8 Prior to installing, it is best to read through this guide and take note of the details for your platform.
9 We have installed Caffe on Ubuntu 14.04, Ubuntu 12.04, OS X 10.9, and OS X 10.8.
11 - [Prerequisites](#prerequisites)
12 - [Compilation](#compilation)
13 - [Hardware questions](#hardware_questions)
17 Caffe depends on several software packages.
19 * [CUDA](https://developer.nvidia.com/cuda-zone) library version 6.0, 5.5, or 5.0 and the latest driver version for CUDA 6 or 319.* for CUDA 5 (and NOT 331.*)
20 * [BLAS](http://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) (provided via ATLAS, MKL, or OpenBLAS).
21 * [OpenCV](http://opencv.org/).
22 * [Boost](http://www.boost.org/) (>= 1.55, although only 1.55 is tested)
23 * `glog`, `gflags`, `protobuf`, `leveldb`, `snappy`, `hdf5`, `lmdb`
24 * For the Python wrapper
25 * `Python 2.7`, `numpy (>= 1.7)`, boost-provided `boost.python`
26 * For the MATLAB wrapper
27 * MATLAB with the `mex` compiler.
29 **CPU-only Caffe**: for cold-brewed CPU-only Caffe uncomment the `CPU_ONLY := 1` in `Makefile.config` to configure and build Caffe without CUDA. This is helpful for cloud or cluster deployment.
33 Caffe requires the CUDA `nvcc` compiler to compile its GPU code and CUDA driver for GPU operation.
34 To install CUDA, go to the [NVIDIA CUDA website](https://developer.nvidia.com/cuda-downloads) 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 Install the library and the latest standalone driver separately; the driver bundled with the library is usually out-of-date.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.
36 Caffe requires BLAS as the backend of its matrix and vector computations.
37 There are several implementations of this library.
40 * [ATLAS](http://math-atlas.sourceforge.net/): free, open source, and so the default for Caffe.
41 + Ubuntu: `sudo apt-get install libatlas-base-dev`
42 + CentOS/RHEL: `sudo yum install libatlas-devel`
43 + OS X: already installed as the [Accelerate / vecLib Framework](https://developer.apple.com/library/mac/documentation/Darwin/Reference/ManPages/man7/Accelerate.7.html).
44 * [Intel MKL](http://software.intel.com/en-us/intel-mkl): commercial and optimized for Intel CPUs, with a free trial and [student](http://software.intel.com/en-us/intel-education-offerings) licenses.
46 2. Set `BLAS := mkl` in `Makefile.config`
47 * [OpenBLAS](http://www.openblas.net/): free and open source; this optimized and parallel BLAS could require more effort to install, although it might offer a speedup.
49 2. Set `BLAS := open` in `Makefile.config`
51 ### Python and/or MATLAB wrappers (optional)
55 The main requirements are `numpy` and `boost.python` (provided by boost). `pandas` is useful too and needed for some examples.
57 You can install the dependencies with
59 pip install -r /path/to/caffe/python/requirements.txt
61 but we highly recommend first installing the [Anaconda](https://store.continuum.io/cshop/anaconda/) Python distribution, which provides most of the necessary packages, as well as the `hdf5` library dependency.
63 For **Ubuntu**, if you use the default Python you will need to `apt-get install` the `python-dev` package to have the Python headers for building the wrapper.
65 For **OS X**, Anaconda is the preferred Python. If you decide against it, please use Homebrew -- but beware of potential linking errors!
67 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!
69 *Caffe's Python interface works with Python 2.7. Python 3 or earlier Pythons are your own adventure.*
73 Install MATLAB, and make sure that its `mex` is in your `$PATH`.
75 *Caffe's MATLAB interface works with versions 2012b, 2013a/b, and 2014a.*
77 ### The rest of the dependencies
81 On **Ubuntu**, most of the dependencies can be installed with
83 sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev
85 And on **CentOS / RHEL**, you can install via yum with
87 sudo yum install protobuf-devel leveldb-devel snappy-devel opencv-devel boost-devel hdf5-devel
89 and for **Ubuntu 14.04** the rest of the dependencies can be installed with
91 sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev protobuf-compiler
93 For **Ubuntu 12.04 and CentOS / RHEL** the only exceptions to package installation are the Google flags library, Google logging library, and LMDB. To install these, do:
96 wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz
97 tar zxvf glog-0.3.3.tar.gz
102 wget https://github.com/schuhschuh/gflags/archive/master.zip
105 mkdir build && cd build
106 export CXXFLAGS="-fPIC" && cmake .. && make VERBOSE=1
109 git clone git://gitorious.org/mdb/mdb.git
110 cd mdb/libraries/liblmdb
113 Note that glog does not compile with the most recent gflags version (2.1), so before that is resolved you will need to build with glog first.
117 On **OS X**, we highly recommend using the [Homebrew](http://brew.sh/) package manager, and ideally starting from a clean install of the OS (or from a wiped `/usr/local`) to avoid conflicts.
