scalene
perspective
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scalene | perspective | |
---|---|---|
32 | 45 | |
11,163 | 7,535 | |
1.9% | 3.9% | |
9.3 | 9.3 | |
3 days ago | 6 days ago | |
Python | C++ | |
Apache License 2.0 | Apache License 2.0 |
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
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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
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How can I find out why my python is so slow?
Use this my fren: https://github.com/plasma-umass/scalene
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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
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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.
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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
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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
perspective
- Ask HN: How Can I Make My Front End React to Database Changes in Real-Time?
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The Design Philosophy of Great Tables (Software Package)
Why do you want to render to canvas?
Perspective seems to be the most performant html table. It is more focused on extremely fast updates than styling, although it looks good.
Glide is a newcomer that also renders to canvas.
https://github.com/finos/perspective
https://github.com/glideapps/glide-data-grid
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Show HN: WhatTheDuck – open-source, in-browser SQL on CSV files
SQL workbench also uses https://perspective.finos.org/ for tables. It's a WASM table library which pairs nicely with duckdb and works well with large tables.
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React Spreadsheet 2 – Your Own Google Sheets
Yes. We are working on adding support for aggregation and pivoting using https://github.com/finos/perspective
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Show HN: DataSheetGrid, an Airtable-like React component
I haven't looked extensively at react-datasheet. It looks like it is trying to build more of a full product than the other data tables.
I have used ag-grid extensively, its an impressive product. Some pieces are a little awkward to use, particularly auto-sizing. But generally ag-grid has thought of most functionality and has a solution. The creator of ag-grid had a great interview on Javascript Jabber [1].
The other serious data table component that I have seen is FinOS Perspective [2]. This is extremely high performance, also more specialized and probably harder to customize. I think Perspective renders to a canvas element from Rust/C++ compiled to WASM (not 100% sure). It is also made for streaming updates.
AG-Grid supports streaming updates... but only in the commercial version.
Eventually the data model for these types of tables becomes tricky. I will be investigating parquet-wasm for my use case. Hit me up if you want to collaborate.
[1] https://blog.ag-grid.com/javascript-jabber-podcast/
[2] https://perspective.finos.org/
- Perspective Market Simulation
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ChDB: Embedded OLAP SQL Engine Powered by ClickHouse
Something like https://github.com/finos/perspective ? We use an OLAP(-y) WASM engine to provide query-ability to our data visualization tool, and doing the calculations in the browser is cheaper and simpler than a server-side database for datasets that fit in browser memory.
- Show HN: Udsv.js – A faster CSV parser in 5KB (min)
- Perspective 2.0, Open Source WebAssembly-Powered BI
What are some alternatives?
flask-profiler - a flask profiler which watches endpoint calls and tries to make some analysis.
ag-Grid - The best JavaScript Data Table for building Enterprise Applications. Supports React / Angular / Vue / Plain JavaScript.
palanteer - Visual Python and C++ nanosecond profiler, logger, tests enabler
arquero - Query processing and transformation of array-backed data tables.
pytest-austin - Python Performance Testing with Austin
nocodb - 🔥 🔥 🔥 Open Source Airtable Alternative
memray - Memray is a memory profiler for Python
datapane - Build and share data reports in 100% Python
pyshader - Write modern GPU shaders in Python!
ClickHouse - ClickHouse® is a free analytics DBMS for big data
viztracer - VizTracer is a low-overhead logging/debugging/profiling tool that can trace and visualize your python code execution.
SandDance - Visually explore, understand, and present your data.