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A
alphabet
- Variable in class net.joeclark.proceduralgeneration.
MarkovTextGenerator
analyzeWord(String)
- Method in class net.joeclark.proceduralgeneration.
MarkovTextGenerator
andAddPriors(Double)
- Method in class net.joeclark.proceduralgeneration.
ClusterChainGenerator
Add or change a 'prior' relative probability for each sequence of clusters not seen in the training data, so some truly random generation of sequences not seen before is possible.
andAddPriors()
- Method in class net.joeclark.proceduralgeneration.
ClusterChainGenerator
Add priors, using the default value MultiOrderMarkovChain.DEFAULT_PRIOR.
B
buildModelFromObservations()
- Method in class net.joeclark.proceduralgeneration.
MarkovTextGenerator
C
ClusterChainGenerator
- Class in
net.joeclark.proceduralgeneration
A class that uses a vowel/consonant clustering algorithm to generate new random text.
ClusterChainGenerator()
- Constructor for class net.joeclark.proceduralgeneration.
ClusterChainGenerator
Initialize a new ClusterChainGenerator.
clusterize(String)
- Method in class net.joeclark.proceduralgeneration.
ClusterChainGenerator
Utility function to break a String down into a
List<String>
of its component clusters.
D
datasetLength
- Variable in class net.joeclark.proceduralgeneration.
MarkovTextGenerator
DEFAULT_MAX_LENGTH
- Static variable in class net.joeclark.proceduralgeneration.
ClusterChainGenerator
12
DEFAULT_MAX_LENGTH
- Static variable in class net.joeclark.proceduralgeneration.
MarkovTextGenerator
12
DEFAULT_MAX_LENGTH
- Static variable in class net.joeclark.proceduralgeneration.
RandomDrawGenerator
2147483647
DEFAULT_MIN_LENGTH
- Static variable in class net.joeclark.proceduralgeneration.
ClusterChainGenerator
4
DEFAULT_MIN_LENGTH
- Static variable in class net.joeclark.proceduralgeneration.
MarkovTextGenerator
4
DEFAULT_MIN_LENGTH
- Static variable in class net.joeclark.proceduralgeneration.
RandomDrawGenerator
1
DEFAULT_ORDER
- Static variable in class net.joeclark.proceduralgeneration.
MarkovTextGenerator
3
DEFAULT_PRIOR
- Static variable in class net.joeclark.proceduralgeneration.
MarkovTextGenerator
0.005
DoubleTextGenerator
- Class in
net.joeclark.proceduralgeneration
A RandomTextGenerator which combines the output of two other RandomTextGenerators.
DoubleTextGenerator(RandomTextGenerator, RandomTextGenerator, String)
- Constructor for class net.joeclark.proceduralgeneration.
DoubleTextGenerator
Instantiate a new DoubleTextGenerator
E
endFilter
- Variable in class net.joeclark.proceduralgeneration.
MarkovTextGenerator
ENGLISH_VOWELS
- Static variable in class net.joeclark.proceduralgeneration.
ClusterChainGenerator
'a','e','i','o','u','y','w'
equals(Object)
- Method in class net.joeclark.proceduralgeneration.
ClusterChainGenerator
G
generateOne()
- Method in class net.joeclark.proceduralgeneration.
ClusterChainGenerator
generateOne()
- Method in class net.joeclark.proceduralgeneration.
DoubleTextGenerator
generateOne()
- Method in class net.joeclark.proceduralgeneration.
MarkovTextGenerator
generateOne()
- Method in class net.joeclark.proceduralgeneration.
RandomDrawGenerator
generateOne()
- Method in interface net.joeclark.proceduralgeneration.
RandomTextGenerator
Generate a random text string, using the implementation's default configuration.
getDatasetLength()
- Method in class net.joeclark.proceduralgeneration.
ClusterChainGenerator
getEndFilter()
- Method in class net.joeclark.proceduralgeneration.
ClusterChainGenerator
getEndFilter()
- Method in class net.joeclark.proceduralgeneration.
MarkovTextGenerator
getEndFilter()
- Method in class net.joeclark.proceduralgeneration.
RandomDrawGenerator
getMaxLength()
- Method in class net.joeclark.proceduralgeneration.
ClusterChainGenerator
getMaxLength()
- Method in class net.joeclark.proceduralgeneration.
MarkovTextGenerator
getMaxLength()
- Method in class net.joeclark.proceduralgeneration.
RandomDrawGenerator
getMinLength()
- Method in class net.joeclark.proceduralgeneration.
ClusterChainGenerator
getMinLength()
- Method in class net.joeclark.proceduralgeneration.
MarkovTextGenerator
getMinLength()
- Method in class net.joeclark.proceduralgeneration.
RandomDrawGenerator
getOrder()
- Method in class net.joeclark.proceduralgeneration.
MarkovTextGenerator
getPrior()
- Method in class net.joeclark.proceduralgeneration.
MarkovTextGenerator
getStartFilter()
- Method in class net.joeclark.proceduralgeneration.
ClusterChainGenerator
getStartFilter()
- Method in class net.joeclark.proceduralgeneration.
MarkovTextGenerator
getStartFilter()
- Method in class net.joeclark.proceduralgeneration.
RandomDrawGenerator
getVowels()
- Method in class net.joeclark.proceduralgeneration.
ClusterChainGenerator
H
hashCode()
- Method in class net.joeclark.proceduralgeneration.
ClusterChainGenerator
I
isTrained()
- Method in class net.joeclark.proceduralgeneration.
ClusterChainGenerator
isTrained()
- Method in class net.joeclark.proceduralgeneration.
MarkovTextGenerator
isTrained()
- Method in class net.joeclark.proceduralgeneration.
RandomDrawGenerator
L
LATIN_VOWELS
- Static variable in class net.joeclark.proceduralgeneration.
ClusterChainGenerator
A long list of characters I found on the Unicode table (unicode-table.com) which seem to be variants of a,e,i,o,u,y for various Latin-based alphabets.
M
makeObservations(Stream<String>)
- Method in class net.joeclark.proceduralgeneration.
MarkovTextCasePreservingGenerator
makeObservations(Stream<String>)
- Method in class net.joeclark.proceduralgeneration.
MarkovTextGenerator
MarkovTextCasePreservingGenerator
- Class in
net.joeclark.proceduralgeneration
An alternative implementation of MarkovTextGenerator that learns and reproduces upper/lower case usage in the training data.
MarkovTextCasePreservingGenerator()
- Constructor for class net.joeclark.proceduralgeneration.
MarkovTextCasePreservingGenerator
MarkovTextGenerator
- Class in
net.joeclark.proceduralgeneration
A class that uses a Markov model, trained on a
Stream<String>
of example text, to generate new, random strings similar to the training data.
MarkovTextGenerator()
- Constructor for class net.joeclark.proceduralgeneration.
MarkovTextGenerator
Initialize a new MarkovTextGenerator.
maxLength
- Variable in class net.joeclark.proceduralgeneration.
MarkovTextGenerator
minLength
- Variable in class net.joeclark.proceduralgeneration.
MarkovTextGenerator
model
- Variable in class net.joeclark.proceduralgeneration.
MarkovTextGenerator
N
net.joeclark.proceduralgeneration
- package net.joeclark.proceduralgeneration
O
observations
- Variable in class net.joeclark.proceduralgeneration.
MarkovTextGenerator
order
- Variable in class net.joeclark.proceduralgeneration.
MarkovTextGenerator
P
prior
- Variable in class net.joeclark.proceduralgeneration.
MarkovTextGenerator
R
random
- Variable in class net.joeclark.proceduralgeneration.
MarkovTextGenerator
RandomDrawGenerator
- Class in
net.joeclark.proceduralgeneration
Draws text strings at random from a training dataset (but lowercase regardless of training data's case)
RandomDrawGenerator()
- Constructor for class net.joeclark.proceduralgeneration.
RandomDrawGenerator
Initialize a new RandomDrawGenerator.
RandomTextGenerator
- Interface in
net.joeclark.proceduralgeneration
An interface for an object that produces randomly-generated text strings on demand.
S
setEndFilter(String)
- Method in class net.joeclark.proceduralgeneration.
ClusterChainGenerator
setEndFilter(String)
- Method in class net.joeclark.proceduralgeneration.
MarkovTextCasePreservingGenerator
setEndFilter(String)
- Method in class net.joeclark.proceduralgeneration.
MarkovTextGenerator
setEndFilter(String)
- Method in class net.joeclark.proceduralgeneration.
RandomDrawGenerator
setMaxLength(int)
- Method in class net.joeclark.proceduralgeneration.
ClusterChainGenerator
setMaxLength(int)
- Method in class net.joeclark.proceduralgeneration.
MarkovTextGenerator
setMaxLength(int)
- Method in class net.joeclark.proceduralgeneration.
RandomDrawGenerator
setMaxOrder(int)
- Method in class net.joeclark.proceduralgeneration.
ClusterChainGenerator
setMinLength(int)
- Method in class net.joeclark.proceduralgeneration.
ClusterChainGenerator
setMinLength(int)
- Method in class net.joeclark.proceduralgeneration.
MarkovTextGenerator
setMinLength(int)
- Method in class net.joeclark.proceduralgeneration.
RandomDrawGenerator
setOrder(int)
- Method in class net.joeclark.proceduralgeneration.
MarkovTextGenerator
setPrior(double)
- Method in class net.joeclark.proceduralgeneration.
MarkovTextGenerator
setPriors(Double)
- Method in class net.joeclark.proceduralgeneration.
ClusterChainGenerator
setRandom(Random)
- Method in class net.joeclark.proceduralgeneration.
ClusterChainGenerator
setRandom(Random)
- Method in class net.joeclark.proceduralgeneration.
MarkovTextGenerator
setRandom(Random)
- Method in class net.joeclark.proceduralgeneration.
RandomDrawGenerator
setSeparator(String)
- Method in class net.joeclark.proceduralgeneration.
DoubleTextGenerator
setStartFilter(String)
- Method in class net.joeclark.proceduralgeneration.
ClusterChainGenerator
setStartFilter(String)
- Method in class net.joeclark.proceduralgeneration.
MarkovTextCasePreservingGenerator
setStartFilter(String)
- Method in class net.joeclark.proceduralgeneration.
MarkovTextGenerator
setStartFilter(String)
- Method in class net.joeclark.proceduralgeneration.
RandomDrawGenerator
setVowels(Character[])
- Method in class net.joeclark.proceduralgeneration.
ClusterChainGenerator
startFilter
- Variable in class net.joeclark.proceduralgeneration.
MarkovTextGenerator
T
train(Stream<String>)
- Method in class net.joeclark.proceduralgeneration.
ClusterChainGenerator
Ingest a new set of training data.
train(Stream<String>)
- Method in class net.joeclark.proceduralgeneration.
MarkovTextGenerator
Build the Markov chain model based on a training dataset.
train(Stream<String>)
- Method in class net.joeclark.proceduralgeneration.
RandomDrawGenerator
Ingest a new set of training data, overwriting any data that was previously trained.
W
withEndFilter(String)
- Method in class net.joeclark.proceduralgeneration.
ClusterChainGenerator
withEndFilter(String)
- Method in class net.joeclark.proceduralgeneration.
MarkovTextGenerator
withEndFilter(String)
- Method in class net.joeclark.proceduralgeneration.
RandomDrawGenerator
withMaxLength(int)
- Method in class net.joeclark.proceduralgeneration.
ClusterChainGenerator
withMaxLength(int)
- Method in class net.joeclark.proceduralgeneration.
MarkovTextGenerator
withMaxLength(int)
- Method in class net.joeclark.proceduralgeneration.
RandomDrawGenerator
withMaxOrder(int)
- Method in class net.joeclark.proceduralgeneration.
ClusterChainGenerator
Sets the 'MaxOrder' parameter of the internal Markov chain.
withMinLength(int)
- Method in class net.joeclark.proceduralgeneration.
ClusterChainGenerator
withMinLength(int)
- Method in class net.joeclark.proceduralgeneration.
MarkovTextGenerator
withMinLength(int)
- Method in class net.joeclark.proceduralgeneration.
RandomDrawGenerator
withOrder(int)
- Method in class net.joeclark.proceduralgeneration.
MarkovTextGenerator
withPrior(double)
- Method in class net.joeclark.proceduralgeneration.
MarkovTextGenerator
withRandom(Random)
- Method in class net.joeclark.proceduralgeneration.
ClusterChainGenerator
withRandom(Random)
- Method in class net.joeclark.proceduralgeneration.
MarkovTextGenerator
withRandom(Random)
- Method in class net.joeclark.proceduralgeneration.
RandomDrawGenerator
withStartFilter(String)
- Method in class net.joeclark.proceduralgeneration.
ClusterChainGenerator
withStartFilter(String)
- Method in class net.joeclark.proceduralgeneration.
MarkovTextGenerator
withStartFilter(String)
- Method in class net.joeclark.proceduralgeneration.
RandomDrawGenerator
withVowels(Character[])
- Method in class net.joeclark.proceduralgeneration.
ClusterChainGenerator
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Price Calculation
We are trying to get the most accurate prices on the site by analysing the Steam Market. Combined with the Market Rates we can have a stable & varied inventory.
Example
Let's say that you have a knife. The Steam market price of that knife is estimated to $50 and we are giving you 90% of the market price(the Market Rate). The mathematics part is going to look like this:
Discounts
Can be combined (Souvenir worth below 0.5 dollar would have 2 discounts)
Unaccepted Items
- 'Too Low': Items worth a below 10 cents is rarely accepted.
- 'Overstock': We have too many of the specific item.
- 'Unaccepted': Usually means an unstable market value(Low sell volume / Jumping prices).
- 'Too High': We don't accept this item because the price is too high price.
Our Cut
When trading items in the same type(knives to knives) the price will differ with 3-5%. We think this is fair, due to the fact that we are taking risk for every item we trade (values can crash). Also, we lay a lot of time on the bot and we are willing to get a little profit, since this is how trading works!


Pick a mentor from the list to see the price and available courses. More about mentors read at mentors pages.


CYOA Template
Hello! Welcome to this CYOA.
This is where you would put your introduction text.
Make Your First Choice
This is where you would explain what these choices are.
choice 1
Hello friends!
Make Your Second Choice
This is where you would explain what these choices are.
choice 1
Hello friends!


Add student to your classes
select class
{{group.name}}
cancel
cancel
person
save
Add group student
name of group
enter year
cancel
cancel
person
save


Here at Sunshine Farm we are more than happy to welcome any and all farming neighbors! You'll be pleased to hear that we have some new friends at Boob World Farm! Coy and Tawnee are at it again with a new farming game plan and a couple of fresh avatars. Hot damn!
 Even more news from the Farm. Tawnee of Coy and Tawnee at Sunshine Farm and Boob World Farm got a new job as a fancy city slicker -- Associate Solution Developer at eventcore! Cheers to that! However, work on the farm may come to a bit of a halt what with all the city work that girl will be up to. Tarnation!
We grow a large variety of crops at Sunshine Farm. We specialize in growing coffee plants, green tea bushes, and ancient fruit. Melons, tomatoes, potatoes, and green beans are also plentiful. As far as fruit trees go, we have cherry, apple, and apricot.
This section will be all about the aminals on the farm. We have cows, chickens, goats, ducks, a pig, a dinosaur, and a kitty cat on the farm.
Artisan Goods enhabit a very special place in our hearts here at Sunshine Farm. Duck mayo, cherry jelly, green tea, and truffle oil are some of our prized Artisan Goods!


Module::WorkflowAgent - Workflow implementation agent
This module provides ability to invoke a workflow and monitor it's progress over the nework. It also provides all the logic that propage and monitor the workflow over the network.
This action will invoke a workflow either as a local action or as a remote action, depending on if our configuration supports it.
ARG0
is an instance of a Module::Workflow::Definition object. The workflow will continue from the
currently ACTIVE action of the definition.
ARG1
is either a string that represents the name of the sender or a hash of permissions that the
requesting entity has.
This action will abort a workflow that is in progress, notifying all the child nodes too.
ARG0
is the definition id of the workflow you want to abort.
(Here are some thoughts regarding the lookup mechanism. You will find the refered part of the code if you search for 'TODO: See FOOD FOR THOUGHT' )
The whole purpose of the LOOKUPS hash and the whole lookup state is to fetch as much information as possible from all the endpoints that can are capable to handle the action in order to avoid re-requesting lookup information and thus polluting the public channel.
Now, in order to pile up responses in the same lookup hash we need an indexing key right?. Ideally we will could use the action name... This will do the following:
- We request "Who can handle 'Action'" - We pile all the responses under the same 'Action' - We pick the first available target - If somebody else wants to handle this action, we pick the next available from the same pile, etc...
This sounds very nice, but there is something important. We are not requesting "Who can handle 'Action'" but we are requesting "Who can handle 'Action' under context { context }"!
This means that if the context of an action is invalid the endpoint will not respond at all! (And thus if we try to invoke this action again and we use the cached information we are going to miss those targets that might not fail this time).
How to include all of them? Without polluting the public message bus?? What would be the ideal index???
A not-so-good solution will be to use the workflow definition ID (Which is generated once by the first invoking node) PLUS the action name. This is GOOD because usually when an entity rejects the action's context it will keep rejecting it for the entire workflow. It is BAD, because of the 'usually' in the previous sentence...
If you have a better idea... go for it!
Developed by Ioannis Charalampidis<EMAIL_ADDRESS>2011-2012 at PH/SFT, CERN


Before Change
np.testing.assert_array_almost_equal(loss_gradient_value[99, 14, :, 0], loss_grad_exp[loss_name])
except NotImplementedError as e:
warnings.warn(UserWarning(e))
def test_pickle(image_dl_estimator, image_dl_estimator_defended, tmp_path):
full_path = os.path.join(tmp_path, "my_classifier.p")
After Change
np.testing.assert_array_almost_equal(loss_gradient_value[99, 14, :, 0], loss_grad_exp[loss_name])
except ARTTestException as e:
add_warning(e)
def test_pickle(image_dl_estimator, image_dl_estimator_defended, tmp_path):
try:


@* * Copyright 2023 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import views.html.helpers.injected.{ContinueButton, SaveAndContinueButtonHelper} @this( form: FormWithCSRF, continueButton: ContinueButton, hmrcPageHeading: HmrcPageHeadingHelper, saveAndContinueButton: SaveAndContinueButtonHelper ) @( heading: String, caption: String, postAction: Call, isIndividual: Boolean, isConfirmed: Boolean = false, saveAndContinue: Boolean = false, saveAndReturnReference: Option[String] = None, sectionCaption: Boolean = true )(contentBlock: Html)(implicit request: Request[_], messages: Messages) @if(sectionCaption) { @hmrcPageHeading(PageHeading( text = heading, section = Some(caption) )) } else {


 If you want to become a programer first you need to ask some couple of questions to you, what kind of programer do you want to be? how can i start? from where should i start?. Dont worry friends you will get answers to all your questions here in code with king
To know about me click, CLICK ME.
CLICK ME


Total value
Tax
Installments
Policy
Installment
Value
Base premium 11%
Commission 17%
Tax 10%
Total cost


15
1 Then Eliphaz the Temanite answered and said,
2 "Should a wise man answer with useless knowledge
and fill himself with the east wind?
3 Should he reason with unprofitable talk
or with speeches with which he can do no good?
4 Indeed, you diminish respect for God;
you obstruct devotion to him,
5 for your iniquity teaches your mouth;
you choose to have the tongue of a crafty man.
6 Your own mouth condemns you, not mine;
indeed, your own lips testify against you.
7 Are you the first man that was born?
Were you brought into existence before the hills?
8 Have you heard the secret knowledge of God?
Do you limit wisdom to yourself?
9 What do you know that we do not know?
What do you understand that is not also in us?
10 With us are both the gray-headed and the very aged men
who are much older than your father.
11 Are the consolations of God too small for you,
the words that are gentle toward you?
12 Why does your heart carry you away?

What is he who is born of a woman that he should be righteous?
15 See, God puts no trust even in his holy ones;
indeed, the heavens are not clean in his sight;
16 how much less clean is one who is abominable and corrupt,
a man who drinks iniquity like water!
17 I will show you; listen to me;
I will announce to you the things I have seen,
18 the things that wise men have passed down from their fathers,
the things that their ancestors did not hide.
19 These were their ancestors, to whom alone the land was given,
and among whom no stranger ever passed.
20 The wicked man twists in pain all his days,
the number of years that are laid up for the oppressor to suffer.
21 A sound of terrors is in his ears;
while he is in prosperity, the destroyer will come upon him.
22 He does not think that he will return out of darkness;
the sword waits for him.
23 He goes to various places for bread, saying, 'Where is it?'


I have had much experience with psychedelic substances in the past, but have not taken any for a few years until now. I am also into spiritually and practice Yoga and meditation several times daily.
I am not on any medication currently or for the past four years.
9:30 PM: I drank a small cup of water which I a added about 5 grams of Kratom and stirred.
10:30 PM: The effects have come on, but unlike the first time I tried Kratom, this time I decidedly do not like what I'm feeling. I was clearer and more energetic before and now am beginning to feel cloudy, and down in a way. I guess people who like painkillers might enjoy Kratom but I wasnít liking the experience too much.
So I decided since I was experimenting I would give morning glory seeds a chance as well. I counted about two hundred and fifty seeds and after blessing them and picking an intention (mine was to gain insight into my life) I ate them raw and drank water to wash them down.
11:00 PM: begin to feel some effects but maybe placebo. Feels like the beginning of a trip like on mushrooms used to be like.
12:00 PM: After overcoming come fears and nauseous feelings I perk up. Now I feel relaxed and loved. Not euphoric by any standards but very good and relaxed and slightly psychedelic. The thing about eating the seeds raw (maybe also with other preparations Iím not sure) is that all my sense of taste is warped. I drink water but it tastes salty much like the seeds. I eat a bit of food but again cant taste much. Strange.
At one point I started playing guitar in my room. I felt that I was playing without effort. The words came naturally too. I made a bunch of heartfelt songs within a few minutes. Ofcourse the next day I had forgotten them ,) my voice sounded so beautiful and I felt in touch with the most creative part of me. Very loving, tender and innocent. Very nice feeling.
Then I headed to bed at some point because I realized my 'trip' wasnít going to be very intense. Once in bed I had no desire to listen to music, but simply to listen to my thoughts. My thoughts were altered. Not the thoughts themselves but rather the quality of them. They were much more eloquent than usual. No effort was needed to figure things out. They flowed in and out of each other with amazing clarity. I gathered one or two basic insights about myself, but realized mainly that there wasnít too much insight I needed to gather about myself and that I was pretty clear on who I was. That was reliving for sure since I was expecting some huge challenge to be overcome or some huge doubt to arise. They never did.
Conclusion: I realized the spirit of morning glory seeds is very nice and friendly (to me). They are gentle and alter the quality of thinking just enough while still being comfortable (at this dosage). The seeds have potential for teaching, and yes also for relaxing and enjoying a different experience in my life as long as I am happy, peaceful and loving when I eat them.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


This webpage is freely generated with BibBase.
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Once consulted go back to Francesco' papers page.


Server-less Cookies With a Flash HTML Template
First be aware.
This method uses Javascript, which doesn't communicate with Flash within IE on the Mac. If you have an important business need for using a cookie you would be better off with a server side tool, which doesn't suffer from this limitation. But for artists and experimenters, this method is pretty nice. The best use of it may be to save a string on the hard drive if you want to distribute via CD. A cookie only saves one string. If you want to save more than one string, you might want to use something like a stack or a list and concatenate the string values together before saving them. On reentry pop the values back off the stack or list to get back your information.
If you have more elaborate needs for saving files from a Flash CD you can look at ASsaver at http://www.ashzfall.com/products/afsaver/ I haven't used it, but it looks pretty nice.
I'd love to hear about browsers that it doesn't work with. Does it work with Opera? I don't know. I'm sorry, but I just haven't had time for serious testing of this. Thus "Be Aware".
Also, be sure to test with cookies disabled to make sure that your movie degrades gracefully if cookies are turned off.
So how do you use the cookieJar HTML template?
First place the cookieJar.html file in your Flash HTML template directory. On windows this default would be C:/Program Files/Macromedia/Flash/HTML/.
Before you publish your fla, go to File/Publish on the menu. Choose the HTML tab and change the Template to CookieJar.
When you want to update the value of the cookie send an FS Command with "setCookies" as the command and the string you want to save as the argument.
For example this code could be put in a button.
On (Release)
FS Command ("setCookies", "Peanut Butter")
End On
Clicking this button would then set the value of the cookie to the string "Peanut Butter". The Javascript code will then also update the "/:CookieJar" variable in Flash to "Peanut Butter" as well. The code tells the cookie to persist for a year from the last time it was set.
I would suggest that you don't use the "/:CookieJar" variable directly as the second argument in the call to FS Command. If you want to use a variable, create it separately, calling it "/:CookieValue", for example. That would make it easier to debug and also would avoid situations where Javascript and ActionScript both are trying to change the CookieJar variable at the same time.
So you would write to CookieValue and send that to the JavaScript code. The JavaScript code writes to CookieJar which you only read from.
There is an example .fla that you can use to examine this more. You can look at it here.
So how does it work?
When you first come to the page there is an OnLoad handler in the HTML that is called when the page first finishes loading. This handler calls the FSCommand Function with "GetCookies" as the command. This function gets the cookie value if it it exists, otherwise it initializes the cookie with the string value "initialized" and saves it for the user.
Now the Flash movie can call the FSCommand function further with "setCookies" and "a string value" as described above, which will update the cookie value to what was set.
A Final Note
Sorry that this tutorial is so sparse, but I initially created this code half a year ago now and there are so many options that I could cover that if I try to do a more complete job of it, I realize I'd never finish. So, hopefully this is a full enough description for it to be useful to some folk. I'd prefer if you direct any questions to the were-here forum, rather than e-mailing me. That way other programmers can help and things can be described only once. I'll help if I can, but I am pretty over-committed as it is, so no promises.
Good luck with all.
Mr. Cort


Is it your turn at the bar?
Don't worry, just start a round and add your drinks. We aren't paying though.
{{nightOut.timestamp | date:"EEEE dd, MMMM y"}}
£{{nightOut.getCost() | number:'1.2-2'}}
Night Out Total
Don't worry, just start a round and add your drinks. We aren't paying though.
{{nightOut.timestamp | date:"EEEE dd, MMMM y"}}
Night Out Total


| AbstractNodJob |
| AbstractNodJob.QueryUnsatException |
| AclLine |
| BaseQuerySynthesizer |
| Field |
| FirstUnsatQuerySynthesizer |
| IngressLocation
Location where reachability analysis begins.
|
| IngressLocation.Type |
| IpAccessListSpecializer
|
| IpSpaceSpecializer
|
| MultipathInconsistencyQuerySynthesizer |
| MultipathInconsistencyQuerySynthesizer.Builder |
| NodContext |
| NodFirstUnsatResult |
| NodJobResult |
| NodProgram |
| NodSatResult |
| QuerySynthesizer
A synthesizer that generates the query portion of a Batfish reachability AST program
|
| ReachabilityProgram |
| ReachabilityProgram.Builder |
| ReachabilityQuerySynthesizer |
| ReachabilityQuerySynthesizer.Builder |
| SatQuerySynthesizer |
| SmtInput |
| StandardReachabilityQuerySynthesizer |
| StandardReachabilityQuerySynthesizer.Builder |
| Synthesizer |
| SynthesizerInput
The input to reachability program synthesis, including settings and marshalled information from
configurations and data-plane
|
| SynthesizerInputImpl |
| SynthesizerInputImpl.Builder |
| Z3ContextJob |


| Class | Description |
|---|---|
| AllocationDescription |
Protobuf type
tensorflow.AllocationDescription |
| AllocationDescription.Builder |
Protobuf type
tensorflow.AllocationDescription |
| AllocationDescriptionProtos | |
| AllocationRecord |
An allocation/de-allocation operation performed by the allocator.
|
| AllocationRecord.Builder |
An allocation/de-allocation operation performed by the allocator.
|
| AllocatorMemoryUsed |
Protobuf type
tensorflow.AllocatorMemoryUsed |
| AllocatorMemoryUsed.Builder |
Protobuf type
tensorflow.AllocatorMemoryUsed |
| ApiDef |
Used to specify and override the default API & behavior in the
generated code for client languages, from what you would get from
the OpDef alone.
|
| ApiDef.Arg |
Protobuf type
tensorflow.ApiDef.Arg |
| ApiDef.Arg.Builder |
Protobuf type
tensorflow.ApiDef.Arg |
| ApiDef.Attr |
Description of the graph-construction-time configuration of this
Op.
|
| ApiDef.Attr.Builder |
Description of the graph-construction-time configuration of this
Op.
|
| ApiDef.Builder |
Used to specify and override the default API & behavior in the
generated code for client languages, from what you would get from
the OpDef alone.
|
| ApiDef.Endpoint |
If you specify any endpoint, this will replace all of the
inherited endpoints.
|
| ApiDef.Endpoint.Builder |
If you specify any endpoint, this will replace all of the
inherited endpoints.
|
| ApiDefProtos | |
| ApiDefs |
Protobuf type
tensorflow.ApiDefs |
| ApiDefs.Builder |
Protobuf type
tensorflow.ApiDefs |
| AssetFileDef |
An asset file def for a single file or a set of sharded files with the same
name.
|
| AssetFileDef.Builder |
An asset file def for a single file or a set of sharded files with the same
name.
|
| AttrValue |
Protocol buffer representing the value for an attr used to configure an Op.
|
| AttrValue.Builder |
Protocol buffer representing the value for an attr used to configure an Op.
|
| AttrValue.ListValue |
LINT.IfChange
Protobuf type
tensorflow.AttrValue.ListValue |
| AttrValue.ListValue.Builder |
LINT.IfChange
Protobuf type
tensorflow.AttrValue.ListValue |
| AttrValueProtos | |
| AutoParallelOptions |
Protobuf type
tensorflow.AutoParallelOptions |
| AutoParallelOptions.Builder |
Protobuf type
tensorflow.AutoParallelOptions |
| CallableOptions |
Defines a subgraph in another `GraphDef` as a set of feed points and nodes
to be fetched or executed.
|
| CallableOptions.Builder |
Defines a subgraph in another `GraphDef` as a set of feed points and nodes
to be fetched or executed.
|
| CollectionDef |
CollectionDef should cover most collections.
|
| CollectionDef.AnyList |
AnyList is used for collecting Any protos.
|
| CollectionDef.AnyList.Builder |
AnyList is used for collecting Any protos.
|
| CollectionDef.Builder |
CollectionDef should cover most collections.
|
| CollectionDef.BytesList |
BytesList is used for collecting strings and serialized protobufs.
|
| CollectionDef.BytesList.Builder |
BytesList is used for collecting strings and serialized protobufs.
|
| CollectionDef.FloatList |
FloatList is used for collecting float values.
|
| CollectionDef.FloatList.Builder |
FloatList is used for collecting float values.
|
| CollectionDef.Int64List |
Int64List is used for collecting int, int64 and long values.
|
| CollectionDef.Int64List.Builder |
Int64List is used for collecting int, int64 and long values.
|
| CollectionDef.NodeList |
NodeList is used for collecting nodes in graph.
|
| CollectionDef.NodeList.Builder |
NodeList is used for collecting nodes in graph.
|
| CondContextDef |
Protocol buffer representing a CondContext object.
|
| CondContextDef.Builder |
Protocol buffer representing a CondContext object.
|
| ConfigProto |
Session configuration parameters.
|
| ConfigProto.Builder |
Session configuration parameters.
|
| ConfigProto.Experimental |
Everything inside Experimental is subject to change and is not subject
to API stability guarantees in
https://www.tensorflow.org/guide/version_compat.
|
| ConfigProto.Experimental.Builder |
Everything inside Experimental is subject to change and is not subject
to API stability guarantees in
https://www.tensorflow.org/guide/version_compat.
|
| ConfigProtos | |
| ControlFlowContextDef |
Container for any kind of control flow context.
|
| ControlFlowContextDef.Builder |
Container for any kind of control flow context.
|
| ControlFlowProtos | |
| CostGraphDef |
Protobuf type
tensorflow.CostGraphDef |
| CostGraphDef.Builder |
Protobuf type
tensorflow.CostGraphDef |
| CostGraphDef.Node |
Protobuf type
tensorflow.CostGraphDef.Node |
| CostGraphDef.Node.Builder |
Protobuf type
tensorflow.CostGraphDef.Node |
| CostGraphDef.Node.InputInfo |
Inputs of this node.
|
| CostGraphDef.Node.InputInfo.Builder |
Inputs of this node.
|
| CostGraphDef.Node.OutputInfo |
Outputs of this node.
|
| CostGraphDef.Node.OutputInfo.Builder |
Outputs of this node.
|
| CostGraphProtos | |
| CriticalSectionDef |
Protocol buffer representing a CriticalSection.
|
| CriticalSectionDef.Builder |
Protocol buffer representing a CriticalSection.
|
| CriticalSectionExecutionDef |
Protocol buffer representing a CriticalSection execution.
|
| CriticalSectionExecutionDef.Builder |
Protocol buffer representing a CriticalSection execution.
|
| CriticalSectionProtos | |
| DebuggedSourceFile |
Protobuf type
tensorflow.DebuggedSourceFile |
| DebuggedSourceFile.Builder |
Protobuf type
tensorflow.DebuggedSourceFile |
| DebuggedSourceFiles |
Protobuf type
tensorflow.DebuggedSourceFiles |
| DebuggedSourceFiles.Builder |
Protobuf type
tensorflow.DebuggedSourceFiles |
| DebugOptions |
EXPERIMENTAL.
|
| DebugOptions.Builder |
EXPERIMENTAL.
|
| DebugProtos | |
| DebugTensorWatch |
EXPERIMENTAL.
|
| DebugTensorWatch.Builder |
EXPERIMENTAL.
|
| DeviceAttributes |
Protobuf type
tensorflow.DeviceAttributes |
| DeviceAttributes.Builder |
Protobuf type
tensorflow.DeviceAttributes |
| DeviceAttributesProtos | |
| DeviceLocality |
Protobuf type
tensorflow.DeviceLocality |
| DeviceLocality.Builder |
Protobuf type
tensorflow.DeviceLocality |
| DeviceStepStats |
Protobuf type
tensorflow.DeviceStepStats |
| DeviceStepStats.Builder |
Protobuf type
tensorflow.DeviceStepStats |
| ErrorCodesProtos | |
| FunctionDef |
A function can be instantiated when the runtime can bind every attr
with a value.
|
| FunctionDef.Builder |
A function can be instantiated when the runtime can bind every attr
with a value.
|
| FunctionDefLibrary |
A library is a set of named functions.
|
| FunctionDefLibrary.Builder |
A library is a set of named functions.
|
| FunctionProtos | |
| GPUOptions |
Protobuf type
tensorflow.GPUOptions |
| GPUOptions.Builder |
Protobuf type
tensorflow.GPUOptions |
| GPUOptions.Experimental |
Protobuf type
tensorflow.GPUOptions.Experimental |
| GPUOptions.Experimental.Builder |
Protobuf type
tensorflow.GPUOptions.Experimental |
| GPUOptions.Experimental.VirtualDevices |
Configuration for breaking down a visible GPU into multiple "virtual"
devices.
|
| GPUOptions.Experimental.VirtualDevices.Builder |
Configuration for breaking down a visible GPU into multiple "virtual"
devices.
|
| GradientDef |
GradientDef defines the gradient function of a function defined in
a function library.
|
| GradientDef.Builder |
GradientDef defines the gradient function of a function defined in
a function library.
|
| GraphDef |
Represents the graph of operations
Protobuf type
tensorflow.GraphDef |
| GraphDef.Builder |
Represents the graph of operations
Protobuf type
tensorflow.GraphDef |
| GraphOptions |
Protobuf type
tensorflow.GraphOptions |
| GraphOptions.Builder |
Protobuf type
tensorflow.GraphOptions |
| GraphProtos | |
| GraphTransferConstNodeInfo |
Protobuf type
tensorflow.GraphTransferConstNodeInfo |
| GraphTransferConstNodeInfo.Builder |
Protobuf type
tensorflow.GraphTransferConstNodeInfo |
| GraphTransferGraphInputNodeInfo |
Protobuf type
tensorflow.GraphTransferGraphInputNodeInfo |
| GraphTransferGraphInputNodeInfo.Builder |
Protobuf type
tensorflow.GraphTransferGraphInputNodeInfo |
| GraphTransferGraphOutputNodeInfo |
Protobuf type
tensorflow.GraphTransferGraphOutputNodeInfo |
| GraphTransferGraphOutputNodeInfo.Builder |
Protobuf type
tensorflow.GraphTransferGraphOutputNodeInfo |
| GraphTransferInfo |
Protocol buffer representing a handle to a tensorflow resource.
|
| GraphTransferInfo.Builder |
Protocol buffer representing a handle to a tensorflow resource.
|
| GraphTransferInfoProto | |
| GraphTransferNodeInfo |
Protobuf type
tensorflow.GraphTransferNodeInfo |
| GraphTransferNodeInfo.Builder |
Protobuf type
tensorflow.GraphTransferNodeInfo |
| GraphTransferNodeInput |
Protobuf type
tensorflow.GraphTransferNodeInput |
| GraphTransferNodeInput.Builder |
Protobuf type
tensorflow.GraphTransferNodeInput |
| GraphTransferNodeInputInfo |
Protobuf type
tensorflow.GraphTransferNodeInputInfo |
| GraphTransferNodeInputInfo.Builder |
Protobuf type
tensorflow.GraphTransferNodeInputInfo |
| GraphTransferNodeOutputInfo |
Protobuf type
tensorflow.GraphTransferNodeOutputInfo |
| GraphTransferNodeOutputInfo.Builder |
Protobuf type
tensorflow.GraphTransferNodeOutputInfo |
| HistogramProto |
Serialization format for histogram module in
core/lib/histogram/histogram.h
Protobuf type
tensorflow.HistogramProto |
| HistogramProto.Builder |
Serialization format for histogram module in
core/lib/histogram/histogram.h
Protobuf type
tensorflow.HistogramProto |
| InterconnectLink |
Protobuf type
tensorflow.InterconnectLink |
| InterconnectLink.Builder |
Protobuf type
tensorflow.InterconnectLink |
| KernelDef |
Protobuf type
tensorflow.KernelDef |
| KernelDef.AttrConstraint |
Protobuf type
tensorflow.KernelDef.AttrConstraint |
| KernelDef.AttrConstraint.Builder |
Protobuf type
tensorflow.KernelDef.AttrConstraint |
| KernelDef.Builder |
Protobuf type
tensorflow.KernelDef |
| KernelDefProtos | |
| LocalLinks |
Protobuf type
tensorflow.LocalLinks |
| LocalLinks.Builder |
Protobuf type
tensorflow.LocalLinks |
| LogMemoryProtos | |
| MemoryLogRawAllocation |
Protobuf type
tensorflow.MemoryLogRawAllocation |
| MemoryLogRawAllocation.Builder |
Protobuf type
tensorflow.MemoryLogRawAllocation |
| MemoryLogRawDeallocation |
Protobuf type
tensorflow.MemoryLogRawDeallocation |
| MemoryLogRawDeallocation.Builder |
Protobuf type
tensorflow.MemoryLogRawDeallocation |
| MemoryLogStep |
Protobuf type
tensorflow.MemoryLogStep |
| MemoryLogStep.Builder |
Protobuf type
tensorflow.MemoryLogStep |
| MemoryLogTensorAllocation |
Protobuf type
tensorflow.MemoryLogTensorAllocation |
| MemoryLogTensorAllocation.Builder |
Protobuf type
tensorflow.MemoryLogTensorAllocation |
| MemoryLogTensorDeallocation |
Protobuf type
tensorflow.MemoryLogTensorDeallocation |
| MemoryLogTensorDeallocation.Builder |
Protobuf type
tensorflow.MemoryLogTensorDeallocation |
| MemoryLogTensorOutput |
Protobuf type
tensorflow.MemoryLogTensorOutput |
| MemoryLogTensorOutput.Builder |
Protobuf type
tensorflow.MemoryLogTensorOutput |
| MemoryStats |
For memory tracking.
|
| MemoryStats.Builder |
For memory tracking.
|
| MetaGraphDef |
NOTE: This protocol buffer is evolving, and will go through revisions in the
coming months.
|
| MetaGraphDef.Builder |
NOTE: This protocol buffer is evolving, and will go through revisions in the
coming months.
|
| MetaGraphDef.MetaInfoDef |
Meta information regarding the graph to be exported.
|
| MetaGraphDef.MetaInfoDef.Builder |
Meta information regarding the graph to be exported.
|
| MetaGraphProtos | |
| NameAttrList |
A list of attr names and their values.
|
| NameAttrList.Builder |
A list of attr names and their values.
|
| NamedTensorProto |
A pair of tensor name and tensor values.
|
| NamedTensorProto.Builder |
A pair of tensor name and tensor values.
|
| NamedTensorProtos | |
| NodeDef |
Protobuf type
tensorflow.NodeDef |
| NodeDef.Builder |
Protobuf type
tensorflow.NodeDef |
| NodeExecStats |
Time/size stats recorded for a single execution of a graph node.
|
| NodeExecStats.Builder |
Time/size stats recorded for a single execution of a graph node.
|
| NodeOutput |
Output sizes recorded for a single execution of a graph node.
|
| NodeOutput.Builder |
Output sizes recorded for a single execution of a graph node.
|
| NodeProto | |
| OpDef |
Defines an operation.
|
| OpDef.ArgDef |
For describing inputs and outputs.
|
| OpDef.ArgDef.Builder |
For describing inputs and outputs.
|
| OpDef.AttrDef |
Description of the graph-construction-time configuration of this
Op.
|
| OpDef.AttrDef.Builder |
Description of the graph-construction-time configuration of this
Op.
|
| OpDef.Builder |
Defines an operation.
|
| OpDefProtos | |
| OpDeprecation |
Information about version-dependent deprecation of an op
Protobuf type
tensorflow.OpDeprecation |
| OpDeprecation.Builder |
Information about version-dependent deprecation of an op
Protobuf type
tensorflow.OpDeprecation |
| OpList |
A collection of OpDefs
Protobuf type
tensorflow.OpList |
| OpList.Builder |
A collection of OpDefs
Protobuf type
tensorflow.OpList |
| OptimizerOptions |
Options passed to the graph optimizer
Protobuf type
tensorflow.OptimizerOptions |
| OptimizerOptions.Builder |
Options passed to the graph optimizer
Protobuf type
tensorflow.OptimizerOptions |
| QueueRunnerDef |
Protocol buffer representing a QueueRunner.
|
| QueueRunnerDef.Builder |
Protocol buffer representing a QueueRunner.
|
| QueueRunnerProtos | |
| ReaderBaseProtos | |
| ReaderBaseState |
For serializing and restoring the state of ReaderBase, see
reader_base.h for details.
|
| ReaderBaseState.Builder |
For serializing and restoring the state of ReaderBase, see
reader_base.h for details.
|
| RemoteFusedGraphExecuteInfo |
Protocol buffer representing a handle to a tensorflow resource.
|
| RemoteFusedGraphExecuteInfo.Builder |
Protocol buffer representing a handle to a tensorflow resource.
|
| RemoteFusedGraphExecuteInfo.TensorShapeTypeProto |
Protobuf type
tensorflow.RemoteFusedGraphExecuteInfo.TensorShapeTypeProto |
| RemoteFusedGraphExecuteInfo.TensorShapeTypeProto.Builder |
Protobuf type
tensorflow.RemoteFusedGraphExecuteInfo.TensorShapeTypeProto |
| RemoteFusedGraphExecuteInfoProto | |
| ResourceHandle | |
| ResourceHandleProto |
Protocol buffer representing a handle to a tensorflow resource.
|
| ResourceHandleProto.Builder |
Protocol buffer representing a handle to a tensorflow resource.
|
| RewriterConfig |
Graph rewriting is experimental and subject to change, not covered by any
API stability guarantees.
|
| RewriterConfig.Builder |
Graph rewriting is experimental and subject to change, not covered by any
API stability guarantees.
|
| RewriterConfig.CustomGraphOptimizer |
Message to describe custom graph optimizer and its parameters
Protobuf type
tensorflow.RewriterConfig.CustomGraphOptimizer |
| RewriterConfig.CustomGraphOptimizer.Builder |
Message to describe custom graph optimizer and its parameters
Protobuf type
tensorflow.RewriterConfig.CustomGraphOptimizer |
| RewriterConfigProtos | |
| RPCOptions |
Protobuf type
tensorflow.RPCOptions |
| RPCOptions.Builder |
Protobuf type
tensorflow.RPCOptions |
| RunMetadata |
Metadata output (i.e., non-Tensor) for a single Run() call.
|
| RunMetadata.Builder |
Metadata output (i.e., non-Tensor) for a single Run() call.
|
| RunOptions |
Options for a single Run() call.
|
| RunOptions.Builder |
Options for a single Run() call.
|
| RunOptions.Experimental |
Everything inside Experimental is subject to change and is not subject
to API stability guarantees in
https://www.tensorflow.org/guide/version_compat.
|
| RunOptions.Experimental.Builder |
Everything inside Experimental is subject to change and is not subject
to API stability guarantees in
https://www.tensorflow.org/guide/version_compat.
|
| SavedModel |
SavedModel is the high level serialization format for TensorFlow Models.
|
| SavedModel.Builder |
SavedModel is the high level serialization format for TensorFlow Models.
|
| SavedModelProtos | |
| SaveSliceInfoDef |
Protobuf type
tensorflow.SaveSliceInfoDef |
| SaveSliceInfoDef.Builder |
Protobuf type
tensorflow.SaveSliceInfoDef |
| ScopedAllocatorOptions |
Protobuf type
tensorflow.ScopedAllocatorOptions |
| ScopedAllocatorOptions.Builder |
Protobuf type
tensorflow.ScopedAllocatorOptions |
| SignatureDef |
SignatureDef defines the signature of a computation supported by a TensorFlow
graph.
|
| SignatureDef.Builder |
SignatureDef defines the signature of a computation supported by a TensorFlow
graph.
|
| StepStats |
Protobuf type
tensorflow.StepStats |
| StepStats.Builder |
Protobuf type
tensorflow.StepStats |
| StepStatsProtos | |
| Summary |
A Summary is a set of named values to be displayed by the
visualizer.
|
| Summary.Audio |
Protobuf type
tensorflow.Summary.Audio |
| Summary.Audio.Builder |
Protobuf type
tensorflow.Summary.Audio |
| Summary.Builder |
A Summary is a set of named values to be displayed by the
visualizer.
|
| Summary.Image |
Protobuf type
tensorflow.Summary.Image |
| Summary.Image.Builder |
Protobuf type
tensorflow.Summary.Image |
| Summary.Value |
Protobuf type
tensorflow.Summary.Value |
| Summary.Value.Builder |
Protobuf type
tensorflow.Summary.Value |
| SummaryDescription |
Metadata associated with a series of Summary data
Protobuf type
tensorflow.SummaryDescription |
| SummaryDescription.Builder |
Metadata associated with a series of Summary data
Protobuf type
tensorflow.SummaryDescription |
| SummaryMetadata |
A SummaryMetadata encapsulates information on which plugins are able to make
use of a certain summary value.
|
| SummaryMetadata.Builder |
A SummaryMetadata encapsulates information on which plugins are able to make
use of a certain summary value.
|
| SummaryMetadata.PluginData |
Protobuf type
tensorflow.SummaryMetadata.PluginData |
| SummaryMetadata.PluginData.Builder |
Protobuf type
tensorflow.SummaryMetadata.PluginData |
| SummaryProtos | |
| TensorConnection |
Defines a connection between two tensors in a `GraphDef`.
|
| TensorConnection.Builder |
Defines a connection between two tensors in a `GraphDef`.
|
| TensorDescription |
Protobuf type
tensorflow.TensorDescription |
| TensorDescription.Builder |
Protobuf type
tensorflow.TensorDescription |
| TensorDescriptionProtos | |
| TensorInfo |
Information about a Tensor necessary for feeding or retrieval.
|
| TensorInfo.Builder |
Information about a Tensor necessary for feeding or retrieval.
|
| TensorInfo.CooSparse |
For sparse tensors, The COO encoding stores a triple of values, indices,
and shape.
|
| TensorInfo.CooSparse.Builder |
For sparse tensors, The COO encoding stores a triple of values, indices,
and shape.
|
| TensorProto |
Protocol buffer representing a tensor.
|
| TensorProto.Builder |
Protocol buffer representing a tensor.
|
| TensorProtos | |
| TensorShapeProto |
Dimensions of a tensor.
|
| TensorShapeProto.Builder |
Dimensions of a tensor.
|
| TensorShapeProto.Dim |
One dimension of the tensor.
|
| TensorShapeProto.Dim.Builder |
One dimension of the tensor.
|
| TensorShapeProtos | |
| TensorSliceProto |
Can only be interpreted if you know the corresponding TensorShape.
|
| TensorSliceProto.Builder |
Can only be interpreted if you know the corresponding TensorShape.
|
| TensorSliceProto.Extent |
Extent of the slice in one dimension.
|
| TensorSliceProto.Extent.Builder |
Extent of the slice in one dimension.
|
| TensorSliceProtos | |
| ThreadPoolOptionProto |
Protobuf type
tensorflow.ThreadPoolOptionProto |
| ThreadPoolOptionProto.Builder |
Protobuf type
tensorflow.ThreadPoolOptionProto |
| TypesProtos | |
| ValuesDef |
Protocol buffer representing the values in ControlFlowContext.
|
| ValuesDef.Builder |
Protocol buffer representing the values in ControlFlowContext.
|
| VariableDef |
Protocol buffer representing a Variable.
|
| VariableDef.Builder |
Protocol buffer representing a Variable.
|
| VariableProtos | |
| VariantTensorDataProto |
Protocol buffer representing the serialization format of DT_VARIANT tensors.
|
| VariantTensorDataProto.Builder |
Protocol buffer representing the serialization format of DT_VARIANT tensors.
|
| VersionDef |
Version information for a piece of serialized data
There are different types of versions for each type of data
(GraphDef, etc.), but they all have the same common shape
described here.
|
| VersionDef.Builder |
Version information for a piece of serialized data
There are different types of versions for each type of data
(GraphDef, etc.), but they all have the same common shape
described here.
|
| VersionsProtos | |
| WhileContextDef |
Protocol buffer representing a WhileContext object.
|
| WhileContextDef.Builder |
Protocol buffer representing a WhileContext object.
|
| Enum | Description |
|---|---|
| ApiDef.Visibility |
Protobuf enum
tensorflow.ApiDef.Visibility |
| AttrValue.ValueCase | |
| Code |
The canonical error codes for TensorFlow APIs.
|
| CollectionDef.KindCase | |
| ControlFlowContextDef.CtxtCase | |
| DataType |
LINT.IfChange
Protobuf enum
tensorflow.DataType |
| GraphTransferInfo.Destination |
Protobuf enum
tensorflow.GraphTransferInfo.Destination |
| OptimizerOptions.GlobalJitLevel |
Control the use of the compiler/jit.
|
| OptimizerOptions.Level |
Optimization level
Protobuf enum
tensorflow.OptimizerOptions.Level |
| RewriterConfig.MemOptType |
Protobuf enum
tensorflow.RewriterConfig.MemOptType |
| RewriterConfig.NumIterationsType |
Enum controlling the number of times to run optimizers.
|
| RewriterConfig.Toggle |
Protobuf enum
tensorflow.RewriterConfig.Toggle |
| RunOptions.TraceLevel |
TODO(pbar) Turn this into a TraceOptions proto which allows
tracing to be controlled in a more orthogonal manner?
|
| Summary.Value.ValueCase | |
| TensorInfo.EncodingCase | |
| TensorSliceProto.Extent.HasLengthCase |
Copyright © 2020. All rights reserved.


