1, Installation of Anaconda
Anaconda Download
- Official website installation Anaconda
- (recommended) Tsinghua image source installation Open source software mirror station of Tsinghua University
Click the above link to enter the website, click the corresponding link and jump to the installation package download page
Download the version you need (for example, Anaconda3-2021.05-Linux-x86_64.sh), which is generally several versions lower than the latest version on the official website, but does not affect the use
Click OK
Anaconda installation
Next, find the installation package download location, open it with the terminal and enter it
bash Anaconda3-2021.05-Linux-x86_64.sh #Run the installation file, where the following statement is the name of your corresponding installation package
Then you can install it without brain. You should know all the English inside. When you encounter [ENTER], press ENTER, and when you encounter yes/no, ENTER yes
Finally, after the installation is completed, you will be prompted that the installation is successful. Then close the terminal and reopen the terminal. The shortcut key is Ctrl/Command + Alt + T. you will find that the word (base) appears at the beginning of the terminal statement, which proves that Anaconda installation is successful.
Anaconda verification
conda --version #Or the following statement conda -V #Check the version number of conda. If no error is reported, Anaconda installation is successful
Content reference from: Ubuntu 18.04 installing Anaconda3
2, Installation of Nvidia graphics card driver
Nvidia driver download and installation
Update, global update
sudo apt upgrade #UPDATE statement
sudo add-apt-repository ppa:graphics-drivers #Add NVIDA graphics card driver library ubuntu-drivers devices #Show installable drivers
Select your favorite driver number for installation. The latest version with the largest version number
sudo apt install nvidia-driver-495 #Installing the 4595 drive
Wait for installation. Generally, the driver installation program will pop up when the installation progress is about 70%. What we have to do is
1. Esc determines the user agreement
2. Remember to set the password and press Enter
3. Repeat the password and press Enter
4. Continue until installation is complete
Nvidia driver activation
Note: read carefully
Restart will pop up the driver installation interface, blue screen, don't panic!!
Control the keyboard and select the second E or something. I can't remember the details. I can't find the follow-up supplement
Then select Continue, yes or something
Until you enter the password interface, enter the just set password. Note that the password is invisible. It doesn't matter if you enter an error. You can re-enter it. Press enter after entering
Finally, select the Reboot option and the system will restart again
At this point, the installation of the graphics card driver is completed
When restarting, you must restart and prepare to activate the graphics card driver. This is actually the command before you pay attention to the above. What you fear is that you are at a loss after restarting. What to do if you are insufficient will lead to an error
shutdown -r now #Restart command, you can restart manually or enter this code at the terminal
Nvidia drive verification
After restart, open the terminal, enter the following code, and the following information appears to prove that the installation of the graphics card driver is completed
nvidia-smi #
Content reference from: Simple record: install GPU driver, CUDA and cuDNN
3, CUDA installation
CUDA Download
Enter CUDA official website CUDA official website
Check the CUDA version number in the above NVIDIA SMI information and download the corresponding version, but it is recommended not to download the too new version. Download according to the version number recommended on the official website of pytoch. The version of 11.3 is recommended here
CUDA installation
Then enter the 11.3 version of CUDA's official website to start the selection, and then the download code appears. It is recommended to select deb(network)
Enter the corresponding code under the official website of the terminal in turn
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-ubuntu2004.pin sudo mv cuda-ubuntu2004.pin /etc/apt/preferences.d/cuda-repository-pin-600 sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/7fa2af80.pub sudo add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/ /" sudo apt-get updatesudo apt-get -y install cuda
CUDA verification
Then, add the environment variable and check the CUDA version. The version number indicates that the CUDA installation is successful
export PATH=$PATH:/usr/local/cuda/bin #Add environment variable nvcc -V #Check CUDA version
Basically, the gcc version of ubuntu is higher now. Use the following code to lower the version before proceeding with the next steps
cat /proc/driver/nvidia/version #View the original gcc version sudo apt install build-essential sudo apt -y install gcc-8 g++-8 #Install gcc 8, which can be adjusted according to the actual situation of error reporting. sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-8 8 sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-8 8 sudo update-alternatives --config gcc #Adjust the default gcc, and install gcc 8 without execution
Install CUDA samples for CUDA calibration
cuda-install-samples-11.x.sh ~ #Install cuda 11.x samples into the ~ directory and replace x with your version number cd ~/NVIDIA_CUDA-11.x_Samples #Enter the Sample directory make #It takes a little time. If the system version does not match, the gcc version may cause an error ./1_Utilities/deviceQuery/deviceQuery #Perform inspection procedures
The following results appear, and Result = PASS appears in the last line, which proves that CUDA works normally
Content reference from: Simple record: install GPU driver, CUDA and cuDNN
4, Pytoch installation
Replace the conda mirror source
Replace the conda Tsinghua image source, because the Internet is very slow. Don't ask me how I know. I want to cry without tears
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/ conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2/ conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/ conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/ conda config --set show_channel_urls yes
Create conda virtual environment
Create conda virtual environment
#Create a virtual environment called python with built-in Python version 3.8. You can decide what name you think of conda create -n pytorch python=3.8
Install pytoch!!!
Install pytoch. The following 11.3 is the version number of cuda you downloaded above. Don't make a mistake. You have to download more than 2G. Wait patiently. It's still relatively fast
conda install pytorch torchvision cudatoolkit=11.3
After installation, enter the virtual environment
conda activate pytorch #You can type whatever name you choose
Run python
python #Just enter python
Verify whether the pytorch package is successfully installed and whether the graphics card is called
be careful
Pytorch people call him pytorch, but the bag they pour in is torch
import torch torch.cuda.is_available()
True appears, which proves that the pytorch package is installed successfully and the graphics card is called successfully
