charts/bitnami/pytorch

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 3.1.0
• 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 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

Global parameters

Name	Description	Value
global.imageRegistry	Global Docker image registry	""
global.imagePullSecrets	Global Docker registry secret names as an array	[]
global.storageClass	Global StorageClass for Persistent Volume(s)	""

Common parameters

Name	Description	Value
nameOverride	String to partially override common.names.fullname template (will maintain the release name)	""
fullnameOverride	String to fully override common.names.fullname template	""
extraDeploy	Array of extra objects to deploy with the release	[]

PyTorch parameters

Name	Description	Value
image.registry	PyTorch image registry	docker.io
image.repository	PyTorch image repository	bitnami/pytorch
image.tag	PyTorch image tag (immutable tags are recommended)	1.9.1-debian-10-r6
image.pullPolicy	Image pull policy	IfNotPresent
image.pullSecrets	Specify docker-registry secret names as an array	[]
image.debug	Specify if debug logs should be enabled	false
git.registry	Git image registry	docker.io
git.repository	Git image repository	bitnami/git
git.tag	Git image tag (immutable tags are recommended)	2.33.0-debian-10-r70
git.pullPolicy	Git image pull policy	IfNotPresent
git.pullSecrets	Specify docker-registry secret names as an array	[]
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 repository	bitnami/bitnami-shell
volumePermissions.image.tag	Init container volume-permissions image tag (immutable tags are recommended)	10-debian-10-r233
volumePermissions.image.pullPolicy	Init container volume-permissions image pull policy	IfNotPresent
volumePermissions.image.pullSecrets	Specify docker-registry secret names as an array	[]
volumePermissions.resources.limits	The resources limits for the container	{}
volumePermissions.resources.requests	The requested resources for the container	{}
service.type	Kubernetes service type	ClusterIP
service.port	Scheduler Service port	49875
service.nodePort	Specify the nodePort value for the LoadBalancer and NodePort service types.	""
service.annotations	Provide any additional annotations which may be required. This can be used to	{}
entrypoint.file	Main entrypoint to your application	""
entrypoint.args	Args required by your entrypoint	[]
mode	Run PyTorch in standalone or distributed mode. Possible values: standalone, distributed	standalone
hostAliases	Deployment pod host aliases	[]
worldSize	Number of nodes that will run the code	""
port	PyTorch master port. MASTER_PORT will be set to this value	49875
configMap	Config map that contains the files you want to load in PyTorch	""
cloneFilesFromGit.enabled	Enable in order to download files from git repository	false
cloneFilesFromGit.repository	Repository that holds the files	""
cloneFilesFromGit.revision	Revision from the repository to checkout	""
cloneFilesFromGit.extraVolumeMounts	Add extra volume mounts for the Git container	[]
extraEnvVars	Additional environment variables	[]
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.	[]
securityContext.enabled	Enable security context	true
securityContext.fsGroup	Group ID for the container	1001
securityContext.runAsUser	User ID for the container	1001
resources.limits	The resources limits for the container	{}
resources.requests	The requested resources for the container	{}
livenessProbe.enabled	Enable livenessProbe	true
livenessProbe.initialDelaySeconds	Initial delay seconds for livenessProbe	5
livenessProbe.periodSeconds	Period seconds for livenessProbe	5
livenessProbe.timeoutSeconds	Timeout seconds for livenessProbe	5
livenessProbe.failureThreshold	Failure threshold for livenessProbe	5
livenessProbe.successThreshold	Success threshold for livenessProbe	1
readinessProbe.enabled	Enable readinessProbe	true
readinessProbe.initialDelaySeconds	Initial delay seconds for readinessProbe	5
readinessProbe.periodSeconds	Period seconds for readinessProbe	5
readinessProbe.timeoutSeconds	Timeout seconds for readinessProbe	1
readinessProbe.failureThreshold	Failure threshold for readinessProbe	5
readinessProbe.successThreshold	Success threshold for readinessProbe	1
persistence.enabled	Use a Persistent Volume Claim to persist data	true
persistence.mountPath	Data volume mount path	/bitnami/pytorch
persistence.accessModes	Persistent Volume Access Mode	["ReadWriteOnce"]
persistence.size	Size of data volume	8Gi
persistence.storageClass	Persistent Volume Storage Class	""
persistence.annotations	Persistent Volume Claim annotations	{}
extraVolumes	Array to add extra volumes (evaluated as a template)	[]
extraVolumeMounts	Array to add extra mounts (normally used with extraVolumes, evaluated as a template)	[]

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.

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

To 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

• https://docs.bitnami.com/tutorials/resolve-helm2-helm3-post-migration-issues/
• https://helm.sh/docs/topics/v2_v3_migration/
• https://helm.sh/blog/migrate-from-helm-v2-to-helm-v3/