检测安装Tensorflow后是否成功调用GPU

在安装好Tensorflow及其相关部件后，我们可以通过以下方法去检测Tensorflow是否成功调用了GPU。

目录

本人配置注明：

检测Tensorflow是否调用GPU

方法一

方法二

本人配置注明：

本人tensorflow、CuDA等部件版本如下：

Tensorflow 2.7.0

python 3.7.11

关于各部件版本对应问题，我会尽快发帖说明。

检测Tensorflow是否调用GPU

方法一

输入以下命令：

import tensorflow.compat.v1 as tfsess = tf.Session(config=tf.ConfigProto(log_device_placement=True))

[！！！此处需要 import tensorflow.compat.v1 as tf 。如果直接 import tensorflow as tf，则会报错'AttributeError: module 'tensorflow' has no attribute 'Session' ']

Device mapping:

/job:localhost/replica:0/task:0/device:GPU:0 -> device: 0, name: NVIDIA GeForce RTX 3050 Ti Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.6

2023-03-03 13:20:44.274717: 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.

2023-03-03 13:20:45.877778: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 1657 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3050 Ti Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.6

2023-03-03 13:20:54.015703: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 1657 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3050 Ti Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.6

2023-03-03 13:21:04.540028: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 1657 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3050 Ti Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.6

2023-03-03 13:25:34.047020: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /device:GPU:0 with 1657 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3050 Ti Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.6

2023-03-03 13:25:34.047944: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /device:GPU:0 with 1657 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3050 Ti Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.6

2023-03-03 13:26:04.474123: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /device:GPU:0 with 1657 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3050 Ti Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.6

2023-03-03 13:27:33.161191: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 1657 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3050 Ti Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.6

方法二

输入以下命令：

import tensorflow as tfprint(tf.test.is_gpu_available())

若结果返回为True，则显示GPU可用。

以上仅个人在配置使用Tensorflow过程中所作总结，希望对大家有用。

来源地址：https://blog.csdn.net/qq_46390120/article/details/129318555