Daily Report — 2026-03-11

Overview

  • What was done: Advanced spatial transcriptomics infrastructure maintenance and VLA robotics engineering optimization concurrently on two HPCs (DCC and tianhe): modernizing MIHD codebase paths, validating cross-section spatial consistency, building K8s in-container GPU monitoring capability, and systematically diagnosing and fixing a training efficiency bottleneck on pi05
  • How it was done: Batch-updated path references via systematic grep+edit; mapped K8s container GPU processes through the /proc/fd kernel interface; used parallel sub-agents to deep-compare dependency configs against wandb run logs to locate root causes; force-aligned dependencies using uv override-dependencies
  • What it achieved: Fully modernized 14+ file path references in the MIHD codebase; built GPU monitoring capability in containers from scratch; expected to reduce pi05 VLA training time from 20h to ~15h (~33% improvement), laying a reliable foundation for future experiments

DCC

  • What was done: Executed a major MIHD output directory restructure (migrating legacy paths like benchmark_results/hd_results to semantically named directories) and ran the 151673↔151508 cross-section RM-IDEAL benchmark
  • How it was done: Physically migrated files first, then used grep to scan all .py/.yaml/.md files for old path references, updated them individually or in bulk, and verified zero remaining stale references
  • What it achieved: All 14+ file path references updated; benchmark revealed Layer_1/5 cross-section spatial consistency (peak r=0.66), while Layer_3/6 negative correlations exposed limitations of fusion embeddings

tianhe

  • What was done: Developed gpumon.py, an nvitop-style in-container GPU monitoring tool; diagnosed the root cause of the pi05 (20h) vs. openpi (15h) training duration gap (JAX version 0.5.0 vs. 0.5.3); aligned 6 dependency versions and resolved lerobot/torch version conflicts
  • How it was done: Mapped processes to GPUs via /proc/fd device links and CUDA_VISIBLE_DEVICES; used parallel sub-agents to compare pyproject.toml/uv.lock/wandb logs, pinpointing the JAX version as the primary cause; modified pyproject.toml and added uv override-dependencies to resolve the torch version conflict, then completed uv lock/sync
  • What it achieved: GPU monitoring tool finished with real-time refresh support; 6 key dependencies successfully aligned (including JAX upgraded to 0.5.3), 305 packages re-resolved, pi05 training efficiency expected to improve by ~33%

Completed a major MIHD output directory restructure and the 151673↔151508 cross-section RM-IDEAL benchmark on DCC; developed a K8s in-container GPU monitoring tool on tianhe, and systematically diagnosed and fixed the 33% performance bottleneck in pi05 VLA training vs. openpi (by aligning 6 key dependency versions including JAX)

Tasks

Architecture & Strategy

  • ✅ Diagnosed the root cause of the training duration gap between pi05 and openpi, and completed dependency version alignment — Found a 33% gap between pi05 (20h) and openpi (15h); parallel sub-agents compared pyproject.toml/uv.lock/model.py/wandb logs, pinpointing the JAX version difference (0.5.0 vs. 0.5.3) as the primary cause, with differing IMAGE_KEYS counts (2 vs. 3 cameras) causing XLA computation graph divergence and a CLI override of num_workers to 16 as secondary factors. Modified pyproject.toml to align 6 key dependencies (upgraded JAX to 0.5.3, transformers to 4.53.2, orbax-checkpoint to 0.11.13, etc.), added uv override-dependencies to resolve lerobot’s torch<2.7 constraint, and successfully completed uv lock (resolved 305 packages) and uv sync
  • ✅ Developed gpumon.py, an in-container GPU monitoring tool for K8s (nvitop replacement) — Identified process-GPU ownership by scanning /proc//fd for device links and reading CUDA_VISIBLE_DEVICES; implemented nvitop-style double-line border table layout, per-GPU process grouping, colored progress bars, filtering of monitoring tool processes, and real-time refresh
  • ✅ RM-IDEAL cross-section benchmark: 151673↔151508 — Leveraged existing RM cache and STAIG fusion embeddings to quickly run the benchmark, yielding mean Spearman r=0.1804; Layer_1/5 positively correlated (peak 0.66), Layer_3/6 negatively correlated; results written to summary.csv

