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AI:paddlepaddle2.6,paddleorc2.8,cuda12,cudnn,nccl,python10环境

1.安装英伟达显卡驱动
首先需要到NAVIDIA官网去查自己的电脑是不是支持GPU运算。
网址是：CUDA GPUs | NVIDIA Developer。打开后的界面大致如下，只要里边有对应的型号就可以用GPU运算，并且每一款设备都列出来相关的计算能力（Compute Capability）。

系统层面查看当前安装的显卡型号：

  1. (py10_paddleocr2.8_gpu_wubo) root@jettech-WS-C621E-SAGE-Series:~# lspci | grep nvida
  2. (py10_paddleocr2.8_gpu_wubo) root@jettech-WS-C621E-SAGE-Series:~# lspci | grep VGA
  3. 3b:00.0 VGA compatible controller: NVIDIA Corporation GV104 [GeForce GTX 1180] (rev a1)
  4. 5e:00.0 VGA compatible controller: NVIDIA Corporation GV104 [GeForce GTX 1180] (rev a1)
  5. 86:00.0 VGA compatible controller: NVIDIA Corporation GV104 [GeForce GTX 1180] (rev a1)
  6. af:00.0 VGA compatible controller: NVIDIA Corporation GV104 [GeForce GTX 1180] (rev a1)

如果是ubuntu系统：明确了显卡性能后，接下来就开始在ubuntu系统安装对应的显卡驱动。

首先，检测NVIDIA图形卡和推荐的驱动程序的模型，在终端输入：

  1. (py10_paddleocr2.8_gpu_wubo) root@jettech-WS-C621E-SAGE-Series:~# ubuntu-drivers devices
  2. WARNING:root:_pkg_get_support nvidia-driver-530: package has invalid Support PBheader, cannot determine support level
  3. WARNING:root:_pkg_get_support nvidia-driver-515-server: package has invalid Support PBheader, cannot determine support level
  4. WARNING:root:_pkg_get_support nvidia-driver-525-server: package has invalid Support PBheader, cannot determine support level
  5. == /sys/devices/pci0000:3a/0000:3a:00.0/0000:3b:00.0 ==
  6. modalias : pci:v000010DEd00001E87sv00001458sd000037A8bc03sc00i00
  7. vendor   : NVIDIA Corporation
  8. driver   : nvidia-driver-530 - distro non-free recommended
  9. driver   : nvidia-driver-470-server - distro non-free
  10. driver   : nvidia-driver-440 - third-party non-free
  11. driver   : nvidia-driver-515 - third-party non-free
  12. driver   : nvidia-driver-450-server - distro non-free
  13. driver   : nvidia-driver-515-server - distro non-free
  14. driver   : nvidia-driver-418-server - distro non-free
  15. driver   : nvidia-driver-418 - third-party non-free
  16. driver   : nvidia-driver-460 - third-party non-free
  17. driver   : nvidia-driver-450 - third-party non-free
  18. driver   : nvidia-driver-470 - third-party non-free
  19. driver   : nvidia-driver-455 - third-party non-free
  20. driver   : nvidia-driver-495 - third-party non-free
  21. driver   : nvidia-driver-525 - third-party non-free
  22. driver   : nvidia-driver-465 - third-party non-free
  23. driver   : nvidia-driver-525-server - distro non-free
  24. driver   : nvidia-driver-410 - third-party non-free
  25. driver   : nvidia-driver-520 - third-party non-free
  26. driver   : nvidia-driver-510 - third-party non-free
  27. driver   : xserver-xorg-video-nouveau - distro free builtin

具体可以使用下面的命令安装：

  1. (py10_paddleocr2.8_gpu_wubo) root@jettech-WS-C621E-SAGE-Series:~#  ubuntu-drivers autoinstall

或者去官网下载驱动再手动安装的方式，命令官网上有。

下载 NVIDIA 官方驱动 | NVIDIA

NVIDIA GeForce 驱动程序 - N 卡驱动 | NVIDIA

安装完成后重启系统，然后在终端中输入命令检测是否安装成功：

  1. (py10_paddleocr2.8_gpu_wubo) root@jettech-WS-C621E-SAGE-Series:~# nvidia-smi
  2. (py10_paddleocr2.8_gpu_wubo) root@jettech-WS-C621E-SAGE-Series:~# nvidia-smi
  3. Fri Jul 12 15:43:58 2024       
  4. +---------------------------------------------------------------------------------------+
  5. | NVIDIA-SMI 530.41.03              Driver Version: 530.41.03    CUDA Version: 12.1     |
  6. |-----------------------------------------+----------------------+----------------------+
  7. | GPU  Name                  Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
  8. | Fan  Temp  Perf            Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
  9. |                                         |                      |               MIG M. |
  10. |=========================================+======================+======================|
  11. |   0  NVIDIA GeForce RTX 2080         Off| 00000000:3B:00.0 Off |                  N/A |
  12. | 32%   41C    P8                3W / 225W|      8MiB /  8192MiB |      0%      Default |
  13. |                                         |                      |                  N/A |
  14. +-----------------------------------------+----------------------+----------------------+
  15. |   1  NVIDIA GeForce RTX 2080         Off| 00000000:5E:00.0 Off |                  N/A |
  16. | 27%   41C    P8                4W / 225W|      8MiB /  8192MiB |      0%      Default |
  17. |                                         |                      |                  N/A |
  18. +-----------------------------------------+----------------------+----------------------+
  19. |   2  NVIDIA GeForce RTX 2080         Off| 00000000:86:00.0 Off |                  N/A |
  20. | 27%   36C    P8                1W / 225W|      8MiB /  8192MiB |      0%      Default |
  21. |                                         |                      |                  N/A |
  22. +-----------------------------------------+----------------------+----------------------+
  23. |   3  NVIDIA GeForce RTX 2080         Off| 00000000:AF:00.0 Off |                  N/A |
  24. | 31%   43C    P8                9W / 225W|     80MiB /  8192MiB |      0%      Default |
  25. |                                         |                      |                  N/A |
  26. +-----------------------------------------+----------------------+----------------------+
  27.                                                                                          
  28. +---------------------------------------------------------------------------------------+
  29. | Processes:                                                                            |
  30. |  GPU   GI   CI        PID   Type   Process name                            GPU Memory |
  31. |        ID   ID                                                             Usage      |
  32. |=======================================================================================|
  33. |    0   N/A  N/A     52177      G   /usr/lib/xorg/Xorg                            4MiB |
  34. |    1   N/A  N/A     52177      G   /usr/lib/xorg/Xorg                            4MiB |
  35. |    2   N/A  N/A     52177      G   /usr/lib/xorg/Xorg                            4MiB |
  36. |    3   N/A  N/A     52177      G   /usr/lib/xorg/Xorg                           28MiB |
  37. |    3   N/A  N/A     52282      G   /usr/bin/gnome-shell                         46MiB |
  38. +---------------------------------------------------------------------------------------+
  39. (py10_paddleocr2.8_gpu_wubo) root@jettech-WS-C621E-SAGE-Series:~#

上图显示cuda最高支持12.1版本

驱动版本Driver Version: 530.41.03

显卡型号：NVIDIA GeForce RTX 2080

显卡num：共计4个 每个显存大小8G

2.安装CUDA
首先要知道硬件支持的CUDA版本：
在上图右上角我们看到“CUDA Version：12.1”，这个表明对于这款显卡，我们后面要装的CUDA版本最高不能超过12.1。

其次要明确CUDA版本需求：

本文最终的目的是装好深度学习环境，这里指的是最终能够正常的使用pytorch[facebook公司]和paddlepaddle【百度公司】或TensorFlow【google公司】。这三款是当前使用比较多的深度学习框架，pytorch[facebook]侧重于科研和模型验证，paddlepaddle更适合工业级深度学习开发部署（当然也可以使用tensorflow）。

为了能够使用他们，我们接下来需要按照顺序安装CUDA、cuDNN、nccl、paddlepaddle、pytorch【省略】安装paddleocr。

在正式安装前我们首先要来确定当前的版本一致性，否则装到后面就会发现各种版本问题了。

接下来我们先看paddlepaddle和pytorch官网目前稳定版所支持的cuda。

paddlepaddle目前官网安装界面如下图所示：

pytorch官网安装界面：

尽量选择两个框架都支持的了，并且本机驱动也支持的CUDA版本。

接下来开始安装：

首先在英伟达官网下载cuda12进行安装即可。

照runfile（local）安装的方式简单，只需要在终端输入图中下方的两条NVIDIA推荐的命令就好了。

  1. 2中方式
  2. 1）交互
  3. ./cuda_xxxxxxx_linux.run
  4. 2）静默
  5. ./cuda_xxxxxxx_linux.run --silent --toolkit --samples
  1. (py10_paddleocr2.8_gpu_wubo) root@jettech-WS-C621E-SAGE-Series:~#  vim ~/.bashrc
  3. export PATH=/usr/local/cuda-12.0/bin${PATH:+:${PATH}}
  4. export LD_LIBRARY_PATH=/usr/local/cuda-12.0/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

最后，更新环境变量配置:

  1. source ~/.bashrc

至此cuda安装完成，输入nvcc -V命令查看cuda信息。

  1. (py10_paddleocr2.8_gpu_wubo) root@jettech-WS-C621E-SAGE-Series:~# nvcc -V
  2. nvcc: NVIDIA (R) Cuda compiler driver
  3. Copyright (c) 2005-2022 NVIDIA Corporation
  4. Built on Mon_Oct_24_19:12:58_PDT_2022
  5. Cuda compilation tools, release 12.0, V12.0.76
  6. Build cuda_12.0.r12.0/compiler.31968024_0

如果想要卸载CUDA（例如重新安装了驱动等情况），需要使用下面的命令：

  1. cd /usr/local/cuda-xx.x/bin/
  2. sudo ./cuda-uninstaller
  3. sudo rm -rf /usr/local/cuda-xx.x

3.安装CUDNN

cuDNN（CUDA Deep Neural Network library） 是由NVIDIA开发的一个深度学习GPU加速库。

目的和功能： cuDNN旨在提供高效、标准化的原语（基本操作）来加速深度学习框架（例如TensorFlow、PyTorch）在NVIDIA GPU上的运算。

专门为深度学习设计：cuDNN提供了为深度学习任务高度优化的函数，如：

  • 卷积操作
  • 池化操作
  • 激活函数
  • 归一化等

安装CUDNN的过程相对比较简单。上官网进行下载。

选择对应的CUDA版本，单击后选择cuDNN Library for Linux（x86_64）下载安装包。

然后打开终端输入类似下面的命令进行解压并拷贝安装：

  1. cp -Pcudnn*/include/cudnn*.h cuda/include/
  2. cp -P cudnn*/lib/libcudnn*  cuda/lib64/
  3. chmod a+r cuda/include/cudnn*.h cuda/lib64/libcudnn*

其实，cuDNN的安装本质上就是复制一堆的文件到CUDA中去。

我们可以使用如下的命令查看cuDNN的信息：

CUDN + cuDNN安装完成，我们可以监控一下gpu状态：

  1. watch -n 1 nvidia-smi

4.安装NCCL

由于深度学习分布式训练需要nccl支持，可以调用多张显卡计算，因此本小节来安装nccl。

首先从官网下载对应版本的nccl.

  1. (py10_paddleocr2.8_gpu_wubo) root@jettech-WS-C621E-SAGE-Series:/usr/local# tar -xf nccl_2.19.3-1+cuda12.0_x86_64.txz  
  2. (py10_paddleocr2.8_gpu_wubo) root@jettech-WS-C621E-SAGE-Series:/usr/local# ln -sf nccl_2.19.3-1+cuda12.0_x86_64 nccl 
  3. (py10_paddleocr2.8_gpu_wubo) root@jettech-WS-C621E-SAGE-Series:/usr/local# cd include/^C
  4. (py10_paddleocr2.8_gpu_wubo) root@jettech-WS-C621E-SAGE-Series:/usr/local# cat /etc/ld.so.conf.d/nccl_2.19.3-1+cuda12.0.conf 
  5. /usr/local/nccl/lib
  6. (py10_paddleocr2.8_gpu_wubo) root@jettech-WS-C621E-SAGE-Series:/usr/local/include# ln -sf ../nccl/include nccl

