gamma
polars
| gamma | polars | |
|---|---|---|
| 8 | 144 | |
| 381 | 26,378 | |
| - | 3.4% | |
| 0.0 | 10.0 | |
| over 1 year ago | about 14 hours ago | |
| Rust | Rust | |
| GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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.
gamma
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Announcing dfdx - an deep learning library built with const generics
There's other differences in how nn layers are implemented if you compare the source of linear layers: https://github.com/coreylowman/dfdx/blob/main/src/nn/linear.rs vs https://github.com/c0dearm/mushin/blob/main/src/nn/layers/linear.rs
- Which areas in tech have the most job density using Rust?
- Mushin: Automatic Differentiation in the GPU with Rust
- Making a better Tensorflow thanks to strong typing
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Using const generics to build neural networks
https://github.com/c0dearm/gamma/pull/1 first pr :)
- What’s everyone working on this week (14/2021)?
polars
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Why Python's Integer Division Floors (2010)
This is because 0.1 is in actuality the floating point value value 0.1000000000000000055511151231257827021181583404541015625, and thus 1 divided by it is ever so slightly smaller than 10. Nevertheless, fpround(1 / fpround(1 / 10)) = 10 exactly.
I found out about this recently because in Polars I defined a // b for floats to be (a / b).floor(), which does return 10 for this computation. Since Python's correctly-rounded division is rather expensive, I chose to stick to this (more context: https://github.com/pola-rs/polars/issues/14596#issuecomment-...).
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Polars
https://github.com/pola-rs/polars/releases/tag/py-0.19.0
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Stuff I Learned during Hanukkah of Data 2023
That turned out to be related to pola-rs/polars#11912, and this linked comment provided a deceptively simple solution - use PARSE_DECLTYPES when creating the connection:
- Polars 0.20 Released
- Segunda linguagem
- Polars: Dataframes powered by a multithreaded query engine, written in Rust
- Summing columns in remote Parquet files using DuckDB
- Polars 0.34 is released. (A query engine focussing on DataFrame front ends)
What are some alternatives?
robusta - Easy interop between Rust and Java
vaex - Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualization and exploration of big tabular data at a billion rows per second 🚀
Epic-Asset-Manager - A frontend to Assets purchased on Epic Games Store
modin - Modin: Scale your Pandas workflows by changing a single line of code
doc_panic_checker
datafusion - Apache DataFusion SQL Query Engine
neurotic - The compile-time feedforward neural network library that no one asked for
DataFrames.jl - In-memory tabular data in Julia
python-compiler - A compiler for a subset of Python using LLVM
datatable - A Python package for manipulating 2-dimensional tabular data structures
dfdx - Deep learning in Rust, with shape checked tensors and neural networks
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing