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Welcome to the OpenCL-caffe wiki!
#OpenCL caffe Wiki
- a. System: Ubuntu >=12.04 with AMD's GPU
- b. OpenCL runtime environment
- c. [clBLAS] (https://github.com/clMathLibraries/clBLAS)
##Caffe dependency The OpenCL caffe project is based on Berkeley's deep learning framework Caffe, so first need to install the dependency by following the Caffe's installation: http://caffe.berkeleyvision.org/installation.html, pay attention that CUDA SDK is also required because some *.cu files to be cleaned up are still in the project. And more information about Caffe can be found in the homepage;
Go to the root directory of OpenCL-Caffe (CAFFEROOT in the following)
mkdir build
cd build
cmake ..
make
make runtest
We redirect all log files generated by caffe to a log subdir. Under the caffe directory, mkdir log. This will create the log dir for all the generated log files. To give an example, to check loss of the training, grep -ni loss log/caffe.INFO
- CIFAR data take cifar10 (http://www.cs.toronto.edu/~kriz/cifar.html) for example. cd data/cifar10, then run ./get_cifar10.sh. After the download is finished, cd examples/cifar10, run ./create_cifar10.sh;
- ImageNet data
Under the directory CAFFEROOT/ choose the network you want to train, eg ./examples/imagenet/train_alexnet.sh
./build/tools/caffe time -model=models/bvlc_alexnet/deploy.prototxt -gpu 0 even though our code will print out timing info, but it is not accurate in this test case. you should check out log/caffe.INFO for the timing.