Bharat brtkwr

Prereqs for Kata containers on Kubernetes workshop

To fully participate in the workshop, you will require access to a Kubernetes cluster with the following properties:

Version >= v1.12 kubernetes
That is scratch/disposable (i.e. not a live cluster you care about)
That supports virtualisation (Kata runs containers in VMs, so the kubelet must be running on a node that is able to run a VM. - see below)
preferably CRI-O installed as the CRI runtime on the cluster, along with the matching runc as the default container runtime.
- Kata Containers also works with containerd, but the workshop demo will be conducted on a stack configured with CRI-O. you are welcome to follow along with containerd and adapt as necessary if you wish. See this page

Exporting your 2FA tokens from Authy to transfer them into another 2FA application

IMPORTANT - Update regarding deprecation of Authy desktop apps

Past August 2024, Authy stopped supported the desktop version of their apps:
See Authy is shutting down its desktop app | The 2FA app Authy will only be available on Android and iOS starting in August for details.

And indeed, after a while, Authy changed something in their backend which now prevents the old desktop app from logging in. If you are already logged in, then you are in luck, and you can follow the instructions below to export your tokens.

If you are not logged in anymore, but can find a backup of the necessary files, then restore those files, and re-install Authy 2.2.3 following the instructions below, and it should work as expected.

Docker - How to cleanup (unused) resources

Once in a while, you may need to cleanup resources (containers, volumes, images, networks) ...

delete volumes

// see: https://github.com/chadoe/docker-cleanup-volumes

$ docker volume rm $(docker volume ls -qf dangling=true)

$ docker volume ls -qf dangling=true | xargs -r docker volume rm

	{ config, lib, pkgs, modulesPath, ... }:

	{
	boot.initrd.availableKernelModules = [ "ahci" "xhci_pci" "virtio_pci" "sr_mod" "virtio_blk" ];
	boot.initrd.kernelModules = [ ];
	boot.kernelModules = [ "kvm-intel" ];
	boot.extraModulePackages = [ ];
	boot.loader.grub.enable = true;
	boot.loader.grub.version = 2;
	boot.loader.grub.efiSupport = false;

	#!/usr/bin/swift

	// DISCLAIMER

	// This script modifies an unencrypted file associated with the trial version of Final Cut Pro.
	// Under the DMCA (17 U.S.C. § 1201), this modification does not qualify as circumvention of a technological
	// protection measure (TPM), as it does not involve bypassing encryption, authentication, or similar protections.
	// Distributing this code is therefore legal under the DMCA.

	// This script is intended for educational and research purposes, such as exploring trial-related file structures,

	#!/usr/bin/env python
	from bs4 import BeautifulSoup
	from twilio.rest import TwilioRestClient
	import json
	import os
	import re
	import requests


	url = 'https://postmates.com/los-angeles'

	import gym
	import random
	import time
	import numpy as np
	import tensorflow as tf
	from tensorflow.core.framework import summary_pb2

	# Summary
	run_params = 'truncated_normal_8x92x64x4_012_'
	writer = tf.summary.FileWriter('logs/run_' + run_params + str(time.time()))

	""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
	import numpy as np
	import cPickle as pickle
	import gym

	# hyperparameters
	H = 200 # number of hidden layer neurons
	batch_size = 10 # every how many episodes to do a param update?
	learning_rate = 1e-4
	gamma = 0.99 # discount factor for reward

	# Implementation of a simple MLP network with one hidden layer. Tested on the iris data set.
	# Requires: numpy, sklearn>=0.18.1, tensorflow>=1.0

	# NOTE: In order to make the code simple, we rewrite x * W_1 + b_1 = x' * W_1'
	# where x' = [x \| 1] and W_1' is the matrix W_1 appended with a new row with elements b_1's.
	# Similarly, for h * W_2 + b_2
	import tensorflow as tf
	import numpy as np
	from sklearn import datasets
	from sklearn.model_selection import train_test_split