No error when trying to watch non-existent directory

When I try to watch a directory that does not exist, I do not get any error. Instead the ready event triggers.
on
Windows 7 x64
iojs v2.3.4
chokidar v1.0.5

Seems like right to me. Chokidar would not throw any errors — instead it will wait until the directory is created and then it would notify you about the creation.

Yup, this is expected behavior. Use fs.stat or something like it if you need to know whether a path exists.

Oh Ok!!
But wouldn't it be useful behaviour if it did - since it seems query the disk anyway (rather than querying the disk an extra time using fs.stat)? I am just trying to understand the motivation behind the design choice...

The motivation is the ability to watch non-existent paths, anticipating their creation. Not sure how to expose via the API in a useful way when it is determined a watched path does not exist. Emitting an error would not be appropriate. Do you have any suggestions?

With ignoreInitial: false, the absence of any add or addDir events before ready effectively tells you.

seems reasonable...
As far as testing absence of add, its possible! But personally I would not prefer it - unnecessarily set a state variable in add and addDir which I then have to test in ready.
But it would be nice to set a boolean say, exists, might be in the ready event callback, just for convenience (though it would be doing the exact same thing inside).
Let me know what you think...

Maybe something returned with ready, but a boolean probably won't cut it since multiple paths and glob patterns can be passed to watch

Sounds good...

I was only worried about the check for if any match is made at all or not. Glob checking may return a list of matches or maybe only just check if the base directory has been matched and accessed?

Something simple we could try would be passing Object.keys[this._watched] with the ready event. If you get an empty array, you know nothing was found.

I was thinking along the same lines. But isn't this an overkill when compared to Object.keys[this._watched].length. I am wondering if there would be any other use cases for having the full list would required?
Also, I wonder if a full list deserves a separate function like watcher.watched() since then I would argue that we should make this information available anytime?

The same processing and memory consumption is required to do Object.keys(this._watched).length, so no, I don't see how it's overkill to just pass the array pointer. Someone may have a use for the full list.
watcher.watched() may be a good idea too. I would favor naming it something more like watcher.getWatchedList() though.

Although, if we do the latter, then is there any point to passing it with the ready event anymore? You'd be able to fulfill your use case just by calling the function.
watcher.on('ready', function() {
  if (!watcher.getWatchedList().length) oopsThePathDoesntExist();
});

I meant overkill in terms of Principle of Least Privilege.
If you want to implement watcher.watched()/watcher.getWatchedList() (I like brevity but verbose names have their own merit), then I would think changing the ready event would become redundant. But imo its the better solution.

You said the same think while my browser didn't refresh. Solution 2 it should be!

@paulmillr any opinion on the method name?

watcher.getWatchedList sounds right. watched should be object / list if it exists

It's currently tracked internally as the this._watched object

Just another thought: It would be nice if the watcher.getWatchedList() subtracts the cwd Object.keys(this._watched).
Alternatively watcher.getWatchedList() could carry a boolean parameter for enabling this.

While testing, I found that watcher._watched seems to watch parent of the specified directory. Is this correct and is there any reason for that?

Yes, that's how it can see whether the non-existent path gets added. Already a bit trickier than anticipated.
So the result probably needs to be run through the same filters that are used to determine whether to expose events that are detected.

Sorry, I don't seem to understand why watching a parent is necessary to detect if the path is non existent. It seems to me that this means you are only able to watch contents of any directory and you need to watch the parent just to see if it contains the specified directory. Is that it? I thought  fs.watch watched the specified for?

I meant specified folder (not for). Sorry

No, this isn't about detecting that the path doesn't exist.
Chokidar deliberately watches the parent in order to detect if the path gets created later.
https://github.com/paulmillr/chokidar/blob/master/test.js#L356-L392

That can't be right. Here is a small test I performed:
I set chokidar too watch C:\foo\bar where foo exists and foo\bar does not! I then log watcher._watched in the all event all callback with the event. I also log in the ready callback.
Initially my output is [] at ready,
When I add bar the outputs are ['C:\\','C:\\foo'],
And only when I perform some other operation in foo does the output become  ['C:\\','C:\\foo','C:\\foo\\bar'].
If Chokidar deliberately watches the parent in order to detect if the path gets created later, then C:\\foo should have been watched at the time ready event is triggered. What am I missing here?

Initially my output is [] at ready

This is a timing issue; ready event comes before the fallback to the parent is executed. Try adding a timeout around where you log watcher._watched. The relevant code that sets a watcher on the parent when a supplied path is not found is at https://github.com/paulmillr/chokidar/blob/master/index.js#L429-L433
I'm not sure why C:\\ ends up being watched in the scenario you've described, although I doubt it's actually watching all of C:\\ recursively (you'd notice the impact on your system resources).

I did the timeout thing and that checks out.
The C:\\, however, does not. So I did it with one level deeper. Apparantly, it watches parent and grandparent directories. This is certainly odd...

Yes it does seem odd. I'll have to try to reproduce and trace what's happening on a windows VM when I get a chance. Haven't reproduced it on OS X with fsevents disabled, which should be following the same flow of logic, but of course there are still going to be lots of platform differences.
We're substantially digressing from the original topic though.
Seems that you could do this today to achieve a satisfactory resolution:
watcher.on('ready', function() {
  if (!Object.keys(watcher._watched).length) oopsThePathDoesntExist();
});

And that we ought to add a getWatchedList method that will provide something similar, but also filter out artifacts of the internal implementation details.

This is a timing issue; ready event comes before the fallback to the parent is executed.

Btw, I may try to change this behavior too, so anyone implementing the above sample code using the internal _watched property should not be surprised if it stops working in a future version.

This new thing was a consequence of the testing something along the lines above sample code, so I though I should point it out - yes, we are digressing but I think its relevant. Anyway, I will wait until the next version bump before I start implementing the above code. Thanks for looking into it!

Well if you want it now, use the semver tilde operator for ~1.0.5 and that should be safe. Then with a minor bump (to 1.1.0 assuming I get to this before something else that necessitates a minor bump), you'll be able to switch it to getWatchedList().
Yes the grandparent watching thing is good to know, and I appreciate that you've reported your observations.

Really appreciate all your effort! Thanks a lot!!!!!

Hi.  I'm unsure of whether or not chokidar can watch folders that don't exist yet?  My scenario on Windows:
watching folder C:\reports\2014-09-03 -- a folder that doesn't exist yet.  If I manually create the folder there are no events fired.  Is C:\reports being watched? Should I receive an event when folder 2015-09-03 gets created?  Will it now be watched?
I am using entire event handling - i.e.
var watcher = chokidar.watch(['C:\reports\2014-09-03'],
	                {ignored: /[\/\\]\./, persistent: true});

	watcher
	.on('add', function(path, stats)
	    {
			dirChange({type: 'File',  action: 'create', path: path , stats:stats });
	    })
	.on('addDir', function(path, stats)
	    {
			dirChange({type: 'Directory',  action: 'create', path: path , stats:stats });
	    })
	.on('change', function(path, stats)
	    {
			dirChange({type: 'File',  action: 'change', path: path , stats:stats });
	    })
	.on('unlink', function(path, stats)
	    {
			dirChange({type: 'File',  action: 'delete', path: path , stats:stats });
	    })
	.on('unlinkDir', function(path, stats)
	    {
			dirChange({type: 'Directory',  action: 'delete', path: path , stats:stats });
	    })
	.on('error', function(error)
	    {
	     console.log('chokidar watch error occurred ' + error);
	    })
	.on('ready', function()
	    {
			console.log('Initial scan complete. Ready for changes.');
	    })

@rfossella the answer to all your questions should be yes. If you have evidence to the contrary please describe it in a new issue.
You could take a look at the 'watch non-existent paths' section of the tests.


[ArPow] Remove roslyn-analyzers patch

When completing https://github.com/dotnet/installer/pull/11978, a patch was required in roslyn-analyzers to remove test projects. The patch is addressed in roslyn-analyzers with https://github.com/dotnet/roslyn-analyzers/pull/5469. Once the change to roslyn-analyzers is picked up by source-build, the patch can be removed.

Resolved with https://github.com/dotnet/installer/pull/12066


fix: lines count

Codecov Report
Attention: Patch coverage is 65.71429% with 12 lines in your changes are missing coverage. Please review.

Project coverage is 44.05%. Comparing base (a93e788) to head (a9a2848).
Report is 9 commits behind head on master.

Files
Patch %
Lines

pkg/parser/parser.go
65.71%
8 Missing and 4 partials :warning:

Additional details and impacted files
@@            Coverage Diff             @@
##           master      #10      +/-   ##
==========================================
+ Coverage   43.33%   44.05%   +0.72%
==========================================
  Files           6        6
  Lines        1050     1085      +35
==========================================
+ Hits          455      478      +23
- Misses        532      540       +8
- Partials       63       67       +4

Flag
Coverage Δ

unittests
44.05% <65.71%> (+0.72%)
:arrow_up:

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Vector type and iterator opportunities

Good day!
I have a snippet of code like this:
dockNode := imgui.InternalDockBuilderGetNode(dockId)
tabs := dockNode.TabBar().Tabs()

Method Tabs() returns Vector type and in my mind such type must have some type of iterator, but it isn't.
Can you help me to realize how can I use this type to iterate over all TabItems in TabBar?
Thank you in advance!

Hi
sorry I didn't respond - I had to miss this issue somehow.
I think you should do something like this:
        slice := []int{1, 2, 3, 4, 5}
        ptr := &slice[0]
        convertedSlice := (*[5]int)(unsafe.Pointer(ptr))[:]
        fmt.Println(convertedSlice)

I'll try to add it to cimgui-go

I've tried to add a generic version for that and there is a problem:
to add some iterator I need to know base type, when our type T is in fact a pointer. I've tried to make it a base type and Data field just a pointer to T but I found out that it sometimes can be string (char*) and its quite difficult to find out what to do then.
I'll think about that

@38dh91 its done now. Sorry that it took so many time.
use (Vector[T]).Slice() to get slice of your vector and d whatever you want with it.


Unable to create Cognito App

What can we improve?
I am having difficulty using the code in CognitoApp.java to deploy Cognito resources.  Specifically, I am encountering the following error message:
staging-imhere-Cognito |  9/14 | 7:30:36 PM | CREATE_FAILED        | AWS::Lambda::Function
 | cognito/AWS679f53fac002430cb0da5b7982bd2287 (AWS679f53fac002430cb0da5b7982bd22872D164C4C)
Resource handler returned message: "The runtime parameter of nodejs14.x is no longer supported for creating or updating
AWS Lambda functions. We recommend you use the new runtime (nodejs20.x) while creating or updating functions.
(Service: Lambda, Status Code: 400, Request ID: 559877dd-d3d2-4230-9ff2-22eddc8d7da3)"
(RequestToken: f7321b78-ee01-7dbd-bd43-4a0ff8abcf6c, HandlerErrorCode: InvalidRequest)

It's not clear to me what code is trying to create a Lambda function.  I don't see anything in either CognitoApp.java or CognitoStack.java that stands out to me as doing this.
A Google search for this error message gave me some pointers that perhaps it was an outdated version of the CDK.  But I just updated to the most recent version of CDK 2 (2.130.0 build bd6e5ee) but this didn't help.
I ran into a similar issue with the version of Postgres and was able to find a method call I could make to specify the desired version of Postgres, but can't find anything similar here.
Any ideas?
Version
latest
Suggested Changes
No response

I think that the lambda is auto-generated to retrieve the client secret from the user pool.
{
  "DescribeCognitoUserPoolClient": {
    "id": "DescribeCognitoUserPoolClient",
    "path": "ApplicationStack/userPoolClient/DescribeCognitoUserPoolClient",
    "children": {
      "Provider": {
        "id": "Provider",
        "path": "ApplicationStack/userPoolClient/DescribeCognitoUserPoolClient/Provider",
        "constructInfo": {
          "fqn": "aws-cdk-lib.aws_lambda.SingletonFunction",
          "version": "2.130.0"
        }
      }
    }
  }
}

By the way, did you update the cdk version in the package.json?

@giuliopulina Thanks for the pointer.  Where did that code come from? I have a few guesses but am pretty new to AWS / CDK.
I hadn't updated package.json, but had updated the global version of cdk.
I just updated the version in package.json and ran npm update.  Then I tried to run the Cognito deployment app again, but got the same result.
I do suspect it's related to the creation of the UserPoolClient since the error occurs right after this log statement is generated:
CREATE_IN_PROGRESS   | AWS::IAM::Policy                      |
cognito/userPoolClient/DescribeCognitoUserPoolClient/CustomResourcePolicy
(userPoolClientDescribeCognitoUserPoolClientCustomResourcePolicy05AFFF63) Resource creation Initiated

Any other ideas?

@giuliopulina Thanks for the pointer. Where did that code come from? I have a few guesses but am pretty new to AWS / CDK.
I hadn't updated package.json, but had updated the global version of cdk.
I just updated the version in package.json and ran npm update. Then I tried to run the Cognito deployment app again, but got the same result.
I do suspect it's related to the creation of the UserPoolClient since the error occurs right after this log statement is generated:
CREATE_IN_PROGRESS   | AWS::IAM::Policy                      |
cognito/userPoolClient/DescribeCognitoUserPoolClient/CustomResourcePolicy
(userPoolClientDescribeCognitoUserPoolClientCustomResourcePolicy05AFFF63) Resource creation Initiated

Any other ideas?

The code that I posted are from the Cloudformation templates generated from the "cdk synth" command, run in my local environment (specifically, tree.json file).
I have built a different version of the Stratospheric application using a different CDK structure, but the core is the same.
Just for my understanding, are you running your npm/cdk command from your local machine or from the CI (Github Actions)?
If, as I imagine are you running the commands from your local environment, I would suggest to try to run the following commands from your cdk folder:

npm install
npm list
(output should be like this)
--<EMAIL_ADDRESS>npm run cognito:deploy

Thanks for the explanation on the cdk synth output; that was basically what I was thinking was happening.
I'm running the code from my local machine, not a GitHub action.  When I run npm list, I get:
<EMAIL_ADDRESS>C:\Users\mcfal\source\imhere\imhere_server_deploy
└──<EMAIL_ADDRESS>
So that seems correct.
Any other ideas of things to check for?

Thanks for the explanation on the cdk synth output; that was basically what I was thinking was happening.
I'm running the code from my local machine, not a GitHub action. When I run npm list, I get:
<EMAIL_ADDRESS>C:\Users\mcfal\source\imhere\imhere_server_deploy
└──<EMAIL_ADDRESS>
So that seems correct.
Any other ideas of things to check for?

Unfortunately not many other ideas, without looking at the project :( if you could share it on a Github, that would surely help to figure out the issue.
The only thing that comes into my mind is to try to re-run 'npm bootstrap' with the new CDK because maybe there are some defaults that need to be overridden.

I tried re-running the bootstrap command, with no luck.  At first it didn't want to do anything, so I deleted that stack.  This caused a few problems but I was able to resolve them using the AWS console, and then re-bootstrap the CDK.  Re-running npm rum cognito:deploy caused an error at the same location.
My code is hosted on BitBucket rather than GitHub, but I've created a GitHub repo and pushed the code to git@github.com:ryanmcfall/cdk_deploy.git.  There's no value for the accountId parameter in ckd.json; obviously you'd need to change that / pass it on the command line to test anyway.
Any thoughts on this would be greatly appreciated!

Ok I think I found the issue:

your project depends on latest released version of stratospheric-dev/constructs (release 0.1.13).
unfortunately, this version of stratospheric-dev/constructs contains a reference to aws-cdk version 2.43.1, that is a very old version and doesn't contain this commit that updates the default Node runtime for custom resources to v18.
This is causing the lambda functions to be generated with Node14 runtime, thus making your deployment fail.

I think the cleanest solution is to wait for the maintainers (@thombergs @BjoernKW @rieckpil) to release a new version of the project.
Temporarily, you can checkout the stratospheric-dev/constructs project locally, upgrade the cdk dependency to 2.130.0 version, build and install it in local maven repo and update your app to use the SNAPSHOT.
I also found a strange thing in cdk-constructs project: the latest release version is 0.1.13, but the current pom.xml version is 0.1.12-SNAPSHOT: it should be 0.1.14-SNAPSHOT.

@giuliopulina thanks for all your help following up on this.  I will give this a shot and see what I can do.

I can confirm this approach works.:

Check out the most recent commit to the cdk-construct repo's main branch (9191b42)
Make two changes to pom.xml:

Modify the property aws-cdk-lib.version to have the value 2.130.0
Change the <version> property to have the value 0.1.14-SNAPSHOT

Execute mvn install to install that version of the project to my local repository
Changed the reference to cdk-constructs in my deployment project's pom.xml from version 0.1.13 to 0.1.14-SNAPSHOT
Ran npm cognito:deploy

Thanks for this discussion and finding a workaround. I'm closing this for now since the issue has been resolved. We'll release a new version of https://github.com/stratospheric-dev/cdk-constructs soon and update to that version.


fix: upgrade the turbo package in Yarn 4

Description
This fixes a failed install when running npx @turbo/codemod@latest update while using Yarn 4.

yarn add turbo@latest --dev -W is not a valid command:

Using yarn version 4.1.0:

It seems to me that -W is not a valid flag for most yarn commands. The only command I could find is yarn workspaces foreach, but that's not the command being issued in this code.
I don't have a great understanding of this codebase, so I did my best to create a version that works. It looks to me that the code is trying to be resilient against which directory the user is at. So I replicated that behavior using yarn workspaces foreach --all --include .. It's a trick to run a specific command in the root of the workspace regardless of current location.
I wasn't able to build the repo. I was hoping for a TS only way to build only the codemod package, but I didn't find it. I currently don't have the time to setup the Go & Rust toolchain and I've never used either. I tested this by manually assembling the command from the code and testing that in my repo.
Testing Instructions

Go to a repo with an outdated version of Turbo & Yarn 4 as the package manager.
Run the codemod command
It should now work after this change.

Hey, thanks for the contribution! I unfortunately tried this with Yarn 1 and it doesn't work there. So we'd be trading Yarn 1 for Yarn 4. 😄
Is it possible you could run yarn --version prior to that code block and then fork the behavior based on the version that comes back? I know that adds some difficulty to things but I'd like to be making an explicit move towards completeness than trading one for the other.

@anthonyshew It seems like there was already a code block for detecting the Yarn version. And somehow I put the change in the wrong one, oops. Because I wasn't able to test this I didn't detect this. I have now rebased the PR to put the codeblock in the correct position.

(Alternatively, if you'd like to leave it to me to pick up what you've written here and adapt it for those requirements, I can do that as well. I've had it happen to me where I tried to submit a PR and the maintainer asked for more and I thought "I had time for the simple way, not the hard way", so I'd completely understand. However, some folks like to see their name in the release notes so don't want to take that away from you!)

In any case you can always take my code and make it shippable. My name in the release notes is not that important to me. My main concern is improving the project and removing a small annoyance from my workflow 😄

Hi! 👋  I've run into this issue as well and was looking to fix it before seeing that this issue already existed.
I found that even though this code technically works:
case "yarn":
      // yarn 2.x and 3.x (berry)
      if (gte(packageManagerVersion, "2.0.0")) {
        return renderCommand([
          "yarn",
          "add",
          `turbo@${to}`,
          installType === "devDependencies" && "--dev",
        ]);
        // yarn 1.x
      }
      return renderCommand([
        "yarn",
        "add",
        `turbo@${to}`,
        installType === "devDependencies" && "--dev",
        isUsingWorkspaces && "-W",
      ]);

The packageManagerVersion gotten in the getAvailablePackageManagers method does not seem to be correct since it's running on a tmp directory instead of the repository root. According to Yarn's documentation, its binary preferably should be on each repository. So this way, when I run @turbo/codemod update on my repository which has a local Yarn version, it detects version 1 (that is installed by default globally) when it should be the local version 4.
I found that if I disable running on tmp folder for each command, the returned version is actually correct:
async function exec(command: string, args: Array<string> = [], opts?: Options) {
  // run the check from tmpdir to avoid corepack conflicting -
  // this is no longer needed as of https://github.com/nodejs/corepack/pull/167
  // but we'll keep the behavior for those on older versions)
  const execOptions: Options = {
    // REMOVE THIS LINE HERE
    // cwd: os.tmpdir(),
    env: { COREPACK_ENABLE_STRICT: "0" },
    ...opts,
  };
  try {
    const { stdout } = await execa(command, args, execOptions);
    return stdout.trim();
  } catch {
    return undefined;
  }
}

But I'm unaware if this breaks other package managers. @anthonyshew do you think this is a viable fix?

@tomcastro It may be. Could you open up a separate PR with that so I can take a look at it?

@anthonyshew sure thing, created this PR

@atimmer, hope you don't mind but I'm going to try something on this PR.
It looks like you're both on the right track:

We're finding the wrong binary for yarn berry versions. The tmpdir meant we were exiting the repository's context, so we could never find yarn 2+ (since they don't allow(? encourage?) global installations).
This meant we were calling the wrong command. The -W is right for yarn 1, so we were trying to use it, even though the repo is berry.

I've tacked on a couple commits that I think should level out these behaviors and have hand tested this implementation on my machine. I'm going to ask a teammate to look this over because:

These package-manager-binary-finding things are finnicky.
The tests were passing before, and still are now. I believe this is because our tests don't run against real package manager installations.

@anthonyshew awesome! let us know if we can help with anything else 🙌


bug: starknet_getTransactionReceipt returns a "Internal server error"

Bug Report
Madara version:
I am currently using the latest version of Madara fetching Starknet mainnet via Deoxys implementation
Current behavior:
When I try to do:
curl --request POST \
     --url https://deoxys.kasar.io/rpc \
     --header 'accept: application/json' \
     --header 'content-type: application/json' \
     --data '
{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "starknet_getTransactionReceipt",
  "params": [
    "0x3bade3c1dc9d6c0c4cadf562012df2df032066f6e2174bf572f817af268d1fd"
  ]
}
'

For example, with https://voyager.online/tx/0x3bade3c1dc9d6c0c4cadf562012df2df032066f6e2174bf572f817af268d1fd beeing a known Starknet mainnet block I receive:
{"jsonrpc":"2.0","error":{"code":500,"message":"Internal server error"},"id":1}

For the same call using a blast rpc key I receive the right data
For the same address using getTransactionByHash I receive the right data
Picked up 10 random tx between block 0-10k and get the same error

Yes, I wasn't in archive mode. Still I think we should return State already discarded for {:?}, tx_hash in that case instead of an Internal server error

I created #1214 to improve the user experience

Tested in --prunning archive mode, and my Madara still panics
Tested with recent blocks, and my Madara still panics

Calling your attention on this since anyone could shutdown a Madara node trough this rpc call


can't use custom imageloader

Hello
As I see in docs,  to use custom ImageLoader, need to do that
ImagePicker.create(activity).imageLoader(ImageLoader customImageLoader)
but don't exist public method imageLoader(), see screenshot https://prnt.sc/qfgbj5.
I.m using 2.2.0 version of library
What need to do to set ImageLoader?
Thanks

Hi @androidPixl
Sorry, the docs kinda messy right now. You can use ImagePickerComponentHolder for custom image loader
Sample:
https://github.com/esafirm/android-image-picker/blob/master/sample/src/main/java/com/esafirm/sample/MainActivity.java#L99


Coloured text not working on signs

Spark link
https://spark.lucko.me/jo037OD0oA
Expected behavior
When using colour codes on a sign, I would expect the colour of the text to be a colour. For example: “&6Test” would be “Test” in gold.
Observed/Actual behavior
After typing my text and colour code, the sign will update and the text remains the default black.
Steps/models to reproduce
Write on a regular sign (any type): “&6Test”
Purpur version
18.07 01:00:06 [Server] INFO Checking version, please wait...
18.07 01:00:06 [Server] INFO Current: git-Purpur-2016 (MC: 1.20.1)*
18.07 01:00:06 [Server] INFO Previous: git-Purpur-2015 (MC: 1.20.1)
18.07 01:00:06 [Server] INFO * You are running the latest version
Agreements

[X] I am running the latest version of Purpur available from https://purpurmc.org/downloads.
[X] I have searched for and ensured there isn't already an open issue regarding this.
[ ] I ticked all the boxes without actually reading them
[X] My version of Minecraft is supported by Purpur.

Other
All relevant permissions have been used and it has been enabled in the purpur.yml file.

+1 same issue here, same perms etc. Only happened after updating to 1.20.1 from 1.19.4

Maybe someone WHEN it will be fixed?


 I think different libraries should offer different flavours. Axios, Got, Ky they all do same / similar things and have different opinions on how to organise and think about an HTTP client. The risk otherwise is that we lose our direction and become a washed out copy of everything else.
And if I understood you correct then there would be massive breaking API changes for no reason other than trying to be modern.
I think you need to be more specific what exactly is the idea you want to pursue.

all those new libs are usually introduce new features, like: AbortSignals or nice "syntax sugar" patterns that makes code simpler. Author of the library should decide on their own what patterns are useful and what are harmful, that is why I call it SYNC with other libraries.

Please take a look on GOT documentation, its a nice comparison table and list of features: https://github.com/sindresorhus/got
copy/paste from their readme
Main API

[x] Promise API
[x] Options
[x] Stream API
[x] Pagination API
[x] Advanced HTTPS API
[x] HTTP/2 support
[x] Response class

Timeouts and retries

[x] Advanced timeout handling
[x] Retries on failure
[x] Errors with metadata

Advanced creation

[x] Hooks
[x] Instances
[x] Progress events & other events
[x] Plugins
[x] Compose

Cache, Proxy and UNIX sockets

[x] RFC compliant caching
[x] Proxy support
[x] Unix Domain Sockets

Integration

[x] TypeScript support
[x] AWS
[x] Testing

Migration guides

Request migration guide

(Note that Request is unmaintained)

Axios
Node.js

Related, but small, update: #557 will add support for abort signals


Error after upgrading to latest webpack 2.1 beta

In a project that uses Microsoft.AspNetCore.ReactServices I upgraded webpack to the latest beta (that I read will turn final soon) and got this error:
Exception: Call to Node module failed with error: TypeError: Cannot read property 'forEach' of undefined
at Object.addReactHotModuleReplacementBabelTransform (node_modules\aspnet-webpack-react\HotModuleReplacement.js:3:33)
at attachWebpackDevMiddleware (node_modules\aspnet-webpack\WebpackDevMiddleware.js:61:38)

this happens on the line:
app.UseWebpackDevMiddleware(new WebpackDevMiddlewareOptions
{
    HotModuleReplacement = true,
    ReactHotModuleReplacement = true
});

I think this is caused by the renaming (in webpack 2.1 beta 23) of module loaders to module rules, which I had to change in my webpack.config.
The line that causes the error is:
webpackConfig.module.loaders.forEach(function (loaderConfig) {

If I change this line to
webpackConfig.module.rules.forEach(function (loaderConfig) {

Everything seems to work as before.

Thanks for reporting this!
aspnet-webpack-react version 1.0.3 is now published and includes a fix for this.


App.js text is clipping super boundaries  on mobile

iPhone XR, iOS 12, safari browser.

https://i.imgur.com/fcqVCv2.png

Resolved a while ago! Thanks for the feedback, Brandon!


Having issue where photo index evaluates to Infinity

Hi!
Firstly, thanks for the amazing package very useful - love it.
I have been using parts of this in a way that it maybe wasn't entirely intended and I have encountered an error with the onScroll method.
My use case is showing the images in a grid of my own, that on clicking creates a new <PhotoBrowser element starting the index with the image that was touched.
This all works great but I get into a weird state where the component is rendered one extra time and this time it causes a error,

I have tracked it down to the _onScroll() function specifically event.layoutMeasurement.width in some cases evaluates to 0 and then of course we divide by 0 somewhere (Infinity) and then the library tries to look at the image at index Infinity.
My temporary work around is to set the value to
const layoutWidth = event.layoutMeasurement.width || Dimensions.get('window').width; which ensures that everything works as expected - this has been tested on iPhone 4s, 5, 5c, 5s, 6, 6s and several android phones, and works as expected.
I have forked this so I can use it in my project, but would like to see if you have a more elegant solution or ideas of the bigger issue.
Thanks

Hey,
I am having the same issue - using the initialIndex prop leads to the error message you posted above on opening rendering the component and also on selecting an image from the grid.
I am having this problem exclusively on IOS, not on android.
However, i don't understand how this is related to the infinite width fix you mentioned.
It looks like this.state.currentMedia is somehow undefined in the FullScreenContainer.
Maybe there is a quick fix for this, after dismissing the error message, the right image is shown anyhow.
Cheers

Hey @JamesWatling, sorry for my late answer and thank you for your time and contribution.
Could you also create a new row in the Example project that reproduces the problem you're experiencing? I'll try creating one if it's not practical for you to create it.

I agree with @1st8 , the issue seems to be with currentMedia being undefined. For now I re-order the list so that selected image is first, will try to get a PR for this if i can manage to fix this. Has anybody managed to fix this yet?

This should be fixed with #13.


deploy docs to gh-pages

Refs https://github.com/badges/shields/issues/5944
This bit adds an action to build the docs and push the output to gh-pages on a push to master. Once we get this merged, I'll look at swapping over the CNAME in CloudFlare and shutting down netlify (interestingly I think the docs have stopped deploying to netlify properly at some point and nobody noticed - hopefully putting them here will make it more obvious that they're working)
Note: its hard to verify this works in PR form, but I have tested this on a fork

Warnings

:warning:

This PR modified package.json, but not package-lock.json - Perhaps you need to run npm install?

Messages

:book:
:sparkles: Thanks for your contribution to Shields, @chris48s!

  Generated by :no_entry_sign: dangerJS against f3767b062d8687f7cba09f27a3299793e93358bd


Latest patch release increases MSRV

The use of extended_key_value_attributes requires 1.54.0 or newer.
I don't see mention of a minimum supported Rust version in the README or Cargo.toml and note that CI tests on latest stable, beta, and nightly so perhaps this is not considered an issue but wanted to note the the latest patch release (1.1.8) breaks support for older Rust versions. The 1.1.6 release works at least as far back as 1.51.0.
Errors when compiling on 1.53.0:
https://gist.github.com/wezm/a46027bb12038bf396f00502eec112b7

Thank you for catching this. (And sorry for the very long delay in responding. I've added a co-maintainer for this crate to help take better care of it. :) )
I had thought this crate documented an MSRV policy, but it indeed does not. I've added one, copied from the MSRV policy of the flate2 crate.


added missing part from 2019-08-01

During SDK generation I have noticed that Rotate Certificates API was added to 2019-08-01 in one PR while new API swagger 2019-10-01 was merged without that new API.
verified with the team that this should be also included in 2019-10-01

Can one of the admins verify this patch?

It is exact same API spec as 2019-08-01.

Automation for azure-sdk-for-go


SpotX: Add version 1.2

SpotX blocks ads and updates for the desktop version of Spotify, disabling podcasts and more.

/verify

I think this app should go to the Nonportable bucket.
The script downloads and installs Spotify via Spotify's installer, and we cannot simply uninstall the app by scoop uninstall spotx.


Got error when running the code "sudo add-apt-repository ppa:webupd8team/java" during the evaluation stage.

Got error when running the code "sudo add-apt-repository ppa:webupd8team/java" during the evaluation stage.

not sure if this could help but please take a look and discuss with them https://github.com/OpenDriveLab/DriveLM/issues/37

Can you also try to change the apt source? https://github.com/bckim92/language-evaluation/issues/3

Can you also try to change the apt source? bckim92/language-evaluation#3

I have tried, but got same error.


Fix service and cluster update missing reason

Please provide a description of this PR:
For: https://github.com/istio/istio/issues/35867

/cherry-pick release-1.12

@kebe7jun: new pull request created: #35938

In response to this:

/cherry-pick release-1.12

Instructions for interacting with me using PR comments are available here.  If you have questions or suggestions related to my behavior, please file an issue against the kubernetes/test-infra repository.


trailing slash appended after param when using io.l5d.path identifier

When a slash precedes ?foo=bar param another slash is appended as trailing slash to the url.
Example: /foo/bar/?foo=bar is forwarded as /foo/bar/?foo=bar/
This causes problems with parsing the params by upstream service.

Thanks, Maciej!

This would be related to a bug in this fix:
https://github.com/BuoyantIO/linkerd/pull/711/files/0281c61224af19498f212c7b76a8af6ffe1e04b6


[Bug]: Dungeon crasht bei bestimmten Befehlen

What happened?
Wenn man einen Befehl sendet, welcher wie ein echter Befehl anfängt, aber nicht dem Befehl entspricht crasht der Dungeon.
z.B. sagasfdfhdsdf, gehen
Location
Host
Relevant log output
D:\Docker\House of MUD\HouseOfMUD\backend\game\dist\src\worker\action\action-handler.js:111
        action.performAction(user, actionArguments);
               ^

TypeError: Cannot read properties of undefined (reading 'performAction')
    at ActionHandlerImpl.processAction (D:\Docker\House of MUD\HouseOfMUD\backend\game\dist\src\worker\action\action-handler.js:111:16)
    at DungeonController.<anonymous> (D:\Docker\House of MUD\HouseOfMUD\backend\game\dist\src\worker\controller\dungeon-controller.js:96:40)
    at Generator.next (<anonymous>)
    at D:\Docker\House of MUD\HouseOfMUD\backend\game\dist\src\worker\controller\dungeon-controller.js:8:71
    at new Promise (<anonymous>)
    at __awaiter (D:\Docker\House of MUD\HouseOfMUD\backend\game\dist\src\worker\controller\dungeon-controller.js:4:12)
    at DungeonController.handleAmqpMessages (D:\Docker\House of MUD\HouseOfMUD\backend\game\dist\src\worker\controller\dungeon-controller.js:87:16)
    at DungeonController.<anonymous> (D:\Docker\House of MUD\HouseOfMUD\backend\game\dist\src\worker\controller\dungeon-controller.js:50:30)
    at Generator.next (<anonymous>)
    at D:\Docker\House of MUD\HouseOfMUD\backend\game\dist\src\worker\controller\dungeon-controller.js:8:71
    at new Promise (<anonymous>)
    at __awaiter (D:\Docker\House of MUD\HouseOfMUD\backend\game\dist\src\worker\controller\dungeon-controller.js:4:12)
    at D:\Docker\House of MUD\HouseOfMUD\backend\game\dist\src\worker\controller\dungeon-controller.js:45:54
    at D:\Docker\House of MUD\HouseOfMUD\backend\game\dist\src\worker\amqp\amqp-adapter.js:286:29
    at Channel.BaseChannel.dispatchMessage (D:\Docker\House of MUD\HouseOfMUD\backend\game\node_modules\amqplib\lib\channel.js:484:12)
    at Channel.BaseChannel.handleDelivery (D:\Docker\House of MUD\HouseOfMUD\backend\game\node_modules\amqplib\lib\channel.js:493:15)
Dungeon 62780b3990e7e9386de83d8e exited with code 1

Fixed by not using regexes for static commands


Feature Request: History of regenerated responses

Currently, regenerating a response delete the old one, it would be helpful to keep the old response in history. Similar to what ChatGPT does.

I like this idea too.


Restoration Framework is not restoring value

Expected Behavior

Set restorable value
Hot restart or relaunch app
Have value restored

Actual Behavior

Set restorable value
Hot restart or relaunch app
Value is not restored

Device Case Success Matrix

Hot restart
Relaunch (debug)
Relaunch (release)

Android 10 device
❌
❌
❌

Chrome
❌
❌
❌

iOS 14.4 device
❌
❌
❌

iOS 14.4 simulator
❌
❌
N/A

MCVE
import 'package:flutter/material.dart';

void main() {
  runApp(MyApp());
}

class MyApp extends StatelessWidget {
  @override
  Widget build(BuildContext context) {
    return MaterialApp(
      title: 'Restoration Framework',
      restorationScopeId: 'Restoration Framework',
      home: MyHomePage(title: 'Restoration Framework'),
    );
  }
}

class MyHomePage extends StatefulWidget {
  MyHomePage({Key key, this.title}) : super(key: key);

  final String title;

  @override
  _MyHomePageState createState() => _MyHomePageState();
}

class _MyHomePageState extends State<MyHomePage> with RestorationMixin {
  @override
  String get restorationId => 'MyHomePage';

  final _counter = RestorableInt(0);

  void _incrementCounter() {
    setState(() {
      _counter.value++;
    });
  }

  @override
  void restoreState(RestorationBucket oldBucket, bool initialRestore) {
    registerForRestoration(_counter, '_counter');
  }

  @override
  void dispose() {
    _counter.dispose();
    super.dispose();
  }

  @override
  Widget build(BuildContext context) {
    return Scaffold(
      appBar: AppBar(
        title: Text(widget.title),
      ),
      body: Center(
        child: Column(
          mainAxisAlignment: MainAxisAlignment.center,
          children: <Widget>[
            Text(
              'You have pushed the button this many times:',
            ),
            Text(
              '${_counter.value}',
              style: Theme.of(context).textTheme.headline4,
            ),
          ],
        ),
      ),
      floatingActionButton: FloatingActionButton(
        onPressed: _incrementCounter,
        tooltip: 'Increment',
        child: Icon(Icons.add),
      ),
    );
  }
}

[✓] Flutter (Channel dev, 1.27.0-4.0.pre, on macOS 11.2.1 20D74 darwin-x64, locale en-US)
    • Flutter version 1.27.0-4.0.pre at /Users/lukepighetti/development/flutter
    • Framework revision f8cd24de95 (11 days ago), 2021-02-16 11:24:17 -0800
    • Engine revision 1d537824d6
    • Dart version 2.13.0 (build 2.13.0-30.0.dev)
...
[✓] Xcode - develop for iOS and macOS
    • Xcode at /Applications/Xcode.app/Contents/Developer
    • Xcode 12.4, Build version 12D4e
    • CocoaPods version 1.10.1
...
[✓] Connected device (2 available)
    • SM S102DL (mobile)         • RF8N737S2CR     • android-arm    • Android 10 (API 29)
    • Luke's iPhone (mobile)     • xxxxxxx         • ios            • iOS 14.4

try reading the docs. its purpose to restore session state when app gets killed in background. try enabling "don't keep activities" to test it out

Closing this because Restoration Framework is apparently not intended for restoring state between app launches. Only if Android kills your app when it's in the background. In other words it's not a replacement for a storage system like Shared Preferences or Hive.


Sokeio 1.0.1: Error:  Class Sokeio\Admin\Livewire\Pages\ThemeOptions\admin

When you trying to create a new theme for Sokeio it returns:
Error: Class "Sokeio\Admin\Livewire\Pages\ThemeOptions\admin" not found in file /home/vagrant/projecttest/vendor/sokeio/admin/src/Livewire/Pages/ThemeOptions/Index.php on line 22

@kamekun  I Fixed it!


Allow passing db context via op_kwargs

For queries where users don't want to pass a table, object, this feature
will allow users to define context at runtime using op_kwargs.
example:
@aql.transform
def test_astro():
    return "SELECT * FROM actor"

with dag:
    actor_table = test_astro(database="pagile", conn_id="my_postgres_conn")

So what is the interface/example that you would like to work?

@ashb the interface mentioned in the first comment, namely:
@aql.transform
def test_astro():
    return "SELECT * FROM actor"

with dag:
    actor_table = test_astro(database="pagile", conn_id="my_postgres_conn")

The idea here is that a user can decide  the database and conn_id at runtime without needing to pass in a table.

Do you want it to be at the call time, because the other option is something like this:
@aql.transform(database="pagile", conn_id="my_postgres_conn")
def test_astro():
    return "SELECT * FROM actor"

with dag:
    actor_table = test_astro()

Right now Operator/Task arguments are passed to the decorator, and the only thing that can be passed to the decorated function are actual func parameters.
We could look at changing that (not sure if we'd need a change in Airflow or if we can just do that in our custom decorator) but there might be confusion about which parameter goes where?

If we did want your first example to work you'd have to overide __call__ on the decorator to be something like this:
     def __call__(self, *args, **kwargs) -> XComArg:
        operator_kwargs = {**self.kwargs]
        operator_kwargs.update((k, v) for (k,v) in kwargs.items() if k not in self.function_arg_names())
        function_kwargs = {k: v for (k,v) in kwargs.items() if k in self.function_arg_names() }
        op = self.operator_class(
            python_callable=self.function,
            op_args=args,
            op_kwargs=function_kwargs,
            multiple_outputs=self.multiple_outputs,
            **self.kwargs,
            **operator_kwargs
        )
        if self.function.__doc__:
            op.doc_md = self.function.__doc__
        return XComArg(op)

I think that might work, but I'm not sure how predictable it might be. Take this example:
@aql.transform
def test_astro(table):
    return "SELECT * FROM {{ table }}"

with dag:
    actor_table = test_astro(table=some_table, database="pagile", conn_id="my_postgres_conn"

Is it clear to users what args go where, and what the valid options are?


Deprecated garbage-collector APIs and GC APIs in runtime-defintions

The APIs in "@fluidframework/garbage-collector" package are only used within the container-runtime since the move of summarizer node to container-runtime. This change deprecates all but 1 items in the garbage-collector package. All these items have been copied over to container-runtime/gc or to runtime-utils. The remaining 1 item will be handled in a separate PR since it involves couple other changes.
The garbage collection APIs in runtime-utils/summary.ts are used only withing the container-runtime package. These have been deprecated. They have been copied to the garbage-collector package.
All usages to these deprecated items have been removed. They will be removed in the next branch.

@ChumpChief You started with a similar approach for summarizer node but decided to not copy it over to container-runtime. Any specific reason for that other than maintaining two copies in main?

FYI, I stand corrected about @internal not doing anything functional. Not on its own, but with the proper api-extractor settings, we could know when we're trying to mix @public and @internal things in ways we shouldn't; e.g. if we've marked something @internal and a @public thing exposes it as part of its interface (for example in a method parameter), we can cause the api-extractor build to fail so we're made aware and fix the inconsistency. @Josmithr opened a few PRs around that, like #14835 .

FYI, I stand corrected about @internal not doing anything functional. Not on its own, but with the proper api-extractor settings, we could know when we're trying to mix @public and @internal things in ways we shouldn't; e.g. if we've marked something @internal and a @public thing exposes it as part of its interface (for example in a method parameter), we can cause the api-extractor build to fail so we're made aware and fix the inconsistency. @Josmithr opened a few PRs around that, like #14835 .

I would highly recommend using our strict API-Extractor config, as it will highlight a lot of API incompatibility issues and enforce useful invariants.

FYI, I stand corrected about @internal not doing anything functional. Not on its own, but with the proper api-extractor settings, we could know when we're trying to mix @public and @internal things in ways we shouldn't; e.g. if we've marked something @internal and a @public thing exposes it as part of its interface (for example in a method parameter), we can cause the api-extractor build to fail so we're made aware and fix the inconsistency. @Josmithr
        Joshua Smithrud (HE/HIM)
        FTE opened a few PRs around that, like #14835 .

I would highly recommend using our strict API-Extractor config, as it will highlight a lot of API incompatibility issues and enforce useful invariants.

What does it mean for this PR? Since Joshua's PR is still in progress, should I wait for it to go in?

I'd say don't wait for Josh's PRs to merge this one. Moving to the strict API-extractor config for the packages touched here should probably be done as its own thing anyway, in case it highlights other @internal/@public tags that need to be updated.

I merged this PR since I need it for a couple of follow up changes as well. @alexvy86 @Josmithr If there is anything needed to do with the strict API-extractor config that's coming in, please let me know and I am happy to do it as a follow up.

I merged this PR since I need it for a couple of follow up changes as well. @alexvy86 @Josmithr If there is anything needed to do with the strict API-extractor config that's coming in, please let me know and I am happy to do it as a follow up.

The incompatible release tag check in particular will not be robust until #14836 goes through, we have published a new build-common, and updated deps on it.
That said, the strict config does already exist, and provides other useful invariant enforcement. Additionally, incompatible release tags will be noted (as comments in the generated API reports), they just won't fail the build. So it might be worth doing a PR that updates this package's api-extractor config to extend the strict variant and work through the build failures / any warnings generated in the report.
You can also temporarily add an override for the release tags compatibility rule in the package config to make them build failures (what I did in #14835).
I'm also happy to help provide context / answer questions if you do decide to take this on 🙂


Raise exception when attempting to subcube an empty set

Would it be better to return empty?  Please chime in if you have an opinion.

Coverage decreased (-0.05%) to 72.685% when pulling 6a36066943c3958179a51356bd4052402ef86f15 on keflavich:fail_slice_when_empty into ebba89786b8f1556bc14f05b43b1cb2d03687e98 on radio-astro-tools:master.

I like returning empty. Then doing operations like looping over subcubes requires an if statement instead of try/excepts everywhere.

Coverage decreased (-0.05%) to 72.685% when pulling 78662bad7f682c443d29a9ceb2c8d480142f605f on keflavich:fail_slice_when_empty into ebba89786b8f1556bc14f05b43b1cb2d03687e98 on radio-astro-tools:master.

Coverage decreased (-0.05%) to 72.685% when pulling 78662bad7f682c443d29a9ceb2c8d480142f605f on keflavich:fail_slice_when_empty into ebba89786b8f1556bc14f05b43b1cb2d03687e98 on radio-astro-tools:master.


fix(CI): adjust job queue and remove wasteful runs

The order is now tuned in for the job sequence.
This also uses workflow_run to execute only when linting, building, and unit testing are done—fewer jobs in general and forces pacing the order of needs for checks.
In the future, I will probably refactor this further so the E2E workflows use workflow_call as well, reducing the runs even more, but for now, here is the behavior:

On PR events: "Lint --> Build --> Test" and parallel run E2E.
On push events (main, etc.): Lint --> Build --> Test (5 different kinds of jobs) --> E2E

To summarize the discussion (so I don't forget) the CI job order reflects the priorities: 1) is the code quality okay? (i.e. lint passed) 2) does it build? 3) do the unit tests still pass 4) (optional) do the e2e tests pass. If any stage fails, the following steps are skipped since there's no need for them.

Correct.


fix issue with text file encoding gzip

The issue is discussed at:
https://github.com/CollectionFS/Meteor-CollectionFS/issues/664

@raix does this fix look correct to you? I haven't looked at this code recently and I don't think I fully understand the root issue. I'm sure it is a fix, but I don't want to cause issues for other types of downloads.

my concern is that we could risk having a connection "hanging"?

I think the connection is finished already when res.end is called.
Node throws an error in stream_writable.js because the writable state
finished is true.
BR
Remy
On Tue, Sep 8, 2015 at 8:48 PM, Morten N.O. Nørgaard Henriksen <
<EMAIL_ADDRESS>wrote:

my concern is that we could risk having a connection "hanging"?
—
Reply to this email directly or view it on GitHub
https://github.com/CollectionFS/Meteor-http-methods/pull/43#issuecomment-138666724
.

It is very difficult to know whether the logic is correct when just fixing for a single test case. I think most likely the correct fix will be something like this:
      function sendResponseIfDone() {
          res.statusCode = self.statusCode;
        // If no streams are waiting
        if (self._streamsWaiting === 0 &&
            (self.statusCode === 200 || self.statusCode === 206) &&
            self.done &&
            !self._responseSent) {
          self._responseSent = true;
          res.end(resultBuffer);
        }
      }

That should stop it from trying to call res.end twice, but without gzip-specific check. @RemyNoulin can you try that fix? Otherwise we can check finished state, too.

It works fine with your change, I tested with text, mp3 and gif files.
On Tue, Sep 8, 2015 at 10:37 PM, Eric Dobbertin<EMAIL_ADDRESS>wrote:

It is very difficult to know whether the logic is correct when just fixing
for a single test case. I think most likely the correct fix will be
something like this:
  function sendResponseIfDone() {
      res.statusCode = self.statusCode;
    // If no streams are waiting
    if (self._streamsWaiting === 0 &&
        (self.statusCode === 200 || self.statusCode === 206) &&
        self.done &&
        !self._responseSent) {
      self._responseSent = true;
      res.end(resultBuffer);
    }
  }

That should stop it from trying to call res.end twice, but without
gzip-specific check. @RemyNoulin https://github.com/RemyNoulin can you
try that fix? Otherwise we can check finished state, too.
—
Reply to this email directly or view it on GitHub
https://github.com/CollectionFS/Meteor-http-methods/pull/43#issuecomment-138692183
.

we should still make sure the stream is ended:
if (!res.finished) {
} // else don't try

I released this fix (including res.finished check) in 0.0.30. Please add<EMAIL_ADDRESS>to your app to ensure it's using this version and see if that fixes your issues.


Fix StackBlitz edit feature by selecting the closest example

Description
This PR is a proposal to fix the fact that the StackBlitz edit button creates a StackBlitz env only with the first example of the page due to event.target.closest('.bd-content') and that pages only have one .bd-content.
For example https://twbs-bootstrap.netlify.app/docs/5.1/components/accordion/#flush will create a StackBlitz env with the content of https://twbs-bootstrap.netlify.app/docs/5.1/components/accordion/#example.
I've changed the selector in order to have the closest example of the edit button (the sibling example).
Types of changes

[x] Bug fix (non-breaking change which fixes an issue)

Checklist

[x] I have read the contributing guidelines
[x] My code follows the code style of the project (using npm run lint)
[x] My change introduces changes to the documentation
[x] I have updated the documentation accordingly
[x] All new and existing tests passed

Related issues
N/A
Live previews

[x] Accordions

[x] Example
[x] Flush
[x] Always open

Before going further here I'd like a first feedback on this PR :)
I propose to tackle this topic with multiple steps:

Hide all the non-working examples (for different reasons) in this PR based on this new parameter https://github.com/twbs/bootstrap/pull/36088/commits/113a17328f3b1dcd2a69ca6ca33c7385d02fc3bb
Create a tracking issue by copying the list actually in the description of this PR (that can be split in multiple sub-issues) to reintroduce progressively edit button after having found a solution

What do you think?

Thanks for your feedback @GeoSot. This PR now contains only the fix and https://github.com/twbs/bootstrap/pull/36091 contains the tracking of the examples to hide or fix + the new parameter allowing to hide one example.

Thank you to @julien-deramond . As always, fast and effective


asciicast v2 compatibility

Hey @marionebl ! Great job with this project 👍
I'll be releasing asciinema 2.0 in the coming days, which uses new asciicast v2 file format, and was wondering if anything needs to be implemented to support it in svg-term-cli (I couldn't locate the actual code which reads the asciicast here). There are already many recordings on asciinema.org in v2 format made using develop branch of asciinema.
Let me know if you need any help!

Hey @sickill, I'll investigate this weekend - can you post links to some v2 casts here? Thanks!

For reference: The dependency chain for the code that reads the asciicast is:
svg-term-cli > svg-term > load-asciicast
load-asciicast in turn uses asciinema-player/player/asciicast/load.

I see, ok, then you should be covered is load supports v2 for a while now 👍
Example v2 recording on asciinema.org:
https://asciinema.org/a/1RdOYnYtFebN6Xv7dv3thdBlt
And direct link to its asciicast file:
https://asciinema.org/a/1RdOYnYtFebN6Xv7dv3thdBlt.cast

Works as expected:

Thanks for reaching out 👍


zh_TW.json

txAdmin in-game menu not tested. Reason: Can't change menu language

Hey thanks for the PR!

Unfortunately the menu is still not translatable because even the translation keys for English are not final, we changing it every few days. ;
You need to remove the .vs folder;
sorry for my ignorance, but how different is Chinese from Chinese traditional? I would like to prevent micro-segregation of translations, if possible.

As soon as the menu translation is ready, I'll commit the English translation to all locale files, and you can edit after that, okay?

Hey thanks for the PR!

Unfortunately the menu is still not translatable because even the translation keys for English are not final, we changing it every few days. ;
You need to remove the .vs folder;
sorry for my ignorance, but how different is Chinese from Chinese traditional? I would like to prevent micro-segregation of translations, if possible.

As soon as the menu translation is ready, I'll commit the English translation to all locale files, and you can edit after that, okay?

1.Sorry for commit the .vs file. Visual Studio automatic generate for that.

The orginal chinese locale is not 100% translate and some the China word usage are different from Hong Kong and Taiwan. Also few peoples in Hong Kong and Taiwan can't read simplified chinese. The original chinese locale also contain some problem that the sentence combine with those parameter will become wiered. But I don't really know the simplified chinese so just make up a traditional chinese one for improving on the sentences and complete the whole translation.

Ok no problem. Love your in-game menu work

I will hold this PR a bit more, when the menu english translation is finally available, I'll mention you here so you know you can update your translations, okay?
Thanks again for the PR!

It's ok.

Unfortunately we were not ready for enabling menu locales and this caused issues.
I don't know when we will have it ready.
I will close this PR, can you please create a new pr just changing the specific translation file with the keys that are already present in the other zh.json file?
I removed all the nui menu stuff from it.


arch: arm: SecureFault_IRQn for non-CMSIS-compliant MCUs

Define IRQ number for SecureFault Handler when building Secure
Firmware for non-CMSIS-compliant ARM Cortex-M MCUs.
Signed-off-by: Ioannis Glaropoulos<EMAIL_ADDRESS>

Codecov Report

Merging #6711 into master will increase coverage by <.01%.
The diff coverage is n/a.

@@            Coverage Diff             @@
##           master    #6711      +/-   ##
==========================================
+ Coverage   54.07%   54.07%   +<.01%
==========================================
  Files         442      442
  Lines       41916    41916
  Branches     7970     7970
==========================================
+ Hits        22665    22666       +1
+ Misses      16051    16050       -1
  Partials     3200     3200

Impacted Files
Coverage Δ

kernel/posix/pthread.c
69.5% <0%> (+0.5%)
:arrow_up:

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
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refactor(p2wdb): Refactored OrbitDB Adapter

PR related to this trello task --> https://trello.com/c/mA1bl8nZ

:tada: This PR is included in version 1.11.0 :tada:
Your semantic-release bot :package::rocket:


[#5441] Only send quota emails if quota enforcement is on

Resolves #5441

Pull Request Checklist:

[ ] Positive Test Case Written by Dev

[ ] Automated Testing

[ ] Sufficient User and Developer Documentation

[ ] Passing Jenkins Build

[ ] Peer Code review and approval

Positive Test Case

[Enter positive test case here]

closing for now, as I believe we have a solution in https://github.com/hydroshare/hydroshare/pull/5461


How to use other format Code128B ?

Could you please tell me how can I use Code128B and other format like Code128A,Code128C

well I checked it, its  already setup in the code.


typescript types?

Noticed that infernojs was written in typescript and this project was referenced in the docs for infernojs, but when I searched I couldn't find any types for this project. Would be nice to have given the good support in the main library.

Hi @Kethku
Thanks for your question.
Incompose was first just an adoption of recompose for inferno. That's why all files are "pure" JS files. I have plans to change them to TS but unfortunately I can't give you a date till when I am able to port them.


[Bug]: Model loading crashes with enable_lora and 8 GPUs

Your current environment

The output of `python collect_env.py`
PyTorch version: 2.4.0+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.3 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.35

Python version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-5.15.0-1063-nvidia-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.2.140
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: Tesla V100-SXM2-16GB
GPU 1: Tesla V100-SXM2-16GB
GPU 2: Tesla V100-SXM2-16GB
GPU 3: Tesla V100-SXM2-16GB
GPU 4: Tesla V100-SXM2-16GB
GPU 5: Tesla V100-SXM2-16GB
GPU 6: Tesla V100-SXM2-16GB
GPU 7: Tesla V100-SXM2-16GB

Nvidia driver version: 535.183.06
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:                         x86_64
CPU op-mode(s):                       32-bit, 64-bit
Address sizes:                        46 bits physical, 48 bits virtual
Byte Order:                           Little Endian
CPU(s):                               80
On-line CPU(s) list:                  0-79
Vendor ID:                            GenuineIntel
Model name:                           Intel(R) Xeon(R) CPU E5-2698 v4 @ 2.20GHz
CPU family:                           6
Model:                                79
Thread(s) per core:                   2
Core(s) per socket:                   20
Socket(s):                            2
Stepping:                             1
CPU max MHz:                          3600.0000
CPU min MHz:                          1200.0000
BogoMIPS:                             4390.20
Flags:                                fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single pti intel_ppin ssbd ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm rdt_a rdseed adx smap intel_pt xsaveopt cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts md_clear flush_l1d
Virtualization:                       VT-x
L1d cache:                            1.3 MiB (40 instances)
L1i cache:                            1.3 MiB (40 instances)
L2 cache:                             10 MiB (40 instances)
L3 cache:                             100 MiB (2 instances)
NUMA node(s):                         2
NUMA node0 CPU(s):                    0-19,40-59
NUMA node1 CPU(s):                    20-39,60-79
Vulnerability Gather data sampling:   Not affected
Vulnerability Itlb multihit:          KVM: Mitigation: VMX disabled
Vulnerability L1tf:                   Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable
Vulnerability Mds:                    Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Meltdown:               Mitigation; PTI
Vulnerability Mmio stale data:        Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed:               Not affected
Vulnerability Spec rstack overflow:   Not affected
Vulnerability Spec store bypass:      Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1:             Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:             Mitigation; Retpolines; IBPB conditional; IBRS_FW; STIBP conditional; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Mitigation; Clear CPU buffers; SMT vulnerable

Versions of relevant libraries:
[pip3] mypy==1.11.0
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.26.4
[pip3] onnxruntime==1.18.1
[pip3] torch==2.4.0
[pip3] torchaudio==2.4.1
[pip3] torchvision==0.19.0
[pip3] triton==3.0.0```

🐛 Describe the bug
While loading the model with LLM using 8 GPUs on a single node with enable_lora=true, vllm crashes without any error messages (even with all trace options enabled).
Error:
(VllmWorkerProcess pid=397111) INFO 10-15 15:30:32 model_runner.py:1329] Capturing the model for CUDA graphs. This may lead to unexpected consequences if the model is not static. To run the model in eager mode, set 'enforce_eager=True' or use '--enforce-eager' in the CLI.
(VllmWorkerProcess pid=397111) INFO 10-15 15:30:32 model_runner.py:1333] CUDA graphs can take additional 1~3 GiB memory per GPU. If you are running out of memory, consider decreasing `gpu_memory_utilization` or enforcing eager mode. You can also reduce the `max_num_seqs` as needed to decrease memory usage.
(VllmWorkerProcess pid=397117) INFO 10-15 15:31:13 model_runner.py:1456] Graph capturing finished in 42 secs.
(VllmWorkerProcess pid=397110) INFO 10-15 15:31:13 model_runner.py:1456] Graph capturing finished in 42 secs.
(VllmWorkerProcess pid=397113) INFO 10-15 15:31:13 model_runner.py:1456] Graph capturing finished in 41 secs.
(VllmWorkerProcess pid=397108) INFO 10-15 15:31:13 model_runner.py:1456] Graph capturing finished in 41 secs.
(VllmWorkerProcess pid=397112) INFO 10-15 15:31:13 model_runner.py:1456] Graph capturing finished in 41 secs.
(VllmWorkerProcess pid=397111) INFO 10-15 15:31:13 model_runner.py:1456] Graph capturing finished in 41 secs.
INFO 10-15 15:31:13 model_runner.py:1456] Graph capturing finished in 41 secs.
(VllmWorkerProcess pid=397109) INFO 10-15 15:31:13 model_runner.py:1456] Graph capturing finished in 41 secs.
/usr/lib/python3.10/multiprocessing/resource_tracker.py:224: UserWarning: resource_tracker: There appear to be 1 leaked shared_memory objects to clean up at shutdown
  warnings.warn('resource_tracker: There appear to be %d '
Killed

Code to reproduce:
from vllm import LLM, SamplingParams

llm = LLM(model="meta-llama/Meta-Llama-3.1-8B-Instruct",
            enable_lora=True,
            max_lora_rank=128,
            dtype=torch.float16,
            max_model_len=1024,
            gpu_memory_utilization=0.75,
            tensor_parallel_size=8)

Full trace log
WARNING 10-15 15:29:22 config.py:1656] Casting torch.bfloat16 to torch.float16.
INFO 10-15 15:29:22 config.py:899] Defaulting to use mp for distributed inference
INFO 10-15 15:29:22 llm_engine.py:226] Initializing an LLM engine (v0.6.1.dev238+ge2c6e0a82) with config: model='meta-llama/Meta-Llama-3.1-8B-Instruct', speculative_config=None, tokenizer='meta-llama/Meta-Llama-3.1-8B-Instruct', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, override_neuron_config=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.float16, max_seq_len=1024, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=8, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), observability_config=ObservabilityConfig(otlp_traces_endpoint=None, collect_model_forward_time=False, collect_model_execute_time=False), seed=0, served_model_name=meta-llama/Meta-Llama-3.1-8B-Instruct, use_v2_block_manager=False, num_scheduler_steps=1, multi_step_stream_outputs=False, enable_prefix_caching=False, use_async_output_proc=True, use_cached_outputs=False, mm_processor_kwargs=None)
WARNING 10-15 15:29:25 multiproc_gpu_executor.py:53] Reducing Torch parallelism from 40 threads to 1 to avoid unnecessary CPU contention. Set OMP_NUM_THREADS in the external environment to tune this value as needed.
INFO 10-15 15:29:25 custom_cache_manager.py:17] Setting Triton cache manager to: vllm.triton_utils.custom_cache_manager:CustomCacheManager
INFO 10-15 15:29:27 selector.py:217] Cannot use FlashAttention-2 backend for Volta and Turing GPUs.
INFO 10-15 15:29:27 selector.py:116] Using XFormers backend.
(VllmWorkerProcess pid=397108) INFO 10-15 15:29:27 selector.py:217] Cannot use FlashAttention-2 backend for Volta and Turing GPUs.
(VllmWorkerProcess pid=397108) INFO 10-15 15:29:27 selector.py:116] Using XFormers backend.
(VllmWorkerProcess pid=397117) INFO 10-15 15:29:27 selector.py:217] Cannot use FlashAttention-2 backend for Volta and Turing GPUs.
(VllmWorkerProcess pid=397117) INFO 10-15 15:29:27 selector.py:116] Using XFormers backend.
(VllmWorkerProcess pid=397109) INFO 10-15 15:29:27 selector.py:217] Cannot use FlashAttention-2 backend for Volta and Turing GPUs.
(VllmWorkerProcess pid=397109) INFO 10-15 15:29:27 selector.py:116] Using XFormers backend.
(VllmWorkerProcess pid=397111) INFO 10-15 15:29:27 selector.py:217] Cannot use FlashAttention-2 backend for Volta and Turing GPUs.
(VllmWorkerProcess pid=397112) INFO 10-15 15:29:27 selector.py:217] Cannot use FlashAttention-2 backend for Volta and Turing GPUs.
(VllmWorkerProcess pid=397111) INFO 10-15 15:29:27 selector.py:116] Using XFormers backend.
(VllmWorkerProcess pid=397112) INFO 10-15 15:29:27 selector.py:116] Using XFormers backend.
(VllmWorkerProcess pid=397113) INFO 10-15 15:29:27 selector.py:217] Cannot use FlashAttention-2 backend for Volta and Turing GPUs.
(VllmWorkerProcess pid=397110) INFO 10-15 15:29:27 selector.py:217] Cannot use FlashAttention-2 backend for Volta and Turing GPUs.
(VllmWorkerProcess pid=397113) INFO 10-15 15:29:27 selector.py:116] Using XFormers backend.
(VllmWorkerProcess pid=397110) INFO 10-15 15:29:27 selector.py:116] Using XFormers backend.
/usr/local/lib/python3.10/dist-packages/xformers/ops/fmha/flash.py:211: FutureWarning: `torch.library.impl_abstract` was renamed to `torch.library.register_fake`. Please use that instead; we will remove `torch.library.impl_abstract` in a future version of PyTorch.
  @torch.library.impl_abstract("xformers_flash::flash_fwd")
(VllmWorkerProcess pid=397112) /usr/local/lib/python3.10/dist-packages/xformers/ops/fmha/flash.py:211: FutureWarning: `torch.library.impl_abstract` was renamed to `torch.library.register_fake`. Please use that instead; we will remove `torch.library.impl_abstract` in a future version of PyTorch.
(VllmWorkerProcess pid=397111) /usr/local/lib/python3.10/dist-packages/xformers/ops/fmha/flash.py:211: FutureWarning: `torch.library.impl_abstract` was renamed to `torch.library.register_fake`. Please use that instead; we will remove `torch.library.impl_abstract` in a future version of PyTorch.
(VllmWorkerProcess pid=397109) /usr/local/lib/python3.10/dist-packages/xformers/ops/fmha/flash.py:211: FutureWarning: `torch.library.impl_abstract` was renamed to `torch.library.register_fake`. Please use that instead; we will remove `torch.library.impl_abstract` in a future version of PyTorch.
(VllmWorkerProcess pid=397117) /usr/local/lib/python3.10/dist-packages/xformers/ops/fmha/flash.py:211: FutureWarning: `torch.library.impl_abstract` was renamed to `torch.library.register_fake`. Please use that instead; we will remove `torch.library.impl_abstract` in a future version of PyTorch.
(VllmWorkerProcess pid=397113) /usr/local/lib/python3.10/dist-packages/xformers/ops/fmha/flash.py:211: FutureWarning: `torch.library.impl_abstract` was renamed to `torch.library.register_fake`. Please use that instead; we will remove `torch.library.impl_abstract` in a future version of PyTorch.
(VllmWorkerProcess pid=397110) /usr/local/lib/python3.10/dist-packages/xformers/ops/fmha/flash.py:211: FutureWarning: `torch.library.impl_abstract` was renamed to `torch.library.register_fake`. Please use that instead; we will remove `torch.library.impl_abstract` in a future version of PyTorch.
(VllmWorkerProcess pid=397108) /usr/local/lib/python3.10/dist-packages/xformers/ops/fmha/flash.py:211: FutureWarning: `torch.library.impl_abstract` was renamed to `torch.library.register_fake`. Please use that instead; we will remove `torch.library.impl_abstract` in a future version of PyTorch.
(VllmWorkerProcess pid=397112)   @torch.library.impl_abstract("xformers_flash::flash_fwd")
(VllmWorkerProcess pid=397111)   @torch.library.impl_abstract("xformers_flash::flash_fwd")
(VllmWorkerProcess pid=397109)   @torch.library.impl_abstract("xformers_flash::flash_fwd")
(VllmWorkerProcess pid=397117)   @torch.library.impl_abstract("xformers_flash::flash_fwd")
(VllmWorkerProcess pid=397113)   @torch.library.impl_abstract("xformers_flash::flash_fwd")
(VllmWorkerProcess pid=397110)   @torch.library.impl_abstract("xformers_flash::flash_fwd")
(VllmWorkerProcess pid=397108)   @torch.library.impl_abstract("xformers_flash::flash_fwd")
(VllmWorkerProcess pid=397109) /usr/local/lib/python3.10/dist-packages/xformers/ops/fmha/flash.py:344: FutureWarning: `torch.library.impl_abstract` was renamed to `torch.library.register_fake`. Please use that instead; we will remove `torch.library.impl_abstract` in a future version of PyTorch.
(VllmWorkerProcess pid=397113) /usr/local/lib/python3.10/dist-packages/xformers/ops/fmha/flash.py:344: FutureWarning: `torch.library.impl_abstract` was renamed to `torch.library.register_fake`. Please use that instead; we will remove `torch.library.impl_abstract` in a future version of PyTorch.
(VllmWorkerProcess pid=397111) /usr/local/lib/python3.10/dist-packages/xformers/ops/fmha/flash.py:344: FutureWarning: `torch.library.impl_abstract` was renamed to `torch.library.register_fake`. Please use that instead; we will remove `torch.library.impl_abstract` in a future version of PyTorch.
(VllmWorkerProcess pid=397110) /usr/local/lib/python3.10/dist-packages/xformers/ops/fmha/flash.py:344: FutureWarning: `torch.library.impl_abstract` was renamed to `torch.library.register_fake`. Please use that instead; we will remove `torch.library.impl_abstract` in a future version of PyTorch.
(VllmWorkerProcess pid=397112) /usr/local/lib/python3.10/dist-packages/xformers/ops/fmha/flash.py:344: FutureWarning: `torch.library.impl_abstract` was renamed to `torch.library.register_fake`. Please use that instead; we will remove `torch.library.impl_abstract` in a future version of PyTorch.
(VllmWorkerProcess pid=397108) /usr/local/lib/python3.10/dist-packages/xformers/ops/fmha/flash.py:344: FutureWarning: `torch.library.impl_abstract` was renamed to `torch.library.register_fake`. Please use that instead; we will remove `torch.library.impl_abstract` in a future version of PyTorch.
(VllmWorkerProcess pid=397117) /usr/local/lib/python3.10/dist-packages/xformers/ops/fmha/flash.py:344: FutureWarning: `torch.library.impl_abstract` was renamed to `torch.library.register_fake`. Please use that instead; we will remove `torch.library.impl_abstract` in a future version of PyTorch.
(VllmWorkerProcess pid=397109)   @torch.library.impl_abstract("xformers_flash::flash_bwd")
(VllmWorkerProcess pid=397113)   @torch.library.impl_abstract("xformers_flash::flash_bwd")
(VllmWorkerProcess pid=397111)   @torch.library.impl_abstract("xformers_flash::flash_bwd")
(VllmWorkerProcess pid=397110)   @torch.library.impl_abstract("xformers_flash::flash_bwd")
(VllmWorkerProcess pid=397112)   @torch.library.impl_abstract("xformers_flash::flash_bwd")
(VllmWorkerProcess pid=397108)   @torch.library.impl_abstract("xformers_flash::flash_bwd")
(VllmWorkerProcess pid=397117)   @torch.library.impl_abstract("xformers_flash::flash_bwd")
/usr/local/lib/python3.10/dist-packages/xformers/ops/fmha/flash.py:344: FutureWarning: `torch.library.impl_abstract` was renamed to `torch.library.register_fake`. Please use that instead; we will remove `torch.library.impl_abstract` in a future version of PyTorch.
  @torch.library.impl_abstract("xformers_flash::flash_bwd")
(VllmWorkerProcess pid=397113) INFO 10-15 15:29:30 multiproc_worker_utils.py:218] Worker ready; awaiting tasks
(VllmWorkerProcess pid=397111) INFO 10-15 15:29:30 multiproc_worker_utils.py:218] Worker ready; awaiting tasks
(VllmWorkerProcess pid=397117) INFO 10-15 15:29:30 multiproc_worker_utils.py:218] Worker ready; awaiting tasks
(VllmWorkerProcess pid=397109) INFO 10-15 15:29:30 multiproc_worker_utils.py:218] Worker ready; awaiting tasks
(VllmWorkerProcess pid=397108) INFO 10-15 15:29:30 multiproc_worker_utils.py:218] Worker ready; awaiting tasks
(VllmWorkerProcess pid=397112) INFO 10-15 15:29:30 multiproc_worker_utils.py:218] Worker ready; awaiting tasks
(VllmWorkerProcess pid=397110) INFO 10-15 15:29:30 multiproc_worker_utils.py:218] Worker ready; awaiting tasks
INFO 10-15 15:29:33 utils.py:992] Found nccl from library libnccl.so.2
(VllmWorkerProcess pid=397110) INFO 10-15 15:29:33 utils.py:992] Found nccl from library libnccl.so.2
(VllmWorkerProcess pid=397111) INFO 10-15 15:29:33 utils.py:992] Found nccl from library libnccl.so.2
(VllmWorkerProcess pid=397108) INFO 10-15 15:29:33 utils.py:992] Found nccl from library libnccl.so.2
(VllmWorkerProcess pid=397112) INFO 10-15 15:29:33 utils.py:992] Found nccl from library libnccl.so.2
(VllmWorkerProcess pid=397108) INFO 10-15 15:29:33 pynccl.py:63] vLLM is using nccl==2.20.5
(VllmWorkerProcess pid=397113) INFO 10-15 15:29:33 utils.py:992] Found nccl from library libnccl.so.2
(VllmWorkerProcess pid=397110) INFO 10-15 15:29:33 pynccl.py:63] vLLM is using nccl==2.20.5
INFO 10-15 15:29:33 pynccl.py:63] vLLM is using nccl==2.20.5
(VllmWorkerProcess pid=397109) INFO 10-15 15:29:33 utils.py:992] Found nccl from library libnccl.so.2
(VllmWorkerProcess pid=397111) INFO 10-15 15:29:33 pynccl.py:63] vLLM is using nccl==2.20.5
(VllmWorkerProcess pid=397112) INFO 10-15 15:29:33 pynccl.py:63] vLLM is using nccl==2.20.5
(VllmWorkerProcess pid=397113) INFO 10-15 15:29:33 pynccl.py:63] vLLM is using nccl==2.20.5
(VllmWorkerProcess pid=397109) INFO 10-15 15:29:33 pynccl.py:63] vLLM is using nccl==2.20.5
(VllmWorkerProcess pid=397117) INFO 10-15 15:29:33 utils.py:992] Found nccl from library libnccl.so.2
(VllmWorkerProcess pid=397117) INFO 10-15 15:29:33 pynccl.py:63] vLLM is using nccl==2.20.5
(VllmWorkerProcess pid=397111) WARNING 10-15 15:29:34 custom_all_reduce.py:128] Custom allreduce is disabled because it's not supported on more than two PCIe-only GPUs. To silence this warning, specify disable_custom_all_reduce=True explicitly.
(VllmWorkerProcess pid=397117) WARNING 10-15 15:29:34 custom_all_reduce.py:128] Custom allreduce is disabled because it's not supported on more than two PCIe-only GPUs. To silence this warning, specify disable_custom_all_reduce=True explicitly.
(VllmWorkerProcess pid=397108) WARNING 10-15 15:29:34 custom_all_reduce.py:128] Custom allreduce is disabled because it's not supported on more than two PCIe-only GPUs. To silence this warning, specify disable_custom_all_reduce=True explicitly.
(VllmWorkerProcess pid=397112) WARNING 10-15 15:29:34 custom_all_reduce.py:128] Custom allreduce is disabled because it's not supported on more than two PCIe-only GPUs. To silence this warning, specify disable_custom_all_reduce=True explicitly.
(VllmWorkerProcess pid=397109) WARNING 10-15 15:29:34 custom_all_reduce.py:128] Custom allreduce is disabled because it's not supported on more than two PCIe-only GPUs. To silence this warning, specify disable_custom_all_reduce=True explicitly.
(VllmWorkerProcess pid=397113) WARNING 10-15 15:29:34 custom_all_reduce.py:128] Custom allreduce is disabled because it's not supported on more than two PCIe-only GPUs. To silence this warning, specify disable_custom_all_reduce=True explicitly.
WARNING 10-15 15:29:34 custom_all_reduce.py:128] Custom allreduce is disabled because it's not supported on more than two PCIe-only GPUs. To silence this warning, specify disable_custom_all_reduce=True explicitly.
(VllmWorkerProcess pid=397110) WARNING 10-15 15:29:34 custom_all_reduce.py:128] Custom allreduce is disabled because it's not supported on more than two PCIe-only GPUs. To silence this warning, specify disable_custom_all_reduce=True explicitly.
INFO 10-15 15:29:34 shm_broadcast.py:241] vLLM message queue communication handle: Handle(connect_ip='<IP_ADDRESS>', local_reader_ranks=[1, 2, 3, 4, 5, 6, 7], buffer=<vllm.distributed.device_communicators.shm_broadcast.ShmRingBuffer object at 0x7ff469fa4760>, local_subscribe_port=54189, remote_subscribe_port=None)
INFO 10-15 15:29:34 model_runner.py:1014] Starting to load model meta-llama/Meta-Llama-3.1-8B-Instruct...
(VllmWorkerProcess pid=397108) INFO 10-15 15:29:34 model_runner.py:1014] Starting to load model meta-llama/Meta-Llama-3.1-8B-Instruct...
(VllmWorkerProcess pid=397117) INFO 10-15 15:29:34 model_runner.py:1014] Starting to load model meta-llama/Meta-Llama-3.1-8B-Instruct...
(VllmWorkerProcess pid=397110) INFO 10-15 15:29:34 model_runner.py:1014] Starting to load model meta-llama/Meta-Llama-3.1-8B-Instruct...
(VllmWorkerProcess pid=397112) INFO 10-15 15:29:34 model_runner.py:1014] Starting to load model meta-llama/Meta-Llama-3.1-8B-Instruct...
(VllmWorkerProcess pid=397113) INFO 10-15 15:29:34 model_runner.py:1014] Starting to load model meta-llama/Meta-Llama-3.1-8B-Instruct...
(VllmWorkerProcess pid=397109) INFO 10-15 15:29:34 model_runner.py:1014] Starting to load model meta-llama/Meta-Llama-3.1-8B-Instruct...
(VllmWorkerProcess pid=397111) INFO 10-15 15:29:34 model_runner.py:1014] Starting to load model meta-llama/Meta-Llama-3.1-8B-Instruct...
(VllmWorkerProcess pid=397113) INFO 10-15 15:29:34 selector.py:217] Cannot use FlashAttention-2 backend for Volta and Turing GPUs.
(VllmWorkerProcess pid=397113) INFO 10-15 15:29:34 selector.py:116] Using XFormers backend.
(VllmWorkerProcess pid=397110) INFO 10-15 15:29:34 selector.py:217] Cannot use FlashAttention-2 backend for Volta and Turing GPUs.
(VllmWorkerProcess pid=397110) INFO 10-15 15:29:34 selector.py:116] Using XFormers backend.
(VllmWorkerProcess pid=397111) INFO 10-15 15:29:34 selector.py:217] Cannot use FlashAttention-2 backend for Volta and Turing GPUs.
INFO 10-15 15:29:34 selector.py:217] Cannot use FlashAttention-2 backend for Volta and Turing GPUs.
(VllmWorkerProcess pid=397111) INFO 10-15 15:29:34 selector.py:116] Using XFormers backend.
INFO 10-15 15:29:34 selector.py:116] Using XFormers backend.
(VllmWorkerProcess pid=397112) INFO 10-15 15:29:34 selector.py:217] Cannot use FlashAttention-2 backend for Volta and Turing GPUs.
(VllmWorkerProcess pid=397108) INFO 10-15 15:29:34 selector.py:217] Cannot use FlashAttention-2 backend for Volta and Turing GPUs.
(VllmWorkerProcess pid=397112) INFO 10-15 15:29:34 selector.py:116] Using XFormers backend.
(VllmWorkerProcess pid=397108) INFO 10-15 15:29:34 selector.py:116] Using XFormers backend.
(VllmWorkerProcess pid=397117) INFO 10-15 15:29:34 selector.py:217] Cannot use FlashAttention-2 backend for Volta and Turing GPUs.
(VllmWorkerProcess pid=397117) INFO 10-15 15:29:34 selector.py:116] Using XFormers backend.
(VllmWorkerProcess pid=397109) INFO 10-15 15:29:34 selector.py:217] Cannot use FlashAttention-2 backend for Volta and Turing GPUs.
(VllmWorkerProcess pid=397109) INFO 10-15 15:29:34 selector.py:116] Using XFormers backend.
INFO 10-15 15:29:35 weight_utils.py:242] Using model weights format ['*.safetensors']
(VllmWorkerProcess pid=397113) INFO 10-15 15:29:35 weight_utils.py:242] Using model weights format ['*.safetensors']
(VllmWorkerProcess pid=397117) INFO 10-15 15:29:35 weight_utils.py:242] Using model weights format ['*.safetensors']
(VllmWorkerProcess pid=397110) INFO 10-15 15:29:35 weight_utils.py:242] Using model weights format ['*.safetensors']
(VllmWorkerProcess pid=397109) INFO 10-15 15:29:35 weight_utils.py:242] Using model weights format ['*.safetensors']
(VllmWorkerProcess pid=397112) INFO 10-15 15:29:35 weight_utils.py:242] Using model weights format ['*.safetensors']
(VllmWorkerProcess pid=397111) INFO 10-15 15:29:35 weight_utils.py:242] Using model weights format ['*.safetensors']
(VllmWorkerProcess pid=397108) INFO 10-15 15:29:35 weight_utils.py:242] Using model weights format ['*.safetensors']
Loading safetensors checkpoint shards:   0% Completed | 0/4 [00:00<?, ?it/s]
Loading safetensors checkpoint shards:  25% Completed | 1/4 [00:11<00:33, 11.06s/it]
Loading safetensors checkpoint shards:  50% Completed | 2/4 [00:20<00:20, 10.05s/it]
Loading safetensors checkpoint shards:  75% Completed | 3/4 [00:22<00:06,  6.55s/it]
Loading safetensors checkpoint shards: 100% Completed | 4/4 [00:44<00:00, 12.38s/it]
Loading safetensors checkpoint shards: 100% Completed | 4/4 [00:44<00:00, 11.03s/it]

