這是機器未來的第2篇文章，由機器未來原創

寫在前面：

• ??博客簡介：專注AIoT領域，追逐未來時代的脈搏，記錄路途中的技術成長！

• ??專欄簡介：記錄博主從0到1掌握物體檢測工作流的過程，具備自定義物體檢測器的能力

• ??面向人群：具備深度學習理論基礎的學生或初級開發者

• ??專欄計劃：接下來會逐步發布跨入人工智能的系列博文，敬請期待

• ??Python零基礎快速入門系列

• ??快速入門Python數據科學系列

• ??人工智能開發環境搭建系列

• ??機器學習系列

• ??物體檢測快速入門系列

• ??自動駕駛物體檢測系列

• ??......

@[toc]

1. 概述

windows GPU深度學習開發環境的安裝包含顯卡驅動、cuda、cuDNN深度學習加速包、anaconda、tensorflow的安裝以及安裝源的配置，理解了本文，還可以安裝pytorch等其他開發框架。

2. GPU工具鏈安裝

2.1 GPU工具鏈的組成

Nvidia顯卡、顯卡驅動、cuda工具套件、cuDNN工具包四部分構成。

目前支持深度學習的顯卡基本上就是N卡，不論是硬件性能，還是最新的論文支持基本上都是N卡，所以不用在這里糾結了，采購時一定要上N卡。

2.2 安裝nvidia顯卡驅動

下載地址：https://www.nvidia.com/download/index.aspx?lang=en-us

根據提供的下載鏈接選擇電腦對應的最新顯卡驅動安裝即可，這里需要記錄一下顯卡驅動的版本，安裝cuda要用，我這臺老電腦顯卡驅動的文件名為425.31-notebook-win10-64bit-international-whql.exe，版本是425.31。

2.3 安裝cuda-通用并行計算架構平臺

• ??https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html

  根據上一個步驟下載的顯卡驅動的版本，選擇可支持的最新cuda toolkit版本，從上圖中可以看到支持425.31驅動版本的cuda toolkit版本是CUDA 10.1 (10.1.105 general release, and updates) ，因此選擇下載cuda10.1的版本。

• ??顯卡驅動cuda版本對照表

• ??https://developer.nvidia.com/cuda-toolkit-archive

• ??cuda下載地址：

• ??下載最新版本的cuda10.1

• ??選擇配置

• ??下載完成后，文件名為cuda_10.1.243_426.00_win10.exe，雙擊一直下一步安裝即可, 默認安裝路徑為C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1。

2.4 安裝cuDNN-深度學習GPU加速庫

• ??https://developer.nvidia.com/rdp/cudnn-archive

??cuDNN版本的選擇 以tensorflow常見cuda和cuDNN搭配為主。

下載的cuda版本為10.1版本，從列表中查找支持cuda10.1的最新的cuDNN版本是cuDNN7.6.5，下載后的cuDNN文件為cudnn-10.1-windows10-x64-v7.6.5.32.zip

• ??將下載的cudnn-10.1-windows10-x64-v7.6.5.32.zip解壓，然后將解壓后cuda文件夾下的文件或文件夾，完全拷貝到C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1目錄下即可。

2.5 測試GPU環境

PS?C:\Users\zhoushimin>?nvidia-smi
Mon?Apr?04?22:00:37?2022
+-----------------------------------------------------------------------------+
|?NVIDIA-SMI?426.00???????Driver?Version:?426.00???????CUDA?Version:?10.1?????|
|-------------------------------+----------------------+----------------------+
|?GPU??Name????????????TCC/WDDM?|?Bus-Id????????Disp.A?|?Volatile?Uncorr.?ECC?|
|?Fan??Temp??Perf??Pwr:Usage/Cap|?????????Memory-Usage?|?GPU-Util??Compute?M.?|
|===============================+======================+======================|
|???0??GeForce?GT?650M????WDDM??|?00000000:01:00.0?N/A?|??????????????????N/A?|
|?N/A???36C????P0????N/A?/??N/A?|?????40MiB?/??2048MiB?|?????N/A??????Default?|
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
|?Processes:???????????????????????????????????????????????????????GPU?Memory?|
|??GPU???????PID???Type???Process?name?????????????????????????????Usage??????|
|=============================================================================|
|????0????????????????????Not?Supported???????????????????????????????????????|+-----------------------------------------------------------------------------+

如果提示'nvidia-smi' 不是內部或外部命令，也不是可運行的程序，則可能環境變量沒有配置好，需要將 C:\Program Files\NVIDIA Corporation\NVSMI 目錄添加到系統環境變量中關閉命令提示符，重新打開輸入?nvidia-smi?就可以看到上面的輸出結果了。

PS?C:\Users\zhoushimin>?nvcc?-V
nvcc:?NVIDIA?(R)?Cuda?compiler?driver
Copyright?(c)?2005-2019?NVIDIA?Corporation
Built?on?Sun_Jul_28_19:12:52_Pacific_Daylight_Time_2019Cuda?compilation?tools,?release?10.1,?V10.1.243

可以看到cuda版本為10.1

type?"C:\Program?Files\NVIDIA?GPU?Computing?Toolkit\CUDA\v10.1\include\cudnn.h"?|?less

按回車鍵，直到輸出結果如下：

#ifndef?CUDNN_VERSION_H_
#define?CUDNN_VERSION_H_

#define?CUDNN_MAJOR?7
#define?CUDNN_MINOR?6
#define?CUDNN_PATCHLEVEL?5

#define?CUDNN_VERSION?(CUDNN_MAJOR?*?1000?+?CUDNN_MINOR?*?100?+?CUDNN_PATCHLEVEL)
#endif?/*?CUDNN_VERSION_H?*/

可知cuDNN的版本為7.6.5

如果找不到相關信息，可以使用如下命令測試

type?"C:\Program?Files\NVIDIA?GPU?Computing?Toolkit\CUDA\v10.1\include\cudnn_version.h"?|?less

3. 安裝Anaconda

3.1 概述

Anaconda，中文大蟒蛇，是一個開源的Python發行版本，其包含了conda、Python等180多個科學包及其依賴項。其簡化了python軟件包的安裝，并且自動關聯依賴，自動下載依賴的軟件包，避免不必要的沖突，另外，anaconda最重要的功能就是創建虛擬環境，實現各種項目開發框架及版本的隔離。

3.2 下載anaconda

Anaconda的官方下載速度較慢，推薦使用清華大學的源下載，盡量選擇日期較新的版本，根據操作系統版本選擇對應的版本。

• ??清華大學 https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/

3.3 安裝Anaconda

注意事項：不要有中文路徑，安裝過程無腦下一步即可。 安裝完畢之后，python、pip等軟件均已安裝完畢。

3.4 測試Anaconda

在命令提示符輸入conda -V查看conda版本，以確認conda環境是否生效。

PS?C:\Users\zhoushimin>?conda?-Vconda?4.10.3

3.5 配置Anaconda

由于Anaconda官方服務器在國外，安裝python軟件包時下載速度巨慢，因此需要配置國內安裝源，在這里使用清華大學的第三方源。

??首先找到配置文件【.condarc】，其在C盤用戶目錄下，如圖：

如果不存在，則直接創建即可。

channels:
-?defaults
show_channel_urls:?true
default_channels:
-?https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
-?https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/r
-?https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/msys2
custom_channels:
conda-forge:?https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
msys2:?https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
bioconda:?https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
menpo:?https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
pytorch:?https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
pytorch-lts:?https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloudsimpleitk:?https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud

conda?clean?-i

conda?create?-n?myenv?numpy

測試時會發現下載包的速度杠杠的，測試完畢后，刪除myenv虛擬環境

conda?remove?-n?myenv?--all

conda?config?--remove-key?channels

4. 配置pip國內安裝源

使用pip安裝軟件包時，有些安裝包下載速度特別慢，可以選擇國內的安裝源。

4.1 安裝源列表

以下安裝源可以選擇：

#?清華大學?
https://pypi.tuna.tsinghua.edu.cn/simple/

#?阿里云?
http://mirrors.aliyun.com/pypi/simple/

#?中國科技大學?https://pypi.mirrors.ustc.edu.cn/simple

4.2 安裝源的使用方式

4.2.1 臨時使用

以安裝tensorflow使用阿里云安裝源為例，在-i后面指定安裝源即可

pip?install?tensorflow_gpu==2.3?-i?http://mirrors.aliyun.com/pypi/simple/

4.2.2 永久使用

做如下配置后，無需再使用-i選項

pip?install?pip?-U?#升級?pip?到最新的版本后進行配置：pip?config?set?global.index-url?https://mirrors.aliyun.com/pypi/simple/

