Compute Engine provides graphics processing units (GPUs) that you can add to your virtual machines (VMs). You can use these GPUs to accelerate specific workloads on your VMs such as machine learning and data processing.
This page explains how to create a VM with attached GPUs. If you want to add GPUs to existing VMs, see Adding or removing GPUs.
For more information about what you can do with GPUs and what types of GPU hardware are available, read GPUs on Compute Engine.
Before you begin
- If you want to use the command-line examples in this guide, do the following:
- Install or update to the latest version of the gcloud command-line tool.
- Set a default region and zone.
- If you want to use the API examples in this guide, set up API access.
- Read about GPU pricing on Compute Engine to understand the cost to use GPUs on your VMs.
- Read about restrictions for VMs with GPUs.
- Check your GPU quota.
- Choose an operating system image:
- If you are using GPUs for machine learning, you can use a Deep Learning VM image for your VM. The Deep Learning VM images have GPU drivers pre-installed and include packages, such as TensorFlow and PyTorch. You can also use the Deep Learning VM images for general GPU workloads. For information about the images available and the packages installed on the images, see Choosing an image.
- You can also use any public image or custom image, but some images might require a unique driver or install process that is not covered in this document. You must identify which drivers are appropriate for your images. For steps to install drivers, see installing GPU drivers.
Checking GPU quota
To protect Compute Engine systems and users, new projects have a global GPU quota, which limits the total number of GPUs you can create in any supported zone.
Use the regions describe command
to ensure that you have sufficient GPU quota in the region where you
want to create VMs with GPUs.
gcloud compute regions describe REGION
Replace REGION with the
region that you want to check for GPU quota.
If you need additional GPU quota, request a quota increase. When you request a GPU quota, you must request a quota for the GPU types that you want to create in each region and an additional global quota for the total number of GPUs of all types in all zones.
If your project has an established billing history, it will receive quota automatically after you submit the request.
Overview
To create a VM with attached GPUs, complete the following steps:
Create the VM. The method used to create a VM depends on the GPU model.
- To create a VM with A100 GPUs, see Creating VMs with attached GPUs (A100 GPUs).
- To create a VM with any other available model, see Creating VMs with attached GPUs (other GPU types).
For the VM to use the GPU, you need to install the GPU driver on your VM.
If you enabled NVIDIA® GRID virtual workstations, install GRID® drivers for virtual workstations.
Creating VMs with attached GPUs (A100 GPUs)
This section covers how to create VMs with attached NVIDIA® A100 GPUs. For other GPU types, see Creating VMs with attached GPUs (other GPU types).
Limitations (A100)
a2-megagpu-16gmachine types are not supported on Windows operating system. When using Windows operating systems, choose a different machine type. For a list of machine types, see NVIDIA® A100 GPUs.For Windows VMs that use A2 machine types, you cannot do a quick format of the attached local SSDs. To format these local SSDs, you must do a full format by using the diskpart utility and specifying
format fs=ntfs label=tmpfs.
Console
In the Google Cloud Console, go to the VM instances page.
Click Create instance.
Specify a Name for your instance. See Resource naming convention.
Select a region and zone where GPUs are available. See the list of available A100 GPU zones.
From the Machine configuration section, complete the following steps .
- Under Machine family, click GPU.
- Under Series, select A2.
- Under Machine type, select the
A2 machine type that you want.
- Expand the CPU platform and GPU section.
Under CPU platform and GPU, review the GPU type and Number of GPUs.
To configure your boot disk, in the Boot disk section, click Change. This opens the Boot disk configuration page.
From the Boot disk configuration page, complete the following steps .
- In the Public images tab, choose a supported Compute Engine image or Deep Learning VM image.
- Specify a boot disk size of at least 40 GB.
- Click Save to confirm your boot disk options.
Configure any other VM settings that you require. For example, you can change the Preemptibility settings to configure your VM as a preemptible instance. This reduces the cost of your VM and the attached GPUs. Read GPUs on preemptible instances to learn more.
