學習新技術有兩座大山，一座是配置環境，另一座是調試bug
最近想了解一下新版的tensorflow，於是乎就打算配置一下環境跑幾個模型玩玩，誰知道竟然是這麼坎坷。
安裝tensorflow2.2 版本 cpu和gpu版的時候
直接pip install tensorflow
肯定是不行的，慢到令人發狂
在這裏插入圖片描述
平常安裝工具都使用清華源
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple tensorflow
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple tensorflow-gpu
但是不知道爲什麼，使用清華源地址，網絡老是莫名其妙的中斷，但是本地測了一下網絡也沒啥問題。
於是乎換了一個包不是很全的豆瓣地址
http://pypi.douban.com/simple/
竟然cpu和gpu版穩穩當當的一遍安裝成功
然後噩夢從此開始
打印一下版本號，看一下環境
import tensorflow as tf
print(tf.__version__)
Failed to load the native TensorFlow runtime.
Traceback (most recent call last):
File "<ipython-input-6-64156d691fe5>", line 1, in <module>
import tensorflow as tf
File "C:\Users\HonorVan\AppData\Roaming\Python\Python36\site-packages\tensorflow\__init__.py", line 24, in <module>
from tensorflow.python import pywrap_tensorflow # pylint: disable=unused-import
File "C:\Users\HonorVan\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\__init__.py", line 49, in <module>
from tensorflow.python import pywrap_tensorflow
File "C:\Users\HonorVan\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\pywrap_tensorflow.py", line 74, in <module>
raise ImportError(msg)
ImportError: Traceback (most recent call last):
File "C:\Users\HonorVan\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\pywrap_tensorflow.py", line 58, in <module>
from tensorflow.python.pywrap_tensorflow_internal import *
File "C:\Users\HonorVan\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 28, in <module>
_pywrap_tensorflow_internal = swig_import_helper()
File "C:\Users\HonorVan\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 24, in swig_import_helper
_mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description)
File "D:\ProgramData\Anaconda3\lib\imp.py", line 243, in load_module
return load_dynamic(name, filename, file)
File "D:\ProgramData\Anaconda3\lib\imp.py", line 343, in load_dynamic
return _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/errors
for some common reasons and solutions. Include the entire stack trace
above this error message when asking for help.
於是查看官網安裝錯誤說明和google一下。
有人說python版本和tensorflow版本不匹配，要麼降低python版本，要麼降低tensorflow版本。
但是去看了一下官網，沒有問題，我的當前環境爲python3.6.5，安裝tensorflow的版本爲2.2，win10 64位，pip 20.0，OK呀~ 但是爲什麼呢？
有了官網的定心丸，於是開始搜索其他原因，想着先把gpu版卸了，先配置cpu版的。
卸完gpu版還存這個問題。
於是又打開google。
說是缺少一個類庫MSVCP140.DLL。
於是https://www.microsoft.com/en-us/download/details.aspx?id=53587下載安裝。
cpu版算是搞定了。
接下來就是萬惡的gpu加速版了。
依然使用豆瓣源安裝沒啥大問題。
然後安裝cuda和cudnn也沒啥大問題，記得環境變量path配一下。把cudnn的文件複製到cuda的目錄裏就OK了。
但是tf對cuda和cudnn的版本也有要求。
通過報錯，瞭解只能對應裝10.1版本的cuda，10.0的還不行。
然後再找10.1cuda對應的cudnn7.6.5。這個顯卡加速庫算是安裝成功了。
然後又來個驅動和運行版本不對應。
CUDA driver version is insufficient for CUDA runtime versionailed.
看了些文章，說是要卸載工具包，在對應版本安裝。
pip uninstall cudnn ； pip uninstall cudatoolkit
但是看了一眼pip list 根本沒有這個東西。
也有人說更新一下顯卡驅動。
我的筆記本買的比較早了顯卡比較垃圾。
於是找到官網https://www.nvidia.cn/Download/index.aspx?lang=cn
更新了一下。
見證奇蹟的時刻。
終於成功了。
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 Version
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/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
安裝個環境折騰了一下午。
終於翻過了一座大山。
cpu版比較簡單，gpu版坑比較多。