RAPIDS

This template is for developing rapids.ai dependent products; rapids.ai enables graphics processing unit accelerated data science.

Remote Development

Via a remote development image. The image depends on .devcontainer/Dockerfile & .devcontainer/requirements.txt, and is built via

 docker build . --file .devcontainer/Dockerfile -t rapids

Run an instance of the image, i.e., a container, via

docker run --rm --gpus all -i -t -p 127.0.0.1:10000:8888 -w /app 
	--mount type=bind,src="$(pwd)",target=/app rapids

or

docker run --rm --gpus all -i -t -p 127.0.0.1:10000:8888 -w /app 
	--mount type=bind,src="$(pwd)",target=/app 
	-v ~/.aws:/home/rapids/.aws rapids

Opt for the second option if you need to interact with Amazon Web Services. Note:

• --rm: automatically remove container
• -i: interactive mode
• -t: tag
• -p: publish maps the host port 10000 to container port 8888
• -v ~/.aws:/home/rapids/.aws enables interactions with Amazon Web Services, if such interactions are required. It maps the local Amazon Web Services credentials directory to a temporary .aws directory within the container.

Get the name of the container via:

docker ps --all

Remote Development & Integrated Development Environments

In aid of productivity, use/attach an IDE (integrated development environment) to the running container. The IntelliJ IDEA set up involves connecting to a machine's Docker daemon, the steps are

• Settings $\rightarrow$ Build, Execution, Deployment $\rightarrow$ Docker $\rightarrow$ WSL: {select the linux operating system}
• View $\rightarrow$ Tool Window $\rightarrow$ Services
  Within the Containers section connect to the running instance of interest, or ascertain connection to the running instance of interest.

Visual Studio Code has its container attachment instructions; study Attach Container.

Code Analysis

The GitHub Actions script main.yml conducts code analysis within a Cloud GitHub Workspace. Depending on the script, code analysis may occur on push to any repository branch, or on push to a specific branch.

The sections herein outline remote code analysis.

pylint

The directive

pylint --generate-rcfile > .pylintrc

generates the dotfile .pylintrc of the static code analyser pylint. Analyse a directory via the command

python -m pylint --rcfile .pylintrc {directory}

The .pylintrc file of this template project has been amended to adhere to team norms, including

• Maximum number of characters on a single line.

  max-line-length=127

• Maximum number of lines in a module.

  max-module-lines=135

pytest

The pytest framework is for test writing. This template includes a test for the program src/algorithms/example.py. The program's corresponding test program is tests/algorithms/test_example.py, which is run via

python -m pytest -p no:cacheprovider /app/tests/algorithms/test_example.py

The directive no:cacheprovider will prevent the storage of test cache, hence prevents the need for a .pytest_cache directory.

flake8

For code & complexity analysis. A directive of the form

python -m flake8 --count --select=E9,F63,F7,F82 --show-source 
	--statistics src/{directory.name}

inspects issues in relation to logic (F7), syntax (Python E9, Flake F7), mathematical formulae symbols (F63), undefined variable names (F82). Additionally

python -m flake8 --count --exit-zero --max-complexity=10 --max-line-length=127 
	--statistics src/{directory.name}

inspects complexity.

References

Images via Docker Hub Container Image Library

• RAPIDS
• About Development Containers
• Development Containers

Images via NVIDIA GPU Cloud (NGC)

• Starting
• Images & Containers
• rapids

Additionally

• CUDF
• Installing RAPIDS