Is it possible to insert link with the footnote?

Something about {% footnoteref "css-counters" "[CSS counters](https://example.com) are, in
essence, variables maintained by CSS whose values may be
incremented by CSS rules to track how many times they’re used." %}
CSS counters{% endfootnoteref %} that deserves a footnote explaining
what they are.

Apparently, it doesn't render the link, does it not?

Hello! I assume you’re referring to eleventy-plugin-footnotes? The example section explains how to use Markdown in footnotes: https://github.com/KittyGiraudel/eleventy-plugin-footnotes#example

Thanks for your response. I did.
{% assign CCMA_footnote = "Please [click on the region](http://dhr.alabama.gov/counties/daycare_agency.aspx) you desire to obtain information about the appropriate Child Care Management Agency.", "" %}

If you think you’re income eligible, you must download and complete the Child Care Assistance Application form and mail it to your local {% footnoteref "CCMA" CCMA_footnote %}Child Care Management Agency{% endfootnoteref %}.

But apparently, it renders
Please click on the region you desire to obtain information about the appropriate Child Care Management Agency. ↩

Right, but you’re not parsing the content of your footnote as Markdown. It’s not enough to just store it in a variable, you also need to actually parse the content as Markdown so that when it’s injected as a footnote, it’s already HTML and not Markdown anymore.
For instance:
{% assign content = "My [content](https://google.com)" | markdown | replace: "
", "" | replace: "
", "" %}

The markdown filter is not a native Liquid filter unfortunately, so you’ll have to author it yourself in your 11ty configuration. Here is how I did it: https://github.com/KittyGiraudel/site/blob/main/.eleventy.js#L42

Thank you and sorry for being noob. If the article is written in Markdown, do I still need to parse the content as Markdown? Kinda confused.
But anyway I tried to add the filter and used your example, I've got markdown is not defined eventhough I have added the config in my eleventy.js.
{% assign content = "My [content](https://google.com)" | markdown | replace: "
", "" | replace: "
", "" %}

If the article is written in Markdown, do I still need to parse the content as Markdown? Kinda confused.

Yes, because this variable assignment is just a string. It’s not parsed as Markdown. It’s treated as authored, as a string.

If the article is written in Markdown, do I still need to parse the content as Markdown? Kinda confused.

It seems that you added the filter to your configuration like I did in mine, but you didn’t actually define the markdown function. That’s not a templating error, that’s a JavaScript error. Your configurations is invalid. You need to author the function to parse Markdown content. In my case, this is my function:
https://github.com/KittyGiraudel/site/blob/e9f12a3b24f90ba8aa3637cdbc8e255a64fe6bf4/.eleventy.js#L115-L119
And it relies on markdown-it, so the library needs to be installed and imported as well.

Thank you very much for your patience, my friend. That works! Appreciate your time. Have a good day ahead.

No worries, take care!


fix qp bug (run_example script)

thanks @NatOnera

:white_check_mark: Build SMT 0.6.744 completed (commit https://github.com/SMTorg/SMT/commit/cccd1ad4ff by @relf)


⚠ BCR Bot has degraded performance

In 888b694, BCR Bot (https://bcr.alternativasinteligentes.com/BCRApi/web/chatbot) experienced degraded performance:

HTTP code: 200
Response time: 999 ms

Resolved: BCR Bot performance has improved in 0f1a839.


How to click on a Button in a column?

I have added three buttons in a HorizontalLayout with addComponentColumn.
How can I click one of these buttons? I've only found _clickRenderer which probably would click on the component.

Hi, that is correct, _clickRenderer() will only click on a component that implements ClickNotifier.
You need to get the HorizontalLayout via _getCellComponent(), then locate the button out of HorizontalLayout (e.g. via _get()), then click on the button (e.g. via _click()).
Please see https://github.com/mvysny/karibu-testing/tree/master/karibu-testing-v10#clicking-renderers for more details.

Hi @mvysny
thanks a lot for your help. Works like a charm.


Dynamic IDs for accessibility

Some aria parameters require IDs. A few examples include: aria-controls and aria-labelledby. It would be nice to have a way to generate dynamic ids...
If there was a way to say you want dynamic ids for an element and then you can concatenate a suffix for child elements.
An example would be a drop down. You generate an ID for the dropdown, say dropDown1, then the button will get id="dropdown1-handle" aria-controls="dropDown1-content" and the drop down content will get id="dropDown1-content" aria-labelledby="dropdown1-handle"

Here are some ideas.
I can see adding an id() function that keeps track of IDs so you'd to the following:
<x x-with="id('dropDown')" x-as="id">
    <button id="{id~'-handle'}" aria-controls="{id~'-content'}">Click Me</button>

...

</x>

Let's also consider a helper function idGroup() that would take an ID and then return a special object that could handle suffixes. So the above example could look like the following:
<x x-with="idGroup('dropDown')" x-as="id">
    <button id="id.handle" aria-controls="id.content">Click Me</button>

...

</x>

That sounds great! idGroup would also allow to pass the prefix into sub components (if we ever want to pass the id deeper)


Installation Failed With error Microsoft Visual 9.0++ is required

Here is the Error Log
copying lib\Crypto\Signature_init_.py -> build\lib.win32-2.7\Crypto\Signature
running build_ext
warning: GMP or MPIR library not found; Not building Crypto.PublicKey._fastmath.
building 'Crypto.Random.OSRNG.winrandom' extension
error: Microsoft Visual C++ 9.0 is required. Get it from http://aka.ms/vcpython27

Hi @shardulsrivastava, Please visit http://www.voidspace.org.uk/python/pycrypto-2.6.1/, download a suitable PyCrypto distribution from there and install it in your system. Then try to install linuxacademy-dl again.


Fix scrollable view on android. Test in genymotion.

If will crash on android if does not modify.

accepted


sidebar.width is ignored

In the config, there's a default value of:
"sidebar.width": "50px"

Any attempt to change this value is ignored.  I tried searching the codebase for its use, and it isn't referenced anywhere.
Can we please add support for this option? That sidebar is too small for my preference.

Aha, found the hard-coded value:
https://github.com/onivim/oni/blob/bfd84e63956c1150386d3244b8f050a0bfccf2e5/browser/src/Services/Sidebar/SidebarContentSplit.tsx#L85
Now I just need to figure out how to inject the config value to that line....

@keforbes you can import an/the instance of configuration from Configuration.ts  then in the width: 200px replace that with width: ${configuration.getValue("sidebar.width")}

I'm actually looking to add a command to increase and decrease the size of the sidebar soon hopefully not quite the dragging with the mouse because that harder just increasing and decreasing that number based on a function call

@Akin909 As far as I know, and tested, the sidebar.width configuration does not exists or does not have an effect when configured. At least on the latest stable release.

@badosu it's in the default config as an option its currently set to 50px, but the value is hard coded to 200px in the relevant component

@Akin909 I am aware of that, but as you can see on SidebarSplit and SidebarView it is received as a prop that is never passed.
Unless I am missing somewhere else, this configuration is never used.
I am chiming in because this is the issue that annoys me the most and for longest using Oni, see also https://github.com/onivim/oni/issues/1414.

@badosu apologies getting wires crossed I wasn't sure what you meant earlier I thought you meant that the key sidebar.width didnt exist in the config but you and @keforbes are right it isn't hooked up at all. I had a brief look last night and tweaking the component he pointed to works though if theres already been a prop which should represent the width somewhere then passing that would be much preferable to injecting the config in like that since we dont do that anywhere else in that way, was more of half baked than a full fledged solution

I'm still hoping I can fix this issue even though most of this conversation is going over my head.  So where should the config value be injected? ISidebarViewProps? ISidebarState?

@keforbes I inspected the elements with the developer tools and seems like your first hunch is right, SidebarContentWrapper is the one controlling the width, currently at 200px.
If possible, may I suggest you, when changing this to replace the default unit measurement from pixels to em or another scalable unit. Currently 200px is too small for high-resolution displays.

Yes, I was able to tweak that value to confirm it affects the size.  But from what I could understand of your conversation with @Akin909, I shouldn't just replace that hard-coded value with ${configuration.getValue("sidebar.width")}.  I thought you guys were saying I should set the config value to a key in one of those props objects and then access the props value in SidebarContentWrapper.  That's where I'm confused.

@keforbes That was a bad assessment from my part, I got confused with the width props inside SidebarView and mistakenly thought it was the part controlling it. With regard to the approach, I am not qualified to tell a better method.

One possibility is that the intended was to sidebar.width to control that and it may have been at some point, but with different content providers for the sidebar it was refactored and this is a legacy artifact.


Defined Type Wrappers for Resources do not allow non-standard use with hard set parameters

username, password, host and port can only be determined by init.pp in the following resource wrappers: cli.pp, deployment.pp, reload.pp, resource.pp, restart.pp.
The easy fix for this is to add the username, password, host and port parameters to the classes and set the defaults to the existing values in the manifests.

