做西装的网站,进一步加强舆情管控,校园推广的方式有哪些,七牛cdn wordpress1.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#xff1a;CUDA GPUs | NVIDIA Developer。打开后的界面大致如下#xff0c;只要里边有对应的型号就可以用GPU运算#xff0c;并且每一款设备都列出来相关的计算能力#xff08;Compu…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
(py10_paddleocr2.8_gpu_wubo) rootjettech-WS-C621E-SAGE-Series:~# lspci | grep nvida
(py10_paddleocr2.8_gpu_wubo) rootjettech-WS-C621E-SAGE-Series:~# lspci | grep VGA
3b:00.0 VGA compatible controller: NVIDIA Corporation GV104 [GeForce GTX 1180] (rev a1)
5e:00.0 VGA compatible controller: NVIDIA Corporation GV104 [GeForce GTX 1180] (rev a1)
86:00.0 VGA compatible controller: NVIDIA Corporation GV104 [GeForce GTX 1180] (rev a1)
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
(py10_paddleocr2.8_gpu_wubo) rootjettech-WS-C621E-SAGE-Series:~# ubuntu-drivers devices
WARNING:root:_pkg_get_support nvidia-driver-530: package has invalid Support PBheader, cannot determine support level
WARNING:root:_pkg_get_support nvidia-driver-515-server: package has invalid Support PBheader, cannot determine support level
WARNING:root:_pkg_get_support nvidia-driver-525-server: package has invalid Support PBheader, cannot determine support level/sys/devices/pci0000:3a/0000:3a:00.0/0000:3b:00.0
modalias : pci:v000010DEd00001E87sv00001458sd000037A8bc03sc00i00
vendor : NVIDIA Corporation
driver : nvidia-driver-530 - distro non-free recommended
driver : nvidia-driver-470-server - distro non-free
driver : nvidia-driver-440 - third-party non-free
driver : nvidia-driver-515 - third-party non-free
driver : nvidia-driver-450-server - distro non-free
driver : nvidia-driver-515-server - distro non-free
driver : nvidia-driver-418-server - distro non-free
driver : nvidia-driver-418 - third-party non-free
driver : nvidia-driver-460 - third-party non-free
driver : nvidia-driver-450 - third-party non-free
driver : nvidia-driver-470 - third-party non-free
driver : nvidia-driver-455 - third-party non-free
driver : nvidia-driver-495 - third-party non-free
driver : nvidia-driver-525 - third-party non-free
driver : nvidia-driver-465 - third-party non-free
driver : nvidia-driver-525-server - distro non-free
driver : nvidia-driver-410 - third-party non-free
driver : nvidia-driver-520 - third-party non-free
driver : nvidia-driver-510 - third-party non-free
driver : xserver-xorg-video-nouveau - distro free builtin
具体可以使用下面的命令安装
(py10_paddleocr2.8_gpu_wubo) rootjettech-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
(py10_paddleocr2.8_gpu_wubo) rootjettech-WS-C621E-SAGE-Series:~# nvidia-smi
(py10_paddleocr2.8_gpu_wubo) rootjettech-WS-C621E-SAGE-Series:~# nvidia-smi
Fri Jul 12 15:43:58 2024
---------------------------------------------------------------------------------------
| NVIDIA-SMI 530.41.03 Driver Version: 530.41.03 CUDA Version: 12.1 |
|-------------------------------------------------------------------------------------
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
||
| 0 NVIDIA GeForce RTX 2080 Off| 00000000:3B:00.0 Off | N/A |
| 32% 41C P8 3W / 225W| 8MiB / 8192MiB | 0% Default |
| | | N/A |
-------------------------------------------------------------------------------------
| 1 NVIDIA GeForce RTX 2080 Off| 00000000:5E:00.0 Off | N/A |
| 27% 41C P8 4W / 225W| 8MiB / 8192MiB | 0% Default |
| | | N/A |
-------------------------------------------------------------------------------------
| 2 NVIDIA GeForce RTX 2080 Off| 00000000:86:00.0 Off | N/A |
| 27% 36C P8 1W / 225W| 8MiB / 8192MiB | 0% Default |
| | | N/A |
-------------------------------------------------------------------------------------
| 3 NVIDIA GeForce RTX 2080 Off| 00000000:AF:00.0 Off | N/A |
| 31% 43C P8 9W / 225W| 80MiB / 8192MiB | 0% Default |
| | | N/A |
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
||
| 0 N/A N/A 52177 G /usr/lib/xorg/Xorg 4MiB |
| 1 N/A N/A 52177 G /usr/lib/xorg/Xorg 4MiB |
| 2 N/A N/A 52177 G /usr/lib/xorg/Xorg 4MiB |
| 3 N/A N/A 52177 G /usr/lib/xorg/Xorg 28MiB |
| 3 N/A N/A 52282 G /usr/bin/gnome-shell 46MiB |
---------------------------------------------------------------------------------------
(py10_paddleocr2.8_gpu_wubo) rootjettech-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 Version12.1”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runfilelocal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。
2中方式
1交互
./cuda_xxxxxxx_linux.run
2静默
./cuda_xxxxxxx_linux.run --silent --toolkit --samples
(py10_paddleocr2.8_gpu_wubo) rootjettech-WS-C621E-SAGE-Series:~# vim ~/.bashrcexport PATH/usr/local/cuda-12.0/bin${PATH::${PATH}}
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:
source ~/.bashrc
至此cuda安装完成输入nvcc -V命令查看cuda信息。
(py10_paddleocr2.8_gpu_wubo) rootjettech-WS-C621E-SAGE-Series:~# nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2022 NVIDIA Corporation
Built on Mon_Oct_24_19:12:58_PDT_2022
Cuda compilation tools, release 12.0, V12.0.76
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
cd /usr/local/cuda-xx.x/bin/
sudo ./cuda-uninstaller
sudo rm -rf /usr/local/cuda-xx.x
3.安装CUDNN cuDNNCUDA 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 Linuxx86_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
cp -Pcudnn*/include/cudnn*.h cuda/include/
cp -P cudnn*/lib/libcudnn* cuda/lib64/
chmod ar 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
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. (py10_paddleocr2.8_gpu_wubo) rootjettech-WS-C621E-SAGE-Series:/usr/local# tar -xf nccl_2.19.3-1cuda12.0_x86_64.txz
(py10_paddleocr2.8_gpu_wubo) rootjettech-WS-C621E-SAGE-Series:/usr/local# ln -sf nccl_2.19.3-1cuda12.0_x86_64 nccl
(py10_paddleocr2.8_gpu_wubo) rootjettech-WS-C621E-SAGE-Series:/usr/local# cd include/^C
(py10_paddleocr2.8_gpu_wubo) rootjettech-WS-C621E-SAGE-Series:/usr/local# cat /etc/ld.so.conf.d/nccl_2.19.3-1cuda12.0.conf
/usr/local/nccl/lib
(py10_paddleocr2.8_gpu_wubo) rootjettech-WS-C621E-SAGE-Series:/usr/local/include# ln -sf ../nccl/include nccl
没安装之前报错 安装之后 import paddlepaddle.utils.run_check()
Running verify PaddlePaddle program ...
I0712 17:30:32.906308 16653 program_interpreter.cc:212] New Executor is Running.
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
W0712 17:30:32.940363 16653 gpu_resources.cc:164] device: 0, cuDNN Version: 8.0.
I0712 17:30:35.770787 16653 interpreter_util.cc:624] Standalone Executor is Used.
PaddlePaddle works well on 1 GPU.Modified FLAGS detected
FLAGS(nameFLAGS_selected_gpus, current_value2, default_value)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.Modified FLAGS detected
FLAGS(nameFLAGS_selected_gpus, current_value3, default_value)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.Modified FLAGS detected
FLAGS(nameFLAGS_selected_gpus, current_value1, default_value)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.Modified FLAGS detected
FLAGS(nameFLAGS_selected_gpus, current_value0, default_value)I0712 17:30:39.014600 17094 tcp_utils.cc:181] The server starts to listen on IP_ANY:40265
I0712 17:30:39.014768 17094 tcp_utils.cc:130] Successfully connected to 127.0.0.1:40265
I0712 17:30:41.528342 17096 tcp_utils.cc:130] Successfully connected to 127.0.0.1:40265
I0712 17:30:41.528888 17096 process_group_nccl.cc:129] ProcessGroupNCCL pg_timeout_ 1800000
I0712 17:30:41.739022 17097 tcp_utils.cc:130] Successfully connected to 127.0.0.1:40265
I0712 17:30:41.776871 17097 process_group_nccl.cc:129] ProcessGroupNCCL pg_timeout_ 1800000
I0712 17:30:41.817867 17095 tcp_utils.cc:130] Successfully connected to 127.0.0.1:40265
I0712 17:30:41.840788 17095 process_group_nccl.cc:129] ProcessGroupNCCL pg_timeout_ 1800000
I0712 17:30:41.851110 17094 process_group_nccl.cc:129] ProcessGroupNCCL pg_timeout_ 1800000
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
W0712 17:30:43.394407 17096 gpu_resources.cc:164] device: 2, cuDNN Version: 8.0.
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
W0712 17:30:43.566882 17097 gpu_resources.cc:164] device: 3, cuDNN Version: 8.0.
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
W0712 17:30:43.629004 17095 gpu_resources.cc:164] device: 1, cuDNN Version: 8.0.
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
W0712 17:30:43.659112 17094 gpu_resources.cc:164] device: 0, cuDNN Version: 8.0.
I0712 17:30:46.433609 17096 process_group_nccl.cc:132] ProcessGroupNCCL destruct
I0712 17:30:46.433516 17095 process_group_nccl.cc:132] ProcessGroupNCCL destruct
I0712 17:30:46.435761 17097 process_group_nccl.cc:132] ProcessGroupNCCL destruct
I0712 17:30:46.437583 17094 process_group_nccl.cc:132] ProcessGroupNCCL destruct
I0712 17:30:46.843884 17168 tcp_store.cc:289] receive shutdown event and so quit from MasterDaemon run loop
PaddlePaddle works well on 4 GPUs.
PaddlePaddle is installed successfully! Lets start deep learning with PaddlePaddle now. 验证NCCL https://github.com/NVIDIA/nccl-tests
(py10_paddlepaddle2.6_paddleocr2.8_cuda12_gpu_wubo) rootjettech-WS-C621E-SAGE-Series:/data/wubo/paddleocr/env/nccl# ls
nccl-tests-2.13.9 nccl-tests-2.13.9.tar.gz
(py10_paddlepaddle2.6_paddleocr2.8_cuda12_gpu_wubo) rootjettech-WS-C621E-SAGE-Series:/data/wubo/paddleocr/env/nccl# cd nccl-tests-2.13.9/
(py10_paddlepaddle2.6_paddleocr2.8_cuda12_gpu_wubo) rootjettech-WS-C621E-SAGE-Series:/data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9# ls^C
(py10_paddlepaddle2.6_paddleocr2.8_cuda12_gpu_wubo) rootjettech-WS-C621E-SAGE-Series:/data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9#
(py10_paddlepaddle2.6_paddleocr2.8_cuda12_gpu_wubo) rootjettech-WS-C621E-SAGE-Series:/data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9# ls
doc LICENSE.txt Makefile README.md src verifiable
(py10_paddlepaddle2.6_paddleocr2.8_cuda12_gpu_wubo) rootjettech-WS-C621E-SAGE-Series:/data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9# make
make -C src build BUILDDIR/data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/build
make[1]: 进入目录“/data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/src”
Compiling timer.cc /data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/build/timer.o
Compiling /data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/build/verifiable/verifiable.o
Compiling all_reduce.cu /data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/build/all_reduce.o
Compiling common.cu /data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/build/common.o
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
Compiling all_gather.cu /data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/build/all_gather.o
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
Compiling broadcast.cu /data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/build/broadcast.o
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
Compiling reduce_scatter.cu /data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/build/reduce_scatter.o
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
Compiling reduce.cu /data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/build/reduce.o
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
Compiling alltoall.cu /data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/build/alltoall.o
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
Compiling scatter.cu /data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/build/scatter.o
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
Compiling gather.cu /data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/build/gather.o
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
Compiling sendrecv.cu /data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/build/sendrecv.o
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
Compiling hypercube.cu /data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/build/hypercube.o
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
make[1]: 离开目录“/data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9/src”
(py10_paddlepaddle2.6_paddleocr2.8_cuda12_gpu_wubo) rootjettech-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
# nThread 1 nGpus 8 minBytes 8 maxBytes 134217728 step: 2(factor) warmup iters: 5 iters: 20 agg iters: 1 validation: 1 graph: 0
#
# Using devices
jettech-WS-C621E-SAGE-Series: Test CUDA failure common.cu:894 invalid device ordinal.. jettech-WS-C621E-SAGE-Series pid 24945: Test failure common.cu:844
(py10_paddlepaddle2.6_paddleocr2.8_cuda12_gpu_wubo) rootjettech-WS-C621E-SAGE-Series:/data/wubo/paddleocr/env/nccl/nccl-tests-2.13.9# ls build/all_reduce_perf ^C
(py10_paddlepaddle2.6_paddleocr2.8_cuda12_gpu_wubo) rootjettech-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
# nThread 1 nGpus 4 minBytes 8 maxBytes 268435456 step: 2(factor) warmup iters: 5 iters: 20 agg iters: 1 validation: 1 graph: 0
#
# Using devices
# Rank 0 Group 0 Pid 25570 on jettech-WS-C621E-SAGE-Series device 0 [0x3b] NVIDIA GeForce RTX 2080
# Rank 1 Group 0 Pid 25570 on jettech-WS-C621E-SAGE-Series device 1 [0x5e] NVIDIA GeForce RTX 2080
# Rank 2 Group 0 Pid 25570 on jettech-WS-C621E-SAGE-Series device 2 [0x86] NVIDIA GeForce RTX 2080
# Rank 3 Group 0 Pid 25570 on jettech-WS-C621E-SAGE-Series device 3 [0xaf] NVIDIA GeForce RTX 2080
#
# out-of-place in-place
# size count type redop root time algbw busbw #wrong time algbw busbw #wrong
# (B) (elements) (us) (GB/s) (GB/s) (us) (GB/s) (GB/s) 8 2 float sum -1 15.71 0.00 0.00 0 15.63 0.00 0.00 016 4 float sum -1 17.28 0.00 0.00 0 15.91 0.00 0.00 032 8 float sum -1 17.18 0.00 0.00 0 16.18 0.00 0.00 064 16 float sum -1 17.14 0.00 0.01 0 15.87 0.00 0.01 0128 32 float sum -1 17.09 0.01 0.01 0 16.30 0.01 0.01 0256 64 float sum -1 17.23 0.01 0.02 0 15.90 0.02 0.02 0512 128 float sum -1 17.28 0.03 0.04 0 16.38 0.03 0.05 01024 256 float sum -1 17.13 0.06 0.09 0 15.81 0.06 0.10 02048 512 float sum -1 17.63 0.12 0.17 0 15.80 0.13 0.19 04096 1024 float sum -1 17.22 0.24 0.36 0 15.99 0.26 0.38 08192 2048 float sum -1 16.61 0.49 0.74 0 16.11 0.51 0.76 016384 4096 float sum -1 18.69 0.88 1.31 0 18.36 0.89 1.34 032768 8192 float sum -1 23.44 1.40 2.10 0 23.02 1.42 2.14 065536 16384 float sum -1 34.72 1.89 2.83 0 34.55 1.90 2.85 0131072 32768 float sum -1 63.00 2.08 3.12 0 62.87 2.08 3.13 0262144 65536 float sum -1 93.22 2.81 4.22 0 93.98 2.79 4.18 0524288 131072 float sum -1 148.2 3.54 5.31 0 148.1 3.54 5.31 01048576 262144 float sum -1 294.1 3.57 5.35 0 289.8 3.62 5.43 02097152 524288 float sum -1 595.3 3.52 5.28 0 592.2 3.54 5.31 04194304 1048576 float sum -1 1319.9 3.18 4.77 0 1317.6 3.18 4.77 08388608 2097152 float sum -1 3014.5 2.78 4.17 0 3100.5 2.71 4.06 016777216 4194304 float sum -1 6966.1 2.41 3.61 0 7025.2 2.39 3.58 033554432 8388608 float sum -1 13814 2.43 3.64 0 13829 2.43 3.64 067108864 16777216 float sum -1 28272 2.37 3.56 0 28100 2.39 3.58 0134217728 33554432 float sum -1 55028 2.44 3.66 0 55975 2.40 3.60 0268435456 67108864 float sum -1 111871 2.40 3.60 0 111223 2.41 3.62 0
# Out of bounds values : 0 OK
# Avg bus bandwidth : 2.23175
# 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
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
(py10_paddleocr2.8_gpu_wubo) rootjettech-WS-C621E-SAGE-Series:/data/wubo/paddleocr/env# conda create --name py10_paddleocr2.8_gpu_wubo python3.10
6. 安装PaddlePaddle
这里参照官网进行安装即可 (py10_paddleocr2.8_gpu_wubo) rootjettech-WS-C621E-SAGE-Series:/data/wubo/paddleocr/env# python -m pip install paddlepaddle-gpu2.6.1.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
import paddle
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
import torch
print(torch.cuda.is_available())
8.安装paddleocr客户端 命令行模式
