GPU: NVIDIA GeForce 930M (Compute Capability = 5.0) CUDA/cuDNN version: 10 Python version: 3.7 (Use Anaconda3 env) TensorFlow version: tensorflow-gpu 1.13.1 Step1: 检查硬件 硬件要求:NVIDIA® GPU card with CUDA® Compute Capability 3.5 or higher. 1. 确认电脑配备GPU 打开 设备管理器 (De...
gpu_ok = tf.test.is_gpu_available() print("tf version:",version,"\nuse GPU",gpu_ok) 1. 2. 3. 4. 输出 tf version: 2.0.0 use GPU True 1. 2. 当然这里如何判断是否代码真的在GPU上面跑,执行以下代码 import tensorflow.compat.v1 as tf tf.disable_v2_behavior() a = tf.constant([1....
Metal device set to: Apple M1 ['/device:CPU:0', '/device:GPU:0'] 2022-02-09 11:52:55.468198: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built ...
TensorFlow的学习需要有专门的Nvida的GPU显卡的电脑,GPU的显存最好在4G以上,我以前那台电脑只有1G显示只能学习一些非常初级模型,稍微复杂的模型就无法运行。对于没有GPU独显的同学,可以使用百度的AI Studio,上面提供免费的GPU服务器,每天可以免费使用8小时,并且直接安装好百度的PaddlePaddle,系统已经配置好,也可以快速学习...
\core\common_runtime\gpu\gpu_device.cc:1053]Created TensorFlow device(/job:localhost/replica:0/task:0/device:GPU:0with4740MB memory)->physical GPU(device:0,name:GeForce GTX1060,pci busid:0000:01:00.0,compute capability:6.1)2018-06-1018:28:01.331056:E T:\src\github\tensorflow\tensorflow\...
>Kernel driver in use:nvidiaKernel modules:nouveau,nvidia_drm,nvidia 这里是tensorflow官方给出的gpu支持:https://www.tensorflow.org/install/gpu cuda和cudnn的安装 tensorflow-gpu要想正常运行,除了必要的gpu驱动,还依赖cuda和cudnn两个sdk。 下面是tensorflow-gpu版本依赖的cuda和cudnn的版本:...
\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1053] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0with4740MB memory) -> physical GPU (device:0, name: GeForce GTX1060, pci busid:0000:01:00.0, compute capability:6.1)2018-06-1018:28:...
在Ubuntu上安装Tensorflow(GPU版)后,可以采取一些性能优化措施,以便更高效地利用GPU加速。例如,可以通过设置内存growth方式来减少内存占用:```pythonimport tensorflow as tfgpus = tf.config.experimental.list_physical_devices(‘GPU’)if gpus:try: Specify a particular GPU to use. Default operation is to use...
available()print("tf version:",version,"\nuse GPU",gpu_ok)>>>tfversion:2.0.>>>useGPUTrue...
ACK基于Scheduling Framework机制,实现GPU拓扑感知调度,即在节点的GPU组合中选择具有最优训练速度的组合。本文介绍如何使用GPU拓扑感知调度来提升TensorFlow分布式训练的训练速度。 前提条件 已创建ACK Pro集群,且集群的实例规格类型选择为GPU云服务器。更多信息,请参...