Empty mBA result for PASCAL-S dataset

Hi, can I know what is the reason of being NaN for mBA result only in PASCAL-S dataset?

First of all, bounday accuracy (mBA) is not designed for Low Resolution datasets (DUTS-TE, DUT-OMRON, ECSSD, HKU-IS, PASCAL-S). Second, unlike other datasets, PASCAL-S has multiple values other than 255 according to each object's saliency. In other words, while other datasets are labeled either 0 or 255 for each pixel, PASCAL-S has multiple values including 0 and 255 such as 128.
Long story short, because of such difference, mBA shows NaN.
Thanks

Thank you so much for your answer


Transition Debugger plugin to the Run plugin and cleanup editor Plugin

Description of Changes

[x] Wrote at least one-line docstrings (for any new functions)
[x] Added unit test(s) covering the changes (if testable)

[ ] Included a screenshot or animation (if affecting the UI, see Licecap)

Use the Run plugin for the debugger
move run settings from editor config to run config
make run_cell_copy work with the debugger by appending %%debug to the cell copy
Added buttons in the debugger plugin to start debugging from the debugger plugin
remove legacy run functions and signals from the editor plugin
remove focus_to_editor option that was now mostly unused (spyder-ide/spyder#17028 and spyder-ide/spyder#3221 are still ok) (It was a strange option anyway because focus_to_editor means "do nothing" and not focus_to_editor means focus to the console)
move debug-specific code from ipyconsole run function to the debugging plugin
run_cell doesn't have to deal with run_cell_copy because the copy is run as a selection
separate extra actions and context modifier (from line is not an extra action but a modifier)

Issue(s) Resolved

Fixes #
Affirmation
By submitting this Pull Request or typing my (user)name below,
I affirm the Developer Certificate of Origin
with respect to all commits and content included in this PR,
and understand I am releasing the same under Spyder's MIT (Expat) license.

I certify the above statement is true and correct:

@ccordoba12 This is ready for review

Quick review:

Added buttons in the debugger plugin to start debugging from the debugger plugin

I don't like this change because it makes the interface more complicated and crowded, and the toolbar of the Debugger plugin doesn't look good now:

So, please revert it.
Instead, I'd like to add the "Stop debugging" button (i.e. the blue square one) to the main toolbar, because I think it's missing to quickly stop debugging.

remove focus_to_editor option that was now mostly unused

What happens now if the user wants to run/debug something and move the focus automatically the IPython console?

What happens now if the user wants to run/debug something and move the focus automatically to the IPython console?

This feature doesn't work in the master now, and I am not sure why the user would want the focus to jump around. Wouldn't it make more sense to keep the focus where it is? What would be the usecase if that feature was fixed?

@ccordoba12 I reverted it:

You can launch from the debugger toolbar or the menu

This feature doesn't work in the master now

Yeah, I forgot about it. Then it's fine to remove that option.

One more thing: I noticed that you removed the force_wdir kwarg and I'd like to ask you: why is that not necessary anymore? I thought we needed something like that to run local code in remote kernels.

One more thing: I noticed that you removed the force_wdir kwarg and I'd like to ask you: why is that not necessary anymore? I thought we needed something like that to run local code in remote kernels.

All the logit is now in run_script


Need a full example

A full DSN example covering AdventureWorks or something similar would be needed. Examples to configure unixODBC for SQL Server are almost non-existent.

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

ID: 3cfdda44-e9f6-d18a-6fbb-311b12847f4e
Version Independent ID: 4755f473-a601-79d8-1de9-15e7bc18c51e
Content: Connecting using ODBC - SQL Server
Content Source: docs/connect/odbc/linux-mac/connection-string-keywords-and-data-source-names-dsns.md
Product: sql
Technology: connectivity
GitHub Login: @David-Engel
Microsoft Alias: v-daenge

@sb08854 Thanks for the feedback.
When you say "full DSN example", do you just mean the contents of the odbc.ini file? Because you can use any of the keywords and values listed in the DSN and Connection String Keywords and Attributes link at the top of the article to build your DSN entry. They are the same keywords you would use to build a connection string. And there are numerous connection strings examples around.
I'm adding some clarification in the article to note that DSNs are optional and how the simple DSN is the equivalent of a simple connection string. I'm hoping that clears things up for you.

David:
Thanks for responding and adding clarifications.
Here’s what I meant by a full DSN example:
http://www.unixodbc.org/odbcinst.html
Need a similar example for SQL Server 2019.
True that DSN and connection string are same info, but having a quick jumpstart sample would immensely help devs save time.
Srinivasan Balram, CTO
Marlabs
From: David Engel<EMAIL_ADDRESS>Reply-To: MicrosoftDocs/sql-docs<EMAIL_ADDRESS>Date: Monday, May 11, 2020 at 5:32 PM
To: MicrosoftDocs/sql-docs<EMAIL_ADDRESS>Cc: Srinivasan Balram<EMAIL_ADDRESS>Mention<EMAIL_ADDRESS>Subject: Re: [MicrosoftDocs/sql-docs] Need a full example (#4743)
@sb08854https://github.com/sb08854 Thanks for the feedback.
When you say "full DSN example", do you just mean the contents of the odbc.ini file? Because you can use any of the keywords and values listed in the DSN and Connection String Keywords and Attributeshttps://docs.microsoft.com/en-us/sql/connect/odbc/dsn-connection-string-attribute?view=sql-server-ver15 link at the top of the article to build your DSN entry. They are the same keywords you would use to build a connection string. And there are numerous connection strings examples around.
I'm adding some clarification in the article to note that DSNs are optional and how the simple DSN is the equivalent of a simple connection string. I'm hoping that clears things up for you.
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Hi whether is it the right place to ask or not. I just started working on ODBC driver 17 and want to know a little bit more about it.

ODBC uses TLS for it's communication or it can work without TLS also?
What happens if I disable all TLS(1.0, 1.1, 1.2) from server will it still connect to sql database?

I go through everywhere but did not find this detailed information about it or you can tell me reference also where I can find about it?

@arzoo1995 -- this could help:
https://support.microsoft.com/en-us/help/3135244/tls-1-2-support-for-microsoft-sql-server


Support for conversion ceylon sequential to native js array.

Currently is conversion supported only for Tuple type.
//Ceylon
shared String[] names = ["one", "two"];

// JS - names is backed by Tuple.
// so we can use nativeArray function to convert into js array
var nativeJsArray = getNames().nativeArray()

Another example where nativeArray is missing.
//Ceylon
shared String[] names = {"one", "two"}.sequence();

// JS - names is backed by ArraySequence.
// where nativeArray function is missing
var nativeJsArray = getNames().nativeArray() // RUNTIME ERROR

It should be supported for all sequential types.

It seems that we're not doing this one. However I wonder it seems like Tuple should not expose its nativeArray() method. @chochos isn't that a problem?
Anyway, I'm closing this issue.

Tuple.nativeArray() is only used in an internal method of the js language module. I can modify nativeArray to return a copy (using slice(0)) instead of returning its internal array, to avoid any problems with the internal structure of the Tuple.


Delete unnecessary spaces

Delete unnecessary spaces: When copying from the web page, there is an unnecessary space after the code.
Fix indent: Indentation is misaligned.

@changeworld : Thanks for your contribution! The author, @msmbaldwin, has been notified to review your proposed change.

Thanks for the contribution, @changeworld!  These fixes look good, so I #sign-off.

@changeworld - Thank you for your submission. We are not able to accept these kinds of changes (formatting and spacing changes) in the repo at this time.


Training model with over 100 intents

Rasa NLU version - 0.11.3
Used backend / pipeline - spacy
Operating system - Windows
I have a few questions regarding how to train the a model with multiple intents.

What is the best way to train abbreviations or single words? e.g. when someone only says NLP (Natural Language Processing) as an expression.

As we have over a 100 intents, what is the best way to train the model, any techniques to use to ensure accuracy.

How does the NLP calculate the confidence of the intent as sometimes it picks a wrong intent which does not contain a word of the expression in the training data.

Thanks

You should train abbreviations just like normal text. One thing to look out for: they are usually not part of the word vector space and therefore their influence on the intent classification is limited - we are currently working on a model that can get around this limitation. Otherwise, it is good to add the intent_featurizer_ngrams to the processing pipeline.
Best is to train the model and take a look at the evaluation to see which intents get confused. This will provide you insights: 1. where to collect more training data 2. if it makes sense to restructure your intents
The meaning of the confidence scores depends on the underlying ML model used. For the sklearn classifier it is the distance from the hyperplane separating the classes. We don't use similarity based models (which compute the similarity of a test sentence with the training data), depending on how much training data you have, an SVM can be wrong while at the same time producing a high confidence.


updating log profile fails... setRetention

This is autogenerated. Please review and update as needed.
Describe the bug
Command Name
az monitor log-profiles update
Errors:
Parameter 'LogProfileResource.location' can not be None.
Traceback (most recent call last):
python3.7/site-packages/knack/cli.py, ln 206, in invoke
    cmd_result = self.invocation.execute(args)
cli/core/commands/__init__.py, ln 523, in execute
    raise ex
cli/core/commands/__init__.py, ln 581, in _run_jobs_serially
    results.append(self._run_job(expanded_arg, cmd_copy))
...
python3.7/site-packages/msrest/serialization.py, ln 221, in validate
    Serializer.validate(value, debug_name, **self._validation.get(attr_name, {}))
python3.7/site-packages/msrest/serialization.py, ln 662, in validate
    raise ValidationError("required", name, True)
msrest.exceptions.ValidationError: Parameter 'LogProfileResource.location' can not be None.

