Infrastructure Collapse and Recovery: Restarting EAGLE-3 Hidden State Extraction After a Ceph Cluster Failure

The Message

Here is what happened: I had to kill the VM because the ceph cluster below the VM ran out of space quite catastrophically. New 15TB Nvme disk was attached directly to the host, kpro6, and the /data volume was moved to it. The container is up again, asses what happened inside, ideally restart the hidden state extraction; Seems like post-boot something auto-started on GPUs, probably vllm/sglang from previous setup.

This single message from the user, delivered at a critical juncture in a multi-day machine learning engineering effort, encapsulates an infrastructure disaster, a recovery plan, and a set of precise instructions for the AI assistant. It appears at message index 4192 in a long conversation spanning the development of an EAGLE-3 speculative decoding drafter for the Kimi-K2.5 large language model. To understand the weight of this message, one must appreciate what was at stake.

The Context: What Was Lost

At the moment this message was sent, the assistant and user were deep into the EAGLE-3 training pipeline. Hidden state extraction — the process of running 37,312 synthetic training samples through the Kimi-K2.5 model to capture internal representations — was approximately 49% complete. The extraction had been running for roughly 10 hours at a steady rate of ~1.09 samples per second (~2,582 tokens per second), producing 18,421 extracted samples consuming 2.3 TB of disk space. The process was humming along with an estimated 4.8 hours remaining. The assistant had been monitoring progress, checking logs, and verifying that the token-count matching approach was producing zero errors.

Then the Ceph cluster — the distributed storage layer underlying the VM — ran out of space "quite catastrophically." This is not a graceful degradation. When a Ceph cluster exhausts its storage capacity, writes fail, VMs can freeze or crash, and data corruption becomes a real risk. The user made the difficult but correct call: kill the VM before the storage failure caused irrecoverable data corruption.

Why This Message Was Written: The Reasoning and Motivation

The user's message serves multiple purposes simultaneously. First, it provides a post-mortem explanation of what happened — essential context for the assistant to understand the current state of the system. Second, it communicates the remediation taken: a new 15 TB NVMe drive was physically attached directly to the hypervisor host (kpro6), bypassing the failed Ceph layer entirely, and the /data volume was migrated to this new drive. Third, it gives clear instructions: assess the state inside the container and restart the hidden state extraction. Fourth, it flags a known issue — that something auto-started on the GPUs after boot, likely vLLM or SGLang from a previous systemd service configuration.

The motivation is straightforward but urgent. The hidden state extraction is the data foundation for training the EAGLE-3 drafter. Without it, no training can proceed. Every hour of downtime is an hour where the eight NVIDIA RTX PRO 6000 Blackwell GPUs (each with 96 GB of VRAM) sit idle. At an estimated cost of thousands of dollars per hour for this hardware, the user's urgency is palpable. The message is a call to action: assess, recover, resume.

Input Knowledge Required

To fully understand this message, one needs to grasp several layers of the technical stack. At the infrastructure level, one must understand that the VM was running on a Proxmox VE hypervisor with Ceph as the distributed storage backend. Ceph provides replicated block storage across multiple hosts, but when it runs out of space, all I/O operations stall or fail. The user mentions "kpro6" — the hostname of the hypervisor — and the fact that a new NVMe drive was attached directly to it, bypassing Ceph entirely. This is a significant architectural change: the /data volume moved from network-backed distributed storage to a local physical NVMe drive, which would actually improve I/O performance and reliability for the extraction workload.

At the application layer, one must understand the EAGLE-3 training pipeline. The hidden state extraction script (02b_extract_hidden_states_sglang.py) runs on a SGLang inference server that has been patched with a custom hidden state dump mechanism. The server loads the Kimi-K2.5 INT4 model across all 8 GPUs using tensor parallelism (--tp-size 8). The extraction script sends each training sample to the server, captures the model's internal hidden states from specific layers, and saves them as .pt files organized in row-batched subdirectories. The process is designed to be resumable — it checks for existing output files before processing each sample.

One also needs to understand the systemd service infrastructure. The user's observation that "something auto-started on GPUs" points to the presence of systemd services like vllm-kimi-k25-int4.service that were configured with Wants=multi-user.target or similar, causing them to launch automatically on boot. This is a common pattern for production ML deployments but becomes a problem when you need to repurpose the GPUs for a different workload.

The Assistant's Response: A Systematic Recovery

The assistant's response to this message (visible in the subsequent messages [msg 4193] through [msg 4211]) demonstrates a textbook disaster recovery procedure. The first action was to create a structured todo list with clear priorities: assess container state, kill auto-started GPU processes, verify /data volume integrity, re-apply the hidden state dump patch if needed, and restart the SGLang extraction server.

The assessment revealed that the vLLM service had indeed auto-started, consuming 75.8 GB per GPU with 0% utilization — the model was loaded but idle, wasting resources. The assistant identified the systemd service (vllm-kimi-k25-int4.service), stopped it, and disabled it to prevent future auto-starts. This was a critical step: without freeing the GPUs, the SGLang extraction server could not start.

The data integrity check showed that the hidden states survived the migration intact — all 18,421 samples were present across the row-batched subdirectories. The merged JSON data and stats files were also intact. The SGLang code patch for hidden state dumping was still applied (since /root is on persistent storage, not /tmp). The extraction script itself was lost (it was in /tmp, which is ephemeral), but the assistant was able to copy it from the development environment.

Assumptions and Potential Pitfalls

The user made several assumptions in this message, all of which proved correct. The assumption that "something auto-started on GPUs" was accurate — the vLLM service had indeed launched on boot. The assumption that the container was "up again" was correct. The assumption that the /data volume migration preserved the extracted data was validated.

However, there were implicit risks. The Ceph cluster running out of space "catastrophically" could have caused silent data corruption even if the files appeared intact. The assistant's subsequent checks (verifying file counts per subdirectory, checking for the 3 known errors from the previous run) were prudent validation steps. The fact that the extraction script was in /tmp and lost on reboot was a minor setback but easily remedied.

Output Knowledge Created

This message and the assistant's response created several important artifacts. First, a clear post-mortem of the infrastructure failure was documented in the conversation, providing a record for future reference. Second, the systemd service was disabled, preventing the same issue on future reboots. Third, the recovery procedure itself — assess, kill, verify, restart — became a reusable pattern. Fourth, the extraction was successfully restarted and would go on to complete all 37,312 samples with zero additional errors, ultimately producing the training data for the EAGLE-3 drafter that achieved 74.7% validation accuracy.

The Thinking Process

The user's thinking in this message is a model of concise technical communication under pressure. Rather than panicking or providing a stream of consciousness, they distilled the situation into four clear facts: the cause (Ceph OOM), the remediation (new NVMe, data migration), the current state (container is up), and the known issue (auto-started GPU processes). The instruction to "assess what happened inside" shows an understanding that the assistant needs to discover the current state independently rather than being told every detail. The phrase "ideally restart the hidden state extraction" communicates priority without micromanaging — it's a goal, not a step-by-step command.

This message also reveals the user's trust in the assistant. They provided high-level context and trusted the assistant to perform the detailed assessment and recovery. This trust was well-placed: the assistant's systematic recovery restored the extraction pipeline within minutes of receiving the message, and the full 37,312-sample dataset was completed without further incident.

Conclusion

Message 4192 is a pivotal moment in the EAGLE-3 training saga. It marks the transition from a catastrophic infrastructure failure to a successful recovery, demonstrating the resilience of well-designed ML pipelines when paired with systematic disaster recovery procedures. The user's clear communication and the assistant's methodical response turned a potential multi-day setback into a brief interruption, ultimately enabling the training of a high-quality speculative decoding drafter.