Restarting Synthetic Data Generation: Fixing Timeouts in the EAGLE-3 Training Pipeline

The Message

ssh root@10.1.230.174 'nohup /root/ml-env/bin/python3 /root/eagle3-train/01b_generate_synthetic.py \
    --model-path /shared/kimi-k2.5-int4 \
    --output-dir /data/eagle3/synth_25k/prepared \
    --dataset mlabonne/open-perfectblend \
    --max-samples 25000 \
    --max-completion-tokens 8192 \
    --max-seq-len 16384 \
    --concurrency 128 \
    --vllm-url http://localhost:8000 \
    --min-completion-tokens 50 \
    > /data/eagle3/synth_25k/inference.log 2>&1 &
echo "PID: $!"'
PID: 337810

This single bash command, issued at message index 2913, represents the culmination of an intensive debugging cycle in the EAGLE-3 training pipeline for the Kimi-K2.5 model. On its surface, it is merely a script invocation—a Python data generation job launched via SSH on a remote machine. But in context, this message is a carefully calibrated restart of a critical data pipeline that had just failed with an 8% timeout rate, losing nearly 2,700 completed samples. The command encodes hard-won lessons about distributed inference at scale, the interplay between concurrency and latency in large language model serving, and the practical engineering decisions required to build high-quality synthetic training data for speculative decoding.

Why This Message Was Written: The Context of Failure

To understand why this particular message exists, one must trace back through the preceding conversation. The team was building an EAGLE-3 draft model—a lightweight speculative decoding head that predicts the next several tokens of the main Kimi-K2.5 model, allowing the large model to be evaluated less frequently and dramatically speeding up inference. Training such a draft model requires high-quality data: actual input-output pairs showing how the target model (Kimi-K2.5) responds to diverse prompts. The team had chosen the mlabonne/open-perfectblend dataset as their source of questions, and had written 01b_generate_synthetic.py to feed each question independently through the vLLM inference server running Kimi-K2.5, capturing both the model's reasoning trace and its final answer.

The initial run had used 200 concurrent requests (--concurrency 200) with the OpenAI Python client's default timeout of 60 seconds. This combination proved disastrous. At high concurrency, each individual request could take minutes to complete—the vLLM server was saturated with 200 in-flight requests, all competing for GPU compute and KV cache memory. Individual requests would queue, wait for their turn in the continuous batching loop, and then slowly generate their 8,000-token completions. Many exceeded the 60-second client timeout, raising exceptions that the script caught and logged as errors. By the time the user intervened (see [msg 2899]), the log showed 222 errors out of 2,700 completed samples—an 8% loss rate that would only worsen as average completion lengths climbed.

The user's observation was precise and actionable. They had been monitoring the vLLM metrics and noticed dramatic throughput dips—from 1,500 tok/s down to 182 tok/s, then 334 tok/s—which correlated with the timeout errors. Their suggestion to "tune to 128 parallel and increase timeout" was the key insight that shaped this restart command.

How Decisions Were Made: The Engineering Trade-offs

The assistant's response to the timeout crisis involved several interconnected decisions, each visible in the preceding messages ([msg 2900] through [msg 2912]).

Reducing concurrency from 200 to 128. This was not arbitrary. The assistant had previously benchmarked the Kimi-K2.5 INT4 model at various concurrency levels and found that peak throughput occurred around C=128, with performance degrading at higher concurrency due to KV cache pressure and scheduling overhead. By dropping to 128, the assistant traded raw throughput for stability—fewer concurrent requests means each request gets served faster, reducing the tail latency that causes timeouts. The measured throughput at C=128 was approximately 1,536 tok/s, which was close to the C=200 throughput of ~1,600 tok/s. The cost in throughput was minimal, but the benefit in reduced timeout probability was substantial.

Increasing the client timeout from 60s to 1800s (30 minutes). The default 60-second timeout in the OpenAI Python client is designed for interactive use, where a response is expected within seconds. For a batch generation job producing 8,000-token completions at high concurrency, 60 seconds was absurdly short. The 1800-second timeout was a generous upper bound—at 1,500 tok/s, an 8,000-token completion takes about 5.3 seconds of actual generation time, but at C=128 with queuing and scheduling delays, the wall-clock time could be several minutes. The 30-minute timeout effectively eliminated client-side timeout as a failure mode, shifting the reliability burden entirely to the server.

Adding streaming save and resume support. The original script wrote results only at the end, meaning all 2,700 completed samples were lost when the process was killed. The assistant rewrote the script to stream each result to a JSONL file as it completed, and to skip samples already present in that file on restart. This was a defensive engineering choice—it acknowledged that long-running batch jobs can and will fail, and that incremental persistence is essential for productivity.

Fixing the reasoning field extraction. A subtle bug had been discovered: the script was checking reasoning_content (the OpenAI API field name) instead of reasoning (the attribute name on the response message object in the vLLM implementation). This meant that even successful requests were not capturing the model's reasoning trace—the very data the pipeline needed most. The assistant fixed this and added logic to reconstruct the full token sequence with the correct special tokens (<|thinking|> at token 163606 and <|response|> at token 163607) wrapping the reasoning content.

Assumptions Made by the User and Agent

Several assumptions underpin this restart command, some explicit and some implicit.

That the vLLM server is stable at C=128. The assistant assumed that the server, which had been running for hours, would continue to serve requests without crashing or degrading. This was a reasonable assumption given the server's track record, but it was untested at the new concurrency level with the longer timeout.

That the 1800s timeout is sufficient. This assumed that no single request would take more than 30 minutes to complete. Given that the average completion was around 1,000 tokens and the maximum was 8,000, and that throughput was ~1,500 tok/s, this was a safe margin—but it did not account for pathological cases where a request might get stuck in the scheduler or encounter a hardware fault.

That the open-perfectblend dataset is representative of the distribution the EAGLE-3 model needs to handle. The assistant was generating training data from a single dataset of 25,000 questions. This assumed that the diversity of questions in open-perfectblend would produce a sufficiently broad distribution of reasoning traces and responses to train an effective draft model. If the dataset was biased toward certain topics or styles, the EAGLE-3 model would inherit that bias.

That the vLLM server's /data volume has sufficient space. The assistant had verified that /data had 2.8 TB free, but the hidden state extraction step (which would follow inference) could consume hundreds of gigabytes. The assumption was that the combined inference output (~500 MB of JSONL) and hidden states (potentially hundreds of GB) would fit within the available space.

Input Knowledge Required

To understand this message, one must grasp several layers of technical context.

The EAGLE-3 architecture. EAGLE-3 is a speculative decoding framework where a lightweight "draft" model predicts the next several tokens of a large "verifier" model. The draft model is trained on the hidden states of the verifier, learning to anticipate its outputs. This requires training data that pairs input prompts with the verifier's actual output tokens and their corresponding hidden states.

The vLLM serving stack. The Kimi-K2.5 INT4 model is deployed behind a vLLM inference server with an OpenAI-compatible API. The script communicates with this server via HTTP, sending chat completion requests and receiving responses. Understanding the relationship between concurrency, batch size, KV cache pressure, and tail latency is essential for diagnosing timeout issues.

The remote infrastructure. The command targets a machine at 10.1.230.174 with a Python environment at /root/ml-env/bin/python3, a model at /shared/kimi-k2.5-int4, and a 3TB data volume at /data. The assistant is working from a separate development machine and copying scripts via SCP before executing them remotely.

Output Knowledge Created

This message creates several forms of knowledge and artifacts.

The inference log at /data/eagle3/synth_25k/inference.log. This file captures the script's progress output, including per-sample status updates, error messages, and timing information. It serves as the primary monitoring channel for the generation run.

The raw responses file at /data/eagle3/synth_25k/prepared/raw_responses.jsonl. Each successfully completed request appends a JSON object containing the sample ID, the full response text (with reasoning and content separated), token counts, and timing metadata. This file is the input to the next pipeline step (hidden state extraction).

The prepared dataset directory. The script also tokenizes each response and saves it in a format suitable for training, including loss masks that mark which tokens should contribute to the training loss (only model-generated tokens, not the prompt).

The process ID (337810). This allows the assistant and user to monitor, kill, or adjust the process if needed.

The Thinking Process Visible in the Reasoning

The assistant's reasoning is visible across the messages leading up to this command. Several patterns stand out.

Diagnostic rigor. When the user reported timeouts, the assistant did not immediately change parameters. Instead, it killed the process, read the script to understand the current timeout configuration, identified the root cause (default 60s timeout), and then systematically addressed multiple failure modes in a single edit cycle. This reflects a methodical approach to debugging: understand the system before changing it.

Defensive engineering. The addition of streaming saves and resume support shows an awareness that long-running jobs are fragile. Rather than assuming the new configuration would work perfectly, the assistant built in resilience against future failures. This is a hallmark of production-oriented engineering.

Prioritization of data quality over quantity. The decision to reduce concurrency from 200 to 128, accepting a slight throughput reduction for dramatically improved reliability, reflects a clear understanding that the goal is not maximum throughput but maximum usable data. An 8% error rate means 8% of samples are wasted; reducing that to near-zero is worth a small throughput hit.

Calibrated optimism. The assistant estimated the total run time at ~5 hours based on the current throughput and average completion length. This estimate was shared with the user, setting expectations while acknowledging uncertainty ("the avg might go up as the dataset has harder questions later"). This balance of transparency and confidence is characteristic of effective technical communication.

Broader Significance

This message captures a pivotal moment in the EAGLE-3 training pipeline—the transition from a failed first attempt to a corrected second attempt. It illustrates a fundamental truth about large-scale ML engineering: the data pipeline is often the hardest part. Training a model is computationally expensive, but generating high-quality training data at scale requires solving a different set of problems: server reliability, timeout handling, incremental persistence, and careful parameter tuning.

The command also embodies a specific philosophy of working with large models: treat the inference server as a black box with known performance characteristics, and tune your client parameters to match those characteristics rather than fighting them. The assistant did not try to optimize the server (e.g., by adjusting vLLM's scheduling parameters or KV cache management); instead, it adapted the client's concurrency and timeout settings to work within the server's natural operating envelope. This is a pragmatic choice that respects the complexity of the serving stack and focuses effort where it has the highest leverage.

Finally, the message demonstrates the value of human-in-the-loop debugging. The user's observation of the throughput dips and their specific suggestion to reduce concurrency provided the key insight that the assistant operationalized. In a fully automated system, the timeout errors might have been logged and ignored, or the system might have retried indefinitely without changing parameters. The human ability to recognize a pattern—"the timeouts correlate with throughput dips at high concurrency"—and translate it into a concrete parameter change is precisely the kind of reasoning that current AI systems struggle to perform autonomously. The collaboration between user and assistant in this debugging cycle is a model of effective human-AI teamwork.