没安装之前报错：

安装之后：

  1. >>> import paddle
  2. >>> paddle.utils.run_check()
  3. Running verify PaddlePaddle program ... 
  4. I0712 17:30:32.906308 16653 program_interpreter.cc:212] New Executor is Running.
  5. W0712 17:30:32.906838 16653 gpu_resources.cc:119] Please NOTE: device: 0, GPU Compute Capability: 7.5, Driver API Version: 12.1, Runtime API Version: 12.0
  6. W0712 17:30:32.940363 16653 gpu_resources.cc:164] device: 0, cuDNN Version: 8.0.
  7. I0712 17:30:35.770787 16653 interpreter_util.cc:624] Standalone Executor is Used.
  8. PaddlePaddle works well on 1 GPU.
  9. ======================= Modified FLAGS detected =======================
  10. FLAGS(name='FLAGS_selected_gpus', current_value='2', default_value='')
  11. =======================================================================
  12. I0712 17:30:38.527948 17096 tcp_utils.cc:107] Retry to connect to 127.0.0.1:40265 while the server is not yet listening.
  13. ======================= Modified FLAGS detected =======================
  14. FLAGS(name='FLAGS_selected_gpus', current_value='3', default_value='')
  15. =======================================================================
  16. I0712 17:30:38.738694 17097 tcp_utils.cc:107] Retry to connect to 127.0.0.1:40265 while the server is not yet listening.
  17. ======================= Modified FLAGS detected =======================
  18. FLAGS(name='FLAGS_selected_gpus', current_value='1', default_value='')
  19. =======================================================================
  20. I0712 17:30:38.817551 17095 tcp_utils.cc:107] Retry to connect to 127.0.0.1:40265 while the server is not yet listening.
  21. ======================= Modified FLAGS detected =======================
  22. FLAGS(name='FLAGS_selected_gpus', current_value='0', default_value='')
  23. =======================================================================
  24. I0712 17:30:39.014600 17094 tcp_utils.cc:181] The server starts to listen on IP_ANY:40265
  25. I0712 17:30:39.014768 17094 tcp_utils.cc:130] Successfully connected to 127.0.0.1:40265
  26. I0712 17:30:41.528342 17096 tcp_utils.cc:130] Successfully connected to 127.0.0.1:40265
  27. I0712 17:30:41.528888 17096 process_group_nccl.cc:129] ProcessGroupNCCL pg_timeout_ 1800000
  28. I0712 17:30:41.739022 17097 tcp_utils.cc:130] Successfully connected to 127.0.0.1:40265
  29. I0712 17:30:41.776871 17097 process_group_nccl.cc:129] ProcessGroupNCCL pg_timeout_ 1800000
  30. I0712 17:30:41.817867 17095 tcp_utils.cc:130] Successfully connected to 127.0.0.1:40265
  31. I0712 17:30:41.840788 17095 process_group_nccl.cc:129] ProcessGroupNCCL pg_timeout_ 1800000
  32. I0712 17:30:41.851110 17094 process_group_nccl.cc:129] ProcessGroupNCCL pg_timeout_ 1800000
  33. W0712 17:30:43.391786 17096 gpu_resources.cc:119] Please NOTE: device: 2, GPU Compute Capability: 7.5, Driver API Version: 12.1, Runtime API Version: 12.0
  34. W0712 17:30:43.394407 17096 gpu_resources.cc:164] device: 2, cuDNN Version: 8.0.
  35. W0712 17:30:43.564615 17097 gpu_resources.cc:119] Please NOTE: device: 3, GPU Compute Capability: 7.5, Driver API Version: 12.1, Runtime API Version: 12.0
  36. W0712 17:30:43.566882 17097 gpu_resources.cc:164] device: 3, cuDNN Version: 8.0.
  37. W0712 17:30:43.627422 17095 gpu_resources.cc:119] Please NOTE: device: 1, GPU Compute Capability: 7.5, Driver API Version: 12.1, Runtime API Version: 12.0
  38. W0712 17:30:43.629004 17095 gpu_resources.cc:164] device: 1, cuDNN Version: 8.0.
  39. W0712 17:30:43.656805 17094 gpu_resources.cc:119] Please NOTE: device: 0, GPU Compute Capability: 7.5, Driver API Version: 12.1, Runtime API Version: 12.0
  40. W0712 17:30:43.659112 17094 gpu_resources.cc:164] device: 0, cuDNN Version: 8.0.
  41. I0712 17:30:46.433609 17096 process_group_nccl.cc:132] ProcessGroupNCCL destruct 
  42. I0712 17:30:46.433516 17095 process_group_nccl.cc:132] ProcessGroupNCCL destruct 
  43. I0712 17:30:46.435761 17097 process_group_nccl.cc:132] ProcessGroupNCCL destruct 
  44. I0712 17:30:46.437583 17094 process_group_nccl.cc:132] ProcessGroupNCCL destruct 
  45. I0712 17:30:46.843884 17168 tcp_store.cc:289] receive shutdown event and so quit from MasterDaemon run loop
  46. PaddlePaddle works well on 4 GPUs.
  47. PaddlePaddle is installed successfully! Let's start deep learning with PaddlePaddle now.

验证NCCL

  1.  https://github.com/NVIDIA/nccl-tests
  2. (py10_paddlepaddle2.6_paddleocr2.8_cuda12_gpu_wubo) root@jettech-WS-C621E-SAGE-Series:/data/wubo/paddleocr/env/nccl# ls
  3. nccl-tests-2.13.9  nccl-tests-2.13.9.tar.gz
  4. (py10_paddlepaddle2.6_paddleocr2.8_cuda12_gpu_wubo) root@jettech-WS-C621E-SAGE-Series:/data/wubo/paddleocr/env/nccl# cd nccl-tests-2.13.9/
  5. (py10_paddlepaddle2.6_paddleocr2.8_cuda12_gpu_wubo) root@jettech-WS-C621E-SAGE-Series:/data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9# ls^C
  6. (py10_paddlepaddle2.6_paddleocr2.8_cuda12_gpu_wubo) root@jettech-WS-C621E-SAGE-Series:/data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9# 
  7. (py10_paddlepaddle2.6_paddleocr2.8_cuda12_gpu_wubo) root@jettech-WS-C621E-SAGE-Series:/data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9# ls
  8. doc  LICENSE.txt  Makefile  README.md  src  verifiable
  9. (py10_paddlepaddle2.6_paddleocr2.8_cuda12_gpu_wubo) root@jettech-WS-C621E-SAGE-Series:/data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9# make
  10. make -C src build BUILDDIR=/data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/build
  11. make[1]: 进入目录“/data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/src”
  12. Compiling  timer.cc                            > /data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/build/timer.o
  13. Compiling /data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/build/verifiable/verifiable.o
  14. Compiling  all_reduce.cu                       > /data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/build/all_reduce.o
  15. Compiling  common.cu                           > /data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/build/common.o
  16. Linking  /data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/build/all_reduce.o > /data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/build/all_reduce_perf
  17. Compiling  all_gather.cu                       > /data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/build/all_gather.o
  18. Linking  /data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/build/all_gather.o > /data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/build/all_gather_perf
  19. Compiling  broadcast.cu                        > /data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/build/broadcast.o
  20. Linking  /data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/build/broadcast.o > /data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/build/broadcast_perf
  21. Compiling  reduce_scatter.cu                   > /data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/build/reduce_scatter.o
  22. Linking  /data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/build/reduce_scatter.o > /data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/build/reduce_scatter_perf
  23. Compiling  reduce.cu                           > /data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/build/reduce.o
  24. Linking  /data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/build/reduce.o > /data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/build/reduce_perf
  25. Compiling  alltoall.cu                         > /data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/build/alltoall.o
  26. Linking  /data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/build/alltoall.o > /data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/build/alltoall_perf
  27. Compiling  scatter.cu                          > /data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/build/scatter.o
  28. Linking  /data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/build/scatter.o > /data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/build/scatter_perf
  29. Compiling  gather.cu                           > /data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/build/gather.o
  30. Linking  /data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/build/gather.o > /data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/build/gather_perf
  31. Compiling  sendrecv.cu                         > /data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/build/sendrecv.o
  32. Linking  /data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/build/sendrecv.o > /data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/build/sendrecv_perf
  33. Compiling  hypercube.cu                        > /data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/build/hypercube.o
  34. Linking  /data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/build/hypercube.o > /data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/build/hypercube_perf
  35. make[1]: 离开目录“/data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/src”
  36. (py10_paddlepaddle2.6_paddleocr2.8_cuda12_gpu_wubo) root@jettech-WS-C621E-SAGE-Series:/data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9# ./build/all_reduce_perf -b 8 -e 128M -f 2 -g 8
  37. # nThread 1 nGpus 8 minBytes 8 maxBytes 134217728 step: 2(factor) warmup iters: 5 iters: 20 agg iters: 1 validation: 1 graph: 0
  38. #
  39. # Using devices
  40. jettech-WS-C621E-SAGE-Series: Test CUDA failure common.cu:894 'invalid device ordinal'
  41.  .. jettech-WS-C621E-SAGE-Series pid 24945: Test failure common.cu:844
  42. (py10_paddlepaddle2.6_paddleocr2.8_cuda12_gpu_wubo) root@jettech-WS-C621E-SAGE-Series:/data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9# ls build/all_reduce_perf ^C
  43. (py10_paddlepaddle2.6_paddleocr2.8_cuda12_gpu_wubo) root@jettech-WS-C621E-SAGE-Series:/data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9# ./build/all_reduce_perf -b 8 -e 256M -f 2 -g4
  44. # nThread 1 nGpus 4 minBytes 8 maxBytes 268435456 step: 2(factor) warmup iters: 5 iters: 20 agg iters: 1 validation: 1 graph: 0
  45. #
  46. # Using devices
  47. #  Rank  0 Group  0 Pid  25570 on jettech-WS-C621E-SAGE-Series device  0 [0x3b] NVIDIA GeForce RTX 2080
  48. #  Rank  1 Group  0 Pid  25570 on jettech-WS-C621E-SAGE-Series device  1 [0x5e] NVIDIA GeForce RTX 2080
  49. #  Rank  2 Group  0 Pid  25570 on jettech-WS-C621E-SAGE-Series device  2 [0x86] NVIDIA GeForce RTX 2080
  50. #  Rank  3 Group  0 Pid  25570 on jettech-WS-C621E-SAGE-Series device  3 [0xaf] NVIDIA GeForce RTX 2080
  51. #
  52. #                                                              out-of-place                       in-place          
  53. #       size         count      type   redop    root     time   algbw   busbw #wrong     time   algbw   busbw #wrong
  54. #        (B)    (elements)                               (us)  (GB/s)  (GB/s)            (us)  (GB/s)  (GB/s)       
  55.            8             2     float     sum      -1    15.71    0.00    0.00      0    15.63    0.00    0.00      0
  56.           16             4     float     sum      -1    17.28    0.00    0.00      0    15.91    0.00    0.00      0
  57.           32             8     float     sum      -1    17.18    0.00    0.00      0    16.18    0.00    0.00      0
  58.           64            16     float     sum      -1    17.14    0.00    0.01      0    15.87    0.00    0.01      0
  59.          128            32     float     sum      -1    17.09    0.01    0.01      0    16.30    0.01    0.01      0
  60.          256            64     float     sum      -1    17.23    0.01    0.02      0    15.90    0.02    0.02      0
  61.          512           128     float     sum      -1    17.28    0.03    0.04      0    16.38    0.03    0.05      0
  62.         1024           256     float     sum      -1    17.13    0.06    0.09      0    15.81    0.06    0.10      0
  63.         2048           512     float     sum      -1    17.63    0.12    0.17      0    15.80    0.13    0.19      0
  64.         4096          1024     float     sum      -1    17.22    0.24    0.36      0    15.99    0.26    0.38      0
  65.         8192          2048     float     sum      -1    16.61    0.49    0.74      0    16.11    0.51    0.76      0
  66.        16384          4096     float     sum      -1    18.69    0.88    1.31      0    18.36    0.89    1.34      0
  67.        32768          8192     float     sum      -1    23.44    1.40    2.10      0    23.02    1.42    2.14      0
  68.        65536         16384     float     sum      -1    34.72    1.89    2.83      0    34.55    1.90    2.85      0
  69.       131072         32768     float     sum      -1    63.00    2.08    3.12      0    62.87    2.08    3.13      0
  70.       262144         65536     float     sum      -1    93.22    2.81    4.22      0    93.98    2.79    4.18      0
  71.       524288        131072     float     sum      -1    148.2    3.54    5.31      0    148.1    3.54    5.31      0
  72.      1048576        262144     float     sum      -1    294.1    3.57    5.35      0    289.8    3.62    5.43      0
  73.      2097152        524288     float     sum      -1    595.3    3.52    5.28      0    592.2    3.54    5.31      0
  74.      4194304       1048576     float     sum      -1   1319.9    3.18    4.77      0   1317.6    3.18    4.77      0
  75.      8388608       2097152     float     sum      -1   3014.5    2.78    4.17      0   3100.5    2.71    4.06      0
  76.     16777216       4194304     float     sum      -1   6966.1    2.41    3.61      0   7025.2    2.39    3.58      0
  77.     33554432       8388608     float     sum      -1    13814    2.43    3.64      0    13829    2.43    3.64      0
  78.     67108864      16777216     float     sum      -1    28272    2.37    3.56      0    28100    2.39    3.58      0
  79.    134217728      33554432     float     sum      -1    55028    2.44    3.66      0    55975    2.40    3.60      0
  80.    268435456      67108864     float     sum      -1   111871    2.40    3.60      0   111223    2.41    3.62      0
  81. # Out of bounds values : 0 OK
  82. # Avg bus bandwidth    : 2.23175 
  83. #

