Tensorflow

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.…

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.…

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.…