The Art of the Graceful Resume: Restarting a 2.3TB Hidden State Extraction After Infrastructure Collapse

In the sprawling conversation of an opencode coding session, most messages are dense with novel action — debugging a new error, implementing a fresh feature, or making a strategic pivot. But some of the most revealing messages are the quiet ones: the restart after a crash. Message [msg 4212] is precisely such a moment. On its surface, it is a single bash command that relaunches a Python extraction script. But beneath that simplicity lies a carefully orchestrated resumption of a pipeline that had been violently interrupted by a Ceph storage cluster running out of space, a VM kill, a disk migration, and a full system reboot.

The Context: A Pipeline Interrupted

To understand why this message exists, one must appreciate what preceded it. The session had been engaged in a massive data engineering effort: extracting hidden states from the Kimi-K2.5 model (a 1-trillion-parameter MoE architecture) across 37,312 training samples, each producing multi-gigabyte tensor files containing the model's internal representations. These hidden states would serve as training data for an EAGLE-3 draft model — a speculative decoding drafter designed to accelerate inference. The extraction had been running for many hours, steadily consuming samples at ~1.09 samples per second, writing tensors to a 3.5TB working directory, when disaster struck.

The user reported: "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."

This is the nightmare scenario for any long-running ML pipeline. A storage backend failure cascades into a VM kill, which means all in-memory state is lost, all processes are terminated, and any files that were being written at the moment of failure may be corrupted. The extraction had reached approximately 18,424 out of 37,312 samples — 49.4% complete — when the system went down.

The Recovery: Systematic Verification Before Action

Before message [msg 4212] could be written, the assistant executed a meticulous recovery protocol spanning messages [msg 4193] through [msg 4211]. This protocol is worth examining because it reveals the assumptions and knowledge required to understand the eventual restart command.

First, the assistant verified that the GPUs were usable. Upon reboot, a systemd service had auto-started vLLM, loading the Kimi-K2.5 model across all 8 GPUs and consuming ~76GB of memory each. The assistant killed the process, disabled the service, and confirmed that all GPUs showed 0 MiB usage — clean and ready.

Second, the assistant verified data integrity. The new 12TB NVMe disk showed 2.4TB used out of 12TB available. The hidden states directory contained 2.3TB of data spread across 10 subdirectories (rows_0-2000 through rows_18000-20000). A manual count confirmed 18,421 .pt files — matching the pre-crash count of 18,424 minus 3 known errors from earlier in the run. The merged dataset (train.jsonl) and its stats.json were intact. No corruption was detected.

Third, the assistant verified the software stack. The SGLang hidden state dump patch — a critical modification to deepseek_v2.py that enables capturing intermediate activations — was still applied (the /root filesystem survived reboot). However, the extraction script itself had been in /tmp, which was wiped. The assistant SCP'd a fresh copy. The /dev/shm tmpfs was clean and empty.

Fourth, the assistant restarted the SGLang server with the HS dump environment variable (SGLANG_HS_DUMP_DIR=/dev/shm/sglang_hs), waited 680 seconds for it to load the model across 8 GPUs, and verified that the health endpoint returned 200 and that HS dump files appeared in /dev/shm.

Only after all four verification steps passed did the assistant proceed to message [msg 4212].## The Message Itself: A Single Command Bearing the Weight of Hours

The subject message reads:

HS dump working. Now launch extraction — it will auto-resume by skipping existing .pt files: ``bash ssh -o ConnectTimeout=10 root@10.1.230.174 'rm -rf /dev/shm/sglang_hs/req_*; nohup ~/ml-env/bin/python3 -u /tmp/02b_extract_hidden_states_sglang.py --prepared-data /data/eagle3/synth_100k/merged/train.jsonl --output-dir /data/eagle3/synth_100k/hidden_states --max-seq-len 8192 --hs-dump-dir /dev/shm/sglang_hs > /data/eagle3/synth_100k/logs/extraction2.log 2>&1 &' `` Extraction resumed

This is deceptively simple. The assistant communicates the key insight — "it will auto-resume by skipping existing .pt files" — in a single sentence, then executes a command that encapsulates hours of prior engineering work.

The Decisions Embedded in the Command

Every flag and argument in this command reflects a deliberate choice informed by the crash recovery:

rm -rf /dev/shm/sglang_hs/req_*: This clears any stale request directories from the previous server instance. During the earlier extraction, the HS dump mechanism created directories like req_22391 and req_22392 in shared memory. These are server-side request identifiers, not our sample indices, and leaving them could confuse the extraction script or cause naming collisions. The assistant explicitly cleans them before starting.

nohup ... &: The extraction is launched in a nohup'd background process, detached from the SSH session. This is essential because SSH connections can drop, and killing the SSH client would otherwise terminate the child process. Given that this extraction will run for approximately 4.8 more hours, process persistence is critical.

-u flag on Python: Unbuffered output ensures that log lines appear immediately in the log file rather than being buffered in memory. The previous extraction suffered from log lag — the tail command showed sample 18,420 while the server-side counter was at 22,391. Unbuffered output mitigates this diagnostic problem.

--prepared-data /data/eagle3/synth_100k/merged/train.jsonl: This references the merged dataset, which contains all 37,312 samples with their input token IDs, output token IDs, and reasoning structures. The assistant had verified earlier that this file and its stats.json were intact after the crash.

--output-dir /data/eagle3/synth_100k/hidden_states: This is the same output directory as before, containing the 18,421 already-extracted .pt files. The auto-resume logic works by checking for the existence of each sample's output file before processing it — if the file exists, the sample is skipped. This is why the assistant could confidently say "it will auto-resume."

--max-seq-len 8192: This limits the sequence length to 8,192 tokens. The earlier training had encountered a Triton shared-memory OOM at 16,384 tokens, forcing a reduction. This parameter reflects that painful lesson.

--hs-dump-dir /dev/shm/sglang_hs: Points to the tmpfs directory where the SGLang server writes hidden state tensors. The extraction script polls this directory for completed requests, reads the tensors, and saves them to the output directory. Using /dev/shm (RAM-backed) for the intermediate dump avoids disk I/O contention during the extraction's hottest path.

Logging to extraction2.log: The 2 suffix distinguishes this log from the original extraction.log, which contained the pre-crash run. This is a small but wise decision — it preserves the historical record while starting fresh diagnostics.

Assumptions Made

The assistant makes several assumptions in this message, all grounded in the preceding verification:

The extraction script's auto-resume logic works correctly. The script checks for existing .pt files before processing each sample. This assumes the naming convention is deterministic (sample index maps to a predictable filename) and that partially-written files from the crash won't be mistaken for complete ones. The assistant had verified that all 18,421 existing files were complete (same size pattern as other directories), so this assumption is well-supported.
The SGLang server remains healthy. The extraction script sends requests to localhost:8000 and expects the server to respond with generated tokens and hidden state dumps. The assistant verified the health endpoint and confirmed HS dump files were appearing, but a server crash during the 4.8-hour extraction would silently kill the pipeline.
The 3 errors from the previous run won't recur. The original extraction had 3 errors (samples with missing files in rows 4000-10000). The assistant doesn't explicitly handle these — they'll either be re-extracted successfully or fail again. The assumption is that the errors were transient (e.g., network hiccups or OOM spikes) rather than systematic.
The new NVMe disk can sustain the write throughput. The previous extraction wrote 2.3TB of data. The new disk has 9.4TB available, so capacity is fine. But the assistant assumes the sequential write pattern of ~1 MB/s per sample won't overwhelm the disk or cause I/O bottlenecks that stall the extraction process.
The model weights on /shared are unchanged. The assistant verified the config.json exists but didn't checksum the model shards. If the crash caused silent corruption on the shared storage, the server might produce incorrect hidden states. This is a low-probability risk given that /shared is on a ZFS rpool, but it's an unverified assumption nonetheless.## The Thinking Process: What the Assistant Doesn't Say But Implies The assistant's reasoning in this message is a masterclass in operational pragmatism. Consider what is not said: There is no attempt to diagnose why the extraction stopped at 18,424. The assistant had observed that the process was in state Dl (uninterruptible sleep, waiting on disk I/O) and that the log hadn't advanced for hours. The likely cause was the Ceph storage failure — the process was blocked trying to write to a filesystem that had become unresponsive. When the VM was killed, the process simply ceased to exist. The assistant correctly recognizes that the root cause (Ceph exhaustion) has been resolved by the disk migration, so re-running from the same point is safe. There is no attempt to salvage or re-process the 3 failed samples. The assistant could have written special logic to retry them, but instead accepts the 99.98% success rate (18,421 out of 18,424 attempted) and moves on. This is a deliberate tradeoff: spending engineering time on 0.02% of data would delay the overall pipeline with negligible benefit. There is no verification that the new extraction will produce identical hidden states to the old one. The model weights are the same, the server configuration is the same, and the input prompts are the same. But floating-point accumulation in GPU kernels can produce bit-level differences across runs, especially with different CUDA graph configurations or NCCL settings. The assistant implicitly assumes that any such differences are negligible for training the EAGLE-3 drafter — a reasonable assumption given that the drafter will see many samples and learn statistical patterns, not exact values.

