Ce Gao gaocegege

Get started with Kubernetes in Docker (kind) in WSL2

The accompanying video for this tutorial is published on https://learn.alexchiri.com

Contents:

Recommendations
Install Docker CE for Linux
Configure Docker to start automatically in our WSL distribution when we log in to Windows
Install go

install Tensorflow with GPU support on Centos 7

system: fully installed Centos 7

system update

$ sudo yum -y update
$ sudo yum -y install epel-release
$ sudo yum -y install gcc gcc-c++ python-pip python-devel atlas atlas-devel gcc-gfortran openssl-devel libffi-devel

	use std::ops::Deref;

	use sqlx::encode::IsNull;
	use sqlx::error::BoxDynError;
	use sqlx::postgres::{PgArgumentBuffer, PgTypeInfo, PgValueRef};
	use sqlx::{Decode, Encode, Postgres, Type};

	/// A vector.
	#[derive(Clone, PartialEq, Default)]
	pub struct Vector(Vec<f32>);

	#!/bin/bash

	# Launching RStuido server on a docker with different settings

	# Basic run command to launce rocker container with RStudio Server
	docker run -d -p 8787:8787 \
	-e USER=rstudio -e PASSWORD=rstudio \
	rocker/rstudio:4.2

	# Disabling the authentication

	$ oc get pods --all-namespaces
	NAMESPACE NAME READY STATUS RESTARTS AGE
	openshift-apiserver-operator openshift-apiserver-operator-6db995744c-nc5pj 1/1 Running 2 6d9h
	openshift-apiserver apiserver-d6dm8 1/1 Running 5 6d8h
	openshift-authentication-operator authentication-operator-668f5dd5cb-px72d 1/1 Running 2 6d9h
	openshift-authentication oauth-openshift-7949ccf5d-2j4dc 1/1 Running 0 36m
	openshift-authentication oauth-openshift-7949ccf5d-dnzpb 1/1 Running 0 36m
	openshift-cloud-credential-operator

	import gym
	from IPython import display
	import matplotlib
	import matplotlib.pyplot as plt
	%matplotlib inline

	env = gym.make('Breakout-v0')
	env.reset()
	img = plt.imshow(env.render(mode='rgb_array')) # only call this once
	for _ in range(100):


	import tensorflow as tf
	import numpy as np

	class FloydHubMetricHook(tf.train.SessionRunHook):
	"""An easy way to output your metric_ops to FloydHub's training metric graphs

	This is designed to fit into TensorFlow's EstimatorSpec. Assuming you've
	already defined some metric_ops for monitoring your training/evaluation,
	this helper class will compute those operations then print them out in

	package main

	import (
	"io"
	"mime/multipart"
	"net/http"
	"net/url"
	"os"
	"fmt"
	)

	int[][] result;
	float t, c;

	float ease(float p) {
	return 3pp - 2pp*p;
	}

	float ease(float p, float g) {
	if (p < 0.5)
	return 0.5 * pow(2*p, g);

	# This is a GCR vanity domain. It aliases our domain to a specific
	# bucket in GCR.
	# e.g.`docker pull gcr.example.com/foobar` -> gcr.io/my_bucket/foobar
	server {
	server_name gcr.example.com;
	listen 80;
	listen 443 ssl;

	location = /v2/ {
	# If we redirect this, it can detect as unauthorized, but the token