Project settings

Settings define what the agent optimizes and how an experiment runs. They're the project-level defaults the agent respects on every experiment.

View

autolab settings

Shows the current objective, constraints, run/setup commands, and the assigned LLM key.

Edit

Pass any flag to edit those fields, then it prints the updated settings:

autolab settings --objective "minimize validation bits-per-byte"
autolab settings --constraints "no extra params; train < 2h on 8xH100"
autolab settings --run "python train.py --config big.yaml" --prep "uv sync"

Flag	Meaning
--objective	What to optimize — the research goal (your metric, in words).
--constraints	Rules the agent must respect.
--run	How one experiment runs.
--prep	Environment setup before the run (e.g. uv sync).
--name	Display name.
--description	Short project description.
--source	Source repo URL.
--coding-timeout	Coding-step time budget in seconds (120–7200; default 600). Retried attempts automatically get 2x. Pass 0 to restore the default.
--watchdog	Run watchdog on/off (default on): the agent reviews each running job every few minutes and decides keep-or-kill instead of hard-killing at a fixed timeout.
--watchdog-interval	Seconds between run watchdog check-ins (60–3600; default 300). Pass 0 to restore the default.

Clear a field with an empty string

autolab settings --constraints ""

Coding-step time budget

Each experiment starts with a coding step: the agent edits code on your node, sanity-checks it, and pushes a branch. That step has a wall-clock budget (default 10 minutes) which includes the time its shell commands take. If your project's environment setup or smoke tests are inherently slow — big datasets, cold caches, busy nodes — raise it instead of letting attempts time out:

autolab settings --coding-timeout 1800   # 30 minutes

Retried coding attempts automatically get 2x the configured budget.

Run watchdog (long jobs aren't hard-killed)

While an experiment is running, the agent checks in on it every ~5 minutes instead of killing it at a fixed timeout: it reads the recent logs (each line stamped with when Autolab received it), compares elapsed time against its own estimate, and explicitly decides keep running or kill now with a reason shown on the job page. A healthy training run that outlives its estimate keeps going; a crash-looping, diverged, or no-longer-useful run is stopped within minutes. Silent runs are still killed by the mechanical stall guard, and dead nodes still recover automatically.

autolab settings --watchdog off            # legacy behavior: hard-kill at the estimate
autolab settings --watchdog-interval 600   # check every 10 minutes

Objective, metric & constraints

Autolab optimizes a single objective stated in plain language — minimize val_loss, maximize MMLU, or even an LLM-as-judge comparison. The agent reads the metric out of your run's logs and the objective text, so be specific about the number you care about. Constraints are the guardrails: parameter budgets, time limits, "don't touch the tokenizer", and so on.

Stop policy & cost cap

By default a project never stops — the agent keeps researching until you intervene, pausing only for the spend cap, an objective that is truly complete or impossible, or a hard blocker that needs you (no usable node, missing credentials, repeated infrastructure failure). To stop earlier, set a termination condition (--stop-policy) and/or a hard spend cap (--max-cost) when you create the project:

autolab init -y --name nanochat --objective "min val loss" \
  --stop-policy "stop when val loss reaches 3.0" \
  --max-cost 100

Adjust them later from the dashboard project settings.

Collaborators

Managing who can access the project has its own subcommand — see Collaborators:

autolab settings collab          # list members

Delete a project

Owners only, and permanent:

autolab settings delete          # confirms first
autolab settings delete --yes    # skip the confirmation

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Changing the objective or constraints steers the live agent on its next experiment. To pause it first, see Drive the agent.

Appearance (web dashboard)

The dashboard has dark and light themes and follows your OS preference by default. The small toggle at the bottom of the sidebar cycles System -> Dark -> Light. The choice applies instantly, is saved per browser, and covers every page including sign-in.