Upgrade slf4j-nop from 1.7.36 to 2.0.15

About this PR
📦 Updates org.slf4j:slf4j-nop from 1.7.36 to 2.0.15 ⚠
Usage
✅ Please merge!
I'll automatically update this PR to resolve conflicts as long as you don't change it yourself.
If you'd like to skip this version, you can just close this PR. If you have any feedback, just mention me in the comments below.
Configure Scala Steward for your repository with a .scala-steward.conf file.
Have a fantastic day writing Scala!

⚙ Adjust future updates
Add this to your .scala-steward.conf file to ignore future updates of this dependency:
updates.ignore = [ { groupId = "org.slf4j", artifactId = "slf4j-nop" } ]

Or, add this to slow down future updates of this dependency:
dependencyOverrides = [{
  pullRequests = { frequency = "30 days" },
  dependency = { groupId = "org.slf4j", artifactId = "slf4j-nop" }
}]

labels: library-update, early-semver-major, semver-spec-major, commit-count:1

Superseded by #152.


Aggregation not working

When I query SELECT value FROM "test"."ten_hours".CPU WHERE VM_ID='1'
I get this:

and when I query this: SELECT mean(value) FROM "test"."ten_hours".CPU WHERE VM_ID='1' GROUP BY time(1m)
I get:

which is probably not the right result or am I doing something wrong?
InfluxDB 0.9 rc24

I have observed the same thing. And this gets even weirder when you add a where condition on time, for example in my case I have only 1 points in the series foo:

time
value

2015-04-15T13:27:46.672Z
10

And my query is: select sum(value) from foo where time > now() - 2w group by time(1w), and I gets back:

time
sum

2015-04-02T00:00:00Z
null

2015-04-09T00:00:00Z
10

2015-04-16T00:00:00Z
null

Beside the fact that it is returning 3 periods (seems like one before and one after) it is taking the beginning of the week (which is my group by period) differently than in influx 0.8, where beginning of the week was always Sunday

I am not sure if this is an expected behavior but I am seeing null values returned for time buckets where points are missing,
My time series has these points,
2015-04-24 00:53:30 +0000 UTC, 1
2015-04-24 00:53:40 +0000 UTC, 2
2015-04-24 00:53:55 +0000 UTC, 4
2015-04-24 00:54:00 +0000 UTC, 5
2015-04-24 00:54:05 +0000 UTC, 6
2015-04-24 00:54:15 +0000 UTC, 7

This works as expected,

SELECT sum(value) FROM metric WHERE (time >= '2015-04-24T00:53:30Z' AND time < '2015-04-24T00:54:16Z') GROUP BY time(10s)
2015-04-24 00:53:30 +0000 UTC, 1
2015-04-24 00:53:40 +0000 UTC, 2
2015-04-24 00:53:50 +0000 UTC, 4
2015-04-24 00:54:00 +0000 UTC, 11
2015-04-24 00:54:10 +0000 UTC, 7

Specifying a bigger time bound returns empty values,

SELECT sum(value) FROM metric WHERE (time >= '2015-04-24T00:53:20Z' AND time < '2015-04-24T00:54:26Z') GROUP BY time(10s)
2015-04-24 00:53:20 +0000 UTC, null
2015-04-24 00:53:30 +0000 UTC, 1
2015-04-24 00:53:40 +0000 UTC, 2
2015-04-24 00:53:50 +0000 UTC, 4
2015-04-24 00:54:00 +0000 UTC, 11
2015-04-24 00:54:10 +0000 UTC, 7
2015-04-24 00:54:20 +0000 UTC, null
Is this expected? I am using rc24.

@fahadullah you can use fill(0) at the end to return 0 for intervals that doesn't contain data

I would have preferred to not get any time buckets where points are missing. I haven't gone back and checked if this is how it worked with 0.8.8 or if this is only with 0.9.

@fahadullah this is how 0.9 works. Things have changed a lot from 0.8 to 0.9 even though it seems like a minor release :).
You'll find  many things either work differently or are yet still to be polished, I can tell you that by experience :smile:

Check out the docs - it can do what you want.
http://influxdb.com/docs/v0.9/query_language/querying_data.html#filling-intervals-with-no-data
Philip
On Thursday, April 23, 2015, Marcos Nils<EMAIL_ADDRESS>wrote:

@fahadullah https://github.com/fahadullah this is how 0.9 works. Things
have changed a lot from 0.8 to 0.9 even though it seems like a minor
release :).
You'll find many things either work differently or are yet still to be
polished, I can tell you that by experience [image: :smile:]
—
Reply to this email directly or view it on GitHub
https://github.com/influxdb/influxdb/issues/2308#issuecomment-95770357.

Thanks. That's very helpful.

Great, I must have skipped that one. Thank you and sorry for an invalid issue.


CLI: Scaffold CLI with commands and flags

:tada: This issue has been resolved in version 0.12.0 :tada:
The release is available on:

GitHub release
npm package (@latest dist-tag)

Your semantic-release bot :package::rocket:


chore(main): release 1.1.0

:robot: I have created a release beep boop
1.1.0 (2024-05-22)
Features

add chi Recoverer middleware (3b0d73f)
add dumper (a6d95b5)
add tags to docker image (fd670a3)
refactor config to yaml (061f49c)

Bug Fixes

add registry to docker image (b48afab)
fix build (d8697e1)
fix go.sum (322bbe3)
remove port from application (6214a00)

This PR was generated with Release Please. See documentation.

:robot: Release is at https://github.com/wert2all/timeline-backend/releases/tag/v1.1.0 :sunflower:


Documentation: Impove CASE_DEFAULT documentation

What should we add?
From https://qiskit.org/documentation/stubs/qiskit.circuit.SwitchCaseOp.html:

The special value CASE_DEFAULT can be used to represent a default condition.

But it doesn't explain how to import CASE_DEFAULT and there is no hyperlink.

Yeah, it's meant to hyperlink to an entry right underneath this table of the control-flow ops, but I must have screwed up the Sphinx syntax.
I'll fix it tomorrow, because I don't want to put up a new PR right now while we're in a CI concurrency crunch for the 0.25.0rc1 release today (or early tomorrow if we can't get through CI).

I've made #10484 that hopefully should make a bunch of improvements here.


refactor Parameter out of Instrument

from @AdriaanRol discussion in #38

I noticed that the parameter class is located inside the instrument folder. Previously this made sense but I think that in recent PRs #39 and #40 we have made the notion of a parameter more abstract and indepenent of the instrument. Would it make sense to separate/refactor this?

Yes, yes it would make sense

Possibly to be fixed in. #403 !


chore: fixed boolean was not represented as a possible type for schemas

Description
Fixed schema could not be represented as a boolean.

@magicmatatjahu sorry for pinging you twice, my implementation had some cached code so didnt catch the failing build.


Push  to package registry

developer can use jar using jar repository.
jar is currently hosted on GitHub package registry

https://help.github.com/en/articles/configuring-apache-maven-for-use-with-github-package-registry#publishing-a-package
options are : github package registry
or maven central repo

follow instruction here on https://github.com/bahadurbaniya/Date-Converter-Bikram-Sambat-to-English-Date#jar-from-github-package-registry to use jar from GitHub pacakge registry


Add python version to transcriber image

Without this (lifted from tts.py) I got an error installing numba when deploying to Modal.

Thank you, this makes sense! Without this we use whatever Python version you have locally, and assuming there's some issue with 3.8 or 3.11.


CLI --debug flag

For running everything serially so it's easier to debug concurrency issues.
In the future it might do other things too to aid in debugging. Suggestions welcome.

:+1:

Currently, ava does not have a single config store, so we have to come up with some way to share configuration between scripts. Right now, I can see 2 ways to implement this, without major code changes:

ava --debug sets environment variable, so that other scripts (files) see it: process.env.AVA_DEBUG = true. Also, it will be possible to enable debug mode by AVA_DEBUG=true ava.
Have a config.js with module.exports = {} and set config values there (all scripts that require('./config') will get the same object).

What do you think?

Config would also be handy for #27

Do you mean for child processes? Or just for the AVA scripts in general?

@Qix- Sorry, did not understand your question

I re-read your comment; I think see what you mean now.
I'd have to test, but I'd suspect that ad-hoc environment variables added to process.env would be passed down to child processes too, which means that's a plausible way of storing configuration so child AVA processes will be aware of configuration. Unless that's not how @sindresorhus plans on implementing child processes.

Yes, child processes will get modified process.env object (node.js docs say so).

I don't understand how this is related to a --debug flag? Right now it doesn't use any child-processes, but just require's the test files.
As for sharing config with sub-processes, that's better discussed in #1. But tl;dr, child_process.fork and message passing.

@sindresorhus I was talking about the ways to indicate, that user specified --debug flag and wants tests to run serially. For example, as for now, how would lib/test.js know, that cli.js has debug = true?

It could just read argv: process.argv.indexOf('--debug') !== -1. But more likely the CLI will pass it as an option to the lib/test.js interface.


Minimal implementation of Sovereign chain context to support packet relaying

Summary
The chain context dependencies that are required for the default packet relayer to work is specified in the constraint closure here: https://github.com/informalsystems/hermes-sdk/blob/ed9161c174d56829ffad258a660235e74be51102/crates/relayer/relayer-components/src/components/default/closures/relay/packet_relayer.rs#L46-L85
To support packet relaying, the sovereign chain context would need to at minimum implement the traits specified in the constraint closure. This issue would track the major implementation progress.
Types

[ ] HasClientStateType
[ ] HasConsensusStateType

Queries

[ ] CanQueryChainStatus
[ ] CanQueryClientState
[ ] CanQueryConsensusState
[ ] CanQueryConsensusStateHeight
[ ] CanQueryPacketIsReceived

Payloads

[ ] CanBuildCreateClientPayload
[ ] CanBuildUpdateClientPayload
[ ] CanBuildReceivePacketPayload
[ ] CanBuildAckPacketPayload
[ ] CanBuildTimeoutUnorderedPacketPayload

Messages

[ ] CanBuildCreateClientMessage
[ ] CanBuildUpdateClientMessage
[ ] CanBuildAckPacketMessage
[ ] CanBuildTimeoutUnorderedPacketMessage
[ ] CanBuildReceivePacketMessage

Transaction

[x] CanSendMessages - #100

Done in previous work.


Only respond to API requests once the server is ready

@grobie Here you are. Not nice, but it's legacy code anyway. ;o)

Fixes #3258 .

This seems to work fine in the canary.


the z coordinate of pose tracking should not be affaced by swinging arms.

Please make sure that this is a solution issue.
System information (Please provide as much relevant information as possible)

Have I written custom code (as opposed to using a stock example script provided in Mediapipe): No
OS Platform and Distribution (e.g., Linux Ubuntu 16.04, Android 11, iOS 14.4):ios 15.5
MediaPipe version: 0.8.10
Bazel version: 5.2
Solution (e.g. FaceMesh, Pose, Holistic): pose
Programming Language and version ( e.g. C++, Python, Java): OC

Describe the expected behavior:
3D body tracking is amazing with mediapipe, but the accuracy of the z coordinate requires improvement. For example, swinging only arms should not affect the z coordinate of other static joints, such as the nose, ears, etc. However, when I only move my arms, my nose and other facial joints are swinging, too. I believe it is caused by the depthAI that weights all pixels including other joints. Can the correlation among different joints be decoupled? or how to mitigate the coupling of various joints? Can we impose the depth data from the lidar or depth camera to improve the z-axis accuracy? Thanks.

