Set up a Datalab VM instance

Set up a Datalab VM instance

Datalab is scheduled to be deprecated on August 11, 2022. Vertex AI Workbench provides a notebook-based environment that offers capabilities beyond Datalab. We recommend that you use Vertex AI Workbench for new projects and migrate your Datalab notebooks to Vertex AI Workbench. For more information, see Deprecation information. To get help migrating Datalab projects to Vertex AI Workbench see Get help.

Objective: This page shows you how to use the datalab command line tool to set up and open Google Cloud Datalab.

Before you begin

1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

2. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   Go to project selector

3. Make sure that billing is enabled for your Cloud project. Learn how to check if billing is enabled on a project.

4. Enable the Google Compute Engine and Cloud Source Repositories APIs.

   Enable the APIs

5. Install and initialize the Google Cloud CLI.

6. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   Go to project selector

7. Make sure that billing is enabled for your Cloud project. Learn how to check if billing is enabled on a project.

8. Enable the Google Compute Engine and Cloud Source Repositories APIs.

   Enable the APIs

9. Install and initialize the Google Cloud CLI.

If you can't install gcloud CLI because you are using an unsupported device or platform, such as Chromebook, see Starting Cloud Datalab on a Chromebook.

Steps to set up and open Cloud Datalab

From a terminal window on your local machine:

1. Update your Google Cloud CLI components:

   gcloud components update
   

   If you installed the Google Cloud CLI through apt or yum, use those package managers to update the components:

   sudo apt update && sudo apt upgrade google-cloud-sdk
   

   sudo yum upgrade google-cloud-sdk
   

2. Install the datalab component for the Google Cloud CLI:

   gcloud components install datalab
   

   If you installed the Google Cloud CLI through apt or yum, use those package managers to update the components:

   sudo apt update && sudo apt install google-cloud-sdk-datalab
   

   sudo yum install google-cloud-sdk-datalab
   

3. Create a Cloud Datalab instance. The name of the instance must start with a lowercase letter, followed by up to 62 lowercase letters, numbers, or hyphens, and cannot end with a hyphen.

   datalab create datalab-instance-name
   

   If the command returns an error, re-run the command with the following debug flag to help diagnose the problem: datalab create --verbosity=debug datalab-instance-name.
   To create a Cloud Datalab instance with GPUs. To create a Cloud Datalab VM with one K80 GPU (see GPU Pricing), use the following beta command:

   datalab beta create-gpu datalab-instance-name
   

   To specify more than one K80 GPU, use the --accelerator-count number flag. For more information, run:

   datalab beta create-gpu --help

4. Open the Cloud Datalab home page in your browser.

   http://localhost:8081
   

   

The connection to your Datalab instance remains open while the datalab command is active. If the terminal command window is closed or interrupted, the connection will terminate, and you will need to run the following command to reestablish the connection:

 datalab connect instance-name

Clean up

To avoid incurring charges to your Google Cloud account for the resources used on this page, follow these steps.

1. You incur charges from the time of creation to the time of deletion of the Cloud Datalab VM instance (see Cloud Datalab Pricing). You are also charged for the Persistent Disk where notebooks are stored. The Persistent Disk remains after the deletion of the VM until you delete it. The following command deletes both the VM instance and its Persistent Disk.

   datalab delete --delete-disk instance-name
   

2. Other cleanup tasks. Additional resources are created by the datalab create command, and will be reused by other Cloud Datalab instances that you create. You can run the following commands to delete the additional resources listed below if you do not expect to create additional Cloud Datalab instances.

   • Delete the datalab-network-allow-ssh firewall rule, which allows SSH connections to your Cloud Datalab instances:

     gcloud compute firewall-rules delete datalab-network-allow-ssh
     

   • Delete the datalab-network Virtual Private Cloud (VPC) network, to which Datalab instances are connected by default.

     gcloud compute networks delete datalab-network
     

     • Delete the datalab-notebooks Cloud Source Repository, which is set up for you to store your notebooks (see Working with notebooks if you wish to backup notebooks before deleting the repo).

       gcloud source repos delete datalab-notebooks
       

What's next

1. Browse the /datalab/docs/intro Cloud Datalab notebook folder to become familiar with the capabilities of Cloud Datalab. You will find tutorials and samples for using Google Cloud Platform services and for performing common data analysis tasks.
2. You can view the datalab server VM logs with the Cloud Platform Console Logs Explorer.
3. Read Cloud Datalab How-to Guides.
4. Learn more about the options available in the datalab command line tool by running datalab --help.
5. Learn about Using Datalab in a team environment.