jannegpriv

Installation steps for K3s dashboard

The steps below requires that you have followed the installation steps for installing K3s on RPIs.

NOTE: The following files can be found in the following repository.

Installation steps for K3s dashboard. On master node, create a folder called dashboard:

dvgodoy

## Gist originally developed by @craffel and improved by @ljhuang2017

import matplotlib.pyplot as plt
import numpy as np

def draw_neural_net(ax, left, right, bottom, top, layer_sizes, coefs_, intercepts_, n_iter_, loss_):
    '''
    Draw a neural network cartoon using matplotilb.
    
    :usage:

vitorreus

#!/bin/bash
#This script creates 1 master, 2 slaves and 3 sentinels services in docker swarm.

docker network create \
  --driver overlay \
  testes_network

SWARM_MASTER=192.168.205.125

#Master

josepjaume

rust-server:
  image: didstopia/rust-server
  restart: always
  mem_limit: 15g
  ports:
    - "28015:28015"
    - "28015:28015/udp"
    - "28016:28016"
    - "80:8080"
  volumes:

criccomini

import os
import unittest

from airflow.models import DagBag

class TestDags(unittest.TestCase):
    """
    Generic tests that all DAGs in the repository should be able to pass.
    """

criccomini

#!/bin/bash

# Runs airflow-dags tests.

# Set Nose defaults if no arguments are passed from CLI.
CWD="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
NOSE_ARGS=$@
if [ -z "$NOSE_ARGS" ]; then
  NOSE_ARGS=" \
    --with-coverage \

d0c-s4vage

#!/usr/bin/env python
# encoding: utf-8

import imp
import os
import pdb
import pip
import readline
from pip.commands.search import SearchCommand
import sys

nuria

from celery import Celery
from celery.signals import after_task_publish,task_success,task_prerun,task_postrun

# first argument, current module
app = Celery('tasks')
app.config_from_object('celeryconfig')

# To instantiate celery and import this module 
# do: celery -A task worker --loglevel=info
# after, once celery is running, instantiate a python console:

mathias-brandewinder

namespace Discretization

// Recursive minimal entropy partitioning,
// based on Fayyad & Irani 1993. 
// See the following article, section 3.3,
// for a description of the algorithm:
// http://www.math.unipd.it/~dulli/corso04/disc.pdf
// Note: this can certainly be optimized.
module MDL =

yoavram

import requests

#http://docs.python-requests.org/en/latest/user/quickstart/#post-a-multipart-encoded-file

url = "http://localhost:5000/"
fin = open('simple_table.pdf', 'rb')
files = {'file': fin}
try:
  r = requests.post(url, files=files)
	print r.text