Sparsezoo

New Features:
* SparseZoo V2 UI and backend which includes better performance and user experience for discovering and using models and recipes
* SparseZoo Additions:
* YOLOv5 and YOLOv5p6 additional sparsified models ([view]( https://sparsezoo.neuralmagic.com/?architectures=yolov5))
* YOLOv8 baseline and sparsified models ([view](https://sparsezoo.neuralmagic.com/?architectures=yolov8))
* oBERTa NLP baseline and sparsified models ([view](https://sparsezoo.neuralmagic.com/?architectures=oberta))
* RoBERTa NLP baseline and sparsified models ([view](https://sparsezoo.neuralmagic.com/?architectures=roberta))
* `sparsezoo.analyze` CLI to enable easy analysis of ONNX models including performance and sparsity metrics (263) (281)
* `sparsezoo.deployment_package` CLI to enable easy packaging of models from the SparseZoo for deployments (261)
* Product usage analytics tracking; to disable, run the command `export NM_DISABLE_ANALYTICS=True` (287)

Changes:
* `ModelAnalysis.from_onnx(...)` updated to accept `ModelProto` objects rather than just ONNX files. (253)

Resolved Issues:
* None

Known Issues:
* If running on a system with no internet access, SparseZoo, SparseML, and DeepSparse CLIs/APIs are crashing. Hotfix forthcoming.

New Features:
* More performant YOLOv5s and YOLOv5l sparse quantize models
* YOLOv5 sparse quantized models for [m](https://sparsezoo.neuralmagic.com/models/cv%2Fdetection%2Fyolov5-s%2Fpytorch%2Fultralytics%2Fcoco%2Fpruned85-none), [l](https://sparsezoo.neuralmagic.com/models/cv%2Fdetection%2Fyolov5-l%2Fpytorch%2Fultralytics%2Fcoco%2Fpruned94-none), [x](https://sparsezoo.neuralmagic.com/models/cv%2Fdetection%2Fyolov5-x%2Fpytorch%2Fultralytics%2Fcoco%2Fpruned70_quant-none-vnni) versions
* YOLOv5p6 sparse quantized models for n, s, m, l, x versions
* NLP multi-label use case models for [BERT-base](https://sparsezoo.neuralmagic.com/models/nlp%2Fmultilabel_text_classification%2Fbert-large%2Fpytorch%2Fhuggingface%2Fgoemotions%2Fpruned90_quant-none), DistilBERT, and [BERT-Large](https://sparsezoo.neuralmagic.com/models/nlp%2Fmultilabel_text_classification%2Fbert-large%2Fpytorch%2Fhuggingface%2Fgoemotions%2Fpruned90_quant-none) on the [GoEmotions dataset](https://sparsezoo.neuralmagic.com/?order_by=modified&descending=true&page=1&keywords=&dataset=goemotions)
* Initial oBERTa models (RoBERTa style models) for SQuAD and GLUE tasks

Changes:
* None

Resolved Issues:
* Due to a breaking change in NumPy, its version was pinned to <=1.21.6 to prevent crashes from happening across SparseZoo, SparseML, and DeepSparse.

Known Issues:
* None

This is a patch release for 1.3.0 that contains the following changes:

- NumPy version pinned to <=1.21.6 to avoid deprecation warning/index errors in pipelines.

New Features:
* BERT models added for the GoEmotions multi-label dataset.
* BERT models added for SQuAD 2.0 dataset.
* oBERTa base models added for GLUE datasets.
* YOLOv5 and YOLOv5p6 models added for transfer learning.

Changes:
* Minimum Python version changed to 3.7.
* Benchmarking and accuracy metrics for a model propagated to the root Python class.

Resolved Issues:
* None

Known Issues:
* None

New Features:
* SparseZoo ONNX analysis API added to enable easy model analysis for sparsity, quantization, flops, parameters, and more.
* BERT document classification models added, trained on the IMDB dataset.

Changes:
* `Tokenizer_config.json` added as required file for transformers models.
* Minimum Python version changed to 3.7 as 3.6 as reached EOL.

Resolved Issues:
* SparseZoo README updated to reflect new APIs and flows that were released with 1.1 release.

Known Issues:
* None

New Features:
* Python 3.10 supported.
* SparseZoo APIs refactored, focusing on better and easier-to-use functionality. The model class is now the core interface and works the same for both SparseZoo stubs as well as local folders and enables storing models from the SparseZoo in any local folder.
* Model Analysis API added, enabling detailed layer and operator information for operations, pruning, quantization, parameter counts, and more.
* BERT-base-cased models added.
* BioBERT models added.
* Quantized BERT-base MLM models added for support for Information Retrieval pipelines.
* Compound (structured pruning, unstructured pruning, quantization) ResNet-50 models added.

Changes:
* None

Resolved Issues:
* None

Known Issues:
* None