Monitoring

The App’s Hosted training jobs view is the live window onto a training run — loss curves, reward trajectories, learning-rate schedule, structured logs, and one-click cancel / retry.

Open it from the left sidebar under Training. The URL lands at /<org>/training?workspace=<workspace>&project=<project> with the job list on the left and the detail pane on the right.

Job list

The left sidebar lists every training job in the active workspace + project. Each row shows:

• Name — the optional display name or the backend/trainer pair.
• Model — the base model being adapted.
• Status — a coloured dot plus the status label.
• Duration — wall-clock time since the job started.

A search box filters by name, ID, status, model, dataset, trainer type, or backend. Pagination loads in batches of 100; click through to load more. + New job in the top-right opens the CLI-submission guide.

Detail pane

Selecting a job populates the right-hand detail pane:

• Header — job name, status badge, and a one-line summary of the form “Training <model> with <trainer> on <dataset> from <capability>@<version>.” Live RL jobs also surface the world goal when one was provided.
• Action buttons — Cancel (while the job is queued or running) or Retry (on terminal jobs).
• Summary stats — four tiles: backend, trainer, dataset, duration.
• Tracked metrics — scalar tiles followed by the four metric charts and a step-by-step history table. See below.
• Job details card — model, backend, trainer type, algorithm, capability version, status, run ref, project ref.
• Artifacts & refs card — the job’s artifact_refs JSON (minus internal worker fields).
• Live logs — structured log entries with timestamp, level, message, and an optional data payload.

Tracked metrics

The scalar tiles above the charts change per run, but typically include steps, examples, tokens, grad accum, and — when populated — best and latest training loss, eval loss, and mean reward.

Up to four echarts instances render whenever the job’s metrics carry the relevant series:

Chart	Reads	Notes
Loss	train_loss, val_loss	SFT only today; the validation line only appears when an eval dataset was used.
Learning rate	learning_rate	Log-scaled y-axis. SFT only today.
Accuracy	accuracy	Renders only when a trainer emits an accuracy series. The Tinker SFT and RL trainers don’t emit it today.
Reward	reward	Renders only when a trainer emits a step-keyed reward series. The Tinker RL trainer emits scalar train/reward_mean only — no step array — so this chart is empty for current Tinker jobs.

The x-axis uses steps when present, falling back to epochs. Charts whose series are all missing or empty aren’t rendered — you won’t see an empty box.

Beneath the charts, a History table lists every step with its train loss, val loss, accuracy, reward, and learning rate, so you can scrub through the full run.

Note

The view fetches once on mount and again on Refresh. It does not poll or stream — watch a job, or click Refresh when you want to update. Terminal jobs keep their final state indefinitely.

Actions

Cancel — same behavior as dn train cancel. Queued jobs flip to cancelled immediately; running jobs enter a cancel-requested state until the worker finishes cleanup.

Retry — same behavior as client.retry_training_job. Terminal jobs only; metrics and artifacts are cleared before requeue.

Where to go next

• Running training jobs covers the same lifecycle from the CLI and SDK.
• Outputs describes the artifacts, metrics, and logs the Training view is reading from.