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Showing 16 open source projects for "raspberry-gpio-python"
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Best-of Python
A ranked list of awesome Python open-source libraries
...If you like to add or update projects, feel free to open an issue, submit a pull request, or directly edit the projects.yaml. Contributions are very welcome! Ranked list of awesome python libraries for web development. Correctly generate plurals, ordinals, indefinite articles; convert numbers. Libraries for loading, collecting, and extracting data from a variety of data sources and formats. Libraries for data batch- and stream-processing, workflow automation, job scheduling, and other data pipeline tasks. -
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Bytewax
Python Stream Processing
Bytewax is a Python framework that simplifies event and stream processing. Because Bytewax couples the stream and event processing capabilities of Flink, Spark, and Kafka Streams with the friendly and familiar interface of Python, you can re-use the Python libraries you already know and love. Connect data sources, run stateful transformations, and write to various downstream systems with built-in connectors or existing Python libraries. -
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SageMaker Spark Container
Docker image used to run data processing workloads
...The container images in this repository are used to build the pre-built container images that are used when running Spark jobs on Amazon SageMaker using the SageMaker Python SDK. The pre-built images are available in the Amazon Elastic Container Registry (Amazon ECR), and this repository serves as a reference for those wishing to build their own customized Spark containers for use in Amazon SageMaker. -
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Pyper
Concurrent Python made simple
Pyper is a Python-native orchestration and scheduling framework designed for modern data workflows, machine learning pipelines, and any task that benefits from a lightweight DAG-based execution engine. Unlike heavier platforms like Airflow, Pyper aims to remain lean, modular, and developer-friendly, embracing Pythonic conventions and minimizing boilerplate. -
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Lithops
A multi-cloud framework for big data analytics
Lithops is an open-source serverless computing framework that enables transparent execution of Python functions across multiple cloud providers and on-prem infrastructure. It abstracts cloud providers like IBM Cloud, AWS, Azure, and Google Cloud into a unified interface and turns your Python functions into scalable, event-driven workloads. Lithops is ideal for data processing, ML inference, and embarrassingly parallel workloads, giving you the power of FaaS (Function-as-a-Service) without vendor lock-in. ... -
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Pathway
Python ETL framework for stream processing, real-time analytics, LLM
...Unlike traditional batch processing frameworks, Pathway continuously updates the results of your data logic as new events arrive, functioning more like a database that reacts in real-time. It supports Python, integrates with modern data tools, and offers a deterministic dataflow model to ensure reproducibility and correctness. -
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Fondant
Production-ready data processing made easy and shareable
Fondant is a modular, pipeline-based framework designed to simplify the preparation of large-scale datasets for training machine learning models, especially foundation models. It offers an end-to-end system for ingesting raw data, applying transformations, filtering, and formatting outputs—all while remaining scalable and traceable. Fondant is designed with reproducibility in mind and supports containerized steps using Docker, making it easy to share and reuse data processing components.... -
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CocoIndex
ETL framework to index data for AI, such as RAG
CocoIndex is an open-source framework designed for building powerful, local-first semantic search systems. It lets users index and retrieve content based on meaning rather than keywords, making it ideal for modern AI-based search applications. CocoIndex leverages vector embeddings and integrates with various models and frameworks, including OpenAI and Hugging Face, to provide high-quality semantic understanding. It’s built for transparency, ease of use, and local control over your search... -
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Siddhi Core Libraries
Stream Processing and Complex Event Processing Engine
...Agile development experience with SQL-like query language and graphical drag-and-drop editor supporting event simulation. Lightweight runtime that can natively run on Kubernetes, Docker, VM, or bare metal, and embedded in any Java or Python application. Scalable, and highly available distributed event processing on Kubernetes, with NATS Streaming and Siddhi Kubernetes Operator. -
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PULSAR
Distributed pub-sub messaging system
...Persistent message storage based on Apache BookKeeper. IO-level isolation between write and read operations. Flexible messaging models with high-level APIs for Java, Go, Python, C++, Node.js, WebSocket and C#. -
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text-dedup
All-in-one text de-duplication
text-dedup is a Python library that enables efficient deduplication of large text corpora by using MinHash and other probabilistic techniques to detect near-duplicate content. This is especially useful for NLP tasks where duplicated training data can skew model performance. text-dedup scales to billions of documents and offers tools for chunking, hashing, and comparing text efficiently with low memory usage. -
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Padasip
Python Adaptive Signal Processing
Padasip (Python Adaptive Signal Processing) is a Python library tailored for adaptive filtering and online learning applications, particularly in signal processing and time series forecasting. It includes a variety of adaptive filter algorithms such as LMS, RLS, and their variants, offering real-time adaptation to changing environments. The library is lightweight, well-documented, and ideal for research, prototyping, or teaching purposes. -
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Wally
Distributed Stream Processing
Wally is a fast-stream-processing framework. Wally makes it easy to react to data in real-time. By eliminating infrastructure complexity, going from prototype to production has never been simpler. When we set out to build Wally, we had several high-level goals in mind. Create a dependable and resilient distributed computing framework. Take care of the complexities of distributed computing "plumbing," allowing developers to focus on their business logic. Provide high-performance & low-latency... -
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Cosmos DB Spark
Apache Spark Connector for Azure Cosmos DB
Azure Cosmos DB Spark is the official connector for Azure CosmosDB and Apache Spark. The connector allows you to easily read to and write from Azure Cosmos DB via Apache Spark DataFrames in Python and Scala. It also allows you to easily create a lambda architecture for batch-processing, stream-processing, and a serving layer while being globally replicated and minimizing the latency involved in working with big data. -
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Sed.py is a python module to provide a easy way to do text stream processing. Just like the name of module, it likes to do the work that sed can do. But not in sed's way, it's in Python's way. To use this module, the knowledge of regexp is necessary.
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StreamMine is a distributed event processing (streaming) infrastructure. You can create low-latency, fault-tolerant stream processing functionality with any stream-oriented operators that can be implemented in Python.
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