SparseML v0.7.0

New Features:

• Support added for
  • PyTorch 1.9.0.
  • Python 3.9.
  • ONNX versions 1.8 - 1.10.
• PyTorch QATWrapper class to support quantization of custom modules through recipes added.
• PyTorch image classification sparse transfer learning recipe and tutorial created.
• Generic benchmarking API provided that can be overwritten for specific framework implementations.
• M-FAC (WoodFisher) pruning implemented along with relat3ed documentation, and tutorials for one-shot and training-aware: https://arxiv.org/abs/2004.14340

Changes:

• Performance sensitivity analysis tests updated to respect new information coming from a change in the DeepSparse analysis API.

Resolved Issues:

• Repeated apply calls no longer occur for PyTorch pruning masks.
• Neural Magic dependencies no longer require only matching major.minor versions (allow any bug version).
• Support added for getting nightly versions if installed for framework info and Neural Magic package versions.

Known Issues:

• Hugging Face transformers integrations with num_epochs override from recipes is not currently working. The workaround is to set the num_epochs argument to the maximum number of epochs in the recipe.