Add 3-phase execution with IPFS cache and hash-based task claiming
New files:
- claiming.py - Redis Lua scripts for atomic task claiming
- tasks/analyze.py - Analysis Celery task
- tasks/execute.py - Step execution with IPFS-backed cache
- tasks/orchestrate.py - Plan orchestration (run_plan, run_recipe)
New API endpoints (/api/v2/):
- POST /api/v2/plan - Generate execution plan
- POST /api/v2/execute - Execute a plan
- POST /api/v2/run-recipe - Full 3-phase pipeline
- GET /api/v2/run/{run_id} - Get run status
Features:
- Hash-based task claiming prevents duplicate work
- Parallel execution within dependency levels
- IPFS-backed cache for durability
- Integration with artdag planning module
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
gilesb
373
tasks/orchestrate.py
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373
tasks/orchestrate.py
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"""
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Plan orchestration tasks.
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Coordinates the full 3-phase execution:
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1. Analyze inputs
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2. Generate plan
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3. Execute steps level by level
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Uses IPFS-backed cache for durability.
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"""
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import json
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import logging
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import os
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from pathlib import Path
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from typing import Dict, List, Optional
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from celery import current_task, group, chain
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# Import from the Celery app
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import sys
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from celery_app import app
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from claiming import get_claimer
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from cache_manager import get_cache_manager
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# Import artdag modules
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try:
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from artdag import Cache
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from artdag.analysis import Analyzer, AnalysisResult
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from artdag.planning import RecipePlanner, ExecutionPlan, Recipe
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except ImportError:
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Cache = None
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Analyzer = None
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AnalysisResult = None
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RecipePlanner = None
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ExecutionPlan = None
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Recipe = None
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from .execute import execute_step
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logger = logging.getLogger(__name__)
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# Cache directories
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CACHE_DIR = Path(os.environ.get('CACHE_DIR', '/data/cache'))
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ANALYSIS_CACHE_DIR = CACHE_DIR / 'analysis'
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PLAN_CACHE_DIR = CACHE_DIR / 'plans'
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@app.task(bind=True, name='tasks.run_plan')
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def run_plan(
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self,
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plan_json: str,
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run_id: Optional[str] = None,
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) -> dict:
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"""
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Execute a complete execution plan.
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Runs steps level by level, with parallel execution within each level.
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Results are stored in IPFS-backed cache.
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Args:
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plan_json: JSON-serialized ExecutionPlan
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run_id: Optional run ID for tracking
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Returns:
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Dict with execution results
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"""
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if ExecutionPlan is None:
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raise ImportError("artdag.planning not available")
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plan = ExecutionPlan.from_json(plan_json)
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cache_mgr = get_cache_manager()
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logger.info(f"Executing plan {plan.plan_id[:16]}... ({len(plan.steps)} steps)")
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# Build initial cache_ids mapping (step_id -> cache_id)
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cache_ids = {}
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for step in plan.steps:
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cache_ids[step.step_id] = step.cache_id
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# Also map input hashes
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for name, content_hash in plan.input_hashes.items():
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cache_ids[name] = content_hash
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# Group steps by level
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steps_by_level = plan.get_steps_by_level()
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max_level = max(steps_by_level.keys()) if steps_by_level else 0
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results_by_step = {}
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total_cached = 0
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total_executed = 0
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for level in range(max_level + 1):
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level_steps = steps_by_level.get(level, [])
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if not level_steps:
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continue
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logger.info(f"Executing level {level}: {len(level_steps)} steps")
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# Check which steps need execution
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steps_to_run = []
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for step in level_steps:
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# Check if cached
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cached_path = cache_mgr.get_by_content_hash(step.cache_id)
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if cached_path:
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results_by_step[step.step_id] = {
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"status": "cached",
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"cache_id": step.cache_id,
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"output_path": str(cached_path),
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}
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total_cached += 1
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else:
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steps_to_run.append(step)
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if not steps_to_run:
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logger.info(f"Level {level}: all steps cached")
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continue
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# Build input cache_ids for this level
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level_cache_ids = dict(cache_ids)
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# Execute steps in parallel
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tasks = [
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execute_step.s(step.to_json(), plan.plan_id, level_cache_ids)
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for step in steps_to_run
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]
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job = group(tasks)
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async_results = job.apply_async()
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# Wait for completion
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try:
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step_results = async_results.get(timeout=3600)
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except Exception as e:
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logger.error(f"Level {level} execution failed: {e}")
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return {
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"status": "failed",
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"error": str(e),
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"level": level,
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"results": results_by_step,
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"run_id": run_id,
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}
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# Process results
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for result in step_results:
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step_id = result.get("step_id")
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cache_id = result.get("cache_id")
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results_by_step[step_id] = result
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cache_ids[step_id] = cache_id
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if result.get("status") in ("completed", "cached", "completed_by_other"):
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total_executed += 1
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elif result.get("status") == "failed":
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logger.error(f"Step {step_id} failed: {result.get('error')}")
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return {
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"status": "failed",
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"error": f"Step {step_id} failed: {result.get('error')}",
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"level": level,
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"results": results_by_step,
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"run_id": run_id,
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}
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# Get final output
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output_step = plan.get_step(plan.output_step)
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output_cache_id = output_step.cache_id if output_step else None
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output_path = None
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output_ipfs_cid = None
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if output_cache_id:
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output_path = cache_mgr.get_by_content_hash(output_cache_id)
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output_ipfs_cid = cache_mgr.get_ipfs_cid(output_cache_id)
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return {
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"status": "completed",
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"run_id": run_id,
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"plan_id": plan.plan_id,
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"output_cache_id": output_cache_id,
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"output_path": str(output_path) if output_path else None,
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"output_ipfs_cid": output_ipfs_cid,
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"total_steps": len(plan.steps),
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"cached": total_cached,
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"executed": total_executed,
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"results": results_by_step,
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}
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@app.task(bind=True, name='tasks.run_recipe')
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def run_recipe(
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self,
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recipe_yaml: str,
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input_hashes: Dict[str, str],
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features: List[str] = None,
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run_id: Optional[str] = None,
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) -> dict:
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"""
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Run a complete recipe through all 3 phases.
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1. Analyze: Extract features from inputs
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2. Plan: Generate execution plan
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3. Execute: Run the plan
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Args:
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recipe_yaml: Recipe YAML content
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input_hashes: Mapping from input name to content hash
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features: Features to extract (default: ["beats", "energy"])
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run_id: Optional run ID for tracking
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Returns:
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Dict with final results
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"""
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if RecipePlanner is None or Analyzer is None:
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raise ImportError("artdag modules not available")
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if features is None:
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features = ["beats", "energy"]
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cache_mgr = get_cache_manager()
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logger.info(f"Running recipe with {len(input_hashes)} inputs")
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# Phase 1: Analyze
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logger.info("Phase 1: Analyzing inputs...")
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ANALYSIS_CACHE_DIR.mkdir(parents=True, exist_ok=True)
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analyzer = Analyzer(cache_dir=ANALYSIS_CACHE_DIR)
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analysis_results = {}
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for name, content_hash in input_hashes.items():
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# Get path from cache
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path = cache_mgr.get_by_content_hash(content_hash)
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if path:
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try:
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result = analyzer.analyze(
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input_hash=content_hash,
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features=features,
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input_path=Path(path),
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)
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analysis_results[content_hash] = result
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logger.info(f"Analyzed {name}: tempo={result.tempo}, beats={len(result.beat_times or [])}")
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except Exception as e:
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logger.warning(f"Analysis failed for {name}: {e}")
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else:
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logger.warning(f"Input {name} ({content_hash[:16]}...) not in cache")
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logger.info(f"Analyzed {len(analysis_results)} inputs")
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# Phase 2: Plan
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logger.info("Phase 2: Generating execution plan...")
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recipe = Recipe.from_yaml(recipe_yaml)
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planner = RecipePlanner(use_tree_reduction=True)
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plan = planner.plan(
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recipe=recipe,
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input_hashes=input_hashes,
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analysis=analysis_results,
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)
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logger.info(f"Generated plan with {len(plan.steps)} steps")
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# Save plan for debugging
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PLAN_CACHE_DIR.mkdir(parents=True, exist_ok=True)
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plan_path = PLAN_CACHE_DIR / f"{plan.plan_id}.json"
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with open(plan_path, "w") as f:
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f.write(plan.to_json())
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# Phase 3: Execute
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logger.info("Phase 3: Executing plan...")
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result = run_plan(plan.to_json(), run_id=run_id)
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return {
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"status": result.get("status"),
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"run_id": run_id,
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"recipe": recipe.name,
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"plan_id": plan.plan_id,
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"output_path": result.get("output_path"),
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"output_cache_id": result.get("output_cache_id"),
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"output_ipfs_cid": result.get("output_ipfs_cid"),
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"analysis_count": len(analysis_results),
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"total_steps": len(plan.steps),
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"cached": result.get("cached", 0),
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"executed": result.get("executed", 0),
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"error": result.get("error"),
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}
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@app.task(bind=True, name='tasks.generate_plan')
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def generate_plan(
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self,
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recipe_yaml: str,
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input_hashes: Dict[str, str],
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features: List[str] = None,
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) -> dict:
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"""
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Generate an execution plan without executing it.
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Useful for:
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- Previewing what will be executed
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- Checking cache status
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- Debugging recipe issues
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Args:
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recipe_yaml: Recipe YAML content
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input_hashes: Mapping from input name to content hash
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features: Features to extract for analysis
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Returns:
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Dict with plan details
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"""
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if RecipePlanner is None or Analyzer is None:
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raise ImportError("artdag modules not available")
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if features is None:
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features = ["beats", "energy"]
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cache_mgr = get_cache_manager()
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# Analyze inputs
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ANALYSIS_CACHE_DIR.mkdir(parents=True, exist_ok=True)
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analyzer = Analyzer(cache_dir=ANALYSIS_CACHE_DIR)
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analysis_results = {}
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for name, content_hash in input_hashes.items():
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path = cache_mgr.get_by_content_hash(content_hash)
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if path:
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try:
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result = analyzer.analyze(
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input_hash=content_hash,
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features=features,
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input_path=Path(path),
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)
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analysis_results[content_hash] = result
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except Exception as e:
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logger.warning(f"Analysis failed for {name}: {e}")
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# Generate plan
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recipe = Recipe.from_yaml(recipe_yaml)
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planner = RecipePlanner(use_tree_reduction=True)
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plan = planner.plan(
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recipe=recipe,
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input_hashes=input_hashes,
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analysis=analysis_results,
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)
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# Check cache status for each step
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steps_status = []
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for step in plan.steps:
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cached = cache_mgr.has_content(step.cache_id)
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steps_status.append({
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"step_id": step.step_id,
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"node_type": step.node_type,
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"cache_id": step.cache_id,
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"level": step.level,
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"cached": cached,
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})
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cached_count = sum(1 for s in steps_status if s["cached"])
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return {
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"status": "planned",
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"recipe": recipe.name,
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"plan_id": plan.plan_id,
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"total_steps": len(plan.steps),
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"cached_steps": cached_count,
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"pending_steps": len(plan.steps) - cached_count,
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"steps": steps_status,
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"plan_json": plan.to_json(),
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}
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