1 查看是否有GPU

 下载和安装 Python 3.8

 下载和安装 PyCharm

 

2 下载 Anaconda

https://www.anaconda.com/

https://www.anaconda.com/products/individual

https://repo.anaconda.com/archive/Anaconda3-2020.11-Windows-x86_64.exe

 

3 安装 Anaconda

 

 

 

 

 

 

 

 

• Anaconda Navigator ：用于管理工具包和环境的图形用户界面，后续涉及的众多管理命令也可以在 Navigator 中手工实现。
• Jupyter notebook ：基于web的交互式计算环境，可以编辑易于人们阅读的文档，用于展示数据分析的过程。
• qtconsole ：一个可执行 IPython 的仿终端图形界面程序，相比 Python Shell 界面，qtconsole 可以直接显示代码生成的图形，实现多行代码输入执行，以及内置许多有用的功能和函数。
• Spyder ：一个使用Python语言、跨平台的、科学运算集成开发环境。

 

4 打开Anaconda

Run as administrator

 

 

5 管理虚环境

创建虚拟环境，为自己的程序安装单独的虚拟环境.
创建一个名称为 myenvpy38 的虚拟环境并指定python版本为3.8
conda create -n myenvpy38 python=3.8

environment location: E:\Eprogramfiles\Anaconda3\envs\myenvpy38

其中 E:\Eprogramfiles\Anaconda3\ 是anaconda的安装路径。

切换虚拟环境
切换到这个环境, 用activae命令，后面加上要切换的环境名称
conda activate myenvpy38

 

查看所有的环境
如果忘记了名称我们可以先用
conda env list


# To deactivate an active environment, use
# conda deactivate

 

conda env list

 

 

conda list

 

 

安装第三方包
 conda install packageName
 或者
 pip install packageName


卸载第三方包
 conda remove packageName
  或者
  pip uninstall packageName

6 安装PyTorch

 

以下步骤安装不成功：

https://pytorch.org/

 

conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch The following packages will be downloaded: package | build ---------------------------|----------------- cudatoolkit-10.2.89 | h74a9793_1 317.2 MB libuv-1.40.0 | he774522_0 255 KB lz4-c-1.9.3 | h2bbff1b_0 131 KB mkl-service-2.3.0 | py38h196d8e1_0 47 KB ninja-1.10.2 | py38h6d14046_0 247 KB pillow-8.1.0 | py38h4fa10fc_0 664 KB pytorch-1.7.1 |py3.8_cuda102_cudnn7_0 768.1 MB pytorch torchaudio-0.7.2 | py38 2.7 MB pytorch torchvision-0.8.2 | py38_cu102 7.2 MB pytorch ------------------------------------------------------------ Total: 1.07 GB The following NEW packages will be INSTALLED: blas pkgs/main/win-64::blas-1.0-mkl cudatoolkit pkgs/main/win-64::cudatoolkit-10.2.89-h74a9793_1 freetype pkgs/main/win-64::freetype-2.10.4-hd328e21_0 intel-openmp pkgs/main/win-64::intel-openmp-2020.2-254 jpeg pkgs/main/win-64::jpeg-9b-hb83a4c4_2 libpng pkgs/main/win-64::libpng-1.6.37-h2a8f88b_0 libtiff pkgs/main/win-64::libtiff-4.1.0-h56a325e_1 libuv pkgs/main/win-64::libuv-1.40.0-he774522_0 lz4-c pkgs/main/win-64::lz4-c-1.9.3-h2bbff1b_0 mkl pkgs/main/win-64::mkl-2020.2-256 mkl-service pkgs/main/win-64::mkl-service-2.3.0-py38h196d8e1_0 mkl_fft pkgs/main/win-64::mkl_fft-1.2.0-py38h45dec08_0 mkl_random pkgs/main/win-64::mkl_random-1.1.1-py38h47e9c7a_0 ninja pkgs/main/win-64::ninja-1.10.2-py38h6d14046_0 numpy pkgs/main/win-64::numpy-1.19.2-py38hadc3359_0 numpy-base pkgs/main/win-64::numpy-base-1.19.2-py38ha3acd2a_0 olefile pkgs/main/noarch::olefile-0.46-py_0 pillow pkgs/main/win-64::pillow-8.1.0-py38h4fa10fc_0 pytorch pytorch/win-64::pytorch-1.7.1-py3.8_cuda102_cudnn7_0 six pkgs/main/win-64::six-1.15.0-py38haa95532_0 tk pkgs/main/win-64::tk-8.6.10-he774522_0 torchaudio pytorch/win-64::torchaudio-0.7.2-py38 torchvision pytorch/win-64::torchvision-0.8.2-py38_cu102 typing_extensions pkgs/main/noarch::typing_extensions-3.7.4.3-py_0 xz pkgs/main/win-64::xz-5.2.5-h62dcd97_0 zstd pkgs/main/win-64::zstd-1.4.5-h04227a9_0 Proceed ([y]/n)? y Downloading and Extracting Packages torchaudio-0.7.2 | 2.7 MB | ######5 | 9% pytorch-1.7.1 | 768.1 MB | | 0% torchvision-0.8.2 | 7.2 MB | #2 | 2% ninja-1.10.2 | 247 KB | ################################################################################## | 100% mkl-service-2.3.0 | 47 KB | ################################################################################## | 100% libuv-1.40.0 | 255 KB | ################################################################################## | 100% pillow-8.1.0 | 664 KB | ################################################################################## | 100% cudatoolkit-10.2.89 | 317.2 MB | ###3 | 4% lz4-c-1.9.3 | 131 KB | ################################################################################## | 100% CondaHTTPError: HTTP 000 CONNECTION FAILED for url <https://conda.anaconda.org/pytorch/win-64/torchaudio-0.7.2-py38.tar.bz2> Elapsed: - An HTTP error occurred when trying to retrieve this URL. HTTP errors are often intermittent, and a simple retry will get you on your way. CondaHTTPError: HTTP 000 CONNECTION FAILED for url <https://conda.anaconda.org/pytorch/win-64/pytorch-1.7.1-py3.8_cuda102_cudnn7_0.tar.bz2> Elapsed: - An HTTP error occurred when trying to retrieve this URL. HTTP errors are often intermittent, and a simple retry will get you on your way. CondaHTTPError: HTTP 000 CONNECTION FAILED for url <https://conda.anaconda.org/pytorch/win-64/torchvision-0.8.2-py38_cu102.tar.bz2> Elapsed: - An HTTP error occurred when trying to retrieve this URL. HTTP errors are often intermittent, and a simple retry will get you on your way. ("Connection broken: ConnectionResetError(10054, 'An existing connection was forcibly closed by the remote host', None, 10054, None)", ConnectionResetError(10054, 'An existing connection was forcibly closed by the remote host', None, 10054, None)) (myenvpy38) E:\Eprogramfiles\Anaconda3\myenv> 改变安装策略： 1 查看显卡对应的 CUDA C盘搜索 nvcuda64.dll，右键，属性

 

 2 下载 cuda_11.0.3

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

http://developer.download.nvidia.com/compute/cuda/11.0.3/local_installers/cuda_11.0.3_451.82_win10.exe

文件3G左右，用迅雷下载比较快

 

3 安装 cuda_11.0.3

默认都是必须安装在C盘，超过4.5GB空间。自定义安装的时候可以选择路径 e:\Eprogramfiles\cuda11\dev\，大部分文件仍然安装到C盘了（C:\Program Files\NVIDIA GPU Computing Toolkit）

检查是否安装成功

e:\Eprogramfiles\cuda11\dev\bin>nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2020 NVIDIA Corporation
Built on Wed_Jul_22_19:09:35_Pacific_Daylight_Time_2020
Cuda compilation tools, release 11.0, V11.0.221
Build cuda_11.0_bu.relgpu_drvr445TC445_37.28845127_0

e:\Eprogramfiles\cuda11\dev\bin>

 

 

 

4 下载与 cuda 相应的 cudnn

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

https://developer.nvidia.com/compute/machine-learning/cudnn/secure/8.0.4/11.0_20200923/cudnn-11.0-windows-x64-v8.0.4.30.zip

 

解压 cudnn-11.0-windows-x64-v8.0.4.30.zip

 

前面安装的cuda的路径下也有这三个对应的文件夹（bin,include,lib），我们要做的就是用cudnn的三个文件夹覆盖cuda中对应的三个文件夹.直接粘过去就行了！

测试是否将cudnn安装好
首先进入CUDA的安装路径 -> extras -> demo_suite,  E:\Eprogramfiles\cuda11\dev\extras\demo_suite 里面有两个测试程序，一个是bandwidthTest.exe，一个是deviceQuery.exe

然后可以在demo_suite这个文件夹下打开cmd,运行那两个exe，结果如下图

 

E:\Eprogramfiles\cuda11\dev\extras\demo_suite>bandwidthTest.exe
[CUDA Bandwidth Test] - Starting...
Running on...

 Device 0: GeForce GTX 1050
 Quick Mode

 Host to Device Bandwidth, 1 Device(s)
 PINNED Memory Transfers
   Transfer Size (Bytes)        Bandwidth(MB/s)
   33554432                     12564.8

 Device to Host Bandwidth, 1 Device(s)
 PINNED Memory Transfers
   Transfer Size (Bytes)        Bandwidth(MB/s)
   33554432                     12848.8

 Device to Device Bandwidth, 1 Device(s)
 PINNED Memory Transfers
   Transfer Size (Bytes)        Bandwidth(MB/s)
   33554432                     95124.9

Result = PASS

NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled.