118 In the following, we assume that you're using Anaconda Python and Homebrew.
120 To install the OpenCV dependency, we'll need to provide an additional source for Homebrew:
122 brew tap homebrew/science
124 If using Anaconda Python, a modification is required to the OpenCV formula.
125 Do `brew edit opencv` and change the lines that look like the two lines below to exactly the two lines below.
127 -DPYTHON_LIBRARY=#{py_prefix}/lib/libpython2.7.dylib
128 -DPYTHON_INCLUDE_DIR=#{py_prefix}/include/python2.7
130 **NOTE**: We find that everything compiles successfully if `$LD_LIBRARY_PATH` is not set at all, and `$DYLD_FALLBACK_LIBRARY_PATH` is set to to provide CUDA, Python, and other relevant libraries (e.g. `/usr/local/cuda/lib:$HOME/anaconda/lib:/usr/local/lib:/usr/lib`).
131 In other `ENV` settings, things may not work as expected.
133 #### 10.8-specific Instructions
135 Simply run the following:
137 brew install --build-from-source --with-python boost
138 brew install --with-python protobuf
139 for x in snappy leveldb gflags glog szip lmdb homebrew/science/opencv; do brew install $x; done
141 Building boost from source is needed to link against your local Python (exceptions might be raised during some OS X installs, but **ignore** these and continue). If you do not need the Python wrapper, simply doing `brew install boost` is fine.
143 **Note** that the HDF5 dependency is provided by Anaconda Python in this case.
144 If you're not using Anaconda, include `hdf5` in the list above.
146 #### 10.9-specific Instructions
148 In OS X 10.9, clang++ is the default C++ compiler and uses `libc++` as the standard library.
149 However, NVIDIA CUDA (even version 6.0) currently links only with `libstdc++`.
150 This makes it necessary to change the compilation settings for each of the dependencies.
152 We do this by modifying the Homebrew formulae before installing any packages.
153 Make sure that Homebrew doesn't install any software dependencies in the background; all packages must be linked to `libstdc++`.
155 The prerequisite Homebrew formulae are
157 boost snappy leveldb protobuf gflags glog szip lmdb homebrew/science/opencv
159 For each of these formulas, `brew edit FORMULA`, and add the ENV definitions as shown:
163 ENV.append "CXXFLAGS", "-stdlib=libstdc++"
164 ENV.append "CFLAGS", "-stdlib=libstdc++"
165 ENV.append "LDFLAGS", "-stdlib=libstdc++ -lstdc++"
166 # The following is necessary because libtool likes to strip LDFLAGS:
167 ENV["CXX"] = "/usr/bin/clang++ -stdlib=libstdc++"
170 To edit the formulae in turn, run
172 for x in snappy leveldb protobuf gflags glog szip boost lmdb homebrew/science/opencv; do brew edit $x; done
176 for x in snappy leveldb protobuf gflags glog szip lmdb homebrew/science/opencv; do brew uninstall $x; brew install --build-from-source --fresh -vd $x; done
177 brew install --build-from-source --with-python --fresh -vd boost
179 **Note** that `brew install --build-from-source --fresh -vd boost` is fine if you do not need the Caffe Python wrapper.
181 **Note** that the HDF5 dependency is provided by Anaconda Python in this case.
182 If you're not using Anaconda, include `hdf5` in the list above.
184 **Note** that in order to build the caffe python wrappers you must install boost using the --with-python option:
186 brew install --build-from-source --with-python --fresh -vd boost
190 There is an unofficial Windows port of Caffe at [niuzhiheng/caffe:windows](https://github.com/niuzhiheng/caffe). Thanks [@niuzhiheng](https://github.com/niuzhiheng)!
194 Now that you have the prerequisites, edit your `Makefile.config` to change the paths for your setup.
195 The defaults should work, but uncomment the relevant lines if using Anaconda Python.
197 cp Makefile.config.example Makefile.config
198 # Adjust Makefile.config (for example, if using Anaconda Python)
203 If there is no GPU in your machine, you should switch to CPU-only Caffe by uncommenting `CPU_ONLY := 1` in `Makefile.config`.
205 To compile the Python and MATLAB wrappers do `make pycaffe` and `make matcaffe` respectively.
206 Be sure to set your MATLAB and Python paths in `Makefile.config` first!
208 *Distribution*: run `make distribute` to create a `distribute` directory with all the Caffe headers, compiled libraries, binaries, etc. needed for distribution to other machines.
210 *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).
212 Now that you have installed Caffe, check out the [MNIST demo](mnist.html) and the pretrained [ImageNet example](imagenet.html).
214 ## Hardware Questions
216 **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 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.
218 **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.
220 Once installed, check your times against our [reference performance numbers](performance_hardware.html) to make sure everything is configured properly.
222 Refer to the project's issue tracker for [hardware/compatibility](https://github.com/BVLC/caffe/issues?labels=hardware%2Fcompatibility&page=1&state=open).