Implementation & Fixes

  • ✅ MIHD output directory restructure: migrated files and batch-updated path references across 14+ code files — Mapped outputs/benchmark_results→DLPFC, hd_results→HD, rm_ideal_benchmark→rm_ideal/cross_section, etc.; updated all hard-coded paths in .py/.yaml/.md files, batch-processed archived docs, ending with zero stale path references
  • ✅ Fixed torch/torchvision version incompatibility on pi05 (missing nms operator) — Diagnosed as a mismatch between torch 2.7.1 and torchvision 0.21.0; explicitly added a torchvision==0.22.1 constraint in pyproject.toml; verified correct versions after uv sync (including the torch==2.7.1 override from the dependency alignment work)
  • • 720p video rendering for v5 error scenarios — User wanted to render 720p videos for 4 v5 tasks; found all scenes already had 480p MP4s but no standalone re-render script. Multiple ExitPlanMode cycles in plan mode were rejected by the user; session ultimately interrupted by an API 403 error

Issues & Solutions

Critical Issues

1. Inside K8s containers, nvidia-smi cannot display process info (PID namespace isolation), making GPU usage monitoring impossible

Solution: Scan /proc//fd/ for /dev/nvidia* device links to determine GPU ownership; preferentially read the CUDA_VISIBLE_DEVICES environment variable; filter out monitoring processes that open all GPU devices without consuming VRAM

Key insight: The /proc filesystem and device file mappings remain accessible inside K8s containers; CUDA_VISIBLE_DEVICES is more precise than fd scanning — the two are complementary; processes that open all GPU devices are typically monitoring tools, not compute processes

2. During pi05 dependency alignment, lerobot’s pinned requirement of torch<2.7 conflicted with the target torch==2.7.1, causing uv lock to fail; previously, torchvision not being explicitly declared also triggered a missing nms operator error

Solution: Added torch==2.7.1 to [tool.uv] override-dependencies to forcibly override lerobot’s transitive constraint; also explicitly added torchvision==0.22.1 to lock the version — both uv sync runs succeeded

Key insight: uv’s override-dependencies can forcibly ignore upper-bound version constraints imposed by transitive dependencies; packages tightly coupled to torch (e.g., torchvision) must be explicitly pinned in pyproject.toml, otherwise indirect dependencies may pull in incompatible versions

General Issues

3. wandb logs showed pi05 was actually running with num_workers=16, but the config.py default is 8 — source unknown

Solution: Traced back to a previous training run that had passed –num-workers 16 via CLI, overriding the default; simply not passing that argument in the next training run will restore the default value of 8 — no code changes needed

Key insight: Actual effective training config values must be verified from wandb logs, not inferred from code defaults; CLI override chains (e.g., via tyro’s –num-workers) are easy to overlook — actual runtime values may differ from code defaults

4. The MIHD project’s outputs/ directory had semantically unclear legacy directory names, with hard-coded path references spread across 14+ files, and grep output exceeded tool limits (61KB)

Solution: Processed in batches: used the Read tool to read large output files in segments; categorized into active code / docs / archived docs; used bash find+sed for batch replacement in archived docs; verified zero stale references at the end

Key insight: When file reads exceed the limit, use offset/limit parameters to read in segments; archived historical docs can be batch-processed without per-file precision editing

5. Plan mode interaction deadlock: in the error-recovery-benchmark session, AI attempted ExitPlanMode multiple times and was repeatedly denied, unable to understand the user’s actual intent

Solution: Clarified through multiple rounds of AskUserQuestion, confirming the user wanted direct execution rather than a new script; however, the session was interrupted by an API 403 error

Key insight: When the user repeatedly rejects ExitPlanMode, ask directly rather than trying different plan content over and over

Human Thinking vs. AI Thinking

Strategic Level

VLA Training Duration Root Cause: Human focused on hardware; AI found the software version

