Only Mark Node Schedulable If Agent Marked Unschedulable

This PR changes the agent to mark a node schedulable only if the agent was responsible for marking it unschedulable.
Prior to this change, the agent would always mark a node as schedulable when starting up. This could result in a node that had been marked unschedulable by an external process (eg. by an admin using kubectl) being marked schedulable by it's update agent if the agent restarted for some reason.

Can one of the admins verify this patch?

Can one of the admins verify this patch?

Can one of the admins verify this patch?

Instance of https://github.com/jenkinsci/ghprb-plugin/issues/292.
Honestly, we should probably move CoreOS projects related to k8s over to prow now that they've finished moving upstream off jenkins.

In addition to @euank's comments, if you don't mind squashing your cleanup and merge commits together, that would be awesome. Probably just the first three commits you have should cover it pretty well.
Just ping us whenever that is done and we will give it a final review.

Will do! A bit swamped but have this on my to-do list.

@hankjacobs any updates on this? just a friendly ping, no rush.

@euank @sdemos Good to go. Thanks for your patience!

Thanks for pushing the fix! I'll take a look at this again sometime soon.
My mistake with the issue reference, I had the link in my copy buffer and accidentally put it in a comment on an unrelated issue.

@sdemos All set with the requested changes. I haven't had a chance to test these changes since I had tore down my original testing infrastructure a while back. I may have some time to set it up tomorrow but no guarantees. Would you be able to test? If not, no biggie.

Changes look good to me. I can test it out. Thanks for the quick fixes!


Add format painter button to WYSIWYG toolbar

Requested in workshop at Apereo conference...
Add button to allow you to copy formatting from one chunk of text to another. Like format painter button in Word.

There was a ticket created 9 YEARS!! ago for this feature for ckeditor but looks like it's still not made it through... several requests in different places as well...
https://dev.ckeditor.com/ticket/1142

Our oldest issue (3 years) doesn't seem too bad now

I was thinking that :-)

Paste formatting button added with this commit: https://github.com/thexerteproject/xerteonlinetoolkits/commit/84b8da87ed07fac1aa7de924a45df2a1496c0ad0


Consider an lbufferless path

Currently, the renderer uses a separate light buffer.
The old renderer did not!
Now that shader preprocessing is happening at run-time, it is easier to experiment with other shader arrangement. It might be worth producing a set of shaders that produce a lit image without the intermediate light buffer step and measuring the performance. It will certainly reduce memory bandwidth requirements.

For this to work well, a shader needs an immutable snapshot of the environment that was used to compile it. This is needed so that renderers can interrogate shaders to determine whether or not they're compatible with the current G-Buffer/L-Buffer/I-Buffer configuration and fail early if they aren't.


Add support for including custom .condarc configuration when building installer

Motivation
By default, the Conda configuration .condarc file is stored in a location outside of all Conda installations and is applied to all Conda environments.
MicroDrop requires packages that are available in the default Conda channels, but are instead provided in channels such as conda-forge, wheeler-microfluidics, etc.  Therefore, it would be helpful to have such channels added to the .condarc Conda configuration.
Notes
Fortunately, Conda provides support for a system configuration file per Conda installation, by including a .condarc file in the root of a Conda installation (i.e., <Conda prefix>/.condarc).
There is also support (see allow_other_channels) for optionally allowing users to supplement the channels defined in the system .condarc.
Proposed solution
During the build of a MicroDrop installer:

Create a .condarc configuration including channels required for MicroDrop

Ideally, the list channels of channels could be customized to allow, for example, nightly/unstable installer builds using unstable source channels (e.g., paver build_installer -c <channel> -c <channel>)

Use post_install in .miniconda-recipe/construct.yaml to write .condarc to Conda prefix upon installation.

Other thoughts

Maybe we should try to use a similar mechanism to (1.1) above to specify a custom list of channels in the construct.yaml configuration?

Should the custom override the channels section in the construct.yaml file?
Maybe use --override-channels flag to define behaviour?

From the output of conda config --help, it looks like there are three default paths searched for .condarc files:

User's home directory (e.g., \Users\<username>\.condarc on Windows)
Root of Conda installation (i.e., <root prefix>\.condarc)
Root of Conda environment (i.e., <root prefix>\envs\<environment name>\.condarc)

Note that these can all supplement each other, allowing, for example, a user to explicitly add additional channels.
Questions

[ ] How is the channel priority resolved when the same channels are listed in different orderings between these .condarc files?


Cannot close the window because it does not have a parent. The parent window was closed or you are attempting to close the root browser window where tests were launched.

What is your Test Scenario?

I would like to open a new window with a different application under different url. Sign in there and do some actions then return to the parent window to continue my test.
As an example the code below uses github as a platform to sign in.
What is the Current behavior?

The test fails because the window cannot be closed.
1) Cannot close the window because it does not have a parent. The parent window was closed or
      you are attempting to close the root browser window where tests were launched.

What is the Expected behavior?

The test should succeed and close the recently opened window.
What is your web application and your TestCafe test code?

Your complete test code (or attach your test files):
import { Selector, t } from 'testcafe';

const GITHUB_LOGIN = 'YOUR GITHUB LOGIN';
const GITHUB_PASSWORD = 'YOUR GITHUB PASSWORD';

export const resize = () => t.resizeWindow(1366, 700);

const getWindowUrl = () => t.eval(() => document.documentURI) as Promise<string>;

async function openAsNewWindow(windowUrl: string) {
  const parentUrl = await getWindowUrl();

  const childWindow = await t.openWindow(windowUrl);
  const childUrl = await getWindowUrl();

  await t.expect(childUrl).eql(windowUrl);

  return async () => {
    await t.closeWindow(childWindow);
    await t.expect(getWindowUrl()).eql(parentUrl);
  };
}

fixture('New window')
  .page('https://github.com/join?source=login')
  .beforeEach(resize);

test.only('should open a new window', async () => {
  await t.typeText(Selector('input[type="text"][name="user[login]"]'), 'TestCafe');

  const closeWindow = await openAsNewWindow('https://github.com/login');

  await t
    .typeText(Selector('input[type="text"][name="login"]'), GITHUB_LOGIN)
    .typeText(Selector('input[type="password"][name="password"]'), GITHUB_PASSWORD)
    .click(Selector('input[type="submit"][value="Sign in"]'));

  await closeWindow();

  await t.click(Selector('input[type="submit"][value="Sign in"]'));
});

Your complete test report:

15:32 $ npm run test

><EMAIL_ADDRESS>test /Users/john/Desktop/Dev/myApp/e2e
> npm run clean && npm run env && testcafe chrome src -S -s screenshots

><EMAIL_ADDRESS>clean /Users/john/Desktop/Dev/myApp/e2e
> rm -rf ./screenshots && mkdir screenshots

><EMAIL_ADDRESS>env /Users/john/Desktop/Dev/myApp/e2e
> rm -rf ./.env && cp ./.env.dev ./.env

 Running tests in:
 - Chrome 85.0.4183.121 / macOS 10.14.6

New window
 ✖ should open a new window
  (screenshots:
 /Users/john/Desktop/Dev/myApp/e2e/screenshots/2020-10-01_15-32-55/test-1/Chrome_85.0.4183.121_macOS_10.14.6/errors/1.png)

   1) Cannot close the window because it does not have a parent. The parent window was closed or
      you are attempting to close the root browser window where tests were launched.

      Browser: Chrome 85.0.4183.121 / macOS 10.14.6
      Screenshot:

   /Users/john/Desktop/Dev/myApp/e2e/screenshots/2020-10-01_15-32-55/test-1/Chrome_85.0.4183.121_macOS_10.14.6/errors/1.png

         19 |    const pageWindowUrl = await getWindowUrl();
         20 |
         21 |    await t.expect(pageWindowUrl).eql(loginUrl);
         22 |
         23 |    return async () => {
       > 24 |      await t.closeWindow(pageWindow);
         25 |
         26 |      const currentMainWindowUrl = await getWindowUrl();
         27 |      await t.expect(currentMainWindowUrl).eql(mainWindowUrl);
         28 |    };
         29 |  }

         at PageObject.<anonymous>

   (/Users/john/Desktop/Dev/myApp/e2e/src/PageObject.ts:24:15)
         at <anonymous>

   (/Users/john/Desktop/Dev/myApp/e2e/src/PageObject.ts:7:71)
         at __awaiter

   (/Users/john/Desktop/Dev/myApp/e2e/src/PageObject.ts:3:12)
         at <anonymous>

   (/Users/john/Desktop/Dev/myApp/e2e/src/PageObject.ts:23:23)
         at Object.<anonymous>

   (/Users/john/Desktop/Dev/myApp/e2e/src/myTest.spec.ts:47:9)
         at fulfilled
      (/Users/john/Desktop/Dev/myApp/e2e/src/myTest.spec.ts:4:58)

 1/1 failed (34s)
npm ERR! code ELIFECYCLE
npm ERR! errno 1
npm ERR<EMAIL_ADDRESS>test: `npm run clean && npm run env && testcafe chrome src -S -s screenshots`
npm ERR! Exit status 1
npm ERR!
npm ERR! Failed at the<EMAIL_ADDRESS>test script.
npm ERR! This is probably not a problem with npm. There is likely additional logging output above.

npm ERR!      /Users/john/.npm/_logs/2020-10-01T13_33_33_023Z-debug.log

Steps to Reproduce:

Use your own login / password to be able to login to github in the test.

The problem doesn't occur if a form is not submitted in the freshly opened window.
Is there anything specific that could break TestCafe window management mechanism?
Your Environment details:

testcafe version: 1.9.3
node.js version: v12.18.3
command-line arguments: testcafe chrome src -S -s screenshots
browser name and version: Chrome 85.0.4183.121
platform and version: macOS 10.14.6
other: n/a

Hello. Thank you for your input. I was able to reproduce the issue.
I examined our code, and it looks like your assumption is correct. The issue occurs only after the form is submitted in the child window. We'll try to fix the issue in the near future.

@Farfurix @AlexKamaev Many thanks guys for fixing the bug!


[Feature]: Ignore ".only" - CLI flag

🚀 Feature Proposal
A CLI flag that will force all tests to run, ignoring .only statements.
Motivation
This would be helpful to be sure files aren't checked with test files containing .only and thus fooling CI as a valid build.
Example

test file is pushed mistakenly having ONE .only test case between many more test cases
test file has one failing test, now skipped because the sibling .only test
Jest was invoked with the proposed flag, runs all tests regardless the .only one, test fails, nothing was harmed

Pitch
otherwise this would have to be another build step, that looks from source code containing .only in tests, which would be annoying to do since some other lib API could have the same signature.
demo: https://github.com/facebook/jest/pull/11947

You can use https://github.com/jest-community/eslint-plugin-jest/blob/main/docs/rules/no-focused-tests.md to avoid this issue. I don't think adding an ability to ignore it to Jest itself is worthwhile - focusing on some tests is valid and if you commit it without meaning to the lint rule can pick it up


Cryptography with Py

This is GUI based Cryptography application that using OneTimePad for encryption of files or text messages sent between two clients connected via a Socket connection. Each user can transfer files and Texts easily giving just a key for encryption. If during the transfer the message is siphoned it will be in an encrypted form and will not be easy to decipher

PR for Issue #103


Take a look into Nuitka

MacOS users often have problems with the python ecosystem, so why don't we offer simple binaries (maybe even over brew?)
https://github.com/Nuitka/Nuitka could be the solution.

