This subpart implements policy on personal conflicts of interest by employees of Government contractors as required by 41 U.S.C. 2303.
As used in this subpart—
“Acquisition function closely associated with inherently governmental functions” means supporting or providing advice or recommendations with regard to the following activities of a Federal agency:
(1) Planning acquisitions.
(2) Determining what supplies or services are to be acquired by the Government, including developing statements of work.
(3) Developing or approving any contractual documents, to include documents defining requirements, incentive plans, and evaluation criteria.
(4) Evaluating contract proposals.
(5) Awarding Government contracts.
(6) Administering contracts (including ordering changes or giving technical direction in contract performance or contract quantities, evaluating contractor performance, and accepting or rejecting contractor products or services).
(7) Terminating contracts.
(8) Determining whether contract costs are reasonable, allocable, and allowable.
“Covered employee” means an individual who performs an acquisition function closely associated with inherently governmental functions and is—
(1) An employee of the contractor; or
(2) A subcontractor that is a self-employed individual treated as a covered employee of the contractor because there is no employer to whom such an individual could submit the required disclosures.
“Personal conflict of interest” means a situation in which a covered employee has a financial interest, personal activity, or relationship that could impair the employee’s ability to act impartially and in the best interest of the Government when performing under the contract. (A de minimis interest that would not “impair the employee’s ability to act impartially and in the best interest of the Government” is not covered under this definition.)
(1) Among the sources of personal conflicts of interest are—
(i) Financial interests of the covered employee, of close family members, or of other members of the covered employee’s household;
(ii) Other employment or financial relationships (including seeking or negotiating for prospective employment or business); and
(iii) Gifts, including travel.
(2) For example, financial interests referred to in paragraph (1) of this definition may arise from—
(i) Compensation, including wages, salaries, commissions, professional fees, or fees for business referrals;
(ii) Consulting relationships (including commercial and professional consulting and service arrangements, scientific and technical advisory board memberships, or serving as an expert witness in litigation);
(iii) Services provided in exchange for honorariums or travel expense reimbursements;
(iv) Research funding or other forms of research support;
(v) Investment in the form of stock or bond ownership or partnership interest (excluding diversified mutual fund investments);
(vi) Real estate investments;
(vii) Patents, copyrights, and other intellectual property interests; or
(viii) Business ownership and investment interests.
The Government’s policy is to require contractors to—
(a) Identify and prevent personal conflicts of interest of their covered employees; and
(b) Prohibit covered employees who have access to non-public information by reason of performance on a Government contract from using such information for personal gain.
(a) By use of the contract clause at 52.203-16, as prescribed at 3.1106, the contracting officer shall require each contractor whose employees perform acquisition functions closely associated with inherently Government functions to—
(1) Have procedures in place to screen covered employees for potential personal conflicts of interest by—
(i) Obtaining and maintaining from each covered employee, when the employee is initially assigned to the task under the contract, a disclosure of interests that might be affected by the task to which the employee has been assigned, as follows:
(A) Financial interests of the covered employee, of close family members, or of other members of the covered employee’s household.
(B) Other employment or financial relationships of the covered employee (including seeking or negotiating for prospective employment or business).
(C) Gifts, including travel; and
(ii) Requiring each covered employee to update the disclosure statement whenever the employee’s personal or financial circumstances change in such a way that a new personal conflict of interest might occur because of the task the covered employee is performing.
(2) For each covered employee—
(i) Prevent personal conflicts of interest, including not assigning or allowing a covered employee to perform any task under the contract for which the Contractor has identified a personal conflict of interest for the employee that the Contractor or employee cannot satisfactorily prevent or mitigate in consultation with the contracting agency;
(ii) Prohibit use of non-public information accessed through performance of a Government contract for personal gain; and
(iii) Obtain a signed non-disclosure agreement to prohibit disclosure of non-public information accessed through performance of a Government contract.
(3) Inform covered employees of their obligation—
(i) To disclose and prevent personal conflicts of interest;
(ii) Not to use non-public information accessed through performance of a Government contract for personal gain; and
(iii) To avoid even the appearance of personal conflicts of interest;
(4) Maintain effective oversight to verify compliance with personal conflict-of-interest safeguards;
(5) Take appropriate disciplinary action in the case of covered employees who fail to comply with policies established pursuant to this section; and
(6) Report to the contracting officer any personal conflict-of-interest violation by a covered employee as soon as identified. This report shall include a description of the violation and the proposed actions to be taken by the contractor in response to the violation, with follow-up reports of corrective actions taken, as necessary.
(b) If a contractor reports a personal conflict-of-interest violation by a covered employee to the contracting officer in accordance with paragraph (b)(6) of the clause at 52.203-16, Preventing Personal Conflicts of Interest, the contracting officer shall—
(1) Review the actions taken by the contractor;
(2) Determine whether any action taken by the contractor has resolved the violation satisfactorily; and
(3) If the contracting officer determines that the contractor has not resolved the violation satisfactorily, take any appropriate action in consultation with agency legal counsel.
(a) In exceptional circumstances, if the contractor cannot satisfactorily prevent a personal conflict of interest as required by paragraph (b)(2)(i) of the clause at 52.203-16, Preventing Personal Conflicts of Interest, the contractor may submit a request, through the contracting officer, for the head of the contracting activity to—
(1) Agree to a plan to mitigate the personal conflict of interest; or
(2) Waive the requirement to prevent personal conflicts of interest.
(b) If the head of the contracting activity determines in writing that such action is in the best interest of the Government, the head of the contracting activity may impose conditions that provide mitigation of a personal conflict of interest or grant a waiver.
(c) This authority shall not be redelegated.
If the contracting officer suspects violation by the contractor of a requirement of paragraph (b), (c)(3), or (d) of the clause at 52.203-16, Preventing Personal Conflicts of Interest, the contracting officer shall contact the agency legal counsel for advice and/or recommendations on a course of action.
(a) Insert the clause at 52.203-16, Preventing Personal Conflicts of Interest, in solicitations and contracts that—
(1) Exceed the simplified acquisition threshold; and
(2) Include a requirement for services by contractor employee(s) that involve performance of acquisition functions closely associated with inherently governmental functions for, or on behalf of, a Federal agency or department.
(b) If only a portion of a contract is for the performance of acquisition functions closely associated with inherently governmental functions, then the contracting officer shall still insert the clause, but shall limit applicability of the clause to that portion of the contract that is for the performance of such services.
(c) Do not insert the clause in solicitations or contracts with a self-employed individual if the acquisition functions closely associated with inherently governmental functions are to be performed entirely by the self-employed individual, rather than an employee of the contractor.


Porcupine Wake-Word Conversion
How to:
- Create a new Porcupine wake-word via https://console.picovoice.ai.
- Choose 'WebAssembly (WASM)' as target platform.
- Download and extract the ZIP file.
- Look for the file named '..._b64.txt' and drag it over the drop-zone (or copy content).
- Press 'Convert' to get array representation.
- Check/adjust automatically generated download parameters and press 'Download as SEPIA file'.
- Follow these instructions to import the file into SEPIA.
Drop-Zone
Result
Download


I think problem solving is an essential tool for this industry I am trying to get into. Problem solving is used every day and is a key part of the work. Therefore, the better you are at problem solving, the better you can become at coding/programming. So, how do we become better at problem solving? There are many techniques for problem solving and all work in a different way. For example, one technique is called Pseudocode.
Pseudocode is an excellent problem-solving tool, particularly for writing complex algorithms. It is a great method for uncovering unclear decisions, hidden side effects, and for defining all inputs, outputs and interactions needed to effectively solve a problem. Pseudocode help by breaking large problems into smaller sub groups of problems.
Another technique I use is reading the error message. Error message – The error message is “what’s wrong”- good error messages are helpful and tell you what you should do. These messages show you what went wrong in our code and even tell us which line gave us the error. For example, I forgot to add the closing curly for a bracket which meant that the code would not run. The error message prompted me “unexpected end of input” followed by telling me which line the error occurred.
One other method that I learnt, but subconsciously did anyway was the rubber ducky method. The rubber ducky method is a technique that involves explaining code line-by-line to inanimate objects (e.g. a rubber duck or in my case talking to my dog Hugo). The way this method works is that when you explain a problem to someone and then hitting upon the solution in the process of explaining the problem. This method doesn’t always work but when it does it feels like magic.
During my learning experience this week I faced many problems while coding. One simple problem was during the Super FizzBuzz exercise. For the first part of the exercise, I managed to solve the fizzbuzz function no problem as I was only dealing with a number. When I switched to the super_fizzbuzz function I thought it would have been quite straightforward too. I understood that I needed a for loop to access all elements in the array and that I needed the same conditions from the previous function. For some reason my code was not working and it took me a while to solve it. The problem-solving techniques I used were ‘trying something’, reading the error messages but I managed to solve the problem by Googling the answer. I found out that my conditions for the multiples of 15 ((arr[i] % 3 == 0) && (arr[i] % 5 == 0)) should have been at the beginning instead of the end. At first I put the individual conditions as ‘if’ statements before it when I should of put the double condition statement before the individual ones. I learnt that it makes sense to put the statement with the most conditions before individual ones as it is passed through a more specific statement before being passed through more straightforward statements.
One problem I felt like I solved in an elegant way was the deBee function in the Built-in methods exercises. The goal was to make a function that removed all the “buzz” and “Buzz” words from the string given. The first step I knew to do was to separate the words which I did using the split(“ ”) function. Then I used the filter function to look up the words “buzz” and “Buzz”. I knew that I had to look for two separate words as one of them is capitalized. I ended up solving this problem in an elegant way by using the .toLowerCase() function which converted the “Buzz” into “buzz”. It may not seem super exciting but I felt amazing when this method worked for me. I honestly love learning how to think differently in order to solve problems and have been enjoying the struggle of learning how to code so far.


--- layout: default ---
The following examples give you an overview of available Substance features. You can clone the examples repository from Github to run them on your computer.
Open Example | View Source


{% extends 'blog/base.html' %} {% load static %} {% block content %}
Sidney Sheldon was a bestselling author of crime and thriller novels, in addition to writing Broadway plays and movies. He passed away in 2007, but his eighteen novels continue to find new readers every day, having sold more than 300 million copies worldwide. If you’re new to Sidney Sheldon and are wondering which book to pick up first, let Goodreads help you decide! This list ranks the top-rated Sidney Sheldon novels according to their average Goodreads user ratings.


Comedian Ross Noble to perform first UK dates of 2020 in Shropshire
by Mat GrowcottComedian Ross Noble will be testing new material with a set of shows in Shropshire next year.
The newly-announced show will see the comedy icon come to Oakengates Theatre on April 18, with further shows at Shrewsbury's Theatre Severn on April 20 and 21.
The performance in Telford, which will last about an hour and 40 minutes, will act as a warm-up gig ahead of a national tour, and will be Noble's first performance in the UK in 2020.
The show will then tour all over England, Scotland and Wales before coming back to Birmingham on May 16.
Noble is most famous for his appearances on television, including Have I Got News for You and QI, and for his participation in radio panel games such as Just a Minute and I'm Sorry I Haven't a Clue. He tours extensively around the world.
In 2017 he was nominated for a Laurence Olivier Award for playing Igor in a musical adaption of Mel Brooks' Young Frankenstein.


Callum Hudson-Odoi, Tyrone Mings and Nick Pope start for England against Kosovo
Follow live updates from Kosovo vs England on the Sky Sports website and app. You can then watch highlights from 15 minutes after full-time on the Sky Sports website, app and YouTube channel
Callum Hudson-Odoi, Tyrone Mings and Nick Pope will start for England against Kosovo in their final European Qualifier.
Burnley goalkeeper Pope - first called up in March 2018 - finally earns his first England start, while Chelsea winger Hudson-Odoi makes only his second start as Gareth Southgate explores his squad options.
Aston Villa defender Mings wins only his second England cap alongside Harry Maguire, while Declan Rice is deployed in midfield in another of Southgate's five changes from the side that thrashed Montenegro 7-0.
Raheem Sterling returns as expected following the altercation with Joe Gomez that led to the Manchester City forward being dropped last time out.
James Maddison was a doubt with a calf strain but is named on the bench, as is John Stones.
England team to face Kosovo
Starting XI: Pope, Alexander-Arnold, Maguire, Mings, Chilwell, Winks, Rice, Oxlade-Chamberlain, Hudson-Odoi, Sterling, Kane.
Subs: Trippier, Pickford, Rose, Stones, Tomori, Mount, Maddison, Sancho, Rashford, Wilson, Abraham, D Henderson.
Watch England highlights for free on Sky Sports
Watch highlights of Kosovo vs England for free with Sky Sports.
The best bits will be available on skysports.com, the Sky Sports app and the Sky Sports Football YouTube channel from 15 minutes after full-time - and you won't need a Sky Sports subscription to tune in.


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This is the official extensions page for OverlayScrollbars.
Here you can find Extensions written by myself or by the community.
If you wanna know how to write your own Extension, please read the
documentation and check the
examples.
In case you've written your own Extension and you to want make it available for everyone, please open a issue on
github.
After I've reviewed your extension it will be listed on this page.


<resourceid>
The <resourceid> element provides an identifier for applications that must use their own identifier scheme, such as context-sensitive help systems and databases.
Category: Prolog (metadata) elements


{% extends "_includes/base.html" %} {% block content %}
Dear {{ account.display_name }},
Please click here to reset your password.


Zechariah
1
1 In the eighth month of the second year of Darius' reign, the word of Yahweh came to Zechariah the son of Berechiah, the son of Iddo the prophet, saying,
2 "Yahweh was exceedingly angry with your fathers!
3 Say to them, 'Yahweh of hosts says this:
"Turn to me!"—this is Yahweh of hosts' declaration—
"and I will return to you!" says Yahweh of hosts.
4 Do not be like your fathers to whom the prophets cried out previously, saying, "Yahweh of hosts says this: turn from your evil ways and wicked practices!" But they would not hear and did not pay attention to me.'" This is Yahweh's declaration.
5 "Your fathers, where are they? And the prophets, are they here forever?
6 But my words and my decrees that I commanded my servants the prophets,
have they not overtaken your fathers?
So they repented and said, 'Yahweh of hosts's plans are made in keeping with his word and practice, and he has carried them out.'"
7 On the twenty-fourth day of the eleventh month, which is the month of Shebat, in the second year of Darius' reign, the word of Yahweh came to Zechariah the son of Berechiah, the son of Iddo the prophet, saying,
8 "I saw in the night, and, look! a man was riding on a red horse, and he was among the myrtle trees that are in the valley; and behind him there were red, reddish-brown, and white horses."
9 I said, "What are these things, Lord?" And the angel that talked with me said to me, "I will show you what these things are."
10 Then the man who stood among the myrtle trees answered and said, "These are those Yahweh has sent out to roam throughout the earth."
11 They answered the angel of Yahweh who stood among the myrtle trees; they said to him, "We have been roaming throughout the earth; see, all the earth sits still and is at rest."
12 Then the angel of Yahweh answered and said, "Yahweh of hosts, how long will you show no compassion to Jerusalem and to the cities of Judah that have suffered indignation these seventy years?"
13 Yahweh answered the angel who had talked with me with good words, words of comfort.
14 So the angel who had talked with me said to me, "Call out and say, 'Yahweh of hosts says this:
"I have been jealous for Jerusalem
and for Zion with a great passion!
15 And I am very greatly angry with the nations that are at ease;
for I was but a little displeased, and they helped
forward the affliction."
16 Therefore Yahweh of hosts says this:
"I have returned to Jerusalem with mercies. My house will be built within her"'
—this is Yahweh of hosts' declaration—"and
the measuring line will be stretched out over Jerusalem!"
17 Again call out, saying,
'Yahweh of hosts says this: 'My cities will once again overflow with goodness,
and Yahweh will again
comfort Zion, and he will once again choose Jerusalem."
18 Then I lifted up my eyes and saw four horns!
19 I spoke to the angel that talked with me, "What are these?" He answered me, "These are the horns that have scattered Judah, Israel, and Jerusalem."
20 Then Yahweh showed me four blacksmiths.
21 I said, "What are these people coming to do?"  But these people are coming to drive them out, to cast down the horns of the nations who lifted up any horn against the land of Judah to scatter her."


HAUNTED PALACE, THE (director: Roger Corman; screenwriters: Charles Beaumont/from an H.P. Lovecraft story "The Case of Charles Dexter Ward" & an Edgar Allan Poe poem; cinematographer: Floyd Crosby; editor: Ronald Sinclair; cast: Vincent Price (Charles Dexter Ward), Debra Paget (Ann Ward), Lon Chaney, Jr. (Simon), Frank Maxwell (Dr. Willet), Leo Gordon (Ezra Weeden), Elisha Cook, Jr. (Micah/Peter Smith), John Dierkes (Jacob West), Cathy Merchant (Hester); Runtime: 86; American International; 1963)
Reviewed by Dennis Schwartz
For those who love the following in their horror tales: schlock, an old-fashioned Grand Guignol story, and a hammy performance from its star, it should be noted that this film has all that. Vincent Price is totally maddening in a dual role as the warlock Joseph Kerwin and as the innocent great-great-grandson Charles Dexter Ward, who returns sometime in the late 19th century to Arkham, a small village in New England, to inherit the palace of the warlock Joseph Kerwin -- someone he bears an uncanny resemblance to. He was burned-to-the-stake by these villagers 110 years ago for practicing evil magic and cursed the town before he died, swearing revenge by giving a wonderfully damming speech before his demise.
Warning: spoilers to follow.
Arriving by carriage to Arkham to claim their inheritance, Charles and his wife Ann (Debra Paget) are told that the town is haunted and that they are not wanted here. They are frightened when they witness the town mutants in the street, those who were born with severe birth defects, but refuse to leave without visiting their castle. When they enter the Burning Man Tavern to ask for directions to their castle, they are shunned except by Dr. Willet (Maxwell). He will tell them of the legend of the palace they inherited and of how his great-great-grandfather was accused of being a warlock, who came to the village 150 years ago and built out of stone transported from Europe his palace, where he conducted secret experiments in the occult. It was believed he communicated with the dark underground gods and tried to bring them back to life. He brought to his palace young woman by putting them in a trance, as his hope was for them to give birth to a new species on Earth, one with the powers derived from hell. When his first wife died, he had Hester (Cathy Merchant), the beautiful fiancée of one of the town's leading citizens, Ezra Weeden (Leo Gordon), come live with him, thereby engaging that man's wrath. The warlock used the book of Necronomican to get the spells and chants and formulas to possibly unlock the secrets of the other world for the black magician. One night Ezra and Micah (Elisha) spotted a trance-like young woman crossing the cemetery and entering the warlock's palace and got the villagers to burn him at the stake...taking the law into their own hands.
In his palace, Charles is greeted by his hulking manservant Lon Chaney Jr., who tells him that he is here to serve him. Soon a battle of wills begins to take place, as the spirit of the warlock tries to enter the spirit of Charles and take control of his body. Charles has a strong will of his own and they battle for control, with Charles being the good guy and the warlock hellbent on revenge to the relatives who acted against him. He also wants to resurrect Hester from the dead and continue with Simon and another assistant hidden in the palace's secret chamber, Jacob (Dierkes), his secret experiments in the occult. Well...you can imagine how disconcerting that is to those who hang out in the Burning Man Tavern, as they get wind of what's going on in the palace and decide to act in the old-fashioned way to rid the village of that threat!
The film was atmospheric -- plenty of cobwebs, dark corners, and ghoulish creatures around to add to the eerie tale. I suppose it was chilling enough but it still lacked that missing magical ingredient to make this a more satisfying film. I think the problem was that the story dragged after the exciting opening scene and never could pick up steam again. This turns out to be a minor film from Roger Corman's oeuvre, not up to his other more daringly told horror tales... yet, it is enjoyable in a goofy sort of way.
It was the last film role for Debra Paget, she married her third husband, a Chinese-American oil millionaire and retired (they divorced in 1971).
REVIEWED ON 12/26/2000 GRADE: C+
Dennis Schwartz: "Ozus' World Movie Reviews"
http://www.sover.net/~ozus
<EMAIL_ADDRESS>© ALL RIGHTS RESERVED DENNIS SCHWARTZ
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
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Related links: index of all rec.arts.movies.reviews reviews


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Spectrum of mutations and sequence variants in the FALDH gene in patients with Sjögren-Larsson syndrome.
The gene encoding the human fatty aldehyde dehydrogenase (FALDH) is located on 17p11.2, causing Sjögren-Larsson syndrome (SLS) when mutated. SLS is an autosomal recessive disorder characterized by a combination of mental retardation, congenital ichthyosis, and spastic di- or tetraplegia. We report here on studies of 16 SLS families from Europe and the Middle East, which resulted in identification of 11 different mutations. The spectrum of mutations characterized in the present study are five nucleotide substitutions resulting in amino acid changes, five frameshift mutations introducing a stop codon, and one in-frame deletion with insertion at the same position. We also observed silent sequence variants in the FALDH gene and a base pair substitution in exon 5 that alters aspartic acid to asparagine, all of which are considered polymorphisms.


