Resume an interrupted evaluation

A long evaluation can be interrupted: Ctrl-C, OOM, a metric raising, a network blip. opik.evaluate_resume(experiment_id, ...) continues from where the original evaluate(...) stopped — replaying only the runs that didn't finish, keeping the runs that did.

Quick start

import opik
from opik.evaluation.metrics import Equals

def my_task(item):
    return {"output": call_my_model(item["input"])}

result = opik.evaluate_resume(
    experiment_id="<id of the experiment to resume>",
    task=my_task,
    scoring_metrics=[Equals()],
)

The returned EvaluationResult covers the whole experiment, not just the runs this call executed. You don't pass dataset, nb_samples, or experiment_name — resume reads them back from the experiment.

What resume does

• Keeps every run that already completed. Outputs and feedback scores are preserved as-is; the task is not re-invoked for them.
• Replays only the runs that didn't complete. Failed task, failed scoring, never-reached items, and missing runs for items with trial_count > 1 all replay.
• Returns one merged result. EvaluationResult.test_results covers both the kept runs and the freshly replayed ones.

When evaluate_resume is the wrong tool

• You want to re-score an existing experiment with new metrics. Use opik.evaluate_experiment(...) — it scores existing runs without re-running the task.
• You want to add more items to the experiment. Resume only iterates the items the original evaluation saw. Start a fresh evaluate() against the larger dataset.
• You changed the task implementation or the metrics between calls. Providing the same task and scoring_metrics you used originally is the caller's responsibility. Resume calls your new task and runs your new metrics only for the missing runs; already-completed runs keep their original outputs and feedback scores. If the change should affect every run, start a fresh evaluate().

Requirements

To call evaluate_resume, the experiment must have been created by:

• A Python SDK version that supports resume.
• An evaluate(...) call against a versioned dataset.

If either condition isn't met, evaluate_resume raises opik.exceptions.ExperimentNotResumable.

If the original evaluate(...) used a custom dataset_sampler or explicit dataset_item_ids, resume also needs a local checkpoint that was written next to the experiment id. Run resume from the same machine that ran the original call — otherwise opik.exceptions.LocalCheckpointMissing is raised. Evaluations without a sampler or explicit ids do not need to run on the same machine.