Building and installing Caffe2

Caffe2 can be built and installed from source code quite easily. Installing Caffe2 from source gives us more flexibility and control over our application setup. The build and install process has four stages:

1. Installing dependencies
2. Installing acceleration libraries

3. Building Caffe2
4. Installing Caffe2

Installing dependencies

We first need to install packages that Caffe2 is dependent on, as well as the tools and libraries required to build it.

1. First, obtain information about the newest versions of Ubuntu packages by querying their online repositories using the apt-get tool:

$ sudo apt-get update

2. Next, using the apt-get tool, install the libraries that are required to build Caffe2, and that Caffe2 requires for its operation:

$ sudo apt-get install -y --no-install-recommends \
      build-essential \
      cmake \
      git \
      libgflags2 \
      libgoogle-glog-dev \
      libgtest-dev \
      libiomp-dev \
      libleveldb-dev \
      liblmdb-dev \
      libopencv-dev \
      libopenmpi-dev \
      libsnappy-dev \
      libprotobuf-dev \
      openmpi-bin \
      openmpi-doc \
      protobuf-compiler \
      python-dev \
      python-pip                          

These packages include tools required to download Caffe2 source code (Git) and to build Caffe2 (build-essential, cmake, and python-dev). The rest are libraries that Caffe2 is dependent on, including Google Flags (libgflags2), Google Log (libgoogle-glog-dev), Google Test (libgtest-dev), LevelDB (libleveldb-dev), LMDB (liblmdb-dev), OpenCV (libopencv-dev), OpenMP (libiomp-dev), OpenMPI (openmpi-bin and openmpi-doc), Protobuf (libprotobuf-dev and protobuf-compiler), and Snappy (libsnappy-dev).

3. Finally, install the Python Pip tool and use it to install other Python libraries such as NumPy and Protobuf Python APIs that are useful when working with Python:

$ sudo apt-get install -y --no-install-recommends python-pip                   

$ pip install --user \
      future \
      numpy \
      protobuf

Installing acceleration libraries

Using Caffe2 to train DL networks and using them for inference involves a lot of math computation. Using acceleration libraries of math routines and deep learning primitives helps Caffe2 users by speeding up training and inference tasks. Vendors of CPUs and GPUs typically offer such libraries, and Caffe2 has support to use such libraries if they are available on your system.

Intel Math Kernel Library (MKL) is key to faster training and inference on Intel CPUs. This library is free for personal and community use. It can be downloaded by registering here: https://software.seek.intel.com/performance-libraries. Installation involves uncompressing the downloaded package and running the install.sh installer script as a superuser. The library files are installed by default to the /opt/intel directory. The Caffe2 build step, described in the next section, finds and uses the BLAS and LAPACK routines of MKL automatically, if MKL was installed at the default directory.

CUDA and CUDA Deep Neural Network (cuDNN) libraries are essential for faster training and inference on NVIDIA GPUs. CUDA is free to download after registering here: https://developer.nvidia.com/cuda-downloads. cuDNN can be downloaded from here: https://developer.nvidia.com/cudnn. Note that you need to have a modern NVIDIA GPU and an NVIDIA GPU driver already installed. As an alternative to the GPU driver, you could use the driver that is installed along with CUDA. Files of the CUDA and cuDNN libraries are typically installed in the /usr/local/cuda directory on Linux. The Caffe2 build step, described in the next section, finds and uses CUDA and cuDNN automatically if installed in the default directory.

Building Caffe2

Using Git, we can clone the Git repository containing Caffe2 source code and all the submodules it requires:

$ git clone --recursive https://github.com/pytorch/pytorch.git && cd pytorch

$ git submodule update --init

Notice how the Caffe2 source code now exists in a subdirectory inside the PyTorch source repository. This is because of Facebook's cohabitation plan for these two popular DL frameworks as it endeavors to merge the best features of both frameworks over a period of time.

Caffe2 uses CMake as its build system. CMake enables Caffe2 to be easily built for a wide variety of compilers and operating systems.

To build Caffe2 source code using CMake, we first create a build directory and invoke CMake from within it:

$ mkdir build
$ cd build
$ cmake ..

CMake checks available compilers, operating systems, libraries, and packages, and figures out which Caffe2 features to enable and compilation options to use. These options can be seen listed in the CMakeLists.txt file present at the root directory. Options are listed in the form of option(USE_FOOBAR "Use Foobar library" OFF). You can enable or disable those options by setting them to ON or OFF in CMakeLists.txt.

These options can also be configured when invoking CMake. For example, if your Intel CPU has support for AVX/AVX2/FMA, and you would wish to use those features to speed up Caffe2 operations, then enable the USE_NATIVE_ARCH option as follows:

$ cmake -DUSE_NATIVE_ARCH=ON ..

Installing Caffe2

CMake produces a Makefile file at the end. We can build Caffe2 and install it on our system using the following command:

$ sudo make install

This step involves building a large number of CUDA files, which can be very slow. It is recommended to use the parallel execution feature of make to use all the cores of your CPU for a faster build. We can do this by using the following command:

$ sudo make -j install

Using the make install method to build and install makes it difficult to update or uninstall Caffe2 later.
Instead, I prefer to create a Debian package of Caffe2 and install it. That way, I can uninstall or update it conveniently. We can do this using the checkinstall tool.

To install checkinstall, and to use it to build and install Caffe2, use the following commands:

$ sudo apt-get install checkinstall
$ sudo checkinstall --pkgname caffe2

This command also produces a Debian .deb package file that you can use to install on other computers or share with others. For example, on my computer, this command produced a file named caffe2_20181207-1_amd64.deb.

If you need a faster build, use the parallel execution feature of make along with checkinstall:

$ sudo checkinstall --pkgname caffe2 make -j install

If you need to uninstall Caffe2 in the future, you can now do that easily using the following command:

$ sudo dpkg -r caffe2