hellerbarde

Latency numbers every programmer should know

L1 cache reference ......................... 0.5 ns
Branch mispredict ............................ 5 ns
L2 cache reference ........................... 7 ns
Mutex lock/unlock ........................... 25 ns
Main memory reference ...................... 100 ns             
Compress 1K bytes with Zippy ............. 3,000 ns  =   3 µs
Send 2K bytes over 1 Gbps network ....... 20,000 ns  =  20 µs
SSD random read ........................ 150,000 ns  = 150 µs

Read 1 MB sequentially from memory ..... 250,000 ns = 250 µs

sloria

The Best of the Best Practices (BOBP) Guide for Python

A "Best of the Best Practices" (BOBP) guide to developing in Python.

In General

Values

• "Build tools for others that you want to be built for you." - Kenneth Reitz
• "Simplicity is alway better than functionality." - Pieter Hintjens

rxaviers

People

:bowtie:	😄 :smile:	😆 :laughing:
😊 :blush:	😃 :smiley:	☺️ :relaxed:
😏 :smirk:	😍 :heart_eyes:	😘 :kissing_heart:
😚 :kissing_closed_eyes:	😳 :flushed:	😌 :relieved:
😆 :satisfied:	😁 :grin:	😉 :wink:
😜 :stuck_out_tongue_winking_eye:	😝 :stuck_out_tongue_closed_eyes:	😀 :grinning:
😗 :kissing:	😙 :kissing_smiling_eyes:	😛 :stuck_out_tongue:

Chaser324

Whether you're trying to give back to the open source community or collaborating on your own projects, knowing how to properly fork and generate pull requests is essential. Unfortunately, it's quite easy to make mistakes or not know what you should do when you're initially learning the process. I know that I certainly had considerable initial trouble with it, and I found a lot of the information on GitHub and around the internet to be rather piecemeal and incomplete - part of the process described here, another there, common hangups in a different place, and so on.

In an attempt to coallate this information for myself and others, this short tutorial is what I've found to be fairly standard procedure for creating a fork, doing your work, issuing a pull request, and merging that pull request back into the original project.

Creating a Fork

Just head over to the GitHub page and click the "Fork" button. It's just that simple. Once you've done that, you can use your favorite git client to clone your repo or j

datagrok

When you cancel a Jenkins job

Unfinished draft; do not use until this notice is removed.

We were seeing some unexpected behavior in the processes that Jenkins launches when the Jenkins user clicks "cancel" on their job. Unexpected behaviors like:

• apparently stale lockfiles and pidfiles
• overlapping processes
• jobs apparently ending without performing cleanup tasks
• jobs continuing to run after being reported "aborted"

PurpleBooth

Project Title

One Paragraph of project description goes here

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

loleg

from picamera.array import PiRGBArray
from picamera import PiCamera
import time
import sys
import cv2
import zbar
import Image

# Debug mode
DEBUG = False

lifthrasiir

아래 메일은 2017-02-12 21:43(이하 한국 표준시)에 한겨레 기사에 대한 의견으로서 기사에 제시된 김재섭 기자의 메일로 보낸 내용이다. 메일에서 사실 관계 등의 오류가 있다면 모두 나의 실수이다.

2017-02-13 14:53에 덧붙임: 더 이상 gist를 비공개로 할 이유가 없어졌으므로 공개로 전환. 이 메일에 대한 답변은 받았으나 공개할 만큼 중요한 반론이 들어 있진 않으며 공개 여부도 묻지 않았으므로 공개하지 않는다. 아래 글 자체에도 다양한 비문과 오자가 있으나 본래 보낸 내용을 살리기 위해 전혀 수정을 하지 않기로 했음을 양해 바람.

2017-02-13 19:00에 덧붙임: 이 기사의 후속으로 구글코리아 측의 기자간담회가 올라갔다. 새 기사에 대해서는 특이한 게 없으므로 노코멘트. 또한 위의 기사 링크를 미디어다음에서 한겨레 웹사이트로 가도록 수정.

원문

안녕하십니까, 귀하께서 작성하신 (물론 저는 그 진위를 알 수 없습니다만, 적어도 그렇게 나와 있는) 기사에 대한 의견을 제기하고자 메일을 씁니다. 이 메일은 저의 개인 의견이며 저를 고용하고 있는 회사나 단체 등의 의견을 전혀 대변하지 않음을 혹시나 싶지만 미리 밝혀 둡니다.

480

MacOS X + oh my zsh + powerline fonts + visual studio code (vscode) terminal settings

Thank you everybody, Your comments makes it better

Install oh my zsh

http://ohmyz.sh/

sh -c "$(curl -fsSL https://raw.github.com/ohmyzsh/ohmyzsh/master/tools/install.sh)"

mserranom

Feature Store

Uber Michelangelo

https://eng.uber.com/michelangelo/

Finding good features is often the hardest part of machine learning and we have found that building and managing data pipelines is typically one of the most costly pieces of a complete machine learning solution.

A platform should provide standard tools for building data pipelines to generate feature and label data sets for training (and re-training) and feature-only data sets for predicting. These tools should have deep integration with the company’s data lake or warehouses and with the company’s online data serving systems. The pipelines need to be scalable and performant, incorporate integrated monitoring for data flow and data quality, and support both online and offline training and predicting. Ideally, they should also generate the features in a way that is shareable across teams to reduce duplicate work and increase data quality. They should also provide strong guard rails and controls to encourage and empower users to adop