File size: 3,496 Bytes
6c5f29f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 | from __future__ import annotations
import json, os, subprocess
from pathlib import Path
import modal
ROOT = Path(__file__).resolve().parent.parent
REMOTE_ROOT = "/root/project"
IGNORE = [
".git", ".git-archives", "__pycache__",
"dreamerv3/.venv", "dreamerv3/pilot_logs",
"dreamerv3/smoke_logs", "dreamerv3/cw_modal_runs",
"dreamerv3/paper_runs_smoke", "results*",
"seq_results", "llm_memory_validation/modal_run",
"llm_memory_validation/learned_run",
"llm_memory_validation/competitor_run_v2",
"llm_memory_validation/counterfactual_run",
"llm_memory_validation/counterfactual_utility_regressor_run",
"llm_memory_validation/counterfactual_staged_run",
"llm_memory_validation/neurips_fast_results",
"llm_memory_validation/neurips_micro_results",
"llm_memory_validation/neurips_full_results",
]
app = modal.App("bsc-budget-sweep")
results_volume = modal.Volume.from_name("neurips-bsc-results", create_if_missing=True)
hf_cache_volume = modal.Volume.from_name("llm-memory-counterfactual-bsc-hf-cache", create_if_missing=True)
image = (
modal.Image.debian_slim(python_version="3.11")
.apt_install("git")
.pip_install(
"torch>=2.4.0",
"transformers>=4.51.0",
"accelerate>=1.6.0",
"scikit-learn>=1.5.0",
"scipy>=1.14.0",
"matplotlib>=3.9.0",
"sentencepiece>=0.2.0",
"safetensors>=0.4.5",
"huggingface_hub[hf_transfer]>=0.30.2",
"numpy>=2.0.0",
"tqdm",
"datasets",
)
.env({
"PYTHONUNBUFFERED": "1",
"HF_HUB_ENABLE_HF_TRANSFER": "1",
"TOKENIZERS_PARALLELISM": "false",
"MPLBACKEND": "Agg",
})
.add_local_dir(ROOT, REMOTE_ROOT, copy=True, ignore=IGNORE)
)
@app.function(
image=image,
gpu="A100-40GB",
cpu=12,
memory=65536,
timeout=60 * 60 * 4,
volumes={
"/results": results_volume,
"/root/.cache/huggingface": hf_cache_volume,
},
)
def run_sweep() -> dict:
env = os.environ.copy()
env["PYTHONPATH"] = REMOTE_ROOT + os.pathsep + env.get("PYTHONPATH", "")
env["HF_HOME"] = "/root/.cache/huggingface"
env["HF_HUB_CACHE"] = "/root/.cache/huggingface/hub"
env["TRANSFORMERS_CACHE"] = "/root/.cache/huggingface/hub"
env["MPLBACKEND"] = "Agg"
env["PYTHONIOENCODING"] = "utf-8"
output_dir = "/results/neurips_full_results"
os.makedirs(output_dir, exist_ok=True)
script = os.path.join(REMOTE_ROOT, "llm_memory_validation", "run_complete_sweep.py")
result = subprocess.run(
["python", script],
cwd=REMOTE_ROOT,
env=env,
capture_output=True,
text=True,
timeout=7200,
)
results_volume.commit()
results_path = Path(output_dir) / "full_results.json"
payload = {
"returncode": result.returncode,
"stdout_tail": result.stdout[-5000:] if len(result.stdout) > 5000 else result.stdout,
"stderr_tail": result.stderr[-5000:] if len(result.stderr) > 5000 else result.stderr,
"results_exist": results_path.exists(),
}
if results_path.exists():
payload["results"] = json.loads(results_path.read_text(encoding="utf-8"))
for fig in ["budget_sweep.png", "ablations.png"]:
fp = Path(output_dir) / fig
payload[f"{fig}_exists"] = fp.exists()
return payload
@app.local_entrypoint()
def main():
payload = run_sweep.remote()
print(json.dumps(payload, indent=2, default=str)) |