Hi @fchen09 ,
Could you please share any reference video and also if any code changes to investigate further on this issue.

At this stage, pardon me cannot share the video. Let me draw how it is going on in the figure. When I swing my arms, the body was lean back and forth depending on whether the arm is backward or forward. Any solution to the unwanted swing? Thanks.

Hi @fchen09 ,
We have closed issue which is having answer for queries on Z co-ordinate , could you please check that closed issue.


Translating beyond current line

As the title says.
Is there a way to translate beyond the current line?
Can I change something in "config.ini" to make it translate more at a time?
I haven't seen it explained anywhere, I apologise in advance if I've missed it somewhere.

Not entirely sure what you mean.
If you are talking about translating the resources of a game before they are used, then no, since all games handles resources differently.

Not entirely sure what you mean.
If you are talking about translating the resources of a game before they are used, then no, since all games handles resources differently.

That was pretty much what I meant. Thank you.
It's easier to swallow it isn't made that way when you know the option isn't readily available somewhere. That it's because it would require separate plugins game by game.
My problem is that I read fast.. So when I have to wait a little second each line, even the short ones, things are slowed down significantly.
I appreciate your answer.

That was pretty much what I meant. Thank you.
It's easier to swallow it isn't made that way when you know the option isn't readily available somewhere. That it's because it would require separate plugins game by game.
My problem is that I read fast.. So when I have to wait a little second each line, even the short ones, things are slowed down significantly.
I appreciate your answer.


Generate cert-manager changediff from capi upgrader

Description of changes:
This is mostly for completion and correctness, but clusterctl doesn't need this info. It will help with logging and with a future upgrade plan command
By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.

[APPROVALNOTIFIER] This PR is APPROVED
This pull-request has been approved by: g-gaston
The full list of commands accepted by this bot can be found here.
The pull request process is described here

Needs approval from an approver in each of these files:

OWNERS [g-gaston]

Approvers can indicate their approval by writing /approve in a comment
Approvers can cancel approval by writing /approve cancel in a comment

[APPROVALNOTIFIER] This PR is APPROVED
This pull-request has been approved by: danbudris, g-gaston
The full list of commands accepted by this bot can be found here.
The pull request process is described here

Needs approval from an approver in each of these files:

OWNERS [danbudris,g-gaston]

Approvers can indicate their approval by writing /approve in a comment
Approvers can cancel approval by writing /approve cancel in a comment

/lgtm


Adding Twitch embed shortcode

Going to close that, the shortcode issues are fixed in the next release and Twitch is not that common to put in Gutenberg directly.


Put CommitLog#beginTimeInLock field in PutMessageLock impl class

Put CommitLog#beginTimeInLock field in PutMessageLock impl class

#3460


[PORT] Add installation update to ActivityHandler

Port this change from botbuilder-dotnet/master branch:
https://github.com/microsoft/botbuilder-dotnet/pull/4294

Fixes https://github.com/microsoft/botbuilder-dotnet/issues/4287
Changed projects

Microsoft.Bot.Builder
Microsoft.Bot.Builder.Tests

separate issue tracking this already


can't use --path with ls

NixOS
bash shell

Output of restic version
restic 0.9.5 compiled with go1.12.1 on linux/amd64
How did you run restic exactly?
export RCLONE_CONFIG_PASS=somepass
restic -r rclone:onedrive:somename ls --path=/home/bbigras/something

What backend/server/service did you use to store the repository?
onedrive with rclone
Expected behavior
Seing the files from the last snapshot containing the specified path.

Actual behavior
I get:
enter password for repository:
repository <something> opened successfully, password is correct
Ignoring "", it is not a snapshot id
Ignoring filters as there are explicit snapshot ids given

Steps to reproduce the behavior
do the steps from "How did you run restic exactly". Probably don't even need a real restic repo.

Do you have any idea what may have caused this?
I guess restic thinks a snapshot id was specified when it wasn't and then ignored the filter.
Do you have an idea how to solve the issue?
no
Did restic help you or made you happy in any way?
restic is awesome.

restic -r rclone:onedrive:somename ls --path=/home/bbigras/something

Is this the directory that contains the restic files? Can you please check with:
rclone lsd onedrive:somename

rclone lsd onedrive:somename
          -1 2019-05-24 16:13:37       159 data
          -1 2019-05-24 16:16:47         2 index
          -1 2019-05-24 16:09:21         1 keys
          -1 2019-05-24 16:12:04         0 locks
          -1 2019-05-24 16:27:16         7 snapshots

You are right, I was able to reproduce it on 2 of my repositories (Windows & Linux). Contrary to what the help message states the restic ls command seems to require a snapshotID (or latest).


Sara-Arash /Markets

Description
add markets page
Type of change

[ ] Bug fix
[x] New feature
[ ] Refactor code
[ ] Update translation text
[ ] Breaking change
[ ] Dependency update
[ ] This change requires a documentation update
[ ] Release

@mustofa-binary I already compress video now it's around 5MB. about the background I'm going to remove it :+1:

📝Changed routes:

/home/_markets
/

(view full size)
(view full size)

…vg/_market-commodities
…kets-svg/_market-forex

(view full size)
(view full size)

…kets-svg/_market-stock
…-svg/_market-synthetic

(view full size)
(view full size)

And 38 other routes:

/markets/_available-options
/markets/_available-trades
/markets/_hero
/markets/_other-markets
/markets/_what-trade
/markets/_why-trade
/markets/commodities/_commodities
/markets/commodities/_digital-options
/markets/commodities/_margin
/markets/forex/_digital-options
/markets/forex/_forex
/markets/forex/_margin
/markets/forex/_multipliers
/markets/globe.mp4
/markets/globe
/markets
/markets/stock/_digital-options
/markets/stock/_margin
/markets/stock/_stock-indices
/markets/sub-markets/_americas
/markets/sub-markets/_asia-oceania
/markets/sub-markets/_continuous-indices
/markets/sub-markets/_crash-boom
/markets/sub-markets/_daily-reset-indices
/markets/sub-markets/_energy
/markets/sub-markets/_europe
/markets/sub-markets/_exotic-pairs
/markets/sub-markets/_major-pairs
/markets/sub-markets/_metals
/markets/sub-markets/_minor-pairs
/markets/sub-markets/_smartfx
/markets/sub-markets/_step-indices
/markets/sub-markets/_submarkets
/markets/sub-markets/_volatility-indices
/markets/synthetic/_digital-options
/markets/synthetic/_margin
/markets/synthetic/_multipliers
/markets/synthetic/_synthetic-indices

1 deleted route:

/home/_simple-steps

Commit de8c8860466ceb55f9dc0e208376fa7c13a4b80b (https://deriv-com-git-fork-sara-fs-markets.binary.sx).


template destroy

Hi there,
I've installed your template. Since then I can<t use middleman. Is there  a way to reverse everything?
Regards,
Etienne

@EtienneLareau sorry to hear you have experienced problems. What version of middleman are you using? I have not tested this template with middleman above version 4.
Also can you let me know the exact steps you took (i.e. what commands you used?)


Update public-api.asciidoc

I believe we should use ElasticApm.startTransaction() and ElasticApm.startSpan() for creating custom transactions and spans. Please correct me if I'm mistaken.

Codecov Report

:exclamation: No coverage uploaded for pull request base (0.6@75d743f). Click here to learn what that means.
The diff coverage is n/a.

@@         Coverage Diff          @@
##             0.6   #160   +/-   ##
====================================
  Coverage       ?    77%
  Complexity     ?    957
====================================
  Files          ?     99
  Lines          ?   3301
  Branches       ?    293
====================================
  Hits           ?   2542
  Misses         ?    602
  Partials       ?    157

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update 75d743f...73a70bb. Read the comment docs.


allow configuring the type used for a C# class

this allows specifying the type for a class via the TranspilationSourceAttribute. Relates to #133
Example:
[TranspilationSource(TypescriptType = "{color: 'blue'|'red'}")]
public class AttributeAnnotatedClass5
{
    public string Color { get; init; }
}

becomes this:
type AttributeAnnotatedClass5 = {color: 'blue'|'red'}

This only works at the class level, not the field level. Doing this at the field level would require some more work and I didn't need it.

I'm not in favor of this change.
Is there a reason you cannot define an enum for the Color property and instead must resort to this?

because that's not my actual use case. My actual use case is that I have a struct that is just a wrapper around a string with validation around it on the server side. There are converters in place so when it's serialized and deserialized it's just treated as a string. So I want to just tell Tapper to treat my struct as a string. Here's an example
public struct WsId {
    public string Value {get;set;}
}

expected TS type
type WsId = string;

this totally works, but right now after I run the type generator I have to rewrite the type from:
type WsId = {Value: string} into type WsId = string

After consideration, it was determined that Tapper does not need this feature.


Add Swisscom Application Cloud to the picklist

Added the Swisscom AppCloud (https://developer.swisscom.com/) to the picklist for easier usage.

+1 this would be very nice!

Looks great! Let's merge!
@hochm, the other vendors on this list have sponsored a small account on their platform. I would really like everyone to iterate on this code without the fear of breaking compatibility. If you have any ideas on how this can be done, please reach out. https://twitter.com/dwayneforde
Cheers


⚠ GitLab Server has degraded performance

In d67dc23, GitLab Server (https://gitlab01.its-telekom.eu) experienced degraded performance:

HTTP code: 200
Response time: 6744 ms

Resolved: GitLab Server performance has improved in a32b9d2.


How to require an specific OS

The Win32Service PHP extension can be built and used only on the Microsoft Windows operating system.
How to specify this constraint on composer.json file?

Not possible at the moment, but something we can look into :+1:

I'd be interested in having a look at this feature! What about the following syntax?
{
    "name": "myvendor/myext",
    "php-ext": {
        "extension-name": "myext",
        "configure-options": [
            // ...
        ],
        "os-families": ['Windows', 'Darwin'] // not compatible with Linux here for example
    }
}

The values contained in os-families would have to match PHP_OS_FAMILY.
Also, it would be possible to use the wildcard, like "os-families": ["*"] (or "os-families": "*"?) if one wants to be explicit on supporting all operating systems.

I think it'd be good to have a way to negate something if you want to closely match PHP_OS_FAMILY, because I suspect most things are gonna be Windows-only, Linux/BSD/Solaris/Darwin-only, or everything. I don't think there are many that support everything except Solaris or everything except BSD.
So it could be nicer with `["!Windows"] or something? I'm just thinking it would suck if people list Linux and forget to include BSD/Solaris/Darwin and then things turn out not installable for no good reason.

I agree with you, by reading at your comment it feels more natural to me to work with a blacklist instead of a whitelist. Also it saves us from handling the wildcard. I'd rather use os-families-exclude instead of the bit-less explicit ! operator indeed.

Some examples for reference:
only Win:
"os-families-exclude": ["BSD", "Darwin", "Solaris", "Linux", "Unknown"]

only non-Win:
"os-families-exclude": ["Windows"]

only OSX:
"os-families-exclude": ["Windows", "BSD", "Solaris", "Linux", "Unknown"]

So what I was suggesting is to include BOTH options sorry it was clearly not clear :D
only Win:
"os-families": ["Windows"]

only non-Win:
"os-families-exclude": ["Windows"]

only OSX:
"os-families-exclude": ["Darwin"]

That way you can target whatever you want with the least amount of listing-distros and possibly forgetting things.
Obviuosly the "Unknown" wildcard is a bit problematic here.. But I guess those fall in the category of "rather just allow it and see if it builds 🤷🏻♂ "?

I’m okay to use both keys. The wildcard or “Unknown” is not necessary.
If the extension maintainer wants to exclude the unknown OS, it must list all included OS.
If the extension maintainer wants to include “unknown” it uses the exclude key or nothing.
IMO it’s necessary to make an exhaustive list of OS names used in the lists.

IMO it’s necessary to make an exhaustive list of OS names used in the lists.

This list is already defined: https://www.php.net/manual/en/reserved.constants.php#constant.php-os-family
That list is where Unknown comes from.
One thing that should be defined is the behaviour if both keys are used, a few examples:
{
  "php-ext": {
    "os-families": ["Windows"],
    "os-families-exclude": ["Windows"]
  }
}

{
  "php-ext": {
    "os-families": ["Windows", "Linux"],
    "os-families-exclude": ["Linux"]
  }
}

I would suggest that perhaps the most natural method for processing would be (pseudo-code):
allowedPlatforms = Windows BSD Darwin Solaris Linux Unknown

if "os-families" is specified: replace allowedPlatforms with the list

if "os-families-exclude" is specified: subtract from the allowedPlatforms

I think I'd declare it in the json schema as being a oneOf, so that either key should be provided, but not both, and probably need to check in the loader code to throw if both are provided because this is nonsense, even if we can reconcile it per your rules,  it's just clearer to disallow it IMO.

That is probably safer :100: :+1:

Both keys are declared nonsense. However, the maintainer must check if the composer.json is valid before tagging the version.
A command validate  must be added to help to validate the file.

I updated https://github.com/ThePHPF/pie-design/pull/29/files to reflect our discussion.
@macintoshplus the validate command seems a nice idea to me. This may be worth discuss about it in a new issue 🙂

A command validate must be added to help to validate the file.

composer validate already exists

Cool, less code to write. But, it's interesting to add this information to the documentation. Right?


IgnoredReturnValue checking specific type in all functions

The current IgnoredReturnValue implementation requires annotation on the function (https://github.com/detekt/detekt/blob/main/detekt-rules-errorprone/src/main/kotlin/io/gitlab/arturbosch/detekt/rules/bugs/IgnoredReturnValue.kt#L132C38-L132C60). It might be useful to make this optional, so you could specify a type which must always be handled.
For example, with returnValueTypes: '*.Result' the following code should be reported:
sealed class Result<out T, out E>
data class Success<out T>(val value: T) : Result<T, Nothing>()
data class Failure<out E>(val reason: E) : Result<Nothing, E>()

fun main() {
    Success(123)
}

This issue is stale because it has been open 90 days with no activity. Please comment or this will be closed in 7 days.


Document CloudRunJob better in README

It's currently:

missing install cloud run
how to register CloudRunJob block.

Actually the cloud run job block is the most interesting feature of prefect 2.x. As it works similar to how vertex works in prefect 1.x; and provides an actual serverless environment to run pipelines in which is extremely powerful. Would be nice if this pip package can easily be added without building a custom prefect image.

Thanks for providing this feedback. To clarify, would using EXTRA_PIP_PACKAGES work for you?


host vm_id should be string, not Buffer.

the vm_id should be a hex string!
                  "environment": {
                     "host": {
                        "vm_id": {
                           "type": "Buffer",
                           "data": [
                              48,
                              101,
                              102,
                              56,
                              53,
                              49,
                              48,
                              52,
                              45,
                              99,
                              50,
                              101,
                              102,
                              45,
                              52,
                              57,
                              52,
                              49,
                              45,
                              57,
                              48,
                              48,
                              51,
                              45,
                              48,
                              48,
                              100,
                              51,
                              50,
                              49,
                              50,
                              49,
                              48,
                              52,
                              57,
                              57
                           ]
                        }
                     },
                     "os":

resolved with #91


Can't access Hatchet web admin GUI

Describe the bug
Fresh deploy of r2r in docker r2r serve --docker --config-name=local_llm_neo4j_kg. All containers come up healthy but when I attempt to access the Hatchet GUI to monitor running workflows I get a blank page that reads:
"Log in to Hatchet
No login methods are enabled."
To Reproduce
Steps to reproduce the behavior:

Launch r2r with r2r serve --docker --config-name=local_llm_neo4j_kg
Navigate to http://r2r-host.local:7274

Expected behavior
Expect to see that Hatchet GUI and to be able to login with default credentials per https://r2r-docs.sciphi.ai/cookbooks/orchestration
Screenshots

Desktop (please complete the following information):

OS: Ubuntu
Browser Firefox 130.0 (64-bit)
Version 24.04.1 LTS

Additional context
r2r version
3.1.14

I have seen this issue before, it seems to be something to do with caching inside the Hatchet web application.
Can you try clearing your cookies or logging in via incognito to see if you then correctly route to the login page?

Nailed it again, Sir. Logging in an Incognito window worked as expected. Thanks, much!


⚠ SkipTheCommericals has degraded performance

In e6e0ca1, SkipTheCommericals ($STC) experienced degraded performance:

HTTP code: 200
Response time: 3674 ms

Resolved: SkipTheCommericals performance has improved in 541c955.


[RFR][TA]Nodejs Provider Analysis

Resolves https://issues.redhat.com/browse/MTA-3372
Jenkins run

@abrugaro @sshveta Could you please review this PR? Thanks!

Moved to https://github.com/konveyor-ecosystem/kantra-cli-tests/pull/32


New term request from NORD

These terms were requested by NORD.
@nicolevasilevsky, could you please review these and let me know if you think these terms should be added in Mondo. I am especially questioning the last one.
We also need to follow-up about which terms should be classified underneath these if we were to create them.
PRRT2-Associated Paroxysmal Movement Disorders
Definition: (as per NORD report) PRRT2-associated paroxysmal movement disorders are a group of rare
movement and seizure disorders caused by changes (disease-causing variants or mutations) in the
PRRT2 gene. They include a spectrum of specific disorders including paroxysmal kinesigenic dyskinesia
(PKD), benign familial infantile epilepsy (BFIE), paroxysmal kinesigenic dyskinesia with infantile
convulsions (PKD/IC) and hemiplegic migraine (HM). In addition, PRRT2 pathogenic variants have been
identified in other childhood-onset movement disorders and different types of seizure conditions, such
as paroxysmal torticollis, episodic ataxia and familial paroxysmal non-kinesigenic dyskinesia. It’s
important to note that these disorders can also have different genetic causes.
References: https://rarediseases.org/rare-diseases/prrt2-associated-paroxysmal-movement-
disorders/#references and also: https://pubmed.ncbi.nlm.nih.gov/32916768/ and
https://www.frontiersin.org/articles/10.3389/fneur.2021.629747/full#B9
EEF1A2-Related Neurodevelopmental Disorder
Definition: (as per NORD report): EEF1A2-related neurodevelopmental disorder is a very rare disorder
caused by harmful variants in the EEF1A2 gene that have arisen spontaneously. There are many
different types of these variants, and the effects are very variable, sometimes even in children with the
same variant. Most children, but not all, will have epilepsy, developmental delay, and intellectual
disability. Some also have challenging behaviors and/or movement disorders.
Note that this condition is also known as: Developmental and epileptic encephalopathy 33 MONDO
14625:
https://www.ebi.ac.uk/ols4/ontologies/mondo/classes/http%253A%252F%252Fpurl.obolibrary.org%25
2Fobo%252FMONDO_0014625 OMIM 616409
(Another condition that may also have some overlapping features is Intellectual developmental disorder,
autosomal dominant 38 DOID:0070068 OMIM: 616393)
References: https://rarediseases.org/rare-diseases/eef1a2-related-neurodevelopmental-
disorder/#references
ZC4H2-Associated Rare Disorders
Definition: (as per NORD report):  is an ultra-rare genetic condition with central and peripheral
nervous system involvement caused by harmful changes (pathogenic variants) of
the ZC4H2 gene. ZC4H2 is located on the X chromosome and encodes the ZC4H2 (zinc finger
C4H2-type containing) protein essential for normal development. ZARD can manifest in a broad
range of clinical severity. Clinical presentations of affected individuals who carry the same
pathogenic ZC4H2 gene variant can vary within families and between families. 1-3  Males and
females can be affected.
References: https://rarediseases.org/rare-diseases/zard/#references

Hi Sabrina and Nicole: Thank you for the follow-up!
As commented, the EEF1A2-Related Neurodevelopmental Disorder is also known as Developmental and epileptic encephalopathy 33 MONDO
14625: link: https://www.ebi.ac.uk/ols4/ontologies/mondo/classes/http%253A%252F%252Fpurl.obolibrary.org%25
For the ZC4H2-associated disorders we do not need to add a new term. MONDO already list this condition as Wieacker-Wolff syndrome.  I would ask to add ZC4H2-associated disorders as a synonym. Thank you! Gioconda

Other neurodevelopmental disease are due to a variation in EEF1A2. Therefore EEF1A2-Related Neurodevelopmental should not be added as a synonym.

[ ] add exact synonym: "ZC4H2-associated disorder" to Mondo 'Wieacker-Wolff syndrome (spectrum)' (MONDO:0025445); use the following reference: https://rarediseases.org/rare-diseases/zard/#references
According to OMIM, the 2 children of this Mondo term are both associated with ZC4H2, and no other diseases are reported to be associated with this gene. Therefore I think it is ok to add "ZC4H2-associated disorder" as a synonym.

To do (2) :  create a new term: PRRT2-Associated Paroxysmal Movement Disorders
@galyea123 , could you please help with the classification?

what should the parent term be?
what should the children be? according to the definition you proposed, the children should be paroxysmal kinesigenic dyskinesia (PKD), benign familial infantile epilepsy (BFIE), paroxysmal kinesigenic dyskinesia with infantile convulsions (PKD/IC), hemiplegic migraine (HM). However, when these terms exist in Mondo, they are not associated with the PRRT2 gene.

Thank you!

I think that paroxysmal movement disorder can be the parent term. ORDO:306768 or maybe classify all of these terms as "gene-related disorders"
As per the children, all of these conditions have more than one cause, as per example, paroxysmal kinesigenic dyskinesia (PKD) can be caused by different gene mutations and other factors. https://translationalneurodegeneration.biomedcentral.com/articles/10.1186/s40035-021-00231-8
The following GeneReview article may be useful: https://www.ncbi.nlm.nih.gov/books/NBK475803/
Thank you!  Gioconda
Thank you, Gioconda

this can be closed, this is done. I linked the wrong issue on the PR


Synergy Casks added

Adding a new cask

[x] Checked there aren’t open pull requests for the same cask.
[x] Checked there aren’t closed issues where that cask was already refused.
[x] When naming the cask, followed the token reference.
[x] Commit message includes cask’s name.
[x] brew cask audit --download {{cask_file}} is error-free.
[x] brew cask style --fix {{cask_file}} left no offenses.
[x] brew cask install {{cask_file}} worked successfully.
[x] brew cask uninstall {{cask_file}} worked successfully.

Thank you for the contribution. It needed some fixes, so they were made in ea70191faa2f1a427648c71b26a1b7532397ff30. Your contribution is still included (and still credited to you), with the appropriate modifications. Please feel free to ask about any of the changes.


DTLS handshake.. help

Question
Leshan v1.0.0 handshaking
I'm going to use reshan v1.0.0 to build an interface with a GUI. We have different registration procedures. It was a method of generating our own number using the IMEI value of the terminal and putting it in the handshake request using the value.
Handshaking process
1. client hello
2. hello verify request
3. server hello, server hello Done
4. client key exchange, change cipher Spec, encrypted handshake Message

That's it for the handshake and the handshake is over. I think the handshake will continue only if we send our own number value in the request, so which part of the reshan 1.0.0 (https://github.com/eclipse/leshan/tree/leshan-1.0.0) code should we modify for registration?
I'm not good at JAVA and I can't find the vastness of the leshan code.
Which part should I modify so that I can send it in a request when I do DTLS communication?
What I've found so far is that I think it's a source related to calcium ->dtls -> hanshaker, but it's a class file and I think it's impossible to modify the calcium library directly.
I think it's possible to modify just two files: leshan-server-demo / leshan-client-demo.
I need you to help me.

I'm sorry but this is very hard to me to understand you.
1) Why Leshan v1.0.0 ?
It seems you are using Leshan v1.0.0, you should not. If you want to use leshan v1.x you should use the last one (OR at least one without any known security issues : https://github.com/eclipse/leshan/security/policy#versions-security-state)
2) Reusing demo ?

I'm going to use ~r~Leshan v1.0.0 to build an interface with a GUI.

I think it's possible to modify just two files: leshan-server-demo / leshan-client-demo.

I don't get what you try to achieve ? It seems you are building a server and/OR a client with a GUI based on Leshan library but you are talking about modifying demos ? I'm not sure to get you...  Please read the first 2 questions/response of the FAQ : https://github.com/eclipse/leshan/wiki/F.A.Q.
3) calcium ?
Are you talking about Californium ?
4) Please better describe your problem

It was a method of generating our own number using the IMEI value of the terminal and putting it in the handshake request using the value.

What does it mean ? Are you using DTLS with PSK ? and you are generating PSK identity and/or PSK key from IMEI ?
Do you ask for API at Leshan client or sever side or both ?

1) Reasons for using Leshan
As of version 1.0.0, the client has already been developed.
It was developed by an outsourced company, and we have to use the same environment 1.0.0 for our own development.
2) Reusing demo ?
Leshan's clients are not important. Tens of thousands of devices are already communicating with outsourced LWM2M servers.
We are only going to build a server.
So, I was thinking of modifying and distributing using only Leshan-server-demo.
However, outsourcing companies have built registration a little differently.

IMEI is used to create a value called ICCID. This ICCID is stored in DB.
When registering, use the endpoint (IMEI) to search the ICCID in the DB.
Through this handshaking, registration succeeds.

This is the packet information of the outsourcing company.

This is the packet information I receive.

question

There are Leshan-server-demo and Leshan-server-cf, but I'm not sure which files to modify and deploy.
In the process of handshaking, the subcontractor receives a ConnectionID, but I do not know which part of the code enables Leshan to receive this part.
I wonder where the Hanshaking is started and performed in the Leshan code.

As of version 1.0.0, the client has already been developed.
It was developed by an outsourced company, and we have to use the same environment 1.0.0 for our own development.

That's mean you are using version with know security issues... it smells not good at all...

So, I was thinking of modifying and distributing using only Leshan-server-demo.

leshan-server-demo is a demo as its name indicates... It should not used in production.

However, outsourcing companies have built registration a little differently.
IMEI is used to create a value called ICCID. This ICCID is stored in DB.
When registering, use the endpoint (IMEI) to search the ICCID in the DB.
Through this handshaking, registration succeeds.

I don't understand how this ICCID is used for DTLS handshaking. So the question is still :
"What does it mean ? Are you using DTLS with PSK ? do you use this ICCID as PSK key ?"
OR describe more precisely what you try to achieve at DTLS level ... or I can not help you.
If you are not able to clarify what you need,  maybe you need to better understand protocol you are using and so read some specifications about :

LWM2M : https://github.com/eclipse/leshan/wiki/Lightweight-M2M-Specification
DTLS : https://www.rfc-editor.org/rfc/rfc6347

There are Leshan-server-demo and Leshan-server-cf, but I'm not sure which files to modify and deploy.

You should not modify it, you should depends on it.
The right way to do is to create a new project which depends on leshan-server-cf. (see https://github.com/eclipse/leshan/wiki/Getting-Started-:-Server)

In the process of handshaking, the subcontractor receives a ConnectionID, but I do not know which part of the code enables Leshan to receive this part.

This is probably about RFC 9146 - Connection Identifier for DTLS 1.2.
You need to change the californium configuration, see : https://github.com/eclipse-californium/californium/blob/2.7.4/californium-core/src/main/java/org/eclipse/californium/core/network/config/NetworkConfig.java#L299-L323
If you have question about Californium/Scandium API, ask directly at : https://github.com/eclipse-californium/californium/issues

I wonder where the Hanshaking is started and performed in the Leshan code.

This is done in Scandium code, see DTLSConnector
But you should not have to dig in that part of the code, if you respect DTLS RFC.

By the way, if you want to define credentials of your device at server side this is with SecurityStore.
See : https://github.com/eclipse/leshan/blob/leshan-1.4.2/leshan-server-cf/src/main/java/org/eclipse/leshan/server/californium/LeshanServerBuilder.java#L180-L192

I don't understand how this ICCID is used for DTLS handshaking. So the question is still :
"What does it mean ? Are you using DTLS with PSK ? do you use this ICCID as PSK key ?"

A: yes , We use the PSK key as ICCID.
So the handshaking process needs to be modified.
Is it okay to change Californium/Scandium and Californium configurations?

How it is sent ?

Receive identity through psk client prams during the handshaking process. As a Hex value, I get an Identity, but this value is the same as our IMEI when encoded.

Match ICCID in DB using IMEI

When sending Change Cipher Spec, EncryTed Handshake Message to the terminal, ICCID also sends a request.

Receive identity through psk client prams during the handshaking process. As a Hex value, I get an Identity, but this value is the same as our IMEI when encoded.

Short version : you are using IMEI as PSK identity.
So again, just implement your own SecurityStore.
You have a method which returns SecurityInfo by PSK Identity, just implement it. It's here you will search your ICCID from PSK Identity (IMEI in your case).

...  ICCID also sends a request.

Above you explain that ICCID is a value ... ("IMEI is used to create a value called ICCID. This ICCID is stored in DB.")
How could a data or a value send a request ?
I don't know if this is my English or yours but it's really hard to me to understand you.

Then, when I handshaking in Leshan, which part do I need to modify to receive the connection ID?

See Californium  API already pointed above https://github.com/eclipse/leshan/issues/1420#issuecomment-1467843388
When you create your Leshan server, you can set the Californium NetworkConfig with this :
https://github.com/eclipse/leshan/blob/2bd9632af703c57cf5a945980c720a359db04110/leshan-server-cf/src/main/java/org/eclipse/leshan/server/californium/LeshanServerBuilder.java#L281-L287
Config should first be created with :
https://github.com/eclipse/leshan/blob/2bd9632af703c57cf5a945980c720a359db04110/leshan-server-cf/src/main/java/org/eclipse/leshan/server/californium/LeshanServerBuilder.java#L388-L395

https://github.com/eclipse/leshan/wiki/F.A.Q.#how-to-activate-more-log-

Could we close this issue ?


add zh_cn.json

Thank you for the localization! I will hold off on merging this because I am about to add some new features. I'll post another comment here before I make the next release.

ok

v1.1.0 has been released. I added localization for the netherite puller pipe, rigid pipes, dense pipes, and pipe probe messages.


Export CSV  problem with UTF-8 (Persian)

as the previous version, the export csv not working with UTF-8 encodiing

What was the original value in row 2? How did you enter this value? And how did you export?

the value is: صفحه اصلی
in persian means "Main Page"
database is utf-8 and i export simply from system

"export from system" isn't helpful. Which button did you click in which UI?
I've just entered this value into a new page as the title, and exported it via admin/reports in the "Pages with no content" report - and it shows up fine. Can you attach a sanitised database export, as well as the CSV file you've loaded there? Also, which tool are you opening this CSV file in - Excel? Which version? Any settings when you opened it?
Encoding issues are complex, you really need to help us with more information here.

No feedback in over two months, closing

potentially fixed by https://github.com/silverstripe/silverstripe-framework/pull/7833 as it now adds the UTF8 BOM


Update workflow dependencies

Update nox and poetry

Codecov Report

Merging #80 (40fd831) into master (334466b) will not change coverage.
The diff coverage is n/a.

@@            Coverage Diff            @@
##            master       #80   +/-   ##
=========================================
  Coverage   100.00%   100.00%
=========================================
  Files            6         6
  Lines          190       190
  Branches        10        10
=========================================
  Hits           190       190

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update 334466b...40fd831. Read the comment docs.


Profile shortlinks don't work

There's an option to copy a shortlink from the user profile page:

As far as I can tell, none of the resulting links work for any users. They're in the format https://ldj.am/$11.

They seem to work now --- not sure what was going on in September before LD54.

If I remember correctly ldj.am expired, and though I was quick to remedy
that, dotfm was slow to reactivate it.
I reeeeally wish they would allow .am domain transfers. 😡


Replace words 'master', 'slave' with something else

This is a Feature Request

What would you like to be enhanced

Replace words 'master', 'slave' with something else.

https://kubernetes.io/docs/tutorials/configuration/configure-redis-using-configmap/
https://kubernetes.io/docs/tasks/configure-pod-container/translate-compose-kubernetes/
...

For example, in https://kubernetes.io/docs/tutorials/stateless-application/guestbook/ ,
the terms 'redis-leader' and 'redis-follower' are used.
Why is this needed
It seems good to avoid to use the words like 'master' and 'slave'.
Comments

/language en

I think leader ad follower sounds good to me. But there are other alternative names like conductor and follower, primary and standby, source and sink, primary and replica. Do let me know which one sounds good? Would love to raised a PR.

It depends on the context too. Where applicable, replace Kubernetes master with control plane. Primary/secondary is a good substitute for controllers with that master/slave terminology.
Worth doing multiple smaller PRs for this so that instances can be discussed and reviewed.

