SyntaxNet fails to build with GPU support

[nryant]

I've been trying for over a day to get SyntaxNet to build with GPU support, and while every attempt passes all tests, invariably the version of TensorFlow that it compiles lacks GPU support:

ldd models/syntaxnet/bazel-bin/syntaxnet/parser_trainer.runfiles/external/org_tensorflow/ensorflow/python/_pywrap_tensorflow.so
    linux-vdso.so.1 =>  (0x00007ffc2cbd6000)
    libdl.so.2 => /lib/x86_64-linux-gnu/libdl.so.2 (0x00007f1ba0e88000)
    libm.so.6 => /lib/x86_64-linux-gnu/libm.so.6 (0x00007f1ba0b82000)
    libpthread.so.0 => /lib/x86_64-linux-gnu/libpthread.so.0 (0x00007f1ba0964000)
    libstdc++.so.6 => /usr/lib/x86_64-linux-gnu/libstdc++.so.6 (0x00007f1ba05e8000)
    libgcc_s.so.1 => /lib/x86_64-linux-gnu/libgcc_s.so.1 (0x00007f1ba03d1000)
    libc.so.6 => /lib/x86_64-linux-gnu/libc.so.6 (0x00007f1ba000c000)
    /lib64/ld-linux-x86-64.so.2 (0x00007f1ba2f7a000)

I've done this with both the current version of SyntaxNet (a4b7bb9) and also the original release (32ab5a5) with the following system setup:

• Ubuntu 14.0.4 LTS
• TITAN X
• CUDA 7.5
• cuDNN v4
• g++ 4.8.4
• bazel 0.2.2b
• Python 2.7.10

NOTE that I've never had trouble compiling TensorFlow separately. Has anyone experienced similar issues recently?

[flashxing]

I have the same problem with you. Have you solved this?

[todtom]

@nryant Hi, I have the same problem, could you tell me how to solve this?

[David-Ba]

Hi, I had the same problem and managed to build SyntaxNet with GPU support with the following steps:

1. Make sure you have the following environment variables set:
   CUDA_HOME="[path_to_cuda_top_directory]" LD_LIBRARY_PATH="[path_to_cuda_lib64_directory] :$LD_LIBRARY_PATH" PATH="[path_to_cuda_bin_directory]:$PATH"
2. Add the line build --config=cuda to tools/bazel.rc
3. Add the line cxx_builtin_include_directory: "/usr/local/cuda-7.5/targets/x86_64-linux/include” to tensorflow/third_party/gpus/crosstool/CROSSTOOL (with the cuda part pointing to your Cuda installation)
4. Force Tensorflow to use Cuda by changing the //conditions:default part in syntaxnet/syntaxnet.bzl from if_false to if_true
5. Do the same thing for tensorflow/third_party/gpus/cuda/build_defs.bzl
6. Build SyntaxNet using this command: bazel test -c opt --config=cuda --define using_cuda_nvcc=true --define using_gcudacc=true syntaxnet/... util/utf8/...

Two tests will fail because SyntaxNet cannot find the Cuda dependencies for some reason (cf. test logs). It seems that the LD_LIBRARY_PATH variable is not set in the test environment. When running the parser_eval and parser_trainer script, however, it should be no problem. Running SyntaxNet on the example in this stage might cause a CUDA_OUT_OF_MEMORY error. A fix for this is available here: #173

Side note: I used Ubuntu 14.04, Cuda 7.5, and cuDNN 4.0.7

[todtom]

@David-Ba I'm not sure why the bazel.rc set crosstool_top to //third_party/gpus/crosstool, maybe the first line of tools/bazel.rc need to be modified like //tensorflow/third_party/gpus/crosstool and followed your 6 steps and some additional error occured.

command is ~/tools/tensorflow/models/syntaxnet$ bazel test -c opt --config=cuda --define using_cuda_nvcc=true --define using_gcudacc=true syntaxnet/... util/utf8/...
and show these messages

INFO: Found 68 targets and 17 test targets...
INFO: From Compiling external/org_tensorflow/tensorflow/core/kernels/spacetodepth_op_gpu.cu.cc:
nvcc warning : option '--relaxed-constexpr' has been deprecated and replaced by option '--expt-relaxed-constexpr'.
nvcc warning : option '--relaxed-constexpr' has been deprecated and replaced by option '--expt-relaxed-constexpr'.
/usr/include/string.h: In function 'void* __mempcpy_inline(void*, const void*, size_t)':
/usr/include/string.h:652:42: error: 'memcpy' was not declared in this scope
   return (char *) memcpy (__dest, __src, __n) + __n;
                                          ^
ERROR: /home/hjm/.cache/bazel/_bazel_hjm/1e0c52c2d9671225fb0df00406e3d29b/external/org_tensorflow/tensorflow/core/kernels/BUILD:1445:1: output 'external/org_tensorflow/tensorflow/core/kernels/_objs/depth_space_ops_gpu/external/org_tensorflow/tensorflow/core/kernels/spacetodepth_op_gpu.cu.pic.o' was not created.
ERROR: /home/hjm/.cache/bazel/_bazel_hjm/1e0c52c2d9671225fb0df00406e3d29b/external/org_tensorflow/tensorflow/core/kernels/BUILD:1445:1: not all outputs were created.
INFO: Elapsed time: 33.099s, Critical Path: 32.81s
//syntaxnet:arc_standard_transitions_test                             NO STATUS
//syntaxnet:beam_reader_ops_test                                      NO STATUS
//syntaxnet:binary_segment_state_test                                 NO STATUS
//syntaxnet:char_properties_test                                      NO STATUS
//syntaxnet:graph_builder_test                                        NO STATUS
//syntaxnet:lexicon_builder_test                                      NO STATUS
//syntaxnet:morphology_label_set_test                                 NO STATUS
//syntaxnet:parser_features_test                                      NO STATUS
//syntaxnet:parser_trainer_test                                       NO STATUS
//syntaxnet:reader_ops_test                                           NO STATUS
//syntaxnet:segmenter_utils_test                                      NO STATUS
//syntaxnet:sentence_features_test                                    NO STATUS
//syntaxnet:shared_store_test                                         NO STATUS
//syntaxnet:tagger_transitions_test                                   NO STATUS
//syntaxnet:text_formats_test                                         NO STATUS
//util/utf8:unicodetext_unittest                                      NO STATUS

 Executed 0 out of 17 tests: 1 fails to build and 16 were skipped.

I'm new to tensorflow, I only want to get the parsed tree faster with using gpus . I'm sincerIy sorry if there are some silly questions.

I used Ubuntu 16.04, Cuda 7.5, and cuDNN 4.0.7, Geforce GTX TITANX

btw. Syntaxnet was running successfully on cpus, but too slow. And some experiments coded with theano worked well on GPUS .

[David-Ba]

@todtom Yes, I set crosstool_top in tools/bazel.rc to cuda --crosstool_top=@org_tensorflow//third_party/gpus/crosstool. I forgot to mention that. Also, I am not sure whether this is the way to go. I just looked around the config files and changed them to what I thought is right. However, I have not encountered your error so far. Maybe do a bazel clean and then rebuild. It helps sometimes.

[todtom]

bazel clean seems not working for me. Can anyone help me?

[calberti]

Thanks @David-Ba for your detailed answer!
@todtom: the issue running bazel clean seems unrelated to GPU support. Can you open a new issue or ask on stack overflow to get more help if needed?

