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# Copyright 2013-2023 Lawrence Livermore National Security, LLC and other
# Spack Project Developers. See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: (Apache-2.0 OR MIT)
from spack.package import *
class PyTorchGeometric(PythonPackage):
"""PyTorch Geometric (PyG) is a geometric deep learning extension
library for PyTorch. It consists of various methods for deep
learning on graphs and other irregular structures, also known as
geometric deep learning, from a variety of published papers. In
addition, it consists of an easy-to-use mini-batch loader for many
small and single giant graphs, multi gpu-support, a large number
of common benchmark datasets (based on simple interfaces to create
your own), and helpful transforms, both for learning on arbitrary
graphs as well as on 3D meshes or point clouds."""
homepage = "https://github.com/pyg-team/pytorch_geometric"
pypi = "torch-geometric/torch_geometric-2.1.0.post1.tar.gz"
license("MIT")
version(
"2.1.0.post1", sha256="32347402076ccf60fa50312825178f1e3e5ce5e7b3b3a8b2729ac699da24525d"
)
version("1.6.3", sha256="347f693bebcc8a621eda4867dafab91c04db5f596d7ed7ecb89b242f8ab5c6a1")
version("1.6.0", sha256="fbf43fe15421c9affc4fb361ba4db55cb9d3c64d0c29576bb58d332bf6d27fef")
variant("cuda", default=False, description="Enable CUDA support")
# setup.py
depends_on("python@3.7:", when="@2:", type=("build", "run"))
depends_on("python@3.6:", type=("build", "run"))
depends_on("py-setuptools", type="build")
depends_on("py-tqdm", type=("build", "run"))
depends_on("py-numpy", type=("build", "run"))
depends_on("py-scipy", type=("build", "run"))
depends_on("py-jinja2", type=("build", "run"))
depends_on("py-requests", type=("build", "run"))
depends_on("py-pyparsing", when="@2:", type=("build", "run"))
depends_on("py-scikit-learn", type=("build", "run"))
# README.md
depends_on("py-torch-scatter+cuda", when="+cuda", type=("build", "run"))
depends_on("py-torch-scatter~cuda", when="~cuda", type=("build", "run"))
depends_on("py-torch-sparse+cuda", when="+cuda", type=("build", "run"))
depends_on("py-torch-sparse~cuda", when="~cuda", type=("build", "run"))
# Optional dependencies
depends_on("py-torch-cluster+cuda", when="+cuda", type=("build", "run"))
depends_on("py-torch-cluster~cuda", when="~cuda", type=("build", "run"))
depends_on("py-torch-spline-conv+cuda", when="+cuda", type=("build", "run"))
depends_on("py-torch-spline-conv~cuda", when="~cuda", type=("build", "run"))
# Undocumented dependencies
depends_on("py-torch", type=("build", "run"))
# Historic or optional dependencies
with when("@:1"):
depends_on("py-pytest-runner", type="build")
depends_on("py-networkx", type=("build", "run"))
depends_on("py-python-louvain", type=("build", "run"), when="@1.6.2:")
depends_on("py-numba", type=("build", "run"))
depends_on("py-pandas", type=("build", "run"))
depends_on("py-rdflib", type=("build", "run"))
depends_on("py-googledrivedownloader", type=("build", "run"))
depends_on("py-h5py~mpi", type=("build", "run"))
depends_on("py-ase", type=("build", "run"))
def setup_build_environment(self, env):
if "+cuda" in self.spec:
cuda_arches = list(self.spec["py-torch"].variants["cuda_arch"].value)
for i, x in enumerate(cuda_arches):
cuda_arches[i] = "{0}.{1}".format(x[0:-1], x[-1])
env.set("TORCH_CUDA_ARCH_LIST", str.join(" ", cuda_arches))
env.set("FORCE_CUDA", "1")
env.set("CUDA_HOME", self.spec["cuda"].prefix)
else:
env.set("FORCE_CUDA", "0")
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