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version: "2.2"

services:
  # setup:
  #   image: docker.elastic.co/elasticsearch/elasticsearch:${STACK_VERSION}
  #   volumes:
  #     - certs:/usr/share/elasticsearch/config/certs
  #   user: "0"
  #   command: >
  #     bash -c '

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Docker Volumes & Networking

2 Types of Mounts:

1.-Volume Mounting:
-When you use the command:
docker volume create “data_volume”

docker volume ls

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FluxCD & Killerkoda K3S

*Killercoda install automatically GIT, DOCKER and K3S Kubernetes

Creating SSH Key “gitlabKey1” and storing it in GITLAB User Settings:
ssh-keygen -t ed25519 -C “killercoda1”
eval "$(ssh-agent -s)"
ssh-add ~/.ssh/id_ed25519
cat ~/.ssh/id_ed25519.pub        (EL CODIGO QUE SE GENERA SE COPIA Y SE PEGA EN GITLAB USER SETTINGS “SSH KEYS”)

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SQL Databases Config para DevOps:

1.-Creando DB y Tablas:

mysql -h 0.0.0.0 -u root -p  (primero te conectas a mysql como “root” y colocare el password configurado por el “docker compose” con el q cree este mysql image)

CREATE DATABASE dantito;   (se crea la DB llamada: dantito)

USE dantito;

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1.To find the current Context, you can simply run the following command:
kubectl config current-context

2.To list all of the available Kubernetes Contexts in our .KubeConfig file:
kubectl config get-contexts

3.To switch to a different Kubernetes Context that exists in our .KubeConfig file:
kubectl config use-context <context-name>

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1.you add the repo:
helm repo add bitnami https://charts.bitnami.com/bitnami 

2.After added, you verify is in ur local machine:
helm search repo 

3.If you wanna do some changes in the YAML files of NGINX (as an example), you pull the files to make changes:
helm pull bitnami/nginx --untar=true

Before INSTALL the YAML project, we have to make sure the KubeConfig is correct configured, for ex. with EKS would be:

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1.-After install pyenv with: pip install virtualenv

2.-select the folder of the project where you wanna create the vEnv.

3.-in terminal run this:
virtualenv pyEnv2.7 -p /usr/bin/python2.7

"pyEnv27"     is the name of the virtualEnv Folder
and
"-p /usr/bin/python2.7"   is the specific route where python2.7 is located natively in MAC

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following this url theory:  https://stackoverflow.com/questions/4490274/returning-with


1st Case '&&' :

-   return a && b
-Explanation:

Will be equivalent to:
  if (a = true) return b;

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'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

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Followed this: https://www.pyimagesearch.com/2016/11/14/installing-keras-with-tensorflow-backend/   
(lo hice para Python2.7)

1.Create a virtualEnv and open it

2. Once is inside, in the console, write this command: export TF_BINARY_URL= (look in Tensorflow WEB the URL: https://www.tensorflow.org/install/pip  y buscar sección "Package Location" y "macOS (CPU-only)" )
I used for my MAC:  "export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.12.0-py2-none-any.whl


3. pip install --upgrade $TF_BINARY_URL