Search Results for "java-ml"
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Showing 12384 open source projects for "java-ml"
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Rubix ML
A high-level machine learning and deep learning library for PHP
Rubix ML is a free open-source machine learning (ML) library that allows you to build programs that learn from your data using the PHP language. We provide tools for the entire machine learning life cycle from ETL to training, cross-validation, and production with over 40 supervised and unsupervised learning algorithms. In addition, we provide tutorials and other educational content to help you get started using ML in your projects. -
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JSON-java
A reference implementation of a JSON package in Java
JSON is a light-weight language-independent data interchange format. The JSON-Java package is a reference implementation that demonstrates how to parse JSON documents into Java objects and how to generate new JSON documents from the Java classes. -
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OptScale
FinOps and MLOps platform to run ML/AI and regular cloud workloads
Run ML/AI or any type of workload with optimal performance and infrastructure cost. OptScale allows ML teams to multiply the number of ML/AI experiments running in parallel while efficiently managing and minimizing costs associated with cloud and infrastructure resources. OptScale MLOps capabilities include ML model leaderboards, performance bottleneck identification and optimization, bulk run of ML/AI experiments, experiment tracking, and more. ... -
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java-client
Java language binding for writing Appium Tests
Java language binding for writing Appium Tests conforms to W3C WebDriver Protocol. Since version 8 Appium Java Client had several major changes, which might require to update your client code. Make sure to follow the v7 to v8 Migration Guide in order to streamline the migration process. -
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Tribuo
Tribuo - A Java machine learning library
Tribuo* is a machine learning library written in Java. It provides tools for classification, regression, clustering, model development, and more. It provides a unified interface to many popular third-party ML libraries like xgboost and liblinear. With interfaces to native code, Tribuo also makes it possible to deploy models trained by Python libraries (e.g. scikit-learn, and pytorch) in a Java program. -
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lightning AI
The most intuitive, flexible, way for researchers to build models
Build in days not months with the most intuitive, flexible framework for building models and Lightning Apps (ie: ML workflow templates) which "glue" together your favorite ML lifecycle tools. Build models and build/publish end-to-end ML workflows that "glue" your favorite tools together. Models are “easy”, the “glue” work is hard. Lightning Apps are community-built templates that stitch together your favorite ML lifecycle tools into cohesive ML workflows that can run on your laptop or any cluster. ... -
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TensorFlow.js
TensorFlow.js is a library for machine learning in JavaScript
TensorFlow.js is a library for machine learning in JavaScript. Develop ML models in JavaScript, and use ML directly in the browser or in Node.js. Use off-the-shelf JavaScript models or convert Python TensorFlow models to run in the browser or under Node.js. Retrain pre-existing ML models using your own data. Build and train models directly in JavaScript using flexible and intuitive APIs. Tensors are the core datastructure of TensorFlow.js They are a generalization of vectors and matrices to potentially higher dimensions. ... -
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KServe
Standardized Serverless ML Inference Platform on Kubernetes
KServe provides a Kubernetes Custom Resource Definition for serving machine learning (ML) models on arbitrary frameworks. It aims to solve production model serving use cases by providing performant, high abstraction interfaces for common ML frameworks like Tensorflow, XGBoost, ScikitLearn, PyTorch, and ONNX. It encapsulates the complexity of autoscaling, networking, health checking, and server configuration to bring cutting edge serving features like GPU Autoscaling, Scale to Zero, and Canary Rollouts to your ML deployments. ... -
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Lux
The Lux Programming Language
...The language is mostly inspired by the following 3 languages. Clojure (syntax, overall look & feel), Haskell (functional programming), and Standard ML (module system). They are implemented as plain-old data-structures whose expressions get eval'ed by the compiler and integrated into the type-checker. The main difference between Lux & Standard ML is that Standard ML separates interfaces/signatures and implementations/structures. -
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Kepler
Kepler (Kubernetes-based Efficient Power Level Exporter)
Kepler (Kubernetes-based Efficient Power Level Exporter) uses eBPF to probe performance counters and other system stats, use ML models to estimate workload energy consumption based on these stats, and exports them as Prometheus metrics. -
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Java Design Patterns
Design patterns implemented in Java
A comprehensive, community-maintained collection of design pattern implementations in Java, offering well-documented, educational examples to help developers understand and apply common architectural solutions. All designs should be as simple as possible. You should start with KISS, YAGNI, and Do The Simplest Thing That Could Possibly Work principles. Complexity and patterns should only be introduced when they are needed for practical extensibility. -
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Playwright for Java
Java version of the Playwright testing and automation library
Playwright Java is the Java version of the Playwright testing and automation library, enabling reliable end-to-end testing for modern web applications. -
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ImGui Java
JNI based binding for Dear ImGui
imgui-java is a comprehensive Java binding for Dear ImGui, the popular immediate-mode GUI library used for real-time tools and debug interfaces. This binding allows Java applications—including those using LWJGL or JOGL—to leverage ImGui’s fast and flexible GUI features without switching languages. With support for native backends and OpenGL rendering, imgui-java is perfect for game engines, development tools, and editors that need an intuitive GUI system. ... -
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commonmark-java
Java library for parsing and rendering CommonMark (Markdown)
