Async Evolution Pipeline

Shinka runs evolution through ShinkaEvolveRunner. Use proposal concurrency to control throughput and emulate prior sync behavior.

Quick Start

from shinka.core import ShinkaEvolveRunner, EvolutionConfig
from shinka.launch import LocalJobConfig
from shinka.database import DatabaseConfig


evo_config = EvolutionConfig(
    num_generations=50,
    max_proposal_jobs=1,  # sync-like proposal behavior
    llm_models=["gpt-5-mini"],
)

runner = ShinkaEvolveRunner(
    evo_config=evo_config,
    job_config=LocalJobConfig(eval_program_path="evaluate.py"),
    db_config=DatabaseConfig(),
)

runner.run()

In async contexts (for example notebooks/async apps), use:

await runner.run_async()

Concurrency Knobs

max_evaluation_jobs: max concurrent evaluation jobs.
max_proposal_jobs: max concurrent proposal generation jobs.
max_db_workers: max async database worker threads.

max_proposal_jobs=1 gives sequential proposal generation behavior.

ShinkaEvolveRunner Parameters

ShinkaEvolveRunner(
    evo_config=EvolutionConfig(...),
    job_config=JobConfig(...),
    db_config=DatabaseConfig(...),
    verbose=True,
    max_evaluation_jobs=2,
    max_proposal_jobs=None,    # defaults to evo_config.max_proposal_jobs
    max_db_workers=None,       # defaults to evo_config.max_db_workers
)

Recommended Settings

Scale	max_evaluation_jobs	max_proposal_jobs
Sequential-like	1-4	1
Small	<= 10	2-5
Medium	10-50	5-10
Large	50+	10-20

Troubleshooting

Too many requests: reduce max_proposal_jobs.
Memory pressure: lower max_proposal_jobs and max_evaluation_jobs.
DB contention: lower max_db_workers.
File I/O errors: ensure aiofiles installed.