Python Panels

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Writing a panel

• Run (or Cmd/Ctrl+Enter) executes the draft. Nothing runs as you type.
• The first run boots the Python runtime, which takes 10–30 seconds. Later runs reuse it and take about 2 seconds. Changing the Packages list starts a fresh runtime.
• Save becomes available after one successful run. It stores the code in the dashboard’s configuration.
• The Templates menu has six starter panels: a metric line chart, a run comparison table, a correlation heatmap, per-run subplots, a logs viewer and a leaderboard. Picking one replaces your draft code and packages, and asks first if you have unsaved edits.

import streamlit as st
import mlop

ctx = mlop.get_context()
st.title(f"{ctx.project}: {len(ctx.runs)} runs selected")

df = await mlop.get_metrics(metrics=["train/loss"])
st.line_chart(df, x="step", y="value", color="run_name")

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Reading run data

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Packages

• Preinstalled: numpy, pandas, pillow, altair and their dependencies.
• Add through the Packages field: matplotlib, seaborn (which pulls in matplotlib) and plotly. Up to 20 entries.

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Security

• A panel runs in a sandboxed iframe with no access to your cookies, browser storage or the surrounding page, and no network access.
• Data reaches the panel only through the mlop calls above. Pluto runs each one as a read-only query under your own session, scoped to the organization and project you are viewing. A panel can never read data you couldn’t already see, and can never change anything.
• Panels on a shared dashboard run in each viewer’s browser with that viewer’s access. Treat panel code like any other dashboard content: review a panel’s code before relying on it.

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Panels on the dashboard

• Panels load when scrolled into view, with progress shown while the runtime loads, packages install and the script runs.
• When you change the selected runs:
  • with Re-run automatically when the selected runs change enabled, the panel re-runs about half a second after you stop clicking;
  • otherwise it keeps showing its last result and displays a Runs changed — Refresh button.
• Fullscreen keeps the panel running, so it doesn’t restart. Press Esc or the button again to exit.
• Panels follow the app’s light/dark theme. Switching theme restarts each panel, which takes about 10 seconds.

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Limitations

• Data calls must be awaited. Wrapping them in an ordinary synchronous function won’t work; use top-level await or an async def.
• matplotlib and seaborn figures are static images (st.pyplot). For interactive charts use st.plotly_chart, st.line_chart or Altair.
• Startup time: the first render takes 10–30 seconds; later reruns take about 2 seconds. The browser caches the runtime after the first panel you load.
• Memory: each panel is its own Python runtime of about 100–200 MB, so keep the number of panels on one dashboard modest.
• Fixed package set: only the packages listed above.
• Size: code is limited to 64 KB per panel.
• AI assistants read panels as code. The MCP dashboard tools return a panel’s source rather than an image of its output, because rendering it would mean running your Python on the server.