[chrhad]

I have followed the 6 steps provided by @David-Ba as follows:

1. Make sure you have the following environment variables set:
   CUDA_HOME="[path_to_cuda_top_directory]" LD_LIBRARY_PATH="[path_to_cuda_lib64_directory] :$LD_LIBRARY_PATH" PATH="[path_to_cuda_bin_directory]:$PATH"
2. Add the line build --config=cuda to tools/bazel.rc
3. Add the line cxx_builtin_include_directory: "/usr/local/cuda-7.5/targets/x86_64-linux/include” to tensorflow/third_party/gpus/crosstool/CROSSTOOL (with the cuda part pointing to your Cuda installation)
4. Force Tensorflow to use Cuda by changing the //conditions:default part in syntaxnet/syntaxnet.bzl from if_false to if_true
5. Do the same thing for tensorflow/third_party/gpus/cuda/build_defs.bzl
6. Build SyntaxNet using this command: bazel test -c opt --config=cuda --define using_cuda_nvcc=true --define using_gcudacc=true syntaxnet/... util/utf8/...

and set crosstool_top in tools/bazel.rc to build:cuda --crosstool_top=@org_tensorflow//third_party/gpus/crosstool

Yet, the installation returns error as follows:
ERROR: no such target '@org_tensorflow//third_party/gpus/crosstool:crosstool': target 'crosstool' not declared in package 'third_party/gpus/crosstool' defined by /home/christian/.cache/bazel/_bazel_christian/d9875fd54a23cac839e874ac491a28bb/external/org_tensorflow/third_party/gpus/crosstool/BUILD.

Reverting the crostool_top back to build:cuda --crosstool_top=//third_party/gpus/crosstool returns the following error:
ERROR: no such package 'third_party/gpus/crosstool': BUILD file not found on package path.

Have I missed anything? My CUDA version is 7.0, with CUDNN version 4.0.7.

[hfxunlp]

ERROR:no such package 'third_party/gpus/crosstool': BUILD file not found on package path.

[a2tm7a]

Same error as @chrhad and @anoidgit. Can someone help with it

[wq343580510]

ERROR:no such package 'third_party/gpus/crosstool': BUILD file not found on package path.
@David-Ba follow the instruction，It seems that many people have encountered this problem。

[TheodoreGalanos]

Same here. Tried copy/pasting it in the folder from syntaxnet/tensorflow/third_party but the BUILD file wasn't associated with smth like that (at least my beginner level view of that error).

Is there any updates? It seems like a small issue to my untrained eyes.

[ducdauge]

Hi guys. I had a similar problem but I might have found a solution here. Quoting it:

running the same command from the tensorflow serving repository root will fail (with errors) for 2 reasons:

1. the crosstool in tools/bazel.rc is invalid (AFAIK). change @org_tensorflow//third_party/gpus/crosstool to @local_config_cuda//crosstool:toolchain.

2. the cuda_configure repository rule will fail (haven't looked in to why exactly), but essentially an bazel clean --expunge && export TF_NEED_CUDA=1 will fix this.

Then, run bazel query 'kind(rule, @local_config_cuda//...)' again and all is well (for me at least); the cuda tool chain should be created in $(bazel info output_base)/external/local_config_cuda/cuda

Afterwards, bazel test -c opt --config=cuda --define using_cuda_nvcc=true --define using_gcudacc=true syntaxnet/... util/utf8/... failed just 1 test, but I had some memory issues with the GPU. I solved them adding config.gpu_options.allow_growth = True to the relevant files

[Vimos]

Using the method offered by @ducdauge , I was able to build.

But still met 2 problems.

Problem 1: nccl not found

In file included from external/org_tensorflow/tensorflow/contrib/nccl/kernels/nccl_manager.cc:15:0:
external/org_tensorflow/tensorflow/contrib/nccl/kernels/nccl_manager.h:23:44: fatal error: external/nccl_archive/src/nccl.h: No such file or directo

Problem 1 is solved via commenting out nccl refered in tensorflow/serving#327

Problem 2: Tests failure

At global scope:
cc1plus: warning: unrecognized command line option '-Wno-self-assign'
FAIL: //syntaxnet:reader_ops_test (see /data/home/vimos/.cache/bazel/_bazel_vimos/8c5df8ecbe273164beccb9b372c94778/execroot/syntaxnet/bazel-out/local_linux-opt/testlogs/syntaxnet/reader_ops_test/test.log).
FAIL: //syntaxnet:graph_builder_test (see /data/home/vimos/.cache/bazel/_bazel_vimos/8c5df8ecbe273164beccb9b372c94778/execroot/syntaxnet/bazel-out/local_linux-opt/testlogs/syntaxnet/graph_builder_test/test.log).

