!URGENT: Fix bug where all plugins get deleted

So the 'plugins deleting' bug started happening for me today. Annoying, but good, as I've managed to debug and fix it.
Turns out (see the commit) that the plugins support path was being searched twice for plugins on launch, so the 'allBundles' array had two duplicates of the all the plugins in App Sup/QS/PlugIns.
In turn, when QS went about loading the plugins, on trying to load the plugins in the App Sup folder the second time it thought they were duplicates and so deleted them (although they were actually the same files)
Line 468 correctly adds
/Library/...app support path
~/Library/...app support path
/Network/Library/...app support path

already, so there was no need to add ~/Library/...app support path again.
It'd be interesting to see what people who aren't having this problem get if they set a breakpoint (WITHOUT this merged in) just before 	for (NSString *currPath in bundleSearchPaths) { and see what is in the array bundleSearchPaths

hmmm...but bundleSearchPaths is an NSSet. It shouldn't even be possible to have duplicates in there, right?
And that's exactly what happens for me: On line 471 it creates the correct path and tries to add it to the set, but since it is already in there, it doesn't add it (or overwrites the old one or whatever).
So, the interesting question here is: Why doesn't this work for you? Doesn QSApplicationSupportSubPath(@"PlugIns", NO) create a different path than NSSearchPathForDirectoriesInDomains() + appSupportSubpath? Or does NSSet not work correctly for you?

Oh oops it IS an NSSet, I completely looked over that.
Well, turns out when I was debugging this earlier I realised that the
QSApplicationSupportSubPath(@"PlugIns", NO) method gives a path starting
/users/patrick... whereas the previous method gives a path starting
/Users/patrick... - the capital is probably what's making the difference
here.
On 15 January 2012 12:42, Henning Jungkurth <
<EMAIL_ADDRESS>
wrote:

hmmm...but bundleSearchPaths is an NSSet. It shouldn't even be possible to
have duplicates in there, right?
And that's exactly what happens for me: On line 471 it creates the correct
path and tries to add it to the set, but since it is already in there, it
doesn't add it (or overwrites the old one or whatever).
So, the interesting question here is: Why doesn't this work for you? Doesn
QSApplicationSupportSubPath(@"PlugIns", NO) create a different path than
NSSearchPathForDirectoriesInDomains() + appSupportSubpath? Or does
NSSet not work correctly for you?

Reply to this email directly or view it on GitHub:
https://github.com/quicksilver/Quicksilver/pull/636#issuecomment-3498660

It looks like the problem is in QSApplicationSupportSubPath. It gives you a path that doesn't exist. /Users/ always starts with a capital letter. Or maybe more correctly in QSController init, because that's were QSApplicationSupportPath is populate, which is used by QSApplicationSupportSubPath. Or maybe we can go back even further: QSController init reads it from the user defaults, so where ever the app support path was put in the user defaults was wrong. Maybe we should fix that.
But removing the line you removed shouldn't hurt...UNLESS the app support folder is not in ~/Library/Application Support/Quicksilver/ for whatever reason. Can that happen? I think so, because otherwise it wouldn't make sense to save that path in the user defaults.
Maybe some further investigation is required.

Interesting. I'd briefly looked at the QSController's init method, but
hadn't thought to change there.
I think the best solution would be to fix the QSApplicationSupportPath
method (or - (NSString *)applicationSupportFolder in QSController), but
looking into it:
I don't have a string @"QSApplicationSupportPath" in my prefs
The path must be coming from QSController.m:97. Strange that there's
something wrong there. I've just stepped through it and it is indeed this
line that's the problem. I have no idea what can be done to change this.
Interestingly enough, in Folder.h:355 (open click 'FSFindFolder') Apple
have defined a constant to find the Dropbox folder. Pretty handy :)
On 15 January 2012 12:58, Henning Jungkurth <
<EMAIL_ADDRESS>
wrote:

It looks like the problem is in QSApplicationSupportSubPath. It gives
you a path that doesn't exist. /Users/ always starts with a capital
letter. Or maybe more correctly in QSController init, because that's were
QSApplicationSupportPath is populate, which is used by
QSApplicationSupportSubPath. Or maybe we can go back even further:
QSController init reads it from the user defaults, so where ever the app
support path was put in the user defaults was wrong. Maybe we should fix
that.
But removing the line you removed shouldn't hurt...UNLESS the app support
folder is not in ~/Library/Application Support/Quicksilver/ for whatever
reason. Can that happen? I think so, because otherwise it wouldn't make
sense to save that path in the user defaults.
Maybe some further investigation is required.

Reply to this email directly or view it on GitHub:
https://github.com/quicksilver/Quicksilver/pull/636#issuecomment-3498749

According to here (4th line) you can:

find the location of the Application Support folder, use
NSSearchPathForDirectoriesInDomainshttp://www.cocoadev.com/index.pl?NSSearchPathForDirectoriesInDomains
(on
Mac OS X v10.4 or later) or Carbon's
FSFindFolder?http://www.cocoadev.com/index.pl?edit=FSFindFolder
.

I've tried the NSSearchPath... method and it seems to work. Please take a
look at the updated commits to this branch.
Gaaargh annoying Apple :(
Now fix the dodgy Services stuff in Lion please :)
On 15 January 2012 13:50, Patrick Robertson<EMAIL_ADDRESS>
Interesting. I'd briefly looked at the QSController's init method, but
hadn't thought to change there.
I think the best solution would be to fix the QSApplicationSupportPath
method (or - (NSString *)applicationSupportFolder in QSController), but
looking into it:
I don't have a string @"QSApplicationSupportPath" in my prefs
The path must be coming from QSController.m:97. Strange that there's
something wrong there. I've just stepped through it and it is indeed this
line that's the problem. I have no idea what can be done to change this.
Interestingly enough, in Folder.h:355 (open click 'FSFindFolder') Apple
have defined a constant to find the Dropbox folder. Pretty handy :)
On 15 January 2012 12:58, Henning Jungkurth <
<EMAIL_ADDRESS>
wrote:

It looks like the problem is in QSApplicationSupportSubPath. It gives
you a path that doesn't exist. /Users/ always starts with a capital
letter. Or maybe more correctly in QSController init, because that's were
QSApplicationSupportPath is populate, which is used by
QSApplicationSupportSubPath. Or maybe we can go back even further:
QSController init reads it from the user defaults, so where ever the app
support path was put in the user defaults was wrong. Maybe we should fix
that.
But removing the line you removed shouldn't hurt...UNLESS the app support
folder is not in ~/Library/Application Support/Quicksilver/ for whatever
reason. Can that happen? I think so, because otherwise it wouldn't make
sense to save that path in the user defaults.
Maybe some further investigation is required.

Reply to this email directly or view it on GitHub:
https://github.com/quicksilver/Quicksilver/pull/636#issuecomment-3498749

Sorry, 'here' in the previous message was:
http://www.cocoadev.com/index.pl?ApplicationSupportFolder
On 15 January 2012 14:02, Patrick Robertson<EMAIL_ADDRESS>
According to here (4th line) you can:

find the location of the Application Support folder, use
NSSearchPathForDirectoriesInDomainshttp://www.cocoadev.com/index.pl?NSSearchPathForDirectoriesInDomains (on
Mac OS X v10.4 or later) or Carbon's FSFindFolder?http://www.cocoadev.com/index.pl?edit=FSFindFolder
.

I've tried the NSSearchPath... method and it seems to work. Please take a
look at the updated commits to this branch.
Gaaargh annoying Apple :(
Now fix the dodgy Services stuff in Lion please :)
On 15 January 2012 13:50, Patrick Robertson<EMAIL_ADDRESS>
Interesting. I'd briefly looked at the QSController's init method, but
hadn't thought to change there.
I think the best solution would be to fix the QSApplicationSupportPath
method (or - (NSString *)applicationSupportFolder in QSController),
but looking into it:
I don't have a string @"QSApplicationSupportPath" in my prefs
The path must be coming from QSController.m:97. Strange that there's
something wrong there. I've just stepped through it and it is indeed this
line that's the problem. I have no idea what can be done to change this.
Interestingly enough, in Folder.h:355 (open click 'FSFindFolder') Apple
have defined a constant to find the Dropbox folder. Pretty handy :)
On 15 January 2012 12:58, Henning Jungkurth <
<EMAIL_ADDRESS>
wrote:

It looks like the problem is in QSApplicationSupportSubPath. It gives
you a path that doesn't exist. /Users/ always starts with a capital
letter. Or maybe more correctly in QSController init, because that's were
QSApplicationSupportPath is populate, which is used by
QSApplicationSupportSubPath. Or maybe we can go back even further:
QSController init reads it from the user defaults, so where ever the app
support path was put in the user defaults was wrong. Maybe we should fix
that.
But removing the line you removed shouldn't hurt...UNLESS the app
support folder is not in ~/Library/Application Support/Quicksilver/ for
whatever reason. Can that happen? I think so, because otherwise it wouldn't
make sense to save that path in the user defaults.
Maybe some further investigation is required.

Reply to this email directly or view it on GitHub:
https://github.com/quicksilver/Quicksilver/pull/636#issuecomment-3498749

Let me get this straight: The FSFindFolder method gave you an incorrect path (with the lower case u in /users/...).
And the NSSearchPath... method gives you the correct path? Great, let's use that :-)
And I just noticed: /users/ is actually not incorrect. It seems to be an (hidden?) alias of /Users/
That's why it still found the path. It just was different for the NSSet comparison, but not the actual path.

Yep:
FSFindFolder gave me /users/... NSSearchPath... gives me /Users/...
My two extra commits revert my first change and change the FSFindFolder
method to NSSearchPath
:)
P.S. I don't think /users/ is 'hidden', it must be something else. $ ls -a / doesn't show it in Terminal
On 15 January 2012 15:17, Henning Jungkurth <
<EMAIL_ADDRESS>
wrote:

Let me get this straight: The FSFindFolder method gave you an incorrect
path (with the lower case u in /users/...).
And the NSSearchPath... method gives you the correct path? Great, let's
use that :-)
And I just noticed: /users/ is actually not incorrect. It seems to be an
(hidden?) alias of /Users/
That's why it still found the path. It just was different for the NSSet
comparison, but not the actual path.

Reply to this email directly or view it on GitHub:
https://github.com/quicksilver/Quicksilver/pull/636#issuecomment-3499497

P.S. I don't think /users/ is 'hidden', it must be something else. $ ls -a / doesn't show it in Terminal

Right. It seems like paths are actually case-insensitive, even though they pretend to be case-sensitive. Great.

Whilst most Unix filesystems are case sensitive, Mac has always been case
insensitive
http://en.wikipedia.org/wiki/Filename#Case:_upper.2C_lower.2C_preservation
On 15 January 2012 15:31, Henning Jungkurth <
<EMAIL_ADDRESS>
wrote:

P.S. I don't think /users/ is 'hidden', it must be something else. $ ls -a / doesn't show it in Terminal

Right. It seems like paths are actually case-insensitive, even though they
pretend to be case-sensitive. Great.

Reply to this email directly or view it on GitHub:
https://github.com/quicksilver/Quicksilver/pull/636#issuecomment-3499587

Yeah, I didn't know about that until today.

Apple added a case-sensitive version of HFS+ around 10.5 or so, but it’s not used by default on the system partition. I wonder if users experiencing this bug had enabled this option at some point.
@pjrobertson did this also explain why the newer iTunes plug-in wasn’t getting installed, or are you still seeing that problem?

Interestingly enough, in Folder.h:355 (open click 'FSFindFolder') Apple have defined a constant to find the Dropbox folder.

Surely that’s referring to ~/Public/Drop Box and not the third party Dropbox utility, right?

Surely thats referring to ~/Public/Drop Box and not the third party
Dropbox utility, right?

Aah I hadn't thought of that. It was added in 10.7 though, so who knows....

Apple added a case-sensitive version of HFS+ around 10.5 or so, but its
not used by default on the system partition. I wonder if users experiencing
this bug had enabled this option at some point.

 But the bug did appear
after I upgraded to 10.7.3 (11D42), but not right away. Unlikely that Apple
would have changed this, plus other users have reported the bug.
On 15 January 2012 18:52, Rob McBroom <
<EMAIL_ADDRESS>
wrote:

Apple added a case-sensitive version of HFS+ around 10.5 or so, but its
not used by default on the system partition. I wonder if users experiencing
this bug had enabled this option at some point.
@pjrobertson did this also explain why the newer iTunes plug-in wasnt
getting installed, or are you still seeing that problem?

Interestingly enough, in Folder.h:355 (open click 'FSFindFolder') Apple
have defined a constant to find the Dropbox folder.

Surely thats referring to ~/Public/Drop Box and not the third party
Dropbox utility, right?

I think you can pick it when you’re doing a clean install. Not sure if it can be enabled after the fact.

Sooo.... can we merge this in, pretty please?
If I don't remember to run a Quicksilver built from this branch, I have to restore a backup of my PlugIns folder every time. Currently I'm having to do it about 10 times a day... :-)

I don't have time to test this today, but the code looks good.

I thought it was still a work in progress. I will try to test it today.


timeout limit option

Hello, I would like to ask you to add option to set timeout limit of connection.
Now I just add this line to OutlineApiClient.php:
$requestData['connect_timeout'] = "5";
before:
return $client->request($method, $requestUrl, $requestData);
and it works, cuz guzzlehttp support this option.
It's very useful if outline server doesn't response and several long laced connection to it can paralyzed work of current server.

Hi!
It's really a good idea. I'll do it after the holidays

Hi, will this feature be implemented in code soon?

Yes, I'm working on it now


[argo-ui] css loads Heebo font from the internet

Is this a BUG REPORT or FEATURE REQUEST?:
Bug report
What happened:
While loading the Argo UI, the CSS tries to load Heebo from fonts.googleapis,.com.
BUT, in air-gapped environments, users workstations do NOT have any internet connection
-> It can take more than 1 to 2 minutes before the page renders (I guess CSS falls back to browser fonts instead)
-> The fallback font used in the left navbar popovers is rendered all blurred
What you expected to happen:
Bundle the fonts in the docker image and serve them from disk
Environment:

Argo version: v2.3.0
Kubernetes version : v1.13.4

Any update on this?

same issue
access https://fonts.googleapis.com/css?family=Heebo:300,400,500,700

Should combine with #2760


Return the sum of Two Numbers

Description
I also included some comments in front of some lines just to explain what some stuff is doing to the beginner.
Program Name -  Returnadditionoftwonumbers.cpp
Issue No.- #240

Type issue number after hash(#)

Checklist(self check)

[x] Yes, this task assigned to me.
[x] My code follows the style guidelines(Clean Code) of this project
[x] I have performed a self-review of my own code
[x] My Filename is in camelCase format
[x] I have added Problem statement, Problem link in file.

Also please add
very easy
beginner
and approved label to it.

I will mark label

Thank you


md-list-item: text is not selectable

What is affected?
Component
Description
As reported in #5145 for md-dialog, even for md-list-item the text is not selectable.
Text in a list in a web page should always be selectable, it's an expected behavior
Reproduction
try to select text in a list in https://material-web.dev/components/list/
Workaround
I'm using an  inside md-list-item to make text selectable
Is this a regression?
No or unsure. This never worked, or I haven't tried before.
Affected versions
Failing in 1.1.1
Browser/OS/Node environment
Browser: Chrome 120.0.6099.217

are there updates?

There aren't any updates to report on this


Lacks Complete Examples - Confusing

Overall the content in this topic is fairly clear. The largest complaint I have is the lack of examples for the more complex uses. How do you actually call a function method and pass Bindings in the call. Your code examples only brush across the more in-depth topics.  Because there are no end to end examples it is very confusing.

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

ID: 7d575e82-637b-32de-e64d-75adf4b6d976
Version Independent ID: e346a973-fcb9-0c10-c1ed-17acd806ced9
Content: Triggers and bindings in Azure Functions
Content Source: articles/azure-functions/functions-triggers-bindings.md
Service: azure-functions
GitHub Login: @ggailey777
Microsoft Alias: glenga

Hi @laultman Thank you for your feedback! We have reviewed this feedback and assigned this issue to the author to investigate further and take the right course of action.

@mike-urnun-msft  @jason-j-MSFT do you have an end-to-end example of Azure function (in javascript) creation and its binding to consume event hub ?

@laultman since this issue seems to be about triggering a function, would it be helpful if we added a link to the testing article?


[Data Editor][Revert] Foreign Keys get duplicated

DBeaver - Version 5.1.5 EE
OS - Windows 10
DBs - MariaDB, Oracle,...
Steps to reproduce:

Delete a Reference from one of the tables
Revert

Expected result:
Deleted Reference reappears in the 'Properties' tab and in the Datgabase Navigator
Actual result:

Deleted reference is not displayed
Related Foreign Key is duplicated

Workaround:
Refreshing all involved tables restores the proper picture

Generally it shouldn't be possible to drop references. Because they don't belong to current table.

Cannot reproduce on 6.0.2


Adding missing links

Missing links to original http://wiki.netbeans.org and corrected a typo

Let's add this.


south migration for django app?

I got an error with google:
DataError at /auth/complete/google-oauth2/
value too long for type character varying(16)

No where in django model source code (of python social auth) has 16 length, and looked at postgresql DB, and UID of social_auth_usersocialauth has 16 length!
I can fix this by manually altering postgresql schema.
It would be good for django users to have a documentation about DB schema change.
Especially since "syncdb" database is different from the python social auth model code. (wonder why!)

There are migrations for django and south in the app.


Add better system fonts

Add a system font stack to support different OSes for a more comfortable & pleasing look. This exact stack is used by https://github.com/ as well

@jekyllbot: :shipit:


API example response's customer keys are not 32 bytes

i think i'm tripping... i'm trying to build this request body in node.js: http://docs.bigchaindb.apiary.io/#reference/0/transactions-collection/push-a-new-transaction
i used https://github.com/cryptocoinjs/bs58 and got:
const bs58 = require('bs58')
const key = bs58.decode(body.transaction.current_owner)
console.log(key.length) // 33
console.log(new Buffer(key).toString('hex')) // 03fce0773b9fba633dd7b08e1828b91036efe197a16334f49f0ac65efe3591f8eb'

i've also tried https://www.npmjs.com/package/base-58, reaching the same result.
is there a specific base-58 algorithm we should be using?

Hi Jonathan,
that's weird indeed.
can you maybe provide:

the version of bigchaindb you are running
the raw transaction

thanks

Hi @jonathanong
We recently changed the signature algorithm and forgot to update the documentation on apiary. We were using  ecdsa and we are now using ed25519
With ecdsa the key as an extra byte in the beginning stating how the key was compressed.
A valid ed25519 public key would be 76wcv6iHcX5bYVyAxJYDbQXXexaRPMH1M6B6yHBp8ey1. Can you try with this key?

@diminator didn't even reach that point! i was just trying to replicate the request body
@r-marques ah ok. thanks. let me try that

#232
Closing this since the questions seemed to have been answered.


refactor(HACBS-2550): use git resolver for verify-ec task

LGTM. But this requires changes in the release-service to supply the right params when creating the pipelineruns, right? So these two changes should be done in sync somehow. I don't see anything about the release-service changes in the acceptance criteria in Jira though.
(And it's another example of a breaking change that once applied in release-service, all the previous pipeline versions will be broken anyway. Hence why keeping pipeline versions is not very useful anyway. Nothing to do with your change, just saying.)
Also, I would appreciate if you added a brief explanation on why this is done in the PR description / commit message. (I know it's in Jira, but still.)

My plan was to create the new pipeline versions, then create an e2e-tests PR to update the pipeline versions used and pair it with the release-service pr. Since e2e doesn't use latest pipelines, I figured merging this first would be fine (and is kind of the only way to do it)

My plan was to create the new pipeline versions, then create an e2e-tests PR to update the pipeline versions used and pair it with the release-service pr. Since e2e doesn't use latest pipelines, I figured merging this first would be fine (and is kind of the only way to do it)

Sounds good. Except you didn't add new pipeline versions in this PR... But I see they were all added in https://github.com/redhat-appstudio/release-service-bundles/pull/183 and nobody updated e2e for that yet I suppose. So probably it's ok in this case.

My plan was to create the new pipeline versions, then create an e2e-tests PR to update the pipeline versions used and pair it with the release-service pr. Since e2e doesn't use latest pipelines, I figured merging this first would be fine (and is kind of the only way to do it)

Sounds good. Except you didn't add new pipeline versions in this PR... But I see they were all added in #183 and nobody updated e2e for that yet I suppose. So probably it's ok in this case.

Yeah, I talked with Rob and his change doesn't have any effect yet (until he completes some other work) so I said I would combine with him and take care of updating e2e


Validate --report option

I found the need for this the hard way haha.
I was using the tool on a large repo and I set the --reportoption to a directory instead of a file so when the execution finished, I had no report :(

great catch!

This is a experience thing.. but what if we remove --csv and enforce either .json or .csv if we are checking for .json as we are here. I'm happy to implement that in another release.

I like the idea! I think it's enough if the help for the --reportoption is clear about it.

Check out https://github.com/zricethezav/gitleaks/pull/114


[App] Line legend in parallel coordinates plot is not visible

Describe the bug
When exploring data in a parallel coordinates plot and pointing the cursor to a line at the bottom of the plot. The legend will be displayed below the line such that it is outside the visible area of the plot
To Reproduce
Steps to reproduce the behavior:

Create a sweep
Add two runs
Point cursor at a line the is next to the bottom of the plot
See that it is not visible or partly visible

Expected behavior
I would expect to see the legend above the line. This is the behaviour for other plots.
Screenshots

Operating System

OS: Windows 10
Browser: Brave Version 1.22.67 Chromium: 89.0.4389.90 (Offizieller Build) (64-Bit)

Thanks for reporting this @mjohenneken, we're tracking it in our roadmap and will update this ticket when it's fixed.


[Question] Performance & RAM usage

Hi, I would like to get Reticulum running on a MT7628 router board with 64MB RAM. Do you think the CPU performance & RAM size are enough?

No. The current Python implementation, at idle, is around 400 megs according to top. I had a 1 gig server that ran well until a file transfer was requested, then it would crash due to lack of memory. Upgrading to 4 gigs shows it peaks just under 2 gigs. So for a simple router, I'd say 1 gig RAM is about the minimum, maybe 512 with a super lean OS. 2-4 gigs is the best bet for something running a node.
Other implementations aren't complete, and we can't judge their memory footprint at this time. I expect them to be lower once mature, but until they're complete, it's not very helpful to speculate.


COLRv1 version

There is a COLRv1 version of this font used in this Codepen demo https://codepen.io/cssgrid/pen/xxLWPOe
Is this going to be made available?

