Awesome
CaffeMex
v2.3:
A multi-GPU & memory-reduced MAT-Caffe on LINUX and WINDOWS
For now it is an unstable version has worked well in plenty of works.
Contributors & Bug report: [Yu Liu] liuyuisanai@gmail.com [Hongyang Li] yangli@ee.cuhk.edu.hk
What's New in Version 2.3?
1.Reduce 30% GPU memory usage
2.More stable for detection task (fix 0-count issue in a few layers)
3.Add center loss layer
Installation for LINUX
1.cp Makefile.config.example Makefile.config and customize your own config.
2.make -j && make matcaffe
Installation for WINDOWS
1.Find 'windows/' and you will know. By merging R-FCN https://github.com/daijifeng001/R-FCN
Feature
1.Support both windows and linux platforms
2.Reduce 30% GPU memory usage (by merging Yuanjun Xiong's caffe http://yjxiong.me/)
3.Lastest Matlab interface
4.Compatible with original caffe
Running on single machine with single/multiple GPU(s)
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Installation this caffe
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Interfaces: Different interfaces between our caffe and origin's:
% reset all solvers and nets
Caffe.reset_all()
% init a solver and set it handle in my_solver
my_solver = Caffe.get_solver('solver_proto_path, gpu_id')
% make a snapshot for my_solver
my_solver.snapshot('snapshot_path_and_name')
% restore a snapshot
my_solver.use_caffemodel('snapshot_path_and_name')
% set phase. It is useful if you use batch norm layer
my_solver.set_phase('train/test')
% set input data, format: cellA{cellB{matrix}}
% data{i}{j} means the j-th input on card i
my_solver.set_input_data(data)
% forward after set input data, usually used for test
my_solver.forward_prefilled()
% you can also replace the two above by this:
my_solver.forward(data)
% train one or more step after you set input data
my_solver.step(1) or step(N)
% get outputs, usually used for get losses
my_solver.get_output()
% get specified net handle:
my_solver.nets{k}
The other interfaces are same as origin caffe.
Running on cluster
1.Copy your datas and codes to cluster's shared disk (such as /mnt/lustre)
2.Write following shell script and save on your management node:
#!/usr/bin/env sh
MV2_USE_CUDA=1 MV2_ENABLE_AFFINITY=0 MV2_SMP_USE_CMA=0 srun --gres=gpu:4 -n1 --ntasks-per-node=1 --kill-on-bad-exit=1 matlab -nodisplay -r "run /FULL/PATH/TO/YOUR/MATLAB/SCRIPT.m"
3.Run the shell script.