You can create a group of VMs that have attached GPUs by using the bulk creation process. With the bulk creation process, you get upfront validation where the request fails fast if it is not feasible. Also if you use the region flag, the bulk creation API automatically chooses the zone that has the capacity to fulfill the request. For more information about the bulk creation, see About bulk creation of VMs.
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 Google Cloud CLI.
- Set a default region and zone.
- If you want to use the API examples in this guide, set up API access.
- To review additional prerequisite steps such as selecting an OS image and checking GPU quota, review the overview document.
Overview
When creating VMs with attached GPUs using the bulk creation method, you can choose
to create VMs in a region (such as us-central1
) or in a specific zone such as
(us-central1-a
).
If you choose to specify a region, Compute Engine places the VMs in any zone within the region that supports GPUs.
Create groups of accelerator-optimized VMs
The accelerator-optimized machine family is available in A2 standard and ultra machine types, and the G2 standard machine type.
Each accelerator-optimized machine type has a fixed number of a specific NVIDIA GPU model attached.
- For A2 accelerator-optimized machine types, NVIDIA A100 GPUs are attached. These are available in both A100 40GB and A100 80GB options.
- For G2 accelerator-optimized machine types, NVIDIA L4 GPUs are attached.
You create a group of accelerator-optimized VMs by using either the Google Cloud CLI, or the Compute Engine API.
gcloud
To create a group of VMs, use the gcloud compute instances bulk create
command. For more
information about the parameters and how to use this command, see
Create VMs in bulk.
The following optional flags are shown in the example command:
The
--preemptible
flag which configures your VM as a preemptible VM instance. This reduces the cost of your VM and the attached GPUs. For more information, see GPUs on preemptible instances.The
--accelerator
flag to specify a virtual workstation. NVIDIA RTX Virtual Workstations (vWS) are supported for only G2 VMs.
Example
This example creates two VMs that have attached GPUs by using the following specifications:
- VM names:
my-test-vm-1
,my-test-vm-2
- Each VM has two GPUs attached, specified by using the appropriate accelerator-optimized machine type
gcloud compute instances bulk create \ --name-pattern="my-test-vm-#" \ --region=REGION \ --count=2 \ --machine-type=MACHINE_TYPE \ --boot-disk-size=200 \ --image=IMAGE \ --image-project=IMAGE_PROJECT \ --on-host-maintenance=TERMINATE --restart-on-failure \ [--preemptible] \ [--accelerator=type=nvidia-l4-vws,count=VWS_ACCELERATOR_COUNT]
Replace the following:
REGION
: the region for the VMs. This region must support your selected GPU model.MACHINE_TYPE
: the machine type that you selected. Choose from one of the following:- An A2 machine type.
- A G2 machine type. G2 machine types also
support custom memory. Memory must be a multiple of 1024 MB and
within the supported memory range. For example, to create a VM
with 4 vCPUs and 19 GB of memory specify
--machine-type=g2-custom-4-19456
.
IMAGE
: an operating system image that supports GPUs.If you want to use the latest image in an image family, replace the
--image
flag with the--image-family
flag and set its value to an image family that supports GPUs. For example:--image-family=rocky-linux-8-optimized-gcp
.You can also specify a custom image or Deep Learning VM Images.
IMAGE_PROJECT
: the Compute Engine image project that the OS image belongs to. If using a custom image or Deep Learning VM Images, specify the project that those images belong to.VWS_ACCELERATOR_COUNT
: the number of virtual GPUs that you need.
If successful, the output is similar to the following:
NAME ZONE my-test-vm-1 us-central1-b my-test-vm-2 us-central1-b Bulk create request finished with status message: [VM instances created: 2, failed: 0.]
API
Use the instances.bulkInsert
method with the
required parameters to create multiple VMs in a zone. For more
information about the parameters and how to use this command, see
Create VMs in bulk.
Example
This example creates two VMs that have attached GPUs by using the following specifications:
- VM names:
my-test-vm-1
,my-test-vm-2
Each VM has two GPUs attached, specified by using the appropriate accelerator-optimized machine type
POST https://compute.googleapis.com/compute/v1/projects/PROJECT_ID/regions/REGION/instances/bulkInsert { "namePattern":"my-test-vm-#", "count":"2", "instanceProperties": { "machineType":MACHINE_TYPE, "disks":[ { "type":"PERSISTENT", "initializeParams":{ "diskSizeGb":"200", "sourceImage":SOURCE_IMAGE_URI }, "boot":true } ], "name": "default", "networkInterfaces": [ { "network": "projects/PROJECT_ID/global/networks/default" } ], "scheduling":{ "onHostMaintenance":"TERMINATE", "automaticRestart":true } } }
Replace the following:
PROJECT_ID
: your project IDREGION
: the region for the VMs. This region must support your selected GPU model.MACHINE_TYPE
: the machine type that you selected. Choose from one of the following:- An A2 machine type.
