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| #!/usr/bin/env python3 | |
| """Feather 7-day production bootstrap: retina + train.""" | |
| import os, sys, subprocess, time | |
| from pathlib import Path | |
| CACHE_ROOT = Path.home() / '.cache' / 'autoresearch' | |
| FEATHER_ROOT = Path('/workspace/feather') | |
| os.chdir(FEATHER_ROOT) | |
| # ---- retina.npz: synthetic generation ---- | |
| retina_path = CACHE_ROOT / 'retina.npz' | |
| if not retina_path.exists(): | |
| print('[bootstrap] generating synthetic retina.npz...', flush=True) | |
| import numpy as np | |
| VOCAB = int(os.environ.get('HYDRA_VOCAB', '65536')) | |
| NBITS = int(os.environ.get('HYDRA_SDR_N_BITS', '16384')) | |
| TARG = int(os.environ.get('HYDRA_SDR_TARGET_ACTIVE', '327')) | |
| rng = np.random.default_rng(42) | |
| sdr = np.zeros((VOCAB, NBITS), dtype=bool) | |
| for i in range(VOCAB): | |
| sdr[i, rng.choice(NBITS, size=TARG, replace=False)] = True | |
| retina_path.parent.mkdir(parents=True, exist_ok=True) | |
| np.savez_compressed(str(retina_path), sdr=sdr, vocab_size=VOCAB, n_bits=NBITS, target_active=TARG) | |
| print(f'[bootstrap] retina.npz written ({retina_path.stat().st_size} bytes)', flush=True) | |
| else: | |
| print(f'[bootstrap] retina.npz already cached ({retina_path.stat().st_size} bytes)', flush=True) | |
| # ---- train.py ---- | |
| print('[bootstrap] launching train.py...', flush=True) | |
| sys.exit(subprocess.run([sys.executable, '-u', 'train.py']).returncode) | |