Click the Create button to create and start the VM.
gcloud
To create and start a VM use the
gcloud compute instances create
command with the following flags. VMs with GPUs cannot live
migrate, make sure that you set the --maintenance-policy TERMINATE flag.
The --preemptible is an optional flag that configures your VM as a
preemptible instance. This reduces the cost of your VM and the attached GPUs.
For more information, see
GPUs on preemptible instances.
gcloud compute instances create VM_NAME \
--machine-type MACHINE_TYPE \
--zone ZONE \
--boot-disk-size DISK_SIZE \
[--image IMAGE | --image-family IMAGE_FAMILY] \
--image-project IMAGE_PROJECT \
--maintenance-policy TERMINATE --restart-on-failure \
[--preemptible]
Replace the following:
VM_NAME: the name for the new VM.MACHINE_TYPE: the A2 machine type that you selected for the VM.ZONE: the zone for the VM. This zone must support A100 GPUs.DISK_SIZE: the size of your boot disk in GB. Specify a boot disk size of at least 40 GB.IMAGEorIMAGE_FAMILYthat supports GPUs. Specify one of the following:IMAGE: the required version of a public image. For example,--image debian-10-buster-v20200309.IMAGE_FAMILY: an image family. This creates the VM from the most recent, non-deprecated OS image. For example, if you specify--image-family debian-10, Compute Engine creates a VM from the latest version of the OS image in the Debian 10 image family.
You can also specify a custom image or Deep Learning VM image.
IMAGE_PROJECT: the Compute Engine image project that the image family belongs to. If using a custom image or Deep Learning VM image, specify the project that those images belong to.
API
In the API, create a POST request to the
instances.insert method.
VMs with GPUs cannot live migrate, make sure you set the onHostMaintenance
parameter to TERMINATE.
The "preemptible": true is an optional parameter that configures
your VM as a preemptible instance. This reduces the cost of
your VM and the attached GPUs. For more information, see
GPUs on preemptible instances.
POST https://compute.googleapis.com/compute/v1/projects/PROJECT_ID/zones/ZONE/instances
{
"machineType": "projects/PROJECT_ID/zones/ZONE/machineTypes/MACHINE_TYPE",
"disks":
[
{
"type": "PERSISTENT",
"initializeParams":
{
"diskSizeGb": "DISK_SIZE",
"sourceImage": "projects/IMAGE_PROJECT/global/images/family/IMAGE_FAMILY"
},
"boot": true
}
],
"name": "VM_NAME",
"networkInterfaces":
[
{
"network": "projects/PROJECT_ID/global/networks/NETWORK"
}
],
"scheduling":
{
"onHostMaintenance": "terminate",
"automaticRestart": true,
["preemptible": true]
},
}
Replace the following:
VM_NAME: the name of the VM.PROJECT_ID: your project ID.ZONE: the zone for the VM. This zone must support A100 GPUs.MACHINE_TYPE: a A2 machine type that you selected for the VM.IMAGEorIMAGE_FAMILY: specify one of the following:IMAGE: the required version of a public image. For example,"sourceImage": "projects/debian-cloud/global/images/debian-10-buster-v20200309"IMAGE_FAMILY: an image family. This creates the VM from the most recent, non-deprecated OS image. For example, if you specify"sourceImage": "projects/debian-cloud/global/images/family/debian-10", Compute Engine creates a VM from the latest version of the OS image in the Debian 10 image family.
You can also specify a custom image or Deep Learning VM image.
IMAGE_PROJECT: the Compute Engine image project that the image family belongs to. If using a custom image or Deep Learning VM image, specify the project that those images belong to.DISK_SIZE: the size of your boot disk in GB. Specify a boot disk size of at least 40 GB.NETWORK: the VPC network that you want to use for the VM. You can specifydefaultto use your default network.
Next: Install the GPU driver on your VM, so that your system can use the device. For A100 GPUs, CUDA 11 is required.
Examples (A100 GPUs)
In these examples, VMs are created with attached NVIDIA® A100 GPUs
by using the gcloud command-line tool. However, you can also use either
the Google Cloud Console or the
Compute Engine API to create these VMs.
The following examples show how to create VMs using the following images:
DLVM image
Using DLVM images is the easiest way to get started because these images already have the NVIDIA drivers and CUDA libraries pre-installed.
These images also provide performance optimizations.
The following DLVM images are supported for NVIDIA® A100:
common-cu110: NVIDIA driver and CUDA pre-installedtf-ent-1-15-cu110: NVIDIA driver, CUDA, TensorFlow Enterprise 1.15.3 pre-installedtf2-ent-2-1-cu110: NVIDIA driver, CUDA, TensorFlow Enterprise 2.1.1 pre-installedtf2-ent-2-3-cu110: NVIDIA driver, CUDA, TensorFlow Enterprise 2.3.1 pre-installedpytorch-1-6-cu110: NVIDIA driver, CUDA, Pytorch 1.6
For more information about the DLVM images that are available, and the packages installed on the images, see the Deep Learning VM documentation.
Create a VM using the
tf2-ent-2-3-cu110image and thea2-highgpu-1gmachine type. In this example, optional flags such as boot disk size and scope are specified.gcloud compute instances create VM_NAME \ --project PROJECT_ID \ --zone us-central1-c \ --machine-type a2-highgpu-1g \ --maintenance-policy TERMINATE --restart-on-failure \ --image-family tf2-ent-2-3-cu110 \ --image-project deeplearning-platform-release \ --boot-disk-size 200GB \ --metadata "install-nvidia-driver=True,proxy-mode=project_editors" \ --scopes https://www.googleapis.com/auth/cloud-platform
Replace the following:
VM_NAME: the name of your VMPROJECT_ID: your project ID.
The preceding example command also generates a AI Platform Notebook for the VM. To access the notebook, in the Google Cloud Console, go to the AI Platform page.
Public or custom image
You can create VMs with attached GPUs that use either a public image that is available on Compute Engine or a custom image.
To create a VM using the most recent, non-deprecated image from the
CentOS 7 image family and the
a2-highgpu-1g
machine type, complete the following steps:
Create the VM. In this example, optional flags such as boot disk type and size are also specified.
gcloud compute instances create VM_NAME \ --project PROJECT_ID \ --zone us-central1-c \ --machine-type a2-highgpu-1g \ --maintenance-policy TERMINATE --restart-on-failure \ --image-family centos-7 \ --image-project centos-cloud \ --boot-disk-size 200GB \ --boot-disk-type pd-ssdReplace the following:
VM_NAME: the name of your VMPROJECT_ID: your project ID.
Install NVIDIA driver and CUDA. For NVIDIA® A100 GPUs, CUDA version 11 or higher is required.
COS
You can create VMs with attached GPUs that use Container-optimized (COS) images.
To create a VM using the cos-85-lts image and
the a2-highgpu-1g machine type, complete
the following steps from your local client. The following examples can be
run on a Mac or Linux client:
If one doesn't already exist, create a
/tmpdirectory.mkdir /tmp
Add a configuration file
/cloud-init.yamlto the/tmpdirectory.This information is required to set up your Container-optimized VM and also installs NVIDIA driver and CUDA when the VM boots up.
cat <<'EOF' > /tmp/cloud-init.yaml #cloud-config write_files: - path: /etc/systemd/system/cos-gpu-installer.service permissions: 0755 owner: root content: | [Unit] Description=Run the GPU driver installer container Requires=network-online.target gcr-online.target After=network-online.target gcr-online.target [Service] User=root Type=oneshot RemainAfterExit=true Environment=INSTALL_DIR=/var/lib/nvidia ExecStartPre=/bin/mkdir -p ${INSTALL_DIR} ExecStartPre=/bin/mount --bind ${INSTALL_DIR} ${INSTALL_DIR} ExecStartPre=/bin/mount -o remount,exec ${INSTALL_DIR} ExecStart=/usr/bin/docker run --privileged \ --net=host \ --pid=host \ --volume ${INSTALL_DIR}:/usr/local/nvidia \ --volume /dev:/dev \ --volume /:/root \ --env NVIDIA_DRIVER_VERSION=450.80.02 \ gcr.io/cos-cloud/cos-gpu-installer:v20200701 StandardOutput=journal+console StandardError=journal+console runcmd: - systemctl daemon-reload - systemctl enable cos-gpu-installer.service - systemctl start cos-gpu-installer.service EOFCreate a Container-optimized VM using the
cos-85-ltsimage family and thea2-highgpu-1g.You need to provide the configuration file by using the
-metadata-from-file user-dataflag.In this example, the optional flag boot disk size is also specified.
gcloud compute instances create VM_NAME \ --project PROJECT_ID \ --zone us-central1-a \ --machine-type a2-highgpu-1g \ --maintenance-policy TERMINATE --restart-on-failure \ --image-family cos-85-lts \ --image-project cos-cloud \ --boot-disk-size 200GB \ --metadata-from-file user-data=/tmp/cloud-init.yaml
Replace the following:
VM_NAME: the name of your VMPROJECT_ID: your project ID.
After the VM is created, log in to the VM and run the following command to verify that the NVIDIA driver is installed.
/var/lib/nvidia/bin/nvidia-smi
It takes approximately 5 minutes for the driver to be installed.
Multi-Instance GPUs (A100)
A Multi-Instance GPU partitions a single NVIDIA A100 GPU within the same VM into as many as seven independent GPU instances. They run simultaneously, each with its own memory, cache and streaming multiprocessors. This setup enables the A100 GPU to deliver guaranteed quality-of-service (QoS) at up to 7x higher utilization compared to earlier GPU models.
For more information about using Multi-Instance GPUs, see NVIDIA Multi-Instance GPU User Guide.
To create Multi-Instance GPUs, complete the following steps:
Create a VM with attached A100 GPUs.
Enable NVIDIA GPU drivers.
Enable Multi-Instance GPUs and reboot the VM.
sudo nvidia-smi -mig 1 sudo reboot
Review the Multi-Instance GPU shapes that are available.
sudo nvidia-smi mig --list-gpu-instance-profiles
The output is similar to the following:
+--------------------------------------------------------------------------+ | GPU instance profiles: | | GPU Name ID Instances Memory P2P SM DEC ENC | | Free/Total GiB CE JPEG OFA | |==========================================================================| | 0 MIG 1g.5gb 19 7/7 4.75 No 14 0 0 | | 1 0 0 | +--------------------------------------------------------------------------+ | 0 MIG 2g.10gb 14 3/3 9.75 No 28 1 0 | | 2 0 0 | +--------------------------------------------------------------------------+ | 0 MIG 3g.20gb 9 2/2 19.62 No 42 2 0 | | 3 0 0 | +--------------------------------------------------------------------------+ | 0 MIG 4g.20gb 5 1/1 19.62 No 56 2 0 | | 4 0 0 | +--------------------------------------------------------------------------+ | 0 MIG 7g.40gb 0 1/1 39.50 No 98 5 0 | | 7 1 1 | +--------------------------------------------------------------------------+
Create the Multi-Instance GPU (GI) and associated compute instances (CI) that you want. You can create these instances by specifying either the full or shortened profile name, profile ID, or a combination of both. For more information, see Creating GPU Instances.
The following example creates two
MIG 3g.20gbGPU instances by using a combination of the shortened profile name (3g.20gb), and the profile ID (9).The
-Cflag is also specified which creates the associated compute instances for the required profile.sudo nvidia-smi mig -cgi 9,3g.20gb -C
The output is similar to the following:
Successfully created GPU instance ID 2 on GPU 0 using profile MIG 3g.20gb (ID 9) Successfully created compute instance ID 0 on GPU 0 GPU instance ID 2 using profile MIG 3g.20gb (ID 2) Successfully created GPU instance ID 1 on GPU 0 using profile MIG 3g.20gb (ID 9) Successfully created compute instance ID 0 on GPU 0 GPU instance ID 1 using profile MIG 3g.20gb (ID 2)
Check that the two Multi-Instance GPUs are created:
sudo nvidia-smi mig -lgi
The output is similar to the following:
+----------------------------------------------------+ | GPU instances: | | GPU Name Profile Instance Placement | | ID ID Start:Size | |====================================================| | 0 MIG 3g.20gb 9 1 0:4 | +----------------------------------------------------+ | 0 MIG 3g.20gb 9 2 4:4 | +----------------------------------------------------+
Check that both the GIs and corresponding CIs are created.
