# Docker Quickstart

***

### 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:

```bash
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:

```bash
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
```

{% hint style="info" %}
TensorWave's officially supported images can be found [here](https://hub.docker.com/u/tensorwavehq).
{% endhint %}

***

### 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:

```bash
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:

```yaml
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:

```bash
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.

{% hint style="warning" %}
If one or both of these commands fails to execute successfully, please double check your running commands.
{% endhint %}

***