# Porting Large Datasets to LeRobot Dataset v3.0

This tutorial explains how to port large-scale robotic datasets to the LeRobot Dataset v3.0 format. We'll use the **DROID 1.0.1** dataset as our primary example, which demonstrates handling multi-terabyte datasets with thousands of shards across SLURM clusters.

## File Organization: v2.1 vs v3.0

Dataset v3.0 fundamentally changes how data is organized and stored:

**v2.1 Structure (Episode-based)**:

```
dataset/
├── data/chunk-000/episode_000000.parquet
├── data/chunk-000/episode_000001.parquet
├── videos/chunk-000/camera/episode_000000.mp4
└── meta/episodes.jsonl
```

**v3.0 Structure (File-based)**:

```
dataset/
├── data/chunk-000/file-000.parquet        # Multiple episodes per file
├── videos/camera/chunk-000/file-000.mp4   # Consolidated video chunks
└── meta/episodes/chunk-000/file-000.parquet  # Structured metadata
```

This transition from individual episode files to file-based chunks dramatically improves performance and reduces storage overhead.

## What's New in Dataset v3.0

Dataset v3.0 introduces significant improvements for handling large datasets:

### 🏗️ **Enhanced File Organization**

- **File-based structure**: Episodes are now grouped into chunked files rather than individual episode files
- **Configurable file sizes**: for data and video files
- **Improved storage efficiency**: Better compression and reduced overhead

### 📊 **Modern Metadata Management**

- **Parquet-based metadata**: Replaced JSON Lines with efficient parquet format
- **Structured episode access**: Direct pandas DataFrame access via `dataset.meta.episodes`
- **Per-episode statistics**: Enhanced statistics tracking at episode level

### 🚀 **Performance Enhancements**

- **Memory-mapped access**: Improved RAM usage through PyArrow memory mapping
- **Faster loading**: Significantly reduced dataset initialization time
- **Better scalability**: Designed for datasets with millions of episodes

## Prerequisites

Before porting large datasets, ensure you have:

- **LeRobot installed** with v3.0 support. Follow our [Installation Guide](./installation).
- **Sufficient storage**: Raw datasets can be very large (e.g., DROID requires 2TB)
- **Cluster access** (recommended for large datasets): SLURM or similar job scheduler
- **Dataset-specific dependencies**: For DROID, you'll need TensorFlow Dataset utilities

## Understanding the DROID Dataset

[DROID 1.0.1](https://droid-dataset.github.io/droid/the-droid-dataset) is an excellent example of a large-scale robotic dataset:

- **Size**: 1.7TB (RLDS format), 8.7TB (raw data)
- **Structure**: 2048 pre-defined TensorFlow dataset shards
- **Content**: 76,000+ robot manipulation trajectories from Franka Emika Panda robots
- **Scope**: Real-world manipulation tasks across multiple environments and objects
- **Format**: Originally in TensorFlow Records/RLDS format, requiring conversion to LeRobot format
- **Hosting**: Google Cloud Storage with public access via `gsutil`

The dataset contains diverse manipulation demonstrations with:

- Multiple camera views (wrist camera, exterior cameras)
- Natural language task descriptions
- Robot proprioceptive state and actions
- Success/failure annotations

### DROID Features Schema

```python
DROID_FEATURES = {
    # Episode markers
    "is_first": {"dtype": "bool", "shape": (1,)},
    "is_last": {"dtype": "bool", "shape": (1,)},
    "is_terminal": {"dtype": "bool", "shape": (1,)},

    # Language instructions
    "language_instruction": {"dtype": "string", "shape": (1,)},
    "language_instruction_2": {"dtype": "string", "shape": (1,)},
    "language_instruction_3": {"dtype": "string", "shape": (1,)},

    # Robot state
    "observation.state.gripper_position": {"dtype": "float32", "shape": (1,)},
    "observation.state.cartesian_position": {"dtype": "float32", "shape": (6,)},
    "observation.state.joint_position": {"dtype": "float32", "shape": (7,)},

    # Camera observations
    "observation.images.wrist_left": {"dtype": "image"},
    "observation.images.exterior_1_left": {"dtype": "image"},
    "observation.images.exterior_2_left": {"dtype": "image"},

    # Actions
    "action.gripper_position": {"dtype": "float32", "shape": (1,)},
    "action.cartesian_position": {"dtype": "float32", "shape": (6,)},
    "action.joint_position": {"dtype": "float32", "shape": (7,)},

    # Standard LeRobot format
    "observation.state": {"dtype": "float32", "shape": (8,)},  # joints + gripper
    "action": {"dtype": "float32", "shape": (8,)},  # joints + gripper
}
```

## Approach 1: Single Computer Porting

### Step 1: Install Dependencies

For DROID specifically:

```bash
pip install tensorflow
pip install tensorflow_datasets
```

For other datasets, install the appropriate readers for your source format.

### Step 2: Download Raw Data

Download DROID from Google Cloud Storage using `gsutil`:

```bash
# Install Google Cloud SDK if not already installed
# https://cloud.google.com/sdk/docs/install

# Download the full RLDS dataset (1.7TB)
gsutil -m cp -r gs://gresearch/robotics/droid/1.0.1 /your/data/

# Or download just the 100-episode sample (2GB) for testing
gsutil -m cp -r gs://gresearch/robotics/droid_100 /your/data/
```

> [!WARNING]
> Large datasets require substantial time and storage:
>
> - **Full DROID (1.7TB)**: Several days to download depending on bandwidth
> - **Processing time**: 7+ days for local porting of full dataset
> - **Upload time**: 3+ days to push to Hugging Face Hub
> - **Local storage**: ~400GB for processed LeRobot format

### Step 3: Port the Dataset

```bash
python examples/port_datasets/port_droid.py \
    --raw-dir /your/data/droid/1.0.1 \
    --repo-id your_id/droid_1.0.1 \
    --push-to-hub
```

### Development and Testing

For development, you can port a single shard:

```bash
python examples/port_datasets/port_droid.py \
    --raw-dir /your/data/droid/1.0.1 \
    --repo-id your_id/droid_1.0.1_test \
    --num-shards 2048 \
    --shard-index 0
```

This approach works for smaller datasets or testing, but large datasets require cluster computing.

## Approach 2: SLURM Cluster Porting (Recommended)

For large datasets like DROID, parallel processing across multiple nodes dramatically reduces processing time.

### Step 1: Install Cluster Dependencies

```bash
pip install datatrove  # Hugging Face's distributed processing library
```

### Step 2: Configure Your SLURM Environment

Find your partition information:

```bash
sinfo --format="%R"  # List available partitions
sinfo -N -p your_partition -h -o "%N cpus=%c mem=%m"  # Check resources
```

Choose a **CPU partition** - no GPU needed for dataset porting.

### Step 3: Launch Parallel Porting Jobs

```bash
python examples/port_datasets/slurm_port_shards.py \
    --raw-dir /your/data/droid/1.0.1 \
    --repo-id your_id/droid_1.0.1 \
    --logs-dir /your/logs \
    --job-name port_droid \
    --partition your_partition \
    --workers 2048 \
    --cpus-per-task 8 \
    --mem-per-cpu 1950M
```

#### Parameter Guidelines

- **`--workers`**: Number of parallel jobs (max 2048 for DROID's shard count)
- **`--cpus-per-task`**: 8 CPUs recommended for frame encoding parallelization
- **`--mem-per-cpu`**: ~16GB total RAM (8×1950M) for loading raw frames

> [!TIP]
> Start with fewer workers (e.g., 100) to test your cluster configuration before launching thousands of jobs.

### Step 4: Monitor Progress

Check running jobs:

```bash
squeue -u $USER
```

Monitor overall progress:

```bash
jobs_status /your/logs
```

Inspect individual job logs:

```bash
less /your/logs/port_droid/slurm_jobs/JOB_ID_WORKER_ID.out
```

Debug failed jobs:

```bash
failed_logs /your/logs/port_droid
```

### Step 5: Aggregate Shards

Once all porting jobs complete:

```bash
python examples/port_datasets/slurm_aggregate_shards.py \
    --repo-id your_id/droid_1.0.1 \
    --logs-dir /your/logs \
    --job-name aggr_droid \
    --partition your_partition \
    --workers 2048 \
    --cpus-per-task 8 \
    --mem-per-cpu 1950M
```

### Step 6: Upload to Hub

```bash
python examples/port_datasets/slurm_upload.py \
    --repo-id your_id/droid_1.0.1 \
    --logs-dir /your/logs \
    --job-name upload_droid \
    --partition your_partition \
    --workers 50 \
    --cpus-per-task 4 \
    --mem-per-cpu 1950M
```

> [!NOTE]
> Upload uses fewer workers (50) since it's network-bound rather than compute-bound.

## Dataset v3.0 File Structure

Your completed dataset will have this modern structure:

```
dataset/
├── meta/
│   ├── episodes/
│   │   └── chunk-000/
│   │       └── file-000.parquet    # Episode metadata
│   ├── tasks.parquet               # Task definitions
│   ├── stats.json                  # Aggregated statistics
│   └── info.json                   # Dataset information
├── data/
│   └── chunk-000/
│       └── file-000.parquet        # Consolidated episode data
└── videos/
    └── camera_key/
        └── chunk-000/
            └── file-000.mp4        # Consolidated video files
```

This replaces the old episode-per-file structure with efficient, optimally-sized chunks.

## Migrating from Dataset v2.1

If you have existing datasets in v2.1 format, use the migration tool:

```bash
python src/lerobot/datasets/v30/convert_dataset_v21_to_v30.py \
    --repo-id your_id/existing_dataset
```

This automatically:

- Converts file structure to v3.0 format
- Migrates metadata from JSON Lines to parquet
- Aggregates statistics and creates per-episode stats
- Updates version information

## Performance Benefits

Dataset v3.0 provides significant improvements for large datasets:

- **Faster loading**: 3-5x reduction in initialization time
- **Memory efficiency**: Better RAM usage through memory mapping
- **Scalable processing**: Handles millions of episodes efficiently
- **Storage optimization**: Reduced file count and improved compression

