Repository containing scaffolding for a Python 3-based data science project with GPU acceleration. Project organization is based on ideas from Good Enough Practices for Scientific Computing.
Simply follow the instructions to create a new project repository from this template.
- Put each project in its own directory, which is named after the project.
- Put external scripts or compiled programs in the
bin
directory. - Put raw data and metadata in a
data
directory. - Put text documents associated with the project in the
doc
directory. - Put all Docker related files in the
docker
directory. - Install the Conda environment into an
env
directory. - Put all notebooks in the
notebooks
directory. - Put files generated during cleanup and analysis in a
results
directory. - Put project source code in the
src
directory. - Name all files to reflect their content or function.
After adding any necessary dependencies to the Conda environment.yml
file you can create the
environment in a sub-directory of your project directory by running the following command.
$ conda env create --prefix ./env --file environment.yml
Once the new environment has been created you can activate the environment with the following command.
$ conda activate ./env
Note that the env
directory is not under version control as it can always be re-created from
the environment.yml
file as necessary.
If you add (remove) dependencies to (from) the environment.yml
file after the environment has
already been created, then you can update the environment with the following command.
$ conda env update --prefix ./env --file environment.yml --prune
In order to build Docker images for your project and run containers with GPU acceleration you will need to install Docker, Docker Compose and the NVIDIA Docker runtime.
Detailed instructions for using Docker to build and image and launch containers can be found in
the docker/README.md
.