Search Results for "model-builder" - Page 8
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Showing 2464 open source projects for "model-builder"
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virtualenv
Virtual Python environment builder
virtualenv is a tool to create isolated Python environments. Since Python 3.3, a subset of it has been integrated into the standard library under the venv module. It creates an environment that has its own installation directories, that doesn’t share libraries with other virtualenv environments (and optionally doesn’t access the globally installed libraries either). The basic problem being addressed is one of dependencies and versions, and indirectly permissions. Imagine you have an... -
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KeyboardLayoutGuide
KeyboardLayoutGuide, back from when it didn't exist
KeyboardLayoutGuide, back from when it didn't exist. Simply constrain your views to the KeyboardLayoutGuide's top anchor the way you would do it natively. If you add your view in Interface Builder, don't forget to enable the "Remove at build time" checkbox for the bottom constraint. By default, KeyboardLayoutGuide will align your item with the bottom safe area. This is a behaviour that can be opt out by using keyboardLayoutGuideNoSafeArea instead of keyboardLayoutGuide. -
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Visual Studio Code
Modern IDE and code editor from Microsoft for Mac, Windows, and Linux
Visual Studio Code combines the simplicity of a code editor with what developers need for their core edit-build-debug cycle. It provides comprehensive code editing, navigation, and understanding support along with lightweight debugging, a rich extensibility model, and lightweight integration with existing tools. Visual Studio Code is a distribution of the Code - OSS repository with Microsoft-specific customizations released under a traditional Microsoft product license. Visual Studio Code is updated monthly with new features and bug fixes. You can download it for Windows, macOS, and Linux on Visual Studio Code's website. ... -
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Device
Light weight tool for detecting the current device and screen size
Device detects the current device model and screen size. Carthage is a decentralized dependency manager that builds your dependencies and provides you with binary frameworks. Run Carthage update to build the framework and drag the built Device. framework into your Xcode project. The Swift Package Manager is a tool for automating the distribution of Swift code and is integrated into the swift compiler. -
Stop Cyber Threats with VM-Series Next-Gen Firewall on AzureGain integrated visibility across all traffic in a single pass. Deploy Palo Alto Networks VM-Series to determine application identity and content while automating security policy updates via rich APIs.
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Rust Programming Language
Empowering everyone to build reliable and efficient software
The Rust Programming Language is a language that empowers you to build reliable and efficient software. It runs blazingly fast and is memory-efficient, so it can power performance-critical services and run on embedded devices. It has a rich type system and ownership model, ensuring both thread and memory safety. Consisting of a standard library, great documentation and a friendly compiler, plus a top-notch build tool, package manager, auto-formatter and many other great tools, it’s the language of choice for increased productivity. Hundreds of companies the world over are using Rust to power an amazing range of cross-platform solutions. ... -
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Passmark
The open-source Playwright library for AI browser regression testing
...It is designed to simplify and accelerate testing workflows by allowing AI models to interpret human-readable instructions and translate them into executable browser actions. One of its defining features is a cache-first execution model, where AI is used initially to discover how to perform actions, and those actions are then stored and replayed at native speed in future runs. This approach significantly reduces reliance on AI during repeated test executions, improving both performance and cost efficiency. The framework also incorporates multi-model validation, using multiple AI systems to verify assertions and reduce the risk of incorrect results. -
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Unsloth-MLX
Bringing the Unsloth experience to Mac users via Apple's MLX framework
...It supports loading and training Hugging Face models with fine-tuning strategies like SFT, DPO, ORPO, and GRPO and even handles exporting models to formats like GGUF for downstream use, although some limitations apply with quantized models. Users can write and test training pipelines directly on macOS before scaling up, accelerating development cycles and lowering entry barriers for model refinement. -
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Tunix
A JAX-native LLM Post-Training Library
...The library is organized around modular pipelines for data loading, rollout, optimization, and evaluation, letting practitioners swap components without rewriting the whole stack. Examples and reference configs demonstrate end-to-end runs for common model families, helping teams reproduce baselines before customizing. Tunix also leans into research ergonomics: logging, checkpointing, and metrics are built in, and the code is written to be hackable rather than monolithic. Overall it aims to shorten the path from an off-the-shelf base model to a well-aligned, task-ready model using scalable JAX primitives. -
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Spring Data MongoDB
Provide support to increase developer productivity in Java
The primary goal of the Spring Data project is to make it easier to build Spring-powered applications that use new data access technologies such as non-relational databases, map-reduce frameworks, and cloud-based data services. The Spring Data MongoDB project aims to provide a familiar and consistent Spring-based programming model for new datastores while retaining store-specific features and capabilities. The Spring Data MongoDB project provides integration with the MongoDB document database. Key functional areas of Spring Data MongoDB are a POJO-centric model for interacting with a MongoDB Document and easily writing a repository-style data access layer. ... -
Your monitoring isn't a stack. It's a pile. Fix that.Replace Datadog, New Relic, and Sentry without adding three more dashboards.
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Tribuo
Tribuo - A Java machine learning library
...Tribuo's Models, Datasets, and Evaluations have provenance, meaning they know exactly what parameters, transformations, and files were used to create them. Provenance data allows each model to be rebuilt verbatim from scratch and for evaluations to track the models and datasets used for each experiment. -
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Opacus
Training PyTorch models with differential privacy
...Open source, modular API for differential privacy research. Everyone is welcome to contribute. ML practitioners will find this to be a gentle introduction to training a model with differential privacy as it requires minimal code changes. Differential Privacy researchers will find this easy to experiment and tinker with, allowing them to focus on what matters. -
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Deepchecks
Test Suites for validating ML models & data
...Deepchecks accompany you through various validation and testing needs such as verifying your data’s integrity, inspecting its distributions, validating data splits, evaluating your model and comparing between different models. While you’re in the research phase, and want to validate your data, find potential methodological problems, and/or validate your model and evaluate it. To run a specific single check, all you need to do is import it and then to run it with the required (check-dependent) input parameters. More details about the existing checks and the parameters they can receive can be found in our API Reference. ... -
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Haiku
JAX-based neural network library
...Haiku is a simple neural network library for JAX that enables users to use familiar object-oriented programming models while allowing full access to JAX’s pure function transformations. Haiku is designed to make the common things we do such as managing model parameters and other model state simpler and similar in spirit to the Sonnet library that has been widely used across DeepMind. It preserves Sonnet’s module-based programming model for state management while retaining access to JAX’s function transformations. Haiku can be expected to compose with other libraries and work well with the rest of JAX. ... -
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Deep Java Library (DJL)
An engine-agnostic deep learning framework in Java
Deep Java Library (DJL) is an open-source, high-level, engine-agnostic Java framework for deep learning. DJL is designed to be easy to get started with and simple to use for Java developers. DJL provides native Java development experience and functions like any other regular Java library. You don't have to be a machine learning/deep learning expert to get started. You can use your existing Java expertise as an on-ramp to learn and use machine learning and deep learning. You can use your... -
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DeepCTR
Package of deep-learning based CTR models
DeepCTR is a Easy-to-use,Modular and Extendible package of deep-learning based CTR models along with lots of core components layers which can be used to easily build custom models. You can use any complex model with model.fit(), and model.predict(). Provide tf.keras.Model like interface for quick experiment. Provide tensorflow estimator interface for large scale data and distributed training. It is compatible with both tf 1.x and tf 2.x. With the great success of deep learning,DNN-based techniques have been widely used in CTR prediction task. ... -
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Llama Stack
Composable building blocks to build Llama Apps
Llama-Stack is an open-source framework designed to facilitate the deployment and fine-tuning of large language models (LLMs) for various natural language processing tasks. -
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Bouncer
Laravel Eloquent roles and abilities
Bouncer is an elegant, framework-agnostic approach to managing roles and abilities for any app using Eloquent models. Bouncer is an elegant, framework-agnostic approach to managing roles and abilities for any app using Eloquent models. With an expressive and fluent syntax, it stays out of your way as much as possible: use it when you want, ignore it when you don't. Bouncer works well with other abilities you have hard-coded in your own app. Your code always takes precedence: if your code... -
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Operator Lifecycle Manager
A management framework for extending Kubernetes with Operators
...Kubernetes clusters are being kept up to date using elaborate update mechanisms today, more often automatically and in the background. Operators, being cluster extensions, should follow that. OLM has a concept of catalogs from which Operators are available to install and keep up to date. In this model, OLM allows maintainers granular authoring of the update path and gives commercial vendors a flexible publishing mechanism using channels. -
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Node.js
Cross-platform JavaScript runtime environment
Node.js is a free, open-source, cross-platform JavaScript runtime environment that allows developers to run JavaScript outside of the browser. It is built on Chrome’s V8 engine and is designed for building fast, scalable network applications. Node.js enables developers to create servers, APIs, command-line tools, and full-stack web applications using a single programming language. Its non-blocking, event-driven architecture makes it highly efficient for handling concurrent requests. The... -
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graphql-compose-mongoose
Mongoose model converter to GraphQL types with resolvers
This is a plugin for graphql-compose, which derives GraphQLType from your Mongoose model. Also derives a bunch of internal GraphQL Types. Provide all CRUD resolvers, including graphql connection, also provide basic search via operators. GraphQL Compose Mongoose is a tool to automatically generate a GraphQL API from Mongoose models, simplifying GraphQL schema creation. -
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Moon
A lightweight game server framework implemented with Actor Model
Moon is a lightweight game server framework based on the actor model. One worker thread can have one or more actors (services), which communicate with each other through message queues. There are many features for game server development. -
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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 and backgrounds of end users, SDK writers and runner writers. -
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jCasbin
An authorization library that supports access control models
An authorization library that supports access control models like ACL, RBAC, ABAC for Golang, Java, C/C++, Node.js, Javascript, PHP, Laravel, Python, .NET (C#), Delphi, Rust, Ruby, Swift (Objective-C), Lua (OpenResty), Dart (Flutter) and Elixir. In Casbin, an access control model is abstracted into a CONF file based on the PERM metamodel (Policy, Effect, Request, Matchers). So switching or upgrading the authorization mechanism for a project is just as simple as modifying a configuration. Besides memory and file, Casbin policy can be stored into lots of places. Currently, dozens of databases are supported, from MySQL, Postgres, Oracle to MongoDB, Redis, Cassandra, AWS S3. jCasbin is a powerful and efficient open-source access control library for Java projects. ... -
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Seldon Core
An MLOps framework to package, deploy, monitor and manage models
...Seldon Core, our open-source framework, makes it easier and faster to deploy your machine learning models and experiments at scale on Kubernetes. Seldon Core serves models built in any open-source or commercial model building framework. You can make use of powerful Kubernetes features like custom resource definitions to manage model graphs. And then connect your continuous integration and deployment (CI/CD) tools to scale and update your deployment. Built on Kubernetes, runs on any cloud and on-premises. Framework agnostic, supports top ML libraries, toolkits and languages. ... -
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SageMaker Training Toolkit
Train machine learning models within Docker containers
...Amazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models. To train a model, you can include your training script and dependencies in a Docker container that runs your training code. A container provides an effectively isolated environment, ensuring a consistent runtime and reliable training process. The SageMaker Training Toolkit can be easily added to any Docker container, making it compatible with SageMaker for training models. ...