That file is one of the COLRv1 builds in https://github.com/googlefonts/color-fonts. Specifically fonts/noto-glyf_colr_1.ttf (COLRv1 with outlines in the glyf table). We probably won't publish COLRv1 here until it's a bit more broadly usable.


Show login options when using multiple SSO login methods

Summary
When multiple SSO's are available and email/password is not were were not showing the LoginOptions screen. This PR should fix it.
Ticket Link
https://mattermost.atlassian.net/browse/MM-43607
Fixes: #6183
Release Note
Fixed Login when multiple SSO methods are enabled while email/password is disabled.

@josephbaylon can you please give priority to this PR ?

Building app in separate branch.

Successfully building:
https://circleci.com/gh/mattermost/mattermost-mobile/21898
https://circleci.com/gh/mattermost/mattermost-mobile/21897

Artifact links:
https://output.circle-artifacts.com/output/job/ffb372d9-3e7c-4e10-aa9f-65472890fdac/artifacts/0/tmp/artifacts/Mattermost_Beta.apk
https://output.circle-artifacts.com/output/job/32b7bc73-9ce4-4656-bc11-03199f9e4006/artifacts/0/tmp/artifacts/Mattermost_Beta.ipa

@josephbaylon have you tested with only one SSO and without SSO ?

I updated my comment above to reflect what I tested

Cherry pick is scheduled.


Getting RequestError: 401 while using OAuth

Describe the bug
I have setup everything exactly as specified in the basic example, but I get 401 error while using oauth with credentials generated from cobalt. If I don't use oauth then everything is working fine.
Code
accessToken and refreshToken are generated using cobalt
import { YtdlCore } from '@ybd-project/ytdl-core';

const NORMAL_OAUTH2 = new YtdlCore.OAuth2({
	accessToken: process.env.GOOGLE_OAUTH2_ACCESS_TOKEN as string,
	refreshToken: process.env.GOOGLE_OAUTH2_REFRESH_TOKEN as string,
	expiryDate: process.env.GOOGLE_OAUTH2_EXPIRY_DATE as string,
});

const ytdl = new YtdlCore({
	oauth2: NORMAL_OAUTH2,
});

ytdl
  .getBasicInfo(youtubeLink)
  .then((info) => {
    console.log(info);
  })
  .catch((err) => {
    console.error('🚀 ~ err ~ : ', err);
  });

Error Details (Log)
[  INFO!  ]: Since PoToken and VisitorData are not specified, they are generated automatically.
[  DEBUG  ]: To speed up processing, html5Player and signatureTimestamp are pre-fetched and cached.
Server is running on port 3000
[ SUCCESS ]: Successfully generated a poToken.

RequestError: Status Code: 401
    at Function.request (/Users/tanish/dev/server/yt_2_mp3/node_modules/@ybd-project/ytdl-core/src/core/Fetcher.ts:31:23)
    at processTicksAndRejections (node:internal/process/task_queues:95:5)
    at async /Users/tanish/dev/server/yt_2_mp3/node_modules/@ybd-project/ytdl-core/src/core/clients/Base.ts:46:28 {
  statusCode: 401
}

Environment

Video URL: https://www.youtube.com/watch?v=IPYTxAHeR_o
@ybd-project/ytdl-core version: ^5.1.4-2
Node.js version: v20.17.0
Operating system: macOs

Thank you for your report.
We will investigate this issue.

it's 2024-09-15T05:42:43.119Z

@ybd-project it's 2024-09-15T05:42:43.119Z

Thank you.

The results of the survey suggest the following factors

the refreshToken or accessToken is not copied correctly or is most likely incorrect. Please generate them again and make sure they are copied properly.
the IP may be banned.

@ybd-project Thanks, I generated the new credentials and then it was working fine.

This issue has been resolved and will be closed.


Helm does not wait for Rancher install/upgrade when replicas=1

What kind of request is this (question/bug/enhancement/feature request): enhancement
Steps to reproduce (least amount of steps as possible):

Deploy the Rancher chart with helm using --set replicas=1 and --wait

Result:
The helm install completes immediately without waiting for the Rancher pod to become ready
Other details that may be helpful:
Due to logic in helm, a wait will not occur when maxUnavailable is non-zero and there is a single replica.

https://helm.sh/docs/intro/using_helm/#helpful-options-for-installupgraderollback

With the maxSurge: 1 upgrades and re-deploys will still start a new Rancher pod before terminating the running pod.

Available to test in v2.6.0-rc1

The enhancement is validated in rancher:v2.6.0-rc1
prerequisite:

use RKE CLI (version v1.2.11) to create a single-node RKE cluster for installing Rancher.

rke up --config ./rancher-cluster.yml

install cert-manager into the cluster

helm install \
  cert-manager jetstack/cert-manager \
  --namespace cert-manager \
  --version v1.4.0 \
  --create-namespace \
  --set installCRDs=true

Case 1: Fresh Install
Steps:

run the following command to install rancher

 helm install rancher2 rancher-latest/rancher \
  --namespace cattle-system \
  --version v2.6.0-rc1 \
  --set hostname=my-hostname.com --set replicas=1 --create-namespace --wait

Results:

the Helm command does not return until the rancher pod is in the Ready state
Rancher is installed successfully

Case 2: Upgrade
Steps:

run the following command to upgrade rancher

 helm upgrade rancher2 rancher-latest/rancher \
  --namespace cattle-system \
  --version v2.6.0-rc1 \
  --set hostname=my-hostname.com --set replicas=1 --create-namespace --wait --set rancherImageTag=master-head

Results:

A new pod is created
the Helm command does not return until the new pod is in the Ready state
then the old pod is removed
Rancher is upgraded successfully

@sowmyav27 Please review and close the issue


No decrease of wer when fine tuning wav2vec 2.0

I am trying to replicate the paper by fine-tuning the Wa2Vec 2.0 No finetuning base model with 1h of librilight.

Both the lexicon and the language model seem fine as I used them for testing the model already fine tuned and got similar result then reported in the paper.
An example of log:
2020-10-02 12:23:48 | INFO | fairseq.trainer | begin training epoch 40
2020-10-02 12:23:55 | INFO | fairseq_cli.train | begin validation on "valid" subset
2020-10-02 12:24:01 | INFO | valid | {"epoch": 40, "valid_loss": "1942.87", "valid_ntokens": "5368", "valid_nsentences": "30", "valid_nll_loss": "10.858", "valid_uer": "79.49", "valid_wer": "99.797", "valid_raw_wer": "111.663", "valid_wps": "0", "valid_wpb": "5368", "valid_bsz": "30", "valid_num_updates": "317", "valid_best_wer": "99.696"}
2020-10-02 12:24:01 | INFO | fairseq_cli.train | begin save checkpoint

2020-10-02 12:24:04 | INFO | fairseq_cli.train | end of epoch 40 (average epoch stats below)
2020-10-02 12:24:04 | INFO | train | {"epoch": 40, "train_loss": "2211.27", "train_ntokens": "6130.5", "train_nsentences": "32", "train_nll_loss": "11.542", "train_wps": "3088.9", "train_ups": "0.5", "train_wpb": "6130.5", "train_bsz": "32", "train_num_updates": "317", "train_lr": "2.51408e-05", "train_gnorm": "645.876", "train_loss_scale": "16", "train_train_wall": "1", "train_wall": "684"}

Do you have any idea where I should look at ? Thank you for your help

are you finetuning a large model (based on your --max-tokens)? you need to do it on 24 gpus. or you can do it on 4 or 5 (similar to 6 you are using here), but then you need to set --update-freq 5
for smaller model you use a batch size 3x bigger and then you need 8 gpus (or 2 with --update-freq 4)

I am fine tuning the small one. I will retry adding the following:
--distributed-world-size 4
--update-freq 2 (8/4)
--max-tokens 1280000*3
Am I correct ?

yes. if it still doesnt work try printing examples from ctc.py

I used the following parameters :
--distributed-world-size 2
--update-freq 4
--max-tokens 1280000*3
I am still having the same issue. Predicting on a small set of Librispeech:
{'1255-90413-0013': 'NO',
 '1255-90413-0011': 'NO',
 '1255-90413-0021': 'NO',
 '1255-90413-0019': 'NO',
 '1255-90413-0005': 'IN NO',
 '1255-90413-0024': 'IN NO',
 '1255-90413-0017': 'NO',
 '1255-90413-0004': 'NO',
 '1255-90413-0002': 'NOW NO NO',
 '1255-90413-0020': 'AN',
 '1255-90413-0023': 'NO',
 '1255-90413-0015': 'NO NO',
 '1255-90413-0006': 'NO',
 '1255-90413-0009': 'NO NO',
 '1255-90413-0026': 'IN NO',
 '1255-90413-0028': 'NO NO',
 '1255-90413-0025': 'NO NO',
 '1255-90413-0000': 'NO',
 '1255-90413-0027': 'NO NO',
 '1255-90413-0012': 'IN NO'}

if you share your entire training log i can take a look quickly.
you can also get the params that my models were finetuned from the checkpoints. just torch.load() the checkpoint (which is a python dictionary) and look in at the "args" key

I will have a look.
Please find enclosed my log (I stopped after 147 epochs as valid_uer was increasing)
log-wav2vec-finetuned-small.txt
Thank you very much for your help

so you only trained for 436 updates in that log. one of the params is --freeze-finetuning-updates. it is set to 10k by default and all it does is train a linear layer on top of the model (that by itself does not lead to any meaningful results). then the rest of the 3k updates are used to finetune the entire network.
in practice this freezing makes results very slighly more accurate, but you can also just set it to 0 so it starts finetuning right away (you may have to lower LR a bit, or not). or you can train it all the way to 13k updates (set --validate-interval-updates to 10k or so, because otherwise you will be validating every epoch and it will take days to train)

I set --freeze-finetuning-updates 0 and will let it run overnight.
Thank you very much for your help.

@alexeib I am sorry to ask a stupid question, is that the "max-tokens" control the batch size while training? And how does it control?

its the number of frames per batch. we typically do dynamic batching in fairseq meaning that the batch size is determined by total length of sequence as opposed to number of examples. but you can also use --batch-size to do traditional batching.
the errors you see are due to some recent changes we have been making to fairseq arch. the first one should be fixed. you can replace --post-process by --remove-bpe for now. i will fix it tomorrow

@ArtemisZGL Same issues here! Were you able to resolve the issue, 'Wav2VecCtc' object has no attribute 'max_decoder_positions'? I'm still stuck there.
Thanks!

can you share your command?

@alexeib Here's my command:
subset=dev-clean
tsv_dir=~/Deep-Learning/Dataset/LibriSpeech/wav2vec2   # contains the letter dict and word dict files, and preprocessed tsv, ltr and wrd files
model_path=~/Deep-Learning/Repos/fairseq/pretrained-models/wav2vec2/wav2vec_small_100h.pt
results_dir=~/Deep-Learning/Repos/fairseq/my/results
kenlm_path=~/Deep-Learning/Repos/kenlm/build/bin

python examples/speech_recognition/infer.py $tsv_dir --task audio_pretraining \
--nbest 1 --path $model_path --gen-subset $subset --results-path $results_dir --w2l-decoder kenlm \
--lm-model $kenlm_path --lm-weight 2 --word-score -1 --sil-weight 0 --criterion ctc --labels ltr --max-tokens 4000000 \
--remove-bpe letter

@sythello sorry, I did not have solved that problem yet, and I just use the valid function in train.py to validate the model.

