1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
|
{
lib,
stdenv,
buildPythonPackage,
fetchurl,
# buildInputs
llvmPackages,
# build-system
distutils,
# dependencies
ml-dtypes,
absl-py,
astunparse,
flatbuffers,
gast,
google-pasta,
grpcio,
h5py,
libclang,
numpy,
opt-einsum,
packaging,
protobuf,
requests,
six,
tensorboard,
termcolor,
typing-extensions,
wrapt,
isPy3k,
mock,
config,
cudaSupport ? config.cudaSupport,
cudaPackages,
zlib,
python,
addDriverRunpath,
}:
# We keep this binary build for three reasons:
# - the source build doesn't work on Darwin.
# - the source build is currently brittle and not easy to maintain
# - the source build doesn't work on NVIDIA Jetson platforms
let
packages = import ./binary-hashes.nix;
inherit (cudaPackages) cudatoolkit cudnn;
isCudaJetson = cudaSupport && cudaPackages.flags.isJetsonBuild;
in
buildPythonPackage rec {
pname = "tensorflow" + lib.optionalString cudaSupport "-gpu";
version = packages."${"version" + lib.optionalString isCudaJetson "_jetson"}";
format = "wheel";
src =
let
pyVerNoDot = lib.strings.stringAsChars (x: lib.optionalString (x != ".") x) python.pythonVersion;
platform = stdenv.system;
cuda = lib.optionalString cudaSupport (if isCudaJetson then "_jetson" else "_gpu");
key = "${platform}_${pyVerNoDot}${cuda}";
in
fetchurl (packages.${key} or (throw "tensorflow-bin: unsupported configuration: ${key}"));
buildInputs = [ llvmPackages.openmp ];
build-system = [
distutils
];
nativeBuildInputs =
lib.optionals cudaSupport [ addDriverRunpath ]
++ lib.optionals isCudaJetson [ cudaPackages.autoAddCudaCompatRunpath ];
dependencies = [
absl-py
astunparse
flatbuffers
gast
google-pasta
grpcio
h5py
libclang
ml-dtypes
numpy
opt-einsum
packaging
protobuf
requests
six
tensorboard
termcolor
typing-extensions
wrapt
]
++ lib.optional (!isPy3k) mock;
preConfigure = ''
unset SOURCE_DATE_EPOCH
# Make sure that dist and the wheel file are writable.
chmod u+rwx -R ./dist
pushd dist
for f in tensorflow-*+nv*.whl; do
# e.g. *nv24.07* -> *nv24.7*
mv "$f" "$(sed -E 's/(nv[0-9]+)\.0*([0-9]+)/\1.\2/' <<< "$f")"
done
popd
'';
postFixup =
# When using the cpu-only wheel, the final package will be named `tensorflow_cpu`.
# Then, in each package requiring `tensorflow`, our pythonRuntimeDepsCheck will fail with:
# importlib.metadata.PackageNotFoundError: No package metadata was found for tensorflow
# Hence, we manually rename the package to `tensorflow`.
lib.optionalString ((builtins.match ".*tensorflow_cpu.*" src.url) != null) ''
(
cd $out/${python.sitePackages}
dest="tensorflow-${version}.dist-info"
mv tensorflow_cpu-${version}.dist-info "$dest"
(
cd "$dest"
substituteInPlace METADATA \
--replace-fail "tensorflow_cpu" "tensorflow"
substituteInPlace RECORD \
--replace-fail "tensorflow_cpu" "tensorflow"
)
)
''
# Note that we need to run *after* the fixup phase because the
# libraries are loaded at runtime. If we run in preFixup then
# patchelf --shrink-rpath will remove the cuda libraries.
+ (
let
# rpaths we only need to add if CUDA is enabled.
cudapaths = lib.optionals cudaSupport [
cudatoolkit.out
cudatoolkit.lib
cudnn
];
libpaths = [
(lib.getLib stdenv.cc.cc)
zlib
];
rpath = lib.makeLibraryPath (libpaths ++ cudapaths);
in
lib.optionalString stdenv.hostPlatform.isLinux ''