4.2.3 恢復官方安裝源

如果安裝源出現異常，恢復官方安裝源的方式如下：

pip?config?unset?global.index-url`

5. 安裝tensorflow

5.1 創建tensorflow虛擬環境

為了隔離不同項目可能對應的不同tensorflow版本或pytorch或python版本，強烈建議使用conda創建不同的虛擬環境以實現各種不同的開發環境的隔離。

我們需要根據開發框架、cuda版本等信息綜合選擇合適的開發環境，強烈不建議自行搭建開發環境適配開源框架，你會懷疑人生，時間周期可能是星期級別！

5.1.1 查看開源框架支持的python和tensorflow版本

博主的需求是安裝物體檢測API[OBJECT DETECTION API]，根據項目的最低配置可以了解到支持的是tensorflow-2.2 python3.6

5.1.2 查看cuda可支持的tensorflow版本

-?查看cuda、tensorflow對照關系表
????-?[經過測試的構建配置](https://tensorflow.google.cn/install/source?hl=zh-cn#gpu)?????![11](https://robot-futures-oss-zone.oss-cn-hangzhou.aliyuncs.com/imgs/11.png)

5.1.3 根據開源框架支持的tensorflow版本、cuda版本，選擇tensorflow-2.2，python-3.6，根據這些信息創建虛擬環境

conda?create?-n?[env_name]?python=[python?version]

詳細命令如下：

conda?create?-n?tensorflow-2.2-py36?python=3.6

輸出如下：

PS?C:\Users\zhoushimin>?conda?create?-n?tensorflow-2.2-py36?python=3.6
Collecting?package?metadata?(repodata.json):?done
Solving?environment:?done


==>?WARNING:?A?newer?version?of?conda?exists.?<==
current?version:?4.10.3
latest?version:?4.12.0

Please?update?conda?by?running

$?conda?update?-n?base?-c?defaults?conda



##?Package?Plan?##

environment?location:?D:\Tools\Anaconda3\envs\tensorflow-2.2-py36

added?/?updated?specs:
-?python=3.6


The?following?packages?will?be?downloaded:

package????????????????????|????????????build
---------------------------|-----------------
certifi-2021.5.30??????????|???py36haa95532_0?????????142?KB??defaults
pip-21.2.2?????????????????|???py36haa95532_0?????????2.1?MB??defaults
python-3.6.13??????????????|???????h3758d61_0????????17.7?MB??defaults
setuptools-58.0.4??????????|???py36haa95532_0?????????976?KB??defaults
wincertstore-0.2???????????|???py36h7fe50ca_0??????????13?KB??defaults
------------------------------------------------------------
Total:????????20.9?MB

The?following?NEW?packages?will?be?INSTALLED:

certifi????????????anaconda/pkgs/main/win-64::certifi-2021.5.30-py36haa95532_0
pip????????????????anaconda/pkgs/main/win-64::pip-21.2.2-py36haa95532_0
python?????????????anaconda/pkgs/main/win-64::python-3.6.13-h3758d61_0
setuptools?????????anaconda/pkgs/main/win-64::setuptools-58.0.4-py36haa95532_0
sqlite?????????????anaconda/pkgs/main/win-64::sqlite-3.38.2-h2bbff1b_0
vc?????????????????anaconda/pkgs/main/win-64::vc-14.2-h21ff451_1
vs2015_runtime?????anaconda/pkgs/main/win-64::vs2015_runtime-14.27.29016-h5e58377_2
wheel??????????????anaconda/pkgs/main/noarch::wheel-0.37.1-pyhd3eb1b0_0
wincertstore???????anaconda/pkgs/main/win-64::wincertstore-0.2-py36h7fe50ca_0

