1, the installation CUDA, cuDNN, Anaconda, Tensorflow-GPU and pyTorch:
Ready to install the file:
@ 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 public cudnn_samples_v7 templates Downloads Video examples.desktop picture libcudnn7_7. + 4.2.24-1 cuda10.0_amd64.deb document libcudnn7-dev_7.4.2.24-1 + cuda10.0_amd64.deb-doc_7.4.2.24-1 + download libcudnn7 cuda10.0_amd64.deb music NVIDIA_CUDA-10.0_Samples Desktop
After you install the nvidia-smi GPU query with parameters:
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 |
+-----------------------------------------------------------------------------+
After installing the inquiry CUDA version with nvcc -V:
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
After the installation queries CUDA CUDA CUDA GPU and comes with sample parameters:
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
Create a virtual environment in Anaconda to install cuDNN, TesnorFlow the GPU version, and pyTorch software.
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
reference:
https://blog.csdn.net/feifeiyechuan/article/details/94451052
https://blog.csdn.net/H_O_W_E/article/details/77370456
2, the use of Anaconda to create a virtual environment:
View virtual environment installed:
conda info -e
Specifies the version of Python to create a virtual environment:
conda create --name py36 python=3.6
View virtual environment dependencies installed:
conda list -n py36
Install dependencies to the virtual environment:
conda install -n py36 cudnn
Activate the virtual environment:
source activate py36
Exit the virtual environment:
source deactivate
Virtual environment to install OpenCV-Python:
conda install -n py36 --channel https://conda.anaconda.org/menpo opencv3
reference:
https://zhuanlan.zhihu.com/p/44398592
https://zhuanlan.zhihu.com/p/55739118