|hoangthienan95 09f0ce87b9 Initial commit||3 months ago|
|bin||3 months ago|
|data||3 months ago|
|doc||3 months ago|
|docker||3 months ago|
|notebooks||3 months ago|
|results||3 months ago|
|src||3 months ago|
|.gitignore||3 months ago|
|LICENSE||3 months ago|
|README.md||3 months ago|
|environment.yml||3 months ago|
|postBuild||3 months ago|
|requirements.txt||3 months ago|
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.
After adding any necessary dependencies for your project to the Conda
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
If you wish to use any JupyterLab extensions included in the
files then you need to activate the environment and rebuild the JupyterLab application using the
following commands to source the
conda activate $ENV_PREFIX # optional if environment already active source postBuild
For your convenience these commands have been combined in a shell script
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
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
after the environment has already been created, then you can re-create the environment with the
$ 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.
Detailed instructions for using Docker to build and image and launch containers can be found in