关于docker:cuda11镜像

pull

docker pull nvidia/cuda:11.2.0-cudnn8-devel-ubi7
 
docker tag  nvidia/cuda:11.2.0-cudnn8-devel-ubi7 registry.cn-hangzhou.aliyuncs.com/mkmk/all:nvidia-cuda-11.2.0-cudnn8-devel-ubi7
 
docker push registry.cn-hangzhou.aliyuncs.com/mkmk/all:nvidia-cuda-11.2.0-cudnn8-devel-ubi7
 

gpu tf

 
docker run --gpus all -it tensorflow/tensorflow:latest-gpu bash
 
 
 
docker pull tensorflow/tensorflow:2.4.1-gpu
 
docker pull tensorflow/tensorflow:2.4.1-gpu-jupyter
 
docker tag tensorflow/tensorflow:2.4.1-gpu  registry.cn-hangzhou.aliyuncs.com/mkmk/all:tf-2.4.1-gpu
 
docker tag tensorflow/tensorflow:2.4.1-gpu-jupyter  registry.cn-hangzhou.aliyuncs.com/mkmk/all:tf-2.4.1-gpu-jupyter
 
docker push registry.cn-hangzhou.aliyuncs.com/mkmk/all:tf-2.4.1-gpu
 
docker push registry.cn-hangzhou.aliyuncs.com/mkmk/all:tf-2.4.1-gpu-jupyter

应用镜像

 
register_url='192.168.170.100:5000'
 
docker stop gpu-jupyter1  && docker rm gpu-jupyter1 
 
docker run -it --gpus=all   --name gpu-jupyter1  -p  8888:8888 ${register_url}/tensorflow/tensorflow:2.4.1-gpu-jupyter 
# 将输入的 ip 批改为 实在 ip
 
 
 
# 残缺 命令 调试 应用
docker run -d --gpus=all   --name gpu-jupyter1  -p  8888:8888 ${register_url}/tensorflow/tensorflow:2.4.1-gpu-jupyter  bash -c  "source /etc/bash.bashrc && jupyter notebook --notebook-dir=/tf --ip 0.0.0.0 --no-browser --allow-root"
 

测试 是否 应用的 gpu

import tensorflow as tf
tf.test.is_gpu_available(
    cuda_only=False,
    min_cuda_compute_capability=None
)
 
# 简化一点
print("is_gpu:", tf.test.is_gpu_available())
 
 
# 查看 所有的 可用设施
from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())
 
 
# 加法 
 
# cpu
%%time
 
with tf.device("/device:CPU:0"):
    a=tf.zeros([1000,1000])
    print("a on gpu:",a.device.endswith('GPU:0'))
    for i in range(10000):
        b=tf.add(a,a)
 
a on gpu: False
CPU times: user 7.74 s, sys: 1.2 s, total: 8.94 s
Wall time: 3.39 s
 
 
# gpu
%%time
 
with tf.device("/device:GPU:0"):
    a=tf.zeros([1000,1000])
    print("a on gpu:",a.device.endswith('GPU:0'))
    for i in range(10000):
        b=tf.add(a,a)
 
a on gpu: True
CPU times: user 900 ms, sys: 1.22 s, total: 2.12 s
Wall time: 2.12 s
 
 

来来来聊一晚