Converted script to cli-tool

Added some arguments and refactored code to be used as a CLI tool

You have replaced all single quotes with double quotes, effectively editing almost every line of code.
Please resubmit the pull request without that. Don't do style changes on any code that you haven't touched otherwise, thanks.

Fair enough, my linter did that. This was really just a 10 minute fix to introduce some additional arguments for my own needs. I'm not going to spend much more time on this.
Awesome program though!


Further improvements towards Rsc-based compilation of a case study project

Travis seems to be stuck, so I ran sbt ci on my local machine, and it was a success. Merging.


Widget labels not aligned

cc @dclarizio @simon3z

@moolitayer I think they're aligned... not as we may expect them though... they seem "aligned to the center".

@martinpovolny I'm thinking this may become a non-issue once we have C3 charts up and running.


Add ability to register custom coersions

Now that we're moving away from a centralized Transproc function registry, we need someplace to put coersions for domain value objects, so that:
BookMapper < ROM::Mapper
  model Book
  attribute :isbn, type: :isbn
end

will coerce Book#isbn to an ISBN type value object.
You can do this currently by calling ROM::Processor::Transproc::Functions.import but that's gobal, and deeply nested enough that it screams private.  Cleaner, more explicit support in the context of the mappers would be good.

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Maybe we should use existing mapper dsl:
setup = ROM.setup(:memory)

module MyCoercers
  def self.to_batman(*args)
    Batman.new(*args)
  end
end

setup.mappers do
  coercers MyCoercers
end

We could also add support for instantiating structs through:
module Transproc::Coercions
  def self.to_struct(model, *args)
    model.new(*args)
  end
end

class MyMapper < ROM::Mapper
  attribute :location, type: :struct, model: Location
end

mapper = MyMapper.build
mapper.call [{ location: [123, 312] }] # would turn it into a location instance

A lot of the API style is to create a subclass which registers itself. What are your thoughts on:
class ISBNCoercer < ROM::Coercer
  register_as :isbn

  def self.call(value)
    value.to_s
  end
end

Also, the concept of prefixing with to_ feels too indirect and requires familiarity with transproc.

@c0 I don't think this should be class, because there is nothing to instantiate
module ISBNCoercer
  extend ROM::Coercer

  def self.call(value)
  end
end

Here ROM::Coercer could in turn

extend Transproc::Registry (allowing to include external modules and/or their singleton methods),
define register_as method for ROM env

Please don't add any more register_as :(

@AMHOL (1) why? (2) if so, we can simply go with Transproc::Registry

It makes me sad to see state held on a class level like that

Also, Transproc is an optional dependency, and ROM has an adapter between mappers and Transproc, so I think extending Transproc::Registry in a ROM core component is a no-go

@AMHOL I'm with you and am all for removing state. I pointed it out because it would be consistent with the rest of the API.

@AMHOL  "Don’t be sad" (c) Magical Deer
Ahh, yes you're right about dependency. Then registering a coerser is no-go.
But I wonder if the whole idea of coercer is coupled to specific mapper. If so, we could mix transproc to mappers and use mappers as local registries except for global one.

No, it's not good to be tied to a specific (single) mapper:
that means, either I have to base-class all my internal mappers, or I have to redo the corecer for every  mapper that happens to use that type.  Since one of the compelling things about value objects is that they can be freely scattered everywhere needed, that could be a lot of mappers.

@cflipse What about base ROM::Mapper. We could extend it by Transproc::Registry and then add shortcuts #[] (aliased as #t) to access its singleton methods from every mapper instance, not to mention mapper classes.

This would mean we have dependency on Transproc. But maybe that would not be a bad thing. Hmm.

@solnic Well, in ROM::Mapper we have that dependency.
We could (as @AMHOL pointed, if I understood it correctly) use other mapper engine, and this make global ROM dependency no-go.

@nepalez technically speaking ROM uses its default transformer compiler to turn a mapper header into transproc function, but it's not a real dependency, it is decoupled from the implementation pov

Ahh, i see. But this make me curious whether we really expect somebody some time decide to write its own compiler to substitute Transproc? I'd rather expect somebody to write another mapper based on the same transformer :)

@nepalez I think it's nice to keep it separate and leave that option, who knows what people could come up with :) But I'm not 100% sure if it's worth the effort.

...that's why I'm so eager to see how your abstract_mapper + faceter evolve. If we could establish a solid mapper AST that can be used to produce fast mappers then we could have compilers that turn that AST to whatever, be it a transproc function or something else. I also hope that optimizations could be really advanced so that we can keep things very fast.

...I'm trying to faster the process by reading Dragon Book. Seems like this was not a cute idea.

I'm fine with base mapper, if that implied dependency is good with others

I think the beauty of swappable dependencies, is swappable behaviour, who knows what you could twist and bend ROM to do if you were to replace the transproc dependency, but without the option, we end up with a monkey-patch it and run philosophy that I would avoid at all costs. Middleman adapters FTW IMO.

This issue was moved to rom-rb/rom-mapper#8


fix syntax errors in get_win32_password

receive patterns require a return
functions must end with end
must use length/1 vs size/1 on strings

Additionally, is it possible with circle-ci to not allow a merge if the build is failing?

@starbelly that isn't a circleci thing, it is github. It should be working just as it does with travis where it requires override the failure to merge.


Synth

Probably Tone.js but we can make custom audio worklets if Tone isn't xen enough.

We have a basic synth now. I'll make a new ticket when a more advanced synth can be integrated.


⚠ Bofrost.lu has degraded performance

In 1d683e1, Bofrost.lu (https://www.bofrost.lu) experienced degraded performance:

HTTP code: 200
Response time: 1267 ms

Resolved: Bofrost.lu performance has improved in 85889f7.


migrate: install rustfmt from rustup (#815)

Should we include rustup installation in our installation scripts?
Besides rustfmt, sniprun also needs cargo to compile, so I think it might be acceptable to add it.
cc @Jint-lzxy @ClSlaid @volpan

Sure! But how can we ensure proper installation (of rustup) on non-Windows platforms?

Sure! But how can we ensure proper installation (of rustup) on non-Windows platforms?

IMO the most direct way is add rustup to wiki.

Sure! But how can we ensure proper installation (of rustup) on non-Windows platforms?

I think we can just check if cargo and rustc is installed?

IMO the most direct way is adding rustup to wiki/Prerequisites.
EDIT: for rust users

OK, I had added it in the prerequisites.

cargo, rustc and rustfmt are added by default when installing toolchain:


Add callsign to viewable feedback

Hi,
I have made a few changes to the publicly accessible feedback page. This is my first 'play around' with core so I'm still learning, any feedback/comments with improvements for next time are much appreciated.

I propose we remove the "Message From" which displays the staff member who sent the feedback (which seems to confuse some members) with a more useful "Callsign" to display the position the user was on when the feedback was received. I haven't placed this inside of an if statement as any feedback sent to the user should have a position attached, however, this can be easily changed.

All the best!

I take it the callsign is the callsign of the controller / pilot not the callsign of the person leaving the feedback?

I take it the callsign is the callsign of the controller / pilot not the callsign of the person leaving the feedback?

Correct. It gets the answer the person leaving the feedback provided from the feedback_answers table.


Safari supports BigInt

BigInts are reported as an error but are supported by the latest Safari versions: https://caniuse.com/bigint
Example error:

Wow thanks for the quick fix!


'-Wl -ld_classic ' in OTHER_LDFLAGS gets added on every Pod installation regardless if it already exists

Description
After updating to react-native 0.72.5 every time pod install is run, the '-Wl -ld_classic ' flags are added to OTHER_LDFLAGS even if they already exist.
I guess there is no check in-place for if the flags already exists in this patch for XCode 15 as the code suggests that it will add the flags every time if XCode >= 15 and remove if not.
I also noticed that the in first addition the -Wl flag is not appended correctly as a separate entry but as part of $(inherited)-Wl which may or may not be part of the problem.
React Native Version
0.72.5
Output of npx react-native info
System:
  OS: macOS 14.0
  CPU: (12) arm64 Apple M2 Max
  Memory: 622.66 MB / 32.00 GB
  Shell:
    version: "5.9"
    path: /bin/zsh
Binaries:
  Node:
    version: 18.15.0
    path: ~/Library/Caches/fnm_multishells/69989_1696588949148/bin/node
  Yarn:
    version: 1.22.19
    path: ~/Library/Caches/fnm_multishells/69989_1696588949148/bin/yarn
  npm:
    version: 9.8.1
    path: ~/Library/Caches/fnm_multishells/69989_1696588949148/bin/npm
  Watchman:
    version: 2<IP_ADDRESS>
    path: /opt/homebrew/bin/watchman
Managers:
  CocoaPods:
    version: 1.13.0
    path: /Users/xxx/.rvm/gems/ruby-3.0.0/bin/pod
SDKs:
  iOS SDK:
    Platforms:
      - DriverKit 23.0
      - iOS 17.0
      - macOS 14.0
      - tvOS 17.0
      - watchOS 10.0
  Android SDK: Not Found
IDEs:
  Android Studio: 2022.3 AI-223.8836.35.2231.10811636
  Xcode:
    version: 15.0/15A240d
    path: /usr/bin/xcodebuild
Languages:
  Java:
    version: <IP_ADDRESS>
    path: /Users/xxx/.jenv/shims/javac
  Ruby:
    version: 3.0.0
    path: /Users/xxx/.rvm/rubies/ruby-3.0.0/bin/ruby
npmPackages:
  "@react-native-community/cli": Not Found
  react:
    installed: 18.2.0
    wanted: 18.2.0
  react-native:
    installed: 0.72.5
    wanted: 0.72.5
  react-native-macos: Not Found
npmGlobalPackages:
  "*react-native*": Not Found
Android:
  hermesEnabled: true
  newArchEnabled: false
iOS:
  hermesEnabled: true
  newArchEnabled: false

Steps to reproduce
Reproduction repo url: https://github.com/xeroxoid/rn-xcode15-flag-repro/tree/main/ReproducerApp

run pod install
check OTHER_LDFLAGS in project build settings (targets are inherited)

Snack, screenshot, or link to a repository
Other Linker Flags after running pod install 3 times:

Thanks for reporting. We already provided a fix for it and it will be included in 0.72.6. https://github.com/facebook/react-native/commit/67ebc7c7d5e04405d59b8cd29dd6cf7d33115cd5

Will this be included on a follow-up for 0.70.14 ?

0.70.14 doesn't have that issue, as we fixed it in this commit.
Make sure to cleanup the project and that the xcodebuild --version output is correct:

if it outputs Xcode 15, it adds the flags if they are not there
if it outputs Xcode 14, it removes the flags if they are present.

I do not have these flags locally, I am running Xcode 15.2 and I set the min deployment version in Xcode to iOS 16. Don't have issues when I build locally but when I run on GitHub Actions it's adding these flags (which breaks my Fastlane lane because I have a check to make sure the repo is clean before building). Based on the Xcode 15 release notes these flags shouldn't be needed if the deployment target is iOS 15+.
I am using React Native 0.73.6.

@simon-assistiq I'm sorry that you are facing this issue. The code that adds those flags is here and the is_using_xcode_15_0 is here.
Make sure that you are using the right version of Xcode in Github Actions. I feel that there is some misconfigurations between your setup and GH Actions.

Oh so these changes are only supposed to show up in 15.0 and not later versions?

According to logs, GitHub Actions is using the same version of Xcode (15.2, 15C500b)

Though now it looks likely that it's happening because I call the Xcode step after the yarn / pod install steps


Broken source links in documentation of 1.3.0.post0

📚 Documentation
The part of the link that is bugged is generated in conf.py by the function:
def _get_version_str():
    if any(s in torchmetrics.__version__ for s in ("dev", "rc")):
        version_str = "master"
    else:
        version_str = f"v{torchmetrics.__version__}"
    return version_str

This function assumes that torchmetrics.__version__  (from __about__.py) is the same as the tag name, except for a "v" prefix in the tag name (that _get_version_str adds, as you can see on the code I displayed above).
The problem here is that torchmetrics.__version__ is "1.3.0.post0" and the tag name is v1.3.0.post instead of v1.3.0.post0.
The generated links thus end up with the additional "0" in them, which leads to a 404 error.

I think the code is fine and my wrong with naming the git tag, I shall be more vigilant... so created a new tag with the same commit SHA.

Yep it works now, thanks for the fix :)


Pass ServiceConfig's maxRequestSize into HttpRequestParser

@DanSimon @benblack86 @jlbelmonte @aliyakamercan @amotamed
https://github.com/tumblr/colossus/issues/542

Wondering if the maxInitBufferSize parameter to bytes() should also come from ServiceConfig.requestBufferSize.

Codecov Report

Merging #549 into master will increase coverage by <.01%.
The diff coverage is 100%.

@@            Coverage Diff             @@
##           master     #549      +/-   ##
==========================================
+ Coverage   84.83%   84.83%   +<.01%
==========================================
  Files          98       98
  Lines        4312     4313       +1
  Branches      342      361      +19
==========================================
+ Hits         3658     3659       +1
  Misses        654      654

Impacted Files
Coverage Δ

...ain/scala/colossus/protocols/http/HttpServer.scala
68.75% <100%> (ø)
:arrow_up:

...la/colossus/protocols/http/HttpRequestParser.scala
95.83% <100%> (+0.18%)
:arrow_up:

...cala/colossus/protocols/http/HttpClientCodec.scala
40% <100%> (ø)
:arrow_up:

...cala/colossus/protocols/http/HttpServerCodec.scala
80% <100%> (ø)
:arrow_up:

Continue to review full report at Codecov.

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ServiceConfig.maxRequestSize is passed into ServiceServer.controllerConfig.inputMaxSize. Should we change reference.conf accordingly?

My initial thoughts are yes we should add to reference.conf and no to maxInitBufferSize since I think we should leave that until people ask for it (hopefully never?!). I think passing ServiceConfig is fine since it is passed by reference so there should be no performance difference between passing an int and a ServiceConfig object (unless cpu memory is affected?).

So ServiceConfig.requestBufferSize is not the size of a byte buffer, but rather the maximum number of pending requests for a single connection before the connection starts automatically failing requests.  The Http parsers won't need that value.
But maxInputBufferSize in bytes() and inputMaxSize in ControllerConfig should both be set to ServiceConfig.maxRequestSize.
So you should not have to pass in the entire ServiceConfig to the parsers.  I don't think that should be done anyway since the parser doesn't need to know about any of the other settings and we should always aim to keep things as focused as possible.  If in the future a parser needs multiple values from the config, we would group those into another case class inside of ServiceConfig.

@DanSimon In Parsing.scala:
def bytes(num: Int, maxSize: DataSize, maxInitBufferSize: DataSize): Parser[Array[Byte]] = { ... }

Which parameter did you mean by maxInputBufferSize? If it's maxInitBufferSize, I recall you said a while ago that this should be small, which is why it's currently hardcoded to 1 KB (https://github.com/tumblr/colossus/pull/505).

Ah yeah I misread, I meant maxSize when talking about coming from config.  You're correct that maxInitBufferSize should be small and can stay hardcoded at 1KB.  It could potentially be an issue if a service is constantly parsing large (multi-MB) requests, but would need some experimentation to be really sure before we make it configurable.

bump

bump, added docs


[WIP] GPG signature verification

I start to implement API and extracting information for the git cli via gogits/git-module#13. It may need to add some little parts to the gogits/go-gogs-client to be more specific to GPG key api.
Related to gogits/gogs#2321 and gogits/gogs#2981

@sapk Make sure you are not doing double work here. @bkcsoft indicates two weeks ago to be working on this too: https://github.com/gogits/gogs/issues/2321#issuecomment-218274684

I have not started on that one yet, and this one does verification which
mine wouldn't (only stupidly show that it had been signed)
WRT people working on things. PR fast, mark it WIP. If no PR for a feature
exists, no one is working on it :laughing:

@bkcsoft Totally of topic, but it would be nice to have people link to the branch where they are working on it (I know it's visible, but not clickable), so that people could file PRs to that feature's branch to contribute :) Just imagining an ideal world of decentralized collaboration ;)
Good to see you are also actively maintaining this project next to Unknwon!

@stevenroose I had not noticed that they were not clickable :laughing: Yes, link to branch would be nice (even though the PR will update when the branch is pushed to...)

@sapk still working on this? I found OpenPGP.js and realized that verification could be done in the frontend to off-load the backend (not that it requires off-loading, but nice to keep the load in the frontend if it's possible :laughing: ) Thoughts?

I didn't tackle the part of the of the verification and I didn't have some time to go further but i will re-put myself on it on the next days. Doing it in the front-end could done easely. it just need the API to get the user public key from gogs like https://developer.github.com/v3/users/keys/. ON the other i don't thinks it would be to much work for the backen to do it. I have to think about it but at first at will implement the API because in both case there will be needed (or at least usefull)

any news ? :)

sadly no time yet :-(

??

@Unknwon why did you close this?

@sapk not sure why this was closed, but could you maybe send this PR to gitea as well? 🙂

I'd kindly invite you to join the Gitea community where a more open approach to community-driven software development is used :)


Document how to view the very first message in a stream

We may want to mention in a user docs page how to view the first message in a stream (maybe as a tip on the page about all the search filters?).  Here's how to do it:
https://chat.zulip.org/#narrow/near/1/stream/documentation

I'm taking this up @timabbott

Fixed via de0329b4f94b49c5cfd7a154bc023078575b9245


required permissions

Describe the bug
A clear and concise description of what the bug is.
To Reproduce
Steps to reproduce the behavior.
Expected behavior
A clear and concise description of what you expected to happen.
Environment summary
Install Method (e.g. pip, interactive script, apt-get, Docker, MSI, edge build) / CLI version (az --version) / OS version / Shell Type (e.g. bash, cmd.exe, Bash on Windows)
Additional context
Add any other context about the problem here.

Closing due to no actionable information.


Invalid Resolution Rule Tweaks

Describe the bug
A couple issues regarding the invalid resolution rules:

'Any of the outcomes don’t answer the market question ONLY. (outcomes cannot introduce a secondary question)' <- This is worded awkwardly and the sentence in parentheses should start with a capital letter at the least, with the sentence period coming after the end of the parentheses. Perhaps something like "Market outcomes introduce a secondary question" is sufficient.

The last two rules say "the market should resolve as invalid." and "it has a high probability as resolving as invalid". This is redundant with the beginning of the invalid resolution rules which say "A market is invalid if:". The other rules aren't structured that way either, so none of them should be. The rules should be consistent in language structure.

I should add that the newly created rule "If markets do not have a resolution source, they should resolve using general knowledge." is not a reason why a market should be invalid. The preface of the invalid resolution rules states "A market is invalid if:".. so "A market is invalid if If markets do not have a resolution source, they should resolve using general knowledge" doesn't make sense.


Handle forking, track canonical chain

Closes #19
Keeps track of the canonical chain using total difficulty, the current fork choice metric for the eth mainnet. Does not keep track of forks other than the canonical chain, instead replaces the canonical chain.
Also I think we should consider removing the verifiedBlocks and finalizedBlocks mappings, and replacing them with view functions that calculate how many confirmations a block on the canonical chain has from the canonical head.

Refactored verified and finalized blocks into view functions based on confirmations. Let me know how many confirmations seem good for this purpose.

Ready for review, canonical chain tracking along with testing for canonical chain. Implemented test contract folder, where test contracts can inherit main contracts to expose extra testing functionality. Also includes a couple fixes to the environment/cli.

This should be mergable pending test result reproduction @gupadhyaya

@brucdarc can you resolve the merge conflicts?

@brucdarc can you resolve the merge conflicts?

Done


[SPARK-10833] [BUILD] [WIP] Inline, organize BSD/MIT licenses in LICENSE

Work in progress -- don't merge yet
In the course of https://issues.apache.org/jira/browse/LEGAL-226 it came to light that the guidance at http://www.apache.org/dev/licensing-howto.html#permissive-deps means that permissively-licensed dependencies has a different interpretation than we (er, I) had been operating under. "pointer ... to the license within the source tree" specifically means a copy of the license within Spark's distribution, whereas at the moment, Spark's LICENSE has a pointer to the project's license in the other project's source tree.
The remedy is simply to inline all such license references (i.e. BSD/MIT licenses) or include their text in "licenses" subdirectory and point to that.
Along the way, we can also treat other BSD/MIT licenses, whose text has been inlined into LICENSE, in the same way.
The LICENSE file can continue to provide a helpful list of BSD/MIT licensed projects and a pointer to their sites. This would be over and above including license text in the distro, which is the essential thing.

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Test build #43029 has started for   PR 8919 at commit c71700d.

Test build #43028 has finished for   PR 8919 at commit d05ed16.

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Anybody care too much one way or the other about the license stuff? I've done my best to manage that aspect, and this represents my current best judgment about how these licenses should be bundled. It's a non-functional change, so I feel reasonably good going ahead with it.

Hey Sean - looks good to me, but can't claim to be nearly as deep as you are on this stuff!


Allow split actions to be edited and implement IOU action completeSplitBill

Details

Fixed Issues

$ https://github.com/Expensify/Expensify/issues/322303
Tests

[ ] Verify that no errors appear in the JS console

Offline tests

QA Steps

[ ] Verify that no errors appear in the JS console

PR Author Checklist

[ ] I linked the correct issue in the ### Fixed Issues section above
[ ] I wrote clear testing steps that cover the changes made in this PR

[ ] I added steps for local testing in the Tests section
[ ] I added steps for the expected offline behavior in the Offline steps section
[ ] I added steps for Staging and/or Production testing in the QA steps section
[ ] I added steps to cover failure scenarios (i.e. verify an input displays the correct error message if the entered data is not correct)
[ ] I turned off my network connection and tested it while offline to ensure it matches the expected behavior (i.e. verify the default avatar icon is displayed if app is offline)
[ ] I tested this PR with a High Traffic account against the staging or production API to ensure there are no regressions (e.g. long loading states that impact usability).

[ ] I included screenshots or videos for tests on all platforms
[ ] I ran the tests on all platforms & verified they passed on:

[ ] Android / native
[ ] Android / Chrome
[ ] iOS / native
[ ] iOS / Safari
[ ] MacOS / Chrome / Safari
[ ] MacOS / Desktop

[ ] I verified there are no console errors (if there's a console error not related to the PR, report it or open an issue for it to be fixed)
[ ] I followed proper code patterns (see Reviewing the code)

[ ] I verified that any callback methods that were added or modified are named for what the method does and never what callback they handle (i.e. toggleReport and not onIconClick)
[ ] I verified that the left part of a conditional rendering a React component is a boolean and NOT a string, e.g. myBool && <MyComponent />.
[ ] I verified that comments were added to code that is not self explanatory
[ ] I verified that any new or modified comments were clear, correct English, and explained "why" the code was doing something instead of only explaining "what" the code was doing.
[ ] I verified any copy / text shown in the product is localized by adding it to src/languages/* files and using the translation method

[ ] If any non-english text was added/modified, I verified the translation was requested/reviewed in #expensify-open-source and it was approved by an internal Expensify engineer. Link to Slack message:

[ ] I verified all numbers, amounts, dates and phone numbers shown in the product are using the localization methods
[ ] I verified any copy / text that was added to the app is grammatically correct in English. It adheres to proper capitalization guidelines (note: only the first word of header/labels should be capitalized), and is approved by marketing by adding the Waiting for Copy label for a copy review on the original GH to get the correct copy.
[ ] I verified proper file naming conventions were followed for any new files or renamed files. All non-platform specific files are named after what they export and are not named "index.js". All platform-specific files are named for the platform the code supports as outlined in the README.
[ ] I verified the JSDocs style guidelines (in STYLE.md) were followed

[ ] If a new code pattern is added I verified it was agreed to be used by multiple Expensify engineers
[ ] I followed the guidelines as stated in the Review Guidelines
[ ] I tested other components that can be impacted by my changes (i.e. if the PR modifies a shared library or component like Avatar, I verified the components using Avatar are working as expected)
[ ] I verified all code is DRY (the PR doesn't include any logic written more than once, with the exception of tests)
[ ] I verified any variables that can be defined as constants (ie. in CONST.js or at the top of the file that uses the constant) are defined as such
[ ] I verified that if a function's arguments changed that all usages have also been updated correctly
[ ] If a new component is created I verified that:

[ ] A similar component doesn't exist in the codebase
[ ] All props are defined accurately and each prop has a /** comment above it */
[ ] The file is named correctly
[ ] The component has a clear name that is non-ambiguous and the purpose of the component can be inferred from the name alone
[ ] The only data being stored in the state is data necessary for rendering and nothing else
[ ] If we are not using the full Onyx data that we loaded, I've added the proper selector in order to ensure the component only re-renders when the data it is using changes
[ ] For Class Components, any internal methods passed to components event handlers are bound to this properly so there are no scoping issues (i.e. for onClick={this.submit} the method this.submit should be bound to this in the constructor)
[ ] Any internal methods bound to this are necessary to be bound (i.e. avoid this.submit = this.submit.bind(this); if this.submit is never passed to a component event handler like onClick)
[ ] All JSX used for rendering exists in the render method
[ ] The component has the minimum amount of code necessary for its purpose, and it is broken down into smaller components in order to separate concerns and functions

[ ] If any new file was added I verified that:

[ ] The file has a description of what it does and/or why is needed at the top of the file if the code is not self explanatory

[ ] If a new CSS style is added I verified that:

[ ] A similar style doesn't already exist
[ ] The style can't be created with an existing StyleUtils function (i.e. StyleUtils.getBackgroundAndBorderStyle(themeColors.componentBG))

[ ] If the PR modifies code that runs when editing or sending messages, I tested and verified there is no unexpected behavior for all supported markdown - URLs, single line code, code blocks, quotes, headings, bold, strikethrough, and italic.
[ ] If the PR modifies a generic component, I tested and verified that those changes do not break usages of that component in the rest of the App (i.e. if a shared library or component like Avatar is modified, I verified that Avatar is working as expected in all cases)
[ ] If the PR modifies a component related to any of the existing Storybook stories, I tested and verified all stories for that component are still working as expected.
[ ] If the PR modifies a component or page that can be accessed by a direct deeplink, I verified that the code functions as expected when the deeplink is used - from a logged in and logged out account.
[ ] If a new page is added, I verified it's using the ScrollView component to make it scrollable when more elements are added to the page.
[ ] If the main branch was merged into this PR after a review, I tested again and verified the outcome was still expected according to the Test steps.
[ ] I have checked off every checkbox in the PR author checklist, including those that don't apply to this PR.

Screenshots/Videos

Web

Mobile Web - Chrome

Mobile Web - Safari

Desktop

iOS

Android

Updated @s77rt! All comments addressed and I just added displaying errors next to missing fields
https://github.com/Expensify/App/assets/9680864/596a1cfa-6df0-47ab-8350-e91c28039539

@s77rt you should be able to test against staging now

Yeah working on that bug, I think I may need a backend change to fix it, although it's shown there it's actually not set in the transaction's modifedCreated, but only created.

Test steps updated.

Split requests are not editable when initialising

That's expected @s77rt, we assume the user will rely on SmartScan to complete the split bill for them (which is implemented in the backend now).

Question: If the receipt is scanned, should we expect the options: amount, merchant, etc. to be disabled?

Minor bug: If we create the split request and click split quickly an error arises
https://github.com/Expensify/App/assets/16493223/29b68b21-7506-4ac2-af1a-6ef473024e19

Seems like an Onyx issue when the transaction is not ready yet in the component? will investigate

I will trigger a build now

Minor bug: If we create the split request and click split quickly an error arises

After the receipt got scanned, getting same error and can't complete the split
https://github.com/Expensify/App/assets/16493223/84448d7c-d179-49a4-800c-6007a5f3f6bf

Isnt it because we only set the draft transaction when you edit something but if you just go to details and click split without changing anything then there is no draft yet which is where we grab the transaction from https://github.com/Expensify/App/blob/7a6f4b2d9c7c050867acc9cc3de115fd7afaa5b8/src/pages/EditSplitBillPage.js#L66-L69

If there's no draft transaction we still grab the original transaction though? investigating

ah just noticed that's in completeSplitBill, you're probably right @mountiny

Use the links below to test this build in android and iOS. Happy testing!

:test_tube::test_tube: Use the links below to test this build in android and iOS. Happy testing! :test_tube::test_tube:

android :robot:
iOS :apple:

https://ad-hoc-expensify-cash.s3.amazonaws.com/android/29064/index.html
https://ad-hoc-expensify-cash.s3.amazonaws.com/ios/29064/index.html

desktop :computer:
web :spider_web:

https://ad-hoc-expensify-cash.s3.amazonaws.com/desktop/29064/NewExpensify.dmg
https://29064.pr-testing.expensify.com

After the receipt got scanned, getting same error and can't complete the split

How was that transaction got filled? you entered the details manually then clicked on 'split'?

It was filled by backend

Weird, when it gets filled by the backend it should no longer be editable

Are you testing against prod? There is a backend PR that just got deployed 16mins ago. Can you try again?

Should be fixed now, can you try again please? Thanks for catching this!

Weird bug

Should be fixed now, can you try again please? Thanks for catching this!

Once the receipt is scanned the options are filled automatically and disabled

Yup that's how it should work.

The first testing steps may be a little confusing

Start a split bill from Global Create by using the Scan tab and attaching a receipt, choose a new user.
Tap on the split action to reveal the split details page.
Make sure the page is editable.

The first step is to start split bill and complete it (up until clicking Split)

On mWeb the split details is not scrollable
https://github.com/Expensify/App/assets/16493223/ce9c2098-f45c-48d8-92b9-ed642a743998

On mweb/native the form error overlaps with the rest of elements

Reviewer Checklist

[ ] I have verified the author checklist is complete (all boxes are checked off).
[ ] I verified the correct issue is linked in the ### Fixed Issues section above
[ ] I verified testing steps are clear and they cover the changes made in this PR

[ ] I verified the steps for local testing are in the Tests section
[ ] I verified the steps for Staging and/or Production testing are in the QA steps section
[ ] I verified the steps cover any possible failure scenarios (i.e. verify an input displays the correct error message if the entered data is not correct)
[ ] I turned off my network connection and tested it while offline to ensure it matches the expected behavior (i.e. verify the default avatar icon is displayed if app is offline)

[ ] I checked that screenshots or videos are included for tests on all platforms
[ ] I included screenshots or videos for tests on all platforms
[ ] I verified tests pass on all platforms & I tested again on:

[ ] Android: Native
[ ] Android: mWeb Chrome
[ ] iOS: Native
[ ] iOS: mWeb Safari
[ ] MacOS: Chrome / Safari
[ ] MacOS: Desktop

[ ] If there are any errors in the console that are unrelated to this PR, I either fixed them (preferred) or linked to where I reported them in Slack
[ ] I verified proper code patterns were followed (see Reviewing the code)

[ ] I verified that any callback methods that were added or modified are named for what the method does and never what callback they handle (i.e. toggleReport and not onIconClick).
[ ] I verified that the left part of a conditional rendering a React component is a boolean and NOT a string, e.g. myBool && <MyComponent />.
[ ] I verified that comments were added to code that is not self explanatory
[ ] I verified that any new or modified comments were clear, correct English, and explained "why" the code was doing something instead of only explaining "what" the code was doing.
[ ] I verified any copy / text shown in the product is localized by adding it to src/languages/* files and using the translation method
[ ] I verified all numbers, amounts, dates and phone numbers shown in the product are using the localization methods
[ ] I verified any copy / text that was added to the app is grammatically correct in English. It adheres to proper capitalization guidelines (note: only the first word of header/labels should be capitalized), and is approved by marketing by adding the Waiting for Copy label for a copy review on the original GH to get the correct copy.
[ ] I verified proper file naming conventions were followed for any new files or renamed files. All non-platform specific files are named after what they export and are not named "index.js". All platform-specific files are named for the platform the code supports as outlined in the README.
[ ] I verified the JSDocs style guidelines (in STYLE.md) were followed

[ ] If a new code pattern is added I verified it was agreed to be used by multiple Expensify engineers
[ ] I verified that this PR follows the guidelines as stated in the Review Guidelines
[ ] I verified other components that can be impacted by these changes have been tested, and I retested again (i.e. if the PR modifies a shared library or component like Avatar, I verified the components using Avatar have been tested & I retested again)
[ ] I verified all code is DRY (the PR doesn't include any logic written more than once, with the exception of tests)
[ ] I verified any variables that can be defined as constants (ie. in CONST.js or at the top of the file that uses the constant) are defined as such
[ ] If a new component is created I verified that:

[ ] A similar component doesn't exist in the codebase
[ ] All props are defined accurately and each prop has a /** comment above it */
[ ] The file is named correctly
[ ] The component has a clear name that is non-ambiguous and the purpose of the component can be inferred from the name alone
[ ] The only data being stored in the state is data necessary for rendering and nothing else
[ ] For Class Components, any internal methods passed to components event handlers are bound to this properly so there are no scoping issues (i.e. for onClick={this.submit} the method this.submit should be bound to this in the constructor)
[ ] Any internal methods bound to this are necessary to be bound (i.e. avoid this.submit = this.submit.bind(this); if this.submit is never passed to a component event handler like onClick)
[ ] All JSX used for rendering exists in the render method
[ ] The component has the minimum amount of code necessary for its purpose, and it is broken down into smaller components in order to separate concerns and functions

[ ] If any new file was added I verified that:

[ ] The file has a description of what it does and/or why is needed at the top of the file if the code is not self explanatory

[ ] If a new CSS style is added I verified that:

[ ] A similar style doesn't already exist
[ ] The style can't be created with an existing StyleUtils function (i.e. StyleUtils.getBackgroundAndBorderStyle(themeColors.componentBG)

[ ] If the PR modifies code that runs when editing or sending messages, I tested and verified there is no unexpected behavior for all supported markdown - URLs, single line code, code blocks, quotes, headings, bold, strikethrough, and italic.
[ ] If the PR modifies a generic component, I tested and verified that those changes do not break usages of that component in the rest of the App (i.e. if a shared library or component like Avatar is modified, I verified that Avatar is working as expected in all cases)
[ ] If the PR modifies a component related to any of the existing Storybook stories, I tested and verified all stories for that component are still working as expected.
[ ] If the PR modifies a component or page that can be accessed by a direct deeplink, I verified that the code functions as expected when the deeplink is used - from a logged in and logged out account.
[ ] If a new page is added, I verified it's using the ScrollView component to make it scrollable when more elements are added to the page.
[ ] If the main branch was merged into this PR after a review, I tested again and verified the outcome was still expected according to the Test steps.
[ ] I have checked off every checkbox in the PR reviewer checklist, including those that don't apply to this PR.

Screenshots/Videos

Web

https://github.com/Expensify/App/assets/16493223/62170b25-c330-4568-a694-4a60ba92488b

Mobile Web - Chrome

https://github.com/Expensify/App/assets/16493223/07d2fd3b-c44a-4810-a7b1-eaed2c42e152

Mobile Web - Safari

https://github.com/Expensify/App/assets/16493223/c0746c50-e846-464c-905c-9c2131aa7cb7

Desktop

https://github.com/Expensify/App/assets/16493223/c9e69383-c905-4d6e-b648-b9c6af3b4d70

iOS

https://github.com/Expensify/App/assets/16493223/0e0fe0dc-527d-4b85-bd55-1ba1373bd55b

Android

https://github.com/Expensify/App/assets/16493223/5527ec56-537a-4356-a24a-c44bd9f1b7e5

@s77rt any other issues you found other than the two UI issues on mWeb?

Can you check please if the issue exists with regular splits as well?

Weird, why is linting failed based on files I haven't changed in this PR?

any other issues you found other than the two UI issues on mWeb?

So far that's all.

Can you check please if the issue exists with regular splits as well?

They don't (or at least I cannot reproduce them - maybe because of the much more available space)

@youssef-lr we just fixed the lint error in this PR. Please merge main again

Not getting an error when the amount is unset

@s77rt fixed the console warnings related to MenuItem as well as the amount issue, still trying to figure out the transaction prop warning issue.

TIL

Wow really interesting! I expected to false too

I have no idea how to fix this..

maybe it's due to how we fetch the transaction from Onyx using reportActions that are also from Onyx?

Strange how the error is The prop transaction.isRequired is marked as required instead of The prop transaction is marked as required.

Okay got it. It should be
transaction: transactionPropTypes.isRequired

and not
transaction: PropTypes.shape(transactionPropTypes).isRequired

transactionPropTypes already is a shape. Same fix needed for draftTransaction in SplitBillDetailsPage and EditSplitBillPage

@s77rt this is ready, can you pleaase complete the checklist so we can get this PR to staging today. Thanks!

Conflicts

@mountiny Can you please fix this prop type for draftTransaction as well

Is the mweb bug supposed to be fixed later?

Note: we merged this PR as is and will address the remaining issues as a followup tomorrow @youssef-lr

:hand: This PR was not deployed to staging yet because QA is ongoing. It will be automatically deployed to staging after the next production release.

Thanks folks!!!

🚀 Deployed to production by https://github.com/jasperhuangg in version: 1.3.83-11 🚀

platform
result

🤖 android 🤖
skipped 🚫

🖥 desktop 🖥
skipped 🚫

🍎 iOS 🍎
skipped 🚫

🕸 web 🕸
skipped 🚫

🚀 Deployed to production by https://github.com/jasperhuangg in version: 1.3.83-11 🚀

platform
result

🤖 android 🤖
skipped 🚫

🖥 desktop 🖥
skipped 🚫

🍎 iOS 🍎
skipped 🚫

🕸 web 🕸
skipped 🚫

🚀 Deployed to production by https://github.com/jasperhuangg in version: 1.3.83-11 🚀

platform
result

🤖 android 🤖
skipped 🚫

🖥 desktop 🖥
skipped 🚫

🍎 iOS 🍎
skipped 🚫

🕸 web 🕸
skipped 🚫

🚀 Deployed to production by https://github.com/jasperhuangg in version: 1.3.83-11 🚀

platform
result

🤖 android 🤖
failure ❌

🖥 desktop 🖥
success ✅

🍎 iOS 🍎
failure ❌

🕸 web 🕸
success ✅


2018-06-25 10:34:50.968306: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 8.56M (8971776 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY

When I run the below code it gives me the CUDA_ERROR_OUT_OF_MEMORY
mona@Mona:~/code/handpose/handtracking$ python detect_multi_threaded.py
/home/mona/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
{'im_width': 320.0, 'im_height': 180.0, 'score_thresh': 0.2, 'num_hands_detect': 2} Namespace(display=1, fps=1, height=200, num_hands=2, num_workers=4, queue_size=5, video_source=0, width=300)
>> loading frozen model for worker
> ====== loading HAND frozen graph into memory
>> loading frozen model for worker
> ====== loading HAND frozen graph into memory
>> loading frozen model for worker
> ====== loading HAND frozen graph into memory
>> loading frozen model for worker
> ====== loading HAND frozen graph into memory
2018-06-25 10:34:50.323937: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2018-06-25 10:34:50.341322: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2018-06-25 10:34:50.345967: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2018-06-25 10:34:50.352734: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2018-06-25 10:34:50.634127: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1356] Found device 0 with properties:
name: TITAN Xp major: 6 minor: 1 memoryClockRate(GHz): 1.582
pciBusID: 0000:01:00.0
totalMemory: 11.90GiB freeMemory: 10.78GiB
2018-06-25 10:34:50.634172: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1435] Adding visible gpu devices: 0
2018-06-25 10:34:50.702435: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1356] Found device 0 with properties:
name: TITAN Xp major: 6 minor: 1 memoryClockRate(GHz): 1.582
pciBusID: 0000:01:00.0
totalMemory: 11.90GiB freeMemory: 10.44GiB
2018-06-25 10:34:50.702479: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1435] Adding visible gpu devices: 0
2018-06-25 10:34:50.703274: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1356] Found device 0 with properties:
name: TITAN Xp major: 6 minor: 1 memoryClockRate(GHz): 1.582
pciBusID: 0000:01:00.0
totalMemory: 11.90GiB freeMemory: 10.44GiB
2018-06-25 10:34:50.703303: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1435] Adding visible gpu devices: 0
2018-06-25 10:34:50.710124: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1356] Found device 0 with properties:
name: TITAN Xp major: 6 minor: 1 memoryClockRate(GHz): 1.582
pciBusID: 0000:01:00.0
totalMemory: 11.90GiB freeMemory: 10.30GiB
2018-06-25 10:34:50.710171: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1435] Adding visible gpu devices: 0
2018-06-25 10:34:50.903013: I tensorflow/core/common_runtime/gpu/gpu_device.cc:923] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-06-25 10:34:50.903050: I tensorflow/core/common_runtime/gpu/gpu_device.cc:929]      0
2018-06-25 10:34:50.903056: I tensorflow/core/common_runtime/gpu/gpu_device.cc:942] 0:   N
2018-06-25 10:34:50.903294: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1053] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9864 MB memory) -> physical GPU (device: 0, name: TITAN Xp, pci bus id: 0000:01:00.0, compute capability: 6.1)
2018-06-25 10:34:50.948216: I tensorflow/core/common_runtime/gpu/gpu_device.cc:923] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-06-25 10:34:50.948250: I tensorflow/core/common_runtime/gpu/gpu_device.cc:929]      0
2018-06-25 10:34:50.948255: I tensorflow/core/common_runtime/gpu/gpu_device.cc:942] 0:   N
2018-06-25 10:34:50.948392: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1053] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 262 MB memory) -> physical GPU (device: 0, name: TITAN Xp, pci bus id: 0000:01:00.0, compute capability: 6.1)
2018-06-25 10:34:50.950178: I tensorflow/core/common_runtime/gpu/gpu_device.cc:923] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-06-25 10:34:50.950208: I tensorflow/core/common_runtime/gpu/gpu_device.cc:929]      0
2018-06-25 10:34:50.950214: I tensorflow/core/common_runtime/gpu/gpu_device.cc:942] 0:   N
2018-06-25 10:34:50.950366: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1053] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 223 MB memory) -> physical GPU (device: 0, name: TITAN Xp, pci bus id: 0000:01:00.0, compute capability: 6.1)
2018-06-25 10:34:50.951312: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 223.88M (234749952 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
>  ====== Hand Inference graph loaded.
>  ====== Hand Inference graph loaded.
2018-06-25 10:34:50.957782: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1435] Adding visible gpu devices: 0
2018-06-25 10:34:50.957830: I tensorflow/core/common_runtime/gpu/gpu_device.cc:923] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-06-25 10:34:50.957838: I tensorflow/core/common_runtime/gpu/gpu_device.cc:929]      0
2018-06-25 10:34:50.957844: I tensorflow/core/common_runtime/gpu/gpu_device.cc:942] 0:   N
2018-06-25 10:34:50.957961: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1053] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 262 MB memory) -> physical GPU (device: 0, name: TITAN Xp, pci bus id: 0000:01:00.0, compute capability: 6.1)
2018-06-25 10:34:50.959096: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1435] Adding visible gpu devices: 0
2018-06-25 10:34:50.959128: I tensorflow/core/common_runtime/gpu/gpu_device.cc:923] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-06-25 10:34:50.959135: I tensorflow/core/common_runtime/gpu/gpu_device.cc:929]      0
2018-06-25 10:34:50.959140: I tensorflow/core/common_runtime/gpu/gpu_device.cc:942] 0:   N
2018-06-25 10:34:50.959247: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1053] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 223 MB memory) -> physical GPU (device: 0, name: TITAN Xp, pci bus id: 0000:01:00.0, compute capability: 6.1)
2018-06-25 10:34:50.962115: I tensorflow/core/common_runtime/gpu/gpu_device.cc:923] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-06-25 10:34:50.962147: I tensorflow/core/common_runtime/gpu/gpu_device.cc:929]      0
2018-06-25 10:34:50.962156: I tensorflow/core/common_runtime/gpu/gpu_device.cc:942] 0:   N
2018-06-25 10:34:50.962296: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1053] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 19 MB memory) -> physical GPU (device: 0, name: TITAN Xp, pci bus id: 0000:01:00.0, compute capability: 6.1)
2018-06-25 10:34:50.963309: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 19.88M (20840448 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-25 10:34:50.963921: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 17.89M (18756608 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-25 10:34:50.964525: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 16.10M (16881152 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-25 10:34:50.965136: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 14.49M (15193088 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-25 10:34:50.965748: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 13.04M (13673984 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-25 10:34:50.966395: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 11.74M (12306688 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-25 10:34:50.967035: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 10.56M (11076096 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-25 10:34:50.967671: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 9.51M (9968640 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-25 10:34:50.968306: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 8.56M (8971776 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
>  ====== Hand Inference graph loaded.
2018-06-25 10:34:50.978706: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1435] Adding visible gpu devices: 0
2018-06-25 10:34:50.978781: I tensorflow/core/common_runtime/gpu/gpu_device.cc:923] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-06-25 10:34:50.978801: I tensorflow/core/common_runtime/gpu/gpu_device.cc:929]      0
2018-06-25 10:34:50.978818: I tensorflow/core/common_runtime/gpu/gpu_device.cc:942] 0:   N
2018-06-25 10:34:50.979073: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1053] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 19 MB memory) -> physical GPU (device: 0, name: TITAN Xp, pci bus id: 0000:01:00.0, compute capability: 6.1)
>  ====== Hand Inference graph loaded.
2018-06-25 10:34:51.014461: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1435] Adding visible gpu devices: 0
2018-06-25 10:34:51.014504: I tensorflow/core/common_runtime/gpu/gpu_device.cc:923] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-06-25 10:34:51.014512: I tensorflow/core/common_runtime/gpu/gpu_device.cc:929]      0
2018-06-25 10:34:51.014517: I tensorflow/core/common_runtime/gpu/gpu_device.cc:942] 0:   N
2018-06-25 10:34:51.014617: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1053] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9864 MB memory) -> physical GPU (device: 0, name: TITAN Xp, pci bus id: 0000:01:00.0, compute capability: 6.1)
2018-06-25 10:34:51.542720: E tensorflow/stream_executor/cuda/cuda_dnn.cc:455] could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR
2018-06-25 10:34:51.542755: E tensorflow/stream_executor/cuda/cuda_dnn.cc:427] could not destroy cudnn handle: CUDNN_STATUS_BAD_PARAM
2018-06-25 10:34:51.542773: F tensorflow/core/kernels/conv_ops.cc:713] Check failed: stream->parent()->GetConvolveAlgorithms( conv_parameters.ShouldIncludeWinogradNonfusedAlgo<T>(), &algorithms)
>> loading frozen model for worker
> ====== loading HAND frozen graph into memory
2018-06-25 10:34:52.231795: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2018-06-25 10:34:52.321816: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1356] Found device 0 with properties:
name: TITAN Xp major: 6 minor: 1 memoryClockRate(GHz): 1.582
pciBusID: 0000:01:00.0
totalMemory: 11.90GiB freeMemory: 331.88MiB
2018-06-25 10:34:52.321844: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1435] Adding visible gpu devices: 0
2018-06-25 10:34:52.548327: I tensorflow/core/common_runtime/gpu/gpu_device.cc:923] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-06-25 10:34:52.548361: I tensorflow/core/common_runtime/gpu/gpu_device.cc:929]      0
2018-06-25 10:34:52.548370: I tensorflow/core/common_runtime/gpu/gpu_device.cc:942] 0:   N
2018-06-25 10:34:52.548511: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1053] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 50 MB memory) -> physical GPU (device: 0, name: TITAN Xp, pci bus id: 0000:01:00.0, compute capability: 6.1)
>  ====== Hand Inference graph loaded.
2018-06-25 10:34:52.554345: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1435] Adding visible gpu devices: 0
2018-06-25 10:34:52.554370: I tensorflow/core/common_runtime/gpu/gpu_device.cc:923] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-06-25 10:34:52.554377: I tensorflow/core/common_runtime/gpu/gpu_device.cc:929]      0
2018-06-25 10:34:52.554382: I tensorflow/core/common_runtime/gpu/gpu_device.cc:942] 0:   N
2018-06-25 10:34:52.554472: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1053] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 50 MB memory) -> physical GPU (device: 0, name: TITAN Xp, pci bus id: 0000:01:00.0, compute capability: 6.1)

I added the following lines from config = tf.ConfigProto()
config.gpu_options.allow_growth = True
session = tf.Session(config=config, ...)
as
config = tf.ConfigProto()
config.gpu_options.allow_growth = True

sess = tf.Session(graph=detection_graph, config=config)

