Serverless

🚀 WasmEdge 0.10.0 is released!

In version 0.10.0, WasmEdge provides a brand new plug-in mechanism to make native extensions easier to develop and install, improves compatibility with LLVM 14, and supports new WebAssembly specs, proposals, and features. New plug-in system for native host functions Many enhancements to the WasmEdge socket API (e.g., microservices and web service clients in WasmEdge) Support for new WebAssembly proposals and specs WasmEdge C API enhancements Other features and bug fixes New plug-in system for native Host Functions The host funtion is the bridge that allows WebAssembly programs to access functionalities and features provided by native libraries.…

😎 WasmEdge 0.9.1 is released! Better networking support, Android support, better developer experience, and more.

WasmEdge 0.9.1 is released! This release features integrated high-performance networking, JavaScript streaming SSR and Fetch API support, a new bindgen framework, Android and OpenHarmony OS support, expanded Kubernetes support, and improved memory management. WebAssembly extensions Streaming SSR functions Android and OpenHarmony Kubernetes Bug fixes and performance enhancements WebAssembly extensions WasmEdge aims to support all standard and optional standard WebAssembly extension proposals. It also supports non-standard and experimental extensions that are important to cloud-native use cases, such as networking and data passing.…

WebAssembly Serverless Functions in AWS Lambda

Serverless functions save developers a ton of trouble managing the backend infrastructure. It also simplifies the development process as developers only need to focus on the business logic. This article is a step-by-step guide on how to write and deploy your own WebAssembly serverless functions on AWS Lambda, Amazon's serverless computing platform. In our demo, WebAssembly functions are executed with the WasmEdge runtime. The figure below shows the overall architecture of our solution.…

😎 WasmEdge 0.8.2 is released. Networking support, Rust SDK, better compatibility and more.

WasmEdge 0.8.2 is finally released. Better cross-platform compatibility. Run WasmEdge applications on Mac OS X (Intel & M1), Windows 10, as well as IoT devices with ARM 32-bit chips. Networking support. Make network requests and run an HTTP server from your WasmEdge application. New and improved APIs. Embed WasmEdge functions in C, Rust, and Golang applications. Generic binary output for WasmEdge’s AoT compiler. Take advantage of AOT performance enhancements in cloud native platforms.…

Rust and WebAssembly Serverless functions in Vercel

Vercel is a leading platform for developing and hosting Jamstack applications. Unlike traditional web apps, where the UI is dynamically generated at runtime from a server, a Jamstack application consists of a static UI (in HTML and JavaScript) and a set of serverless functions to support dynamic UI elements via JavaScript. There are many benefits to the Jamstack approach. But perhaps one of the most significant benefits is performance. Since the UI is no longer generated at runtime from a central server, there is much less load on the server and we can now deploy the UI via edge networks such as CDNs.…

WebAssembly Serverless Functions in Netlify

Netlify is a leading platform for developing and hosting Jamstack applications. In fact, the term Jamstack was coined by Netlify founder Mathias Biilmann in 2015. Netlify also hosts the annual Jamstack Conf. A Jamstack application consists of a static UI (in HTML and JavaScript) and a set of serverless functions to support dynamic UI elements via JavaScript. There are many benefits to the Jamstack approach. But perhaps one of the most significant benefits is performance.…

Serverless AI functions on Tencent Cloud

Tencent is a leading serverless infrastructure provider in the public cloud. With the WasmEdge custom runtime for Tencent Serverless Cloud Function (SCF), you can write a few lines of simple code to turn any Tensorflow model into a serverless function and then offer it as a web service. A template project containing source code and configuration files is available here. Out of the box, you can deploy the template to Tencent Cloud and have a working web app for image classification.…

A WASI-like extension for Tensorflow

AI inference is a computationally intensive task that could benefit greatly from the speed of Rust and WebAssembly. However, the standard WebAssembly sandbox provides very limited access to the native OS and hardware, such as multi-core CPUs, GPU and specialized AI inference chips. It is not ideal for the AI workload. The popular WebAssembly System Interface (WASI) provides a design pattern for sandboxed WebAssembly programs to securely access native host functions.…

AI as A Service on WebAssembly

AI inference is an ideal use case for Function as a Service (FaaS). The inference operation is transactional, often happens on the edge, need to scale up and down quickly, and integrates with other network services for input and output. However, the inference is also ill-suited for today's FaaS infrastructure. Inference is computationally intensive and time sensitive. FaaS today has long cold start time, and poor runtime performance due to the heavy runtime software stack and inefficient programming languages.…

AI as a Service: Face Detection Using MTCNN

The MTCNN is a class of Multi-task Cascaded Convolutional Network models. They are very good at detection faces and facial features. You can train (or retrain) MTCNN models with your own faces dataset so that it can accurately detect faces for your application. Second State FaaS provides a Rust API to run Tensorflow-based MTCNN models at native speeds. In this article, we will use the original MTCNN model trained in the FaceNet dataset as an example.…