Building native (linux) binaries seems to work, if we supply another script calling pluploader manually:
import pluploader.main

pluploader.main.main()

Now I need to get access to a Mac and try Nuitka there.

A brew recipe without nuitka seems to be easier, I guess. Let's go this route and see how far we can go.

A brew recipe without nuitka seems to be easier, I guess. Let's go this route and see how far we can go.


fix: 🐛 convert mediator affirmation expiry to Date and handle expiry of null

Description

Breaking Changes

JIRA Link

Checklist

[ ] Updated the Readme.md (if required) ?

/fast-forward

:tada: This PR is included in version 27.1.0-alpha.2 :tada:
The release is available on:

npm package (@alpha dist-tag)
GitHub release

Your semantic-release bot :package::rocket:


Adding documentation fot the lights API

Overview
Provides documentation for the lights API.
Changes

This PR migrates this document @plarios put together for the lights API to readthedocs.
Adds a table with the initial values for the LED that @BrettMonoDrive put together here

Test
This is the corresponding build

Good catch! It should be ready for another look. Rebuilt the branch here


feat: add segment.io as telemetry service

this PR:

remove previous telemetry implementation
add SegmentIO to handle telemetry
SegmentIO has a JavaScript function(destination) to forward telemetry data to DataDog
fix cmd version command (required for telemetry data) #491

closes #499 closes #491
Signed-off-by: João Vanzuita<EMAIL_ADDRESS>

Is this PR ready for review? Are we removing the old telemetry?
As I understood, we would continue to feed metric to the old place until we feel ready to replace.
https://github.com/kubefirst/kubefirst/issues/499#issuecomment-1268546969

Is this PR ready for review? Are we removing the old telemetry?

that's right, I was waiting the today's release first to ask for review.

As I understood, we would continue to feed metric to the old place until we feel ready to replace. #499 (comment)

that's also correct, we're actually doing that following this approach: starting from this PR and upcoming releases, the installer uses SegmentIO, for previous kubefirst versions App Runner is still in place to handle telemetry.

So, to calculate new install we would have to add the 2 dashboards?

So, to calculate new install we would have to add the 2 dashboards?

previous and this PR uses the same metric names[1], there were no required changes in the dashboard
https://github.com/kubefirst/kubefirst/pull/508/files#diff-63e3490c1939ebc33c6a79bf6843562e7d8e2104842fbed9b44b520413ca47edR14


superset-frontend proxy is not working when superset backend decides to use zstd encoding

Bug description
Hi guys, my company is using Apache Superset and we just found weird situation.
Our new developer were using superset-frontend and he saw weird characters being displayed instead of "normal page".
After few hours of investigation we realised the problem was superset (python server) which served content using zstd encoding (instead of br encoding which other devs were using).
zstd encoding is not supported by superset's node js proxy (see: https://github.com/apache/superset/blob/10c78960a7c5e5628aaceb584c206a81f7fd12c8/superset-frontend/webpack.proxy-config.js#L123)
We managed to fix the problem using exactly same version of python packages from working machine, but it looks like some superset dependency was updated to serve using zstd by default. Unfortunately it looks like zstd is not supported in node.js yet, so we cannot just easily support zstd in webpack.proxy-config.js
Does anyone know which exact package can cause this? I tried to grep superset and werkzeug code for zstd but found nothing.
Thank you for your suggestions.
How to reproduce the bug

Install latest superset
Start superset backend
Start superset frontend dev-server (npm run dev-server)
Go to dev-server and you will see bunch of question marks and weird characters instead of expected markup

Screenshots/recordings
No response
Superset version
master / latest-dev
Python version
3.11
Node version
18 or greater
Browser
Chrome
Additional context
No response
Checklist

[X] I have searched Superset docs and Slack and didn't find a solution to my problem.
[X] I have searched the GitHub issue tracker and didn't find a similar bug report.
[X] I have checked Superset's logs for errors and if I found a relevant Python stacktrace, I included it here as text in the "additional context" section.

@jakubjosef so that would be on a fresh install from pypi as in pip install apache-superset ?
Doing a bit of guessing here, since all of our CI installs the pin packages in requirements/development.txt, maybe the unpinned version of Superset has this issue? If that's the case it points towards:

as a workaround, you leveraging docker images build on top of pinned deps, or using the pinned deps in your installation scripts
for us, we could run some CI tests against a vanilla pip install apache-superset, so at least we know when an open dependency breaks a build - which would inform pin-pointing the issue and changing the version range for that dep

but I think we'll face the same issue eventually

My suggestion would be either:

use the base docker, which installs the pinned deps ahead of installing the apache-superset package
fetch the requirements/base.txt from the repo (with the associated version tag you're trying to build as in git checkout 4.0.0) and pip install those pin deps ahead of installing the superset package (that has loose dependency ranges). At that point might as well pip install -e . to install from the repo as opposed to pypi, but should be same as pip install apache-superset==4.0.0

On our side, there's a question as to whether we should publish a more strict or "with depenencies pinned" version of the package to work around all this....

Hi @jakubjosef thanks for reporting this, and for the research into the cause. I ran into the same issue and found that adding COMPRESS_REGISTER = False into my superset_config.py file fixes it for me when developing locally. We'll look into either documenting this somewhere or fixing it more longer-term.

I also encountered the same problem. After modifying config.py =>COMPRESS_REGISTER = False, the page no longer had strange symbols, but there were many resources on the page that failed to load; May I ask how I can solve this problem?
superset:3.1.3
python: 3.9.7
node: 16.20.2
npm: 8.19.4

For me the culprit package was Flask-Compress 1.15.
Rolling back Flask-Compress to 1.14 and removing zstandard solved the problem.
You can pip install pipdeptree and then run pipdeptree -r -p zstandard to see what depends on zstandard.

@tyrius02 Thank you for identifying what exact package version is causing this problem, it can help others. We were not able to identify exact cause when we faced this problem.

$ cd superset-frontend
$ npm i simple-zstd -D

Edit superset-frontend/webpack.proxy-config.js add zstd support :
const {ZSTDDecompress} = require('simple-zstd');

...
function processHTML(proxyResponse, response) {
  ...

  } else if (responseEncoding === 'zstd') {
    uncompress = ZSTDDecompress();
  }
 ...
}

@rebareba Great, thank you for providing this code!

Sounds like we're discussing this bug... I'm excited to hear we're on the verge of a real fix here!

Fixed by https://github.com/apache/superset/pull/30034
Thanks to all (especially @rebareba) for the help here!


Scenario for Asset that only cares about total booking amount

Hi , Our team is dicussing a scenario described below, hope that you can help us figure out the best practice.
For example, there's a pool asset, which can Contain 500 people at most.
In the morning, we allow only 200 people to book the pool (08:00 ~12:00).
In the afternoon, we allow all 500 people to book the pool (14:00 ~18:00).
If we use current model , we will have to build 500 asset for this pool,
and create periods for morning (08:00 ~12:00) and afternoon(14:00 ~18:00).
Maybe there's better ways to handle this type of asset, which only cares about total booking amount?

Under the current design, we should create 500 "Player" assets in the "Pool" category with the blacklist mode. 200 of the player assets should have "reject" schedules all afternoon.
The real issue is that if the player number reaches 10000+, we will face a performance problem. Adding a new Quantity property in the Asset entity is a solution, but it may cause more complexities.
Do you have any other ideas?

Let's return to your dynamic quantity need. If the Quantity property in the Asset entity adds, the dynamic-quantity scene can implement on the app development side. You can create a new aggregate root named AssetPeriodQuantity and then override the AssetOccupancyManager to use it.


[Android, Java] Fix JNI Type casting issue

Fix #27621
In the case of a 32-bit device, there is a problem that uint32_t is not cast to Java long boxed type.
To solve this issue, I changed it so that it can be saved as a normal value by casting to a local variable of jni type in advance.
However, the ZAP creation script has some problems, so this script also needs to be modified.
https://github.com/project-chip/zap/pull/1092

I updated ZAP version. And, I ran zap_regen_all.py script.
I also uploaded zap generated file.


Update to 1.18

Title says it all.

As I said on curseforge, I'll start porting later this week.

Speaking of which I started porting your mod.
Here: https://github.com/Samlegamer/Rereskillable-Rereforked/tree/1.18.1
It's not all there (buggy gui), but I think I ported 75%!

I've fixed the UI issues with my version, should be on curseforge soon.


Disable non-functional tooltip

IDE Information
Latest IDEA
Bug Description
When searching through a file and iterating between occurrences, the edit tooltip shows up on each and every search result - not only being annoying, but also not actually working (the shortcut indicated doesn't work if focus is in search box).

Additional context
No response

This IDE extension clearly turns into Microsoft Clippy.


Documentation is wrong

This page says:
To get full details of all the add-ins plus the assigned users and groups, pipe the output of the Get-OrganizationAddIn cmdlet to the Format-List cmdlet, as shown in the following example.
Get-OrganizationAddIn |Format-List
This does not work! Get-OrganizationAddIn retrieves only most generic AddIn properties like Name, ProductID. It does not return AssignedUser and AssignedGroups properties. They are only returned if Get-OrganizationAddIn cmdlet is run with -ProductId parameter.
This goes against PowerShell typical usage scenarios and makes cmdlets piping useless.

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

ID: 680b165b-5c8b-66c4-f9f4-0e20b8666f19
Version Independent ID: cb17922d-a250-90d8-79d9-ee4662208eec
Content: Use the Centralized Deployment PowerShell cmdlets to manage add-ins
Content Source: Enterprise/use-the-centralized-deployment-powershell-cmdlets-to-manage-add-ins.md
Service: o365-administration
GitHub Login: @twernermsft
Microsoft Alias: twerner

Hello @SDedik
Thanks for noticing, I could reproduce, I am proposing thru a PR, improving that Ps line to get full details of all add-ins in a one-liner.
Thank you


Adds snapshot_convertor skeleton project

What is this PR trying to achieve?
Adding a queue backed autoscaling scala service to perform elasticsearch snapshot conversions. In order to make information available to the general public.
Who is this change for?
🎭
Have the following been considered/are they needed?

[ ] Deployed new versions
[ ] Run terraform apply.

Part of https://github.com/wellcometrust/platform/issues/1397

Looks good, useful for seeing what needs to change for a new service.


Uncaught Error: spawn /bin/sh EAGAIN

[Enter steps to reproduce below:]

Opened atom from terminal while running fish shell.
Waited... Silent-sync package started its synchronisation.

Atom Version: 0.190.0
System: Mac OS X 10.10.3
Thrown From: Atom Core
Stack Trace
Uncaught Error: spawn /bin/sh EAGAIN
At events.js:141

Error: spawn /bin/sh EAGAIN
    at exports._errnoException (util.js:734:11)
    at Process.ChildProcess._handle.onexit (child_process.js:1035:32)
    at child_process.js:1127:20
    at process._tickCallback (node.js:357:13)

Commands
  6x -7:30.3 core:close (atom-text-editor.editor.is-focused)

Config
{
  "core": {
    "themes": [
      "one-dark-ui",
      "one-dark-syntax"
    ]
  },
  "editor": {
    "invisibles": {},
    "fontSize": 12
  }
}

Installed Packages
# User
script, v2.18.0
silent-sync, v1.1.5

# Dev
No dev packages

This sounds like a duplicate of https://github.com/atom/atom/issues/4692. Can you check out that issue and leave a comment there if the suggestion doesn't help? Also, do you see the same problem in another shell?


3.5.0 release?

It'd be great to get the bug fixes from the C++ client as soon as we can :)

I started a 3.5.0 release candidate last week: https://lists.apache.org/thread/67b1p5kpfx08wq0cy9vjpcq5dyygspw7
However, I found a regression of the C++ client (https://github.com/apache/pulsar-client-cpp/issues/420) two days ago and tried to fix that issue. So we need to fix it and release C++ client 3.5.1 first.

The wheels are available in PyPI now: https://pypi.org/project/pulsar-client/3.5.0/#files


random crashes on "Cannot set property 'channel' of undefined"

node_modules/botkit/lib/Slackbot_worker.js:468
        msg.channel = src.channel;
                    ^

TypeError: Cannot set property 'channel' of undefined

Sometimes my bot will randomly crash with the error above.
The code is really simple:
controller.hears(
	['joke', 'humor'],
	['direct_message', 'direct_mention' , 'mention', 'ambient'],
	function(bot, message) {
		require('./modules/joking')(bot, message)
	});

Where joking/index.js has:
'use strict';

module.exports = function(bot, message) {
	bot.reply(message, "I work to buy a car to go to work.");
}

As you can see the message is being properly passed around.
  It's the only way for this error to occur.

Ok, I'll take a look. Thanks!

@marcobarbosa Did you intend to close this issue?  Or did you find the problem/is this issue now resolved?

I intended to close it.
The response is taken from an array of phrases with a random index. The logic for the random index has to be the issue then :)
Thanks for the help!

Sure thing!  Glad I could help.  Gotta keep an eye out for those off-by-one errors... ;-)

Hey @dossy , decided to pick up where I left and the bot still crashes.
Take this full example:
// modules/laughing.js
'use strict';

module.exports = function(bot, message) {
	var randomInt = require('./../utils/randomInt.js');
	var replies = [
		'hahaha',
		'haha',
		'LOL',
		':joy:',
		':laughing:',
		':grin:'
	];
	bot.reply(message, replies[ randomInt(0, replies.length - 1) ]);
}

// randomInt.js
'use strict';

module.exports = function(min, max) {
	return Math.floor(Math.random() * (max - min + 1)) + min;
}

// index.js
// (...)
controller.hears(
	['haha', 'hehe', 'lol'],
	['ambient'],
	function(bot,message) {
		require('./modules/laughing')(bot, message)
	});

It will crash with the same error as I mentioned before. The response is not undefined?

Um, your randomInt() function doesn't do what you probably think it does.  Given a randomInt(0, n), it can return values from 0 to n.  For example, imagine if n = 10:
Math.floor(0.999999 * (10 - 0 + 1)) + 0 = Math.floor(0.999 * 11) = 10.
In which case, replies[n] will result in undefined as JavaScript arrays are zero-indexed, e.g., only contains elements from 0 to n - 1 where n is the length of the array.

Hi Dossy,
I appreciate your thorough reply. Thanks for that.
If you look at bot.reply you can see that I'm decrementing n by one to account for the zero-indexed array:
bot.reply(message, replies[ randomInt(0, replies.length - 1) ]);

But I think this issue should remain closed. As you said, the message must be undefined for this to occur. I'll use another random function for this and check for the message before calling reply.
Thanks again for the help.

Ah, you're right, you are calling randomInt(0, n - 1) -- and you're able to reproduce the problem with that exact code you provided, and nothing else, against the latest version of BotKit?  That does sound like a bug that ought to be fixed, if it is indeed a bug?

Currently running 0.0.15 but AFAIK I'm the only one with this issue?
I'll test a few more times when I have the time and let you know if I find something else.
Meanwhile, I'll just check for the message before posting it, like:
var reply = replies[randomInt(0, replies.length - 1)];
if (reply) bot.reply(message, reply);

Thanks!

You should really update, as that version has seen many updates since the most recent! If the problem still exists let us know.


CFE_TBL_Load should produce clearer message when it has a file header mismatch

Is your feature request related to a problem? Please describe.
CFE_TBL_LoadFromFile compares the application's name + table name with the string in the header of the file. If you configure the cfe_es_startup.scr with a different application name than what is expected in the header, you get an obtuse "Fail to load Tbl '.' from '/cf/.tbl'" not making it clear that it's purely a mismatch of the header.
Describe the solution you'd like
At minimum it should indicate that the matter is a mis-match of the header and the expected header (which means that the file is there, for example). Better would be to report the actual contents of the header and the expected value. The EID for the error is unique so CFE_TBL_Load can produce an event with more specific text.
Describe alternatives you've considered
Could have CFE_TBL_LoadFromFile generate the event, but that's an "internal" fn so probably not.
Additional context
Add any other context about the feature request here.
Requester Info
<EMAIL_ADDRESS>

Note that CFE_TBL_Load writes to syslog instead of generating events...I'd like to discuss at the CCB whether this is correct behavior or whether the table code should be generating events. The description for WriteToSysLog states:
"This routine writes a formatted string to the cFE system log.  This can be used to record very low-level errors that can't be reported using the Event Services. This function is used in place of printf for flight software. It should be used for significant startup events, critical errors, and conditionally compiled debug software."
I wouldn't classify loading tables as "significant startup events, critical errors..."

@CDKnightNASA we just created the "ready for CCB" label for cases like this :). Can you add the label to this issue?

CCB 20200212 - Good idea, need to think through implementation: events VS sys_log and best way to handle it.

[ ] I need to confirm that ES can/does log to syslog if configured or if it's otherwise unable to generate event messages on the bus.

@CDKnightNASA will you have time to work this one soon, or should I remove the current milestone?  Happy to go either way, just likely won't get in this release cycle if not addressed soon.
Or is the syslog/event discussion a good candidate for a new issue, and this one could be closed with just updating the current message?

working on it now

Some comments: No EVS does not send events to SysLog if EVS can't publish to the SB. (In fact, EVS does not even check the return value of CFE_SB_SendMsg()--I am thinking I should file a bug against EVS that it should check the return value and, if not CFE_SUCCESS, should report an error to SysLog? About the only error that is likely from SendMsg() is a CFE_SB_BUF_ALOC_ERR, but it is possible!
Second - the logic for CFE_TBL_Load() is that it logs to syslog in the main line and then at the end of the function, it checks the status and if it's not success, then it reports an event depending on the value of status. Seems clunky and difficult to debug.
One other comment -- a lot of the messages report the contents of the Status variable as a hex number. Given that the Status is set to a #define decimal value, I'm very confused why hex.


Several Knights changes

A few things:

General Knight now displays the sword in a different position, it holds it pointing laterally. Sometimes with the random rotation the sword now go through the knight on its side, could we fix captain's position somehow? Maybe the captain can go on the front and the other two more on the back, as we do on real models.

General Knight model changes UVs on shoulders, horse's head's plate and horse's back plates and now their borders are golden. Also on horse's front barding, golden too in both armies (no red or blue, I saw no conflict). Because it means higher status. No changes on texture for it, the texture already had all that.

Added one extra troop, knight bannerman. It now displays a huge banner to help identifying the General base. Took it from the third knight model, erased the lance and added the banner.

Changed all rider's texture because of the bannerman adition. Took the same banner texture from the infantry (long spear and banner). I didn't erase any other part from the previous texture, there was room enough for it.

Very nice, especially the knight with the banners, merged


Expose API Base URL option

There are projects like Helicone which provide a wrapper around the OpenAI API for tracking prompts/response quality over time, error rates, understanding pricing and token usage etc. Additionally some people might implement OpenAI API-compatible open source model servers e.g. based on LLama etc. So it would be great if the user could change the baseURL in the settings to use other services.
From the Helicone docs, the implementation should be pretty easy:
import { Configuration, OpenAIApi } from "openai";

const configuration = new Configuration({
  apiKey: process.env.OPENAI_API_KEY,
  basePath: "https://oai.hconeai.com/v1",
});

const openai = new OpenAIApi(configuration);

Great idea. Tried this change, and while the app works, nothing is displayed on the Helicone dashboard.
I've asked the Helicone discord if there are issues with the /v1/chat/completions API, or with stream: true (for getting the streaming output).

Done and deployed. Check https://github.com/enricoros/nextjs-chatgpt-app/commit/7154db7f72e39cf09e6e1f27c7d7e371d5448787 for instructions and the note about Helicone adding support for chatGPT/streaming on the dashboard.


Include service time response (from http header) in gatling reports.

Hi gatling guys!
There is x-envoy-upstream-service-time header. It shows real service response time without network latency.
I would like to have the server time included in gatling reports. The header name can differ depending on tool and should configurable.

Is it feasible to add the functionality?
If not. Is there a way to write a workaround to add this to gatling reports?

Regards

Hi,
I'm afraid that's not possible at the moment.
Actually, we're considering introducing an API for reporting custom metrics (counts and durations) but this will be an Enterprise only feature.
I can't think of a workaround.
Regards
PS: questions are best asked on our community forum: https://community.gatling.io/

Thank you for your response. I will use forum next time.
Happy Easter!


Update image ghcr.io/zoriya/kyoo_transcoder to 24c0409

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GUVNOR-2259 - Correctly limiting parameters to the Guvnor REST operations

Please let me know if you think this is a good idea?
@Rikkola, @manstis, let me know..

Will add unicode tests..

@mrietveld Please ping me when you've added the i18n tests.

@mrietveld is this still relevant?

I give up: I haven't been able to find a valid way to test i18n.


Merge the test cases under testing/testsuites into the testing folder

Summary
The test cases in the testsuites folder are not stored in a reasonable location, so they are merged into the testing folder.
Impact
Testing

[Experimental Bot, please feedback here]
Does this PR meet the NuttX Requirements? Please be concise
No.  While it provides a summary, it lacks crucial details.
Missing Information:

Summary:  Lacks specifics. Why is the current location unreasonable?  What part of the code is changed (directory structure only?  Makefiles?  Build system logic?) How does the merge work (simple move, scripts, etc.)? Are there related issues?
Impact:  Completely empty. This section needs to address all listed impact areas. Moving tests can have wide-ranging consequences, especially on build systems and user workflows.  At a minimum, expect build impact (Makefile changes) and user impact (locating tests).  Documentation almost certainly needs updating.
Testing: Empty.  Must provide build host and target details.  "Testing logs" should show the tests running before and after the change to demonstrate the change doesn't break anything and achieves the desired outcome.  Just saying "it works" is insufficient.  Show how you proved it works.

Example of an improved Summary:
The current location of test cases in testsuites makes it difficult to discover and run tests related to specific modules. This PR moves all tests under the testing directory, organizing them by module.  This change simplifies test discovery and execution and streamlines the testing process.  This addresses the lack of organized testing highlighted in NuttX Issue #XYZ.  The change involves modifying the directory structure, updating Makefiles to reflect the new locations, and updating the CI scripts to run the relocated tests.

@txy-21 please remove .o from the patch and fix the conflict


Dead links in README

They are:
https://developers.fractal.is/unity-sdk/introduction
should be ( think ) :
https://docs.fractal.is/unity-sdk/unity-sdk

fixed, thanks !


is it time to rerun the benchmarks?

Hi SGLang team,
I have just tried SGLang for the first time - and it was probably one of the easiest projects to setup and launch - it literally took me a few minutes to go from 0 to serving - awesome!!! and thank you for making it so easy on the user.
I have just benchmarked vllm=0.6.2 vs sglang=0.3.2 on 2 H100s w/ 8b llama3 and tp=2 and I get vllm slightly faster than sglang performance, yet the benchmark section shows a very different picture. Would it be possible to re-benchmark and tell me if I am missing on some optimization flags to see the results you get - I'm just checking the baseline at the moment - so no quantization and such. Will get there a bit later. FWIW, I have just benchmarked and vllm had a massive throughput speed up made in v0.6.2 over its v0.5 https://x.com/StasBekman/status/1844886291378470966 - which is probably why the benchmark on your site needs a refresher.
Thank you!
Below are the stats and command lines so that it's reproducible by others.
vllm=0.6.2 w/ normal
============ Serving Benchmark Result ============
Successful requests:                     50
Benchmark duration (s):                  5.30
Total input tokens:                      12180
Total generated tokens:                  11255
Request throughput (req/s):              9.43
Output token throughput (tok/s):         2121.93
Total Token throughput (tok/s):          4418.26
---------------Time to First Token----------------
Mean TTFT (ms):                          367.92
Median TTFT (ms):                        375.01
P99 TTFT (ms):                           378.64
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          5.80
Median TPOT (ms):                        6.53
P99 TPOT (ms):                           6.75
---------------Inter-token Latency----------------
Mean ITL (ms):                           6.54
Median ITL (ms):                         6.87
P99 ITL (ms):                            8.56
==================================================

vllm=0.6.2 w/ --num-scheduler-steps 8
============ Serving Benchmark Result ============
Successful requests:                     50
Benchmark duration (s):                  4.44
Total input tokens:                      12180
Total generated tokens:                  11249
Request throughput (req/s):              11.27
Output token throughput (tok/s):         2535.33
Total Token throughput (tok/s):          5280.50
---------------Time to First Token----------------
Mean TTFT (ms):                          242.44
Median TTFT (ms):                        231.79
P99 TTFT (ms):                           279.11
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          6.42
Median TPOT (ms):                        5.82
P99 TPOT (ms):                           12.75
---------------Inter-token Latency----------------
Mean ITL (ms):                           44.78
Median ITL (ms):                         44.70
P99 ITL (ms):                            101.35
==================================================

sglang==0.3.2
============ Serving Benchmark Result ============
Successful requests:                     50
Benchmark duration (s):                  5.47
Total input tokens:                      12180
Total generated tokens:                  11514
Request throughput (req/s):              9.14
Output token throughput (tok/s):         2104.62
Total Token throughput (tok/s):          4330.98
---------------Time to First Token----------------
Mean TTFT (ms):                          240.62
Median TTFT (ms):                        242.29
P99 TTFT (ms):                           326.44
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          8.84
Median TPOT (ms):                        7.19
P99 TPOT (ms):                           26.74
---------------Inter-token Latency----------------
Mean ITL (ms):                           7.04
Median ITL (ms):                         6.53
P99 ITL (ms):                            10.16
==================================================

the servers
vllm:
python -m vllm.entrypoints.openai.api_server \
    --host <IP_ADDRESS> --port 9999 \
    --model meta-llama/Meta-Llama-3-8B-Instruct \
    --tokenizer meta-llama/Meta-Llama-3-8B-Instruct \
    --dtype=bfloat16 \
    --seed 42 \
    --gpu_memory_utilization 0.8 \
    --num-scheduler-steps 8 \
    -tp 2

sglang:
python -m sglang.launch_server --port 9999 --tp 2  --model-path meta-llama/Meta-Llama-3-8B-Instruct

the benchmark client
git clone https://github.com/vllm-project/vllm
cd vllm/benchmarks
wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
mkdir results
python benchmark_serving.py \
    --backend vllm \
    --model meta-llama/Meta-Llama-3-8B-Instruct \
    --dataset-name sharegpt \
    --dataset-path ShareGPT_V3_unfiltered_cleaned_split.json \
    --port 9999 \
    --save-result \
    --result-dir results \
    --result-filename test.json \
    --num-prompts 50 \
    --request-rate inf \
    --seed 42

Hi @stas00
First of all, thank you for your issue.
Your description reveals several issues, which I will point out here. If you have any questions, we can continue the discussion.
Regarding the figure in the README, you can refer to https://github.com/sgl-project/sglang/tree/main/benchmark/blog_v0_2, which provides a detailed description of versions and reproduction methods. Regarding the performance improvement of vLLM v0.6, we have also conducted a benchmark that can be found at https://github.com/sgl-project/sglang/tree/main/benchmark/benchmark_vllm_060. vLLM v0.6 has indeed improved significantly, but there are some limitations. Whether it's --num-scheduler-steps or --multi-step-stream-outputs, when enabled or disabled, compared to the baseline, TTFT or ITL may worsen. Meanwhile, other metrics improve. You need to understand the tradeoff behind this, rather than being attracted by a single metric.
Meanwhile, there are some issues with the parameters when you benchmark SGLang. For your testing scenario, you should use --disable-radix and --enable-torch-compile. Additionally, for an 8B model, using tp 1 is sufficient, there's no need to use tp 2, etc.
Overall, there are many aspects to consider with benchmarking. Both the configuration of the benchmark and configuration of the server itself can significantly impact the results. We need to focus on the overall performance metrics rather than local ones.

Thank you for your reply, Yineng.
Thank you for sharing the vllm==0.6.0 vs sglang benchmark. This is great, and fits right into the OP
Your frontpage shows vllm throughput being much much worse than sglang.

The benchmark you have shared shows that vllm is slightly worse, which is a very different situation. That's why I was suggesting a new visual is needed to show the updated reality.
Please note the first results table I shared doesn't use --num-scheduler-steps and I was comparing apples to apples, since the setup I had to benchmark was using tp=2, I had to benchmark sglang with tp=2 as well.
But let's finish the vllm vs sglang discussion as I wasn't seeking to provoke - was just hoping for a fair representation of vllm as it currently appears to be very inferior on that plot you have published many months ago.
===============================
If I get the resources my intention is to support multiple inference backends in our team's inference framework and switch between them depending on which backend performs better than others in each particular use-case - or because of a better stability.
Let's move to how do I make SGLang shine. Thank you for sharing the tips that I should add --disable-radix and --enable-torch-compile. Tomorrow is a holiday, so I'm looking forward to re-running the benchmarks on Tue.
And it sounds like a very low TTFT is one of the main objectives of SGLang, correct? We currently do mainly offline generation, so TTFT doesn't matter, but it'll become hugely important when later we will be facing the user. But that's why I was benchmarking throughput.
One other thing I was puzzling over is how could I do outlines-style structured json generation, there I pass the target json schema and everything is taken care of automatically - with SGLang it appears I need to manually create a regex - any reason why this can't be automated? I really liked your blog about switching to prefill when doing structured generation and knowing that the next few tokens are fixed and require no generation and wanted to try it out in practice. Though I am starting to diverge here and should probably start a different thread on this one.

@stas00 If you are concerned about offline scenarios, focusing on throughput, you should maximize the batch size and make full use of VRAM, meaning the KV Cache usage should be as high as possible. However, in your benchmark command, only 50 requests were made. Running Llama 3.1 8B under two H100 devices is far from reaching the true limit of throughput with these 50 requests.

I conducted a simple benchmark with 2000 prompts, which you can use as a reference. The commands are generally consistent with what you provided above. Among them, vLLM is version 0.6.3 and SGLang is the latest main version. The startup command for vLLM removed --gpu_memory_utilization 0.8 and added --disable-log-requests.
From the results, it can be seen that in the maximum throughput scenarios you are concerned about, SGLang consistently outperforms vLLM. (24022.63 vs 22116.03)
# SGLang
============ Serving Benchmark Result ============
Successful requests:                     2000
Benchmark duration (s):                  34.55
Total input tokens:                      453502
Total generated tokens:                  376493
Request throughput (req/s):              57.89
Output token throughput (tok/s):         10896.87
Total Token throughput (tok/s):          24022.63
---------------Time to First Token----------------
Mean TTFT (ms):                          4878.89
Median TTFT (ms):                        4804.10
P99 TTFT (ms):                           8589.78
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          194.03
Median TPOT (ms):                        74.07
P99 TPOT (ms):                           1647.41
---------------Inter-token Latency----------------
Mean ITL (ms):                           78.71
Median ITL (ms):                         34.42
P99 ITL (ms):                            518.89
==================================================

# vLLM
============ Serving Benchmark Result ============
Successful requests:                     2000
Benchmark duration (s):                  37.58
Total input tokens:                      453502
Total generated tokens:                  377607
Request throughput (req/s):              53.22
Output token throughput (tok/s):         10048.22
Total Token throughput (tok/s):          22116.03
---------------Time to First Token----------------
Mean TTFT (ms):                          15410.77
Median TTFT (ms):                        15314.09
P99 TTFT (ms):                           32016.97
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          30.23
Median TPOT (ms):                        28.58
P99 TPOT (ms):                           78.09
---------------Inter-token Latency----------------
Mean ITL (ms):                           25.85
Median ITL (ms):                         19.51
P99 ITL (ms):                            273.56
==================================================

This is very useful, @zhyncs

I'm able to reproduce that SGLang consistently outperforms vLLM - but do we agree that the difference isn't 2x as it appears to be from the benchmark on the front page? (bringing back to the OP)

you suggested to add 2 flags --disable-radix --enable-torch-compile but in my benchmarking they make the results worse - any suggestions if perhaps I'm doing wrong?

looks like vllm had a speed regression 0.6.2 => 0.6.3 https://github.com/vllm-project/vllm/issues/9476 - I get a better throughput with 0.6.2, though it's still behind SGLang.

Here are the benchmark results:
2000 concurrent requests benchmark
Benchmark
git clone https://github.com/vllm-project/vllm
cd vllm/benchmarks
wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
mkdir results
python benchmark_serving.py \
    --backend vllm \
    --model meta-llama/Meta-Llama-3-8B-Instruct \
    --dataset-name sharegpt \
    --dataset-path ShareGPT_V3_unfiltered_cleaned_split.json \
    --port 9999 \
    --save-result \
    --result-dir results \
    --result-filename test.json \
    --num-prompts 2000 \
    --request-rate inf \
    --seed 42

sglang==0.3.2
python -m sglang.launch_server --host $(hostname) --host <IP_ADDRESS> --port 9999 --tp 2  --model-path  meta-llama/Meta-Llama-3-8B-Instruct

============ Serving Benchmark Result ============
Successful requests:                     2000
Benchmark duration (s):                  30.80
Total input tokens:                      453502
Total generated tokens:                  376822
Request throughput (req/s):              64.94
Output token throughput (tok/s):         12235.46
Total Token throughput (tok/s):          26960.73
---------------Time to First Token----------------
Mean TTFT (ms):                          2724.71
Median TTFT (ms):                        2866.76
P99 TTFT (ms):                           3483.93
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          85.08
Median TPOT (ms):                        51.94
P99 TPOT (ms):                           367.09
---------------Inter-token Latency----------------
Mean ITL (ms):                           56.85
Median ITL (ms):                         34.19
P99 ITL (ms):                            419.42
==================================================

sglang==0.3.2 + --disable-radix --enable-torch-compile
python -m sglang.launch_server --host $(hostname) --host <IP_ADDRESS> --port 9999 --tp 2  --model-path  meta-llama/Meta-Llama-3-8B-Instruct \
--disable-radix --enable-torch-compile

============ Serving Benchmark Result ============
Successful requests:                     2000
Benchmark duration (s):                  39.77
Total input tokens:                      453502
Total generated tokens:                  376435
Request throughput (req/s):              50.29
Output token throughput (tok/s):         9464.84
Total Token throughput (tok/s):          20867.41
---------------Time to First Token----------------
Mean TTFT (ms):                          5574.30
Median TTFT (ms):                        5403.57
P99 TTFT (ms):                           9824.15
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          208.52
Median TPOT (ms):                        84.46
P99 TPOT (ms):                           1794.57
---------------Inter-token Latency----------------
Mean ITL (ms):                           84.73
Median ITL (ms):                         39.16
P99 ITL (ms):                            908.29
==================================================

vllm==0.6.2
python -m vllm.entrypoints.openai.api_server \
    --host <IP_ADDRESS> --port 9999 \
    --model meta-llama/Meta-Llama-3-8B-Instruct \
    --tokenizer meta-llama/Meta-Llama-3-8B-Instruct \
    --dtype=bfloat16 \
    --seed 42 \
    --num-scheduler-steps 8 \
    --disable-log-requests \
    -tp 2

============ Serving Benchmark Result ============
Successful requests:                     2000
Benchmark duration (s):                  37.56
Total input tokens:                      453502
Total generated tokens:                  377235
Request throughput (req/s):              53.24
Output token throughput (tok/s):         10042.39
Total Token throughput (tok/s):          22115.08
---------------Time to First Token----------------
Mean TTFT (ms):                          13418.88
Median TTFT (ms):                        13693.80
P99 TTFT (ms):                           27527.70
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          19.64
Median TPOT (ms):                        18.37
P99 TPOT (ms):                           74.99
---------------Inter-token Latency----------------
Mean ITL (ms):                           137.43
Median ITL (ms):                         136.93
P99 ITL (ms):                            506.59
==================================================

vllm==0.6.3.post1
python -m vllm.entrypoints.openai.api_server \
    --host <IP_ADDRESS> --port 9999 \
    --model meta-llama/Meta-Llama-3-8B-Instruct \
    --tokenizer meta-llama/Meta-Llama-3-8B-Instruct \
    --dtype=bfloat16 \
    --seed 42 \
    --num-scheduler-steps 8 \
    --disable-log-requests \
    -tp 2

============ Serving Benchmark Result ============
Successful requests:                     2000
Benchmark duration (s):                  43.14
Total input tokens:                      453502
Total generated tokens:                  378114
Request throughput (req/s):              46.36
Output token throughput (tok/s):         8764.77
Total Token throughput (tok/s):          19277.04
---------------Time to First Token----------------
Mean TTFT (ms):                          16705.17
Median TTFT (ms):                        15436.29
P99 TTFT (ms):                           36631.51
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          36.76
Median TPOT (ms):                        33.73
P99 TPOT (ms):                           110.12
---------------Inter-token Latency----------------
Mean ITL (ms):                           30.63
Median ITL (ms):                         23.22
P99 ITL (ms):                            318.24
==================================================

The radix prefix cache has overhead for SGLang. We've recently worked to reduce this, but a new version hasn't been released yet. Therefore, I recommend adding --disable-radix for benchmark. When running small models like Llama 8b, I suggest enabling torch compile. It outperforms gpt-fast and is beneficial for latency-sensitive scenarios. Ultimately, the choice is yours.

ref https://github.com/sgl-project/sgl-learning-materials/blob/main/slides/the_first_sglang_online_meetup.pdf

You can see my results above - either or both --disable-radix --enable-torch-compile impact throughput for the worse.

--disable-radix theoretically does not reduce throughput. In my impression, when enabling radix cache in the ShareGPT dataset, the benefits of cache sharing are less than the overhead itself.

That's very cool, @zhyncs! Thank you for running additional benchmarks.
Adding --num-scheduler-steps 8 --multi-step-stream-outputs=False to vllm should make things faster in vllm 0.6.3+ according to this thread https://github.com/vllm-project/vllm/issues/9476

python3 -m vllm.entrypoints.openai.api_server --model meta-llama/Llama-3.1-8B-Instruct --disable-log-requests --max_model_len 2048 --num-scheduler-steps 8 --multi-step-stream-outputs=False
python3 -m sglang.bench_serving --backend vllm --request-rate 46

============ Serving Benchmark Result ============
Backend:                                 vllm
Traffic request rate:                    46.0
Successful requests:                     1000
Benchmark duration (s):                  32.53
Total input tokens:                      224442
Total generated tokens:                  190594
Total generated tokens (retokenized):    190565
Request throughput (req/s):              30.74
Input token throughput (tok/s):          6899.73
Output token throughput (tok/s):         5859.18
----------------End-to-End Latency----------------
Mean E2E Latency (ms):                   3663.89
Median E2E Latency (ms):                 2412.93
---------------Time to First Token----------------
Mean TTFT (ms):                          92.97
Median TTFT (ms):                        82.02
P99 TTFT (ms):                           289.57
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          19.66
Median TPOT (ms):                        19.51
P99 TPOT (ms):                           36.61
---------------Inter-token Latency----------------
Mean ITL (ms):                           147.97
Median ITL (ms):                         139.65
P99 ITL (ms):                            369.77
==================================================

@stas00 Thanks for your advice. Cool It gets better. And the performance is still worse than TensorRT LLM and SGLang.

The TTFT is indeed much worse, but vllm's throughput is higher than SGLang's according to the updated numbers you have shared.
And cycling back to the OP, it's hopefully very clear now that the plots on the SGLang's front page need a refresh to bring them to the up-to-date reality ;)

As described above https://github.com/sgl-project/sglang/issues/1639#issuecomment-2438584930 I use this benchmark config to test the online scenario. If we want to test the max throughput for offline, we should maximize the batch size and make full use of VRAM, meaning the KV Cache usage should be as high as possible. At this time, SGLang's throughput is also higher.

The readme and documentation are all updated. We will release more up-to-date benchmark results in the following blog series.
May I know your requirements and workloads better? What kind of optimizations do you want?

@stas00 May I know your requirements and workloads better? What kind of optimizations do you want? We can see how we can make SGLang better for your workloads.

Thank you, @merrymercy
I don't think we have any special requests at this point. Our team has been using vllm and I've been poking around looking for the best solutions for different use-cases - your blog posts have been very awesome so I had to try sglang out and found it super-easy to use and the outcomes were great too - I communicated that to the team so we shall see how things transpire - might take some time since vllm is quite excellent as well.


[Bug]: Cmake regex error while trying to make install

What Operating System(s) are you seeing this problem on?
Linux (x86-64)
dlib version
19.24.5
Python version
No response
Compiler
gcc 11.4.0
Expected Behavior
installation successfully done
Current Behavior
Gives error whie sudo make install
Steps to Reproduce
mkdir build
cd build
cmake .. //sucessfull
make //sucessfull
sud make install // gives an error
Anything else?

Try without + characters in your file path.  Like don't put + in your directory name.
I don't know that this is anything I can control.


Add Methylseq indexes

Needed for https://github.com/nf-core/methylseq/pull/350

Are these still useful?

Yeah once I get around to getting it working!

I tested that these work on the scatter branch...merging this now


[bitwarden vault] ... cannot reach the custom url option in settings

Extension
https://www.raycast.com/jomifepe/bitwarden
Description
Hi --
In the settings, I am unable to tab or scroll down to the option to input my self hosted custom url. I am unable to resize the Raycast settings box in order to do so. See screenshot

Steps To Reproduce
Raycast settings
Current Behaviour
No response
Expected Behaviour
No response

Hello, I'm sorry, but it's unlikely that this issue is related to the Bitwarden extension.

Apologies, you're right -- thanks for responding. It actually happens for all menus. My resolution/etc is standard, can't seem to figure out how to increase the size of Raycast settings menus.


java.lang.VerifyError on Lollipop when using release APK

I get this error when I install my release APK on a 5.x device. The error does not occur when I push the same code from Android Studio to the device, or if I run it on 4.x.
java.lang.VerifyError: Verifier rejected class com.myapp.android.ui.activity.MainActivity$$ViewInjector due to bad method void com.myapp.android.ui.activity.MainActivity$$ViewInjector.reset(com.myapp.android.ui.activity.MainActivity) (declaration of 'com.myapp.android.ui.activity.MainActivity$$ViewInjector' appears in /data/app/com.myapp.android-2/base.apk)
       at java.lang.Class.classForName(Class.java)
       at java.lang.Class.forName(Class.java:308)
       at java.lang.Class.forName(Class.java:272)
       at butterknife.ButterKnife.findInjectorForClass(ButterKnife.java:298)
       at butterknife.ButterKnife.inject(ButterKnife.java:271)
       at butterknife.ButterKnife.inject(ButterKnife.java:184)
       at com.myapp.android.ui.activity.MyDrawerActivity.onCreate(MyDrawerActivity.java:31)

Line 31 is just ButterKnife.inject(this);. I inject my Toolbar and a custom NavigationDrawer in the class.
@InjectView(R.id.toolbar) Toolbar mToolbar;
@InjectView(R.id.nav_drawer) MyNavigationDrawer mNavigationDrawer;

Is there something that would be different with the Butterknife codegen when using gradle assembleRelease? I am not using ProGuard at all.
Here are my other Android build settings:
# Android SDK settings
ANDROID_BUILD_MIN_SDK_VERSION=14
ANDROID_BUILD_TARGET_SDK_VERSION=21
ANDROID_BUILD_SDK_VERSION=21
ANDROID_BUILD_TOOLS_VERSION=21.1.2

Checked on a Moto X 2014 running Lollipop, looks like this is related to 5.0, not just Nexus devices.

Crash only happens when running ART.
Logcat:
I/art     (21354): Verification error in void com.myapp.android.ui.activity.MainActivity$$ViewInjector.inject(butterknife.ButterKnife$Finder, com.myapp.android.ui.activity.MainActivity, java.lang.Object)
I/art     (21354): void com.myapp.android.ui.activity.MainActivity$$ViewInjector.inject(butterknife.ButterKnife$Finder, com.myapp.android.ui.activity.MainActivity, java.lang.Object) failed to verify: register v4 has type Reference: com.myapp.android.ui.activity.MainActivity but expected Reference: com.myapp.android.ui.activity.LoggedInNavActivitya.lang.Object): [0x0]
I/art     (21354): Verification error in void com.myapp.android.ui.activity.MainActivity$$ViewInjector.reset(com.myapp.android.ui.activity.MainActivity)
I/art     (21354): void com.myapp.android.ui.activity.MainActivity$$ViewInjector.reset(com.myapp.android.ui.activity.MainActivity) failed to verify: register v1 has type Reference: com.myapp.android.ui.activity.MainActivity but expected Reference: com.myapp.android.ui.activity.LoggedInNavActivity
E/art     (21354): Verification failed on class com.myapp.android.ui.activity.MainActivity$$ViewInjector in /data/app/com.myapp.android-1/base.apk because: Verifier rejected class com.myapp.android.ui.activity.MainActivity$$ViewInjector due to bad method void com.myapp.android.ui.activity.MainActivity$$ViewInjector.reset(com.myapp.android.ui.activity.MainActivity)

Are you using ProGuard or retrolambda or something that could be mucking up your bytecode?

No proguard or retrolamba.
I see the issue now. LoggedInNavActivity is no longer in my codebase. MainActivity used to extend it, but I did some refactoring and the file is now deleted. It doesn't show up in a code search in my repo at all.

Are you doing clean builds and seeing this?

Yes. Let me try to blow out the build folder manually though.

That worked. Why would ART have a problem but not Dalvik?
Sounds like the codegen aggressively caches. Is this a Gradle bug, or working as intended?

I'm not really sure. It's all completely opaque to butter knife's annotation processor. I'm really surprised that a clean build wasn't adequate enough to clear the output directly. What's worse is that it seems like this was actually a compiled class file since the source files we generate would have just failed the build. I can only guess that it only fails on ART because every class has to go through the AOT compilation whereas on Dalvik everything was lazy.


Fix for invalid_request

A fix for this issue:
https://github.com/seoda/socialite-apple/issues/2

@yujiod did you have a chance to review this? Thanks

@yujiod bump

@cschalenborgh Is there any way to override this function?

@cschalenborgh Is there any way to override this function?

You can try this: https://matthewsetter.com/series/tooling/composer/forked-repositories/


Added an example and updated a parameter desc

Docs Build status updates of commit c066ca1:
:white_check_mark: Validation status: passed

File
Status
Preview URL
Details

azureadps-1.0/MSOnline/Set-MsolDomainFederationSettings.md
:white_check_mark:Succeeded
View (azureadps-1.0)

For more details, please refer to the build report.
Note: Broken links written as relative paths are included in the above build report. For broken links written as absolute paths or external URLs, see the broken link report.
For any questions, please:Try searching the docs.microsoft.com contributor guidesPost your question in the Docs support channel

Fixes #688.

@billmath , please review the changes I made on this cmdlet based on what we discussed.

#sign-off

@CelesteDG , please review this.

@CelesteDG can you please review and merge?


..when does it become official?

Still waiting for official confirmation, not sure I'll believe it until I have it in writing from HR sorry I mean P&OD 😉


tiled layer do not display

var options = {
        transparent: true,
        format: 'image/png'
      }
 var source = window.L.WMS.source(
        'http://e/geoserver/FAST/wms',
        options
      )
      source.addSubLayer('layer')

works fine. but
var options = {
transparent: true,
format: 'image/png',
tiled:true      }
the tile is 0*256 and not show
leaflet 1.3 ,chrome latest

Did you compare URLs of requests to WMS in these two cases ?
Just checked, works normal with my own WMS service, sending several requests with &tiled=true&width=256&height=256 instead of single one.

with some test. it is ok.
i used a css framwork to set img's max-width:100%
fix by add
.leaflet-container img {
  max-width: none;
}


Keep logs every time the scrapers run

This is for gathering whether we need to spend ages on optimising the translator

This should be covered now that we're going to use SumoLogic, yes?

Correct, closing


[Feature] Prevent Uploading Old Files

Is there an existing issue for this?

[X] I have searched the existing issues

Is your feature request related to a problem? Please describe.
I am listing the files I have uploaded. When I add a new file, the same files are uploaded again. What can I do to prevent it from reloading files that have already been uploaded?
Describe the solution you'd like
Files uploaded as data-upload="false" can be prevented from being uploaded again.
Describe alternatives you've considered
Files uploaded as data-upload="false" can be prevented from being uploaded again.

Yes please. When I use "load" endpoint, the retrieve file is upload to the server again.

You have to mark already uploaded files as "local", meaning the file is already on the server.
https://pqina.nl/filepond/docs/api/instance/properties/#files

@rikschennink yes, I marked as "local". Also I tried with restore endpoint, marking as "limbo", and the file is upload again. The only way to stop upload again is setting "instantUpload" to false.


docs: add CHAOSS metrics (EN + ZH) + refactoring

Description
Added new CHAOSS metrics (both EN & ZH), fixed links, and minor bugs.
🦄 Tested on local, build running successfully!
cc: @Peng99999, @frank-zsy  /  Partially resolves #24
Before submitting the PR, please take the following into consideration

[x] It's really useful if your PR references an issue where it is discussed ahead of time. In many cases, features are absent for a reason. If you don't have an issue, please create one.
[x] Prefix your PR title with feat: , fix: , chore: , docs:, or refactor:.
[x] The description should clearly illustrate what problems it solves.
[x] Ensure that the commit messages follow our guidelines.
[x] Resolve merge conflicts (if any).
[x] Make sure that the current branch is upto date with the main branch.

Really amazing, you even change the CHAOSS URL to zh-CN for ZH language. I will modify the Chinese part.

/preview

@Neilblaze I will merge the PR and congrats on finishing your OSPP task~ I will add X-lab metrics part later.

I will merge the PR and congrats on finishing your OSPP task~ I will add X-lab metrics part later.

Thank you @frank-zsy! 🥳
Also, just to let you know, I received an email from OSPP (a few days ago) that I must submit my Final Project Report by the 30th instant (I think 彭佳恒 also referred to the same on Slack). So, I'll start working on it now and will let you know if I need anything from you.
cc: @Peng99999

I will merge the PR and congrats on finishing your OSPP task~ I will add X-lab metrics part later.

Thank you @frank-zsy! 🥳 Also, just to let you know, I received an email from OSPP (a few days ago) that I must submit my Final Project Report by the 30th instant (I think 彭佳恒 also referred to the same on Slack). So, I'll start working on it now and will let you know if I need anything from you.
cc: @Peng99999

yeah, I have received the same email. Please don’t forget to write the OSPP final project report😄

@frank-zsy , @Peng99999 I've prepared the Final Project Report & it's accessible 👉🏼  " here "
Feedbacks are greatly appreciated! 🙏🏼
Once I receive the green signal, I'll submit! 😄

@Neilblaze Great report. There are several things:

I would like to use open source rather than open-source as an adj.
I think you can add a table about the contributions tracker part, like besides 4 PRs merged, there are 32 commits and 96 discussions, along with 2387 lines added. The numbers are really impressive.

@frank-zsy Noted! ✅
Thanks for the review, I will update the report & submit it by tomorrow! 😃

@Neilblaze I have seen the final report you submitted on the OSPP website. Once the review period begins (Oct. 1), I will approve your final report. The website looks more refined and visually appealing now, thank you for making a significant contribution. 👍👏  We also welcome you to continue contributing in the future.😃

@Peng99999 Thanks for the positive feedback, really appreciate that!
And to your point, yes, absolutely! I'd love to continue contributing to the OpenDigger website and improve it further! 😄


UIPFAUTH-57 Added stripes-authority-components to stripesDeps.

Description
Added stripes-authority-components to stripesDeps
Issues
UIPFAUTH-57

I also faced this test failure previously, it can be addressed the same way.

I also faced this test failure previously, it can be addressed the same way.

Thanks, I tried it but it looks like stripes-authority-components is not a part of stripes export, so it didn't work here. I think I need to mock some ui-quick-marc components


[BUG] Notebook wrongly generated in the download

Issue description
On the right side of the screen when you open a demo it gives you the option to download the notebook. It has been detected that the notebook generated in several demos does not render well
List of demos with this problem

Getting Started:

tutorial_qubit_rotation
tutorial_backprop
tutorial_adjoint_diff
tutorial_plugins_hybrid
....
....

Optimization:

tutorial_qaoa_intro
tutorial_quantum_natural_gradient
...
...

QML

Expresibility_fourier_series
tutorial_kernel_modules
tutorial_kernel_based_training
...
...

with all the ones I have tried does not work. However one has been detected where at least the code is well rendered:
https://pennylane.ai/qml/demos/tutorial_vqt.html

Hi @KetpuntoG, this is a known issue with the build pipeline. Could you confirm whether or not these notebooks look similar to what you are seeing here?

No @co9olguy , the problem looks different. In the link you passed me it seems that it doesn't understand the cells as python but they appear. When downloading the notebooks from pennylane, the code cells disappear.