and still get the same error as you see here:
Also, it gets stuck in the last line ending in compute capability: 6.1
mona@Mona:~/code/handpose/handtracking$ python detect_multi_threaded.py
/home/mona/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
{'im_width': 320.0, 'im_height': 180.0, 'score_thresh': 0.2, 'num_hands_detect': 2} Namespace(display=1, fps=1, height=200, num_hands=2, num_workers=4, queue_size=5, video_source=0, width=300)
>> loading frozen model for worker
> ====== loading HAND frozen graph into memory
>> loading frozen model for worker
> ====== loading HAND frozen graph into memory
>> loading frozen model for worker
> ====== loading HAND frozen graph into memory
>> loading frozen model for worker
> ====== loading HAND frozen graph into memory
2018-06-25 10:47:11.156972: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2018-06-25 10:47:11.180850: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2018-06-25 10:47:11.211547: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2018-06-25 10:47:11.236604: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2018-06-25 10:47:11.423600: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1356] Found device 0 with properties:
name: TITAN Xp major: 6 minor: 1 memoryClockRate(GHz): 1.582
pciBusID: 0000:01:00.0
totalMemory: 11.90GiB freeMemory: 10.77GiB
2018-06-25 10:47:11.423652: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1435] Adding visible gpu devices: 0
2018-06-25 10:47:11.499971: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1356] Found device 0 with properties:
name: TITAN Xp major: 6 minor: 1 memoryClockRate(GHz): 1.582
pciBusID: 0000:01:00.0
totalMemory: 11.90GiB freeMemory: 10.61GiB
2018-06-25 10:47:11.499999: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1435] Adding visible gpu devices: 0
2018-06-25 10:47:11.530682: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1356] Found device 0 with properties:
name: TITAN Xp major: 6 minor: 1 memoryClockRate(GHz): 1.582
pciBusID: 0000:01:00.0
totalMemory: 11.90GiB freeMemory: 10.42GiB
2018-06-25 10:47:11.530734: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1435] Adding visible gpu devices: 0
2018-06-25 10:47:11.539197: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1356] Found device 0 with properties:
name: TITAN Xp major: 6 minor: 1 memoryClockRate(GHz): 1.582
pciBusID: 0000:01:00.0
totalMemory: 11.90GiB freeMemory: 10.29GiB
2018-06-25 10:47:11.539239: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1435] Adding visible gpu devices: 0
2018-06-25 10:47:11.690564: I tensorflow/core/common_runtime/gpu/gpu_device.cc:923] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-06-25 10:47:11.690599: I tensorflow/core/common_runtime/gpu/gpu_device.cc:929]      0
2018-06-25 10:47:11.690608: I tensorflow/core/common_runtime/gpu/gpu_device.cc:942] 0:   N
2018-06-25 10:47:11.690829: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1053] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9877 MB memory) -> physical GPU (device: 0, name: TITAN Xp, pci bus id: 0000:01:00.0, compute capability: 6.1)
2018-06-25 10:47:11.737120: I tensorflow/core/common_runtime/gpu/gpu_device.cc:923] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-06-25 10:47:11.737156: I tensorflow/core/common_runtime/gpu/gpu_device.cc:929]      0
2018-06-25 10:47:11.737164: I tensorflow/core/common_runtime/gpu/gpu_device.cc:942] 0:   N
2018-06-25 10:47:11.737345: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1053] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 266 MB memory) -> physical GPU (device: 0, name: TITAN Xp, pci bus id: 0000:01:00.0, compute capability: 6.1)
>  ====== Hand Inference graph loaded.
2018-06-25 10:47:11.746520: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1435] Adding visible gpu devices: 0
2018-06-25 10:47:11.746573: I tensorflow/core/common_runtime/gpu/gpu_device.cc:923] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-06-25 10:47:11.746583: I tensorflow/core/common_runtime/gpu/gpu_device.cc:929]      0
2018-06-25 10:47:11.746591: I tensorflow/core/common_runtime/gpu/gpu_device.cc:942] 0:   N
2018-06-25 10:47:11.746725: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1053] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 266 MB memory) -> physical GPU (device: 0, name: TITAN Xp, pci bus id: 0000:01:00.0, compute capability: 6.1)
2018-06-25 10:47:11.767820: I tensorflow/core/common_runtime/gpu/gpu_device.cc:923] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-06-25 10:47:11.767851: I tensorflow/core/common_runtime/gpu/gpu_device.cc:929]      0
2018-06-25 10:47:11.767858: I tensorflow/core/common_runtime/gpu/gpu_device.cc:942] 0:   N
2018-06-25 10:47:11.767886: I tensorflow/core/common_runtime/gpu/gpu_device.cc:923] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-06-25 10:47:11.767904: I tensorflow/core/common_runtime/gpu/gpu_device.cc:929]      0
2018-06-25 10:47:11.767910: I tensorflow/core/common_runtime/gpu/gpu_device.cc:942] 0:   N
2018-06-25 10:47:11.767997: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1053] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 203 MB memory) -> physical GPU (device: 0, name: TITAN Xp, pci bus id: 0000:01:00.0, compute capability: 6.1)
2018-06-25 10:47:11.768033: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1053] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 203 MB memory) -> physical GPU (device: 0, name: TITAN Xp, pci bus id: 0000:01:00.0, compute capability: 6.1)
2018-06-25 10:47:11.768997: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 203.69M (213581824 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-25 10:47:11.769227: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 203.69M (213581824 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-25 10:47:11.770416: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 183.32M (192223744 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-25 10:47:11.770917: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 164.99M (173001472 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-25 10:47:11.771418: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 148.49M (155701504 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-25 10:47:11.771908: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 133.64M (140131584 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-25 10:47:11.772398: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 120.28M (126118656 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
>  ====== Hand Inference graph loaded.
2018-06-25 10:47:11.772948: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 108.25M (113506816 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-25 10:47:11.773446: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 97.42M (102156288 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-25 10:47:11.773938: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 87.68M (91940864 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-25 10:47:11.774431: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 78.91M (82746880 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-25 10:47:11.774925: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 71.02M (74472192 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-25 10:47:11.775428: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 63.92M (67025152 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-25 10:47:11.775925: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 57.53M (60322816 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-25 10:47:11.776422: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 51.78M (54290688 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-25 10:47:11.776927: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 46.60M (48861696 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-25 10:47:11.777428: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 41.94M (43975680 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-25 10:47:11.777453: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1435] Adding visible gpu devices: 0
2018-06-25 10:47:11.777485: I tensorflow/core/common_runtime/gpu/gpu_device.cc:923] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-06-25 10:47:11.777493: I tensorflow/core/common_runtime/gpu/gpu_device.cc:929]      0
2018-06-25 10:47:11.777498: I tensorflow/core/common_runtime/gpu/gpu_device.cc:942] 0:   N
2018-06-25 10:47:11.777771: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1053] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 203 MB memory) -> physical GPU (device: 0, name: TITAN Xp, pci bus id: 0000:01:00.0, compute capability: 6.1)
2018-06-25 10:47:11.777985: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 37.74M (39578112 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-25 10:47:11.778566: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 33.97M (35620352 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-25 10:47:11.779093: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 30.57M (32058368 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-25 10:47:11.779608: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 27.52M (28852736 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-25 10:47:11.780125: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 24.76M (25967616 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-25 10:47:11.780640: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 22.29M (23371008 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-25 10:47:11.781157: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 20.06M (21033984 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-25 10:47:11.781678: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 18.05M (18930688 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-25 10:47:11.782203: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 16.25M (17037824 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-25 10:47:11.782733: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 14.62M (15334144 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-25 10:47:11.783295: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 13.16M (13800960 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-25 10:47:11.783848: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 11.84M (12420864 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-25 10:47:11.784398: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 10.66M (11179008 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-25 10:47:11.784961: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 9.59M (10061312 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-06-25 10:47:11.785524: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 8.64M (9055232 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
>  ====== Hand Inference graph loaded.
2018-06-25 10:47:11.795805: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1435] Adding visible gpu devices: 0
2018-06-25 10:47:11.795869: I tensorflow/core/common_runtime/gpu/gpu_device.cc:923] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-06-25 10:47:11.795885: I tensorflow/core/common_runtime/gpu/gpu_device.cc:929]      0
2018-06-25 10:47:11.795894: I tensorflow/core/common_runtime/gpu/gpu_device.cc:942] 0:   N
2018-06-25 10:47:11.796063: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1053] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 203 MB memory) -> physical GPU (device: 0, name: TITAN Xp, pci bus id: 0000:01:00.0, compute capability: 6.1)
>  ====== Hand Inference graph loaded.
2018-06-25 10:47:11.802724: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1435] Adding visible gpu devices: 0
2018-06-25 10:47:11.802768: I tensorflow/core/common_runtime/gpu/gpu_device.cc:923] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-06-25 10:47:11.802778: I tensorflow/core/common_runtime/gpu/gpu_device.cc:929]      0
2018-06-25 10:47:11.802786: I tensorflow/core/common_runtime/gpu/gpu_device.cc:942] 0:   N
2018-06-25 10:47:11.802908: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1053] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9877 MB memory) -> physical GPU (device: 0, name: TITAN Xp, pci bus id: 0000:01:00.0, compute capability: 6.1)
2018-06-25 10:47:12.321811: E tensorflow/stream_executor/cuda/cuda_dnn.cc:455] could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR
2018-06-25 10:47:12.321842: E tensorflow/stream_executor/cuda/cuda_dnn.cc:427] could not destroy cudnn handle: CUDNN_STATUS_BAD_PARAM
2018-06-25 10:47:12.321856: F tensorflow/core/kernels/conv_ops.cc:713] Check failed: stream->parent()->GetConvolveAlgorithms( conv_parameters.ShouldIncludeWinogradNonfusedAlgo<T>(), &algorithms)
>> loading frozen model for worker
> ====== loading HAND frozen graph into memory
2018-06-25 10:47:13.078694: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2018-06-25 10:47:13.166140: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1356] Found device 0 with properties:
name: TITAN Xp major: 6 minor: 1 memoryClockRate(GHz): 1.582
pciBusID: 0000:01:00.0
totalMemory: 11.90GiB freeMemory: 335.69MiB
2018-06-25 10:47:13.166167: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1435] Adding visible gpu devices: 0
2018-06-25 10:47:13.372075: I tensorflow/core/common_runtime/gpu/gpu_device.cc:923] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-06-25 10:47:13.372106: I tensorflow/core/common_runtime/gpu/gpu_device.cc:929]      0
2018-06-25 10:47:13.372111: I tensorflow/core/common_runtime/gpu/gpu_device.cc:942] 0:   N
2018-06-25 10:47:13.372235: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1053] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 54 MB memory) -> physical GPU (device: 0, name: TITAN Xp, pci bus id: 0000:01:00.0, compute capability: 6.1)
>  ====== Hand Inference graph loaded.
2018-06-25 10:47:13.377552: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1435] Adding visible gpu devices: 0
2018-06-25 10:47:13.377573: I tensorflow/core/common_runtime/gpu/gpu_device.cc:923] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-06-25 10:47:13.377578: I tensorflow/core/common_runtime/gpu/gpu_device.cc:929]      0
2018-06-25 10:47:13.377582: I tensorflow/core/common_runtime/gpu/gpu_device.cc:942] 0:   N
2018-06-25 10:47:13.377661: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1053] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 54 MB memory) -> physical GPU (device: 0, name: TITAN Xp, pci bus id: 0000:01:00.0, compute capability: 6.1)

use the following command and use kill -9 PID for any Python process:
$ sudo fuser -V /dev/nvidia*


Migrate from String to byte[] keys and add new "INCREMENTING_KEY" type

Per some internal conversations, this is an experiment in tuning the KeyDistributor components to generate byte[] instead of Java String objects. It's a little lighter weight and some synthetic microbenchmarks make it look like we may be able to generate keys faster.
I'm also experimenting with an incrementing key that will approximate "random" key values by just incrementing an integer value and letting it eventually wrap.

Note: The key generation, in general, is very flawed regardless of performance. There's no knobs for the distribution of the randomness (e.g. uniform, gaussian, etc.). sigh.

I'll need help testing this...I've only done unit testing on my laptop. While my changes to the greater projects compile...this code relies on generics and could have runtime exceptions.