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

Put any pre-requisite steps here...
az monitor log-profiles update --name {} --set {}

Expected Behavior
Environment Summary
Darwin-18.7.0-x86_64-i386-64bit
Python 3.7.4
Shell: bash

azure-cli 2.0.63 *

Extensions:
aks-preview 0.4.14

Additional Context

resolved: added location= to the string
please update the online docs for the update option


fix: Click event firing twice in Next.js applications

Summary
This package would have some strange behaviors in Next.js where the file upload window would pop up twice when the upload button was clicked. Upon investigation it was seen that the click event fired twice when the component was clicked. Adding a event.stopPropagaiton to the UploadWrapper and the input component stops this behavior, allowing the component to function as intended.
Key Changes

Added event.stopPropagation to the UploadWrapper and input components.

Check List

[ ] The changes to the "Readme" file(if needed)
[ ] The changes not breaking any old rule of the library or usage

Added necessary colon to title.

All necessary changes to pass semantic checks have been completed. Please revisit this pull rquest.

@room9design Thank you so much for your contribution.  could you please fix the semantic pull request issue?

I'm confused, is there a place other than inside Github where I need to change the pull request title?

@KarimMokhtar I'm confused, is there a place other than inside Github where I need to change the pull request title?

I think you need to change the commit itself from “Fix” to “fix”

@KarimMokhtar Recommitted with updated commit message.


Sounds not working

Not sure if the client itself has sound settings that I've changed accidentally and unknowingly. But I have no game sounds at all and can't seem to find out why. Sounds work on osrs client, and konduit. But not runelite. Please help me fix this

Someone mentioned in Discord that this can happen when the client is started in low detail mode - I can confirm this is one potential cause. Try disabling low detail mode and restarting the client.

Duplicate of #696

So how am I able to fix this issue?
On Thu, 5/17/18, Tomas Slusny<EMAIL_ADDRESS>wrote:
Subject: Re: [runelite/runelite] Sounds not working (#2782)
To: "runelite/runelite"<EMAIL_ADDRESS>Cc: "tonydelo1"<EMAIL_ADDRESS>"Author"<EMAIL_ADDRESS>Date: Thursday, May 17, 2018, 1:52 PM
Duplicate of #696
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Check https://github.com/runelite/runelite/issues/696


Allow for imports when path includes a query string. Fixes #4181

Fixes #4181
Summary
Jest fails to apply a transform to a file if the import path includes query parameters. A detailed description can be found here.
Test plan
Added tests to ensure this is functioning properly.

What happens when I want to do: require('foo?a') and require('foo?b')? What will happen in various bundlers that support this syntax? If they are two JS files, will they be the same module instance or will they evaluate the possibly same code twice? Are they to be considered separate modules, or the same ones?
With this change to Jest, what is the behavior and how does it diverge or match those bundlers?

Codecov Report

Merging #4549 into master will increase coverage by 0.78%.
The diff coverage is 100%.

@@            Coverage Diff             @@
##           master    #4549      +/-   ##
==========================================
+ Coverage   56.04%   56.82%   +0.78%
==========================================
  Files         185      186       +1
  Lines        6268     6305      +37
  Branches        3        3
==========================================
+ Hits         3513     3583      +70
+ Misses       2754     2721      -33
  Partials        1        1

Impacted Files
Coverage Δ

packages/jest-resolve/src/index.js
37.27% <100%> (+0.57%)
:arrow_up:

.../eslint-plugin-jest/src/rules/no_disabled_tests.js
94.44% <0%> (-2.78%)
:arrow_down:

packages/jest-diff/src/diff_strings.js
98.86% <0%> (-0.03%)
:arrow_down:

...es/jest-cli/src/lib/handle_deprecation_warnings.js
0% <0%> (ø)
:arrow_up:

packages/jest-snapshot/src/index.js
42.22% <0%> (ø)
:arrow_up:

packages/jest-config/src/deprecated.js
75% <0%> (ø)
:arrow_up:

packages/jest-config/src/normalize.js
78.14% <0%> (ø)
:arrow_up:

packages/jest-cli/src/pattern_prompt.js
100% <0%> (ø)
:arrow_up:

packages/jest-cli/src/lib/update_global_config.js
90% <0%> (ø)
:arrow_up:

packages/pretty-format/src/plugins/dom_element.js
100% <0%> (ø)
:arrow_up:

... and 169 more

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At a high-level, my primary goal was to circumvent the error thrown by Jest's resolver for module paths that included query string. For my specific use-case, I do not necessarily care about the resulting imported module. I am mocking the image file entirely.
However, I do believe you raise good questions. Unfortunately, I'm not confident on the best answers to them nor the approach to resolving them. If you have a desired outcome for your scenarios or recommendations on implementing the changes, it'd be appreciated if you provide some guidance. I am happy to explore executing your recommendations.
Thank you in advance. I greatly appreciate your time and the investments your team has made in Jest. Our team loves Jest!

I'm going to close this PR because of the unanswered questions but I am not opposed to merging this if somebody takes the time to fully think this through. For now, I recommend using moduleNameMapper to strip of the bits of the URL at the end.

Is this something we can otherwise solve with a custom resolver so that we don't have to ship this within Jest by default?

This was my first try too. As far as I understand Jest's internals, the resolver is used for only mapping from a module name to a module filesystem path. And this mapping is 1:1 and also cached. The file content loaded from the mapped path is also cached in a two layers of caching. All this happens outside of the resolver's code.

Maybe we can make some adjustments to enable use of a custom resolver for this.

This would be the right modular way to go. Shell I try creating a proof of concept PR then?

Go for it!

Here it comes with more text than code 😂
https://github.com/facebook/jest/pull/6282

@cpojer btw, if the resource queries topic is not so well fitting into Jest, maybe there is another way to hook into and get dynamic mocking possible? I'm super excited with what's new in Jest 23 and I'd be happy to see even more test automation (like we've done in Introscope) built into Jest 24 ;)


Fixes all of the remaining Grunt code styling warnings.

@rwaldron this gets us to the point where Grunt is finally satisfied:

I'm happy to rebase if you'd like us to get #143 gets merged first.


Spring Cloud Gateway doesn't add X-Forwarded-Prefix header

I have working zuul config and wanted to convert it to spring cloud gateway but I'm hitting a problem with the spring data rest / hateos response from my back-end api.
I have my gateway at http://localhost:8888 and access the api with http://localhost:8888/api/foo, the backend api server is at http://localhost:7777
The problem is that the /api is missing from the hateoas generated URIs in the response coming back.
The zuul config I'm replicating is as follows
application.properties
server.port=8888

zuul.routes.api.path=/api/**
zuul.routes.api.url=http://localhost:7777

zuul.add-proxy-headers=true
ribbon.eureka.enabled=false

The config for spring cloud gateway is as follows
application.properties
server.port=8888

spring.cloud.gateway.forwarded.enabled=true
spring.cloud.gateway.x-forwarded.enabled=true
spring.cloud.gateway.x-forwarded.forEnabled=true
spring.cloud.gateway.x-forwarded.protoEnabled=true

java api routes config
    @Bean
    public RouteLocator routeLocator(RouteLocatorBuilder builder) {
        return builder.routes()
                .route("api", r ->
                        r.path("/api/{segment}")
                            .filters(f -> f
//                                    .stripPrefix(1)
                                    .setPath("/{segment}")
                            )
                            .uri("http://localhost:7777")
                )
                .build();
    }

I've tried with and without the x-forwarded properties and it's the same with setPath and stripPrefix and I'm out of ideas now.

Did a bit more investigation comparing headers added by zuul and S-C-G and it seems the difference is the X-Forwarded-Prefix header isn't being added

I've created a minimal app for another gateway issue with chains of proxies that I'll be opening but it also shows this issue https://github.com/RobMaskell/gatewayissues.
To reproduce, bootRun the 'api' and 'web' modules in the IDE and access http://locahost:8888/api/ and look at the resulting json response, note the lack of /api in the Spring Hateoas generated URIs (because it uses spring URI builder that picks up on x-forwarded-prefix).
Don't know if this is an enhancement or needs documenting as unsupported but I feel Spring projects should be consistent in support for headers especially in simple cases like the StripPrefix filter.
If considered a valid bug or enhancement then I can look at submitting a PR for StripPrefix at least.

I am also hitting this in Kubernetes (on AWS). I saw the same behaviour with nginx ingress before adding the annotation 'nginx.ingress.kubernetes.io/x-forwarded-prefix: "true"'. I wonder if the problem is just that XForwardedHeadersFilter doesn't included X-Forwarded-Prefix. I am trying to find out. (I notice from RobMaskell/gatewayissues that @RobMaskell was able to add a filter with "- AddRequestHeader=x-forwarded-prefix, /api" but I am using discovery locator so don't have a fixed path I can use to work around this.)

There is a header called X-Original-URI that contains the path with the prefix in front. From this it is possible to extract the prefix. I tried overriding the XForwardedHeadersFilter class and found that it did then set the prefix and the links in the hateoas are then correct.

I don't remember seeing that header in my case so maybe that will just work for you and not be a generic solution?

Might be that it's an nginx thing. @RobMaskell Were you getting to the gateway via an ingress?

@ryandawsonuk an Ingress yes but a garden variety GCP HTTP LB Ingress


[MRG] Return variance of decision tree predictions

Minor modifications to sklearn's tree predict method to return predictions.

@glouppe @betatim

Nice! I didn't realise that the impurity in a leaf would be the variance. It is such a simple solution: totally rad bro!
Some thoughts:

[ ] should we use a different name instead of overloading DecisionTreeRegressor? I am +0 on a new name but don't have a good suggestion right now 😦
[ ] could you add a test like (or similar, if we make a better one we should backport it to GBRT)

rgr = DecisionTreeRegressor(max_depth=3)
rng = np.random.RandomState(1)
N = 10000
X = np.ones((N, 1))
y = rng.normal(size=N)
rgr.fit(X, y)
estimates = rgr.predict(X, return_variance=True)
assert_almost_equal([0, 1], np.mean(estimates, axis=1))

[ ] do we need to check that the impurity criterion is set to MSE?

School time: why is the impurity the variance? It must be connected to the splitting criterion being MSE right? If there is an easy way to see this I'd love a short explanation. Or is it as simple as this?

School time: why is the impurity the variance? It must be connected to the splitting criterion being MSE right? If there is an easy way to see this I'd love a short explanation. Or is it as simple as this?

It matches with the variance because the prediction at a leaf is the mean node value. See eq. 3.22 in https://arxiv.org/pdf/1407.7502.pdf

should we use a different name instead of overloading DecisionTreeRegressor? I am +0 on a new name but don't have a good suggestion right now

Not sure this is necessary. I am fine with reusing the same name, as we are anyway overloading sklearn's tree with a drop-in replacement that should be compatible wherever sklearn's is.

do we need to check that the impurity criterion is set to MSE? (MSE is the default and only choice right now)

+1, better be safe

Thanks Manoj! Besides the small comments, +1 for this.

👍 from me too

@betatim It is good to note that the mean square is the same as the variance in predictions since the predicted value is the mean. Thanks for that! (Not that it matters but I did not see that the split criterion is set to "mse" and not "impurity").

I'm going to merge when Travis passes. Feel free to address any nitpicks in follow-up PR's. Thanks!

Nice work 🎆


chore: community templates individual commits

Do not review or merge

/build-deploy-preview

No longer needed.


Samanta Content Evaluator

Frontend:
Create a frontend page with no unnecessary scrolling or spacing issues.
Sample:

Experience Button:

On click :
Validate user-entered email address.
If valid:

Check occurrences via "User Email Info" API and show in popup.
If occurrences are:

0: Hit "Register User" API, change button text to "Check".
2, 3, 4, 5: Change button text to "Check".
6 or above: Change button text to "Contribute".

Sample:

Check Button:

On click :
Hit "Samanta Content" API.
Show result in a popup with occurrences.

Sample:

Contribute Button:

If occurrences are 4 or 5 :
Add a "Contribute" button alongside "Check" button in the popup.
On click, redirect user to https://dowellpay.online/contribute-payment/?product_number=UXLIVINGLAB001.
If occurrences are 6 or above :
Show "Contribute" button in the popup.
On click, redirect user to https://dowellpay.online/contribute-payment/?product_number=UXLIVINGLAB001.

Coupon Functionality:

If occurrences are 6 or above :
Provide a field to enter a coupon code and redeem.
Upon successful redemption, show the result.

Email Result:

After generating results :
Provide option to send the result to user's email.

Sample:

Rating Scale:

Alongside the result :
Offer a rating scale for user feedback.

Sample:

Content Input Field Design: The content input field should not be a text area.

Contribute Button Position and Color: Change the color of the contribute button and position it on the left, while placing the check button on the right.

Experience Button Behavior: Check if content length exceeds 60 characters; if below 60, disable the experience button to prevent user interaction.

Modal White Spaces: In the Samanta Content Evaluator modal, reduce unnecessary white spaces to eliminate scrolling and enable users to view all content seamlessly.


cant enable modules (IVR,RingGroups,TimeConditions)

Hi,
Im installing modules like Ring Groups, IVR and more. But nothing shown in Application menu?
What am i doing wrong?

I think this is related to this #119
I had the same issue that Ring Groups doesn't show up. I tried to go into the shell of the container and using this command to install the module.
fwconsole ma install ringgroups

Then, refresh the page and it works.
Hope this will help

Problem seems to have been resolved with latest Freepbx Upstream release. Cannot Recreate on new tiredofit/freepbx:15-4.0.0 release.


Pytest integration

Allow using dishka container to provide fixtures

im one of the pytest maintainers, im very curious about enabling this as i want to start using dishka more in a number of projects that have some needs and we will need pytest fixtures
we are plying with ideas on how to repressent pytest fixtures in a more interoperable manner and would love to get input on both - making pytest fixutres more easy to integrate as well as easing other di tools to cooperate with pytest nicely
a key pain point is probably that pytest currently works name based instead of type based

I though about several approaches here:

Generate fixures manually. So the code will be like

x = dishka_fixture("x", Class)

which will be transformed to
def x(dishka_container):
   return dishka_container.get(Class)

Decorate tests and fixtures using @inject decorator as in all integrations. So it will erase all FromDishka arguments and add dishka_container

@inject
def test(a, x: FromDishka[Class]):
   ...

will be transofmed to something similar to
def test(a, dishka_container):
   _real_test(a, dishka_container(Class))

Automatically wrap all tests and fixtures like in 2. I tried to achieve it here: https://github.com/reagento/dishka/commit/dc8c8a099dd1079a248205c6e383aabc606723dd but it works only with tests, not fixtures

As a PoC
def dishka_fixture(cls: Any):
    def temp_fixture(dishka_container):
        return dishka_container.get(cls)

    return pytest.fixture(temp_fixture)

@pytest.fixture
def dishka_container():
    p = Provider(scope=Scope.APP)
    p.provide(lambda: 12, provides=int)
    return make_container(p)

someint = dishka_fixture(int)

def test_x(someint):
    assert someint == 12

It's critical to set the name, else thing's will turn bleak

@RonnyPfannschmidt is it? I did only small test and that code looks working. I would prefer not to set name twice for same fixture

I may be missremember the exact factory parsing behavior
I'll validate later

@RonnyPfannschmidt is there any hook to modify any fixture/test?

There's no hook to modify dependency injection yet
Same goes for discovery of fixtures
I'd like a discussion on enabling discovery of more fixtures and matching them to parameters

@RonnyPfannschmidt
a) we can patch fixtures and tests so they will call container by themselves. It doesn't require modification of pytest DI engine. But it requires some kind middlewares/hooks which will be applied to any created test/fixture. I showed prototype for wrapping tests here https://github.com/reagento/dishka/commit/dc8c8a099dd1079a248205c6e383aabc606723dd
b) probably pytest can be modified to lookup fixtures not only by name, but by types. In this case we need some fixture factory protocol in pytest implemented by dishka. This looks much more complicated for me

not to mention that pytest has own fixture scopes
instead of patching tests and fixtures, i'd like to have a way to be a intermediate between the container and the pytest system (so the normal fill fixture mechanisms can be used)
my current impression is that in the case of pytest, a certain focus on controlling the call signatures from the test framework is necessary

Regarding scopes my point is that main logic of scopes is related to application running inside test, so here we do not need anything. If user needs some logic on synchronizing container scope for tests (e.g, Scope.RUNTIME=="session" and Scope.APP=="funcion") it can be implemented in his dishka_container fixture
On topic of container managements I am interested in implementing integration which will be available on current versions of pytest, but it will be manual (using @inject or dishka_fixture as shown above). But for autoinjected I definitely need changes in pytest logic.
Do you have any issues/plans for this in pytest repo?

No plans or issues yet, the pre planning stage is literally starting now (in part because dishka is nicely inspiring)

Another open question here is pytest-asyncio support. We have 2 versions of container now: sync and async. I guess, user might want to use async container to get fixtures for non-async test. And vice versa.

https://github.com/pytest-dev/pytest/pull/12473 is working on replacing fixtures with a more specific object
i believe that can be a starting point for more


sdxl_train_network.py: Lora trains poorly and produces terrible results

I tried 10 times to train lore on Kaggle and google colab, and each time the training results were terrible even after 5000 training steps on 50 images.
I use this sequence of commands:
%cd /content/kohya_ss/finetune
!python3 merge_captions_to_metadata.py --caption_extention .txt /content/dataset /content/dataset.json
!python3 prepare_buckets_latents.py --bucket_reso_steps 64 --min_bucket_reso 1024 --max_bucket_reso 1024 /content/dataset /content/dataset.json /content/dataset_lat.json "femboysLover/blue_pencil-fp16-XL"

%%writefile /content/prompt.txt
masterpiece, best quality, one eye closed, solo, 1girl --w 1024 --h 1024 --d 123

%cd /content/kohya_ss
!python3 "./sdxl_train_network.py" --in_json "/content/dataset_lat.json" --pretrained_model_name_or_path="femboysLover/blue_pencil-fp16-XL" --train_data_dir="/content/dataset" --resolution="1024,1024" --output_dir="/content/out" --network_alpha="16"  --network_dim "32" --save_model_as=safetensors --network_module=networks.lora --output_name="last" --no_half_vae --learning_rate="0.0001" --lowram --lr_scheduler="constant" --train_batch_size="4" --max_train_steps="5000" --save_every_n_steps="100" --mixed_precision="fp16" --save_precision="fp16" --seed="12345"  --optimizer_type="AdamW8bit" --min_snr_gamma=5 --mem_eff_attn --gradient_checkpointing --full_fp16 --xformers --sample_sampler=euler_a --sample_prompts="/content/prompt.txt" --sample_every_n_steps="50" --network_train_unet_only --cache_text_encoder_outputs

I noticed this in the sdxl_train_network execution log:
number of images (including repeats) / 各bucketの画像枚数（繰り返し回数を含む）
bucket 0: resolution (256, 1024), count: 8
bucket 1: resolution (512, 512), count: 42
mean ar error (without repeats): 0.0

Could this break LoRA?  And if so, how to fix it?

You don't want to train SDXL with 256x1024 and 512x512 images; those are too small. You should use 1024x1024 resolution for 1:1 aspect ratio and 512x2048 for 1:4 aspect ratio. Did you disable upscaling bucket resolutions?

