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Running on Zero
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import uuid
import threading
import time as _time
from io import BytesIO
from datetime import datetime, timezone
from huggingface_hub import hf_hub_download, CommitOperationAdd, CommitOperationDelete
def _img_to_jpeg(img, quality=85):
if img is None:
return None
buf = BytesIO()
img.convert("RGB").save(buf, format="JPEG", quality=quality)
return buf.getvalue()
def _build_table(pil_inputs, output_pil, prompt, seed, steps, guidance_scale,
input_width, input_height, duration_seconds, success, error_message, now):
import json as _json
import pyarrow as pa
img_struct = pa.struct([("bytes", pa.binary()), ("path", pa.string())])
hf_meta = _json.dumps({"info": {"features": {
"timestamp": {"dtype": "float64", "_type": "Value"},
"prompt": {"dtype": "string", "_type": "Value"},
"seed": {"dtype": "int32", "_type": "Value"},
"steps": {"dtype": "int32", "_type": "Value"},
"guidance_scale": {"dtype": "float32", "_type": "Value"},
"input_images": {"feature": {"_type": "Image"}, "_type": "Sequence"},
"output_image": {"_type": "Image"},
"duration_seconds": {"dtype": "float32", "_type": "Value"},
"input_width": {"dtype": "int32", "_type": "Value"},
"input_height": {"dtype": "int32", "_type": "Value"},
"success": {"dtype": "bool", "_type": "Value"},
"error_message": {"dtype": "string", "_type": "Value"},
}}}).encode()
schema = pa.schema([
("timestamp", pa.float64()),
("prompt", pa.string()),
("seed", pa.int32()),
("steps", pa.int32()),
("guidance_scale", pa.float32()),
("input_images", pa.list_(img_struct)),
("output_image", img_struct),
("duration_seconds", pa.float32()),
("input_width", pa.int32()),
("input_height", pa.int32()),
("success", pa.bool_()),
("error_message", pa.string()),
], metadata={b"huggingface": hf_meta})
def _img(b):
return {"bytes": b, "path": None}
input_jpegs = [_img_to_jpeg(img) for img in pil_inputs]
output_jpeg = _img_to_jpeg(output_pil)
return pa.table({
"timestamp": pa.array([now.timestamp()], type=pa.float64()),
"prompt": pa.array([prompt], type=pa.string()),
"seed": pa.array([int(seed)], type=pa.int32()),
"steps": pa.array([int(steps)], type=pa.int32()),
"guidance_scale": pa.array([float(guidance_scale)], type=pa.float32()),
"input_images": pa.array([[_img(b) for b in input_jpegs]], type=pa.list_(img_struct)),
"output_image": pa.array([_img(output_jpeg) if output_jpeg else None], type=img_struct),
"duration_seconds": pa.array([float(duration_seconds)], type=pa.float32()),
"input_width": pa.array([int(input_width)], type=pa.int32()),
"input_height": pa.array([int(input_height)], type=pa.int32()),
"success": pa.array([bool(success)], type=pa.bool_()),
"error_message": pa.array([str(error_message)], type=pa.string()),
}, schema=schema)
def _write_parquet(table):
import tempfile
import pyarrow.parquet as pq
with tempfile.NamedTemporaryFile(suffix=".parquet", delete=False) as tmp:
path = tmp.name
pq.write_table(table, path)
return path
def _make_path(now, uid):
return f"data/{now.strftime('%Y-%m-%d-%H%M%S')}-{uid}.parquet"
def _list_existing_files(api, repo_id):
try:
entries = list(api.list_repo_tree(repo_id, repo_type="dataset", path_in_repo="data"))
except Exception as e:
print(f"[log] could not list existing files (empty repo?): {e}")
return []
return sorted(f.path for f in entries if f.path.endswith(".parquet"))
def _build_add_ops(batch):
return [CommitOperationAdd(path_in_repo=p, path_or_fileobj=local)
for p, local in batch]