Thank you all for your comments!
@AvineshTripathi

I wondering if the label kind/feature is appropriate for this issue

This label was added because I used the "Feature Request" issue template.
Let's remove this! 😊
/remove-kind feature

This issue is a duplicate of issue #21621.
/triage duplicate

/close
as a duplicate

/remove-triage accepted


Segmentation Fault when trying to compile tcc

Hello.
On my system I built lacc and make test and amke test-selfhost are working.
I tried lacc tcc.c and it got me a segmentation fault..
http://repo.or.cz/tinycc.git

Hi. Thank you for reporting this issue. There was an error in how include paths were constructed, which should be fixed in d5c7d09badb66349879d97a52e64c33cb8f5175d.
I see that tcc depends on some extensions that are currently not supported by lacc, such as zero-length arrays and short bitfields, so it will still not compile. I will work on adding these features.


feat(metrics): convert log to tracing

This isn't quite ready but I would still like some eyes on it.


size mismatch between gt and init_labels gave ValueError: Found input variables with inconsistent numbers of samples

thank you so much for sharing~we're very interested in your work and tried it on our data which looks like as below

the input data size was checked before the training (the label is an array of zero created by codes in customdataset), and it seems that a size mismatch between gt and init_labels then occured at the line  init_nmi = normalized_mutual_info_score(gt, init_labels) during the training and gave ValueError: Found input variables with inconsistent numbers of samples.
could you help us to fix this issu please?
thank you
sincerely
Zhao
Singularity> python3 DeepDPM.py --gpus 0,1,2 --offline --batch-size 512 --max_epoch 20 --dir ./pretrained_embeddings/raw/XCIT_NANO_12_P16_224/
train_codes.size() torch.Size([74770, 128])
train_labels.size() torch.Size([74770])
/usr/local/lib/python3.8/dist-packages/pytorch_lightning/utilities/warnings.py:53: LightningDeprecationWarning: pytorch_lightning.utilities.warnings.rank_zero_deprecation has been deprecated in v1.6 and will be removed in v1.8. Use the equivalent function from the pytorch_lightning.utilities.rank_zero module instead.
  new_rank_zero_deprecation(
/usr/local/lib/python3.8/dist-packages/pytorch_lightning/utilities/warnings.py:58: LightningDeprecationWarning: The `pytorch_lightning.loggers.base.DummyLogger` is deprecated in v1.7 and will be removed in v1.9. Please use `pytorch_lightning.loggers.logger.DummyLogger` instead.
  return new_rank_zero_deprecation(*args, **kwargs)
Sequential()
GPU available: True (cuda), used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
`Trainer(limit_train_batches=1.0)` was configured so 100% of the batches per epoch will be used..
`Trainer(limit_val_batches=1.0)` was configured so 100% of the batches will be used..
/usr/local/lib/python3.8/dist-packages/torch/utils/hooks.py:59: UserWarning: backward hook <function Subclustering_net.__init__.<locals>.<lambda> at 0x7f09d872fb80> on tensor will not be serialized.  If this is expected, you can decorate the function with @torch.utils.hooks.unserializable_hook to suppress this warning
  warnings.warn("backward hook {} on tensor will not be "
Initializing distributed: GLOBAL_RANK: 0, MEMBER: 1/3
Initializing distributed: GLOBAL_RANK: 1, MEMBER: 2/3
Initializing distributed: GLOBAL_RANK: 2, MEMBER: 3/3
----------------------------------------------------------------------------------------------------
distributed_backend=nccl
All distributed processes registered. Starting with 3 processes
----------------------------------------------------------------------------------------------------

/usr/local/lib/python3.8/dist-packages/pytorch_lightning/callbacks/model_checkpoint.py:616: UserWarning: Checkpoint directory /mnt/iribhm/people/zzhang/DeepDPM/DeepDPM-main_vincent4/checkpoints exists and is not empty.
  rank_zero_warn(f"Checkpoint directory {dirpath} exists and is not empty.")
LOCAL_RANK: 2 - CUDA_VISIBLE_DEVICES: [0,1,2]
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2]
LOCAL_RANK: 1 - CUDA_VISIBLE_DEVICES: [0,1,2]
/usr/local/lib/python3.8/dist-packages/pytorch_lightning/core/optimizer.py:381: RuntimeWarning: Found unsupported keys in the optimizer configuration: {'scheduler'}
  rank_zero_warn(

  | Name              | Type              | Params
--------------------------------------------------------
0 | cluster_net       | MLP_Classifier    | 6.5 K
1 | subclustering_net | Subclustering_net | 6.6 K
--------------------------------------------------------
13.1 K    Trainable params
0         Non-trainable params
13.1 K    Total params
0.052     Total estimated model params size (MB)
/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/connectors/data_connector.py:203: UserWarning: num_workers>0, persistent_workers=False, and strategy=ddp_spawn may result in data loading bottlenecks. Consider setting persistent_workers=True (this is a limitation of Python .spawn() and PyTorch)
  rank_zero_warn(
Epoch 0:   0%|                                                                                | 0/66 [00:00<?, ?it/s]/usr/local/lib/python3.8/dist-packages/pytorch_lightning/loops/optimization/optimizer_loop.py:135: UserWarning: `training_step` returned `None`. If this was on purpose, ignore this warning...
  self.warning_cache.warn("`training_step` returned `None`. If this was on purpose, ignore this warning...")
Epoch 0:  74%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                       | 49/66 [00:07<00:02,  6.32it/s, loss=nanInitializing clusters params using Kmeans...                                                                                | 0/17 [00:00<?, ?it/s]
Initializing clusters params using Kmeans...
Epoch 0: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 66/66 [00:15<00:00,  4.22it/s, loss=nan]/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:536: PossibleUserWarning: It is recommended to use `self.log('cluster_net_train/val/avg_val_loss', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices.
  warning_cache.warn(
Epoch 0: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 66/66 [00:15<00:00,  4.22it/s, loss=nanInitializing clusters params using Kmeans...
gt.size() torch.Size([24924])
init_labels.size() torch.Size([8410])
gt.size() torch.Size([24924])
init_labels.size() torch.Size([8410])
gt.size() torch.Size([24924])
init_labels.size() torch.Size([8410])
Traceback (most recent call last):
  File "DeepDPM.py", line 456, in <module>
    train_cluster_net()
  File "DeepDPM.py", line 436, in train_cluster_net
    trainer.fit(model, train_loader, val_loader)
  File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/trainer.py", line 696, in fit
    self._call_and_handle_interrupt(
  File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/trainer.py", line 648, in _call_and_handle_interrupt
    return self.strategy.launcher.launch(trainer_fn, *args, trainer=self, **kwargs)
  File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/strategies/launchers/multiprocessing.py", line 103, in launch
    mp.start_processes(
  File "/usr/local/lib/python3.8/dist-packages/torch/multiprocessing/spawn.py", line 198, in start_processes
    while not context.join():
  File "/usr/local/lib/python3.8/dist-packages/torch/multiprocessing/spawn.py", line 160, in join
    raise ProcessRaisedException(msg, error_index, failed_process.pid)
torch.multiprocessing.spawn.ProcessRaisedException:

-- Process 2 terminated with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/torch/multiprocessing/spawn.py", line 69, in _wrap
    fn(i, *args)
  File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/strategies/launchers/multiprocessing.py", line 129, in _wrapping_function
    results = function(*args, **kwargs)
  File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/trainer.py", line 737, in _fit_impl
    results = self._run(model, ckpt_path=self.ckpt_path)
  File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/trainer.py", line 1168, in _run
    results = self._run_stage()
  File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/trainer.py", line 1254, in _run_stage
    return self._run_train()
  File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/trainer.py", line 1285, in _run_train
    self.fit_loop.run()
  File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/loops/loop.py", line 201, in run
    self.on_advance_end()
  File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/loops/fit_loop.py", line 286, in on_advance_end
    epoch_end_outputs = self.trainer._call_lightning_module_hook("training_epoch_end", epoch_end_outputs)
  File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/trainer.py", line 1552, in _call_lightning_module_hook
    output = fn(*args, **kwargs)
  File "/mnt/iribhm/people/zzhang/DeepDPM/DeepDPM-main_vincent4/src/clustering_models/clusternet_modules/clusternetasmodel.py", line 375, in training_epoch_end
    init_nmi = normalized_mutual_info_score(gt, init_labels)
  File "/usr/local/lib/python3.8/dist-packages/sklearn/metrics/cluster/_supervised.py", line 1028, in normalized_mutual_info_score
    labels_true, labels_pred = check_clusterings(labels_true, labels_pred)
  File "/usr/local/lib/python3.8/dist-packages/sklearn/metrics/cluster/_supervised.py", line 71, in check_clusterings
    check_consistent_length(labels_true, labels_pred)
  File "/usr/local/lib/python3.8/dist-packages/sklearn/utils/validation.py", line 387, in check_consistent_length
    raise ValueError(
ValueError: Found input variables with inconsistent numbers of samples: [24924, 8410]

Hi @Vz09,
As a quick fix - as you anyway do not have any labels, you could comment out the following lines from the code:
"init_nmi = normalized_mutual_info_score(gt, init_labels)
init_ari = adjusted_rand_score(gt, init_labels)" (lines 373-4 at src/clustering_models/clusternet_modules/clusternetasmodel.py), we will upload an official fix in the future.
That said, please make sure (using debugging) that the number of samples you are training on (self.codes) is indeed equal to the number of samples you have in the dataset, as if this is not the case there might be some problem with the dimension configuration and this will need more attention.

Hi @Vz09, As a quick fix - as you anyway do not have any labels, you could comment out the following lines from the code: "init_nmi = normalized_mutual_info_score(gt, init_labels) init_ari = adjusted_rand_score(gt, init_labels)" (lines 373-4 at src/clustering_models/clusternet_modules/clusternetasmodel.py), we will upload an official fix in the future.
That said, please make sure (using debugging) that the number of samples you are training on (self.codes) is indeed equal to the number of samples you have in the dataset, as if this is not the case there might be some problem with the dimension configuration and this will need more attention.

Thank you for your reply~
Does the print added in the code of class customdataset allow to check that the number of samples of my training equals the number of samples I had in the dataset? if yes, they are equal (74770).
class CustomDataset(MyDataset):
    def __init__(self, args):
        super().__init__(args)
        self.transformer = transforms.Compose([transforms.ToTensor()])
        self._data_dim = 0

    def get_train_data(self):
        train_codes = torch.Tensor(torch.load(os.path.join(self.data_dir, "train_data.pt")))
        if self.args.transform_input_data:
            train_codes = transform_embeddings(self.args.transform_input_data, train_codes)
        if self.args.use_labels_for_eval:
            train_labels = torch.load(os.path.join(self.data_dir, "train_labels.pt"))
        else:
            train_labels = torch.zeros((train_codes.size()[0]))
        self._data_dim = train_codes.size()[1]
        print("train_codes.size()", train_codes.size())
        print("train_labels.size()", train_labels.size())
        train_set = TensorDatasetWrapper(train_codes, train_labels)
        del train_codes
        del train_labels
        return train_set

I tried to comment the two lines you talked about, that indeed allowed to continue to epoch 25, then another error occurred...
train_codes.size() torch.Size([74770, 128])
train_labels.size() torch.Size([74770])
Sequential()
/usr/local/lib/python3.8/dist-packages/pytorch_lightning/utilities/warnings.py:53: LightningDeprecationWarning: pytorch_lightning.utilities.warnings.rank_zero_deprecation has been deprecated in v1.6 and will be removed in v1.8. Use the equivalent function from the pytorch_lightning.utilities.rank_zero module instead.
  new_rank_zero_deprecation(
/usr/local/lib/python3.8/dist-packages/pytorch_lightning/utilities/warnings.py:58: LightningDeprecationWarning: The `pytorch_lightning.loggers.base.DummyLogger` is deprecated in v1.7 and will be removed in v1.9. Please use `pytorch_lightning.loggers.logger.DummyLogger` instead.
  return new_rank_zero_deprecation(*args, **kwargs)
GPU available: True (cuda), used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
`Trainer(limit_train_batches=1.0)` was configured so 100% of the batches per epoch will be used..
`Trainer(limit_val_batches=1.0)` was configured so 100% of the batches will be used..
/usr/local/lib/python3.8/dist-packages/torch/utils/hooks.py:59: UserWarning: backward hook <function Subclustering_net.__init__.<locals>.<lambda> at 0x7f6643376af0> on tensor will not be serialized.  If this is expected, you can decorate the function with @torch.utils.hooks.unserializable_hook to suppress this warning
  warnings.warn("backward hook {} on tensor will not be "
Initializing distributed: GLOBAL_RANK: 5, MEMBER: 6/8
Initializing distributed: GLOBAL_RANK: 4, MEMBER: 5/8
Initializing distributed: GLOBAL_RANK: 7, MEMBER: 8/8
Initializing distributed: GLOBAL_RANK: 1, MEMBER: 2/8
Initializing distributed: GLOBAL_RANK: 0, MEMBER: 1/8
Initializing distributed: GLOBAL_RANK: 6, MEMBER: 7/8
Initializing distributed: GLOBAL_RANK: 2, MEMBER: 3/8
Initializing distributed: GLOBAL_RANK: 3, MEMBER: 4/8
----------------------------------------------------------------------------------------------------
distributed_backend=nccl
All distributed processes registered. Starting with 8 processes
----------------------------------------------------------------------------------------------------

