Your code. In the spotlight.

A source profiler for Python and Spark

📜 Overview

General Information
Repository
Build
Code

💡 Introduction

Profile Python source line by line, including notebook cells and Spark actions. Reports contain source snapshots, timings, memory measurements, and definition links in one HTML file. Library work is attributed to the calling project line.

📦 Install

Python 3.11–3.15 is supported. Install in your script or notebook's environment:

uv pip install -U linescope

Extra	Adds
linescope[notebook]	IPython cell magics and inline reports
linescope[scalene]	CPU sampling and GPU metrics on Python 3.11–3.14
linescope[full]	Both extras

For unreleased changes:

uv pip install -U git+https://github.com/tvdboom/linescope.git@main

Spark and Databricks use the runtime's PySpark and SDK. Enable Spark observation with spark=True or --spark.

🚀 Use

💻 Terminal

linescope --memory --output report.html your_script.py
linescope --output report.html -m your_package.your_module

The report opens in a browser. Add --display none to save without opening it. Put profiler options before the target; subsequent arguments go to your program.

🐍 Python

from linescope import profile

with profile(memory=True, display="none") as session:
    total = sum(value * value for value in range(100_000))

session.save("report.html")

Trace is the default. Select backend="scalene" for sampling or GPU collection, or backend="tachyon" for sampling on Python 3.15.

📓 Notebook

%load_ext linescope

%%profile --display cell-summary

total = sum(value * value for value in range(100_000))

📘 Documentation

Relevant links
⭐ About	See what LineScope measures and how reports present it.
🚀 Getting started	Install LineScope and generate your first report.
👨‍💻 User guide	Choose a backend, configure profiling, and read reports.
📓 Notebooks and Databricks	Profile individual cells or a whole notebook session.
⚡ Spark	Trace Spark actions back to source lines and interpret plan metrics.
✨ AI suggestions	Request performance suggestions for profiled code from a report.
🎛️ API reference	Look up Python APIs, configuration, and result models.
💻 CLI reference	Look up command-line options for profiling scripts and modules.
❔ FAQ	Find answers to common profiling questions.
🔧 Contributing	Set up a development environment and run the project checks.
🌳 Dependencies	Review required packages and optional integrations.
📃 License	Read the MIT license terms.