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When deploying and training a model using Windows Subsystem for Linux (WSL2), I need to use the GPU to speed up the training process. However, I encountered a dilemma: the video memory requirements of the model exceeded the capacity limit of the independent video card. In Windows systems, when the independent video memory is insufficient, the system will automatically call the shared video memory to meet the demand. Is there any way to implement a similar function in the WSL2 environment, that is, to call the shared video memory when the independent video memory is insufficient to continue training the model?
The text was updated successfully, but these errors were encountered:
If this a feature request, please reply with '/feature'. If this is a question, reply with '/question'. Otherwise please attach logs by following the instructions below, your issue will not be reviewed unless they are added. These logs will help us understand what is going on in your machine.
How to collect WSL logs
Download and execute collect-wsl-logs.ps1 in an administrative powershell prompt:
Once completed please upload the output files to this Github issue.
Click here for more info on logging
If you choose to email these logs instead of attaching to the bug, please send them to [email protected] with the number of the github issue in the subject, and in the message a link to your comment in the github issue and reply with '/emailed-logs'.
If this a feature request, please reply with '/feature'. If this is a question, reply with '/question'. Otherwise please attach logs by following the instructions below, your issue will not be reviewed unless they are added. These logs will help us understand what is going on in your machine.
When deploying and training a model using Windows Subsystem for Linux (WSL2), I need to use the GPU to speed up the training process. However, I encountered a dilemma: the video memory requirements of the model exceeded the capacity limit of the independent video card. In Windows systems, when the independent video memory is insufficient, the system will automatically call the shared video memory to meet the demand. Is there any way to implement a similar function in the WSL2 environment, that is, to call the shared video memory when the independent video memory is insufficient to continue training the model?
The text was updated successfully, but these errors were encountered: