Pack project PRI next to WinMD

Today, C++ apps that try to use XAML/WinUI types authored with C#/WinRT fail at runtime because they can't find the xaml resources.
E.g.
WinRT originate error - 0x80004005 : 'Cannot locate resource from 'ms-appx:///WinUIComponentCs/NameReporter.xaml'.'.
Evelyn and I found that the component's PRI file needs to live in same folder as the WinMD.
This PR updates our build logic to reflect this requirement.
Fixes #1118

Confirmed that both C++ and C# apps that use nuget references to authored components work with custom user controls.


Tray support?

Is there any plan to support the tray? As far as I know, the tray has not been implemented yet. That's the only thing stopping me from using this bar.

I'm currently focused on resolving some bugs.
My next step will be to update the codebase to iced 13 and add the multi-monitor support.
I never thought about implementing the tray because I personally didn't use it :sweat_smile:
What do you have in mind in terms of functionality and UI/UX?

Oh, trey, this is a good offer, even though I don't use it myself, but I think it will be very useful to many!

The tray is very necessary for some background programs. for example, I have set up synchronization of some files via Seafile. clicking on the icons with the right mouse button also usually brings up a list of quick useful actions.

I made a list of possible features. It's a lot of stuff and I don't know if I have enough time to implement and maintain everything.

show icons of running programs
show icons of "pinned" programs (even when they are not running and indicate when they are running)
"pin" a program via a config file (plus: maybe adding a way to pin/unpin a program directly from the tray bar)
left-click on an icon will launch the program if is not running otherwise,

only one instance: it will focus the program window
multiple instances: open a menu to select the instance

right-click to open a menu with some options.

show all the possible instances with the corresponding close button
open a new instance
close every instance

To get the list of active programs I could use hyprland but it will not display the list of background programs. For that, I don't know how to do

@MalpenZibo
The tray does not have to perform so many functions. It should show applications running in the background, allow you to kill a process or open a window, and so on. There is a protocol for implementing the tray, you do not need to use hyprland here, perhaps you can look at an example of the implementation in waybar

I only found this: https://github.com/elkowar/eww/blob/master/crates/eww/src/widgets/systray.rs but it's not a fact that this is what is needed, I'm not very good at rust

I also found this. https://github.com/vars1ty/HybridBar/blob/main/src/widgets/tray_widget.rs

It's just that each program provides its own tray icon.

I'm currently working on this feature. I found everything necessary to implement the dbus interface but right now I don't have too much time so it could take a while

Here some progress

It could take I while before finish this feature. Right now I'm able to get the application Icon of every application registered to the dbus interface. I still need to implement the application menu, maybe I could split the implementation into two different PR and complete the icon visualization first

I think I need to buy a better monitor)

Well, Linux's tray support status is not in great shape. Even in my setup, the Slack icon is quite blurry, which seems to be a common issue.
I'll try my best but I receive the pixel data directly from the dbus interface and the quality is not so great.

Oh, sad, but not critical

I got a slightly better result installing this arch package libappindicator-gtk2 and libappindicator-gtk3. But I'm not sure that can resolve anything, I just have tried this arch linux wiki suggestion https://wiki.archlinux.org/title/KDE#Blurry_icons_in_System_tray (see chapter 6.7.9 Blurry icons in system tray)


The use of Data Element /DMO/AGENCY_ID is not permitted.

After importing and activating the packages this Error comes up in my ADTs when trying create a database table.
This message appears at every line where I try to use a data element of the flight package.
What's wrong?

Hello,
can you specify the type of system you are using (SAP BTP ABAP Environment, SAP S/4HANA Cloud private edition, SAP S/4HANA Cloud public edition)?
Which software component / language version was used for installing the /DMO/ sample content?
Which software component are you using for your table zrap100_atrav000 ?
The error you are getting indicates that you are not allowed to access the /DMO/ artifacts. This can happen if you have installed the /DMO/ content in a package that uses the language version "Standard ABAP". Or you are trying to consume the /DMO/ artifacts in a package that is not having "ZLOCAL" as the software component.
Best Regards, Volker

Hello,
thanks for your reply.

Im working on S4HANA ON PREMISE.
This is the package with the sample content
This is the package where table zrap100_atrav000 is installed:

There is no Superpackage in my /DMO/FLIGHT.

 The /DMO/FLIGHT has Standard ABAP as Language Version.
Can I simply change it or do I have to remove the package and install it again in a package with ABAP for Cloud Development set?
Thank you for your help!
Best regards
Chris

Hello Chris,
thanks for the detailed explanation.
Changing the language version of a package doesn't change the language version of the contained artifacts. It only defaults the language version for new artifacts.
In your case I'd propose to work in a Z-package that is based on the software component LOCAL. WIth this you work entirely in language version "Standard ABAP" and should not get the initially mentioned errors.
Best Regards, Volker

It works now! Thank you for your support.
In my case there was no LOCAL Package created yet. So I created one named $LOCAL and set up "Standard ABAP" as language version. After that I created my tutorial package in this $LOCAL package, so both (/DMO/FLIGHT and Tutorial Package) are on the same language version.
As VolkerDrees already mentioned, I accidentally set up the /DMO/FLIGHT Package as "Standard ABAP" language version. Problem with that is that this Language version has waaaay less syntax restrictions as the "ABAP Cloud Development" language version. Sooo if you install the Flight data with this language version, you can't later access it through a package what is installed with the "ABAP Cloud Development" language version, due to high syntax restrictions.

Hi @VolkerDrees , I am using ABAP Trail 2022 system.
I have created z* package with software component as local but still facing similar issue.

But there was another ztable created in same Zpackage yesterday for August CDS challenge activated with out any issue.
Could you please let me know what is the issue and how I can practise ABAP trail system with custom objects.

Hello babu4abap,
in the language version "ABAP for Cloud Development" you can only access released artifacts or artifacts belonging to the same software component. I assume the package /DMO/FLIGHT in your case has a different software component - and therefore the problem occurs. So either make sure to use the same software component in both packages, or switch to "Standard ABAP" as the language version.


replace main to mainClass in docs

Outdated samples has been updated
groovy dsl:

kotlin dsl:

Messages

:book:

Thank you very much for making our website better :heart:!

  Generated by :no_entry_sign: dangerJS against bfa1cbed1ba8304791a0ef30a7904d883dc38179


Uncompatible with Foundry version 10

According to the error: Metadata validation failed for module "healthEstimate": The file "lib/color-picker.min.js" included by module healthEstimate does not exist.
Hope this helps!

Have you tried updating Health Estimate after you upgraded to V10? I fixed that issue at the start of the V10 Prototype https://github.com/mclemente/healthEstimate/commit/899109758e6b97b896b890f8743555fcc37bf2a3

Unfortunately Health Estimate isn't even available in my list of modules. And reinstalling always brings be back to that error.

Everything is working fine now, thanks for your help!


libva-vdpau-driver rename from vaapiVdpau breaks common-gpu-nvidia when config.allowAliases is false

This PR https://github.com/NixOS/nixpkgs/pull/317542 breaks the use of nixos-hardware.nixosModules.common-gpu-nvidia-nonprime If you are using config.allowAliases of false. Assume that we need to use the new libva-vdpau-driver name:
Related partial stack error when using config.allowAliases of false:

       error: undefined variable 'vaapiVdpau'

       at /nix/store/s4q588p6gh8jnibxcd67xc1h2g10baps-source/common/gpu/nvidia/default.nix:7:5:

            6|   hardware.graphics.extraPackages = with pkgs; [
            7|     vaapiVdpau
             |     ^
            8|   ];

Can you make a pull request request that uses the new package if it's present?
(if pkgs ? libva-vdpau-driver then pkgs.libva-vdpau-driver else pkgsvaapiVdpau)

@Mic92 Absolutely. PR submitted here: https://github.com/NixOS/nixos-hardware/pull/1018


Having EXTENT in method definition parsed as an error

class OpenEdge.DataAdmin.Rest.RestQueryFactory implements IRestQueryFactory:
    method private char extent GetNamedQuery(pcname as char) :
        define variable cInfo as character extent no-undo.
        return cInfo.
    end.
end class.

Registered: https://github.com/usagi-coffee/tree-sitter-abl/issues/96

Fixed with version 0.0.44 of tree-sitter-abl


Renaming changes to remove instances of Metric from class names to co…

Description
Renaming changes to adhere to Metric Spec/Go-SDK
All instance of MetricRecord were renamed to ExportRecord.
All instances of MetricExporter were renamed to Exporter.
Reason: The spec and Go-SDK it was based on refers to the metric exporter uses that naming.
Concern: Not sure how OpenTelemetry Python (docs)[https://opentelemetry-python.readthedocs.io/en/stable/getting-started.html] is updated so this might briefly introduce some naming inconsistencies.
Relates to issues #1375 and #1307
Type of change

[x] Spec Compliance (non-breaking change which further complies sdk with spec)
[x] This change requires a documentation update

How Has This Been Tested?
I ran tox to make sure nothing had been accidentally broken.
Checklist:

[x] Followed the style guidelines of this project
[x] Unit tests have been added
[x] Documentation has been updated

Filed upstream


Feature/add k8s event interpretation

Currently, Acto waits for system convergence by observing a silence of K8s events within a configured
time window. Such a wait of convergence is performed after applying each test case to ensure that the
collected states reflect the system's final states. However, such a wait can be futile when the input CR
carries faulty mutations (ie. invalid container image urls) or represents un-schedulable resources given the
test cluster topology and hardware constraints. In these cases, the cluster gets stuck in an unhealthy state
and corresponding K8s events will not cease to exist, causing Acto to wait until the configured timeout
(480 seconds by default). Therefore, this PR aims to interpret K8s events received during the
convergence wait and identify opportunities for aborting the wait to accelerate the total test runtime.

@Robertation256 Thanks for the PR! Could you follow this doc to format the code? https://github.com/xlab-uiuc/acto/blob/main/docs/development.md
If you use vscode, installing the black, pylint, and isort should be enough. The github pre-commit hook can also perform the tasks

@Robertation256 Thanks for the PR! Could you follow this doc to format the code? https://github.com/xlab-uiuc/acto/blob/main/docs/development.md
If you use vscode, installing the black, pylint, and isort should be enough. The github pre-commit hook can also perform the tasks

Done!


Changes suggested not working Error: NotImplemented: Support for codec 'zstd' not built

Describe the usage question you have. Please include as many useful details as  possible.
I'm trying to follow the  visiumhd_analysis_vignette.Rmd.
While trying to run '''object <- Load10X_Spatial(data.dir = data_dir, bin.size = c(8,16))'''
I'm getting the following error
Error: NotImplemented: Support for codec 'zstd' not built
In order to read this file, you will need to reinstall arrow with additional features enabled.
Set one of these environment variables before installing:

Sys.setenv(LIBARROW_MINIMAL = "false") (for all optional features, including 'zstd')
Sys.setenv(ARROW_WITH_ZSTD = "ON") (for just 'zstd')

See https://arrow.apache.org/docs/r/articles/install.html for details
Even when i follow the instrucutions and try and use the Sys.setenv and install the package, its still giving me the same error. How do i overcome this?
Component(s)
R

Which OS are you on? I assume MacOS which means you get binaries by default and will need to add type="source" when installing arrow.

Which OS are you on? I assume MacOS which means you get binaries by default and will need to add type="source" when installing arrow.

I am on a macos,  Should it take a long time to download from source? I have tried that and the package wont install from, it just freezes at this point

install.packages("arrow", type = "source")
trying URL 'https://cran.rstudio.com/src/contrib/arrow_16.1.0.tar.gz'
Content type 'application/x-gzip' length 4439675 bytes (4.2 MB)
==================================================
downloaded 4.2 MB

installing source package ‘arrow’ ...
** package ‘arrow’ successfully unpacked and MD5 sums checked
** using staged installation
*** pkg-config not found.
*** Found local C++ source: 'tools/cpp'
*** Building libarrow from source
For build options and troubleshooting, see the install guide:
https://arrow.apache.org/docs/r/articles/install.html
**** cmake 3.26.4
**** pkg-config not installed, setting ARROW_DEPENDENCY_SOURCE=BUNDLED
****  S3/GCS  support  requires version >= 1.0.2 of openssl-devel (rpm), libssl-dev (deb), or openssl (brew) ; building with  ARROW_S3=OFF and ARROW_GCS=OFF
**** arrow

Which OS are you on? I assume MacOS which means you get binaries by default and will need to add type="source" when installing arrow.

I've tried that and im still getting the same errors

installing from source
installing to /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/library/00LOCK-arrow/00new/arrow/libs
** R
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
** building package indices
** testing if installed package can be loaded from temporary location
** checking absolute paths in shared objects and dynamic libraries
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path

DONE (arrow)

The downloaded source packages are in
‘/private/var/folders/zh/2vp0syx15h1bpjmnr_jfn1j5y0bfr3/T/RtmpNVwsps/downloaded_packages’

data_dir <- file.path("filepath")
object <- Load10X_Spatial(data.dir = data_dir, bin.size = c(8,16))
Error: NotImplemented: Support for codec 'zstd' not built
In order to read this file, you will need to reinstall arrow with additional features enabled.
Set one of these environment variables before installing:

Sys.setenv(LIBARROW_MINIMAL = "false") (for all optional features, including 'zstd')
Sys.setenv(ARROW_WITH_ZSTD = "ON") (for just 'zstd')

See https://arrow.apache.org/docs/r/articles/install.html for details

AHH!! I think its working, thank you very much


Training process terminated with exit code 139 if add_image called inside training_step

🐛 Bug
A call to self.logger.experiment.add_image inside trainer.training_step terminates the training process.
Similar code inside trainer.on_train_batch_end or trainer.on_train_epoch_end works as expected.
To Reproduce
import os
import torch
from torch import nn
import torch.nn.functional as F
from torchvision import transforms
from torchvision.datasets import MNIST
from torch.utils.data import DataLoader, random_split

import pytorch_lightning as pl

class DataModule(pl.LightningDataModule):
    def __init__(self):
        super().__init__()

    def train_dataloader(self):
        ds_train = MNIST(os.getcwd(), download=True, transform=transforms.ToTensor())
        dl_train = DataLoader(ds_train, num_workers=6)
        return dl_train

    def val_dataloader(self):
        return self.train_dataloader()

class LitAutoEncoder(pl.LightningModule):
    def __init__(self):
        super().__init__()
        self.encoder = nn.Sequential(nn.Linear(28 * 28, 64), nn.ReLU(), nn.Linear(64, 3))
        self.decoder = nn.Sequential(nn.Linear(3, 64), nn.ReLU(), nn.Linear(64, 28 * 28))

    def forward(self, x):
        embedding = self.encoder(x)
        return embedding

    def training_step(self, batch, batch_idx):
        x, y = batch
        x = x.view(x.size(0), -1)
        z = self.encoder(x)
        x_hat = self.decoder(z)
        loss = F.mse_loss(x_hat, x)

        # =========>>>> work as expected
        self.log("train_loss", loss)

        # =========>>>> work as expected (though do not make sense)
        self.logger.experiment.add_text('my_text', f'abcdef{loss}')

        # =========>>>> following code fails
        # =========>>>> Process finished with exit code 139 (interrupted by signal 11: SIGSEGV)
        self.logger.experiment.add_image('my_image', batch[0][0], self.global_step)

        return loss

    def on_train_batch_end(self, outputs, batch, batch_idx) -> None:
        # =========>>>> similar code works here
        self.logger.experiment.add_image('my_image', batch[0][0], self.global_step)

    def configure_optimizers(self):
        optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
        return optimizer

def main():
    autoencoder = LitAutoEncoder()
    trainer = pl.Trainer(
        accelerator="gpu",
        devices=1,
        log_every_n_steps=10
    )

    dm = DataModule()
    trainer.fit(model=autoencoder, datamodule=dm)

if __name__ == '__main__':
    main()

Expected behavior
According to documentation logging images inside trainer.training_step should work.
Environment

CUDA:

GPU:

NVIDIA GeForce RTX 3090

available:         True
version:           11.3

Packages:

numpy:             1.21.6
pyTorch_debug:     False
pyTorch_version:   1.11.0+cu113
pytorch-lightning: 1.6.0~1.6.3
tqdm:              4.64.0

System:

OS:                Linux
architecture:

64bit
ELF

processor:         x86_64
python:            3.9.11
version:           20.04.1-Ubuntu SMP Mon Apr 4 09:38:31 UTC 2022

cc @otaj @awaelchli @edward-io @rohitgr7

self.logger.experiment.add_image('my_image', batch[0][0], self.global_step)

is it terminating right at the first call or after some time? I tried your example for 1 epoch and it's working for me.

It is the add_image inside training_step that causes the problem.  (I just update the sample so the problem-related line is now uncommented.)
The crash happens at batch 2 or batch 3, which is almost at the first call.

I use gdb to trace the problems:
>>> gdb python
(gdb) run c.py

and get the following report:

@rohitgr7 I just find a closely related issue that has been closed #11925.

yep! I did uncomment the line and ran it and it worked. Maybe some issue with your matplotlib installation?
Also tried on colab, and it seems to be working there.

I get three servers at hand and two of them reproduce this crash. The third server and the colab work fine.
The crash is not related to the python version (tested on 3.7-3.9), GPU availability, or lightning version (1.6.0-1.6.3).
Seems that there is something wrong with Pillow.
Pillow > 9.0.1 breaks tensorboard writer and reducing it to 8.4.0 solves the problem.
Will investigate more tomorrow.

After some checks, find that the only problematic Pillow version is 9.1.0.
Pillow == 8.4.0, 9.0.0 and 9.0.1 works fine for me. Other lower version is not compatible with torchvision==0.12.0 so not tested.
Wrapping the logging code with with torch.no_grad(): does not stop the crash.
The behavior is rather consistent across all my servers and Colab. The crash happens near step 2 or step 33.
The servers crash with either exit code 139 (interrupted by signal 11: SIGSEGV) or Segmentation fault (core dumped).
Colab kernel breaks and requires a start.

Thanks to @semaphore-egg
downgrade Pillow version from 9.1.0 to 9.0.1 solved my problem.


Onboard Camel K Tekton tasks

We should onboard a Camel K task on Tekton hub in order to generalize the concepts exposed in Camel K CICD blog. Ideally we should rework the task provided in this example in order to let the user leverage kamel cli in any kind of pipeline work.

This work is a prerequisite to enable an external pipeline for #4281


Error at boot-up trying to upgrade to PEX 2.0

I tried to update my server from PEX 1.x to PEX 2.0 and I get two errors at bootup and PEX 2.0 is not running. Hes my whole log of the bootup:
log.txt

I have tried build 146 still don't convert anything or even load. If I delete the PermissionsEx folder it loads fine, just get set my player as OP or admin. If I have to rebuild my perms that's fine I was going to go over the whole thing to make work better, get rid or redundant perms and make sure perms are in the correct group and assigned to correct world.

[23:32:48] [Server thread/WARN]: **** SERVER IS RUNNING IN OFFLINE/INSECURE MODE!

As long as you're getting this error, PEX will not function correctly as offline mode is not supported.
If you're using Bungeecord to connect multiple servers, you need to go to their documentation and find out how to set the server up correctly for that offline mode requirement.


Create notebook to reserve and configure resource for the experiment

In reserve.md, add material to:

reserve and configure a resource following the pattern of this example for format, section titles, etc.
but instead of a regular bare metal server, reserve a GPU server as in this example
and use the CC-Ubuntu22.04-CUDA disk image instead of CC-Ubuntu20.04
also, install the specific libraries needed for this material
and of course, retrieve this repo at the end.

Review comments:

Write notebook markdown inside cells, use Makefile to convert to notebook, run and test
Notebook markdown should not include output
The reason for using a CUDA image is so that the student should not need the whole installation process, if it is not necessary. So we should see if it works using the CUDA image with minimal extra installs.
Can keep the line that tests if TF can see the GPU -

node.run('python3 -c \'import tensorflow as tf; print(tf.config.list_physical_devices("GPU"))\'')

GPUs very not visible without the installation of libcudnn8=<IP_ADDRESS>-1+cuda12.2

Review -

project name should not be included in commit version
add a note "installing libcudnnX because it is the most recent one supported by TFY"
install specific library versions
clarify the instruction "Make sure that 8888 port on your local machine is free before running this command." Instead, say something like "if you get the error message X it means Y, and you should then do Z"
retrieve materials onto the jupyter server (and start the jupyter server from that directory) following the example of https://github.com/teaching-on-testbeds/deep-nets-reproducing/blob/main/reserve.ipynb ("retrieve the materials" and "Run a JupyterHub server")


Update README

Is there a way to keep the examples on the class path an link / embed them in the README?
That would allow to compile them with the project and make sure they reflect API changes.

@culmat Thanks, and sorry for the (very) long delay!

Is there a way to keep the examples on the class path an link / embed them in the README?

Not sure, I'm going to try some things...


story board add volunteer view

it will be in the paper

@saihati Can you attach your storyboard view please?


A valid Caffe installation was not found on your system. Use the envvar CAFFE_ROOT to indicate a valid installation.

# when I input ./digits-devserver, an error occur,
# this is the error information, my caffe is in /home/mx/caffe/
A valid Caffe installation was not found on your system.
Use the envvar CAFFE_ROOT to indicate a valid installation.
Traceback (most recent call last):
File "./digits-devserver", line 8, in
import digits.config
File "/home/mx/digits/digits/config/init.py", line 7, in
from . import caffe
File "/home/mx/digits/digits/config/caffe.py", line 227, in
executable, version, flavor = load_from_path()
File "/home/mx/digits/digits/config/caffe.py", line 51, in load_from_path
raise ValueError('Caffe executable not found in PATH')
ValueError: Caffe executable not found in PATH

Hello, with #1091 merged now you need to use environment variables to configure DIGITS. The environment variables are listed there. In your case you need to set the CAFFE_ROOT variable: export CAFFE_ROOT=/home/mx/caffe/.

