#####################################################################

# Core Project metadata

#####################################################################

[project]

name = "nvidia-physicsnemo"

authors = [

{ name="NVIDIA PhysicsNeMo Team"},

]

description = "A deep learning framework for AI-driven multi-physics systems"

readme = "README.md"

requires-python = ">=3.11,<3.15"

license = "Apache-2.0"

license-files = ["LICENSE.txt", "THIRD_PARTY_NOTICES.md"]

classifiers = [

"Programming Language :: Python :: 3",

"Operating System :: OS Independent",

]

dynamic = ["version", "optional_dependencies"]

dependencies = [

"warp-lang>=1.17.0",

"nvtx>=0.2.16",

"treelib>=1.8.0",

"numpy>=2.2.6",

"torch>=2.13.0",

"tqdm>=4.70.1",

"requests>=2.34.2",

# Retain a direct floor for urllib3's decompression-bomb security fixes.

"urllib3>=2.8.0",

"GitPython>=3.1.62",

"s3fs>=2026.9.0",

"packaging>=26.0",

"timm>=1.0.30",

"einops>=0.8.2",

"h5py>=3.16.0",

"cftime>=1.6.6",

"jaxtyping>=0.3.11",

"termcolor>=3.3.0",

"hydra-core>=1.3.7",

"tensordict[zarr]>=0.15.3,<0.16.0",

"omegaconf>=2.3.1",

"importlib-metadata>=9.0.1",

"psutil>=7.2.2",

"fsspec>=2026.9.0",

]

[project.urls]

Homepage = "https://github.com/NVIDIA/physicsnemo"

Documentation = "https://docs.nvidia.com/physicsnemo/index.html#core"

Issues = "https://github.com/NVIDIA/physicsnemo/issues"

Changelog = "https://github.com/NVIDIA/physicsnemo/blob/main/CHANGELOG.md"

#####################################################################

# Flags for UV compatibility

#####################################################################

[tool.uv]

managed = true

conflicts = [

[

{ extra = "cu12" },

{ extra = "cu13" },

],

[

{ extra = "natten-cu12" },

{ extra = "natten-cu13" },

],

[

{ extra = "transformer-engine-cu12" },

{ extra = "transformer-engine-cu13" },

],

[

{ extra = "cu12" },

{ extra = "natten-cu13" },

],

[

{ extra = "cu12" },

{ extra = "transformer-engine-cu13" },

],

[

{ extra = "cu13" },

{ extra = "natten-cu12" },

],

[

{ extra = "cu13" },

{ extra = "transformer-engine-cu12" },

],

[

{ extra = "natten-cu12" },

{ extra = "transformer-engine-cu13" },

],

[

{ extra = "natten-cu13" },

{ extra = "transformer-engine-cu12" },

],

]

[tool.uv.extra-build-dependencies]

torch-sparse = ["torch"]

torch-cluster = ["torch"]

torch-scatter = ["torch"]

transformer-engine-torch = [{ requirement = "torch", match-runtime = true }]

[[tool.uv.index]]

name = "nvidia"

url = "https://pypi.nvidia.com"

explicit = true # Only use for packages that explicitly specify this index

# Select PyTorch wheels from the same CUDA major version as RAPIDS and CuPy.

# These indexes apply to uv; pip users must select the wheel index explicitly.

[[tool.uv.index]]

name = "pytorch-cu130"

url = "https://download.pytorch.org/whl/cu130"

explicit = true

# PyTorch 2.13 is the newest release with CUDA 12.9 wheels. CUDA 12.9 keeps

# Blackwell kernels and satisfies RAPIDS' nvJitLink>=12.9 requirement; later

# PyTorch releases publish CUDA 12 wheels only for CUDA 12.6, which stops at

# sm_90. Linux only: this index has no Windows wheels.

[[tool.uv.index]]

name = "pytorch-cu129"

url = "https://download.pytorch.org/whl/cu129"

explicit = true

# NATTEN pre-built wheel indexes (flat HTML pages), split by CUDA version AND

# torch version. The full path is required because natten encodes both CUDA

# and torch versions in local version identifiers (e.g. 0.21.7+torch2130cu126).

# Keep these URLs and the torch pins in the natten extras in sync: the binary

# extensions must use the same PyTorch ABI as the runtime.

# NATTEN publishes no CUDA 12.9 build; its CUDA 12.6 wheels run on the CUDA

# 12.9 PyTorch runtime but contain kernels only through sm_90.

# See https://natten.org/install/ for available builds.

[[tool.uv.index]]

name = "natten-cu126-whl"

url = "https://whl.natten.org/cu126/torch2.13.0"

format = "flat"

explicit = true

[[tool.uv.index]]

name = "natten-cu130-whl"

url = "https://whl.natten.org/cu130/torch2.13.0"

format = "flat"

explicit = true

[tool.uv.sources]

# When a CUDA 12 extra is active, torch/torchvision come from the CUDA 12.9

# index; with a CUDA 13 extra, they come from the CUDA 13.0 index. The NATTEN

# and Transformer Engine extras select the index too, so their CUDA binaries

# match PyTorch even without cu12/cu13. Every extra listed here must also

# declare torch and torchvision.

# When no CUDA extra is active (bare install), they come from PyPI.

# On macOS, CUDA extras should not be used (no GPU builds available).

torch = [

{ index = "pytorch-cu129", extra = "cu12" },

{ index = "pytorch-cu129", extra = "natten-cu12" },

{ index = "pytorch-cu129", extra = "transformer-engine-cu12" },

{ index = "pytorch-cu130", extra = "cu13" },

{ index = "pytorch-cu130", extra = "natten-cu13" },

{ index = "pytorch-cu130", extra = "transformer-engine-cu13" },

]

torchvision = [

{ index = "pytorch-cu129", extra = "cu12" },

{ index = "pytorch-cu129", extra = "natten-cu12" },

{ index = "pytorch-cu129", extra = "transformer-engine-cu12" },

{ index = "pytorch-cu130", extra = "cu13" },

{ index = "pytorch-cu130", extra = "natten-cu13" },

{ index = "pytorch-cu130", extra = "transformer-engine-cu13" },

]

cuml-cu13 = { index = "nvidia" }

cuml-cu12 = { index = "nvidia" }

pylibraft-cu13 = { index = "nvidia" }

pylibraft-cu12 = { index = "nvidia" }

nvidia-dali-cuda130 = { index = "nvidia" }

nvidia-dali-cuda120 = { index = "nvidia" }

# When natten-cu12 or natten-cu13 extras are active, pull natten from

# the pre-built wheel index instead of building from source via PyPI.

natten = [

{ index = "natten-cu126-whl", extra = "natten-cu12" },

{ index = "natten-cu130-whl", extra = "natten-cu13" },

]

# transformer-engine-torch is an sdist whose setup.py dynamically pins

# transformer_engine_cu{12,13} based on torch.version.cuda in the build

# environment. Without this override, uv lock freezes whichever CUDA

# variant happened to be in the build env, which broke CI when PyTorch

# 2.12 flipped the PyPI default to cu130 (the cu13 binary then got

# pulled into a CUDA 12.8 runtime, producing missing libcublas.so.13

# errors at import time). We provide the CUDA-agnostic subset of the

# dependencies here; the matching cuXX binary is pulled by the explicit

# transformer-engine-cu12 / transformer-engine-cu13 extras below.

# Static deps mirror TE 2.19's build_tools/pytorch.py::install_requirements().

[[tool.uv.dependency-metadata]]

name = "transformer-engine-torch"

requires-dist = [

"torch>=2.1",

"einops",

"onnxscript",

"onnx",

"packaging",

"pydantic",

"nvdlfw-inspect",

"nvidia-cudnn-frontend>=1.28.0",

]

#####################################################################

# Flags Controlling the local build of physicsnemo

#####################################################################

[build-system]

requires = ["hatchling>=1.32.4"]

build-backend = "hatchling.build"

[tool.hatch.version]

path = "physicsnemo/__init__.py"

[tool.hatch.build.targets.wheel]

packages = ["physicsnemo"]

[tool.hatch.build.targets.sdist]

exclude = [

"/.github",

"/docs",

"/examples",

"/test",

"/CODING_STANDARDS",

"/benchmarks",

]

#####################################################################

# Local Development Requirements (pytest, etc)

#####################################################################

[dependency-groups]

dev = [

"asv>=0.6.6",

"coverage>=7.16.2",

"interrogate>=1.7.0",

"onnx>=1.23.1",

"onnxscript>=0.7.2",

"pre-commit>=4.6.2",

"pytest>=9.1.1",

"pytest-dependency>=0.6.1",

"pytest-timeout>=2.4.0",

"ruff>=0.14.8",

"pytest-testmon>=2.2.0",

"ipykernel>=7.4.0",

"import-linter>=2.15",

]