5.安装anconda

首先下载Anaconda3
在[清华镜像]下载Linux版本的anaconda
清华镜像官网Anaconda下载

里选择的是Anaconda3-5.0.0-Linux-x86_64.sh

在用户文件夹下新建一个名为anaconda的文件夹，并将刚刚下载的文件放在此文件夹中，执行以下命令：

  1. bash Anaconda3-5.0.0-Linux-x86_64.sh

需要都很多页协议，不断按回车键跳过。
出现询问时就输入yes
之后选择默认的安装目录，按回车确定。
出现询问是否初始化或配置环境变量就输入yes
安装完成。
创建虚拟环境

  1. (py10_paddleocr2.8_gpu_wubo) root@jettech-WS-C621E-SAGE-Series:/data/wubo/paddleocr/env# conda create --name py10_paddleocr2.8_gpu_wubo python=3.10

6. 安装PaddlePaddle

这里参照官网进行安装即可：

  1. (py10_paddleocr2.8_gpu_wubo) root@jettech-WS-C621E-SAGE-Series:/data/wubo/paddleocr/env# python -m pip install paddlepaddle-gpu==2.6.1.post120 -f https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html

最后进行验证。

使用 python 或 python3 进入python解释器，输入：

GPU版本

  1. import paddle 
  2. paddle.utils.run_check()

如果出现PaddlePaddle is installed successfully!，说明您已成功安装。同时会显示当前可以并行使用的GPU数量。

7.安装Pytorch

参照官网命令进行安装：

最后验证安装是否成功。

打开Python,输入以下命令：

  1. import torch
  2. print(torch.cuda.is_available())

8.安装paddleocr客户端 命令行模式

标签： paddlepaddle python 人工智能
本文转载自: https://blog.csdn.net/Michaelwubo/article/details/140380986
版权归原作者 Michaelwubo 所有， 如有侵权，请联系我们删除。
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