more changres

examples/dataset/prompt.txt

@@ -0,0 +1,20 @@
+Goal:
+Create a high and low policy, the high policy take obs + text and return text, its a VLM
+The low policy is a VLA that take text returned by the high and ouptut actions
+
+
+High policy is ran every one second or when user send prompt
+
+
+Synthetic data generation:
+D demo -> teleop data with a global task annotation
+D label -> segment data into short skills (one to three seconds)
+D syn: p-gen will create the high level prompt user might gave to p-hi 
+Given D label prompt p-gen to imagine appropriate action, take images, ALL PRIOR skill labels in the episode: ℓ̂₀, …, ℓ̂ₜ₋₁
+
+“Given the scene + all previous steps + current needed skill ℓ̂₅,
+generate a user request that logically leads to ℓ̂₅.”
+
+Train:
+Phi(lt| images, global label) cross entropy - next token predictions
+Plow(At| images, qt, lt)

examples/port_datasets/convert_to_video_parallel.sh

@@ -0,0 +1,141 @@
+#!/bin/bash
+#SBATCH --time=24:00:00
+#SBATCH --partition=hopper-cpu
+#SBATCH --cpus-per-task=96
+#SBATCH --output=/fsx/jade_choghari/logs/launcher_%j.out
+#SBATCH --error=/fsx/jade_choghari/logs/launcher_%j.err
+# Activate conda environment
+# Load conda
+source /fsx/jade_choghari/miniforge3/etc/profile.d/conda.sh
+conda activate lerobot
+set -e  # Exit on error
+
+# Input dataset
+REPO_ID="lerobot"
+ROOT="/fsx/jade_choghari/vlabench-primitive/"
+
+# Output paths
+OUTPUT_DIR="/fsx/jade_choghari/vlabench-primitive-encoded"
+OUTPUT_REPO_ID="vlabench-primitive-encoded"
+LOGS_DIR="/fsx/jade_choghari/logs/convert_video"
+
+# Video encoding settings
+VCODEC="libsvtav1"
+PIX_FMT="yuv420p"
+GOP_SIZE=2
+CRF=30
+FAST_DECODE=0
+
+# Parallelization settings
+NUM_WORKERS=24           # Number of parallel SLURM workers
+NUM_IMAGE_WORKERS=4       # Threads per worker for image saving
+
+# SLURM settings
+PARTITION="hopper-cpu"  # Change to your CPU partition name
+CPUS_PER_TASK=32           # CPUs per worker
+MEM_PER_CPU="4G"          # Memory per CPU
+TIME_LIMIT="24:00:00"     # Time limit per job
+
+###############################################################################
+# STEP 1: Parallel Video Conversion
+###############################################################################
+rm -rf "${OUTPUT_DIR}"
+mkdir -p "${OUTPUT_DIR}"
+
+echo "=============================================="
+echo "STEP 1: Starting parallel video conversion"
+echo "  Workers: ${NUM_WORKERS}"
+echo "  Input: ${REPO_ID} (root: ${ROOT})"
+echo "  Output: ${OUTPUT_DIR}"
+echo "=============================================="
+
+python /admin/home/jade_choghari/lerobot/examples/port_datasets/slurm_convert_to_video.py\
+    --repo-id "${REPO_ID}" \
+    --root "${ROOT}" \
+    --output-dir "${OUTPUT_DIR}" \
+    --output-repo-id "${OUTPUT_REPO_ID}" \
+    --vcodec "${VCODEC}" \
+    --pix-fmt "${PIX_FMT}" \
+    --g ${GOP_SIZE} \
+    --crf ${CRF} \
+    --fast-decode ${FAST_DECODE} \
+    --num-image-workers ${NUM_IMAGE_WORKERS} \
+    --logs-dir "${LOGS_DIR}" \
+    --job-name "convert_video" \
+    --slurm 1 \
+    --workers ${NUM_WORKERS} \
+    --partition "${PARTITION}" \
+    --cpus-per-task ${CPUS_PER_TASK} \
+    --mem-per-cpu "${MEM_PER_CPU}" \
+    --time-limit "${TIME_LIMIT}"
+
+echo ""
+echo "✓ Parallel conversion jobs submitted!"
+echo "  Monitor with: squeue -u \$USER"
+echo "  Check logs in: ${LOGS_DIR}/convert_video"
+echo ""
+echo "Wait for all jobs to complete before running Step 2."
+echo "You can check completion with: squeue -u \$USER | grep convert_video"
+echo ""
+echo "After all jobs complete, run Step 2 to aggregate shards:"
+echo "  bash convert_to_video_parallel.sh aggregate"
+
+###############################################################################
+# STEP 2: Aggregate Shards (run this after Step 1 completes)
+###############################################################################
+
+if [ "$1" == "aggregate" ]; then
+    echo ""
+    echo "=============================================="
+    echo "STEP 2: Aggregating video shards"
+    echo "  Shards: ${NUM_WORKERS}"
+    echo "  Input: ${OUTPUT_DIR}/shard_XXXX"
+    echo "  Output: ${OUTPUT_DIR}_final"
+    echo "=============================================="
+
+    python slurm_aggregate_video_shards.py \
+        --shards-dir "${OUTPUT_DIR}" \
+        --output-dir "${OUTPUT_DIR}_final" \
+        --output-repo-id "${OUTPUT_REPO_ID}" \
+        --num-shards ${NUM_WORKERS} \
+        --logs-dir "${LOGS_DIR}" \
+        --job-name "aggregate_video" \
+        --slurm 1 \
+        --partition "${PARTITION}" \
+        --cpus-per-task 16 \
+        --mem-per-cpu "8G" \
+        --time-limit "08:00:00"
+
+    echo ""
+    echo "✓ Aggregation job submitted!"
+    echo "  Monitor with: squeue -u \$USER | grep aggregate_video"
+    echo "  Check logs in: ${LOGS_DIR}/aggregate_video"
+    echo ""
+    echo "After completion, your final dataset will be in:"
+    echo "  ${OUTPUT_DIR}_final"
+fi
+
+###############################################################################
+# Helpful information
+###############################################################################
+
+if [ "$1" != "aggregate" ]; then
+    echo ""
+    echo "=============================================="
+    echo "WORKFLOW SUMMARY"
+    echo "=============================================="
+    echo ""
+    echo "1. Step 1 is now running - it will:"
+    echo "   - Split episodes across ${NUM_WORKERS} workers"
+    echo "   - Each worker converts its episodes to video"
+    echo "   - Creates shard datasets in ${OUTPUT_DIR}/shard_XXXX"
+    echo ""
+    echo "2. After Step 1 completes, run Step 2:"
+    echo "   bash convert_to_video_parallel.sh aggregate"
+    echo ""
+    echo "3. Step 2 will merge all shards into a single dataset"
+    echo ""
+    echo "=============================================="
+fi
+
+

examples/port_datasets/slurm_aggregate_video_shards.py

@@ -0,0 +1,266 @@
+#!/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Aggregate video dataset shards into a single dataset.
+
+After parallel conversion using slurm_convert_to_video.py, this script merges
+all the shard datasets into one final dataset.
+
+Example usage:
+    python slurm_aggregate_video_shards.py \
+        --shards-dir /fsx/jade_choghari/libero_video \
+        --output-dir /fsx/jade_choghari/libero_video_final \
+        --output-repo-id lerobot_video \
+        --num-workers 100 \
+        --partition cpu_partition \
+        --cpus-per-task 16
+"""
+
+import argparse
+import logging
+from pathlib import Path
+
+from datatrove.executor import LocalPipelineExecutor
+from datatrove.executor.slurm import SlurmPipelineExecutor
+from datatrove.pipeline.base import PipelineStep
+
+
+class AggregateVideoShards(PipelineStep):
+    """Pipeline step that aggregates video dataset shards."""
+
+    def __init__(
+        self,
+        shards_dir: str | Path,
+        output_dir: str | Path,
+        output_repo_id: str,
+        num_shards: int,
+    ):
+        super().__init__()
+        self.shards_dir = Path(shards_dir)
+        self.output_dir = Path(output_dir)
+        self.output_repo_id = output_repo_id
+        self.num_shards = num_shards
+
+    def run(self, data=None, rank: int = 0, world_size: int = 1):
+        """Aggregate all shards into a single dataset."""
+        from lerobot.datasets.dataset_tools import merge_datasets
+        from lerobot.datasets.lerobot_dataset import LeRobotDataset
+        from lerobot.utils.utils import init_logging
+
+        init_logging()
+
+        # Only worker 0 performs aggregation
+        if rank != 0:
+            logging.info(f"Worker {rank} skipping - only worker 0 performs aggregation")
+            return
+
+        logging.info(f"Starting aggregation of {self.num_shards} shards")
+
+        # Collect all shard datasets
+        shard_datasets = []
+        for shard_idx in range(self.num_shards):
+            shard_dir = self.shards_dir / f"shard_{shard_idx:04d}"
+            if not shard_dir.exists():
+                logging.warning(f"Shard directory not found: {shard_dir}")
+                continue
+
+            # Find the repo_id for this shard
+            shard_repo_id = f"{self.output_repo_id}_shard_{shard_idx:04d}"
+            try:
+                shard_dataset = LeRobotDataset(shard_repo_id, root=shard_dir)
+                shard_datasets.append(shard_dataset)
+                logging.info(
+                    f"Loaded shard {shard_idx}: {shard_dataset.meta.total_episodes} episodes, "
+                    f"{shard_dataset.meta.total_frames} frames"
+                )
+            except Exception as e:
+                logging.error(f"Failed to load shard {shard_idx}: {e}")
+                continue
+
+        if len(shard_datasets) == 0:
+            raise ValueError(f"No valid shards found in {self.shards_dir}")
+
+        logging.info(f"Successfully loaded {len(shard_datasets)} shards, starting merge")
+
+        # Merge all shards
+        self.output_dir.mkdir(parents=True, exist_ok=True)
+        merged_dataset = merge_datasets(
+            shard_datasets,
+            output_repo_id=self.output_repo_id,
+            output_dir=self.output_dir,
+        )
+
+        logging.info("✓ Aggregation complete!")
+        logging.info(f"Merged dataset saved to: {self.output_dir}")
+        logging.info(f"Total episodes: {merged_dataset.meta.total_episodes}")
+        logging.info(f"Total frames: {merged_dataset.meta.total_frames}")
+
+
+def make_aggregate_executor(
+    shards_dir,
+    output_dir,
+    output_repo_id,
+    num_shards,
+    job_name,
+    logs_dir,
+    partition,
+    cpus_per_task,
+    mem_per_cpu,
+    time_limit,
+    slurm=True,
+):
+    """Create executor for shard aggregation."""
+    kwargs = {
+        "pipeline": [
+            AggregateVideoShards(
+                shards_dir=shards_dir,
+                output_dir=output_dir,
+                output_repo_id=output_repo_id,
+                num_shards=num_shards,
+            ),
+        ],
+        "logging_dir": str(logs_dir / job_name),
+    }
+
+    if slurm:
+        # Only need 1 worker for aggregation
+        kwargs.update(
+            {
+                "job_name": job_name,
+                "tasks": 1,
+                "workers": 1,
+                "time": time_limit,
+                "partition": partition,
+                "cpus_per_task": cpus_per_task,
+                "sbatch_args": {"mem-per-cpu": mem_per_cpu},
+            }
+        )
+        executor = SlurmPipelineExecutor(**kwargs)
+    else:
+        kwargs.update(
+            {
+                "tasks": 1,
+                "workers": 1,
+            }
+        )
+        executor = LocalPipelineExecutor(**kwargs)
+
+    return executor
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Aggregate video dataset shards into a single dataset",
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+
+    parser.add_argument(
+        "--shards-dir",
+        type=Path,
+        required=True,
+        help="Directory containing shard_XXXX subdirectories",
+    )
+    parser.add_argument(
+        "--output-dir",
+        type=Path,
+        required=True,
+        help="Output directory for the aggregated dataset",
+    )
+    parser.add_argument(
+        "--output-repo-id",
+        type=str,
+        required=True,
+        help="Repository ID for the aggregated dataset",
+    )
+    parser.add_argument(
+        "--num-shards",
+        type=int,
+        required=True,
+        help="Number of shards to aggregate (should match --workers from conversion)",
+    )
+    parser.add_argument(
+        "--logs-dir",
+        type=Path,
+        required=True,
+        help="Path to logs directory for datatrove",
+    )
+    parser.add_argument(
+        "--job-name",
+        type=str,
+        default="aggregate_video_shards",
+        help="Job name for SLURM",
+    )
+    parser.add_argument(
+        "--slurm",
+        type=int,
+        default=1,
+        help="Launch over SLURM (1) or locally (0)",
+    )
+    parser.add_argument(
+        "--partition",
+        type=str,
+        required=True,
+        help="SLURM partition (use CPU partition)",
+    )
+    parser.add_argument(
+        "--cpus-per-task",
+        type=int,
+        default=16,
+        help="Number of CPUs per task (aggregation can use more CPUs)",
+    )
+    parser.add_argument(
+        "--mem-per-cpu",
+        type=str,
+        default="8G",
+        help="Memory per CPU",
+    )
+    parser.add_argument(
+        "--time-limit",
+        type=str,
+        default="08:00:00",
+        help="Time limit for SLURM job",
+    )
+
+    args = parser.parse_args()
+
+    # Convert slurm flag to boolean
+    slurm = args.slurm == 1
+
+    # Create and run executor
+    executor = make_aggregate_executor(
+        shards_dir=args.shards_dir,
+        output_dir=args.output_dir,
+        output_repo_id=args.output_repo_id,
+        num_shards=args.num_shards,
+        job_name=args.job_name,
+        logs_dir=args.logs_dir,
+        partition=args.partition,
+        cpus_per_task=args.cpus_per_task,
+        mem_per_cpu=args.mem_per_cpu,
+        time_limit=args.time_limit,
+        slurm=slurm,
+    )
+
+    logging.info("Starting shard aggregation")
+    executor.run()
+    logging.info("Aggregation job submitted/completed")
+
+
+if __name__ == "__main__":
+    main()
+
+

examples/port_datasets/slurm_convert_to_video.py

@@ -0,0 +1,539 @@
+#!/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Parallelize video conversion using SLURM and Datatrove.
+
+This script converts an image-based LeRobot dataset to video format by distributing 
+episodes across multiple workers for parallel processing.
+
+Example usage:
+    python slurm_convert_to_video.py \
+        --repo-id lerobot \
+        --root /fsx/jade_choghari/libero/ \
+        --output-dir /fsx/jade_choghari/libero_video \
+        --output-repo-id lerobot_video \
+        --workers 100 \
+        --partition cpu_partition \
+        --cpus-per-task 8 \
+        --logs-dir ./logs
+"""
+
+import argparse
+import logging
+from pathlib import Path
+
+from datatrove.executor import LocalPipelineExecutor
+from datatrove.executor.slurm import SlurmPipelineExecutor
+from datatrove.pipeline.base import PipelineStep
+
+
+class ConvertEpisodesToVideo(PipelineStep):
+    """Pipeline step that converts episodes to videos in parallel."""
+
+    def __init__(
+        self,
+        repo_id: str,
+        root: str | None,
+        output_dir: str,
+        output_repo_id: str | None,
+        vcodec: str = "libsvtav1",
+        pix_fmt: str = "yuv420p",
+        g: int = 2,
+        crf: int = 30,
+        fast_decode: int = 0,
+        num_image_workers: int = 4,
+    ):
+        super().__init__()
+        self.repo_id = repo_id
+        self.root = root
+        self.output_dir = Path(output_dir)
+        self.output_repo_id = output_repo_id or f"{repo_id}_video"
+        self.vcodec = vcodec
+        self.pix_fmt = pix_fmt
+        self.g = g
+        self.crf = crf
+        self.fast_decode = fast_decode
+        self.num_image_workers = num_image_workers
+
+    def run(self, data=None, rank: int = 0, world_size: int = 1):
+        """Process a shard of episodes."""
+        from datasets.utils.tqdm import disable_progress_bars
+
+        from lerobot.datasets.lerobot_dataset import LeRobotDataset
+        from lerobot.scripts.lerobot_edit_dataset import (
+            encode_episode_videos,
+        )
+        from lerobot.utils.utils import init_logging
+        import logging
+        init_logging()
+        disable_progress_bars()
+
+        logging.info(f"Worker {rank}/{world_size} starting video conversion")
+
+        # Load source dataset
+        dataset = LeRobotDataset(self.repo_id, root=self.root)
+        total_episodes = dataset.meta.total_episodes
+
+        # Determine which episodes this worker processes
+        episodes_per_worker = total_episodes // world_size
+        remainder = total_episodes % world_size
+
+        if rank < remainder:
+            # First 'remainder' workers get one extra episode
+            start_ep = rank * (episodes_per_worker + 1)
+            end_ep = start_ep + episodes_per_worker + 1
+        else:
+            start_ep = remainder * (episodes_per_worker + 1) + (rank - remainder) * episodes_per_worker
+            end_ep = start_ep + episodes_per_worker
+
+        episode_indices = list(range(start_ep, end_ep))
+
+        logging.info(
+            f"Worker {rank} processing episodes {start_ep} to {end_ep-1} ({len(episode_indices)} episodes)"
+        )
+
+        if len(episode_indices) == 0:
+            logging.info(f"Worker {rank} has no episodes to process")
+            return
+
+        # Create shard-specific output directory
+        import shutil
+        shard_output_dir = self.output_dir / f"shard_{rank:04d}"
+        
+        # Remove existing directory to avoid conflicts with LeRobotDatasetMetadata.create
+        if shard_output_dir.exists():
+            logging.warning(f"Shard directory {shard_output_dir} already exists, removing it")
+            shutil.rmtree(shard_output_dir)
+
+        # Import conversion function
+        from lerobot.scripts.lerobot_edit_dataset import convert_dataset_to_videos
+
+        logging.info(
+            f"Worker {rank} converting {len(episode_indices)} episodes with codec {self.vcodec}, CRF {self.crf}"
+        )
+
+        # Convert this shard's episodes with remapped indices
+        # We need to remap episode indices to start from 0 for proper file structure
+        self._convert_shard_to_videos(
+            dataset=dataset,
+            shard_output_dir=shard_output_dir,
+            shard_repo_id=f"{self.output_repo_id}_shard_{rank:04d}",
+            episode_indices=episode_indices,
+        )
+
+        logging.info(f"Worker {rank} completed successfully")
+
+    def _convert_shard_to_videos(
+        self,
+        dataset,
+        shard_output_dir,
+        shard_repo_id,
+        episode_indices,
+    ):
+        """Convert a shard's episodes to videos with proper index remapping."""
