https://tuatini.me/building-tensorflow-as-a-standalone-project/
Here you'll learn how to build Tensorflow either for your x86_64 machine or for the raspberry pi 3 as a standalone shared library which can be interfaced from the C++ API.
(This tutorial couldn't be possible without the help of the people from the References section)
Watch out for the "For the Rpi" dropdown menus to know what commands are related to the Rpi and which ones aren't.
If you don't want to go through the pain of all the coming steps to compile tensorflow on the Raspberry pi 3 I prepared the precompiled tensorflow lib for the test project here. You can download it and jump directly to "Compile the test project".
Contents
Install USB Memory as Swap (Rpi)
For the Rpi
Overview
These instructions were crafted for a Raspberry Pi 3 Model B running a vanilla copy of Ubuntu 16.04-xenial or an x86_64 machine also running Ubuntu 16.04.
1. Install the basic dependencies
First, update apt-get to make sure it knows where to download everything.
sudo apt-get update
Next, install some base dependencies and tools we'll need later.
For Bazel:
sudo apt-get install pkg-config zip g++ zlib1g-dev unzip default-jdk autoconf automake libtool
For TensorFlow:
# For Python 2.7
sudo apt-get install python-pip python-numpy swig python-dev
sudo pip install wheel
# For Python 3.3+
sudo apt-get install python3-pip python3-numpy swig python3-dev
sudo pip3 install wheel
For the Rpi
Finally, for cleanliness, make a directory that will hold the Protobuf, Bazel, and TensorFlow repositories.
mkdir tf
cd tf
2. Install a Memory Drive as Swap for Compiling
For the Rpi
3. Build Bazel
Here we'll need to build bazel from sources on the Rpi platform. If you are on a x86_64 machine then just install bazel following these instructions.
For the Rpi
4. Compiling TensorFlow
First things first, clone the TensorFlow repository and move into the newly created directory.
For the RpiFor the x86_64
Now let's configure the build:
./configure
You have bazel 0.5.1- installed.
Please specify the location of python. [Default is /usr/bin/python]: /usr/bin/python
Do you wish to build TensorFlow with MKL support? [y/N] N
No MKL support will be enabled for TensorFlow
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] N
No 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 support will be enabled for TensorFlow
Do you wish to build TensorFlow with VERBS support? [y/N] N
No VERBS support will be enabled for TensorFlow
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] N
No CUDA support will be enabled for TensorFlow
Do you wish to build TensorFlow with MPI support? [y/N] N
MPI support will not be enabled for TensorFlow
Configuration finished
Note: if you want to build for Python 3, specify /usr/bin/python3 for Python's location and /usr/local/lib/python3.5/dist-packages for the Python library path.
Now we can use it to build TensorFlow!
To build the shared library to use in C++ (for bindings in other languages you need to compile the C interface with //tensorflow:libtensorflow.so instead):
For the RpiFor the x86_64
Now we'll move the shared libraries and headers to a test folder instead of copying them system wide.
To do so lets create our project structure:
mkdir ../tf_test
then open test.cpp:
nano ../tf_test/test.cpp
and copy this code:
#include "tensorflow/cc/client/client_session.h"
#include "tensorflow/cc/ops/standard_ops.h"
#include "tensorflow/core/framework/tensor.h"
int main() {
using namespace tensorflow;
using namespace tensorflow::ops;
Scope root = Scope::NewRootScope();
// Matrix A = [3 2; -1 0]
auto A = Const(root, { {3.f, 2.f}, {-1.f, 0.f}});
// Vector b = [3 5]
auto b = Const(root, { {3.f, 5.f}});
// v = Ab^T
auto v = MatMul(root.WithOpName("v"), A, b, MatMul::TransposeB(true));
std::vector outputs;
ClientSession session(root);
// Run and fetch v
TF_CHECK_OK(session.Run({v}, &outputs));
// Expect outputs[0] == [19; -3]
LOG(INFO) << outputs[0].matrix();
return 0;
}
Before moving on we also need to compile the Protobuf dependencies with:
mkdir /tmp/proto
tensorflow/contrib/makefile/download_dependencies.sh
cd tensorflow/contrib/makefile/downloads/protobuf/
./autogen.sh
./configure --prefix=/tmp/proto/
make
make install
Same thing for Eigen:
mkdir /tmp/eigen
cd ../eigen
mkdir build_dir
cd build_dir
cmake -DCMAKE_INSTALL_PREFIX=/tmp/eigen/ ../
make install
cd ../../../../../..
Now copy the libraries to the project folder:
mkdir ../tf_test/lib
cp bazel-bin/tensorflow/libtensorflow_cc.so ../tf_test/lib/
cp /tmp/proto/lib/libprotobuf.a ../tf_test/lib/
Then the includes files:
mkdir -p ../tf_test/include/tensorflow
cp -r bazel-genfiles/* ../tf_test/include/
cp -r tensorflow/cc ../tf_test/include/tensorflow
cp -r tensorflow/core ../tf_test/include/tensorflow
cp -r third_party ../tf_test/include
cp -r /tmp/proto/include/* ../tf_test/include
cp -r /tmp/eigen/include/eigen3/* ../tf_test/include
5. Compile the test project
Finally do some cleanup and compile the test file with:
cd ../tf_test/
find . -name "*.cc" -type f -delete
g++ -std=c++11 -Wl,-rpath='$ORIGIN/lib' -Iinclude -Llib test.cpp -ltensorflow_cc -o exec
You can now execute your program with:
./exec
When you execute your program you may see some warnings such as: The TensorFlow library wasn't compiled to use SSE4.1 instructions. This tutorial didn't include these special flags to speedup Tensorflow. I let the reader figure out what compilation flags are adapted to his platform.
Now it's done, congrats!
6. Cleaning Up
For the Rpi
And you're done! You deserve a break.
7. References
Building tensorflow on the raspberry pi
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