Slow completions when running cells

Reported by @ale-dg here https://github.com/microsoft/vscode/issues/206119#issuecomment-1996165888
Yes, but consider I did not use Pylance. Would you like me to test with it? I just activated it and opened the log trace for python and just for indexing it finishes its memory.
2024-03-13 17:51:01.092 [info] [Info - 17:51:01] (4192) Heap stats: total_heap_size=1220MB, used_heap_size=1156MB, total_physical_size=1218MB, total_available_size=2900MB, heap_size_limit=4096MB
2024-03-13 17:51:01.108 [info] [Warn - 17:51:01] (4192) Workspace indexing has hit its upper limit: 2000 files

Don, what enables this log output? I don't see it the two logs above:
traceVerbose(`Jupyter completion time: ${stopWatch.elapsedTime}`);

Thought it might indicate how much time the kernel is taking to compute completions.

That sounds to me like it's the kernel completions then. If it works fine without running any cells, then just Pylance is involved. If it slows down only when the kernel is involved, then it's both Pylance and the Jupyter Kernel providing the completions. Given the cpu profile you sent, Pylance isn't taking any time to compute completions, so I'm going to transfer this issue to the Jupyter extension.

Well... I can't give any input on that since I am not so familiar on how all the internals work. Would it be ok if I downgrade for being able to complete some projects? I will gladly upgrade back once you are ok with some updates to test.
Thanks,
Best

Downgrading just Pylance fixes the problem?

It fixes all the problems.

Oh. It would be interesting if you could keep everything at the latest except for pylance. Move that back to say 2023.8.30

These versions have worked for me... and apparently for a lot people around:
VSCode 1.82.3, Jupyter v2023.8.1002501831, Pylance v2023.8.30

Oh. It would be interesting if you could keep everything at the latest except for pylance. Move that back to say 2023.8.30, but leave Jupyter, Python, and VS code at the latest version.

Alright... I'll test it... give me a sec

Before I do anything.... do you need me to get another cpuprofile or anything? or just a normal feedback?

Before I do anything.... do you need me to get another cpuprofile or anything? or just a normal feedback?

Just normal feedback. The 2023.8.30 version doesn't have the capability to take a cpu profile anyway.

Just finished a quick test... it goes just a bit faster, but the lag is still noticeable.

Thanks a lot for the help.
Can you try the opposite then? Downgrade just jupyter but put Pylance back to the latest.

The same result as before. A bit faster, but still lags

@ale-dg If it's not too much trouble, could you do the profiling again for us? The previous profile is too large and contains too much noise. This time, could you start profiling and perform just one action that you feel is slow, wait for it to finish, and then stop profiling? That will help us find the spot you are complaining about. The previous one is just too large, so we can't identify the spot where you say you're having a performance issue.
If you are experiencing multiple performance issues, it is better to have multiple profile data sets rather than one gigantic profile that spans all different issues.
for example, Although I have forgotten to mention that when you hit escape to close the hints' window, the lag goes away, but when you jump to another parameter, it lags again because the window loads again.
it would be nice to have 2 profile data, one without perf issue and one with the perf issue for this specific action you did.

@ale-dg If it's not too much trouble, could you do the profiling again for us? The previous profile is too large and contains too much noise. This time, could you start profiling and perform just one action that you feel is slow, wait for it to finish, and then stop profiling? That will help us find the spot you are complaining about. The previous one is just too large, so we can't identify the spot where you say you're having a performance issue.

Hi @heejaechang, that's why I wanted to open it so I can point to you exactly where the issue began and the best thing I could tell you was "there is a big time lapse where the notebook was executing and then I began typing", but the only thing I know that opens *.cpuprofile is VSCode and as I mentioned above, it crashed when I tried to open it (at least in the version I have). It's not that I don't want to help you, but every time I need to retrieve something (logs, profiles ,etc), I have to rerun the notebook and it takes around 10 min so I can get the errors. If you could give any pointers on how to open the file, then I can tell you the time-stamps where the issues began because I kind of remember the times, but not the exact times.

If you are experiencing multiple performance issues, it is better to have multiple profile data sets rather than one gigantic profile that spans all different issues.
for example, Although I have forgotten to mention that when you hit escape to close the hints' window, the lag goes away, but when you jump to another parameter, it lags again because the window loads again.

I get what you are saying, maybe this was my mistake for not getting it earlier.
Best

@ale-dg it is regular v8 cpu profiling data, so you should be able to open it with either chrome or edge.
open developer tool

make sure to explicitly search for *.cpuprofile and open fg one.
now, you should see something like this

...
By the way, just to set expectations, the user sees only one window, vscode, but underneath it, there are multiple processes and threads running to support various aspects of vscode. The profiling data doesn't capture every single process/component running for vscode (in other words, it is not system-wide profiling); it just profiles the pylance server. So, we can pinpoint if it is the pylance server having performance issues, but if not, we can't tell which components in vscode are having performance issues. We just know it is not pylance.

Had I known earlier haha..
Well... even so, I did the two profiles. I have separated them in the folders in the attached zip. The lagging I did it after running the cells (and it went faster than the release, although there is still some lagging) and the escape I did it after ending the lag profiling.
Now can I downgrade before I forget what I was working on? 😜 😬
Best
profiles_2.zip

@ale-dg
Reload VS Code and try again and let me know if that makes a difference.
Sorry we're asking for so many logs and informatino,
thanks for being patient, given this has been identified as an issue with Jupyter extension when you run cells,
I'd like to try the following

Disable kernel completions
Please can you add the following setting to your user settings

"jupyter.enableKernelCompletions": false

Optionally open your setting and untick (remove the check) for the following

This should make it much better (if not completely solve the problem).

If the above does not work, please let me know
If it does work, then please enable verbose logging and share the logs for Jupyer (with the latest versions)
Note, please try typing (editing) code to replicate the delays you experienced in completions.
As you mentioned here https://github.com/microsoft/vscode-jupyter/issues/15369#issuecomment-1998599256

Literally I have just downgraded everything because nobody answered me 😂😂😂
Let me update everything again

@DonJayamanne naturally everything with the stable releases, right? Not with pre-releases...

@ale-dg sorry, I was looking at the new log you shared. as far as I can see from the log, pylance is working as expected. I dont see any work on FG taking more than 300ms at the largest, most of them are around 5~20ms window for completion. and most of time server is on idle as you probably can see from dev tool yourself.

@ale-dg sorry, I was looking at the new log you shared. as far as I can see from the log, pylance is working as expected. I dont see any work on FG taking more than 300ms at the largest, most of them are around 5~20ms window for completion. and most of time server is on idle as you probably can see from dev tool yourself.

@heejaechang  Yes, I saw it... but still don't understand why it lags :S

@ale-dg
Sorry we're asking for so many logs and information, thanks for being patient & you have been ver helpful,
Given this has been identified as an issue with Jupyter extension when you run cells, I'd like to try the following

Disable kernel completions
Please can you add the following setting to your user settings

"jupyter.enableKernelCompletions": false

Optionally open your setting and untick (remove the check) for the following

This should make it much better (if not completely solve the problem).

If the above does not work, please let me know
If it does work, then please enable verbose logging and share the logs for Jupyer (with the latest versions)
Note, please try typing (editing) code to replicate the delays you experienced in completions.
As you mentioned here Slow completions when running cells #15369 (comment)

@DonJayamanne It didn't work, it keeps lagging like the first time (so, very slow)...

Pleaser can you share the jupyter logs
And confirm that the delay is only experienced after starting a kennel

@DonJayamanne mate... I just finished setting up again my smooth VSCode platform for moving on with my work... It will be very helpful if you could let me know as fast as possible to get any info, because it honestly takes me quite sometime to get it and basically all day I have been helping with this. And also it takes me sometime to go back to the proper set-up for working smoothly and to get the info you ask for.
I can confirm that I set "jupyter.enableKernelCompletions": false in the settings.json, restarted VSCode, ran it, tested it, restarted VSCode again, and tested it again.

Hi,
Below the logs for Jupyter. I just left it until the cell where I do some changes. There is an error because of the delay; I'm not sure if it didn't close the bracket or if I deleted it without intent.
Best
2-Jupyter-MD.log

@ale-dg thank you very much for helping on us on this!
@DonJayamanne I think when he says hint, I think he meant signature help not completion. basically, I think what he is saying is if he types with signature help up, typing is slow, but if he dismiss signature help, typing is fast.
I looked our cpuprofile and all signature call is again within expected range and completion after signature help doesnt show anything pops out.
does jupyter show its own signature help as well?

Just to not confuse anybody... I mean these windows (old images, but you get the idea):

Good evening all,
@rchiodo, @DonJayamanne, @DonJayamanne here's a new thing I just tested... these are the versions of the active extensions:
Extensions (4)

Extension
Author (truncated)
Version

python
ms-
2024.2.1

vscode-pylance
ms-
2024.3.1

jupyter
ms-
2023.8.1002501831

jupyter-renderers
ms-
1.0.17

The version of VSCode is:
Version: 1.82.3
Commit: fdb98833154679dbaa7af67a5a29fe19e55c2b73
Date: 2023-10-02T11:09:17.749Z
Electron: 25.8.1
ElectronBuildId: 24153832
Chromium: 114.0.5735.289
Node.js: 18.15.0
V8: <IP_ADDRESS>-electron.0
OS: Darwin arm64 23.4.0
No lag after running and typing. The notebook still runs at the same speed though (a bit slow for my liking, but the same speed as before), but the lag when typing has gone and even when the windows above still pop-up.
Best

1 is completion.
2 is signature help
3 is tooltip of completion item.
when you say you dismissed hint, which one are you referring to?

1 is completion. 2 is signature help 3 is tooltip of completion item.
when you say you dismissed hint, which one are you referring to?

when I say hint, I mean 2 - Signature Help (hint window). If I hit Escape, it goes away and then everything runs fast and it "unstucks" 1 - Completion (which I guess I have referred to as auto complete).
3 - Tooltip only appears on certain themes, which I have not enabled since doing all the tests.
Best

Hi all,
Hope you're doing well. Just on a follow up here... was it possible to find the problem with the slow processing of Jupyter? As mentioned before, with the downgraded versions everything has been working properly this week, so also wanted you to inform you about it.
Best

@ale-dg

Just on a follow up here... was it possible to find the problem with the

We have made some progress in identifying a few issues, but I do not think you are running into the same issues.
Hence the follow up questions.
Please can you try again with the following changes (& re-loading VS Code)

Add setting to your user settings "jupyter.enableKernelCompletions": false
Uninstall all extensions except Python, Jupyter and Notebook Renderers
Disable auto save using the setting as follows
Ensure you are using the latest version of VS Code and latest versions of Python and Jupyter extensions

Finally re-load VS Code and test again

If possible, please share a screen recording of the issue (you can use something like gifcap.dev or similar)

was it possible to find the problem with the slow processing of Jupyter?

Finally, based on the comments, you seem to be running into a few issues

when I say hint, I mean 2 - Signature Help (hint window). If I hit Escape, it goes away and then everything runs fast and it "unstucks" 1 - Completion (which I guess I have referred to as auto complete).
.. was it possible to find the problem with the slow processing of Jupyter? As

I'd like to get clarifications.
Is the delay in typing text and getting completions.
Or is the delay in just typing text in the editors.
Or are there delays in running code in Jupyter?
I'm sorry for asking these questions, and sorry we haven't been able to get to the bottom of this,
& once again thank you very much for the information and patience,
I am certain we can get to the bottom of this and believe we can resolve this,
Again, please remember to uninstall all extensions except ones mentioned, and reload VS Code.

@DonJayamanne

Create a simple notebook with 1 cell containing print("Hello World")

Add setting to your user settings "jupyter.enableKernelCompletions": false

Uninstall all extensions except Python, Jupyter and Notebook Renderers

Disable auto save using the setting as follows

Ensure you are using the latest version of VS Code and latest versions of Python and Jupyter extensions

Finally re-load VS Code and test again
If possible, please share a screen recording of the issue (you can use something like gifcap.dev or similar)

I have made the changes above, but nothing happens. No completions, no nothing, therefore no issue, it is just like working in an old notebook pad (with colours though). Not sure what would you like me to record here because honestly it won't show anything.

was it possible to find the problem with the slow processing of Jupyter?

Finally, based on the comments, you seem to be running into a few issues

when I say hint, I mean 2 - Signature Help (hint window). If I hit Escape, it goes away and then everything runs fast and it "unstucks" 1 - Completion (which I guess I have referred to as auto complete).
.. was it possible to find the problem with the slow processing of Jupyter? As

I'd like to get clarifications. Is the delay in typing text and getting completions. Or is the delay in just typing text in the editors. Or are there delays in running code in Jupyter?

For both, when having the newest version of everything. I mean, I can understand the delay in running the code can happen for many issues, but not that it sometimes would crash during execution or when restarting the kernel (which I guess you have been working in other opened issues).
As for the delays, when I begin to execute cells (even from the first cells), the lags begin, something like this:

type ord = OrdinalEncoder(
lag lag lag lag lag lag lag
signature help pops (if I hit escape when this appears, the lag disappears until I jump to another parameter)
lag lag lag lag
type han (i.e. I want to type in "handle_unknown")
lag lag lag lag lag lag lag
completion appears
lag lag lag lag
allows to select in the completion and/or finishes the sentence (if I typed it completely and correctly)
lag lag lag lag lag lag...

... and It keeps like it for the rest of the notebook, hence there is also a delay in receiving the command for executing the cells and so on and so on, which has been also reported in some issues all around the main VSCode repro, not in the Jupyter's one.
So, when you are working, it looks like a disconnection between the keyboard, VSCode and the screen.
Best

As for the delays, when I begin to execute cells (even from the first cells), the lags begin, something like this:

Do you notice the same delays when you do NOT execute any cells?
Do you notice the same delays for a notebook with just one cell?

Thanks again,
As mentioned, we have identified a number of areas that contribute to some of the delays observed.
However still struggling to understand the scenario you are running into .

Do you notice the same delays when you do NOT execute any cells?

When not executing cells, it runs lightning fast. @rchiodo asked for that test last time we were hooked on this and when not executing cells all the signature helps and completions work absolutely fine.

Do you notice the same delays for a notebook with just one cell?
No. What I noticed by the time I reported it is that it builds up with the number of executions and not specifically with the number of cells, therefore the larger the notebook the more executions there will be. But you could easily run 10 cells 10 times and the lag will build up. Obviously the more complex things we try to do (so, the more we "push" the kernel), it builds up faster.

When you run into the delays can you run the following command from the command palette and let me kow what you get in the output Developer: Measure Extension Host Latency

I'll have to install all the normal extensions. I'll do it and come back with you.

This is what I get back

Roundtrip latency: 2.510ms
Up: 6060.6 Mbps
Down: 4166.7 Mbps

Hi,
I have made a short video. Just for you to see what I mean with the lags
https://github.com/microsoft/vscode-jupyter/assets/106413328/c9e291c4-74c8-4a7b-a562-8aaebe07994e

Thanks
When you experience the delays, save the notebook
Charge the settings to disable autostart of jupyter (search for the string 'autostart jupyter'
Reload vscode, wait a few seconds till the kernel is auto selected
Start typing (without running any cells), do you still experience the same perf issues

Hi @DonJayamanne,
Sorry it took me some days to answer.
I have just tried it and the lag keeps happening exactly as in the video above. As I have been mentioning, when no cells are executed, the lag does not happen. It only happens when I start working on large notebooks and especially with markdowns. Also recently I tried it with an old notebook which had some markdowns and the lag appeared (sorry I can't share these notebooks, they're sensitive).
The only solution for not having lag is to downgrade Jupyter to the August 2023 release (somehow it doesn't hangs and the kernel rarely dies), and the most stable solution is to definitely downgrade everything to August, which makes everything go smoother for working.
Best

Duplicate of https://github.com/microsoft/vscode-jupyter/issues/14459#issuecomment-2027895444

@ale-dg @rabyj
Please can you

Download the latest version of VS Code insiders (if you have it installed, then please install todays update)
Then install the latest Jupyter and Python pre-release versions
Please test this out and let me know how it goes.

Note:

Please disable auto save feature when testing this.
Please disable all extensions except Python, Jupyter, Notebook Renderers and Pylance
Please disable kernel completions (from settings)
Please share a screen recording of the issue
Please could you enable logging as follows:

Open settings & go into Jupyter->Logging
Change the value to verbose
Reload VS Code,
Attempt to repro the issue & then copy the output from the Jupyter output panel.

@rabyj I'm assuming the delays you are experiencing are to do with typing in cells and not with execution, is that correct?
@ale-dg @rabyj
If you are able to consistently repro the perf issues then please share the following CPU profile logs

Reload VS Code
Open a Jupyter notebook, run a cell, wait a few seconds (I want to ensure Jupyter and Python extension have completely activated and a kernel has started before we gather any logs)
First start Profiling the Running Extensions and then replicate the issue

Once you have replicated the issue, stop profiling, save the CPU profile and then share the CPU Profile
Steps here Profile the Running Extensions
Please let me know how soon after you started profiling did you experience the delays

After the above, capture the CPU Profile logs for the Renderer Process.

As earlier, start profilng, then replicate the issue, wait a few seconds and then save the logs
Steps here https://github.com/microsoft/vscode/wiki/Performance-Issues#profiling-the-renderer-process

Thank you again and really do appreciate your help in this space

@rabyj I'm assuming the delays you are experiencing are to do with typing in cells and not with execution, is that correct?

Yes indeed, sudden freezes during typing, lasting for a few seconds, quite often. Worse than when I was working with big notebook months ago. It either got worse recently, or I just wasn't working with big enough notebooks before. I  am noting that I use several small markdown cell to delineate sections, but I don't know if they're making it worse.
I don't really have time to test more, sorry, am in a rush to write a paper. I found this issue when trying to figure out what was up with the freezing. Won't be able to contribute until that's finished.

Hi @DonJayamanne ,
Just for tracking purposes since you have mentioned this in another issue, I'm uploading here the profiles as well.
Best
Archive.zip

cking purposes since you have mentioned this in another issue, I'm uploading here the profiles as well.

You are most kind.

@ale-dg

I have just tested with the largest notebook I have which includes a lot of markdowns and it indeed runs faster.

Are you able to share this notebook, perhaps via email,
If there's some personal information, then can you try to obfuscate some code/text and share it.
I'd like to ensure the same packages are in the notebook and the same volume of code and perhaps output as well.

@isidorn @mjbvz  Based on the logs, the renderer process is busy here rendering the Parameters widget with Markdown content.

@mjbvz should be able to help here. Or you can investigate our paramater widgets class yourself, since I am not sure it has an owner.

@ale-dg

I have just tested with the largest notebook I have which includes a lot of markdowns and it indeed runs faster.

Are you able to share this notebook, perhaps via email, If there's some personal information, then can you try to obfuscate some code/text and share it. I'd like to ensure the same packages are in the notebook and the same volume of code and perhaps output as well.

@DonJayamanne sorry, mate. For the particular notebook which I have been using to test, I can't. It goes beyond the personal data but also the information contained in the markdowns, the data and the graphs themselves. For starters the data comes from 7 different sources with a 70 to 80 MB size per file,  each containing 75 columns and I end up having something like 150 columns after re-working them. Then the total coding cells are around 180 and the total markdown "cells" are around 120 - 130 or so (some with several paragraphs), so it will be too much time-consuming to change them or even to try. Maybe someone in the other issue has a particular one?
I'll try to find one I did sometime ago, but I don't remember it has too much markdowns nor too much coding into it...
Best

Additional note: the intellisence seems especially slow when using plotly functions in my case, for example make_subplots, fig.add_trace and go.Box. For almost every keyword argument I try to add, it freezed up for a few seconds. Very frustrating.
If I reload the window, as expected, it works fine. After working for a bit, it starts lagging/freezing again. Feels like when there are too many variables it starts having a hard time, but might just be a correlation.

@DonJayamanne @amunger I have created this notebook which uses an open dataset of penguins. As you can see, even compressed is quite heavy and it began to lag after cell 70 or so (exactly when I began to work on the ColumnTransformer section).
Btw, I must mention today I received a couple of updates for Jupyter and VSCode Insiders:
VS Code version: Code - Insiders 1.89.0-insider (50a6f4f200c5a33f47997eb6a8966e50cf219e21, 2024-04-12T05:51:02.558Z)
OS version: Darwin arm64 23.4.0
Modes:

Extension
Author (truncated)
Version

catppuccin-vsc
Cat
3.13.0

catppuccin-vsc-icons
Cat
1.11.0

catppuccin-vsc-pack
cat
1.0.2

vscode-pull-request-github
Git
0.87.2024041104

rainbow-csv
mec
3.11.0

black-formatter
ms-
2024.2.0

python
ms-
2024.5.11021008

vscode-pylance
ms-
2024.4.101

jupyter
ms-
2024.4.2024041201

jupyter-renderers
ms-
1.0.17

(2 theme extensions excluded)
Best
penguins_classification.ipynb.zip

@DonJayamanne Please put together simple steps that demonstrates that problem. I gave that notebook a try but IntelliSense worked fine

@DonJayamanne Please put together simple steps that demonstrates that problem. I gave that notebook a try but IntelliSense worked fine

@mjbvz from a user's perspective, what I can tell you what happens is recorded in a video here https://github.com/microsoft/vscode-jupyter/issues/14459#issuecomment-2050850128.
When you execute a bunch of cells with markdowns, the UI begins to lag and feels like there's a disconnection between the keyboard and the screen. Everyone who checks the profiles see IntelliSense working fine, even I have confirmed it above together with @heejaechang, but the reality is different. The problem builds the longer the notebook is and/or the more cells you execute. So you could also have only 10 cells executed 10 times and the lag builds.
Hope it helps.
Best

@mjbvz btw, it's also recorded here https://github.com/microsoft/vscode-jupyter/issues/15369#issuecomment-2019172042

@DonJayamanne Is the parameter hints widget is being fed an absolutely massive string of markdown or some very oddly formatted markdown containing html? Are there some pylance logs you can collect that would could show this?
Can you please also create a notebook with easy setup steps. I can't easily run the provided notebooks due to all the dependencies

@heejaechang pleaser can you provide instructions to get the logs so we can see the markdown contents sent to vscode from the language server.

@DonJayamanne @mjbvz I did some last month... would these work? See them in this comment: https://github.com/microsoft/vscode-jupyter/issues/15369#issuecomment-1998698042
Best

Also there are some logs here, should you want to check them as well (coming from another issue https://github.com/microsoft/pylance-release/issues/5614)
https://github.com/microsoft/pylance-release/issues/5614#issuecomment-1996158984
https://github.com/microsoft/pylance-release/issues/5614#issuecomment-1996301022
https://github.com/microsoft/pylance-release/issues/5614#issuecomment-1998258867

Would this be related as well? Please see https://github.com/microsoft/pylance-release/issues/5748 and  https://github.com/microsoft/pylance-release/issues/5173

@DonJayamanne @mjbvz @heejaechang as you can see above, I have referenced this issue to another in Pylance. I attached the same log (from stable versions - VSCode, Jupyter, Pylance, etc.)  since even by opening the same notebook as above, Pylance crashed 🫠
Hope it helps
Best
Pylance.log

@mjbvz
I think I can replicate this, not sure, but I can see 300ms spent in rendering the parameter hints on my machine.
I would like to think I have a fairly beefy machine & 300ms is a lot when typing.

& then I get a message similar to the following in the dev tools:
WARN [perf] Renderer reported VERY LONG TASK (297ms), starting profiling session '44cde74f-3e84-495a-bcca-b1688b64c7cb'
Here's are the packages that need to be installed
pip install ipykernel scikit-learn seaborn statsmodels plotly catppuccin-matplotlib pingouin

Here is the first and second cell
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.patches import Polygon
import plotly.express as px
import plotly.graph_objects as go
import mplcatppuccin
import warnings
from scipy.stats import iqr
import scipy.stats as stats
import statsmodels.api as sm
from statsmodels.formula.api import ols
from pingouin import ttest
from sklearn.preprocessing import (
    StandardScaler,
    Normalizer,
    PowerTransformer,
    QuantileTransformer,
    RobustScaler,
    FunctionTransformer,
    MinMaxScaler,
    MaxAbsScaler,
)
from sklearn.pipeline import Pipeline
from statsmodels.graphics.gofplots import ProbPlot
from sklearn.compose import ColumnTransformer
from sklearn.model_selection import train_test_split, cross_validate
from sklearn.preprocessing import LabelEncoder
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import StratifiedKFold
warnings.simplefilter('ignore')

%matplotlib inline
%config InlineBackend.figure_format = 'retina'
pd.set_option('display.max_columns', None)
pd.set_option('display.precision', 2)
pd.set_option('display.colheader_justify', 'left')

mpl.style.use('latte')

& second cell to replic the issue
from sklearn.preprocessing import OrdinalEncoder

ord = OrdinalEncoder()

Adding brackets or parameters to OridinalEncoder in the last line causes issues.
Note: Its very difficult to replicate this issue, i say that because I can no longer repro this issue
Here is the MD that I captured for two of the calls.
For some reason it gets called multiple times (i could see this getting called almost 20-30 times).
All when I add just the ( opening bracket.
One of the Mardown content passed into sanitize call
<p>Encode categorical features as an integer array.</p><p>The input to this transformer should be an array-like of integers or\nstrings, denoting the values taken on by categorical (discrete) features.\nThe features are converted to ordinal integers. This results in\na single column of integers (0 to n_categories - 1) per feature.</p><p>Read more in the <code>User Guide <preprocessing_categorical_features></code>.\nFor a comparison of different encoders, refer to:<br><code>sphx_glr_auto_examples_preprocessing_plot_target_encoder.py</code>.</p><h2 id="parameters">Parameters</h2>\n<p>categories : 'auto' or a list of array-like, default='auto'<br>    Categories (unique values) per feature:</p><ul>\n<li>'auto' : Determine categories automatically from the training data.</li>\n<li>list : <code>categories[i]</code> holds the categories expected in the ith\ncolumn. The passed categories should not mix strings and numeric\nvalues, and should be sorted in case of numeric values.</li>\n</ul>\n<p>    The used categories can be found in the <code>categories_</code> attribute.</p><p>dtype : number type, default=np.float64<br>    Desired dtype of output.</p><p>handle_unknown : {'error', 'use_encoded_value'}, default='error'<br>    When set to 'error' an error will be raised in case an unknown\ncategorical feature is present during transform. When set to\n'use_encoded_value', the encoded value of unknown categories will be\nset to the value given for the parameter <code>unknown_value</code>. In<br>    <code>inverse_transform</code>, an unknown category will be denoted as None.</p><p>unknown_value : int or np.nan, default=None<br>    When the parameter handle_unknown is set to 'use_encoded_value', this\nparameter is required and will set the encoded value of unknown\ncategories. It has to be distinct from the values used to encode any of\nthe categories in <code>fit</code>. If set to np.nan, the <code>dtype</code> parameter must\nbe a float dtype.</p><p>encoded_missing_value : int or np.nan, default=np.nan<br>    Encoded value of missing categories. If set to <code>np.nan</code>, then the <code>dtype</code>\nparameter must be a float dtype.</p><p>min_frequency : int or float, default=None<br>    Specifies the minimum frequency below which a category will be\nconsidered infrequent.</p><ul>\n<li><p>If <code>int</code>, categories with a smaller cardinality will be considered\ninfrequent.</p></li>\n<li><p>If <code>float</code>, categories with a smaller cardinality than\n<code>min_frequency * n_samples</code>  will be considered infrequent.</p></li>\n</ul>\n<p>max_categories : int, default=None<br>    Specifies an upper limit to the number of output categories for each input\nfeature when considering infrequent categories. If there are infrequent\ncategories, <code>max_categories</code> includes the category representing the\ninfrequent categories along with the frequent categories. If <code>None</code>,\nthere is no limit to the number of output features.</p><p>    <code>max_categories</code> do **not** take into account missing or unknown\ncategories. Setting <code>unknown_value</code> or <code>encoded_missing_value</code> to an\ninteger will increase the number of unique integer codes by one each.\nThis can result in up to <code>max_categories + 2</code> integer codes.</p><h2 id="attributes">Attributes</h2>\n<p>categories_ : list of arrays<br>    The categories of each feature determined during <code>fit</code> (in order of\nthe features in X and corresponding with the output of <code>transform</code>).\nThis does not include categories that weren't seen during <code>fit</code>.</p><p>n_features_in_ : int<br>    Number of features seen during <code>fit</code>.</p><p>feature_names_in_ : ndarray of shape (<code>n_features_in_</code>,)<br>    Names of features seen during <code>fit</code>. Defined only when <code>X</code>\nhas feature names that are all strings.</p><p>infrequent_categories_ : list of ndarray<br>    Defined only if infrequent categories are enabled by setting\n<code>min_frequency</code> or <code>max_categories</code> to a non-default value.\n<code>infrequent_categories_[i]</code> are the infrequent categories for feature\n<code>i</code>. If the feature <code>i</code> has no infrequent categories\n<code>infrequent_categories_[i]</code> is None.</p><h2 id="see-also">See Also</h2>\n<p>OneHotEncoder : Performs a one-hot encoding of categorical features. This encoding<br>    is suitable for low to medium cardinality categorical variables, both in\nsupervised and unsupervised settings.<br>TargetEncoder : Encodes categorical features using supervised signal<br>    in a classification or regression pipeline. This encoding is typically\nsuitable for high cardinality categorical variables.<br>LabelEncoder : Encodes target labels with values between 0 and<br>    <code>n_classes-1</code>.</p><h2 id="notes">Notes</h2>\n<p>With a high proportion of <code>nan</code> values, inferring categories becomes slow with\nPython versions before 3.10. The handling of <code>nan</code> values was improved\nfrom Python 3.10 onwards, (c.f.<br><code>bpo-43475 <https://github.com/python/cpython/issues/87641></code>_).</p><h2 id="examples">Examples</h2>\n<p>Given a dataset with two features, we let the encoder find the unique\nvalues per feature and transform the data to an ordinal encoding.</p><div class="code" data-code="id#108">>>> from sklearn.preprocessing import OrdinalEncoder\n>>> enc = OrdinalEncoder()\n>>> X = [['Male', 1], ['Female', 3], ['Female', 2]]\n>>> enc.fit(X)\nOrdinalEncoder()\n>>> enc.categories_\n[array(['Female', 'Male'], dtype=object), array([1, 2, 3], dtype=object)]\n>>> enc.transform([['Female', 3], ['Male', 1]])\narray([[0., 2.],\n       [1., 0.]])</div><div class="code" data-code="id#109">>>> enc.inverse_transform([[1, 0], [0, 1]])\narray([['Male', 1],\n       ['Female', 2]], dtype=object)</div><p>By default, <code>OrdinalEncoder</code> is lenient towards missing values by\npropagating them.</p><div class="code" data-code="id#110">>>> import numpy as np\n>>> X = [['Male', 1], ['Female', 3], ['Female', np.nan]]\n>>> enc.fit_transform(X)\narray([[ 1.,  0.],\n       [ 0.,  1.],\n       [ 0., nan]])</div><p>You can use the parameter <code>encoded_missing_value</code> to encode missing values.</p><div class="code" data-code="id#111">>>> enc.set_params(encoded_missing_value=-1).fit_transform(X)\narray([[ 1.,  0.],\n       [ 0.,  1.],\n       [ 0., -1.]])</div><p>Infrequent categories are enabled by setting <code>max_categories</code> or <code>min_frequency</code>.\nIn the following example, "a" and "d" are considered infrequent and grouped\ntogether into a single category, "b" and "c" are their own categories, unknown\nvalues are encoded as 3 and missing values are encoded as 4.</p><div class="code" data-code="id#112">>>> X_train = np.array(\n...     [["a"] * 5 + ["b"] * 20 + ["c"] * 10 + ["d"] * 3 + [np.nan]],\n...     dtype=object).T\n>>> enc = OrdinalEncoder(\n...     handle_unknown="use_encoded_value", unknown_value=3,\n...     max_categories=3, encoded_missing_value=4)\n>>> _ = enc.fit(X_train)\n>>> X_test = np.array([["a"], ["b"], ["c"], ["d"], ["e"], [np.nan]], dtype=object)\n>>> enc.transform(X_test)\narray([[2.],\n       [0.],\n       [1.],\n       [2.],\n       [3.],\n       [4.]])</div>

