How do I send the saved data to server in json form ?

Inside index.html (I'm working on Java web app. So I changed it to index.jsp), You will find following callback-
fb.on('save', function(payload){
console.log(payload);
}
So here you need to write code to send the data to server, as follows -
fb.on('save', function(payload){
console.log(payload);
$.post(url,
{
payload: payload
},
function(response){
// If you want to perform some task using response, Do it here.
}
);
}

Did you get this solved or require any further support?

Thanks for the reply. I just saw it. I will check it out.
Also is it possible to convert this into something like typeform? How hard it will be and what files do I need to change?

Can anyone help me...
I need to create a field as same as the address field....when i duplicate the field i cant able to edit the html what we defined in script


[FEAT] - Integrate GetAccountStateQuery from ledger API

Internal
Describe the feature you'd like
We added a query for getting the account state to the ledger API. This query should be integrated into consensus once the PR is merged in ledger.
Additional context / screenshots
IntersectMBO/cardano-ledger#3977

Resolved by https://github.com/IntersectMBO/ouroboros-consensus/pull/1083


Add distro build scripts

Closes #5

I added a build script for generating the artifacts via electron-builder. It also would handle publishing and nightly builds if wished.
We used it in https://github.com/camunda/camunda-modeler and it worked fine for us 👍 Let me know if something needs to be changed/ improved/ removed.

Let's keep it simple (to begin with) and remove the nightly builds :) We are fine with daily ones at the moment :)

I will update that accordingly! I will reach out to you once updated.

I will update that accordingly! I will reach out to you once updated.

@lipis should be alright now

Also there is no need to force push.. we always squash merge :)

For the next PRs you can create branches directly in this repo.. :)

Yup! 🚀


skype carousel playing video using CardAction of type playVideo

I have a CardAction that i want to display in a carousel and i have set the cardaction type as playVideo and its value as a valid https url for the mp4 to play but i could not get it to work.. anyone manage to get this playVideo working? how is this suppose to work?
var reply = context.MakeMessage();
reply.Text = "some text here";
reply.Attachments = new List < Attachment > ();
var actions = new List < CardAction > ();
for (int i = 0; i < 2; i++) {
    actions.Add(new CardAction {
        Title = "Play Video",
        Value = "https://server.com/iknowthisisavalidvideofile.mp4",
        Type = ActionTypes.PlayVideo
    });
}
reply.AttachmentLayout = AttachmentLayoutTypes.Carousel;
for (int i = 0; i < 5; i++) {
    reply.Attachments.Add(new HeroCard {
        Title = $ "{i}. Velit nonumy dolor in amet eros laoreet ipsum nonumy soluta",
        Subtitle = $ "{i}. Word this plutonian more window il raven no nevermore faster",
        Buttons = actions
    }.ToAttachment());
}
await context.PostAsync(reply);
context.Wait(MessageReceived);

this is what i would see if i run it on https://web.skype.com/en/?ecsoverride=developer

on a skype mac client this is what i can see but when i click on the play video button nothing is happening

and this is what the bot emulator spits out to me
"attachments": [{
    "contentType": "application/vnd.microsoft.card.hero",
    "content": {
        "title": "0. Velit nonum ....",
        "subtitle": "0. Word this plutonian more ....",
        "buttons": [{
            "type": "playVideo",
            "title": "Play Video",
            "value": "https://server.com/iknowthisisavalidvideofile.mp4"
        }]
    }
}]

can someone from the team please give me a bit of guidance here... little help @Stevenic ??

maybe @msft-shahins you could help? would appreciate it if you could at least pass this to skype devs? TIA

Today I have updated my windows skype client to <IP_ADDRESS> the type playVideo now actually opens the browser and plays the video.. which is good.. now my question is.. any plans on actually playing to video on the same conversation skype window? how does the playVideo type supposed to work?
note: i have win skype client version <IP_ADDRESS> and invoking the "check updates" button within skype did not pull down the new version of <IP_ADDRESS>.. i have to download the <IP_ADDRESS> installer for me to get the latest build.

Unfortunately playing video in a Skype card is not yet supported. You will need to send video as regular attachments instead.

but if supported what is the behavior to expect? i just want to know so that i can gear my bot towards it in the future.. i have a requirement to have it played on the same skype conversation window.. let me know

Playing video in a Skype card is not supported.

can u at least let us know whether or not this will be supported soon or will be added on your backlog? this kind of information is important to me so just give an indication of whether this feature will be supported at all in the near future..


os: rethink our vmware config strategy

Related issues: https://github.com/coreos/docs/pull/598 and https://github.com/coreos/coreos-cloudinit/pull/393
We've reached the point where we need to rethink how we're telling users to consume our VMware images. A few problems I see:

coreos.autologin is basically required, whether we like it or not. It's hard to get SSH keys onto the box. Doesn't speak well for other security story.
Network config is a pain. This gets better with the backdoor stuff doc'd in https://github.com/coreos/coreos-cloudinit/pull/393, but its still a bit unclear how to use this effectively.
Creating config-drives is a pain. If you embed network config in them, it means they have to be custom made per machine, which makes reusing it to configure a bunch of machines impossible.
Cloning VMs isn't mentioned, but it will break on you unless you reset the machine ID. Cloning is a fairly common operation for VMware users since they are normally creating more pets-like machines than cattle. We need to warn or better handle this situation.

Open questions:

Can we ship the backdoor stuff in the OEM so that network config is no longer an issue? Is it possible to do this if we assume the user configs their interfaces in the VMware interface a certain way? Maybe we offer that "its easy if we can assume you do X", otherwise go custom with Y and do it by hand.
If we remove the network config, we can start to reuse the config-drives easier. The story for creating and editing config-drives is still sub-optimal. What can we do to make this easier?
Should we skip all of this and tell users to hand configure their VMs, much like they are used to doing? Then we add some language to improve the cloning experience, and then tell them to do the hand config once, and then clone from there. If the networking config is dynamic via the backdoor, this might be a pretty nice workflow.

We already ship the backdoor stuff in "vmware" and "vmware_ova".

@crawford But, do we use it to make any assumptions about the machine? I don't see where we pull all of this together to make the experience better.

@robszumski what assumptions would we make?

@crawford something like:
For easy config, configure interface X to have your public IP. Configure interface Y to have your private IP. If you do this, you can use $public_ipv4 and $private_ipv4 as you regularly would.
Any deviation from this configuration should follow the custom VMware setup section.

I think it's sufficient to show example configs which demonstrate the behavior. Implicit behavior is always trouble. Always.

I guess I'm pitching two sections, one for easy and one for custom. They can be on a single page or two as we see fit. It needs to be very clear how to choose between them, however.

There should only be custom. We have already learned the hard way and are creating Ignition. I'm not going to wilfully make the same mistakes all over again.

Ignition doesn't really help out in this circumstance. The main issues I see are specification of the VM hardware attributes (which we can guide them), the method of providing config (CC or ignition) is cumbersome, and we don't provide any guidance for cloning and other "pet" operations like updating config.

When I mentioned Ignition, I was alluding to explicit configuration is better than implicit. Providing the config is not cumbersome at all; they just edit their machine definition. Updating the config (though it shouldn't be done) can be easily done by editing the machine definition.

Closing due to inactivity.


Two links to the automation scripts at the top of the document are broken

[Enter feedback here]

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

ID: 886426b5-8548-1cfc-ccd2-fd03e549c967
Version Independent ID: 599aea76-47e4-dccc-799c-2d8acf549546
Content: Windows Update - Additional resources - Windows Deployment
Content Source: windows/deployment/update/windows-update-resources.md
Product: w10
Technology: windows
GitHub Login: @jaimeo
Microsoft Alias: jaimeo

@rothschild86 . Same issue has been already esculated by other user with report #9104
This article already replied, he is working for new
hosting location to update scripts.
so please close this again.

Duplicate of #9104
#please-close


Missing mail state after init_app()

When using an App class with the application factory approach with flask v1.1.1, the mail.state after running init_app() has a None value:
from flask import Flask
from flask_mail import Mail

mail = Mail()

class App(Flask):
    def __init__(self):
        Flask.__init__(self, __name__)
        # load config etc.

        # does not write the state into global mail object:
        mail.init_app(self)

        # workaround:
        mail.state = mail.init_app(self)

This means, the provided documentation is not correct.
You would need the workaround of assigning the state in order to be able to import mail from this module and run mail.send(message) (else the connection host won’t be available).
Another approach is to get mail from the current app’s extensions (as e.g. flask-security does it), like:
mail = current_app.extensions.get('mail')
mail.send(message)

Sorry, not an issue actually. I accidentally had the TESTING environment var set to True, so email sending was suppressed. The mail.state is still not set if you use init_app() after the Mail() initialization without an app, but this is not an issue for sending emails.

This is a real issue in my opinion. Not setting the state in init_app() prevents sending mail if it was first initialized without an app, hence either the documentation needs to mention that or the code needs to be corrected.


Api changes for explorer

This implements:

https://github.com/ooni/backend/issues/639
https://github.com/ooni/backend/issues/640

Rebased

LGTM!


Provide API for row right click

I need to make a context menu on row right click.
Instead of writing a row wrapper - which i did currently, i would like to pass onRowRightClick or onRowContextMenu (to be consistent with element event) to the table and get the event callback.
Implementing this is quite straightforward as i saw in code and should not take much time - i could provide a PR.

I don't like continuing to add onRow* properties to Table. I wish I had designed that part of the API differently.
If you're willing to create a PR with all of the necessary code changes (to Table and defaultRowRenderer) and docs changes (Table.md), and it passes lint check, etc. I guess I'll merge it and it can go into the next release. 😄
I'm going to close this issue since I don't plan on taking action on it. But send a PR my way if you'd like!

For anyone finding this from Google, this was implemented in #741, though it doesn't seem to have made it to the TypeScript defs


[Bitwarden] InstalledCLINotFoundError: Bitwarden CLI not found at /opt/homebrew/bin/bw

Extension
https://www.raycast.com/jomifepe/bitwarden
Description
I'm using Nix as my package manager but the extention seem to search bw only at a specific path.
The path of Nix package manager is ~/.nix-profile/bin/bw(which bw)
Steps To Reproduce

Install nix
Install bw-cli via nix (nix-env -iA nixpkgs.bitwarden-cli)
Log into Bitwarden

Current Behaviour
After login getting: InstalledCLINotFoundError: Bitwarden CLI not found at /opt/homebrew/bin/bw
Expected Behaviour
Searching for bw-cli and working without error.

Hello, the extension currently is not able to see if you have the CLI installed in some unpredictable place, only in /opt/homebrew/bin/bw and /usr/local/bin/bw, but you can always specify your path (~/.nix-profile/bin/bw) in the extension preferences.

