Search Results for "frameworks"
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Showing 112 open source projects for "frameworks"
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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,... -
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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... -
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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... -
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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... -
Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
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DMF: Distributed Multiplatform Framework
Development framework including code generator and UML ex/import
A component based programming framework. This project is aimed to support various target frameworks. A wxWidgets based GUI application is the major sample which also provides rapid database GUI design with UML import and export (db reverse engineering). -
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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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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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PyText
A natural language modeling framework based on PyTorch
PyText is a deep-learning based NLP modeling framework built on PyTorch. PyText addresses the often-conflicting requirements of enabling rapid experimentation and of serving models at scale. It achieves this by providing simple and extensible interfaces and abstractions for model components, and by using PyTorch’s capabilities of exporting models for inference via the optimized Caffe2 execution engine. We use PyText at Facebook to iterate quickly on new modeling ideas and then seamlessly... -
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setupdocx
Multidocument automation by templates - for sphinx, mkdocs, epydoc ...
The ‘setupdocx‘ provides a control layer for continuous documentation by the simplified creation, packaging, and installation of documentation. The provided commands are distributed as entry points and optional base classes for further customization into 'setup.py' - setuptools / distutils. Manages arbitrary document templates for the supported builder, supports multiple builds with arbitrary document layouts, designs, and patched contents. The current release supports the following... -
Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
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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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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... -
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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'... -
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PREESM provides a complete framework for fast prototyping. It generates a simulation and an optimized multiprocessor code from graph descriptions of algorithm and architecture. Please visit the project homepage at http://preesm.sourceforge.net.
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PopperFramework
Declarative PageObject description
PopperFramework is a java framework which allows you to create PageObjects for your webapplication-tests in a declarative way. That means you just have to define an interface defining the structure of your website and add some annotation with the basic information like locators. PopperFramework will generate the implementation of that PageObject for you. Key-Goal of PopperFramework is to allow you as a developer of sureface-tests to concentrate on your business => writing PageObjets and... -
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BOSS Flow
Workflow control and configuration java framework
A framework to simply understandable control flows in applications. It is easy to manage flow by XML and annotations. There are many simple ways to catch actions such as transition, step entry or leaving, including control the decision similar to 'if' or 'switch' statements. -
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MOBIDICK (MOdular But IntegrateD applICation frameworK) is dedicated to gather as less as possible Java frameworks in order to complete full needs of large business application and allow regeneration of code, even when java frameworks change.
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Open source development for FluidEarth (https://fluidearth.net/default.aspx) which supports the OpenMI Standard (http://openmi.sourceforge.net) Here are OpenMI Association approved applications.
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Essential ORM Modeler
Design your ORM application in a platform independent way.
Essential ORM Modeler allows you to model your application using an extension of UML class models, generating code for distinct ORM frameworks (JPA, SQLAlchemy or Ruby´s ActiveRecord). The notation, based on persistence patterns, is platform independent. This tool is a plugin of eclipse. -
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JavATE is a set of Java libraries that enables application development using the domain driven methodology.
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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...
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Deduced is used to edit and generate model classes. It also provides tools to implement code deduction patterns. It also simplifies data exchange and persistance.
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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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Go! is a framework that enables development of user space device drivers in Linux. Go! allows Linux device drivers to be written using UML and C++, and deployed in user space with minimal effects on performance.
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This is a collection of tools and a code library to assist engineers who are developing SystemVerilog based verification environments. Components include utility libraries, scoreboard and shutdown manager implementation, register tool, etc.
