=["drift_quality_gate"], diff --git a/template/pipelines/training.py b/template/pipelines/training.py index 1b1e766..db27e26 100644 --- a/template/pipelines/training.py +++ b/template/pipelines/training.py @@ -2,40 +2,35 @@ from typing import List, Optional +import random from steps import ( data_loader, -{%- if hyperparameters_tuning %} - hp_tuning_select_best_model, - hp_tuning_single_search, -{%- endif %} model_evaluator, model_trainer, notify_on_failure, notify_on_success, + train_data_preprocessor, + train_data_splitter, +{%- if hyperparameters_tuning %} + hp_tuning_select_best_model, + hp_tuning_single_search, +{%- endif %} {%- if metric_compare_promotion %} - promote_get_metric, - promote_metric_compare_promoter_in_model_registry, + compute_performance_metrics_on_current_data, + promote_with_metric_compare, {%- else %} - promote_latest_in_model_registry, + promote_latest_version, {%- endif %} - promote_get_versions, - promote_model_version_in_model_control_plane, - train_data_preprocessor, - train_data_splitter, ) from zenml import pipeline, get_pipeline_context -from zenml.integrations.mlflow.steps.mlflow_deployer import ( - mlflow_model_registry_deployer_step, -) -from zenml.integrations.mlflow.steps.mlflow_registry import mlflow_register_model_step from zenml.logger import get_logger {%- if hyperparameters_tuning %} {%- else %} from zenml.artifacts.external_artifact import ExternalArtifact -from utils.get_model_from_config import get_model_from_config +from utils import get_model_from_config {%- endif %} logger = get_logger(__name__) @@ -74,7 +69,7 @@ def {{product_name}}_training( # of one step as the input of the next step. pipeline_extra = get_pipeline_context().extra ########## ETL stage ########## - raw_data, target = data_loader() + raw_data, target, _ = data_loader(random_state=random.randint(0,100)) dataset_trn, dataset_tst = train_data_splitter( dataset=raw_data, test_size=test_size, @@ -123,6 +118,7 @@ def {{product_name}}_training( model=ExternalArtifact(value=best_model), {%- endif %} target=target, + name=pipeline_extra["mlflow_model_name"], ) model_evaluator( model=model, @@ -133,55 +129,22 @@ def {{product_name}}_training( fail_on_accuracy_quality_gates=fail_on_accuracy_quality_gates, target=target, ) - mlflow_register_model_step( - model, - name=pipeline_extra["mlflow_model_name"], - ) - ########## Promotion stage ########## - latest_version, current_version = promote_get_versions( - after=["mlflow_register_model_step"], - ) {%- if metric_compare_promotion %} - latest_deployment = mlflow_model_registry_deployer_step( - id="deploy_latest_model_version", - registry_model_name=pipeline_extra["mlflow_model_name"], - registry_model_version=latest_version, - replace_existing=True, - ) - latest_metric = promote_get_metric( - id="get_metrics_latest_model_version", - dataset_tst=dataset_tst, - deployment_service=latest_deployment, - ) - - current_deployment = mlflow_model_registry_deployer_step( - id="deploy_current_model_version", - registry_model_name=pipeline_extra["mlflow_model_name"], - registry_model_version=current_version, - replace_existing=True, - after=["get_metrics_latest_model_version"], - ) - current_metric = promote_get_metric( - id="get_metrics_current_model_version", + latest_metric,current_metric = compute_performance_metrics_on_current_data( dataset_tst=dataset_tst, - deployment_service=current_deployment, + after=["model_evaluator"] ) - was_promoted, promoted_version = promote_metric_compare_promoter_in_model_registry( + promote_with_metric_compare( latest_metric=latest_metric, current_metric=current_metric, - latest_version=latest_version, - current_version=current_version, ) + last_step = "promote_with_metric_compare" {%- else %} - promoted_version = promote_latest_in_model_registry( - latest_version=latest_version, - current_version=current_version, - ) - was_promoted = True + promote_latest_version(after=["model_evaluator"]) + last_step = "promote_latest_version" {%- endif %} - promote_model_version_in_model_control_plane(was_promoted) - notify_on_success(after=["promote_model_version_in_model_control_plane"]) + notify_on_success(after=[last_step]) ### YOUR CODE ENDS HERE ### diff --git a/template/steps/__init__.py b/template/steps/__init__.py index 3e0eb05..02dc87e 100644 --- a/template/steps/__init__.py +++ b/template/steps/__init__.py @@ -17,12 +17,10 @@ from .inference import inference_predict from .promotion import ( {%- if metric_compare_promotion %} - promote_get_metric, - promote_metric_compare_promoter_in_model_registry, + compute_performance_metrics_on_current_data, + promote_with_metric_compare, {%- else %} - promote_latest_in_model_registry, + promote_latest_version, {%- endif %} - promote_get_versions, - promote_model_version_in_model_control_plane, ) from .training import model_evaluator, model_trainer diff --git a/template/steps/etl/data_loader.py b/template/steps/etl/data_loader.py index 4bcb59e..31f21aa 100644 --- a/template/steps/etl/data_loader.py +++ b/template/steps/etl/data_loader.py @@ -17,8 +17,9 @@ @step def data_loader( - is_inference: bool = False, -) -> Tuple[Annotated[pd.DataFrame, "dataset"], Annotated[str, "target"],]: + random_state: int, + is_inference: bool = False +) -> Tuple[Annotated[pd.DataFrame, "dataset"], Annotated[str, "target"],Annotated[int,"random_state"]]: """Dataset reader step. This is an example of a dataset reader step that load Breast Cancer dataset. @@ -33,6 +34,7 @@ def data_loader( Args: is_inference: If `True` subset will be returned and target column will be removed from dataset. + random_state: Random state for sampling Returns: The dataset artifact as Pandas DataFrame and name of target column. @@ -42,7 +44,10 @@ def data_loader( inference_size = int(len(dataset.target) * 0.05) target = "target" dataset: pd.DataFrame = dataset.frame - inference_subset = dataset.sample(inference_size, random_state=42) + inference_subset = dataset.sample( + inference_size, + random_state=random_state + ) if is_inference: dataset = inference_subset dataset.drop(columns=target, inplace=True) @@ -51,4 +56,4 @@ def data_loader( dataset.reset_index(drop=True, inplace=True) logger.info(f"Dataset with {len(dataset)} records loaded!") ### YOUR CODE ENDS HERE ### - return dataset, target + return dataset, target, random_state diff --git a/template/steps/inference/inference_predict.py b/template/steps/inference/inference_predict.py index c0b52fb..f5d6145 100644 --- a/template/steps/inference/inference_predict.py +++ b/template/steps/inference/inference_predict.py @@ -4,16 +4,15 @@ from typing_extensions import Annotated import pandas as pd -from zenml import step -from zenml.integrations.mlflow.model_deployers.mlflow_model_deployer import ( - MLFlowDeploymentService, +from zenml import step, get_step_context +from zenml.integrations.mlflow.steps.mlflow_deployer import ( + mlflow_model_registry_deployer_step, ) from zenml.model import ArtifactConfig @step def inference_predict( - deployment_service: MLFlowDeploymentService, dataset_inf: pd.DataFrame, ) -> Annotated[pd.Series, "predictions", ArtifactConfig(overwrite=False)]: """Predictions step. @@ -23,19 +22,26 @@ def inference_predict( 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 to use different input data - and model version in registry. See the documentation for more information: + In this example, the step can be configured to use different input data. + See the documentation for more information: https://docs.zenml.io/user-guide/advanced-guide/configure-steps-pipelines Args: - deployment_service: Deployed model service. dataset_inf: The inference dataset. Returns: - The processed dataframe: dataset_inf. + The predictions as pandas series """ ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### + pipeline_extra = get_step_context().pipeline_run.config.extra + promoted_version = str(get_step_context().model_config._get_model_version().number) + + deployment_service = mlflow_model_registry_deployer_step.entrypoint( + registry_model_name=pipeline_extra["mlflow_model_name"], + registry_model_version=promoted_version, + replace_existing=True, + ) predictions = deployment_service.predict(request=dataset_inf) predictions = pd.Series(predictions, name="predicted") deployment_service.deprovision(force=True) diff --git a/template/steps/promotion/__init__.py b/template/steps/promotion/__init__.py index afeb1cb..623727e 100644 --- a/template/steps/promotion/__init__.py +++ b/template/steps/promotion/__init__.py @@ -2,10 +2,8 @@ {%- if metric_compare_promotion %} -from .promote_get_metric import promote_get_metric -from .promote_metric_compare_promoter_in_model_registry import promote_metric_compare_promoter_in_model_registry +from .compute_performance_metrics_on_current_data import compute_performance_metrics_on_current_data +from .promote_with_metric_compare import promote_with_metric_compare {%- else %} -from .promote_latest_in_model_registry import promote_latest_in_model_registry +from .promote_latest_version import promote_latest_version {%- endif %} -from .promote_get_versions import promote_get_versions -from .promote_model_version_in_model_control_plane import promote_model_version_in_model_control_plane diff --git a/template/steps/promotion/promote_get_versions.py b/template/steps/promotion/promote_get_versions.py deleted file mode 100644 index bb5ff3c..0000000 --- a/template/steps/promotion/promote_get_versions.py +++ /dev/null @@ -1,53 +0,0 @@ -# {% include 'template/license_header' %} - - -from typing import Tuple -from typing_extensions import Annotated - -from zenml import get_step_context, step -from zenml.client import Client -from zenml.logger import get_logger -from zenml.model_registries.base_model_registry import ModelVersionStage - -logger = get_logger(__name__) - -model_registry = Client().active_stack.model_registry - - -@step -def promote_get_versions() -> ( - Tuple[Annotated[str, "latest_version"], Annotated[str, "current_version"]] -): - """Step to get latest and currently tagged model version from Model Registry. - - This is an example of a model version extraction step. It will retrieve 2 model - versions from Model Registry: latest and currently promoted to target - environment (Production, Staging, etc). - - Returns: - The model versions: latest and current. If not current version - returns same - for both. - """ - - ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### - pipeline_extra = get_step_context().pipeline_run.config.extra - none_versions = model_registry.list_model_versions( - name=pipeline_extra["mlflow_model_name"], - stage=None, - ) - latest_versions = none_versions[0].version - logger.info(f"Latest model version is {latest_versions}") - - target_versions = model_registry.list_model_versions( - name=pipeline_extra["mlflow_model_name"], - stage=ModelVersionStage(pipeline_extra["target_env"]), - ) - current_version = latest_versions - if target_versions: - current_version = target_versions[0].version - logger.info(f"Currently promoted model version is {current_version}") - else: - logger.info("No currently promoted model version found.") - ### YOUR CODE ENDS HERE ### - - return latest_versions, current_version diff --git a/template/steps/promotion/promote_model_version_in_model_control_plane.py b/template/steps/promotion/promote_model_version_in_model_control_plane.py deleted file mode 100644 index 725311b..0000000 --- a/template/steps/promotion/promote_model_version_in_model_control_plane.py +++ /dev/null @@ -1,26 +0,0 @@ -# {% include 'template/license_header' %} - - -from zenml import get_step_context, step -from zenml.logger import get_logger - -logger = get_logger(__name__) - - -@step -def promote_model_version_in_model_control_plane(promotion_decision: bool): - """Step to promote current model version to target environment in Model Control Plane. - - Args: - promotion_decision: Whether to promote current model version to target environment - """ - - ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### - if promotion_decision: - target_env = get_step_context().pipeline_run.config.extra["target_env"].lower() - model_version = get_step_context().model_config._get_model_version() - model_version.set_stage(stage=target_env, force=True) - logger.info(f"Current model version was promoted to '{target_env}'.") - else: - logger.info("Current model version was not promoted.") - ### YOUR CODE ENDS HERE ### diff --git a/template/steps/promotion/{% if metric_compare_promotion %}promote_get_metric.py{% endif %} b/template/steps/promotion/{% if metric_compare_promotion %}promote_get_metric.py{% endif %} deleted file mode 100644 index 592ff05..0000000 --- a/template/steps/promotion/{% if metric_compare_promotion %}promote_get_metric.py{% endif %} +++ /dev/null @@ -1,53 +0,0 @@ -# {% include 'template/license_header' %} - - -from typing_extensions import Annotated - -import pandas as pd -from sklearn.metrics import accuracy_score -from zenml import step -from zenml.client import Client -from zenml.integrations.mlflow.services import MLFlowDeploymentService -from zenml.logger import get_logger - -logger = get_logger(__name__) - -model_registry = Client().active_stack.model_registry - - -@step -def promote_get_metric( - dataset_tst: pd.DataFrame, - deployment_service: MLFlowDeploymentService, -) -> Annotated[float, "metric"]: - """Get metric for comparison for one model deployment. - - This is an example of a metric calculation step. It get a model deployment - service and computes metric on recent test 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 to use different input data. - See the documentation for more information: - - https://docs.zenml.io/user-guide/advanced-guide/configure-steps-pipelines - - Args: - dataset_tst: The test dataset. - deployment_service: Model version deployment. - - Returns: - Metric value for a given deployment on test set. - - """ - - ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### - X = dataset_tst.drop(columns=["target"]) - y = dataset_tst["target"].to_numpy() - logger.info("Evaluating model metrics...") - - predictions = deployment_service.predict(request=X) - metric = accuracy_score(y, predictions) - deployment_service.deprovision(force=True) - ### YOUR CODE ENDS HERE ### - return metric diff --git a/template/steps/promotion/{% if metric_compare_promotion %}promote_metric_compare_promoter_in_model_registry.py{% endif %} b/template/steps/promotion/{% if metric_compare_promotion %}promote_metric_compare_promoter_in_model_registry.py{% endif %} deleted file mode 100644 index 8498876..0000000 --- a/template/steps/promotion/{% if metric_compare_promotion %}promote_metric_compare_promoter_in_model_registry.py{% endif %} +++ /dev/null @@ -1,87 +0,0 @@ -# {% include 'template/license_header' %} - -from typing_extensions import Annotated, Tuple -from zenml import get_step_context, step -from zenml.client import Client -from zenml.logger import get_logger -from zenml.model_registries.base_model_registry import ModelVersionStage - -logger = get_logger(__name__) - -model_registry = Client().active_stack.model_registry - - -@step -def promote_metric_compare_promoter_in_model_registry( - latest_metric: float, - current_metric: float, - latest_version: str, - current_version: str, -)->Tuple[Annotated[bool, "was_promoted"], Annotated[int,"promoted_version"]]: - """Try to promote trained model. - - This is an example of a model promotion step. It gets precomputed - metrics for 2 model version: latest and currently promoted to target environment - (Production, Staging, etc) and compare than in order to define - if newly trained model is performing better or not. If new model - version is better by metric - it will get relevant - tag, otherwise previously promoted model version will remain. - - If the latest version is the only one - it will get promoted automatically. - - 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 to use different input data. - See the documentation for more information: - - https://docs.zenml.io/user-guide/advanced-guide/configure-steps-pipelines - - Args: - latest_metric: Recently trained model metric results. - current_metric: Previously promoted model metric results. - latest_version: Recently trained model version. - current_version:Previously promoted model version. - - """ - - ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### - pipeline_extra = get_step_context().pipeline_run.config.extra - should_promote = True - - if latest_version == current_version: - logger.info("No current model version found - promoting latest") - else: - logger.info( - f"Latest model metric={latest_metric:.6f}\n" - f"Current model metric={current_metric:.6f}" - ) - if latest_metric >= current_metric: - logger.info( - "Latest model versions outperformed current versions - promoting latest" - ) - else: - logger.info( - "Current model versions outperformed latest versions - keeping current" - ) - should_promote = False - - promoted_version = current_version - if should_promote: - if latest_version != current_version: - model_registry.update_model_version( - name=pipeline_extra["mlflow_model_name"], - version=current_version, - stage=ModelVersionStage.ARCHIVED, - ) - model_registry.update_model_version( - name=pipeline_extra["mlflow_model_name"], - version=latest_version, - stage=ModelVersionStage(pipeline_extra["target_env"]), - ) - promoted_version = latest_version - - logger.info( - f"Current model version in `{pipeline_extra['target_env']}` is `{promoted_version}`" - ) - ### YOUR CODE ENDS HERE ### - return should_promote, int(promoted_version) diff --git a/template/steps/promotion/{% if metric_compare_promotion %}promote_with_metric_compare.py{% endif %} b/template/steps/promotion/{% if metric_compare_promotion %}promote_with_metric_compare.py{% endif %} index b44c58b..8f56939 100644 --- a/template/steps/promotion/{% if metric_compare_promotion %}promote_with_metric_compare.py{% endif %} +++ b/template/steps/promotion/{% if metric_compare_promotion %}promote_with_metric_compare.py{% endif %} @@ -56,11 +56,11 @@ def promote_with_metric_compare( ) if latest_metric >= current_metric: logger.info( - "Latest model versions outperformed current versions - promoting latest" + "Latest model version outperformed current version - promoting latest" ) else: logger.info( - "Current model versions outperformed latest versions - keeping current" + "Current model version outperformed latest version - keeping current" ) should_promote = False diff --git a/template/steps/promotion/{% if not metric_compare_promotion %}promote_latest_in_model_registry.py{% endif %} b/template/steps/promotion/{% if not metric_compare_promotion %}promote_latest_in_model_registry.py{% endif %} deleted file mode 100644 index 02f6ec9..0000000 --- a/template/steps/promotion/{% if not metric_compare_promotion %}promote_latest_in_model_registry.py{% endif %} +++ /dev/null @@ -1,52 +0,0 @@ -# {% include 'template/license_header' %} - -from typing_extensions import Annotated -from zenml import get_step_context, step -from zenml.client import Client -from zenml.logger import get_logger -from zenml.model_registries.base_model_registry import ModelVersionStage - -logger = get_logger(__name__) - -model_registry = Client().active_stack.model_registry - - -@step -def promote_latest_in_model_registry( - latest_version:str, - current_version:str, -) -> Annotated[int,"promoted_version"]: - """Promote latest trained model. - - This is an example of a model promotion step, which promotes the - latest trained model to the current version. - - Args: - latest_version: Recently trained model version. - current_version: Current model version, if present. - - """ - - ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### - logger.info(f"Promoting latest model version `{latest_version}`") - pipeline_extra = get_step_context().pipeline_run.config.extra - if latest_version != current_version: - model_registry.update_model_version( - name=pipeline_extra["mlflow_model_name"], - version=current_version, - stage=ModelVersionStage(ModelVersionStage.ARCHIVED), - metadata={}, - ) - model_registry.update_model_version( - name=pipeline_extra["mlflow_model_name"], - version=latest_version, - stage=ModelVersionStage(pipeline_extra["target_env"]), - metadata={}, - ) - promoted_version = latest_version - - logger.info( - f"Current model version in `{pipeline_extra['target_env']}` is `{promoted_version}`" - ) - ### YOUR CODE ENDS HERE ### - return int(promoted_version) diff --git a/template/steps/training/model_trainer.py b/template/steps/training/model_trainer.py index fb26e0d..9c85d15 100644 --- a/template/steps/training/model_trainer.py +++ b/template/steps/training/model_trainer.py @@ -6,11 +6,12 @@ import mlflow import pandas as pd from sklearn.base import ClassifierMixin -from zenml import step +from zenml import step, log_artifact_metadata from zenml.client import Client from zenml.integrations.mlflow.experiment_trackers import MLFlowExperimentTracker from zenml.logger import get_logger from zenml.model import ModelArtifactConfig +from zenml.integrations.mlflow.steps.mlflow_registry import mlflow_register_model_step logger = get_logger(__name__) @@ -30,6 +31,7 @@ def model_trainer( dataset_trn: pd.DataFrame, model: ClassifierMixin, target: str, + name: str, ) -> Annotated[ClassifierMixin, "model", ModelArtifactConfig()]: """Configure and train a model on the training dataset. @@ -56,6 +58,7 @@ def model_trainer( dataset_trn: The preprocessed train dataset. model: The model instance to train. target: Name of target columns in dataset. + name: The name of the model. Returns: The trained model artifact. @@ -70,6 +73,15 @@ def model_trainer( dataset_trn.drop(columns=[target]), dataset_trn[target], ) + + mlflow_register_model_step.entrypoint( + model, + name=name, + ) ### YOUR CODE ENDS HERE ### + log_artifact_metadata( + output_name="model", + model_registry_version = Client().active_stack.model_registry.list_model_versions(name=name)[-1].version + ) return model diff --git a/template/steps/{% if hyperparameters_tuning %}hp_tuning{% endif %}/hp_tuning_single_search.py b/template/steps/{% if hyperparameters_tuning %}hp_tuning{% endif %}/hp_tuning_single_search.py index e088d50..edcfc86 100644 --- a/template/steps/{% if hyperparameters_tuning %}hp_tuning{% endif %}/hp_tuning_single_search.py +++ b/template/steps/{% if hyperparameters_tuning %}hp_tuning{% endif %}/hp_tuning_single_search.py @@ -11,7 +11,7 @@ from zenml import step from zenml.logger import get_logger -from utils.get_model_from_config import get_model_from_config +from utils import get_model_from_config logger = get_logger(__name__) diff --git a/template/utils/misc.py b/template/utils/misc.py deleted file mode 100644 index 936a57f..0000000 --- a/template/utils/misc.py +++ /dev/null @@ -1,33 +0,0 @@ -# {% include 'template/license_header' %} - - -import string - -import pandas as pd -from sklearn.datasets import make_classification - - -def generate_random_data(n_samples: int) -> pd.DataFrame: - """Generate random data for model input. - - Args: - n_samples: Number of records to generate. - - Returns: - pd.DataFrame: Generated dataset for classification task. - """ - n_features = 20 - X, y = make_classification( - n_samples=n_samples, - n_features=n_features, - n_classes=2, - random_state=42, - ) - dataset = pd.concat( - [ - pd.DataFrame(X, columns=list(string.ascii_uppercase[:n_features])), - pd.Series(y, name="target"), - ], - axis=1, - ) - return dataset From 825c48f553d2c9591bc993d40338a51d6a9e85db Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Fri, 3 Nov 2023 14:09:40 +0100 Subject: [PATCH 03/28] brush up --- template/pipelines/batch_inference.py | 1 - template/steps/etl/data_loader.py | 3 --- ...compute_performance_metrics_on_current_data.py{% endif %} | 1 - ...are_promotion %}promote_with_metric_compare.py{% endif %} | 2 -- ...t metric_compare_promotion %}promote_latest.py{% endif %} | 4 ---- template/steps/training/model_evaluator.py | 5 +---- 6 files changed, 1 insertion(+), 15 deletions(-) diff --git a/template/pipelines/batch_inference.py b/template/pipelines/batch_inference.py index 393f499..2cc62b9 100644 --- a/template/pipelines/batch_inference.py +++ b/template/pipelines/batch_inference.py @@ -6,7 +6,6 @@ drift_quality_gate, {%- endif %} inference_data_preprocessor, - inference_get_current_version, inference_predict, notify_on_failure, notify_on_success, diff --git a/template/steps/etl/data_loader.py b/template/steps/etl/data_loader.py index 31f21aa..9e0f9a8 100644 --- a/template/steps/etl/data_loader.py +++ b/template/steps/etl/data_loader.py @@ -7,13 +7,10 @@ import pandas as pd from sklearn.datasets import load_breast_cancer from zenml import step -from zenml.client import Client from zenml.logger import get_logger logger = get_logger(__name__) -artifact_store = Client().active_stack.artifact_store - @step def data_loader( diff --git a/template/steps/promotion/{% if metric_compare_promotion %}compute_performance_metrics_on_current_data.py{% endif %} b/template/steps/promotion/{% if metric_compare_promotion %}compute_performance_metrics_on_current_data.py{% endif %} index 06017ad..11e4ea4 100644 --- a/template/steps/promotion/{% if metric_compare_promotion %}compute_performance_metrics_on_current_data.py{% endif %} +++ b/template/steps/promotion/{% if metric_compare_promotion %}compute_performance_metrics_on_current_data.py{% endif %} @@ -6,7 +6,6 @@ from typing_extensions import Annotated import pandas as pd from sklearn.metrics import accuracy_score from zenml import step, get_step_context -from zenml.client import Client from zenml.integrations.mlflow.steps.mlflow_deployer import ( mlflow_model_registry_deployer_step, ) diff --git a/template/steps/promotion/{% if metric_compare_promotion %}promote_with_metric_compare.py{% endif %} b/template/steps/promotion/{% if metric_compare_promotion %}promote_with_metric_compare.py{% endif %} index 8f56939..8a1e7a7 100644 --- a/template/steps/promotion/{% if metric_compare_promotion %}promote_with_metric_compare.py{% endif %} +++ b/template/steps/promotion/{% if metric_compare_promotion %}promote_with_metric_compare.py{% endif %} @@ -1,8 +1,6 @@ # {% include 'template/license_header' %} -from typing_extensions import Annotated, Tuple from zenml import get_step_context, step -from zenml.client import Client from zenml.logger import get_logger from utils import get_model_versions, promote_in_model_registry diff --git a/template/steps/promotion/{% if not metric_compare_promotion %}promote_latest.py{% endif %} b/template/steps/promotion/{% if not metric_compare_promotion %}promote_latest.py{% endif %} index 7b583e4..0fd3561 100644 --- a/template/steps/promotion/{% if not metric_compare_promotion %}promote_latest.py{% endif %} +++ b/template/steps/promotion/{% if not metric_compare_promotion %}promote_latest.py{% endif %} @@ -1,11 +1,7 @@ # {% include 'template/license_header' %} -from typing_extensions import Annotated from zenml import get_step_context, step -from zenml.client import Client -from zenml.enums import ModelStages from zenml.logger import get_logger -from zenml.model import ModelConfig from utils import get_model_versions, promote_in_model_registry diff --git a/template/steps/training/model_evaluator.py b/template/steps/training/model_evaluator.py index 8d0d0f8..8847ef6 100644 --- a/template/steps/training/model_evaluator.py +++ b/template/steps/training/model_evaluator.py @@ -1,9 +1,6 @@ # {% include 'template/license_header' %} -from typing import Tuple -from typing_extensions import Annotated - import mlflow import pandas as pd from sklearn.base import ClassifierMixin @@ -25,7 +22,7 @@ def model_evaluator( min_train_accuracy: float = 0.0, min_test_accuracy: float = 0.0, fail_on_accuracy_quality_gates: bool = False, -): +)->None: """Evaluate a trained model. This is an example of a model evaluation step that takes in a model artifact From 3b1fbeac952528b31df88fe29bf7b1a20f14339b Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Fri, 3 Nov 2023 14:10:37 +0100 Subject: [PATCH 04/28] black --- template/steps/etl/data_loader.py | 14 +++++++------- .../steps/etl/inference_data_preprocessor.py | 6 +++++- template/steps/training/model_evaluator.py | 2 +- template/steps/training/model_trainer.py | 4 +++- template/utils/__init__.py | 2 +- template/utils/get_model_versions.py | 17 +++++++++++------ template/utils/promote_in_model_registry.py | 8 +++----- 7 files changed, 31 insertions(+), 22 deletions(-) diff --git a/template/steps/etl/data_loader.py b/template/steps/etl/data_loader.py index 9e0f9a8..5fdc7c3 100644 --- a/template/steps/etl/data_loader.py +++ b/template/steps/etl/data_loader.py @@ -14,9 +14,12 @@ @step def data_loader( - random_state: int, - is_inference: bool = False -) -> Tuple[Annotated[pd.DataFrame, "dataset"], Annotated[str, "target"],Annotated[int,"random_state"]]: + random_state: int, is_inference: bool = False +) -> Tuple[ + Annotated[pd.DataFrame, "dataset"], + Annotated[str, "target"], + Annotated[int, "random_state"], +]: """Dataset reader step. This is an example of a dataset reader step that load Breast Cancer dataset. @@ -41,10 +44,7 @@ def data_loader( inference_size = int(len(dataset.target) * 0.05) target = "target" 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) diff --git a/template/steps/etl/inference_data_preprocessor.py b/template/steps/etl/inference_data_preprocessor.py index fa37bfa..9a20f61 100644 --- a/template/steps/etl/inference_data_preprocessor.py +++ b/template/steps/etl/inference_data_preprocessor.py @@ -14,7 +14,11 @@ def inference_data_preprocessor( dataset_inf: pd.DataFrame, preprocess_pipeline: Pipeline, target: str, -) -> Annotated[pd.DataFrame, "dataset_inf", ArtifactConfig(overwrite=False, artifact_name="inference_dataset")]: +) -> Annotated[ + pd.DataFrame, + "dataset_inf", + ArtifactConfig(overwrite=False, artifact_name="inference_dataset"), +]: """Data preprocessor step. This is an example of a data processor step that prepares the data so that diff --git a/template/steps/training/model_evaluator.py b/template/steps/training/model_evaluator.py index 8847ef6..f31458d 100644 --- a/template/steps/training/model_evaluator.py +++ b/template/steps/training/model_evaluator.py @@ -22,7 +22,7 @@ def model_evaluator( min_train_accuracy: float = 0.0, min_test_accuracy: float = 0.0, fail_on_accuracy_quality_gates: bool = False, -)->None: +) -> None: """Evaluate a trained model. This is an example of a model evaluation step that takes in a model artifact diff --git a/template/steps/training/model_trainer.py b/template/steps/training/model_trainer.py index 9c85d15..15c9f0c 100644 --- a/template/steps/training/model_trainer.py +++ b/template/steps/training/model_trainer.py @@ -81,7 +81,9 @@ def model_trainer( ### YOUR CODE ENDS HERE ### log_artifact_metadata( output_name="model", - model_registry_version = Client().active_stack.model_registry.list_model_versions(name=name)[-1].version + model_registry_version=Client() + .active_stack.model_registry.list_model_versions(name=name)[-1] + .version, ) return model diff --git a/template/utils/__init__.py b/template/utils/__init__.py index abfeb63..9f3c19a 100644 --- a/template/utils/__init__.py +++ b/template/utils/__init__.py @@ -3,4 +3,4 @@ from .get_model_from_config import get_model_from_config from .get_model_versions import get_model_versions -from .promote_in_model_registry import promote_in_model_registry \ No newline at end of file +from .promote_in_model_registry import promote_in_model_registry diff --git a/template/utils/get_model_versions.py b/template/utils/get_model_versions.py index dff9025..3a58b9b 100644 --- a/template/utils/get_model_versions.py +++ b/template/utils/get_model_versions.py @@ -5,18 +5,23 @@ from zenml.model import ModelConfig from zenml.models.model_models import ModelVersionResponseModel + def _get_mr_version(model_version: ModelVersionResponseModel) -> str: - return model_version.get_model_object("model").metadata["model_registry_version"].value + return ( + model_version.get_model_object("model").metadata["model_registry_version"].value + ) + def get_model_versions( - model_version: ModelVersionResponseModel, - target_env: str -)->Tuple[Annotated[str,"latest_version"],Annotated[str,"current_version"]]: + model_version: ModelVersionResponseModel, target_env: str +) -> Tuple[Annotated[str, "latest_version"], Annotated[str, "current_version"]]: latest_version = _get_mr_version(model_version) try: - current_model_version = ModelConfig(name=model_version.model.name,version=target_env)._get_model_version() + current_model_version = ModelConfig( + name=model_version.model.name, version=target_env + )._get_model_version() current_version = _get_mr_version(current_model_version) except KeyError: current_version = latest_version - return latest_version,current_version \ No newline at end of file + return latest_version, current_version diff --git a/template/utils/promote_in_model_registry.py b/template/utils/promote_in_model_registry.py index 88b2a01..b6bd606 100644 --- a/template/utils/promote_in_model_registry.py +++ b/template/utils/promote_in_model_registry.py @@ -7,11 +7,9 @@ logger = get_logger(__name__) + def promote_in_model_registry( - latest_version:str, - current_version:str, - model_name:str, - target_env: str + latest_version: str, current_version: str, model_name: str, target_env: str ): model_registry = Client().active_stack.model_registry if latest_version != current_version: @@ -26,4 +24,4 @@ def promote_in_model_registry( version=latest_version, stage=ModelVersionStage(target_env), metadata={}, - ) \ No newline at end of file + ) From d7a4a429bef2f0601df995e25a0f9f0f2f47b5c1 Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Fri, 3 Nov 2023 14:25:05 +0100 Subject: [PATCH 05/28] add comments --- template/steps/inference/inference_predict.py | 2 ++ ..._performance_metrics_on_current_data.py{% endif %} | 2 ++ template/steps/training/model_trainer.py | 4 +++- template/utils/get_model_versions.py | 11 +++++++++++ template/utils/promote_in_model_registry.py | 8 ++++++++ 5 files changed, 26 insertions(+), 1 deletion(-) diff --git a/template/steps/inference/inference_predict.py b/template/steps/inference/inference_predict.py index f5d6145..0e4814b 100644 --- a/template/steps/inference/inference_predict.py +++ b/template/steps/inference/inference_predict.py @@ -37,11 +37,13 @@ def inference_predict( pipeline_extra = get_step_context().pipeline_run.config.extra promoted_version = str(get_step_context().model_config._get_model_version().number) + # deploy predictor service deployment_service = mlflow_model_registry_deployer_step.entrypoint( registry_model_name=pipeline_extra["mlflow_model_name"], registry_model_version=promoted_version, replace_existing=True, ) + # run prediction and prepare output predictions = deployment_service.predict(request=dataset_inf) predictions = pd.Series(predictions, name="predicted") deployment_service.deprovision(force=True) diff --git a/template/steps/promotion/{% if metric_compare_promotion %}compute_performance_metrics_on_current_data.py{% endif %} b/template/steps/promotion/{% if metric_compare_promotion %}compute_performance_metrics_on_current_data.py{% endif %} index 11e4ea4..836c01f 100644 --- a/template/steps/promotion/{% if metric_compare_promotion %}compute_performance_metrics_on_current_data.py{% endif %} +++ b/template/steps/promotion/{% if metric_compare_promotion %}compute_performance_metrics_on_current_data.py{% endif %} @@ -53,12 +53,14 @@ def compute_performance_metrics_on_current_data( if latest_version != current_version: metrics = {} for version in [latest_version,current_version]: + # deploy predictor service deployment_service = mlflow_model_registry_deployer_step.entrypoint( registry_model_name=pipeline_extra["mlflow_model_name"], registry_model_version=version, replace_existing=True, ) + # predict and evaluate predictions = deployment_service.predict(request=X) metrics[version]=accuracy_score(y, predictions) deployment_service.deprovision(force=True) diff --git a/template/steps/training/model_trainer.py b/template/steps/training/model_trainer.py index 15c9f0c..9896165 100644 --- a/template/steps/training/model_trainer.py +++ b/template/steps/training/model_trainer.py @@ -74,16 +74,18 @@ def model_trainer( dataset_trn[target], ) + # register mlflow model mlflow_register_model_step.entrypoint( model, name=name, ) - ### YOUR CODE ENDS HERE ### + # 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, ) + ### YOUR CODE ENDS HERE ### return model diff --git a/template/utils/get_model_versions.py b/template/utils/get_model_versions.py index 3a58b9b..87130c8 100644 --- a/template/utils/get_model_versions.py +++ b/template/utils/get_model_versions.py @@ -7,6 +7,11 @@ def _get_mr_version(model_version: ModelVersionResponseModel) -> str: + """Read metadata of linked model object to fetch version in Model Registry. + + Args: + model_version: Model Version response from Model Control Plane. + """ return ( model_version.get_model_object("model").metadata["model_registry_version"].value ) @@ -15,6 +20,12 @@ def _get_mr_version(model_version: ModelVersionResponseModel) -> str: def get_model_versions( model_version: ModelVersionResponseModel, target_env: str ) -> Tuple[Annotated[str, "latest_version"], Annotated[str, "current_version"]]: + """Get latest and currently promoted model versions from Model Control Plane. + + Args: + model_version: latest model version in Model Control Plane comming from context + target_env: Target stage to search for currently promoted version + """ latest_version = _get_mr_version(model_version) try: current_model_version = ModelConfig( diff --git a/template/utils/promote_in_model_registry.py b/template/utils/promote_in_model_registry.py index b6bd606..aedf995 100644 --- a/template/utils/promote_in_model_registry.py +++ b/template/utils/promote_in_model_registry.py @@ -11,6 +11,14 @@ def promote_in_model_registry( latest_version: str, current_version: str, model_name: str, target_env: str ): + """Promote model version in model registry to a given stage. + + Args: + latest_version: version to be promoted + current_version: currently promoted version + model_name: name of the model in registry + target_env: stage for promotion + """ model_registry = Client().active_stack.model_registry if latest_version != current_version: model_registry.update_model_version( From 727130c065d3037f5ef8e209c9d05d1dce1a64a1 Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Fri, 3 Nov 2023 14:37:22 +0100 Subject: [PATCH 06/28] fix renaming issue --- ...tric_compare_promotion %}promote_latest_version.py{% endif %}} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename template/steps/promotion/{{% if not metric_compare_promotion %}promote_latest.py{% endif %} => {% if not metric_compare_promotion %}promote_latest_version.py{% endif %}} (100%) diff --git a/template/steps/promotion/{% if not metric_compare_promotion %}promote_latest.py{% endif %} b/template/steps/promotion/{% if not metric_compare_promotion %}promote_latest_version.py{% endif %} similarity index 100% rename from template/steps/promotion/{% if not metric_compare_promotion %}promote_latest.py{% endif %} rename to template/steps/promotion/{% if not metric_compare_promotion %}promote_latest_version.py{% endif %} From e17f9a7b7fe00f810ff57402fdf37363a9d2767d Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Fri, 3 Nov 2023 15:32:16 +0100 Subject: [PATCH 07/28] more MCP --- template/pipelines/training.py | 4 +-- .../hp_tuning_select_best_model.py | 28 ++++++++----------- .../hp_tuning_single_search.py | 15 ++++++---- 3 files changed, 23 insertions(+), 24 deletions(-) diff --git a/template/pipelines/training.py b/template/pipelines/training.py index db27e26..15aa654 100644 --- a/template/pipelines/training.py +++ b/template/pipelines/training.py @@ -98,9 +98,7 @@ def {{product_name}}_training( target=target, ) after.append(step_name) - best_model = hp_tuning_select_best_model( - search_steps_prefix=search_steps_prefix, after=after - ) + best_model = hp_tuning_select_best_model(after=after) {%- else %} model_configuration = pipeline_extra["model_configuration"] best_model = get_model_from_config( diff --git a/template/steps/{% if hyperparameters_tuning %}hp_tuning{% endif %}/hp_tuning_select_best_model.py b/template/steps/{% if hyperparameters_tuning %}hp_tuning{% endif %}/hp_tuning_select_best_model.py index 0f0bcc3..d199a16 100644 --- a/template/steps/{% if hyperparameters_tuning %}hp_tuning{% endif %}/hp_tuning_select_best_model.py +++ b/template/steps/{% if hyperparameters_tuning %}hp_tuning{% endif %}/hp_tuning_select_best_model.py @@ -6,40 +6,36 @@ from sklearn.base import ClassifierMixin from zenml import get_step_context, step -from zenml.client import Client from zenml.logger import get_logger logger = get_logger(__name__) @step -def hp_tuning_select_best_model( - search_steps_prefix: str, -) -> Annotated[ClassifierMixin, "best_model"]: +def hp_tuning_select_best_model() -> Annotated[ClassifierMixin, "best_model"]: """Find best model across all HP tuning attempts. This is an example of a model hyperparameter tuning step that takes in prefix of steps called previously to search for best hyperparameters. It will loop other them and find best model of all according to metric. - Args: - search_steps_prefix: Prefix of steps used for grid search before. - Returns: The best possible model class and its' parameters. """ ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### - run_name = get_step_context().pipeline_run.name - run = Client().get_pipeline_run(run_name) + model_version = get_step_context().model_config._get_model_version() best_model = None best_metric = -1 - for run_step_name, run_step in run.steps.items(): - if run_step_name.startswith(search_steps_prefix): - if "best_model" in run_step.outputs: - model: ClassifierMixin = run_step.outputs["best_model"].load() - metric: float = run_step.outputs["metric"].load() - if best_model is None or best_metric < metric: - best_model = model + # consume artifacts attached to current model version in Model Control Plane + for full_artifact_name in model_version.artifact_object_ids: + # if artifacts comes from one of HP tuning steps + if full_artifact_name.endswith("hp_result"): + hp_output = model_version.artifacts[full_artifact_name]["1"] + model: ClassifierMixin = hp_output.load() + # fetch metadata we attached earlier + metric = float(hp_output.metadata["metric"].value) + if best_model is None or best_metric < metric: + best_model = model ### YOUR CODE ENDS HERE ### return best_model diff --git a/template/steps/{% if hyperparameters_tuning %}hp_tuning{% endif %}/hp_tuning_single_search.py b/template/steps/{% if hyperparameters_tuning %}hp_tuning{% endif %}/hp_tuning_single_search.py index edcfc86..e88ed42 100644 --- a/template/steps/{% if hyperparameters_tuning %}hp_tuning{% endif %}/hp_tuning_single_search.py +++ b/template/steps/{% if hyperparameters_tuning %}hp_tuning{% endif %}/hp_tuning_single_search.py @@ -1,14 +1,14 @@ # {% include 'template/license_header' %} -from typing import Any, Dict, Tuple +from typing import Any, Dict from typing_extensions import Annotated import pandas as pd from sklearn.base import ClassifierMixin from sklearn.metrics import accuracy_score from sklearn.model_selection import RandomizedSearchCV -from zenml import step +from zenml import step, log_artifact_metadata from zenml.logger import get_logger from utils import get_model_from_config @@ -24,7 +24,7 @@ def hp_tuning_single_search( dataset_trn: pd.DataFrame, dataset_tst: pd.DataFrame, target: str, -) -> Tuple[Annotated[ClassifierMixin, "best_model"], Annotated[float, "metric"]]: +) -> Annotated[ClassifierMixin, "hp_result"]: """Evaluate a trained model. This is an example of a model hyperparameter tuning step that takes @@ -47,7 +47,7 @@ def hp_tuning_single_search( target: Name of target columns in dataset. Returns: - The best possible model parameters for given config. + The best possible model for given config. """ model_class = get_model_from_config(model_package, model_class) @@ -79,5 +79,10 @@ def hp_tuning_single_search( cv.fit(X=X_trn, y=y_trn) y_pred = cv.predict(X_tst) 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), + ) ### YOUR CODE ENDS HERE ### - return cv.best_estimator_, score + return cv.best_estimator_ From 745859be206d08e5c1acebee732bc8ae4183456f Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Fri, 3 Nov 2023 15:38:14 +0100 Subject: [PATCH 08/28] update docstring --- .../hp_tuning_select_best_model.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/template/steps/{% if hyperparameters_tuning %}hp_tuning{% endif %}/hp_tuning_select_best_model.py b/template/steps/{% if hyperparameters_tuning %}hp_tuning{% endif %}/hp_tuning_select_best_model.py index d199a16..89336c0 100644 --- a/template/steps/{% if hyperparameters_tuning %}hp_tuning{% endif %}/hp_tuning_select_best_model.py +++ b/template/steps/{% if hyperparameters_tuning %}hp_tuning{% endif %}/hp_tuning_select_best_model.py @@ -15,9 +15,9 @@ def hp_tuning_select_best_model() -> Annotated[ClassifierMixin, "best_model"]: """Find best model across all HP tuning attempts. - This is an example of a model hyperparameter tuning step that takes - in prefix of steps called previously to search for best hyperparameters. - It will loop other them and find best model of all according to metric. + This is an example of a model hyperparameter tuning step that loops + other artifacts linked to model version in Model Control Plane to find + the best hyperparameter tuning output model of all according to the metric. Returns: The best possible model class and its' parameters. From 6f42d4a69e6a4c368cf9ed9babf2d8e428e49fef Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Fri, 3 Nov 2023 17:01:07 +0100 Subject: [PATCH 09/28] update readme --- README.md | 64 +++++++++++++++++++++++++++------- assets/01_etl.png | Bin 69666 -> 36376 bytes assets/02_hp.png | Bin 67179 -> 36659 bytes assets/03_train.png | Bin 35958 -> 24775 bytes assets/04_promotion.png | Bin 193345 -> 29325 bytes assets/05_batch_inference.png | Bin 189800 -> 89114 bytes 6 files changed, 52 insertions(+), 12 deletions(-) diff --git a/README.md b/README.md index 1fff7e4..610a517 100644 --- a/README.md +++ b/README.md @@ -18,8 +18,7 @@ Both pipelines are inside a shared Model Control Plane model context - training * Search for an optimal model object architecture and tune its hyperparameters * Train the model object and evaluate its performance on the holdout set * Compare a recently trained model object with one promoted earlier - * If a recently trained model object performs better, then stage it as a new inference model object in the model registry - * On success of the current model object, then stage newly created Model Control Plane version as the one used for inference + * If a recently trained model object performs better, then stage it as a new inference model in the Model Control Plane * [CD] Batch Inference * Load the inference dataset and preprocess it reusing object fitted during training * Perform data drift analysis reusing training dataset of the inference Model Control Plane version as a reference @@ -120,7 +119,50 @@ We also output `preprocess_pipeline` as an output artifact from `train_data_prep To ensure the high quality of ML models many ML Engineers go for automated hyperparameter tuning or even automated model architecture search. In this example, we are using prepared data from ETL to spin up a search of the best model parameters for different architectures in parallel. -To create parallel processing of computationally expensive operations we use a loop over predefined potential architectures and respective parameters search grid and create one step for each candidate. After the steps are created we need to collect results (one best model per each search step) in a `hp_tuning_select_best_model` step to define the final winner and pass it to training. To ensure that collection goes smoothly and in full we use an `after` statement populated with all search steps names, so the selector job will wait for the completion of all searches. +To create parallel processing of computationally expensive operations we use a loop over predefined potential architectures and respective parameters search grid and create one step for each candidate. Inside each hyperparameter tuning step instance, we run random search cross-validation to find the best parameters and after that evaluate the result using the metric of interest (accuracy_score in this example). We attach a computed metric to the output artifact as metadata to be used later in `hp_tuning_select_best_model`. +
+ Code snippet 💻 + +```python +from zenml import log_artifact_metadata + +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), +) +``` +
+ +After the steps are executed we need to collect results (one best model per each search step) in a `hp_tuning_select_best_model` step to define the final winner and pass it to training. We use the Model Control Plane capabilities to pull correct artifacts from previous steps and fetch their metadata for final evaluation before actual training. +
+ Code snippet 💻 + +```python +from zenml import get_step_context + +model_version = get_step_context().model_config._get_model_version() + +best_model = None +best_metric = -1 +# consume artifacts attached to current model version in Model Control Plane +for full_artifact_name in model_version.artifact_object_ids: + # if artifacts comes from one of HP tuning steps + if full_artifact_name.endswith("hp_result"): + hp_output = model_version.artifacts[full_artifact_name]["1"] + model: ClassifierMixin = hp_output.load() + # fetch metadata we attached earlier + metric = float(hp_output.metadata["metric"].value) + if best_model is None or best_metric < metric: + best_model = model +``` +
+ + +To ensure that collection goes smoothly and in full we use an `after` statement populated with all search steps names, so the selector job will wait for the completion of all searches. + + You can find more information about the current state of [Hyperparameter Tuning using ZenML in the documentation](https://docs.zenml.io/user-guide/advanced-guide/pipelining-features/hyper-parameter-tuning). @@ -185,9 +227,9 @@ Once the model object is trained and evaluated on meeting basic quality standard In this example, we are implementing promotion based on metric comparison to decide on the spot and avoid more complex approaches like Champion/Challengers shadow deployments. In other projects, other promotion techniques and strategies can be used. -To achieve this we would retrieve the model registry version from [Model Registry](https://docs.zenml.io/stacks-and-components/component-guide/model-registries): latest (the one we just trained) and current (the one having a proper tag). Next, we need to deploy both model objects using [Model Deployer](https://docs.zenml.io/stacks-and-components/component-guide/model-deployers) and run predictions on the testing set for both of them. Next, we select which one of the model registry versions has a better metric value and associate it with the inference tag. +To achieve this we would retrieve the model version from the Model Control Plane: latest (the one we just trained) and current (the one having a proper tag). Next, we need to deploy both model objects using [Model Deployer](https://docs.zenml.io/stacks-and-components/component-guide/model-deployers) and run predictions on the testing set for both of them. Next, we select which one of the model registry versions has a better metric value. If the newly trained model is performing better we promote it to the inference stage in the Model Control Plane. -As a last step we promote the current Model Control Plane version to the inference stage if a promotion decision was made. By doing so we ensure that the best Model Control Plane version would be used for inference later on and ensure seamless integration of relevant artifacts from training pipeline in the batch inference pipeline. +By doing so we ensure that the best-performing version will be used for inference later on and ensure seamless integration of relevant artifacts from the training pipeline in the batch inference pipeline. ### [Continuous Deployment] Batch Inference The Batch Inference pipeline is designed to run with inference Model Control Plane version context. This ensures that we always infer only on quality-assured Model Control Plane version and provide seamless integration of required artifacts created during training of this Model Control Plane version. @@ -208,7 +250,7 @@ model_config: The process of loading data is similar to training, even the same step function is used, but with the `is_inference` flag. -But inference flow has an important difference - there is no need to fit preprocessing `Pipeline`, rather we need to reuse one fitted during training on the train set, to ensure that the model object gets the expected input. To do so we will use [ExternalArtifact](https://docs.zenml.io/user-guide/advanced-guide/pipelining-features/configure-steps-pipelines#pass-any-kind-of-data-to-your-steps) with lookup by `model_artifact_name` only to get the preprocessing pipeline fitted during the quality assurance training run. This is possible, since we configured batch inference pipeline to run inside a Model Control Plane version context. +But inference flow has an important difference - there is no need to fit preprocessing sklearn `Pipeline`, rather we need to reuse one fitted during training on the train set, to ensure that the model object gets the expected input. To do so we will use [ExternalArtifact](https://docs.zenml.io/user-guide/advanced-guide/pipelining-features/configure-steps-pipelines#pass-any-kind-of-data-to-your-steps) with lookup by `model_artifact_name` only to get the preprocessing pipeline fitted during the quality-assured training run. This is possible since we configured the batch inference pipeline to run inside a Model Control Plane version context.
Code snippet 💻 @@ -230,9 +272,9 @@ df_inference = inference_data_preprocessor( [📂 Code folder](template/steps/%7B%25%20if%20data_quality_checks%20%25%7Ddata_quality%7B%25%20endif%20%25%7D) -On the drift reporting stage we will use [standard step](https://docs.zenml.io/stacks-and-components/component-guide/data-validators/evidently#the-evidently-data-validator) `evidently_report_step` to build Evidently report to assess certain data quality metrics. `evidently_report_step` has a number of options, but for this example, we will build only `DataQualityPreset` metrics preset to get a number of NA values in reference and current datasets. +In the drift reporting stage, we will use [standard step](https://docs.zenml.io/stacks-and-components/component-guide/data-validators/evidently#the-evidently-data-validator) `evidently_report_step` to build Evidently report to assess certain data quality metrics. `evidently_report_step` has a number of options, but for this example, we will build only `DataQualityPreset` metrics preset to get a number of NA values in reference and current datasets. -We pass `dataset_trn` from training pipeline as a `reference_dataset` here. To do so we will use [ExternalArtifact](https://docs.zenml.io/user-guide/advanced-guide/pipelining-features/configure-steps-pipelines#pass-any-kind-of-data-to-your-steps) with lookup by `model_artifact_name` only to get the training dataset used during quality-assured training run. This is possible, since we configured batch inference pipeline to run inside a Model Control Plane version context. +We pass `dataset_trn` from the training pipeline as a `reference_dataset` here. To do so we will use [ExternalArtifact](https://docs.zenml.io/user-guide/advanced-guide/pipelining-features/configure-steps-pipelines#pass-any-kind-of-data-to-your-steps) with lookup by `model_artifact_name` only to get the training dataset used during quality-assured training run. This is possible since we configured the batch inference pipeline to run inside a Model Control Plane version context. After the report is built we execute another quality gate using the `drift_quality_gate` step, which assesses if a significant drift in the NA count is observed. If so, execution is stopped with an exception. @@ -242,11 +284,9 @@ You can follow [Data Validators docs](https://docs.zenml.io/stacks-and-component [📂 Code folder](template/steps/inference) -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) for reuse. - -As we performed promotion as part of the training pipeline it is very easy to fetch the needed model registry version from [Model Registry](https://docs.zenml.io/stacks-and-components/component-guide/model-registries) and deploy it for inference with [Model Deployer](https://docs.zenml.io/stacks-and-components/component-guide/model-deployers). +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. -Once the model registry version is deployed the only thing left over is to call `.predict()` on the deployment service object and put those 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) with zero effort. We additionally annotated the `predictions` output with `ArtifactConfig(overwrite=False)` to ensure that this artifact will get linked to a Model Control Plane version in a versioned fashion. 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 metadata of `model` artifact linked to the inference model version of the Model Control Plane to create a deployment service and run `.predict()` to put those 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 `ArtifactConfig(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.
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- +

-Both pipelines are inside a shared Model Control Plane model context - training pipeline creates and promotes new Model Control Plane version and inference pipeline is reading from inference Model Control Plane version. This makes those pipelines closely connected, while ensuring that only quality assured Model Control Plane versions are used to produce predictions delivered to stakeholders. +All pipelines are leveraging the Model Control Plane to bring all parts together - the training pipeline creates and promotes a new Model Control Plane version with a trained model object in it, deployment pipeline uses the inference Model Control Plane version (the one promoted during training) to create a deployment service and inference pipeline using deployment service from the inference Model Control Plane version and store back new set of predictions as a versioned data artifact for future use. This makes those pipelines closely connected while ensuring that only quality-assured Model Control Plane versions are used to produce predictions delivered to stakeholders. * [CT] Training * Load, split, and preprocess the training dataset * Search for an optimal model object architecture and tune its hyperparameters * Train the model object and evaluate its performance on the holdout set * Compare a recently trained model object with one promoted earlier - * If a recently trained model object performs better, then stage it as a new inference model in the Model Control Plane + * If a recently trained model object performs better - stage it as a new inference model object in model registry + * On success of the current model object - stage newly created Model Control Plane version as the one used for inference +* [CD] Deployment + * Deploy a new prediction service based on the model object connected to the inference Model Control Plane version. * [CD] Batch Inference * Load the inference dataset and preprocess it reusing object fitted during training * Perform data drift analysis reusing training dataset of the inference Model Control Plane version as a reference * Run predictions using a model object from the inference Model Control Plane version - * Store predictions as a versioned artifact and link it to the inference Model Control Plane version + * Store predictions as an versioned artifact and link it to the inference Model Control Plane version It showcases the core ZenML concepts for supervised ML with batch predictions: @@ -231,6 +234,27 @@ To achieve this we would retrieve the model version from the Model Control Plane By doing so we ensure that the best-performing version will be used for inference later on and ensure seamless integration of relevant artifacts from the training pipeline in the batch inference pipeline. +### [Continuous Deployment] Deployment + +The Deployment pipeline is designed to run with inference Model Control Plane version context. This ensures that we always infer only on quality-assured Model Control Plane version and provide seamless integration of required artifacts created during training of this Model Control Plane version. +This is achieved by providing this configuration in `deployer_config.yaml` used to configure our pipeline: +```yaml +model_config: + name: your_product_name + version: production +``` + +

