Python 即使指定了其他gpu,skflow也会在gpu0中分配内存
我在4 GPU Amazon实例上遇到了这个问题,使用了一个简单的示例脚本:Python 即使指定了其他gpu,skflow也会在gpu0中分配内存,python,amazon-web-services,amazon-ec2,tensorflow,skflow,Python,Amazon Web Services,Amazon Ec2,Tensorflow,Skflow,我在4 GPU Amazon实例上遇到了这个问题,使用了一个简单的示例脚本: import skflow import tensorflow as tf from sklearn import datasets iris = datasets.load_iris() X_train, X_test, y_train, y_test = cross_validation.train_test_split(iris.data, iris.target, test_size=0.2, ran
import skflow
import tensorflow as tf
from sklearn import datasets
iris = datasets.load_iris()
X_train, X_test, y_train, y_test = cross_validation.train_test_split(iris.data, iris.target,
test_size=0.2, random_state=42)
def my_model(X, y):
with tf.device('/gpu:1'):
layers = skflow.ops.dnn(X, [1000, 500, 150], keep_prob=0.5) # many neurons to see the impac on memory
with tf.device('/cpu:0'):
return skflow.models.logistic_regression(layers, y)
classifier = skflow.TensorFlowEstimator(model_fn=my_model, n_classes=3)
classifier.fit(X_train, y_train)
在启动脚本之前,nvidia smi
的结果是:
Fri Feb 19 11:30:22 2016
+------------------------------------------------------+
| NVIDIA-SMI 346.46 Driver Version: 346.46 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GRID K520 Off | 0000:00:03.0 Off | N/A |
| N/A 40C P0 41W / 125W | 2247MiB / 4095MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 1 GRID K520 Off | 0000:00:04.0 Off | N/A |
| N/A 36C P0 40W / 125W | 2113MiB / 4095MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 2 GRID K520 Off | 0000:00:05.0 Off | N/A |
| N/A 41C P0 43W / 125W | 53MiB / 4095MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 3 GRID K520 Off | 0000:00:06.0 Off | N/A |
| N/A 39C P0 41W / 125W | 1816MiB / 4095MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
在脚本运行时:
Fri Feb 19 11:30:53 2016
+------------------------------------------------------+
| NVIDIA-SMI 346.46 Driver Version: 346.46 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GRID K520 Off | 0000:00:03.0 Off | N/A |
| N/A 40C P0 46W / 125W | 3926MiB / 4095MiB | 26% Default |
+-------------------------------+----------------------+----------------------+
| 1 GRID K520 Off | 0000:00:04.0 Off | N/A |
| N/A 37C P0 42W / 125W | 3926MiB / 4095MiB | 17% Default |
+-------------------------------+----------------------+----------------------+
| 2 GRID K520 Off | 0000:00:05.0 Off | N/A |
| N/A 41C P0 44W / 125W | 92MiB / 4095MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 3 GRID K520 Off | 0000:00:06.0 Off | N/A |
| N/A 39C P0 42W / 125W | 1856MiB / 4095MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
因此,内存被分配给GPU0,即使代码中没有任何部分提到它。你知道这种行为是从哪里来的吗?这引起了一个问题,因为我们在这个实例上是多个用户,即使没有人使用它,GPU0也会饱和。 < P>如果您只对GPU1感兴趣,我会考虑将脚本包装成将代码> CUADAVISILBYX设备(参见)到<代码> 1 。这样,脚本只能看到一个GPU(它的id看起来像是
0
)。如果您将其设置为2,3
,您将分别获得ID为0,1
的GPU。我们发现的一个解决方法是修改skflow.TensorFlowEstimator
罪魁祸首是
with self._graph.as_default():
tf.set_random_seed(self.tf_random_seed)
self._global_step = tf.Variable(
0, name="global_step", trainable=False)
在skflow.TensorFlowEstimator.setup\u training()
中,我们将其修改为
with self._graph.as_default(), tf.device("/gpu:{0}".format(self.gpu_number)):
tf.set_random_seed(self.tf_random_seed)
self._global_step = tf.get_variable('global_step', [],
initializer=tf.constant_initializer(0), trainable=False)
在类中添加属性
gpu\U编号
,并在skflow.TensorFlowEstimator中使用allow\u soft\u placement=True
初始化会话
。\u setup\u training()
谢谢您的回答。你想提交一份PR,让它更通用,这样其他人就可以更容易地使用它吗?@YuanTang我一点都不熟悉pull请求,但我会在这个周末尝试一下。太好了!只需分叉存储库,在本地克隆分叉存储库,然后处理分叉回购。完成修改后,当您转到tensorflow/skflow中的pull request部分时,单击跨叉比较
。如果您有任何问题,请告诉我,我很乐意为您提供帮助。