0.6.0 Credits 3 peers for every 1 peer Debited?

Summary
I noticed that my new 0.6.0 peer was running 3* outbound balance for every 1* inbound balance.  And no files were uploaded nor download via this new node.  Every pushsync-received chunk has this pattern, regardless of whether or not a return receipt was successfully read from the downstream peers.
time="2021-05-03T17:53:33Z" level=error msg="pushsync: chunk 17843c7eef1b400b3047d93ab142bba687912fff170d9f08113583920b2fcc32 receive receipt from peer 1f8812c5af5bd4442ae6a407da2af4b1144c6a0a3f93cdb82d850c34365e7066: stream reset"
time="2021-05-03T17:53:33Z" level=error msg="pushsync: chunk 17843c7eef1b400b3047d93ab142bba687912fff170d9f08113583920b2fcc32 receive receipt from peer 18dc8ed96c0dd6bd70bb5994a804eb12c7e8c124e44232a44b34bfcd5264f341: stream reset"
time="2021-05-03T17:53:33Z" level=error msg="pushsync: chunk 17843c7eef1b400b3047d93ab142bba687912fff170d9f08113583920b2fcc32 receive receipt from peer 069f988f229a426cb1169bc8c1589d9668789246fa95216e64ae6323f78571b6: stream reset"
time="2021-05-03T17:53:33Z" level=trace msg="debiting peer a1940b1df17c5a22396fd92b2658872addab1cfcd182a4e6f292b20019c0ba62 with price<PHONE_NUMBER>0, new balance is<PHONE_NUMBER>0"
time="2021-05-03T17:53:33Z" level=trace msg="crediting peer 069f988f229a426cb1169bc8c1589d9668789246fa95216e64ae6323f78571b6 with price<PHONE_NUMBER>0, new balance is -1369000000000"
time="2021-05-03T17:53:33Z" level=trace msg="crediting peer 18dc8ed96c0dd6bd70bb5994a804eb12c7e8c124e44232a44b34bfcd5264f341 with price<PHONE_NUMBER>0, new balance is -2188000000000"
time="2021-05-03T17:53:33Z" level=trace msg="crediting peer 1f8812c5af5bd4442ae6a407da2af4b1144c6a0a3f93cdb82d850c34365e7066 with price<PHONE_NUMBER>0, new balance is -2228000000000"
time="2021-05-03T18:29:40Z" level=error msg="pushsync: chunk 1885f8b0531ea51dca617679bfd4acd71777937e8a128517665df953f2f8bd68 receive receipt from peer 18dc8ed96c0dd6bd70bb5994a804eb12c7e8c124e44232a44b34bfcd5264f341: stream reset"
time="2021-05-03T18:29:40Z" level=error msg="pushsync: chunk 1885f8b0531ea51dca617679bfd4acd71777937e8a128517665df953f2f8bd68 receive receipt from peer 1f8812c5af5bd4442ae6a407da2af4b1144c6a0a3f93cdb82d850c34365e7066: stream reset"
time="2021-05-03T18:29:40Z" level=error msg="pushsync: chunk 1885f8b0531ea51dca617679bfd4acd71777937e8a128517665df953f2f8bd68 receive receipt from peer 069f988f229a426cb1169bc8c1589d9668789246fa95216e64ae6323f78571b6: stream reset"
time="2021-05-03T18:29:40Z" level=trace msg="debiting peer a1940b1df17c5a22396fd92b2658872addab1cfcd182a4e6f292b20019c0ba62 with price<PHONE_NUMBER>0, new balance is<PHONE_NUMBER>0"
time="2021-05-03T18:29:40Z" level=trace msg="crediting peer 1f8812c5af5bd4442ae6a407da2af4b1144c6a0a3f93cdb82d850c34365e7066 with price<PHONE_NUMBER>0, new balance is -2255000000000"
time="2021-05-03T18:29:40Z" level=trace msg="crediting peer 18dc8ed96c0dd6bd70bb5994a804eb12c7e8c124e44232a44b34bfcd5264f341 with price<PHONE_NUMBER>0, new balance is -2211000000000"
time="2021-05-03T18:29:40Z" level=trace msg="crediting peer 069f988f229a426cb1169bc8c1589d9668789246fa95216e64ae6323f78571b6 with price<PHONE_NUMBER>0, new balance is -1398000000000"
time="2021-05-03T18:29:49Z" level=error msg="pushsync: chunk 0e143738c0d818603b4b038c2215ceeef01ccaa641faa336cf14b6668a915310 receive receipt from peer 069f988f229a426cb1169bc8c1589d9668789246fa95216e64ae6323f78571b6: stream reset"
time="2021-05-03T18:29:49Z" level=trace msg="debiting peer a1940b1df17c5a22396fd92b2658872addab1cfcd182a4e6f292b20019c0ba62 with price<PHONE_NUMBER>0, new balance is<PHONE_NUMBER>0"
time="2021-05-03T18:29:49Z" level=trace msg="crediting peer 1f8812c5af5bd4442ae6a407da2af4b1144c6a0a3f93cdb82d850c34365e7066 with price<PHONE_NUMBER>0, new balance is -2284000000000"
time="2021-05-03T18:29:49Z" level=trace msg="crediting peer 18dc8ed96c0dd6bd70bb5994a804eb12c7e8c124e44232a44b34bfcd5264f341 with price<PHONE_NUMBER>0, new balance is -2240000000000"
time="2021-05-03T18:29:49Z" level=trace msg="crediting peer 069f988f229a426cb1169bc8c1589d9668789246fa95216e64ae6323f78571b6 with price<PHONE_NUMBER>0, new balance is -1426000000000"
Steps to reproduce
Fire up a new clean node and wait for a chunk to arrive via pushsync.  Then look at your /balances and your --verbosity 5 (trace) logs.
Expected behavior
At most I would expect at most a single credit for one downstream peer for each debit of an upstream peer.  At this rate, it would guarantee every node operator to lose BZZ tokens into the swarm!
Actual behavior
See logs above.
The full log is available at:
https://ipfs.io/ipfs/QmUUtrDXjRinPamWeR65taq9DPAoL15deCuAf3yo92EKEc?filename=bee6-0.out
/balances output:
{
"balances": [
{
"peer": "069f988f229a426cb1169bc8c1589d9668789246fa95216e64ae6323f78571b6",
"balance": -2786000000000
},
{
"peer": "18dc8ed96c0dd6bd70bb5994a804eb12c7e8c124e44232a44b34bfcd5264f341",
"balance": -3563000000000
},
{
"peer": "1f8812c5af5bd4442ae6a407da2af4b1144c6a0a3f93cdb82d850c34365e7066",
"balance": -3599000000000
},
{
"peer": "8ec163755407cbae8a6fa56b48cf645009c86a6299d94a202d0e43b48fcb64fc",
"balance":<PHONE_NUMBER>000
},
{
"peer": "a1940b1df17c5a22396fd92b2658872addab1cfcd182a4e6f292b20019c0ba62",
"balance":<PHONE_NUMBER>000
}
]
}
/settlements output (definitely losing gBZZ on this node.  3.034/1.000 sent vs received!)
{
"totalreceived":<PHONE_NUMBER>000,
"totalsent":<PHONE_NUMBER>0000,
"settlements": [
{
"peer": "1f8812c5af5bd4442ae6a407da2af4b1144c6a0a3f93cdb82d850c34365e7066",
"received": 0,
"sent":<PHONE_NUMBER>000
},
{
"peer": "18dc8ed96c0dd6bd70bb5994a804eb12c7e8c124e44232a44b34bfcd5264f341",
"received": 0,
"sent":<PHONE_NUMBER>000
},
{
"peer": "069f988f229a426cb1169bc8c1589d9668789246fa95216e64ae6323f78571b6",
"received": 0,
"sent":<PHONE_NUMBER>000
},
{
"peer": "8ec163755407cbae8a6fa56b48cf645009c86a6299d94a202d0e43b48fcb64fc",
"received":<PHONE_NUMBER>000,
"sent": 0
}
]
}

This is explained by #1640 . In the newest Bee, Bee nodes still pay for forwarding requests. This will be updated, such that only originating nodes pay, such that a forwarder won't make a loss on such preemptive retries.


takeWhile and dropWhile

The Ramda functions takeWhile and dropWhile should both have the type signature:
(a -> Boolean) -> List a -> List a

I mentioned takeUntil and dropUntil in the Gitter room, but thinking on it more they don't bring much to the table (the way I was thinking of them):
const takeUntil = pred => S.takeWhile(S.complement(pred));

I agree that we should provide {take,drop}While but not {take,drop}Until. :)

On Gitter I asked if anyone wanted findIndex as it would be useful for implementing these functions and if I write it as a helper I can't think of a reason not to expose it.

I'm suspicious of indexes. Ideally these functions would be defined in terms of Foldable or some other type class. A linked list, for example, could satisfy Foldable without supporting index-based member access. The complicating factor is the String type (as is so often the case). I'm not sure how useful these functions are for strings, though. Consider:
> dropWhile(equals('\t'), '\t\tfoo\tbar')
'foo\tbar'

This could be achieved with the help of a regular expression:
> '\t\tfoo\tbar'.replace(/^\t*/, '')
'foo\tbar'

What do you think of requiring Foldable? The only thing that concerns me is the lack of consistency with take and drop.

A linked list, for example, could satisfy Foldable without supporting index-based member access.

An index could still represent the position of a node in a linked list though.
As for strings, I would just add them for consistency. But I would also keep in mind that many developers shy away from regular expressions because of readability and familiarity/usability.
And I think I understand the reason strings aren't considered Foldable in JS. The string "hello" if considered as Foldable must be interpreted as a list of characters ['h', 'e', 'l', 'l', 'o']. But JS doesn't have characters, just strings. So does that make it [['h'], ['e'], ['l'], ['l'], ['o']]. But then I can keep expanding ad infinitum.
What I'm not sure of, because I haven't thought enough about it is why a str.length === 1 can't be considered a character in the context of a fold.

I just realized there's an inconsistency in the return type as well
take :: Integer -> List a -> Maybe (List a)

takeWhile :: (a -> Boolean) -> List a -> List a

There's no need to return a Maybe as the predicate will not be run on empty Lists.
I'm still inclined to include strings, but will leave that discussion for the PR.

Oh. I just saw the note here concerning String and the List type https://github.com/sanctuary-js/sanctuary/blob/88d1af05c69d77b31d8be77c5f49acbf68a262b2/index.js#L2639

Will Foldable work without Applicative? How do we get an initial value if takeWhile and dropWhile are implemented in terms of reduce?

Here's a possible implementation:
function takeWhile(pred, xs) {
  var take = true;
  function reducer(xs, x) {
    return take && pred(x) ? append(x, xs) : (take = false, xs);
  }
  return Z.reduce(reducer, Z.empty(xs.constructor), xs);
}

The generalized append is already proving useful!

Yes. I was testing a similar implementation with @silly-goat. But that does add the monoid requirement.

Will using append not clone the array on every iteration?

@svozza Yes. It's not a practical implementation. Just POC for using reduce.

Will using append not clone the array on every iteration?

Good point. We could also provide a fast path for arrays.


Add missing whitespace in community page

Changes:

A white space has been added to the community/index.hbs page

Screenshots of the change:

In general it is recommended to not hard code spaces in the template files but instead add spaces as part of a quoted string in the localization file, this is because if we hard coded in spaces it can cause problem with other language's translation that doesn't use spaces in the same way or at all.

In all the translations, the value (here) in the localization file has no space at the end. So, I thought using a space in the template itself would have an effect.
Ok will correct it for all the translations.
What about these?
https://github.com/processing/p5.js-website/blob/main/src/templates/pages/community/index.hbs#L50
https://github.com/processing/p5.js-website/blob/main/src/templates/pages/community/index.hbs#L56

Thanks @lf32 for catching this. In this case, I think you can add a whitespace in the end of the line 979, so it'd be "contributors conferences held at the ".

Every time changes are made, why is there a tweak in "reference.js"?

Is there any issue here?

Thanks :)


chore: add git build script for multiple projects

Checklist:

[x] I have read freeCodeCamp's contribution guidelines.
[x] My pull request has a descriptive title (not a vague title like Update index.md)

Closes #57
Accomplishments:

For each project listed in .freeCodeCamp/tooling/git/build.js > PROJECT_LIST, create a new branch named <project>
Run lesson seed and make commit with commit message (<lesson_number>)
Push branch to origin ---- I have no idea why... probably need to push to upstream?
FIX: Change parser to not require a lesson after final lesson

Well Tom, this was more painful than expected, but that is mostly because I overestimated my RegEx prowess.
Basic idea:

Tom builds/makes a project(s)
Tom submits second-to-last PR to repo with project
Tom runs npm run build:git
Tom pushes created branch(es) up to repo
Branch(es) is pulled down when course is started
Branch(es) is used for seed

Currently, even lessons without seed get a commit - it is an empty commit. I thought this would make the most sense 🤷♂


Using WebChat in SharePoint Script Editor

I have followed the approach mentioned in path - https://github.com/billba/botchattest
As of now this link contains two .html(s),
\botchattest-master\Index.html
\botchattest-master\botchat\Index.html
I have merged the code from both the .html(s) and created one html. So, that I can use the complete code snippet in my SharePoint page in Script Editor Web Part.
I created a Web Part Page in my SharePoint site and added a Script Editor web part, and saved required .JS and .CSS in Style Library Folder.
Here, the "Click" event of Hero card is get post-back and my whole page is getting refreshed. However when I have used in normal html page (), it's working as expected.
Please Find ZIP, Where it is working fine in HTML Page:
SPBot.zip
Web Part - SP Page
I pasted below code in my web part page in Script Editor Web Part:
WPCode.txt
Thanks in Advance!
Regards,
Viren

I haven't looked at your code, but it doesn't make sense to merge the two index.html files. One is for the botchat control, one is for the page its embedded in.

Thanks @billba. Now, I have again used the 2 pages logic in share-point page. Since, I cannot serve .html page from SharePoint, it's downloading that page instead of opening, so I have renamed the page .html to .aspx
I have pasted below iFrame tag in my web part page in Script Editor web part. It's working as expected.
Thanks for your help and information. Now, I am closing this issue.

<iframe id="botchat" src="../Style Library/botchat/index.aspx?s=<SecureId>" width="450" height="500">

Thanks,
Viren

Hey viren...billba asked that I look at your issue as I have more experience with SharePoint. I got it working pretty easily using the same steps you used. Only difference is I put my index.aspx/css/js in the SiteAssets library instead of Style Library. I would be really careful in how SharePoint does relative paths with site collections. Here is my embed code as sample (note: "Chatbot" is a subsite off the root site collection of my SharePoint Online tenant):
<iframe id="botchat" src="/chatbot/SiteAssets/index.aspx?t=<directline secret>" width="320" height="500">

@richdizz Thank you so much for your input.


Add provider list and finish group names to material modal, fixes #436

Definition of Done

[ ] The code does at least what is defined in the ticket and does not affect any other functionality
[ ] The PR has a meaningful title
[ ] There is a link to the issue-id
[ ] There are no 'hacks' or shortcuts refactoring is preferred
[ ] The happy case of the app was tested at least once manually

@webholics this is fixed now

Conflicts und tests failen

@webholics fixed


Pie chart bug when large number in series

When there is a large number in a series of 3, then there is a weird graphical bug.
e.g.
var data = {
series: [5, 100000, 4]
};
var data = {
series: [5, 4, 100000]
};
Strangely, this is fine:
var data = {
series: [100000, 5, 4]
};

I met this problem too, and I just try to preprocess the data by changing it into percent and set min value as 0.001
var total = data.reduce(function(a,b){return a+b;});
if(total<=0) return;
data = data.map(function(x,i,a){
    var result = +(x/total).toFixed(2);
    return result || 0.001;
});

I did something similar as a workaround. Thanks @yuanoook

This should no longer be an issue in 0.9.8


Java Backend: Applying "expand macros" step for spec rules earlier, before concretize config

@denis-bogdanas please provide a test for this which fails on the previous version, but works on this version. I think the following should work.
file main.k:
module DATA
    imports INT
    imports BOOL

    syntax Bool ::= #range ( Int, Int )
    rule #range(5, I) => 0 <=Int I andBool I <Int 32  [macro]
    rule #range(8, I) => 0 <=Int I andBool I <Int 256 [macro]
endmodule

module MAIN
    imports DATA

    configuration <k> $PGM:Pgm </k>

    syntax Pgm ::= "a" | "b"

    rule <k> a => b ... </k>
endmodule

file test-spec.k:
requires "main.k"

module VERIFICATION
    imports MAIN

    rule #range(8, X) => true requires #range(5, X) [smt-lemma]
endmodule

module TEST-SPEC
    imports VERIFICATION

    rule <k> _:Pgm => a ... </k>
endmodule

Then call kompile --boundary-cells k test-spec.k --def-module VERIFICATION. This should trigger the weird check before this change which causes the smt-lemma to incorrectly be interpreted as a proof obligation and fails the entire proof, but shouldn't trigger this after this change.

But you should double-check that it triggers it.

Test added, with some modifications. It fails on K-master.


Incorrect locals assignment in Matrix2X2.GetDeterminant() implementation

Summary
The Matrix2X2<T>.GetDeterminant() implementation computes (M11*M21)-(M12*M22) instead of (M11*M22)-(M12*M21), due to an inverted assignment of locals c and d.
Steps to reproduce

Platform: Desktop
Framework Version: .NET 8
API: not relevant
API Version: not relevant

Create a new .NET 8 console project and add Silk.NET.Maths package reference
Paste the following code

using System;
using Silk.NET.Maths;

//     | m11 m12 |
// m = |         |
//     | m21 m22 |
// det(m) = m11*m22 - m21*m22
var m = new Matrix2X2<float>(2, 3, 4, 5);
Console.WriteLine($"Determinant (expected): {m.M11 * m.M22 - m.M21 * m.M12}");
Console.WriteLine($"Determinant (actual)  : {m.GetDeterminant()}");

Check the result

Determinant (expected): -2
Determinant (actual)  : -7

Comments
I can provide a PR if needed.

Good spot, PRs welcome!

Also @HurricanKai for second opinion on the maths (this means nothing to me)


Improve and optimize Chinese Translation

Improve and optimize Chinese Translation
Reference: https://www.mcbbs.net/thread-1461023-1-1.html#post_28814848

Thank you for updating the translations.


Documentation for Ingestion Server

Problem Description
Hello @aldenstpage ,
I see that currently there is no documentation for ingestion server module and it supports Falcon Api.
I would like to add documentation for the ingestion server.
Solution Description
I would like to add swagger api spec as done in analytics module.
Once approved I would start working on it.

I don't think we plan on extending that API. In the beginning, it looked like we were going to need an API for managing ingestion tasks on a more granular level, but it ended up not being necessary. I would like to remove some of the endpoints in the future because that API isn't especially reliable (in particular changes to the ingestion architecture have made the percent complete field in the task endpoint completely wrong). There are other places where we are more sorely in need of documentation.


Store customer and invoice-issuer data next to invoice to prevent updates

The data of the customer and the invoice-issuer should be somehow persisted so they wont change in already generated invoice.

Maybe this can be solved by saving invoices as pdf files #11  which can not be adjusted later.


Build failure with recent version of clang

When trying to build with a recent version of clang (a218706cba90248be0c60bd6a8f10dbcf0270955), it fails with the following error:
clang -MMD -MT build/ninja/linux/release/x86-64/rpmalloc-a8e50b6/rpmalloc-65d008a.o -MF build/ninja/linux/release/x86-64/rpmalloc-a8e50b6/rpmalloc-65d008a.o.d -I.  -DRPMALLOC_COMPILE=1 -funit-at-a-time -fstrict-aliasing -fvisibility=hidden -fno-stack-protector -fomit-frame-pointer -fno-math-errno -ffinite-math-only -funsafe-math-optimizations -fno-trapping-math -ffast-math -D_GNU_SOURCE=1 -W -Werror -pedantic -Wall -Weverything -Wno-c++98-compat -Wno-padded -Wno-documentation-unknown-command -Wno-implicit-fallthrough -Wno-static-in-inline -Wno-reserved-id-macro -Wno-disabled-macro-expansion -std=c11 -m64 -g -DBUILD_RELEASE=1 -O3 -funroll-loops -DENABLE_PRELOAD=1 -DENABLE_OVERRIDE=1 --target=x86_64-linux-gnu --sysroot=/b/s/w/ir/x/w/cipd/sysroot -c rpmalloc/rpmalloc.c -o build/ninja/linux/release/x86-64/rpmalloc-a8e50b6/rpmalloc-65d008a.o
rpmalloc/rpmalloc.c:869:6: error: mixing declarations and code is incompatible with standards before C99 [-Werror,-Wdeclaration-after-statement]
        int flags = MAP_PRIVATE | MAP_ANONYMOUS | MAP_UNINITIALIZED;
            ^
rpmalloc/rpmalloc.c:1108:11: error: mixing declarations and code is incompatible with standards before C99 [-Werror,-Wdeclaration-after-statement]
                        size_t remain_count = reserved_count - _memory_heap_reserve_count;
                               ^

This is because -Weverything enables all warnings, even those that are incompatible with the selected standard ("because its point is to enable all warnings, no matter if they make sense or not. (Maybe you want to build your code with a newer standard but keep it somewhat compatible with the old standard or something like that)").
Adding -Wno-declaration-after-statement to the clang command-line suppress this warning and allow the build to succeed.

Sigh, warning about standards before C99 when -std=c11 is present on command line, wonderful ... I'll just add that to the command line then.

Well, that happens, thanks for the quick fix :) I confirm that this fixes the issue for us!


Add "Contributing" section to README

Esp.:

Format using isort --profile black .
Format using ruff format
Lint using ruff check .

Since we added isort to the ruff configs, it should be sufficient to run ruff check --fix . and ruff format ..

Just added a makefile that bundles the two ruff commands into one (make ruff) in #197.

That's true! Note to self: for conform.nvim this translates to:
formatters_by_ft = {
  python = {
-    'isort',
+    'ruff_fix',
    'ruff_format'
  },
}


Issue #2727 - Introducing PhysicalFilePathResult and VirtualFilePathResult

To handle app and physical file system paths separately.
Removing FilePathResult

/cc: @Eilon @pranavkm

:watch:

Travis and appeyor don't like this PR

Updated..

:shipit:

Thanks! https://github.com/aspnet/Mvc/commit/ddc74e5396c5a09e950c5d0029bc23d452bc7783

This would be a shock for migrating from MVC5, where the File controller method is assumed to mean 'physical'.


Add mGage integration into phoenix

We have mGage for UK, Brazil, and Mexico. We need to add this integration into drupal so that users in those countries are able to have a similar transactional text message experience as US users receive.
Ideally we could find a way to add these configurations to https://www.dosomething.org/admin/config/dosomething/opt_in for each country.
Something else to consider, I believe that currently as a Brazilian user when I register with my cell phone number the site is trying to add me to Mobile Commons. This isn't really a huge deal right now because Mobile Commons should just bounce those users. But ideally, we wouldn't be sending these users over to Mobile Commons at all.
@marahml @mshmsh5000 @DeeZone @sergii-tkachenko @angaither
Related:

https://github.com/DoSomething/mbc-registration-mobile/issues/23
https://github.com/DoSomething/phoenix/issues/5939

Great, thanks @mikefantini! A couple of questions for the group:

Can we just take the CGG functionality and replicate it for the BR + MX auth forms?
@DeeZone @angaither I believe as of yesterday, all prev. phone numbers from AGG were pushed to mGage?

Looping in @classicfreddie here too

@mikefantini The Message Broker mbc-registration-mobile app keys on user_country and application_id. The mobile number is added to the appropreate SMS service (Mobile Commons (2 accounts: US / CA) - mGage: 2 lists, MX / BR)) based on these two key values.

Users from user_country 'US', 'CA', 'BR" or "MX" and the CGG application_id are processed.
Users from user_country 'US' and 'CA' and the Drupal application_id are processed.
All other submissions with mobile numbers are skipped.

Logic that defines the current flow: https://github.com/DoSomething/mbc-registration-mobile/blob/master/src/MBC_RegistrationMobile_ServiceDirector.php#L50-L97
The mGage settings are currenty hard coded into the mbc-registration-mobile app (mbc-registration-mobile app). Addition to the Drupal app is different than the Mobile Commons requirments, the values should include:

opt_in_ID
opt_in_keyword

@marahml Yes, last years CGG (not AGG as you inquired about above) have been imported to mGage lists.

@angaither thoughts on who best to take this on, on the Phoenix side?

I can do that if @angaither is OK with that.

:+1: I believe the bulk of this is already done, will just need an addtional op in field here
https://www.dosomething.org/admin/config/dosomething/opt_in

@DeeZone can you explain what the opt_in_keyword is?

@angaither

I believe the bulk of this is already done

Details on what needs to be done: https://github.com/DoSomething/phoenix/issues/5939 The additional fields but adjustments in several other locations.

is that per campaign? or do we have a default one that can get overridden per campaign?

A value mGage requires in addition to the opt_in_id value. Unlike Mobile Commons opt in which has a default keyword, mGage mimics the user sending an actual keyword to a shortcode.

Both the opt_in_id and opt_in_keyword (mGage) values should have a default value by country (MX/BR).
The default values could be overridden at the campaign level just like the current Mobile COmmons settin. The campaign specific opt_in_id / opt_in_keyword settings should only be available in campaigns with MX/BR language versions.
Campaigns with a US English version should only have the opt_in_id setting (Mobile Commons), not opt_in_keyword.
global-en campaigns should not have opt_in settings. SMS (Mobile Commons / mGage) messaging outside of US, MX, BR and CA is not supported.
I suspect this is leading to later global version having campaigns with opt_insettings by language within the campaign. One campaign, several languages, different opt_in settings.

vNext


ensure scrollbars appear as expected on code previews

fixes #96 by making the combining the scrolling elements, so it isn't two separate elements that are handles scrolling.
added a div around the code, as using the outer div for scrolling on the X axis gave unexpected behavior for the copy button

Thank you, love it!


LIST(impl): Add map implementation

Can I get a code review to see if these are the kinds of PRs you want? I'm versed in rebase if you need changes to the commit structure too.

Sorry for the slow reply (Sunday and whatnot). Going to review this now. On a side note, do you by any chance have docker set up on your local machine? Travis is failing and nobody I know can seem to reproduce the error that Travis is reporting. See: https://travis-ci.org/frptools/collectable/jobs/259185345
Apparently if you run the Travis test instance in Docker, it'll show the issue up. Unfortunately my machine seems to fail when installing prerequisites for Docker, so I haven't been able to figure the issue out yet, which means that pull requests always show up as build failures.

Code's all good, and builds fine for me locally. Merged. Thanks!

Unfortunately, Docker doesn't want to install on my machine either and I haven't been able to find out why.
Thanks for the review, I'll move on to some more challenging PRs (wanted to make sure I understood the PR process first).

Travis is finally building. Your future PRs should now actually reflect this.

Thanks, that's helpful


Compilation suddenly fails with "[Package] is already loaded".

Release Type: Official Release/GitHub (please choose appropriate option)
Official Release
Version: Version number and/or git branch
Xenko Game Studio <IP_ADDRESS>-beta02-0550
Platform(s): Does the problem occur on Windows, Android...?
Windows 10
Describe the bug
When attempting to run the game from Xenko or Visual Studio build fails with error:
Error:Assets [C:/Users/user/Documents/Xenko Projects/FirstPersonShooter/Assets/EffectCompileLog.xkeffectlog] with location [EffectCompileLog] from Package [C:/Users/user/Documents/Xenko Projects/FirstPersonShooter/FirstPersonShooter.Windows/FirstPersonShooter.Windows.xkpkg] is already loaded from package [C:/Users/user/Documents/Xenko Projects/FirstPersonShooter/FirstPersonShooter.Game/FirstPersonShooter.Game.xkpkg]
[C:\Users\user\.nuget\packages\xenko.core.assets.compilerapp\<IP_ADDRESS>-beta02-0550\build\Xenko.Core.Assets.CompilerApp.targets(68,5)]: Error:The command ""C:\Users\user\.nuget\packages\xenko.core.assets.compilerapp\<IP_ADDRESS>-beta02-0550\build\..\lib\net472\Xenko.Core.Assets.CompilerApp.exe"  --disable-auto-compile --project-configuration "Debug" --platform=Windows --project-configuration=Debug --property:RuntimeIdentifier=win7-x86 --output-path="C:\Users\user\Documents\Xenko Projects\FirstPersonShooter\Bin\Windows\Debug\data" --build-path="C:\Users\user\Documents\Xenko Projects\FirstPersonShooter\FirstPersonShooter.Windows\obj\xenko\assetbuild\data" --package-file="C:\Users\user\Documents\Xenko Projects\FirstPersonShooter\FirstPersonShooter.Windows\FirstPersonShooter.Windows.csproj" --log-pipe="net.pipe://localhost/Xenko.Core.Assets.Editor.9ef52b60-21f2-460b-a64a-a75da53dc9a7" --verbose" exited with code **1.**

To Reproduce
Just going back and forth editing code in VS and looking around (but not editing) things in Game Studio.  I was playing with the First Person Shooter sample specifically when it fell apart.
Expected behavior
Game should build and then run.
Log and callstacks
log.txt
Additional context
I also tried clean and rebuild.  No change.

Duplicate of #301


add lazy.js to test/perf

Hey,
Got myself tinkering with this and decide to add lazy.js to test/perf, most of the time 2nd to most, but it beat it 4x in zip 2 x 100000 integers test. I didn't update the readme since I'm running node v5.7.1.

Hey @syarul, thanks.  Cool to see how well lazy.js performs!
I was using lodash and Array mostly as familiar reference points.  The reactive libs are more for dealing with asynchronous events over time rather than for synchronous data processing.  That said, I don't think it hurts to add another data point.
I'm not surprised that lazy.js beats most on zip :)  Asynchronous zip is tricky, and there's probably plenty of room for improving most's implementation!  Since events are async, the reactive libs all have to do buffering and bookkeeping to account for speed differences.
We're about to release 0.19.0, so I'll take a closer look at this after that lands.

Sure thing.

Hey @syarul, sorry for the lag on this.
After much thought and discussion, we've decided that we should remove lodash and Array from the perf test suite.  The main reason is that they fulfill very different use cases.  They're interesting points of reference, but including them can give the wrong impression: that most, rxjs, etc. should be used for synchronous collection processing.
Removing them gives a more apples-to-apples comparison, which is probably more helpful to reactive users.
So, I'll close this and open a new issue to remove lodash and Array.


Renamed API Framework

Hey,
I accidentally noticed the other day that my tiny framework is mentioned here :) Thank you very much!
I'd like to send you a PR, because I recently renamed the project and gave it more robust foundations. And of couse, I continue supporting the container-interop interface!
P.S.: I hope that the standard gets more adoption one day, as it's a great idea!

:+1: merging!


Is it work with raspberry pie 3 ?

Hi,
Is this tool working with raspberry pie 3?
I want create a bot with it on my pie.

Hi @mnlbox if you mean integrating TagUI to Raspberry Pi through REST API, there are recent updates that allow advance API calls (using different methods GET, POST, PUT etc, different headers and so on). That can integrate with a Raspberry Pi running its own REST webservice.
If you mean literally running TagUI on it, in theory that's possible but I have not tried that. Seen some articles online saying some people compile the PhantomJS binaries directly for the Raspberry. Also saw this which suggests maybe can be done without compiling.
https://quaintproject.wordpress.com/2015/04/26/how-to-install-casperjs-on-the-raspberry-pi/
The packaged installation for Linux is running PhantomJS 64bit. You may want to try that and let me know if you have any questions? I don't have Raspberry, but your idea of running TagUI on it is very exciting! So let me know if anything I can help with.

Closing the issue for now but do let me know if you have any questions or anything I can help with!


我无法将它更改到D盘

Microsoft PowerToys version
0.68.1
Installation method
GitHub
Running as admin
None
Area(s) with issue?
General
Steps to reproduce
PowerToysReport_2023-06-10-10-30-38.zip
✔ Expected Behavior
可以从C盘更改至D盘
❌ Actual Behavior
设置里只有修改和卸载
Other Software
No response

Not able to repro this, going to close. Let us know if you see this again!

😡😡😡
---- 回复的原邮件 ----
发件人 Kayla @.> 日期 2024年10月30日 22:34 收件人 @.> 抄送至<EMAIL_ADDRESS>主题 Re: [microsoft/PowerToys] 我无法将它更改到D盘 (Issue #26771)
Not able to repro this, going to close. Let us know if you see this again!
—
Reply to this email directly, view it on GitHub, or unsubscribe.
You are receiving this because you authored the thread.Message ID: @.***>


Groups in Identity Access Policies

Groups
All Azure AD groups used as part of these recommendations must be created as a Microsoft 365 group not a Security group. This requirement is important for the deployment of sensitivity labels when securing documents in Microsoft Teams and SharePoint later on. For more information, see the article Learn about groups and access rights in Azure Active Directory
Which Azure AD groups are we talking about here? Further explanation when they need to be M365 groups would be good.
We wouldn't use M365 groups to include or exclude users from conditional access policies for example.

Document Details
⚠ Do not edit this section. It is required for learn.microsoft.com ➟ GitHub issue linking.

ID: b77aedd2-47a9-fcf5-3cc3-b46fe8548314
Version Independent ID: 6942764e-b1c3-a1a1-007f-e34d44c5c696
Content: Common Zero Trust identity and device access policies - Microsoft 365 for enterprise - Office 365
Content Source: microsoft-365/security/office-365-security/identity-access-policies.md
Service: microsoft-365-security
Sub-service: mdo
GitHub Login: @MicrosoftGuyJFlo
Microsoft Alias: joflore

@MicrosoftGuyJFlo Please help us in resolving this issue. Thanks

The groups are those that are mentioned in the assigning policies section that follows under include or exclude. They get reused in other articles in the guidance. #please-close


Microtasks are no longer dispatched after a runtime syntax error

If a syntax error is encountered at runtime, it's not detected synchronously while main() is on the stack, and the isolate recovers using Isolate.setErrorsFatal(false), microtasks in that isolate will cease to be dispatched. For example:
import 'dart:async';
import 'dart:isolate';

void badFn() {continue;}

void main() {
  var errorPort = new ReceivePort();
  Isolate.current.setErrorsFatal(false);
  Isolate.current.addErrorListener(errorPort.sendPort);
  errorPort.listen((message) {
    print("caught error");
    Timer.run(() => print("after timer"));
    scheduleMicrotask(() => print("after microtask"));
  });

  scheduleMicrotask(() => badFn());
}

This prints "caught error" and "after timer", but not "after microtask".

See also #23684, which proposes to make runtime syntax errors thrown like other exceptions.


Add toJson() for result class

Pleas add if it is possible to generated classes(*.g.dart) .toJson() method
class MyTableItemsResult { fields ... toJson(){..return all fields in json format} }

For what do you need json serialization here? You can probably get close to this already by enabling the raw_result_set_data builder option. Then you can use QueryRow.data to get access to the underlying fields.
If you need more complex serialization features, I suggest writing your own data class and then using custom result classes.


How can I dynamically change the selected values?

Can anybody tell me how could I dynamically change the selected values for this plugin using jQuery? Thanks

You need to use .choose function on the dropdown object.
Please refer to the documentation.

The example in that part of the documentation is apparently wrong. Here's how:
var myDropdown = $('selector').dropdown(_options_).data();
// change value
myDropdown.choose('some value in the options list');  // if single choice
myDropdown.choose(['value a', 'value c', 'value z']); // if multiple choice


error in python train_c3d_ucf101.py

2018-04-12 10:47:08.094678: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2018-04-12 10:47:08.094730: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2018-04-12 10:47:08.094744: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2018-04-12 10:47:08.094758: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2018-04-12 10:47:08.094772: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
2018-04-12 10:47:08.352114: I tensorflow/core/common_runtime/gpu/gpu_device.cc:887] Found device 0 with properties:
name: GeForce GTX TITAN X
major: 5 minor: 2 memoryClockRate (GHz) 1.2155
pciBusID 0000:03:00.0
Total memory: 11.92GiB
Free memory: 222.94MiB
2018-04-12 10:47:08.352169: I tensorflow/core/common_runtime/gpu/gpu_device.cc:908] DMA: 0
2018-04-12 10:47:08.352181: I tensorflow/core/common_runtime/gpu/gpu_device.cc:918] 0:   Y
2018-04-12 10:47:08.352205: I tensorflow/core/common_runtime/gpu/gpu_device.cc:977] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX TITAN X, pci bus id: 0000:03:00.0)
2018-04-12 10:47:21.296425: W tensorflow/core/common_runtime/bfc_allocator.cc:273] Allocator (GPU_0_bfc) ran out of memory trying to allocate 27.00MiB.  Current allocation summary follows.
2018-04-12 10:47:21.296492: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (256):   Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.296501: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (512):   Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.296510: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (1024):  Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.296518: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (2048):  Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.296527: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (4096):  Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.296537: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (8192):  Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.296546: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (16384):         Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.296555: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (32768):         Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.296563: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (65536):         Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.296571: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (131072):        Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.296579: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (262144):        Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.296587: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (524288):        Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.296596: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (1048576):       Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.296604: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (2097152):       Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.296613: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (4194304):       Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.296621: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (8388608):       Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.296631: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (16777216):      Total Chunks: 1, Chunks in use: 0 22.94MiB allocated for chunks. 13.50MiB client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.296640: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (33554432):      Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.296648: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (67108864):      Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.296657: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (134217728):     Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.296665: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (268435456):     Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.296673: I tensorflow/core/common_runtime/bfc_allocator.cc:660] Bin for 27.00MiB was 16.00MiB, Chunk State:
2018-04-12 10:47:21.296689: I tensorflow/core/common_runtime/bfc_allocator.cc:666]   Size: 22.94MiB | Requested Size: 13.50MiB | in_use: 0, prev:   Size: 256B | Requested Size: 4B | in_use: 1
2018-04-12 10:47:21.296698: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x420a3c0000 of size 1280
2018-04-12 10:47:21.296705: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x420a3c0500 of size 256
2018-04-12 10:47:21.296711: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x420a3c0600 of size 256
2018-04-12 10:47:21.296717: I tensorflow/core/common_runtime/bfc_allocator.cc:687] Free at 0x420a3c0700 of size 24049920
2018-04-12 10:47:21.296722: I tensorflow/core/common_runtime/bfc_allocator.cc:693]      Summary of in-use Chunks by size:
2018-04-12 10:47:21.296730: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 2 Chunks of size 256 totalling 512B
2018-04-12 10:47:21.296738: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 1280 totalling 1.2KiB
2018-04-12 10:47:21.296745: I tensorflow/core/common_runtime/bfc_allocator.cc:700] Sum Total of in-use chunks: 1.8KiB
2018-04-12 10:47:21.296757: I tensorflow/core/common_runtime/bfc_allocator.cc:702] Stats:
Limit:                    24051712
InUse:                        1792
MaxInUse:                 24051712
NumAllocs:                      20
MaxAllocSize:             24049920
2018-04-12 10:47:21.296769: W tensorflow/core/common_runtime/bfc_allocator.cc:277] *___________________________________________________________________________________________________
2018-04-12 10:47:21.300202: W tensorflow/core/common_runtime/bfc_allocator.cc:273] Allocator (GPU_0_bfc) ran out of memory trying to allocate 27.00MiB.  Current allocation summary follows.
2018-04-12 10:47:21.300224: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (256):   Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.300234: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (512):   Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.300242: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (1024):  Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.300250: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (2048):  Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.300258: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (4096):  Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.300266: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (8192):  Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.300274: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (16384):         Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.300282: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (32768):         Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.300290: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (65536):         Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.300298: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (131072):        Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.300308: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (262144):        Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.300315: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (524288):        Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.300323: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (1048576):       Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.300331: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (2097152):       Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.300340: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (4194304):       Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.300348: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (8388608):       Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.300358: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (16777216):      Total Chunks: 1, Chunks in use: 0 22.94MiB allocated for chunks. 13.50MiB client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.300368: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (33554432):      Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.300378: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (67108864):      Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.300385: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (134217728):     Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.300394: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (268435456):     Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.300403: I tensorflow/core/common_runtime/bfc_allocator.cc:660] Bin for 27.00MiB was 16.00MiB, Chunk State:
2018-04-12 10:47:21.300414: I tensorflow/core/common_runtime/bfc_allocator.cc:666]   Size: 22.94MiB | Requested Size: 13.50MiB | in_use: 0, prev:   Size: 256B | Requested Size: 4B | in_use: 1
2018-04-12 10:47:21.300422: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x420a3c0000 of size 1280
2018-04-12 10:47:21.300429: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x420a3c0500 of size 256
2018-04-12 10:47:21.300436: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x420a3c0600 of size 256
2018-04-12 10:47:21.300442: I tensorflow/core/common_runtime/bfc_allocator.cc:687] Free at 0x420a3c0700 of size 24049920
2018-04-12 10:47:21.300449: I tensorflow/core/common_runtime/bfc_allocator.cc:693]      Summary of in-use Chunks by size:
2018-04-12 10:47:21.300457: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 2 Chunks of size 256 totalling 512B
2018-04-12 10:47:21.300465: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 1280 totalling 1.2KiB
2018-04-12 10:47:21.300475: I tensorflow/core/common_runtime/bfc_allocator.cc:700] Sum Total of in-use chunks: 1.8KiB
2018-04-12 10:47:21.300483: I tensorflow/core/common_runtime/bfc_allocator.cc:702] Stats:
Limit:                    24051712
InUse:                        1792
MaxInUse:                 24051712
NumAllocs:                      20
MaxAllocSize:             24049920
2018-04-12 10:47:21.300492: W tensorflow/core/common_runtime/bfc_allocator.cc:277] *___________________________________________________________________________________________________
2018-04-12 10:47:21.302355: W tensorflow/core/common_runtime/bfc_allocator.cc:273] Allocator (GPU_0_bfc) ran out of memory trying to allocate 27.00MiB.  Current allocation summary follows.
2018-04-12 10:47:21.302386: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (256):   Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.302394: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (512):   Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.302402: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (1024):  Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.302412: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (2048):  Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.302420: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (4096):  Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.302428: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (8192):  Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.302436: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (16384):         Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.302444: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (32768):         Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.302453: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (65536):         Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.302461: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (131072):        Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.302469: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (262144):        Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.302476: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (524288):        Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.302484: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (1048576):       Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.302492: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (2097152):       Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.302500: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (4194304):       Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.302508: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (8388608):       Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.302520: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (16777216):      Total Chunks: 1, Chunks in use: 0 22.94MiB allocated for chunks. 13.50MiB client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.302528: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (33554432):      Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.302536: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (67108864):      Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.302545: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (134217728):     Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.302553: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (268435456):     Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.302564: I tensorflow/core/common_runtime/bfc_allocator.cc:660] Bin for 27.00MiB was 16.00MiB, Chunk State:
2018-04-12 10:47:21.302576: I tensorflow/core/common_runtime/bfc_allocator.cc:666]   Size: 22.94MiB | Requested Size: 13.50MiB | in_use: 0, prev:   Size: 256B | Requested Size: 4B | in_use: 1
2018-04-12 10:47:21.302584: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x420a3c0000 of size 1280
2018-04-12 10:47:21.302589: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x420a3c0500 of size 256
2018-04-12 10:47:21.302596: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x420a3c0600 of size 256
2018-04-12 10:47:21.302600: I tensorflow/core/common_runtime/bfc_allocator.cc:687] Free at 0x420a3c0700 of size 24049920
2018-04-12 10:47:21.302607: I tensorflow/core/common_runtime/bfc_allocator.cc:693]      Summary of in-use Chunks by size:
2018-04-12 10:47:21.302613: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 2 Chunks of size 256 totalling 512B
2018-04-12 10:47:21.302620: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 1280 totalling 1.2KiB
2018-04-12 10:47:21.302627: I tensorflow/core/common_runtime/bfc_allocator.cc:700] Sum Total of in-use chunks: 1.8KiB
2018-04-12 10:47:21.302635: I tensorflow/core/common_runtime/bfc_allocator.cc:702] Stats:
Limit:                    24051712
InUse:                        1792
MaxInUse:                 24051712
NumAllocs:                      20
MaxAllocSize:             24049920
2018-04-12 10:47:21.302644: W tensorflow/core/common_runtime/bfc_allocator.cc:277] *___________________________________________________________________________________________________
2018-04-12 10:47:21.911379: W tensorflow/core/common_runtime/bfc_allocator.cc:273] Allocator (GPU_0_bfc) ran out of memory trying to allocate 64.00MiB.  Current allocation summary follows.
2018-04-12 10:47:21.911405: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (256):   Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.911414: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (512):   Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.911421: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (1024):  Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.911428: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (2048):  Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.911436: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (4096):  Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.911444: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (8192):  Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.911453: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (16384):         Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.911463: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (32768):         Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.911473: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (65536):         Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.911482: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (131072):        Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.911492: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (262144):        Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.911500: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (524288):        Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.911508: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (1048576):       Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.911517: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (2097152):       Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.911525: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (4194304):       Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.911533: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (8388608):       Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.911545: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (16777216):      Total Chunks: 1, Chunks in use: 0 22.94MiB allocated for chunks. 13.50MiB client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.911554: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (33554432):      Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.911564: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (67108864):      Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.911572: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (134217728):     Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.911580: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (268435456):     Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.911589: I tensorflow/core/common_runtime/bfc_allocator.cc:660] Bin for 64.00MiB was 64.00MiB, Chunk State:
2018-04-12 10:47:21.911596: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x420a3c0000 of size 1280
2018-04-12 10:47:21.911604: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x420a3c0500 of size 256
2018-04-12 10:47:21.911611: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x420a3c0600 of size 256
2018-04-12 10:47:21.911619: I tensorflow/core/common_runtime/bfc_allocator.cc:687] Free at 0x420a3c0700 of size 24049920
2018-04-12 10:47:21.911626: I tensorflow/core/common_runtime/bfc_allocator.cc:693]      Summary of in-use Chunks by size:
2018-04-12 10:47:21.911634: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 2 Chunks of size 256 totalling 512B
2018-04-12 10:47:21.911642: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 1280 totalling 1.2KiB
2018-04-12 10:47:21.911649: I tensorflow/core/common_runtime/bfc_allocator.cc:700] Sum Total of in-use chunks: 1.8KiB
2018-04-12 10:47:21.911659: I tensorflow/core/common_runtime/bfc_allocator.cc:702] Stats:
Limit:                    24051712
InUse:                        1792
MaxInUse:                 24051712
NumAllocs:                      20
MaxAllocSize:             24049920
2018-04-12 10:47:21.911669: W tensorflow/core/common_runtime/bfc_allocator.cc:277] *___________________________________________________________________________________________________
2018-04-12 10:47:21.924118: W tensorflow/core/common_runtime/bfc_allocator.cc:273] Allocator (GPU_0_bfc) ran out of memory trying to allocate 128.00MiB.  Current allocation summary follows.
2018-04-12 10:47:21.924160: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (256):   Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.924177: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (512):   Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.924194: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (1024):  Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.924211: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (2048):  Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.924227: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (4096):  Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.924242: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (8192):  Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.924257: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (16384):         Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.924272: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (32768):         Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.924287: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (65536):         Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.924302: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (131072):        Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.924317: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (262144):        Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.924332: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (524288):        Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.924347: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (1048576):       Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.924362: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (2097152):       Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.924377: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (4194304):       Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.924392: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (8388608):       Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.924412: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (16777216):      Total Chunks: 1, Chunks in use: 0 22.94MiB allocated for chunks. 13.50MiB client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.924428: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (33554432):      Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.924443: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (67108864):      Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.924458: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (134217728):     Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.924473: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (268435456):     Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
2018-04-12 10:47:21.924491: I tensorflow/core/common_runtime/bfc_allocator.cc:660] Bin for 128.00MiB was 128.00MiB, Chunk State:
2018-04-12 10:47:21.924505: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x420a3c0000 of size 1280
2018-04-12 10:47:21.924517: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x420a3c0500 of size 256
2018-04-12 10:47:21.924530: I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x420a3c0600 of size 256
2018-04-12 10:47:21.924541: I tensorflow/core/common_runtime/bfc_allocator.cc:687] Free at 0x420a3c0700 of size 24049920
2018-04-12 10:47:21.924552: I tensorflow/core/common_runtime/bfc_allocator.cc:693]      Summary of in-use Chunks by size:
2018-04-12 10:47:21.924564: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 2 Chunks of size 256 totalling 512B
2018-04-12 10:47:21.924580: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 1280 totalling 1.2KiB
2018-04-12 10:47:21.924594: I tensorflow/core/common_runtime/bfc_allocator.cc:700] Sum Total of in-use chunks: 1.8KiB
2018-04-12 10:47:21.924610: I tensorflow/core/common_runtime/bfc_allocator.cc:702] Stats:
Limit:                    24051712
InUse:                        1792
MaxInUse:                 24051712
NumAllocs:                      20
MaxAllocSize:             24049920
2018-04-12 10:47:21.924628: W tensorflow/core/common_runtime/bfc_allocator.cc:277] *___________________________________________________________________________________________________
Traceback (most recent call last):
File "train_c3d_ucf101.py", line 261, in
tf.app.run()
File "/home/lyynew/anaconda2/lib/python2.7/site-packages/tensorflow/python/platform/app.py", line 48, in run
_sys.exit(main(_sys.argv[:1] + flags_passthrough))
File "train_c3d_ucf101.py", line 258, in main
run_training()
File "train_c3d_ucf101.py", line 204, in run_training
sess.run(init)
File "/home/lyynew/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 778, in run
run_metadata_ptr)
File "/home/lyynew/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 982, in _run
feed_dict_string, options, run_metadata)
File "/home/lyynew/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1032, in _do_run
target_list, options, run_metadata)
File "/home/lyynew/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1052, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InternalError: Dst tensor is not initialized.
[[Node: cond_6/Switch_1/_157 = _Recvclient_terminated=false, recv_device="/job:localhost/replica:0/task:0/gpu:0", send_device="/job:localhost/replica:0/task:0/cpu:0", send_device_incarnation=1, tensor_name="edge_390_cond_6/Switch_1", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"]]

I search about solve method about CUDA_ERROR_OUT_OF_MOMERY https://blog.csdn.net/py496980138/article/details/78843217
but useless

https://stackoverflow.com/questions/45404689/tensorflow-how-to-train-2-cnnindependent-on-2-gpus-cuda-error-out-of-memory-e

i don't know where to add codes to make my gpu work


Override QC Validation with ProofOfStakeCutoverValidator

Override QC Validation with ProofOfStakeCutoverValidator
Implement QC validation override

[!WARNING]
This pull request is not mergeable via GitHub because a downstack PR is open. Once all requirements are satisfied, merge this PR as a stack on Graphite.
Learn more

Current dependencies on/for this PR:

feature/pos-syncing-and-steady-state

PR #926

PR #927

PR #928

PR #929   👈

This stack of pull requests is managed by Graphite.


[Feature Request] add username-password feature

Hi,
I would appreciate if there was any login/password feature.

Implemented in af125ecce14546b446ef1d87e28c451444796976, and should be available in the latest beta: v2.8.0-beta. It's been minimally tested, so definitely let me know if it's not working as expected.

Implemented in af125ec, and should be available in the latest beta: v2.8.0-beta. It's been minimally tested, so definitely let me know if it's not working as expected.

Thank you! I will test it and let you know.


Bump more files to Sorbet typed: strict

[x] Have you followed the guidelines in our Contributing document?
[x] Have you checked to ensure there aren't other open Pull Requests for the same change?
[x] Have you added an explanation of what your changes do and why you'd like us to include them?
[ ] Have you written new tests for your changes? Here's an example.
[ ] Have you successfully run brew style with your changes locally?
[x] Have you successfully run brew typecheck with your changes locally?
[ ] Have you successfully run brew tests with your changes locally?

I had a break for a while, but inspiration struck tonight so here’s some more effort for https://github.com/Homebrew/brew/issues/17297.

Sorry for the delay here, I thought the style issues were the only ones. But I'll get around to fixing the actual typechecking failures hopefully tonight!


Horizantal scroll not working

Describe the Issue
The code snipper should have horizantal scroll but it does not and half code is cut and not scrollable
Affected Page
https://www.freecodecamp.org/learn/javascript-algorithms-and-data-structures-v8/learn-basic-string-and-array-methods-by-building-a-music-player/step-13
Steps to Reproduce

Go to https://www.freecodecamp.org/learn/javascript-algorithms-and-data-structures-v8/learn-basic-string-and-array-methods-by-building-a-music-player/step-13
Click on code snipper
Scroll left or right
it won't scroll

Expected behavior
The code snippet should scroll horizontol
Screenshots

System

Device: Laptop
OS: macOS
Browser: Chrome
Version 121.0.6167.184 (Official Build) (arm64)

Additional context
No response

Perhaps this issue is similar to another one?

@mahassan Can you be very very explicit about what you are doing when trying to scroll the code snippet. We need to know exactly how you are trying to scroll.
And yes, as @mmatsumoto1026 suggested, have a look at that previous issue. You may have to use your mouse on the horizontal scrollbar because of the limitations of the monaco editor.

Hi Is this still open?

Closing as duplicate


Default menu icons (task #5348)

This PR changes the way icons are being used in Module Menus by introducing the following fallback mechanism to retrieve the icon from different resources. Note that this works only for module menus coming from csv-migrations plugin and does not affect menus coming from other plugins or any other source.

Use menu item icon specified in menus.json
Otherwise, use table icon specified in config.json
Otherwise, use default icon specified in config Menu.default_menu_item_icon

Codecov Report

Merging #635 into master will increase coverage by 0.78%.
The diff coverage is 0%.

@@             Coverage Diff              @@
##             master     #635      +/-   ##
============================================
+ Coverage     31.43%   32.21%   +0.78%
- Complexity     1162     1680     +518
============================================
  Files           104      104
  Lines          3983     4979     +996
============================================
+ Hits           1252     1604     +352
- Misses         2731     3375     +644

Impacted Files
Coverage Δ
Complexity Δ

src/Event/Plugin/Menu/View/ModuleMenuListener.php
0% <0%> (ø)
18 <4> (+5)
:arrow_up:

src/Controller/UsersController.php
67.15% <0%> (-0.98%)
46% <0%> (+23%)

...ent/Plugin/Search/Model/ChildListItemsListener.php
0% <0%> (ø)
42% <0%> (+21%)
:arrow_up:

src/Event/Controller/Api/LookupActionListener.php
0% <0%> (ø)
88% <0%> (+44%)
:arrow_up:

src/Shell/Task/MigrationsTask.php
0% <0%> (ø)
4% <0%> (+2%)
:arrow_up:

...c/Event/Plugin/Menu/View/DashboardMenuListener.php
0% <0%> (ø)
20% <0%> (+10%)
:arrow_up:

src/Event/Plugin/Menu/View/ModuleIndexListener.php
0% <0%> (ø)
14% <0%> (+7%)
:arrow_up:

src/Event/Plugin/Menu/View/SearchViewListener.php
0% <0%> (ø)
8% <0%> (+4%)
:arrow_up:

src/Shell/Task/Upgrade20180726084300Task.php
0% <0%> (ø)
12% <0%> (+6%)
:arrow_up:

src/Feature/Config.php
100% <0%> (ø)
14% <0%> (+7%)
:arrow_up:

... and 25 more

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update 735f34e...46f3a20. Read the comment docs.


Java JDK versions

[Enter feedback here]
Switched to OpenJDK 11 but that breaks the Android Device Manager and the emulators cannot be created/edited/deleted. To get that working you have to set Visual Studio - Options - Xamarin - JDK path to OpenJDK version 8. So the Android tools break when using the new version 11.

Document Details
⚠ Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.

ID: 7607155a-9a56-d752-c5ac-c3aeb6b128bc
Version Independent ID: 9a62d421-d009-30b3-843e-a92bfc21c061
Content: .NET MAUI installation - .NET MAUI
Content Source: docs/get-started/installation.md
Product: dotnet-mobile
Technology: dotnet-maui
GitHub Login: @davidbritch
Microsoft Alias: dabritch

Hi @lywyn
Yes, afraid that's everyone's experience. You need OpenJDK 8 to manipulate emulators, but you need OpenJDK 11 to actually build apps. It's something that will hopefully be fixed for the next preview.


New Rule: jsx-no-target-blank

New rule to protect against a pretty serious security vulnerability: https://mathiasbynens.github.io/rel-noopener
Using target='_blank' to open a link in a new tab is inherently unsafe and should always be accompanied by rel='noopener noreferrer'

Coverage increased (+0.02%) to 96.817% when pulling 10c727ac38e100df74746dd0424ee97ff9a8c62a on Gasparila:master into 63d33cb7f0e46484636fe178f8c8ffaaeaa024e9 on yannickcr:master.

Nice :) but why specific to JSX/react?

why specific to JSX/react?

It's definitely specific to JSX. I suppose not necessarily React, though. @eddie can you suggest a more appropriate place to open this PR?

@dmnd You are right (specific to jsx).. It was early when I wrote that comment. Here is probably the best place :+1:

Merged. Thanks!


Save transperency-log data into a file

sigrun-rekor-transparency-log-ledger.json

Current rekor-api does not allow this

Check latest version

Query for all tlogs, use latest one

ask in slack


Copy paste warnings in cell output not possible

How are you mainly using the Jupyter extension?

[X] Notebooks (.ipynb)
[ ] Interactive Window and or Cell Scripts (ex. .py files with #%%)
[ ] Both

What happened?
CTRL + C does not copy cell output warnings (warnings.warn()). Furthermore it is not possible to select the text, right click, and copy it. It is not possible to right click normal cell output text and click copy either.

Related: microsoft/vscode-jupyter#1190
What flavor of VS Code are you using?
Stable
Jupyter Extension Version
v2021.8.2041215044

Jupyter logs
See ![image](https://user-images.githubusercontent.com/3297866/133282778-017147cd-c3f2-4c15-a18c-0dadda41d085.png)
(Ironically I can't select this text)

What coding language are you working with?
Python
VS Code Version?
Version: 1.60.0 (user setup)
Commit: e7d7e9a9348e6a8cc8c03f877d39cb72e5dfb1ff
Date: 2021-09-01T10:41:52.311Z
Electron: 13.1.8
Chrome: 91.0.4472.164
Node.js: 14.16.0
V8: 9.1.269.39-electron.0
OS: Windows_NT x64 10.0.19042
Python Extension Version
v2021.9.1230869389
Python Version
3.8.11.final.0
Anaconda Version
conda version : 4.10.3
conda-build version : 3.21.4
What OS are you running on?
Windows
Running Jupyter locally or remotely?
N/A or Not sure
Code of Conduct

[X] I agree to follow this project's Code of Conduct

Same issue when copying output ie for the piece of code: np.matmul

?

This works for me right now, at least in Insiders. @IanMatthewHuff can you share the notebook you tried where this didn't work?

@roblourens It's funny I'm getting odd inconsistent behavior with this on Mac + current insiders currently. My first time I tried a Cmd+C it did copy the text from the warning. But now after adding a basic output cell I was unable to copy anything from the notebook, either warning text or output text.
But that was on a build of VS Code Insiders from the end of last week. I updated to today and now I'm not able to repro. Was there a very recent expected fix here? I'm going to play with it a bit more to see if I can repro.

There was https://github.com/microsoft/vscode/issues/131937, but that would only affect copying outside the webview, not copying inside the webview

Wasn't able to get it to repro this morning in Insiders.
@ArtificialTruth could you try your scenario using vscode-insiders build? Can you repro the same issue there?


Adding excludes option

This PR adds an excludes: [] option which can be used to exclude certain paths of the versioned object from being versioned.
Excludes are defined as follows
ExcludeSchema.plugin(patchHistory, {
  mongoose,
  name: 'excludePatches',
  excludes: ['/hidden', '/object/hiddenProperty', '/array/*/hiddenProperty'],
})

A exclude of /hidden would exclude all paths that start with /hidden.
For arrays I introduced the * symbol to specifiy a specific property inside the array to be excluded. One could ofcourse also specify a number to only exclude one array element.
Problems
Add the moment I have one failing test. This tests what patches are written when an object is saved initially.
At the moment I do the comparison on the path-property of the json-patch. Since json-patch adds only the top-level-properties when an object is added: e.g. op:'add', path:'/object', value:'{ hiddenProperty: 'test' }' it would take a little more effort to make the exclude work for this kind of operation.
@codepunkt Do you think this behaviour would be necessary for consistency or can we only support the excludes option for updates() and not for initial writing?

At the moment I do the comparison on the path-property of the json-patch. Since json-patch adds only the top-level-properties when an object is added: e.g. op:'add', path:'/object', value:'{ hiddenProperty: 'test' }' it would take a little more effort to make the exclude work for this kind of operation.

I added a comparison to make exclude work for this case.
@codepunkt Please take your time to review the changes introduced. Especially in 9cf4bab. I tried to comment the method deepRemovePath() to full extend, but sure I didn't. If there is something unclear, feel free to ask.
I enhanced the test for most cases I could think of this evening but there are probably more to cover, which brings me to my question.
I'd really like this feature in the release, because I need it 😄. The logic is "rather" complicated and cause of that I would take it more as a beta-feature than as a fully tested one. Can't think of a case that would break it right now, but you can never be sure..
Would you include it in the release or do you say we need to do more test to be sure it works as intended?
Of course I'll provide documentation in the README if you choose to include it. Thanks in advance for your review.

I haven't been using mongodb or mongoose for a few years, so i can't comment on these parts. However, i think i understand the exclude use case.
I'm unsure about the deepRemovePath stuff because i don't understand how fast-json-patch diffentiates between nested paths and having objects as the value.
Documentation in the readme would be nice!

Hey! At first thanks for your review, I appreciate it!
I'll try to include your suggestions and make the code more understandable. Probably should've done this one more time before pushing it, but yesterday it seems reasonably good to me.

I'm unsure about the deepRemovePath stuff because i don't understand how fast-json-patch diffentiates between nested paths and having objects as the value.

As I understand it, the first added property is always a top-level-property, so it should always result in something like op:'add', path:'/top-level, value:'{ ... }'.
Every other operation results in the lower-most property path it can get, to resemble the operation.
Taking this object as a starting point: { top: {} }
Adding a third-level-property /top/second/third gives a patch like op:'add', path:'/top, value:'{ second: { third: { ... }}}'.
Replacing this property after will give  op:'replace', path:'/top/second/third, value:'{ ... }'.
Removing this property after will give  op:'remove', path:'/top/second/third.
Does that clear it up a little? I'd say that these cases are covered, because at first I check if the starting-path is the same
const deepRemovePath = (patch, pathToExclude) => {
  const patchPath = getArrayFromPath(patch.path)

  // first check if the base path of the json-patch overlaps with the path we want to exclude
  if (isPathCovered(patchPath, pathToExclude)) {
    let value = patch.value

and here I start to traverse at the matching index in pathToExclude
// because the paths overlap start at patchPath.length
    // e.g.
    // patch: { path:'/object', value:{ property: 'test' } }
    // pathToExclude: '/object/property'
    // need to start at array idx 1, because value starts at idx 0
    for (let i = patchPath.length; i < pathToExclude.length - 1; i++) {

Documentation will take place tomorrow, I think I need some sleep.
PS.: It is hard to break down the code, and especially the thoughts I had while writing it and how I expect it to work. I hope it is understandable. Thanks again for the review!!!

It's understandable, you're doing a really good job here!

@codepunkt Thanks for approving!
If you are fine with the README changes feel free to merge! If you want me to include another case or specify something further let me know!
Also if any bugs encounter with this feature, summon me and I'll have a look!

@vinerich I don't see any README changes yet 😀

@codepunkt There you go. The commit didn't work because I don't have my MongoDB on localhost and forgot to change it back after stash 😄


[keymgr] return OpDoneSuccess when lifecycle is off during an operation

@tjaychen
since LC is off, expect any OP will return failed status. WDYT?

/edascratch/weicai-opentitan/ot/fix_lc/keymgr-sim-vcs/0.keymgr_lc_disable/out

good catch @weicaiyang , it turns out this one the en dropped during the middle of transitioning to invalid.
I forgot to mark operations as invalid during that state.  Can you see if this helps?

good catch @weicaiyang , it turns out this one the en dropped during the middle of transitioning to invalid.
I forgot to mark operations as invalid during that state. Can you see if this helps?

thanks for quickly fixing this. the change looks good to me.


Exposed DeletionIterator and CompactMetas functions.

Required for CLI for deletions in Thanos: https://github.com/thanos-io/thanos/pull/3421
Signed-off-by: Bartlomiej Plotka<EMAIL_ADDRESS>

Ok (: Addressed comments. PTAL @roidelapluie @brian-brazil

:+1:


Support timed events

This PR supports sending multiple, timestamped events as a general composer API.

Support optional timestamp for each event
Support sending multiple events in a UDP packet
Simplify log_probe using the new Event and EventMessage structs

@strohel @goodhoko thank you for your review! I'm sorry for taking so long to get back to this. I addressed all of the pending comments and will go ahead and merge this.


Editor hiccup: ambiguis behaviour when handling orElse in firstMatching

This issue was originally filed by<EMAIL_ADDRESS>
What steps will reproduce the problem?

define a variable of type List<String>
use firstMatching method on it
add an orElse function with no parameters, as per the help definition: E orElse()

What is the expected output? What do you see instead?
Expected: working or an error like "function definition must be compatible with (E value)" (as an example)
Actual: the warning "extra argument"
Versions
Dart Editor version 0.3.2_r17657
Dart SDK version <IP_ADDRESS>_r17657
Please provide any additional information below.
This is a general problem. If a function with a certain definition is required then the error or the editor should somehow suggest what the correct definition is.
Here is a concrete example:
List<String> test = [ 'a', 'b', 'c' ];
test.firstMatching((String s) => s == 'b', () => null); // warning: "extra argument", highlights: "() => null"
Here is the help text from the editor:
E firstMatching(bool test(E value), E orElse())
Returns the first element that satisfies the given predicate [f].
If none matches, the result of invoking the [orElse] function is returned. By default, when [orElse] is null, a
 [StateError] is thrown.

I think this can be closed. The error message has since become more clear:
List<String> test = [ 'a', 'b', 'c' ];
test.firstWhere((String s) => s == 'b', () => null);
// warning: 1 positional arguments expected, but 2 found

https://dartpad.dartlang.org/ec462ddf20d97b254edb

Thanks for checking!


Switch asyncComponent to React Lazy/Suspense

Here's an up-for-grabs ticket for someone that wants to play with React Lazy and Suspense. We are currently using a component called AsyncComponent to dynamically import and use components in some extensions. React now supports this natively, so we can remove these unnecessary components:
See
https://github.com/OHIF/Viewers/blob/c5a4860209bf3f04f667c902cb2e47e1e47d8ab4/extensions/dicom-pdf/src/asyncComponent.js#L10-L35
https://github.com/OHIF/Viewers/blob/c5a4860209bf3f04f667c902cb2e47e1e47d8ab4/extensions/dicom-pdf/src/index.js#L4-L7
Instead of using AsyncComponent, we can use React.Lazy and wrap the import in the React.Suspense HOC. See https://reactjs.org/docs/code-splitting.html#reactlazy

I shall take this up.

Alright, so this is how I have the index.js file in one of the extensions:
const Component = React.lazy(() => {
  return import('./OHIFCornerstoneViewport');
});

function OHIFCornerstoneViewport() {
  return (
    <React.Suspense fallback={
Loading...
}>
      <Component />
    </React.Suspense>
  );
}

I have replaced the AsyncComponent with a call to React.Lazy and updated the component by wrapping <React.Suspense> around it. However, it now throws the following error:

OHIFCornerstoneViewport.js?6845:139 Uncaught TypeError: Cannot read property 'studies' of undefined

I continue to debug this error but since I'm not yet completely familiar with the codebase, any guidance would be appreciated.


Security: Kong allows remote clients to forge remote IP

Summary
We use a Layer 3 load balancer (relayD) to route requests to Kong. Currently with the way kong is setup, the default nginx LUA template includes the following entry:
set_real_ip_from <IP_ADDRESS>/0;
Per the NGINX Documentation: http://nginx.org/en/docs/http/ngx_http_realip_module.html#set_real_ip_from

Defines trusted addresses that are known to send correct replacement addresses. If the special value unix: is specified, all UNIX-domain sockets will be trusted.

It would probably be more ideal if we could supply range(s) of trusted internal IPs instead in the kong configuration, which would be inserted into the LUA config automatically.
Here is a full example:
Command:
kyle$ curl -H "X-Real-IP: <IP_ADDRESS>" -H "X-Forwarded-For: <IP_ADDRESS>" -X GET https://api.kong.qa/kyle
Log Entry on Kong Server:
<IP_ADDRESS> - - [20/Sep/2016:12:34:04 -0700] "GET /kyle HTTP/1.1" 404 249 "-" "curl/7.43.0"
Steps To Reproduce

curl -H "X-Real-IP: <IP_ADDRESS>" -H "X-Forwarded-For: <IP_ADDRESS>" -X GET https://YOUR_KONG_INSTANCE_OR_LOAD_BALANCER
Check your access log files (/usr/local/kong/logs/access.log)

Additional Details & Logs

Kong version 0.9.1
Operating System CentOS Linux release 7.2.1511 (Core) - Kernel: 3.10.0-327.28.2.el7.x86_64

Note: somehow related to #1615 (asking for a better configurable behavior of the ngx_http_realip module.

@thibaultcha I submitted a PR ^

Moved to @2202. Thanks!


Instead of using ibkr real time data I want to use the real time data from some place else: tda-api

Instead of
ticks_bid_ask = client.tables["ticks_bid_ask"]
client.request_tick_data_realtime(rc, dhib.TickDataType.BID_ASK)
I want to use tda-api level one streaming data: https://tda-api.readthedocs.io/en/latest/streaming.html

how can tda-api be integrated into Deephaven docker and
tick_bid_ask be replace by tda-api streaming data

Sorry for the delayed response.  Let me give two answers.

If you want to use the TDA data with IB, you can populate a table using the TDA API.  That table can then be used in queries with the IB API.  You can do this using a DynamicTableWriter or a TablePublisher or Deephaven's new "function generated tables" (not yet documented).
If you want to trade using only TDA without IB, you will want to create some code similar to the IB plugin, but specific to TDA.  From looking at the TDA API example, this can be done.  If you only implement the part of the API that you care about, it shouldn't be too much work.

For either path, I highly recommend getting on the Deephaven Community Slack channel.  Many users and developers there can help walk you through each of these paths.  To get on slack, go to https://deephaven.io/slack.


Fix/gateway tests

Before submitting this PR, please make sure:

[X] Your code builds clean without any errors or warnings
[X] You are using approved title ("feat/", "fix/", "docs/", "refactor/")

A description of the changes proposed in the pull request:

Move bronze connectors tests to /test-bronze folder
Fix tests in /test folder
Move clob connector tests to /test-bronze folder. This needed because the CLOB tests which are based on the Injective connector currently call Injective network endpoints. While tests pass when you run Jest on the /clob folder alone, the network calls cause conflicts with other test suites when run together.

2023-08-22 09:28:16 | info | 	Injective endpoints: {"indexer":"https://k8s.global.mainnet.exchange.grpc-web.injective.network","grpc":"https://k8s.global.mainnet.chain.grpc-web.injective.network","rpc":"https://k8s.global.mainnet.tm.injective.network","rest":"https://k8s.global.mainnet.lcd.injective.network","chronos":"https://k8s.global.mainnet.chronos.grpc-web.injective.network","explorer":"https://k8s.global.mainnet.explorer.grpc-web.injective.network"}

Ideally, the clob tests should be refactored to use a different connector OR patched so that the Injective network calls don't occur.

Merged to development and part of 1.19.0


🐞[Bug]: Changing the styling of Heading tags to make them visible

Is there an existing issue for this?

[X] I have searched the existing issues

Describe the bug
As you can see that the heading section are not visible, so I will figure out the issue and applying the required styling to make it visible
Expected behavior
I will figure out the cause for the isssue and work on the styling
Add ScreenShots
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What browsers are you seeing the problem on?
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Hello @varshith257 can you please assign the issue.
Thank you!!

Hi @varshith257 can you please assign the issue

@Kishita16 It's already assigned to @bharat-c27 . I recommend you to go through old issues opened a long back and tag me there if you are willing to work on them

Okay!
Thank you


Create new StreamWriter client

When using client.Message(obj) each Message() call makes a call to reflect.TypeOf() to lookup the type field for the underlying protocol.  If only one type of object will every be sent by one client, it makes sense to avoid making that call every time for performance.  A new type of client should be created that can only send one type of struct, but does so with no additional overhead to JSON marshalling.
NewStreamWriter(connection, type_store, struct_type)

done in 40b19bd