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解决TensorFlow程序无限制占用GPU的方法

这篇文章主要介绍了解决TensorFlow程序无限制占用GPU的方法,文中通过示例代码介绍的非常详细,对大家的学习或者工作具有一定的参考学习价值,需要的朋友们下面随着小编来一起学习学习吧

今天遇到一个奇怪的现象,使用tensorflow-gpu的时候,出现内存超额~~如果我训练什么大型数据也就算了,关键我就写了一个y=W*x…显示如下图所示:

程序如下:

import tensorflow as tf

w = tf.Variable([[1.0,2.0]])
b = tf.Variable([[2.],[3.]])

y = tf.multiply(w,b)

init_op = tf.global_variables_initializer()

with tf.Session() as sess:
 sess.run(init_op)
 print(sess.run(y))

出错提示:

占用的内存越来越多,程序崩溃之后,整个电脑都奔溃了,因为整个显卡全被吃了

2018-06-10 18:28:00.263424: I T:\src\github\tensorflow\tensorflow\core\platform\cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2018-06-10 18:28:00.598075: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1356] Found device 0 with properties: 
name: GeForce GTX 1060 major: 6 minor: 1 memoryClockRate(GHz): 1.6705
pciBusID: 0000:01:00.0
totalMemory: 6.00GiB freeMemory: 4.97GiB
2018-06-10 18:28:00.598453: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1435] Adding visible gpu devices: 0
2018-06-10 18:28:01.265600: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:923] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-06-10 18:28:01.265826: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:929]  0 
2018-06-10 18:28:01.265971: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:942] 0: N 
2018-06-10 18:28:01.266220: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1053] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 4740 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1060, pci bus id: 0000:01:00.0, compute capability: 6.1)
2018-06-10 18:28:01.331056: E T:\src\github\tensorflow\tensorflow\stream_executor\cuda\cuda_driver.cc:936] failed to allocate 4.63G (4970853120 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-10 18:28:01.399111: E T:\src\github\tensorflow\tensorflow\stream_executor\cuda\cuda_driver.cc:936] failed to allocate 4.17G (4473767936 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-10 18:28:01.468293: E T:\src\github\tensorflow\tensorflow\stream_executor\cuda\cuda_driver.cc:936] failed to allocate 3.75G (4026391040 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-10 18:28:01.533138: E T:\src\github\tensorflow\tensorflow\stream_executor\cuda\cuda_driver.cc:936] failed to allocate 3.37G (3623751936 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-10 18:28:01.602452: E T:\src\github\tensorflow\tensorflow\stream_executor\cuda\cuda_driver.cc:936] failed to allocate 3.04G (3261376768 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-10 18:28:01.670225: E T:\src\github\tensorflow\tensorflow\stream_executor\cuda\cuda_driver.cc:936] failed to allocate 2.73G (2935238912 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-10 18:28:01.733120: E T:\src\github\tensorflow\tensorflow\stream_executor\cuda\cuda_driver.cc:936] failed to allocate 2.46G (2641714944 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-10 18:28:01.800101: E T:\src\github\tensorflow\tensorflow\stream_executor\cuda\cuda_driver.cc:936] failed to allocate 2.21G (2377543424 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-10 18:28:01.862064: E T:\src\github\tensorflow\tensorflow\stream_executor\cuda\cuda_driver.cc:936] failed to allocate 1.99G (2139789056 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-10 18:28:01.925434: E T:\src\github\tensorflow\tensorflow\stream_executor\cuda\cuda_driver.cc:936] failed to allocate 1.79G (1925810176 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-10 18:28:01.986180: E T:\src\github\tensorflow\tensorflow\stream_executor\cuda\cuda_driver.cc:936] failed to allocate 1.61G (1733229056 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-10 18:28:02.043456: E T:\src\github\tensorflow\tensorflow\stream_executor\cuda\cuda_driver.cc:936] failed to allocate 1.45G (1559906048 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-10 18:28:02.103531: E T:\src\github\tensorflow\tensorflow\stream_executor\cuda\cuda_driver.cc:936] failed to allocate 1.31G (1403915520 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-10 18:28:02.168973: E T:\src\github\tensorflow\tensorflow\stream_executor\cuda\cuda_driver.cc:936] failed to allocate 1.18G (1263524096 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-10 18:28:02.229387: E T:\src\github\tensorflow\tensorflow\stream_executor\cuda\cuda_driver.cc:936] failed to allocate 1.06G (1137171712 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-10 18:28:02.292997: E T:\src\github\tensorflow\tensorflow\stream_executor\cuda\cuda_driver.cc:936] failed to allocate 976.04M (1023454720 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-10 18:28:02.356714: E T:\src\github\tensorflow\tensorflow\stream_executor\cuda\cuda_driver.cc:936] failed to allocate 878.44M (921109248 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-10 18:28:02.418167: E T:\src\github\tensorflow\tensorflow\stream_executor\cuda\cuda_driver.cc:936] failed to allocate 790.59M (828998400 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-10 18:28:02.482394: E T:\src\github\tensorflow\tensorflow\stream_executor\cuda\cuda_driver.cc:936] failed to allocate 711.54M (746098688 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY

分析原因:

显卡驱动不是最新版本,用__驱动软件__更新一下驱动,或者自己去下载更新。

TF运行太多,注销全部程序冲洗打开。

由于TF内核编写的原因,默认占用全部的GPU去训练自己的东西,也就是像meiguo一样优先政策吧

这个时候我们得设置两个方面:

  • 选择什么样的占用方式?优先占用__还是__按需占用
  • 选择最大占用多少GPU,因为占用过大GPU会导致其它程序奔溃。最好在0.7以下

先更新驱动:

再设置TF程序:

注意:单独设置一个不行!按照网上大神博客试了,结果效果还是很差(占用很多GPU)

设置TF:

  • 按需占用
  • 最大占用70%GPU

修改代码如下:

import tensorflow as tf

w = tf.Variable([[1.0,2.0]])
b = tf.Variable([[2.],[3.]])

y = tf.multiply(w,b)

init_op = tf.global_variables_initializer()

cOnfig= tf.ConfigProto(allow_soft_placement=True)
gpu_optiOns= tf.GPUOptions(per_process_gpu_memory_fraction=0.7)
config.gpu_options.allow_growth = True
with tf.Session(cOnfig=config) as sess:
 sess.run(init_op)
 print(sess.run(y))

成功解决:

2018-06-10 18:21:17.532630: I T:\src\github\tensorflow\tensorflow\core\platform\cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2018-06-10 18:21:17.852442: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1356] Found device 0 with properties: 
name: GeForce GTX 1060 major: 6 minor: 1 memoryClockRate(GHz): 1.6705
pciBusID: 0000:01:00.0
totalMemory: 6.00GiB freeMemory: 4.97GiB
2018-06-10 18:21:17.852817: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1435] Adding visible gpu devices: 0
2018-06-10 18:21:18.511176: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:923] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-06-10 18:21:18.511397: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:929]  0 
2018-06-10 18:21:18.511544: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:942] 0: N 
2018-06-10 18:21:18.511815: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1053] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 4740 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1060, pci bus id: 0000:01:00.0, compute capability: 6.1)
[[2. 4.]
 [3. 6.]]

参考资料:

主要参考博客

错误实例

到此这篇关于解决TensorFlow程序无限制占用GPU的方法 的文章就介绍到这了,更多相关TensorFlow 占用GPU内容请搜索以前的文章或继续浏览下面的相关文章希望大家以后多多支持!


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