Football Analytics
Dates
Leagues
Bundesliga
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Ringgit continues downtrend at close
KUALA LUMPUR, Nov 20 — The ringgit ended lower against the US dollar today in line with most emerging Asian currencies, following the escalation of the US-China trade war, said a dealer.
At 6pm, the ringgit finished at 4.1640/1670 against the greenback from yesterday’s close of 4.1560/1590.
The dealer said the continuous trade war risk would likely cause a further weakening of China’s yuan performance and by extension could weaken the ringgit due to its very tight correlation.
China’s yuan slipped to a new two-week low in overnight trading after US President Donald Trump threatened to raise new tariffs on Chinese imports if ongoing trade negotiations fail.
“At the moment, more investors have shifted their interest towards safe-haven currencies,” the dealer said.
The local note also traded mostly lower against other major currencies.
The ringgit was weaker against the Singapore dollar at 3.0579/0613 from 3.0534/0567 yesterday, depreciated against the yen to 3.8399/8437 from 3.8202/8240 and fell against the euro to 4.6066/6104 from 4.5990/5040.
However, the local note went up vis-a-vis the British pound to 5.3749/3808 from 5.3829/3884. — Bernama


---
layout: post
title: Emma Week 11, 12, Memorial Day Weekend
categories: []
tags: []
status: publish
type: post
published: true
meta:
_original_post_id: ''
_original_import_author: John Duprey
author:
---
-
-
jduprey
- May 31st, 2005
| Pictures of Emma from weeks 11 and 12 are up along with a slew of other pics from Memorial day weekend. We had a lot of fun on Sunday at Sodus Bay with the Van Ee's. We had a nice picnic. Emma got passed around a lot and Mason was fairly well behaved. Monday we discovered that one of Mason play mates is also his litter mate - his brother! Small world! The Van Ee brothers are trying to lure me into the sick and twisted world of "World of Warcraft." Time will tell if their treachery was successful. On on the verge of buying a new digital camera to replace my aging (OLD) Kodak DC215. Stay tuned.. |
|


Enumerables
November 4, 2015 | Coding
Overview
The Enumerable mixin
provides collection classes with several traversal and searching methods, and with the ability to sort. The class must provide a method each, which yields successive members of the collection. If Enumerable#max, #min,
code. or #sort
is used, the objects in the collection must also implement a meaningful <=>
operator, as these methods rely on an ordering between members of the collection.
An example of how to use Ruby's Enumerable is as follows:
Comments box


|
PyHodl
0.2.6
Framework to download, update, analyze and plot your crypto-transactions. Completely off-line and security-oriented.
|
Public Member Functions | |
| def | __init__ (self, wallets, base_currency="USD") |
| def | plot_balances (self) |
| def | plot_price (self, wallet) |
| def | plot_delta_buy_sells (self, wallet) |
| def | plot_buy_sells (self, wallet) |
| def | plot_crypto_fiat_balance (self) |
| def | show (self, title, x_label="Time", y_label="value") |
| Public Member Functions inherited from pyhodl.charts.balances.BalancePlotter | |
| def | __init__ (self, wallets) |
| def | plot_balances (self) |
| def | plot_delta_balances (self) |
| def | show (self, title, x_label="Time", y_label="Balances") |
| Public Member Functions inherited from pyhodl.charts.balances.CryptoPlotter | |
| def | __init__ (self, wallets) |
| def | show (self, title, x_label="Time", y_label="Amount") |
| def | get_wallet_by_coin (self, coin) |
Public Attributes | |
| base_currency | |
| wallets_value | |
| Public Attributes inherited from pyhodl.charts.balances.BalancePlotter | |
| portfolio | |
| Public Attributes inherited from pyhodl.charts.balances.CryptoPlotter | |
| wallets | |
| axis | |
Additional Inherited Members | |
| Static Public Member Functions inherited from pyhodl.charts.balances.CryptoPlotter | |
| def | compute_knn_trend (x_data, y_val, smooth_points) |
| def | compute_trend (x_data, y_val, smooth_points) |
| def | plot (x_data, y_val, label, with_trend=False) |
Plots coins-equivalent of your wallet
| def pyhodl.charts.balances.FiatPlotter.__init__ | ( | self, | |
| wallets, | |||
base_currency = "USD" |
|||
| ) |
| def pyhodl.charts.balances.FiatPlotter.plot_balances | ( | self | ) |
:return: void Plots balances for transaction of coin
| def pyhodl.charts.balances.FiatPlotter.plot_buy_sells | ( | self, | |
| wallet | |||
| ) |
:param wallet: Wallet Coin wallet to plot :return: void Plots buy/sells points of coin against coin price
| def pyhodl.charts.balances.FiatPlotter.plot_crypto_fiat_balance | ( | self | ) |
:return: void Total balance for wach coin
| def pyhodl.charts.balances.FiatPlotter.plot_delta_buy_sells | ( | self, | |
| wallet | |||
| ) |
:param wallet: Wallet Coin wallet to plot :return: void Plots buy/sells points of coin
| def pyhodl.charts.balances.FiatPlotter.plot_price | ( | self, | |
| wallet | |||
| ) |
:param wallet: Wallet Coin wallet to plot :return: void Plots price of coin wallet
| def pyhodl.charts.balances.FiatPlotter.show | ( | self, | |
| title, | |||
x_label = "Time" , |
|||
y_label = "value" |
|||
| ) |
| pyhodl.charts.balances.FiatPlotter.base_currency |
| pyhodl.charts.balances.FiatPlotter.wallets_value |


Table of Contents
Deeper Understanding of Batch Normalization
Preprocessing for Deep Learning
Self Normalizing Neural Networks
An Overview of Normalization Methods in Deep Learning
Normalization
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Normalization:
Normalization, aka Feature Scaling, is a method used to normalize the range of independent variables or features of data. It is generally performed during the data preprocessing step.Motivation:
Since the range of values of raw data varies widely, in some machine learning algorithms, objective functions will not work properly without normalization (e.g. dot-product-based functions are scale sensitive).
Moreover, normalizing the inputs leads to spherical contours of the objective which makes the optimization easier (for vanilla sgd) and speeds up the convergence. -
Input Normalization:
Rescaling (min-max normalization):
is the simplest method and consists in rescaling the range of features to scale the range in \([0, 1]\) or \([−1, 1]\). Selecting the target range depends on the nature of the data. To rescale to \([0, 1]\) range:$$x'=\dfrac{x-{\text{min}}(x)}{ {\text{max}}(x)-{\text{min}}(x)}$$
To rescale a range between an arbitrary set of values \([a, b]\), the formula becomes:
$${\displaystyle x'=a+{\frac {(x-{\text{min}}(x))(b-a)}{ {\text{max}}(x)-{\text{min}}(x)}}}$$
where \(a,\: b\) are the min-max values.
(Zero-) Centering - Mean Normalization:
- Define the mean \(\mu_j\) of each feature of the datapoints \(x^{(i)}\):
$$\mu_{j}=\frac{1}{m} \sum_{i=1}^{m} x_{j}^{(i)}$$
- Replace each \(x_j^{(i)}\) with \(x_j - \mu_j\)
Standardization (Z-score Normalization):
Feature standardization makes the values of each feature in the data have zero-mean (when subtracting the mean in the numerator) and unit-variance. Subtract the mean from each feature, then, divide the values of each feature by its standard deviation.$$x' = \frac{x - \bar{x}}{\sigma}$$
(Scaling to) Unit Length Normalization:
Another option that is widely used in machine-learning is to scale the components of a feature vector such that the complete vector has length one. This usually means dividing each component by the Euclidean length of the vector:$${\displaystyle x'={\frac {x}{\left\|{x}\right\|}}}$$
We can use \(L_1\) norm or other metrics based on problem.
- Define the mean \(\mu_j\) of each feature of the datapoints \(x^{(i)}\):
-
Activation Normalization:
Extends the idea of input normalization to deeper networks. Interprets activations in a layer as a featurized input (abstract representation of the input) and aims to normalize those layer outputs/activations.The difference however, is in the target mean and variance we want to achieve. Unlike inputs, we might not want to force the activations to have mean\(=0\) and variance\(=1\). E.g. if we are using the sigmoid activation; mean\(=0\) and variance\(=1\) will utilize the linear part of sigmoid. So, changing the mean and variance will allow the network to take advantage of the non-linearity.
In practice, it’s much more common to normalize the outputs before applying the activation (e.g. in Batch-Norm)
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Statistical Normalization - Transformations:
Let \(X\) be \(n \times d\) design matrix of sample pts.
Each row \(i\) of \(X\) is a sample pt \(X_i^T\).Centering Transform:
AKA Mean Normalization is just removing the mean from each observation. It centers the data around \(0\).$$X' = X - \mathbf{\mu}$$
where \(\mathbf{\mu}\) is the mean of all the rows of \(X\).
Decorrelation Transform:
removes only the correlations but leaves variances intact,$$Z = X'V$$
where \(\text{Var}(X') = \Sigma = \dfrac{1}{n}X'^TX' = V\Lambda V^T\), and \(\text{Var}(Z) = \Lambda\) is the sample covariance matrix.
It transforms the sample points to the eigenvector coordinate system.
Standardization Transform:
sets variances to \(1\) but leaves correlations intact,$$X'_s = \dfrac{X - \mathbf{\mu}}{\mathbf{\sigma}_ X}$$
where \(\mathbf{\sigma}_ X\) is the standard deviation of all the rows of \(X\).
Sphering Transform:
Rescales the uncorrelated matrix \(Z\) in order to obtain a covariance matrix corresponding to the identity matrix. To do that we scale our decorrelated data by dividing each dimension by the square-root of its corresponding eigenvalue.$$W = X'\Sigma^{-1/2} = X'(V \Lambda^{-1/2} V^T)$$
this is ZCA whitening.
Whitening Transform:
The Whitening Transformation is a linear transformation that transforms a vector of random variables with a known covariance matrix into a set of new variables whose covariance is the identity matrix, meaning that they are uncorrelated and each have variance 1. The transformation is called “whitening” because it changes the input vector into a white noise vector.
= Centering + SpheringThen \(W\) has covariance matrix \(I . \left[\text { If } X_{i} \sim \mathcal{N}(\mu, \Sigma), \text { then approximately, } W_{i} \sim \mathcal{N}(0, I)\right]\).
Notes:
- Decorrelation: intuitively, it means that we want to rotate the data until there is no correlation anymore.
- Centering seems to make it easier for hidden units to get into a good operating region of the sigmoid or ReLU
- Standardization (normalizing variance) makes the objective function better conditioned, so gradient descent converges faster
Batch Normalization
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Batch Normalization:
Batch Normalization is a normalization method that normalizes activations in a network across the mini-batch. For each feature, batch normalization computes the mean and variance of that feature in the mini-batch. It then subtracts the mean and divides the feature by its mini-batch standard deviation.
This restricts the activations to have \(0\) mean and unit variance.
This is followed by an affine transformation of the normalized activations to rescale the mean and variance, in a learnable way, to whatever the network sees fit. This is done because restricting the mean and variance of the activations might hinder the network from taking advantage of the freedom of setting the distribution of the activations to something that might help the later layers learn faster. This means that the expressiveness of the network does not change.Notes:
- Scale Invariance: Batch Norm renders the loss function of neural networks scale invariant.
I.E. scaling the weights by a constant does not change the output, or the loss, of the batch normalized network.- Implications of Scale Invariance - Exponentially Growing Learning Rate:
- It is possible to use exponentially growing learning rate schedule when training neural networks with batch normalization.
- The paper establishes the following Equivalence:
- Weight decay with constant learning rate
- No weight decay and an exponentially growing learning rate
This equivalence holds for other normalization layers as well, Group Normalization, Layer Normalization, Instance Norm, etc.
- Implications of Scale Invariance - Exponentially Growing Learning Rate:
- Weight Decay and BN:
- This Paper shows that weight decay on a BN-network is just tweaking the effective learning rate.
- Without weight decay, the gradient of a BN-network is always orthogonal to the current value of the parameter vector and therefore increases the scale of weights (reduces effective learning rate).
Intuitively, you can compensate the decrease of effective learning rate by using an exponential learning rate scheme or just simply weight decay.
- Scale Invariance: Batch Norm renders the loss function of neural networks scale invariant.
-
Effectiveness of Batch Normalization:
There are many different proposed reasons that try to explain the wide success and effectiveness of the method. We summarize here some of those reasons and give an intuition to why those reasons apply.Summarizing the intuition of why BN works:
The overall intuition is that batch normalization makes the loss surface “easier to navigate”, making optimization easier, enabling the use of higher learning rates, and improving model performance across multiple tasks.
Further, there is a regularization effect when using BN induced by added noise to the estimate of the mean and variance of the data due to using mini-batches instead. -
Internal Covariate Shift as an Intuitive but Wrong Motivation for the Success of BN:
The correlation between batch normalization and internal covariate shift is widely accepted but was not supported by experimental results. Scholars recently show with experiments that the hypothesized relationship is not an accurate one. Rather, the enhanced accuracy with the batch normalization layer seems to be independent of internal covariate shift.Two Problems with the ICS Explanation:
- Even if the mean and variance are constant, the distribution of activations can still change. Why are the mean and variance so important?
- If we introduce \(\gamma\) and \(\beta\), the mean and variance will deviate from \(0\) and \(1\) anyway. What then, is the point of batch normalization?
-
what is internal covariate shift? Internal covariate shift is a phenomenon that occurs in machine learning when the distribution of the input data that a model receives changes during the training process. This can happen when the model is trained on a batch of data, but the distribution of that data is different from the distribution of the data that the model will see when it is deployed in the real world. As a result, the model’s performance can degrade over time, and it may not be able to generalize well to new data. This is a common problem in deep learning, where models can have millions of parameters and can be very sensitive to changes in the input data. To address internal covariate shift, it is important to carefully monitor the distribution of the input data and to use techniques such as batch normalization to ensure that the model remains stable and performs well over time.
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what are the criticisms of internal covariate shift? There is some debate in the machine learning community about the importance of internal covariate shift and whether it is a significant problem in practice. Some researchers have argued that internal covariate shift is not a major concern, and that other factors, such as the quality of the data and the choice of model architecture, are more important for achieving good performance. Others have suggested that internal covariate shift is a real phenomenon, but that it can be addressed using existing techniques such as batch normalization. There is also some disagreement about the best way to measure internal covariate shift, with some researchers advocating for more sophisticated methods than others. Overall, the topic is still an active area of research, and it is likely that the understanding of internal covariate shift will continue to evolve.
I’m not aware of any specific criticisms of internal covariate shift. However, some researchers have raised concerns about the use of batch normalization, which is a common technique used to address internal covariate shift in deep learning models. One concern is that batch normalization can make models less interpretable, because it adds additional layers to the model that can make it harder to understand how the model is making predictions. Additionally, batch normalization can add computational complexity to the model, which can make training more difficult and can increase the time and resources needed to train the model. Overall, the use of batch normalization and other techniques to address internal covariate shift is an active area of research, and there is ongoing debate about the best ways to address this issue.
-
Is internal covariate shift a real concern? Yes, internal covariate shift is a real concern in machine learning, especially in deep learning. When the distribution of the input data that a model receives changes during training, the model’s performance can degrade over time. This can happen when the model is trained on a batch of data, but the distribution of that data is different from the distribution of the data that the model will see when it is deployed in the real world. As a result, the model may not be able to generalize well to new data, and its performance may suffer. This can be a particularly significant problem in deep learning, where models can have millions of parameters and can be very sensitive to changes in the input data. To address internal covariate shift, it is important to carefully monitor the distribution of the input data and to use techniques such as batch normalization to ensure that the model remains stable and performs well over time.
-
Suppressing Higher-Order Effects (Goodfellow):
Quick Intuition:
- In a neural network, changing one weight affects subsequent layers, which then affect subsequent layers, and so on.
- This means changing one weight can make the activations fly all over the place in complex ways.
- This forces us to use lower learning rates to prevent the gradients from exploding, or – if we use sigmoid activations – to prevent the gradients from disappearing.
- Batch normalization to the rescue! Batch normalization reparameterizes the network to make optimization easier.
- With batch normalization, we can control the magnitude and mean of the activations independent of all other layers.
- This means weights don’t fly all over the place (as long as we have sensible initial means and magnitudes), and we can optimize much easier.
Further Intuition:
Deep Neural networks have higher-order interactions, which means changing weights of one layer might also affect the statistics of other layers in addition to the loss function. These cross layer interactions, when unaccounted lead to internal covariate shift. Every time we update the weights of a layer, there is a chance that it affects the statistics of a layer further in the neural network in an unfavorable way.
Convergence may require careful initializing, hyperparameter tuning and longer training durations in such cases. However, when we add the batch normalized layer between the layers, the statistics of a layer are only affected by the two hyperparameters \(\gamma\) and \(\beta\). Now our optimization algorithm has to adjust only two hyperparameters to control the statistics of any layer, rather than the entire weights in the previous layer. This greatly speeds up convergence, and avoids the need for careful initialization and hyperparameter tuning. Therefore, Batch Norm acts more like a check pointing mechanism.Notice that the ability to arbitrarily set the mean and the standard deviation of a layer also means that we can recover the original distribution if that was sufficient for proper training.
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Induced Smoothness of the Optimization Landscape (Santurkar et al.):
In a new paper, the authors claim that BN works because it makes the loss surface smoother. Concretely, it improves the \(\beta\)-smoothness or the Lipschitzness of the function. This smoothness induces a more predictive and stable behavior of the gradients, allowing for faster training. -
Batch Norm as a Regularizer:
BN also acts a regularizer. The mean and the variance estimated for each batch is a noisier version of the true mean, and this injects randomness in our optima search. This helps in regularization.
-
Length-Direction Decoupling:
It is argued that the success of batch normalization could be at least partially credited to the length-direction decoupling effect that the method provides.By interpreting the batch normalization procedure as the reparametrization of weight space, it could be shown that the length and the direction of the weights are separated after the procedure, and they could thus be trained separately.
This property could then be used to prove the faster convergence of problems with batch normalization:
-
Limitations/Problems of BN:
When doing normalization, we ideally want to the use the global mean and variance to standardize our data. Computing this for each layer is far too expensive though, so we need to approximate using some other measures. BN approximates the mean and variance with those of the mini batch, which is a noisy estimate. Although, we motivated some of the effectiveness of BN to its regularizing effects due to this, same, noisy estimate, this estimate can be problematic in the following scenarios:
- Small Batch Sizes:
If the batch size is \(1\), the variance is \(0\) so batch normalization cannot be applied. Slightly larger mini-batch sizes won’t have this problem, but small mini-batches make our estimates very noisy and can negatively impact training, meaning batch normalization imposes a certain lower bound on our batch size. - Recurrent Connections in an RNN:
In an RNN, the recurrent activations of each time-step will have different statistics. This means that we have to fit a separate batch normalization layer for each time-step. This makes the model more complicated and – more importantly – it forces us to store the statistics for each time-step during training. - Dependence of the loss between samples in a mini-batch:
BN makes the loss value for each sample in a mini-batch dependent on other samples in the mini-batch. For instance, if a sample causes a certain layer’s activations to become much larger, this will make the mean and variance larger as well. This will change the activation for all other samples in the mini-batch as well. Furthermore, the mini-batch statistics will depend on the mini-batch size as well (a smaller mini-batch size will increase the random variation in the mean and variance statistics). The problem arises not when training a model on a single machine, but when we start to conduct distributed training, things can get ugly. As mentioned in this paper, we need to take extra care in choosing the batch size and learning rate in the presence of batch normalization when doing distributed training. If two different machines use different batch sizes, they will indirectly be optimizing different loss functions: this means that the value of \(\gamma\) that worked for one machine is unlikely to work for another machine. This is why the authors stressed that the batch size for each worker must be kept constant across all machines. - BN parameters in Fine-Tuning Applications:
When Fine-Tuning a larger network by freezing all the layers except the last layer; it is unclear if one should use the mean and variance computed on the original dataset or use the mean and variance of the mini-batches. Though most frameworks use the mini-batch statistics, if we are using a different mini-batch size there will be a mismatch between the optimal batch normalization parameters and the parameters in the network.
If there is a mis-match in the mini-batch sizes it seems to be better to use the statistics of the original dataset instead.
Further Discussion
- Small Batch Sizes:
Weight Normalization
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Weight Normalization:
Weight Normalization is a normalization method that, instead of normalizing the mini-batch, normalizes the weights of the layer.WN reparameterizes the weights \(\boldsymbol{w}\) of any layer in the network in the following way:
$$\boldsymbol{w}=\frac{g}{\|\boldsymbol{v}\|} \boldsymbol{v} \:\:\:\:\:\:\: $$
where \(\boldsymbol{v}\) is a \(k\)-dimensional vector, \(g\) is a scalar, and \(\|\boldsymbol{v}\|\) denotes the Euclidean norm of \(\boldsymbol{v}\).
This reparameterization has the effect of fixing the Euclidean norm of the weight vector \(\boldsymbol{w}\): we now have \(\|\boldsymbol{w}\| = g\), independent of the parameters \(\boldsymbol{v}\).Weight Normalization separates the norm of the weight vector from its direction without reducing expressiveness:
- For variance, this has a similar effect to dividing the inputs by the standard deviation in batch normalization.
- As for the mean, the authors of the paper proposed using a method called “mean-only batch normalization” together with weight normalization.
- Advantages:
- Since WN separates the norm of the weight vector from its direction, and then optimizes both \(g\) and \(\boldsymbol{v}\) using gradient descent. This change in learning dynamics makes optimization easier.
- It makes the mean and variance independent of the batch; since now they are connected to the weights of the network.
- It is often much faster than BN. In CNNs, the number of weights tends to be far smaller than the number of inputs, meaning weight normalization is computationally cheaper compared to batch normalization (CNNs share weights).
-
Mean-Only Batch Normalization:
Although weight normalization on its own can assist training, the authors of the paper proposed using a method called “mean-only batch normalization” in conjunction with weight normalization.Mean-Only Batch Normalization is a method that subtracts out the mean of the mini-batch but does not divide the inputs by the standard deviation or rescales them.
Though this method counteracts some of the computational speed-up of weight normalization, it is cheaper than batch-normalization since it does not need to compute the standard deviations. The authors claim that this method provides the following benefits:
- It makes the mean of the activations independent from \(\boldsymbol{v}\):
Weight normalization independently cannot isolate the mean of the activations from the weights of the layer, causing high-level dependencies between the means of each layer. Mean-only batch normalization can resolve this problem. - It adds “gentler noise” to the activations:
One of the side-effects of batch normalization is that it adds some stochastic noise to the activations as a result of using noisy estimates computed on the mini-batches. This has a regularization effect in some applications but can be potentially harmful in some noise-sensitive domains like reinforcement learning. The noise caused by the mean estimations, however, are “gentler” since the law of large numbers ensures the mean of the activations is approximately normally distributed.
Thus, weight normalization can still work in settings with a smaller mini-batch size.
- It makes the mean of the activations independent from \(\boldsymbol{v}\):
Layer Normalization
- Layer Normalization:
Layer Normalization is a normalization method developed by Hinton that, instead of normalizing the inputs across batches like BN, normalizes the inputs across the features:$$\begin{aligned} \mu_{i} &=\frac{1}{m} \sum_{j=1}^{m} x_{i j} \\ \sigma_{i}^{2} &=\frac{1}{m} \sum_{j=1}^{m}\left(x_{i j}-\mu_{i}\right)^{2} \\ \hat{x}_{i j} &=\frac{x_{i j}-\mu_{i}}{\sqrt{\sigma_{i}^{2}+\epsilon}} \end{aligned}$$
This is deceptively similar to the batch norm equations:
$$\begin{aligned} \mu_{j} &=\frac{1}{m} \sum_{i=1}^{m} x_{i j} \\ \sigma_{j}^{2} &=\frac{1}{m} \sum_{i=1}^{m}\left(x_{i j}-\mu_{j}\right)^{2} \\ \hat{x}_{i j} &=\frac{x_{i j}-\mu_{j}}{\sqrt{\sigma_{j}^{2}+\epsilon}} \end{aligned}$$
where \(x_{i j}\) is the \(i,j\)-th element of the input, the first dimension represents the batch and the second represents the feature (I have modified the notation from the original papers to make the contrast clearer).
In batch normalization, the statistics are computed across the batch and are the same for each example in the batch. In contrast, in layer normalization, the statistics are computed across each feature and are independent of other examples.This means that layer normalization is not a simple reparameterization of the network, unlike the case of weight normalization and batch normalization, which both have the same expressive power as an unnormalized neural network. The layer normalized model, thus, has different invariance properties than the other methods.
- Advantages:
- The independence between inputs means that each input has a different normalization operation, allowing arbitrary mini-batch sizes to be used.
- The experimental results show that layer normalization performs well for recurrent neural networks.
- Analysis of the Invariance Properties of LN:
Instance Normalization
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Instance Normalization:
Instance Normalization is similar to layer normalization but with an extra restriction: it computes the mean/standard deviation and normalize across each channel in each training example.Motivation:
In Style Transfer problems, the network should be agnostic to the contrast of the original image.
Therefore, it is specific to images and not trivially extendable to RNNs.Experimental results show that instance normalization performs well on style transfer when replacing batch normalization. Recently, instance normalization has also been used as a replacement for batch normalization in GANs.
Group Normalization
- Group Normalization:
Group Normalization computes the mean and standard deviation over groups of channels for each training example.
You can think of GN as being half way between layer normalization and instance normalization:- When we put all the channels into a single group, it becomes Layer Normalization
- When we put each channel in a different group, it becomes Instance Normalization
-
Motivation/Advantages:
Layer Norm and Instance Norm were significantly inferior to BN on image recognition tasks. Group Normalization was able to achieve much closer performance to BN with a batch-size of 32 on ImageNet and outperformed it on smaller batch sizes.For tasks like object detection and segmentation that use much higher resolution images (and therefore cannot increase their batch size due to memory constraints), Group Normalization was shown to be a very effective normalization method.
- Effectiveness of Group Normalization:
Why is group normalization so effective compared to layer normalization and instance normalization?
Layer Norm makes an implicit assumption that all channels are “equally important” when computing the mean. This assumption is often not true in convolution layers.
For instance, neurons near the edge of an image and neurons near the center of an image will have very different activation statistics. This means that computing different statistics for different channels can give models much-needed flexibility.Instance Norm, on the other hand, assumes that the channels are completely independent from each other. Channels in an image are not completely independent though, so being able to leverage the statistics of nearby channels is an advantage group normalization has over instance normalization.
Batch ReNormalization
- Batch ReNormalization:
Batch ReNormalization is an extension of BN for applying batch normalization to small batch sizes.In the Authors words: “an effective extension to ensure that the training and inference models generate the same outputs that depend on individual examples rather than the entire mini-batch”
The authors propose to use a moving average while also taking the effect of previous layers on the statistics into account. Their method is – at its core – a simple reparameterization of normalization with the moving average. If we denote the moving average mean and standard deviation as \(\mu\) and \(\sigma\) and the mini-batch mean and standard deviation as \(\mu_B\) and \(\sigma_B\), the batch renormalization equation is:
$$\frac{x_{i}-\mu}{\sigma}=\frac{x_{i}-\mu_{\mathcal{B}}}{\sigma_{\mathcal{B}}} \cdot r+d, \quad \text { where } r=\frac{\sigma_{\mathcal{B}}}{\sigma}, \quad d=\frac{\mu_{\mathcal{B}}-\mu}{\sigma}$$
In other words, we multiply the batch normalized activations by \(r\) and add \(d\), where both \(r\) and \(d\) are computed from the mini-batch statistics and moving average statistics. The trick here is to not backpropagate through \(r\) and \(d\). Though this means we ignore some of the effects of previous layers on previous mini batches, since the mini batch statistics and moving average statistics should be the same on average, the overall effect of this should cancel out on average as well.
-
Motivation (derivation):
The basic idea behind batch renormalization comes from the fact that we do not use the individual mini-batch statistics for batch normalization during inference. Instead, we use a moving average of the mini-batch statistics. This is because a moving average provides a better estimate of the true mean and variance compared to individual mini-batches.Why don’t we use the moving average during training?
The answer has to do with the fact that during training, we need to perform backpropagation. In essence, when we use some statistics to normalize the data, we need to backpropagate through those statistics as well. If we use the statistics of activations from previous mini-batches to normalize the data, we need to account for how the previous layer affected those statistics during backpropagation. If we ignore these interactions, we could potentially cause previous layers to keep on increasing the magnitude of their activations even though it has no effect on the loss. This means that if we use a moving average, we would need to store the data from all previous mini-batches during training, which is far too expensive.
- Performance:
Unfortunately, batch renormalization’s performance still degrades when the batch size decreases (though not as badly as batch normalization), meaning group normalization still has a slight advantage in the small batch size regime.
Batch-Instance Normalization
-
Batch-Instance Normalization:
Batch-Instance Normalization is an extension of instance normalization that attempts to account for differences in contrast and style in images. It is basically, just, an interpolation between batch normalization and instance normalization.Denoting the batch normalized outputs and the instance normalized outputs as \(\hat{x}^{(B)}\) and \(\hat{x}^{(I)}\) each, the batch-instance normalized output can be expressed as:
$$\mathbf{y}=\left(\rho \cdot \hat{\mathbf{x}}^{(B)}+(1-\rho) \cdot \hat{\mathbf{x}}^{(I)}\right) \cdot \gamma+\beta$$
The interesting aspect of batch-instance normalization is that the balancing parameter \(\rho\) is learned through gradient descent.
-
Motivation:
The problem with instance normalization is that it completely erases style information. Though this is beneficial in certain settings like style transfer, it can be problematic in settings like weather classification where the style (e.g. the brightness of the image) can be a crucial feature. In other words, the degree of style information that should be removed is dependent on the task at hand. Batch-instance normalization attempts to deal with this by learning how much style information should be used for each task and feature map (channel).
Thus, B-IN extends instance normalization to account for differences in contrast and style in images.
-
Performance:
Batch-instance normalization outperformed batch normalization on CIFAR-10/100, ImageNet, domain adaptation, and style transfer. In image classification tasks, the value of \(\rho\) tended to be close to \(0\) or \(1\), meaning many layers used either instance or batch normalization almost exclusively. In addition, layers tended to use batch normalization more than instance normalization, which fits the intuition proposed by the authors that instance normalization serves more as a method to eliminate unnecessary style variation. On style transfer – on the other hand – the model tended to use instance normalization more, which makes sense given style is much less important in style transfer.The authors also found that in practice, using a higher learning rate for \(\rho\) improves performance.
One important contribution of batch-instance normalization is that it showed that models could learn to adaptively use different normalization methods using gradient descent. This raises the question: could models learn to use an even wider variety of normalization methods?
This nicely leads us to the next normalization method: Switchable Normalization.
Switchable Normalization
-
Switchable Normalization:
Switchable Normalization is a method that uses a weighted average of different mean and variance statistics from batch normalization, instance normalization, and layer normalization. Similar to batch-instance normalization, the weights were learned through backpropagation.
-
Motivation:
Given the different methods proposed for normalization, common questions arise, including:
Is batch normalization still the best normalization method out-of-the-box? What if we combine different normalization methods? What if the best normalization method actually differs depending on the depth of the layer?
Switchable Normalization aims to answer those questions.
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Performance:
The authors showed that switch normalization could potentially outperform batch normalization on tasks such as image classification and object detection.Perhaps more interestingly, the paper showed that the statistics of instance normalization were used more heavily in earlier layers, whereas layer normalization was preferred in the later layers, and batch normalization being used in the middle. Smaller batch sizes lead to a preference towards layer normalization and instance normalization, as is expected.
Spectral Normalization
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Spectral Normalization:
Spectral Normalization is a method proposed to improve the training of GANs by limiting the Lipschitz constant of the discriminator.
The authors restrict the Lipschitz constant by normalizing the weight matrices by their largest eigenvalue (or their spectral norm – hence the name). The largest eigenvalue is computed using the power method which makes the computational cost of this method very cheap. (Compared to weight normalization, spectral normalization does not reduce the rank of the weight matrix.)1
SN is not designed to be a replacement for batch normalization, but it gives us a very interesting look into normalization in deep learning in general.
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Performance:
Experimental results show that spectral normalization improves the training of GANs with minimal additional tuning.
Further Exploration/Discussions
Though recent papers have explored different normalization methods at different depths of the network, there are still many dimensions that can be explored. In this paper, the authors show that \(L_1\) normalization performs better than batch normalization, suggesting that as we understand batch normalization better, we might be able to come up with more principled methods of normalization. This means that we might see new normalization methods use different statistics instead of changing what they compute the statistics over.
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Most likely a wrong statement ↩


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November 18, 2019
Miss BumBum Suzy Cortez Proclaims She Is ‘Fire’ In A Peacock Bikini, And Fans Wholeheartedly Agree
Brazilian bombshell Suzy Cortez has once again dropped jaws with her newest Instagram post, where she modeled a peacock printed bikini by a luxurious pool. Suzy seemed to feel so confident in her own sexiness that she titled the upload “fire,” and added two flame emoji. Fans most definitely agreed with the assessment.
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Suzy kept the rest of her makeup simple, and left her hair styled so that it fell in natural soft waves down towards her waist.
She was posed with her hands behind her head as she smoldered into the camera. Suzy was also sure to jut out her hip to flaunt the curve of the perky posterior that twice earned her the title of Miss BumBum.
In just an hour, the upload already earned over 3,400 likes and over 50 comments. Many of the replies were fans wholeheartedly agreeing with Suzy’s assessment that she looked like fire in the photo.
“Fire hot… very beautiful,” raved one follower, who mirrored Suzy’s emoji use with two fire ones.
Loading...
“Stunning beauty,” added a second awestruck fan.
“So beautiful,” gushed a third, along with two red hearts.
“I’m your 100 percent fan,” proclaimed a fourth.
The Brazilian beauty often treats her followers to a number of steamy shots, and is very active on Instagram. In fact, just earlier this morning, she posted a black and white shot where she wore a thong why laying in bed, as covered by The Inquisitr.


At Export Origins our philosophy is rooted in sustainable healthy living for greater longevity, wellness, and happiness. We have combined our passion and expertise to offer a curated selection of pure, ethically sourced, and premium quality products from our U.S. partners.
We are passionate about advancing international trade in a sustainable way; and it is our goal to facilitate access of better-for-you products for everyone to enjoy without compromising our people, environment, and animals.


Lopud is a small island in the Adriatic sea a few miles north west of Dubrovnik. The island can only be reached by boat and there are no cars.
Lopud Villas.com
Lopud is a small island in the Adriatic sea a few miles north west of Dubrovnik. The island can only be reached by boat and there are no cars.


👻 GISigh: Brendan Hoover's Blog about data.
Welcome to my Blog
Here is my last prediction for the 2020 Presidential Election. I have Biden winning convincingly, but will note that Nevada, Arizona, Florida, Georgia, and North Carolina are all within polling error. Trump must win all those states to win the presidency (except Nevada). I don’t think it would be particularly odd for a Republican to win those states. If the networks can call Florida tonight for Biden, it’s a wrap. However, close calls in these three East-cost-toss-up states means the race is likely tight. On the flip side, North Carolina may also be the state that tips things in Biden’s favor, where he is polling above 3% in his favor. Since I’ve been using 538’s data, I also stole their color scheme, hehe.
There was some movement overnight. In the last several days high quality polls came in for Pennslyvania and moved the state into 'leans Biden' in my model. Those polls show Biden with a 6 - 7% edge, which is well outside normal polling error for a Trump victory in the state. Polls also moved in Biden's favor in Georgia and Florida. It's worth noting we are in herding season. Read about herding here.
The election polls remain pretty consistent. I've updated the legend and more clearly labeled the "toss up" states. Biden needs to find 17 electoral votes in the toss up states. He may be able to find all of that in Pennslyvania where Biden currently has a 5.3% polling lead.
Not much changed today, so let’s look at what happens if the polls are off by 5% in Trump’s favor, which is what happened in 2016. In this scenario, Biden still comes up ahead, but by a much closer margin. One can imagine a lot of court battles in this scenario. It’s worth noting that I forgot to include Washington, D.C. yesterday. So, Biden’s numbers are +3 there. I’ve added those here. I’ve not included any Bayes here and color the map by 20% intervals. .
Well, it doesn't look like much changed today, but here's a bit about how I made the above map.
First, I input the moduls I'll need. Then I pull the data from 538 and put it in a pandas dataframe. The nice thing about pulling the data from 538 is that I don't have to download it each day. The script takes care of that and it's easy peasy.
Some election polls are better than others based on various factors like sampling design. I don't have any idea which ones are better, so I relied on 538's rankings. These are conveniently already in the dataset. So, I created a weighting scheme based on the letter grade, which can be seen in the below code snippet. It's pretty straight forward. An A+ is weighted more heavily than a poll with a C rating. See the code snippet below #Rank Weighting. I also applied a weighted decay function of 30 days. This basically means that more recent days have the most impact, but as far back as 30 days still have some impact on weighting. This allows us to consider most recent polls, but not toss out the other polls for 30 days.
Much like the master bedroom in MTV’s Cribs, the ARIMA model is where the magic happens. I iterate through each state and apply the weighting from above. I separate the Biden and Trump from each other in each state, which I think they’d both appreciate, and do some smoothing using a 7 day moving average. I then run the ARIMA model using an Auto_Arima function, which makes life easy because I don’t have to make any choices. The function runs a number of scenarios and chooses the one with lowest AIC. Again, Auto_Arima is run on each state and each candidate independently. Interestingly, this is how 538 runs their model (state by state), the Economist uses National Polling. I’m a bit little skeptical of using National Polls, but they make a nice case for it here.
There are a number of other models I could have run, but ARIMA is a pretty solid first go for Time Series.
Now that I’ve run the ARIMA models and made some predictions on the percentage of votes for each state for Biden and Trump, I use those as my likelihoods in a simple Bayesian model. This is about as simple a Bayes model as there is. I use those state likelihoods and pulled in some more data from 538 to use as my priors. Specifically, I use Vote Share of Incumbent and Vote Share of Challenger as my priors for each state. Determining these values on my own would take a lot of heavy lifting, so I cheat or “leverage” (as the kids say) some of 538’s heavy lifting. With those data it’s easy to compute the probability of each candidate winning a state. You’ll notice that a state like Illinois is probably woefully underestimated. I chose the vote shares as a conservative estimate. Still, in the end, my outcome is very close to that of 538, which isn’t bad for a short exercise.
Everything up to this point was data preparation to input to the map. If you’ve read this far (gods bless you) you’ve probably noticed that I was using geopandas, which pretty much has the same functionality as a normal pandas dataframe, but with the added bonus of being geographically located. With a little wrangling and the Bokeh package it’s pretty easy to make a pretty decent map.
And since this is all automated, I can run it every day until the election and see if there have been any big changes.
I used an ARIMA model and some simple Bayesian Statistics to predict the November 3rd election. The model uses every poll available at 538 here.
Since I don’t know anything about polls and which ones are good, I used 538’s poll ratings to derive a weighted mean for each day. I also weighted the average using a 30-day decay, which smarter people than me seem to think is a good decay function for elections. For missing dates, I used a simple interpolation and computed a 7-day rolling mean. I fed this data into the ARIMA model. By using the auto_arima function available in the pmdarima python package, I was able to automate the modeling and can run it every day until the election.
To get Bayesian priors for each candidate winning the state, I again turned to 538 here.
I used the Vote share of the incumbent (Trump) and Vote share of the challenger (Biden) for each state as a Bayesian prior. I chose these as priors because they are conservative estimates, they probably underestimate the probability of Biden winning states like California and Illinois and Trump winning states like North Dakota and Montana. However, these priors fit well for the toss up states, so they were a good choice for a short modeling exercise. I used the final percentage from the ARIMA models as my likelihood for each candidate and calculated the Bayesian posteriors, which are displayed in the above map.
My model is slightly more bullish on Biden than the 538 model, but not by much.


Force-directed graph drawing (Wikipedia)
Force Directed Graph is one way to visualize Node-Link data by using force-directed layout algorithms.
The example below is using Coulomb’s Law and Hooke’s Law (and Damping effect) to calculate the kinetic energy of the graph that is to be minimized.
The Processing source code is available at https://github.com/zshiba/visualization.


{{
employee?.applicationUser.fname +
" " +
employee?.applicationUser.lname || "Demo Name"
}}
{{ "@" + employee?.applicationUser.userName || "@userName" }}
{{ employee?.applicationUser.email ||<EMAIL_ADDRESS>}}
{{
employee?.applicationUser.address || "Demo Address"
}}


RUC meridional water vapor transport
Here is the time-average water vapor transport from the RUC integration for the PIRCS-1B period (June 1- July 31, 1993), and six other models that have participated in both PIRCS-1A and PIRCS-1B.
Last Updated on 11/13/00
By Chris Anderson


Control Panel
My Profile
User Accounts
Organisations / Clients
Device Database
Notifications
Logic Engine
Themes & Styles
Account Settings


All Packages Class Hierarchy This Package Previous Next Index
Interface javax.servlet.ServletConfig
- public interface ServletConfig
This interface is implemented by services in order to pass
configuration information to a servlet when it is first loaded. A
service writer implementing this interface must write methods for the
servlet to use to get its initialization parameters and the context
in which it is running.
The ServletConfig interface can also be implemented by servlets.
(GenericServlet does this.) When implemented by a servlet, the
methods in the interface make getting the configuration data more
convenient. For example, a servlet could implement
getServletContext by writing,
public ServletContext getServletContext() {
return getServletConfig().getServletContext();
}
making access to the servlet's context object a single method
invocation (a call to getServletContext()
).
-
getInitParameter(String)
-
Returns a string containing the value of the named
initialization parameter of the servlet, or null if the
parameter does not exist.
-
getInitParameterNames()
- Returns the names of the servlet's initialization parameters
as an enumeration of strings, or an empty enumeration if there
are no initialization parameters.
-
getServletContext()
- Returns the context for the servlet.
getServletContext
public abstract ServletContext getServletContext()
- Returns the context for the servlet.
getInitParameter
public abstract String getInitParameter(String name)
- Returns a string containing the value of the named
initialization parameter of the servlet, or null if the
parameter does not exist. Init parameters have a single string
value; it is the responsibility of the servlet writer to
interpret the string.
- Parameters:
- name - the name of the parameter whose value is requested
getInitParameterNames
public abstract Enumeration getInitParameterNames()
- Returns the names of the servlet's initialization parameters
as an enumeration of strings, or an empty enumeration if there
are no initialization parameters.
All Packages Class Hierarchy This Package Previous Next Index


V 1.0 - VR Step Lite released
VR Step is a virtual reality plugin for Unity that allows your users to walk in place to move with no extra instrumentation. It is designed to be used with Google VR, and works on any VR-ready Android or IPhone device. You can now design full, open worlds that your users can explore freely in VR, and you won't limit your userbase by requiring things like bluetooth controllers.
This lite version uses very basic step detection and has somewhat limited options, but it is still a very powerful tool in the right hands. See Gravity Pull VR on the IOS and Android app stores for an example of this plugin in action. Currently, only Google VR is supported, but the Pro version will have full GearVR, Oculus, and Vive support.
We recommend that you first import the Google VR SDK before importing the VR Step asset. VR Step should work correctly with any version of the Google VR SDK, but the included prefabs do require at least the Cardboard SDK v0.6.0 release. If you are using an older version of the SDK, the demo scene or the WIPPlayer prefab may not work, so skip to the "Adding the scripts manually" section.
You may import whatever components you like, but you should import the entire "Scripts" folder at minimum.
Included in the folder "DemoScene" is a demo scene that demonstrates the walking in place. Simply load that scene and click "Play" to try it out in the editor. To simulate a detected step, hit the Up Arrow on your keyboard. To look around, hold the "Alt" key and move your mouse (this is from Google VR, the camera and mouse movement are not part of VR Step)
You can look at the WIPPlayer object in the scene to see how it is composed.
The WIPPlayer prefabs are an easy way to instantly have a controllable, walking-in-place controlled VR camera in your scene. Simply drag the prefab into a scene and move the top-level WIPPlayer object to position the player.
We've included a prefab for you. It is controlled with a RigidBody component.
Do not include multiple of these prefabs in the same scene
The top-level object of the WIPPlayer object has the Google VR Viewer script attached to it. For information on Google VR scripts, see the Google VR documentation.
You'll note that the prefab has 2 children, a "PlayerRigidBody" object, which is the main driver of movement, and a "StepDetector" object, which is the object that runs the step detection.
The GvrHead object is parented to the PlayerRigidBody object. It's important to note that the "Track Position" boolean is UNCHECKED, and the "Target" attribute is set to the top-level parent object (WIPPlayer). This is an important setting, without it you will see strange behaviour
The bulk of the settings are on the PlayerRigidBodoy object. See the sections below on each component for more information
If you need to configure the plugin manually, follow these steps
The Step Detector script uses the accelerometer and gyroscope input from the phone to detect when the player has taken a step. These values are read through Unity's Input class. If you need to read the accelerometer and gyroscope from another source, you will have to modify the script.
The StepDetector acts as a Singleton object within a scene, so you should only ever have 1 of them in a scene.
We recommend not changing the default values on the StepDetector component, but they are available for customization if you need them. Below are the values exposed by the inspector.
Filter Num Samples to Average - This value affects signal smoothing. Higher values will smooth the accelerometer signal more and filter out noise, but increase latency. Lower values will have more noise, but lower latency. The recommended number is 4.
Step Min Threshold - This value acts as a sort of "sensitivity" setting. It represents the slope of step detection values required to detect a step. A higher value will REDUCE the number of steps required, and if it is too high your players will have to stomp or jump hard to get a step detected. A lower value will INCREASE the number of steps required, and if it is too low will result in almost any head movement registering as a step. We recommend values between .2 and .5. The default value of .35 has shown good results for us in testing.
Simulate Steps with Key - If you do not want to use the up arrow in the editor to simulate steps, you can change it to another key.
If you would like to write your own scripts that respond to steps, you can register them with the StepDetector by using the static methods "AddStepAction" and "RemoveStepAction". An exteremely basic example is included in the script "PlaySoundOnStep".
Generally, you would want to register a function to be called in that object's "Start" function. The function must take no arguments and must return nothing. Here is an example:
public class PlaySoundOnStep : MonoBehaviour {
public AudioSource sound;}
void Start() {VRStep.StepDetector.AddStepAction(OnStep);}
void OnStep() {sound.Play();}
The WIPController is what drives movement. Generally you would have a RigidBody component also attached to the object, but if you do not you can write a custom script to pull the velocity generated by the script and use it to drive your own controller.
The WIPController uses the time between steps to define how quickly the player moves. If they are running, the time between steps is small, so they will move quickly. If they are walking slowly, the time between steps is larger, so they will move more slowly. If the time between steps gets large enough, they will stop. These values are shown below.
Max Forward Velocity- This is the maximum velocity that the character will move
Min Forward Velocity- This is the minimum velocity that the character will move
Damping value- Every FixedUpdate frame, the velocity is multiplied by this value to slow them down. We recommend a value of .95 or higher. 1 will do no damping, and your character will only slow down if no steps have been detected in a time defined by the Stopping Step Time value.
Min Step Time- If the time between steps is this or lower, velocity is set to max forward velocity
Max Step Time- If the time between steps is this or higher, velocity is set to the min forward velocity
Stopping Step Time- If the time between steps is this or higher, velocity is set to 0.
Rotate Towards Gaze - If checked, the object this script is attached to will be rotated to align with the gaze of the cardboardhead object (not including looking up and down). If not checked, there is no rotation, and you will have to set the rotation manually. The movement is always in the forward vector (the Z vector of the transform, or transform.forward).
Forward Transform - The reference to the Transform that is pointing forward, usually you want this to be the GVR head object. The up/down tilt orientation of this object is ignored.
Jump Enabled - Whether or not jumping should be allowed.
Jump Detection Threshold -
On Ground - Whether or not the object is currently on the ground. This is set by the CheckIfOnGround function automatically
Ground Layer Mask - The layers that count as ground, assign this layer to the objects that are on the ground
Jump Grace Period - The amount of time after leaving the ground that the user can still jump, to avoid frustration for your users. A setting of about .3 works well, but you can experiment.
While WIP lets your users explore large environments freely, it can still be somewhat tiring to walk in place too much. You may want to design your game around minimal amounts of walking, unless you are making an exercise game. For example, in our game Gravity Pull, you manipulate boxes in a portal-like environment to solve puzzles. Initially you had to walk up to a box to pick it up, but if you missed your target when throwing it, people had to walk way too much. Instead, we made it so that players can pull objects towards them from any distance.
Be careful about what velocity values you use. While WIP does seem to reduce the risk of simulator sickness, high velocities can still make some people feel sick.
Follow Google VR's guidelines to designing VR games and VR UIs.