Here is a comparison of what the web shows and what the download shows:

After download:

This is very strange 🤔 I wonder if the affected demos share anything in common that causes this (the python cells to be ignored).


Change S0 calculation to enforce Again < Hard < Good < Easy.py

Relevant comment: https://github.com/ankitects/anki/pull/3367#issuecomment-2308395460
These changes will prevent situations where Again = Hard, or Hard = Good, or anything like that. But we still need to do something about the other problem. Read my comment above.

This change may induce a problem: if the initial stability of again is very large (perhaps due to the lack of data, the user rarely presses again in the first learning), the initial stabilities of hard, good and easy will be enforced correctly.

Can you at least keep the clampings I proposed?

I mean the clamps can induce the problem. So I cannot keep it.

Alright, close this PR, but please come up with a way to enforce strict monotonicty, aka "less than", not "less than or equal to"

OK, I will consider it. Could you open a new issue to record this feature?


Added logs endpoint to display on the dashboard

Describe changes
I implemented a global method that hooks as the step is launching and leverages the python logging handler interface + the artifact store interface to write and read a log file from the artifact store. Some notes:

A new logs table is introduced that keeps a track of the relationship between step_id and artifact_store_id. This relationship is neccessary to fetch the logs in the /logs endpoint
I also added pipeline_run_id in the logs table to do the same for pipeline runs for future facing purposes

I am looking for people to take a look at the models, schemas, and tables and get feedback on whether this design makes any sense.
Questions:

[ ] Is the path to the artifact store that I am using ever going to clash with the artifacts themselves?

Pre-requisites
Please ensure you have done the following:

[ ] I have read the CONTRIBUTING.md document.
[ ] If my change requires a change to docs, I have updated the documentation accordingly.
[ ] If I have added an integration, I have updated the integrations table and the corresponding website section.
[ ] I have added tests to cover my changes.

Types of changes

[ ] Bug fix (non-breaking change which fixes an issue)
[ ] New feature (non-breaking change which adds functionality)
[ ] Breaking change (fix or feature that would cause existing functionality to change)
[ ] Other (add details above)

@schustmi any opinions on the tests that were added with this PR?

@schustmi Regarding this comment

To me the "when" and "interval" are actually pretty confusing and I wouldn't know how to use them exactly. Do we actually need to even expose them, I don't see any way for users to configure this anyway?

We arent really "exposing" them right? Not sure what you mean

@schustmi Regarding this comment

To me the "when" and "interval" are actually pretty confusing and I wouldn't know how to use them exactly. Do we actually need to even expose them, I don't see any way for users to configure this anyway?

We arent really "exposing" them right? Not sure what you mean

We expose them as arguments of the function that creates the log handler, and that just adds complexity which isn't needed as we don't set these values anywhere in the code.

@fa9r One test added for you and @schustmi <3

No that’s already resolved
On Tue 23. May 2023 at 06:36, Alexej Penner @.***>
wrote:

@.**** commented on this pull request.
In src/zenml/artifact_stores/step_logging_utils.py
https://github.com/zenml-io/zenml/pull/1526#discussion_r1201505626:

+) -> str:

"""Generates and prepares a URI for the log file for a step.

Args:

   artifact_store: The artifact store on which the artifact will be stored.

   pipeline_run_id: The id of the pipeline run.

   step_name: Name of the step.

   log_key: The unique identification key of the log file.

Returns:

   The URI of the output artifact.

"""
if log_key is None:

   log_key = str(uuid4())

logs_base_uri = os.path.join(

Is this still an open question?
—
Reply to this email directly, view it on GitHub
https://github.com/zenml-io/zenml/pull/1526#discussion_r1201505626, or
unsubscribe
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You are receiving this because you authored the thread.Message ID:
@.***>

--
Hamza Tahir
Co-Creator & CTO
[image: ZenML] https://zenml.io/
Github  https://github.com/maiot-io/zenml Twitter
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ZenML Inc./GmbH, Schellingstr. 36, 80799 Munich
HRB Munich 268487, MD/GF: Adam Probst, Hamza Tahir


Download PDF run sheets

DATA="token=XYZABC123&content=pdf&record=LA04513&event=baseline_arm_1&instrument=eeg_run_sheet&returnFormat=json"
CURL=`which curl`
$CURL -H "Content-Type: application/x-www-form-urlencoded" \
      -H "Accept: application/json" \
      -X POST \
      -d $DATA \
      -o /tmp/export.pdf \
      https://redcap.partners.org/redcap/api/

Run a bash loop to obtain caselist to provide to the above API call
Run a program daily, independent of this app, to download PDF run sheets and put them in Pronet/PHOENIX/PROTECTED/PronetWU/processed/WU01590/eeg/ses-20220921/pdf_run_sheets/WU01590.pdf
Remember that we shall need two API calls--one for PRONET and one for PRESCIENT

https://github.com/AMP-SCZ/eeg-qc-dash/blob/4b1120b32b6984455271a8c48f762bc53b1cb500/app.py#L393-L395
Under session hyperlink, add a PDF hyperlink:
html.Td([html.A(ses,
            href=d.replace(ROOTDIR,URL_PREFIX)+f'/{sub}_{ses}_runSheet.txt'
            target='_blank'),
         html.Br(),
         html.A(PDF,
         href=d.replace(ROOTDIR,URL_PREFIX)+f'/{sub}_{ses}_runSheet.pdf'
            target='_blank')
        ])


Fix typo in comment

@andresnava : Thanks for your contribution to the Azure documentation! The author, @alexkarcher-msft, has been notified to review your proposed change.

Looks perfect!
#sign-off


Add risk validation

Allow users to validate risk level of an investigation (Not edit it yet!) https://trello.com/c/DU95hA6H/842-add-validation-status
Description

Added new fields to investigation "risk_validated_at", "risk_validated_by"
Add a new row in the risk section of show investigation page to represent risk validation
Link to new page for user to confirm risk is validated
Tests

Checklist:

[x] Have you documented your changes in the pull request description?
[ ] Does the change present any security considerations?
[ ] Is any gem functionality overloaded? Eg: Devise controller methods being overloaded.
[ ] Has acceptance criteria been tested by a peer?
[ ] Has the CHANGELOG been updated? (If change is worth telling users about.)

General testing (author)

[ ] Test without JavaScript
[ ] Test on small screen

Accessibility testing (author)

[ ] Works keyboard only
[ ] Tested with one screen reader
[ ] Zoom page to 400% - content still visible
[ ] Disable CSS - does content make sense and appear in a logical order?

so minor issues

Back link on interim page to read as "Back"
when validated, it should show date but not the time stamp(I think)
3.Accessibility issue - when click on validation error in summary it should focus the field
When case is validated by other team - Then it should send standard case updated notification - case validation added by "Nasir Khan(team name)

Submit button needs to be updated to Continue

The Validate call to action should only show when a user/team has been added to the case with edit permissions. At the moment the call to action is showing for teams not added to the case.
See below, no CTA to change/set risk level (as expected), but able to validate the case.

I think the Validate call to action is persisting for all teams?
We need to restrict the 'role' to one team for now - need to think about how we test this, because when we merge to main branch it needs to be a different team to those being used in review apps.

so minor issues

Back link on interim page to read as "Back"
when validated, it should show date but not the time stamp(I think)
Accessibility issue - when click on validation error in summary it should focus the field
When case is validated by other team - Then it should send standard case updated notification - case validation added by "Nasir Khan(team name)

@nasirkhanbeis I think we want to persist with back to allegation... as we do with other interim pages? (see setting case risk level and opening/closing cases)


fix mktemp on busybox

per discussion in https://github.com/facebook/flow/issues/1605, mktemp on busybox requires 6 X's in its template

@facebook-github-bot shipit

@facebook-github-bot shipit


tree-kill nw when cli get ctrl+c, etc

https://github.com/segmentio/nightmare/commit/275be9e78ba7101dac6f6e09440fa2ff033eca86

note: killing nw requires "tree-kill"
fixme: process.onexit cant kill nw even if "end" called

fix #10

@egoist btw, I guess there is something wrong with cross-spawn-async,
as child-process-promise was working fine.
Maybe change the way to fix #10 without replacing the library?

Emm... I don't know what's going on for now, in fact lately I don't have much time maintaining this repo, if you can help I'd like to transfer this repo to you 😁

Yeah, I'd like to. If you are really busy, I'm happy to take this repo.
Btw, it is a cool project. Our team is using it building NW apps for company. :smile:

Oops, you should remove or rename fritx/nwjs first.
I've also added to owners of nwjs on NPM.

OK, done. It's my pleasure. Thanks!
Btw, I just found<EMAIL_ADDRESS>works fine with Ctrl+C (on Windows). It ends nw.exe immediately (but without saying "bye!").
But with<EMAIL_ADDRESS>it doesn't end nw.exe.
My PR made it kill nw.exe but it takes much more time compared to 0.13.x

I guess we have to review this commit https://github.com/egoist/nwjs/commit/9e1d2d29e1e7724b52fe260d26ec64b9f2043062

@fritx sadly I find that rename repo doesn't help 😂
OK, let's look into this issue first.

It's weird on my device, 0.14 works the same as 0.13 😂

LOL :joy: thanks, buddy.
i know this issue exist in window, but i don't have time recently. i will check it ASAP. Thanks.

@egoist sorry, I can't reproduce the bug on Windows now. It's wierd :astonished:
Closing this first.

@egoist yup, I can reproduce the issue now.
In Windows 7 at my office.
WIth v1.3.1 it works fine, but with v1.4.0 Ctrl+C doesn't kill the nw process.
So I have to switch to 1.3.1 or my fork to get on my work.


[AnVIL Catalog] Validate custom events are accessible on the GA4 Query Explorer for dev and prod

Need
Custom events are to be included in analytics reports.
Approach
Add a custom dimension in GA for every custom parameter specified in the following tags in the AnVIL Catalog Google Tag Manager.
For any parameter that is missing, follow the CC Google Analytics Design Document (specifically the Configuring a Custom Event and Custom Events sections).
Definition of Done
Confirm every custom parameter is visible in analytics reports for AnVIL Dataset Catalog - GA4 and AnVIL Dataset Catalog CC Staging - GA4. Specifically, document the parameters below with checkboxes to indicate they have been checked:
AnVIL Dataset Catalog - GA4

tag - entity_selected

[x] entity_name

tag - entity_table_sorted

[x] entity_name
[x] column_name
[x] sort_direction

tag - filter_selected

[x] filter_name
[x] filter_value

AnVIL Dataset Catalog CC Staging - GA4

tag - entity_selected

[x] entity_name

tag - entity_table_sorted

[x] entity_name
[x] column_name
[x] sort_direction

tag - filter_selected

[x] filter_name
[x] filter_value

@MillenniumFalconMechanic This one should be all working now!

Thanks @jpaten!