The chart on the left displays current surf trends for Torrey Pines. The x and y axes correspond to time/date and wave height (ft) respectively. The direction of the arrows represents wave direction, as it would be represented on a compass. The scale of the arrows relates to the period of the waves (s). On the right is a visualization of the selected wave height on a 6ft tall man with a 9ft surfboard next to him.
Data courtesy of CDIP


You can change the behaviour of Ameol by starting the program with some Command Line Switches.
To automate certain tasks within Ameol when using the CIX Forums using the Ameol Script Language.
Some actions within Ameol are more easily performed using Keyboard Shortcuts.


Knob Picker
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Vérifier


Share with us some of your favorite things, and be entered for a chance to win a mystery box of FAVORITETHINGS!™®*
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I like making websites and apps for fun and profit.
Partial to JavaScript but open to anything.
Talk Wellington
Pickup Pong
Spinvision


Prathamesh Mandke, pkmandke AT vt DOT edu
Japnit Singh Sethi, japss96 AT vt DOT edu
Department of Electrical and Computer Engineering
Virginia Tech
Abstract
In this work, we consider the low light image enhancement problem of recovering an enhanced normal light version of a low contrast image which suffers from poor visibilty. Image enhancement is an inherently ill-posed problem since a given low light image can have many possible normal light equivalents. Further, the presence of noise along with spatial variations in contrast and brightness pose additional challenges in solving this problem.
We seek to explore, compare and contrast various deep learning based approaches to the problem in supervised and unsupervised settings. In our experiments, we obtain performance benchmarks with 3 methods viz., 1) CLAHE, which is a conventional (non deep learning) histogram equalization approach 2) EnlightenGAN an unsupervised state-of-the-art low light enhancement model based on GANs and 3) multiple variations of U-Net based auto-encoder architectures with certain modifications. We observe that the Generative model performs best across these methods while CLAHE performs worst. The auto-encoder based architectures are able to retrieve most information from the low light images but the images look less natural. We provide qualitative as well as quantitative benchmarks to support these claims.
Introduction
Low light image enhancement is a widely studied problem in Computer Vision, where the goal is to recover an enhanced normal light version of an image with low contrast or visibility. Low light image enhancement finds widespread applications in domains such as autonomous driving and surveillance where mission critical computer vision systems rely on images captured in low lighting conditions for decision making. As mentioned earlier, this task is inherently ill-posed since a given low-light image can have multiple potential normal light equivalents. A simple example could be where a snapshot of a mountain range taken at night (pitch dark) could map to a normal light version during peak sunshine or at dusk. Besides, other artifacts such as image noise or spatially varying brightness and contrast pose additional difficulties. For example, certain regions of an image may need to be enhanced more than others in terms of contrast. Thus, a conventional Computer Vision approach that increases the overall image contrast may not give visually pleasing results.
Approach
Most approaches to the image enhancement problem can be categorized as either histogram based methods (such as CLAHE) or learning based methods. Learning based approaches can further be subdivided into supervised or unsupervised based on whether paired low-light and normal-light images are available. Further, deep learning methods are based on either the convolutional auto-encoder framework or the Generative Adversarial Models (GANs) based framework.
As mentioned earlier, the goal of this work is to compare and contrast various different methods across each of the aforementioned approaches to the problem. To this end, we choose to work with: 1) CLAHE - an adaptive histogram equalization based technique, 2) EnlightenGAN - an unsupervised GAN based framework and 3) multiple variations of the U-Net based auto-encoder framework which has shown great success for image-to-image translation problems. We present the results of benchmarking these methods supported with qualitative and quantitative comparisons. For CLAHE, we use the OpenCV implementation of the algorithm. To experiment with EnlightenGAN, we use the codebase provided by the authors (GitHub) and retrain the model using 3 NVIDIA GPUs based on the technique described in the paper. We implement the U-Net based auto-encoder variants from scratch using the PyTorch framework in Python. A brief overview of the aforementioned approaches follows.
CLAHE and EnlightenGAN
CLAHE (Contrast Limited Adaptive Histogram Equalization) improves over vanilla histogram equalization by considering small patches of the image to perform local equalization instead of globally over the entire image. This helps avoid any unwanted excess enhancement in regions with very high or very low contrast. Further, contrast limiting (contrast values beyond a threshold are clipped) is applied to reduce the effect of noise amplification as shown in the figure above.
EnlightenGAN is an unsupervised GAN based low light image enhancement method proposed by Jiang et al.. The framework learns to enhance low light images without any paired supervision at training time. The main highlights of this work are a self-attention module in the Generator model along with a dual global-local discriminator structure that helps the model handle both the fine and coarse details in the image with relative ease.
U-Net based auto-encoders
To explore the problem in a supervised setting, we implement U-Net based auto-encoder architectures and explore multiple versions of the same. Image enhancement falls under a wider class of problems known as image-to-image translation in which the U-Net architecture has shown great success. In this work, we explore two variations of the original U-net architecture. While the original model uses feature concatenation in the residual connections, we instead perform arithmetic addition of the corresponding feature maps in the encoder and decoder drawing inspiration from the ResNet architecture. This helps reduce the feature maps sizes in the decoder while also helping the decoding process with features extracted from the original image. In addition, we explore the effect of two upsampling strategies in the decoder namely transpose convolutions and bilinear upsampling. While bilinear upsampling has been a widely used upsampling strategy, the transpose convolution operation (popularized by the DCGAN paper) serves as a learnable upsampling layer in convolutional auto-encoders. As shown in the figure above, the overall architecture mainly consists of two parts, the encoder which consists of convolutional layers which use a stride of 2 to achieve the downsampling of the feature maps and the decoder which upsamples the feature map from the encoder using either the transpose convolutional layers (as shown) or bilinear upsampling. The skip connections, which add together feature maps from corresponding levels of the encoder and the decoder help the decoder in retrieving features of the input image which make reconstruction easier. In addition, the final output image of the decoder is added (element-wise) with the original input image to generate the normal light output. This way, the model only learns to predict the difference between the input low-light and the output normal-light image which is often an easier task than predicting the output image directly. The figure below shows the auto-encoder architecture with bilinear upsampling instead of transpose convolution in the decoder.
Experiments and Results
In this section, we present the experimental setup and results of our experiments with the CLAHE technique, EnlightenGAN and the U-Net based auto-encoders.
Experimental Setup
CLAHE and ENGAN
To begin with, we work with Contrast Limited Adaptive Histogram Equalization which is a conventional computer vision algorithm as described in the previous section. This technique does not require any data for training. We use the OpenCV's python based implementation of CLAHE. There are two parameters viz., clip limit for the contrast threshold and the grid or window size to use for applying histogram equalization.
To experiment with EnlightenGAN, we reuse the author's codebase which is based on the PyTorch deep learning framework. (Note that we do not attach the code or the dataset for ENGAN with the submission, since we have not made any modifications from the author's original code.) The author's have curated a custom unpaired dataset with 914 low light and 1016 normal light images which we use for training. We re-train the EnlightenGAN model with the default setting for the EnlightenGAN architecture as described here. The training takes ~3hrs when distributed accross 3 NVIDIA TITAN RTX GPUs. The Figure below shows the training curve for 200 epochs visualized using the visdom tool.
U-Net based auto-encoders
Programmer's Guide
We implement the different variations of the U-net based auto-encoder architectures (described in the Approach section above) from scratch in Python (v3.7) using the PyTorch (v1.6) deep learning library. We adopt the codebase structure from this GitHub repository which makes it easy to abstract away common constructs of dataset loading as well as model training, evaluation and visualization. In particular, we adopt the base classes for different entities (such as models and dataloaders) and the options handler for all our experiments with the U-Net auto-encoders. Based on this structure, we implement a model class for the auto-encoder (models/autoencoder_model.py) which includes code for initializing the model along with training and testing it based on custom options. We also implement a generic dataloader for any dataset having training/validation/testing data (see data/trainval_dataloader.py). The LoL dataset can be loaded by our custom dataloader defined in data/lol_dataset.py. We also implement the two versions (transpose convolution and bilinear upsampling) of the auto-encoder described earlier (see models/networks.py). We also explore versions of these architectures with smaller number of filters (argument f=16) but the results are not conclusive and hence have not been mentioned. train.py and test.py are generic scripts that can be used to run an instance of training/testing the model. We train all models on a compute node with NVIDIA Titan RTX GPUs.
Implementation Details
There are two variants of the model with bilinear upsampling and transpose convolutions as the upsampling strategies. We train each of these models with two loss functions: 1) Only the Mean Square Error (MSE) between the predicted image and the ground truth normal light image and 2) MSE along with the Structure Similarity Index. The models are trained to minimize the Mean Square Error between the output and the ground truth normal light image while (optionally) maximizing the Structure Similarity Index Measure (SSIM). (Note: The trainable version of the SSIM at this URL is used in our work.) Thus, the model is trained to minimize: Loss = MSE + lambda * (1 - SSIM) which is the overall loss function where the weight of the SSIM term can be controlled with the lambda parameter. Note that while the MSE forces pixel wise similarity between the predicted and ground truth natural light images, the SSIM strives to achieve more better visual pleasing image enhancement. This results in 4 different training configurations: 1) Bilinear upsampling + MSE Loss 2) Bilinear upsampling + MSE and SSIM Loss 3) Transpose Convolutional + MSE Loss and 4) Transpose Convolution + MSE and SSIM Loss. Instances all these configurations are trained using the 485 images from the training set of the LOw-Light (LOL) Dataset curated by Wei et al. that is available publicly at this HTTPS URL. The data is split into 450/485 images for training and 35 for validation with image resized to a fixed size of 320x320px while maintaining the aspect ratio. This resizing is achieved by the function *MakeSquared* implemented in data/utils.py. The MSE Loss along with the Peak Signal to Noise Ratio (PSNR) for training/validation for the Bilinear+MSE configuration is shown in the figure above. (We do not present the loss curves for all 4 configurations to save space and avoid redundancy.) Note that we use the MSE (and not it's square root RMSE) in the loss function since it provides a smooth convex surface amenable for back-propagation through the network.
Metrics
Along with qualitative visualization, we rely on 2 commonly used quantitative metrics viz. Mean Square Error (MSE) and Structure Similarity Index (SSIM) for evaluating our results. The MSE is a pixel wise mean square error between the true and predicted normal light image. A lower value of MSE indicates better similarity between two images. The Structure Similarity Index (SSIM) measures the perceptual similarity between two images and gives a a more general idea of how similar two images appear to a viewer. The value of SSIM ranges between 0 and 1 with a higher value indicating better structural similarity. We use Sci-kit Learn's implementation of SSIM and MSE in our work.
In order to choose optimal contrast limit value for CLAHE, we perform a hyper-parameter search over the 485 train images from the LoL paired image dataset curated by Wei et al.. We fix the grid or window size for CLAHE to 2 and plot the mean RMSE and SSIM across all images as a function of the clip limit to pick the optimal value of clip limit such that RMSE is minimum while SSIM is maximum. The plots are shown in the Figure above. The code to generate these plots along with the CLAHE algorithm has been submitted in included in the file clahe.py. In particular, it can be seen that with the window size fixed, the RMSE has a minimum at a clip limit of 30. while the SSIM steadily falls with the clip limit. Thus, we select 30 as an optimal value of the contrast threshold and apply CLAHE on all the images from the evaluation subset of the LoL dataset.
Results
In order to evaluate these different techniques, we use the test subset of the LOw-Light (LOL) Dataset curated by Wei et al. that is available publicly at this HTTPS URL. This dataset consists of paired low light and normal light images which we use to compute the metrics described below.
Discussion
The gallery of images above displays the qualitative results of 4 sample images from the test set for CLAHE, ENGAN and various combinations of the auto-encoder architecture. We train ENGAN on the original dataset curated by the author's consisting of 914 low light and 1016 normal light unpaired images and apply the trained model on the same evaluation subset of the LoL dataset. It can be seen that ENGAN despite being an unsupervised technique, performs best across all methods. To obtain the results for CLAHE, we select a window size of 2 and select the clip limit (contrast threshold) of 30 based on the hyper-parameter search described in the above sub-section. It is evident that CLAHE fails to fully recover the color component of the images while enhancing the contrast and lighting conditions. One explanation for this could be that CLAHE works by equalizing the luminosity component of the L*a*b color space version of the low light image and combines this enhanced L channel with the a*b components to construct the normal light version. Thus, it fails to consider the color spaces in the context of enhancement and results in poor reconstruction. At the same time, since CLAHE simply adjusts the luminosity it is able to recover the details of the original image with considerable fidelity - one aspect where auto-encoder methods struggle. The auto-encoder variants perform slightly better than CLAHE while being somewhat inferior to ENGAN. While the transpose convolution based architectures (with RMSE and RMSE+SSIM losses) tend to generate slightly sharper images compared to their bilinear counter-parts, there does not seem to be any considerable difference between these variants. There is, however, one subtle artifact that plagues the transpose convolution based models which we discuss in the analysis section below. In the models with transpose convolutions, when trained to minimize RMSE while also maximizing SSIM the resulting images (last row) seem considerably sharper with lesser black patch-like artifacts as compared to the model trained with only RMSE. This highlights the importance of the SSIM which enforces not a pixel-wise match constraint but a holistic structural similarity between the prediction and ground truth. It is interesting to observe that all the CNN based models are able to recover minor details (such as the digital clock time reading) with considerable fidelity while enhancement.
Failure Cases
In the results of the auto-encoder architecture with bilinear upsampling trained only to minimize the MSE loss, we observe certain unnatural pixelated artifacts in the test set images. We experiment with multiple hyper-parameter combinations in an attempt to recover from these issues but to no avail. We believe that simply enforcing a pixel-wise MSE constraint on the images does not enable the model to learn a generalizable enhancement function. Thus, the model fails to recover details that seem to be outlier regions in the context of the data it was trained on. This pixelation is also observed in a few instances of the other auto-encoder variants albeit to a relatively lesser degree. Additionally, it can also be observed that in the last image above, the digital clock time is barely visible indicating a poor performance in recovering the details.
Analysis
In the results from the transpose convolution based models, an interesting artifact was observed. In all the images above, a subtle square-like repetitive pattern is observed. The images above are cropped versions of the predicted normal light images to make it easier to observe the artifacts. These type of artifacts have been previously observed while using transpose convolutions in multiple contexts. Odena et al. from Google Brain noted artifacts as being checker board like patterns that arise because of the nature of the transpose convolution operation that results in an uneven overlap between the filter trying to fill a cell in the output feature map. They aver that deconvolution overlap, random initialization and loss functions are the main causes behind these artifacts and that an alternative upsampling strategy such as bilinear interpolation could be an easy way to overcome them. Indeed, these artifacts are not observed in our experiments with bilinear upsampling. Aitken et al. propose an initialization method for sub-pixel convolution known as convolution NN resize that helps tackle the issue, which could be used in future works as an alternative to the vanilla transpose convolution in this direction.
Quantitative Results
To obtain quantitative results, we compute the mean SSIM and MSE between true normal light images and the predictions over the evaluation (test) dataset (LoL) for the different approaches. The table below shows the mean MSE and SSIM values for CLAHE, ENGAN and the different variations of the U-Net auto-encoder. The SSIM is computed with a window size of 11 (default) using the skimage library. The mean MSE is computed after scaling the images between 0 to 1.
| MSE | SSIM | |
| CLAHE | 0.04 | 0.4455 |
| ENGAN | 0.024 | 0.675 |
| Bilinear with MSE Loss | 0.013 | 0.517 |
| Bilinear with MSE+SSIM Loss | 0.015 | 0.553 |
| Transpose Conv with MSE Loss | 0.013 | 0.557 |
| Transpose Conv with MSE+SSIM Loss | 0.016 | 0.538 |
It is interesting to observe that despite the qualitative difference between the different methods being significant, quantitatively they do not seem to differ as much. For instance, the Mean SSIM value is 0.4455 for CLAHE while being 0.675 for ENGAN despite the ENGAN output being far superior than CLAHE. Similarly, the difference in RMSE is not significant either. The SSIM is a measure of how structurally similar the images are while MSE measures a more fine pixel-wise similarity which. Neither of these measures, however, convey a complete idea of similarity between the images being compared. In general, for image to image translation problems, it is often difficult to rely solely on quantitative metrics for performance evaluation and a qualitative examination of the output is a must. Considering the SSIM for the different measures, it can be seen that the ENGAN has the highest value of 0.67 with CLAHE being the lowest and the different auto-encoder variants being in-between. This corroborates the qualitative observations from the previous sub-section. Comparing the different auto-encoder based variants, it can be seen that they do not differ by a significant amount either in terms of MSE or SSIM. In terms of SSIM, it seems that the model transpose convolution for upsampling trained to minimize the MSE loss performs best with an SSIM of 0.557 while the simple scheme of bilinear upsampling with MSE loss performs worst with an SSIM value of 0.517. This low value also supports our observation of certain pixelations in the predicted output for bilinear + MSE case. In addition, it seems that the checkerboard like artifacts observed in case of transpose convolutions do not seem to have a significant effect on the metrics. This somewhat underscores the idea that quantitative metrics do not convey the complete picture of model performance in case of image-to-image translation problems.
Conclusion and Future Work
This report has described a qualitative as well as quantitative comparison between 3 different approaches towards the Low Light Image Enhancement problem. On the one hand, we explore CLAHE - a conventional computer vision algorithm based on histogram equalization, while ENGAN is a state-of-the-art unsupervised image enhancement technique. In addition, we also consider multiple variants of the U-Net based auto-encoder architecture with transpose convolutions and residual skip connections trained to minimize the MSE Loss while also (optionally) maximizing the SSIM metric. Qualitative results indicate that ENGAN, when trained with ~1k images, performs best among all techniques, with the images closely resembling the ground truth normal light versions. The results from CLAHE do not appear to recover the color aspects of the original image with full fidelity. Among the 4 different variants of the auto-encoder based models, quantitative comparisons of MSE and SSIM do not indicate a significant difference. Qualitatively, the transpose convolution based model trained to minimize MSE while maximizing SSIM performs best with least artifacts. On the other hand, using bilinear upsampling along with an MSE Loss leads to certain pixelated artifacts as discussed in the above section. Other 2 variants viz., the bilinear upsampling with MSE+SSIM loss and transpose convolution with MSE Loss have a few dark patches in the reconstructed images. In models that use transpose convolutions for upsampling, we observe certain checkerboard-like artifacts as discussed above. Aside from using bilinear upsampling in lieu of transpose convolutions, the convolution NN resize initialization scheme could be used in future work to tackle these checkerboard artifacts. As a future work, various other approaches to the image enhancement problem such as the "Deep Retinex Decomposition" work by Wei et al. [5] could be benchmarked against these methods to better understand the scenarios where a particular architecture or loss function performs better.
References
[1] Ronneberger O., Fischer P., Brox T. (2015) U-Net: Convolutional Networks for Biomedical Image Segmentation. In: Navab N., Hornegger J., Wells W., Frangi A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science, vol 9351. Springer, Cham. https://doi.org/10.1007/978-3-319-24574-4_28
[2] Jiang, Yifan, et al. "Enlightengan: Deep light enhancement without paired supervision." arXiv preprint arXiv:1906.06972 (2019).
[3] He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
[4] Pisano ED, Zong S, Hemminger BM, DeLuca M, Johnston RE, Muller K, Braeuning MP, Pizer SM. Contrast limited adaptive histogram equalization image processing to improve the detection of simulated spiculations in dense mammograms. J Digit Imaging. 1998 Nov;11(4):193-200. doi: 10.1007/BF03178082. PMID: 9848052; PMCID: PMC3453156.
[5] Wei, Chen, et al. "Deep retinex decomposition for low-light enhancement." arXiv preprint arXiv:1808.04560 (2018).
[6] Z. Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli, "Image quality assessment: From error visibility to structural similarity," IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600-612, Apr. 2004.
[7] Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2 (NIPS'14). MIT Press, Cambridge, MA, USA, 2672–2680.
[8] URL: https://docs.opencv.org/master/d5/daf/tutorial_py_histogram_equalization.html
[9] Visdom tool by Facebook Research. URL: https://github.com/facebookresearch/visdom
[10] PyTorch library. URL: https://pytorch.org
[11] Radford, A., Metz, L. & Chintala, S. (2015). Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. URL
[12] Aitken, A., Ledig, C., Theis, L., Caballero, J., Wang, Z., & Shi, W. (2017). Checkerboard artifact free sub-pixel convolution: A note on sub-pixel convolution, resize convolution and convolution resize. ArXiv, abs/1707.02937.
[13] Odena, et al., "Deconvolution and Checkerboard Artifacts", Distill, 2016. URL


