Daily Report — 2026-03-01

Today’s Overview

What was done: Advanced the spatial transcriptomics toolchain (STHD/MIHD) and robotics learning benchmark (Error Recovery Benchmark) across DCC/tianhe dual servers. DCC focused on VisiumHD multimodal data analysis and repo architecture refactoring; tianhe focused on model evaluation, configuration bug fixes, error scenario generation, and Pi0.5 LoRA fine-tuning launch.
How it was done: DCC used a safe refactoring flow of grep dependency analysis → batch migration → dry_run validation, plus 4 HD path adaptation bug fixes to complete the multimodal pipeline. tianhe used TensorBoard API to monitor training, compared HDF5 dataset structures to pinpoint configuration bugs, iteratively debugged image format adaptation, and sequentially fixed 4 training startup bugs including JAX environment variables.
Why it matters: The MIHD repo underwent major streamlining (deleted 41 files / ~250K lines of code), establishing a clear modular architecture. The Coffee BC-RNN root cause was identified and fixed. Pi0.5 LoRA 9-task parallel training runs stably on 6×A800. The Error Recovery Benchmark obtained its first BC-RNN high-success-rate data and Pi0.5 baseline evaluation, with a complete infrastructure documentation system established.

DCC

What was done: Completed in-depth improvements to STHD CLAUDE.md, VisiumHD three-annotation visualization (pathologist/STHD/STAIG), MIHD pipeline HD adaptation bug fixes, STAIG fusion end-to-end run, scGPT 11-slice KMeans visualization, MIHD 6-phase cleanup refactor, and back-filled the 2026-02-28 daily report.
How it was done: Stepped through HD data paths (discovered the r_big//4 mapping pattern), fixed 4 issues in vision encoder/fusion/mclust; refactoring followed the sequence “grep verify → create new module → update references → delete old files”, with dry_run confirming all 440 planned experiments passed; replaced unavailable mclust with KMeans.
Why it matters: MIHD achieved its first pca×uni2×staig_fusion multimodal fusion on VisiumHD (Silhouette=0.343 vs PCA 0.086). The repo deleted 41 files / ~250K lines of code + 5.3MB of images, and the drop_feature bug was fixed. scGPT 11-slice visualization was compressed from an estimated 6+ hours to 2 minutes.

tianhe

What was done: Completed Pi0.5 9-task base model evaluation (4.2% SR), fixed missing Coffee BC-RNN object observation key, developed BC-RNN Stack_D0 error scenario generation pipeline (11 scenarios + MP4s), cleared ~530GB of idle GPU VRAM, monitored BC-RNN 9-task training, organized project files v4.17/v4.18, and successfully launched Pi0.5 LoRA 9-task parallel fine-tuning (after fixing 4 bugs).
How it was done: Executed GPU tasks on the an49 node via SLURM srun --overlap; compared HDF5 dataset structures to locate the Coffee configuration bug; iteratively fixed the 4-layer robomimic BC-RNN image observation issue; fixed 4 training startup bugs (JAX_PLATFORMS / norm_stats path / boolean parameter / W&B); used CPU + subsampling + parallelism to reduce norm_stats computation from 10+ minutes to 2.5 minutes.
Why it matters: BC-RNN Stack task reached 64% SR at epoch 22, validating data quality. The Coffee bug fix lays the groundwork for retraining complex tasks. Pi0.5 LoRA runs stably on 6×A800 (77.7GB/GPU, 100% utilization), with complete 9-task fine-tuning infrastructure in place.

Parallel progress across four projects on DCC and tianhe servers: DCC completed VisiumHD three-annotation visualization and STAIG fusion multimodal run (Silhouette 0.343), and executed a 6-phase large-scale cleanup refactor of the MIHD repo (deleted ~250K lines of code); tianhe completed Pi0.5 9-task base model evaluation (4.2% SR), diagnosed and fixed missing Coffee BC-RNN object observation key, developed the BC-RNN Stack_D0 error scenario generation pipeline (fixed 4-layer image bug, generated 11 scenarios), and successfully launched Pi0.5 LoRA 9-task parallel fine-tuning at 100% GPU utilization on 6×A800 after fixing 4 startup bugs.

