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    Create an AI-optimized Slurm cluster

    This document outlines the steps to configure and deploy Slurm clusters that use A4X, A4, A3 Ultra, A3 Mega, or A3 High machine types. To learn more about these accelerator-optimized machine types, see GPU machine types .

    For other methods to create an AI-optimized Slurm cluster in Google Cloud, see the following:

    Limitations

    Depending on the machine series that the VMs in your Slurm cluster use, the following limitations apply:

    A4X

    • You don't receive sustained use discounts or flexible committed use discounts for instances that use these machine types. Additionally, A4X Max machine types don't support resource-based committed use discounts .
    • You can only create instances in certain regions and zones .
    • You can't use Persistent Disk (regional or zonal). You can only use Google Cloud Hyperdisk .
    • These machine types are only available on the NVIDIA Grace platform .
    • Machine type changes aren't supported for A4X Max or A4X. To switch to or from one of these machine types, you must create a new A4X Max or A4X instance.
    • You can't run Windows operating systems on these machine types.
    • For A4X instances, when you use ethtool -S to monitor GPU networking, the physical port counters that end in _phy don't update. This is expected behavior for instances that use the MRDMA Virtual Function (VF) architecture. For more information, see MRDMA functions and network monitoring tools .
    • A4X Max and A4X instances don't support the following:
    • You can't attach Hyperdisk ML disks created before February 4, 2026 to A4X machine types.

    A4

    • You don't receive sustained use discounts and flexible committed use discounts for instances that use an A4 machine type.
    • You can only use an A4 machine type in certain regions and zones .
    • You can't use Persistent Disk (regional or zonal). You can only use Google Cloud Hyperdisk .
    • The A4 machine type is only available on the Emerald Rapids CPU platform .
    • You can't change the machine type of an instance to or from A4 machine type. You must create a new instance with this machine type.
    • A4 machine types don't support sole-tenancy .
    • You can't run Windows operating systems on an A4 machine type.
    • For A4 instances, when you use ethtool -S to monitor GPU networking, physical port counters that end in _phy don't update. This is expected behavior for instances that use the MRDMA Virtual Function (VF) architecture. For more information, see MRDMA functions and network monitoring tools .
    • You can't attach Hyperdisk ML disks that were created before February 4, 2026 to A4 machine types.

    A3 Ultra

    • You don't receive sustained use discounts and flexible committed use discounts for instances that use an A3 Ultra machine type.
    • You can only use an A3 Ultra machine type in certain regions and zones .
    • You can't use Persistent Disk (regional or zonal). You can only use Google Cloud Hyperdisk .
    • The A3 Ultra machine type is only available on the Emerald Rapids CPU platform .
    • Machine type changes aren't supported for A3 Ultra machine type. To switch to or from this machine type, you must create a new instance.
    • You can't run Windows operating systems on an A3 Ultra machine type.
    • A3 Ultra machine types don't support sole-tenancy .
    • For A3 Ultra instances, when you use ethtool -S to monitor GPU networking, physical port counters that end in _phy don't update. This is expected behavior for instances that use the MRDMA Virtual Function (VF) architecture. For more information, see MRDMA functions and network monitoring tools .

    A3 Mega

    • You don't receive sustained use discounts and flexible committed use discounts for instances that use an A3 Mega machine type.
    • You can only use an A3 Mega machine type in certain regions and zones .
    • You can't use regional Persistent Disk on an instance that uses an A3 Mega machine type.
    • The A3 Mega machine type is only available on the Sapphire Rapids CPU platform .
    • Machine type changes aren't supported for A3 Mega machine type. To switch to or from this machine type, you must create a new instance.
    • You can't run Windows operating systems on an A3 Mega machine type.

    A3 High

    Before you begin

    Before creating a Slurm cluster, if you haven't already done so, complete the following steps:

    1. Choose a consumption option : your choice of consumption option determines how you get and use GPU resources.

      To learn more, see Choose a consumption option .

    2. Obtain capacity : the process to obtain capacity differs for each consumption option.

      To learn about the process to obtain capacity for your chosen consumption option, see Capacity overview .

    3. Ensure that you have enough Filestore capacity quota : you need to have enough Filestore quota in your target region before deploying. The required minimum capacity depends on the machine types in your cluster:
      • A4, A4X, A3 Ultra, and A3 Mega : requires a minimum of 10 TiB (10,240 GiB) of HIGH_SCALE_SSD (zonal) capacity.
      • A3 High : requires a minimum of 2.5 TiB (2,560 GiB) of BASIC_SSD (standard) capacity.

      To check quota or request a quota increase, see the following:

    4. Install Cluster Toolkit : to provision Slurm clusters, you must use Cluster Toolkit version v1.62.0 or later.

      To install Cluster Toolkit, see Set up Cluster Toolkit .

    In the Google Cloud console, activate Cloud Shell.

    Activate Cloud Shell

    At the bottom of the Google Cloud console, a Cloud Shell session starts and displays a command-line prompt. Cloud Shell is a shell environment with the Google Cloud CLI already installed and with values already set for your current project. It can take a few seconds for the session to initialize.

    Required roles

    To ensure that the Compute Engine default service account has the necessary permissions to deploy a Slurm cluster, ask your administrator to grant the following IAM roles to the Compute Engine default service account:

    For more information about granting roles, see Manage access to projects, folders, and organizations .

    Your administrator might also be able to give the Compute Engine default service account the required permissions through custom roles or other predefined roles .

    Set up a storage bucket

    Cluster blueprints use Terraform modules to provision Cloud infrastructure. A best practice when working with Terraform is to store the state remotely in a version enabled file. On Google Cloud, you can create a Cloud Storage bucket that has versioning enabled.

    To create this bucket and enable versioning from the CLI, run the following commands:

    gcloud storage buckets create gs:// BUCKET_NAME 
\
    --project= PROJECT_ID 
\
    --default-storage-class=STANDARD --location= BUCKET_REGION 
\
    --uniform-bucket-level-access
gcloud storage buckets update gs:// BUCKET_NAME 
--versioning

    Replace the following:

    Open the Cluster Toolkit directory

    To use Slurm with Google Cloud, you must install Cluster Toolkit . After you install the toolkit, ensure that you are in the Cluster Toolkit directory by running the following command:

    cd cluster-toolkit

    This cluster deployment requires Cluster Toolkit v1.62.0 or later. To check your version, you can run the following command:

    ./gcluster --version

    Create a deployment file

    Create a deployment file that you can use to specify the Cloud Storage bucket, set names for your network and subnetwork, and set deployment variables such as project ID, region, and zone.

    To create a deployment file, follow the steps for your required machine type and consumption option.

    A4X

    To create your deployment file, use a text editor to create a YAML file named a4xhigh-slurm-deployment.yaml and add the following content.

    terraform_backend_defaults:
  type: gcs
  configuration:
    bucket: BUCKET_NAME 
vars:
  deployment_name: DEPLOYMENT_NAME 
project_id: PROJECT_ID 
region: REGION 
zone: ZONE 
a4x_cluster_size: NUMBER_OF_VMS 
a4x_reservation_name: RESERVATION_NAME

    Replace the following:

    • BUCKET_NAME : the name of your Cloud Storage bucket, which you created in the previous section.
    • DEPLOYMENT_NAME : a name for your deployment. If creating multiple clusters, ensure that you select a unique name for each one.
    • PROJECT_ID : your project ID.
    • REGION : the region that has the reserved machines.
    • ZONE : the zone where you want to provision the cluster. If you're using a reservation-based consumption option, the region and zone information was provided by your account team when the capacity was delivered .
    • NUMBER_OF_VMS : the number of A4X VMs in your cluster. You can specify any number of VMs. However, A4X VMs are physically interconnected by a multi-node NVLink system in groups of 18 VMs (72 GPUs) to form an NVLink domain.

      For optimal network performance, we recommend that you specify a value that is a multiple of 18 VMs (for example, 18, 36, or 54). When you create an A4X cluster, the A4X blueprint automatically creates and applies a compact placement policy with a GPU topology of 1x72 for each group of 18 VMs. For more information about A4X topology, see A4X fundamentals .

    • RESERVATION_NAME : the name of your reservation .

    A4

    The parameters that you need to add to your deployment file depend on the consumption option that you're using for your deployment. Select the tab that corresponds to your consumption option's provisioning model.

    Reservation-bound

    To create your deployment file, use a text editor to create a YAML file named a4high-slurm-deployment.yaml and add the following content.

    terraform_backend_defaults:
  type: gcs
  configuration:
    bucket: BUCKET_NAME 
vars:
  deployment_name: DEPLOYMENT_NAME 
project_id: PROJECT_ID 
region: REGION 
zone: ZONE 
a4h_cluster_size: NUMBER_OF_VMS 
a4h_reservation_name: RESERVATION_NAME

    Replace the following:

    • BUCKET_NAME : the name of your Cloud Storage bucket, which you created in the previous section.
    • DEPLOYMENT_NAME : a name for your deployment. If creating multiple clusters, ensure that you select a unique name for each one.
    • PROJECT_ID : your project ID.
    • REGION : the region that has the reserved machines.
    • ZONE : the zone where you want to provision the cluster. If you're using a reservation-based consumption option, the region and zone information was provided by your account team when the capacity was delivered .
    • NUMBER_OF_VMS : the number of VMs that you want for the cluster.
    • RESERVATION_NAME : the name of your reservation .

    Flex-start

    To create your deployment file, use a text editor to create a YAML file named a4high-slurm-deployment.yaml and add the following content.

    terraform_backend_defaults:
  type: gcs
  configuration:
    bucket: BUCKET_NAME 
vars:
  deployment_name: DEPLOYMENT_NAME 
project_id: PROJECT_ID 
region: REGION 
zone: ZONE 
a4h_cluster_size: NUMBER_OF_VMS 
a4h_dws_flex_enabled: true

    Replace the following:

    • BUCKET_NAME : the name of your Cloud Storage bucket, which you created in the previous section.
    • DEPLOYMENT_NAME : a name for your deployment. If creating multiple clusters, ensure that you select a unique name for each one.
    • PROJECT_ID : your project ID.
    • REGION : the region where you want to provision your cluster.
    • ZONE : the zone where you want to provision your cluster.
    • NUMBER_OF_VMS : the number of VMs that you want for the cluster.

    This deployment provisions static compute nodes , which means that the cluster has a set number of nodes at all times. If you want to enable your cluster to autoscale instead, use examples/machine-learning/a4-highgpu-8g/a4high-slurm-blueprint.yaml file and edit the values of node_count_static and node_count_dynamic_max to match the following:

    node_count_static: 0
      node_count_dynamic_max: $(vars.a4h_cluster_size)

    Spot

    To create your deployment file, use a text editor to create a YAML file named a4high-slurm-deployment.yaml and add the following content.

    terraform_backend_defaults:
  type: gcs
  configuration:
    bucket: BUCKET_NAME 
vars:
  deployment_name: DEPLOYMENT_NAME 
project_id: PROJECT_ID 
region: REGION 
zone: ZONE 
a4h_cluster_size: NUMBER_OF_VMS 
a4h_enable_spot_vm: true

    Replace the following:

    • BUCKET_NAME : the name of your Cloud Storage bucket, which you created in the previous section.
    • DEPLOYMENT_NAME : a name for your deployment. If creating multiple clusters, ensure that you select a unique name for each one.
    • PROJECT_ID : your project ID.
    • REGION : the region where you want to provision your cluster.
    • ZONE : the zone where you want to provision your cluster.
    • NUMBER_OF_VMS : the number of VMs that you want for the cluster.

    A3 Ultra

    The parameters that you need to add to your deployment file depend on the consumption option that you're using for your deployment. Select the tab that corresponds to your consumption option's provisioning model.

    Reservation-bound

    To create your deployment file, use a text editor to create a YAML file named a3ultra-slurm-deployment.yaml and add the following content.

    terraform_backend_defaults:
  type: gcs
  configuration:
    bucket: BUCKET_NAME 
vars:
  deployment_name: DEPLOYMENT_NAME 
project_id: PROJECT_ID 
region: REGION 
zone: ZONE 
a3u_cluster_size: NUMBER_OF_VMS 
a3u_reservation_name: RESERVATION_NAME

    Replace the following:

    • BUCKET_NAME : the name of your Cloud Storage bucket, which you created in the previous section.
    • DEPLOYMENT_NAME : a name for your deployment. If creating multiple clusters, ensure that you select a unique name for each one.
    • PROJECT_ID : your project ID.
    • REGION : the region that has the reserved machines.
    • ZONE : the zone where you want to provision the cluster. If you're using a reservation-based consumption option, the region and zone information was provided by your account team when the capacity was delivered .
    • NUMBER_OF_VMS : the number of VMs that you want for the cluster.
    • RESERVATION_NAME : the name of your reservation .

    Flex-start

    To create your deployment file, use a text editor to create a YAML file named a3ultra-slurm-deployment.yaml and add the following content.

    terraform_backend_defaults:
  type: gcs
  configuration:
    bucket: BUCKET_NAME 
vars:
  deployment_name: DEPLOYMENT_NAME 
project_id: PROJECT_ID 
region: REGION 
zone: ZONE 
a3u_cluster_size: NUMBER_OF_VMS 
a3u_dws_flex_enabled: true

    Replace the following:

    • BUCKET_NAME : the name of your Cloud Storage bucket, which you created in the previous section.
    • DEPLOYMENT_NAME : a name for your deployment. If creating multiple clusters, ensure that you select a unique name for each one.
    • PROJECT_ID : your project ID.
    • REGION : the region where you want to provision your cluster.
    • ZONE : the zone where you want to provision your cluster.
    • NUMBER_OF_VMS : the number of VMs that you want for the cluster.

    This deployment provisions static compute nodes , which means that the cluster has a set number of nodes at all times. If you want to enable your cluster to autoscale instead, use examples/machine-learning/a3-ultragpu-8g/a3ultra-slurm-blueprint.yaml file and edit the values of node_count_static and node_count_dynamic_max to match the following:

    node_count_static: 0
      node_count_dynamic_max: $(vars.a3u_cluster_size)

    Spot

    To create your deployment file, use a text editor to create a YAML file named a3ultra-slurm-deployment.yaml and add the following content.

    terraform_backend_defaults:
  type: gcs
  configuration:
    bucket: BUCKET_NAME 
vars:
  deployment_name: DEPLOYMENT_NAME 
project_id: PROJECT_ID 
region: REGION 
zone: ZONE 
a3u_cluster_size: NUMBER_OF_VMS 
a3u_enable_spot_vm: true

    Replace the following:

    • BUCKET_NAME : the name of your Cloud Storage bucket, which you created in the previous section.
    • DEPLOYMENT_NAME : a name for your deployment. If creating multiple clusters, ensure that you select a unique name for each one.
    • PROJECT_ID : your project ID.
    • REGION : the region where you want to provision your cluster.
    • ZONE : the zone where you want to provision your cluster.
    • NUMBER_OF_VMS : the number of VMs that you want for the cluster.

    A3 Mega

    The parameters that you need to add to your deployment file depend on the consumption option that you're using for your deployment. Select the tab that corresponds to your consumption option's provisioning model.