Pull Request sent


Feat: add pod restarts count

Signed-off-by: Zhiyu Wang<EMAIL_ADDRESS>Description of your changes

Fixes #
I have:

[x] Read and followed KubeVela's contribution process.
[x] Related Docs updated properly. In a new feature or configuration option, an update to the documentation is necessary.
[x] Run yarn lint to ensure this PR is ready for review.
[ ] Added backport release-x.y labels to auto-backport this PR if necessary.

Special notes for your reviewer

@zhiyuone Thanks for your PR.


Pre-trained models

Hi,
I noticed the checkpoints.pth files only contain the weights of the prediction layers, and not of the backbone. Did I understand correctly? Are they supposed to be used with the provided features?
Thanks,
Chiara

Hi Chiara,
The code uses the provided features. These features are extracted from a TBN model trained on the source domain.
The backbone model isn't needed to replicate the results. I will upload the weights of the backbone after the holidays as I currently don't have access to them.
Best,
Jonathan

Ok, thanks. I will wait for the weights to be uploaded.
Chiara

Ok, thanks. I will wait for the weights to be uploaded.
Chiara

Hi Chiara,
The weights of the base model for feature extraction can be found here: https://www.dropbox.com/s/2ct6d750kzfn9qr/TBN_DA.pth.tar?dl=0
The code for backbone model is found here: https://github.com/ekazakos/temporal-binding-network
This should be enough to relplicate the extracted features, therefore closing the issue.

Hi Chiara,
The weights of the base model for feature extraction can be found here: https://www.dropbox.com/s/2ct6d750kzfn9qr/TBN_DA.pth.tar?dl=0
The code for backbone model is found here: https://github.com/ekazakos/temporal-binding-network
This should be enough to relplicate the extracted features, therefore closing the issue.


This is a welcome issue!

Welcome to the course!

@leighanneduvall
@nkatangoor
@katiem0
@jlipek
@amehta76
@dthompson-hstm
@sardesai-hstm
@mikeyg314
@anitha0530
@rachana-hstm

Thanks Evgeny!

Hello world!

I can see it.

Comments

Got it!

Hello

Got it


feat: Document native webpack support

Closes #37

I just realized we need to update https://webpack.js.org/configuration/devtool/#devtool to add the debug ids option

Oh wait it's documented here: https://webpack.js.org/plugins/source-map-dev-tool-plugin/#options
Should we also add it to https://webpack.js.org/configuration/devtool/#devtool?

Possibly, but I'm not sure of a good way to add it without making that table matrix twice as long 😂


feat(User): add User#associateWithAuthData & #disassociateAuthData

https://github.com/leancloud/docs/issues/2342

Codecov Report

Merging #519 into master will decrease coverage by 0.05%.
The diff coverage is 20%.

@@            Coverage Diff             @@
##           master     #519      +/-   ##
==========================================
- Coverage   67.02%   66.96%   -0.06%
==========================================
  Files          36       36
  Lines        2972     2976       +4
==========================================
+ Hits         1992     1993       +1
- Misses        980      983       +3

Impacted Files
Coverage Δ

src/user.js
60.36% <20%> (-0.43%)
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Way to obtain control limitations for each control on each module

The feature you'd like
Hey there! First let me say that I've used Tone.js on a few projects now and it's always loads of fun - thank you for maintaining the project.
Right now I'm working on a UI that will let users control each Tone effect. So for example, I'm making a React component that maps to the controls of Chebyshev. I've never heard of a Chebyshev, so I'm just looking at the API docs and I see that order is a Positive and that's A number greater than or equal to 0. Great! But what does that really mean? A number between 0-1000000? Like I'm making a range element that takes a min, max, and step. So I try 0-10 with a step of 0.01, but that causes the app to crash. So I look through the code and see it's 1-100 and must be an integer (step 1). Perfect! So I plug that in and get a Unhandled Rejection (InternalError): too much recursion at a setting of 1. By now I can't tell if this is a Tone bug or my misunderstanding of the range 😂
This is just one control on one effect and I imagine anyone making a UI for Tone or making sound procedurally must go through this process. Even with TypeScript and marking order with Positive wouldn't really be enough because order is a specific type of Positive.
tl;dr: I think for each component/effect, it would be useful to have min/max/step data on each control similar to how there's initial data for modules. UI developers can plug/play for their control elements and programmatic Tonesters can use it in their generative functions.
Any alternatives you've considered
Honestly a giant control tree would be boss:
{
  'Chebyshev': {
    module: Chebyshev,
    controls: {
      'order': {
        min: 1,
        max: 100,
        step: 1
      },
      'oversampling': [
        'none',
        '2x',
        '4x',
      ]
    }
  },
  'Auto Wah': { /* ...etc... */ }
}

Probably too UI-focused though. I feel like just having this data on the individual modules would be more than enough.

Take a look at the data.json which is distributed as part of the npm package. A lot of that information is already in there. You'll see in the code for Chebyshev for example that there is a min and max field in the documentation which is parsed into that json file. I think that you could parse the data.json file and probably generate a simplified description without the rest of the documentation data like you're describing.
A lot of the code also has an assertRange attribute which throws an error if the value is not within the given range (for values which are listed as Positive for example). I guess the value should be set to 2-100 instead of 1-100 if you're getting an error with Chebyshev.

Oh, that is interesting; data.json does have a lot of this stuff. It also highlights a problem with my original feature request: since individual elements inherit from other elements they can end up with a ton of configurable pieces. I imagine trying to manage all of that manually would be a nightmare (although at the same time, I guess if it's inheriting bits, it could inherit the min/max/step if they were set in code).
Just looking at the order though, it's clear there would be an issue with just parsing the JSON:
{
  "id": 15450,
  "name": "__set",
  "kind": 1048576,
  "kindString": "Set signature",
  "flags": {
    "isExported": true
  },
  "comment": {
    "shortText": "The order of the Chebyshev polynomial which creates the equation which is applied to the incoming\nsignal through a Tone.WaveShaper. The equations are in the form:\n```\norder 2: 2x^2 + 1\norder 3: 4x^3 + 3x\n```",
    "tags": [
      {
        "tag": "min",
        "text": "1"
      },
      {
        "tag": "max",
        "text": "100\n"
      }
    ]
  },
  "parameters": [
    {
      "id": 15451,
      "name": "order",
      "kind": 32768,
      "kindString": "Parameter",
      "flags": {
        "isExported": true
      },
      "type": {
        "type": "intrinsic",
        "name": "number"
      }
    }
  ],
  "type": {
    "type": "intrinsic",
    "name": "void"
  }
}

There's no step (and this control requires an int) and the min/max values are formatted for text output (the max includes a \n). Is this file made by typedoc? Makes me wonder if there's a way to autogenerate the file I'm suggesting in a similar way, but rather than outputting JSON for docs outputting JSON for devs to use. Just seems like it would be cool to have a way to instantly bootstrap a modular system without ever having to look at the docs for control constraints; Create-React-App for new web audio nerds. Speaking of which, do you know of an OS project using Tone that's a full modular system using the pre-baked Tone modules? Maybe I could see how they do things.
Really though, the more I think about it it seems like there really should be a way to autogenerate this data: for most of these inputs the type matches the expectation (unlike Chebyshev's order which is a "Positive" but constrained to 1-100 in steps of 1, Chorus' depth is a Normal which can accept anything between 0-1) so it would just be a matter of overwriting the type's base expectation or using a more accurate type. Then ??? and profit. I'm not familiar with TypeScript, so I'm not sure how to grab those types into something more basic for people to use.


Differentiator framework missing with Carthage

I'm using RxDataSources through Carthage but when I try to update RxDataSources (from 1.0.4 to 3.0.1) I have a crash when launching the application with this message:
dyld: Library not loaded: @rpath/Differentiator.framework/Differentiator
  Referenced from: /var/containers/Bundle/Application/[...]
  Reason: image not found

It turns out Carthage also build the Differentiator.framework as well. But I feel like this is something that should be written somewhere since we expect to only use RxDataSources.framework, and maybe you should prefix the the framework by Rx so we don't forget to link it and see right away for what it's used... :)

@Antoine4011 Finally, Antoine, how did you solve that issue? I'm new to Carthage, and I can't find where I should add or remove things to make it work once again ...
Thanks !

Just ran into this.

