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:
Install docker: 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 iscpu
orgpu
(for cuda 9.) orgpu10
(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.