New home for cdm-utilities with more flexible package layout/structure
For now the library requires python version at least 3.9
. If your install gives an
error about not being able to find compatible version of a library, check
python --version
If you're using an older python version, you'll need to upgrade.
The simplest way to use this repo is to set up a conda env or environment of your choice
and then, after git clone
-ing this directory, simply issue
make install
or
pip install .
A virtual environment can be defined and created through an environment.yml file. For example, the Conda environment conda-env-cdm can be created with this snippet included in environment.yml
name: conda-env-cdm
channels:
- pytorch
- nvidia
- conda-forge
dependencies:
- python == 3.10
- pandas
- pyyaml
- minio
- requests
- ipykernel
- conda-build
- pip
- pip:
- git+https://github.com/clinical-data-mining/msk_cdm.git
If you're contributing to the code please run make install_precommit_hooks
from the
root of the repository to install the pre-commit hooks. They will probably require you
to git add
a second time after trying the first round of git commit
(one of the
hooks is the Black linter, which modifies the source file, so you need to try to
git commit
twice if it does).
You can run unit tests by issuing make test
from the root dir.
If you're updating this repo, please make a Pull Request via a git branch. If the change is to the core functionality (i.e. modifies the core code in this repo possibly used by other projects/peple) please request a code review.
This repo uses mkdocs. One can install all dependencies using pip:
pip install -r requirements.txt
Then to run locally:
mkdocs serve --dev-addr <Default 127.0.0.1:8000>
Run:
mkdocs gh-deploy