Set up a "Hello world" project with two apps

Acheieved Milestones 💪

[x] Create a new Rails API app called 'hello-rails-back-end'.
[x] Initialize your project with Git.
[x] Create a table message and create 5 different greetings.
[x] Create an API endpoint that selects a random greeting from your table (you will need a controller with an action and rails route).

Hi @Tekhlay
While you made a great effort in this project, unfortunately, I cannot proceed to review your code.
Invalid Code Review Request
Kindly add link to the front repo in the backend readme.
Your Code Review Request will be marked as invalid in your Dashboard, so please submit a new one once you are ready 🙏
Cheers and Happy coding!👏👏👏
Feel free to leave any questions or comments in the PR thread if something is not 100% clear.
Please, remember to tag me in your question so I can receive the notification.
Please, do not open a new Pull Request for re-reviews. You should use the same Pull Request submitted for the first review, either valid or invalid unless it is requested otherwise.

Invalid Code Review Request does not count into the code reviews limit.

Hi @Tekhlay,
Your project is complete!  There is nothing else to say other than... it's time to merge it :shipit:
Congratulations! 🎉
Highlights

The Pull Request has a proper title and description. ✔
You have added a descriptive Readme file. Good Job. ✔
There is no linter error is present. ✔
Project has met all the technical aspects you needed for this project. Keep rocking! ✔

Optional suggestions
Every comment with the [OPTIONAL] prefix won't stop the approval of this PR. However, I strongly recommend you to take them into account as they can make your code better. Some of them were simply missed by the previous reviewer and addressing them will really improve your application.
Cheers and Happy coding!👏👏👏
Feel free to leave any questions or comments in the PR thread if something is not 100% clear.
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Explanation for frontmatter

It seems that a lot of users that never worked with frontmatter or related technologies are trying to manipulate the DOM inside frontmatter, so we should have it detailed that DOM manipulation is not possible within frontmatter because it runs on the server, at build time, and not on the client/browser.

Hell yes, I am actually going to go and learn more about frontmatter and textmate just to get a bit versed in this Astro tech

Good point y'all, I'll take a look as well and see what I can source

I honestly do think, ince we write up on the frontmatter, we might just hit the seo roof and get most of the traffic, cause there is actually not much of coherent, structured, content out there, this pushes the convo out further, when Fred was talking in the RFC 22/06, about the possibility of having an open standard for markdown component files for astro, and potentially something wider, I totally see what he means by it, there is nothing really out there, apart from use frontmatter with Yaml and Toml, Jekyll and a few other parsing repos that kinda explains what's it all about,

@aFuzzyBear Did research myself as well and agree to your last comment and will propose some content for the documentation. We can also see if we can contribute together maybe.

@ewatch that would be amazing bud, Ill pull some stuff together and find a spare gdoc to stick it in, and we can see what we can do with it from there, =)

I honestly do think, ince we write up on the frontmatter, we might just hit the seo roof and get most of the traffic, cause there is actually not much of coherent, structured, content out there, this pushes the convo out further, when Fred was talking in the RFC 22/06, about the possibility of having an open standard for markdown component files for astro, and potentially something wider, I totally see what he means by it, there is nothing really out there, apart from use frontmatter with Yaml and Toml, Jekyll and a few other parsing repos that kinda explains what's it all about,

Ooo, I love this. let's super optimize that page and see what happens!

Makes sense to me to take some time to explain what you can and can't do in the component frontmatter. I think even just having a really clear .astro example with docs would help users. Something like
---
// Welcome to your first Astro Component!
// you can use the frontmatter to write JavaScript (or TypeScript!) that will run during the build.
// Note: this code never gets sent to the browser, so you don't have access to the DOM.
// A console.log, for example, will run in the CLI during your build.
// You do, however, have the ability to reference any variables in your template.
const foo = 42;
---

My favorite number is {foo}

FYI: the docs have been successfully merged back into the main Astro repo: https://github.com/snowpackjs/astro/tree/main/docs
This issue will be migrated over soon, but if anyone can take a look at this issue before then, please remember to create a PR against the main astro repo instead.


versions: allow v3 of tls and random providers

Providers random and tls have new versions available that use v2 of the provider SDK and drop support for Terraform 0.11. This allows these new versions so that this is not a breaking change but users who require these new versions don't get a conflict.
https://github.com/hashicorp/terraform-provider-random/blob/master/CHANGELOG.md#300-october-09-2020
https://github.com/hashicorp/terraform-provider-tls/blob/master/CHANGELOG.md#300-october-14-2020

Merged as 445627e1c367245ed73039c6131b6c23631972bf, thanks!


[FEATURE REQUEST] Channel deprecation/sunset

It would be great to have a way to flag that a channel is deprecated or is going to disappear/sunset at some point. We already thought about a property like deprecated: true but that would be insufficient if we want to understand when the channel is going to finally disappear and what alternatives do I have.
I thought on something like this, following a bit the idea behind the sunset header definition for HTTP by @dret.
deprecated: true
sunset:
  datetime: 2020-01-21T18:33:19Z # Optional
  message: Stop using it now! # Optional
  externalDocs: myjira.com/issue-7 # Optional (Thanks Michael Davis)
  alternativeChannel: /alternative/channel/name # Optional

I'm sure some folks over at JSON Schema and OpenAPI already had this discussion. Any thoughts? @philsturgeon @MikeRalphson @relequestual
Source of discussion here: https://asyncapi.slack.com/archives/C34F2JV0U/p1579542224002900

Back in a days I worked for few years on a platfrom for publishing microservices, we had marketplace and all that stuff so you can imagine we had hundreds of different APIs and we used RAML. The pain was exactly when we new we have to deprecate something. We ended up with writing information manually in release notes, and we had a plan to invent a kind of notification system where we would put proper data about deprecation in a machine-readable format. If we would already have all of those in a specification writing such notification center would be much better as we already had all those services in a system and they all were exposing RAML files on the root of the service.
My suggestion is:
deprecated:
  sunset: 2020-01-21T18:33:19Z # Optional
  reason: Stop using it now! # Required
  externalDocs: myjira.com/issue-7 # Optional (Thanks Michael Davis)

deprecated: # not boolean but an object, if it is there in the spec for me it is like true
sunset: this is how we called the closing of above-mentioned platform :) and it is also a word that has a bit positive "sound". This would be date fields like in the HTTP header proposal
reason: I like it more than a "message" or a "description". I took it from https://www.apollographql.com/docs/graphql-tools/schema-directives/. This would be required field
externalDocs: great suggestion by Michael, I would use it in the above described use case as a field where I provide a link to release notes with more details and migration instruction
alternativeChannel: this is a detail that should be in release notes, or in a reason if someone is too lazy. I can't imagine what I can use this field for programmatically. I would not automatically switch to a new channel, deprecations are things that need closer manual attention.

I like where this is heading and the idea of all the extra properties is very good and we would most definitely use a lot of the optional ones..
The sunset property can be confusing though, especially for people that are not native english speakers. (I always found it a confusing word myself).
I would use a more descriptive property like for example "endDate", "purgeDate", "removalDate", ...
Externaldocs as plural suggests an array payload instead of string

@fmvilas sorry I never replied to the above, must have missed it. I believe these are the currently open related OAS issues:

https://github.com/OAI/OpenAPI-Specification/issues/1973
https://github.com/OAI/OpenAPI-Specification/issues/1616
https://github.com/OAI/OpenAPI-Specification/issues/782

I don't believe we've looked to address this in JSON Schema.
It would be nice to have it uniformly across API specs and JSON Schema.
I'm open to anything we can get agreement on which is useful.
As @dret noted, there are some existing draft standards for this. @philsturgeon wrote an article which links to both and has some good points for discussion: https://blog.stoplight.io/deprecating-api-endpoints

On 2021-11-08 12:00, Ben Hutton wrote:

I don't believe we've looked to address this in JSON Schema.
It would be nice to have it uniformly across API specs and JSON Schema.

that would be interesting but challenging. currently, deprecation/sunset
work at the resource level, so at a granularity level that to some
extent is "above" JSON schema.

I'm open to anything we can get agreement on which is useful.

same here. these are extremely standard things to do in pretty much any
API out there, so any support we can provide would be useful for pretty
much everybody.

Looking for a champion! I consider this feature can be very useful TBH.


The probelm with redux

I 'v been researching on redux for a while,
still unable to use it in to my project.
Redux makes things more complicated, even if you just want to implement a list retrieved from server.
you have to write much more boilerplate codes , in order to get redux works.
I really doubt if all the efforts worth the cost.
does anyone have the same doubt?

Uh no. No one is advocating the use of Redux in simple projects where you can reason about the state easily. You can do it but Redux really shines in more complex projects where state management is a serious issue.

You definitely don't need Redux if you just want to show data retrieved from a server. Check this article and maybe this project.

This is a bug tracker, and not intended as a discussion forum.  FWIW, Dan did just write a post on this topic: https://medium.com/@dan_abramov/you-might-not-need-redux-be46360cf367 .


feat: parse old cookies and convert to new format

Summary

Read old cookie
Pass old cookie values as options
Save cookies as usual
Unit tests
Integration tests

Manual verification

Remove all amplitude cookies
Load web app with old amplitude sdk
init old amplitude sdk
Take note of userId, sessionId, deviceId
Update web app code and swap sdk
Load web app with new amplitude sdk
init new amplitude sdk without user id
Compare userId, sessionId, deviceId

Checklist

[x] Does your PR title have the correct title format?
Does your PR have a breaking change?:  No

@qingzhuozhen @justin-fiedler bumping this up, when you get the chance 😄


screenshots and support for cypress-cucumber-preprocessor

Hello @boxblinkracer, firstly, just want to say thank you for this plugin. 🙏  I've been trying it out with my project that uses Cypress v10.8.0 and it seems to be working just fine so far.  I have two questions, I would have asked them in the Discussions tab but I don't see it so asking them in this issue instead.
Q1.  When my test fails, I see its status as failed in testrail. But I don't see the screenshot anywhere. Where in the test run/ test case result would it be, if it were to exist in TestRail?
here is my Cypress.env.json file :
{
    "testrail": {
      "domain": "xxx.testrail.io/",
      "username": "xyz",
      "password": "xyz",
      "projectId": "1",
      "suiteId": "2",
      "runId": "4",
      "screenshots": true
    }
  }

I can see the cypress/screenshots folder populate with the screenshot when the test fails but I am not seeing the screenshot in TestRail.
Q2. Can this package also be used for Cypress projects that use the Cypress-cucumber preprocessor? i.e. https://www.npmjs.com/package/cypress-cucumber-preprocessor? If yes, could you please share how to supply the testIDs for something like Scenario Outlines which have multiple tests within them?
Thank you for your help!

Hi
first of all thank you so much for your kind words
great that you like it
Q1)
so the screenshot should usually be inside a result (please see my screenshot attached).
in your case i think the configuration might have both modes...so I guess runId is set, so inside run R4 in the result of the testcase.

Q2) i have no idea :D
i didnt use it yet
so the integration basically extracts the name of the test....so if cucumber is wrapping cypress tests then in the core a test with a name and therefore a testcase id should exist
ill create another ticket for it, but also, i'm a bit out of time at the moment
so i cannot guarantee a fast implementation :)

subscribe to the @swatinigam 's  Q2 . any information regarding cucumber scenarios and testrail integration would be much appreciated :pray:

Hi there
sorry for not replying earlier (no worries...still mainained, just released v2.6)
i have never used it so far, so I need to dig deeper (but im totally open to support it)
if anyone has a plug and play template that could help me to get started, feel free to share it :)

so, this is finally done
i will continue in the other ticket and close this one
https://github.com/boxblinkracer/cypress-testrail/issues/14


Add sync db CLI

@gpang Add sync CLI

I will add integration test once the test PR is merged

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jenkins, test this please

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@gpang PTAL, comments addressed

Merged build finished. Test PASSed.

@gpang comments addressed, PTAL

alluxio-bot, merge this please

alluxio-bot, cherry-pick this to branch-2.1 please


Added Bech32 Testnet prefix for litecoin

Hrm that didn't quite do it :D
I think v<IP_ADDRESS> was created mistakenly, we should be using 0.0.34.

I thought you've tagged it mistakenly because I guess we will get the conflicts again with 0.0.34


Produce static binaries on Linux

Tested it on Arch Linux and Ubuntu, should only trigger for Linux systems. Possible solution for #38

Awesome! Merging it right now, thank you very much for your contribution (again)!


specialized_mesh_pipeline example panics when hovering over mesh

Bevy version
main
bisected to #15800.
Relevant system information
SystemInfo { os: "MacOS 14.5 ", kernel: "23.5.0", cpu: "Apple M1 Max", core_count: "10", memory: "64.0 GiB" }
AdapterInfo { name: "Apple M1 Max", vendor: 0, device: 0, device_type: IntegratedGpu, driver: "", driver_info: "", backend: Metal }
What you did

cargo run --example specialized_mesh_pipeline
hover over over the triangles

What went wrong
thread 'Compute Task Pool (3)' panicked at crates/bevy_picking/src/mesh_picking/ray_cast/intersections.rs:145:29:
index out of bounds: the len is 3 but the index is 3
note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace
Encountered a panic in system `bevy_picking::mesh_picking::update_hits`!
Encountered a panic in system `bevy_app::main_schedule::Main::run_main`!

I'm pretty sure something is malformed in the mesh. This looks like the exact issue spotted by @tbillington and called out as not a problem in https://github.com/bevyengine/bevy/pull/15800#discussion_r1797081408 by @Jondolf.

It looks like it is because the mesh doesn't have normals.

The line the panic is referring to is indexing the third vertex position for a triangle, not indexing a normal. The code does check if Mesh::ATTRIBUTE_NORMAL exists at all, and should work if it doesn't. I believe normals should only be an issue if there are less normals than there are vertices, which is considered malformed by Bevy anyway.
The panic happens because the example defines the mesh's indices as [0, 1, 2, 0, 2, 3]. Index 3 points to nothing since the mesh only has three vertices. Not sure why it's set up like that or how it's even meant to work.

Cool, you're right, thanks. Opened a tiny PR to fix.


Add Waynesburg Pennsylvania!!!

Please consider adding an email link for the town of Waynesburg, Pennsylvania.

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Email addresses (at least one) of the government officials you’d like to receive the message
Detailed message concerning the budgeting decisions at stake in your community

Until you provide this content, we cannot post your city.
Follow our email style guidelines for quicker acceptance. Remember that messages must be city-specific and focused on defunding the police. If your message is copied from an external source, please provide a link to the original source.
Your request will be closed until you reply with the necessary content. Thank you!


Permission problems with system LaunchAgent/Daemons

I was migrating from a somewhat more relaxed laptop setup to a new one that has a few security tools on it and I had problems when macprefs was trying to chown on /Library/LaunchAgents
https://github.com/clintmod/macprefs/blob/master/startup_items.py#L45-L56
I'm able to chown the directory by hand when I omit -R and on most .plist items but anything owned by the security tools in question prevented me from doing a chown, even if it was to the same user/group it already had (root:wheel) that macprefs is trying to apply.
Makes me wonder if it is best to only chown the files that macprefs copied over? Or fail but warn, not totally bail out of macprefs, this would, however, need a file list to chown on instead of a full dir.

Looks like this project is abandoned, it would be nice to be able to skip non readable objects


Handle disconnects during dual-funding flow

During the dual-funding flow, a disconnect will lead to a bad state. (e.g. disconnect occurs while in state WaitForFundingCreated) Specifically, the UTXOs remain "reserved", and the corresponding sats are stuck in the user's swap-in wallet until they restart the app.
This PR addresses the problem by assigning the UTXOs to a reserved.pending state when the flow is started. And from there:

if ChannelEvents.Created is seen, UTXOs move to reserved.reserved state
if Disconnected is seen, UTXOs are removed from reserved.pending

It would be nice to add a unit test for this. But, if I understand correctly, this would require injecting Electrum messages into the ElectrumClient. Which isn't possible now (it's a private API). But the electrum-client-rework branch might help us here.

This code change is potentially quite dangerous, there is a risk that we end up creating multiple dual-funding attempts in parallel with the same utxos, which means none of them will work because they double-spend each other, and Phoenix doesn't have double-spend detection so the user will only see that channels are stuck in a weird state.
If we really want to go in that direction, that needs to be thoroughly tested, otherwise it will introduce more issues than the one it solves.


Alchemy balances

Type of change
Other
Does it contain breaking changes?
YES
A new env variable has been added: Alchemy api key
Description
Replaced all usage of the Aragon SDK with the Alchemy SDK.
This includes fetching of DAO transfers and DAO balances.
Changes

[x] Converted implementation of useDaoBalances hook to use the Alchemy SDK instead of Aragon SDK
[x] Converted implementation of useDaoTransfers hook to use the Alchemy SDK instead of Aragon SDK
[x] Created a hook useTokenFetch which provides a wrapper to allow for a more efficient version of the fetchTokenInfo function by caching previously fetched addresses. The useTokenInfo hook now uses this hook.
[x] Cleaned up relative imports (changed to use path alias instead, as it should have been)

This pull request has been merged by the SecureSECO DAO.

This pull request could not be merged.
Executed by: 0x1ef2db73affdd73d4e219638b5ffb62a564f17ea
Transaction hash: 0x03884041530300ee3b8c9375303601117d526322cf20803d572d68b07cd5e2f7
Error: Error: Pull request is not mergeable: (SecureSECODAO/dao-webapp#146). Mergeable = null, State = unknown

This pull request could not be merged.
Executed by: 0x1ef2db73affdd73d4e219638b5ffb62a564f17ea
Transaction hash: 0x03884041530300ee3b8c9375303601117d526322cf20803d572d68b07cd5e2f7
Error: Error: Pull request is not mergeable: (SecureSECODAO/dao-webapp#146). Mergeable = null, State = unknown

This pull request could not be merged.
Executed by: 0x1ef2db73affdd73d4e219638b5ffb62a564f17ea
Transaction hash: 0x03884041530300ee3b8c9375303601117d526322cf20803d572d68b07cd5e2f7
Error: Error: Pull request is not mergeable: (SecureSECODAO/dao-webapp#146). Mergeable = null, State = unknown


Swipe up dismisses the keyboard

I have a UIViewController with UITableView and UISearchBar, when I swipe up on UIViewController (not UITableView) the keyboard hides, I tried it while presenting the view as normal (not as stark) and the keyboard not hides when swipe up.

Good day, @fawzirifai !
Most likely this is the correct behavior.
Can you record a video that does not suit you?


fix(web): escape quotes in installer

Shows the current failing case: https://testgrid.kurl.sh/run/docker-daemon-conf


Problem on Uglify for production build

First of all, thank you for this awesome package. It's really cool package.
I use HOC for 4 my components, it's work as expected in development mode.
When I build project in production mode - 2 components receive the classNames object for property from another component.
If I disable webpack.optimize.UglifyJsPlugin from the production build - it also works as expected.
Link to my rep: https://github.com/AndreyShpilevoy/DemUiBoilerplate/tree/master/DEM_MVC_UI
I trying to create Bootstrap-like grid on jss with theme support.

I haven't looked into it too much, but I believe the issue is that you're not using true functions for your components, but are using anonymous functions, like so:
const Container = ({ fluid, children, classNames }) =>

When Uglify is ran, I'm assuming this get's converted to something like const a = () =>, which is causing the duplicate style objects. I'd suggest using true functions, or passing a styleName to the styler function.

Thank you for the fast reply.
I tried to change my arrow functions to named functions but got the same result.
Unfortunately, the problem wasn't solved.

I also changed all those components from stateless to stateful - and even that didn't solve the problem with UglifyJsPlugin .

Weird. Looking into it a bit more.

Messed around with this a bit, the problem resolves itself if you pass mangle: false to the uglify configuration or if you use options.styleName. http://lisperator.net/uglifyjs/mangle
This is definitely a tricky situation to solve. I can probably generate a UID in production.

Yep, if pass mangle: false to the uglify configuration really solved this issue.
But it increased bundled files size average in 51,5%.
So it's definitely a workaround.
I will wait for your fix this problem.
Huge thank you for your time and great work.

Thanks for finding the issue. Definitely something that should be fixed.
Will most likely go with the UID approach. Just need to avoid collisions.

@AndreyShpilevoy I published a small patch, let me know if that works for you.

Thank you!
It's really work. Even with stateless arrow functions.

Sweet. 👍


Gradients do not exist for variables

I have tried to run run_classifer.py, it works well on GPU, then I have made a little fix, it runs well on TPU. However, when I tried to run run_squad.py, I met this bug on GPU and TPU:
Model was constructed with shape Tensor("unique_ids:0", shape=(None, 1), dtype=int32) for input (None, 1), but it was re-called on a Tensor with incompatible shape (None,).
WARNING:tensorflow:Gradients do not exist for variables ['albert_model/pooler_transform/kernel:0', 'albert_model/pooler_transform/bias:0'] when minimizing the loss.
W1211 11:49:55.828053<PHONE_NUMBER>06368 optimizer_v2.py:1043] Gradients do not exist for variables ['albert_model/pooler_transform/kernel:0', 'albert_model/pooler_transform/bias:0'] when minimizing the loss.
WARNING:tensorflow:Model was constructed with shape Tensor("unique_ids:0", shape=(None, 1), dtype=int32) for input (None, 1), but it was re-called on a Tensor with incompatible shape (None,).
W1211 11:49:59.275795<PHONE_NUMBER>06368 network.py:847] Model was constructed with shape Tensor("unique_ids:0", shape=(None, 1), dtype=int32) for input (None, 1), but it was re-called on a Tensor with incompatible shape (None,).
WARNING:tensorflow:Gradients do not exist for variables ['albert_model/pooler_transform/kernel:0', 'albert_model/pooler_transform/bias:0'] when minimizing the loss.
W1211 11:50:02.947960<PHONE_NUMBER>06368 optimizer_v2.py:1043] Gradients do not exist for variables ['albert_model/pooler_transform/kernel:0', 'albert_model/pooler_transform/bias:0'] when minimizing the loss.

@guozhiyu
You can ignore the above warnings. ALBERT model has two outputs pooler_tranformer and full sequence output. pooler transformer is not used while training SQuAD. So there will no gradients for that layer.


GSD Request

--- GSD JSON ---
{
  "vendor_name": "Linux",
  "product_name": "Kernel",
  "product_version": "versions from  to before v4.14.260",
  "vulnerability_type": "unspecified",
  "affected_component": "unspecified",
  "attack_vector": "unspecified",
  "impact": "unspecified",
  "credit": "",
  "references": [
    "https://git.kernel.org/pub/scm/linux/kernel/git/stable/linux.git/commit/?id=311601f114859d586d5ef8833d60d3aa23282161"
  ],
  "extended_references": [
    {
      "type": "commit",
      "value": "311601f114859d586d5ef8833d60d3aa23282161",
      "note": "fixed"
    }
  ],
  "reporter": "joshbressers",
  "reporter_id": 1692786,
  "notes": "",
  "description": "phonet/pep: refuse to enable an unbound pipe\n\nThis is an automated ID intended to aid in discovery of potential security vulnerabilities. The actual impact and attack plausibility have not yet been proven.\nThis ID is fixed in Linux Kernel version v4.14.260 by commit 311601f114859d586d5ef8833d60d3aa23282161. For more details please see the references link."
}
--- GSD JSON ---

This issue has been assigned GSD-2021-1002784


Ability to set Relative path for output directory

As it says on the tin.
Currently relative path as output dir puts files not where one would expect it.

May I ask you where is it? Since I rendered several times (for testing purpose) to relative path I would like to delete them. I tried to check my "tmp" folder, but there is nothing related to this. Thanks for help.

This is where the tempfile is created so it uses standard Python module:
    project_file = tempfile.NamedTemporaryFile(suffix='.blend', delete=False).name

You should be able to run in Blender's Python console:
    import tempfile
    print(tempfile.NamedTemporaryFile())

to see where it creates those.

Do you ever sleep, man? :) Thanks for response.
I tried to educate myself, but no way :) sorry "end user" :)
How can someone recognise path  from this console print?
<tempfile._TemporaryFileWrapper object at 0x10ff5e390>
Search on net without success.
I expected something like this - path as answer

bpy.app.tempdir
'/tmp/blender_8xXx6t/'

I'm interested in this directory just because I understood that thanks to relative path are now files saved somewhere and are not deleted automatically. Right?

My mistake. Object returned by NamedTemporaryFile has a name property (see here):

That name can be retrieved from the name attribute of the returned file-like object.

So the code snippet should have been:
import tempfile
print(tempfile.NamedTemporaryFile().name)

Thanks ! I got it :)


installation instructions

What's the preferred way to install this?

pip install git+https://github.com/keenbrowne/flask-pbj.git
You should then be able to from flask_pbj import api, json, protobuf
I never got around to pushing this to pypi.
Also note that I haven't tested this library with more recent versions of flask. The setup.py has a hard dependency on protobuf 2.5, I also haven't tested protobuf 2.6.1 but I expect it should work.

Installed, thanks!

I just tested with a newer version of flask/protobuf on ubuntu. It's working for me. I imported like so:
from flask.ext.pbj import api, json, protobuf
and then I decorated a route like in the sample.
Hope you find this extension useful :)


BUG - Inject not work for projects stored on iCloud Drive

Describe the bug

Xcode projects stored on iCloud Drive not work with Inject package. Xcode projects stored, for example, on Desktop, works correctly, but on iCloud Drive not. No matter whether Xcode project is small or big. I tested on fresh Xcode project created directly from Xcode and this even not work if it is stored on iCloud Drive.

To Reproduce
Steps to reproduce the behavior:

Add SPM via Xcode
Add "-Xlinker -interposable" (without the double quotes and on separate lines) to the "Other Linker Flags"
Download newest version of Xcode Injection
Unpack it and place under /Applications
Xcode version to compile i under the default location: /Applications/Xcode.app
Run the injection application
Select open project / open recent from it's menu and pick the right workspace file stored on iCloud Drive
Add import Inject
Add .enableInjection() at the end of body definition
Add @ObserveInjection var inject to view struct
Run Xcode project
Change something in the view of Xcode project (for example, Text("Hello World") to Text("Hello)")
Click Cmd + S
See erros in debug console

Expected behavior

Inject will work for projects stored on iCloud Drive

Screenshots

💉 ⚠ Could not locate compile command for "/Users/marcinjedrzejak/Library/Mobile Documents/com~apple~CloudDocs/Developer/AquaVibe/AquaVibe/Screens/Settings/SettingsView.swift" in /Users/marcinjedrzejak/Library/Developer/Xcode/DerivedData/AquaVibe-cajjtsrvofweaaavplnnhceixeel/Logs/Build/.
This could be due to one of the following:

Injection does not work with Whole Module Optimization.
There are restrictions on characters allowed in paths.
File paths in the simulator are case sensitive.
The modified source file is not in the current project.
The source file is an XCTest that has not been run yet.
Xcode has removed the build logs. Edit a file and re-run
or try a build clean then rebuild to make logs available or
consult: "/Users/marcinjedrzejak/Library/Developer/CoreSimulator/Devices/A10C3F60-4DDF-4F05-A94D-0CE117259F24/data/Containers/Data/Application/73D59922-9FDF-4851-83A8-D22F4EED875A/tmp/command.sh". Whatever the problem, if you see
this error it may be worth trying the new implementation
https://github.com/johnno1962/InjectionNext.

Device Info:

Device: iPhone 16 Pro Simulator
OS: iOS 18.0

Did you try the new implementation as it suggests? When you encounter this problem that is probably your best option if you don't want to move the project out of iCloud Drive.

Not fully, in new project from scratch, it works until I change something, for example, with button, then is below error in console. In my other project, I immediately had below error:
🔥 ⚠ Symbols altered, this may not be supported. 256 c.f. 256

That error is a warning as the numbers are the same, I'll change the emoji. Other than that is it working?

View is not updated even console is showing that changes has been done.
Cmd + S, console shows something done in the background, but view in simulator is not refreshed:

Bu do you have a @ObserveInjection var redraw inside that particular view?

I spotted where was issue, apologies, never mind. 'Prepare SwiftUI' option from InjectionNext app assigned the codes to wrong sections. Now looks that is working fine 👍
For summary, for projects stored in local drive, I should use Inject? For projects stored in iCloud Drive, I should use InjectionNext?

InjectionIII and InjectionNext are alternative substrates that operate under and implement Inject. You would always use Inject and you can use the newer InjectionNext when the project is on iCloud Drive or either otherwise.


Versioning issue in Apigility

Just encountered a issue in while using a apigility.
As i am new i am not sure this is issue or existing flow of application.
This is the current situation i Created 3 module in api.

Status - containing ping API
Customer - containing test API
User - containing test API

Eariler http://test.api/ping was working fine
but when i updated user module to version 2 and set its default version to 2.
my ping api of Status module stopped working and returned 404
but when i hit http://test.api/v1/ping
It works fine.
while debugging i found it was due to
'zf-versioning' => array(
'uri' => array(
0 => 'user.rest.test',
),
'default_version' => 2, //<this
),
in module config.
as its default version is changed to 2 but my other api is on default version of 1.

This repository has been closed and moved to laminas-api-tools/api-tools-versioning; a new issue has been opened at https://github.com/laminas-api-tools/api-tools-versioning/issues/2.


Mask for number

Robin, please help.
How can I define the mask, that doesn't allow to enter 0 into the brackets
$(selector).inputmask("(99)-9999999");

@LygutaKsusha ,
Have a try with
Inputmask("(88)-9999999", {
			definitions: {
				"8": {
					validator: "[1-9]",
					cardinality: 1,
					definitionSymbol: "*"
				}
			}
		}).mask(selector);

Yes! It works!!!! Thank you very much!!!!


OOM error while QLoRA+Deepspeed fine tuning of Llama3-70B model on 4xA100-40GB gpus

System Info
OS                        Ubuntu
GPUS                      4XA100-40GB
Python                    3.10.14
accelerate                0.29.3
bitsandbytes              0.43.1
deepspeed                 0.14.2
peft                      0.10.0
transformers              4.40.1
trl                       0.8.6

Who can help?
@pacman100
Information

[X] The official example scripts
[X] My own modified scripts

Tasks

[X] An officially supported task in the examples folder
[ ] My own task or dataset (give details below)

Reproduction

deepspeed_config_z3_qlora_4g.yaml
compute_environment: LOCAL_MACHINE
debug: false
deepspeed_config:
  deepspeed_multinode_launcher: standard
  offload_optimizer_device: none
  offload_param_device: none
  zero3_init_flag: true
  zero3_save_16bit_model: true
  zero_stage: 3
distributed_type: DEEPSPEED
downcast_bf16: 'no'
machine_rank: 0
main_training_function: main
mixed_precision: bf16
num_machines: 1
num_processes: 4
rdzv_backend: static
same_network: true
tpu_env: []
tpu_use_cluster: false
tpu_use_sudo: false
use_cpu: false

run_peft_qlora_deepspeed_stage3_llama3_70b.sh
accelerate launch --config_file "configs/deepspeed_config_z3_qlora_4g.yaml"  train.py \
--seed 100 \
--model_name_or_path "NousResearch/Meta-Llama-3-70B" \
--dataset_name "smangrul/ultrachat-10k-chatml" \
--chat_template_format "chatml" \
--add_special_tokens False \
--append_concat_token False \
--splits "train,test" \
--max_seq_len 2048 \
--num_train_epochs 1 \
--logging_steps 5 \
--log_level "info" \
--logging_strategy "steps" \
--evaluation_strategy "epoch" \
--save_strategy "epoch" \
--bf16 True \
--packing True \
--learning_rate 1e-4 \
--lr_scheduler_type "cosine" \
--weight_decay 1e-4 \
--warmup_ratio 0.0 \
--max_grad_norm 1.0 \
--output_dir "llama-sft-qlora-dsz3" \
--per_device_train_batch_size 2 \
--per_device_eval_batch_size 2 \
--gradient_accumulation_steps 2 \
--gradient_checkpointing True \
--use_reentrant True \
--dataset_text_field "content" \
--use_flash_attn True \
--use_peft_lora True \
--lora_r 8 \
--lora_alpha 16 \
--lora_dropout 0.1 \
--lora_target_modules "all-linear" \
--use_4bit_quantization True \
--use_nested_quant True \
--bnb_4bit_compute_dtype "bfloat16" \
--bnb_4bit_quant_storage_dtype "bfloat16"

Using the official train.py example from https://github.com/huggingface/peft/blob/main/examples/sft/train.py

Log output
[2024-05-02 06:46:54,788] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cuda (auto detect)
 [WARNING]  sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.2
 [WARNING]  using untested triton version (2.2.0), only 1.0.0 is known to be compatible
[2024-05-02 06:46:56,799] torch.distributed.run: [WARNING]
[2024-05-02 06:46:56,799] torch.distributed.run: [WARNING] *****************************************
[2024-05-02 06:46:56,799] torch.distributed.run: [WARNING] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.
[2024-05-02 06:46:56,799] torch.distributed.run: [WARNING] *****************************************
[2024-05-02 06:47:02,228] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-05-02 06:47:02,234] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-05-02 06:47:02,239] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-05-02 06:47:02,241] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cuda (auto detect)
 [WARNING]  sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.2
 [WARNING]  using untested triton version (2.2.0), only 1.0.0 is known to be compatible
 [WARNING]  sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.2
 [WARNING]  using untested triton version (2.2.0), only 1.0.0 is known to be compatible
 [WARNING]  sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.2
 [WARNING]  using untested triton version (2.2.0), only 1.0.0 is known to be compatible
 [WARNING]  sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.2
 [WARNING]  using untested triton version (2.2.0), only 1.0.0 is known to be compatible
[2024-05-02 06:47:02,967] [INFO] [comm.py:637:init_distributed] cdb=None
[2024-05-02 06:47:02,967] [INFO] [comm.py:637:init_distributed] cdb=None
[2024-05-02 06:47:02,972] [INFO] [comm.py:637:init_distributed] cdb=None
[2024-05-02 06:47:02,991] [INFO] [comm.py:637:init_distributed] cdb=None
[2024-05-02 06:47:02,992] [INFO] [comm.py:668:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl
`low_cpu_mem_usage` was None, now set to True since model is quantized.
`low_cpu_mem_usage` was None, now set to True since model is quantized.
`low_cpu_mem_usage` was None, now set to True since model is quantized.
`low_cpu_mem_usage` was None, now set to True since model is quantized.
Loading checkpoint shards: 100%|██████████████████████████████| 30/30 [01:25<00:00,  2.86s/it]
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
Loading checkpoint shards: 100%|██████████████████████████████| 30/30 [01:27<00:00,  2.91s/it]
Loading checkpoint shards: 100%|██████████████████████████████| 30/30 [01:27<00:00,  2.93s/it]
Loading checkpoint shards: 100%|██████████████████████████████| 30/30 [01:27<00:00,  2.93s/it]
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
Size of the train set: 10000. Size of the validation set: 2000
A sample of train dataset: {'content': "<|im_start|>user\nThese instructions apply to section-based themes (Responsive 6.0+, Retina 4.0+, Parallax 3.0+ Turbo 2.0+, Mobilia 5.0+). What theme version am I using?\nOn your Collections pages & Featured Collections sections, you can easily show the secondary image of a product on hover by enabling one of the theme's built-in settings!\nYour Collection pages & Featured Collections sections will now display the secondary product image just by hovering over that product image thumbnail.\nDoes this feature apply to all sections of the theme or just specific ones as listed in the text material?<|im_end|>\n<|im_start|>assistant\nThis feature only applies to Collection pages and Featured Collections sections of the section-based themes listed in the text material.<|im_end|>\n<|im_start|>user\nCan you guide me through the process of enabling the secondary image hover feature on my Collection pages and Featured Collections sections?<|im_end|>\n<|im_start|>assistant\nSure, here are the steps to enable the secondary image hover feature on your Collection pages and Featured Collections sections:\n\n1. Log in to your Shopify account and go to your Online Store.\n2. Click on Customize theme for the section-based theme you are using.\n3. Navigate to the Collection pages or Featured Collections section where you want to enable the secondary image hover feature.\n4. With the section open, click on the section's setting (gear) icon in the top-left corner.\n5. In the settings panel that appears, look for an option labeled 'Image display' or 'Image hover'.\n6. If available, select 'Show secondary image on hover'.\n7. Save the changes and preview the Collection/Featured Collection page to see the effect.\n\nIf you're having trouble finding the setting, the best thing to do is refer to your theme's documentation, since the location and labeling of settings can vary between themes.<|im_end|>\n<|im_start|>user\nCan you provide me with a link to the documentation for my theme?<|im_end|>\n<|im_start|>assistant\nI don't have access to your store's theme information. However, you can usually find the documentation for your theme by going to the shopify theme store, finding your theme and clicking on the 'support' link located in the bottom right corner of the page. Alternatively, you can do a google search for the name of your theme followed by 'documentation' or 'user guide'.<|im_end|>\n<|im_start|>user\nCan you confirm if this feature also works for the Quick Shop section of my theme?<|im_end|>\n<|im_start|>assistant\nThe secondary image hover feature may or may not work for your Quick Shop section, depending on the configuration of your theme. Some themes include this feature in the Quick Shop section by default, while others may require additional customization. To check if this feature is available for the Quick Shop section of your theme, follow these steps:\n\n1. Go to the Quick Shop section where you would like to enable the feature. 2. Click on the Quick Shop settings icon (gear icon) and look for 'Image display' or 'Image hover'. 3. If available, select 'Show secondary image on hover'. 4. Save the changes. If this option is not available in your Quick Shop section settings, you may need to reach out to your theme developer for assistance with customizing your Quick Shop section to include this feature.<|im_end|>\n"}
Size of the train set: 10000. Size of the validation set: 2000
A sample of train dataset: {'content': "<|im_start|>user\nThese instructions apply to section-based themes (Responsive 6.0+, Retina 4.0+, Parallax 3.0+ Turbo 2.0+, Mobilia 5.0+). What theme version am I using?\nOn your Collections pages & Featured Collections sections, you can easily show the secondary image of a product on hover by enabling one of the theme's built-in settings!\nYour Collection pages & Featured Collections sections will now display the secondary product image just by hovering over that product image thumbnail.\nDoes this feature apply to all sections of the theme or just specific ones as listed in the text material?<|im_end|>\n<|im_start|>assistant\nThis feature only applies to Collection pages and Featured Collections sections of the section-based themes listed in the text material.<|im_end|>\n<|im_start|>user\nCan you guide me through the process of enabling the secondary image hover feature on my Collection pages and Featured Collections sections?<|im_end|>\n<|im_start|>assistant\nSure, here are the steps to enable the secondary image hover feature on your Collection pages and Featured Collections sections:\n\n1. Log in to your Shopify account and go to your Online Store.\n2. Click on Customize theme for the section-based theme you are using.\n3. Navigate to the Collection pages or Featured Collections section where you want to enable the secondary image hover feature.\n4. With the section open, click on the section's setting (gear) icon in the top-left corner.\n5. In the settings panel that appears, look for an option labeled 'Image display' or 'Image hover'.\n6. If available, select 'Show secondary image on hover'.\n7. Save the changes and preview the Collection/Featured Collection page to see the effect.\n\nIf you're having trouble finding the setting, the best thing to do is refer to your theme's documentation, since the location and labeling of settings can vary between themes.<|im_end|>\n<|im_start|>user\nCan you provide me with a link to the documentation for my theme?<|im_end|>\n<|im_start|>assistant\nI don't have access to your store's theme information. However, you can usually find the documentation for your theme by going to the shopify theme store, finding your theme and clicking on the 'support' link located in the bottom right corner of the page. Alternatively, you can do a google search for the name of your theme followed by 'documentation' or 'user guide'.<|im_end|>\n<|im_start|>user\nCan you confirm if this feature also works for the Quick Shop section of my theme?<|im_end|>\n<|im_start|>assistant\nThe secondary image hover feature may or may not work for your Quick Shop section, depending on the configuration of your theme. Some themes include this feature in the Quick Shop section by default, while others may require additional customization. To check if this feature is available for the Quick Shop section of your theme, follow these steps:\n\n1. Go to the Quick Shop section where you would like to enable the feature. 2. Click on the Quick Shop settings icon (gear icon) and look for 'Image display' or 'Image hover'. 3. If available, select 'Show secondary image on hover'. 4. Save the changes. If this option is not available in your Quick Shop section settings, you may need to reach out to your theme developer for assistance with customizing your Quick Shop section to include this feature.<|im_end|>\n"}
Size of the train set: 10000. Size of the validation set: 2000
A sample of train dataset: {'content': "<|im_start|>user\nThese instructions apply to section-based themes (Responsive 6.0+, Retina 4.0+, Parallax 3.0+ Turbo 2.0+, Mobilia 5.0+). What theme version am I using?\nOn your Collections pages & Featured Collections sections, you can easily show the secondary image of a product on hover by enabling one of the theme's built-in settings!\nYour Collection pages & Featured Collections sections will now display the secondary product image just by hovering over that product image thumbnail.\nDoes this feature apply to all sections of the theme or just specific ones as listed in the text material?<|im_end|>\n<|im_start|>assistant\nThis feature only applies to Collection pages and Featured Collections sections of the section-based themes listed in the text material.<|im_end|>\n<|im_start|>user\nCan you guide me through the process of enabling the secondary image hover feature on my Collection pages and Featured Collections sections?<|im_end|>\n<|im_start|>assistant\nSure, here are the steps to enable the secondary image hover feature on your Collection pages and Featured Collections sections:\n\n1. Log in to your Shopify account and go to your Online Store.\n2. Click on Customize theme for the section-based theme you are using.\n3. Navigate to the Collection pages or Featured Collections section where you want to enable the secondary image hover feature.\n4. With the section open, click on the section's setting (gear) icon in the top-left corner.\n5. In the settings panel that appears, look for an option labeled 'Image display' or 'Image hover'.\n6. If available, select 'Show secondary image on hover'.\n7. Save the changes and preview the Collection/Featured Collection page to see the effect.\n\nIf you're having trouble finding the setting, the best thing to do is refer to your theme's documentation, since the location and labeling of settings can vary between themes.<|im_end|>\n<|im_start|>user\nCan you provide me with a link to the documentation for my theme?<|im_end|>\n<|im_start|>assistant\nI don't have access to your store's theme information. However, you can usually find the documentation for your theme by going to the shopify theme store, finding your theme and clicking on the 'support' link located in the bottom right corner of the page. Alternatively, you can do a google search for the name of your theme followed by 'documentation' or 'user guide'.<|im_end|>\n<|im_start|>user\nCan you confirm if this feature also works for the Quick Shop section of my theme?<|im_end|>\n<|im_start|>assistant\nThe secondary image hover feature may or may not work for your Quick Shop section, depending on the configuration of your theme. Some themes include this feature in the Quick Shop section by default, while others may require additional customization. To check if this feature is available for the Quick Shop section of your theme, follow these steps:\n\n1. Go to the Quick Shop section where you would like to enable the feature. 2. Click on the Quick Shop settings icon (gear icon) and look for 'Image display' or 'Image hover'. 3. If available, select 'Show secondary image on hover'. 4. Save the changes. If this option is not available in your Quick Shop section settings, you may need to reach out to your theme developer for assistance with customizing your Quick Shop section to include this feature.<|im_end|>\n"}
Size of the train set: 10000. Size of the validation set: 2000
A sample of train dataset: {'content': "<|im_start|>user\nThese instructions apply to section-based themes (Responsive 6.0+, Retina 4.0+, Parallax 3.0+ Turbo 2.0+, Mobilia 5.0+). What theme version am I using?\nOn your Collections pages & Featured Collections sections, you can easily show the secondary image of a product on hover by enabling one of the theme's built-in settings!\nYour Collection pages & Featured Collections sections will now display the secondary product image just by hovering over that product image thumbnail.\nDoes this feature apply to all sections of the theme or just specific ones as listed in the text material?<|im_end|>\n<|im_start|>assistant\nThis feature only applies to Collection pages and Featured Collections sections of the section-based themes listed in the text material.<|im_end|>\n<|im_start|>user\nCan you guide me through the process of enabling the secondary image hover feature on my Collection pages and Featured Collections sections?<|im_end|>\n<|im_start|>assistant\nSure, here are the steps to enable the secondary image hover feature on your Collection pages and Featured Collections sections:\n\n1. Log in to your Shopify account and go to your Online Store.\n2. Click on Customize theme for the section-based theme you are using.\n3. Navigate to the Collection pages or Featured Collections section where you want to enable the secondary image hover feature.\n4. With the section open, click on the section's setting (gear) icon in the top-left corner.\n5. In the settings panel that appears, look for an option labeled 'Image display' or 'Image hover'.\n6. If available, select 'Show secondary image on hover'.\n7. Save the changes and preview the Collection/Featured Collection page to see the effect.\n\nIf you're having trouble finding the setting, the best thing to do is refer to your theme's documentation, since the location and labeling of settings can vary between themes.<|im_end|>\n<|im_start|>user\nCan you provide me with a link to the documentation for my theme?<|im_end|>\n<|im_start|>assistant\nI don't have access to your store's theme information. However, you can usually find the documentation for your theme by going to the shopify theme store, finding your theme and clicking on the 'support' link located in the bottom right corner of the page. Alternatively, you can do a google search for the name of your theme followed by 'documentation' or 'user guide'.<|im_end|>\n<|im_start|>user\nCan you confirm if this feature also works for the Quick Shop section of my theme?<|im_end|>\n<|im_start|>assistant\nThe secondary image hover feature may or may not work for your Quick Shop section, depending on the configuration of your theme. Some themes include this feature in the Quick Shop section by default, while others may require additional customization. To check if this feature is available for the Quick Shop section of your theme, follow these steps:\n\n1. Go to the Quick Shop section where you would like to enable the feature. 2. Click on the Quick Shop settings icon (gear icon) and look for 'Image display' or 'Image hover'. 3. If available, select 'Show secondary image on hover'. 4. Save the changes. If this option is not available in your Quick Shop section settings, you may need to reach out to your theme developer for assistance with customizing your Quick Shop section to include this feature.<|im_end|>\n"}
Using auto half precision backend
PeftModelForCausalLM(
  (base_model): LoraModel(
    (model): LlamaForCausalLM(
      (model): LlamaModel(
        (embed_tokens): Embedding(128264, 8192)
        (layers): ModuleList(
          (0-79): 80 x LlamaDecoderLayer(
            (self_attn): LlamaFlashAttention2(
              (q_proj): lora.Linear4bit(
                (base_layer): Linear4bit(in_features=8192, out_features=8192, bias=False)
                (lora_dropout): ModuleDict(
                  (default): Dropout(p=0.1, inplace=False)
                )
                (lora_A): ModuleDict(
                  (default): Linear(in_features=8192, out_features=8, bias=False)
                )
                (lora_B): ModuleDict(
                  (default): Linear(in_features=8, out_features=8192, bias=False)
                )
                (lora_embedding_A): ParameterDict()
                (lora_embedding_B): ParameterDict()
              )
              (k_proj): lora.Linear4bit(
                (base_layer): Linear4bit(in_features=8192, out_features=1024, bias=False)
                (lora_dropout): ModuleDict(
                  (default): Dropout(p=0.1, inplace=False)
                )
                (lora_A): ModuleDict(
                  (default): Linear(in_features=8192, out_features=8, bias=False)
                )
                (lora_B): ModuleDict(
                  (default): Linear(in_features=8, out_features=1024, bias=False)
                )
                (lora_embedding_A): ParameterDict()
                (lora_embedding_B): ParameterDict()
              )
              (v_proj): lora.Linear4bit(
                (base_layer): Linear4bit(in_features=8192, out_features=1024, bias=False)
                (lora_dropout): ModuleDict(
                  (default): Dropout(p=0.1, inplace=False)
                )
                (lora_A): ModuleDict(
                  (default): Linear(in_features=8192, out_features=8, bias=False)
                )
                (lora_B): ModuleDict(
                  (default): Linear(in_features=8, out_features=1024, bias=False)
                )
                (lora_embedding_A): ParameterDict()
                (lora_embedding_B): ParameterDict()
              )
              (o_proj): lora.Linear4bit(
                (base_layer): Linear4bit(in_features=8192, out_features=8192, bias=False)
                (lora_dropout): ModuleDict(
                  (default): Dropout(p=0.1, inplace=False)
                )
                (lora_A): ModuleDict(
                  (default): Linear(in_features=8192, out_features=8, bias=False)
                )
                (lora_B): ModuleDict(
                  (default): Linear(in_features=8, out_features=8192, bias=False)
                )
                (lora_embedding_A): ParameterDict()
                (lora_embedding_B): ParameterDict()
              )
              (rotary_emb): LlamaRotaryEmbedding()
            )
            (mlp): LlamaMLP(
              (gate_proj): lora.Linear4bit(
                (base_layer): Linear4bit(in_features=8192, out_features=28672, bias=False)
                (lora_dropout): ModuleDict(
                  (default): Dropout(p=0.1, inplace=False)
                )
                (lora_A): ModuleDict(
                  (default): Linear(in_features=8192, out_features=8, bias=False)
                )
                (lora_B): ModuleDict(
                  (default): Linear(in_features=8, out_features=28672, bias=False)
                )
                (lora_embedding_A): ParameterDict()
                (lora_embedding_B): ParameterDict()
              )
              (up_proj): lora.Linear4bit(
                (base_layer): Linear4bit(in_features=8192, out_features=28672, bias=False)
                (lora_dropout): ModuleDict(
                  (default): Dropout(p=0.1, inplace=False)
                )
                (lora_A): ModuleDict(
                  (default): Linear(in_features=8192, out_features=8, bias=False)
                )
                (lora_B): ModuleDict(
                  (default): Linear(in_features=8, out_features=28672, bias=False)
                )
                (lora_embedding_A): ParameterDict()
                (lora_embedding_B): ParameterDict()
              )
              (down_proj): lora.Linear4bit(
                (base_layer): Linear4bit(in_features=28672, out_features=8192, bias=False)
                (lora_dropout): ModuleDict(
                  (default): Dropout(p=0.1, inplace=False)
                )
                (lora_A): ModuleDict(
                  (default): Linear(in_features=28672, out_features=8, bias=False)
                )
                (lora_B): ModuleDict(
                  (default): Linear(in_features=8, out_features=8192, bias=False)
                )
                (lora_embedding_A): ParameterDict()
                (lora_embedding_B): ParameterDict()
              )
              (act_fn): SiLU()
            )
            (input_layernorm): LlamaRMSNorm()
            (post_attention_layernorm): LlamaRMSNorm()
          )
        )
        (norm): LlamaRMSNorm()
      )
      (lm_head): Linear(in_features=8192, out_features=128264, bias=False)
    )
  )
)
trainable params: 103,546,880 || all params: 70,657,384,448 || trainable%: 0.1465478531493122
[2024-05-02 06:48:45,278] [INFO] [logging.py:96:log_dist] [Rank 0] DeepSpeed info: version=0.14.2, git-hash=unknown, git-branch=unknown
trainable params: 103,546,880 || all params: 70,657,384,448 || trainable%: 0.1465478531493122
trainable params: 103,546,880 || all params: 70,657,384,448 || trainable%: 0.1465478531493122
trainable params: 103,546,880 || all params: 70,657,384,448 || trainable%: 0.1465478531493122
Traceback (most recent call last):
  File "/opt/conda/lib/python3.10/site-packages/torch/distributed/c10d_logger.py", line 72, in wrapper
    return func(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py", line 1914, in broadcast
    work = group.broadcast([tensor], opts)
torch.distributed.DistBackendError: NCCL error in: /opt/conda/conda-bld/pytorch_1711403380909/work/torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:1691, unhandled cuda error (run with NCCL_DEBUG=INFO for details), NCCL version 2.19.3
ncclUnhandledCudaError: Call to CUDA function failed.
Last error:
Cuda failure 2 'out of memory'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/root/hmohapa/peft/examples/sft/train.py", line 162, in <module>
    main(model_args, data_args, training_args)
  File "/root/hmohapa/peft/examples/sft/train.py", line 146, in main
    trainer.train(resume_from_checkpoint=checkpoint)
  File "/opt/conda/lib/python3.10/site-packages/trl/trainer/sft_trainer.py", line 361, in train
    output = super().train(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/transformers/trainer.py", line 1859, in train
    return inner_training_loop(
  File "/opt/conda/lib/python3.10/site-packages/transformers/trainer.py", line 2012, in _inner_training_loop
    model, self.optimizer = self.accelerator.prepare(self.model, self.optimizer)
  File "/opt/conda/lib/python3.10/site-packages/accelerate/accelerator.py", line 1266, in prepare
Traceback (most recent call last):
  File "/opt/conda/lib/python3.10/site-packages/torch/distributed/c10d_logger.py", line 72, in wrapper
    return func(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py", line 1914, in broadcast
    result = self._prepare_deepspeed(*args)
  File "/opt/conda/lib/python3.10/site-packages/accelerate/accelerator.py", line 1652, in _prepare_deepspeed
        engine, optimizer, _, lr_scheduler = deepspeed.initialize(**kwargs)work = group.broadcast([tensor], opts)

  File "/opt/conda/lib/python3.10/site-packages/deepspeed/__init__.py", line 181, in initialize
torch.distributed.DistBackendError: NCCL error in: /opt/conda/conda-bld/pytorch_1711403380909/work/torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:1691, unhandled cuda error (run with NCCL_DEBUG=INFO for details), NCCL version 2.19.3
ncclUnhandledCudaError: Call to CUDA function failed.
Last error:
Cuda failure 2 'out of memory'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/root/hmohapa/peft/examples/sft/train.py", line 162, in <module>
    engine = DeepSpeedEngine(args=args,
  File "/opt/conda/lib/python3.10/site-packages/deepspeed/runtime/engine.py", line 262, in __init__
    self._configure_distributed_model(model)
main(model_args, data_args, training_args)  File "/opt/conda/lib/python3.10/site-packages/deepspeed/runtime/engine.py", line 1157, in _configure_distributed_model

  File "/root/hmohapa/peft/examples/sft/train.py", line 146, in main
    trainer.train(resume_from_checkpoint=checkpoint)
  File "/opt/conda/lib/python3.10/site-packages/trl/trainer/sft_trainer.py", line 361, in train
    self._broadcast_model()
  File "/opt/conda/lib/python3.10/site-packages/deepspeed/runtime/engine.py", line 1077, in _broadcast_model
    output = super().train(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/transformers/trainer.py", line 1859, in train
    dist.broadcast(p.data, groups._get_broadcast_src_rank(), group=self.seq_data_parallel_group)
  File "/opt/conda/lib/python3.10/site-packages/deepspeed/comm/comm.py", line 117, in log_wrapper
    return func(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/deepspeed/comm/comm.py", line 224, in broadcast
    return cdb.broadcast(tensor=tensor, src=src, group=group, async_op=async_op)
  File "/opt/conda/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 489, in _fn
    return fn(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/deepspeed/comm/torch.py", line 199, in broadcast
    return torch.distributed.broadcast(tensor=tensor, src=src, group=group, async_op=async_op)
  File "/opt/conda/lib/python3.10/site-packages/torch/distributed/c10d_logger.py", line 74, in wrapper
    msg_dict = _get_msg_dict(func.__name__, *args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/torch/distributed/c10d_logger.py", line 49, in _get_msg_dict
        "args": f"{args}, {kwargs}",return inner_training_loop(

  File "/opt/conda/lib/python3.10/site-packages/torch/_tensor.py", line 461, in __repr__
  File "/opt/conda/lib/python3.10/site-packages/transformers/trainer.py", line 2012, in _inner_training_loop
    return torch._tensor_str._str(self, tensor_contents=tensor_contents)
  File "/opt/conda/lib/python3.10/site-packages/torch/_tensor_str.py", line 677, in _str
    return _str_intern(self, tensor_contents=tensor_contents)
  File "/opt/conda/lib/python3.10/site-packages/torch/_tensor_str.py", line 597, in _str_intern
    tensor_str = _tensor_str(self, indent)
  File "/opt/conda/lib/python3.10/site-packages/torch/_tensor_str.py", line 331, in _tensor_str
    self = self.float()
torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 3.92 GiB. GPU 2 has a total capacity of 39.39 GiB of which 2.00 MiB is free. Process 112078 has 39.38 GiB memory in use. Of the allocated memory 37.36 GiB is allocated by PyTorch, and 76.66 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
    model, self.optimizer = self.accelerator.prepare(self.model, self.optimizer)
  File "/opt/conda/lib/python3.10/site-packages/accelerate/accelerator.py", line 1266, in prepare
    result = self._prepare_deepspeed(*args)
  File "/opt/conda/lib/python3.10/site-packages/accelerate/accelerator.py", line 1652, in _prepare_deepspeed
    engine, optimizer, _, lr_scheduler = deepspeed.initialize(**kwargs)
  File "/opt/conda/lib/python3.10/site-packages/deepspeed/__init__.py", line 181, in initialize
    engine = DeepSpeedEngine(args=args,
  File "/opt/conda/lib/python3.10/site-packages/deepspeed/runtime/engine.py", line 262, in __init__
    self._configure_distributed_model(model)
  File "/opt/conda/lib/python3.10/site-packages/deepspeed/runtime/engine.py", line 1157, in _configure_distributed_model
    self._broadcast_model()
  File "/opt/conda/lib/python3.10/site-packages/deepspeed/runtime/engine.py", line 1077, in _broadcast_model
    dist.broadcast(p.data, groups._get_broadcast_src_rank(), group=self.seq_data_parallel_group)
  File "/opt/conda/lib/python3.10/site-packages/deepspeed/comm/comm.py", line 117, in log_wrapper
    return func(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/deepspeed/comm/comm.py", line 224, in broadcast
    return cdb.broadcast(tensor=tensor, src=src, group=group, async_op=async_op)
  File "/opt/conda/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 489, in _fn
    return fn(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/deepspeed/comm/torch.py", line 199, in broadcast
    return torch.distributed.broadcast(tensor=tensor, src=src, group=group, async_op=async_op)
  File "/opt/conda/lib/python3.10/site-packages/torch/distributed/c10d_logger.py", line 74, in wrapper
    msg_dict = _get_msg_dict(func.__name__, *args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/torch/distributed/c10d_logger.py", line 49, in _get_msg_dict
    "args": f"{args}, {kwargs}",
  File "/opt/conda/lib/python3.10/site-packages/torch/_tensor.py", line 461, in __repr__
    return torch._tensor_str._str(self, tensor_contents=tensor_contents)
  File "/opt/conda/lib/python3.10/site-packages/torch/_tensor_str.py", line 677, in _str
    return _str_intern(self, tensor_contents=tensor_contents)
  File "/opt/conda/lib/python3.10/site-packages/torch/_tensor_str.py", line 597, in _str_intern
    tensor_str = _tensor_str(self, indent)
  File "/opt/conda/lib/python3.10/site-packages/torch/_tensor_str.py", line 331, in _tensor_str
    self = self.float()
torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 3.92 GiB. GPU 1 has a total capacity of 39.39 GiB of which 2.00 MiB is free. Process 112077 has 39.38 GiB memory in use. Of the allocated memory 37.36 GiB is allocated by PyTorch, and 76.66 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
[2024-05-02 06:49:11,955] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 322 closing signal SIGTERM
[2024-05-02 06:49:11,955] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 325 closing signal SIGTERM
[2024-05-02 06:49:13,221] torch.distributed.elastic.multiprocessing.api: [ERROR] failed (exitcode: 1) local_rank: 1 (pid: 323) of binary: /opt/conda/bin/python
Traceback (most recent call last):
  File "/opt/conda/bin/accelerate", line 8, in <module>
    sys.exit(main())
  File "/opt/conda/lib/python3.10/site-packages/accelerate/commands/accelerate_cli.py", line 46, in main
    args.func(args)
  File "/opt/conda/lib/python3.10/site-packages/accelerate/commands/launch.py", line 1060, in launch_command
    deepspeed_launcher(args)
  File "/opt/conda/lib/python3.10/site-packages/accelerate/commands/launch.py", line 764, in deepspeed_launcher
    distrib_run.run(args)
  File "/opt/conda/lib/python3.10/site-packages/torch/distributed/run.py", line 803, in run
    elastic_launch(
  File "/opt/conda/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 135, in __call__
    return launch_agent(self._config, self._entrypoint, list(args))
  File "/opt/conda/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 268, in launch_agent
    raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError:
============================================================
train.py FAILED
------------------------------------------------------------
Failures:
[1]:
  time      : 2024-05-02_06:49:11
  host      : d34443f89434
  rank      : 2 (local_rank: 2)
  exitcode  : 1 (pid: 324)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
------------------------------------------------------------
Root Cause (first observed failure):
[0]:
  time      : 2024-05-02_06:49:11
  host      : d34443f89434
  rank      : 1 (local_rank: 1)
  exitcode  : 1 (pid: 323)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
============================================================

Expected behavior
LoRA adapters should fine tune successfully

I also had OOM with Qlora #1708

@pacman100 Could you please take a look :pray:?


404s after updating AWS SDKs

After upgrading our project's aws-java-sdks from 1.12.565 to 1.12.638 we're seeing the following error with elasticmq queues:
Exception happened when try to receive messages from queue - com.amazonaws.services.sqs.model.AmazonSQSException: null (Service: AmazonSQS; Status Code: 404; Error Code: null; Request ID: null; Proxy: null) 09:28:24 user ERROR c.l.u.s.p.<OurProcessor><PHONE_NUMBER>33092391 Failed to receive message from queue: http://localhost:9324/queue/<our-queue>+ Cause: null (Service: AmazonSQS; Status Code: 404; Error Code: null; Request ID: null; Proxy: null)
Upgrading our version of elasticmq to 1.4.2 (we're currently on 0.13.10 :skull: ) doesn't solve the issue, either.  I'm wondering if there's some dependency I need to add to our project, or some other obvious gotcha that I'm missing.  The last time anyone touched our elasticmq implementation was 6 years ago.
We manage our elasticmq version via a chef cookbook, and manage our dependencies with maven.

I should have left a comment when closing (and I maybe closed the issue as something other than 'not planned').  Our issue was the elasticmq upgrade wasn't being applied properly.  After successfully upgrading and applying the latest version of elasticmq, we no longer experienced the issue.  I'll leave it to @micossow to re-close or investigate as needed.

Thanks for the updates. I tried iupgrading aws-java-sdks to1.12.750 and elasticmq-rest-sqs to 1.6.5 but it failed with the same 404 error. We need to upgrade aws-java-sdks to at least 1.12.638 which has fixes of OWASP high vulnerability (dependent library ion-java-1.0.2). Please provide what version of aws-java-sdks and elasticmq-rest-sqs fixed this 404 error.

@kirangubbi are you using the latest version w ElasticMQ? The latest version is tested against SDK 1.12.699. If you have any issues with the latest version, I would need more details to reproduce the issue.

Thanks for the updates. I tried upgrading aws-java-sdks to1.12.750 and elasticmq-rest-sqs to 1.6.5 but failed with the some date format issue. Then I changed elasticmq-rest-sqs to 1.5.3. It worked.

I am not entirely sure if it relates to ElasticMQ. I just went for 1.5.3 because after that version it has additional support for scala 3 which I thought might be causing this issue.

Ok, I'll close the issue then.


Dataset information is not displayed

Regarding the additional manual ingestion frontend included by SciCat4DAPHNE.
Clicking on a dataset in the list of avaliable datasets, to retrieve further informations, directs to an empty page. However, in the "original" SciCat frontend the details of the dataset are shown.

I guess this is due to the missing configuration in the provided example of the env.js file
missing (lately introduced) keys are
  "REACT_APP_EXTERNAL_DATASETS_URL": "http://###/datasets",
  "REACT_APP_SCICAT_DEFAULT_DS_OWNER": "c",
  "REACT_APP_SCICAT_DEFAULT_DS_GROUP": "b",
  "REACT_APP_SCICAT_DEFAULT_DS_OWNER_GROUP": "a",
  "REACT_APP_ROUTER_BASENAME": "upload"

Now, neither in the SciCat4Daphne frontend nor in the SciCat frontend details are displayed for any of the ingested datasets.
my env.js is as follows:
"""
window.env = {
"REACT_APP_API_URL": "http://localhost/api/v3",
"REACT_APP_API_URL2": "http://localhost",
"REACT_APP_STORE_KEY": "scicat",
"REACT_APP_ROUTER_BASENAME": "upload",
"REACT_APP_EXTERNAL_DATASETS_URL": "http://localhost/datasets",
"REACT_APP_SCICAT_DEFAULT_DS_OWNER": "c",
"REACT_APP_SCICAT_DEFAULT_DS_GROUP": "b",
"REACT_APP_SCICAT_DEFAULT_DS_OWNER_GROUP": "a",
}
"""


feat: implement TransferWaitTransaction to get tx and block

Implement B̶r̶o̶a̶d̶c̶a̶s̶t̶T̶r̶a̶n̶s̶a̶c̶t̶i̶o̶n̶s̶A̶n̶d̶W̶a̶i̶t̶ TransferWaitTransaction to get tx and block from single transaction based on this issue

Hi, you're welcome!
nice points, I fixed them.

Hi, shall I do something for rebase/merge? Or just wait?

Hi, i will merge when next version will be ready

Cool!


Join multiple meetings feature for CDF/LF Zoom account

I am hoping that "Join multiple meetings" feature could be enabled on the Spinnaker Zoom account. I needed this type of account to run the Spinnaker Gardening Days hackathon effectively and still don't have a China-friendly workaround to use for the June hackathon coming up. I will not be able to spin up multiple meetings for hackathon project teams using the Spinnaker account because each account can only have one active meeting (even on different devices).
This feature is only enabled on very large accounts, but it seems like the LF may have the critical mass. It would allow us to monitor events that use multiple zoom meetings for folks who need help, as well as inappropriate content or zoom bombing. Can someone please look into it?

Hi, any update on this? SGD is coming up June 25th and I'll need to figure out a free or inexpensive workaround asap if we can't use this feature

I guess this is a no?

@dnilasor I reached out to Brian Warner who is helping with program management in CDF that should be able to fulfill this request, sorry for the delay, we had a change in program management within CDF.

No worries, I understand! Thanks for your follow-up. I am putting together a workaround in case this doesn't work out but glad he can look into it, as I think everyone at the CDF would likely use this feature.

@dnilasor I'll find you on Slack and we can get this figured out.

@dnilasor the "join multiple meetings" option is not available with our Zoom accounts. I know this is late, but as a workaround for future events, Zoom breakout rooms are an option. Some projects also use Google Meet when needs like this arise. Please keep me posted with how I can help in the future.  -G


Change pack version in Hello.cproject.yml

Change pack version to 1.4.0

@RonanSynnottArm could you please elaborate why you suggest to revert to version 1.4.0 of the BSP rather than fixing it to the currently latest version 1.5.0 released in Feb. 2024? Is there something wrong with that version?

Is 1.5.0 available? The build fails to find this for me.
Also verified in MDK Pack manager.

https://www.keil.arm.com/packs/v2m_mps3_sse_300_bsp-arm/versions/
Have you run the check for updates recently in the MDK PackInstaller
or for CMSIS-Toolbox: cpackget update-index ?

Apologies. It is now updated.
I close this PR.


ngx-translate doesn't translate ng-container element

Current behavior
I have an element:
<ng-container [translate]="'name'">Name
ngx-translate doesn't translate it. But If I change the element to span, it works.
Expected behavior

ngx-translate should be able to translate ng-container element.
How do you think that we should fix this?

Minimal reproduction of the problem with instructions

Environment

ngx-translate version: X.Y.Z
Angular version: X.Y.Z

Browser:
- [ ] Chrome (desktop) version XX
- [ ] Chrome (Android) version XX
- [ ] Chrome (iOS) version XX
- [ ] Firefox version XX
- [ ] Safari (desktop) version XX
- [ ] Safari (iOS) version XX
- [ ] IE version XX
- [ ] Edge version XX

For Tooling issues:
- Node version: XX
- Platform:

Others:

Same for all custom elements, don't know is it ngx-translate or angular issue. I'm using translate pipe instead.

so this is expected result.

I'm not 100% sure. It's not because ng-container itself is not rendered and considerated a comment that a text node inside of it should be ignored. In other words:
<ng-container>something, whaterver it is, text or nodes</ng-container>

produces
something, whaterver it is, text or nodes
<!---->

A text node is still a node, a leaf to be more precise, it doesnt "disappear" because it's wrapped in a ng-container. The ng-container just allows to avoid to output a new node, like a span, that would just make the DOM heavier.


Feat: Support for theme variable names in the color field instead of hex

Summary
When picking a color, it would be a predefined set of theme color variables (i.e. primary, secondary, neutral, etc).
Basic example
{
  name: 'rawFrontmatter.background_color',
  component: 'color',
  label: 'Background Color',
  description: 'Edit the page background color here',
  colorFormat: 'hex',
  colors: [
    { label: 'Primary', color: theme.color.primary, value: 'primary' },
    { label: 'Secondary', color: theme.color.secondary, value: 'secondary' },
    { label: 'Neutral', color: theme.color.neutral, value: 'neutral' },
  ],
  widget: 'block',
},

Motivation
Currently, when colors are selected, the values are hardcoded in the data file. This can be problematic if developers wish to change specific colors in a color palette. If the color field supported theme values, we could leave the color values in the theme and simply render it's color in the color field. This way if the theme ever changes, all the selected colors throughout the site change as well.

You can use data files to set theme colors and then access those through a global screen plugin

You can use data files to set theme colors and then access those through a global screen plugin

@DirtyF oh yeah, I understand that I can use a global data file to define site wide colors. Currently I have a select field with color name options you can choose from. The problem with this is that I cannot see the color swatch associated with the color name in the field. It would be nice to be able to be able to use the color field to do this instead of a select field so I can see the color associated with the name.

@DirtyF oh yeah, I understand that I can use a global data file to define site wide colors. Currently I have a select field with color name options you can choose from. The problem with this is that I cannot see the color swatch associated with the color name in the field. It would be nice to be able to be able to use the color field to do this instead of a select field so I can see the color associated with the name.


Recursive nesting of the same plugin removes the settings menu

Recursive nesting of the same plugin in itself causes the settings menu for the inner copy of the plugin for not being initialised in structure mode
This is mostly due to some plugin rendering issue in structure mode

Wrong report, sorry

so is this fixed already (in 3.1.1)?
I'm a bit confused ;)

It was not an issue in the first place :)

so I have to create different plugins for each level of the menu?
or is the plugin init problem caused by something else?

There was no plugin init issue just a bogus error

Sorry, I'm still confused. :(
So what was/is the cause of the (plugin menu not shown for childs) problem and how can I fix it?


Missing Class files

The progammme loaded perfectly and ran until it produced the following error message:

AI-PhotoSorter % python main.py
Traceback (most recent call last):
File "/Users/robbw/XPLAY/AI-PhotoSorter/main.py", line 4, in
from ImageClassifier import ImageClassifier
ModuleNotFoundError: No module named 'ImageClassifier'

It appears that 'ImageClassifier' and 'PhotoQualityOrganizer' are missing.
Do you intend to provide them?

OOps! Wrote too soon.
I found the missing Class files in image_quality.py & Image_class_train.py respectively.
Now all I have to do is find out why main.py does not detect them.

The problem is that the Class file  import commands were not correct. Changing them in main.py as follows fixed this problem:

from Image_class_train import ImageClassifier
from image_quality import PhotoQualityOrganizer


Support previewing video and audio urls

https://owen-wilson-wow-api.herokuapp.com/

https://jsonhero.io/j/IjYzAVKIQps7


added 'r' to the word 'you' for the Login section.

LOL, I leave out 'r' all the time :)

Met too. I bet there are some more missing in the docs. Thanks!


Support compiling with GCC 13

When compiling with GCC 13, we get errors like this:
In file included from /home/jcmorin/jcmenv/aswf/OpenTimelineIO/src/opentime/rationalTime.cpp:4:
/home/jcmorin/jcmenv/aswf/OpenTimelineIO/src/opentime/rationalTime.h: In function ‘constexpr double opentime::v1_0::fabs(double)’:
/home/jcmorin/jcmenv/aswf/OpenTimelineIO/src/opentime/rationalTime.h:27:9: error: ‘uint64_t’ does not name a type
   27 |         uint64_t i;
      |         ^~~~~~~~
/home/jcmorin/jcmenv/aswf/OpenTimelineIO/src/opentime/rationalTime.h:10:1: note: ‘uint64_t’ is defined in header ‘<cstdint>’; did you forget to ‘#include <cstdint>’?
    9 | #include <limits>
  +++ |+#include <cstdint>
   10 | #include <string>
/home/jcmorin/jcmenv/aswf/OpenTimelineIO/src/opentime/rationalTime.h:29:10: error: ‘union opentime::v1_0::fabs(double)::<unnamed>’ has no member named ‘i’
   29 |     bits.i &= std::numeric_limits<uint64_t>::max() / 2;
      |          ^
/home/jcmorin/jcmenv/aswf/OpenTimelineIO/src/opentime/rationalTime.h:29:35: error: ‘uint64_t’ was not declared in this scope
   29 |     bits.i &= std::numeric_limits<uint64_t>::max() / 2;
      |                                   ^~~~~~~~
/home/jcmorin/jcmenv/aswf/OpenTimelineIO/src/opentime/rationalTime.h:29:35: note: ‘uint64_t’ is defined in header ‘<cstdint>’; did you forget to ‘#include <cstdint>’?
/home/jcmorin/jcmenv/aswf/OpenTimelineIO/src/opentime/rationalTime.h:29:43: error: template argument 1 is invalid
   29 |     bits.i &= std::numeric_limits<uint64_t>::max() / 2;

Like the note says, we need to include cstdint. This is also documented in https://gcc.gnu.org/gcc-13/porting_to.html#header-dep-changes.

