Amazon ECS task definitions for video trancoding workloads

To use video transcoding workloads on Amazon ECS, register Amazon EC2 VT1 instances. After you registered these instances, you can run live and pre-rendered video transcoding workloads as tasks on Amazon ECS. Amazon EC2 VT1 instances use Xilinx U30 media transcoding cards to accelerate live and pre-rendered video transcoding workloads.

Note

For instructions on how to run video transcoding workloads in containers other than Amazon ECS, see the Xilinx documentation.

Considerations

Before you begin deploying VT1 on Amazon ECS, consider the following:

• Your clusters can contain a mix of VT1 and non-VT1 instances.

• You need a Linux application that uses Xilinx U30 media transcoding cards with accelerated AVC (H.264) and HEVC (H.265) codecs.

  Important

  Applications that use other codecs might not have improved performance on VT1 instances.

• Only one transcoding task can run on a U30 card. Each card has two devices that are associated with it. You can run as many transcoding tasks as there are cards for each of your VT1 instance.

• When creating a service or running a standalone task, you can use instance type attributes when configuring task placement constraints. This ensures that the task is launched on the container instance that you specify. Doing so helps ensure that you use your resources effectively and that your tasks for video transcoding workloads are on your VT1 instances. For more information, see How Amazon ECS places tasks on container instances.

  In the following example, a task is run on a vt1.3xlarge instance on your default cluster.

  aws ecs run-task \
       --cluster default \
       --task-definition vt1-3xlarge-xffmpeg-processor \
       --placement-constraints type=memberOf,expression="attribute:ecs.instance-type == vt1.3xlarge"

• You configure a container to use the specific U30 card available on the host container instance. You can do this by using the linuxParameters parameter and specifying the device details. For more information, see Task definition requirements.

Using a VT1 AMI

You have two options for running an AMI on Amazon EC2 for Amazon ECS container instances. The first option is to use the Xilinx official AMI on the AWS Marketplace. The second option is to build your own AMI from the sample repository.

• Xilinx offers AMIs on the AWS Marketplace.

• Amazon ECS provides a sample repository that you can use to build an AMI for video transcoding workloads. This AMI comes with Xilinx U30 drivers. You can find the repository that contains Packer scripts on GitHub. For more information about Packer, see the Packer documentation.

Task definition requirements

To run video transcoding containers on Amazon ECS, your task definition must contain a video transcoding application that uses the accelerated H.264/AVC and H.265/HEVC codecs. You can build a container image by following the steps on the Xilinx GitHub.

The task definition must be specific to the instance type. The instance types are 3xlarge, 6xlarge, and 24xlarge. You must configure a container to use specific Xilinx U30 devices that are available on the host container instance. You can do so using the linuxParameters parameter. The following table details the cards and device SoCs that are specific to each instance type.

Instance Type	vCPUs	RAM (GiB)	U30 accelerator cards	Addressable XCU30 SoC devices	Device Paths
vt1.3xlarge	12	24	1	2	/dev/dri/renderD128,/dev/dri/renderD129
vt1.6xlarge	24	48	2	4	/dev/dri/renderD128,/dev/dri/renderD129,/dev/dri/renderD130,/dev/dri/renderD131
vt1.24xlarge	96	182	8	16	/dev/dri/renderD128,/dev/dri/renderD129,/dev/dri/renderD130,/dev/dri/renderD131,/dev/dri/renderD132,/dev/dri/renderD133,/dev/dri/renderD134,/dev/dri/renderD135,/dev/dri/renderD136,/dev/dri/renderD137,/dev/dri/renderD138,/dev/dri/renderD139,/dev/dri/renderD140,/dev/dri/renderD141,/dev/dri/renderD142,/dev/dri/renderD143

Important

If the task definition lists devices that the EC2 instance doesn't have, the task fails to run. When the task fails, the following error message appears in the stoppedReason: CannotStartContainerError: Error response from daemon: error gathering device information while adding custom device "/dev/dri/renderD130": no such file or directory.