Daily Report — 2026-07-14

Daily Overview

• What was done: Performed a comprehensive audit and cleanup of Tianhe3 supercomputer storage for two users, identified and archived critical OpenPI checkpoints while reclaiming ~1.7TB. Architected the quantization pipeline for GR00T-N1.7 on Qualcomm platforms and successfully deployed the Action-Sketcher inference framework to generate 2D visualization traces for LIBERO tasks. Additionally, defined canonical data specifications for the ErrorRecoveryBenchmark, recovered text from a corrupted video asset, updated scGHT evaluation reports, and performed major infrastructure refactoring on the Gadget toolkit.
• How it was done: Utilized SSH orchestration with Codex/AI agents to execute remote storage audits (dust), Python-based HDF5 metadata inspections, and interactive cleanup scripts. Implemented AIMET SeqMSE and SpinQuant designs for GR00T, resolved MuJoCo EGL/CUDA mapping conflicts for robotics inference, corrected dataset provenance logic, refected the Gadget config system to a single source of truth, and applied OCR techniques to media recovery.
• Impact: Eliminated significant storage bottlenecks on HPC clusters, established a reproducible framework for edge-deployment quantization, generated critical reasoning datasets for robotic policy training, ensured data integrity for benchmark validation, and improved security and maintainability for local development tooling.

Executed a high-volume daily workflow focusing on HPC storage optimization recovering ~1.7TB on Tianhe3, finalizing the GR00T quantization pipeline architecture, deploying and debugging the Action-Sketcher robotics benchmark, correcting ErrorRecoveryBenchmark dataset definitions, and refactoring the Gadget toolkit infrastructure.

Tasks

Architecture & Strategy

• 🔄 GR00T Quantization Pipeline Architecture & Scripting — Defined 4-phase roadmap (FP16 Baseline -> SeqMSE PTQ -> SpinVariant -> Profiling). Created Python infrastructure for AIMet SeqMSE and SpinQuant on the Qwen3 backbone, wrote Slurm scripts, and successfully submitted initial env setup/baseline jobs after resolving timeout issues.
• ✅ Tianhe3 Storage Audit & Cleanup Execution — Audited ~2.7TB across zhaoganlong/tangzijia. Identified OpenPI checkpoints (1.1TB) and MimicGen data as primary consumers. Generated and executed an interactive script to archive step-19999 checkpoints and delete intermediates, reclaiming ~1.7TB.
• ✅ Action-Sketcher Deployment & LIBERO Rollout — Deployed Action-Sketcher to Tianhe3. Modified rollout scripts to capture planning prompts and 2D sketches. Diagnosed MuJoCo EGL/CUDA conflicts, enabled parallel multi-GPU execution on GPUs 4-7, and collected traces for 10 tasks (1 failure).
• ✅ ErrorRecoveryBenchmark Data Qualification — Defined canonical training/eval specs (1,360 scenes each) resolving doc/code conflicts. Executed read-only HDF5 audits on Tianhe3 to validate metadata provenance and identified schema discrepancies in human-demo datasets.
• ✅ Gadget Toolkit Refactoring & Public Release Prep — Refactored Gadget configuration to a single repo-local config.json with fail-fast logic. Scrubbed secrets, reset git history for public GitHub release, and fixed Hugo deployment path resolution issues.

Implementation & Fixes

• ✅ Media Recovery & Research Reporting Updates — Implemented GPU-accelerated OCR pipeline to extract novel text from corrupted MP4 video. Updated MIHD progress report with scGPT-HD ARI results and verified rclone sync stability.

Problems & Solutions

Critical Issues

1. ErrorRecoveryBenchmark Data Provenance Ambiguity: Discrepancy between paper docs, code, and actual HDF5 data regarding dataset sizes and ’training’ definitions.

Solution: User intervened to define canonical counts (1360/1360). AI patched audit scripts to accept heterogeneous metadata schemas (e.g., humandemo_source vs source_dataset).

Key Insight: Documentation often lags code; explicit ‘canonical’ definitions and flexible schema validation are essential for accurate benchmark verification.