#####################################################################

# Optional Dependency groups

#####################################################################

# These groups are self-referential. So, they chain together as you

# move up the physicsnemo hierarchy. A new extra dep in `physicsnemo/nn`

# will also be an extra dep in `physicsnemo/models`, but not the other

# direction.

[project.optional-dependencies]

# CUDA backend extras (mutually exclusive via [tool.uv] conflicts).

# Combine with feature extras, e.g. nvidia-physicsnemo[nn-extras,cu13].

# When neither is specified, torch comes from PyPI and no RAPIDS

# packages are installed.

cu13 = [

"torch>=2.13.0",

"torchvision>=0.28.0",

"cuml-cu13>=26.8.0",

"pylibraft-cu13>=26.8.0",

# RAPIDS' stable Numba 0.64 dependency does not support NumPy 2.5.

"numpy<2.5",

# Keep CuPy and PyTorch on the same CUDA major version (cupy/cupy#9827).

"cupy-cuda13x>=14.2.0",

"nvidia-dali-cuda130>=2.3.0",

]

cu12 = [

# PyTorch 2.14+ has no CUDA 12.9 wheels; see the pytorch-cu129 index.

"torch>=2.13.0,<2.14",

"torchvision>=0.28.0",

"cuml-cu12>=26.8.0",

"pylibraft-cu12>=26.8.0",

"cupy-cuda12x>=14.2.0",

"nvidia-dali-cuda120>=2.3.0",

]

# NATTEN pre-built wheel extras (mutually exclusive via [tool.uv] conflicts).

# Pair with the matching CUDA backend extra, e.g.:

# uv sync --extra cu12 --extra natten-cu12

# uv sync --extra cu13 --extra natten-cu13

# NATTEN 0.21.7 provides wheels for PyTorch 2.13, but not 2.14 yet.

# Without these extras, install NATTEN separately; see https://natten.org/install/.

natten-cu12 = [

"torch==2.13.0",

"torchvision>=0.28.0",

"natten>=0.21.7",

]

natten-cu13 = [

"torch==2.13.0",

"torchvision>=0.28.0",

"natten>=0.21.7",

]

# Transformer Engine extras (mutually exclusive via [tool.uv] conflicts).

# Pair with the matching CUDA backend extra, e.g.:

# pip install nvidia-physicsnemo[cu12,transformer-engine-cu12]

# pip install nvidia-physicsnemo[cu13,transformer-engine-cu13]

# Uses the metapackage's `core_cu12` / `core_cu13` extras (introduced in

# transformer-engine 2.14.0) to explicitly pin the CUDA backend binary,

# so the resolution doesn't depend on torch.version.cuda in the build

# env. See [[tool.uv.dependency-metadata]] above for the matching

# override on transformer-engine-torch's dynamic CUDA dep.

transformer-engine-cu12 = [

"torch>=2.13.0,<2.14",

"torchvision>=0.28.0",

"transformer_engine[core_cu12,pytorch]>=2.19.0",

]

transformer-engine-cu13 = [

"torch>=2.13.0",

"torchvision>=0.28.0",

"transformer_engine[core_cu13,pytorch]>=2.19.0",

]

utils-extras = [

"wandb>=0.30.0",

"mlflow>=3.16.1",

"line_profiler>=5.0.2",

]

mesh-extras = [

"matplotlib>=3.11.2",

"pyvista>=0.49.0",

"vtk>=9.7.1",

]

nn-extras = [

"scipy>=1.17.1",

"nvidia-physicsnemo[utils-extras]",

]

model-extras = [

"nvidia-physicsnemo[nn-extras]",

"pandas>=2.3.3",

"pyvista>=0.49.0",

"vtk>=9.7.1",

]

datapipes-extras = [

"pandas>=2.3.3",

"tfrecord>=1.14.6",

"dask>=2026.8.0",

"netCDF4>=1.7.3",

"xarray>=2026.7.0",

"zarr>=3.1.6",

]

uq-extras = [

"gpytorch>=1.15.2",

]

# Use case specific dependency groups.

gnns = [

"torch_geometric>=2.8.0.post1",

"torch_scatter>=2.1.2",

"torch_sparse>=0.6.18",

"torch_cluster>=1.6.3",

"PyYAML>=6.0.3",

# "nvfuser",

"nvidia-physicsnemo[model-extras]",

]

sym = [

"sympy>=1.14.0",

]

#####################################################################

# Linting configuration

#####################################################################

[tool.ruff]

# Enable flake8/pycodestyle (`E`), Pyflakes (`F`), flake8-bandit (`S`),

# isort (`I`), and performance 'PERF' rules.

lint.select = ["E", "F", "S", "I", "PERF"]

lint.fixable = ["I"]

# Never enforce `E501` (line length violations),

# and `S311` (random number generators)

# and `F722` which breaks jaxtyping annotations

lint.ignore = ["E501", "S311", "F722"]

# Exclude the docs and experimental folders (this applies to both lint and format)

exclude = ["docs", "physicsnemo/experimental"]

[tool.ruff.lint.per-file-ignores]

# Ignore `F401` (import violations) in all `__init__.py` files, and in `docs/*.py`.

"__init__.py" = ["F401"]

"docs/*.py" = ["F401"]

# Ignore `S101` (assertions) in all `test` files.

"test/*.py" = ["S101"]

"examples/**/test_*.py" = ["S101"]

[project.entry-points."physicsnemo.models"]

# AFNO

AFNO = "physicsnemo.models.afno:AFNO"

DistributedAFNO = "physicsnemo.models.afno:DistributedAFNO"

ModAFNO = "physicsnemo.models.afno:ModAFNO"

# Diffusion UNets

SongUNet = "physicsnemo.models.diffusion_unets:SongUNet"

SongUNetPosEmbd = "physicsnemo.models.diffusion_unets:SongUNetPosEmbd"

SongUNetPosLtEmbd = "physicsnemo.models.diffusion_unets:SongUNetPosLtEmbd"

DhariwalUNet = "physicsnemo.models.diffusion_unets:DhariwalUNet"

DiffusionUNet3D = "physicsnemo.models.diffusion_unets:DiffusionUNet3D"

# DiT

DiT = "physicsnemo.models.dit:DiT"

# Diffusion wrappers

CorrDiffRegressionUNet = "physicsnemo.models.diffusion_unets:CorrDiffRegressionUNet"

StormCastUNet = "physicsnemo.models.diffusion_unets:StormCastUNet"

# DLWP

DLWP = "physicsnemo.models.dlwp:DLWP"

# DLWP HEALPix

HEALPixRecUNet = "physicsnemo.models.dlwp_healpix:HEALPixRecUNet"

HEALPixUNet = "physicsnemo.models.dlwp_healpix:HEALPixUNet"

# DPOT

DPOTNet = "physicsnemo.models.dpot:DPOTNet"

# Fengwu

Fengwu = "physicsnemo.models.fengwu:Fengwu"

# FIGConvNet

FIGConvUNet = "physicsnemo.models.figconvnet:FIGConvUNet"

# FLARE

FLARE = "physicsnemo.models.flare:FLARE"

# FNO

FNO = "physicsnemo.models.fno:FNO"

# GeoTransolver

GeoTransolver = "physicsnemo.models.geotransolver:GeoTransolver"

# GLOBE

GLOBE = "physicsnemo.models.globe:GLOBE"

# GraphCast

GraphCastNet = "physicsnemo.models.graphcast:GraphCastNet"

# MeshGraphNets

MeshGraphNet = "physicsnemo.models.meshgraphnet:MeshGraphNet"

BiStrideMeshGraphNet = "physicsnemo.models.meshgraphnet:BiStrideMeshGraphNet"

MeshGraphKAN = "physicsnemo.models.meshgraphnet:MeshGraphKAN"

HybridMeshGraphNet = "physicsnemo.models.meshgraphnet:HybridMeshGraphNet"

# MLP

FullyConnected = "physicsnemo.models.mlp:FullyConnected"

# Pangu

Pangu = "physicsnemo.models.pangu:Pangu"

# Pix2Pix

Pix2Pix = "physicsnemo.models.pix2pix:Pix2Pix"

Pix2PixUnet = "physicsnemo.models.pix2pix:Pix2PixUnet"

# RNNs

One2ManyRNN = "physicsnemo.models.rnn:One2ManyRNN"

Seq2SeqRNN = "physicsnemo.models.rnn:Seq2SeqRNN"

# SRNNs

SRResNet = "physicsnemo.models.srrn:SRResNet"

# SwinRNN

SwinRNN = "physicsnemo.models.swinvrnn:SwinRNN"

# TopoDiff

TopoDiff = "physicsnemo.models.topodiff:TopoDiff"

# Transolver

Transolver = "physicsnemo.models.transolver:Transolver"

# VFGN

VFGN = "physicsnemo.models.vfgn:VFGNLearnedSimulator"