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Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

Polyaxon ⭐ 3,438

MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle

Rumale is a machine learning library in Ruby

Dist Keras ⭐ 611

Distributed Deep Learning, with a focus on distributed training, using Keras and Apache Spark.

Compiled, automatically parallel Python for data science

Deepgraph ⭐ 274

Analyze Data with Pandas-based Networks. Documentation:

Yet another black-box optimization library for Python

Comparative Genomics Toolkit 3

Hypergol ⭐ 54

Hypergol is a Data Science/Machine Learning productivity toolkit to accelerate any projects into production with autogenerated code, standardised structure for data and ML and parallel processing out-of-the-box.

Parallel Corpora Tools ⭐ 39

Tools for filtering and cleaning parallel and monolingual corpora for machine translation and other natural language processing tasks.

Framework which helps you to make parallel/distributed calculations using data pipelines

Ordered Concurrently ⭐ 22

Ordered-concurrently a library for concurrent processing with ordered output in Go. Process work concurrently and returns output in a channel in the order of input. It is useful in concurrently processing items in a queue, and get output in the order provided by the queue.

FluidML is a lightweight framework for developing machine learning pipelines.

Pipelining framework for python. Designed for data analysis but can be useful to other applications

Pdf2dataset ⭐ 8

Converts a whole subdirectory with a big (or small) volume of PDF documents to a dataset (pandas DataFrame) with error tracking and choice of features

Le Traducteur ⭐ 6

A Neural Machine Translation framework built with PyTorch and AllenNLP.

Blog Sniffer ⭐ 6

Sniff all engineering blogs to your timescale database

Parallel coordinates with grand tour for exploratory data visualization of massive and high-dimensional data

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