2. Tianhe3 Storage Quota & Metadata Latency: HPC audits revealed complex interplay between Lustre metadata latency for large HDF5 groups and distributed user quotas distinct from global free space.

Solution: Used dust for efficient directory auditing. Rewrote Python HDF5 inspectors to read only high-level attributes rather than iterating nested groups. Added explicit LFS quota checks within cleanup scripts to prevent denial during archival.

Key Insight: Large-scale HPC audits require attribute-only introspection; global space availability does not guarantee project-specific quota headroom.

3. Action-Sketcher Inference Failures: Encountered CUDA OOM (wrong device loading), MuJoCo EGL device ID mismatches against CUDA_VISIBLE_DEVICES, and SSH proxy DNS failures.

Solution: Patched inference code to explicitly set devices. Mapped MUJOCO_EGL_DEVICE_ID=0 locally while remapping visible GPUs. Resolved SSH connectivity by bypassing DNS failures using cached tunnel IPs and host keys.

Key Insight: Robotics rendering pipelines often decouple EGL indices from CUDA visibility; remote HPC connections may require cryptographic bypasses when DNS/Proxy services are unstable.

4. Quantization Environment & Model Access: Initial Slurm jobs failed due to uv package manager timeouts on large wheels; later blocked by gated HuggingFace models (Cosmos-Reason2-2B).

Solution: Increased UV_HTTP_TIMEOUT in sbatch scripts. Investigated config swapping for Qwen3 but halted pending user HF token approval to preserve baseline integrity.

Key Insight: Network timeouts are critical risks in HPC environment setup; gated models require explicit credential management rather than architectural workarounds.

General Issues

5. SSH Command Quoting & Buffering: Complex Python one-liners failed via SSH due to shell escaping issues; long HDF5 scripts hid progress due to buffering.

Solution: Switched to uploading standalone Python scripts via SCP for complex logic. Enabled unbuffered output (-u or flush=True) in remote scripts to provide real-time feedback.

Key Insight: Remote execution from local CLI tools should favor file transfer over inline commands; explicit flushing is mandatory for long-running remote processes.

6. Video File Corruption: Standard ffmpeg extraction failed due to HEVC NAL unit errors in a download asset.

Solution: Implemented keyframe-seeking strategy (-ss) to skip corrupted packets, using GPU-accelerated RapidOCR to salvage valid segments.

Key Insight: Timestamp-based seeking is more robust than continuous decoding for recovering data from structurally damaged multimedia assets.

Human vs AI Approaches

Strategic Level

Benchmark Data Definition (ErrorRecovery)

Role	Approach
Human	User defined the semantic ground truth for Training vs. Eval sets and specific counts, correcting AI’s initial assumption that code was authoritative.
AI	AI performed the mechanical verification and schema audits but failed to recognize the contradiction between ‘paper reality’ and ‘code reality’ without human guidance.

Difference Analysis: Human provided the strategic intent and business logic; AI handled the tactical execution and technical validation.

Gadget Config Architecture

Role	Approach
Human	User mandated a single source of truth config with fail-fast behavior, rejecting legacy fallbacks.
AI	AI refactored disparate modules to align with this architecural constraint while maintaining transitional safety.

Difference Analysis: Human set the architectural boundary; AI handled the migration complexity.

Strategic Data Retention vs. AI Conservatism (Tianhe3 Cleanup)

Role	Approach
Human	User enforced a strict retention policy (only step 19999 checkpoints), overriding standard backup practices to maximize space savings.
AI	AI initially suggested keeping both final and intermediate checks based on heuristic best practices, adapting only after explicit user constraints.

Difference Analysis: Human prioritized resource optimization; AI defaulted to data preservation until corrected.

GPU Resource Allocation Strategy

Role	Approach
Human	User specified exact task-to-GPU mappings for parallel rollout to optimize runtime efficiency.
AI	AI generated the complex nohup orchestration logic and environment variable management required to execute the user’s plan.

Difference Analysis: Human defined the logical distribution; AI managed the syntactic complexity of process control.

AI Limitations

Critical Limitations

• AI relied on heuristic assumptions for retention policies and model availability, lacking immediate awareness of explicit user constraints (e.g., ‘only step 19999’) or external gating mechanisms without guidance.

General Limitations

• Long-running remote Python scripts defaulted to buffering output, obscuring progress from the user until explicitly patched.
• Initial MuJoCo deployment attempts failed to anticipate EGL/CUDA device ID mapping conflicts, requiring specific domain knowledge to resolve.
• AI initially struggled with complex shell quoting for remote Python execution via SSH, leading to syntax errors that required iterative debugging or file transfer workarounds.

Learnings

Key Learnings

• When running MuJoCo with CUDA_VISIBLE_DEVICES, MUJOCO_EGL_DEVICE_ID must be explicitly set to the local Python process index (usually 0), not the physical GPU ID.
• On Lustre-based HPC clusters, enumerating large HDF5 groups causes metadata latency; reading file attributes is significantly faster than iterating structures for provenance analysis.
• Establishing ‘canonical’ sources of truth for experimental parameters (like dataset sizes) early prevents contradictory work in complex research projects where docs lag code.
• HDF5 datasets in robotics often contain semantic redundancy (e.g., v1 vs v2); metadata inspection is required to confirm identity before deletion or archival.

Practical Learnings

• Robust remote execution from local CLI tools should prefer uploading scripts over inline commands, and enable unbuffered output for long processes to ensure visibility.

Conversation Summaries

GR00T Quantization Pipeline

• Architecture Design and Initial Execution for Qualcomm Deployment 19:32:47.364 | claude_code Established a comprehensive 4-stage roadmap for quantizing GR00T-N1.7 (FP16 Baseline -> SeqMSE PTQ -> SpinVariant -> Profiling). Archived the codebase architecture design, generated rotation/quantization scripts, and submitted Slurm job chains. Progress remains blocked pending user-provided HuggingFace credentials for gated models.

Tianhe3 Storage Management

✅ HPC Storage Audit, Analysis, and Cleanup 19:22:41.312 | codex Conducted a deep audit of ~2.7TB usage on Tianhe3 for users zhaoganlong and tangzijia. Identified OpenPI checkpoints (1.1TB) and MimicGen data as primary consumers. Analyzed HDF5 metadata to distinguish valid checkpoints from temp/cache files. Generated and executed an interactive script that archived final step-19999 checkpoints and deleted ~1.7TB of redundant data.

Action-Sketcher / WorldModel3DVisualPrompt

✅ Deployment, Debugging, and Parallel Rollout on Tianhe3 03:01:24.739 | codex Deployed Action-Sketcher to Tianhe3 and modified rollout scripts to capture planning prompts and 2D sketches. Resolved critical bugs including CUDA OOM (wrong device loading), MuJoCo EGL/CUDA mapping conflicts, and SSH proxy DNS failures. Successfully executed parallel LIBERO rollouts on GPUs 4-7, collecting traces for 10 tasks (1 failure).

ErrorRecoveryBenchmark

✅ Data Qualification and State Survey 22:05:27.672 | codex/claude_code Defined canonical training/eval specs (1,360 scenes) resolving doc/code conflicts. Executed read-only HDF5 audits on Tianhe3 to validate metadata provenance and identified schema discrepancies in human-demo datasets. Developed a recovery plan for missing checkpoints and contradictory documentation.

Gadget (GitHub Toolkit)

✅ Config Unification, Security Scrubbing, and Public Release Prep 20:17:12.343 | claude_code/cursor Refactored Gadget configuration to a single repo-local config.json with fail-fast logic. Scrubbed private paths/secrets, reset git history for public GitHub release, fixed Hugo deployment path issues, and standardized line endings.

Media & Research Reporting

✅ Corrupted Video Recovery and scGHT Report Update 19:20:45.816 | claude_code Implemented GPU-accelerated OCR pipeline to extract ~17k chars from a corrupted video. Updated MIHD progress report with scGPT-HD ARI results. Verified rclone sync stability for daily logging.

Token Usage