scalene
flask-profiler
Our great sponsors
scalene | flask-profiler | |
---|---|---|
32 | 1 | |
11,163 | 744 | |
1.9% | - | |
9.3 | 0.0 | |
about 9 hours ago | over 1 year ago | |
Python | Python | |
Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
scalene
-
Memray – A Memory Profiler for Python
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)
- Scalene: A high-performance CPU GPU and memory profiler for Python
- Scalene: A high-performance, CPU, GPU, and memory profiler for Python
-
How can I find out why my python is so slow?
Use this my fren: https://github.com/plasma-umass/scalene
-
Making Python 100x faster with less than 100 lines of Rust
You should take a look at Scalene - it's even better.
https://github.com/plasma-umass/scalene
-
Blog Post: Making Python 100x faster with less than 100 lines of Rust
I like seeing another Python profiler. The one I've been playing with is Scalene (GitHub). It does some fun things related to letting you see how much things are moving across the system Python memory boundary.
-
Cum as putea sa imbunatatesc timpul de rulare al pitonului?
Ai vazut "Python Performance Matters" by Emery Berger (Strange Loop 2022)? E in principiu o prezentare si demo cu Scalene.
- Scalene - A Python CPU/GPU/memory profiler with optimization proposals
- Scalene: A Python CPU/GPU/memory profiler with optimization proposals
-
OpenAI might be training its AI technology to replace some software engineers, report says
I tried out some features of machine learning models suggesting optimisations on code profiled by scalene and pretty much all of them would make the code less efficient, both time and memory wise. I am not worried. The devil is in the details and ML will not replace all of us anytime soon
flask-profiler
-
Profiling Flask application to improve performance
There are a lot of profiling tools for Python code, and most of them are built-in — like profile or cProfile. Since I’m speaking about Flask application, let’s see what the world has especially for it. There is a beautiful lib called flask-profiler, which has a web interface with some cool features such as route or date filters. But Flask also has a built-in in werkzeug's profiler. It looked awesomely easy in use, so it was the first — and the last — one I tried. To use the built-in profiler you’ll need to add only two lines of code to your project:
What are some alternatives?
palanteer - Visual Python and C++ nanosecond profiler, logger, tests enabler
Shynet - Modern, privacy-friendly, and detailed web analytics that works without cookies or JS.
pytest-austin - Python Performance Testing with Austin
gprof2dot - Converts profiling output to a dot graph.
memray - Memray is a memory profiler for Python
django-leaflet-admin-list - The Django Leaflet Admin List package provides an admin list view featured by the map and bounding box filter for the geo-based data of the GeoDjango.
pyshader - Write modern GPU shaders in Python!
kube-opex-analytics - 🎨 Kubernetes Usage Analytics and Accounting for Cost Allocation and Capacity Planning - Hourly Trends, Daily and Monthly Accounting - Prometheus Exporter - Built-in & Grafana Dashboards.
viztracer - VizTracer is a low-overhead logging/debugging/profiling tool that can trace and visualize your python code execution.
Redash - Make Your Company Data Driven. Connect to any data source, easily visualize, dashboard and share your data.
Dask - Parallel computing with task scheduling
rqmonitor - Flask based more dynamic and actionable frontend dashboard for monitoring Redis Queue 👩🏿💻 http://python-rq.org