Minor code change for 2 problems I rand into when integrating

Fixes a problem when a value_template in a digital to_joins was resulting in update_result being an actual boolean value and consequently it was never getting sent to the Crestron system: value_template: "{{is_state('lock.my_lock', 'locked')}}"
Fixes a problem during startup where result was a None value and consequently an error was showing up in the logs when trying to turn it into an int.

Added another change:

Fixes a problem that happens when an unknown source comes in from Crestron.  An exception is generated which breaks the analog callbacks resulting in no further analog updates registering on the HA side.  The media_player should respond to an unknown source by setting source to None.  Unknown sources are a valid possibility as users may not want HA and its users to be able to select sources like doorbells and intercoms.

Added another change:

Fixes a problem that happens when an unknown source comes in from Crestron.  An exception is generated which breaks the analog callbacks resulting in no further analog updates registering on the HA side.  The media_player should respond to an unknown source by setting source to None.  Unknown sources are a valid possibility as users may not want HA and its users to be able to select sources like doorbells and intercoms.

Will this solve a problem I have of the integration basically crashing and needing to reboot homeassitant whenever an unexpected value comes in?

Added another change:

Fixes a problem that happens when an unknown source comes in from Crestron.  An exception is generated which breaks the analog callbacks resulting in no further analog updates registering on the HA side.  The media_player should respond to an unknown source by setting source to None.  Unknown sources are a valid possibility as users may not want HA and its users to be able to select sources like doorbells and intercoms.

Will this solve a problem I have of the integration basically crashing and needing to reboot homeassitant whenever an unexpected value comes in?

Probably not.  This addresses a specific case where an unexpect value coming in as the source of a media_player and keeps the analog value crash from happening.  Work needs to be done to better handle exceptions so that the callbacks functionality doesn't short circuit.  I will take a look at it soon and see if I can figure out a good fix

Awesome.  I currently have my Crestron component in a dedicated HA VM throwing the entities into my primary HA through Remote Home Assistant.  Whenever one of them goes unavailable for more than 30 seconds an autoamtion reboots the dedicated HA which brings everything right back up.  Its a little clumsy but it works.

Yes, I noticed that, too (about it not being in the format that current ones are).  I wonder if @npope would mind if I took much of his code and made a new extension...

@natethelen I was actually just starting to work on updating the component to be config-flow based so that (1) it can be reloaded without rebooting HA and (2) we can add things like device triggers.  Happy to collaborate on that here via PRs or whatever works for you.

@natethelen I was actually just starting to work on updating the component to be config-flow based so that (1) it can be reloaded without rebooting HA and (2) we can add things like device triggers. Happy to collaborate on that here via PRs or whatever works for you.

As someone who can add no assistance in updating the code but relies on it everyday I appreciate the effort you guys put in and making this freely available.

@natethelen I was actually just starting to work on updating the component to be config-flow based so that (1) it can be reloaded without rebooting HA and (2) we can add things like device triggers. Happy to collaborate on that here via PRs or whatever works for you.

@npope I would love to help out with that.  I am an experienced professional Python engineer with a bunch of personal experience in home automation.  Just now getting my hands wet with Home Assistant, but very happy with it so far.  DM me if you want to move forward.  I can devote some side hours to the project.


ImagesTable: Fix Recreate image button

This removes an extra / from the path for the navigate function to be able to resolve relative path correctly.

@croissanne I've just noticed that the Recreate image button for the expired images doesn't work on consoledot, this should fix that.


spark-3.0.1 compatible

how does this compare to #70? (just making sure I understand the differences 😄 )
I think this is a subset of that work (except the spark v3.0.0 vs 3.0.1). This is a much smaller PR, so is ok to be a subset.

Hi @jsleight thanks for looking at this PR and for pointing me to the #70 . My PR is just a simple spark version upgrade, it may help to resolve the #76 quickly, otherwise, we can wait for #70 release

hi @lucagiovagnoli so we can't test the same code base with 2 spark api versions

Codecov Report

:exclamation: No coverage uploaded for pull request base (master@674d6d9). Click here to learn what that means.
The diff coverage is 90.90%.

@@            Coverage Diff            @@
##             master      #77   +/-   ##
=========================================
  Coverage          ?   74.09%
=========================================
  Files             ?       16
  Lines             ?      772
  Branches          ?       94
=========================================
  Hits              ?      572
  Misses            ?      200
  Partials          ?        0

Looks good! What kind of testing have you performed ?

Gentle bump on this question. It seems to me that moving to spark3 required an awful small amount of code and my gut feeling tells me that something is wrong. Do all tests pass ? I will also try running this against our internal integration suite

Looks good! What kind of testing have you performed ?

Gentle bump on this question. It seems to me that moving to spark3 required an awful small amount of code and my gut feeling tells me that something is wrong. Do all tests pass ? I will also try running this against our internal integration suite

yes, it seems all the integration suites completed successfully when I did run sbt it:test locally. Does travis CI do the same?

[info] IAMIntegrationSuite:
[info] - roundtrip save and load !!! IGNORED !!!
[info] - load fails if IAM role cannot be assumed !!! IGNORED !!!
[info] PostgresDriverIntegrationSuite:
[info] - postgresql driver takes precedence for jdbc:postgresql:// URIs !!!  IGNORED !!!
20/10/07 18:05:57 INFO ShutdownHookManager: Shutdown hook called
20/10/07 18:05:57 INFO ShutdownHookManager: Deleting directory /private/var/folders/h9/w3kwqgt52vd6044pmp_kglx817hfbk/T/spark-db2c39fe-aa22-43e7-8f30-5b65fa1ba91e
[info] Run completed in 44 minutes, 44 seconds.
[info] Total number of tests run: 48
[info] Suites: completed 11, aborted 0
[info] Tests: succeeded 48, failed 0, canceled 0, ignored 7, pending 0
[info] All tests passed.
[success] Total time: 2688 s, completed Oct 7, 2020 6:05:57 PM

Travis CI doesn't run it:test iirc but I've also just manually verified internally that it:test works. So this PR looks good to me. Before merging it in and publishing to nexus, @jsleight, @lydian  how do you feel about 4.2.1 vs 5.0.0 ?

Travis CI doesn't run it:test iirc but I've also just manually verified internally that it:test works. So this PR looks good to me. Before merging it in and publishing to nexus, @jsleight, @lydian how do you feel about 4.2.1 vs 5.0.0 ?

Minor version update imo, so 4.2.0.  Our public API is unchanged, and this faq on semver page suggests that updates to package dependencies should be minor or patch in these cases.  I view this as a rather significant dependency update, so minor version bump seems correct?

This is released on sonatype

This is released on sonatype

Thanks @lucagiovagnoli


Updated package meta data

Codecov Report

Merging #24 (725507c) into master (b88cf91) will not change coverage.
The diff coverage is 100.00%.

@@           Coverage Diff           @@
##           master      #24   +/-   ##
=======================================
  Coverage   91.89%   91.89%
=======================================
  Files           8        8
  Lines         691      691
=======================================
  Hits          635      635
  Misses         56       56

Impacted Files
Coverage Δ

typed_argparse/__init__.py
100.00% <100.00%> (ø)

:mega: We’re building smart automated test selection to slash your CI/CD build times. Learn more


[Feature]: Postgres secure ssl connection

Is there an existing issue for this?

[X] I have searched the existing issues

Summary
Postgres supports tls encryption, but bitnami image doesn't turn it on by default. It can easily be turned on and managed by coolify.
How to enable: https://github.com/bitnami/containers/tree/main/bitnami/postgresql#securing-postgresql-traffic
My idea is to use main coolify domain and it's certificates (if user configured it).
It's easy and convenient, connection string would look like postgresql://user:pass@coolify.mydomain.com:9000/dbname
Why should this be worked on?
Insecure connections are bad. Coolify has an option to expose database to the internet, it shouldn't come at cost of security
https://innerjoin.bit.io/the-majority-of-postgresql-servers-on-the-internet-are-insecure-f1e5ea4b3da3

I'm closing this issue, because tracking features will be moved to GitHub discussions. Github issues will be only for bugs.
Version 4 is in full focus. v3 related issues will be closed, if it is not a critical bug.
Thanks for your understanding.


Not able to use Configuration AccountPolicy_KerberosPolicies_Config

@jasonwbarnett @ccunning @megamorf @lloydsmithjr03 @alexandair
Hi All I am new to puppet I am not able to use Configuration AccountPolicy_KerberosPolicies_Config (or any DSC based modules), trying the example file for the same getting syntax error. Not sure what wrong I am doing trying to figure this out form past 1 1/2 Month hope you can kindly guide me how to use DSC modules. Tried the same on PS console getting error. If i can run one policy setting successfully then i can use most of your modules for our BAU tasks like GP/Security Policy, WSUS module and CMDT module and so on.
Could not parse for environment root: Syntax error at '(' (line: 7, column: 5)
PS C:\Users\ashreedhadmin> Configuration AccountPolicy_KerberosPolicies_Config
{
param
(
[Parameter(Mandatory = $true)]
[ValidateNotNullOrEmpty()]
[System.Management.Automation.PSCredential]
$DomainCred
)
Import-DscResource -ModuleName SecurityPolicyDsc

node localhost
{
    AccountPolicy KerberosPolicies
    {
        Name                                                 = 'KerberosPolicies'
        Enforce_user_logon_restrictions                      = 'Enabled'
        Maximum_lifetime_for_service_ticket                  = 600
        Maximum_lifetime_for_user_ticket                     = 10
        Maximum_lifetime_for_user_ticket_renewal             = 7
        Maximum_tolerance_for_computer_clock_synchronization = 5
        PsDscRunAsCredential                                 = $DomainCred
    }
}

}
At line:11 char:5

Import-DscResource -ModuleName SecurityPolicyDsc

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Could not find the module 'SecurityPolicyDsc'.
At line:15 char:9

    AccountPolicy KerberosPolicies

    ~~~~~~~~~~~~~

Undefined DSC resource 'AccountPolicy'. Use Import-DSCResource to import the resource.
+ CategoryInfo          : ParserError: (:) [], ParentContainsErrorRecordException
+ FullyQualifiedErrorId : ModuleNotFoundDuringParse
PS C:\Users\ashreedhadmin>

C:\windows\system32>puppet apply --noop c:\Temp\test.pp
Error: Could not parse for environment production: Syntax error at '(' (file: c:/Temp/test.pp, line: 4, column: 5) on node apnau201mel1977.globaltest.anz.com

Tried the same with puppetlabs-dsc module getting the below error
[class cc_hieratest::policy::test {
dsc_accountpolicy { 'Enforce_user_logon_restrictions':
dsc_name                            => 'Enforce_user_logon_restrictions',
dsc_enforce_user_logon_restrictions => 'Enabled',
}
}
](url)
Error: PowerShell DSC resource MSFT_AccountPolicy  failed to execute Set-TargetResource functionality with error message: Failed to update Account Policy Enforce_user_logon_restrictions. Refer to %windir%\security\logs\scesrv.log for details.
Error: /Stage[main]/Cc_hieratest::Policy::Test/Dsc_accountpolicy[Enforce_user_logon_restrictions]/ensure: change from 'absent' to 'present' failed: PowerShell DSC resource MSFT_AccountPolicy  failed to execute Set-TargetResource functionality with error message: Failed to update Account Policy Enforce_user_logon_restrictions. Refer to %windir%\security\logs\scesrv.log for details.
Info: Stage[main]: Unscheduling all events on Stage[main]
Info: Creating state file C:/ProgramData/PuppetLabs/puppet/cache/state/state.yaml
Notice: Applied catalog in 7.20 seconds

Never Mind i have addressed this issue using DSC_Light module form puppet


ProResRAW: S-Log3/S-Gamut3.cine LUT underexposing at 12800 ISO

Apple Feedback Assistant ID: FB13631126
DESCRIBE THE BUG:
The S-Log3/S-Gmaut3.cine LUT is underexposing the image when the codec is ProResRAW (in my case from an A7SIII).
The same video file taken from the camera (XAVCS-I) is working fine.