Today’s Tasks

Architecture & Strategy

✅ VisiumHD barcode mapping discovery and three-annotation visualization — Discovered the r_big//4 mapping pattern (17502/17502 complete match), generated three comparative visualizations: pathologist annotation (4 tissue types: Neoplasm 48.6% / Connective 29.5%, etc.), STHD cell types (85 classes → 11 coarse classes via majority vote, 96.3% match), and STAIG fusion clustering. Established baseline for VisiumHD data analysis.
✅ MIHD pipeline HD dataset adaptation and STAIG fusion end-to-end run — Fixed 4 HD path adaptation bugs (added cropped_fullres.tif pattern to find_spatial_image, passed crop_dir.parent as data_root for vision encoder, loaded spatial coords from adata.obsm[‘spatial’] in fusion stage, added mclust→KMeans fallback). Completed full pca×uni2×staig_fusion pipeline on VisiumHD crop10large (patch extraction 5.5 min, UNI2 GPU inference 5 min, STAIG training 6 min, Silhouette=0.343).
✅ MIHD repo 6-phase cleanup refactor (deleted ~250K lines of code) — Phase 1: deleted 41 dead files (~250K lines + 5.3MB images). Phase 2: migrated shared functions from run_benchmark.py into 6 modules (staig_utils, vision_extractors, Fusion, clustering, etc.) and updated all pipeline/ imports. Phase 3: deleted the run_benchmark.py monolith and 5 dependent models. Phase 4: extracted STAIG shared code and fixed the inverted drop_feature logic bug in BasicContrastive (>= changed to <). Phase 5: updated all documentation. Phase 6: dry_run validated all 440 experiments pass.
✅ Coffee BC-RNN configuration bug diagnosis and fix (object observation key) — By comparing the actual obs key structure of the HDF5 dataset, the AI independently discovered that the BC-RNN config template was missing the object observation key (Coffee requires a 57-dim object state: Pod/Machine/Holder position + relative pose + hinge angle). Added extra_low_dim=[‘object’] for all 9 tasks, regenerated and validated all config files, fixing the root cause of Coffee’s 0% SR.
✅ BC-RNN Stack_D0 error scenario generation pipeline development — Fixed 4 consecutive bugs in the robomimic BC-RNN image observation integration (enable_camera detection, json.loads parsing of checkpoint config, 84×84 resolution auto-detection, HWC→CHW transpose + float32 normalization). Created configs/benchmark_v4_stack.yaml and scripts/batch_visualize_policy_scenes.py. Generated 11 tip_over error scenarios and 11 MP4 visualization videos.
✅ Pi0.5 LoRA 9-task fine-tuning pipeline setup and successful launch — Created train_pi05_benchmark.py (5 subcommands), registered 18 configs in openpi/config.py (9 finetune + 9 inference), fixed IMAGE_KEY_MAP prefix matching bug in vla_server.py. Completed HDF5→LeRobot data conversion for all 9 tasks (task-by-task to avoid OOM segfault). Used CPU + subsampling + parallelism to compute all norm_stats in 2.5 minutes. Fixed 4 startup bugs (JAX_PLATFORMS=cpu, assets path, –no-overwrite, WANDB_MODE=disabled), then successfully launched 6-task parallel training on GPUs 1–6 with XLA_MEM=95% (100% GPU utilization, 77.7GB/GPU).
✅ Pi0.5 Phoenix 9-task base model evaluation — Monitored 50-rollout evaluations across 9 MimicGen tasks and obtained final results: total SR=4.2% (19/450). Stack_D0 (24%) and Stack_D1 (12%) had meaningful success; the remaining 7 tasks were 0–2%. Confirmed the third training run completed successfully to step 99999 (previous two failed due to SLURM time limits and orbax conflicts).
🔄 Coffee BC-RNN rollout visualization and environment initialization investigation — Generated a coffee_d0 rollout video (742KB). After viewing, the user identified object interpenetration/freezing during environment initialization (a simulator bug, not a model issue). Investigation of Coffee env kwargs began but the session was cut off; fix incomplete.

Implementation & Fixes

✅ GPU VRAM cleanup and BC-RNN 9-task training monitoring — Identified and cleared ~530GB of idle VRAM on the an49 node (zhaoganlong Phoenix serve_policy 407GB + two stale VLA servers at 61GB each). Monitored 9 BC-RNN tasks: stack_d0 (64% @ epoch 22), stack_d1 (44%), coffee (0%), stack_three / threading / three_piece_assembly all reached 58–96% @ epoch 300–420.
✅ Infrastructure reference doc creation and project file organization v4.17/v4.18 — Created docs/infrastructure_reference.md (649 scenarios / 9 error types / 6 detectors / 4 injectors / complete pipeline diagram, 13 chapters). v4.17 organization: archived old files in a 4-level archive/ directory, merged VLM tutorials, created EXTERNAL_DEPENDENCIES.md. v4.18 organization: extracted create_env()/load_task_registry() into script_utils.py (eliminating ~210 lines of duplicate code), updated CLAUDE.md with new M14 evaluation command docs.
✅ scGPT 11-slice KMeans visualization — Terminated the time-consuming mclust job (had been running 2 hours, 2/11 complete). Switched to KMeans and completed all 11 DLPFC slices in 2 minutes: average ARI=0.1695, NMI=0.2772.
✅ In-depth STHD CLAUDE.md improvements — Analyzed all core STHD modules. Added documentation for 6 previously missing modules (frontline.py, qcmask.py, etc.), probabilistic model optimization objective, Numba JIT details, pdata TSV format, and patch overlap handling logic.
✅ Back-filled 2026-02-28 daily report (MIHD experiment metrics and visualizations) — Added Chapter 5 (experiment results summary) and Chapter 6 (output file inventory), including the 151673 multimodal benchmark table, Vision Refinement before/after comparison, and 60+ visualization hyperlinks.

Problems & Solutions

Key Issues

1. VisiumHD HD dataset path adaptation: vision encoder image paths, spatial coords loading, and multiple lookups in the fusion stage all assumed DLPFC directory structure and failed

Solution: Three fixes: added cropped_fullres.tif pattern to find_spatial_image(); passed crop_dir.parent as data_root when calling the vision encoder; in the fusion stage, loaded coordinates directly from adata.obsm[‘spatial’] to bypass path lookup.

Key insight: The pipeline was designed assuming a flat DLPFC directory structure (data_root/section_id/). HD data requires mapping at the call site rather than modifying encoder internals. Path abstraction layers should include adapter interfaces for different dataset structures from the start.

2. Long-running task progress invisible: GPU utilization at 0% but VRAM fully loaded — impossible to tell if it was a CPU bottleneck or the model being eagerly loaded and idling

Solution: Set PYTHONUNBUFFERED=1 and added tqdm to key loops. compute_norm_stats.py requires both CUDA_VISIBLE_DEVICES="" and JAX_PLATFORMS=cpu to force CPU mode. Root cause: PaligemmaTokenizer is eagerly loaded onto all visible GPUs during get_config().model, but norm_stats only needs dataset transforms.

Key insight: GPU util=0 can indicate either a CPU preprocessing bottleneck or a model that is eagerly loaded but not computing. The two are distinguishable by looking at nvidia-smi VRAM usage patterns. norm_stats computation requires no model inference at all and should default to CPU mode.

