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Secured HLS setup with Nginx as media server

This example is part of this article.

This is an example for an HLS delivery with basic security. Nginx compiled with nginx-rtmp-module & secure-link is used as media server. Features:

• Domain filtering
• Referrer filtering
• Embed buster

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Install and using numba on mac

• Mac version: 10.11.3
• Python version: 3.5.1

Install

$ brew tap homebrew/versions
$ brew install llvm37

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'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

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CREATE OR REPLACE PROCEDURE SQL_TO_CSV  
(  
 P_QUERY IN VARCHAR2, -- PLSQL文  
 P_DIR IN VARCHAR2, -- 导出的文件放置目录  
 P_FILENAME IN VARCHAR2 -- CSV名  
 )  
 IS  
  L_OUTPUT UTL_FILE.FILE_TYPE;  
  L_THECURSOR INTEGER DEFAULT DBMS_SQL.OPEN_CURSOR;  
  L_COLUMNVALUE VARCHAR2(4000);  

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madlib-postgres-ubuntu

Instructions for installing MADlib and Postgres on Ubuntu 16.04

# Install Postgres 9.6, pl/r and dependencies for MADlib
sudo apt-get install -y cmake postgresql-9.6 postgresql-server-dev-9.6 postgresql-plpython-9.6 postgresql-9.6-plr libkrb5-dev postgresql-client postgresql-contrib

# grab madlib rpm
cd /home/gpadmin/tmp

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vi /usr/bin/xlsx

#!/bin/bash
docker run --rm -v /root/upload:/upload -v /data:/data xlsx xlsx $1 $2

vi /usr/bin/ssconvert

#!/bin/bash
docker run --rm -v /root/upload:/upload ssconvert ssconvert $1 $2 $3

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apt install python-pip pypy
pip install xlsx2csv
vi /usr/local/bin/xlsx2csv
#!/usr/bin/python -> #!/usr/bin/pypy

xlsx2csv -a test.xlsx test

# method 2
npm install -g xlsx
xlsx test.xlsx -o test.csv

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apt install nuitka
nuitka --recurse-all xlsx2csv.py

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git checkout master
git pull origin master
git merge v1.2.2
git push origin master

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set max_worker_processes = 64;
set max_parallel_workers_per_gather = 16;
#设置强制并行，建议off，避免不必要的使用worker
#set force_parallel_mode = on;