Search Results for "cpu-g"

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...

... 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).

I was tired of the wayward UML tools to explain some simple ideas and share with my colleagues, I created this tool to create simple schema simply. Not perfect but do the job.

.... 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.

Model-based development environment for embedded projects. Features a clean and advanced language designed for embedded programming.

This program allows one to virtually see how the different process scheduling algorithms work. The purpose of this program is to act as a recruiting tool for future computer engineers as well as a teaching tool for future Modern OS classes.