Not very good solution (

I was saved by this issue : )


peertube: enable building on Darwin

Description of changes
I develop using an M1 Mac and so having this build on Darwin helped me investigate its build recently. It can serve also as a starting point for anyone who wants to try running PeerTube on a Mac. I don't yet use it (although it looks interesting!), so I haven't tested the functionality.
Putting this up as a proposal since I was just checking out this package. I also don't mind if this is rejected at the moment, for example, on grounds of increasing maintenance work.
Things done

Built on platform(s)

[ ] x86_64-linux
[ ] aarch64-linux
[ ] x86_64-darwin
[x] aarch64-darwin

[ ] For non-Linux: Is sandbox = true set in nix.conf? (See Nix manual)
[ ] Tested, as applicable:

NixOS test(s) (look inside nixos/tests)
and/or package tests
or, for functions and "core" functionality, tests in lib/tests or pkgs/test
made sure NixOS tests are linked to the relevant packages

[ ] Tested compilation of all packages that depend on this change using nix-shell -p nixpkgs-review --run "nixpkgs-review rev HEAD". Note: all changes have to be committed, also see nixpkgs-review usage
[ ] Tested basic functionality of all binary files (usually in ./result/bin/)
22.11 Release Notes (or backporting 22.05 Release notes)

[ ] (Package updates) Added a release notes entry if the change is major or breaking
[ ] (Module updates) Added a release notes entry if the change is significant
[ ] (Module addition) Added a release notes entry if adding a new NixOS module
[ ] (Release notes changes) Ran nixos/doc/manual/md-to-db.sh to update generated release notes

[x] Fits CONTRIBUTING.md.

Thank you for your review!
I updated platforms to [ "x86_64-linux" "x86_64-darwin" "aarch64-darwin" ] since platforms.darwin expands to also include i686-darwin and armv7a-darwin, which this PR doesn't handle.
I'm not sure it makes sense to update the platforms list for the NixOS tests, since they don't (can't (?)) run on Darwin.

I'm not sure it makes sense to update the platforms list for the NixOS tests, since they don't (can't (?)) run on Darwin.

Test should show if peertube will work on darwin platform.

I don't know how to write this test, unfortunately. The NixOS test suggests PeerTube needs PostgreSQL and Redis to be set up in a VM, so a maybe a Darwin VM needs to be spun up, and is that possible?

Test is already there, just need to in file nixos/tests/all-tests.nix replace:
  peertube = handleTestOn ["x86_64-linux"] ./web-apps/peertube.nix {}

to
  peertube = handleTestOn [ "x86_64-linux""x86_64-darwin" "aarch64-darwin" ] ./web-apps/peertube.nix {}

The test includes configuration for NixOS, so I don't see how it can work on a Darwin machine.

Then I'm probably wrong on this point.

@Izorkin Is the lack of a test a blocker in your view? I don't mind whatever you decide. (I just don't think it's possible to write an end-to-end test of this for Darwin in nixpkgs, so we can choose not to merge this.)

@Izorkin Is the lack of a test a blocker in your view?

Current version of PR suits me fine.

This pull request has been mentioned on NixOS Discourse. There might be relevant details there:
https://discourse.nixos.org/t/prs-already-reviewed/2617/648

This needs to be rebased.

@ofborg build peertube.passthru.tests.peertube

@ofborg build peertube.passthru.tests.peertube

@ofborg build peertube peertube.passthru.tests

I am having trouble running the tests on aarch64-linux. I tried building the tests using nix-build -A nixosTests.peertube but no result symlink is generated. I don't know if it's because I am already in a VM (using darwin.builder) or not. Does anyone else have an aarch64-linux who can try out running the tests?


Tween.removeAllTweens chokes on Timelines

Timelines don't have a target, so they cause an error when using removeAllTweens.

This was fixed.


Implement an efficient way to create and compress clips to upload to Zooniverse (using ALVIS)

I tried to upload clips from one of the GU movies to Zooniverse using tut#3 and creating the first 10 clips took ~20 minutes. Is there any way to use Alvis GPU resources or find a more efficient way to generate clips

@victor-wildlife The only way to use the GPU for FFMpeg with Alvis is to build our kso-data-management Docker image with ffmpeg from source. I can create such a version, perhaps with a special tag so that we only use it for Alvis. It will probably take 5-10 times longer to build the image than before though, since this is a major compilation job.
Another option includes looking at optimisations without building ffmpeg from source, e.g. as they do in this article https://jeromewu.github.io/speedup-ffmpeg-without-compiling-from-source-code/. In the example, they achieve a speedup of about 2x, which would bring our extraction time to 1min per clip - not optimal, but better.
What are your thoughts on this?

@jannesgg I think we should go ahead with building ffmpeg from source. It might pay off to ask someone from SNIC/Alvis to provide their suggestions on this as they might have encountered a similar challenge before?

@victor-wildlife I have created a branch with ffmpeg built from source (ffmpeg-cuda). It took 1h10 to build the image. I have also sent a support request to SNIC, so we can see what they suggest.

that's awesome @jannesgg I will try running it on my end and report back

@jannesgg  How can I run the ffmpeg-cuda branch in Alvis? jupyter1.sh get the latest docker image from the main branch right?


[BEAM-12385] Handle VARCHAR and other JDBC specific logical types in AvroUtils.

Handle VARCHAR and other JDBC specific logical types in AvroUtils when converting a Row to GenericRecord.
@jbonofre @timrobertson100 can you help review? How do you suggest adding tests to JDBCIOTest? I can add tests to JdbcIOTest  but would require to introduce unnecessary dependencies on MariaDB or MySQL drivers.

Thank you for your contribution! Follow this checklist to help us incorporate your contribution quickly and easily:

[x] Choose reviewer(s) and mention them in a comment (R: @jbonofre @timrobertson100).
[x] Format the pull request title like [BEAM-XXX] Fixes bug in ApproximateQuantiles, where you replace BEAM-XXX with the appropriate JIRA issue, if applicable. This will automatically link the pull request to the issue.

See the Contributor Guide for more tips on how to make review process smoother.

GitHub Actions Tests Status (on master branch)

See CI.md for more information about GitHub Actions CI.

CC: @iemejia

@iemejia Added tests for date-time and string (varchar) related Logical types.

gentle bump up.

Merged manually, it will be part of Beam 2.32.0 (sorry if it did not make it into 2.31.0 but the branch was cut just before the PR was merged).


Issue in the Discretized_continuous.py

Hi.
When running this file I faced some errors. When I printed the dataset[3]["visits"] I got output like [([], [], [], [18.35])]. Each patient visit has 4 elements and the last one is always float. Am I making some mistake in somewhere with data generation? For this reason, I am getting the following error:

I get an IndexError for v[4].

 ---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
Input In [58], in <cell line: 1>()
     22     if contType == "Age":
     23         firstVisit = False
     24     new_cont = formatCont(
     25         random.uniform(
---> 26             discretization[contType][v[4][-1]], #changing 4 to 3 removes error
     27             discretization[contType][v[4][-1] + 1], #changing 4 to 3 removes error
     28         ),
     29         contType,
     30     )
     31     new_visits.append((v[1], new_labs, new_values, [new_cont]))
     32 p["visits"] = new_visits

IndexError: tuple index out of range

I get a _TypeError if I change v[4] to v[3]

new_cont = formatCont(
     25         random.uniform(
---> 26             discretization[contType][v[3][-1]],
     27             discretization[contType][v[3][-1] + 1],
     28         ),
     29         contType,
     30     )

I would be very grateful if you could point me in the right direction like where to look for solving the issue.

Hello, yes the indices in discretized_convert.py were one too high throughout the file. We have fixed that error and pushed an update. However, the data you discuss at the top of the comments looks like it has already been discretized. The output of generate.py has integers as the v[3] element and that is a float (which we are trying to achieve as an output). Can you check what the output of the generate.py step is?

Here is the first element of the generate.py output.
[{'visits': [([27,
44,
124,
172,
314,
324,
355,
496,
517,
552,
770,
808,
1025,
1149,
1450,
1580,
1601,
1638,
1655,
1813,
1820,
2022,
2080,
2103,
2446,
2458,
2465,
2469,
2572,
2634,
2922,
2931,
3077,
3219,
3306,
3341,
3382,
3453,
3523,
3603,
3787],
[],
[],
[9])],
'labels': array([0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)},

That looks different than the data in your first message (specifically, the final element in the visit tuple is [9] rather than [18.35]. This one should be ready to discretize with the updated code.

I was able to do the discretization. Thanks!


User can assign Server to Client Steering DS from Delivery Services page

I'm submitting a ...

[x] bug report
[ ] new feature / enhancement request
[ ] improvement request (usability, performance, tech debt, etc.)
[ ] other

Traffic Control components affected ...

[ ] CDN in a Box
[ ] Documentation
[ ] Grove
[ ] Traffic Control Client
[ ] Traffic Monitor
[ ] Traffic Ops
[ ] Traffic Ops ORT
[x] Traffic Portal
[ ] Traffic Router
[ ] Traffic Stats
[ ] Traffic Vault
[ ] unknown

Current behavior:

In Traffic Portal, when you go to any Client Steering DS there isn't any option to add Server to the Delivery Service.

But user can go to the Delivery Services page and add Server from the outside.

Expected / new behavior:

Should remove the Manage Servers for the Steering DS from Delivery Service page.
Minimal reproduction of the problem with instructions:

Anything else:

Good catch. Also Manage Server Capabilities should be removed from the context menu for Steering* delivery services.

This is also a problem when you go to "servers" and assigned delivery services. You can assign Client Steering services. When done, it creates some cryptic messages in atstscfg logs.

Ultimately, we should be preventing this from happening via the API.


Fixes issue396

Fixes #396

Sorry for the late reply @26. This PR does not work for me locally (I get errors and UI issues). Does it work locally for you? If so could you upload a video of it?
I'm surprised to see you use MainLoop here. What was the rationale behind that?

Closing due to staleness


Highlight critial information in the backend with colors

ERROR - red, SUCCESS - green, WARNING - yellow

7482505707a5217a890034089904aa2c4ea6306c


i2pd: 2.49.0 -> 2.50.1

Automatic update generated by nixpkgs-update tools. This update was made based on information from https://repology.org/project/i2pd/versions.
meta.description for i2pd is: Minimal I2P router written in C++
meta.homepage for i2pd is: https://i2pd.website
Updates performed

Version update

To inspect upstream changes

Release on GitHub

Compare changes on GitHub

Impact

Checks done (click to expand)

built on NixOS
The tests defined in passthru.tests, if any, passed
found 2.50.0 with grep in /nix/store/fv7jhg02hwzq5xy6va5pzr2glkndn8cf-i2pd-2.50.0
found 2.50.0 in filename of file in /nix/store/fv7jhg02hwzq5xy6va5pzr2glkndn8cf-i2pd-2.50.0

Rebuild report (if merged into master) (click to expand)

1 total rebuild path(s)

1 package rebuild(s)

First fifty rebuilds by attrpath
i2pd

Instructions to test this update (click to expand)

Either download from Cachix:
nix-store -r /nix/store/fv7jhg02hwzq5xy6va5pzr2glkndn8cf-i2pd-2.50.0 \
  --option binary-caches 'https://cache.nixos.org/ https://nix-community.cachix.org/' \
  --option trusted-public-keys '
  nix-community.cachix.org-1:mB9FSh9qf2dCimDSUo8Zy7bkq5CX+/rkCWyvRCYg3Fs=
  cache.nixos.org-1:6NCHdD59X431o0gWypbMrAURkbJ16ZPMQFGspcDShjY=
  '

(The Cachix cache is only trusted for this store-path realization.)
For the Cachix download to work, your user must be in the trusted-users list or you can use sudo since root is effectively trusted.
Or, build yourself:
nix-build -A i2pd https://github.com/r-ryantm/nixpkgs/archive/ef3dbaa08ce108b876f9ad42c350394d3e0c76f0.tar.gz

Or:
nix build github:r-ryantm/nixpkgs/ef3dbaa08ce108b876f9ad42c350394d3e0c76f0#i2pd

After you've downloaded or built it, look at the files and if there are any, run the binaries:
ls -la /nix/store/fv7jhg02hwzq5xy6va5pzr2glkndn8cf-i2pd-2.50.0
ls -la /nix/store/fv7jhg02hwzq5xy6va5pzr2glkndn8cf-i2pd-2.50.0/bin

Pre-merge build results
We have automatically built all packages that will get rebuilt due to
this change.
This gives evidence on whether the upgrade will break dependent packages.
Note sometimes packages show up as failed to build independent of the
change, simply because they are already broken on the target branch.
Result of nixpkgs-review run on x86_64-linux 1

1 package built:

i2pd

Maintainer pings
cc @edwtjo for testing.

Automatic update generated by nixpkgs-update tools. This update was made based on information from https://github.com/PurpleI2P/i2pd/releases.
meta.description for i2pd is: Minimal I2P router written in C++
meta.homepage for i2pd is: https://i2pd.website
Updates performed

Version update

To inspect upstream changes

Release on GitHub

Compare changes on GitHub

Impact
Checks done

built on NixOS
The tests defined in passthru.tests, if any, passed
found 2.50.1 with grep in /nix/store/p170c6qg2cww8bpdlchag97v8465k5ww-i2pd-2.50.1
found 2.50.1 in filename of file in /nix/store/p170c6qg2cww8bpdlchag97v8465k5ww-i2pd-2.50.1

Rebuild report (if merged into master) (click to expand)

1 total rebuild path(s)

1 package rebuild(s)

First fifty rebuilds by attrpath
i2pd

Instructions to test this update (click to expand)

Either download from Cachix:
nix-store -r /nix/store/p170c6qg2cww8bpdlchag97v8465k5ww-i2pd-2.50.1 \
  --option binary-caches 'https://cache.nixos.org/ https://nix-community.cachix.org/' \
  --option trusted-public-keys '
  nix-community.cachix.org-1:mB9FSh9qf2dCimDSUo8Zy7bkq5CX+/rkCWyvRCYg3Fs=
  cache.nixos.org-1:6NCHdD59X431o0gWypbMrAURkbJ16ZPMQFGspcDShjY=
  '

(The Cachix cache is only trusted for this store-path realization.)
For the Cachix download to work, your user must be in the trusted-users list or you can use sudo since root is effectively trusted.
Or, build yourself:
nix-build -A i2pd https://github.com/r-ryantm/nixpkgs/archive/140eaf504b2df016d4e12261f3452be60bdfc829.tar.gz

Or:
nix build github:r-ryantm/nixpkgs/140eaf504b2df016d4e12261f3452be60bdfc829#i2pd

After you've downloaded or built it, look at the files and if there are any, run the binaries:
ls -la /nix/store/p170c6qg2cww8bpdlchag97v8465k5ww-i2pd-2.50.1
ls -la /nix/store/p170c6qg2cww8bpdlchag97v8465k5ww-i2pd-2.50.1/bin

Pre-merge build results
We have automatically built all packages that will get rebuilt due to
this change.
This gives evidence on whether the upgrade will break dependent packages.
Note sometimes packages show up as failed to build independent of the
change, simply because they are already broken on the target branch.
Result of nixpkgs-review run on x86_64-linux 1

1 package built:

i2pd

Maintainer pings
cc @edwtjo for testing.


[bug] "creating collaborative contract" status UI page continues showing a pulsating dot but collab contract has been created OK

Describe the bug
when creating a collab contract, at the final stage where "creating collaborative contract" status message is shown and 'pulsating dots' for waiting indicator is shown, the page never completes. but if you check tez explorer eg tztk ,the contract has completed successfully - it shows successful "create proxy" stage, eg https://tzkt.io/opXuod24EUrSXSYoyKR16PHCbUdfKyKcw1ivRfWNh7dwSQ1MiF2/11268981
seems to be just a UI issue
I used the create collab steps
Ruralidyill saw similar, when creating a collab for TezQuakeAid (Turkey & Syria fundraiser 09 Feb 2023)
and Merch mentioned this may have been happening prior to the recent UI upgrade (new UI)
thanks
Reproduction

Create collab contract as per https://github.com/teia-community/teia-docs/wiki/Collab-contract-Guide#setting-up-a-contract-for-fundraisers
wait for final stage, where UI shows "creating collaborative contract" status message is shown and 'pulsating dots' for waiting indicator is shown
the UI page never completes - the pulsating dots continue for a long time (tried waiting over 5mins)
if you check tez explorer eg tztk ,the contract has completed successfully - it shows successful "create proxy" stage, eg https://tzkt.io/opXuod24EUrSXSYoyKR16PHCbUdfKyKcw1ivRfWNh7dwSQ1MiF2/11268981
this appears to be just a UI issue, feature is working OK, but may be confusing for users as they might think it's not working (especially if they don't check tez explorer or know what to look for there)

Expected behavior
UI page should return to collaborate page or show a 'success' message to indicate to the user that the new collab contract has been created successfully
Platform and versions
- OS: Macbook Pro, MAC OS Monterey 12.2.1
- Browser: Firefox Mac
- Browser Version: 109.0.1

Console output
No response
Additional context
No response

I will try again but this should be fixed

thanks :)
On Fri, 24 Feb 2023 at 01:22, Mel Massadian @.***>
wrote:

I will try again but this should be fixed
—
Reply to this email directly, view it on GitHub
https://github.com/teia-community/teia-ui/issues/251#issuecomment-1441882283,
or unsubscribe
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You are receiving this because you authored the thread.Message ID:
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Try install Gitea instead of GitLab

Try this download url
https://dl.gitea.io/gitea/1.13.6/gitea-1.13.6-linux-arm-6
Git

Putting this on hold due to install difficulties.


bevy_dynamic_plugin: make it possible to handle loading errors

Objective
Currently, bevy_dynamic_plugin simply panics on error. This makes it impossible to handle failures in applications that use this feature.
For example, I'd like to build an optional expansion for my game, that may not be distributed to all users. I want to use bevy_dynamic_plugin for loading it. I want my game to try to load it on startup, but continue without it if it cannot be loaded.
Solution

Make the dynamically_load_plugin function return a Result, so it can gracefully return loading errors.
Create an error enum type, to provide useful information about the kind of error. This adds thiserror to the dependencies of bevy_dynamic_plugin, but that dependency is already used in other parts of bevy (such as bevy_asset), so not a big deal.

I chose not to change the behavior of the builder method in the App extension trait. I kept it as panicking. There is no clean way (that I'm aware of) to make a builder-style API that has fallible methods. So it is either a panic or a warning. I feel the panic is more appropriate.

Changelog
Changed

bevy_dynamic_plugin::dynamically_load_plugin now returns Result instead of panicking, to allow for error handling

There is no clean way (that I'm aware of) to make a builder-style API that has fallible methods

Repeated ? chaining works pretty well! I'm fine to leave that for another PR though; it's more controversial and involved.

bors r+


Fix armv7 cache issue

Description:
Closes #793, see the details in #793.
Before:
setup-node will try to find the cached Node.js in ./arm/, which is never existing.
After this fix:
setup-node will try to find the cached Node.js in ./arm7vl/
Related issue:
#793
Check list:

[ ] Mark if documentation changes are required.
[ ] Mark if tests were added or updated to cover the changes.

I didn't run the code formatter, so the workflow was failed. It should be fixed now.

Amazing work Lous ❤

Hello @louislam. Sorry for the late response. Could you please run the npm ci && npm run build command ?

Hello @louislam. Sorry for the late response. Could you please run the npm ci && npm run build command ?

Done, should be ok now.
ncc: Version 0.33.4
ncc: Compiling file index.js into CJS
ncc: Using<EMAIL_ADDRESS>(local user-provided)
2959kB  dist/setup/index.js
2959kB  [16298ms] - ncc 0.33.4
ncc: Version 0.33.4
ncc: Compiling file index.js into CJS
ncc: Using<EMAIL_ADDRESS>(local user-provided)
2464kB  dist/cache-save/index.js
2464kB  [16030ms] - ncc 0.33.4
root@rs-debian:~/setup-node#


python311Packages.holidays: 0.40 -> 0.41

Diff: https://github.com/dr-prodigy/python-holidays/compare/refs/tags/v0.40...v0.41
Changelog: https://github.com/dr-prodigy/python-holidays/releases/tag/v0.41
Description of changes

Things done

Built on platform(s)

[ ] x86_64-linux
[ ] aarch64-linux
[ ] x86_64-darwin
[ ] aarch64-darwin

For non-Linux: Is sandboxing enabled in nix.conf? (See Nix manual)

[ ] sandbox = relaxed
[ ] sandbox = true

[ ] Tested, as applicable:

NixOS test(s) (look inside nixos/tests)
and/or package tests
or, for functions and "core" functionality, tests in lib/tests or pkgs/test
made sure NixOS tests are linked to the relevant packages

[ ] Tested compilation of all packages that depend on this change using nix-shell -p nixpkgs-review --run "nixpkgs-review rev HEAD". Note: all changes have to be committed, also see nixpkgs-review usage
[ ] Tested basic functionality of all binary files (usually in ./result/bin/)
24.05 Release Notes (or backporting 23.05 and 23.11 Release notes)

[ ] (Package updates) Added a release notes entry if the change is major or breaking
[ ] (Module updates) Added a release notes entry if the change is significant
[ ] (Module addition) Added a release notes entry if adding a new NixOS module

[ ] Fits CONTRIBUTING.md.

Add a :+1: reaction to pull requests you find important.

Result of nixpkgs-review pr 281528 run on x86_64-linux 1

2 packages failed to build:

python312Packages.holidays
python312Packages.holidays.dist

8 packages built:

calcure
calcure.dist
home-assistant-component-tests.holiday
home-assistant-component-tests.workday
python311Packages.holidays
python311Packages.holidays.dist
python311Packages.prophet
python311Packages.prophet.dist


"Image must be grayscale"

I'm getting the following error:
"assert image.shape[2] == 1, 'Image must be grayscale'"
When I do on an image.
img = snowy.load('test.png')
mask = img != 0.0
sdf = snowy.unitize(snowy.generate_sdf(mask))
snowy.show(snowy.hstack([img, mask, sdf]))
I try converting the image to grayscale on ImageMagic but I get similar error
Thanks

I think I found the solution by doing:
img = snowy.load('test.png')
source = snowy.rgb_to_luminance(snowy.extract_rgb(img))
sdf = snowy.unitize(snowy.generate_sdf(source != 0.0))


Issue #7: add some .gitkeep to get project load better in IDE

Signed-off-by: Mickael Istria<EMAIL_ADDRESS>

Thanks @mickaelistria !


[Question]: OSError: (External) CUDA error(700), an illegal memory access was encountered.

请提出你的问题
在做nlp 文档抽取 finetune 过程中，在几个轮次过后，会自动中断训练，中断的时机不确定
训练命令：
python3.7 finetune.py --device cpu  --logging_steps 5 --save_steps 100 --eval_steps 100 --seed 42 --model_name_or_path uie-x-base --output_dir ./checkpointtest1/model_best --train_path train/data/4/train.txt --dev_path train/data/4/dev.txt  --max_seq_len 512  --per_device_train_batch_size  4 --per_device_eval_batch_size 2 --num_train_epochs 80 --learning_rate 1e-5 --do_train --do_eval --do_export --export_model_dir ./checkpointtest1/model_best --overwrite_output_dir --disable_tqdm True --metric_for_best_model eval_f1 --load_best_model_at_end  True --save_total_limit 1
频繁出现，报以下错误：
Exception in thread Thread-4:
Traceback (most recent call last):
File "/usr/local/lib/python3.7/dist-packages/paddlenlp-2.5.2.post0-py3.7.egg/paddlenlp/transformers/tokenizer_utils_base.py", line 698, in convert_to_tensors
tensor = as_tensor(value)
File "/usr/local/lib/python3.7/dist-packages/paddle/tensor/creation.py", line 546, in to_tensor
return _to_tensor_non_static(data, dtype, place, stop_gradient)
File "/usr/local/lib/python3.7/dist-packages/paddle/tensor/creation.py", line 411, in _to_tensor_non_static
stop_gradient=stop_gradient,
OSError: (External) CUDA error(700), an illegal memory access was encountered.
[Hint: Please search for the error code(700) on website (https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__TYPES.html#group__CUDART__TYPES_1g3f51e3575c2178246db0a94a430e0038) to get Nvidia's official solution and advice about CUDA Error.] (at /paddle/paddle/phi/backends/gpu/cuda/cuda_info.cc:259)
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/lib/python3.7/threading.py", line 926, in _bootstrap_inner
self.run()
File "/usr/lib/python3.7/threading.py", line 870, in run
self._target(*self._args, **self._kwargs)
File "/usr/local/lib/python3.7/dist-packages/paddle/fluid/dataloader/dataloader_iter.py", line 218, in _thread_loop
self._thread_done_event)
File "/usr/local/lib/python3.7/dist-packages/paddle/fluid/dataloader/fetcher.py", line 138, in fetch
data = self.collate_fn(data)
File "/usr/local/lib/python3.7/dist-packages/paddlenlp-2.5.2.post0-py3.7.egg/paddlenlp/data/data_collator.py", line 199, in call
return_attention_mask=self.return_attention_mask,
File "/usr/local/lib/python3.7/dist-packages/paddlenlp-2.5.2.post0-py3.7.egg/paddlenlp/transformers/tokenizer_utils_base.py", line 2619, in pad
return BatchEncoding(batch_outputs, tensor_type=return_tensors)
File "/usr/local/lib/python3.7/dist-packages/paddlenlp-2.5.2.post0-py3.7.egg/paddlenlp/transformers/tokenizer_utils_base.py", line 229, in init
self.convert_to_tensors(tensor_type=tensor_type, prepend_batch_axis=prepend_batch_axis)
File "/usr/local/lib/python3.7/dist-packages/paddlenlp-2.5.2.post0-py3.7.egg/paddlenlp/transformers/tokenizer_utils_base.py", line 708, in convert_to_tensors
"Unable to create tensor, you should probably activate truncation and/or padding "
ValueError: Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' 'truncation=True' to have batched tensors with the same length.
Traceback (most recent call last):
File "finetune.py", line 177, in
main()
File "finetune.py", line 147, in main
train_result = trainer.train(resume_from_checkpoint=checkpoint)
File "/usr/local/lib/python3.7/dist-packages/paddlenlp-2.5.2.post0-py3.7.egg/paddlenlp/trainer/trainer.py", line 669, in train
tr_loss_step = self.training_step(model, inputs)
File "/usr/local/lib/python3.7/dist-packages/paddlenlp-2.5.2.post0-py3.7.egg/paddlenlp/trainer/trainer.py", line 1350, in training_step
loss = self.compute_loss(model, inputs)
File "/usr/local/lib/python3.7/dist-packages/paddlenlp-2.5.2.post0-py3.7.egg/paddlenlp/trainer/trainer.py", line 1312, in compute_loss
outputs = model(**inputs)
File "/usr/local/lib/python3.7/dist-packages/paddle/fluid/dygraph/layers.py", line 1012, in call
return self.forward(*inputs, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/paddlenlp-2.5.2.post0-py3.7.egg/paddlenlp/transformers/ernie_layout/modeling.py", line 1174, in forward
image=image,
File "/usr/local/lib/python3.7/dist-packages/paddle/fluid/dygraph/layers.py", line 1012, in call
return self.forward(*inputs, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/paddlenlp-2.5.2.post0-py3.7.egg/paddlenlp/transformers/ernie_layout/modeling.py", line 775, in forward
position_ids=visual_position_ids,
File "/usr/local/lib/python3.7/dist-packages/paddlenlp-2.5.2.post0-py3.7.egg/paddlenlp/transformers/ernie_layout/modeling.py", line 645, in _calc_img_embeddings
visual_embeddings = self.visual_act_fn(self.visual_proj(self.visual(image.astype(paddle.float32))))
File "/usr/local/lib/python3.7/dist-packages/paddle/fluid/dygraph/layers.py", line 1012, in call
return self.forward(*inputs, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/paddlenlp-2.5.2.post0-py3.7.egg/paddlenlp/transformers/ernie_layout/modeling.py", line 560, in forward
features = self.backbone(images_input)
File "/usr/local/lib/python3.7/dist-packages/paddle/fluid/dygraph/layers.py", line 1012, in call
return self.forward(*inputs, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/paddlenlp-2.5.2.post0-py3.7.egg/paddlenlp/transformers/ernie_layout/visual_backbone.py", line 213, in forward
y = block(y)
File "/usr/local/lib/python3.7/dist-packages/paddle/fluid/dygraph/layers.py", line 1012, in call
return self.forward(*inputs, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/paddlenlp-2.5.2.post0-py3.7.egg/paddlenlp/transformers/ernie_layout/visual_backbone.py", line 85, in forward
short = self.short(inputs)
File "/usr/local/lib/python3.7/dist-packages/paddle/fluid/dygraph/layers.py", line 1012, in call
return self.forward(*inputs, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/paddlenlp-2.5.2.post0-py3.7.egg/paddlenlp/transformers/ernie_layout/visual_backbone.py", line 42, in forward
y = self._batch_norm(y)
File "/usr/local/lib/python3.7/dist-packages/paddle/fluid/dygraph/layers.py", line 1012, in call
return self.forward(*inputs, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/paddle/fluid/dygraph/nn.py", line 1375, in forward
self._trainable_statistics, False)
OSError: (External) CUDNN error(8), CUDNN_STATUS_EXECUTION_FAILED.
[Hint: Please search for the error code(8) on website (https://docs.nvidia.com/deeplearning/cudnn/api/index.html#cudnnStatus_t) to get Nvidia's official solution and advice about CUDNN Error.] (at /paddle/paddle/phi/kernels/gpu/batch_norm_kernel.cu:1229)

