From 30f6bd3b3cefc14a97dbcd61dd7fc8b5ea9baa90 Mon Sep 17 00:00:00 2001 From: Safoine El khabich Date: Tue, 12 Nov 2024 16:34:12 +0100 Subject: [PATCH] vertex project --- .../.assets/cloud_mcp.png | Bin 0 -> 237746 bytes .../.assets/cloud_mcp_predictions.png | Bin 0 -> 226866 bytes .../.assets/cloud_mcp_screenshot.png | Bin 0 -> 220224 bytes .../.assets/feature_engineering_pipeline.png | Bin 0 -> 112307 bytes .../.assets/inference_pipeline.png | Bin 0 -> 257666 bytes .../.assets/pipeline_overview.png | Bin 0 -> 407607 bytes .../.assets/training_pipeline.png | Bin 0 -> 64785 bytes .../.copier-answers.yml | 8 + vertex-registry-and-deployer/.dockerignore | 2 + vertex-registry-and-deployer/LICENSE | 15 ++ vertex-registry-and-deployer/README.md | 87 ++++++++++ .../configs/inference.yaml | 16 ++ .../configs/training_sgd.yaml | 16 ++ .../pipelines/__init__.py | 19 +++ .../pipelines/inference.py | 45 ++++++ .../pipelines/training.py | 55 +++++++ vertex-registry-and-deployer/requirements.txt | 5 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100644 vertex-registry-and-deployer/LICENSE create mode 100644 vertex-registry-and-deployer/README.md create mode 100644 vertex-registry-and-deployer/configs/inference.yaml create mode 100644 vertex-registry-and-deployer/configs/training_sgd.yaml create mode 100644 vertex-registry-and-deployer/pipelines/__init__.py create mode 100644 vertex-registry-and-deployer/pipelines/inference.py create mode 100644 vertex-registry-and-deployer/pipelines/training.py create mode 100644 vertex-registry-and-deployer/requirements.txt create mode 100644 vertex-registry-and-deployer/run.py create mode 100644 vertex-registry-and-deployer/steps/__init__.py create mode 100644 vertex-registry-and-deployer/steps/model_deployer.py create mode 100644 vertex-registry-and-deployer/steps/model_promoter.py create mode 100644 vertex-registry-and-deployer/steps/model_register.py create mode 100644 vertex-registry-and-deployer/steps/model_trainer.py diff --git a/vertex-registry-and-deployer/.assets/cloud_mcp.png 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b/vertex-registry-and-deployer/run.py new file mode 100644 index 00000000..c0cf1174 --- /dev/null +++ b/vertex-registry-and-deployer/run.py @@ -0,0 +1,152 @@ +# Apache Software License 2.0 +# +# Copyright (c) ZenML GmbH 2024. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import os +from typing import Optional + +import click +import yaml +from pipelines import ( + inference, + training, +) +from zenml.client import Client +from zenml.logger import get_logger + +logger = get_logger(__name__) + + +@click.command( + help=""" +ZenML Starter project. + +Run the ZenML starter project with basic options. + +Examples: + + \b + # Run the feature engineering pipeline + python run.py --feature-pipeline + + \b + # Run the training pipeline + python run.py --training-pipeline + + \b + # Run the training pipeline with versioned artifacts + python run.py --training-pipeline --train-dataset-version-name=1 --test-dataset-version-name=1 + + \b + # Run the inference pipeline + python run.py --inference-pipeline + +""" +) +@click.option( + "--training-pipeline", + is_flag=True, + default=False, + help="Whether to run the pipeline that trains the model.", +) +@click.option( + "--inference-pipeline", + is_flag=True, + default=False, + help="Whether to run the pipeline that performs inference.", +) +@click.option( + "--no-cache", + is_flag=True, + default=False, + help="Disable caching for the pipeline run.", +) +def main( + training_pipeline: bool = False, + inference_pipeline: bool = False, + no_cache: bool = False, +): + """Main entry point for the pipeline execution. + + This entrypoint is where everything comes together: + + * configuring pipeline with the required parameters + (some of which may come from command line arguments, but most + of which comes from the YAML config files) + * launching the pipeline + + Args: + training_pipeline: Whether to run the pipeline that trains the model. + inference_pipeline: Whether to run the pipeline that performs inference. + no_cache: If `True` cache will be disabled. + """ + client = Client() + + config_folder = os.path.join( + os.path.dirname(os.path.realpath(__file__)), + "configs", + ) + + # Execute Training Pipeline + if training_pipeline: + run_args_train = {} + + # Run the SGD pipeline + pipeline_args = {} + if no_cache: + pipeline_args["enable_cache"] = False + pipeline_args["config_path"] = os.path.join( + config_folder, "training_sgd.yaml" + ) + training.with_options(**pipeline_args)(**run_args_train) + logger.info("Training pipeline with SGD finished successfully!