Distributed quick start

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Distributed quick start {#distributed-quick-start}

Spin up a local coordinator and worker to sanity-check the distributed stack before deeper testing. Use this workflow when onboarding new contributors or verifying environment changes.

Prerequisites {#prerequisites}

  • Python environment with project dependencies installed (pip install -e .[dev]).
  • Separate terminals (or tmux panes) for server and worker processes.
  • Optional: .env file for custom configuration; otherwise defaults apply.

Launch the server {#launch-server}

python -m bytebiota server --host 127.0.0.1 --port 8080 --no-reload
  • Logs show worker capacity, web UI status, and tuning mode.
  • Visit http://127.0.0.1:8080 for the dashboard once workers connect.

Start a local worker {#start-worker}

In a second terminal:

python -m bytebiota worker --preset auto --server-url http://127.0.0.1:8080
  • The worker auto-detects CPU/memory limits, registers with the server, and begins pulling assignments.
  • Use --verbose for detailed execution traces during development.

Verify the run {#verify-run}

  1. Watch the server console for assignment and checkpoint messages.
  2. Load the web dashboard to confirm population growth, worker heartbeats, and seed-bank activity.
  3. Optional: run python -m bytebiota export-diversity --log-file {data_dir}/checkpoints/experiment.jsonl --output diversity.csv after a few minutes to inspect diversity metrics.

Stop and clean up {#cleanup}

  • Press Ctrl+C in the worker terminal first, then stop the server. Both processes flush logs and checkpoints automatically.
  • Remove transient data if needed:
    bash rm -rf data/checkpoints data/logs data/cache