skorch
polars
| skorch | polars | |
|---|---|---|
| 3 | 144 | |
| 5,648 | 26,514 | |
| 0.8% | 3.9% | |
| 6.9 | 10.0 | |
| 5 days ago | 5 days ago | |
| Jupyter Notebook | Rust | |
| BSD 3-clause "New" or "Revised" License | 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.
skorch
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[P] skorch 0.12.0 - HuggingFace integrations for sklearn, M1 support and others
Find a detailled list of changes in the release text.
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[P] ray-skorch - distributed PyTorch on Ray with sklearn API
I'm the principal author of ray-skorch, a library that lets you run distributed PyTorch training on large-scale datasets while providing a familiar, scikit-learn compatible skorch API, integrating well with the rest of the scikit-learn ecosystem.
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Scikit-Learn Version 1.0
There are scikit-learn (sklearn) API-compatible wrappers for e.g. PyTorch and TensorFlow.
Skorch: https://github.com/skorch-dev/skorch
tf.keras.wrappers.scikit_learn: https://www.tensorflow.org/api_docs/python/tf/keras/wrappers...
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?
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]
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 🚀
scikit-learn - scikit-learn: machine learning in Python
modin - Modin: Scale your Pandas workflows by changing a single line of code
pytorch-lightning - Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes.
datafusion - Apache DataFusion SQL Query Engine
sktime - A unified framework for machine learning with time series
DataFrames.jl - In-memory tabular data in Julia
ray-skorch - Distributed skorch on Ray Train
datatable - A Python package for manipulating 2-dimensional tabular data structures
DataFrame - C++ DataFrame for statistical, Financial, and ML analysis -- in modern C++ using native types and contiguous memory storage
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing