tool icon  SparseZoo

Neural network model repository for highly sparse and sparse-quantized models with matching sparsification recipes

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Overview

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.

SparseZoo Flow

Installation

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 sparsezoo

Quick Tour

Python APIS

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)

Common 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)

Console Scripts

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.

Downloading

Download command help

```shell script sparsezoo.download -h

<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

```shell script sparsezoo.download zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned_quant-moderate

#### Searching

Search command help

```shell script
sparsezoo search -h


Searching for all classification models in the computer vision domain

```shell script sparsezoo search –domain cv –sub-domain classification

<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.

Resources

Learning More

Release History

Official builds are hosted on PyPI

Additionally, more information can be found via GitHub Releases.

License

The project is licensed under the Apache License Version 2.0.

Community

Contribute

We appreciate contributions to the code, examples, integrations, and documentation as well as bug reports and feature requests! Learn how here.

Join

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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