bitnami/pytorch

Chart version: 2.1.1
Api version: v2
App version: 1.7.1
Deep learning platform that accelerates the transition from res...
application
Chart Type
Active
Status
Unknown
License
10362
Downloads
https://charts.bitnami.com/bitnami
Set me up:
helm repo add center https://repo.chartcenter.io
Install Chart:
helm install pytorch center/bitnami/pytorch
Versions (0)

PyTorch

PyTorch is a deep learning platform that accelerates the transition from research prototyping to production deployment. It is built for full integration into Python that enables you to use it with its libraries and main packages.

TL;DR

$ helm repo add bitnami https://charts.bitnami.com/bitnami
$ helm install my-release bitnami/pytorch

Introduction

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

Bitnami charts can be used with Kubeapps for deployment and management of Helm Charts in clusters. This Helm chart has been tested on top of Bitnami Kubernetes Production Runtime (BKPR). Deploy BKPR to get automated TLS certificates, logging and monitoring for your applications.

Prerequisites

  • Kubernetes 1.12+
  • Helm 3.0-beta3+
  • 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 repo add bitnami https://charts.bitnami.com/bitnami
$ helm install my-release bitnami/pytorch

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

Tip: List all releases using helm list

Uninstalling the Chart

To uninstall/delete the my-release deployment:

$ helm delete my-release

The command removes all the Kubernetes components associated with the chart and deletes the release.

Parameters

The following table lists the configurable parameters of the PyTorch chart and their default values.

Parameter Description Default
global.imageRegistry Global Docker image registry nil
global.imagePullSecrets Global Docker registry secret names as an array [] (does not add image pull secrets to deployed pods)
global.storageClass Global storage class for dynamic provisioning nil
image.registry PyTorch image registry docker.io
image.repository PyTorch image name bitnami/pytorch
image.tag PyTorch image tag {TAG_NAME}
image.pullPolicy Image pull policy IfNotPresent
image.pullSecrets Specify docker-registry secret names as an array [] (does not add image pull secrets to deployed pods)
image.debug Specify if debug logs should be enabled false
git.registry Git image registry docker.io
git.repository Git image name bitnami/git
git.tag Git image tag {TAG_NAME}
git.pullPolicy Git image pull policy IfNotPresent
git.pullSecrets Specify docker-registry secret names as an array [] (does not add image pull secrets to deployed pods)
nameOverride String to partially override common.names.fullname template with a string (will prepend the release name) nil
fullnameOverride String to fully override common.names.fullname template with a string nil
volumePermissions.enabled Enable init container that changes volume permissions in the data directory (for cases where the default k8s runAsUser and fsUser values do not work) false
volumePermissions.image.registry Init container volume-permissions image registry docker.io
volumePermissions.image.repository Init container volume-permissions image name bitnami/minideb
volumePermissions.image.tag Init container volume-permissions image tag buster
volumePermissions.image.pullPolicy Init container volume-permissions image pull policy Always
volumePermissions.resources Init container resource requests/limit nil
service.type Kubernetes service type ClusterIP
entrypoint.file Main entrypoint to your application ''
entrypoint.args Args required by your entrypoint nil
mode Run PyTorch in standalone or distributed mode (possible values: standalone, distributed) standalone
worldSize Number of nodes that will execute your code nil
port PyTorch master port 49875
configMap Config map that contains the files you want to load in PyTorch nil
cloneFilesFromGit.enabled Enable in order to download files from git repository false
cloneFilesFromGit.repository Repository that holds the files nil
cloneFilesFromGit.revision Revision from the repository to checkout master
extraEnvVars Extra environment variables to add to master and workers pods nil
podAffinityPreset Pod affinity preset. Ignored if affinity is set. Allowed values: soft or hard ""
podAntiAffinityPreset Pod anti-affinity preset. Ignored if affinity is set. Allowed values: soft or hard soft
nodeAffinityPreset.type Node affinity preset type. Ignored if affinity is set. Allowed values: soft or hard ""
nodeAffinityPreset.key Node label key to match Ignored if affinity is set. ""
nodeAffinityPreset.values Node label values to match. Ignored if affinity is set. []
affinity Affinity for pod assignment {} (evaluated as a template)
nodeSelector Node labels for pod assignment {} (evaluated as a template)
tolerations Tolerations for pod assignment [] (evaluated as a template)
resources Pod resources {}
securityContext.enabled Enable security context true
securityContext.fsGroup Group ID for the container 1001
securityContext.runAsUser User ID for the container 1001
livenessProbe.enabled Enable/disable the Liveness probe true
livenessProbe.initialDelaySeconds Delay before liveness probe is initiated 5
livenessProbe.periodSeconds How often to perform the probe 5
livenessProbe.timeoutSeconds When the probe times out 5
livenessProbe.successThreshold Minimum consecutive successes for the probe to be considered successful after having failed. 1
livenessProbe.failureThreshold Minimum consecutive failures for the probe to be considered failed after having succeeded. 5
readinessProbe.enabled Enable/disable the Readiness probe true
readinessProbe.initialDelaySeconds Delay before readiness probe is initiated 5
readinessProbe.periodSeconds How often to perform the probe 5
readinessProbe.timeoutSeconds When the probe times out 1
readinessProbe.successThreshold Minimum consecutive successes for the probe to be considered successful after having failed. 1
readinessProbe.failureThreshold Minimum consecutive failures for the probe to be considered failed after having succeeded. 5
persistence.enabled Use a PVC to persist data true
persistence.mountPath Path to mount the volume at /bitnami/pytorch
persistence.storageClass Storage class of backing PVC nil (uses alpha storage class annotation)
persistence.accessMode Use volume as ReadOnly or ReadWrite ReadWriteOnce
persistence.size Size of data volume 8Gi
persistence.annotations Persistent Volume annotations {}

Specify each parameter using the --set key=value[,key=value] argument to helm install. For example,

$ helm install my-release \
  --set mode=distributed \
  --set worldSize=4 \
    bitnami/pytorch

The above command create 4 pods for PyTorch: one master and three workers.

Alternatively, a YAML file that specifies the values for the parameters can be provided while installing the chart. For example,

$ helm install my-release -f values.yaml bitnami/pytorch

Tip: You can use the default values.yaml

Configuration and installation details

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.

Production configuration

This chart includes a values-production.yaml file where you can find some parameters oriented to production configuration in comparison to the regular values.yaml. You can use this file instead of the default one.

  • Run PyTorch in distributed mode: “`diff
  • mode: standalone
  • mode: distributed - Number of nodes that will run the code: diff - #worldSize: + worldSize: 4 “`

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

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.

Setting Pod’s affinity

This chart allows you to set your custom affinity using the affinity parameter. Find more information about Pod’s affinity in the kubernetes documentation.

As an alternative, you can use of 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.

Troubleshooting

Find more information about how to deal with common errors related to Bitnami’s Helm charts in this troubleshooting guide.

Upgrading

2.1.0

This version introduces bitnami/common, a library chart as a dependency. More documentation about this new utility could be found here. Please, make sure that you have updated the chart dependencies before executing any upgrade.

To 2.0.0

On November 13, 2020, Helm v2 support was formally finished, this major version is the result of the required changes applied to the Helm Chart to be able to incorporate the different features added in Helm v3 and to be consistent with the Helm project itself regarding the Helm v2 EOL.

What changes were introduced in this major version?

  • Previous versions of this Helm Chart use apiVersion: v1 (installable by both Helm 2 and 3), this Helm Chart was updated to apiVersion: v2 (installable by Helm 3 only). Here you can find more information about the apiVersion field.
  • The different fields present in the Chart.yaml file has been ordered alphabetically in a homogeneous way for all the Bitnami Helm Charts

Considerations when upgrading to this version

  • If you want to upgrade to this version from a previous one installed with Helm v3, you shouldn’t face any issues
  • If you want to upgrade to this version using Helm v2, this scenario is not supported as this version doesn’t support Helm v2 anymore
  • If you installed the previous version with Helm v2 and wants to upgrade to this version with Helm v3, please refer to the official Helm documentation about migrating from Helm v2 to v3

Useful links