KOGITO-8414 Added knative-service-discovery.adoc

JIRA: https://issues.redhat.com/browse/KOGITO-8414
This guide is for Knative custom function, which is implemented in https://github.com/kiegroup/kogito-runtimes/pull/2711
The example used by this guide is implemented in https://github.com/kiegroup/kogito-examples/pull/1526
This PR should be merged only after the above PRs are merged.
Please make sure that your PR meets the following requirements:

[x] You have read the contributions doc
[x] Pull Request title is properly formatted: KOGITO-XYZ Subject
[x] Pull Request title contains the target branch if not targeting main: [0.9.x] KOGITO-XYZ Subject
[x] The nav.adoc file has a link to this guide in the proper category
[x] The index.adoc file has a card to this guide in the proper category, with a meaningful description

How to backport a pull request to a different branch?

In order to automatically create a backporting pull request please add one or more labels having the following format backport-<branch-name>, where <branch-name> is the name of the branch where the pull request must be backported to (e.g., backport-7.67.x to backport the original PR to the 7.67.x branch).

NOTE: backporting is an action aiming to move a change (usually a commit) from a branch (usually the main one) to another one, which is generally referring to a still maintained release branch. Keeping it simple: it is about to move a specific change or a set of them from one branch to another.

Once the original pull request is successfully merged, the automated action will create one backporting pull request per each label (with the previous format) that has been added.
If something goes wrong, the author will be notified and at this point a manual backporting is needed.

NOTE: this automated backporting is triggered whenever a pull request on main branch is labeled or closed, but both conditions must be satisfied to get the new PR created.

@ricardozanini as we discussed offline, I've changed the structure to match camel-routes-integration.adoc and moved the guide to the "Integrations" menu.

The whole first part explaining the custom functions should be in https://kiegroup.github.io/kogito-docs/serverlessworkflow/latest/core/custom-functions-support.html IMO and only the "Example" should be in this page

The first part is explaining Knative custom functions (a specific type of custom function), while https://kiegroup.github.io/kogito-docs/serverlessworkflow/latest/core/custom-functions-support.html is for custom functions in general. It explains how to create a custom function.

I agree with @radtriste, @hbelmiro. It would be nice to keep the ubiquity among the custom functions documentation.

The whole first part explaining the custom functions should be in https://kiegroup.github.io/kogito-docs/serverlessworkflow/latest/core/custom-functions-support.html IMO and only the "Example" should be in this page

@radtriste The first part is explaining Knative custom functions (a specific type of custom function), while https://kiegroup.github.io/kogito-docs/serverlessworkflow/latest/core/custom-functions-support.html is for custom functions in general. It explains how to create a custom function.

well we are explaining there the Camel custom functions. could be good to centralize the custom functions type on that page. And then have a specific example in Integrations section, like @ricardozanini did for Camel

Done @radtriste.

Looks good once navbar and index are updated.

Done.


Formatting issues in performance pages

Description:

https://apim.docs.wso2.com/en/latest/Administer/ProductAdministration/PerfromanceTuningAndTestResults/tuning-performance/#apim-level-settings

xmls not formatted
tables are having formatting issues

Suggested Labels:

Suggested Assignees:

Affected Product Version:
3.0
OS, DB, other environment details and versions:
Steps to reproduce:
Related Issues:

Closing this issue as it is tracked via the following two issues.
https://github.com/wso2/docs-apim/issues/284
https://github.com/wso2/docs-apim/issues/283


Fork, Commit, Merge - Medium Issue (Tailwind CSS)

Fork, Commit, Merge - Medium Issue (Tailwind CSS)
Create a Responsive Pricing Table Using Tailwind CSS
Note: You don't have ask permission to start solving the issue or get assigned, since these issues are supposed to be always open for new contributors. The actions-user bot will reset the file back to previous state for the next contributor after your commit is merged. So you can just simply start working with the issue right away!
Navigate to the tasks/tailwind directory from the root of the project.
Then install all the necessary dependencies by running npm i in Terminal.
Also just in case, run npm run watch to make sure that Tailwind CSS is built correctly every time changes are made.
After that you can open the medium directory and index.html file inside of it.

Description
The goal of this task is to design a responsive pricing table using Tailwind CSS. A pricing table typically displays different pricing tiers or plans for a service or product, with details about each plan. The table should be responsive, adjusting its layout for various screen sizes.
Requirements

Use Tailwind CSS classes for styling.
Create at least three pricing tiers (e.g., Basic, Standard, Premium) with different features and prices.
Ensure that the pricing table is visually appealing and easy to read.
Implement responsive design to adapt to different screen sizes (e.g., mobile, tablet, desktop).
Include clear call-to-action (CTA) buttons for each pricing tier.

Task Details

Create an HTML structure for the pricing table.
Apply Tailwind CSS classes to style the table, pricing cards, and buttons.
Populate the pricing tiers with sample data, including plan names, prices, and features.
Implement responsive design using Tailwind CSS utilities to ensure the table looks good on various devices.
Add hover effects or other interactive elements to enhance the user experience.

If you are using VS Code and have for example Live Server extension, you can simply open index.html with the server and check how the page looks. After the card is done, you are ready to make a pull request!
If you do not have Live Server extension, you'll find information about installing and how to use it here.
Check out README.md for more instructions and how to make a pull request.
Feel free to ask any questions here if you have some problems!
Also, kindly give this project a star to enhance its visibility for new developers!

Hey, could i be assigned to this issue? Thanks

Hey, could i be assigned to this issue? Thanks

Hi, go ahead!
As it says on the description:
Note: You don't have ask permission to start solving the issue or get assigned, since these issues are supposed to be always open for new contributors. The actions-user bot will reset the file back to previous state for the next contributor after your commit is merged. So you can just simply start working with the issue right away!


leave quality values produced by ccs in "unpolished.flnc.bam"

Feature request here again. I generate CCS reads with the --polish parameter set to ccs to get the quality values. It would be nice if isoseq3 could also output these values in the "unpolished.flnc.bam" file.
Best,
K

Already implemented and will be available next release! Thank you for your input!


Not possible to delete

Hi,
First i have to say nice work. I have a problem when i extract all files i would like to remove zip file afterwords, but it wont let me and zip is being lock by this class. Im using zip.dispose and also zip.close, but still it is locked. Any help pls ?

Can you debug if the Dispose occurs? and it occurs before you try to delete?

Duplicated as #41
Please read my comment there


Unmarshall problem for struct with two embedded params

For the struct with two embedded params and both with a custom unmarshall function, unmarshall of parent struct will fail for ambiguous selector of unmarshall functions.
e.g., RP: cdn, swagger: link, code generated: link
May no need if flatten all the allof.

This will be fixed via https://github.com/Azure/autorest.go/issues/732.


How to login on Open duo (Android app)?

At first open app(open duo apk), it show with Account ID box.
How to get Account ID?

I believe it’s any name you decide.


feat(app): migrate from browserRouter to hashRouter

:tada: This PR is included in version 1.4.0 :tada:
The release is available on GitHub release
Your semantic-release bot :package::rocket:


Cannot migrate to a materialized view from a normal view

It seems that there isn't an easy way to migrate from a normal database view to a materialized view.  Since update_view generates a DROP MATERIALIZED VIEW view_name statement whenever :materialized is true, it will fail when view_name is a non-materialized view.
So if we'd like to support a way for users to migrate from a normal view to a materialized one, we'll either need to provide a second type of migration generation command or we'll need to add some logic into the existing one, checking for whether the view already exists as a non-materialized view.
You can see my demo repo where the issue exists here: https://github.com/adamnbowen/test-scenic/tree/materialized-migration.  That repo is on Rails 5, but I don't see how the issue would be Rails 5 specific.
Note that in my test repo, you'll need to inject environment variables for your database username, password and port—you can see the config in config/database.yml for the appropriate variables to set.
The commands I ran to generate the migrations were rails g scenic:model ActiveUser and then rails g scenic:model ActiveUser --materialized, where a rake db:migrate on the first migration worked just fine, and the failure only happened after the second migration.
$ rails db:migrate
==<PHONE_NUMBER>2756 UpdateActiveUsersToVersion2: migrating ======================
-- update_view(:active_users, {:version=>2, :revert_to_version=>1, :materialized=>true})
rails aborted!
StandardError: An error has occurred, this and all later migrations canceled:

PG::WrongObjectType: ERROR:  "active_users" is not a materialized view
HINT:  Use DROP VIEW to remove a view.
: DROP MATERIALIZED VIEW "active_users";/Users/adambowen/src/test-scenic/db/migrate/20160206232756_update_active_users_to_version_2.rb:3:in `change'
/Users/adambowen/src/test-scenic/bin/rails:9:in `require'
/Users/adambowen/src/test-scenic/bin/rails:9:in `<top (required)>'
/Users/adambowen/src/test-scenic/bin/spring:13:in `<top (required)>'
bin/rails:3:in `load'
bin/rails:3:in `<main>'
ActiveRecord::StatementInvalid: PG::WrongObjectType: ERROR:  "active_users" is not a materialized view
HINT:  Use DROP VIEW to remove a view.
: DROP MATERIALIZED VIEW "active_users";
/Users/adambowen/src/test-scenic/db/migrate/20160206232756_update_active_users_to_version_2.rb:3:in `change'
/Users/adambowen/src/test-scenic/bin/rails:9:in `require'
/Users/adambowen/src/test-scenic/bin/rails:9:in `<top (required)>'
/Users/adambowen/src/test-scenic/bin/spring:13:in `<top (required)>'
bin/rails:3:in `load'
bin/rails:3:in `<main>'
PG::WrongObjectType: ERROR:  "active_users" is not a materialized view
HINT:  Use DROP VIEW to remove a view.
/Users/adambowen/src/test-scenic/db/migrate/20160206232756_update_active_users_to_version_2.rb:3:in `change'
/Users/adambowen/src/test-scenic/bin/rails:9:in `require'
/Users/adambowen/src/test-scenic/bin/rails:9:in `<top (required)>'
/Users/adambowen/src/test-scenic/bin/spring:13:in `<top (required)>'
bin/rails:3:in `load'
bin/rails:3:in `<main>'
Tasks: TOP => db:migrate
(See full trace by running task with --trace)

Thanks for taking the time to submit such a detailed report. You can migrate to a materialized view by:
drop_view :foo
create_view :foo, version: 2, materialized: true

You would have to write up and down in your migration for this. This is by design because I don't think this warrants a special reversible schema statement and generator flag.

I would like to investigate the feasibility of catching this error and logging a helpful and instructive message. I think that could go a long way to preventing confusion.
I think we would have to catch errors caused by non-existent views in drop_view and check if the named view exists as the other type. If it does, we log a message similar to my message above.

I think that makes sense—It's an uncommon enough problem that it probably doesn't warrant a special type of migration, but providing the error message would at least prevent someone from filing a new issue like I did today ;).
I am having some difficulty getting the down migration written correctly—I'll post in a bit if I'm able to get it working correctly or not.

Okay, no, nevermind on the down migration: my latest commit in the brand gets things solved—I ended up with:
  def up
    drop_view :active_users
    create_view :active_users,
      version: 2,
      materialized: true
  end

  def down
    drop_view :active_users, materialized: true
    create_view :active_users, version: 1
  end

This seems like it would be a common flow, can we add a bit explaining the transition in the readme/docs?

We'd be open to a README PR for sure. It seems like this specific issue has been resolved but follow-ups for documenting or handling the error at migration time are welcome.


Add Rust GitHub action workflow shareable for testing

Add Rust GitHub action workflow shareable for testing

:rocket: This issue has been resolved in v0.4.0. See 0.4.0 for release notes.


Max people enrolled

I have a doubt about the max amount of people you can recognize with F455.
Is 1000 hard coded into the device and is this limitation also applied in host mode.
If I extract the face points, I can store them somewhere else and have more than 1000 faces?.
Thanks.

In device mode indeed we have 1000 limitation due to memory constraints. In host side no reason for such limitation and user/app can manage 10k users easily. where did you see any limit in host mode?


Doubst about the WorkerNavigator

Abraham, everything ok ?
I have managed to randomize the navigator object, but some properties of the WorkerNavigator remains the same. Do you have any suggestions of how to randomize that ?

Hey, I'm enjoying the lively weather and coffee. It's just perfect.
I've not experimented with patching the WorkerNavigator. I've heard it is possible using Chrome DevTools Protocol. There is a tracker issue here.
https://bugs.chromium.org/p/chromium/issues/detail?id=1162071&q=creepjs&can=2

Happy to know tha you are fine.
Thank about the tip, i wil check it out

Even with CDP, this is hard to achieve. Posssible approach is using Target-interception.


Add testing of kubernetes 1.17.0,1.18.0 in travis

#172397180

1.18 is not green, https://github.com/cloudfoundry-incubator/cf-operator/issues/891


Show over dialog?

SuperActivityToast works awesome, but I want to use it from an open dialog and it shows behind the dialog. Can you change it to show above all dialogs?
Alternatively, if you could put the LinearLayout for the SuperCardToast in any views layout instead of just the main layout, this would work as well.

Nemi5150,
Unfortunately, this does not appear possible without adding some sort of System Alert permission to the library which would have to be included in every app using the library. This is especially unfortunate because the SuperActivityToasts with a button do not appear over the keyboard. Hopefully the API will change over time to accommodate this.
Thanks for the suggestion,
John


Export x from './y' results in unexpected identifier error

If I write something like export a from './a' Flow gives me Unexpected identifier. Same if I write
import a from './a'
export a

Can I work around this?
I'm using 0.43

I have since learned that export export a from './a' is sugar for export {default as a} from './a'. Flow understands this, so that solves it for me.
Still the new syntax should be supported at some point, if the proposal is mature enough.
And the fact that Flow trips over
import a from './a'
export a

stil makes it a bug I suspect

l meet this problem too.Did you find some solutions ?@0x80

@Ai01 for me the solution was to write export {default as a} from './a'. So without the shorthand syntax. I haven't tried the other syntax since.

@0x80 l know. thank you very much.

is this a bug or not?

If there are multiple valid ways of writing the same thing and one is resulting in a type error then I'd consider this a bug yes. But I don't think the sugar syntax made it into the official standard yet, so in that sense it's simply not supported yet I assume.

It's not a bug guys. Just invalid syntax
import a from './a.js';

export {
  a
};
export const b = a;
export default a;

http://2ality.com/2014/09/es6-modules-final.html


Excellent App

Thanks for creating and sharing!

That is really great indeed! Very useful.

That is really great indeed! Very useful.

Nice App

Thanks 🛩
Released v1.0.0

This is the only app of this category that works perfectly for me.


Keep backend customer details window fully extensible v2

The current implementation adds functionality to the backend's customer details window by overriding various methods of the default classes. Unfortunately this is done in a matter, which prevents other plugins from extending these parts as well. Also a lot of core code is copied into this plugin and already out of date as of Shopware 5.2. This PR fixes these issues by refactoring the full overrides into selective extensions.

Hello,
thank you for creating this pull request.
I have opened an issue on our Issue Tracker for you. See the issue link: https://issues.shopware.com/issues/PT-7057.
Please use this issue to track the state of your pull request.

It would be great if this fix could be prioritized on your side including a new plugin release after merging. Currently installing / using the backend orders plugin is problematic with our offers and DATEV plugins.

Hey @samuelvogel,
very nice refactoring! Thank you and a happy new year 2017 ;)

Great! Happy new year to you too.
The greatest gift would be a new backend order release with the accumulated fixes 😉


Mod crashes when launching with Modernfix

Describe the bug
Minecraft crashes when used with Modernfix
To Reproduce
Steps to reproduce the behavior:
install Dynamic trees
install modernfix
Screenshots
If applicable, add screenshots to help explain your problem.
Rendering Glitches
Are you using OptiFine (or any equivalent mods that mess with rendering)?
Additional context
Add any other context about the problem here.
Crashes
crash-2024-01-30_14.53.17-client.txt
If the above mods are current then please submit a full crash log. The crash log normally contains the names of mods and their versions. Don't be that guy who submitted a crash report without first checking that the versions are current.
Also, do not paste the crash report into the issue body! Use a paste site like Gist or Pastebin.
Mods List
Dynamictrees-1.20.1-1.3.0- Beta2
modernfix-forge-5.11.1+mc1.20.1

Please confirm if this still happens

On my modpack it only crash the second time I launch it. Removing ModernFix makes everything load correctly :
crash-2024-07-19_18.25.10-fml.txt


Support channels_first in normalization

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

Comparison is base (ee2252e) 79.07% compared to head (33ad433) 64.15%.
Report is 1 commits behind head on master.

Additional details and impacted files
@@             Coverage Diff             @@
##           master   #18807       +/-   ##
===========================================
- Coverage   79.07%   64.15%   -14.92%
===========================================
  Files         336      336
  Lines       34234    34234
  Branches     6694     6694
===========================================
- Hits        27070    21964     -5106
- Misses       5582    10811     +5229
+ Partials     1582     1459      -123

Flag
Coverage Δ

keras
64.15% <ø> (-14.81%)
:arrow_down:

keras-jax
?

keras-numpy
?

keras-tensorflow
?

keras-torch
64.15% <ø> (-0.01%)
:arrow_down:

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


Passing default role only sets default profile

When passing in a default role to assume, only the [default] block in the credentials file gets overwritten. This should overwrite both the default and the named profile blocks instead of just one or the other.

+1.
We just had a use-case come about that describes this exact issue.
When specifying a role (profile) with -r ROLE_NAME it makes that the [default] profile; but doesn't create a profile entry with that name. As @louahola said; it should do both; create a [default] entry AND a named profile entry. Obviously the data would be the same; just a different profile.
Interesting enough; if you dont specify a role; it doesn't create a [default] profile entry; but it does create an entry for each Role that has the SAML Assertions within the claim.

Fixed in #20


[Grid] Static view

Display a time range of 24hrs, creating one column for each hour.
Column width could be set from the outside, but have a lower bounded value, below which is not possible to go.

:tada: This issue has been resolved in version 1.0.0 :tada:
The release is available on:

npm package (@latest dist-tag)
GitHub release

Your semantic-release bot :package::rocket:


ValueError: batch_size should be a positive integer value, but got batch_size=0

Hi,
I am trying to run the training on consep dataset. I run extract_patches.py without issue but when I try to run runtrain.py I got the following error:
(hovernet) [jgibert@oslo hover_net]$ python run_train.py --gpu=0
Detect #GPUS: 0
Using manual seed: 10
Dataset train: 1728
Traceback (most recent call last):
  File "run_train.py", line 305, in <module>
    trainer.run()
  File "run_train.py", line 289, in run
    phase_info, engine_opt, save_path, prev_log_dir=prev_save_path
  File "run_train.py", line 161, in run_once
    fold_idx=fold_idx,
  File "run_train.py", line 130, in _get_datagen
    worker_init_fn=worker_init_fn,
  File "/home/jgibert/anaconda3/envs/hovernet/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 230, in __init__
    batch_sampler = BatchSampler(sampler, batch_size, drop_last)
  File "/home/jgibert/anaconda3/envs/hovernet/lib/python3.6/site-packages/torch/utils/data/sampler.py", line 198, in __init__
    "but got batch_size={}".format(batch_size))
ValueError: batch_size should be a positive integer value, but got batch_size=0

The config.py file:
(hovernet) [jgibert@oslo hover_net]$ cat config.py
import importlib
import random

import cv2
import numpy as np

from dataset import get_dataset

class Config(object):
    """Configuration file."""