INFO 10-15 15:30:20 model_runner.py:1025] Loading model weights took 1.9028 GB
(VllmWorkerProcess pid=397108) INFO 10-15 15:30:20 model_runner.py:1025] Loading model weights took 1.9028 GB
(VllmWorkerProcess pid=397112) INFO 10-15 15:30:20 model_runner.py:1025] Loading model weights took 1.9028 GB
(VllmWorkerProcess pid=397111) INFO 10-15 15:30:20 model_runner.py:1025] Loading model weights took 1.9028 GB
(VllmWorkerProcess pid=397113) INFO 10-15 15:30:20 model_runner.py:1025] Loading model weights took 1.9028 GB
(VllmWorkerProcess pid=397109) INFO 10-15 15:30:20 model_runner.py:1025] Loading model weights took 1.9028 GB
(VllmWorkerProcess pid=397117) INFO 10-15 15:30:20 model_runner.py:1025] Loading model weights took 1.9028 GB
(VllmWorkerProcess pid=397110) INFO 10-15 15:30:20 model_runner.py:1025] Loading model weights took 1.9028 GB
INFO 10-15 15:30:27 distributed_gpu_executor.py:57] # GPU blocks: 29021, # CPU blocks: 16384
(VllmWorkerProcess pid=397117) INFO 10-15 15:30:30 model_runner.py:1329] Capturing the model for CUDA graphs. This may lead to unexpected consequences if the model is not static. To run the model in eager mode, set 'enforce_eager=True' or use '--enforce-eager' in the CLI.
(VllmWorkerProcess pid=397117) INFO 10-15 15:30:30 model_runner.py:1333] CUDA graphs can take additional 1~3 GiB memory per GPU. If you are running out of memory, consider decreasing `gpu_memory_utilization` or enforcing eager mode. You can also reduce the `max_num_seqs` as needed to decrease memory usage.
(VllmWorkerProcess pid=397110) INFO 10-15 15:30:30 model_runner.py:1329] Capturing the model for CUDA graphs. This may lead to unexpected consequences if the model is not static. To run the model in eager mode, set 'enforce_eager=True' or use '--enforce-eager' in the CLI.
(VllmWorkerProcess pid=397110) INFO 10-15 15:30:30 model_runner.py:1333] CUDA graphs can take additional 1~3 GiB memory per GPU. If you are running out of memory, consider decreasing `gpu_memory_utilization` or enforcing eager mode. You can also reduce the `max_num_seqs` as needed to decrease memory usage.
(VllmWorkerProcess pid=397112) INFO 10-15 15:30:32 model_runner.py:1329] Capturing the model for CUDA graphs. This may lead to unexpected consequences if the model is not static. To run the model in eager mode, set 'enforce_eager=True' or use '--enforce-eager' in the CLI.
(VllmWorkerProcess pid=397112) INFO 10-15 15:30:32 model_runner.py:1333] CUDA graphs can take additional 1~3 GiB memory per GPU. If you are running out of memory, consider decreasing `gpu_memory_utilization` or enforcing eager mode. You can also reduce the `max_num_seqs` as needed to decrease memory usage.
(VllmWorkerProcess pid=397108) INFO 10-15 15:30:32 model_runner.py:1329] Capturing the model for CUDA graphs. This may lead to unexpected consequences if the model is not static. To run the model in eager mode, set 'enforce_eager=True' or use '--enforce-eager' in the CLI.
(VllmWorkerProcess pid=397108) INFO 10-15 15:30:32 model_runner.py:1333] CUDA graphs can take additional 1~3 GiB memory per GPU. If you are running out of memory, consider decreasing `gpu_memory_utilization` or enforcing eager mode. You can also reduce the `max_num_seqs` as needed to decrease memory usage.
(VllmWorkerProcess pid=397113) INFO 10-15 15:30:32 model_runner.py:1329] Capturing the model for CUDA graphs. This may lead to unexpected consequences if the model is not static. To run the model in eager mode, set 'enforce_eager=True' or use '--enforce-eager' in the CLI.
(VllmWorkerProcess pid=397113) INFO 10-15 15:30:32 model_runner.py:1333] CUDA graphs can take additional 1~3 GiB memory per GPU. If you are running out of memory, consider decreasing `gpu_memory_utilization` or enforcing eager mode. You can also reduce the `max_num_seqs` as needed to decrease memory usage.
INFO 10-15 15:30:32 model_runner.py:1329] Capturing the model for CUDA graphs. This may lead to unexpected consequences if the model is not static. To run the model in eager mode, set 'enforce_eager=True' or use '--enforce-eager' in the CLI.
INFO 10-15 15:30:32 model_runner.py:1333] CUDA graphs can take additional 1~3 GiB memory per GPU. If you are running out of memory, consider decreasing `gpu_memory_utilization` or enforcing eager mode. You can also reduce the `max_num_seqs` as needed to decrease memory usage.
(VllmWorkerProcess pid=397109) INFO 10-15 15:30:32 model_runner.py:1329] Capturing the model for CUDA graphs. This may lead to unexpected consequences if the model is not static. To run the model in eager mode, set 'enforce_eager=True' or use '--enforce-eager' in the CLI.
(VllmWorkerProcess pid=397109) INFO 10-15 15:30:32 model_runner.py:1333] CUDA graphs can take additional 1~3 GiB memory per GPU. If you are running out of memory, consider decreasing `gpu_memory_utilization` or enforcing eager mode. You can also reduce the `max_num_seqs` as needed to decrease memory usage.
(VllmWorkerProcess pid=397111) INFO 10-15 15:30:32 model_runner.py:1329] Capturing the model for CUDA graphs. This may lead to unexpected consequences if the model is not static. To run the model in eager mode, set 'enforce_eager=True' or use '--enforce-eager' in the CLI.
(VllmWorkerProcess pid=397111) INFO 10-15 15:30:32 model_runner.py:1333] CUDA graphs can take additional 1~3 GiB memory per GPU. If you are running out of memory, consider decreasing `gpu_memory_utilization` or enforcing eager mode. You can also reduce the `max_num_seqs` as needed to decrease memory usage.
(VllmWorkerProcess pid=397117) INFO 10-15 15:31:13 model_runner.py:1456] Graph capturing finished in 42 secs.
(VllmWorkerProcess pid=397110) INFO 10-15 15:31:13 model_runner.py:1456] Graph capturing finished in 42 secs.
(VllmWorkerProcess pid=397113) INFO 10-15 15:31:13 model_runner.py:1456] Graph capturing finished in 41 secs.
(VllmWorkerProcess pid=397108) INFO 10-15 15:31:13 model_runner.py:1456] Graph capturing finished in 41 secs.
(VllmWorkerProcess pid=397112) INFO 10-15 15:31:13 model_runner.py:1456] Graph capturing finished in 41 secs.
(VllmWorkerProcess pid=397111) INFO 10-15 15:31:13 model_runner.py:1456] Graph capturing finished in 41 secs.
INFO 10-15 15:31:13 model_runner.py:1456] Graph capturing finished in 41 secs.
(VllmWorkerProcess pid=397109) INFO 10-15 15:31:13 model_runner.py:1456] Graph capturing finished in 41 secs.
/usr/lib/python3.10/multiprocessing/resource_tracker.py:224: UserWarning: resource_tracker: There appear to be 1 leaked shared_memory objects to clean up at shutdown
  warnings.warn('resource_tracker: There appear to be %d '
Killed

What is strange is that it works if I use 4 GPUs (tensor_parallel_size=4).
An easy workaround is to set enable_lora=False and to use the merged model with the adapter directly.

I don't have  V100 GPUs, so I tested the code using 8xA800 GPUs. I can' t reproduce the issue you described.

I tried on a similar type of machine with similar GPUs but on a fresh install and it still crashes at the same point...


Add How To page

[ ] Remove current About page to Menu
[ ] Add How To page placeholder in the bottom tabs
[ ] Add bullet point texts

Text for 'how to' page:
FIT Count quick guide text.zip
I've tried to make it as short as I can but let me know if you think it is still too long!

Sorry, minor edits, have deleted previous version:
FIT Count quick guide text.zip


UE4.23.1

Hi Wongfei,
Can I reach out personally to you, to ask how we could use MediaPipe for UE4.23.1?
Thank you for your time, it is valued.

copy dir
"ue4-mediapipe-plugin\Plugins\MediaPipe" to "yourproject423\Plugins\MediaPipe"
delete
"yourproject423\Plugins\MediaPipe\Binaries"
"yourproject423\Plugins\MediaPipe\Intermediate"
edit
"yourproject423\Plugins\MediaPipe\MediaPipe.uplugin"
change
"EngineVersion": "4.26.0"
to
"EngineVersion": "4.23.0"
edit
"yourproject423\yourproject423.uproject"
add after modules
"Plugins": [
	{
		"Name": "MediaPipe",
		"Enabled": true
	}
]

launch the project

Thank you so much! I'll let you know if anything comes up.
Sincerely / Met vriendelijke groeten,
Aaron van der Brugge
On Tue, Mar 8, 2022 at 4:29 PM wongfei @.***> wrote:

copy dir
"ue4-mediapipe-plugin\Plugins\MediaPipe" to
"yourproject423\Plugins\MediaPipe"
delete
"yourproject423\Plugins\MediaPipe\Binaries"
"yourproject423\Plugins\MediaPipe\Intermediate"
edit
"yourproject423\Plugins\MediaPipe\MediaPipe.uplugin"
change
"EngineVersion": "4.26.0"
to
"EngineVersion": "4.23.0"
edit
"yourproject423\yourproject423.uproject"
add after modules
"Plugins": [
{
"Name": "MediaPipe",
"Enabled": true
}
]
launch the project
—
Reply to this email directly, view it on GitHub
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or unsubscribe
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@.***>

So I'm having trouble opening the project in Unity 2.26. I want to see what the scenes look like because it will help me understand how I am supposed to use this plugin. So I open it in Unity 2.26.2, but it asks to rebuild and then I get this error:

Please help?

Ah, so I see the plugin only supports Windows. Is there any way to open the project on a Mac?
Will an HTML5 package work?

So I tried it on Windows but we are not able to open the project at all in 4.26. What could be the issue?

upload "ue4-mediapipe-plugin\Saved\Logs\MediaPipeDemo.log"

It doesn't make a Saved/Logs folder, I checked. :-/
[image: Screen Shot 2022-03-11 at 4.40.57 PM.png]
This is the state of the folder after trying to open it. Please don't mind
the XCode project, I was trying out some stuff to fix it.
Same issue on Windows. Even with the plugin disabled. Using UE4.26.2
Thanks for your time.
Sincerely / Met vriendelijke groeten,
Aaron van der Brugge
On Fri, Mar 11, 2022 at 4:34 PM wongfei @.***> wrote:

upload "ue4-mediapipe-plugin\Saved\Logs\MediaPipeDemo.log"
—
Reply to this email directly, view it on GitHub
https://github.com/wongfei/ue4-mediapipe-plugin/issues/26#issuecomment-1064850736,
or unsubscribe
https://github.com/notifications/unsubscribe-auth/AKZ2ZW72GPQJIRSBG6FYLN3U7LZQXANCNFSM5QFCRZGQ
.
Triage notifications on the go with GitHub Mobile for iOS
https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675
or Android
https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub.
You are receiving this because you authored the thread.Message ID:
@.***>


Call to deprecated method loadRevision()/deleteRevision() of class Drupal\Core\Entity\EntityStorageInterface round 3

Bug report
$ composer show mglaman/*
mglaman/phpstan-drupal 1.2.1 Drupal extension and rules for PHPStan
 $ composer show phpstan/*
phpstan/extension-installer       1.3.1   Composer plugin for automatic installation of PHPStan extensions
phpstan/phpdoc-parser             1.24.2  PHPDoc parser with support for nullable, intersection and generic types
phpstan/phpstan                   1.10.41 PHPStan - PHP Static Analysis Tool
phpstan/phpstan-deprecation-rules 1.1.4   PHPStan rules for detecting usage of deprecated classes, methods, properties, constants and traits.
phpstan/phpstan-phpunit           1.3.15  PHPUnit extensions and rules for PHPStan

Symthom is the same as in #549 or #586
  170    Call to deprecated method deleteRevision() of class Drupal\Core\Entity\EntityStorageInterface:
         in drupal:10.1.0 and is removed from drupal:11.0.0. Use
         \Drupal\Core\Entity\RevisionableStorageInterface::deleteRevision instead.

or
  170    Call to deprecated method loadRevision() of class Drupal\Core\Entity\EntityStorageInterface:
         in drupal:10.1.0 and is removed from drupal:11.0.0. Use
         \Drupal\Core\Entity\RevisionableStorageInterface::loadRevision instead.

Code snippet that reproduces the problem
The code calls a custom entity storage with the following definition
final class Foo extends SqlContentEntityStorage implements FooInterface {}

interface FooInterface extends ContentEntityStorageInterface {}

No it is not :(

Looks like this is still happening even on 10.2.x-dev https://git.drupalcode.org/project/layout_builder_component_attributes/-/jobs/794515