TensorFlow:如何验证它是否在GPU上运行

Question

我正在寻找一种简单的方法来验证我的TF图表实际上是在GPU上运行.

PS.验证cuDNN库是否被使用也是很好的.

Answer 1

有几种方法可以查看操作位置.

1. 将RunOptions和RunMetadata添加到会话调用中,并在Tensorboard中查看操作和计算的位置.请参阅此处的代码:https://www.tensorflow.org/get_started/graph_viz

2. 在会话ConfigProto中指定log_device_placement选项.这会记录到控制放置操作的设备.https://www.tensorflow.org/api_docs/python/tf/ConfigProto

3. 使用nvidia-smi查看终端中的GPU使用情况.

Answer 2

在Python中导入TF时

import tensorflow as tf

您将获得这些日志,指示CUDA库的使用情况

I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcublas.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcudnn.so.5 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcufft.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcurand.so.8.0 locally

此外,当您在Config Proto中构建图形并使用log_device_placement运行会话时,您将获得这些日志(显示它找到了GPU设备):

I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:910] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
I tensorflow/core/common_runtime/gpu/gpu_device.cc:885] Found device 0 with properties: 
name: GeForce GTX 1060 6GB
major: 6 minor: 1 memoryClockRate (GHz) 1.759
pciBusID 0000:01:00.0
Total memory: 5.93GiB
Free memory: 4.94GiB
I tensorflow/core/common_runtime/gpu/gpu_device.cc:906] DMA: 0 
I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 0:   Y 
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1060 6GB, pci bus id: 0000:01:00.0)