Sparseml

New Features:

* Keras sparsification beta supporting pruning and examples with Keras Applications available.
* Training and sparsification integrated with the rwightman/pytorch-image-models repository.
* Training, sparsification, and deployment integrated with the ultralytics/yolov5 repository.
* Integrations with the SparseZoo to run PyTorch and Keras code implemented with recipes directly from the zoo.
* PyTorch sparse-quantized transfer learning notebook available.
* Keras pruned ResNet models implemented.
* Groups of modifiers enabled in SparseML recipes.

Changes:

* Examples directory renamed to integrations.

Resolved Issues:

* GroupedPruningMaskCreator now able to save to PyTorch state dicts.
* Quantization-aware training compatibility issues addressed with PyTorch.
* Docs and readme corrections made for minor issues and broken links.
* Makefile no longer deletes files for docs compilation and cleaning.

Known Issues:

* None

This is a patch release for 0.1.0 that contains the following changes:

- Docs updates: tagline, overview, update to use sparsification for verbiage, fix broken links for recipes
- Flaky decorator added to some sparsity tests so if they fail due to random chance will immediately retest
- Modifier groups enabled for recipes
- DeepSparse nightly build dependencies now match on major.minor and not full version
- Serialization support for MaskedLayer in Keras added
- Support implemented for loading recipes from SparseZoo to common scripts and APIs
- Examples directory renamed to integrations to clarify function
- Rwightman integration added
- Ultralytics integration added
- PyTorch sparse-quantized transfer learning notebook added

Welcome to our initial release on GitHub! Older release notes can be [found here](https://neuralmagic.com/blog/release-notes/).

New Features:

* Keras Alpha for optimizing models using pruning added.
* PyTorch 1.7 is supported.
* PyTorch distributed supported for built-in training flows.
* Torchvision models added to PyTorch ModelRegistry class.
* MakeFile flows and utilities implemented for GitHub repo structure.

Changes:

* Software packaging updated to reflect new GitHub distribution channel, from file naming conventions to license enforcement removal.
* Migration made to use the SparseZoo package for loading pre-trained models and recipes.
* Migration made to use DeepSparse Engine for analyzing and benchmarking ONNX models.
* ONNX and ONNXRuntime dependency versions updated to include latest.

Resolved Issues:

* Infinite recursion resolved for the PyTorch ScheduledOptimizer in nested optimizer flows.
* tf2onnx folding nodes now working with Sparsify.

Known Issues:

* TensorFlow pre-trained models are not pushed currently in the SparseZoo and will fail to load.
* TensorFlow V1 is no longer being built for newer operating systems such as Ubuntu 20.04. Therefore, SparseML with TensorFlow V1 is unsupported on these operating systems as well.