Search Results for "python 2"

... are suitable. A flexible and lightweight library that users can easily use or fork when writing customized training loop code in TensorFlow 2.x. It seamlessly integrates with tf.distribute and supports running on different device types (CPU, GPU, and TPU).

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...

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...

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.

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.

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.