- A G2 machine type. G2 machine types also
support custom memory. Memory must be a multiple of 1024 MB and
within the supported memory range. For example, to create a VM
with 4 vCPUs and 19 GB of memory specify
--machine-type=g2-custom-4-19456
.
SOURCE_IMAGE_URI
: the URI for the specific image or image family that you want to use.For example:
- Specific image:
"sourceImage": "projects/rocky-linux-cloud/global/images/rocky-linux-8-optimized-gcp-v20220719"
- Image family:
"sourceImage": "projects/rocky-linux-cloud/global/images/family/rocky-linux-8-optimized-gcp"
.
When you specify an image family, Compute Engine creates a VM from the most recent, non-deprecated OS image in that family. For more information about when to use image families, see Image family best practices.
- Specific image:
Additional settings:
You can reduce the cost of your VM and the attached GPUs by using preemptible VMs. For more information, see GPUs on preemptible VM instances. To set the VM to be preemptible, add the
"preemptible": true
option to your request."scheduling": { "onHostMaintenance": "terminate", "automaticRestart": true, "preemptible": true }
For G2 VMs, NVIDIA RTX Virtual Workstations (vWS) are supported. To specify a virtual workstation, add the
guestAccelerators
option to your request. ReplaceVWS_ACCELERATOR_COUNT
with the number of virtual GPUs that you need."guestAccelerators": [ { "acceleratorCount": VWS_ACCELERATOR_COUNT, "acceleratorType": "projects/PROJECT_ID/zones/ZONEacceleratorTypes/nvidia-l4-vws" } ]
Limitations
A2 standard
- You don't receive sustained use discounts for VMs that use A2 standard machine types.
- You can only use A2 standard machine types in certain regions and zones.
- The A2 standard machine types is only available on the Cascade Lake platform.
- You can't use regional persistent disks on VMs that use A2 standard machine types.
- You can't use the
a2-megagpu-16g
A2 standard machine type on Windows operating systems. When using Windows operating systems, choose a different A2 standard machine type. - You can't do a quick format of the attached Local SSDs on Windows VMs that use A2 standard
machine types. To format these Local SSDs, you must do a full format by using the diskpart
utility and specifying
format fs=ntfs label=tmpfs
.
A2 ultra
- You don't receive sustained use discounts for VMs that use A2 ultra machine types.
- You can only use A2 ultra machine types in certain regions and zones.
- The A2 ultra machine types is only available on the Cascade Lake platform.
- You can't use regional persistent disks on VMs that use A2 ultra machine types.
- You can't do a quick format of the attached Local SSDs on Windows VMs that use A2 ultra
machine types. To format these Local SSDs, you must do a full format by using the diskpart
utility and specifying
format fs=ntfs label=tmpfs
. - You can't change the machine type for a VM that uses the A2 ultra machine family. If you need to use a different A2 ultra machine type, or any other machine family, you must create a new VM.
- You can't change any other machine type to an A2 ultra machine type. If you need to create a VM that uses an A2 ultra machine type, you must create a new VM.
G2 standard
- You don't receive sustained use discounts for VMs that use G2 standard machine types.
- You can only use G2 standard machine types in certain regions and zones.
- The G2 standard machine types is only available on the Cascade Lake platform.
- You can't use regional persistent disks on VMs that use G2 standard machine types.
- Standard persistent disks (
pd-standard
) are not supported on VMs that use G2 standard machine types. For supported disk types, see Supported disk types for G2. - You can't create Multi-Instance GPUs on G2 standard machine types.
- You can't use Deep Learning VM Images as boot disks for your VMs that use G2 standard machine types.
- The current default driver for Container-Optimized OS doesn't support L4 GPUs running on
G2 machine types. Container-Optimized OS also only support a select set of drivers.
If you want to use Container-Optimized OS on G2 machine types, review the following notes:
- Use a Container-Optimized OS version that supports the minimum recommended
NVIDIA driver version
525.60.13
or later. For more information, review the Container-Optimized OS release notes. - When you install the driver,
specify the latest available version that works for the L4 GPUs.
