droid/scripts/MULTIGPU_PREPROCESSING_README.md

# Multi-GPU DROID Preprocessing Guide

Process the entire DROID dataset in parallel using independent GPU processes (no inter-GPU communication).

## Overview

- **Mode**: Independent processing (no DDP, no communication)
- **Recommended**: Single machine with 8 GPUs
- **Optional**: Two machines with 16 GPUs total
- **Dataset**: Full DROID train split (~70k episodes)
- **Output**: 1000 tracked points per view with visibility masks
- **Preview videos**: 20 total (distributed across GPUs)
- **Processing time**: ~10-20 seconds per episode

## Quick Start

### Single Machine (8 GPUs) - Recommended

```bash
cd /root/workspace/code/wmrl/Dual-Dynamics-Models/DROID-main
bash scripts/run_multigpu_single_machine.sh
```

**How it works:**
- 8 independent processes (one per GPU)
- No communication between processes
- Each GPU processes episodes where `(episode_idx % 8) == gpu_id`
- GPU 0: episodes 0, 8, 16, 24, ...
- GPU 1: episodes 1, 9, 17, 25, ...
- GPU 2: episodes 2, 10, 18, 26, ...
- etc.

### Two Machines (16 GPUs) - Optional

If you want faster processing with 16 GPUs across 2 machines:

#### Machine 1 (GPUs 0-7, Global IDs 0-7)

```bash
cd /root/workspace/code/wmrl/Dual-Dynamics-Models/DROID-main
bash scripts/run_multigpu_machine1.sh
```

#### Machine 2 (GPUs 0-7, Global IDs 8-15)

```bash
cd /root/workspace/code/wmrl/Dual-Dynamics-Models/DROID-main
bash scripts/run_multigpu_machine2.sh
```

**Important**: Both machines should use the **same shared output directory** (e.g., `/mnt/kevin/data/droid_processed_1000pts`) accessible via NFS or similar.

## Monitoring Progress

### Check logs:
```bash
# Single machine
tail -f /mnt/kevin/data/droid_processed_1000pts/log_gpu0.txt

# Or for two machines:
# Machine 1
tail -f /mnt/kevin/data/droid_processed_1000pts/log_machine0_gpu0.txt

# Machine 2
tail -f /mnt/kevin/data/droid_processed_1000pts/log_machine1_gpu0.txt
```

### Check all running processes:
```bash
ps aux | grep preprocess_droid_multigpu
```

### Check GPU utilization:
```bash
nvidia-smi
```

### Monitor specific GPU:
```bash
watch -n 1 nvidia-smi
```

## How It Works

### Episode Distribution
- **Total episodes**: ~70,832 in DROID train split
- **Per GPU**: ~4,427 episodes (70,832 / 16)
- Each GPU processes: `episodes where (episode_idx % 16) == global_gpu_id`

### Global GPU Mapping
| Machine | Local GPU | Global GPU | Episodes Range |
|---------|-----------|------------|----------------|
| 1       | 0         | 0          | 0, 16, 32, ... |
| 1       | 1         | 1          | 1, 17, 33, ... |
| ...     | ...       | ...        | ...            |
| 1       | 7         | 7          | 7, 23, 39, ... |
| 2       | 0         | 8          | 8, 24, 40, ... |
| 2       | 1         | 9          | 9, 25, 41, ... |
| ...     | ...       | ...        | ...            |
| 2       | 7         | 15         | 15, 31, 47, ...|

### Preview Video Distribution
- **Total**: 20 preview videos
- **Per GPU**: ~1-2 videos
- Distributed evenly across episode range

## Output Structure

```
/mnt/kevin/data/droid_processed_1000pts/
├── data/
│   ├── episode_000000.npz  # Processed by GPU 0
│   ├── episode_000001.npz  # Processed by GPU 1
│   ├── episode_000002.npz  # Processed by GPU 2
│   └── ...
├── preview_videos/
│   ├── preview_episode_000000.mp4
│   ├── preview_episode_004427.mp4
│   └── ... (20 total)
├── metadata_gpu00.json  # GPU 0 stats
├── metadata_gpu01.json  # GPU 1 stats
├── ...
├── metadata_gpu15.json  # GPU 15 stats
├── log_machine0_gpu0.txt
├── log_machine0_gpu1.txt
├── ...
└── log_machine1_gpu7.txt
```

### NPZ File Contents (per episode)
```python
{
    'episode_idx': int,                              # Episode index
    'uuid': str,                                     # Calibration UUID
    'images_exterior': (T, 180, 320, 3),            # Exterior camera frames
    'images_wrist': (T, 180, 320, 3),               # Wrist camera frames
    'actions': (T, 7),                               # Robot actions (Euler XYZ)
    'mesh_vertices_2d_exterior': (T, 7, 2),         # Ground truth mesh (7 points)
    'mesh_vertices_vis_exterior': (T, 7),           # Mesh visibility mask
    'tracks_exterior': (T, 1000, 2),                # 1000 tracked points
    'tracks_vis_exterior': (T, 1000),               # Track visibility mask
    'tracks_wrist': (T, 1000, 2),                   # 1000 tracked points
    'tracks_vis_wrist': (T, 1000),                  # Track visibility mask
    'mesh_indices': (7,),                            # [0,1,2,3,4,5,6]
}
```

## After Completion

### Merge Metadata
Once all GPUs finish, merge the metadata files:

```bash
cd /root/workspace/code/wmrl/Dual-Dynamics-Models/DROID-main
python scripts/merge_multigpu_metadata.py \
    --output-dir /mnt/kevin/data/droid_processed_1000pts \
    --num-gpus 16
```

This will:
- Check all 16 GPU metadata files
- Report total processed/skipped episodes
- Create `metadata_merged.json` with full statistics
- Identify any missing/incomplete GPUs

### Verify Output
```bash
# Count processed episodes
ls -1 /mnt/kevin/data/droid_processed_1000pts/data/episode_*.npz | wc -l

# Count preview videos
ls -1 /mnt/kevin/data/droid_processed_1000pts/preview_videos/preview_*.mp4 | wc -l

# Check total size
du -sh /mnt/kevin/data/droid_processed_1000pts/
```

## Episode Filtering

Episodes are **skipped** (not processed) if they meet any of these criteria:

1. ❌ **No refined extrinsics** - Missing calibration data
2. ❌ **Too short** - Less than 10 valid frames
3. ❌ **Too long** - More than 400 valid frames (configurable)
4. ❌ **Insufficient mesh visibility** - Less than 2 mesh vertices visible in first frame

**Note**: Episodes > 400 frames are **skipped entirely**, not truncated. This ensures all saved trajectories are complete.

## Configuration

### Edit Output Directory
Modify in both launcher scripts:
```bash
OUTPUT_DIR="/mnt/kevin/data/droid_processed_1000pts"
```

### Adjust Preview Count
Modify in both launcher scripts:
```bash
PREVIEW_TOTAL=20  # Change to desired number
```

### Adjust Max Frames
Modify in both launcher scripts:
```bash
MAX_FRAMES=400  # Episodes longer than this will be SKIPPED (not truncated)
```

## Troubleshooting

### GPU Out of Memory
- Reduce `MAX_FRAMES` to 200 or 300
- CoTracker uses ~6-8GB VRAM for 400 frames

### Process Killed/Crashed
- Check log file: `cat /mnt/kevin/data/droid_processed_1000pts/log_machine*_gpu*.txt`
- Resume by re-running the launcher (already-processed episodes will be skipped by file existence check)

### Uneven Load Distribution
- Some GPUs may finish faster if they get episodes with fewer frames or more skipped episodes
- This is normal and expected

### Network Storage Issues
- If using NFS, ensure good network bandwidth
- Consider using local storage and merging later if NFS is slow

## Estimated Timing

### Single Machine (8 GPUs)
- **Per episode**: 10-20 seconds (avg ~15s with 400 max frames)
- **Per GPU**: ~8,854 episodes × 15s = ~36.8 hours
- **Total wall time**: ~36-48 hours (all GPUs in parallel)
- **Output size**: ~500GB-1TB (depends on episode lengths and compression)

### Two Machines (16 GPUs)
- **Per episode**: 10-20 seconds (avg ~15s with 400 max frames)
- **Per GPU**: ~4,427 episodes × 15s = ~18.4 hours
- **Total wall time**: ~18-24 hours (all GPUs in parallel)
- **Output size**: ~500GB-1TB (depends on episode lengths and compression)

## Manual Single-GPU Test

Before full run, test on a single GPU:

```bash
cd /root/workspace/code/wmrl/Dual-Dynamics-Models/DROID-main

CUDA_VISIBLE_DEVICES=0 /mnt/kevin/envs/miniconda3/envs/atm_ati_vdm_droid/bin/python \
    scripts/preprocess_droid_multigpu.py \
    --gpu-id 0 \
    --machine-id 0 \
    --num-gpus 1 \  # Test with just 1 GPU
    --output-dir /tmp/droid_test \
    --max-frames 100 \
    --preview-total 3
```

## Stopping All Processes

```bash
# Machine 1
pkill -f preprocess_droid_multigpu

# Machine 2
pkill -f preprocess_droid_multigpu
```

## Point Distribution Details

### Exterior View (1000 points)
- **First 7 indices**: Mesh vertices (tracked by CoTracker)
  - Also saved separately as ground truth in `mesh_vertices_2d_exterior`
- **Indices 7-999 (993 points)**: Arm-shaped sampling
  - Gaussian clouds around visible mesh vertices (σ=15px)
  - Lines connecting mesh vertices
  - Uniform random fill

### Wrist View (1000 points)
- **First 300 indices**: Sparse uniform across full image
- **Indices 300-999 (700 points)**: Dense in bottom 60%-100% of image
  - Where gripper typically appears in wrist camera

### Visibility Masks
- Value range: [0.0, 1.0]
- 0.0 = occluded/invisible
- 1.0 = fully visible
- This&That dataloader uses threshold of 0.7 by default

## Next Steps

After preprocessing completes:
1. Run metadata merge script
2. Verify output with sample loading test
3. Update This&That VDM dataloader to use new DROID RLDS dataset
4. Begin ControlNet training with 1000-point tracks