Input Knowledge Required

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

SGLang's hidden state dump mechanism: The SGLANG_HS_DUMP_DIR environment variable triggers a patch in the model's forward pass that saves intermediate activations (the "auxiliary" hidden states from multiple transformer layers) to disk. Without knowing this, the purpose of /dev/shm/sglang_hs is opaque.

The EAGLE-3 training pipeline: The extracted hidden states serve as training targets for a speculative decoding drafter. The drafter learns to predict the base model's hidden states from a compressed representation, enabling faster autoregressive generation. The scale — 37,312 samples, 87.8M tokens, 2.3TB of tensors — reflects the drafter's need for diverse training data covering the model's behavior across many contexts.

The crash recovery context: The message references "auto-resume" behavior that is only meaningful if you know the extraction script checks for existing output files. The assistant had previously designed this script with resumability in mind — a foresight that now saves hours of re-computation.

Infrastructure constraints: The use of /dev/shm (tmpfs) for intermediate storage, the NCCL environment variables for inter-GPU communication, the --tp-size 8 for tensor parallelism across 8 GPUs, and the --mem-fraction-static 0.88 for memory management all reflect the specific hardware configuration (8× RTX PRO 6000 Blackwell GPUs with 96GB each, connected via PCIe).

Output Knowledge Created

This message produces several forms of knowledge:

A restartable pipeline pattern: The combination of (1) deterministic output filenames, (2) existence-check-based skipping, and (3) clean separation between intermediate (tmpfs) and persistent (NVMe) storage creates a template for fault-tolerant data extraction pipelines. Any ML engineer facing a similar crash recovery scenario could adapt this pattern.

Validation of the recovery procedure: The systematic verification protocol — check GPUs, check data integrity, check software state, restart server, verify server health, launch extraction — is itself a reusable artifact. The assistant demonstrated that even after a catastrophic infrastructure failure (Ceph exhaustion → VM kill → disk migration → reboot), a long-running extraction can be resumed with zero data loss.

Confidence in the pipeline's robustness: By successfully resuming from 49.4% completion without special handling, the assistant proved that the extraction script's design decisions (deterministic naming, idempotent output, graceful skipping) were correct. This confidence carries forward into the training phase, where the same dataset will be used to train the EAGLE-3 drafter.

Mistakes and Incorrect Assumptions

While the message is largely sound, a few potential issues deserve examination:

The log file naming could cause confusion: Using extraction2.log instead of appending to extraction.log means the historical record is split across two files. If someone later needs to trace the full extraction timeline, they'll need to merge timestamps from both files. A better approach might have been to continue appending to the original log with a clear restart marker.

No explicit error handling for the 3 previously failed samples: The auto-resume logic will attempt to re-extract these samples. If they fail again due to a systematic issue (e.g., a specific input format that crashes the server), the extraction will hang or produce 3 fewer samples than expected. The assistant doesn't set up monitoring or alerting for this edge case.

The assumption that /dev/shm cleanup is sufficient: The rm -rf /dev/shm/sglang_hs/req_* command clears request directories, but it doesn't verify that the SGLang server's internal state is clean. If the server had any residual state from the pre-crash run (e.g., cached KV entries in GPU memory), the new extraction might encounter unexpected behavior. However, since the server was fully restarted (not just resumed), this risk is minimal.

No verification of disk write performance: The new NVMe disk showed 9.4TB available, but the assistant didn't benchmark write throughput. If the disk is significantly slower than the previous Ceph-backed storage, the extraction could become I/O-bound and take much longer than the estimated 4.8 hours. In practice, a local NVMe should outperform networked Ceph, so this assumption likely holds — but it's unverified.

The Broader Significance

Message [msg 4212] is a testament to the importance of designing for failure. The extraction script's auto-resume capability wasn't a feature added in response to the crash — it was built from the start, anticipating exactly this scenario. The assistant's recovery protocol — verify, clean, restart, verify again — mirrors the principles of chaos engineering: assume infrastructure will fail, and design systems that survive.

In the context of the full session, this message marks the transition from recovery to forward progress. After hours of debugging, killing rogue processes, checking disk integrity, and waiting for server initialization, the assistant finally issues the command that puts the pipeline back on track. The remaining 18,891 samples will be extracted over the next ~4.8 hours, and the EAGLE-3 training pipeline will proceed to achieve 74.7% validation accuracy — a result that would have been impossible without this graceful resume.

The quiet confidence of "HS dump working. Now launch extraction — it will auto-resume by skipping existing .pt files" belies the complexity of what came before. It is the voice of an engineer who has verified every link in the chain and trusts the system to hold.