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DeepSparse
Sparsity-aware neural network inference engine for GPU-class performance on CPUs.
SparseML
Open-source libraries for applying sparsification recipes to neural networks with a few lines of code.
SparseZoo
Open-source model repository for sparse and sparse-quantized models.
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Natural Language Processing (NLP)
Get started with Sparse NLP use cases. Apply your data and redeploy on CPUs with ease.
Compute Vision
Get started with sparse computer vision use cases. Apply your data and deploy on CPUs with ease.
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Installation

The Neural Magic Platform contains several products: DeepSparse (available in two editions, Community and Enterprise), SparseML, and SparseZoo.

Each package is installed with PyPI. It is recommended to install in a virtual environment to encapsulate your local environment.

Installing the Neural Magic Platform

To begin using the Neural Magic Platform, run the following command, which installs standard setups for deployment with DeepSparse and model training/optimization with SparseML:

pip install deepsparse[server] sparseml[torch,torchvision]

Now, you are ready to install one of the Neural Magic products.

Installing Products

DeepSparse Community
Install DeepSparse Community for performant inference on CPUs in development or testing environments.
DeepSparse Enterprise
Install DeepSparse Enterprise for performant inference on CPUs in production deployments.
SparseML
Install SparseML to apply SOTA sparsification algorithms to models easily.
SparseZoo
Install SparseZoo to download pre-sparsified models and recipes.
Quick Tour
DeepSparse Community Installation