Add --null option to rtx env to produce machine-parsable output

Currently, the only option we have to control the output from rtx env is the --shell flag, which controls which shell syntax is produced. Unfortunately, all of the shells supported have syntactic quirks that make using the result programmatically take extra work.
The system env command has a --null option (-0 for short) that prints the environment with entries separated by ASCII NUL characters rather than newlines. Also, in general env does not include quotes around the values. These two features allow the output to be very easily parsed without requiring shell syntax parsing.
 Consider an environment value like FOO="\'", which would be printed by rtx env as something like
export FOO='\'\'''

New Feature: rtx env --null
I would like to suggest that rtx env learn a --null option (and -0 for short), and that rtx env -0 produce output in the same format as env -0—that is, with no export keyword (or similar), with no quotes, and with ASCII NUL separators instead of newlines. These should probably conflict with the --shell option, since this output isn't meant to be read by any shell.
An alternative would be for --shell to learn a new value like env. For one thing, that feels slightly misleading, since the output isn't intended for any shell. Also, I feel like there's value in using the same flags as system env for discoverability.
Inspiration
I'm investigating options for integrating rtx into a long-running multi-project emacs session, and one of the hurdles I've run into is how to get the environment from rtx into emacs. I think rtx env would tell me everything I need to know if I ran it from the buffer's directory, but then I have to deal with parsing the output.
I could alternative use rtx exec -- env -0, but that dumps the entire environment, not just the values rtx wants to set—not the end of the world, but it feels wasteful since emacs already knows most of it. I could maybe get around that by using env -i rtx exec -- env -0 to run rtx in an empty envirnoment so there are no other values to print. That doesn't currently work (#507), but even if it did it would at least require merging the PATH it prints into the PATH in emacs, and possibly other tool-specific values as well. Also also, the user may have RTX_ flags set that need to be available.
On the whole, using rtx env feels like the right solution, if it could produce a machine-parsable format.

I'm more inclined to support json over having things be null-delimited. Otherwise I'd eventually have to deal with escaping nulls and end up with a custom format. It's also more human-readable.

Whoah, ok. Sad there wasn't any discussion of pros and cons, but I'll see what I can do with JSON in emacs.

And for what it's worth, I'm not sure I understand what you mean by

Otherwise I'd eventually have to deal with escaping nulls and end up with a custom format.

The format produced by env -0 is a safe format, no escaping required. Environment variable names are not permitted to contain =, and their values are considered to end at the first NUL character. This is true in POSIX) and in Windows.

oh ok I somehow thought you could put null values into a variable—thanks for correcting me on that point. I know it's common to use null with find -print0 | xargs -0, but I haven't come across it very often in other CLI use-cases. I am more familiar with using json or just making sure output is easy to parse with awk rather than using nulls.
I would prefer by default using json in rtx for machine-readable output since it can be used to represent structure in a way that null delimitation couldn't. That doesn't apply to rtx env though. I think rtx env --json makes sense to have regardless of your use-case. If you find json is hard to work with for your emacs use-case I can look into adding null delimitation as well.


Orientation works in Android Emulator but not on Phone

The orientation changes work fine when I run it on the Android emulator, but when I test it on my phone the Orientation.addOrientationListener(this._orientationDidChange); never gets called. It's running Android 6.0.1.
I linked everything as written in the README and added the following to AndroidManifest.xml
      <activity
        android:name=".MainActivity"
        android:label="@string/app_name"
        android:configChanges="keyboard|keyboardHidden|orientation|screenSize">

Does anyone experience the same issues or got it working on a phone running Android 6.0.1?

Portrait mode was enabled on the phone. After setting it to Auto-rotate in the settings, it works. :+1:


Latest Hygieia code has broken Sonar

Hi Team,
Hygieia dashboard was working fine with Jenkins, Sonar, Stash integrated. Jira was not working. I was told that latest hygieia code has fixes for Jira. I got the latest code deployed.
Now i see Sonar is broken. Please look into the issue at the earliest.
the last commit included in my build is 30e7e19df795dea683f88df8a858d6855fb44b4c

It has also broken the product dashboard as well

@anandsit - what errors are you getting?....it is working fine on my setup.

I have met similar issue as what I post on the gitter
I've got one item in the database:

However UI does not show it:

This Sonar instance is just upgraded to Version 5.6.3 which is running in 5.2 months before
Reversely, I've another Sonar instance in Version 5.6.3, and it is running with Hygieia correctly.
Before, I set both Sonar to Hygieia, but currently only one is running fine.

#Sonar server(s) (required) - Can provide multiple
sonar.servers[0]=http://sonar.badSonar.com/
sonar.servers[1]=http://<IP_ADDRESS>:9000/

for me , not even this job/project names are being displayed..
no error either. :(

Can you clear your bro

Browser cache

yes, switching to chrome worked!, Thankyou.

yes!! browser cache issue. now resolved.
thanks. Good that i was busy over weekend and did not downgrade ;-)


fix cut off labels for time graphs

http://manage.dimagi.com/default.asp?188934
Before:

After:

top y-label still a little cut off, but there isn't a trivial way to fix this, can deal with it by increasing y-max


Gulp task doesn't copy angular.min.js and angular-animate.min.js

Default gulp task example doesn't copy angular[-animate].min.js files into docs/js as well as marked.js.
Only angular-bootstrap[-prettify].js are copied.
Tested with<EMAIL_ADDRESS>

Same here.

I have the same problem

Yeah got me too. This issue has been fixed but if you're using npm you might be having some problems still. Please see #80.

A new release has been made a couple days ago with support for npm 3. Please test:-)

+1


Add "JCL" ( Job Control Language) as a "Known" Language?

Hi,
This page https://code.visualstudio.com/docs/languages/identifiers mentions many known language identifiers.
Can a new language entry be added for "Job Control Language" (JCL)?
Language: JCL
Identifier: jcl
Thanks!

For example " https://marketplace.visualstudio.com/items?itemName=IBM.zopeneditor " provides language with id "jcl"


Continuously clicking bottom navigation tabs leads to "java.lang.IllegalArgumentException" saveBackStack.....

Issue with NavigationAdvancedSample
Version used: 2.4.0-alpha01 (used in sample) & 2.4.0-alpha03 (latest)
Devices/Android versions reproduced on: ALL
There would be a crash when pressing the bottom navigation icons continuously (like mad) and shifting between fragments (by pressing random nav icons).
(Log screenshot & screen recording attached)
https://user-images.githubusercontent.com/83884225/123041316-c84e3300-d412-11eb-813c-decffc95ed45.mp4

....
End

Same here

Same here
https://issuetracker.google.com/issues/191791847

Checkout this

Same issue when multi touch all nav menus.

Yep, see b/188896069.


parens problems

This works
     * @param {...(string|number)} props The property names to pick

This doesn't
     * @param {(...(string|number))} props The property names to pick

JSDoc parser recommends the second example (the one that doesn't work with doctrine)
to work around an issue in their parser.
Related to #149.

When you say "it doesn't work", what does that mean? Are you saying there's an error thrown? Something else?

Like #149 this is from the demo page.

This still has issues in v1.1.0.

Thanks for this issue. We have decided to officially end-of-life JSDoc support in ESLint. As a result, this repository and project will no longer be maintained.
Thanks for understanding and we apologize for any inconvenience.


C#-Wrapper Build Errors - HandleRef missing

Required Info

Camera Model
D435

Firmware Version
<IP_ADDRESS>

Operating System & Version
Win 10

Kernel Version (Linux Only)
-

Platform
PC

SDK Version
2.0

Issue Description
I am trying to develop a application in c# with the intel real sense, using the .NET Wrapper.
After following the instructions from /wrappers/csharp i build the cs-tutorial-1-depth with errors, see Build.txt file.
Build.txt
Can you help me fixing these errors?

Hi @Tkong1
I'm not 100% sure what happened during the build, but I suspect it has something to do with the wrapper targeting .NET framework 3.5 by default (to enable Unity integration). Can you double check you have it installed or modify the project to target other version?

@dorodnic thank you for your prompt reply.
I set the Target Framework to .NET Framework 3.5 (with reopening visual studio) and build the project again, with the same errors.
I hope i can provide you more Information to the issue, see screenshot.

@dorodnic fixed the most problems with updating to Visual Studio 15.6.7


Fix typos and clarify wording

I'd keep the comparison to && to POSIX shell, but I'll merge this and add it back.


Omnisharp randomly ignores editorconfig settings, requires restart

Issue Description
I have editorconfig and roslyn analyzers support both enabled in my omnisharp preferences, and a .editorconfig in my repo root that applies for all projects in the tree.
Omnisharp generally does pick up on this and respect the settings, however seemingly at random Omnisharp will drop the editorconfig settings and revert to the default rule set and make recommendations against the editorconfig. Specifically, this is present in editorconfig:
dotnet_diagnostic.SA1101.severity = none
dotnet_style_qualification_for_field = false:error

After some time, when the bug triggers I'll start seeing recommendations for SA1101 to remove 'this'. Restarting omnisharp fixes the issue (temporarily).
Steps to Reproduce
Load C# project and edit it
Expected Behavior
omnisharp respects editorconfig; don't have to constantly restart omnisharp
Actual Behavior
Have to restart omnisharp every few minutes
Logs
OmniSharp log
Debug log has some details from a private repo; reach out to me directly for logs
C# log
See above
Environment information
VSCode version: 1.50.1
C# Extension: 1.23.5
Mono Information
        OmniSharp using built-in mono
Dotnet Information
.NET Core SDK (reflecting any global.json):
 Version:   3.1.403
 Commit:    9e895200cd
Runtime Environment:
OS Name:     Mac OS X
OS Version:  10.15
OS Platform: Darwin
RID:         osx.10.15-x64
Base Path:   /usr/local/share/dotnet/sdk/3.1.403/
Host (useful for support):
Version: 3.1.9
Commit:  774fc3d6a9
.NET Core SDKs installed:
3.1.403 [/usr/local/share/dotnet/sdk]
.NET Core runtimes installed:
Microsoft.AspNetCore.App 3.1.9 [/usr/local/share/dotnet/shared/Microsoft.AspNetCore.App]
Microsoft.NETCore.App 3.1.9 [/usr/local/share/dotnet/shared/Microsoft.NETCore.App]
To install additional .NET Core runtimes or SDKs:
https://aka.ms/dotnet-download

Visual Studio Code Extensions

Extension
Author
Version

aadb2c
AzureADB2CTools
1.2.93

azure-account
ms-vscode
0.9.3

azurerm-vscode-tools
msazurermtools
0.13.0

code-spell-checker
streetsidesoftware
1.9.2

cortex-debug
marus25
0.3.7

cpptools
ms-vscode
1.0.1

csharp
ms-dotnettools
1.23.5

CursorColumnSelectOnTrueLines
martinzimmermannapps
0.0.2

docomment
k--kato
0.1.18

EditorConfig
EditorConfig
0.15.1

gitlens
eamodio
10.2.3

go
golang
0.18.1

kubernetes-context
Okteto
0.1.0

markdown-all-in-one
yzhang
3.3.0

markdown-mermaid
bierner
1.8.1

markdown-preview-enhanced
shd101wyy
0.5.13

mssql
ms-mssql
1.9.0

prettier-vscode
esbenp
5.7.1

python
ms-python
2020.10.332292344

remote-wsl
ms-vscode-remote
0.50.1

scala
scala-lang
0.4.5

sublime-keybindings
ms-vscode
4.0.7

terraform
hashicorp
2.2.3

vscode-azurefunctions
ms-azuretools
1.0.0

vscode-azureresourcegroups
ms-azuretools
0.2.0

vscode-azurestorage
ms-azuretools
0.11.0

vscode-cosmosdb
ms-azuretools
0.15.0

vscode-docker
ms-azuretools
1.7.0

vscode-dotnet-adapter
derivitec-ltd
1.4.3

vscode-dotnet-runtime
ms-dotnettools
0.1.2

vscode-drawio
hediet
1.0.3

vscode-html-css
ecmel
0.2.3

vscode-icons
vscode-icons-team
11.0.0

vscode-kubernetes-tools
ms-kubernetes-tools
1.2.1

vscode-markdownlint
DavidAnson
0.37.2

vscode-node-azure-pack
ms-vscode
0.0.11

vscode-solution-explorer
fernandoescolar
0.3.11

vscode-test-explorer
hbenl
2.19.1

vscode-yaml
redhat
0.12.0

vsliveshare
ms-vsliveshare
1.0.3121

xml
DotJoshJohnson
2.5.1

@stewartadam Not sure if it is a typo, but editorconfig files do not have a ".json" extension. They should simply be named .editorconfig

Thx, it was a typo -- can confirm the file is .editorconfig in my repo.

