Kernel dying on NimbleBox while executing code? Debug using these steps

Resources being allocated to your NimbleBox instance are available only to you, and no-one else.

Kernel dying on a NimbleBox instance can be due to one of the following reasons:

  1. The RAM is being overloaded while trying to load a large dataset.
  2. The instance has automatically shut down due to the auto-shutdown timer.

To check the above, you can open a new terminal window from the Jupyter or VS Code menu and monitor the hardware by running the following commands:

  • nvidia-smi : to check what processes are running on GPU and the amount of memory allocated.
  • htop : to check which processes are using RAM and the amount of RAM left.

Some ways to fix the overloading of RAM is to reduce the batch size or model size.

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