| # Reprocess 4,976 DROID Episodes Across 5 Machines |
|
|
| ## Quick Start |
|
|
| Run this command on each of the 5 machines: |
|
|
| ```bash |
| cd /root/workspace/code/wmrl/Dual-Dynamics-Models/DROID-main |
| bash scripts/reprocess_all_5machines.sh <MACHINE_ID> |
| ``` |
|
|
| Where `<MACHINE_ID>` is: |
| - **Machine 1**: `0` |
| - **Machine 2**: `1` |
| - **Machine 3**: `2` |
| - **Machine 4**: `3` |
| - **Machine 5**: `4` |
|
|
| ## Episode Distribution |
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|
| Each machine processes a subset of the 4,976 episodes: |
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|
| | Machine | Episodes | Count | |
| |---------|--------------|-------| |
| | 0 | 0 → 995 | 995 | |
| | 1 | 995 → 1990 | 995 | |
| | 2 | 1990 → 2985 | 995 | |
| | 3 | 2985 → 3980 | 995 | |
| | 4 | 3980 → 4976 | 996 | |
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|
| Each machine distributes its episodes across 8 GPUs (~124 episodes per GPU). |
|
|
| ## Monitoring Progress |
|
|
| ### Check logs |
| ```bash |
| # Watch GPU 0 log in real-time |
| tail -f /mnt/kevin/data/droid_processed_1000pts/reprocessing_logs/m0_gpu0.log |
| |
| # Check all GPU logs on machine 0 |
| ls -lh /mnt/kevin/data/droid_processed_1000pts/reprocessing_logs/m0_*.log |
| ``` |
|
|
| ### Check completion |
| ```bash |
| # Count completed GPUs on machine 0 |
| grep 'Complete:' /mnt/kevin/data/droid_processed_1000pts/reprocessing_logs/m0_*.log | wc -l |
| # Should output: 8 (when all GPUs done) |
| |
| # Check success/error counts |
| grep -h 'Success:' /mnt/kevin/data/droid_processed_1000pts/reprocessing_logs/m0_*.log |
| ``` |
|
|
| ### Monitor GPU usage |
| ```bash |
| watch -n 1 nvidia-smi |
| # Should see all 8 GPUs at 70-90% utilization |
| ``` |
|
|
| ## Verify Results |
|
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| After all machines complete, verify track counts: |
|
|
| ```bash |
| cd /root/workspace/code/wmrl/Dual-Dynamics-Models/DROID-main |
| |
| /mnt/kevin/envs/miniconda3/envs/atm_ati_vdm_droid/bin/python -c " |
| import numpy as np |
| from pathlib import Path |
| import random |
| |
| data_dir = Path('/mnt/kevin/data/droid_processed_1000pts/data') |
| npz_files = sorted(data_dir.glob('episode_*.npz')) |
| |
| # Sample 50 random episodes |
| samples = random.sample(npz_files, 50) |
| |
| correct = 0 |
| wrong = 0 |
| |
| for npz_path in samples: |
| data = np.load(npz_path, allow_pickle=True) |
| wrist_count = data['tracks_wrist'].shape[1] |
| |
| if wrist_count == 1105: |
| correct += 1 |
| else: |
| wrong += 1 |
| print(f'{npz_path.name}: wrist={wrist_count}') |
| |
| print(f'\nResults from 50 random samples:') |
| print(f' Correct (1105): {correct}') |
| print(f' Wrong: {wrong}') |
| " |
| ``` |
|
|
| Expected output: All 50 samples should have 1105 wrist points. |
|
|
| ## Troubleshooting |
|
|
| ### Stop all reprocessing |
| ```bash |
| pkill -f 'python.*episode' |
| ``` |
|
|
| ### Restart a specific machine |
| ```bash |
| # Stop |
| pkill -f 'python.*episode' |
| |
| # Wait a few seconds |
| sleep 5 |
| |
| # Restart |
| bash scripts/reprocess_all_5machines.sh <MACHINE_ID> |
| ``` |
|
|
| ### Check for stuck processes |
| ```bash |
| ps aux | grep python | grep episode |
| ``` |
|
|
| ## Estimated Time |
|
|
| - **Per episode**: ~30-60 seconds (depending on length) |
| - **Per GPU**: ~124 episodes × 45 sec avg = ~93 minutes |
| - **Total time**: ~1.5-2 hours (all machines in parallel) |
|
|
| ## What Gets Updated |
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| Each `.npz` file will be updated with: |
| - ✓ `tracks_wrist`: 1112 → **1105 points** (7 mesh + 98 grid + 1000 random) |
| - ✓ `tracks_exterior`: Already correct (1105 points) |
| - ✓ `mesh_vertices_2d_wrist_fixed`: Already exists |
| - ✓ All other fields: Unchanged |
|
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| ## Features |
|
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| - ✅ **GPU distribution**: All 40 GPUs (5 machines × 8 GPUs) utilized |
| - ✅ **OOM handling**: Automatic retry with batching (batch_size=600) |
| - ✅ **Correct track counts**: 1105 points for both views |
| - ✅ **Progress tracking**: Individual log files per GPU |
| - ✅ **Resumable**: Skips already-processed episodes (checks track count) |
| |
