Question

我在Python中开发了一个使用Tensorflow的应用程序和另一个使用GPU的模型。我有一台带有许多GPU（3xNVIDIA GTX1080）的PC，由于所有型号都试图使用所有可用的GPU，导致OUT_OF_MEMORY_ERROR，我发现你可以用Python

将特定的GPU分配给Python脚本

class FCN:
  def __init__(self):
    os.environ['CUDA_VISIBLE_DEVICES'] = '1'
    self.keep_probability = tf.placeholder(tf.float32, name="keep_probabilty")
    self.image = tf.placeholder(tf.float32, shape=[None, IMAGE_SIZE, IMAGE_SIZE, 3], name="input_image")
    self.annotation = tf.placeholder(tf.int32, shape=[None, IMAGE_SIZE, IMAGE_SIZE, 1], name="annotation")

    self.pred_annotation, logits = inference(self.image, self.keep_probability)
    tf.summary.image("input_image", self.image, max_outputs=2)
    tf.summary.image("ground_truth", tf.cast(self.annotation, tf.uint8), max_outputs=2)
    tf.summary.image("pred_annotation", tf.cast(self.pred_annotation, tf.uint8), max_outputs=2)
    self.loss = tf.reduce_mean((tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,
                                                                               labels=tf.squeeze(self.annotation,
                                                                                                 squeeze_dims=[3]),
                                                                               name="entropy")))
    tf.summary.scalar("entropy", self.loss)

...

这里我附上了我的FCN课程的片段

FCN.py

在同一个文件if __name__ == "__main__": fcn = FCN() fcn.train_model() images_dir = '/home/super/datasets/MeterDataset/full-dataset-gas-images/' for img_file in os.listdir(images_dir): fcn.segment(os.path.join(images_dir, img_file))中，我有一个使用该类的主要内容，当Tensorflow打印输出时，我可以看到只使用了1个GPU，正如我所料。

2018-01-09 11:31:57.351029: I tensorflow/core/common_runtime/gpu/gpu_device.cc:955] Found device 0 with properties: 
name: GeForce GTX 1080
major: 6 minor: 1 memoryClockRate (GHz) 1.7335
pciBusID 0000:09:00.0
Total memory: 7.92GiB
Free memory: 7.60GiB
2018-01-09 11:31:57.351047: I tensorflow/core/common_runtime/gpu/gpu_device.cc:976] DMA: 0 
2018-01-09 11:31:57.351051: I tensorflow/core/common_runtime/gpu/gpu_device.cc:986] 0:   Y 
2018-01-09 11:31:57.351057: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1045] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080, pci bus id: 0000:09:00.0)

输出：

def main(args):
  start_time = datetime.now()

  font = cv2.FONT_HERSHEY_SIMPLEX

  results_file = "../results.txt"
  if os.path.exists(results_file):
    os.remove(results_file)

  results_file = open(results_file, "a")

  fcn = FCN()

当我尝试从另一个脚本实例化FCN对象时出现问题。

__init__()

此处对象的创建始终使用所有3个GPU，而不是使用仅分配到2018-01-09 11:41:02.537548: I tensorflow/core/common_runtime/gpu/gpu_device.cc:976] DMA: 0 1 2 2018-01-09 11:41:02.537555: I tensorflow/core/common_runtime/gpu/gpu_device.cc:986] 0: Y Y Y 2018-01-09 11:41:02.537558: I tensorflow/core/common_runtime/gpu/gpu_device.cc:986] 1: Y Y Y 2018-01-09 11:41:02.537561: I tensorflow/core/common_runtime/gpu/gpu_device.cc:986] 2: Y Y Y 2018-01-09 11:41:02.537567: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1045] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080, pci bus id: 0000:0b:00.0) 2018-01-09 11:41:02.537571: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1045] Creating TensorFlow device (/gpu:1) -> (device: 1, name: GeForce GTX 1080, pci bus id: 0000:09:00.0) 2018-01-09 11:41:02.537574: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1045] Creating TensorFlow device (/gpu:2) -> (device: 2, name: GeForce GTX 1080, pci bus id: 0000:05:00.0)方法。

这里是不受欢迎的输出：

function convertToRoman(num) {
  var toTen = ["", "I", "II", "III", "IV", "V", "VI", "VII", "VIII", "IX", "X"];
  var toHungred = ["", "X" ,"XX", "XXX", "XL", "L", "LX", "LXX", "LXXX", "XC", "C"];
  var toThousend = ["", "C", "CC", "CCC", "CD", "D", "DC", "DCC", "DCCC", "CM", "M"];  			
  var arrString = String(num).split("");  
  var arr = [];           
  if (arrString.length == 3 ){  				
  	  	arr.push(toThousend[arrString[+0]]);
  	  	arr.push(toHungred[arrString[+1]]);
  	  	arr.push(toTen[arrString[+2]]);
   } 
  else if (arrString.length == 2 ){  				
   	  arr.push(toHungred[arrString[+0]]);
  	  arr.push(toTen[arrString[+1]]);  			  	
  }
  else if (arrString.length == 1 ){  				
  	arr.push(toTen[arrString[+0]]);  			  	  	
  }
  else if (arrString.length == 4 ) {
    for (var i =1; i<=[arrString[+0]]; i++) {
                  arr.push("M");                  
                }
    arr.push(toThousend[arrString[+1]]);
  	arr.push(toHungred[arrString[+2]]);
  	arr.push(toTen[arrString[+3]]);
  }              
  console.log (arr.join(""));
}

convertToRoman(36);

Answer 1

以下是您可以做的事情：

运行已设置CUDA_VISIBLE_DEVICES环境变量的脚本，为discussed here：
```
CUDA_VISIBLE_DEVICES=1 python another_script.py
```
为Session构造函数提供显式配置：
```
config = tf.ConfigProto(device_count={'GPU': 1})
sess = tf.Session(config=config)
```
...强制tensorflow只使用一个GPU，无论有多少GPU可用。您还可以通过visible_device_list设置细粒度的设备列表（详见config.proto）。

在Tensorflow中限制GPU设备

1 个答案: