Daily Log — 2026-02-27

Today’s Overview

  • What was done: Analyzed the lab’s HDD_POOL storage distribution, documented the Slurm GPU node request and stable connection workflow, and queried the full M14 baseline evaluation results (Pi0, Pi0.5, BC-RNN)
  • How it was done: Scanned per-user directory sizes via parallel shell commands, queried Slurm cluster partition status and account permissions, and read JSON result files under outputs/evaluation_logs
  • Why it matters: Clarified the cluster GPU resource request process and pam_slurm_adopt restrictions, confirmed M14’s key findings (learned policies suffer severe distribution shift on error scenarios), and established the motivation for M15 LoRA fine-tuning experiments

Completed storage analysis on the Tianhe HPC cluster, documented the Slurm GPU node request workflow, and confirmed M14 baseline evaluation results (Pi0/BC-RNN achieved near-zero success rates on error recovery scenarios)

Today’s Tasks

Architecture & Strategy

  • ✅ M14 Baseline Evaluation Results Query (Pi0, Pi0.5, BC-RNN) — Read evaluation logs under outputs/evaluation_logs and confirmed full M14 results: BC-RNN success rate 0.28%, Pi0/Pi0.5 success rate 0%, Random baseline’s Recovery Progress was actually the highest (0.0199)

Implementation & Fixes

  • ✅ Slurm GPU Node Request and Connection Workflow — Documented the available Slurm commands on the tianhexy-a cluster (srun/salloc/sbatch, etc.), idle nodes in the ai and xy-a800 partitions, pam_slurm_adopt restrictions, and the tmux+salloc stable session approach
  • ✅ HDD_POOL Storage Analysis — Analyzed per-user disk usage under the entire sysu_gbli2xy_1 directory, identified the largest user (chenjunye ~5.1TB) and shared directories (miniconda3, robotics_dataset, VLA_data); overall filesystem utilization at 81%
  • ❌ Locating the “evaluate on training set” Change History — User recalled requesting that Pi0.5 & BC-RNN evaluation be changed to run on the training set, but AI found no corresponding changes in the codebase, git history, or memory files; could not trace it after the session ended

Issues & Solutions

Critical Issues

1. Direct SSH to Compute Node Rejected by pam_slurm_adopt (Access denied: you have no active jobs on this node)

Solution: Must first allocate resources via salloc to obtain a JOBID, then enter the node via srun --jobid=<JOBID> --pty bash, or SSH in after having an active job; recommended approach is tmux + salloc for a stable persistent session

Key Insight: This cluster uses a pam_slurm_adopt security policy that completely blocks SSH to compute nodes for users without active jobs — behavior different from typical HPC clusters

General Issues

2. AI Unable to Locate the “evaluate on training set” Change Mentioned by User

Solution: Unresolved; AI suggested the user describe the change in more detail (was it fixed initial conditions? or a train/test split?) to help re-locate it

Key Insight: Conversation history across sessions is invisible to AI — if a change was not committed to code or written to MEMORY.md, it is completely unrecoverable

3. du Command Repeatedly Timed Out Scanning Large Directories (miniconda3, chenshiyu, VLA_data, etc.) at Both 120s and 300s

Solution: Switched to a tmpdir + background parallel process approach (each directory forked independently) with a 600s timeout, ultimately obtaining sizes for VLA_data (248GB) and several other directories; 5 directories still could not be scanned within a reasonable time due to estimated size exceeding 2TB each

Key Insight: du speed on a shared Lustre filesystem is constrained by both inode count and network latency; for very large directories, only parallel + timeout estimation is feasible

Human Thinking vs. AI Thinking

Strategic Level

Memory of Past Operations

Role Approach
Human User recalled asking in a previous session to change evaluation to “evaluate on training set,” and directly asked for the results
AI AI cannot access cross-session history; could only search the codebase for traces, ultimately unable to reproduce the user’s recollection

Analysis: Humans have continuous memory of their own past actions, while AI’s memory depends on changes landing in code or being explicitly written to MEMORY.md

