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# Copyright 2013-2022 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 PyDgl(CMakePackage, PythonExtension):
"""Deep Graph Library (DGL).
DGL is an easy-to-use, high performance and scalable Python package for
deep learning on graphs. DGL is framework agnostic, meaning if a deep graph
model is a component of an end-to-end application, the rest of the logics
can be implemented in any major frameworks, such as PyTorch, Apache MXNet
or TensorFlow."""
homepage = "https://www.dgl.ai/"
git = "https://github.com/dmlc/dgl.git"
maintainers = ["adamjstewart"]
version("master", branch="master", submodules=True)
version("0.4.3", tag="0.4.3", submodules=True)
version("0.4.2", tag="0.4.2", submodules=True)
variant("cuda", default=True, description="Build with CUDA")
variant("openmp", default=True, description="Build with OpenMP")
variant(
"backend",
default="pytorch",
description="Default backend",
values=["pytorch", "mxnet", "tensorflow"],
multi=False,
)
depends_on("cmake@3.5:", type="build")
depends_on("cuda", when="+cuda")
depends_on("llvm-openmp", when="%apple-clang +openmp")
# Python dependencies
# See python/setup.py
extends("python")
depends_on("python@3.5:", type=("build", "run"))
depends_on("py-pip", type="build")
depends_on("py-wheel", type="build")
depends_on("py-setuptools", type="build")
depends_on("py-cython", type="build")
depends_on("py-numpy@1.14.0:", type=("build", "run"))
depends_on("py-scipy@1.1.0:", type=("build", "run"))
depends_on("py-networkx@2.1:", type=("build", "run"))
depends_on("py-requests@2.19.0:", when="@0.4.3:", type=("build", "run"))
# Backends
# See https://github.com/dmlc/dgl#installation
depends_on("py-torch@1.2.0:", when="@0.4.3: backend=pytorch", type="run")
depends_on("py-torch@0.4.1:", when="backend=pytorch", type="run")
depends_on("mxnet@1.5.1:", when="@0.4.3: backend=pytorch", type="run")
depends_on("mxnet@1.5.0:", when="backend=mxnet", type="run")
depends_on("py-tensorflow@2.1:", when="@0.4.3: backend=tensorflow", type="run")
depends_on("py-tensorflow@2.0:", when="backend=tensorflow", type="run")
depends_on("py-tfdlpack", when="backend=tensorflow", type="run")
build_directory = "build"
# https://docs.dgl.ai/install/index.html#install-from-source
def cmake_args(self):
args = []
if "+cuda" in self.spec:
args.append("-DUSE_CUDA=ON")
else:
args.append("-DUSE_CUDA=OFF")
if "+openmp" in self.spec:
args.append("-DUSE_OPENMP=ON")
if self.spec.satisfies("%apple-clang"):
args.extend(
[
"-DOpenMP_CXX_FLAGS=" + self.spec["llvm-openmp"].headers.include_flags,
"-DOpenMP_CXX_LIB_NAMES=" + self.spec["llvm-openmp"].libs.names[0],
"-DOpenMP_C_FLAGS=" + self.spec["llvm-openmp"].headers.include_flags,
"-DOpenMP_C_LIB_NAMES=" + self.spec["llvm-openmp"].libs.names[0],
"-DOpenMP_omp_LIBRARY=" + self.spec["llvm-openmp"].libs[0],
]
)
else:
args.append("-DUSE_OPENMP=OFF")
if self.run_tests:
args.append("-DBUILD_CPP_TEST=ON")
else:
args.append("-DBUILD_CPP_TEST=OFF")
return args
def install(self, spec, prefix):
with working_dir("python"):
args = std_pip_args + ["--prefix=" + prefix, "."]
pip(*args)
# Work around installation bug: https://github.com/dmlc/dgl/issues/1379
install_tree(prefix.dgl, prefix.lib)
def setup_run_environment(self, env):
# https://docs.dgl.ai/install/backend.html
backend = self.spec.variants["backend"].value
env.set("DGLBACKEND", backend)
@property
def import_modules(self):
modules = [
"dgl",
"dgl.nn",
"dgl.runtime",
"dgl.backend",
"dgl.function",
"dgl.contrib",
"dgl._ffi",
"dgl.data",
"dgl.runtime.ir",
"dgl.backend.numpy",
"dgl.contrib.sampling",
"dgl._ffi._cy2",
"dgl._ffi._cy3",
"dgl._ffi._ctypes",
]
if "backend=pytorch" in self.spec:
modules.extend(["dgl.nn.pytorch", "dgl.nn.pytorch.conv", "dgl.backend.pytorch"])
elif "backend=mxnet" in self.spec:
modules.extend(["dgl.nn.mxnet", "dgl.nn.mxnet.conv", "dgl.backend.mxnet"])
elif "backend=tensorflow" in self.spec:
modules.extend(
["dgl.nn.tensorflow", "dgl.nn.tensorflow.conv", "dgl.backend.tensorflow"]
)
return modules
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