Remove .value from metadata access

[AlexejPenner]

This relates to zenml-io/zenml#3096

Summary by CodeRabbit

• New Features

  • Enhanced the quickstart notebook with structured markdown cells for better understanding of the MLOps process using ZenML.
  • Introduced new code snippets for loading models and making predictions.
  • Added a comparison mechanism in the model promotion process to evaluate model accuracy before promotion.

• Bug Fixes

  • Improved handling of metadata in the inference pipeline for better clarity and functionality.

• Documentation

  • Updated explanations and added visual aids in the quickstart notebook to support learning objectives.

• Refactor

  • Simplified the extraction of metadata values in the run script for improved readability and efficiency.

[coderabbitai]

Walkthrough

The changes encompass modifications to three files: quickstart.ipynb, run.py, and model_promoter.py. The notebook has been enhanced with structured markdown cells, improved code clarity, and additional visual aids related to the ZenML framework. The run.py script has been updated to simplify metadata access, while maintaining its command-line interface. Lastly, the model_promoter.py file introduces a new mechanism for model accuracy comparison during promotion, including error handling for existing models.

Changes

File	Change Summary
template/quickstart.ipynb	Enhanced clarity and functionality with added markdown cells, improved metadata handling in the inference function, and new visual aids for learning.
template/run.py	Simplified access to random_state and target values in metadata; no changes to command-line interface or execution logic.
template/steps/model_promoter.py	Added logic to compare current model accuracy with an existing model; implemented error handling for model retrieval and updated promotion logic.

Sequence Diagram(s)

sequenceDiagram
    participant User
    participant Notebook
    participant RunScript
    participant ModelPromoter

    User->>Notebook: Open quickstart.ipynb
    Notebook->>RunScript: Execute inference pipeline
    RunScript->>RunScript: Access metadata
    RunScript->>ModelPromoter: Check model accuracy
    ModelPromoter->>ModelPromoter: Compare accuracies
    ModelPromoter-->>RunScript: Return promotion status
    RunScript-->>Notebook: Provide results

Loading

🐇 "In the notebook, knowledge blooms,
With markdown and images, it dispels the glooms.
Run scripts now simpler, no layers to peel,
Models promoted with accuracy's seal.
A hop into ZenML, where learning takes flight,
Join the journey, explore day and night!" 🌟

---

📜 Recent review details

Configuration used: CodeRabbit UI
Review profile: CHILL

📥 Commits

Files that changed from the base of the PR and between 4dbef59 and acb4f47.

📒 Files selected for processing (3)

• template/quickstart.ipynb (1 hunks)
• template/run.py (1 hunks)
• template/steps/model_promoter.py (0 hunks)

💤 Files with no reviewable changes (1)

• template/steps/model_promoter.py

🧰 Additional context used

🔇 Additional comments (4)

template/run.py (1)

210-211: LGTM! Simplified metadata access.

The changes simplify the access to random_state and target from the preprocess_pipeline_artifact.run_metadata. This is consistent with the PR objective of removing .value from metadata access.

To ensure consistency across the codebase, please run the following script to check for any remaining instances of .value being used to access metadata:

If the script returns any results, those instances should be updated to match this new pattern of direct metadata access.

template/quickstart.ipynb (3)

Line range hint 979-1001: LGTM: Inference pipeline structure is well-organized.

The overall structure of the inference function is well-organized and follows good practices:

• It separates concerns by having distinct steps for data loading, preprocessing, and prediction.
• It correctly uses client.get_artifact_version to retrieve the preprocessing pipeline, which helps maintain consistency between training and inference.

However, as noted in the previous comment, the use of hardcoded values for random_state and target is a point of concern.

---

Line range hint 1-1001: Overall: High-quality tutorial with a minor concern in the inference function.

This notebook provides an excellent introduction to ZenML and MLOps concepts, covering data loading, feature engineering, model training, and inference. The code is well-structured and follows good practices throughout.

The main point of concern is in the inference function, where dynamic metadata retrieval was replaced with hardcoded values. While this might simplify the immediate implementation, it could potentially limit the flexibility and reusability of the code.