I can confirm the problem. I also have to restart OmniSharp constantly to get rid of warnings that appear because the .editorconfig settings are not taken into account.
Unfortunately I'm unable to deliver a repro because I can't identify the cause. The whole thing happens after a few minutes.
A restart of VS Code does not bring any relief.

fwiw, I've noticed it pretty consistently on Azure Function projects if I run 'dotnet clean' and delete the .vscode/ folder. When subsequently opening the folder in VS Code, a few prompts show up to restore projects, initialize the project for use with VS Code, and initialize the project for use with Functions.

Started seeing the same behavior with C# extension 1.23.6. For me, the trigger that causes OmniSharp to start ignoring the .editorconfig file is rebuild (dotnet build --no-incremental).
When I load the workspace for the first time, everything works fine and all settings are respected. The moment I rebuild, OmniSharp outputs the following:
[info]: OmniSharp.MSBuild.ProjectManager
        Update project: DataAccess
[dbug]: OmniSharp.Roslyn.CSharp.Services.Diagnostics.CSharpDiagnosticWorkerWithAnalyzers
        Project (ProjectId, #05540e1b-acf3-411e-a483-fcf6e1193800 - ) updated, reanalyzing its diagnostics.
[info]: OmniSharp.MSBuild.ProjectManager
        Update project: DataModel
[dbug]: OmniSharp.Roslyn.CSharp.Services.Diagnostics.CSharpDiagnosticWorkerWithAnalyzers
        Project (ProjectId, #6fc4b559-c765-4dd6-9195-76957c62eae4 - ) updated, reanalyzing its diagnostics.
[info]: OmniSharp.MSBuild.ProjectManager
        Update project: Server.Domain
[dbug]: OmniSharp.Roslyn.CSharp.Services.Diagnostics.CSharpDiagnosticWorkerWithAnalyzers
        Project (ProjectId, #2b300a91-eb43-4573-91bc-96d45496b6e0 - ) updated, reanalyzing its diagnostics.
[info]: OmniSharp.MSBuild.ProjectManager
        Update project: Graph
[dbug]: OmniSharp.Roslyn.CSharp.Services.Diagnostics.CSharpDiagnosticWorkerWithAnalyzers
        Project (ProjectId, #119d988c-3db9-4f3d-9483-7c4e5e1abcdf - ) updated, reanalyzing its diagnostics.
[info]: OmniSharp.MSBuild.ProjectManager
        Update project: ExcelExport
[dbug]: OmniSharp.Roslyn.CSharp.Services.Diagnostics.CSharpDiagnosticWorkerWithAnalyzers
        Project (ProjectId, #833150d6-577d-4c1a-8fc4-997b72a1f13e - ) updated, reanalyzing its diagnostics.

After that, all settings in the .editorconfig file are ignored and the only thing that fixes it is to restart omnisharp.

I am seeing the same behavior in my Unity projects and that happens everytime I create a C# file inside the Unity editor.  An omnisharp restart gets rid of that but it is annoying in the long run.

This appears to be happening for me.. when I run dotnet build it works, but when I click 'Debug Test' on a unit test, it shows all kinds of build errors like CA1707 and CA1000


Update types.d.ts

added interface Output to exports - this is necessary when decomposing the webpack.Configuration obj
What kind of change does this PR introduce?
Bugfix
Did you add tests for your changes?
No
Does this PR introduce a breaking change?
No
What needs to be documented once your changes are merged?
Nothing

Summary

copilot:summary
Details

copilot:walkthrough

Hi @exp0rs, if you look at the top of this file you have changed: you might notice the following:
https://github.com/webpack/webpack/blob/6c6320a17eaf4742e00f82d592903d2166a1cb65/types.d.ts#L1-L5
This means that if you want to expose these types you need to properly do so via JSDoc annotations in the code itself or in type definitions files. Please look at the other examples the codebase for more information.

thnx, sorry

No problem, I will close the PR for now, if you see a bug or have a feature request for types, please open an issue along with your PR so you can reference it. This provides important information for us to understand why this change is being made.


Added Universal JS Day

Thanks! :D

Thanks for making the contribution and my apologies for the late reply. As this is unfortunately now outdated, I'm going to close the PR. I hope this doesn't discourage you from opening another PR in the future 😃


Decouple backdrop click and ESC press on mat-dialog

Feature request:
Both ESC and backdrop click closes an active dialog. This can be disabled at the moment, but only all together. These two behaviours should be decoupled.
What is the expected behavior?
MatDialogConfig should have 2 extra properties:
// ...
disableCloseOnEscape: boolean,
disableCloseOnBackdropClick: boolean

What is the use-case or motivation for changing an existing behavior?
So we can disable closing on backdrop click, but keep close on ESC press. It would be very helpful for inaccurate mouse users :).

The suggested approach for this is to set disableClose and then use keydownEvents or backdropClick to re-enable the desired behavior.
See https://github.com/angular/material2/pull/3460 and linked threads for more context.

@willshowell thanks for your comment. I am aware that there are possible workarounds. For example, I wrote something like this:
const dialogRef = this.dialog.open(MyDialogComponent, {
  disableClose: true
});
dialogRef.keydownEvents()
  .filter((e: KeyboardEvent) => e.code === 'Escape')
  .take(1)
  .subscribe((e: KeyboardEvent) => {
    dialogRef.close();
  });

But wouldn't it be a nice to have it out of box? I believe it could be useful for many people.

@salemdar the team (at least in the past) has held pretty strong to this. See https://github.com/angular/material2/pull/3460#issuecomment-319420262 for a better explanation than I can give.

Yep, as per Will's comment, we don't really want to decouple these behaviors.


Feature Request: 2 possible useful Community Tools which could be added to the current collection

Hi,
I do use very often these 2 scripts which are from Kenneth Fisher, "http://www.sqlstudies.com",

https://github.com/sqlstudent144/SQL-Server-Scripts/blob/main/sp_SrvPermissions.sql
https://github.com/sqlstudent144/SQL-Server-Scripts/blob/master/sp_DBPermissions.sql

maybe you could consider implementing them into the Community Tools Collection

To be included in the next release. 🚀


Icons for action on rows

I'm submitting a ...  (check one with "x")
[ ] bug report => search github for a similar issue or PR before submitting
[*] feature request
[ ] support request => Please do not submit support request here

Current behavior

It would be nice to have action buttons(e.g edit/delete). I am not sure if there is already a way to do this.
Expected behavior

We should be able configure action icons on rows, which should just emit an event with the row when the icon is clicked. We might be able to do this with templates, but not sure if we can get a reference to the row from a template.
Reproduction of the problem

What is the motivation / use case for changing the behavior?

Please tell us about your environment:

Table version: 0.7.x

Angular version: 2.0.x

Browser: [all | Chrome XX | Firefox XX | IE XX | Safari XX | Mobile Chrome XX | Android X.X Web Browser | iOS XX Safari | iOS XX UIWebView | iOS XX WKWebView ]

Language: [all | TypeScript X.X | ES6/7 | ES5]

Ya, you can do this with templates. See the demos for example

@amcdnl is it possible to use a custom template for a single cell only? Because i dont want to create a column template for each col if I only want to add an "options" column with 3 icons.

hey, here is a snippet on how to get what you want
 <ngx-datatable-column name="id" >
                                <template let-column="column" ngx-datatable-header-template>
                                    actions
                                </template>
                                <template let-row="row" let-value="value" ngx-datatable-cell-template>

                                </template>
                            </ngx-datatable-column>

and events in your component:
    detail(row: any) {
        this.router.navigateByUrl('/details/' + row.id);
    }

    delete(row: any) {

    }

    send(row: any) {

    }

Hopefully this will give you some more info

Hello @amcdnl, First thanks all workers this plugin. I like know if is possible use mix of list automatic and template. I see what @CanKattwinkel ask de same question, but in my project when I put the template the list hidden. This is corret?
Thanks

Hi, I am developing an internal framework and I am using this table. A question: how can I insert my own custom component for action buttons for each row?


IE11: Nested flex container makes trouble if no min-width is set

In IE11 the following example does not work correctly if you shrink the window width:
http://codepen.io/anon/pen/mEjYYY
In Chrome, Firefox and Edge the behaviour is correct.
Another example:
http://codepen.io/anon/pen/AXgGkY
Correct:

IE11:

A min-width of 1 px seems to solve the problem. Very odd...

In your case you're using the default flex shrink and basis values. If you set them (along with optionally a min-width) everything seems to handle fine:
http://codepen.io/anon/pen/NALrrz
I know this is probably a reduced test case from a larger issue, so I think I'd need more context as to how this came up and what specifically the problem is.

Your example is not what I want... Please see the second example in my first post.
Correct:

IE11:

My point was that flex items with text in them will behave differently from flex items with fixed-width things in them (like in your most recent screenshot). I was also making sure you were aware that you weren't setting the flex shrink/basis properties.
While I agree that there's conflicting behavior going on here, it's not clear how to generalize these differences into a clear "bug" with a clear workaround. Without that, I can't add it to the list of flexbugs.

My example with the colored boxes has only containers with a fixed size (200px width and 20px height). And the workaround is "min-width: 1px;".

If someone has this exact problem, then sure min-width: 1px could be a workaround. But recommending min-width: 1px for any nested flex box layout is obviously too broad a recommendation.
I can't add this to the flexbugs list unless we can identify a generic set of situations in which this bug occurs and where the min-width: 1px fix always works.

2.5 years later and min-width: 1px or flex: 1 1 0% still works.


RowView can not set width props

No matter how it is set, the width will always be defaultContainerWidth. cannot cover

@hi-asan doing this will work for ya -
dimensions={{width: 800}}

@hi-asan  lemme know if the above does it for ya. Closing

Yes, the problem has been resolved. Thank you very much. I plan to adjust the changes I made in the source code back


[DOC] Add derivative work credits

Reference Issues/PRs

What does this implement/fix? Explain your changes.
Credits in the readme and document index were already given but I forgot to add them in their respective code files with proper copyright. They include reference of derivative works from scikit-learn, scikit-portfolio, Riskfolio-Lib and statsmodel (all under MIT or BSD 3)
Does your contribution introduce a new dependency? If yes, which one?
No
What should a reviewer concentrate their feedback on?
Is the syntax correct for this kind of credits?
Did you add any tests for the change?
N.A.
Any other comments?
No
PR checklist

For all contributions

[x] The PR title starts with either [ENH], [MNT], [DOC], or [BUG]. [BUG] - bugfix, [MNT] - CI, test framework, [ENH] - adding or improving code, [DOC] - writing or improving documentation or docstrings.
[ ] I've added myself to the list of contributors
How to: add yourself to the all-contributors file in the skfolio root directory (not the CONTRIBUTORS.md).
[ ] Optionally, I've added myself and possibly others to the CODEOWNERS file - do this if you want to become the owner or maintainer of an estimator you added.

For new estimators

[ ] I've added the estimator to the API reference in docs/api.rst.
[ ] I've added one or more illustrative usage examples to the docstring and the examples section.

Thx, remaining credits have been added


dotnet: cleanup; point dotnet-sdk alias to 5.0; remove unsupported SDKs

Motivation for this change

point dotnet-sdk alias to 5.0
cleanup by removing unsupported dotnet SDKs (2.1 and 3.0)
fixes https://github.com/NixOS/nixpkgs/issues/112428

Things done

Built on platform(s)

[ ] x86_64-linux
[ ] aarch64-linux
[ ] x86_64-darwin
[ ] aarch64-darwin

[ ] For non-Linux: Is sandbox = true set in nix.conf? (See Nix manual)
[ ] Tested via one or more NixOS test(s) if existing and applicable for the change (look inside nixos/tests)
[ ] Tested compilation of all packages that depend on this change using nix-shell -p nixpkgs-review --run "nixpkgs-review wip"
[ ] Tested execution of all binary files (usually in ./result/bin/)
21.11 Release Notes (or backporting 21.05 Release notes)

[ ] (Package updates) Added a release notes entry if the change is major or breaking
[ ] (Module updates) Added a release notes entry if the change is significant
[ ] (Module addition) Added a release notes entry if adding a new NixOS module

[x] Fits CONTRIBUTING.md.

@kuznero What do you think? Do you like the change?

@IvarWithoutBones what do you think?


experiment_id ssp370 activity_id

experiment_id ssp370 activity_id:
ScenarioMIP and AerChemMIP co-sponsor ssp370, but I think that in the DRS and directory structure ScnearioMIP should appear, so the order should be changed to "ScenarioMIP AerChemMIP"  (currently they are reversed).
Karl

Fine with me, Karl, thank you.

Certainly ScenarioMIP should be the main sponsor of ssp370.

This change can be made because both interested parties have agreed.


I want to send some data using restricted MAM then wait for some time and add more data to the same Merkle Tree is that possible?

I am trying to save some IPFS hashes using MAM. I send each hash to Merkel tree after 15 minutes, I want all my messages to be on only one Merkle tree. So the root address could give me access to all messages but after 15 minutes it creates a new root with a new Merkle tree. Is that possible to make a permanent  Merkle tree or I am missing something?
my code for pushing ipfs hash
const pushData = async(_secretKey,_provider,packet) =>{
 let mamState = Mam.init(_provider,seed)
 mamState = Mam.changeMode(mamState, 'restricted', _secretKey)
 const trytes = asciiToTrytes(packet)
 const message = Mam.create(mamState, trytes)

 // Save new mamState
 mamState = message.state

 // Attach the payload
 await Mam.attach(message.payload, message.address, 3, 9)

 return message.root
}

my code for fetching  ipfs hashs
const fetchData = async(_sideKey,_provider,_root)=>{
 mamState = Mam.init(_provider,seed)
 mamState = Mam.changeMode(mamState, 'restricted', _sideKey)
 const result = await Mam.fetch(_root, 'restricted', _sideKey)
 return result.messages
}

If I  use pushData  to save some IPFS hashes in sequence the main root address work, else if I wait some time between sending messages it creates a new Merkel tree could anyone tell me why and how to solve it?

I needed to make the 'mamState' global and it solves the problem.


Add flamegraph profiling using flame and flamer

This is just an initial change to get the ball rolling for flame profiling, we can add more scopes later.

There's not a ton of information here that would be helpful to optimization (yet), but it's really interesting to see!

This is pretty cool! Is it mandatory to add the flame-it attribute to each function you wish to track? Can we just follow all functions?

Off topic: why does the importlib loading take so long? Is it a whole lot of code?

re: importlib: The main problem is that it is a fair bit of code, and RustPython isn't all that optimized. See #1047.
re: automatically tagging all fns: see llogiq/flamer#45. I tried to do that initially, and there were a lot of issues with it. Even so, manually tagging all fns in frame.rs with #[flame] gave way too much irrelevant data, and the file size was huge as well (36MB compared to the 1MB with the current version here). e.g., do we really need to see how long each individual exception creation function takes?

I had to use a modified local clone of flame to get this flamegraph due to a bug (TyOverby/flame#47), but something interesting that you can see is that in _install_external_importers, when it imports _frozen_importlib_external, a fairly large portion of the time spent in __import__ is before it even starts executing the module (Frame::run(<module>)). Maybe we can detect when it's importing _frozen_importlib_external from _frozen_importlib and use Rust to import it?

Here it is with a simple optimization to skip __import__ in that case: link. Shaved off 10ms, not bad!

CPython actually have large parts of the __import__ function implemented in C. We can do the same in Rust for optimization.


filter collection objects by creator/updater

Feature or enhancement
We have different sets of people working on different projects within the same TaxonWorks Project.  It would be very helpful if it was easier to filter collection objects by creator/updater. This would allow us to more easily work only on images/collection objects within our respective projects.
The two places this would work best with our workflows are:

The TODO/breakdown list page.  It would be great to be able to filter the 'hexagons' by creator/updater.  If that's too complicated, it would help to be able to type in a page number to jump to.
in the Filter Collection Objects Task. Likewise, it would be helpful to be able to sort by creator/updater.

Thank you!
Julia + Sophia
Location
https://sfg.taxonworks.org/tasks/collection_objects/filter and https://sfg.taxonworks.org/tasks/accessions/breakdown/sqed_depiction/todo_map
Screenshot, napkin sketch of interface, or conceptual description
For the collection object filter, it would be easy to add filters for record creator and record updater.
For the TODO/breakdown list, it would be great if we could filter by record attributes.  But if not, at the very least it would speed things up to have an option in the navigate bar to jump to a page number.  Eg.  ( Jump to: [type page number here] [Go button] )
Your role
Collection digitization technicians

in the Filter Collection Objects Task. Likewise, it would be helpful to be able to sort by creator/updater

This one is done.
The other (TODO) is now a separate issue: #3043.  Closing this.


merge from arlac77/template-github,arlac77/template-kronos-module

package.json

chore(package): add >=14.1.0 remove >=14.0.0 (engines.node)

.travis.yml

chore(travis): (jobs.include)
chore(travis): remove 14.0.0 (jobs.include.node_js)

:tada: This PR is included in version 10.1.2 :tada:
The release is available on:

npm package (@latest dist-tag)
GitHub release

Your semantic-release bot :package::rocket:


Guard against race condition when removing spies

It seems that -- extremely rarely -- it's possible for a spy to be removed, nulling out its $element, but still be seen by the handler.  My guess is that the handler gets queued in Angular's internal deferred actions, then before it can execute the context is destroyed and the spy removed (but not removed from the context's list due to the ordering).  Then when the handler executes it can activate a spy with a null $element, which causes a crash.  I didn't bother to track down the root cause precisely because it's so rare (one occurrence in over a year), and so easy to protect against.

Thanks!


Magic number incorrect for N3 Mainnet and Testnet

> CONST.MAGIC_NUMBER.TestNet
844378958
> CONST.MAGIC_NUMBER.MainNet
5195086

Testnet magic shoud be 877933390
Mainnet magic should be 860833102

I think you might need to update your neon-js version as they've changed a month ago with this PR https://github.com/CityOfZion/neon-js/pull/779
https://github.com/CityOfZion/neon-js/blob/820e20d3160e46d019c3f65e84d3f9270ffa1588/packages/neon-core/src/consts.ts#L9-L10


EAGLE-326 fix a typo in documentation for application management configuration

this will be fixed in eaglemonitoring git repo

only change "author" in commit "e4cad7b"


Fix package names in release Dockerfile

@bparees saves the release \o/

what did i do?
[merge]

@smarterclayton apparently this fixed the cross compile issue for STI.  I don't know the logic behind it, perhaps @mfojtik can explain why the amd64 packages work.

ah, found the IRC discussion:
(10:55:02 AM) vbatts: mfojtik: i don't think this build/release image has all the bootstrapping needed
(10:55:33 AM) vbatts: mfojtik: the build is failing on darwin/amd64, but the golang-pkg-darwin-amd64.noarch rpm is not installed


Replace React.PropTypes / PropTypes

I updated the react to version 15.5.0 and saw an error in the console:

Warning: Accessing PropTypes via the main React package is deprecated. Use the prop-types package from npm instead.

We need to update components to use prop-types package. More info in official blog: https://facebook.github.io/react/blog/2017/04/07/react-v15.5.0.html
// Before (15.4 and below)
import React from 'react';

class Component extends React.Component {
  render() {
    return
{this.props.text}
;
  }
}

Component.propTypes = {
  text: React.PropTypes.string.isRequired,
}

// After (15.5)
import React from 'react';
import PropTypes from 'prop-types';

class Component extends React.Component {
  render() {
    return
{this.props.text}
;
  }
}

Component.propTypes = {
  text: PropTypes.string.isRequired,
};

Thanks @hoosin. Sure be nice to get this merged.

@souhe are you planning to merge this soon plz ?

Was going to look into this myself...but since @hoosin is a great guy, he already did the work for me! Would love it if this were merged. Console warnings are no bueno

@souhe Merge it please.

@souhe could you please merge this?

It's fixed by another PR. See current react-scrollbar version.
But anyway @hoosin thank you for your work, and sorry for delay :)


AXILITE independent channels

2020.10.06 added support for for independent channels. In cases where the target being accessed by the core only supports one channel access at a time each await completions must be added around each access. Perhaps a config parameter for this feature would be better

Hi,
We belive this is outside the standard of the AXI-Lite; could you clearify the use case?
Br,
Marius

Before the update the BFM would not issue a Read and Write cycle simultaneously. In our case we have a couple of very simple slaves that do not support this. (Perhaps not strictly AXI4-Lite compliant but they do work with any host that we are using.
The other issue I suppose if testing for 100% AXI-Lite Compliancy, would be that one might want to be  able to check that the client behaves itself with sequential access.
To be clear I can work around this with await completions but need to go through all of my test code and add them.

Hi,
I will create a ticket for adding a configuration option so that the VIP can be run in this mode.
Br,
Marius

Hi,
This is fixed in v2021.10.22.
A new element in the BFM config record is added. This is called 'force_single_pending_transaction' and can be set to true to require that a transaction is completed before starting the next transaction.


the mod keeps on saying it needs fabric api even though i have it installed

What minecraft version?

I've tried it against the 4 Minecraft versions Blåhaj supports, I could not reproduce. If possible, upload a crash log.
Closing as cannot reproduce.


[BCHDCC-80] Keyword Match Notification Banner

Description

If there is no exact match when a user searches a keyword, inform the user that the keyword they searched did not populate exact match results, but related results are being displayed.
For example, when a user searches for "Baby food" and the combination of the 2 words were not found, the page should display a banner indicating the user that the exact match was not found, but similar/partial matches will be displayed.
If the word being searched is a partial match, but not an exact match, the no exact matches banner does not get displayed. For example, if the user searches "covid", there are many locations with organizations names with the word "covid" in the title, but none of them have an exact match to only the keyword "covid". Although it isn't an exact match, it is a partial match in the title.
Keyword match notification banner is accessible to screen readers.
Motivation and Context

BCHDCC-80 - Implement a notification banner when search is not a match

There was no frontend design that I could follow, this is a simple banner that can be updated with a different design in the future. This is a first, simple design iteration.
This ticket depends on parts of the implementation in BCHDCC-79
Screenshots

No results with an exact result match for "baby food"

mobile

No keyword match banner notification for search "covid" because there are results that have "covid" in their title

Libraries Added
Migrations to Run: Yes / No
Tests to Run
Internal Checklist

[x] My code follows the code style of this project (https://rubystyle.guide/).
[ ] I have added tests to cover my changes.
[x] All new and existing tests passed.

@anna-dorigo I wasn't able to completely reproduce the search/punctuation filtering problems, but I added an extra layer of punctuation filter to the search - let me know if this fixes it.


Transaction Builder: Button to fetch next sequence number for account

For this item:

PR here: https://github.com/stellar/laboratory/pull/68


Feature Request: Update the Blog Navbar Search Functionality when The Blogs are Opened

Issue Description:
The search bar in the blog's navbar (when viewing a specific blog) does not perform any actions when a user types into it. It should be updated to allow users to search for specific parts of blog posts based on entered keywords.
Steps to Reproduce:
1). Open the blog's website.
2). Click on any blog.
3). Type any text into the search bar in the navbar.
4). Observe that no search results are displayed or filtered as expected.
Expected Behavior:
The search bar should filter or display results based on the user's input. For example, as the user types, relevant parts of the blog should appear.
Hey @AkshitLakhera please assign me this task with appropriate labels:) i will implement the search functionality so user can easily navigate to parts of long blogs to save time

Definitely it is nice  feature to have.Go ahead ,I am assigning this to you.Feel free to reach me if you need any help.
Looking forward to it.


Consider implementing a ParallaxRecyclerView

Google is moving from the old ListView to the more flexible RecyclerView. It would be very useful to have a parallax-enabled version of that as well.

I know about that project - https://github.com/kanytu/android-parallax-recyclerview
please let me know if its not good enough and this one need to be ported


Add registration

Additions:

created registration middleware
4 registration middleware tests
Changes
removed temporary registration stub from route

Looking forward to any feedback. Thank you!

still need to resolve merge conflict

still need to resolve merge conflict

To Do

[ ] add check existing user middleware
[ ] add tests for 200, 400, 504, check existing user

Notes

Added isFormValid, hashForm, generateCookie to registrationMiddleware
Added registrationLogger module to reduce bloat in registrationMiddleware
Response object now sends back user data excluding password (should we return _id?)
Tests now use MongoDB memory server

To Do

[ ] add check existing user middleware
[ ] add tests for 200, 400, 504, check existing user

Notes

Added isFormValid, hashForm, generateCookie to registrationMiddleware
Added registrationLogger module to reduce bloat in registrationMiddleware
Response object now sends back user data excluding password (should we return _id?)
Tests now use MongoDB memory server

Form validation now skips checking for role_id.
Email now properly extracted from req in case of errors.
Requests sent from frontend form add a default role_id in order for common-menu API to work properly.

Additional
Will add back role_id to validation  and remove default role_id to req once new form is complete


Signing manifesto

adding myself to support

Signing manifesto

Added in https://github.com/opentofu/manifesto/pull/1137


Introduce deny default networkpolicy to all namespaces

This change will add a network policy to all platform namespaces and then additional polices to allow traffic that is required.
There are currently two limitations with this solution which is for the most part caused by running managed clusters.

Access to the api server cant be limited to just the specific IP as it is dynamic and resolved through DNS. OSS Calico does not support DNS based egress rules and using the IP from Terraform would be risky if that IP changes in the middle of the night. The current solution is to allow pods that need api server access egress permission to all public IPs. We shoudl re visit this in the future.
Controller which implement Kubernetes webhooks have to allow traffic from the api server. I think I have figured out the CIDR used in AKS but we should keep an eye on this to make sure that this is correct all of the time. We still need to figure out the network policy to use in EKS to make this possible,

You might already know this but apparently there are two ways AKS talks to Azure.
One way is tunnelfrontand the other one is called aks-link I don't think we need to take this in to consideration since the network policies is already in place for them but good to think about.


Questions About Frames extracting

In the Line 85 of SwinBERT/create_image_frame_tsv.py.
"  current_image_path = previous_image_path "
Does it mean when the amount of extracted images is less than num_frames, you will pad them to num_frames with the last image? This step is a little confused to me. Is the result of it different from the one which do not copy the last image?

I follow “/prepro/extract_ youcook2_ frms.sh"  executes "./prepro/extract_ frames.py", but   it doesn't seem to work, and the following results are obtained:

python ./prepro/extract_frames.py
--video_root_dir ./datasets/MSRVTT-v2/videos
--save_dir ./datasets/MSRVTT-v2/
--video_info_tsv ./datasets/MSRVTT-v2/val.img.tsv
--num_frames 32
0it [00:00, ?it/s]`

Is my operation incorrect?
Thank you very much ~

In the Line 85 of SwinBERT/create_image_frame_tsv.py. " current_image_path = previous_image_path "
Does it mean when the amount of extracted images is less than num_frames, you will pad them to num_frames with the last image? This step is a little confused to me. Is the result of it different from the one which do not copy the last image?

Hi! Have you reproduced the results in paper? May I ask did you adjust the value of 'loss_sparse_w' and the 'learning_rate' in command? For the 'loss_sparse_w', I guess it's the regularization hyperparameter of $Loss_{SPARSE}$ , i.e. the $\lambda$ in the paper. In the appendix, it seems like for MSR-VTT, the model performs best when $\lambda$ = 5. But the why the default value of 'loss_sparse_w' in command is 0.5? Do I need to adjust it to 5? Thank you a lot!


Fix sum KL distribution across both latent dims.

Logits have shape TensorShape([2, 32, 32]), I believe you want to sum over the last two latent dimensions when you are computing the KL divergence loss.

My bad! As discussed offline the Categorical consumes one dimension, so the Independent distribution was correct.


CancelledError:  [Derived]RecvAsync is cancelled. 	 [[{{node Reshape_17/_52}}]] 	 [[GroupCrossDeviceControlEdges_0/RMSprop/RMSprop/Const/_57]] [Op:__inference_distributed_function_24912]

Please make sure that this is a bug. As per our GitHub Policy, we only address code/doc bugs, performance issues, feature requests and build/installation issues on GitHub. tag:bug_template
System information

Have I written custom code (as opposed to using a stock example script provided in TensorFlow): Yes
OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Windows 10 Home
Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device: NaN
TensorFlow installed from (source or binary): pip
TensorFlow version (use command below): 2.0.0
Python version: 3.7.4
Bazel version (if compiling from source):
GCC/Compiler version (if compiling from source):
CUDA/cuDNN version: v10.1
GPU model and memory: GTX 1060 6GB

You can collect some of this information using our environment capture
script
You can also obtain the TensorFlow version with: 1. TF 1.0: python -c "import tensorflow as tf; print(tf.GIT_VERSION, tf.VERSION)" 2. TF 2.0: python -c "import tensorflow as tf; print(tf.version.GIT_VERSION, tf.version.VERSION)"
Describe the current behavior
During fitting the data, it gives Cancelled Error in the very first batch
Describe the expected behavior
To "Fit" the model without error
Code to reproduce the issue
max_len_text=275
max_len_summary=28
from keras import backend as K
K.clear_session()
latent_dim = 500
encoder_inputs = Input(shape=(max_len_text,))
enc_emb = Embedding(x_voc_size, latent_dim,trainable=True)(encoder_inputs)
encoder_lstm1 = LSTM(latent_dim,return_sequences=True,return_state=True)
encoder_output1, state_h1, state_c1 = encoder_lstm1(enc_emb)
encoder_lstm2 = LSTM(latent_dim,return_sequences=True,return_state=True)
encoder_output2, state_h2, state_c2 = encoder_lstm2(encoder_output1)
encoder_lstm3=LSTM(latent_dim, return_state=True, return_sequences=True)
encoder_outputs, state_h, state_c= encoder_lstm3(encoder_output2)
decoder_inputs = Input(shape=(None,))
dec_emb_layer = Embedding(y_voc_size, latent_dim,trainable=True)
dec_emb = dec_emb_layer(decoder_inputs)
decoder_lstm = LSTM(latent_dim, return_sequences=True, return_state=True)
decoder_outputs,decoder_fwd_state, decoder_back_state = decoder_lstm(dec_emb,initial_state=[state_h, state_c])
attn_layer = AttentionLayer(name='attention_layer')
attn_out, attn_states = attn_layer([encoder_outputs, decoder_outputs])
decoder_concat_input = Concatenate(axis=-1, name='concat_layer')([decoder_outputs, attn_out])
decoder_dense = TimeDistributed(Dense(y_voc_size, activation='softmax'))
decoder_outputs = decoder_dense(decoder_concat_input)
model = Model([encoder_inputs, decoder_inputs], decoder_outputs)
model.compile(optimizer='rmsprop', loss='sparse_categorical_crossentropy')
es = EarlyStopping(monitor='val_loss', mode='min', verbose=1)
Model_training=model.fit([X_train,Y_train[:,:-1]], Y_train.reshape(Y_train.shape[0],Y_train.shape[1], 1)[:,1:]  ,epochs=50,callbacks=[es],batch_size=256, validation_data=([X_test,Y_test[:,:-1]],Y_test.reshape(Y_test.shape[0],Y_test.shape[1], 1)[:,1:]))
Provide a reproducible test case that is the bare minimum necessary to generate the problem.
Other info / logs
Include any logs or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached.
Train on 314860 samples, validate on 78716 samples
Epoch 1/50
256/314860 [..............................] - ETA: 5:22WARNING:tensorflow:Early stopping conditioned on metric val_loss which is not available. Available metrics are:

CancelledError                            Traceback (most recent call last)
 in
1 Model_training=model.fit([X_train,Y_train[:,:-1]], Y_train.reshape(Y_train.shape[0],Y_train.shape[1], 1)[:,1:]
2                          ,epochs=50,callbacks=[es],batch_size=256, validation_data=([X_test,Y_test[:,:-1]],
----> 3                          Y_test.reshape(Y_test.shape[0],Y_test.shape[1], 1)[:,1:]))
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_core\python\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
726         max_queue_size=max_queue_size,
727         workers=workers,
--> 728         use_multiprocessing=use_multiprocessing)
729
730   def evaluate(self,
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in fit(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, **kwargs)
322                 mode=ModeKeys.TRAIN,
323                 training_context=training_context,
--> 324                 total_epochs=epochs)
325             cbks.make_logs(model, epoch_logs, training_result, ModeKeys.TRAIN)
326
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in run_one_epoch(model, iterator, execution_function, dataset_size, batch_size, strategy, steps_per_epoch, num_samples, mode, training_context, total_epochs)
121         step=step, mode=mode, size=current_batch_size) as batch_logs:
122       try:
--> 123         batch_outs = execution_function(iterator)
124       except (StopIteration, errors.OutOfRangeError):
125         # TODO(kaftan): File bug about tf function and errors.OutOfRangeError?
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py in execution_function(input_fn)
84     # numpy translates Tensors to values in Eager mode.
85     return nest.map_structure(_non_none_constant_value,
---> 86                               distributed_function(input_fn))
87
88   return execution_function
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_core\python\eager\def_function.py in call(self, *args, **kwds)
455
456     tracing_count = self._get_tracing_count()
--> 457     result = self._call(*args, **kwds)
458     if tracing_count == self._get_tracing_count():
459       self._call_counter.called_without_tracing()
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_core\python\eager\def_function.py in _call(self, *args, **kwds)
485       # In this case we have created variables on the first call, so we run the
486       # defunned version which is guaranteed to never create variables.
--> 487       return self._stateless_fn(*args, **kwds)  # pylint: disable=not-callable
488     elif self._stateful_fn is not None:
489       # Release the lock early so that multiple threads can perform the call
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_core\python\eager\function.py in call(self, *args, **kwargs)
1821     """Calls a graph function specialized to the inputs."""
1822     graph_function, args, kwargs = self._maybe_define_function(args, kwargs)
-> 1823     return graph_function._filtered_call(args, kwargs)  # pylint: disable=protected-access
1824
1825   @property
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_core\python\eager\function.py in _filtered_call(self, args, kwargs)
1139          if isinstance(t, (ops.Tensor,
1140                            resource_variable_ops.BaseResourceVariable))),
-> 1141         self.captured_inputs)
1142
1143   def _call_flat(self, args, captured_inputs, cancellation_manager=None):
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_core\python\eager\function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
1222     if executing_eagerly:
1223       flat_outputs = forward_function.call(
-> 1224           ctx, args, cancellation_manager=cancellation_manager)
1225     else:
1226       gradient_name = self._delayed_rewrite_functions.register()
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_core\python\eager\function.py in call(self, ctx, args, cancellation_manager)
509               inputs=args,
510               attrs=("executor_type", executor_type, "config_proto", config),
--> 511               ctx=ctx)
512         else:
513           outputs = execute.execute_with_cancellation(
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_core\python\eager\execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
65     else:
66       message = e.message
---> 67     six.raise_from(core._status_to_exception(e.code, message), None)
68   except TypeError as e:
69     keras_symbolic_tensors = [
C:\ProgramData\Anaconda3\lib\site-packages\six.py in raise_from(value, from_value)
CancelledError:  [Derived]RecvAsync is cancelled.
[[{{node Reshape_17/_52}}]]
[[GroupCrossDeviceControlEdges_0/RMSprop/RMSprop/Const/_57]] [Op:__inference_distributed_function_24912]
Function call stack:
distributed_function

Also experienced this, also with LSTM models. Training runs for a while eg. 100ish batches, then this error comes up.

@solalatus In my case only first batch is executed and then this error is encountered. Can it be something related to GPU memory?

Well, not the first batch, the first couple hundred batches. So nothing coming from the graph definition, more like some memory leak or operations bug.

@AniTho ,
Can you share a simple and standalone code to reproduce the issue?Thanks!

@oanush the problem was solved after downgrading tensorflow to 1.14

I have exprerienced the exact same error message in TF 2.0.0
The problem can be reproduced with one of the tutorial on the Tensorflow website:
https://www.tensorflow.org/tutorials/text/text_classification_rnn
Problem happens right after the training is started ( in the 1st epoch):
CancelledError:  [Derived]RecvAsync is cancelled.
[[{{node Adam/Adam/update/AssignSubVariableOp/_41}}]]
[[Reshape_11/_38]] [Op:__inference_distributed_function_6315]
Function call stack:
distributed_function
The problem seems to be related to the GPU, if I executed tensorflow witth CPU only, it does not crash. I however use the tensorflow-rocm (with a Vega 56 card), but it's probably not a coincidence that I get the exact same error message than mentioned above.
Have I written custom code (as opposed to using a stock example script provided in TensorFlow): Yes
OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Elementary OS
Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device: NaN
TensorFlow installed from (source or binary): pip
TensorFlow version (use command below): 2.0.0
Python version: 3.7.5
Bazel version (if compiling from source):
GCC/Compiler version (if compiling from source):
CUDA/cuDNN version: ROCM 2.9
GPU model and memory: Vega 56 8Gb

Closing as per @AniTho comment. Thank you

I don't quite understand. How is changing the TF version a solution? An explanation as to why this error is reproducible for TF 2.0 would be much appreciated, as well as a clarification whether or not this error is a TF bug or a configuration issue.

I don't quite understand. How is changing the TF version a solution? An explanation as to why this error is reproducible for TF 2.0 would be much appreciated, as well as a clarification whether or not this error is a TF bug or a configuration issue.

I myself faced this today, and for me it was cudnn version issue

I had same issue with latest TF 2.0.0 / CUDNN.  I see lots of hits on this issue when searching.
I think this issue should be reopened @oanush as TF 2.0 experiences problem with toy / example solution.  Migrating to a previous version isn't a fix.
The code seems to be around here.
https://github.com/tensorflow/tensorflow/blob/81f844c1ff2bee0c3a98a7fff7b308ad77d85309/tensorflow/core/framework/rendezvous.h
I am testing on a smallish 6GB GEForce GTX 1660 Ti so perhaps it's just running out of memory and giving a bad error?  Might be an Nvidia driver issue rather than Tensorflow's interface.
Adding validation_split seems to cause kernel shutdowns and the error above in jupyter.
history = model.fit(x, y, batch_size=256, epochs=2, validation_split=0.33)

Setting
TF_FORCE_GPU_ALLOW_GROWTH=1

in system environment (Windows) and restarting the shell / restarting Jupyter worked.
Perhaps this needs to be set by default or parameter documented / set as part of Tensorflow api?
tf.config.experimental.set_memory_growth(gpu, True)

nvidia-smi

+-----------------------------------------------------------------------------+
| NVIDIA-SMI 441.66       Driver Version: 441.66       CUDA Version: 10.2     |
|-------------------------------+----------------------+----------------------+
| GPU  Name            TCC/WDDM | Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  GeForce GTX 166... WDDM  | 00000000:01:00.0  On |                  N/A |
| 27%   38C    P8    13W / 120W |   6028MiB /  6144MiB |      8%      Default |
+-------------------------------+----------------------+----------------------+

@asears can you recheck your cudnn version??

cudnn-7.6.5-cuda10.0_0  - fit crashed again even after setting the environment variable so perhaps not  a fix.

cudnn-7.6.5-cuda10.0_0 - fit crashed again even after setting the environment variable so perhaps not a fix.

Facing the same issue with the same cudnn and cuda version.
Just following this tutorial: https://www.tensorflow.org/tutorials/text/text_classification_rnn
It occurs randomly in the 1st epoch or 2nd on my computer.
I tried in Google Colab, successfully ran one time without error.

same issue here with LSTM on GPU
appears to be solved by:
 import os os.environ["TF_FORCE_GPU_ALLOW_GROWTH"]="true"

same issue here with LSTM on GPU
appears to be solved by:
import os
os.environ["TF_FORCE_GPU_ALLOW_GROWTH"]="true"

EDIT: spoke too soon, somehow this works on one machine but not another?

It's working on my laptop as well using this method

Getting the same error, with AND without the TF_FORCE_GPU_ALLOW_GROWTH flag set.
CancelledError:  [_Derived_]RecvAsync is cancelled. 	 [[{{node ConstantFolding/assert_greater_equal_1/Assert/AssertGuard/switch_pred/_30_const_false/_117}}]] [Op:__inference_train_function_240852]

Getting the same error, with AND without the TF_FORCE_GPU_ALLOW_GROWTH flag set.
CancelledError: [_Derived_]RecvAsync is cancelled. [[{{node ConstantFolding/assert_greater_equal_1/Assert/AssertGuard/switch_pred/_30_const_false/_117}}]] [Op:__inference_train_function_240852]

I guess you need cudnn 8.0.2 because cudnn 7.6.4 supports cuda 10.2 (based on Nvidia documentation).

Getting the same error, with AND without the TF_FORCE_GPU_ALLOW_GROWTH flag set.
CancelledError: [_Derived_]RecvAsync is cancelled. [[{{node ConstantFolding/assert_greater_equal_1/Assert/AssertGuard/switch_pred/_30_const_false/_117}}]] [Op:__inference_train_function_240852]

I guess you need cudnn 8.0.2 because cudnn 7.6.4 supports cuda 10.2 (based on Nvidia documentation).

I upgraded to cudnn 8.0.3 / cuda 11.0 and tensorflow2.3.0 built from source, and the problem persists.

@Zahlii no idea then

Bringing this back to life - Getting the same error with both Cuda 11.1 and 10.1 in tf 2.3.1.
My error is slightly different from the ones above in terms of the text I get back:
`[Derived]RecvAsync is cancelled.
[[{{node Adam/Adam/update/AssignSubVariableOp/_27}}]]
[[gradient_tape/sequential/embedding/embedding_lookup/Reshape/_24]] [Op:__inference_train_function_2598]
Function call stack:
train_function`

I am receiving a similar error.
CancelledError:  [Derived]RecvAsync is cancelled.
[[{{node Adam/Adam/update/AssignSubVariableOp/_33}}]]
[[gradient_tape/sequential_1/embedding_1/embedding_lookup/Reshape/_30]] [Op:__inference_train_function_11493]
I am using tf 2.3.1 and Cuda Toolkit 11.1

Same issue, tf 2.3.1 cuda 10.1, with MirroredStrategy on 2 GPU and 1CPU machine.

Same issue!
nvcc: NVIDIA (R) Cuda compiler driver                                                                                Copyright (c) 2005-2019 NVIDIA Corporation                                                                                Built on Sun_Jul_28_19:07:16_PDT_2019                                                                                Cuda compilation tools, release 10.1, V10.1.243

And tf version 2.3.0
Training seems to stop mid way!
#Epoch 1/10
WARNING:tensorflow:From /home/aman_arora/.local/lib/python3.7/site-packages/tensorflow/python/data/ops/multi_device_iterator_ops.py:601: get_next_as_optional (from tensorflow.python.data.ops.iterator_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Iterator.get_next_as_optional()` instead.
INFO:tensorflow:batch_all_reduce: 213 all-reduces with algorithm = nccl, num_packs = 1
INFO:tensorflow:batch_all_reduce: 213 all-reduces with algorithm = nccl, num_packs = 1
INFO:tensorflow:batch_all_reduce: 213 all-reduces with algorithm = nccl, num_packs = 1
INFO:tensorflow:batch_all_reduce: 213 all-reduces with algorithm = nccl, num_packs = 1
 7845/16191 [=============>................] - ETA: 40:32 - loss: 0.2051
---------------------------------------------------------------------------
CancelledError                            Traceback (most recent call last)
~/.local/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py in _method_wrapper(self, *args, **kwargs)
    106   def _method_wrapper(self, *args, **kwargs):
    107     if not self._in_multi_worker_mode():  # pylint: disable=protected-access
--> 108       return method(self, *args, **kwargs)
    109
    110     # Running inside `run_distribute_coordinator` already.
~/.local/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
   1096                 batch_size=batch_size):
   1097               callbacks.on_train_batch_begin(step)
-> 1098               tmp_logs = train_function(iterator)
   1099               if data_handler.should_sync:
   1100                 context.async_wait()
~/.local/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
    778       else:
    779         compiler = "nonXla"
--> 780         result = self._call(*args, **kwds)
    781
    782       new_tracing_count = self._get_tracing_count()
~/.local/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
    805       # In this case we have created variables on the first call, so we run the
    806       # defunned version which is guaranteed to never create variables.
--> 807       return self._stateless_fn(*args, **kwds)  # pylint: disable=not-callable
    808     elif self._stateful_fn is not None:
    809       # Release the lock early so that multiple threads can perform the call
~/.local/lib/python3.7/site-packages/tensorflow/python/eager/function.py in __call__(self, *args, **kwargs)
   2827     with self._lock:
   2828       graph_function, args, kwargs = self._maybe_define_function(args, kwargs)
-> 2829     return graph_function._filtered_call(args, kwargs)  # pylint: disable=protected-access
   2830
   2831   @property
~/.local/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _filtered_call(self, args, kwargs, cancellation_manager)
   1846                            resource_variable_ops.BaseResourceVariable))],
   1847         captured_inputs=self.captured_inputs,
-> 1848         cancellation_manager=cancellation_manager)
   1849
   1850   def _call_flat(self, args, captured_inputs, cancellation_manager=None):
~/.local/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
   1922       # No tape is watching; skip to running the function.
   1923       return self._build_call_outputs(self._inference_function.call(
-> 1924           ctx, args, cancellation_manager=cancellation_manager))
   1925     forward_backward = self._select_forward_and_backward_functions(
   1926         args,
~/.local/lib/python3.7/site-packages/tensorflow/python/eager/function.py in call(self, ctx, args, cancellation_manager)
    548               inputs=args,
    549               attrs=attrs,
--> 550               ctx=ctx)
    551         else:
    552           outputs = execute.execute_with_cancellation(
~/.local/lib/python3.7/site-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
     58     ctx.ensure_initialized()
     59     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
---> 60                                         inputs, attrs, num_outputs)
     61   except core._NotOkStatusException as e:
     62     if name is not None:
CancelledError:  [_Derived_]RecvAsync is cancelled.
 [[{{node div_no_nan/ReadVariableOp_3/_596}}]] [Op:__inference_train_function_70284]
Function call stack:
train_function

@oanush why was this issue closed?

