Python 虽然安装了Tensorflow GPU，但我的脚本似乎没有在GPU上执行
我有一台装有cuda 10.1和tensorflow以及tensorflow gpu 1.14.0的机器。我正在运行一个python脚本，用VirtualNV训练CNN。我在源代码中指出我要使用GPU，如下所示：Python 虽然安装了Tensorflow GPU，但我的脚本似乎没有在GPU上执行,python,tensorflow,Python,Tensorflow,我有一台装有cuda 10.1和tensorflow以及tensorflow gpu 1.14.0的机器。我正在运行一个python脚本，用VirtualNV训练CNN。我在源代码中指出我要使用GPU，如下所示： import os os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"; os.environ["CUDA_VISIBLE_DEVICES"]="0"; 然而，当我运行脚本时，培训阶段需要花费很多时间才能完成。以下是我的nvidia smi的输出
import os
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID";
os.environ["CUDA_VISIBLE_DEVICES"]="0";
>>> import tensorflow as tf
>>> sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
>>> with tf.Session() as sess:
devices = sess.list_devices()
>>> from tensorflow.python.client import device_lib
>>> print(device_lib.list_local_devices())
##########################
# Install Miniconda
##########################
mkdir -p ~/install
cd ~/install
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
# I guess on a mac you should do
# wget https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh
#########################
# install nvidia driver
# so these are the linux (ubuntu) commands
# for mac, maybe one should follow the scheme
# removing nvidia drivers first
# and then download newest nvidia driver
# and install it
# and reboot
#
# If you are using a laptop without gpu, just skip this block
#########################
sudo apt purge nvidia-* # remove all nvidia driver first
sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt install nvidia-driver-418
sudo apt install nvidia-cuda-toolkit
# reboot
sudo reboot
#########################
# install machine learning stuff keras tensorflow-gpu
#
# if you are installing in a laptop without gpu,
# replace 'tensorflow-gpu' by 'tensorflow'!
#########################
conda create --name keras
conda activate keras
conda install python ipython jupyter pandas scipy seaborn scikit-learn tensorflow-gpu keras pytest openpyxl graphviz
#########################
# finally, test a successful installation by:
# entering:
ipython # and there trying:
from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())
# should list gpu
# sth like:
physical_device_desc: "device: 0, name: GeForce GTX 1050 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1"
, name: "/device:XLA_GPU:0"
device_type: "XLA_GPU"
memory_limit: 17179869184
locality {
}
incarnation: 14085000268159177816
physical_device_desc: "device: XLA_GPU device"
]