SparseML v1.5.0

New Features:

• PyTorch 1.13 support (#1143)
• Enabled patch versions for torchvision 0.14.x (#1557)
• YOLOv8 sparsification pipelines (view)
• Per layer distillation support for PyTorch Distillation modifier (#1272)
• Torchvision training pipelines:
  • Wandb, TensorBoard, and console logging (#1299)
  • DataParallel module (#1332)
  • Distillation (#1310)
• Product usage analytics tracking; to disable, run the command export NM_DISABLE_ANALYTICS=True (#1487)

Changes:

• Transformers and YOLOv5 integrations migrated from auto install to install from PyPI packages. Going forward, pip install sparseml[transformers] and pip install sparseml[yolov5] will need to be used.
• Error message updated when utilizing wandb loggers and wandb is not installed in the environment, telling user to pip install wandb. (#1374)
• Keras and TensorFlow tests have been removed; these are no longer actively supported pathways.
• scikit-learn now replaced with sklearn to stay current with dependency name changes. (#1294)

Resolved Issues:

• Using recipes that utilized the legacy PyTorch QuantizationModifier with DDP when restoring weights for sparse transfer no longer crashes. (#1490)
• If labels were not being set correctly when utilizing a distillation teacher different from the student with token classification pipelines, training runs would crash. (#1414)
• Q/DQ folding fixed on ONNX export for quantization nodes occurring before Softmax in transformer graphs; performance issues would result for some transformer models in DeepSparse. (#1343)
• Inaccurate metrics calculations for torchvision training pipelines led to discrepancies in top1 and top5 accuracies by ~1%. (#1341)

Known Issues:

• None