Thanks very much ,but every time I use digits,I must input CAFFE_ROOT=/home/mx/caffe/ in terminal,
maybe I should write the environment variables into a file, how to do it?

# These instructions are for bash
$ echo $SHELL
/bin/bash

# Check the current value of your envvar
$ echo $CAFFE_ROOT

# Add the envvar to ~/.profile so it will load automatically when you login
$ echo "export CAFFE_ROOT=/home/username/caffe/" >> ~/.profile

# Load the new configuration
$ source ~/.profile

# Check the new envvar value
$ echo $CAFFE_ROOT
/home/username/caffe/

If you need more help or if you use a different shell, you can probably find what you need via Google. Setting environment variables is a good skill to learn.

I am having the same problem, however I have completed these steps and I have CAFFE_ROOT pointing to the correct place. https://github.com/NVIDIA/DIGITS/issues/1390

Did you install with a deb package? If so, please follow these instructions for changing the configuration:
https://github.com/NVIDIA/DIGITS/blob/digits-5.0/docs/UbuntuInstall.md#troubleshooting

In my case, when I try to set a CAFFE_ROOT value, it says my path is not valid for some reason.
And CAFFE_ROOT is empty.  I installed my caffe through aptitude dependencies of CUDA, and I need to upgrade it to 0.16v in order to fix a GPU memory error.

I have installed all of the prerequisites. I have the CAFFE_ROOT in /home/user/caffe which is a path.
| _ / | | / |
| |) | | ( || | | | _
|/|| || |___/ 6.1.1
A valid Caffe installation was not found on your system.
Use the envvar CAFFE_ROOT to indicate a valid installation.
Traceback (most recent call last):
File "/usr/lib/python2.7/runpy.py", line 174, in _run_module_as_main
"main", fname, loader, pkg_name)
File "/usr/lib/python2.7/runpy.py", line 72, in _run_code
exec code in run_globals
File "/home/mikulis/digits/digits/main.py", line 70, in
main()
File "/home/mikulis/digits/digits/main.py", line 53, in main
import digits.config
File "digits/config/init.py", line 7, in
from . import ( # noqa
File "digits/config/caffe.py", line 230, in
executable, version, flavor = load_from_path()
File "digits/config/caffe.py", line 54, in load_from_path
raise ValueError('Caffe executable not found in PATH')
ValueError: Caffe executable not found in PATH
thank you so much,
eric


Renaming cleanup

Summary of Changes
General app cleanup:

Group react components under components/ folder
Group helpers into utils
Group provider and context related files into `store/' folder
Align file renaming (camelCase for .ts files, PascalCase for .tsx)

Related Issues
Pull Request Checklist
This is just a reminder about the most common mistakes. Please make sure that you tick all appropriate boxes. But please read our contribution guide at least once; it will save you a few review cycles!
If an item doesn't apply to your pull request, check it anyway to make it apparent that there's nothing to do.

[ ] All commits contain a DCO Signed-off-by line (we use the DCO GitHub app to enforce this).
[ ] Updated LICENSE-3RD-PARTY.md for any added dependencies or vendored components.
[ ] Run prettier: npm run style-format
[ ] Updated documentation for changed code and new or modified features.

If you have any questions to any of the points above, just submit and ask! This checklist is here to help you, not to deter you from contributing!
PR template adapted from the Python attrs project.

These two files are automatically generated by Xcode. Better not to apply LINTing to them or change them else we'll just bounce back and forth between them. We should add ios/ and android/ to the LINT/Prettier exclusion.
ios/Media.xcassets/Contents.json
ios/Media.xcassets/AppIcon.appiconset/Contents.json

I'll revert these. Seems to get picked up by Linter and Prettier. Might need to add a exclusion for these.


Datapoint -> VisionTensor; datapoint[s] -> vision_tensor[s]

Alternative to #7894

Closed by https://github.com/pytorch/vision/pull/7894


Improve documentation of name in from_array

This attempts to explain what the advantages and disadvantages of
name=False, and more strongly encourage its use. Motivated by #7208.
I also fixed some typos along the way.
[skipci]

[x] Passes black dask / flake8 dask

@jsignell might be of interest to you since you were looking at #7208.


fix: fix Layout wording for SM sizes

Overview

PR Checklist

[ ] Related to designs:
[ ] Related to JIRA ticket: ABC-123
[ ] I have run this code to verify it works
[ ] This PR includes unit tests for the code change

📬Published Alpha Packages<EMAIL_ADDRESS>
🚀 Styleguide deploy preview ready!https://60ca6696c9bd1e00c9abb19e--gamut-preview.netlify.appDeploy Logs

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HarmonicBalance fail to be installed on mac device with arm64 cpu

This happens bc  an old version of ffmpeg package is used. This issue can be resolved by simply install<EMAIL_ADDRESS>first then install HarmonicBalance.
Related: https://github.com/JuliaIO/FFMPEG.jl/issues/58

This would be solved if Plots was put in an extension.


az group create... --location westus failed

Problem encountered on https://dotnet.microsoft.com/learn/aspnet/deploy-microservice-tutorial/azure-resources
Operating System: windows
azure cli command:
az group create --name myMicroserviceResources --location westus
failed with:

request failed: Error occurred in request., ConnectionError: HTTPSConnectionPool(host='management.azure.com', port=443): Max retries exceeded with url: /subscriptions/4142177e-d22e-44a9-a424-fc9dac2789a6/resourcegroups/myMicroserviceResources?api-version=2019-07-01 (Caused by NewConnectionError('<urllib3.connection.VerifiedHTTPSConnection object at 0x04FDD430>: Failed to establish a new connection: [Errno 11001] getaddrinfo failed',))

worked when I did:
az group create --name myMicroserviceResources --location eastus
After following this.

The .NET team does not own Azure CLI - have you tried contacting Azure support regarding this?

Hi, not I just clicked on the 'I ran into an issue' button during the tutorial.  Is there a contact url or something?

Contacts are listed at https://azure.microsoft.com/en-us/support/options/. If you're seeing problems region-wide in westus, create a support ticket (blue box near bottom-left of that page). There's also the less formal route of using social media to contact their support team (black box near bottom-right of that page).
For Azure CLI bugs and issues, check out the issue tracker at https://github.com/Azure/cli.

I'm not familiar with the az tools to know this for certain but the error may be an issue with your DNS server, not Azure ("getaddrinfo failed").

Given that it worked for eastus, I think we should close this issue.


Add build docs check

make html SPHINXOPTS="-W" to treat warnings as errors

adding the build_docs check from https://github.com/Infleqtion/server-superstaq/blob/main/.github/workflows/ci.yml to client

Added check, but not sure if this should be closed as warnings are not being treated as errors yet


Implement assumeThat() static method to replace JUnit implementation

Summary
I'd like to use all of the benefits of fluent assertJ style assertThat() method with assumeThat() method, with as it's couterpart in JUnit would through AssumptionViolatedException and allow to skip test execution on failed assumptions.
The reason is that I use that kind of assumptions pretty heavily and don't want to depend on JUnit specifically. There is great power in AssertJ already, and I'd like it to be available not just for assertions, but also for the assumptions.
Looks like this work was already initiated at org.assertj.swing.junit.util.Assumptions.assumeThat, but as for me it's proper place is in assertj-core library.
Example
assumeThat(someString).isNotBlank();

Java 8 specific ?

NO  : create Pull Request from the 2.x branch

I definitely like the idea, I want to make sure though what the scope is (is that replicating all assertions in an assumeThat fashion ?) to avoid maintenance costs.

this was also suggested in https://github.com/joel-costigliola/assertj-core/issues/266

At bare minimum I'd go with what JUnit has:
static void	assumeFalse(boolean b)
static void	assumeNoException(Throwable e)
static void	assumeNotNull(Object... objects)
static void	assumeNull(Object... objects)
static void	assumeTrue(boolean b)

Most importantly, ability to work with custom org.assertj.assertions.core.Condition in the fashion of is(), has(), and for collections - are(), allOf(), anyOf(), - will unlock assumeThat() for most purposes, and later based on usage we would be able to figure out which other methods are to be on priority list.

@falkoner good point about supporting conditions.
I'm ok to add this feature but we need to find a way to avoid hard dependency on JUnit (I might have idea for that).

static void	assumeFalse(boolean b)
static void	assumeNoException(Throwable e)
static void	assumeNotNull(Object... objects)
static void	assumeNull(Object... objects)
static void	assumeTrue(boolean b)
Most importantly, ability to work with custom org.assertj.assertions.core.Condition in the fashion of is(), has(), and for collections - are(), allOf(), anyOf(), - will unlock assumeThat() for most purposes, and later based on usage we would be able to figure out which other methods are to be on priority list.


Performance when moving the/zooming the graph

The example you provide in the Readme shows serious performance issues (tested on Chrome 60 and Firefox 46).
The position/size of the labels changes with noticeable delay when moving/zooming the graph. The overall performance does not feel very smooth.

Thank you. Soon, the rendering procedure will be rewriting.

#3 Performance was improved in new extension version.


Set bufferSize to null to fix Windows 10 timeout issue

Fixes #44, not tested as I do not have a Windows 10 machine.

@clue shouldn't that be ^0.5 || ^0.4.5 since we also support 0.5?


bug: fish and powershell autocompletion support missing in the description of the autocompletion command

Expected Behaviour

Add these to the description of the autocompletion command:
👉 https://github.com/alexellis/arkade/blob/1e43d6ead0ef958e9caf08e0ec23b49fe6563ffb/cmd/completion.go#L60-L63
Current Behaviour

arkade completion --help                                                                                ─╯

Outputs shell completion for the given shell (bash or zsh)
This depends on the bash-completion binary.  Example installation instructions:
OS X:
	$ brew install bash-completion
	$ source $(brew --prefix)/etc/bash_completion
	$ arkade completion bash > ~/.arkade-completion  # for bash users
	$ arkade completion zsh > ~/.arkade-completion   # for zsh users
	$ source ~/.arkade-completion
Ubuntu:
	$ apt-get install bash-completion
	$ source /etc/bash-completion
	$ source <(arkade completion bash) # for bash users
	$ source <(arkade completion zsh)  # for zsh users
Additionally, you may want to output the completion to a file and source in your .bashrc

Are you a GitHub Sponsor (Yes/No?)
Check at https://github.com/sponsors/alexellis

[x] Yes
[ ] No

Possible Solution

add this description above to the description of the autocompletion command:
fish:

  $ yourprogram completion fish | source

  # To load completions for each session, execute once:
  $ yourprogram completion fish > ~/.config/fish/completions/yourprogram.fish

PowerShell:

  PS> arkade completion powershell | Out-String | Invoke-Expression

  # To load completions for every new session, run:
  PS> arkade completion powershell > arkade.ps1
  # and source this file from your PowerShell profile.
`

Steps to Reproduce (for bugs)

Context

Your Environment

What Kubernetes distribution are you using?

kubectl version

Operating System and version (e.g. Linux, Windows, MacOS):

uname -a

cat /etc/os-release

What arkade version is this?

$ arkade version
Version: 0.8.5
Git Commit: ef3938e08a51af9eacb3c920c1f5222eac76f0ab

Go ahead 👍


fix: allow generate-formula-api to support renamed formulae

[x] Have you followed the guidelines in our Contributing document?
[ ] Have you checked to ensure there aren't other open Pull Requests for the same change?
[x] Have you added an explanation of what your changes do and why you'd like us to include them?
[ ] Have you written new tests for your changes? Here's an example.
[ ] Have you successfully run brew style with your changes locally?
[ ] Have you successfully run brew typecheck with your changes locally?
[ ] Have you successfully run brew tests with your changes locally?

It feels incredibly naive, but I think this should support formula renames in the API.

Everything here looks reasonable, but I think this will only work for brew install, not brew upgrade. brew install would also not warn when using the old name.
On upgrade, we implicitly check formula_renames.json in Formulary. I don't see a way of doing the same with this naive approach. Tap#formula_renames should do the same when using the API, so I think the API JSON needs to have a "formula_renames" field.

The HTML redirects is a good idea and that makes sense to do.
I however don't think the bit copying the JSON is a good idea. brew will not treat that as a rename and it may confuse API consumers as they'll now need to cross-check the name field to know if it's a real formula or not.
In terms of supporting renames in the install-from-api code in brew, I think the data is already there in terms of oldnames. Let me have a look and see what can be done there.

I however don't think the bit copying the JSON is a good idea. It may confuse API consumers as they'll now need to cross-check the name field to know if it's a real formula or not, and inflates the JSON by a fair bit. For the mega formula.json, it'll actually appear as two of the same entry as it's just a big array.

Thinking about it more actually: if it's just the direct JSON for individual formulae and not the formula.json file then it's probably ok

The HTML redirects is a good idea and that makes sense to do.

I'm not opposed but I'm not strongly in favour either given we've not really had requests for this.

I however don't think the bit copying the JSON is a good idea. It may confuse API consumers as they'll now need to cross-check the name field to know if it's a real formula or not, and inflates the JSON by a fair bit. For the mega formula.json, it'll actually appear as two of the same entry as it's just a big array.

Also agreed.
Now that https://github.com/Homebrew/brew/pull/15320 is implemented I think it'd be good to do the bare minimum required to get this data in place. I don't think we need to provide JSON or HTML files for the old names; no-one has asked for this really.

It seems I misunderstood the problem with formula renames. What needs to be solved for the API? I thought it was discoverability but that doesn't seem to be the case.

What needs to be solved for the API?

Nothing anymore since https://github.com/Homebrew/brew/pull/15320 is merged.
We can keep the additional redirects added in this PR though.


Activate a pipenv environment without creating a new shell?

Is it possible to activate a pipenv environment without creating a new shell?
Example:
user@host:~/some_project$ pipenv activate
(some_project-OH5ik22OJ) user@host:~/some_project$

(Edit:) I should mention that this is possible by running
user@host:~/some_project$ . `pipenv --venv`/bin/activate

But I'm more interested in the "why spawn a new shell?"
Why am I asking?
I guess I don't really see the advantage of spawning a new shell. I do, however, see disadvantages:

When exiting the shell, your CWD changes to the path you were at when you spawned the shell, not the path you were in when exiting the shell
After exiting, you don't get your command history from when you were inside the shell

Examples of the disadvantages:
user@host:~/some_project$echo "outside of shell"
outside of shell
user@host:~/some_project$pipenv shell
<snip>
(some_project-AGMeXx6P) user@host:~/some_project$cd src
(some_project-AGMeXx6P) user@host:~/some_project/src$ echo "inside shell"
inside shell
(some_project-AGMeXx6P) user@host:~/some_project/src$ exit
user@host:~/some_project$ history | tail -n 10                # dir changed!
   1  echo "outside of shell"
   2  pipenv shell
   3  history | tail -n 10                                    # `echo "inside shell"` missing!

Describe you environment

Windows, WSL, and Linux
Python version: 3.5.2
Pipenv version: 8.2.6

@dougthor42, spawning a shell is the safe elegant solution to modify the environment without the need to maintain code for restoring it for every shell. When you exit from the shell, your previous env restored by the system.
Regarding the issues:

restoring the working directory - sounds like a feature
history not saved - this looks like a bug

When you exit from the shell, your previous env restored by the system.

So how is this different from simply running deactivate, like a standard venv?

restoring the working directory - sounds like a feature

For some, perhaps 😄

So how is this different from simply running deactivate, like a standard venv?

From the user point of view: "deactivate" is a non-standard venv command, and it works only if your shell is supported. "exit" is the standard way to exit a shell, and it works in any shell.
From the maintainer point of view: You don't need to maintain deactivate for all shells.

You can use pipenv —venv to get the location of the virtualenv and activate it like a normal virtualenv.

@techalchemy Yeah I realized that right after I posted, so it's part of the edit:
@nirs Ah, I didn't realized that deactivate had shell-specific code in it.
So I guess this issue devolves into a bug with the command history not being saved. Should I post that to the pew project or is it a bug with something else?

Activation and deactivation is shell and OS specific and requires extensive manipulation of paths which goes far beyond the scope of what pipenv is trying to do.  pipenv shell is mainly a convenience operation, it is intended that you would primarily use pipenv run to do your heavy lifting.
As for command history, you can refer to the other issues on this topic: #876 #297

On windows, that is probably a pew bug.  I think @techalchemy sums it up.  Modifying the shell is beyond the scope of what pipenv in trying to accomplish!  Thanks for using pipenv @dougthor42!

Yeah too many platforms involved which is why we don’t use activate in the first place. You can add something to [scripts] to do something like pipenv run activate

I've simply written a little shell function called activate (similar to the virtualenv deactivate) which automatically activates the virtualenv by searching throug .venv and $WORKON_HOME.
It's part of my ever growing (and increasingly unreadable) package of shell scripts:
https://github.com/WoLpH/dotfiles/blob/fb8cadf59676328b6f8fe5623030251667fb56be/_zshrc#L293-L316

@WoLpH Your shell function activate looks useful, although it seems like it only works if PIPENV_VENV_IN_PROJECT is set to something. Am I right?

@Flimm you're right, I have that enabled by default so I forgot to mention that.
Here's a version that should work in all cases and sets slightly more environment variables:
activate(){
    activate_file=$(pipenv --venv)/bin/activate
    if [ -e "$activate_file" ]; then
        . $activate_file

        # the pipenv shell normally enables these as well
        export PYTHONDONTWRITEBYTECODE=1
        export PIPENV_ACTIVE=1

        if [ -f "${VIRTUAL_ENV}/.project" ]; then
            cd $(cat "${VIRTUAL_ENV}/.project")
        fi
        return
    fi
}

I used . $(pipenv --venv)/bin/activate to avoid execute "Permission denied" error

function activate
  . $(pipenv --venv)/bin/activate.fish
end
funcsave activate

I just had the same issue. Recently, I wanted to test Nix (the package manager), which could help in managing multiple versions of Python (having to compile from source Python38 for one single project was annoying, I'm sorry for those who need even more...)
Because Nix is already managing most of the environment variables, the idea was to create a shell.nix to automagically download the right version of python in my project, with pipenv and any other software requirements. If there was a pipenv activate, I could just add that to the shell hook, but no, I have to use the solution provided by @WoLpH.
Having such an option would allow any piece of software providing a shell to upgrade itself in the environment...


How can I get the model weights file defined in a config file with the key MODEL.WEIGHTS when I train OneFormer and set resume=False?

Appreciate your effort in research work of OneFormer.
I got this error when I started an independant training:

I find it occurs because of the call of the method trainer.resume_or_load(resume=args.resume), which is located at line 424 of file train_net.py. It will try to load checkpoint from the MODEL.WEIGHTS of the config file. And I don't konw how to get it, could someone help me？
This may be a low-level mistake, I'm a student, please don't take offense.

Hi, @qgq99, thanks for your interest in our work. Happy to answer your question, nothing offensive.
Could you share your training command? When you execute the command, you may pass a custom path for the pretrained weights using something like the command below for Swin-L OneFormer:
python train_net.py --dist-url 'tcp://<IP_ADDRESS>:50163' \
    --num-gpus 8 \
    --config-file configs/ade20k/swin/oneformer_swin_large_bs16_160k.yaml \
    MODE..WEIGHTS <PATH-TO-CHECKPOINT-HERE> \
    OUTPUT_DIR outputs/ade20k_swin_large WANDB.NAME ade20k_swin_large

You can get the pretrained weights using the instructions here.

Hi, @praeclarumjj3, thank you for your reply!
The training command I used is in the same format as shown in the file GETTING_STARTED.md, specifically as follows:
python train_net.py --dist-url 'tcp://<IP_ADDRESS>:50163' \
    --num-gpus 2 \
    --config-file configs/ade20k/oneformer_swin_tiny_bs16_160k.yaml \
    OUTPUT_DIR outputs/ade20k_swin_large WANDB.NAME ade20k_swin_tiny

@qgq99, you need to specify MODE.WEIGHTS, as I suggested in my previous comment. Please try the following command:
python train_net.py --dist-url 'tcp://<IP_ADDRESS>:50163' \
    --num-gpus 2 \
    --config-file configs/ade20k/oneformer_swin_tiny_bs16_160k.yaml \
    MODEL.WEIGHTS <PATH-TO-CHECKPOINT-HERE> \
    OUTPUT_DIR outputs/ade20k_swin_large WANDB.NAME ade20k_swin_tiny


Trying to join using Twitter/Facebook

At the moment, it is not possible to join Joind using Twitter / X or Facebook. There are some buttons available at the index, but both buttons are giving errors. Twitter / X will create the app and fails afterwards. Facebook will show an error-message directly.
Is this something, which can be fixed? If not, I guess it is better to remove both buttons.

Thanks for letting us know!
Due to the past shitstorm on "twitter" at least that one will not be fixed.
But I assume removing both is the best option here.

I completely understand the choice. If you want, I can create a PR later this week.


unexpected token export

Using just the basic stuff from the readme.

Possible dup of https://github.com/wobsoriano/solid-apexcharts/issues/1


midi controller crashes CT after assigning command and triggering it

Hi Rob,
I wonder why you are checking if cv is in shortcutA here:
https://github.com/coldtype/coldtype/blob/6b6bf53c1477231bde128ade4032b08c16f762e7/coldtype/renderer/winman/midi.py#L69-L71
For me cv is a controller value as integer (e.g. 0 or 127, so the check crashes as it expects a string when comparing with shortcutA (which is sth. like "render_all" as you know). I tried casting the cv to a string to avoid the crash, but obviously that still makes little sense, as it won’t be part of shortcutA anyway.
My .coldtype.py looks sth like this:
...
    "Maschine Mikro MK3": {
        "note_on": {
            40: "overlay_timeline",
            42: "prev_prev",
            43: "prev_next",
            44: "clear_last_render",
        },
        "controller": {
            58: "render_all",
            ...
	}
    }
...

so when I change abovementioned lines to
if shortcutA:
    self.on_shortcut(shortcutA, cn)
# Note that I also removed the `shortcutA.get(cv)`, as that will crash as well.

