Deploy container instances that use GPU resources

To run certain compute-intensive workloads on Azure Container Instances, deploy your container groups with GPU resources. The container instances in the group can access one or more NVIDIA Tesla GPUs while running container workloads such as CUDA and deep learning applications.

This article shows how to add GPU resources when you deploy a container group by using a YAML file or Resource Manager template. You can also specify GPU resources when you deploy a container instance using the Azure portal.

Prerequisites

• If you would like to use this sku for your production container deployments, create an Azure Support request to increase the limit.

Preview limitations

In preview, the following limitations apply when using GPU resources in container groups.

Region availability

Regions	OS	Available GPU SKUs
China East 2	Linux	V100

Support will be added for additional regions over time.

Supported OS types: Linux only

Additional limitations: GPU resources can't be used when deploying a container group into a virtual network.

About GPU resources

Count and SKU

To use GPUs in a container instance, specify a GPU resource with the following information:

• Count - The number of GPUs: 1, 2, or 4.

• SKU - The GPU SKU: V100. Each SKU maps to the NVIDIA Tesla GPU in one the following Azure GPU-enabled VM families:

  SKU	VM family
  V100	NCv3

Maximum resources per SKU

OS	GPU SKU	GPU count	Max CPU	Max Memory (GB)	Storage (GB)
Linux	V100	1	6	112	50
Linux	V100	2	12	224	50
Linux	V100	4	24	448	50

When deploying GPU resources, set CPU and memory resources appropriate for the workload, up to the maximum values shown in the preceding table. These values are currently larger than the CPU and memory resources available in container groups without GPU resources.

Things to know

• Deployment time - Creation of a container group containing GPU resources takes up to 8-10 minutes. This is due to the additional time to provision and configure a GPU VM in Azure.

• Pricing - Similar to container groups without GPU resources, Azure bills for resources consumed over the duration of a container group with GPU resources. The duration is calculated from the time to pull your first container's image until the container group terminates. It does not include the time to deploy the container group.

  See pricing details.

• CUDA drivers - Container instances with GPU resources are pre-provisioned with NVIDIA CUDA drivers and container runtimes, so you can use container images developed for CUDA workloads.

  We support up through CUDA 11 at this stage. For example, you can use the following base images for your Dockerfile:

  • nvidia/cuda:11.4.2-base-ubuntu20.04
  • tensorflow/tensorflow:devel-gpu

  Note

  To improve reliability when using a public container image from Docker Hub, import and manage the image in a private Azure container registry, and update your Dockerfile to use your privately managed base image. Learn more about working with public images.

YAML example

One way to add GPU resources is to deploy a container group by using a YAML file. Copy the following YAML into a new file named gpu-deploy-aci.yaml, then save the file. This YAML creates a container group named gpucontainergroup specifying a container instance with a V100 GPU. The instance runs a sample CUDA vector addition application. The resource requests are sufficient to run the workload.

additional_properties: {}
apiVersion: '2021-09-01'
name: gpucontainergroup
properties:
  containers:
  - name: gpucontainer
    properties:
      image: k8s-gcrio.azureedge.net/cuda-vector-add:v0.1
      resources:
        requests:
          cpu: 1.0
          memoryInGB: 1.5
          gpu:
            count: 1
            sku: V100
  osType: Linux
  restartPolicy: OnFailure

Deploy the container group with the az container create command, specifying the YAML file name for the --file parameter. You need to supply the name of a resource group and a location for the container group such as chinaeast2 that supports GPU resources.

az container create --resource-group myResourceGroup --file gpu-deploy-aci.yaml --location chinaeast2

The deployment takes several minutes to complete. Then, the container starts and runs a CUDA vector addition operation. Run the az container logs command to view the log output:

az container logs --resource-group myResourceGroup --name gpucontainergroup --container-name gpucontainer

Output:

[Vector addition of 50000 elements]
Copy input data from the host memory to the CUDA device
CUDA kernel launch with 196 blocks of 256 threads
Copy output data from the CUDA device to the host memory
Test PASSED
Done

Resource Manager template example

Another way to deploy a container group with GPU resources is by using a Resource Manager template. Start by creating a file named gpudeploy.json, then copy the following JSON into it. This example deploys a container instance with a V100 GPU that runs a TensorFlow training job against the MNIST dataset. The resource requests are sufficient to run the workload.

{
    "$schema": "https://schema.management.azure.com/schemas/2015-01-01/deploymentTemplate.json#",
    "contentVersion": "1.0.0.0",
    "parameters": {
      "containerGroupName": {
        "type": "string",
        "defaultValue": "gpucontainergrouprm",
        "metadata": {
          "description": "Container Group name."
        }
      }
    },
    "variables": {
      "containername": "gpucontainer",
      "containerimage": "mcr.microsoft.com/azuredocs/samples-tf-mnist-demo:gpu"
    },
    "resources": [
      {
        "name": "[parameters('containerGroupName')]",
        "type": "Microsoft.ContainerInstance/containerGroups",
        "apiVersion": "2021-09-01",
        "location": "[resourceGroup().location]",
        "properties": {
            "containers": [
            {
              "name": "[variables('containername')]",
              "properties": {
                "image": "[variables('containerimage')]",
                "resources": {
                  "requests": {
                    "cpu": 4.0,
                    "memoryInGb": 12.0,
                    "gpu": {
                        "count": 1,
                        "sku": "V100"
                  }
                }
              }
            }
          }
        ],
        "osType": "Linux",
        "restartPolicy": "OnFailure"
        }
      }
    ]
}

Deploy the template with the az deployment group create command. You need to supply the name of a resource group that was created in a region such as chinaeast2 that supports GPU resources.

az deployment group create --resource-group myResourceGroup --template-file gpudeploy.json

The deployment takes several minutes to complete. Then, the container starts and runs the TensorFlow job. Run the az container logs command to view the log output:

az container logs --resource-group myResourceGroup --name gpucontainergrouprm --container-name gpucontainer

Output:

2018-10-25 18:31:10.155010: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
2018-10-25 18:31:10.305937: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Found device 0 with properties:
name: Tesla V100 major: 3 minor: 7 memoryClockRate(GHz): 0.8235
pciBusID: ccb6:00:00.0
totalMemory: 11.92GiB freeMemory: 11.85GiB
2018-10-25 18:31:10.305981: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1120] Creating TensorFlow device (/device:GPU:0) -> (device: 0, name: Tesla V100, pci bus id: ccb6:00:00.0, compute capability: 3.7)
2018-10-25 18:31:14.941723: I tensorflow/stream_executor/dso_loader.cc:139] successfully opened CUDA library libcupti.so.8.0 locally
Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.
Extracting /tmp/tensorflow/input_data/train-images-idx3-ubyte.gz
Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.
Extracting /tmp/tensorflow/input_data/train-labels-idx1-ubyte.gz
Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.
Extracting /tmp/tensorflow/input_data/t10k-images-idx3-ubyte.gz
Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.
Extracting /tmp/tensorflow/input_data/t10k-labels-idx1-ubyte.gz
Accuracy at step 0: 0.097
Accuracy at step 10: 0.6993
Accuracy at step 20: 0.8208
Accuracy at step 30: 0.8594
...
Accuracy at step 990: 0.969
Adding run metadata for 999

Clean up resources

Because using GPU resources may be expensive, ensure that your containers don't run unexpectedly for long periods. Monitor your containers in the Azure portal, or check the status of a container group with the az container show command. For example:

az container show --resource-group myResourceGroup --name gpucontainergroup --output table

When you're done working with the container instances you created, delete them with the following commands:

az container delete --resource-group myResourceGroup --name gpucontainergroup -y
az container delete --resource-group myResourceGroup --name gpucontainergrouprm -y

Next steps

• Learn more about deploying a container group using a YAML file or Resource Manager template.
• Learn more about GPU optimized VM sizes in Azure.