Search Results for "cpu speed"

go-web-framework-benchmark is a benchmark suite for comparing the performance of Go web frameworks. It was created to measure full HTTP request processing instead of only route matching speed. The project runs small real HTTP servers for many stable frameworks and tests a simple /hello endpoint. It can add configurable handler delay to simulate business work such as database calls, cache access, disk writes, socket activity, or microservice calls. The benchmark uses wrk and scripts to collect throughput, latency, allocation, concurrency, pipelining, and CPU-bound results. ...

...You can represent quantization/dynamic shape/image pre-processing and even derivation in one model. After training, just put everything into your model and inference it on any platform at ease. Speed and precision problems won't bother you anymore due to the same core inside. In training, GPU memory usage could go down to one-third at the cost of only one additional line, which enables the DTR algorithm. Gain the lowest memory usage when inferencing a model by leveraging our unique pushdown memory planner. NOTE: MegEngine now supports Python installation on Linux-64bit/Windows-64bit/MacOS(CPU-Only)-10.14+/Android 7+(CPU-Only) platforms with Python from 3.5 to 3.8. ...

Execute JavaScript from Go. For scripts that are large or are repeatedly run in different contexts, it is beneficial to compile the script once and used the cached data from that compilation to avoid recompiling every time you want to run it. The v8go library would welcome contributions from anyone able to get an external windows build of the V8 library linking with v8go, using the version of V8 checked out in the deps/v8 git submodule, and documentation of the process involved. This process...

Mobile AI Compute Engine (or MACE for short) is a deep learning inference framework optimized for mobile heterogeneous computing on Android, iOS, Linux and Windows devices. Runtime is optimized with NEON, OpenCL and Hexagon, and Winograd algorithm is introduced to speed up convolution operations. The initialization is also optimized to be faster. Chip-dependent power options like big.LITTLE scheduling, Adreno GPU hints are included as advanced APIs. UI responsiveness guarantee is sometimes...

Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license. Expressive architecture encourages application and innovation. Models and optimization are defined by configuration without hard-coding.

Caffe2 is a lightweight, modular, and scalable deep learning framework. Building on the original Caffe, Caffe2 is designed with expression, speed, and modularity in mind. Caffe2 is a deep learning framework that provides an easy and straightforward way for you to experiment with deep learning and leverage community contributions of new models and algorithms. You can bring your creations to scale using the power of GPUs in the cloud or to the masses on mobile with Caffe2’s cross-platform...