    Reservation-bound

    To create your deployment file, create a YAML file named a3mega-slurm-deployment.yaml and add the following content.

    terraform_backend_defaults:
  type: gcs
  configuration:
    bucket: BUCKET_NAME 
vars:
  deployment_name: DEPLOYMENT_NAME 
project_id: PROJECT_ID 
region: REGION 
zone: ZONE 
a3m_cluster_size: NUMBER_OF_VMS 
a3m_reservation_name: RESERVATION_NAME

    Replace the following:

    • BUCKET_NAME : the name of your Cloud Storage bucket, which you created in the previous section.
    • DEPLOYMENT_NAME : a name for your deployment. If creating multiple clusters, ensure that you select a unique name for each one.
    • PROJECT_ID : your project ID.
    • REGION : the region that has the reserved machines.
    • ZONE : the zone where you want to provision the cluster. If you're using a reservation-based consumption option, the region and zone information was provided by your account team when the capacity was delivered .
    • NUMBER_OF_VMS : the number of VMs that you want for the cluster.
    • RESERVATION_NAME : the name of your reservation .

    Flex-start

    To create your deployment file, create a YAML file named a3mega-slurm-deployment.yaml and add the following content.

    terraform_backend_defaults:
  type: gcs
  configuration:
    bucket: BUCKET_NAME 
vars:
  deployment_name: DEPLOYMENT_NAME 
project_id: PROJECT_ID 
region: REGION 
zone: ZONE 
a3m_cluster_size: NUMBER_OF_VMS 
a3m_dws_flex_enabled: true

    Replace the following:

    • BUCKET_NAME : the name of your Cloud Storage bucket, which you created in the previous section.
    • DEPLOYMENT_NAME : a name for your deployment. If creating multiple clusters, ensure that you select a unique name for each one.
    • PROJECT_ID : your project ID.
    • REGION : the region where you want to provision your cluster.
    • ZONE : the zone where you want to provision your cluster.
    • NUMBER_OF_VMS : the number of VMs that you want for the cluster.

    This deployment provisions static compute nodes , which means that the cluster has a set number of nodes at all times. If you want to enable your cluster to autoscale instead, use examples/machine-learning/a3-megagpu-8g/a3mega-slurm-blueprint.yaml file and edit the values of node_count_static and node_count_dynamic_max to match the following:

    node_count_static: 0
      node_count_dynamic_max: $(vars.a3m_cluster_size)

    Spot

    To create your deployment file, create a YAML file named a3mega-slurm-deployment.yaml and add the following content.

    terraform_backend_defaults:
  type: gcs
  configuration:
    bucket: BUCKET_NAME 
vars:
  deployment_name: DEPLOYMENT_NAME 
project_id: PROJECT_ID 
region: REGION 
zone: ZONE 
a3m_cluster_size: NUMBER_OF_VMS 
a3m_enable_spot_vm: true

    Replace the following:

    • BUCKET_NAME : the name of your Cloud Storage bucket, which you created in the previous section.
    • DEPLOYMENT_NAME : a name for your deployment. If creating multiple clusters, ensure that you select a unique name for each one.
    • PROJECT_ID : your project ID.
    • REGION : the region where you want to provision your cluster.
    • ZONE : the zone where you want to provision your cluster.
    • NUMBER_OF_VMS : the number of VMs that you want for the cluster.

    A3 High

    The parameters that you need to add to your deployment file depend on the consumption option that you're using for your deployment. Select the tab that corresponds to your consumption option's provisioning model.

    Reservation-bound

    To create your deployment file, create a YAML file named a3high-slurm-deployment.yaml and add the following content.

    terraform_backend_defaults:
  type: gcs
  configuration:
    bucket: BUCKET_NAME 
vars:
  deployment_name: DEPLOYMENT_NAME 
project_id: PROJECT_ID 
region: REGION 
zone: ZONE 
a3h_cluster_size: NUMBER_OF_VMS 
a3h_reservation_name: RESERVATION_NAME

    Replace the following:

    • BUCKET_NAME : the name of your Cloud Storage bucket, which you created in the previous section.
    • DEPLOYMENT_NAME : a name for your deployment. If creating multiple clusters, ensure that you select a unique name for each one.
    • PROJECT_ID : your project ID.
    • REGION : the region that has the reserved machines.
    • ZONE : the zone where you want to provision the cluster. If you're using a reservation-based consumption option, the region and zone information was provided by your account team when the capacity was delivered .
    • NUMBER_OF_VMS : the number of VMs that you want for the cluster.
    • RESERVATION_NAME : the name of your reservation .

