深度学习入门——给Ubuntu系统安装CUDA、cuDNN、Anaconda、Tensorflow-GPU和pyTorch
内容导读
互联网集市收集整理的这篇技术教程文章主要介绍了深度学习入门——给Ubuntu系统安装CUDA、cuDNN、Anaconda、Tensorflow-GPU和pyTorch，小编现在分享给大家，供广大互联网技能从业者学习和参考。文章包含8906字，纯文字阅读大概需要13分钟。
内容图文
1、安装CUDA、cuDNN、Anaconda、Tensorflow-GPU和pyTorch：
准备安装文件：
ubuntu@ubuntu:~$ ls anaconda3 NVIDIA-Linux-x86_64-440.31.run Anaconda3-5.1.0-Linux-x86_64.sh snap cuda_10.0.130_410.48_linux.run 公共的 cudnn_samples_v7 模板 Downloads 视频 examples.desktop 图片 libcudnn7_7.4.2.24-1+cuda10.0_amd64.deb 文档 libcudnn7-dev_7.4.2.24-1+cuda10.0_amd64.deb 下载 libcudnn7-doc_7.4.2.24-1+cuda10.0_amd64.deb 音乐 NVIDIA_CUDA-10.0_Samples 桌面
安装后用 nvidia-smi 查询GPU参数：
ubuntu@ubuntu:~$ nvidia-smi
Sun Mar 1 20:24:23 2020
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 440.31 Driver Version: 440.31 CUDA Version: 10.2 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce RTX 207... Off | 00000000:01:00.0 On | N/A |
| 40% 18C P8 9W / 215W | 137MiB / 7979MiB | 7% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 1020 G /usr/lib/xorg/Xorg 135MiB |
+-----------------------------------------------------------------------------+
安装后用 nvcc -V 查询CUDA版本：
ubuntu@ubuntu:~$ nvcc -V nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2018 NVIDIA Corporation Built on Sat_Aug_25_21:08:01_CDT_2018 Cuda compilation tools, release 10.0, V10.0.130
安装CUDA后用CUDA自带的样例查询GPU和CUDA参数：
ubuntu@ubuntu:/usr/local/cuda/samples/1_Utilities/deviceQuery$ sudo ./deviceQuery
./deviceQuery Starting...
CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 1 CUDA Capable device(s)
Device 0: "GeForce RTX 2070 SUPER"
CUDA Driver Version / Runtime Version 10.2 / 10.0
CUDA Capability Major/Minor version number: 7.5
Total amount of global memory: 7979 MBytes (8366784512 bytes)
(40) Multiprocessors, ( 64) CUDA Cores/MP: 2560 CUDA Cores
GPU Max Clock rate: 1785 MHz (1.78 GHz)
Memory Clock rate: 7001 Mhz
Memory Bus Width: 256-bit
L2 Cache Size: 4194304 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: 65536 bytes
Total amount of shared memory per block: 49152 bytes
Total number of registers available per block: 65536
Warp size: 32
Maximum number of threads per multiprocessor: 1024
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: 2147483647 bytes
Texture alignment: 512 bytes
Concurrent copy and kernel execution: Yes with 3 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
Device supports Unified Addressing (UVA): Yes
Device supports Compute Preemption: Yes
Supports Cooperative Kernel Launch: Yes
Supports MultiDevice Co-op Kernel Launch: Yes
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 = 10.2, CUDA Runtime Version = 10.0, NumDevs = 1
Result = PASS
在 Anaconda下创建虚拟环境来安装cuDNN， TesnorFlow 的 GPU 版，以及pyTorch等软件。
ubuntu@ubuntu:~$ source activate py36 (py36) ubuntu@ubuntu:~$ conda list # packages in environment at /home/ubuntu/.conda/envs/py36: # # Name Version Build Channel _libgcc_mutex 0.1 main _tflow_select 2.1.0 gpu absl-py 0.9.0 py36_0 astor 0.8.0 py36_0 blas 1.0 mkl c-ares 1.15.0 h7b6447c_1001 ca-certificates 2020.1.1 0 certifi 2016.2.28 py36_0 cudatoolkit 10.0.130 0 cudnn 7.6.5 cuda10.0_0 cupti 10.0.130 0 gast 0.2.2 py36_0 google-pasta 0.1.8 py_0 grpcio 1.14.1 py36h9ba97e2_0 h5py 2.10.0 py36h7918eee_0 hdf5 1.10.4 hb1b8bf9_0 intel-openmp 2020.0 166 keras-applications 1.0.8 py_0 keras-preprocessing 1.1.0 py_1 libgcc-ng 9.1.0 hdf63c60_0 libgfortran-ng 7.3.0 hdf63c60_0 libprotobuf 3.11.4 hd408876_0 libstdcxx-ng 9.1.0 hdf63c60_0 markdown 3.1.1 py36_0 mkl 2020.0 166 mkl-service 2.3.0 py36he904b0f_0 mkl_fft 1.0.15 py36ha843d7b_0 mkl_random 1.1.0 py36hd6b4f25_0 numpy 1.18.1 py36h4f9e942_0 numpy-base 1.18.1 py36hde5b4d6_1 openssl 1.0.2u h7b6447c_0 opt_einsum 3.1.0 py_0 Pillow 7.0.0 <pip> pip 20.0.2 <pip> pip 9.0.1 py36_1 protobuf 3.11.4 py36he6710b0_0 python 3.6.2 0 readline 6.2 2 scipy 1.4.1 py36h0b6359f_0 setuptools 36.4.0 py36_1 six 1.14.0 py36_0 sqlite 3.13.0 0 tensorboard 1.15.0 pyhb230dea_0 tensorflow 1.15.0 gpu_py36h5a509aa_0 tensorflow-base 1.15.0 gpu_py36h9dcbed7_0 tensorflow-estimator 1.15.1 pyh2649769_0 tensorflow-gpu 1.15.0 h0d30ee6_0 termcolor 1.1.0 py36_1 tk 8.5.18 0 torch 1.4.0+cu92 <pip> torchvision 0.5.0+cu92 <pip> webencodings 0.5.1 py36_1 werkzeug 0.16.1 py_0 wheel 0.29.0 py36_0 wrapt 1.11.2 py36h7b6447c_0 xz 5.2.4 h14c3975_4 zlib 1.2.11 h7b6447c_3
参考：
https://blog.csdn.net/feifeiyechuan/article/details/94451052
https://blog.csdn.net/H_O_W_E/article/details/77370456
2、使用Anaconda创建虚拟环境：
查看已安装的虚拟环境：
conda info -e
指定Python版本创建虚拟环境：
conda create --name py36 python=3.6
查看虚拟环境安装过的依赖包：
conda list -n py36
给虚拟环境安装依赖包：
conda install -n py36 cudnn
激活虚拟环境：
source activate py36
退出虚拟环境：
source deactivate
给虚拟环境安装OpenCV-Python：
conda install -n py36 --channel https://conda.anaconda.org/menpo opencv3
参考：
https://zhuanlan.zhihu.com/p/44398592
https://zhuanlan.zhihu.com/p/55739118
https://zhuanlan.zhihu.com/p/94744929
https://blog.csdn.net/mjl960108/article/details/80141467
内容总结
以上是互联网集市为您收集整理的深度学习入门——给Ubuntu系统安装CUDA、cuDNN、Anaconda、Tensorflow-GPU和pyTorch全部内容，希望文章能够帮你解决深度学习入门——给Ubuntu系统安装CUDA、cuDNN、Anaconda、Tensorflow-GPU和pyTorch所遇到的程序开发问题。 如果觉得互联网集市技术教程内容还不错，欢迎将互联网集市网站推荐给程序员好友。
内容备注
版权声明：本文内容由互联网用户自发贡献，该文观点与技术仅代表作者本人。本站仅提供信息存储空间服务，不拥有所有权，不承担相关法律责任。如发现本站有涉嫌侵权/违法违规的内容， 请发送邮件至 gblab@vip.qq.com 举报，一经查实，本站将立刻删除。
内容手机端
扫描二维码推送至手机访问。
来源：【匿名】