Assessment After Catastrophe: Verifying System Integrity Post-Crash in the EAGLE-3 Training Pipeline

The Message

ssh -o ConnectTimeout=10 root@10.1.230.174 'df -h /data /shared /root; echo "==="; ls /data/eagle3/synth_100k/ 2>/dev/null; echo "==="; ls /shared/kimi-k2.5-int4/config.json 2>/dev/null && echo "model ok" || echo "model MISSING"'
Filesystem                    Size  Used Avail Use% Mounted on
scratch/subvol-129-disk-0      12T  2.4T  9.4T  21% /data
rpool/data/shared             1.7T  761G  920G  46% /shared
rpool/data/subvol-129-disk-0  800G   37G  764G   5% /=
===
hidden_states
logs
merged
prepared
===
/shared/kimi-k2.5-int4/config.json
model ok

This single message, at first glance a routine filesystem check, is in fact a pivotal moment of triage after a catastrophic infrastructure failure. It represents the first clear signal that the entire EAGLE-3 training pipeline — weeks of work, terabytes of data, and thousands of dollars in compute — may have survived a VM crash and disk migration intact. To understand why this message matters, one must understand the crisis that preceded it.

The Crisis: When the Storage Layer Fails

The EAGLE-3 training pipeline for the Kimi-K2.5 model had been running for days. Hidden state extraction — the process of capturing the internal representations of a large language model across 37,312 training samples — was at the 49% mark when disaster struck. The Ceph distributed storage cluster underlying the virtual machine ran out of space "quite catastrophically," as the user put it. The VM was killed. A new 15TB NVMe drive was physically attached directly to the host machine (bypassing the failed Ceph layer), and the /data volume was migrated to it. The container was restarted.

When the user returned, they reported that GPUs showed no activity — something had interrupted the extraction. The assistant's first task was to assess the damage. This message is the culmination of that assessment, the moment when the assistant confirms that the critical data assets are still in place.

What the Command Actually Checks

The SSH command is deceptively simple, but it performs three distinct checks, each answering a specific question about system integrity:

1. Filesystem mounts and capacity (df -h /data /shared /root): The assistant needs to verify that the new NVMe disk is properly mounted at /data and that the other critical mount points (/shared for model weights, / for the root filesystem) are intact. The output confirms the new 12TB scratch volume at /data with 9.4TB available — a healthy 21% utilization. The /shared volume (1.7TB, 46% used) and root volume (800GB, 5% used) are also present. Critically, the filesystem name scratch/subvol-129-disk-0 confirms this is the new direct-attached NVMe, not the old Ceph-backed volume.

2. Data directory structure (ls /data/eagle3/synth_100k/): The extraction pipeline organizes data into several subdirectories: hidden_states (the actual extracted representations, ~3.5TB), logs (extraction progress logs), merged (the merged training dataset), and prepared (preprocessed data). The fact that all four directories exist means the data survived the crash and disk migration. This is the most important check — if hidden_states were missing, weeks of work would be lost.

3. Model weights availability (ls /shared/kimi-k2.5-int4/config.json): The target model — a 1-trillion-parameter Mixture-of-Experts model quantized to INT4 — must be present for extraction to resume. The config.json file is the canonical indicator that the model directory is intact. The output confirms "model ok."

The Thinking Process: What This Message Reveals

This message sits at a specific point in the assistant's reasoning chain. The assistant had already:

  1. Discovered that vLLM had auto-started on boot via a systemd service (the vllm-kimi-k25-int4.service), consuming all 8 GPUs with 75.8GB each
  2. Stopped and disabled that service, killed the processes, and confirmed GPUs were freed (0MiB usage across all GPUs)
  3. Now needed to verify that the data and model survived before proceeding to restart extraction The assistant is operating under several implicit assumptions: - The data survived the crash: The Ceph failure could have corrupted in-flight writes. The extraction process writes hidden states as individual .pt files (PyTorch tensors) to the filesystem. If a write was in progress when the VM died, that file could be corrupted. The assistant assumes the directory listing is sufficient evidence of integrity, though a deeper check (e.g., verifying file checksums or attempting to load a sample) might be warranted. - The new disk is reliable: The 15TB NVMe is a single point of failure now. Unlike the Ceph cluster which provided distributed redundancy, this is a single drive. The assistant doesn't question this trade-off — it accepts the new architecture. - The model weights are sufficient: Checking only config.json is a lightweight proxy for model integrity. The actual model consists of dozens of sharded weight files totaling ~600GB. If any of those were corrupted during the Ceph failure, extraction would fail at runtime. The assistant trusts that the model directory is intact based on this single file check.

Input Knowledge Required

To understand this message, one needs:

Output Knowledge Created

This message produces several critical pieces of knowledge:

  1. The new disk is properly mounted and has sufficient space: 9.4TB available on a 12TB drive. The extraction needs to write ~3.5TB of hidden states (already partially written), plus training will need temporary space. 9.4TB is ample.
  2. The data directories survived: hidden_states, logs, merged, and prepared all exist. This means the extraction can resume from where it left off, rather than starting from scratch.
  3. The model weights are accessible: The Kimi-K2.5 INT4 model is present at /shared/kimi-k2.5-int4. No model restoration is needed.
  4. The root filesystem is healthy: 764GB free on the 800GB root volume, indicating no filesystem corruption from the crash.

The Broader Significance

This message represents a turning point in the session. Before it, the assistant was in crisis mode — discovering auto-started processes, killing them, freeing GPUs. After it, the assistant can proceed to restart the extraction, confident that the infrastructure is sound. The message is the "all clear" signal.

The compactness of the command is notable. In three lines, the assistant checks storage, data integrity, and model availability — the three pillars on which the entire pipeline depends. This is a textbook example of triage: identify the minimal set of checks that can confirm whether a system is recoverable, execute them in parallel (via a single SSH command with chained shell operations), and interpret the results against domain knowledge.

The message also reveals a subtle but important operational principle: when recovering from infrastructure failure, verify the data before restarting the workload. The temptation would be to immediately restart the extraction script and hope for the best. Instead, the assistant methodically confirms that the foundation is solid before proceeding. This discipline — check first, then act — is what separates robust automation from fragile scripts.

For anyone studying the EAGLE-3 training pipeline, this message captures the moment of maximum uncertainty and its resolution. The data survived. The model survived. The pipeline can continue.