Codecov Report

Merging #1605 (589f6b7) into main (23ff3f9) will not change coverage.
The diff coverage is n/a.

Additional details and impacted files

@@           Coverage Diff           @@
##             main    #1605   +/-   ##
=======================================
  Coverage   79.91%   79.91%
=======================================
  Files         197      197
  Lines       21731    21731
  Branches     4339     4339
=======================================
  Hits        17366    17366
  Misses       2213     2213
  Partials     2152     2152

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94.31% <ø> (ø)

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add (failing) test reversing contour starting & ending w/ CurveTo

Fixes #370

I implemented @behdad's idea of starting from the last point of the contour so we retain any implied closing line in the original contour. If the original contour has an explicit closing line then the reversed contour is identical to the one that FontTools' ReverseContourPen would produce.
With this (plus https://github.com/googlefonts/fontc/pull/340) I finally got Oswald's glyf table to be 100% identical to the one fontmake produces! 🎉


[chip-test] chip_sw_sysrst_ctrl_in_irq

Test point name
chip_sw_sysrst_ctrl_in_irq
Host side component
SystemVerilog+Rust
OpenTitanTool infrastructure implemented
Unknown
Contact person
msfschaffner
Checklist
Please fill out this checklist as items are completed. Link to PRs and issues as appropriate.

[x] Check if existing test covers most or all of this testpoint (if so, either extend said test to cover all points, or skip the next 3 checkboxes)
[x] Device-side (C) component developed
[x] Bazel build rules developed
[x] Host-side component developed
[x] HJSON test plan updated with test name (so it shows up in the dashboard)
[ ] Test added to dvsim nightly regression (and passing at time of checking)

I've started to develop the test. For progress please check: https://github.com/lowRISC/opentitan/pull/15594

I have completed the PR: https://github.com/lowRISC/opentitan/pull/15594 and now waiting for reviews. After merging I will check nightly regressions and close this issue.

PR merged, waiting for the nightly reports to close this Issue in case the test passes.

This test passing in the regression

Tests
latest
2022.10.26
2022.10.25
2022.10.24
2022.10.23
2022.10.22
2022.10.21
2022.10.17/12
2022.10.17/1
2022.10.17
Suite

196
chip_sw_sysrst_ctrl_in_irq
100
100
100
--
--
--
--
--
--
--
chip_sw_sysrst_ctrl_in_irq


Collaboration

Hi there.
I met this project in an event this past weekend. I'm interested to join the project but I'm willing to learn. I don't have (yet) enough coding skills (even though I can code some python). Where should I start? Thanks!

Hi and welcome, @victorwestmann. =]
I think the wiki is a good start point, there is a lot of information there. We have a gitter.im[0] channel too =]
0 - https://gitter.im/quokkaproject/quokka
[ ]'s (hugs)

Welcome @victorwestmann :smile:
A good start is try to running project in your machine and try to running tests.
[]'s

Welcome @victorwestmann there is a roadmap file https://github.com/quokkaproject/quokka/blob/master/roadmap.md with some prioritized issues.

Thanks!


Use sorted properties when saving configuration in NSwagStudio

It's quite annoying to see all JSON properties jumping around when saving new version of nswag.json (migrating from v13 to v14). We could introduce a one time breaking change which will make Studio save file following ordered JSON property names so that later VCS diffs etc would look nicer.
Included change for NSwag.Sample.NET80/nswag.json which shows how obsolete properties are removed and they are now sorted correctly after save via NSwagStudio.

NET 8 sample's nswag.json before:
{
  "runtime": "Net80",
  "defaultVariables": null,
  "documentGenerator": {
    "aspNetCoreToOpenApi": {
      "project": "NSwag.Sample.NET80.csproj",
      "msBuildProjectExtensionsPath": null,
      "configuration": null,
      "runtime": null,
      "targetFramework": null,
      "noBuild": false,
      "verbose": true,
      "workingDirectory": null,
      "requireParametersWithoutDefault": false,
      "apiGroupNames": null,
      "defaultPropertyNameHandling": "Default",
      "defaultReferenceTypeNullHandling": "Null",
      "defaultDictionaryValueReferenceTypeNullHandling": "NotNull",
      "defaultResponseReferenceTypeNullHandling": "NotNull",
      "defaultEnumHandling": "Integer",
      "flattenInheritanceHierarchy": false,
      "generateKnownTypes": true,
      "generateEnumMappingDescription": false,
      "generateXmlObjects": false,
      "generateAbstractProperties": false,
      "generateAbstractSchemas": true,
      "ignoreObsoleteProperties": false,
      "allowReferencesWithProperties": false,
      "excludedTypeNames": [],
      "serviceHost": null,
      "serviceBasePath": null,
      "serviceSchemes": [],
      "infoTitle": "My Title",
      "infoDescription": null,
      "infoVersion": "1.0.0",
      "documentTemplate": null,
      "documentProcessorTypes": [],
      "operationProcessorTypes": [],
      "typeNameGeneratorType": null,
      "schemaNameGeneratorType": null,
      "contractResolverType": null,
      "serializerSettingsType": null,
      "useDocumentProvider": true,
      "documentName": "v1",
      "aspNetCoreEnvironment": null,
      "createWebHostBuilderMethod": null,
      "startupType": null,
      "allowNullableBodyParameters": true,
      "output": "openapi.json",
      "outputType": "Swagger2",
      "assemblyPaths": [],
      "assemblyConfig": null,
      "referencePaths": [],
      "useNuGetCache": false
    }
  },
  "codeGenerators": {}
}

After save

Can't you just update the nswag.json with the new version and then the props should be stable as the order in c# stays? Do we really need reorder? I think alpha order is not optimal as the props which belong together are shuffled...


Custom seeder base class

Add to config seed_base_class

I realized that was asked this query https://github.com/robmorgan/phinx/issues/773

The ConfigInterface also needs this method. Which results in another minor version bump.

@rquadling, I added methods in the ConfigInterface

Thanks.

Is this looking to get merged anytime soon? Currently needing this on a project I am using.

I also need this on a project I'm working on - is this going to get merged?

Hi. Things have been a little slow. Will tag this for me to look at over the weekend.
Thank you for your patience.
Some unit tests would be nice.

I also need this on a project I'm working on - is this going to get merged?

I'm currently working on repeatables and I've incorporated this functionality into my branch. Not yet production ready but as I'm doing this both for Phinx and my employment, things are moving a lot faster.
Anyone wanting to clean up the PR would help the current codebase.

Looks like this can move forward then once the conflicts are resolved.

will attempt to resolve conflicts.
created copy of this branch for now: https://github.com/cakephp/phinx/pull/1512

this one can be closed, changes requested here are implemented in #1512

@dead23angel you can close this one in favor of https://github.com/cakephp/phinx/pull/1512

Closing in favor of #1512


fix: use gson 'lazily parsed number' for 'object to number' strategy

This commit makes a small change to our Gson factory so that it sets the "object to number" strategy to LAZILY_PARSED_NUMBER. The default value (DOUBLE) does not provide the fidelity needed when performing marshalling/unmarshalling steps involving generic datastructures like Map<String,Object>.
Signed-off-by: Phil Adams<EMAIL_ADDRESS>

Discussed this with @RICELLIS on slack and we agreed that moving forward with this change should be ok (famous last words perhaps :joy: ).

:tada: This PR is included in version 9.17.4 :tada:
The release is available on GitHub release
Your semantic-release bot :package::rocket:


GVRTextViewSceneObject: Add REALTIME as a new frequency

The GVRTextViewSceneObject is only refreshed at the specified frequency if
anything has changed.

Sorry, but in my opinion,
we could keep improving the GVRTextView to simplify the use of it and keep GVRTextViewSceneObject  deprecated.

OK. I will check with others about the de-deprecation. I also prefer GVRTextView because it is lighter.

@ragner
We keep falling back to the GVRTextViewSceneObject because it is just more straightforward and easy to use. This is the only reason; I am not fond of it either but was basically forced to use it in certain situations.
As long as the construction is a two-phase thing for GVRViewSceneObjects we will have to keep the GVRTextViewSceneObject around. Also I think we might have to view a textWidget somewhat differently than any-android-view (we may switch to an implementation that is not based on Android views for text for example). In a way the GVRViewSceneObject and GVRTextViewSceneObject are complementary, not competing with each other.
I personally think the de-deprecation is reasonable considering that for text specifically we keep using the deprecated one more frequently.

Overall the code looks good to me. @ragner Since you voiced your concern I will wait for us all to come to an agreement before merging it.

Think it would be cleaner to switch out the internals of one and replace it with the other (whichever is better). Having two ways to create a textview is not ideal.

More or less tried exactly that. We had apps that deadlocked (GLSurfaceView gets callbacks on the UI thread and obtains locks that its GL thread locks on too) if you just start waiting on the UI thread while on the GL thread which a swap like the suggested would lead to. Nowadays we can create everything off the GL thread but not all apps take advantage of that and onInit is still happening on the GL thread. Once we hide the GL thread from the apps things will be much easier.

@liaxim  I agree that the use of GVRTextViewSceneObject is easier than GVRViewSceneObject.
With the merge of https://github.com/Samsung/GearVRf/pull/468 is possible to improve GVRViewSceneObject/GVRTextView to simplify its construction to one phase too.

@ragner Are you ok with merging this the way it is?

Yes, it is ok.


Make NIOSSLCertificate._subjectAlternativeNames() return type non-optional

Motivation
NIOSSLCertificate._subjectAlternativeNames() can currently return nil or an empty _SubjectAlternativeNames collection. There is no semantic difference in these two cases.
Modification

support a nil nameStack in _SubjectAlternativeNames
make NIOSSLCertificate._subjectAlternativeNames() return type non-optional

Result
We now return an empty _SubjectAlternativeNames instead of nil
Source Compatibility
This is a non-breaking change even though this is public API because it has an underscore prefix. It is therefore unstable and allowed to change in a minor release.

Merging over the API breakage checker.


UI should present any errors occurred during any user action

Error's are being shown using Alert MUI component on top of the respective view.
Done