Job Progress

job.get_status() tells you the coarse state of a job — Running, Completed, and so on. Often you want more: is a running job labeling normally, still recruiting annotators, or genuinely stalled? job.get_progress() answers that in one call.

Reading Progress

from rapidata import RapidataClient

client = RapidataClient()

job = client.job.get_job_by_id("...")
progress = job.get_progress()

print(f"{progress.state}: {progress.completion_percentage:.1f}% done")

if progress.recruiting:
    r = progress.recruiting
    print(f"{r.graduated} annotators ready, {r.distilling} still qualifying")

get_progress() does not block — it reports the current progress and returns immediately.

What Progress Contains

A JobProgress has three fields:

Field	Description
state	The job's current state, the same value returned by get_status().
completion_percentage	How much of the requested labeling is done, from 0 to 100.
recruiting	A RecruitingMetrics snapshot of the annotator pool, or None for curated audiences.

The Recruiting Funnel

Custom audiences recruit their own pool of annotators, and recruiting shows how that pool is distributed. The counts are mutually exclusive — each annotator is in exactly one bucket:

Field	Description
graduated	Annotators who passed qualification and are eligible to work now.
distilling	Annotators still going through qualification.
dropped	Annotators removed from the pool (score too low, limits reached, etc.).
inactive	Previously graduated or distilling annotators who went quiet.

This is what lets you tell recruiting from stalling: a running job at 0% with graduated == 0 is still assembling its pool, not stuck.

The same snapshot is available straight from the audience, which is useful when one audience feeds several jobs:

audience = client.audience.get_audience_by_id("aud_...")
print(audience.get_recruiting_metrics())

Note

Only custom audiences recruit their own pool. For curated audiences, job.get_progress().recruiting is None, and audience.get_recruiting_metrics() returns all zeros.

Preliminary Results

To look at the responses collected so far without waiting for the job to finish, pass preliminary_results=True:

snapshot = job.get_results(preliminary_results=True)
df = snapshot.to_pandas()

The snapshot is not final and may not include every datapoint. If the job has already completed, the final results are returned instead.

Pausing a Job

job.pause() stops a job from collecting responses; job.resume() lets it continue. Responses gathered before the pause are kept.

job = client.job.get_job_by_id("...")
job.pause()
print(job.get_status())  # Paused

job.resume()

While a job is Paused, get_results() and display_progress_bar() raise an error instead of waiting, since the job will not complete until it is resumed.

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