    def __init__(self):
        self.seed = 10

        self.logging = True

        # turn on debug flag to trace some parallel processing problems more easily
        self.debug = False

        model_name = "hovernet"
        model_mode = "original" # choose either `original` or `fast`

        if model_mode not in ["original", "fast"]:
            raise Exception("Must use either `original` or `fast` as model mode")

        nr_type = 5 # number of nuclear types (including background)

        # whether to predict the nuclear type, availability depending on dataset!
        self.type_classification = True

        # shape information -
        # below config is for original mode.
        # If original model mode is used, use [270,270] and [80,80] for act_shape and out_shape respectively
        # If fast model mode is used, use [256,256] and [164,164] for act_shape and out_shape respectively
        aug_shape = [540, 540] # patch shape used during augmentation (larger patch may have less border artefacts)
        act_shape = [270, 270] # patch shape used as input to network - central crop performed after augmentation
        out_shape = [80, 80] # patch shape at output of network

        if model_mode == "original":
            if act_shape != [270,270] or out_shape != [80,80]:
                raise Exception("If using `original` mode, input shape must be [270,270] and output shape must be [80,80]")
        if model_mode == "fast":
            if act_shape != [256,256] or out_shape != [164,164]:
                raise Exception("If using `original` mode, input shape must be [256,256] and output shape must be [164,164]")

        self.dataset_name = "consep" # extracts dataset info from dataset.py
        self.log_dir = "logs/" # where checkpoints will be saved

        # paths to training and validation patches
        self.train_dir_list = [
            "/mnt/isilon/Breast_HMAR/HoverNet/consep/consep/train/270x270_128x128"
        ]
        self.valid_dir_list = [
            "/mnt/isilon/Breast_HMAR/HoverNet/consep/consep/valid/270x270_128x128"
        ]

        self.shape_info = {
            "train": {"input_shape": act_shape, "mask_shape": out_shape,},
            "valid": {"input_shape": act_shape, "mask_shape": out_shape,},
        }

        # * parsing config to the running state and set up associated variables
        self.dataset = get_dataset(self.dataset_name)

        module = importlib.import_module(
            "models.%s.opt" % model_name
        )
        self.model_config = module.get_config(nr_type, model_mode)

Any hints why I got this error? Thanks!

I think this is because you do not have a GPU. (detected 0). The batch_size is being calculated by multiplying the batch_size*nr_gpus, so if you do not have a GPU this variable is set to 0.

@Tato14 if you check torch.cuda.device_count() from your conda shell, does it give you a positive integer?

@Tato14 if you do have a GPU then try running without the --gpu=0

Hi, sorry for the late response. If I hardcode batch_size, the error dissapear. It seems that --gpu=0 is not properly managed. If I changed to --gpu=2, run_train.py picks the correct GPU


rallystatuses directory is not created

The cron entry /etc/cron.d/osc_rally (or actually the command executed /usr/local/bin/run_rally.py assumes the existence of /home/nagiososc/rallystatuses and will try to make it, if it does not exist.
On one cloud this directory was not created, and thus there where no historic files. This is possibly caused by a failing rally, but no logging is present

(by xavpaice)
The dir won't be created till after fcbtest.rally has run, but is created before it's needed.  If os.mkdir(HISTORY_FOLDER, mode=0o755) had failed, the exception would have been dumped into syslog as cron stderr output.
I'm unable to reproduce this, is there some situation we can use to reproduce this?

(by eric-chen)
This charm is no longer being actively maintained. Please consider using the new Canonical Observability Stack instead.
(https://charmhub.io/topics/canonical-observability-stack)
I will close this feature request


Fixed PrivateNet master account

examples/algob.config-template.js is already updated, I took master account details from examples/algob.config-template.js.


use mamba for release ci/cd on pypi pipeline

this is a 2 parter:

we should use mamba for the publish-pypi.yml pipeline
we need to update the documentation to reflect that users will need to manually install the documentation packages. the best way to do this is probably to create a requirements_docs.yml and let them know they need to use that to install the sphinx dependencies.

The publish-pypi.yml pipeline is already updated with mamba. Was the latest release automatically published in pypi using mamba @fabiolsilva , @pjmaechling?

@wsavran I believe you already wrote in the contribution instructions  (https://github.com/SCECcode/pycsep/blob/main/CONTRIBUTING.md#developer-installation) to install pycsep with pip install -e .[all].  That should install all the docs dependencies, which are specified in pycsep setup.py.   Will make an update in the docs, where that is missing.


Fix no valid mods text

This fixes the no valid moderators text to use the same formatting as the no verified moderators text, and prevents them from showing at the same time. If no moderators are valid, then the no verified moderators text shouldn't be visible.
This PR is marked WIP because it overlaps with some of the other PRs that haven't been merged yet, and probably needs to be updated once they're merged into master.
Closes #1305

@jjeffryes can this be closed? It looks like it's included in 1306.

@jjeffryes ok #1306 is in. Can you merge master into this one so I could see what changed? Also, if you wouldn't mind fixing the conflict?


Revert "Integ Test for RemotePublication can be disabled by rolling restart of masters"

Reverts opensearch-project/OpenSearch#15677
Test found flaky in https://build.ci.opensearch.org/job/gradle-check/47335/testReport/junit/org.opensearch.gateway.remote/RemoteStatePublicationIT/testRemotePublicationDisabledByRollingRestart/

Created #15767, will mute this test rather than reverting. This will cause conflict for other PRs otherwise.


MaxWidthChars for Entry control (incl. patch)

I wanted to set the maximum visible width of an Entry control today for an entry form. For example, only 10 characters should be visible = control width. I noticed that

Entry.SetMaxLength sets the maximum characters the user can enter (useful),
Entry.SetWidth(charcount) sends a resize request, but when an Entry is inside a container (Grid in my case), then this is not enough. Therefore, the ...
Entry.SetMaxWidthChars(charcount) function is needed.

Unfortunately, this function is not wrapped in gotk3.
See the gtk3 documentation about the functions here:

gtk_entry_get_max_width_chars
gtk_entry_set_max_width_chars

Patch for gotk3: Paste the following into gtk/gtk.go file:
// SetMaxWidthChars() is a wrapper around gtk_entry_set_max_width_chars().
func (v *Entry) SetMaxWidthChars(nChars int) {
	C.gtk_entry_set_max_width_chars(v.native(), C.gint(nChars))
}

// GetMaxWidthChars() is a wrapper around gtk_entry_get_max_width_chars().
func (v *Entry) GetMaxWidthChars() int {
	c := C.gtk_entry_get_max_width_chars(v.native())
	return int(c)
}

Already tested this today. I am using libgtk-3.0 version 3.18.9 for reference.
I could file a PR, but it's really just a matter of copy & paste into any place in gtk/gtk.go.
Please include this change into gotk3.

Please send a pull request, even for small changes. It will trigger the automated test suite and help us to make sure everything is fine.
Thank you.


I want to add a RDS to my component in AWS

As a developer i want to create a component in kubernetes with an RDS in AWS

This is either replaced by #60 or irrelevant for the next foreseeable future


Omitted Declaration

Not sure if this is an issue or an option setting I'm not aware of. In the following rule, for example,
.rule {
padding: 1.56em;
padding-bottom: .56em;
}
The minified result omits the first padding declaration, leaving only "padding-bottom: .56em". Is there a way to make the minified version include both padding declarations? Thanks.

OK, so I figured out that setting the option advanced:false solves the issue, but now I don't see any css rules when I try to inspect an element in Chrome's dev tools.

Turned out to be a Chrome thing. Closing . . .

:+1: it definitely works with both advanced turned on and off - just tested on the latest 3.3.

Oh, OK. I should have been a little more explicit in detailing the how the rule is written in the Sass file, as it appears to make a difference:
.rule {
padding: $general-component-padding;
padding-bottom: $general-component-padding - 1em;
}
So, with advanced turned on, the first padding declaration is omitted from the minified file. Both declarations are present when advanced is turned off.

Please correct me if I'm wrong but Sass is a pre-processor so at the minification step both of these should have numeric values rather than variables, right?

Yes, the variables have numeric values at the minification step. Thus, the unminified CSS file contains:
.rule {
padding: 1.56em;
padding-bottom: .56em;
}
The result following minification of this file with advanced turned off is:
.rule {padding:1.56em;padding-bottom:.56em}
With advanced turned on, it is:
.rule {padding-bottom:.56em}

Can you find out which version of clean-css do you use with npm ls?

Yup, I'm using 3.3.5
OK, I think I've tracked down the issue. I have a clearfix mixin, which I omitted from the example in order to keep things tidy. it looks like this:
@mixin clearfix {
&:before, &:after {
content: " ";
display: table;
}
&:after {
clear: both;
}
zoom: 1;
/* for IE 7 */
}
So, this is what the whole rule looks like, pre-minification, with the clearfix:
.rule {
zoom: 1;
/* for IE 7 */
border-top: 1px solid #dddcdc;
background: #f7f7f7;
padding: 1.56em;
padding-bottom: 0.56em;
margin-bottom: 2em;
}
.rule:before,
.rule:after {
content: " ";
display: table;
}
.rule:after {
clear: both;
}
This is what it looks like post-minification:
.rule{zoom:1;border-top:1px solid #dddcdc;background:#f7f7f7;padding-bottom:.56em;margin-bottom:2em}
This is what it looks like post-minification with advanced set to false:
.rule{zoom:1;border-top:1px solid #dddcdc;background:#f7f7f7;padding:1.56em;padding-bottom:.56em;margin-bottom:2em}.rule:after,.rule:before{content:" ";display:table}.rule:after{clear:both}.
To sum up:
When minified with clearfix included, the first padding declaration and clearfix declarations are missing (except zoom:1)
When advanced is set to false, all works as expected with clearfix.
When the clearfix mixin is removed, all else works as expected.

Thanks for coming back, I'll give it another look.

