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mosaicml/streaming

0
View on GitHub↗
1,521 stars·199 forks·Python·Apache-2.0·11 viewsstreaming.docs.mosaicml.com↗

Streaming

A Data Streaming Library for Efficient Neural Network Training

Features

  • Stream Processing - Streams large datasets for distributed model training.
  • Computation and Optimization - Data streaming library for efficient neural network training.

Star history

Star history chart for mosaicml/streamingStar history chart for mosaicml/streaming

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

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Projects sharing features with Streaming

These projects share indexed features with Streaming. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • airtai/faststreamairtai avatar

    airtai/faststream

    5,234View on GitHub↗

    FastStream is an asynchronous Python framework designed for building event-driven microservices. It provides a unified abstraction layer for interacting with various message brokers, enabling developers to manage event production and consumption through a consistent interface while maintaining access to native provider-specific features. The framework centers on a decorator-based routing model that binds application logic directly to broker topics, supported by a built-in dependency injection container that resolves resources at runtime. The framework distinguishes itself through its deep int

    Python
    View on GitHub↗5,234
  • aklivity/zillaaklivity avatar

    aklivity/zilla

    690View on GitHub↗

    🦎 A multi-protocol edge & service proxy. Seamlessly interface web apps, IoT clients, & microservices to Apache Kafka® via declaratively defined, stateless APIs.

    Java
    View on GitHub↗690
  • apache/beamapache avatar

    apache/beam

    8,612View on GitHub↗

    Apache Beam is a distributed data pipeline framework and unified data processing model designed to handle both bounded batch data and unbounded real-time streams. It provides a system for building scalable, data-parallel workflows that operate across compute clusters using a single programming model. The framework utilizes a cross-runner pipeline abstraction that decouples the data processing logic from the underlying execution backend, allowing the same pipeline to run on different distributed compute engines. It supports multi-language pipeline development by translating high-level code fro

    Java
    View on GitHub↗8,612
  • adapter-hub/adaptersadapter-hub avatar

    adapter-hub/adapters

    2,815View on GitHub↗

    A Unified Library for Parameter-Efficient and Modular Transfer Learning

    Python
    View on GitHub↗2,815
Compare all 30 related projects→

Frequently asked questions

What does mosaicml/streaming do?

A Data Streaming Library for Efficient Neural Network Training

What are the main features of mosaicml/streaming?

The main features of mosaicml/streaming are: Stream Processing, Computation and Optimization.

Which projects share features with mosaicml/streaming?

Projects with overlapping indexed features include: airtai/faststream — FastStream is an asynchronous Python framework designed for building event-driven microservices. It provides a unified… aklivity/zilla — 🦎 A multi-protocol edge & service proxy. Seamlessly interface web apps, IoT clients, & microservices to Apache Kafka®… apache/beam — Apache Beam is a distributed data pipeline framework and unified data processing model designed to handle both bounded… apache/flink — Apache Flink is a distributed processing engine designed for both high-throughput, low-latency data streams and finite… apache/kafka — Kafka is a distributed event streaming platform designed for capturing, storing, and processing real-time data streams… adapter-hub/adapters — A Unified Library for Parameter-Efficient and Modular Transfer Learning.