I need some apps with multi dex

Hi again:)
would you please introduce me some popular apps with multi dex which has 2,3,4,5 dex files...?

instagram and youtube have each four classes.dex

and a program with 2 or 3 files....?

This one here has two classes: https://github.com/androguard/androguard/blob/master/examples/tests/multidex/multidex.apk
The AOSP browser has 3...
but I also saw APKs with >20 classes.dex and I can not give you an individual example for each ;)
If you want examples for each, just download some APK collections and scan over them.

No need, it's enough. TnQ:)

I need a real app with 2-dex files include method calls and manifest.xml :(

You can generate them on your own... Just grab some random app from f-droid in source code, and compile to multidex. Then you can create any number of dex files you like.


Bug-fix

Send 404 if a user with no user profile tries to create an APD
Get distinct trustee IDs when setting admin policy when APD becomes active.
(Multiple policies were being set when multiple APDs belonging to same trustee
were set to active state)
Change session ID key in JSON sent from callApd to createToken

retest this please

retest this please

retest this please

retest this please

There is an occasional bug happening in createApd.existingUrl due to Mockito. Not able to reproduce locally. The report is here

retest this please


[BUG] Governance Action Proposal submission form validation

Context & versions

Governance Action Proposal submission form with an empty value is supported. Taking space as a filled character.

Steps to reproduce

Create a governance action proposal form with info type
Add space for all fields, the continue button is visible and ready to submit
Also try for treasury type. add valid address and fill other field with space

Actual behaviour

Info type Governance Action proposal

Treasury type Governance Action proposal

I am closing this issue because this functionality has been moved to the proposal creation process. (https://github.com/IntersectMBO/govtool/issues/1564)


Unable to cast object of type 'EFSecondLevelCache.Core.Contracts.EFCacheKey' to type **

I used redis for cahce provider in my app, when the first time to fetch data from db, it will return a empty result and cache the result to the redis, but i found the cached data is incorrent, the cached value is '{"CacheDependencies": []}' and the value type is 'EFSecondLevelCache.Core.Contracts.EFCacheKey, EFSecondLevelCache.Core, Version=<IP_ADDRESS>, Culture=neutral, PublicKeyToken=null' , so it willl throw a castexception when attemp read again.
Code:
var entity=context.Set<ProductCategorie>().Where(m=>!m.Deleted).Cacheable().FirstOrDefault();
Error:
System.InvalidCastException:“Unable to cast object of type  'EFSecondLevelCache.Core.Contracts.EFCacheKey' to type 'Jazea.Chain.Domain.Entity.ProductCategorie'.”

It works when use ToList() or Count().
public static void AddCache(IServiceCollection services)
{
    var provider = services.BuildServiceProvider();
    var config = provider.GetService<IConfigurationRoot>();
    var redisConnStr = config.GetValue<string>("RedisConnetionString");
    services.AddEFSecondLevelCache();
    services.AddSingleton(typeof(ICacheManager<>), typeof(BaseCacheManager<>));
    var jss = new JsonSerializerSettings()
    {
        NullValueHandling = NullValueHandling.Ignore,
        ReferenceLoopHandling = ReferenceLoopHandling.Ignore
    };
    services.AddSingleton<ICacheManagerConfiguration>(
        new CacheManager.Core.ConfigurationBuilder()
            .WithJsonSerializer(jss, jss)
            .WithRedisConfiguration("Redis", redisConnStr)
            .WithRedisCacheHandle("Redis")
            .WithExpiration(ExpirationMode.None, TimeSpan.FromHours(1))
            .Build()
    );
}

public void Configure(IApplicationBuilder app, ILoggerFactory loggerFactory)
{
    loggerFactory.AddConsole(Configuration.GetSection("Logging"));
    loggerFactory.AddDebug();
    app.UseEFSecondLevelCache();
    app.Map("/api", c => c.UseMvc());
}

Because i  set NullValueHandling = NullValueHandling.Ignore, so the cached value is incomplete.
The complate info is :
{
  "Key": null,
  "KeyHash": null,
  "CacheDependencies": []
}

It will throw InvalidCastException while reading the cahed value .

Please update your NuGet packages (all of them). You are not using the latest versions.
I can't reproduce your issue. I added Redis related settings and services. I can run both unit tests and also the sample web app with the same FirstOrDefault method:

The whole purpose of using the CacheManager as the infrastructure is to do not fighting with all of the available cache providers. EFSecondLevelCache.Core doesn't know anything about Redis or .... It just uses the public API of the CacheManager to insert/update/get info.

It's fixed via https://github.com/VahidN/EFSecondLevelCache.Core/commit/15839e39b10ef581139fe558a64d0adf85a8c074 (Improved null object detection).
PM> update-package


Request for Multilingual Support

Hello,
Great work on the 16personalities API! It currently only retrieves questions in English. It would be helpful if you could add support for other languages.
For reference, you can find the list of supported languages on the official site here: 16personalities Languages.

Hi there
I can look after this project in a week
Currently they've kept cloudflare antibot protection
Will figure out some way further and will add multilingual support as well.


ci: Run embedded build on PRs

Backport 836f909d3 so we run embedded CI on 0.11 release branch PRs.
Backport 14790aa7c2c6 and ffa1a805133 to fix embedded alpine build on
0.11 release branch.

yay, looks good


What is the purpose of Graphics.UI.FLTK.LowLevel.Base.Button?

I see that Button extends Base.Button, but the four functions mentioned in its docs (handle, hide, resize, and showWidget) all exist at the Widget level.

These are functions that can be overridden on the Haskell side and are typically the bits of the application that need the most debugging. Having them "pinned" to Button makes for much nicer error messages on the Haskell side, makes it easier to work with gdb when needed and, maybe this is fixed now, but I found it throws off profiling as well.

Gotcha, thanks!


Save distribution rasters with terra so they are SpatRasters

Currently, distribution rasters (.rds files saved to output/distributions) are raster::RasterLayers, but in the migration to use terra, it would be better if they were saved as terra::SpatRaster objects. The heavy lifting for this change is going to be in functions/predict_sdm.R, and there might be some adaptation necessary in the templates/template-distribution-[sdm method].R files, too.

I think this is a good idea. I can start to work on this over the break.

Done (over a bunch of different commts, starting with 21dcb8d6 on 29 Dec 2022).  Changes were needed in several different scripts.


amd-ucodegen: init at 0-unstable-2017-06-07

Description of changes
amd-ucodegen is a tool that can be used to generate AMD microcode containers as used by the Linux kernel. It accepts raw AMD microcode files such as those generated by MCExtractor as input. The generated output file can be installed in /lib/firmware/amd-ucode.
The patch is needed for working with newer CPU microcodes.
The implemented test uses platomav CPU microcode database as inputs.

Things done

Built on platform(s)

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

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

[ ] sandbox = relaxed
[ ] sandbox = true

[x] Tested, as applicable:

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

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

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

[x] Fits CONTRIBUTING.md.

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

Doesn't that apply only to Nixos modules?
I followed what is written here: https://github.com/NixOS/nixpkgs/blob/master/pkgs/README.md#writing-inline-package-tests

Tests are fine as-is, NixOS tests are only needed if we're using some aspect of the VM environment.

Result of nixpkgs-review pr 331119 run on x86_64-linux 1

1 package built:

amd-ucodegen


Allow accessing to-be-deleted stores in upgraders (#105)

My attempt at making to-be-deleted stores still available via upgrader's transaction by specifically putting them back into a special upgrader schema. Not sure if this is the right approach but the added tests for shorter table rename and primary key change seem to pass with it.

Thanks. Looking at the code, this seems correct. And as all tests pass and you provided great unit tests, seems fine to merge in.
Thanks a lot for taking the action and do the PR!


I would like to know how to run tlt converter generated .trt engine file in jetson-inference

I have trained a detectnet model using transfer learning toolkit. The material in this repo mentioned about inference using onnx and caffe models, I would like to know steps related to .trt engine.

Hi Shaik, there is an overload of tensorNet::Create() that can load a TRT engine directly.  However, supporting the model would require additional pre/post-processing code to be added like there is in detectNet.cpp for the other models.

From: Shaik Ahmad<EMAIL_ADDRESS>Sent: Tuesday, December 1, 2020 3:56:01 AM
To: dusty-nv/jetson-inference<EMAIL_ADDRESS>Cc: Subscribed<EMAIL_ADDRESS>Subject: [dusty-nv/jetson-inference] I would like to know how to run tlt converter generated .trt engine file in jetson-inference (#824)
I have trained a detectnet model using transfer learning toolkit. The material in this repo mentioned about inference using onnx and caffe models, I would like to know steps related to .trt engine.
—
You are receiving this because you are subscribed to this thread.
Reply to this email directly, view it on GitHubhttps://github.com/dusty-nv/jetson-inference/issues/824, or unsubscribehttps://github.com/notifications/unsubscribe-auth/ADVEGKZ4HOONSLHSVG6O5STSSSVSDANCNFSM4UIW3Q4Q.

@dusty-nv Thank you very much for your response. Can you guide me to implement that in python. We can add this to the repo.

The core pre/post-processing is implemented in C++/CUDA.   For example here is where the pre-processing code starts:
https://github.com/dusty-nv/jetson-inference/blob/8498115a4ed8967c7e23f5ae27b5aa2fa1d1431b/c/detectNet.cpp#L716
The Python bindings simply call into this C++ code.

I have trained a detectnet model using transfer learning toolkit. The material in this repo mentioned about inference using onnx and caffe models, I would like to know steps related to .trt engine.

