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initcron / instavote-appset-vote-prod.yaml
Last active February 25, 2025 14:34
Create only vote app in Prod
apiVersion: argoproj.io/v1alpha1
kind: ApplicationSet
metadata:
name: instavote
namespace: argocd
spec:
generators:
- matrix:
generators:
- git:
# This is an auto-generated file. DO NOT EDIT
apiVersion: v1
kind: ServiceAccount
metadata:
namespace: argo-rollouts
labels:
app.kubernetes.io/component: argo-rollouts-dashboard
app.kubernetes.io/name: argo-rollouts-dashboard
app.kubernetes.io/part-of: argo-rollouts
name: argo-rollouts-dashboard
@initcron
initcron / instavote-dev.yaml
Created February 23, 2025 13:34
ApplicationSet spec to iterate over a list of charts available in the Git Repo using Git Generator: https://argo-cd.readthedocs.io/en/stable/operator-manual/applicationset/Generators-Git/
apiVersion: argoproj.io/v1alpha1
kind: ApplicationSet
metadata:
name: instavote-dev
namespace: argocd
spec:
generators:
- git:
repoURL: https://github.com/initcron/instavote-gitops
revision: HEAD
@initcron
initcron / instavote-apps.yaml
Created February 23, 2025 13:33
ApplicationSet to iternate over a matrix of 2 charts (services = vote, redis) and 3 environments 2x3 = 6 application specs.
apiVersion: argoproj.io/v1alpha1
kind: ApplicationSet
metadata:
name: instavote
namespace: argocd
spec:
generators:
- matrix:
generators:
- git:
@initcron
initcron / gist:9b6f14bd6f3c835651ec1b4cfcdea72b
Last active February 6, 2025 05:37
kubectl create secret generic git-credentials --from-literal=username=<YOUR_GIT_USERNAME> --from-literal=password=<YOUR_GIT_PERSONAL_ACCESS_TOKEN> --namespace=<YOUR_NAMESPACE>
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
generateName: vote-ci-
spec:
entrypoint: main
arguments:
parameters:
- name: repo-url
value: "https://github.com/xxxxxx/vote.git"

Setting up Argo Locally on existing Kubernetes Cluster

Create Argo Namespace

kubectl create namespace argo
kubectl get ns

Setup Argo Version and Install it

Does an AI Platform Engineer Need Data Science Essentials?

Short Answer: Yes, but only selectively.

As an AI Platform Engineer, the focus is on building, deploying, and optimizing AI/ML models at scale, not on developing new ML algorithms or performing deep data science research. However, to work effectively with Data Scientists and MLOps workflows, an AI Platform Engineer should understand key Data Science essentials related to:

Understanding ML model workflows (How data moves through AI/ML pipelines)
Feature Engineering & Feature Stores (How data is prepped for models)
Fine-tuning & Inference Optimization (How models are trained and served efficiently)
Evaluating Model Performance (Ensuring models meet production-quality standards)

Mini Project Spec: Host Your Resume on AWS S3

Introduction:

In this mini-project, you will learn how to use Amazon S3 to host your resume, creating a publicly accessible web-based version of your professional profile. This is a practical skill that can be applied in real-world scenarios, such as job applications or networking events.

Project Goal:

  • To host an HTML version of your resume on AWS S3.
@initcron
initcron / wp_cleanup_cache.sh
Created December 4, 2024 16:22
Cleanup WP Cache
#!/bin/bash
sudo su
curl -O https://raw.githubusercontent.com/wp-cli/builds/gh-pages/phar/wp-cli.phar
chmod +x wp-cli.phar
sudo mv wp-cli.phar /usr/local/bin/wp
wp transient delete --all --path=/srv/www/wordpress --allow-root
wp option get siteurl --path=/srv/www/wordpress --allow-root
wp option get home --path=/srv/www/wordpress --allow-root
@initcron
initcron / test-notebook.yml
Created November 29, 2024 07:25
Tests with Databrics Job Trigger
name: Test and Trigger Databricks Job
# Trigger the workflow on push to the main branch
on:
push:
branches:
- main
jobs:
test: