sagemaker-python-sdk/sagemaker-train/pyproject.toml at a02fce33efa4fbfce5d389ecc4905a323b9a3e18 · aws/sagemaker-python-sdk · GitHub

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[build-system]
requires = ["setuptools>=64", "wheel"]
build-backend = "setuptools.build_meta"

[project]
name = "sagemaker-train"
dynamic = ["version"]
description = "Open source library for training and deploying models on Amazon SageMaker."
readme = "README.rst"
requires-python = ">=3.9"
authors = [
  { name = "Amazon Web Services" },
]
keywords = [
  "AI",
  "AWS",
  "Amazon",
  "ML",
  "MXNet",
  "Tensorflow",
  "PyTorch",
  "HuggingFace"
]
classifiers = [
  "Development Status :: 3 - Alpha",
  "Intended Audience :: Developers",
  #"License :: OSI Approved :: Apache Software License",
  "Natural Language :: English",
  "Programming Language :: Python",
  "Programming Language :: Python :: 3.9",
  "Programming Language :: Python :: 3.10",
  "Programming Language :: Python :: 3.11",
  "Programming Language :: Python :: 3.12",
]
dependencies = [
    "sagemaker-core>=2.2.0",
    "graphene>=3,<4",
    "typing_extensions>=4.9.0",
    "tblib>=1.7.0",
    "PyYAML>=6.0,<7.0",
    "paramiko>=2.11.0",
    "jinja2>=3.0,<4.0",
    "sagemaker-mlflow>=0.0.1,<1.0.0",
    "mlflow>=3.0.0,<4.0.0",
]

[project.urls]
Homepage = "https://github.com/aws/sagemaker-python-sdk"

[project.optional-dependencies]
test = [
    "pytest",
    "pytest-cov",
    "pytest-xdist",
    "mock",
    "pydantic",
    "pandas",
    "scipy",
    "omegaconf",
    "graphene",
    "IPython"
]

[tool.setuptools.packages.find]
where = ["src/"]
include = ["sagemaker*"]
namespaces = true

[tool.setuptools.dynamic]
version = { file = "VERSION"}

[tool.pytest.ini_options]
addopts = ["-vv"]
testpaths = ["tests"]
markers = [
    "serial: marks tests that must run serially (not in parallel)",
]

[tool.black]
line-length = 100

[tool.setuptools]
include-package-data = true