Proceed?([y]/n)??y

等待執行完畢。

5.2 切換至虛擬環境，安裝tensorflow

conda?activate?tensorflow-2.2-py36

pip?install?tensorflow==2.2.0

Successfully?built?termcolor
Installing?collected?packages:?urllib3,?pyasn1,?idna,?charset-normalizer,?zipp,?typing-extensions,?six,?rsa,?requests,?pyasn1-modules,?oauthlib,?cachetools,?requests-oauthlib,?importlib-metadata,?google-auth,?dataclasses,?werkzeug,?tensorboard-plugin-wit,?protobuf,?numpy,?markdown,?grpcio,?google-auth-oauthlib,?absl-py,?wrapt,?termcolor,?tensorflow-estimator,?tensorboard,?scipy,?opt-einsum,?keras-preprocessing,?h5py,?google-pasta,?gast,?astunparse,?tensorflowSuccessfully?installed?absl-py-1.0.0?astunparse-1.6.3?cachetools-4.2.4?charset-normalizer-2.0.12?dataclasses-0.8?gast-0.3.3?google-auth-1.35.0?google-auth-oauthlib-0.4.6?google-pasta-0.2.0?grpcio-1.44.0?h5py-2.10.0?idna-3.3?importlib-metadata-4.8.3?keras-preprocessing-1.1.2?markdown-3.3.6?numpy-1.19.5?oauthlib-3.2.0?opt-einsum-3.3.0?protobuf-3.19.4?pyasn1-0.4.8?pyasn1-modules-0.2.8?requests-2.27.1?requests-oauthlib-1.3.1?rsa-4.8?scipy-1.4.1?six-1.16.0?tensorboard-2.2.2?tensorboard-plugin-wit-1.8.1?tensorflow-2.2.0?tensorflow-estimator-2.2.0?termcolor-1.1.0?typing-extensions-4.1.1?urllib3-1.26.9?werkzeug-2.0.3?wrapt-1.14.0?zipp-3.6.0

5.3 測試tensorflow gpu環境

創建gpu_tf_test.py，拷貝如下代碼：

import?numpy?as?np
import?tensorflow?as?tf
from?tensorflow?import?keras
from?tensorflow.keras.models?import?Sequential
from?tensorflow.keras.layers?import?Dense
from?tensorflow.keras.optimizers?import?SGD

import?time?

#?config?=?tf.ConfigProto()
#?config.gpu_options.allow_growth?=?True
#?session?=?tf.Session(config=config)

#?啟用GPU
from?tensorflow.compat.v1?import?ConfigProto#?tf?2.x的寫法
config?=ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction=0.9
tf.compat.v1.Session(config=config)

#?構建數據集
X_data?=?np.linspace(-1,1,1000)[:,?np.newaxis]
noise?=?np.random.normal(0,0.05,X_data.shape)
y_data?=?np.square(X_data)?+?noise?+?0.5

print("shape")
print(X_data.shape)

#?構建神經網絡

model?=?Sequential()
model.add(Dense(10,?input_shape=(1,),?kernel_initializer='normal',?activation='relu'))
#model.add(Dense(5,?activation='relu'))
#?vs?分類為softmax激活
model.add(Dense(10000,?kernel_initializer='normal'))
model.add(Dense(10000,?kernel_initializer='normal'))
model.add(Dense(100,?kernel_initializer='normal'))
model.add(Dense(1,?kernel_initializer='normal'))
#sgd?=?SGD(lr=0.001)
model.compile(loss='mean_squared_error',?optimizer="sgd")?#adam
#?訓練?epoch?=?10,?30,?50,?view?the?results
start?=?time.time()
model.fit(X_data,?y_data,?epochs=50,?batch_size=16,?verbose=1)
end?=?time.time()

#?在原數據上預測
y_predict=model.predict(X_data)
#print(y_predict)
model.summary()

print("training?time?{}".format(end?-?start))

執行測試

#?切換至虛擬環境
conda?activate?tensorflow-2.2-py36
#?執行測試python?gpu_tf_test.py

輸出如下：

(tensorflow-2.2-py36)?C:\Users\zhoushimin\Desktop>python?gpu_test.py
2022-04-05?00:24:35.790114:?I?tensorflow/stream_executor/platform/default/dso_loader.cc:44]?Successfully?opened?dynamic?library?cudart64_101.dll
2022-04-05?00:24:39.180693:?I?tensorflow/compiler/xla/service/service.cc:168]?XLA?service?0x211ab3407a0?initialized?for?platform?Host?(this?does?not?guarantee?
that?XLA?will?be?used).?Devices:
2022-04-05?00:24:39.181152:?I?tensorflow/compiler/xla/service/service.cc:176]???StreamExecutor?device?(0):?Host,?Default?Version
2022-04-05?00:24:39.185023:?I?tensorflow/stream_executor/platform/default/dso_loader.cc:44]?Successfully?opened?dynamic?library?nvcuda.dll
2022-04-05?00:24:39.957288:?I?tensorflow/core/common_runtime/gpu/gpu_device.cc:1561]?Found?device?0?with?properties:?
pciBusID:?0000:01:00.0?name:?GeForce?GT?650M?computeCapability:?3.0
coreClock:?0.835GHz?coreCount:?2?deviceMemorySize:?2.00GiB?deviceMemoryBandwidth:?59.60GiB/s
2022-04-05?00:24:39.958038:?I?tensorflow/stream_executor/platform/default/dso_loader.cc:44]?Successfully?opened?dynamic?library?cudart64_101.dll
2022-04-05?00:24:39.965471:?I?tensorflow/stream_executor/platform/default/dso_loader.cc:44]?Successfully?opened?dynamic?library?cublas64_10.dll
2022-04-05?00:24:39.972214:?I?tensorflow/stream_executor/platform/default/dso_loader.cc:44]?Successfully?opened?dynamic?library?cufft64_10.dll
2022-04-05?00:24:39.975560:?I?tensorflow/stream_executor/platform/default/dso_loader.cc:44]?Successfully?opened?dynamic?library?curand64_10.dll
2022-04-05?00:24:39.985029:?I?tensorflow/stream_executor/platform/default/dso_loader.cc:44]?Successfully?opened?dynamic?library?cusolver64_10.dll
2022-04-05?00:24:39.989813:?I?tensorflow/stream_executor/platform/default/dso_loader.cc:44]?Successfully?opened?dynamic?library?cusparse64_10.dll
2022-04-05?00:24:40.014427:?I?tensorflow/stream_executor/platform/default/dso_loader.cc:44]?Successfully?opened?dynamic?library?cudnn64_7.dll
2022-04-05?00:24:40.016214:?I?tensorflow/core/common_runtime/gpu/gpu_device.cc:1657]?Ignoring?visible?gpu?device?(device:?0,?name:?GeForce?GT?650M,?pci?bus?id:?0000:01:00.0,?compute?capability:?3.0)?with?Cuda?compute?capability?3.0.?The?minimum?required?Cuda?capability?is?3.5.
2022-04-05?00:24:40.063972:?I?tensorflow/core/common_runtime/gpu/gpu_device.cc:1102]?Device?interconnect?StreamExecutor?with?strength?1?edge?matrix:
2022-04-05?00:24:40.064381:?I?tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]??????0?
2022-04-05?00:24:40.064707:?I?tensorflow/core/common_runtime/gpu/gpu_device.cc:1121]?0:???N
2022-04-05?00:24:40.068691:?I?tensorflow/compiler/xla/service/platform_util.cc:139]?StreamExecutor?cuda?device?(0)?is?of?insufficient?compute?capability:?3.5?required,?device?is?3.0
2022-04-05?00:24:40.069704:?I?tensorflow/compiler/jit/xla_gpu_device.cc:161]?Ignoring?visible?XLA_GPU_JIT?device.?Device?number?is?0,?reason:?Internal:?no?supported?devices?found?for?platform?CUDA
shape
(1000,?1)
2022-04-05?00:24:40.099506:?I?tensorflow/core/common_runtime/gpu/gpu_device.cc:1561]?Found?device?0?with?properties:?
pciBusID:?0000:01:00.0?name:?GeForce?GT?650M?computeCapability:?3.0