I had this issue but I could fix it.
In my case I had something along the lines:
train_data = get_dataset('train').padded_batch(train_batch_size)
valid_data = get_dataset('valid').padded_batch(valid_batch_size)

model.fit(
  train_data,
  validation_data=valid_data
)

Where train_batch_size != valid_batch_size which got me:
tensorflow.python.framework.errors_impl.CancelledError:  [_Derived_]RecvAsync is cancelled.
	 [[{{node Mean/_183}}]] [Op:__inference_test_function_88423]

Function call stack:
test_function

Solution
Apparently this problem might occur if we use different batch sizes. Using only one batch size solved the issue for me.

I had this issue but I could fix it.
In my case I had something along the lines:
train_data: tf.data.Dataset = get_dataset('train').padded_batch(train_batch_size)
valid_data: tf.data.Dataset = get_dataset('valid').padded_batch(valid_batch_size)

model.fit(
  train_data,
  validation_data=valid_data
)

Where train_batch_size != valid_batch_size which got me:
tensorflow.python.framework.errors_impl.CancelledError:  [_Derived_]RecvAsync is cancelled.
	 [[{{node Mean/_183}}]] [Op:__inference_test_function_88423]

Function call stack:
test_function

Solution
Apparently this problem might occur if we use different batch sizes. Using only one batch size solved the issue for me.

I never use batch, that means my batch size always is 1.

I still have the same problem. I try to fix it as much as I can. need help T^T
My project is image caption. My code is from https://www.kaggle.com/wikiabhi/image-caption-generator dataset flickr30k and uses colab.

Also encountering the same error.  I'm using relatively simple Keras code as follows:
`def build_model(in_dim, embedding_dim, maxlength):
inputs = tf.keras.layers.Input(shape=(None,))
embeddings = tf.keras.layers.Embedding(in_dim, embedding_dim, input_length=maxlength)(inputs)
x = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(LSTM_units, return_sequences=True))(embeddings)
x = tf.keras.layers.Dropout(0.1)(x)
x = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(LSTM_units, return_sequences=True))(x)
x = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(LSTM_units, return_sequences=True))(x)
x = tf.keras.layers.Dropout(0.1)(x)
x = tf.keras.layers.LSTM(LSTM_units, return_sequences=True)(x)
x_h = tf.keras.layers.concatenate([tf.keras.layers.GlobalAveragePooling1D()(x),
                                 tf.keras.layers.GlobalMaxPool1D()(x)])
x_h = tf.keras.layers.Dropout(0.2)(x_h)
x_h = tf.keras.layers.Dense(dense_units, activation='relu')(x_h)
x_h = tf.keras.layers.Dropout(0.3)(x_h)
x_h = tf.keras.layers.add([x_h, tf.keras.layers.Dense(dense_units, activation='relu')(x_h)])

outputs = tf.keras.layers.Dense(num_classes, activation='softmax')(x_h)

model = keras.Model(inputs=inputs, outputs=outputs)
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
return model

model_1 = build_model(num_chars, embedding_dimension, max_length)
callbacks = [tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.2, verbose=1, mode='max', patience=2, min_lr=0.000001),
tf.keras.callbacks.ModelCheckpoint('/content/saved_at_{epoch}.h5', monitor='val_loss', save_best_only=True)
]
history = model_1.fit(x_train,
np.array(y_train),
epochs=epochs,
batch_size=batch_size,
callbacks=callbacks,
validation_split=val_split,
class_weight=class_weights
)`
And the stack is:
`Epoch 1/20
CancelledError                            Traceback (most recent call last)
 in ()
10                      callbacks=callbacks,
11                      validation_split=val_split,
---> 12                      class_weight=class_weights
13                      )
8 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
58     ctx.ensure_initialized()
59     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
---> 60                                         inputs, attrs, num_outputs)
61   except core._NotOkStatusException as e:
62     if name is not None:
CancelledError:  [Derived]RecvAsync is cancelled.
[[{{node Adam/Adam/update/AssignSubVariableOp/_57}}]]
[[gradient_tape/functional_1/embedding/embedding_lookup/Reshape/_54]] [Op:__inference_train_function_18787]
Function call stack:
train_function`
I'm running in Colab, so everything should be up to date.  Attempting to use import os os.environ["TF_FORCE_GPU_ALLOW_GROWTH"]="true" results in `---------------------------------------------------------------------------
InternalError                             Traceback (most recent call last)
 in ()
13                      callbacks=callbacks,
14                      validation_split=val_split,
---> 15                      class_weight=class_weights
16                      )
6 frames
/usr/local/lib/python3.6/dist-packages/six.py in raise_from(value, from_value)
InternalError: GPU sync failed`
Just returned to Tensorflow after a monthlong hiatus, but I've never seen this before.  Not sure why it's a closed issue, since it's clearly been around for a year or so.

I ran into this problem again (see my stackoverflow question).
My training runs for multiple hours until at some point I am getting this error:
2020-12-05 18:06:59.383572: W tensorflow/core/framework/op_kernel.cc:1767] OP_REQUIRES failed at cudnn_rnn_ops.cc:1517 : Unknown: CUDNN
_STATUS_BAD_PARAM
in tensorflow/stream_executor/cuda/cuda_dnn.cc(1484): 'cudnnSetTensorNdDescriptor( tensor_desc.get(), data_type, sizeof(dims) / sizeof(dims[0]), dims, strides)'
2020-12-05 18:06:59.383906: W tensorflow/core/framework/op_kernel.cc:1767] OP_REQUIRES failed at cudnn_rnn_ops.cc:1517 : Unknown: CUDNN_STATUS_BAD_PARAM
in tensorflow/stream_executor/cuda/cuda_dnn.cc(1484): 'cudnnSetTensorNdDescriptor( tensor_desc.get(), data_type, sizeof(dims) / sizeof(dims[0]), dims, strides)'
2020-12-05 18:06:59.384114: W tensorflow/core/framework/op_kernel.cc:1767] OP_REQUIRES failed at cudnn_rnn_ops.cc:1517 : Unknown: CUDNN_STATUS_BAD_PARAM
in tensorflow/stream_executor/cuda/cuda_dnn.cc(1484): 'cudnnSetTensorNdDescriptor( tensor_desc.get(), data_type, sizeof(dims) / sizeof(dims[0]), dims, strides)'
Traceback (most recent call last):
  File "asr/bin/train_keras.py", line 300, in <module>
    app.run(main)
  File "/home/sfalk/miniconda3/envs/asr2/lib/python3.8/site-packages/absl/app.py", line 300, in run
    _run_main(main, args)
  File "/home/sfalk/miniconda3/envs/asr2/lib/python3.8/site-packages/absl/app.py", line 251, in _run_main
    sys.exit(main(argv))
  File "asr/bin/train_keras.py", line 236, in main
    model.fit(
  File "/home/sfalk/miniconda3/envs/asr2/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py", line 108, in _method_wrapper
    return method(self, *args, **kwargs)
  File "/home/sfalk/miniconda3/envs/asr2/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py", line 1098, in fit
    tmp_logs = train_function(iterator)
  File "/home/sfalk/miniconda3/envs/asr2/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py", line 780, in __call__
    result = self._call(*args, **kwds)
  File "/home/sfalk/miniconda3/envs/asr2/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py", line 807, in _call
    return self._stateless_fn(*args, **kwds)  # pylint: disable=not-callable
  File "/home/sfalk/miniconda3/envs/asr2/lib/python3.8/site-packages/tensorflow/python/eager/function.py", line 2829, in __call__
    return graph_function._filtered_call(args, kwargs)  # pylint: disable=protected-access
  File "/home/sfalk/miniconda3/envs/asr2/lib/python3.8/site-packages/tensorflow/python/eager/function.py", line 1843, in _filtered_call
    return self._call_flat(
  File "/home/sfalk/miniconda3/envs/asr2/lib/python3.8/site-packages/tensorflow/python/eager/function.py", line 1923, in _call_flat
    return self._build_call_outputs(self._inference_function.call(
  File "/home/sfalk/miniconda3/envs/asr2/lib/python3.8/site-packages/tensorflow/python/eager/function.py", line 545, in call
    outputs = execute.execute(
  File "/home/sfalk/miniconda3/envs/asr2/lib/python3.8/site-packages/tensorflow/python/eager/execute.py", line 59, in quick_execute
    tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
tensorflow.python.framework.errors_impl.CancelledError:  [_Derived_]RecvAsync is cancelled.
         [[{{node div_no_nan/ReadVariableOp_5/_1620}}]]
         [[GroupCrossDeviceControlEdges_2/Identity_6/_1699]] [Op:__inference_train_function_159151]

Function call stack:
train_function

@Izeple Could you provide and share a colab where we can reproduce this issue?

I have upgraded to 2.4.0 and CUDA 11 / cuDNN 8 but the error still persists (https://github.com/tensorflow/tensorflow/issues/45594).

I think I have fixed the issue. The root of this was bucket_by_sequence_length and me setting drop_remainder=False.
What seems to happen here is that there are batches which do not have enough samples s.t. there weren't enough examples for all cards. Since I set drop_remainder=True I didn't get this error anymore. So, make sure that you are not running into the same issue.

if your are facing this error in colab then its due to dataset size.. reduce the dataset size then try it..

Why is this issue closed? This is still ongoing.

Having this issue with TF 2.4. What is the resolution and why is this closed.

The issue is closed because, well, not sure.
While I can't really help without specifics, immediate recommendations are:
-Reduce model size
-Reduce dataset size
-Use smaller batches
-Batch items into a tf.dataset object
Interestingly, I finished training a large 23M param GRU on the same dataset as before without encountering issues.  Try unrolling the RNN layers and batching items in buckets of  64

For me the issue was gpu memory settings, I ran:
`import tensorflow as tf
if 'session' in locals() and session is not None:
print('Close interactive session')
session.close()
import tensorflow as tf
print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))
gpus = tf.config.list_physical_devices('GPU')
if gpus:
try:
# Currently, memory growth needs to be the same across GPUs
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
logical_gpus = tf.config.experimental.list_logical_devices('GPU')
print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
except RuntimeError as e:
# Memory growth must be set before GPUs have been initialized
print(e)`
at the start of my jupyter notebook and my LSTM training went through fine.  I was using keras 2.4.3 with an 8GB Nividia GPU.

