學習新技術有兩座大山，一座是安裝配置環境，另一座是調試bug。

對于想學習人工智能開發的人來說，安裝配置tensorflow是必不可少的一步，這個過程對于初次接觸的人來說，到處都是火坑。下面大家跟我一起，看看都會踩到哪些雷！

一、安裝tensorflow

如果安裝tensorflow CPU版本，可以用這個命令：

pip install tensorflow

如果安裝tensorflow GPU版本，可以用這個命令：

pip install tensorflow-gpu

但這樣的話，因為訪問的是國外的網站，速度慢的會讓你懷疑人生！

所以我強烈建議大家用國內清華源的鏡像

如果安裝CPU版：

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

如果安裝GPU版：

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

但有時使用清華源的地址，網絡總是莫名其妙的中斷，碰到這種情況可以換豆瓣源的鏡像地址

https://pypi.douban.com/simple

試試，一般就OK了。

二、測試tensorflow安裝是否成功

打印一下版本號，看一下安裝是否成功

import tensorflow as tfprint(tf.__version__)

如果正常顯示tensorflow的版本號，則一切OK；但如果出現下面的錯誤提示：

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: 找不到指定的模塊。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.

有人說是因為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，比如我的當前環境為python3.6.5，安裝tensorflow的版本為2.2，win10 64位，pip 20.0，版本完全匹配呀~ 那是為什么呢？

如果你前面配置的是CPU版，可能是缺少類庫MSVCP140.DLL。

那么在這個地址https://www.microsoft.com/en-us/download/details.aspx?id=53587下載安裝即可。

這樣就能搞定CPU版的tensorflow。

三、搞定tensorflow GPU版的問題

如果你安裝的是GPU版，那么接著安裝cuda和cudnn，正常next就可以了，只需要注意環境變量path配一下，把cudnn的文件復制到cuda的目錄里就OK了。

但是tensorflow對cuda和cudnn的版本也有要求。

tensorflow2.2，通過官網了解只能對應裝10.1版本的cuda，10.0的還不行。

然后再找10.1cuda對應的cudnn7.6.5。這個顯卡加速庫就算是安裝成功了。

但如果報驅動和運行版本不對應的錯誤：

CUDA driver version is insufficient for CUDA runtime versionailed.

可能需要卸載工具包，再安裝對應的版本。

pip uninstall cudnn ； pip uninstall cudatoolkit

但在卸載前要先用pip list 命令看看有沒有這些東東。如果沒有，你可以嘗試更新顯卡驅動，到英偉達官網https://www.nvidia.cn/Download/index.aspx?lang=cn選擇對應的顯卡型號更新即可。

比如我的顯卡是老掉渣的GTX1060，最后顯示一切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

總的來說CPU版比較簡單，GPU版坑比較多。

總結

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