+ +

+ +The deployment pipeline takes the model object trained earlier from the inference Model Control Plane version and produces a prediction service, which can be used by external tools or by another pipeline. In this case it will be used by a batch prediction pipeline later on. Prepared prediction service is linked to the same inference Model Control Plane version. + +``` +NOTE: In this template a prediction service is only created for local orchestrators, but you can redefine step definition to fit the needs of your infrastructure. +``` + + ### [Continuous Deployment] Batch Inference The Batch Inference pipeline is designed to run with inference Model Control Plane version context. This ensures that we always infer only on quality-assured Model Control Plane version and provide seamless integration of required artifacts created during training of this Model Control Plane version. This is achieved by providing this configuration in `inference_config.yaml` used to configure our pipeline: @@ -241,7 +265,7 @@ model_config: ```

- +

### [Continuous Deployment] Batch Inference: ETL Steps @@ -286,7 +310,12 @@ 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 metadata of `model` artifact linked to the inference model version of the Model Control Plane to create a deployment service and run `.predict()` to put those 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 `ArtifactConfig(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. This is achieved because we additionally annotated the `predictions` output with `ArtifactConfig(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. + +``` +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. +``` +
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-Both pipelines are inside a shared Model Control Plane model context - training pipeline creates and promotes new Model Control Plane version and inference pipeline is reading from inference Model Control Plane version. This makes those pipelines closely connected, while ensuring that only quality-assured Model Control Plane versions are used to produce predictions delivered to stakeholders. +All pipelines are leveraging the Model Control Plane to bring all parts together - the training pipeline creates and promotes a new Model Control Plane version with a trained model object in it, deployment pipeline uses the inference Model Control Plane version (the one promoted during training) to create a deployment service and inference pipeline using deployment service from the inference Model Control Plane version and store back new set of predictions as a versioned data artifact for future use. This makes those pipelines closely connected while ensuring that only quality-assured Model Control Plane versions are used to produce predictions delivered to stakeholders. * [CT] Training * Load, split, and preprocess the training dataset * Search for an optimal model object architecture and tune its hyperparameters @@ -117,6 +117,8 @@ Both pipelines are inside a shared Model Control Plane model context - training * Compare a recently trained model object with one promoted earlier * If a recently trained model object performs better - stage it as a new inference model object in model registry * On success of the current model object - stage newly created Model Control Plane version as the one used for inference +* [CD] Deployment + * Deploy a new prediction service based on the model object connected to the inference Model Control Plane version. * [CD] Batch Inference * Load the inference dataset and preprocess it reusing object fitted during training * Perform data drift analysis reusing training dataset of the inference Model Control Plane version as a reference @@ -143,13 +145,16 @@ The project loosely follows [the recommended ZenML project structure](https://do ``` . ├── configs # pipelines configuration files +│ ├── deployer_config.yaml # the configuration of the deployment pipeline │ ├── inference_config.yaml # the configuration of the batch inference pipeline │ └── train_config.yaml # the configuration of the training pipeline ├── pipelines # `zenml.pipeline` implementations │ ├── batch_inference.py # [CD] Batch Inference pipeline +│ ├── deployment.py # [CD] Deployment pipeline │ └── training.py # [CT] Training Pipeline ├── steps # logically grouped `zenml.steps` implementations │ ├── alerts # alert developer on pipeline status +│ ├── deployment # deploy trained model objects │ ├── data_quality # quality gates built on top of drift report │ ├── etl # ETL logic for dataset │ ├── hp_tuning # tune hyperparameters and model architectures From 42a2404170b6233bc4428343bdb1e5489be5eb76 Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Mon, 6 Nov 2023 14:23:51 +0100 Subject: [PATCH 21/28] fix naming issue --- template/pipelines/batch_inference.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/template/pipelines/batch_inference.py b/template/pipelines/batch_inference.py index 7f74df8..56ddf6d 100644 --- a/template/pipelines/batch_inference.py +++ b/template/pipelines/batch_inference.py @@ -33,7 +33,7 @@ def {{product_name}}_batch_inference(): ########## ETL stage ########## df_inference, target, _ = data_loader( random_state=ExternalArtifact( - model_artifact_pipeline_name="e2e_use_case_training", + model_artifact_pipeline_name="{{product_name}}_training", model_artifact_name="random_state", ), is_inference=True From 60a2b116ef3a7bf09b3362613688ff626b07aab2 Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Mon, 6 Nov 2023 15:03:23 +0100 Subject: [PATCH 22/28] fix template --- template/configs/train_config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/template/configs/train_config.yaml b/template/configs/train_config.yaml index 1f7280b..7751de1 100644 --- a/template/configs/train_config.yaml +++ b/template/configs/train_config.yaml @@ -19,7 +19,7 @@ steps: model_trainer: parameters: name: {{ product_name }} -{%- if hyperparameters_tuning %} +{%- if metric_compare_promotion %} compute_performance_metrics_on_current_data: parameters: target_env: {{ target_environment }} From 44932e59390c67fa6115d8fbee02bdbbf1716ed6 Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Mon, 6 Nov 2023 15:35:09 +0100 Subject: [PATCH 23/28] fix template --- template/configs/train_config.yaml | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/template/configs/train_config.yaml b/template/configs/train_config.yaml index 7751de1..517812d 100644 --- a/template/configs/train_config.yaml +++ b/template/configs/train_config.yaml @@ -24,10 +24,12 @@ steps: parameters: target_env: {{ target_environment }} promote_with_metric_compare: +{%- else %} + promote_latest_version: +{%- endif %} parameters: mlflow_model_name: {{ product_name }} target_env: {{ target_environment }} -{%- endif %} notify_on_success: parameters: notify_on_success: False From bc81c5efed6f29bb61ca68c15e13353c3b1399ed Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Tue, 7 Nov 2023 15:14:16 +0100 Subject: [PATCH 24/28] try fix tests for macos --- .github/actions/e2e_template_test/action.yml | 4 ++++ tests/test_template.py | 2 +- 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/.github/actions/e2e_template_test/action.yml b/.github/actions/e2e_template_test/action.yml index 9802e08..73cc182 100644 --- a/.github/actions/e2e_template_test/action.yml +++ b/.github/actions/e2e_template_test/action.yml @@ -20,6 +20,10 @@ inputs: required: false default: '3.9' +env: + # fork fix for macos + OBJC_DISABLE_INITIALIZE_FORK_SAFETY: YES + runs: using: "composite" steps: diff --git a/tests/test_template.py b/tests/test_template.py index ca3429b..fc6f836 100644 --- a/tests/test_template.py +++ b/tests/test_template.py @@ -78,7 +78,7 @@ def generate_and_run_project( # MLFlow Deployer not supported on Windows # MLFlow `service daemon is not running` error on MacOS - if platform.system().lower() not in ["windows", "macos", "darwin"]: + if platform.system().lower() not in ["windows"]: # run the project call = [sys.executable, "run.py"] From bc5ec6a89bef7079e4a7c5a1a84e191f4656bee5 Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Tue, 7 Nov 2023 18:37:10 +0100 Subject: [PATCH 25/28] try fix tests for macos --- .github/actions/e2e_template_test/action.yml | 4 ---- .github/workflows/ci.yml | 2 ++ 2 files changed, 2 insertions(+), 4 deletions(-) diff --git a/.github/actions/e2e_template_test/action.yml b/.github/actions/e2e_template_test/action.yml index 73cc182..9802e08 100644 --- a/.github/actions/e2e_template_test/action.yml +++ b/.github/actions/e2e_template_test/action.yml @@ -20,10 +20,6 @@ inputs: required: false default: '3.9' -env: - # fork fix for macos - OBJC_DISABLE_INITIALIZE_FORK_SAFETY: YES - runs: using: "composite" steps: diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 6fe0ba6..4d0b1e9 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -27,6 +27,8 @@ jobs: ZENML_DEBUG: true ZENML_ANALYTICS_OPT_IN: false ZENML_LOGGING_VERBOSITY: INFO + # fork fix for macos + OBJC_DISABLE_INITIALIZE_FORK_SAFETY: YES steps: - name: Check out repository code uses: actions/checkout@v3 From 7965112636ef05ca0893ad1a2f01756725ee8e7c Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Wed, 8 Nov 2023 18:31:34 +0100 Subject: [PATCH 26/28] add cool pics --- README.md | 12 ++++++------ assets/01_etl.png | Bin 36376 -> 73625 bytes assets/02_hp.png | Bin 36659 -> 84184 bytes assets/03_train.png | Bin 24775 -> 27297 bytes assets/04_promotion.png | Bin 29325 -> 62288 bytes assets/05_deployment.png | Bin 47825 -> 46098 bytes assets/06_batch_inference.png | Bin 98598 -> 184934 bytes template/.assets/00_pipelines_composition.png | Bin 368948 -> 1787317 bytes template/README.md | 2 +- 9 files changed, 7 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index a1f5aea..f991418 100644 --- a/README.md +++ b/README.md @@ -9,7 +9,7 @@ what the ZenML templates are all about. This project template is a great starting point for anyone looking to deepen their knowledge of ZenML. It consists of three pipelines with the following high-level setup:

- +

All pipelines are leveraging the Model Control Plane to bring all parts together - the training pipeline creates and promotes a new Model Control Plane version with a trained model object in it, deployment pipeline uses the inference Model Control Plane version (the one promoted during training) to create a deployment service and inference pipeline using deployment service from the inference Model Control Plane version and store back new set of predictions as a versioned data artifact for future use. This makes those pipelines closely connected while ensuring that only quality-assured Model Control Plane versions are used to produce predictions delivered to stakeholders. @@ -104,7 +104,7 @@ model_config: [📂 Code folder](template/steps/etl/)

- +

Usually at the very beginning of every training pipeline developers are acquiring data to work with in later stages. In this example, we are using [the Breast Cancer Dataset](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_breast_cancer.html) to showcase steps but avoid high computational costs. @@ -117,7 +117,7 @@ We also output `preprocess_pipeline` as an output artifact from `train_data_prep [📂 Code folder](template/steps/%7B%25%20if%20hyperparameters_tuning%20%25%7Dhp_tuning%7B%25%20endif%20%25%7D)

- +

To ensure the high quality of ML models many ML Engineers go for automated hyperparameter tuning or even automated model architecture search. In this example, we are using prepared data from ETL to spin up a search of the best model parameters for different architectures in parallel. @@ -175,7 +175,7 @@ You can find more information about the current state of [Hyperparameter Tuning [📂 Code folder](template/steps/training/)

- +

Having the best model architecture and its hyperparameters defined in the previous section makes it possible to train a quality model object. Also, model training is the right place to bring an [Experiment Tracker](https://docs.zenml.io/stacks-and-components/component-guide/experiment-trackers) into the picture - we will log all metrics and model object itself into the [Experiment Tracker](https://docs.zenml.io/stacks-and-components/component-guide/experiment-trackers), so we can register our model object in a [Model Registry](https://docs.zenml.io/stacks-and-components/component-guide/model-registries) and pass it down to a [Model Deployer](https://docs.zenml.io/stacks-and-components/component-guide/model-deployers) easily and traceable. We will use information from the active stack to make the implementation agnostic of the underlying infrastructure. @@ -223,7 +223,7 @@ def e2e_example_training(...): [📂 Code folder](template/steps/promotion/)

- +

Once the model object is trained and evaluated on meeting basic quality standards, we would like to understand whether it is good enough to beat the existing model object used in inference. This is a very important step, as promoting a weak model object as inference might have a huge negative impact. @@ -265,7 +265,7 @@ model_config: ```

- +

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All pipelines are leveraging the Model Control Plane to bring all parts together - the training pipeline creates and promotes a new Model Control Plane version with a trained model object in it, deployment pipeline uses the inference Model Control Plane version (the one promoted during training) to create a deployment service and inference pipeline using deployment service from the inference Model Control Plane version and store back new set of predictions as a versioned data artifact for future use. This makes those pipelines closely connected while ensuring that only quality-assured Model Control Plane versions are used to produce predictions delivered to stakeholders. From f275b4cf5e5cfb82db758ea1ac2e3b0ba80b1f1c Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Wed, 8 Nov 2023 18:39:04 +0100 Subject: [PATCH 27/28] add image optimizer here --- .github/workflows/image-optimizer.yml | 26 ++++++++++++++++++++++++++ 1 file changed, 26 insertions(+) create mode 100644 .github/workflows/image-optimizer.yml diff --git a/.github/workflows/image-optimizer.yml b/.github/workflows/image-optimizer.yml new file mode 100644 index 0000000..dddbd1e --- /dev/null +++ b/.github/workflows/image-optimizer.yml @@ -0,0 +1,26 @@ +name: Compress Images +on: + pull_request: + # Run Image Actions when JPG, JPEG, PNG or WebP files are added or changed. + # See https://help.github.com/en/actions/automating-your-workflow-with-github-actions/workflow-syntax-for-github-actions#onpushpull_requestpaths for reference. + paths: + - '**.jpg' + - '**.jpeg' + - '**.png' + - '**.webp' +jobs: + build: + # Only run on non-draft PRs within the same repository. + if: github.event.pull_request.head.repo.full_name == github.repository && github.event.pull_request.draft == false + name: calibreapp/image-actions + runs-on: ubuntu-latest + steps: + - name: Checkout Repo + uses: actions/checkout@v3 + + - name: Compress Images + uses: calibreapp/image-actions@main + with: + # The `GITHUB_TOKEN` is automatically generated by GitHub and scoped only to the repository that is currently running the action. By default, the action can’t update Pull Requests initiated from forked repositories. + # See https://docs.github.com/en/actions/reference/authentication-in-a-workflow and https://help.github.com/en/articles/virtual-environments-for-github-actions#token-permissions + githubToken: ${{ secrets.GITHUB_TOKEN }} From c7d318ce4cfc650b9ef56488088f6fd13b86ac8b Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Wed, 8 Nov 2023 17:39:49 +0000 Subject: [PATCH 28/28] Optimised images with calibre/image-actions --- assets/01_etl.png | Bin 73625 -> 65157 bytes assets/02_hp.png | Bin 84184 -> 72813 bytes assets/03_train.png | Bin 27297 -> 21240 bytes assets/04_promotion.png | Bin 62288 -> 49039 bytes assets/05_deployment.png | Bin 46098 -> 36657 bytes assets/06_batch_inference.png | Bin 184934 -> 165972 bytes template/.assets/00_pipelines_composition.png | Bin 1787317 -> 998415 bytes 7 files changed, 0 insertions(+), 0 deletions(-) diff 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zWNvf!0+ttVTT3|C$#b1N_a8F^G4T0r#W5@8LE1mAWfWKBa25NJNy<>}KVVuZQ`aEN zHbdE>-P+g7I2aL6RViLG;|Z+e6A~CQ{WB^`?4!nOObbAz-FPOmCO`#IMYSfVR1Q7% zYnAQz*jP$xn&T(>2*{j+X~n#D(8+eEAeh~Jw#~w^JqE()6OmchP<$WDM73}oYxwi{ z9?!_}UWP)UAL5*n?Dl(Q0k)zSM2XZMO5;sBeOy0=X-r8SL4Af*<)Yf5dZ)H From 661f60bbb6c8d4ae644ba95289210b244ecbd6da Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Mon, 6 Nov 2023 08:49:24 +0100 Subject: [PATCH 10/28] get rid of deployer --- template/pipelines/batch_inference.py | 1 + template/steps/inference/inference_predict.py | 20 ++++++------------- ...ance_metrics_on_current_data.py{% endif %} | 20 ++++++++----------- 3 files changed, 15 insertions(+), 26 deletions(-) diff --git a/template/pipelines/batch_inference.py b/template/pipelines/batch_inference.py index 2cc62b9..7f74df8 100644 --- a/template/pipelines/batch_inference.py +++ b/template/pipelines/batch_inference.py @@ -33,6 +33,7 @@ def {{product_name}}_batch_inference(): ########## ETL stage ########## df_inference, target, _ = data_loader( random_state=ExternalArtifact( + model_artifact_pipeline_name="e2e_use_case_training", model_artifact_name="random_state", ), is_inference=True diff --git a/template/steps/inference/inference_predict.py b/template/steps/inference/inference_predict.py index 0e4814b..12495eb 100644 --- a/template/steps/inference/inference_predict.py +++ b/template/steps/inference/inference_predict.py @@ -5,9 +5,6 @@ import pandas as pd from zenml import step, get_step_context -from zenml.integrations.mlflow.steps.mlflow_deployer import ( - mlflow_model_registry_deployer_step, -) from zenml.model import ArtifactConfig @@ -34,19 +31,14 @@ def inference_predict( The predictions as pandas series """ ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### - pipeline_extra = get_step_context().pipeline_run.config.extra - promoted_version = str(get_step_context().model_config._get_model_version().number) - - # deploy predictor service - deployment_service = mlflow_model_registry_deployer_step.entrypoint( - registry_model_name=pipeline_extra["mlflow_model_name"], - registry_model_version=promoted_version, - replace_existing=True, - ) + model_version = get_step_context().model_config._get_model_version() + + # get predictor + predictor = model_version.get_model_object("model").load() + # run prediction and prepare output - predictions = deployment_service.predict(request=dataset_inf) + predictions = predictor.predict(dataset_inf) predictions = pd.Series(predictions, name="predicted") - deployment_service.deprovision(force=True) ### YOUR CODE ENDS HERE ### return predictions diff --git a/template/steps/promotion/{% if metric_compare_promotion %}compute_performance_metrics_on_current_data.py{% endif %} b/template/steps/promotion/{% if metric_compare_promotion %}compute_performance_metrics_on_current_data.py{% endif %} index 836c01f..e7f8a1b 100644 --- a/template/steps/promotion/{% if metric_compare_promotion %}compute_performance_metrics_on_current_data.py{% endif %} +++ b/template/steps/promotion/{% if metric_compare_promotion %}compute_performance_metrics_on_current_data.py{% endif %} @@ -6,10 +6,8 @@ from typing_extensions import Annotated import pandas as pd from sklearn.metrics import accuracy_score from zenml import step, get_step_context -from zenml.integrations.mlflow.steps.mlflow_deployer import ( - mlflow_model_registry_deployer_step, -) from zenml.logger import get_logger +from zenml.model import ModelConfig from utils import get_model_versions @@ -50,20 +48,18 @@ def compute_performance_metrics_on_current_data( # Get model version numbers from Model Control Plane latest_version, current_version = get_model_versions(model_version, target_env) + # Get predictors + predictors = { + latest_version: model_version.get_model_object("model").load(), + current_version: ModelConfig(name=model_version.model.name,version=target_env)._get_model_version().get_model_object("model").load(), + } + if latest_version != current_version: metrics = {} for version in [latest_version,current_version]: - # deploy predictor service - deployment_service = mlflow_model_registry_deployer_step.entrypoint( - registry_model_name=pipeline_extra["mlflow_model_name"], - registry_model_version=version, - replace_existing=True, - ) - # predict and evaluate - predictions = deployment_service.predict(request=X) + predictions = predictors[version].predict(X) metrics[version]=accuracy_score(y, predictions) - deployment_service.deprovision(force=True) else: metrics = {latest_version:1.0,current_version:0.0} ### YOUR CODE ENDS HERE ### From 2202ae37ee339ed71167e64d9bf047cd2b5fd0e1 Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Mon, 6 Nov 2023 10:08:56 +0100 Subject: [PATCH 11/28] don't rely on MR versions anymore --- ...ance_metrics_on_current_data.py{% endif %} | 14 +++---- ...}promote_with_metric_compare.py{% endif %} | 14 +++---- ...ion %}promote_latest_version.py{% endif %} | 11 +++--- template/utils/__init__.py | 2 +- template/utils/get_model_versions.py | 38 ++++++++++--------- 5 files changed, 39 insertions(+), 40 deletions(-) diff --git a/template/steps/promotion/{% if metric_compare_promotion %}compute_performance_metrics_on_current_data.py{% endif %} b/template/steps/promotion/{% if metric_compare_promotion %}compute_performance_metrics_on_current_data.py{% endif %} index e7f8a1b..f69d463 100644 --- a/template/steps/promotion/{% if metric_compare_promotion %}compute_performance_metrics_on_current_data.py{% endif %} +++ b/template/steps/promotion/{% if metric_compare_promotion %}compute_performance_metrics_on_current_data.py{% endif %} @@ -7,7 +7,6 @@ import pandas as pd from sklearn.metrics import accuracy_score from zenml import step, get_step_context from zenml.logger import get_logger -from zenml.model import ModelConfig from utils import get_model_versions @@ -39,28 +38,27 @@ def compute_performance_metrics_on_current_data( ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### pipeline_extra = get_step_context().pipeline_run.config.extra target_env = pipeline_extra["target_env"].lower() - model_version = get_step_context().model_config._get_model_version() X = dataset_tst.drop(columns=["target"]) y = dataset_tst["target"].to_numpy() logger.info("Evaluating model metrics...") # Get model version numbers from Model Control Plane - latest_version, current_version = get_model_versions(model_version, target_env) + latest_version, current_version = get_model_versions(target_env) # Get predictors predictors = { - latest_version: model_version.get_model_object("model").load(), - current_version: ModelConfig(name=model_version.model.name,version=target_env)._get_model_version().get_model_object("model").load(), + latest_version.number: latest_version.get_model_object("model").load(), + current_version.number: current_version.get_model_object("model").load(), } if latest_version != current_version: metrics = {} - for version in [latest_version,current_version]: + for version in [latest_version.number,current_version.number]: # predict and evaluate predictions = predictors[version].predict(X) metrics[version]=accuracy_score(y, predictions) else: - metrics = {latest_version:1.0,current_version:0.0} + metrics = {latest_version.number:1.0,current_version.number:0.0} ### YOUR CODE ENDS HERE ### - return metrics[latest_version],metrics[current_version] + return metrics[latest_version.number],metrics[current_version.number] diff --git a/template/steps/promotion/{% if metric_compare_promotion %}promote_with_metric_compare.py{% endif %} b/template/steps/promotion/{% if metric_compare_promotion %}promote_with_metric_compare.py{% endif %} index 8a1e7a7..2563264 100644 --- a/template/steps/promotion/{% if metric_compare_promotion %}promote_with_metric_compare.py{% endif %} +++ b/template/steps/promotion/{% if metric_compare_promotion %}promote_with_metric_compare.py{% endif %} @@ -3,7 +3,7 @@ from zenml import get_step_context, step from zenml.logger import get_logger -from utils import get_model_versions, promote_in_model_registry +from utils import get_model_versions, promote_in_model_registry, get_model_registry_version logger = get_logger(__name__) @@ -43,9 +43,9 @@ def promote_with_metric_compare( should_promote = True # Get model version numbers from Model Control Plane - latest_version, current_version = get_model_versions(model_version, target_env) + latest_version, current_version = get_model_versions(target_env) - if latest_version == current_version: + if latest_version.number == current_version.number: logger.info("No current model version found - promoting latest") else: logger.info( @@ -70,14 +70,14 @@ def promote_with_metric_compare( # Promote in Model Registry promote_in_model_registry( - latest_version=latest_version, - current_version=current_version, + latest_version=get_model_registry_version(latest_version), + current_version=get_model_registry_version(current_version), model_name=pipeline_extra["mlflow_model_name"], target_env=pipeline_extra["target_env"] ) - promoted_version = latest_version + promoted_version = get_model_registry_version(latest_version) logger.info( - f"Current model version in `{pipeline_extra['target_env']}` is `{promoted_version}`" + f"Current model version in `{pipeline_extra['target_env']}` is `{promoted_version}` registered in Model Registry" ) ### YOUR CODE ENDS HERE ### diff --git a/template/steps/promotion/{% if not metric_compare_promotion %}promote_latest_version.py{% endif %} b/template/steps/promotion/{% if not metric_compare_promotion %}promote_latest_version.py{% endif %} index 0fd3561..b116b17 100644 --- a/template/steps/promotion/{% if not metric_compare_promotion %}promote_latest_version.py{% endif %} +++ b/template/steps/promotion/{% if not metric_compare_promotion %}promote_latest_version.py{% endif %} @@ -3,7 +3,7 @@ from zenml import get_step_context, step from zenml.logger import get_logger -from utils import get_model_versions, promote_in_model_registry +from utils import get_model_versions, promote_in_model_registry, get_model_registry_version logger = get_logger(__name__) @@ -19,20 +19,19 @@ def promote_latest_version() -> None: ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### pipeline_extra = get_step_context().pipeline_run.config.extra target_env = pipeline_extra["target_env"].lower() - model_version = get_step_context().model_config._get_model_version() # Get model version numbers from Model Control Plane - latest_version, current_version = get_model_versions(model_version, target_env) + latest_version, current_version = get_model_versions(target_env) logger.info(f"Promoting latest model version `{latest_version}`") # Promote in Model Control Plane - model_version.set_stage(stage=target_env, force=True) + get_step_context().model_config._get_model_version().set_stage(stage=target_env, force=True) logger.info(f"Current model version was promoted to '{target_env}'.") # Promote in Model Registry promote_in_model_registry( - latest_version=latest_version, - current_version=current_version, + latest_version=get_model_registry_version(latest_version), + current_version=get_model_registry_version(current_version), model_name=pipeline_extra["mlflow_model_name"], target_env=pipeline_extra["target_env"] ) diff --git a/template/utils/__init__.py b/template/utils/__init__.py index 9f3c19a..61581ce 100644 --- a/template/utils/__init__.py +++ b/template/utils/__init__.py @@ -2,5 +2,5 @@ from .get_model_from_config import get_model_from_config -from .get_model_versions import get_model_versions +from .get_model_versions import get_model_versions, get_model_registry_version from .promote_in_model_registry import promote_in_model_registry diff --git a/template/utils/get_model_versions.py b/template/utils/get_model_versions.py index 87130c8..59d3c63 100644 --- a/template/utils/get_model_versions.py +++ b/template/utils/get_model_versions.py @@ -2,37 +2,39 @@ from typing import Tuple from typing_extensions import Annotated +from zenml import get_step_context from zenml.model import ModelConfig from zenml.models.model_models import ModelVersionResponseModel -def _get_mr_version(model_version: ModelVersionResponseModel) -> str: - """Read metadata of linked model object to fetch version in Model Registry. - - Args: - model_version: Model Version response from Model Control Plane. - """ - return ( - model_version.get_model_object("model").metadata["model_registry_version"].value - ) - - def get_model_versions( - model_version: ModelVersionResponseModel, target_env: str -) -> Tuple[Annotated[str, "latest_version"], Annotated[str, "current_version"]]: + target_env: str, +) -> Tuple[ + Annotated[ModelVersionResponseModel, "latest_version"], + Annotated[ModelVersionResponseModel, "current_version"], +]: """Get latest and currently promoted model versions from Model Control Plane. Args: - model_version: latest model version in Model Control Plane comming from context target_env: Target stage to search for currently promoted version + + Returns: + Latest and currently promoted model versions from the Model Control Plane """ - latest_version = _get_mr_version(model_version) + latest_version = get_step_context().model_config._get_model_version() try: - current_model_version = ModelConfig( - name=model_version.model.name, version=target_env + current_version = ModelConfig( + name=latest_version.model.name, version=target_env )._get_model_version() - current_version = _get_mr_version(current_model_version) except KeyError: current_version = latest_version return latest_version, current_version + +def get_model_registry_version(model_version: ModelVersionResponseModel): + """Get model version in model registry from metadata of a model in the Model Control Plane. + + Args: + model_version: the Model Control Plane version response + """ + return model_version.get_model_object("model").metadata["model_registry_version"].value From 146a1bd437787541699f8fffa6c78f473607aea3 Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Mon, 6 Nov 2023 10:09:03 +0100 Subject: [PATCH 12/28] rename --- template/utils/{get_model_versions.py => model_versions.