Bringing this back to life - Getting the same error with both Cuda 11.1 and 10.1 in tf 2.3.1 when using GRU.
I am running Win10.
The suggestions above with
1) import os
   os.environ["TF_FORCE_GPU_ALLOW_GROWTH"]="true"

2) TF_FORCE_GPU_ALLOW_GROWTH=1

3) physical_devices = tf.config.list_physical_devices('GPU')
   tf.config.experimental.set_memory_growth(physical_devices[0], True)

Do not work.
My error is slightly different from the ones above in terms of the text I get back:
	 [[{{node Adam/Adam/update/AssignSubVariableOp/_27}}]]
	 [[gradient_tape/sequential/embedding/embedding_lookup/Reshape/_24]] [Op:__inference_train_function_2598]

Function call stack:
train_function```

In my case(on Google Colab), I used 256 batch_size when first fitting model.(worked well)
But using kerastuner for hyperparameters tuning, I got the error similar to yours.
Solved it with making lower the training BATCH_SIZE from 256 to 100 when tuning my BiLSTM model using kerastuner.

Folks, I am facing this issue right now and opened a different issue https://github.com/tensorflow/tensorflow/issues/50296, please comment there and provide information if someone else is facing the same problem.
For me limiting GPU memory growth is not working!

I just face this issue. Using LSTM and embedded layer. However, when I reduce 65k data to 50k data, the problem is solved. Anyone knows why is that? using TF 2.5.0

When I checked logs in command prompt it shows this but however using cpu it works fine
total_region_allocated_bytes_:<PHONE_NUMBER> memory_limit_:<PHONE_NUMBER> available bytes: 0 curr_region_allocation_bytes_:<PHONE_NUMBER>


feat: admin create faqs

close #298
what does this PR do
This pr allow admin user to create FAQS
How should this be manually tested?
Authenticate a User:
Send a POST request to /api/v1/faqs to authenticate a user and obtain a JWT token.
Request Body
{
  "question": "string",
  "answer": "string",
  "category": "string",
  "tags:""
}

Note: category and tags are optional
Response
{
   "status": true,
   "status_code": 201,
   "message": "FAQ created successfully",
   "data":{
       "question": "wheres  you",
       "answer": "John",
       "category": "question",
       "tags": "CD",
       "id": "a9a8f1db-aedf-4863-9720-281f8b70cc70",
       "created_at": "2024-07-26T08:49:22.996Z",
       "updated_at": "2024-07-26T08:49:22.996Z"
   }
}

403 Forbidden if the request is not made by an admin.
{
     "status": false,
     "status_code": 403,
     "error":{
          "status": "Forbidden request",
          "message": "User not authorized to perform operation",
          "status_code": 403
     },
     "message": "User not authorized to perform operation"
}

Invalid request
{
      "status": false,
      "status_code": 400,
      "error": {
          "status": "Bad request",
          "message": "Faqs question exist",
          "status_code": 400
      }
     "message": "Faqs question exist"
}

Checklist of what you did

[x]  The endpoint /admin/faqs should accept a POST request.
[x]  The request body must include question,  answer.
[x]  The request body may optionally include category and tags.
[x]  Only authenticated administrators should be able to access this endpoint.
[x]  On success, the endpoint should return a 201 Created status with the created FAQ object.
[x]  If the input data is invalid, the endpoint should return a 400 Bad Request status with an appropriate error message.
[x]  If the request is admin, the endpoint should return a 403 Unauthorized status.

Test

[x]  Ensure the endpoint correctly identifies if the requesting user is signed in.
[x]  Ensure the endpoint correctly identifies if the requesting user is an admin.
[x]  Ensure the endpoint to handle all expected errors.
[x]  Ensure the endpoint to successfully create a FAQs.

Test Screenshot

@Demandtech it should be

feat:
not
Feat:

@CFCIfe @Homoakin619 @davidolanrewaju @Heba-WebDev  All requested changes have been made... Thanks

New pr for this feature got merged today


Integrated Terminal Doesnt Render properly

Issue Type: Bug
Hello,
Powershell is currently selected as integrated terminal.
When I press 'Ctrl + ~' to request terminal, a blank window appears at the bottom of my editor with no prompt.
If I enter any misc. command, the command works and then the terminal prompt then appears normally from that point onwards.
I have tried switching to windows prompt and also to Git bash. Windows prompt produces the same outcome, but with git bash, instead of appearing as an integrated terminal, it appears as a separate window, outside of the editor.
Have not tried doing a clean reinstall of VS Code yet.
Is there a way for terminal prompt to show properly first render onwards?
Thanks
VS Code version: Code 1.42.1
(c47d83b293181d9be64f27ff093689e8e7aed054, 2020-02-11T14:45:59.656Z)
OS version: Windows_NT x64 10.0.17763

System Info

Item
Value

CPUs
AMD A8-9600 RADEON R7, 10 COMPUTE CORES 4C+6G   (4 x 3094)

GPU Status
2d_canvas: enabledflash_3d: enabledflash_stage3d: enabledflash_stage3d_baseline: enabledgpu_compositing: enabledmetal: disabled_offmultiple_raster_threads: enabled_onoop_rasterization: disabled_offprotected_video_decode: unavailable_offrasterization: enabledskia_renderer: disabled_offsurface_control: disabled_offsurface_synchronization: enabled_onvideo_decode: enabledviz_display_compositor: enabled_onviz_hit_test_surface_layer: disabled_offwebgl: enabledwebgl2: enabled

Load (avg)
undefined

Memory (System)
14.95GB (8.76GB free)

Process Argv

Screen Reader
no

VM
0%

Extensions (5)
Extension|Author (truncated)|Version

---|---|---
prettier-vscode|esb|3.20.0
auto-using|Fud|0.7.9
csharp|ms-|1.21.11
LiveServer|rit|5.6.1
vscodeintellicode|Vis|1.2.5

This is a bug in Windows 10 1809, unfortunately we have no way to work around the issue so you will need to wait until you get the next version of Windows 10 (1903).
/duplicate https://github.com/Microsoft/vscode/issues/57803


Could not build all modules with target STC15F104E

When I run make TARGET=STC15F104E the compilation fails for me because of:

uart2 module (multiple problems)
stc_gpio, stc_adc and stc_pca demos (linking problems)
stc_spi demo (function 'delay_ms' implicit declaration)
stc_uart2 demo (because uart2 module could not be built)

Could you please help me fixing it?

Thanks for the bug report.  STC15F uses a different timer for UART2 than STC12C causing the compilation problem.  This has been fixed in the trunk.

Thank you.


major refactor of downloads, environment creation

As a prelude to #75/#76, we talked about splitting out the downloader pieces into smaller things.
This is that... and entirely too much else. I basically couldn't track where all the if statements were going, and this is what I came up with to make future providers of installers more tractable... micromamba wouldn't look like what's in there, but we'll get there when we get there, I guess.

move most of the validation to parse time to fail earlier

more can be done here

strategy pattern (one per very short file, except miniconda download) applied to:

installers sources
base tools (e.g. mamba, conda-build)
environment creation
baseline packages (e.g. python, though now can add more)

does at most one solve, seems quite snappy
more consistent top-level logging approach
standardized most functions to take...

a frozen inputs (as from the yaml, this could still be improved
a dynamic grab-bag of things (e.g. useMamba, the parsed env file)

some more typing could be applied here

normalize package spec string creation
removed most of the any types
removed the (Error|Success)Result pattern, leaning almost entirely on throw and one top-level catch (except around settings)

Anyhow... I would worry about this bitrotting pretty fast, as any other features/fixes change, but it's also going to be a bear to review. So sorry! I can try to pull it apart into smaller incremental pieces, but basically did the whole thing in one go when my network was patchy.

Hi @bollwyvl as always thanks for this. This is a bit too much for a single PR so if we could split this, that would be great. Also less chance of breaking things.

Question what is the deal with underscored files?

Just for sorting. could be changed.

too much for a single PR

Sure, fair. Will add a top-level plan of action...


chore: type ResultSet.tsx

SUMMARY
As titled. Also includes a couple small refactors/bug fixes that i came across as I was working
TEST PLAN
CI
ADDITIONAL INFORMATION

[ ] Has associated issue:
[ ] Changes UI
[ ] Requires DB Migration.
[ ] Confirm DB Migration upgrade and downgrade tested.
[ ] Introduces new feature or API
[ ] Removes existing feature or API

to: @ktmud @graceguo-supercat @nytai @bkyryliuk

Thanks for the quick stamps! I'm going to wait for @bkyryliuk to take a look as well since the CTAS logic was the main functional change here and i want to make sure it looks good to him


Upgrade to nextjs v14 and remove Adyen languages not supported

Closes #396

What I did

How to test

Checklist

[x] Make sure your changes are tested (stories and/or unit, integration, or end-to-end tests).
[x] Make sure to add/update documentation regarding your changes.
[x] You are NOT deprecating/removing a feature.

:tada: This PR is included in version 4.3.0 :tada:
The release is available on GitHub release
Your semantic-release bot :package::rocket:


