Open Source Python UML Tools

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.

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.

The torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision. We recommend Anaconda as Python package management system. Torchvision currently supports Pillow (default), Pillow-SIMD, which is a much faster drop-in replacement for Pillow with SIMD, if installed will be used as the default. Also, accimage, if installed can be activated by calling torchvision.set_image_backend('accimage'), libpng, which can be installed via conda conda install libpng or any of the package managers for debian-based and RHEL-based Linux distributions, and libjpeg, which can be installed via conda conda install jpeg or any of the package managers for debian-based and RHEL-based Linux distributions. It supports libjpeg-turbo as well. libpng and libjpeg must be available at compilation time in order to be available. TorchVision also offers a C++ API that contains C++ equivalent of python models.

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.

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.

DAE Tools is a cross-platform equation-based object-oriented modelling, simulation and optimisation software. It is not a modelling language nor a collection of numerical libraries but rather a higher level structure – an architectural design of interdependent software components providing an API for: - Model development/specification - Activities on developed models, such as simulation, optimisation, sensitivity analysis and parameter estimation - Processing of the results, such as plotting and exporting to various file formats - Report generation - Code generation, co-simulation and model exchange The following class of problems can be solved by DAE Tools: - Initial value problems of implicit form - Index-1 DAE systems - With lumped or distributed parameters - Steady-state or dynamic - Continuous with some elements of event-driven systems

Multi-categorization Recommendation Adjusting (MRA) is to optimize the results of recommendation based on traditional(basic) recommendation models, through introducing objective category information and taking use of the feature that users always get the habits of preferring certain categories. Besides this, there are two advantages of this improved model: 1) it can be easily applied to any kind of existing recommendation models. And 2) a controller is set in this improved model to provide controllable adjustment range, which thereby makes it possible to provide optional modes of recommendation aiming different kinds of users.

Modelio-Open is a project hosting a set of open source extensions (SoaML, SysML and UML Testing Profile) for a previous version (1.2) of the Modelio Free tool . Currently, the lastest version (2.x) of Modelio modeling and generation tool is available at http://modelio.org/downloads/download-modelio.html. All extensions are downloadable at http://forge.modelio.org/projects.

O PETRILab é um software multiplataforma desenvolvido inteiramente em Python. Ele permite a modelagem e simulação de Redes de Petri Interpretadas para Controle (RPIC), tendo suporte a todos seus elementos: lugares, transições, arcos ordinários e inibidores, eventos, condições e ações. Com uma interface gráfica simples e intuitiva, o usuário consegue modelar e simular passo-a-passo sua RPIC de forma rápida e prática, afim de estudá-la e aprimorá-la. Além disso, o software conta com uma conversão automática de RPICs em diagramas Ladder, como proposto no artigo de M. V. Moreira e J. C. Basílio [1]. Para utilizar o programa, baixe a versão mais recente em https://sourceforge.net/projects/petrilab/files/, extraia no local de preferência e execute o arquivo 'petrilab.pyw'. [1] M. V. Moreira e J. C. Basílio, “Bridging the Gap Between Design and Implementation of Discrete-Event Controllers”.IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERIN

This project provides a set of Python tools for creating various kinds of neural networks, which can also be powered by genetic algorithms using grammatical evolution. MLP, backpropagation, recurrent, sparse, and skip-layer networks are supported.

Our goal is to develop a full working solver for ATA (with 1 clock) in Python, with MTL to ATA support. The decidability for the emptiness problem was proposed by Lasota and Walukiewicz. The MTL to ATA was proposed by Ouaknine and Worrell.

Pretty Damn Quick (PDQ) analytically solves queueing network models of computer and manufacturing systems, data networks, etc., written in conventional programming languages. Generic or customized reports of predicted performance measures are output.

SPE is a python IDE with auto indentation&completion,call tips,syntax coloring&highlighting,uml viewer,class explorer,source index,todo list,pycrust shell,file browsers,drag&drop,Blender support.Spe ships with wxGlade,PyChecker and Kiki.

In Systems Biology models are created in various formats (Matlab, Java, C/C++, Python, ...). "Annotate Your Model" will help you to link your model to biological web resources by creating a CSV file containing MIRIAM annotations.

Framework applying all MDE (Model Driven Engineering) concepts with : a core MetaMeta Model based on the OMG's MOF, a model transformation engine, a code generation engine (in discussion...) and a set of MetaModel suitable for use with this framework.

That project aims at providing a clean API, and the corresponding C++ implementation, for the basis of Airline IT Business Object Model (BOM), ie, to be used by several other Open Source projects, such as RMOL, Air-Sched, Travel-CCM, OpenTREP, etc.

Coral is a tool and a development platform to create and transform models and modeling languages. It can be used to edit UML models, to develop editors for other modeling languages and to implement MDA and QVT-like model transformations.

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.

This is a suite of tools for farmers it includes local market prices for their sales, weather reports, other features useful to farmers.

Gaphor is a UML modeling environment written in Python. Gaphor is small and very extensible. The repository is located at http://github.com/gaphor/gaphor.

A Python programming environment providing memory sizing, profiling and analysis, and a specification language that can formally specify aspects of Python programs and generate tests and documentation from a common source.

This is an educational example of how to run a numerical model (in Fortran) from Python, including data handling (netCDF), configuration with a config file, etc.

Institute of Technology, Blanchardstown Computer Science code by the class of 2007-2011 on course BN104. In this project we are open sourcing all of our project work to the public in the hopes it can be reused, built-upon, and used in education.

KML is a knowledge base with support of logical modeling. Advanced model is used to represent knowledge as a set of statements similar to natural language sentences. This project hosts a set of model storage library and server (vrb-ols) and clients.

Konzept is a small class diagram editor. Major design goal was usability. The project was inspired by the static diagram editor of the Toolkit of Conceptual Modelling. Konzept is a pure Qt application written in Python.