Distributed TensorFlow

TensorFlow is an open source software library for numerical computation using data flow graphs, and it supports distributed computing, allowing 'data parallel' or 'model parallel' on different servers. This means data scientists can now scale out distributed training to 100s of GPUs using TensorFlow.

• https://www.tensorflow.org
• https://www.tensorflow.org/deploy/distributed

Prerequisites

• Kubernetes cluster v1.8+

Chart Details

This chart will create a TensorFlow cluster, and distribute a computation graph across that cluster.

Installing the Chart

• To install the chart with the release name mnist:

  $ helm install --name mnist incubator/distributed-tensorflow

• To install with custom values via file:

  $ helm install  --values values.yaml  --name mnist  incubator/distributed-tensorflow
  

  Below is an example of the custom value file values.yaml with GPU support.

  worker:
    number: 2
    podManagementPolicy: Parallel
    image:
      repository: cheyang/distributed-tf
      tag: 1.6.0-gpu
    port: 9090
    gpuCount: 1
  
  ps:
    number: 2
    podManagementPolicy: Parallel
    image:
      repository: cheyang/distributed-tf
      tag: 1.6.0
      pullPolicy: IfNotPresent
    port: 8080
  
  # optimize for training
  hyperparams:
    batchsize: 20
    learningrate: 0.001
    trainsteps: 10000
  

Notice: you can check the details of docker image from Docker hub

Uninstalling the Chart

• To uninstall/delete the mnist deployment:

   $ helm delete mnist

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 Service Distributed Tensorflow chart and their default values.

Parameter	Description	Default
worker.image.repository	TensorFlow Worker Server's image repository	cheyang/distributed-tf
worker.image.tag	TensorFlow Worker Server's image tag	gpu
worker.image.pullPolicy	image pullPolicy for the worker	IfNotPresent
worker.gpuCount	Set the gpu to be allocated and allowed for the Pods	0
ps.image.repository	TensorFlow Parameter Server's image repository	cheyang/distributed-tf
ps.image.tag	TensorFlow Parameter Server's image tag	1.6.0-gpu
ps.image.pullPolicy	image pullPolicy for the ps	IfNotPresent