Argo Workflows on Kubernetes - Core Concepts

Create a mini Kubernetes cluster locally with minikube on Windows 10, using kubectl to deploy applications and manage resources, supported by VirtualBox or Hyper-V hypervisors.

install kubectl on Windows 10 by downloading the latest release, placing the executable in a program files folder, updating your account path in environment variables, and verifying with kubectl version.

Choose a hypervisor between Hyper-V and watchbox for Windows 10. Hyper-V is available only on Windows 10 Pro, Enterprise, and Education, not on Windows 10 Home.

Enable Hyper-V on Windows 10 by running the administrator command line, enabling the Hyper-V feature, rebooting, and verifying via system information that the hypervisor is active.

Start minikube with Hyper-V on Windows 10 to spin up a Kubernetes cluster, use kubectl to inspect nodes, and stop the cluster when finished.

Discover the latest changes to the Argo installation flow, and access the Argo server user interface via http://localhost:2746 in your browser.

Install Argo workflows on a Kubernetes cluster by creating a namespace and applying quickstart manifests from GitHub, then configure the workflow controller, Argo server, and Minneola storage for artifacts.

Deploy and run your first Argo workflow in the Aagot namespace using hello world example, view its execution in the Argo server UI, and inspect the YAML, templates, and logs.

Explore the core concept of a workflow definition in Argo Workflows on Kubernetes, focusing on a YAML structure with apiVersion, kind, metadata, spec, templates, and entryPoint.

Create a workflow with a container template, defining metadata and a unique name, then set the entry point and use a python 3.8 image to echo a success.

Create an Argo workflow from a script template on Kubernetes, specify the Python 3.8 slim image, and verify the script template executes successfully.

Highlight the idea that ratings are the currency of the internet, focusing on stars and text reviews to boost search visibility and support ongoing work.

Create a workflow using the resource template to trigger another workflow, defining name, entry point, and script template with Python, and observe the created workflows and logs.

Explore template indicators that call other templates, including steps templates that define tasks as a series of steps, and learn to model tasks as a dependency graph with prerequisites.

Create a workflow in Argo Workflows on Kubernetes using the steps template as the entry point, with a unique name and three serial steps that run a Python script.

Extend the steps template to run step two and step three in parallel in Argo workflows, by adjusting indentation and deleting the first hyphen, then observe step four after completion.

Rewrite Argo workflows from the steps template to the dag template, defining tasks and dependencies in a directed acyclic graph to control execution order and enable parallelism.

Install a mini Kubernetes cluster with Argo workflows, learn the core concepts, and create a workflow for each concept, preparing you to explore advanced features in the full course.