Run Lifecycle

Start, resume, and end a Pluto run from Python.

A run starts with pluto.init() and ends with either run.finish() (mark it complete on the server) or run.close() (release local resources without changing the run’s server-side state). This page covers the parameters for each, plus how to resume a finished run.

Starting a Run

import pluto

run = pluto.init(
    project="my-project",
    name="experiment-1",
    config={"lr": 1e-3, "batch_size": 32},
    tags=["baseline"],
)

Common parameters:

Parameter	Type	Description
project	str	Project name (auto-created if it doesn’t exist).
name	str	Human-readable run name. Pluto also auto-generates a sequential display ID like MMP-42.
config	dict	Hyperparameters. Snapshot at init; for in-run edits use run.update_config().
tags	list[str]	Initial tag set. Edit later with run.add_tags() / run.remove_tags().
run_id	int | str	Resume an existing run by numeric ID or display ID. Used with resume=True.
resume	bool	If True, re-open a previously finished run instead of creating a new one.
fork_run_id	int | str	Fork from a parent run. See Run Forking.
fork_step	int	Required with fork_run_id. Step number to fork at.
inherit_config	bool	When forking, deep-merge the parent’s config into the child’s. Defaults to True.
inherit_tags	bool	When forking, copy the parent’s tags. Defaults to False.

Resuming a Finished Run

run = pluto.init(project="my-project", run_id="MMP-42", resume=True)
run.log({"eval/accuracy": 0.94}, step=10000)
run.finish()

Both numeric IDs (12345) and display IDs ("MMP-42") are accepted.

Ending a Run

The SDK exposes two teardown methods:

Method	Local cleanup	Marks the run on the server	Use when
run.finish()	Yes	Yes — transitions to COMPLETED (or FAILED on uncaught exception)	The training loop is done and you want the run to leave the active state.
run.close()	Yes	No — server status is unchanged	A short-lived process attached to an ongoing run and shouldn’t end it.

Both stop the monitor thread, drain the sync queue, and close HTTP clients. run.finish() also fires automatically on interpreter shutdown via atexit. run.close() unregisters the atexit hook, so a process that calls close() won’t accidentally complete the run when it exits.

When to Use close() Instead of finish()

close() supports multi-process workflows where one process attaches to an active run and shouldn’t take it down on exit. Two common cases:

• Eval job appending to a live training run. The training loop holds the “real” finish(). The eval process opens the same run with resume=True, writes metrics, and close()s — leaving the run active.
• Side process uploading artifacts while the main run is still producing data elsewhere.

# In a side process attaching to a still-running training job
run = pluto.init(project="my-project", run_id="MMP-42", resume=True)
run.log({"eval/loss": 0.32}, step=current_train_step)
run.close()  # local teardown only, run stays active on the server

close() is idempotent and thread-safe; subsequent finish() calls on a closed run are no-ops.