I can get my controllers to work, but I guess then I am bypassing some guards that you tried to achieve there. I just don’t really get those. Do the CC controllers work for you?

Hi Mark, sorry for the confusion there! The "controller" spec is slightly different, you need to provide a 0-127 integer value as well as the shortcut(s) to perform.
For instance, I've got a slider set so the min value jumps to the beginning of an animation and the max value jumps to the end:
"controller": {
   84: { 0: "jump_home", 127: "jump_end" }
}

Does that make sense?

Hi Rob,
This works fantastically! Thank you so much.
I am using 58: { 0: "", 127: "render_all" }  now, as I want to use the DAW control buttons to control CT. Works like a charm. Deliberately not assigning anything to the 0 value, so that it doesn’t run the shortcut twice on CC down and release. Only thing is that it then prints shortcut! {0: '', 127: 'render_all'} 0 ... No shortcut/action 58, after running successfully. But I don’t mind that at all.
Lovely! Now I can use the labeled buttons on my Maschine Mikro to control Coldtype, much easier to "remember" my main actions. Thanks so much for making this possible :)

I managed to get most shortcuts to work, however a few are behaving slightly unexpected. Here is my current state with some annotations:
# "Coldtype Template" (Controller Editor)
"note_on": {
    44: "clear_last_render",
    48: "prev_prev",
    49: "prev_next",
    50: "jump_prev",
    51: "jump_next",
    42: "load_prev_in_directory",
    43: "load_next_in_directory",
    36: "play_preview", # "Play"-Button CC, only works when Type "Note" e.g. "C1", Mode "Gate", Value "127" --- Not working when assigning as controller (see below at CC `57`)
},
"controller": {
    # Buttons set on
    #   Mode "Trigger"
    #   Value "127"
    #   Action "Down"
    # in Controller Editor
    8:  { 0: "", 127: "quit" }, # works
    58: { 0: "", 127: "render_all" }, # works
    53: { 0: "", 127: "restart_app" }, # works
    57: { 0: "", 127: "play_preview" }, # only STOPS when preview playback is running, no matter which mode it is set to, `No shortcut/action 57`
    59: { 0: "", 127: "play_to_end" }, # only STOPS when preview playback is running, no matter which mode it is set to, prints `No shortcut/action 59`
    55: { 0: "", 127: "toggle_time_viewer" }, # only works when preview playback is running
    56: { 0: "", 127: "toggle_xray" }, # works all the time, also when preview is stopped
    7: { 1: "load_prev_in_directory", 127: "load_next_in_directory" }, # encoder works when set as `relative`, step `1`, does not work when assigning "prev_prev" "or prev_next" instead
}

What is odd for example is that "toggle_time_viewer" does only work, when the preview play is running, while "toggle_xray" works no matter what. The respectiv v keyboard key works also no matter the viewers playing state.

Any more ideas about this?
I also tried with my self built MIDI controller, which sends perfectly fine signals as seen with the MIDI Monitor app.
But I still cannot get some of the commands working. For instance the prev_prev, prev_next or play_preview
Coldtype still prints the correct assignment to the console: shortcut! {0: '', 127: 'play_preview'} 127 and only stops when preview playback is running, no matter which mode it is set to. In case of my CC57, it has its mode set to "Trigger".
Anyway, the key question is, why do some commands work, and some don’t? I have the commands from the keyboard.py.
Thanks in advance :)

Was just able to look into this a little and think I may have fixed it, though I haven't been quite able to repro some of what you're talking about — are you able to pip install coldtype to your venv w/ the latest from coldtype/main? (i.e. pip install "coldtype[viewer] @ git+https://github.com/coldtype/coldtype")

Thanks!
I installed a brand new Coldtype 0.10.14 just this weekend, when I started to spam your repository with issues and questions again. So if you didn’t update Coldtype since then, I guess I am on the latest.

Definitely not spam! Great to have someone asking questions — and this one's definitely a bug in the library. I meant could you install a pre-release version directly from git (installing with that command above will install the latest commits to main from coldtype, rather than from any released version), as I don't quite want to release it if it doesn't fix your issue.
As for repro, I was able to repro play_preview not working at all, but didn't get some of the other ones to work in the partial states you're mentioning.

Cool, thanks a lot.
I will pull the latest github state and then try again. I will make a proper overview once more with what works and what does not! 🙌


com.kazurayam.materialstore.facet package should move to its own subproject

The com.kazurayam.materialstore.facet.textgrid package was developed to propose a solution to a question in Katalon Forum:
https://forum.katalon.com/t/comparing-2-html-table-s-in-katalon-studio/62233
It is a special case that applies the materialstore to solve some specifc problem.
This package is not a part of basic trunk of the materialstore library.
Therefore it should have its own subproject.

done at 0.9.2-SNAPSHOT


How is the in-memory token cache working

Hey, I'm using the "@azure/identity": "3.2.3", and using the new DefaultAzureCredential().getToken() to get a token, I noticed on readme that the token is cached. I just want to make sure every time when I call the getToken method, it is retrieving the token from cache. I found the ExpiringAccessTokenCache class in this sdk, but I couldn't find a place where using that class. Could someone walk me through how is the token caching working?

My understanding is that in-memory caching works automatically and configuration is only needed if you want persistent caching across instances: https://github.com/Azure/azure-sdk-for-js/blob/main/sdk/identity/identity/TOKEN_CACHING.md
@KarishmaGhiya can correct me if I'm wrong though. 😄

Thank you, within the token valid period, we get the same token, however, by looking at the logs (see the screenshot below) I notice every time it sends a get token request to an url, is the token coming from cache or from a network call?

Also, I'm getting the log SetAppTokenProvider has saved the token in cache every time when the getToken method gets called, is this correct? My understanding is that SetAppTokenProvider is not defined if I get the above log. Why is the  SetAppTokenProvider always not defined if the token is cached? Appreciated if someone could explain this in details.

Thank you, within the token valid period, we get the same token, however, by looking at the logs (see the screenshot below) I notice every time it sends a get token request to an url, is the token coming from cache or from a network call?

If the token is not expired, it should be coming from the cache not from the network call

Also, I'm getting the log SetAppTokenProvider has saved the token in cache every time when the getToken method gets called, is this correct? My understanding is that SetAppTokenProvider is not defined if I get the above log. Why is the SetAppTokenProvider always not defined if the token is cached? Appreciated if someone could explain this in details.

Yes that is the logging that I had added to ensure that the token was getting saved in the cache.

Thank you, but is it right every time when I make a call I receive that
log? Coz why it saves to cache for each call?
On Thu, 13 Jul 2023 at 20:37, KarishmaGhiya @.***>
wrote:

Also, I'm getting the log SetAppTokenProvider has saved the token in cache
every time when the getToken method gets called, is this correct? My
understanding is that SetAppTokenProvider is not defined if I get the
above log. Why is the SetAppTokenProvider always not defined if the token
is cached? Appreciated if someone could explain this in details. [image:
Screenshot 2023-07-13 at 11 51 35 AM]
https://user-images.githubusercontent.com/60414595/253124553-f259ff5a-115d-4cc9-b4c1-2ae215f4fee8.png
Yes that is the logging that I had added to ensure that the token was
getting saved in the cache.
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Thank you, no, the token is valid for 24 hours, it was just few mins between each calls. I got the same token for each call, however, that log also appears for each call, which is why I wanted to confirm is the token coming from cache or a network call.

Also, I'm getting the log SetAppTokenProvider has saved the token in cache every time when the getToken method gets called, is this correct? My understanding is that SetAppTokenProvider is not defined if I get the above log. Why is the SetAppTokenProvider always not defined if the token is cached? Appreciated if someone could explain this in details.

Yes that is the logging that I had added to ensure that the token was getting saved in the cache. This is weird. Yes this shouldn't be happening. What is the time difference between your subsequent get token calls? Is the token expired between these calls?

Thank you, no, the token is valid for 24 hours, it was just few mins between each calls. I got the same token for each call, however, that log also appears for each call, which is why I wanted to confirm is the token coming from cache or a network call.

Any updates on this?

@revanth-pf Were you making exact same consecutive requests? Were there any differences in the scopes, claims, etc.?
Can you share the code that I can reproduce on my end?

@KarishmaGhiya I'm a colleague of @yixuanhou, yes we both made the same requests using the same code and the behaviour is what was reported earlier. We'll share the code shortly, thanks

getToken = async () => {
    const azureCredentialInstance = new ManagedIdentityCredential(
      getEnv("AZURE_CLIENT_ID")
    );

// we save the scope in azure keyvault, so it's the same scope always
    const scope = await getSecret("scope-name", `kv-name`);

    return await azureCredentialInstance.getToken(scope);
  };

// example of the getSecret function
export const getSecret = async (
  secret: string,
  vaultNameOrURL?: string,
  credential?: TokenCredential
): Promise<string> => {
  vaultNameOrURL ??= getEnv("VAULT_NAME");
  const url = makeKeyVaultUrl(vaultNameOrURL);

  try {
    credential = new DefaultAzureCredential();
    const secretClient = new SecretClient(url, credential);
    const { value } = await secretClient.getSecret(secret);
    if (!value) throw new Error("Secret value undefined");
    return value;
  } catch (e) {
    console.error(e);
    throw new Error(`Error retrieving secret ${secret}`);
  }
};

Please see the sample code above.

You are creating a different credential for the two service calls -

Default Azure Credential -
js credential = new DefaultAzureCredential();  const secretClient = new SecretClient(url, credential);  const { value } = await secretClient.getSecret(secret);
Managed Identity Credential-
`js
getToken = async () => {
const azureCredentialInstance = new ManagedIdentityCredential(
getEnv("AZURE_CLIENT_ID")
);

// we save the scope in azure keyvault, so it's the same scope always
const scope = await getSecret("scope-name", kv-name);
return await azureCredentialInstance.getToken(scope);

};
`
As a result you will receive separate logs from the SetAppTokenProvider for these different instances of the credentials.

Hey @KarishmaGhiya, thanks for getting back on this. Please allow me to elaborate the code snippets posted above. These snippets are from our internal library.
As a library user, I would call getToken() (the wrapper method defined by us) the purpose of which is to get a token from the MSI directly. The wrapper method handles the creation of msi instance and passing it the scope parameter etc. The scope parameter's value has been stored in key vault in our case,  therefore our wrapper calls the key vault to get the scope value (the value of scope variable would never change). Finally, the wrapper function makes a call to ManagedIdentityCredential().getToken(scope) to get a token from the msi and pass it on to the library user. This token will be used for internal application purposes to cater other needs.
So, to speak to your suggestions, yes, we do use different instances such as the DefaultAzureCredential() for key vault's SecretClient() and ManagedIdentityCredential(). We aren't concerned on receiving different tokens for those individual calls. However, when I do call ManagedIdentityCredential().getToken(scope) multiple times via our wrapper the expectation is that the ManagedIdentityCredential().getToken(scope) should return a token from the cache when available.
To test this hypothesis, we ran a test on our wrapper method getToken() and called it 10 times back to back in a loop. We saw the ManagedIdentityCredential().getToken(scope) always reaching out to the ip that starts with 169.254 to get a token instead of getting it from cache on subsequent runs based on the libraries internal logs.
With the above said, is it expected for the library to hit the cache in subsequent runs even if it ends up creating a new instance of ManagedIdentityCredential().getToken(scope) on every invocation or should the ManagedIdentityCredential() instance be treated as a singleton for the caching to work?
Thanks,
Revanth

With the above said, is it expected for the library to hit the cache in subsequent runs even if it ends up creating a new instance of ManagedIdentityCredential().getToken(scope) on every invocation or should the ManagedIdentityCredential() instance be treated as a singleton for the caching to work?

@revanth-pf The caching doesn't work against multiple instances of ManagedIdentityCredential. If I want internal token caching to work i need to call the getToken(scope) on the same instance of ManagedIdentityCredential every time.

To test this hypothesis, we ran a test on our wrapper method getToken() and called it 10 times back to back in a loop. We saw the ManagedIdentityCredential().getToken(scope) always reaching out to the ip that starts with 169.254 to get a token instead of getting it from cache on subsequent runs based on the libraries internal logs.

Was this on the same instance of ManagedIdentityCredential() or was a new instance created everytime?

We are creating a new instance of ManagedIdentityCredential() on every call for getToken(). We tried your suggestion regarding using the same instance of ManagedIdentityCredential()
The following is the log that we now see in AI,

I believe that it's being fetched from the cache?

Yes this looks correct

@KarishmaGhiya @xirzec Thank you for your help on this

@KarishmaGhiya thank you for this

I thought the DefaultAzureCredential will end up using the
ManagedIdentityCredential because we have the Azure client id env car setup.
Even if I use the same DefaultAzureCredential for the two calls. I saw the
rest api call in logs (see the very first screenshot), was that expected?
On Fri, 21 Jul 2023 at 04:08, KarishmaGhiya @.***>
wrote:

You are creating a different credential for the two service calls -

Default Azure Credential -
js credential = new DefaultAzureCredential(); const secretClient = new
SecretClient(url, credential); const { value } = await
secretClient.getSecret(secret);
Managed Identity Credential-
`js
getToken = async () => {
const azureCredentialInstance = new ManagedIdentityCredential(
getEnv("AZURE_CLIENT_ID")
);

// we save the scope in azure keyvault, so it's the same scope always
const scope = await getSecret("scope-name", kv-name);
return await azureCredentialInstance.getToken(scope);
};
`
As a result you will receive separate logs from the SetAppTokenProvider
for these different instances of the credentials.
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Markdnwn automatic numbers only autofills "1."

VSCode Version: Code 1.16.1 (27492b6bf3acb0775d82d2f87b25a93490673c6d, 2017-09-14T16:38:23.027Z)
OS Version: Windows_NT x64 10.0.15063
Extensions:

Extension
Author (truncated)
Version

vscode-great-icons
emm
2.1.16

vsc-material-theme
Equ
1.0.3

ayu
tea
0.8.0

markdown-all-in-one
yzh
0.10.2

(3 theme extensions excluded)

Steps to Reproduce:

Open a .md file
Type the number "1." with a space and some text after it
Press enter and watch it type "1." instead of continuing to 2

Reproduces without extensions: Yep!

This behavior comes from your markdown extension. Please file the issue against them: https://github.com/neilsustc/vscode-markdown/issues


[SPARK-34012][SQL][3.0] Keep behavior consistent when conf spark.sqllegacy.parser.havingWithoutGroupByAsWhere is true with migration guide

What changes were proposed in this pull request?
In https://github.com/apache/spark/pull/22696 we support HAVING without GROUP BY means global aggregate
But since we treat having as Filter before, in this way will cause a lot of analyze error, after https://github.com/apache/spark/pull/28294 we use UnresolvedHaving to instead Filter to solve such problem, but break origin logical about treat SELECT 1 FROM range(10) HAVING true as SELECT 1 FROM range(10) WHERE true   .
This PR fix this issue and add UT.
NOTE: This backport comes from https://github.com/apache/spark/pull/31039
Why are the changes needed?
Keep consistent behavior of migration guide.
Does this PR introduce any user-facing change?
No
How was this patch tested?
added UT

FYI @maropu @cloud-fan

FYI @maropu @cloud-fan

Looks fine if the tests pass.

Looks fine if the tests pass.

Test build #133702 has started for PR 31049 at commit 0a0f1b9.

Test build #133702 has started for PR 31049 at commit 0a0f1b9.

Kubernetes integration test starting
URL: https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder-K8s/38290/

Kubernetes integration test starting
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Kubernetes integration test status failure
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Kubernetes integration test status failure
URL: https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder-K8s/38290/

Refer to this link for build results (access rights to CI server needed):
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Test build #133702 has finished for PR 31049 at commit 0a0f1b9.

This patch passes all tests.
This patch merges cleanly.
This patch adds no public classes.

Test build #133702 has finished for PR 31049 at commit 0a0f1b9.

This patch passes all tests.
This patch merges cleanly.
This patch adds no public classes.

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Thanks! Merged to branch-3.0.

Thanks! Merged to branch-3.0.


Fixes and changes to Shadow Fiend

Changed Shadow Pool talent damage from 50 magic dmg over 2 seconds to 80 + 10% max hp over 2 seconds

Fixed the permanent extra cap to soul stacks gained trought the Shadow Harem talent being lost upon respawn if you had the Nevermore's Recall talent active.

Changed how Soul Frenzy can be lost: Before you would lose the buff if ANY shadow raze missed, even the extra ones. Now you will lose it only if you miss with ALL the razes.

Fixed the "close" version of Shadow Raze not refreshing the Requiem of Souls' debuff when you had the Soul Rend talent active.

Updated the Aghanim Scepter to be on par to Vanilla:

Still the same for the ultimate

Will now increase the soul cap on Necromastery (and stacks additivelly with the Shadow Harem talent)

Updated the tooltips to reflect the changes, Link to the pastebin: https://pastebin.com/fbpwWPNX

Luafied Pangolier and (by force) applied a small imbafication on his Q (changed the targeting, since volvo didn't make vector targeting available to modders yet).
Also added some filler talents to make it work with our talent system (check the tooltips for info)
Link to the tooltips: https://pastebin.com/n75DR1A6

Updated Pangolier lua file:

Made a progress towards Rolling Thunder imba modifier, but it's still incomplete so i still use vanilla version of the modifier
Added voicelines to the abilities, will add more voice lines as the time goes on!
Updated Shield Crash re-cast interaction: if he hit at least one hero when he has already the buff, the duration is refreshed and the stack count is upgraded if the new damage reduction would be higher (before it would outright reapply the modifier, thus losing the damage reduction if it was lower too).
Updated Swashubckle to make it not work on attack immune enemies (ethereal)


multi-page annotation ML Backend

This is the labeling interface I used, how to connect the tesseract backend for this?
<View>
  <Repeater on="$document" indexFlag="{{idx}}" mode="pagination">
      <Image name="page_{{idx}}" value="$document[{{idx}}].page" inline="true"/>

    	<Labels name="labels_{{idx}}" toName="page_{{idx}}">
            <Label value="Text" background="green"/>
            <Label value="Handwriting" background="blue"/>
      	</Labels>

      <Rectangle name="bbox_{{idx}}" smart="true" toName="page_{{idx}}" strokeWidth="3"/>

      <TextArea name="transcription_{{idx}}" toName="page_{{idx}}"
            editable="true"
            perRegion="true"
            required="true"
            maxSubmissions="1"
            rows="5"
            placeholder="Recognized Text"
            displayMode="region-list"
            />

      <View style="display: flex; justify-content: center;">
        <Header value=" Rate this page:"/>
        <Rating name="rating_{{idx}}" toName="page_{{idx}}"/>
      </View>
  </Repeater>
</View>

<!-- {
"document": [
    {
      "page": "https://htx-pub.s3.amazonaws.com/demo/images/demo_stock_purchase_agreement/0001.jpg"
    },
    {
      "page": "https://htx-pub.s3.amazonaws.com/demo/images/demo_stock_purchase_agreement/0002.jpg"
    },
    {
      "page": "https://htx-pub.s3.amazonaws.com/demo/images/demo_stock_purchase_agreement/0003.jpg"
    }
  ]
} -->

You have modify tesseract ml backend here:
https://github.com/heartexlabs/label-studio-ml-backend/blob/master/label_studio_ml/examples/tesseract/tesseract.py#L50
to_name / from_name should be rethinked.

Another thing is I added the json input with local file path instead of URL, it don't works. Is there anyways?

Check this:
https://labelstud.io/guide/storage.html#Tasks-with-local-storage-file-references

And one more question is how can get the idx from the context?

Sorry what context do you mean?

@makseq

To send the text to the right textarea tag in the

You need to know the idx of the page you annotate, right?
But how to find the idx of that page?

You have <Image name="page_{{idx}}" value="$document[{{idx}}].page" inline="true"/> in your labeling config, this image serves some specific page. When you annotate this image, labels with this image ids will be created. TextArea should be linked with this image too, so it will be toName="page_12" from Image name="page_12".


Cannot install plugin to k8s instance of grafana

Hello. I could not install volkovlabs-env-datasource, can you please help. While string grafana I'got
logger=plugin.loader t=2022-11-23T07:09:16.316292106Z level=info msg="Plugin registered" pluginID=input
logger=plugin.loader t=2022-11-23T07:09:16.334529842Z level=warn msg="Could not find root URL that matches running application URL" plugin=volkovlabs-env-datasource appUrl=https://grafana.preview30.k8s-cluster02.mydomain.com/ rootUrls=[http://localhost:3000/]
logger=plugin.loader t=2022-11-23T07:09:16.334574642Z level=warn msg="Skipping loading plugin due to problem with signature" pluginID=volkovlabs-env-datasource status=invalid

Grafana configuration is changing using env variables:
- name: GF_DEFAULT_APP_MODE
  value: development
- name: GF_SERVER_DOMAIN
  value: grafana.preview30.k8s-cluster02.appulate.dev
- name: GF_SERVER_ROOT_URL
  value: https://grafana.preview30.k8s-cluster02.appulate.dev
- name: GF_SERVER_HTTP_PORT
  value: '3000'
- name: GF_SECURITY_ADMIN_PASSWORD
  value: foobar
- name: GF_USERS_ALLOW_SIGN_UP
  value: 'false'
- name: GF_SECURITY_CSRF_TRUSTED_ORIGINS
  value: https://grafana.preview30.k8s-cluster02.appulate.dev
- name: GF_SECURITY_CSRF_ADDITIONAL_HEADERS
  value: X-Forwarded-Host
- name: GF_INSTALL_PLUGINS
  value: "https://github.com/VolkovLabs/volkovlabs-env-datasource/releases/download/v2.2.0/volkovlabs-env-datasource-2.2.0.zip;volkovlabs-env-datasource"
- name: GF_PLUGINS_ALLOW_LOADING_UNSIGNED_PLUGINS
  value: volkovlabs-env-datasource

Can you help to do it right way? =)

@nrukavkov Environment Data Source is a Private plugin signed forrootUrls=[http://localhost:3000/]. I will update README to make it clear.
It's unable to load because your domain is set to https://grafana.preview30.k8s-cluster02.mydomain.com/  according to the error message:
appUrl=https://grafana.preview30.k8s-cluster02.mydomain.com/ rootUrls=[http://localhost:3000/]

We recommend using NGINX in front of the Grafana for an extra level of protection, which also allows keeping the default domain as http://localhost:3000.
Other options are:

Build the plugin and sign it for your custom domain.
Allow Unsigned plugins: https://volkovlabs.io/plugins/grafana-allow-unsigned/

Please let me know if you have any questions.

@nrukavkov To allow loading unsigned plugins to work like in your configuration, you will need to remove the MANIFEST.txt file from the archive and host it locally.

@MkrierPharmanity I will in the upcoming release.


Error: Cannot look up attributes in a type object

As reported in this gist: https://gist.github.com/ab5tract/4c2b8cc439d8470cbfda
I will post back a golf, if I find one.

More golf-like and useful, I hope:
https://gist.github.com/ab5tract/414e6b6cda21d7f167e27b322932fd14

Contents of the golf, for easier access:
`use v6;
use OO::Monitors;
monitor Grid {
has @!grid;
submethod BUILD() {
    @!grid = [23, 42];
}

}
my $g = Grid.new;
dd $g;`
The error is:

Invocant requires an instance of type Lock, but a type object was passed.  Did you forget a .new?

and other variations on the theme, including:

Cannot invoke this object (REPR: Null; VMNull)

Fixed BUILD and new methods in monitors and added tests.


Unable to resolve module typescript/2.9/node_modules

Hi there,
thanks for this library! Hopefully it will be used in many RN EOS projects.
So I tried to install it in 1) this copied repo and 2) my app, and followed the instructions given.
Unfortunately in both cases  I get the following error.
I use Mac OS High Sierra, yarn 1.70, npm 6.20
error: bundling failed: Error: Unable to resolve module<EMAIL_ADDRESS>The module ../Library/Caches/typescript/2.9/node_modules/@types/reactcould not be found from/Users/admin/EOSwallet/App.js. Indeed, none of these files exist:

The tips at the rn-nodeify repo did not help help. Trying to install typescript manually as well.

rn-nodeify won't work with modules that are added using npm link.
modules that contain a .babelrc will cause problems with the latest react-native version (0.20 at this time), remove them after installation (rm node_modules//.babelrc)
when installing a package from git, the postinstall hook isn't triggered, run it manually instead (npm run postinstall)
restart the react-native packager after installing a module!
removing the packager cache helps as well sometimes (rm -fr $TMPDIR/react-)
rn-nodeify currently uses npm to install shims. PRs are welcome to make it compatible with yarn

Gonna try last tip and let you know about the results:

use npm@3. npm@5 has some issues that cause node_modules to disappear. See:

using npm@3 did not work

fixed. Bug in VScode, although I was not even using Typescript
#52675

Seems like its been solved.


Update Post “best-way-to-treat-your-home-for-termites-white-ants”

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PR #74 introduce a bug in viterbi_decode

I just checked the merged PR #74 and it comes out that it introduces a bug. You can reproduce the issue by running test_conv_encode_viterbi_decode in test_convcode.py.
This bug is undetected by Travis due to the flag -a '!slow' in the configuration file. I'm about to open a PR to remove this flag and we could discuss about this change there (see #77).
https://github.com/veeresht/CommPy/blob/b104c99ee52df10b3e37571741d1460ac24b60cd/.travis.yml#L34
Let's now discuss on the bug introduced in #74. The new _compute_branch_metric with LRU caching is called as follow:
https://github.com/veeresht/CommPy/blob/b104c99ee52df10b3e37571741d1460ac24b60cd/commpy/channelcoding/convcode.py#L623
However, with a hard decoding type, the function calls .astype on the arguments. This is obviously not possible on tuples.
https://github.com/veeresht/CommPy/blob/b104c99ee52df10b3e37571741d1460ac24b60cd/commpy/channelcoding/convcode.py#L575-L578
It is not possible to just keep the tuples provided since hamming_dist relies on numpy capabilities.
https://github.com/veeresht/CommPy/blob/b104c99ee52df10b3e37571741d1460ac24b60cd/commpy/utilities.py#L112-L132
I don't have time now to explore if this change should be reverted or if there is a better solution. Indeed, converting back and forth from numpy arrays to tuples may lead to a overhead greater than the gain obtained with the LRU cache. I'll try to have a look in the next days but I can't be sure on when it will be possible. @eSoares do you have any suggestions ?

Well spotted!
I always try to also run all the tests in my IDE before PR, but this one was not caught...
I was departing from work when the email hit, so I didn't look to much, but from a couple of quick tests (similar to the one in the PR #74 ) with vs without the caching was something like:

With caching+bugfix: 0:02:52.711836
Without caching: 0:06:06.118742

The bugfix that I made was the following chance:
@functools.lru_cache(maxsize=128, typed=False)
def _compute_branch_metrics(decoding_type, _r_codeword: tuple, _i_codeword_array: tuple):
    r_codeword = np.array(list(_r_codeword))
    i_codeword_array = np.array(list(_i_codeword_array))
   ....

So, converting back still looks an improvement over no caching at all.
But tomorrow I will run a couple of more tests.

Great! Thanks for the help :+1:

Why do you use a list before building the numpy array? This should give the same results with less type changes
def _compute_branch_metrics(decoding_type, _r_codeword: tuple, _i_codeword_array: tuple):
    r_codeword = np.array(_r_codeword)
    i_codeword_array = np.array(_i_codeword_array)

Yes, my mistake. No need to unwrap to list before constructing an numpy array.
PR with the fix done.


fixes #469 made bulk import scan split tolerant

Made MetadataScanner check structure and retry when linked list broken
Removed TabletIterator
Made code that used TabletIterator use MetadataScanner

I am seeing WALSunnyDayIT fail with this PR

Merged in commit 9feb5e1


restructure table and fix select row issues

table is now a single object (no more tabset panels)
made the new table select input match the look of previous tabset panels
got the selected rows to remain consistent when changing selected columns (clicking different buttons)

this has been integrated, not sure why it didn't get brought in via the pull request but I'll take the blame for that.


FEATURE:

Feature Summary
Add a dynamic space-themed background effect with floating stars on the website.
The background enhancement will make the website more visually appealing and immersive, fitting well with the theme of the "Alien Invasion Defense" game.
Description
Currently, the background is static, which doesn’t fully match the adventurous feel of the game. Adding a space-themed background effect, like floating stars, will create a more engaging experience and complement the game's interstellar theme.
Proposed Solution
Implement a dynamic background with subtle, animated floating stars to give the illusion of a space environment. This could be achieved using CSS animations or JavaScript for smoother transitions, ensuring it doesn’t interfere with the website's performance.
Alternatives Considered
Static Space Background - A non-animated starry background, but it may not be as captivating.
Planet or Nebula Graphics - While adding planets or nebulas could be visually interesting, it might feel too cluttered or distract from the game elements.
Screenshots/Logs
No response
Additional Information

[X] I have searched for existing feature requests
[X] I am willing to help implement this feature
[X] I can provide more details or clarification if needed

I want to add these feature .
Assign this work to me


Submissions under review already listed

Submissions that I currently have submitted and are still flagged as "under review" under the "My Submissions" page, show up in some parts of the website (but not in others).

Visible:

on the submission page, see, e.g., https://metriq.info/Submission/46.
under a Task, see, e.g., https://metriq.info/Task/34

Not visible:

on the homepage if I click on "latest".

Not-yet-approved submissions are now not included in method/task/tag views.
We could restrict privileges to see by-index submission detail views, but do we need to do so? No one is going to browse into a submission via clicking a link, this way. Not restricting the by-index detail view lets users immediately share their submission with a private audience, but not in the homepage and not in SOTA charts. I don't see a particular problem with that, and it might be fine to leave that part as-is. Yes, thumbnail and submission URL links could be malicious, but the submitter would have to personally publicize the detail view, (or via a personal proxy, I guess,) in order for anyone to be exposed.
(Please do re-open the issue, if anyone disagrees.)


Release Google.Cloud.Dataproc.V1 version 5.3.0

Changes in this release:
New features

Enable REST transport in C# (commit a6c4606)

Created release for Google.Cloud.Dataproc.V1-5.3.0


Allow saving empty files

Fixes #3931
Description
For some reason, we've been disallowing saving empty files to runs. I don't think it's quite right, but maybe I'm missing something?
This PR removes this constraint.
Testing
CI.
Checklist

Include reference to internal ticket "Fixes WB-NNNN" (and github issue "Fixes #NNNN" if applicable)

@vanpelt could you shed some light on why we quietly disallow saving/uploading empty files? asking you as i traced that condition all the way to https://github.com/wandb/wandb/commit/1cd03d19f91bfda01edbf7f4afe032265d6311ef


"This time-out applies only if the debug attribute in the element is set to false"

Unfortunately HttpRuntimeSection does not have a Debug property, and its related <httpRuntime> tag has no debug attribute either.
Instead, users should check CompilationSection and <compilation> tag, https://docs.microsoft.com/en-us/dotnet/api/system.web.configuration.compilationsection.debug?view=netframework-4.7.2

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

ID: 3899f4c6-9bee-d28e-9037-9e844f1782c9
Version Independent ID: 21abbc0c-17e2-9d5c-c14c-e3393edc1cf9
Content: HttpRuntimeSection.ExecutionTimeout Property (System.Web.Configuration)
Content Source: xml/System.Web.Configuration/HttpRuntimeSection.xml
Product: dotnet-api
GitHub Login: @dotnet-bot
Microsoft Alias: dotnetcontent

Thanks, @lextm, for your feedback. Would you like to submit a pull request that addresses the issue?
//cc @davidsh, another System.Web issue.

//cc @davidsh, another System.Web issue.

@Tratcher for System.Web issues

@shirhatti can you route?

@Jinhuafei


Empty File Message

Describe the improvement that you'd like
It would be nice if the editor showed a small message when the file is empty. I realize that the file being empty means it's empty, but an extra indicator would be nice!

Thanks for your suggestion. I will consider using a pop-up message or extra status bar to indicate an empty file. If you have any embodied ideas, please let me know.


Creating a New Visual Does Not Resolve JSON Schema Files on SDK v3

Hi folks,
On SDK 3.1.5, if I instantiate a new visual (defaulting to API 2.6.0) the JSON schema locations in my .vscode/settings.json resolve to the location previously used for the api, meaning that the intellisense in VS Code does not work for these files.
On instantiation, the json.schemas key is as follows:
    "json.schemas": [
        {
            "fileMatch": [
                "/pbiviz.json"
            ],
            "url": "./.api/v1.6.0/schema.pbiviz.json"
        },
        {
            "fileMatch": [
                "/capabilities.json"
            ],
            "url": "./.api/v1.6.0/schema.capabilities.json"
        },
        {
            "fileMatch": [
                "/dependencies.json"
            ],
            "url": "./.api/v1.6.0/schema.dependencies.json"
        }
    ]

This needs to be corrected as follows to work:
    "json.schemas": [
        {
            "fileMatch": [
                "/pbiviz.json"
            ],
            "url": "./node_modules/powerbi-visuals-api/schema.pbiviz.json"
        },
        {
            "fileMatch": [
                "/capabilities.json"
            ],
            "url": "./node_modules/powerbi-visuals-api/schema.capabilities.json"
        },
        {
            "fileMatch": [
                "/dependencies.json"
            ],
            "url": "./node_modules/powerbi-visuals-api/schema.dependencies.json"
        }
    ]

Is this a fix that can be applied at the SDK level at all?
Thanks very much,
Daniel

@dm-p Thanks for report. We will fix it in the next version.


Add Discourse SSO support

This enable users of the Almond's discourse community to log in
with their existing Almond credentials.
(Or more correctly, it forces contributors to the discourse to
make an account on Almond, which gives us users)
Fixes #201

This pull request introduces 1 alert when merging 4da52b5d12533b3ab5fc8db15b12d45586f8def6 into ed2122c207137d01d58516cbb84c9864cf00622a - view on LGTM.com
new alerts:

1 for Missing rate limiting

Comment posted by LGTM.com

Pull Request Test Coverage Report for Build 555

17 of 19 (89.47%)  changed or added relevant lines in 3 files are covered.
2 unchanged lines in 1 file lost coverage.
Overall coverage increased (+0.1%) to 59.839%

Changes Missing Coverage
Covered Lines
Changed/Added Lines
%

routes/user.js
14
16
87.5%

Files with Coverage Reduction
New Missed Lines
%

routes/user.js
2
40.26%

Totals

Change from base Build 551:
0.1%

Covered Lines:
4561

Relevant Lines:
7131

💛  - Coveralls


[BUG - Release] Crash when exiting game with watches to be logged to file

Branch or Release
3.0.1
Game and Engine Version
Deep Rock Galactic, 4.27
Describe the bug
Everything running as normal, using watches on some stuff fine, with logging to file selected. But when I close the game, I get a crash dmp and none of the data that was sent to the watches on the GUI was output into any file. I thought it's meant to get sent to watches.meta.json but that file seems to only be populated when you save a watch to be auto set when you next load up the game.
Mods directory
No mods installed.
To Reproduce
It's a bit odd, I cannot reliably repro the crash part issue with the same setup steps as below, however my watched data (except saving watch for auto-load) are never saved to any file.
Steps to reproduce the behavior:

Launch game
Add watch to /Script/GameplayCameras.MatineeCameraShake:ReceiveIsFinished
Load into mission (so there's a few entries in there)
Exit game
Crash (sometimes)

Expected behavior
Game to not crash, and the write to file setting on a watch to actually write the data to a file.
Screenshots, UE4SS Log, and .dmp file
UE4SS.log
crash_2024_02_29_22_57_44.dmp
Desktop:

OS: Win10

I'm able to reproduce both the crash and the fact that it's not being written to file.

The watches.meta.json file is not meant to receive logs of whatever the user watches, it just stores metadata for automatically regaining a watch if the user ticks "Auto-create on startup" checkbox to the right of the watch name.

I don't yet know why it's crashing, but it's not saving to file because it appears that the feature was only implemented for properties.
This shouldn't be very hard to implement for functions, give me a few moments.

The watches not working on functions is addressed by #419

#419 hopefully now also fixes the crash.

it appears that the feature was only implemented for properties.

Is it intentional that I can't set a watch on an array property? But I can set a watch on any other property type.

Yes, ArrayProperty and also StructProperty watches have not been implemented, not sure why.
There doesn't appear to be any problems with StructProperty.
I'm not aware of any ArrayProperties in defualt UE that I can test with but I'll add both StructProperty and ArrayProperty to #419 and you can test it and let me know if it works.

it appears that the feature was only implemented for properties.

Is it intentional that I can't set a watch on an array property? But I can set a watch on any other property type.

Yes, ArrayProperty and also StructProperty watches have not been implemented, not sure why. There doesn't appear to be any problems with StructProperty. I'm not aware of any ArrayProperties in defualt UE that I can test with but I'll add both StructProperty and ArrayProperty to #419 and you can test it and let me know if it works.

Forgot to reply, they do seem to work.

This crash is caused by watches being processed after UE has started shutting down and freeing objects which means the functions and properties being watched might not exist any longer.
The changes introduced by #419 might make this happen a bit less often but it will still happen.
I'm not sure how to properly fix this problem without using the GUObjectArray lock.

To clarify, FUObjectArray::ObjObjectsCritical is not enough.
That lock isn't locked when UE shuts down.
I think we need FGCCSyncObject::Critical, and I'm not sure we can access that very easily.
We can maybe use FUObjectArray::UObjectDeleteListenersCritical because that's locked when UObjectArray shuts down but it might already be too late, not sure.
It would be easier to access than FGCCSyncObject::Critical because we already implement FUObjectArray, all we need to do is add it to UVTD, and it's right after UObjectDeleteListeners which we already implement.


[wip] cleanup of p2p-fullblocktest Python test

This PR should not be merged yet. It is 'wip' for discussion on cleanups to Python code. Present reformatting were done after review of this recently modified Python test script (p2p-fullblocktest).
More consistency and adherence to Python's PEP8 would be helpful for faster review of changes.
While reviewing the recent PR which touched this script, I didn't find bugs in it. I subjected it to a few commonly used tools to see what they would flag. The tools used were:

autopep8 (with --max-line-length 120)
dewildcard (https://github.com/quentinsf/dewildcard)
flake8 (to determine unused imports after dewildcard)
pylint

I would recommend that autopep8 / flake8 / pylint be run on all project Python code before committing, to check for style issues. Usually these tools can be installed with pip install <toolname>.
There does not seem to be a pep8 configuration file in the source tree.
Some minor manual corrections were needed:

removed unused includes since the dewildcard script unfortunately expanded wildcards completely
moved trailing comments on code line to preceding line (autopep8 does this only where max line length exceeded)
removed trailing whitespace

The result passes pep8 with the only remaining errors being only 'too long line'.
There is still an unresolved TODO in the script, but it was there before.
This PR can be closed at any time by the maintainer, should he decide that the formatting changes should not be committed (and it may not be worth doing this on a single file basis)

In general, the Python code is very lacking in docstrings.
It seems I cannot comment on unchanged lines in a PR, so I just add the pylint warnings that I think could still be considered:
R:148,4:FullBlockTest.get_tests: Too many statements (126/50)
W:149,8:FullBlockTest.get_tests: Attribute 'genesis_hash' defined outside init

I think this is a great idea.
The python tests don't really have anyone that cares for it and while it would be nice to have devs run the tools you write, we know it will be forgotten on a regular basis.
As such I'd be very much in favour of simply running the tool on a regular basis and committing that. Maybe even automated if that is safe.

I'll look into creating a PEP8 config file that can be used commonly for various Bitcoin projects including Classic. It would help porting if the Python code were formatted similarly between them.
I think it should only be necessary to have minimal rule waivers from PEP8, e.g. on max line length.
We could add a contrib/ script to run some standard reformatting on some Python files before checking them in, or at regular intervals like you said. Whoever wants to could run this from a git hook.

teamcity gave the thumbs up on this PR at this time.

@zander do you plan on integrating the TC results with GitHub so we can see the results of test runs? I can provide some sample code on how to do it. =)

do you plan on integrating the TC results with GitHub so we can see the results of test runs?

I have no principle objections to that, not entirely sure what kind of information you are looking for.

Basically just would be great to see the TC status in the PR. When a PR is submitted, I assume it kicks off a TC job. Would be great to see the status directly on GitHub so committers know their builds are passing.

ftrader:
23 days ago you wrote:

This PR should not be merged yet. It is 'wip' for discussion on cleanups to Python code.

I am a fan of the idea to do work in a topic-branch, as you seem to use here. To make things stable and finish stuff before we integrate it into master. So, I'm happy with the work you did and how you approach it :)
I'm just unsure why you opened a PR for it while it seems to not be ready for weeks, and you didn't do any visible work after the initial PR opened.
Isn't it enough to just have a topic branch in your own repo?

It is 'wip' for discussion

I think I'm going to close it for now so it doesn't clutter the PR list. I'm fine with having it in my own repo, and re-opening a PR when I think it's appropriate.
The purpose was mainly to share some tools and procedures that could be used to clean up the rather messy Python scripts. I'll do that for BU first, if that goes alright I'll revisit Classic.


[Feature]: Add logo Boss Info

Library name
live-desktop @lederhq/live-common
Describe your feature request
We request to add the Boss Info token for the ledger applications:
Token name: Boss Info
Token symbol: BOSS
Blockchain: ERC20 Ethereum
Token smart contract adress: 0x2e880962a9609aa3eab4def919fe9e917e99073b
Token etherscan.iko link: https://etherscan.io/token/0x2e880962a9609aa3eab4def919fe9e917e99073b
logo:

https://developers.ledger.com/docs/token/introduction/

I did this with the request form in your distributed link. But the description for the pull request to add the icon ist out of date. Please check that out.
Thats the reason that we added this issue.

I did this with the request form in your distributed link. But the description for the pull request to add the icon ist out of date. Please check that out. Thats the reason that we added this issue.

what is our of date on the icon side of things exactly?

https://github.com/LedgerHQ/ledger-live-common/pull/1838/commits/bccc67e49f18d2a1edd286d3a2b30ba522a37778
You give this sample to add a pull request. That is out of date.
I contacted the support because it was also not possible to create an new branch on the new source ....
They wrote me that I have to open an issuse.

From you site:

How to add token icon?
When a Token is added in Ledger Live without an icon, it will display the first letter of the token’s name. To add an icon, you must open a Pull Request in the Ledger Live repository. You can check this PR for an example.

You can either create the icon or find it on cryptoicons.co or cryptologos.cc.
Some rules about the icon:
Don’t use Linear gradients, defs, masks, clip-path, filters and inline styles
Only use svg, path, circle, ellipse, rect, polyline, line tags if needed and add fill=”black”
Feel free to look at the other icons to see what is expected.

If you click check this PR the description there is out of date

If you click check this PR the description there is out of date

what do you mean sorry?