Repository for sample controller. Complements sample-apiserver

Go client for Kubernetes.

Golang package for generating API documentation from httptest. See example output

a web application performance improvement training

Dapr is a portable, event-driven, runtime for building distributed applications across cloud and edge.

Production-Grade Container Scheduling and Management

Connect, secure, control, and observe services.

Dapr user documentation, used to build docs.dapr.io

Easy and Repeatable Kubernetes Development

Serve, optimize and scale PyTorch models in production

The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (V…

Pure bash script to test and wait on the availability of a TCP host and port

Tensorflow + Go, the gopher way

Build Better Websites. Create modern, resilient user experiences with web fundamentals.

Super simple build framework with fast, repeatable builds and an instantly familiar syntax – like Dockerfile and Makefile had a baby.

Natural Gradient Boosting for Probabilistic Prediction

GoMock is a mocking framework for the Go programming language.

Probabilistic reasoning and statistical analysis in TensorFlow

Fast, testable, Scala services built on TwitterServer and Finagle

The Go language implementation of gRPC. HTTP/2 based RPC

Sample apps and code written for Google Cloud in the Go programming language.

Official CORS gin's middleware

Package gorilla/mux is a powerful HTTP router and URL matcher for building Go web servers with 🦍

Bayesian Deep Learning Benchmarks

High-quality implementations of standard and SOTA methods on a variety of tasks.

Gin is a HTTP web framework written in Go (Golang). It features a Martini-like API with much better performance -- up to 40 times faster. If you need smashing performance, get yourself some Gin.

Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts (Neurips 2020)

Code to accompany the paper Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning

Code to accompany the paper 'Improving model calibration with accuracy versus uncertainty optimization'.

Enabling PyTorch on XLA Devices (e.g. Google TPU)