Run training and watch jobs

Studio’s Home and Job detail pages: trigger a run, watch it stream, inspect a single job.

Run training and watch jobs

Studio’s Home page (#/) is split into two columns: Run training on the left, the Jobs list on the right. Clicking a job in the list navigates to Job detail (#/jobs/:id), which is where the live event stream and the loss chart live.

Run training

The Run training panel calls /api/manifest once when the page loads. The response is the project’s createArkor({ trainer }) summary, which the panel uses to label the action:

• Run training: <trainer name> once a trainer is found.
• No trainer in src/arkor/index.ts yet. Add createTrainer(...) and pass it to createArkor. if the bundle imported but exposed nothing.
• Couldn't read manifest: <error> if the build itself failed (typo in src/arkor/, etc.).

The button is disabled while a run is in flight and while no trainer has resolved.

When you click it, Studio sends POST /api/train. The backend spawns arkor start in a subprocess and streams its stdout / stderr back as raw text. The pre-formatted log box auto-scrolls; what you see is exactly what the spawned arkor start would print in a terminal.

There is no input form for picking the trainer or passing flags: Studio always runs the trainer registered through createArkor, and arkor start reuses .arkor/build/index.mjs if it already exists. Edits to src/arkor/ are not picked up automatically across multiple clicks on the same page; reload the Run training page (or run arkor build from a terminal) between edits and the next click. See CLI § build / start for the precise rebuild rules.

Jobs list

The Jobs list polls GET /api/jobs once at mount, then every 5 seconds. There is no manual refresh button; the interval is fixed.

Column	Source
Status	Job.status (queued / running / completed / failed / cancelled). The cell carries a CSS class for colouring.
Name	Job.name. Links to #/jobs/<id>.
Created	new Date(Job.createdAt).toLocaleString().
ID	Job.id, monospaced.

The list shows whatever order the backend returned. There is no client-side filter, search, or pagination. When the project has no jobs yet, the panel reads No jobs yet..

Job detail

#/jobs/:id opens a Server-Sent Events connection to GET /api/jobs/:id/events via EventSource. The page listens for five named events plus a stream sentinel:

Event	Effect on the page
training.started	Status flips to running. The raw line [started] <data> is appended to the event log.
training.log	The step and loss are appended to the loss chart’s data array. The raw line [log] step=<n> loss=<value> joins the event log.
checkpoint.saved	Raw line [checkpoint] <data> joins the event log. The chart is not affected.
training.completed	Status flips to completed. Artifact count is shown below the status (artifacts: <n>).
training.failed	Status flips to failed. The error message from the event payload is shown beneath the status.
end	The page closes the EventSource. No reconnect.

Stream errors append [stream error] to the log; reconnect is left to the browser’s EventSource retry behaviour.

The event log keeps only the last 50 lines (older lines drop off as new ones arrive). It is a raw <pre> block, intended for quick inspection rather than full forensic logs; for the complete history, look at the cloud-api directly.

The loss chart is a single SVG path (640 × 200) drawn from training.log events that include a numeric loss. It uses min-max scaling on the y-axis and the step number on the x-axis. There is no zoom, no tooltip, and no export. If no training.log event has arrived yet, the chart slot reads No training.log events yet..

Things this page does not do

• No cancel button. To stop a running job, call trainer.cancel() from your own code that drives the trainer. Studio does not expose this in the UI today.
• No artifact browser. The page reports the artifact count for completed jobs but does not list or link to individual artifacts. For full artifact access, use the cloud-api or the SDK’s onCompleted({ artifacts }) callback during the run.
• No mid-run inference. The Playground is for completed jobs only (see Playground). For live inspection during a run, use the SDK’s onCheckpoint({ infer }).