Frequently we run into issues while developing pytorch that fail only for a particular build configuration that is very hard to reproduce on your local machine. Facebook uses docker containers for running CI setups for various build configurations, which you can also use for building your own local Docker images in order to reproduce the issue easily. This post is how you can use the Docker functionality on QGPU in order to build such a Docker image on QGPU1.

First steps

The first step is to ask an admin (Pearu/Sameer/Dharhas) to add you to the docker group on QGPU1. Once you’re on this group, find the Amazon ECR API access keys on the facebook quip document. Docker and nvidia-docker are already installed so you need not install them.

Then install the Amazon ECR client for your user from here. Then run aws ecr get-login which will prompt you for login credentials, and then subsequently run for automatic login.

In order to know which docker image you need, you must know its full name first. The name can be found from the circleCI build. The amazon ECR name can be a little different so just find the name from the output of the aws ecr describe-repositories command. Use in this manner to find which repo you need (typically the name of your failing build on circleCI):

aws ecr describe-repositories | grep -C 3 xenial

From the output of this pickup the exact repositoryName value and replace it in the repo_name variable in the below ruby script in order to get the full string that be the name of your docker image:

require 'json'

repo_name = "pytorch/pytorch-linux-xenial-cuda10.2-cudnn7-py3-gcc7"

puts "logging in..."
`aws ecr get-login --no-include-email | bash`

images = JSON.parse `aws ecr describe-images --repository-name #{repo_name}`
image_tag = images['imageDetails'][-1]['imageTags'][0]
repo_info = JSON.parse `aws ecr describe-repositories --repository-names #{repo_name}`
uri = repo_info['repositories'][0]['repositoryUri']

puts "Docker image: #{uri}:#{image_tag}"

You can then create your own docker image using a Dockerfile like so:

# Insert the SHA key after the image name.
FROM <insert docker image name here>


RUN conda install -y hypothesis

RUN cd workspace \
    && git submodule sync --recursive \
    && git submodule update --init --recursive \
    && TORCH_CUDA_ARCH_LIST=Turing python install --cmake

The default docker container is built for a different GPU in some cases so it is important to specify the TORCH_CUDA_ARCH_LIST env variable.

Further reading