Today I’ll check the state of Vue 3 support of GraphQL libraries. According to Awesome Vue there are the following libs w.r.t. GraphQL:
In addition there is
At the time of writing the UI frameworks supporting Vue 3 are:
This article assumes you’ve already installed and setup Ubuntu 20.04 LTS on Windows 10 running WSL2.
This might sound strange… but do not install VSCode in Ubuntu but in Windows instead.
If you installed VSCode in WSL2-Ubuntu (according to the installation docs described here) by mistake and run
code in WSL2-Ubuntu you’ll see a notification as follows:
If you want to run logic on an embedded system in a deterministic manner often there is no way around a “low end” Real Time Operating System (RTOS) like FreeRTOS. A “high end” RTOS like e.g. an Embedded Linux distribution would simply not beeing exact and controllable enough and behaves more like a “high level” general purpose operating system (GPOS) in many aspects. Getting determinism into a system may be easier using a “low end” RTOS. “From buttom up” a custom embedded OS (super loop) is no option if it’s necessary to execute tasks concurrently with an adequat response time…
This is important to understand because it implies that whatever authorization technology you use you’ll need to use another technology for authentication supplementary to authorization.
Authentication is not part of this blog post. However as a reference for further reading there is e.g. the authentication technology OpenID Connect. OpenID Connect can be used with JWT as well…
In the majority of use cases ONNX will be the machine learning interoperability for you. Of course it’s evolving, but there is a lot of support for training frameworks, support for algorithms and inference hardware acceleration already.
When you are working with artificial intelligence you’ll learn that there a lot of different frameworks to train models, runtimes to execute models, potentially compilers to improve runtime of interences and other tooling. When it comes to inference runtime optimization (including optimization of potentially very costly pre-processing) the hardware architectures the models may be deployed onto can make a significant difference.
In this post we’ll build upon a website which uses Gatsby for the end user facing part of the website and NetlifyCMS for the admin user facing part of the website. If you’ve not setup a website yet you’ll find some help in one of my other posts How to Setup a Powerful and Free JAMstack Website about how to setup a website with GitLab for hosting the code.
Ideally you should care about ML model deployment patterns before you deploy machine learning models into a production data pipeline. You can refactor design decisions afterwards but w.r.t. some patterns it might get very hard to fix a sub-optimal design afterwards. I’ll not deep dive into this but to give some short hint… it relates to model life cycle management in a lot of cases.
This term consists o two parts:
I’ve developed several small websites in my spare time before. I’ve primarily used static site generators and JAMstack setups. However I’m still primarily a system and backend developer. I’ ve never planned to and I’ll never create such amazing interactive websites like the one shown below. You’ll see that Ionic is not suitable for such use cases anyway later.
Nevertheless in 2020 I had the pleasure to design a MVP for an industrial IoT application. Beeing a one (developer) man show at that time in an early stage startup one requirement…
Software Developer for rapid prototype or high quality software with interest in distributed systems and high performance on premise server applications.