TensorFlow and Go on Raspberry Pi

This is a guide which I ran through for building libtensorflow on Raspberry Pi.

0. Used Hardwares and Softwares

All steps were taken on my Raspberry Pi 3 B model with:

  • Minimum GPU memory allocated (16MB)
  • 1GB of swap memory
  • External USB HDD (as root partition)

and software versions were:

  • Raspbian (Stretch) / gcc 6.3.0
  • Tensorflow 1.3.0
  • Protobuf 3.1.0
  • Bazel 0.5.1

Before the beginning, I had to install dependencies:

for protobuf

$ sudo apt-get install autoconf automake libtool

for bazel

$ sudo apt-get install pkg-config zip g++ zlib1g-dev unzip oracle-java8-jdk

1. Install Protobuf

I cloned the protobuf’s repository:

$ git clone https://github.com/google/protobuf.git

and started building:

$ cd protobuf
$ git checkout v3.1.0
$ ./autogen.sh
$ ./configure
$ make -j 4
$ sudo make install
$ sudo ldconfig

It took less than an hour to finish.

I could see the version of installed protobuf with:

$ protoc --version
libprotoc 3.1.0

2. Install Bazel

a. download

I got a zip file of bazel from here and unzipped it:

$ wget https://github.com/bazelbuild/bazel/releases/download/0.5.1/bazel-0.5.1-dist.zip
$ unzip -d bazel bazel-0.5.1-dist.zip

b. edit bootstrap files

In the unzipped directory, I opened the scripts/bootstrap/compile.sh file:

$ cd bazel
$ vi scripts/bootstrap/compile.sh

searched for lines that looked like following:

run "${JAVAC}" -classpath "${classpath}" -sourcepath "${sourcepath}" \
      -d "${output}/classes" -source "$JAVA_VERSION" -target "$JAVA_VERSION" \
      -encoding UTF-8 "@${paramfile}"

and appended -J-Xmx500M to the last line so that the whole lines would look like:

run "${JAVAC}" -classpath "${classpath}" -sourcepath "${sourcepath}" \
      -d "${output}/classes" -source "$JAVA_VERSION" -target "$JAVA_VERSION" \
      -encoding UTF-8 "@${paramfile}" -J-Xmx500M

It was for enlarging the max heap size of Java.

c. compile

After that, started building with:

$ chmod u+w ./* -R
$ ./compile.sh

It also took about an hour.

d. install

After the compilation had finished, I could find the compiled binary in output directory.

Copied it into /usr/local/bin directory:

$ sudo cp output/bazel /usr/local/bin/

3. Build libtensorflow.so

(I referenced this document for following processes)

a. download

Got the tensorflow go code with:

$ go get -d github.com/tensorflow/tensorflow/tensorflow/go

b. edit files

In the downloaded directory, I checked out the latest tag and replaced lib64 to lib in the files with:

$ cd ${GOPATH}/src/github.com/tensorflow/tensorflow
$ git fetch --all --tags --prune
$ git checkout tags/v1.3.0
$ grep -Rl 'lib64' | xargs sed -i 's/lib64/lib/g'

Raspberry Pi still runs on 32bit OS, so they had to be changed like this.

After that, I commented #define IS_MOBILE_PLATFORM out in tensorflow/core/platform/platform.h:

// Since there's no macro for the Raspberry Pi, assume we're on a mobile
// platform if we're compiling for the ARM CPU.
//#define IS_MOBILE_PLATFORM	// <= commented this line

If it is not commented out, bazel will build for mobile platforms like iOS or Android, not Raspberry Pi.

To do this easily, just run:

$ sed -i "s|#define IS_MOBILE_PLATFORM|//#define IS_MOBILE_PLATFORM|g" tensorflow/core/platform/platform.h

Finally, it was time to configure and build tensorflow.

c. configure and build

$ ./configure

I had to answer to some questions here.

Then I started building libtensorflow.so with:

$ bazel build -c opt --copt="-mfpu=neon-vfpv4" --copt="-funsafe-math-optimizations" --copt="-ftree-vectorize" --copt="-fomit-frame-pointer" --jobs 1 --local_resources 1024,1.0,1.0 --verbose_failures --genrule_strategy=standalone --spawn_strategy=standalone //tensorflow:libtensorflow.so

My Pi became unresponsive many times during this process, but I kept it going on.

d. install

After a long time of struggle, (it took nearly 7 hours for me!)

I finally got libtensorflow.so compiled in bazel-bin/tensorflow/.

So I copied it into /usr/local/lib/:

$ sudo cp ./bazel-bin/tensorflow/libtensorflow.so /usr/local/lib/
$ sudo chmod 644 /usr/local/lib/libtensorflow.so
$ sudo ldconfig

All done. Time to test!

4. Go Test

I ran a test for validating the installation:

$ go test github.com/tensorflow/tensorflow/tensorflow/go

then I could see:

ok      github.com/tensorflow/tensorflow/tensorflow/go  0.350s

Ok, it works!

Edit: As this instruction says, I had to regenerate operations before the test:

$ go generate github.com/tensorflow/tensorflow/tensorflow/go/op

5. Further Test

I wanted to see a simple go program running, so I wrote this code:

// sample.go
package main

import (

	tf "github.com/tensorflow/tensorflow/tensorflow/go"

// Sorry - I don't have a good example yet :-P
func main() {
	tensor, _ := tf.NewTensor(int64(42))

	if v, ok := tensor.Value().(int64); ok {
		fmt.Printf("The answer to the life, universe, and everything: %v\n", v)

and ran it with go run sample.go:

The answer to the life, universe, and everything: 42

See the result?

From now on, I can write tensorflow applications in go, on Raspberry Pi! :-)

98. Trouble shooting

Build failure due to a problem with Eigen

Back in the day with Tensorflow 1.2.0, I encountered this issue while building, but it’s still not fixed yet in 1.3.0.

So I had to work around this problem again by editing tensorflow/workspace.bzl from:

	name = "eigen_archive",
	urls = [
	sha256 = "ca7beac153d4059c02c8fc59816c82d54ea47fe58365e8aded4082ded0b820c4",
	strip_prefix = "eigen-eigen-f3a22f35b044",
	build_file = str(Label("//third_party:eigen.BUILD")),


	name = "eigen_archive",
	urls = [
	sha256 = "a34b208da6ec18fa8da963369e166e4a368612c14d956dd2f9d7072904675d9b",
	strip_prefix = "eigen-eigen-d781c1de9834",
	build_file = str(Label("//third_party:eigen.BUILD")),

and starting again from the beginning:

$ bazel clean
$ ./configure
$ bazel build -c opt --copt="-mfpu=neon-vfpv4" --copt="-funsafe-math-optimizations" --copt="-ftree-vectorize" --copt="-fomit-frame-pointer" --jobs 1 --local_resources 1024,1.0,1.0 --verbose_failures --genrule_strategy=standalone --spawn_strategy=standalone //tensorflow:libtensorflow.so


Then I could build it without further problems.

I hope it would be fixed on future releases.

99. Wrap-up

Installing TensorFlow on Raspberry Pi is not easy yet. (There’s a kind project which makes it super easy though!)

Installing libtensorflow.so is a lot more difficult, because it takes too much time to build it.

But it is worth trying; managing TensorFlow graphs in golang will be handy for people who don’t love python - just like me.

999. If you need one,

You don’t have time to build it yourself, but still need the compiled file?

Good, take it here.

I cannot promise, but will try keeping it up-to-date whenever a newer version of tensorflow comes out.