这个问题比较难定位，请搜索OSError: (External) CUDNN error(8), CUDNN_STATUS_EXECUTION_FAILED.报错的可能
[Hint: Please search for the error code(8) on website (https://docs.nvidia.com/deeplearning/cudnn/api/index.html#cudnnStatus_t) to get Nvidia's official solution and advice about CUDNN Error.] (at /paddle/paddle/phi/kernels/gpu/batch_norm_kernel.cu:1229)

启动命令的device为啥是cpu?

是gpu 这里写错了

我调小了batch_size train batch_size =2  eval batch_size = 1  训练时显示GPU利用率100%，评估时利用率60%左右， 不一会儿就中断了，偶尔可以训练一段时间
:训练命令如下：
python3.7  -u -m paddle.distributed.launch --gpus "0"  finetune.py   --device gpu  --logging_steps 5  --save_steps 100  --eval_steps 100  --seed 42  --model_name_or_path uie-x-base  --output_dir checkpoint-auto-4-2/model_best  --train_path train/data/4/train.txt  --dev_path train/data/4/dev.txt   --max_seq_len 512   --per_device_train_batch_size  2  --per_device_eval_batch_size 1  --num_train_epochs 80  --learning_rate 1e-5  --do_train  --do_eval  --do_export  --export_model_dir checkpoint-auto-4-2/model_best  --overwrite_output_dir  --disable_tqdm True  --metric_for_best_model eval_f1  --load_best_model_at_end  True  --save_total_limit 1
当 train batch_size = 1 eval batch_size  =1 时  会立即训练中断 报错如下：
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Error: /paddle/paddle/phi/kernels/gpu/bce_loss_kernel.cu:42 Assertion (x >= static_cast<T>(0)) && (x <= one) failed. Input is expected to be within the interval [0, 1], but received nan.
Traceback (most recent call last):
File "finetune.py", line 177, in
main()
File "finetune.py", line 147, in main
train_result = trainer.train(resume_from_checkpoint=checkpoint)
File "/usr/local/lib/python3.7/dist-packages/paddlenlp-2.5.2.post0-py3.7.egg/paddlenlp/trainer/trainer.py", line 669, in train
tr_loss_step = self.training_step(model, inputs)
File "/usr/local/lib/python3.7/dist-packages/paddlenlp-2.5.2.post0-py3.7.egg/paddlenlp/trainer/trainer.py", line 1350, in training_step
loss = self.compute_loss(model, inputs)
File "/usr/local/lib/python3.7/dist-packages/paddlenlp-2.5.2.post0-py3.7.egg/paddlenlp/trainer/trainer.py", line 1312, in compute_loss
outputs = model(**inputs)
File "/usr/local/lib/python3.7/dist-packages/paddle/fluid/dygraph/layers.py", line 1012, in call
return self.forward(*inputs, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/paddlenlp-2.5.2.post0-py3.7.egg/paddlenlp/transformers/ernie_layout/modeling.py", line 1174, in forward
image=image,
File "/usr/local/lib/python3.7/dist-packages/paddle/fluid/dygraph/layers.py", line 1012, in call
return self.forward(*inputs, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/paddlenlp-2.5.2.post0-py3.7.egg/paddlenlp/transformers/ernie_layout/modeling.py", line 739, in forward
visual_bbox = self._calc_visual_bbox(self.config["image_feature_pool_shape"], bbox, visual_shape)
File "/usr/local/lib/python3.7/dist-packages/paddlenlp-2.5.2.post0-py3.7.egg/paddlenlp/transformers/ernie_layout/modeling.py", line 687, in _calc_visual_bbox
axis=-1,
File "/usr/local/lib/python3.7/dist-packages/paddle/tensor/manipulation.py", line 1839, in stack
return _C_ops.stack(x, axis)
OSError: (External) CUDA error(719), unspecified launch failure.
[Hint: Please search for the error code(719) on website (https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__TYPES.html#group__CUDART__TYPES_1g3f51e3575c2178246db0a94a430e0038) to get Nvidia's official solution and advice about CUDA Error.] (at /paddle/paddle/phi/backends/gpu/cuda/cuda_info.cc:252)
Exception in thread Thread-2:
Traceback (most recent call last):
File "/usr/local/lib/python3.7/dist-packages/paddlenlp-2.5.2.post0-py3.7.egg/paddlenlp/transformers/tokenizer_utils_base.py", line 698, in convert_to_tensors
tensor = as_tensor(value)
File "/usr/local/lib/python3.7/dist-packages/paddle/tensor/creation.py", line 554, in to_tensor
return to_tensor_static(data, dtype, stop_gradient)
File "/usr/local/lib/python3.7/dist-packages/paddle/tensor/creation.py", line 472, in to_tensor_static
output = assign(data)
File "/usr/local/lib/python3.7/dist-packages/paddle/tensor/creation.py", line 1868, in assign
value_name: values,
File "/usr/local/lib/python3.7/dist-packages/paddle/fluid/layer_helper.py", line 45, in append_op
return self.main_program.current_block().append_op(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/paddle/fluid/framework.py", line 4046, in append_op
attrs=kwargs.get("attrs", None),
File "/usr/local/lib/python3.7/dist-packages/paddle/fluid/framework.py", line 3037, in init
self.desc.infer_shape(self.block.desc)
RuntimeError: (NotFound) The kernel assign_value is not registered.
[Hint: Expected iter != kernels.end(), but received iter == kernels.end().] (at /paddle/paddle/phi/core/kernel_factory.cc:197)
[operator < assign_value > error]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/lib/python3.7/threading.py", line 926, in _bootstrap_inner
self.run()
File "/usr/lib/python3.7/threading.py", line 870, in run
self._target(*self._args, **self._kwargs)
File "/usr/local/lib/python3.7/dist-packages/paddle/fluid/dataloader/dataloader_iter.py", line 218, in _thread_loop
self._thread_done_event)
File "/usr/local/lib/python3.7/dist-packages/paddle/fluid/dataloader/fetcher.py", line 138, in fetch
data = self.collate_fn(data)
File "/usr/local/lib/python3.7/dist-packages/paddlenlp-2.5.2.post0-py3.7.egg/paddlenlp/data/data_collator.py", line 199, in call
return_attention_mask=self.return_attention_mask,
File "/usr/local/lib/python3.7/dist-packages/paddlenlp-2.5.2.post0-py3.7.egg/paddlenlp/transformers/tokenizer_utils_base.py", line 2619, in pad
return BatchEncoding(batch_outputs, tensor_type=return_tensors)
File "/usr/local/lib/python3.7/dist-packages/paddlenlp-2.5.2.post0-py3.7.egg/paddlenlp/transformers/tokenizer_utils_base.py", line 229, in init
self.convert_to_tensors(tensor_type=tensor_type, prepend_batch_axis=prepend_batch_axis)
File "/usr/local/lib/python3.7/dist-packages/paddlenlp-2.5.2.post0-py3.7.egg/paddlenlp/transformers/tokenizer_utils_base.py", line 708, in convert_to_tensors
