tf安装

1.        首先打开 anaconda prompt
2.        新建环境

   • conda create -n tf-gpu python=3.8

   •  y
3. 　　激活创建的环境
   • conda activate tf-gpu
4. 　　选择最终的cuda和cudnn搭配组合

   • conda install cudatoolkit=10.1.243
     conda install cudnn=7.6.5
     pip install tensorflow-gpu==2.2.0

5. 　　使用下面命令检查是否安装完成

   • python
     
     import tensorflow as tf
     
     print(tf.__version__)
     print(tf.test.gpu_device_name())
     print(tf.config.experimental.set_visible_devices)
     print('GPU:', tf.config.list_physical_devices('GPU'))
     print('CPU:', tf.config.list_physical_devices(device_type='CPU'))
     print(tf.config.list_physical_devices('GPU'))
     print(tf.test.is_gpu_available())
     # 输出可用的GPU数量
     print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))
     # 查询GPU设备

      

6.  会存在错误protobuf库需要降级
   1. 原理同numpy
   2. 卸载升级
      1. 　　pip uninstall protobuf
      2.        pip install protobuf==3.20.0
   3. 错误如下:　　

      File "C:\Tools\Anaconda3\envs\tf-gpu\lib\site-packages\tensorflow\core\framework\resource_handle_pb2.py", line 16, in <module>
          from tensorflow.core.framework import tensor_shape_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__shape__pb2
        File "C:\Tools\Anaconda3\envs\tf-gpu\lib\site-packages\tensorflow\core\framework\tensor_shape_pb2.py", line 36, in <module>
          _descriptor.FieldDescriptor(
        File "C:\Tools\Anaconda3\envs\tf-gpu\lib\site-packages\google\protobuf\descriptor.py", line 561, in __new__
          _message.Message._CheckCalledFromGeneratedFile()
      TypeError: Descriptors cannot not be created directly.
      If this call came from a _pb2.py file, your generated code is out of date and must be regenerated with protoc >= 3.19.0.
      If you cannot immediately regenerate your protos, some other possible workarounds are:
       1. Downgrade the protobuf package to 3.20.x or lower.
       2. Set PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python (but this will use pure-Python parsing and will be much slower).

7. 　　　numpy会存在不兼容该问题
   
   1. http://t.csdnimg.cn/kI7PO

   2. # 先卸载安装numpy
      pip uninstall numpy
      
      # 然后安装旧版本的
      conda install numpy==1.19.2
      
      
      
      

8. 安装过程中存在的大部分问题, 大部分可以通过调整镜像源进行修改
   1. 　　参考: http://t.csdnimg.cn/Vytxe
   2.        参考:http://t.csdnimg.cn/PQHVu
9. 　　如果想使用jupyter notebook打开　　

   • 　　 jupyter notebook中添加Anaconda中的虚拟环境

     首先，还是在Anaconda Prompt中打开虚拟环境，忘了怎么进入虚拟环境的翻看上面1.3，进入虚拟环境之后输入下面代码，等待安装完成就行

     1. •   　　　　

          pip install ipykernel ipython -i http://pypi.douban.com/simple --trusted-host pypi.douban.com

           

          继续输入下面代码，大概意思就是将内核装填进tensorflow这个虚拟环境中

        • 参考: http://t.csdnimg.cn/LnFSJ

        •  

          ipython kernel install --user --name tensorflow