nixpkgs/pkgs/development/python-modules/tensorflow/default.nix
Michael Lingelbach a0b992fdf8 tensorflow: 1.15.0 -> 1.15.1
* Apply glibc 2.3 patch
* build tensorflow with bazel_1
* Bump openssl version to 1.1

(cherry picked from commit 34296980d1)
2020-03-25 16:42:27 +00:00

430 lines
13 KiB
Nix

{ stdenv, pkgs, bazel_1, buildBazelPackage, lib, fetchFromGitHub, fetchpatch, symlinkJoin
, addOpenGLRunpath
# Python deps
, buildPythonPackage, isPy3k, pythonOlder, pythonAtLeast, python
# Python libraries
, numpy, tensorflow-tensorboard, backports_weakref, mock, enum34, absl-py
, future, setuptools, wheel, keras-preprocessing, keras-applications, google-pasta
, functools32
, opt-einsum
, termcolor, grpcio, six, wrapt, protobuf, tensorflow-estimator
# Common deps
, git, swig, which, binutils, glibcLocales, cython
# Common libraries
, jemalloc, openmpi, astor, gast, grpc, sqlite, openssl, jsoncpp, re2
, curl, snappy, flatbuffers, icu, double-conversion, libpng, libjpeg, giflib
# Upsteam by default includes cuda support since tensorflow 1.15. We could do
# that in nix as well. It would make some things easier and less confusing, but
# it would also make the default tensorflow package unfree. See
# https://groups.google.com/a/tensorflow.org/forum/#!topic/developers/iRCt5m4qUz0
, cudaSupport ? false, nvidia_x11 ? null, cudatoolkit ? null, cudnn ? null, nccl ? null
# XLA without CUDA is broken
, xlaSupport ? cudaSupport
# Default from ./configure script
, cudaCapabilities ? [ "3.5" "5.2" ]
, sse42Support ? builtins.elem (stdenv.hostPlatform.platform.gcc.arch or "default") ["westmere" "sandybridge" "ivybridge" "haswell" "broadwell" "skylake" "skylake-avx512"]
, avx2Support ? builtins.elem (stdenv.hostPlatform.platform.gcc.arch or "default") [ "haswell" "broadwell" "skylake" "skylake-avx512"]
, fmaSupport ? builtins.elem (stdenv.hostPlatform.platform.gcc.arch or "default") [ "haswell" "broadwell" "skylake" "skylake-avx512"]
# Darwin deps
, Foundation, Security
}:
assert cudaSupport -> nvidia_x11 != null
&& cudatoolkit != null
&& cudnn != null;
# unsupported combination
assert ! (stdenv.isDarwin && cudaSupport);
let
withTensorboard = pythonOlder "3.6";
cudatoolkit_joined = symlinkJoin {
name = "${cudatoolkit.name}-merged";
paths = [
cudatoolkit.lib
cudatoolkit.out
# for some reason some of the required libs are in the targets/x86_64-linux
# directory; not sure why but this works around it
"${cudatoolkit}/targets/${stdenv.system}"
];
};
cudatoolkit_cc_joined = symlinkJoin {
name = "${cudatoolkit.cc.name}-merged";
paths = [
cudatoolkit.cc
binutils.bintools # for ar, dwp, nm, objcopy, objdump, strip
];
};
# Needed for _some_ system libraries, grep INCLUDEDIR.
includes_joined = symlinkJoin {
name = "tensorflow-deps-merged";
paths = [
pkgs.protobuf
jsoncpp
];
};
tfFeature = x: if x then "1" else "0";
version = "1.15.1";
variant = if cudaSupport then "-gpu" else "";
pname = "tensorflow${variant}";
pythonEnv = python.withPackages (_:
[ # python deps needed during wheel build time (not runtime, see the buildPythonPackage part for that)
numpy
keras-preprocessing
protobuf
wrapt
gast
astor
absl-py
termcolor
keras-applications
setuptools
wheel
] ++ lib.optionals (!isPy3k)
[ future
functools32
mock
]);
bazel-build = buildBazelPackage {
name = "${pname}-${version}";
bazel = bazel_1;
src = fetchFromGitHub {
owner = "tensorflow";
repo = "tensorflow";
rev = "v${version}";
sha256 = "1j8vysfblkyydrr67qr3i7kvaq5ygnjlx8hw9a9pc95ac462jq7i";
};
patches = [
# Work around https://github.com/tensorflow/tensorflow/issues/24752
./no-saved-proto.patch
# Fixes for NixOS jsoncpp
./system-jsoncpp.patch
# https://github.com/tensorflow/tensorflow/pull/29673
(fetchpatch {
name = "fix-compile-with-cuda-and-mpi.patch";
url = "https://github.com/tensorflow/tensorflow/pull/29673/commits/498e35a3bfe38dd75cf1416a1a23c07c3b59e6af.patch";
sha256 = "1m2qmwv1ysqa61z6255xggwbq6mnxbig749bdvrhnch4zydxb4di";
