CODEWITHSUNDEEP
multithreadingmachine-learningneural-networkcaffeopenblas
magic282
magic282

Reputation: 101

How to use multi CPU cores to train NNs using caffe and OpenBLAS

I am learning deep learning recently and my friend recommended me caffe. After install it with OpenBLAS, I followed the tutorial, MNIST task in the doc. But later I found it was super slow and only one CPU core was working.

The problem is that the servers in my lab don't have GPU, so I have to use CPUs instead.

I Googled this and got some page like this . I tried to export OPENBLAS_NUM_THREADS=8 and export OMP_NUM_THREADS=8. But caffe still used one core.

How can I make caffe use multi CPUs?

Many thanks.

Upvotes: 4

Views: 5767

Answers (3)

dipendra009
dipendra009

Reputation: 299

While building OpenBLAS, you have to set the flag USE_OPENMP = 1 to enable OpenMP support. Next set Caffe to use OpenBLAS in the Makefile.config, please export the number of threads you want to use during runtime by setting up OMP_NUM_THREADS=n where n is the number of threads you want.

Upvotes: 1

GieBrchs
GieBrchs

Reputation: 136

@Karthik. That also works for me. One interesting discovery that I made was that using 4 threads reduces forward/backward pass during the caffe timing test by a factor of 2. However, increasing the thread count to 8 or even 24 results in f/b speed that is less than what I get with OPENBLAS_NUM_THREADS=4. Here are times for a few thread counts (tested on NetworkInNetwork model).

[#threads] [f/b time in ms]
1 223
2 150
4 113
8 125
12 144

For comparison, on a Titan X GPU the f/b pass took 1.87 ms.

Upvotes: 2

Karthik Hegde
Karthik Hegde

Reputation: 183

I found that this method works:

When you build the caffe, in your make command, do use this for 8 cores: make all -j8 and make pycaffe -j8

Also, make sure OPENBLAS_NUM_THREADS=8 is set.

This question has a full script for the same.

Upvotes: -2

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