Dataproc optional Jupyter component

You can install additional components like Jupyter when you create a Dataproc cluster using the Optional components feature. This page describes the Jupyter component.

The Jupyter component is a Web-based single-user notebook for interactive data analytics and supports the JupyterLab Web UI. The Jupyter Web UI is available on port 8123 on the cluster's first master node.

Launch notebooks for multiple users. You can create a Dataproc-enabled Vertex AI Workbench instance or install the Dataproc JupyterLab plugin on a VM to to serve notebooks to multiple users.

Configure Jupyter. Jupyter can be configured by providing dataproc:jupyter cluster properties. To reduce the risk of remote code execution over unsecured notebook server APIs, the default dataproc:jupyter.listen.all.interfaces cluster property setting is false, which restricts connections to localhost (127.0.0.1) when the Component Gateway is enabled (Component Gateway activation is required when installing the Jupyter component).

The Jupyter notebook provides a Python kernel to run Spark code, and a PySpark kernel. By default, notebooks are saved in Cloud Storage in the Dataproc staging bucket, which is specified by the user or auto-created when the cluster is created. The location can be changed at cluster creation time using the dataproc:jupyter.notebook.gcs.dir cluster property.

Work with data files. You can use a Jupyter notebook to work with data files that have been uploaded to Cloud Storage. Since the Cloud Storage connector is pre-installed on a Dataproc cluster, you can reference the files directly in your notebook. Here's an example that accesses CSV files in Cloud Storage:

df = spark.read.csv("gs://bucket/path/file.csv")
df.show()

See Generic Load and Save Functions for PySpark examples.

Install Jupyter

Install the component when you create a Dataproc cluster. The Jupyter component requires activation of the Dataproc Component Gateway.

Note: Only when using image version 1.5, installation of the Jupyter component also requires installation of the Anaconda component.

Console

1. Enable the component.
   • In the Google Cloud console, open the Dataproc Create a cluster page. The Set up cluster panel is selected.
   • In the Components section:
     • Under Optional components, select the Jupyter component.
     • Under Component Gateway, select Enable component gateway (see Viewing and Accessing Component Gateway URLs).

gcloud CLI

To create a Dataproc cluster that includes the Jupyter component, use the gcloud dataproc clusters create cluster-name command with the --optional-components flag.

Latest default image version example

The following example installs the Jupyter component on a cluster that uses the latest default image version.

gcloud dataproc clusters create cluster-name \
    --optional-components=JUPYTER \
    --region=region \
    --enable-component-gateway \
    ... other flags

REST API

The Jupyter component can be installed through the Dataproc API using SoftwareConfig.Component as part of a clusters.create request.

• Set the EndpointConfig.enableHttpPortAccess property to true as part of the clusters.create request to enable connecting to the Jupyter notebook Web UI using the Component Gateway.

Open the Jupyter and JupyterLab UIs

Click the Google Cloud console Component Gateway links to open in your local browser the Jupyter notebook or JupyterLab UI running on the cluster master node.

Select "GCS" or "Local Disk" to create a new Jupyter Notebook in either location.

Attach GPUs to master and worker nodes

You can add GPUs to your cluster's master and worker nodes when using a Jupyter notebook to:

1. Preprocess data in Spark, then collect a DataFrame onto the master and run TensorFlow
2. Use Spark to orchestrate TensorFlow runs in parallel
3. Run Tensorflow-on-YARN
4. Use with other machine learning scenarios that use GPUs