Create a Deep Learning VM instance from Cloud Marketplace

This page shows you how to create a Deep Learning VM Images instance from Cloud Marketplace within the Google Cloud console without using the command line.

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 Google Cloud project.

4. 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

5. Make sure that billing is enabled for your Google Cloud project.

6. Choose a specific Deep Learning VM image to use. Your choice depends on your preferred framework and processor type.
7. If you are using GPUs with your Deep Learning VM, check the quotas page to ensure that you have enough GPUs available in your project. If GPUs are not listed on the quotas page or you require additional GPU quota, request a quota increase.

Creating an instance

1. Go to the Deep Learning VM Cloud Marketplace page in the Google Cloud console.

   Go to the Deep Learning VM Cloud Marketplace page

2. Click Get started.

3. Enter a Deployment name, which will be the root of your VM name. Compute Engine appends -vm to this name when naming your instance.

4. Select a Zone.

5. Under Machine type, select the specifications that you want for your VM. Learn more about machine types.

6. Under GPUs, select the GPU type and Number of GPUs. If you don't want to use GPUs, click the Delete GPU button and skip to step 7. Learn more about GPUs.

   1. Select a GPU type. Not all GPU types are available in all zones. Find a combination that is supported.
   2. Select the Number of GPUs. Each GPU supports different numbers of GPUs. Find a combination that is supported.

7. Select a machine learning Framework.

8. If you're using GPUs, an NVIDIA driver is required. You can install the driver yourself, or select Install NVIDIA GPU driver automatically on first startup.

9. You have the option to select Enable access to JupyterLab via URL instead of SSH (Beta). Enabling this Beta feature lets you access your JupyterLab instance using a URL. Anyone who is in the Editor or Owner role in your Google Cloud project can access this URL. Currently, this feature only works in the United States, the European Union, and Asia.

10. Select a boot disk type and boot disk size.

11. Select the networking settings that you want.

12. Click Deploy.

If you choose to install NVIDIA drivers, allow 3-5 minutes for installation to complete.

After the VM is deployed, the page updates with instructions for accessing the instance.

What's next

For instructions on connecting to your new Deep Learning VM instance through the Google Cloud console or command line, read Connecting to Instances. Your instance name is the Deployment name you specified with -vm appended.