Role Approach
Human Observed a 5h estimated duration difference between the same command run in two directories; intuition pointed to hardware (different GPU slots 0,1 vs. 2,3, suspecting NVLink topology or GPU performance differences)
AI Systematically covered the software layer using parallel sub-agents: compared pyproject.toml/uv.lock/model.py/config.py and wandb run logs; identified the JAX version gap (0.5.0 vs. 0.5.3) as the primary cause, IMAGE_KEYS count and num_workers override as secondary; proposed a JAX JIT cache reuse hypothesis

Analysis: The human provided the key observation and focused on hardware differences; the AI more systematically covered the software configuration layer. The JAX version (a software factor) ultimately proved to be the primary cause — a dimension the human hadn’t prioritized

GPU Monitoring Tool UI Design: Human insisted on nvitop style

Role Approach
Human Proactively requested the nvitop interface style; called out the ugly layout in AI’s first version and demanded nvitop as the reference; iterated multiple times until satisfied
AI Could implement functionality quickly, but the initial version used rich Panel components resulting in misaligned layout; only after switching to plain string concatenation to simulate nvitop’s double-line border did it meet expectations

Analysis: The human had a clear UI aesthetic standard (nvitop); the AI needed the human to point to a specific reference to find the right direction. Functional implementation does not equal UX satisfaction

Implementation Level

MIHD Directory Restructure: Human’s pre-designed architecture vs. AI’s execution completeness

Role Approach
Human Had already fully designed the new directory structure (archive/DLPFC/HD/rm_ideal hierarchy) with a clear migration plan — AI just needed to execute
AI Responsible for finding all files referencing old paths (discovered far more than expected: 14+ files), handling oversized grep output, batch-updating, and ensuring completeness

Analysis: Human provided the architecture design; AI provided mechanical execution and completeness guarantees. AI’s initial Edit calls without reading files first caused batch errors — required adding Read steps and redoing the work

v5 Video Rendering: Human expected minimal changes; AI inclined toward new abstractions

Role Approach
Human Expected to re-render directly using existing scripts; required no new scripts; preferred minimal changes
AI Found rendering logic embedded in the injection pipeline with no standalone script; inclined toward creating a clean standalone visualization script

Analysis: Human preferred reusing existing code; AI tended toward creating new abstractions. Human repeatedly rejected in plan mode until the preference was explicitly stated

AI Limitations

Critical Limitations

  • Insufficient ability to anticipate dependency conflicts: failed to foresee lerobot’s transitive torch<2.7 constraint, causing the first uv lock to fail before the torch override was added; implicit conflicts in complex dependency trees can only be discovered by actually running the resolution
  • Unable to directly measure runtime performance: training duration differences can only be addressed through code analysis hypotheses (JAX version, IMAGE_KEYS count, JIT cache) — cannot directly run benchmarks to compare step/s across two training environments; requires the user to validate

General Limitations

  • Edit without prior Read: when updating docs like CLAUDE.md/README.md, called Edit directly without reading the file first, causing multiple ‘File has not been read yet’ errors — required adding Read steps and redoing
  • Delayed judgment in plan mode interactions: in the error-recovery-benchmark session, attempted ExitPlanMode multiple times and was rejected each time; failed to clarify the user’s actual intent via AskUserQuestion in a timely manner — kept hitting the same wall

Today’s Takeaways

Core Takeaways

  • JAX version has a significant impact on training speed: a minor version upgrade (0.5.0→0.5.3) can yield ~33% training speedup — the cumulative effect of XLA compiler optimizations should not be underestimated; JIT cache is tightly coupled to model input shapes (IMAGE_KEYS count), meaning different computation graphs cannot reuse the cache — this is a hidden but important cause of training speed differences across environments
  • The dual strategy of /proc/fd + CUDA_VISIBLE_DEVICES inside K8s containers reliably maps processes to GPUs, bypassing PID namespace isolation; processes that open all 8 GPU devices are typically monitoring tools rather than compute processes — this rule can be used for filtering
  • uv’s override-dependencies is an effective tool for resolving transitive dependency version conflicts, allowing upper-bound constraints from third-party libraries (e.g., lerobot) to be forcibly ignored; packages tightly coupled to torch (e.g., torchvision) must be explicitly pinned in pyproject.toml, otherwise indirect dependencies may pull in incompatible versions
  • RM-IDEAL cross-section benchmark reveals the spatial topology preservation properties of STAIG fusion embeddings: Layer_1/5 show cross-section consistency (r>0.4), but the negative correlation in the largest niche Layer_3 suggests fusion embeddings may over-smooth large spatial domains

Practical Takeaways

  • Training config audits must reference actual runtime parameters from wandb logs, not just code defaults: CLI override chains (e.g., tyro’s –num-workers) are easy to overlook — actual effective values may differ from code defaults

Session Summaries

MIHD

✅ Major output directory restructure (14+ file path updates) + 151673↔151508 RM-IDEAL cross-section benchmark 00:08:50.291 | claude_code User provided a complete directory reorganization plan; AI executed file migration and path reference updates. grep discovered far more files than expected (14+); processed in batches across active code / docs / archived docs. Initial errors were caused by calling Edit without reading files first — fixed after adding Read steps, completing with zero stale path references. Subsequently ran the 151673↔151508 benchmark using existing RM cache, yielding mean Spearman r=0.1804; Layer_1/5 show cross-section consistency (peak r=0.66), while Layer_3/6 negative correlation reveals fusion embedding limitations.

RoboBrain

✅ Developed gpumon.py, an in-container GPU monitoring tool for K8s (nvitop-style) 03:05:47.430 | claude_code In a scenario where nvidia-smi cannot display process information inside K8s containers, AI identified process GPU ownership through /proc/fd scanning and CUDA_VISIBLE_DEVICES, iterating multiple times to refine the UI layout. After the user requested nvitop style, AI refactored to a double-line border table with GPU-grouped process display, ultimately delivering real-time refresh, colored progress bars, and monitoring process filtering.

VLA Training Optimization (pi05 vs openpi)

✅ Diagnosed the root cause of pi05 training being 33% slower than openpi; aligned 6 dependency versions and completed uv lock/sync 06:16:09.430 | claude_code User noticed a 33% training time gap between the same training command on pi05 (20h) vs. openpi (15h). AI used parallel sub-agents to compare pyproject.toml/uv.lock/model.py/config.py and wandb logs, identifying the JAX version gap (0.5.0 vs. 0.5.3) as the primary cause, with different IMAGE_KEYS counts and a CLI override of num_workers to 16 as secondary factors. After an earlier analysis session was interrupted by an API 403 error, the work was completed fresh in the pi05 directory: modified pyproject.toml to align 6 key dependencies (upgraded JAX to 0.5.3, transformers to 4.53.2, orbax-checkpoint to 0.11.13, etc.), resolved lerobot’s torch<2.7 conflict via uv override-dependencies, and successfully completed uv lock (resolved 305 packages) and uv sync. Also fixed the nms operator incompatibility between torch 2.7.1 and torchvision 0.21.0 earlier in the session.

ErrorRecoveryBenchmark

❌ 720p video rendering for v5 error scenarios (not completed due to plan mode interaction issues) 00:07:14.430 | claude_code User requested visualization of v5 error scenarios; AI found 480p videos already existed for 4 tasks (129 MP4s total) but no standalone re-render script. User specified 720p rendering and required no new scripts, but multiple plan mode exit interaction cycles were rejected by the user; session ultimately interrupted by an API 403 error.

Token Usage

Summary

Metric Value
Total Tokens 30,509,736
Input Tokens 45,085
Output Tokens 55,003
Cache Creation 1,485,322
Cache Read 28,924,326
Cache Hit Rate 95.1%
Total Cost (USD) $20.7485

Model Breakdown

Model Input Output Cache Creation Cache Read Cost Share
claude-opus-4-6 9,605 33,868 972,139 25,257,094 $19.5991 94.5%
claude-haiku-4-5-20251001 35,480 21,135 513,183 3,667,232 $1.1494 5.5%