Add number of search limit to exessive search results and Help

Number is missing from results page shown if number exceeeded:
Number also needs adding to "Nature of the search" section in Help (currently text is 500, as a place holder.

Ready for test3

Thanks @Vino-S, @PatReynolds to take a look and test.

The number is now added in the message but I can't see anything on the Help page (may not be looking in correct place...)

Hi @DeniseColbert
You will not find it on the test3 Help page, but it is on test3 in draft form.
Note: when FreeBMD(1) "refuses" to do a search because it is too big, it basically removes the middle block of the search page and substitutes  the following text block;

It's done a Pearl page: https://www.freebmd.org.uk/cgi/search.pl . Its only possible to directly call the search page using this filename (you can't call the error message).
I believe that only one of the three FreeBMD(1) main (general) help pages cover this problem of the search being too big : it's in https://www.freebmd.org.uk/search-help.shtml which is called by hyperlink from the FreeBMD(1) Help page. That page is on test3 as a draft https://test3.freebmd.org.uk/cms/help/draft-how-best-to-search and the text can be found in first paragraph of the Searching the FreeBMD Database. section. That draft page, I believe was done by Comfort, but it was never progressed beyond what you see; but some of its material was put into the test3 Help page.
The current test3 Help page does not have any sub-pages, nor any hyperlinks to other FreeBMD2 pages; and it appears to have been made by selecting moving material from Help -subpages on FreeBMD(1) onto what is now the test3 Help page (as mentioned above).
At some point in time a decision will need to be made about enhancing the test3 Help page and deciding what missing information should be added to the page directly and what needs to be added via hyperlinks.
Note: there are quite a few draft sub-pages of Help, but none have yet made it through review (a page with hyperlinks, for example, cannot be sign off until all the hyperlinked pages have been sign off).

Hi @richpomfret
The View limit on FreeBMD(1) is stated to be 3000 and that is the same number in the error meassage I get on test3 if I try and search for: First name = John, Surname=Smith, no limit on dates, "place" nor event.
That same limit of 3000 is given on the FreeBMD(1) Help page and also appears on https://test3.freebmd.org.uk/cms/help/draft-help-1st-edited-draft-draft  in the first paragraph of the Nature of the search section.
Note: the image given at the top of this story, with no number in the error message, is still the case for test2. But test3 is OK.
Out of interest, I started putting limits on my search until I stopped getting the error message on test3, and then slightly relaxed them. I succesfully got 2866 births for John Smith between March 1837 and March of 1843; and 2901 births for John Smith between December 1938 and March 1844. So the 3000 limit is probably what is set on test3.
Note: Civil registration with the GRO did not start until six months into that year, the third quarter July-Sept 1873. I don't think there is any easy way of fixing this: there is no data to search for the first six months of 1873, that the search starts with the September quarter of 1873.

Thank you @PhilipClarke712. @PatReynolds can you comment?

Test2: Number of results still missing, e.g. search John SMITH births gives:

Test 3:

What is needed is the text (e.g.
Too Many search results  32302 results

Not necessary to do this update now (as per Pat's last request). Can move back to backlog/future milestone.

Done on test 3 and Beta


Container starts even if exception is thrown during initialization

Helidon container starts even if exception is thrown during initialization
Environment Details

Helidon Version: master 2.0.0-SNAPSHOT
Helidon MP
JDK version: 11
OS: Ubuntu

Problem Description
Helidon container starts even if exception is thrown during initialization(after deployment)
Steps to reproduce
void init(@Observes @Priority(PLATFORM_AFTER + 101) @Initialized(ApplicationScoped.class) Object e, BeanManager bm) {
   throw new RuntimeException("Survive this!");
}

The exception is correctly thrown on initialization, but the CDI.current() returned a CDI instance.


release: 0.20.3

https://github.com/leptonai/leptonai/actions/runs/9345796772

https://github.com/leptonai/leptonai/actions/runs/9346427598

https://github.com/leptonai/leptonai/actions/runs/9346504062

https://github.com/leptonai/leptonai/actions/runs/9346816364


Servlet Multipart Configs related to file size aren't working

Hi Team,
For specifying the maximum size of a file that is acceptable to be uploaded to the server, we specify these options in the application properties
spring.servlet.multipart.maxFileSize=15MB
spring.servlet.multipart.maxRequestSize=15MB

These are working for a regular spring boot service running on it's default web server i.e Tomcat, but the same options does not seem to work when included in spring cloud gateway running on it's default web server i.e Netty
A spring boot service throws MaxFileLimitExceeded option when I try to upload a file(mp4 video) > 15MB in size

Servlet.service() for servlet [dispatcherServlet] in context with path [] threw exception [Request processing failed; nested exception is org.springframework.web.multipart.MaxUploadSizeExceededException: Maximum upload size exceeded;

Whereas spring cloud gateway does not block the request and allow to proceed through

Spring Boot version: 2.3.5.RELEASE & Java version: 1.8
Spring Cloud Train version: 2021.0.1 & Java version: 11

There is no size limit for uploading files on spring-cloud-gateway, check the specific application service configuration

There is no size limit for uploading files on spring-cloud-gateway, check the specific application service configuration

What if I want to impose the size limit as I'd do on Zuul gateway with the below config
spring.servlet.multipart.maxFileSize=15MB
spring.servlet.multipart.maxRequestSize=15MB
How do I achieve the same via Spring cloud gateway? If that's not possible, are there any alternatives?

spring.servlet.multipart.maxFileSize=15MB
spring.servlet.multipart.maxRequestSize=15MB
These don't work in spring-cloud-gateway, custom filter restricts files

Got it. Thanks

Gateway is not a servlet application, hence none of those will work. see https://stackoverflow.com/questions/57424649/spring-webflux-set-max-files-size-for-uploading-files


One pre-trained model for all datasets or one for each?

Hi @gzerveas , when I read your paper, I thought you pre-trained ONE model based on all datasets and fine-tuned on each dataset for the specific classification/regression task. However, based on the README in this repository, it seems that each dataset has a pre-trained model. Which method did you use in your experiments?

@Roxyi The datasets are very disparate, both in terms of their characteristics (e.g dimensionality, time series length, sparsity), but more importantly, in terms of the natural quantities they encode (e.g. absorption frequency spectra, ECG/EEG signals, weather data etc). It would therefore make little to no sense training a single model on all datasets: there is little, if any, shared information, and the result would be catastrophic forgetting. I personally wouldn't trust a method that claims to perform better on time series of biomedical signals, after being pretrained on predicting traffic data of the road network of San Fransisco, or absorption spectra of chemical substances.
What happens instead is that the models are pretrained in an un-/self-supervised way to "fill in the gaps" in the time-series of a dataset of interest, and then are fine-tuned in a supervised way (using the provided labels in the dataset) to predict the label classes or physical quantities (in regression). There may be one or two exceptions for this: for example, the BeijingPM25 and BeijingPM10 datasets share time series data and only differ in the labels; for those, indeed a single model was pretrained in a self-supervised way, and then was fine-tuned for each one of these datasets.

@gzerveas Got you, that makes sense. Thank you for the explanation.


Tutorials: Correlation IDs

What?
Come up with an idea of a tutorial with the purpose of explaining correlation IDs and how to define them using AsyncAPI.
Ideally, there should be some working code at the end of the tutorial so people can see the value of both, having correlation IDs and having them defined on AsyncAPI.
Why?
To introduce the "correlation IDs" feature and its purpose.

Hi @fmvilas ,
Is this issue still open? I’d love to work on it. Let me know!

@derberg are we gonna move forward with it


Page routing silently falls back to generic handler when named handler does not exist

For example, if Index.cshtml contains
<form method="post" asp-page-handler="Integral">
    <!-- ... -->
    <input type="submit" />
</form>

and the corresponding page model contains only
public void OnGet(...)
{
    // ...
}

public IActionResult OnPost(...)
{
    // ...
}

the OnPost(...) handler will execute. I expected a 404 in this scenario.
The behaviour is the same for @page and @page "{handler?}".
The chosen handler method is logged. But, there's no mention of failing to find the OnPostIntegral(...) handler. Should at least warn for this case because it indicates a developer error.

What URL got generated in the rendered <form ...> tag?

What URL got generated in the rendered <form ...> tag?

With @page "{handler?}" in Index.cshtml, <form> tag helper generates action="/Integral". With bare @page, <form> tag helper generates action="/?handler=Integral". I'm more surprised the first does not result in a 404 than the second.

This is by design. This is how routing works.

@danroth27, @DamianEdwards, @Eilon do you think we should change the design here to not silently redirect but to behave exactly as instructed?

@rynowak where else do we have fallbacks to a more-generic route (a shorter template, not a template that ends with /*). I.e. is your "how routing works" statement specific to pages?
@mkArtakMSFT if this is how page routing works, even when using the handler route value option, :ok: (mostly). Suggest we at least need some logging to help users figure out what's going on when the fallback was not intended e.g. a typo in the name of the handler method or in the asp-page-handler value.

@dougbu, don't we already have logging where we choose which handler to choose? If not, please file a new issue specifically for that.

@mkArtakMSFT we log about the handler chosen but not the handlers considered (that I've seen).
In any case, the larger issue might be documentation, might be a missing feature, or something else. Took a bit of experimentation i.e. perhaps missing documentation, but I've found handlers are 100% optional even in page models containing handlers. For example, a page model containing just public void OnGet() {} still accepts POST requests. There's no obvious way to restrict the HTTP methods a page accepts i.e. perhaps a missing feature.
To get the behaviour I wanted i.e. to make sure I had all the handlers my page expected, I switched to @page "{handler=default}" and changed the page model to
public class IndexModel : PageModel
{
    public IActionResult OnGet()
    {
        return new NotFoundObjectResult($"Executing unsupported handler '{RouteData.Values["handler"]}'.");
    }

    public IActionResult OnPost()
    {
        return new NotFoundObjectResult($"Executing unsupported handler '{RouteData.Values["handler"]}'.");
    }

    public IActionResult OnGetDefault([FromHeader, Required] string host)
    {
        // ...
        return Page();
    }

    public IActionResult OnPostDefault([MinLength(6)] string host)
    {
        // ...
        return Page();
    }

    public IActionResult OnPostIntegrals([MinLength(6), Required] int[] ints)
    {
        // ...
        return Page();
    }
}

This caught my typo in the OnPostIntegrals method name and returned an error message making it easy to see.

This is all by design. Nothing here makes me want to change any of it.

@DamianEdwards :ok: then should I move this issue to the Docs repo?

If you feel something should be added to docs regarding how handler selection works, go for it.

Thanks folks! Closing by-design.


reminder - shift efi changes to last step after modules

"I guess I should shift the EFI changes to the last step after the modules " - as per telegram - placing here.

Already did, thanks!


Are maps from Mapsforge supported

I've not seen any mentioning of Mapsforge maps which are also vector based. Are these supported?

I don't know anything about Mapsforge - but if they use a standard vector map format then I don't see why not. Give it a try and let us know how it works out!


add more comments to explain niivue code base to other devs

Now that NiiVue is being use by other groups, it is a good time to make sure the code base is commented well so that others can use and understand the code easily.
Perhaps this is a good task for an AI bot....

Also, as part of this work, maybe work on breaking larger functions into smaller, bite-sized pieces.

I think some updates can be made to the document, as some APIs cannot be obtained and the classification is somewhat messy. This is my personal suggestion

I'd like to see more official documentation and tutorials, API principles and instructions, and more API interfaces and features. Hope Niivue gets better and better!


Fix soil sensor

Updating soil moisture sensor in greenpithumb.py to match the new parameters we added to the constructor in #53.
Fixes the mode to be BCM so that it matches the numbering on fritzing diagrams on the Raspberry Pi cobbler.
Fixes the wiring config example because the pins were reversed, causing the calculation to start at 1023 going down rather than 0 going up.

This change is

Coverage decreased (-0.4%) to 85.111% when pulling 64372a90933765e6ab0e0e4a4d3dd050692fd8a9 on mtlynch:fix-soil-sensor into 270b1a832073cf9b2acd51fe3e10a9c9313a18e2 on JeetShetty:master.


