mltrail-verifiable-provenan.../mltrail_verifiable_provenan.../reprobundle.py

44 lines
1.6 KiB
Python

from dataclasses import dataclass, asdict
from typing import List, Dict, Any, Optional
import json
import hashlib
from .contracts import Environment
@dataclass
class ReproBundle:
"""A compact, content-addressed snapshot describing everything needed to
deterministically replay a run (code commit, environment, dataset refs,
model ref, and a minimal run manifest).
This is intentionally small and deterministic so it can be cheaply
exchanged between peers and used to compute Merkle-like bundle hashes.
"""
code_commit: str
environment: Environment
dataset_refs: List[Dict[str, Any]]
model_ref: Dict[str, Any]
run_manifest: Dict[str, Any]
container_hash: Optional[str] = None
def to_dict(self) -> Dict[str, Any]:
# Convert to a canonical dict suitable for hashing/serialization.
return {
"code_commit": self.code_commit,
"environment": self.environment.to_dict(),
"container_hash": self.container_hash,
"dataset_refs": sorted(self.dataset_refs, key=lambda d: d.get("id") or ""),
"model_ref": self.model_ref,
"run_manifest": self.run_manifest,
}
def compute_bundle_hash(self) -> str:
"""Compute a deterministic SHA-256 digest over the canonical bundle
representation. Uses JSON with sorted keys so identical bundles always
produce the same digest.
"""
payload = self.to_dict()
serialized = json.dumps(payload, sort_keys=True, default=str).encode("utf-8")
return hashlib.sha256(serialized).hexdigest()