Functionally validated the INCREMENTING_KEYS type works.

Going to try creating a comparison between the byte array key distributors and the String ones.


[Version] update version to 1.8.3

Release 1.8.2 for Tizen 7.0 M2. Start implementation for next release (v1.8.3).
Signed-off-by: Jaeyun<EMAIL_ADDRESS>

:memo: TAOS-CI Version: 1.5.20200925. Thank you for submitting PR #217. Please a submit 1commit/1PR (one commit per one PR) policy to get comments quickly from reviewers. Your PR must pass all verificiation processes of cibot before starting a review process from reviewers. If you are new member to join this project, please read manuals in documentation folder and wiki page. In order to monitor a progress status of your PR in more detail, visit http://ci.nnstreamer.ai/.


How to set a initial index

I want to set a index for picker view  , for example  3

+1, same big problem

+1, same big problem


Update property syntax in README.md

In the Declarative Rendering section of README.md it seems to be using lit-html syntax rather than how we use properties in LitElement where we'd need to specify the 'this' keyword.
So rather than:
attribute: html
we should have
attribute: html
Reference Issue
Fixes #197

CLA signed

Thanks for the PR. Can you please verify that the CLA is signed? The bot is still complaining.

Ah sorry different email on my google account and Github - I've used the Github email to sign the CLA now. Hopefully it goes green :-)


Implement EIP-4844

What was wrong?
EIP-4844 was not implemented in the specs
Related to Issue #848
How was it fixed?
Implemented EIP-4844. Updated the evm tools to account for the changes.
Cute Animal Picture

The currently failing CI is because of missing py.typed file in the consensus-specs that we use for the point evaluation pre-compile. Have already raised a PR


how do i use unretweet api?

https://dev.twitter.com/rest/reference/post/statuses/unretweet/%3Aid

see https://github.com/tweepy/tweepy/pull/736

This has now been released with Tweepy v3.6.0.


chore(third_party): upgrade to deno_std 0.167.0

Description
Upgrade deno_ std to v0.167 to extend support for some node libs, such as crypto
note: I just updated the submodule, I don't know if I need to do any extra configuration
Release Notes

https://github.com/denoland/deno_std/releases/tag/0.167.0

If you want to use some new Node polyfills from deno-std/node, you will also need to add new entries to NODE_POLYFILLS in server/src/module_loader.rs. You can refer to the analogous list from Deno sources:
https://github.com/denoland/deno/blob/main/cli/node/mod.rs

@honzasp Can you please send a pull request to add those entries?


validates User ID as a string

Coverage remained the same at 100.0% when pulling 1fcb1b6e8e7ff58ed9898023a2f195b38a88b43a on rashid/user-id-string-validation into 619f3bca6e655b43123105e7bed245081bc508c0 on master.

@mikeng13 Please review changes.
NOTE: This validator is only validates 'user_id'. However all inputs validator is implemented in different branch.

build

@mikeng13 All inputs validation is implemented in PR- https://github.com/optimizely/ruby-sdk/pull/103
You may close this PR and review the other one.


Enable temporary survey

Overview
Re-enables the temporary survey popup until December 13th, 2020.
Connects #1151
Demo

Testing Instructions

Run ./scripts/server
Navigate to :6543/. You should see the survey popup with the updated link. Close the popup.
Refresh the page. You should no longer see the popup.

Checklist

[ ] fixup! commits have been squashed
[ ] CI passes after rebase
[ ] CHANGELOG.md updated with summary of features or fixes, following Keep a Changelog guidelines

I've added a section, outside of the component function, which looks for and removes the SURVEY_DIALOG_HAS_BEEN_DISPLAYED key from local storage. We will most likely want to remove this code in the future, once it's had a chance to serve its purpose. (Possibly when we next update the survey URL and start/end dates).


Annotations appear twice in Stackdriver Trace

Please answer these questions before submitting a bug report.
What version of OpenCensus are you using?
0.13.2
What JVM are you using (java -version)?
8
What did you do?
Simply starting a span and adding an annotation via span.addAnnotation("start"). Exporting it via the Stackdriver exporter.
What did you expect to see?
The annotation appears once in the Stackdriver Trace UI
What did you see instead?
The annotation is sometimes duplicated with the exact same timestamp (sometimes it's only once there). When I add the Logging Exporter, I can see that the annotation is only once attached to the span.

Huh, that's no good. We just filed a bug to track this internally and will take a look ASAP.

Thanks for having a look!

I can confirm this behaviour as well. I have been running a Jaeger instance (using the Zipkin exporter) at the same time as well and the duplicated annotations only showed up in the Stackdriver UI.

Hi all,
I think we found the issue. We believe this is because of failures/retries (one deadline request which gets to the backend and is executed) then we retry to send the same Spans. In the backend SD does a proto merge in this case which for repeated fields like annotation this cause duplicates.
SD team is working to a backend fix to avoid this problem and I started a discussion on the specs repo about OC supporting retries (https://github.com/census-instrumentation/opencensus-specs/issues/119)

I will close this issue because we think that we fixed the problem on the Stackdriver side. Feel free to reopen if you still see this problem.


fix  brace-style

avoid warning  Opening curly brace appears on the same line as controlling statement  brace-style

I've already pushed it.


Adding import

How it is imported will depend on how it is loaded so this won't be valid for everyone.

I had trouble yesterday figuring this out, so I thought I'd share it.
Which other ways do exist? Maybe you could show them all, like in the "Installation" section where you're showing multiple ways.

If you add the script tag to the page than it is added as a global so there is no need to import.

So what about something like this?:
// If you don't add it via `<script>`:
import SignaturePad from "signature_pad";

Where it says

This library is provided as UMD (Universal Module Definition) and ES6 module.

We could change it to:

This library is provided as UMD (Universal Module Definition) and ES6 module with a default export.

I didn't know the signature_pad part of this line - so I had to guess it:

import SignaturePad from "signature_pad";

This info is what I'm trying to get into README ;-)
Or is this always the same as in yarn add signature_pad - so that everybody knows it anyway?

Ya that is just the package name. It is always the same to install and import.

OK, thanks, then let's leave it as it is ;-)


"Edit Card #n" removes one per line format

Using Opera 38.0.2, Safari 9.1.1. When clicking Edit Card, it removes the one per line format.
EX:
Responsibilities:
Resp1
Resp2
Resp3

becomes
Responsibilities
Resp1,Resp2,Resp3

when edit is clicked.

Confirmed to be an issue in all browsers. I'm pretty sure I know where this is happening, so I'll try to get a fix in tonight.

Fixed in 9259e1fdbd866e0b6c2e01175d92c3167794a949


HDF5 issue with parallel execution on clusters

Hello,
I am trying to run Mach3 step test problem on a cluster using MPI. The program raises an error related to HDF5 when the mesh file or solution file is being written in the "out" folder. Sometime when I change the number of cores the simulation runs and solution files are written just fine but after some time into the simulation the HDF5 error will be raised again.  Here is a sample of the error that I get:
ERROR: ERROR: LoadError: LoadError: HDF5.API.H5Error: Error getting attribute name
libhdf5 Stacktrace:
[1] H5Aget_name: Invalid arguments to routine/Inappropriate type
not an attribute
Stacktrace:
[1] macro expansion
@ ~/.julia/packages/HDF5/wWr4z/src/api/HDF5.API.H5Error: Error getting attribute name
libhdf5 Stacktrace:error.jl:18 [inlined]
[2]
[1] h5a_get_name(H5Aget_name: Invalid arguments to routine/Inappropriate typeattr_id::
not an attribute
The operating system on the cluster is Red Hat.  I am using openmpi_4.0.0_gcc version and the file system is GPFS.

Thanks for reaching out, @peyvanahmad! Could you let me know a little more about the circumstances of when the error occurs:

Does it happen only when running in parallel or also when running in serial?

How many ranks do you use and how far into the simulation does it fail (i.e., after how many time steps)?
Do you use the cluster's HDF5 library (by setting JULIA_HDF5_PATH or the one auto-installed as a JLL package by Julia?
If you use the cluster's HDF5 library: Do you try to write to the files in parallel or do you use Trixi's current serialized I/O approaach?
Can you reproduce the error with a different MPI library on your cluster, i.e., something other than openmpi_4.0.0_gcc?

Finally, it would be helpful if you could post the full error message, or at least the full stacktrace to figure out in which routine the error occurs.

It happens when running in parallel.
It happens sporadically. Sometimes the simulation goes through to the end
in parallel and sometimes the HDF5 error occurs. When I run on a single
computational node the error appears less frequently.
I used 32 ranks on a single computational node (I have attached the
complete error file here). The error happened at 88,000 time step
I use the auto-installed HDF5 package by Julia.
I try to run the code with a different MPI library but openmpi_4.0.0_gcc is
the one that is very robust.
On Sat, Apr 2, 2022 at 11:55 PM Michael Schlottke-Lakemper <
@.***> wrote:

Thanks for reaching out, @peyvanahmad
https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Fpeyvanahmad&data=04|01|apeyva2%40groute.uic.edu|afbb35cf341342f1ca9708da1525da83|e202cd477a564baa99e3e3b71a7c77dd|0|0|637845549565766903|Unknown|TWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D|3000&sdata=1vEAgx9ToM10LtEUgAhlve%2BDuQQF2Xtd%2B%2Bt7PbvBOB0%3D&reserved=0!
Could you let me know a little more about the circumstances of when the
error occurs:

Does it happen only when running in parallel or also when running in
serial?

How many ranks do you use and how far into the simulation does it
fail (i.e., after how many time steps)?
Do you use the cluster's HDF5 library (by setting JULIA_HDF5_PATH
https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fjuliaio.github.io%2FHDF5.jl%2Fstable%2F%23Using-custom-or-system-provided-HDF5-binaries&data=04|01|apeyva2%40groute.uic.edu|afbb35cf341342f1ca9708da1525da83|e202cd477a564baa99e3e3b71a7c77dd|0|0|637845549565766903|Unknown|TWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D|3000&sdata=SFE6PCcaOQMCFCikoCr%2BLPdKnF2r5a7SSBaGnumUPQo%3D&reserved=0
or the one auto-installed as a JLL package by Julia?
If you use the cluster's HDF5 library: Do you try to write to the
files in parallel or do you use Trixi's current serialized I/O approaach?
Can you reproduce the error with a different MPI library on your
cluster, i.e., something other than openmpi_4.0.0_gcc?

Finally, it would be helpful if you could post the full error message, or
at least the full stacktrace to figure out in which routine the error
occurs.
—
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I have attached the complete error file here

Unfortunately I cannot find anything - can you please try again to add it to the GitHub issue on the website directly? Also, it would be great if you can include the elixir you're using the exact Julia command you used for starting it.


burstable CPU% are incorrect.  As is your calculation formula for credits.

"Base CPU Performance of VM (%)" seems incorrect in many places.
https://learn.microsoft.com/en-us/azure/virtual-machines/sizes-b-series-burstable
https://learn.microsoft.com/en-us/azure/virtual-machines/bsv2-series
https://learn.microsoft.com/en-us/azure/virtual-machines/basv2
example:
Standard_B32als_v2	32	64	60%	960	384
Standard_B32as_v2	32	128	40%	960	384
You've listed the same 384 credits for both of these 32CPU machines, so the % should be the same.  Either the % is wrong, or your credits/hour is wrong.
And on this page,
https://learn.microsoft.com/en-us/azure/virtual-machines/b-series-cpu-credit-model/b-series-cpu-credit-model
((40%*32)/2 - (60%*32)/2)/100 = (6.4 - 9.6)/100 = -3.2 credits per minute
Since when does (6.4 - 9.6) / 100 = -3.2?
You've already done the proper math with the 40% and 60% values by recognizing the 40% is actually 0.4.
Such that (0.432)/2 - (0.632)/2 = 6.4 - 9.6
(Another way to say it is that % is inherently a divide by 100, which you've already done. To have the /100 outside of the brackets makes your formula look like garbage anyway.)

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

ID: 0cd2e24b-0fd3-d698-2840-3f8206933026
Version Independent ID: 832dc371-480a-c74a-e956-e1904473af31
Content: B-series burstable - Azure Virtual Machines
Content Source: articles/virtual-machines/sizes-b-series-burstable.md
Service: virtual-machines
Sub-service: sizes
GitHub Login: @rishabv90
Microsoft Alias: risverma

formatting is also inconsistent.  In the v1 chart, you don't have the % sign in the values, but in v2 charts, you do.

https://learn.microsoft.com/en-us/azure/virtual-machines/basv2
Standard_B2ats_v2	2	1	20%	60	24
claims 24/h credit increase, but I just started a Linux machine that has nothing running and can clearly see in Metrics that it's only increasing by 12/h

@jeffl2022
Thanks for your feedback! We will investigate and update as appropriate.

Hi @rishabv90 Please review it.

@jeffl2022
Thanks for bringing this to our attention.
I'm going to assign this to the document author so they can take a look at it accordingly.

ya, there's lots much wrong.  So many mistakes.
example:

Had a discussion with @rishabv90, and according to him, these PRs/issues are duplicates of what they have already addressed through ICMs. Closing this issue. Should this issue be still existing, request to file new issue to work on. Thanks!
#please-close


Convert /bin/update -> AWS Lambda

Codecov Report

Merging #312 into master will not change coverage.
The diff coverage is n/a.

@@           Coverage Diff           @@
##           master     #312   +/-   ##
=======================================
  Coverage   22.18%   22.18%
=======================================
  Files          69       69
  Lines        1920     1920
  Branches      262      262
=======================================
  Hits          426      426
  Misses       1426     1426
  Partials       68       68

Impacted Files
Coverage Δ

...er-problems-backend/src/lambda/time_submissions.rs
0% <ø> (ø)
:arrow_up:

...er-problems-backend/src/lambda/user_submissions.rs
0% <ø> (ø)
:arrow_up:

atcoder-problems-backend/src/lambda/user_info.rs
0% <ø> (ø)
:arrow_up:

Continue to review full report at Codecov.

Legend - Click here to learn more
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About the code

im working in the ABSA and reading your paper which is amazing. But there is not anything about your code. Could please send it to<EMAIL_ADDRESS>!

i am also interested to your idea of this paper. Could please send the code to<EMAIL_ADDRESS>Thanks a lot.

Hi, I am also interested in your work regarding this paper. If you don't mind, could you please share the code to<EMAIL_ADDRESS>please?
Thanks you!


Two bug fixes

Hi ekalinin,
We discovered a bug where the callback in the RobotsParser constructor gets called multiple times, due to large robots.txt files being returned through the request object in multiple chunks. The solution was to alter the response's data listener to collect the chunks, and add the response's end listener to combine the chunks and then call the callback.
We also found a bug which crashes the parser if a rule in robots.txt is malformed (if it has an unquoted %). And we handle it by simply ignoring the rule (rather than guessing what it is supposed to match).
Both of these problems were caused by Wikipedia's robots.txt file, if you are interested in replicating the bugs.
Thanks,
Steve

Hi,
Great patch. Applied.
Thaks.


Rollup Primary Membership to Account and Contact

As a user, I want my most recent membership record to be rolled up as a lookup to the contact and account record.
Acceptance criteria:

The field on the contact and account are lookups
Set to the newest record as defined as: does not expire or greatest end date on a membership of current status
Membership can be excluded by the Type (set by a custom setting)
Job can be set with a trigger

Backlog:

Job to set the rollup via batch

Complication:

What automation is setting the people to expired status when their end date passes?

Discussion related to membership rollups:

Do we want to use lookups (ability to pull down anything you want via formulas)?
Do we want to use rollups (view start/end dates & status)?


getOcspResponderUrl in CertificateLoader sometimes fails

I haven't yet reproduced this in a simple script, but apparently sometimes the extension <IP_ADDRESS>.<IP_ADDRESS>.1 is parsed as 'id-ad-ocsp' (what getOcspResponderUrl searches for, defined upstream in phpseclib) and sometimes as 'id-pkix-ocsp' (defined in AsnUtil). It's possibly due to when the certificate is actually parsed, I'll try to come up with a reproducible example.
At any point, the search in getOcspResponderUrl should probably either look up the numeric OID (if available) or at least consider the alternate name as well.

I managed to reproduce the problem, apparently after AsnUtil::loadOIDs() is called (for whatever reason), the new name for the OID takes over:
<?php
include 'vendor/autoload.php';
use web_eid\ocsp_php\certificate\CertificateLoader;
use web_eid\ocsp_php\util\AsnUtil;

$cert_data = file_get_contents('tests/_resources/revoked.crt');

print("Before loadOIDs\n");
print_aia_exts((new CertificateLoader())->fromString($cert_data));

AsnUtil::loadOIDs();
print("After loadOIDs\n");
print_aia_exts((new CertificateLoader())->fromString($cert_data));

//// helper function
function print_aia_exts($loader) {
    printf("Found OCSP URL: %s\n", $loader->getOcspResponderUrl());
    $opts = $loader->getCert()->getExtension("id-pe-authorityInfoAccess");
    foreach ($opts as $opt) {
       printf("Access Method: %s, Access Location: %s\n", $opt["accessMethod"], $opt["accessLocation"]["uniformResourceIdentifier"]);
    }
}

output:
Before loadOIDs
Found OCSP URL: http://ocsp.int-x3.letsencrypt.org
Access Method: id-ad-ocsp, Access Location: http://ocsp.int-x3.letsencrypt.org
Access Method: id-ad-caIssuers, Access Location: http://cert.int-x3.letsencrypt.org/
After loadOIDs
Found OCSP URL:
Access Method: id-pkix-ocsp, Access Location: http://ocsp.int-x3.letsencrypt.org
Access Method: id-ad-caIssuers, Access Location: http://cert.int-x3.letsencrypt.org/

Sorry for the quick and dirty example, I hope it showcases the issue clearly. At the very list it's a surprising behaviour that should at least be mentioned somewhere, I chased it for several days :)

Thanks a lot, we will schedule it for fixing. It will take some time.

Thank you for the feedback.
I will close this issue since the ocsp-php code was moved to web-eid-authtoken-validation-php repository.
We are happy to accept your proposals in the web-eid-authtoken-validation-php repository: https://github.com/web-eid/web-eid-authtoken-validation-php.


Extension seems to hide assignees menu from issues and PRs

Refined GitHub seems to be removing or otherwise hiding the assignees menu from issues and PRs. I tried disabling and re-enabling the extension to test it, and it always disapears when enabled, and reappears when disabled.
Oddly, the assignees menu shows, and I can interact with it when there is already someone assigned, but does not show when there is no one assigned.
No assignees (Refined GitHub enabled):

With Assignees (Refined GitHub disabled):

The same thing happens when viewing a PR.

Forgot to add version info:
Browser: Firefox Dev Edition 74.0
OS: Win 10 Pro 1809
Extension: 20.2.19

Empty sections are completely removed if RGH doesn’t think you can edit them. Here either this detection is broke for Assignees, or some other selector needs to be updated

I'm not a full member of the Creative Commons org, I'm a collaborator, so maybe that's affecting it? Is there a toggle for this in settings? I love RGH, but I can't use it if there's no way to turn this off :/

This is the clean-sidebar feature you can disable temporarily in the options.
The detection happens in the DOM: if there’s a “settings” icon, then you can edit it, so the section should not be removed.

Yep, just found it and was about to comment with the name of the setting. It does have a gear icon on it, so I'm not sure what the deal is.
Do you consider this to be a proper bug that needs fixing? If so, I could investigate further if I can find the time.

Do you consider this to be a proper bug that needs fixing?

Yep, it's a bug

Sounds good, will investigate soon :)

@fregante wasn't this fixed?

@yakov116 thanks! I forgot to close it in https://github.com/sindresorhus/refined-github/pull/2916

I'm still having this issue Firefox 85.0.2 (64-bit), addon version 21.2.9.


add week 6 milestones

Project
Text2SQL

@Apoorvgarg-creator Kindly check

@Apoorvgarg-creator Kindly check

Kindly add PR link

and remove headings that aren't used

@Apoorvgarg-creator the changes were made to the dataset itself, there was errors with that. Not with our code. So I have not made a pr for that yet. After the dataset itself is finalised I will push that. As for heading, I belive I have only added milestones. What should I remove?

Remove the screenshot, contribution and learnings headings

@Apoorvgarg-creator done