Search Results for "word processing for mac"
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Showing 208 open source projects for "word processing for mac"
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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gse
Go efficient multilingual NLP and text segmentation
Go efficient multilingual NLP and text segmentation; support English, Chinese, Japanese and others. Gse is implements jieba by golang, and try add NLP support and more feature. Support common, search engine, full mode, precise mode and HMM mode multiple word segmentation modes. Support user and embed dictionary, Part-of-speech/POS tagging, analyze segment info, stop and trim words. Support multilingual: English, Chinese, Japanese and others. Support Traditional Chinese. Support HMM cut text... -
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Kapacitor
Open source framework for processing, monitoring, and alerting
Open source framework for processing, monitoring, and alerting on time series data. Kapacitor is a real-time data processing engine for monitoring and alerting, specifically designed to work with time-series data from InfluxDB. -
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Apache Flink
Stream processing framework with powerful stream
Apache Flink is a distributed engine for stateful computations over data streams and batches, designed for low-latency processing at scale. Its core runtime executes dataflow graphs with fine-grained backpressure and checkpointing, allowing applications to recover consistently from failures. Flink’s event-time model and watermarks enable accurate out-of-order processing, windowing, and complex time semantics that typical real-time systems struggle with. Developers program against high-level... -
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SageMaker Spark Container
Docker image used to run data processing workloads
Apache Spark™ is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing. The SageMaker Spark Container is a Docker image used to run batch data... -
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Spring Batch
Spring Batch is a framework for writing batch applications using Java
A lightweight, comprehensive batch framework designed to enable the development of robust batch applications vital for the daily operations of enterprise systems. Spring Batch provides reusable functions that are essential in processing large volumes of records, including logging/tracing, transaction management, job processing statistics, job restart, skip, and resource management. It also provides more advanced technical services and features that will enable extremely high-volume and high... -
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Apache Spark
A unified analytics engine for large-scale data processing
Apache Spark is a unified engine for large-scale data processing, offering APIs for batch jobs, streaming, machine learning, and graph computation. It builds on resilient distributed datasets (RDDs) and the newer DataFrame/Dataset abstractions to provide fault-tolerant, in-memory computation across clusters. Spark’s execution engine handles scheduling, shuffles, caching, and data locality so users can focus on transformations rather than infrastructure plumbing. With Spark Streaming... -
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Apache Beam
Unified programming model for Batch and Streaming
Apache Beam is an open source, unified programming model to define both batch and streaming data-parallel processing pipelines, as well as certain language-specific SDKs for constructing pipelines and Runners. These pipelines are executed on one of Beam’s supported distributed processing back-ends, which include Apache Apex, Apache Flink, Apache Spark, and Google Cloud Dataflow. Beam is especially useful for Embarrassingly Parallel data processing tasks, and caters to the different needs... -
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Membrane Core
The core of Membrane Framework, multimedia processing framework
membrane_core is the foundation of the Membrane multimedia framework for Elixir, providing the abstractions and runtime needed to build real-time audio and video pipelines. It models media processing as a graph of lightweight, supervised OTP processes—elements connected by links—so work is isolated, fault-tolerant, and easy to scale or reconfigure at runtime. The core defines a clear lifecycle and callback API for elements, plus concepts like buffers, events, and capabilities/format... -
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NNCF
Neural Network Compression Framework for enhanced OpenVINO
NNCF (Neural Network Compression Framework) is an optimization toolkit for deep learning models, designed to apply quantization, pruning, and other techniques to improve inference efficiency. -
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Benthos
Fancy stream processing made operationally mundane
Benthos is a high performance and resilient stream processor, able to connect various sources and sinks in a range of brokering patterns and perform hydration, enrichments, transformations and filters on payloads. It comes with a powerful mapping language, is easy to deploy and monitor, and ready to drop into your pipeline either as a static binary, docker image, or serverless function, making it cloud native as heck. Delivery guarantees can be a dodgy subject. Benthos processes and... -
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Pachyderm
Data-Centric Pipelines and Data Versioning
Data-driven pipelines automatically trigger based on detecting data changes. Automatic immutable data lineage and data versioning of all data types. Autoscaling and parallel processing built on Kubernetes for resource orchestration. Uses standard object stores for data storage with automatic deduplication. Runs across all major cloud providers and on-premises installations. Automatic and intelligent versioning of even the largest data sets of unstructured and structured data. Git-like... -
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Broadway
Concurrent and multi-stage data ingestion and data processing
Broadway is a data processing library for Elixir designed to handle high-throughput, concurrent workloads with ease. It provides an abstraction for defining pipelines that consume data from sources like RabbitMQ, Kafka, Amazon SQS, or custom producers. Each pipeline is fault-tolerant and backpressure-aware, ensuring stable throughput even under load. The library integrates seamlessly with GenStage and OTP supervision trees, making it highly resilient in production. Developers can enrich... -
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Watermill
Building event-driven applications the easy way in Go
Go library for building event-driven applications. Our goal was to create a tool that is easy to understand, even by junior developers. It doesn't matter if you want to do Event-driven architecture, CQRS, Event Sourcing or just stream MySQL Binlog to Kafka. Watermill was designed to process hundreds of thousands of messages per second. Every component is built in a way that allows you to configure it for your needs. You can also implement your own middleware for the router. Watermill is... -
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Hexagon
Hexagon is a microservices toolkit written in Kotlin
Hexagon is a microservices toolkit written in Kotlin. Its purpose is to ease the building of server applications (Web applications or APIs) that run inside a cloud platform. The Hexagon Toolkit provides several libraries to build server applications. These libraries provide single standalone features and are referred to as "Ports". -
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spacy-transformers
Use pretrained transformers like BERT, XLNet and GPT-2 in spaCy
spaCy supports a number of transfer and multi-task learning workflows that can often help improve your pipeline’s efficiency or accuracy. Transfer learning refers to techniques such as word vector tables and language model pretraining. These techniques can be used to import knowledge from raw text into your pipeline, so that your models are able to generalize better from your annotated examples. You can convert word vectors from popular tools like FastText and Gensim, or you can load in any... -
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BentoML
Unified Model Serving Framework
BentoML simplifies ML model deployment and serves your models at a production scale. Support multiple ML frameworks natively: Tensorflow, PyTorch, XGBoost, Scikit-Learn and many more! Define custom serving pipeline with pre-processing, post-processing and ensemble models. Standard .bento format for packaging code, models and dependencies for easy versioning and deployment. Integrate with any training pipeline or ML experimentation platform. Parallelize compute-intense model inference... -
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Celery
Distributed task queue (development branch)
Celery is a simple, flexible, and reliable distributed system to process vast amounts of messages, while providing operations with the tools required to maintain such a system. It’s a task queue with focus on real-time processing, while also supporting task scheduling. Celery has a large and diverse community of users and contributors, you should come join us on IRC or our mailing-list. Celery is Open Source and licensed under the BSD License. A task queue’s input is a unit of work called a... -
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Asynq
Simple, reliable, and efficient distributed task queue in Go
Asynq is a Go library for queueing tasks and processing them asynchronously with workers. It's backed by Redis and is designed to be scalable yet easy to get started. Client puts tasks on a queue. Server pulls tasks off queues and starts a worker goroutine for each task. Tasks are processed concurrently by multiple workers. Task queues are used as a mechanism to distribute work across multiple machines. A system can consist of multiple worker servers and brokers, giving way to high... -
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Botonic
Build chatbots and conversational experiences using React
Botonic is a full-stack Javascript framework to create chatbots and modern conversational apps that work on multiple platforms, web, mobile and messaging apps (Messenger, Whatsapp, Telegram, etc). Building modern applications on top of messaging apps like Whatsapp or Messenger is much more than creating simple text-based chatbots. Botonic is a full-stack serverless framework that combines the power of React and Tensorflow.js to create amazing experiences at the intersection of text and... -
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Argo Workflows
Workflow engine for Kubernetes
Argo Workflows is an open source container-native workflow engine for orchestrating parallel jobs on Kubernetes. Argo Workflows is implemented as a Kubernetes CRD (Custom Resource Definition). Define workflows where each step in the workflow is a container. Model multi-step workflows as a sequence of tasks or capture the dependencies between tasks using a directed acyclic graph (DAG). Easily run compute intensive jobs for machine learning or data processing in a fraction of the time using... -
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Piscina
A fast, efficient Node.js Worker Thread Pool implementation
Piscina is a fast, efficient Node.js worker thread pool implementation, designed to perform heavy CPU-bound tasks in parallel, thereby improving application performance. -
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concretecms
Repository for Concrete CMS development
Do you want a CMS that both developers and editors love? You will spend less time building, managing extensions, and training clients with Concrete CMS. Your clients know how to use a word processor without any training. Would you like their website editing experience to be just as simple? Concrete CMS was designed as an extendable platform for building beautiful websites clients love to manage on their own. The core has lots of built-in features, so you’re not forced to use an ecosystem of... -
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go-web-framework-benchmark
Go web framework benchmark
go-web-framework-benchmark is a benchmark suite for comparing the performance of Go web frameworks. It was created to measure full HTTP request processing instead of only route matching speed. The project runs small real HTTP servers for many stable frameworks and tests a simple /hello endpoint. It can add configurable handler delay to simulate business work such as database calls, cache access, disk writes, socket activity, or microservice calls. The benchmark uses wrk and scripts to... -
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Krayin CRM
Free & Opensource Laravel CRM solution for SMEs and Enterprises
Krayin CRM is a hand-tailored CRM framework built on some of the hottest open-source technologies such as Laravel (a PHP framework) and Vue.js a progressive Javascript framework. Free & Opensource Laravel CRM solution for SMEs and Enterprises for complete customer lifecycle management. We also have a forum for any type of concerns, feature requests, or discussions. Please visit: Krayin CRM Forums. -
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Commanded
Use Commanded to build Elixir CQRS/ES applications
Commanded is an Elixir framework for implementing CQRS (Command Query Responsibility Segregation) and Event Sourcing patterns. It provides domain-driven design tools—aggregates, commands, events, and projections—backed by an event store (e.g. PostgreSQL).
