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Install tensorflow's Python interface

We follow the virtual environment approach to install the tensorflow's Python interface. The full instruction can be found on the tensorflow's official website. Now we assume that the Python interface will be installed to virtual environment directory $tensorflow_venv

virtualenv --system-site-packages -p python3 $tensorflow_venv
source $tensorflow_venv/bin/activate
pip install --upgrade pip
pip install --upgrade tensorflow==1.12.0

To verify the installation,

python -c "import tensorflow as tf; tf.enable_eager_execution(); print(tf.reduce_sum(tf.random_normal([1000, 1000])))"

One should remember to activate the virtual environment every time he/she runs deepmd training program dp_train.

Install tensorflow's C++ interface

The tensorflow's C++ interface will be compiled from the source code. Firstly one installs bazel. It is highly recommended that the bazel version 0.15.0 is used. A full instruction of bazel installation can be found here.

cd /some/workspace
wget https://github.com/bazelbuild/bazel/releases/download/0.15.0/bazel-0.15.0-dist.zip
mkdir bazel-0.15.0
cd bazel-0.15.0
unzip ../bazel-0.15.0-dist.zip
./compile.sh
export PATH=`pwd`/output:$PATH

Firstly get the source code of the tensorflow

cd /some/workspace
git clone https://github.com/tensorflow/tensorflow tensorflow -b v1.12.0 --depth=1
cd tensorflow

DeePMD-kit is compiled by cmake, so we need to compile and integrate tensorflow with cmake projects. The rest of this section basically follows the instruction provided by Tuatini. Now execute

./configure

You will answer a list of questions that help configure the building of tensorflow. It is recommended to build for Python3. You may want to answer the question like this (please replace $tensorflow_venv by the virtual environment directory):

Please specify the location of python. [Default is $tensorflow_venv/bin/python]:

The library path for Python should be set accordingly.

Now build the shared library of tensorflow:

bazel build -c opt --verbose_failures //tensorflow:libtensorflow_cc.so

You may want to add options --copt=-msse4.2, --copt=-mavx, --copt=-mavx2 and --copt=-mfma to enable SSE4.2, AVX, AVX2 and FMA SIMD accelerations, respectively. It is noted that these options should be chosen according to the CPU architecture. If the RAM becomes an issue of your machine, you may limit the RAM usage by using --local_resources 2048,.5,1.0.

Now I assume you want to install tensorflow in directory $tensorflow_root. Create the directory if it does not exists

mkdir -p $tensorflow_root

Before moving on, we need to compile the dependencies of tensorflow, including Protobuf, Eigen, nsync and absl. Firstly, protobuf

mkdir /tmp/proto
sed -i 's;PROTOBUF_URL=.*;PROTOBUF_URL=\"https://mirror.bazel.build/github.com/google/protobuf/archive/v3.6.0.tar.gz\";g' tensorflow/contrib/makefile/download_dependencies.sh
tensorflow/contrib/makefile/download_dependencies.sh
cd tensorflow/contrib/makefile/downloads/protobuf/
./autogen.sh
./configure --prefix=/tmp/proto/
make
make install

Then Eigen

mkdir /tmp/eigen
cd ../eigen
mkdir build_dir
cd build_dir
cmake -DCMAKE_INSTALL_PREFIX=/tmp/eigen/ ../
make install

nsync

mkdir /tmp/nsync
cd ../../nsync
mkdir build_dir
cd build_dir
cmake -DCMAKE_INSTALL_PREFIX=/tmp/nsync/ ../
make
make install

And absl

cd ../../absl
bazel build
mkdir -p $tensorflow_root/include/
rsync -avzh --include '*/' --include '*.h' --exclude '*' absl $tensorflow_root/include/
cd ../../../../..

Now, copy the libraries to the tensorflow's installation directory:

mkdir $tensorflow_root/lib
cp bazel-bin/tensorflow/libtensorflow_cc.so $tensorflow_root/lib/
cp bazel-bin/tensorflow/libtensorflow_framework.so $tensorflow_root/lib/
cp /tmp/proto/lib/libprotobuf.a $tensorflow_root/lib/
cp /tmp/nsync/lib64/libnsync.a $tensorflow_root/lib/

Then copy the headers

mkdir -p $tensorflow_root/include/tensorflow
cp -r bazel-genfiles/* $tensorflow_root/include/
cp -r tensorflow/cc $tensorflow_root/include/tensorflow
cp -r tensorflow/core $tensorflow_root/include/tensorflow
cp -r third_party $tensorflow_root/include
cp -r /tmp/proto/include/* $tensorflow_root/include
cp -r /tmp/eigen/include/eigen3/* $tensorflow_root/include
cp -r /tmp/nsync/include/*h $tensorflow_root/include

Now clean up the source files in the header directories:

cd $tensorflow_root/include
find . -name "*.cc" -type f -delete

The temporary installation directories for the dependencies can be removed:

rm -fr /tmp/proto /tmp/eigen /tmp/nsync