python-gpu-data-science-project

Repository containing scaffolding for a Python 3-based data science project with GPU acceleration.

Creating a new project from this template

Simply follow the instructions to create a new project repository from this template.

Project organization

Project organization is based on ideas from Good Enough Practices for Scientific Computing.

1. Put each project in its own directory, which is named after the project.
2. Put external scripts or compiled programs in the bin directory.
3. Put raw data and metadata in a data directory.
4. Put text documents associated with the project in the doc directory.
5. Put all Docker related files in the docker directory.
6. Install the Conda environment into an env directory.
7. Put all notebooks in the notebooks directory.
8. Put files generated during cleanup and analysis in a results directory.
9. Put project source code in the src directory.
10. Name all files to reflect their content or function.

Using Conda

Creating the Conda environment

As this template repo is designed to help users get set up with a GPU-accelerated Python data science project the environment.yml file already includes key NVIDIA CUDA components in particular versions 10.1 of both cudatoolkit and cupti. Currently CUDA 10.1 is version supported by the major machine learning and deep learning libraries such as RAPIDS, PyTorch, TensorFlow, etc. Note that the nvcc compiler is not included in the cudatoolkit pacakge (if you need nvcc then consider using the cudatoolkit-dev package instead).

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
. 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

Listing the full contents of the Conda environment

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

Updating the Conda environment

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

Using Docker

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.