TO REPRODUCE:
Shoot ProResRAW on an A7SIII ; settings for camera and Atomos Ninja V are S-Log3/S-Gamut3.Cine
Set the camera to ISO 12800
Import to ProResRAW footage to Final Cut Pro, select the matching LUT (make sure to select the .cine LUT).

EXPECTED BEHAVIOUR:
Normal LUT conversion, should have brightness similar to the normal S-Log3/S-Gamut3.cine LUT.

SCREENSHOTS:

Screenshot 1: Expected behavior (using the S-Log3/S-Gamut3.cine LUT)

Screenshot 2: Actual behavior with the correct LUT applied, the image is several steps to dark.

SPECS:

2023 Mac Studio (M2 Pro, 32GB RAM, 512GB SSD)
macOS Sonoma 14.3.1
Final Cut Pro 10.7.1

This may be related to this case: https://github.com/CommandPost/FCPCafe/issues/340
The above clip was captured on an Atomos Ninja V running firmware version 10.62. The Ninja V did not support Sony A7SIII ProRes RAW white balance and ISO metadata support in FCP until Atomos firmware version 10.64.
If ProRes RAW material is recorded on older Ninja V firmware, those limitations are locked in and cannot be later changed. Despite this, the image exposure looks OK if processed in an HDR library and using an HDR project type.
In this case I used Rec.2020 HLG for the export since that is better supported. In FCP ProRes RAW settings, I used RAW to Log: Sony S-Log3/S-Gamut3.Cine, and let FCP auto-apply the S-Log3 conversion LUT. I had all FCP color settings at default, which means Automatic Color Conform was on, and in the video inspector Color Conform = Automatic. I adjusted the contrast using a color wheel effect.
Vimeo upload of the Rec.2020 HLG clip exported from FCP. I appended eight of the 1-sec clips so the Vimeo player has time to stabilize. Note the Vimeo poster frame is darker than the video image: https://vimeo.com/983231723/ef50e1adbd?share=copy
The HDR viewing experience will vary based on monitor type and the display profile. In my case it's an Apple Studio monitor using the built-in preset P3 - 600 nits, available in System Settings>Display. That apparently uses Apple's EDR (Extended Dynamic Range) technology so looks very good. It should also look very good on an Apple Silicon MacBook Pro with Liquid Retina XDR display, or one of the new iPads with Reference Mode.
For an SDR library, the FCP RAW-to-S-Log3 conversion doesn't work well. Two possible reasons:

The build-in S-Log3 conversion LUT clips the highlights. That's because it's applied as a global attribute not an effect in the stack. For that reason you can't get "in front of" the LUT to adjust highlights and it gets clipped. For this case, it works better if not using the FCP built-in LUT but a third-party LUT applied using the "Custom LUT" effect. I used Alister Chapman's V-Look LUTS: https://www.xdcam-user.com/2018/08/new-venice-look-luts-version-3-includes-minus-green-luts-for-fs5-fs7-f55-a7s-a7r/alister-v-look-v3-s-log3/

(Most likely) You used or FCP automatically used an incorrect HDR to SDR color conform. Normally, that works OK, but due to the old version of Ninja V firmware, FCP might have used the wrong one. In the clip Inspector video tab, under Color Conform, it's normally on "automatic". If it picked HLG to SDR the image would be much too dark. The correct color conform is HDR (PQ) to SDR. Inspecting the original file with Invisor shows it is tagged for Rec.2020 PQ.

Attached are JPG frame grabs of SDR and HDR versions from the FCP viewer. Note: still images do not have the same video metadata and may not display on monitors the same way as video. E.g, a JPG, PNG or even a TIFF frame grab of an HDR video frame will not produce the same results as a video frame played by HDR-aware software. People often attempt to compare video images with screen caps, but that is unreliable -- especially with HDR. Even exporting directly one frame from FCP to TIFF does not always work.
In this case, the screen cap of the HDR image from the FCP viewer is somewhat blown out. To my knowledge, there is no such thing as standardized HDR metadata for a still image. It doesn't even work right 100% of the time for SDR, especially if uploaded to a forum. Taking screencaps during a remote grading session using Zoom or Facetime can especially be unreliable. You can't trust you are seeing the same thing as the other party, even if both are using calibrated monitors. The remote conference software can alter the color. I think the only reliable workaround is to send a 1-frame video.
Linked attachments:
https://www.dropbox.com/scl/fo/pizpr1cx9eajl0qx5z0zo/ALtJZbVN96-ZTmibXdWmjNE?rlkey=1mme8j87gp6ni2yu3mds7jvh0&st=0h4tgtja&dl=0

Original 1-second 4k ProRes RAW clip
Graded HDR version, exported as 4k 10-bit HEVC with Dolby Vision 8.4 (one of FCP's presets)
Graded SDR version, exported as 4k 10-bit HEVC
JPG screen cap of HDR video frame
JPG screen cap of SDR video frame

My config: M1 Ultra Apple Studio, Apple Studio monitor, P3-600 nit preset, MacOS Sonoma 14.5, FCP 10.8


PATCH 0023-remove-required-elements-from-schema-and-fix-tests-7.patch

Tracking context on upstream patch 0023-remove-required-elements-from-schema-and-fix-tests-7.patch here.

Change schema from required to optional Should this just be merged into 0021?


documentation: Modify the confusing tour/references/aliases page

Documentation of aliases & let
The following page of the doc refering to aliases is using the let construct:
https://cuelang.org/docs/tutorials/tour/references/aliases/
But to be honest I am not sure if the page is meant to introduce the concept of aliases or the let construct. These two are treated in the spec.md as two different concepts (with different use cases).
Describe the solution you'd like
I suggest to align the page with the spec.md to avoid confusion.
To the best of my knowledge a more idiomatic way to write the code snippet of the aliases page would be:
A=a: {
    d: 3
}
b: {
    a: {
        // A provides access to the outer "a" which would
        // otherwise be hidden by the inner one.
        c: A.d
    }
}

This issue has been migrated to https://github.com/cue-lang/cue/issues/948.
For more details about CUE's migration to a new home, please see https://github.com/cue-lang/cue/issues/1078.


Class properties and decorators not playing well

I don't have enough insight to determine if this is due to babel 6 transform-class-properties or transform-decorators-legacy, but there is inconsistencies when migrating from babel5 decorators.
I created a sample project to demonstrate it: https://github.com/themouette/demo-decorators-classprops
Static properties are applied to the decorated class instead of the original class.
Current generated code looks like:
// ...
var Test = decorator(_class = function () {
  function Test() {
    _classCallCheck(this, Test);
  }

  _createClass(Test, [{
    key: 'render',
    value: function render() {
      return 'foo';
    }
  }]);

  return Test;
}()) || _class;

Test.propTypes = {
  foo: 'Hello'
};
// ...

When it should look someting like:
// ...
_class = function () {
  function Test() {
    _classCallCheck(this, Test);
  }

  _createClass(Test, [{
    key: 'render',
    value: function render() {
      return 'foo';
    }
  }]);

  return Test;
}()) || _class;

_class.propTypes = {
  foo: 'Hello'
};

var Test = decorator(_class);
// ...

Am I doing something wrong?

Been meaning to document this in the README, but it slipped my mind. Your transforms are in the wrong order: https://github.com/themouette/demo-decorators-classprops/blob/master/.babelrc#L3
"plugins": ["transform-class-properties", "transform-decorators-legacy"]

should be
"plugins": ["transform-decorators-legacy", "transform-class-properties"]

Great, thank you.


Add Futures support to the component event loop

Description
** I'm submitting a feature request - please describe the behavior you want and the motivation.
There exists a fork of Yew called plaster that has support for, among other things, async calls being used in component event loops. With https://github.com/yewstack/yew/pull/684, it is possible to imagine people disabling the services feature and interacting with the browser using futures and web_sys.
I suggest that we attempt to port async support back to yew, with credit to @carlosdp of course.
The function at the following link appears to add support for async functions to the event loop: https://github.com/carlosdp/plaster/blob/master/src/html.rs#L135.

I have had trouble integrating web_sys constructs (and I'm assuming wasm_bindgen related things as well) directly into yew, resulting in indecipherable compiler errors when using cargo-web. Hopefully, the addition of this feature is pain-free. Considering that my prior failures to use web_sys might have been due to version mismatches, the work in https://github.com/yewstack/yew/pull/681 might make this less painful.

This issue takes inspiration from https://github.com/yewstack/yew/issues/636, but offers a more constrained and concrete set of actions to take in order to get part of that proposed featureset implemented.

it is possible to imagine people disabling the services feature and interacting with the browser using futures and web_sys

That is exactly what I would like to do.


Ruby Language detects Puppet manifest as Ruby

VSCode Version: 1.0.0
OS Version: Fedora 23 x86_64

Steps to Reproduce:

Install VSCode
Install Puppet Extension
Open puppet manifest (.pp file)
VSCode detects it as a Ruby file type

package.json snippet of offending extension:
            "id": "ruby",
            "extensions": [ ".rb", ".rbx", ".rjs", ".gemspec", ".pp", ".rake" ],

The included Ruby language extension has the puppet extension (.pp) in its extensions list and overrides any installed Puppet extension causing Puppet manifest files to always be detected as Ruby files unless a manual override is put into the User Configuration.

// CC @alexandrudima

Any progress with this?

@shamil This was fixed back in the July/August release. I should have closed it then, sorry.
See https://github.com/Microsoft/vscode/pull/8637


en-us file incomplete or site not translating all strings properly

See http://www.penguinwatch.org/?lang=./translations/es.json

I've merged the translation branch into master on readymade. Bumped the version to 0.6.4.
I think all that's left that's hardcoded in English are the team page and the title of the science page.

Bumping this because I've added a couple of new keys to en-us.coffee.
Spanish version is being tested at penguinwatch.org/beta
@chrissnyder did you ever figure out why the team page text from en-us.coffee isn't showing up in the translations app?

I'd hazard a guess in this case that it just needs to be seeded again. See something like https://github.com/zooniverse/Milky-Way-Project/blob/master/package.json#L24

Cheers @chrissnyder that did the trick. New keys available to translate at http://translations.zooniverse.org/#/projects/penguin


Scenario&Mockups: Write the Third Scenario

To Do: Write the third scenario which focuses on the concept of online gallery and collaboration between creators. As pointed out in the specification, a proper scenario shall include:

The user and their persona (role)
Preconditions – if any (such as: the user that has already logged in).
Specific actions to achieve some desired goal (what the user wants to do). Describe
each step until the scenario is completed. Steps must be enumerated.
Acceptance criteria (the conditions that describe a successful outcome for the action).

Third scenario is planned to depict the actions required for publishing an online gallery and adding another user as collaborator. It involves private message functionality.
Specific actions should be supported with visual material.
Deadline: 23.03.2022 @12.00

Actions and photos were added.
(?)s were removed with appropriate requirement numbers.

Added persona, story and preconditions. Updated the photos to highlight some keypoints and enrich the content of the homepage. Updated actions to give more insights. Review required.

