Option 1: Create a new container with Apex

Dockerfile installs the latest Apex on top of an existing image. Run

docker build -t image_with_apex .

By default, Dockerfile uses NVIDIA's Pytorch container as the base image, which requires an NVIDIA GPU Cloud (NGC) account. If you don't have an NGC account, you can sign up for free by following the instructions here.

Alternatively, you can supply your own base image via the BASE_IMAGE build-arg. Any BASE_IMAGE you supply must have Pytorch and Cuda installed, for example:

docker build --build-arg BASE_IMAGE=pytorch/pytorch:0.4-cuda9-cudnn7-devel -t image_with_apex .

If you want to rebuild your image, and force the latest Apex to be cloned and installed, make any small change to the SHA variable in Dockerfile.

Warning: Currently, Pytorch's default non-devel image on Dockerhub pytorch/pytorch:0.4_cuda9_cudnn7 contains Pytorch installed with prebuilt binaries. It does not contain NVCC, which means it is not an eligible candidate for <base image>.

Option 2: Install Apex in a running container

Instead of building a new container, it is also a viable option to clone Apex on bare metal, mount the Apex repo into your container at launch by running, for example,

docker run --runtime=nvidia -it --rm --ipc=host -v /bare/metal/apex:/apex/in/container <base image>

then go to /apex/in/container within the running container and python setup.py install.