Open Source Linux UML Tools
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UML Tools for Linux
View 1278 business solutionsBrowse free open source UML tools and projects for Linux 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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Gemini 3 and 200+ AI Models on One PlatformBuild generative AI apps with Vertex AI. Switch between models without switching platforms.
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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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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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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Earn up to 16% annual interest with Nexo.Put idle assets to work with competitive interest rates, borrow without selling, and trade with precision. All in one platform. Geographic restrictions, eligibility, and terms apply.
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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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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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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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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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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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AI-generated apps that pass security reviewRetool lets you generate dashboards, admin panels, and workflows directly on your data. Type something like “Build me a revenue dashboard on my Stripe data” and get a working app with security, permissions, and compliance built in from day one. Whether on our cloud or self-hosted, create the internal software your team needs without compromising enterprise standards or control.
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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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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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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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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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Constructs
Define composable configuration models through code
Constructs are classes that define a "piece of system state". Constructs can be composed together to form higher-level building blocks which represent a more complex state. Constructs are often used to represent the desired state of cloud applications. For example, in the AWS CDK, which is used to define the desired state for AWS infrastructure using CloudFormation, the lowest-level construct represents a resource definition in a CloudFormation template. These resources are composed to represent higher-level logical units of a cloud application, etc. -
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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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OrientDB
DBMS supporting graph, document, full-text and geospatial models
OrientDB is an Open Source Multi-Model NoSQL DBMS with the support of Native Graphs, Documents, Full-Text search, Reactivity, Geo-Spatial and Object Oriented concepts. It's written in Java and it's amazingly fast. No expensive run-time JOINs, connections are managed as persistent pointers between records. You can traverse thousands of records in no time. Supports schema-less, schema-full and schema-mixed modes. Has a strong security profiling system based on user, roles and predicate security and supports SQL amongst the query languages. Thanks to the SQL layer it's straightforward to use for people skilled in the Relational world. OrientDB adheres to the NoSQL movement even though it supports ACID Transactions and SQL as query language. In this way it's easy to start using it without having to learn too much new stuff. -
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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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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 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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Image classification models for Keras
Keras code and weights files for popular deep learning models
All architectures are compatible with both TensorFlow and Theano, and upon instantiation the models will be built according to the image dimension ordering set in your Keras configuration file at ~/.keras/keras.json. For instance, if you have set image_dim_ordering=tf, then any model loaded from this repository will get built according to the TensorFlow dimension ordering convention, "Width-Height-Depth". Pre-trained weights can be automatically loaded upon instantiation (weights='imagenet' argument in model constructor for all image models, weights='msd' for the music tagging model). Weights are automatically downloaded if necessary, and cached locally in ~/.keras/models/. This repository contains code for the following Keras models, VGG16, VGG19, ResNet50, Inception v3, and CRNN for music tagging. -
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PyMC3
Probabilistic programming in Python
PyMC3 allows you to write down models using an intuitive syntax to describe a data generating process. Fit your model using gradient-based MCMC algorithms like NUTS, using ADVI for fast approximate inference — including minibatch-ADVI for scaling to large datasets, or using Gaussian processes to build Bayesian nonparametric models. PyMC3 includes a comprehensive set of pre-defined statistical distributions that can be used as model building blocks. Sometimes an unknown parameter or variable in a model is not a scalar value or a fixed-length vector, but a function. A Gaussian process (GP) can be used as a prior probability distribution whose support is over the space of continuous functions. PyMC3 provides rich support for defining and using GPs. Variational inference saves computational cost by turning a problem of integration into one of optimization. PyMC3's variational API supports a number of cutting edge algorithms, as well as minibatch for scaling to large datasets. -
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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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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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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. -
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statsmodels
Statsmodels, statistical modeling and econometrics in Python
statsmodels is a Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests, and statistical data exploration. An extensive list of result statistics are available for each estimator. The results are tested against existing statistical packages to ensure that they are correct. The package is released under the open source Modified BSD (3-clause) license. Generalized linear models with support for all of the one-parameter exponential family distributions. Markov switching models (MSAR), also known as Hidden Markov Models (HMM). Vector autoregressive models, VAR and structural VAR. Vector error correction model, VECM. Robust linear models with support for several M-estimators. statsmodels supports specifying models using R-style formulas and pandas DataFrames.