Next steps:

1. Investigate why the metadata retrieval was commented out. Were there issues with this approach?
2. If possible, consider reverting to dynamic metadata retrieval to maintain flexibility.
3. If hardcoded values must be used, add comments explaining the rationale and any plans to make this more dynamic in the future.
4. Consider adding error handling or default values in case the metadata retrieval fails, rather than relying solely on hardcoded values.

To ensure the notebook runs correctly with these changes, please test the entire workflow from start to finish, paying special attention to the inference step.

---

985-986: Consider keeping dynamic metadata retrieval.

The change from dynamically retrieving random_state and target to using hardcoded values might reduce the flexibility of the code. While this simplifies the immediate implementation, it could make the code less adaptable to different scenarios or datasets.

Could you provide the rationale for this change? If there were issues with the metadata retrieval, it might be worth investigating and fixing those instead of using hardcoded values.

To verify if the metadata retrieval was working correctly before, we can run the following script:

This script will help us understand if the metadata was available and correctly structured in the artifact.

---

Thank you for using CodeRabbit. We offer it for free to the OSS community and would appreciate your support in helping us grow. If you find it useful, would you consider giving us a shout-out on your favorite social media?

❤️ Share

• X
• Mastodon
• Reddit
• LinkedIn

🪧 Tips

Chat

There are 3 ways to chat with CodeRabbit:

• Review comments: Directly reply to a review comment made by CodeRabbit. Example:
  • I pushed a fix in commit <commit_id>, please review it.
  • Generate unit testing code for this file.
  • Open a follow-up GitHub issue for this discussion.
• Files and specific lines of code (under the "Files changed" tab): Tag @coderabbitai in a new review comment at the desired location with your query. Examples:
  • @coderabbitai generate unit testing code for this file.
  • @coderabbitai modularize this function.
• PR comments: Tag @coderabbitai in a new PR comment to ask questions about the PR branch. For the best results, please provide a very specific query, as very limited context is provided in this mode. Examples:
  • @coderabbitai gather interesting stats about this repository and render them as a table. Additionally, render a pie chart showing the language distribution in the codebase.
  • @coderabbitai read src/utils.ts and generate unit testing code.
  • @coderabbitai read the files in the src/scheduler package and generate a class diagram using mermaid and a README in the markdown format.
  • @coderabbitai help me debug CodeRabbit configuration file.

Note: Be mindful of the bot's finite context window. It's strongly recommended to break down tasks such as reading entire modules into smaller chunks. For a focused discussion, use review comments to chat about specific files and their changes, instead of using the PR comments.

CodeRabbit Commands (Invoked using PR comments)

• @coderabbitai pause to pause the reviews on a PR.
• @coderabbitai resume to resume the paused reviews.
• @coderabbitai review to trigger an incremental review. This is useful when automatic reviews are disabled for the repository.
• @coderabbitai full review to do a full review from scratch and review all the files again.
• @coderabbitai summary to regenerate the summary of the PR.
• @coderabbitai resolve resolve all the CodeRabbit review comments.
• @coderabbitai configuration to show the current CodeRabbit configuration for the repository.
• @coderabbitai help to get help.

Other keywords and placeholders

• Add @coderabbitai ignore anywhere in the PR description to prevent this PR from being reviewed.
• Add @coderabbitai summary to generate the high-level summary at a specific location in the PR description.
• Add @coderabbitai anywhere in the PR title to generate the title automatically.

CodeRabbit Configuration File (.coderabbit.yaml)

• You can programmatically configure CodeRabbit by adding a .coderabbit.yaml file to the root of your repository.
• Please see the configuration documentation for more information.
• If your editor has YAML language server enabled, you can add the path at the top of this file to enable auto-completion and validation: # yaml-language-server: $schema=https://coderabbit.ai/integrations/schema.v2.json

Documentation and Community

• Visit our Documentation for detailed information on how to use CodeRabbit.
• Join our Discord Community to get help, request features, and share feedback.
• Follow us on X/Twitter for updates and announcements.