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caffe/docs/installation.md

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Installation

Installation

Prior to installing, have a glance through this guide and take note of the details for your platform. We install and run Caffe on Ubuntu 14.04 and 12.04, OS X 10.10 / 10.9 / 10.8, and AWS. The official Makefile and Makefile.config build are complemented by an automatic CMake build from the community.

When updating Caffe, it's best to make clean before re-compiling.

Prerequisites

Caffe has several dependencies:

  • CUDA is required for GPU mode.
    • library version 7.0 and the latest driver version are recommended, but 6.* is fine too
    • 5.5, and 5.0 are compatible but considered legacy
  • BLAS via ATLAS, MKL, or OpenBLAS.
  • Boost >= 1.55
  • protobuf, glog, gflags, hdf5

Optional dependencies:

  • OpenCV >= 2.4 including 3.0
  • IO libraries: lmdb, leveldb (note: leveldb requires snappy)
  • cuDNN for GPU acceleration (v3)

Pycaffe and Matcaffe interfaces have their own natural needs.

  • For Python Caffe: Python 2.7 or Python 3.3+, numpy (>= 1.7), boost-provided boost.python
  • For MATLAB Caffe: MATLAB with the mex compiler.

cuDNN Caffe: for fastest operation Caffe is accelerated by drop-in integration of NVIDIA cuDNN. To speed up your Caffe models, install cuDNN then uncomment the USE_CUDNN := 1 flag in Makefile.config when installing Caffe. Acceleration is automatic. The current version is cuDNN v3; older versions are supported in older Caffe.

CPU-only Caffe: for cold-brewed CPU-only Caffe uncomment the CPU_ONLY := 1 flag in Makefile.config to configure and build Caffe without CUDA. This is helpful for cloud or cluster deployment.

CUDA and BLAS

Caffe requires the CUDA nvcc compiler to compile its GPU code and CUDA driver for GPU operation. To install CUDA, go to the NVIDIA CUDA website and follow installation instructions there. Install the library and the latest standalone driver separately; the driver bundled with the library is usually out-of-date. Warning! The 331.* CUDA driver series has a critical performance issue: do not use it.

For best performance, Caffe can be accelerated by NVIDIA cuDNN. Register for free at the cuDNN site, install it, then continue with these installation instructions. To compile with cuDNN set the USE_CUDNN := 1 flag set in your Makefile.config.

Caffe requires BLAS as the backend of its matrix and vector computations. There are several implementations of this library. The choice is yours:

  • ATLAS: free, open source, and so the default for Caffe.
  • Intel MKL: commercial and optimized for Intel CPUs, with a free trial and student licenses.
    1. Install MKL.
    2. Set up MKL environment (Details: Linux, OS X). Example: source /opt/intel/mkl/bin/mklvars.sh intel64
    3. Set BLAS := mkl in Makefile.config
  • OpenBLAS: free and open source; this optimized and parallel BLAS could require more effort to install, although it might offer a speedup.
    1. Install OpenBLAS
    2. Set BLAS := open in Makefile.config

Python and/or MATLAB Caffe (optional)

Python

The main requirements are numpy and boost.python (provided by boost). pandas is useful too and needed for some examples.

You can install the dependencies with

for req in $(cat requirements.txt); do pip install $req; done

but we suggest first installing the Anaconda Python distribution, which provides most of the necessary packages, as well as the hdf5 library dependency.

To import the caffe Python module after completing the installation, add the module directory to your $PYTHONPATH by export PYTHONPATH=/path/to/caffe/python:$PYTHONPATH or the like. You should not import the module in the caffe/python/caffe directory!

Caffe's Python interface works with Python 2.7. Python 3.3+ should work out of the box without protobuf support. For protobuf support please install protobuf 3.0 alpha (https://developers.google.com/protocol-buffers/). Earlier Pythons are your own adventure.

MATLAB

Install MATLAB, and make sure that its mex is in your $PATH.

Caffe's MATLAB interface works with versions 2015a, 2014a/b, 2013a/b, and 2012b.

Windows

There is an unofficial Windows port of Caffe at niuzhiheng/caffe:windows. Thanks @niuzhiheng!

Compilation

Caffe can be compiled with either Make or CMake. Make is officially supported while CMake is supported by the community.

Compilation with Make

Configure the build by copying and modifying the example Makefile.config for your setup. The defaults should work, but uncomment the relevant lines if using Anaconda Python.

cp Makefile.config.example Makefile.config
# Adjust Makefile.config (for example, if using Anaconda Python, or if cuDNN is desired)
make all
make test
make runtest
  • For CPU & GPU accelerated Caffe, no changes are needed.
  • For cuDNN acceleration using NVIDIA's proprietary cuDNN software, uncomment the USE_CUDNN := 1 switch in Makefile.config. cuDNN is sometimes but not always faster than Caffe's GPU acceleration.
  • For CPU-only Caffe, uncomment CPU_ONLY := 1 in Makefile.config.

To compile the Python and MATLAB wrappers do make pycaffe and make matcaffe respectively. Be sure to set your MATLAB and Python paths in Makefile.config first!

Distribution: run make distribute to create a distribute directory with all the Caffe headers, compiled libraries, binaries, etc. needed for distribution to other machines.

Speed: for a faster build, compile in parallel by doing make all -j8 where 8 is the number of parallel threads for compilation (a good choice for the number of threads is the number of cores in your machine).

Now that you have installed Caffe, check out the MNIST tutorial and the reference ImageNet model tutorial.

Compilation with CMake

In lieu of manually editing Makefile.config to configure the build, Caffe offers an unofficial CMake build thanks to @Nerei, @akosiorek, and other members of the community. It requires CMake version >= 2.8.7. The basic steps are as follows:

mkdir build
cd build
cmake ..
make all
make install
make runtest

See PR #1667 for options and details.

Hardware

Laboratory Tested Hardware: Berkeley Vision runs Caffe with K40s, K20s, and Titans including models at ImageNet/ILSVRC scale. We also run on GTX series cards (980s and 770s) and GPU-equipped MacBook Pros. We have not encountered any trouble in-house with devices with CUDA capability >= 3.0. All reported hardware issues thus-far have been due to GPU configuration, overheating, and the like.

CUDA compute capability: devices with compute capability <= 2.0 may have to reduce CUDA thread numbers and batch sizes due to hardware constraints. Your mileage may vary.

Once installed, check your times against our reference performance numbers to make sure everything is configured properly.

Ask hardware questions on the caffe-users group.