Hi @AhmadShaik, my team and I are facing the same problem.
If you are working on it i would be interested to know how are you trying to solve it and how it is going. If you need any help we could work on it

Thanks @dusty-nv for the work you've done on this repository, really well done.
I'm also interested in this subject, and noticed the post on the NVIDIA Forum and the TLT Model Export and Deployment section which references the need to pre process the image and post process the Tensor after inference.
If I run with PeopleNet and a command line like this:
detectnet --model=/jetson-inference/data/resnet34_peoplenet_pruned.etlt_b1_gpu0_fp16.engine --input-blob=input_1 --output-cvg=output_cov/Sigmoid --output-bbox=output_bbox/BiasAdd --labels=/jetson-inference/data/labels_peoplenet.txt  file:///jetson-inference/data/sample_1080p_h264.mp4 file:///jetson-inference/data/detectnet_peoplenet_out_sample_1080p_h264.mp4

Where resnet34_peoplenet_pruned.etlt_b1_gpu0_fp16.engine is the output of the execution on deepstream and labels_peoplenet.txt is coming from the Peoplenet zip file, I get an output like detectnet_peoplenet.log running against the sample_1080p_h264.mp4 and an output video here which doesn't show the correct output but does show that bag detection (but not people detection) is occurring.  This might not be the best video for people detection with the blurred faces/backs to the camera or perhaps something about the fact that preprocessing is missing means detections aren't working properly.
I'm wondering if anyone can provide any pointers about the best way I could learn how to write the code I'd need to add in order to pre/post-process for a TLT model .engine file.  It looks like the preprocess step might be as simple as a call to the right cudaTensorXXX function with the right parameters but it's not obvious to me where these parameters would come from.  For postprocessing I need to know how to map the raw outputs the information necessary to fill in the Detections object.
I'm guessing this is related to the Spec File but if so the associations aren't obvious to me.
Any pointers or tips are much appreciated.  Also if this is generally not going to be a project that's possible for someone at less than expert level AI skills to complete that feedback would also be really useful.

Thanks @dusty-nv for the work you've done on this repository, really well done.
I'm also interested in this subject, and noticed the post on the NVIDIA Forum and the TLT Model Export and Deployment section which references the need to pre process the image and post process the Tensor after inference.
If I run with PeopleNet and a command line like this:
detectnet --model=/jetson-inference/data/resnet34_peoplenet_pruned.etlt_b1_gpu0_fp16.engine --input-blob=input_1 --output-cvg=output_cov/Sigmoid --output-bbox=output_bbox/BiasAdd --labels=/jetson-inference/data/labels_peoplenet.txt  file:///jetson-inference/data/sample_1080p_h264.mp4 file:///jetson-inference/data/detectnet_peoplenet_out_sample_1080p_h264.mp4

Where resnet34_peoplenet_pruned.etlt_b1_gpu0_fp16.engine is the output of the execution on deepstream and labels_peoplenet.txt is coming from the Peoplenet zip file, I get an output like detectnet_peoplenet.log running against the sample_1080p_h264.mp4 and an output video here which doesn't show the correct output but does show that bag detection (but not people detection) is occurring.  This might not be the best video for people detection with the blurred faces/backs to the camera or perhaps something about the fact that preprocessing is missing means detections aren't working properly.
I'm wondering if anyone can provide any pointers about the best way I could learn how to write the code I'd need to add in order to pre/post-process for a TLT model .engine file.  It looks like the preprocess step might be as simple as a call to the right cudaTensorXXX function with the right parameters but it's not obvious to me where these parameters would come from.  For postprocessing I need to know how to map the raw outputs the information necessary to fill in the Detections object.
I'm guessing this is related to the Spec File but if so the associations aren't obvious to me.
Any pointers or tips are much appreciated.  Also if this is generally not going to be a project that's possible for someone at less than expert level AI skills to complete that feedback would also be really useful.

Hi Dan, I don't have support in the jetson-inference library for the TLT models - it would require the addition of the pre/post-processing code to support the models.   Instead I recommend running the TLT models through DeepStream, which is documented on each model's page on NGC.
Adding support for these TLT models is something I've been meaning to do but haven't gotten around to yet.  Also since these models are intended for production use-cases the DeepStream runtime may be a better fit.

From: Dan Walkes<EMAIL_ADDRESS>Sent: Saturday, January 9, 2021 11:14:00 PM
To: dusty-nv/jetson-inference<EMAIL_ADDRESS>Cc: Dustin Franklin<EMAIL_ADDRESS>Mention<EMAIL_ADDRESS>Subject: Re: [dusty-nv/jetson-inference] I would like to know how to run tlt converter generated .trt engine file in jetson-inference (#824)
Thanks @dusty-nvhttps://github.com/dusty-nv for the work you've done on this repository, really well done.
I'm also interested in this subject, and noticed the post on the NVIDIA Forumhttps://forums.developer.nvidia.com/t/running-jetsons-detectnet-cpp-with-peoplenet-pruned-model/145313/3?u=danwalkes1 and the TLT Model Export and Deploymenthttps://docs.nvidia.com/metropolis/TLT/tlt-getting-started-guide/text/faqs.html#model-export-and-deployment section which references the need to pre process the image and post process the Tensor after inference.
If I run with PeopleNet and a command line like this:
detectnet --model=/jetson-inference/data/resnet34_peoplenet_pruned.etlt_b1_gpu0_fp16.engine --input-blob=input_1 --output-cvg=output_cov/Sigmoid --output-bbox=output_bbox/BiasAdd --labels=/jetson-inference/data/labels_peoplenet.txt  file:///jetson-inference/data/sample_1080p_h264.mp4 file:///jetson-inference/data/detectnet_peoplenet_out_sample_1080p_h264.mp4
Where resnet34_peoplenet_pruned.etlt_b1_gpu0_fp16.engine is the output of the execution on deepstream and labels_peoplenet.txt is coming from the Peoplenet zip file, I get an output like detectnet_peoplenet.loghttps://github.com/dusty-nv/jetson-inference/files/5792195/detectnet_peoplenet.log running against the sample_1080p_h264.mp4 and an output video herehttps://drive.google.com/file/d/1ukJ-QgQOjjqEc37s5DezpJTobqgcbT60/view?usp=sharing which doesn't show the correct output but does show that bag detection (but not people detection) is occurring. This might not be the best video for people detection with the blurred faces/backs to the camera or perhaps something about the fact that preprocessing is missing means detections aren't working properly.
I'm wondering if anyone can provide any pointers about the best way I could learn how to write the code I'd need to add in order to pre/post-process for a TLT model .engine file. It looks like the preprocess step might be as simple as a call to the right cudaTensorXXX function with the right parameters but it's not obvious to me where these parameters would come from. For postprocessing I need to know how to map the raw outputs the information necessary to fill in the Detections object.
I'm guessing this is related to the Spec Filehttps://docs.nvidia.com/metropolis/TLT/tlt-getting-started-guide/text/creating_experiment_spec.html?highlight=dims# but if so the associations aren't obvious to me.
Any pointers or tips are much appreciated. Also if this is generally not going to be a project that's possible for someone at less than expert level AI skills to complete that feedback would also be really useful.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHubhttps://github.com/dusty-nv/jetson-inference/issues/824#issuecomment-757411872, or unsubscribehttps://github.com/notifications/unsubscribe-auth/ADVEGK7T2BNTBQ42QNNWIHDSZESQRANCNFSM4UIW3Q4Q.

Hi Dan, I don't have support in the jetson-inference library for the TLT models - it would require the addition of the pre/post-processing code to support the models.   Instead I recommend running the TLT models through DeepStream, which is documented on each model's page on NGC.
Adding support for these TLT models is something I've been meaning to do but haven't gotten around to yet.  Also since these models are intended for production use-cases the DeepStream runtime may be a better fit.

From: Dan Walkes<EMAIL_ADDRESS>Sent: Saturday, January 9, 2021 11:14:00 PM
To: dusty-nv/jetson-inference<EMAIL_ADDRESS>Cc: Dustin Franklin<EMAIL_ADDRESS>Mention<EMAIL_ADDRESS>Subject: Re: [dusty-nv/jetson-inference] I would like to know how to run tlt converter generated .trt engine file in jetson-inference (#824)
Thanks @dusty-nvhttps://github.com/dusty-nv for the work you've done on this repository, really well done.
I'm also interested in this subject, and noticed the post on the NVIDIA Forumhttps://forums.developer.nvidia.com/t/running-jetsons-detectnet-cpp-with-peoplenet-pruned-model/145313/3?u=danwalkes1 and the TLT Model Export and Deploymenthttps://docs.nvidia.com/metropolis/TLT/tlt-getting-started-guide/text/faqs.html#model-export-and-deployment section which references the need to pre process the image and post process the Tensor after inference.
If I run with PeopleNet and a command line like this:
detectnet --model=/jetson-inference/data/resnet34_peoplenet_pruned.etlt_b1_gpu0_fp16.engine --input-blob=input_1 --output-cvg=output_cov/Sigmoid --output-bbox=output_bbox/BiasAdd --labels=/jetson-inference/data/labels_peoplenet.txt  file:///jetson-inference/data/sample_1080p_h264.mp4 file:///jetson-inference/data/detectnet_peoplenet_out_sample_1080p_h264.mp4
Where resnet34_peoplenet_pruned.etlt_b1_gpu0_fp16.engine is the output of the execution on deepstream and labels_peoplenet.txt is coming from the Peoplenet zip file, I get an output like detectnet_peoplenet.loghttps://github.com/dusty-nv/jetson-inference/files/5792195/detectnet_peoplenet.log running against the sample_1080p_h264.mp4 and an output video herehttps://drive.google.com/file/d/1ukJ-QgQOjjqEc37s5DezpJTobqgcbT60/view?usp=sharing which doesn't show the correct output but does show that bag detection (but not people detection) is occurring. This might not be the best video for people detection with the blurred faces/backs to the camera or perhaps something about the fact that preprocessing is missing means detections aren't working properly.
I'm wondering if anyone can provide any pointers about the best way I could learn how to write the code I'd need to add in order to pre/post-process for a TLT model .engine file. It looks like the preprocess step might be as simple as a call to the right cudaTensorXXX function with the right parameters but it's not obvious to me where these parameters would come from. For postprocessing I need to know how to map the raw outputs the information necessary to fill in the Detections object.
I'm guessing this is related to the Spec Filehttps://docs.nvidia.com/metropolis/TLT/tlt-getting-started-guide/text/creating_experiment_spec.html?highlight=dims# but if so the associations aren't obvious to me.
Any pointers or tips are much appreciated. Also if this is generally not going to be a project that's possible for someone at less than expert level AI skills to complete that feedback would also be really useful.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHubhttps://github.com/dusty-nv/jetson-inference/issues/824#issuecomment-757411872, or unsubscribehttps://github.com/notifications/unsubscribe-auth/ADVEGK7T2BNTBQ42QNNWIHDSZESQRANCNFSM4UIW3Q4Q.

Hi Dan, I don't have support in the jetson-inference library for the TLT models - it would require the addition of the pre/post-processing code to support the models.   Instead I recommend running the TLT models through DeepStream, which is documented on each model's page on NGC.
Adding support for these TLT models is something I've been meaning to do but haven't gotten around to yet.  Also since these models are intended for production use-cases the DeepStream runtime may be a better fit.

From: Dan Walkes<EMAIL_ADDRESS>Sent: Saturday, January 9, 2021 11:14:00 PM
To: dusty-nv/jetson-inference<EMAIL_ADDRESS>Cc: Dustin Franklin<EMAIL_ADDRESS>Mention<EMAIL_ADDRESS>Subject: Re: [dusty-nv/jetson-inference] I would like to know how to run tlt converter generated .trt engine file in jetson-inference (#824)
Thanks @dusty-nvhttps://github.com/dusty-nv for the work you've done on this repository, really well done.
I'm also interested in this subject, and noticed the post on the NVIDIA Forumhttps://forums.developer.nvidia.com/t/running-jetsons-detectnet-cpp-with-peoplenet-pruned-model/145313/3?u=danwalkes1 and the TLT Model Export and Deploymenthttps://docs.nvidia.com/metropolis/TLT/tlt-getting-started-guide/text/faqs.html#model-export-and-deployment section which references the need to pre process the image and post process the Tensor after inference.
If I run with PeopleNet and a command line like this:
detectnet --model=/jetson-inference/data/resnet34_peoplenet_pruned.etlt_b1_gpu0_fp16.engine --input-blob=input_1 --output-cvg=output_cov/Sigmoid --output-bbox=output_bbox/BiasAdd --labels=/jetson-inference/data/labels_peoplenet.txt  file:///jetson-inference/data/sample_1080p_h264.mp4 file:///jetson-inference/data/detectnet_peoplenet_out_sample_1080p_h264.mp4
Where resnet34_peoplenet_pruned.etlt_b1_gpu0_fp16.engine is the output of the execution on deepstream and labels_peoplenet.txt is coming from the Peoplenet zip file, I get an output like detectnet_peoplenet.loghttps://github.com/dusty-nv/jetson-inference/files/5792195/detectnet_peoplenet.log running against the sample_1080p_h264.mp4 and an output video herehttps://drive.google.com/file/d/1ukJ-QgQOjjqEc37s5DezpJTobqgcbT60/view?usp=sharing which doesn't show the correct output but does show that bag detection (but not people detection) is occurring. This might not be the best video for people detection with the blurred faces/backs to the camera or perhaps something about the fact that preprocessing is missing means detections aren't working properly.
I'm wondering if anyone can provide any pointers about the best way I could learn how to write the code I'd need to add in order to pre/post-process for a TLT model .engine file. It looks like the preprocess step might be as simple as a call to the right cudaTensorXXX function with the right parameters but it's not obvious to me where these parameters would come from. For postprocessing I need to know how to map the raw outputs the information necessary to fill in the Detections object.
I'm guessing this is related to the Spec Filehttps://docs.nvidia.com/metropolis/TLT/tlt-getting-started-guide/text/creating_experiment_spec.html?highlight=dims# but if so the associations aren't obvious to me.
Any pointers or tips are much appreciated. Also if this is generally not going to be a project that's possible for someone at less than expert level AI skills to complete that feedback would also be really useful.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHubhttps://github.com/dusty-nv/jetson-inference/issues/824#issuecomment-757411872, or unsubscribehttps://github.com/notifications/unsubscribe-auth/ADVEGK7T2BNTBQ42QNNWIHDSZESQRANCNFSM4UIW3Q4Q.

Hi Dan, I don't have support in the jetson-inference library for the TLT models - it would require the addition of the pre/post-processing code to support the models.   Instead I recommend running the TLT models through DeepStream, which is documented on each model's page on NGC.
Adding support for these TLT models is something I've been meaning to do but haven't gotten around to yet.  Also since these models are intended for production use-cases the DeepStream runtime may be a better fit.

From: Dan Walkes<EMAIL_ADDRESS>Sent: Saturday, January 9, 2021 11:14:00 PM
To: dusty-nv/jetson-inference<EMAIL_ADDRESS>Cc: Dustin Franklin<EMAIL_ADDRESS>Mention<EMAIL_ADDRESS>Subject: Re: [dusty-nv/jetson-inference] I would like to know how to run tlt converter generated .trt engine file in jetson-inference (#824)
Thanks @dusty-nvhttps://github.com/dusty-nv for the work you've done on this repository, really well done.
I'm also interested in this subject, and noticed the post on the NVIDIA Forumhttps://forums.developer.nvidia.com/t/running-jetsons-detectnet-cpp-with-peoplenet-pruned-model/145313/3?u=danwalkes1 and the TLT Model Export and Deploymenthttps://docs.nvidia.com/metropolis/TLT/tlt-getting-started-guide/text/faqs.html#model-export-and-deployment section which references the need to pre process the image and post process the Tensor after inference.
If I run with PeopleNet and a command line like this:
detectnet --model=/jetson-inference/data/resnet34_peoplenet_pruned.etlt_b1_gpu0_fp16.engine --input-blob=input_1 --output-cvg=output_cov/Sigmoid --output-bbox=output_bbox/BiasAdd --labels=/jetson-inference/data/labels_peoplenet.txt  file:///jetson-inference/data/sample_1080p_h264.mp4 file:///jetson-inference/data/detectnet_peoplenet_out_sample_1080p_h264.mp4
Where resnet34_peoplenet_pruned.etlt_b1_gpu0_fp16.engine is the output of the execution on deepstream and labels_peoplenet.txt is coming from the Peoplenet zip file, I get an output like detectnet_peoplenet.loghttps://github.com/dusty-nv/jetson-inference/files/5792195/detectnet_peoplenet.log running against the sample_1080p_h264.mp4 and an output video herehttps://drive.google.com/file/d/1ukJ-QgQOjjqEc37s5DezpJTobqgcbT60/view?usp=sharing which doesn't show the correct output but does show that bag detection (but not people detection) is occurring. This might not be the best video for people detection with the blurred faces/backs to the camera or perhaps something about the fact that preprocessing is missing means detections aren't working properly.
I'm wondering if anyone can provide any pointers about the best way I could learn how to write the code I'd need to add in order to pre/post-process for a TLT model .engine file. It looks like the preprocess step might be as simple as a call to the right cudaTensorXXX function with the right parameters but it's not obvious to me where these parameters would come from. For postprocessing I need to know how to map the raw outputs the information necessary to fill in the Detections object.
I'm guessing this is related to the Spec Filehttps://docs.nvidia.com/metropolis/TLT/tlt-getting-started-guide/text/creating_experiment_spec.html?highlight=dims# but if so the associations aren't obvious to me.
Any pointers or tips are much appreciated. Also if this is generally not going to be a project that's possible for someone at less than expert level AI skills to complete that feedback would also be really useful.
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Thanks @dusty-nv for the response.
I have successfully run the models in DeepStream and I agree this would be the best way to use the models if I were starting with a blank slate.
A bit more background: I'm trying to leverage the TLT models in an existing application which cannot be easily re-architected to support DeepStream.  So I was looking at whether it would be possible to use TLT models with TensorRT instead as an alternative to a more thourough re-architect of an existing proprietary application in the short term.
Regarding:

it would require the addition of the pre/post-processing code to support the models

I understand at a high level what needs to be done but I'm unclear about the possible ways to accomplish this.  Specifically I'm wondering which of the three approaches below could work:

Write the code myself, using either some spec I haven't found yet or leveraging some existing DeepStream code I haven't found yet.
Fund an AI expert to help write this who would know how to do this based on existing documentation or source and a deeper knowledge than I have about how the tensors are formatted.
Wait for you or an NVIDIA employee to do this, since there's not enough publicly available information shared about the tensors and it's not possible/practical to determine this without additional documentation.

I know option 3 is possible based on what you have already mentioned here but I'm wondering if options 1 or 2 are worth pursuing.

Thanks @dusty-nv for the response.
I have successfully run the models in DeepStream and I agree this would be the best way to use the models if I were starting with a blank slate.
A bit more background: I'm trying to leverage the TLT models in an existing application which cannot be easily re-architected to support DeepStream.  So I was looking at whether it would be possible to use TLT models with TensorRT instead as an alternative to a more thourough re-architect of an existing proprietary application in the short term.
Regarding:

it would require the addition of the pre/post-processing code to support the models

I understand at a high level what needs to be done but I'm unclear about the possible ways to accomplish this.  Specifically I'm wondering which of the three approaches below could work:

Write the code myself, using either some spec I haven't found yet or leveraging some existing DeepStream code I haven't found yet.
Fund an AI expert to help write this who would know how to do this based on existing documentation or source and a deeper knowledge than I have about how the tensors are formatted.
Wait for you or an NVIDIA employee to do this, since there's not enough publicly available information shared about the tensors and it's not possible/practical to determine this without additional documentation.

I know option 3 is possible based on what you have already mentioned here but I'm wondering if options 1 or 2 are worth pursuing.

I heard from the TLT team that there is a Python-based tool called tlt-infer included in the TLT SDK that runs TLT models with TensorRT.  You could try checking that out and see how the pre/post-processing is done there, and/or adopt that code into your application.

I heard from the TLT team that there is a Python-based tool called tlt-infer included in the TLT SDK that runs TLT models with TensorRT.  You could try checking that out and see how the pre/post-processing is done there, and/or adopt that code into your application.

Thanks for the suggestion @dusty-nv
I gave this a shot with nvcr.io/nvidia/tlt-streamanalytics:v2.0_py3 and I see when I run this container that the tlt-infer is just a python wrapper on iva.common.magnet_infer which is a compiled python file.  So unless there's a way to get the original python source for iva.common.magnet_infer and friends this looks like it might be a dead end.  I can ask about this on the TLT forum if that's a better place to post this question.

Thanks for the suggestion @dusty-nv
I gave this a shot with nvcr.io/nvidia/tlt-streamanalytics:v2.0_py3 and I see when I run this container that the tlt-infer is just a python wrapper on iva.common.magnet_infer which is a compiled python file.  So unless there's a way to get the original python source for iva.common.magnet_infer and friends this looks like it might be a dead end.  I can ask about this on the TLT forum if that's a better place to post this question.

Hmm ok, yea - they should know more about it on the TLT forums.  Thanks.

Hmm ok, yea - they should know more about it on the TLT forums.  Thanks.

OK thank you for your help, I've started a new thread at https://forums.developer.nvidia.com/t/source-code-documentation-for-tlt-infer-and-tlt-tensorrt/165684

OK thank you for your help, I've started a new thread at https://forums.developer.nvidia.com/t/source-code-documentation-for-tlt-infer-and-tlt-tensorrt/165684


The game crashes when you beat a level somtimes when you die sometimes at random

Probably fixed in 3.0


add fan @gennadygennady

Organized  tags with indentation based on the starting letter for improved readability

@gennadygennady better keep the formatting as is

@yegor256 ok, i will rollback it. But why?

I also found that this formatting should be updated; these tabs are inconsistent now

@yegor256 ok, i will rollback it. But why?

+1 I also found that this formatting should be updated; these tabs are inconsistent now


This one:
https://github.com/anoma/vamp-ir/commit/bdbac967bb8d7f56cbf06f042fa2ce6123d5b551
But then the library didn't compile for some users due to some dependency updates, but that issue was fixed in this PR from yesterday:
https://github.com/anoma/vamp-ir/commit/72ec820a1d6788f8618e2ca1e799b9a358709a83

Thanks!


array_shuffle

A simple way to shuffle any array.
function array_shuffle(arr){
	var i, j, k;
	var size = array_length(arr);
	for (i = 0; i < size; i += 1){
	    j = irandom_range(i, size - 1);
	    if (i != j){
	        k = arr[i];
	        arr[i] = arr[j];
	        arr[j] = k;
	    }
	}
}

Already handled by built-in array_shuffle (returning new array) and array_shuffle_ext (changing the existing array).
These are not in the current LTS, but should make it to the 2025 LTS mainline, and it seems like a good moment for Community Toolbox to get its own LTS-compliant versio. ^^

Then this should be close.


[utils] Add a CSV-output mode to rusage script.

Just a little more scripting work for gathering very-high-level stats.

@swift-ci please smoke test and merge

@swift-ci please smoke test and merge


async_setup_platforms -- deprecated

Home Assistant 2023.5.2
Supervisor 2023.04.1
Operating System 10.1
Frontend 20230503.3 - latest
Hello and thanks to the devs for creating this great HACS plugin. I noticed with the 2023.5.2 Home Assistant release that my Tailwind integration was failing at startup. After some research, I found that the async_setup_platform function has been marked as deprecated and set to fail in 2023.3.  It seems that it is now indeed failing. To correct the issue, I have modified "init.py" file in HACS - Custom Components - tailwind_iq3, line 74
From:  hass.config_entries.async_setup_platforms(entry, PLATFORMS)
To: await hass.config_entries.async_forward_entry_setups(entry, PLATFORMS)
I restarted Home Assistant and the Tailwind integration seems to be working as expected. Hopefully this is the permanent fix and can be updated in the HACS package for all.
Thanks

@ericober Thanks for the detailed report, and you're welcome.  The deprecation you mention was addressed in v0.4.0 (released Feb 2nd), though I'd suggest updating to v.0.5.0 (released Feb 26th).  If HACS hasn't offered you the update, you should be able to install the update by going to HACS -> Integrations -> Tailwind iQ3, opening the menu in the top right and choosing "Redownload" - if it doesn't offer you the latest version(s), try the "Update information" option in the same menu.

@pauln Thank you very much. Pulled the latest as HACS was not giving me the notification for an update.


Fixed return of dependencies a proper json object

Description:
Fix the previous version!

Pull Request Test Coverage Report for Build 2575

1 of 1 (100.0%)  changed or added relevant line in 1 file are covered.
No unchanged relevant lines lost coverage.
Overall coverage decreased (-0.01%) to 68.55%

Totals

Change from base Build 2570:
-0.01%

Covered Lines:
1258

Relevant Lines:
1770

💛  - Coveralls

Nice catch but when i apply your patch than "Content-Type" is missing from the response.


setup pixel repo to use requirements.txt

@hovey

No longer relevant, since we use pyproject.toml.


Navigator: no reparent/paste into a conditional

Problem:
Currently, reparenting/pasting into a conditional means we reparent/paste into the active branch of the conditional.
However, according the slot-like plans we would like to eliminate this behavior, and only allow reparenting/pasting directly into a clause (and even them in a different way, but that is not in the scope of this PR)
Fix:

targetElementSupportsChildren should return false for conditionals - this guarantees you can not drag into a conditional in the navigator
I removed the whole insertion logic into conditional logic from insertJSXElementChild - this allowed me to remove the metadata parameter (which was only needed to check the actual condition value)
I added a new test to make sure you can not drag into a conditional in the navigator
I removed the paste tests for conditionals (because we do not want to allow that neither)

Missing:
When you drag after or before elements in the navigator, we don't check if the future parent supports children or not. See this line https://github.com/concrete-utopia/utopia/blob/master/editor/src/components/navigator/navigator-item/navigator-item-dnd-container.tsx#L97 (When the target is not a reparent target, we don't check whether the element supports children or not.)
The reparent will not happen because I removed the code from insertJSXElementChild, but we still see the drop target highlight.
This should be fixed in a subsequent PR, but the hover behavior is so different in the slot-like world, that I would only fix this when that design is implemented.

There are some paste tests in navigator-conditionals.spec.browser2.tsx as well, which I assume should also be removed?

There are some paste tests in navigator-conditionals.spec.browser2.tsx as well, which I assume should also be removed?

@seanparsons Those tests check pasting into clauses, and not directly into the conditional expression itself. So those are still valid and important (otherwise they would be broken)


Is OCSP stapling enabled if custom SSL cert/key defined in tls directive ?

I tested Caddy with Caddyfile with specific defined custom cert/key on port 445 but both cipherscan and testssl are reporting OCSP as not enabled or not offered ? According to https://caddyserver.com/docs/automatic-https OCSP is automatically enabled for tls but is that only if Caddy auto generates the LE SSL cert and HTTPS setup ?
Plan is to put Caddy up against other web servers on the same server similar to https://h2ohttp2.centminmod.com/flags.html so trying to make sure Caddy configuration is as close to the rest of the web servers and that I have Caddy properly configured. Not sure what I am missing for OCSP support in Caddy ? Thanks !
Caddyfile using a LE SSL cert I already generated from my Centmin Mod LEMP stack LE integration
le12.http2ssl.xyz:445
gzip {
        level 6
}
browse
#tls off
tls /etc/letsencrypt/live/le12.http2ssl.xyz/fullchain.pem /etc/letsencrypt/live/le12.http2ssl.xyz/privkey.pem {
        protocols tls1.0 tls1.2
        ciphers ECDHE-RSA-AES128-GCM-SHA256 ECDHE-ECDSA-AES128-GCM-SHA256 ECDHE-RSA-AES128-CBC-SHA ECDHE-RSA-AES256-CBC-SHA ECDHE-ECDSA-AES256-CBC-SHA ECDHE-ECDSA-AES128-CBC-SHA RSA-AES128-CBC-SHA RSA-AES256-CBC-SHA ECDHE-RSA-3DES-EDE-CBC-SHA RS$
}
root /home/nginx/domains/le12.http2ssl.xyz/public

fastcgi / <IP_ADDRESS>:9000 {
        ext   .php
        split .php
        index index.php
}

log /opt/caddy/le12.http2ssl.xyz-access.log {
        rotate {
                size 100 # Rotate after 100 MB
                age  14  # Keep log files for 14 days
                keep 10  # Keep at most 10 log files
        }
}

cipherscan output for caddy on port 445
cipherscan le12.http2ssl.xyz:445
.......................
Target: le12.http2ssl.xyz:445

prio  ciphersuite                  protocols              pfs                 curves
1     ECDHE-RSA-AES128-GCM-SHA256  TLSv1.2                ECDH,P-256,256bits  prime256v1,secp384r1,secp521r1
2     ECDHE-RSA-AES128-SHA         TLSv1,TLSv1.1,TLSv1.2  ECDH,P-256,256bits  prime256v1,secp384r1,secp521r1
3     ECDHE-RSA-AES256-SHA         TLSv1,TLSv1.1,TLSv1.2  ECDH,P-256,256bits  prime256v1,secp384r1,secp521r1
4     AES128-SHA                   TLSv1,TLSv1.1,TLSv1.2  None                None
5     AES256-SHA                   TLSv1,TLSv1.1,TLSv1.2  None                None
6     ECDHE-RSA-DES-CBC3-SHA       TLSv1,TLSv1.1,TLSv1.2  ECDH,P-256,256bits  prime256v1,secp384r1,secp521r1
7     DES-CBC3-SHA                 TLSv1,TLSv1.1,TLSv1.2  None                None

Certificate: trusted, 2048 bits, sha256WithRSAEncryption signature
TLS ticket lifetime hint: None
OCSP stapling: not supported
Cipher ordering: server
Curves ordering: server - fallback: no
Server supports secure renegotiation
Server supported compression methods: NONE
TLS Tolerance: yes

cipherscan output for nginx on port 443
cipherscan le12.http2ssl.xyz:443
............................
Target: le12.http2ssl.xyz:443

prio  ciphersuite                  protocols              pfs                 curves
1     ECDHE-RSA-CHACHA20-POLY1305  TLSv1.2                ECDH,P-256,256bits  prime256v1
2     ECDHE-RSA-AES128-GCM-SHA256  TLSv1.2                ECDH,P-256,256bits  prime256v1
3     ECDHE-RSA-AES256-GCM-SHA384  TLSv1.2                ECDH,P-256,256bits  prime256v1
4     DHE-RSA-AES128-GCM-SHA256    TLSv1.2                DH,2048bits         None
5     DHE-RSA-AES256-GCM-SHA384    TLSv1.2                DH,2048bits         None
6     ECDHE-RSA-AES128-SHA256      TLSv1.2                ECDH,P-256,256bits  prime256v1
7     ECDHE-RSA-AES128-SHA         TLSv1,TLSv1.1,TLSv1.2  ECDH,P-256,256bits  prime256v1
8     ECDHE-RSA-AES256-SHA384      TLSv1.2                ECDH,P-256,256bits  prime256v1
9     ECDHE-RSA-AES256-SHA         TLSv1,TLSv1.1,TLSv1.2  ECDH,P-256,256bits  prime256v1
10    DHE-RSA-AES128-SHA256        TLSv1.2                DH,2048bits         None
11    DHE-RSA-AES128-SHA           TLSv1,TLSv1.1,TLSv1.2  DH,2048bits         None
12    DHE-RSA-AES256-SHA256        TLSv1.2                DH,2048bits         None
13    DHE-RSA-AES256-SHA           TLSv1,TLSv1.1,TLSv1.2  DH,2048bits         None
14    AES128-GCM-SHA256            TLSv1.2                None                None
15    AES256-GCM-SHA384            TLSv1.2                None                None
16    AES128-SHA256                TLSv1.2                None                None
17    AES256-SHA256                TLSv1.2                None                None
18    AES128-SHA                   TLSv1,TLSv1.1,TLSv1.2  None                None
19    AES256-SHA                   TLSv1,TLSv1.1,TLSv1.2  None                None

Certificate: trusted, 2048 bits, sha256WithRSAEncryption signature
TLS ticket lifetime hint: 3600
OCSP stapling: supported
Cipher ordering: server
Curves ordering: server - fallback: no
Server supports secure renegotiation
Server supported compression methods: NONE
TLS Tolerance: yes

No - OCSP stapling is only implemented for managed ACME certificates.

Thanks Matt - any change of getting OCSP working on custom defined tls directive certs ?

Yeah, I don't see why not -- just a matter of manpower right now!

no worries - definitely understand time and resource limitations of working on an open source project :)

@centminmod I believe this is fixed upstream in the branch; when I merge this into master will you be able to test it again by building from source?


New feedback - ID 195

User input 1 (by - sharepoint ID195):
K testing updated Power Automate

Deleting as this was only a test


[docs] Improve the interoperability guide

[ ] I have followed (at least) the PR section of the contributing guide.

Is there any issue that this PR should close? Otherwise, LGTM!

No, it was created as a suggestion from a comment on a previous PR


Support ISteamWebAPIUtil interface

The following endpoints must be implemented:

[x] GetServerInfo
[x] GetSupportedAPIList

Implemented by 8ee9a4213a9b3e2cf3d6ab558eb269738c8a42ec


Default CoreSettings Tagline to empty

Currently user gets a tagline they didn't input after blog setup, it's better to remove this default value and let user enter it in the Admin Settings.

On another look, it's not bad to give a default value, user can update this in Settings.


Added New Problem

Added Easy and Moderate Problem. @Coding-Cartel/cartels

looks great. good approach used.


Remove UserModel reference from Home controller

Description
Remove the UserModel reference from Home Controller (referenced on https://github.com/codeigniter4/appstarter/pull/4).
Checklist:

[X] Securely signed commits

This is the right place :)
Can you GPG-sign the commit?

This is the right place :)
Can you GPG-sign the commit?
I thought it was done. Had to upload the key to GitHub. Thanks!


How to diffrentiate empty or whitespace-only input from errornous data?

json_error_t status;
m_handle=json_load_callback(loadCallback,&readhandler,0,&status);
if(m_handle==nullptr)
	{
	if(status.position!=0)
		{
		exceptionRaise(ErrorMessage("#0;:#1;: error: #2;."
			,{readhandler.nameGet(),status.line,status.text}));
		}
	m_handle=json_object();
	}

This works as long as the input is truly empty, but it fails for whitespace-only data. Currently, data is passed to libjansson as if
grep '^[[:space]:]*//@' "$READHANDLER_NAME" | sed 's/\/\/@//'

This works perfectly as long as the data is at beginning of the file. Otherwise, parse errors are reported at the wrong line. Within the current framework, the cleanest solution is to pass empty lines to libjansson so that it is internal linecounter keeps incrementing. But with the current logic, an exception will be thrown ("Expected '[', '{' at EOF"). What is really needed here is an error code system, or can I rely on
if(strcmp("Expected '[', '{' at EOF",status.text))
       {exceptionRaise(...);}

sed 's/\/\/@//'

What are you trying to achieve with this sed call?
From Maike issue #40 it looks like you might want to extract those portions of text from a file that are in a line of their own except for a marker that consists of line-comment markers from usual programming languages (like # or //), followed immediately by an @ sign. The portion to be extracted is the parts right from that marker. A sed invocation for that sort of filter could be:
sed -nre 's~^\s*(//|#)@~~p' -- gm_programs.py

The -n option and p(rint) flag to the s(ubstitution) replace the grep filter.
If you want to only get lines that have a non-whitespace character right of the @ sign, you could pipe the above to | grep -Pe '\S' or extend the sed regexp to:
sed -nre 's~^\s*(//|#)@(.*\S)~\2~p' -- gm_programs.py

While the latter is short, it might become buggy on some version of sed if input lines are very long. Also if you add parens to the comment markup match, you'd have to adjust the reference number on the right. Since without the N command (append next line) your input buffer will never start with a newline, we can use an initial newline as a marker for interesting lines, so we can do more s~~~ubstitutions before we print:
sed -nre '
  # mark interesting lines, remove comment markup:
  s~^\s*(//|#)@~\n~
  # trim end-of-line whitespace, and our marker if
  # nothing visible had remained in that line:
  s~\s+$~~
  # if the line still is marked, remove our marker and print:
  s~^\n~~p
  ' -- gm_programs.py

or condensed:
sed -nre 's~^\s*(//|#)@~\n~;s~\s+$~~;s~^\n~~p' -- gm_programs.py

I prefer selective printing over deleting of lines because d's control flow behavior changes depending on whether the -n option is present.

That said, empty or whitespace-only input is not a valid JSON value, so for a compliant JSON parser, empty or whitespace-only input is just one special case of errornous data.

@mk-pmb I remove the comment mark on each line. Notice that they have not to appear at beginning of line. My sed was the simplest possible, but does not cover every case.
So JSON cannot be empty. That is interesting. I would have distinguished empty from error, because my situation is:

Empty => Nothing to do
Syntax error => Throw an error

As it is now, I can identify empty by looking at the offset where jansson complains (it must be zero), but this does not hold for whitespace only data, which is what the filter spits out if there are no marked lines. So this means I have to write a custom parser that separates these cases?

It would probably be a nice feature of jansson if it could report the special error cases "empty" and "whitespace only". Until it does, you'll probably have to check that yourself.

It appears that adding that feature would break the ABI. Currently, it looks like
struct json_error_t{
      const char* text;
      const char* source;
      int line;
      int column;
      int position;
 };

This feature request would replace the first field of this struct an enum containing integer values for each possible value. And then there would be a function that converts the status code (JSON_NO_ERROR,JSON_EMPTY,... ) to a message. Thus it breaks the ABI :-( . Other solutions include a second set of json_load_* functions which takes an json_error2_t.


Provide option to re-use configurations in angular.json

I am working on a project with different languages.
This requires that each language is a separate configuration in the angular.json, eg
"configurations": {
  "prod-en": {
    "optimization": true,
    "outputHashing": "all",
    "sourceMap": false,
    "extractCss": true,
    "namedChunks": false,
    "aot": true,
    "extractLicenses": true,
    "vendorChunk": false,
    "buildOptimizer": true,
    "fileReplacements": [
      {
        "replace": "src/environments/environment.ts",
        "with": "src/environments/environment.prod.ts"
      }
    ],
    "i18nLocale": "en",
    "baseHref": "/en/"
  },
  "prod-fr": {
    "optimization": true,
    "outputHashing": "all",
    "sourceMap": false,
    "extractCss": true,
    "namedChunks": false,
    "aot": true,
    "extractLicenses": true,
    "vendorChunk": false,
    "buildOptimizer": true,
    "fileReplacements": [
      {
        "replace": "src/environments/environment.ts",
        "with": "src/environments/environment.prod.ts"
      }
    ],
    "i18nLocale": "fr",
    "baseHref": "/fr/"
  },
  …
]

This very soon becomes a maintenance nightmare and is definitely not DRY.
And all this only for enabling different languages.
The languages could be configured using ng build --i18n-locale=en …, but unfortunately ng serve does not provide these options.
(Before v6, i18n could be configured using CLI parameters for ng serve.)
This becomes worse when one wants to have a different set of options for ng serve (hashing, disable optimisations, source maps, etc.).
Then, one has to add another set of configurations (e.g. 'dev-en', 'dev-fr'), containing only the i18n options.
A third dimension (the first two being 'locale' and 'buildType') are different environment files.
Thus, if my application is deployed in three languages on three different environments, I need 9 different configurations for the build, and up to 9 more for local testing.
Thus, a mechanism to easily mix and match different sets of configurations would be great.
Or am I missing another feature that would solve my problem?

what about ng build -c=prod-fr

I know how to build these configurations. But as stated above (in my edited post), the current system can easily lead to (for example) 18 different configurations. And most of them share a large part.
This leads to a large angular.json file, which also is not very easy to read due to so man duplications. And editing all of them is error-prone.

Of course I can use ng build --i18nLocale, but I cannot use ng serve --i18nLocale, so I still need more configuration, bloating up the angular.json.
The issue is not only with languages, but generally with different build options, environments etc.


Globbing from doc doesn't work - results in 'stat **/*.yaml: no such file or directory'

The globbing example in the README section doesn't work:
https://github.com/datreeio/action-datree#examples
      - name: Run Datree Policy Check
        uses: datreeio/action-datree@main
        with:
          path: '**/*.yaml'

results in:
stat **/*.yaml: no such file or directory

I realized this appears to be broken in the CLI tool and not just the action, so have raised:
https://github.com/datreeio/datree/issues/618

Thanks, closing this, we'll follow up in the new issue you opened.


Notification Request

Notification is appear  for all download file in when app is first open .But download file are already pause before app are exist.bro.

I expect bro. when click action button after show notification bro thank you

May i request bro :)

This is because after the create task, it will immediately receive a state, so the NotificationFactory will create a Notification based on this state, so you can slightly modify your NotificationFactory to determine whether the first time you receive the state should be able to solve this problem

bro thank you for answer . Please can you show me sample bro?

This problem is not cause in version 1.1.9 bro . But 1.1.9 is ui stuck . This version is perfect bro . pendingIntent is work and no ui stuck bro :D thank. But i have above problem bro. If you don't mine please advice how to modify for my notificationFactory bro. Thank you

Ok bro thank you.:D

public class CustomNotificationFactoryImpl implements NotificationFactory {
private boolean isFirst = true;
private HashMap<RealMission, NotificationCompat.Builder> map = new HashMap<>();
@Nullable
@Override
public Notification build(@NotNull Context context, @NotNull RealMission mission, @NotNull Status status) {
    if (isFirst) {
        isFirst = false;
        return null;
    }

    NotificationCompat.Builder builder = get(mission, context);
    if (status instanceof Normal) {

    } else if () {

    }
}

private NotificationCompat.Builder get(RealMission mission, Context context) {
    NotificationCompat.Builder builder = map.get(mission);
    if (builder == null) {
        builder = createNotificationBuilder(mission, context);
        map.put(mission, builder);
    }
}

private NotificationCompat.Builder createNotificationBuilder(RealMission mission, Context context) {
    return new NotificationCompat.Builder(context, "")
            .setSmallIcon(R.drawable.ic_download)
            .setContentTitle("title");
}

}

yeah work perfectly bro .Save my problem Thank you so much .

bro is not work when download file is one or more :(


Add closed-form damped harmonic oscillator algorithm to Animated.spring

Motivation
As I was working on mimicking iOS animations for my ongoing work with react-navigation, one task I had was to match the "push from right" animation that is common in UINavigationController.
I was able to grab the exact animation values for this animation with some LLDB magic, and found that the screen is animated using a CASpringAnimation with the parameters:

stiffness: 1000
damping: 500
mass: 3

After spending a considerable amount of time attempting to replicate the spring created with these values by CASpringAnimation by specifying values for tension and friction in the current Animated.spring implementation, I was unable to come up with mathematically equivalent values that could replicate the spring exactly.
After doing some research, I ended up disassembling the QuartzCore framework, reading the assembly, and determined that Apple's implementation of CASpringAnimation does not use an integrated, numerical animation model as we do in Animated.spring, but instead solved for the closed form of the equations that govern damped harmonic oscillation (the differential equations themselves are here, and a paper describing the math to arrive at the closed-form solution to the second-order ODE that describes the DHO is here).
Though we can get the currently implemented RK4 integration close by tweaking some values, it is, the current model is at it's core, an approximation. It seemed that if I wanted to implement the CASpringAnimation behavior exactly, I needed to implement the analytical model (as is implemented in CASpringAnimation) in Animated.
Implementation
We add three new optional parameters to Animated.spring (to both the JS and native implementations):

stiffness, a value describing the spring's stiffness coefficient
damping, a value defining how the spring's motion should be damped due to the forces of friction (technically called the viscous damping coefficient).
mass, a value describing the mass of the object attached to the end of the simulated spring

Just like if a developer were to specify bounciness/speed and tension/friction in the same config, specifying any of these new parameters while also specifying the aforementioned config values will cause an error to be thrown.
Defaults for Animated.spring across all three implementations (JS/iOS/Android) stay the same, so this is intended to be a non-breaking change.
If stiffness, damping, or mass are provided in the config, we switch to animating the spring with the new damped harmonic oscillator model (DHO as described in the code).
The DHO animation algorithm will calculate the position of the spring at time t explicitly and in an analytical fashion, and use this calculation to update the animation's value.
Also, docs have been updated to cover the new configuration options (and also I added docs for Animated configuration options that were missing, such as restDisplacementThreshold, etc).
Test Plan
Run tests. Run NativeAnimation example in RNTester.

Nice work! I'll have a closer look tomorrow, could you also add a test for the native implementations? Here are the test for the other spring implementation: https://github.com/facebook/react-native/blob/master/ReactAndroid/src/test/java/com/facebook/react/animated/NativeAnimatedNodeTraversalTest.java#L265 and https://github.com/facebook/react-native/blob/master/RNTester/RNTesterUnitTests/RCTNativeAnimatedNodesManagerTests.m#L269.

@janicduplessis yah for sure. Sorry I missed them!

Awesome work! Just wanted to discuss a few things loosely related to the code:

As you have an analytic formula for x(t) maybe it would be better to also calculate velocity analytically instead of using an approximation here:

var velocity = deltaTime > 0 ? (position - this._lastPosition) / deltaTime : 0;

Calculating it analytically will yield more precise results and I wonder if they could be noticeable. One example where this can be noted is when you use animated tracking with spring (like in "chatheads"), in which case parameters of spring changes with the motion of the finger and the velocity from the previous spring is passed to the next spring.
Calculating velocity analytically is just a matter of calculating time derivative for the equation you have for x(t).

I understand your concern about not making this a breaking change but I wonder what would it take to make this new implementation compatible with the old one so that we can replace RK4 completely. I think this would help in maintaining this quite complicated codebase as we already have spring implementation in three places (js, android, iOS) and now we multiply it by two.
From my understanding the difficulty here is to translate properties of RK4 impl spring (tension and friction) into DHO properties (stiffness, damping and mass). I understand that even if we find a proper mapping the two implementation would yield slightly different results.

From the RK4 implementation it appears like the tension parameter is actually used as a "stiffness" param and friction seems to play the same role as "damping constant":
var aAcceleration = this._tension *
        (this._toValue - tempPosition) - this._friction * tempVelocity;

And this is from the paper you linked:

Assuming mass = 1, this._tensions plays the same role as k (stiffness) and this._friction corresponds to c (damping constant)

I was wandering how CASpringAnimation handles frame drops. The RK4 implementation has a limit on time delta that can be animated in a single animation loop. So for example when loop is stuck for like 10 frames then we advance only by four frames. This should supposedly provide a better experience as the distance between the frames where we update position is going to be shorter and therefore harder to notice but not sure how this works in practice. I can imagine that adding this to DHO impl shouldn't be very difficult (e.g we add another variable that keeps track of the time offset and as long as the time delta between loop calls is small enough we just add that to the time offset and if it's longer than we add some constant number of milliseconds). Even though that would not be very difficult to add I'm not sure if it's worth it, so would like to hear your thoughts.

Thanks @kmagiera for the thoughts.
On analytic solution for velocity:
I'll take a look at this further today. Just have to do the math. I think it's a reasonable ask.
On replacing the current RK4 solution with DHO:
I can play around with this. You're right that it theoretically should work, although, I also thought that I would be able to get the RK4 method to take mass into account by doing as you say -- treating tension as k, friction as c, and dividing k and c by m. But that didn't work. I'll investigate this further.
On frame drops:
I'm not sure how CASpringAnimation handles frame drops (probably not very common for it to ever encounter any, given that core animation runs out of process in a higher priority thread than the app). Re this particular implementation however, I've already added it to the JS and Android implementations: https://github.com/facebook/react-native/pull/15322/files#diff-f048d92ca0be679bc38d38147b311100R682 (JS) and https://github.com/facebook/react-native/pull/15322/files#diff-3205235300ac0a6c2dbcf7d2a7264117R161 (Android). This matches the behavior in the RK4 implementation. I didn't add it the iOS implementation, as I didn't see that we were accounting for frame drops anywhere else in the native Animated implementations, but maybe I should add the same logic here.

I'll let you figure out the maths :P

I think a small change in the spring curve should not be noticeable so if we are able to convert the RK4 parameters to something similar with DHO we should get rid of the RK4 implementation. Might as well deprecate some parameters to keep the config simpler since there will now be 3 different ways to pass the spring config.

Unless we can figure out how CASpringAnimation handles frame drops I think we should add the same logic on iOS for both DHO and RK4 (if we decide to keep that implementation). It was probably forgotten when it was implemented on iOS, I'd like to keep the 3 implementations as close as possible.

@janicduplessis all sounds good. Worked on this a bit today. Hope to have a final version tomorrow

This looks great. Quick note, I removed the "Core Team" label as we're currently using this label to track PRs that have already been assigned internally to a Facebook employee for further review. I wouldn't want to set the expectation that adding the label will automatically flag it for internal review - that process is still done manually. That said, if you want to get this reviewed by a FB employee, let me know!

@janicduplessis @kmagiera this is ready for re-review.
I've taken all of @kmagiera's comments and implemented them.

Velocity is now calculated analytically (that derivative for the underdamped spring is NARLY).
The analytic DHO model is now the only model across the three platforms. Tension/friction/bounciness/speed can still be specified as before. Tension/friction does indeed correspond directly to stiffness/damping with a mass of 1, as @kmagiera posited.
I added the "max delta time" functionality that existed in Android/JS to the iOS implementation

@hramos sorry for adding the label! Originally I was looking for internal review, but generally, that's probably not necessary
...unless @vjeux wants to comment since he wrote a bunch of this initially. Hi @vjeux!

Awesome detective work :) @willbailey will likely be interested as he implemented rebound and the RK4 algorithm, I just copy pasted his implementation in RN

As the others have said, fantastic work and sleuthing, @skevy!
Would you be interested in breaking this out into its own library?  Rebound is great, but it's encumbered by the PATENTS clause (for better or worse, many companies refuse to use any dependencies with a PATENTS clause).  It's also 12K minified.
If there was an accurate and unencumbered springs microlibrary, the JavaScript ecosystem would be better for it.  Animated could wrap it for React Native, and we'd wrap it for material-motion.

Nice work! (such math!)
Out of curiosity, what is the use case for isInteraction?

I think it is if you want your animation to not block InteractionManager.runAfterInteractions, could be useful for a looping animation.

@appsforartists yah, I could take a crack at this. I'll DM you on twitter and we can chat about it.

@skevy Sorry https://github.com/facebook/react-native/commit/5590b1b5ad058bcc377521464b025bc4808a22f4 landed so you'll need to rebase :)
The code is now in animations/SpringAnimation.js. Could you also just let/const + prettier since I did it for most files in Animated now.

Not sure what your policy is on introducing external dependencies, but this algorithm now exists in its own repo: https://github.com/skevy/wobble/  Would be nice to have it in one canonical place, rather than two.

@janicduplessis this is rebased now and ready for final review.
Thanks for your patience!
cc @kmagiera

@appsforartists fwiw I decided to keep the implementation implemented separately here for now, given the fact that RN also contains Obj-C/Java implementations of the same algorithm, and I think it's reasonable to keep them colocated. Maybe we can change this in the future, but I think it's easier for now.

Thanks for working on this. I'll import it and let some internal tests run, and if all goes well, I'll land it on Monday.

This is almost ready to land. I need to perform some manual steps before doing so. Stay tuned.


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ping missing from openthread/codelab_otsim Docker image

Minor problem, but ping is missing from the openthread/codelab_otsim Docker image.
> root@ebc7d981f006:~/src/openthread# ping6 -c 4 fdca:f078:8ce4:39be:b258:c4b4:24b6:d7d
bash: ping6: command not found

We can either update the Codelab to instruct the reader to run apt install inetutls-ping,
or the image can be updated to include the tool.

Already a logged issue for this #68 , let's use that one.


[5.0] Remove reference to deprecated OSAtomicCompareAndSwap32

Inside our library we are using function which was deprecated in iOS 10 && macOS 12 SDKs. It’s referenced here. We should remove the reference to this function and use the analogous function from stdatomic.h
How it affects current users
There is no affection.

Discussion needed
When doing this tak, I've encountered the problem with using functions from stdatomic.h directly in Swift.
Problem
The issue is that although stdatomic.h is public under current SDKs and it's added to module map of Darwin.C module,  the import Darwin.C.stdatomic doesn't expose required atomic_compare_exchange_strong_explicit function. It's a known bug: https://openradar.appspot.com/27161329 but it hasn't been fixed at the moment.
Possible solutions
There are three possible solutions I've found:

add stdatomic.h to module map of RxBluetoothKit. I can be done either directly or using a wrapping module that makes the Swift-friendly adjustments to the stdatomic interface. This is the approach that swift-atomic library takes, see here

write our own implementation of atomic compare and swap, similar to what RxSwift has done on Linux

ignore the deprecation warning and continue using OSAtomicCompareAndSwap32. This is the approach that RxSwift took on Darwin platforms

Discussion
The first solution is following the Apple hint, so it might be the clearest. However, it's complicating the project structure and possibly might break in the future, if the availability of stdatomic.h would change in next SDK release.
The second solution is non-invasive, but least optimised. It needs performance consideration.
The third solution is a non-solution, and it might also break in the future, when deprecation will turn into lack of availability of Darwin.libkern.OSAtomic.
@Kacper20 @Cierpliwy What do you think?

Hi @siejkowski,
Thanks so much for your analysis — it's very thorough! We've been talking about getting rid of the complicated scanning logic as it's pain to maintain and isn't really usable.
Because our custom operator & scheduling is used only in scanning and it's also the class in which we see this deprecation we'll most surely remove all of the related code. Based on this I'm closing this issue. If we'll change our minds we'll come back to this topic and it'll be reopened. It's also a great resource for other developers having the same problems.
I will make an issue soon about the details of scan refactor.


PDF optimisation: original width height as data attributes on images

Preview environment images deployed!
The preview environment has been updated:
client image tag: preview-1420-a619fb1ebf2045161bd7e4423e7bd8c4234ebb5a-20231127.1630
storybook image tag: preview-1420-a619fb1ebf2045161bd7e4423e7bd8c4234ebb5a-20231127.1630
Client URL: https://pr-1420--epp.elifesciences.org
Storybook URL: https://pr-1420--epp-storybook.elifesciences.org
Note: Updated images can take a few minutes to roll out.

Preview environment created!
The preview environment has been deployed to the cluster.
Client URL: https://pr-1420--epp.elifesciences.org
Storybook URL: https://pr-1420--epp-storybook.elifesciences.org
Note: If this is a new environment, it can take a few minutes for DNS to update.

There is currently an issue causing all preprints to return 500 error when this branch is deployed.

I tested the page load times locally on this branch for pdf and fulltext pages (restating image server to reduce the effect of IIIF caching):
> docker compose restart image-server && sleep 10 && time curl http://localhost:8080/reviewed-preprints/85111v1 > /dev/null
[+] Restarting 1/1
real  0m13.041s
user  0m0.009s
sys 0m0.018s

> docker compose restart image-server && sleep 10 && time curl http://localhost:8080/reviewed-preprints/85111v1/pdf > /dev/null
[+] Restarting 1/1
real  0m14.693s
user  0m0.007s
sys 0m0.013s

Not great. So I quickly wrote and pushed a small patch to make sure we only collect the image data on /pdf page:
> docker compose restart image-server && sleep 10 && time curl http://localhost:8080/reviewed-preprints/85111v1 > /dev/null
[+] Restarting 1/1
real  0m0.488s
user  0m0.005s
sys 0m0.012s

> docker compose restart image-server && sleep 10 && time curl http://localhost:8080/reviewed-preprints/85111v1/pdf > /dev/null
[+] Restarting 1/1
real  0m13.035s
user  0m0.008s
sys 0m0.015s

Much more acceptable.


[CT-166] [Bug] accepted_values test causing  OSError: File name too long

Is there an existing issue for this?

[X] I have searched the existing issues

Current Behavior
We have 12 accepted values defined in the accepted_values test list. When running this, it gives this OSError
OSError: [Errno 36] File name too long: '/home/********/project/project-name/target/test.folder.source_accepted_values_very_long_accepted_values_strings.123456789.html'

Expected Behavior
Maybe a better way to generate test file names? Or maybe document somewhere that there could be a limit of number of accepted values defined in the list?
Steps To Reproduce
No response
Relevant log output
No response
Environment
- OS: macOS Big Sur, version 11.5.2
- Python: 3.9.5
- dbt: 1.0.0

What database are you using dbt with?
snowflake
Additional Context
No response

Hi, @treeforest456! Thanks for the report, and I'm sorry to hear you're having issues running tests. We have run into issues with the way we internally uniquely identify test nodes before and it seems the file path that is too long for your file system is caused in part by the unique identifier being too large.
I left a comment on a previous issue to propose a long-term fix, and I wanted to point you to a proposed short-term fix in this ticket which is identified as a good first issue for a community PR, and I believe would solve your issue as well.
Unfortunately, I cannot think of any workarounds for your particular issue that do not involve a code change to dbt.
Since this ticket specifically calls out the inability to write test results because of the filepath length, I do not believe this issue is a duplicate and will leave it open until resolved.


Set Refresh Interval for Feeds

Is your feature request related to a problem? Please describe.
Each feed's refresh interval is 5 minutes, which I think it's a bit much. I don't want to get rate limited from a few of my feeds because of the numerous refreshes.
Describe the solution you'd like
It would be nice to set a refresh interval for each feed.

This is tricky because CommaFeed is at its core multi-user. A feed is only fetched once for all users that are subscribed to that feed, so a setting like that could not be set by a user, because it will have an impact on other users.
Since 5.0.0, there is however the new commafeed.feed-refresh.interval-empirical setting that will make the feed engine refresh feeds at intervals based on the feed's activity.

How do I use/change the setting?

See the Configuration section of the README


Add animated flip card using reactjs

Project title
Animated flip card
Project Description
I want to add animated flip card using reactjs that would be userfriendly.
Stack:
And in order to tick the check box just but x inside them for example - [x] like this. Please delete options that are not relevant.

[] Html
[] CSS
[] JavaScript
[] ReactJS

/assign

can you please assign me this issue?

@Tanuj-rai assigned to u

@Tanuj-rai are you working on this PR or I close this?


Some Micro keybindings don't work in Windows Terminal.

Description of the problem or steps to reproduce
Specifications

Im using Micro in Windows Terminal, but not all keybindings work.
Commit hash:
OS: Win10
Terminal: Windows Terminal

Duplicate of           python -m pip install --upgrade meson jinja2
.


Generate notifications when a user is mentioned in a whisper

This fixes a bug where the whisper type was not checked correctly, and
a notification would not be generated if there was a mention on a whisper.
Change-type: patch
Signed-off-by: Lucian Buzzo<EMAIL_ADDRESS>

@balena-ci I self-certify!


Echo bad id names in not-found error message

When one or more ids in a manifest file are not found, include the ids of the bad datasets.
One or more ID's in the manifest file were invalid....
Should be: The following IDs were not found: Then list the ids.

@shirey If say 2 id's out of 10 are incorrect, should the 8 that are correct proceed and the user can try again with the 2 that were unsuccessful, or should it not proceed until every id is valid


Updated from v6 to v9 ... allow: geolocation no longer works for iFrames

I am using
  attributes: {
    iframe: {
      allow: 'geolocation'
    }
  },

in my iFrame, and it was working ... but then due to another issue I was having rendering remote popups, I realized I had inadvertently gotten V6 when I did yarn add zoid .... because it was the latest one tagged i guess.
Hoping this would solve my popup problems, I updated zoid to v9.0.42 and then ran the same code.  I had to fix the react driver issue by using import * as zoid from 'zoid/dist/zoid.frameworks.js' but that was the ONLY code I changed.
When I reloaded my test parent page and clicked a button in my child component that initiated geolocation check, I received:
Error Code = 1 - Geolocation has been disabled in this document by Feature Policy.
in my browser console.
I checked the iFrame element and it does render with allow="geolocation" but that seems not be working anymore.
Any thoughts on why this might be?
Reverting to v6 for now....

Hmm. All zoid does is add the attribute. Struggling to think of a reason why the new version would change anything here...

Any updates? Just noticed chrome 90 will rename Feature Policy is to Permissions Policy in here, I have a geolocation call inside the iframe, and it's not working anymore, the error message now is Geolocation has been disabled in this document by permissions policy.


Route caching breaks the Model Route Binding in Middleware

Laravel Version: 7.2.1
PHP Version: 7.4.0
Database Driver & Version: MySQL 8.0.18

Description:
After caching the routes using artisan route:cache the Route Model Binding does not work.
Steps To Reproduce:
Route:
Route::group(
    [
        'prefix' => '{country}',
    ],
    function () {
        Route::get('/', 'PageController@homepage')->name('homepage.country');
    }
);

Middleware:
public function handle($request, Closure $next)
    {
        $country = $request->route()->parameter('country');
        $request->attributes->set('country', $country);

        return $next($request);
    }

Controller:
   public function homepage(Request $request)
    {
        /** @var Country|null $country */
        $country = $request->attributes->get('country');

        dd($country);
    }

RouteServiceProvider:
        Route::bind(
            'country',
            function ($value) {
                return Country::where('slug', $value)->firstOrFail();
            }
        );

Before caching (country is an Object):

After running php artisan route:cache (country is just the slug):

Where is that middleware placed in the stack?

It's the last in the web group:
            'web' => [
                EncryptCookies::class,
                AddQueuedCookiesToResponse::class,
                StartSession::class,
                // \Illuminate\Session\Middleware\AuthenticateSession::class,
                ShareErrorsFromSession::class,
                VerifyCsrfToken::class,
                SubstituteBindings::class,
                ResolveCountry::class, // <----
            ],

I can't recreate this on 7.2.2.

I can't recreate this on 7.2.0 either.

Just updated to 7.2.2 and still getting the same results.

Well, your best bet might be to write a failing test for the framework then. I can't recreate it.

Getting a similar issue with route caching ever since Laravel 7
Link to Laracasts

I'm able of reproducing this issue, but it's unrelated to middleware. I'm using explicit route model bindings and, after caching, it raises an exception because apparently the model bindings cease to work.
In the web routes:
Route::model('user', App\User::class);
Then a bit further down:
Route::get('profile/{user}', 'UserController@showProfile');
When route caching isn't used, the profile page loads fine. When route caching is used, this exception is raised instead:
must be an instance of App\User, string given
Looking at the route parameters in the exception page reveals that it simply passsed the "user" portion as the int, rather than resolving it to the User model (the string mentioned in the exception is the user ID, "123").

Looking again at the documentation, this is probably because my model binding is being declared inside the routes file itself, rather than in RouteServiceProvider, but I honestly wasn't expecting it to blow up like this once routes were cached.

Closing this as we can't reproduce this.

In case it can help someone else in the future, I had a similar issue which was caused by a Laravel Shift moving my custom route bindings into the route registration callback:
public function boot()
{
    $this->configureRateLimiting();
    $this->routes(function() {
        // If you put Route::bind() calls here, they won't work when routes are cached.
        Route::prefix('api')
            ->middleware('api')
            ->namespace($this->namespace)
            ->group(base_path('routes/api.php'));

        Route::middleware('web')
            ->namespace($this->namespace)
            ->group(base_path('routes/web.php'));
    }
}

Which works, but only if you don't cache the routes.
Moving the bindings to the "root" of the boot method fixed it, but it took a while to figure out why.

RouteServiceProvider

wow thanks, I was scratching my head for hours trying to figure out what the issue was. this feels like something that should be documented more clearly

In case it can help someone else in the future, I had a similar issue which was caused by a Laravel Shift moving my custom route bindings into the route registration callback:
public function boot()
{
    $this->configureRateLimiting();
    $this->routes(function() {
        // If you put Route::bind() calls here, they won't work when routes are cached.
        Route::prefix('api')
            ->middleware('api')
            ->namespace($this->namespace)
            ->group(base_path('routes/api.php'));

        Route::middleware('web')
            ->namespace($this->namespace)
            ->group(base_path('routes/web.php'));
    }
}

Which works, but only if you don't cache the routes. Moving the bindings to the "root" of the boot method fixed it, but it took a while to figure out why.

wow thanks, I was scratching my head for hours trying to figure out what the issue was. this feels like something that should be documented more clearly!

I have same issue on Laravel 10, after cache. all my bind from RouteServiceProvider not available.


docker-compose-nexus-mongo-no-secty.yml can't run normally

Retrieve and run the nightly-build docker-compose-nexus-mongo-no-secty.yml
core-metadata, support-scheduler, and device-virtual are trying to use redis database, the error logs are as following:

level=ERROR ts=2020-02-24T05:39:06.27239039Z app=edgex-core-metadata source=handler.go:47 msg="Could not dial Redis: dial tcp: lookup edgex-redis on <IP_ADDRESS>:53: no such host"

level=ERROR ts=2020-02-24T05:38:51.898329218Z app=edgex-support-scheduler source=init.go:63 msg="Failed to load schedules and events Could not dial Redis: dial tcp: lookup edgex-redis on <IP_ADDRESS>:53: no such host"

level=ERROR ts=2020-02-24T05:39:06.275358333Z app=device-virtual source=service.go:149 msg="DeviceServicForName failed: 500 - Could not dial Redis: dial tcp: lookup edgex-redis on <IP_ADDRESS>:53: no such host\n"

This issue is currently affecting blackbox testing.


fix: use chain specific net dir for known peers

Adds a chain specific net dir using #1846. Tested locally:
2023-04-13T00:38:19.323306Z  INFO Wrote network peers to file peers_file="/Users/dan/Library/Application Support/reth/net/mainnet/known-peers.json"

Codecov Report

Merging #2218 (5dc5f60) into main (703f0fd) will decrease coverage by 57.01%.
The diff coverage is 0.00%.

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@@             Coverage Diff             @@
##             main    #2218       +/-   ##
===========================================
- Coverage   71.98%   14.97%   -57.01%
===========================================
  Files         471      471
  Lines       56526    56528        +2
===========================================
- Hits        40690     8465    -32225
- Misses      15836    48063    +32227

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Coverage Δ

integration-tests
14.97% <0.00%> (-3.43%)
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Impacted Files
Coverage Δ

bin/reth/src/args/network_args.rs
0.00% <0.00%> (-5.46%)
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bin/reth/src/node/mod.rs
0.00% <0.00%> (-7.32%)
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... and 349 files with indirect coverage changes
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