>         "gas": "0x186a0",
1840c1840
<         "gas": "0x13238",
---
>         "gas": "0x186a0",
1851c1851
<         "gas": "0x13238",
---
>         "gas": "0x186a0",
1862c1862
<         "gas": "0x13244",
---
>         "gas": "0x186a0",
1873c1873
<         "gas": "0x13238",
---
>         "gas": "0x186a0",
1884c1884
<         "gas": "0x13244",
---
>         "gas": "0x186a0",
1895c1895
<         "gas": "0x13238",
---
>         "gas": "0x186a0",
1906c1906
<         "gas": "0x13238",
---
>         "gas": "0x186a0",
1917c1917
<         "gas": "0x1322c",
---
>         "gas": "0x186a0",
1928c1928
<         "gas": "0x13238",
---
>         "gas": "0x186a0",
1939c1939
<         "gas": "0x13238",
---
>         "gas": "0x186a0",
1950c1950
<         "gas": "0x1322c",
---
>         "gas": "0x186a0",
1961c1961
<         "gas": "0x13238",
---
>         "gas": "0x186a0",
1972c1972
<         "gas": "0x1322c",
---
>         "gas": "0x186a0",
1983c1983
<         "gas": "0x13238",
---
>         "gas": "0x186a0",
1994c1994
<         "gas": "0x13244",
---
>         "gas": "0x186a0",
2005c2005
<         "gas": "0x13238",
---
>         "gas": "0x186a0",
2016c2016
<         "gas": "0x13238",
---
>         "gas": "0x186a0",
2027c2027
<         "gas": "0x1322c",
---
>         "gas": "0x186a0",
2038c2038
<         "gas": "0x13238",
---
>         "gas": "0x186a0",
2049c2049
<         "gas": "0x1322c",
---
>         "gas": "0x186a0",
2060c2060
<         "gas": "0x13238",
---
>         "gas": "0x186a0",
2071c2071
<         "gas": "0x1f1ce",
---
>         "gas": "0x24ade",
2083c2083
<         "gas": "0x2bb38",
---
>         "gas": "0x30d40",
2094c2094
<         "gas": "0x1322c",
---
>         "gas": "0x186a0",
2105c2105
<         "gas": "0xb42b7",
---
>         "gas": "0xb994b",
2116c2116
<         "gas": "0x23787",
---
>         "gas": "0x291b3",
2128c2128
<         "gas": "0x0",
---
>         "gas": "0x5208",
2139c2139
<         "gas": "0x2b8b4",
---
>         "gas": "0x30d40",
2150c2150
<         "gas": "0x0",
---
>         "gas": "0x5208",
2161c2161
<         "gas": "0x4c91",
---
>         "gas": "0xa0f9",
2172c2172
<         "gas": "0x4c91",
---
>         "gas": "0xa0f9",
2183c2183
<         "gas": "0x0",
---
>         "gas": "0x5208",
2194c2194
<         "gas": "0x4c91",
---
>         "gas": "0xa0f9",
2205c2205
<         "gas": "0x4c91",
---
>         "gas": "0xa0f9",
2216c2216
<         "gas": "0xa7f8",
---
>         "gas": "0xfa00",
2227c2227
<         "gas": "0x2328",
---
>         "gas": "0x7530",
2238c2238
<         "gas": "0x2b0b0",
---
>         "gas": "0x30d40",
2249c2249
<         "gas": "0x10d88",
---
>         "gas": "0x15f90",
2260c2260
<         "gas": "0x0",
---
>         "gas": "0x5208",
2271c2271
<         "gas": "0x7d63",
---
>         "gas": "0xd1fb",
2283c2283
<         "gas": "0xcd6a",
---
>         "gas": "0x121d2",
2293c2293
<         "error": "out of gas",
---
>         "error": "Out of gas",
2295c2295
<         "gas": "0x0",
---
>         "gas": "0x5208",
2306c2306
<         "gas": "0x2b8f0",
---
>         "gas": "0x30d40",
2317c2317
<         "gas": "0x2b8cc",
---
>         "gas": "0x30d40",
2328c2328
<         "gas": "0x2b8e4",
---
>         "gas": "0x30d40",
2339c2339
<         "gas": "0x5468",
---
>         "gas": "0xa8d0",
2349c2349
<         "error": "execution reverted",
---
>         "error": "Reverted",
2351c2351
<         "gas": "0x132e8",
---
>         "gas": "0x186a0",
2355c2355
<         "revertReason": "value too low!",
---
>         "revertReason": "revert: value too low!",
2364c2364
<         "gas": "0x95ec",
---
>         "gas": "0xea60",
2375c2375
<         "gas": "0x9604",
---
>         "gas": "0xea60",
2386c2386
<         "gas": "0x9604",
---
>         "gas": "0xea60",
2397c2397
<         "gas": "0x3e8",
---
>         "gas": "0x55f0",
2408c2408
<         "gas": "0x3e8",
---
>         "gas": "0x55f0",
2419c2419
<         "gas": "0x3e8",
---
>         "gas": "0x55f0",
2430c2430
<         "gas": "0x3e8",
---
>         "gas": "0x55f0",
2441c2441
<         "gas": "0x3e8",
---
>         "gas": "0x55f0",
2452c2452
<         "gas": "0x3e8",
---
>         "gas": "0x55f0",
2463c2463
<         "gas": "0x3e8",
---
>         "gas": "0x55f0",
2474c2474
<         "gas": "0x3e8",
---
>         "gas": "0x55f0",
2485c2485
<         "gas": "0x3e8",
---
>         "gas": "0x55f0",
2496c2496
<         "gas": "0x3e8",
---
>         "gas": "0x55f0",
2507c2507
<         "gas": "0x3e8",
---
>         "gas": "0x55f0",
2518c2518
<         "gas": "0x3e8",
---
>         "gas": "0x55f0",
2529c2529
<         "gas": "0x3e8",
---
>         "gas": "0x55f0",
2540c2540
<         "gas": "0x3e8",
---
>         "gas": "0x55f0",
2551c2551
<         "gas": "0x3e8",
---
>         "gas": "0x55f0",
2562c2562
<         "gas": "0x3e8",
---
>         "gas": "0x55f0",
2573c2573
<         "gas": "0x3e8",
---
>         "gas": "0x55f0",
2584c2584
<         "gas": "0x3e8",
---
>         "gas": "0x55f0",
2595c2595
<         "gas": "0x3e8",
---
>         "gas": "0x55f0",
2606c2606
<         "gas": "0x3e8",
---
>         "gas": "0x55f0",
2617c2617
<         "gas": "0x31aa3",
---
>         "gas": "0x373ef",
2629c2629
<         "gas": "0x3e8",
---
>         "gas": "0x55f0",
2640c2640
<         "gas": "0x3e8",
---
>         "gas": "0x55f0",
2651c2651
<         "gas": "0x3e8",
---
>         "gas": "0x55f0",
2662c2662
<         "gas": "0x3e8",
---
>         "gas": "0x55f0",
2673c2673
<         "gas": "0x3e8",
---
>         "gas": "0x55f0",
2684c2684
<         "gas": "0x3e8",
---
>         "gas": "0x55f0",
2695c2695
<         "gas": "0x3e8",
---
>         "gas": "0x55f0",
2706c2706
<         "gas": "0x3e8",
---
>         "gas": "0x55f0",
2717c2717
<         "gas": "0x3e8",
---
>         "gas": "0x55f0",
2728c2728
<         "gas": "0x3e8",
---
>         "gas": "0x55f0",
2739c2739
<         "gas": "0x3e8",
---
>         "gas": "0x55f0",
2750c2750
<         "gas": "0x3dab1",
---
>         "gas": "0x42e4d",
2761c2761
<         "gas": "0x0",
---
>         "gas": "0x5208",
2772c2772
<         "gas": "0x2b8d8",
---
>         "gas": "0x30d40",
2784c2784
<         "gas": "0x0",
---
>         "gas": "0x5208",
2795c2795
<         "gas": "0x0",
---
>         "gas": "0x5208",
2806c2806
<         "gas": "0x3ab5f",
---
>         "gas": "0x403d7",
2818c2818
<         "gas": "0x3538",
---
>         "gas": "0x8740",
2829c2829
<         "gas": "0x11fb38",
---
>         "gas": "0x124f80",
2840c2840
<         "gas": "0x9d6d",
---
>         "gas": "0xf1e1",
2852c2852
<         "gas": "0x0",
---
>         "gas": "0x5208",
2863c2863
<         "gas": "0x2b8a8",
---
>         "gas": "0x30d40",
2875c2875
<         "gas": "0x2b8c0",
---
>         "gas": "0x30d40",
2887c2887
<         "gas": "0x0",
---
>         "gas": "0x5208",
2898c2898
<         "gas": "0x2b8e4",
---
>         "gas": "0x30d40",
2910c2910
<         "gas": "0x0",
---
>         "gas": "0x5208",
2921c2921
<         "gas": "0x0",
---
>         "gas": "0x5208",
2932c2932
<         "gas": "0x0",
---
>         "gas": "0x5208",
2943c2943
<         "gas": "0x0",
---
>         "gas": "0x5208",
2954c2954
<         "gas": "0x0",
---
>         "gas": "0x5208",
2965c2965
<         "gas": "0x0",
---
>         "gas": "0x5208",
2976c2976
<         "gas": "0x0",
---
>         "gas": "0x5208",
2987c2987
<         "gas": "0x0",
---
>         "gas": "0x5208",
2998c2998
<         "gas": "0x0",
---
>         "gas": "0x5208",
3009c3009
<         "gas": "0x0",
---
>         "gas": "0x5208",
3020c3020
<         "gas": "0x0",
---
>         "gas": "0x5208",

Is this relating to us vs Geth vs Erigon having different starting gas limit for the traces or something?

ref https://github.com/paradigmxyz/reth/issues/3678
https://github.com/ethereum/go-ethereum/pull/27029
https://github.com/paradigmxyz/reth/blob/5062b7ea866960a99fb3b459550034fb6c37e766/crates/revm/revm-inspectors/src/tracing/mod.rs#L210-L214

I don't know about Geth. The above is Erigon. Sorry if I confused things...but...it's a shame if there's a difference between Geth/Erigon/Reth. The chances of perfect accounting are pretty close to zero if we can get the results of RPC queries identical across clients.

using latest reth (0.1.0-alpha.16 92908b1) this seems fixed
curl shows the transaction reverting, altho the reported gas of 100000 still differs from Erigon's 78392 (as in https://github.com/paradigmxyz/reth/issues/4951#issuecomment-1852839762)
Trueblocks chifra scrape passed this block successfully, and continues to sync


how to install CRDs with kubectl in k8s deploy scenario

Page
https://docs.alluxio.io/os/user/stable/en/deploy/Running-Alluxio-On-Kubernetes.html#csi
Summary
Deploy alluxio with kubectl according to the documentation;
And During the installation of CSI driver, failed to execute the following command:
kubectl create -f alluxio-csi-controller-rbac.yaml
serviceaccount/csi-controller-sa created
clusterrole.rbac.authorization.k8s.io/alluxio-external-provisioner-role created
clusterrolebinding.rbac.authorization.k8s.io/alluxio-csi-provisioner-binding created

kubectl create -f alluxio-csi-controller.yaml
deployment.apps/alluxio-csi-controller created

kubectl apply -f alluxio-csi-driver.yaml
error: resource mapping not found for name: "alluxio" namespace: "" from "alluxio-csi-driver.yaml": no matches for kind "CSIDriver" in version "storage.k8s.io/v1beta1"
ensure CRDs are installed first

so how to install CRDs with kubectl;

Then please use helm-generate.sh (see here for usage) to generate related templates. All CSI related templates will be under ${ALLUXIO_HOME}/integration/kubernetes/csi.

I also try to use helm-generate.sh to re-generate CSI templates, but not found CRD templates.
alluxio-csi-controller-rbac.yaml.template
alluxio-csi-controller.yaml.template
alluxio-csi-driver.yaml.template
alluxio-csi-fuse-configmap.yaml.template
alluxio-csi-nodeplugin.yaml.template
alluxio-nginx-pod.yaml.template
alluxio-pv.yaml.template
alluxio-pvc-static.yaml.template
alluxio-pvc.yaml.template
alluxio-storage-class.yaml.template

@ssz1997 can you take a look?

@wwq2333 Sorry I missed this one eariler. It seems to me that your k8s version maybe outdated. We require the k8s cluster version to be at least 1.17. Can you help confirm the version of your k8s cluster?

@wwq2333 A workaround for you is to modify the apiVersion field in your alluxio-csi-driver.yaml.template to storage.k8s.io/v1, instead of storage.k8s.io/v1beta1.
Helm may have trouble comparing version strings of 1.24.x. Could you check the version of your Helm? To support k8s 1.24.x, you need helm version of 3.9.x. See https://helm.sh/docs/topics/version_skew/.


Create panIntoView method for overlays

The panIntoView method should check the rendered dimensions of the overlay element (and children) and pan the map to try to accommodate the contents in the viewport.  By default, if the overlay contents are too large for the viewport, the top-left corner could be shown.

Want to back this issue? Place a bounty on it! We accept bounties via Bountysource.

autoPan works great, thanks!


Pastanaga Editor: Title field is not selected when creating a new page

Create a new document via the add menu
-> The title field is not automatically selected

I think the title field should be automatically selected so the user can type in the title directly.

I would like to work on this issue.
As I went through the code I figured a way to implement this, but it needs accessing the DOM element with id field-title from the componentDidMount method of Form component.
Shall I proceed with this method? or Can there be better way to do this?


Automatically enqueue frontend assets of blocks defined in block.json for dynamic blocks

WordPress Core automatically enqueues frontend assets like the viewScript only on pages where the block is actively being used for static blocks. Dynamic blocks currently need to handle that step manually.
Proposed Solution:
Since WordPress already registers all the assets we can add a check whether an asset exists and then automatically enqueue it from within the render_callback function.

This is no longer required because WordPress Core actually changed it's behavior and is now also automatically enqueueing all assets that are assigned to a block for dynamic blocks on the frontend 🎉


Breakage

This may be another clang issue. I will update clang.

Indeed. Updating clang did the trick.