3. Coffee BC-RNN loss converging normally (−7.66 → −15.4) but SR stuck at 0%, while Stack with the same config reached 64% SR at epoch 20

Solution: Compared the actual obs key structure of the HDF5 dataset and found that Coffee requires a 57-dim object state (essential for multi-stage precise manipulation: Pod/Machine/Holder position + relative pose + hinge angle), which was missing from the config template. Added extra_low_dim=[‘object’] for all 9 tasks and regenerated all configs.

Key insight: BC-RNN config observation keys must match task complexity. Coffee is a multi-stage precision task; 84×84 images alone cannot provide sufficient spatial resolution. The symptom of a config bug (normal loss but SR=0%) closely resembles insufficient model capacity and can only be distinguished by comparing dataset structure.

4. robomimic BC-RNN image observation integration failure: 4 consecutive issues (enable_camera / checkpoint config format / resolution / image format)

Solution: ① Auto-enable camera by detecting rgb modality from the checkpoint config field (stored as a JSON string — requires json.loads, not direct dict access). ② Read actual image dimensions (84×84) from shape_metadata and pass to create_env. ③ In _prepare_image_obs(), manually apply HWC→CHW transpose and uint8→float32/255 normalization.

Key insight: robomimic checkpoint config is stored as a JSON string (not a dict — counterintuitive). When bypassing the standard rollout pipeline, format conversions normally handled automatically by ObsUtils.process_obs must be done manually.

5. Pi0.5 LoRA training startup: 4 consecutive blocking bugs — JAX CPU forcing broken, norm_stats path wrong, boolean parameter format wrong, W&B proxy blocking

Solution: ① JAX CPU mode requires both CUDA_VISIBLE_DEVICES="" and JAX_PLATFORMS=cpu (the latter cannot be omitted). ② norm_stats actually writes to assets//benchmark//norm_stats.json, not checkpoints/. ③ argparse boolean flags use –no-overwrite, not –overwrite=False. ④ HPC compute nodes have no outbound network; must pre-set WANDB_MODE=disabled.

Key insight: JAX XLA backend detection is independent of CUDA environment variables. openpi norm_stats paths are tightly bound to dataset_name and write under assets/. HPC compute nodes should disable all external-network-dependent logging systems by default.

6. MIHD run_benchmark.py monolith heavily imported by pipeline/ — cannot be deleted directly; inverted drop_feature logic in BasicContrastive.py

Solution: Used systematic grep to confirm all reference points, then migrated in “create new module → update references → delete old file” order. Fixed drop_feature: changed >= drop_prob to < drop_prob, and imported the corrected version from staig_utils.

Key insight: Migration order is critical in large refactors. Same-named functions across 3 files may have 3 different variants; boundary conditions must be carefully compared before merging (> / >= / < differences can cause completely opposite behavior).

General Issues

7. SLURM srun hangs on a node that already has an interactive session; orbax checkpoint resume conflict (Destination …/5000 already exists)

Solution: Add –overlap to srun to allow shared execution. orbax resume requires explicitly setting overwrite=True or pre-clearing the existing step directory.

Key insight: SLURM interactive jobs use exclusive allocation by default. orbax resume=True does not automatically overwrite existing step directories — a common pitfall in the JAX training ecosystem.

8. Bash tool completely non-functional on tianhe nodes (echo/true/pwd all exit code 1); unable to execute any shell commands

Solution: Used Read/Write/Edit/Glob tools for all file operations (Write can implicitly create parent directories, replacing mkdir). Tasks requiring shell execution were explicitly flagged as items for the user to run.

Key insight: The Claude Code file tool set can substitute for most Bash file operations. When Bash is unavailable, proactively switch strategies and inform the user rather than blocking.