You don't want to train SDXL with 256x1024 and 512x512 images; those are
too small. You should use 1024x1024 resolution for 1:1 aspect ratio and
512x2048 for 1:4 aspect ratio. Did you disable upscaling bucket resolutions?

I fixed the bucket resolution.
%cd /content/kohya_ss/finetune
!python3 prepare_buckets_latents.py --bucket_reso_steps 64
--min_bucket_reso 1024 --max_bucket_reso 1024 --max_resolution 1024,1024
/content/dataset /content/dataset.json /content/dataset_lat.json
"femboysLover/blue_pencil-fp16-XL"

%cd /content/kohya_ss
!python3 "./sdxl_train_network.py" --in_json "/content/dataset_lat.json"
--network_weights "/content/out/last-step00000050.safetensors"
--pretrained_model_name_or_path="femboysLover/blue_pencil-fp16-XL"
--train_data_dir="/content/dataset" --resolution="1024,1024"
--output_dir="/content/out" --network_alpha="16" --network_dim "32"
--save_model_as=safetensors --network_module=networks.lora
--output_name="last" --no_half_vae --learning_rate="5e-5" --lowram
--lr_scheduler="constant" --train_batch_size="4" --max_train_steps="5000"
--save_every_n_steps="50" --mixed_precision="fp16" --save_precision="fp16"
--seed="12345" --optimizer_type="AdamW8bit" --min_snr_gamma=5
--mem_eff_attn --gradient_checkpointing --full_fp16 --xformers
--sample_sampler=euler_a --sample_prompts="/content/prompt.txt"
--sample_every_n_steps="50" --network_train_unet_only
--cache_text_encoder_outputs

The result is still terrible, with a high LR it turns out to be some kind
of mess, and with a low LR the model simply slowly degrades and the style
changes.
I tried it on different datasets, currently as a test I’m using 48 images
from safebooru for the one_closed_eye tag.
Message ID: @.***>

Here is an example image at 600 training steps
%cd /content/kohya_ss
!python3 "./sdxl_train_network.py" --in_json "/content/dataset_lat.json"  --pretrained_model_name_or_path="femboysLover/blue_pencil-fp16-XL" --train_data_dir="/content/dataset" --resolution="1024,1024" --output_dir="/content/out" --network_alpha="32"  --network_dim "64" --save_model_as=safetensors --network_module=networks.lora --output_name="last" --no_half_vae --learning_rate="1.0" --scale_weight_norms=1 --lr_scheduler="adafactor" --lr_scheduler_num_cycles="1" --train_batch_size="4" --max_train_steps="5000" --save_every_n_steps="50" --mixed_precision="fp16" --save_precision="fp16" --seed="12345"  --optimizer_type="adafactor" --mem_eff_attn --gradient_checkpointing --full_fp16 --xformers --sample_sampler=euler_a --sample_prompts="/content/prompt.txt" --sample_every_n_steps="50" --network_train_unet_only --cache_text_encoder_outputs --lowram

.0001

50 steps

200 steps

%cd /content/kohya_ss
!python3 "./sdxl_train_network.py" --in_json "/content/dataset_lat.json"  --pretrained_model_name_or_path="femboysLover/blue_pencil-fp16-XL" --train_data_dir="/content/dataset" --resolution="1024,1024" --output_dir="/content/out" --network_alpha="32"  --network_dim "64" --save_model_as=safetensors --network_module=networks.lora --output_name="last" --no_half_vae --learning_rate="0.0001" --scale_weight_norms=1 --lr_scheduler="adafactor" --lr_scheduler_num_cycles="1" --train_batch_size="4" --max_train_steps="5000" --save_every_n_steps="50" --mixed_precision="fp16" --save_precision="fp16" --seed="12345"  --optimizer_type="adafactor" --mem_eff_attn --gradient_checkpointing --full_fp16 --xformers --sample_sampler=euler_a --sample_prompts="/content/prompt.txt" --sample_every_n_steps="50" --network_train_unet_only --cache_text_encoder_outputs --lowram

I've never used the full_fp16 or lowram settings before, so I don't know if those could be negatively affecting your results. Are you using the "#_trigger class" naming convention in your image inputs folder?

I've never used the full_fp16 or lowram settings before, so I don't know if those could be negatively affecting your results. Are you using the "#_trigger class" naming convention in your image inputs folder?

No, I don't use it, as this is so much I know for dreambooth training, and I train LoRA based on tags with safebooru

Could you please test with the basic settings, such as AdamW optimizer, constant scheduler, network_alpha=1, learning rate=1e-4?

NaN loss and black images
%cd /content/kohya_ss
!python3 "./sdxl_train_network.py" --in_json "/content/dataset_lat.json"  --pretrained_model_name_or_path="femboysLover/blue_pencil-fp16-XL" --train_data_dir="/content/dataset" --resolution="1024,1024" --output_dir="/content/out" --network_alpha="1"  --network_dim "64" --save_model_as=safetensors --network_module=networks.lora --output_name="last" --no_half_vae --learning_rate="1e-4" --lr_scheduler="constant" --train_batch_size="4" --max_train_steps="5000" --save_every_n_steps="50" --mixed_precision="fp16" --save_precision="fp16" --seed="12345"  --optimizer_type="adamw" --mem_eff_attn --gradient_checkpointing --full_fp16 --xformers --sample_sampler=euler_a --sample_prompts="/content/prompt.txt" --sample_every_n_steps="50" --network_train_unet_only --cache_text_encoder_outputs --lowram

bf16

It seems google colab with T4 does not support bf16

I'm guessing you are using this implementation in Colab.
Couple of things I'd like to check

What is the original resolution of the images you are using?
You seem to have selected the "cache_text_encoder_outputs", have you tried training without that?
You mentioned Dreambooth training, but AFAIK, that needs regularizartion images, which I have been able to get working yet. (If you did, TEACH ME SEMPAI)
Can you paste the output from the "Bucketing and Latents Caching" step?
Have you tried setting a VAE?
You mentioned testing a few datasets, have you tried on different models?
I think you are training on the model you'd like to use. Prehaps try training on base SDXL model then apply the Lora on that model?
It seems like you are trying to train the "one eye closed" concept. If that's the case, I'm under the impression that for best results, you'd need a larger than 50 image sample size, and to carefully tag everything about the images. (I like to refer to this resource as a guide)

I've been getting decent results on my first try and here are my bucketing and latent caching output (Ignore the total numbers, I'm obsessed and went overboard)

Found 379 images.

And Training Config output

[sdxl_arguments]
cache_text_encoder_outputs = false
no_half_vae = true
min_timestep = 0
max_timestep = 1000
shuffle_caption = true
lowram = true

[model_arguments]
pretrained_model_name_or_path = "stabilityai/stable-diffusion-xl-base-1.0"
vae = "/content/vae/sdxl_vae.safetensors"

[dataset_arguments]
debug_dataset = false
in_json = "/content/LoRA/meta_lat.json"
train_data_dir = "/content/drive/MyDrive/LoRA/train_2"
dataset_repeats = 5
keep_tokens = 0
resolution = "1024,1024"
color_aug = false
token_warmup_min = 1
token_warmup_step = 0

[training_arguments]
output_dir = "/content/drive/MyDrive/kohya-trainer/output/Blue_Waifu"
output_name = "Blue_Waifu"
save_precision = "fp16"
save_every_n_epochs = 1
train_batch_size = 5
max_token_length = 225
mem_eff_attn = false
sdpa = true
xformers = false
max_train_epochs = 3
max_data_loader_n_workers = 8
persistent_data_loader_workers = true
gradient_checkpointing = true
gradient_accumulation_steps = 1
mixed_precision = "fp16"

[logging_arguments]
log_with = "tensorboard"
logging_dir = "/content/LoRA/logs"
log_prefix = "Blue_Waifu"

[sample_prompt_arguments]
sample_every_n_epochs = 1
sample_sampler = "euler_a"

[saving_arguments]
save_model_as = "safetensors"

[optimizer_arguments]
optimizer_type = "AdaFactor"
learning_rate = 0.0001
max_grad_norm = 0
optimizer_args = [ "scale_parameter=False", "relative_step=False", "warmup_init=False",]
lr_scheduler = "constant_with_warmup"
lr_warmup_steps = 100

[additional_network_arguments]
no_metadata = false
network_module = "networks.lora"
network_dim = 32
network_alpha = 16
network_args = []
network_train_unet_only = true

[advanced_training_config]
save_state = false
save_last_n_epochs_state = false
multires_noise_iterations = 6
multires_noise_discount = 0.3
caption_dropout_rate = 0
caption_tag_dropout_rate = 0.1
caption_dropout_every_n_epochs = 0
min_snr_gamma = 5

[prompt]
negative_prompt = "lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, "
width = 1024
height = 1024
scale = 12
sample_steps = 28
[[prompt.subset]]
prompt = "masterpiece, best quality, face focus, cute, 1girl, green hair, sweater, looking at viewer, upper body, beanie, outdoors, night, turtleneck"

I'm guessing you are using this implementation in Colab.
Couple of things I'd like to check
1. What is the original resolution of the images you are using?

2. You seem to have selected the "cache_text_encoder_outputs", have you tried training without that?

3. You mentioned Dreambooth training, but AFAIK, that needs regularizartion images, which I haven't been able to get working yet. (If you did, TEACH ME SEMPAI)

4. Can you paste the output from the "Bucketing and Latents Caching" step?

5. Have you tried setting a VAE?

6. You mentioned testing a few datasets, have you tried on different models?

7. I think you are training on the model you'd like to use. Prehaps try training on base SDXL model then apply the Lora on that model?

