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¶
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. |
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¶
A project's termination condition (--stop-policy) and hard spend cap
(--max-cost) are set when you create it:
autolab init -y --name nanochat --objective "min val loss" \
--stop-policy "stop after 30 experiments with no improvement" \
--max-cost 100
Adjust them later from the dashboard project settings.
Collaborators¶
Managing who can access the project has its own subcommand — see Collaborators:
Delete a project¶
Owners only, and permanent:
Changing the objective or constraints steers the live agent on its next experiment. To pause it first, see Drive the agent.