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Files

T

Szymon Piotr KrasuskiandKubernetes Prow Robot 19b4b54a39 [stable/distributed-tensorflow] Add custom variable to set number of training steps. (#16436 )

* Add custom variable to set number of trainign steps.

By default script run in cheyang/distributed-tensorflow container
runs with flag train_steps set to 0 which means that
script will be running inifitely,

However, it's to possible to set this flag to fixed number
which will make script to run only passed number of training
steps and stop.

This change adds custom variable in hyperparams that lets user set
custom value for training steps.

Signed-off-by: Szymon <krasuski.szymon.piotr@gmail.com>

* Bump chart version

Signed-off-by: Szymon <krasuski.szymon.piotr@gmail.com>

2019-10-21 08:51:37 -07:00

templates

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.helmignore

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Chart.yaml

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distributed-tf.jpg

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README.md

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values.yaml

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README.md

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.

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`