The Art of Visualizing Progress: Writing a Training Plotter for EAGLE-3

A Single Message That Bridges Monitoring and Understanding

In the middle of a marathon EAGLE-3 training session on a cluster of RTX PRO 6000 Blackwell GPUs, a seemingly small event occurs: the assistant writes a Python plotting script. The message itself is deceptively brief — a single tool call that creates a file at /home/theuser/glm-kimi-sm120-rtx6000bw/eagle3-train/plot_training.py, followed by the laconic confirmation "Wrote file successfully." Yet this message, <msg id=4301>, represents a critical inflection point in the conversation: the moment when raw numerical metrics are transformed into visual insight, when the assistant shifts from passive monitoring to active analysis, and when the human collaborator's request for a graph crystallizes into executable code.

The Context: A Training Run in Full Swing

To understand why this message was written, we must first appreciate the situation that produced it. The assistant and user have been engaged in an intensive multi-day effort to train an EAGLE-3 speculative decoding drafter for the Kimi-K2.5 language model. After overcoming a cascade of obstacles — a VM crash and disk migration, a Triton shared-memory out-of-memory error at sequence length 16384, DataLoader packing inefficiencies, and multiple rounds of OOM debugging — the training is finally running smoothly.

The current run, launched in <msg id=4287>, uses 4 GPUs with batch_size=8, max_seq_len=8192, and ttt-steps=5 (Tree-of-Thoughts-with-Temperature depth). The key metrics from <msg id=4298> show remarkable progress: after only 60% of epoch 1, the drafter is already achieving 74-81% next-token prediction accuracy (full_acc_0), with conditional accuracy staying above 50% even at depth 4. The estimated acceptance length is ~3.2-3.5 tokens — a dramatic improvement over the previous 10K-sample drafter's 2.1 tokens.

The user's request in <msg id=4299> — "graph learning progress so far" — is deceptively simple. It asks for a visual representation of the training dynamics, but the underlying need is deeper: to understand whether the model is genuinely learning, whether convergence is stable, whether there are signs of overfitting or plateaus, and whether the trajectory justifies continuing the full 5-epoch plan. The assistant has been providing textual summaries of metrics, but a graph reveals patterns — loss curves, accuracy trends, the relationship between different TTT depths — that numbers alone cannot convey.

The Message Itself: What Was Written

The subject message <msg id=4301> contains a single tool call:

[assistant] [write] /home/theuser/glm-kimi-sm120-rtx6000bw/eagle3-train/plot_training.py
Wrote file successfully.

The actual content of plot_training.py is not displayed in the message — the conversation data only shows the tool invocation and its result. However, we can infer its structure from the subsequent execution in <msg id=4302>:

python3 /home/theuser/glm-kimi-sm120-rtx6000bw/eagle3-train/plot_training.py /tmp/train_metrics_raw.txt /tmp/eagle3_training_progress.png

The script takes two command-line arguments: an input file containing the raw training metrics (already fetched from the remote server in <msg id=4300>) and an output path for the generated PNG image. The input file contains 16,868 lines of JSON-formatted metric dictionaries, each recording loss and accuracy values for all 5 TTT steps at a given training iteration.

The Reasoning Behind the Script Design

The assistant's decision to write a dedicated Python script rather than using an inline plotting command or a Jupyter notebook reveals several assumptions and design choices.

First, the choice of matplotlib/seaborn over lighter alternatives. The script almost certainly uses matplotlib (and likely seaborn for aesthetics) — the standard tools for ML training visualization. This assumes the environment has these libraries installed, which is reasonable given that the machine is a full ML development workstation with PyTorch and associated tooling.

Second, the decision to parse raw log lines rather than use a structured logging framework. The training script (04_train.py) outputs metrics as JSON-formatted log lines via the speculators.metrics logger. The plotting script must parse these lines, extract the JSON dictionaries, and organize them by TTT depth. This is a pragmatic choice: it avoids modifying the training pipeline to add native plotting support, and it allows the plotter to work with any log file retroactively.

Third, the choice of PNG output over interactive formats. A static PNG image is the simplest way to share results in a terminal-based workflow. The assistant could have generated an interactive HTML plot or a PDF, but PNG is universally viewable, can be displayed inline in the chat interface, and is lightweight for a single training curve.

Fourth, the assumption that 16,868 lines contain 4,217 usable steps. The execution output in <msg id=4302> reveals: "Parsed 4217 steps (from 16868 total metric lines)". The 4:1 ratio (16,868 / 4 = 4,217) confirms that the script correctly accounts for the 4-GPU distributed training setup — each training step produces one metric line per rank, and the script deduplicates or averages them. This is a non-trivial parsing decision that shows the assistant understood the data's structure.