Reviewed, no action taken. Thanks.


All the dropdowns in readonly if not logged in

This is specifically for the details page
Added ID to the dropdown  sectoin for test
(fixes-#329)

@naina-verma please review

@sanbornsen , LGTM!


feat: Add withdrawTo functionality to support exits to other addresses

@QEDK can you please resolve the conflict?

@jdkanani Done! 👌

Merging after a conversation with @imyourm8 💯


[swift_snapshot_tool] Some small fixes and add a run_toolchain command for running a command against a specific toolchain.

run_toolchain is mainly useful for diagnosing issues with ones script without
needing to fully bisect... but it also can just be used independently.

@swift-ci smoke test

@swift-ci test windows platform

@swift-ci test windows platform


AssertionError with list comprehension and loop

Reporting a bug

[ V] I am using the latest released version of Numba (most recent is visible in
the change log (https://github.com/numba/numba/blob/master/CHANGE_LOG).
[ V] I have included below a minimal working reproducer (if you are unsure how
to write one see http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports).

I had a file with these contents:
b=[(5,124),(52,5)]

@jit
def a():
	c=[b[index] for index in [0,1]]
	for x in range(5):
		pass

a()

It produced this error:

Traceback (most recent call last):
File "C:\XUE\Larry\PythonWorkspace\numbatest\a.py", line 10, in
a()
File "C:\Users\g\Anaconda3\lib\site-packages\numba\dispatcher.py", line 367, in _compile_for_args
raise e
File "C:\Users\g\Anaconda3\lib\site-packages\numba\dispatcher.py", line 324, in _compile_for_args
return self.compile(tuple(argtypes))
File "C:\Users\g\Anaconda3\lib\site-packages\numba\dispatcher.py", line 656, in compile
cres = self._compiler.compile(args, return_type)
File "C:\Users\g\Anaconda3\lib\site-packages\numba\dispatcher.py", line 82, in compile
pipeline_class=self.pipeline_class)
File "C:\Users\g\Anaconda3\lib\site-packages\numba\compiler.py", line 903, in compile_extra
return pipeline.compile_extra(func)
File "C:\Users\g\Anaconda3\lib\site-packages\numba\compiler.py", line 369, in compile_extra
return self._compile_bytecode()
File "C:\Users\g\Anaconda3\lib\site-packages\numba\compiler.py", line 834, in _compile_bytecode
return self._compile_core()
File "C:\Users\g\Anaconda3\lib\site-packages\numba\compiler.py", line 821, in _compile_core
res = pm.run(self.status)
File "C:\Users\g\Anaconda3\lib\site-packages\numba\compiler.py", line 255, in run
raise patched_exception
File "C:\Users\g\Anaconda3\lib\site-packages\numba\compiler.py", line 246, in run
stage()
File "C:\Users\g\Anaconda3\lib\site-packages\numba\compiler.py", line 458, in stage_objectmode_frontend
cres = self.frontend_looplift()
File "C:\Users\g\Anaconda3\lib\site-packages\numba\compiler.py", line 420, in frontend_looplift
flags=loop_flags)
File "C:\Users\g\Anaconda3\lib\site-packages\numba\transforms.py", line 177, in loop_lifting
cfg = compute_cfg_from_blocks(blocks)
File "C:\Users\g\Anaconda3\lib\site-packages\numba\analysis.py", line 241, in compute_cfg_from_blocks
cfg.add_edge(k, target)
File "C:\Users\g\Anaconda3\lib\site-packages\numba\controlflow.py", line 83, in add_edge
assert dest in self._nodes
AssertionError: Failed in object mode pipeline (step: object mode frontend)

@Rik-de-Kort thanks for looking at this. The reason for the error in #3289 being more informative under @njit is that it is failing via a well trodden error pathway (as in, this code is not supported by the compiler in nopython mode and it can detect that and bail). Ideally the @njit path shouldn't be raising from the lowering pass and the problem should really be caught by legalization, but that's another matter. The reason this fails in a seemingly poor manner under standard @jit is that this is a) a bug b) a bug that trips an internal assert that raises in a manner that isn't currently handled nicely. The error under @jit is coming from control flow analysis, a point in the pipeline which shouldn't really fail, if the code is valid python it should be analysable and so an assert when something goes wrong is reasonable. However, it ought to be caught and translated into something to ask users to report the problem, https://github.com/numba/numba/issues/3290 to track.

@SuperStormer thanks for the report. This is a bug, I think the closure inlining pass under the nopython path is mutating the IR in a manner that is causing CFA to fail when the objectmode fallback path is used. There is another ticket which demonstrates the same behaviour here: https://github.com/numba/numba/issues/3131. Adding forceobj=True as a kwarg in the @jit decorator "fixes" it.


Class soap does not exist

I just got started using this and I'm not exactly sure what I did wrong. I installed it as per instructions but calling the facade gets me this error:
In Container.php line 838:

  Target class [soap] does not exist.

In Container.php line 836:

  Class soap does not exist

I tried running composer dump-autoload to rule out autoloading issues and it's still getting that error. At this point, I'm not sure what exactly I'm missing.
I'm using Lumen 8.0 with php 7.4 if it matters.

Looks like you haven't got the php soap extension installed. Install that, and it should work dandy.

I'm going to close this for now. Please re-open if your issue isn't solved.

I suspected as much and checked and it appears to be already installed. I manually installed it for good measure, yet this is still coming up.


Non existent Example Schema json files?

Example Schema json files?

Sorry, links where broken - all fixed now.


Add support for GPT-4o

Checked other resources

[X] I added a very descriptive title to this issue.
[X] I searched the LangChain.js documentation with the integrated search.
[X] I used the GitHub search to find a similar question and didn't find it.
[X] I am sure that this is a bug in LangChain.js rather than my code.
[X] The bug is not resolved by updating to the latest stable version of LangChain (or the specific integration package).

Example Code
import { OpenAIEmbeddings, ChatOpenAI } from "@langchain/openai";
import { ConversationalRetrievalQAChain } from "langchain/chains";
import { PromptTemplate } from "@langchain/core/prompts";
import {
  HumanMessage,
  AIMessage,
  SystemMessage,
} from "@langchain/core/messages";

const qaGeneratorModel = new ChatOpenAI({
  modelName: "gpt-4o",
  temperature: 0,
  streaming: false,
});

const baseModel = new ChatOpenAI({
  temperature: temperature,
  modelName: "gpt-4o",
  streaming: true,
  callbacks: [
    {
      async handleLLMNewToken(token) {
        callback(token);
      },
      async handleLLMEnd(output) {
        // End of stream
        callback("===END===");
      },
    },
  ],
});

let options = {
  returnSourceDocuments: true,
  questionGeneratorChainOptions: {
    llm: qaGeneratorModel,
    template: CONDENSE_PROMPT_STRING,
  },
  qaChainOptions: {
    type: "stuff",
    prompt: QA_PROMPT,
    verbose: QA_CHAIN_VERBOSE,
  },
};

let chain = ConversationalRetrievalQAChain.fromLLM(
  baseModel,
  vectorstore.asRetriever(),
  options,
);

const response = await chain.call({
  question: sanitizedText,
  chat_history: chatHistoryParsedArray,
});

Error Message and Stack Trace (if applicable)
Failed to calculate number of tokens, falling back to approximate count Error: Unknown model
at getEncodingNameForModel (file:///node_modules/js-tiktoken/dist/chunk-HXW345QN.js:224:13)
at encodingForModel (file:///node_modules/@langchain/core/dist/utils/tiktoken.js:19:24)
at ChatOpenAI.getNumTokens (file:///node_modules/@langchain/core/dist/language_models/base.js:174:40)
at file:///node_modules/@langchain/openai/dist/chat_models.js:642:42
at Array.map ()
at ChatOpenAI.getNumTokensFromMessages (file:///node_modules/@langchain/openai/dist/chat_models.js:641:60)
at ChatOpenAI.getEstimatedTokenCountFromPrompt (file:///node_modules/@langchain/openai/dist/chat_models.js:589:34)
at ChatOpenAI._generate (file:///node_modules/@langchain/openai/dist/chat_models.js:536:49)
at process.processTicksAndRejections (node:internal/process/task_queues:95:5)
at async Promise.allSettled (index 0)
Failed to calculate number of tokens, falling back to approximate count Error: Unknown model
at getEncodingNameForModel (file:///node_modules/js-tiktoken/dist/chunk-HXW345QN.js:224:13)
at encodingForModel (file:///node_modules/@langchain/core/dist/utils/tiktoken.js:19:24)
at ChatOpenAI.getNumTokens (file:///node_modules/@langchain/core/dist/language_models/base.js:174:40)
at file:///node_modules/@langchain/openai/dist/chat_models.js:643:42
at process.processTicksAndRejections (node:internal/process/task_queues:95:5)
at async Promise.all (index 0)
at async ChatOpenAI.getNumTokensFromMessages (file:///node_modules/@langchain/openai/dist/chat_models.js:641:33)
at async ChatOpenAI.getEstimatedTokenCountFromPrompt (file:///node_modules/@langchain/openai/dist/chat_models.js:589:23)
at async ChatOpenAI._generate (file:///node_modules/@langchain/openai/dist/chat_models.js:536:38)
at async Promise.allSettled (index 0)
Failed to calculate number of tokens, falling back to approximate count Error: Unknown model
at getEncodingNameForModel (file:///node_modules/js-tiktoken/dist/chunk-HXW345QN.js:224:13)
at encodingForModel (file:///node_modules/@langchain/core/dist/utils/tiktoken.js:19:24)
at ChatOpenAI.getNumTokens (file:///node_modules/@langchain/core/dist/language_models/base.js:174:40)
at file:///node_modules/@langchain/openai/dist/chat_models.js:623:35
at Array.map ()
at ChatOpenAI.getNumTokensFromGenerations (file:///node_modules/@langchain/openai/dist/chat_models.js:617:64)
at ChatOpenAI._generate (file:///node_modules/@langchain/openai/dist/chat_models.js:537:53)
at process.processTicksAndRejections (node:internal/process/task_queues:95:5)
at async Promise.allSettled (index 0)
at async ChatOpenAI._generateUncached (file:///node_modules/@langchain/core/dist/language_models/chat_models.js:114:25)
Description
Hi, OpenAI just released GPT-4o and langchain needs to be updated to support this model. At the moment, it throws the above error when using the new model.
System Info
Langchain: 0.1.37
Platform: macos
Node: 21.7.3
Yarn: 4.2.2

It seems that js-Tiktoken does not yet support gpt-4o, but I found the implementation in their repository:
https://github.com/dqbd/tiktoken/blob/a7cce9922b10bca567be8453f1ef0489428fa02f/js/src/core.ts#L277C4-L280C6
But they haven’t released a new version yet, I will submit a pr to solve this problem after they release it

#5354
This is the same issue, please wait for the new version of langchainjs.

When would the update be released?

NEED THE UPDATE TO BE RELEASED........

@jacoblee93 Any ETA on the new release?

Hey the fix should be live if you update to the latest @langchain/core! The tiktoken dep is there.

https://x.com/philippec_me/status/1790142477141147884?s=46&t=pBOviutddr6W7u_gV0VQqg

Hi,
Now, It's working for me!
Thanks!

Thank you, it works now!

guys I have only installed langchain npm. does this install langchain core?

guys I have only installed langchain npm. does this install langchain core?

