---
description: Understand GPU support in Docker Compose
keywords: documentation, docs, docker, compose, GPU access, NVIDIA, samples
title: Enable GPU access with Docker Compose
linkTitle: Enable GPU support
weight: 90
aliases:
- /compose/gpu-support/
---
Compose services can define GPU device reservations if the Docker host contains such devices and the Docker Daemon is set accordingly. For this, make sure you install the [prerequisites](/manuals/engine/containers/resource_constraints.md#gpu) if you haven't already done so.
The examples in the following sections focus specifically on providing service containers access to GPU devices with Docker Compose.
You can use either `docker-compose` or `docker compose` commands. For more information, see [Migrate to Compose V2](/manuals/compose/releases/migrate.md).
## Enabling GPU access to service containers
GPUs are referenced in a `compose.yaml` file using the [device](/reference/compose-file/deploy.md#devices) attribute from the Compose Deploy specification, within your services that need them.
This provides more granular control over a GPU reservation as custom values can be set for the following device properties:
- `capabilities`. This value specifies as a list of strings (eg. `capabilities: [gpu]`). You must set this field in the Compose file. Otherwise, it returns an error on service deployment.
- `count`. This value, specified as an integer or the value `all`, represents the number of GPU devices that should be reserved (providing the host holds that number of GPUs). If `count` is set to `all` or not specified, all GPUs available on the host are used by default.
- `device_ids`. This value, specified as a list of strings, represents GPU device IDs from the host. You can find the device ID in the output of `nvidia-smi` on the host. If no `device_ids` are set, all GPUs available on the host are used by default.
- `driver`. This value is specified as a string, for example `driver: 'nvidia'`
- `options`. Key-value pairs representing driver specific options.
> [!IMPORTANT]
>
> You must set the `capabilities` field. Otherwise, it returns an error on service deployment.
>
> `count` and `device_ids` are mutually exclusive. You must only define one field at a time.
For more information on these properties, see the [Compose Deploy Specification](/reference/compose-file/deploy.md#devices).
### Example of a Compose file for running a service with access to 1 GPU device
```yaml
services:
test:
image: nvidia/cuda:12.9.0-base-ubuntu22.04
command: nvidia-smi
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: 1
capabilities: [gpu]
```
Run with Docker Compose:
```console
$ docker compose up
Creating network "gpu_default" with the default driver
Creating gpu_test_1 ... done
Attaching to gpu_test_1
test_1 | +-----------------------------------------------------------------------------+
test_1 | | NVIDIA-SMI 450.80.02 Driver Version: 450.80.02 CUDA Version: 11.1 |
test_1 | |-------------------------------+----------------------+----------------------+
test_1 | | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
test_1 | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
test_1 | | | | MIG M. |
test_1 | |===============================+======================+======================|
test_1 | | 0 Tesla T4 On | 00000000:00:1E.0 Off | 0 |
test_1 | | N/A 23C P8 9W / 70W | 0MiB / 15109MiB | 0% Default |
test_1 | | | | N/A |
test_1 | +-------------------------------+----------------------+----------------------+
test_1 |
test_1 | +-----------------------------------------------------------------------------+
test_1 | | Processes: |
test_1 | | GPU GI CI PID Type Process name GPU Memory |
test_1 | | ID ID Usage |
test_1 | |=============================================================================|
test_1 | | No running processes found |
test_1 | +-----------------------------------------------------------------------------+
gpu_test_1 exited with code 0
```
On machines hosting multiple GPUs, the `device_ids` field can be set to target specific GPU devices and `count` can be used to limit the number of GPU devices assigned to a service container.
You can use `count` or `device_ids` in each of your service definitions. An error is returned if you try to combine both, specify an invalid device ID, or use a value of count that’s higher than the number of GPUs in your system.
```console
$ nvidia-smi
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 450.80.02 Driver Version: 450.80.02 CUDA Version: 11.0 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 Tesla T4 On | 00000000:00:1B.0 Off | 0 |
| N/A 72C P8 12W / 70W | 0MiB / 15109MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
| 1 Tesla T4 On | 00000000:00:1C.0 Off | 0 |
| N/A 67C P8 11W / 70W | 0MiB / 15109MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
| 2 Tesla T4 On | 00000000:00:1D.0 Off | 0 |
| N/A 74C P8 12W / 70W | 0MiB / 15109MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
| 3 Tesla T4 On | 00000000:00:1E.0 Off | 0 |
| N/A 62C P8 11W / 70W | 0MiB / 15109MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
```
## Access specific devices
To allow access only to GPU-0 and GPU-3 devices:
```yaml
services:
test:
image: tensorflow/tensorflow:latest-gpu
command: python -c "import tensorflow as tf;tf.test.gpu_device_name()"
deploy:
resources:
reservations:
devices:
- driver: nvidia
device_ids: ['0', '3']
capabilities: [gpu]
```