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138 строки
7.6 KiB
Markdown
138 строки
7.6 KiB
Markdown
---
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title: Installation
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---
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# Installation
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Prior to installing, have a glance through this guide and take note of the details for your platform.
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We install and run Caffe on Ubuntu 14.04 and 12.04, OS X 10.10 / 10.9 / 10.8, and AWS.
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The official Makefile and `Makefile.config` build are complemented by an automatic CMake build from the community.
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- [Prerequisites](#prerequisites)
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- [Compilation](#compilation)
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- [Hardware](#hardware)
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- Platforms: [Ubuntu guide](install_apt.html), [OS X guide](install_osx.html), and [RHEL / CentOS / Fedora guide](install_yum.html)
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When updating Caffe, it's best to `make clean` before re-compiling.
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## Prerequisites
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Caffe has several dependencies:
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* [CUDA](https://developer.nvidia.com/cuda-zone) is required for GPU mode.
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* library version 7.0 and the latest driver version are recommended, but 6.* is fine too
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* 5.5, and 5.0 are compatible but considered legacy
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* [BLAS](http://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) via ATLAS, MKL, or OpenBLAS.
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* [Boost](http://www.boost.org/) >= 1.55
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* `protobuf`, `glog`, `gflags`, `hdf5`
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Optional dependencies:
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* [OpenCV](http://opencv.org/) >= 2.4 including 3.0
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* IO libraries: `lmdb`, `leveldb` (note: leveldb requires `snappy`)
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* cuDNN for GPU acceleration (v3)
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Pycaffe and Matcaffe interfaces have their own natural needs.
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* For Python Caffe: `Python 2.7` or `Python 3.3+`, `numpy (>= 1.7)`, boost-provided `boost.python`
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* For MATLAB Caffe: MATLAB with the `mex` compiler.
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**cuDNN Caffe**: for fastest operation Caffe is accelerated by drop-in integration of [NVIDIA cuDNN](https://developer.nvidia.com/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.
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**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.
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### CUDA and BLAS
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Caffe requires the CUDA `nvcc` compiler to compile its GPU code and CUDA driver for GPU operation.
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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.
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For best performance, Caffe can be accelerated by [NVIDIA cuDNN](https://developer.nvidia.com/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`.
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Caffe requires BLAS as the backend of its matrix and vector computations.
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There are several implementations of this library. The choice is yours:
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* [ATLAS](http://math-atlas.sourceforge.net/): free, open source, and so the default for Caffe.
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* [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.
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1. Install MKL.
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2. Set `BLAS := mkl` in `Makefile.config`
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* [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.
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1. Install OpenBLAS
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2. Set `BLAS := open` in `Makefile.config`
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### Python and/or MATLAB Caffe (optional)
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#### Python
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The main requirements are `numpy` and `boost.python` (provided by boost). `pandas` is useful too and needed for some examples.
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You can install the dependencies with
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for req in $(cat requirements.txt); do pip install $req; done
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but we suggest first installing the [Anaconda](https://store.continuum.io/cshop/anaconda/) Python distribution, which provides most of the necessary packages, as well as the `hdf5` library dependency.
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To import the `caffe` Python module after completing the installation, add the module directory to your `$PYTHONPATH` by `export PYTHONPATH=/path/to/caffe/python:$PYTHONPATH` or the like. You should not import the module in the `caffe/python/caffe` directory!
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*Caffe's Python interface works with Python 2.7. Python 3.3+ should work out of the box without protobuf support. For protobuf support please install protobuf 3.0 alpha (https://developers.google.com/protocol-buffers/). Earlier Pythons are your own adventure.*
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#### MATLAB
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Install MATLAB, and make sure that its `mex` is in your `$PATH`.
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*Caffe's MATLAB interface works with versions 2015a, 2014a/b, 2013a/b, and 2012b.*
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#### Windows
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There is an unofficial Windows port of Caffe at [niuzhiheng/caffe:windows](https://github.com/niuzhiheng/caffe). Thanks [@niuzhiheng](https://github.com/niuzhiheng)!
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## Compilation
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Caffe can be compiled with either Make or CMake. Make is officially supported while CMake is supported by the community.
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### Compilation with Make
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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.
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cp Makefile.config.example Makefile.config
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# Adjust Makefile.config (for example, if using Anaconda Python, or if cuDNN is desired)
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make all
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make test
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make runtest
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- For CPU & GPU accelerated Caffe, no changes are needed.
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- 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.
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- For CPU-only Caffe, uncomment `CPU_ONLY := 1` in `Makefile.config`.
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To compile the Python and MATLAB wrappers do `make pycaffe` and `make matcaffe` respectively.
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Be sure to set your MATLAB and Python paths in `Makefile.config` first!
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**Distribution**: run `make distribute` to create a `distribute` directory with all the Caffe headers, compiled libraries, binaries, etc. needed for distribution to other machines.
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**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).
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Now that you have installed Caffe, check out the [MNIST tutorial](gathered/examples/mnist.html) and the [reference ImageNet model tutorial](gathered/examples/imagenet.html).
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### Compilation with CMake
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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.
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The basic steps are as follows:
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mkdir build
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cd build
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cmake ..
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make all
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make install
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make runtest
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See [PR #1667](https://github.com/BVLC/caffe/pull/1667) for options and details.
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## Hardware
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**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.
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**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.
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Once installed, check your times against our [reference performance numbers](performance_hardware.html) to make sure everything is configured properly.
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Ask hardware questions on the [caffe-users group](https://groups.google.com/forum/#!forum/caffe-users).
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