| @split | |
| @login_required | |
| def hello(): | |
| return render_template('hello.html') | |
| @hello.for_mimetype("application/json") | |
| @login_required | |
| def hello(): | |
| return jsonify({"hello":"flask"}) |
| #!/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 |
| #!/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 \ |
| 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 = |
| 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: |
| rust-server: | |
| image: didstopia/rust-server | |
| restart: always | |
| mem_limit: 15g | |
| ports: | |
| - "28015:28015" | |
| - "28015:28015/udp" | |
| - "28016:28016" | |
| - "80:8080" | |
| volumes: |
| 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. | |
| """ |
| 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 |
| #!/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 |
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:
