Chainguard Container for pytorch-fips

A minimal, wolfi-based FIPS compliant 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 Containers are regularly-updated, secure-by-default container images.

Download this Container Image

For those with access, this container image is available on cgr.dev:

docker pull cgr.dev/ORGANIZATION/pytorch-fips:latest

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

Compatibility Notes

Chainguard’s PyTorch FIPS container is a FIPS-enabled container image similar to the pytorch/pytorch image, with several key differences:

• Chainguard images are using Wolfi Linux distribution, the pytorch/pytorch images are based on Ubuntu 22.04
• Chainguard images are running as nonroot user with home in /home/nonroot directory, while the pytorch/pytorch images are running as root user
• Chainguard images do not ship a full shell by default and their entrypoint is Python, while pytorch/pytorch uses Bash as the entrypoint (NOTE: Chainguard also provides -dev images that have a full shell and entrypoint can be set to /bin/sh or /bin/bash for such images)

Compatibility Package

If you're running an older version of CUDA not supported by the container, you have the option to install CUDA compatibility packages.

First, install the compatibility package for your specific host OS using the NVIDIA package repository. Make sure to install the package specific to your current version of CUDA.

Once the compatibility package has been installed, you can run the container in compatibility mode:

docker run --rm -it \
 -e LD_LIBRARY_PATH="/usr/local/cuda-12.9/compat" \
 cgr.dev/$ORGANIZATION/pytorch-fips

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:

Set the following environment variable to the name of your organization or manually replace $ORGANIZATION in the following command:

ORGANIZATION=my-organization

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

Testing GPU Access

If your environment has connected GPUs, you can check that PyTorch has access. First, set the following environment variable to the name of your organization or manually replace $ORGANIZATION in the following command:

ORGANIZATION=my-organization

docker run --rm -it --gpus all cgr.dev/$ORGANIZATION/pytorch-fips: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, place it in a folder on your host machine, and run the script in a PyTorch FIPS container from a volume.

First, set the following environment variable to the name of your organization or manually replace $ORGANIZATION in the following command:

ORGANIZATION=my-organization

mkdir -p pytorch-test &&\
 curl https://raw.githubusercontent.com/chainguard-dev/pytorch-getting-started/refs/heads/main/model_builder.py > pytorch-test/model_builder.py &&\
 docker run --rm -it -v "$PWD/pytorch-test:/tmp/pytorch-test" cgr.dev/$ORGANIZATION/pytorch-fips:latest /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

ORGANIZATION=my-organization

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

What are Chainguard Containers?

Chainguard Containers are minimal container images that are secure by default.

In many cases, the Chainguard Containers tagged as :latest contain only an open-source application and its runtime dependencies. These minimal container images typically do not contain a shell or package manager. Chainguard Containers are built with Wolfi, our Linux undistro designed to produce container images that meet the requirements of a more secure software supply chain.

The main features of Chainguard Containers include:

• Minimal design, without unnecessary software bloat
• Daily builds to ensure container images are up-to-date with available security patches
• High quality build-time SBOMs attesting to the provenance of all artifacts within the image
• Verifiable signatures provided by Sigstore
• Reproducible builds with Cosign and apko (read more about reproducibility)

For cases where you need container images with shells and package managers to build or debug, most Chainguard Containers come paired with a -dev variant.

Although the -dev container image variants have similar security features as their more minimal versions, they feature additional software that is typically not necessary in production environments. We recommend using multi-stage builds to leverage the -dev variants, copying application artifacts into a final minimal container that offers a reduced attack surface that won’t allow package installations or logins.

Learn More

To better understand how to work with Chainguard Containers, please visit Chainguard Academy and Chainguard Courses.

In addition to Containers, Chainguard offers VMs and Libraries. Contact Chainguard to access additional products.

Trademarks

This software listing is packaged by Chainguard. The trademarks set forth in this offering are owned by their respective companies, and use of them does not imply any affiliation, sponsorship, or endorsement by such companies.