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CE-EE Node Activate
Estimated reading time: 4 minutesThe Docker Engine 18.09
release introduces a new feature called CE-EE Node Activate, which allows a user to perform an in-place seamless activation of the Enterprise engine feature set on an existing Docker Engine - Community node through the Docker command line.
CE-EE Node Activate will apply a license, and switch the Docker engine to the Enterprise engine binary.
Requirements:
- The Docker Engine - Community version must be 18.09 or higher.
- All of the Docker packages must be installed: docker-cli, docker-server, and containerd.
Limitations
- This feature is only supported on x86 Linux nodes.
- Windows nodes are not currently supported.
- Node-level Engine activation between Docker Engine - Community and EE is only supported in the same version of Docker Enterprise Engine for Docker.
- Prior versions of Docker Engine - Community do not support this feature.
Notes on patching after running Docker Engine - Community to EE Node Activation
Docker recommends replacing the apt or yum repository from Docker Engine - Community with the EE repository that appears on your hub/store account after starting the trial or paid license. This allows apt/yum upgrade operations to work as expected and keep them current as long as your license is still valid and has not expired.
Note: You can use the
docker engine update
command. However, if you continue to use the Docker Engine - Community packages, the OS package will no longer replace the active daemon binary during apt/yum updates, so you are responsible for performing thedocker engine update
operation periodically to keep your engine up to date.
Docker Engine 18.09 Docker Engine - Community to EE Node Activation Process
The activation can be performed either online with connection to Docker Hub, or offline.
- Check the current Docker version. Both the Docker client and server (
containerd
) need to be installed. Your output may vary slightly from what is displayed on this page.
$ docker version
Client:
Version: 18.09.0
API version: 1.39
Go version: go1.10.4
Git commit: 4d60db4
Built: Wed Nov 7 00:48:22 2018
OS/Arch: linux/amd64
Experimental: false
Server: Docker Engine - Community
Engine:
Version: 18.09.0
API version: 1.39 (minimum version 1.12)
Go version: go1.10.4
Git commit: 4d60db4
Built: Wed Nov 7 00:19:08 2018
OS/Arch: linux/amd64
Experimental: false
- Log into the Docker engine from the command line.
Note: When running the command
docker login
, the shell stores the credentials in the current user’s home directory. RHEL and Ubuntu-based Linux distributions have different behavior for sudo. RHEL sets $HOME to point to/root
while Ubuntu leaves$HOME
pointing to the user’s home directory who ransudo
and this can cause permission and access problems when switching betweensudo
and non-sudo’d commands.
For Ubuntu or Debian:
$ docker login
Login with your Docker ID to push and pull images from Docker Hub. If you don't have a Docker ID, head over to https://hub.docker.com to create one.
Username: beluga
Password:
WARNING! Your password will be stored unencrypted in /home/docker/.docker/config.json.
Configure a credential helper to remove this warning. See
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Login Succeeded
For CentOS, use sudo
:
$ sudo docker login
Login with your Docker ID to push and pull images from Docker Hub. If you don't have a Docker ID, head over to https://hub.docker.com to create one.
Username: beluga
Password:
WARNING! Your password will be stored unencrypted in /home/docker/.docker/config.json.
Configure a credential helper to remove this warning. See
https://docs.docker.com/engine/reference/commandline/login/#credentials-store
Login Succeeded
-
Download your Docker Enterprise license and distribute it to your Docker engines.
-
Activate the EE license. You must use
sudo
even if your user is part of the docker group.
$ sudo docker engine activate --license ee-engine-license-1000-nodes.lic
License: Quantity: 400 Nodes Expiration date: 2019-05-12 License is currently active
18.09.0: resolved
e3cba72cdf2e: done [==================================================>] 434B/434B
3642e2b52398: done [==================================================>] 1.161kB/1.161kB
eb6fe2abc4ad: done [==================================================>] 4.544MB/4.544MB
f2f08b0292f2: done [==================================================>] 25.65MB/25.65MB
a539281ee17b: done [==================================================>] 1.122MB/1.122MB
515c4dc2b0fe: done [==================================================>] 333.9kB/333.9kB
2cf04a6ee63e: done [==================================================>] 4.84kB/4.84kB
Successfully activated engine.
Restart docker with 'systemctl restart docker' to complete the activation.
- Check the Docker Engine version. The engine server will become EE, and the engine client will stay CE.
$ docker version
Client:
Version: 18.09.0
API version: 1.39
Go version: go1.10.4
Git commit: 4d60db4
Built: Wed Nov 7 00:48:22 2018
OS/Arch: linux/amd64
Experimental: false
Server: Docker Engine - Enterprise
Engine:
Version: 18.09.0
API version: 1.39 (minimum version 1.12)
Go version: go1.10.4
Git commit: 33a45cd
Built: Wed Nov 7 00:19:46 2018
OS/Arch: linux/amd64
Experimental: false
NOTE: Your output may vary slightly from what is displayed on this page.
- If you are running a Swarm cluster with CE, please repeat these steps on each node.
Offline CE-EE node activation
For offline CE-EE node activation, you’ll need to get the Docker Enterprise Engine onto the system. The recommended model is to download the EE .deb
or .rpm
packages manually and copy them to the target systems. Afterward, download the license manually, and copy that license to the target systems. Use the --license <path/to/license.file>
command line option to the activate command.


Python while loopThe while loop is also known as a pre-tested loop. In general, a while loop allows a part of the code to be executed as long as the given condition is true. It can be viewed as a repeating if statement. The while loop is mostly used in the case where the number of iterations is not known in advance. The syntax is given below. Here, the statements can be a single statement or the group of statements. The expression should be any valid python expression resulting into true or false. The true is any non-zero value. Example 1Output: 1 2 3 4 5 6 7 8 9 10 Example 2Output: Enter the number?10 10 X 1 = 10 10 X 2 = 20 10 X 3 = 30 10 X 4 = 40 10 X 5 = 50 10 X 6 = 60 10 X 7 = 70 10 X 8 = 80 10 X 9 = 90 10 X 10 = 100 Infinite while loopIf the condition given in the while loop never becomes false then the while loop will never terminate and result into the infinite while loop. Any non-zero value in the while loop indicates an always-true condition whereas 0 indicates the always-false condition. This type of approach is useful if we want our program to run continuously in the loop without any disturbance. Example 1Output: Hi! we are inside the infinite while loop (infinite times) Example 2Output: Enter the number?102 Entered value is 102 Enter the number?102 Entered value is 102 Enter the number?103 Entered value is 103 Enter the number?103 (infinite loop) Using else with Python while loopPython enables us to use the while loop with the while loop also. The else block is executed when the condition given in the while statement becomes false. Like for loop, if the while loop is broken using break statement, then the else block will not be executed and the statement present after else block will be executed. Consider the following example. Output: 1 2 3 4 5 The while loop exhausted Example 2Output: 1 2
Next TopicPython break statement
|
Python while loopThe while loop is also known as a pre-tested loop. In general, a while loop allows a part of the code to be executed as long as the given condition is true. It can be viewed as a repeating if statement. The while loop is mostly used in the case where the number of iterations is not known in advance. The syntax is given below. Here, the statements can be a single statement or the group of statements. The expression should be any valid python expression resulting into true or false. The true is any non-zero value. Example 1Output: 1 2 3 4 5 6 7 8 9 10 Example 2Output: Enter the number?10 10 X 1 = 10 10 X 2 = 20 10 X 3 = 30 10 X 4 = 40 10 X 5 = 50 10 X 6 = 60 10 X 7 = 70 10 X 8 = 80 10 X 9 = 90 10 X 10 = 100 Infinite while loopIf the condition given in the while loop never becomes false then the while loop will never terminate and result into the infinite while loop. Any non-zero value in the while loop indicates an always-true condition whereas 0 indicates the always-false condition. This type of approach is useful if we want our program to run continuously in the loop without any disturbance. Example 1Output: Hi! we are inside the infinite while loop (infinite times) Example 2Output: Enter the number?102 Entered value is 102 Enter the number?102 Entered value is 102 Enter the number?103 Entered value is 103 Enter the number?103 (infinite loop) Using else with Python while loopPython enables us to use the while loop with the while loop also. The else block is executed when the condition given in the while statement becomes false. Like for loop, if the while loop is broken using break statement, then the else block will not be executed and the statement present after else block will be executed. Consider the following example. Output: 1 2 3 4 5 The while loop exhausted Example 2Output: 1 2
Next TopicPython break statement
|
Python while loopThe while loop is also known as a pre-tested loop. In general, a while loop allows a part of the code to be executed as long as the given condition is true. It can be viewed as a repeating if statement. The while loop is mostly used in the case where the number of iterations is not known in advance. The syntax is given below. Here, the statements can be a single statement or the group of statements. The expression should be any valid python expression resulting into true or false. The true is any non-zero value. Example 1Output: 1 2 3 4 5 6 7 8 9 10 Example 2Output: Enter the number?10 10 X 1 = 10 10 X 2 = 20 10 X 3 = 30 10 X 4 = 40 10 X 5 = 50 10 X 6 = 60 10 X 7 = 70 10 X 8 = 80 10 X 9 = 90 10 X 10 = 100 Infinite while loopIf the condition given in the while loop never becomes false then the while loop will never terminate and result into the infinite while loop. Any non-zero value in the while loop indicates an always-true condition whereas 0 indicates the always-false condition. This type of approach is useful if we want our program to run continuously in the loop without any disturbance. Example 1Output: Hi! we are inside the infinite while loop (infinite times) Example 2Output: Enter the number?102 Entered value is 102 Enter the number?102 Entered value is 102 Enter the number?103 Entered value is 103 Enter the number?103 (infinite loop) Using else with Python while loopPython enables us to use the while loop with the while loop also. The else block is executed when the condition given in the while statement becomes false. Like for loop, if the while loop is broken using break statement, then the else block will not be executed and the statement present after else block will be executed. Consider the following example. Output: 1 2 3 4 5 The while loop exhausted Example 2Output: 1 2
Next TopicPython break statement
|
Python while loopThe while loop is also known as a pre-tested loop. In general, a while loop allows a part of the code to be executed as long as the given condition is true. It can be viewed as a repeating if statement. The while loop is mostly used in the case where the number of iterations is not known in advance. The syntax is given below. Here, the statements can be a single statement or the group of statements. The expression should be any valid python expression resulting into true or false. The true is any non-zero value. Example 1Output: 1 2 3 4 5 6 7 8 9 10 Example 2Output: Enter the number?10 10 X 1 = 10 10 X 2 = 20 10 X 3 = 30 10 X 4 = 40 10 X 5 = 50 10 X 6 = 60 10 X 7 = 70 10 X 8 = 80 10 X 9 = 90 10 X 10 = 100 Infinite while loopIf the condition given in the while loop never becomes false then the while loop will never terminate and result into the infinite while loop. Any non-zero value in the while loop indicates an always-true condition whereas 0 indicates the always-false condition. This type of approach is useful if we want our program to run continuously in the loop without any disturbance. Example 1Output: Hi! we are inside the infinite while loop (infinite times) Example 2Output: Enter the number?102 Entered value is 102 Enter the number?102 Entered value is 102 Enter the number?103 Entered value is 103 Enter the number?103 (infinite loop) Using else with Python while loopPython enables us to use the while loop with the while loop also. The else block is executed when the condition given in the while statement becomes false. Like for loop, if the while loop is broken using break statement, then the else block will not be executed and the statement present after else block will be executed. Consider the following example. Output: 1 2 3 4 5 The while loop exhausted Example 2Output: 1 2
Next TopicPython break statement
|


Once the JVM sees invokedynamic
for the first time, it calls the bootstrap method.
The lambda bootstrap method will use the InnerClassLambdaMetafactory
to generate an inner class for the lambda at runtime.
Then the bootstrap method encapsulates the generated inner class inside a special type of CallSite
known as ConstantCallSite
.
This type of CallSite
would never change after setup.
Therefore, after the first setup for each lambda, the JVM will always use the fast path to directly call the lambda logic.
Although this is the most efficient type of invokedynamic
, it's certainly not the only available option.
As a matter of fact, Java provides MutableCallSite
and VolatileCallSite
to accommodate for more dynamic requirements.
So, in order to implement lambda expressions, instead of creating anonymous inner classes at compile-time, Java creates them at runtime via invokedynamic
.
One might argue against deferring inner class generation until runtime.
However, the invokedynamic
approach has a few advantages over the simple compile-time solution.
First, the JVM does not generate the inner class until the first use of lambda. Hence, we won't pay for the extra footprint associated with the inner class before the first lambda execution.
Additionally, much of the linkage logic is moved out from the bytecode to the bootstrap method.
Therefore, the invokedynamic
bytecode is usually much smaller than alternative solutions.
The smaller bytecode can boost startup speed.
Suppose a newer version of Java comes with a more efficient bootstrap method implementation.
Then our invokedynamic
bytecode can take advantage of this improvement without recompiling.
This way we can achieve some sort of forwarding binary compatibility.
Basically, we can switch between different strategies without recompilation.
Finally, writing the bootstrap and linkage logic in Java is usually easier than traversing an AST to generate a complex piece of bytecode.
So, invokedynamic
can be (subjectively) less brittle.


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Basic course-design demo of assets
I was sitting in a chair in the patent office at Bern, when all of a sudden a thought occurred to me: ‘If a person falls freely, he will not feel his own weight.


Leveraging our experience with Node.js performance, Fastify has been built from the ground up to be as fast as possible.
All the code used for our benchmarks is available on GitHub.
Here's a brief summary on how fastify overhead performed against the some other well known Node.js web frameworks:
{{ contents | safe }}{{ framework.requests_sec }} req/sec {% if framework.test %}(Check the code){% endif %}
Please note that this is a synthetic, "hello world" benchmark that aims to evaluate the framework overhead. The overhead that each framework has on your application depends on your application, you should always benchmark if performance matters to you.
Do you want to provide feedback on our benchmarks? Open an issue on GitHub and we will get back to you!


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