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename template/utils/{get_model_versions.py => model_versions.py} (100%) diff --git a/template/utils/get_model_versions.py b/template/utils/model_versions.py similarity index 100% rename from template/utils/get_model_versions.py rename to template/utils/model_versions.py From 1d9ff867059b00cef1dadbb1c1ae042cb27c71a6 Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Mon, 6 Nov 2023 10:12:00 +0100 Subject: [PATCH 13/28] rename --- template/utils/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/template/utils/__init__.py b/template/utils/__init__.py index 61581ce..ca47d21 100644 --- a/template/utils/__init__.py +++ b/template/utils/__init__.py @@ -2,5 +2,5 @@ from .get_model_from_config import get_model_from_config -from .get_model_versions import get_model_versions, get_model_registry_version +from .model_versions import get_model_versions, get_model_registry_version from .promote_in_model_registry import promote_in_model_registry From dec25ecf846fd0b0e33017fa1697a4e065e8a0e8 Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Mon, 6 Nov 2023 10:13:01 +0100 Subject: [PATCH 14/28] typo --- ...ompare_promotion %}promote_with_metric_compare.py{% endif %} | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/template/steps/promotion/{% if metric_compare_promotion %}promote_with_metric_compare.py{% endif %} b/template/steps/promotion/{% if metric_compare_promotion %}promote_with_metric_compare.py{% endif %} index 2563264..fae5813 100644 --- a/template/steps/promotion/{% if metric_compare_promotion %}promote_with_metric_compare.py{% endif %} +++ b/template/steps/promotion/{% if metric_compare_promotion %}promote_with_metric_compare.py{% endif %} @@ -62,7 +62,7 @@ def promote_with_metric_compare( ) should_promote = False - promoted_version = current_version + promoted_version = get_model_registry_version(current_version) if should_promote: # Promote in Model Control Plane model_version.set_stage(stage=target_env, force=True) From f75972f21170fa8a0a82d12340c7eaa11d15d264 Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Mon, 6 Nov 2023 10:33:52 +0100 Subject: [PATCH 15/28] use step level configs in yaml --- template/inference_config.yaml | 23 +++--- template/pipelines/training.py | 1 - template/steps/alerts/notify_on.py | 7 +- ...ance_metrics_on_current_data.py{% endif %} | 9 +-- ...}promote_with_metric_compare.py{% endif %} | 15 ++-- ...ion %}promote_latest_version.py{% endif %} | 15 ++-- template/train_config.yaml | 74 +++++++++++-------- 7 files changed, 83 insertions(+), 61 deletions(-) diff --git a/template/inference_config.yaml b/template/inference_config.yaml index 6d9d2e1..d152dde 100644 --- a/template/inference_config.yaml +++ b/template/inference_config.yaml @@ -1,5 +1,6 @@ # {% include 'template/license_header' %} +# environment configuration settings: docker: required_integrations: @@ -12,15 +13,19 @@ settings: - mlflow - sklearn - slack -extra: - mlflow_model_name: {{ product_name }} -{%- if target_environment == 'production' %} - target_env: Production -{%- else %} - target_env: Staging -{%- endif %} - notify_on_success: False - notify_on_failure: True + +# configuration of steps +steps: + notify_on_success: + parameters: + notify_on_success: False + +# configuration of the Model Control Plane model_config: name: {{ product_name }} version: {{ target_environment }} + +# pipeline level extra configurations +extra: + notify_on_failure: True + diff --git a/template/pipelines/training.py b/template/pipelines/training.py index 15aa654..b89eb96 100644 --- a/template/pipelines/training.py +++ b/template/pipelines/training.py @@ -116,7 +116,6 @@ def {{product_name}}_training( model=ExternalArtifact(value=best_model), {%- endif %} target=target, - name=pipeline_extra["mlflow_model_name"], ) model_evaluator( model=model, diff --git a/template/steps/alerts/notify_on.py b/template/steps/alerts/notify_on.py index ae183a9..c063a58 100644 --- a/template/steps/alerts/notify_on.py +++ b/template/steps/alerts/notify_on.py @@ -35,8 +35,9 @@ def notify_on_failure() -> None: @step(enable_cache=False) -def notify_on_success() -> None: +def notify_on_success( + notify_on_success: bool +) -> None: """Notifies user on pipeline success.""" - step_context = get_step_context() - if alerter and step_context.pipeline_run.config.extra["notify_on_success"]: + if alerter and notify_on_success: alerter.post(message=build_message(status="succeeded")) diff --git a/template/steps/promotion/{% if metric_compare_promotion %}compute_performance_metrics_on_current_data.py{% endif %} b/template/steps/promotion/{% if metric_compare_promotion %}compute_performance_metrics_on_current_data.py{% endif %} index f69d463..6bb12e9 100644 --- a/template/steps/promotion/{% if metric_compare_promotion %}compute_performance_metrics_on_current_data.py{% endif %} +++ b/template/steps/promotion/{% if metric_compare_promotion %}compute_performance_metrics_on_current_data.py{% endif %} @@ -5,7 +5,7 @@ from typing_extensions import Annotated import pandas as pd from sklearn.metrics import accuracy_score -from zenml import step, get_step_context +from zenml import step from zenml.logger import get_logger from utils import get_model_versions @@ -15,6 +15,7 @@ logger = get_logger(__name__) @step def compute_performance_metrics_on_current_data( dataset_tst: pd.DataFrame, + target_env: str, ) -> Tuple[Annotated[float, "latest_metric"],Annotated[float, "current_metric"]]: """Get metrics for comparison during promotion on fresh dataset. @@ -23,7 +24,8 @@ def compute_performance_metrics_on_current_data( 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 to use different input data. + In this example, the step can be configured to use different input data + and target environment stage for promotion. See the documentation for more information: https://docs.zenml.io/user-guide/advanced-guide/configure-steps-pipelines @@ -36,9 +38,6 @@ def compute_performance_metrics_on_current_data( """ ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### - pipeline_extra = get_step_context().pipeline_run.config.extra - target_env = pipeline_extra["target_env"].lower() - X = dataset_tst.drop(columns=["target"]) y = dataset_tst["target"].to_numpy() logger.info("Evaluating model metrics...") diff --git a/template/steps/promotion/{% if metric_compare_promotion %}promote_with_metric_compare.py{% endif %} b/template/steps/promotion/{% if metric_compare_promotion %}promote_with_metric_compare.py{% endif %} index fae5813..9e9dc73 100644 --- a/template/steps/promotion/{% if metric_compare_promotion %}promote_with_metric_compare.py{% endif %} +++ b/template/steps/promotion/{% if metric_compare_promotion %}promote_with_metric_compare.py{% endif %} @@ -12,6 +12,8 @@ logger = get_logger(__name__) def promote_with_metric_compare( latest_metric: float, current_metric: float, + mlflow_model_name: str, + target_env: str, )->None: """Try to promote trained model. @@ -26,7 +28,8 @@ def promote_with_metric_compare( 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 to use different input data. + In this example, the step can be configured to use precomputed model metrics + and target environment stage for promotion. See the documentation for more information: https://docs.zenml.io/user-guide/advanced-guide/configure-steps-pipelines @@ -37,9 +40,6 @@ def promote_with_metric_compare( """ ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### - pipeline_extra = get_step_context().pipeline_run.config.extra - target_env = pipeline_extra["target_env"].lower() - model_version = get_step_context().model_config._get_model_version() should_promote = True # Get model version numbers from Model Control Plane @@ -65,6 +65,7 @@ def promote_with_metric_compare( promoted_version = get_model_registry_version(current_version) if should_promote: # Promote in Model Control Plane + model_version = get_step_context().model_config._get_model_version() model_version.set_stage(stage=target_env, force=True) logger.info(f"Current model version was promoted to '{target_env}'.") @@ -72,12 +73,12 @@ def promote_with_metric_compare( promote_in_model_registry( latest_version=get_model_registry_version(latest_version), current_version=get_model_registry_version(current_version), - model_name=pipeline_extra["mlflow_model_name"], - target_env=pipeline_extra["target_env"] + model_name=mlflow_model_name, + target_env=target_env.capitalize(), ) promoted_version = get_model_registry_version(latest_version) logger.info( - f"Current model version in `{pipeline_extra['target_env']}` is `{promoted_version}` registered in Model Registry" + f"Current model version in `{target_env}` is `{promoted_version}` registered in Model Registry" ) ### YOUR CODE ENDS HERE ### diff --git a/template/steps/promotion/{% if not metric_compare_promotion %}promote_latest_version.py{% endif %} b/template/steps/promotion/{% if not metric_compare_promotion %}promote_latest_version.py{% endif %} index b116b17..17299a1 100644 --- a/template/steps/promotion/{% if not metric_compare_promotion %}promote_latest_version.py{% endif %} +++ b/template/steps/promotion/{% if not metric_compare_promotion %}promote_latest_version.py{% endif %} @@ -9,7 +9,10 @@ logger = get_logger(__name__) @step -def promote_latest_version() -> None: +def promote_latest_version( + mlflow_model_name: str, + target_env: str +) -> None: """Promote latest trained model. This is an example of a model promotion step, which promotes the @@ -17,22 +20,20 @@ def promote_latest_version() -> None: """ ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### - pipeline_extra = get_step_context().pipeline_run.config.extra - target_env = pipeline_extra["target_env"].lower() - # Get model version numbers from Model Control Plane latest_version, current_version = get_model_versions(target_env) logger.info(f"Promoting latest model version `{latest_version}`") # Promote in Model Control Plane - get_step_context().model_config._get_model_version().set_stage(stage=target_env, force=True) + model_version = get_step_context().model_config._get_model_version() + model_version.set_stage(stage=target_env, force=True) logger.info(f"Current model version was promoted to '{target_env}'.") # Promote in Model Registry promote_in_model_registry( latest_version=get_model_registry_version(latest_version), current_version=get_model_registry_version(current_version), - model_name=pipeline_extra["mlflow_model_name"], - target_env=pipeline_extra["target_env"] + model_name=mlflow_model_name, + target_env=target_env.capitalize(), ) ### YOUR CODE ENDS HERE ### diff --git a/template/train_config.yaml b/template/train_config.yaml index 1f6f088..1f7280b 100644 --- a/template/train_config.yaml +++ b/template/train_config.yaml @@ -1,5 +1,6 @@ # {% include 'template/license_header' %} +# environment configuration settings: docker: required_integrations: @@ -12,18 +13,51 @@ settings: - mlflow - sklearn - slack -extra: - mlflow_model_name: {{ product_name }} -{%- if target_environment == 'production' %} - target_env: Production -{%- else %} - target_env: Staging + +# configuration of steps +steps: + model_trainer: + parameters: + name: {{ product_name }} +{%- if hyperparameters_tuning %} + compute_performance_metrics_on_current_data: + parameters: + target_env: {{ target_environment }} + promote_with_metric_compare: + parameters: + mlflow_model_name: {{ product_name }} + target_env: {{ target_environment }} {%- endif %} - notify_on_success: False + notify_on_success: + parameters: + notify_on_success: False + +# configuration of the Model Control Plane +model_config: + name: {{ product_name }} + license: {{ open_source_license }} + description: {{ product_name }} E2E Batch Use Case + audience: All ZenML users + use_cases: | + The {{project_name}} project demonstrates how the most important steps of + the ML Production Lifecycle can be implemented in a reusable way remaining + agnostic to the underlying infrastructure, and shows how to integrate them together + into pipelines for Training and Batch Inference purposes. + ethics: No impact. + tags: + - e2e + - batch + - sklearn + - from template + - ZenML delivered + create_new_model_version: true + +# pipeline level extra configurations +extra: notify_on_failure: True {%- if hyperparameters_tuning %} - # This set contains all the models that you want to evaluate - # during hyperparameter tuning stage. + # This set contains all the model configurations that you want + # to evaluate during hyperparameter tuning stage. model_search_space: random_forest: model_package: sklearn.ensemble @@ -67,7 +101,7 @@ extra: start: 1 end: 10 {%- else %} - # This model configuration will be used for the training stage. + # This model configuration will be used for the training stage. model_configuration: model_package: sklearn.tree model_class: DecisionTreeClassifier @@ -75,22 +109,4 @@ extra: criterion: gini max_depth: 5 min_samples_leaf: 3 -{%- endif %} -model_config: - name: {{ product_name }} - license: {{ open_source_license }} - description: {{ product_name }} E2E Batch Use Case - audience: All ZenML users - use_cases: | - The {{project_name}} project demonstrates how the most important steps of - the ML Production Lifecycle can be implemented in a reusable way remaining - agnostic to the underlying infrastructure, and shows how to integrate them together - into pipelines for Training and Batch Inference purposes. - ethics: No impact. - tags: - - e2e - - batch - - sklearn - - from template - - ZenML delivered - create_new_model_version: true \ No newline at end of file +{%- endif %} \ No newline at end of file From 689ca5a3a12b26a4d00563026ed197c47f3d11f6 Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Mon, 6 Nov 2023 10:37:31 +0100 Subject: [PATCH 16/28] move configs around --- template/README.md | 37 ++++++++++---------- template/{ => configs}/inference_config.yaml | 0 template/{ => configs}/train_config.yaml | 0 template/run.py | 2 ++ 4 files changed, 21 insertions(+), 18 deletions(-) rename template/{ => configs}/inference_config.yaml (100%) rename template/{ => configs}/train_config.yaml (100%) diff --git a/template/README.md b/template/README.md index 89e0cfa..d2516b0 100644 --- a/template/README.md +++ b/template/README.md @@ -142,23 +142,24 @@ The project loosely follows [the recommended ZenML project structure](https://do ``` . -├── pipelines # `zenml.pipeline` implementations -│ ├── batch_inference.py # [CD] Batch Inference pipeline -│ └── training.py # [CT] Training Pipeline -├── steps # logically grouped `zenml.steps` implementations -│ ├── alerts # alert developer on pipeline status -│ ├── data_quality # quality gates built on top of drift report -│ ├── etl # ETL logic for dataset -│ ├── hp_tuning # tune hyperparameters and model architectures -│ ├── inference # inference on top of the model from the registry -│ ├── promotion # find if a newly trained model will be new inference -│ └── training # train and evaluate model -├── utils # helper functions +├── configs # pipelines configuration files +│ ├── inference_config.yaml # the configuration of the batch inference pipeline +│ └── train_config.yaml # the configuration of the training pipeline +├── pipelines # `zenml.pipeline` implementations +│ ├── batch_inference.py # [CD] Batch Inference pipeline +│ └── training.py # [CT] Training Pipeline +├── steps # logically grouped `zenml.steps` implementations +│ ├── alerts # alert developer on pipeline status +│ ├── data_quality # quality gates built on top of drift report +│ ├── etl # ETL logic for dataset +│ ├── hp_tuning # tune hyperparameters and model architectures +│ ├── inference # inference on top of the model from the registry +│ ├── promotion # find if a newly trained model will be new inference +│ └── training # train and evaluate model +├── utils # helper functions ├── .dockerignore -├── inference_config.yaml # the configuration of the batch inference pipeline -├── Makefile # helper scripts for quick start with integrations -├── README.md # this file -├── requirements.txt # extra Python dependencies -├── run.py # CLI tool to run pipelines on ZenML Stack -└── train_config.yaml # the configuration of the training pipeline +├── Makefile # helper scripts for quick start with integrations +├── README.md # this file +├── requirements.txt # extra Python dependencies +└── run.py # CLI tool to run pipelines on ZenML Stack ``` diff --git a/template/inference_config.yaml b/template/configs/inference_config.yaml similarity index 100% rename from template/inference_config.yaml rename to template/configs/inference_config.yaml diff --git a/template/train_config.yaml b/template/configs/train_config.yaml similarity index 100% rename from template/train_config.yaml rename to template/configs/train_config.yaml diff --git a/template/run.py b/template/run.py index 123b830..8f9eec2 100644 --- a/template/run.py +++ b/template/run.py @@ -159,6 +159,7 @@ def main( pipeline_args["config_path"] = os.path.join( os.path.dirname(os.path.realpath(__file__)), + "configs" "train_config.yaml", ) pipeline_args[ @@ -171,6 +172,7 @@ def main( run_args_inference = {} pipeline_args["config_path"] = os.path.join( os.path.dirname(os.path.realpath(__file__)), + "configs", "inference_config.yaml", ) pipeline_args[ From 67ef03550a113bbcac3a2b02a5d916497d0b97ee Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Mon, 6 Nov 2023 10:38:14 +0100 Subject: [PATCH 17/28] typo --- template/run.