sudo nvidia-smi
The output is similar to the following:
Tue May 18 18:32:22 2021 +-----------------------------------------------------------------------------+ | NVIDIA-SMI 450.80.02 Driver Version: 450.80.02 CUDA Version: 11.0 | |-------------------------------+----------------------+----------------------+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |===============================+======================+======================| | 0 A100-SXM4-40GB Off | 00000000:00:04.0 Off | On | | N/A 43C P0 52W / 350W | 22MiB / 40537MiB | N/A Default | | | | Enabled | +-------------------------------+----------------------+----------------------+ +-----------------------------------------------------------------------------+ | MIG devices: | +------------------+----------------------+-----------+-----------------------+ | GPU GI CI MIG | Memory-Usage | Vol| Shared | | ID ID Dev | BAR1-Usage | SM Unc| CE ENC DEC OFA JPG| | | | ECC| | |==================+======================+===========+=======================| | 0 1 0 0 | 11MiB / 20096MiB | 42 0 | 3 0 2 0 0 | | | 0MiB / 32767MiB | | | +------------------+----------------------+-----------+-----------------------+ | 0 2 0 1 | 11MiB / 20096MiB | 42 0 | 3 0 2 0 0 | | | 0MiB / 32767MiB | | | +------------------+----------------------+-----------+-----------------------+ +-----------------------------------------------------------------------------+ | Processes: | | GPU GI CI PID Type Process name GPU Memory | | ID ID Usage | |=============================================================================| | No running processes found | +-----------------------------------------------------------------------------+
Creating VMs with attached GPUs (other GPU types)
You can create a VM with one or more GPUs using the Google Cloud Console,
the gcloud command-line tool, or the
Compute Engine API.
This section describes how to create VMs using the following GPU types:
- NVIDIA® T4:
nvidia-tesla-t4 - NVIDIA® T4 Virtual Workstation with NVIDIA®
GRID®:
nvidia-tesla-t4-vws - NVIDIA® V100:
nvidia-tesla-v100 - NVIDIA® P100:
nvidia-tesla-p100. NVIDIA® P100 Virtual Workstation with NVIDIA® GRID®:
nvidia-tesla-p100-vwsNVIDIA® P4:
nvidia-tesla-p4NVIDIA® P4 Virtual Workstation with NVIDIA® GRID®:
nvidia-tesla-p4-vwsNVIDIA® K80:
nvidia-tesla-k80
Console
In the Google Cloud Console, go to the VM instances page.
Click Create instance.
Specify a Name for your instance. See Resource naming convention.
Select a region and zone. See the list of available zones with GPUs.
From the Machine configuration page, complete the following steps:
- Under Series, select N1.
- Under Machine type, select the N1 machine type that you want. Alternatively, you can specify custom machine type settings if desired.
- Expand the CPU platform and GPU section.
- Click Add GPU.
- Specify the GPU type and Number of GPUs.
If your GPU model supports virtual workstations, and you plan on running graphics-intensive workloads on this VM, select Enable Virtual Workstation (NVIDIA GRID).
For information about NVIDIA® GRID virtual workstations, see NVIDIA® GRID® GPUs for graphics workloads.
To select your operating system, in the Boot disk section, click Change. This opens the Boot disk configuration page.
From the Boot disk configuration page, complete the following steps:
- In the Public images tab, choose a supported Compute Engine image or Deep Learning VM image.
- Specify a boot disk size of at least 40 GB.
- Click Save to confirm your boot disk options.
Configure any other instance settings that you require. For example, you can change the Preemptibility settings to configure your instance as a preemptible instance. This reduces the cost of your instance and the attached GPUs. Read GPUs on preemptible instances to learn more.
Click the Create button to create and start the VM.
gcloud
To create and start a VM use the
gcloud compute instances create
command with the following flags.
The --preemptible is an optional flag that configures your
VM as a preemptible instance. This reduces the cost of your
VM and the attached GPUs. For more information, see
GPUs on preemptible instances.
gcloud compute instances create VM_NAME \
--machine-type MACHINE_TYPE \
--zone ZONE \
--boot-disk-size DISK_SIZE \
--accelerator type=ACCELERATOR_TYPE,count=ACCELERATOR_COUNT \
[--image IMAGE | --image-family IMAGE_FAMILY] \
--image-project IMAGE_PROJECT \
--maintenance-policy TERMINATE --restart-on-failure \
[--preemptible]
Replace the following:
VM_NAME: the name for the new VM.MACHINE_TYPE: the machine type that you selected for your VM.ZONE: the zone for the VM. This zone must support the GPU type.DISK_SIZE: the size of your boot disk in GB. Specify a boot disk size of at least 40 GB.IMAGEorIMAGE_FAMILYthat supports GPUs. Specify one of the following:IMAGE: the required version of a public image. For example,--image debian-10-buster-v20200309.IMAGE_FAMILY: an image family. This creates the VM from the most recent, non-deprecated OS image. For example, if you specify--image-family debian-10, Compute Engine creates a VM from the latest version of the OS image in the Debian 10 image family.
You can also specify a custom image or Deep Learning VM image.
IMAGE_PROJECT: the Compute Engine image project that the image family belongs to. If using a custom image or Deep Learning VM image, specify the project that those images belong to.ACCELERATOR_COUNT: the number of GPUs that you want to add to your VM. See GPUs on Compute Engine for a list of GPU limits based on the machine type of your VM.ACCELERATOR_TYPE: the GPU model that you want to use. If you plan on running graphics-intensive workloads on this VM, use one of the virtual workstation models.Choose one of the following values:
- NVIDIA® T4:
nvidia-tesla-t4 - NVIDIA® T4 Virtual Workstation with NVIDIA®
GRID®:
nvidia-tesla-t4-vws - NVIDIA® P4:
nvidia-tesla-p4 - NVIDIA® P4 Virtual Workstation with NVIDIA®
GRID®:
nvidia-tesla-p4-vws - NVIDIA® P100:
nvidia-tesla-p100 - NVIDIA® P100 Virtual Workstation with NVIDIA®
GRID®:
nvidia-tesla-p100-vws - NVIDIA® V100:
nvidia-tesla-v100 - NVIDIA® K80:
nvidia-tesla-k80
- NVIDIA® T4:
Example
For example, you can use the following gcloud command to start an
Ubuntu 16.04 VM with 1 NVIDIA K80 GPU and 2 vCPUs in the
us-east1-d zone.
gcloud compute instances create gpu-instance-1 \
--machine-type n1-standard-2 \
--zone us-east1-d \
--boot-disk-size 40GB \
--accelerator type=nvidia-tesla-k80,count=1 \
--image-family ubuntu-1604-lts \
--image-project ubuntu-os-cloud \
--maintenance-policy TERMINATE --restart-on-failure
API
Identify the GPU type that you want to add to your VM. Submit a GET request to list the GPU types that are available to your project in a specific zone.
The "preemptible": true is an optional parameter that configures
your VM as a preemptible instance. This reduces the cost of
your VM and the attached GPUs. For more information, see
GPUs on preemptible instances.
GET https://compute.googleapis.com/compute/v1/projects/PROJECT_ID/zones/ZONE/acceleratorTypes
Replace the following:
PROJECT_ID: project ID.ZONE: zone from which you want to list the available GPU types.
In the API, create a POST request to the
instances.insert method.
