Daily Report — 2026-02-27
Daily Overview
- What was done: Audited shared disk resources, configured Slurm GPU allocation workflows, and analyzed M14 baseline evaluation results.
- How it was done: Executed systematic shell commands for filesystem and cluster state inspection, leveraged AI code search tools, and synthesized session logs into actionable strategies.
- Impact: Established stable compute pipelines for future training jobs and validated the critical distribution shift challenge requiring targeted domain adaptation.
Conducted cluster storage audits and GPU scheduling analysis while reviewing baseline policy evaluation metrics for the Error Recovery Benchmark project.
Tasks
Architecture & Strategy
- ✅ HDD_POOL Storage Audit — Analyzed directory sizes across user accounts in the XYFS01 shared system to identify top consumers and estimate total usage.
- ✅ Slurm GPU Node Configuration — Explored available partitions, command options, and secure connection methods to prepare for AI training workloads.
- ✅ M14 Baseline Evaluation Review — Investigated current evaluation scripts and results for Pi0/BC-RNN policies on error recovery scenes.
Problems & Solutions
Critical Issues
1. Direct SSH to Slurm GPU nodes is blocked by cluster security policies.
Solution: Implemented salloc/tmux and sbatch workflows instead, verified via documentation checks.
Key Insight: Cluster relies on pam_slurm_adopt enforcing job-bound access; session persistence requires terminal multiplexers or batch submission rather than direct network connections.
General Issues
2. Scanning massive directories caused severe timeouts during storage audit.
Solution: Used parallel background processes with explicit timeout limits and depth-limited du commands to gather partial results safely.
Key Insight: Lustre filesystem metadata operations are highly I/O intensive; safe fallback strategies and partitioned scanning are necessary for large-scale audits.
Human vs AI Approaches
Strategic Level
Evaluation dataset configuration scope
| Role | Approach |
|---|---|
| Human | User recalled a previous instruction to switch evaluation to the training set and expected immediate execution based on implicit context. |
| AI | AI searched filesystem history, found no record of such a change, analyzed existing config files, and asked for precise technical definition regarding seed resets vs data splits. |
Difference Analysis: Human operated from high-level project planning memory; AI strictly followed stateless verification protocols, highlighting the gap between intended pipeline changes and actual codebase implementation status.
AI Limitations
General Limitations
- Failed to accurately estimate sizes of extremely large directories due to filesystem I/O timeouts, requiring iterative fallback strategies instead of automated completion.
- Lacks persistent memory of past vague instructions, leading to confusion when asked about prior code modifications without explicit file references.
Learnings
Key Learnings
- Baseline policies achieving near-zero success on injected error scenes confirms a severe distribution shift, validating the architectural pivot toward human-in-the-loop fine-tuning (M15).
- Cluster security policies like pam_slurm_adopt strictly gate compute node access; stable jobs require multiplexers or batch scripts rather than direct network connections.
Conversation Summaries
XYFS_HDD_Storage_Audit
✅ HDD_POOL Storage Audit 04:28:09.784 | claude_code User requested a comprehensive analysis of shared storage allocation. AI executed recursive directory scans, successfully analyzed personal usage (~29GB), and systematically audited other users’ directories. Identified several massive directories causing timeouts, estimated ~15TB confirmed plus >10TB pending, and highlighted that 81% of the filesystem is utilized.
ErrorRecoveryBenchmark
✅ M14 Baseline Results Review 05:57:24.584 | claude_code Investigated previous baseline evaluations for Pi0, Pi0.5, and BC-RNN policies on error recovery tasks. AI located output logs in outputs/evaluation_logs/, compiled metrics showing near-zero success rates across all models, and confirmed that out-of-distribution error scenes break standard policy generalization.
✅ Slurm & GPU Node Setup 05:18:36.275 | claude_code Explored cluster environment to prepare for AI training workloads. AI verified available Slurm commands, queried partition states, and identified idle A800 nodes in ai and xy-a800 partitions. Discovered that direct SSH is blocked by security policies, recommending salloc paired with tmux for stable resource allocation.
🔍 Pi0.5 Evaluation Scope Clarification 07:57:48.342 | claude_code User inquired about modifying evaluation to use the training set, referencing past instructions. AI performed deep codebase searches for evaluation scripts and training configs, found current setups already use MimicGen tasks, but lacked context on the specific pipeline change requested, prompting clarification on seed resets vs data splits.