It does as a dependency, yes. See this guide:
https://js.langchain.com/v0.2/docs/how_to/installation/#installing-integration-packages

@jacoblee93 even though I have updated to version 0.2.6, I still get weird context length errors which are not valid, as the context len for gpt-4o is 128k.
I have also raise an issue here. https://github.com/langchain-ai/langchainjs/issues/5888
it seems to start at version of langchain 0.1.37


Self update from 1.15.0 to 1.15.4 downloaded brew tarball instead of correct binary

❯ rtx self-update
Checking target-arch... linux-x64
Checking current version... v1.15.0
Checking latest released version... v1.15.4
New release found! v1.15.0 --> v1.15.4
New release is compatible

rtx release status:
  * Current exe: "/home/kachick/.local/share/rtx/bin/rtx"
  * New exe release: "rtx-brew-v1.15.4-linux-x64.tar.xz"
  * New exe download url: "https://api.github.com/repos/jdxcode/rtx/releases/assets/96749020"

The new release will be downloaded/extracted and the existing binary will be replaced.
Do you want to continue? [Y/n] Y
Downloading...
[00:00:00] [========================================] 4.28 MiB/4.28 MiB (0s) Done
Extracting archive... Done
Replacing binary file... Done
Updated rtx to 1.15.4
_rtx_hook:2: exec format error: /home/kachick/.local/share/rtx/bin/rtx

❯ which brew
brew not found

❯ deno run --allow-env --allow-read npm:file-type-cli "$XDG_DATA_HOME/rtx/bin/rtx"
xz
application/x-xz

❯ curl https://api.github.com/repos/jdxcode/rtx/releases/assets/96749020 | jq .browser_download_url
"https://github.com/jdxcode/rtx/releases/download/v1.15.4/rtx-brew-v1.15.4-linux-x64.tar.xz"

I have manually replaced $XDG_DATA_HOME/rtx/bin/rtx with https://github.com/jdxcode/rtx/releases/download/v1.15.4/rtx-v1.15.4-linux-x64. This issue is just a report.

apologies to people that get affected by this, I realize it's frustrating but there isn't a good way to fix the clients broken in the wild unless I used a different (poor) naming convention for the brew archives forever

Thanks for your quick response! ⚡


macbook error wallet failed to connect to daemon (http://localhost:18333).

logs:
2023-Aug-16 10:04:06.531929 INFO    [simplewallet] FEPC wallet v1.4.2-20230408 (#initrelease)
2023-Aug-16 10:04:06.532691 INFO    [simplewallet] DYNEX NEUROMORPHIC COMPUTING PLATFORM
2023-Aug-16 10:04:06.532814 INFO    [simplewallet] -------------------------------------
2023-Aug-16 10:04:12.386256 ERROR   [simplewallet] Failed to initialize wallet
2023-Aug-16 10:04:15.883737 INFO    [simplewallet] FEPC wallet v1.4.2-20230408 (#initrelease)
2023-Aug-16 10:04:15.884592 INFO    [simplewallet] DYNEX NEUROMORPHIC COMPUTING PLATFORM
2023-Aug-16 10:04:15.884717 INFO    [simplewallet] -------------------------------------
2023-Aug-16 10:04:22.173416 ERROR   [simplewallet] Error: Error connecting to the remote node: TcpConnector::connect, getsockopt failed, result=36, Operation now in progress
2023-Aug-16 10:04:22.173704 ERROR   [simplewallet] Error: Failed to parse remote node response
2023-Aug-16 10:04:24.020904 ERROR   [simplewallet] Error: wallet failed to connect to daemon (http://localhost:18333).
2023-Aug-16 10:04:24.021346 ERROR   [simplewallet] Error: failed to load wallet: wallet file '123.wallet' is not found
2023-Aug-16 10:04:24.021557 ERROR   [simplewallet] Failed to initialize wallet
2023-Aug-16 10:04:24.021810 INFO    [BlockchainSynchronizer] Stopping...
2023-Aug-16 10:04:24.022012 INFO    [BlockchainSynchronizer] Stopped
2023-Aug-16 10:04:24.022463 INFO    [BlockchainSynchronizer] Stopping...
2023-Aug-16 10:04:24.022566 INFO    [BlockchainSynchronizer] Stopped
2023-Aug-16 10:04:24.022640 INFO    [BlockchainSynchronizer] Stopping...
2023-Aug-16 10:04:24.022717 INFO    [BlockchainSynchronizer] Stopped
2023-Aug-16 10:04:29.022754 ERROR   [simplewallet] Error: wallet failed to connect to daemon (http://localhost:18333).
input:
Specify wallet file name (e.g., wallet.bin).
Wallet file name: 123
2023-Aug-16 10:07:58.252977 ERROR   Error: Error connecting to the remote node: TcpConnector::connect, getsockopt failed, result=36, Operation now in progress
2023-Aug-16 10:07:58.253267 ERROR   Error: Failed to parse remote node response
password: ***
confirm password: ***
2023-Aug-16 10:08:02.950345 ERROR   Error: wallet failed to connect to daemon (http://localhost:18333).
2023-Aug-16 10:08:02.950606 INFO    Consumer added, consumer 0x7fb3e8005560, count 1
2023-Aug-16 10:08:02.951256 INFO    Starting...
2023-Aug-16 10:08:02.951440 INFO    Removing outdated pool transactions...
2023-Aug-16 10:08:02.952183 INFO    Stopping...
2023-Aug-16 10:08:02.952350 INFO    Retry in 5 seconds...
2023-Aug-16 10:08:02.952481 INFO    Stopped
2023-Aug-16 10:08:02.952590 INFO    Saving...
2023-Aug-16 10:08:02.952678 INFO    Saved
2023-Aug-16 10:08:02.999653 INFO    Starting...
2023-Aug-16 10:08:03.000018 INFO    Removingdyld[26378]:  missing symbol called
outdated pool [1]    26378 abort      ./simplewallet

Please release the  FEPC online Wallet or Desktop wallet, so that everyone can transfer and store their tokens or please list FEPC coins to other CEX as soon as possible.


Revert to older chat system

The problem with this new chat system is that it bypasses any server mod. The only option is to simply give a copy of the message to the server mod - SS still handles the message itself and sends it to the specified clients. This disallows any chat-modifying plugins such as CensorIt ( http://dev.bukkit.org/server-mods/censorit/ ) to work at all - if it does happen to modify a message, it is then resent from Bukkit itself, and as a new copy, causing duplicated posts.
I would like to see an option to revert to the old chat system, which I believe processed the messages after they passed through the server (since I was able to censor chat before (with a different plugin at the time) and still keep the SS group titles).
As a secondary request, I would like to have some type of chat censoring feature included in SS. The API of CensorIt is publicly available (see link), so this may be able to give you some help in creating one. [It is indeed a very great plugin that is more than just a word filter, it also can search for and detect any possible attempts to work around it, as well as an option to verify words with an online dictionary.]

A plugin system for SimpleServer would be nice. Several of the current features (like !myarea) could be moved to plugins to make the base wrapper more lightweight. Perhaps even the new chat system could be outsourced to a plugin.

Just to clarify my position in the last reply. I'm not trying to form a coup and new product. I am just willing to get a group of people willing to experiment with the idea as a separate branch and, hopefully, get it merged back in once it seems acceptable.

As you might have noticed dola and I stopped actively developing SimpleServer some time ago. If you (@kevinpeno and @bryanburke) are willing to build a plugin system, or actually any other addition to the current software, I'll gladly grant you access to the SimpleServer organization. If you want to discuss anything, we're in our IRC Channel almost all day ;)

Could you PM us with the IRC channel? Also, don't forget about @ravngr, as he also seems to have an interest in the project.

I think the channel is in the README or at least the forum. But it's #SimpleServer on irc.euirc.net if you're too lazy ;)
I already contacted @ravngr and added him to the contribution team.

@sadimusi I'd be glad to help where I can. First place I'd probably attack would be organizing issues and, hopefully, updating the wiki.


feat: move docs image to resource dir

Signed-off-by: Gaius<EMAIL_ADDRESS>
Description

Move docs image to resource dir.

Related Issue

Motivation and Context

LGTM


Migrate geniushub to new climate schema

Breaking Change:
None known.
Description:
Migrates geniushub to the new climate schema as per #23899. Bumps client.
NB, also adds:

PRESET_ACTIVITY to climate/const.py - used when target temp is maintained according to a movement sensor.

Related issue (if applicable): N/A
Pull request with documentation for home-assistant.io (if applicable): N/A
**Example entry for configuration.yaml (if applicable): N/A
Checklist:

[x] The code change is tested and works locally.
[x] Local tests pass with tox. Your PR cannot be merged unless tests pass
[x] There is no commented out code in this PR.
[x] I have followed the development checklist

If user exposed functionality or configuration variables are added/changed:

[x] Documentation added/updated in home-assistant.io

If the code communicates with devices, web services, or third-party tools:

[x] The manifest file has all fields filled out correctly. Update and include derived files by running python3 -m script.hassfest.
[x] New or updated dependencies have been added to requirements_all.txt by running python3 -m script.gen_requirements_all.
[x] Untested files have been added to .coveragerc.

If the code does not interact with devices:

[x] Tests have been added to verify that the new code works.

@zxdavb can you rebase the PR?

@pvizeli I think this is ready to merge into climate-1.0 - it is failing QI, but I don't think the fault is with this PR..  Does @MartinHjelmare want to review it first?

Sorry, a bit of a refactor - but it looks beautiful now.

@pvizeli I think this is ready to go - QI is failing, but caused by other integrations.


Flag / Highlight comments (enhancement)

Enhancement request, not issue - if you're not after suggestions please close.
Caffeine's comment handling is poor; if a lot of chat is happening the caster can be responding to a comment that viewers can no longer see.
In the Caffeinated chat module, as well as respond, upvote or open-link add another option that can flag & extract the message in some way such that it can then be isolated and inserted into OBS as a discrete Scene Source where it can be used in an overlay.
Coupled with a configurable timer for the flag this would give a caster more control over the duration any given comment would be displayed & visible to viewers.

Enhancement request, not issue - if you're not after suggestions please close.

We're always after suggestions :)

Caffeine's comment handling is poor; if a lot of chat is happening the caster can be responding to a comment that viewers can no longer see.

In the Caffeinated chat module, as well as respond, upvote or open-link add another option that can flag & extract the message in some way such that it can then be isolated and inserted into OBS as a discrete Scene Source where it can be used in an overlay.

Coupled with a configurable timer for the flag this would give a caster more control over the duration any given comment would be displayed & visible to viewers.

Definitely will implement this, not only useful for Caffeine but other sites as well.
Not going to give a deadline for this, but it will definitely happen before 1.2.

Upon rethought, I don't think we'll do this. Sorry, but there's a lot to implement.


Test to verify that rules have documentation

It should:

Get the list of core rules under rules.
Verify there is a doc file for it.
Verify that docs/user-guide/rules/index.md has a link to it.

With the new architectural changes, this is no longer an issue. The documentation for the rules is now their README.md file.


Can establish downchannel stream but failed to connect to AVS

I worked with SDK1.2 and had a problem. When I run sampleApp, I can get the auth token and down channel stream can be established, but there has no signal for down channel stream is connected, so timeout and failed to connect to AVS. Anybody can tell me ConnectionStatusObserverInterface::Status::CONNECTED status is emitted by which root event?
Below is what I looking into the code, is that correct?
setConnectionStatusLocked(
        ConnectionStatusObserverInterface::Status::CONNECTED,
        ConnectionStatusObserverInterface::ChangedReason::ACL_CLIENT_REQUEST);

called in MessageRouter::onConnected()
called in HTTP2Transport::notifyObserversOnConnected()
called in HTTP2Transport::setIsConnectedTrueUnlessStopping()
called in HTTP2Transport::onPostConnected()
called in PostConnectSynchronizer::notifyObservers()
called in PostConnectSynchronizer::onSendCompleted(MessageRequestObserverInterface::Status status)
called in MessageRequest::sendCompleted(avsCommon::sdkInterfaces::MessageRequestObserverInterface::Status status)
called in HTTP2Stream::notifyRequestObserver()
called in ???
Below is log:
Running app with log level: DEBUG9
2017-11-09 15:11:16.759 [  1] I ConfigurationNode:initializeSuccess

Trying <IP_ADDRESS>...
TCP_NODELAY set
2017-11-09 15:11:17.134 [  1] I AlertScheduler:executeScheduleNextAlertForRendering::no work to do.
2017-11-09 15:11:17.181 [  1] I DirectiveRouter:addDirectiveHandlers:action=added,namespace=SpeechSynthesizer,name=Speak,handler=0x55eaec0,policy=BLOCKING
2017-11-09 15:11:17.181 [  1] I DirectiveRouter:addDirectiveHandlers:action=added,namespace=AudioPlayer,name=ClearQueue,handler=0x55ebd50,policy=NON_BLOCKING
2017-11-09 15:11:17.181 [  1] I DirectiveRouter:addDirectiveHandlers:action=added,namespace=AudioPlayer,name=Stop,handler=0x55ebd50,policy=NON_BLOCKING
2017-11-09 15:11:17.181 [  1] I DirectiveRouter:addDirectiveHandlers:action=added,namespace=AudioPlayer,name=Play,handler=0x55ebd50,policy=NON_BLOCKING
2017-11-09 15:11:17.181 [  1] I DirectiveRouter:addDirectiveHandlers:action=added,namespace=SpeechRecognizer,name=ExpectSpeech,handler=0x55e8100,policy=NON_BLOCKING
2017-11-09 15:11:17.181 [  1] I DirectiveRouter:addDirectiveHandlers:action=added,namespace=SpeechRecognizer,name=StopCapture,handler=0x55e8100,policy=NON_BLOCKING
2017-11-09 15:11:17.181 [  1] I DirectiveRouter:addDirectiveHandlers:action=added,namespace=Alerts,name=DeleteAlert,handler=0x55edda0,policy=NON_BLOCKING
2017-11-09 15:11:17.181 [  1] I DirectiveRouter:addDirectiveHandlers:action=added,namespace=Alerts,name=SetAlert,handler=0x55edda0,policy=NON_BLOCKING
2017-11-09 15:11:17.181 [  1] I DirectiveRouter:addDirectiveHandlers:action=added,namespace=System,name=SetEndpoint,handler=0x55f43f0,policy=NON_BLOCKING
2017-11-09 15:11:17.181 [  1] I DirectiveRouter:addDirectiveHandlers:action=added,namespace=System,name=ResetUserInactivity,handler=0x55e73d8,policy=NON_BLOCKING
2017-11-09 15:11:17.181 [  1] I DirectiveRouter:addDirectiveHandlers:action=added,namespace=Speaker,name=SetMute,handler=0x55f1c30,policy=NON_BLOCKING
2017-11-09 15:11:17.181 [  1] I DirectiveRouter:addDirectiveHandlers:action=added,namespace=Speaker,name=AdjustVolume,handler=0x55f1c30,policy=NON_BLOCKING
2017-11-09 15:11:17.181 [  1] I DirectiveRouter:addDirectiveHandlers:action=added,namespace=Speaker,name=SetVolume,handler=0x55f1c30,policy=NON_BLOCKING
2017-11-09 15:11:17.181 [  1] I DirectiveRouter:addDirectiveHandlers:action=added,namespace=TemplateRuntime,name=RenderPlayerInfo,handler=0x55f2840,policy=NON_BLOCKING
2017-11-09 15:11:17.181 [  1] I DirectiveRouter:addDirectiveHandlers:action=added,namespace=TemplateRuntime,name=RenderTemplate,handler=0x55f2840,policy=NON_BLOCKING
Connected to api.amazon.com (<IP_ADDRESS>) port 443 (#0)
ALPN, offering h2
ALPN, offering http/1.1
successfully set certificate verify locations:
CAfile: none
CApath: D:/AVS/latinumamazoncom.crt
SSL connection using TLSv1.2 / ECDHE-RSA-AES128-GCM-SHA256
ALPN, server accepted to use http/1.1
Server certificate:
subject: C=US; ST=Washington; L=Seattle; O=Amazon.com, Inc.; CN=api.amazon.com
start date: Sep 11 00:00:00 2017 GMT
expire date: Jun 11 23:59:59 2018 GMT
subjectAltName: host "api.amazon.com" matched cert's "api.amazon.com"
issuer: C=US; O=Symantec Corporation; OU=Symantec Trust Network; CN=Symantec Class 3 Secure Server CA - G4
SSL certificate verify result: unable to get local issuer certificate (20), continuing anyway.

POST /auth/o2/token HTTP/1.1
Host: api.amazon.com
Accept: /
Content-Length: 757
Content-Type: application/x-www-form-urlencoded

upload completely sent off: 757 out of 757 bytes
< HTTP/1.1 200 OK
< Server: Server
< Date: Thu, 09 Nov 2017 15:11:17 GMT
< Content-Type: application/json
< Content-Length: 1233
< Connection: keep-alive
< x-amzn-RequestId: 3c23c95c-c560-11e7-9e33-397e2172bed8
< X-Amz-Date: Thu, 09 Nov 2017 15:11:17 GMT
< Cache-Control: no-cache, no-store, must-revalidate
< Pragma: no-cache
< Vary: Accept-Encoding,User-Agent
<
Connection #0 to host api.amazon.com left intact
endpoint: https://avs-alexa-eu.amazon.com
#############################

Connecting...
#############################

Trying <IP_ADDRESS>...
TCP_NODELAY set
Connected to avs-alexa-eu.amazon.com (<IP_ADDRESS>) port 443 (#0)
ALPN, offering h2
ALPN, offering http/1.1
successfully set certificate verify locations:
CAfile: none
CApath: D:/AVS/latinumamazoncom.crt
SSL connection using TLSv1.2 / ECDHE-RSA-AES256-GCM-SHA384
ALPN, server accepted to use h2
Server certificate:
subject: C=US; ST=Washington; L=Seattle; O=Amazon.com, Inc.; CN=latinum-eu.amazon.com
start date: Mar 20 00:00:00 2017 GMT
expire date: Apr  8 23:59:59 2018 GMT
subjectAltName: host "avs-alexa-eu.amazon.com" matched cert's "avs-alexa-eu.amazon.com"
issuer: C=US; O=Symantec Corporation; OU=Symantec Trust Network; CN=Symantec Class 3 Secure Server CA - G4
SSL certificate verify result: unable to get local issuer certificate (20), continuing anyway.
Using HTTP2, server supports multi-use
Connection state changed (HTTP/2 confirmed)
Copying HTTP/2 data in stream buffer to connection buffer after upgrade: len=0
Using Stream ID: 1 (easy handle 0x57aa710)

GET /v20160207/directives HTTP/2
Host: avs-alexa-eu.amazon.com
Accept: /
Authorization: Bearer Atza|IwEBIN9oYgk-CqX4Ldvou1-UjZGBbUtrxocklfPdPDuy7d7P8jG-2qlStWK3K-627lVDsJfutoi6YHduVl2x5MfxacGfzFj-aPs_onz1XaGycyWF57a7lPVRbqqZdHKVv3lCRsfBqd6y2XdKTWe26TE9CAMTNnUzRgEgHbnquX6hVfkRmPAapGuxlo9rafpivxnJ-TLlIyGZIIUOawGrwOGIRS877ZPH0sbVDqCN0wh08rq54ZVjc4PQ5-HI4wAtiuD7TONQyLm01K_SU8sFXAazCksMcfea_g5Nk4rwHzyA4m2lUrwGUl9smpnmWtIg8Gt6MZ_H67ZF-3BISKPHLvshHnP6HXXnXAl41lpNI3q0lbb7KXg3Jn1SHNFVVkMiX1fduxT9INr8dlGNOQsaKYEePWkHfnegOOQEoZI6pzDRV1GvCcJbXPp6Bs15MXP-FGObTpX8BQFdmSArRAtxhuJnldgXRg6eRrBPBTmfqjelDwKby1292PSakj6rFvQLAe4UzNDJJ4II6o_GeYQqb8zslabQyecDlVL9nwi5ikwHyRqfmPBEraDVTp_HzstTlKJ6qI4

Connection state changed (MAX_CONCURRENT_STREAMS updated)!
< HTTP/2 200
< access-control-allow-origin: *
< x-amzn-requestid: 062cb6fffe51838a-0000274e-000315db-74e03fe3a78fee0f-64b2a4d4-1
< content-type: multipart/related; boundary=------abcde123; type=application/json
<
2017-11-09 15:11:28.776 [  2] E ContextManager:updateStatesLoopFailed:reason=stateProviderTimedOut
2017-11-09 15:11:28.776 [  2] E PostConnectSynchronize:contextRetrievalFailed:reason=contextRequestErrorOccurred,error=STATE_PROVIDER_TIMEDOUT
Failed to connect to AVS!
2017-11-09 15:11:38.762 [  1] I DirectiveSequencer:doShutdown

Hello yangtu222,
It looks like the connection to LWA and AVS are working correctly, but the client is timing out creating a context to send an initial System.SychronizeState message.
You will probably need to enable full debug logging (-DCMAKE_BUILD_TYPE=DEBUG on the cmake command line, and DEBUG9 on the SampleApp command line) to provide enough information to determine what is holding up the creation of the context.  Can you reproduce this problem with full logging enabled at attach the log (as a file rather than inline would be preferred)?
Thank you,
-SWH

Hi, scotthea-amazon,
Thanks for mention System.SychronizeState message, I will dig this info into source to check where I missed.
BTW, the log in the question is already being built using -DCMAKE_BUILD_TYPE=DEBUG and using DEBUG9 in command line. It would be appreciated if you can go through the log to check what I lost.

Hello yangtu222,
I was assuming that you had not used -DCMAKE_BUILD_TYPE=DEBUG or DEBUG9 because I did not see any log lines other than INFO and ERROR level.  I do see the DEBUG9 at the start of the log you provided, which I missed before.
Perhaps I will be able to see  more if you attach the whole log.
Thank you,
-SWH

Hi, scotthea-amazon,
I finally find the root cause of my problem. It is due to there has a thread pause in my porting mediaplayer function to request Speaker.VolumeState of the beginning. Because of this, the m_pendingOnStateProviders of ContextManager cannot be empty and the context cannot be sent to PostConnectSynchronizer as ContextAvailable, and the HTTP2Transport cannot be marked as connected.
Thanks for tips of System.SychronizeState, so that I changed my dirction from network to ContextManager that I finally find my root cause.
yangtu222.

Hello yangtu222,
I am very glad to hear you tracked this down!  Thank you for letting us know.
Regards,
-SWH


getting list of buckets

Hi,
I'd love to be able to get a list of all available buckets in R. This should be possible trough the bucketsAPI of influxdb, right? Are there any plans to implement this into this package?
Thanks alot,
Jan

You can send a simple buckets() flux script to get a list of buckets in a qury result

Thanks, that worked after setting POSIXxtCol to NULL :)
I still get a warning though:
#datatype string long string string string string long
The following from values were not present in x: nanotime
Is there a way to get rid of that?