Java library for parsing and rendering Markdown text according to the CommonMark specification (and some extensions). Provides classes for parsing input to an abstract syntax tree of nodes (AST), visiting and manipulating nodes, and rendering to HTML. It started out as a port of commonmark.js, but has since evolved into a full library with a nice API. -
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Testcontainers Java
Testcontainers is a Java library that supports JUnit tests
Testcontainers for Java is a Java library that supports JUnit tests, providing lightweight, throwaway instances of common databases, Selenium web browsers, or anything else that can run in a Docker container. Use a containerized instance of a MySQL, PostgreSQL or Oracle database to test your data access layer code for complete compatibility, but without requiring complex setup on developers' machines and safe in the knowledge that your tests will always start with a known DB state. ... -
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GraphQL Java
GraphQL Java implementation
...The schema describes what queries are possible and what fields you can get back. (Note: when we refer to schema here, we always refer to a "GraphQL Schema", which is not related to other schemas like "JSON Schema" or "Database Schema") GraphQL Java is the Java (server) implementation for GraphQL. There are several repositories in the GraphQL Java Github org. The most important one is the GraphQL Java Engine which is the basis for everything else. The GraphQL Java Engine is only concerned with executing queries. It doesn't deal with any HTTP or JSON related topics. -
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docker-java
Java Docker API Client
Java Docker API Client. To start using docker-java , you need to add at least two dependencies. You will need an instance of DockerClientConfig to tell the library how to access Docker, which credentials to use to pull from Docker registries, etc. The builder is available and allows you to configure every property of the client. Should you need to customize the Jackson's ObjectMapper used by docker-java, you can create your own DockerClientConfig. -
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ML.NET
Open source and cross-platform machine learning framework for .NET
With ML.NET, you can create custom ML models using C# or F# without having to leave the .NET ecosystem. ML.NET lets you re-use all the knowledge, skills, code, and libraries you already have as a .NET developer so that you can easily integrate machine learning into your web, mobile, desktop, games, and IoT apps. ML.NET offers Model Builder (a simple UI tool) and ML.NET CLI to make it super easy to build custom ML Models. -
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Language support for Java for VS Code
Java Language Support for Visual Studio Code
Provides Java ™ language support via Eclipse ™ JDT Language Server, which utilizes Eclipse ™ JDT, M2Eclipse and Buildship. Now that Java extension will publish platform-specific versions, it will embed a JRE for supported platforms such as win32-x64, Linux-x64, Linux-arm64, darwin-x64, and darwin-arm64. The embedded JRE is used to launch the Language Server for Java. -
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DVC
Data Version Control | Git for Data & Models
DVC is built to make ML models shareable and reproducible. It is designed to handle large files, data sets, machine learning models, and metrics as well as code. Version control machine learning models, data sets and intermediate files. DVC connects them with code and uses Amazon S3, Microsoft Azure Blob Storage, Google Drive, Google Cloud Storage, Aliyun OSS, SSH/SFTP, HDFS, HTTP, network-attached storage, or disc to store file contents. -
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OpenAI-Java
OpenAI Api Client in Java
OpenAI-Java is the official Java client library provided by OpenAI for interacting with the OpenAI API. It is designed to make it easier for Java applications to call endpoints like chat completions, embeddings, function calling, streaming, and other model services using idiomatic Java patterns. You configure the client (often via environment variables or system properties), then build parameter objects (e.g. -
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Flama
Fire up your models with the flame
Flama is a python library which establishes a standard framework for development and deployment of APIs with special focus on machine learning (ML). The main aim of the framework is to make ridiculously simple the deployment of ML APIs, simplifying (when possible) the entire process to a single line of code. The library builds on Starlette, and provides an easy-to-learn philosophy to speed up the building of highly performant GraphQL, REST and ML APIs. Besides, it comprises an ideal solution for the development of asynchronous and production-ready services, offering automatic deployment for ML models. -
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SageMaker Spark
A Spark library for Amazon SageMaker
SageMaker Spark is an open-source Spark library for Amazon SageMaker. With SageMaker Spark you construct Spark ML Pipelines using Amazon SageMaker stages. These pipelines interleave native Spark ML stages and stages that interact with SageMaker training and model hosting. With SageMaker Spark, you can train on Amazon SageMaker from Spark DataFrames using Amazon-provided ML algorithms like K-Means clustering or XGBoost, and make predictions on DataFrames against SageMaker endpoints hosting your trained models, and, if you have your own ML algorithms built into SageMaker compatible Docker containers, you can use SageMaker Spark to train and infer on DataFrames with your own algorithms -- all at Spark scale. ... -
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JDA Java Discord API
Java wrapper for the popular chat & VOIP service
JDA strives to provide a clean and full wrapping of the Discord REST api and its Websocket-Events for Java. This library is a helpful tool that provides the functionality to create a discord bot in java. Discord is currently prohibiting creation and usage of automated client accounts (AccountType.CLIENT). We have officially dropped support for client login as of version 4.2.0! Note that JDA is not a good tool to build a custom discord client as it loads all servers/guilds on startup unlike a client which does this via lazy loading instead. ... -
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ChatGPT Java API
Java SDK for seamless integration with ChatGPT APIs
ChatGPT-Java is a Java SDK created by PlexPt that offers a convenient, out-of-the-box integration with the OpenAI ChatGPT API (including models like GPT-4o, GPT-5) for Java applications. The library supports chat completions, token counting, function calls, streaming responses, proxy configuration, custom API host, and integration with frameworks such as Spring (via SseEmitter).
