Notebook integrations

Google Colab (cloud)

Typical behavior:

  1. Start the backend in Colab.

  2. Open a raw TCP bore tunnel from Colab.

  3. Start Studio locally and bridge to Colab with weightslab tunnel.

  4. Open Studio locally and control the remote run in real time.

Colab-side startup (inside notebook):

import weightslab as wl

# Starts gRPC backend and auto-deploys a bore tunnel endpoint.
# The notebook output prints: weightslab tunnel bore.pub:<port>
wl.serve(serving_grpc=True, serving_bore=True)

# Keep backend available while you interact from local Studio
wl.keep_serving()

Alternative Colab flow (manual tunnel command):

bore local 50051 --to bore.pub

Local machine flow:

# Terminal 1: Studio UI
weightslab start

# Terminal 2: bridge to Colab endpoint printed by the notebook
weightslab tunnel bore.pub:12345

See Weights Studio UI and User Commands Reference for tunnel details.

Local Jupyter Notebook

From the Studio landing page (no backend connected), you can bootstrap or reopen a local notebook workflow. This runs as a standalone local Jupyter server process.

In-training notebook (UI embedded)

When a backend is connected, Studio can open its embedded notebook panel. This notebook runs inside the training process and sees live objects directly (df, model, optimizer, checkpoints).

Python code execution in cells:

import matplotlib.pyplot as plt

# Explore available signal columns
signal_cols = [c for c in df.columns if c.startswith("signals")]
print(signal_cols[:10])

# Example: quick stats for loss-like columns
loss_cols = [c for c in signal_cols if "loss" in c.lower()]
print(df[loss_cols].describe().T[["mean", "std", "min", "max"]])

# Example: plot recent values for the first available loss-like signal
if loss_cols:
    c = loss_cols[0]
    recent = df[c].dropna().tail(200)
    plt.figure(figsize=(8, 3))
    plt.plot(recent.values)
    plt.title(f"Recent values for {c}")
    plt.xlabel("Recent samples")
    plt.ylabel("Signal value")
    plt.grid(alpha=0.25)
    plt.show()

Agent query cells (> prompts):

> Generate Python code to plot training loss statistics over the last 200 samples
> including mean, std, min, max, and one line chart.

The assistant writes the code into the cell; then you run that generated code as a normal Python cell.

See the “Integrated Notebooks” section of Weights Studio UI.