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 use 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 3.6-3.9, 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 sparsezoo
The Python APIs respect this format enabling you to search and download models. Some code examples are given below.
Searching the Zoo
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())
Searching Optimized Versions
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)
Users can specify the directory where models (temporarily during download) and its required credentials will be saved in your working machine.
SPARSEZOO_MODELS_PATH is the path where the downloaded models will be saved temporarily. Default
SPARSEZOO_CREDENTIALS_PATH is the path where
credentials.yaml will be saved. Default
In addition to the Python APIs, a console script entry point is installed with the package
This enables easy interaction straight from your console/terminal.
Download command help
<br>Download ResNet-50 Model ```shell script sparsezoo.download zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/base-none
Download pruned and quantized ResNet-50 Model
#### Searching Search command help ```shell script sparsezoo search -h
Searching for all classification models in the computer vision domain
<br>Searching for all ResNet-50 models ```shell script sparsezoo search --domain cv --sub-domain classification \ --architecture resnet_v1 --sub-architecture 50
For a more in-depth read, check out SparseZoo documentation.
Official builds are hosted on PyPI
Additionally, more information can be found via GitHub Releases.
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 to our **Deep Sparse Community Slack**. We are growing the community member by member and happy to see you there. Bugs, feature requests, or additional questions can also be posted to our GitHub Issue Queue.
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