- c5.xlarge AWS instance: 4 CPUs, 8 GB RAM
- Ubuntu 18.04
- Go 1.12
- Python 2.7
- Vips 8.7.4
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#/bin/sh | |
ffmpeg -i input -c:v libx264 -preset slow -profile:v high -crf 18 -coder 1 -pix_fmt yuv420p -movflags +faststart -g 30 -bf 2 -c:a aac -b:a 384k -profile:a aac_low output |
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http -v -j POST https://hkt-mobile-api.nowtv.now.com/09/1/getLiveURL mode=prod format=HLS callerReferenceNo=20190707123320 channelno=332 deviceType=5 deviceId=7634de3e186676d537OA | |
http -v -j POST https://api.viu.now.com/p8/2/getLiveURL mode=prod format=HLS callerReferenceNo=20190707123320 channelno=100 deviceType=5 deviceId=7634de3e186676d537 |
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import boto3 | |
def lambda_handler(event, context): | |
is_test = context.function_name == 'test' # this value is injected by SAM local | |
instances = boto3.resource('ec2').instances.all() | |
copyable_tag_keys = ["Team", "Billing", "BillingTag", "Env", "Project"] | |
for instance in instances: | |
copyable_tags = [t for t in instance.tags |
FWIW: I didn't produce the content presented here (the outline from Edmond Lau's book). I've just copy-pasted it from somewhere over the Internet, but I cannot remember what exactly the original source is. I was also not able to find the author's name, so I cannot give him/her the proper credits.
- By Edmond Lau
- Highly Recommended 👍
- http://www.theeffectiveengineer.com/
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http://www.oreilly.com/data/free/files/2014-data-science-salary-survey.pdf | |
http://www.oreilly.com/data/free/files/2015-data-science-salary-survey.pdf | |
http://www.oreilly.com/data/free/files/Data_Analytics_in_Sports.pdf | |
http://www.oreilly.com/data/free/files/advancing-procurement-analytics.pdf | |
http://www.oreilly.com/data/free/files/ai-and-medicine.pdf | |
http://www.oreilly.com/data/free/files/analyzing-data-in-the-internet-of-things.pdf | |
http://www.oreilly.com/data/free/files/analyzing-the-analyzers.pdf | |
http://www.oreilly.com/data/free/files/architecting-data-lakes.pdf | |
http://www.oreilly.com/data/free/files/being-a-data-skeptic.pdf | |
http://www.oreilly.com/data/free/files/big-data-analytics-emerging-architecture.pdf |
workshop github guideline: https://github.com/aws-samples/aws-workshop-for-kubernetes
if you wanna follow the guideline, make sure you:
- setup cloud9, follow the instructions under the heading: Create AWS Cloud9 Environment
- setup Kubernetes multi master cluster, follow the instructions under the heading: Create a Kubernetes Cluster with kops and Kubernetes Cluster Context. Suggest to create a multi-master cluster as the examples will be easier to follow.
- Pod
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#!/bin/sh | |
# The bandwidth to simulate, here about 56kilobit per second. This is layer 2 bandwidth, so TCP/UDP and IP overhead will apply | |
BW="56kbps" | |
# _Half_ the latency that we aim for. Since this applies to both the WAN port and Wi-Fi, the delay is applied twice, so this actually puts it at around 120+ms | |
LATENCY="60ms" | |
# Chance of packet loss. Also applied to both interfaces, so it is 1%. | |
LOSS="0.5%" | |
# The device name of your wifi device. | |
WIFI="wlan0" |
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#!/bin/bash | |
# | |
# template script for generating CentOS container for LXC | |
# | |
# | |
# lxc: linux Container library | |
# Authors: |
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#install required packages | |
apt-get update | |
apt-get upgrade -y | |
apt-get install -y git curl build-essential libssl-dev libcurl4-openssl-dev nodejs | |
#optional | |
apt-get install libsqlite3-dev sqlite3 | |
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