Another sample of mardown content passed into another call
<p>Encode categorical features as an integer array.</p><p>The input to this transformer should be an array-like of integers or\nstrings, denoting the values taken on by categorical (discrete) features.\nThe features are converted to ordinal integers. This results in\na single column of integers (0 to n_categories - 1) per feature.</p><p>Read more in the <code>User Guide <preprocessing_categorical_features></code>.\nFor a comparison of different encoders, refer to:<br><code>sphx_glr_auto_examples_preprocessing_plot_target_encoder.py</code>.</p><h2>Parameters</h2>\n<p>categories : 'auto' or a list of array-like, default='auto'<br>    Categories (unique values) per feature:</p><ul>\n<li>'auto' : Determine categories automatically from the training data.</li>\n<li>list : <code>categories[i]</code> holds the categories expected in the ith\ncolumn. The passed categories should not mix strings and numeric\nvalues, and should be sorted in case of numeric values.</li>\n</ul>\n<p>    The used categories can be found in the <code>categories_</code> attribute.</p><p>dtype : number type, default=np.float64<br>    Desired dtype of output.</p><p>handle_unknown : {'error', 'use_encoded_value'}, default='error'<br>    When set to 'error' an error will be raised in case an unknown\ncategorical feature is present during transform. When set to\n'use_encoded_value', the encoded value of unknown categories will be\nset to the value given for the parameter <code>unknown_value</code>. In<br>    <code>inverse_transform</code>, an unknown category will be denoted as None.</p><p>unknown_value : int or np.nan, default=None<br>    When the parameter handle_unknown is set to 'use_encoded_value', this\nparameter is required and will set the encoded value of unknown\ncategories. It has to be distinct from the values used to encode any of\nthe categories in <code>fit</code>. If set to np.nan, the <code>dtype</code> parameter must\nbe a float dtype.</p><p>encoded_missing_value : int or np.nan, default=np.nan<br>    Encoded value of missing categories. If set to <code>np.nan</code>, then the <code>dtype</code>\nparameter must be a float dtype.</p><p>min_frequency : int or float, default=None<br>    Specifies the minimum frequency below which a category will be\nconsidered infrequent.</p><ul>\n<li><p>If <code>int</code>, categories with a smaller cardinality will be considered\ninfrequent.</p></li>\n<li><p>If <code>float</code>, categories with a smaller cardinality than\n<code>min_frequency * n_samples</code>  will be considered infrequent.</p></li>\n</ul>\n<p>max_categories : int, default=None<br>    Specifies an upper limit to the number of output categories for each input\nfeature when considering infrequent categories. If there are infrequent\ncategories, <code>max_categories</code> includes the category representing the\ninfrequent categories along with the frequent categories. If <code>None</code>,\nthere is no limit to the number of output features.</p><p>    <code>max_categories</code> do **not** take into account missing or unknown\ncategories. Setting <code>unknown_value</code> or <code>encoded_missing_value</code> to an\ninteger will increase the number of unique integer codes by one each.\nThis can result in up to <code>max_categories + 2</code> integer codes.</p><h2>Attributes</h2>\n<p>categories_ : list of arrays<br>    The categories of each feature determined during <code>fit</code> (in order of\nthe features in X and corresponding with the output of <code>transform</code>).\nThis does not include categories that weren't seen during <code>fit</code>.</p><p>n_features_in_ : int<br>    Number of features seen during <code>fit</code>.</p><p>feature_names_in_ : ndarray of shape (<code>n_features_in_</code>,)<br>    Names of features seen during <code>fit</code>. Defined only when <code>X</code>\nhas feature names that are all strings.</p><p>infrequent_categories_ : list of ndarray<br>    Defined only if infrequent categories are enabled by setting\n<code>min_frequency</code> or <code>max_categories</code> to a non-default value.\n<code>infrequent_categories_[i]</code> are the infrequent categories for feature\n<code>i</code>. If the feature <code>i</code> has no infrequent categories\n<code>infrequent_categories_[i]</code> is None.</p><h2>See Also</h2>\n<p>OneHotEncoder : Performs a one-hot encoding of categorical features. This encoding<br>    is suitable for low to medium cardinality categorical variables, both in\nsupervised and unsupervised settings.<br>TargetEncoder : Encodes categorical features using supervised signal<br>    in a classification or regression pipeline. This encoding is typically\nsuitable for high cardinality categorical variables.<br>LabelEncoder : Encodes target labels with values between 0 and<br>    <code>n_classes-1</code>.</p><h2>Notes</h2>\n<p>With a high proportion of <code>nan</code> values, inferring categories becomes slow with\nPython versions before 3.10. The handling of <code>nan</code> values was improved\nfrom Python 3.10 onwards, (c.f.<br><code>bpo-43475 <https://github.com/python/cpython/issues/87641></code>_).</p><h2>Examples</h2>\n<p>Given a dataset with two features, we let the encoder find the unique\nvalues per feature and transform the data to an ordinal encoding.</p><div data-code="id#108">>>> from sklearn.preprocessing import OrdinalEncoder\n>>> enc = OrdinalEncoder()\n>>> X = [['Male', 1], ['Female', 3], ['Female', 2]]\n>>> enc.fit(X)\nOrdinalEncoder()\n>>> enc.categories_\n[array(['Female', 'Male'], dtype=object), array([1, 2, 3], dtype=object)]\n>>> enc.transform([['Female', 3], ['Male', 1]])\narray([[0., 2.],\n       [1., 0.]])</div><div data-code="id#109">>>> enc.inverse_transform([[1, 0], [0, 1]])\narray([['Male', 1],\n       ['Female', 2]], dtype=object)</div><p>By default, <code>OrdinalEncoder</code> is lenient towards missing values by\npropagating them.</p><div data-code="id#110">>>> import numpy as np\n>>> X = [['Male', 1], ['Female', 3], ['Female', np.nan]]\n>>> enc.fit_transform(X)\narray([[ 1.,  0.],\n       [ 0.,  1.],\n       [ 0., nan]])</div><p>You can use the parameter <code>encoded_missing_value</code> to encode missing values.</p><div data-code="id#111">>>> enc.set_params(encoded_missing_value=-1).fit_transform(X)\narray([[ 1.,  0.],\n       [ 0.,  1.],\n       [ 0., -1.]])</div><p>Infrequent categories are enabled by setting <code>max_categories</code> or <code>min_frequency</code>.\nIn the following example, "a" and "d" are considered infrequent and grouped\ntogether into a single category, "b" and "c" are their own categories, unknown\nvalues are encoded as 3 and missing values are encoded as 4.</p><div data-code="id#112">>>> X_train = np.array(\n...     [["a"] * 5 + ["b"] * 20 + ["c"] * 10 + ["d"] * 3 + [np.nan]],\n...     dtype=object).T\n>>> enc = OrdinalEncoder(\n...     handle_unknown="use_encoded_value", unknown_value=3,\n...     max_categories=3, encoded_missing_value=4)\n>>> _ = enc.fit(X_train)\n>>> X_test = np.array([["a"], ["b"], ["c"], ["d"], ["e"], [np.nan]], dtype=object)\n>>> enc.transform(X_test)\narray([[2.],\n       [0.],\n       [1.],\n       [2.],\n       [3.],\n       [4.]])</div>

@DonJayamanne @mjbvz
Do you need the yml or the requirements txt? I can get you both if it helps you to get the environment running easier.

@ale-dg Thank you very mcuh.
However even without those packages I can see some delays in the display of the parameter hints

@mjbvz I have no knowledge of the code, but found the following to be inresteing
element.innerHTML = sanitizeRenderedMarkdown(markdown, markdownHtmlDoc.body.innerHTML) as unknown as string;
Given the fact taht the markdown has already been sanitialized a few lines earlier, why do we need to sanitize this again.
Feel like we can remove this extra sanitization call.

@DonJayamanne I haven't tried the exact same code because normally when working with ML I use notebook, not normal py files. I can try to do it tomorrow rendering the plots in the interactive window, just to see how it goes.
Although your comment comes nicely because now I am writing a streamlit interface (on a completely different Python environment) in VSCode insiders which must be a py file and it SOMETIMES lags, not as frequently as with notebooks and not as dramatically, but it does. It's very light though, I don't think it would even show on a profile because I have tried to repro the exact same delay and it didn't happen.
What I can tell you is that if I paste a piece of code from one window to another (I did it from a ipynb to a py), it lags for some seconds while IntelliSense recognises the code and the lag stays for a while, then it comes back to normal.
Best

From my  test and t looking at  code and taking into account you’re Jupyter extension works , this  issue has no relation to Jupyter execution
ie If you copy the exact same code (all the cells into a single python file), you will then run into the same perf issues
anyways, that’s fine for now, I think we have all the information we need as I can replicate some of the delays at my end


Map as a global variable

I was trying to set a map as a global variable, but I get a parsing error about using : in the first key.
For example declare variable $TEST as map(*) := map { 1: 2, 3: 4 }
I was using the BaseX flavour if that makes any difference

Hey
This is a duplicate of #166 and #156. Current map syntax is based on early implementation of maps in Saxon.


Add Support for Agent-to-Agent Communication

Problem
The current Agent Protocol specification focuses solely on human-agent interactions through threads and runs. But I think many applications will require many specialized agents working together. Currently, there's no specified way for:

Agents to discover other agents and their capabilities
Agents to communicate directly with each other
Coordinating work between multiple agents
Delegating tasks between agents
Sharing context and knowledge between agents

Proposed Solution
Add new endpoints that enable agent discovery and communication:
# Agent Registry
GET /agents
Response:
{
  "agents": [{
    "id": "agent-123",
    "name": "Research Assistant",
    "capabilities": {
      "tasks": ["research", "summarize"],
      "input_types": ["text", "pdf"],
      "output_types": ["text", "json"]
    },
    "status": "active"
  }]
}

# Agent Communication
POST /threads/{thread_id}/messages
{
  "from_agent": "agent_id",
  "to_agent": "agent_id",
  "content": {},
  "type": "request|response"
}

# Agent Task Delegation
POST /threads/{thread_id}/delegate
{
  "delegate_to": "agent_id",
  "permissions": ["read", "write", "execute"],
  "task": {
    "type": "task_type",
    "input": {},
    "constraints": {}
  }
}

# Agent Shared State Creation
POST /threads/{thread_id}/shared_state
{
  "namespace": "shared_namespace",
  "access": ["agent_id1", "agent_id2"],
  "state": {}
}

This solution would provide:

A registry where agents can publish their capabilities and discover others
Standard message formats for agent-to-agent communication. I think this would need a specific format optimized for agent-to-agent communication.
Integration with existing thread model for conversation history

What do you think ?

I dont think this should be part of it yet! Most multi agent systems dont work reliabely in prod. The Agent To Agent comm can be just a tool. And often is just abstracted in SDK´s although its just a tool call under the hood.
So a more general, better approach would be having a call tool as api  Tool API schema defined here.


Reconnect Ambassador to Scout

We killed the Scout connection while working on the grand refactor; we need it back again for any release to happen.

Fixed in 0b8e4ef.


other required 100% o2 compliant parts

What other parts are necessary for constructing an o2 mixing solution that's viable?
Dependent on finalizing pneumatic architecture, see RespiraWorks/SystemDesign#5

Why "other"? As opposed to what? This needs a better title.


How to change the default training time?

I have about 400 story and 4k examples. I'm using Botfront local version in windows matching. When I train them, it's failing because of time out.
How can I change the default training time?

@pheel @kenrilea @MatthieuJnon bro, would you please help me out 🥺.

@pheel @kenrilea @MatthieuJnon bro, would you please help me out 🥺.

What version are you using @Abu-Ishak ? On recent versions there should be no timeout and if you want one you should be able to set it using the env variable TRAINING_TIMEOUT

What version are you using @Abu-Ishak ? On recent versions there should be no timeout and if you want one you should be able to set it using the env variable TRAINING_TIMEOUT

@MatthieuJnon thanks for your reply. I'm using botfront version: 0.26.0 . Does the timeout parameter use mS? If I want to increase my training time to 60 min, then how can I write it?

@MatthieuJnon thanks for your reply. I'm using botfront version: 0.26.0 . Does the timeout parameter use mS? If I want to increase my training time to 60 min, then how can I write it?

@Abu-Ishak, if you upgrade to >=0.27.0, you will get no timeout by default. Current is 0.27.5.

@Abu-Ishak, if you upgrade to >=0.27.0, you will get no timeout by default. Current is 0.27.5.


code quality test

This could prevent issues like this https://github.com/red-hat-data-services/odh-manifests/pull/500 and also may improve our code pointing to eventual bugs in the codebase.
At the moment both JSON and YAML check can be incorporated after we decide how we want to run it and finalize naming and place of the execution scripts.
Next steps:

to decide what rules we want to follow for our Dockerfiles and add the check for them too
check similar approach of code static analysis also for https://github.com/opendatahub-io/kubeflow (note there are some CI checks already in https://github.com/kubeflow/kubeflow/tree/master/.github/workflows)

Created PR upstream https://github.com/opendatahub-io/notebooks/pull/430.


backend: Routes on bidding

Bidding on Items
look at supabase tables
POST /api/bids - Submits a bid for a specific item, provided by a qualified user (U).
GET /api/bids/:item_id - Retrieves all bids for a particular item.
PUT /api/bids/:id/accept - Marks a bid as accepted, finalizing the deal with the buyer.
PUT /api/bids/:id/reject - Rejects a specific bid.

// GET /api/users/:id - Fetches profile data for a specific user by their ID.
// DELETE /api/users/:id - Soft-deletes a user’s account (for deactivation or suspension).

Completed

Completed


additional algorithms run

AutoCombat (environment specified)
Polynomial (home made)
CovBat (environment missing)
ComBat++
ComBAT-GAM - non-linear
RELIEF

AUtocombat, and ComBAT-GAM are done. RELIEF is not neccesary

@MDijsselhof I'm adding you as an asignee to this because you are the natural reviewer due to the fact you will need to run all the notebooks for these algorithms to get results. Once it goes into review column, please review.

This issue will now only cover neurocombat, open nested combat, autocombat. Other algorithm implementations have their own issue. The issue is partially blocked by waiting for permission on the "H" dataset.

Done.


my restful server is strongloop , param format is filter[skip] it's have problem

my code is
var query = { filter[skip] : 10};
Restangular.all("products").getList("products",query );
error code is
product.factory.js:27 Uncaught SyntaxError: Unexpected token [

var query = { filter[skip] : 10}; // invalid JavaScript

var query = { filter['skip'] : 10}; // valid JavaScript


Kimdv/remove convenience initializers

@swift-ci please test

Will add them to this PR

@ahoppen should TupleExprElementSyntax I also replace the string with an PartialSyntaxNodeString and then change to label: label.map { TokenSyntax("\($0)") }

Also removed the one from IfStmtSyntax as there is one in SyntaxNodeWithBody.swift

Also removed TernaryExprSyntax as we can use ExprSyntax now

@swift-ci please test

@ahoppen should TupleExprElementSyntax I also replace the string with an PartialSyntaxNodeString and then change to label: label.map { TokenSyntax("\($0)") }

For TupleExprElementSyntax it’s fine since we aren’t parsing the string – we are using it as the contents of an identifier.

@swift-ci Please test

@swift-ci please test


"Smartcard feature (including user certificate)" is missing in "Windows Hello for Business features" section

[Enter feedback here]
Not for this page but for the "Windows Hello for Business features" (1 level up)
a) I miss the "Smartcard feature" enabled with Windows Certificate Trust described here:

It should stated that Windows Hello could be used for Smartcard purposes (= User ceritficate). Please describe the features that it can be used for:
Authentication against IIS Websites?
Authentication for RDP
Signing Office Documents (works we tested that - you can sign Word documents with hello Smartcard certificate)
Signing PDFs?
CodeSigning (including CodeSign EKU in Windows Hello Certificate Template on PKI)?
E-Mail Signing S/MIME (including e-mail protection EKU in Windows Hello Certificate Template on PKI)?

b)The GPO setting for Windows Hello : "Use Windows Hello for Business as smart card certificates" should be describe
-> As far as I understand this GPO allows the smartcard unlock just with the Windows Hello Sign-In and does not have the need to always unlock with fingprint / PIN during sign-in session. This needs to be clarified why and where this should be used. Examples needed.
Be

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

ID: 4e456736-63a3-672a-814f-047cc66f0f98
Version Independent ID: 80cd84e4-6ea5-c707-f362-98fd507fcbfa
Content: Remote Desktop - Microsoft 365 Security
Content Source: windows/security/identity-protection/hello-for-business/hello-feature-remote-desktop.md
Product: w10
GitHub Login: @mapalko
Microsoft Alias: mapalko

@mapalko Please help us in resolving this issue. Thanks

@yogkumgit can you reassign to @paolomatarazzo

Hello @identityiskey and thank you for the feedback! Unfortunately, since you opened this issue, the table of content has been reorganized and some articles retired. Can you please reopen the issue, if still present, linking the correct page? I can't find the page you're pointing out: "Windows Hello for Business features" (1 level up)


Added back shields

Only build from bundle is done at the moment. These shields are based on the pipeline functionality in Jenkins and they will automatically perform all tests approved by the validate_test.py script in the test folder.

Great, you need to resubmit this PR to the dev branch!


Selection issue in AnimationPlayer editor plugin

Godot version: 3.1.stable.official
Windows 7 Home Premium (x86)
I can't move cursor above Animations window if I pressed for selection

I can confirm this on commit https://github.com/godotengine/godot/commit/584ca0f156cec64c259382895e105cf27566a987 (on Linux).
Note that this bug doesn't occur if you move the cursor outside the editor window then back in (while holding the left mouse button).

Still valid in c844cc8


Please invite me to the Community Organization

Name
Shubham Verma
Discord Username (if applicable) E.g John#412
No response
Questions & suggestions?
I would love to be part of this awesome community:)

Hey why I am not getting my name in people section?


Cannot connect to Github from separate nhost accounts.

Hello 🙂
I'm having trouble connecting my nhost project to a github repository. The NHost app is already installed on github and several repositories are already linked to other NHost workspaces on different accounts.
As shown on the screenshots, I can only "Connect to Github", then "Configure the app".
3 repositories are supposed to be accessible, but none is available when back on my dashboard.
Thanks for the help 🙂

Is the repos you're connecting in a github organization?
I have experienced a similar issue, and it seems to work fine one repos owned by individual accounts, but not orgs

Yes it is indeed in a github org. However, the same workflow works on 2 different nhost accounts, linked repos in the same organization :/

Yes, this is currently a permission issue that we're tracking internally. Would you mind sending me an email to the email attached to<EMAIL_ADDRESS>I can add the repo manually (include the subdomain and the full name of the repo e.g. org/name-of-the-repo)

This issue is blocking for us. We want to use Nhost for multiple projects for our clients (maybe dozens in the future) but it's currently not possible because of that.


Bazel can't recognize python binary path

Hi, can you help me with this issue:
ERROR: An error occurred during the fetch of repository 'local_execution_config_python':    Traceback (most recent call last):         File "C:/users/user/_bazel_user/bkk7hz7b/external/org_tensorflow/third_party/py/python_configure.bzl", line 213, column 33, in _create_local_python_repository                 python_lib = _get_python_lib(repository_ctx, python_bin)         File "C:/users/user/_bazel_user/bkk7hz7b/external/org_tensorflow/third_party/py/python_configure.bzl", line 130, column 21, in _get_python_lib                 result = execute(repository_ctx, [python_bin, "-c", cmd])         File "C:/users/user/_bazel_user/bkk7hz7b/external/org_tensorflow/third_party/remote_config/common.bzl", line 230, column 13, in execute                 fail( Error in fail: Repository command failed Traceback (most recent call last):   File "<string>", line 1, in <module> NameError: name 'system' is not defined ERROR: C:/users/user/mediapipe_repo/mediapipe/WORKSPACE:476:14: fetching local_python_configure rule //external:local_execution_config_python: Traceback (most recent call last):         File "C:/users/user/_bazel_user/bkk7hz7b/external/org_tensorflow/third_party/py/python_configure.bzl", line 213, column 33, in _create_local_python_repository                 python_lib = _get_python_lib(repository_ctx, python_bin)         File "C:/users/user/_bazel_user/bkk7hz7b/external/org_tensorflow/third_party/py/python_configure.bzl", line 130, column 21, in _get_python_lib                 result = execute(repository_ctx, [python_bin, "-c", cmd])         File "C:/users/user/_bazel_user/bkk7hz7b/external/org_tensorflow/third_party/remote_config/common.bzl", line 230, column 13, in execute                 fail( Error in fail: Repository command failed Traceback (most recent call last):   File "<string>", line 1, in <module> NameError: name 'system' is not defined ERROR: Analysis of target '//mediapipe/examples/desktop/hello_world:hello_world' failed; build aborted: Repository command failed Traceback (most recent call last):   File "<string>", line 1, in <module> NameError: name 'system' is not defined INFO: Elapsed time: 6.055s INFO: 0 processes. FAILED: Build did NOT complete successfully (38 packages loaded, 11 targets config\ ured)
I've tried this solution #724 but still didn't work for me. I also tried to recognize the path by run sys.executable on python interpreter but it still didn't work. I'm using the latest version of Bazel, python 3.10 and Git Bash
This is the command that i run
C:\Users\User\mediapipe_repo\mediapipe>bazel build -c opt --define MEDIAPIPE_DISABLE_GPU=1 --action_env PYTHON_BIN_PATH="C://Python_3.10//python.exe" mediapipe/examples/desktop/hello_world

Update:
I've tried to run the command as below
bazel build -c opt \   --define MEDIAPIPE_DISABLE_GPU=1 \   --action_env PYTHON_BIN_PATH=$(C://Python_3.10//python.ex) \   mediapipe/examples/desktop/hello_world
and seems the error occured when i ran bazel build mediapipe/examples/desktop/hello_world. This is the error:
ERROR: An error occurred during the fetch of repository 'local_execution_config_python':    Traceback (most recent call last):         File "C:/users/user/_bazel_user/bkk7hz7b/external/org_tensorflow/third_party/py/python_configure.bzl", line 213, column 33, in _create_local_python_repository                 python_lib = _get_python_lib(repository_ctx, python_bin)         File "C:/users/user/_bazel_user/bkk7hz7b/external/org_tensorflow/third_party/py/python_configure.bzl", line 130, column 21, in _get_python_lib                 result = execute(repository_ctx, [python_bin, "-c", cmd])         File "C:/users/user/_bazel_user/bkk7hz7b/external/org_tensorflow/third_party/remote_config/common.bzl", line 230, column 13, in execute                 fail( Error in fail: Repository command failed Python was not found; run without arguments to install from the Microsoft Store, or disable this shortcut from Settings > Manage App Execution Aliases. ERROR: C:/users/user/mediapipe_repo/mediapipe/WORKSPACE:476:14: fetching local_python_configure rule //external:local_execution_config_python: Traceback (most recent call last):         File "C:/users/user/_bazel_user/bkk7hz7b/external/org_tensorflow/third_party/py/python_configure.bzl", line 213, column 33, in _create_local_python_repository                 python_lib = _get_python_lib(repository_ctx, python_bin)         File "C:/users/user/_bazel_user/bkk7hz7b/external/org_tensorflow/third_party/py/python_configure.bzl", line 130, column 21, in _get_python_lib                 result = execute(repository_ctx, [python_bin, "-c", cmd])         File "C:/users/user/_bazel_user/bkk7hz7b/external/org_tensorflow/third_party/remote_config/common.bzl", line 230, column 13, in execute                 fail( Error in fail: Repository command failed Python was not found; run without arguments to install from the Microsoft Store, or disable this shortcut from Settings > Manage App Execution Aliases. ERROR: Analysis of target '//mediapipe/examples/desktop/hello_world:hello_world' failed; build aborted: Repository command failed Python was not found; run without arguments to install from the Microsoft Store, or disable this shortcut from Settings > Manage App Execution Aliases. INFO: Elapsed time: 2.433s INFO: 0 processes. FAILED: Build did NOT complete successfully (38 packages loaded, 11 targets config\ ured)

Could you check if the python environment path is set C:\Python_3.10\ . If so, try running this command

Hi @nurshafiza, It seems the command used to reproduce this issue is not clear. Could you provide the clear command and steps  to reproduce the issue. Thank you!

Could you check if Python Environment Path is set C:\Python_3.10\ .                                                                                If so try running this command bazel build -c opt --define MEDIAPIPE_DISABLE_GPU=1 --action_env PYTHON_BIN_PATH="C:\Python_3.10\python.exe" mediapipe/examples/desktop/hello_world . If It still doesnt work try installing numpy and then try again.

I have a similar problem
DEBUG: C:/users/ruentgen/_bazel_programmerruent/badkprfe/external/org_tensorflow/third_party/repo.bzl:132:14:
Warning: skipping import of repository 'pybind11' because it already exists.
INFO: Build option --action_env has changed, discarding analysis cache.
INFO: Repository local_execution_config_python instantiated at:
C:/users/ruentgen/documents/mediapipe/WORKSPACE:479:14: in
C:/users/ruentgen/_bazel_programmerruent/badkprfe/external/org_tensorflow/tensorflow/workspace2.bzl:879:19: in workspace
C:/users/ruentgen/_bazel_programmerruent/badkprfe/external/org_tensorflow/tensorflow/workspace2.bzl:86:27: in _tf_toolchains
C:/users/ruentgen/_bazel_programmerruent/badkprfe/external/org_tensorflow/tensorflow/tools/toolchains/remote_config/configs.bzl:6:28: in initialize_rbe_configs
C:/users/ruentgen/_bazel_programmerruent/badkprfe/external/org_tensorflow/tensorflow/tools/toolchains/remote_config/rbe_config.bzl:158:27: in _tensorflow_local_config
Repository rule local_python_configure defined at:
C:/users/ruentgen/_bazel_programmerruent/badkprfe/external/org_tensorflow/third_party/py/python_configure.bzl:279:41: in
ERROR: An error occurred during the fetch of repository 'local_execution_config_python':
Traceback (most recent call last):
File "C:/users/ruentgen/_bazel_programmerruent/badkprfe/external/org_tensorflow/third_party/py/python_configure.bzl", line 213, column 33, in _create_local_python_repository
python_lib = _get_python_lib(repository_ctx, python_bin)
File "C:/users/ruentgen/_bazel_programmerruent/badkprfe/external/org_tensorflow/third_party/py/python_configure.bzl", line 130, column 21, in _get_python_lib
result = execute(repository_ctx, [python_bin, "-c", cmd])
File "C:/users/ruentgen/_bazel_programmerruent/badkprfe/external/org_tensorflow/third_party/remote_config/common.bzl", line 230, column 13, in execute
fail(
Error in fail: Repository command failed
Traceback (most recent call last):
File "", line 1, in
NameError: name 'system' is not defined
ERROR: C:/users/ruentgen/documents/mediapipe/WORKSPACE:479:14: fetching local_python_configure rule //external:local_execution_config_python: Traceback (most recent call last):
File "C:/users/ruentgen/_bazel_programmerruent/badkprfe/external/org_tensorflow/third_party/py/python_configure.bzl", line 213, column 33, in _create_local_python_repository
python_lib = _get_python_lib(repository_ctx, python_bin)
File "C:/users/ruentgen/_bazel_programmerruent/badkprfe/external/org_tensorflow/third_party/py/python_configure.bzl", line 130, column 21, in _get_python_lib
result = execute(repository_ctx, [python_bin, "-c", cmd])
File "C:/users/ruentgen/_bazel_programmerruent/badkprfe/external/org_tensorflow/third_party/remote_config/common.bzl", line 230, column 13, in execute
fail(
Error in fail: Repository command failed
Traceback (most recent call last):
File "", line 1, in
NameError: name 'system' is not defined
ERROR: Analysis of target '//mediapipe/examples/desktop/hello_world:hello_world' failed; build aborted: Repository command failed
Traceback (most recent call last):
File "", line 1, in
NameError: name 'system' is not defined
INFO: Elapsed time: 0.397s
INFO: 0 processes.
FAILED: Build did NOT complete successfully (37 packages loaded, 11 targets configured)

Could you check if Python Environment Path is set C:\Python_3.10\ . If so try running this command bazel build -c opt --define MEDIAPIPE_DISABLE_GPU=1 --action_env PYTHON_BIN_PATH="C:\Python_3.10\python.exe" mediapipe/examples/desktop/hello_world . If It still doesnt work try installing numpy and then try again.

Thanks! this solution works well for me

Hi @nurshafiza, Glad to hear that issue has been fixed. Could we move this issue to close status as it has been resolved. Thank you!

Hi @nurshafiza, Glad to hear that issue has been fixed. Could we move this issue to close status as it has been resolved. Thank you!

Yes, thank you so much for your reply!

Hi guys I'm trying to build the hello_workd solution and I get this similar problem I already tried this steps but it does not fix the problem
ERROR: An error occurred during the fetch of repository 'local_execution_config_python': Traceback (most recent call last): File "C:/users/ruentgen/_bazel_programmerruent/badkprfe/external/org_tensorflow/third_party/py/python_configure.bzl", line 213, column 33, in _create_local_python_repository python_lib = _get_python_lib(repository_ctx, python_bin) File "C:/users/ruentgen/_bazel_programmerruent/badkprfe/external/org_tensorflow/third_party/py/python_configure.bzl", line 130, column 21, in _get_python_lib result = execute(repository_ctx, [python_bin, "-c", cmd]) File "C:/users/ruentgen/_bazel_programmerruent/badkprfe/external/org_tensorflow/third_party/remote_config/common.bzl", line 230, column 13, in execute fail( Error in fail: Repository command failed Traceback (most recent call last): File "", line 1, in NameError: name 'system' is not defined ERROR: C:/users/ruentgen/documents/mediapipe/WORKSPACE:479:14: fetching local_python_configure rule //external:local_execution_config_python: Traceback (most recent call last): File "C:/users/ruentgen/_bazel_programmerruent/badkprfe/external/org_tensorflow/third_party/py/python_configure.bzl", line 213, column 33, in _create_local_python_repository python_lib = _get_python_lib(repository_ctx, python_bin) File "C:/users/ruentgen/_bazel_programmerruent/badkprfe/external/org_tensorflow/third_party/py/python_configure.bzl", line 130, column 21, in _get_python_lib result = execute(repository_ctx, [python_bin, "-c", cmd]) File "C:/users/ruentgen/_bazel_programmerruent/badkprfe/external/org_tensorflow/third_party/remote_config/common.bzl", line 230, column 13, in execute fail( Error in fail: Repository command failed Traceback (most recent call last): File "", line 1, in NameError: name 'system' is not defined ERROR: Analysis of target '//mediapipe/examples/desktop/hello_world:hello_world' failed; build aborted: Repository command failed Traceback (most recent call last): File "", line 1, in NameError: name 'system' is not defined INFO: Elapsed time: 0.397s INFO: 0 processes. FAILED: Build did NOT complete successfully (37 packages loaded, 11 targets configured)

Have you solved it?

https://github.com/google-ai-edge/mediapipe/issues/4622#issuecomment-2268186119


Pat capon39 patch 1

Flipped tool access sentences following discussion with Christina to have the action first, before the software name (as not all users will know what the software has to offer, but should be familiar with the action).
Also went through to correct instances of 'CellRanger' to be 'Cell Ranger' as per the 10X Genomics website.

@PatCapon39 can you address the comments above please

I don't see any above comments sorry Cam?


Improved description extra weight

Improved explanation of the description at Extra Weight according to the English original in the national language

@iternio I made a translation using deepl, much better than google translate.
Instead of translating it by deepl to the corresponding language, what about implementing the improved description in English language? So native speaker can translate it there own.


List of collectibles is cut off

Title/Description
The Safe below has around 170 ERC-721 tokens stored. However, only 20 are being displayed in the Collectibles list.
Environment

Safe: https://gnosis-safe.io/app/#/safes/0x4dEBDD6CEe25b2F931D2FE265D70e1a533B02453/balances/collectibles
App Version: 2.11.0.
Environment:

production (mainnet)

I'm closing this issue because it was fixed when we migrated from Opensea to Gnosis
CC @francovenica


[charts/vault] Add PodDisruptionBudget to vault Helm Chart

Q
A

Bug fix?
no

New feature?
yes

API breaks?
no

Deprecations?
no

Related tickets
N/A

License
Apache 2.0

What's in this PR?
Adds a PodDisruptionBudget to the vault Helm Chart, turned on by default with maxUnavailable set to 1, to avoid issues for people running the default configuration of 1 replica.
Also adds the option to set a priorityClassName to the StatefulSet Pods.
Also adds the option (but no change to default behaviour) to set maxUnavailable on the vault-secret-webhook Helm Chart PDB.
Why?
Avoids the possibility of downtime if multiple Vault Pods get evicted at once.
Checklist

[x] Code meets the Developer Guide
[x] User guide and development docs updated (if needed)
[x] Related Helm chart(s) updated (if needed)

I'm pretty sure the tests aren't failing because of anything I did.


Don't insert extra newline in TFIDFSim's score explanation

Ref: https://github.com/apache/lucene-solr/commit/f0bfcbc7d8fbc5bb2791da60af559e8b0ad6eed6

I just came to my attention that the change provided in this PR is consistent with Java Lucene  5.5.  LuceneNet is of course is targeting 4.8 but where it makes sense, on occasion, we incorporate changes from a later version.  Now that I realize that 5.5 implements this change, it makes sense to me to accept this pull request since it feels like it fixes a bug that got fixed in 5.5.  Thank you for this PR.

@rauhs
This is a port of Lucene 4.8.0 (for the most part). Strictly speaking, we are supposed to be sticking with that version, but we have let a few patches slip in from later versions of Lucene because they would make it difficult to work with and are in places that are difficult or impossible to replace by end users.
But this appears to be part of a larger design change to TFIDFSimilarity that occurred in Lucene 5.x, rather than an actual bug that needs to be fixed.
Is there some reason you have for needing this change ahead of schedule?

First: Thanks for the quick feedback!
There is no real bug. I have just noticed the annoying newline in my explanations for a few months of working with 4.8 lucene and today was the day that it has bothered me too much so I went into the git repos and realized it was fixed in the Java version. (See my link).
So no, there is no real reason other than getting rid of this annoying newline everywhere which makes reading the explanation a bit easier.

But this appears to be part of a larger design change to TFIDFSimilarity that occurred in Lucene 5.x, rather than an actual bug that needs to be fixed.

I don't think this change was part of that rework. Here it was just about the newline.
I'm fine with a rejection here, no big deal to me. I can live with the newline. Whatever you prefer. Cheers

OK, so I'm confused.
The source from the following two locations are very different and I thought they should be the same.  Based on the first link, the change is just a small patch, based on the 2nd link it's part of a much bigger rework of the ExplainScore method.
https://svn.apache.org/viewvc/lucene/dev/branches/branch_5x/lucene/core/src/java/org/apache/lucene/search/similarities/TFIDFSimilarity.java?view=markup&pathrev=1641847
https://github.com/apache/lucene/blob/branch_5x/lucene/core/src/java/org/apache/lucene/search/similarities/TFIDFSimilarity.java
Can anyone explain why these two versions of branch_5x are so different for this file?

@rclabo - That is a good question, and I wish I had a good answer :).
The first link is for SVN and I suspect it is out of date as they are using Git now. Of course the SVN link says it was updated 6 years, 3 months ago and the Git link says it was updated Feb 2016. So that is at least a 2 year gap between these branches.
Do note that branches may continually be patched, but tags are fixed points in time that you can tie to a specific release.

While I'd still love to know why the two branch_5x links have different code for the file (different subversions of 5?) I can now see that the PR does simply apply a small change that existed in version 5.0 of java lucene  prior to the larger rework of the ExplainScore method that happened in 5.2


Improve CI

Improve the CI by adding a format and warning checks.
Fix also a spec not running in the GitHub Actions runner.

I haven't an IOCTL working inside the CI, yet. I put a pending.

I think we should pick a more general ioctl. How about SIOCGSTAMP?
https://manpages.ubuntu.com/manpages/bionic/man2/ioctl_list.2.html

Ah, actually the FIO ioctls are generic ioctls that can be called on any file descriptor. Those should work in CI.
https://man.openbsd.org/ioctl.2

Hmm, I tried testing this, and you need to create a socket, but I'm only getting back 0.
require "socket"
require "ioctl"

FIOSETOWN       = 0x8901_u32
SIOCSPGRP       = 0x8902_u32
FIOGETOWN       = 0x8903_u32
SIOCGPGRP       = 0x8904_u32
SIOCATMARK      = 0x8905_u32
SIOCGSTAMP_OLD  = 0x8906_u32      # Get stamp (timeval)
SIOCGSTAMPNS_OLD = 0x8907_u32     # Get stamp (timespec)

value : Int32 = 0
socket = TCPSocket.new("example.com",80)

IOCTL.ioctl(socket.fd, FIOGETOWN, pointerof(value))

puts value

I've finally found a simple ioctl that works. Even if an integer passed as an argument returns 0, what's important is to have successful ioctl call.


Reboot: why is @viewport manually vendor prefixed?

I'm a little confused why Autoprefixer is a build requirement, but there are still vendor-prefixes?  Don't want to nit-pick, but not sure if an ancient version of Opera & other browsers need support...

In that particular case, I'm pretty sure it's future-proofing since so far only IE/Edge implements the relevant CSS standard: http://caniuse.com/#feat=css-deviceadaptation

IIRC Fx is dropping -moz- prefixes for new rules; they have a dev branch & use flags.
I can't find documentation for the -webkit- prefix, but possible since it is pre-blink addition.
Interesting footnote in MDN's @viewport entry.
But I still think prefixe fixing belong in autoprefixer, & not main .scss code  :dancers:

I can't find documentation for the -webkit- prefix

WebKit doesn't currently implement @viewport (see caniuse link).

Oh, thanks.

Dropped the unused and unsupported ones in de944e0.


[zh] updated setup/production-environment/tools/kops.md

original file: https://kubernetes.io/zh-cn/docs/setup/production-environment/tools/kops/

@Michelle951 ,thank you for your contribution.
You must sign the CLA for teams of kubernetes/website to do code reiview.
Please click on the red button NOT COVERED line in the first comment.
If you don't know how to do it, here is the CLA tutorial.

@Michelle951 ,thank you for your contribution.
You must sign the CLA for teams of kubernetes/website to do code reiview.
Please click on the red button NOT COVERED line in the first comment.
If you don't know how to do it, here is the CLA tutorial.

