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{ stdenv , bazel_3 , buildBazelPackage , isPy3k , lib , fetchFromGitHub , symlinkJoin
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, addOpenGLRunpath
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# Python deps
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, buildPythonPackage , pythonOlder , pythonAtLeast , python
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# Python libraries
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, numpy , tensorflow-tensorboard_2 , absl-py
, future , setuptools , wheel , keras-preprocessing , google-pasta
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, opt-einsum , astunparse , h5py
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, termcolor , grpcio , six , wrapt , protobuf , tensorflow-estimator_2
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, dill , flatbuffers-python , tblib , typing-extensions
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# Common deps
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, git , pybind11 , which , binutils , glibcLocales , cython , perl
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# Common libraries
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, jemalloc , mpi , gast , grpc , sqlite , boringssl , jsoncpp
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, curl , snappy , flatbuffers-core , lmdb-core , icu , double-conversion , libpng , libjpeg_turbo , giflib
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# 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
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, cudaSupport ? false , cudatoolkit ? null , cudnn ? null , nccl ? null
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, mklSupport ? false , mkl ? null
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, tensorboardSupport ? true
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# XLA without CUDA is broken
, xlaSupport ? cudaSupport
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# Default from ./configure script
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, cudaCapabilities ? [ " s m _ 3 5 " " s m _ 5 0 " " s m _ 6 0 " " s m _ 7 0 " " s m _ 7 5 " " c o m p u t e _ 8 0 " ]
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, sse42Support ? stdenv . hostPlatform . sse4_2Support
, avx2Support ? stdenv . hostPlatform . avx2Support
, fmaSupport ? stdenv . hostPlatform . fmaSupport
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# Darwin deps
, Foundation , Security
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} :
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assert cudaSupport -> cudatoolkit != null
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&& cudnn != null ;
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# unsupported combination
assert ! ( stdenv . isDarwin && cudaSupport ) ;
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assert mklSupport -> mkl != null ;
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let
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withTensorboard = ( pythonOlder " 3 . 6 " ) || tensorboardSupport ;
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cudatoolkit_joined = symlinkJoin {
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name = " ${ cudatoolkit . name } - m e r g e d " ;
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paths = [
cudatoolkit . lib
cudatoolkit . out
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] ++ lib . optionals ( lib . versionOlder cudatoolkit . version " 1 1 " ) [
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# 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 } / t a r g e t s / ${ stdenv . system } "
] ;
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} ;
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cudatoolkit_cc_joined = symlinkJoin {
name = " ${ cudatoolkit . cc . name } - m e r g e d " ;
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paths = [
cudatoolkit . cc
binutils . bintools # for ar, dwp, nm, objcopy, objdump, strip
] ;
} ;
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# Needed for _some_ system libraries, grep INCLUDEDIR.
includes_joined = symlinkJoin {
name = " t e n s o r f l o w - d e p s - m e r g e d " ;
paths = [
jsoncpp
] ;
} ;
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tfFeature = x : if x then " 1 " else " 0 " ;
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version = " 2 . 4 . 1 " ;
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variant = if cudaSupport then " - g p u " else " " ;
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pname = " t e n s o r f l o w ${ variant } " ;
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pythonEnv = python . withPackages ( _ :
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[ # python deps needed during wheel build time (not runtime, see the buildPythonPackage part for that)
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# This list can likely be shortened, but each trial takes multiple hours so won't bother for now.
absl-py
astunparse
dill
flatbuffers-python
gast
google-pasta
grpcio
h5py
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keras-preprocessing
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numpy
opt-einsum
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protobuf
setuptools
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six
tblib
tensorflow-estimator_2
tensorflow-tensorboard_2
termcolor
typing-extensions
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wheel
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wrapt
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] ) ;
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bazel-build = buildBazelPackage {
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name = " ${ pname } - ${ version } " ;
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bazel = bazel_3 ;
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src = fetchFromGitHub {
owner = " t e n s o r f l o w " ;
repo = " t e n s o r f l o w " ;
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rev = " v ${ version } " ;
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sha256 = " s h a 2 5 6 - J 6 2 Q f P 4 5 g 5 n x N 9 N q a 1 t A G y c 4 v D 2 J K h 5 0 d d H L r d 6 / q s Y = " ;
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} ;
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patches = [
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# Relax too strict Python packages versions dependencies.
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./relax-dependencies.patch
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# Add missing `io_bazel_rules_docker` dependency.
./workspace.patch
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] ;
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# On update, it can be useful to steal the changes from gentoo
# https://gitweb.gentoo.org/repo/gentoo.git/tree/sci-libs/tensorflow
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nativeBuildInputs = [
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which pythonEnv cython perl
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] ++ lib . optional cudaSupport addOpenGLRunpath ;
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buildInputs = [
jemalloc
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mpi
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glibcLocales
git
# libs taken from system through the TF_SYS_LIBS mechanism
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grpc
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sqlite
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boringssl
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jsoncpp
curl
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pybind11
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snappy
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flatbuffers-core
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icu
double-conversion
libpng
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libjpeg_turbo
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giflib
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lmdb-core
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] ++ lib . optionals cudaSupport [
cudatoolkit
cudnn
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] ++ lib . optionals mklSupport [
mkl
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] ++ lib . optionals stdenv . isDarwin [
Foundation
Security
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] ;
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# arbitrarily set to the current latest bazel version, overly careful
TF_IGNORE_MAX_BAZEL_VERSION = true ;
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# Take as many libraries from the system as possible. Keep in sync with
# list of valid syslibs in
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# https://github.com/tensorflow/tensorflow/blob/master/third_party/systemlibs/syslibs_configure.bzl
TF_SYSTEM_LIBS = lib . concatStringsSep " , " [
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" a b s l _ p y "
" a s t o r _ a r c h i v e "
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" a s t u n p a r s e _ a r c h i v e "
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" b o r i n g s s l "
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# Not packaged in nixpkgs
# "com_github_googleapis_googleapis"
# "com_github_googlecloudplatform_google_cloud_cpp"
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" c o m _ g i t h u b _ g r p c _ g r p c "
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# Multiple issues with custom protobuf.
# First `com_github_googleapis` fails to configure. Can be worked around by disabling `com_github_googleapis`
# and related functionality, but then the next error is about "dangling symbolic link", and in general
# looks like that's only the beginning: see
# https://stackoverflow.com/questions/55578884/how-to-build-tensorflow-1-13-1-with-custom-protobuf
# "com_google_protobuf"
# Fails with the error: external/org_tensorflow/tensorflow/core/profiler/utils/tf_op_utils.cc:46:49: error: no matching function for call to 're2::RE2::FullMatch(absl::lts_2020_02_25::string_view&, re2::RE2&)'
# "com_googlesource_code_re2"
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" c u r l "
" c y t h o n "
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" d i l l _ a r c h i v e "
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" d o u b l e _ c o n v e r s i o n "
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" e n u m 3 4 _ a r c h i v e "
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" f l a t b u f f e r s "
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" f u n c t o o l s 3 2 _ a r c h i v e "
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" g a s t _ a r c h i v e "
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" g i f "
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" h w l o c "
" i c u "
" j s o n c p p _ g i t "
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" l i b j p e g _ t u r b o "
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" l m d b "
" n a s m "
# "nsync" # not packaged in nixpkgs
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" o p t _ e i n s u m _ a r c h i v e "
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" o r g _ s q l i t e "
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" p a s t a "
" p c r e "
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" p n g "
" p y b i n d 1 1 "
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" s i x _ a r c h i v e "
" s n a p p y "
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" t b l i b _ a r c h i v e "
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" t e r m c o l o r _ a r c h i v e "
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" t y p i n g _ e x t e n s i o n s _ a r c h i v e "
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" w r a p t "
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" z l i b "
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] ;
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INCLUDEDIR = " ${ includes_joined } / i n c l u d e " ;
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PYTHON_BIN_PATH = pythonEnv . interpreter ;
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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 } / b i n " ;
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GCC_HOST_COMPILER_PATH = lib . optionalString cudaSupport " ${ cudatoolkit_cc_joined } / b i n / g c c " ;
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TF_CUDA_COMPUTE_CAPABILITIES = lib . concatStringsSep " , " cudaCapabilities ;
postPatch = ''
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# bazel 3.3 should work just as well as bazel 3.1
rm - f . bazelversion
'' + l i b . o p t i o n a l S t r i n g ( ! w i t h T e n s o r b o a r d ) ''
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# 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
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sed - i ' /tensorboard ~ = /d ' tensorflow/tools/pip_package/setup.py
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'' ;
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# https://github.com/tensorflow/tensorflow/pull/39470
NIX_CFLAGS_COMPILE = [ " - W n o - s t r i n g o p - t r u n c a t i o n " ] ;
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preConfigure = let
opt_flags = [ ]
++ lib . optionals sse42Support [ " - m s s e 4 . 2 " ]
++ lib . optionals avx2Support [ " - m a v x 2 " ]
++ lib . optionals fmaSupport [ " - m f m a " ] ;
in ''
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patchShebangs configure
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# dummy ldconfig
mkdir dummy-ldconfig
echo " # ! ${ stdenv . shell } " > dummy-ldconfig/ldconfig
chmod + x dummy-ldconfig/ldconfig
export PATH = " $ P W D / d u m m y - l d c o n f i g : $ P A T H "
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export PYTHON_LIB_PATH = " $ N I X _ B U I L D _ T O P / s i t e - p a c k a g e s "
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export CC_OPT_FLAGS = " ${ lib . concatStringsSep " " opt_flags } "
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mkdir - p " $ P Y T H O N _ L I B _ P A T H "
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# To avoid mixing Python 2 and Python 3
unset PYTHONPATH
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'' ;
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configurePhase = ''
runHook preConfigure
./configure
runHook postConfigure
'' ;
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hardeningDisable = [ " f o r m a t " ] ;
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bazelBuildFlags = [
" - - c o n f i g = o p t " # optimize using the flags set in the configure phase
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]
++ lib . optionals ( mklSupport ) [ " - - c o n f i g = m k l " ] ;
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bazelTarget = " / / t e n s o r f l o w / t o o l s / p i p _ p a c k a g e : b u i l d _ p i p _ p a c k a g e / / t e n s o r f l o w / t o o l s / l i b _ p a c k a g e : l i b t e n s o r f l o w " ;
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removeRulesCC = false ;
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# Without this Bazel complaints about sandbox violations.
dontAddBazelOpts = true ;
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fetchAttrs = {
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# cudaSupport causes fetch of ncclArchive, resulting in different hashes
sha256 = if cudaSupport then
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" 1 i 7 z 2 a 7 b c 2 q 1 v n 1 h 9 n x 1 x c 6 g 1 r 1 c b y 2 x v b c s 2 0 f j 9 h 6 c 2 f g a w 9 j 4 "
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else
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" 0 s 8 q 5 r x q 8 a b r 5 0 c 5 j p w v 9 6 n c f c 0 k 8 j w 7 w 7 0 r i 8 v i q y 0 3 1 g 9 v 9 v 4 5 " ;
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} ;
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buildAttrs = {
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outputs = [ " o u t " " p y t h o n " ] ;
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preBuild = ''
patchShebangs .
'' ;
installPhase = ''
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mkdir - p " $ o u t "
tar - xf bazel-bin/tensorflow/tools/lib_package/libtensorflow.tar.gz - C " $ o u t "
# Write pkgconfig file.
mkdir " $ o u t / l i b / p k g c o n f i g "
cat > " $ o u t / l i b / p k g c o n f i g / t e n s o r f l o w . p c " < < 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 " $ P W D / d i s t "
cp - Lr " $ P W D / d i s t " " $ p y t h o n "
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'' ;
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postFixup = lib . optionalString cudaSupport ''
find $ out - type f \ ( - name ' * . so' - or - name ' * . so . * ' \ ) | while read lib ; do
addOpenGLRunpath " $ l i b "
done
'' ;
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requiredSystemFeatures = [
" b i g - p a r a l l e l "
] ;
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} ;
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meta = with lib ; {
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description = " C o m p u t a t i o n u s i n g d a t a f l o w g r a p h s f o r s c a l a b l e m a c h i n e l e a r n i n g " ;
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homepage = " h t t p : / / t e n s o r f l o w . o r g " ;
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license = licenses . asl20 ;
maintainers = with maintainers ; [ jyp abbradar ] ;
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platforms = with platforms ; linux ++ darwin ;
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broken = ! ( xlaSupport -> cudaSupport ) ;
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} ;
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} ;
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in buildPythonPackage {
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inherit version pname ;
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disabled = ! isPy3k ;
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src = bazel-build . python ;
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# 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
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'' ;
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setupPyGlobalFlags = [ " - - p r o j e c t _ n a m e ${ pname } " ] ;
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# tensorflow/tools/pip_package/setup.py
propagatedBuildInputs = [
absl-py
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astunparse
dill
flatbuffers-python
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gast
google-pasta
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grpcio
h5py
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keras-preprocessing
numpy
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opt-einsum
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protobuf
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six
tblib
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tensorflow-estimator_2
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termcolor
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typing-extensions
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wrapt
] ++ lib . optionals withTensorboard [
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tensorflow-tensorboard_2
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] ;
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nativeBuildInputs = lib . optional cudaSupport addOpenGLRunpath ;
postFixup = lib . optionalString cudaSupport ''
find $ out - type f \ ( - name ' * . so' - or - name ' * . so . * ' \ ) | while read lib ; do
addOpenGLRunpath " $ l i b "
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patchelf - - set-rpath " ${ cudatoolkit } / l i b : ${ cudatoolkit . lib } / l i b : ${ cudnn } / l i b : ${ nccl } / l i b : $ ( p a t c h e l f - - p r i n t - r p a t h " $ lib " ) " " $ l i b "
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done
'' ;
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# Actual tests are slow and impure.
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# TODO try to run them anyway
# TODO better test (files in tensorflow/tools/ci_build/builds/*test)
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checkPhase = ''
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$ { python . interpreter } < < EOF
# A simple "Hello world"
import tensorflow as tf
hello = tf . constant ( " H e l l o , w o r l d ! " )
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tf . print ( hello )
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# Fit a simple model to random data
import numpy as np
np . random . seed ( 0 )
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tf . random . set_seed ( 0 )
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model = tf . keras . models . Sequential ( [
tf . keras . layers . Dense ( 1 , activation = " l i n e a r " )
] )
model . compile ( optimizer = " s g d " , loss = " m s e " )
x = np . random . uniform ( size = ( 1 , 1 ) )
y = np . random . uniform ( size = ( 1 , ) )
model . fit ( x , y , epochs = 1 )
EOF
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'' ;
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# Regression test for #77626 removed because not more `tensorflow.contrib`.
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passthru = {
deps = bazel-build . deps ;
libtensorflow = bazel-build . out ;
} ;
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inherit ( bazel-build ) meta ;
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}