Fork an experiment

Forking refers to creating a run based on an existing experiment run. This way, you can restart a run at a particular step.

To fork an experiment, create a run and specify the parent run and step to fork from:

from neptune_scale import Run


run = Run(
    experiment_name="swim-further",
    fork_run_id="likable-barracuda",  # parent run ID
    fork_step=102,
)

To get a link to the experiment in the Neptune web app:

run = Run(experiment_name=...)
run.get_experiment_url()

For details, see Construct Neptune URLs.

Forking and inheritance

The new run inherits all metrics up to and including the fork point specified by the fork_step argument.

A child run inherits the following metadata from its parent:

Metadata	Attribute type or path	How you log it
Metrics	FloatSeries attributes	run.log_metrics=(...)
Tags	StringSet	run.add_tags(tags=...) or via tags management in the app
Description	sys/description	run.log_configs(data={"sys/description": "..."}) or in the app

Configs and other single values logged with log_configs() aren't inherited automatically.

Set step for series

When logging metrics to the child run, steps after the fork point become part of the experiment lineage:

run = Run(
    ...,
    fork_step: 100,
)

for step in epoch:
    # your training loop
    run.log_metrics(
        data={"loss": 0.11, "acc": 0.81},
        step=101,                              # or 100.1, for example
    )

When viewing charts in the web app, if there's a gap between the fork step and the next logged step, Neptune linearly interpolates the values between those steps.

note

Before and at the fork step, the child run can log values without errors:

• If the pre-fork step exists in the parent run, new values logged at that step are ignored.
• If the pre-fork step doesn't exist in the parent run, you can display such data points in Runs mode with the Show inherited metrics setting disabled.