Development Setup

Development Setup#

See also CONTRIBUTING.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

Remote Graphistry integration tests are opt-in because they require a live server and credentials:

TEST_REMOTE_INTEGRATION=1 \
GRAPHISTRY_API_TOKEN=<jwt> \
python -m pytest graphistry/tests/compute/test_chain_let_remote_integration.py

Use GRAPHISTRY_USERNAME/GRAPHISTRY_PASSWORD instead of GRAPHISTRY_API_TOKEN when token auth is not available. For service-account style authentication in application code, prefer personal_key_id + personal_key_secret. Optional env vars: GRAPHISTRY_SERVER and GRAPHISTRY_TEST_DATASET_ID.

Docs#

Automatically build via ReadTheDocs from inline definitions.

To manually build, see docs/.

Ignore files#

You may need to add ignore rules:

  • ruff: pyproject.toml (or 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

CI runs on every PR and updates them

Cypher Surface Growth Guard#

CI includes cypher-frontend-surface-guard, which enforces bounded growth for:

  • graphistry/compute/gfql/cypher/lowering.py total line count

  • CompiledCypherQuery, CompiledGraphBinding, CompiledCypherGraphQuery dataclass field/property counts

Guard implementation + baseline:

  • Script: bin/ci_cypher_surface_guard.py

  • Baseline: bin/ci_cypher_surface_guard_baseline.json

If growth is intentional, regenerate baseline in your branch and include explicit PR rationale:

python bin/ci_cypher_surface_guard.py --write-baseline

Then commit both code changes and baseline update together.

Type Hygiene Guard#

bin/lint.sh (run by the python-lint-types matrix on py3.8-3.14) runs bin/ci_type_hygiene_guard.py, a stdlib-only AST check over graphistry/ (tests excluded, matching mypy.ini). It exists to catch the defect classes that keep coming back in code review, so a reviewer does not have to.

Check

What it flags

missing-annotations

a parameter or return without a type annotation (same ground as ruff ANN001/002/003/201/202/204/205/206)

explicit-any

Any anywhere inside an annotation

explicit-cast

a cast(...) call

bare-generic

unsubscripted list / dict / List / Dict / … in an annotation

plottable-setattr

setattr() onto a parameter annotated as a Plottable

plottable-attr-write

param.attr = ... onto a parameter annotated as a Plottable

vocab-str-param

a closed-vocabulary parameter (table, kind, direction, how, mode, engine) annotated as plain str

Enforcement is a per-file count ratchet against bin/ci_type_hygiene_baseline.json: a file may not gain findings, and a file absent from the baseline must have zero. Existing debt is grandfathered, new and moved code is not.

./bin/ci_type_hygiene_guard.py             # what CI runs
./bin/ci_type_hygiene_guard.py --report    # totals per check
./bin/ci_type_hygiene_guard.py --list plottable-setattr
./bin/ci_type_hygiene_guard.py --strict    # show files that improved; time to retighten

When a finding is genuinely correct, annotate that line and say why:

setattr(res, f"_{kind}_dbscan", dbscan)  # hygiene-ok: plottable-setattr -- res is a fresh copy

Do not raise a cap with --update-baseline to make a new finding go away. Lowering caps after fixing debt is the intended use; commit the code change and the baseline update together.

Comment Density Guard#

bin/lint.sh (the same python-lint-types matrix lane as the type-hygiene guard) runs bin/ci_comment_density_guard.py, a stdlib-only tokenize + ast check over graphistry/. It enforces the “Encoding: names, tests, and structure — not prose” rules in agents/skills/review/SKILL.md, which were the last rule class on that stack still enforced only by human review.

Check

What it flags

comment-block

a run of 2+ adjacent full-line # comments (3+ for a Sphinx #: run)

perf-claim

performance / complexity / benchmark vocabulary in a comment or docstring

issue-rationale

a standalone comment or a docstring citing #<issue> as the explanation

comment-block is a form rule and reads # comments only. perf-claim and issue-rationale are content rules — the claim does not become admissible by moving into a docstring — so they read docstrings too. Tests are exempt from comment-block and issue-rationale (a test may explain its oracle) but not from perf-claim: measurement belongs in pyg-bench wherever it is written.

The line limit is 127, so a constraint that genuinely cannot be expressed by a name or a test fits on one line. regress and A/B count only next to performance vocabulary (they also name correctness concepts), and a comment that points at pyg-bench is a pointer to the measurement rather than a claim.

Enforcement is a per-file count ratchet against bin/ci_comment_density_baseline.json, exactly like the type-hygiene guard.

./bin/ci_comment_density_guard.py             # what CI runs
./bin/ci_comment_density_guard.py --report    # totals per check
./bin/ci_comment_density_guard.py --list comment-block
./bin/ci_comment_density_guard.py --strict    # show files that improved; time to retighten

The fix is almost never a suppression: extract a helper whose NAME states the rule, or write the test whose NAME states it. When a comment genuinely earns its place and still cannot fit, annotate it:

# guard-ok: comment-block -- openCypher 9.1 §4.2 wording, quoted verbatim

GFQL Cache Registry#

Every process-lifetime cache in graphistry/compute/gfql/** registers itself in graphistry/compute/gfql/cache_registry.py, at its own definition site, as either clearable (keyed by caller input; gfql_clear_caches() empties it) or an exempt process singleton with a written reason. The module docstring is the spec; graphistry/tests/compute/gfql/test_clear_caches_covers_every_cache.py fails CI on any unregistered memo. Never clear a cache by name lookup – registration hands over the bound clear handle precisely because a name-based clear once shipped a silent no-op and a wrong published benchmark number.

Conventions behind the checks#

  • Never write to a caller’s Plottable. Caching by setattr keyed on id() leaked results across gfql() calls and returned stale answers after an in-place frame mutation (issue #1825). Return a new object, or thread a per-execution cache.

  • Prefer engine-agnostic SeriesT / DataFrameT plus a localized # type: ignore over Any plus call-site cast().

  • Parameterize generics. list[str] needs from __future__ import annotations on py3.8 lanes; List[str] works without it. Both satisfy the check – only the unsubscripted form is flagged.

  • A fixed vocabulary is a Literal, not a str. A column name is legitimately str; an engine name, a table=/kind= of 'nodes'/'edges', a how=, or a direction= is not. Reuse an existing alias where one exists (e.g. GraphEntityKind = Literal['nodes', 'edges'] in graphistry/models/compute/features.py) rather than declaring a new one. vocab-str-param only knows the six parameter names above – it is a floor, not a full check; reviewers still own the general case.

Ruff additionally rejects getattr(x, "const") / setattr(x, "const", v) (B009/B010) outright; today’s offenders are grandfathered in pyproject.toml’s per-file-ignores and are retired with ruff check --fix --select B009,B010 <file>.

GPU CI#

Today, no CI lane executes cuDF. ci.yml never sets TEST_CUDF and no lane installs cudf, and ci-gpu.yml is disabled: its jobs are gated on the GRAPHISTRY_ENABLE_GPU_PUBLIC repository variable (unset), it needs the gpu_public self-hosted runner, and a gpu-disabled-guard job hard-fails any manual trigger. So a TEST_CUDF=1 receipt is developer-local evidence only – a cuDF-gated test can contradict the CPU contract, or rot outright, and stay green on master indefinitely. Treat a GPU claim in a PR as unprotected until a GPU lane exists: re-run it yourself rather than trusting the last receipt.

bin/ci_gpu_gate_audit.py (lane gpu-gate-audit) keeps the size of that gap visible: it counts the cuDF gates, requires each to be attributable (a reason= naming TEST_CUDF, so pytest -rs names what was not run rather than reporting a bare s) and to actually read the flag from the environment, and cross-checks this note against whether any workflow sets TEST_CUDF. Wiring a real GPU lane retires the note; deleting the note without wiring a lane fails the audit. The audit is static – it proves the gates are well formed, never that the gated assertions hold.

GPU CI can be manually triggered by core dev team members, once the lane is re-enabled:

  1. Push intended changes to protected branches gpu-public or master

  2. Manually trigger action ci-gpu on one of the above branches

GPU tests can also be run locally via ./docker/test-gpu-local.sh , or directly with TEST_CUDF=1 pytest ... on a RAPIDS-equipped box.

Debugging Tips#

  • Use the unit tests

  • use the logging module per-file

Publish: Merge, Tag, & Upload#

  1. Update CHANGELOG.md in your PR branch

    • Convert ## [Development] section to ## [X.Y.Z - YYYY-MM-DD]

    • Document all changes following Keep a Changelog format

    • Commit and push to PR branch

  2. Merge the PR to master (via GitHub UI or gh pr merge)

  3. Switch to master and pull the merged changes

    git checkout master
git pull --ff-only origin master
git status --short  # should be empty before tagging

  4. Tag the repository with the new version number (semantic versioning X.Y.Z)

    git tag X.Y.Z
git push origin refs/tags/X.Y.Z

  5. Confirm the publish Github Action published to pypi

    • Auto-triggers on tag push

    • Expected gate: on tag-triggered releases, the final Publish distribution to PyPI job can pause in waiting until a maintainer approves Review deployments for environment pypi-release.

    • If the run is waiting, open the run page and approve Review deployments, then wait for the PyPI job to complete.

    • If manually triggering (workflow_dispatch), choose release_mode:

      • evidence: build + SBOM + provenance + evidence artifacts only (no publish)

      • test: includes TestPyPI publish, skips PyPI (uses synthetic runner-local version 0.0.dev<run_id> to avoid local-version upload rejection)

      • release: TestPyPI + PyPI publish (restricted to master, with pypi-release approval)

    • Do not rerun publish for a version that is already on PyPI (duplicate-file uploads are rejected)

    • Verify version appears on PyPI: curl -s https://pypi.org/pypi/graphistry/json | jq -r '.info.version'

    • Verify release evidence artifacts from the workflow run:

      • built distributions (dist/*.whl, dist/*.tar.gz)

      • SBOM (evidence/sbom-cyclonedx.json)

      • GitHub build provenance attestation for built distributions (dist/*.whl, dist/*.tar.gz)

    • Keep the PyPI Trusted Publisher binding aligned with this workflow:

      • repository: graphistry/pygraphistry

      • workflow file: .github/workflows/publish-pypi.yml

      • environment: pypi-release

      • refs: tag pushes and workflow_dispatch on master only

    • This workflow publishes with attestations enabled for both TestPyPI and PyPI.

  6. Toggle version as active at ReadTheDocs

  7. Create GitHub Release with detailed release notes

    gh release create X.Y.Z --title "vX.Y.Z - Brief Title" --notes "Release notes in markdown..."

    Or create via GitHub UI: https://github.com/graphistry/pygraphistry/releases/new?tag=X.Y.Z

    Release notes should include:

    • Critical fixes and breaking changes (if any)

    • Major features from current and recent versions

    • Links to full CHANGELOG and installation instructions

    • Highlight important API changes, new capabilities, and use cases

CI Dependency Lockfiles#

CI uses per-Python-version hashed lockfiles for supply chain security:

  • Generation: A generate-lockfiles CI job runs bin/generate-lockfiles.sh to produce lockfiles for all profile × Python version combos. Most are uploaded as artifacts, not committed.

  • ReadTheDocs lockfile: requirements/rtd-py3.12.lock is committed because .readthedocs.yml consumes it directly. Update it when changing RTD’s Python version, docs/pygraphviz extras, setup.py dependency constraints that affect docs, or RTD install steps:

    PROFILES=rtd VERSIONS=3.12 ./bin/generate-lockfiles.sh

    CI’s check-rtd-lockfile job regenerates only the RTD profile using the committed lockfile’s --exclude-newer timestamp and fails if requirements/rtd-py3.12.lock is out of sync. To fix a red check-rtd-lockfile, rerun the command above and commit the resulting lockfile.

  • Spark lockfile: requirements/spark-py3.14.lock is committed because the test-spark job installs a small Spark-specific smoke-test environment without the broader test extras. Update requirements/spark-py3.14.in when changing the direct Spark smoke dependencies, then regenerate and commit the lockfile:

    PROFILES=spark VERSIONS=3.14 ./bin/generate-lockfiles.sh

    CI’s check-spark-lockfile job uses the committed lockfile’s --exclude-newer timestamp and fails if requirements/spark-py3.14.lock is out of sync.

  • 6-day cooldown: --exclude-newer ensures no package published in the last 6 days is included, mitigating 0-day supply chain attacks. UV_EXCLUDE_NEWER is also set globally as belt-and-suspenders.

  • Hash verification: --require-hashes on install ensures tamper-proof installs (except AI/umap profiles where torch conflicts prevent it).

  • Adding a dependency: After modifying most setup.py extras, CI automatically regenerates artifact lockfiles. If the change affects ReadTheDocs docs dependencies, also update and commit requirements/rtd-py3.12.lock.

  • Emergency override: Set COOLDOWN_DAYS=0 in bin/generate-lockfiles.sh to disable the 6-day cooldown for urgent patches.