Bharat brtkwr

Beta support for raw block volumes is available in K8s 1.13: https://kubernetes.io/blog/2019/03/07/raw-block-volume-support-to-beta/

The following in-tree volumes types support raw blocks:

AWS EBS
Azure Disk
Cinder

Running Kata Containers in Minikube for Kubernetes 1.14+

minikube is an easy way to try out a kubernetes (k8s) cluster locally. It utilises running a single node k8s stack in a local VM.

Kata Containers is an OCI compatible container runtime that runs container workloads inside VMs.

Wouldn't it be nice if you could use kata under minikube to get an easy out of the box experience to try out Kata? Well, turns out with a little bit of config and setup that is already supported, you can!

Comments

This is the best Q-learning algorithm implemented on TensorFlow that I stumbled upon before I found https://github.com/brtknr/general-gym-player.

The reason I decided not to use this particular script was because I did not quite understand why there is a need to specify a value of epsilon for choosing action. It felt a bit arbitrary defined to me and not generally applicable to other environments.

DQN implementation of Keras-RL used with epsilon-greedy per-episode decay policy.

Requirements that can be installed using pip:

keras
keras-rl
tensorflow-gpu
gym

Forked and modified from the original to be compatible with the following:

This has been forked from the original and modified to be compatible with:

tensorflow.__version__ = 0.12.1
gym.version.VERSION = 0.7.3

Run with defaults on terminal

$ python vpg.py

	apiVersion: v1
	kind: Namespace
	metadata:
	name: debug
	---
	apiVersion: apps/v1
	kind: DaemonSet
	metadata:
	name: debug-app
	namespace: debug

	#!/bin/sh


	# this script does absolutely ZERO error checking. however, it worked
	# for me on a RHEL 6.3 machine on 2012-08-08. clearly, the version numbers
	# and/or URLs should be made variables. cheers, zmil...@cs.wisc.edu


	mkdir mosh
	cd mosh

	import numpy as np
	import pickle
	import tensorflow as tf
	import matplotlib.pyplot as plt
	import math

	import gym
	env = gym.make('LunarLander-v2')
	print ('Shape of the observation space is', env.observation_space.shape)