Visualization¶

ROSE does not ship a plotting module — visualizing a run's progress is left to whichever tool already fits your workflow: raw IterationState fields, the native file tracker, or an MLflow/ClearML dashboard if tracking.backend is already wired up.

Real-time, in-loop¶

Every iteration yields an IterationState with iteration, metric_value, and metric_history (the full list of past metric values so far):

import matplotlib.pyplot as plt

async for state in learner.start(max_iter=20):
    print(f"[iter {state.iteration}] mse={state.metric_value:.4f}")

plt.plot(state.metric_history)
plt.xlabel("iteration")
plt.ylabel("mse")
plt.show()

This is the fastest path when you just want a quick look right after a local run.

Native file tracker¶

For HPC runs where you want a durable, preemption-safe record, attach HPC_FileTracker (see examples/integrations/tracking/basic.py) — it appends one JSON line per iteration:

learner.add_tracker(HPC_FileTracker("run.jsonl"))

Replay and plot after the run, or from a different machine entirely:

import pandas as pd

df = pd.read_json("run.jsonl", lines=True)
iterations = df[df.event == "iteration"]
iterations.plot(x="iteration", y="mse", logy=True)

Any numeric value your tasks return as a dict (e.g. n_labeled, train_mse) is captured automatically and available as its own column.

MLflow / ClearML dashboards¶

If tracking.backend: mlflow or tracking.backend: clearml is set in your spec (or you attach MLflowTracker/ClearMLTracker directly in the Python API), every iteration's metrics are already logged with no extra code. The respective web UIs are the recommended way to compare runs and overlay parallel-learner series:

• MLflow integration — mlflow ui --port 5000, metric curves per run, run comparison.
• ClearML integration — Scalars tab, parallel learners shown as separate series under the same title.