Human Reasoning vs. AI Reasoning

Strategic Level

Proactive discovery and optimization decisions around abnormal GPU resource usage

Role	Approach
Human	Repeatedly and proactively noticed GPU util=0 with VRAM fully loaded (VisiumHD patch extraction stuck on CPU, norm_stats loading the full model, an49 idling with 530GB VRAM). Proactively stopped and demanded root cause analysis. Applied knowledge of JAX memory mechanics to explicitly request XLA_PYTHON_CLIENT_MEM_FRACTION=0.95, and expanded training GPUs from 1–4 to 1–6 (with a global view of cluster resources).
AI	Tended to wait for tasks to complete rather than proactively auditing resource usage. Responded reactively to GPU anomalies. In the norm_stats case, traced source code through the agent to provide precise root cause identification (PaligemmaTokenizer eager loading). Passively analyzed memory optimization trade-offs; did not proactively suggest using GPUs 5–6.

Role

Approach

Human

Repeatedly and proactively noticed GPU util=0 with VRAM fully loaded (VisiumHD patch extraction stuck on CPU, norm_stats loading the full model, an49 idling with 530GB VRAM). Proactively stopped and demanded root cause analysis. Applied knowledge of JAX memory mechanics to explicitly request XLA_PYTHON_CLIENT_MEM_FRACTION=0.95, and expanded training GPUs from 1–4 to 1–6 (with a global view of cluster resources).

Tended to wait for tasks to complete rather than proactively auditing resource usage. Responded reactively to GPU anomalies. In the norm_stats case, traced source code through the agent to provide precise root cause identification (PaligemmaTokenizer eager loading). Passively analyzed memory optimization trade-offs; did not proactively suggest using GPUs 5–6.

Analysis: The human proactively detected system-level anomalies via nvidia-smi and prior knowledge and provided optimization direction. The AI has an advantage in root cause analysis depth but lacks proactive monitoring awareness and failed to notice the available GPU 5–6 resources.

Execution control and pacing (multiple ExitPlanMode rejections)

Role	Approach
Human	For large-scale file operations, bulk GPU cluster job submissions, and training launches (all irreversible actions), repeatedly rejected the AI’s requests for automatic execution, insisting on reviewing plans first or starting from a single task for incremental validation.
AI	After completing planning, tended to immediately request execution authorization, preferring to complete all work at once and maximize parallelism and automation.

Analysis: The human maintained strict review pacing for irreversible actions. Incremental validation (run a single task first, then batch) is more appropriate in resource-constrained HPC environments. The AI systematically underestimated the necessity of review and incremental verification.

Independent discovery of BC-RNN bug root cause

Role	Approach
Human	Noticed the anomaly of normal Coffee loss but 0% SR, and requested a diagnosis without offering any specific hypothesis.
AI	Systematically compared Coffee/Stack config files → inspected the actual HDF5 dataset obs key structure (found object: 57 dims) → analyzed task complexity differences → independently concluded “missing object key” (with no human hypothesis guiding the process).

Analysis: The AI’s ability to locate root causes through systematic data/code comparison exceeds human intuition. The human identified that a problem existed based on an anomalous pattern; the AI found the root cause through structured exploration.

Distinguishing Coffee simulator bug from model learning failure

Role	Approach
Human	After watching the rollout video, immediately identified object interpenetration/freezing during environment initialization as a simulator bug, not a model issue.
AI	Based on 0% SR, initially explained the problem as the model failing to learn the task, tending to attribute it to data, hyperparameters, or task difficulty.

Analysis: Visual intuition and simulator experience allow the human to quickly distinguish physical errors from learning failures. The AI lacks direct perception of simulator visual anomalies and tends to attribute simulator bugs to model capability issues.

Pragmatic engineering decisions (tool replacement and diagnosis scope)

Role	Approach
Human	Directly decided to replace mclust with KMeans (more efficient than fixing a dependency under environment constraints); for the Pi0.5 training failure, chose “diagnose only, do not fix” (the third run already completed successfully to step 99999 — check evaluation results before deciding whether to retrain).
AI	Tended toward fixing existing tools (installing rpy2) or providing complete diagnosis + fix plans, without prioritizing the key constraint that “an existing checkpoint is already evaluatable.”

Analysis: The human makes more pragmatic resource prioritization decisions. The AI favors completeness and fix-oriented approaches, sometimes missing key constraints (the fact that “the third run already succeeded”).

AI Limitations

Significant Limitations

Lack of proactive long-running task health checks: Relies on the user to notice anomalies like GPU idling or stuck processes, rather than checking proactively. Should set up active monitoring for all tasks exceeding 5 minutes, rather than waiting for output or responding reactively.
Lack of preventive validation before integrating external tools/libraries: Did not pre-check availability of mclust/rpy2, CLI interfaces of external scripts (compute_norm_stats.py parameter format), memory consumption of large batch operations (LeRobot conversion segfault), or image format conventions (robosuite HWC vs robomimic CHW). This led to multiple rounds of failure before fixes. The 4-layer image observation bug is a concentrated example of systemic lack of foresight.
Overly specific path and environment assumptions: MIHD pipeline hardcoded the DLPFC directory structure; scripts incorrectly assumed norm_stats outputs to checkpoints/; JAX CPU forcing omitted JAX_PLATFORMS=cpu; HDF5 code failed to anticipate that HPC compute nodes have no outbound network (W&B). All of these caused first-run failures.
Attribution bias: Misidentified the Coffee simulator environment initialization bug (object interpenetration/freezing) as insufficient model learning capacity, only corrected after the user watched the video. Lacks the ability to directly perceive visual simulator anomalies.
Tendency toward bulk automated execution: Did not proactively ask users whether step-by-step validation was needed in large pipelines; required multiple user interventions to control execution granularity. After killing old processes, did not realize the launcher had restarted child processes with new PIDs, requiring 3 rounds of kill operations.

General Limitations

Cannot diagnose root causes when the Bash tool fails; can only passively work around it. SubAgent exploration reports sometimes rely on documentation inference rather than actual filesystem scans, returning conclusions that don’t match the actual directory structure.

Today’s Learnings

Core Learnings

VisiumHD coordinate mapping: 2µm bin r_big//4 gives the 8µm grid row (same for col). Annotation barcode format: s_008um_{row:05d}_{col:05d}-1. MIHD achieved pca×uni2×staig_fusion Silhouette=0.343 on VisiumHD, significantly outperforming pure PCA’s 0.086, validating multimodal fusion effectiveness on HD data.
robomimic BC-RNN inference key configs: Checkpoint config is stored as a JSON string (requires json.loads, not direct dict access); shape_metadata records actual image dimensions; when bypassing the standard rollout, must manually apply HWC→CHW transpose and uint8→float32/255 normalization (normally handled automatically by ObsUtils.process_obs in the standard robomimic rollout).
JAX/Pi0.5 training key configs: ① Forcing CPU requires both CUDA_VISIBLE_DEVICES="" and JAX_PLATFORMS=cpu. ② XLA_PYTHON_CLIENT_MEM_FRACTION=0.95 is effective for A800 80GB (61→77.7GB). ③ HPC nodes must pre-set WANDB_MODE=disabled. ④ argparse boolean flags use –no-overwrite, not –flag=False. ⑤ openpi norm_stats path is assets//benchmark//norm_stats.json (not checkpoints/).
BC-RNN config and task complexity matching: Coffee requires a 57-dim object state (essential for multi-stage precision manipulation). Stack’s simple stacking requires only images. orbax resume=True does not automatically overwrite existing step directories — requires explicit overwrite=True (a universal pitfall in the JAX training ecosystem).
Large repo refactoring methodology: Systematically verify all import dependencies via grep → execute in “create new → update references → delete old” order → validate with dry_run. Before merging same-named function variants, carefully compare boundary conditions (> vs >= vs < can cause completely opposite behavior).
BC-RNN vs Pi0.5 capability comparison: BC-RNN on simple tasks (Stack D0/D1, Threading D0) reaches 64–100% SR by epoch 22. Complex multi-step tasks (Coffee, ThreePieceAssembly D1) have clear capability ceilings (near 0% even at 600 epochs). Pi0.5 base model at 4.2% SR; LoRA fine-tuning effectiveness pending validation.
norm_stats computation acceleration: Requires only dataset transforms, with no model inference at all. CPU mode + –max-frames 10000 subsampling + 9-task parallelism reduced computation from 10+ minutes to 2.5 minutes (156 batches is statistically sufficient).

Practical Learnings

SLURM HPC debugging tips: Use srun –overlap to attach commands to a node that already has an interactive session (direct SSH is blocked by pam_slurm_adopt). nohup bash script child processes don’t exit when the parent exits — when killing, handle the launcher and child processes separately.

Session Summaries

STHD

✅ STHD codebase analysis and in-depth CLAUDE.md improvements 00:06:57.489 | claude_code Read all core STHD modules and added documentation for 6 previously missing modules (frontline.py, qcmask.py, roi.py, sim.py, etc.), the probabilistic model optimization objective, Numba JIT parallelization details, pdata TSV format, and patch overlap handling logic. Explored the VisiumHD shared data directory and confirmed both crop10 and crop10large have STHD prediction results.

MIHD

✅ VisiumHD three-annotation visualization + STAIG fusion end-to-end + scGPT KMeans visualization 00:06:22.389 | claude_code Discovered the r_big//4 barcode mapping pattern (100% match) and generated three comparative visualizations: pathologist / STHD / STAIG fusion. Fixed 4 HD path adaptation bugs and completed the full pca×uni2×staig_fusion pipeline (Silhouette=0.343 vs PCA 0.086). Terminated the time-consuming mclust job and switched to KMeans, compressing 11-slice visualization from 6+ hours to 2 minutes (ARI=0.1695).

✅ MIHD repo 6-phase cleanup refactor + back-filled 2026-02-28 daily report 02:46:18.544 | claude_code Executed the user-provided 6-phase refactoring plan: deleted 41 dead files (~250K lines), migrated shared functions from run_benchmark.py into 6 modules, deleted the monolith and 5 dependent models, extracted STAIG shared code and fixed the inverted drop_feature logic bug, updated all documentation, and validated all 440 experiments pass via dry_run. Concurrently back-filled the 2026-02-28 daily report with the 151673 multimodal benchmark table and 60+ visualization hyperlinks.

Error Recovery Benchmark

🔄 Pi0.5 9-task evaluation complete + Coffee BC-RNN config fix + v4.17 file organization + Pi0.5 LoRA data preparation 00:09:05.761 | claude_code Obtained Pi0.5 evaluation results (total SR=4.2%, Stack peak at 24%). Diagnosed the two previous Pi0.5 training failures (SLURM time limit + orbax conflict) and confirmed the third run completed at step 99999. AI independently identified the root cause of Coffee’s 0% SR (missing 57-dim object observation key), fixed and validated all 9 config files. Completed v4.17 file organization (archive/ 4-level directory, VLM tutorial merge, EXTERNAL_DEPENDENCIES.md). Completed LeRobot data conversion for 8/9 tasks, wrote training launch scripts; ExitPlanMode rejected.

🔄 BC-RNN Stack_D0 error scenario generation pipeline (4 bug fixes) + infrastructure docs + GPU cleanup + training monitoring 01:10:59.841 | claude_code Iteratively fixed 4 image observation bugs (enable_camera detection / json.loads parsing / 84×84 resolution auto-detection / HWC→CHW transpose), successfully generating 11 tip_over error scenarios and MP4 videos. Cleared 530GB of idle VRAM on an49. Created docs/infrastructure_reference.md (649 scenarios / 9 error types / full component documentation, 13 chapters). Monitored 7 BC-RNN tasks (stack near perfect, coffee completely failing). Discovered coffee rollout video has a simulator environment initialization bug; fix incomplete.

