为什么Keras以“裸露"为模型? CPU更快? [英] Why Keras model on "bare" CPU is faster?

问题描述

运行Keras模型...不好的是，不使用CPU扩展速度更快(应该相反). 查看下面的输出.

Running a Keras model... the bad thing is that it is faster not to use CPU extentions (it should be the other way around). Look at the output below.

是否有一个可以在其中设置inter_op_parallelism选项的配置文件?

Is there a config file where I can set inter_op_parallelism option ?

 Using TensorFlow backend.
 2018-10-18 17:21:32.620461: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
 2018-10-18 17:21:32.621535: I tensorflow/core/common_runtime/process_util.cc:69] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.
 Results: -33.20 (23.69) MSE

 real   2m55.990s
 user   4m8.784s
 sys    3m50.192s

---

 Using TensorFlow backend.
 2018-10-18 17:25:04.773578: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
 Results: -32.57 (23.16) MSE

 real   1m48.847s
 user   2m15.792s
 sys    0m13.440s

推荐答案

在这里，您是我与keras一起使用的代码，只需将其放在代码顶部即可.

Here you are the code I'm using with keras, just put it on the top of your code.

from keras import backend as K
import tensorflow as tf

NUM_PARALLEL_EXEC_UNITS = 6

config = tf.ConfigProto(intra_op_parallelism_threads = NUM_PARALLEL_EXEC_UNITS, 
         inter_op_parallelism_threads = 1, 
         allow_soft_placement = True, 
         device_count = {'CPU': NUM_PARALLEL_EXEC_UNITS })

session = tf.Session(config=config)

K.set_session(session)

import os

os.environ["OMP_NUM_THREADS"] = str(NUM_PARALLEL_EXEC_UNITS)
os.environ["KMP_BLOCKTIME"] = "30"
os.environ["KMP_SETTINGS"] = "1"
os.environ["KMP_AFFINITY"]= "granularity=fine,verbose,compact,1,0"

注意:我对结果有些失望.仅使用这些参数，我可以达到最高150％的速度.

Note: I'm a little bit disappointed with the results. I could reach maximum 150% speeding up only playing with these parameters.

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