beam-ds 2.0.3

Beam Datascience package

BeamDS Package (beam data-science)

This BeamDS implementation follows the guide at https://packaging.python.org/tutorials/packaging-projects/

prerequisits:

install the build package:

python -m pip install --upgrade build

Packages to install:

tqdm, loguru, tensorboard

to reinstall the package after updates use:

1. Now run this command from the same directory where pyproject.toml is located:

python -m build

2. reinstall the package with pip:

pip install dist/*.whl --force-reinstall

Building the Beam-DS docker image

The docker image is based on the latest official NVIDIA pytorch image. To build the docker image from Ubuntu host, you need to:

1. update nvidia drivers to the latest version: https://linuxconfig.org/how-to-install-the-nvidia-drivers-on-ubuntu-20-04-focal-fossa-linux

2. install docker: https://docs.docker.com/desktop/linux/install/ubuntu/

3. Install NVIDIA container toolkit: https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#install-guide

4. Install and configure NVIDIA container runtime: https://stackoverflow.com/a/61737404

Build the sphinx documentation

Follow https://github.com/cimarieta/sphinx-autodoc-example

Profiling your code with Scalene

Scalene is a high-performance python profiler that supports GPU profiling. To analyze your code with Scalene use the following arguments:

scalene --reduced-profile --outfile OUTFILE.html --html --- your_prog.py <your additional arguments>

Uploading the package to PyPi

1. Install twine:

python -m pip install --user --upgrade twine

2. Build the package:

python -m build

3. Upload the package:

python -m twine upload --repository pypi dist/*