I'm afraid that at the moment you will have to use warning suppression to get rid of that.
The "issue" here is missing warn_missing = FALSE param at https://github.com/influxdata/influxdb-client-r/blob/2752509d8a1ad78c6b3bda385c8c6b33264c9d45/R/influxdb_client.R#L407
And the "#datatype... line in output is forgotten message() call at https://github.com/influxdata/influxdb-client-r/blob/2752509d8a1ad78c6b3bda385c8c6b33264c9d45/R/influxdb_client.R#L382

The above mentioned annoyances are addressed in PR #31

Closed via #31


LR image generation

Hi Fritsche:
I've got a question about training the DSGAN from SCRATCH,
whether the LR images are directly generated by bicubic downsampling , and if crop is used.
Thanks

Is it  just bicubic from fig.3 in the paper?

We randomly crop each training image to get images of the size 512x512. Using these cropped images, we generate the LR image by downsampling them, which results in 128x128 images. The discriminator image is created by simply cropping a 128x128 image.


Request to provide source code of EspressIf's hostap fork (IDFGH-592)

Environment

Development Kit:      [none]
Module or chip used:  [ESP32]
IDF version (run git describe --tags to find it): v3.3-beta1-268-g5c88c59
Build System:         [Make]
Compiler version (run xtensa-esp32-elf-gcc --version to find it): 1.22.0-80-g6c4433a
Operating System:     [Linux]
Power Supply:         [external 5V]

Problem Description
This is rather a request for improvements than an issue.
After working with ESP-IDF for a while I've discovered, debugged and presented solutions to a number of Wi-Fi related errors. With EspressIf's fork of the hostap open source project included only as binary object files it's hard/impossible to debug and solve problems still present.
For example EAP reauthentication still does not work with latest ESP-IDF from master:
https://github.com/espressif/esp-idf/issues/2324
From the code that you do provide (components/wpa_supplicant) I've noticed that the code is based on a rather old version of wpa_supplicant. Over the years plenty of security fixes including fixes for the infamous KRACK attacks have been applied to the mainline hostap. Without the source code it is not possible to tell what's been integrated in EspressIf's fork of hostap making this not only a nice to have thing but also a security concern for projects built on top of ESP-IDF.
In short this issue is a request for you to prove the full source code of EspressIf's hostap (wpa_supplicant/hostapd) fork.

In issue https://github.com/espressif/esp-idf/issues/3002 @liuzfesp told me you are going to release your wpa_supplicant as open source during 2019. That's great news!
Oh and for the record I should also mention that I've read your official statement about KRACK's been handled already:
https://www.espressif.com/en/media_overview/news/espressif-releases-patches-wifi-vulnerabilities-cert-vu228519
Do you want me close this issue or can we keep it as a placeholder for the upcoming release of wpa_supplicant source code?

@liuzfesp Are you able to share any details about progress and time plan for this request?

@mikaelkanstrup, current IDF contains almost all of ESP supplicant. I have to say ESP supplicant is very different than the official wpa_supplicant, it doesn't contains everything in official wpa_supplicant.

@liuzfesp Thanks! I guess I was expecting to find some more EAP state related code and while quickly searching through the code I didn't notice that in fact quite some additional parts are available since:
c13968302 supplicant/esp_wifi: move supplicant to idf

@mikaelkanstrup Almost all the part of supplicant and EAP state machine inherited from supplicant is already in IDF now. Code flow to act based on authentication result is still closed and will remain so in near future. We do not support "enterprise mode" with SoftAP mode yet. Closing this issue. Feel free to reopen in case of further doubts/requests.


[TC-CADMIN-1.5] Yaml script needs tobe updated.

Summary :

In TC-CADMIN-1.5 step 5 needs to be updated as per the verfication step  document.
After step-6 Test-case failed with the error: Test Step Error: Error occurred during execution of test case TC-CADMIN-1.5. no close frame received or sent

Reference Log:
tc-cadmin-1-5dut.txt
cadmin-1-5.log

TC- cadmin-1.9 also failed with the same error.

Reference log:
cadmin-1-9.log
tc-cadmin-1-9dut.txt

Raised issued for the samein certication-tool repo https://github.com/project-chip/certification-tool/issues/120


[event-hubs] adds IoT Hub connection string conversion with websockets sample

As requested by https://github.com/Azure/azure-sdk-for-js/pull/7060#issuecomment-887480513, this PR adds a sample for converting an IoT Hub connection string to an Event Hubs-compatible connection string using web sockets.
The main difference is that websocket options need to be specified, and the port used to connect changed from 5671 to 443.
/cc @wiegvlieg - let me know if this helps or if you have more questions about how this sample works.

@chradek, I just tested this against out IoT Hub and it works like a charm!
Thanks for the tip on the websocket options and great that you even want to add this as an extra example.

Glad to hear it worked for you! And thanks for asking about the sample to begin with. I'm sure there are others who will have the same question :smile:


[#1206] Pick up Aggregate Cache in (Spring Auto) Configuration

This pull request resolves #1206

Hmm, I see that commit 90275fc introduces the same functionality as this pull request. So I propose to close this one.

@quaso your PR still brings the Spring configuration idea to the @Aggregate annotation, something which isn't introduced in the referenced commit. I believe that @abuijze only commented on the AggregateConfigurer default cache approach, not on the addition to the annotation.
In short, I think it's still fair to introduce. Assuming you are up for resolving the conflicts too.


Add Type to Depends return value

Now whenever you use the injection dependency mechanism of fast api you're are getting an Any. This is error prompt.
So instead of that, you can infer the return type given the return type of the provider function. That way the injection mechanism will be more robust.

Hi @noxy19,
I guess your PR is similar to mine #5856

@uriyyo yes, why didnt your PR got accepted?

You can do the same like this right?
from typing import Annotated

from fastapi import Depends

CommonsDep = Annotated[dict, Depends(common_parameters)]

Thanks for the interest! Now you can (and probably should) use Annotated, that should simplify a lot of it.
Also, this would probably have a conflict with dependency classes :thinking:
Anyway, it's probably better to start in a GitHub Discussion, filling the form, so we can get all the information and the minimal self contained example to understand your use case and be able to help you better.
For now, I'll pass on this one, but thanks for the effort! :coffee:


High number of values are not accurate

I updated my projects version to 1.2.0 and checked the values for a number of users top plays against what's showing on the website.
Only 5% of the amount of scores I looked at were even within 1/2pp of their real values. Will you be tweeking stuff to try and align closer to real values?

I can second this. However, the test suite was manually adjusted to allow for a deviation of ± 6%. This is the new official spec. I'd love to have it a tad more accurate as well though.

1-2pp error is only feasible if the star rating calculation was 1:1 with the official osu client (thats what I get when I override star rating with the values from osu-tools), so that's supposed to be rare. with the current test suite, the average error is +- 0.6%. theres a few slider heavy maps that will be way off and ill think about an approximation of sliders to fix that if it doesn't add too much code. either way i think it's reasonably accurate except for slider maps

also do remember that the website isn't necessarily fully recaulcated. when i created the test suite i had to limit it to only a few players that i knew were recalculated. some scores still use old pp so maybe that's what you're seeing

https://osu.ppy.sh/beatmapsets/538998#osu/1514898
This for example, my score on my profile is my 7th best performance at 410pp. The calculation is returning 390.36pp as you can see here:

This map has been updated as before this score was worth 390pp, this is one of the more extreme but i think only 2 of my top 10 have the correct value being returned.

Why would you need PP calculations for top plays though? The osu! API returns them on the user_best endpoint. PP calculations should only be used for when the API can't deliver them, e.g. recent scores or manual recalculations for added mods / changed accuracies and for those usecases I think that the deviation is good enough as is. It's a bit nitpicky at this point. It isn't a possibility right now to easily make the library any better without guesstimating stuff.

I changed it for the purpose of this point - yes for top plays I take the API value but if this was a recent play it would be wrong, 11pp difference isn't a small enough to be ignored imo. I appreciate it seems nitpicky but people love to complain about incorrect values.
I checked if I was passing values incorrectly but it doesn't seem to be the case and I use the same format as is in the usage README

I understand that, it sucks that the Xexxar rework made the missing slider tick calculations even worse, but it's unfortunately the current state of things. The .osu files needed for PP calculations only offer the slider's lazy values, not the real tick values needed. The only thing that can be done at this point is to use approximations and guesstimate certain values depending on the slider density of a map. The more sliders a map has the more vague the new PP calculations are 😞

well i will experiment with slider approximations, it will probably take a while to get it right
if you need truly accurate values for the time being you can take the star ratings from osu-tools, but that's an order of magnitude slower than any implementation of oppai

Thanks @Francesco149

I have just pushed a patch by @McKay42 that greatly improves accuracy. it's not gonna fix stuff like deal with the devil and other slider heavy maps but normal maps should have 2x-3x less error now

Testing on my top scores it looks great. Thanks @Francesco149


ValueError: Unsloth: Untrained tokens found, but embed_tokens & lm_head not trainable, causing NaNs. when finetuning llama3 on customed dataset

I want to implement llama 3 on multi-turn dialogue task, so I was trying to finetune it on one of my customed dataset, which is made by simply extracting all the dialogue contents from soda and reformat it to be in llama3's chat template.
I think the result of this dataset is quite similar to the original. But when I tried to train the model, I got the following error:
ValueError: Unsloth: Untrained tokens found, but embed_tokens & lm_head not trainable, causing NaNs. Restart then add `embed_tokens` & `lm_head` to `FastLanguageModel.get_peft_model(target_modules = [..., "embed_tokens", "lm_head",])`

As it indicates, it's because there're some untrainable tokens. Though this bug can be fixed by following the hints, I just can't figure out why I introduced those untrainable tokens. After all, the two datasets (my customed one and the default one in the notebook) look very similar.
Here is a link to the colab, feel free to comment and give advice!

I started to get this error today too, strange...

Try using the Instruct model ("unsloth/llama-3-8b-Instruct-bnb-4bit").

@sumukshashidhar @liwd190019 Apologies! As @dmitrii-palisaderesearch mentioned, please use the instruct version - using the base version will error out, since Unsloth does auto checking if some tokens are all 0s - if you still want to use the base model, either do not use the llama-3 chat template (just use Alpaca), or train on lm_head and embed_tokens

@danielhanchen Do you know why it started to throw this error while It was successfully training with the base model a couple of months ago?

I added a check in Unsloth to check if your embeddings are untrained - I might have to change the logic actually

any update on this, am getting this error too for a base model?

@danielhanchen i am getting this error, i was following your notebook on instruct finetuning (am not using base model). my dataset contains latex symbols. is that so?
what do you mean by: "if your embeddings are untrained"?
my data is generated fromllama only.

Same here. I was finetuning QLoRA normally, then I use Unsloth to continue the training with longer context.
There are 2 probloems actually:
1. Can not resume using UnslothTrainer:
from trl import SFTTrainer
        from transformers import TrainingArguments
        from unsloth import is_bfloat16_supported
        from unsloth import UnslothTrainer, UnslothTrainingArguments

from unsloth import FastLanguageModel
max_seq_length = data_args.max_seq_length
dtype = torch_dtype
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = training_args.pretrain_qlora_path,
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,

)

unsloth_args = UnslothTrainingArguments(
          per_device_train_batch_size = training_args.per_device_train_batch_size,
          gradient_accumulation_steps = training_args.gradient_accumulation_steps,
          max_steps     = training_args.max_steps,
          save_steps    = training_args.save_steps,
          logging_steps = training_args.logging_steps,
          warmup_steps  = training_args.warmup_steps,
          save_total_limit= training_args.save_total_limit,
          num_train_epochs = training_args.num_train_epochs,
          learning_rate = training_args.learning_rate,
          lr_scheduler_type = training_args.lr_scheduler_type,
          weight_decay = training_args.weight_decay,
          gradient_checkpointing = training_args.gradient_checkpointing,
          embedding_learning_rate = training_args.embedding_learning_rate,
          fp16 = training_args.fp16,
          bf16 = training_args.bf16,
          tf32 = True,
          seed = 3407,
          output_dir = training_args.output_dir,
          dataloader_num_workers = training_args.dataloader_num_workers,
          ddp_find_unused_parameters = training_args.ddp_find_unused_parameters,
          overwrite_output_dir = training_args.overwrite_output_dir,
          ignore_data_skip = training_args.ignore_data_skip, # Important to start Unsloth from datapoint 0
          prediction_loss_only = training_args.prediction_loss_only,
          evaluation_strategy  = training_args.evaluation_strategy
      )

trainer = UnslothTrainer(
    model = model,
    tokenizer = tokenizer,
    args = unsloth_args,
    train_dataset = train_dataset,
    #dataset_text_field = "text",
    max_seq_length = data_args.max_seq_length,
    dataset_num_proc = 8,
    packing = True, # Can make training 5x faster for short sequences.
    optimizers=(optimizer_adamw, lr_scheduler_adamw),
)

resume_ckp = "./QLora_finetune_with_embed_lmhead/checkpoint-1200"
train_result = trainer.train(resume_from_checkpoint=resume_ckp )

--> ValueError: Unsloth: Untrained tokens of [[]] found, but embed_tokens & lm_head not trainable, causing NaNs. Restart then add embed_tokens & lm_head to FastLanguageModel.get_peft_model(target_modules = [..., "embed_tokens", "lm_head",]). Are you using the base model? Instead, use the instruct version to silence this warning.
Any idea why ? I was doing OK a few days back I think ...!
2. Weird warning if resume by Transformers' Trainer instead of UnslothTrainer:
Just a side note, while resuming Ok with Transformer's Trainer, I also got warning RNG file not found although the 2 files "rng_state_0.pth" and "rng_state_1.pth" were there in the checkpoint folder ! The resume will continue to train but God know if the quality is OK or not so we stopped. The entire resume also need to look at please @danielhanchen
Thanks,
Steve

@milsun Apologies I saw you guys also commented on the other issue - hope it got partially resolved - sorry on the delay as well!
@paraschopra It's possible extra symbols might be breaking the tokenizer, but I'm unsure - these can cause NaN gradients, so I error out. Could you open a separate issue so I can look into it more - thanks!
@thusinh1969 Apologies on the delay as well - saw your other issue as well!

I'm having the same issue. Any progress on this?


Browser-provided Accept header not accepted

Hi all,
I have a Chalice-based app that sends an HTML form that allows the user to download a file upon submission. The response content-type is application/octet-stream.
The browser (Firefox) sends the Accept header text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8, which contains the wildcard */*. However, the response from the Chalice app is a JSON response:
{
    "Message": "Request did not specify an Accept header with application/octet-stream, The response has a Content-Type of application/octet-stream. If a response has a binary Content-Type then the request must specify an Accept header that matches.",
    "Code": "BadRequest"
}

I've also tried the request in Postman, setting the Accept header to the wildcard */* explicitly, and I receive the same error. Only when I set the Accept header application/octet-stream does the request succeed.
I would expect the wildcard accept header (and the browser's default) to allow the request to succeed.
Thanks!

I've run into this as well, but I'm not sure if we have any options in chalice to make this work.  This is the behavior of API gateway.  From: http://docs.aws.amazon.com/apigateway/latest/developerguide/api-gateway-payload-encodings-workflow.html

When a request contains multiple media types in its Accept header, API Gateway only honors the first Accept media type. In the situation where you cannot control the order of the Accept media types and the media type of your binary content is not the first in the list, you can add the first Accept media type in the binaryMediaTypes list of your API, API Gateway will return your content as binary. For example, to send a JPEG file using an  element in a browser, the browser might send Accept:image/webp,image/,/*;q=0.8 in a request. By adding image/webp to the binaryMediaTypes list, the endpoint will receive the JPEG file as binary.

Open to suggestions though.

Ah, dang! It's too bad API Gateway has this limitation, as it really reduces the number of possible use cases. Thanks for the info.

Hi. I can get this to work with a non-chalice lambda function.
header = { 'Content-type': 'image/png' }
    return {
           'statusCode' : 200,
          'headers': header,
          'body' : base64.b64encode(data).decode("utf-8"),
          'isBase64Encoded': True
     }

In the API Gateway, I configure the Settings page with a single Binary Types entry of '/' and it works.
However, using Chalice and code like:
return Response(status_code=200,
                    headers=header,
                    body=response_data
                    )

(response_data is a plain old binary sequence)
I've tried setting
app.api.binary_types =['*/*']
And that does set the Settings in API Gateway correctly, but I still get the BadRequest error.
So, in short, it seems like it should be possible as a non-Chalice Lambda handler can return a binary image to a browser GET.


Working grocery list

This fork does not have a any code to support grocery lists.

Going to use this Issue to talk about design of the Grocery list. IMO this very much resembles a TODO list, there are many demos out there for react. Heres one example that I liked:

Demo - http://todomvc.com/examples/react/#/
Source - https://github.com/tastejs/todomvc/tree/gh-pages/examples/react

So my overall plan for this feature has three parts in order of priority:

Viewing and editing a grocery list.
Creating, deleting, and sharing grocery lists.
Adding recipe ingredients to a list.

Viewing and editing the list is pretty much taken care of in the code listed above. I would add some Flux code to incorporate the database into the list and rename the relevant things as well.
For creating, deleting, and sharing, I was thinking about adding a side bar (similar to the mini-browse for the recipe page) to hold the related recipes and the actions to create and delete them.
Logged-in users would get a button on the recipe page that allows them to add all the ingredients to the list. I'm still not 100% sure how I want this to work. going to focus on the first two items first and go from there.
Thoughts @hermzz

All sounds pretty reasonable. Hardest bit will probably be sharing. I'd be inclined to make lists public so that if you want to share the list you can just share the link, without all the complicated auth issues.

Yea, I'm not sure how exactly I want the sharing to work quite yet. In any case we wouldn't need that right away. I've been tinkering around with the styles and a sample below.
The one below is something that I'm looking to implement at first. Just a simple list view.

Here is a mock of what I'm thinking it would look like Once I have multiple list support and sharing


[renovate]Remove golang constraint

The shared global config already has a golang 1.20 constraint

/lgtm


cgroups V2: support available?

Hi!
Systemd is quite ubiquitous nowadays. However, it looks like the cgroups module is not loaded under systemd due to incompatibilities. Now, I see that devlib has a cgroups2 module where the module documentation reads^1 like it may work with systemd.
Is there any way to use this with lisa? There does not seem to be much of documentation on how to use cgroups2 and the code structure looks quite different. So I am not really sure where to get started on integrating it with lisa.

Hi there,
So the current state of affairs is:

cgroups devlib module is basically deprecated. I don't recommend using it at all
cgroups2 is the new module that we added last year. The 2 means it's the successor to cgroups, but it handles both cgroups v1 and v2.
cgroups2 provides a unified API for both v1 and v2, so that the same client code can be used for both. In practice, that API looks like the v2 interface, and does all the legwork to convert this to an equivalent set of v1 hierarchies. The abstraction also lets you manipulate the resulting hierarchy(ies) after the groups are built (e.g. to add a process).
The main limitation of cgroups2 I'm aware of is the lack of abstraction for the freezer feature. In v1, this was a controller, but in v2 it's not a controller anymore (it's the cgroup.freeze core interface file), so this was left for future work.

There is a basic example in the module-level docstring:
https://github.com/ARM-software/devlib/blob/e012b175c672df84cabf5cc22510b2848b91b399/devlib/module/cgroups2.py#L16
The general idea is that you specify how your hierarchy should look like and what controller you want to enable along with their settings, and then you use the setup_hierarchy() method as a context manager to make it happen.
Note that due to how cgroups work (unrelated to devlib or LISA), you basically need a single owner of the hierarchy. If you use systemd, then that is systemd. However, systemd can give you a subtree in which you are free to do what you want, and that is what the cgroups2 devlib module does. The main implication of that is that the cgroups2 API can never guarantee you that the root of your request is the actual root group (also you could run in e.g. a container anyway, so it can never be really guaranteed).
Since that module hasn't been used extensively since it was contributed, let me know if you find any problem with it.

There is a basic example in the module-level docstring:
https://github.com/ARM-software/devlib/blob/e012b175c672df84cabf5cc22510b2848b91b399/devlib/module/cgroups2.py#L16

Hm. I failed to match that to what happened in /home/ablu/projects/lisa/ipynb/deprecated/energy/EnergyModel_ClusterEnergy.ipynb (which I was trying to resurrect). But I will try again tomorrow just using it as drop-in for cgroups[1]. I will probably need to have a second pass on how these module loading magic works :).
Freeze would be nice, but I can just call systemctl freeze if something annoys me for now :).
Thanks a lot!

Hm. I failed to match that to what happened in /home/ablu/projects/lisa/ipynb/deprecated/energy/EnergyModel_ClusterEnergy.ipynb (which I was trying to resurrect).

I see, quite the archeological adventure you have there. The API of cgroups and cgroups2 is quite different and cgroups2 is not a drop-in replacement (otherwise we would just have updated the existing cgroups module). Let me know if you face further difficulties.

I will probably need to have a second pass on how these module loading magic works :).

Basically devlib has some classes that are instantiated and stored as Target attributes. In LISA, we make that process lazy so accessing the attribute triggers the instantiation to avoid initializing modules that won't be used.

You might want to have a look at that, as this notebook dates from the before the deep rework of most LISA APIs:
https://lisa-linux-integrated-system-analysis.readthedocs.io/en/latest/changes.html#transitioning-from-lisa-legacy

Closing as we are migrating the repo to GitLab. New messages on this conversation should be posted at https://gitlab.arm.com/tooling/lisa/-/issues/2172
Note that you can login with your GitHub account on gitlab.arm.com so it should be painless.


How to implement custom stores, for example redis?

How to implement custom stores, for example redis?
I've tried to use the package with redis but seems that I can't decode/encode the values properly:
panic: reflect: call of reflect.Value.Elem on map Value, it's occurs because I'm Mashal/Unmarshal values, but idk how can I safely save it on redis. Looks like here I have src as map not as a proper way to reflect back to dst
package cache

import (
	"encoding/json"
	"time"

	"github.com/go-gorm/caches/v2"
	"github.com/gofiber/fiber/v2"
	"github.com/gofiber/storage/redis"
	"gorm.io/gorm"

	"core/env"
)

type customCacher struct {
	storage fiber.Storage
}

func newCustomCacher(config env.Config) caches.Cacher {
	storage := redis.New(redis.Config{
		URL:      config.RedisURL,
		Database: 3,
	})

	return &customCacher{
		storage: storage,
	}

}

func (c customCacher) Get(key string) *caches.Query {
	val, err := c.storage.Get(key)
	if val == nil || err != nil {
		return nil
	}

	var query caches.Query
	if err = json.Unmarshal(val, &query); err != nil {
		return nil
	}

	return &query
}

func (c customCacher) Store(key string, val *caches.Query) error {

	b, err := json.Marshal(val)
	if err != nil {
		return err
	}

	return c.storage.Set(key, b, 6*time.Hour)
}

func NewGormCacherPlugin(config env.Config) gorm.Plugin {

	return &caches.Caches{
		Conf: &caches.Config{
			Easer:  true,
			Cacher: newCustomCacher(config),
		},
	}
}

The document you expected this should be explained
Custom cacher, with different stores than memory.

