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Common Warning Messages

Quick reference for common warning messages.

Warning Meaning Action
UserWarning: The dataloader does not have many workers Slow data loading Increase num_workers
UserWarning: Trying to infer the batch_size Can't determine batch size Explicitly set in datamodule
UserWarning: The number of training batches is very small Epoch finishes quickly Increase dataset size or reduce batch size
FutureWarning: Passing (type, 1) for ndim Deprecated numpy usage Update to latest version
UserWarning: Mixed precision is not supported on CPU Using wrong accelerator Switch to GPU or remove precision flag

Understanding Warnings

UserWarning

User warnings are informational and usually don't break training: - Data loader warnings: See Slow Training - Batch size warnings: Usually safe to ignore if training works - Mixed precision warnings: See Device Errors

FutureWarning

Future warnings indicate deprecated features: - Update dependencies to latest versions - Check AutoTimm documentation for new APIs

DeprecationWarning

Deprecation warnings suggest updating code: - Review recent changes in package documentation - Update to new recommended patterns

Suppressing Warnings

import warnings

# Suppress specific warning
warnings.filterwarnings("ignore", message=".*dataloader.*")

# Suppress all warnings (not recommended)
warnings.filterwarnings("ignore")