Photo by Austin Distel on UnsplashTech Stack: Python 3.7, Airflow (1.10.10), Docker GitHub link: All of the code can be found here. Airflow + SlackSlack is an increasingly popular chat app used in the workplace. Apache Airflow is an open source platform for orchestrating workflows. One of the biggest advantages to using Airflow is the versatility around its hooks and operators. Hooks are interface
Note: The recipes in this article will still work, but I recommend that you use the notebook API now. Do: gcloud beta notebooks --help The simplest way to launch a notebook on GCP is to go through the workflow from the GCP console. Go to AI Platform and click on Notebook Instances. You can create a new instance from the user interface: Create a new notebook instance from the UIOnce the instance is
Both NVidia and Google recently released dev board targeted towards EdgeAI and also at a cost point to attract developers, makers and hobbyists. Both the dev boards are primarily for inference, but support limited transfer learning re-training. The Edge TPU supports transfer learning training using weight imprinting technique. Both of the dev kits consists of a SOM (System-on-Module) connected to
In the 1.10 release, Airflow introduced a new executor to run workers at scale: the Kubernetes executor. In this article we’ll look into: What is Airflow and which problem it solvesThe Kubernetes executor and how it compares to the Celery executorAn example deployment on minikubeTL;DRAirflow has a new executor that spawns worker pods natively on Kubernetes. There’s a Helm chart available in this g
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