Python CNTK在4 GPU中1比特SGD与正常SGD的速度比较

我使用Ubuntu（python 3.4）在Azure NC24 GPU虚拟机中安装了CNTK的版本。该机器有4个NVIDIA K80 GPU。构建信息：

            Build type: release
            Build target: GPU
            With 1bit-SGD: yes
            With ASGD: yes
            Math lib: mkl
            CUDA_PATH: /usr/local/cuda-8.0
            CUB_PATH: /usr/local/cub-1.4.1
            CUDNN_PATH: /usr/local
            Build Branch: HEAD
            Build SHA1: 8e8b5ff92eff4647be5d41a5a515956907567126
            Built by Source/CNTK/buildinfo.h$$0 on bbdadbf3455d
            Build Path: /home/philly/jenkins/workspace/CNTK-Build-Linux

我正在分布式模式下运行CIFAR示例：

mpiexec -n 4 python TrainResNet_CIFAR10_Distributed.py -n resnet20 -q 32

Finished Epoch [1]: [Training] loss = 1.675002 * 50176, metric = 62.5% * 50176 112.019s (447.9 samples per second)
Finished Epoch [1]: [Training] loss = 1.675002 * 50176, metric = 62.5% * 50176 112.019s (447.9 samples per second)
Finished Epoch [1]: [Training] loss = 1.675002 * 50176, metric = 62.5% * 50176 112.018s (447.9 samples per second)
Finished Epoch [1]: [Training] loss = 1.675002 * 50176, metric = 62.5% * 50176 112.019s (447.9 samples per second)
Finished Epoch [2]: [Training] loss = 1.247423 * 50176, metric = 45.4% * 50176 8.210s (6111.3 samples per second)
Finished Epoch [2]: [Training] loss = 1.247423 * 50176, metric = 45.4% * 50176 8.210s (6111.4 samples per second)
Finished Epoch [2]: [Training] loss = 1.247423 * 50176, metric = 45.4% * 50176 8.210s (6111.8 samples per second)
Finished Epoch [2]: [Training] loss = 1.247423 * 50176, metric = 45.4% * 50176 8.210s (6111.6 samples per second)
...
...
Finished Epoch [160]: [Training] loss = 0.037745 * 49664, metric = 1.2% * 49664 7.883s (6300.4 samples per second)
Finished Epoch [160]: [Training] loss = 0.037745 * 49664, metric = 1.2% * 49664 7.883s (6299.7 samples per second)
Finished Epoch [160]: [Training] loss = 0.037745 * 49664, metric = 1.2% * 49664 7.884s (6299.7 samples per second)
Finished Epoch [160]: [Training] loss = 0.037745 * 49664, metric = 1.2% * 49664 7.884s (6299.2 samples per second)

但是，当我使用1比特SGD运行时，我得到：

mpiexec -n 4 python TrainResNet_CIFAR10_Distributed.py -n resnet20 -q 1 -a 50000

...
Finished Epoch [160]: [Training] loss = 0.059290 * 49664, metric = 2.1% * 49664 10.055s (4939.1 samples per second)
Finished Epoch [160]: [Training] loss = 0.059290 * 49664, metric = 2.1% * 49664 10.056s (4938.9 samples per second)
Finished Epoch [160]: [Training] loss = 0.059290 * 49664, metric = 2.1% * 49664 10.056s (4938.9 samples per second)
Finished Epoch [160]: [Training] loss = 0.059290 * 49664, metric = 2.1% * 49664 10.056s (4938.9 samples per second)

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如前所述，它应该比正常的对应物快。非常感谢您的帮助。

当GPU之间的通信时间比小批量的计算时间长时，1位sgd是一种有效的策略

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上面的实验有两个“问题”：你正在训练的模型参数很少（计算量没有那么多），4个GPU在同一台机器上（与通过网络进行通信相比，通信没有那么糟糕）。此外，在机器内部，CNTK使用的是比1位使用的通用MPI实现优化得多的。更新：在发表此评论时，默认情况下不使用NCCL。

谢谢。因此，如果我处理一个更大的问题（比如说ImageNet和ResNet 152），我应该看到一个加速吗？是的，这可能会有所帮助，尽管ResNet通常没有像VGG这样的一些旧网络那样有那么多的参数，它们在最后使用完全连接的层。