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#!/bin/bash

# Function which takes a namespace, and waits until ip is assigned to it.
function wait_for_ip() {
    namespace=$1
    echo "Waiting for IP for namespace: $namespace"
    while [ -z $ip ]; do
        ip=$(kubectl get ingress -n $namespace -o jsonpath="{.items[*].status.loadBalancer.ingress[*].ip}")
        [ -z "$ip" ]
    done

PyDataBlog

apiVersion: apps/v1
kind: Deployment
metadata:
  name: myapp
  namespace: ingress-demo
spec:
  replicas: 10
  selector:
    matchLabels:
      app: myapp

PyDataBlog

apiVersion: v1
kind: Service
metadata:
  name: hello-svc
  namespace: ingress-demo
spec:
  selector:
    app: myapp
  ports:
  - port: 8080

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apiVersion: v1
kind: Namespace
metadata:
  name: ingress-demo

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apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  annotations:
    kubernetes.io/ingress.class: "nginx"
    nginx.ingress.kubernetes.io/ssl-redirect: "true"
  name: hello-world-ingress
  namespace: ingress-demo
  labels:
    name: hello-world-ingress

PyDataBlog

apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  annotations:
    kubernetes.io/ingress.class: "nginx"
    nginx.ingress.kubernetes.io/ssl-redirect: "true"
  name: dashboard-ingress
  namespace: kubernetes-dashboard
  labels:
    name: dashboard-ingress

PyDataBlog

apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  annotations:
    kubernetes.io/ingress.class: "nginx"
    nginx.ingress.kubernetes.io/ssl-redirect: "true"
  name: logs-ingress
  namespace: kube-system
  labels:
    name: logs-ingress

PyDataBlog

cost = [10, 10, 10, 10, 1, 1, 1]

function count_no_improvements(x)
    counter = 0

    for i in 1:size(x, 1)
        if (i > 1) && abs(x[i] - x[i-1]) < (1e-6 * x[i])
            counter += 1
        else
            counter = 0

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using Plots
using MLJBase
using StableRNGs # Seeding generator for reproducibility

# Generate fake data
X, y = make_blobs(10_000, 3; centers=2, as_table=false, rng=2020);
X = Matrix(X');
y = reshape(y, (1, size(X, 2)));
f(x) =  x == 2 ? 0 : x
y2 = f.(y);

PyDataBlog

"""
    Train the network using the desired architecture that best possible
    matches the training inputs (DMatrix) and their corresponding ouptuts(Y)
    over some number of iterations (epochs) and a learning rate (η).
"""
function train_network(layer_dims , DMatrix, Y;  η=0.001, epochs=1000, seed=2020, verbose=true)
    # Initiate an empty container for cost, iterations, and accuracy at each iteration
    costs = []
    iters = []
    accuracy = []