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This page shows you how to create a PyTorch Deep Learning VM Images instance
with PyTorch and other tools pre-installed. You can create
a PyTorch instance from Cloud Marketplace within
the Google Cloud console or 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 a PyTorch Deep Learning VM instance from the Cloud Marketplace
To create a PyTorch Deep Learning VM instance
from the Cloud Marketplace, complete the following steps:
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.
Under Framework, select PyTorch 1.8 + fast.ai 2.1
(CUDA 11.0).
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.
Creating a PyTorch Deep Learning VM instance from the command line
To use the Google Cloud CLI to create a new a Deep Learning VM
instance, you must first install and initialize the Google Cloud CLI:
--image-family must be either pytorch-latest-cpu
or pytorch-VERSION-cpu
(for example, pytorch-1-13-cpu).
--image-project must be deeplearning-platform-release.
With one or more GPUs
Compute Engine offers the option of adding one or more GPUs to your
virtual machine instances. GPUs offer faster processing for many complex data
and machine learning tasks. To learn more about GPUs, see GPUs on
Compute Engine.
To create a Deep Learning VM instance with the
latest PyTorch image family and one
or more attached GPUs, enter the following at the command line:
--image-family must be either pytorch-latest-gpu
or pytorch-VERSION-CUDA-VERSION
(for example, pytorch-1-10-cu110).
--image-project must be deeplearning-platform-release.
--maintenance-policy must be TERMINATE. To learn more, see
GPU Restrictions.
--accelerator specifies the GPU type to use. Must be
specified in the format
--accelerator="type=TYPE,count=COUNT".
For example, --accelerator="type=nvidia-tesla-p100,count=2".
See the GPU models
table
for a list of available GPU types and counts.
--metadata is used to specify that the NVIDIA driver should be installed
on your behalf. The value is install-nvidia-driver=True. If specified,
Compute Engine loads the latest stable driver on the first boot
and performs the necessary steps (including a final reboot to activate the
driver).
If you've elected to install NVIDIA drivers, allow 3-5 minutes for installation
to complete.
It may take up to 5 minutes before your VM is fully provisioned. In this
time, you will be unable to SSH into your machine. When the installation is
complete, to guarantee that the driver installation was successful, you can
SSH in and run nvidia-smi.
When you've configured your image, you can save a snapshot of your
image so that you can start derivitave instances without having to wait
for the driver installation.
Creating a preemptible instance
You can create a preemptible Deep Learning VM instance. A preemptible
instance is an instance you can create and run at a much lower price than
normal instances. However, Compute Engine might stop (preempt) these
instances if it requires access to those resources for other tasks.
Preemptible instances always stop after 24 hours. To learn more about
preemptible instances, see Preemptible VM
Instances.
To create a preemptible Deep Learning VM instance:
Follow the instructions located above to create a new instance using the
command line. To the gcloud compute instances create command, append the
following:
--preemptible
What's next
For instructions on connecting to your new Deep Learning VM instance
through the Google Cloud console or command line, see 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 2025-08-25 UTC."],[[["\u003cp\u003eThis guide explains how to create a PyTorch Deep Learning VM instance, either through the Google Cloud Marketplace or using the command line interface.\u003c/p\u003e\n"],["\u003cp\u003eYou can configure your instance with or without GPUs, and if using GPUs, an NVIDIA driver installation is required, which can be done automatically.\u003c/p\u003e\n"],["\u003cp\u003eWhen creating an instance, options such as machine type, zone, framework, and networking settings, along with specific configurations for GPUs, can be customized.\u003c/p\u003e\n"],["\u003cp\u003eUsing the command line, you can create instances with specified PyTorch versions, and it includes provisions for creating instances with or without GPUs, along with specifying details like image family and project.\u003c/p\u003e\n"],["\u003cp\u003ePreemptible instances, which offer a lower price but can be terminated, are available for creation and can be done by adding a \u003ccode\u003e--preemptible\u003c/code\u003e flag to your gcloud command.\u003c/p\u003e\n"]]],[],null,["# Create a PyTorch Deep Learning VM instance\n\nThis page shows you how to create a PyTorch Deep Learning VM Images instance\nwith PyTorch and other tools pre-installed. You can create\na PyTorch instance from Cloud Marketplace within\nthe Google Cloud console or 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. 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 a PyTorch Deep Learning VM instance from the Cloud Marketplace\n-----------------------------------------------------------------------\n\nTo create a PyTorch Deep Learning VM instance\nfrom the Cloud Marketplace, complete the following steps:\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. Under **Framework** , select **PyTorch 1.8 + fast.ai 2.1\n (CUDA 11.0)**.\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\nCreating a PyTorch Deep Learning VM instance from the command line\n------------------------------------------------------------------\n\nTo use the Google Cloud CLI to create a new a Deep Learning VM\ninstance, you must first install and initialize the [Google Cloud CLI](/sdk/docs):\n\n1. Download and install the Google Cloud CLI using the instructions given on [Installing Google Cloud CLI](/sdk/downloads).\n2. Initialize the SDK using the instructions given on [Initializing Cloud\n SDK](/sdk/docs/initializing).\n\nTo use `gcloud` in Cloud Shell, first activate Cloud Shell using the\ninstructions given on [Starting Cloud Shell](/shell/docs/starting-cloud-shell).\n\n### Without GPUs\n\nTo create a Deep Learning VM instance\nwith the latest PyTorch image family and a\nCPU, enter the following at the command line: \n\n export IMAGE_FAMILY=\"pytorch-latest-cpu\"\n export ZONE=\"us-west1-b\"\n export INSTANCE_NAME=\"my-instance\"\n\n gcloud compute instances create $INSTANCE_NAME \\\n --zone=$ZONE \\\n --image-family=$IMAGE_FAMILY \\\n --image-project=deeplearning-platform-release\n\nOptions:\n\n- `--image-family` must be either `pytorch-latest-cpu`\n or `pytorch-`\u003cvar translate=\"no\"\u003eVERSION\u003c/var\u003e`-cpu`\n (for example, `pytorch-1-13-cpu`).\n\n- `--image-project` must be `deeplearning-platform-release`.\n\n### With one or more GPUs\n\nCompute Engine offers the option of adding one or more GPUs to your\nvirtual machine instances. GPUs offer faster processing for many complex data\nand machine learning tasks. To learn more about GPUs, see [GPUs on\nCompute Engine](/compute/docs/gpus).\n\nTo create a Deep Learning VM instance with the\nlatest PyTorch image family and one\nor more attached GPUs, enter the following at the command line: \n\n export IMAGE_FAMILY=\"pytorch-latest-gpu\"\n export ZONE=\"us-west1-b\"\n export INSTANCE_NAME=\"my-instance\"\n\n gcloud compute instances create $INSTANCE_NAME \\\n --zone=$ZONE \\\n --image-family=$IMAGE_FAMILY \\\n --image-project=deeplearning-platform-release \\\n --maintenance-policy=TERMINATE \\\n --accelerator=\"type=nvidia-tesla-v100,count=1\" \\\n --metadata=\"install-nvidia-driver=True\"\n\nOptions:\n\n- `--image-family` must be either `pytorch-latest-gpu`\n or `pytorch-`\u003cvar translate=\"no\"\u003eVERSION\u003c/var\u003e`-`\u003cvar translate=\"no\"\u003eCUDA-VERSION\u003c/var\u003e\n (for example, `pytorch-1-10-cu110`).\n\n- `--image-project` must be `deeplearning-platform-release`.\n\n- `--maintenance-policy` must be `TERMINATE`. To learn more, see\n [GPU Restrictions](/compute/docs/gpus#restrictions).\n\n- `--accelerator` specifies the GPU type to use. Must be\n specified in the format\n `--accelerator=\"type=`\u003cvar translate=\"no\"\u003eTYPE\u003c/var\u003e`,count=`\u003cvar translate=\"no\"\u003eCOUNT\u003c/var\u003e`\"`.\n For example, `--accelerator=\"type=nvidia-tesla-p100,count=2\"`.\n See the [GPU models\n table](/compute/docs/gpus#other_available_nvidia_gpu_models)\n for a list of available GPU types and counts.\n\n Not all GPU types are supported in all regions. For details, see\n [GPU regions and zones availability](/compute/docs/gpus/gpu-regions-zones).\n- `--metadata` is used to specify that the NVIDIA driver should be installed\n on your behalf. The value is `install-nvidia-driver=True`. If specified,\n Compute Engine loads the latest stable driver on the first boot\n and performs the necessary steps (including a final reboot to activate the\n driver).\n\nIf you've elected to install NVIDIA drivers, allow 3-5 minutes for installation\nto complete.\n\nIt may take up to 5 minutes before your VM is fully provisioned. In this\ntime, you will be unable to SSH into your machine. When the installation is\ncomplete, to guarantee that the driver installation was successful, you can\nSSH in and run `nvidia-smi`.\n\nWhen you've configured your image, you can save a snapshot of your\nimage so that you can start derivitave instances without having to wait\nfor the driver installation.\n\nCreating a preemptible instance\n-------------------------------\n\nYou can create a preemptible Deep Learning VM instance. A preemptible\ninstance is an instance you can create and run at a much lower price than\nnormal instances. However, Compute Engine might stop (preempt) these\ninstances if it requires access to those resources for other tasks.\nPreemptible instances always stop after 24 hours. To learn more about\npreemptible instances, see [Preemptible VM\nInstances](/compute/docs/instances/preemptible).\n\nTo create a preemptible Deep Learning VM instance:\n\n- Follow the instructions located above to create a new instance using the\n command line. To the `gcloud compute instances create` command, append the\n following:\n\n ```\n --preemptible\n ```\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, see [Connecting to\nInstances](/compute/docs/instances/connecting-to-instance). Your instance name\nis the **Deployment name** you specified with `-vm` appended."]]
