Logger and Signals

Weightslab logger behavior is similar in spirit to TensorBoard: it tracks scalar evolution and per-sample context.

What gets logged

• Scalar signals (losses, metrics)

• Per-sample signal vectors

• Optional predictions/targets for deeper analysis

Start services

import weightslab as wl

wl.serve(serving_cli=True, serving_grpc=True)

Custom signals

Use the signal decorator for static or dynamic signals.

import numpy as np
import weightslab as wl

@wl.signal(name="brightness")
def brightness(ctx):
    image = ctx.image
    if image is None:
        return 0.0
    return float(np.mean(image))

@wl.signal(name="weighted_train_loss", subscribe_to="train_loss/CE", compute_every_n_steps=10)
def weighted_train_loss(ctx):
    if ctx.subscribed_value is None:
        return 0.0
    return 0.5 * float(ctx.subscribed_value)

Use in training loop

wl.save_signals(
    signals={"train_loss/CE": loss},
    batch_ids=batch_ids,
    preds_raw=logits,
    targets=targets,
    step=step,
    log=True,
)

Notes

• Static signals can be batch-computed with wl.compute_signals(dataset_or_loader).

• Dynamic signals are triggered from subscribed metrics/signals.

• Call wl.keep_serving() at script end if you need services to remain available.