ODROID-C2 (Quad Cortex A53 2GHz) have performance advantage than Raspberry Pi 3(Quad A53 1.2GHz) http://www.jeffgeerling.com/blog/2016/review-odroid-c2-compared-raspberry-pi-3-and-orange-pi-plus

I tried to install with reference to [https://github.com/samjabrahams/tensorflow-on-raspberry-pi] . But I can't install... 64bit ubuntu run on ODROID-C2, there is mismatch architecture? (aarch64 and armv7l)

Next I tried to build from source code with reference to https://github.com/samjabrahams/tensorflow-on-raspberry-pi/blob/master/GUIDE.md But I got some errors...

.Unrecognized option: -client
Error: Could not create the Java Virtual Machine.
Error: A fatal exception has occurred. Program will exit.

Here is memo which steps to run tensorflow v0.10.0rc only CPU build on python3 on ubuntu16.04(aarch64) with bazel(master branch newer than 0.3.1)


Build Steps

Use virtualenv to build modules.

  1. Build protobuf
  2. Build grpc-java compiler
  3. Build bazel with above
  4. Build tensorflow with bazel


  • I use ODORID-C2 with EMMC 32GB.
  • Flash ubuntu64-16.04-minimal-odroid-c2-20160803.img.xz from http://odroid.com/dokuwiki/doku.php?id=en:odroid_flashing_tools
  • I make 2GB swap partition on EMMC and replace kernel to use zswap. It seems to be not necessary.

First make work direcotry with virtualenv.

sudo apt-get install python3 python-virtualenv python3-virtualenv
virtualenv -p python3 --system-site-packages work
cd work
source bin/activate

Install some packages for build

# For protobuf, grpc-java, bazel
sudo apt-get install openjdk-8-jdk automake autoconf curl zip unzip libtool

# For Tensorflow
sudo apt-get install python3-numpy python3-dev swig zlib1g-dev

I upload some patches and build script to https://github.com/neo-titans/odroid/tree/master/build_tensorflow

Build protobuf

See https://github.com/bazelbuild/bazel/tree/master/third_party/protobuf

It looks me grpc-java needs v3.0.0-beta-3. But protobuf in bazel needs v3.0.0-beta-2 ? So make two version with make static link.

# For grpc-java build
git clone https://github.com/google/protobuf.git
cd protobuf
git checkout tags/v3.0.0-beta-3
LDFLAGS=-static ./configure --prefix=$(pwd)/../
sed -i -e 's/LDFLAGS = -static/LDFLAGS = -all-static/' ./src/Makefile
make -j4
make install

# For bazel build
git checkout tags/v3.0.0-beta-2
LDFLAGS=-static ./configure --prefix=$(pwd)/../
sed -i -e 's/LDFLAGS = -static/LDFLAGS = -all-static/' ./src/Makefile
make -j4
cd ..

Build grpc-java compiler

See https://github.com/grpc/grpc-java/blob/v0.15.0/compiler I built v0.15.0 with protoc v3.0.0-beta3

git clone https://github.com/grpc/grpc-java.git
cd grpc-java/
git checkout tags/v0.15.0

Add some patch to build on arm and as static link.

patch -p0 < ../../grpc-java.v0.15.0.patch

Finally build with built protoc

CXXFLAGS="-I$(pwd)/../include" LDFLAGS="-L$(pwd)/../lib" ./gradlew java_pluginExecutable -Pprotoc=$(pwd)/../bin/protoc
cd ..

Build bazel

Build master(47be2a4) which is newer than 0.3.1

git clone https://github.com/bazelbuild/bazel.git
cd bazel
git checkout 47be2a40c601b5e4737f7a6825fad7e7f6ce0302

Copy protoc and grpc-java from built binary

cp ../protobuf/src/protoc third_party/protobuf/protoc-linux-aarch64.exe
cp ../grpc-java/compiler/build/exe/java_plugin/protoc-gen-grpc-java third_party/grpc/protoc-gen-grpc-java-0.15.0-linux-aarch64.exe

Add some patch to build on arm and start to compile.

patch -p0 < ../../bazel.47be2a4.patch

Copy bazel into bin

cp output/bazel ../bin/
cd ..

Build TensorFlow

Get source v0.10.0rc0(3cb3995) and patch to build farmhash from https://github.com/tensorflow/tensorflow/issues/851

git clone --recurse-submodules https://github.com/tensorflow/tensorflow
cd tensorflow
git checkout tags/v0.10.0rc0 # 3cb39956e622b322e43547cf2b6e337020643f21
patch -p0 < ../../tensorflow.v0.10.0rc0.patch

Configure and build

PYTHON_BIN_PATH=$(pwd)/../bin/python TF_NEED_GCP=0 TF_NEED_CUDA=0 ./configure
bazel build -c opt --local_resources 1536,0.5,1.0 --verbose_failures //tensorflow/tools/pip_package:build_pip_package
bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
cd ..

I didn't check without 2GB swap. But I think it works without swap.

Finally install my build package and run example.

pip install /tmp/tensorflow_pkg/tensorflow-0.10.0rc0-py3-none-any.whl
time python lib/python3.5/site-packages/tensorflow/models/image/mnist/convolutional.py

Performance comparison

I ran once and took about 110 minutes on ODROID-C2. Then it consumes power 5 Watt during run. (Idle 2 Watt)

Step 8500 (epoch 9.89), 767.3 ms
Minibatch loss: 1.604, learning rate: 0.006302
Minibatch error: 0.0%
Validation error: 0.9%
Test error: 0.8%

real 110m40.107s
user 394m27.710s
sys  1m8.660s

And I ran with official package on QNAP TS-453A (Ubuntu16.04 LXC) which had Intel N3150 (Quad 1.6GHz Atom Braswell)

$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.10.0rc0-cp35-cp35m-linux_x86_64.whl
$ pip3 install --upgrade $TF_BINARY_URL
$ time python lib/python3.5/site-packages/tensorflow/models/image/mnist/convolutional.py
Step 8500 (epoch 9.89), 516.9 ms
Minibatch loss: 1.605, learning rate: 0.006302
Minibatch error: 0.0%
Validation error: 0.8%
Test error: 0.9%

real 74m30.912s
user 273m53.475s
sys  1m3.437s

And MacBook Pro (Retina, 13-inch, Late 2013) which had Intel Corei5 (Dual 2.4GHz Hyper Threading Haswell)

$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-0.10.0rc0-py3-none-any.whl
$ pip3 install --upgrade $TF_BINARY_URL
$ python lib/python3.5/site-packages/tensorflow/models/image/mnist/convolutional.py
Step 8500 (epoch 9.89), 265.3 ms
Minibatch loss: 1.615, learning rate: 0.006302
Minibatch error: 1.6%
Validation error: 0.9%
Test error: 0.8%

real 38m28.159s
user 114m13.666s
sys  11m24.914s

TITAN X is about x100 faster than ODORID-C2... It is not practical for learning...