config.list_physical_devices('GPU'): print("Name:", gpu.name, " Type:", gpu.device_type) print(tf.config.get_device_details(gpu.name)) 4.检查GPU是否支持TensorFlow 通过tf.config.experimental可以检查你的GPU是否支持TensorFlow。
Latest supported Visual C++ Redistributable downloads 同时装了Tensorflow CPU和GPU版本后可以选择使用GPU...
#3. 从tensorflow官方网可知:https://www.tensorflow.org/install/pip#windows-native## TensorFlow 2.10 was the last TensorFlow release that supported GPU on native-Windows.## Starting with TensorFlow 2.11, you will need to install TensorFlow in WSL2, or install tensorflow-cpu and, optionally, try ...
Note:GPU support on native-Windows is only available for 2.10 or earlier versions, starting in TF 2.11, CUDA build is not supported for Windows. For using TensorFlow GPU on Windows, you will need to build/install TensorFlow inWSL2or use tensorflow-cpu with TensorFlow-DirectML-Plugin 注意:本机...
NVIDIA GPU Drivers NVIDIA Container Toolkit For supported versions, see theFramework Containers Support Matrixand theNVIDIA Container Toolkit Documentation. No other installation, compilation, or dependency management is required.It is not necessary to install the NVIDIA CUDA Toolkit. ...
华为云帮助中心为你分享云计算行业信息,包含产品介绍、用户指南、开发指南、最佳实践和常见问题等文档,方便快速查找定位问题与能力成长,并提供相关资料和解决方案。本页面关键词:tensorflow list gpu。
pip install tensorflow-gpu==2.4.0 #测试tensorflow python #可以看到python版本 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 运行 import tensorflow as tf tf.config.list_physical_devices() # gpu版的安装的信息 #检查在本机有没有安装cuda cudnn ...
华为云帮助中心为你分享云计算行业信息,包含产品介绍、用户指南、开发指南、最佳实践和常见问题等文档,方便快速查找定位问题与能力成长,并提供相关资料和解决方案。本页面关键词:tensorflow list gpu。
WARNING:tensorflow:NCCLisnotsupported when using virtual GPUs, fallingback to reduction to one device INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0','/job:localhost/replica:0/task:0/device:GPU:1') ...
tensorflow.python.client import device_lib import numpy as np print(tf.__version__) print(f"Tensor Flow Version: {tf.__version__}") print(f"Keras Version: {tf.keras.__version__}")print() gpu = len(tf.config.list_physical_devices('GPU'))>0 print("GPU is","available"ifgpuelse"...