Torch Multi-GPU support #480
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Nice work! Everything seems to be working for me. I'm trying out the NCCL integration now... One things I've noticed is that we should update the Torch warning box for creating new classification/generic models. |
| % make CUDA_HOME=/usr/local/cuda test | ||
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| > NOTE: if the above command fails due to missing libraries you may explicitely point the makefile to the location of your NVidia driver. For example: |
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Typo - should be "explicitly"
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Thanks, I always make this spelling mistake!
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This is working for me both with and without NCCL. Looks good except for the little doc nitpicks above. |
This commit enables data parallelism, i.e. train batches are evenly spread across selected GPUs. Strong scaling is applied by default, i.e. batch size is unchanged and each GPU receives its share during training. For better performance, installation of the NCCL module is recommended. On a 2xTitanX machine (with NCCL): - GoogleNet: 51m4s (1 GPU) -> 34m3s (2 GPUs) - Alexnet: 12m31s (1 GPU) -> 10m38s (2 GPUs) - LeNet*: 53s (1GPU) -> 1m30s (2 GPUs) * the overhead of inter-GPU communication seems to outweigh the compute gain on LeNet
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Close #138
This commit enables data parallelism, i.e. train
batches are evenly spread across selected GPUs.
Strong scaling is applied by default, i.e. batch
size is unchanged and each GPU receives its share
during training.
For better performance, installation of the NCCL
module is recommended.
On a 2xTitanX machine (with NCCL):
outweigh the compute gain on LeNet