\n\n") + + if inference_pipeline: + run_args_inference = {} + pipeline_args = {"enable_cache": False} + pipeline_args["config_path"] = os.path.join( + config_folder, "inference.yaml" + ) + + # Configure the pipeline + inference_configured = inference.with_options(**pipeline_args) + + # Fetch the production model + with open(pipeline_args["config_path"], "r") as f: + config = yaml.load(f, Loader=yaml.SafeLoader) + zenml_model = client.get_model_version( + config["model"]["name"], config["model"]["version"] + ) + preprocess_pipeline_artifact = zenml_model.get_artifact( + "preprocess_pipeline" + ) + + # Use the metadata of feature engineering pipeline artifact + # to get the random state and target column + random_state = preprocess_pipeline_artifact.run_metadata[ + "random_state" + ].value + target = preprocess_pipeline_artifact.run_metadata["target"].value + run_args_inference["random_state"] = random_state + run_args_inference["target"] = target + + # Run the pipeline + inference_configured(**run_args_inference) + logger.info("Inference pipeline finished successfully!") + + +if __name__ == "__main__": + main() diff --git a/vertex-registry-and-deployer/steps/__init__.py b/vertex-registry-and-deployer/steps/__init__.py new file mode 100644 index 00000000..b88f9536 --- /dev/null +++ b/vertex-registry-and-deployer/steps/__init__.py @@ -0,0 +1,29 @@ +# Apache Software License 2.0 +# +# Copyright (c) ZenML GmbH 2024. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from .model_register import ( + model_register, +) +from .model_promoter import ( + model_promoter, +) +from .model_trainer import ( + model_trainer, +) +from .model_deployer import ( + model_deployer, +) diff --git a/vertex-registry-and-deployer/steps/model_deployer.py b/vertex-registry-and-deployer/steps/model_deployer.py new file mode 100644 index 00000000..b06abbbb --- /dev/null +++ b/vertex-registry-and-deployer/steps/model_deployer.py @@ -0,0 +1,65 @@ +# Apache Software License 2.0 +# +# Copyright (c) ZenML GmbH 2023. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from typing import Any + +from typing_extensions import Annotated +from zenml import ArtifactConfig, get_step_context, step +from zenml.client import Client +from zenml.integrations.gcp.services.vertex_deployment import ( + VertexAIDeploymentConfig, + VertexDeploymentService, +) +from zenml.logger import get_logger + +logger = get_logger(__name__) + + +@step(enable_cache=False) +def model_deployer( + model_registry_uri: str, +) -> Annotated[ + VertexDeploymentService, ArtifactConfig(name="vertex_deployment", is_deployment_artifact=True) +]: + """Model deployer step. + + Args: + model_registry_uri: URI of the model in the model registry. + + Returns: + The deployed model service. + """ + zenml_client = Client() + current_model = get_step_context().model + model_deployer = zenml_client.active_stack.model_deployer + breakpoint() + vertex_deployment_config = VertexAIDeploymentConfig( + location="europe-west1", + name="zenml-vertex-quickstart", + model_name=current_model.name, + description="An example of deploying a model using the MLflow Model Deployer", + model_id=model_registry_uri, + ) + service = model_deployer.deploy_model( + config=vertex_deployment_config, + service_type=VertexDeploymentService.SERVICE_TYPE, + ) + + logger.info( + f"The deployed service info: {model_deployer.get_model_server_info(service)}" + ) + return service diff --git a/vertex-registry-and-deployer/steps/model_promoter.py b/vertex-registry-and-deployer/steps/model_promoter.py new file mode 100644 index 00000000..0c1851ad --- /dev/null +++ b/vertex-registry-and-deployer/steps/model_promoter.py @@ -0,0 +1,72 @@ +# Apache Software License 2.0 +# +# Copyright (c) ZenML GmbH 2024. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from zenml import get_step_context, step +from zenml.client import Client +from zenml.logger import get_logger + +logger = get_logger(__name__) + + +@step +def model_promoter(accuracy: float, stage: str = "production") -> bool: + """Model promoter step. + + This is an example of a step that conditionally promotes a model. It takes + in the accuracy of the model and the stage to promote the model to. If the + accuracy is below 80%, the model is not promoted. If it is above 80%, the + model is promoted to the stage indicated in the parameters. If there is + already a model in the indicated stage, the model with the higher accuracy + is promoted. + + Args: + accuracy: Accuracy of the model. + stage: Which stage to promote the model to. + + Returns: + Whether the model was promoted or not. + """ + is_promoted = False + + if accuracy < 0.8: + logger.info( + f"Model accuracy {accuracy*100:.2f}% is below 80% ! Not promoting model." + ) + else: + logger.info(f"Model promoted to {stage}!") + is_promoted = True + + # Get the model in the current context + current_model = get_step_context().model + + # Get the model that is in the production stage + client = Client() + try: + stage_model = client.get_model_version(current_model.name, stage) + # We compare their metrics + prod_accuracy = ( + stage_model.get_artifact("sklearn_classifier").run_metadata["test_accuracy"].value + ) + if accuracy > float(prod_accuracy): + # If current model has better metrics, we promote it + is_promoted = True + current_model.set_stage(stage, force=True) + except KeyError: + # If no such model exists, current one is promoted + is_promoted = True + current_model.set_stage(stage, force=True) + return is_promoted diff --git a/vertex-registry-and-deployer/steps/model_register.py b/vertex-registry-and-deployer/steps/model_register.py new file mode 100644 index 00000000..fecd5233 --- /dev/null +++ b/vertex-registry-and-deployer/steps/model_register.py @@ -0,0 +1,28 @@ +# model_register.py + +from typing_extensions import Annotated +from zenml import ArtifactConfig, get_step_context, step +from zenml.client import Client +from zenml.logger import get_logger + +logger = get_logger(__name__) + +@step(enable_cache=False) +def model_register() -> Annotated[str, ArtifactConfig(name="model_registry_uri")]: + """Model registration step.""" + # Get the current model from the context + current_model = get_step_context().model + + client = Client() + model_registry = client.active_stack.model_registry + model_version = model_registry.register_model_version( + name=current_model.name, + version=str(current_model.version), + model_source_uri=current_model.get_model_artifact("sklearn_classifier").uri, + description="ZenML model registered after promotion", + ) + logger.info( + f"Model version {model_version.version} registered in Model Registry" + ) + + return model_version.model_source_uri \ No newline at end of file diff --git a/vertex-registry-and-deployer/steps/model_trainer.py b/vertex-registry-and-deployer/steps/model_trainer.py new file mode 100644 index 00000000..1b154d64 --- /dev/null +++ b/vertex-registry-and-deployer/steps/model_trainer.py @@ -0,0 +1,119 @@ + +# Apache Software License 2.0 +# +# Copyright (c) ZenML GmbH 2024. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from typing import Optional, Tuple, Union + +import pandas as pd +from sklearn.base import ClassifierMixin +from sklearn.datasets import load_breast_cancer +from sklearn.linear_model import SGDClassifier +from sklearn.model_selection import train_test_split +from sklearn.pipeline import Pipeline +from sklearn.preprocessing import MinMaxScaler +from typing_extensions import Annotated +from zenml import ArtifactConfig, log_artifact_metadata, step +from zenml.logger import get_logger + +logger = get_logger(__name__) + +@step +def model_trainer( + random_state: int = 42, + test_size: float = 0.2, + drop_na: bool = True, + normalize: bool = True, + target: str = "target", + min_train_accuracy: float = 0.3, + min_test_accuracy: float = 0.3, +) -> Tuple[ + Annotated[ClassifierMixin, ArtifactConfig(name="sklearn_classifier", is_model_artifact=True)], + Annotated[float, ArtifactConfig(name="accuracy")], +]: + # Load the dataset + dataset = load_breast_cancer(as_frame=True).frame + dataset.reset_index(drop=True, inplace=True) + logger.info(f"Dataset with {len(dataset)} records loaded!") + + # Split the dataset + dataset_trn, dataset_tst = train_test_split( + dataset, + test_size=test_size, + random_state=random_state, + shuffle=True, + ) + + # Separate features and target + X_trn = dataset_trn.drop(columns=[target]) + y_trn = dataset_trn[target] + X_tst = dataset_tst.drop(columns=[target]) + y_tst = dataset_tst[target] + + # Preprocess the data + preprocess_steps = [] + if drop_na: + preprocess_steps.append(("drop_na", NADropper())) + if normalize: + preprocess_steps.append(("normalize", MinMaxScaler())) + preprocess_pipeline = Pipeline(preprocess_steps) + + X_trn = preprocess_pipeline.fit_transform(X_trn) + X_tst = preprocess_pipeline.transform(X_tst) + + # Train the model + model = SGDClassifier() + logger.info(f"Training model {model}...") + + model.fit(X_trn, y_trn) + + # Evaluate the model + trn_acc = model.score(X_trn, y_trn) + tst_acc = model.score(X_tst, y_tst) + logger.info(f"Train accuracy={trn_acc*100:.2f}%") + logger.info(f"Test accuracy={tst_acc*100:.2f}%") + + messages = [] + if trn_acc < min_train_accuracy: + messages.append( + f"Train accuracy {trn_acc*100:.2f}% is below {min_train_accuracy*100:.2f}%!" + ) + if tst_acc < min_test_accuracy: + messages.append( + f"Test accuracy {tst_acc*100:.2f}% is below {min_test_accuracy*100:.2f}%!" + ) + else: + for message in messages: + logger.warning(message) + + log_artifact_metadata( + metadata={ + "train_accuracy": float(trn_acc), + "test_accuracy": float(tst_acc), + }, + artifact_name="sklearn_classifier", + ) + + return model, tst_acc + +class NADropper: + """Support class to drop NA values in sklearn Pipeline.""" + + def fit(self, *args, **kwargs): # noqa: D102 + return self + + def transform(self, X: Union[pd.DataFrame, pd.Series]): # noqa: D102 + return X.dropna() \ No newline at end of file