I set --freeze-finetuning-updates 0 and will let it run overnight.
Thank you very much for your help.

@ezerhouni  hi, i meet the same question as you, have you sovled it?

Hi @alexeib,
I am following the recent implementation of wav2vec2 for fine-tuning:
https://huggingface.co/blog/fine-tune-wav2vec2-english
Settings:
Pretrained model: "facebook/wav2vec2-base-960h",
gradient_checkpointing=True,
ctc_loss_reduction="mean",
pad_token_id=processor.tokenizer.pad_token_id
attention_dropout=0.1,
hidden_dropout=0.1,
feat_proj_dropout=0.0,
mask_time_prob=0.05,
layerdrop=0.1,
gradient_checkpointing=True,
ctc_loss_reduction="mean",
group_by_length=True,
per_device_train_batch_size=32,
evaluation_strategy="steps",
num_train_epochs=1500,
fp16=True,
save_steps=400, #this would mean every 400 steps model gets saved which also means Google drive gets full
eval_steps=400,
logging_steps=400,
learning_rate=0.0005,
warmup_steps=500,
save_total_limit=2,
Issue:

Step
Training Loss
Validation Loss
Wer
Runtime
Samples Per Second

400
5.063200
4.566135
1.000000
0.715900
6.984000

800
5.115200
4.514411
1.000000
0.732400
6.827000

1200
5.119200
4.485986
1.000000
0.724300
6.903000

**The training loss is marginally decreasing and WER is still 1. What can be done to improve and faster training with better accuracy.
I also tried with a higher learning rate but training loss was still very poor, it seems the model is not converging.**
Regards.

Hi,
I also encountered the same problem. While trying to fine-tune the base model on custom dataset, no matter what I do WER and UER does not decrease.

--freeze-finetuning-updates is already set to 0
tried cosine lr scheduler (with this the UER was fluctuating between 97 and 100)
changed learning rate and update freq
tried to debug valid_step, model only predicts blank token "|"
tried different parameter setups for wav2vec_ctc model, such as dropout rates, mask probabilities, mask lengths
tried on different subsets of my custom dataset to see if the issue is data related

Here is details of the setup I used,

fairseq version v0.10.2 (build by cloning and pip install --editable)
pytorch 1.7.1
cuda 10.1
1 Titan RTX 24 GB
python 3.8.10
os: Ubuntu 18.04

You can find the training config and train log files of one of the many trials below.
hydra_train.log
config.txt
Please share if you have any idea about what is going on. If you need any further information just ask me, I will provide it.
Thanks for your consideration.

Hi,
I also encountered the same problem. While trying to fine-tune the base model on custom dataset, no matter what I do WER and UER does not decrease.

--freeze-finetuning-updates is already set to 0
tried cosine lr scheduler (with this the UER was fluctuating between 97 and 100)
changed learning rate and update freq
tried to debug valid_step, model only predicts blank token "|"
tried different parameter setups for wav2vec_ctc model, such as dropout rates, mask probabilities, mask lengths
tried on different subsets of my custom dataset to see if the issue is data related

Here is details of the setup I used,

fairseq version v0.10.2 (build by cloning and pip install --editable)
pytorch 1.7.1
cuda 10.1
1 Titan RTX 24 GB
python 3.8.10
os: Ubuntu 18.04

You can find the training config and train log files of one of the many trials below.
hydra_train.log
config.txt
Please share if you have any idea about what is going on. If you need any further information just ask me, I will provide it.
Thanks for your consideration.

there is something wrong with your finetuning dataset. you have extremely low nll_loss (i.e. loss normalized by token) right from the beginning. usually this loss starts high and decreases (for letter based models you start getting < 100 results after it decreases around below 4). did you split your examples into letters with spaces in between (and word boundary tokens between words)?

Yes, I prepared .ltr files according to libri_labes.py's outputs.
The dataset I am using is a mixed files from open source (common voice, vox and openSLR) Turkish speech to text datasets. All preprocessed into mono channel 16000 Hz wav files which was the same setup for the pretraining data as well.
What I don't understand is that training loss seems to decrease meaning it converges to somewhere. But that "somewhere" is not a happy place for validation data. I also tried to use only Common Voice data for both training and validation just to make sure train and validation data come from the same distribution, it was not effective either.

Hello,
I also am experiencing this problem. I am trying to fine-tune the Wav2Vec 2.0 Large (960h) on a small technical domain in English using the pre-trained libri960_big.pt model.
I cannot get my valid WER nor raw valid WER to decrease and my UER goes between 96 and 99 consistently.
Like @harunuz, I also did the following:

--freeze-finetuning-updates set to 0
tried cosine lr scheduler (no improvement)
changed learning rate and update freq (no improvement)
printed results from ctc.py for debugging and my model is mostly predicting the tokens 'H', 'L', and '' which eventually, as training continues, devolves to just word delimiters '|'.
tried various parameter setups but no change is detected

I also re-recorded my audio files in a quieter setting using Audacity with the sampling rate set to 16K and mono-channel (in contrast to the original audio files that had to be re-sampled and set from stereo to mono channel using SoX). I re-ran again with this new data and had the same results.
I use Google Colab Pro for my setup:
fairseq v0.10.2 (also built by cloning and pip install --editable)
pytorch 1.9.0+cu102
CUDA 11.2
Tesla V100
python 3.7.10
I used the base_10m config from fairseq's finetuning configs with the following changes for training:
!fairseq-hydra-train
distributed_training.distributed_world_size=1
task.data='/path/to/manifest_file'
model.w2v_path='/path/to/libri960_big.pt'
model.freeze_finetune_updates=0
checkpoint.save_dir='/path/to/save/directory'
dataset.validate_interval=5
dataset.validate_after_updates=0
dataset.valid_subset='valid'
dataset.batch_size=1
dataset.num_workers=1
optimization.max_epoch=30
+optimization.update_freq='[24]'
--config-dir /content/fairseq/examples/wav2vec/config/finetuning
--config-name base_10m \
Here is my training log:
hydra_train.log
Note that the distributed_world_size, batch_size, num_workers, update_freq, and validation interval are all set to conserve memory as Colab is prone to OOM errors with only one GPU. I only train to 30 epochs because the model gets stuck predicting only word delimiter tokens at about 26 epochs and no improvement is made after that.
Like harunuz, I have low nll_loss from the beginning. I double-checked my .ltr, .wrd, dict.ltr.txt, and tsv files to make sure I hadn't incorrectly preprocessed my data. Everything looks good from my perspective.
dict.ltr.txt:
A 1
B 2
C 3
D 4
E 5
F 6
G 7
H 8
I 9
J 10
K 11
L 12
M 13
N 14
O 15
P 16
Q 17
R 18
S 19
T 20
U 21
V 22
W 23
X 24
Y 25
Z 26
' 27
| 28
 29
Sample from train.ltr:
A L R I G H T | T H E | T I M E | I S | A P P R O X I M A T E L Y |
Sample from valid.ltr:
I ' M | G O I N G | T O | Y O U | K N O W | S T E P | O N E | H E R E | I S | T O | R E M O V E | I T |
Sample from train.wrd:
ALRIGHT THE TIME IS APPROXIMATELY
Sample from valid.wrd:
I'M GOING TO YOU KNOW STEP ONE HERE IS TO REMOVE IT
I agree with harunuz on the confusion regarding the decrease in training loss. The only possibility I can imagine behind it is that the model is more focused on bringing down the training loss than accuracy so it has learned that single characters and word delimiters cost less than other, longer guesses (in the sense of less insertions, deletions, etc.), leading to the predictions I'm seeing during training. This is just a theory, I could be very wrong about this.
Do I merely need more data? Is there a quality other than sampling rate and the number of channels I should check with my audio data? I've already cleaned my transcriptions and so I know that there are no unusual characters there and that the transcriptions match the audio files. Is the problem with how I set up validation? I feel like I am missing one small detail that is causing this problem but I can't find what it is.
Thank you in advance for any help you can provide! I have been struggling with this for awhile.

Yes, I prepared .ltr files according to libri_labes.py's outputs.
The dataset I am using is a mixed files from open source (common voice, vox and openSLR) Turkish speech to text datasets. All preprocessed into mono channel 16000 Hz wav files which was the same setup for the pretraining data as well.
What I don't understand is that training loss seems to decrease meaning it converges to somewhere. But that "somewhere" is not a happy place for validation data. I also tried to use only Common Voice data for both training and validation just to make sure train and validation data come from the same distribution, it was not effective either.

best advice i can offer is try to carefully examine your finetuning setup - maybe print some examples from ctc.py. There is something wrong, the loss values are super low and should be much higher. it is like everything is mapped to a single token (like "unk") and thats the only thing the model predicts. you sure your dict and labels match ? case wise etc

Hello,
I also am experiencing this problem. I am trying to fine-tune the Wav2Vec 2.0 Large (960h) on a small technical domain in English using the pre-trained libri960_big.pt model.
I cannot get my valid WER nor raw valid WER to decrease and my UER goes between 96 and 99 consistently.
Like @harunuz, I also did the following:

--freeze-finetuning-updates set to 0
tried cosine lr scheduler (no improvement)
changed learning rate and update freq (no improvement)
printed results from ctc.py for debugging and my model is mostly predicting the tokens 'H', 'L', and '' which eventually, as training continues, devolves to just word delimiters '|'.
tried various parameter setups but no change is detected

I also re-recorded my audio files in a quieter setting using Audacity with the sampling rate set to 16K and mono-channel (in contrast to the original audio files that had to be re-sampled and set from stereo to mono channel using SoX). I re-ran again with this new data and had the same results.
I use Google Colab Pro for my setup:
fairseq v0.10.2 (also built by cloning and pip install --editable)
pytorch 1.9.0+cu102
CUDA 11.2
Tesla V100
python 3.7.10
I used the base_10m config from fairseq's finetuning configs with the following changes for training:
!fairseq-hydra-train
distributed_training.distributed_world_size=1
task.data='/path/to/manifest_file'
model.w2v_path='/path/to/libri960_big.pt'
model.freeze_finetune_updates=0
checkpoint.save_dir='/path/to/save/directory'
dataset.validate_interval=5
dataset.validate_after_updates=0
dataset.valid_subset='valid'
dataset.batch_size=1
dataset.num_workers=1
optimization.max_epoch=30
+optimization.update_freq='[24]'
--config-dir /content/fairseq/examples/wav2vec/config/finetuning
--config-name base_10m \
Here is my training log:
hydra_train.log
Note that the distributed_world_size, batch_size, num_workers, update_freq, and validation interval are all set to conserve memory as Colab is prone to OOM errors with only one GPU. I only train to 30 epochs because the model gets stuck predicting only word delimiter tokens at about 26 epochs and no improvement is made after that.
Like harunuz, I have low nll_loss from the beginning. I double-checked my .ltr, .wrd, dict.ltr.txt, and tsv files to make sure I hadn't incorrectly preprocessed my data. Everything looks good from my perspective.
dict.ltr.txt:
A 1
B 2
C 3
D 4
E 5
F 6
G 7
H 8
I 9
J 10
K 11
L 12
M 13
N 14
O 15
P 16
Q 17
R 18
S 19
T 20
U 21
V 22
W 23
X 24
Y 25
Z 26
' 27
| 28
29
Sample from train.ltr:
A L R I G H T | T H E | T I M E | I S | A P P R O X I M A T E L Y |
Sample from valid.ltr:
I ' M | G O I N G | T O | Y O U | K N O W | S T E P | O N E | H E R E | I S | T O | R E M O V E | I T |
Sample from train.wrd:
ALRIGHT THE TIME IS APPROXIMATELY
Sample from valid.wrd:
I'M GOING TO YOU KNOW STEP ONE HERE IS TO REMOVE IT
I agree with harunuz on the confusion regarding the decrease in training loss. The only possibility I can imagine behind it is that the model is more focused on bringing down the training loss than accuracy so it has learned that single characters and word delimiters cost less than other, longer guesses (in the sense of less insertions, deletions, etc.), leading to the predictions I'm seeing during training. This is just a theory, I could be very wrong about this.
Do I merely need more data? Is there a quality other than sampling rate and the number of channels I should check with my audio data? I've already cleaned my transcriptions and so I know that there are no unusual characters there and that the transcriptions match the audio files. Is the problem with how I set up validation? I feel like I am missing one small detail that is causing this problem but I can't find what it is.
Thank you in advance for any help you can provide! I have been struggling with this for awhile.