Thanks, Jakub. Have you had a chance to look at this? Please let me know if you need clarification on anything.

Not yet as I was out for a while. But I will.

I have retested it in the latest 3.3 and master (pre 3.4) and it works fine now.
Advanced on:
.rule{zoom:1;border-top:1px solid #dddcdc;background:#f7f7f7;padding:1.56em 1.56em .56em;margin-bottom:2em}.rule:after,.rule:before{content:" ";display:table}.rule:after{clear:both}

Advanced off:
.rule{zoom:1;border-top:1px solid #dddcdc;background:#f7f7f7;padding:1.56em;padding-bottom:.56em;margin-bottom:2em}.rule:after,.rule:before{content:" ";display:table}.rule:after{clear:both}

Sorry you had to wait a bit.


[Session] Persist updates from inactive agent

If we have an agent in memory (but it isn't the active one), it's still possible that its session would get refreshed (e.g. if we have a dangling interval call somewhere that we didn't clean up). In that case, it's important that we actually persist the session since refreshing it has already consumed the stored refresh token.
It's possible this may be one of the causes of the reported unexpected logouts.
This PR changes the logic so that any agent's update event leads to updating persisted tokens — even if it's not the active agent. This should mean that, whenever we get a new session from the server (for any agent), it will get written into the persisted storage.
Note that in theory, we could have multiple agents for the same account "active" at the same time, which I think would mean that they might start "fighting" with each other. I'll think more about this case and maybe send some follow-up send https://github.com/bluesky-social/social-app/pull/4183.
Test Plan
Mostly tests cause I'm not sure how to actually trigger refresh or expiry.

merged via https://github.com/bluesky-social/social-app/pull/4183


[KIT Digital Twin] Update to Operating Model V2.1

A new version of the Catena-X Operating Model was published:
https://catena-x.net/fileadmin/user_upload/Publikationen_und_WhitePaper_des_Vereins/CX_Operating_Modelv2.1_final.pdf
it contains now the information about the decentralized digital twin registry etc. So it should be the basis for the Digital Twin KIT.
Context: https://eclipse-tractusx.github.io/docs-kits/kits/Digital Twin Kit/Adoption View Digital Twin Kit#customer-journey

Has this been solved with PR #654 ?

Good catch


Add module-info.java

Closes #3.

@Thihup question for you - IntelliJ complains about the module-info.java file and refuses to compile/run tests because it's using source version as Java 8. Did you run into a similar issue with your IDE?

Nevermind, I figured it out 🙂. I had to set the compiler source and target version to Java 9 so that IntelliJ wouldn't complain.


Drop snap

This PR completely drops SNAP, which also enables:

Windows support.
Build against musl.

Besides, I have also enabled -Wall -Wextra -Wpedantic, and UBSan in CI, which should hopefully help the development a lot.
This is a rather significant change, and perhaps breaks some functionalities (I have spotted quite a few). So please give it as much test as you can. Thanks!

Awesome! Thanks


DiagnosticListener no longer works on .NET Core 3.1 or .NET 5

Describe the bug
SqlClient 5.1.0 dropped support for .NET Core 3.1, and targeted .NET 6 instead.  However, there are still .NET Standard 2.0 and 2.1 targets, so applications running .NET Core 3.1 or .NET 5.0 can still install the newer version - but in doing so they'll get the .NET Standard version of SqlClient.
Unfortunately, the SqlDiagnosticListener class is unimplemented for .NET Standard.  Thus, if a .NET Core 3.1 or .NET 5.0 application updates to the latest version, they will silently lose all diagnostic messages from Sql Client at runtime.
We found this by a test breaking in our CI.  See https://github.com/getsentry/sentry-dotnet/issues/2189
To reproduce
Make a .NET Core 3.1 or .NET 5 app that uses Microsoft.Data.SqlClient version 5.0.1 and subscribes to diagnostic listeners.  Perform any operation so you can see the diagnostic information.
Then update to Microsoft.Data.SqlClient version 5.1.0.  Notice there are no compilation errors, but the diagnostic data is no longer emitted.
Expected behavior
Any of the following would have been acceptable:

Diagnostic data still is emitted (requiring implementation of SqlDiagnosticListener for .NET Standard)
The application didn't compile due to the breaking change (which would require some msbuild trickery)
The version number of the release should have been 6.0.0 to indicate a significant breaking change.

Yes, I understand both .NET Core 3.1 and .NET 5 are out of support, but that doesn't mean it's fine to release changes that will break behavior in them without bumping the major.

I should also point out that I see a lot of other files in this repositoty that are split between .NetCoreApp.cs vs .NetStandard.cs extensions.  So there may be a lot of other things that changed behavior, not just diagnostic listeners.

Here's a minimal reproduction.
using System;
using System.Diagnostics;
using Microsoft.Data.SqlClient;

namespace SqlClientDiagDemo
{
    static class Program
    {
        static void Main()
        {
            var subscriber = new Subscriber();
            DiagnosticListener.AllListeners.Subscribe(subscriber);

            // This is enough to get the first diag message.
            // We don't actually have to connect.
            using var connection = new SqlConnection();
        }
    }

    class Subscriber : IObserver<DiagnosticListener>
    {
        public void OnCompleted() { }

        public void OnError(Exception error) { }

        public void OnNext(DiagnosticListener listener)
        {
            Console.WriteLine($"Subscribing to {listener.Name}");
        }
    }
}

Target netcoreapp3.1 and add a package reference to Microsoft.Data.SqlClient 5.0.1.  Run and it will give the output:
Subscribing to SqlClientDiagnosticListener

Now update to version 5.1.0 and run again - the output will be empty.

@mattjohnsonpint we will look into this and get back to you soon.

Thanks. I mainly was bringing it to your attention because I believe it will affect our shared customers.

@mattjohnsonpint basically, we cannot support TFMs that are not supported by Microsoft, such as netcoreapp3.1 or net5, but regarding Net standard versions we will look into it.

If you want to continue to run on .NET Core 3.1 and .NET5, then you can continue to run with MDS 5.0.x.

If you want to continue to run on .NET Core 3.1 and .NET5, then you can continue to run with MDS 5.0.x.

Sorry, but how is this an answer?  According to #152, this library is supposed to follow SemVer.  Dropping the older TFMs created a serious breaking change.  IMHO, 5.1.0 should be de-listed and re-released as 6.0.0.  The major version bump is the signal that people need so they know not to take this update.
At least update the changelog and FAQ to describe the behavior caused by the breaking change.

@DavoudEshtehari @JRahnama - I'm also concerned that the issue was closed by a contributor without input from a member of the @dotnet platform team.  Is the response the official Microsoft position?

@mattjohnsonpint This is the result from the bug triage meeting and @lcheunglci is officially in dotnet team. It seems a good idea adding it under FAQ.
@David-Engel

@mattjohnsonpint We are not de-listing 5.1 and bumping to 6.0. There were no API-level breaking changes in 5.1. Just like OS versions and SQL Server versions regularly drop out of support, we dropped support for out-of-support versions of .NET in 5.1.
And this was specifically noted in the release notes:
https://github.com/dotnet/SqlClient/blob/main/release-notes/5.1/5.1.0.md#breaking-changes
I recommend upgrading to a version of .NET which is in support.

@DavoudEshtehari - Thanks for the clarification, I'm glad this was brought up in triage.
@lcheunglci - I didn't mean any offense, I was just confused about your role here.  From my view on GitHub, you just appear as a regular contributor and not as a MS employee or collaborator of this repo, nor as a member of the @dotnet team. Please consider updating your org visibility to public to prevent confusion in the future.
@David-Engel - The release notes mention that .NET Core 3.1 support was dropped, but actually the library continues to work with .NET Core 3.1 and .NET 5.0 due to them being covered by the .NET Standard implementation - which has a slightly reduced feature set.   The release notes do not mention which behavioral changes are breaking, but certainly SqlDiagnosticListener support is one of them (and there may be others).
My understanding was that this library followed the same breaking change rules as the rest of the .NET platform, which covers behavioral changes in addition to source and binary API changes.  In particular, it states here:

Platform Support
✓ Allowed

An operation previously not supported on a specific platform, is now supported

✗ Disallowed

An operation previously supported on a specific platform is no longer supported, or now requires a specific service-pack

As far as recommending upgrading - absolutely.  I will recommend to any customer of ours (Sentry) that complains when they are no longer seeing telemetry data for SQL queries appear in their dashboard that they either need to update to a supported version of .NET or downgrade to M.D.S. 5.0.1.
I can't speak for the other libraries and applications out there that may have built features reliant on SqlDataListener that will also be broken in the same way.  For example, the SqlClient Instrumentation for OpenTelemetry also uses this feature.  I believe Application Insights in Azure Monitor does as well.  I'm not sure how many others do, which is why I raised the issue here.
Of course, nobody has to take this update - but without a detailed reading of the release notes, they'd have no signal that the update was not safe for them.  They'd just silently lose the telemetry data.

Digging a little deeper, it appears that the reason there's no diagnostic listener support for .NET Standard is that it was intentionally removed in #535 because "... they are not available for .NET Standard 2.0."  That is not exactly correct.  They are not part of the standard, but they are indeed available - via the System.Diagnostics.DiagnosticSource NuGet package.  M.D.S. already has this dependency, transitively through Microsoft.Data.SqlClient => Azure.Identity => Azure.Core => System.Diagnostics.DiagnosticSource.
Indeed, there are no compilation errors using the SqlDiagnosticListener.NetCoreApp.cs implementation in place for SqlDiagnosticListener.NetStandard.cs.
Furthermore, the existing implementation will also work on .NET Framework, which would address #1529. It only needs a package reference to System.Runtime.Loader added (which the netcore project already has).
Would you be open to a PR to enable SqlDiagnosticListener on .NET Standard and .NET Framework using the existing implementation for .NET Core?  That would resolve my concern.
Thanks.

