Tabrizian

Scheduling

Can we apply the Reinforcement Learning problem for the ML scheduling while considering the case that the jobs may be preempted?

Paper	State Representation	Action Representation	Reward Function	RL Customizations	Policy Representation	Goal
Resource Management with Deep Reinforcement Learning (HotNets '16)	An image showing the resource allocation of the cluster of resources and resource demands of the jobs waiting to be scheduled.	Whether to admit a job or not	Negative of the total slowdown	REINFORCE + Negating the value function in order to reduce the variance.	Simple feed forward network	Reducing the average job slowdown
Device Placement Optimization with Reinforcement Learning (ICML '17)	A computation graph + a list of devices	The given placement strategy	Square root of the total running time in a given placement or a large const

Tabrizian

export OVPN_DATA="ovpn-data-example"

Tabrizian

body {
	background-color: #c5d2ea;
	margin-left: 20px;
	font-family: "Helvetica Neue", Helvetica, sans-serif;
}

.student {
	margin-bottom: 5px;
	font-size: 150%;
}

Tabrizian

https://www.mkyong.com/java/how-to-send-http-request-getpost-in-java/

Tabrizian

http://ceit.aut.ac.ir/~gradtalk/files/MohammadGhaffarian/Presentation.pdf

http://dl5.downloadha.com/hosein/Animation/April2018/Captain.Tsubasa.2018/S01/PersianDubbed/
Captain.Tsubasa.2018.S01E02.PersianDubbed.720p.WEB-DL.x264-DLHA_www.Downloadha.com_.mkv

http://ceit.aut.ac.ir/~gradtalk/files/MohammadGhaffarian/Presentation.pdf

http://dl5.downloadha.com/Behnam/2018/May/Education/Polite-English-in-Forty-
Minutes_Downloadha.com_.zip

Tabrizian

import com.mongodb.MongoClient;
import com.mongodb.client.MongoCollection;
import com.mongodb.client.MongoDatabase;
import static com.mongodb.client.model.Filters.*;
import com.mongodb.client.model.CreateCollectionOptions;
import com.mongodb.client.model.ValidationOptions;
import org.bson.Document;

import java.util.Arrays;

Tabrizian

-------------------------------------------------------------------------------
-- Company:
-- Engineer:
--
-- Create Date: 07/05/2017 08:40:01 AM
-- Design Name:
-- Module Name: root - Behavioral
-- Project Name:
-- Target Devices:
-- Tool Versions:

Tabrizian

https://docs.google.com/presentation/d/19SXk8Rc8EuuXTigDP-O8YJt2bNaNXH3L9cGq7WwUeMg/edit#slide=id.gc6f73a04f_0_9

Tabrizian

mongoimport --db test --collection restaurants --drop --file ~/downloads/primer-dataset.json
db.users.find( {} )
{ <field1>: <value1>, ... }
db.users.find( { status: "A" } )
db.users.find( { status: { $in: [ "P", "D" ] } } )
db.users.find( { status: "A", age: { $lt: 30 } } )
{ $or: [ { status: "A" }, { age: { $lt: 30 } } ] }

Tabrizian

[
     {
       "_id": 1,
       "name": "sue",
       "age": 19,
       "type": 1,
       "status": "P",
       "favorites": { "artist": "Picasso", "food": "pizza" },
       "finished": [ 17, 3 ],
       "badges": [ "blue", "black" ],