pyproject.toml

[build-system]
requires = ["hatchling", "hatch-vcs"]
build-backend = "hatchling.build"


[project]
name = "pytensor_ml"
dynamic = ["version"]
description = "A(nother) deep learning library, built on top of Pytensor"
readme = "README.md"
requires-python = ">=3.12"
authors = [{name="Jesse Grabowski", email='jessegrabowski@gmail.com'}, {name="Ricardo Vieira"}]
license = "Apache-2.0"
license-files = ["LICENSE"]
classifiers = [
  "Development Status :: 2 - Pre-Alpha",
  "Intended Audience :: Science/Research",
  "Programming Language :: Python",
  "Topic :: Scientific/Engineering",
  "Operating System :: Microsoft :: Windows",
  "Operating System :: POSIX",
  "Operating System :: Unix",
  "Operating System :: MacOS",
  "Programming Language :: Python :: 3",
  "Programming Language :: Python :: 3.12",
  "Programming Language :: Python :: 3.13",
  "Programming Language :: Python :: 3.14",
]
keywords = [
  "neural network",
  "deep learning",
  "autograd",
  "gradient descent",
  "pymc"
]

dependencies = [
  "pytensor>=3.3.3,<4.0.0",
  "numpy",
]

[project.urls]
Homepage = "https://github.com/pymc-devs/pytensor-ml"
Documentation = "https://pytensor-ml.readthedocs.io"
Source = "https://github.com/pymc-devs/pytensor-ml"
Tracker = "https://github.com/pymc-devs/pytensor-ml/issues"

[project.optional-dependencies]
dev = [
    "safetensors",
    "cloudpickle",
    "pre-commit",
    "pytest",
    "pytest-cov",
    "scikit-learn",
    "mypy",
    "jax",
    "pyyaml",
]
examples = [
    "matplotlib",
    "tqdm",
]

[tool.hatch.version]
source = "vcs"

[tool.hatch.build.hooks.vcs]
version-file = "pytensor_ml/_version.py"


[tool.pytest.ini_options]
minversion = "6.0"
xfail_strict=true
# A checkpoint test reads a published snapshot of several GB from disk, so it runs only when asked
# for: `pytest -m checkpoint`.
addopts = "-m 'not checkpoint'"
markers = [
    "checkpoint: needs a full published checkpoint on disk, not a fixture",
]
filterwarnings = [
    "error",
    'ignore:\nArviZ is undergoing a major refactor.*:FutureWarning',
    # pytensor silences this on import, but our "error" rule above resets the
    # warnings filter per-test and undoes it. Numba emits it from the numba
    # linker, which is pytensor 3's default.
    "ignore::numba.core.errors.NumbaPerformanceWarning",
    # torch.compile pulls in inductor, which imports torch.utils.mkldnn, whose class bodies still call
    # the deprecated torch.jit.script_method. Raising here aborts the import half-done, so every later
    # torch-mode test retries it and fails the same way. The message past this prefix depends on the
    # Python version.
    "ignore:`torch.jit.script_method` is:DeprecationWarning",
    # pytensor hands torch its constants as the read-only numpy arrays it stores them in.
    "ignore:The given NumPy array is not writable:UserWarning",
]

[tool.ruff]
line-length = 100
target-version = "py312"
exclude = ["pytensor_ml/_version.py"]

[tool.ruff.lint]
select = ["D", "E", "F", "I", "UP", "W", "RUF", "TID252"]
ignore = [
  "E501",  # Line too long
  "RUF001", # String contains ambiguous character (such as Greek letters)
  "RUF002", # Docstring contains ambiguous character (such as Greek letters)
  "RUF012", # Mutable class attributes should be annotated with `typing.ClassVar`
  "RUF059", # Unpacked variable is never used (noisy for `X, y = fixture` unpacking)
  "D100",
  "D101",
  "D102",
  "D103",
  "D104",
  "D105",
  "D107",
  "D200",
  "D202",
  "D203",
  "D204",
  "D205",
  "D209",
  "D212",
  "D213",
  "D301",
  "D400",
  "D401",
  "D403",
  "D413",
  "D415",
  "D417",
]
fixable = ["ALL"]

[tool.ruff.lint.per-file-ignores]
# An __init__.py re-exports its package's API and imports submodules for their registration side effects.
"**/__init__.py" = ["F401"]


[tool.ruff.lint.isort]
lines-between-types = 1


[tool.mypy]
python_version = "3.12"
ignore_missing_imports = true
strict_equality = true
warn_redundant_casts = true
warn_unused_configs = true
warn_unused_ignores = true
# pytensor ops are typed to return Any, so returning their results from a function annotated with a
# concrete tensor type fires constantly without catching real bugs in our code.
warn_return_any = false
warn_unreachable = true
files = ["pytensor_ml"]

[tool.pixi.workspace]
channels = ["conda-forge"]
platforms = ["osx-arm64", "linux-64", "win-64"]

# Autodoc imports pytensor_ml, so the docs environment needs the runtime stack alongside sphinx.
# Read the Docs cannot read a pixi environment, so it builds from conda_envs/environment-docs.yml;
# the two lists have to move together.
[tool.pixi.feature.docs.dependencies]
python = ">=3.12"
pytensor = ">=3.3.3,<3.4.0"
numpy = "*"
safetensors = "*"
# The gallery extension renders notebook thumbnails with matplotlib.
matplotlib = "*"
ipython = "*"
jupyter = "*"
sphinx = ">=7"
pydata-sphinx-theme = "*"
myst-nb = "*"
numpydoc = "*"
sphinx-copybutton = "*"
sphinx-design = "*"
sphinx-codeautolink = "*"
sphinx-sitemap = "*"
sphinx-notfound-page = "*"
sphinx-autobuild = "*"
jupyter-sphinx = "*"
sphinxcontrib-bibtex = "*"

[tool.pixi.feature.docs.pypi-dependencies]
pytensor_ml = { path = ".", editable = true }

[tool.pixi.feature.docs.tasks]
docs-build = { cmd = "sphinx-build -b html source build/html", cwd = "docs" }
# Rebuilds on save and serves the result at http://localhost:8000. The ignored paths are the ones a
# build writes back into source/ -- the gallery pages and staged notebooks, their thumbnails, and the
# autosummary stubs -- which would otherwise retrigger the watcher and rebuild forever. They are the
# same set docs/.gitignore lists, and have to move together.
docs-serve = { cmd = """\
    sphinx-autobuild source build/html --port 8000 --open-browser \
    --ignore '*/source/examples/*' \
    --ignore '*/source/_thumbnails/*' \
    --ignore '*/source/api/generated/*'\
""", cwd = "docs" }

[tool.pixi.environments]
docs = { features = ["docs"] }