Create a Deep Learning VM instance from Cloud Marketplace
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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
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.
In the Google Cloud console, on the project selector page,
select or create a Google Cloud project.
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
Go to the Deep Learning VM Cloud Marketplace page in
the Google Cloud console.
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.
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.
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.
Select a boot disk type and boot disk size.
Select the networking settings that you want.
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.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-09-02 UTC."],[[["\u003cp\u003eThis guide outlines how to create a Deep Learning VM instance directly from the Google Cloud Marketplace within the console, eliminating the need for command-line operations.\u003c/p\u003e\n"],["\u003cp\u003eBefore creating the instance, you must select a specific Deep Learning VM image based on your preferred framework and processor type, and check that enough GPU quota is available if you are planning to use GPUs.\u003c/p\u003e\n"],["\u003cp\u003eThe instance creation process involves selecting a deployment name, zone, machine type, optional GPU settings, and machine learning framework, then it includes selecting a boot disk and networking settings before deployment.\u003c/p\u003e\n"],["\u003cp\u003eIf you are planning to use GPUs, you will need to install the NVIDIA drivers, which can be done automatically on the first startup, and you also have the choice of enabling JupyterLab access via URL.\u003c/p\u003e\n"],["\u003cp\u003eAfter deployment, you will be provided instructions to access the instance, and the instance name is created by appending \u003ccode\u003e-vm\u003c/code\u003e to the deployment name that was chosen during setup.\u003c/p\u003e\n"]]],[],null,["# Create a Deep Learning VM instance from Cloud Marketplace\n\nThis page shows you how to create a Deep Learning VM Images instance\nfrom Cloud Marketplace within the\nGoogle Cloud console without using the command line.\n\nBefore you begin\n----------------\n\n- Sign in to your Google Cloud account. If you're new to Google Cloud, [create an account](https://console.cloud.google.com/freetrial) to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.\n- In the Google Cloud console, on the project selector page,\n select or create a Google Cloud project.\n\n | **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.\n\n [Go to project selector](https://console.cloud.google.com/projectselector2/home/dashboard)\n-\n [Verify that billing is enabled for your Google Cloud project](/billing/docs/how-to/verify-billing-enabled#confirm_billing_is_enabled_on_a_project).\n\n- In the Google Cloud console, on the project selector page,\n select or create a Google Cloud project.\n\n | **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.\n\n [Go to project selector](https://console.cloud.google.com/projectselector2/home/dashboard)\n-\n [Verify that billing is enabled for your Google Cloud project](/billing/docs/how-to/verify-billing-enabled#confirm_billing_is_enabled_on_a_project).\n\n1. [Choose a specific Deep Learning VM\n image to use](/deep-learning-vm/docs/images). Your choice depends on your preferred framework and processor type.\n2. If you are using GPUs with your Deep Learning VM, check the [quotas page](https://console.cloud.google.com/quotas) 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\n quota increase](/compute/quotas#requesting_additional_quota).\n\nCreating an instance\n--------------------\n\n1. Go to the Deep Learning VM Cloud Marketplace page in\n the Google Cloud console.\n\n [Go to the Deep Learning VM Cloud Marketplace page](https://console.cloud.google.com/marketplace/details/click-to-deploy-images/deeplearning)\n2. Click **Get started**.\n\n3. Enter a **Deployment name** , which will be the root of your VM name.\n Compute Engine appends `-vm` to this name when naming your instance.\n\n4. Select a **Zone**.\n\n5. Under **Machine type** , select the specifications that you\n want for your VM.\n [Learn more about machine types.](/compute/docs/machine-types)\n\n6. Under **GPUs** , select the **GPU type** and **Number of GPUs** .\n If you don't want to use GPUs,\n click the **Delete GPU** button\n and skip to step 7. [Learn more about GPUs.](/gpu)\n\n 1. Select a **GPU type** . Not all GPU types are available in all zones. [Find a combination that is supported.](/compute/docs/gpus)\n 2. Select the **Number of GPUs** . Each GPU supports different numbers of GPUs. [Find a combination that is supported.](/compute/docs/gpus)\n7. Select a machine learning **Framework**.\n\n8. If you're using GPUs, an NVIDIA driver is required.\n You can install the driver\n yourself, or select **Install NVIDIA GPU driver automatically\n on first startup**.\n\n9. You have the option to select **Enable access to JupyterLab via URL\n instead of SSH (Beta)**. Enabling this Beta feature lets you\n access your JupyterLab\n instance using a URL. Anyone who is in the Editor or Owner role in your\n Google Cloud project can access this URL.\n Currently, this feature only works in\n the United States, the European Union, and Asia.\n\n10. Select a boot disk type and boot disk size.\n\n11. Select the networking settings that you want.\n\n12. Click **Deploy**.\n\nIf you choose to install NVIDIA drivers, allow 3-5 minutes for installation\nto complete.\n\nAfter the VM is deployed, the page updates with instructions for\naccessing the instance.\n\nWhat's next\n-----------\n\nFor instructions on connecting to your new Deep Learning VM instance\nthrough the Google Cloud console or command line, read [Connecting to\nInstances](/compute/docs/instances/connecting-to-instance). Your instance name\nis the **Deployment name** you specified with `-vm` appended."]]