coreClock:?0.835GHz?coreCount:?2?deviceMemorySize:?2.00GiB?deviceMemoryBandwidth:?59.60GiB/s
2022-04-05?00:24:40.100197:?I?tensorflow/stream_executor/platform/default/dso_loader.cc:44]?Successfully?opened?dynamic?library?cudart64_101.dll
2022-04-05?00:24:40.100541:?I?tensorflow/stream_executor/platform/default/dso_loader.cc:44]?Successfully?opened?dynamic?library?cublas64_10.dll
2022-04-05?00:24:40.100821:?I?tensorflow/stream_executor/platform/default/dso_loader.cc:44]?Successfully?opened?dynamic?library?cufft64_10.dll
2022-04-05?00:24:40.101140:?I?tensorflow/stream_executor/platform/default/dso_loader.cc:44]?Successfully?opened?dynamic?library?curand64_10.dll
2022-04-05?00:24:40.101432:?I?tensorflow/stream_executor/platform/default/dso_loader.cc:44]?Successfully?opened?dynamic?library?cusolver64_10.dll
2022-04-05?00:24:40.101674:?I?tensorflow/stream_executor/platform/default/dso_loader.cc:44]?Successfully?opened?dynamic?library?cusparse64_10.dll
2022-04-05?00:24:40.101915:?I?tensorflow/stream_executor/platform/default/dso_loader.cc:44]?Successfully?opened?dynamic?library?cudnn64_7.dll
2022-04-05?00:24:40.103257:?I?tensorflow/core/common_runtime/gpu/gpu_device.cc:1657]?Ignoring?visible?gpu?device?(device:?0,?name:?GeForce?GT?650M,?pci?bus?id:?0000:01:00.0,?compute?capability:?3.0)?with?Cuda?compute?capability?3.0.?The?minimum?required?Cuda?capability?is?3.5.
2022-04-05?00:24:40.104045:?I?tensorflow/core/common_runtime/gpu/gpu_device.cc:1102]?Device?interconnect?StreamExecutor?with?strength?1?edge?matrix:
2022-04-05?00:24:40.104239:?I?tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]
2022-04-05?00:24:40.139180:?W?tensorflow/core/framework/cpu_allocator_impl.cc:81]?Allocation?of?400000000?exceeds?10%?of?free?system?memory.
2022-04-05?00:24:41.717374:?W?tensorflow/core/framework/cpu_allocator_impl.cc:81]?Allocation?of?400000000?exceeds?10%?of?free?system?memory.
2022-04-05?00:24:41.799759:?W?tensorflow/core/framework/cpu_allocator_impl.cc:81]?Allocation?of?400000000?exceeds?10%?of?free?system?memory.
Epoch?1/50
2022-04-05?00:24:42.666531:?W?tensorflow/core/framework/cpu_allocator_impl.cc:81]?Allocation?of?400000000?exceeds?10%?of?free?system?memory.
2022-04-05?00:24:42.841301:?W?tensorflow/core/framework/cpu_allocator_impl.cc:81]?Allocation?of?400000000?exceeds?10%?of?free?system?memory.
63/63?[==============================]?-?36s?567ms/step?-?loss:?0.2708
Epoch?2/50
63/63?[==============================]?-?35s?559ms/step?-?loss:?0.0703
Epoch?3/50
63/63?[==============================]?-?39s?614ms/step?-?loss:?0.0350
Epoch?4/50
63/63?[==============================]?-?41s?643ms/step?-?loss:?0.0140
Epoch?5/50
63/63?[==============================]?-?41s?650ms/step?-?loss:?0.0144
Epoch?6/5030/63?[=============>................]?-?ETA:?21s?-?loss:?0.0091

從日志中可以看到顯卡GeForce GT 650M已經加載成功了，cuda也加載成功了，因為GPU性能較弱，僅3.0，不滿足最小要求3.5，直接使用的cpu在運行。

6. 總結

總體來說，深度學習安裝環境的安裝還是比較麻煩的，涉及到

• ??依賴新卡驅動版本號

• ??依賴cuda版本和開發框架匹配的cuDNN版本

• ??依賴開源項目支持的版本

一般來說，一個項目一個環境，避免環境沖突。一個項目一個環境可以通過anaconda來實現，也可以使用docker來實現隔離。有一些框架在windows平臺支持不好，例如目標檢測框架MMDetection，盡可能還是使用linux環境來做開發。

以上是我的學習總結，有問題歡迎交流。

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