bitnamicharts/pytorch repository overview

⁠Bitnami Secure Images Helm chart for PyTorch

PyTorch is a deep learning platform that accelerates the transition from research prototyping to production deployment. Bitnami image includes Torchvision for specific computer vision support.

Overview of PyTorch⁠

Trademarks: This software listing is packaged by Bitnami. The respective trademarks mentioned in the offering are owned by the respective companies, and use of them does not imply any affiliation or endorsement.

⁠ TL;DR

helm install my-release oci://REGISTRY_NAME/REPOSITORY_NAME/pytorch

⁠ Introduction

This chart bootstraps a PyTorch⁠ deployment on a Kubernetes⁠ cluster using the Helm⁠ package manager.

Python is built for full integration into Python that enables you to use it with its libraries and main packages.

⁠ Before you begin

• Kubernetes 1.23+
• Helm 3.8.0+
• PV provisioner support in the underlying infrastructure
• ReadWriteMany volumes for deployment scaling

⁠ Installing the chart

To install the chart with the release name my-release:

helm install my-release oci://REGISTRY_NAME/REPOSITORY_NAME/pytorch

These commands deploy PyTorch on the Kubernetes cluster in the default configuration. The Parameters⁠ section lists the parameters that can be configured.

⁠ Configuration and installation details

This section describes resource settings, rolling tags, loading files, pod affinity, backup, and other options.

⁠ Resource requests and limits

Bitnami charts allow setting resource requests and limits for all containers inside the chart deployment. These are inside the resources value (check parameter table). Setting requests is essential for production workloads and these should be adapted to your specific use case.

To make this process easier, the chart contains the resourcesPreset values, which automatically sets the resources section according to different presets. Check these presets in the bitnami/common chart⁠. However, in production workloads using resourcesPreset is discouraged as it may not fully adapt to your specific needs. Find more information on container resource management in the official Kubernetes documentation⁠.

⁠ Rolling VS Immutable tags⁠

It is strongly recommended to use immutable tags in a production environment. This ensures your deployment does not change automatically if the same tag is updated with a different image.

Bitnami will release a new chart updating its containers if a new version of the main container, significant changes, or critical vulnerabilities exist.

⁠ Loading your files

The PyTorch chart supports three different ways to load your files. In order of priority, they are:

1. Existing config map
2. Files under the files directory
3. Cloning a git repository

This means that if you specify a config map with your files, it won't look for the files/ directory nor the git repository.

In order to use use an existing config map, set the configMap=my-config-map parameter.

To load your files from the files/ directory you don't have to set any option. Just copy your files inside and don't specify a ConfigMap.

Finally, if you want to clone a git repository you can use those parameters:

cloneFilesFromGit.enabled=true
cloneFilesFromGit.repository=https://github.com/my-user/my-repo
cloneFilesFromGit.revision=master

⁠ Set pod affinity

This chart allows you to set your custom affinity using the affinity parameter. Find more information about pod affinity in the Kubernetes documentation⁠.

As an alternative, you can use the preset configurations for pod affinity, pod anti-affinity, and node affinity available at the bitnami/common⁠ chart. To do so, set the podAffinityPreset, podAntiAffinityPreset, or nodeAffinityPreset parameters.

⁠ Backup and restore

To back up and restore Helm chart deployments on Kubernetes, you need to back up the persistent volumes from the source deployment and attach them to a new deployment using Velero⁠, a Kubernetes backup/restore tool. Find the instructions for using Velero in this guide⁠.

⁠ FIPS parameters

The FIPS parameters only have effect if you are using images from the Bitnami Secure Images catalog⁠.

For more information on this new support, please refer to the FIPS Compliance section⁠.

⁠ Persistence

The Bitnami PyTorch⁠ image can persist data. If enabled, the persisted path is /bitnami/pytorch by default.

The chart mounts a Persistent Volume⁠ at this location. The volume is created using dynamic volume provisioning.

⁠ Adjust permissions of persistent volume mountpoint

As the image run as non-root by default, it is necessary to adjust the ownership of the persistent volume so that the container can write data into it.

By default, the chart is configured to use Kubernetes Security Context to automatically change the ownership of the volume. However, this feature does not work in all Kubernetes distributions. As an alternative, this chart supports using an initContainer to change the ownership of the volume before mounting it in the final destination.

You can enable this initContainer by setting volumePermissions.enabled to true.

⁠ Parameters

The following subsections list global, common, and component-specific parameters.

⁠ Global parameters

⁠ Common parameters

⁠ PyTorch parameters

Note: the README for this chart is longer than the DockerHub length limit of 25000, so it has been trimmed. The full README can be found at https://techdocs.broadcom.com/us/en/vmware-tanzu/bitnami-secure-images/bitnami-secure-images/services/bsi-app-doc/apps-charts-pytorch-index.html⁠