/usr/local/lib/python3.8/dist-packages/pytorch_lightning/callbacks/model_checkpoint.py:616: UserWarning: Checkpoint directory /mnt/iribhm/people/zzhang/DeepDPM/DeepDPM-main_vincent4/checkpoints exists and is not empty.
  rank_zero_warn(f"Checkpoint directory {dirpath} exists and is not empty.")
LOCAL_RANK: 7 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]
LOCAL_RANK: 4 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]
LOCAL_RANK: 5 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]
LOCAL_RANK: 2 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]
LOCAL_RANK: 3 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]
LOCAL_RANK: 1 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]
LOCAL_RANK: 6 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]
/usr/local/lib/python3.8/dist-packages/pytorch_lightning/core/optimizer.py:381: RuntimeWarning: Found unsupported keys in the optimizer configuration: {'scheduler'}
  rank_zero_warn(

  | Name              | Type              | Params
--------------------------------------------------------
0 | cluster_net       | MLP_Classifier    | 6.5 K
1 | subclustering_net | Subclustering_net | 6.6 K
--------------------------------------------------------
13.1 K    Trainable params
0         Non-trainable params
13.1 K    Total params
0.052     Total estimated model params size (MB)
/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/connectors/data_connector.py:203: UserWarning: num_workers>0, persistent_workers=False, and strategy=ddp_spawn may result in data loading bottlenecks. Consider setting persistent_workers=True (this is a limitation of Python .spawn() and PyTorch)
  rank_zero_warn(

Training: 0it [00:00, ?it/s]
Training:   0%|          | 0/99 [00:00<?, ?it/s]
Epoch 0:   0%|          | 0/99 [00:00<?, ?it/s]
Epoch 0:   1%|          | 1/99 [00:19<31:47, 19.46s/it]
Epoch 0:   1%|          | 1/99 [00:19<31:47, 19.46s/it, loss=nan]
Epoch 0:   2%|▏         | 2/99 [00:19<15:44,  9.74s/it, loss=nan]
Epoch 0:   2%|▏         | 2/99 [00:19<15:44,  9.74s/it, loss=nan]
....I kipped intermediate log
Epoch 25:  74%|███████▎  | 73/99 [00:22<00:07,  3.31it/s, loss=0]
Epoch 25:  74%|███████▎  | 73/99 [00:22<00:07,  3.31it/s, loss=0]
Epoch 25:  75%|███████▍  | 74/99 [00:22<00:07,  3.35it/s, loss=0]
Epoch 25:  75%|███████▍  | 74/99 [00:22<00:07,  3.35it/s, loss=0]

Validation: 0it [00:00, ?it/s]

Validation:   0%|          | 0/25 [00:00<?, ?it/s]

Validation DataLoader 0:   0%|          | 0/25 [00:00<?, ?it/s]

Validation DataLoader 0:   4%|▍         | 1/25 [00:00<00:00, 173.97it/s]
Epoch 25:  76%|███████▌  | 75/99 [00:44<00:14,  1.70it/s, loss=0]

Validation DataLoader 0:   8%|▊         | 2/25 [00:00<00:00, 134.73it/s]
Epoch 25:  77%|███████▋  | 76/99 [00:44<00:13,  1.72it/s, loss=0]

Validation DataLoader 0:  12%|█▏        | 3/25 [00:00<00:00, 98.68it/s]
Epoch 25:  78%|███████▊  | 77/99 [00:44<00:12,  1.74it/s, loss=0]

Validation DataLoader 0:  16%|█▌        | 4/25 [00:00<00:00, 58.51it/s]
Epoch 25:  79%|███████▉  | 78/99 [00:44<00:11,  1.76it/s, loss=0]

Validation DataLoader 0:  20%|██        | 5/25 [00:00<00:00, 67.64it/s]
Epoch 25:  80%|███████▉  | 79/99 [00:44<00:11,  1.78it/s, loss=0]

Validation DataLoader 0:  24%|██▍       | 6/25 [00:00<00:00, 76.02it/s]
Epoch 25:  81%|████████  | 80/99 [00:44<00:10,  1.81it/s, loss=0]codes.size():  torch.Size([3154, 128])
logits.size():  torch.Size([9347, 1])
K:  1

Validation DataLoader 0:  28%|██▊       | 7/25 [00:00<00:00, 79.10it/s]
Epoch 25:  82%|████████▏ | 81/99 [00:44<00:09,  1.83it/s, loss=0]

Validation DataLoader 0:  32%|███▏      | 8/25 [00:00<00:00, 75.19it/s]
Epoch 25:  83%|████████▎ | 82/99 [00:44<00:09,  1.85it/s, loss=0]

Validation DataLoader 0:  36%|███▌      | 9/25 [00:00<00:00, 73.14it/s]
Epoch 25:  84%|████████▍ | 83/99 [00:44<00:08,  1.87it/s, loss=0]

Validation DataLoader 0:  40%|████      | 10/25 [00:00<00:00, 70.16it/s]
Epoch 25:  85%|████████▍ | 84/99 [00:44<00:07,  1.89it/s, loss=0]

Validation DataLoader 0:  44%|████▍     | 11/25 [00:00<00:00, 73.41it/s]
Epoch 25:  86%|████████▌ | 85/99 [00:44<00:07,  1.91it/s, loss=0]

Validation DataLoader 0:  48%|████▊     | 12/25 [00:00<00:00, 77.05it/s]
Epoch 25:  87%|████████▋ | 86/99 [00:44<00:06,  1.94it/s, loss=0]

Validation DataLoader 0:  52%|█████▏    | 13/25 [00:00<00:00, 77.63it/s]
Epoch 25:  88%|████████▊ | 87/99 [00:44<00:06,  1.96it/s, loss=0]

Validation DataLoader 0:  56%|█████▌    | 14/25 [00:00<00:00, 76.06it/s]
Epoch 25:  89%|████████▉ | 88/99 [00:44<00:05,  1.98it/s, loss=0]

Validation DataLoader 0:  60%|██████    | 15/25 [00:00<00:00, 78.72it/s]
Epoch 25:  90%|████████▉ | 89/99 [00:44<00:04,  2.00it/s, loss=0]

Validation DataLoader 0:  64%|██████▍   | 16/25 [00:00<00:00, 76.24it/s]
Epoch 25:  91%|█████████ | 90/99 [00:44<00:04,  2.02it/s, loss=0]

Validation DataLoader 0:  68%|██████▊   | 17/25 [00:00<00:00, 73.49it/s]
Epoch 25:  92%|█████████▏| 91/99 [00:44<00:03,  2.05it/s, loss=0]

Validation DataLoader 0:  72%|███████▏  | 18/25 [00:00<00:00, 72.70it/s]
Epoch 25:  93%|█████████▎| 92/99 [00:44<00:03,  2.07it/s, loss=0]codes.size():  torch.Size([3154, 128])
logits.size():  torch.Size([9347, 1])
K:  1

Validation DataLoader 0:  76%|███████▌  | 19/25 [00:00<00:00, 71.92it/s]
Epoch 25:  94%|█████████▍| 93/99 [00:44<00:02,  2.09it/s, loss=0]codes.size():  torch.Size([3154, 128])
logits.size():  torch.Size([9347, 1])
K:  1

Validation DataLoader 0:  80%|████████  | 20/25 [00:00<00:00, 73.28it/s]
Epoch 25:  95%|█████████▍| 94/99 [00:44<00:02,  2.11it/s, loss=0]

Validation DataLoader 0:  84%|████████▍ | 21/25 [00:00<00:00, 75.34it/s]
Epoch 25:  96%|█████████▌| 95/99 [00:44<00:01,  2.13it/s, loss=0]

Validation DataLoader 0:  88%|████████▊ | 22/25 [00:00<00:00, 77.29it/s]
Epoch 25:  97%|█████████▋| 96/99 [00:44<00:01,  2.16it/s, loss=0]

Validation DataLoader 0:  92%|█████████▏| 23/25 [00:00<00:00, 79.32it/s]
Epoch 25:  98%|█████████▊| 97/99 [00:44<00:00,  2.18it/s, loss=0]

Validation DataLoader 0:  96%|█████████▌| 24/25 [00:00<00:00, 81.38it/s]
Epoch 25:  99%|█████████▉| 98/99 [00:44<00:00,  2.20it/s, loss=0]

Validation DataLoader 0: 100%|██████████| 25/25 [00:00<00:00, 83.41it/s]
Epoch 25: 100%|██████████| 99/99 [00:44<00:00,  2.22it/s, loss=0]
Epoch 25: 100%|██████████| 99/99 [00:44<00:00,  2.22it/s, loss=0]

                                                                        codes.size():  torch.Size([3154, 128])
logits.size():  torch.Size([9347, 1])
K:  1
codes.size():  torch.Size([3154, 128])
logits.size():  torch.Size([9347, 1])
K:  1
codes.size():  torch.Size([3154, 128])
logits.size():  torch.Size([9347, 1])
K:  1
codes.size():  torch.Size([3154, 128])
logits.size():  torch.Size([9347, 1])
K:  1
codes.size():  torch.Size([3154, 128])
logits.size():  torch.Size([9347, 1])
K:  1
Traceback (most recent call last):
  File "DeepDPM.py", line 456, in <module>
    train_cluster_net()
  File "DeepDPM.py", line 436, in train_cluster_net
    trainer.fit(model, train_loader, val_loader)
  File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/trainer.py", line 696, in fit
    self._call_and_handle_interrupt(
  File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/trainer.py", line 648, in _call_and_handle_interrupt
    return self.strategy.launcher.launch(trainer_fn, *args, trainer=self, **kwargs)
  File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/strategies/launchers/multiprocessing.py", line 103, in launch
    mp.start_processes(
  File "/usr/local/lib/python3.8/dist-packages/torch/multiprocessing/spawn.py", line 198, in start_processes
    while not context.join():
  File "/usr/local/lib/python3.8/dist-packages/torch/multiprocessing/spawn.py", line 160, in join
    raise ProcessRaisedException(msg, error_index, failed_process.pid)
torch.multiprocessing.spawn.ProcessRaisedException:

-- Process 2 terminated with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/torch/multiprocessing/spawn.py", line 69, in _wrap
    fn(i, *args)
  File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/strategies/launchers/multiprocessing.py", line 129, in _wrapping_function
    results = function(*args, **kwargs)
  File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/trainer.py", line 737, in _fit_impl
    results = self._run(model, ckpt_path=self.ckpt_path)
  File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/trainer.py", line 1168, in _run
    results = self._run_stage()
  File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/trainer.py", line 1254, in _run_stage
    return self._run_train()
  File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/trainer.py", line 1285, in _run_train
    self.fit_loop.run()
  File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/loops/loop.py", line 201, in run
    self.on_advance_end()
  File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/loops/fit_loop.py", line 286, in on_advance_end
    epoch_end_outputs = self.trainer._call_lightning_module_hook("training_epoch_end", epoch_end_outputs)
  File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/trainer.py", line 1552, in _call_lightning_module_hook
    output = fn(*args, **kwargs)
  File "/mnt/iribhm/people/zzhang/DeepDPM/DeepDPM-main_vincent4/src/clustering_models/clusternet_modules/clusternetasmodel.py", line 448, in training_epoch_end
    ) = self.training_utils.comp_cluster_params(
  File "/mnt/iribhm/people/zzhang/DeepDPM/DeepDPM-main_vincent4/src/clustering_models/clusternet_modules/utils/training_utils.py", line 146, in comp_cluster_params
    mus = compute_mus(
  File "/mnt/iribhm/people/zzhang/DeepDPM/DeepDPM-main_vincent4/src/clustering_models/clusternet_modules/utils/clustering_utils/clustering_operations.py", line 278, in compute_mus
    mus = compute_mus_soft_assignment(codes, logits, K)
  File "/mnt/iribhm/people/zzhang/DeepDPM/DeepDPM-main_vincent4/src/clustering_models/clusternet_modules/utils/clustering_utils/clustering_operations.py", line 227, in compute_mus_soft_assignment
    [
  File "/mnt/iribhm/people/zzhang/DeepDPM/DeepDPM-main_vincent4/src/clustering_models/clusternet_modules/utils/clustering_utils/clustering_operations.py", line 228, in <listcomp>
    (logits[:, k].reshape(-1, 1) * codes).sum(axis=0) / denominator[k]
RuntimeError: The size of tensor a (9347) must match the size of tensor b (3154) at non-singleton dimension 0

then I tried to comment the following lines of clusternetasmodel.py, it continued till epoch 44 and another error occurred again....
  if not freeze_mus:
                (
                    self.pi,
                    self.mus,
                    self.covs,
                ) = self.training_utils.comp_cluster_params(
                    self.train_resp,
                    self.codes.view(-1, self.codes_dim),
                    self.pi,
                    self.K,
                    self.prior,
                )

Epoch 44:  74%|███████▎  | 73/99 [00:25<00:09,  2.86it/s, loss=0]
Epoch 44:  74%|███████▎  | 73/99 [00:25<00:09,  2.86it/s, loss=0]
Epoch 44:  75%|███████▍  | 74/99 [00:25<00:08,  2.90it/s, loss=0]
Epoch 44:  75%|███████▍  | 74/99 [00:25<00:08,  2.90it/s, loss=0]

Validation: 0it [00:00, ?it/s]

Validation:   0%|          | 0/25 [00:00<?, ?it/s]

Validation DataLoader 0:   0%|          | 0/25 [00:00<?, ?it/s]

Validation DataLoader 0:   4%|▍         | 1/25 [00:00<00:00, 149.64it/s]
Epoch 44:  76%|███████▌  | 75/99 [00:48<00:15,  1.56it/s, loss=0]

Validation DataLoader 0:   8%|▊         | 2/25 [00:01<00:13,  1.67it/s]
Epoch 44:  77%|███████▋  | 76/99 [00:49<00:14,  1.54it/s, loss=0]

Validation DataLoader 0:  12%|█▏        | 3/25 [00:01<00:12,  1.80it/s]
Epoch 44:  78%|███████▊  | 77/99 [00:49<00:14,  1.55it/s, loss=0]

Validation DataLoader 0:  16%|█▌        | 4/25 [00:01<00:08,  2.38it/s]
Epoch 44:  79%|███████▉  | 78/99 [00:49<00:13,  1.57it/s, loss=0]

Validation DataLoader 0:  20%|██        | 5/25 [00:01<00:06,  2.93it/s]
Epoch 44:  80%|███████▉  | 79/99 [00:49<00:12,  1.59it/s, loss=0]

Validation DataLoader 0:  24%|██▍       | 6/25 [00:01<00:05,  3.45it/s]
Epoch 44:  81%|████████  | 80/99 [00:49<00:11,  1.61it/s, loss=0]

Validation DataLoader 0:  28%|██▊       | 7/25 [00:01<00:04,  4.00it/s]
Epoch 44:  82%|████████▏ | 81/99 [00:49<00:11,  1.63it/s, loss=0]

Validation DataLoader 0:  32%|███▏      | 8/25 [00:01<00:03,  4.55it/s]
Epoch 44:  83%|████████▎ | 82/99 [00:49<00:10,  1.65it/s, loss=0]

Validation DataLoader 0:  36%|███▌      | 9/25 [00:01<00:03,  5.08it/s]
Epoch 44:  84%|████████▍ | 83/99 [00:49<00:09,  1.67it/s, loss=0]

Validation DataLoader 0:  40%|████      | 10/25 [00:01<00:02,  5.58it/s]
Epoch 44:  85%|████████▍ | 84/99 [00:49<00:08,  1.69it/s, loss=0]

Validation DataLoader 0:  44%|████▍     | 11/25 [00:01<00:02,  6.07it/s]
Epoch 44:  86%|████████▌ | 85/99 [00:49<00:08,  1.71it/s, loss=0]

Validation DataLoader 0:  48%|████▊     | 12/25 [00:01<00:01,  6.51it/s]
Epoch 44:  87%|████████▋ | 86/99 [00:49<00:07,  1.73it/s, loss=0]

Validation DataLoader 0:  52%|█████▏    | 13/25 [00:01<00:01,  6.99it/s]
Epoch 44:  88%|████████▊ | 87/99 [00:49<00:06,  1.74it/s, loss=0]

Validation DataLoader 0:  56%|█████▌    | 14/25 [00:01<00:01,  7.47it/s]
Epoch 44:  89%|████████▉ | 88/99 [00:49<00:06,  1.76it/s, loss=0]

Validation DataLoader 0:  60%|██████    | 15/25 [00:01<00:01,  7.93it/s]
Epoch 44:  90%|████████▉ | 89/99 [00:49<00:05,  1.78it/s, loss=0]

Validation DataLoader 0:  64%|██████▍   | 16/25 [00:01<00:01,  8.39it/s]
Epoch 44:  91%|█████████ | 90/99 [00:49<00:04,  1.80it/s, loss=0]

Validation DataLoader 0:  68%|██████▊   | 17/25 [00:01<00:00,  8.84it/s]
Epoch 44:  92%|█████████▏| 91/99 [00:49<00:04,  1.82it/s, loss=0]

Validation DataLoader 0:  72%|███████▏  | 18/25 [00:01<00:00,  9.21it/s]
Epoch 44:  93%|█████████▎| 92/99 [00:49<00:03,  1.84it/s, loss=0]

Validation DataLoader 0:  76%|███████▌  | 19/25 [00:01<00:00,  9.62it/s]
Epoch 44:  94%|█████████▍| 93/99 [00:49<00:03,  1.86it/s, loss=0]

Validation DataLoader 0:  80%|████████  | 20/25 [00:01<00:00, 10.02it/s]
Epoch 44:  95%|█████████▍| 94/99 [00:49<00:02,  1.88it/s, loss=0]

Validation DataLoader 0:  84%|████████▍ | 21/25 [00:02<00:00, 10.41it/s]
Epoch 44:  96%|█████████▌| 95/99 [00:50<00:02,  1.90it/s, loss=0]

Validation DataLoader 0:  88%|████████▊ | 22/25 [00:02<00:00, 10.86it/s]
Epoch 44:  97%|█████████▋| 96/99 [00:50<00:01,  1.92it/s, loss=0]

Validation DataLoader 0:  92%|█████████▏| 23/25 [00:02<00:00, 11.22it/s]
Epoch 44:  98%|█████████▊| 97/99 [00:50<00:01,  1.94it/s, loss=0]

Validation DataLoader 0:  96%|█████████▌| 24/25 [00:02<00:00, 11.67it/s]
Epoch 44:  99%|█████████▉| 98/99 [00:50<00:00,  1.96it/s, loss=0]

Validation DataLoader 0: 100%|██████████| 25/25 [00:02<00:00, 12.13it/s]
Epoch 44: 100%|██████████| 99/99 [00:50<00:00,  1.98it/s, loss=0]
Epoch 44: 100%|██████████| 99/99 [00:50<00:00,  1.98it/s, loss=0]

                                                                        Traceback (most recent call last):
  File "DeepDPM.py", line 456, in <module>
    train_cluster_net()
  File "DeepDPM.py", line 436, in train_cluster_net
    trainer.fit(model, train_loader, val_loader)
  File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/trainer.py", line 696, in fit
    self._call_and_handle_interrupt(
  File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/trainer.py", line 648, in _call_and_handle_interrupt
    return self.strategy.launcher.launch(trainer_fn, *args, trainer=self, **kwargs)
  File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/strategies/launchers/multiprocessing.py", line 103, in launch
    mp.start_processes(
  File "/usr/local/lib/python3.8/dist-packages/torch/multiprocessing/spawn.py", line 198, in start_processes
    while not context.join():
  File "/usr/local/lib/python3.8/dist-packages/torch/multiprocessing/spawn.py", line 160, in join
    raise ProcessRaisedException(msg, error_index, failed_process.pid)
torch.multiprocessing.spawn.ProcessRaisedException:

-- Process 2 terminated with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/torch/multiprocessing/spawn.py", line 69, in _wrap
    fn(i, *args)
  File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/strategies/launchers/multiprocessing.py", line 129, in _wrapping_function
    results = function(*args, **kwargs)
  File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/trainer.py", line 737, in _fit_impl
    results = self._run(model, ckpt_path=self.ckpt_path)
  File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/trainer.py", line 1168, in _run
    results = self._run_stage()
  File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/trainer.py", line 1254, in _run_stage
    return self._run_train()
  File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/trainer.py", line 1285, in _run_train
    self.fit_loop.run()
  File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/loops/loop.py", line 201, in run
    self.on_advance_end()
  File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/loops/fit_loop.py", line 286, in on_advance_end
    epoch_end_outputs = self.trainer._call_lightning_module_hook("training_epoch_end", epoch_end_outputs)
  File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/trainer.py", line 1552, in _call_lightning_module_hook
    output = fn(*args, **kwargs)
  File "/mnt/iribhm/people/zzhang/DeepDPM/DeepDPM-main_vincent4/src/clustering_models/clusternet_modules/clusternetasmodel.py", line 462, in training_epoch_end
    ) = self.training_utils.init_subcluster_params(
  File "/mnt/iribhm/people/zzhang/DeepDPM/DeepDPM-main_vincent4/src/clustering_models/clusternet_modules/utils/training_utils.py", line 190, in init_subcluster_params
    mus, covs, pis = init_mus_and_covs_sub(
  File "/mnt/iribhm/people/zzhang/DeepDPM/DeepDPM-main_vincent4/src/clustering_models/clusternet_modules/utils/clustering_utils/clustering_operations.py", line 134, in init_mus_and_covs_sub
    codes_k = codes[indices_k]
IndexError: The shape of the mask [9347] at index 0 does not match the shape of the indexed tensor [3154, 128] at index 0

I don't know if further coding comment impacts the results of clustering or not....
could you explain more about problem with the dimension configuration please? how should I check or change please?
thank you
sincerely
Zhao


Update cortx-aws-k8s-installation.md for Motr + Hare cortx cluster

Added a Note for Motr + Hare cluster in cortx level QSG ( section 3.5 )

View rendered doc/cortx-aws-k8s-installation.md

Corresponding JIRA ticket is https://jts.seagate.com/browse/UDX-6985
@amit-si-seagate @osowski   Can someone review and approve this PR? Thanks.


Application Insights is greyed out

I'm trying to follow the instructions to enable snapshot debugging for my service but the Application Insights button is greyed out.  There is no indication why or how to fix it.

