Search Results for "as built"
Sort By:
Showing 27 open source projects for "as built"
View related business solutions-
AI-powered service management for IT and enterprise teamsGive your IT, operations, and business teams the ability to deliver exceptional services—without the complexity. Maximize operational efficiency with refreshingly simple, AI-powered Freshservice.
-
Go From AI Idea to AI App FastAccess Gemini 3 and 200+ models. Build chatbots, agents, or custom models with built-in monitoring and scaling.
-
1
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. ... -
2
TensorFlow.js
TensorFlow.js is a library for machine learning in JavaScript
...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. -
3
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,... -
4
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... -
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.
-
5
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). -
6
...FOML can reason about: 1. The model meta-data (meta-model level reasoning, or syntax reasoning) 2. Data level: reason about a model and its data (instance model, i.e., diagram) FOML is built as a semantic layer on top of PathLP - a compact logic rule language of guarded path expressions, an adaptation of a subset of F-logic.
-
7
...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.
-
8
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... -
9
PyText
A natural language modeling framework based on PyTorch
...We use PyText at Facebook to iterate quickly on new modeling ideas and then seamlessly ship them at scale. Distributed-training support built on the new C10d backend in PyTorch 1.0. Mixed precision training support through APEX (trains faster with less GPU memory on NVIDIA Tensor Cores). Extensible components that allows easy creation of new models and tasks. -
Try Google Cloud Risk-Free With $300 in CreditUse your credit across every product. Compute, storage, AI, analytics. When it runs out, 20+ products stay free. You only pay when you choose to.
-
10
SageMaker Chainer Containers
Docker container for running Chainer scripts to train and host Chainer
...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. The "base" Dockerfile encompasses the installation of the framework and all of the dependencies needed. ... -
11
JAS-mine
Toolkit for agent-based & dynamic microsimulation modelling
...With the aim to develop large-scale, data-driven models, the main architectural choice of JAS-mine is to use whenever possible standard, open-source tools already available in the software development community. It has built-in utilities for communicating with an underlying relational database. In addition, the platform provides standard tools which are frequently used both in agent-based modelling and dynamic microsimulations, like design of experiments (DOE), run-time monitoring and visualization with plots and graphs (GUI), I/O communication and statistical analysis. ... -
12
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). ... -
13
Edward
A probabilistic programming language in TensorFlow
...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. -
14
astah-anycode-plugin
Code generation from UML to any language (Java, Objective-C, PHP...)
...It enables to generate source code from UML class diagrams to various languages (Java, JPA, PHP, C#, Objective-C...) with custom templates. It now makes source code generation easy thanks to built-in directives. -
15
StarUML NS
SINVAS(StarUML NS) is re-developed by StarUML developers in Qt.
...By strengthening the advantage of existing StarUML version and making up for the shortcoming of, the project’s goal wanted to be a modeling platform that could illustrate the modeling from possessed information, beyond UML modeling tool. Because a refactoring and the work of separation of enterprise business function are performing all together in order to make StarUML NS, enterprise solution built, into open-source, the Community Edition of StarUML NS is not completely finished. All of you may test this community edition, and then please send us any suggestions, the bugs about function; thereby we really believe that your participation will be contributed to the advancement in the product. -
16
Framework for creation of board games. Chess is built-in, Monopoly comes next. It addresses: 1) Players 2) Game authors 3) AI developers 4) Model-based developers. Developed by software professionals using EMF. New games are configured in few hours.
-
17
Jensor is a light-weight, low-overhead Java Profiler built using Java BCI. Jensor provides innovative analysis modules like JVM Replay, Pattern Analysis and Tagging Engine for technical as well as business analysis based on the same underlying data.
-
18
SPSens
Stochastic parameter sensitivity analysis for chemical networks
...Additionally the package includes basic stochastic simulation algorithms. The package includes several example networks which can be easily modified for other networks. Serial and parallel MPI implementations can be built and called from the command line. The serial implementation of the package can also be called from Matlab via the MEX interface with limited functionality. It supports many user specified options, including the use of antithetic sampling that can be used in some cases to increase efficiency. -
19
The Latiz Palette Modeling Environment is a systems modeling event driven simulation environment written in Java and built on the NetBeans platform.
-
20
The project is built with oaW (openArchitectureWare) maybe the most popular framework of MDSD now days. It provide a small DSL for modeling and can be extended (the code generated) through a cartridge mechanism. Give it a chance :) , feedback are welcome
-
21
Complex list management with an extremely versatile interface. Built-in sorting (with abstract variants), finding, iteration, and element manipulation. Supports const-interface elements. Transparent 'virtual array storage' (VAS) for large element types
-
22
(Pre-Alpha) Cross-Platform ERD to Database Design Tool built in Java. Currently Supports PostgreSQL. Not Feature-Complete.
-
23
This is a software visualizer built entirely on non-iconic glyphs, with support for color-impaired operators. Renderings produced show software as a diagram, not as a written language. The SableCC language interpreter is involved.
-
24
QVT-Parser parses QVT-Relations model-transformation specifications from textual syntax into EMF QVT-models. QVT defines a language for expressing model-to-model transformations. The parser is is built using ANTLR and EMF.
-
25
A suite of tools and components assisting with model driven software development built upon a modular MDA/MDD generator framework implemented in Java(TM) supporting arbitrary import (design) formats, meta models, and output (code) formats.
