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memray

Profile Python memory allocations and generate temporal flamegraph HTML reports.

383|92|Updated Apr 30, 2022
Search Tags:#performance analysis#memory profiling#flamegraph#memray#python debugging#temporal allocations
One-click install
npx skills add https://github.com/scverse/spatialdata --skill memray
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: memray
Source: https://github.com/scverse/spatialdata/tree/main/.claude/skills/memray
Command: npx skills add https://github.com/scverse/spatialdata --skill memray

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It helps you pinpoint where and when a Python program allocates memory so you can diagnose leaks, spikes, or inefficient allocation patterns instead of guessing.

Core Features & Use Cases

  • Profile memory over time: Use temporal flamegraphs to understand allocation behavior across the run, not just peak usage.
  • Generate a browser-friendly report: Produce an interactive HTML flamegraph from a recorded allocation trace.
  • Practical debugging workflow: Compare reports between runs to validate whether changes reduced churn or leaks.

Use Case: You run a data processing script in spatial omics and suspect memory growth during specific steps; this skill profiles allocations and visualizes where the memory pressure originates.

Quick Start

Profile your script by running it with the memray-run step (for example: pixi run -e profiling memray-run your_script.py).

Frequently Asked Questions about memray

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I diagnose a memory leak in a Python data pipeline?▼

A temporal flamegraph visualizes memory allocation behavior across the entire runtime of a Python program, helping you understand memory growth and allocation spikes over time rather than just showing peak usage.

How do I profile Python memory allocations on macOS and Linux?▼

To profile Python memory allocations on macOS and Linux, run your script using the memray-run command in the pixi profiling environment to record an output trace file.

Can I generate a browser-friendly report from a Python allocation trace?▼

Yes, you can generate a browser-friendly report by converting a recorded .bin trace file into an interactive HTML flamegraph using the memray-flame tool, which opens directly in your browser.

What is a temporal flamegraph and how does it help debug memory churn?▼

A temporal flamegraph visualizes memory allocation behavior across the entire runtime of a Python program, helping you understand memory growth and allocation spikes over time rather than just showing peak usage.

Does this memory profiling approach work for spatial omics batch scripts?▼

Yes, this memory profiling approach works for spatial omics batch scripts by tracking allocation behavior during specific data processing steps to identify where memory pressure originates.

How do I validate if changes reduced memory churn in a Python script?▼

You can validate if changes reduced memory churn by comparing temporal flamegraph reports between different runs of your Python script to observe the differences in allocation patterns.

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