SparseML v0.12.0
·
3 commits
to release/0.12
since this release
New Features:
- SparseML recipe stages support: recipes can be chained together to enable easier prototyping with compound sparsification.
- SparseML image classification CLIs implemented to enable easy commands for training models like ResNet-50:
sparseml.image_classification.train --help
- FFCV support provided for PyTorch image classification pipelines.
- Masked language modeling CLI added for Hugging Face transformers integration:
sparseml.transformers.masked_language_modeling --help
- DistilBERT support provided for Hugging Face transformers integration.
Changes:
- Modifiers logging upgraded to standardize logging across SparseML and integrations with hyperparameter stores like Weights and Biases.
- Hugging Face Transformers integration updated to the latest state from the main upstream branch.
- Ultralytics YOLOv5 Integration updated to the latest state from the main upstream branch.
- Quantization-aware training graphs updated to enable better recovery and to provide optional support for deployment environments like TensorRT.
Resolved Issues:
- MFAC Pruning modifier multiple minor issues addressed that were preventing proper functioning in recipes leading to exceptions.
- Distillation loss for transformers integration was not calculated correctly when inputs were multidimensional.
- Minor fixes made across modifiers and transformers integration.
Known Issues:
- None