tensorflow-gpu-data-science-project

Repository containing scaffolding for a Python 3-based data science project with GPU acceleration using on the TensorFlow ecosystem.

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.

Building the Conda environment

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 ./envof your project directory by running the following commands.

export ENV_PREFIX=$PWD/env
mamba 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.

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

Ibex

The most efficient way to build Conda environments on Ibex is to launch the environment creation script as a job on the debug partition via Slurm. For your convenience a Slurm job script ./bin/create-conda-env.sbatch is included. The script should be run from the project root directory as follows.

sbatch ./bin/create-conda-env.sbatch

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.

$ mamba env create --prefix $ENV_PREFIX --file environment.yml --force

Installing the NVIDIA CUDA Compiler (NVCC) (Optional)

Installing the NVIDIA CUDA Toolkit manually is only required if your project needs to use the nvcc compiler. Note that even if you have not written any custom CUDA code that needs to be compiled with nvcc, if your project uses packages that include custom CUDA extensions for PyTorch then you will need nvcc installed in order to build these packages.

If you don't need nvcc, then you can skip this section as conda will install a cudatoolkit package which includes all the necessary runtime CUDA dependencies (but not the nvcc compiler).

Workstation

You will need to have the appropriate version of the NVIDIA CUDA Toolkit installed on your workstation. If using the most recent versionf of PyTorch, then you should install NVIDIA CUDA Toolkit 11.2 (documentation).

After installing the appropriate version of the NVIDIA CUDA Toolkit you will need to set the following environment variables.

$ export CUDA_HOME=/usr/local/cuda-11.2
$ export PATH=$CUDA_HOME/bin:$PATH
$ export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH

Ibex

Ibex users do not neet to install NVIDIA CUDA Toolkit as the relevant versions have already been made available on Ibex by the Ibex Systems team. Users simply need to load the appropriate version using the module tool.

$ module load cuda/11.2.2

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.