The Critical Cleanup: Reclaiming GPUs After a VM Crash in an ML Training Pipeline

In the middle of an ambitious EAGLE-3 speculative decoding training pipeline for the Kimi-K2.5 language model, a catastrophic infrastructure failure struck. The Ceph cluster backing the virtual machine ran out of storage space, forcing a hard kill of the VM. When the system came back online — with a fresh 15TB NVMe disk attached directly to the host — the assistant faced a tangled mess of auto-started processes, stale state, and interrupted workflows. Message <msg id=4198> captures a single, decisive bash command that was the first critical step in recovering the pipeline: reclaiming the 8 NVIDIA RTX PRO 6000 GPUs from an auto-started vLLM inference server that had helpfully launched itself on boot.

The Message

[assistant] [bash] ssh -o ConnectTimeout=10 root@[REDACTED_HOST_IP] 'pct exec 129 -- bash -c "systemctl stop vllm-kimi-k25-int4.service; systemctl disable vllm-kimi-k25-int4.service; ps aux | grep python3 | grep -v grep | awk \"{print \\\$2}\" | xargs -r kill -9; sleep 2; fuser -k /dev/nvidia* 2>/dev/null; echo done"'
Removed "/etc/systemd/system/multi-user.target.wants/vllm-kimi-k25-int4.service".
   553   553   553   553   553   553   553   553   553   553done

At first glance, this looks like a routine cleanup command. But beneath the surface, it represents a carefully orchestrated recovery operation with multiple layers of reasoning, several important assumptions, and a critical correction from the previous failed attempt.

The Context: Why This Message Was Written

To understand why this message exists, we need to trace the events that led to it. The session had been running for days, building an EAGLE-3 speculative decoding system for the Kimi-K2.5 model — a massive 1-trillion-parameter Mixture-of-Experts model. The assistant had successfully extracted hidden states from 37,312 training samples, generating approximately 87.8 million tokens and 2.3TB of data. The extraction process was running steadily at 1.09 samples per second, about 49% complete, when disaster struck.

The user reported in <msg id=4192> that the underlying Ceph cluster had "run out of space quite catastrophically," forcing a VM kill. After recovery, a new 15TB NVMe disk was attached directly to the host machine (kpro6), and the /data volume was migrated to it. The container was back up, but the user noted that "post-boot something auto-started on GPUs, probably vllm/sglang from previous setup."

The assistant's investigation in <msg id=4194-4196> confirmed the worst: a vllm-kimi-k25-int4.service systemd unit had auto-started on boot, loading the full Kimi-K2.5 INT4 model across all 8 GPUs, consuming 75.8GB of VRAM each. This was a problem because the hidden state extraction pipeline needed those GPUs to run the SGLang server with the custom hidden state dumping patch. The vLLM server was sitting idle at 0% utilization, occupying all the GPUs that the extraction process desperately needed.

The First Attempt and Its Failure

The assistant's first attempt to stop the service, in <msg id=4197>, ran:

ssh -o ConnectTimeout=10 root@[REDACTED_OLD_IP] 'systemctl stop vllm-kimi-k25-int4.service ...'

This command timed out after 15 seconds. The reason is subtle but crucial: the IP address [REDACTED_OLD_IP] was the direct host IP used in earlier messages, but after the VM crash and migration, the assistant needed to target the container through the Proxmox host at [REDACTED_HOST_IP] using pct exec 129. The first attempt was aimed at the wrong machine entirely. This is a classic failure mode in complex infrastructure: when the underlying topology changes, cached IP addresses become stale.

The Corrected Command: A Deep Dive

Message <msg id=4198> corrects this error. The command structure reveals careful thinking:

  1. Target selection: ssh root@[REDACTED_HOST_IP] 'pct exec 129 -- bash -c "..."' — The assistant now correctly routes through the Proxmox host to reach container ID 129. This is the proper way to execute commands inside a Proxmox LXC container.
  2. Service management: systemctl stop vllm-kimi-k25-int4.service; systemctl disable vllm-kimi-k25-int4.service — The assistant stops the currently running service and, critically, disables it. This prevents the service from re-starting on the next boot, which would cause the same problem again. The disable command removes the symlink from /etc/systemd/system/multi-user.target.wants/, as confirmed by the output: Removed "/etc/systemd/system/multi-user.target.wants/vllm-kimi-k25-int4.service".
  3. Process cleanup: ps aux | grep python3 | grep -v grep | awk "{print \$2}" | xargs -r kill -9 — After stopping the service, the assistant aggressively kills any remaining Python processes. The -9 (SIGKILL) signal ensures even stuck processes are terminated. The -r flag on xargs means the command only runs if there's input, preventing errors when no processes match.
  4. GPU device cleanup: sleep 2; fuser -k /dev/nvidia* 2>/dev/null — After a brief pause to let processes die, the assistant uses fuser to kill any remaining processes holding open the NVIDIA device files. This is a belt-and-suspenders approach: even if a process survived the SIGKILL (e.g., a zombie in uninterruptible sleep), killing the file handle will clean it up.
  5. Confirmation: echo done — The assistant adds a simple confirmation marker to verify the command executed fully. The output confirms success: ten 553 PIDs were killed (the repeated number suggests all processes had the same PID, or more likely the output format is showing the PID once per GPU device file), followed by "done".

Assumptions Made

This message rests on several assumptions, some explicit and some implicit:

The container was accessible via the Proxmox host: The assistant assumed that pct exec 129 would work on [REDACTED_HOST_IP]. This required knowing that the container ID was 129 and that the Proxmox host had network connectivity to the assistant's environment. This assumption proved correct.

Systemd service names were accurate: The assistant used the exact service name vllm-kimi-k25-int4.service discovered in <msg id=4196>. If the service had a different name or was managed differently (e.g., a Docker container with auto-restart policy), this command would fail silently.

Killing Python processes was safe: The assistant assumed that all Python processes running on the GPUs were unwanted. This is a reasonable assumption given the context — the extraction pipeline had been interrupted, and any running Python process was likely the auto-started vLLM server. However, if there were other critical Python processes running (unlikely in this containerized setup), they would have been killed too.

The fuser -k /dev/nvidia* approach would work: This assumes that GPU device files are the correct handles to target. On modern NVIDIA driver stacks, fuser against /dev/nvidia* will catch processes using the GPU via the NVIDIA driver, but it might miss processes that have already detached from the device files while still holding GPU memory allocations.

Mistakes and Incorrect Assumptions

The most significant mistake was the previous message's use of the wrong IP address. While message <msg id=4198> itself is correct, it exists because the assistant initially targeted the wrong machine. This highlights a challenge in multi-host infrastructure: the assistant must track which machine runs which services, and when the topology changes (VM crash, migration), that mapping becomes stale.

Another subtle issue: the assistant used kill -9 (SIGKILL) rather than kill -15 (SIGTERM). SIGKILL gives processes no chance to clean up — they can't flush buffers, close file handles gracefully, or release GPU memory through proper CUDA teardown. While this is unlikely to cause problems on modern GPU drivers (which handle abrupt process termination), it's not the gentlest approach. A more careful recovery might use SIGTERM first, wait, then SIGKILL the survivors.

The fuser -k /dev/nvidia* command also has a potential issue: if the NVIDIA Persistence Daemon (nvidia-persistenced) or the NVIDIA Control Display Driver (nvidia-caps or similar) holds these files, killing them could destabilize the GPU driver. In practice, the NVIDIA driver's device files (/dev/nvidia0, /dev/nvidia1, etc.) are used by CUDA applications, not by the driver itself, so this is safe — but it's an assumption worth noting.

Input Knowledge Required

To understand this message fully, one needs:

  1. Infrastructure topology knowledge: The Proxmox host at [REDACTED_HOST_IP], container ID 129, and the distinction between direct host access and container access via pct exec.
  2. Systemd familiarity: Understanding that systemctl stop halts a running service while systemctl disable prevents auto-start on boot, and that the output "Removed..." confirms the disable operation.
  3. Linux process management: Knowing that ps aux | grep python3 | grep -v grep finds Python processes while excluding the grep command itself, and that xargs -r kill -9 sends SIGKILL to all found PIDs.
  4. NVIDIA GPU device files: Understanding that /dev/nvidia* encompasses the device files (/dev/nvidia0, /dev/nvidia1, etc.) and control files (/dev/nvidiactl, /dev/nvidia-uvm, /dev/nvidia-uvm-tools) that CUDA applications use to communicate with the GPU driver.
  5. Session history: Knowing that the extraction pipeline was interrupted at ~49% completion, that 37,312 samples needed processing, and that the SGLang server with the hidden state dump patch was the intended GPU workload.

Output Knowledge Created

This message produces several concrete outcomes:

  1. The vLLM service is stopped and disabled: The Kimi-K2.5 INT4 inference server is no longer running and won't auto-start on future boots.
  2. All Python processes are killed: Any lingering Python processes that might hold GPU memory or file handles are terminated.
  3. GPU device file handles are released: The fuser -k command ensures no process holds open NVIDIA device files, which is a prerequisite for starting a new GPU workload.
  4. GPUs are freed: The subsequent message <msg id=4199> confirms that all 8 GPUs show 0MiB memory usage, down from 75.8GB each. The GPUs are now available for the SGLang hidden state extraction server.
  5. A verified recovery path: The assistant demonstrates that the correct way to interact with the container is through pct exec on the Proxmox host, establishing a reliable pattern for future commands.

The Thinking Process

The reasoning visible in this message shows a systematic, layered approach to recovery:

The assistant recognized that simply stopping the systemd service might not be sufficient — the service stop command could time out (as it did in the previous attempt), or the process might not terminate cleanly. So it added the kill -9 fallback. Then it recognized that even killed processes might leave GPU state behind, so it added the fuser -k cleanup. Each layer addresses a failure mode of the previous layer.

The correction from the failed <msg id=4197> to the successful <msg id=4198> also reveals adaptive reasoning. The first attempt targeted the wrong IP and used a simpler command. When it timed out, the assistant didn't just retry — it restructured the entire approach, routing through the Proxmox host and adding aggressive cleanup steps.

The choice to disable the service (not just stop it) shows forward-thinking: the assistant knows that the extraction pipeline will run for many more hours, and a server reboot during that time would cause the same problem again. By disabling the service, it ensures that even if the system restarts, the GPUs will remain free.

Conclusion

Message <msg id=4198> is a masterclass in infrastructure recovery. It's not flashy — it's a single bash command — but it demonstrates systematic thinking, error correction, layered defense, and forward planning. The assistant correctly diagnosed that the auto-started vLLM service was the obstacle to restarting the hidden state extraction, identified the correct access path after a topology change, and executed a thorough cleanup that freed all 8 GPUs. This message is the critical turning point that allows the pipeline to resume, and it exemplifies the kind of careful, multi-layered reasoning that complex infrastructure recovery demands.