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Top 16 Python Profiling Projects
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scalene
Scalene: a high-performance, high-precision CPU, GPU, and memory profiler for Python with AI-powered optimization proposals
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viztracer
VizTracer is a low-overhead logging/debugging/profiling tool that can trace and visualize your python code execution.
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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python-benchmark-harness
A micro/macro benchmark framework for the Python programming language that helps with optimizing your software.
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profiling-code
Collection of examples and links that uses different profiling tools to show memory usage and timings.
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I collected a list of profilers (also memory profilers, also specifically for Python) here: https://github.com/albertz/wiki/blob/master/profiling.md
Currently I actually need a Python memory profiler, because I want to figure out whether there is some memory leak in my application (PyTorch based training script), and where exactly (in this case, it's not a problem of GPU memory, but CPU memory).
I tried Scalene (https://github.com/plasma-umass/scalene), which seems to be powerful, but somehow the output it gives me is not useful at all? It doesn't really give me a flamegraph, or a list of the top lines with memory allocations, but instead it gives me a listing of all source code lines, and prints some (very sparse) information on each line. So I need to search through that listing now by hand to find the spots? Maybe I just don't know how to use it properly.
I tried Memray, but first ran into an issue (https://github.com/bloomberg/memray/issues/212), but after using some workaround, it worked now. I get a flamegraph out, but it doesn't really seem accurate? After a while, there don't seem to be any new memory allocations at all anymore, and I don't quite trust that this is correct.
There is also Austin (https://github.com/P403n1x87/austin), which I also wanted to try (have not yet).
Somehow this experience so far was very disappointing.
(Side node, I debugged some very strange memory allocation behavior of Python before, where all local variables were kept around after an exception, even though I made sure there is no reference anymore to the exception object, to the traceback, etc, and I even called frame.clear() for all frames to really clear it. It turns out, frame.f_locals will create another copy of all the local variables, and the exception object and all the locals in the other frame still stay alive until you access frame.f_locals again. At that point, it will sync the f_locals again with the real (fast) locals, and then it can finally free everything. It was quite annoying to find the source of this problem and to find workarounds for it. https://github.com/python/cpython/issues/113939)
Project mention: Ask HN: C/C++ developer wanting to learn efficient Python | news.ycombinator.com | 2024-04-10* https://github.com/gaogaotiantian/viztracer get a timeline of execution vs call-stack (great to discover what's happening deep inside pandas)
It also lets you run tools like https://github.com/jrfonseca/gprof2dot on the profiling results to generate comprehensive flowcharts (call graph) for your program.
There is a "live" flamegraph TUI that uses Austin for those interested in Python profiling https://github.com/P403n1x87/austin-tui. The data collected can be exported and then converted into pprof, and analysed further with flameshow etc...
In this blog, I will present secimport — a toolkit for creating and running sandboxed applications in Python that utilizes eBPF (bpftrace) to secure Python runtimes.
Project mention: Show HN: I developed the most user-friendly Python profiling tool available | news.ycombinator.com | 2023-10-29
Here are a few tools that are actively maintained that can help you understand and profile the performance of your Python code, from Django apps to Celery workers to desktop GUI apps:
NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
Python Profiling related posts
- Memray – A Memory Profiler for Python
- Show HN: I developed the most user-friendly Python profiling tool available
- Flameshow: A Terminal Flamegraph Viewer
- Visualizing Pythons process?
- Map a python project
- What are some features you wish Python had?
- Scanning Function calls in a script - is there a tool?
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Index
What are some of the best open-source Profiling projects in Python? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | scalene | 11,163 |
| 2 | viztracer | 4,325 |
| 3 | gprof2dot | 3,093 |
| 4 | pyheat | 825 |
| 5 | austin-tui | 616 |
| 6 | dd-trace-py | 491 |
| 7 | secimport | 158 |
| 8 | timebudget | 156 |
| 9 | python-benchmark-harness | 147 |
| 10 | pytest-austin | 116 |
| 11 | ansible-trace | 90 |
| 12 | whyslow | 27 |
| 13 | dask-memusage | 24 |
| 14 | pylaprof | 13 |
| 15 | austin-web | 7 |
| 16 | profiling-code | 0 |
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