Dashboard#

The AReno dashboard is a web interface for launching training and serving tasks, preparing plans with an agent, and monitoring jobs. Local jobs and Modal jobs share the same task forms and job views. Modal-specific setup is covered in the Modal jobs subtopic.

Start and stop#

With AReno installed, start the background dashboard server:

areno dashboard --start

Open http://127.0.0.1:8765. To use a different port, pass --port. To stop the background server:

areno dashboard --stop

For foreground operation, run:

python -m areno.dashboard.server --host 127.0.0.1 --port 8765

Local training and serving require a supported local backend; starting the interface does not provide one. The bundled frontend is ready to use. From a repository checkout, rebuild changed frontend source with pnpm --dir dashboard build. After changing dashboard Python code, restart the server; a page refresh alone does not update the backend.

Find your way around#

View

Purpose

Overview

Review runtime health, active work, selected job information, and quick actions.

Jobs

Browse tasks and open job details, metrics, samples, configuration, and logs.

Runtime

Inspect the local environment and runtime checks before starting local work.

Launcher

Configure a Train or Serve task using the shared forms and presets.

Agent

Discuss tasks, inspect jobs through tools, and review proposed execution plans.

Launch training or serving#

Open Launcher and choose Train or Serve. Select a preset or fill in the model, dataset, algorithm, and execution parameters. Advanced fields expose additional options without requiring a command-line invocation.

For training, use a dataset loader compatible with the selected algorithm and actual dataset fields. A successful dataset download alone does not guarantee that its rows satisfy the training schema. See Writing dataset loaders and reward functions and Dataset formats for examples and field requirements.

Review runtime checks before launching a local task. GPU count, tensor parallelism, model architecture, and available memory must agree. The checks provide feedback; running a training or serving task still uses the configured backend. After launch, open the task in Jobs to follow execution.

The Run on Modal switch uses the same Train/Serve form with additional cloud resource fields. See Modal jobs for its setup and confirmation flow.

Work with the agent#

Open Agent, configure its provider in Settings, and describe the task or ask about a selected job. The agent can inspect live job information, metrics, logs, and repository examples through dashboard tools. Supply the intended model and dataset rather than relying on invented repository paths.

Review proposed task plans before confirming actions that start work. Modal execution plans support inline parameter editing, addition, and deletion; saving revalidates the plan. Chat preserves your position when you scroll up or focus a plan field. Conversation history is available from the Agent history tab.

The + button next to the chat input opens managed dataset attachments for Modal workflows. File and repository-URL import details are documented under Modal jobs.

Inspect jobs#

The Jobs list shows status, stage, step, metrics, and elapsed time. Open a job for its detailed views:

  • Metrics show recorded scalar series. Charts reduce rendered points for long runs while retaining the recorded metric history.

  • Runtime timeperf shows per-step timing segments when the trainer reports timing metrics. It remains empty before those measurements are available.

  • Rollout Sample shows captured prompts, completions, and associated record fields. One sample per rollout step is captured by default; ARENO_LOG_COMPLETIONS=0 disables capture in the training environment.

  • Config shows the task settings; multi-stage workflows display individual stage sections and their parameters.

  • Logs show recent task output. The view follows new lines when you are at the bottom and preserves your position when you scroll up.

The stage indicator reflects reported trainer state. Elapsed time freezes when a job stops, exits, fails, or succeeds. Use the stop action to terminate a task; closing its detail view does not stop it. For metric tags, timing semantics, and rollout sample contents, see Observability.

Settings and troubleshooting#

The dashboard gear and the Agent Settings button open the same settings panel. Configure the Agent provider there. Modal credentials are a separate connection in that panel; their persistence and reconnect behavior are covered in Modal jobs.

If a view remains empty, check that the intended job is selected, inspect its status and logs, and verify that it has reached the stage that emits the data. Local scalar recording uses --metrics-log-dir. A stopped job will not produce new training output. Runtime environment checks help diagnose local setup issues; see Troubleshooting for backend-specific failures.

After an update, rebuild the frontend if its source changed, restart the Python server if backend code changed, and refresh the page. Dashboard records and Modal workflow data persist separately from the browser UI.