    Flex-start

    To create your deployment file, create a YAML file named a3high-slurm-deployment.yaml and add the following content.

    terraform_backend_defaults:
  type: gcs
  configuration:
    bucket: BUCKET_NAME 
vars:
  deployment_name: DEPLOYMENT_NAME 
project_id: PROJECT_ID 
region: REGION 
zone: ZONE 
a3h_cluster_size: NUMBER_OF_VMS 
a3h_dws_flex_enabled: true

    Replace the following:

    • BUCKET_NAME : the name of your Cloud Storage bucket, which you created in the previous section.
    • DEPLOYMENT_NAME : a name for your deployment. If creating multiple clusters, ensure that you select a unique name for each one.
    • PROJECT_ID : your project ID.
    • REGION : the region where you want to provision your cluster.
    • ZONE : the zone where you want to provision your cluster.
    • NUMBER_OF_VMS : the number of VMs that you want for the cluster.

    This deployment provisions static compute nodes , which means that the cluster has a set number of nodes at all times. If you want to enable your cluster to autoscale instead, use examples/machine-learning/a3-highgpu-8g/a3high-slurm-blueprint.yaml file and edit the values of node_count_static and node_count_dynamic_max to match the following:

    node_count_static: 0
      node_count_dynamic_max: $(vars.a3h_cluster_size)

    Spot

    To create your deployment file, create a YAML file named a3high-slurm-deployment.yaml and add the following content.

    terraform_backend_defaults:
  type: gcs
  configuration:
    bucket: BUCKET_NAME 
vars:
  deployment_name: DEPLOYMENT_NAME 
project_id: PROJECT_ID 
region: REGION 
zone: ZONE 
a3h_cluster_size: NUMBER_OF_VMS 
a3h_enable_spot_vm: true

    Replace the following:

    • BUCKET_NAME : the name of your Cloud Storage bucket, which you created in the previous section.
    • DEPLOYMENT_NAME : a name for your deployment. If creating multiple clusters, ensure that you select a unique name for each one.
    • PROJECT_ID : your project ID.
    • REGION : the region where you want to provision your cluster.
    • ZONE : the zone where you want to provision your cluster.
    • NUMBER_OF_VMS : the number of VMs that you want for the cluster.

    Provision a Slurm cluster

    Cluster Toolkit provisions the cluster based on the deployment file you created in the previous step and the default cluster blueprint. For more information about the software that is installed by the blueprint, including NVIDIA drivers and CUDA, learn more about Slurm custom images .

    To provision the cluster, run the command for your machine type from the Cluster Toolkit directory. This step takes approximately 20-30 minutes.

    A4X

    ./gcluster deploy -d a4xhigh-slurm-deployment.yaml examples/machine-learning/a4x-highgpu-4g/a4xhigh-slurm-blueprint.yaml --auto-approve

    A4

    ./gcluster deploy -d a4high-slurm-deployment.yaml examples/machine-learning/a4-highgpu-8g/a4high-slurm-blueprint.yaml --auto-approve

    A3 Ultra

    ./gcluster deploy -d a3ultra-slurm-deployment.yaml examples/machine-learning/a3-ultragpu-8g/a3ultra-slurm-blueprint.yaml --auto-approve

    A3 Mega

    ./gcluster deploy -d a3mega-slurm-deployment.yaml examples/machine-learning/a3-megagpu-8g/a3mega-slurm-blueprint.yaml --auto-approve

    A3 High

    ./gcluster deploy -d a3high-slurm-deployment.yaml examples/machine-learning/a3-highgpu-8g/a3high-slurm-blueprint.yaml --auto-approve

    Connect to the Slurm cluster

    To access your cluster, you must login to the Slurm login node. To login, you can use either Google Cloud console or Google Cloud CLI.

    Console

    1. Go to the Compute Engine> VM instancespage.

      Go to the VM instances page

    2. Locate the login node. It should have a name with the pattern DEPLOYMENT_NAME + login-001 .

    3. From the Connectcolumn of the login node, click SSH.

    gcloud

    To connect to the login node, complete the following steps:

    1. Identify the login node by using the gcloud compute instances list command .

      gcloud compute instances list \
  --zones=  ZONE 
 
\
  --filter="name ~ login" --format "value(name)"

      If the output lists multiple Slurm clusters, you can identify your login node by the DEPLOYMENT_NAME that you specified.

    2. Use the gcloud compute ssh command to connect to the login node.

      gcloud compute ssh LOGIN_NODE 
\
  --zone=  ZONE 
 
--tunnel-through-iap

      Replace the following:

      • ZONE : the zone where the VMs for your cluster are located.
      • LOGIN_NODE : the name of the login node, which you identified in the previous step.

    Test network performance on the Slurm cluster

    We recommended that you validate the functionality of provisioned clusters. To do so, use NCCL tests , which are NVIDIA Collective Communications Library (NCCL) tests that are optimized for the Google environment.

    Redeploy the Slurm cluster

    If you need to increase the number of compute nodes or add new partitions to your cluster, you might need to update configurations for your Slurm cluster by redeploying. Redeployment can be sped up by using an existing image from a previous deployment. To avoid creating new images during a redeploy, specify the --only flag.

    To redeploy the cluster using an existing image do the following:

    1. Connect to the cluster

    2. Run the command for your required machine type:

      A4X

      ./gcluster deploy -d a4xhigh-slurm-deployment.yaml examples/machine-learning/a4x-highgpu-4g/a4xhigh-slurm-blueprint.yaml --only cluster-env,cluster -w --auto-approve

      A4

      ./gcluster deploy -d a4high-slurm-deployment.yaml examples/machine-learning/a4-highgpu-8g/a4high-slurm-blueprint.yaml --only cluster-env,cluster -w --auto-approve

      A3 Ultra

      ./gcluster deploy -d a3ultra-slurm-deployment.yaml examples/machine-learning/a3-ultragpu-8g/a3ultra-slurm-blueprint.yaml --only cluster-env,cluster -w --auto-approve

      A3 Mega

      ./gcluster deploy -d a3mega-slurm-deployment.yaml examples/machine-learning/a3-megagpu-8g/a3mega-slurm-blueprint.yaml --only cluster-env,cluster -w --auto-approve

      A3 High

      ./gcluster deploy -d a3high-slurm-deployment.yaml examples/machine-learning/a3-highgpu-8g/a3high-slurm-blueprint.yaml --only cluster-env,cluster -w --auto-approve

      This command is only for redeployments where an image already exists, it only redeploys the cluster and its infrastructure.

    Destroy the Slurm cluster

    By default, the A4X, A4, and A3 Ultra blueprints enable deletion protection on the Filestore instance. To delete the Filestore instance when you destroy the Slurm cluster, disable deletion protection before running the destroy command. For instructions, see set or remove deletion protection on an existing instance .

    1. Disconnect from the cluster if you haven't already.

    2. Before running the destroy command, navigate to the root of the Cluster Toolkit directory. By default, DEPLOYMENT_FOLDER is located at the root of the Cluster Toolkit directory.

    3. To destroy the cluster, run:

    ./gcluster destroy DEPLOYMENT_FOLDER 
--auto-approve

    Replace the following:

    • DEPLOYMENT_FOLDER : the name of the deployment folder. It's typically the same as DEPLOYMENT_NAME .

    When destruction is complete you should see a message similar to the following:

    Destroy complete! Resources: xx destroyed.

    To learn how to cleanly destroy infrastructure and for advanced manual deployment instructions, see the deployment folder located at the root of the Cluster Toolkit directory: DEPLOYMENT_FOLDER /instructions.txt

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    View Site in Mobile | Classic
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