jph00

#!/usr/bin/env bash
###
# NB: You probably don't want this gist any more.
#     Instead, use this version from `fastsetup`:
#     https://github.com/fastai/fastsetup/blob/master/setup-conda.sh
###

set -e

cd

pshapiro

jbielick

from datetime import datetime, timedelta

from airflow.models import Variable
from airflow import DAG
from airflow.operators.bash_operator import BashOperator

private_key = Variable.get("git_deploy_private_key_secret")
repo_url = Variable.get("git_remote_url")

default_args = {

webframp

Probably one of the easiest things you'll ever do with gpg

Install Keybase: https://keybase.io/download and Ensure the keybase cli is in your PATH

First get the public key

keybase pgp export | gpg --import

Next get the private key

audreyfeldroy

• Update HISTORY.rst
• Commit the changes:

git add HISTORY.rst
git commit -m "Changelog for upcoming release 0.1.1."

• Update version number (can also be patch or major)

bumpversion minor

pbugnion

This gist lets you keep IPython notebooks in git repositories. It tells git to ignore prompt numbers and program outputs when checking that a file has changed.

To use the script, follow the instructions given in the script's docstring.

For further details, read this blogpost.

The procedure outlined here is inspired by this answer on Stack Overflow.

eteq

#!/usr/bin/env python
from __future__ import division, print_function

"""
This script generates a file to use for building authorea papers, and then runs
latex on them.

Requires python >= 2.6 (3.x should work, too)

The key assumptions are:

dan-blanchard

For ETS's SKLL project, we found out the hard way that Travis-CI's support for numpy and scipy is pretty abysmal. There are pre-installed versions of numpy for some versions of Python, but those are seriously out of date, and scipy is not there are at all. The two most popular approaches for working around this are to (1) build everything from scratch, or (2) use apt-get to install more recent (but still out of date) versions of numpy and scipy. Both of these approaches lead to longer build times, and with the second approach, you still don't have the most recent versions of anything. To circumvent these issues, we've switched to using Miniconda (Anaconda's lightweight cousin) to install everything.

A template for installing a simple Python package that relies on numpy and scipy using Miniconda is provided below. Since it's a common s

wpm

from pyspark import SparkContext

import numpy as np

from sklearn.cross_validation import train_test_split, Bootstrap
from sklearn.datasets import make_classification
from sklearn.metrics import accuracy_score
from sklearn.tree import DecisionTreeClassifier

def run(sc):

dln

# -*- mode: ruby -*-

Vagrant.configure("2") do |config|
  config.vm.box = "precise64"
  config.vm.box_url = "http://files.vagrantup.com/precise64.box"
  config.vm.network :public_network, :bridge => "eth0"
  config.vm.synced_folder "data", "/data"
  config.vm.synced_folder "mesos", "/mesos"
  config.vm.synced_folder "/home/dln/src/mesos-docker/target/scala-2.10", "/mesos/mesos-docker"
  config.vm.synced_folder "salt", "/srv/salt"