FAQ
Frequently Asked Questions
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A: First, use the top and nvidia-smi commands to check CPU and GPU usage, respectively. If the CPU is consistently at 100% and the GPU is idle, it is highly likely that the program is stuck on a GPU call; in that case, please refer to the answer to the previous question.If the issue is not caused by the reasons mentioned above, you’ll need to debug the code. We recommend adding print statements to log output at key lines of code, then running the program to pinpoint the exact line where it’s get
If installing certain dependencies causes JupyterLab’s dependencies to be inadvertently upgraded or downgraded, affecting JupyterLab’s operation, you can log in to the instance via SSH and run the command jupyter-lab to verify. If the command returns an error, this confirms the issue. In that case, you can resolve it by reinstalling via pip install jupyterlab and then restarting the instance.
If you notice continuous memory growth while plotting or memory growth caused by switching models when using the SD WebUI in the AUTOMATIC1111 project, you can refer to the following methods to resolve the issue
If you encounter the following error in the VS output while establishing a remote connection via VS Code, you can refer to the solution below
First, use the nvidia-smi command to check GPU usage
1. If you encounter issues with SSH-based connections using VSCode, PyCharm, or similar tools, please troubleshoot and resolve them as follows: 2. If you encounter connection issues when using XShell or MobaXterm, please refer to the documentation; there is no need to continue reading the troubleshooting steps below.
First, use the nvidia-smi command to check GPU usage. If you find that the program has closed but there is still GPU memory in use, this indicates that a residual process is occupying the memory. In that case, free it up as follows:
Note: Files deleted using the rm command cannot be recovered.