Input Knowledge Required

To write this script, the assistant needed to know:

  1. The log format: Each line is a JSON dictionary under the [speculators.metrics] prefix, containing keys like train.loss_0, train.full_acc_0, train.cond_acc_0, etc., for TTT steps 0 through 4.
  2. The distributed training structure: With 4 GPUs using torchrun, each rank logs independently, so the script must handle 4× redundancy in the raw data.
  3. The training configuration: The script needs to know that ttt-steps=5 means there are 5 prediction depths (0 through 4), each with its own loss and accuracy metrics.
  4. The output format: PNG at a specific path, suitable for display in the conversation interface.
  5. The file system layout: The script is written to a local path (/home/theuser/.../plot_training.py), not to the remote server, because the raw metrics have been copied locally in <msg id=4300>.

Output Knowledge Created

This message produces two kinds of output:

Immediate output: The plot_training.py script itself — a reusable tool for visualizing EAGLE-3 training progress. This script can be run again at any point during or after training to generate updated graphs.

Downstream output: The PNG image generated in <msg id=4302> and displayed in <msg id=4303>. This image becomes the basis for the user's understanding of training dynamics and informs decisions about whether to continue training, adjust hyperparameters, or deploy the checkpoint.

More broadly, the message creates knowledge about the training trajectory — not just the current metrics, but their evolution over time. A loss curve reveals whether the model is still learning (decreasing loss), plateauing (flat loss), or overfitting (diverging validation loss). Accuracy curves across TTT depths show whether deeper speculation is improving or degrading relative to shallower predictions.

The Thinking Process: What We Can Infer

While the subject message does not contain explicit reasoning (it is a tool call, not a narrative response), the thinking behind it is visible in the surrounding conversation.

In <msg id=4298>, the assistant provides a detailed textual analysis of the training metrics, computing estimated acceptance length from the accuracy values. This shows the assistant is already thinking about how to interpret the numbers. When the user asks for a graph, the assistant immediately understands the need: to visualize the trends that the textual summary only hints at.

The assistant's thinking likely proceeds as follows:

  1. "The user wants to see learning curves. I need to extract the metrics from the log file, parse them, and plot them."
  2. "The raw data is on the remote server — I already fetched it locally in the previous step (msg 4300)."
  3. "I need a script that handles the JSON parsing, averages across ranks, and plots loss and accuracy for each TTT depth."
  4. "I'll write it to the project directory so it can be reused."
  5. "After writing, I'll execute it and display the result." This chain of reasoning is implicit but clear from the sequence of actions: fetch data, write parser, execute parser, display result.

Assumptions and Potential Mistakes

The script makes several assumptions that could be wrong:

Assumption: All metric lines are valid JSON. If the training log contains truncated or malformed JSON lines (e.g., from concurrent logging), the parser could fail or produce incorrect results. The execution shows success, so this assumption held.

Assumption: The 4:1 line-to-step ratio is consistent. The script assumes exactly 4 ranks log each step. If a rank crashes and restarts (which happened earlier in the session), the ratio could be off, leading to incorrect step counting.

Assumption: Loss and accuracy are monotonically improving. The script likely plots raw values without smoothing. If the training has high variance (common with small batch sizes or noisy data), the curves could be jagged and hard to interpret.

Assumption: PNG is sufficient. A static image cannot show interactive details like hover-over values or zoom. For deep analysis, the user might prefer an interactive plot or the raw data itself.

No obvious mistakes are visible in the message or its execution — the script ran successfully and produced a valid image. However, one subtle issue is that the script was written to the local machine but the training is running on a remote server (10.1.230.174). The metrics were copied locally in <msg id=4300>, so this is correct, but it means the script cannot be run directly on the remote server without modification.

The Broader Significance

This message, for all its brevity, represents a fundamental pattern in ML engineering: the transition from data collection to data understanding. The assistant had been monitoring training through raw metrics and textual summaries — watching loss values scroll by, checking GPU utilization, computing acceptance length estimates. But the user's request for a graph forced a shift from monitoring to analysis, from numbers to insight.

The act of writing plot_training.py is also an act of tool-building. Rather than manually plotting with a one-off command, the assistant creates a reusable script that can be invoked at any future point. This is characteristic of effective ML workflows: automation is invested in early, so that analysis can be repeated effortlessly.

Furthermore, the script serves as documentation. Its parsing logic encodes the structure of the training log format, making explicit what was previously implicit. Anyone reading the script can understand exactly which metrics are tracked and how they relate to the training configuration.

Conclusion

Message <msg id=4301> is a quiet but pivotal moment in a complex ML training session. It is the point where raw data becomes visual knowledge, where monitoring becomes understanding, and where a one-off request becomes a reusable tool. The assistant's decision to write a dedicated Python script — rather than an ad-hoc plotting command — reflects a deeper understanding of the ML workflow: that visualization is not a one-time event but an iterative process, and that the tools for insight should be as reusable as the models they help us understand.

In the context of the full conversation, this message is the bridge between the "doing" phase (training the model) and the "knowing" phase (understanding what was trained). It is a reminder that in machine learning, the code we write to see our models is just as important as the code we write to build them.