Metrics

Time-series data that changes over the course of a run: loss, accuracy, learning rate, and any custom scalars you want to chart.

Streams

Every metric value lives on a stream. A stream is just a named series identified by a prefix, like train or eval. You select one by calling exp.metrics(prefix), then .log(...) appends a point.

The unprefixed exp.metrics.log(...) writes to the root stream, which is the right place for run-wide scalars like epoch or step.

Basic Logging

Log scalars by keyword. Nested dicts automatically route to child streams:

from ml_dash import Experiment

with Experiment(prefix="alice/project/my-experiment").run as exp:
    for epoch in range(10):
        train_loss, train_acc = train_one_epoch(model)
        eval_loss, eval_acc = evaluate(model)

        exp.metrics.log(
            epoch=epoch,
            train=dict(loss=train_loss, accuracy=train_acc),
            eval=dict(loss=eval_loss, accuracy=eval_acc),
        )

The train=dict(...) form is equivalent to exp.metrics("train").log(...). Pick whichever reads better at the call site.

Explicit Streams

When metrics for a stream are produced at different points in the loop, address each stream directly and flush once you have a coherent snapshot:

for epoch in range(10):
    exp.metrics("train").log(loss=train_loss, accuracy=train_acc)
    exp.metrics("eval").log(loss=eval_loss, accuracy=eval_acc)
    exp.metrics.log(epoch=epoch).flush()

Stream names are arbitrary; system, lr, grad_norm, etc. all work the same way.

Reading

Read points back by index range:

result = exp.metrics("train").read(start_index=0, limit=10)
for point in result["data"]:
    print(point["index"], point["data"])

Per-Batch Logging

Logging every batch with .log() is fine but produces a lot of points. To accumulate batch-level values and emit one summarized row per epoch, see Buffering.

For configuration like learning rate and batch size, use Parameters. For text events and structured logs, see Logging.

Next: Learn about Files to upload models, plots, and artifacts.