Neural network model repository for highly sparse and sparse-quantized models with matching sparsification recipes
SparseZoo is a constantly-growing repository of sparsified (pruned and pruned-quantized) models with matching sparsification recipes for neural networks. It simplifies and accelerates your time-to-value in building performant deep learning models with a collection of inference-optimized models and recipes to prototype from. Read more about sparsification here.
Available via API and hosted in the cloud, the SparseZoo contains both baseline and models sparsified to different degrees of inference performance vs. baseline loss recovery. Recipe-driven approaches built around sparsification algorithms allow you to take the models as given, transfer-learn from the models onto private datasets, or transfer the recipes to your architectures.
The GitHub repository contains the Python API code to handle the connection and authentication to the cloud.
This repository is tested on Python 3.6+, and Linux/Debian systems. It is recommended to install in a virtual environment to keep your system in order.
Install with pip using:
pip install sparsezooThe Python APIs respect this format enabling you to search and download models. Some code examples are given below.
from sparsezoo import Zoo
models = Zoo.search_models(domain="cv", sub_domain="classification")
print(models)from sparsezoo.models.classification import resnet_50
model = resnet_50()
model.download()
print(model.onnx_file.downloaded_path())from sparsezoo import Zoo
from sparsezoo.models.classification import resnet_50
search_model = resnet_50()
sparse_models = Zoo.search_sparse_models(search_model)
print(sparse_models)In addition to the Python APIs, a console script entry point is installed with the package sparsezoo.
This enables easy interaction straight from your console/terminal.
Download command help
sparsezoo.download -h
Download ResNet-50 Model
sparsezoo.download zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/base-none
Download pruned and quantized ResNet-50 Model
sparsezoo.download zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned_quant-moderateSearch command help
sparsezoo search -h
Searching for all classification models in the computer vision domain
sparsezoo search --domain cv --sub-domain classification
Searching for all ResNet-50 models
sparsezoo search --domain cv --sub-domain classification \
--architecture resnet_v1 --sub-architecture 50For a more in-depth read, check out SparseZoo documentation.
Official builds are hosted on PyPI
- stable: sparsezoo
- nightly (dev): sparsezoo-nightly
Additionally, more information can be found via GitHub Releases.
The project is licensed under the Apache License Version 2.0.
We appreciate contributions to the code, examples, integrations, and documentation as well as bug reports and feature requests! Learn how here.
For user help or questions about SparseZoo, sign up or log in: Deep Sparse Community Discourse Forum and/or Slack. We are growing the community member by member and happy to see you there.
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