Attached Image not showing Face even though it does contain many with 1% confidence level

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

Have I written custom code (as opposed to using a stock example script provided in Mediapipe):
OS Platform and Distribution (e.g., Linux Ubuntu 16.04, Android 11, iOS 14.4):
MediaPipe version:
Bazel version:
Solution (e.g. FaceMesh, Pose, Holistic):
Programming Language and version ( e.g. C++, Python, Java):

Describe the expected behavior:
Standalone code you may have used to try to get what you need :
If there is a problem, provide a reproducible test case that is the bare minimum necessary to generate the problem. If possible, please share a link to Colab/repo link /any notebook:
Other info / Complete Logs :
Include any logs or source code that would be helpful to
diagnose the problem. If including tracebacks, please include the full
traceback. Large logs and files should be attached:

Hi @surapuramakhil ,
Could you provide steps to reproduce this issue.

same instructions as of there in this issue https://github.com/google/mediapipe/issues/3596

Hi @surapuramakhil ,
Marking this as a duplicate  ,please comment on older issue https://github.com/google/mediapipe/issues/3596


use doFinally in the DseConnection#executeRequest to reset warnings

What this PR does:
The  ClientWarn.instance.resetWarnings(); is not called if the reactive sequence ends up in error. The error handler does call the convertExceptionWithWarnings which calls the ClientWarn.instance.getAndClearWarnings(), however, although name suggest different the getAndClearWarnings() is not clearing the thread local.
Proposed fix ensures that after either onSuccess or onError handlers are called, we reset the state.
I would be very thankful if anybody has an idea about properly testing this?
Which issue(s) this PR fixes:
No ticket.
Checklist

[ ] Changes manually tested
[x] CLA Signed: DataStax CLA

Sounds good. Might actually fix a problem with mismatched warnings (another request that runs on a thread where clearing failed). Will not do anything for the Map retention problem but that is something DSE side needs to address.


When used with Image: Buffer throws ValueError on sending update

I am using the tutorial http://holoviews.org/user_guide/Streaming_Data.html, section ''A simple example: Brownian motion '' as a start and try to modify it for use with hv.Image.
The initial DynamicMap is displayed, but on Buffer.send(...) I get an error.
import pandas as pd
import numpy as np
import holoviews as hv
from holoviews.streams import Buffer
hv.extension('bokeh')

# returns 3x3 array with random values
def imgdata():
    imgdata = {}
    m, n = 3, 3
    for item in list(range(0,m)): imgdata[str(item)] = np.random.randn(n)
    return pd.DataFrame(data=imgdata).values

example = imgdata()
value_stream = Buffer(example, length=1, index=False)
img_dmap = hv.DynamicMap(hv.Image, streams=[value_stream])
img_dmap

This gives me the following image:

Now I try try to send new image values
value_stream.send(imgdata())

and get a ValueError
Traceback (most recent call last):
  File "C:\Users\mdk\Anaconda3\envs\paneldev\lib\site-packages\holoviews\plotting\util.py", line 272, in get_plot_frame
    return map_obj[key]
  File "C:\Users\mdk\Anaconda3\envs\paneldev\lib\site-packages\holoviews\core\spaces.py", line 1324, in __getitem__
    val = self._execute_callback(*tuple_key)
  File "C:\Users\mdk\Anaconda3\envs\paneldev\lib\site-packages\holoviews\core\spaces.py", line 1097, in _execute_callback
    retval = self.callback(*args, **kwargs)
  File "C:\Users\mdk\Anaconda3\envs\paneldev\lib\site-packages\holoviews\core\spaces.py", line 733, in __call__
    ret = self.callable(*args, **kwargs)
  File "C:\Users\mdk\Anaconda3\envs\paneldev\lib\site-packages\holoviews\element\raster.py", line 321, in __init__
    raise ValueError('Density along Image axes could not be determined. '
ValueError: Density along Image axes could not be determined. If the data contains only one coordinate along the x- or y-axis ensure you declare the bounds and/or density.

As I did not find any documentation on using Buffer with Image, my approach might be not the correct one...

This is a usage question and should probably be asked on Stackoverflow :) My guess is the problem comes from Image using 2D array data, whereas Buffer supplies tabular data (i.e. 1D).

The reason I decided to open an issue is that the initial image is created, and only the update (using the same method as for initialization) is not working.
But you might be right, it could still be a usage error rather than an holoviews error.

While I still did not find the possible error in my code above, the following code using Stream is working without errors:
import holoviews as hv
from holoviews import streams
from holoviews.streams import Stream
import pandas as pd
import numpy as np
hv.extension('bokeh')

def set_df():
    imgdata = {}
    m, n = 2, 2
    for item in list(range(0,m)): imgdata[str(item)] = np.random.randn(n)
    return pd.DataFrame(imgdata)

def set_images(df=set_df()):
    return hv.Image(df.values)

DF = Stream.define('DataFrame', df=set_df())

dmap = hv.DynamicMap(set_images, streams=[DF(df=set_df())])
dmap

Updates with:
dmap.event(df=set_df())

So I am strongly suspecting that I misused Buffer in some way...

So I am strongly suspecting that I misused Buffer in some way...

As I said, it's for tabular data only.


Reproducing Rubik's cube

Hi all,
I am unable to reproduce performance on Rubik's cube with default settings. I also played around with it and typically very little movement happens, a couple fingers just wiggle a bit but cube doesn't really move.
I have heard this is possible because my computer is not fast enough.
If so, is there some setting to freeze real time simulation, but compute the optimal plan offline with a certain number of compute, then play it back in the sim?

The video results for this task are generated with a MacBook Pro with M1 Pro chip.
Make sure you build MJPC in Release mode. For my laptop I needed to change the real time rate to less than 50% (you can do this with '-' key). You can also set the number of rollout trajectories to 10 if your machine has less CPU cores.

Thank you for the reply I will try the minus sign to slow things down.
I was unable to get the vscode instructions working so instead I did:
mkdir build
cmake ..
make .
Is this release mode or do we need more settings?

Yes I saw those instructions but was not able to follow it.
Can you provide more instructions?

I think the first time I opened the project in VSCode I saw the configuration popup, but I don't see it anymore and not sure how to make it come back.
I clicked the wrench to change the activekit to clang12 (this is a must in Ubuntu 20 or it doesnt build). Then I change the button to the right of build to release, and click build. But I get this:

Even though I set the build target as release, when I check the string, it says this:
[proc] Executing command: /usr/bin/cmake --no-warn-unused-cli -DCMAKE_BUILD_TYPE:STRING=Debug -DCMAKE_EXPORT_COMPILE_COMMANDS:BOOL=TRUE -DCMAKE_C_COMPILER:FILEPATH=/usr/bin/clang-12 -DCMAKE_CXX_COMPILER:FILEPATH=/usr/bin/clang++-12 -S/samsung4tb/mujoco_mpc -B/samsung4tb/mujoco_mpc/build -G Ninja
and at the end it still errors.
I can use this command manually in terminal to build it, but not sure if I am missing performance optimizations necessary to reproduce:
rli14@fitzroy:/samsung4tb/mujoco_mpc/build$ CXX=/usr/bin/clang++-12 CC=/usr/bin/clang-12 cmake -DCMAKE_BUILD_TYPE=Release ..

Send me an email so we can get on a call this week and I can help you figure out the build.


too picky about precedence (violated is AssigningExpression right)

The typechecker accepts
class A(shared variable Anything b = null) {}
A().b = () => "violated is AssigningExpression right";

but ceylon.ast throws:
Assertion failed: Check precedence
        unviolated is ThenElseExpression left = expressionToCeylon(assignOperation.leftTerm, update)
        violated is AssigningExpression right = expressionToCeylon(assignOperation.rightTerm, update)
ceylon.language.AssertionError "Assertion failed: Check precedence
        unviolated is ThenElseExpression left = expressionToCeylon(assignOperation.leftTerm, update)
        violated is AssigningExpression right = expressionToCeylon(assignOperation.rightTerm, update)"
        at ceylon.ast.redhat.assignOperationToCeylon_.assignOperationToCeylon(AssignOperation.ceylon:16)
        at ceylon.ast.redhat.assignmentOperationToCeylon_.assignmentOperationToCeylon(AssignmentOperation.ceylon:20)
        at ceylon.ast.redhat.assignmentStatementToCeylon_.assignmentStatementToCeylon(AssignmentStatement.ceylon:16)
        at ceylon.ast.redhat.expressionStatementToCeylon_.expressionStatementToCeylon(ExpressionStatement.ceylon:20)

It looks like the parser accepts any expression to the right-hand side of any assigning expression (including +=), so precedence just doesn’t exist on the rhs apparently.


problem

Estou recebendo esse erro na hora de dar o fetch nos arquivos do repositório.

@joserribeiro26 can you please share the repo link you used? is it a super large repo?

@joserribeiro26 Hey can you tell me the repo you used i tried a few large ones even react from meta. All seem to work fine?

I have the same issue and I setup a public test repo (with just a readme file) to confirm if it was related to a private repo or not and although it worked, it also got the same error later when I tried again. It will intermittently work and then stop working again.
Application error: a client-side exception has occurred (see the browser console for more information).
https://github.com/[SonixSquad/Testrepo](https://github.com/SonixSquad/Testrepo)

Heres the dev console details provided on the error specifics
Failed to load resource: the server responded with a status of 500 ()
create-420d82159595630e.js:1  Error fetching repository files: TypeError: Cannot read properties of undefined (reading 'map')
at v (create-420d82159595630e.js:1:15857)
v @ create-420d82159595630e.js:1
framework-1751fec5da7e0462.js:25  TypeError: Cannot read properties of undefined (reading 'length')
at S (create-420d82159595630e.js:1:9004)
at ld (framework-1751fec5da7e0462.js:25:58401)
at od (framework-1751fec5da7e0462.js:25:72731)
at i (framework-1751fec5da7e0462.js:25:120263)
at uI (framework-1751fec5da7e0462.js:25:99019)
at framework-1751fec5da7e0462.js:25:98886
at uM (framework-1751fec5da7e0462.js:25:98893)
at ux (framework-1751fec5da7e0462.js:25:95645)
at uS (framework-1751fec5da7e0462.js:25:94200)
at x (framework-1751fec5da7e0462.js:49:1364)
l5 @ framework-1751fec5da7e0462.js:25
main-48a2be0192cdda09.js:1  TypeError: Cannot read properties of undefined (reading 'length')
at S (create-420d82159595630e.js:1:9004)
at ld (framework-1751fec5da7e0462.js:25:58401)
at od (framework-1751fec5da7e0462.js:25:72731)
at i (framework-1751fec5da7e0462.js:25:120263)
at uI (framework-1751fec5da7e0462.js:25:99019)
at framework-1751fec5da7e0462.js:25:98886
at uM (framework-1751fec5da7e0462.js:25:98893)
at ux (framework-1751fec5da7e0462.js:25:95645)
at uS (framework-1751fec5da7e0462.js:25:94200)
at x (framework-1751fec5da7e0462.js:49:1364)
Z @ main-48a2be0192cdda09.js:1
main-48a2be0192cdda09.js:1  A client-side exception has occurred, see here for more info: https://nextjs.org/docs/messages/client-side-exception-occurred

I have the same issue and I setup a public test repo (with just a readme file) to confirm if it was related to a private repo or not and although it worked, it also got the same error later when I tried again. It will intermittently work and then stop working again.
Application error: a client-side exception has occurred (see the browser console for more information).
[https://github.com/[SonixSquad/Testrepo](https://github.com/SonixSquad/Testrepo)](https://github.com/[SonixSquad/Testrepo](https://github.com/SonixSquad/Testrepo))

Hey thanks for this, going to try a bunch tomorrow and check why, assume its function timing out or GitHub api throwing error since github auth for cloning repos was removed when actual auth was added, Fixes coming tomorrow thanks

Thank you all for your support! The issue has been resolved—it was related to the GitHub API key, but everything is working now, some repos may be super large and vercel function will timeout eventually but most should work just tried with mutliple different repos back to back to back on deployed version...
I'll be rolling out more updates soon, including improvements to image rendering and better code rendering features, especially now that we have paying customers. Stay tuned for more updates, and thanks again for your purchases!


