Jupyter Notebooks

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This is a static snapshot from the time of the Kubeflow 0.2 release.
For up-to-date information, see the latest version.

Bringing up a Jupyter Notebook

The jupyterhub component deployed JupyterHub and a corresponding load balancer service. You can check its status using the kubectl command line.

kubectl get svc -n=${NAMESPACE}

NAME               TYPE           CLUSTER-IP      EXTERNAL-IP   PORT(S)        AGE
...
tf-hub-0           ClusterIP      None            <none>        8000/TCP       1m
tf-hub-lb          ClusterIP      10.11.245.94    <none>        80/TCP         1m
...

By default we are using ClusterIPs for the JupyterHub UI. This can be changed to one of the following:

  • NodePort (for non-cloud) by issuing

    ks param set jupyterhub serviceType NodePort
ks apply ${KF_ENV}

  • LoadBalancer (for cloud) by issuing

    ks param set jupyterhub serviceType LoadBalancer
ks apply ${KF_ENV}

however this will leave your Jupyter notebook open to the Internet.

To connect to your Jupyter Notebook locally:

PODNAME=`kubectl get pods --namespace=${NAMESPACE} --selector="app=tf-hub" --output=template --template="{{with index .items 0}}{{.metadata.name}}{{end}}"`
kubectl port-forward --namespace=${NAMESPACE} $PODNAME 8000:8000

Then, open http://127.0.0.1:8000 in your browser.

You should see a sign in prompt.

  1. Sign in using any username/password

  2. Click the “Start My Server” button, and you will be greeted by a dialog screen.

  3. Select a CPU or GPU image from the Image dropdown menu depending on whether you are doing CPU or GPU training, or whether or not you have GPUs in your cluster. We currently offer a cpu and gpu image for each tensorflow minor version(eg: 1.4.1,1.5.1,1.6.0). Or you can type in the name of any TF image you want to run.

  4. Allocate memory, CPU, GPU, or other resources according to your need (1 CPU and 2Gi of Memory are good starting points)

    • To allocate GPUs, make sure that you have GPUs available in your cluster
    • Run the following command to check if there are any nvidia gpus available: kubectl get nodes "-o=custom-columns=NAME:.metadata.name,GPU:.status.allocatable.nvidia\.com/gpu"
    • If you have GPUs available, you can schedule your server on a GPU node by specifying the following json in Extra Resource Limits section: {"nvidia.com/gpu": "1"}

  5. Click Spawn

    • The images are 10’s of GBs in size and can take a long time to download depending on your network connection

    • You can check the status of your pod by doing

      kubectl -n ${NAMESPACE} describe pods jupyter-${USERNAME}

      • Where ${USERNAME} is the name you used to login

      • GKE users if you have IAP turned on the pod will be named differently

        jupyter-accounts-2egoogle-2ecom-3USER-40DOMAIN-2eEXT

  6. You should now be greeted with a Jupyter Notebook interface.

The image supplied above can be used for training Tensorflow models with Jupyter. The images include all the requisite plugins, including Tensorboard that you can use for rich visualizations and insights into your models.

To test the install, we can run a basic hello world (adapted from mnist_softmax.py )

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

import tensorflow as tf

x = tf.placeholder(tf.float32, [None, 784])

W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))

y = tf.nn.softmax(tf.matmul(x, W) + b)

y_ = tf.placeholder(tf.float32, [None, 10])
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))

train_step = tf.train.GradientDescentOptimizer(0.05).minimize(cross_entropy)

sess = tf.InteractiveSession()
tf.global_variables_initializer().run()

for _ in range(1000):
  batch_xs, batch_ys = mnist.train.next_batch(100)
  sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})

correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))

Paste the example into a new Python 3 Jupyter notebook and execute the code. This should result in a 0.9014 accuracy result against the test data.

Please note that when running on most cloud providers, the public IP address will be exposed to the internet and is an unsecured endpoint by default. For a production deployment with SSL and authentication, refer to the documentation.

Submitting k8s resources from Jupyter Notebook

The Jupyter Notebook pods are assigned the jupyter-notebook service account. This service account is bound to jupyter-notebook role which has namespace-scoped permissions to the following k8s resources:

  • pods
  • deployments
  • services
  • jobs
  • tfjobs
  • pytorchjobs

This means that you can directly create these k8s resources directly from your jupyter notebook. kubectl is already installed in the notebook, so you can create k8s resources running the following command in a jupyter notebook cell

!kubectl create -f myspec.yaml

Building docker images from Jupyter Notebook on GCP

If using Jupyter Notebooks on GKE, you can submit docker image builds to Cloud Build which builds your docker images and pushes them to Google Container Registry.

Activate the attached service account using

!gcloud auth activate-service-account --key-file=${GOOGLE_APPLICATION_CREDENTIALS}

If you have a Dockerfile in your current directory, you can submit a build using

!gcloud container builds submit --tag gcr.io/myproject/myimage:tag .

Advanced build documentation for docker images is available here