you only have 10 examples.. how long is each example?
you dont need update freq = 24 because you probably fit the entire training set in a single example.
are you sure your labels are in the same order as audio in your tsv file?
have you tried finetuning a 10m/5m/1m subsets from libri-light? those should work out of the box

Hello,
I also am experiencing this problem. I am trying to fine-tune the Wav2Vec 2.0 Large (960h) on a small technical domain in English using the pre-trained libri960_big.pt model.
I cannot get my valid WER nor raw valid WER to decrease and my UER goes between 96 and 99 consistently.
Like @harunuz, I also did the following:

--freeze-finetuning-updates set to 0
tried cosine lr scheduler (no improvement)
changed learning rate and update freq (no improvement)
printed results from ctc.py for debugging and my model is mostly predicting the tokens 'H', 'L', and '' which eventually, as training continues, devolves to just word delimiters '|'.
tried various parameter setups but no change is detected

I also re-recorded my audio files in a quieter setting using Audacity with the sampling rate set to 16K and mono-channel (in contrast to the original audio files that had to be re-sampled and set from stereo to mono channel using SoX). I re-ran again with this new data and had the same results.
I use Google Colab Pro for my setup:
fairseq v0.10.2 (also built by cloning and pip install --editable)
pytorch 1.9.0+cu102
CUDA 11.2
Tesla V100
python 3.7.10
I used the base_10m config from fairseq's finetuning configs with the following changes for training:
!fairseq-hydra-train
distributed_training.distributed_world_size=1
task.data='/path/to/manifest_file'
model.w2v_path='/path/to/libri960_big.pt'
model.freeze_finetune_updates=0
checkpoint.save_dir='/path/to/save/directory'
dataset.validate_interval=5
dataset.validate_after_updates=0
dataset.valid_subset='valid'
dataset.batch_size=1
dataset.num_workers=1
optimization.max_epoch=30
+optimization.update_freq='[24]'
--config-dir /content/fairseq/examples/wav2vec/config/finetuning
--config-name base_10m
Here is my training log:
hydra_train.log
Note that the distributed_world_size, batch_size, num_workers, update_freq, and validation interval are all set to conserve memory as Colab is prone to OOM errors with only one GPU. I only train to 30 epochs because the model gets stuck predicting only word delimiter tokens at about 26 epochs and no improvement is made after that.
Like harunuz, I have low nll_loss from the beginning. I double-checked my .ltr, .wrd, dict.ltr.txt, and tsv files to make sure I hadn't incorrectly preprocessed my data. Everything looks good from my perspective.
dict.ltr.txt:
A 1
B 2
C 3
D 4
E 5
F 6
G 7
H 8
I 9
J 10
K 11
L 12
M 13
N 14
O 15
P 16
Q 17
R 18
S 19
T 20
U 21
V 22
W 23
X 24
Y 25
Z 26
' 27
| 28
29
Sample from train.ltr:
A L R I G H T | T H E | T I M E | I S | A P P R O X I M A T E L Y |
Sample from valid.ltr:
I ' M | G O I N G | T O | Y O U | K N O W | S T E P | O N E | H E R E | I S | T O | R E M O V E | I T |
Sample from train.wrd:
ALRIGHT THE TIME IS APPROXIMATELY
Sample from valid.wrd:
I'M GOING TO YOU KNOW STEP ONE HERE IS TO REMOVE IT
I agree with harunuz on the confusion regarding the decrease in training loss. The only possibility I can imagine behind it is that the model is more focused on bringing down the training loss than accuracy so it has learned that single characters and word delimiters cost less than other, longer guesses (in the sense of less insertions, deletions, etc.), leading to the predictions I'm seeing during training. This is just a theory, I could be very wrong about this.
Do I merely need more data? Is there a quality other than sampling rate and the number of channels I should check with my audio data? I've already cleaned my transcriptions and so I know that there are no unusual characters there and that the transcriptions match the audio files. Is the problem with how I set up validation? I feel like I am missing one small detail that is causing this problem but I can't find what it is.
Thank you in advance for any help you can provide! I have been struggling with this for awhile.

you only have 10 examples.. how long is each example?
you dont need update freq = 24 because you probably fit the entire training set in a single example.
are you sure your labels are in the same order as audio in your tsv file?
have you tried finetuning a 10m/5m/1m subsets from libri-light? those should work out of the box
your nll loss is > 14 which is what one would expect to start at, except its not going down which means the model is not learning anything

Each example is about 10-25 seconds. I also got more recordings and ran it with the larger data set but still had the same results.
Ah okay, I misunderstood the function of update freq then. Thank you for the clarification.
Yes, I double-checked my labels to make sure they are in the same order as in the tsv file and they all are.
I hadn't but I did try them (libri-light's 1hr/10m/5min/1m) this week and I got the same results. I think I figured out the problem though. I believe there are several dependency issues in my installation of flashlight's python bindings (previously wav2letter) in Colab and so it is not doing any decoding during training. I'm actually not sure these python bindings can be successfully installed in Colab due to its environment set up, but I'll keep trying.  I apologize for wasting your time with such an unrelated problem. Thank you for your help!

Yes, I prepared .ltr files according to libri_labes.py's outputs.
The dataset I am using is a mixed files from open source (common voice, vox and openSLR) Turkish speech to text datasets. All preprocessed into mono channel 16000 Hz wav files which was the same setup for the pretraining data as well.
What I don't understand is that training loss seems to decrease meaning it converges to somewhere. But that "somewhere" is not a happy place for validation data. I also tried to use only Common Voice data for both training and validation just to make sure train and validation data come from the same distribution, it was not effective either.

For the record, I finally figured out what is wrong with my experiments. It was neither the finetuning data, nor the installation mistakes. It was the pretraining itself. I realized that my pretrained model was a disaster. I had got over almost 98% accuracy on validation data in pretraining. However, my dataset was not well-prepared. The sound files had long silences in them and the validation split was not a very good evaluation material.
I tried finetuning the proposed large model that was pretrained on multilingual dataset. And in like 30k updates, I got pretty good results.
Much thanks for your time though @alexeib

This also happened to me when I forgot to put a space in the vocabulary:
vocab=['a','b','c',.......]
to
vocab=[' ','a','b','c',.......]


ReplicationFactor not properly set when deleting topic at runtime

setting the spring.cloud.stream.kafka.binder.replicationFactor at spring boot startup results in the correct replicationFactor. Deleting the topic at runtime e.g.
/opt/kafka/bin/kafka-topics.sh --zookeeper zookeeper --delete --topic sometopic
will always result in a topic being recreated with replicationFactor 1.

Hi, @rzeijde: Could you describe your use-case, please?

Closing the issue due to inactivity.


Message Queues

Write about Message Queues and RabbitMQ

Research done and chapter written by @zythaarcz . This issue was previously part of #31 but is now a subtask of #98
I am now moving this issue to implement done, as Tomas has already finished writing it

I have finished reviewing this chapter. It is currently placed as chapter 9.4 in the report. I am closing this issue, as I see no need for everyone to verify it.


Add shutDown method to PluginMessageHandler

This method deterministically and synchronously cleans up resources that cannot be dealt with in a deinitializer due to possible errors thrown during the clean up. Usually this includes closure of file handles, sockets, shutting down external processes and IPC resources set up for these processes, etc.
This method is required for swift-plugin-server to properly clean up file handles opened for communication with Wasm macros, as prototyped in https://github.com/swiftlang/swift/pull/73031.
For the default non-Wasm macros implementations this change has no effect, since the shutDown implementation for those is empty.

@swift-ci test

@swift-ci test


Review CI setup

The nightly job does not seem to execute front-end tooling.
I don't know how the publish job could've worked if the front-end tooling had missing src/ in the paths. See #21.

Good call!
I would add "Use PyPI trusted publishing to the list" (https://docs.pypi.org/trusted-publishers/using-a-publisher/, https://github.com/wagtail/cookiecutter-wagtail-package/blob/main/{{ cookiecutter.__project_name_kebab }}/.github/workflows/publish.yml#L31-L52)

The addition of the src/ folder was recent - we haven't published a version since then. Thanks for picking up on that.
The CI setup is built on https://github.com/wagtail/cookiecutter-wagtail-package so we might be able to backport some of the recent fixes from there here, where it makes sense.

This is now complete.


Link to article explaining how to enable HTTP API

The documentation surrounding the HTTP API is a little misleading. When I read the documentation, I came to the conclusion that I needed to setup my own gRPC Gateway service. Only after asking a question in the Discord channel did I learn about the article that explains how to enable the HTTP API.

I have read the CLA Document and I hereby sign the CLA

Thanks @alsbury!


LAND-1568: Add contact on new post fix

PR Checklist

[x] Includes changes to desk files
app/channels.hoon
[x] Describes how you tested the PR locally (test ship vs livenet)
Livenet moons
[x] If a new feature, includes automated tests
N/A
[x] Comments added anywhere logic may be confusing without context
N/A

nvm let's just do develop


Fix CI so it runs on PRs, update README, and fix broken test

#31 didn't have the proper Go tests run so this should fix that, and fix the test that got accidentally broken.

Build & test is now passing again!


Document alternative ways of reloading

I find method_name(true) isn't very intention-revealing whereas the
supported method_name(reload: true) or method_name(:reload) are
much clearer alternatives and I keep having to check the source to
see if it is possible, so why not document it in the README?
See: https://github.com/matthewrudy/memoist/blob/d0101e8bdcb129bb4b609055db0107be2c645821/lib/memoist.rb#L52-L57

@olivierlacan does the reload: true one work?
The problem with this method is that it's confused by the fact it can be applied to a method with any arity.

@matthewrudy It does.


Doc issues / suggested improvements

The quickstart code snippet contains the macos binary, not the linux binary.
Maybe mentioning those would be helpful.

Same doc new section. In section three you use @latest as a version, while above you are using the current version (today, 0.8.0)..i guess this needs to be aligned.

Same thing in code generation. There you also using latest

In code generation you mention <version>. I guess we have the exact version somewhere. So I think it make sense to inline the version here.

