Open Source Mac UML Tools
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UML Tools for Mac
View 1412 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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brModelo 3.2
ER Databese Model
Tool used to Database ER model Ferramenta para modelagem ER em bancos de dados. -
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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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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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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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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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NClass is a free tool to easily create UML class diagrams with full C# and Java language support.
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The GR module for Julia
Plotting for Julia based on GR
This is the GR module for Julia. It places a Julia interface to GR, a universal framework for visualization applications. GR allows users to create high quality, engaging visualizations, everything from 2D graphs to complex 3D scenes. With this module simply type in Julia 'using gr', and you can instantly start calling functions in the GR framework API. GR is based on an implementation of a Graphical Kernel System (GKS) and OpenGL. As a self-contained system, integration into existing applications is quick and easy-- simply use a direct call from Julia with ccall syntax. -
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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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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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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 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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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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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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odaba
ODABA is an terminology-oriented database management system and develo
ODABA is an terminology-oriented database management system (TODBMS) on a high conceptual level. It provides a number of enhanced features based on natural language analysis. Terminology orientation allows designing user oriented data models. Enhanced event model and Active Data Link technology support rapid development of GUI applications. Several development, documentation, test and analyzing tools are provided in order to support developers and administrators. .NET interface and a powerful object-orjented script language (OSI) improve development efficiency. -
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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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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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NOTE: The GROOVE codebase has moved to https://github.com/nl-utwente-groove
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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.js models
Pretrained models for TensorFlow.js
This repository hosts a set of pre-trained models that have been ported to TensorFlow.js. The models are hosted on NPM and unpkg so they can be used in any project out of the box. They can be used directly or used in a transfer learning setting with TensorFlow.js. To find out about APIs for models, look at the README in each of the respective directories. In general, we try to hide tensors so the API can be used by non-machine learning experts. New models should have a test NPM script. You can run the unit tests for any of the models by running "yarn test" inside a directory. Use off-the-shelf JavaScript models or convert Python TensorFlow models to run in the browser or under Node.js. Build and train models directly in JavaScript using flexible and intuitive APIs. Develop ML models in JavaScript, and use ML directly in the browser or in Node.js. -
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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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DeepCTR-Torch
Easy-to-use,Modular and Extendible package of deep-learning models
DeepCTR-Torch is an easy-to-use, Modular and Extendible package of deep-learning-based CTR models along with lots of core components layers that can be used to build your own custom model easily.It is compatible with PyTorch.You can use any complex model with model.fit() and model.predict(). With the great success of deep learning, DNN-based techniques have been widely used in CTR estimation tasks. The data in the CTR estimation task usually includes high sparse,high cardinality categorical features and some dense numerical features. Low-order Extractor learns feature interaction through product between vectors. Factorization-Machine and it’s variants are widely used to learn the low-order feature interaction. High-order Extractor learns feature combination through complex neural network functions like MLP, Cross Net, etc. -
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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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jFuzzyLogic is a java implementation of a Fuzzy Logic software package. It implements a complete Fuzzy inference system (FIS) as well as Fuzzy Control Logic compliance (FCL) according to IEC 61131-7 (formerly 1131-7).
