conda安装tensorflow-gpu简洁版_win10 tensorflow2.2 安装注意事项

学习新技术有两座大山，一座是安装配置环境，另一座是调试bug。

对于想学习人工智能开发的人来说，安装配置tensorflow是必不可少的一步，这个过程对于初次接触的人来说，到处都是火坑。下面大家跟我一起，看看都会踩到哪些雷！

一、安装tensorflow

如果安装tensorflow CPU版本，可以用这个命令：

pip install tensorflow

如果安装tensorflow GPU版本，可以用这个命令：

pip install tensorflow-gpu

但这样的话，因为访问的是国外的网站，速度慢的会让你怀疑人生！

所以我强烈建议大家用国内清华源的镜像

如果安装CPU版：

pip install -i https://pypi.tuna.tsinghua.edu.cn/simple tensorflow

如果安装GPU版：

pip install -i https://pypi.tuna.tsinghua.edu.cn/simple tensorflow-gpu

但有时使用清华源的地址，网络总是莫名其妙的中断，碰到这种情况可以换豆瓣源的镜像地址

https://pypi.douban.com/simple

试试，一般就OK了。

二、测试tensorflow安装是否成功

打印一下版本号，看一下安装是否成功

import tensorflow as tfprint(tf.__version__)

如果正常显示tensorflow的版本号，则一切OK；但如果出现下面的错误提示：

Failed to load the native TensorFlow runtime.

Traceback (most recent call last):File "<ipython-input-6-64156d691fe5>", line 1, in <module>import tensorflow as tfFile "C:UsersHonorVanAppDataRoamingPythonPython36site-packagestensorflow__init__.py", line 24, in <module>from tensorflow.python import pywrap_tensorflow  # pylint: disable=unused-importFile "C:UsersHonorVanAppDataRoamingPythonPython36site-packagestensorflowpython__init__.py", line 49, in <module>from tensorflow.python import pywrap_tensorflowFile "C:UsersHonorVanAppDataRoamingPythonPython36site-packagestensorflowpythonpywrap_tensorflow.py", line 74, in <module>raise ImportError(msg)ImportError: Traceback (most recent call last):File "C:UsersHonorVanAppDataRoamingPythonPython36site-packagestensorflowpythonpywrap_tensorflow.py", line 58, in <module>from tensorflow.python.pywrap_tensorflow_internal import *File "C:UsersHonorVanAppDataRoamingPythonPython36site-packagestensorflowpythonpywrap_tensorflow_internal.py", line 28, in <module>_pywrap_tensorflow_internal = swig_import_helper()File "C:UsersHonorVanAppDataRoamingPythonPython36site-packagestensorflowpythonpywrap_tensorflow_internal.py", line 24, in swig_import_helper_mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description)File "D:ProgramDataAnaconda3libimp.py", line 243, in load_modulereturn load_dynamic(name, filename, file)File "D:ProgramDataAnaconda3libimp.py", line 343, in load_dynamicreturn _load(spec)
ImportError: DLL load failed: 找不到指定的模块。Failed to load the native TensorFlow runtime.See https://www.tensorflow.org/install/errorsfor some common reasons and solutions.  Include the entire stack trace
above this error message when asking for help.

有人说是因为python版本和tensorflow版本不匹配，要么降低python版本，要么降低tensorflow版本。

如果不是python版本和tensorflow版本不匹配，比如我的当前环境为python3.6.5，安装tensorflow的版本为2.2，win10 64位，pip 20.0，版本完全匹配呀~ 那是为什么呢？

如果你前面配置的是CPU版，可能是缺少类库MSVCP140.DLL。

那么在这个地址https://www.microsoft.com/en-us/download/details.aspx?id=53587下载安装即可。

这样就能搞定CPU版的tensorflow。

三、搞定tensorflow GPU版的问题

如果你安装的是GPU版，那么接着安装cuda和cudnn，正常next就可以了，只需要注意环境变量path配一下，把cudnn的文件复制到cuda的目录里就OK了。

但是tensorflow对cuda和cudnn的版本也有要求。

tensorflow2.2，通过官网了解只能对应装10.1版本的cuda，10.0的还不行。

然后再找10.1cuda对应的cudnn7.6.5。这个显卡加速库就算是安装成功了。

但如果报驱动和运行版本不对应的错误：

CUDA driver version is insufficient for CUDA runtime versionailed.

可能需要卸载工具包，再安装对应的版本。

pip uninstall cudnn ； pip uninstall cudatoolkit

但在卸载前要先用pip list 命令看看有没有这些东东。如果没有，你可以尝试更新显卡驱动，到英伟达官网https://www.nvidia.cn/Download/index.aspx?lang=cn选择对应的显卡型号更新即可。

比如我的显卡是老掉渣的GTX1060，最后显示一切OK。

I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library nvcuda.dll
I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: 
pciBusID: 0000:01:00.0 name: GeForce GTX 1060 computeCapability: 6.1
coreClock: 1.6705GHz coreCount: 10 deviceMemorySize: 6.00GiB deviceMemoryBandwidth: 178.99GiB/s
I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll
I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll
I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll
I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll
I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll
I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll
I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x22345614f70 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default VersionI tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: 
pciBusID: 0000:01:00.0 name: GeForce GTX 1060 computeCapability: 6.1
coreClock: 1.6705GHz coreCount: 10 deviceMemorySize: 6.00GiB deviceMemoryBandwidth: 178.99GiB/s
I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll
I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll
I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll
I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll
I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll
I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll
I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:
I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0 
I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N 
I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 4700 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1060, pci bus id: 0000:01:00.0, compute capability: 6.1)
I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x223673b0e90 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): GeForce GTX 1060, Compute Capability 6.1

总的来说CPU版比较简单，GPU版坑比较多。

本文来自互联网用户投稿，该文观点仅代表作者本人，不代表本站立场。本站仅提供信息存储空间服务，不拥有所有权，不承担相关法律责任。如若转载，请注明出处：http://www.mzph.cn/news/505400.shtml

如若内容造成侵权/违法违规/事实不符，请联系多彩编程网进行投诉反馈email:809451989@qq.com，一经查实，立即删除！