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Repository containing scaffolding for a Python 3-based data science project.
Simply follow the instructions to create a new project repository from this template.
Project organization is based on ideas from Good Enough Practices for Scientific Computing.
bin
directory.data
directory.doc
directory.docker
directory.env
directory.notebooks
directory.results
directory.src
directory.After adding any necessary dependencies for your project to the Conda environment.yml
file
(or the requirements.txt
file), you can create the environment in a sub-directory of your
project directory by running the following command.
ENV_PREFIX=$PWD/env
conda env create --prefix $ENV_PREFIX --file environment.yml --force
Once the new environment has been created you can activate the environment with the following command.
conda activate $ENV_PREFIX
Note that the ENV_PREFIX
directory is not under version control as it can always be re-created as
necessary.
If you wish to use any JupyterLab extensions included in the environment.yml
and requirements.txt
files then you need to activate the environment and rebuild the JupyterLab application using the
following commands to source the postBuild
script.
conda activate $ENV_PREFIX # optional if environment already active
source postBuild
For your convenience these commands have been combined in a shell script ./bin/create-conda-env.sh
.
Running the shell script will create the Conda environment, activate the Conda environment, and build
JupyterLab with any additional extensions. The script should be run from the project root directory as
follows.
./bin/create-conda-env.sh
The list of explicit dependencies for the project are listed in the environment.yml
file. To see
the full lost of packages installed into the environment run the following command.
conda list --prefix $ENV_PREFIX
If you add (remove) dependencies to (from) the environment.yml
file or the requirements.txt
file
after the environment has already been created, then you can re-create the environment with the
following command.
$ conda env create --prefix $ENV_PREFIX --file environment.yml --force
If you have added any JupyterLab extensions or made any other changes to the postBuild
script, then you
should re-create the entire Conda environment by re-running the bin/create-conda-env.sh
scipt as follows.
./bin/create-conda-env.sh
In order to build Docker images for your project and run containers you will need to install Docker and Docker Compose.
Detailed instructions for using Docker to build and image and launch containers can be found in
the docker/README.md
.
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