Numba
Dask
Our great sponsors
Numba | Dask | |
---|---|---|
124 | 32 | |
9,350 | 11,906 | |
1.7% | 1.6% | |
9.9 | 9.7 | |
6 days ago | 6 days ago | |
Python | Python | |
BSD 3-clause "New" or "Revised" License | BSD 3-clause "New" or "Revised" 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.
Numba
-
Mojo🔥: Head -to-Head with Python and Numba
Around the same time, I discovered Numba and was fascinated by how easily it could bring huge performance improvements to Python code.
-
Is anyone using PyPy for real work?
Simulations are, at least in my experience, numba’s [0] wheelhouse.
-
Python Algotrading with Machine Learning
A super-fast backtesting engine built in NumPy and accelerated with Numba.
-
PYTHON vs OCTAVE for Matlab alternative
Regarding speed, I don't agree this is a good argument against Python. For example, it seems no one here has yet mentioned numba, a Python JIT compiler. With a simple decorator you can compile a function to machine code with speeds on par with C. Numba also allows you to easily write cuda kernels for GPU computation. I've never had to drop down to writing C or C++ to write fast and performant Python code that does computationally demanding tasks thanks to numba.
-
Codon: Python Compiler
Just for reference,
* Nuitka[0] "is a Python compiler written in Python. It's fully compatible with Python 2.6, 2.7, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 3.10, and 3.11."
* Pypy[1] "is a replacement for CPython" with builtin optimizations such as on the fly JIT compiles.
* Cython[2] "is an optimising static compiler for both the Python programming language and the extended Cython programming language... makes writing C extensions for Python as easy as Python itself."
* Numba[3] "is an open source JIT compiler that translates a subset of Python and NumPy code into fast machine code."
* Pyston[4] "is a performance-optimizing JIT for Python, and is drop-in compatible with ... CPython 3.8.12"
-
Two-tier programming language
Taichi (similar to numba) is a python library that allows you to write high speed code within python. So your program consists of slow python that gets interpreted regularly, and fast python (fully type annotated and restricted to a subset of the language) that gets parallellized and jitted for CPU or GPU. And you can mix the two within the same source file.
-
Been using Python for 3 years, never used a Class.
There are also just-in-time compilers available for some Python features, that compile those parts to machine code. That includes Numba (usable as a library within CPython) and Pypy (an alternative Python implementation that includes a JIT compiler to improve performance). There’s also Cython, which is a superset of Python that allows more directly interfacing with C and C++ functions, and compiling the resulting combined code.
-
Is there a language with lisp syntax but C semantics?
this was a submission from u/bpecsek and shows that lisp with sbcl can do quite well on bench-marking. but keep in mind that these sort of benchmarks can't tell you much about real world applications. moreover if you are really concerned about niche performance you need to start thinking about compilers. heck with an appropriate compiler even python can go wrooom
- [D] Yann LeCun's Hot Take about programming languages for ML
-
Python Developer Seeking Input: Is it Worth Learning Rust for FFI?
- if no purpose built libraries are faster, use numba (http://numba.pydata.org/) to speed up your code. Optionally you can also use Taichi (https://www.taichi-lang.org/) instead of numba.
Dask
- The Distributed Tensor Algebra Compiler (2022)
-
A peek into Location Data Science at Ola
Data scientists work on phenomenally large datasets, and Dask is a handy tool for exploration within the confines of a single cloud VM or their local PCs. Location data visualization is an essential part of deciding further algorithm development and roadmap for projects. This lays the foundation for data engineering and science to work at scale, with petabytes of data.
- File format for large data with many columns
-
What is the best way to save a csv.file in number only ? PC hangs when my file is more than 2GB
Dask
-
Large Scale Hydrology: Geocomputational tools that you use
We're using a lot of Python. In addition to these, gridMET, Dask, HoloViz, and kerchunk.
-
msgspec - a fast & friendly JSON/MessagePack library
I wrote this for speeding up the RPC messaging in dask, but figured it might be useful for others as well. The source is available on github here: https://github.com/jcrist/msgspec.
-
What does it mean to scale your python powered pipeline?
Dask: Distributed data frames, machine learning and more
-
Data pipelines with Luigi
To do that, we are efficiently using Dask, simply creating on-demand local (or remote) clusters on task run() method:
- Dask – a flexible library for parallel computing in Python
- Distributed computing in python??
What are some alternatives?
NetworkX - Network Analysis in Python
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
cupy - NumPy & SciPy for GPU
Pyjion - Pyjion - A JIT for Python based upon CoreCLR
Kedro - Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.
SymPy - A computer algebra system written in pure Python
statsmodels - Statsmodels: statistical modeling and econometrics in Python
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Interactive Parallel Computing with IPython - IPython Parallel: Interactive Parallel Computing in Python