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Development Setup

See also CONTRIBUTE.md and ARCHITECTURE.md

Development is setup for local native and containerized Python coding & testing, and with automatic GitHub Actions for CI + CD. The server tests are like the local ones, except against a wider test matrix of environments.

LFS

We are starting to use git lfs for data:

# install git lfs: os-specific commands below
git lfs install
git lfs checkout

git lfs: ubuntu

curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash
sudo apt-get install git-lfs

Docker

Install

cd docker && docker compose build && docker compose up -d

For just CPU tests, you can focus on test-cpu and use the run instructions below:

cd docker && docker compose build test-cpu

Run local tests without rebuild

Containerized call to pytest for CPU + GPU modes:

cd docker

# cpu - pandas
./test-cpu-local.sh

# cpu - fast & targeted
WITH_LINT=0 WITH_TYPECHECK=0 WITH_BUILD=0 ./test-cpu-local.sh graphistry/tests/test_hyper_dask.py::TestHypergraphPandas::test_hyper_to_pa_mixed2

# gpu - pandas, cudf, dask, dask_cudf; test only one file
./test-gpu-local.sh graphistry/tests/test_hyper_dask.py

Connector tests (currently neo4j-only): cd docker && WITH_NEO4J=1 ./test-cpu-local.sh (optional WITH_SUDO=" ")

  • Will start a local neo4j (docker) then enable+run tests against it

Docs

Automatically build via ReadTheDocs from inline definitions.

To manually build, see docs/.

Ignore files

You may need to add ignore rules:

  • flake8: bin/lint.sh
  • mypi: mypi.ini
  • sphinx: docs/source/conf.py

Remote

Some databases like Neptune can be easier via cloud editing, especially within Jupyter:

git clone https://github.com/graphistry/pygraphistry.git
git checkout origin/my_branch
pip install --user -e .
git diff

and

import logging
logging.basicConfig(level=logging.DEBUG)

import graphistry
graphistry.__version__

CI

GitHub Actions: See .github/workflows

Debugging Tips

  • Use the unit tests
  • use the logging module per-file

Publish: Merge, Tag, & Upload

  1. Merge the desired PR to master and switch to master head (git checkout master && git pull)

  2. Manually update CHANGELOG.md

  3. Tag the repository with a new version number. We use semantic version numbers of the form X.Y.Z.

    git tag X.Y.Z
git push --tags

  4. Confirm the publish Github Action published to pypi, or manually run it for the master branch

  5. Toggle version as active at ReadTheDocs