Implementation Level

Scope of Storage Analysis

Role Approach
Human Seeing that AI only analyzed their own directory (29GB), the user proactively asked to expand the analysis to all users in the group for a global view
AI AI defaulted to only analyzing the user’s requested directory (tangzijia), and did not proactively suggest or expand to other group members

Analysis: Humans have a global perspective and team awareness; AI tends to execute the narrowest literal interpretation of a task

AI Limitations

Critical Limitations

  • Missing cross-session memory: The “evaluate on training set” change mentioned by the user was never recorded in MEMORY.md or the codebase, making it completely unrecoverable for AI — it could only ask the user for clarification

General Limitations

  • Inefficient du scanning strategy: AI made multiple sequential attempts (120s → 300s → 600s timeouts) before finally switching to a parallel approach, wasting several interaction rounds
  • Initial analysis scope too narrow: AI did not proactively suggest analyzing the entire group directory and only expanded after the user explicitly requested it

Today’s Takeaways

Core Findings

  • M14 key conclusion: BC-RNN success rate 0.28%, Pi0/Pi0.5 at 0%, Random’s Recovery Progress (0.0199) was actually higher than all learned policies — indicating severe out-of-distribution generalization failure for learned policies under error injection scenarios, directly justifying M15 LoRA fine-tuning

Practical Findings

  • The tianhexy-a cluster uses pam_slurm_adopt; GPU nodes do not support direct SSH. GPUs in the ai and xy-a800 partitions are not registered with GRES, so no --gres flag is needed when requesting; tmux + salloc is the recommended approach for stable sessions
  • The lab’s HDD_POOL filesystem is at 81% utilization; the largest user is chenjunye (~5.1TB); shared miniconda3 and robotics_dataset each occupy approximately 1–2TB; historical checkpoints should be cleaned up regularly

Session Summaries

✅ M14 Baseline Evaluation Results Query (Pi0, Pi0.5, BC-RNN, Random) 05:57:24.584 | claude_code User asked about the progress of previously launched Pi0 and BC-RNN evaluations. AI read the outputs/evaluation_logs directory and summarized the full M14 results: across 649 PickPlace error scenarios, BC-RNN success rate was 0.28%, Pi0/Pi0.5 were completely at 0%, and Random had the highest Recovery Progress. AI noted this directly justifies M15 LoRA fine-tuning.

✅ Slurm GPU Node Request and Stable Connection Full Workflow 05:18:36.275 | claude_code User wanted to understand cluster Slurm commands and GPU node request procedures. AI explored the tianhexy-a cluster configuration, idle nodes in the ai and xy-a800 partitions, and account permissions, discovering that pam_slurm_adopt blocks direct SSH to compute nodes. AI ultimately provided three complete solutions: salloc interactive allocation, sbatch batch processing, and tmux stable sessions.

✅ HDD_POOL Storage Analysis: Per-User Directory Usage 04:28:09.784 | claude_code User requested an analysis of the lab’s shared storage. AI first analyzed the user’s own directory (29GB), then expanded to all group users at the user’s request. Through multiple rounds of parallel du scanning (switching to a tmpdir+parallel strategy after several timeouts), AI obtained sizes for most directories, identified chenjunye (~5.1TB) as the largest user, with overall filesystem utilization at 81% and 1.5PB remaining.

🔄 Locating Pi0.5 & BC-RNN Training Set Evaluation Change Records 07:57:48.382 | claude_code User recalled asking for evaluation to be changed to run on the training set and asked for the results. AI found no corresponding traces in the codebase, git history, or MEMORY.md. After the user interrupted the search and asked about files like evaluate_mimicgen.py, AI still found no relevant changes. The session ended with AI asking the user to describe the specific change, without resolution.

Token Usage

Summary

Metric Value
Total Tokens 2,398,136
Input Tokens 1,229
Output Tokens 4,345
Cache Creation 161,084
Cache Read 2,231,478
Cache Hit Rate 93.3%
Total Cost (USD) $0.4475

Model Breakdown

Model Input Output Cache Creation Cache Read Cost Share
claude-haiku-4-5-20251001 1,229 4,345 161,084 2,231,478 $0.4475 100.0%