EdgeTrain v0.2.0: Scoring and prioritization for dynamic parameter updates

This version introduces a refined adaptive training strategy with a constant pruning ratio. Key updates:

• Score Calculation: This version now computes an accuracy score and a memory score based on resource usage and model performance.
• Parameter Prioritization: Accuracy and memory scores are weighted according to default or user-defined priority weighting schemes to idenfity a priority order for parameter adjustment. Only the top priority paramater is adjusted in each epoch.
  • Batch size priority is weighted by memory usage.
  • Learning rate priority is inversely weighted by accuracy improvement (i.e. increases if accuracy stagnates).
• Fixed Pruning Ratio: Pruning is constant and is stripped at the end.
• Code Quality Improvements: Added pre-commit hooks and CI linting for consistency.

Bug Fix: Circular Import Issue

Fixed a circular import issue in the package to allow for proper imports at package level rather than module level.

EdgeTrain v0.1.0-alpha: Initial Release

EdgeTrain is a utility package designed for machine learning training with limited resources. This initial alpha release includes functionality to dynamically adjust batch sizes and learning rates based on available CPU/GPU resources, helping optimize training processes on edge devices.