pyproject.toml

[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"

[project]
name = "uni2ts"
dynamic = [
  "version",
]
dependencies = [
  "torch>=2.1,<2.5",
  "lightning>=2.0",
  "gluonts~=0.14.3",
  "numpy~=1.26.0",
  "scipy~=1.11.3",
  "einops==0.7.*",
  "jaxtyping~=0.2.24",
  "python-dotenv==1.0.0",
  "hydra-core==1.3",
  "orjson",
  "tensorboard",
  "multiprocess",
  "huggingface_hub>=0.23.0",
  "safetensors",
  "datasets~=2.17.1",
  "jax[cpu]",
]
requires-python = ">=3.10"
authors = [
  {name = "Gerald Woo", email = "gwoo@salesforce.com"},
  {name = "Chenghao Liu", email = "chenghao.liu@salesforce.com"},
  {name = "Akshat Kumar"},
  {name = "Caiming Xiong"},
  {name = "Silvio Savarese"},
  {name = "Doyen Sahoo"},
]
maintainers = [
  {name = "Gerald Woo", email = "gwoo@salesforce.com"},
  {name = "Juncheng Liu", email = "juncheng.liu@salesforce.com"}
]
description = "Unified Training of Universal Time Series Forecasting Transformers"
readme = "README.md"
license = {file = "LICENSE.txt"}
keywords = ["Time Series Forecasting", "Transformer", "Deep Learning", "PyTorch"]
classifiers = [
  "Development Status :: 3 - Alpha",
  "Programming Language :: Python",
  "Topic :: Scientific/Engineering :: Artificial Intelligence",
]

[project.optional-dependencies]
dev = [
  "pytest==7.4.3",
  "pytest_timeout==2.2.0",
  "hatch",
  "black[jupyter]==24.2.0",
  "isort",
  "pre-commit",
]
notebook = [
  "jupyter",
  "ipywidgets",
  "matplotlib",
]
build-lotsa = [
  "buildings_bench",
  "pyreadr",
  "tables",
  "subseasonal-data"
]

[tool.hatch.version]
path = "src/uni2ts/__about__.py"

[tool.hatch.build]
packages = ["src/uni2ts"]

[tool.isort]
profile = "black"

[tool.black]
line-length = 88
target-version = ['py310']

[tool.pytest.ini_options]
pythonpath = [
  ".", "src",
]