align with core changes

[avishniakov]

zenml-io/zenml#2375

Summary by CodeRabbit

• Refactor
  • Updated the dataset loading process in pipelines for improved efficiency and maintainability.
• Documentation
  • Revised Quickstart guide to reflect changes in dataset loading methods.

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Commits

Files that changed from the base of the PR and between 3897c2e and 2390337.

Walkthrough

The recent update signifies a shift in how datasets are managed and retrieved within the project's infrastructure. By transitioning from the ExternalArtifact interface to utilizing the Client class from zenml.client, both the training function and the quickstart notebook now adopt a more streamlined approach for dataset artifact handling. This change enhances the efficiency and simplicity of loading datasets into pipelines, reflecting an effort to refine and optimize data operations.

Changes

File Path	Change Summary
template/.../training.py	Replaced ExternalArtifact with Client from zenml.client for dataset artifact retrieval.
template/quickstart.ipynb	Switched from ExternalArtifact to Client().get_artifact_version for loading datasets.

🐇✨
In the world of code, where changes abide,
A rabbit hopped along, with a joyful stride.
"From artifacts old, to clients anew,
Together we leap, to vistas in view!"
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Review Status

Actionable comments generated: 0

Configuration used: CodeRabbit UI

Commits

Files that changed from the base of the PR and between 6dbf10f and 3897c2e.

Files selected for processing (2)

• template/pipelines/training.py (2 hunks)
• template/quickstart.ipynb (4 hunks)

Additional comments: 7

template/pipelines/training.py (3)

• 7-8: The import changes correctly reflect the transition from using ExternalArtifact to the Client class for dataset artifact retrieval. This aligns with the PR objectives.
• 47-49: The instantiation of the Client class and the use of get_artifact_version for dataset retrieval are correctly implemented. However, ensure that name_id_or_prefix is the correct parameter name expected by get_artifact_version and that it can accept UUID objects directly, as the function is being passed train_dataset_id and test_dataset_id which are of type Optional[UUID].
• 44-52: > 📝 NOTE

This review was outside the diff hunks and was mapped to the diff hunk with the greatest overlap. Original lines [50-62]

The logic for model training, evaluation, and promotion within the training function appears to be unchanged and correctly implemented. Ensure that the rest of the pipeline, including model_trainer, model_evaluator, and model_promoter steps, are compatible with the new dataset artifact objects returned by get_artifact_version.

template/quickstart.ipynb (4)

• 145-145: The import statement correctly includes the Client class, aligning with the PR objectives to use the Client for dataset artifact retrieval in the notebook. This is a necessary change for the subsequent use of Client().get_artifact_version.
• 145-145: The instantiation of the Client class is correctly placed at the beginning of the relevant code cell, ensuring that the client is available for artifact retrieval operations throughout the notebook. This setup is consistent with best practices for structuring code in Jupyter notebooks.
• 631-632: The usage of client.get_artifact_version with id as the parameter for fetching dataset artifacts is correct. However, ensure that the id parameter is the correct way to reference artifacts by their UUID and that the function call matches the expected signature in the Client class. This is crucial for the correct operation of the training pipeline within the notebook.
• 984-984: The use of client.get_artifact_version for fetching the preprocessing pipeline artifact by its ID is correctly implemented. This demonstrates a consistent approach to artifact retrieval throughout the notebook, aligning with the PR objectives. Ensure that the preprocess_pipeline_id is correctly passed and utilized in subsequent steps for preprocessing and inference.