Repository containing scaffolding for a Python 3-based data science project using on the TensorFlow ecosystem.
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.
- 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 that should be downloaded via conda
to the environment.yml
file
and any dependencies that should be downloaded via pip
to the requirements.txt
file you create the
Conda environment in a sub-directory ./env
of your project directory by running the following commands.
$ 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
(/path/to/env) $
Note that the ./env
directory is not under version control as it can always be re-created from
the ./bin/create-conda-environment.sh
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
After building the Conda environment you can check that Horovod has been built with support for TensorFlow and MPI with the following command.
$ conda activate ./env # optional if environment already active
(/path/to/env) $ horovodrun --check-build
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
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
.