如何在张量流中获取当前可用的GPU? [英] How to get current available GPUs in tensorflow?

问题描述

我有一个使用分布式TensorFlow的计划，我看到TensorFlow可以使用GPU进行培训和测试.在群集环境中，每台机器可能具有0个或1个或更多个GPU，我想将TensorFlow图运行到尽可能多的机器上的GPU中.

I have a plan to use distributed TensorFlow, and I saw TensorFlow can use GPUs for training and testing. In a cluster environment, each machine could have 0 or 1 or more GPUs, and I want to run my TensorFlow graph into GPUs on as many machines as possible.

我发现运行tf.Session()时，TensorFlow在如下所示的日志消息中提供了有关GPU的信息:

I found that when running tf.Session() TensorFlow gives information about GPU in the log messages like below:

I tensorflow/core/common_runtime/gpu/gpu_init.cc:126] DMA: 0 
I tensorflow/core/common_runtime/gpu/gpu_init.cc:136] 0:   Y 
I tensorflow/core/common_runtime/gpu/gpu_device.cc:838] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0)

我的问题是如何从TensorFlow获取有关当前可用GPU的信息?我可以从日志中获取已加载的GPU信息，但我想以一种更复杂的编程方式来实现. 我还可以使用CUDA_VISIBLE_DEVICES环境变量有意地限制GPU，所以我不想知道一种从OS内核获取GPU信息的方法.

My question is how do I get information about current available GPU from TensorFlow? I can get loaded GPU information from the log, but I want to do it in a more sophisticated, programmatic way. I also could restrict GPUs intentionally using the CUDA_VISIBLE_DEVICES environment variable, so I don't want to know a way of getting GPU information from OS kernel.

简而言之，如果机器中有两个GPU，我想要一个类似tf.get_available_gpus()的函数，该函数将返回['/gpu:0', '/gpu:1'].我该如何实施?

In short, I want a function like tf.get_available_gpus() that will return ['/gpu:0', '/gpu:1'] if there are two GPUs available in the machine. How can I implement this?

推荐答案

有一个未公开的方法，称为

There is an undocumented method called device_lib.list_local_devices() that enables you to list the devices available in the local process. (N.B. As an undocumented method, this is subject to backwards incompatible changes.) The function returns a list of DeviceAttributes protocol buffer objects. You can extract a list of string device names for the GPU devices as follows:

from tensorflow.python.client import device_lib

def get_available_gpus():
    local_device_protos = device_lib.list_local_devices()
    return [x.name for x in local_device_protos if x.device_type == 'GPU']

请注意(至少达到TensorFlow 1.4)，调用device_lib.list_local_devices()将运行一些初始化代码，默认情况下，这些初始化代码将在所有设备上分配所有GPU内存(此问题.

Note that (at least up to TensorFlow 1.4), calling device_lib.list_local_devices() will run some initialization code that, by default, will allocate all of the GPU memory on all of the devices (GitHub issue). To avoid this, first create a session with an explicitly small per_process_gpu_fraction, or allow_growth=True, to prevent all of the memory being allocated. See this question for more details.

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