Mr. Yap mryap

Viewing IPython notebooks in GitLab

GitLab is open source software to collaborate on code (a GitHub clone to run on your own server). Clicking a blob (a file in a repository) in GitLab shows a nice rendering if GitLab supports the file type (e.g., images, Markdown documents), or its content as plain text otherwise. The patch described here adds support to GitLab for rendering IPython notebooks (.ipynb files).

Useful Docker commands

The Basics

Build and tag an image from a Dockerfile :

docker build -t name/tag .

Run image:

Using Git to Manage a Live Web Site

Overview

As a freelancer, I build a lot of web sites. That's a lot of code changes to track. Thankfully, a Git-enabled workflow with proper branching makes short work of project tracking. I can easily see development features in branches as well as a snapshot of the sites' production code. A nice addition to that workflow is that ability to use Git to push updates to any of the various sites I work on while committing changes.

	Latency Comparison Numbers (~2012)
	----------------------------------
	L1 cache reference 0.5 ns
	Branch mispredict 5 ns
	L2 cache reference 7 ns 14x L1 cache
	Mutex lock/unlock 25 ns
	Main memory reference 100 ns 20x L2 cache, 200x L1 cache
	Compress 1K bytes with Zippy 3,000 ns 3 us
	Send 1K bytes over 1 Gbps network 10,000 ns 10 us
	Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD

	import numpy as np
	import pandas as pd
	import pandas.io.ga as ga

	hosts = ['blog.example.com', 'www.example.com']
	account_id = "12345678"

	# construct a list of filters
	# following the regex =~ contains syntax
	# documented in the google API docs

	# Reproducible Research using Docker and R

	# Challenges of reproducibility

	- dependencies
	- isolation and transparency
	- portability of computationational environment
	- extendability and resuse
	- ease of use

	with
	dau as (
	-- This part of the query can be pretty much anything.
	-- The only requirement is that it have three columns:
	-- dt, user_id, inc_amt
	-- Where dt is a date and user_id is some unique identifier for a user.
	-- Each dt-user_id pair should be unique in this table.
	-- inc_amt represents the amount of value that this user created on dt.
	-- The most common case is
	-- inc_amt = incremental revenue from the user on dt

	FROM python:2.7
	ADD . /code
	WORKDIR /code
	RUN pip install -r requirements.txt