computervision-recipes/docker

Docker Support

The Dockerfile in this directory will build Docker images with all the dependencies and code needed to run example notebooks or unit tests included in this repository.

Multiple environments are supported by using multistage builds. In order to efficiently build the Docker images in this way, Docker BuildKit is necessary. The following examples show how to build and run the Docker image for CPU and GPU environments. Note on some platforms, one needs to manually specify the environment variable for DOCKER_BUILDKITto make sure the build runs well. For example, on a Windows machine, this can be done by the powershell command as below, before building the image

$env:DOCKER_BUILDKIT=1

Once the container is running you can access Jupyter notebooks at http://localhost:8888.

Building and Running with Docker

CPU environment

DOCKER_BUILDKIT=1 docker build -t computervision:cpu --build-arg ENV="cpu" .
docker run -p 8888:8888 -d computervision:cpu

GPU environment

DOCKER_BUILDKIT=1 docker build -t computervision:gpu --build-arg ENV="gpu" .
docker run --runtime=nvidia -p 8888:8888 -d computervision:gpu

Build Arguments

There are several build arguments which can change how the image is built. Similar to the ENV build argument these are specified during the docker build command.

Build Arg	Description
ENV	Environment to use, options: cpu, gpu
BRANCH	Git branch of the repo to use (defaults to master)
ANACONDA	Anaconda installation script (defaults to miniconda3 4.6.14)

Example using the staging branch:

DOCKER_BUILDKIT=1 docker build -t computervision:cpu --build-arg ENV="cpu" --build-arg BRANCH="staging" .

In order to see detailed progress with BuildKit you can provide a flag during the build command: --progress=plain

Running tests with docker

docker run -it computervision:cpu pytests tests/unit