def _build_delete_ops(existing_files, n_new, max_files):
total_after = len(existing_files) + n_new
if max_files <= 0 or total_after <= max_files:
return []
n_delete = total_after - max_files
return [CommitOperationDelete(path_in_repo=p) for p in existing_files[:n_delete]]
def _delete_temp_files(batch):
for _, local in batch:
try:
os.unlink(local)
except Exception:
pass
def _squash_if_needed(api, repo_id):
marker = "metadata/last_squash.txt"
today = datetime.now(timezone.utc).strftime("%Y-%m-%d")
try:
try:
local = hf_hub_download(repo_id=repo_id, filename=marker,
repo_type="dataset", token=api.token)
if open(local).read().strip() == today:
return
except Exception as e:
print(f"[log] squash marker not found ({e}), proceeding with squash")
api.super_squash_history(repo_id=repo_id, repo_type="dataset")
api.upload_file(path_or_fileobj=today.encode(), path_in_repo=marker,
repo_id=repo_id, repo_type="dataset")
print(f"[log] squashed history for {repo_id}")
except Exception as e:
print(f"[log] squash warning: {e}")
class LogUploader:
def __init__(self, token, repo_id, max_files=5000, batch_interval=60):
self._token = token
self._repo_id = repo_id
self._max_files = max_files
self._batch_interval = batch_interval
self._pending = []
self._lock = threading.Lock()
if token and repo_id:
threading.Thread(target=self._loop, daemon=True, name="log-uploader").start()
def log_inference(self, pil_inputs, output_pil, prompt, seed, steps, guidance_scale,
input_width, input_height, duration_seconds, success, error_message=""):
if not self._token or not self._repo_id:
print(f"[log] skipped — token={'set' if self._token else 'missing'}, repo={'set' if self._repo_id else 'missing'}")
return
t0 = _time.perf_counter()
try:
now = datetime.now(timezone.utc)
table = _build_table(pil_inputs, output_pil, prompt, seed, steps, guidance_scale,
input_width, input_height, duration_seconds, success, error_message, now)
local_path = _write_parquet(table)
path_in_repo = _make_path(now, uuid.uuid4().hex[:8])
self._enqueue(path_in_repo, local_path)
print(f"[log] queued {path_in_repo} (pending={len(self._pending)})")
except Exception as e:
import traceback as _tb
print(f"[log] WARNING: {e}\n{_tb.format_exc()}")
print(f"[log] log_inference total: {_time.perf_counter() - t0:.3f}s")
def _enqueue(self, path_in_repo, local_path):
with self._lock:
self._pending.append((path_in_repo, local_path))
def _drain(self):
with self._lock:
batch = self._pending[:]
self._pending.clear()
return batch
def _requeue(self, batch):
with self._lock:
self._pending[:0] = batch
def _loop(self):
while True:
_time.sleep(self._batch_interval)
self._flush()
def _flush(self):
batch = self._drain()
if not batch:
return
try:
self._commit_batch(batch)
_delete_temp_files(batch)
except Exception as e:
print(f"[log] batch upload warning: {e}")
self._requeue(batch)
def _commit_batch(self, batch):
from huggingface_hub import HfApi
api = HfApi(token=self._token)
api.create_repo(repo_id=self._repo_id, repo_type="dataset", private=True, exist_ok=True)
existing = _list_existing_files(api, self._repo_id)
add_ops = _build_add_ops(batch)
del_ops = _build_delete_ops(existing, len(batch), self._max_files)
api.create_commit(
repo_id=self._repo_id, repo_type="dataset",
operations=add_ops + del_ops,
commit_message=f"[log] batch {len(batch)}" + (f", prune {len(del_ops)}" if del_ops else ""),
)
print(f"[log] committed {len(batch)} file(s), pruned {len(del_ops)}")
_squash_if_needed(api, self._repo_id)
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