remove async stuff

examples/rac/rac_data_collection_openarms_rtc.py

@@ -102,9 +102,6 @@ class RaCRTCDatasetConfig:
     num_image_writer_threads_per_camera: int = 4
     video_encoding_batch_size: int = 1
     rename_map: dict[str, str] = field(default_factory=dict)
-    # Async video encoding: encode videos in background without blocking recording
-    # Set to number of worker processes (0 = half of CPUs, None = disabled)
-    async_video_encoder_workers: int | None = 0
 
 
 @dataclass
@@ -725,9 +722,6 @@ def rac_rtc_collect(cfg: RaCRTCConfig) -> LeRobotDataset:
                     num_processes=cfg.dataset.num_image_writer_processes,
                     num_threads=cfg.dataset.num_image_writer_threads_per_camera * len(robot_raw.cameras),
                 )
-            if cfg.dataset.async_video_encoder_workers is not None:
-                workers = None if cfg.dataset.async_video_encoder_workers == 0 else cfg.dataset.async_video_encoder_workers
-                dataset.start_video_encoder(num_workers=workers)
         else:
             dataset = LeRobotDataset.create(
                 cfg.dataset.repo_id,
@@ -740,7 +734,6 @@ def rac_rtc_collect(cfg: RaCRTCConfig) -> LeRobotDataset:
                 image_writer_threads=cfg.dataset.num_image_writer_threads_per_camera
                 * len(robot_raw.cameras if hasattr(robot_raw, "cameras") else []),
                 batch_encoding_size=cfg.dataset.video_encoding_batch_size,
-                video_encoder_workers=cfg.dataset.async_video_encoder_workers,
             )
 
         # Load policy
@@ -853,8 +846,7 @@ def rac_rtc_collect(cfg: RaCRTCConfig) -> LeRobotDataset:
                     dataset.clear_episode_buffer()
                     continue
 
-                use_async = cfg.dataset.async_video_encoder_workers is not None
-                dataset.save_episode(async_video_encoding=use_async)
+                dataset.save_episode()
                 recorded += 1
 
                 if recorded < cfg.dataset.num_episodes and not events["stop_recording"]:

src/lerobot/datasets/lerobot_dataset.py

@@ -69,7 +69,6 @@ from lerobot.datasets.utils import (
     write_tasks,
 )
 from lerobot.datasets.video_utils import (
-    AsyncVideoEncoder,
     VideoFrame,
     concatenate_video_files,
     decode_video_frames,
@@ -694,12 +693,10 @@ class LeRobotDataset(torch.utils.data.Dataset):
 
         # Unused attributes
         self.image_writer = None
-        self.video_encoder = None
         self.episode_buffer = None
         self.writer = None
         self.latest_episode = None
         self._current_file_start_frame = None  # Track the starting frame index of the current parquet file
-        self._pending_video_encoding = None  # Track pending async video encoding
 
         self.root.mkdir(exist_ok=True, parents=True)
 
@@ -1070,21 +1067,9 @@ class LeRobotDataset(torch.utils.data.Dataset):
 
     def finalize(self):
         """
-        Close the parquet writers and stop async workers. This function needs to be called after data 
-        collection/conversion, else footer metadata won't be written to the parquet files.
+        Close the parquet writers. This function needs to be called after data collection/conversion, else footer metadata won't be written to the parquet files.
         The dataset won't be valid and can't be loaded as ds = LeRobotDataset(repo_id=repo, root=HF_LEROBOT_HOME.joinpath(repo))
         """
-        # Finalize any pending video encoding from the last episode
-        if hasattr(self, '_pending_video_encoding') and self._pending_video_encoding:
-            prev_ep_idx, prev_temp_paths, prev_video_keys = self._pending_video_encoding
-            logging.info(f"Finalizing last episode {prev_ep_idx} videos...")
-            self._wait_video_encoder()
-            for vkey, vpath in zip(prev_video_keys, prev_temp_paths):
-                self._save_episode_video(vkey, prev_ep_idx, vpath)
-            self._pending_video_encoding = None
-        
-        self._wait_video_encoder()
-        self.stop_video_encoder()
         self._close_writer()
         self.meta._close_writer()
 
@@ -1166,7 +1151,6 @@ class LeRobotDataset(torch.utils.data.Dataset):
         self,
         episode_data: dict | None = None,
         parallel_encoding: bool = True,
-        async_video_encoding: bool = False,
     ) -> None:
         """
         This will save to disk the current episode in self.episode_buffer.
@@ -1174,7 +1158,6 @@ class LeRobotDataset(torch.utils.data.Dataset):
         Video encoding is handled automatically based on batch_encoding_size:
         - If batch_encoding_size == 1: Videos are encoded immediately after each episode
         - If batch_encoding_size > 1: Videos are encoded in batches.
-        - If async_video_encoding=True: Encoding runs in background (requires start_video_encoder())
 
         Args:
             episode_data (dict | None, optional): Dict containing the episode data to save. If None, this will
@@ -1182,8 +1165,6 @@ class LeRobotDataset(torch.utils.data.Dataset):
                 None.
             parallel_encoding (bool, optional): If True, encode videos in parallel using ProcessPoolExecutor.
                 Defaults to True on Linux, False on macOS as it tends to use all the CPU available already.
-            async_video_encoding (bool, optional): If True and video_encoder is started, encode videos
-                asynchronously in background processes without blocking. Defaults to False.
         """
         episode_buffer = episode_data if episode_data is not None else self.episode_buffer
 
@@ -1218,41 +1199,8 @@ class LeRobotDataset(torch.utils.data.Dataset):
         ep_metadata = self._save_episode_data(episode_buffer)
         has_video_keys = len(self.meta.video_keys) > 0
         use_batched_encoding = self.batch_encoding_size > 1
-        use_async_encoding = (
-            async_video_encoding 
-            and hasattr(self, "video_encoder") 
-            and self.video_encoder is not None
-        )
 
         if has_video_keys and not use_batched_encoding:
-            if use_async_encoding:
-                # Pipeline: finalize PREVIOUS episode's encoding while current is submitted
-                # This allows encoding to happen during the NEXT episode's recording
-                if hasattr(self, '_pending_video_encoding') and self._pending_video_encoding:
-                    prev_ep_idx, prev_temp_paths, prev_video_keys = self._pending_video_encoding
-                    logging.info(f"Finalizing previous episode {prev_ep_idx} videos...")
-                    self.video_encoder.wait_until_done()
-                    for vkey, vpath in zip(prev_video_keys, prev_temp_paths):
-                        self._save_episode_video(vkey, prev_ep_idx, vpath)
-                    self._pending_video_encoding = None
-                
-                # Submit current episode encoding (will run during next episode recording)
-                temp_paths = []
-                for video_key in self.meta.video_keys:
-                    img_dir = self._get_image_file_dir(episode_index, video_key)
-                    temp_path = Path(tempfile.mkdtemp(dir=self.root)) / f"{video_key}_{episode_index:03d}.mp4"
-                    temp_paths.append(temp_path)
-                    self.video_encoder.submit(
-                        imgs_dir=img_dir,
-                        video_path=temp_path,
-                        fps=self.fps,
-                        episode_index=episode_index,
-                        video_key=video_key,
-                    )
-                # Store for finalization during next episode
-                self._pending_video_encoding = (episode_index, temp_paths, list(self.meta.video_keys))
-                logging.info(f"Episode {episode_index} videos queued for encoding (will finalize next episode)")
-            else:
             video_paths = self._encode_multiple_temporary_episode_videos(self.meta.video_keys, episode_index)
             for video_key, video_path in zip(self.meta.video_keys, video_paths):
                 ep_metadata.update(self._save_episode_video(video_key, episode_index, video_path))
@@ -1271,8 +1219,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
 
         if not episode_data:
             # Reset episode buffer and clean up temporary images (if not already deleted during video encoding)
-            delete_images = len(self.meta.image_keys) > 0 and not use_async_encoding
-            self.clear_episode_buffer(delete_images=delete_images)
+            self.clear_episode_buffer(delete_images=len(self.meta.image_keys) > 0)
 
     def _batch_save_episode_video(self, start_episode: int, end_episode: int | None = None) -> None:
         """
@@ -1544,30 +1491,6 @@ class LeRobotDataset(torch.utils.data.Dataset):
         if self.image_writer is not None:
             self.image_writer.wait_until_done()
 