For example,
sudo cos-extensions install gpu -- -version=525.60.13
.
- Use a Container-Optimized OS version that supports the minimum recommended
NVIDIA driver version
- You must use the Google Cloud CLI or the Compute Engine API to
create G2 VMs
for the following scenarios:
- You want to specify custom memory values.
- You want to customize the number of visible CPU cores.
Create groups of N1-general purpose VMs
You create a group of VMs with attached GPUs by using either the Google Cloud CLI, or the Compute Engine API.
This section describes how to create multiple VMs using the following GPU types:
NVIDIA GPUs:
- NVIDIA T4:
nvidia-tesla-t4
- NVIDIA P4:
nvidia-tesla-p4
- NVIDIA P100:
nvidia-tesla-p100
- NVIDIA V100:
nvidia-tesla-v100
- NVIDIA K80:
nvidia-tesla-k80
. See NVIDIA K80 EOL.
NVIDIA RTX Virtual Workstation (vWS) (formerly known as NVIDIA GRID):
- NVIDIA T4 Virtual Workstation:
nvidia-tesla-t4-vws
- NVIDIA P4 Virtual Workstation:
nvidia-tesla-p4-vws
NVIDIA P100 Virtual Workstation:
nvidia-tesla-p100-vws
For these virtual workstations, an NVIDIA RTX Virtual Workstation (vWS) license is automatically added to your VM.
gcloud
To create a group of VMs, use the gcloud compute instances bulk create
command.
For more information about the parameters and how to use this command, see
Create VMs in bulk.
Example
The following example creates two VMs with attached GPUs using the following specifications:
- VM names:
my-test-vm-1
,my-test-vm-2
- VMs created in any zone in
us-central1
that supports GPUs - Each VM has two T4 GPUs attached, specified by using the accelerator type and accelerator count flags
- Each VM has GPU drivers installed
- Each VM uses the Deep Learning VM image
pytorch-latest-gpu-v20211028-debian-10
gcloud compute instances bulk create \ --name-pattern="my-test-vm-#" \ --count=2 \ --region=us-central1 \ --machine-type=n1-standard-2 \ --accelerator type=nvidia-tesla-t4,count=2 \ --boot-disk-size=200 \ --metadata="install-nvidia-driver=True" \ --scopes="https://www.googleapis.com/auth/cloud-platform" \ --image=pytorch-latest-gpu-v20211028-debian-10 \ --image-project=deeplearning-platform-release \ --on-host-maintenance=TERMINATE --restart-on-failure
If successful, the output is similar to the following:
NAME ZONE my-test-vm-1 us-central1-b my-test-vm-2 us-central1-b Bulk create request finished with status message: [VM instances created: 2, failed: 0.]
API
Use the instances.bulkInsert
method with the
required parameters to create multiple VMs in a zone.
For more information about the parameters and how to use this command, see
Create VMs in bulk.
Example
The following example creates two VMs with attached GPUs using the following specifications:
- VM names:
my-test-vm-1
,my-test-vm-2
- VMs created in any zone in
us-central1
that supports GPUs - Each VM has two T4 GPUs attached, specified by using the accelerator type and accelerator count flags
- Each VM has GPU drivers installed
- Each VM uses the Deep Learning VM image
pytorch-latest-gpu-v20211028-debian-10
Replace PROJECT_ID
with your project ID.
POST https://compute.googleapis.com/compute/v1/projects/PROJECT_ID/region/us-central1/instances/bulkInsert { "namePattern":"my-test-vm-#", "count":"2", "instanceProperties": { "machineType":"n1-standard-2", "disks":[ { "type":"PERSISTENT", "initializeParams":{ "diskSizeGb":"200", "sourceImage":"projects/deeplearning-platform-release/global/images/pytorch-latest-gpu-v20211028-debian-10" }, "boot":true } ], "name": "default", "networkInterfaces": [ { "network": "projects/PROJECT_ID/global/networks/default" } ], "guestAccelerators": [ { "acceleratorCount": 2, "acceleratorType": "nvidia-tesla-t4" } ], "scheduling":{ "onHostMaintenance":"TERMINATE", "automaticRestart":true }, "metadata":{ "items":[ { "key":"install-nvidia-driver", "value":"True" } ] } } }
What's next?
- To monitor GPU performance, see Monitor GPU performance.
- To optimize GPU performance, see Optimize GPU performance.
- To handle GPU host maintenance, see Handle GPU host maintenance events.