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/template/run.py b/template/run.py index 8f9eec2..184ae5f 100644 --- a/template/run.py +++ b/template/run.py @@ -159,7 +159,7 @@ def main( pipeline_args["config_path"] = os.path.join( os.path.dirname(os.path.realpath(__file__)), - "configs" + "configs", "train_config.yaml", ) pipeline_args[ From dcfb09c1dd13dac82c07d281374e4291c629fbcf Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Mon, 6 Nov 2023 13:20:35 +0100 Subject: [PATCH 18/28] add deployment pipeline --- template/configs/deployer_config.yaml | 31 +++++++++++ template/pipelines/__init__.py | 1 + template/pipelines/deployment.py | 22 ++++++++ template/run.py | 14 ++++- template/steps/__init__.py | 1 + template/steps/deployment/__init__.py | 4 ++ .../steps/deployment/deployment_deploy.py | 55 +++++++++++++++++++ template/steps/inference/inference_predict.py | 18 +++++- 8 files changed, 142 insertions(+), 4 deletions(-) create mode 100644 template/configs/deployer_config.yaml create mode 100644 template/pipelines/deployment.py create mode 100644 template/steps/deployment/__init__.py create mode 100644 template/steps/deployment/deployment_deploy.py diff --git a/template/configs/deployer_config.yaml b/template/configs/deployer_config.yaml new file mode 100644 index 0000000..d152dde --- /dev/null +++ b/template/configs/deployer_config.yaml @@ -0,0 +1,31 @@ +# {% include 'template/license_header' %} + +# environment configuration +settings: + docker: + required_integrations: + - aws +{%- if data_quality_checks %} + - evidently +{%- endif %} + - kubeflow + - kubernetes + - mlflow + - sklearn + - slack + +# configuration of steps +steps: + notify_on_success: + parameters: + notify_on_success: False + +# configuration of the Model Control Plane +model_config: + name: {{ product_name }} + version: {{ target_environment }} + +# pipeline level extra configurations +extra: + notify_on_failure: True + diff --git a/template/pipelines/__init__.py b/template/pipelines/__init__.py index 3d65248..ac1e395 100644 --- a/template/pipelines/__init__.py +++ b/template/pipelines/__init__.py @@ -3,3 +3,4 @@ from .batch_inference import {{product_name}}_batch_inference from .training import {{product_name}}_training +from .deployment import {{product_name}}_deployment diff --git a/template/pipelines/deployment.py b/template/pipelines/deployment.py new file mode 100644 index 0000000..aa4a21c --- /dev/null +++ b/template/pipelines/deployment.py @@ -0,0 +1,22 @@ +# {% include 'template/license_header' %} + +from steps import deployment_deploy,notify_on_success,notify_on_failure + +from zenml import pipeline + + +@pipeline(on_failure=notify_on_failure) +def {{product_name}}_deployment(): + """ + Model deployment pipeline. + + This is a pipeline deploys trained model for future inference. + """ + ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### + # Link all the steps together by calling them and passing the output + # of one step as the input of the next step. + ########## Deployment stage ########## + deployment_deploy() + + notify_on_success(after=["deployment_deploy"]) + ### YOUR CODE ENDS HERE ### diff --git a/template/run.py b/template/run.py index 184ae5f..4f40e7d 100644 --- a/template/run.py +++ b/template/run.py @@ -8,7 +8,7 @@ from zenml.artifacts.external_artifact import ExternalArtifact from zenml.logger import get_logger -from pipelines import {{product_name}}_batch_inference, {{product_name}}_training +from pipelines import {{product_name}}_batch_inference, {{product_name}}_training, {{product_name}}_deployment logger = get_logger(__name__) @@ -168,6 +168,18 @@ def main( {{product_name}}_training.with_options(**pipeline_args)(**run_args_train) logger.info("Training pipeline finished successfully!") + # Execute Deployment Pipeline + run_args_inference = {} + pipeline_args["config_path"] = os.path.join( + os.path.dirname(os.path.realpath(__file__)), + "configs", + "deployer_config.yaml", + ) + pipeline_args[ + "run_name" + ] = f"{{product_name}}_deployment_run_{dt.now().strftime('%Y_%m_%d_%H_%M_%S')}" + {{product_name}}_deployment.with_options(**pipeline_args)(**run_args_inference) + # Execute Batch Inference Pipeline run_args_inference = {} pipeline_args["config_path"] = os.path.join( diff --git a/template/steps/__init__.py b/template/steps/__init__.py index 02dc87e..5a8fcea 100644 --- a/template/steps/__init__.py +++ b/template/steps/__init__.py @@ -24,3 +24,4 @@ {%- endif %} ) from .training import model_evaluator, model_trainer +from .deployment import deployment_deploy diff --git a/template/steps/deployment/__init__.py b/template/steps/deployment/__init__.py new file mode 100644 index 0000000..56ca367 --- /dev/null +++ b/template/steps/deployment/__init__.py @@ -0,0 +1,4 @@ +# {% include 'template/license_header' %} + + +from .deployment_deploy import deployment_deploy diff --git a/template/steps/deployment/deployment_deploy.py b/template/steps/deployment/deployment_deploy.py new file mode 100644 index 0000000..bb55bfa --- /dev/null +++ b/template/steps/deployment/deployment_deploy.py @@ -0,0 +1,55 @@ +# {% include 'template/license_header' %} + + +from typing import Optional +from typing_extensions import Annotated + +from zenml import step, get_step_context +from zenml.client import Client +from zenml.logger import get_logger +from zenml.model import DeploymentArtifactConfig +from zenml.integrations.mlflow.steps.mlflow_deployer import ( + mlflow_model_registry_deployer_step, +) +from zenml.integrations.mlflow.services.mlflow_deployment import ( + MLFlowDeploymentService, +) +from utils import get_model_registry_version + +logger = get_logger(__name__) + +@step +def deployment_deploy() -> Annotated[Optional[MLFlowDeploymentService], "mlflow_deployment", DeploymentArtifactConfig()]: + """Predictions step. + + This is an example of a predictions step that takes the data 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. + In this example, the step can be configured to use different input data. + See the documentation for more information: + + https://docs.zenml.io/user-guide/advanced-guide/configure-steps-pipelines + + Args: + dataset_inf: The inference dataset. + + Returns: + The predictions as pandas series + """ + ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### + if Client().active_stack.orchestrator.flavor == "local": + model_version = get_step_context().model_config._get_model_version() + + # deploy predictor service + deployment_service = mlflow_model_registry_deployer_step.entrypoint( + registry_model_name=model_version.model.name, + registry_model_version=get_model_registry_version(model_version), + replace_existing=True, + ) + else: + logger.warning("Skipping deployment as the orchestrator is not local.") + deployment_service = None + ### YOUR CODE ENDS HERE ### + return deployment_service diff --git a/template/steps/inference/inference_predict.py b/template/steps/inference/inference_predict.py index 12495eb..6cd1a2b 100644 --- a/template/steps/inference/inference_predict.py +++ b/template/steps/inference/inference_predict.py @@ -1,12 +1,18 @@ # {% include 'template/license_header' %} +from typing import Optional from typing_extensions import Annotated import pandas as pd from zenml import step, get_step_context +from zenml.logger import get_logger from zenml.model import ArtifactConfig +from zenml.integrations.mlflow.services.mlflow_deployment import ( + MLFlowDeploymentService, +) +logger = get_logger(__name__) @step def inference_predict( @@ -34,10 +40,16 @@ def inference_predict( model_version = get_step_context().model_config._get_model_version() # get predictor - predictor = model_version.get_model_object("model").load() + predictor_service:Optional[MLFlowDeploymentService] = model_version.get_deployment("mlflow_deployment").load() + if predictor_service is not None: + # run prediction from service + predictions = predictor_service.predict(request=dataset_inf) + else: + logger.warning("Predicting from loaded model instead of deployment service as the orchestrator is not local.") + # run prediction from memory + predictor = model_version.get_model_object("model").load() + predictions = predictor.predict(dataset_inf) - # run prediction and prepare output - predictions = predictor.predict(dataset_inf) predictions = pd.Series(predictions, name="predicted") ### YOUR CODE ENDS HERE ### From 04c3701d780cff653e77acf46fad29dafaa95ff9 Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Mon, 6 Nov 2023 13:22:05 +0100 Subject: [PATCH 19/28] black a bit --- template/steps/alerts/notify_on.py | 4 +--- template/steps/deployment/deployment_deploy.py | 9 ++++++++- template/steps/inference/inference_predict.py | 10 ++++++++-- .../hp_tuning_select_best_model.py | 2 +- template/utils/model_versions.py | 7 +++++-- 5 files changed, 23 insertions(+), 9 deletions(-) diff --git a/template/steps/alerts/notify_on.py b/template/steps/alerts/notify_on.py index c063a58..d9fe9e9 100644 --- a/template/steps/alerts/notify_on.py +++ b/template/steps/alerts/notify_on.py @@ -35,9 +35,7 @@ def notify_on_failure() -> None: @step(enable_cache=False) -def notify_on_success( - notify_on_success: bool -) -> None: +def notify_on_success(notify_on_success: bool) -> None: """Notifies user on pipeline success.""" if alerter and notify_on_success: alerter.post(message=build_message(status="succeeded")) diff --git a/template/steps/deployment/deployment_deploy.py b/template/steps/deployment/deployment_deploy.py index bb55bfa..fd5d0d4 100644 --- a/template/steps/deployment/deployment_deploy.py +++ b/template/steps/deployment/deployment_deploy.py @@ -18,8 +18,15 @@ logger = get_logger(__name__) + @step -def deployment_deploy() -> Annotated[Optional[MLFlowDeploymentService], "mlflow_deployment", DeploymentArtifactConfig()]: +def deployment_deploy() -> ( + Annotated[ + Optional[MLFlowDeploymentService], + "mlflow_deployment", + DeploymentArtifactConfig(), + ] +): """Predictions step. This is an example of a predictions step that takes the data in and returns diff --git a/template/steps/inference/inference_predict.py b/template/steps/inference/inference_predict.py index 6cd1a2b..d92e4de 100644 --- a/template/steps/inference/inference_predict.py +++ b/template/steps/inference/inference_predict.py @@ -14,6 +14,7 @@ logger = get_logger(__name__) + @step def inference_predict( dataset_inf: pd.DataFrame, @@ -40,12 +41,17 @@ def inference_predict( model_version = get_step_context().model_config._get_model_version() # get predictor - predictor_service:Optional[MLFlowDeploymentService] = model_version.get_deployment("mlflow_deployment").load() + predictor_service: Optional[MLFlowDeploymentService] = model_version.get_deployment( + "mlflow_deployment" + ).load() if predictor_service is not None: # run prediction from service predictions = predictor_service.predict(request=dataset_inf) else: - logger.warning("Predicting from loaded model instead of deployment service as the orchestrator is not local.") + logger.warning( + "Predicting from loaded model instead of deployment service " + "as the orchestrator is not local." + ) # run prediction from memory predictor = model_version.get_model_object("model").load() predictions = predictor.predict(dataset_inf) diff --git a/template/steps/{% if hyperparameters_tuning %}hp_tuning{% endif %}/hp_tuning_select_best_model.py b/template/steps/{% if hyperparameters_tuning %}hp_tuning{% endif %}/hp_tuning_select_best_model.py index 89336c0..ea78e7f 100644 --- a/template/steps/{% if hyperparameters_tuning %}hp_tuning{% endif %}/hp_tuning_select_best_model.py +++ b/template/steps/{% if hyperparameters_tuning %}hp_tuning{% endif %}/hp_tuning_select_best_model.py @@ -15,7 +15,7 @@ def hp_tuning_select_best_model() -> Annotated[ClassifierMixin, "best_model"]: """Find best model across all HP tuning attempts. - This is an example of a model hyperparameter tuning step that loops + This is an example of a model hyperparameter tuning step that loops other artifacts linked to model version in Model Control Plane to find the best hyperparameter tuning output model of all according to the metric. diff --git a/template/utils/model_versions.py b/template/utils/model_versions.py index 59d3c63..083926b 100644 --- a/template/utils/model_versions.py +++ b/template/utils/model_versions.py @@ -17,7 +17,7 @@ def get_model_versions( Args: target_env: Target stage to search for currently promoted version - + Returns: Latest and currently promoted model versions from the Model Control Plane """ @@ -31,10 +31,13 @@ def get_model_versions( return latest_version, current_version + def get_model_registry_version(model_version: ModelVersionResponseModel): """Get model version in model registry from metadata of a model in the Model Control Plane. Args: model_version: the Model Control Plane version response """ - return model_version.get_model_object("model").metadata["model_registry_version"].value + return ( + model_version.get_model_object("model").metadata["model_registry_version"].value + ) From 1bf00241642cec142d07fe32cb44b9c134023e4f Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Mon, 6 Nov 2023 14:21:08 +0100 Subject: [PATCH 20/28] update readme --- README.md | 43 +++++++++++++++--- assets/05_deployment.png | Bin 0 -> 47825 bytes ...h_inference.png => 06_batch_inference.png} | Bin 89114 -> 98598 bytes template/.assets/00_pipelines_composition.png | Bin 141526 -> 368948 bytes template/README.md | 11 +++-- 5 files changed, 44 insertions(+), 10 deletions(-) create mode 100644 assets/05_deployment.png rename assets/{05_batch_inference.png => 06_batch_inference.png} (56%) diff --git a/README.md b/README.md index 610a517..a1f5aea 100644 --- a/README.md +++ b/README.md @@ -7,23 +7,26 @@ to top it all off, a simple but useful CLI. This is exactly what the ZenML templates are all about. This project template is a great starting point for anyone looking to deepen their knowledge of ZenML. -It consists of two pipelines with the following high-level setup: +It consists of three pipelines with the following high-level setup: