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import json
import re
import sqlite3
from dataclasses import dataclass
from datetime import datetime, timezone
from pathlib import Path
from typing import Any
from adam.models import Job, JobStatus, SystemSnapshot
def _utc_now() -> str:
return datetime.now(timezone.utc).isoformat()
def _json(value: Any) -> str:
return json.dumps(value, sort_keys=True)
def _safe_json(value: str, fallback: Any) -> Any:
try:
parsed = json.loads(value or "")
except (TypeError, ValueError, json.JSONDecodeError):
return fallback
return parsed if isinstance(parsed, type(fallback)) else fallback
def _safe_int(value: Any, default: int = 0) -> int:
try:
if isinstance(value, bool):
return default
return int(value)
except (TypeError, ValueError):
return default
def _safe_float(value: Any, default: float = 0.0) -> float:
try:
if isinstance(value, bool):
return default
return float(value)
except (TypeError, ValueError):
return default
def _loss_from_logs(logs: list[str]) -> float | None:
for line in reversed(logs):
match = re.search(r"\bloss(?:\s*[:=]\s*|\s+)(-?\d+(?:\.\d+)?(?:e[+-]?\d+)?)", line, re.I)
if match:
try:
return float(match.group(1))
except ValueError:
return None
return None
def _duration_seconds(job: Job) -> int:
if not job.started_at:
return 0
try:
start = datetime.fromisoformat(job.started_at)
end = datetime.fromisoformat(job.ended_at) if job.ended_at else datetime.now(timezone.utc)
return max(0, int((end - start).total_seconds()))
except ValueError:
return 0
def _image_count(path: str) -> int:
folder = Path(path).expanduser()
if not folder.is_dir():
return 0
try:
return sum(
1 for item in folder.rglob("*")
if item.is_file() and item.suffix.casefold() in {".png", ".jpg", ".jpeg", ".webp", ".bmp"}
)
except OSError:
return 0
@dataclass(slots=True)
class ExperimentRun:
id: str
job_id: str
timestamp: str
model_architecture: str
model_name: str
trigger_word: str
base_model: str
dataset_path: str
dataset_name: str
dataset_item_count: int
epochs: int
batch_size: int
learning_rate: float
optimizer: str
scheduler: str
resolution: int
seed: int
status: str
training_time_seconds: int
final_loss: float | None
output_folder: str
checkpoint_paths: list[str]
preview_images: list[str]
peak_vram_gb: float | None
hardware: dict[str, Any]
settings: dict[str, Any]
generation_settings: dict[str, Any]
notes: str = ""
quality_score: int | None = None
@classmethod
def from_row(cls, row: sqlite3.Row) -> "ExperimentRun":
payload = dict(row)
for key in ("checkpoint_paths", "preview_images"):
payload[key] = _safe_json(payload.get(key, "[]"), [])
for key in ("hardware", "settings", "generation_settings"):
payload[key] = _safe_json(payload.get(key, "{}"), {})
payload["quality_score"] = (
_safe_int(payload["quality_score"]) if payload.get("quality_score") is not None else None
)
return cls(**payload)
class ExperimentStore:
def __init__(self, root: Path) -> None:
self.root = root.resolve()
self.path = self.root / "data" / "experiments.sqlite3"
self.path.parent.mkdir(parents=True, exist_ok=True)
self._init_db()
def connect(self) -> sqlite3.Connection:
connection = sqlite3.connect(self.path)
connection.row_factory = sqlite3.Row
return connection
def _init_db(self) -> None:
with self.connect() as db:
db.execute(
"""
CREATE TABLE IF NOT EXISTS experiments (
id TEXT PRIMARY KEY,
job_id TEXT UNIQUE NOT NULL,
timestamp TEXT NOT NULL,
model_architecture TEXT NOT NULL,
model_name TEXT NOT NULL,
trigger_word TEXT NOT NULL DEFAULT '',
base_model TEXT NOT NULL,
dataset_path TEXT NOT NULL,
dataset_name TEXT NOT NULL,
dataset_item_count INTEGER NOT NULL,
epochs INTEGER NOT NULL,
batch_size INTEGER NOT NULL,
learning_rate REAL NOT NULL,
optimizer TEXT NOT NULL,
scheduler TEXT NOT NULL,
resolution INTEGER NOT NULL,
seed INTEGER NOT NULL,
status TEXT NOT NULL,
training_time_seconds INTEGER NOT NULL,
final_loss REAL,
output_folder TEXT NOT NULL,
checkpoint_paths TEXT NOT NULL,
preview_images TEXT NOT NULL,
peak_vram_gb REAL,
hardware TEXT NOT NULL,
settings TEXT NOT NULL,
generation_settings TEXT NOT NULL,
notes TEXT NOT NULL DEFAULT '',
quality_score INTEGER
)
"""
)
self._migrate_columns(db)
@staticmethod
def _migrate_columns(db: sqlite3.Connection) -> None:
existing = {row["name"] for row in db.execute("PRAGMA table_info(experiments)").fetchall()}
columns = {
"id": "TEXT PRIMARY KEY",
"job_id": "TEXT NOT NULL DEFAULT ''",
"timestamp": "TEXT NOT NULL DEFAULT ''",
"model_architecture": "TEXT NOT NULL DEFAULT ''",
"model_name": "TEXT NOT NULL DEFAULT ''",
"trigger_word": "TEXT NOT NULL DEFAULT ''",
"base_model": "TEXT NOT NULL DEFAULT ''",
"dataset_path": "TEXT NOT NULL DEFAULT ''",
"dataset_name": "TEXT NOT NULL DEFAULT ''",
"dataset_item_count": "INTEGER NOT NULL DEFAULT 0",
"epochs": "INTEGER NOT NULL DEFAULT 0",
"batch_size": "INTEGER NOT NULL DEFAULT 0",
"learning_rate": "REAL NOT NULL DEFAULT 0",
"optimizer": "TEXT NOT NULL DEFAULT ''",
"scheduler": "TEXT NOT NULL DEFAULT ''",
"resolution": "INTEGER NOT NULL DEFAULT 0",
"seed": "INTEGER NOT NULL DEFAULT 0",
"status": "TEXT NOT NULL DEFAULT ''",
"training_time_seconds": "INTEGER NOT NULL DEFAULT 0",
"final_loss": "REAL",
"output_folder": "TEXT NOT NULL DEFAULT ''",
"checkpoint_paths": "TEXT NOT NULL DEFAULT '[]'",
"preview_images": "TEXT NOT NULL DEFAULT '[]'",
"peak_vram_gb": "REAL",
"hardware": "TEXT NOT NULL DEFAULT '{}'",
"settings": "TEXT NOT NULL DEFAULT '{}'",
"generation_settings": "TEXT NOT NULL DEFAULT '{}'",
"notes": "TEXT NOT NULL DEFAULT ''",
"quality_score": "INTEGER",
}
for name, definition in columns.items():
if name not in existing and name != "id":
db.execute(f"ALTER TABLE experiments ADD COLUMN {name} {definition}")
def record_job(self, job: Job, snapshot: SystemSnapshot | None = None) -> ExperimentRun | None:
training_steps = [step for step in job.plan.steps if step.tool_id.endswith("_trainer")]
if not training_steps:
return None
step = training_steps[-1]
args = dict(step.arguments)
architecture = step.tool_id.removesuffix("_trainer")
dataset_path = str(args.get("dataset_dir", ""))
output_folder = str(job.output_folder or args.get("output_dir", ""))
preview_images = [job.preview_path] if job.preview_path else []
checkpoints = []
if output_folder:
folder = Path(output_folder)
if folder.is_dir():
try:
checkpoints = [
str(path)
for path in sorted(folder.rglob("*"))
if path.is_file() and path.suffix.casefold() in {".safetensors", ".ckpt", ".pt", ".bin"}
][-10:]
discovered_previews = [
str(path)
for path in sorted(folder.rglob("*"))
if path.is_file()
and path.suffix.casefold() in {".png", ".jpg", ".jpeg", ".webp", ".bmp"}
and any(token in path.name.casefold() for token in ("preview", "sample", "epoch"))
][-12:]
preview_images = list(dict.fromkeys([*preview_images, *discovered_previews]))
except OSError:
checkpoints = []
hardware = {}
peak_vram = None
if snapshot is not None:
hardware = {
"gpu_name": snapshot.gpu_name,
"gpu_percent": snapshot.gpu_percent,
"vram_used_gb": snapshot.vram_used_gb,
"vram_total_gb": snapshot.vram_total_gb,
"memory_used_gb": snapshot.memory_used_gb,
"memory_total_gb": snapshot.memory_total_gb,
"cpu_percent": snapshot.cpu_percent,
"gpu_temperature": snapshot.gpu_temperature,
}
peak_vram = snapshot.vram_used_gb or None
run = ExperimentRun(
id=f"EXP-{job.id}",
job_id=job.id,
timestamp=job.ended_at or _utc_now(),
model_architecture=architecture,
model_name=str(args.get("model_name", job.plan.project_name)),
trigger_word=str(args.get("trigger_word", "")),
base_model=str(args.get("base_model", args.get("base_model_path", ""))),
dataset_path=dataset_path,
dataset_name=Path(dataset_path).name if dataset_path else "",
dataset_item_count=_image_count(dataset_path),
epochs=_safe_int(args.get("epochs")),
batch_size=_safe_int(args.get("batch_size")),
learning_rate=_safe_float(args.get("learning_rate")),
optimizer=str(args.get("optimizer", "")),
scheduler=str(args.get("scheduler", args.get("sampler", ""))),
resolution=_safe_int(args.get("resolution")),
seed=_safe_int(args.get("seed", args.get("preview_seed", 0))),
status=job.status.value,
training_time_seconds=_duration_seconds(job),
final_loss=_loss_from_logs(job.logs),
output_folder=output_folder,
checkpoint_paths=checkpoints,
preview_images=preview_images,
peak_vram_gb=peak_vram,
hardware=hardware,
settings=args,
generation_settings={},
)
with self.connect() as db:
db.execute(
"""
INSERT INTO experiments (
id, job_id, timestamp, model_architecture, model_name, trigger_word, base_model,
dataset_path, dataset_name, dataset_item_count, epochs, batch_size,
learning_rate, optimizer, scheduler, resolution, seed, status,
training_time_seconds, final_loss, output_folder, checkpoint_paths,
preview_images, peak_vram_gb, hardware, settings, generation_settings
) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
ON CONFLICT(job_id) DO UPDATE SET
timestamp=excluded.timestamp,
trigger_word=excluded.trigger_word,
status=excluded.status,
training_time_seconds=excluded.training_time_seconds,
final_loss=excluded.final_loss,
output_folder=excluded.output_folder,
checkpoint_paths=excluded.checkpoint_paths,
preview_images=excluded.preview_images,
peak_vram_gb=excluded.peak_vram_gb,
hardware=excluded.hardware,
settings=excluded.settings
""",
(
run.id, run.job_id, run.timestamp, run.model_architecture, run.model_name,
run.trigger_word, run.base_model, run.dataset_path, run.dataset_name, run.dataset_item_count,
run.epochs, run.batch_size, run.learning_rate, run.optimizer, run.scheduler,
run.resolution, run.seed, run.status, run.training_time_seconds, run.final_loss,
run.output_folder, _json(run.checkpoint_paths), _json(run.preview_images),
run.peak_vram_gb, _json(run.hardware), _json(run.settings),
_json(run.generation_settings),
),
)
return run
def list_runs(self, search: str = "", architecture: str = "", dataset: str = "", limit: int = 200) -> list[ExperimentRun]:
clauses = []
params: list[Any] = []
if search:
clauses.append("(id LIKE ? OR job_id LIKE ? OR model_name LIKE ? OR dataset_name LIKE ? OR dataset_path LIKE ? OR output_folder LIKE ? OR notes LIKE ? OR status LIKE ?)")
term = f"%{search}%"
params.extend([term, term, term, term, term, term, term, term])
if architecture:
clauses.append("model_architecture = ?")
params.append(architecture)
if dataset:
clauses.append("dataset_name LIKE ?")
params.append(f"%{dataset}%")
where = " WHERE " + " AND ".join(clauses) if clauses else ""
with self.connect() as db:
rows = db.execute(
"SELECT * FROM experiments" + where + " ORDER BY timestamp DESC LIMIT ?",
[*params, int(limit)],
).fetchall()
return [ExperimentRun.from_row(row) for row in rows]
def get(self, run_id: str) -> ExperimentRun | None:
with self.connect() as db:
row = db.execute("SELECT * FROM experiments WHERE id = ?", (run_id,)).fetchone()
return ExperimentRun.from_row(row) if row else None
def update_notes(self, run_id: str, notes: str, quality_score: int | None) -> None:
with self.connect() as db:
db.execute(
"UPDATE experiments SET notes = ?, quality_score = ? WHERE id = ?",
(notes, quality_score, run_id),
)
def compare(self, run_ids: list[str]) -> list[dict[str, Any]]:
runs = [run for run_id in run_ids if (run := self.get(run_id)) is not None]
fields = [
"model_architecture", "epochs", "final_loss", "training_time_seconds",
"resolution", "batch_size", "learning_rate", "scheduler",
"peak_vram_gb", "dataset_name", "quality_score",
]
rows = []
for field in fields:
values = {run.id: getattr(run, field) for run in runs}
comparable = {str(value) for value in values.values()}
rows.append({"field": field, "changed": len(comparable) > 1, **values})
return rows
def clone_request(self, run_id: str) -> str:
run = self.get(run_id)
if run is None:
return ""
options = {
key: value
for key, value in run.settings.items()
if key not in {"dataset_dir", "model_name", "epochs", "output_dir", "resume_from"}
}
return (
f"From the {run.dataset_name or run.dataset_path} dataset, train a "
f"{run.model_architecture.upper()} model for {run.epochs} epochs. "
f"Name the model {run.model_name} Clone. "
"[ADAM_TRAINING_OPTIONS:" + json.dumps(options, sort_keys=True) + "] "
"[ADAM_TRAINER:" + run.model_architecture + "]"
)
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