tune
fugue
| tune | fugue | |
|---|---|---|
| 1 | 11 | |
| 33 | 1,883 | |
| - | 1.6% | |
| 1.3 | 6.4 | |
| about 1 year ago | 12 days ago | |
| Python | Python | |
| 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.
tune
fugue
- FLaNK Stack Weekly 22 January 2024
-
Daft: A High-Performance Distributed Dataframe Library for Multimodal Data
Please integrate it with Fugue.
https://github.com/fugue-project/fugue
- Fugue: A unified interface for distributed computing
- [Discussion] Open Source beats Google's AutoML for Time series
- Ask HN: How do you test SQL?
-
Replacing Pandas with Polars. A Practical Guide
Fugue is an interesting library in this space , though I haven’t tried it
https://github.com/fugue-project/fugue
A unified interface for distributed computing. Fugue executes SQL, Python, and Pandas code on Spark, Dask and Ray without any rewrites.
-
The hand-picked selection of the best Python libraries and tools of 2022
fugue — distributed computing done easy
-
[P] Open data transformations in Python, no SQL required
This looks similar to fugue, am I right? How do they compare?
-
What the Duck?!
I am looking forward to how Substrait could help removing this friction. It aims to provide a standardised intermediate query language (lower level than SQL) to connect frontend user interfaces like SQL or data frame libraries with backend analytical computing engines. It is linked to the Arrow ecosystem. Something like Ibis or Fugue could become the front and DuckDB the backend engine.
-
Pyspark now provides a native Pandas API
There's dask-sql, but I think it is being abandoned for fugue-project. I'm actually excited for this project as it is trying to provide a backend agnostic solution, which would seem like a difficult, lofty goal. I wish them luck.
What are some alternatives?
FEDOT - Automated modeling and machine learning framework FEDOT
modin - Modin: Scale your Pandas workflows by changing a single line of code
Distributed-Systems-Guide - Distributed Systems Guide
data-science-ipython-notebooks - Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
Optimus - :truck: Agile Data Preparation Workflows made easy with Pandas, Dask, cuDF, Dask-cuDF, Vaex and PySpark
vizier - Python-based research interface for blackbox and hyperparameter optimization, based on the internal Google Vizier Service.
mlToolKits - learningOrchestra is a distributed Machine Learning integration tool that facilitates and streamlines iterative processes in a Data Science project.
bagua - Bagua Speeds up PyTorch
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
wandb - 🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.
xarray - N-D labeled arrays and datasets in Python