我最近安装了tensorflow (Windows CPU版本)，收到了以下消息:
成功安装tensorflow-1.4.0 tensorflow-tensorboard-0.4.0rc2
然后当我试图逃跑的时候
import tensorflow as tf
hello = tf.constant('Hello, TensorFlow!')
sess = tf.Session()
sess.run(hello)
'Hello, TensorFlow!'
a = tf.constant(10)
b = tf.constant(32)
sess.run(a + b)
42
sess.close()
(我是在https://github.com/tensorflow/tensorflow上找到的)
我收到了以下信息:
2017-11-02 01:56:21.698935: I C:\tf_jenkins\home\workspace\rel-win\M\windows\PY\36\tensorflow\core\platform\cpu_feature_guard。cc:137]你的CPU支持这个TensorFlow二进制文件没有被编译使用的指令:AVX AVX2
但当我逃跑时
import tensorflow as tf
hello = tf.constant('Hello, TensorFlow!')
sess = tf.Session()
print(sess.run(hello))
它正常运行并输出Hello, TensorFlow!，这表明安装确实是成功的，但还有一些地方是错误的。
你知道问题是什么以及如何解决它吗?
他提供了一次名单，被人删了却看到答案是
下载软件包列表
输出:
F:\temp\Python>python test_tf_logics_.py
[0, 0, 26, 12, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0]
[ 0 0 0 26 12 0 0 0 2 0 0 0 0 0 0 0 0]
[ 0 0 26 12 0 0 0 2 0 0 0 0 0 0 0 0 0]
2022-03-23 15:47:05.516025: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-03-23 15:47:06.161476: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 10 MB memory: -> device: 0, name: NVIDIA GeForce GTX 1060 6GB, pci bus id: 0000:01:00.0, compute capability: 6.1
[0 0 2 0 0 0 0 7 0 0 0 0 0 0 0 0 0]
...
他提供了一次名单，被人删了却看到答案是
下载软件包列表
输出:
F:\temp\Python>python test_tf_logics_.py
[0, 0, 26, 12, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0]
[ 0 0 0 26 12 0 0 0 2 0 0 0 0 0 0 0 0]
[ 0 0 26 12 0 0 0 2 0 0 0 0 0 0 0 0 0]
2022-03-23 15:47:05.516025: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-03-23 15:47:06.161476: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 10 MB memory: -> device: 0, name: NVIDIA GeForce GTX 1060 6GB, pci bus id: 0000:01:00.0, compute capability: 6.1
[0 0 2 0 0 0 0 7 0 0 0 0 0 0 0 0 0]
...
我发现解决这个问题最简单的方法是卸载所有东西，然后安装特定版本的tensorflow-gpu:
卸载tensorflow:
pip uninstall tensorflow
卸载tensorflow-gpu:(确保运行这个，即使你不确定你是否安装了它)
pip uninstall tensorflow-gpu
安装特定的tensorflow-gpu版本:
pip install tensorflow-gpu==2.0.0
pip install tensorflow_hub
pip install tensorflow_datasets
你可以通过在python文件中添加以下代码来检查是否有效:
from __future__ import absolute_import, division, print_function, unicode_literals
import numpy as np
import tensorflow as tf
import tensorflow_hub as hub
import tensorflow_datasets as tfds
print("Version: ", tf.__version__)
print("Eager mode: ", tf.executing_eagerly())
print("Hub Version: ", hub.__version__)
print("GPU is", "available" if tf.config.experimental.list_physical_devices("GPU") else "NOT AVAILABLE")
运行文件，然后输出如下所示:
Version: 2.0.0
Eager mode: True
Hub Version: 0.7.0
GPU is available
希望这能有所帮助
