Search Results for "framework-3-offline"

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,...

...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.

Implemented classes of 3d objects: * Vector * Point * Line * Plane * LineSegment Yet incompletely implemented classes: * Triangle * Disk (closed circle) * Union (a collection of 3d objects) Each object has methods for finding its sizes, containing box or containing sphere. It finds intersection and distance or closest to another object part of itself. It also can tell if it contains the other object or is it contained by that. Where appropriate, it's easy to check...

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.

...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. All "final" Dockerfiles build images using base images that use the tagging scheme.

Snot The Second, the rubber boxes King, declared a holy war against evil plastic balls to defeat himself and his crew lives. Excentric crossplatform 2D labyrinth game written in Python. Just try to reach exit of each level.

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 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.

Pythonisa is a Python code analyzer is a simple code analyzer for Python that creates a class diagram after executing your code.

The Location Containment Object Model(LCOM) is a simulation framework written in Python. LCOM provides a rule-based solution to handling partial object containment, object migration, message passing, and simulation observation.

Mantikhor is an *information representation* language. Like RDF, it models information as directed-edge arc-node graphs, although Mantikhor's use of such constructs is more structured and constrained.

This project contains a set of tools for formal verification and static analysis of VHDL design.

An AToM3 definition of a CASE framework for creating Zope products with files in the filesystem. The framework consists of a modelling notation ZProduct and a suit of related transformations.

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.