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

ID: 279763aa-b638-e2bc-cdb8-52c7bfe426ee
Version Independent ID: df3a411a-b8da-fccb-9c0b-aeb60a7c8baa
Content: Enable Snapshot Debugger for .NET apps in Azure App Service - Azure Monitor
Content Source: articles/azure-monitor/app/snapshot-debugger-appservice.md
Service: azure-monitor
Sub-service: application-insights
GitHub Login: @brahmnes
Microsoft Alias: bfung

@clarkezone , Thanks for brining this to our attention. We will investigate it further and update you shortly.

When the button for Application Insights is greyed out that usually means the combination OS+Runtime stack for your App Service Web App is not currently supported for codeless monitoring. I believe currently this would happen if your OS is Linux and is using any runtime except Node.js where codeless monitoring does currently work with Linux.

And just to clarify further, if for example you are running a Linux + .NET Core web app, you can still use Application Insights monitoring but you have to take a manual dependency on the App Insights SDK in your project rather than being able to use the codeless monitoring solution that is offered by the greyed out button.

@clarkzone can you confirm if the web app you are running is Linux based? The doc currently states that this is only supported for Windows, but we can definitely make that more clear and specifically call out the scenario you are experiencing.
I can also recommend to the PM/Engineering team that they add a tooltip so that the greyed out icon tells you why it is greyed out rather than just making you have to search in the docs.

Yes, I was using .NET Core Linux.  The tooltip would be super helpful.  I just added App Insights to my project manually.  I now see some telemetry showing up but the Application Insights button is still greyed out in the app service.. any ideas?

I know we have a preview experience for Profiler for .NET Core apps on Linux which is described here: https://docs.microsoft.com/azure/azure-monitor/app/profiler-aspnetcore-linux, but for snapshot debugger I am honestly not sure. @jogrima is the PM who owns snapshot debugger he should be able to weigh in on if this is supported, and if so how to get things up and running.

@mrbullwinkle - Thanks for providing more insights on this.
@clarkezone - I hope the solution provided by @mrbullwinkle has answered your question.
Thanks again for bringing this to our attention. At this moment there are no doc updates, so we will now proceed to close this thread. If there are further questions regarding this matter, please tag me in your reply. We will gladly continue the discussion and we will reopen the issue.


Error when I try db push following the documentation

II'm following all the steps in the guide to make changes in the schema and update those changes in planetscale database. connected to planetscale locally but when I run  prisma db run I get this:
`npx prisma db push
Environment variables loaded from .env
Prisma schema loaded from prisma\schema.prisma
Datasource "db": MySQL database "platforms" at "<IP_ADDRESS>:3309"
⚠  There might be data loss when applying the changes:
• You are about to drop the column gh_username on the User table, which still
contains 1 non-null values.
• You are about to drop the column image on the User table, which still contains 1 non-null values.
• You are about to drop the column name on the User table, which still contains 1 non-null values.
√ Do you want to ignore the warning(s)? ... yes
Error: P3022
Direct execution of DDL (Data Definition Language) SQL statements is disabled on this database. Please read more here how to handle this: https://pris.ly/d/migrate-no-direct-ddl `
So this link refers to the old docs. I really need to change that database schema...kinda stuck here. Really appreciate some help.

Hey @yosle, are you connecting to a development branch when you do this? The error message makes it sound like you might be on a production branch which does not allow DDL.

I have the same problem. I followed the docs and get the same error. After following this article,
https://shadcn.com/next-prisma-planetscale-vercel I run into the problem that I can't open two dev branches with the hobby plan.
Any other ideas?

@Manubi It looks like that blog post is also using the older recommended way.
In the new way, you never create a shadow branch, just one where you are developing in alongside your production branch. You can check out the document I link in my last comment for more information. The TLDR is:

Make dev branch
Change DATABASE_URL to the connection string for the dev branch.
Make your Prisma changes locally then npx prisma db push
Your changes are pushed to the dev branch and ready to open a deploy request to your production branch whenever you are ready.

You can also still use pscale connect if you would like, just make sure to have the right DATABASE_URL in your env vars.

@yosle I reached out to the Prisma team and we got the URL from the error updated so that it links to somewhere that explains more about development and production branches, including the error message. Since that is now improved, I'm going to close this issue for now. Feel free to follow up in a comment with any more questions about it.


Interop tests are failing

Many cloud to cloud and cloud to prod tests are failing. Interop test runs take >1h.
https://grpc-testing.appspot.com/job/gRPC_interop_master/2594/

@yang-g CC

Most of those tests have output indicating success, but they also timed out. Maybe the worker got starved out.

This is because ESF has a health checking bug when we enable port sharing for serving both Autobahn and gRPC over LOAS2. A fix will come in soon.

Restarted ESF with the fix. Should work now.

Leaving this config for some more time for @yang-g to debug. If we don't find a solution today, we'll modify the config. Currently all interop tests time out.

Building all interop docker images is failing with:
Error response from daemon: Error running deviceCreate (createSnapDevice) dm_task_run failed
https://grpc-testing.appspot.com/job/gRPC_interop_master/2607/console

something badly wrong with the grpc-canary-interop1
Even after restart, I see
jenkins@grpc-canary-interop1:/home/jtattermusch$ docker run -it debian:jessie /bin/bash Error response from daemon: Error running deviceCreate (createSnapDevice) dm_task_run failed
I'll probably nuke the VM and recreate it from scratch.

I recreated grpc-canary-interop1 from scratch.

Thank you, Jan!

I rebuilt the backend with some fixes. Local tests show success. We should see success on Jenkins starting from now.

Good work, let's see if the tests start to succeed.

Some problems building the java image: https://github.com/grpc/grpc-java/issues/1370

On friday, the last update was that most of the tests were actually succeeding, but after they exited, the docker container continued running, which eventually resulted in a timeout.
Today morning, I switched the storage option for all jenkins workers to overlayFS (it was devicemapper before, see #4988). Finally, the interop tests seem to be passing again.
https://grpc-testing.appspot.com/job/gRPC_interop_master/2706/
Closing this for now, let's reopen if we see major interop tests problems during the day.


Anti-shittery law

You must obey orders given to you by crew members based on the station's chain of command, except where such orders would conflict with the First Law.

Changing second law from

You must obey orders given to you by crew members, except where such orders would conflict with the First Law.

to

You must obey orders given to you by crew members based on the station's chain of command, except where such orders would conflict with the First Law.

makes it so that AI no longer has to open doors for crew that doesn't have access to them. The only situations in which they can now is if a person is threatening suicide, but I doubt any crew is smart enough to do this. If it becomes a problem, the first law can also be changed to remove the "through inaction" part of it


I cannot find get_radar_data.py

Question
What would you like to ask?
in [hunt.js] (link),
// Auto-generated from get_radar_data.py
// DO NOT EDIT THIS FILE DIRECTLY

but there's no get_radar_data.py...

Its name has changed get_hunt_data.py


Maximal estimate error

Hi, is there a formula for cpc or hll sketches to calculate max error for estimate ? I wonder what are the practices for assuring correctness(high accuracy) of execution while using sketches ?
Sketches, probablistic models for performing such operations as distinct counting looks great in both terms storage and performance, however crucial thing for me is whether there are hard boundaries for accuracy and error ?

Error is probabilistic, and for larger values is approximately Gaussian. Bounds estimates are provided in terms of 1, 2, and 3 standard deviations, with a decreasing probability of exceeding each of the bounds, respectively. That same trend persists indefinitely, but we don't quantify it.

As Jon said, for these algorithms there are no hard bounds. There is a distribution of error similar to normal distribution with ever decreasing probability to have an estimate farther and farther away from the true value. The sketch can give you upper and lower bounds for three confidence intervals that roughly correspond to 1, 2 and 3 standard deviations of the normal distribution: roughly 67%, 95% and 99% confidence. There are formulas in the papers. Perhaps we need better documentation on the web site. We have an accuracy table for Theta sketch, but not for HLL and CPC yet.

Ok, now I got It. I was wondering whether I have missed certain information, or It is the way it works. Thank you guys.
@AlexanderSaydakov that's a good point about documentation, for sure the other two sketches could benefit from having documentation as good as theta sketch.


Adding new events dynamically?

Hey,
first of all i want to thank you for your great calendar plugin! Im using an ajax call to fetch all events, preprocessing them on client side, to bring them i the needed format and saving them to a scope variable. In chrome it is working fine, but firefox and internet explorer arent showing any events in the calendar. Is there a problem with data binding in your directive? Because of setting the events variable after processing the directive?
Thanks in advance!
Cheers,
Antony

That's weird, it shouldn't be happening, please could you provide a plunker, codepen etc which demonstrates your issue? Also are you using the latest version of the calendar?

No offence but i was missing a conversion bug of javascripts Date function...sorry. When using moment(date).toDate() everything works like a charm. Maybe IE and FF cant handle every format given to the Date function, i was using something like "2015-11-14 01:09:00". Momentjs handeled it correctly, so everythings fine now!
Can be closed.

Oh yeah there are lots of weird discrepancies of date handling between browsers which is one of the reasons that moment.js exists, I'm glad you got it sorted :smile:


az find failed with an unexpected error

This is autogenerated. Please review and update as needed.
Describe the bug
Command Name
az find
Errors:
HTTPSConnectionPool(host='app.aladdin.microsoft.com', port=443): Max retries exceeded with url: /api/v1.0/examples?query=az+sql+db+show-connection-string&clientType=AzureCli&context=%7B%22sessionId%22%3A+%223da80348-0e2a-401e-a2d8-1cf36dd1167f%22%2C+%22subscriptionId%22%3A+%229624cf99-973c-43cc-b512-b216a5f3395f%22%2C+%22versionNumber%22%3A+%222.5.1%22%7D (Caused by SSLError(SSLError("bad handshake: Error([('SSL routines', 'tls_process_server_certificate', 'certificate verify failed')],)",),))
Traceback (most recent call last):
urllib3\urllib3\contrib\pyopenssl.py, ln 456, in wrap_socket
pip-install-7cl6xofh\pyOpenSSL\OpenSSL\SSL.py, ln 1934, in do_handshake
...

To Reproduce:
Steps to reproduce the behavior. Note that argument values have been redacted, as they may contain sensitive information.

Put any pre-requisite steps here...
az find {}

Expected Behavior
Environment Summary
Windows-10-10.0.18362-SP0
Python 3.6.6
Installer:

azure-cli 2.5.1

Extensions:
interactive 0.4.4

Additional Context

Likely related to this previously reported bug with SSLErrors via the CLI:
https://github.com/Azure/azure-cli/issues/8632

az find

@bestbear, looks like you are behind a proxy. Could you check https://github.com/Azure/azure-cli/blob/dev/doc/use_cli_effectively.md#working-behind-a-proxy and see if it solves the issue?

Duplicate of #1572


Add AlphaOmega Recording Interface

This PR adds the AlphaOmegaRecordingInterface to the GUIDE, which passes on the test data.

Looks good 👍


Changed way to get Primitive from keysetHandle

In version 1.2.2, keysetHandle.getPrimitive(Aead.class) method does not exist, so we could use AeadFactory.getPrimitive(keysetHandle);

If you look at the documentation in the 1.2 branch, you'll see that it matches the 1.2.2 implementation.
https://github.com/google/tink/blob/1.2/docs/JAVA-HOWTO.md
On the other hand, the documentation within the master branch matches the implementation contained within the master branch.
We're going to keep it this way for consistency. However, we do plan to launch a site to help make the distinction between the versions and their respective documentation more clear.
Thanks!