Create an A3, A2, or G2 VM (original) (raw)

Linux Windows

This document explains how to create a VM that uses a machine type from the A3 High, A3 Mega, A3 Edge, A2, and G2 machine series. To learn more about creating VMs with attached GPUs, seeOverview of creating an instance with attached GPUs.

Before you begin

Required roles

To get the permissions that you need to create VMs, ask your administrator to grant you theCompute Instance Admin (v1) (roles/compute.instanceAdmin.v1) IAM role on the project. For more information about granting roles, see Manage access to projects, folders, and organizations.

This predefined role contains the permissions required to create VMs. To see the exact permissions that are required, expand the Required permissions section:

Required permissions

The following permissions are required to create VMs:

You might also be able to get these permissions with custom roles or other predefined roles.

Create a VM that has attached GPUs

You can create an A3 High, A3 Mega, A3 Edge, A2, or G2 accelerator-optimized VM by using the Google Cloud console, Google Cloud CLI, or REST.

To make some customizations to your G2 VMs, you might need to use the Google Cloud CLI or REST. See G2 limitations.

Console

  1. In the Google Cloud console, go to the Create an instance page.
    Go to Create an instance
  2. Specify a Name for your VM. SeeResource naming convention.
  3. Select a region and zone where GPUs are available. See the list of availableGPU regions and zones.
  4. In the Machine configuration section, select the GPUs machine family.
    1. Complete one of the following steps to select either a predefined or custom machine type based on the machine series:
      • For all GPU machine series, you can select a predefined machine type as follows:
        1. In the GPU type list, select your GPU type.
        * For A3 High, A3 Mega, or A3 Edge accelerator-optimized VMs, selectNVIDIA H100 80GB, or NVIDIA H100 80GB MEGA.
        * For A2 accelerator-optimized VMs, select either NVIDIA A100 40GBor NVIDIA A100 80GB.
        * For G2 accelerator-optimized VMs, select NVIDIA L4.
        2. In the Number of GPUs list, select the number of GPUs.
      • For the G2 machine series, you can select a custom machine type as follows:
        1. In the GPU type list, select NVIDIA L4.
        2. In the Machine type section, select Custom.
        3. To specify the number of vCPUs and the amount of memory for the instance, drag the sliders or enter the values in the text boxes. The console displays an estimated cost for the instance as you change the number of vCPUs and memory.
    2. Optional: The G2 machine series supportsNVIDIA RTX Virtual Workstations (vWS) for graphics workloads. If you plan on running graphics-intensive workloads on your G2 VM, select Enable Virtual Workstation (NVIDIA GRID).
  5. In the Boot disk section, clickChange. This opens the Boot disk configuration page.
  6. On the Boot disk configuration page, do the following:
    1. On the Public images tab, choose asupported Compute Engine imageor Deep Learning VM Images.
    2. Specify a boot disk size of at least 40 GB.
    3. To confirm your boot disk options, click Select.
  7. Optional: Configure provisioning model. For example, if your workload is fault-tolerant and can withstand possible VM preemption, consider using Spot VMs to reduce the cost of your VMs and the attached GPUs. For more information, seeGPUs on Spot VMs. To do this, complete the following steps:
    1. In the Availability policies section, select Spotfrom the VM provisioning model list. This setting disables automatic restart and host maintenance options for the VM.
    2. Optional: In the On VM termination list, select what happens when Compute Engine preempts the VM:
      • To stop the VM during preemption, select Stop (default).
      • To delete the VM during preemption, select Delete.
  8. To create and start the VM, click Create.

gcloud

To create and start a VM, use thegcloud compute instances createcommand with the following flags. VMs with GPUs can't live migrate, make sure that you set the --maintenance-policy=TERMINATE flag.

The following optional flags are shown in the sample command:

Replace the following:

REST

Send a POST request to theinstances.insert method. VMs with GPUs can't live migrate, make sure you set the onHostMaintenanceparameter 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": "SOURCE_IMAGE_URI" }, "boot": true } ], "name": "VM_NAME", "networkInterfaces": [ { "network": "projects/PROJECT_ID/global/networks/NETWORK" } ], "scheduling": { "onHostMaintenance": "terminate", ["automaticRestart": true] }, }

Replace the following:

Install drivers

For the VM to use the GPU, you need toInstall the GPU driver on your VM.

Examples

In these examples, most of the VMs are created by using the Google Cloud CLI. However, you can also use either the Google Cloud console orREST to create these VMs.

The following examples show how to create VMs using the following images:

COS (A3 Edge/High)

You can create either a3-edgegpu-8g or a3-highgpu-8g VMs that have attached H100 GPUs by usingContainer-optimized (COS) images.

For detailed instructions on how to create these a3-edgegpu-8g ora3-highgpu-8gVMs that use Container-Optimized OS, seeCreate an A3 VM with GPUDirect-TCPX enabled.

Public OS image (G2)

You can create VMs that have attached GPUs that use either apublic image that is available on Compute Engine or a custom image.

To create a VM using the most recent, non-deprecated image from theRocky Linux 8 optimized for Google Cloud image familythat uses the g2-standard-8machine type and has an NVIDIA RTX Virtual Workstation, complete the following steps:

  1. 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=ZONE \
    --machine-type=g2-standard-8 \
    --maintenance-policy=TERMINATE --restart-on-failure \
    --network-interface=nic-type=GVNIC \
    --accelerator=type=nvidia-l4-vws,count=1 \
    --image-family=rocky-linux-8-optimized-gcp \
    --image-project=rocky-linux-cloud \
    --boot-disk-size=200GB \
    --boot-disk-type=pd-ssd
    Replace the following:
    • VM_NAME: the name of your VM
    • PROJECT_ID : your project ID.
    • ZONE: the zone for the VM.
  2. Install NVIDIA driver and CUDA. For NVIDIA L4 GPUs, CUDA version XX or higher is required.

DLVM image (A2)

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:

For more information about the DLVM images that are available, and the packages installed on the images, see theDeep Learning VM documentation.

  1. Create a VM using the tf2-ent-2-3-cu110 image 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 ZONE \
    --machine-type a2-highgpu-1g \
    --maintenance-policy TERMINATE \
    --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 VM
    • PROJECT_ID : your project ID.
    • ZONE: the zone for the VM
  2. The preceding example command also generates aVertex AI Workbench user-managed notebooks instancefor the VM. To access the notebook, in the Google Cloud console, go to theVertex AI Workbench > User-managed notebookspage.
    Go to the User-managed notebooks page

Multi-Instance GPU (A3 and A2 VMs only)

A Multi-Instance GPUpartitions a single NVIDIA H100 or 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 NVIDIA H100 or A100 GPU to deliver guaranteed quality-of-service (QoS) at up to 7x higher utilization compared to earlier GPU models.

You can create up to seven Multi-instance GPUs. For A100 40GB GPUs, each Multi-instance GPU is allocated 5 GB of memory. With the A100 80GB and H100 80GB GPUs the allocated memory doubles to 10 GB each.

For more information about using Multi-Instance GPUs, seeNVIDIA Multi-Instance GPU User Guide.

To create Multi-Instance GPUs, complete the following steps:

  1. Create an A3 High, A3 Mega, A3 Edge, or A2 accelerator-optimized VM.
  2. Enable NVIDIA GPU drivers.
  3. Enable Multi-Instance GPUs..
    sudo nvidia-smi -mig 1
  4. 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.10gb 19 7/7 9.62 No 16 1 0 |
| 1 1 0 |
+-----------------------------------------------------------------------------+
| 0 MIG 1g.10gb+me 20 1/1 9.62 No 16 1 0 |
| 1 1 1 |
+-----------------------------------------------------------------------------+
| 0 MIG 1g.20gb 15 4/4 19.50 No 26 1 0 |
| 1 1 0 |
+-----------------------------------------------------------------------------+
| 0 MIG 2g.20gb 14 3/3 19.50 No 32 2 0 |
| 2 2 0 |
+-----------------------------------------------------------------------------+
| 0 MIG 3g.40gb 9 2/2 39.25 No 60 3 0 |
| 3 3 0 |
+-----------------------------------------------------------------------------+
....... 5. 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, seeCreating GPU Instances.
The following example creates two MIG 3g.20gb GPU instances by using the profile ID (9).
The -C flag is also specified which creates the associated compute instances for the required profile.
sudo nvidia-smi mig -cgi 9,9 -C 6. Check that the two Multi-Instance GPUs are created:
sudo nvidia-smi mig -lgi 7. Check that both the GIs and corresponding CIs are created.
sudo nvidia-smi
The output is similar to the following:
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 525.125.06 Driver Version: 525.125.06 CUDA Version: 12.0 |
|-------------------------------+----------------------+----------------------+
| 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 NVIDIA H100 80G... Off | 00000000:04:00.0 Off | On |
| N/A 33C P0 70W / 700W | 39MiB / 81559MiB | N/A Default |
| | | Enabled |
+-------------------------------+----------------------+----------------------+
| 1 NVIDIA H100 80G... Off | 00000000:05:00.0 Off | On |
| N/A 32C P0 69W / 700W | 39MiB / 81559MiB | 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 | 19MiB / 40192MiB | 60 0 | 3 0 3 0 3 |
| | 0MiB / 65535MiB | | |
+------------------+----------------------+-----------+-----------------------+
| 0 2 0 1 | 19MiB / 40192MiB | 60 0 | 3 0 3 0 3 |
| | 0MiB / 65535MiB | | |
+------------------+----------------------+-----------+-----------------------+
......
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+

What's next?