That's it 🙃

Closed as fixed via #105


Last Alexa Sensor - Stops Working

Hassio: 0.99.2
HACS Alexa Media Player: 2.2.1
The last alexa sensor seems to stop working after sometime (less than 1 day, longer than a few hours).  To restore the sensor functionality I have to restart Home Assistant.
Here's my config for the last_alexa sensor:
 # Last Called Echo Device
  - platform: template
    sensors:
      last_alexa:
        entity_id:
          - media_player.bedroom
          - media_player.kitchen
          - media_player.katelynn_s_room
          - media_player.valerie_s_room
          - media_player.noah_s_room
          - media_player.basement
          - media_player.baby_s_room
          - media_player.garage
        value_template: >
          {{ states.media_player | selectattr('attributes.last_called','eq',True) | map(attribute='entity_id') | first }}

I've summoned Alexa through 2 other devices this morning, but it fails to update the sensor:

After restarting Home Assistant, the sensor will update to the last called device during the period when it stopped updating the sensor.
Please let me know if you need anything further.

We don't support the custom sensor however if you have some logs detailing your interaction when it's not updating we can take a look. It's possible you're not logged in anymore and you need to refresh your pickle file. Are media updates showing?

You might be right with the authentication issue.  In restarting HA a number of times, it would resolve the issue but only for a short period of time.  I restarted the host yesterday and upon the system coming back up, I was prompted to re-auth and a new pickle file was created.  So far it's been running through the night and is still updating the sensor.  I'll close this for now, thanks for the prompt reply.


Amazon Web Services : EC2

Does anyone have an clear and working example of a .ftpconfig for an AWS EC2 sftp connection? (using a pem-file)

I'm also having issues. I can connect with terminal over SSH and in FileZilla so I know my host name, user and key are good.

Same here, Phillip, everything works in Coda 2.5 : an application on osx i try to get away from but atom won't let me... sigh..

Been using this to connect to EC2 instances just fine
{
    "protocol": "sftp",
    "host": "host-endpoint",
    "port": 22,
    "user": "username",
    "remote": "remote/working/directory",
    "local": "",
    "agent": "",
    "privatekey": "path/to/local/pemfile",
    "promptForPass": true,
    "keepalive": 10000,
    "keyboardInteractive": false
}

Why would you set promotForPass to true? Which pass should you enter?The user is "ec2-user" right?

promptForPass is for the pem/ppk file password (if set).  Not for the ec2 user password.
What you're thinking of is the keyboardInteractive field.  Which allows username/password logins to the target machine.
I only allow private key authentication for my EC2 instance logins, so I set keyboardInteractive to false.

Thanks for the helpful information @jackalstomper
Linking to https://github.com/mgrenier/remote-ftp/issues/7 for when someone updates the docs.

This is my config that's still not working:
{
    "protocol": "sftp",
    "host": "PUBLIC_DNS",
    "port": 22,
    "user": "USERNAME",
    "pass": "",
    "promptForPass": false,
    "remote": "/opt/bitnami/apps/wordpress/htdocs",
    "local": "PATH_TO_LOCAL_DIR",
    "agent": "",
    "privatekey": "PATH_TO_LOCAL_KEY",
    "passphrase": "",
    "hosthash": "",
    "ignorehost": true,
    "connTimeout": 10000,
    "keepalive": 10000,
    "keyboardInteractive": false,
    "watch": [],
    "watchTimeout": 500
}

@philliptmaddox Any error messages? Is your privatekey unencrypted and readable to the user who executes Atom? If the key can't be read it's unfortunately just silently ignored
You could try to set "debug":true in the .ftpconfig and see what the developer console reads...

The private key is unencrypted and should not be the issue.
This is all I'm getting: Remote FTP: Error: All configured authentication methods failed

Did you try an absolute path?

Yes, thats the issue! Use an absolute path instead of the ~ bit for your homedir.

I've now added an error popup when the privateKey file can't be read :)

Still doesn't work: Remote FTP: Error: All configured authentication methods failed . (nothing showing up in console)
{
"protocol": "sftp",
"host": "<IP_ADDRESS>",
"port": 22,
"user": "ec2-user",
"promptForPass": false,
"remote": "/opt/app/current/www",
"agent": "",
"privatekey": "/Users/stef/Documents/AWS/awsec2specifickeyforthiswebsite.pem",
"passphrase": "",
"ignorehost": true,
"conTimeout": 10000,
"keepalive": 10000,
"debug": true,
"keyboardInteractive": false,
"watch": [],
"watchTimeout": 500
}

@jpxd that did it. Thanks!
@Stefanbracke if you're using bitnami, try 'bitnami' as the user instead of ec2-user

If you're using Windows, you should be adding C: before /Users
Similar for other OS... it is best if you're starting from the absolute root of the drive the key is on and address your way to the folder without any relative paths

I had an issue where it couldn't read the private key file on my local computer
I had ~/.ssh permissions set to 700
I had ~/.ssh/key set to 600
to fix it
I set ~/.ssh permissions set to 770
I set ~/.ssh/key set to 660


Minimize html

Adds the minimize HTML package
note: there is also a CSS and Javascript minimizer but they would be two additional packages

Also remove the clean-html script

removed clean-html and resolved conflicts


How to submit my page to the "made with" section?

I built a product customizer using FabricJS + BabylonJS and i'd like to show it in the "made with" section.
You can see the page HERE

please open a thread on our forum @PirateJC  will help you from there ;D


<IPython.core.display.Image object>

When I want to run the code in my own computer. It cannot shoe me any images or videos.

The notebooks are indeed meant to be run in Jupyter. The code itself is not Jupyter specific, but in a script you would add plt.show() for some matplotlib plots, and some specific widgets will not work outside of a notebook. The ! mo cells and similar cells that run shell commands are also jupyter/ipython specific.
Can you tell a bit more about your use case? For most notebook code, using ipython should work. You may need to install tk (depending how you installed Python), then you can do ipython --matplotlib tk -i 001-hello-world.py and the image from the code that was exported from the 001 notebook shows up as expected.

Closing this issue, please feel free to open a new issue if you have more information or a new question.


Endless loading time when creating a playlist

Expected Behavior
When creating a playlist a playlist should be created , when creating a playlist with added music numbers i want a playlist with music nummbers added
Actual Behavior
As soon you click the add playlist button, a loading sign appears , and stand there forever
Steps to Reproduce the Problem

when i go back to drive, i can open the playlist but no music is added.

Specifications

Browser: google chrome
OS: windows 10
desktop pc

I have same problem
Browser: Mozilla Firefox 71.0 64-bit
OS: Linux Mint 19.3 Cinnamon
Laptop Dell Inspiron 3542


Remove need for GITHUB_TOKEN

Hey there,
thanks a lot for this action!
It was unclear to me why this action wants the GITHUB_TOKEN since that can be quite a bit of a security risk.
A colleague of mine had a look and he thinks it isn't actually necessary.
Since he doesn't have time to make an issue I'll summarize his points here:

That particular action could be written without the token without a problem. They use it to retrieve the changelist for the PR, which they could get easily from the local git repository just by having head and base ref.

For the annotations, you also don't need the token: see the docs for setting an error message.

You can just echo "::error file=app.js,line=1,col=5,endColumn=7::Missing semicolon" in your script and it will create an annotation in app.js, line 1 with "missing semicolon". Absolutely no need for API calls, and much easier this way.

Hope this helps you improve this action even further!

TL;DR I'm open to suggested changes in the README, but not removing the token all together.
It is required to

get list of file changes for private repos
post thread comments. This action also removes duplicate previously posted thread comments (created by the bot). FWIW, we only remove a thread comment if the comment's body starts with a special HTML comment (<!-- cpp-linter-action -->) and the comment was created by the generic bot's user ID.

I agree that it could be a security risk (if you don't trust the src code openly available here). Note that the README does mention that the token is only required in the above situations. Furthermore, we include a link (in the README) to the GitHub docs about configuring the token (which also talks about or links to other docs about configuring the token's privileges).
Ultimately, this is a user decision. The token can be omitted if changed-files-only is false and thread-comments is false. thread-comments default value was recently changed to false after exploring private repo support (thread comments can't be supported for private repos).

get list of file changes for private repos

Why don't you just compare the head with the base ref of the repository to get the file changes?
Is the git history not part of the checkout?

if you don't trust the src code openly available here

I mean even if you do trust the code in its current state, most people will follow the readme and use the @master version of the action so you'd also have to trust that the repo is never compromised.
I don't think that's likely to happen but it seems like an unnecessary risk.

Is the git history not part of the checkout?

It is only if you set fetch-depth to 0 for actions/checkout@v2+. We're not trying to assume what the workflow does outside this action.
We're also supporting push and PR events, so base refs aren't always going to be available.
It is true that the README should demonstrate using the latest release version tag, but that increases maintainability in the README. @shenxianpeng Are you ok with using shenxianpeng/cpp-linter-action@v1 instead of ``shenxianpeng/cpp-linter-action@master` in the README?

@2bndy5 Of course no problem, I'll update it right away.

Ok, the README now demonstrates using the v1 tag. We also published a v1.4.2...
@StefanFabian There's also nothing stopping users from using a commit's SHA instead of a branch or tag. I recently helped a project that opted for using actions pinned to a specific commit (for the same security reason you mentioned).


Production Build Error

Failed to modify the code from this file
./node_modules/@polymer/lit-element/lit-elelment.js:12

Sorry don't know what build you're running and why it's trying to modify that file.

same problem! I use create-react-app create a program and use wired-elements,but when I run yarn bulid,appear this

This is likely because the target of the build is set to ES5. There may be other solutions out there. Don't think it is specific to wired-elements. But anyone using lit-element
https://github.com/facebook/create-react-app/blob/master/packages/react-scripts/template/README.md#npm-run-build-fails-to-minify

Same problem here 👍

Same here. Did anyone find the solution?

The easiest work around is the use the bundled version in your app
<script<EMAIL_ADDRESS>

This demo CodeSandbox shows usage of wired-elements in create-react-app.
https://codesandbox.io/embed/xrll5wyl8w
(uses the latest wired-elements build)


Mac: java/rmi/activation/rmidViaInheritedChannel/InheritedChannelNotServerSocket.java TestFailedException: TEST FAILED: rmid not destroyed in: 60000 milliseconds

19:55:39 ACTIVATION_LIBRARY: Activation System available after 0 milliseconds
19:55:39 RMID: getting a reference to the activation system
19:55:39 RMID: obtained a reference to the activation system
19:55:39 TEST FAILED: reference to the activation system was null
19:55:39 RMID: caught exception trying to shutdown rmid
19:55:39 RMID: TEST FAILED: reference to the activation system was null
19:55:39 TestFailedException: TEST FAILED: reference to the activation system was null
19:55:39 	at TestLibrary.bomb(TestLibrary.java:121)
19:55:39 	at TestLibrary.bomb(TestLibrary.java:124)
19:55:39 	at RMID.shutdown(RMID.java:291)
19:55:39 	at RMID.destroy(RMID.java:313)
19:55:39 	at ActivationLibrary$DestroyThread.run(ActivationLibrary.java:220)
19:55:39 RMID: testlibrary finished shutting down rmid
19:55:39 RMID: RMID's process still not terminated after more than 200 milliseconds
19:55:39 RMID: RMID's process still not terminated after more than 400 milliseconds

@sophia-guo I am guessing this is java8.
Just a thought that for any new issues we put a link to the test (either to the job in Jenkins, although we don't keep too many of those around, or the test in the playlist with a note on how to run to reproduce, in this case I guess this test belongs in this test suite: https://github.com/AdoptOpenJDK/openjdk-tests/blob/master/openjdk_regression/playlist.xml#L337 and could be rerun by "make jdk_rmi_SE80_0"

Yes, should add more information to reproduce.
All issues created by now are java8 specific. Locally we don't have a separate ProblemList.txt for java9. Though we haven't got a chance to see whole java9 test build behavior by default  tests will be exclude in java9 test build.
To rerun:

Search the class name in ProblemList.txt find the exclude line and comment the line
(Here: java/rmi/activation/rmidViaInheritedChannel/InheritedChannelNotServerSocket.java 154 macosx-all)
Above the line find the group test belongs to
(Here, jdk_rmi)
make jdk_rmi_SE80_0

Probably need to update README again. :-)

It is a duplicate of openjdk bug https://bugs.openjdk.java.net/browse/JDK-8170562

Closing as now listed in summary issue #345


feature request: possibility of partitioning by block_num (postgres)

Potential use case:

Several replicas of low-size databases to handle queries covering recent activity
Fewer replicas of full history to handle rarer queries


Document object lifetime semantics across languages

https://github.com/TheFoundryVisionmongers/OpenAssetIO/pull/353#discussion_r859535937

We already have docs for our python semantics across language. This is about the c-api, which far way, and is looking to require more holistic long term planning.