E:\Eprogramfiles\cuda11\dev\extras\demo_suite>deviceQuery.exe
deviceQuery.exe Starting...

 CUDA Device Query (Runtime API) version (CUDART static linking)

Detected 1 CUDA Capable device(s)

Device 0: "GeForce GTX 1050"
  CUDA Driver Version / Runtime Version          11.0 / 11.0
  CUDA Capability Major/Minor version number:    6.1
  Total amount of global memory:                 4096 MBytes (4294967296 bytes)
  ( 5) Multiprocessors, (128) CUDA Cores/MP:     640 CUDA Cores
  GPU Max Clock rate:                            1493 MHz (1.49 GHz)
  Memory Clock rate:                             3504 Mhz
  Memory Bus Width:                              128-bit
  L2 Cache Size:                                 524288 bytes
  Maximum Texture Dimension Size (x,y,z)         1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384)
  Maximum Layered 1D Texture Size, (num) layers  1D=(32768), 2048 layers
  Maximum Layered 2D Texture Size, (num) layers  2D=(32768, 32768), 2048 layers
  Total amount of constant memory:               zu bytes
  Total amount of shared memory per block:       zu bytes
  Total number of registers available per block: 65536
  Warp size:                                     32
  Maximum number of threads per multiprocessor:  2048
  Maximum number of threads per block:           1024
  Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
  Max dimension size of a grid size    (x,y,z): (2147483647, 65535, 65535)
  Maximum memory pitch:                          zu bytes
  Texture alignment:                             zu bytes
  Concurrent copy and kernel execution:          Yes with 5 copy engine(s)
  Run time limit on kernels:                     Yes
  Integrated GPU sharing Host Memory:            No
  Support host page-locked memory mapping:       Yes
  Alignment requirement for Surfaces:            Yes
  Device has ECC support:                        Disabled
  CUDA Device Driver Mode (TCC or WDDM):         WDDM (Windows Display Driver Model)
  Device supports Unified Addressing (UVA):      Yes
  Device supports Compute Preemption:            Yes
  Supports Cooperative Kernel Launch:            Yes
  Supports MultiDevice Co-op Kernel Launch:      No
  Device PCI Domain ID / Bus ID / location ID:   0 / 1 / 0
  Compute Mode:
     < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >

deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 11.0, CUDA Runtime Version = 11.0, NumDevs = 1, Device0 = GeForce GTX 1050
Result = PASS

 

5 安装PyTorch

=====================================================

 conda activate myenvpy38

镜像源配置一下, 仍然特别慢
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
conda config --set show_channel_urls yes

conda install pytorch torchvision torchaudio cudatoolkit=11.0 -c pytorch

 =====================================================

 

在下载的过程中下载torch1.7.1的时候比较慢，下载的过程中还会超时，故直接拷贝下载地址下载whl文件，安装whl文件。

单独下载：

https://download.pytorch.org/whl/torch_stable.html

https://download.pytorch.org/whl/cu110/torchvision-0.8.2%2Bcu110-cp38-cp38-win_amd64.whl

https://download.pytorch.org/whl/cu110/torch-1.7.1%2Bcu110-cp38-cp38-win_amd64.whl

https://download.pytorch.org/whl/torchaudio-0.7.2-cp38-none-win_amd64.whl

 

 conda activate myenvpy38

 

pip --default-timeout=1000 install -U numpy  -i http:///simple/ --trusted-host

pip --default-timeout=1000 install -U matplotlib.pyplot -i http:///simple/ --trusted-host
pip --default-timeout=1000 install -U matplotlib  -i http:///simple/ --trusted-host
 
pip --default-timeout=1000 install -U pandas -i http:///simple/ --trusted-host

pip --default-timeout=1000 install -U sklearn -i http:///simple/ --trusted-host

pip --default-timeout=1000 install -U typing-extensions -i http:///simple/ --trusted-host

 

安装有先后顺序，先torch

 E:\Eprogramfiles\Anaconda3\envs\myenvpy38>pip install "D:\software\torch-1.7.1+cu110-cp38-cp38-win_amd64.whl"

  E:\Eprogramfiles\Anaconda3\envs\myenvpy38>pip install D:\software\torchaudio-0.7.2-cp38-none-win_amd64.whl

 E:\Eprogramfiles\Anaconda3\envs\myenvpy38>pip install "D:\software\torchvision-0.8.2+cu110-cp38-cp38-win_amd64.whl"