-    def start_video_encoder(self, num_workers: int | None = None) -> None:
-        """
-        Start async video encoder for on-the-fly video encoding during recording.
-        
-        Args:
-            num_workers: Number of encoding worker processes. Defaults to half of available CPUs.
-        """
-        if hasattr(self, "video_encoder") and self.video_encoder is not None:
-            logging.warning("Replacing existing AsyncVideoEncoder")
-            self.video_encoder.stop()
-        
-        self.video_encoder = AsyncVideoEncoder(num_workers=num_workers)
-
-    def stop_video_encoder(self, wait: bool = True) -> None:
-        """Stop async video encoder and wait for pending tasks."""
-        if hasattr(self, "video_encoder") and self.video_encoder is not None:
-            self.video_encoder.stop(wait=wait)
-            self.video_encoder = None
-
-    def _wait_video_encoder(self) -> None:
-        """Wait for async video encoder to finish pending tasks."""
-        if hasattr(self, "video_encoder") and self.video_encoder is not None:
-            self.video_encoder.wait_until_done()
-
     def _encode_temporary_episode_video(self, video_key: str, episode_index: int) -> Path:
         """
         Use ffmpeg to convert frames stored as png into mp4 videos.
@@ -1606,14 +1529,8 @@ class LeRobotDataset(torch.utils.data.Dataset):
         image_writer_threads: int = 0,
         video_backend: str | None = None,
         batch_encoding_size: int = 1,
-        video_encoder_workers: int | None = None,
     ) -> "LeRobotDataset":
-        """Create a LeRobot Dataset from scratch in order to record data.
-        
-        Args:
-            video_encoder_workers: If set, starts async video encoder with this many workers.
-                Set to 0 for half of available CPUs. For on-the-fly encoding during recording.
-        """
+        """Create a LeRobot Dataset from scratch in order to record data."""
         obj = cls.__new__(cls)
         obj.meta = LeRobotDatasetMetadata.create(
             repo_id=repo_id,
@@ -1628,17 +1545,12 @@ class LeRobotDataset(torch.utils.data.Dataset):
         obj.revision = None
         obj.tolerance_s = tolerance_s
         obj.image_writer = None
-        obj.video_encoder = None
         obj.batch_encoding_size = batch_encoding_size
         obj.episodes_since_last_encoding = 0
 
         if image_writer_processes or image_writer_threads:
             obj.start_image_writer(image_writer_processes, image_writer_threads)
 
-        if video_encoder_workers is not None:
-            workers = None if video_encoder_workers == 0 else video_encoder_workers
-            obj.start_video_encoder(num_workers=workers)
-
         # TODO(aliberts, rcadene, alexander-soare): Merge this with OnlineBuffer/DataBuffer
         obj.episode_buffer = obj.create_episode_buffer()
 
@@ -1652,7 +1564,6 @@ class LeRobotDataset(torch.utils.data.Dataset):
         obj.writer = None
         obj.latest_episode = None
         obj._current_file_start_frame = None
-        obj._pending_video_encoding = None
         # Initialize tracking for incremental recording
         obj._lazy_loading = False
         obj._recorded_frames = 0

src/lerobot/datasets/video_utils.py

@@ -16,12 +16,8 @@
 import glob
 import importlib
 import logging
-import multiprocessing
-import os
-import queue
 import shutil
 import tempfile
-import threading
 import warnings
 from dataclasses import dataclass, field
 from pathlib import Path
@@ -404,170 +400,6 @@ def encode_video_frames(
         raise OSError(f"Video encoding did not work. File not found: {video_path}.")
 
 
-def _video_encode_worker(task_queue: multiprocessing.JoinableQueue, result_queue: multiprocessing.Queue):
-    """Worker process that encodes videos from a queue."""
-    import gc
-    worker_pid = os.getpid()
-    logging.info(f"[VideoEncoder Worker {worker_pid}] Started")
-    
-    while True:
-        try:
-            item = task_queue.get()
-            if item is None:
-                task_queue.task_done()
-                logging.info(f"[VideoEncoder Worker {worker_pid}] Shutting down")
-                break
-            
-            imgs_dir, video_path, fps, episode_index, video_key, callback_data = item
-            try:
-                encode_video_frames(imgs_dir, video_path, fps, overwrite=True)
-                shutil.rmtree(imgs_dir)
-                result_queue.put(("success", episode_index, video_key, str(video_path), callback_data))
-                logging.info(f"[VideoEncoder Worker {worker_pid}] Done ep {episode_index} {video_key}")
-            except Exception as e:
-                logging.error(f"[VideoEncoder Worker {worker_pid}] Error: {e}")
-                result_queue.put(("error", episode_index, video_key, str(e), callback_data))
-            
-            task_queue.task_done()
-            gc.collect()
-        except Exception as e:
-            logging.error(f"[VideoEncoder Worker {worker_pid}] Fatal error: {e}")
-
-
-# Use spawn context on Linux to avoid fork issues with threading
-_mp_ctx = multiprocessing.get_context("spawn")
-
-
-class AsyncVideoEncoder:
-    """
-    Async video encoder that processes video encoding in background processes.
-    
-    This enables on-the-fly video encoding during data collection without blocking
-    the main recording loop. Uses a configurable number of worker processes.
-    
-    Args:
-        num_workers: Number of encoding worker processes. Defaults to half of available CPUs.
-        max_queue_size: Maximum number of pending encoding tasks. Defaults to 10.
-    """
-    
-    def __init__(self, num_workers: int | None = None, max_queue_size: int = 10):
-        if num_workers is None:
-            num_workers = max(2, os.cpu_count() // 2)
-        
-        self.num_workers = num_workers
-        self._stopped = False
-        self._lock = threading.Lock()
-        
-        self.task_queue = _mp_ctx.JoinableQueue(maxsize=max_queue_size)
-        self.result_queue = _mp_ctx.Queue()
-        self.pending_tasks: dict[tuple[int, str], Any] = {}
-        
-        self.workers = []
-        for i in range(num_workers):
-            p = _mp_ctx.Process(
-                target=_video_encode_worker, 
-                args=(self.task_queue, self.result_queue),
-                name=f"VideoEncoder-{i}"
-            )
-            p.start()
-            self.workers.append(p)
-            logging.info(f"Started video encoder worker {i} with PID {p.pid}")
-        
-        self._result_thread = threading.Thread(target=self._process_results, daemon=True)
-        self._result_thread.start()
-        
-        logging.info(f"AsyncVideoEncoder started with {num_workers} workers (PIDs: {[w.pid for w in self.workers]})")
-    
-    def _process_results(self):
-        """Background thread to process completed encoding results."""
-        while not self._stopped:
-            try:
-                result = self.result_queue.get(timeout=0.1)
-                status, episode_index, video_key, data, callback_data = result
-                
-                with self._lock:
-                    key = (episode_index, video_key)
-                    if key in self.pending_tasks:
-                        task = self.pending_tasks.pop(key)
-                        if task.get("callback"):
-                            task["callback"](status, episode_index, video_key, data, callback_data)
-                
-                if status == "error":
-                    logging.error(f"Video encoding failed for ep {episode_index}, {video_key}: {data}")
-                else:
-                    logging.info(f"Video encoded: ep {episode_index}, {video_key} -> {data}")
-            except queue.Empty:
-                continue
-            except Exception as e:
-                if not self._stopped:
-                    logging.error(f"Result processing error: {e}")
-    
-    def submit(
-        self,
-        imgs_dir: Path,
-        video_path: Path,
-        fps: int,
-        episode_index: int,
-        video_key: str,
-        callback: callable = None,
-        callback_data: Any = None,
-    ):
-        """Submit a video encoding task."""
-        if self._stopped:
-            raise RuntimeError("AsyncVideoEncoder has been stopped")
-        
-        with self._lock:
-            self.pending_tasks[(episode_index, video_key)] = {
-                "callback": callback,
-                "video_path": video_path,
-            }
-        
-        logging.info(f"Queuing video encode: ep {episode_index} {video_key}, queue size: {self.task_queue.qsize()}")
-        self.task_queue.put((imgs_dir, video_path, fps, episode_index, video_key, callback_data))
-    
-    @property
-    def pending_count(self) -> int:
-        """Number of pending encoding tasks."""
-        with self._lock:
-            return len(self.pending_tasks)
-    
-    def wait_until_done(self, timeout: float | None = None):
-        """Wait for all pending tasks to complete and flush memory."""
-        logging.info(f"Waiting for {self.pending_count} pending video encodes...")
-        self.task_queue.join()
-        logging.info("All video encodes complete")
-    
-    def flush(self):
-        """Wait for current tasks and clear memory. Call between episodes."""
-        self.wait_until_done()
-        import gc
-        gc.collect()
-    
-    def stop(self, wait: bool = True):
-        """Stop all workers and clean up resources."""
-        if self._stopped:
-            return
-        
-        self._stopped = True
-        logging.info("Stopping AsyncVideoEncoder...")
-        
-        for _ in self.workers:
-            self.task_queue.put(None)
-        
-        if wait:
-            for w in self.workers:
-                w.join(timeout=10.0)
-        
-        for w in self.workers:
-            if w.is_alive():
-                logging.warning(f"Force terminating worker {w.pid}")
-                w.terminate()
-        
-        self.task_queue.close()
-        self.result_queue.close()
-        logging.info("AsyncVideoEncoder stopped")
-
-
 def concatenate_video_files(
     input_video_paths: list[Path | str], output_video_path: Path, overwrite: bool = True
 ):