WebAssembly

Second State joins the Autoware Foundation

Second State has joined the Autoware Foundation as an industry member. Through collaboration with the Foundation and member organizations, Second State aims to create and promote an open-source virtualization runtime for Software Defined Vehicles (SDVs). According to a recent paper on IEEE Spectrum, a typical car has 50+ software vendors. Traditional automotive engineering keeps those heterogeneous software components safe via hardware isolation. That is to enclose each vendor’s software in its own hardware known as an ECU.…

🥳 WasmEdge (formerly SSVM) is now a CNCF Project

The WasmEdge Runtime (formerly SSVM) is a lightweight and high-performance WebAssembly (WASM) VM optimized for edge computing. It is widely used in scenarios ranging from cloud serverless functions, SaaS, blockchain smart contracts, IoT, to automotive real time applications. WasmEdge has recently been accepted by the CNCF Foundation as a sandbox project, which is the world's first official CNCF WebAssembly runtime project. We envision that it will fill an important role in CNCF’s open source cloud computing landscape as a mission critical lightweight runtime for edge clouds.…

Deploying Tensorflow models in production with less than 50 lines of code

Serverless Tensorflow functions in public clouds For software developers and students, artificial intelligence pays. In 2021, the average annual salary for Tensorflow (a popular AI framework) developers is $148,508. Skills in artificial intelligence are now mandatory in even entry level programming jobs. In fact, it is quite easily to follow an online tutorial and train your own Tensorflow model for tasks such as image recognition and natural language processing. You only need some basic Python knowledge to do the training and then run the model for a demo.…

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.…

Serverless Functions in Rust Challenge #1

Learn how to create simple yet high performance serverless functions, for media processing, machine learning, and AI inference, in Rust. Everyone gets a swag bag full of Serverless goodies for completing the challenges. In addition, you will receive a digital certificate and public Github commits to demonstrate your skills of Rust and serverless computing. Create a serverless function in Rust First, please review the Getting Started Guide to learn the basic concepts.…

Access commands and programs in the host operating system

WASI enables WebAssembly programs to call standard library functions in the host operating system. It does so through a fine-grained security model known as “capability-based security”. The WebAssembly VM owner can grant access to host system resources when the VM starts up. The program cannot access any resources (e.g., file folders) that are not explicitly allowed. Now, why limit ourselves to standard library functions? The same approach can be used to call just any host functions from WebAssembly.…

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.…

AI as a Service: Image Classification Using MobileNet

MobileNet is a class of Convolutional Neural Networks (CNN) models for computer vision applications. The most common application for MobileNet models is image classification. You can train (or retrain) a MobileNet model to recognize objects that are interesting to your application (e.g., to classify birds in a bird watching application). Second State FaaS provides a Rust API to run Tensorflow-based MobileNet models at native speeds. In this article, we will use a MobileNet model trained from the ImageNet dataset as an example.…