Search Results for "beam"
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Showing 78 open source projects for "beam"
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Beam
A type-safe, non-TH Haskell SQL library and ORM
...Backends can be written and maintained independently of this repository. For example, the beam-MySQL and beam-firebird backends are packaged independently. -
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Apache Beam
Unified programming model for Batch and Streaming
Beam is especially useful for Embarrassingly Parallel data processing tasks, and caters to the different needs and backgrounds of end users, SDK writers and runner writers. -
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Scio
A Scala API for Apache Beam and Google Cloud Dataflow
Scio is a Scala API developed by Spotify that builds on Apache Beam to enable expressive batch and streaming data pipelines, optimized for running on Google Cloud Dataflow. Inspired by Spark and Scalding, it provides scalable, type‑safe, and production-grade data processing, with built-in support for BigQuery, Pub/Sub, Cassandra, Elasticsearch, Redis, TensorFlow IO, and more. -
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Rustler
Safe Rust bridge for creating Erlang NIF functions
Rustler is a library for writing Erlang NIFs in safe Rust code. That means there should be no ways to crash the BEAM (Erlang VM). The library provides facilities for generating the boilerplate for interacting with the BEAM, handles encoding and decoding of Erlang terms, and catches rust panics before they unwind into C. The library provides functionality for both Erlang and Elixir, however Elixir is favored as of now. The code you write in a Rust NIF should never be able to crash the BEAM. ... -
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Hex
Package manager for the Erlang ecosystem
Hex is the official package manager for the Erlang ecosystem, supporting languages like Elixir and Erlang that run on the BEAM virtual machine. It integrates seamlessly with build tools such as Mix and Rebar3, allowing developers to fetch, publish, and manage packages efficiently. Hex provides a centralized repository, ensuring that packages are easily discoverable and maintainable, thereby streamlining the development workflow within the BEAM community. -
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Big-AGI
AI suite powered by state-of-the-art models and providing advanced AI
...It unifies access to multiple large language models (LLMs) and AI services through a modern web UI that emphasizes efficient interaction, flexibility, and extensibility, enabling users to conduct multi-model chats, execute code, generate images, and perform voice or text-based tasks all in one place. The workspace includes advanced features like Beam, which enables multi-model consensus and comparative responses to improve reliability and reduce hallucination, and robust persona management to tailor responses to specific roles or workflows. Big-AGI can be self-hosted or deployed in cloud environments, giving users full control over data and model access limits and avoiding vendor lock-in. -
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Elixir WebRTC
An Elixir implementation of the W3C WebRTC API
...Its main value is bringing WebRTC primitives into Elixir so developers can build real-time products with the fault tolerance and concurrency strengths of the BEAM. -
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AtomVM
Tiny Erlang VM
Brings Erlang, Elixir and other functional languages to really small systems. AtomVM implements from scratch a minimal Erlang VM that supports a subset of ErlangVM features and that is able to run unmodified BEAM binaries on really small systems like MCUs. There is much more information, including a more complete "Getting Started Guide," extensive documentation, examples, and contact information available on the AtomVM project website. AtomVM aims to be easily portable to new platforms with a minimum effort, so additional platforms might be supported in a near future. ... -
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vLLM
A high-throughput and memory-efficient inference and serving engine
vLLM is a fast and easy-to-use library for LLM inference and serving. High-throughput serving with various decoding algorithms, including parallel sampling, beam search, and more. -
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Google Cloud Dataflow Template Pipelines
Cloud Dataflow Google-provided templates for solving data tasks
DataflowTemplates is the source repository for Google-provided Dataflow templates that are intended to solve large-scale in-cloud data processing tasks without requiring users to build everything from scratch in a full development environment. The repository is centered on templated pipelines powered by Google Cloud Dataflow and Apache Beam, making it easier to run common integration and movement jobs such as data import, export, backup, restore, and bulk API operations. Its structure shows support for multiple generations of templates, including v1 and v2 implementations, as well as related metadata, YAML assets, plugins, and Python components that support broader template execution and maintenance. ... -
11
Gleam
A friendly language for building type-safe, scalable systems!
Gleam's powerful static type system helps find and prevent bugs at compile time, long before it reaches your users. It also serves as a productive refactoring tool, enabling programmers to confidently make large changes to unfamiliar code, quickly and with low risk. For problems, the type system can't solve (such as your server being hit by a bolt of lightning) the Erlang virtual machine provides well-tested mechanisms for gracefully handling failure. Hunting down bugs can be stressful so... -
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Open Beam Profiler
Profiling and analysis of laser beams with ImageJ
This open-source software enables profiling and analysis of laser beams. It can be used with a variety of cameras supported by ImageJ and Micro-Manager, or images from any other source. It provides all common analysis methods like D4Sigma, knife-edge, as well as fitting of Hermite-Gauss, Laguerre-Gauss and super-Gauss functions. -
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Phoenix Framework
Peace of mind from prototype to production
Phoenix is a high-performance, productive web development framework written in Elixir. It runs on the Erlang VM (BEAM). It is designed to support both traditional request/response web applications. It also supports real-time, soft-real-time applications via WebSockets, channels, PubSub, and presence features. Phoenix emphasizes fault tolerance, scalability, and developer productivity. It provides tools like code generators, LiveView integration, templating, routing, and a flexible plug pipeline. ... -
14
Dialyxir
Mix tasks to simplify use of Dialyzer in Elixir projects
Mix tasks to simplify use of Dialyzer in Elixir projects. Elixir 1.6 is required, to support the new pretty printing feature. If your project is not yet on 1.6, continue to specify 0.5 in your mix deps. Warning messages have been greatly improved, but are filtered through the legacy formatter to support your existing ignore files. You can optionally use the new Elixir term format for ignore files. You may want to use the --format short argument in your CI pipelines. There are several... -
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TIGRE
TIGRE: Tomographic Iterative GPU-based Reconstruction Toolbox
TIGRE is an open-source toolbox for fast and accurate 3D tomographic reconstruction for any geometry. Its focus is on iterative algorithms for improved image quality that have all been optimized to run on GPUs (including multi-GPUs) for improved speed. It combines the higher-level abstraction of MATLAB or Python with the performance of CUDA at a lower level in order to make it both fast and easy to use. TIGRE is free to download and distribute: use it, modify it, add to it, and share it. Our... -
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Learn Elixir
Learn the Elixir programming language to build functional web apps
learn-elixir is a practical, beginner-friendly guide to learning Elixir and the Erlang VM (BEAM), emphasizing why Elixir scales—lightweight processes, immutable data, robust GC, and supervisors—and how to apply those strengths in real projects. The repo walks you from installation on macOS, Ubuntu, and Windows to an interactive workflow using iex, Livebook, and even a one-line Docker run for a zero-install setup. It teaches Elixir’s core types and syntax with short, runnable examples, then moves into real project work using Mix for generating apps, compiling, running tasks, managing dependencies, and creating docs. ... -
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Elixir Koans
Elixir learning exercises
Elixir Koans is an interactive learning project designed to teach the fundamentals of the Elixir programming language through a series of self-guided coding exercises. Inspired by the style of koans used in other programming communities, it provides incomplete code snippets and failing tests that learners must solve to progress. Each exercise builds on the previous one, gradually introducing core concepts such as pattern matching, recursion, processes, and concurrency in Elixir. By debugging... -
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TorchAudio
Data manipulation and transformation for audio signal processing
The aim of torchaudio is to apply PyTorch to the audio domain. By supporting PyTorch, torchaudio follows the same philosophy of providing strong GPU acceleration, having a focus on trainable features through the autograd system, and having consistent style (tensor names and dimension names). Therefore, it is primarily a machine learning library and not a general signal processing library. The benefits of PyTorch can be seen in torchaudio through having all the computations be through PyTorch... -
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whisper-timestamped
Multilingual Automatic Speech Recognition with word-level timestamps
Multilingual Automatic Speech Recognition with word-level timestamps and confidence. Whisper is a set of multi-lingual, robust speech recognition models trained by OpenAI that achieve state-of-the-art results in many languages. Whisper models were trained to predict approximate timestamps on speech segments (most of the time with 1-second accuracy), but they cannot originally predict word timestamps. This repository proposes an implementation to predict word timestamps and provide a more... -
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Discord.SortedSet
Elixir SortedSet backed by a Rust-based NIF
SortedSet NIF is a performant and reliable sorted set data structure for Elixir, implemented in Rust using the Rustler crate to take advantage of native performance while maintaining seamless integration with the BEAM ecosystem. It provides ordering and uniqueness guarantees, with all terms stored according to Elixir’s built-in sorting rules. Internally, it uses a vector of vectors layout rather than a single vector to minimize costly reallocations, allowing efficient bucket pointer copying instead of expensive term copying during growth. This design achieves a balance between performance and simplicity, and developers can customize bucket sizes for specific workloads, with a default of 500 offering solid performance across common scenarios. ... -
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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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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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GenStage
Producer and consumer actors with back-pressure for Elixir
GenStage is a specification and set of behaviours for building demand-driven data pipelines on the BEAM. It formalizes the roles of producers, consumers, and producer-consumers, using back-pressure so that fast producers don’t overwhelm downstream stages. Developers implement callbacks like handle_demand and handle_events to control how items are emitted, transformed, and consumed across asynchronous boundaries. Because stages are OTP processes, you gain fault tolerance, supervised restarts, and concurrency tuned via configurable demand and partitioning. ... -
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AtomAI
Deep and Machine Learning for Microscopy
AtomAI is a Pytorch-based package for deep and machine-learning analysis of microscopy data that doesn't require any advanced knowledge of Python or machine learning. The intended audience is domain scientists with a basic understanding of how to use NumPy and Matplotlib. It was developed by Maxim Ziatdinov at Oak Ridge National Lab. The purpose of the AtomAI is to provide an environment that bridges the instrument-specific libraries and general physical analysis by enabling the seamless... -
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observer_cli
Visualize Erlang/Elixir Nodes On The Command Line
Observer CLI is a library to be dropped into any beam nodes, to be used to assist DevOps people to diagnose problems in production nodes. Provide a high-performance tool usable both in development and production settings. Focus on important and detailed information about real-time running systems. Keep minimal consumption. Increments are values that are mostly useful when compared to a previous one to have an idea of what they're doing because otherwise, they'd never stop increasing: bytes in and out of the node, number of garbage collector runs, words of memory that were garbage collected, and the global reductions count for the node. ...