Include the acceleratorType parameter to specify which GPU type you want to use, and
include the acceleratorCount parameter to specify how many GPUs you want
to add. Also set the onHostMaintenance parameter to TERMINATE.
POST https://compute.googleapis.com/compute/v1/projects/PROJECT_ID/zones/ZONE/instances
{
"machineType": "projects/PROJECT_ID/zones/ZONE/machineTypes/MACHINE_TYPE",
"disks":
[
{
"type": "PERSISTENT",
"initializeParams":
{
"diskSizeGb": "DISK_SIZE",
"sourceImage": "projects/IMAGE_PROJECT/global/images/family/IMAGE_FAMILY"
},
"boot": true
}
],
"name": "VM_NAME",
"networkInterfaces":
[
{
"network": "projects/PROJECT_ID/global/networks/NETWORK"
}
],
"guestAccelerators":
[
{
"acceleratorCount": ACCELERATOR_COUNT,
"acceleratorType": "projects/PROJECT_ID/zones/ZONE/acceleratorTypes/ACCELERATOR_TYPE"
}
],
"scheduling":
{
"onHostMaintenance": "terminate",
"automaticRestart": true,
["preemptible": true]
},
}
Replace the following:
VM_NAME: the name of the VM.PROJECT_ID: your project ID.ZONE: the zone for the VM. This zone must support the GPU type.MACHINE_TYPE: the machine type that you selected for the VM. See GPUs on Compute Engine to see what machine types are available based on your desired GPU count.IMAGEorIMAGE_FAMILY: specify one of the following:IMAGE: the required version of a public image. For example,"sourceImage": "projects/debian-cloud/global/images/debian-10-buster-v20200309"IMAGE_FAMILY: an image family. This creates the VM from the most recent, non-deprecated OS image. For example, if you specify"sourceImage": "projects/debian-cloud/global/images/family/debian-10", Compute Engine creates a VM from the latest version of the OS image in the Debian 10 image family.
You can also specify a custom image or Deep Learning VM image.
IMAGE_PROJECT: the Compute Engine image project that the image family belongs to. If using a custom image or Deep Learning VM image, specify the project that those images belong to.DISK_SIZE: the size of your boot disk in GB. Specify a boot disk size of at least 40 GB.NETWORK: the VPCnetwork that you want to use for the VM. You can specifydefaultto use your default network.ACCELERATOR_COUNT: the number of GPUs that you want to add to your VM. See GPUs on Compute Engine for a list of GPU limits based on the machine type of your VM.ACCELERATOR_TYPE: the GPU model that you want to use. If you plan on running graphics-intensive workloads on this VM, use one of the virtual workstation models.Choose one of the following values:
- NVIDIA® T4:
nvidia-tesla-t4 - NVIDIA® T4 Virtual Workstation with NVIDIA®
GRID®:
nvidia-tesla-t4-vws - NVIDIA® P4:
nvidia-tesla-p4 - NVIDIA® P4 Virtual Workstation with NVIDIA®
GRID®:
nvidia-tesla-p4-vws - NVIDIA® P100:
nvidia-tesla-p100 - NVIDIA® P100 Virtual Workstation with NVIDIA®
GRID®:
nvidia-tesla-p100-vws - NVIDIA® V100:
nvidia-tesla-v100 - NVIDIA® K80:
nvidia-tesla-k80
- NVIDIA® T4:
Next: To ensure that your system can use the GPUs, complete the following steps:
- Install the GPU drivers.
- If you enabled NVIDIA® GRID® virtual workstations, install GRID® drivers for virtual workstations.
What's next?
- Learn more about GPUs on Compute Engine.
- Add Local SSDs to your instances. Local SSD devices pair well with GPUs when your apps require high-performance storage.
- Create groups of GPU instances using instance templates.
- To monitor GPU performance, see Monitoring GPU performance.
- To optimize GPU performance, see Optimizing GPU performance.
- To handle GPU host maintenance, see Handling GPU host maintenance events.
- Try the Running TensorFlow Inference Workloads at Scale with TensorRT5 and NVIDIA T4 GPU tutorial.