alferov

# Delete all containers
docker rm $(docker ps -aq)
# Delete all images
docker rmi $(docker images -q)
# Delete all untagged images
docker rmi $(docker images -q --filter "dangling=true")

References:

jamesottaway

version: '2.0'
services:
  couchpotato:
    image: linuxserver/couchpotato
    ports:
      - 5050:5050
    volumes:
      - couchpotato:/config:rw
      - movies:/movies:rw
      - downloads:/downloads:rw

viksit

"""Asynchronous requests in Flask with gevent"""

from time import time

from flask import Flask, Response

from gevent.pywsgi import WSGIServer
from gevent import monkey

import requests

subfuzion

Common Options

-#, --progress-bar Make curl display a simple progress bar instead of the more informational standard meter.

-b, --cookie <name=data> Supply cookie with request. If no =, then specifies the cookie file to use (see -c).

-c, --cookie-jar <file name> File to save response cookies to.

ernestum

import numpy as np
from scipy.ndimage.interpolation import map_coordinates
from scipy.ndimage.filters import gaussian_filter

def elastic_transform(image, alpha, sigma, random_state=None):
    """Elastic deformation of images as described in [Simard2003]_.
    .. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for
       Convolutional Neural Networks applied to Visual Document Analysis", in
       Proc. of the International Conference on Document Analysis and
       Recognition, 2003.

UnaNancyOwen

Building QVTK(VTK6+Qt5) with Visual Studio

Download

1. VTK 6.3.0(VTK-6.3.0.zip)をダウンロードしてファイルを解凍する。(C:\VTK-6.3.0) http://www.vtk.org/VTK/resources/software.html#latestcand
   https://github.com/Kitware/VTK/tree/v6.3.0

2. 以下の手順を参考にQt 5.5.1(qt-everywhere-opensource-src-5.5.1.zip)をビルドする。　　

amroamroamro

Python version of the MATLAB code in this Stack Overflow post: https://stackoverflow.com/a/18648210/97160

The example shows how to determine the best-fit plane/surface (1st or higher order polynomial) over a set of three-dimensional points.

Implemented in Python + NumPy + SciPy + matplotlib.

jlafon

An introduction to DynamoDB

DynamoDB is a powerful, fully managed, low latency, NoSQL database service provided by Amazon. DynamoDB allows you to pay for dedicated throughput, with predictable performance for "any level of request traffic". Scalability is handled for you, and data is replicated across multiple availability zones automatically. Amazon handles all of the pain points associated with managing a distributed datastore for you, including replication, load balancing, provisioning, and backups. All that is left is for you to take your data, and its access patterns, and make it work in the denormalized world of NoSQL.

Modeling your data

The single most important part of using DynamoDB begins before you ever put data into it: designing the table(s) and keys. Keys (Amazon calls them primary keys) can be composed of one attribute, called a hash key, or a compound key called the hash and range key. The key is used to uniquely identify an item in a table. The choice of the primary key is particularl

amatellanes

/* Useful celery config.

app = Celery('tasks',
             broker='redis://localhost:6379',
             backend='redis://localhost:6379')

app.conf.update(
    CELERY_TASK_RESULT_EXPIRES=3600,
    CELERY_QUEUES=(
        Queue('default', routing_key='tasks.#'),

thomasfr

[Unit]
Description=Keeps a tunnel to 'remote.example.com' open
After=network.target

[Service]
User=autossh
# -p [PORT]
# -l [user]
# -M 0 --> no monitoring
# -N Just open the connection and do nothing (not interactive)