Thanks @mattjohnsonpint for digging deeper into the root cause of the issue.  I'll reopen this issue, and feel free to open a PR to enable SqlDiagnosticListener and you'll be able to verify that it builds from our pipeline.


[WB-6572] Exception handling for Project in CLI

https://wandb.atlassian.net/browse/WB-6572
Description
What does the PR do?
Adds logic to handle the validation of the project name in CLI.
In a case where the project name is invalid, the program will exit with a minimal traceback (decided not to hide the traceback completely for the user to understand that execution was unsuccessful)
For example:
project_name = "Launch Test: PyTorch Fashion???"
run = wandb.init(project=project_name)

will have the following output in the command line:

Testing
How was this PR tested?
Release Notes
Below, please enter user-facing release notes as one or more bullet points.
If your change is not user-visible, write NO RELEASE NOTES instead, with no bullet points.
------------- BEGIN RELEASE NOTES ------------------
NO RELEASE NOTES
------------- END RELEASE NOTES --------------------

@kptkin This is far simple and much better than the original, but I'm seeing a bunch of errors in wandb files which I don't think we need to show.
After your change the error says this (which is much better but not perfect):
ERROR Settings field `project`: Invalid project name "Launch Test: PyTorch Fashion???", cannot contain characters "/,\,#,?,%,:", found ":,?"
Traceback (most recent call last):
  File "main.py", line 5 in <module>
    run = wandb.init(project=project_name)
... more content here
... including info about wandb sdk files

Could we change it to something more readable like:
ERROR The project name "Launch Test: PyTorch Fashion???" has invalid characters. The characters "/,\,#,?,%,:" are not supported. Please remove ":,?" from the project name.
Traceback (most recent call last):
  File "main.py", line 5 in <module>
    run = wandb.init(project=project_name)
# No more content here
# Don't include a trace into wandb files
# Don't repeat the same error message


Warning portion does not have file marking context.

The warning portion uses the CodeLang data structure to easily check if anything is worth warning about.  Unfortunately if it finds something, it can give any context as to where the warning occurred.
It seems like every single leap element of the Codelang structure, and possibly some higher level stuff(not sure though) is going to need to carry around the file marker of where they physically exist in the file.  This is just an extra data member, and it shouldn't break anything in terms of the rest of the code.

Marked as bug because it's a crucial design flaw

Ok, most of the file location stuff is there alongside the actual values in the CodeLang structure.  When more of the warning stuff is implemented, the warning stuff might have to add a few more of these location things for values that are not represented as wstrings.
For example, things like class specifiers get split up into bools and enums, so we don't track that as a Token_Value right now.


Update pyproject.toml

Pull Request Test Coverage Report for Build<PHONE_NUMBER>
Details

0 of 0   changed or added relevant lines in 0 files are covered.
2 unchanged lines in 1 file lost coverage.
Overall coverage remained the same at 99.123%

Files with Coverage Reduction
New Missed Lines
%

cvx/cla/types.py
2
97.87%

Totals

Change from base Build<PHONE_NUMBER>:
0.0%

Covered Lines:
226

Relevant Lines:
228

💛  - Coveralls


[WIP] feat(Node): make zone.js work in Node

This is a port of @mlaval #78 to zone 0.5
relates to https://github.com/angular/angular/issues/1904 & the short term plan described there
/cc @tbosch @IgorMinar
Edit:

before merging we should discuss if the short term plan defined in the linked PR makes sense,
I'd like to find a good solution to distribute both zone.js and zone-microtask.js (have different repos ?)

@btford It is not comprehensive, see the linked PR for more info

@vicb: does Zone work in node now?

Not that I know

In that case, I can take a look at how node environments can be supported. Seems like it could be interesting.
Is that OK or have you got separate plans to tackle this?

That would be great !

also see continuation-local-storage

obsolete. Latest zone works on node.

What versions?

v0.6.4.  (But we don't patch node's API)

right, this is problematic.  it seems like a fool's errand to try to patch every async API method in every possible environment.  hope the proposal works out--from where I sit, the only feasible solution is something at the specification level.


Clarify the homeserver's behaviour for user searching

Rendered: see 'docs' status check

The homeserver can implement its own algorithm, but is suggested to ensure the requesting user can reasonably see the other users.
The text is intentionally left vague for whether or not the API is local users only to give the homeserver some flexibility in this area.
General cleanup is also included in this commit.
Fixes https://github.com/matrix-org/matrix-doc/issues/1108

@ara4n this should be updated - when you get a chance, please take a look.


[Chapter 09] Where is the Dockerfile for pystock-inference-api ?

The code required for this section is in the pystock-inference-api folder .

Can't find
https://github.com/PacktPublishing/Machine-Learning-Engineering-with-MLflow/tree/master/Chapter09/psystock-inference-api

@webmakaka: This section in particular didn't contain any docker file. If i remember correctly, at this point we assume that the reader would be quite familiar with the details of the docker image and we will focus on the docker file.


Consider deploying AWS stuff ahead of time to shift the focus from AWS things to OpenShift things

Prep system
bastion host
VPC

done


Handle when github sends "null" string as the name

Purpose

Handle when github sends "null" string as the name filled in Github profile

Codecov Report

:exclamation: No coverage uploaded for pull request base (master@a9eee45). Click here to learn what that means.
The diff coverage is n/a.

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=========================================
  Coverage          ?   16.44%
=========================================
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  Lines             ?     1569
  Branches          ?      274
=========================================
  Hits              ?      258
  Misses            ?     1181
  Partials          ?      130

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feat: lint and prettier setup

ESLint and Prettier setup
Add lint and format check to Github Actions workflow
Adds contributing guidelines and development setup
Makes initial refactoring for future introduction of eslint and prettier for next release

Closing in favor of https://github.com/wfpena/angular-wysiwyg/pull/15


[BUG] save_checkpoint() missing some params

Describe the bug
I'm using deepspeed to train GNN model from pgn (https://github.com/pyg-team/pytorch_geometric). But after I save the model with engine.save_checkpoint(), I couldn't load the checkpoint because of missing parameters.
To Reproduce
I provide a code snippet.
I launch save() function with deepspeed --include="localhost:2,3" train.py  --deepspeed  --deepspeed_config config/ds_config.json (I've tried no-ZeRO, ZeRO1 and ZeRO2 settings, all failed).
def save():
    from torch_geometric.nn import GATConv
    parser = argparse.ArgumentParser()
    parser.add_argument("--local_rank", default=0, type=int,
                        help="local_rank for distributed training on gpus")
    parser = deepspeed.add_config_arguments(parser)
    args = parser.parse_args()
    os.environ['MASTER_ADDR'] = 'localhost'  #
    os.environ['MASTER_PORT'] = '12345'  #
    args.local_rank = int(os.environ['LOCAL_RANK'])

    model = GATConv(4, 2, heads=2, edge_dim=3)
    engine, _, _, _ = deepspeed.initialize(args=args, model=model, model_parameters=model.parameters())
    engine.save_checkpoint('Model/tmp_model', tag='checkpoint-0')

And load checkpoint with python launcher.
def load():
    from torch_geometric.nn import GATConv
    model = GATConv(4, 2, heads=2, edge_dim=3)
    model = load_state_dict_from_zero_checkpoint(model, "Model/tmp_model", 'checkpoint-0')

output
[2022-02-24 02:43:46,824] [INFO] [zero_to_fp32.py:425:load_state_dict_from_zero_checkpoint] Extracting fp32 weights
Processing zero checkpoint 'Model/tmp_model/checkpoint-0'
Detected checkpoint of type zero stage 2, world_size: 2
Reconstructed fp32 state dict with 6 params 44 elements
[2022-02-24 02:43:46,826] [INFO] [zero_to_fp32.py:428:load_state_dict_from_zero_checkpoint] Overwriting model with fp32 weights
Traceback (most recent call last):
  File "/home/huangbz/_project2/train.py", line 344, in <module>
    model = load_state_dict_from_zero_checkpoint(model, "Model/tmp_model", 'checkpoint-0')
  File "/home/huangbz/.conda/envs/Graph/lib/python3.6/site-packages/deepspeed/utils/zero_to_fp32.py", line 430, in load_state_dict_from_zero_checkpoint
    model.load_state_dict(state_dict, strict=False)
  File "/home/huangbz/.conda/envs/Graph/lib/python3.6/site-packages/torch/nn/modules/module.py", line 1468, in load_state_dict
    load(self)
  File "/home/huangbz/.conda/envs/Graph/lib/python3.6/site-packages/torch/nn/modules/module.py", line 1466, in load
    load(child, prefix + name + '.')
  File "/home/huangbz/.conda/envs/Graph/lib/python3.6/site-packages/torch/nn/modules/module.py", line 1463, in load
    state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs)
  File "/home/huangbz/.conda/envs/Graph/lib/python3.6/site-packages/torch/nn/modules/module.py", line 1372, in _load_from_state_dict
    hook(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)
  File "/home/huangbz/.conda/envs/Graph/lib/python3.6/site-packages/torch_geometric/nn/dense/linear.py", line 131, in _lazy_load_hook
    weight = state_dict[prefix + 'weight']
KeyError: 'lin_dst.weight'

System info (please complete the following information):

GPU count and types [e.g. two machines with x8 A100s each]
Python version: 3.6
PGN version: 2.0.3

@skpig, apologies for the late reply. Can you please check if this problem remains the latest tree?

Yes, it remains in the current master branch. @tjruwase
I've already dug into the code and found the missing params. Here I also provide a simpler net that also reproduces the bug.
class MyNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.linear1 = nn.Linear(1,2)
        self.linear2 = self.linear1

    def forward(self, x):
        return self.linear1(self.linear2(x))

@tjruwase  any advice?

@skpig, yes it is possible that DeepSpeed model checkpointing might miss some model states. To help with this situation, save_checkpoint() accepts a client_state argument which is a dict that the client use to store any arbitrary state information. At checkpoint load time, load_checkpoint() will return this client_state dict for the client to restore as appropriate. See a suggested usage below. Can you try this?
client_state = {...}
engine.save_checkpoint(..., client_state=client_state)
....
load_path, client_state = engine.load_checkpoint(...)
... = client_state

@skpig, apologies for the confusion. It is possible that I may have misunderstood your original question. I thought you wanted to save some additional parameters that are either not part of the module or won't be properly saved by torch.save(). DeepSpeed uses torch.save() internally. So, I suggest trying two things:

Confirm that you can torch.save() and torch.load() work for you as expected. In other words, don't use deepspeed.

Regarding checkpoint loading. Yes, this happens after the model and engine has been initialized. Please see the sequence below:
  model = GATConv(4, 2, heads=2, edge_dim=3)
  engine, _, _, _ = deepspeed.initialize(args=args, model=model, model_parameters=model.parameters())
  load_path, client_state = engine.load_checkpoint(load_dir='Model/tmp_model', 'checkpoint-0')

In the meantime, I will try to run your code snippet on my side.

@skpig, how is it going? Just wanted to share that the following script worked for me. Can you try it on your side with the following commands:

To save:  python test.py
To load:  python test.py --load

import argparse
import os
import deepspeed
import torch_geometric
from torch_geometric.nn import GATConv

ds_config = {
    "train_batch_size": 1,
    "train_micro_batch_size_per_gpu": 1,
    "optimizer": {
        "type": "Adam"
    }
}

def init_engine():
    model = GATConv(4, 2, heads=2, edge_dim=3)
    engine, _, _, _ = deepspeed.initialize(model=model, model_parameters=model.parameters(), config=ds_config)
    return engine

def load():
    engine = init_engine()
    load_path, client_state = engine.load_checkpoint(load_dir='/tmp/Model/tmp_model', tag='checkpoint-0')
    print(f'successfully loaded checkpoint from {load_path}')

def save():
    engine = init_engine()
    engine.save_checkpoint('/tmp/Model/tmp_model', tag='checkpoint-0')

parser = argparse.ArgumentParser()
parser.add_argument('--load', action='store_true', help='Load checkpoint')
args = parser.parse_args()

if args.load:
    load()
else:
    save()


Issue #8445 - Made the SMS 2FA note as a Warning

Made the SMS 2FA note as a Warning
Fixes #8445

@keyurpd 	Thanks!


Add back noisy simulation of Pulse job for FakeBackendV2 after Aer adds support for BackendV2

What is the expected enhancement?
https://github.com/Qiskit/qiskit-terra/pull/7643 implemented mocked IBM Quantum Systems backends using the new BackendV2 interface.
However, noisy simulation of pulse job using FakeBackendV2 is not possible because Aer doesn't support generating PulseSystemModel from V2 backends yet. This issue has been raised in Aer: https://github.com/Qiskit/qiskit-aer/issues/1478 and is slated to be fixed in Aer 0.11.0. After Aer adds support for BackendV2, we should revert the changes done in this commit https://github.com/Qiskit/qiskit-terra/pull/7643/commits/68fef20665e5c33a3d9b259946d074f926cfaecc to allow pulse simulation.
Related to https://github.com/Qiskit/qiskit-terra/issues/7829

@cometta Great! Following up with our Slack conversations, please open a pull request to fix this issue and request me to review. If you are unsure about how to open a PR, feel free to reach out to me on GitHub or Slack.


[feat] Create <rh-popover>

Description
Create a <rh-popover> element based on <pf-popover>
We already have a popover page at ux dot. We should problably add a coming soon message or some such to it. Also need to update its incorrect status.
Acceptance Criteria

[x] Design done
[ ] Development done

Images
No response
Design file

Popover in our Design System library

Other resources
No response

@marionnegp and @coreyvickery can you ensure that the design file linked above is the right one?

@markcaron Yes, very similar to PatternFly's.

@markcaron, can I take a stab at developing this one? Seems like we can reuse a lot of what's been done for <pf-popover>.


All typed arrays equal

const array: Uint8Array = new Float32Array(0);

This is difference types, but typescript not see errors =\

duplicate of #5388


Check all second factors for a single user

At the moment only one second factor per user is checked: https://github.com/sandstorm/NeosTwoFactorAuthentication/blob/e58561bf3dedf5ea08c4999a0e4a03de264a0728/Classes/Security/Authentication/Provider/PersistentUsernameAndPasswordWithSecondFactorProvider.php#L46
The provider should check all factors and match if one was correct.

was solved with 491ec39b and release in version 1.0.2


SafeNet HSM

Check plugin for SafeNet HSM status.

Oops, basket wasn't generated via check2basket but manually on the test system.
Is this a problem?


Make PSR-6 implementation optional

Hello @jyggen
I'm trying to make an adapter from Laravel cache facilities to the psr 6 interfaces. It's going great at the moment.
My question is this: Should this package not include some kind of concrete implementation of these interfaces as per default? like you did beforehand usingtedivm/stash?

Yeah, haven't decided which approach to take though. Currently I use the in-memory client from fig/cache-util for testing, might go for that or just write a lightweight one specifically for this package. Another idea is just make it work without a cache implementation altogether, but that requires some code changes.

That last option is sounding reasonable, because i think caching is just a little bit out of scope of this package.
Or i could be wrong here but we could just make it easy for the consumer of the package to implement caching on their own, i'm not really sure how this would work but something like the ability to make use of the package caching or to implement their own, and disable caching altogether.

Any news regarding this?

Yeah, I pushed a commit earlier today that should make caching optional. Let me know how if you run into any issues.

I think we should extract the if(! $item === null) check to a readable method name. I'll make a pr for it soon.


Add expo/round filtering to project status

When seeing what projects haven't been judges, add a filter at the top of the page

check if each assignment within Project is completed ==> if they're not completed ==> then add them to filtered thing


log4ahk.layout: Stack trace of functions called

Implement Stack Trace of functions calles
use %T as placeholder (see https://metacpan.org/pod/Log::Log4perl::Layout::PatternLayout)

This is the first layout element to support curlies


Sous chefs adoption

Description
Describe what this change achieves
Issues Resolved
List any existing issues this PR resolves
Check List

[ ] All tests pass. See TESTING.md for details.
[ ] New functionality includes testing.
[ ] New functionality has been documented in the README if applicable.

@detjensrobert that would be nice but I don't really have the time to do that with all the other cookbooks we need to adopt. Feel free to tackle it!


OCR issue

When I import a scanned PDF, I encounter an error ( first screenshot ) , and the OCR option is not available for me in the expert tab. Even if I activate pdf options from auto to on I still encounter another error (as shown in the second screenshot). What could be the problem?

I recommend DocTR model instead of scanned/OCR handling.  Unstructured is vastly slower for OCR and less accurate.
I can't tell much from the 2nd error you provided as it's only single line.  I can't tell where in code that is from etc.


Mac failed run the Quick Start Guide

System Info
Mac M4 + Ollama + sample docker script
Information

[X] The official example scripts
[ ] My own modified scripts

🐛 Describe the bug
HOST = "localhost"  # Replace with your host
PORT = 5001       # Replace with your port
MODEL_NAME='meta-llama/Llama-3.2-3B-Instruct'

from llama_stack_client import LlamaStackClient

client = LlamaStackClient(base_url=f'http://{HOST}:{PORT}')

response = client.inference.chat_completion(
    messages=[
        {"role": "system", "content": "You are a friendly assistant."},
        {"role": "user", "content": "Write a two-sentence poem about llama."}
    ],
    model_id=MODEL_NAME,
)

print(response.completion_message.content)

Error logs
INFO:     <IP_ADDRESS>:57444 - "POST /alpha/inference/chat-completion HTTP/1.1" 500 Internal Server Error
07:40:59.395 [INFO] [/alpha/inference/chat-completion] HTTP Request: POST http://host.docker.internal:11434/api/generate "HTTP/1.1 200 OK"
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/site-packages/llama_stack/distribution/server/server.py", line 187, in endpoint
    return await maybe_await(value)
  File "/usr/local/lib/python3.10/site-packages/llama_stack/distribution/server/server.py", line 151, in maybe_await
    return await value
  File "/usr/local/lib/python3.10/site-packages/llama_stack/distribution/routers/routers.py", line 123, in chat_completion
    return await provider.chat_completion(**params)
  File "/usr/local/lib/python3.10/site-packages/llama_stack/providers/remote/inference/ollama/ollama.py", line 221, in chat_completion
    return await self._nonstream_chat_completion(request)
  File "/usr/local/lib/python3.10/site-packages/llama_stack/providers/remote/inference/ollama/ollama.py", line 273, in _nonstream_chat_completion
    assert isinstance(r, dict)
AssertionError

Expected behavior
Run smoothly

This bug has long been fixed but looks like there are stale packages out there. Can you tell me what exact docker command you used? Could you also list the docker image version by doing docker image ls | grep llamastack

docker run -it \
  -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
  -v ~/.llama:/root/.llama \
  llamastack/distribution-ollama \
  --port $LLAMA_STACK_PORT \
  --env INFERENCE_MODEL=$INFERENCE_MODEL \
  --env OLLAMA_URL=http://host.docker.internal:11434

Docker image version: llamastack/distribution-ollama   latest    a7bb50aad98a

Oh no, :latest is pointing to an older docker. I am going to update the tag to point to the actual latest commit.

@chrish2 I updated latest to point to the correct version. Can you make sure you re-pull it?


Update release note for 15.9.1

Update release note for 15.9.1

@MSraye : Thanks for your contribution! The author, @stevestein, has been notified to review your proposed change.

#sign-off


Segmentation fault on long command-line parameter

# ./xgboost b=aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa
Segmentation fault

The cause is a classical buffer overrun in https://github.com/dmlc/rabit/blob/8f61535b83e650331459d7f33a1615fa7d27b7bd/src/allreduce_base.cc#L65
This bug prevents using xgboost with long filenames.
Also, this bug is most likely a security vulnerability if an attacker can control at least one key or value of a command-line parameter.

Note that a similar bug and vulnerability is also in https://github.com/dmlc/xgboost/blob/c85b9012c63f279854b15e7d2b866c8a4dbfb2f8/src/cli_main.cc#L336
which would likely be triggerable by
# ./xgboost a b=aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa

once the first bug is fixed.


single-pool: js

using the new experimental web3js

closing this and letting the branch sit dormant. my current plan now is to flesh out the js lib on web3legacy and aim to make its web3next-first at a later date, when both libraries can be used together. its not possible for existing wallets to work with web3next outputs, this will ahve to wait until the web3legacy interface is reimplemented on top of web3next


<EMAIL_ADDRESS>update to latest version

Update PDF-XChange Editor to latest version.

Add 32 bit version.

Update homepage.

[x] I have read the Contributing Guide.

/verify

/verify


Fixed DateTimeFormatter format function logic and registered format_datetime Velox function

Summary: Updated logic in DateTimeFormatter format function so that year 0 is BC and CENTURY_OF_ERA token format supports negative years. Registered format_datetime Velox function and wrote tests to verify correctness.
Differential Revision: D37259813

@ConnorDevlin2000 some high-level comments.
In PR title, and description, use active verbs. Limit description to 80 characters on each line.
Example: Fix DateTimeFormatter format function logic.
Looks like format_datetime registration moved to another PR.


Fixed JFXRadioButton with graphics

@rufio1987 before merging with master can you explain the issue?

There were 2 problems:
1 - A JFXRadioButton with graphics had the class list "getChildren().get(i).getStyleClass()" empty, so it would throw an arrayoutofboundsException. (lines 233/239)
2 - The JFXRadioButton with no text (but only graphic) threw another exception because the getChildren().get(1) had no textProperty (lines 212/217)

Thanks @rufio1987 for pointing out the issue 👍 , I'd rather fix it a bit different though.
Regards,


Unable to authorize from remote server

I can't authorize identi.ca from my datamost.com-account when logging in. On the Authorization page, when clicking on [Authorize], a connection can't be established. Is it due to registrations being disabled?

AFAIK, that's a problem with identi.ca still using Pump.io v3.x.
For now, depending on what you need to do, using a non-web client might help.

Yep. Unfortunately there's nothing we can do. We need @evanp to upgrade identi.ca.

Wait, what? Since when did the authorization protocol change?

It didn't, but for a while there was a crasher bug in the auth code.

https://github.com/pump-io/pump.io/blob/master/CHANGELOG.md#411---2017-07-14

@evanp has identi.ca already been updated?

Yes


CCLE maf files

Hi PoisonAlien, I am very very new to bioinformatics and I apologize if this is a dumb questions. Would it be possible to show how you can apply your tools to CCLE maf file? I tried separating the file to individual lineage maf files but having difficulty  in running MutsigCV on them. I am sorry if I am not making sense. My question is that if you can apply your tool for CCLE maf file.
Thanks.
Shwetha

Hello,
Yes you can process any data as long as it complies to MAF format. Here is a snippet on how to generate CCLE maf object
#Download CCLE maf file (details here: https://depmap.org/portal/download/)
download.file("https://ndownloader.figshare.com/files/34989940", destfile = "ccle.maf")
ccle = data.table::fread(file = "ccle.maf")
#Change some column names
tsb_idx = which(colnames(ccle) == "DepMap_ID")
tsa2_idx = which(colnames(ccle) == "Alternate_Allele")
colnames(ccle)[tsa2_idx] = "Tumor_Seq_Allele2"
colnames(ccle)[tsb_idx] = "Tumor_Sample_Barcode"

#Cell line annotations (DepMap ID to many..)
download.file("https://ndownloader.figshare.com/files/35020903", destfile = "ccle.clinicalInfo.csv")
sampinfo = data.table::fread(input = "ccle.clinicalInfo.csv")
colnames(sampinfo)[1] = "Tumor_Sample_Barcode"

#Make MAF
ccle = maftools::read.maf(maf = ccle, clinicalData = sampinfo)


PS/Chief Nodes not terminating & Worker Nodes not accurately terminating with tf.estimator.train_and_evaluate

I'm using tf.estimator.train_and_evaluate of TF 1.9.
When the training reaches the max_steps and max_steps is less than the dataset's size, the evaluator and the non-chief workers will terminate as expected. But, the ps nodes and the chief worker do not terminate automatically.
When the max_steps is larger than the dataset's size, only one of the workers will terminate, which I guess is the one that reaches the end of the dataset. The remaining workers and ps nodes stuck.

Thank you for your post. We noticed you have not filled out the following field in the issue template. Could you update them if they are relevant in your case, or leave them as N/A? Thanks.
Have I written custom code
OS Platform and Distribution
TensorFlow installed from
TensorFlow version
Bazel version
CUDA/cuDNN version
GPU model and memory
Exact command to reproduce
Mobile device

It has been 15 days with no activity and the awaiting response label was assigned. Is this still an issue?

It is.

Can you describe your setup a bit more? How many workers / ps nodes, any sample code / minimal example you can provide so that we can reproduce and debug it?

@rohan100jain I make a minimal example for linear regression. First, run python gen_data.py to generate data. Then, run
python model.py --ps_hosts=<IP_ADDRESS>:2222 --chief_hosts=<IP_ADDRESS>:2223 --worker_hosts=<IP_ADDRESS>:2224 --task_index=0 --job_name=ps
python model.py --ps_hosts=<IP_ADDRESS>:2222 --chief_hosts=<IP_ADDRESS>:2223 --worker_hosts=<IP_ADDRESS>:2224 --task_index=0 --job_name=chief
python model.py --ps_hosts=<IP_ADDRESS>:2222 --chief_hosts=<IP_ADDRESS>:2223 --worker_hosts=<IP_ADDRESS>:2224 --task_index=0 --job_name=worker
python model.py --ps_hosts=<IP_ADDRESS>:2222 --chief_hosts=<IP_ADDRESS>:2223 --worker_hosts=<IP_ADDRESS>:2224 --task_index=0 --job_name=evaluator
the code is as follows:
gen_data.py
import random
import tensorflow as tf
import os

if __name__ == "__main__":
    if not os.path.exists(os.path.join('.', 'data')):
        os.makedirs(os.path.join('.', 'data'))
    train_out = tf.python_io.TFRecordWriter('data/train.tfrecord')
    for _ in range(10000):
        x1 = (random.randint(1, 10000) - 5000) / 10000.0
        x2 = (random.randint(1, 10000) - 5000) / 10000.0
        x3 = (random.randint(1, 10000) - 5000) / 10000.0
        y = 3 * x1 - 2 * x2 + 5 * x3
        example = tf.train.Example(features=tf.train.Features(
            feature={
                'x': tf.train.Feature(float_list=tf.train.FloatList(value=[x1, x2, x3])),
                'y': tf.train.Feature(float_list=tf.train.FloatList(value=[y])),
            }
        ))
        train_out.write(example.SerializeToString())
    train_out.close()
    test_out = tf.python_io.TFRecordWriter('data/test.tfrecord')
    for _ in range(1000):
        x1 = (random.randint(1, 10000) - 5000) / 10000.0
        x2 = (random.randint(1, 10000) - 5000) / 10000.0
        x3 = (random.randint(1, 10000) - 5000) / 10000.0
        y = 3 * x1 - 2 * x2 + 5 * x3
        example = tf.train.Example(features=tf.train.Features(
            feature={
                'x': tf.train.Feature(float_list=tf.train.FloatList(value=[x1, x2, x3])),
                'y': tf.train.Feature(float_list=tf.train.FloatList(value=[y])),
            }
        ))
        test_out.write(example.SerializeToString())
    test_out.close()

model.py
import tensorflow as tf
import os
import json

FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_string("ps_hosts", '', "Comma-separated list of hostname:port pairs")
tf.app.flags.DEFINE_string("chief_hosts", '', "Comma-separated list of hostname:port pairs")
tf.app.flags.DEFINE_string("worker_hosts", '', "Comma-separated list of hostname:port pairs")
tf.app.flags.DEFINE_string("job_name", '', "One of 'ps', 'worker', 'chief', 'evaluator'")
tf.app.flags.DEFINE_integer("task_index", 0, "Index of task within the job")

def input_fn(file_dir, num_epochs, num_workers=1, task_index=0):
    def _parse_fn(record):
        features = {
            "x": tf.FixedLenFeature([3], tf.float32),
            "y": tf.FixedLenFeature([1], tf.float32)
        }
        parsed = tf.parse_single_example(record, features)
        label = parsed.pop("y")
        return parsed, label
    dataset = tf.data.Dataset.list_files(file_dir, shuffle=False)
    dataset = dataset.apply(tf.contrib.data.parallel_interleave(tf.data.TFRecordDataset, cycle_length=1))
    dataset = dataset.map(_parse_fn, num_parallel_calls=32).prefetch(10)
    dataset = dataset.apply(tf.contrib.data.shuffle_and_repeat(buffer_size=4, count=num_epochs))
    if num_workers > 1:
        dataset = dataset.shard(num_workers, task_index)
    dataset = dataset.batch(4)
    iterator = dataset.make_one_shot_iterator()
    batch_features, batch_labels = iterator.get_next()
    return batch_features, batch_labels

def set_dist_env():
    ps_hosts = FLAGS.ps_hosts.split(',')
    chief_hosts = FLAGS.chief_hosts.split(',')
    worker_hosts = FLAGS.worker_hosts.split(',')
    task_index = FLAGS.task_index
    job_name = FLAGS.job_name
    tf_config = {
        'cluster': {'chief': chief_hosts, 'worker': worker_hosts, 'ps': ps_hosts},
        'task': {'type': job_name, 'index': task_index}
    }
    print(json.dumps(tf_config))
    os.environ['TF_CONFIG'] = json.dumps(tf_config)

