Sparseml

This is a patch release for 1.5.0 that contains the following changes:
- Latest 1.5-supported transformers datasets are incompatible with pandas 2.0. Future releases will support later datasets versions so this is to restrict pandas to < 2.0. (1634 )

This is a patch release for 1.5.0 that contains the following changes:
- Propagated `datasets_dir` argument in YOLOv8 training command to address missing args error. (1620)

New Features:
* PyTorch 1.13 support (1143)
* Enabled patch versions for torchvision 0.14.x (1557)
* YOLOv8 sparsification pipelines ([view](https://github.com/neuralmagic/sparseml/tree/main/src/sparseml/yolov8))
* 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

This is a patch release for 1.4.0 that contains the following changes:
- Support implemented for overriding ONNX inputs with static and dynamic shapes. (1476)

This is a patch release for 1.4.0 that contains the following changes:
- The auto install for transformers was failing due to the cutover from sklearn to scikit-learn package naming intermittently; this is no longer failing. (1294)
- Python sparsification loggers now on instantiation they print out the directory. (1432)
- ONNX models in YOLOv5 were improperly exported for some shapes; more shapes are now supported for dynamic models. (1442)
- SparseML YOLOv5 validation commands were creating folders under the "zoo:" name for the SparseZoo stub; folders are now created under their ids.
- Image classificaiton training script no longer fails if optional dependency tensorboard is not installed. (1456)
- Torchvision sparsification script now properly overrides final layer of torchvision native models. (1455)

This is a patch release for 1.4.0 that contains the following changes:
- Auto batch reduction for YOLOv5 on quantization aware training was failing with an OOM error. This has now been fixed and logic simplified to always scale the input batch size down by 4 and increase the gradient accumulation steps by 4 (https://github.com/neuralmagic/yolov5/commit/2c7058d75ab6798f85287d3f2e6cebf5dc5d25ec)
- Older YOLOv5 sparse models, examples, and commands were failing to start in the 1.4 release due to the removal of hyps/hyp.finetune.yaml, data/hyps/hyp.finetune.yaml, models_v5.0/yolov5s.yaml files. These have now been added back in to ensure those training commands will still work and complete successfully. (https://github.com/neuralmagic/yolov5/commit/5308d00dfdcb37742eb22c741c125eecddede435, https://github.com/neuralmagic/yolov5/commit/a1ebe6a9a2460a95ff62b88e91c56802264014a1, https://github.com/neuralmagic/yolov5/commit/88aea22397f95f7889f098a515af4ff7a9cb2ed5)
- YOLOv5 models were crashing when trying to resume training from a checkpoint using the --resume arg. This is now fixed and --resume is supported again. (https://github.com/neuralmagic/yolov5/commit/bd2bc0b193dca24ed6da403c68f3120b17cd039e)
- Hugging Face token classification training CLI no longer fails when teacher and student labels are in different order. It now pulls the token configuration from the teacher model for the student. (1400)