0.0.9

Version 0.0.9 (deprecated)

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I have fixed the issue where long-running ML training tasks (when retries>=3, like the one with 49 Random Forest trials) would cause a "network error". This happened because the server was not sending any data while waiting for the training to finish, leading to a timeout.

Changes Made

• 1. Asynchronous Subprocess Streaming
     Updated the ExecutorService (executor.py) to run pipeline scripts using asynchronous subprocesses.
     Instead of waiting for the script to finish, it now reads the output line by line. Each line is sent immediately to the frontend as a status update.

This keeps the network connection active and provides real-time feedback.

• 2. Verbose ML Templates
     Updated the ML templates to provide more progress information during training:
• • • AutoML: Now prints the current algorithm being optimized.
• • • Random Forest: Now prints when SHAP value calculation starts.
• • • Scikit-Learn: Enabled verbose=1 in RandomizedSearchCV to show trial progress in the logs.

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Engineer: Antonio Martínez