I am working on these failures right now, it seems that they are memory related issues.

[utkrist]

Following is the summary of what worked for me. It is based on prev comments and other sources.

0. Install all the dependencies for syntaxnet

1. Choose non NFS location for bazel temp directory related files. I choose '/tmp/bazeltemp'. Add this line to .bashrc:
   export TEST_TMPDIR=/tmp/bazeltemp

2. Install bazel using installer (I choose 0.4.5). Here, bin and bazelrc can be in NFS location
   $ chmod +x bazel-version-installer-os.sh
$ ./bazel-version-installer-os.sh --bin=$HOME/bin --base=/tmp/bazeltemp/base --bazelrc=$HOME/.bazelrc

3. Make following edit in configure file of tensorflow: models/syntaxnet/tensorflow/configure
   Replace bazel clean --expunge with bazel clean --expunge_async

4. $ ./configure
   Experiment with different options if you like to

Please specify the location of python. [Default is /home/anaconda2/bin/python]:
Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native]:
Do you wish to use jemalloc as the malloc implementation? [Y/n] y
jemalloc enabled
Do you wish to build TensorFlow with Google Cloud Platform support? [y/N] n
No Google Cloud Platform support will be enabled for TensorFlow
Do you wish to build TensorFlow with Hadoop File System support? [y/N] y
Hadoop File System support will be enabled for TensorFlow
Do you wish to build TensorFlow with the XLA just-in-time compiler (experimental)? [y/N] n
No XLA JIT support will be enabled for TensorFlow
Found possible Python library paths:
/home/anaconda2/lib/python2.7/site-packages
Please input the desired Python library path to use. Default is [/home/anaconda2/lib/python2.7/site-packages]

Using python library path: /home/anaconda2/lib/python2.7/site-packages
Do you wish to build TensorFlow with OpenCL support? [y/N] n
No OpenCL support will be enabled for TensorFlow
Do you wish to build TensorFlow with CUDA support? [y/N] y
CUDA support will be enabled for TensorFlow
Please specify which gcc should be used by nvcc as the host compiler. [Default is /usr/bin/gcc]:
Please specify the CUDA SDK version you want to use, e.g. 7.0. [Leave empty to use system default]: 8.0
Please specify the Cudnn version you want to use. [Leave empty to use system default]: 5.1
Please specify the location where cuDNN 5.1 library is installed. Refer to README.md for more details. [Default is /opt/software/cuda/cuda-8.0]:
Please specify a list of comma-separated Cuda compute capabilities you want to build with.
You can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus.
Please note that each additional compute capability significantly increases your build time and binary size.
[Default is: "3.5,5.2"]

5. Follow the instructions below:
   a. Make sure you have the following environment variables set in .bashrc

   CUDA_HOME="[path_to_cuda_top_directory]"
LD_LIBRARY_PATH="[path_to_cuda_lib64_directory] :$LD_LIBRARY_PATH"
PATH="[path_to_cuda_bin_directory]:$PATH"

   For example my .basrhc has following

     export ORACLE_HOME=/opt/software/oracle/product/12.1.0/client
     export PATH=${PATH}:${ORACLE_HOME}/bin
     export PATH=/home/IAIS/uadhikari/anaconda2/bin:$PATH

     export CUDA_HOME=/opt/software/cuda/cuda-8.0
     export CUDA_TOOLKIT_PATH=${CUDA_HOME}
     export LD_LIBRARY_PATH=${CUDA_HOME}/lib64:${CUDA_HOME}/extras/CUPTI/lib64:$LD_LIBRARY_PATH
     export PATH=${CUDA_HOME}/bin:${PATH}

     export JAVA_HOME=/opt/software/jdk1.8.0_51
     export PATH=/tmp/bazeltemp/bin:$PATH
     export CUDNN_HOME=${CUDA_HOME}
     export TEST_TMPDIR=/tmp/bazeltemp

b. Add the line `build --config=cuda to tools/bazel.rc` (I added as first line in the file)

c. In the file tensorflow/third_party/gpus/crosstool/CROSSTOOL,
replace every `cxx_builtin_include_directory: "%{cuda_include_path}"`
with `cxx_builtin_include_directory: "your/cuda/home/path/include"`

d. Force Tensorflow to use Cuda by changing the //conditions:default part in syntaxnet/syntaxnet.bzl from `if_false` to `if_true`.

e. Do the same thing for tensorflow/third_party/gpus/cuda/build_defs.bzl

5. $ bazel clean --expunge_async

6. Carefully run each of these
   $ export TF_NEED_CUDA=1
$ export CUDA_TOOLKIT_PATH=$CUDA_HOME
$ export TF_CUDA_VERSION=8.0
$ export TF_CUDNN_VERSION=5.1
$ export CUDNN_INSTALL_PATH=$CUDA_HOME

7. This has to be run in in models/syntaxnet folder
   $ bazel test -c opt --config=cuda --define using_cuda_nvcc=true --define using_gcudacc=true syntaxnet/... util/utf8/...
   If you get error about cross tool or local_config_cuda, goto step 5 and try again

8. If you get error about nccl:
   comment out the dependency for nccl in: tensorflow/tensorflow/contrib/BUILD as mentioned in
   bazel GPU build error with fatal error: external/nccl_archive/src/nccl.h: No such file or directory serving#327
   Goto step 5 and try again

I hope this works for you.

[jhowliu]

Hi @utkrist,

I followed your instructions, but I had some test failed.

The log says message below.

exec ${PAGER:-/usr/bin/less} "$0" || exit 1
-----------------------------------------------------------------------------
2017-04-18 10:54:49.948817: F external/org_tensorflow/tensorflow/core/framework/allocator_registry.cc:42] Check failed: !CheckForDuplicates(name, priority) Allocator with name: [DefaultCPUAllocator] and priority: [100] already registered
external/bazel_tools/tools/test/test-setup.sh: line 159:  2453 Aborted 

Have you ever seen it ?

(I used the latest tensorflow, cuda-8.0 and bazel 0.4.5)

[utkrist]

@jhowliu I suggest that you downgrade your bazel to 0.4.2 and try the instructions again. If it still does not work then let me know. I will then make a fresh install in new machine and will try to reproduce your error.

[jhowliu]

Hi @utkrist ,
Thanks for your suggestions.
It worked when I used bazel 0.4.5 and tensorflow at a7d6015. (It still worked with bazel 0.4.2)

But I got the another trouble about ran of the memory when I use the demo example.
Please take a look the log.
I have tried your instruction given in #173 but still not work.

Thanks again.

[utkrist]

Hi @jhowliu,
Did you mange to solve the problem?

[jhowliu]

@utkrist
I tried the many version of bazel and lots of instructions i could find but still out of memory.
Maybe my gpu is not enough memory to use the syntaxnet.

so i will use the syntaxnet with cpu until solve the problem.
do you have any ideas ?

[udnaan]

these instructions do no work on MacOS.

[smirnovevgeny]

I've spent a day for reading syntaxnet gpu issues to build docker container.
This container passes sentences through parsey_universal with Russian-SynTagRus model.
docker pull evgenysmirnov/syntaxnet:cuda
https://hub.docker.com/r/evgenysmirnov/syntaxnet/

[zerodarkzone]

I have some tests fails with an OUT_OF_MEMORY error.
I built it with Bazel 0.5.4, Cuda 8.0 and Cudnn 6.0.23
Any update on this error?

[lifeiteng]

@zerodarkzone try bazel test --jobs 1 ...