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Local JupyterLab connecting to Databricks via SSH

This package allows to connect to a remote Databricks cluster from a locally running Jupyter Lab:

1 Prerequisites

  1. Operating System

    Either Macos or Linux. Windows is currently not supported

  2. Anaconda installation

    A recent version of Anaconda with Python >= 3.5 The tool conda must be newer then 4.7.5

  3. Databricks CLI

    To install Databricks CLI and configure profile(s) for your cluster(s), please refer to AWS / Azure

    Whenever $PROFILE is used in this documentation, it refers to a valid Databricks CLI profile name, stored in a shell environment variable.

  4. SSH access to the Databricks cluster

    Configure your Databricks clusters to allow ssh access, see Configure SSH access

    Only clusters with valid ssh configuration are visible to databrickslabs_jupyterlab.

  5. Databricks Runtime

    The project has been tested with Databricks runtimes 5.5 LTS, 6.0, 6.1 and 6.2 with Python 3 (both ML and standard runtime). Note, it does not support Python 2.7

2 Installation

  1. Install databrickslabs_jupyterlab Create a new conda environment and install databrickslabs_jupyterlab with the following commands:

    (base)$ conda create -n db-jlab python=3.6
(base)$ conda activate db-jlab
(db-jlab)$ pip install --upgrade databrickslabs-jupyterlab==1.0.9

    The prefix (db-jlab)$ for all command examples in this document assumes that the databrickslabs_jupyterlab conda enviromnent db-jlab is activated.

  2. Bootstrap databrickslabs_jupyterlab Bootstrap the environment for databrickslabs_jupyterlab with the following command, which will finish with showing the usage:

    (db-jlab)$ databrickslabs-jupyterlab -b

3 Getting started

Ensure, ssh access is correctly configured, see Configure SSH access

3.1 Starting Jupyter Lab

  1. Create a kernel specification In the terminal, create a jupyter kernel specification for a Databricks CLI profile $PROFILE and start Jupyter Lab with the following command:

    (db-jlab)$ databrickslabs-jupyterlab $PROFILE -k

  2. Start Jupyter Lab Start Jupyter Lab using databrickslabs-jupyterlab

    (db-jlab)$ databrickslabs-jupyterlab $PROFILE -l -c

    The command with -l is a save version for the standard command to start Jupyter Lab (jupyter lab) that ensures that the kernel specificiation is updated. If -c is provided it also copies the PAT to the clipboard for the next step in the notebook.

    A new kernel is available in the kernel change menu (see here for an explanation of the kernel name structure)

3.2 Using Spark in the Notebook

  1. Check whether the notebook is properly connected

    When the notebook successfully connected to the cluster, the status bar at the bottom of Jupyter lab should show ...|Idle [Connected]:

    kernel ready

    If this is not the case, see Troubleshooting

  2. Get a remote Spark Session in the notebook

    To connect to the remote Spark context, enter the following two lines into a notebook cell:

    from databrickslabs_jupyterlab.connect import dbcontext
dbcontext()

    This will request you to add the token copied to clipboard above:

    Get Token

    After pressing Enter, you will see

    Spark Success

    Note: databrickslabs-jupyterlab $PROFILE -c let's you quickly copy the token again to the clipboard so that you can simply paste the token to the input box.

  3. Test the Spark access

    To check the remote Spark connection, enter the following lines into a notebook cell:

    import socket
print(socket.gethostname(), is_remote())

a = sc.range(10000).repartition(100).map(lambda x: x).sum()
print(a)

    It will show that the kernel is actually running remotely and the hostname of the driver. The second part quickly smoke tests a Spark job.

    Get Token

Success: Your local Jupyter Lab is successfully contected to the remote Databricks cluster

4 Advanced topics

5 Project Support

Please note that all projects in the /databrickslabs github account are provided for your exploration only, and are not formally supported by Databricks with Service Level Agreements (SLAs). They are provided AS-IS and we do not make any guarantees of any kind. Please do not submit a support ticket relating to any issues arising from the use of these projects.

Any issues discovered through the use of this project should be filed as GitHub Issues on the Repo. They will be reviewed as time permits, but there are no formal SLAs for support.

6 Test notebooks

To work with the test notebooks in ./examples the remote cluster needs to have the following libraries installed:

  • mlflow==1.0.0
  • spark-sklearn