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An evaluation job runs a set of tasks on one or more models. Each attempt by one model at one task is a trace: it gets its own environment instance, and HUD records every step. Jobs are how you benchmark models, check that new tasks are solvable, and collect rollouts for training.

Before you run

  • A taskset with at least one task.
  • A successful build of every environment the tasks use, and any runtime variables their templates need.
  • Credits. Starting a job is not checked, but its environments are billed and its model calls are refused once your team fails the credit check.

Start a job

Select tasks on a taskset’s Tasks tab, then Run task. Or open the + menu in the sidebar and select Evaluation job, then pick the taskset. The Run tasks dialog sets: The run button shows how many traces will start: tasks × models × repetitions. HUD creates the job and all of its traces at once, opens the job in a new tab, and starts each trace as capacity frees up. Every trace is billed for model usage and environment runtime. See billing and usage.

Follow a job

The job finishes when every trace has. It ends as error if any trace errored, otherwise as cancelled if any trace was cancelled, otherwise as completed.

Read the results

Treat no-score as 0% in the Results menu counts traces without a reward as 0 in the charts.

Keep bad traces out of the score

Invalidate a trace that should not count, such as one that hit an infrastructure failure. It stays on the job, but the leaderboard and reward summaries skip it. Show invalidated traces in the Results menu brings them back into the charts, and Revalidate all makes them count again.

Cancel, export, and share

  • Cancel Job, in Details while the job runs, stops its pending and running traces and shuts down their instances.
  • Export → Copy JSON or Download JSON gives the job’s hud eval configuration, so you can run it again or adapt it from the terminal.
  • Share opens Share Job: invite people or teams as Viewer or Editor, or make the job public. Public viewers see Traces and Overview, not Usage.
To run the same evaluation from the terminal or Python, see running an eval. To start one over HTTP, see runs and evals.