Thank you for the instructions. I used a wrong user ID for this commit, and have just reset the author. Thanks

/lgtm

/approve


Update user guide

Update the user guide to follow the contents in the notion
Closes issue : https://github.com/AY2324S1-CS2103T-T12-1/tp/issues/21

LGTM!!


"Filter with the same name already exists! Aborting"

Hi,
The plugin doesn't work in my case as expected (objects are not attached). During loading I get the following output:
Loading grasp-fix plugin
GazeboGraspFix: Using disable_collisions_on_attach 0
GazeboGraspFix: Using update rate 4
GazeboGraspFix: Using max_grip_count 100
GazeboGraspFix: Using grip_count_threshold 4
GazeboGraspFix: Using release_tolerance 0.1005
GazeboGraspFix: Adding collision scoped name yumi::gripper_r_finger_l::gripper_r_finger_l_collision
GazeboGraspFix: Adding collision scoped name yumi::gripper_r_finger_r::gripper_r_finger_r_collision
[Err] [ContactManager.cc:282] Filter with the same name already exists! Aborting
Subscribing contact manager to topic

Is it crucial to avoid the error that comes from the contact manager to make the plugin work?
If yes, have you experienced this before / do you know a fix?  (Googling the error lead me to this: https://bitbucket.org/osrf/gazebo/issues/960/allow-multiple-contactsensor-per-collision   which should be solved in the Gazebo version I'm using [7.0.0])

Thanks!

I played around with the parameters and got it somehow to work.

I played around with the parameters and got it somehow to work.

Excuse me! Could I ask you how you got it work.


Conflicting peer dependency<EMAIL_ADDRESS>

npm ERR! Conflicting peer dependency<EMAIL_ADDRESS>npm ERR! node_modules/react
npm ERR!   peer<EMAIL_ADDRESS>from<EMAIL_ADDRESS>npm ERR!   node_modules/@wojtekmaj/enzyme-adapter-react-17
npm ERR!     dev<EMAIL_ADDRESS>from the root project

$ node --version
v18.0.0
$ npm --version
8.6.0

My guess is that this is happening due to the -0 on ^17.0.0-0. Can that be dropped?

That should be a perfectly valid version as per NPM's own tooling.


Add a way to retrieve object storage by name

Could we have a way to retrieve an object storage by using it's name?
It would make the object_storage datasource more useful than having to find the objectStorageId through the API.
Thanks

the LIST API is for search purposes where you can pass a name, not to get one single element, not like GET Api /objectStorageId that will always gives 1 element or none.


nginx: lua atpanic: Lua VM crashed, reason: not enough memory

Is there an existing issue for this?

[X] I have searched the existing issues

Kong version ($ kong version)
2.7.1
Current Behavior
We are upgrading to Kong 2.7.1 from Kong 2.0.3 and when we are doing that experiencing out of memory error.
We never experienced this issue with 2.0.3 and it has been running perfectly for a long time now.
But right after we started upgrading, started seeing this issue and had to revert back.
We are using C5.2xlarge instances.
Some of the errors are:
malloc(2048) failed (12: Out of memory)
nginx: lua atpanic: Lua VM crashed, reason: not enough memory
malloc(262144) failed (12: Out of memory) while reading response header from upstream
Expected Behavior
No response
Steps To Reproduce
No response
Anything else?
No response

wei-xin: fairyasky

Hi Team,
Any update on this?
TIA

Hi @kollol-chowdhury-incontact, could you provide more detailed logs and steps to reproduce this issue?

Hi @mayocream , we have not been able to reproduce the issue in lower env and faced this issue twice in prod. We observed that we start getting intermittent 404 error with the message no Route matched with the values. After we do kong reload,it works for like 3mins,and then starts giving the same 404 error. When we checked the logs we found many out of memory error.
We have kong 2.0.3 running without any issue.Facing this with /kong 2.7.1
Also observed that http_allocated_gc has increased drastically in kong 2.7.1 . In kong 2.0.3 it used to be of 0.5 MiB,whereas now it reaches 250MiB . Is this normal?
Facing lots of challenge because of this issue.


Error on rad patch list

A few hours ago I went through part 1 of the tutorial, and submitted a patch with a new garden via radicle. I came back to check on the status, typed rad patch list, and got this:
Invalid radicle input:
  machine-id: 12D3KooWAmB1gB9kceaGfKaUHfhcmfUzXdJEbzRnT73mF1DrknSk
  radicle-error: Exception validation-failure {:info "Did not satisfy predicate."
:name "edit author == original author"}
  src/Radicle/Internal/PrimFns.hs:165:32 throwErrorHere
[ipfs]:159:12
[ipfs]:159:13
[ipfs]:92:2
[ipfs]:93:3
[ipfs]:48:3
[ipfs]:7:2
[ipfs]:107:5
src/Radicle/Internal/Core.hs:351:12 annotate
src/Radicle/Internal/PrimFns.hs:154:16 List
[ipfs]:32:6
[ipfs]:32:7
[ipfs]:118:3
[ipfs]:71:6
[ipfs]:71:7
[ipfs]:75:7
src/Radicle/Internal/Core.hs:386:26 annotate
src/Radicle/Internal/Eval.hs:65:23 Lambda

I'm using commit ba9ef05402dc8dd2223a4b9c996faa341dc76653, built with stack. rad issue list seems to work fine.
The commit hash of the patch I submitted was 6c6e30a5bf152997d14508b37910ddd7f3635416.
Let me know if there's any other useful information I can provide.

Thank you for reporting the issue!
The error comes from an unauthorised patch edit that was sent to the machine. This should have been prevented by on-machine validations. We fixed the the bug and are currently working on updating the machine. After that you should be able to propose your patch without problems.
I will let you know once we made the upgrade.

@zenhack Can you confirm this is now working for you? (You may need to restart daemon-radicle.)

@jameshaydon, yep, seems to be working.


[ie/NeteaseMusic] more metadata

IMPORTANT: PRs without the template will be CLOSED
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In the --embed-metadata option, add album title to the file metadata.(#8530)
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In order to be accepted and merged into yt-dlp each piece of code must be in public domain or released under Unlicense. Check all of the following options that apply:

[x] I am the original author of this code and I am willing to release it under Unlicense
[ ] I am not the original author of this code but it is in public domain or released under Unlicense (provide reliable evidence)

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[x] Fix or improvement to an extractor (Make sure to add/update tests)
[ ] New extractor (Piracy websites will not be accepted)
[ ] Core bug fix/improvement
[ ] New feature (It is strongly recommended to open an issue first)

Copilot Summary

🤖 Generated by Copilot at d3d3824
Summary
🎵🏷🛠

Add more metadata fields to NetEaseMusicIE extractor. Use existing helper functions to parse the JSON response from the API and extract fields such as album_artist, album, thumbnail and average_rating.

NetEaseMusic tracks
More metadata extracted
Winter of content

Walkthrough

Add metadata fields for NetEaseMusic tracks (link) by using traverse_obj to access the JSON response and helper functions to convert the values

Can you update the tests please

In the future, run the test, let it fail, and copy the missing test dict keys/values from the error output. Do not write the download tests by hand


Docs link on PyPI incorrect (server not found)

@ulinja
The link from the PyPI page for hyprpy seems to be incorrect:
https://hyprpy.docs.lobbes.dev/

This produces a server not found error in the browser.
Might want to go over there and change it to the working URL that I see on your repo:
https://hyprpy.lobbes.dev/

Hi @RedBearAK
thanks a lot for pointing this out, will be fixed in the next release!


How can we reset the input value?

Hi, have a look at #2 which allows you to access the input element.
Might add an easier option at some point. (Also, I'm happy to review your PR if you want to implement it yourself 😃).

Thanks, I hope to get back to it later.


Dark mode inverting colours on websites

Describe the bug
Elements like logos or nation flags have their colours inverted.
To Reproduce
Steps to reproduce the behavior:
Set the browser in dark theme
Expected behavior
Such elements shouldn't be inverted.
Screenshots

Smartphone (please complete the following information):

Device: Realme XT
Android Version : Android 13 AOSP
Kiwi Browser Version 116.0.5845.70

Additional context
The version based on v112 didn't have this issue.
Tho it's similar to a previously reported issue #352

Same, it also happens on favicons in bookmarks, and some gifs like this one.


[Workers] Document minimal_subrequests compatibility flag

This new flag is live, and will become default next month.

@codewithkristian, are you able to see why this build failed? The logs aren't accessible for me.


update docker for 0.7.0

Why change the URLs to http from https?

Odd problem with urllib.
On Wed, Feb 17, 2016 at 8:02 AM Alex Rothberg<EMAIL_ADDRESS>wrote:

Why change the URLs to http from https?
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Reply to this email directly or view it on GitHub
https://github.com/tensorflow/tensorflow/pull/1114#issuecomment-185271733
.

Care to elaborate?

@jendap, what was the problem there?
On Sat, Feb 20, 2016 at 11:49 Alex Rothberg<EMAIL_ADDRESS>wrote:

Care to elaborate?
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https://github.com/tensorflow/tensorflow/pull/1114#issuecomment-186670176
.


Real-Time Supply Chain with Apache Kafka in the Food and Retail Industry / Walmart

URL:
https://www.kai-waehner.de/blog/2022/02/25/real-time-supply-chain-with-apache-kafka-in-food-retail-industry/
Review (1-2 sentences):
I see a lot of discussions around the topic do you really need streaming data? Here is a real-world example from one of the biggest retailers of how to use and why to use real-time data streaming. This article is more an overview one a technical one. Just for taking a look at use cases where they adopted it.

Published in Digest #20.


Accept a function as 'average' method argument

Check if 'key' inside 'average' method is a function. If so, pass it as it is to the 'sum' method and divide the result over the items length

Yes, the Laravel collection accepts a callback as argument.
https://github.com/illuminate/collections/blob/1e70a82b09cffea032d336ee720056cf05dd31d2/Collection.php#L75
On 30 Apr 2021, at 7:34 a.m., Daniel Eckermann @.***> wrote:

Hi @DaLooKhttps://github.com/DaLooK
Thanks for this PR.
The goal for Collect.js is to mirror the API of Laravel Collections.
Do you know if this is implemented in Laravel Collection?
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Yes, the Laravel collection accepts a callback as argument.
https://github.com/illuminate/collections/blob/1e70a82b09cffea032d336ee720056cf05dd31d2/Collection.php#L75

Great! Could you please provide a test for this also?