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https://github.com/eywalker/nvidia-docker-compose

Simple wrapper for docker-compose to use GPU enabled docker under nvidia-docker
https://github.com/eywalker/nvidia-docker-compose

docker docker-compose nvidia-docker

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Simple wrapper for docker-compose to use GPU enabled docker under nvidia-docker

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# `nvidia-docker-compose`

`nvidia-docker-compose` is a simple python script that wraps [`docker-compose`](https://docs.docker.com/compose/) to allow `docker-compose` to work with GPU enabled Docker containers as made available with [`nvidia-docker`](https://github.com/NVIDIA/nvidia-docker)!



## Dependencies

`nvidia-docker-compose` requires following dependencies to be installed on the system:

* Docker engine

* `nvidia-docker`



It also depends on the `docker-compose`, `PyYAML` and `Jinja2` Python packages, which would be installed automatically during the installation step described below.



## `nvidia-docker-compose` and `nvidia-docker2`

As some of you may know, NVIDIA is working on the release of `nvidia-docker2` that integrates much more tightly with Docker infrastructure (more details can be found at the master branch of their project repo https://github.com/NVIDIA/nvidia-docker). One of the huge benefit of this new integration is that you would no longer need `nvidia-docker-compose` to launch docker-compose with GPU capacity. Refer to the issue [#23](https://github.com/eywalker/nvidia-docker-compose/issues/23) for how you could configure `nvidia-docker2` to work with `docker-compose`.



## Before you install

`nvidia-docker-compose` depends on `nvidia-docker` to properly function and above all, it depends on all extra Docker volumes that are automatically created when you run `nvidia-docker`. Before you install and run `nvidia-docker-compose`, please make sure to test run `nvidia-docker` at least once to ensure that all volumes are set up and are functioning correctly. In particular, I recommend that you run the following command:



```bash

$ nvidia-docker run --rm nvidia/cuda nvidia-smi

```

If this runs and properly lists all available GPUs on your machine, then you are ready to proceed! If not, please refer to `nvidia-docker` documentation and helps to make sure that it functions properly before using `nvidia-docker-compose`.



## Installing

To install the script, simply run:



```bash

$ pip install nvidia-docker-compose

```



If you are using system Python, it may be necessary to run the above command with `sudo` upfront.



## Using `nvidia-docker-compose`

The `nvidia-docker-compose` is a drop-in replacement for the `docker-compose`. Simply run as you would run `docker-compose`:

```bash

$ nvidia-docker-compose ...

```

Depending on how your system is configured, you may need to run the script with `sudo` (i.e. if you usually need `sudo` to run `docker`, you will need `sudo`).



Running `nvidia-docker-compose` generates a new YAML config file `nvidia-docker-compose.yml` locally. It is safe to delete this file in-between usages and I recommend you add this to your `.gitignore` file if you are going to use `nvidia-docker-compose` within a Git repository. Once generated, you can also use the `nvidia-docker-compose.yml` directly to launch GPU enabled containers directly with the standard `docker-compose`. You can do so as:

```bash

$ docker-compose -f nvidia-docker-compose.yml ...

```



## Running flexibly on multi-GPU setup



When working on multi-GPU setup, you would often want to run separate container for each GPU or at least limit the visibility of GPUs to only specific Docker containers. If you are not afraid to dig in, you would discover that you can control visibility of GPUs to each container by selectively including `/dev/nvidia*` under the `devices` section (i.e. `/dev/nvidia0` for the first GPU, and so on) . However, doing this manually would mean that you will have to interfere with the function of `nvidia-docker` and `nvidia-docker-compose`, and previously there was no natural way to specify which service in the `docker-compose.yml` should be run with which GPUs. This is further complicated by the fact that different machine would have different numbers of GPUs, and thus keeping a service with `/dev/nvidia4` under `devices` section on a 2 GPU machine could cause an error.



### Specifying GPU target

*New from version 0.4.0*

`nvidia-docker-compose` now allows you to specify which GPU a specific service should be run with by including `/dev/nvidia*` under the `devices` heading. As in the following



```yaml

version: "2"

services

  process1:

    image: nvidia/cuda

    devices:

      - /dev/nvidia0

  process2:

    image: nvidia/cuda

    devices:

      - /dev/nvidia1

      - /dev/nvidia2

```



The service `process1` will now only see the first GPU (`/dev/nvidia0`) while the service `process2` will see second and third GPU (`/dev/nvidia0` and `/dev/nvidia1`). If you don't specify any `/dev/nvidia*` under devices section, the service will automatically see all available GPUs as have been the case previously.



Although this feature will allow you to finely control which service sees which GPU(s), it is still rather inflexible as will require you to adjust the `docker-compose.yml` per computer device. This is precisely where the Jinja2 templating can help you! 



### Using [Jinja2](http://jinja.pocoo.org/) in `docker-compose.yml` file 

*New from version 0.4.0*



To support the relatively common use case of wanting to launch as many compute containers (with the same configuration) as the number of GPUs available on the target machine, `nvidia-docker-compose` now supports use of [Jinja2](http://jinja.pocoo.org/). Combined with the ability to specify GPU targeting, you can now write `docker-compose` config that adapts flexibility to the GPU availability. For an example if you prepare the following template and save it as `docker-compose.yml.jinja`:



```yaml

version: "2"

services:

  {% for i in range(N_GPU) %}

  notebook{{i}}:

    image: eywalker/tensorflow:cuda

    ports:

      - "300{{i}}:8888"

    devices:

      - /dev/nvidia{{i}}

    volumes:

      - ./notebooks:/notebooks

  {% endfor %}

```



and specify the target Jinja2 template with `-t`/`--template` flag when you run:



```bash

$ nvidia-docker-compose --template docker-compose.yml.jinja ...

```



It will pick up the Jinja template, process it and expand it to the following `docker-compose.yml`:



```yaml

version: "2"

services:

  notebook0:

    image: eywalker/tensorflow:cuda

    ports:

      - "3000:8888"

    devices:

      - /dev/nvidia0

    volumes:

      - ./notebooks:/notebooks

  notebook1:

    image: eywalker/tensorflow:cuda

    ports:

      - "3001:8888"

    devices:

      - /dev/nvidia1

    volumes:

      - ./notebooks:/notebooks

  notebook2:

    image: eywalker/tensorflow:cuda

    ports:

      - "3002:8888"

    devices:

      - /dev/nvidia2

    volumes:

      - ./notebooks:/notebooks

```

on a 3 GPU machine. The Jinja variable `N_GPU` automatically reflects the available number of the GPUs on the system. This `docker-compose.yml` is then processed by `nvidia-docker-compose` just like any other config file to launch GPU enabled containers.



### Generating Compose File Only



If you want to generate GPU-enabled compose file for later use, `-G`/`--generate` flag will make `nvidia-docker-compose` exit after generating the compose file without running `docker-compose`.



```bash

$ nvidia-docker-compose -G ...

```



## Additional command line options

For additional configurations such as specifying alternate `nvidia-docker-plugin` host address, alternate target docker compose file name (instead of the default `nvidia-docker-compose.yml`), refer to the command line help at:



```bash

$ nvidia-docker-compose -h

```



## How it works

`nvidia-docker-compose` is a simple Python script that performs two actions:

* parse `docker-compose` config file (defaults to `docker-compose.yml`) and creates a new config YAML `nvidia-docker-compose.yml` with configurations necessary to run GPU enabled containers. Configuration parameters are read from `nvidia-docker-plugins`.

* run `docker-compose` with the newly generated config file `nvidia-docker-compose.yml`