Dls 8121 - added ALF code without labels for pages

changed app name to be correct and fixed application in tests

https://github.com/hmrc/app-config-base/pull/8726

https://github.com/hmrc/app-config-production/pull/18459


Discrepancy in the intro and deployment method described later in the article

The intro to this article describes it as:

This quickstart shows how to deploy a Node.js app to Azure Web Apps. You create the web app using the Azure CLI, and you use Git to deploy sample Node.js code to the web app.

However, there is no other mention of Git based deployment later in the article. It expressly describes how the user should navigate to the ZipDeploy URL and upload a compressed ZIP file.
I'd rather you correct the intro text for this article, or, better still, add the missing content for Git deployment (while keeping the Zip deployment method intact).
This might allow the user to choose whichever method they feel fit for use.

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

ID: 4b0165d7-6462-842c-76ec-1a1f344fa51a
Version Independent ID: 3b2c77ed-d9eb-cf20-0ace-908b88927e54
Content: Create a Node.js web app in Azure
Content Source: articles/app-service/app-service-web-get-started-nodejs.md
Service: app-service-web
GitHub Login: @cephalin
Microsoft Alias: cephalin;cfowler

@jagged3dge The article was modified to use ZipDeploy and we missed updating the description. I will update it. Thank you for reporting this.

Changed merged w/ latest update.

#please-close


model-server-api has to be an api dependency in model-client

IModelClientV2 uses ModelQuery as a parameter which requires this dependency to be of type 'api'.

:tada: This PR is included in version 1.11.1 :tada:
The release is available on GitHub release
Your semantic-release bot :package::rocket:


Fix the gradient sign for QM9q and SPICE

A quick fix for #116 until a proper solution decided.

@stefdoerr could you review?


How do I schedule for 1 year?

Hey there how do I schedule for 1 year?

Did you try 1 year later? and create the label

Thank you ;) It works.


Bump mdbook version

Is there any good reason we aren't using the latest version of mdbook? All that was required was to move the theme directory into src.

@AndyGauge Fixed

This one makes me nervous.

@AndyGauge You were right to be nervous, this has broken how we handle links.
0.2.0

This release changes how links are handled in mdBook. Previously, relative links were interpreted relative to the book's root. In 0.2.0+ links are relative to the page they are in. This has several advantages, such as making links work in other markdown viewers like GitHub. You will likely have to change links in your book to accommodate this change.

This makes sense, but we include the table from a subpage in the main page (e.g. science.md includes the table for mathematics.md), so I see no way of getting this to work with the way we handle things. Currently, either the science or mathematics tables will link to the wrong thing.

Apologies for not noticing this when upgrading. In future, we can pass the build through something like mdbook-linkchecker, which I have verified will catch this error. It also lets us check external links, meaning we might be able to use it instead of link-checker as well.

Its fine, we just need to update links to be absolute.

Production doesn't work for me right now. Clicking Vector Sum from the science table leads to the right page, whereas clicking Vector Sum from the mathematics table leads to a missing page (Note the science/science url).


⚠ Bofrost.at has degraded performance

In aa49307, Bofrost.at (https://www.bofrost.at) experienced degraded performance:

HTTP code: 200
Response time: 823 ms

Resolved: Bofrost.at performance has improved in 5b68721.


fix(readme): update readme with correct param for approving primary name

:tada: This PR is included in version 2.5.1-alpha.2 :tada:
The release is available on:

npm package (@alpha dist-tag)
GitHub release

Your semantic-release bot :package::rocket:

:tada: This PR is included in version 2.5.2-alpha.1 :tada:
The release is available on:

npm package (@alpha dist-tag)
GitHub release

Your semantic-release bot :package::rocket:

:tada: This PR is included in version 2.5.2 :tada:
The release is available on:

npm package (@latest dist-tag)
GitHub release

Your semantic-release bot :package::rocket:


How to draw mask image in skija?

I want to draw the two pictures together to realize the function of picture mask, just like css style mask-image ,
So I want to know if any ways to use mask-image in skaji?
Looking forward to your reply
thanks

It sounds like this issue should be closed...


dsAnalyze & array data.

As it stands, dsAnalyze doesn't work the way AnalyzeStudy used to - assuming data is a vector of structures, and then applying a function handle to each element of data. Thus, even if data is a vector, if your analysis function operates on a scalar data object, you get a scalar data object out of dsAnalyze (the result of applying the function to the first element of data). This makes it hard to implement parallel execution (as in https://github.com/DynaSim/DynaSim/issues/162).
I would just change dsAnalyze to operate on vector data structures, but there seems to be some functionality built in for dsAnalyze to produce figures, and output figure handles. I don't know why this functionality would be baked into dsAnalyze and not dsPlot, but I'm hesitant to break it by merging in the fix I need. So, I need input here.

dsAnalyze is the former AnalyzeResults.  ds.analyzeStudy is still there.  AFAIK, dsAnalyze only works on a single data structure, while ds.analyzeStudy was meant to work on an array of structs. I have almost finished merging the functionality on the branch for #98.

@benpolletta, are you sure that there is now a missing functionality, and not just functionality that had never been implemented?

ae34c79f1ab2d27518cb06f8470ebc0ed9ea4785


Signature dictionary /Contents encryption

I've noticed that signature dictionary /Contents value is not encrypted even if the PDF is encrypted.
Adobe Acrobat Reader DC handles such PDFs properly.
Conversely, it cannot handle properly an encrypted PDF with the signature dictionary /Contents value encrypted.
This pull request makes QPDF handle the signature dictionary /Contents value as unencrypted even if the PDF is encrypted.
Also tests for this is added.
Please review this pull request.

I've noticed that this pull request needs to change QPDFObjectHandle::getParsedOffset().
So I've merged the branch and change it.
If you do not want the two branches to merge with merge commit, but rather want the commits to be linear, make such an integrated branch / pull request.

I am reviewing your changes and preparing pull request that I will be prepared to merge after your final okay that it meets your needs. Your changes are high quality, which makes my work easy. I'll let you know when I'm ready. Kids are playing at a neighbor's house, so I have some time. :-)

Incorporated by #376


new layout loaded all_posts length: 3 no post_url, skipping

when i run the code
this keeps happening till i stop the code again
[WDM] - Driver [C:\Users\pc.wdm\drivers\geckodriver\win64\v0.35.0\geckodriver.exe] found in cache
new layout loaded
all_posts length: 3
no post_url, skipping
no post_url, skipping
no post_url, skipping
all_posts length: 7
no post_url, skipping
no post_url, skipping
no post_url, skipping
no post_url, skipping

me too


Add openvms detection

This has been tested with OpenVMS 8.3.

Added openvms detection
Removed the hardcoding of ssh_connection setting platform to unix.
In os_common.rb we can now more accurately determine the platform by using uname_s method. Openvms does not have understand uname_s and doesnt generate an error detectable by the command object. However you can check the message coming from that for the generic openvms error.

Looking at the lint testing part now

@chris-rock not sure on this final error. All the lint testing should pass now.

awesome @briandoodyie I think we are getting very close...

@briandoodyie Anything I can help with?

Hmmm the ssh access will work and was the original change that was made. It made everything a lot simpler but doesnt windows now also support ssh. Might be assumption that just doesnt work now. thoughts? @chris-rock

@briandoodyie I wanna clean this up as well, I just want to do this in two steps.

@briandoodyie I go with the current PR. Thanks @briandoodyie for putting all the effort into that


Does this work with a self-hosted Sentry?

Or we should wait for a future release?

It does work with self hosted sentry however currently the general iOS story on self hosted sentry is not great because of the lack of system symbols. So YMMV.

So, what should we set in the build script in the following steps?
export SENTRY_ORG=YOUR_ORG_SLUG
export SENTRY_PROJECT=YOUR_PROJECT_SLUG
export SENTRY_AUTH_TOKEN=YOUR_AUTH_TOKEN
...

You set the values from the URLs for the slug and the auth token you get from your local sentry. Additionally you will need to export SENTRY_API_URL as mentioned in the docs. You can find a bit of information on these in the CLI docs: http://docs.sentry.io/learn/cli/

Cool, thanks @mitsuhiko.
We were missing the SENTRY_URL export.

When I try to export SENTRY_URL, I get an error

error: http error: generic error (400)

What's wrong?
(Jenkins + Fastlane)

@zhelyabuzhsky i strongly recommend using forum.sentry.io for support. I can't tell you whats wrong like this though, you would need to enable debug output.


Permit to call static function for ProviderSqlSource

The goal of this pullrequest is to permit the call of static functions form the ProviderSqlSource rather than instanciate a bean before calling the function.
I have just added the test to know if the function is a static function or not and invoke the function without an instance if the function is static.

Hi @tnicolas29200 , Thanks for your contribution. I think this change is a reasonable suggestion. However, this PR supports only no argument method. I will submit #1131 that supports allowed method argument patterns.


Fixing C++ breakage from copybara import.

This is just weird and makes MSVC unhappy. Some kind of implicit cast/initializer-list/inline-construction wonkiness that no one should ever do.

🤦 had made local changes in llvm and the submodule update failed
false alarm, but now I'm just seeing C++ undefined behavior everywhere :P


Excess input spilling

If the assembling-machine or furnace is being filled by loaders, which WILL fill the machine to the max allowed, then the machine ends up spilling the excess on upgrade, and enough items are spilled to exceed the bounding box of the machine resulting in the spilled items not being deleted.
Also, it would be nice that if anything is spilled, it only deletes the item corresponding to the machine it just replaced.

As can be seen, enough items are spilled to result in the stone furnaces and steel furnaces to end up outside of the box where these would be deleted.

How does this happen? Do all entities inside assemblers get put on ground on replacing?

Only seems to be the entities past the stack size of the ingredient.   Loaders, which are a native type to factorio will just feed the machine till it won't take any more,  while inserters specifically feed the machine for a limited quantity of ingredients, typically 10 of, or 2 crafts worth, whichever is greater.

Ive set spill to false, you can test if this fixes the problem

This does indeed fix the problem.  No more free furnaces/assembling machines.

Thanks for reporting and testing! Released 0.2.5


Twig extension : a new filter

There is a filter called date using php DateTime Object but it doesn't allow formatting as the locale of the user. That's why I wanted to created a new filter using the extension "Intl". This works fine when I put the finction in "core.php" but when I want to extend twig extension with "Twig_Filter_Method()", no php native functions and classes is recognized. I can't use php function inside the new function created by me.
For example : "Fatal error: Class 'Application\BlogBundle\Twig\DateTime' not found" or "Fatal error: Class 'Application\BlogBundle\Twig\IntlDateFormatter' not found".
What's wrong ? et why twig doesn't use a filter for convert the date about locale.

btw, see #30 for a PR adding such a filter in this repo.


