To manually specify the program to use a specific GPU, you can achieve it by: 1. Use the NVIDIA control panel under Windows, add the program in "Program settings" and select the target GPU; 2. Developers can use the CUDA_VISIBLE_DEVICES environment variable to limit the GPU number; 3. The Linux system switches the default GPU through the prime-select command or temporarily specify it with special variables; 4. After setting, use task manager, nvidia-smi or code to check whether it is effective. After the operation, you may need to restart the application or system and verify the results again.
Sometimes you may encounter this situation: your computer has two or more graphics cards installed, but a program uses the one you don't want it to use. For example, if you want a certain game to go to the independent graphics card, it uses the core graphics card; or if you want to specify a GPU running task when doing deep learning, the system automatically allocates other cards. At this time, you need to manually specify the program to use a specific GPU .

The following are some common methods, suitable for Windows systems and some Linux scenarios, which are simple to operate but are easily overlooked.

1. Set up through NVIDIA control panel under Windows system
If your computer is an NVIDIA graphics card combination (such as a core graphics card), this is the most direct way.
- Open the NVIDIA Control Panel
- Go to "Manage 3D Settings" → "Program Settings" tab
- Under "Select a preferred refresh rate for this program", click "Add"
- Browse and select the program you want to force (such as steam.exe or python.exe)
- Then in the "Preferred Refresh Rate", "Power Management Mode", etc., find "Preferred GPU" or "Preferred Adapter" and select the graphics card you want it to use
- Just save the application
Tips: If you are not sure about the startup path of the program, you can run the program first, and then right-click its process → open file location in the task manager, so that you can find the correct exe path.
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2. Specify the GPU using command line parameters or environment variables (for developers)
If you are running some programs that support CUDA (such as training models and running PyTorch scripts), you can specify which GPU to use by setting environment variables:
CUDA_VISIBLE_DEVICES=1 python your_script.py
The above command will allow the program to only see GPU No. 1 (number starts from 0), thus forcing the use of this card.
Note: This method is only effective for applications that support CUDA, and these variables will not be recognized by ordinary games or software.
3. Switch GPU through Prime Select under Linux system
If you are using Ubuntu or other Linux distributions and have dual graphics cards with NVIDIA and Intel, you can use the following command to view the current default GPU:
prime-select query
The output may be intel
or nvidia
. You can switch through the following command:
sudo prime-select nvidia
Effective after restart. It can also be temporarily switched for specific programs, but it needs to be used with variables such as __NV_PRIME_RENDER_OFFLOAD
, which is a little more complicated.
4. Check whether it is successful: Confirm that the program really uses the GPU you want to use
Don't think that everything will be fine after setting it up. It's better to verify it:
You can use Task Manager → Performance → GPU on Windows to view the usage of each GPU
NVIDIA users can also use the
nvidia-smi
command to monitor GPU usage in real timeIf you are running deep learning tasks, you can add it to the code:
import torch print(torch.cuda.current_device())
To confirm the currently used GPU number
Basically these are the methods. Just select the appropriate setting method according to your specific needs and system type. Some settings may require restarting the application or even the system to take effect. Remember to check it again after the operation.
The above is the detailed content of How to force a program to use a specific GPU. For more information, please follow other related articles on the PHP Chinese website!

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