Search Results for "cpu-z"

Community Z Tools Project (CZT): Tools for editing, typechecking and animating Z specifications and related notations. Includes a Java framework for building formal methods tools. NOTE: development of CZT has now moved to GitHub: https://github.com/community-z-users/czt

...Deep learning applications require complex, multi-stage data processing pipelines that include loading, decoding, cropping, resizing, and many other augmentations. These data processing pipelines, which are currently executed on the CPU, have become a bottleneck, limiting the performance and scalability of training and inference. DALI addresses the problem of the CPU bottleneck by offloading data preprocessing to the GPU. Additionally, DALI relies on its own execution engine, built to maximize the throughput of the input pipeline.

Bento is a project that encapsulates Packer templates for building Vagrant base boxes. A subset of templates are built and published to the bento org on Vagrant Cloud. These published boxes serve as the default boxes for kitchen-vagrant.

The POCO C++ Libraries are powerful cross-platform C++ libraries for building network- and internet-based applications that run on desktop, server, mobile, IoT, and embedded systems. Whether building automation systems, industrial automation, IoT platforms, air traffic management systems, enterprise IT application and infrastructure management, security and network analytics, automotive infotainment and telematics, financial or healthcare, C++ developers have been trusting the POCO C++...

...The application Ob open four browsers, Tesseract applications container, TAL, Chatosx, AndroMac, AndroEmu, MySMS, Shutdown, Updater, Search for, MyMaps, SOA, BLI, Xcoder, BasicProg, BasicProg2, VM, D,M&Z Suite Basic and D,M&Z Suite Advanced (POSIX based).

...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, transforms etc. They've been tested for machine learning workloads on Amazon EC2, Amazon ECS and Amazon EKS services as well.

pyTorch Tutorials is an open-source collection of hands-on tutorials designed to teach developers how to build neural networks with the PyTorch framework. It covers the fundamentals of PyTorch from basic tensor operations to constructing full neural network models, making it suitable for beginners and intermediate learners alike. The project is structured around clear, executable Python scripts and Jupyter notebooks that demonstrate regression, classification, convolutional networks,...

...The Docker images are built from the Dockerfiles in docker/. The Dockerfiles are grouped based on the version of TensorFlow Serving they support. Each supported processor type (e.g. "cpu", "gpu", "ei") has a different Dockerfile in each group. If your are testing locally, building the image is enough. But if you want to your updated image in SageMaker, you need to publish it to an ECR repository in your account. You can also run your container locally in Docker to test different models and input inference requests by hand.

...This is fine for things like language features but can be a frustrating overhead for libraries that allow for compile-time code transformation as an optimization. babel-plugin-macros defines a standard interface for libraries that want to use compile-time code transformation without requiring the user to add a babel plugin to their build system (other than babel-plugin-macros, which is ideally already in place). There is cpu usage/time overhead; the client needs to run the code to generate these classes every time the page loads. There is code bundle size overhead; the client needs to receive a CSS parser in order to generate these class names, and shipping this makes the amount of js the client needs to parse larger. Eval-ing out code at compile time that will be baked into the runtime code, for instance, to get a list of directories in the filesystem.

MorphMPI simulates an ABI for MPI. Applications compiled using MorphMPI can easily be relinked to use any MPI implementation on a compatible OS-cpu combination.

GNUDE is a complete suite of GNU C, C++, Fortran, and Java Cross Compilers, and the GDB CPU Simulator and Debugger for embedded microprocessor applications development. Targets development for ARM7, ARM9, and XScale applications.

CodeXpand Engine is a Scripting engine written with C++. It work with COMPILED SCRIPT linked at Run Time with Host Applications. The SCRIPT COMPILER generate the CPU bytecode requested from the Host CPU where the application (& the scripts) will run.