scalene
High-performance CPU, GPU, and memory profiler for Python with AI-powered optimization
GraphCanon updated 2w · GitHub synced 2w
Decision brief
Scalene is a high-performance CPU, GPU, and memory profiler for Python that uses AI to suggest optimizations.
Good fit when
- When you need precise profiling of both CPU and GPU performance in Python applications
- For optimization suggestions powered by artificial intelligence
Avoid when
- If your project does not involve Python, as Scalene is specific to this language
- Avoid if your system lacks necessary dependencies like Visual C++ Redistributable on Windows
Observed Jul 17, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Very active (2d since push)
- As of 2w
- Provenance
- Not a fork · Organization account
- As of 2w
- Security (OSV)
- 18 low (18 low)
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install scaleneSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Scalene profiles the performance of Python programs on CPUs and GPUs, offering precise profiling with suggestions for optimization.
Capability facts
- CLI
- CLI entrypoint
Source: pyproject.toml:[project.scripts] · Aug 4, 2026
- Languages
- python
Source: github.language+pyproject.toml · Aug 4, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 4, 2026)
python3 -m pip install -U scaleneSource link
Source: README excerpt (regex_v1, Aug 4, 2026)
Using the Scalene VS Code Extension:Source link
Tags
README
Quick Start
Installing Scalene:
python3 -m pip install -U scalene
or
conda install -c conda-forge scalene
Using Scalene:
After installing Scalene, you can use Scalene at the command line, or as a Visual Studio Code extension.
Using the Scalene VS Code Extension:
First, install the Scalene extension from the VS Code Marketplace or by searching for it within VS Code by typing Command-Shift-X (Mac) or Ctrl-Shift-X (Windows). Once that's installed, click Command-Shift-P or Ctrl-Shift-P to open the Command Palette. Then select "Scalene: AI-powered profiling..." (you can start typing Scalene and it will pop up if it's installed). Run that and, assuming your code runs for at least a second, a Scalene profile will appear in a webview.
Commonly used command-line options:
Scalene uses a verb-based command structure with two main commands: run (to profile) and view (to display results).
---
## Installation
<details open>
<summary>Using <code>pip</code> (Mac OS X, Linux, Windows, and WSL2)</summary>
Scalene is distributed as a `pip` package and works on Mac OS X, Linux (including Ubuntu in [Windows WSL2](https://docs.microsoft.com/en-us/windows/wsl/wsl2-index)) and Windows platforms.
> **Note for Windows users**
>
> Starting with Scalene 2.0, Windows supports full memory profiling. If you
> encounter issues, ensure you have the [Visual C++ Redistributable](https://aka.ms/vs/17/release/vc_redist.x64.exe)
> installed. If building from source, you will need Visual C++ Build Tools and CMake.
>
You can install it as follows:
```console
% pip install -U scalene
or
% python3 -m pip install -U scalene
You may need to install some packages first.
See https://stackoverflow.com/a/19344978/4954434 for full instructions for all Linux flavors.
For Ubuntu/Debian:
% sudo apt install git python3-all-dev
Using conda (Mac OS X, Linux, Windows, and WSL2)
% conda install -c conda-forge scalene
Scalene is distributed as a conda package and works on Mac OS X, Linux (including Ubuntu in Windows WSL2) and Windows platforms.
Note for Windows users
Starting with Scalene 2.0, Windows supports full memory profiling. If you encounter issues, ensure you have the Visual C++ Redistributable installed.
On ArchLinux
You can install Scalene on Arch Linux via the AUR
package. Use your favorite AUR helper, or
manually download the PKGBUILD and run makepkg -cirs to build. Note that this will place
libscalene.so in /usr/lib; modify the below usage instructions accordingly.
For agents
This page has a .md twin and JSON over the API.