How do I interpret obscure errors in low-level libraries like NumPy during AI training

Question

Can you tell me How do I interpret obscure errors in low-level libraries like NumPy during AI training?

Answer 1

To interpret obscure errors in low-level libraries like NumPy during AI training, you can refer to the following:

• Traceback Analysis: Analyze the full error message and traceback to locate the source of the issue.
• Input Shapes and Types: Ensure input tensors or arrays have correct shapes and types.
• Gradient Flow: Check for NaN or infinite values in gradients or activations.
• Use Debugging Tools: Use debuggers like pdb or logging to track intermediate steps.

Here is the code snippet you can refer to:

In the above code, we are using the following key points:

• Traceback: Read through the traceback to identify where the error originates.
• Input validation: Check shapes, types, and NaN/Inf values in data.
• Debugger: Use tools like pdb or logging to isolate the problem.