Add log_table function for mlflow

[hiyuchang]

Description

As the title says.

Checklist

Please check the following items before code is ready to be reviewed.

• Code has passed all tests
• Docstrings have been added/updated in Google Style
• Documentation has been updated
• Code is ready for review

[gemini-code-assist]

Summary of Changes

Hello @hiyuchang, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request introduces a new capability to log tabular data, specifically pandas DataFrames, to MLflow. This enhancement allows for better tracking and visualization of experimental data, such as experiences or metrics, over different training steps within the MLflow ecosystem.

Highlights

• MLflow Integration: Implemented the log_table function within trinity/utils/monitor.py to enable logging of pandas DataFrames to MLflow.
• Data Preparation: Before logging, a 'step' column is added to the input DataFrame, ensuring that the logged data includes the current step information.
• Artifact Logging: The DataFrame is logged as a JSON artifact using mlflow.log_table, with the artifact file named after the provided table name.

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[gemini-code-assist]

Code Review

This pull request implements the log_table method for the MlflowMonitor, which was previously a placeholder. The change adds the current step to the provided DataFrame and logs it as a table artifact in MLflow. My review focuses on a potential issue where the input DataFrame is modified in-place, which could lead to unexpected side effects. I've provided a suggestion to address this by creating a copy of the DataFrame before logging, ensuring the function is free of side effects.