# Specifying GPUs in an Amazon ECS task definition
<a name="ecs-gpu-specifying"></a>

To use the GPUs on a container instance and the Docker GPU runtime, make sure that you designate the number of GPUs your container requires in the task definition. You can specify a numeric value or `ALL`. When you specify `ALL`, all GPUs on the container instance are allocated to the container. As containers that support GPUs are placed, the Amazon ECS container agent pins the desired number of physical GPUs to the appropriate container. The number of GPUs reserved for all containers in a task cannot exceed the number of available GPUs on the container instance the task is launched on. For more information, see [Creating an Amazon ECS task definition using the console](create-task-definition.md).

**Important**  
If your GPU requirements aren't specified in the task definition, the task uses the default Docker runtime.

The following shows the JSON format for the GPU requirements in a task definition:

```
{
  "containerDefinitions": [
     {
        ...
        "resourceRequirements" : [
            {
               "type" : "GPU", 
               "value" : "{{2}}"
            }
        ],
     },
...
}
```

You can also specify `ALL` as the value instead of a number to allocate all GPUs on the container instance to the container.

The following example demonstrates the syntax for a Docker container that specifies a GPU requirement. This container uses two GPUs, runs the `nvidia-smi` utility, and then exits.

```
{
  "containerDefinitions": [
    {
      "memory": 80,
      "essential": true,
      "name": "gpu",
      "image": "nvidia/cuda:11.0.3-base",
      "resourceRequirements": [
         {
           "type":"GPU",
           "value": "2"
         }
      ],
      "command": [
        "sh",
        "-c",
        "nvidia-smi"
      ],
      "cpu": 100
    }
  ],
  "family": "example-ecs-gpu"
}
```

The following example task definition shows a TensorFlow container that prints the number of available GPUs. The task runs on Amazon ECS Managed Instances, requires one GPU, and uses a `g4dn.xlarge` instance.

```
{
  "family": "tensorflow-gpu",
  "networkMode": "awsvpc",
  "executionRoleArn": "arn:aws:iam::account-id:role/ecsTaskExecutionRole",
  "containerDefinitions": [
    {
      "name": "tensorflow",
      "image": "tensorflow/tensorflow:latest-gpu",
      "essential": true,
      "command": [
        "python",
        "-c",
        "import tensorflow as tf; print('Num GPUs Available: ', len(tf.config.list_physical_devices('GPU')))"
      ],
      "resourceRequirements": [
        {
          "type": "GPU",
          "value": "1"
        }
      ],
      "logConfiguration": {
        "logDriver": "awslogs",
        "options": {
          "awslogs-group": "/ecs/tensorflow-gpu",
          "awslogs-region": "region",
          "awslogs-stream-prefix": "ecs"
        }
      }
    }
  ],
  "requiresCompatibilities": [
    "MANAGED_INSTANCES"
  ],
  "cpu": "4096",
  "memory": "8192",
}
```