Output API refactor, interface side [WIP]

For each of rstan (@bgoodri ) and pystan/httpstan (@ariddell ) what do you want to write a std::vector<double> and a std::tuple<algorithm specific types> to?
Based on routing signatures from #2557
Handling HMC output
Model-file defined:

relay.write_parameter_names(std::vector<std::string>);

relay.write_parameters(std::vector<double>);

relay.write_unconstrained_parameters(std::vector<double>);

relay.write_parameter_gradients(std::vector<double>);

relay.write_parameter_momenta(std::vector<double>);

relay.write_generated_quantity_names(std::vector<std::string>);

relay.write_generated_quantities(std::vector<double>);

relay.write_transformed_parameter_names(std::vector<std::string>);

relay.write_transformed_parameters(std::vector<double>);

On the interface-side handling these requires providing a writer implementing:

`Writer& operator<<(Writer& writer, std::vectorstd::string names);
Writer& operator<<(Writer& writer, std::vector<double> parameters);
Writer& operator<<(Writer& writer, stan::stage stage);

The writer must also:

Set up where output will go on construction and how it is handled on construction
Length-check that the parameters vector is the same length as the names vector, throw on failure.

The writer is the right place to:

Thin
Select parameter subsets for output

Algorithm-defined:

relay.write_iteration(int iteration);

relay.write_stage(std::string stage); <- this should be, "warmup"/"sampling" for MCMC, could indicate different warmup stages within HMC with adaptation, could be "optimizing"/"converged" for optimization.  Since we often want to know the timing information stage-wise the relay class could run a timer and record how long each stage took as well.

relay.write_log_density(double);

relay.write_stepsize(double)

relay.write_tree_depth(int);

relay.write_acceptance_rate(double);

relay.write_energy(double);

relay.write_n_leapfrog(int);

relay.write_divergence(bool);

Writer& operator<<(Writer& writer, std::vector<double> parameters);

relay.write_mass_matrix(Eigen::Vectord mm); <- @mitzimorris

relay.write_mass_matrix(Eigen::Matrixd mm);<- @mitzimorris

relay.write_trajectory(???); <- @martinmodrak

Logger:

relay.debug(std::string msg);
relay.info(std::string msg);
relay.warn(std::string msg);
relay.error(std::string msg);
relay.fatal(std::string msg);
relay.debug(std::stringstream msg);
relay.info(std::stringstream msg);
relay.warn(std::stringstream msg);
relay.error(std::stringstream msg);
relay.fatal(std::stringstream msg);

Handling BFGS/LBFGS optimization output:
Model-file-defined signatures

relay.write_parameters(std::vector<double>);
relay.write_unconstrained_parameters(std::vector<double>);
relay.write_transformed_parameters(std::vector<double>);
relay.write_generated_quantities(std::vector<double>);
relay.write_hessian(Eigen::Matrixd hessian) (CHECK: is this generated or just rstan? Q: should we generate this?)
relay.write_parameter_gradients(std::vector<double>);

Algorithm-defined signatures:

relay.write_iteration(int iteration);
relay.write_stage(std::string stage); <- this should be, "warmup"/"sampling" for MCMC, could indicate different warmup stages within HMC with adaptation, could be "optimizing"/"converged" for optimization.  Since we often want to know the timing information stage-wise the relay class could run a timer and record how long each stage took as well.
relay.write_log_density(double);
relay.write_dx_magnitude(double);
relay.write_grad_magnitude(double);
relay.write_alpha(double);
relay.write_alpha_0(double);
relay.write_n_evals(double);
"notes" <- Huh, should be broken out
termination/convergence messages

Logger:

relay.debug(std::string msg);
relay.info(std::string msg);
relay.warn(std::string msg);
relay.error(std::string msg);
relay.fatal(std::string msg);
relay.debug(std::stringstream msg);
relay.info(std::stringstream msg);
relay.warn(std::stringstream msg);
relay.error(std::stringstream msg);
relay.fatal(std::stringstream msg);

Handling Newton optimization output:
Model-file-defined signatures

relay.write_parameters(std::vector<double>);
relay.write_unconstrained_parameters(std::vector<double>);
relay.write_transformed_parameters(std::vector<double>);
relay.write_generated_quantities(std::vector<double>);
relay.write_hessian(Eigen::Matrixd hessian) (CHECK: is this generated or just rstan? Q: should we generate this?)
relay.write_parameter_gradients(std::vector<double>);

Algorithm-defined signatures:

relay.write_iteration(int iteration);
relay.write_stage(std::string stage); <- this should be, "warmup"/"sampling" for MCMC, could indicate different warmup stages within HMC with adaptation, could be "optimizing"/"converged" for optimization.  Since we often want to know the timing information stage-wise the relay class could run a timer and record how long each stage took as well.
relay.write_log_density(double);
relay.write_delta_log_density(double);

Logger:

relay.debug(std::string msg);
relay.info(std::string msg);
relay.warn(std::string msg);
relay.error(std::string msg);
relay.fatal(std::string msg);
relay.debug(std::stringstream msg);
relay.info(std::stringstream msg);
relay.warn(std::stringstream msg);
relay.error(std::stringstream msg);
relay.fatal(std::stringstream msg);

Handling ADVI output:
Model-file-defined signatures

relay.write_parameters(std::vector<double>);
relay.write_unconstrained_parameters(std::vector<double>);
relay.write_transformed_parameters(std::vector<double>);
relay.write_generated_quantities(std::vector<double>);
relay.write_hessian(Eigen::Matrixd hessian) (CHECK: is this generated or just rstan? Q: should we generate this?)
relay.write_parameter_gradients(std::vector<double>);

Algorithm-defined signatures:

relay.write_iteration(int iteration);
relay.write_stage(std::string stage); <- this should be, "warmup"/"sampling" for MCMC, could indicate different warmup stages within HMC with adaptation, could be "optimizing"/"converged" for optimization.  Since we often want to know the timing information stage-wise the relay class could run a timer and record how long each stage took as well.
relay.write_ELBO(double);
relay.write_delta_ELBO_mean(double);
relay.write_delta_ELBO_med(double);
relay.write_iteration_time(double);   // standardize? time_type?
relay.write_eta(double); (called once)

Logger:

relay.debug(std::string msg);
relay.info(std::string msg);
relay.warn(std::string msg);
relay.error(std::string msg);
relay.fatal(std::string msg);
relay.debug(std::stringstream msg);
relay.info(std::stringstream msg);
relay.warn(std::stringstream msg);
relay.error(std::stringstream msg);
relay.fatal(std::stringstream msg);

Rcpp is going to map a std::vector<double> to a numeric matrix and a
std::tuple to a list, both of which are fine.
On Tue, Jun 26, 2018 at 8:34 AM Krzysztof Sakrejda<EMAIL_ADDRESS>wrote:

For each of rstan (@bgoodri https://github.com/bgoodri ) and
pystan/httpstan (@ariddell https://github.com/ariddell ) what do you
want to write a std::vector and a std::tuple to?
Based on routing signatures from #2557
https://github.com/stan-dev/stan/issues/2557
Handling HMC output Model-file defined:

relay.write_parameter_names(std::vectorstd::string);
relay.write_parameters(std::vector);
relay.write_unconstrained_parameters(std::vector);
relay.write_parameter_gradients(std::vector);
relay.write_parameter_momenta(std::vector);
relay.write_generated_quantity_names(std::vectorstd::string);
relay.write_generated_quantities(std::vector);
relay.write_transformed_parameter_names(std::vectorstd::string);
relay.write_transformed_parameters(std::vector);
On the interface-side handling these requires providing a writer
implementing:

`Writer& operator<<(Writer& writer, std::vectorstd::string names);
Writer& operator<<(Writer& writer, std::vector parameters);
Writer& operator<<(Writer& writer, stan::stage stage);

The writer must also:

Set up where output will go on construction and how it is handled on
construction
Length-check that the parameters vector is the same length as the
names vector, throw on failure.

The writer is the right place to:

Thin
Select parameter subsets for output

Algorithm-defined:

relay.write_iteration(int iteration);
relay.write_stage(std::string stage); <- this should be,
"warmup"/"sampling" for MCMC, could indicate different warmup stages within
HMC with adaptation, could be "optimizing"/"converged" for optimization.
Since we often want to know the timing information stage-wise the relay
class could run a timer and record how long each stage took as well.
relay.write_log_density(double);
relay.write_stepsize(double)
relay.write_tree_depth(int);
relay.write_acceptance_rate(double);
relay.write_energy(double);
relay.write_n_leapfrog(int);
relay.write_divergence(bool);
Writer& operator<<(Writer& writer, std::vector parameters);
relay.write_mass_matrix(Eigen::Vectord mm); <- @mitzimorris
https://github.com/mitzimorris
relay.write_mass_matrix(Eigen::Matrixd mm);<- @mitzimorris
https://github.com/mitzimorris
relay.write_trajectory(???); <- @martinmodrak
https://github.com/martinmodrak
Logger:

relay.debug(std::string msg);
relay.info(std::string msg);
relay.warn(std::string msg);
relay.error(std::string msg);
relay.fatal(std::string msg);
relay.debug(std::stringstream msg);
relay.info(std::stringstream msg);
relay.warn(std::stringstream msg);
relay.error(std::stringstream msg);
relay.fatal(std::stringstream msg);

Handling BFGS/LBFGS optimization output: Model-file-defined signatures

relay.write_parameters(std::vector);
relay.write_unconstrained_parameters(std::vector);
relay.write_transformed_parameters(std::vector);
relay.write_generated_quantities(std::vector);
relay.write_hessian(Eigen::Matrixd hessian) (CHECK: is this
generated or just rstan? Q: should we generate this?)
relay.write_parameter_gradients(std::vector);

Algorithm-defined signatures:

relay.write_iteration(int iteration);
relay.write_stage(std::string stage); <- this should be,
"warmup"/"sampling" for MCMC, could indicate different warmup stages within
HMC with adaptation, could be "optimizing"/"converged" for optimization.
Since we often want to know the timing information stage-wise the relay
class could run a timer and record how long each stage took as well.
relay.write_log_density(double);
relay.write_dx_magnitude(double);
relay.write_grad_magnitude(double);
relay.write_alpha(double);
relay.write_alpha_0(double);
relay.write_n_evals(double);
"notes" <- Huh, should be broken out
termination/convergence messages

Logger:

relay.debug(std::string msg);
relay.info(std::string msg);
relay.warn(std::string msg);
relay.error(std::string msg);
relay.fatal(std::string msg);
relay.debug(std::stringstream msg);
relay.info(std::stringstream msg);
relay.warn(std::stringstream msg);
relay.error(std::stringstream msg);
relay.fatal(std::stringstream msg);

Handling Newton optimization output: Model-file-defined signatures

relay.write_parameters(std::vector);
relay.write_unconstrained_parameters(std::vector);
relay.write_transformed_parameters(std::vector);
relay.write_generated_quantities(std::vector);
relay.write_hessian(Eigen::Matrixd hessian) (CHECK: is this
generated or just rstan? Q: should we generate this?)
relay.write_parameter_gradients(std::vector);

Algorithm-defined signatures:

relay.write_iteration(int iteration);
relay.write_stage(std::string stage); <- this should be,
"warmup"/"sampling" for MCMC, could indicate different warmup stages within
HMC with adaptation, could be "optimizing"/"converged" for optimization.
Since we often want to know the timing information stage-wise the relay
class could run a timer and record how long each stage took as well.
relay.write_log_density(double);
relay.write_delta_log_density(double);

Logger:

relay.debug(std::string msg);
relay.info(std::string msg);
relay.warn(std::string msg);
relay.error(std::string msg);
relay.fatal(std::string msg);
relay.debug(std::stringstream msg);
relay.info(std::stringstream msg);
relay.warn(std::stringstream msg);
relay.error(std::stringstream msg);
relay.fatal(std::stringstream msg);

Handling ADVI output: Model-file-defined signatures

relay.write_parameters(std::vector);
relay.write_unconstrained_parameters(std::vector);
relay.write_transformed_parameters(std::vector);
relay.write_generated_quantities(std::vector);
relay.write_hessian(Eigen::Matrixd hessian) (CHECK: is this
generated or just rstan? Q: should we generate this?)
relay.write_parameter_gradients(std::vector);

Algorithm-defined signatures:

relay.write_iteration(int iteration);
relay.write_stage(std::string stage); <- this should be,
"warmup"/"sampling" for MCMC, could indicate different warmup stages within
HMC with adaptation, could be "optimizing"/"converged" for optimization.
Since we often want to know the timing information stage-wise the relay
class could run a timer and record how long each stage took as well.
relay.write_ELBO(double);
relay.write_delta_ELBO_mean(double);
relay.write_delta_ELBO_med(double);
relay.write_iteration_time(double); // standardize? time_type?
relay.write_eta(double); (called once)

