I'm trying to build TF Lite Android example app following this documentation.
System – macOS High Siera 10.13.6
gcc version 4.2.1
However I get the following error:
ERROR: No default_toolchain found for cpu 'arm64-v8a'. Valid cpus are: [
darwin,
darwin_x86_64,
k8,
ios_x86_64,
ios_i386,
ios_armv7,
ios_arm64,
watchos_i386,
watchos_armv7k,
tvos_x86_64,
tvos_arm64,
armeabi-v7a,
]
The error is due to bazel not supporting arm64, this is supported here is a workaround, the other way will be to just drop arm64-v8a from compilation flags.
Related
I'm looking the TF Lite Android App
Which can be found on GIT: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/java/demo
How can I compile the tensorflow lite framework to use the optimized "atom" cpu type?
Is it possible to compile it on a MAC os with the CPU optimizations for the "atom" cpu?
I want to run the app on an Android device (SDK 22) with an "Intel Atom" Processor.
When I run the application without any changes through Android Studio the rate was about 1200ms per frame.
Compering the same APK installed on my Galaxy S9 (arm - snapdragon processor) was about 30ms per frame.
In the "build.gradle" there is this section:
dependencies {
...
compile 'org.tensorflow:tensorflow-lite:0.0.0-nightly'
...
}
So it's seems that it's downloading the framework,
How can I compile it locally with the CPU optimization and set the app to use it instead of downloading the non optimized nightly version?
I tried to run this tutorial :
Installing TensorFlow from Sources with the cpu flags but not sure exactly how it's helping me with the Android scenario..
Assuming that your Atom device is x86, use the --fat_apk_cpu flag to specify the x86 ABI:
$ bazel build -c opt --cxxopt='--std=c++11' \
--fat_apk_cpu=x86 \
//tensorflow/contrib/lite/java/demo/app/src/main:TfLiteCameraDemo
Switch x86 with x86_64 if you're building for a 64-bit device.
The built APK, available at bazel-bin/tensorflow/contrib/lite/java/demo/app/src/main/TfLiteCameraDemo.apk, will contain the x86 .so file:
$ zipinfo bazel-bin/tensorflow/contrib/lite/java/demo/app/src/main/TfLiteCameraDemo.apk | grep lib
-rw---- 2.0 fat 1434712 b- defN 80-Jan-01 00:00 lib/x86/libtensorflowlite_jni.so
If your device is connected, you can use bazel mobile-install instead of bazel build to directly install the app:
$ bazel mobile-install -c opt --cxxopt='--std=c++11' \
--fat_apk_cpu=x86 \
--start_app \
//tensorflow/contrib/lite/java/demo/app/src/main:TfLiteCameraDemo
The objective of my experiment is to build tensorflow on Jetson TK1 arm based embedded board. Since pre-builts of tensorflow for arm architecture are not given by the official releases, I was forced to the option of building it from source.
To build tensorflow, we need Bazel which should be also build from source. Now I got stuck here, not able to build bazel at all.
I have referred various blogs and github projects and tried to follow the instructions everyone said it worked for them.
1) Tensorflow on Raspberry-pi
2) Jetson Hacks building Tensorflow from source
3) Official Documentation
Steps Followed:
$ sudo apt-get install build-essential openjdk-8-jdk python zip
$ wget https://github.com/bazelbuild/bazel/releases/download/0.4.5/bazel-0.4.5-dist.zip
$ unzip -d bazel bazel-0.4.5-dist.zip
$ cd bazel
$ sudo ./compile.sh
Error Log:
ERROR: /build/bazel/src/main/protobuf/BUILD:25:2: Java compilation in rule '//src/main/protobuf:extra_actions_base_java_proto' failed: Worker process sent response with exit code: 1.
java.lang.InternalError: Cannot find requested resource bundle for locale en_US
at com.sun.tools.javac.util.JavacMessages.getBundles(JavacMessages.java:128)
at com.sun.tools.javac.util.JavacMessages.getLocalizedString(JavacMessages.java:147)
at com.sun.tools.javac.util.JavacMessages.getLocalizedString(JavacMessages.java:140)
at com.sun.tools.javac.util.Log.localize(Log.java:673)
at com.sun.tools.javac.util.Log.printLines(Log.java:485)
at com.sun.tools.javac.api.JavacTaskImpl.handleExceptions(JavacTaskImpl.java:156)
at com.sun.tools.javac.api.JavacTaskImpl.doCall(JavacTaskImpl.java:93)
at com.sun.tools.javac.api.JavacTaskImpl.call(JavacTaskImpl.java:87)
at com.google.devtools.build.buildjar.javac.BlazeJavacMain.compile(BlazeJavacMain.java:104)
at com.google.devtools.build.buildjar.SimpleJavaLibraryBuilder$1.invokeJavac(SimpleJavaLibraryBuilder.java:163)
at com.google.devtools.build.buildjar.ReducedClasspathJavaLibraryBuilder.compileSources(ReducedClasspathJavaLibraryBuilder.java:52)
at com.google.devtools.build.buildjar.SimpleJavaLibraryBuilder.compileJavaLibrary(SimpleJavaLibraryBuilder.java:166)
at com.google.devtools.build.buildjar.SimpleJavaLibraryBuilder.run(SimpleJavaLibraryBuilder.java:178)
at com.google.devtools.build.buildjar.BazelJavaBuilder.processRequest(BazelJavaBuilder.java:90)
at com.google.devtools.build.buildjar.BazelJavaBuilder.runPersistentWorker(BazelJavaBuilder.java:67)
at com.google.devtools.build.buildjar.BazelJavaBuilder.main(BazelJavaBuilder.java:44)
Caused by: java.util.MissingResourceException: Can't find bundle for base name com.google.errorprone.errors, locale en_US
at java.util.ResourceBundle.throwMissingResourceException(ResourceBundle.java:1573)
at java.util.ResourceBundle.getBundleImpl(ResourceBundle.java:1396)
at java.util.ResourceBundle.getBundle(ResourceBundle.java:854)
at com.sun.tools.javac.util.JavacMessages.lambda$add$0(JavacMessages.java:106)
at com.sun.tools.javac.util.JavacMessages.getBundles(JavacMessages.java:125)
... 15 more
Target //src:bazel failed to build
INFO: Elapsed time: 291.995s, Critical Path: 258.92s
ERROR: Could not build Bazel
To make sure the error is independent of the architecture, I have tried to build Bazel in x86_64 PC. Even there I am getting the same error. I have seen people created the similar issue in bazel github group, none solved.
Version 0.4.5 is very old. We just released 0.12.0, could you try that one?
I am learning tesnorflow from this blog:
http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/
The code i am running is :
https://github.com/dennybritz/cnn-text-classification-tf/blob/master/train.py
I have installed tensorflow from sourcse in a virtual enviroment,in CPU only enviroment using followinbg bazel build command: bazel build --config=mkl ...
here is the exact error:
"2018-01-16 03:15:27.783040: F tensorflow/core/kernels/mkl_maxpooling_op.cc:157] Check failed: dnnPoolingCreateForward_F32( &prim_pooling_fwd, primAttr, algorithm, lt_user_input, params.kernel_size, params.kernel_stride, params.in_offset, dnnBorderZerosAsymm) == E_SUCCESS (-127 vs. 0)
Aborted
"
I have debugged error to the line where sess.run is written, i have beleived it has something to do it mkl_maxpooling, as i had installed tensorflow with mkl optimization of INTEL cpu's
Given below are the steps that I followed:
Build tensorflow 1.4 from source with mkl as mentioned in the question
Cloned the git repo "https://github.com/dennybritz/cnn-text-classification-tf.git"
Ran "python train.py" from "cnn-text-classification-tf" directory(created from git clone)
Code ran without any errors. So it seems like the tensorflow was not properly built from the source. Please confirm that there were no errors while building tensorflow from source.
I was trying to using syntaxnet and I have finished most of processes. Upgrade bazel version to 0.43 in case of errors (Ubuntu 16.04 Ver, Anaconda python 2.7).
However, I am having a troubles with ./configure part. I am reading the official instruction via tensorflow github.
git clone --recursive https://github.com/tensorflow/models.git
cd models/syntaxnet/tensorflow
**./configure**
cd ..
bazel test syntaxnet/... util/utf8/...
# On Mac, run the following:
bazel test --linkopt=-headerpad_max_install_names \
syntaxnet/... util/utf8/...
Following logs will help you to understand what’s going on my machine. Thanks for the advice
Please specify the location of python. [Default is /home/ryan/anaconda2/bin/python]:
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] n
No Hadoop File System support will be enabled for TensorFlow
Found possible Python library paths:
/home/ryan
/home/ryan/pynaoqi-python2.7
/home/ryan/anaconda2/lib/python2.7/site-packages
Please input the desired Python library path to use. Default is [/home/ryan]
/home/ryan/anaconda2/lib/python2.7/site-packages
Do you wish to build TensorFlow with GPU support? [y/N] y
GPU 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 location where CUDA 8.0 toolkit is installed. Refer to README.md for more details. [Default is /usr/local/cuda]:
Please specify the Cudnn version you want to use. [Leave empty to use system default]: 5.0
Please specify the location where cuDNN 5.0 library is installed. Refer to README.md for more details. [Default is /usr/local/cuda]:
Invalid path to cuDNN toolkit. Neither of the following two files can be found:
/usr/local/cuda-8.0/lib64/libcudnn.so.5.0
/usr/local/cuda-8.0/libcudnn.so.5.0
.5.0
Please specify the Cudnn version you want to use. [Leave empty to use system default]:
Please specify the location where cuDNN library is installed. Refer to README.md for more details. [Default is /usr/local/cuda]:
libcudnn.so resolves to libcudnn.5
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"]:
INFO: Options provided by the client:
Inherited 'common' options: --isatty=1 --terminal_columns=120
INFO: Reading options for 'clean' from /home/ryan/git_ryan/models/syntaxnet/tensorflow/tools/bazel.rc:
Inherited 'build' options: --force_python=py2 --host_force_python=py2 --python2_path=/home/ryan/anaconda2/bin/python --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true --define PYTHON_BIN_PATH=/home/ryan/anaconda2/bin/python --spawn_strategy=standalone --genrule_strategy=standalone
**INFO: Reading options for 'clean' from /etc/bazel.bazelrc:
Inherited 'build' options: --action_env=PATH --action_env=LD_LIBRARY_PATH --action_env=TMPDIR --test_env=PATH --test_env=LD_LIBRARY_PATH
Unrecognized option: --action_env=PATH
ERROR: /home/ryan/git_ryan/models/syntaxnet/tensorflow/tensorflow/tensorflow.bzl:568:26: Traceback (most recent call last):
File "/home/ryan/git_ryan/models/syntaxnet/tensorflow/tensorflow/tensorflow.bzl", line 562
rule(attrs = {"srcs": attr.label_list..."), <3 more arguments>)}, <2 more arguments>)
File "/home/ryan/git_ryan/models/syntaxnet/tensorflow/tensorflow/tensorflow.bzl", line 568, in rule
attr.label_list(cfg = "data", allow_files = True)
expected ConfigurationTransition or NoneType for 'cfg' while calling label_list but got string instead: data.
ERROR: com.google.devtools.build.lib.packages.BuildFileContainsErrorsException: error loading package '': Extension file 'tensorflow/tensorflow.bzl' has errors.
Configuration finished**
I think the version of your bazel is too high for Syntaxnet. you can try bazel-0.3.1 please.
I've reached the Setting up TensorFlow for Development section of the Tensorflow 0.9 tutorial, and I'm getting an error when trying to add support for GPU.
\> bazel build -c opt --config=cuda //tensorflow/tools/pip_package:build_pip_package
ERROR: /home/.../tensorflow/tensorflow/core/kernels/BUILD:1489:1:
undeclared inclusion(s) in rule
'//tensorflow/core/kernels:batchtospace_op_gpu': this rule is missing
dependency declarations for the following files included by
'tensorflow/core/kernels/batchtospace_op_gpu.cu.cc'
I do not get this error if I exclude the --config=cuda directive, however.
Setup:
Elementary OS 0.3
tensorflow 0.9
cuda-7.5
cudnn 5.0.5
gcc 4.8.4
try adding
cxx_builtin_include_directory: "/usr/local/cuda-8.0/include"
to
third_party/gpus/crosstool/CROSSTOOL
https://github.com/tensorflow/tensorflow/issues/3431