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README.md

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 install 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 1.8+ with Beta APIs enabled
  • PV provisioner support in the underlying infrastructure

Installing the Chart

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

$ helm install --name my-release bitnami/pytorch

The command deploys PyTorch on the Kubernetes cluster in the default configuration. The configuration 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.

Configuration

The following table lists the configurable parameters of the MinIO 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)
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 pytorch.fullname template with a string (will prepend the release name) nil
fullnameOverride String to fully override pytorch.fullname template with a string 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
nodeSelector Node labels for pod assignment {}
tolerations Toleration labels for pod assignment []
affinity Map of node/pod affinities {}
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 --name 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 --name my-release -f values.yaml bitnami/pytorch

Tip: You can use the default values.yaml

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.

$ helm install --name my-release -f ./values-production.yaml bitnami/pytorch
  • Run PyTorch in distributed mode:
- mode: standalone
+ mode: distributed
  • Number of nodes that will run the code:
- #worldSize:
+ worldSize: 4

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:

$ helm install --name my-release \
  --set configMap=my-config-map \
  bitnami/pytorch

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:

$ helm install --name my-release \
  bitnami/pytorch

Finally, if you want to clone a git repository:

$ helm install --name my-release \
  --set cloneFilesFromGit.enabled=true \
  --set cloneFilesFromGit.repository=https://github.com/my-user/my-repo \
  --set cloneFilesFromGit.revision=master \
  bitnami/pytorch

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.

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