})
(fetchpatch {
name = "backport-pr-18950.patch";
url = "https://github.com/tensorflow/tensorflow/commit/73640aaec2ab0234d9fff138e3c9833695570c0a.patch";
sha256 = "1n9ypbrx36fc1kc9cz5b3p9qhg15xxhq4nz6ap3hwqba535nakfz";
})
./tf-1.15-bazel-1.0.patch
(fetchpatch {
# be compatible with gast >0.2 instead of only gast 0.2.2
name = "gast-update.patch";
url = "https://github.com/tensorflow/tensorflow/commit/85751ad6c7f5fd12c6c79545d96896cba92fa8b4.patch";
sha256 = "077cpj0kzyqxzdya1dwh8df17zfzhqn7c685hx6iskvw2979zg2n";
})
./lift-gast-restriction.patch
# cuda 10.2 does not have "-bin2c-path" option anymore
# https://github.com/tensorflow/tensorflow/issues/34429
./cuda-10.2-no-bin2c-path.patch
];
# On update, it can be useful to steal the changes from gentoo
# https://gitweb.gentoo.org/repo/gentoo.git/tree/sci-libs/tensorflow
nativeBuildInputs = [
swig which pythonEnv
] ++ lib.optional cudaSupport addOpenGLRunpath;
buildInputs = [
jemalloc
openmpi
glibcLocales
git
# libs taken from system through the TF_SYS_LIBS mechanism
# grpc
sqlite
openssl
jsoncpp
pkgs.protobuf
curl
snappy
flatbuffers
icu
double-conversion
libpng
libjpeg
giflib
re2
pkgs.lmdb
] ++ lib.optionals cudaSupport [
cudatoolkit
cudnn
nvidia_x11
] ++ lib.optionals stdenv.isDarwin [
Foundation
Security
];
# arbitrarily set to the current latest bazel version, overly careful
TF_IGNORE_MAX_BAZEL_VERSION = true;
# Take as many libraries from the system as possible. Keep in sync with
# list of valid syslibs in
# https://github.com/tensorflow/tensorflow/blob/master/third_party/systemlibs/syslibs_configure.bzl
TF_SYSTEM_LIBS = lib.concatStringsSep "," [
"absl_py"
"astor_archive"
"boringssl"
# Not packaged in nixpkgs
# "com_github_googleapis_googleapis"
# "com_github_googlecloudplatform_google_cloud_cpp"
"com_google_protobuf"
"com_googlesource_code_re2"
"curl"
"cython"
"double_conversion"
"flatbuffers"
"gast_archive"
"gif_archive"
# Lots of errors, requires an older version
# "grpc"
"hwloc"
"icu"
"jpeg"
"jsoncpp_git"
"keras_applications_archive"
"lmdb"
"nasm"
# "nsync" # not packaged in nixpkgs
"opt_einsum_archive"
"org_sqlite"
"pasta"
"pcre"
"png_archive"
"six_archive"
"snappy"
"swig"
"termcolor_archive"
"wrapt"
"zlib_archive"
];
INCLUDEDIR = "${includes_joined}/include";
PYTHON_BIN_PATH = pythonEnv.interpreter;
TF_NEED_GCP = true;
TF_NEED_HDFS = true;
TF_ENABLE_XLA = tfFeature xlaSupport;
CC_OPT_FLAGS = " ";
# https://github.com/tensorflow/tensorflow/issues/14454
TF_NEED_MPI = tfFeature cudaSupport;
TF_NEED_CUDA = tfFeature cudaSupport;
TF_CUDA_PATHS = lib.optionalString cudaSupport "${cudatoolkit_joined},${cudnn},${nccl}";
GCC_HOST_COMPILER_PREFIX = lib.optionalString cudaSupport "${cudatoolkit_cc_joined}/bin";
GCC_HOST_COMPILER_PATH = lib.optionalString cudaSupport "${cudatoolkit_cc_joined}/bin/gcc";
TF_CUDA_COMPUTE_CAPABILITIES = lib.concatStringsSep "," cudaCapabilities;
postPatch = ''
# https://github.com/tensorflow/tensorflow/issues/20919
sed -i '/androidndk/d' tensorflow/lite/kernels/internal/BUILD
# Tensorboard pulls in a bunch of dependencies, some of which may
# include security vulnerabilities. So we make it optional.
# https://github.com/tensorflow/tensorflow/issues/20280#issuecomment-400230560
sed -i '/tensorboard >=/d' tensorflow/tools/pip_package/setup.py
'';
preConfigure = let
opt_flags = []
++ lib.optionals sse42Support ["-msse4.2"]
++ lib.optionals avx2Support ["-mavx2"]
++ lib.optionals fmaSupport ["-mfma"];
in ''
patchShebangs configure
# dummy ldconfig
mkdir dummy-ldconfig
echo "#!${stdenv.shell}" > dummy-ldconfig/ldconfig
chmod +x dummy-ldconfig/ldconfig
export PATH="$PWD/dummy-ldconfig:$PATH"
export PYTHON_LIB_PATH="$NIX_BUILD_TOP/site-packages"
export CC_OPT_FLAGS="${lib.concatStringsSep " " opt_flags}"
mkdir -p "$PYTHON_LIB_PATH"
# To avoid mixing Python 2 and Python 3
unset PYTHONPATH
'';
configurePhase = ''
runHook preConfigure
./configure
runHook postConfigure
'';
# FIXME: Tensorflow uses dlopen() for CUDA libraries.
NIX_LDFLAGS = lib.optionalString cudaSupport "-lcudart -lcublas -lcufft -lcurand -lcusolver -lcusparse -lcudnn";
hardeningDisable = [ "format" ];
bazelFlags = [
# temporary fixes to make the build work with bazel 0.27
"--incompatible_no_support_tools_in_action_inputs=false"
"--incompatible_use_native_patch=false"
];
bazelBuildFlags = [
"--config=opt" # optimize using the flags set in the configure phase
];
bazelTarget = "//tensorflow/tools/pip_package:build_pip_package //tensorflow/tools/lib_package:libtensorflow";
fetchAttrs = {
# So that checksums don't depend on these.
TF_SYSTEM_LIBS = null;
# cudaSupport causes fetch of ncclArchive, resulting in different hashes
sha256 = if cudaSupport then
"1p544yk7jcspgc4qr4amw11ds16c2an5yxvagx5pmwawz0s083pf"
else
"1dqbw3k3avqiy9xpgs44l6z65ab5rjjlxwig8z7gcl7fw9h6sbq9";
};
buildAttrs = {
outputs = [ "out" "python" ];
preBuild = ''
patchShebangs .
'';
installPhase = ''
mkdir -p "$out"
tar -xf bazel-bin/tensorflow/tools/lib_package/libtensorflow.tar.gz -C "$out"
# Write pkgconfig file.
mkdir "$out/lib/pkgconfig"
cat > "$out/lib/pkgconfig/tensorflow.pc" << EOF
Name: TensorFlow
Version: ${version}
Description: Library for computation using data flow graphs for scalable machine learning
Requires:
Libs: -L$out/lib -ltensorflow
Cflags: -I$out/include/tensorflow
EOF
# build the source code, then copy it to $python (build_pip_package
# actually builds a symlink farm so we must dereference them).
bazel-bin/tensorflow/tools/pip_package/build_pip_package --src "$PWD/dist"
cp -Lr "$PWD/dist" "$python"
'';
postFixup = lib.optionalString cudaSupport ''
find $out -type f \( -name '*.so' -or -name '*.so.*' \) | while read lib; do
addOpenGLRunpath "$lib"
done
'';
};
meta = with stdenv.lib; {
description = "Computation using data flow graphs for scalable machine learning";
homepage = http://tensorflow.org;
license = licenses.asl20;
maintainers = with maintainers; [ jyp abbradar ];
platforms = with platforms; linux ++ darwin;
# The py2 build fails due to some issue importing protobuf. Possibly related to the fix in
# https://github.com/akesandgren/easybuild-easyblocks/commit/1f2e517ddfd1b00a342c6abb55aef3fd93671a2b
broken = !(xlaSupport -> cudaSupport) || !isPy3k;
};
};
in buildPythonPackage {
inherit version pname;
src = bazel-build.python;
# Upstream has a pip hack that results in bin/tensorboard being in both tensorflow
# and the propagated input tensorflow-tensorboard, which causes environment collisions.
# Another possibility would be to have tensorboard only in the buildInputs
# https://github.com/tensorflow/tensorflow/blob/v1.7.1/tensorflow/tools/pip_package/setup.py#L79
postInstall = ''
rm $out/bin/tensorboard
'';
setupPyGlobalFlags = [ "--project_name ${pname}" ];
# tensorflow/tools/pip_package/setup.py
propagatedBuildInputs = [
absl-py
astor
gast
google-pasta
keras-applications
keras-preprocessing
numpy
six
protobuf
tensorflow-estimator
termcolor
wrapt
grpcio
opt-einsum
] ++ lib.optionals (!isPy3k) [
mock
future
functools32
] ++ lib.optionals (pythonOlder "3.4") [
backports_weakref enum34
] ++ lib.optionals withTensorboard [
tensorflow-tensorboard
];
nativeBuildInputs = lib.optional cudaSupport addOpenGLRunpath;
postFixup = lib.optionalString cudaSupport ''
find $out -type f \( -name '*.so' -or -name '*.so.*' \) | while read lib; do
addOpenGLRunpath "$lib"
done
'';
# Actual tests are slow and impure.
# TODO try to run them anyway
# TODO better test (files in tensorflow/tools/ci_build/builds/*test)
checkPhase = ''
${python.interpreter} <<EOF
# A simple "Hello world"
import tensorflow as tf
hello = tf.constant("Hello, world!")
sess = tf.Session()
sess.run(hello)
# Fit a simple model to random data
import numpy as np
np.random.seed(0)
tf.random.set_random_seed(0)
model = tf.keras.models.Sequential([
tf.keras.layers.Dense(1, activation="linear")
])
model.compile(optimizer="sgd", loss="mse")
x = np.random.uniform(size=(1,1))
y = np.random.uniform(size=(1,))
model.fit(x, y, epochs=1)
EOF
'';
passthru.libtensorflow = bazel-build.out;
inherit (bazel-build) meta;
}