"Unable to create tensor, you should probably activate truncation and/or padding "
ValueError: Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' 'truncation=True' to have batched tensors with the same length.
LAUNCH INFO 2023-08-07 10:28:10,123 Pod failed
LAUNCH ERROR 2023-08-07 10:28:10,123 Container failed !!!
Container rank 0 status failed cmd ['/usr/bin/python3.7', '-u', 'finetune.py', '--device', 'gpu', '--logging_steps', '5', '--save_steps', '100', '--eval_steps', '100', '--seed', '42', '--model_name_or_path', 'uie-x-base', '--output_dir', 'checkpoint-auto-4-2/model_best', '--train_path', 'train/data/4/train.txt', '--dev_path', 'train/data/4/dev.txt', '--max_seq_len', '512', '--per_device_train_batch_size', '1', '--per_device_eval_batch_size', '1', '--num_train_epochs', '80', '--learning_rate', '1e-5', '--do_train', '--do_eval', '--do_export', '--export_model_dir', 'checkpoint-auto-4-2/model_best', '--overwrite_output_dir', '--disable_tqdm', 'True', '--metric_for_best_model', 'eval_f1', '--load_best_model_at_end', 'True', '--save_total_limit', '1'] code 1 log log/workerlog.0
env {'GREP_COLOR': '1;31', 'CUDNN_VERSION': '<IP_ADDRESS>', 'LC_ALL': 'en_US.UTF-8', 'LD_LIBRARY_PATH': '/usr/local/lib/python3.7/dist-packages/cv2/../../lib64:/usr/local/TensorRT-<IP_ADDRESS>/lib:/usr/local/cuda-11.2/targets/x86_64-linux/lib:/usr/local/nvidia/lib:/usr/local/nvidia/lib64', 'LANG': 'en_US.UTF-8', 'HOSTNAME': 'f82f758c2aa9', 'OLDPWD': '/paddle/PaddleNLP-2.5.2/applications/information_extraction/document/train', 'WITH_GPU': 'ON', 'NVIDIA_VISIBLE_DEVICES': 'all', 'NCCL_VERSION': '2.8.4', 'GOPATH': '/root/gopath', 'PWD': '/paddle/PaddleNLP-2.5.2/applications/information_extraction/document', 'HOME': '/root', 'GOROOT': '/usr/local/go', 'CLICOLOR': '1', 'DEBIAN_FRONTEND': 'noninteractive', 'GREP_OPTIONS': '--color=auto', 'LIBRARY_PATH': '/usr/local/cuda/lib64/stubs', 'TERM': 'xterm', 'WITH_AVX': 'ON', 'CUDA_VERSION': '11.2.1', 'NVIDIA_DRIVER_CAPABILITIES': 'compute,utility', 'CUDA_VISIBLE_DEVICES': '0', 'SHLVL': '1', 'LANGUAGE': 'en_US.UTF-8', 'NVIDIA_REQUIRE_CUDA': 'cuda>=11.2 brand=tesla,driver>=418,driver<419 brand=tesla,driver>=440,driver<441 driver>=450,driver<451', 'PATH': '/home/cmake-3.16.0-Linux-x86_64/bin:/usr/local/gcc-8.2/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/local/go/bin:/root/gopath/bin', 'PS1': '\[\033[1;33m\]λ \[\033[1;37m\]\h \[\033[1;32m\]\w \[\033[0m\]', '_': '/usr/bin/python3.7', 'CUSTOM_DEVICE_ROOT': '', 'OMP_NUM_THREADS': '1', 'QT_QPA_PLATFORM_PLUGIN_PATH': '/usr/local/lib/python3.7/dist-packages/cv2/qt/plugins', 'QT_QPA_FONTDIR': '/usr/local/lib/python3.7/dist-packages/cv2/qt/fonts', 'POD_NAME': 'zvyhkr', 'PADDLE_MASTER': '<IP_ADDRESS>:45402', 'PADDLE_GLOBAL_SIZE': '1', 'PADDLE_LOCAL_SIZE': '1', 'PADDLE_GLOBAL_RANK': '0', 'PADDLE_LOCAL_RANK': '0', 'PADDLE_NNODES': '1', 'PADDLE_TRAINER_ENDPOINTS': '<IP_ADDRESS>:45403', 'PADDLE_CURRENT_ENDPOINT': '<IP_ADDRESS>:45403', 'PADDLE_TRAINER_ID': '0', 'PADDLE_TRAINERS_NUM': '1', 'PADDLE_RANK_IN_NODE': '0', 'FLAGS_selected_gpus': '0'}
LAUNCH INFO 2023-08-07 10:28:10,123 ------------------------- ERROR LOG DETAIL -------------------------
)
Exception in thread Thread-2:
Traceback (most recent call last):
File "/usr/local/lib/python3.7/dist-packages/paddlenlp-2.5.2.post0-py3.7.egg/paddlenlp/transformers/tokenizer_utils_base.py", line 698, in convert_to_tensors
tensor = as_tensor(value)
File "/usr/local/lib/python3.7/dist-packages/paddle/tensor/creation.py", line 554, in to_tensor
return to_tensor_static(data, dtype, stop_gradient)
File "/usr/local/lib/python3.7/dist-packages/paddle/tensor/creation.py", line 472, in to_tensor_static
output = assign(data)
File "/usr/local/lib/python3.7/dist-packages/paddle/tensor/creation.py", line 1868, in assign
value_name: values,
File "/usr/local/lib/python3.7/dist-packages/paddle/fluid/layer_helper.py", line 45, in append_op
return self.main_program.current_block().append_op(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/paddle/fluid/framework.py", line 4046, in append_op
attrs=kwargs.get("attrs", None),
File "/usr/local/lib/python3.7/dist-packages/paddle/fluid/framework.py", line 3037, in init
self.desc.infer_shape(self.block.desc)
RuntimeError: (NotFound) The kernel assign_value is not registered.
[Hint: Expected iter != kernels.end(), but received iter == kernels.end().] (at /paddle/paddle/phi/core/kernel_factory.cc:197)
[operator < assign_value > error]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/lib/python3.7/threading.py", line 926, in _bootstrap_inner
self.run()
File "/usr/lib/python3.7/threading.py", line 870, in run
self._target(*self._args, **self._kwargs)
File "/usr/local/lib/python3.7/dist-packages/paddle/fluid/dataloader/dataloader_iter.py", line 218, in _thread_loop
self._thread_done_event)
File "/usr/local/lib/python3.7/dist-packages/paddle/fluid/dataloader/fetcher.py", line 138, in fetch
data = self.collate_fn(data)
File "/usr/local/lib/python3.7/dist-packages/paddlenlp-2.5.2.post0-py3.7.egg/paddlenlp/data/data_collator.py", line 199, in call
return_attention_mask=self.return_attention_mask,
File "/usr/local/lib/python3.7/dist-packages/paddlenlp-2.5.2.post0-py3.7.egg/paddlenlp/transformers/tokenizer_utils_base.py", line 2619, in pad
return BatchEncoding(batch_outputs, tensor_type=return_tensors)
File "/usr/local/lib/python3.7/dist-packages/paddlenlp-2.5.2.post0-py3.7.egg/paddlenlp/transformers/tokenizer_utils_base.py", line 229, in init
self.convert_to_tensors(tensor_type=tensor_type, prepend_batch_axis=prepend_batch_axis)
File "/usr/local/lib/python3.7/dist-packages/paddlenlp-2.5.2.post0-py3.7.egg/paddlenlp/transformers/tokenizer_utils_base.py", line 708, in convert_to_tensors
"Unable to create tensor, you should probably activate truncation and/or padding "
ValueError: Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' 'truncation=True' to have batched tensors with the same length.

