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Top 17 Python Profiler 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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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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vardbg
A simple Python debugger and profiler that generates animated visualizations of program flow, useful for algorithm learning.
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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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Xpedite
A non-sampling profiler purpose built to measure and optimize performance of C++ low latency/real time systems
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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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lineprofilergui
A Qt GUI to run and display line by line profiling data using line_profiler for python
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platforminfo
System info made easy - Python library to find system info on your computer. Requires python ≥ 3.7
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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)
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)
I was wondering if you have any synchronous routes in your app? We have an open issue regarding those and would love some ideas for solutions :)
Project mention: Ask HN: C/C++ developer wanting to learn efficient Python | news.ycombinator.com | 2024-04-10
Every minor detail matters and adds to the total import time as part of the cold start. We need to optimize our code and imports. If you use Python, you can analyze your code with a tool like Tuna and optimize your libraries (perhaps replace slower ones) and your imports.
Can anyone suggest a good non-sampling profiler for low latency apps? The only one I can think of is https://github.com/morganstanley/Xpedite but it's not even clear that it is being maintained anymore.
Project mention: Sys.monitoring: Python Execution event monitoring | news.ycombinator.com | 2023-12-29sys.monitoring is a legendary feature for python Recently, I came across a profiler based on sys.monitoring.
akarsu(https://github.com/furkanonder/akarsu) is new generation profiler based on sys.monitoring. You must try guys!
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 Profiler related posts
- Ask HN: C/C++ developer wanting to learn efficient Python
- Minha jornada de otimização de uma aplicação django
- Memray – A Memory Profiler for Python
- Sys.monitoring: Python Execution event monitoring
- Akarsu – New Generation Profiler Based on PEP669
- I want some feedback on my new Python library
- How to profile an asynchronous FastAPI server
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Index
What are some of the best open-source Profiler projects in Python? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | memray | 12,510 |
| 2 | scalene | 11,163 |
| 3 | pyinstrument | 6,105 |
| 4 | memory_profiler | 4,210 |
| 5 | vprof | 3,948 |
| 6 | tuna | 1,262 |
| 7 | vardbg | 1,079 |
| 8 | pyheatmagic | 1,020 |
| 9 | flask-profiler | 744 |
| 10 | Xpedite | 148 |
| 11 | python-benchmark-harness | 147 |
| 12 | memprof | 128 |
| 13 | akarsu | 58 |
| 14 | dask-memusage | 24 |
| 15 | pylaprof | 13 |
| 16 | lineprofilergui | 10 |
| 17 | platforminfo | 3 |
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