+        import shutil
+        import pandas as pd
+        from tqdm import tqdm
+
+        from lerobot.datasets.lerobot_dataset import LeRobotDatasetMetadata
+        from lerobot.datasets.utils import write_info, write_stats, write_tasks
+        from lerobot.datasets.video_utils import encode_video_frames, get_video_info
+        from lerobot.scripts.lerobot_edit_dataset import (
+            save_episode_images_for_video,
+            _copy_data_without_images,
+        )
+        from lerobot.utils.constants import OBS_IMAGE
+
+        # Check that it's an image dataset
+        if len(dataset.meta.video_keys) > 0:
+            raise ValueError(
+                f"This operation is for image datasets only. Video dataset provided: {dataset.repo_id}"
+            )
+
+        # Get all image keys
+        hf_dataset = dataset.hf_dataset.with_format(None)
+        img_keys = [key for key in hf_dataset.features if key.startswith(OBS_IMAGE)]
+
+        if len(img_keys) == 0:
+            raise ValueError(f"No image keys found in dataset {dataset.repo_id}")
+
+        logging.info(f"Converting {len(episode_indices)} episodes with {len(img_keys)} cameras")
+
+        # Create new features dict, converting image features to video features
+        new_features = {}
+        for key, value in dataset.meta.features.items():
+            if key not in img_keys:
+                new_features[key] = value
+            else:
+                # Convert image key to video format
+                new_features[key] = value.copy()
+                new_features[key]["dtype"] = "video"
+
+        # Create new metadata for video dataset
+        new_meta = LeRobotDatasetMetadata.create(
+            repo_id=shard_repo_id,
+            fps=dataset.meta.fps,
+            features=new_features,
+            robot_type=dataset.meta.robot_type,
+            root=shard_output_dir,
+            use_videos=True,
+            chunks_size=dataset.meta.chunks_size,
+            data_files_size_in_mb=dataset.meta.data_files_size_in_mb,
+            video_files_size_in_mb=dataset.meta.video_files_size_in_mb,
+        )
+
+        # Create temporary directory for image extraction
+        temp_dir = shard_output_dir / "temp_images"
+        temp_dir.mkdir(parents=True, exist_ok=True)
+
+        # Process each episode with REMAPPED indices (0, 1, 2, ...)
+        all_episode_metadata = []
+        fps = int(dataset.fps)
+
+        try:
+            for new_ep_idx, orig_ep_idx in enumerate(tqdm(episode_indices, desc="Converting episodes")):
+                # Get episode metadata from source using ORIGINAL index
+                src_episode = dataset.meta.episodes[orig_ep_idx]
+                episode_length = src_episode["length"]
+                episode_duration = episode_length / dataset.fps
+
+                video_metadata = {}
+
+                # Encode videos for each camera
+                for img_key in img_keys:
+                    # Save images temporarily using ORIGINAL episode index
+                    imgs_dir = temp_dir / f"episode_{orig_ep_idx:06d}" / img_key
+                    save_episode_images_for_video(
+                        dataset, imgs_dir, img_key, orig_ep_idx, self.num_image_workers
+                    )
+
+                    # Determine chunk and file indices using NEW (remapped) episode index
+                    chunk_idx = new_ep_idx // new_meta.chunks_size
+                    file_idx = new_ep_idx % new_meta.chunks_size
+
+                    # Create video path in the new dataset structure
+                    video_path = new_meta.root / new_meta.video_path.format(
+                        video_key=img_key, chunk_index=chunk_idx, file_index=file_idx
+                    )
+                    video_path.parent.mkdir(parents=True, exist_ok=True)
+
+                    # Encode video
+                    encode_video_frames(
+                        imgs_dir=imgs_dir,
+                        video_path=video_path,
+                        fps=fps,
+                        vcodec=self.vcodec,
+                        pix_fmt=self.pix_fmt,
+                        g=self.g,
+                        crf=self.crf,
+                        fast_decode=self.fast_decode,
+                        overwrite=True,
+                    )
+
+                    # Clean up temporary images
+                    shutil.rmtree(imgs_dir)
+
+                    # Store video metadata
+                    video_metadata[img_key] = {
+                        f"videos/{img_key}/chunk_index": chunk_idx,
+                        f"videos/{img_key}/file_index": file_idx,
+                        f"videos/{img_key}/from_timestamp": 0.0,
+                        f"videos/{img_key}/to_timestamp": episode_duration,
+                    }
+
+                # Build episode metadata using NEW index
+                episode_meta = {
+                    "episode_index": new_ep_idx,
+                    "length": episode_length,
+                    "dataset_from_index": new_ep_idx * episode_length,
+                    "dataset_to_index": (new_ep_idx + 1) * episode_length,
+                }
+
+                # Add video metadata
+                for img_key in img_keys:
+                    episode_meta.update(video_metadata[img_key])
+
+                # Add data chunk/file info
+                if "data/chunk_index" in src_episode:
+                    episode_meta["data/chunk_index"] = src_episode["data/chunk_index"]
+                    episode_meta["data/file_index"] = src_episode["data/file_index"]
+
+                all_episode_metadata.append(episode_meta)
+
+            # Copy and transform data files (removing image columns)
+            _copy_data_without_images(dataset, new_meta, episode_indices, img_keys)
+
+            # Save episode metadata
+            episodes_df = pd.DataFrame(all_episode_metadata)
+            episodes_path = new_meta.root / "meta" / "episodes" / "chunk-000" / "file-000.parquet"
+            episodes_path.parent.mkdir(parents=True, exist_ok=True)
+            episodes_df.to_parquet(episodes_path, index=False)
+
+            # Update metadata info
+            new_meta.info["total_episodes"] = len(episode_indices)
+            new_meta.info["total_frames"] = sum(ep["length"] for ep in all_episode_metadata)
+            new_meta.info["total_tasks"] = dataset.meta.total_tasks
+            new_meta.info["splits"] = {"train": f"0:{len(episode_indices)}"}
+
+            # Update video info for all image keys using the actual first video file
+            for img_key in img_keys:
+                if not new_meta.features[img_key].get("info", None):
+                    # Use the first actually created video file
+                    chunk_idx = all_episode_metadata[0][f"videos/{img_key}/chunk_index"]
+                    file_idx = all_episode_metadata[0][f"videos/{img_key}/file_index"]
+                    video_path = new_meta.root / new_meta.video_path.format(
+                        video_key=img_key, chunk_index=chunk_idx, file_index=file_idx
+                    )
+                    new_meta.info["features"][img_key]["info"] = get_video_info(video_path)
+
+            write_info(new_meta.info, new_meta.root)
+
+            # Copy stats and tasks
+            if dataset.meta.stats is not None:
+                # Remove image stats
+                new_stats = {k: v for k, v in dataset.meta.stats.items() if k not in img_keys}
+                write_stats(new_stats, new_meta.root)
+
+            if dataset.meta.tasks is not None:
+                write_tasks(dataset.meta.tasks, new_meta.root)
+
+        finally:
+            # Clean up temporary directory
+            if temp_dir.exists():
+                shutil.rmtree(temp_dir)
+
+
+def make_convert_executor(
+    repo_id,
+    root,
+    output_dir,
+    output_repo_id,
+    vcodec,
+    pix_fmt,
+    g,
+    crf,
+    fast_decode,
+    num_image_workers,
+    job_name,
+    logs_dir,
+    workers,
+    partition,
+    cpus_per_task,
+    mem_per_cpu,
+    time_limit,
+    slurm=True,
+):
+    """Create executor for parallel video conversion."""
+    kwargs = {
+        "pipeline": [
+            ConvertEpisodesToVideo(
+                repo_id=repo_id,
+                root=root,
+                output_dir=output_dir,
+                output_repo_id=output_repo_id,
+                vcodec=vcodec,
+                pix_fmt=pix_fmt,
+                g=g,
+                crf=crf,
+                fast_decode=fast_decode,
+                num_image_workers=num_image_workers,
+            ),
+        ],
+        "logging_dir": str(logs_dir / job_name),
+    }
+
+    if slurm:
+        kwargs.update(
+            {
+                "job_name": job_name,
+                "tasks": workers,  # Number of parallel tasks = number of workers
+                "workers": workers,
+                "time": time_limit,
+                "partition": partition,
+                "cpus_per_task": cpus_per_task,
+                "sbatch_args": {"mem-per-cpu": mem_per_cpu},
+            }
+        )
+        executor = SlurmPipelineExecutor(**kwargs)
+    else:
+        kwargs.update(
+            {
+                "tasks": workers,
+                "workers": 1,  # Local mode: sequential
+            }
+        )
+        executor = LocalPipelineExecutor(**kwargs)
+
+    return executor
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Parallelize video conversion across SLURM workers",
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+
+    # Input/Output paths
+    parser.add_argument(
+        "--repo-id",
+        type=str,
+        required=True,
+        help="Repository ID of the source image dataset",
+    )
+    parser.add_argument(
+        "--root",
+        type=str,
+        default=None,
+        help="Root directory containing the source dataset (default: HF cache)",
+    )
+    parser.add_argument(
+        "--output-dir",
+        type=str,
+        required=True,
+        help="Output directory for the video dataset",
+    )
+    parser.add_argument(
+        "--output-repo-id",
+        type=str,
+        default=None,
+        help="Repository ID for output dataset (default: <repo_id>_video)",
+    )
+
+    # Video encoding parameters
+    parser.add_argument(
+        "--vcodec",
+        type=str,
+        default="libsvtav1",
+        help="Video codec",
+    )
+    parser.add_argument(
+        "--pix-fmt",
+        type=str,
+        default="yuv420p",
+        help="Pixel format",
+    )
+    parser.add_argument(
+        "--g",
+        type=int,
+        default=2,
+        help="GOP size (group of pictures)",
+    )
+    parser.add_argument(
+        "--crf",
+        type=int,
+        default=30,
+        help="Constant rate factor (quality)",
+    )
+    parser.add_argument(
+        "--fast-decode",
+        type=int,
+        default=0,
+        help="Fast decode tuning",
+    )
+    parser.add_argument(
+        "--num-image-workers",
+        type=int,
+        default=4,
+        help="Number of threads per worker for saving images",
+    )
+
+    # SLURM parameters
+    parser.add_argument(
+        "--logs-dir",
+        type=Path,
+        required=True,
+        help="Path to logs directory for datatrove",
+    )
+    parser.add_argument(
+        "--job-name",
+        type=str,
+        default="convert_to_video",
+        help="Job name for SLURM",
+    )
+    parser.add_argument(
+        "--slurm",
+        type=int,
+        default=1,
+        help="Launch over SLURM (1) or locally (0)",
+    )
+    parser.add_argument(
+        "--workers",
+        type=int,
+        default=100,
+        help="Number of parallel workers (each processes different episodes)",
+    )
+    parser.add_argument(
+        "--partition",
+        type=str,
+        required=True,
+        help="SLURM partition (use CPU partition)",
+    )
+    parser.add_argument(
+        "--cpus-per-task",
+        type=int,
+        default=8,
+        help="Number of CPUs per SLURM task",
+    )
+    parser.add_argument(
+        "--mem-per-cpu",
+        type=str,
+        default="4G",
+        help="Memory per CPU",
+    )
+    parser.add_argument(
+        "--time-limit",
+        type=str,
+        default="08:00:00",
+        help="Time limit for SLURM job",
+    )
+
+    args = parser.parse_args()
+
+    # Convert slurm flag to boolean
+    slurm = args.slurm == 1
+
+    # Create and run executor
+    executor = make_convert_executor(
+        repo_id=args.repo_id,
+        root=args.root,
+        output_dir=args.output_dir,
+        output_repo_id=args.output_repo_id,
+        vcodec=args.vcodec,
+        pix_fmt=args.pix_fmt,
+        g=args.g,
+        crf=args.crf,
+        fast_decode=args.fast_decode,
+        num_image_workers=args.num_image_workers,
+        job_name=args.job_name,
+        logs_dir=args.logs_dir,
+        workers=args.workers,
+        partition=args.partition,
+        cpus_per_task=args.cpus_per_task,
+        mem_per_cpu=args.mem_per_cpu,
+        time_limit=args.time_limit,
+        slurm=slurm,
+    )
+
+    logging.info(f"Starting video conversion with {args.workers} workers")
+    executor.run()
+    logging.info("All workers submitted/completed")
+
+
+if __name__ == "__main__":
+    main()
+
+

slurm-21724787.out

@@ -0,0 +1,7 @@
+==============================================
+STEP 1: Starting parallel video conversion
+  Workers: 2
+  Input: lerobot (root: /fsx/jade_choghari/vlabench-primitive/)
+  Output: /fsx/jade_choghari/vlabench-primitive-encoded
+==============================================
+python: can't open file '/admin/home/jade_choghari/lerobot/slurm_convert_to_video.py': [Errno 2] No such file or directory