✅ BC-RNN first-batch evaluation + v4.18 code organization + Pi0.5 LoRA pipeline validation and successful launch (4 bug fixes) 21:48:48.390 | claude_code Obtained first-batch evaluation via TensorBoard API (stack_d0=64% @ epoch 22, coffee=0%). Implemented v4.18 cleanup (extracted script_utils.py to eliminate ~210 lines of duplicate code, updated CLAUDE.md). Validated the coffee_d0 full chain (1000 demos / 2.4GB) and completed 9-dataset conversions task-by-task (switched from batch to task-by-task after batch segfault). Fixed 4 Pi0.5 training startup bugs (JAX_PLATFORMS / assets path / –no-overwrite / W&B), expanded to GPUs 1–6 + XLA_MEM=0.95, and ultimately ran 9-task parallel LoRA fine-tuning stably across 6×A800 at 77.7GB/GPU and 100% utilization.

Token Usage

Overview

Metric	Value
Total Tokens	89,955,645
Input Tokens	145,331
Output Tokens	187,297
Cache Created	3,559,531
Cache Read	86,063,486
Cache Hit Rate	96.0%
Total Cost (USD)	$54.9985

Model Breakdown

Model	Input	Output	Cache Created	Cache Read	Cost	Share
claude-opus-4-6	23,709	111,184	2,047,068	70,767,659	$51.0761	92.9%
claude-haiku-4-5-20251001	121,622	76,113	1,512,463	15,295,827	$3.9223	7.1%

Usage by Device

Device	Total Tokens	Input	Output	Cost
DCC	8,323,086	4,161	22,420	$6.0188
tianhe	81,632,559	141,170	164,877	$48.9797

Daily Report — 2026-03-01#

Today’s Overview#

DCC#

tianhe#

Today’s Tasks#

Architecture & Strategy#

Implementation & Fixes#

Problems & Solutions#

Key Issues#

1. VisiumHD HD dataset path adaptation: vision encoder image paths, spatial coords loading, and multiple lookups in the fusion stage all assumed DLPFC directory structure and failed#

2. Long-running task progress invisible: GPU utilization at 0% but VRAM fully loaded — impossible to tell if it was a CPU bottleneck or the model being eagerly loaded and idling#

3. Coffee BC-RNN loss converging normally (−7.66 → −15.4) but SR stuck at 0%, while Stack with the same config reached 64% SR at epoch 20#

4. robomimic BC-RNN image observation integration failure: 4 consecutive issues (enable_camera / checkpoint config format / resolution / image format)#

5. Pi0.5 LoRA training startup: 4 consecutive blocking bugs — JAX CPU forcing broken, norm_stats path wrong, boolean parameter format wrong, W&B proxy blocking#

6. MIHD run_benchmark.py monolith heavily imported by pipeline/ — cannot be deleted directly; inverted drop_feature logic in BasicContrastive.py#

General Issues#

7. SLURM srun hangs on a node that already has an interactive session; orbax checkpoint resume conflict (Destination …/5000 already exists)#

8. Bash tool completely non-functional on tianhe nodes (echo/true/pwd all exit code 1); unable to execute any shell commands#

Human Reasoning vs. AI Reasoning#

Strategic Level#

Proactive discovery and optimization decisions around abnormal GPU resource usage#

Execution control and pacing (multiple ExitPlanMode rejections)#

Independent discovery of BC-RNN bug root cause#

Distinguishing Coffee simulator bug from model learning failure#

Pragmatic engineering decisions (tool replacement and diagnosis scope)#

AI Limitations#

Significant Limitations#

General Limitations#

Today’s Learnings#

Core Learnings#

Practical Learnings#

Session Summaries#

STHD#

MIHD#

Error Recovery Benchmark#

Token Usage#

Overview#

Model Breakdown#

Usage by Device#