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Showing 19 open source projects for "building"
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Secure File Transfer for Windows with Cerberus by RedwoodCerberus supports unlimited users and connections on a single IP, with built-in encryption, 2FA, and a browser-based web client — all deployable in under 15 minutes with a 25-day free trial.
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Full-stack observability with actually useful AI | Grafana CloudBuilt on open standards like Prometheus and OpenTelemetry, Grafana Cloud includes Kubernetes Monitoring, Application Observability, Incident Response, plus the AI-powered Grafana Assistant. Get started with our generous free tier today.
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Mongoose
Elegant mongodb object modeling for node.js
...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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TensorFlow.js
TensorFlow.js is a library for machine learning in JavaScript
...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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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. -
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PyMC3
Probabilistic programming in Python
...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. ... -
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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SageMaker Hugging Face Inference Toolkit
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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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. ... -
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Mimosa is a modeling and simulation platform, covering the process from building conceptual models to running the simulations. The specification uses ontologies and an extensible set of formalisms for the dynamics. The simulation kernel is based on DEVS.
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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... -
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SageMaker Chainer Containers
Docker container for running Chainer scripts to train and host Chainer
SageMaker Chainer Containers is an open-source library for making the Chainer framework run on Amazon SageMaker. This repository also contains Dockerfiles which install this library, Chainer, and dependencies for building SageMaker Chainer images. Amazon SageMaker utilizes Docker containers to run all training jobs & inference endpoints. The Docker images are built from the Dockerfiles specified in Docker/. The Docker files are grouped based on Chainer version and separated based on Python version and processor type. The Docker images, used to run training & inference jobs, are built from both corresponding "base" and "final" Dockerfiles. ... -
Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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Physhun is a Java framework for building and executing finite state machines in J2SE and J2EE environments. State models are defined as collections of Spring beans, and do not use proprietary modeling languages. The framework is simple and flexible.
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The Java Light Component Framework (JLCF) is a framework for developing modular applications for the java language. It allows designing an application using building blocks with well-defined inputs and outputs (aka software components). Using this well-defined modular approach, the application has a clear and explicit design which facilitates the development and maintenance of a software system. JLCF focuses on usability on a simple programming model and also provides advanced features such as interceptors, callbacks and dynamic component replacement.
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ROO is an (OWL) ontology construction tool based on Protégé 4. ROO makes the ontology building process easier as it provides guidance about the steps involved and it allows to enter knowledge using an easy to learn controlled natural language.
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SchemaForms is a toolkit for building HTML form from an XML data file and an XML-Schema specification. Supports basic data types (string, checkbox, enumeration,..) but is easily extensible to custom data types.
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Galois sub-hierarchy builder
Visualize Galois sub-hierarchy of 2d matrix
..."Efficiently computing a linear extension of the sub-hierarchy of a Concept Lattice" by Anne Berry, Marianne Huchard, Ross M. McConnell, Alain Sigayret and Jeremy P. Spinrad. "Performances of Galois Sub-hierarchy-building algorithms" by Gabriela Arevalo, Anne Berry, Marianne Huchard, Guillaume Perrot and Alain Sigayret In you're in need of other GSH algorithms or their realizations in C++, you might want to investigate a project called Galicia on sourceforge. This program is free software and is licensed under GNU General Public License as published by the Free Software Foundation. ... -
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The Adobe Source Libraries (ASL) are a collection of C++ libraries building foundation technology to allow the construction of commercial applications by assembling generic algorithms through declarative descriptions.
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OpenMDE
An ecosystem for model-driven engineering on the JavaEE platform.
The vision is to enable the incorporation of best practices, standard patterns and tools for building robust, scalable, distributed applications with a rich domain model without forcing any lock-in with any particular vendor or framework. For more information visit: http://www.openmde.org. -
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CodaServer is a database that makes building business applications easy. It uses a SQL-like language that lets you describe your workflow and business processes, and uses this information to enforce your business rules and guarantee data integrity.
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Jello Framework is an AOA based java web application framework,it includes many distinct viewpoints such as None Config, BPC Model,DMM persistence technology and so on,supports rapid web application building.
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UMMF is an open-source implementation of UML-compatible meta-meta-model, for the purposes of reifying meta-model implementations and to provide a platform for building flexible model and meta-model transformation tools.
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