[DOCS]: Rewrite The Reference Docs For Users

Rewrite/replace the reference docs for the Tanstack APIs and make sure they are standard and intuitive enough for users and external users.
A preferable way to go about it will be to use TSdoc to auto-generate them 😁.
Related to https://github.com/TanStack/form/issues/746.

Please not that work on this has already begun here:
https://github.com/TanStack/form/pull/735

Please not that work on this has already begun here:
https://github.com/TanStack/form/pull/735

Oh, of course no problem at all 🤲.


Addressing issue #246

Addressing Issue #246

@kwekuako - can you please assist to approve and merge the content for this PR based on issue https://github.com/MicrosoftDocs/OfficeDocs-O365ITPro/issues/246?
Thank you.

@AndreaBarr - This PR can be closed. Thank you.


add_alpha is false even if vertex color has alpha component

Describe the bug
add_alpha = mat is not None and add_alpha and not (mat.blend_method is None or mat.blend_method == 'OPAQUE')
This expression gives False with our pipeline-
# ...
        if not obj.data.color_attributes and obj.active_material:
            obj.data.color_attributes.new(name='Col', type='FLOAT_COLOR', domain='CORNER')

            color = obj.active_material.diffuse_color

            for datum in obj.data.attributes.active_color.data:
                datum.color = color

            obj.data.materials.clear()
# ...

This pipeline of ours may be a bit of a legacy, but bear with us- because we only use vertex colors in our models, we turn a material on an object with only the base diffuse color into vertex color.
PS: Whether or not we clear the materials and whether or not we assign Alpha Blend does not seem to affect the result.
Expected behavior
What we expect is that the alpha component of the vertex colors also get picked up and exported.
.blend file/.gltf (mandatory)
prism.zip
Our export scripts are at https://github.com/automancy/automancy
Version

OS: (arch) linux
Blender Version: 4.2.0 Alpha (from blender-git aur package)
GLTF exporter revision: 858b36e23b444145b5c33dce245159282744f130 (symlink'd to blender addons folder for debug)

Forgot to mention: vc_alpha_name is not None.

Hello,
Seems your materials have "OPAQUE" blend_method, so no alpha will be exported.
Can you please upload a test file when you already apply all your modification done by your eporter, just before calling the bpy.ops.export_scene.gltf?

I'm also experiencing this bug again #2206

Seems your materials have "OPAQUE" blend_method, so no alpha will be exported.

There shouldn't even be a material- not in our case.

Can you please upload a test file when you already apply all your modification done by your eporter, just before calling the bpy.ops.export_scene.gltf?

Here:
a.zip

Your VC will be exported only when used as base color multiplier of the base color, as described in glTF specification.

That's weird- we haven't read the spec, but shouldn't you be able to simply have vertex colors?

@The0Dev

I'm also experiencing this bug again https://github.com/KhronosGroup/glTF-Blender-IO/issues/2206

Will be fixed in #2237
@msparkles
This will be fixed in #2233


Prepare for a gpt-4o model version update in Azure OpenAI Service

Is the update done intantly and how does that fit with  "azd  up" when still using the "old" api  version
I assume bicep param updating is required - before or after?

Good question, I'll ask around as to what the best practice is for version updating.


JVM crashes using AdoptOpenJDK 15 OpenJ9 and Gradle 6.7.1

I tested it in a Debian 10 container with Gradle 6.7.1 and AdoptOpenJDK 15 OpenJ9. It crashed immediately with a JVM dump.
./gradlew tasks
Unhandled exception
Type=Segmentation error vmState=0x00000000
…

Java Version:
java --version
openjdk 15.0.1 2020-10-20
OpenJDK Runtime Environment AdoptOpenJDK (build 15.0.1+9)
Eclipse OpenJ9 VM AdoptOpenJDK (build openj9-0.23.0, JRE 15 Linux amd64-64-Bit Compressed References 20201022_81 (JIT enabled, AOT enabled)
OpenJ9   - 0394ef754
OMR      - 582366ae5
JCL      - ad583de3b5 based on jdk-15.0.1+9)

I did the same test with AdoptOpenJDK 15 Hotspot and everything worked out of the box in the same environment.
This issue might be in the wrong repository as this issue is not related to hombrew-opendjdk, but I do not know where else to post it.

If it's an OpenJ9-specific segmentation error, I'd advise raising an OpenJ9 issue here: https://github.com/eclipse/openj9

@adamfarley thanks. Opened the issue here: https://github.com/eclipse/openj9/issues/11508

This one can be closed.


Startup crash using Docker

Hi, I'm having issues deploying versions from 1.3.5 to 1.3.10. (I haven't tested earlier versions).
Here are my logs:
CLI: Matterbridge.loadInstance() failed with error: SystemError [ERR_SYSTEM_ERROR]: A system error occurred: uv_os_get_passwd returned ENOENT (no such file or directory)

CLI: Matterbridge.loadInstance() failed with error: SystemError [ERR_SYSTEM_ERROR]: A system error occurred: uv_os_get_passwd returned ENOENT (no such file or directory)

CLI: Matterbridge.loadInstance() failed with error: SystemError [ERR_SYSTEM_ERROR]: A system error occurred: uv_os_get_passwd returned ENOENT (no such file or directory)

CLI: Matterbridge.loadInstance() failed with error: SystemError [ERR_SYSTEM_ERROR]: A system error occurred: uv_os_get_passwd returned ENOENT (no such file or directory)

CLI: Matterbridge.loadInstance() failed with error: SystemError [ERR_SYSTEM_ERROR]: A system error occurred: uv_os_get_passwd returned ENOENT (no such file or directory)

CLI: Matterbridge.loadInstance() failed with error: SystemError [ERR_SYSTEM_ERROR]: A system error occurred: uv_os_get_passwd returned ENOENT (no such file or directory)

CLI: Matterbridge.loadInstance() failed with error: SystemError [ERR_SYSTEM_ERROR]: A system error occurred: uv_os_get_passwd returned ENOENT (no such file or directory)

CLI: Matterbridge.loadInstance() failed with error: SystemError [ERR_SYSTEM_ERROR]: A system error occurred: uv_os_get_passwd returned ENOENT (no such file or directory)

CLI: Matterbridge.loadInstance() failed with error: SystemError [ERR_SYSTEM_ERROR]: A system error occurred: uv_os_get_passwd returned ENOENT (no such file or directory)

I also attach my docker-compose.yaml file:
version: "3"

services:
  matter-bridge:
    image: luligu/matterbridge:${VERSION-latest}
    container_name: matter-bridge

    restart: always

    volumes:
      - ${PLUGIN_PATH}:/Matterbridge
      - ${STORAGE_PATH}:/.matterbridge

    environment:
      PUID: ${PUID-1000}
      PGID: ${PGID-1000}

    user: ${PUID-1000}:${PGID-1000}

    network_mode: host

Upon reviewing the generated folders, I found only the following file with this content:
File: 0d0a2ac6163e36f3f2349c77671004edfe3d5e1abbef93cbb583acec8d01481b
Content:
{"key":"storageNames","value":["matterbridge"]}

Finally, it was my fault. I'm leaving the corrected .yaml file and closing the issue.
Regards
version: "3"

services:
  matter-bridge:
    image: luligu/matterbridge:${VERSION-latest}
    container_name: matter-bridge

    restart: always

    volumes:
      - ${PLUGIN_PATH}:/root/Matterbridge
      - ${STORAGE_PATH}:/root/.matterbridge

    network_mode: host


tutorial.md: docker-server value for Docker Hub

See comment in https://github.com/tektoncd/pipeline/issues/2616
Most people are used to 'docker.io' as server for Docker Hub and not 'index.docker.io'. The tutorial doesn't work with 'docker.io', so defining the value clearly in case of widely-used Docker Hub may be useful.
Best,
Didier

Changes

Submitter Checklist
These are the criteria that every PR should meet, please check them off as you
review them:

[ ] Includes tests (if functionality changed/added)
[ ] Includes docs (if user facing)
[ ] Commit messages follow commit message best practices
[ ] Release notes block has been filled in or deleted (only if no user facing changes)

See the contribution guide for more details.
Double check this list of stuff that's easy to miss:

If you are adding a new binary/image to the cmd dir, please update
the release Task to build and release this image.

Reviewer Notes
If API changes are included, additive changes must be approved by at least two OWNERS and backwards incompatible changes must be approved by more than 50% of the OWNERS, and they must first be added in a backwards compatible way.
Release Notes

/ok-to-test

/kind documentation

@jerop : no interest in merging this typo fix? Didier

@jerop : no interest in merging this typo fix? Didier

@didier-durand i believe @jerop made a comment here https://github.com/tektoncd/pipeline/pull/3298#discussion_r496759397 and it probably can get merged quickly after that when this is addressed,

@didier-durand i believe @jerop made a comment here https://github.com/tektoncd/pipeline/pull/3298#discussion_r496759397 and it probably can get merged quickly after that when this is addressed,


boing example

random is deprecated
and there are some string comparisons to nil
I'm new to nim so just replaced nil with "" (no idea if that's the same thing in nim)
I'd do a PR but need to learn a bit about nim....

Thanks, all fixed now.


[T4A3][T16-A2]Varun Gupta

Ready for review

Do take note of the title format change I've made.

@varung97 added some comments, please code this PR after you're done reading the comments


Bump subcharts and dependencies to fix security vulnerabilities

reported by artifacthub scanning

The helm chart was not rebased on 0.7.3 so the cluster could not came up because the Nginx version was moved to values.yaml. I had to bump it to 0.8.0 though because two service related tests were failing.


mpl2: ensure we don't get stuck in cluster placement

Fix #4335
We were getting stuck in when synchronizing the threads results.
Running Secure-CI

Secure-CI passed.


can  achieve such a jump effect

@AllenFang  can  achieve such a jump effect

HI @li164902697 As I mention on README.md, react-bootstrap-table already stop implement new feature and start move to react-bootstrap-table2.  So it's sorry that I'm not going to implement this feature but you can still custom the pagination bar to achieve your requirements.
Check this you can learn how to custom the pagination panel, actually, it's not very hard you just add some button and input I think.


⚠ Secret Website 3 has degraded performance

In 7818342, Secret Website 3 ($SECRET_WEBSITE_3) experienced degraded performance:

HTTP code: 200
Response time: 5691 ms

Resolved: Secret Website 3 performance has improved in 6434e81.


Feature Suggestion: Allow to monitor when you are added as a reviewer of a pull request

Hey,
Nice tool.
I have a feature suggestion: Would it be possible to add the possibility to monitor also the PRs you are asked for reviewing?
i.e: Someone creates a new PR, add you as a reviewer, then you get a notification informing about this PR. #

Hi @danielfranca , thanks for using PR monitor and for your suggestion. I've checked the GitHub api and couldn't find a query that gives the result you described. Please let me know if you have any ideas on how to implement it.


Content: Code refactoring

Code refactoring
Description
The idea was to writing script to insert new generated flights data to DB using cron job.
Eventually project should be deep refactoring.
Changes

Updated .gitignore file
Added composer support
Added main cron jobs script

Closing to prevent accidental merge

Closing to prevent accidental merge