Remote key escrow

Remote key escrow via a GRPC implementation of the trustmanager.Storage interface.
Still finishing it up but intend for Notary Signer to be able to act as an escrow backend with configurable APIs (so it can host either or both of: current key storage for server and/or the new GRPC trustmanager.Storage server).
By implementing the low level trustmanager.Storage interface, passwords, and therefore key encryption and decryption, still happen at the client, so compromise of the remote key storage does not equate to compromise of the escrowed keys.
Signed-off-by: David Lawrence<EMAIL_ADDRESS>(github: endophage)

This is about as tested as it's going to get. The compiled .pb.go is hurting coverage but I'm not going to write tests for generated code.

@ecordell the full repo will be coming in follow up PRs, with something identical or at least very, very close to that interface. This was a nice self contained piece of functionality (that is also somewhat necessary for the remote full repository).
The dependency injection PR from @n4ss is also foundational work for the full remote repo.

Could we also add trustmanager/remoteks/*.pb.go to the codecov ignore section?

I think I've addressed everything here. Just rebased, tests passed locally, hopefully circle agrees.

I've re-kicked the yubikey tests, and they past.  Codecov is being weird.  :(   This otherwise LGTM, modulo a codecov ignore change (which does not account for the codecov weirdness atm)


installation failed on macos 10.14.6

Problem encountered on https://dotnet.microsoft.com/en-us/learn/dotnet/hello-world-tutorial/install
Operating System: macos 10.14.6
Provide details about the problem you're experiencing. Include your operating system version, exact error message, code sample, and anything else that is relevant.
After installing the sdk using the link, the dotnet command at the terminal failed as described in the hello-world tutorial:
 ❯ dotnet
zsh: correct dotnet to done [nyae]? n
zsh: command not found: dotnet

As the first page states, you need to be on macOS 10.15 or later versions.
Here's the support matrix: https://docs.microsoft.com/en-us/dotnet/core/install/macos#supported-releases

Thank you for the link and the unreasonably fast response! I got to the tutorial without going through that page. Can I get away with saying "not my fault"? :)


Critical! User creation API failing

Environment: NTP Production.
Tried to create a user through postman(details below). On first request, got service error, On subsequent requests getting 'username/email already exists' error. Not able to find the user/email in the user list query. However, able to login using the emailid.
"firstName": "Bindu",
"lastName": "Thirumalai",
"provider":"tiss",
"email"<EMAIL_ADDRESS>"userName":"BinduThirumalai",
"emailVerified": true

@Jayaprakash8887 - i think this issue is resolved now. Please close if CreateUser is working now.


How to fire onchange event

Hello, I got an issues of index out of range issues while input text to make a tag, but before tag creation, it shows errors of index out of range

Please provide me with more details so i can reproduce your error in my environment.

I believe this issue has to do with using TextFieldTags without a validator:
TextFieldTags(
  tagsStyler: TagsStyler(
    tagDecoration: BoxDecoration(color: Colors.blue),
  ),
  textFieldStyler: TextFieldStyler(
    textFieldFilled: true,
    hintText: "Tags",
    helperText: null,
    textFieldBorder: OutlineInputBorder(borderSide: BorderSide.none),
  ),
  onTag: (tag) {
    //todo
  },
  onDelete: (tag) {
    //todo
  },
)

Upon entering a tag, I received an exception: Null check operator used on a null value


Newly acquired items no longer show when connecting to an ongoing game

Describe the bug
Previously when connecting to an ongoing Archipelago multiworld all the items other players gave you since the last time you played. But the current version doesn't do that anymore. It still displays items when I get them during the game, but I miss seeing all the items I got since last I connected.
To Reproduce
Steps to reproduce the behavior:

Create an Archipelago game
Connect to it
Save and quit
Get given an item (can just cheat it in with /send)
Reconnect to the game
The item will show up in RecentItemsDisplay, if installed, but not in the bottom left of the screen

Expected behavior
All the received items should show on-screen when connecting.
Screenshots
N/A
Additional context
I unfortunately do not remember the last version it worked in, but it's been a while (probably over 6 months) since I last did an HK Arch run, so it could be anywhere in that range...

The behavior listed was an intentional change, especially when receiving many items it can keep the item queue in the corner blocked for a long time. I figured that generally in cases where it's relevant, players would use the tracker to see what items they had gotten offline.
Potentially I could consider this a feature request but would want to know more about the use case

Yeah, it can stay blocked for a while for sure, but when using RecentItemsDisplay as well that isn't really a problem. Plus items don't get lost, just delayed.
My group likes to do long (multi-day) randomizers, and we often play asynchronously. That leads to getting a lot of items while I am offline. We found it fun to see all the items we got scroll by one by one. It made it a lot easier (for me at least) to keep track of all the important items I received, and it was a fun group activity to watch each other receive all the new items. With that feature removed it's now much harder for me to keep track of the new items available to me (or even be aware of them, without proactively looking).

I may consider this as an option. It seems to me that the value of the corner display, especially if recent items display is not used, is severely reduced if blocked for several minutes. Plus going looking proactively, either at the tracker or at the inventory will generally be much more efficient. My inclination is that I probably wouldn't want to maintain this, but am not certain


[fix] settings: add recaptcha keys in settings

Added missing settings for recaptcha keys

I'm pretty sure recaptcha isn't used anywhere in the code. We only use django-simple-captcha

I added this before reading the detailed install instructions from https://github.com/ethstakersclub/ethstakersclub/commit/1e63c3b7e1b137fe7d804633d7524f4035d32718.
It was stated in an error message when trying to run the app.
I'll have to go through the setup instructions and try again to see if it still complains, but I'm guessing not.


fixed scope parameter separator

Fixed the scope parameter to be separated by spaces as stated in the doc.
It is also related to https://github.com/RiskioFr/oauth2-auth0/issues/9

Thank you @smatyas for your contribution


Eliminate babel-eslint and disable stage-1 support?

We do not need babel-eslint if we disable support for experimental features. How do people feel? Do we want to keep stage-1 support?

Only issue: ESLint doesn't support stage 1-3 proposals like Babel does. So our options are:

Keep supporting bleeding edge Stage 1 features via babel-eslint
Remove Babel stage 1-3 support and run ESLint directly. Eliminate babel-eslint

In the event you are looking for an opinoin, here is mine.
I have already removed babel-eslint with the knowledge I only planned on supporting es2015.

Since babel dropped decorator support i have moved down to stage-2 mainly for the object spread functionality, which to me simplifies object combination and immutable updating.

This is one thing that I tend to use frequently but I have not had any
issues linting with eslint and the babelrc included in my PR. It may be a
good idea to update my PR to show that it can be used/how to use it.
On Sat, Apr 2, 2016, 5:59 AM Tristan Smith<EMAIL_ADDRESS>wrote:

Since babel dropped decorator support i have moved down to stage-2 mainly
for the object spread functionality, which to me simplifies object
combination and immutable updating.
—
You are receiving this because you commented.
Reply to this email directly or view it on GitHub
https://github.com/coryhouse/react-slingshot/issues/87#issuecomment-204694414

@coryhouse Shouldn't this be closed based on the changes from es-watch

Yep, good catch. Closing.


I have faced some problem with using axios post request

Hi,
my web server is php, laravel.
and client side is javascript, react native.
I have tried using axios connecting php as like
const instance =  axios.create({
baseURL: url,
});
if(authToken.length != 0) instance.defaults.headers.common['Authorization'] = authToken;
instance.defaults.headers.post['Content-Type'] = 'application/x-www-form-urlencoded';
instance.post(url, {
...body
})
.then(response => console.log(response)
.catch(response => console.log(response);
but it reponse like
Automatically populating $HTTP_RAW_POST_DATA is deprecated and will be removed in a future version

That is an issue in your server and not in axios itself. Please review your code.

i have same issue how i can solve it??


providerID should be nil in additionalUserInfo for signInAnonymouslyWithCompletion:

This makes the behavior consistent between iOS and Android for additionalUserInfo of an anonymous sign-in. Right now the iOS SDK is returning "password" (email sign-in) as the providerID but I think nil would be more appropriate and matches Android.
Also added a test.
Fixes firebase/firebase-ios-sdk#3450

FYI @morganchen12 @renkelvin Only repo members can merge.  I'll do this one.  Thanks @collinjackson!


Enable stapler (but don't require passing test)

Summary
Enable stapler (but don't require passing test)
Changes Made
Enable stapler (but don't require passing test)
TODO
Fix signing.
Motivation
Stapler is required for distribution
Testing
N/A
Screenshots/GIFs
N/A
Checklist
Please ensure all of the following are completed before submitting the PR:

[x] Code has been tested and verified.
[x] Documentation has been updated to reflect the changes.
[x] All tests pass successfully.
[x] Code follows the project's coding guidelines and style.
[x] The branch is up-to-date with the latest changes from the main branch.
[x] Reviewed by at least one other contributor.

Related Issues
This PR addresses the following issue(s): N/A
Additional Notes
N/A

Thanks for submitting a pull request!
We will review it as soon as possible.


Add templets

Users can delete them self on thier page.
Changed some user page names.
Added so that admin can change a users username and email addres.
Changed restrictions on pages to admin and user for easisier debugging.
,
removed a comma synatx error
Fixed loading of page when pressing location button
Added a wall button to header

A modal should be used to confirm that the user want to remove their account, instead of using an alert.

Modal is fixed!


fix(protocol): fix permission bugs

OwnerUUPSUpgradable.pause() and OwnerUUPSUpgradable.unpause() are not permission-checked. Now they are internal functions.
TaikoL1.unpause() was not permission checked, fixed.
Allow certain onlyOwner function to use onlyFromOwnerOrNamed in case we get rid of the security council earlier but would like to keep special named roles for special operations.

@Brechtpd could you verify the permissions again?


Lab 9 - Power Apps Component Framework

Module: 09 Power Component Frameworks
Task: 1.2 & 2.1
Suggestion to do this as well to run properly
Description of issue
Repro steps:

Open solution.cdsproj from Solution folder and update the path
bin/debug
Ex 2 - Task 2.1 -  with https

This is already included in the PR I raised
Add the following tag to the PropertyGroup
bin/debug

Addressed by PR #77 https://github.com/MicrosoftLearning/PL-400_Microsoft-Power-Platform-Developer/pull/77


NOTE: THIS SHOULD PROBABLY NOT BE MERGED.
I put this up here just to make the folks who have been running into the Docker networking  issues mentioned here (https://github.com/OpenMined/Sonar/issues/13) were aware of a fix. See my comment on that issue for more information.

@anoff @carrollgt91 should I go ahead and close this PR?

Yeah, it's likely very out of date at this point anyways 👍

On Sep 2, 2017, at 12:30 PM, iamtrask<EMAIL_ADDRESS>wrote:
@anoff @carrollgt91 should I go ahead and close this PR?
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is it possible to run multiple formatters for a filetype?

i think about moving from format.nvim to this. but i have a few filetypes that needs multiple formatters. like go... i need goimports and gofumpt. in format.nvim it is
                        go = {
                            {
                                cmd = {"goimports -w", "gofumpt -w"},
                                tempfile_postfix = ".tmp"
                            }
                        },

or in yaml i need also kustomize
                        yaml = {
                            {cmd = {"prettier -w", "kustomize cfg fmt"}}
                        },

You can run multiple formatters, you just create a new entry in the same filetype table.
 go = {
  -- goimports
  function()
    return {
      exe = "goimports",
      args = {...}
    }
  end,
  -- gofumpt
  function()
    return {
      exe = "gofmpt",
      args = {...}
    }
  end
}

i really dont get it work. my config looks like this:
https://gist.github.com/xsteadfastx/a87706f22285506fb07f62e96af74701#file-init-lua-L261
goimports just wont work. it doesn nothing. any idea how to debug?

You are using args = {"-w"} and stdin = true. That contradicts itself.
remove the args and it should work

i used this from the examples: https://github.com/mhartington/formatter.nvim/blob/master/CONFIG.md#golang
also lua-fmt... copied from examples... it does nothing. is there a debug more or something?

also updated config: https://gist.github.com/xsteadfastx/e39facac21454d57c317c34e3894b827#file-init-lua-L262

i missed the filetype = {}...oh sorry :)


Getting matrix contains invalid numeric entries error

When trying SparseInt with ViT, I get this error
  File "/home/user/anaconda3/envs/detectron2/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/user/ard/SparseInst/sparseinst/loss.py", line 301, in forward
    indices = [linear_sum_assignment(c[i], maximize=True)
  File "/home/user/ard/SparseInst/sparseinst/loss.py", line 301, in <listcomp>
    indices = [linear_sum_assignment(c[i], maximize=True)
ValueError: matrix contains invalid numeric entries

Here's the config printed
[07/29 18:08:15 detectron2]: Command line arguments: Namespace(config_file='configs/sparse_inst_pvt_b2_li_giam.yaml', dist_url='tcp://<IP_ADDRESS>:50153', eval_only=False, machine_rank=0, num_gpus=4, num_machines=1, opts=['SOLVER.AMP.ENABLED', 'True'], resume=False)
[07/29 18:08:15 detectron2]: Contents of args.config_file=configs/sparse_inst_pvt_b2_li_giam.yaml:
_BASE_: "Base-SparseInst.yaml"
MODEL:
  WEIGHTS: "pretrained_models/pvt_v2_b2_li.pth"
  BACKBONE:
    NAME: "build_pyramid_vision_transformer"
  SPARSE_INST:
    ENCODER:
      IN_FEATURES: ["p2", "p3", "p4"]
  PVT:
    NAME: "b2"
    LINEAR: True
    OUT_FEATURES: ["p2", "p3", "p4"]
OUTPUT_DIR: "output/sparse_inst_pvt_b2_linear_giam"

[07/29 18:08:15 detectron2]: Running with full config:
CUDNN_BENCHMARK: false
DATALOADER:
  ASPECT_RATIO_GROUPING: true
  FILTER_EMPTY_ANNOTATIONS: true
  NUM_WORKERS: 6
  REPEAT_THRESHOLD: 0.0
  SAMPLER_TRAIN: TrainingSampler
DATASETS:
  PRECOMPUTED_PROPOSAL_TOPK_TEST: 1000
  PRECOMPUTED_PROPOSAL_TOPK_TRAIN: 2000
  PROPOSAL_FILES_TEST: []
  PROPOSAL_FILES_TRAIN: []
  TEST:
  - bipacolortest
  TRAIN:
  - bipacolortrain
GLOBAL:
  HACK: 1.0
INPUT:
  CROP:
    ENABLED: false
    SIZE:
    - 0.9
    - 0.9
    TYPE: relative_range
  FORMAT: RGB
  MASK_FORMAT: bitmask
  MAX_SIZE_TEST: 853
  MAX_SIZE_TRAIN: 853
  MIN_SIZE_TEST: 640
  MIN_SIZE_TRAIN:
  - 416
  - 448
  - 480
  - 512
  - 544
  - 576
  - 608
  - 640
  MIN_SIZE_TRAIN_SAMPLING: choice
  RANDOM_FLIP: horizontal
MODEL:
  ANCHOR_GENERATOR:
    ANGLES:
    - - -90
      - 0
      - 90
    ASPECT_RATIOS:
    - - 0.5
      - 1.0
      - 2.0
    NAME: DefaultAnchorGenerator
    OFFSET: 0.0
    SIZES:
    - - 32
      - 64
      - 128
      - 256
      - 512
  BACKBONE:
    FREEZE_AT: 0
    NAME: build_pyramid_vision_transformer
  CSPNET:
    NAME: darknet53
    NORM: ''
    OUT_FEATURES:
    - csp1
    - csp2
    - csp3
    - csp4
  DEVICE: cuda
  FPN:
    FUSE_TYPE: sum
    IN_FEATURES: []
    NORM: ''
    OUT_CHANNELS: 256
  KEYPOINT_ON: false
  LOAD_PROPOSALS: false
  MASK_ON: true
  META_ARCHITECTURE: SparseInst
  PANOPTIC_FPN:
    COMBINE:
      ENABLED: true
      INSTANCES_CONFIDENCE_THRESH: 0.5
      OVERLAP_THRESH: 0.5
      STUFF_AREA_LIMIT: 4096
    INSTANCE_LOSS_WEIGHT: 1.0
  PIXEL_MEAN:
  - 123.675
  - 116.28
  - 103.53
  PIXEL_STD:
  - 58.395
  - 57.12
  - 57.375
  PROPOSAL_GENERATOR:
    MIN_SIZE: 0
    NAME: RPN
  PVT:
    LINEAR: true
    NAME: b2
    OUT_FEATURES:
    - p2
    - p3
    - p4
  RESNETS:
    DEFORM_MODULATED: false
    DEFORM_NUM_GROUPS: 1
    DEFORM_ON_PER_STAGE:
    - false
    - false
    - false
    - false
    DEPTH: 50
    NORM: FrozenBN
    NUM_GROUPS: 1
    OUT_FEATURES:
    - res3
    - res4
    - res5
    RES2_OUT_CHANNELS: 256
    RES5_DILATION: 1
    STEM_OUT_CHANNELS: 64
    STRIDE_IN_1X1: false
    WIDTH_PER_GROUP: 64
  RETINANET:
    BBOX_REG_LOSS_TYPE: smooth_l1
    BBOX_REG_WEIGHTS: &id002
    - 1.0
    - 1.0
    - 1.0
    - 1.0
    FOCAL_LOSS_ALPHA: 0.25
    FOCAL_LOSS_GAMMA: 2.0
    IN_FEATURES:
    - p3
    - p4
    - p5
    - p6
    - p7
    IOU_LABELS:
    - 0
    - -1
    - 1
    IOU_THRESHOLDS:
    - 0.4
    - 0.5
    NMS_THRESH_TEST: 0.5
    NORM: ''
    NUM_CLASSES: 80
    NUM_CONVS: 4
    PRIOR_PROB: 0.01
    SCORE_THRESH_TEST: 0.05
    SMOOTH_L1_LOSS_BETA: 0.1
    TOPK_CANDIDATES_TEST: 1000
  ROI_BOX_CASCADE_HEAD:
    BBOX_REG_WEIGHTS:
    - &id001
      - 10.0
      - 10.0
      - 5.0
      - 5.0
    - - 20.0
      - 20.0
      - 10.0
      - 10.0
    - - 30.0
      - 30.0
      - 15.0
      - 15.0
    IOUS:
    - 0.5
    - 0.6
    - 0.7
  ROI_BOX_HEAD:
    BBOX_REG_LOSS_TYPE: smooth_l1
    BBOX_REG_LOSS_WEIGHT: 1.0
    BBOX_REG_WEIGHTS: *id001
    CLS_AGNOSTIC_BBOX_REG: false
    CONV_DIM: 256
    FC_DIM: 1024
    NAME: ''
    NORM: ''
    NUM_CONV: 0
    NUM_FC: 0
    POOLER_RESOLUTION: 14
    POOLER_SAMPLING_RATIO: 0
    POOLER_TYPE: ROIAlignV2
    SMOOTH_L1_BETA: 0.0
    TRAIN_ON_PRED_BOXES: false
  ROI_HEADS:
    BATCH_SIZE_PER_IMAGE: 512
    IN_FEATURES:
    - res4
    IOU_LABELS:
    - 0
    - 1
    IOU_THRESHOLDS:
    - 0.5
    NAME: Res5ROIHeads
    NMS_THRESH_TEST: 0.5
    NUM_CLASSES: 80
    POSITIVE_FRACTION: 0.25
    PROPOSAL_APPEND_GT: true
    SCORE_THRESH_TEST: 0.05
  ROI_KEYPOINT_HEAD:
    CONV_DIMS:
    - 512
    - 512
    - 512
    - 512
    - 512
    - 512
    - 512
    - 512
    LOSS_WEIGHT: 1.0
    MIN_KEYPOINTS_PER_IMAGE: 1
    NAME: KRCNNConvDeconvUpsampleHead
    NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS: true
    NUM_KEYPOINTS: 17
    POOLER_RESOLUTION: 14
    POOLER_SAMPLING_RATIO: 0
    POOLER_TYPE: ROIAlignV2
  ROI_MASK_HEAD:
    CLS_AGNOSTIC_MASK: false
    CONV_DIM: 256
    NAME: MaskRCNNConvUpsampleHead
    NORM: ''
    NUM_CONV: 0
    POOLER_RESOLUTION: 14
    POOLER_SAMPLING_RATIO: 0
    POOLER_TYPE: ROIAlignV2
  RPN:
    BATCH_SIZE_PER_IMAGE: 256
    BBOX_REG_LOSS_TYPE: smooth_l1
    BBOX_REG_LOSS_WEIGHT: 1.0
    BBOX_REG_WEIGHTS: *id002
    BOUNDARY_THRESH: -1
    CONV_DIMS:
    - -1
    HEAD_NAME: StandardRPNHead
    IN_FEATURES:
    - res4
    IOU_LABELS:
    - 0
    - -1
    - 1
    IOU_THRESHOLDS:
    - 0.3
    - 0.7
    LOSS_WEIGHT: 1.0
    NMS_THRESH: 0.7
    POSITIVE_FRACTION: 0.5
    POST_NMS_TOPK_TEST: 1000
    POST_NMS_TOPK_TRAIN: 2000
    PRE_NMS_TOPK_TEST: 6000
    PRE_NMS_TOPK_TRAIN: 12000
    SMOOTH_L1_BETA: 0.0
  SEM_SEG_HEAD:
    COMMON_STRIDE: 4
    CONVS_DIM: 128
    IGNORE_VALUE: 255
    IN_FEATURES:
    - p2
    - p3
    - p4
    - p5
    LOSS_WEIGHT: 1.0
    NAME: SemSegFPNHead
    NORM: GN
    NUM_CLASSES: 54
  SPARSE_INST:
    CLS_THRESHOLD: 0.005
    DATASET_MAPPER: SparseInstDatasetMapper
    DECODER:
      GROUPS: 4
      INST:
        CONVS: 4
        DIM: 256
      KERNEL_DIM: 128
      MASK:
        CONVS: 4
        DIM: 256
      NAME: GroupIAMDecoder
      NUM_CLASSES: 10
      NUM_MASKS: 100
      OUTPUT_IAM: false
      SCALE_FACTOR: 2.0
    ENCODER:
      IN_FEATURES:
      - p2
      - p3
      - p4
      NAME: InstanceContextEncoder
      NORM: ''
      NUM_CHANNELS: 256
    LOSS:
      CLASS_WEIGHT: 2.0
      ITEMS:
      - labels
      - masks
      MASK_DICE_WEIGHT: 2.0
      MASK_PIXEL_WEIGHT: 5.0
      NAME: SparseInstCriterion
      OBJECTNESS_WEIGHT: 1.0
    MASK_THRESHOLD: 0.45
    MATCHER:
      ALPHA: 0.8
      BETA: 0.2
      NAME: SparseInstMatcher
    MAX_DETECTIONS: 100
  WEIGHTS: sparse_inst_pvt_v2_b2_li_giam_02e25d.pth
OUTPUT_DIR: output/sparse_inst_pvt_b2_linear_giam
SEED: -1
SOLVER:
  AMP:
    ENABLED: true
  AMSGRAD: false
  BACKBONE_MULTIPLIER: 1.0
  BASE_LR: 5.0e-05
  BIAS_LR_FACTOR: 1.0
  CHECKPOINT_PERIOD: 5000
  CLIP_GRADIENTS:
    CLIP_TYPE: value
    CLIP_VALUE: 1.0
    ENABLED: false
    NORM_TYPE: 2.0
  GAMMA: 0.1
  IMS_PER_BATCH: 32
  LR_SCHEDULER_NAME: WarmupMultiStepLR
  MAX_ITER: 1500
  MOMENTUM: 0.9
  NESTEROV: false
  OPTIMIZER: ADAMW
  REFERENCE_WORLD_SIZE: 0
  STEPS:
  - 1166
  - 1388
  WARMUP_FACTOR: 0.001
  WARMUP_ITERS: 1000
  WARMUP_METHOD: linear
  WEIGHT_DECAY: 0.05
  WEIGHT_DECAY_BIAS: null
  WEIGHT_DECAY_NORM: 0.0
TEST:
  AUG:
    ENABLED: false
    FLIP: true
    MAX_SIZE: 4000
    MIN_SIZES:
    - 400
    - 500
    - 600
    - 700
    - 800
    - 900
    - 1000
    - 1100
    - 1200
  DETECTIONS_PER_IMAGE: 100
  EVAL_PERIOD: 60
  EXPECTED_RESULTS: []
  KEYPOINT_OKS_SIGMAS: []
  PRECISE_BN:
    ENABLED: false
    NUM_ITER: 200
VERSION: 2
VIS_PERIOD: 0

Hi @sarmientoj24,  thanks for your interest in SparseInst.
Have you load any pretrained weights?

Yes
On Mon, Aug 1, 2022, 17:37 Tianheng Cheng @.***> wrote:

Hi @sarmientoj24 https://github.com/sarmientoj24, thanks for your
interest in SparseInst.
Have you load any pretrained weights?
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Could you provide the log of the training process?

Could you provide the log of the training process?

Hello, I met the same problem as well when I try to change the optimizer from ADAMW to SGD.

I fell into this problem too. The cause is:
SOLVER:
AMP:
ENABLED: true
Set it to False (use FP32) and the error disappears
I've tried to debug but wasn't able to fix it

I haven't loaded any pre-trained weights. The problem also exists.

I haven't loaded any pre-trained weights. The problem also exists.

when I set AMP to False, the error disappears too, there is a new error which is RuntimeError: CUDA driver error: a PTX JIT compilation failed
Traceback (most recent call last):   File "/data2/cjn/SparseInst-main-new/tools/train_net.py", line 233, in <module>     launch(   File "/home/ultrasonic/anaconda3/envs/detectron2-0.3/lib/python3.8/site-packages/detectron2-0.3-py3.8-linux-x86_64.egg/detectron2/engine/launch.py", line 62, in launch     main_func(*args)   File "/data2/cjn/SparseInst-main-new/tools/train_net.py", line 227, in main     return trainer.train()   File "/home/ultrasonic/anaconda3/envs/detectron2-0.3/lib/python3.8/site-packages/detectron2-0.3-py3.8-linux-x86_64.egg/detectron2/engine/defaults.py", line 431, in train     super().train(self.start_iter, self.max_iter)   File "/home/ultrasonic/anaconda3/envs/detectron2-0.3/lib/python3.8/site-packages/detectron2-0.3-py3.8-linux-x86_64.egg/detectron2/engine/train_loop.py", line 138, in train     self.run_step()   File "/home/ultrasonic/anaconda3/envs/detectron2-0.3/lib/python3.8/site-packages/detectron2-0.3-py3.8-linux-x86_64.egg/detectron2/engine/defaults.py", line 441, in run_step     self._trainer.run_step()   File "/home/ultrasonic/anaconda3/envs/detectron2-0.3/lib/python3.8/site-packages/detectron2-0.3-py3.8-linux-x86_64.egg/detectron2/engine/train_loop.py", line 232, in run_step     loss_dict = self.model(data)   File "/home/ultrasonic/anaconda3/envs/detectron2-0.3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl     result = self.forward(*input, **kwargs)   File "/data2/cjn/SparseInst-main-new/sparseinst/sparseinst.py", line 107, in forward     losses = self.criterion(output, targets, max_shape)   File "/home/ultrasonic/anaconda3/envs/detectron2-0.3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl     result = self.forward(*input, **kwargs)   File "/data2/cjn/SparseInst-main-new/sparseinst/loss.py", line 196, in forward     losses.update(self.get_loss(loss, outputs, targets, indices,   File "/data2/cjn/SparseInst-main-new/sparseinst/loss.py", line 176, in get_loss     return loss_map[loss](outputs, targets, indices, num_instances, **kwargs)   File "/data2/cjn/SparseInst-main-new/sparseinst/loss.py", line 106, in loss_labels     class_loss = sigmoid_focal_loss_jit( RuntimeError: CUDA driver error: a PTX JIT compilation failed

Hi all, I've found that the sigmoid + norm in the decoder will cause the NaN error when FP16 is enabled. In the latest update, we provide a special softmax version of the decoder to avoid numerical errors, and it supports FP16 better than the sigmoid + norm. Sorry for the late reply and hope my suggestion can help you.

Hi all, I've found that the sigmoid + norm in the decoder will cause the NaN error when FP16 is enabled. In the latest update, we provide a special softmax version of the decoder to avoid numerical errors, and it supports FP16 better than the sigmoid + norm. Sorry for the late reply and hope my suggestion can help you.

I got the same problem now. I am using pre-trained weights and trying to train R-50-vd-DCN model. Are there additional steps to use new softmax version?

It seems sigmoid + norm is used by default. Adding MODEL.SPARSE_INST.DECODER.NAME GroupIAMSoftDecoder to the command line solved the problem for me.

It seems sigmoid + norm is used by default. Adding MODEL.SPARSE_INST.DECODER.NAME GroupIAMSoftDecoder to the command line solved the problem for me.

It still does not work...


Change "" tag to the table header

I had changed the table header tag into "" tag and solve issue #17

Then, I merge lah?

Ok,proceed


Duplicate Values in Env

This is related to the issue in #159, and the solution in #160. During that process I came across some suspicious behavior in the way the runtime environment is modified over a computation. It seems that repeated instances of existing local parameters, value equations, and function equations are pushed onto the env redundantly.
In the case of the fix in #160, the issue was related to BoGL attempting to extract a reference or function from the env by checking it from top to bottom. Normally, this isn't a problem, but for each subsequent let, if-then-else, while, function application, or reference, the corresponding evaluation environment may be modified depending on what follows next. This modification is always pushing new elements onto the env as far as I can tell, and so certain aspects like while loops can rapidly increase the environment's size. Despite this, the lazy aspect of haskell appears to let us get away with only needing to evaluate just enough to do a lookup for the first occurrence of a name, and so everything works okay. An example of this would be as follows for an example with a local 'x' and a couple functions:
[("x",True),
("x",True),
("x",True),
("maxX",5),
("explosion",\["x"] -> while True do and(x, x)),
("maxX",5),
("x",True),
("maxX",5),
("explosion",\["x"] -> while True do and(x, x)),
...]

This needs some investigation to see if this is a problem or not later on, but currently it seems benign. It would seem that modifications to the environment, if they produce a new environment under the Reader monad, could be done as replacements instead of additions, but that's just speculation. It is possible that due to lazy evaluation we may not have to worry about this at any point.

Noting that Martin made an excellent suggestion that we should migrate the environment to a map of key:value pairs instead of a list. This would likely give us a small performance bump.


Correction for raw affiliation Collaborative Graduate Program in Molecular Imaging, Western University

Correction needed for raw affiliation Collaborative Graduate Program in Molecular Imaging, Western University
raw_affiliation_name: Collaborative Graduate Program in Molecular Imaging, Western University
new_rors: 02grkyz14
previous_rors: 02agqkc58
works_examples: W4390012273
contact: daccc4a825cdf5ac749dddcb8a8c27db:af93d4bd9b738958 @ uwo.ca

This issue was accepted and ingested by the OpenAlex team on 2024-12-21. The new affiliations should be visible within the next 7 days.


Help with using Tone Generator

Hello Steven, How are you?
I am trying to use your helpful plugin, the tonegenerator, but its seems to create wrong sounds for the frequencies.
To check this, I created an app which generate sounds from 0 to 20,000 using the plugin,  and its going up and down, up and down, you can see it in the attached movie.
While he should go just up, like this movie: https://www.youtube.com/watch?v=H-iCZElJ8m0
Do you have an idea what am i doing wrong?
My code is very simple, you can see it below
Thank you very much for your help!
Tomer Zarfati
    var intFreq = -1;
    $("#playBtnTest4").click(function () {
            intFreq = 0;
            $("#firstFrequency").val(intFreq);
            cordova.plugins.tonegenerator.play(intFreq);
            DisplayStairs();
    });

    function DisplayStairs()
    {
            if (intFreq >= 20000 || intFreq == -1) {
                intFreq = 0;
                return;
            }
            intFreq += 100;
            $("#firstFrequency").val(intFreq);
            cordova.plugins.tonegenerator.frequency(intFreq);
            window.setTimeout(function () { DisplayStairs(); }, 100)
    }

Hi Tomer,
Thanks I've been meaning to give the tone generator some more TLC. Glat to hear someone is using it out there besides me!
Will take a look at your problem this weekend.
Cherio,
Steven

how to generate 200hz sound?
cordova.plugins.tonegenerator.play(200)
or
cordova.plugins.tonegenerator.play(frequency(200))


Messages Are Broken

Every time I try and send a message (whether to friends or random individuals) I get an error message which states "501 Not Implemented - The endpoint you've contacted has not been completed yet." as shown in the pictures attached below. Is there a fix for this? Being able to send people messages in VRChat without having to be in game was extremely useful for me.

messages dont work as the endpoint was removed by VRCHAT because they suck please refer to my pull request https://github.com/pypy-vrc/VRCX/pull/19

It was provided as an unofficial feature, but it is now obsolete. An alternative is to modify the invite message, but I, personally, don't recommend it.


Typo "Azure blob storage"→"Azure Blob Storage"

https://docs.microsoft.com/en-us/sql/relational-databases/extended-events/targets-for-extended-events-in-sql-server?view=sql-server-ver15

@hyoshioka0128 : Thanks for your contribution! The author(s) have been notified to review your proposed change.

@hyoshioka0128 : Thanks for your contribution! The author(s) have been notified to review your proposed change.

@hyoshioka0128 Hello, I like this convenient PR #5995 and its fix.
I am now going to Merge this fix. The fix should be visible live on our Docs website within 24 hours.
We hope to hear from you again.  Thanks.

@hyoshioka0128 Hello, I like this convenient PR #5995 and its fix.
I am now going to Merge this fix. The fix should be visible live on our Docs website within 24 hours.
We hope to hear from you again.  Thanks.


Low speed on AMD GPU

Here is my setup, using ubuntu
AMD 6800 XT 16GB Vram
32GB Ram
Python version: 3.10.12
pytorch version: 2.2.1+rocm5.7
I am getting between 14s-15s/it with flux1-dev-Q2_K.gguf, also Q4_0 and Q6_K, with T5XXL_fp8 and fp16...

I only find examples of Nvidia speeds, so I don't know if I should expect faster results or not.

I'm not sure either what speed to expect as I don't have an AMD card. It looks like it's loading into lowvram mode from your screenshot though. What does your workflow look like?

I also tried different samplers, including basic workflow from comfyui with custom sampler, same results.

This is around the same speed I get on my 6800M. If you're not doing it already, try using the CLI argument --force-fp32

This is around the same speed I get on my 6800M. If you're not doing it already, try using the CLI argument --force-fp32

That works for me, I experienced significant speedup after that... but why?

Thank you very much mamei16 ! It's much faster this way, I went from 15s/it to 8.73s/it

Same question, why? :D

--force-fp32 should work if I just throw this behind "python3 main.py" in the bash window ?

This is around the same speed I get on my 6800M. If you're not doing it already, try using the CLI argument --force-fp32

That works for me, I experienced significant speedup after that... but why?

Thank you very much mamei16 ! It's much faster this way, I went from 15s/it to 8.73s/it

Same question, why? :D

I have no idea :D

--force-fp32 should work if I just throw this behind "python3 main.py" in the bash window ?

Yep!

Do you guys use any other launch arguments? I have a 7800 XT and it's still pretty slow for me, even after forcing fp32.

Do you guys use any other launch arguments? I have a 7800 XT and it's still pretty slow for me, even after forcing fp32. Edit: i get around 8s/it.

I don't know of any other arg that further improves performance. It just seems that the current software does not properly take advantage of AMD GPUs. Even older Nvidia cards perform better.

Do you guys use any other launch arguments? I have a 7800 XT and it's still pretty slow for me, even after forcing fp32. Edit: i get around 8s/it.

My 6800 16GB VRAM 32GB RAM has 18.50s/it with DEV FP16, but 10.83s/it with Q5_KS when using ---force-fp32. Most other models have similar performance like FP16 and FP16 even gets much slower by using --force-fp32.
I've tested --highvram, --gpu-only and some more runtime parameters without significant improvements.

What happens if you change this line in dequant.py from:
        out = dequantize(data, qtype, oshape, dtype=None)

To this:
        out = dequantize(data, qtype, oshape, dtype=torch.float32)

What happens if you change this line in dequant.py from:
        out = dequantize(data, qtype, oshape, dtype=None)

To this:
        out = dequantize(data, qtype, oshape, dtype=torch.float32)

Seems to have no effect.

Same here, no effect after changing dequant.py.
Nevertheless with --force-fp32 there is still a small speed increase for Q8_0 and a 2x speed increase for Q5_KS - with and without changing dequant.py.
I'm on Zluda with ROCm 5.7 and both Q models mentioned are superior to the FP8 model, not least in terms of the quality of the results.

Interesting, thank you for testing. That change would make the dequantization also run in FP32, but it looks like that's not contributing to the slowdown significantly then.

That works for me, I experienced significant speedup after that... but why?

Because by default Comfy tries to use bfloat16 as the compute datatype for FLUX (ref: https://github.com/comfyanonymous/ComfyUI/blob/7df42b9a2364bae6822fbd9e9fa10cea2e319ba3/comfy/supported_models.py#L645). bfloat16 seems to be very slow on AMD through pytorch for some reason (no hardware support and therefore it tries to emulate the datatype? idk)

Thanks for explaining that change and the reference to bfloat16.
BTW, there is no improvement in performance at all when using --force-fp16.
I wanted to see if only a few models profit from --force-fp32:
With --force-fp32:
Q2_K:    9.12s/it
Q3_K_S:  9.59s/it
Q4_0:    9.03s/it
Q4_1:    9.03s/it
Q4_K_S:  9.17s/it
Q5_0:   11.04s/it
Q5_1:   14.29s/it
Q5_K_S: 11.89s/it
Q6_K:   16.17s/it
Q8_0:   20.05s/it
Without any runtime parameters:
Q2_K:   17.96s/it
Q3_K_S: 18.46s/it
Q4_0:   17.90s/it
Q4_1:   17.89s/it
Q4_K_S: 18.04s/it
Q5_0:   18.58s/it
Q5_1:   18.55s/it
Q5_K_S: 18.33s/it
Q6_K:   18.24s/it
Q8_0:   18.64s/it
All tests with SCHNELL 1024x1024, t5xxl_fp16, comparable results with 2 quick DEV tests.
Original FP16 similar to Q8_0.
It seems that with --force-fp32 you sacrifice quality for speed and space - as I would expect from quantized models. For some reason this is not the case without this parameter. Would be interesting to know how this parameter influences other AMD GPUs and NVIDIA GPUs.

(Nearly) doubling the inference speed by setting some environment variables
I managed to get the speed (FLUX dev, 1024x1024, Q6_K, t5xxl_fp8_e4m3fn) on my RX 6900 XT from 7.3 s/it to around 3.85 s/it by running optimization through PyTorch TunableOp. This is still slower than an RTX 3080 (which gets about 2 s/it) which should have comparable hardware performance, but the improvement is nonetheless sizable.
How I did this
This was actually pretty simple once I found out about TunableOp. It requires at least PyTorch v2.3. To use it, I just had to run ComfyUI like this:
PYTORCH_TUNABLEOP_ENABLED=1 PYTORCH_TUNABLEOP_VERBOSE=1 python main.py --novram

The initial optimization run will take like half an hour because TunableOp goes through all variants of the matrix multiplication operations trying to find the fastest one for each individual multiplication, but subsequent runs should be fast because the results are saved to a file (tunableop_results.csv by default, can be changed with the PYTORCH_TUNABLEOP_FILENAME environment variable). I had to run ComfyUI with --novram because otherwise I would run out of GPU memory at one point during the optimization process for some reason. When rerunning ComfyUI without this flag afterwards, optimization for one operation did repeat once for some reason (but without OOM).
Additional notes
Unfortunately, changing image size causes the optimization to be run again (matrix sizes change), but I think the results are saved incrementally so that going through all needed resolutions once should be enough. Also, updating anything related to PyTorch/ROCm will cause the optimization results to be invalid and therefore the optimization needs to be run again. I don't know if this optimization works on Windows, as I'm running Linux.
Additionally, I have edited the line I mentioned in my previous comment from
supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32]

to
supported_inference_dtypes = [torch.float16, torch.float32]

so that ComfyUI would run the inference in fp16. I did not check whether just using --force-fp32 and TunableOp would bring the same performance boost, but using fp16 should anyway be faster. I also tested the non-GGUF fp8 version, and it is slightly faster compared to GGUF, at around 3.3 s/it.
Final words
So yeah, it seems that the low performance is caused at least partly by PyTorch choosing non-optimal matrix multiplication operations on AMD GPUs. There's probably still something wonky going on as I think my card should still be a bit faster, but almost doubling the performance with a couple of environment variables is already pretty good.
I wonder how many other projects would benefit from running TunableOp. It would also be interesting to know whether this effect is only limited to consumer cards, or if this is present even on datacenter-class cards.

(Nearly) doubling the inference speed by setting some environment variables
I managed to get the speed (FLUX dev, 1024x1024, Q6_K, t5xxl_fp8_e4m3fn) on my RX 6900 XT from 7.3 s/it to around 3.85 s/it by running optimization through PyTorch TunableOp. This is still slower than an RTX 3080 (which gets about 2 s/it) which should have comparable hardware performance, but the improvement is nonetheless sizable.
How I did this
This was actually pretty simple once I found out about TunableOp. It requires at least PyTorch v2.3. To use it, I just had to run ComfyUI like this:
PYTORCH_TUNABLEOP_ENABLED=1 PYTORCH_TUNABLEOP_VERBOSE=1 python main.py --novram

The initial optimization run will take like half an hour because TunableOp goes through all variants of the matrix multiplication operations trying to find the fastest one for each individual multiplication, but subsequent runs should be fast because the results are saved to a file (tunableop_results.csv by default, can be changed with the PYTORCH_TUNABLEOP_FILENAME environment variable). I had to run ComfyUI with --novram because otherwise I would run out of GPU memory at one point during the optimization process for some reason. When rerunning ComfyUI without this flag afterwards, optimization for one operation did repeat once for some reason (but without OOM).
Additional notes
Unfortunately, changing image size causes the optimization to be run again (matrix sizes change), but I think the results are saved incrementally so that going through all needed resolutions once should be enough. Also, updating anything related to PyTorch/ROCm will cause the optimization results to be invalid and therefore the optimization needs to be run again. I don't know if this optimization works on Windows, as I'm running Linux.
Additionally, I have edited the line I mentioned in my previous comment from
supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32]

to
supported_inference_dtypes = [torch.float16, torch.float32]

so that ComfyUI would run the inference in fp16. I did not check whether just using --force-fp32 and TunableOp would bring the same performance boost, but using fp16 should anyway be faster. I also tested the non-GGUF fp8 version, and it is slightly faster compared to GGUF, at around 3.3 s/it.
Final words
So yeah, it seems that the low performance is caused at least partly by PyTorch choosing non-optimal matrix multiplication operations on AMD GPUs. There's probably still something wonky going on as I think my card should still be a bit faster, but almost doubling the performance with a couple of environment variables is already pretty good.
I wonder how many other projects would benefit from running TunableOp. It would also be interesting to know whether this effect is only limited to consumer cards, or if this is present even on datacenter-class cards.

Not sure if you're already using it but you can also try adding in flash attention https://github.com/Beinsezii/comfyui-amd-go-fast

I have not tried that! However, looks like it only works on Navi 3 for now, I have an RX 6900 XT which is a Navi 2 card :(
I think I tried to compile Flash Attention for ROCm at some point and it failed so I don't have high hopes of getting it to work this time...

Sorry to hear. I actually went from 46 seconds to 36 seconds a generation using it with Flux Dev, 24 steps, 1280x720

Did you happen to get a HIPBLASLT_TENSILE error when running pytorch tuneable?

I got some warning related to that, but the optimization still worked. Apparently it's possible to disable hipblaslt stuff by setting PYTORCH_TUNABLEOP_HIPBLASLT_ENABLED=0 (all env vars are listed in the TunableOp README).

Thank you! So with the tuning parameter, it took off about 3 seconds. So I'm down to 32.9 seconds on average with 24 steps @ 1280x720

Thanks guys! Was able to get it from 8s/it to 2s/it on my 7800 XT using these flags and flash attention. A lot better now! :)

Did you happen to get a HIPBLASLT_TENSILE error when running pytorch tuneable?

I got some warning related to that, but the optimization still worked. Apparently it's possible to disable hipblaslt stuff by setting PYTORCH_TUNABLEOP_HIPBLASLT_ENABLED=0 (all env vars are listed in the TunableOp README).

I tried it but I get this error
reading tuning results from tunableop_results0.csv
key="PT_VERSION" is not provided for validation.
results validator check failed
Memory access fault by GPU node-1 (Agent handle: 0x2f900b0) on address 0x74e206205000. Reason: Page not present or supervisor privilege.
Abandon (core dumped)

key="PT_VERSION" is not provided for validation.

This sounds almost like the csv file (tunableop_results0.csv) is somehow corrupted. Can you check what its contents look like? Maybe try deleting/renaming the csv file and see if rerunning the optimization works.

Did you happen to get a HIPBLASLT_TENSILE error when running pytorch tuneable?

I got some warning related to that, but the optimization still worked. Apparently it's possible to disable hipblaslt stuff by setting PYTORCH_TUNABLEOP_HIPBLASLT_ENABLED=0 (all env vars are listed in the TunableOp README).

I tried it but I get this error
reading tuning results from tunableop_results0.csv key="PT_VERSION" is not provided for validation. results validator check failed Memory access fault by GPU node-1 (Agent handle: 0x2f900b0) on address 0x74e206205000. Reason: Page not present or supervisor privilege. Abandon (core dumped)

Memory access fault by GPU node-1 (Agent handle: 0x2f900b0) on address 0x74e206205000. Reason: Page not present or supervisor privilege. is a known error experienced by ROCm users for a couple of years now. So far, none of the dozen or so "solutions" I've seen were able to solve that issue for me.
I think the lines before the access fault error are simply a result of tunableop_results0.csv being an empty file.

(Nearly) doubling the inference speed by setting some environment variables
I managed to get the speed (FLUX dev, 1024x1024, Q6_K, t5xxl_fp8_e4m3fn) on my RX 6900 XT from 7.3 s/it to around 3.85 s/it by running optimization through PyTorch TunableOp. This is still slower than an RTX 3080 (which gets about 2 s/it) which should have comparable hardware performance, but the improvement is nonetheless sizable.
How I did this
This was actually pretty simple once I found out about TunableOp. It requires at least PyTorch v2.3. To use it, I just had to run ComfyUI like this:
PYTORCH_TUNABLEOP_ENABLED=1 PYTORCH_TUNABLEOP_VERBOSE=1 python main.py --novram

The initial optimization run will take like half an hour because TunableOp goes through all variants of the matrix multiplication operations trying to find the fastest one for each individual multiplication, but subsequent runs should be fast because the results are saved to a file (tunableop_results.csv by default, can be changed with the PYTORCH_TUNABLEOP_FILENAME environment variable). I had to run ComfyUI with --novram because otherwise I would run out of GPU memory at one point during the optimization process for some reason. When rerunning ComfyUI without this flag afterwards, optimization for one operation did repeat once for some reason (but without OOM).
Additional notes
Unfortunately, changing image size causes the optimization to be run again (matrix sizes change), but I think the results are saved incrementally so that going through all needed resolutions once should be enough. Also, updating anything related to PyTorch/ROCm will cause the optimization results to be invalid and therefore the optimization needs to be run again. I don't know if this optimization works on Windows, as I'm running Linux.
Additionally, I have edited the line I mentioned in my previous comment from
supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32]

to
supported_inference_dtypes = [torch.float16, torch.float32]

so that ComfyUI would run the inference in fp16. I did not check whether just using --force-fp32 and TunableOp would bring the same performance boost, but using fp16 should anyway be faster. I also tested the non-GGUF fp8 version, and it is slightly faster compared to GGUF, at around 3.3 s/it.
Final words
So yeah, it seems that the low performance is caused at least partly by PyTorch choosing non-optimal matrix multiplication operations on AMD GPUs. There's probably still something wonky going on as I think my card should still be a bit faster, but almost doubling the performance with a couple of environment variables is already pretty good.
I wonder how many other projects would benefit from running TunableOp. It would also be interesting to know whether this effect is only limited to consumer cards, or if this is present even on datacenter-class cards.

I'm trying to do this on my machine with 32GB of RAM and a RX 6800XT but i keep getting out of memory errors, also the .csv file is always empty. How much RAM did you need while you did this? I'm using the full model at fp8 mode while using gguf clip loader to load a q5_k_m T5 model along with a lora.

I think CPU RAM usage was around 20 GiB at max, so it should fit in 32 GiB (I have 64). Did you try with --force-fp32 or did you use my edit to supported_models.py? I tried with --force-fp32 and got some OOMs with that. Also, increasing the batch size even from 1 to 2 seems to cause OOMs during tuning. Remember to use --no-vram for the tuning run, otherwise you'll get an OOM.
I did not try with LoRAs though. Maybe doing the tuning once first without LoRA and then adding the LoRA afterwards could work?

I think CPU RAM usage was around 20 GiB at max, so it should fit in 32 GiB (I have 64). Did you try with --force-fp32 or did you use my edit to supported_models.py? I tried with --force-fp32 and got some OOMs with that. Also, increasing the batch size even from 1 to 2 seems to cause OOMs during tuning. Remember to use --no-vram for the tuning run, otherwise you'll get an OOM.
I did not try with LoRAs though. Maybe doing the tuning once first without LoRA and then adding the LoRA afterwards could work?

I tried doing it for sdxl model and after updating rocm+torch to the latest version the OOM issue went away. I'm now struggling with the .csv file not being generated correctly. It creates a .csv file when i first run it, however it stays empty after tuning, and when i try to run it again it complains about "reading tuning results from tunableop_results0.csv key="PT_VERSION" is not provided for validation. results validator check failed". I dont really really know what else might be the problem since it doesn't spit out any error messages aside from that.

I tried doing it for sdxl model and after updating rocm+torch to the latest version the OOM issue went away. I'm now struggling with the .csv file not being generated correctly. It creates a .csv file when i first run it, however it stays empty after tuning, and when i try to run it again it complains about "reading tuning results from tunableop_results0.csv key="PT_VERSION" is not provided for validation. results validator check failed". I dont really really know what else might be the problem since it doesn't spit out any error messages aside from that.

Use ctrl+c on the terminal to turn the comfyui off and you should see a message saying it's writing the result to the file before the terminal closes.

Rocm 6's focus has been to optimize fp8 performance on Navi 3 from the documentation I can find. Might be worth it to try this model with a 6.1 Rocm Linux setup https://civitai.com/models/637170?modelVersionId=724032 I feel you should get better performance than going GGUF. The model is just slightly under 16G, so you'll need to test with your 7800. Works incredibly well on my 7900xtx

Yep I am trying it today, and I am getting 7.18s/it with fp8 flux1 diffusion model and t5xxl_fp8. Need to try different setups (GGUF flux + fp8 t5xxl, fp8 flux1 + GGUF t5xxl), to see how it goes

I tried doing it for sdxl model and after updating rocm+torch to the latest version the OOM issue went away. I'm now struggling with the .csv file not being generated correctly. It creates a .csv file when i first run it, however it stays empty after tuning, and when i try to run it again it complains about "reading tuning results from tunableop_results0.csv key="PT_VERSION" is not provided for validation. results validator check failed". I dont really really know what else might be the problem since it doesn't spit out any error messages aside from that.

Use ctrl+c on the terminal to turn the comfyui off and you should see a message saying it's writing the result to the file before the terminal closes.

Figured it out. It was because i was launching it through comfyui-manager's comfy-cli. You need to launch comfyui through "python main.py" or it wont write the .csv file. Will tinker around with it more when i get the chance.

I tried doing it for sdxl model and after updating rocm+torch to the latest version the OOM issue went away. I'm now struggling with the .csv file not being generated correctly. It creates a .csv file when i first run it, however it stays empty after tuning, and when i try to run it again it complains about "reading tuning results from tunableop_results0.csv key="PT_VERSION" is not provided for validation. results validator check failed". I dont really really know what else might be the problem since it doesn't spit out any error messages aside from that.

Use ctrl+c on the terminal to turn the comfyui off and you should see a message saying it's writing the result to the file before the terminal closes.

Figured it out. It was because i was launching it through comfyui-manager's comfy-cli. You need to launch comfyui through "python main.py" or it wont write the .csv file. Will tinker around with it more when i get the chance.

Same issue on Windows, the csv file remains empty, getting the PT_VERSION error, running from main.py does not work. Any idea on how to fix it? What exactly is generated in the first row of the csv file?

I tried doing it for sdxl model and after updating rocm+torch to the latest version the OOM issue went away. I'm now struggling with the .csv file not being generated correctly. It creates a .csv file when i first run it, however it stays empty after tuning, and when i try to run it again it complains about "reading tuning results from tunableop_results0.csv key="PT_VERSION" is not provided for validation. results validator check failed". I dont really really know what else might be the problem since it doesn't spit out any error messages aside from that.

Use ctrl+c on the terminal to turn the comfyui off and you should see a message saying it's writing the result to the file before the terminal closes.

Figured it out. It was because i was launching it through comfyui-manager's comfy-cli. You need to launch comfyui through "python main.py" or it wont write the .csv file. Will tinker around with it more when i get the chance.

Same issue on Windows, the csv file remains empty, getting the PT_VERSION error, running from main.py does not work. Any idea on how to fix it? What exactly is generated in the first row of the csv file?

Try with --force-fp16. Did it for me

key="PT_VERSION" is not provided for validation.

This sounds almost like the csv file (tunableop_results0.csv) is somehow corrupted. Can you check what its contents look like? Maybe try deleting/renaming the csv file and see if rerunning the optimization works.

But how can I make comfy load the file after the "tuning"?

Same issue on Windows, the csv file remains empty, getting the PT_VERSION error, running from main.py does not work. Any idea on how to fix it? What exactly is generated in the first row of the csv file?

I launched ComfyUI with this command: PYTORCH_TUNABLEOP_ENABLED=1 PYTORCH_TUNABLEOP_VERBOSE=1 PYTORCH_TUNABLEOP_HIPBLASLT_ENABLED=0 python3 main.py --force-fp32 --novram (under Linux)
this is the resulting tunableop_results0.csv for my 6700XT.
Validator,PT_VERSION,2.4.1
Validator,ROCBLAS_VERSION,4.1.0-cefa4a9b-dirty
Validator,ROCM_VERSION,<IP_ADDRESS>-82-5fabb4c
Validator,GCN_ARCH_NAME,gfx1030
GemmTunableOp_float_NT,nt_16384_16384_512,Default,21.33
GemmStridedBatchedTunableOp_float_NN,nn_128_256_4352_B_24,Gemm_Rocblas_18717,0.781374
GemmStridedBatchedTunableOp_float_NN,nn_128_4096_4352_B_24,Gemm_Rocblas_18718,10.5327
GemmStridedBatchedTunableOp_float_TN,tn_4352_256_128_B_24,Gemm_Rocblas_19997,0.718891
GemmStridedBatchedTunableOp_float_TN,tn_4352_4096_128_B_24,Gemm_Rocblas_19997,10.902
GemmTunableOp_float_TN,tn_16384_512_16384,Gemm_Rocblas_19999,25.1117
GemmTunableOp_float_TN,tn_6144_1_3072,Gemm_Rocblas_20194,0.212292
GemmTunableOp_float_TN,tn_3072_4352_15360,Default,39.2634
GemmTunableOp_float_TN,tn_9216_1_3072,Gemm_Rocblas_20192,0.403658
GemmTunableOp_float_TN,tn_3072_256_12288,Gemm_Rocblas_20006,2.09865
GemmTunableOp_float_TN,tn_3072_4096_64,Gemm_Rocblas_19950,0.167817
GemmTunableOp_float_TN,tn_9216_4096_3072,Gemm_Rocblas_19980,21.0174
GemmTunableOp_float_TN,tn_3072_1_256,Gemm_Rocblas_20190,0.0169701
GemmTunableOp_float_TN,tn_3072_1_3072,Gemm_Rocblas_20190,0.12163
GemmTunableOp_float_TN,tn_64_4096_3072,Gemm_Rocblas_20112,0.253881
GemmTunableOp_float_TN,tn_3072_1_768,Gemm_Rocblas_20190,0.0390971
GemmTunableOp_float_TN,tn_12288_256_3072,Default,1.85803
GemmTunableOp_float_TN,tn_18432_1_3072,Gemm_Rocblas_20142,0.903636
GemmTunableOp_float_TN,tn_9216_256_3072,Gemm_Rocblas_19999,1.46172
GemmTunableOp_float_TN,tn_3072_4096_12288,Gemm_Rocblas_19997,29.1898
GemmTunableOp_float_TN,tn_12288_4096_3072,Gemm_Rocblas_19980,28.2916
GemmTunableOp_float_TN,tn_21504_4352_3072,Gemm_Rocblas_19980,52.7649
GemmTunableOp_float_TN,tn_3072_256_4096,Gemm_Rocblas_20006,0.637689
GemmTunableOp_float_TN,tn_3072_4096_3072,Gemm_Rocblas_19963,7.14236
GemmTunableOp_float_TN,tn_3072_256_3072,Gemm_Rocblas_20006,0.524814

I had the same "reading tuning results from tunableop_results0.csv key="PT_VERSION" is not provided for validation. results validator check failed". warning as i had an empty .csv from a previous incomplete run.
The .csv remained empty during inferencing and even after a sample image had been generated, it was populated only after shutting down ComfyUI like others have reported.

Same issue on Windows, the csv file remains empty, getting the PT_VERSION error, running from main.py does not work. Any idea on how to fix it? What exactly is generated in the first row of the csv file?

I launched ComfyUI with this command: PYTORCH_TUNABLEOP_ENABLED=1 PYTORCH_TUNABLEOP_VERBOSE=1 PYTORCH_TUNABLEOP_HIPBLASLT_ENABLED=0 python3 main.py --force-fp32 --novram (under Linux) this is the resulting tunableop_results0.csv for my 6700XT.
Validator,PT_VERSION,2.4.1
Validator,ROCBLAS_VERSION,4.1.0-cefa4a9b-dirty
Validator,ROCM_VERSION,<IP_ADDRESS>-82-5fabb4c
Validator,GCN_ARCH_NAME,gfx1030
GemmTunableOp_float_NT,nt_16384_16384_512,Default,21.33
GemmStridedBatchedTunableOp_float_NN,nn_128_256_4352_B_24,Gemm_Rocblas_18717,0.781374
GemmStridedBatchedTunableOp_float_NN,nn_128_4096_4352_B_24,Gemm_Rocblas_18718,10.5327
GemmStridedBatchedTunableOp_float_TN,tn_4352_256_128_B_24,Gemm_Rocblas_19997,0.718891
GemmStridedBatchedTunableOp_float_TN,tn_4352_4096_128_B_24,Gemm_Rocblas_19997,10.902
GemmTunableOp_float_TN,tn_16384_512_16384,Gemm_Rocblas_19999,25.1117
GemmTunableOp_float_TN,tn_6144_1_3072,Gemm_Rocblas_20194,0.212292
GemmTunableOp_float_TN,tn_3072_4352_15360,Default,39.2634
GemmTunableOp_float_TN,tn_9216_1_3072,Gemm_Rocblas_20192,0.403658
GemmTunableOp_float_TN,tn_3072_256_12288,Gemm_Rocblas_20006,2.09865
GemmTunableOp_float_TN,tn_3072_4096_64,Gemm_Rocblas_19950,0.167817
GemmTunableOp_float_TN,tn_9216_4096_3072,Gemm_Rocblas_19980,21.0174
GemmTunableOp_float_TN,tn_3072_1_256,Gemm_Rocblas_20190,0.0169701
GemmTunableOp_float_TN,tn_3072_1_3072,Gemm_Rocblas_20190,0.12163
GemmTunableOp_float_TN,tn_64_4096_3072,Gemm_Rocblas_20112,0.253881
GemmTunableOp_float_TN,tn_3072_1_768,Gemm_Rocblas_20190,0.0390971
GemmTunableOp_float_TN,tn_12288_256_3072,Default,1.85803
GemmTunableOp_float_TN,tn_18432_1_3072,Gemm_Rocblas_20142,0.903636
GemmTunableOp_float_TN,tn_9216_256_3072,Gemm_Rocblas_19999,1.46172
GemmTunableOp_float_TN,tn_3072_4096_12288,Gemm_Rocblas_19997,29.1898
GemmTunableOp_float_TN,tn_12288_4096_3072,Gemm_Rocblas_19980,28.2916
GemmTunableOp_float_TN,tn_21504_4352_3072,Gemm_Rocblas_19980,52.7649
GemmTunableOp_float_TN,tn_3072_256_4096,Gemm_Rocblas_20006,0.637689
GemmTunableOp_float_TN,tn_3072_4096_3072,Gemm_Rocblas_19963,7.14236
GemmTunableOp_float_TN,tn_3072_256_3072,Gemm_Rocblas_20006,0.524814

I had the same "reading tuning results from tunableop_results0.csv key="PT_VERSION" is not provided for validation. results validator check failed". warning as i had an empty .csv from a previous incomplete run. The .csv remained empty during inferencing and even after a sample image had been generated, it was populated only after shutting down ComfyUI like others have reported.
Edit: after removing the flag --novram it started giving me Memory access fault by GPU node-1 (Agent handle: 0x2f900b0) on address 0x74e206205000. Reason: Page not present or supervisor privilege. and a segmentation fault, even if part of the initial optimization was already done
i fixed this by editing supported_inference_dtypes in ComfyUI/comfy/supported_models.pyand removing the --force-fp32 flag as Exploder98 suggested. Some more entries have been added to my .csv, i am now getting 13.65s/it with flux1-dev-Q5_K_S With --force-fp32 i was getting around 25s/it, stock was giving me 40-45 s/it

Thanks for the info, I got it working but did not notice any improvement. I'm getting around 9s/it with the same card but on zluda and Q8.


arch: deprecating recall action

What is the problem that this fixes or functionality that this introduces? Does it fix any open issues?
Remove RecallAction to clean up the codebase and make Runtime more concise, since the current implementation is Recall is not really working.
I think @enyst's memory representation + condensation idea (https://github.com/OpenDevin/OpenDevin/pull/2709, https://github.com/OpenDevin/OpenDevin/pull/2021) is probably better to implement both recall and summary, other than putting them into Runtime class?
Give a brief summary of what the PR does, explaining any non-trivial design decisions
Other references

@tobitege done! more than happy to!

@tobitege done! more than happy to!

Great!


Expected Type-checking error

We found a strange bug regarding Regent. Please find the code snippet to reproduce this bug:
task main()
  var G_i : double[5]
  var G_k : double[5]
  for i = 0, 4 do
    G_i[i] = 0.0
    G_k[i] = 0.0
  end
  var sum : double[4]
  for i = 0, 4 do
    sum[i] = 0.0
  end
  var t = -1.0
  for l = 0, 4 do
    sum[l] = sum[l] - (G_i - G_k)*t
  end
  format.println("{}", sum[0])
end

regentlib.start(main)

Expected output: Regent should throw an error while type checking.
Resultant output: -5.0
Regent is compiled on the control replication branch, commit hash 5554d31c6

I am a little confused. Can we have operations between arrays and primitive data types in Regent?
If yes, how does it work exactly?

Ok, it looks like + on two arrays results in an element-wise array operation {double[5], double[5]} -> double[5], while a - on two arrays seems to coerce both to pointers and then compute a pointer difference, i.e., {double[5], double[5]} -> int64. And then you can coerce int64 to double so that flows through the rest of your program without throwing an error.
It's a bit strange to diverge from the Terra behavior, but I guess we already provided + and *, so we probably ought to be consistent and provide - as well.
But this might be an argument for recommending regentlib.array in this case, though I think that currently provides even fewer operators than raw arrays. At least it wouldn't be subject to unexpected pointer conversions.


pair_preprocessor_ is None

First, thanks for adding this module - it is a great idea and a great contribution to transparent models.
When running your code there seems to be an old (I think) mapping in line 430 in gamchanger.py
  # Use the ebm's mapping to map level name to bin index     ebm_col_mapping = ebm_copy.pair_preprocessor_.col_mapping_
I think this relates to earlier versions of EBM
When I change this line to
ebm_col_mapping = ebm_copy.feature_groups_
The adjusted model is properly exported

Hi @esoreq, thanks for raising the issue. It seems you are looking at an older version of gamchanger.py? I made a lot of changes in https://github.com/interpretml/gam-changer/commit/093a6b6cc5147265d2fc54418ae42a56fe13fae6, and the lines you refer too seem to be different now.
https://github.com/interpretml/gam-changer/blob/aba94f624c726e63ef7360c5c691aedac3a44bd4/notebook-widget/gamchanger/gamchanger.py#L526-L527
Can you try to update gamchanger to the latest version (0.1.13) and try again? pip install -U gamchanger. Let me know if you still encounter an error. Thanks!!


Investigate using #[automatically_derived] to make Debug-transmogrification less fallible

automatically_derived is an attribute added to derives of internal attributes. Perhaps path_matches_prelude_derive could be changed to use that detect Debug more correctly.

No, it's added to impls, not the derives themselves


NTR:multivesicular body fusion to apical plasma membrane

Hi,
I'd like to request a new term for annotating gene products from this paper:
http://www.ncbi.nlm.nih.gov/pubmed/26459596
GO:0061025 membrane fusion
is_a GO:new multivesicular body fusion to apical plasma membrane
GO:1990182 exosomal secretion
part_of GO:new multivesicular body fusion to apical plasma membrane
Thanks,
--Kimberly

Perhaps make some link to exosome secretion?

label "multivesicular body fusion to apical plasma membrane"@en
definition "The fusion of the membrane of a multivesicular body with the apical plasma membrane, resulting in release of exosomes from the cell"
EquivalentTo: 'membrane fusion'
and (results_in_fusion_of some 'apical plasma membrane')
and (results_in_fusion_of some 'multivesicular body membrane')
and ('part of' some 'exosomal secretion')


carousel always shows one card and a bit of the next

That's okay for very narrow windows, but on a wide window we could show multiple cards, instead we show one VERY WIDE ONE and bit of the next. Waste of space.

Working better now.


fix(sops): decode base64 data before passing to SOPS

Should fix #84
I'm not a Go programmer, so it's possible I've mucked this up, but, it's a pretty simple fix and it does seem to work.

In retrospect, this one should also be updated to use base64.URLEncoding.DecodeString() rather than base64.StdEncoding.DecodeString().


[Bug]: next-web-push example not working

Provide environment information
node v18 on macOs 14.5
"@serwist/next": "latest",
"next": "14.2.1",
"react": "18.2.0",
"react-dom": "18.2.0",
"serwist": "latest",
"web-push": "3.6.7"
Which project is this issue for?
@serwist/next
Link to reproduction - Issues with a link to complete (but minimal) reproduction code help us address them faster
https://github.com/serwist/serwist/tree/main/examples/next-web-push
To reproduce
Run the following command npx degit serwist/serwist/examples/next-web-push my-app and follow the other instruction to complete the installation at https://github.com/serwist/serwist/tree/main/examples/next-web-push
Describe the bug
Hi,
I tried to run the next web push example here by running the npx degit serwist/serwist/examples/next-web-push my-app command and following the other instructions:
https://github.com/serwist/serwist/tree/main/examples/next-web-push
However, it is now working for me. Notifications are not showing.
Could you please confirm whether it's just my problem?
Similarly, I've tried this other example from https://github.com/shadowwalker/next-pwa/tree/master/examples/web-push and it works as expected.
Expected behavior
Notifications should appear.
Screenshots (if relevant)
No response
Additional information (if relevant)
No response

I forgot to subscribe the service worker to the push event... Really sorry about this...

@DuCanhGH I still can't see the notification on browser


Added initialState, a Container.Provider prop that is passed to useHook

While I love the minimalism of the new unstated-next api, it unfortunately appeared to not be able to fulfill one of my team's key use cases, the ability to instantiate container state with dynamic values. To allow for this use case, I added an optional prop that is given to <Container.Provider >, initialState. This is then passed to useHook() when the provider is instantiated, where you can use this value as needed when writing your custom hook.
Example use case:
interface UserContainerState{
  name:string
}

// Fallback state
const defaultState: UserContainerState = {name:"Unknown"}

export function useUserContainer(initialState:UserContainerState | null | undefined) {
  if(!initialState){
    initialState = defaultState
  }
  const [name, setName] = useState(initialState.name||"Unknown");

  return {name, setName };
}

const UserContainer = createContainer(useUserContainer);

const HomePage: React.FC = ()=>{
return (

User 1

        <UserContainer.Provider initialState={{name:"Jim"}}> //userContainer.name will start as 'Jim'
          <UserConsumer/>
        </UserContainer.Provider>

User 2

        <UserContainer.Provider >
          <UserConsumer/> //userContainer.name will start as 'unknown'
        </UserContainer.Provider>

);
}

An alternative method for solving this problem would be to pass all props from Container.Provider to the useHook, which may be a solution that is a little less specifically tied to this particular use case.
I am new to both typescript and contributing to open source, so if I made a mistake in either, let me know and I will be happy to fix what I can.

Good idea, I was thinking about something similar but this is a nicer way of doing it.
Sorry I didn't take this commit. But I took the change and did the types in a backwards compatible way and updated the docs.
You can now do this in v1.1.0

No problem about the commit, I'm just glad I was able to contribute a useful idea :)


question: StructTag on edges

This is my generated model. How to change the tag on edges?
// User is the model entity for the User schema.
type User struct {
	config `json:"-"`
	// ID of the ent.
	ID int `json:"id,omitempty"`
	// Edges holds the relations/edges for other nodes in the graph.
	// The values are being populated by the UserQuery when eager-loading is set.
	Edges UserEdges `json:"edges"` // <-- I want to edit this
}

Hey @masseelch, you can use the edge.Annotation for this. For example:
https://github.com/facebook/ent/blob/d3849f9eb7a9ff6e98f50970918718292e5df944/entc/integration/ent/schema/card.go#L21-L33

Closing this issue, but please feel free to re-open if doesn't answer your question or you need help with that.

I don't really understand what schema.Annotation is.

You can read more about it here - https://entgo.io/docs/schema-annotations
But in your case, it’s simple, just follow the example I mentioned above - https://github.com/facebook/ent/issues/905#issuecomment-719499393

Well, yes, it looks simple. But there is no schema that has an Annotation exported.

Do i need to be on master branch for this?

Yeah, that was it. Thanks. Working now :-)

One little detail: Providing a struct tag on fields or edges will automatically add the json tag. With the schema.Annotation i have to add the tag myself.
Expected behaviour or is this a bug?

Expected behaviour or is this a bug?

This is intended. This annotation gives you the option to override the struct-tag.

Do i need to be on master branch for this?

I'll release a new version in the upcoming days.


Feature/just one

Merge k8s and dev justifies into just one just file.

@jkneubuh watch out that if you don't add -f then if just doesn't find a justfile in the cwd it searches up the tree.
So you can be in a sub directory and still call just foo