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import logging
import os
import signal
import sys
import time
import uuid
from typing import Optional
from skydiscover.config import Config, build_output_dir, load_config
from skydiscover.search.base_database import Program
from skydiscover.search.default_discovery_controller import (
DiscoveryController,
DiscoveryControllerInput,
)
from skydiscover.search.registry import create_database, get_program
from skydiscover.search.route import get_discovery_controller
from skydiscover.search.utils.logging_utils import setup_search_logging
from skydiscover.utils.code_utils import extract_solution_language
from skydiscover.utils.metrics import format_metrics, get_score
logger = logging.getLogger(__name__)
class Runner:
"""Top-level entry point for a discovery run.
Loads config, creates the database and discovery controller, runs the
search loop, and saves checkpoints + best program.
Args:
initial_program_path: path to the starting solution file.
evaluation_file: path to the user's evaluator script (must define evaluate()).
config_path: optional YAML config file (ignored if config is provided).
config: optional pre-built Config object (takes priority over config_path).
output_dir: where to write logs, checkpoints, and best program.
Auto-generated from search type + problem name if omitted.
"""
def __init__(
self,
evaluation_file: str,
initial_program_path: Optional[str] = None,
config_path: Optional[str] = None,
config: Optional[Config] = None,
output_dir: Optional[str] = None,
evaluator_env_vars: Optional[dict[str, str]] = None,
):
self.config = config if config is not None else load_config(config_path)
self.name = self.config.search.type
self.output_dir = output_dir or build_output_dir(
self.name, initial_program_path or "scratch"
)
os.makedirs(self.output_dir, exist_ok=True)
self._setup_logging()
# Load the initial program (can be optional)
self.initial_program_path = initial_program_path
self.initial_program_solution = (
self._load_initial_program() if initial_program_path else None
)
if self.initial_program_solution and not self.config.language:
self.config.language = extract_solution_language(self.initial_program_solution)
if not self.config.language:
self.config.language = "python"
# Set the file extension
ext = os.path.splitext(initial_program_path)[1] if initial_program_path else ".py"
ext = ext or ".py"
self.file_extension = ext if ext.startswith(".") else f".{ext}"
if self.config.file_suffix == ".py":
self.config.file_suffix = self.file_extension
# Create the database
self.database = create_database(self.config.search.type, self.config.search.database)
self.database.language = self.config.language or "python"
self.evaluation_file = evaluation_file
self.evaluator_env_vars = dict(evaluator_env_vars or {})
# Initialize the discovery controller
self.discovery_controller: Optional[DiscoveryController] = None
logger.info(f"Runner ready: search={self.name}, program={self.initial_program_path}")
@property
def initial_score(self) -> Optional[float]:
"""Score of the seed program, or None if unavailable."""
if not self.database or not self.database.programs or not self.initial_program_solution:
return None
seed_solution = self.initial_program_solution
seed_prog = None
for prog in self.database.programs.values():
if prog.solution == seed_solution:
seed_prog = prog
break
if seed_prog is None:
for prog in self.database.programs.values():
if prog.iteration_found == 0:
seed_prog = prog
break
if seed_prog and seed_prog.metrics:
return get_score(seed_prog.metrics)
return None
async def run(
self,
iterations: Optional[int] = None,
checkpoint_path: Optional[str] = None,
) -> Optional[Program]:
"""Entrypoint for the discovery process.
Args:
iterations: max iterations (uses config.max_iterations if None).
checkpoint_path: resume from this checkpoint directory if provided.
Returns:
Best Program found, or None if no valid programs were produced.
"""
max_iterations = iterations if iterations is not None else self.config.max_iterations
start_iteration = 0
if checkpoint_path and os.path.exists(checkpoint_path):
self._load_checkpoint(checkpoint_path)
start_iteration = self.database.last_iteration + 1
logger.info(f"Resuming from iteration {start_iteration}")
else:
start_iteration = self.database.last_iteration
# Create the discovery controller input
controller_input = DiscoveryControllerInput(
config=self.config,
evaluation_file=self.evaluation_file,
database=self.database,
file_suffix=self.config.file_suffix,
output_dir=self.output_dir,
evaluator_env_vars=self.evaluator_env_vars,
)
# Get the discovery controller
self.discovery_controller = get_discovery_controller(controller_input)
# Add initial program to database if not resuming
should_add_initial = (
start_iteration == 0
and len(self.database.programs) == 0
and self.initial_program_solution is not None
)
if should_add_initial:
await self._add_initial_program(start_iteration)
else:
logger.info(
f"Resuming from iteration {start_iteration} with {len(self.database.programs)} programs"
)
# Start the monitor
monitor_server = None
try:
monitor_server = self._start_monitor(max_iterations)
self._setup_human_feedback(monitor_server)
self._setup_monitor_summary(monitor_server)
self._push_existing_to_monitor()
self._install_signal_handlers()
discovery_start = start_iteration + 1 if should_add_initial else start_iteration
self.database.log_status()
def checkpoint_cb(iteration: int) -> None:
self._sync_database()
self._save_checkpoint(iteration)
# MAIN LOOP: Run the discovery
await self.discovery_controller.run_discovery(
discovery_start,
max_iterations,
checkpoint_callback=checkpoint_cb,
)
self._sync_database()
final_iteration = discovery_start + max_iterations - 1
if final_iteration > 0:
self._save_checkpoint(final_iteration)
# Re-evaluate best program in test mode (authoritative score).
best = self._get_best_program()
if best:
try:
test_result = await self.discovery_controller.evaluator.evaluate_program(
best.solution, best.id, mode="test"
)
for k, v in test_result.metrics.items():
best.metrics[f"test_{k}"] = v
logger.info(
f"Test evaluation for best program: {format_metrics(test_result.metrics)}"
)
# Persist test metrics to disk so they survive the run.
self._save_best_program(best)
except Exception as e:
logger.warning(f"Test-mode re-evaluation failed: {e}")
finally:
# Stop the monitor
early_stopped = (
self.discovery_controller is not None
and self.discovery_controller.early_stopping_triggered
)
if self.discovery_controller is not None:
self.discovery_controller.close()
self.discovery_controller = None
if monitor_server:
try:
reason = "early_stopping" if early_stopped else "completed"
monitor_server.push_event({"type": "discovery_complete", "reason": reason})
time.sleep(0.5)
monitor_server.stop()
except Exception:
logger.debug("Failed to stop monitor server", exc_info=True)
# Get the best program
best_program = self._get_best_program()
if best_program:
status = "early stopping" if early_stopped else "completed"
logger.info(f"Discovery {status}. Best: {format_metrics(best_program.metrics)}")
self._save_best_program(best_program)
return best_program
logger.warning("No valid programs found")
return None
# ------------------------------------------------------------------
# Initial program
# ------------------------------------------------------------------
async def _add_initial_program(self, start_iteration: int) -> None:
logger.info("Adding initial program to database")
program_id = str(uuid.uuid4())
initial_image_path = None
if self.config.language == "image":
logger.info("Generating initial image from seed text...")
img_dir = os.path.join(self.output_dir, "generated_images")
try:
result = await self.discovery_controller.llms.generate(
system_message="Generate an image based on the following description. Also provide brief reasoning about your creative choices.",
messages=[{"role": "user", "content": self.initial_program_solution}],
image_output=True,
output_dir=img_dir,
program_id=program_id,
)
initial_image_path = result.image_path
logger.info(f"Initial image: {initial_image_path}")
except Exception as e:
logger.warning(f"Failed to generate initial image: {e}")
eval_input = (
initial_image_path
if self.config.language == "image" and initial_image_path
else self.initial_program_solution
)
eval_result = await self.discovery_controller.evaluator.evaluate_program(
eval_input, program_id
)
metrics = eval_result.metrics
if not initial_image_path and isinstance(metrics.get("image_path"), str):
initial_image_path = metrics.pop("image_path")
program = get_program(
self.config, self.initial_program_solution, program_id, metrics, start_iteration
)
program.artifacts = eval_result.artifacts
if initial_image_path:
program.metadata = program.metadata or {}
program.metadata["image_path"] = initial_image_path
self.database.add(program)
try:
self.database.initial_program_id = program.id
self.database.initial_program_score = get_score(program.metrics or {})
except Exception as e:
logger.warning(f"Failed to set initial program score: {e}")
# ------------------------------------------------------------------
# Monitor and feedback setup
# ------------------------------------------------------------------
def _start_monitor(self, max_iterations: int):
if not self.config.monitor.enabled:
return None
try:
from skydiscover.extras.monitor import MonitorServer, create_monitor_callback
server = MonitorServer(
host=self.config.monitor.host,
port=self.config.monitor.port,
max_solution_length=self.config.monitor.max_solution_length,
)
server.set_config_summary(f"{self.name} | max_iter={max_iterations}")
server.start()
callback = create_monitor_callback(server, self.database, time.time())
self.discovery_controller.monitor_callback = callback
url = f"http://localhost:{server.port}/"
print(f"\n Live monitor: {url}\n", flush=True)
logger.info(f"Live monitor: {url}")
return server
except Exception as e:
logger.warning(f"Failed to start monitor: {e}")
return None
def _setup_human_feedback(self, monitor_server) -> None:
if not (self.config.human_feedback_enabled or monitor_server):
return
try:
from skydiscover.context_builder import HumanFeedbackReader
path = self.config.human_feedback_file or os.path.join(
self.output_dir, "human_feedback.md"
)
mode = getattr(self.config, "human_feedback_mode", "append")
reader = HumanFeedbackReader(path, mode=mode)
self.discovery_controller.feedback_reader = reader
if monitor_server:
monitor_server.set_feedback_reader(reader)
logger.info(f"Human feedback: {path}")
except Exception as e:
logger.warning(f"Failed to set up human feedback: {e}")
def _setup_monitor_summary(self, monitor_server) -> None:
if not (monitor_server and self.config.monitor.summary_model):
return
try:
monitor_server.configure_summary(
model=self.config.monitor.summary_model,
api_key=self.config.monitor.summary_api_key or "",
api_base=self.config.monitor.summary_api_base,
top_k=self.config.monitor.summary_top_k,
interval=self.config.monitor.summary_interval,
)
except Exception as e:
logger.warning(f"Failed to configure AI summary: {e}")
def _push_existing_to_monitor(self) -> None:
if not (self.discovery_controller.monitor_callback and self.database.programs):
return
for prog in self.database.programs.values():
try:
self.discovery_controller.monitor_callback(
prog, getattr(prog, "iteration_found", 0)
)
except Exception:
logger.debug("Monitor callback failed for program %s", prog.id, exc_info=True)
logger.info(f"Pushed {len(self.database.programs)} existing program(s) to monitor")
def _install_signal_handlers(self) -> None:
def on_signal(signum, frame):
logger.info(f"Signal {signum} received, shutting down...")
if self.discovery_controller is not None:
self.discovery_controller.request_shutdown()
def force_exit(signum, frame):
sys.exit(128 + signum)
# After the first termination signal, ensure subsequent SIGINT/SIGTERM
# cause an immediate exit instead of re-running the soft handler.
signal.signal(signal.SIGINT, force_exit)
signal.signal(signal.SIGTERM, force_exit)
signal.signal(signal.SIGINT, on_signal)
signal.signal(signal.SIGTERM, on_signal)
# ------------------------------------------------------------------
# Checkpointing and saving
# ------------------------------------------------------------------
def _sync_database(self) -> None:
"""Ensure we have the controller's latest database"""
db = getattr(self.discovery_controller, "database", None)
if db is not None and db is not self.database:
self.database = db
def _setup_logging(self) -> None:
log_dir = self.config.log_dir or os.path.join(self.output_dir, "logs")
setup_search_logging(log_level=self.config.log_level, log_dir=log_dir, name=self.name)
def _load_initial_program(self) -> str:
with open(self.initial_program_path, "r") as f:
return f.read()
def _save_checkpoint(self, iteration: int) -> None:
checkpoint_dir = os.path.join(self.output_dir, "checkpoints")
checkpoint_path = os.path.join(checkpoint_dir, f"checkpoint_{iteration}")
os.makedirs(checkpoint_path, exist_ok=True)
self.database.save(checkpoint_path, iteration)
best = self._get_best_program()
if best:
with open(
os.path.join(checkpoint_path, f"best_program{self.file_extension}"), "w"
) as f:
f.write(best.solution)
with open(os.path.join(checkpoint_path, "best_program_info.json"), "w") as f:
from skydiscover.search.utils.checkpoint_manager import SafeJSONEncoder
json.dump(
{
"id": best.id,
"generation": best.generation,
"iteration": best.iteration_found,
"current_iteration": iteration,
"metrics": best.metrics,
"language": best.language,
"timestamp": best.timestamp,
"saved_at": time.time(),
},
f,
indent=2,
cls=SafeJSONEncoder,
)
logger.info(f"Checkpoint {iteration}: best={format_metrics(best.metrics)}")
logger.info(f"Checkpoint saved to {checkpoint_path}")
def _load_checkpoint(self, checkpoint_path: str) -> None:
if not os.path.exists(checkpoint_path):
raise FileNotFoundError(f"Checkpoint not found: {checkpoint_path}")
self.database.load(checkpoint_path)
logger.info(f"Loaded checkpoint (iteration {self.database.last_iteration})")
def _get_best_program(self) -> Optional[Program]:
if self.database.best_program_id:
prog = self.database.get(self.database.best_program_id)
if prog:
return prog
return self.database.get_best_program()
def _save_best_program(self, program: Program) -> None:
best_dir = os.path.join(self.output_dir, "best")
os.makedirs(best_dir, exist_ok=True)
code_path = os.path.join(best_dir, f"best_program{self.file_extension}")
with open(code_path, "w") as f:
f.write(program.solution)
info_path = os.path.join(best_dir, "best_program_info.json")
with open(info_path, "w") as f:
from skydiscover.search.utils.checkpoint_manager import SafeJSONEncoder
json.dump(
{
"id": program.id,
"generation": program.generation,
"iteration": program.iteration_found,
"timestamp": program.timestamp,
"parent_id": program.parent_id,
"metrics": program.metrics,
"language": program.language,
"saved_at": time.time(),
},
f,
indent=2,
cls=SafeJSONEncoder,
)
if self.config.language == "image" and program.metadata:
img = program.metadata.get("image_path")
if img and os.path.exists(img):
import shutil
shutil.copy2(img, os.path.join(best_dir, "best_image" + os.path.splitext(img)[1]))
logger.info(f"Best program saved to {best_dir}")
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