deprecated-helm-charts/stable/tensorflow-notebook/README.md

⚠️ Repo Archive Notice

As of Nov 13, 2020, charts in this repo will no longer be updated. For more information, see the Helm Charts Deprecation and Archive Notice, and Update.

TensorFlow Notebook Helm Chart

TensorFlow is an open source software library for numerical computation using data flow graphs, and tensorboard is the tool visualizing TensorFlow programs. Using Jupyter notebook to get into TensorFlow and develop models is the great way for data scientist. With these three tools you are able to start your machine learning work in two minutes.

• https://www.tensorflow.org
• https://www.tensorflow.org/programmers_guide/summaries_and_tensorboard
• http://jupyter.org/

DEPRECATION NOTICE

This chart is deprecated and no longer supported.

Prerequisites

• Kubernetes cluster v1.8+

Chart Details

This chart will deploy the followings:

• Jupyter Notebook with TensorFlow
• Tensorboard

Installing the Chart

• To install the chart with the release name notebook:

  $ helm install --name notebook stable/tensorflow-notebook
  

• To install with custom values via file :

  $ helm install  --values values.yaml  --name notebook  stable/tensorflow-notebook
  

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

  jupyter:
    image:
      repository: tensorflow/tensorflow
      tag: 1.6.0-devel-gpu
      pullPolicy: IfNotPresent
    password: tensorflow
    resources:
      limits:
        nvidia.com/gpu: 1
    requests:
        nvidia.com/gpu: 1
  tensorboard: 
    image:   
      repository: tensorflow/tensorflow
      tag: 1.6.0-devel
      pullPolicy: IfNotPresent
  service:
    type: LoadBalancer
  

Run TensorFlow Example tensorboard_basic.ipynb

Notice: you should set the log_path /output/training_logs

Check the TensorBoard

Uninstalling the Chart

• To uninstall/delete the notebook deployment:

  $ helm delete notebook
  

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 Tensorflow Development chart and their default values.

Parameter	Description	Default
jupyter.image.repository	TensorFlow Development image repository	tensorflow/tensorflow
jupyter.image.tag	TensorFlow Development image tag	1.5.0-devel-gpu
jupyter.password	The password to access jupyter	mytest
jupyter.image.pullPolicy	image pullPolicy for the jupyter	IfNotPresent
tensorboard.image.repository	TensorFlow Development image repository	tensorflow/tensorflow
tensorboard.image.tag	TensorFlow Development image tag	1.5.0-devel-gpu
tensorboard.image.pullPolicy	image pullPolicy for the tensorboard	IfNotPresent
resources	Set the resource to be allocated and allowed for the Pods	{}
service.type	service type	LoadBalancer