Environment configuration

Conda virtual env

The preferred option to setup your environment is through conda environment as follows:

conda create --name <env> --file conda_env.txt

example configuration files for linux-64 (cpu and gpu) and osx-64 are provided in SuperNNova/env.

Docker

You can also use docker:

Create a docker image:

cd env && make {image}

where image is one of cpu or gpu (for cuda 9.) or gpu10 (for cuda 10.)

  • This images contains all of this repository’s dependencies.

  • Image construction will typically take a few minutes

Enter docker environment by calling:

python launch_docker.py --image <image> --dump_dir </path/to/data>

  • Add --image image where image is cpu or gpu (for cuda 9.) or gpu10 (for cuda 10.)

  • Add --dump_dir /path/to/data to mount the folder where you stored the data (see Data walkthrough) into the container. If unspecified, will use the default location (i.e. snndump)

This will launch an interactive session in the docker container, with zsh support.