Thanks for your reply, but the error persists after adding the path manually:
InstalledCLINotFoundError: Bitwarden CLI not found at /Users/jonas/.nix-profile/bin/bw

That is odd, the errors comes from checking if it exists and if raycast has permissions to execute it, everything should be working as expected. I will investigate it.

Yes.


UX improvement for backup comments

Ludusavi version
v0.21.0
Operating system
Windows
Installation method
Standalone
Description
Firstly: When a comment is lengthy and goes beyond the width of the text field, using the arrow keys to change the cursor's position to the left isn't intuitive: The cursor pins itself to the right of the text field as the text shifts making it difficult to read the rest of the comment. Like all other single-line text fields on modern OSes, the text in the field should not shift until the cursor reaches one or the other side of the field. So when going left, wait until reaching the very left to start revealing text beyond the field's width (inverse of what currently happens when going right).
Secondly: The red X button ❌ for the comment is ambiguous. In case it was going to delete or clear my comment, I copied it before pressing it. It only closes the comment field in fact. Perhaps a green checkmark ✅ would be more intuitive? Or an upwards arrow 🔼?
Thanks.
Logs
No response

Agreed, but this is blocked on https://github.com/iced-rs/iced/issues/1285 . Another option might be a multi-line text input ( https://github.com/iced-rs/iced/issues/320 ), although that hasn't been released just yet.

That's a good point. I think visibility off ( , https://fonts.google.com/icons?selected=Material Icons Outlined%3Avisibility_off%3A) might be a good fit.


Allow writefln like ouput during CTFE.

davidl reported this on 2008-03-14T06:45:55Z
Transfered from https://issues.dlang.org/show_bug.cgi?id=1924
CC List

bugzilla (@WalterBright)
razvan.nitu1305
shro8822
smjg

Description
char[] wrongfunc()
{
    char[] result;
    pragma(msg, result);       // fail at this line
    return result;
}
mixin(wrongfunc);

fvbommel commented on 2008-03-14T09:44:16Z
Your 'result' is a variable that happens not to get assigned at runtime, but the fact that it happens not to change doesn't mean it's a constant.

P.S. According to the spec it even needs to be a string literal (which I don't think is what the compiler implements, since I'm pretty sure it allows string constants constructed by templates and ctfe). But either way your code is clearly illegal.

davidl commented on 2008-03-15T00:23:07Z
char[] wrongfunc()
{
    char[] result="void main()";
    result ~= "{}";
    const char[] myresult= result;
    pragma(msg, myresult);       // fail at this line
    return result;
}
mixin(wrongfunc);

abc.d(6): Error: string expected for message, not 'result'

pragma here is clearly for debugging CTFE. I don't want to make it as the following

char[] getresult()
{
    char[] result="void main()";
    result ~= "{}";
    return result;
}
char[] myfunc()
{
    const char[] result = getresult();
    pragma(msg,result);
    return result;
}
mixin(myfunc);

it's way dummier for debugging CTFE, especially if i want to do only some simple inspect on some particular vars.

smjg commented on 2008-11-26T11:13:18Z
From the names pragma(msg) and pragma(writefln), it isn't clear at all that one's while compiling and one's while evaluating.

Better would be to name it something like "ctfeMsg" or "msgCTFE".  And perhaps writefln notation could be supported in both this and regular msg.  This won't break existing code as long as pragma(msg, string) with no further arguments is still defined to write that exact string.

shro8822 commented on 2008-11-26T12:43:43Z

Sorry, I was far less than clear.

writefln in all cases we use it in now executes with the code path, e.g. when the code is executed rather than generated. pragma is compile time in all cases. Seems like a good fit to me.

OTOH as long as it's not something like pragma(D9B01F73-E64A-4FA9-A2DE-C20CD24560BA) I'm not going to object to getting it!

clugdbug commented on 2008-11-27T03:57:58Z
On second thoughts, this isn't an actual proposal. There's a genuine need, but  pragma(msg) inside CTFE is not the solution. I think we should just kill this bug, and create a new one when there's an actual proposal for how it should work.

shro8822 commented on 2008-11-26T10:32:42Z

pragma(writefln,"hello #%d", 1);
vote++;

shro8822 commented on 2008-11-26T11:17:58Z
To date, every use of writefln I've seen has been run time. So it doesn't seem particularly ambiguous at least to me,

smjg commented on 2008-11-26T08:52:17Z
I think we need separate notation for this.  Generating a compiler message while compiling a function is conceptually different from generating a compiler message while evaluating it.

shro8822 commented on 2008-11-27T11:11:38Z
Vote++ or rename and re-tag it.

smjg commented on 2008-11-26T12:05:59Z
(In reply to comment #7)
> To date, every use of writefln I've seen has been run time. So it doesn't seem
> particularly ambiguous at least to me,

That's because at the moment writefln can only be executed at run time.  If it (or something resembling it) becomes executable at compile time, it could just as well be either as and when compiled or during CTFE.

Having a difference between pragma(msg) and pragma(writefln) beyond what they say on the tin would be arbitrary and confusing.

clugdbug commented on 2008-11-26T08:35:34Z
Although a way of debugging CTFE functions would be very nice to have, this is definitely not a bug.

I'm changing this to an enhancement, changed title from: fail to pragma compile time constant

razvan.nitu1305 commented on 2019-10-26T06:50:56Z
This bug report is invalid. The original reported issue is invalid since pragma cannot evaluate at compile time the value of a runtime variable.

As for the other proposed issue - the ability to print when a function is evaluated - it doesn't have to be implemented in the compiler. This can be implemented as a function in phobos.

Changing component from dmd to phobos.

Test case:

import std.stdio;

char[] wrongfunc()
{
    char[] result;
    writeln(result);
    return result;
}

void main()
{
    enum c = wrongfunc();
}

dupe of #9577


images and text exceed the page boundaries

All the images and text are arranged horizontally, exceeding the page boundaries

Fixed
Arrange the content vertically by using flex-direction="column"


Failed apply doesn't clean up domains

On my machine terraform apply fails pretty often, and when terraform apply fails the domains that failed to set up are "stopped" but are not "undefined". So subsequent applys will fail because the domains already exist. Is there a way for us to do cleanup when apply fails?

encountered same issue :(


Does the pair command work?

Is flutter_adb currently supporting the ADB pair command for recent devices?
Also, you're basing your work on the very old AdbLib project. Why not basing it on recent ones like Kadb or libadb-android?

I have not implemented the pair functionality yet, no.
My own usecase for the package is on an android 10 device using self-adb to run certain commands not otherwise possible without requiring root access. Since the pairing functionality was added in android 11 this wasn't useful for me, so I haven't looked into it, I just figured I'd take the hour or 2 to write the readme & example app to submit the package so others might benefit from it, since there wasn't any native dart alternative yet from what I could find.
This is a rewrite of the protocol from scratch in flutter, I simply used cgutman's library for the protocol basics, and ended up somewhat copying his class structure, hence why I added the attribution.
Both of the libraries you linked to are vastly more complex, so would not make a lot of sense for a simple library like this which is rewritten from scratch in native dart code, and at least for me my usecases are all already covered by this simple implementation. I don't think for the basic functionality of opening a shell and running some commands there is any functional difference.
I agree that if you wanted to make a plugin simply to adapt an existing java/kotlin android library, then it would make more sense to use one of those libraries you linked.
Feel free to submit a pull request with the addition of the pair functionality if that is useful to you, I don't expect to be working on any new functionality for this library any time soon, aside from fixing bugs or reviewing pull requests, as I am busy with other projects.

I was looking for a complete implementation of ADB in Dart, because yes it is relatively complicated.
I currently use libadb-android, but it doesn't work for iOS.

i'm waiting too for pair function. pair ip:port code


Add option to config for rotating logfiles

Old achieve logs are not rotated

Add an option in the config.toml like rotate_log_after_days, which gives the user the possibility to set the number of days after the logs files are rotated.

Currently, we rotate the log by size (after 10 MB), not by date. This approach is simple and efficient.
However, we can introduce an option to automatically delete old logs. For instance:
max_backups, which represents the maximum number of old log files to retain.

/bounty 15$

/attempt #755

@b00f I'm unable to push a branch to the repo. Do I need some permissions from your end by any chance?
ERROR: Permission to pactus-project/pactus.git denied to Akshat-Jain.
fatal: Could not read from remote repository.

Please make sure you have the correct access rights
and the repository exists.

/attempt #755

@Akshat-Jain and @himmish any update about this issue?

@b00f I've raised a PR: https://github.com/pactus-project/pactus/pull/802, can you check if it suffices the requirements? Thanks!

@Akshat-Jain That is closed. Please cancel your attempt.

@b00f Already done.


Show date

Please return back the function that support not showing the date card as defualt (show_date: false).

Hi,
there is another way to remove the date display:
The date display is a screen in the queue, you can remove it in the YAML:
esphome:
  name: $devicename
  on_boot:
    then:
      - delay: 30s
      - lambda: |-
          id(rgb8x32)->del_screen("*",3);

or via service call:
service: esphome.ulanzi_del_screen
data:
  icon_name: "*"
  mode: 3

If you want a blank screen use:
service: esphome.esp32mic_blank_screen
data:
  lifetime: 1440
  screen_time: 10

I have devised another method. I added automation to home assistant that delete the screen when I power on the device.
Thanks!!!

Thank you so much!

Hi,
what's your aim? You will only use the display as a clock and nothing else?
The behavior now is: On device start when there is nothing else in the queue, it will display:
clock_screen(24 * 60, 10)
date_screen(24 * 60, 5)

After one day (24*60) both screens are at their end of life and will be removed from the queue => empty queue => and again:
clock_screen(24 * 60, 10)
date_screen(24 * 60, 5)

And so on.

ok I see. My intention is to have it mostly as a clock and push other screens upon trigger as temperature changes, etc.
Just the default of showing the date every few seconds is not something I want.

OK, I will think about a solution.

Hi, the function is there, but I forgot to write it in the docs. Give it a try.

Awesome!  Works like a charm!! 👏
Thanks a lot:)

After adding
show_date: false

The black screen appears every 10 seconds (for 5 seconds).
Adding
  on_boot:
    then:
      - ds1307.read_time:
      - delay: 30s
      - lambda: |-
          id(rgb8x32)->del_screen("*",1);

Didn't solved the problem.

Indeed.  I didn't pay attention cause I had the "delete" lambda on boot as well,  forgot to remove it.
(BTW,  You need to delete mode "3" for date: id(rgb8x32)->del_screen("*",3);,   it works )
Let's check in the next "tick" in one day (24*60) if it's getting reset or not.

A day and a half later,  the Date screen didn't popped back.
So for now we have to keep the:
on_boot:
    then:
      - delay: 30s
      - lambda: |-
          id(rgb8x32)->del_screen("*", 3);

Regardless,  @lubeda do you think it's an easy fix - to remove the blank date screen in the first place?   So we won't have to delete it on boot.


Casting a string tensor to a list of string

I couldn't find an approach to casting a string tensor to a list of string.
For instance, if someone has the following sample_string_tensor:
import tensorflow as tf

batch_size = 4
sample_string_tensor = tf.convert_to_tensor(["sãmple utf-8 stríng - " + str(i) for i in range(n_strings)])
sample_string_tensor
# <tf.Tensor: shape=(4,), dtype=string, numpy=
# array([b's\xc3\xa3mple utf-8 str\xc3\xadng - 0',
#        b's\xc3\xa3mple utf-8 str\xc3\xadng - 1',
#        b's\xc3\xa3mple utf-8 str\xc3\xadng - 2',
#        b's\xc3\xa3mple utf-8 str\xc3\xadng - 3'], dtype=object)>

how to cast to list of string below?
['sãmple utf-8 stríng - 0',
 'sãmple utf-8 stríng - 1',
 'sãmple utf-8 stríng - 2',
 'sãmple utf-8 stríng - 3']

Note there are some no ascii character.

@Ceceu,
You can use sample_string_tensor.numpy() to extract the value from the tensor. Please check this gist for reference.
Also, this question is better asked on StackOverflow since it is not a bug or feature request. There is a larger community that reads questions there.

Thank you @amahendrakar, but that is not the answer. Note when you use sample_string_tensor.numpy() you actually have an array of byte strings (b'example string') which in turn, have to be decoded. A lot of tokenizers requires a string or a list of strings, that's the reason for my issue.
Therefore, I am looking for an approach to decode the array of byte strings from a tensor into a batch of strings. As I have a large dataset to tokenize, this must be done in a vectorized manner  through the map function.
I already submitted a question on StackOverflow, but as no one has been able to answer so far, I thought it best to ask for help directly from those who maintain the Tensorflow.

May be you can use tf.compat.as_str_any() function to this?
Take a look at a toy example:
s = tf.convert_to_tensor('my_string')
print(type(s.numpy()))
print(type(tf.compat.as_str_any(s)))

output:
<class 'bytes'>
<class 'str'>

@ymodak, I don't know why your answer was downvoted since it's correct! It returns the value of a tensor as a string.
The answer to the preliminary question then is:
n_strings = 4
sample_string_tensor = tf.convert_to_tensor(["sãmple utf-8 stríng - " + str(i) for i in range(n_strings)])
sample_string_tensor
list_of_sample_strings = [tf.compat.as_str_any(tensor.numpy()) for tensor in sample_string_tensor]
list_of_sample_strings

@ymodak, I don't know why your answer was downvoted since it's correct! It returns the value of a tensor as a string.
The answer to the preliminary question then is:
n_strings = 4
sample_string_tensor = tf.convert_to_tensor(["sãmple utf-8 stríng - " + str(i) for i in range(n_strings)])
sample_string_tensor
list_of_sample_strings = [tf.compat.as_str_any(tensor.numpy()) for tensor in sample_string_tensor]
list_of_sample_strings

A simple way:
>>> list_of_sample_strings = [x.numpy().decode() for x in sample_string_tensor]

['sãmple utf-8 stríng - 0',
 'sãmple utf-8 stríng - 1',
 'sãmple utf-8 stríng - 2']


Why the asyncronous import style that doesn't match the docs

Summary
This repository uses the import() function instead of the import statement like all the documentation the readme links to for example.
Why was the traditional import syntax I see in the tutorials and the deprecated version of the repository abandoned? Is it simply because wasm files are loaded async? If so, how did it work before?
Thanks in advance. I'm just beginning to get my feet wet.

Webpack requires the dynamic import(). This will be fixed in the future, but for now it is necessary.
That documentation page is outdated.

Thanks.


Switch to starry-night

Maybe better option than shiki? It Produces unifiedjs ast natively.

Considerations:

I don't want to use default github code theme. But there's significantly more kind of token classes to cover.
So, I would copy some themes from vscode themes, and convert them accordingly.


new account replaced the before account when use createUserWithEmailAndPassword

Hello! i need create a new account and i use this code:
<EMAIL_ADDRESS>'12341234').then(
      (success) => {
        console.log(success);
      }
    ).catch(
      (err) => {
        console.log(err.toString());
      }
    );

But this new account replaced the before account.
I need remove auto sign in.
Thanks.

@Leanvitale If I understand correctly, you don't want the user to automatically be logged in, after registration? Just simply do this...
return this.afAuth.auth.createUserWithEmailAndPassword(userEmail, userPassword).then(() => { return this.afAuth.auth.signOut(); });

@Leanvitale Please use stackoverflow for these kind of questions as this repo is dedicated to troubleshooting library issues.


add hostname/domainname to SDK info

Comment from Zendesk:

Zendesk: 10695 linked successfully.

                  Zendesk: #10695
By: Ganesh Palaniappan<EMAIL_ADDRESS>


Export to IndoorGML not working

Hi,
The export to IndoorGML functionality is not working for me. I have done everything as shown in this video:
https://www.youtube.com/watch?v=eW2Tpq2Yk_c
But the 'download' button does not appear.
Any idea how to solve this?
Thank you.

The solution was to have an InFactory server running when exporting to IndoorGML. The export port number should match the port number of the InFactory server. Might be a good idea to add this to the README.


[Makefile] missing make clean items

Currently missing clean items:
*.bcf \
*.run.xml \

You can refer to the official gitignore template for TeX to clean it comprehensively.

已修复


Add optional hostname logging to log messages.

Scope & Purpose
Enterprise companion PR: https://github.com/arangodb/enterprise/pull/656
Docs PR: https://github.com/arangodb/docs/pull/628
Add optional hostname logging to log messages.
Whether or not the hostname is added to each log message can be controlled via
the new startup option --log.hostname. Its default value is the empty string,
meaning no hostname will be added to log messages.
Setting the option to an arbitrary string value will make this string be logged
in front of each regular log message, and inside the hostname attribute in
case of JSON-based logging. Setting the option to a value of auto will use
the hostname as returned by gethostbyname.

[ ] :hankey: Bugfix (requires CHANGELOG entry)
[x] :pizza: New feature (requires CHANGELOG entry, feature documentation and release notes)
[ ] :fire: Performance improvement
[ ] :hammer: Refactoring/simplification
[x] :book: CHANGELOG entry made

Backports:

[x] No backports required

Related Information

[x] Docs PR: https://github.com/arangodb/docs/pull/628
[x] Enterprise PR: https://github.com/arangodb/enterprise/pull/656

Testing & Verification

[x] The behavior in this PR was manually tested
[x] This PR adds tests that were used to verify all changes:

[x] Added new integration tests (e.g. in server_parameters)

Link to Jenkins PR run:
http://<IP_ADDRESS>:8080/view/PR/job/arangodb-matrix-pr/14189/

http://<IP_ADDRESS>:8080/view/PR/job/arangodb-matrix-pr/14203/


EDITOR with VS Code not working on Windows

Guild uses the editor defined in VISUAL or EDITOR environment variables.

I had set the EDITOR variable to code in Windows 10. This opens up the file in the VS Code. But the file is immediately deleted and guild is auto-confirming the default values, So I am not able to change the values in the text editor.

Yes, it worked. Thank you.


Error: /usr/lib64/libstdc++.so.6: version `CXXABI_1.3.5' not found

CentOS 6.5 with nodejs 1.4.28 installed.
When I use npm install to install Ghost it says
Error: /usr/lib64/libstdc++.so.6: version `CXXABI_1.3.5' not found (required by /home/www/domains/Ghost/node_modules/grunt-sass/node_modules/node-sass/vendor/linux-x64/binding.node)

Hey there @twtww, this is an issue with node-sass.
It depends on what you're trying to do, but you don't need to install node-sass if you use the zip file from GitHub/Ghost.org or the npm package - just run npm install --production. If you want to install from git stable or for dev purposes, you can find out more about how to get node-sass installed here: https://github.com/sass/node-sass/issues/517#issuecomment-69072917
It would be great if you could update this issue to contain the details of what you're doing - what install process you're using, which version of Ghost etc. You can find a handy template in our contributing guidelines, thanks.

Going to close this for now.


docs/installation: Add chafa

Thanks for the PR! The dependencies related to image preview are already listed on its dedicated page here https://yazi-rs.github.io/docs/image-preview


about map

Prerequisite

[X] I have searched Issues and Discussions but cannot get the expected help.
[X] I have read the FAQ documentation but cannot get the expected help.
[X] The bug has not been fixed in the latest version (master) or latest version (3.x).

💬 Describe the reimplementation questions
Hello, I found that the map value has been very low during the training of target detection of a category, and it seems that the recall rate is normal. What is the reason for this?

Environment
Pyhon3.7
Expected results
No response
Additional information
No response

This is because the model predicts too many objects. You may consider increase the score_thr in the test_cfg.


Bump lib core to alpha 8

See<EMAIL_ADDRESS>

/retest

Codecov Report

Merging #101 (0c0db00) into main (5eb4b35) will decrease coverage by 0.28%.
The diff coverage is n/a.

@@            Coverage Diff             @@
##             main     #101      +/-   ##
==========================================
- Coverage   18.06%   17.77%   -0.29%
==========================================
  Files          35       36       +1
  Lines        1290     1311      +21
  Branches      336      341       +5
==========================================
  Hits          233      233
- Misses       1057     1078      +21

Impacted Files
Coverage Δ

...ib-utils/src/components/table/VirtualizedTable.tsx
0.00% <0.00%> (ø)

...tils/src/components/table/VirtualizedTableBody.tsx
0.00% <0.00%> (ø)

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issue with allow-unknown-fields

If we have a new field in envoy bootstrap config, and start Envoy with --allow-unknown-fields arg, Envoy still complains that the field is not known.
Looks like the problem is here
https://github.com/envoyproxy/envoy/blob/bcc7a96d9e66bc0f70e3ed52161ec3471f835bdb/source/common/protobuf/utility.cc#L55
Instead of passing ProtoUnknownFieldsMode::Strict, shouldn't we pass what is set via options i.e the static variable proto_unknown_fields ?

@kyessenov Do you have any suggestions on this? If that is the intended behaviour or is this a bug?

Ok. I see in the comments in the PR #4096, you mentioned about not changing this function. So looks like bootstrap configuration parsing is done via this method. Not a big deal.


boyer-moore string search

We should be able to implement boyer moore string searching, optimized to run against the raw zbytes of a zng stream, so that we minimize the amount of record parsing and and io buffer allocation. There's a connection with implementing zng compression support, as a compressed batch of records gives an easy way to identify record boundaries.

Note to self: When this Epic is closed, it's probably worth cutting another set of the zq "perf" numbers so we can quantify the observable benefits from this.


WmiNamespaceSecurity: New resource proposal

Description
I suggest the resource WmiNamespaceSecurity is migrated to ComputerManagementDsc. That would deprecate the module WmiNamespaceSecurityDsc.
Also opened issue https://github.com/PowerShell/WmiNamespaceSecurityDsc/issues/7 to track this.
Proposed properties
See current resource properties
Special considerations or limitations
This is only possible if the we can get this class-based resource and the present mof-based resource currently present in this resource module to work together.

It looks like there is no maintainer on this resource.
Have we ever combined Class based and MOF resources together before? I'd be happy to give this a try once I've moved this resource away from the harness model (to be done soon).

Not that I know of, but we were just discussing it here :)
https://github.com/PowerShell/DscResource.Template/issues/11


GPTQ uint4 quantization broken

System Info
root@laion-gaudi2-00:/home/sdp# docker run -p 8081:80 -v $volume:/data --runtime=habana -e HUGGING_FACE_HUB_TOKEN=$hf_token -e PT_HPU_ENABLE_LAZY_COLLECTIVES=true -e HABANA_VISIBLE_DEVICES=2,3,4,5,6,7 -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --ipc=host tgi_gaudi --model-id $model --sharded true --num-shard 5 --max-input-tokens 4096 --max-total-tokens 8192 --max-batch-prefill-tokens 8242
2024-08-11T22:11:55.546868Z  INFO text_generation_launcher: Args {
model_id: "hugging-quants/Meta-Llama-3.1-70B-Instruct-GPTQ-INT4",
revision: None,
validation_workers: 2,
sharded: Some(
true,
),
num_shard: Some(
5,
),
quantize: None,
speculate: None,
dtype: None,
trust_remote_code: false,
max_concurrent_requests: 128,
max_best_of: 2,
max_stop_sequences: 4,
max_top_n_tokens: 5,
max_input_tokens: Some(
4096,
),
max_input_length: None,
max_total_tokens: Some(
8192,
),
waiting_served_ratio: 0.3,
max_batch_prefill_tokens: Some(
8242,
),
max_batch_total_tokens: None,
max_waiting_tokens: 20,
max_batch_size: None,
cuda_graphs: None,
hostname: "0d708a2172ae",
port: 80,
shard_uds_path: "/tmp/text-generation-server",
master_addr: "localhost",
master_port: 29500,
huggingface_hub_cache: Some(
"/data",
),
weights_cache_override: None,
disable_custom_kernels: false,
cuda_memory_fraction: 1.0,
rope_scaling: None,
rope_factor: None,
json_output: false,
otlp_endpoint: None,
cors_allow_origin: [],
watermark_gamma: None,
watermark_delta: None,
ngrok: false,
ngrok_authtoken: None,
ngrok_edge: None,
tokenizer_config_path: None,
disable_grammar_support: false,
env: false,
max_client_batch_size: 4,
}
2024-08-11T22:11:55.546942Z  INFO hf_hub: Token file not found "/root/.cache/huggingface/token"
/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_0.so.3 is not a symbolic link
/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc.so.2 is not a symbolic link
/sbin/ldconfig.real: /usr/local/lib/libtbbbind.so.3 is not a symbolic link
/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc_proxy.so.2 is not a symbolic link
/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_5.so.3 is not a symbolic link
/sbin/ldconfig.real: /usr/local/lib/libtbb.so.12 is not a symbolic link
2024-08-11T22:11:55.740778Z  INFO text_generation_launcher: Using default cuda graphs [1, 2, 4, 8, 16, 32]
2024-08-11T22:11:55.740792Z  INFO text_generation_launcher: Sharding model on 5 processes
2024-08-11T22:11:55.740878Z  INFO download: text_generation_launcher: Starting download process.
2024-08-11T22:11:58.162183Z  INFO text_generation_launcher: Files are already present on the host. Skipping download.
2024-08-11T22:11:58.543915Z  INFO download: text_generation_launcher: Successfully downloaded weights.
2024-08-11T22:11:58.544105Z  INFO shard-manager: text_generation_launcher: Starting shard rank=0
2024-08-11T22:12:03.786121Z  INFO text_generation_launcher: CLI SHARDED = True DTYPE = bfloat16
2024-08-11T22:12:03.786323Z  INFO text_generation_launcher: CLI SHARDED = 5
2024-08-11T22:12:03.786401Z  INFO text_generation_launcher: CLI server start deepspeed =deepspeed --num_nodes 1 --num_gpus 5 --no_local_rank /usr/local/lib/python3.10/dist-packages/text_generation_server/tgi_service.py --model_id hugging-quants/Meta-Llama-3.1-70B-Instruct-GPTQ-INT4 --revision None --sharded True --dtype bfloat16 --trust_remote_code False --uds_path /tmp/text-generation-server
2024-08-11T22:12:08.551807Z  INFO shard-manager: text_generation_launcher: Waiting for shard to be ready... rank=0
2024-08-11T22:12:18.560690Z  INFO shard-manager: text_generation_launcher: Waiting for shard to be ready... rank=0
2024-08-11T22:12:28.568664Z  INFO shard-manager: text_generation_launcher: Waiting for shard to be ready... rank=0
2024-08-11T22:12:38.578065Z  INFO shard-manager: text_generation_launcher: Waiting for shard to be ready... rank=0
2024-08-11T22:12:43.403761Z ERROR text_generation_launcher: deepspeed --num_nodes 1 --num_gpus 5 --no_local_rank /usr/local/lib/python3.10/dist-packages/text_generation_server/tgi_service.py --model_id hugging-quants/Meta-Llama-3.1-70B-Instruct-GPTQ-INT4 --revision None --sharded True --dtype bfloat16 --trust_remote_code False --uds_path /tmp/text-generation-server  exited with status = 1
2024-08-11T22:12:44.082499Z ERROR shard-manager: text_generation_launcher: Shard complete standard error output:
[WARNING|utils.py:212] 2024-08-11 22:12:02,456 >> optimum-habana v1.13.0.dev0 has been validated for SynapseAI v1.17.0 but habana-frameworks v<IP_ADDRESS>6 was found, this could lead to undefined behavior!
[WARNING|utils.py:225] 2024-08-11 22:12:03,010 >> optimum-habana v1.13.0.dev0 has been validated for SynapseAI v1.17.0 but the driver version is v1.16.2, this could lead to undefined behavior!
/usr/local/lib/python3.10/dist-packages/transformers/deepspeed.py:24: FutureWarning: transformers.deepspeed module is deprecated and will be removed in a future version. Please import deepspeed modules directly from transformers.integrations
warnings.warn(
/usr/local/lib/python3.10/dist-packages/habana_frameworks/torch/hpu/init.py:158: UserWarning: torch.hpu.setDeterministic is deprecated and will be removed in next release. Please use torch.use_deterministic_algorithms instead.
warnings.warn(
/usr/local/lib/python3.10/dist-packages/habana_frameworks/torch/hpu/init.py:158: UserWarning: torch.hpu.setDeterministic is deprecated and will be removed in next release. Please use torch.use_deterministic_algorithms instead.
warnings.warn(
[WARNING|utils.py:212] 2024-08-11 22:12:19,432 >> optimum-habana v1.13.0.dev0 has been validated for SynapseAI v1.17.0 but habana-frameworks v<IP_ADDRESS>6 was found, this could lead to undefined behavior!
[WARNING|utils.py:212] 2024-08-11 22:12:19,435 >> optimum-habana v1.13.0.dev0 has been validated for SynapseAI v1.17.0 but habana-frameworks v<IP_ADDRESS>6 was found, this could lead to undefined behavior!
[WARNING|utils.py:212] 2024-08-11 22:12:19,437 >> optimum-habana v1.13.0.dev0 has been validated for SynapseAI v1.17.0 but habana-frameworks v<IP_ADDRESS>6 was found, this could lead to undefined behavior!
[WARNING|utils.py:212] 2024-08-11 22:12:19,437 >> optimum-habana v1.13.0.dev0 has been validated for SynapseAI v1.17.0 but habana-frameworks v<IP_ADDRESS>6 was found, this could lead to undefined behavior!
[WARNING|utils.py:212] 2024-08-11 22:12:19,440 >> optimum-habana v1.13.0.dev0 has been validated for SynapseAI v1.17.0 but habana-frameworks v<IP_ADDRESS>6 was found, this could lead to undefined behavior!
[WARNING|utils.py:225] 2024-08-11 22:12:20,124 >> optimum-habana v1.13.0.dev0 has been validated for SynapseAI v1.17.0 but the driver version is v1.16.2, this could lead to undefined behavior!
[WARNING|utils.py:225] 2024-08-11 22:12:20,588 >> optimum-habana v1.13.0.dev0 has been validated for SynapseAI v1.17.0 but the driver version is v1.16.2, this could lead to undefined behavior!
[WARNING|utils.py:225] 2024-08-11 22:12:20,747 >> optimum-habana v1.13.0.dev0 has been validated for SynapseAI v1.17.0 but the driver version is v1.16.2, this could lead to undefined behavior!
/usr/local/lib/python3.10/dist-packages/transformers/deepspeed.py:24: FutureWarning: transformers.deepspeed module is deprecated and will be removed in a future version. Please import deepspeed modules directly from transformers.integrations
warnings.warn(
2024-08-11 22:12:20.920 | INFO     | main:main:10 - TGIService: starting tgi service ....
2024-08-11 22:12:20.921 | INFO     | main:main:11 - TGIService: --model_id hugging-quants/Meta-Llama-3.1-70B-Instruct-GPTQ-INT4, --revision None, --sharded True, --speculate None, --dtype bfloat16, --trust_remote_code True, --uds_path /tmp/text-generation-server
The argument trust_remote_code is to be used with Auto classes. It has no effect here and is ignored.
/usr/local/lib/python3.10/dist-packages/transformers/deepspeed.py:24: FutureWarning: transformers.deepspeed module is deprecated and will be removed in a future version. Please import deepspeed modules directly from transformers.integrations
warnings.warn(
/usr/local/lib/python3.10/dist-packages/habana_frameworks/torch/hpu/init.py:158: UserWarning: torch.hpu.setDeterministic is deprecated and will be removed in next release. Please use torch.use_deterministic_algorithms instead.
warnings.warn(
2024-08-11 22:12:21.398 | INFO     | main:main:10 - TGIService: starting tgi service ....
2024-08-11 22:12:21.399 | INFO     | main:main:11 - TGIService: --model_id hugging-quants/Meta-Llama-3.1-70B-Instruct-GPTQ-INT4, --revision None, --sharded True, --speculate None, --dtype bfloat16, --trust_remote_code True, --uds_path /tmp/text-generation-server
/usr/local/lib/python3.10/dist-packages/transformers/deepspeed.py:24: FutureWarning: transformers.deepspeed module is deprecated and will be removed in a future version. Please import deepspeed modules directly from transformers.integrations
warnings.warn(
The argument trust_remote_code is to be used with Auto classes. It has no effect here and is ignored.
2024-08-11 22:12:21.561 | INFO     | main:main:10 - TGIService: starting tgi service ....
2024-08-11 22:12:21.561 | INFO     | main:main:11 - TGIService: --model_id hugging-quants/Meta-Llama-3.1-70B-Instruct-GPTQ-INT4, --revision None, --sharded True, --speculate None, --dtype bfloat16, --trust_remote_code True, --uds_path /tmp/text-generation-server
[WARNING|utils.py:225] 2024-08-11 22:12:21,580 >> optimum-habana v1.13.0.dev0 has been validated for SynapseAI v1.17.0 but the driver version is v1.16.2, this could lead to undefined behavior!
The argument trust_remote_code is to be used with Auto classes. It has no effect here and is ignored.
[WARNING|utils.py:225] 2024-08-11 22:12:21,759 >> optimum-habana v1.13.0.dev0 has been validated for SynapseAI v1.17.0 but the driver version is v1.16.2, this could lead to undefined behavior!
/usr/local/lib/python3.10/dist-packages/habana_frameworks/torch/hpu/init.py:158: UserWarning: torch.hpu.setDeterministic is deprecated and will be removed in next release. Please use torch.use_deterministic_algorithms instead.
warnings.warn(
/usr/local/lib/python3.10/dist-packages/habana_frameworks/torch/hpu/init.py:158: UserWarning: torch.hpu.setDeterministic is deprecated and will be removed in next release. Please use torch.use_deterministic_algorithms instead.
warnings.warn(
/usr/local/lib/python3.10/dist-packages/transformers/deepspeed.py:24: FutureWarning: transformers.deepspeed module is deprecated and will be removed in a future version. Please import deepspeed modules directly from transformers.integrations
warnings.warn(
/usr/local/lib/python3.10/dist-packages/transformers/deepspeed.py:24: FutureWarning: transformers.deepspeed module is deprecated and will be removed in a future version. Please import deepspeed modules directly from transformers.integrations
warnings.warn(
2024-08-11 22:12:22.487 | INFO     | main:main:10 - TGIService: starting tgi service ....
2024-08-11 22:12:22.487 | INFO     | main:main:11 - TGIService: --model_id hugging-quants/Meta-Llama-3.1-70B-Instruct-GPTQ-INT4, --revision None, --sharded True, --speculate None, --dtype bfloat16, --trust_remote_code True, --uds_path /tmp/text-generation-server
The argument trust_remote_code is to be used with Auto classes. It has no effect here and is ignored.
2024-08-11 22:12:22.611 | INFO     | main:main:10 - TGIService: starting tgi service ....
2024-08-11 22:12:22.612 | INFO     | main:main:11 - TGIService: --model_id hugging-quants/Meta-Llama-3.1-70B-Instruct-GPTQ-INT4, --revision None, --sharded True, --speculate None, --dtype bfloat16, --trust_remote_code True, --uds_path /tmp/text-generation-server
The argument trust_remote_code is to be used with Auto classes. It has no effect here and is ignored.
/usr/local/lib/python3.10/dist-packages/habana_frameworks/torch/hpu/init.py:158: UserWarning: torch.hpu.setDeterministic is deprecated and will be removed in next release. Please use torch.use_deterministic_algorithms instead.
warnings.warn(
/usr/local/lib/python3.10/dist-packages/habana_frameworks/torch/hpu/init.py:158: UserWarning: torch.hpu.setDeterministic is deprecated and will be removed in next release. Please use torch.use_deterministic_algorithms instead.
warnings.warn(
============================= HABANA PT BRIDGE CONFIGURATION ===========================
PT_HPU_LAZY_MODE = 1
PT_RECIPE_CACHE_PATH =
PT_CACHE_FOLDER_DELETE = 0
PT_HPU_RECIPE_CACHE_CONFIG =
PT_HPU_MAX_COMPOUND_OP_SIZE =<PHONE_NUMBER>854775807
PT_HPU_LAZY_ACC_PAR_MODE = 0
PT_HPU_ENABLE_REFINE_DYNAMIC_SHAPES = 0
---------------------------: System Configuration :---------------------------
Num CPU Cores : 160
CPU RAM       :<PHONE_NUMBER> KB
Loading 9 checkpoint shards:   0%|          | 0/9 [00:00<?, ?it/s]Traceback (most recent call last):
File "/usr/local/lib/python3.10/dist-packages/text_generation_server/tgi_service.py", line 37, in
main(args)
File "/usr/local/lib/python3.10/dist-packages/text_generation_server/tgi_service.py", line 16, in main
server.serve(
File "/usr/local/lib/python3.10/dist-packages/text_generation_server/server.py", line 223, in serve
asyncio.run(
File "/usr/lib/python3.10/asyncio/runners.py", line 44, in run
return loop.run_until_complete(main)
File "/usr/lib/python3.10/asyncio/base_events.py", line 649, in run_until_complete
return future.result()
File "/usr/local/lib/python3.10/dist-packages/text_generation_server/server.py", line 189, in serve_inner
model = get_model(
File "/usr/local/lib/python3.10/dist-packages/text_generation_server/models/init.py", line 101, in get_model
return CausalLM(
File "/usr/local/lib/python3.10/dist-packages/text_generation_server/models/causal_lm.py", line 629, in init
model = self.get_deepspeed_model(
File "/usr/local/lib/python3.10/dist-packages/text_generation_server/models/causal_lm.py", line 783, in get_deepspeed_model
model = deepspeed.init_inference(model, **ds_inference_kwargs)
File "/usr/local/lib/python3.10/dist-packages/deepspeed/init.py", line 340, in init_inference
engine = InferenceEngine(model, config=ds_inference_config)
File "/usr/local/lib/python3.10/dist-packages/deepspeed/inference/engine.py", line 165, in init
self._apply_injection_policy(config, client_module)
File "/usr/local/lib/python3.10/dist-packages/deepspeed/inference/engine.py", line 412, in _apply_injection_policy
replace_transformer_layer(client_module, self.module, checkpoint, config, self.config)
File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/replace_module.py", line 345, in replace_transformer_layer
replaced_module = replace_module(model=model,
File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/replace_module.py", line 603, in replace_module
replaced_module, _ = _replace_module(model, policy, state_dict=sd)
File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/replace_module.py", line 663, in _replace_module
_, layer_id = _replace_module(child,
File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/replace_module.py", line 663, in _replace_module
_, layer_id = _replace_module(child,
File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/replace_module.py", line 639, in _replace_module
replaced_module = policies[child.class][0](child,
File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/replace_module.py", line 316, in replace_fn
new_module = replace_wo_policy(child, _policy, prefix=prefix, state_dict=state_dict)
File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/replace_module.py", line 299, in replace_wo_policy
return _autotp._replace_module(module)
File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/auto_tp.py", line 464, in _replace_module
self._replace_module(child, name, class_name)
File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/auto_tp.py", line 443, in _replace_module
Loading.load(child, self.state_dict, checking_key, self.mp_group)
File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/auto_tp.py", line 161, in load
module.weight = mp_replace.copy(module.weight.data, state_dict[prefix + 'weight'])
KeyError: 'model.layers.3.self_attn.q_proj.weight'
Traceback (most recent call last):
File "/usr/local/lib/python3.10/dist-packages/text_generation_server/tgi_service.py", line 37, in
Traceback (most recent call last):
File "/usr/local/lib/python3.10/dist-packages/text_generation_server/tgi_service.py", line 37, in
main(args)
File "/usr/local/lib/python3.10/dist-packages/text_generation_server/tgi_service.py", line 16, in main
server.serve(
File "/usr/local/lib/python3.10/dist-packages/text_generation_server/server.py", line 223, in serve
main(args)
File "/usr/local/lib/python3.10/dist-packages/text_generation_server/tgi_service.py", line 16, in main
server.serve(
File "/usr/local/lib/python3.10/dist-packages/text_generation_server/server.py", line 223, in serve
asyncio.run(
File "/usr/lib/python3.10/asyncio/runners.py", line 44, in run
return loop.run_until_complete(main)
File "/usr/lib/python3.10/asyncio/base_events.py", line 649, in run_until_complete
asyncio.run(
File "/usr/lib/python3.10/asyncio/runners.py", line 44, in run
return loop.run_until_complete(main)
File "/usr/lib/python3.10/asyncio/base_events.py", line 649, in run_until_complete
return future.result()
File "/usr/local/lib/python3.10/dist-packages/text_generation_server/server.py", line 189, in serve_inner
model = get_model(
File "/usr/local/lib/python3.10/dist-packages/text_generation_server/models/init.py", line 101, in get_model
return future.result()
File "/usr/local/lib/python3.10/dist-packages/text_generation_server/server.py", line 189, in serve_inner
return CausalLM(
File "/usr/local/lib/python3.10/dist-packages/text_generation_server/models/causal_lm.py", line 629, in init
model = get_model(
File "/usr/local/lib/python3.10/dist-packages/text_generation_server/models/init.py", line 101, in get_model
return CausalLM(
File "/usr/local/lib/python3.10/dist-packages/text_generation_server/models/causal_lm.py", line 629, in init
model = self.get_deepspeed_model(
File "/usr/local/lib/python3.10/dist-packages/text_generation_server/models/causal_lm.py", line 783, in get_deepspeed_model
model = self.get_deepspeed_model(
File "/usr/local/lib/python3.10/dist-packages/text_generation_server/models/causal_lm.py", line 783, in get_deepspeed_model
model = deepspeed.init_inference(model, **ds_inference_kwargs)
File "/usr/local/lib/python3.10/dist-packages/deepspeed/init.py", line 340, in init_inference
model = deepspeed.init_inference(model, **ds_inference_kwargs)
File "/usr/local/lib/python3.10/dist-packages/deepspeed/init.py", line 340, in init_inference
engine = InferenceEngine(model, config=ds_inference_config)
File "/usr/local/lib/python3.10/dist-packages/deepspeed/inference/engine.py", line 165, in init
self._apply_injection_policy(config, client_module)
File "/usr/local/lib/python3.10/dist-packages/deepspeed/inference/engine.py", line 412, in _apply_injection_policy
engine = InferenceEngine(model, config=ds_inference_config)
File "/usr/local/lib/python3.10/dist-packages/deepspeed/inference/engine.py", line 165, in init
self._apply_injection_policy(config, client_module)
File "/usr/local/lib/python3.10/dist-packages/deepspeed/inference/engine.py", line 412, in _apply_injection_policy
replace_transformer_layer(client_module, self.module, checkpoint, config, self.config)
File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/replace_module.py", line 345, in replace_transformer_layer
replace_transformer_layer(client_module, self.module, checkpoint, config, self.config)
File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/replace_module.py", line 345, in replace_transformer_layer
replaced_module = replace_module(model=model,
File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/replace_module.py", line 603, in replace_module
replaced_module = replace_module(model=model,
File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/replace_module.py", line 603, in replace_module
replaced_module, _ = _replace_module(model, policy, state_dict=sd)
File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/replace_module.py", line 663, in _replace_module
replaced_module, _ = _replace_module(model, policy, state_dict=sd)
File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/replace_module.py", line 663, in _replace_module
_, layer_id = _replace_module(child,
File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/replace_module.py", line 663, in _replace_module
_, layer_id = _replace_module(child,
File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/replace_module.py", line 663, in _replace_module
_, layer_id = _replace_module(child,
File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/replace_module.py", line 639, in _replace_module
_, layer_id = _replace_module(child,
File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/replace_module.py", line 639, in _replace_module
replaced_module = policies[child.class][0](child,
File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/replace_module.py", line 316, in replace_fn
replaced_module = policies[child.class][0](child,
File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/replace_module.py", line 316, in replace_fn
new_module = replace_wo_policy(child, _policy, prefix=prefix, state_dict=state_dict)
File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/replace_module.py", line 299, in replace_wo_policy
new_module = replace_wo_policy(child, _policy, prefix=prefix, state_dict=state_dict)
File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/replace_module.py", line 299, in replace_wo_policy
return _autotp._replace_module(module)
File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/auto_tp.py", line 464, in _replace_module
return _autotp._replace_module(module)
File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/auto_tp.py", line 464, in _replace_module
self._replace_module(child, name, class_name)
File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/auto_tp.py", line 443, in _replace_module
self._replace_module(child, name, class_name)
File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/auto_tp.py", line 443, in _replace_module
Loading.load(child, self.state_dict, checking_key, self.mp_group)
File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/auto_tp.py", line 161, in load
module.weight = mp_replace.copy(module.weight.data, state_dict[prefix + 'weight'])
Loading.load(child, self.state_dict, checking_key, self.mp_group)
KeyError  File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/auto_tp.py", line 161, in load
: 'model.layers.3.self_attn.q_proj.weight'
module.weight = mp_replace.copy(module.weight.data, state_dict[prefix + 'weight'])
KeyError: 'model.layers.3.self_attn.q_proj.weight'
Traceback (most recent call last):
File "/usr/local/lib/python3.10/dist-packages/text_generation_server/tgi_service.py", line 37, in
main(args)
File "/usr/local/lib/python3.10/dist-packages/text_generation_server/tgi_service.py", line 16, in main
server.serve(
File "/usr/local/lib/python3.10/dist-packages/text_generation_server/server.py", line 223, in serve
asyncio.run(
File "/usr/lib/python3.10/asyncio/runners.py", line 44, in run
return loop.run_until_complete(main)
File "/usr/lib/python3.10/asyncio/base_events.py", line 649, in run_until_complete
return future.result()
File "/usr/local/lib/python3.10/dist-packages/text_generation_server/server.py", line 189, in serve_inner
model = get_model(
File "/usr/local/lib/python3.10/dist-packages/text_generation_server/models/init.py", line 101, in get_model
return CausalLM(
File "/usr/local/lib/python3.10/dist-packages/text_generation_server/models/causal_lm.py", line 629, in init
model = self.get_deepspeed_model(
File "/usr/local/lib/python3.10/dist-packages/text_generation_server/models/causal_lm.py", line 783, in get_deepspeed_model
model = deepspeed.init_inference(model, **ds_inference_kwargs)
File "/usr/local/lib/python3.10/dist-packages/deepspeed/init.py", line 340, in init_inference
engine = InferenceEngine(model, config=ds_inference_config)
File "/usr/local/lib/python3.10/dist-packages/deepspeed/inference/engine.py", line 165, in init
self._apply_injection_policy(config, client_module)
File "/usr/local/lib/python3.10/dist-packages/deepspeed/inference/engine.py", line 412, in _apply_injection_policy
replace_transformer_layer(client_module, self.module, checkpoint, config, self.config)
File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/replace_module.py", line 345, in replace_transformer_layer
replaced_module = replace_module(model=model,
File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/replace_module.py", line 603, in replace_module
replaced_module, _ = _replace_module(model, policy, state_dict=sd)
File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/replace_module.py", line 663, in _replace_module
_, layer_id = _replace_module(child,
File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/replace_module.py", line 663, in _replace_module
_, layer_id = _replace_module(child,
File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/replace_module.py", line 639, in _replace_module
replaced_module = policies[child.class][0](child,
File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/replace_module.py", line 316, in replace_fn
new_module = replace_wo_policy(child, _policy, prefix=prefix, state_dict=state_dict)
File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/replace_module.py", line 299, in replace_wo_policy
return _autotp._replace_module(module)
File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/auto_tp.py", line 464, in _replace_module
self._replace_module(child, name, class_name)
File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/auto_tp.py", line 443, in _replace_module
Loading.load(child, self.state_dict, checking_key, self.mp_group)
File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/auto_tp.py", line 161, in load
module.weight = mp_replace.copy(module.weight.data, state_dict[prefix + 'weight'])
KeyError: 'model.layers.3.self_attn.q_proj.weight'
Traceback (most recent call last):
File "/usr/local/lib/python3.10/dist-packages/text_generation_server/tgi_service.py", line 37, in
main(args)
File "/usr/local/lib/python3.10/dist-packages/text_generation_server/tgi_service.py", line 16, in main
server.serve(
File "/usr/local/lib/python3.10/dist-packages/text_generation_server/server.py", line 223, in serve
asyncio.run(
File "/usr/lib/python3.10/asyncio/runners.py", line 44, in run
return loop.run_until_complete(main)
File "/usr/lib/python3.10/asyncio/base_events.py", line 649, in run_until_complete
return future.result()
File "/usr/local/lib/python3.10/dist-packages/text_generation_server/server.py", line 189, in serve_inner
model = get_model(
File "/usr/local/lib/python3.10/dist-packages/text_generation_server/models/init.py", line 101, in get_model
return CausalLM(
File "/usr/local/lib/python3.10/dist-packages/text_generation_server/models/causal_lm.py", line 629, in init
model = self.get_deepspeed_model(
File "/usr/local/lib/python3.10/dist-packages/text_generation_server/models/causal_lm.py", line 783, in get_deepspeed_model
model = deepspeed.init_inference(model, **ds_inference_kwargs)
File "/usr/local/lib/python3.10/dist-packages/deepspeed/init.py", line 340, in init_inference
engine = InferenceEngine(model, config=ds_inference_config)
File "/usr/local/lib/python3.10/dist-packages/deepspeed/inference/engine.py", line 165, in init
self._apply_injection_policy(config, client_module)
File "/usr/local/lib/python3.10/dist-packages/deepspeed/inference/engine.py", line 412, in _apply_injection_policy
replace_transformer_layer(client_module, self.module, checkpoint, config, self.config)
File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/replace_module.py", line 345, in replace_transformer_layer
replaced_module = replace_module(model=model,
File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/replace_module.py", line 603, in replace_module
replaced_module, _ = _replace_module(model, policy, state_dict=sd)
File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/replace_module.py", line 663, in _replace_module
_, layer_id = _replace_module(child,
File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/replace_module.py", line 663, in _replace_module
_, layer_id = _replace_module(child,
File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/replace_module.py", line 639, in _replace_module
replaced_module = policies[child.class][0](child,
File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/replace_module.py", line 316, in replace_fn
new_module = replace_wo_policy(child, _policy, prefix=prefix, state_dict=state_dict)
File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/replace_module.py", line 299, in replace_wo_policy
return _autotp._replace_module(module)
File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/auto_tp.py", line 464, in _replace_module
self._replace_module(child, name, class_name)
File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/auto_tp.py", line 443, in _replace_module
Loading.load(child, self.state_dict, checking_key, self.mp_group)
File "/usr/local/lib/python3.10/dist-packages/deepspeed/module_inject/auto_tp.py", line 161, in load
module.weight = mp_replace.copy(module.weight.data, state_dict[prefix + 'weight'])
KeyError: 'model.layers.3.self_attn.q_proj.weight'
Loading 9 checkpoint shards:   0%|          | 0/9 [00:00<?, ?it/s]
Loading 9 checkpoint shards:   0%|          | 0/9 [00:00<?, ?it/s]
Loading 9 checkpoint shards:   0%|          | 0/9 [00:00<?, ?it/s]
Loading 9 checkpoint shards:   0%|          | 0/9 [00:00<?, ?it/s]
Loading 9 checkpoint shards:   0%|          | 0/9 [00:00<?, ?it/s] rank=0
2024-08-11T22:12:44.083532Z ERROR text_generation_launcher: Shard 0 failed to start
2024-08-11T22:12:44.083544Z  INFO text_generation_launcher: Shutting down shards
Error: ShardCannotStart
Information

[X] Docker
[ ] The CLI directly

Tasks

[X] An officially supported command
[ ] My own modifications

Reproduction
docker run -p 8081:80 -v $volume:/data --runtime=habana -e HUGGING_FACE_HUB_TOKEN=$hf_token -e PT_HPU_ENABLE_LAZY_COLLECTIVES=true -e HABANA_VISIBLE_DEVICES=2,3,4,5,6,7 -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --ipc=host tgi_gaudi --model-id $model --sharded true --num-shard 5 --max-input-tokens 4096 --max-total-tokens 8192 --max-batch-prefill-tokens 8242
Expected behavior
it should run.

Wait for next release. It should work.
https://github.com/huggingface/tgi-gaudi/pull/217

The 2.0.6 is released. TGI-gaudi can support the GPTQ unint.


ReferenceError: DrawerJs is not defined

Hi All, I am new to this tool. I wish to add it in my vue web page.
I tried the following to implement it but it throws issue like this.
ReferenceError: DrawerJs is not defined
How to fix this?

import drawerLocalization from './DrawerLocalization'
import drawerJsConfig from './redactorConfig'
import DrawerJs from './Drawer'
var drawer = new DrawerJs.Drawer(null, {
texts: drawerLocalization,
plugins: drawerJsConfig,
defaultImageUrl: '/images/drawer.jpg',
defaultActivePlugin : { name : 'Pencil', mode : 'lastUsed'},
}, 600, 600);
$('#canvas-editor').append(drawer.getHtml());
drawer.onInsert();
})

I solved this issue by downloading all the needed files and saved them to my local.
I loaded those css and js files from my local system.


Producer Component Header documentation Improvements

Added notes related to Producer component attributes.

closing this - need to redo.


Questions over development practices on test.lostmerchants.com

Hi, I have been using the example here https://github.com/Xeio/WanderLost/issues/75#issuecomment-1130934828 to test. But, I am not sure if I am doing this correctly as I have a couple questions.
import signalR from "@microsoft/signalr";

const connection = new signalR.HubConnectionBuilder()
  .withUrl("https://test.lostmerchants.com/MerchantHub")
  .withAutomaticReconnect()
  .build();

connection.keepAliveIntervalInMilliseconds = 4 * 60 * 1000;
connection.serverTimeoutInMilliseconds = 8 * 60 * 1000;

async function start() {
  try {
      await connection.start();
      await connection.invoke("SubscribeToServer", "Shandi");
      await connection.on("UpdateMerchantGroup", async (server) => {
      const merchants = await connection.invoke(
        "GetKnownActiveMerchantGroups",
        server
      )
      console.log(merchants)
      });
  } catch (err) {
      console.log(err);
      setTimeout(() => start(), 5000);
  }
};

connection.onclose(async () => {
  await start();
});

start();

I found that withAutomaticReconnect() doesn't seem to do what it says. So I had to wrap it in a onclose() is this how it's intended to use or is there a bug somewhere within the signalR lib? Without wrapping in that enclose the connection ends and I have to restart the script.

I have this set this test to listen to the test server of SignalR and I wanted a bit quicker development cycle. Is this the endpoint, that allows me to push messages as much as I want to this dev (without getting permanently banned from the site) or am I suppose to wait every 30 minutes?

I may be misunderstanding how this pub sub system works. But I am staring at the website and I am seeing that there should be messages:

But, it doesn't seem like any messages are being picked up on main server for Valtan.

(node:20052) ExperimentalWarning: The Fetch API is an experimental feature. This feature could change at any time
(Use `node --trace-warnings ...` to show where the warning was created)
[2022-08-27T22:33:50.494Z] Information: WebSocket connected to wss://lostmerchants.com/MerchantHub?id=zpJy-rGRAdLNQdoGwwpYgA.

Thanks for your time.

For the most part as long as you're hitting the test site you can do whatever.
Generally, you would only get that "UpdateMerchantGroup" message when a new merchant is submitted. I do notice that in your code it subscribes to the "Shandie" server, but in the UI screenshot you're looking at the "Valtan" server. I'm not sure if that's your issue?
I can't really answer 1, that's more a SignalR question, I don't know if there's any specific oddities around the javascript library. I think someone else mentioned something around auto reconnect in another issue, but it seems fine to just retry manually with a delay like you are if that works.

Generally, you would only get that "UpdateMerchantGroup" message when a new merchant is submitted. I do notice that in your code it subscribes to the "Shandie" server, but in the UI screenshot you're looking at the "Valtan" server. I'm not sure if that's your issue?

Oh yea at the time Shandi didn't have anyone submit any reports so I swapped both my code and the website to show Valtan since someone had published a message there just for a screenshot. But I still can't seem to be messages regardless of what server I am on.

I can't really answer 1, that's more a SignalR question, I don't know if there's any specific oddities around the javascript library. I think someone else mentioned something around auto reconnect in another issue, but it seems fine to just retry manually with a delay like you are if that works.

Okay no problem. I think I can leave it as it for now.

Not sure, the code I prototyped that you linked seemed to work when I limitedly tried it. Just to be clear, you're starting up your client, then going to the test site and submitting a merchant on the same server? The way your code is written it won't get merchants that were already submitted before it starts up and has to be running when the submission happens.

Oh. I think that's probably where I am going wrong in this implementation.
Is there a way to start up the client and get any active merchants at any point?

You'd need to invoke the GetKnownActiveMerchantGroups call after the connection is successful.


Slower prediction of tflite model then expected

Hi,
Why trained object detection model quantized via tf.lite converter works more then 2 seconds per 1 sample on 1 CPU instance,
while expected time ~20 ms mentioned in tf2 detection zoo? Is it normal? What can be wrong?
I completed all necessary steps:

Fine tuned model according to tensorflow-object-detection manual
https://tensorflow-object-detection-api-tutorial.readthedocs.io/en/latest/training.html#exporting-a-trained-model
finetuned model: ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8.tar.gz
https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md

Then froze tf graph using export_tflite_graph_tf2.py from https://github.com/tensorflow/models/tree/master/research/object_detection

Converted model via tf.lite.TFLiteConverter.
It doesn't work for tf.2.3 version  (the same problems as in #(42065), #(41877)) , therefore I used tf-nightly==2.5.0.dev20201126

Made a prediction from tflite model in Jupyter Notebook on 1 CPU instance

interpreter = tf.lite.Interpreter(model_path=str(tflite_model_file), num_threads=1)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
interpreter.set_tensor(input_details[0]["index"], features)
interpreter.invoke()
res = interpreter.get_tensor(output_details[0]["index"])
interpreter.reset_all_variables()
I expect to get ~20 ms per one sample, but got ~2 seconds:
CPU times: user 2.1 s, sys: 2.78 ms, total: 2.11 s
Wall time: 2.11 s
And for ssd_mobilenet_v1_fpn_640x640_coco17_tpu-8 - the latency is more then 2 minutes.
System information

OS Platform and Distribution: RHEL 7.7
TensorFlow installed from (source or binary): from binary
TensorFlow version (or github SHA if from source): tensorflow-gpu==2.3.0

Command used to run the converter or code if you’re using the Python API
converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir=SAVED_MODEL_PATH)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
tflite_model = converter.convert()
Failure details

Producing correct results, but the model is slower than expected

Any other info / logs
Running export_tflite_graph_tf2.py i got many info messages like this:
INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f1bf30107b8>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f1bf3010748>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f1bf3010780>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).
I1127 11:49:05.573841<PHONE_NUMBER>98784 def_function.py:1038] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f1bf30107b8>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f1bf3010748>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f1bf3010780>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).
INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f1bf2fd4400>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f1bf2fd4470>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f1bf2fd4780>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).
I1127 11:49:05.574001<PHONE_NUMBER>98784 def_function.py:1038] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f1bf2fd4400>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f1bf2fd4470>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f1bf2fd4780>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).
INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f1bf2f95b70>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f1bf2f95be0>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f1bf2f95ef0>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).
I1127 11:49:10.198460<PHONE_NUMBER>98784 def_function.py:1038] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f1bf2f95b70>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f1bf2f95be0>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f1bf2f95ef0>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).
INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f1bf2fac898>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f1bf2fac908>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f1bf2facc18>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).
I1127 11:49:10.198984<PHONE_NUMBER>98784 def_function.py:1038] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f1bf2fac898>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f1bf2fac908>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f1bf2facc18>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).
INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f1bf30107b8>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f1bf3010748>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f1bf3010780>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).
I1127 11:49:10.199471<PHONE_NUMBER>98784 def_function.py:1038] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f1bf30107b8>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f1bf3010748>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f1bf3010780>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).
INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f1bf2fd4400>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f1bf2fd4470>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f1bf2fd4780>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).
I1127 11:49:10.199697<PHONE_NUMBER>98784 def_function.py:1038] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f1bf2fd4400>, TensorSpec(shape=(None, 40, 40, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f1bf2fd4470>, TensorSpec(shape=(None, 20, 20, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f1bf2fd4780>, TensorSpec(shape=(None, 10, 10, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).
INFO:tensorflow:Assets written to: exported-model/saved_model/assets
I1127 11:49:11.159580<PHONE_NUMBER>98784 builder_impl.py:775] Assets written to: exported-model/saved_model/assets
INFO:tensorflow:Writing pipeline config file to exported-model/pipeline.config

Quantization logs:
2020-11-27 15:22:43.446327: I tensorflow/cc/saved_model/loader.cc:277] SavedModel load for tags {serve}; Status: success: OK. Took 1257095 microseconds.
2020-11-27 15:22:45.713440: I tensorflow/lite/tools/optimize/quantize_weights.cc:224] Skipping quantization of tensor ssd_mobile_net_v2fpn_keras_feature_extractor/functional_1/Conv1/Conv2D because it has fewer than 1024 elements (864).
2020-11-27 15:22:45.713525: I tensorflow/lite/tools/optimize/quantize_weights.cc:224] Skipping quantization of tensor ssd_mobile_net_v2fpn_keras_feature_extractor/functional_1/expanded_conv_depthwise_BN/FusedBatchNormV3;ssd_mobile_net_v2fpn_keras_feature_extractor/functional_1/expanded_conv_depthwise/depthwise;ssd_mobile_net_v2fpn_keras_feature_extractor/functional_1/block_5_project/Conv2D because it has fewer than 1024 elements (288).
2020-11-27 15:22:45.713543: I tensorflow/lite/tools/optimize/quantize_weights.cc:224] Skipping quantization of tensor ssd_mobile_net_v2fpn_keras_feature_extractor/functional_1/expanded_conv_project/Conv2D because it has fewer than 1024 elements (512).
2020-11-27 15:22:45.713561: I tensorflow/lite/tools/optimize/quantize_weights.cc:224] Skipping quantization of tensor ssd_mobile_net_v2fpn_keras_feature_extractor/functional_1/block_1_depthwise_BN/FusedBatchNormV3;ssd_mobile_net_v2fpn_keras_feature_extractor/functional_1/block_1_depthwise/depthwise;ssd_mobil
e_net_v2fpn_keras_feature_extractor/functional_1/block_12_project/Conv2D because it has fewer than 1024 elements (864).

@valery-kustov
Please share a simple stand alone code to replicate the issue faced or if possible share a colab gist with the issue.


Use constant-time comparison for validating API tokens

When using API tokens to authenticate calls from the application to the Dapr sidecar, comparisons should use constant-time functions to avoid timing attacks (see CWE-208).
This change will have a negative performance impact (by design).

Code used for benchmarking
package main

import (
	"crypto/subtle"
	"testing"
)

var strA = "HELLO-WORLD"
var strB = "HELLO-WORLD"

func CompareNonConstantTime(a, b string) bool {
	return a == b
}
func CompareConstantTime(a, b string) bool {
	return subtle.ConstantTimeCompare([]byte(a), []byte(b)) == 1
}
func BenchmarkCompareNonConstantTime(b *testing.B) {
	for i := 0; i < b.N; i++ {
		CompareNonConstantTime(strA, strB)
	}
}
func BenchmarkCompareConstantTime(b *testing.B) {
	for i := 0; i < b.N; i++ {
		CompareConstantTime(strA, strB)
	}
}

Benchmarking environment (Codespace)
goos: linux
goarch: amd64
cpu: Intel(R) Xeon(R) Platinum 8272CL CPU @ 2.60GHz

Benchmark results and inputs.
Two equal short strings:
var strA = "HELLO-WORLD"
var strB = "HELLO-WORLD"
BenchmarkCompareNonConstantTime-4       658838829                1.807 ns/op           0 B/op          0 allocs/op
BenchmarkCompareConstantTime-4          67086715                19.76 ns/op            0 B/op          0 allocs/op

Two equal very long strings:
var strA = "Ei fu. Siccome immobile,\ndato il mortal sospiro,\nstette la spoglia immemore\norba di tanto spiro,\ncosì percossa, attònita\nla terra al nunzio sta,\nmuta pensando all'ultima\nora dell'uom fatale;\nné sa quando una simile\norma di piè mortale\nla sua cruenta polvere\na calpestar verrà."
var strB = "Ei fu. Siccome immobile,\ndato il mortal sospiro,\nstette la spoglia immemore\norba di tanto spiro,\ncosì percossa, attònita\nla terra al nunzio sta,\nmuta pensando all'ultima\nora dell'uom fatale;\nné sa quando una simile\norma di piè mortale\nla sua cruenta polvere\na calpestar verrà."
BenchmarkCompareNonConstantTime-4       641540404                1.820 ns/op           0 B/op          0 allocs/op
BenchmarkCompareConstantTime-4           4635044               273.6 ns/op           576 B/op          2 allocs/op

Two different short strings, equal length:
var strA = "hello-world!"
var strB = "he11o-w0rld."
BenchmarkCompareNonConstantTime-4       332751294                3.601 ns/op           0 B/op          0 allocs/op
BenchmarkCompareConstantTime-4          69982993                16.15 ns/op            0 B/op          0 allocs/op

Two strings with different length:
var strA = "hello-world!"
var strB = "foo-bar"
BenchmarkCompareNonConstantTime-4<PHONE_NUMBER>               0.7907 ns/op          0 B/op          0 allocs/op
BenchmarkCompareConstantTime-4          100000000               10.33 ns/op            0 B/op          0 allocs/op

A more real-world example where the token is a 256-bit random shared secret, encoded as Base64:
var strA = "YZ5JlrOMAaaDd5ho0dPf7koz68o5fKTqlsRTaF5sfRo"
var strB = "YZ5JlrOMAaaDd5ho0dPf7koz68o5fKTqlsRTaF5sfRo"
BenchmarkCompareNonConstantTime-4       652827076                1.815 ns/op           0 B/op          0 allocs/op
BenchmarkCompareConstantTime-4          15006927                84.90 ns/op           96 B/op          2 allocs/op

Note: this is step 1 of the fix. Will need to also implement some code that adds a delay to requests after a failed auth, to make brute force attacks non practical.

@yaron2 @artursouza  any final thoughts on this? Should this PR be simply abandoned?
At the very least, if this PR can't be merged, I would strongly suggest we consider merging https://github.com/dapr/dapr/pull/4477 to prevent brute-force attacks. Constant-time comparison would be a good addition as it also prevents side-channel attacks, but at least #4477 should offer a decent protection against brute-force.

👋 bot

👋 bot

@ItalyPaleAle please fix conflicts then we can get it.

@yaron2 had concerns

👋 (But I'll fix the merge conflicts if people are interested)


Internationalization in app/controllers/application_controller/*.rb 2/2

Gettexts added or modified.

@mzazrivec please review

Reviewed, looks good, ok to merge.


[FEATURE]improve the blog page

Detailed description
There is excessive animation on the blog page and the cards not not taking up the appropriate space
Context
The excessive hover animation is not looking good at all and tha cards should be aligned at the center of the page to the left ,it would improve the ui of the page
Possible implementation
No response
Additional information
No response
Code of Conduct

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

I would like to work on this issue under gssoc'23

I want to work on this issue as a contributor.
Pleasure Assign so that I can show my skills under GSSoC'23.

Hello @karthiknadar1204 👋🏻
Yes, merge of old code and new aren't clean at all. Do you want to work on it?
Sorry @syedawanya18 will ask @karthiknadar1204 and assign you if no response. :) Thanks for your consideration.

Would love to work on it @mkubdev

Cool! I assigned it to you :) 🚀

@mkubdev Will have to create a separate route for the blog page, you have anything specific in mind that you want me to implement or can i design the page in my own way?

@mkubdev Will have to create a separate route for the blog page, you have anything specific in mind that you want me to implement or can i design the page in my own way?

you can follow our figma v3 design

Also please add the gssoc label for reference, just a suggestion @vinzvinci

EXAMS ARE OVER, WILL SUBMIT PR BY WEEKEND
THANK YOU

@karthiknadar1204
Are you interested in contributing?
If not, please let me know so that I can close the issue.

I had opened this during gssoc'23,will contribute to this.
Thank you

Sure, thanks for letting me know.


The generated boundary protocol cannot be compiled if component's symbol contain '-'

Using gen/boundary
 (gen/boundary "ha" 'duct-framework.component.wow.Wow)

will generate code below
(ns duct-framework.boundary.ha
  (:require
    [duct-framework.component.wow])) ; this is correct

(defprotocol Ha)

(extend-protocol Ha
  duct-framework.component.wow.Wow) ; should be _ instead of -

The correct one should be
(ns duct-framework.boundary.ha
  (:require
    [duct-framework.component.wow]))

(defprotocol Ha)

(extend-protocol Ha
  duct_framework.component.wow.Wow)

Good catch. This'll be fixed by the next version.

Not relevant in 0.9.0.


[Task] Support multiple status pages

Description
当前状态监控界面只有一个，如果服务放的过多需要鼠标上下滚动很久才能看完。
建议增加多个状态界面，实现一个业务系统对应一个状态页面。
serviceIP:1157/status
多状态页参考如下

组件编辑参考如下

事件发布页参考如下

Task List
任务一：实现状态页面自定义地址
任务二：实现自定义状态页面的组件管理
任务三：实现自定义状态页面的事件管理

Bot detected the issue body's language is not English, translate it automatically. 👯👭🏻🧑🤝🧑👫🧑🏿🤝🧑🏻👩🏾🤝👨🏿👬🏿

Description
There is currently only one status monitoring interface. If there are too many services, it will take a long time to scroll up and down with the mouse to see them all.
It is recommended to add multiple status interfaces so that one business system corresponds to one status page.
serviceIP:1157/status
Multi-status page reference is as follows

Component editing reference is as follows

The event release page is as follows

Task List
Task 1: Implement a custom address for the status page
Task 2: Implement component management of custom status pages
Task 3: Implement event management of custom status pages


Fix support for LOAD_FAST_AND_CLEAR

Fix #9300

RE: https://github.com/numba/numba/pull/9315#pullrequestreview-1755908955
addressed in https://github.com/numba/numba/pull/9315/commits/3f5ebbb5ba3857f79acf87f17ab6648ca592d13c
the type inferer will scan for use of _undef_var in call arguments and in return types.

CI is failing due to:

ambiguous cast between undef-to-any and optional cast
objectmode not handling Expr.undef


Check "Extended transaction definition" feature

SPI has added a new method Connection#beginTransaction(TransactionDefinition).

https://github.com/r2dbc/r2dbc-spi/pull/159

Check whether r2dbc-proxy need any updates based on this.

No change is required in r2dbc-proxy.
Since Connection already has the same name method without parameter, [before|after]BeginTransactionOnConnection method on ProxyMethodExecutionListener  covers the new method.

No change is required in r2dbc-proxy.
Since Connection already has the same name method without parameter, [before|after]BeginTransactionOnConnection method on ProxyMethodExecutionListener  covers the new method.


Release 0.7.0-rc

[x] Review closed issues have appropriate tags.
[x] Review closed issues have been applied to the current milestone.
[x] Ensure the next version milestone is created.
[x] Any issues in the current milestone that are not closed, move to next milestone.
[x] Run make gen-changelog to generate the CHANGELOG.md (if release candidate make gen-changelog RELEASE_VERSION=0.7.0-rc)
[x] Ensure the helm tag value is correct (should be the 0.7.0 if a full release, 0.7.0-rc if release candidate)
[x] Ensure the helm Chart version values are correct (should be the 0.7.0 if a full release, 0.7.0-rc if release candidate)
[x] Run make gen-install
[x] Ensure all example images exist on gcr.io/agones-images
[x] If full release, update documentation with updated example images tags
[x] If RC release, update all ⚠⚠⚠ warnings to: "This is currently a release candidate feature"
[x] If full release, remove all instances of "⚠⚠⚠ This is currently a development feature and has not been released ⚠⚠⚠"
[x] If full release, update install docs with the new release version
[x] Create PR with these changes, and merge them with approval
[x] Confirm local git remote upstream points at git@github.com:GoogleCloudPlatform/agones.git
[x] Run git checkout master && git reset --hard upstream/master to ensure your code is in line with upstream  (unless this is a hotfix, then do the same, but for the the release branch)
[x] Run make do-release. (if release candidate make do-release RELEASE_VERSION=0.7.0-rc) to create and push the docker images and helm chart.
[x] Do a helm repo add agones https://agones.dev/chart/stable and verify that the new version is available via the command helm search agones/
[x] Do a helm install and a smoke test to confirm everything is working.
[x] Create a release with the release template

[x] Make a tag with the release version.
[x] Attach all assets found in the release folder to the release.

[x] Send an email to the mailing list with the release details (copy-paste the github release)
[x] If full release, then increment the base_version in build/Makefile
[x] Ensure the helm tag value is set to the next version (should be the 0.7.0+0.1 if a full release, 0.7.0+0.1-rc if release candidate)
[x] Ensure the helm Chart version values is set to the next version (should be the 0.7.0+0.1 if a full release, 0.7.0+0.1-rc if release candidate)
[x] Run make gen-install
[x] Create PR with these changes, and merge them with approval
[x] Close this issue.
[x] If full release, close the current milestone. Congratulations! - the release is now complete! :tada: :clap: :smile: :+1:

Complete! :tada: