Open Source Mac UML Tools
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UML Tools for Mac
View 1458 business solutionsBrowse free open source UML tools and projects for Mac below. Use the toggles on the left to filter open source UML tools by OS, license, language, programming language, and project status.
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PlantUml allows to quickly create some UML diagram using a simple textual description language.
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Open tool platform for the cost effective rigorous development of dependable complex software systems services. This platform is based on the event-B formal method and provides natural support for refinement and mathematical proof.
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brModelo 3.2
ER Databese Model
Tool used to Database ER model Ferramenta para modelagem ER em bancos de dados. -
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NClass is a free tool to easily create UML class diagrams with full C# and Java language support.
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99.99% Uptime for MySQL and PostgreSQL on Google CloudCloud SQL Enterprise Plus gives you a 99.99% availability SLA with near-zero downtime maintenance—typically under 10 seconds. Get 2x better read/write performance, intelligent data caching, and 35 days of point-in-time recovery. Supports MySQL, PostgreSQL, and SQL Server with built-in vector search for gen AI apps. New customers get $300 in free credit.
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Violet is a UML editor with these benefits: Very easy to learn and use. Draws nice-looking diagrams. Completely free. Cross-platform. Violet is intended for developers, students, teachers, and authors who need to produce simple UML diagrams quickly. Want to contribute ? Source code is hosted on github : https://github.com/violetumleditor/violetumleditor
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JGraphT is a free Java class library that provides mathematical graph-theory objects and algorithms. JGraphT supports a rich gallery of graphs and is designed to be powerful, extensible, and easy to use.
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Yaoqiang BPMN Editor
an Open Source BPMN 2.0 / DMN 1.1 Modeler
Yaoqiang BPMN Editor is a graphical editor for business process diagrams, compliant with OMG specifications (BPMN 2.0 / DMN 1.1). -
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Mongoose
Elegant mongodb object modeling for node.js
Mongoose is a MongoDB object modeling tool that was built to answer the need for better ways to model your application data. It's designed to work in an asynchronous environment, providing a simple, straightforward approach to object modeling that skips out on the tedious tasks of writing MongoDB validation, casting and business logic boilerplate. Mongoose offers an uncomplicated schema-based solution, and comes with nifty features like type casting, validation, query building, and business logic hooks right out of the box. Mongoose also has a rich set of plugins made by the community and you can write your own to make Mongoose an even better solution for your needs. -
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Modelio - Modeling environment (UML)
Modeling tool supporting UML, BPMN and other standards
Modelio is an open source modeling environment tool providing support for the latest standards (UML 2, BPMN 2, ...). It can be extended by adding modules which add new functionalities. A large set of modules (free and open source) supporting code management (generation/reverse), modeling standards (TOGAF, SysML, SoaML, ...), document generation, ... is available from the Modelio Store (https://store.modelio.org/). Need help or want to share experience with the Modelio community? Go to our forum: https://www.modelio.org/forum/index.html. -
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The Whole Platform is a technology for engineering the production of software. We provide an Eclipse based Language Workbench for developing, manipulating and transforming languages using a model driven approach.
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Edward
A probabilistic programming language in TensorFlow
A library for probabilistic modeling, inference, and criticism. Edward is a Python library for probabilistic modeling, inference, and criticism. It is a testbed for fast experimentation and research with probabilistic models, ranging from classical hierarchical models on small data sets to complex deep probabilistic models on large data sets. Edward fuses three fields, Bayesian statistics and machine learning, deep learning, and probabilistic programming. Edward is built on TensorFlow. It enables features such as computational graphs, distributed training, CPU/GPU integration, automatic differentiation, and visualization with TensorBoard. Expectation-Maximization, pseudo-marginal and ABC methods, and message passing algorithms. -
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Laravel MongoDB
MongoDB based eloquent model and Query builder for Laravel
This package adds functionalities to the Eloquent model and Query builder for MongoDB, using the original Laravel API. This library extends the original Laravel classes, so it uses exactly the same methods. Make sure you have the MongoDB PHP driver installed. In case your Laravel version does NOT autoload the packages, add the service provider to config/app.php. For usage with Lumen, add the service provider in bootstrap/app.php. In this file, you will also need to enable Eloquent. You must however ensure that your call to $app->withEloquent(); is below where you have registered the MongodbServiceProvider. The service provider will register a MongoDB database extension with the original database manager. There is no need to register additional facades or objects. When using MongoDB connections, Laravel will automatically provide you with the corresponding MongoDB objects. -
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SageMaker Training Toolkit
Train machine learning models within Docker containers
Train machine learning models within a Docker container using Amazon SageMaker. 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. If you use a prebuilt SageMaker Docker image for training, this library may already be included. Write a training script (eg. train.py). Define a container with a Dockerfile that includes the training script and any dependencies. -
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USE is a system for the specification and validation of information systems based on a subset of the Unified Modeling Language (UML) and the Object Constraint Language (OCL). Please report any encountered bugs using the ticket system. The USE documentation can be found on the project homepage linked below. Downloads for the most popular plugins can be found here: http://sourceforge.net/projects/useocl/files/Plugins/
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DRAKON Editor
A free cross-platform editor for the DRAKON visual language.
DRAKON is a diagram language developed within the Russian space program. Its primary objective is presenting complex software systems in a way which is easy to understand by humans. DRAKON's motto: took a glance - understood at once. DRAKON Editor helps software architects, quality specialists and developers. Architects and quality assurers can express a high-level view of how their product works. DRAKON serves them to explain the dynamics of a software system. Software engineers can use DRAKON Editor to build algorithms in Go, Java, Processing.org, D, C#, C, C++, Python, Tcl, Javascript, Erlang and Lua. -
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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. Built on top of TensorFlow.js, the ml5.js library provides access to machine learning algorithms and models in the browser with a concise, approachable API. Comfortable with concepts like Tensors, Layers, Optimizers and Loss Functions (or willing to get comfortable with them)? TensorFlow.js provides flexible building blocks for neural network programming in JavaScript. -
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A fast and reliable tool, written in Java 8, for generating professional UML sequence diagrams from text. Supports actors, con-/destructors, threads, broadcasts, notes, and fragments. Can serve as a Javadoc taglet. Exports PDF, (E)PS, SVG, PNG, ... The source code is hosted at http://github.com/sdedit/sdedit
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AWS Deep Learning Containers
A set of Docker images for training and serving models in TensorFlow
AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet. Deep Learning Containers provide optimized environments with TensorFlow and MXNet, Nvidia CUDA (for GPU instances), and Intel MKL (for CPU instances) libraries and are available in the Amazon Elastic Container Registry (Amazon ECR). The AWS DLCs are used in Amazon SageMaker as the default vehicles for your SageMaker jobs such as training, inference, transforms etc. They've been tested for machine learning workloads on Amazon EC2, Amazon ECS and Amazon EKS services as well. This project is licensed under the Apache-2.0 License. Ensure you have access to an AWS account i.e. setup your environment such that awscli can access your account via either an IAM user or an IAM role. -
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Laravel Media Library
Associate files with Eloquent models
This package can associate all sorts of files with Eloquent models. It provides a simple, fluent API to work with. The Pro version of the package offers Blade, Vue and React components to handle uploads to the media library and to administer the content of a medialibrary collection. The storage of the files is handled by Laravel's Filesystem, so you can use any filesystem you like. Additionally the package can create image manipulations on images and pdfs that have been added in the media library. -
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SageMaker Hugging Face Inference Toolkit
Library for serving Transformers models on Amazon SageMaker
SageMaker Hugging Face Inference Toolkit is an open-source library for serving Transformers models on Amazon SageMaker. This library provides default pre-processing, predict and postprocessing for certain Transformers models and tasks. It utilizes the SageMaker Inference Toolkit for starting up the model server, which is responsible for handling inference requests. For the Dockerfiles used for building SageMaker Hugging Face Containers, see AWS Deep Learning Containers. The SageMaker Hugging Face Inference Toolkit implements various additional environment variables to simplify your deployment experience. The Hugging Face Inference Toolkit allows user to override the default methods of the HuggingFaceHandlerService. SageMaker Hugging Face Inference Toolkit is licensed under the Apache 2.0 License. -
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Amazon SageMaker Examples
Jupyter notebooks that demonstrate how to build models using SageMaker
Welcome to Amazon SageMaker. This projects highlights example Jupyter notebooks for a variety of machine learning use cases that you can run in SageMaker. If you’re new to SageMaker we recommend starting with more feature-rich SageMaker Studio. It uses the familiar JupyterLab interface and has seamless integration with a variety of deep learning and data science environments and scalable compute resources for training, inference, and other ML operations. Studio offers teams and companies easy on-boarding for their team members, freeing them up from complex systems admin and security processes. Administrators control data access and resource provisioning for their users. Notebook Instances are another option. They have the familiar Jupyter and JuypterLab interfaces that work well for single users, or small teams where users are also administrators. Advanced users also use SageMaker solely with the AWS CLI and Python scripts using boto3 and/or the SageMaker Python SDK. -
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TensorFlow Model Garden
Models and examples built with TensorFlow
The TensorFlow Model Garden is a repository with a number of different implementations of state-of-the-art (SOTA) models and modeling solutions for TensorFlow users. We aim to demonstrate the best practices for modeling so that TensorFlow users can take full advantage of TensorFlow for their research and product development. To improve the transparency and reproducibility of our models, training logs on TensorBoard.dev are also provided for models to the extent possible though not all models are suitable. A flexible and lightweight library that users can easily use or fork when writing customized training loop code in TensorFlow 2.x. It seamlessly integrates with tf.distribute and supports running on different device types (CPU, GPU, and TPU). -
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EiffelStudio is an Integrated Development Environment (IDE) that provides comprehensive facilities to programmers. It offers a comprehensive suite of tools that enable programmers to produce correct, reliable, and maintainable software while keeping control of the development process. If you want to create fast, robust, scalable applications, then EiffelStudio™ will offer you a cost-effective solution. Imagine being able to model your system as you think – capturing your requirements and your thought processes with EiffelStudio. When ready to design, you build upon the model you just created, still with EiffelStudio. Then you implement with EiffelStudio. You never need to throw anything out and start over. You don’t need extra tools to go back and safely make changes in your architecture. Roundtrip engineering? It’s built in by design. Testing, metrics and productivity tools? They’re built in. EiffelStudio accommodates quickly and efficiently new thoughts and changes.
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Java 2D & 3D visual entity relationship design & modeling (ERD,SQL) for Oracle,MSSQL,Postgres,MySQL,...,Database change&dictionary management, Swing Data Binding, Apache FOP Renderer for dot matrix printers,Sparx Enterprise Architect Reports + more
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Linguistic Tree Constructor
Syntax tree editor for rapid annotation of existing text
Linguistic Tree Constructor (LTC) is a tool for drawing lingusitic syntax trees of already-existing text. It is a syntax editor, not a text editor, so the text has to exist already. It is best suited for large-scale, rapid creation of hand-annotated treebanks. The user can define their own node categories, and can label each node with labels, also definable by the user. LTC supports "generic", X-Bar and RRG trees. Supports interlinear texts in SIL SFM format.