# This is an array containing all the directories in the tensorflow2
# package that contain .so files.
#
# TODO: Create this list programmatically, and remove paths that aren't
# actually needed.
rrPathArr=(
"$out/${python.sitePackages}/tensorflow/"
"$out/${python.sitePackages}/tensorflow/core/kernels"
"$out/${python.sitePackages}/tensorflow/compiler/mlir/stablehlo/"
"$out/${python.sitePackages}/tensorflow/compiler/tf2tensorrt/"
"$out/${python.sitePackages}/tensorflow/compiler/tf2xla/ops/"
"$out/${python.sitePackages}/tensorflow/include/external/ml_dtypes/"
"$out/${python.sitePackages}/tensorflow/lite/experimental/microfrontend/python/ops/"
"$out/${python.sitePackages}/tensorflow/lite/python/analyzer_wrapper/"
"$out/${python.sitePackages}/tensorflow/lite/python/interpreter_wrapper/"
"$out/${python.sitePackages}/tensorflow/lite/python/metrics/"
"$out/${python.sitePackages}/tensorflow/lite/python/optimize/"
"$out/${python.sitePackages}/tensorflow/python/"
"$out/${python.sitePackages}/tensorflow/python/autograph/impl/testing"
"$out/${python.sitePackages}/tensorflow/python/client"
"$out/${python.sitePackages}/tensorflow/python/data/experimental/service"
"$out/${python.sitePackages}/tensorflow/python/framework"
"$out/${python.sitePackages}/tensorflow/python/grappler"
"$out/${python.sitePackages}/tensorflow/python/lib/core"
"$out/${python.sitePackages}/tensorflow/python/lib/io"
"$out/${python.sitePackages}/tensorflow/python/platform"
"$out/${python.sitePackages}/tensorflow/python/profiler/internal"
"$out/${python.sitePackages}/tensorflow/python/saved_model"
"$out/${python.sitePackages}/tensorflow/python/util"
"$out/${python.sitePackages}/tensorflow/tsl/python/lib/core"
"$out/${python.sitePackages}/tensorflow.libs/"
"${rpath}"
)
# The the bash array into a colon-separated list of RPATHs.
rrPath=$(IFS=$':'; echo "''${rrPathArr[*]}")
echo "about to run patchelf with the following rpath: $rrPath"
find $out -type f \( -name '*.so' -or -name '*.so.*' \) | while read lib; do
echo "about to patchelf $lib..."
chmod a+rx "$lib"
patchelf --set-rpath "$rrPath" "$lib"
${lib.optionalString cudaSupport ''
addDriverRunpath "$lib"
''}
done
''
)
# Symlink nvcc besides TensorFlow so that routines that require JIT can work
# properly.
+ lib.optionalString cudaSupport ''
ln -s ${cudaPackages.cuda_nvcc} "$out/${python.sitePackages}/tensorflow/cuda"
'';
# Upstream has a pip hack that results in bin/tensorboard being in both tensorflow
# and the propagated input tensorboard, which causes environment collisions.
# Another possibility would be to have tensorboard only in the buildInputs
# See https://github.com/NixOS/nixpkgs/pull/44381 for more information.
postInstall = ''
rm $out/bin/tensorboard
'';
pythonImportsCheck = [
"tensorflow"
"tensorflow.python"
"tensorflow.python.framework"
];
meta = {
description = "Computation using data flow graphs for scalable machine learning";
homepage = "http://tensorflow.org";
sourceProvenance = with lib.sourceTypes; [ binaryNativeCode ];
license = lib.licenses.asl20;
maintainers = [ ];
badPlatforms = [ "x86_64-darwin" ];
# unsupported combination
broken = stdenv.hostPlatform.isDarwin && cudaSupport;
};
}
|