Logger:

relay.debug(std::string msg);
relay.info(std::string msg);
relay.warn(std::string msg);
relay.error(std::string msg);
relay.fatal(std::string msg);
relay.debug(std::stringstream msg);
relay.info(std::stringstream msg);
relay.warn(std::stringstream msg);
relay.error(std::stringstream msg);
relay.fatal(std::stringstream msg);

—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
https://github.com/stan-dev/stan/issues/2559, or mute the thread
https://github.com/notifications/unsubscribe-auth/ADOrqoYDHdY2oAoNN1iU9Rh2yTXPue5eks5uAipegaJpZM4U32RL
.

Ditto for PyStan. Cython maps std types in a similar fashion.

The logger thing is happening instead so this is dated, closing.


Issue in save_image utility in torch vision.utils

🐛 Describe the bug
The make_grid utility here - https://github.com/pytorch/vision/blob/main/torchvision/utils.py#L24 -

The normalize option here - https://github.com/pytorch/vision/blob/main/torchvision/utils.py#L43 - shifts the image to 0 and 1 only if normalize = True, which by default is False.
Then in the save_image function here - https://github.com/pytorch/vision/blob/main/torchvision/utils.py#L138 - the images are being multiplied by 255 irrespective of weather the normalize argument is passed or not.

The issue -

Only when normalize = True should we multiply by 255 since if the values aren't between 0 and 1, then the multiplication by 255 will not make sense since the values can overflow above 255 which can cause problems depending on which file format is being used to save the PIL.Image. Also, 255 is very specific to the PNG format. If one wants to save the image as a TIFF file say, then the TIFF format takes care of floating point values for pixels as well (this could be made a feature).

The solution according to me -

if condition in save_image to multiply by 255 only when normalize = True.
255 should only be multiplied if the Image is being saved as a .png image

Versions
Collecting environment information...
PyTorch version: 1.11.0
Is debug build: False
CUDA used to build PyTorch: 11.3
ROCM used to build PyTorch: N/A
OS: CentOS Linux release 7.9.2009 (Core) (x86_64)
GCC version: (GCC) 4.8.5 20150623 (Red Hat 4.8.5-44)
Clang version: Could not collect
CMake version: version <IP_ADDRESS>
Libc version: glibc-2.17
Python version: 3.8.13 (default, Mar 28 2022, 11:38:47)  [GCC 7.5.0] (64-bit runtime)
Python platform: Linux-3.10.0-514.26.2.el7.x86_64-x86_64-with-glibc2.17
Is CUDA available: True
CUDA runtime version: Could not collect
GPU models and configuration:
GPU 0: NVIDIA GeForce RTX 2080 Ti
GPU 1: NVIDIA GeForce RTX 2080 Ti
Nvidia driver version: 470.74
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
Versions of relevant libraries:
[pip3] mypy-extensions==0.4.3
[pip3] numpy==1.21.5
[pip3] numpydoc==1.2
[pip3] torch==1.11.0
[pip3] torchaudio==0.11.0
[pip3] torchvision==0.12.0
[conda] blas                      1.0                         mkl
[conda] cudatoolkit               11.3.1               h2bc3f7f_2
[conda] ffmpeg                    4.3                  hf484d3e_0    pytorch
[conda] mkl                       2021.4.0           h06a4308_640
[conda] mkl-service               2.4.0            py38h7f8727e_0
[conda] mkl_fft                   1.3.1            py38hd3c417c_0
[conda] mkl_random                1.2.2            py38h51133e4_0
[conda] numpy                     1.21.5           py38he7a7128_1
[conda] numpy-base                1.21.5           py38hf524024_1
[conda] numpydoc                  1.2                pyhd3eb1b0_0
[conda] pytorch                   1.11.0          py3.8_cuda11.3_cudnn8.2.0_0    pytorch
[conda] pytorch-mutex             1.0                        cuda    pytorch
[conda] torchaudio                0.11.0               py38_cu113    pytorch
[conda] torchvision               0.12.0               py38_cu113    pytorch

Nice points @meghbhalerao Even I think that make_grid and save_image have valid use cases. I can also see Keras-Cv team deciding to add the same https://github.com/keras-team/keras-cv/pull/451
What are your thoughts Nicolas, I think we should start a rewrite of these two?

Thanks @oke-aditya  - let me know if any help is needed.


chore(nginx): update nginx to 1.11.7

See https://nginx.org/en/CHANGES

Current coverage is 55.23% (diff: 100%)

Merging #295 into master will not change coverage

@@             master       #295   diff @@
==========================================
  Files             6          6
  Lines           382        382
  Methods           0          0
  Messages          0          0
  Branches          0          0
==========================================
  Hits            211        211
  Misses          151        151
  Partials         20         20

Powered by Codecov. Last update 2152eeb...a0e22a7


html5 - addChild not rendering added child to screen in certain condition + possible fix

When creating a new display object but adding it as a child only several frames later,
if nothing else is changed in the stage, the call to addChild doesn't cause the stage to update.
sample code to recreate the issue (used the DisplayABitmap sample and changed the Main a bit):
package;

import openfl.events.Event;
import openfl.display.Bitmap;
import openfl.display.BitmapData;
import openfl.display.Sprite;
import openfl.Assets;

class Main extends Sprite {

	private var _bitmap:Bitmap;
	private var _isFirst:Bool;

	public function new () {
		super ();

		_bitmap = new Bitmap (Assets.getBitmapData ("assets/openfl.png"));
		_bitmap.x = (stage.stageWidth - _bitmap.width) / 2;
		_bitmap.y = (stage.stageHeight - _bitmap.height) / 2;
		_isFirst = true;
		addEventListener(Event.ENTER_FRAME, handleFrame);
	}

	private function handleFrame(e:Event):Void {
		if (!_isFirst) {
			removeEventListener(Event.ENTER_FRAME, handleFrame);
			addChild(_bitmap); // THIS LINE IS NOT WORKING
		}
		_isFirst = false;
	}
}

All I do here is create the bitmap first, then wait for at least one frame to pass,
then add the child.
In the html5 target, the child isn't shown.
In other targets (flash, neko) it is.
A possible fix is to change the code in the html5 codebase of the class DisplayObjectContainer,
In the function addChild, change the line:
child.__setRenderDirty ();

into:
var prevWorldRenderDirty:Int = DisplayObject.__worldRenderDirty;
child.__setRenderDirty ();
if (prevWorldRenderDirty == DisplayObject.__worldRenderDirty) {
	DisplayObject.__worldRenderDirty++;
}

This is fixed in the current version, thank you :smile:


0.2.0 release

Creates a new release as well as adds a bunch of new features

You can specify the allocators a specific ChaiBuffer should use, and construct an Array with that buffer.
You can allocate an Array directly on device. This only works when the Array was previously empty, but this should be the most common use case.
Adds half precision math functions.
Added a conversion from ArrayView< T, N, NDIM > to ArrayView< U, N, NDIM >, the use case I had was to convert between arrays of __half and __half2.
Improved the error logging when you have an LVARRAY_ERROR (or ASSERT) on device.
Added functions to memcpy an ArraySlice, it uses Umpire so it works between memory spaces.
Added a zero method to ArrayView and CRSMatrixView that will use memset instead of launching a kernel to zero it out.
arrayManipulation::resize (which is used by pretty much every class) now uses memset when it will be zero initializing memory. This should be a lot faster for large arrays, it's possible it will be slower for really small things but it would be easy enough to add in a different path for small cases.
Replace the MemorySpace enum with using MemorySpace = camp::resource::Platform.

Closes #172.

Both Spheral and Tribol would like a new release, plus the original release didn't have the Python stuff.

@corbett5 Don't create the release until we setup the auto DOI generation.


[arc] We do not need to resummarize the subregion blocks of a loop when we summarize the loops ARC interesting instructions. This will save compile time.

The reason why this work is not needed is that ARC before any dataflow is
performed first summarizes the interesting instructions in all blocks. This
information is kept up to date by the ARC optimizer as it moves around
retains/releases.
Thus while performing dataflow, all we need to summarize are loops.

@swift-ci Please smoke test


gconv单元测试

Codecov Report

Merging #209 into master will increase coverage by 2.6%.
The diff coverage is n/a.

@@            Coverage Diff            @@
##           master     #209     +/-   ##
=========================================
+ Coverage   73.18%   75.78%   +2.6%
=========================================
  Files         204      204
  Lines       17558    17558
=========================================
+ Hits        12849    13307    +458
+ Misses       4024     3618    -406
+ Partials      685      633     -52

Impacted Files
Coverage Δ

g/os/gspath/gspath_cache.go
81.81% <0%> (-4.55%)
:arrow_down:

g/container/gtree/gtree_btree.go
91.87% <0%> (-0.68%)
:arrow_down:

g/container/gtree/gtree_redblacktree.go
97.36% <0%> (+0.4%)
:arrow_up:

g/util/gconv/gconv_struct.go
76.16% <0%> (+9.3%)
:arrow_up:

g/util/gconv/gconv_map.go
100% <0%> (+46.55%)
:arrow_up:

g/util/gconv/gconv_time.go
100% <0%> (+55%)
:arrow_up:

g/util/gconv/gconv.go
100% <0%> (+59.93%)
:arrow_up:

g/util/gconv/gconv_slice.go
100% <0%> (+84.36%)
:arrow_up:

Continue to review full report at Codecov.

Legend - Click here to learn more
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Powered by Codecov. Last update 4b9db0c...d2a053c. Read the comment docs.


[Bug Report] YouTube gets broken after 2nd song.

Bug Description
After 2 songs the YouTube request gets broken. (Can't paly anything on YouTube until restart)
Steps to Reproduce

Login Voice chat
Do !play command to play youtube
After 2nd song YouTUbe gets broken

Expected Result
Play youtube music.
Debug Output
[02:12:20] [WARN] [LocalAudioTrackExecutor]: Suspicious exception for playback of U4ZPHtzmvCE
com.sedmelluq.discord.lavaplayer.tools.FriendlyException: Please sign in
at dev.lavalink.youtube.track.YoutubeAudioTrack.process(YoutubeAudioTrack.java:102)
at com.sedmelluq.discord.lavaplayer.track.playback.LocalAudioTrackExecutor.execute(LocalAudioTrackExecutor.java:109)
at com.sedmelluq.discord.lavaplayer.player.DefaultAudioPlayerManager.lambda$executeTrack$2(DefaultAudioPlayerManager.java:339)
at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source)
at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source)
at java.base/java.lang.Thread.run(Unknown Source)
[02:12:20] [ERROR] [AudioHandler]: Track U4ZPHtzmvCE has failed to play
com.sedmelluq.discord.lavaplayer.tools.FriendlyException: Please sign in
at dev.lavalink.youtube.track.YoutubeAudioTrack.process(YoutubeAudioTrack.java:102)
at com.sedmelluq.discord.lavaplayer.track.playback.LocalAudioTrackExecutor.execute(LocalAudioTrackExecutor.java:109)
at com.sedmelluq.discord.lavaplayer.player.DefaultAudioPlayerManager.lambda$executeTrack$2(DefaultAudioPlayerManager.java:339)
at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source)
at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source)
at java.base/java.lang.Thread.run(Unknown Source)
Additional Info
Currently using Jmusicbot <IP_ADDRESS> (Tried with 0.4.3) too, but not works.
Checklist

[X] I have looked for information about this within the documentation
[X] I have searched for similar issues on the issues page
[X] I am using an official release from the releases page

JMusicBot Version
<IP_ADDRESS>

Your IP has been identified as a bot by youtube. I had the same issue when using a VPS that was rang banned so I hosted it on my PI at home but yesterday it also got flagged.
Gotta wait for https://github.com/jagrosh/MusicBot/pull/1670 I guess...

where you got version <IP_ADDRESS>?

On their Discord.

Your IP has been identified as a bot by youtube. I had the same issue when using a VPS that was rang banned so I hosted it on my PI at home but yesterday it also got flagged.
Gotta wait for #1670 I guess...

Even if I use a VPN would't fix it? I have a paid VPN.

Your IP has been identified as a bot by youtube. I had the same issue when using a VPS that was rang banned so I hosted it on my PI at home but yesterday it also got flagged.
Gotta wait for #1670 I guess...

Even if I use a VPN would't fix it? I have a paid VPN.

Maybe, until youtube deem your vpn range as "must be logged on" by youtube api, if it's not already the case

Well I talked to A locked NUTS (developer)
This is what he/she said.
Currently using : [JMusicBot-0.4.3-yt-oauth2-348fc3c.jar] Tried with VPN also, some videos not plays at all. :(

Did they give an eta for 4.4?

Well I talked to A locked NUTS (developer)
This is what he/she said.
Currently using : [JMusicBot-0.4.3-yt-oauth2-348fc3c.jar] Tried with VPN also, some videos not plays at all. :(

the yt-oauth worked like a charm for me, thanks for the tip

Apologies for a basic question. Was hoping to test the OAuth YT option - not sure however where to acquire the build which includes it. Running on Docker for what it's worth. Cheers!

Duplicate of #1588

Thank you for the comment, after I "logged on with a fake google" seems working just fine =)

Apologies for a basic question. Was hoping to test the OAuth YT option - not sure however where to acquire the build which includes it. Running on Docker for what it's worth. Cheers!

Hey, I found the experimental build on the Jagrosh Discord
In the Bot Discussion > Support
Look for the first pinned message by Michaili and follow the steps to set up a google account to your bot.
This is experimental and not the main build so use it at your own discretion.

Apologies for a basic question. Was hoping to test the OAuth YT option - not sure however where to acquire the build which includes it. Running on Docker for what it's worth. Cheers!

Hey, I found the experimental build on the Jagrosh Discord
In the Bot Discussion > Support Look for the first pinned message by Michaili and follow the steps to set up a google account to your bot.
This is experimental and not the main build so use it at your own discretion.

Hi thanks the message yeah I use that experimental build, with a Google API. (Made a fake Google Account) and since then everything seems to work, some songs like rap or else which filtered adult content or else, it cannot be player but other then that 99.9% songs are playable rn
So I reccomend to make a fake Google Account and use the API with the bot.


MapUnitPoints

It seems that MapUnitPoints can only be added in the 10 (not the 105) version of the New Database tool.

This most likely reflected failure to save the new .tbx in version 10-compatible format.  "Save As" doesn't work if the file already exists, but ArcMap offers no warnings--you have to check timestamps to see that nothing happened.
There have been several updates to the .tbx files since July 2019, so I suspect this issue is moot. I'm closing it.


Build(deps-dev): Bump eslint-plugin-react-hooks from 4.6.0 to 4.6.2 (#5805)

Pull request opened by the merge tool on behalf of #5805

Merged in bf691a14bbf81ee8907dc0ed1a74b13be87a6712


Allow for custom floating-ui root portal element

Could be possible to allow custom floating-ui root element reference?
Instead of using body to append floating elements like pop-ups, etc
Could it be possible to reference a custom element to append the floating-ui to?
My use case it's embedding an app inside another and I am not allowed to use iframe for encapsulation.
Thanks and sorry if this falls out of scope for this library!

I am using Popover in video player with custom controls. When I am going fullscreen the Popover is not showing as it is appended to the body tag. It would be great if we can use Popover inside desired tag.


Ambiguity Around Authentication/ApplicationCookie/ExpireTimeSpan

This setting is listed twice on this page:

User account lockout settings - Default: 24 hours
Cookie authentication site settings - Default: 14 days

Based on the grouping of the other settings, I'm guessing that for User account lockout settings, the setting name should be Authentication/UserManager/ExpireTimeSpan. Just need to confirm that and update the doc.

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

ID: 6313f348-4130-74f5-320d-b5bd270060c6
Version Independent ID: de470e06-c2d3-354e-ba80-75691a5d8bd4
Content: Set authentication identity for a portal - Power Apps
Content Source: powerapps-docs/maker/portals/configure/set-authentication-identity.md
Service: powerapps
GitHub Login: @sandhangitmsft
Microsoft Alias: sandhan

Thank you for reaching out. It's 24 hours - 1 day. The documentation has been updated and will be visible after some time.


Update apheleia-indent-lisp-buffer to respect local variables

I ran into some issues using apheleia while contributing to a project
that works with indent-tabs-mode disabled. This led to this slight
modification of apheleia-indent-lisp-buffer
This allows for any local values for indent-line-function,
lisp-indent-function to be carried over when
formatting. Additionally, added indent-tabs-mode in order to
prevent adding tabs when unwanted.
If there is any additional documentation you would like me to provide,
let me know and I can update.

Yeah, that makes sense. We should be enabling the major mode before setting the local variables. I think this just needs a changelog entry, no need to update the README as the new behavior is what a user would intuitively expect to happen.

I put it under bugs fixed if that works


🚀 Feature: [Tech Insights plugin] allow the ability to trigger FactRetrievers for a specific component

🔖 Feature description
Create a new POST endpoint in the Tech-Insights Plugin backend, to allow for triggering the FactRetrievers for a specific component.
🎤 Context
Currently, FactRetrievers will only run on a schedule, and the backend plugin does not expose any other way to allow us to trigger a "recalculation of facts" to a specific component.
This means that the users are stuck with stale data (because it will wait until the FactRetriever job gets triggered, to fetch everything again).
I want to allow users of the Tech-Insights plugin to trigger a "recalculation of the facts", if they want, so they don't have to wait for the next CronJob to run.
This would be specially useful in big systems where you have thousands of components, and you don't want to be running that  big job all the time.
This would also allow us to add a Refresh button in the Scorecard Card, so if changes were made to the repository, users could force a Refresh of the Facts, to get the most up-to-date information, without having to wait for the next cronjob to run.
✌ Possible Implementation
Create a POST /facts/:namespace/:kind/:name endpoint, that would trigger the FactRetrieverHandler where entityFilter is set to only filter that specific component.
👀 Have you spent some time to check if this feature request has been raised before?

[X] I checked and didn't find similar issue

🏢 Have you read the Code of Conduct?

[X] I have read the Code of Conduct

Are you willing to submit PR?
Yes I am willing to submit a PR!

Ping @Xantier not sure if you'd seen this?

Ping @Xantier not sure if you'd seen this?

I have now. Thanks @sonikro, this makes perfect sense and would be a good addition to the package. PR would be very much appreciated

+1 on this

Any update on this? Facing the same issue as we only schedule once a week. We want to manually trigger retrieval for new facts

Any update on this? Facing the same issue as we only schedule once a week. We want to manually trigger retrieval for new facts

I have a PR open already. I just got back from vacation I'll try to wrap it up this weekend.

@sonikro What happened with your PR?

@sonikro What happened with your PR?

I've been having some trouble getting the time to wrap it up. If someone wants to continue the work on my PR feel free to do it. If I manage to get some time to work on it next week I'll reopen the PR.


Builds should be able to be ordered by dates (scheduled_on, started_on, ended_on)

Currently the build endpoint allows only sorting by id.

The following need to be modified in order for this to be possible https://github.com/Quansight/conda-store/blob/main/conda-store-server/conda_store_server/server/views/api.py#L546-L550.
As a bonus it would be nice to ensure that the fastapi docs reflect the available options (this would make the issue harder but not necissary).


Pools taking too long to load BUT not showing user there is a delay

Describe the bug
We need to show the user that the UI is loading, or ideally which pools there are and that information is loading. Instead the Pools are popping onto the screen and loading is hidden from the user, which is misleading.
TESTNET
Polygon appears after >30 seconds
https://www.awesomescreenshot.com/video/13438556?key=b97d90fbfd5e24f0faf2f06f2d96537f
MAINNET
https://www.awesomescreenshot.com/video/13438597?key=a22411cfe29690afdef49f3ed0785648
To Reproduce
Steps to reproduce the behavior:

Go to https://testnet.bridge.connext.network/pools
Wait

Can we also keep the token category position fixed while/after it loads? You can see here that the category jumps around a lot near the end of the load: https://share.vidyard.com/watch/4M54AveaKaFNfxMuyksPKL?


feat(multicast): add multicast

add multicast feature

Coverage decreased (-0.6%) to 85.294% when pulling 446ec5d0419fba2ce4fd4c7e28150a92500f0c7c on multicast into c1c8d7698a1b64a9d06790da2a6314feb55908fb on master.


[WIP]Adding the source_updated_at column for volumes table.

Adding DB migration with extra column for the volumes table.
related PRs:

https://github.com/RedHatInsights/topological_inventory-ingress_api-client-ruby/pull/66
https://github.com/RedHatInsights/topological_inventory-ingress_api/pull/91
https://github.com/RedHatInsights/topological_inventory-azure/pull/25
https://github.com/RedHatInsights/topological_inventory-persister/pull/61

based on https://projects.engineering.redhat.com/browse/TPINVTRY-789

@pkomanek you need to increase VERSION https://github.com/RedHatInsights/topological_inventory-core/blob/master/lib/topological_inventory/core/version.rb
And update it in the persister repo's Gemfile

https://github.com/RedHatInsights/topological_inventory-ingress_api-client-ruby/pull/66#issuecomment-630979208

SecurityGroup.network fixed by https://github.com/RedHatInsights/topological_inventory-core/pull/163