Manufacturing engineers and natural science researchers have long suffered from ad-hoc implementations of scientific computing tasks and their hand-craft deployment on clusters. EASIER is a domain specific language, compiler and runtime for efficiently and automatically scaling scientific computing tasks up and out, providing scientific computing developers similar experience to that of developing and deploying large-scale deep learning models.
The dependencies should be fixed to exactly the acceptable minimun versions to ensure the widest compatibility and development consistency.
The Python itself should be fixed to 3.8:
# for conda
conda create -n ENV_NAME python=3.8
conda activate ENV_NAME
# for conda, only run this if you see compile/link errors with `pip` commands
conda install gxx_linux-64
# for other venvs
# TODOTo install the dependencies for development:
# for Ubuntu
sudo apt-get install libopenmpi-dev
pip install Cython==3.0.11 mpi4py==3.1.5 # must be installed separately
pip install -r dev-requirements.txt
pip install -e . # equals `python setup.py develop`├── docker/ # Dockerfiles
├── easier/ # python package
│ ├── core/ # jit compiler implementation
│ └── **/** # numerical algorithms based on eaiser jit compiler
├── tests/ # unit tests
├── dev-requirements.txt
├── README.md
├── setup.py
└── .gitignoreThis project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.