Preparation time
less than 30 mins
Cooking time
10 to 30 mins
Serves
Serves 4-6
This is a popular dish in Bengal, where of course both prawns and coconut abound. It’s normally served in a whole green coconut there, but that’s a bridge too far for us. I would suggest serving it, as in Bengal, as a little course in its own right, but it’s also delicious with some rice and flatbreads.
Put the mustard seeds into a mini-food processor and add two tablespoons of water. Blend for a minute and then add another two tablespoons of water and continue blending until the seeds start to break up (this can take a minute or two of persistent blending; add more water if you’re having trouble). Keep blending until you have a rough paste that resembles wholegrain mustard, adding a splash more water if needed. Set aside.
Heat the vegetable oil in a heavy-based saucepan or karahi over a low-medium heat. Add the onion and fry for 10 minutes. Add the blended mustard paste, turmeric, coconut milk, grated coconut and salt. Bring to a boil then simmer for 4-5 minutes.
Add the prawns and green chillies and simmer for a further 3-4 minutes, or until the prawns are pink and cooked through. Scatter with coriander and serve.


--- layout: post title: no title date: '2016-09-08T01:42:25+02:00' categories: - Sketches - Storyboards picture_url: https://film-Storyboards.tumblr.com/post/150111604731 ---


Hi. My name is Duke Frau. I'm thinking of a number from #{UserNumberBean.minimum} to #{UserNumberBean.maximum}. Can you guess it?


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def | set_access_token |
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def | clear_access_token |
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def | has_access_token |
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def | expiration_time |
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def | set_expiration_time |
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def | clear_expiration_time |
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def | has_expiration_time |
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def | MergeFrom |
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def | ParseASCII |
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def | ParseASCIIIgnoreUnknown |
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def | Equals |
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def | IsInitialized |
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def | ByteSize |
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def | ByteSizePartial |
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def | Clear |
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def | OutputUnchecked |
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def | OutputPartial |
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def | TryMerge |
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int | has_access_token_ = 0 |
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string | access_token_ = "" |
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int | expiration_time_ = 0 |
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int | kaccess_token = 1 |
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int | kexpiration_time = 2 |
| |
The documentation for this class was generated from the following file:
- code/googleappengine-read-only/python/google/appengine/api/app_identity/app_identity_service_pb.py


co
league
Home
Bet History
(current)
FAQ
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Copyright ©


Welcome to the Static Web Server powered by libuv
Click some links below if you wanna play
Also open your dev tools to see the hello world message
Non existing page
README


KubeVirt Quickstart
This demo will deploy KubeVirt on an existing Kubernetes (1.9 or later) or OpenShift Origin (3.9 or later) cluster. For a quick way to bring up a Kubernetes or OpenShift Origin cluster, see Minikube and Minishift.
Deploy KubeVirt
KubeVirt deploys as an add-on to a Kubernetes (1.9 or later) cluster, using the kubectl
tool and the following manifest file:
$ export VERSION=v0.7.0
$ kubectl create \
-f https://github.com/kubevirt/kubevirt/releases/download/$VERSION/kubevirt.yaml
Note: The initial deployment to a new cluster can take a long time, because a number of containers have to be pulled from the internet. Use
watch kubectl get --all-namespaces pods
to monitor the progress.
Deploying KubeVirt on OpenShift Origin
On OpenShift Origin, the following SCCs need to be added prior kubevirt.yaml deployment:
$ oc adm policy add-scc-to-user privileged system:serviceaccount:kube-system:kubevirt-privileged
$ oc adm policy add-scc-to-user privileged system:serviceaccount:kube-system:kubevirt-controller
$ oc adm policy add-scc-to-user privileged system:serviceaccount:kube-system:kubevirt-infra
$ export VERSION=v0.7.0
$ oc apply -f https://github.com/kubevirt/kubevirt/releases/download/${VERSION}/kubevirt.yaml
Install virtctl
Note: This requires
kubectl
from Kubernetes 1.9 or later on the client
An additional binary is provided to get quick access to the serial and graphical ports of a VM, and handle start/stop operations.
The tool is called virtctl
and can be retrieved from the release page of KubeVirt:
$ curl -L -o virtctl \
https://github.com/kubevirt/kubevirt/releases/download/$VERSION/virtctl-$VERSION-linux-amd64
$ chmod +x virtctl
Deploy a VirtualMachine
Once you deployed KubeVirt you are ready to launch a VM:
# Creating a virtual machine
$ kubectl apply -f https://raw.githubusercontent.com/kubevirt/demo/master/manifests/vm.yaml
# After deployment you can manage VMs using the usual verbs:
$ kubectl get vms
$ kubectl get vms -o yaml testvm
# To start an offline VM you can use
$ ./virtctl start testvm
$ kubectl get vmis
$ kubectl get vmis -o yaml testvm
# To shut it down again
$ ./virtctl stop testvm
# To delete
$ kubectl delete vms testvm
# To create your own
$ kubectl create -f $YOUR_VM_SPEC
Accessing VMs (serial console & spice)
# Connect to the serial console
$ ./virtctl console testvm
# Connect to the graphical display
# Note: Requires `remote-viewer` from the `virt-viewer` package.
$ ./virtctl vnc testvm
User Guide
Now that KubeVirt is up an running, you can take a look at the user guide to understand how you can create and manage your own virtual machines.
Appendix: Deploying minikube
- If not installed, install minikube as described here
- Install the kvm2 driver
- Verify nested virtualization is enabled on the machine minikube is being installed on:
$ cat /sys/module/kvm_intel/parameters/nested Y
If not, then enable it as described here
- Download the
minikube
binary
-
Launch minikube with CNI:
$ minikube start \ --vm-driver kvm2 \ --network-plugin cni
- Install
kubectl
via a package manager or download it
KubeVirt on an existing Kubernetes cluster
If you already have a Kubernetes cluster, you can use kubevirt-ansible to deploy KubeVirt.
First clone the kubevirt-ansible repo on your master Kubernetes node.
git clone https://github.com/kubevirt/kubevirt-ansible
cd kubevirt-ansible
KubeVirt with no additional storage
To install KubeVirt without adding additional storage capabilities:
ansible-playbook -i localhost -e cluster=k8s -e storage_role=storage-none playbooks/kubevirt.yml
KubeVirt with storage environment for development and testing
KubeVirt can also be installed with a self-contained storage environment, using Ceph and Cinder, that is geared for non-production use. To install KubeVirt with the demo storage environment, first edit the inventory file and populate the section named “masters”, “etcd”, and “nodes”.
# inventory
# BEGIN CUSTOM SETTINGS
[masters]
# Your master FQDN
[etcd]
# Your etcd FQDN
[nodes]
# Your nodes FQDN's
[nfs]
# Your nfs server FQDN
[glusterfs]
# Your glusterfs nodes FQDN
# Each node should have the "glusterfs_devices" variable, which
# points to the block device that will be used by gluster.
#
# If you run openshift deployment
# You can add your master as schedulable node with option openshift_schedulable=true
# Add at least one node with lable to run on it router and docker containers
# openshift_node_labels="{'region': 'infra','zone': 'default'}"
# END CUSTOM SETTINGS
Once you have your inventory file filled in:
ansible-playbook -i inventory -e cluster=k8s -e storage_role=storage-demo playbooks/kubevirt.yml
KubeVirt with GlusterFS and Heketi storage environment
To install GlusterFS and Heketi on Kubernetes, follow the setup guide in gluster-kubernetes repo. You will need to configure the admin key with gk-deploy and at end of the install note the Heketi URL.
Once you have GlusterFS and Heketi installed on your Kubernetes cluster, you can deploy KubeVirt with the storage-glusterfs role to setup a Secret and a StorageClass to allow you to provision Persistent Volume Claims to store your VM images.
First edit the inventory file and populate the sections “master”, “etcd”, “nodes”, and “glusterfs”.
# inventory
# BEGIN CUSTOM SETTINGS
[masters]
# Your master FQDN
[etcd]
# Your etcd FQDN
[nodes]
# Your nodes FQDN's
[nfs]
# Your nfs server FQDN
[glusterfs]
# Your glusterfs nodes FQDN
# Each node should have the "glusterfs_devices" variable, which
# points to the block device that will be used by gluster.
#
# If you run openshift deployment
# You can add your master as schedulable node with option openshift_schedulable=true
# Add at least one node with lable to run on it router and docker containers
# openshift_node_labels="{'region': 'infra','zone': 'default'}"
# END CUSTOM SETTINGS
Then run this playbook, substituting the namespaces and heketi_url to match your environment:
ansible-playbook -i inventory -e cluster=k8s -e storage_role=storage-glusterfs -e namespace=kube-system -e glusterfs_namespace=kube-system -e glusterfs_name= -e heketi_url=http://<IP_ADDRESS>:8080 playbooks/kubevirt.yml
How it looks in the UI
Provided you deployed kubernetes dashboard, you will see this kind of pods for your vms