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docker pull cgr.dev/chainguard/pytorch
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Sign InA minimal, wolfi-based image for pytorch, a Python package that provides two high-level features: Tensor computation with strong GPU acceleration and Deep neural networks built on a tape-based autograd system.
The image is available on cgr.dev
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PyTorch has some prerequisites which need to be configured in the environment prior to running with GPUs. For examples, please refer to TESTING.md.
Additionally, please refer to the upstream documentation for more information on configuring and using PyTorch.
Assuming the environment prerequisites have been met, below demonstrates how to launch the container:
If your environment has connected GPUs, you can check that PyTorch has access with the following:
As a quick intro, we will use PyTorch to create a very simple deep learning model with two linear layers and an activation function. We’ll create an instance of it and ask it to report on its parameters. Running the below will fetch a model_builder.py script from the Chainguard Images repository, place it in a folder on your host machine, and run the script in a pytorch container from a volume.
You may also consider running this quickstart script based on the official PyTorch quickstart tutorial using the same approach as above.
As a place to get started, you may also use this Helm chart to get PyTorch running
Chainguard Images contain software packages that are direct or transitive dependencies. The following licenses were found in the "latest" version of this image:
Apache-2.0
BSD-3-Clause
GCC-exception-3.1
GPL-2.0-or-later
GPL-3.0-or-later
IJG
LGPL-2.1-or-later
For a complete list of licenses, please refer to this Image's SBOM.
Software license agreementA FIPS validated version of this image is available for FedRAMP compliance. STIG is included with FIPS image.