请问您的paddle和paddle以及cuda版本是什么？我看报错是cuda kernel的问题：
RuntimeError: (NotFound) The kernel assign_value is not registered.
然后如果数据是非官方的话，检查一下数据有没有超长或者超短等问题。


EWS copyFolder will be deprecated

❗ please do not add sensitive information in issues, you can provide extra information via email using issue number as reference ❗
Describe the issue
The copy folder API will be deprecated soon as stated by microsoft, this is implemented by Exchange Filesystem.
https://techcommunity.microsoft.com/t5/exchange-team-blog/upcoming-api-deprecations-in-exchange-web-services-for-exchange/ba-p/2813925
Reporter
Laurens

My bad, only createFolder and removeFolder are implemented, copyFolder is not implemented.


issue1-width

Challenge Size your Images has an issue.
User Agent is: Mozilla/5.0 (Windows NT 6.3; WOW64; rv:50.0) Gecko/20100101 Firefox/50.0.
Please describe how to reproduce this issue, and include links to screenshots if possible.
My code:

<link href="https://fonts.googleapis.com/css?family=Lobster" rel="stylesheet" type="text/css">

CatPhotoApp

Kitty ipsum dolor sit amet, shed everywhere shed everywhere stretching attack your ankles chase the red dot, hairball run catnip eat the grass sniff.

Purr jump eat the grass rip the couch scratched sunbathe, shed everywhere rip the couch sleep in the sink fluffy fur catnip scratched.

it does not work when I try to compile

Hi @michelle65! Thank you for reporting this issue.
There's an issue in your code. You added your class with .classname in the HTML, where it should be classname.
If you need coding help, please click the "Help" button on the challenge. This issue tracker is meant for issues with Free Code Camp itself.
If you think I'm wrong in closing this issue, please reopen it and add further clarification.
Happy coding!


i18n(ko-KR): update astro-glob-outside.mdx

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update astro-glob-outside.mdx
Add a little more detailed explanation

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reference/errors/astro-glob-used-outside.mdx
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Return arbitrary status codes

Is it possible to return arbitrary HTTP status codes, e.g. a 401 Unauthorized when a particular field requires authentication?
I'm currently using a custom private function to wrap resolver functions, which returns {:error, "Must be logged in"} if the user is not authenticated:
query do
  field :posts, list_of(:post) do
    resolve private &App.PostResolver.all/2
  end
end

defp private(fun) do
  fn args, %{context: context} = info ->
    case context do
      %{current_user: user} when not is_nil(user) -> fun.(args, info)
      _ -> {:error, "Must be logged in"}
    end
  end
end

This results in a 200 status code even when an error is returned, which complicates error handling. It would be nice if we could return, e.g. {:error, message: "...", plug_status: 401}.
You can use Plug's exception protocol for this, but it feels kind of wrong:
case context do
  %{current_user: user} when not is_nil(user) -> fun.(args, info)
  _ -> raise App.Unauthorized
end

defmodule App.Unauthorized do
  defexception [message: "Must be logged in", plug_status: 401]
end

This is intentionally not supported. GraphQL does not use HTTP status codes for semantic value. If you want to do authorization in the plug pipeline prior to Absinthe.Plug we cover how to do so here: http://absinthe-graphql.org/guides/context-and-authentication/

You may want to check out master where we support returning more complex error messages. So you could return something like:
{:error, %{message: "Unauthorized", status: 401}}

Thanks, @benwilson512! The only problem with doing authorization prior to Absinthe.Plug is that it's all-or-nothing - you can't whitelist/blacklist specific fields.
It makes sense that GraphQL doesn't use HTTP status codes, though. If one field were successful (200) and another unsuccessful (401), what would the server return? The complex error messages make more sense - thanks for pointing me in the right direction.

If one field were successful (200) and another unsuccessful (401), what would the server return?

Yup yeah this is the core issue. Hopefully the complex error messages will suite your needs!

This is intentionally not supported. GraphQL does not use HTTP status codes for semantic value. If you want to do authorization in the plug pipeline prior to Absinthe.Plug we cover how to do so here: http://absinthe-graphql.org/guides/context-and-authentication/

Updated link for others: https://github.com/absinthe-graphql/absinthe/blob/master/guides/context-and-authentication.md


Proposal: For meshes add a level so that multiple meshes can be stored

Right now there is only one root group for meshes: /data/meshes/. As opposed to particles where where there can be many: /data/particles/electrons/, /data/particles/protons/`, etc.
This is restricting. For example it may be desirable to store internal fields separately from external fields. So to have a structure to enable groups like: /data/meshes/dc_field/, /data/meshes/particle_field/`, would be desirable.
@ax3l: Please put this in the 2.0 project.

@DavidSagan thank you for the proposal!
Can you elaborate a little on what is missing? Can't this be realized with individual names of fields already as in your example?

@ax3l
Looking at this issue again I might be confusing the base standard with the ED-PIC extension. In any case, if I look at the example openPMD datasets I see paths like /data/400/fields/E and there is no way to specify multiple E-fields since meshesPath is fields. My proposal is to add a directory level so that you could have /data/400/fields/dc-field/E, /data/400/fields/ac-field/E, etc.

Why would we need to group the fields further instead of just naming them as a domain-expert would understand?

I don't understand this.

@RemiLehe

I think that Axel's point is that you can always use:
/data/400/fields/Efield_dc and /data/400/fields/Efield_ac (which would be compatible with the current standard) instead of /data/400/fields/dc-field/E and /data/400/fields/ac-field/E.

If I look at "Naming Conventions for mesh records (field records)" section of the EXT_ED-PIC extension it looks like /data/400/fields/Efield_dc would not be compatible since, in this example,  meshesPath is set to "fields/". But this is an extension standard and it looks like the base standard does not impose this so all this may just be my confusing the extension with the base.


Question about GLUE dataset

Hello! Thank you for your excellent code. In the paper, it is mentioned that fine-tuning was done on the GLUE dataset, but the load_data function in the code does not include GLUE. How can the results on the GLUE dataset be reproduced? Thank you very much!

Hi, you can use "QSTLlamaForSequenceClassification" or "QSTOPTForSequenceClassification"  and the huggingface GLUE dataset to test QST's performance.

Hi, you can use "QSTLlamaForSequenceClassification" or "QSTOPTForSequenceClassification" and the huggingface GLUE dataset to test QST's performance.

Should I use the  file "qst.py" or https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py

Do you train on GLUE dataset？

Hi, I've uploaded the glue_qst.py to conduct experiments on the GLUE benchmark and you can run the glue_qsy.sh to run the script.

Hi, I've uploaded the glue_qst.py to conduct experiments on the GLUE benchmark and you can run the glue_qsy.sh to run the script.

Thank you！


Upgrade terraform version to 0.14.5

After this commit applied dbfactory will use 0.14.5 terraform version.
Issue: MM-36454

Summary
Upgrade terraform version to 0.14.5
Ticket Link
https://mattermost.atlassian.net/browse/MM-36454
Release Note
Upgrade terraform version to 0.14.5

The file Makefile is in the blocklist for external contributors. Hence, these changes are not tested by the CI pipeline active until the build is re-triggered by a core committer or the PR is merged. Please be careful when reviewing it.
/cc @mattermost/core-security @mattermost/core-build-engineers

The file Makefile is in the blocklist for external contributors. Hence, these changes are not tested by the CI pipeline active until the build is re-triggered by a core committer or the PR is merged. Please be careful when reviewing it.
/cc @mattermost/core-security @mattermost/core-build-engineers


Fix typo in help channel opening embed

Reported-By: @Preocts in https://discord.com/channels/267624335836053506/429409067623251969/1067965320096124928

Ah well we have #2386 so there probably is no point in merging this. It'll just create conflicts and the impact of the issue this solves is minimal.


Fix issue reloading mount options on restart

On daemon restart the local volume driver will read options that it
persisted to disk, however it was reading an incorrect path, causing
volume options to be silently ignored after a daemon restart.
Fixes #25310

LGTM

LGTM :angel:


Relayer CLI for MsgChannelOpenTry

Crate
primarily cli
Summary
We should be able to send transactions from the relayer CLI for MsgChannelOpenTry:
https://github.com/informalsystems/ibc-rs/blob/12cb1d6187e878eee0e40295fe0b804ddef0b846/modules/src/ics04_channel/msgs.rs#L81-L88
Proposal
The following CLIs should be implemented by the relayer:
relayer tx raw chan-open-try <to-chain-B> <port-id> <channel-id>

For Admin Use

[X] Not duplicate issue
[X] Appropriate labels applied
[X] Appropriate milestone (priority) applied
[ ] Appropriate contributors tagged
[ ] Contributor assigned/self-assigned

Believe this is closed by #403


Expression is ambiguous type error

When I upgraded to macOS 15.0 , project did not built. Showing error: 'Expression' is ambiguous for type lookup in this context. That was in file MySQLCRUD of MariaDB module. When to expect new version of connector fixing this error?

Good day
Problem is solved in branch Expession_error.
See you soon

Good day
Problem is in fork JurjovecBorut, branch Expession_error.
See you soon

Good day
Problem is solved in fork JurjovecBorut, branch Expession_error.
See you soon

Good day
Problem is solved in fork JurjovecBorut, branch master.
See you soon


Fixed a typo. "da" suffix should be separated.

This is the grammatically correct way in Turkish.

Thanks for the report, however, translations are handled at Transifex.