Menu bar 'open settings' and location not working properly

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Description of issue / Feature request
From menu bar icon, Open Settings on the main dropdown does not work. have to go to experimental and 'launch aerial settings'.
also location does not seem to work. I had a screen recording, but not able to attach as GitHub says video is too big. Not able to compress the video to below 10MB
AerialLog.txt

Hey @liquidmetal2
Apologies it's been a busy week and I wasn't able to answer you earlier.

Regarding the first issue, yes, it's an issue with the new System Settings from the macOS Ventura beta you are running.

The experimental trick will work in the meantime, but you can also try the latest beta of Aerial Companion here from last july : https://github.com/AerialScreensaver/AerialCompanion/releases/download/v1.4.0beta2/Aerial.Companion.app.zip
In beta2 you'll see it explicitly says that settings are not the same in screen saver mode and in desktop/fullscreen mode.

This is no longer true, despite what it says. Basically the 3 buttons will give you access to the same settings, except the top one opens macOS's System Settings, while the other two, Companion loads the settings in a window for easier access. I'm working on redesigning things so there's only one button in companion to make things simpler again, but it's not ready yet. Hopefully it will be for Ventura release.

Regarding location, it looks like it's not location but the weather fetch. Can you try opening System Settings > Screen Saver, then try the weather fetch from here ? I think there may be a weird network issue when Companion runs the screen saver, for some reason. It should not affect the screen saver when it runs, it should be able to fetch your data correctly.

Let me know if that clears things up.

Quick update : the weather API uses HTTP and there's a security thing that blocks that nowadays by default, I'll try to see how to fix that. This is only an issue when running Aerial Settings from Companion, as far as I can tell.  The screen saver itself will run fine (but not the experimental stuff).

Hi

For the first item, will check out the beta later, as don’t really want to use beta at the moment on top of Ventura beta!
For the second item about weather location check, yes, doing it via System Settings > Screensaver > Aerial Options and selecting ‘current location’ worked and it shows the location correctly. Of course as you mentioned via the Companion app, it doesn’t work due to the http block; I expect mostly all are now required to be https.

Thanks for your reply and the nice work on Aerial screensaver
Thank you,
Ravindra Holalkere
A developed country is not where the poor have cars, it's where the rich use public transport
														— (Unknown) Mayor of Bogota.

The greatest tragedy in mankind's entire history may be the hijacking of morality by religion.
														—Arthur C Clarke

On 08-Oct-2022, at 20:39, Guillaume Louel @.***> wrote:
Hey @liquidmetal2 https://github.com/liquidmetal2
Apologies it's been a busy week and I wasn't able to answer you earlier.
Regarding the first issue, yes, it's an issue with the new System Settings from the macOS Ventura beta you are running.
The experimental trick will work in the meantime, but you can also try the latest beta of Aerial Companion here from last july : https://github.com/AerialScreensaver/AerialCompanion/releases/download/v1.4.0beta2/Aerial.Companion.app.zip
In beta2 you'll see it explicitly says that settings are not the same in screen saver mode and in desktop/fullscreen mode.
https://user-images.githubusercontent.com/37544189/194713578-9905069a-4820-415e-83b2-128d961fbadb.png
This is no longer true, despite what it says. Basically the 3 buttons will give you access to the same settings, except the top one opens macOS's System Settings, while the other two, Companion loads the settings in a window for easier access. I'm working on redesigning things so there's only one button in companion to make things simpler again, but it's not ready yet. Hopefully it will be for Ventura release.
Regarding location, it looks like it's not location but the weather fetch. Can you try opening System Settings > Screen Saver, then try the weather fetch from here ? I think there may be a weird network issue when Companion runs the screen saver, for some reason. It should not affect the screen saver when it runs, it should be able to fetch your data correctly.
Let me know if that clears things up.
—
Reply to this email directly, view it on GitHub https://github.com/JohnCoates/Aerial/issues/1266#issuecomment-1272339335, or unsubscribe https://github.com/notifications/unsubscribe-auth/AAURJEPTHHIKEI4WNHV3653WCGFA5ANCNFSM6AAAAAAQ2YTLRQ.
You are receiving this because you were mentioned.

@liquidmetal2 second bug is also fixed in this beta of Companion with more fixes for Ventura, I would encourage you to actually update because of the Ventura changes : https://github.com/AerialScreensaver/AerialCompanion/releases/download/v1.5.0b2/Aerial.Companion.app.zip

Ok sure, thanks. Will try it out today.
Thank You,
Ravindra H

"Primitive life is very common and intelligent life is fairly rare. Some would say it has yet to occur on earth.”
																		—Stephen Hawking, Lecture at George Washington University, 2008

"We have, I fear, confused power with greatness"
                                                													—Stewart Udall (he said this decades ago and it holds true for the millennia).

On 16-Oct-2022, at 21:25, Guillaume Louel @.***> wrote:
@liquidmetal2 https://github.com/liquidmetal2 second bug is also fixed in this beta of Companion with more fixes for Ventura, I would encourage you to actually update because of the Ventura changes : https://github.com/AerialScreensaver/AerialCompanion/releases/download/v1.5.0b2/Aerial.Companion.app.zip
—
Reply to this email directly, view it on GitHub https://github.com/JohnCoates/Aerial/issues/1266#issuecomment-1279996994, or unsubscribe https://github.com/notifications/unsubscribe-auth/AAURJELFXMIQA57WROGE66TWDQQQ3ANCNFSM6AAAAAAQ2YTLRQ.
You are receiving this because you were mentioned.


[<Library component: Model|Core|etc...>] AutoTimeMixer is not working

What happened + What you expected to happen
I have tried to use AutoTimeMixer after successfully doing ordinary 'TimeMixer.'
This one worked well when I tried to do it. (ordinary ver)
Note: Y_train_df["unique_id"].nunique() = 5  and H = 288
model = TimeMixer(
    h=H,
    input_size=1440,
    n_series=Y_train_df["unique_id"].nunique(),
    scaler_type='minmax',
    max_steps=500,
    early_stop_patience_steps=10,
    val_check_steps=50,
    learning_rate=1e-3,
    loss=MSE(),
    valid_loss=MAE(),
    batch_size=32,

    d_ff=5,
    e_layers=5,

    accelerator='auto',
    devices='auto',
    enable_model_summary=False,
    enable_progress_bar=True
)

and then, AutoTimeMixer is not working. Both Ray and Optuna are not working. I wonder why does it happen for auto.
I have tried to use many different parameters to match the tensor size only to fail to solve this problem.
The code and logs are in the section below:
Versions / Dependencies
python 3.10.14
reinstalled neuralforecast today
Reproduction script
H = 288

config1 = {
    'n_series': Y_train_df["unique_id"].nunique(),
    'input_size': 1440,
    # 'down_sampling_layers': 5,
    # 'down_sampling_window': 5,
    'scaler_type': 'minmax',
    'batch_size': 64,
    }

config2 = AutoTimeMixer.get_default_config(h=288, backend="optuna", n_series= Y_train_df["unique_id"].nunique() )

def config_o(trial):
    return config1

model = AutoTimeMixer(
    h = H,
    n_series = Y_train_df["unique_id"].nunique(),
    config = config1,
    loss = MSE(),
    valid_loss = MSE(),
    verbose = True,
    backend = "ray",   # the error is the same when it is optuna and use config 2
    num_samples = 5,
    gpus = 1,

)

nf = NeuralForecast(models=[model], freq='10min')

nf.fit(df=Y_train_df, val_size=288)

ERROR LOG

---------------------------------------------------------------------------
ProcessRaisedException                    Traceback (most recent call last)
Cell In[3], [line 60](vscode-notebook-cell:?execution_count=3&line=60)
     [42](vscode-notebook-cell:?execution_count=3&line=42) model = AutoTimeMixer(
     [43](vscode-notebook-cell:?execution_count=3&line=43)     h = H,
     [44](vscode-notebook-cell:?execution_count=3&line=44)     n_series = Y_train_df["unique_id"].nunique(),
   (...)
     [52](vscode-notebook-cell:?execution_count=3&line=52)
     [53](vscode-notebook-cell:?execution_count=3&line=53) )
     [58](vscode-notebook-cell:?execution_count=3&line=58) nf = NeuralForecast(models=[model], freq='10min')
---> [60](vscode-notebook-cell:?execution_count=3&line=60) nf.fit(df=Y_train_df, val_size=288)

File ~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/core.py:544, in NeuralForecast.fit(self, df, static_df, val_size, sort_df, use_init_models, verbose, id_col, time_col, target_col, distributed_config)
    [541](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/core.py:541)     self._reset_models()
    [543](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/core.py:543) for i, model in enumerate(self.models):
--> [544](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/core.py:544)     self.models[i] = model.fit(
    [545](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/core.py:545)         self.dataset, val_size=val_size, distributed_config=distributed_config
    [546](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/core.py:546)     )
    [548](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/core.py:548) self._fitted = True

File ~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_auto.py:429, in BaseAuto.fit(self, dataset, val_size, test_size, random_seed, distributed_config)
    [417](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_auto.py:417)     results = self._optuna_tune_model(
    [418](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_auto.py:418)         cls_model=self.cls_model,
    [419](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_auto.py:419)         dataset=dataset,
   (...)
    [426](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_auto.py:426)         distributed_config=distributed_config,
    [427](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_auto.py:427)     )
    [428](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_auto.py:428)     best_config = results.best_trial.user_attrs["ALL_PARAMS"]
--> [429](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_auto.py:429) self.model = self._fit_model(
    [430](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_auto.py:430)     cls_model=self.cls_model,
    [431](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_auto.py:431)     config=best_config,
    [432](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_auto.py:432)     dataset=dataset,
    [433](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_auto.py:433)     val_size=val_size * self.refit_with_val,
    [434](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_auto.py:434)     test_size=test_size,
    [435](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_auto.py:435)     distributed_config=distributed_config,
    [436](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_auto.py:436) )
    [437](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_auto.py:437) self.results = results
    [439](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_auto.py:439) # Added attributes for compatibility with NeuralForecast core

File ~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_auto.py:362, in BaseAuto._fit_model(self, cls_model, config, dataset, val_size, test_size, distributed_config)
    [358](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_auto.py:358) def _fit_model(
    [359](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_auto.py:359)     self, cls_model, config, dataset, val_size, test_size, distributed_config=None
    [360](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_auto.py:360) ):
    [361](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_auto.py:361)     model = cls_model(**config)
--> [362](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_auto.py:362)     model = model.fit(
    [363](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_auto.py:363)         dataset,
    [364](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_auto.py:364)         val_size=val_size,
    [365](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_auto.py:365)         test_size=test_size,
    [366](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_auto.py:366)         distributed_config=distributed_config,
    [367](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_auto.py:367)     )
    [368](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_auto.py:368)     return model

File ~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_multivariate.py:547, in BaseMultivariate.fit(self, dataset, val_size, test_size, random_seed, distributed_config)
    [543](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_multivariate.py:543) if distributed_config is not None:
    [544](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_multivariate.py:544)     raise ValueError(
    [545](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_multivariate.py:545)         "multivariate models cannot be trained using distributed data parallel."
    [546](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_multivariate.py:546)     )
--> [547](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_multivariate.py:547) return self._fit(
    [548](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_multivariate.py:548)     dataset=dataset,
    [549](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_multivariate.py:549)     batch_size=self.n_series,
    [550](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_multivariate.py:550)     valid_batch_size=self.n_series,
    [551](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_multivariate.py:551)     val_size=val_size,
    [552](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_multivariate.py:552)     test_size=test_size,
    [553](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_multivariate.py:553)     random_seed=random_seed,
    [554](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_multivariate.py:554)     shuffle_train=False,
    [555](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_multivariate.py:555)     distributed_config=None,
    [556](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_multivariate.py:556) )

File ~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_model.py:356, in BaseModel._fit(self, dataset, batch_size, valid_batch_size, val_size, test_size, random_seed, shuffle_train, distributed_config)
    [354](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_model.py:354) model = self
    [355](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_model.py:355) trainer = pl.Trainer(**model.trainer_kwargs)
--> [356](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_model.py:356) trainer.fit(model, datamodule=datamodule)
    [357](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_model.py:357) model.metrics = trainer.callback_metrics
    [358](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_model.py:358) model.__dict__.pop("_trainer", None)

File ~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py:538, in Trainer.fit(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)
    [536](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py:536) self.state.status = TrainerStatus.RUNNING
    [537](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py:537) self.training = True
--> [538](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py:538) call._call_and_handle_interrupt(
    [539](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py:539)     self, self._fit_impl, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path
    [540](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py:540) )

File ~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py:46, in _call_and_handle_interrupt(trainer, trainer_fn, *args, **kwargs)
     [44](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py:44) try:
     [45](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py:45)     if trainer.strategy.launcher is not None:
---> [46](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py:46)         return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
     [47](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py:47)     return trainer_fn(*args, **kwargs)
     [49](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py:49) except _TunerExitException:

File ~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/pytorch_lightning/strategies/launchers/multiprocessing.py:144, in _MultiProcessingLauncher.launch(self, function, trainer, *args, **kwargs)
    [136](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/pytorch_lightning/strategies/launchers/multiprocessing.py:136) process_context = mp.start_processes(
    [137](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/pytorch_lightning/strategies/launchers/multiprocessing.py:137)     self._wrapping_function,
    [138](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/pytorch_lightning/strategies/launchers/multiprocessing.py:138)     args=process_args,
   (...)
    [141](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/pytorch_lightning/strategies/launchers/multiprocessing.py:141)     join=False,  # we will join ourselves to get the process references
    [142](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/pytorch_lightning/strategies/launchers/multiprocessing.py:142) )
    [143](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/pytorch_lightning/strategies/launchers/multiprocessing.py:143) self.procs = process_context.processes
--> [144](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/pytorch_lightning/strategies/launchers/multiprocessing.py:144) while not process_context.join():
    [145](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/pytorch_lightning/strategies/launchers/multiprocessing.py:145)     pass
    [147](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/pytorch_lightning/strategies/launchers/multiprocessing.py:147) worker_output = return_queue.get()

File ~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/torch/multiprocessing/spawn.py:189, in ProcessContext.join(self, timeout)
    [187](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/torch/multiprocessing/spawn.py:187) msg = "\n\n-- Process %d terminated with the following error:\n" % error_index
    [188](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/torch/multiprocessing/spawn.py:188) msg += original_trace
--> [189](https://vscode-remote+ssh-002dremote-002btrain2.vscode-resource.vscode-cdn.net/sswoon/TimeSeries/NeuralForecast_test/~/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/torch/multiprocessing/spawn.py:189) raise ProcessRaisedException(msg, error_index, failed_process.pid)

ProcessRaisedException:

-- Process 2 terminated with the following error:
Traceback (most recent call last):
  File "anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/torch/multiprocessing/spawn.py", line 76, in _wrap
    fn(i, *args)
  File "anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/pytorch_lightning/strategies/launchers/multiprocessing.py", line 173, in _wrapping_function
    results = function(*args, **kwargs)
  File "anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 574, in _fit_impl
    self._run(model, ckpt_path=ckpt_path)
  File "anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 981, in _run
    results = self._run_stage()
  File "anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 1025, in _run_stage
    self.fit_loop.run()
  File "anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py", line 205, in run
    self.advance()
  File "anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py", line 363, in advance
    self.epoch_loop.run(self._data_fetcher)
  File "anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 140, in run
    self.advance(data_fetcher)
  File "anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 250, in advance
    batch_output = self.automatic_optimization.run(trainer.optimizers[0], batch_idx, kwargs)
  File "anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 190, in run
    self._optimizer_step(batch_idx, closure)
  File "anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 268, in _optimizer_step
    call._call_lightning_module_hook(
  File "anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 167, in _call_lightning_module_hook
    output = fn(*args, **kwargs)
  File "anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/pytorch_lightning/core/module.py", line 1306, in optimizer_step
    optimizer.step(closure=optimizer_closure)
  File "anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/pytorch_lightning/core/optimizer.py", line 153, in step
    step_output = self._strategy.optimizer_step(self._optimizer, closure, **kwargs)
  File "anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/pytorch_lightning/strategies/ddp.py", line 270, in optimizer_step
    optimizer_output = super().optimizer_step(optimizer, closure, model, **kwargs)
  File "anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/pytorch_lightning/strategies/strategy.py", line 238, in optimizer_step
    return self.precision_plugin.optimizer_step(optimizer, model=model, closure=closure, **kwargs)
  File "anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/pytorch_lightning/plugins/precision/precision.py", line 122, in optimizer_step
    return optimizer.step(closure=closure, **kwargs)
  File "anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/torch/optim/lr_scheduler.py", line 130, in wrapper
    return func.__get__(opt, opt.__class__)(*args, **kwargs)
  File "anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/torch/optim/optimizer.py", line 484, in wrapper
    out = func(*args, **kwargs)
  File "anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/torch/optim/optimizer.py", line 89, in _use_grad
    ret = func(self, *args, **kwargs)
  File "anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/torch/optim/adam.py", line 205, in step
    loss = closure()
  File "anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/pytorch_lightning/plugins/precision/precision.py", line 108, in _wrap_closure
    closure_result = closure()
  File "anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 144, in __call__
    self._result = self.closure(*args, **kwargs)
  File "anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
    return func(*args, **kwargs)
  File "anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 129, in closure
    step_output = self._step_fn()
  File "anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 317, in _training_step
    training_step_output = call._call_strategy_hook(trainer, "training_step", *kwargs.values())
  File "anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 319, in _call_strategy_hook
    output = fn(*args, **kwargs)
  File "anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/pytorch_lightning/strategies/strategy.py", line 389, in training_step
    return self._forward_redirection(self.model, self.lightning_module, "training_step", *args, **kwargs)
  File "anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/pytorch_lightning/strategies/strategy.py", line 640, in __call__
    wrapper_output = wrapper_module(*args, **kwargs)
  File "anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
    return forward_call(*args, **kwargs)
  File "seoul/anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/torch/nn/parallel/distributed.py", line 1636, in forward
    else self._run_ddp_forward(*inputs, **kwargs)
  File "anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/torch/nn/parallel/distributed.py", line 1454, in _run_ddp_forward
    return self.module(*inputs, **kwargs)  # type: ignore[index]
  File "anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
    return forward_call(*args, **kwargs)
  File "anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/pytorch_lightning/strategies/strategy.py", line 633, in wrapped_forward
    out = method(*_args, **_kwargs)
  File "anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_base_multivariate.py", line 371, in training_step
    output = self(windows_batch)
  File "anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
    return forward_call(*args, **kwargs)
  File "anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/models/timemixer.py", line 645, in forward
    y_pred = self.forecast(insample_y, x_mark_enc, x_mark_dec)
  File "anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/models/timemixer.py", line 576, in forecast
    x = self.normalize_layers[i](x, "norm")
  File "anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
    return forward_call(*args, **kwargs)
  File "anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_modules.py", line 557, in forward
    x = self._normalize(x)
  File "anaconda3/envs/sswoon_llama3/lib/python3.10/site-packages/neuralforecast/common/_modules.py", line 588, in _normalize
    x = x * self.affine_weight
RuntimeError: The size of tensor a (2) must match the size of tensor b (5) at non-singleton dimension 2

Issue Severity
High: It blocks me from completing my task.

[plus] Since I have four GPUs,  I set gpus = 4 and it seems gpus are not detected and freezed. I had to set gpus = 1 to avoid this problem. According to the document, gpus is the number of gpus that I have. I wonder why this is not working either.


Unify error format

Expected Behaviour
Let's say we have the following code:
auth(config)
  .then(token => console.log(token))
  .catch(error => console.log(`Error retrieving access token. ${error}`));

Sometimes jwt-auth throws a native error (eg. if we feed a public key as a private key). Other times it throws a native object (for JSON errors thrown by the API). It will be helpful to have one type of error.
Actual Behaviour
In the case when an object is thrown as an error, the native object toString method will return
[object Object]. Our error looks like Error retrieving the access token. [object Object] which is not optimal.

Can we wrap this into #9?

Yes. But it is @Aaronius fault that he doesn't read the comments from the PR request form. :p

@dompuiu I'm going to mark this as a duplicate of #9 even though your's was the earlier issue. Should have a fix this week.


Update client3:circe, client3:core, ... to 3.9.6

About this PR
📦 Updates

com.softwaremill.sttp.client3:circe
com.softwaremill.sttp.client3:core
com.softwaremill.sttp.client3:zio

from 3.9.1 to 3.9.6
📜 GitHub Release Notes - Version Diff
Usage
✅ Please merge!
I'll automatically update this PR to resolve conflicts as long as you don't change it yourself.
If you'd like to skip this version, you can just close this PR. If you have any feedback, just mention me in the comments below.
Configure Scala Steward for your repository with a .scala-steward.conf file.
Have a fantastic day writing Scala!

⚙ Adjust future updates
Add this to your .scala-steward.conf file to ignore future updates of this dependency:
updates.ignore = [ { groupId = "com.softwaremill.sttp.client3" } ]

Or, add this to slow down future updates of this dependency:
dependencyOverrides = [{
  pullRequests = { frequency = "30 days" },
  dependency = { groupId = "com.softwaremill.sttp.client3" }
}]

labels: library-update, early-semver-patch, semver-spec-patch, commit-count:1

Superseded by #570.


How to run an operator only in namespace mode?

Hello!
How to run a clickhouse operator in only one namespace mode?
By this I mean not only restrictions on working in specific namespaces, but also a change in clusterwide access to the namespace level.
For example:

use Role instead of ClusterRole
use RoleBinding instead if ClusterRoleBinding

try to use
https://github.com/Altinity/clickhouse-operator/blob/master/deploy/operator/clickhouse-operator-install-ansible.yaml
as template
and use something as jinja2 for replace {{ namespace }}

@Slach
I forgot to clarify that the question concerns the helm chart.
I want to deploy the helm chart operator to work only in one dedicated namespace without cluster-level rights.

by default helm chart install operator which watch only one namespace
but with cluster wide access limited access
in this case, you need to modify templates folder in helm chart
and replace ClusterRole to Role and ClusterRoleBinding to RoleBinding
look
https://github.com/Altinity/clickhouse-operator/tree/master/deploy/helm/clickhouse-operator/templates/generated
for details


Topic message scheduling async sample for azure-servicebus

Adding topic message scheduling async sample for azure-servicebus==7.0.0b5.
@KieranBrantnerMagee Let me know if this needs any update.

First, thanks for doing the effort to get this ready; much appreciated!
Second, some quick probing questions:

I'd note we have some samples for scheduling already, for which this looks to be similar.  Is there a specific ask for topic vs. queue samples, or a point of subtlety where the former wouldn't be well-described by the latter?
If the answer to the first bullet is "we have a specific need for this" my next question would be "sync version to match the async version?" (and append URIs for both versions in the samples to README.md, likely alongside the existing schedule breadcrumbs) but I'd be curious to your thoughts on the above first.

Thanks as always.

Sure thing, and yes we had a specific need for this (using topics instead of queues with async).
Regarding inclusion for sync version to match async version and include a link in the README.md, sounds good.  I'll take a look and update here.
Thank you as well!

@KieranBrantnerMagee
Here's an updated version.  Let me know what you think.


fix: readd usage of WalletConnectProvider for solana

Description
Readd the usage of WalletConnectProvider of Solana adapter injecting the UniversalAdapter provider.
Type of change

[ ] Chore (non-breaking change that addresses non-functional tasks, maintenance, or code quality improvements)
[x] Bug fix (non-breaking change which fixes an issue)
[ ] New feature (non-breaking change which adds functionality)
[ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)

Associated Issues
For Linear issues: Closes APKT-xxx
For GH issues: closes #...
Showcase (Optional)
If there is a UI change include the screenshots with before and after state.
If new feature is being introduced, include the link to demo recording.
Checklist

[ ] Code in this PR is covered by automated tests (Unit tests, E2E tests)
[ ] My changes generate no new warnings
[ ] I have reviewed my own code
[ ] I have filled out all required sections
[ ] I have tested my changes on the preview link
[ ] Approver of this PR confirms that the changes are tested on the preview link

I'm closing this PR in favor of #3205 3205


[Error] NotFoundError: Failed to execute 'removeChild' on 'Node': The node to be removed is not a child of this node.

Page
https://registry.platformio.org/libraries/mbed-jackb/EEPROM/installation
Stack
at O (https://cdn.registry.platformio.org/static/js/main.7cce2b10.js:2:456996)
    at Suspense
    at div
    at div
    at Q (https://cdn.registry.platformio.org/static/js/main.7cce2b10.js:2:152703)
    at div
    at main
    at X (https://cdn.registry.platformio.org/static/js/main.7cce2b10.js:2:155477)
    at F (https://cdn.registry.platformio.org/static/js/main.7cce2b10.js:2:456370)
    at R (https://cdn.registry.platformio.org/static/js/main.7cce2b10.js:2:611100)
    at O (https://cdn.registry.platformio.org/static/js/main.7cce2b10.js:2:456996)
    at B (https://cdn.registry.platformio.org/static/js/main.7cce2b10.js:2:456426)
    at b (https://cdn.registry.platformio.org/static/js/main.7cce2b10.js:2:447917)
    at https://cdn.registry.platformio.org/static/js/main.7cce2b10.js:2:562190
    at hl (https://cdn.registry.platformio.org/static/js/main.7cce2b10.js:2:411438)
    at hl (https://cdn.registry.platformio.org/static/js/main.7cce2b10.js:2:411438)

Please refresh the page with Ctrl+R. Does it work now?

Yes, I think this error was momentary, thanks for your reply.


Use absolute paths to avoid issues with relative paths and relativize

This can otherwise lead to issues if one path is relative and the other is not.

Could you provide an example where the current approach is not working? Just to allow me to better understand the intent, which I'd need to maintain this feature/fix later on.

@holgerbrandl yes, totally, I'll try to synthesize one in a bit, I ran into this with one of our projects.

Could you provide an example where the current approach is not working? Just to allow me to better understand the intent, which I'd need to maintain this feature/fix later on.

Les say you have this simple script:
PathTest.kts:
#!/usr/bin/env kscript

@file:Include("./subdir/Sub.kts")

println(helloWorld)

and then subdir/Sub.kts:
#!/usr/bin/env kscript

val helloWorld = "Hello World"

now if you do kscript --idea PathTest.kts it actually throws an NPE because AppHelpers line 323 Paths.get(scriptFile.absolutePath).parent is null and that is used in 332. It is null because the unresolved path of scriptFile is empty and thus the parent is null.
If you would have run kscript --idea <fully_qualified_path>/PathTest.kts it would have worked.
Another issue is if you just go to a different directory (e.g one down from where the script is and call kscript --idea ../PathTest.kts this also does not work without this PR, as with this the scriptDir and includeDir have a different root (well one has a root and the other has none). Which also fails. Again if you give the full path of the script it works.
So this PR will always use the full absolute path for both script and include dir to avoid any of those issues.

Thanks @rossbacher,  for the nice explanation and the fix.


Allow state and input proxies to be logged

Right now if you do console.log('state', state) then weird things happen. Workaround: console.log('state', {...state})

Done by adding all values to the proxy target, which is what the browser logs


[GHSA-ppc3-fpvh-7396] Improper synchronization in Apache Netbeans HTML/Java API

Updates

Description
Source code location

Hi @JLLeitschuh, this change seems to only reference the lines of code deleted in the fix commit already in the advisory. Since the source code location should be used to refer to a web accessible code repository, I am going to reject this one.


GitHub organizations not respected

Nice, but without the additions to github organizations the stats are meaningless

This project is created only to showcase "my stats". I open sourced it in hope that people will contribute in this repository. And It got no contribution in 3+ years. That's why it is only being maintained and not any feature addition.
Recently github-readme-stats had some problem thats why this repo is getting traffic. After some time this repository will be dead again.
But you can definitely add this feature and send me a PR, I will be grateful then.
Thank you! 😁

Unfortunately this is totally not my tech stack. Np I understand that. Maybe somebody with the required skill will find some time. Regards

OMG, you are the creator of albert launcher! Feel blessed that you created an issue. It was nice to talk with you 🙏🏻


Python, Default Template, Silent failure: "Deployment successful. No HTTP triggers found. "

Relevant threads:
https://github.com/microsoft/vscode-azurefunctions/issues/2529
https://github.com/microsoft/Oryx/issues/1774
I create a default HTTP trigger function app. I can run it locally.
When I try to deploy, it fails silently with:
2:50:41 PM debugApp123: **Deployment successful**. deployer = ms-azuretools-vscode deploymentPath = Functions App ZipDeploy. Extract zip. Remote build.
2:51:07 PM debugApp123: Syncing triggers...
2:51:12 PM debugApp123: Querying triggers...
2:51:18 PM debugApp123: **No HTTP triggers found.**

Details:
Python, Runtime v4, Linux premium plan
Manjaro linux, vscode 1.81.1, azure extension 1.12.3
Things I have tried from relevant thread (1):

setting AzureWebJobsFeatureFlags:EnableWorkerIndexing.
removing type hints

I don't even know where to go from here, I sat in a support call for 2 hours and we couldn't solve this.
There are no errors, anywhere.

Checking the .zip files in the webjobstorage /data/SitePackages shows that the function files are present.
Sometimes I also get this error:
4:00:49 PM: Error: The operation was aborted.

Also with no further explanation.

Sorry for the inconvenience. This fix for this issue is merged and will be released soon.

Actually the bug Alex linked is just for Node.js, not Python. @ZirconCode I would recommend you file an issue on the Functions team focused on Python here: https://github.com/Azure/azure-functions-python-worker/issues

Thank you, appreciate the response, after some more exploration, I made a mother of all bug reports here: https://github.com/microsoft/Oryx/issues/2102

I'm facing this same problem right now. Tried all hints posted here, on microsoft forum and stackoverflow. Sometimes I also got
4:00:49 PM: Error: The operation was aborted.` error without any explanations. Any ideas how to fix that?

@karoldziubak use https://github.com/microsoft/vscode-azurefunctions/issues/4176 for tracking the "Error: The operation was aborted." issue.


[T3A7][T01-T3]Naren V

Ready for review

@radneran Some comments added. Please close the PR after reading comments. No big issue in this PR, Good job. Next time try to submit the PR correctly.


Remove library jsdom from jest, if test env = node

Do you want to request a feature or report a bug?
feature
What is the current behavior?
jsdom exists in jest
If the current behavior is a bug, please provide the steps to reproduce and
either a repl.it demo through https://repl.it/languages/jest or a minimal
repository on GitHub that we can yarn install and yarn test.
What is the expected behavior?
jsdom removed from jest
Please provide your exact Jest configuration and mention your Jest, node,
yarn/npm version and operating system.

Set "testEnvironment": "node" in your configuration.


Support for multi-container override label

Added support for the following override labels:

audit.kubernetes.io/allow-automount-service-account-token

audit.kubernetes.io/pod/allow-read-only-root-filesystem-false

container.audit.kubernetes.io/<container-name>/allow-read-only-root-filesystem-false

audit.kubernetes.io/pod/allow-run-as-root

container.audit.kubernetes.io/<container-name>/allow-run-as-root

audit.kubernetes.io/pod/allow-privileged

container.audit.kubernetes.io/<container-name>/allow-privileged

audit.kubernetes.io/pod/allow-privilege-escalation

container.audit.kubernetes.io/<container-name>/allow-privilege-escalation

audit.kubernetes.io/pod/allow-capability-<capability-name>

container.audit.kubernetes.io/<container-name>/allow-capability-

Fixes # (issue)
closes #93
Type of change

[x] Bug fix :bug:
[x] This change requires a documentation update :book:
[x] Breaking changes :warning:

Checklist:

[x] I have :tophat: my changes (A 🎩 specifically includes pulling down changes, setting them up, and manually testing the changed features and potential side effects to make sure nothing is broken)
[x] I have performed a self-review of my own code
[x] I have made corresponding changes to the documentation
[x] I have added tests that prove my fix is effective or that my feature works
[x] New and existing unit tests pass locally with my changes
[x] The test coverage did not decrease

Can you update the checklist and mark the type of change, we are now enforcing everybody to use them

@nschhina i have marked the checklist and type of changes

LGTM, all my suggestions have been addressed. We can 🚢  after @klautcomputing gives his 👍


CONTRIBUTING.md

I would like to add a CONTRIBUTING.mdfile which will contain all contributing guidelines and all git workflow so that all newbies or beginners and even experts can look into it and also the contributors will understand the workflow

I would love to work on this issue as a part of DWOC. Kindly assign it to me

I would love to work on this issue as a part of DWOC. Kindly assign it to me


ASC should have a package dependency on Core Components

Is your feature request related to a problem? Please describe.
ASC should have a package dependency on Core Components since it requires Core Components to be installed.

Fixed by #579


Installing Bleno on Edison fails

Had to re-install everything on my Intel edison but now for some reason im not able to install bleno at all:

I have tested everything, reinstalling Edison multiple times, re-configuring it but it just dont work any more.

It's buried in the screenshot, but have you looked at:

https://github.com/sandeepmistry/noble#prerequisites

That was it, thank you !
Sorry for that, i thought i had installed bluez, thats why i was confused :)

No worries, glad that helped ...


Fix an anchor in the README

GitHub automatically replaces spaces in header anchors with dashes. The anchor target was changed to reflect this.

@Calinou  Thank you :+1:


NIFI-748 Fixed logic around handling partial query results from prove…

…nance repository

Ensured that failures derived form correlating Document to its actual provenance event do fail the entire query and produce partial results with warning messages
Refactored DocsReader.read() operation.
Added test to validate two conditions where the such failures could occur

Grabbed the PR locally and scoped it out.  Nothing substantive to add beyond those comments here already.  Performed some querying of a locally running instance with the new patch applied and didn't notice any tremendous performance impacts.  Will continue to run as this patch goes through it's final paces.

@trkurc @joewitt @apiri
Guys, please see the latest commit. Didn't squash it, so its easier to read and see what's been addressed. In summary:

Since based on the latest comment from Joe it appears that we all agree that DocReader is not really public, i kept the dead constructor out and also made DocReader package private.
Based on Tony's point added Document sorting logic back. At least it will ensure that previous behavior is maintained.
See commit message for more details

I'm mostly okay with package private. On the off chance someone was using this API it will be marginally more challenging to adapt to this change, but is possible.

@trkurc  Personally, I have exactly 0 qualms about changing it to package private. If I choose to take some random util class from a release of Apache Tomcat, for example, and depended on it, I should certainly not be surprised if that class changes from release to release (including incremental releases) - I don't think this is any different.


Fix some dependencies.

Manual fixing of the genrule() generated includes and how they are consumed by libraries.

Codecov Report
All modified and coverable lines are covered by tests :white_check_mark:

Project coverage is 92.92%. Comparing base (46de0f6) to head (29f2eed).

:exclamation: Your organization needs to install the Codecov GitHub app to enable full functionality.
Additional details and impacted files
@@           Coverage Diff           @@
##           master    #2135   +/-   ##
=======================================
  Coverage   92.92%   92.92%
=======================================
  Files         359      359
  Lines       26673    26673
=======================================
  Hits        24787    24787
  Misses       1886     1886

:umbrella: View full report in Codecov by Sentry.
:loudspeaker: Have feedback on the report? Share it here.


fixes image links

simple pointer to docs folder

The error occurred because the index.rst file for the documentation imported and showed the contents of README.rst. As the image links were relative to the index.rst file, they couldn't be found on the github repo. Have resolved this by making index.rst and README.rst independent.


Make sure that the program exits with appropriate error codes

Make sure that the program exits with appropriate error codes in all cases. If there are no errors, exit with 0. Otherwise, exit with a non-zero exit code (e.g., -1).

Close via https://github.com/DukeManh/OSD_SSG/commit/7e5b0c4aa02864d0764b28c80c77dfe4646b1c27


Add test mode and cleanup

Added test mode

without enabling test mode, komoju gateway now requests to https://komoju.com/api/v1
If you want to use sandbox, use test option: KomojuGateway.new(login: 'login', test: true)

Changed to use old-style hash because all other code use old style hash
Added some tests
Add "Komoju" to README

@rramsden @shioyama This is ready for review. can you review please?

Looks good to me :+1: good work!

You can add a default header with the Komoju client using the following
Komoju.connect('frt-mart', url: 'http://localhost:50130/api/v1', default_headers: {'User-Agent' => 'ActiveMerchant'})

:+1: great work thanks

thanks, I'll add default headers and merge

confirmed that unit and remote tests pass. merging

@camelmasa Now komoju gateway requires test option for sandbox requests. please change your code as follows
gateway = ActiveMerchant::Billing::KomojuGateway.new(login: 'secret-key', test: true)


Fix RSpec/MessageChain "better" docs.

The original code results in an error because receive takes a single argument of the method name which it tries to symbolize.
     NoMethodError:
       undefined method `to_sym' for {:bar=>#<Thing:0x00007ff34792c178 @baz=42>}:Hash
       Did you mean?  to_s
                      to_set

I believe what it's trying to do is allow(foo).to receive(:bar).and_return(thing).

Before submitting the PR make sure the following are checked:

[x] Feature branch is up-to-date with master (if not - rebase it).
[x] Squashed related commits together.
[x] N/A Added tests.
[x] N/A Added an entry to the changelog if the new code introduces user-observable changes.
[x] The build (bundle exec rake) passes (be sure to run this locally, since it may produce updated documentation that you will need to commit).

Here's some code showing the equivalency between the bad and better.
require 'rspec'

class Thing
  attr_accessor :baz

  def initialize(baz:)
    @baz = baz
  end
end

class Foo
  def bar
    23
  end
end

RSpec.describe 'receive' do
  let(:foo) { Foo.new }

  shared_examples 'baz is 42' do
    it 'foo.bar.baz is 42' do
      expect(foo.bar.baz).to eq 42
    end
  end

  context 'with receive_message_chain' do
    before do
      allow(foo).to receive_message_chain(:bar, :baz).and_return(42)
    end

    it_behaves_like 'baz is 42'
  end

  context 'with receive.and_return' do
    let(:thing) {
      Thing.new(baz: 42)
    }
    before do
      allow(foo).to receive(:bar).and_return(thing)
    end

    it_behaves_like 'baz is 42'
  end
end


doc: suppression examples would be better using List[NagPackSuppression]

Describe your issue?
The suppression examples use List[dict] rather than List[NagPackSuppression]
For example "Example 1) Default Construct" has
NagSuppressions.add_resource_suppressions(test, [id="AwsSolutions-EC23", reason="lorem ipsum"])
when
NagSuppressions.add_resource_suppressions(test, [NagPackSuppression(id="AwsSolutions-EC23", reason="lorem ipsum")])
would seem to be better
I'd be happy to spend the time changing these if its accepted

Hello
https://constructs.dev/packages/cdk-nag/v/2.4.39?lang=python#suppressing-a-rule
But the typescript version shows the same issue
https://constructs.dev/packages/cdk-nag/v/2.4.39?lang=typescript#suppressing-a-rule
No mention of NagPackSuppression

That is a valid example for TypeScript since the NagPackSuppression type is inferred. We would have to look to see if there are any ways to influence the python documentation generated by jsii.

Would asserting the type help the generation?
NagSuppressions.addResourceSuppressions(test, [
{
id: 'AwsSolutions-EC23', reason: 'lorem ipsum'
},
]);

I think we would need to do something like this https://github.com/aws/jsii/issues/826


refactor: Use generic class and style for check in view

Use BaseBottomSheetFragment for base class'
Use ItemPadding for layout style

Fixes #387

@Shailesh351 @rohanagarwal94 @shreyasnbhat Please review.


[Bug] In Brand new installation with v6 the sidebar menu collapse function doesn't work

Bug report
What I did
I installed a new project with backpack V6 and the permission manager plugin, I added the sidebar menú items

     Authentication

 Users

 Roles

 Permissions

What I expected to happen
The menu should appear collapsed and be displayed with a click.
What happened
The menu appear already opened

Is it a bug in the latest version of Backpack?
PHP VERSION:
PHP 8.2.7 (cli) (built: Jun  8 2023 15:27:40) (NTS)
Copyright (c) The PHP Group
Zend Engine v4.2.7, Copyright (c) Zend Technologies
with Zend OPcache v8.2.7, Copyright (c), by Zend Technologies
with Xdebug v3.2.0, Copyright (c) 2002-2022, by Derick Rethans
LARAVEL VERSION:
<IP_ADDRESS>
BACKPACK PACKAGE VERSIONS:
backpack/basset: 0.16.0
backpack/crud: v6.x-dev
backpack/devtools: v6.x-dev
backpack/editable-columns: v6.x-dev
backpack/generators: v6.x-dev
backpack/permissionmanager: v6.x-dev
backpack/pro: v6.x-dev
backpack/theme-tabler: dev-main

Thanks for the heads up @miquelangeld 🙏
I've sent a PR to update the docs, can you test if the updated docs are working as expected ?
https://github.com/Laravel-Backpack/PermissionManager/pull/328
Going to close this, we will re-open if needed 🙏
Cheers


build: bump jinja2 3.1.x -> 3.1.4 (#206)

Part of canonical/bundle-kubeflow#883

Will close and rebase the branch instead.


LAB_09a-Implement_Web_Apps.md

lab09a.png
Text overextends the background square, should be adapted, maybe split into 2 lines.
LAB_09a-Implement_Web_Apps.md.pdf

Hello contributor,
Thank you for your contribution! We are processing your suggestions and we will update you shortly.
Kind regards,
Microsoft Worldwide Learning Team

https://dev.azure.com/ceapex/CEINTL/_workitems/edit/691937

The image fixed. close it.


Add repetition_penalty aligned with huggingface(porting pr #886 to latest branch)

thanks to @Abraham-Xu  for his great work on pr #886. Since I've been waiting for #886 to be merged and it hasn't been, I've gone ahead and merged it into my fork.

Great job! One more thing, the api-server should add those parameters

I tested this branch
but output of vllm and HF transformers is still  inconsistent

I tested this branch but output of vllm and HF transformers is still inconsistent

I have test Baichuan 13b and found that the differences in the sampling process have been largely eliminated.
Could you please provide more details about the model and the request parameters that led to differences on your end?
It's worth noting that the floating-point calculation order in the forward propagation phase may vary between different inference frameworks, which can result in cumulative errors, especially for long sequences. Unfortunately, a satisfactory solution for this difference has not been found yet.
However, the most important thing is to assess whether the generated results are usable, rather than pursuing complete consistency.

I tested this branch but output of vllm and HF transformers is still inconsistent

I have test Baichuan 13b and found that the differences in the sampling process have been largely eliminated. Could you please provide more details about the model and the request parameters that led to differences on your end?
It's worth noting that the floating-point calculation order in the forward propagation phase may vary between different inference frameworks, which can result in cumulative errors, especially for long sequences. Unfortunately, a satisfactory solution for this difference has not been found yet.
However, the most important thing is to assess whether the generated results are usable, rather than pursuing complete consistency.

I understand and agree not to pursue complete consystency.
but I tested on codellama-13b-hf
codellama was trained by infilling training, so it can fill code in the middle
e.g.  input propmt
def return_sum(a, b):
    <FILL_ME>
    return c

by giving above input propmt, output of hf transformers is always (temperature from 0 - 1.0)
c = a + b

which is a good demonstration of infilling ability
but output of vllm is not
when temperature = 0.1 - 1.0
one output sample is
c = a + b
    return c

c = a + b

    return c

def return_sum_with_print(a, b):
    c = a + b
    print(c)

when temperature = 0, output is :
c = a + b

we found that when temperature != 0, output of vllm will ignore infilling

I merged #1424 since that code looks simpler. But again, thank you for your contribution! Let me know if there is any issue with #1424.

I'm getting inconsistent results between HF and vllm with llama-7b @yanxiyue
## hf
import torch
from transformers import (
    AutoTokenizer,
    AutoModelForCausalLM
)

MODEL_NAME = "huggyllama/llama-7b"
#MODEL_NAME = "huggyllama/llama-13b"

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, torch_dtype=torch.float16).cuda()

prompt = [
    "Hello, what is apple?  ",
]
input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda()
generated_ids = model.generate(
    input_ids,
    do_sample=False,
    repetition_penalty=1.2,
    max_new_tokens=64,
)

texts = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
""" OUTPUT
Hello, what is orange?   \nHello, what are you doing here?\n— _The Wizard of Oz_ , L. Frank Baum (1900)\n#  **What Is a Computer?**\nA computer is an electronic device that can store and process information in the'
"""

## vllm
from vllm import LLM, SamplingParams

# Sample prompts.
prompts = [
    "Hello, what is apple?  ",
]
sampling_params = SamplingParams(
    temperature=0,
    max_tokens=64,
    frequency_penalty=1.2
)

MODEL_NAME = "huggyllama/llama-7b"
#MODEL_NAME = "huggyllama/llama-13b"

llm = LLM(
    model=MODEL_NAME,
    trust_remote_code=True,
)
outputs = llm.generate(prompts, sampling_params)
for output in outputs:
    prompt = output.prompt
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

""" OUTPUT
Hello, what is apple?  \nHello, what is apple?  \nHello, what is apple?  \nHello, what is apple?  \nWhat\'s that you say?  What\'s that you say?"  And so on. The child will repeat the question until he gets an answer
"""


V1 is not applied on MariaDB with existing tables in the schema

I had a MariaDB database with existing tables in the schema. When I provide a script starting with V1__, it is not applied and no warning is given. The schema_version table is initialised on version 1 with type BASELINE. When I provide a script starting with V2__, it is correctly applied and I see two versions in the schema_version table.
The fact that the script is simply ignored without a warning got me on the wrong track for quite some time, looking for classpath issues. Then when I started debugging Flyway, I saw that it did find my script.

Thank you for your report. This really works as designed. If you baseline with V1, Flyway assumes that a V1 script contains the migration to recreate the baseline. Any suggestion on how we could make this clearer in the docs?
Cheers
Axel

It could be mentioned on this page: http://flywaydb.org/documentation/command/baseline.html
But I wouldn't have found it there when I had this problem. I was just gazing at the logs:
MetaDataTableImpl - Creating Metadata table: my_schema.schema_version
DbBaseline - Schema baselined with version: 1
DbMigrate - Current version of schema my_schema: 1
DbMigrate - Schema my_schema is up to date. No migration necessary.
And I kept asking myself: why doesn't it run my script? Perhaps it could log that it assumes the V1 script contains the baseline, and that the script is not ran because the schema is not empty.


cleanup(metrics): remove duplicated new_metric method

What type of PR is this?
/kind cleanup

Any specific area of the project related to this PR?
/area libsinsp

Does this PR require a change in the driver versions?

What this PR does / why we need it:
This change was meant to go in as part of #1920, but somehow got lost in translation. Compilation of Falco will be fixed by falcosecurity/falco#3265.
Which issue(s) this PR fixes:

Fixes #
Special notes for your reviewer:
Does this PR introduce a user-facing change?:

NONE

[APPROVALNOTIFIER] This PR is APPROVED
This pull-request has been approved by: Molter73
The full list of commands accepted by this bot can be found here.
The pull request process is described here

Needs approval from an approver in each of these files:

OWNERS [Molter73]

Approvers can indicate their approval by writing /approve in a comment
Approvers can cancel approval by writing /approve cancel in a comment

/cc @incertum @FedeDP

LGTM label has been added.  Git tree hash: 4c9f35d55723c9803d1f8152387598dfd10301e2

/milestone 0.18.0

[APPROVALNOTIFIER] This PR is APPROVED
This pull-request has been approved by: FedeDP, Molter73
The full list of commands accepted by this bot can be found here.
The pull request process is described here

Needs approval from an approver in each of these files:

OWNERS [FedeDP,Molter73]

Approvers can indicate their approval by writing /approve in a comment
Approvers can cancel approval by writing /approve cancel in a comment


[Snyk] Fix for 3 vulnerabilities

This PR was automatically created by Snyk using the credentials of a real user.Snyk has created this PR to fix one or more vulnerable packages in the `maven` dependencies of this project.
Changes included in this PR

Changes to the following files to upgrade the vulnerable dependencies to a fixed version:

ch-jetty/pom.xml

Vulnerabilities that will be fixed
With an upgrade:

Severity
Priority Score (*)
Issue
Upgrade
Breaking Change
Exploit Maturity

621/1000  Why? Mature exploit, Has a fix available, CVSS 4.7
Cross-site Scripting (XSS) SNYK-JAVA-ORGECLIPSEJETTY-174479
org.eclipse.jetty:jetty-http:  9.2.22.v20170606 -> 9.4.47.v20220610
No
Mature

349/1000  Why? Has a fix available, CVSS 2.7
Improper Input Validation SNYK-JAVA-ORGECLIPSEJETTY-2945452
org.eclipse.jetty:jetty-http:  9.2.22.v20170606 -> 9.4.47.v20220610  org.eclipse.jetty:jetty-server:  9.2.22.v20170606 -> 9.4.47.v20220610  org.eclipse.jetty:jetty-servlets:  9.2.22.v20170606 -> 9.4.47.v20220610
No
No Known Exploit

589/1000  Why? Has a fix available, CVSS 7.5
Web Cache Poisoning SNYK-JAVA-ORGECLIPSEJETTY-32383
org.eclipse.jetty:jetty-http:  9.2.22.v20170606 -> 9.4.47.v20220610  org.eclipse.jetty:jetty-server:  9.2.22.v20170606 -> 9.4.47.v20220610  org.eclipse.jetty:jetty-servlets:  9.2.22.v20170606 -> 9.4.47.v20220610
No
No Known Exploit

(*) Note that the real score may have changed since the PR was raised.
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user and passwd?

when i accept to access the container through ssh,what is the user and the passwd ?

This image does not add any users beyond those provided by the base Ubuntu image. You'll need to create your own image with this image as a base and add a user there.


v6 how to expose input ref for focus() and blur() with the new Field Component?

I need to call focus() and blur() programmatically on my input field. But with the new Field wrapper, I don't see an easy way to get access to the ref of the input field. I think the Field Component should implement the methods focus() and blur() if possible.

Yes would be very nice to have

To clarify, you mean...
focusMyField() {
  this.refs.myFieldRef.focus()
}

render() {
  return (

      <Field name="myField" ref="myFieldRef" component="input"/>
      <button onClick={this.focusMyField.bind(this)}>Focus</button>

  )
}

??

@erikras yes

Ye, makes sense. But we may just need a method to access the rendered field component:
focusMyField() {
  this.refs.myFieldRef.getRenderedComponent().focus()
}

render() {
  return (

      <Field name="myField" ref="myFieldRef" component="input"/>
      <button onClick={this.focusMyField.bind(this)}>Focus</button>

  )
}

Otherwise we might call these methods that are not available on the final component (custom ones).

I've implemented getRenderedComponent(). It will be published in the next alpha release.

Published as v6.0.0-alpha.8.

👍 thanks

@ms88privat Thanks for bringing my attention to react-native-stateless-form. Very interesting project.

Just in case, there is another method for doing this for stateless components: https://github.com/facebook/react/issues/4936#issuecomment-179917137

I've implemented getRenderedComponent(). It will be published in the next alpha release.
Hi there @erikras - I am trying to access the ref from the onSubmitFail in my react-native app. However I cannot access that. Is there some way to get the ref to the node in onSubmitFail? My goal is to scroll to the <TextInput /> element.


Connect to onlyoffice via docker network

I am trying to configure the onlyoffice_redmine plugin using the suggested instruction.
Now there are 6 containers in my Docker: Redmine (my own build on unicorn), Postgres (latest, used only in Redmine), portainer (community edition, latest), nginx (it's called server, latest, has port forwarding 80:80, 443:443), drawio (jgraph/drawio:latest), onlyoffice/documentserver (called onlyoffice, has port forwarding 8083:80).
All containers are connected by an internal network mainNetwork.
As a basic configuration for onlyoffice, I made the following entry in docker-compose:
onlyoffice:
image: onlyoffice/documentserver
container_name: onlyoffice-documentserver
ports:
- 8083:80
networks:
- mainNetwork
I installed the plugin following the suggested instructions and configured it as follows:

And got the following error:

At the same time, the specified address itself is accessible from the redmine container via curl.
Please tell me, what am I doing wrong?

Oh, I think I can guess what the problem is - this docker image cannot work on ports other than 80 and 443 (when trying to edit a test example with port forwarding 81:80 returns an error):

I don't fully understand why connecting onlyoffice with an internal network the problem persists, but it makes no difference.
Please specify this feature of the docker image deployment in the documentation.

OK, I figured out the problem.
It turns out that instead of https://onlyoffice it was necessary to specify htts://<IP_ADDRESS>/ (my local ip address) - then everything works... However, only on ubuntu, and file conversion does not work - it gives an error.
In an ubuntu-based Docker image on a debian host machine, exactly the same configuration causes problems with 403 "forbidden".
And, of course, this plugin is outdated - you should not use show for tasks, as it is written in https://github.com/MayamaTakeshi/redmine_rt/issues/21
I removed this plugin because it conflicts with redmine_ke, and besides, as I wrote above, it does not work (it works very crookedly).


Fix snapping to edges

Currently it was broken due to invisible borders around windows on Win10, also there were some other minor issues with unsnapping. Also the aero snapping wasn't taken into account too.
The current code is the full copy of my mpv snapping code mpv-player/mpv#3890, with one small fix.
The program should be linked with Dwmapi.dll (Dwmapi.lib)

Thanks! I'm merging this, but I think I'll move snapping logic to a separate class afterwards.

It seems this PR introduced a new bug that can be reproduced with the following steps:

Aero-snap the window to the left/right half of the monitor.
Move the window slightly to bottom.
Window stays snapped within the gap and does not return to its original size.

mpv has the same issue.

Thanks, I'm looking into this.


Correct time control to allow more than 1 period

I have misread the specification about TimeControl which is not easy to read. Here are some examples ( taken from #13 )

German leagues (90 minutes with incr. 30 seconds, last period 30 minutes):

40/5400+30:1800+30
or 40/4500:1800+30
I think the first one is the correct one (what if the increment is only valid for the sudden death phase??)

Blitz (3+2)

180+2: Use seconds all the time.

German league (former time control) 2 hours / 40, 1 hour / 20, rest 30 minutes, no increment.

40/7200:20/3600:1800

Blitz (5)

300

So what I have not taken into consideration was, that each time period may have a different description. They are separated by ':'.
So the parsing rules should be changed. As a result, the TimeControl is then an array of periods, with each period being the TimeControl from before.

The one format (40/5400+30:1800+30) is not supported (yet), I will have to do some additional documentation in the spec. This combines movesInSeconds and increment in one period (which is not allowed due to the spec).


SLURM settings not used during flye olc

In the JSON I set additional parameters for SLURM:
"cluster_settings": { "cluster_type": "IPCluster", "processes": 36, "cluster_options": { "scheduler": "slurm", "queue": "PRI_Std", "extra_params": {"mem":200, "cpus-per-task":36, "time":"3-24:00:00"} }
These settings are however ignored during flye in OLC. Although flye is run with -t 36:
flye-assemble -l /.../results/olc/flye-asm-1/flye.log -t 36
The SLURM sbatch is single core:
`  Priority=5000 Nice=0 Account=pri QOS=normal
JobState=RUNNING Reason=None Dependency=(null)
Requeue=1 Restarts=0 BatchFlag=1 Reboot=0 ExitCode=0:0
RunTime=10:21:12 TimeLimit=1-00:00:00 TimeMin=N/A
SubmitTime=2019-03-24T23:39:34 EligibleTime=2019-03-24T23:39:35
StartTime=2019-03-24T23:39:35 EndTime=2019-03-25T23:39:36 Deadline=N/A
NumNodes=1 NumCPUs=1 NumTasks=1 CPUs/Task=1 ReqB:S:C:T=0:0::
TRES=cpu=1,mem=200G,node=1
`
Which means that because 1 CPU is not sufficient to finish the job within 1 day, SLURM cancels the job.
Could you indicate where this part of the pipeline is located in the scripts? I might be able to fix it myself then.

It seems to be a limitation of ipython-cluster-helper. It uses only a select number of 'extra_params':
https://github.com/roryk/ipython-cluster-helper/blob/e1bfe4817b3cea37f5f7a759e705d295aa940c3e/cluster_helper/cluster.py
To control CPUs, you need to change the cluster settings:
"cluster_options": { "scheduler": "slurm", "queue": "PRI_Std", "cores_per_job":36, "extra_params": {"mem":200} }
the mem options used, the others not...


unit tests and a bug fix

This PR

restores a unit test (test_e_g_s_functions)
improves a test set (Tests elbo gradient, and that elbo supports dual numbers)
fixes a bug that was keeping the derivative one place from getting computed if the hessian wasn't also getting computed

These commits are cherry picked from my jcr/cg branch. I figured we should make sure these tests pass on the jr/constraints branch too before merge, though I don't believe they'll present any trouble.

Nevermind, this creates a small merge conflict with the jr/constraints branch. Let's just get that one committed first.


[VS Code] Joining workspace: Could not connect to the server. Check that you are connected to the internet.

[docsmsft.docs-markdown]: 命令“applyTemplate”在 "commands" 部分重复出现。
TMSyntax.ts:47 Overwriting grammar scope name to file mapping for scope source.yaml.
Old grammar file: file:///c%3A/Users/huzhifeng/AppData/Local/Programs/Microsoft%20VS%20Code/resources/app/extensions/yaml/syntaxes/yaml.tmLanguage.json.
New grammar file: file:///c%3A/Users/huzhifeng/.vscode/extensions/pivotal.vscode-concourse-1.1.0/yaml-support/yaml.tmLanguage
e.register @ TMSyntax.ts:47
TMSyntax.ts:47 Overwriting grammar scope name to file mapping for scope source.yaml.
Old grammar file: file:///c%3A/Users/huzhifeng/.vscode/extensions/pivotal.vscode-concourse-1.1.0/yaml-support/yaml.tmLanguage.
New grammar file: file:///c%3A/Users/huzhifeng/.vscode/extensions/pivotal.vscode-manifest-yaml-1.1.0/yaml-support/yaml.tmLanguage
e.register @ TMSyntax.ts:47
TMSyntax.ts:47 Overwriting grammar scope name to file mapping for scope source.yaml.
Old grammar file: file:///c%3A/Users/huzhifeng/.vscode/extensions/pivotal.vscode-manifest-yaml-1.1.0/yaml-support/yaml.tmLanguage.
New grammar file: file:///c%3A/Users/huzhifeng/.vscode/extensions/pivotal.vscode-spring-boot-1.1.0/yaml-support/yaml.tmLanguage
e.register @ TMSyntax.ts:47
log.ts:157  INFO no standard startup: not a new window
4console.ts:134 [Extension Host] Error: No search provider registered for scheme: vsls
    at file:///C:/Users/huzhifeng/AppData/Local/Programs/Microsoft VS Code/resources/app/out/vs/workbench/workbench.main.js:4649:619
    at Array.forEach (<anonymous>)
    at t.searchWithProviders (file:///C:/Users/huzhifeng/AppData/Local/Programs/Microsoft VS Code/resources/app/out/vs/workbench/workbench.main.js:4649:454)
    at file:///C:/Users/huzhifeng/AppData/Local/Programs/Microsoft VS Code/resources/app/out/vs/workbench/workbench.main.js:4648:590
    at n.Class.derive._creator.then (file:///C:/Users/huzhifeng/AppData/Local/Programs/Microsoft VS Code/resources/app/out/vs/workbench/workbench.main.js:169:517)
    at t.doSearch (file:///C:/Users/huzhifeng/AppData/Local/Programs/Microsoft VS Code/resources/app/out/vs/workbench/workbench.main.js:4648:491)
    at t.fileSearch (file:///C:/Users/huzhifeng/AppData/Local/Programs/Microsoft VS Code/resources/app/out/vs/workbench/workbench.main.js:4648:117)
    at e.$checkExists (file:///C:/Users/huzhifeng/AppData/Local/Programs/Microsoft VS Code/resources/app/out/vs/workbench/workbench.main.js:5176:740)
    at t._doInvokeHandler (file:///C:/Users/huzhifeng/AppData/Local/Programs/Microsoft VS Code/resources/app/out/vs/workbench/workbench.main.js:3278:1009)
    at t._invokeHandler (file:///C:/Users/huzhifeng/AppData/Local/Programs/Microsoft VS Code/resources/app/out/vs/workbench/workbench.main.js:3278:675)
    at t._receiveRequest (file:///C:/Users/huzhifeng/AppData/Local/Programs/Microsoft VS Code/resources/app/out/vs/workbench/workbench.main.js:3277:196)
    at t._receiveOneMessage (file:///C:/Users/huzhifeng/AppData/Local/Programs/Microsoft VS Code/resources/app/out/vs/workbench/workbench.main.js:3276:126)
    at file:///C:/Users/huzhifeng/AppData/Local/Programs/Microsoft VS Code/resources/app/out/vs/workbench/workbench.main.js:3273:997
    at e.fire (file:///C:/Users/huzhifeng/AppData/Local/Programs/Microsoft VS Code/resources/app/out/vs/workbench/workbench.main.js:183:466)
    at a (file:///C:/Users/huzhifeng/AppData/Local/Programs/Microsoft VS Code/resources/app/out/vs/workbench/workbench.main.js:337:918)
    at Socket.n._socketDataListener (file:///C:/Users/huzhifeng/AppData/Local/Programs/Microsoft VS Code/resources/app/out/vs/workbench/workbench.main.js:338:137)
    at emitOne (events.js:116:13)
    at Socket.emit (events.js:211:7)
    at addChunk (_stream_readable.js:263:12)
    at readableAddChunk (_stream_readable.js:250:11)
    at Socket.Readable.push (_stream_readable.js:208:10)
    at Pipe.onread (net.js:594:20)
t.log @ console.ts:134
console.ts:134 [Extension Host] rejected promise not handled within 1 second
t.log @ console.ts:134
log.ts:169   ERR ENOENT: no such file or directory, unlink 'C:\Users\huzhifeng\.vscode\extensions\ms-vsliveshare.vsliveshare-0.3.954\cascade.json': Error: ENOENT: no such file or directory, unlink 'C:\Users\huzhifeng\.vscode\extensions\ms-vsliveshare.vsliveshare-0.3.954\cascade.json'
console.ts:134 [Extension Host] rejected promise not handled within 1 second
t.log @ console.ts:134
log.ts:169   ERR Running the contributed command:'vscode.removeFromRecentlyOpened' failed. Illegal argument 'path' - Path to remove from recently opened.: Error: Running the contributed command:'vscode.removeFromRecentlyOpened' failed. Illegal argument 'path' - Path to remove from recently opened.
    at e._executeContributedCommand (c:\Users\huzhifeng\AppData\Local\Programs\Microsoft VS Code\resources\app\out\vs\workbench\node\extensionHostProcess.js:460:23)
    at e.executeCommand (c:\Users\huzhifeng\AppData\Local\Programs\Microsoft VS Code\resources\app\out\vs\workbench\node\extensionHostProcess.js:459:458)
    at Object.executeCommand (c:\Users\huzhifeng\AppData\Local\Programs\Microsoft VS Code\resources\app\out\vs\workbench\node\extensionHostProcess.js:683:333)
    at JoinReloadCommand.registerForDisposingWorkspaceFile (C:\Users\huzhifeng\.vscode\extensions\ms-vsliveshare.vsliveshare-0.3.954\out\src\_new_\commands\JoinPostReloadCommand.js:203:25)
    at JoinReloadCommand.invoke (C:\Users\huzhifeng\.vscode\extensions\ms-vsliveshare.vsliveshare-0.3.954\out\src\_new_\commands\JoinPostReloadCommand.js:129:18)
    at MetaProfilingCommandDecorator.invoke (C:\Users\huzhifeng\.vscode\extensions\ms-vsliveshare.vsliveshare-0.3.954\out\src\_new_\decorators\MetaProfilingCommandDecorator.js:18:38)
    at AuthenticationCommandDecorator.invoke (C:\Users\huzhifeng\.vscode\extensions\

livesharelog.zip

@Priya91 already upload the logs

@netroby Is thsi problem still reproing for you? The logs seem incomplete, did you happen to delete some lines from there? Can you try again, and attach the logs for the recent session.

llog.zip
Still can not connect, I am not willing to delete any log, it just as it was

@netroby Can you go to your browser, and navigate to page https://insiders.liveshare.vsengsaas.visualstudio.com/auth/login ?

Are there any firewall rules that's blocking vscode from making outside connections?

i tried today. the live share working correct. now i close this issue


silent_redirect_uri and refresh tokens

When a refresh token is available, the library uses that to do a silentRenew. Relevant code in UserManager.js:
signinSilent(args = {}) {
        // first determine if we have a refresh token, or need to use iframe
        return this._loadUser().then(user => {
            if (user && user.refresh_token) {
                args.refresh_token = user.refresh_token;
                return this._useRefreshToken(args);
            }
            else {
                args.id_token_hint = args.id_token_hint || (this.settings.includeIdTokenInSilentRenew && user && user.id_token);
                return this._signinSilentIframe(args);
            }
        });
    }

Hence if refresh tokens are available, silent_redirect_uri should not really be needed if I understand it correctly. But the following code in UserSettings.js prevents automaticSilentRenew if no silent_redirect_uri is set:
get automaticSilentRenew() {
        return !!(this.silent_redirect_uri && this._automaticSilentRenew);
    }

This looks like a bug to me, automaticSilentRenew should be possible without a silent_redirect_uri if refresh tokens are available. Or am I missing something?

Yea, good point. I think when I updated for code flow/refresh tokens I didn't revisit that piece of code. Personally I wish I had never added automaticSilentRenew, and in 2.0 I plan to remove it. So yes, it's a bug.

@brockallen a fix would be nice :)

So would a PR :)

Thanks for the response @brockallen. No, doesn't need an urgent fix. I can just specify a silent_redirect_uri even though it's not used. So not a big problem, just something I noticed.

@brockallen Please don't remove automaticSilentRenew. :)

Ok, I think I have fixed this by changing the automaticSilentRenew to not require the silent_redirect_uri

<EMAIL_ADDRESS>released; please have a look and see if it satisfies this issue.

Any chance to test on the beta?

FYI: I ran into this bug in 1.7.0 and it disappeared by upgrading to 1.7.1. Thanks @brockallen !


SSL_set_cipher_list/openssl ciphers ALL:-EXP:-SRP:-SEED:-CAMELLIA:-DSS:-RC2:-DES-CBC-MD5:-DES-CBC3-MD5 | grep DH-DSS-AES256-GCM-SHA384 returns a result when it shouldn't

...I think (I don't think it should?)
Copying from https://github.com/NixOS/nixpkgs/issues/21408
Issue description
The underlying call, SSL_set_cipher_list, appears to not correctly parse the filter list. This occurs both from the commandline, (running openssl ciphers ALL:-EXP:-SRP:-SEED:-CAMELLIA:-DSS:-RC2:-DES-CBC-MD5:-DES-CBC3-MD5 ) and from calling the function directly (as AOSP's conscrypt does)
Steps to reproduce
run openssl ciphers ALL:-EXP:-SRP:-SEED:-CAMELLIA:-DSS:-RC2:-DES-CBC-MD5:-DES-CBC3-MD5 with
Technical details

OpenSSL: 1.0.2j

There is no problem with parsing.  The cipher in question does not match DSS, and hence is not removed via -DSS.  The definition of DSS is as a synonym for aDSS, while this cipher uses fixed DH auth, with DH/DSS key exchange such ciphers match kDHd.
The documentation in ciphers(1) matches this behaviour:
       aDSS, DSS
           cipher suites using DSS authentication, i.e. the certificates carry
           DSS keys.


anisaldoxime, bridgehead (logical-conflict)

This pull request was generated by the 'mq' tool

/trunk merge


Update next and add one-click build via MSBuild

Problem Description
MSBuild solution presently forces to run npm install and npm run build manually. Simplify the build process to spawn npm install && npm run build from the main MSBuild in case if directory hasn't been built yet. Note that this does not rebuild if the static pages have already been built. Ideally the clean might be able to clean the static pages as well, then rebuild will actually rebuild all.
Also updated next to latest.

Thanks


Performance Enhancement: Parallelized Unpacking of Velodyne Packets

Feature Overview
This enhancement introduces a significant performance improvement by parallelizing the process of unpacking Velodyne packets. By leveraging parallel processing techniques, the repository's functionality will experience enhanced efficiency and reduced processing time during the unpacking stage. This improvement is particularly relevant for VLS-128 where large amounts of Velodyne packet data need to be processed quickly and efficiently.
Could you please provide any feedback, suggestions, and guidance on the proposed enhancement @drwnz @amc-nu ?
Here are the performance metrics obtained with profiling scripts:

nebula-vls128-main
nebula-vls128-parallel

d_total AVG
24.8819
6.36068

n_out AVG
213395
213395

d_total STD
9.54071
2.84753

n_out STD
114.332
114.378

d_total AVG % rel to nebula-vls128-main
100
25.5634

n_out AVG % rel to nebula-vls128-main
100
99.9999

d_total STD % rel to nebula-vls128-main
100
29.8461

n_out STD % rel to nebula-vls128-main
100
100.041

@mebasoglu looks interesting, are you able to create a PR for your implementation?

Hello @drwnz , I created the PR: https://github.com/tier4/nebula/pull/62

Hi @mebasoglu, we implemented the per-packet decoding proposed in

#94
#109

in

#148

and will thus close this issue in favor of that approach. The reason we favored the other approach is that packets arrive one-by one anyway, and instead of accumulating them to be able to decode in parallel, decoding them in a single thread as they arrive is even more memory efficient. it is also faster from a latency perspective, as upon arrival of the last packet, only one packet has to be decoded until the pointcloud can be published.
Nevertheless, thank you for your investigation and proposal.


Outdated color codes

Due to the course of time, some colors (especially red/bright red) have become outdated.

a2a3e0a254ec264b351c4668ce6eb693d6936f7e


Feat(eos_validate_state): Added the support of validate_lldp key to skip the VerifyLLDPNeighbors tests

Change Summary
Added the support of validate_lldp key to skip the VerifyLLDPNeighbors tests
Related Issue(s)
Fixes #4675
Component(s) name
arista.avd.eos_validate_state
Proposed changes
Added the support of validate_lldp key to skip the VerifyLLDPNeighbors tests
How to test
Run the molecule eos_validate_state with set validate_lldp to false
Checklist
User Checklist

N/A

Repository Checklist

[x] My code has been rebased from devel before I start
[x] I have read the CONTRIBUTING document.
[x] My change requires a change to the documentation and documentation have been updated accordingly.
[x] I have updated molecule CI testing accordingly. (check the box if not applicable)

on hold till https://github.com/aristanetworks/avd/pull/4679 get merged


Support matchConditions for policy spec

Fix #849
This will add a "Match Conditions" tab to the policy configuration which allows the user to input multiple matchCondition expressions to the policy's spec.
https://github.com/user-attachments/assets/cd4e12c0-7143-456c-8d0a-c361c34a8e04

@kravciak I've fixed those two issues:
https://github.com/user-attachments/assets/cef5c185-425f-4986-b965-fe4955774966


Would be nice to have a command copied to clipboard.

With the current aichat -e command, user has the ability to either approve, revise, explain, or cancel. It would be nice to have another option to copy to clipboard, or to be pasted into commandline without executing, enabling the user to manually revise the command to his liking. I know the revise itself is meant for the literal feature I am asking, but would be nice to manually change it, gives somewhat control over what I would want to run, since llm can only suggest based on my natural language description of what I would want, could be time saving at some times.
Having said that, it would be also nice to have an configuration that can be set, to auto save to clipboard when requested for a command, for instance if aichat -e find the largest file in this dir is ran, the menu comes up as usual, but at the same time, the suggested command that could be ran (showed in the menu) is saved to clipboard, hence if I were to cancel and exit, I could still run the command suggested previously from my clipboard. Same reason as before, convenience, and somewhat time saving
anyways thanks for a cool tool, I can replace warp with this! hehe W for open-source~

We will not support this feature, please copy with your mouse.

This sounds like a reasonable enhancement, revise with another natural language seems very difficult to get right, at least for me. Any reason why the feature will not be supported?


BasemapGallery loading demo data in production release

Hi, I have a hard time initializing BasemapGallery.AvailableBasemaps with a custom list of basemaps. The shown gallery is overridden by the loading of what seems to be demo data. Except for a sample app, why would one want this behavior?
PS: Not using a portal
https://github.com/Esri/arcgis-maps-sdk-dotnet-toolkit/blob/5a3260c89ffc652b921221f517e53e58c9a0d73d/src/Toolkit/Toolkit.Maui/BasemapGallery/BasemapGallery.cs#L37-L45

The shown gallery is overridden by the loading of what seems to be demo data

Could you clarify what you mean by that? Any chance you could provide a small reproducer that demonstrates the issue?

I can see I was a bit short in my description. I'll explain myself more clearly.
If I add this xaml
<toolkit:BasemapGallery />

then I get a list of maps

I would expect to get an empty basemap gallery, I expect to get nothing at all. That is what I think of as "demo basemaps". For me it is a bit unusual that a UI control loads default data.
Then I try this expecting to stop loading the "demo basemaps"
<toolkit:BasemapGallery Portal="{x:Null}" />

But it still continues loading.
Next up is
<toolkit:BasemapGallery Portal="{x:Null}"
                        AvailableBasemaps="{Binding Basemaps}" />

public MapPageViewModel()
{
    LoadBasemapGallery();
}

private async void LoadBasemapGallery()
{
    var aTask = BasemapGalleryItem.CreateAsync(new Basemap(new ArcGISTiledLayer(new Uri(Constants.GeodataServiceUri.Bilder))));
    var bTask = BasemapGalleryItem.CreateAsync(new Basemap(new ArcGISTiledLayer(new Uri(Constants.GeodataServiceUri.VectorNordenBasisTerreng))));
    var cTask = BasemapGalleryItem.CreateAsync(new Basemap(new ArcGISTiledLayer(new Uri(Constants.GeodataServiceUri.VectorNordenGråtoneTerreng))));

    await Task.WhenAll(aTask, bTask, cTask);

    Basemaps.Add(aTask.Result);
    Basemaps.Add(bTask.Result);
    Basemaps.Add(cTask.Result);
}

public ObservableCollection<BasemapGalleryItem> Basemaps { get; } = new();

But it still continues to load the "demo basemaps" and I can't get it to show my custom basemaps definition.
At this point I looked up the source code
https://github.com/Esri/arcgis-maps-sdk-dotnet-toolkit/blob/5a3260c89ffc652b921221f517e53e58c9a0d73d/src/Toolkit/Toolkit.Maui/BasemapGallery/BasemapGallery.cs#L37-L45
..and discovered that the loading of initial "demo basemaps" are triggered from the constructor regardless of anything else. For apps that don't use this initial setup of basemaps I think that is a bit unfortunate. It also leads to many ArcGISWebException because of missing ChallengeHandler setup, thus slowing down app startup.


(change) Remove deprecated key Webhooks

Requirements
In version v0.9.0 the field Webhooks has been deprecated.
In a future version, we want to remove the field to avoid keeping it forever.

This PR will stay open for some time to let people time to migrate to the Notifiers.
The PR will merge in early May.