RamiKrispin

#!/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 

ddysher

$ 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             

alexchiri

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

thomelane

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):

davidhughhenrymack

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 

ZenGround0

package main

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

Bleuje

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

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

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

thockin

      # 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

lyastro

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

SaulLawliet

# coding: utf-8
import json
import re

import requests

# 替换成你自己的经纬度数据
# 查询方式 打开饿了么官网 -> 开发者模式 -> 输入送餐地址 -> 观察请求 -> 找到经纬度数据
latitude = 31.23978
longitude = 121.49968