Daily Report — 2026-02-23

Today’s Overview

  • What was done: Ran three parallel core workstreams across the DCC and Tianhe clusters: comprehensive optimization of MIHD fusion training with full-slide benchmarking, launch and critical bug fix of Error Recovery Benchmark M14 three-way evaluation, and end-to-end setup of Pi0.5 LoRA fine-tuning from scratch to stable operation.
  • How it was done: On the DCC side, eliminated CPU bottlenecks via NumPy vectorization, GPU-native random number generation, and batched Transformer forward passes, while decoupling the encoder architecture. On Tianhe, resolved data compatibility issues and VRAM OOM through progressive debugging (dependency downgrade, source patching, LoRA architecture switch, sbatch submission), and fixed evaluation process crashes with scene-level try-except.
  • Why it matters: QFormer training is expected to be 20–50x faster; STAIG full-slide average ARI=0.546; discovered a polarizing pattern where refine benefits weak but hurts strong fusion methods. Pi0.5 LoRA training (Job 46553) is running stably at 2.0s/step (estimated 53 hours to complete). M14 three-way evaluation resumed after bug fix, with ~519 of 649 scenes confirmed as valid evaluation targets.

DCC

  • What was done: Completed comprehensive optimization of MIHD fusion training on the RTX 5000 Ada GPU node: 3 CPU acceleration implementations, visual encoder architecture decoupling, Vision Refine vs. Baseline ablation experiments (8 fusion methods × with/without refine), and full benchmarking across all 11 DLPFC slides with STAIG.
  • How it was done: Used the conda General environment to run PyTorch training. Applied scipy cdist vectorization for edge weight computation, GPU-native random number generation to eliminate cross-device transfers, and batched Transformer forward passes to replace per-spot loops. Cleared pyc cache to resolve ImportError. Monitored long-running experiments in parallel using background TaskOutput.
  • Why it matters: 10/11 DLPFC slides succeeded with STAIG fusion (151676 known to collapse), average ARI=0.546. qformer+no-refine is the best combination for slide 151673 (ARI=0.4832). Discovered that scan_cluster refine benefits weak fusion but hurts strong fusion.

Tianhe

  • What was done: On the Tianhe cluster (an46/an49/an51), completed the full Pi0.5 LoRA fine-tuning pipeline (data conversion → normalization statistics → LoRA training), and Error Recovery Benchmark M14 three-way evaluation (CPU analysis + GPU evaluation + environment fingerprint crash fix).
  • How it was done: Resolved data format compatibility issues (datasets downgrade, lerobot source patching), VRAM OOM (full fine-tuning → LoRA architecture switch), and process management issues (srun → sbatch) through progressive debugging. Used srun –overlap to launch evaluation processes in parallel. Fixed EnvironmentMismatchError with scene-level try-except.
  • Why it matters: Obtained a step-1000 LoRA checkpoint; Job 46553 is running stably (loss=0.068, 2.0s/step). M14 three-way evaluation (m14_cpu complete, m14_pi05 complete, m14_pi0 in progress) resumed past the scene 122 crash point. Confirmed that ~130 natural_* scenes in the 649-scene database are incompatible, leaving ~519 valid evaluation scenes.

Systematically optimized MIHD spatial transcriptomics fusion training on the DCC node (3x CPU acceleration + architecture decoupling + full-slide benchmarking + Vision Refine ablation experiments). Concurrently on the Tianhe cluster, completed MimicGen data preparation, fixed M14 three-way evaluation environment fingerprint crashes, and resolved Pi0.5 full fine-tuning OOM issues. Successfully brought Pi0.5 LoRA training (Job 46553) to stable operation.

Today’s Tasks

Architecture & Strategy

  • ✅ MIHD fusion training — 3x CPU acceleration (vectorization + GPU-native ops + batching) — Applied comprehensive optimization across STAIGTrainer.py, STAIGTrainerE2E.py, and QFormerFusion.py: replaced O(n²) nested loops with scipy cdist (initialization phase, ~100–500x speedup), moved adaptive_dropout_adj random number generation to GPU (eliminating ~1–2s per-epoch GPU↔CPU sync overhead), and added a batched forward interface to QFormerFusion (forward_batched() with key_padding_mask + pre-built padded tensors, estimated 20–50x speedup).
  • 🔄 Pi0.5 LoRA fine-tuning (Job 46551 → 46553, running) — After full fine-tuning OOM (pi0.5 training state ~62GB exceeds A800 capacity; FSDP=4 ineffective), switched to gemma_2b_lora + gemma_300m_lora architecture (VRAM reduced to ~22.5GB). Submitted Job 46551 via sbatch, which ran to step 3000 before being misidentified as terminated due to stdout buffering. After adding PYTHONUNBUFFERED=1 + stdbuf + ERR trap, restarted as Job 46553 with –resume. Currently running stably on an46 (4×A800) at 2.0s/step, loss=0.068, estimated ~53 hours to complete.
  • 🔄 M14 three-way evaluation (m14_cpu/Pi0/Pi0.5) — launch and environment fingerprint fix — Fixed MUJOCO_EGL_DEVICE_ID mismatch and resumed m14_cpu; launched Pi0 (an49 GPU6, port 5556) and Pi0.5 (an49 GPU5, port 5557) VLA evaluations. All three crashed at scene 122/649 (~130 natural_* scenes with incompatible xml_hash). After adding scene-level EnvironmentMismatchError try-except to collector.py and resuming with –resume, m14_cpu and m14_pi05 completed with exit code 0; m14_pi0 still running.
  • ✅ Critical bug fixes in evaluate_mimicgen.py and collector.py — evaluate_mimicgen.py: added env.seed() for reproducibility, 8D state dimension assert validation, and fixed an in-place array modification bug in _quat2axisangle (replaced with np.clip + copy). collector.py: added per-scene EnvironmentMismatchError catch in collect_on_scenes(), logging a warning and skipping incompatible scenes to prevent the entire process from crashing.
  • ✅ staig_fusion support for arbitrary visual encoders (removing hard UNI dependency) — Renamed the constant (STAIG_FUSION_VISION_ENCODER → STAIG_FUSION_DEFAULT_VISION_ENCODER), added the STAIG_UNI_FAMILY set ({uni, uni2}), and updated branching logic in extraction_planner.py, evaluation_planner.py, phase2_evaluate.py, and run_benchmark.py. UNI-family models automatically use staig_strict preprocessing; all others use standard. Cleared pyc cache to resolve ImportError.
  • ✅ STAIG fusion benchmarking across all 11 DLPFC slides — Ran pca+uni2+staig_fusion and none+uni2+staig_fusion on all 11 DLPFC slides. Fixed the UNI2 patch_size compatibility bug (256×256 → 224×224 resize) and NaN KMeans fallback. 10/11 slides succeeded; average ARI=0.546, NMI=0.639. Slide 151676 is a known collapse (ARI=0).
  • ✅ M13 CPU analysis triple report (baseline report + classifier reliability + error type discriminability) — Ran 4_analyze_results.py, 7_classifier_reliability.py (fixed missing tabulate), and 8_error_type_discriminability.py (fixed int(‘False’) crash) on 726 m14_cpu episodes, producing 6 analysis files. Fleiss’ kappa = −0.02 (poor); drop↔grasp_slip kappa = 0.71 (non-redundancy validation failed); SR = 0 across all scenes (CPU baseline cannot recover).
  • ✅ MIHD Vision Refine vs. Baseline ablation (8 fusion methods) — Ran full ablation on slide 151673 for concat/mean/element_wise_sum/attention/basic_contrastive/adaln_attention/llava_mlp/qformer with and without scan_cluster refine. Generated a three-panel visualization (GT | Baseline | Refine) and summarized results in vision_refine_summary_151673.txt. qformer baseline ARI=0.4832 is the best across all experiments; refine benefits weak fusion (attention +0.086) but hurts strong fusion (qformer −0.054).

Implementation & Fixes

  • ✅ MimicGen dataset preparation (HDF5 → LeRoBot conversion + normalization statistics) — Converted 9 core MimicGen tasks to LeRoBot format (4,500 episodes, 1,034,176 frames). Fixed datasets 4.4.1 incompatibility (downgraded to 3.6.0) and a missing prev_delta_indices attribute bug in lerobot source code. Subsequently computed normalization statistics over 16,159 batches (~3.5 hours), producing norm_stats.json.
  • ✅ Updated 项目全景总结.md (scene count 454 → 649, version v4.12) — Comprehensively updated scene count (454 → 649, 9 error types), milestone progress, short-term goal completion status, version history (v4.11.1 → v4.12), and gap analysis actual values.

Problems & Solutions

Critical Issues

1. Pi0.5 full fine-tuning training state (params + optimizer + EMA) requires ~62GB/GPU. Even with FSDP=4 sharding, replicated_sharding causes each GPU to temporarily hold the full model during initialization, resulting in repeated OOM

Solution: Switched to LoRA fine-tuning (gemma_2b_lora + gemma_300m_lora), reducing trainable parameters by ~90% and VRAM requirements to ~22.5GB/GPU.

Key Insight: JAX FSDP only shards parameter storage — it does not reduce forward pass activation memory. A single A800 80GB cannot support pi0.5 full fine-tuning; LoRA is the only viable option for an A800 cluster. When the warning 'Can't reduce memory use below 62.46GiB' appears, switch to LoRA immediately rather than continuing to experiment with FSDP configurations.

2. All three M14 evaluations crashed at scene 122/649: natural capture scenes (~130 natural_* scenes) have xml_hash mismatches with the current mimicgen environment, triggering EnvironmentMismatchError with no catch handler, causing the entire evaluation process to crash

Solution: Added a per-scene EnvironmentMismatchError try-except in collect_on_scenes() in collector.py, logging a warning and skipping the scene. Restarted all three evaluations with –resume after the fix.

Key Insight: The 649-scene database contains two environment types: ~519 impulse/augmented compatible scenes (xml_hash matches) and ~130 natural_* scenes (generated in a VLA environment with cameras enabled, different xml_hash). The actual valid evaluation target for M14 is ~519 scenes; target episode counts need to be adjusted accordingly.

3. UNI2 (ViT-H/14, patch_size=14) is incompatible with STAIG strict mode’s 256×256 patch input (256/14 = 18.28, not an integer — AssertionError)

Solution: When STAIG mode is detected in the UNI2 extraction pipeline, automatically resize patches from 256×256 to 224×224 before batch inference (224 is divisible by 14).

Key Insight: UNI v1 uses dynamic_img_size=True and accepts arbitrary input sizes; UNI2 is a standard ViT-H/14. STAIG’s 256×256 patch was designed for UNI v1; porting to new models requires adapting to patch_size divisibility constraints.

4. Training log progress was severely distorted by stdout buffering: Pi0.5 training appeared to have only reached ~580 steps, but had actually reached step 3000, causing a false determination that training had stopped

Solution: Added PYTHONUNBUFFERED=1 and stdbuf -oL to both the training script and the sbatch submission script to ensure real-time log flushing. Also added an ERR trap and background GPU memory monitoring.

Key Insight: All long-running training scripts should standardize on PYTHONUNBUFFERED=1. Without it, stdout buffering completely distorts progress monitoring and leads to unnecessary restart operations.

5. Dual Slurm resource management issues: srun –overlap on an already-allocated job blocks and times out; long-running training processes are killed with SIGTERM/SIGKILL (exit code 137/143) when an interactive session times out

Solution: Use srun –jobid=XXXXX –overlap to avoid resource conflicts and run commands on already-allocated nodes. Submit long-running training jobs as independent batch jobs via sbatch, decoupled from the interactive session lifecycle.

Key Insight: Interactive development (srun) and long-running training (sbatch) must be strictly separated. sbatch jobs have independent resource allocation and lifecycle management — this is the correct approach for cluster training.

6. STAIG training loss became NaN from the first epoch for DLPFC slide 151676; KMeans fallback crashed due to NaN

Solution: Added nan_to_num cleanup to the KMeans fallback path. The training collapse itself is a known issue (high temperature tau=30 + unusual data distribution) with no fundamental solution.

Key Insight: Slide 151676 is an anomalous case that likely requires dedicated investigation with adjusted tau/dropout rate/graph construction parameters.

General Issues

7. After multiple training failures, zombie GPU processes (nvitop, previous training runs) occupied 70+ GB of VRAM, preventing new training from acquiring sufficient memory. Most OOM errors were actually caused by zombie processes, not misconfiguration

Solution: Used fuser /dev/nvidiaX and kill -9 to clean up zombie processes one by one, paying particular attention to nvitop, which holds a CUDA context even when no active training is running.

Key Insight: Before starting any training run, always check for and clean up all non-essential GPU processes. Monitoring tools like nvitop are a common source of hidden VRAM consumption.

8. Python .pyc cache caused code modifications to have no effect: renaming a constant missed the reference in phase2_evaluate.py (ImportError); modifying batch_size/fsdp_devices in config.py without clearing the cache caused configuration changes to be silently ignored

Solution: After modifying module constants or configurations, proactively clear all pycache directories and .pyc files to ensure Python recompiles with the latest code.

Key Insight: Python bytecode caching is a common pitfall during rapid iterative development. After renaming constants, always do a global search for all references before committing changes to avoid missing any.

9. MUJOCO_EGL_DEVICE_ID=0 did not match CUDA_VISIBLE_DEVICES=5, causing m14_cpu evaluation to crash immediately with an AssertionError on startup

Solution: Set MUJOCO_EGL_DEVICE_ID to the same GPU number present in the CUDA_VISIBLE_DEVICES string (both set to 5).

Key Insight: robosuite EGL binding is implemented via a string-contains assertion — MUJOCO_EGL_DEVICE_ID must be a GPU number that actually exists in CUDA_VISIBLE_DEVICES, not a remapped relative index.

10. Collection of dataset/script compatibility issues: datasets 4.4.1 incompatible with lerobot 0.1.0 (column access API changes); int(‘False’) crash in scripts/8 (JSONL booleans serialized as strings); tabulate not installed

Solution: Downgraded datasets to 3.6.0 and re-converted the dataset. Added ‘True’/‘False’ string-to-bool conversion and kruskal degenerate-case protection when reading the success field. Ran pip install tabulate.

Key Insight: Python dependency compatibility on shared cluster environments must be verified on first run. Booleans in JSONL can become strings after json.dumps — always explicitly convert when reading.

Human Thinking vs. AI Thinking

Strategic Level

Discovery of the 649-scene database composition architecture

Role Thinking
Human Executed the plan assuming all 649 scenes were evaluable; did not anticipate scene compatibility issues
AI After all three evaluations crashed at scene 122, AI independently identified through EnvironmentMismatchError analysis that the database contains two incompatible environment types (~519 impulse-compatible + ~130 natural_* incompatible). Proposed the scene-level try-except fix and recognized that the actual valid evaluation count should be ~519.

Differential Analysis: This was the most important architectural discovery of the day, identified independently by AI during debugging. The AI’s diagnostic capability demonstrated real value here — but if the scene fingerprint distribution in meta.json had been checked in advance, this could have been prevented before launching evaluation.

CPU performance bottleneck identification and training acceleration

Role Thinking
Human Human intuition identified “CPU-intensive work every epoch” as the root cause of slow training, provided the high-level direction of “preprocessing acceleration,” and designed a phased execution plan upfront (including precise command-line parameters)
AI AI systematically read three core files, precisely identified three specific bottlenecks (initialization O(n²) loop, per-epoch GPU↔CPU transfer, per-spot Python loop), quantified the analysis (2,700 spots × 200 epochs = 540,000 independent forward passes), designed a complete implementation plan with boundary handling, and discovered several runtime bugs not anticipated by the plan during execution

Differential Analysis: Human provided strategic direction and architectural judgment; AI handled quantitative analysis, specific implementation, and debugging adaptation. AI’s estimates were inaccurate for FSDP memory savings (expected FSDP=4 to reduce to ~16GB; still OOM in practice) and pre-validation of new model constraints (UNI2 patch_size).

Multi-GPU parallel training strategy and VRAM management

Role Thinking
Human Proactively suggested using multiple GPUs for parallel training, driving AI to explore FSDP solutions; took a pragmatic stance on starting training with partial data (validate early)
AI AI was overly optimistic about JAX FSDP memory optimization, only switching to LoRA after multiple OOM failures. Correctly judged that dataset format completeness is required (cannot train on partial data). Switching to LoRA was the right direction, but should have been identified earlier from the JAX warnings.

Differential Analysis: The user’s parallelization intuition was correct, but AI had a misunderstanding of FSDP principles (only shards storage, does not reduce activation memory). The architectural solution (LoRA) should have been proposed earlier.

Implementation Level

Plan Mode workflow and AI autonomous execution boundaries

Role Thinking
Human Rejected AI’s automatic ExitPlanMode invocation twice, explicitly requiring plan review before approving execution; understood that the plan is a user decision gate
AI After completing the plan, directly attempted to call ExitPlanMode to advance execution without waiting for user confirmation

Differential Analysis: AI failed to correctly understand Plan Mode semantics — a plan requires explicit user approval and is not a signal for AI to automatically proceed. This reflects insufficient understanding of the “plan as a user decision checkpoint” workflow pattern.

AI Limitations

Critical Limitations

  • Incorrect understanding of JAX FSDP memory optimization principles: estimated FSDP=4 would reduce per-GPU memory to ~16GB (62/4), when in fact FSDP only shards parameter storage — the full model is still required during initialization, and forward pass activation memory is not reduced. Should have switched to LoRA immediately upon seeing the 'Can't reduce memory use below 62.46GiB' warning, rather than continuing to try multiple FSDP configurations.
  • Failed to pre-validate model architecture constraints and data compatibility: did not check ViT-H/14’s patch_size divisibility requirement when implementing UNI2 compatibility; did not check scene fingerprint distribution in meta.json before launching evaluation. Both were only discovered and fixed after runtime crashes, creating unnecessary debugging cycles.
  • Misjudgment in monitoring long-running task progress: did not set PYTHONUNBUFFERED=1 in training scripts, causing log buffering and misreading of progress (580 steps → actually 3,000 steps). Restarted training multiple times without verifying GPU memory was actually released — most OOM errors were actually caused by zombie processes, not misconfiguration.

General Limitations

  • Incomplete cache clearing and reference search after code modifications: renamed a constant without searching all references at once (missed scripts/phase2_evaluate.py); modified config.py without clearing pycache, leading to runtime ImportError or silently ignored configuration changes requiring extra fix steps.
  • Insufficient understanding of Plan Mode workflow: attempted to call ExitPlanMode after completing a plan on multiple occasions (rejected by user twice), failing to understand that a plan is a user decision gate rather than a signal for AI to automatically advance.

Today’s Takeaways

Core Takeaways

  • Pi0.5 full training state (params + AdamW + EMA) requires ~62GB VRAM; a single A800 80GB cannot support full fine-tuning. JAX FSDP only shards parameter storage and does not affect full model loading during initialization or forward activation memory. LoRA fine-tuning (gemma_2b_lora) reduces VRAM requirements to ~22.5GB and is the only viable option for an A800 cluster.
  • The 649-scene database consists of two categories: ~519 impulse/pose_perturb/augmented compatible scenes (xml_hash matches) and ~130 natural_* scenes (generated in a VLA environment with cameras enabled, different xml_hash). M14’s actual valid scene count is ~519; target episode counts and all documentation need to be updated accordingly.
  • Vision Refine shows a polarizing effect on fusion performance: scan_cluster refine (1536d → 256d dimensionality reduction) benefits weak fusion (attention +0.086 ARI) but hurts strong fusion (qformer −0.054). Strong fusion methods like QFormer have the capacity to handle high-dimensional inputs — dimensionality reduction actually loses information. The best result of the day was qformer+no-refine (ARI=0.4832).
  • Vectorization and batching are extremely effective in scientific computing: O(n²) nested loops → cdist matrix operation yields ~100–500x speedup; per-spot Python loops → batched GPU forward yields ~20–50x speedup. These are the core strategies for accelerating spatial transcriptomics training, and also validate the general optimization principle of “replace per-epoch computation with preprocessing.”
  • Slurm cluster training best practices: long-running training must use sbatch (not srun, to avoid SIGTERM on session timeout); all training scripts should standardize on PYTHONUNBUFFERED=1 + stdbuf -oL; use fuser /dev/nvidiaX to clean zombie GPU processes before starting training (monitoring tools like nvitop also hold CUDA contexts); MUJOCO_EGL_DEVICE_ID must exactly match the GPU number in the CUDA_VISIBLE_DEVICES string.
  • STAIG fusion is an end-to-end refine+fuse integrated solution (GCN encodes spatial relationships); stacking scan_cluster refine on the input side creates functional overlap and information redundancy — the two should not be used together. DLPFC slide 151676 has a stable training collapse problem (NaN from epoch 1) requiring dedicated investigation with adjusted tau/dropout rate/graph construction parameters.
  • Preliminary M13 analysis conclusions: Random+BC-RNN CPU baselines achieve SR=0 across all scenes, Fleiss’ kappa=−0.02 (poor), discriminability not significant — because the baseline policies simply cannot recover, there is no SR variance. Meaningful statistical results require VLA (Pi0/Pi0.5) data.

Session Summary

MIHD

✅ MIHD fusion training comprehensive optimization: 3x CPU acceleration + architecture decoupling + Vision Refine ablation + STAIG full-slide benchmark 00:02:10.477 | claude_code Completed six workstreams on the DCC RTX 5000 Ada node: ① Applied three CPU accelerations to STAIGTrainer/STAIGTrainerE2E/QFormerFusion: cdist vectorization (100–500x), GPU-native dropout random number generation, and batched forward (estimated 20–50x); ② Removed staig_fusion’s hard dependency on UNI, added STAIG_UNI_FAMILY set to support arbitrary visual encoders, cleared pyc cache to resolve ImportError; ③ Ran full ablation of 8 fusion methods with/without scan_cluster refine on slide 151673 and generated three-panel visualizations — qformer baseline ARI=0.4832 is the best result; refine benefits weak fusion (attention +0.086) but hurts strong fusion (qformer −0.054); ④ Fixed UNI2 patch_size compatibility bug (256 → 224 resize) and NaN KMeans fallback; ⑤ Completed STAIG fusion benchmark across all 11 DLPFC slides — 10/11 succeeded, average ARI=0.546 (151676 known collapse).

Error Recovery Benchmark

• Pi0.5 LoRA full training pipeline setup + M14 three-way evaluation launch and critical bug fix + M13 CPU analysis 00:00:18.651 | claude_code Completed two parallel workstreams on the Tianhe cluster. [Training track] Converted 9 MimicGen tasks to LeRoBot format (4,500 episodes, 1M frames), fixed datasets version compatibility (downgraded to 3.6.0) and lerobot source bugs; normalization statistics took 3.5 hours; after repeated OOM from full fine-tuning (pi0.5 training state ~62GB exceeds A800 capacity; JAX FSDP ineffective), switched to LoRA architecture and submitted Job 46551 via sbatch, which ran to step 3000 before being misidentified as terminated due to stdout buffering. After adding PYTHONUNBUFFERED=1, restarted as Job 46553 (an46, 2.0s/step, loss=0.068, estimated 53 hours). [Evaluation track] Fixed missing tabulate / int(‘False’) type conversion / kruskal degenerate-case bugs, completed M13 CPU analysis triple report (kappa=−0.02, SR=0 across all scenes, VLA data needed for meaningful statistics). After fixing MUJOCO_EGL_DEVICE_ID mismatch, launched m14_cpu/Pi0/Pi0.5 three-way evaluation — all crashed at scene 122 due to EnvironmentMismatchError (discovered ~130 incompatible natural_* scenes in the 649-scene database). After adding scene-level try-except in collector.py and resuming with –resume, m14_cpu and m14_pi05 completed; m14_pi0 still running. Also updated 项目全景总结.md (scene count 454 → 649, v4.12).

Token Usage

Overview

Metric Value
Total Tokens 17,246,252
Input Tokens 32,552
Output Tokens 1,598
Cache Created 2,043,944
Cache Read 15,168,158
Cache Hit Rate 88.1%
Total Cost (USD) $8.8234

Model Breakdown

Model Input Output Cache Created Cache Read Cost Share
claude-opus-4-6 249 443 670,414 3,372,934 $5.8889 66.7%
claude-haiku-4-5-20251001 32,303 1,155 1,373,530 11,795,224 $2.9345 33.3%

Per-Device Usage

Device Total Tokens Input Output Cost
DCC 2,621,468 8,010 256 $1.2491
Tianhe 14,624,784 24,542 1,342 $7.5743