8. It seems like you are trying to train the "one eye closed" concept. If that's the case, I'm under the impression that for best results, you'd need a larger than 50 image sample size, and to carefully tag everything about the images. (I like to refer to [this resource](https://rentry.org/59xed3) as a guide)

I've been getting decent results on my first try and here are my bucketing and latent caching output (Ignore the total numbers, I'm obsessed and went overboard)

Found 379 images.

And Training Config output

[sdxl_arguments]
cache_text_encoder_outputs = false
no_half_vae = true
min_timestep = 0
max_timestep = 1000
shuffle_caption = true
lowram = true

[model_arguments]
pretrained_model_name_or_path = "stabilityai/stable-diffusion-xl-base-1.0"
vae = "/content/vae/sdxl_vae.safetensors"

[dataset_arguments]
debug_dataset = false
in_json = "/content/LoRA/meta_lat.json"
train_data_dir = "/content/drive/MyDrive/LoRA/train_2"
dataset_repeats = 5
keep_tokens = 0
resolution = "1024,1024"
color_aug = false
token_warmup_min = 1
token_warmup_step = 0

[training_arguments]
output_dir = "/content/drive/MyDrive/kohya-trainer/output/Blue_Waifu"
output_name = "Blue_Waifu"
save_precision = "fp16"
save_every_n_epochs = 1
train_batch_size = 5
max_token_length = 225
mem_eff_attn = false
sdpa = true
xformers = false
max_train_epochs = 3
max_data_loader_n_workers = 8
persistent_data_loader_workers = true
gradient_checkpointing = true
gradient_accumulation_steps = 1
mixed_precision = "fp16"

[logging_arguments]
log_with = "tensorboard"
logging_dir = "/content/LoRA/logs"
log_prefix = "Blue_Waifu"

[sample_prompt_arguments]
sample_every_n_epochs = 1
sample_sampler = "euler_a"

[saving_arguments]
save_model_as = "safetensors"

[optimizer_arguments]
optimizer_type = "AdaFactor"
learning_rate = 0.0001
max_grad_norm = 0
optimizer_args = [ "scale_parameter=False", "relative_step=False", "warmup_init=False",]
lr_scheduler = "constant_with_warmup"
lr_warmup_steps = 100

[additional_network_arguments]
no_metadata = false
network_module = "networks.lora"
network_dim = 32
network_alpha = 16
network_args = []
network_train_unet_only = true

[advanced_training_config]
save_state = false
save_last_n_epochs_state = false
multires_noise_iterations = 6
multires_noise_discount = 0.3
caption_dropout_rate = 0
caption_tag_dropout_rate = 0.1
caption_dropout_every_n_epochs = 0
min_snr_gamma = 5

[prompt]
negative_prompt = "lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, "
width = 1024
height = 1024
scale = 12
sample_steps = 28
[[prompt.subset]]
prompt = "masterpiece, best quality, face focus, cute, 1girl, green hair, sweater, looking at viewer, upper body, beanie, outdoors, night, turtleneck"

here are the image resolutions from the dataset and their number:
(2002, 3508): 1,
 (1100, 1100): 1,
 (2160, 3840): 1,
 (2564, 3624): 1,
 (1278, 1278): 1,
 (512, 512): 1,
 (1270, 945): 1,
 (992, 1403): 1,
 (1254, 1771): 2,
 (2432, 3200): 1,
 (1448, 2048): 1,
 (1920, 2560): 1,
 (5787, 5785): 1,
 (1000, 1432): 1,
 (652, 990): 1,
 (1024, 1024): 2,
 (1542, 2041): 1,
 (2300, 3800): 1,
 (1329, 1873): 1,
 (888, 1013): 1,
 (844, 1341): 1,
 (690, 930): 1,
 (775, 1100): 1,
 (900, 1200): 1,
 (2353, 4093): 1,
 (826, 1200): 1,
 (562, 800): 1,
 (700, 1050): 1,
 (2083, 3542): 1,
 (1250, 1761): 1,
 (800, 1225): 1,
 (2591, 3624): 1,
 (1200, 1389): 1,
 (1576, 3745): 1,
 (1600, 2400): 1,
 (581, 841): 1,
 (1507, 1653): 1,
 (1530, 2047): 1,
 (1447, 2047): 1

yes
I tried dreambooth without regularization, the result is the same as with training with tags

/content/kohya_ss/finetune
2023-09-21 07:51:49.881718: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
found 41 images.
loading existing metadata: /content/dataset.json
load VAE: femboysLover/blue_pencil-fp16-XL
exception occurs in loading vae: femboysLover/blue_pencil-fp16-XL does not appear to have a file named config.json.
retry with subfolder='vae'
Downloading (…)main/vae/config.json: 100% 602/602 [00:00<00:00, 3.40MB/s]
Downloading (…)ch_model.safetensors: 100% 167M/167M [00:00<00:00, 201MB/s]
The config attributes {'force_upcast': True} were passed to AutoencoderKL, but are not expected and will be ignored. Please verify your config.json configuration file.
 83% 34/41 [00:37<00:07,  1.10s/it]/usr/local/lib/python3.10/dist-packages/PIL/Image.py:996: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images
  warnings.warn(
100% 41/41 [00:43<00:00,  1.07s/it]
bucket 0 (768, 1024): 31
bucket 1 (896, 1024): 2
bucket 2 (960, 1024): 1
bucket 3 (1024, 768): 1
bucket 4 (1024, 1024): 6
mean ar error: 0.05769353115186938
writing metadata: /content/dataset_lat.json
done!

yes
I also tried DreamShaperXL, animagine-xl, counterfeitXL, DeepBlue
I'll try, but most likely the result will also be terrible
I tried both 100 and 200 images, but always in the end LoRA was trained to break the generated image, but not the desired concept

I am using same settings as in my this video
the results are super super overtrained nothing like
something seriously broken with SDXL
doing more tests to figure out the issue. dreambooth training on the other hand working amazing
here a tweet where I compared results : https://twitter.com/GozukaraFurkan/status/1704625590437814424
Become A Master Of SDXL Training With Kohya SS LoRAs - Combine Power Of Automatic1111 & SDXL LoRAs

something is so much broken with lora training
5e-5 speed
https://twitter.com/GozukaraFurkan/status/1704867030761984091
i am gonna test my tutorial commit and let you know

I found my error
I were using SDXL 0.9 beta release which have different weights and FP32 :)
ignore my message

@drimeF0, One thing I'm noticing in your bucket is that the images are heavily skewed towards 768 x 1024 with 31 images and only 6 for the 1024 x 1024, and the samples you are generating are 1024 x 1024. This may be resulting in the 1024 x 1024 images being undertrained with insufficient samples.
Can you try generating images with 768 x 1024 dimensions? I suspect the results there may be more decent. One thing I've been doing in a personal copy of the colab, has been modifying the code so that the max_bucket_reso is 1536 and min_bucket_reso is 640 to try to get bucket distribution that's more inline with the recommended for SDXL. I'm reluctant to share my version for now because it's a hacked together version that does dreambooth with regularization and there are parts that are still rough and inelegant, mainly because I want it to work on the free tier. (TLDR, in order to get things to run without overruning the usage time restrictions, bucketing and latent processing is done on manual folder by folder step)
One thing I did for my dataset was cropping and resizing majority of my images into the recommended SDXL training image sizes, cribbed from this tutorial by our good friend @FurkanGozukara.
Since in the training process, images will only be trained against images in the same bucket, one thing I did was crop and rescaled the same image into mulitple sizes corresponding to the respective aspect ratios. You will note that I got lazy towards the end and skip that for several images, resulting in buckets with 1 image in them.
If the images generated at 768 x 1024 are acceptable (You might have to retrain the LoRA at the proper standing learning rate of 1e-4 in order to avoid getting overcooked results), then a quick and dirty way to get the results you want may be to crop and resize most of your images to 1024 x 1024 and use that as your dataset

I already tried the bucket size 1024x1024, the results are the same

@drimeF0, I have to admit I'm stumped. I set out to try recreating your issue in this colab implementation following the majority of the default settings in it, and was able to get results like this:

I would note that I did modify the bucket to have min_bucket_reso of 640 and max_bucket_reso of 1536, but aside from that, the only difference I can note are the following:

This implementation defaults to AdaFactor optimizer which gave me good results with the default settings.
This implementation follows the repeats x epoch method of doing training, while I think I see you using steps instead.

For further reference, this result was training with scraping 98 images from safebooru with the following tags: "one_eye_closed, 1girl, absurdres", tags scraped from safebooru, and the clean_caption option used at step 3.4. Bucketing and Latents Caching.
The results were from the 4th epoch, with the training image repeated 10 times each epoch at batch size 5.
I think... in order for me to try troubleshooting more, I probably need to look at how you are implementing the training in your enviroment. And even then, I may not be able to help if it involves tweaking Kohya's code.

Side note, I have similar issues where the LoRA keeps outputing both eyes closed. I believe that in order to fix this issue, we would need to expand the training data set to include "eyes_closed" images where both eyes are closed, and images where both eyes are open for the LoRA to learn the difference.

Hi, sorry for taking so long to respond. Here's the notebook:
https://colab.research.google.com/drive/14BnL_yiyVGs8WFovba1t3UjcjnZiVrpi?usp=sharing

@drimeF0 I made a copy and modified it with the following changes:

added recursive metadata and bucketing to support multiple concepts
Changed min_bucket_reso to 640 and max_bucket_reso to 1536
used the SDXL VAE for latents and training
changed from steps to using repeats+epoch

I'm still running my intial test with three separate concepts on this modified version. An earlier attempt with only eyes_closed and one_eye_closed is still getting me boths eyes closed @@

eyes_open: -one_eye_closed, -eyes_closed, solo, 1girl , highres
eyes_closed: eyes_closed, solo, 1girl , highres
one_eye_closed: one_eye_closed, solo, 1girl , highres

https://colab.research.google.com/drive/1j8y5AvtdiXl4_8CHk3wd7xxre0u5Vsxf?usp=sharing
I'm getting the impression that this notebook is originally meant to run on Kraggle, since on Google Colab free tier, unless you are closely monitoring, all the work will be lost when Google happily d/c me

While I wasn't successful at training the concept (Which may require training the text encoder as well, and possibly better tagging of the dataset, maybe trying SDP instead of xformers, or just more training, or mentioned below, replacing the keyword tag to wink), I managed to hit step 579 (Epoch 3) without the model collasping.
As a matter of fact, it managed to maintain a very coherent style! (Not sure if this is the normal style expected from this model as I haven't really played with it before.
Epoch 1(step 193):

Epoch 2(step 386):

Epoch 3(step 579):

image prompt: masterpiece, best quality, one eye closed, solo, 1girl --w 768 --h 1024 --d 123
At step 579, I also generated a couple more samples, and they still seem to carry the default styling.

image prompt: masterpiece, best quality, one eye closed, solo, 1girl --w 1024 --h 1024

image prompt: masterpiece, best quality, one eye closed, solo, 1girl --w 832 --h 1216
I'm not likely to get more out of the training at this point due to running it on free colab, and while I can't think of how to better help you train the specific one eye closed concept (Hrm... maybe replace one eye closed with wink? might be a more natural concept to train), I hope that I've at least help you troubleshoot the model collapse issue.

While I wasn't successful at training the concept (Which may require training the text encoder as well, and possibly better tagging of the dataset, maybe trying SDP instead of xformers, or just more training, or mentioned below, replacing the keyword tag to wink), I managed to hit step 579 (Epoch 3) without the model collasping.
As a matter of fact, it managed to maintain a very coherent style! (Not sure if this is the normal style expected from this model as I haven't really played with it before.
Epoch 1(step 193):
Epoch 2(step 386):
Epoch 3(step 579):  image prompt: masterpiece, best quality, one eye closed, solo, 1girl --w 768 --h 1024 --d 123
At step 579, I also generated a couple more samples, and they still seem to carry the default styling.  image prompt: masterpiece, best quality, one eye closed, solo, 1girl --w 1024 --h 1024
 image prompt: masterpiece, best quality, one eye closed, solo, 1girl --w 832 --h 1216
I'm not likely to get more out of the training at this point due to running it on free colab, and while I can't think of how to better help you train the specific one eye closed concept (Hrm... maybe replace one eye closed with wink? might be a more natural concept to train), I hope that I've at least help you troubleshoot the model collapse issue.

thank you very much for your help with the model collapse during training, I will later try to transfer the training code to kaggle and run the code on a lower lr for 5000 training steps, in addition, I will try various datasets

@drimeF0, I suspect that the root of the issue may have something to do with using steps instead of epoch/repeats for training. If I can make a suggestion, instead of using 5000 training steps, try increasing the number of epochs or repeats.
To make the changes, you can edit the --max_train_epochs "8" and --dataset_repeats "5"


Metro Window Drag Issue

Clicking anywhere on Metro Window allowing user to drag. This is making confusion with other drag operations inside Metro Window. Drag and Move should happen only on title bar.

Hi,
@flagbug
@punker76
@xamlfactory
When do you implement this?
" ShowTitleBar="False" should ideally disable drag moving window behavior. Also drag move should work only within the bounds of TitleBarHeight."
I need this? Please help!

I figured out the problem like this:
    private void MetroWindow_MouseDown(object sender, MouseButtonEventArgs e)
    {
        if (e.Source != null && e.Source.GetType() == typeof(MainWindow))
        {
            e.Handled = false;
        }
        else
        {
            e.Handled = true;
        }
    }


Build JNI on Windows

This change makes JNI buildable on Windows. It also cleans up the logic for determining when to build JNI and adds the necessary dependency to MainUtils

Can one of the admins verify this patch?

Can one of the admins verify this patch?

@jenkins-droid test this please

Can one of the admins verify this patch?

Can one of the admins verify this patch?

@jenkins-droid test this please

@gongsu832 : Let me know if there's anything else in the change that you'd like addressed.

Can one of the admins verify this patch?

LGTM @jenkins-droid test this please

Can one of the admins verify this patch?

@gongsu832 : Is there anything blocking this change from going in? Can we commit it?

@jenkins-droid test this please

@gongsu832 Are you ok with this PR? @sstamenova wants to commit this one before looking into fixing #744 for windows. Since this is JNI, you should really be the one giving the final ok on our side, thanks @gongsu832

@AlexandreEichenberger Yes it's OK to merge. Thanks.

Jenkins Linux s390x Build #1289 [push] Build JNI on Windows (#7... started at 10:46

Jenkins Linux amd64 Build #1291 [push] Build JNI on Windows (#7... started at 09:46

Jenkins Linux ppc64le Build #437 [push] Build JNI on Windows (#7... started at 10:49

Jenkins Linux s390x Build #1289 [push] Build JNI on Windows (#7... passed after 41 min

Jenkins Linux amd64 Build #1291 [push] Build JNI on Windows (#7... passed after 48 min

Jenkins Linux ppc64le Build #437 [push] Build JNI on Windows (#7... passed after 58 min


x/pkgsite: "// Output:" matcher for test Examples is noncanonical

The standard logic for extracting "// Output:" comments in example tests is here:
https://cs.opensource.google/go/go/+/master:src/go/doc/example.go;l=113;drc=28f1bf61b7383bd4079d77090e67b3198b75be12
Observe that it has a ^ anchor so that it matches only the start of the comment text.
By contrast, pksite uses this regexp:
https://cs.opensource.google/go/x/pkgsite/+/master:internal/godoc/dochtml/internal/render/render.go;l=24;drc=7431aa5932c48bce425bf3f2dc368c5b1fed6e2c
which lacks the anchor. The consequence is that an example such as this one:
func Example() {
    // A
    // B This Example does not have an "// Output:" comment so it will not run.
    // C
}

is considered executable by pkgsite, and its rendering of the example is abruptly cut off at line C.

This issue can be detected by adding one test case
diff --git a/internal/godoc/dochtml/internal/render/linkify_test.go b/internal/godoc/dochtml/internal/render/linkify_test.go
index d3ea8b83..7b455204 100644
--- a/internal/godoc/dochtml/internal/render/linkify_test.go
+++ b/internal/godoc/dochtml/internal/render/linkify_test.go
@@ -426,6 +426,18 @@ a := 1
 // Output:
 b := 1

+`,
+               },
+               {
+                       "Special \"Output\" comment must on it's own comment block",
+                       `_ = true
+// To test output, use: // output: the output content
+`,
+                       `
+

+_ = true
+// To test output, use: // output: the output content
+

 `,
                },
        } {

And can be fixed quickly by ensuring there is only spaces before the // sequence
diff --git a/internal/godoc/dochtml/internal/render/render.go b/internal/godoc/dochtml/internal/render/render.go
index 3875f3ba..0ad66c9e 100644
--- a/internal/godoc/dochtml/internal/render/render.go
+++ b/internal/godoc/dochtml/internal/render/render.go
@@ -21,7 +21,7 @@ import (

 var (
        // Regexp for example outputs.
-       exampleOutputRx = regexp.MustCompile(`(?i)//[[:space:]]*(unordered )?output:`)
+       exampleOutputRx = regexp.MustCompile(`(?i)^[[:space:]]*//[[:space:]]*(unordered )?output:`)
 )

 type Renderer struct {

Since that there could only be space-like characters before the // Output: block, assuming the the code is formatted.
@findleyr Would you think this issue deserve a fix? If so, maybe I can submit a PR for this. :D


Define a pattern for injecting time so that code that relies on time.Now() is consistently easier to test

Define a pattern for injecting time so that code that relies on time.Now() is consistently easier to test. We had a case (https://github.com/stellar/go/pull/1675) recently that @poliha fixed where a test was intermittently failing because of time.Now() usage in the test meant depending on how long the test took to run the expectations in the test might be wrong.
We use time.Now() in 63 places in our non-test code in this repository which suggests we're writing a significant amount of code that's dependent on the current time.
We should make time something that is injected in all these places so that we can write more reliable tests.
This was something we discussed the possibility of here https://github.com/stellar/go/pull/1675#discussion_r319201330.

Goal:

Easy to fix, shift, or move time predictably
Lightweight

Ideas to explore:

Using a simple struct and interface in our code to make a function satisfying the signature of time.Now() injectable.
Using third party packages that expose a variety of functions and features relating to clocks https://github.com/cloudfoundry/clock https://godoc.org/github.com/facebookgo/clock


pip install pytuya fails

When I try to install pytuya package I get the following error:
pip install pytuya
Collecting pytuya
Using cached https://files.pythonhosted.org/packages/81/7a/a77347f79ab92408acf49d48d15043fa1254405fb501f2ecc6e64f2c7ca9/pytuya-7.0.1.zip
Complete output from command python setup.py egg_info:
Traceback (most recent call last):
File "", line 1, in
File "/tmp/pip-install-yfyZYU/pytuya/setup.py", line 23, in
with codecs.open(os.path.join(here, 'README.md'), encoding='utf-8') as f:
File "/usr/lib/python2.7/codecs.py", line 896, in open
file = builtin.open(filename, mode, buffering)
IOError: [Errno 2] No such file or directory: '/tmp/pip-install-yfyZYU/pytuya/README.md'

Yeah, same here. I tried python2 and python3. I've got pip 18.1 if it matters. Looks like a simple packaging error though (README.md not in the package)?
Version 7.0 still installs fine:
pip install --user pytuya==7.0`
Collecting pytuya==7.0
  Downloading
...
Installing collected packages: pyaes, pytuya
Successfully installed pyaes-1.6.1 pytuya-7.0

Thanks for the heads up.

Should be resolved by d9e72d335b5f234004ef962628a598bdd87613ed - will close in a week.

https://pypi.org/project/pytuya/ version 7.0.2 posted to PyPi


Padrino websockets

Hey @padrino/core-members,
How're things?
Many people have been asking us about WebSockets support in Padrino and even though it's not up fully up to us, I think that we should make it easier and consistent for our users to implement those. After all, streamed communications are a crucial building block of the web :).
That's why I've just open sourced padrino-websockets an agnostic WS implementation for Padrino. Tons of thanks to @stakach for the amazing base and for doing SpiderGazelle a rack web server based on libuv which looks very promissing (highly recommended)!
It will let us do stuff like:
websocket :channel do
  event :test do |context, message|
    "test on channel"
    send_message message
  end
end

websocket :another_channel, map: '/blah' do
  event :test do |context, message|
    "test on channel"
    broadcast message
  end
end

At the moment it only supports SpiderGazelle as its backend but stuff like faye-websocket-ruby should be simple enough to be integrated, giving us support for Goliath, Phusion Passenger >= 4.0 with nginx >= 1.4, Puma >= 2.0, Rainbows and Thin (from its README).
I haven't released it as a gem it might make sense to include it in core and I'd like your advise and thoughts on it.
Cheers!
Darío

Playing with padrino-websockets now with faye.. seems pretty good.


ref(*): refactor to utilize exec/builder logic

Refactors code to utilize logic in the upstream exec/builder pkg
Updates porter dep to latest (v0.25.0-beta.1)

Closes https://github.com/deislabs/porter-terraform/issues/27
Signed-off-by: Vaughn Dice<EMAIL_ADDRESS>

oh gosh, I am awful at seeing that changes were pushed! 🤦♀ If you @ me, I will review faster I promise! 😅


Why output dimension is only select the first?

The code metrics = engine.train(trainx, trainy[:, 0, :, :])  in line84 of train.py  seems only predict one dimension of total D dimension.
But the paper wrote the output dimension is D.

I have the same question.

Hello!  If I want the feature_dimension in the output of gwnet in model.py to be 2 (the default feature_dimension=1 in the source code), how should I modify the gwnet part?
Additionally, I have a question: I understand that the convolution operations in gwnet are performed along the feature dimension, and the output shape of the model is [batch_size, feature_dim, num_nodes, seq_len] where feature_dimension=12 and seq_len=1. However, in line 18 of engine.py, the output is transposed directly, changing the feature dimension to the seq_len dimension. I wonder if this handling is reasonable.


Fix immediate error with default command

SDPA attention implementation is not available for Pythia models on the Transformers library. It works with Llama, which is what is in the distributed runs below.
Fixes #1

 Thank you for your submission! We really appreciate it. Like many open source projects, we ask that you sign our Contributor License Agreement before we can accept your contribution.You have signed the CLA already but the status is still pending? Let us recheck it.


Keyboard shortcut - Ctrl+H

Is it possible to implement a custom keyboard shortcut?
With existing firefox 'manage extension shortcuts' - It is possible to assign a shortcut key - but not possible to change the existing firefox key (Ctrl + H) - It will be nice if we could do this.
As of now, (as an alternative solution) I am using the Shortkeys (https://addons.mozilla.org/en-US/firefox/addon/shortkeys/ - https://github.com/mikecrittenden/shortkeys) extension to do this.
Thanks :)

There is a keyboard shortcut, CTRL+ALT+H. CTRL+H is an existing shortcut that opens the history sidebar. I'll update the readme/UI to mention that. Thanks!
You can also change the shortcut by going to about:addons, clicking the settings dropdown button and clicking Manage Extension Shortcuts.


Add static attribution to attribution control

The attribution control uses the visible layers 'attribution' property to determine what attributions to show. There are instances where you may want a particular attribution to be always shown, such as attribution information for the app, or for OpenLayers itself.
Some have used workarounds (example SO question) that involve manually forcing this attribution into every layers attribution array, meaning it will always display. This feels sub-optimal.
I propose that a new option is added to the Attribution control, along the links of staticAttribution. If this is defined, it is always added the attributions list regardless of the current layers. This assumes that I am not just missing something obvious that already does this.
I have a draft implementation ready to go if you believe something like this is a good idea.
Screen capture of potential implementation

The simplest workaround is to add an empty layer new VectorLayer({source: new VectorSource({attributions: 'Static attribution'})}) but that is still a workaround, an option in the control constructor would be more logical.

Closed via #16031


Fix rst syntax error in ingress-shim.rst

What this PR does / why we need it:
Fix rst syntax error in ingress-shim.rst.
The code block syntax is missing a : so the code is not show in rendered doc.

Thanks!
/ok-to-test
/lgtm
/approve

/release-note-none

This is annoyingly going through e2e testing... I'll be updating our testing infra soon so simple docs changes don't have to go through this whole process!


Why isn't the segmented image output

Regarding the segmented image you mentioned, we think you're referring to the final output in the third stage. As for visualizing it, we save the generated results in inference.py, then load and screenshot them using software like ITK-SNAP.

Regarding the segmented image you mentioned, we think you're referring to the final output in the third stage. As for visualizing it, we save the generated results in inference.py, then load and screenshot them using software like ITK-SNAP.

Thank you for your answer. Do the three stages only use labeled data sets?

How does this error be resolved when implementing inference.py

The parameter y of mix-train requires two inputs and four outputs, but there is only one input and one output in front of it.


Remove unnecessary to_list() calls

Avoids flattening depsets during the analysis phase by passing depsets into ctx.actions.run's input parameter.

Nice performance improvement. Thanks.


fix database.export(json),yaml in chinese will get u787979 problems

when load *.xlsx in 'rb' and have some chinese in data, it will keep unicode in data.  such as '\u8979766'
and when try to export in json or yaml,it will still keep the unicode in format
data = tablib.Dataset()
with open('data.xlsx','rb') as fh:
    data.load(fh,'xlsx')
print(data.dict)
print(json.dumps(data.dict,ensure_ascii=False))

with open('data.json','w') as f:
    f.write(data.export('json'))

with open('data.json','w') as f:
    f.write(json.dumps(data.dict,ensure_ascii=False))

Please could you add tests? This will help ensure there are no future regressions.

Please could you add tests? This will help ensure there are no future regressions.

ok,I will add tests in a few days.

I completed this patch in #484.


"Out of Gas" transaction handling?

Summary
With the recent gas price fluctuations on the goerli test network, many of us are accumulating failed cashout transactions due to "out of gas".  Are these transactions reverted internal to bee or do we need to somehow reprocess them as was mentioned in bee-support?
Steps to reproduce
Do a bunch of cashouts and you'll likely hit an out-of-gas as the goerli network jumps the price to 20gwei instead of 1.
Expected behavior
I would like to believe that the bee node will detect the failed transaction and make the peer cashable again, but having examined the source code, I don't think that is currently the case.
Actual behavior
I haven't done a definitive test yet, but I suspect that the peer is no longer cashable until at least another threshold has been reached.  And even then, I'm not certain that the new check will include the settlement from the failed cashout transaction.
The following script was suggested in bee-support as being necessary to get the out-of-gas transactions to be reprocessed.  I can confirm that it lists my failed transactions, but I'm not sure if I can meet the requirement for a "geth compatible" keystore and password for my node's Ethereum account.
https://gist.github.com/syspulse/dd652cce3bf2e1ddc13272ef4e472726

Any comment from the developers on what can be done to recover these transactions?

I have many cash transactions went into out-of-gas error,  and desperately need help on this point.

If you can send a cheque with a same peer when you have received new cheques, then you will also cash out the old cheques. The reason for this can be found in how cheques are internally implemented (see sw3).
As you can see, what matters is the cumulative amount on the cheque. If you did not cash out a previous cheque, then the chequebook of your peer did not update the paidout[beneficiary] mapping, so you will just receive the payout as if the previous transaction did not happen.
While I understand this is a nuisance, we will not work on fixing this now. Especially since there is a way around this.
Thanks for reporting though and please let me know if you need any further explanations (can be here in a comment or PM).


Capitalize properties on MessageAction to match AttachmentActionCallback

Re: https://github.com/nlopes/slack/pull/379#issuecomment-425755399
Making this match so that it's like other parts of the codebase.

LGTM, mind just rebasing against master? I made a mistake this morning when moving some personal branches around and had to fix master.

@james-lawrence done!

thanks. =)


fixed: legacy word-wrapped TextFields would sometimes leave an extra empty line below

disclaimer: I just pushed buttons and this worked (fixed my test cases, and did not break any text not displaying the bug). I actually have no idea what I'm doing.

Thanks! Just cherry-picked the commit :smile:

xD (I don't know how to make git not messy! D:)

This might help :smile:
http://stackoverflow.com/questions/8134960/how-to-revert-master-branch-to-upstream

Cheers :]
So.. Turns out this "fix" is actually wrong. It was a higher-level problem with another library and this only masked the problem :( This change is actually incorrect (hey, I did include a disclaimer!)
Could this be reverted? Sorry! (now I've made more mess)


HMR not working

I followed the steps in the readme, and I see the correct console output in my app, but my components don't actually re-render. Here is the output I see in the console:
[HMR] Waiting for update signal from WDS...
components.js:56356 [WDS] Hot Module Replacement enabled.
components.js:56356 [WDS] App updated. Recompiling...
components.js:56356 [WDS] App hot update...
components.js:56789 [HMR] Checking for updates on the server...
components.js:56832 [HMR] Updated modules:
components.js:56834 [HMR]  - ./app/javascript/components/SearchBar.js
components.js:56771 [HMR] App is up to date.

When I watch the dom inspector, I see the new script tag added to the head element, but nothing else in the dom changes. Any suggestions on how to debug this?

I updated webpacker and tried this again, and now I have a different problem. My app requests http://localhost:8080/ba9cba921e647214acee.hot-update.json, but the dev server generated 0.ba9cba921e647214acee.hot-update.js. Any idea how to fix that?

Ok, so I updated my publicPath to http://localhost:8080/packs/, and now I see this when I change a component:
[HMR] Updated modules:
[HMR]  - ./app/javascript/components/MyComponent.js

But it the change doesn't actually show up.

I dont see anything wrong here. Can you show the content of your pack file? Are you correctly calling module.hot.accept with the correct path, callback and module?

Here is my pack file:
import SearchBar from 'components/SearchBar'
import WebpackerReact from 'webpacker-react/hmr'
import 'closestPolyfill'
import 'babel-polyfill'
import 'whatwg-fetch'

WebpackerReact.setup({
  SearchBar,
})

if (module.hot) {
  module.hot.accept('components/SearchBar', () => WebpackerReact.renderOnHMR(SearchBar))
}

Which Webpacker version are you using? Webpacker's configuration changed quite a lot recently and I suspect the issue can come from here.
Can you change your Webpack config to output the configuration and show it here?
In config/webpack/development.server.js (or the config file used by webpack-dev-server in your setup), do something like:
config = merge(devConfig, { ... } )
console.log(config);
module.exports = config

I'm on webpacker 1.2, this is what my config looks like:
{ entry:
   { components:
      [ 'react-hot-loader/patch',
        '/<project-path>/app/javascript/packs/components.js.erb' ] },
  output:
   { filename: '[name].js',
     path: '/<project-path>/public/packs',
     publicPath: 'http://localhost:8080/packs/',
     pathinfo: true },
  module: { rules: [ [Object], [Object], [Object], [Object], [Object] ] },
  plugins:
   [ EnvironmentPlugin { keys: [Object], defaultValues: [Object] },
     ExtractTextPlugin { filename: '[name].css', id: 1, options: {} },
     ManifestPlugin { opts: [Object] },
     NamedModulesPlugin { options: {} } ],
  resolve:
   { extensions:
      [ '.coffee',
        '.js',
        '.jsx',
        '.ts',
        '.vue',
        '.sass',
        '.scss',
        '.css',
        '.png',
        '.svg',
        '.gif',
        '.jpeg',
        '.jpg' ],
     modules:
      [ '/<project-path>/app/javascript',
        '/<project-path>/node_modules' ] },
  resolveLoader: { modules: [ 'node_modules' ] },
  stats: { errorDetails: true } }

One difference I see it with my current setup is in entry.components: for you the full path is used, but in my config only the relative path (components.js.erb) is used. As /<project-path>/app/javascript is in resolve.modules it is not needed to have the full path in entry, and I think it can cause an issue with hot reloading as it may not match the component path you use in module.hot.accept
Another difference is that I only use plain .js packs, I dont know if this can make a difference.
Could you try to change the Webpack config with a static entry and see if it works?
{
  entry: {
    components: ['packs/components'],
  }
}

@renchap thanks for the suggestions. I dropped the erb extension to make sure that wasn't the issue, and I tried the static entry, which doesn't seem to change the situation.
You mentioned the entry path has to match the path in hot.accept, which it doesn't. From my console output: components.js:56834 [HMR]  - ./app/javascript/components/SearchBar.js, but the entry path is packs/components or /<project-path>/app/javascript/packs/components.js.

I've run into exactly the same issue.
From my entry:
'orders/summary':
      [ 'react-hot-loader/patch',
        '/Users/tylerboyd/psuite/app/javascript/packs/orders/summary.jsx' ],

From console log:
[WDS] App hot update...
[HMR] Checking for updates on the server...
[HMR] Updated modules:
[HMR]  - ./app/javascript/packs/orders/summary.jsx
[HMR] App is up to date.

I'm accepting orders/order_form which (indirectly) imports the summary file.
However, the changes don't appear on the page when hot reloading occurs.

This looks like to be related with recent changes to Webpacker.
I updated my main application to Webpacker 2.0 and I am seeing the publicPath issue you mentioned in the first post.
I am looking at this and will try to reproduce and fix.

I got HMR working with the latest Webpacker (from Git master).
Your fix for the publicPath issue is not correct. The problem comes from the way webpacker configures it. It is used at 2 places: in the manifest plugin configuration, and in the dev server config.
Webpacker uses manifest.json to know what is the URL for each pack file.
The real fix is to fix the URL in both places (manifest.json and dev server config).
For the lastest Webpack, I changed config/webpack/configuration.js to:
const output = {
  ...
  publicPath: '//localhost:8080/',
}

After restarting webpack-dev-server, public/packs/manifest.json contains the correct URLs for the packfiles (http://localhost:8080/pack.js, without /packs prefix), and serves the assets correctly at this URL.
HMR is working fine with this setup.
Can you please test this change and confirm that it works? Warning: this forces publicPath, so it will no longer work on production. Wrap the change in an if NODE_END == 'development' statement if you want to deploy it.
I will open an issue in Webpacker repo to add dev server support for publicPath.

After trying to understand the real issue, the fix above only masked the problem.
The issue lies here: https://github.com/renchap/webpacker-react/blob/master/javascript/webpacker_react-npm-module/src/configure-hot-module-replacement.js#L11
webpacker now correctly sets output.publicPath, so it is no longer needed to override it. And the override was incorrect, it missed the /packs/ directory.

I just release version 0.3.0 with the bugfix (and some other changes).
Can you please test it and tell me if all works fine for you now?

I'm still having the second issue that came up (ie. the URL isn't the problem, but)

Ok, so I updated my publicPath to http://localhost:8080/packs/, and now I see this when I change a component:
[HMR] Updated modules:
[HMR]  - ./app/javascript/components/MyComponent.js
But it the change doesn't actually show up.

The output looks correct but the components aren't actually updated.

Are you using Webpacker-React 0.3.0, both for the Gem and the NPM module?
Which version of Webpacker are you using?
Is it a change you could reproduce this behaviour with a simple Rails app and put it on GitHub so I can investigate?

Using<EMAIL_ADDRESS>for NPM, 0.3.1 for the Gem.
Webpacker 2.0, pretty sure modules is set to false. Output didn't change when I upgraded webpacker-react versions.
Will try to reproduce.

I was unable to reproduce in a new Rails app. Probably some odd, specific bug in my config.
You can consider this closed IMO 👍

I am interested to know which part of your configuration causes this, if you find out please comment here, it will surely help others and I will add it to the README.

I went through every webpack-related file and changed my app to the rails new app that had HMR working until I fixed my app.
I think I isolated the issue down to:

Removing "transform-es2015-modules-commonjs" from my .babelrc plugins.

Definitely my fault, hopefully posting this will help someone else in the future with the same problem.

Ok, as I thought it is linked to how modules are handled. This Babel plugin, like the preset I wrote before, transforms import statements and this breaks Webpack's handling of modules. As HMR is dependent on modules, it broke. This is why you only saw one module updated in your console, and not the whole chain of modules (from the root component to the updated component).


Fix contributing section link

Auto-generated link was still present in the contributing section.

Thanks @baweaver !

Looks like our CI isn't configured to run on forks. I'll look into this early next week so we can get the checks to pass and merge this for ya.

No rush, just a little text correction I'd noticed.
On Sat, Feb 12, 2022, 12:18 PM Alex Evanczuk @.***>
wrote:

Looks like our CI isn't configured to run on forks. I'll look into this
early next week so we can get the checks to pass and merge this for ya.
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@baweaver We're going to convert this repo to use Github actions rather than build kite, since it seems like a lot of the open source community has rallied around that for open source projects.
For the time being, I'll go ahead and merge this since it's just a README change!


feat(outbox): Opportunity to override topic using cloudevent.topic me…

Description
Opportunity to override the topic in the transactional outbox pattern configured in the state store component
In the metadata state request add the management of the "cloudevent.topic" property with the topic to override that has been defined in the parameter outboxPublishTopic of the state component

Issue reference

implementation proposal request https://github.com/dapr/dapr/issues/8043
Checklist
Please make sure you've  completed the relevant tasks for this PR, out of the following list:

[ ] Code compiles correctly
[ ] Created/updated tests
[ ] Unit tests passing
[ ] End-to-end tests passing
[ ] Extended the documentation / Created issue in the https://github.com/dapr/docs/ repo: dapr/docs#[issue number]
[ ] Specification has been updated / Created issue in the https://github.com/dapr/docs/ repo: dapr/docs#[issue number]
[ ] Provided sample for the feature / Created issue in the https://github.com/dapr/docs/ repo: dapr/docs#[issue number]

Thanks for the PR @luigirende . Can you please a unit test for this change?

Sorry elena-kolevska forgotten to commit the test file, update the repository


Xamarin.iOS Xamarin Form  Cross Platform

MTOUCH: Error MT3001: Could not AOT the assembly in missing  Firebase.CloudMessaging When Run on Actual iPhone Device.
Please Provide Solution

Can you make sure  to switch off "Project Options > Build > iOS Build > Enable incremental builds (needs to be unchecked)
Then clean, build, deploy.
If not can you add "-v -v -v" to the additional mtouch arguments in the project's options (iPhone Build page), rebuild, and get the full build output? So I can look at it when I get home.