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Chainguard Image for pytorch

A 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.

Chainguard Images are regularly-updated, minimal container images with low-to-zero CVEs.

Download this Image

This image is available on cgr.dev:

docker pull cgr.dev/ORGANIZATION/pytorch:latest

Be sure to replace the ORGANIZATION placeholder with the name used for your organization's private repository within the Chainguard registry.

Running pytorch

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:

docker run --rm -i -t \
    --privileged \
    --gpus all \
    cgr.dev/chainguard/pytorch:latest

Testing GPU Access

If your environment has connected GPUs, you can check that PyTorch has access with the following:

docker run --rm -it --gpus all cgr.dev/chainguard/pytorch:latest
Python 3.11.9 (main, Apr  2 2024, 15:40:32) [GCC 13.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import torch
>>> torch.cuda.is_available()
True
>>> torch.cuda.device_count()
1
>>> torch.cuda.get_device_name(0)
'Tesla V100-SXM2-16GB'

Testing PyTorch

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.

mkdir pytorch-test &&\
 curl https://raw.githubusercontent.com/chainguard-images/images/main/images/pytorch/model_builder.py > pytorch-test/model_builder.py &&\
 docker run --rm -it -v "$PWD/pytorch-test:/tmp/pytorch-test" --gpus all cgr.dev/chainguard/pytorch:latest -c "python /tmp/pytorch-test/model_builder.py"

You may also consider running this quickstart script based on the official PyTorch quickstart tutorial using the same approach as above.

Using Helm charts

As a place to get started, you may also use this Helm chart to get PyTorch running

  helm install pytorch \
  --namespace pytorch-space --create-namespace  \
  --set image.registry="cgr.dev" \
  --set image.repository="chainguard/pytorch" \
  --set image.tag=latest \
  --set containerSecurityContext.runAsUser=0 \
  --set containerSecurityContext.runAsNonRoot=false \
  --set containerSecurityContext.allowPrivilegeEscalation=true \
  --wait oci://registry-1.docker.io/bitnamicharts/pytorch

Contact Support

If you have a Zendesk account (typically set up for you by your Customer Success Manager) you can reach out to Chainguard's Customer Success team through our Zendesk portal.

What are Chainguard Images?

Chainguard Images are a collection of container images designed for security and minimalism.

Many Chainguard Images are distroless; they contain only an open-source application and its runtime dependencies. These images do not even contain a shell or package manager. Chainguard Images are built with Wolfi, our Linux undistro designed to produce container images that meet the requirements of a secure software supply chain.

The main features of Chainguard Images include:

-dev Variants

As mentioned previously, Chainguard’s distroless Images have no shell or package manager by default. This is great for security, but sometimes you need these things, especially in builder images. For those cases, most (but not all) Chainguard Images come paired with a -dev variant which does include a shell and package manager.

Although the -dev image variants have similar security features as their distroless versions, such as complete SBOMs and signatures, they feature additional software that is typically not necessary in production environments. The general recommendation is to use the -dev variants only to build the application and then copy all application artifacts into a distroless image, which will result in a final container image that has a minimal attack surface and won’t allow package installations or logins.

That being said, it’s worth noting that -dev variants of Chainguard Images are completely fine to run in production environments. After all, the -dev variants are still more secure than many popular container images based on fully-featured operating systems such as Debian and Ubuntu since they carry less software, follow a more frequent patch cadence, and offer attestations for what they include.

Learn More

To better understand how to work with Chainguard Images, we encourage you to visit Chainguard Academy, our documentation and education platform.

Licenses

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 agreement

Compliance

A FIPS validated version of this image is available for FedRAMP compliance. STIG is included with FIPS image.


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