Color consistency issue in search all window

Color consistency issue in search all window

Hi @gayanper, this is the same problem described in #67, please see my comment and the follow-up conversation that provides information about the problem and how to solve it on your side. As a theme author there is actually no way to fix this and it must be resolved within the JetBrains theme API itself.


Consider better lex based refactoring

Consider better lex-based refactoring based on #1167

The name of the object must include at least the name of the service as a prefix such as actor and feed.


feat(2): add tags to docs

[ ] Add doc's tags route
[x] Create DocTagsListPage component

Closes #3434

Motivation
Learn react
Have you read the Contributing Guidelines on pull requests?
Yes, I think so. ;)
Test Plan
(Write your test plan here. If you changed any code, please provide us with clear instructions on how you verified your changes work. Bonus points for screenshots and videos!)
Related PRs
(If this PR adds or changes functionality, please take some time to update the docs at https://github.com/facebook/docusaurus, and link to your PR here.)

Closing due to https://github.com/facebook/docusaurus/issues/3434#issuecomment-705754605


Update log statements for already created objects

Missed/ misclassified some log statements/ levels. Go back and update the log levels and add more detail where needed.

Completed in [gh-2].


Cannot change autostart in config

Hello,
I would like to change the autostart option globally, so I added it to the config.yml:
tetranz_select2_entity:
    autostart: false

But:

Unrecognized option "autostart" under "tetranz_select2_entity"

Is it normal?

Yeah, that's "normal" because autostart is not a global setting so for now you'll have to set it per instance.
It probably should be global so I'll try to get to that when I have time but anyone is welcome to submit a P.R.


small fixes

Codecov Report
Attention: 82 lines in your changes are missing coverage. Please review.

Comparison is base (87fb2de) 29.58% compared to head (8c05b10) 29.20%.
Report is 4 commits behind head on master.

Files
Patch %
Lines

pkg/controller/secret_controller.go
0.00%
71 Missing :warning:

pkg/juicefs/mount/builder/common.go
0.00%
6 Missing and 1 partial :warning:

pkg/driver/provisioner.go
0.00%
1 Missing :warning:

pkg/juicefs/juicefs.go
66.66%
1 Missing :warning:

pkg/juicefs/mount/builder/secret.go
0.00%
1 Missing :warning:

tests/sanity/fake_juicefs_provider.go
0.00%
1 Missing :warning:

Additional details and impacted files
@@            Coverage Diff             @@
##           master     #851      +/-   ##
==========================================
- Coverage   29.58%   29.20%   -0.38%
==========================================
  Files          45       46       +1
  Lines        5578     5654      +76
==========================================
+ Hits         1650     1651       +1
- Misses       3768     3842      +74
- Partials      160      161       +1

:umbrella: View full report in Codecov by Sentry.
:loudspeaker: Have feedback on the report? Share it here.


Prepare an environment for development by multiple people

[ ] write collabrator.md
[ ] make a lead to collabrator.md in the readme
[ ] write features already added in the current develop branch to the changelog

@anharu2394
I looked at the repositories of other packages, but I think it's better to abolish the develop branch like GitHub-Flow instead of Git-Flow and change it to master.
In case of bug fixes or function additions, I think that it can be operated if you are careful at the time of review so that it will be described in the CHANGELOG.


Transaction Feed Accessibility

Description
Prevents overlap of transaction feed items due to either large text or small screen sizes.

Screen
iOS Before
iOS After
Android Before
Android After

Transaction
Before
After
Before
After

Other changes
Jest snapshots updated
Tested
Tested Locally on iOS and Android
How others should test
Observe the transaction and activity feeds with default text sizing and large text sizing.
Backwards compatibility
Only styles were updated so this should be backwards compatible.

https://valora.testquality.com requirement R68.

Hi @MuckT We have verified this issue on Latest Android internal build V1.17.0 and updated the status in test quality tool.
Test quality tool link:
Android(11.0):

https://valora.testquality.com/project/12128/plan/21240/run/149557/result/2450254

Verified On Devices: Google Pixel 4a (11.0)


D3.2.2 Lasair final user interface design

17/JUL/22

@RoyWilliams distributed a version via email to all the assignees

30/AUG/22

@thespacedoctor submitted this for review

18/NOV/22

approved at 08/NOV/22 Exec Group
made available from publications page


docker-compose up fails with error message

What I'm trying to achieve
Quickly start a localhost setup with docker-compose.yaml
Steps to reproduce the problem

git clone https://github.com/mirumee/saleor && cd saleor
docker-compose up

Returns the error
Unsupported config option for services service: 'celery'

What I expected to happen
Some docker builds and eventually docker containers running.
Working development application at http://localhost:8000
System information
Operating system: Ubuntu 18.04
Browser: firefox
docker-compose: 1.4.2
docker: 18.06.0-ce

Solution to the problem found.
Update version of docker-compose and the up command works.
docker-compose version 1.22.0, build f46880fe


update API documentation translation

Translation for API info wasn't updated properly in the translation file.

.deploy

.deploy

.deploy

.deploy

.deploy


[Snyk] Upgrade vue from 3.4.5 to 3.4.7

This PR was automatically created by Snyk using the credentials of a real user.Snyk has created this PR to upgrade vue from 3.4.5 to 3.4.7.
:information_source: Keep your dependencies up-to-date. This makes it easier to fix existing vulnerabilities and to more quickly identify and fix newly disclosed vulnerabilities when they affect your project.

The recommended version is 2 versions ahead of your current version.
The recommended version was released 21 days ago, on 2024-01-09.

Release notes

Package name: vue

3.4.7 - 2024-01-09Please refer to CHANGELOG.md for details.

3.4.6 - 2024-01-08Please refer to CHANGELOG.md for details.

3.4.5 - 2024-01-04Please refer to CHANGELOG.md for details.

    from vue GitHub release notes

Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open upgrade PRs.
For more information:
🧐 View latest project report
🛠 Adjust upgrade PR settings
🔕 Ignore this dependency or unsubscribe from future upgrade PRs

Outdated


Error in browser with adblock extension + webrtc-adapter

[ ] I have provided steps to reproduce (e.g. a link to a jsfiddle)
[x] I have provided browser name, version and adapter.js version
[x] This issue only happens when adapter.js is used

Note: If the checkboxes above are not checked (which you do after the issue is posted), the issue will be closed.
Versions affected
Browser - Chrome 67.0.3396.87
adapter.js - v6.1.5
adblock extension - now..
Description
it appears only if the adblock extension is installed.
attached the screenshots with the exact places where there is a bug

Steps to reproduce

Install adblock extension
Use application with webrtc-adapter
Try to connect to other user (must execute new RTCPeerConnection(...))

Expected results
works fine, no error
Actual results
Uncaught (in promise) TypeError: Cannot assign to read only property 'iceServers' of object '#<Object>'
window.RTCPeerConnection | @ | chrome_shim.js?fa98:597
WrappedRTCPeerConnection | @ | blob:http://localhos…6d-31ed32b197c2:300
...

IIRC adblock has the nasty property of removing window.RTCPeerConnection.
I don't understand why this would throw at the line you indicate though and not the next one.
You should always check support by checking whether window.RTCPeerConnection and navigator.mediaDevices.getUserMedia exist before calling them.

I was getting a the same error awhile back using Adblock Plus and filed a bug report on their forum. I also defined some quick steps in there for reproducing this issue using the webrtc trickle example.
It looks like this is now happening with the regular Adblock extension as well.
@fippo I'm not sure this is just a case of checking for the existence of webrtc methods on the window object. At least in my case, the window methods are defined.
I believe the issue is caused by adblock wrapping the pc methods and using Object.defineProperty to add properties to the pcConfig object. That would make the iceServers key immutable and cause the error above. AddBlock Plus has their code on github so you can see their implementation here.
This could be solved by creating a copy of the pcConfig object that replaces the immutable properties with mutable properties. Here is an example using the snippet that is throwing the error:
// migrate from non-spec RTCIceServer.url to RTCIceServer.urls
var OrigPeerConnection = window.RTCPeerConnection;
window.RTCPeerConnection = function(pcConfig, pcConstraints) {
  const configCopy = {};
  Object.getOwnPropertyNames(pcConfig).forEach(function(name) {
    configCopy[name] = pcConfig[name];
  });
  if (configCopy && configCopy.iceServers) {
    var newIceServers = [];
    for (var i = 0; i < configCopy.iceServers.length; i++) {
      var server = configCopy.iceServers[i];
      if (!server.hasOwnProperty('urls') &&
          server.hasOwnProperty('url')) {
        utils.deprecated('RTCIceServer.url', 'RTCIceServer.urls');
        server = JSON.parse(JSON.stringify(server));
        server.urls = server.url;
        newIceServers.push(server);
      } else {
        newIceServers.push(configCopy.iceServers[i]);
      }
    }
    configCopy.iceServers = newIceServers;
  }
  return new OrigPeerConnection(configCopy, pcConstraints);

Not sure if this is something you would want to add or not :)

All this monkey patching is causing me headaches.

I don't seem to be getting this error anymore. Still using the same adapter version so it's possible something changed with Adblock. Could also be some other factor I'm forgetting about. @1tu are you still getting this?


Array errors not being enumerated

While working on tests for #82 I found that array object errors were not being populated in getErrors(). At first I thought this was something I did, but I am able to replicate it in master.
Proof of concept:
var SchemaObject = require('schema-object');

var Schema = new SchemaObject({
  stringField: String,
  arrayField:[{
    stringField: String,
    stringFieldWithValidator: {
      type: String,
      maxLength: 8
    }
  }]
});

var instance = new Schema({
  stringField: 'value',
  arrayField: [
    {
      stringField: 'test',
      stringFieldWithValidator: 'StringWayLongerThan8'
    }
  ]
});

console.log(instance.getErrors()); //[]

After a little digging it looks like the validators are being run as the value does not get set to the instance. It appears as if the errors are being set on the SchemaArray instead of the SchemaObject.

I'm having the same issue too. Is very important to me to be able to validate all objects inside an array of a predefined SchemaObject type.
Any workarround on this?


Typos in About

Some typos in the about section:

...how any computer I use should work...
...If you don't like that, then get the hell out.
That being said, I do welcome criticism and corrections with anything I may post.

I'm also a big proponent on not keeping writing and talking one-to-one. There are areas where we can be concise. For example:

I also really just do not care about front end design.

can be

Also, I do not care about front end design.

a0562e4ec286015a2f4ee884d882fbf2d89a5a0b
Thanks Derek.


mpl2: fix weights variations in macro placement

CI for #4339 showed that results were still varying depending on the number of threads.
Testing the changes in this PR with the test case showed consistency across results.
Weights for macro placement don't depend on the number of threads anymore.
Running Secure-CI..

Secure-CI passed.

@maliberty FYI, I as was testing this change, I realized that a bunch of situations where we were seeing some weird orientations could be happening because of this bug.