我正在尝试使用TensorFlow的mpi。有关此类代码的示例，请see this OpenAI baselines PPO code。它告诉我们运行以下命令：
$ mpirun -np 8 python -m baselines.ppo1.run_atari
我有一台带有一个GPU（内存为12GB）和安装了Tensorflow 1.3.0的机器，使用的是Python 3.5.3。当我运行此代码时，我收到以下错误：
2017-09-24 17:29:12.975967: I tensorflow/core/common_runtime/gpu/gpu_device.cc:955] Found device 0 with properties:
name: TITAN X (Pascal)
major: 6 minor: 1 memoryClockRate (GHz) 1.531
pciBusID 0000:01:00.0
Total memory: 11.90GiB
Free memory: 11.17GiB
2017-09-24 17:29:12.975990: I tensorflow/core/common_runtime/gpu/gpu_device.cc:976] DMA: 0
2017-09-24 17:29:12.975996: I tensorflow/core/common_runtime/gpu/gpu_device.cc:986] 0: Y
2017-09-24 17:29:12.976011: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1045] Creating TensorFlow device (/gpu:0) -> (device: 0, name: TITAN X (Pascal), pci bus id: 0000:01:00.0)
2017-09-24 17:29:12.987133: I tensorflow/core/common_runtime/gpu/gpu_device.cc:955] Found device 0 with properties:
name: TITAN X (Pascal)
major: 6 minor: 1 memoryClockRate (GHz) 1.531
pciBusID 0000:01:00.0
Total memory: 11.90GiB
Free memory: 11.17GiB
2017-09-24 17:29:12.987159: I tensorflow/core/common_runtime/gpu/gpu_device.cc:976] DMA: 0
2017-09-24 17:29:12.987165: I tensorflow/core/common_runtime/gpu/gpu_device.cc:986] 0: Y
2017-09-24 17:29:12.987172: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1045] Creating TensorFlow device (/gpu:0) -> (device: 0, name: TITAN X (Pascal), pci bus id: 0000:01:00.0)
[2017-09-24 17:29:12,994] Making new env: PongNoFrameskip-v4
2017-09-24 17:29:13.017845: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:893] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2017-09-24 17:29:13.022347: I tensorflow/core/common_runtime/gpu/gpu_device.cc:955] Found device 0 with properties:
name: TITAN X (Pascal)
major: 6 minor: 1 memoryClockRate (GHz) 1.531
pciBusID 0000:01:00.0
Total memory: 11.90GiB
Free memory: 104.81MiB
2017-09-24 17:29:13.022394: I tensorflow/core/common_runtime/gpu/gpu_device.cc:976] DMA: 0
2017-09-24 17:29:13.022415: I tensorflow/core/common_runtime/gpu/gpu_device.cc:986] 0: Y
2017-09-24 17:29:13.022933: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1045] Creating TensorFlow device (/gpu:0) -> (device: 0, name: TITAN X (Pascal), pci bus id: 0000:01:00.0)
2017-09-24 17:29:13.026338: E tensorflow/stream_executor/cuda/cuda_driver.cc:924] failed to allocate 104.81M (109903872 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
（这只是错误信息的第一部分;它非常长，但我认为这个开头部分是重要的事情。）
但是，如果我使用mpirun -np 1运行，则此命令有效。
我在线搜索，发现repository from Uber表示“要在拥有4个GPU的计算机上运行”，我需要使用：
$ mpirun -np 4 python train.py
我只想确认mpirun -np X表示X受到计算机上GPU数量的限制，假设我们正在运行的是TensorFlow程序。
答案 0 :(得分：0)
在阅读了有关MPI的更多信息之后，我可以确认是的，确实进程数量受GPU数量的限制。理由：
int[] numbers = {0,1,2,3,4,5,6,7,8,9,10};
public void traverseReversed(int[] a) {
traverseReversed(a, 0);
}
private void traverseReversed(int[] a, int i) {
if ( i + 1 < a.length ) {
// Traverse the rest of the array first.
traverseReversed(a, i+1);
}
System.out.println(a[i]);
}
public void test() throws Exception {
System.out.println("Hello world!");
traverseReversed(numbers);
}
命令将运行X＆＃34;副本＆＃34;代码（但每个都有自己的排名）。 See the documentation here。mpirun -np X和python tf_program1.py，而他们都使用TensorFlow并且需要在您的计算机上使用单独的GPU。因此，我似乎被迫使用一个流程。