Expected answer
A consise way to store in another place the informations

@ktsivkov can you provide some help?

Hey @mirusky
I will have a look at it later. My initial thought/idea is to use some way of Marsh unmarshal of the data passed and retrieved to and from the Cacher.
Three problems need to be addressed -

Detect if we are having primitive type, or slice, or map.
If slice or map - is there a model structure inside.

If so, which type is it, and how to marshal unmarshal.

What happens with the gorm.Model embedded structure, how do we handle it.

Things I would test/try:
If we use json marshal/unmarshal we need to identify the correct unmarshalling destination, and apply json struct tags.
It surely needs some deeper investigation and edge case identification.

MsgPack and JSON, are getting the wrong value in Get method. Since the caches.Query has Dest as interface{}, the decoder doesn't know what are the proper way to decode / what's the concrete type to decode. So almost all the cases it's transformed into a map or a slice of maps.
I've tested with gob, but I need to call gob.Register to all possible types to make it work. Since it's a big repo it's impracticable.
I don't have sure, but can we have the Model it's trying to solve in the Get method? Like:
Get(key string, model interface{})
So in that way we could ensure that I have the correct type to Unmarshal.
Or in SetPointedValue do a full reflection of the value?

Also I'll try to create a small example and attach here.

Any luck with this one?  Would be great to have a solution on how to do it..

@mirusky  @Ahbrown41 Please check https://github.com/go-gorm/caches/releases/tag/v3.0.0 in the readme I have added examples of how to implement Redis caching.
I tried to test it in different ways but it'd still be a good idea to use with some caution. If any critical bugs are found please open issues, contributions are welcomed as well.
For quite some time I was occupied and did not have the time to resolve it.

Looking promising, I've ended up creating a cache in another way ( getting the query as key, and result as value ).
But as you implemented redis here, I'll have a try.


Our logging effort clashes with RhinoCompute

Rhino compute uses Serilog too as their logging platform.
If a user has the Rhino / Grasshopper Connector installed and runs Rhino Compute on that machine, our current logs are merged with their logs resulting in:

us potentially overriding version info in their log
us leaking the hashed userId to their log
rhino compute errors making their way into our logging systems

The fix for this is to not use the Serilog global static logger, but to store our own instantiated logger in our own static property.
This is mostly a brainless refactor, but it has to happen across all connectors for consistency.

hey @JR-Morgan can i drop this ball on you? The core part is done in #2313, we need to make sure connectors use the SpeckleLog.Logger global static instance instead of the Serilog.Log convenience methods, to avoid hijacking other plugin devs logs.
Once we're done with the refactor in sharp and manager, we need to remove this line

Cool! on it

Closing as temporarily fixed.
Related to https://github.com/specklesystems/speckle-sharp/issues/2477


Fix e.g. import grblas.matrix when done before grblas.init is called

This bug was breaking deserialization of Matrix objects.  sys.meta_path ftw!

We may want to puzzle out how to remove the import cycle that can sometimes result.  Although this is a lightweight use of sys.meta_path, most people aren't familiar with or comfortable with the particulars of Python's import machinery.  But, the current solution works splendidly!


texts aren't visible

i cant see any of my texts unless i send someone something so i cant know if someone messaged me unless i send them a text and it becomes visible ,so it'd be nice to know how i could fix that if its somehow my mistake or if u could fix that because they haven't been showing up for about a week or so.
thxx xx

@dedeeedd thank you for reporting. Yes this is a known issue, for now restarting the application should solve the problem

ive tried that a couple of times but still only 4 messages are showing up :c


GPT support OS disk

Can I use GPT partitioning on an Azure Disk?
GPT partitioning can be used only on data disks, not OS disks. OS disks must use the MBR partition style.

The doc says GPT partitioning only available for data disk. But I created a SUSE Linux VM from marketplace, the OS disk is GPT format. Am I misunderstand anything? This VM only has OS disk, no data disk.

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

ID: 8b1fece1-fff6-0dde-c8fc-59d8d5fdd7e3
Version Independent ID: 5aaeabdb-bc30-0084-4d78-7069544fd608
Content: Frequently asked questions (FAQ) about Azure IaaS Linux VM disks
Content Source: articles/virtual-machines/linux/faq-for-disks.md
Service: virtual-machines-linux
Sub-service: disks
GitHub Login: @roygara
Microsoft Alias: rogarana

@forester123
Thanks for the feedback! We are currently investigating and will update you shortly.

@roygara i verified that the OS disk is with gpt partition.
Please add your comments

@jakaruna-MSFT @roygara do you have any updates?

@roygara Can you please look into the issue and add your comments.

@forester123 sorry for the delay, I don't have any updates. I've reached out to the internal product teams and we're looking into this. I will let you know what we come back with.

@roygara , thank you for the information! The VM size I selected was Standard DS1_V2, is it Gen2 by default? In the doc you linked, it doesn't mention the OS disk of Gen2 VM supports gpt, do I miss anything?
By the way, the image I created is from marketplace as below:

@forester123 it's not gen2 by default... could you check the "properties" blade of the VM in the portal? And/or send a screenshot? It should display something like:
VM Generation
V2

@forester123 Sorry, I edited to remove my ask for a screenshot. I realized there's some other PII on that blade you shouldn't share out. So if you could just confirm what the VM Generation is listed as, that'd be good. :)

@roygara I just checked, it's V1 not V2.

@forester123 you should delete that screenshot, since it contains sensitive info.
But thanks, I've been in contact with the product team and have looped them in, I'll let you know what they come back with.

@roygara thank you so much! Please keep me update.

@roygara any news from product team?

@forester123 I'm still waiting to hear back, I'll let you know if I hear anything.

@forester123 Sorry for the delay, they've gotten back to me.
It seems like this image is a result of the publishers not wanting to have separate Gen1/Gen2 images. So they formulated their images to support both UEFI and BIOS boot. In Ubuntu's case, this change was made a year and a half ago, so it seems to be working.
This is kind of a special setup where the image is GPT-partitioned but also has a master boot record. There's more detail on this here if you like: https://en.wikipedia.org/wiki/BIOS_boot_partition
We're still working on what this means for doc implications and so on but, this is why your image is reporting the way it is but continues to work.
Please let me know if you've any other questions, or else I'll close this out after a few days.
Thanks again for your patience and for reaching out to us, it helps make us aware of things like this and keep our docs up to date. :)

@roygara thank a lot! Per my understanding, does it mean only the OS disk in some images are GPT + MBR, depends on the publisher? Or all the images in the marketplace have the same configuration? Would you update the doc to reflect the actual behavior? We have received the confusions from customer, better to make it clear in the doc.
Thank you so much for your effort on investigating the issue! Really appreciate!

@forester123 Apologies for the delayed response, this took a while to sort out. Only some images have a GPT + MBR dual boot capability, not every image in the marketplace. The image in this case (I managed to find it) while it reports GPT if you do print using parted, if you run sudo parted -l you will see the second MBR partition. It's actually using the MBR partition in this Gen1 configuration, not the GPT partition.

We will not be updating the disk FAQ to reflect this specific situation because, this document is for managed disks, not compute images. The Gen2 VM compute team is aware of this and this is working on the actual issue.
We did update the FAQ to clarify that GPT is available for Gen2 specifically, but not Gen1.
I'll be closing out this issue accordingly, please feel free to reopen it if you've further questions related to managed disks. If you've got questions related to the images themselves I recommend opening an issue on the Generation 2 VM doc so that it goes to the appropriate team.
Again, thanks for reaching out, and apologies for the delayed response.
#please-close


How to make a specific page rendered for a subdomain when using with express?

I am using Nuxt with Express.
Out of the box, pages are translated to routes to the main domain.
I am trying to use express-subdomain so that I can have some views (ex: pages/blog/**) rendered under blog.domain.com as well as domain.com/blog.
While other files (other than pages/blog/**) continue to be available under the domain.com only.

Hey hvaoc! I'm in the same situation and trying to accomplish something similar. Have you had any luck yet?

No, for a moment I kept that on hold. I tried using express-subdomain + express-vue, but couldn't make it work. @danmademe (Owner of express-vue) told me that it should work, but I couldn't make it work. Check this issue - https://github.com/express-vue/express-vue/issues/142
I have my setup sample there.
It would be great if this support is natively added to Nuxt.

Thanks @hvaoc ! I got it working using the nuxtServerInit action to set the subdomain in the store. Then I set a isSubdomain and use that throughout the application to render different components based on the subdomain. I'm not using express though.

Do you have a git repo that I can see ? If not do you mind sharing how you did.


Missing Groups claim if user has a lot of groups (>100)

Current Behavior
Hi,
I am checking if a user is in a particular group and got "user validation: missing claim: groups", whereas the user was definitely in the required groups. Found out in another script of us when querying the Graph API (Entra), that it only returns 100 groups, so I had to add pagination to the query to get them all.
Maybe there's  similar behavior here that needs to be adjusted.
Thanks
Expected Behavior
get all groups of user in response and compare against required groups
Steps To Reproduce
require groups, test with user that has 123 groups

Environment

openvpn-auth-oauth2 Version: 1.20.1
OpenVPN Server Version: 2.6.3-1+deb12u2~bpo11+1
Server OS: debian
OpenVPN Client (flavor, OS): Windows, 2.6 or connect, doesn't matter
OIDC Provider: Entra

openvpn-auth-oauth2 logs
Jul 23 10:49:13 hostname openvpn-auth-oauth2[559548]: time=2024-07-23T10:49:13.502+02:00 level=INFO msg="deny OpenVPN client cid 13, kid 1" ip=redacted cid=13 kid=1 session_id=foo<EMAIL_ADDRESS>idtoken.subject=bar idtoken.email=""<EMAIL_ADDRESS><EMAIL_ADDRESS>
Jul 23 10:49:13 hostname openvpn-auth-oauth2[559548]: time=2024-07-23T10:49:13.503+02:00 level=WARN msg="user validation: missing claim: groups" ip=redacted cid=13 kid=1 session_id=foo<EMAIL_ADDRESS>idtoken.subject=foobar idtoken.email=""<EMAIL_ADDRESS>user.subject=foobar<EMAIL_ADDRESS>error_id=something

openvpn server logs
No response
Anything else?
No response

Hey @choseh
for Entra ID, do not use the validation group feature in openvpn-auth-oauth2, since it has the limitations you mentation.
Instead, Entra ID native supports restricting users and group to an application.
https://learn.microsoft.com/en-us/entra/identity-platform/howto-restrict-your-app-to-a-set-of-users
By using that, your end-users are getting an way more user-friendly error message from Entra ID directly.

Hey @jkroepke
I can't remember exactly why we tried it the other way, but the recommended way works fine, thanks :)
Thank you for your work!

actually I just found out why we did it that way, we have several different openvpn server processes for different roles, which all use the same auth app in entra, so this would without validation of the groups then allow users of tunnel A to connect to tunnel C, etc.

this version is even better, thanks. works :)