Diagnosing After Disaster: The First Post-Crash Reconnaissance in an ML Training Pipeline

Introduction

In the course of a complex machine learning deployment spanning multiple weeks, infrastructure failures are not just possible—they are inevitable. This article examines a single message from an opencode coding session where an AI assistant, tasked with training an EAGLE-3 speculative decoding drafter for the Kimi-K2.5 language model, confronts the aftermath of a catastrophic storage failure. The message, indexed as <msg id=4195>, represents the first diagnostic step after a VM crash forced by a Ceph cluster running out of disk space, followed by a migration to a new 15TB NVMe drive. It is a moment of reconnaissance: the assistant must assess what survived, what broke, and what auto-started in the chaos of recovery.

The Message

The assistant writes:

75.8 GB per GPU, 0% util — something auto-loaded the model. Let me check what's running and the state of /data, /shared, and the SGLang code.

``bash ssh -o ConnectTimeout=10 root@10.1.230.174 'ps aux | grep -E "python3|sglang|vllm" | grep -v grep' ``

`` root 218 0.0 0.0 29448 16588 ? Ss 17:04 0:00 /usr/bin/python3 /usr/bin/networkd-dispatcher --run-startup-triggers root 239 0.0 0.0 107048 17204 ? Ssl 17:04 0:00 /usr/bin/python3 /usr/share/unattended-upgrades/unattended-upgrade-shutdown --wait-for-signal root 440 0.0 0.2 72455616 1058932 ? Ssl 17:04 0:34 /root/ml-env/bin/python3 -m vllm.entrypoints.openai.api_server --model /shared/kimi-k2.5-int4 --tensor-parallel-size 8 --tool-call-parse... ``

At first glance, this appears to be a simple diagnostic command. But beneath its surface lies a rich tapestry of reasoning, assumptions, and contextual knowledge that reveals how an experienced practitioner approaches infrastructure recovery in a high-stakes ML environment.

Why This Message Was Written: The Reasoning and Motivation

The immediate trigger for this message is the user's report in <msg id=4192>: the VM was killed because the Ceph cluster ran out of space, a new 15TB NVMe disk was attached directly to the host, the /data volume was migrated, and the container is back up. The user explicitly asks the assistant to "assess what happened inside, ideally restart the hidden state extraction." They also note that "something auto-started on GPUs, probably vllm/sglang from previous setup."

The assistant's previous message (<msg id=4194>) had already run nvidia-smi to check GPU state, revealing that all 8 GPUs had approximately 75.8 GB of memory allocated each, with 0% utilization. This is a critical data point: the model weights (the Kimi-K2.5-INT4 quantized model, approximately 75 GB when loaded with tensor parallelism across 8 GPUs) are occupying VRAM, but no computation is happening. This is the classic signature of a server that loaded its weights at boot but is not serving any requests—likely because its startup scripts ran but the hidden state extraction client never reconnected.

The motivation for this specific message is therefore twofold. First, the assistant needs to confirm which process is occupying the GPUs. The nvidia-smi output shows memory allocation but not process identity. Second, the assistant needs to determine whether the auto-started process is the SGLang server needed for extraction, or the vLLM server from a previous deployment phase. These are fundamentally different tools with different codebases, different patch requirements, and different startup procedures. Mistaking one for the other could lead the assistant down a rabbit hole of debugging the wrong server.

The assistant also signals a broader intention: "Let me check what's running and the state of /data, /shared, and the SGLang code." This is a triage plan. The three items—running processes, disk state, and code integrity—represent the three pillars of recovery. Without all three confirmed, restarting extraction would be premature.

How Decisions Were Made: The Diagnostic Approach

The decision to run ps aux filtered for Python processes is a textbook first step in Unix system recovery, but the specific filter choices reveal domain expertise. The assistant filters for python3, sglang, and vllm—the three process families that could legitimately consume GPU memory in this environment. The grep -v grep exclusion is standard practice to avoid matching the grep command itself.

The choice of ssh with -o ConnectTimeout=10 is also telling. The previous message (<msg id=4194>) had already established that the remote host is reachable and responsive. The 10-second timeout is a safety measure: if the host had become unreachable between messages (possible given the recent infrastructure instability), the command would fail gracefully rather than hanging indefinitely.

The assistant does not immediately jump to killing processes or restarting services. This restraint is a deliberate decision. In a post-crash scenario, the first priority is always information gathering, not action. The assistant needs to understand what state the system is in before making any changes. This is especially important because the user mentioned that the container "auto-started" something—the assistant cannot assume that the auto-started process is harmful. It might be a systemd service or a Docker restart policy that the user intentionally configured.

Assumptions Made

Several assumptions underpin this message, some explicit and some implicit.

The most visible assumption is that the GPU memory allocation (75.8 GB per GPU) corresponds to a model load, not some other memory use. This is a reasonable inference: 75.8 GB is approximately the size of the Kimi-K2.5-INT4 model when loaded with 8-way tensor parallelism (the model is ~600B parameters, quantized to INT4, so ~300 GB total, divided across 8 GPUs gives ~37.5 GB per GPU for weights alone, plus KV cache and activation memory). The assistant assumes this is the same model from the previous deployment phase.

A subtler assumption is that the ps aux output will definitively identify the culprit. In practice, GPU memory can be allocated by processes that have already exited (orphaned CUDA contexts), or by processes that are not visible in ps aux (e.g., running in a different container or namespace). The assistant implicitly assumes a standard Linux process model where the GPU-using process is still alive and visible.

The assistant also assumes that the SSH connection will be fast enough to return within the timeout. Given the recent disk migration and potential I/O pressure, this is not guaranteed. The ConnectTimeout=10 mitigates this but does not eliminate the risk.

Perhaps the most important assumption is that the hidden state extraction data survived the crash. The extraction had been running for approximately 18,400 out of 37,312 samples (49%) before the crash, producing ~1.6 TB of hidden state files. If the Ceph failure corrupted the data, or if the migration to the new NVMe disk lost files, the entire extraction might need to be restarted from scratch. The assistant's plan to check /data state implicitly acknowledges this risk, but the current message does not yet verify data integrity.

Mistakes or Incorrect Assumptions

In this specific message, there are no clear mistakes. The diagnostic command is appropriate, the reasoning is sound, and the output confirms the hypothesis. However, one could argue that the assistant is being too cautious. The user explicitly said "something auto-started on GPUs, probably vllm/sglang from previous setup" and asked to "restart the hidden state extraction." The assistant could have immediately killed the vLLM process and restarted SGLang, saving a round trip. Instead, it chose to gather more information first.

This is not a mistake, but it is a design philosophy choice. The assistant prioritizes correctness over speed. In a production environment where a wrong kill could corrupt state or leave the system in an unrecoverable state, this is the right call. But in a time-sensitive training pipeline where every hour of GPU idle time costs money, the user might prefer a more aggressive approach.

The assistant also assumes that the auto-started process is the only thing wrong. It does not yet check whether the SGLang source code (which had been patched for hidden state extraction) survived the crash, or whether the Python environment is still intact. These checks come in subsequent messages, but they are absent from this one.

Input Knowledge Required

To fully understand this message, the reader needs knowledge spanning several domains:

Infrastructure context: The system has 8 NVIDIA RTX PRO 6000 Blackwell GPUs, each with 96 GB of VRAM. The model being deployed is Kimi-K2.5-INT4, a ~600B parameter MoE model quantized to 4-bit precision. The hidden state extraction pipeline uses a patched version of SGLang that dumps intermediate layer activations to /dev/shm/sglang_hs/ during inference.

Session history: The extraction had been running for approximately 18,400 out of 37,312 samples before the crash, at a steady rate of 1.09 samples/s. The training dataset is 100K synthetic prompts, and the extracted hidden states occupy approximately 3.5 TB. The assistant had previously tuned the SGLang server for maximum throughput and applied a custom patch to deepseek_v2.py to capture auxiliary hidden states.

Tool knowledge: The reader must understand that SGLang and vLLM are both inference serving frameworks, but they have different APIs, different speculative decoding implementations, and different codebases. The hidden state extraction patch was applied to SGLang specifically, so if vLLM auto-started instead of SGLang, the extraction server would need to be stopped and SGLang started fresh.

Unix process management: The ps aux output shows process IDs, start times, CPU time, and command lines. The reader must interpret PID 440 (started at 17:04, 34 seconds of CPU time) as a recently started process, and the command line /root/ml-env/bin/python3 -m vllm.entrypoints.openai.api_server --model /shared/kimi-k2.5-int4 --tensor-parallel-size 8 as a vLLM server loading the quantized Kimi-K2.5 model with 8-way tensor parallelism.

Output Knowledge Created

This message produces several concrete pieces of knowledge:

vLLM auto-started, not SGLang: The running process is vLLM (specifically vllm.entrypoints.openai.api_server), not SGLang. This is critical because the hidden state extraction patch was applied to SGLang's deepseek_v2.py file. The vLLM server cannot perform extraction without its own patch, which was never developed. Therefore, the vLLM process must be killed and replaced with the patched SGLang server.
The model weights are intact: The vLLM server successfully loaded /shared/kimi-k2.5-int4, confirming that the model directory survived the crash and disk migration. If the model had been corrupted, vLLM would have failed to start or would have crashed immediately.
The Python environment is functional: The vLLM server started successfully and has been running for 34 seconds without crashing. This confirms that the ml-env virtual environment, with its PyTorch, CUDA, and other dependencies, survived the reboot.
The auto-start mechanism is a systemd service or Docker restart policy: The vLLM process started at 17:04, which is approximately when the container came back up. This timing strongly suggests a systemd service file or Docker restart: always policy that launches vLLM on boot. This knowledge will be useful later when the assistant needs to prevent the auto-start from interfering with the SGLang extraction server.
No extraction data was lost in the crash: This is not yet confirmed by this message, but the fact that vLLM can load the model from /shared suggests that the shared filesystem survived. The /data volume (which holds the extracted hidden states) was migrated to the new NVMe disk, so its contents should be intact. The assistant will verify this in subsequent messages.

The Thinking Process

The assistant's reasoning is visible in the structure of the message itself. The opening line—"75.8 GB per GPU, 0% util — something auto-loaded the model"—is a synthesis of the nvidia-smi output from the previous message. The assistant is connecting two data points: the memory allocation (75.8 GB) and the utilization (0%). The memory allocation tells the assistant that a model is loaded; the utilization tells the assistant that no inference is happening. Together, they imply a server that started but is not receiving requests.

The phrase "something auto-loaded the model" is deliberately vague. The assistant does not yet know whether it is SGLang, vLLM, or even a custom script. The ps aux command is designed to resolve this ambiguity.

The assistant then outlines its broader plan: "check what's running and the state of /data, /shared, and the SGLang code." This is a mental checklist. The three items correspond to three risks:

Running processes: Is the GPU-using process the right one (SGLang) or the wrong one (vLLM)?
Disk state: Did the hidden state data survive the crash and migration?
Code integrity: Did the patched SGLang source code survive? Was the patch reverted by the reboot? The assistant does not execute all three checks in this message. It starts with the most urgent: identifying the rogue process. The disk and code checks will come in subsequent messages, once the process situation is understood. The choice of grep -E "python3|sglang|vllm" is also revealing. The assistant knows that the GPU-using process must be a Python process (because both SGLang and vLLM are Python-based). By filtering for these three terms, the assistant covers all likely candidates while excluding irrelevant Python processes like system scripts.

Conclusion

Message <msg id=4195> is a masterclass in diagnostic restraint. In the aftermath of a catastrophic storage failure and VM crash, the assistant does not panic, does not rush to restart services, and does not make assumptions about what survived. Instead, it methodically gathers information, starting with the most critical unknown: which process is occupying the GPUs.

The message reveals a practitioner who understands that in complex ML infrastructure, the most dangerous action is often the first one. By confirming that vLLM auto-started (not SGLang), the assistant sets the stage for the next steps: killing the vLLM process, verifying data integrity, and restarting the patched SGLang server for hidden state extraction. The extraction will resume from where it left off, thanks to the script's resume capability (skipping existing .pt files), and the training pipeline will continue.

This message also serves as a reminder that ML engineering is not just about training models—it is about building systems that survive failure. The hidden state extraction script's resume capability, the separation of model weights on /shared and extracted data on /data, and the assistant's methodical diagnostic approach all contribute to a system that can recover from infrastructure disasters with minimal data loss. The crash cost time, but it did not cost progress.