Tutorial: Getting Started with Distributed Deep Learning with Caffe on Windows
What is Caffe?
Windows 8.1 on 64bit
Visual Studio 2013 Community
GeForce GT 750M
1. Check for Compatibility
Windows Server 2008
Windows Server 2012.(If you are using Windows 8, upgrade through here: http://windows.microsoft.com/en-ca/windows-8/update-from-windows-8-tutorial)
Make sure your GPU is supported by CUDA: https://developer.nvidia.com/cuda-gpus
Anything with compute capability of >=3.0 should be good.
If you do not have a compatible GPU, you can still use Caffe but it will be magnitudes slower than with a GPU and skip part 2.
Make sure you have a compatible Visual Studios for CUDA support:
Visual Studio 2013
Visual Studio 2013 Community (Download Visual Studio 2013 Community Edition Free)
Visual Studio 2012
Visual Studio 2010
More nVidia documentation at:
2. Install CUDA
3. Install Caffe
Remember to add caffe-windows/3rdparty/bin to your PATH
Open caffe-windows/buildVS2013/MainBuilder.sln in Visual Studio
If you don’t have a compatible GPU, open caffe-windows/build_cpu_only/MainBuilder.sln
Set the GPU compatible mode:
Right click the caffe project and click properties
In the left menu, go to Configuration Properties -> Cuda C/C++ -> Device
In the Code Generation key, modify the compute capabilities to your GPU’s (such as compute_30,sm_30; etc)
Build the solution in release mode
Right click the solution and click Build Solution
(It’s OK if matcafe and pycafe fail)
Download the mnist leveldb from http://pan.baidu.com/s/1mgl9ndu
Extract the folders to caffe-windows/examples/mnist
You should get some output similar to the following when you finish:
I0112 00:06:37.180341 45040 solver.cpp:326] Iteration 10000, loss = 0.00428135
I0112 00:06:37.181342 45040 solver.cpp:346] Iteration 10000, Testing net (#0)
I0112 00:06:51.726634 45040 solver.cpp:414] Test net output #0: accuracy = 0
I0112 00:06:51.726634 45040 solver.cpp:414] Test net output #1: loss = 0.027
0199 (* 1 = 0.0270199 loss)
I0112 00:06:51.726634 45040 solver.cpp:331] Optimization Done.
I0112 00:06:51.726634 45040 caffe.cpp:215] Optimization Done.
Full instructions can be found on the readme of https://github.com/happynear/caffe-windows
Start Time: 23:25:19.38
Finish Time: 23:28:37.62
Finish Time: 0:06:51.91As you can see, even a low-end GPU can train a magnitude faster than a CPU.