Daily Report — 2026-05-09
Daily Overview
- What was done: Fixed training interruptions caused by corrupted MP4 files in LeRobot datasets and managed GPU resource allocation for the Tangzijia team.
- How it was done: Developed Python scripts to diagnose and re-encode corrupted videos from source HDF5, updated data loading code with pre-checks and graceful fallback mechanisms, and used shell commands to identify and terminate specific user jobs.
- Impact: Restored stable training capability by addressing data integrity issues and optimized compute resource availability for the error recovery project.
Resolved corrupted LeRobot video dataset issues by creating diagnostic and repair scripts, implemented robust error handling in PairedDataset, and cleaned up GPU resources for the error_recovery_benchmark project.
Tasks
Architecture & Strategy
- ✅ LeRobot Video Data Diagnosis and Repair — Created diagnose_corrupted_videos.py and repair_corrupted_videos.py to identify 60 corrupted episodes across single and multiple OSS datasets, then re-encode them from original HDF5 sources.
- ✅ PairedDataset Error Handling Optimization — Refactored paired_dataset.py to implement pre-checking of augmented frames, blacklist management for corrupted videos, and prominent console logging when errors occur to aid debugging.
Implementation & Fixes
- ✅ GPU Resource Cleanup for Tangzijia — Identified and terminated lingering PyTorch processes belonging to tangzijia/error_recovery_benchmark on GPUs 4 and 5, freeing ~60GB VRAM per GPU.
- ✅ BOSS Project README Documentation — Added instructions for all scripts in the BOSS directory (data collection, conversion, mapping, validation) to improve project usability.
Problems & Solutions
Critical Issues
1. RuntimeError: InvalidDataError in DataLoader when decoding augmented video frames due to corrupted MP4 files generated during HDF5 conversion.
Solution: Implemented a two-layer fix: 1) Static repair via scripts to re-encode bad episodes from source HDF5; 2) Dynamic handling with pre-checking (disabled post-repair) and try-except blocks that skip corrupted frames without interrupting training.
Key Insight: Corrupted videos in LeRobot datasets are often silent at the file level but fail during specific decode operations; diagnosing them requires explicit PyAV decoding rather than just checking file headers.
2. JAX JIT compilation constraints prevented conditional skipping of forward passes based on corrupted mask status at runtime.
Solution: Accepted that compute for corrupted samples would still run but ensured their loss contribution was masked to zero, preventing gradient pollution without breaking static graph requirements.
Key Insight: Static graph frameworks like JAX require control flow to be independent of tensor values during compilation; workaround strategies must respect these compile-time constraints.
General Issues
3. Initial error handling masked clean dataset errors by only fixing augmented dataset corruption, leading to new InvalidDataError from clean data (boss44_lerobot).
Solution: Realized the scope of corruption extended beyond augmented sets; applied diagnosis and repair logic to all involved LeRobot datasets.
Key Insight: Data pipeline bugs can be distributed across multiple dataset versions; a fix for one part may expose latent issues in another if not verified holistically.
Human vs AI Approaches
Strategic Level
Root Cause Analysis of Data Corruption
| Role | Approach |
|---|---|
| Human | User suspected corruption originated during the HDF5 to LeRobot conversion process and proposed re-encoding from source data rather than just masking errors. |
| AI | AI initially focused on runtime handling (try-except placeholders) before exploring the conversion script logic and creating specialized repair tools. |
Difference Analysis: Human prioritized permanent data integrity solutions over temporary workarounds; AI balanced immediate operational stability with long-term fixes by eventually aligning with the user’s preference for source data correction.
Implementation Level
GPU Process Management Complexity
| Role | Approach |
|---|---|
| Human | User requested stopping specific team’s jobs without affecting others, implying trust in the AI to distinguish process ownership accurately. |
| AI | AI used multiple verification steps (nvidia-smi, ps, tmux, fuser/procfs) to map GPU memory usage to specific process PIDs and verify user ownership via path/string matching before killing processes. |
Difference Analysis: The Human provided the goal (free resources); the AI had to navigate complex container/sandbox permission limitations to execute safe resource clearance.
AI Limitations
Critical Limitations
- AI initially failed to identify that clean datasets (boss44_lerobot) also contained corrupted videos, assuming only augmented datasets needed repair.
General Limitations
- AI encountered permission denied errors when trying to access /dev/nvidia devices via fuser in a sandboxed environment, requiring alternative methods like /proc scanning.
Learnings
Key Learnings
- When fixing data pipeline issues, always verify the root cause applies to all dataset versions (clean vs. augmented) rather than just observing the first error surface.
Practical Learnings
- Adding prominent logging (e.g., ERROR level with clear markers) is critical during data debugging phases to quickly distinguish between expected placeholders and true failures.
Conversation Summaries
RoboBrain PI
🔄 Debugging LeRobot Config AssertionError 11:04:28.272 | claude_code Investigated an AssertionError when running compute_norm_stats.py. The error indicated LeRobot was misinterpreting a local dataset path as a HuggingFace Hub repo ID. User interrupted investigation before the fix was applied.
Error Recovery Benchmark
🔍 GPU Resource Cleanup Request 20:44:10.699 | claude_code User requested stopping all active jobs for user ’tangzijia’ on available GPUs. The session recorded this request, delegating execution to the Codex session.
✅ Identifying and Killing Tangzijia’s GPU Jobs 20:47:17.389 | codex Executed complex process management to stop tangzijia’s training jobs on GPUs 4 and 5. Used nvidia-smi, ps, tmux, and /proc fs scans to verify ownership of PIDs consuming VRAM. Successfully sent SIGTERM to specific PIDs (start_openpi_serve_policy_safe.py, run_pi05_tuning_grid.py) freeing ~60GB per GPU while leaving other users’ work intact.
OpenPI (Chenjunye)
✅ Fixing Corrupted LeRobot Video Decoding Errors 06:19:52.420 | claude_code Addressed persistent InvalidDataError from corrupted MP4s in augmented datasets. Created diagnose_corrupted_videos.py and repair_corrupted_videos.py to re-encode bad episodes from HDF5 sources. Updated paired_dataset.py with pre-checking (later disabled), blacklist skipping, and robust error logging. Documented all BOSS project scripts in README.