flameprof

library0.4pypypi✓ verified 23d ago

Flameprof is a Python library and command-line tool that generates interactive SVG flame graphs from `cProfile` statistics. It visualizes function call hierarchies and their CPU time consumption, offering a compact and understandable alternative to raw `cProfile` output or tools like `gprof2dot`. The current version is 0.4. Releases are sporadic, with major updates occurring infrequently.

observability testing

pip install flameprof

github ↗package ↗

INSTALL

IMPORT

SIG · FLAMEPROF

flameprof

observabilitypythonv0.4

Install

2.4s avg

Import

—

Disk

17MB

Pass rate

10/ 10

Env Coverage10 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.4 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.95 runs

installs and imports cleanly · install 0.0s · import 0.000s · 19.2MB

glibc

py 3.10–3.95 runs

installs and imports cleanly · install 2.4s · import 0.000s · 20MB

17MB installed

● package 17MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

flameprof CLI

✓ flameprof input.prof > output.svg

Flameprof is primarily used as a command-line tool to process cProfile output files, rather than through direct Python imports for its core functionality.

First, create a Python script (`my_app.py`) that you wish to profile. Use Python's built-in `cProfile` module to run your application and save the profiling data to a `.prof` file. Then, use the `flameprof` command-line tool to convert this `.prof` file into an interactive SVG flame graph. Open the generated SVG file in a web browser to view the visualization.

# my_app.py import cProfile import time def func_a(): time.sleep(0.1) def func_b(): time.sleep(0.05) func_a() def main(): for _ in range(3): func_b() func_a() # Profile the application profile_file = 'my_app.prof' cProfile.run('main()', profile_file) # To generate the flame graph, run in your terminal: # flameprof my_app.prof > my_app_flamegraph.svg # Then open my_app_flamegraph.svg in a web browser.

flameprof --version

Debug

Known issues

gotchaFlameprof is primarily a command-line utility. While it is a Python package, its main functionality is exposed via the `flameprof` executable after installation, not typically through direct Python `import` statements for generating flame graphs.

fix

Always invoke `flameprof` from your terminal or shell scripts after generating a cProfile output file.

affects: All versions

gotchaThe input file for `flameprof` must be a statistics file generated by Python's `cProfile` module (e.g., created with `cProfile.run('my_func()', 'output.prof')` or `python -m cProfile -o output.prof myscript.py`). It does not natively support output from other profiling tools like `perf` or `py-spy` without prior conversion.

fix

Ensure your profiling data is in the `cProfile` format. If you need to profile other sources, consider converting their output to a compatible format or using different flame graph generators.

affects: All versions

gotchaUsers have reported instances where `flameprof` can produce 'impossible stacks' or `ZeroDivisionError: float division by zero` when processing certain `cProfile` data, especially with unusual call patterns or highly optimized code.

fix

If encountering these errors, try simplifying the profiled code or updating to the latest `flameprof` version. For critical debugging, consider alternative profiling visualization tools if the issue persists.

affects: 0.3, 0.4

Upgrade

Version history

0.4latest on PyPI · released Dec 25, 2018

Audit

Dependencies

No dependency data recorded yet.

Agent activity

21 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/baverman/flameprof ↗

packagehttps://pypi.org/project/flameprof/ ↗