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import json
import os
import sqlite3
from datetime import datetime
from pathlib import Path
from huggingface_hub import CommitScheduler
try: # absolute imports when installed
from trackio.context_vars import current_scheduler
from trackio.dummy_commit_scheduler import DummyCommitScheduler
from trackio.utils import TRACKIO_DIR
except Exception: # relative imports for local execution on Spaces
from context_vars import current_scheduler
from dummy_commit_scheduler import DummyCommitScheduler
from utils import TRACKIO_DIR
class SQLiteStorage:
@staticmethod
def _get_connection(db_path: Path) -> sqlite3.Connection:
conn = sqlite3.connect(str(db_path))
conn.row_factory = sqlite3.Row
return conn
@staticmethod
def get_project_db_path(project: str) -> Path:
"""Get the database path for a specific project."""
safe_project_name = "".join(
c for c in project if c.isalnum() or c in ("-", "_")
).rstrip()
if not safe_project_name:
safe_project_name = "default"
return TRACKIO_DIR / f"{safe_project_name}.db"
@staticmethod
def init_db(project: str) -> Path:
"""
Initialize the SQLite database with required tables.
Returns the database path.
"""
db_path = SQLiteStorage.get_project_db_path(project)
db_path.parent.mkdir(parents=True, exist_ok=True)
with SQLiteStorage.get_scheduler().lock:
with sqlite3.connect(db_path) as conn:
cursor = conn.cursor()
cursor.execute("""
CREATE TABLE IF NOT EXISTS metrics (
id INTEGER PRIMARY KEY AUTOINCREMENT,
timestamp TEXT NOT NULL,
run_name TEXT NOT NULL,
step INTEGER NOT NULL,
metrics TEXT NOT NULL
)
""")
cursor.execute(
"""
CREATE INDEX IF NOT EXISTS idx_metrics_run_step
ON metrics(run_name, step)
"""
)
conn.commit()
return db_path
@staticmethod
def get_scheduler():
"""
Get the scheduler for the database based on the environment variables.
This applies to both local and Spaces.
"""
if current_scheduler.get() is not None:
return current_scheduler.get()
hf_token = os.environ.get("HF_TOKEN")
dataset_id = os.environ.get("TRACKIO_DATASET_ID")
if dataset_id is None:
scheduler = DummyCommitScheduler()
else:
scheduler = CommitScheduler(
repo_id=dataset_id,
repo_type="dataset",
folder_path=TRACKIO_DIR,
private=True,
squash_history=True,
token=hf_token,
)
current_scheduler.set(scheduler)
return scheduler
@staticmethod
def log(project: str, run: str, metrics: dict):
"""
Safely log metrics to the database. Before logging, this method will ensure the database exists
and is set up with the correct tables. It also uses the scheduler to lock the database so
that there is no race condition when logging / syncing to the Hugging Face Dataset.
"""
db_path = SQLiteStorage.init_db(project)
with SQLiteStorage.get_scheduler().lock:
with SQLiteStorage._get_connection(db_path) as conn:
cursor = conn.cursor()
cursor.execute(
"""
SELECT MAX(step)
FROM metrics
WHERE run_name = ?
""",
(run,),
)
last_step = cursor.fetchone()[0]
current_step = 0 if last_step is None else last_step + 1
current_timestamp = datetime.now().isoformat()
cursor.execute(
"""
INSERT INTO metrics
(timestamp, run_name, step, metrics)
VALUES (?, ?, ?, ?)
""",
(
current_timestamp,
run,
current_step,
json.dumps(metrics),
),
)
conn.commit()
@staticmethod
def bulk_log(
project: str,
run: str,
metrics_list: list[dict],
steps: list[int] | None = None,
timestamps: list[str] | None = None,
):
"""Bulk log metrics to the database with specified steps and timestamps."""
if not metrics_list:
return
if steps is None:
steps = list(range(len(metrics_list)))
if timestamps is None:
timestamps = [datetime.now().isoformat()] * len(metrics_list)
if len(metrics_list) != len(steps) or len(metrics_list) != len(timestamps):
raise ValueError(
"metrics_list, steps, and timestamps must have the same length"
)
db_path = SQLiteStorage.init_db(project)
with SQLiteStorage.get_scheduler().lock:
with SQLiteStorage._get_connection(db_path) as conn:
cursor = conn.cursor()
data = []
for i, metrics in enumerate(metrics_list):
data.append(
(
timestamps[i],
run,
steps[i],
json.dumps(metrics),
)
)
cursor.executemany(
"""
INSERT INTO metrics
(timestamp, run_name, step, metrics)
VALUES (?, ?, ?, ?)
""",
data,
)
conn.commit()
@staticmethod
def get_metrics(project: str, run: str) -> list[dict]:
"""Retrieve metrics for a specific run. The metrics also include the step count (int) and the timestamp (datetime object)."""
db_path = SQLiteStorage.get_project_db_path(project)
if not db_path.exists():
return []
with SQLiteStorage._get_connection(db_path) as conn:
cursor = conn.cursor()
cursor.execute(
"""
SELECT timestamp, step, metrics
FROM metrics
WHERE run_name = ?
ORDER BY timestamp
""",
(run,),
)
rows = cursor.fetchall()
results = []
for row in rows:
metrics = json.loads(row["metrics"])
metrics["timestamp"] = row["timestamp"]
metrics["step"] = row["step"]
results.append(metrics)
return results
@staticmethod
def get_projects() -> list[str]:
"""
Get list of all projects by scanning the database files in the trackio directory.
"""
projects: set[str] = set()
if not TRACKIO_DIR.exists():
return []
for db_file in TRACKIO_DIR.glob("*.db"):
project_name = db_file.stem
projects.add(project_name)
return sorted(projects)
@staticmethod
def get_runs(project: str) -> list[str]:
"""Get list of all runs for a project."""
db_path = SQLiteStorage.get_project_db_path(project)
if not db_path.exists():
return []
with SQLiteStorage._get_connection(db_path) as conn:
cursor = conn.cursor()
cursor.execute(
"SELECT DISTINCT run_name FROM metrics",
)
return [row[0] for row in cursor.fetchall()]
@staticmethod
def get_max_steps_for_runs(project: str, runs: list[str]) -> dict[str, int]:
"""Efficiently get the maximum step for multiple runs in a single query."""
db_path = SQLiteStorage.get_project_db_path(project)
if not db_path.exists():
return {run: 0 for run in runs}
with SQLiteStorage._get_connection(db_path) as conn:
cursor = conn.cursor()
placeholders = ",".join("?" * len(runs))
cursor.execute(
f"""
SELECT run_name, MAX(step) as max_step
FROM metrics
WHERE run_name IN ({placeholders})
GROUP BY run_name
""",
runs,
)
results = {run: 0 for run in runs} # Default to 0 for runs with no data
for row in cursor.fetchall():
results[row["run_name"]] = row["max_step"]
return results
def finish(self):
"""Cleanup when run is finished."""
pass
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