Docker Quickstart

Estimated time: 5 minutes, 6 minutes with buffer.

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Pulling a Docker Image

Your node comes with Docker Engine installed, so all Docker functionality should be available to you on your first connection. Begin by pulling the desired image:

docker pull tensorwavehq/hello_world:latest

You can verify that your image was properly pulled by running the following command and checking for your desired image:

docker images

If the pull was successful, your output should look similar to this:

REPOSITORY                      TAG            IMAGE ID       CREATED          SIZE
tensorwavehq/hello_world        latest         359e600f7aac   2 minutes ago   61.2GB

TensorWave's officially supported images can be found here.

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Running a Docker Container

In order to run your Docker containers with GPU acceleration, you must mount the devices. For certain applications, you must also add the container to a group to utilize your GPUs.

Using the docker run Command

Here's an example command to mount the devices and configure the correct permissions:

docker run --device /dev/kfd --device /dev/dri --group-add video tensorwavehq/hello_world:latest

The usage of each option is as follows:

• --device /dev/kfd

  • This command mounts the main compute interface to your container.

• --device /dev/dri

  • This command mounts the Direct Rendering Interface for your GPU. To restrict access, append /renderD<node>, where the node is the ID of the node you want to mount.

• --group-add video (optional)

  • This command adds your container to the server's video group, which is necessary for certain applications (including PyTorch).

Using docker-compose

The following is an equivalent docker-compose to the command above:

version: '3'
services:
  hello_world:
    image: tensorwavehq/hello_world:latest
    devices:
      - /dev/kfd
      - /dev/dri
    group_add:
      - video

To use it, create a docker-compose.yml file in any subdirectory, and within that subdirectory, run:

docker compose up

Verifying Setup

If done properly, the output of either the run or compose command should be similar to:

CUDA available: True
Number of GPUs: 8
GPU 0: AMD Instinct MI300X
GPU 1: AMD Instinct MI300X
GPU 2: AMD Instinct MI300X
GPU 3: AMD Instinct MI300X
GPU 4: AMD Instinct MI300X
GPU 5: AMD Instinct MI300X
GPU 6: AMD Instinct MI300X
GPU 7: AMD Instinct MI300X

For other containers, to verify that your Docker container has access to your GPUs, run both rocm-smi and rocminfo. These commands will reveal information about the GPUs mounted to your container.

If one or both of these commands fails to execute successfully, please double check your running commands.

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