def model_fn(features, labels, mode, params=None):
    x = features["x"]
    w = tf.get_variable(name='w', dtype=tf.float32, shape=[3, 1], initializer=tf.glorot_normal_initializer())
    b = tf.get_variable(name='b', dtype=tf.float32, shape=[1], initializer=tf.constant_initializer(0.0))
    pred = tf.add(b, tf.matmul(x, w))
    predictions = {"prediction": pred}
    if mode == tf.estimator.ModeKeys.PREDICT:
        export_outputs = {
            tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: tf.estimator.export.PredictOutput(predictions)
        }
        return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions, export_outputs=export_outputs)
    error = tf.reduce_mean(tf.square(labels - pred))
    if mode == tf.estimator.ModeKeys.EVAL:
        return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions, loss=error)
    if mode == tf.estimator.ModeKeys.TRAIN:
        train_op = tf.train.GradientDescentOptimizer(0.001).minimize(error, global_step=tf.train.get_global_step())
        return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions, loss=error, train_op=train_op)

def main(_):
    tr_dir = 'data/train*'
    va_dir = 'data/test*'
    set_dist_env()
    config = tf.estimator.RunConfig().replace(
        session_config=tf.ConfigProto(device_count={'GPU': 0, 'CPU': 32}),
        log_step_count_steps=10000,
        save_summary_steps=10000,
        save_checkpoints_steps=10000)
    e = tf.estimator.Estimator(model_fn=model_fn, model_dir="model", params={}, config=config)
    num_workers = len(FLAGS.worker_hosts.split(",")) + 1
    worker_index = 0
    if FLAGS.job_name == "worker":
        worker_index = int(FLAGS.task_index) + 1
    train_spec = tf.estimator.TrainSpec(input_fn=lambda: input_fn(tr_dir, num_epochs=9999, num_workers=num_workers, task_index=worker_index), max_steps=200000)
    feature_spec = {
        "x": tf.FixedLenFeature([3], tf.float32),
    }
    serving_input_receiver_fn = tf.estimator.export.build_parsing_serving_input_receiver_fn(feature_spec)
    exporters = tf.estimator.LatestExporter("linear", serving_input_receiver_fn)
    eval_spec = tf.estimator.EvalSpec(
        input_fn=lambda: input_fn(va_dir, num_epochs=1),
        steps=1000,
        start_delay_secs=5,
        throttle_secs=10,
        exporters=exporters)
    tf.estimator.train_and_evaluate(e, train_spec, eval_spec)

if __name__ == "__main__":
    tf.logging.set_verbosity(tf.logging.INFO)
    tf.app.run()

Nagging Assignee @rohan100jain: It has been 15 days with no activity and this issue has an assignee. Please update the label and/or status accordingly.

Nagging Assignee @rohan100jain: It has been 30 days with no activity and this issue has an assignee. Please update the label and/or status accordingly.

Nagging Assignee @rohan100jain: It has been 45 days with no activity and this issue has an assignee. Please update the label and/or status accordingly.

@wuciawe  I didn't reproduce this problem with your code in TF r1.10.

@weberxie Do you mean the ps, the chief, and the worker all will terminate properly when the training step reaches the max_steps?

@wuciawe Only the ps still running when step reaches the max_steps.

@weberxie So the problem remains.

@wuciawe PS does not terminate is normal.

@ Have you killled the ps when using the estimator for the distributed tensorflow? I met the same problem,
I try many ways, but it was still failed to shutdowm the ps.
I view the code of the estimator. I only find the ps server is "join()".  but i want to close the PS
thank you

Nagging Assignee @rohan100jain: It has been 14 days with no activity and this issue has an assignee. Please update the label and/or status accordingly.

Nagging Assignee @rohan100jain: It has been 31 days with no activity and this issue has an assignee. Please update the label and/or status accordingly.

Nagging Assignee @rohan100jain: It has been 46 days with no activity and this issue has an assignee. Please update the label and/or status accordingly.

We met same issue in TF 1.12 like above described.
set max_step in TrainSepc to 126800
    model = build_estimator(build_model_column)

    train_spec = tf.estimator.TrainSpec(input_fn=train_input_fn, max_steps=FLAGS.max_step)
    eval_spec = tf.estimator.EvalSpec(input_fn=eval_input_fn, steps=6000, throttle_secs=120)
    for index in range(Config.TRAIN_EPOCHS):
        tf.estimator.train_and_evaluate(model, train_spec, eval_spec)
        tf.logging.info('=' * 60)

Then after 126800 steps, the whole training stop, but chief still not exit
INFO:tensorflow:Saving checkpoints for 1268001 into hdfs://xxx

We test same code in TF 1.7 and TF 1.8, chief can exit as expect, but in TF 1.10 and TF 1.12, chief didn't exit even max_step reached.

We just tested TF 1.13 and found that chief can exit after max_step reached.

@weberxie Interesting. How would you think the lack of automatic resource cleanup is normal for a machine learning framework in production. In a model training workflow, how to notify the workflow controller the termination of the training stage to terminate the remaining PSs and to update the trained model to online service in a programmable way?

It is expected that the cluster resource manager would terminate PS. It could be a programmable framework like Apache Yarn or Kubernetes, or some scripts that kills PS after worker exits, or even a personnel who just does the job manually.

@weberxie Interesting. How would you think the lack of automatic resource cleanup is normal for a machine learning framework in production. In a model training workflow, how to notify the workflow controller the termination of the training stage to terminate the remaining PSs and to update the trained model to online service in a programmable way?

It is expected that the cluster resource manager would terminate PS. It could be a programmable framework like Apache Yarn or Kubernetes, or some scripts that kills PS after worker exits, or even a personnel who just does the job manually.

I thought distributed tf was/would itself be that kind of framework, similar to that spark/flink can recover from failure and terminate after job finished in standalone mode without the help of YARN/K8S.
PS Do the job manually is not expected for production usage.

I’m sorry, but apparently TF is not designed around the way you expected.

We met same issue in TF 1.10 like above described, but no problem in TF 1.13

I met the same issue in TF 1.14.
I have 2 ps ,2 worker .Woker1 is finished successfully. But "worker0" is chief node , stuck in Saving checkpoints for 10002 into hdfs:*****model.ckpt..Ps0 is the evaluate node, is finished successfully,ps1 is stuck in  2020-10-16 09:49:03.411776: I tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc:365] Started server with target: grpc://localhost:2222 20/10/16 01:50:32 WARN util.NativeCodeLoader main: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable

I met the same issue in TF 1.14.
I have 2 ps ,2 worker .Woker1 is finished successfully. But "worker0" is chief node , stuck in Saving checkpoints for 10002 into hdfs:*****model.ckpt..Ps0 is the evaluate node, is finished successfully,ps1 is stuck in  2020-10-16 09:49:03.411776: I tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc:365] Started server with target: grpc://localhost:2222 20/10/16 01:50:32 WARN util.NativeCodeLoader main: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable

I try the same code in TF.1.12, it works!

Hi There,
We are moving this issue to closed status, as you are using an older version of tensorflow(1.x) which is officially considered as end of life. We recommend that you upgrade to 2.4 or later version and let us know if the issue still persists in newer versions.
Please open a new issue for any help you need against 2.x, and we will get you the right help.Thanks!


backport: bitcoin#18152

Issue being fixed or feature implemented
It splits from https://github.com/dashpay/dash/pull/5802 due to non-trivial decisions regards updateNumBlocks usage
What was done?
Backport bitcoin#18152
How Has This Been Tested?
Run unit/functional tests; tried to sync blockchain; reindex and watch a spinner and progress bar.
Breaking Changes
N/A
Checklist:

[x] I have performed a self-review of my own code
[ ] I have commented my code, particularly in hard-to-understand areas
[ ] I have added or updated relevant unit/integration/functional/e2e tests
[ ] I have made corresponding changes to the documentation
[x] I have assigned this pull request to a milestone (for repository code-owners and collaborators only)

Previous discussion is here https://github.com/dashpay/dash/pull/5802#discussion_r1444993223.
I think the cleanest way to fix this is to introduce another SynchronizationState e.g. RESET and use it df3d6cd93e.
Things to test from Console once qt node is fully synced:

mnsync reset once node is fully synced
setnetworkactive false/setnetworkactive true

My results:
073b8e8: No status bar updates
df3d6cd93e: Works just like before this PR

I just realised, that a function setNumBlocks is relevant only for Qt app, and sync_state is used only inside it. let's pass INIT_DOWNLOAD indeed, it is workaround but it would work same.

yeah, technically both these cases are similar to switching back to IBD so using INIT_DOWNLOAD kind of makes sense here too.


Payload added by e.g. rules not updating state on changes due to updated_at check

Describe the problem
Because you check for user updates to update the state or not: https://github.com/auth0/auth0-react/blob/v1.9.0/src/reducer.tsx#L38
any other changes e.g. due to rules (or actions) do not refresh the react state and components that use those values are not updated.
So e.g. if external data is added and that data changes, the state will not update and the SPA will still show the previous content.
What was the expected behavior?
Update the state on getAccessTokenSilently if any of its data changes. Either check accordingly all properties, if there have been changes or just update the data always.
Reproduction
Simplest would be:

Create a rule that adds custom data that changes, e.g. to the idToken using the corresponding application scope.
Easiest would be to just add the current timestamp.
Output that value.
Add a button that forces a token refresh using getAccessTokenSilently.
The values will stay the same as before until a full page refresh is done.

Not 100% sure if it depends if the additional data is added to the id token or the access token - have not gone into that much detail.
Environment

Version of auth0-react used: 1.9.0
Which browsers have you tested in? Chrome, Firefox, etc.

I ran into this issue as well. I did a little research and it looks like the updated_at check was added to resolve this issue. However, in my testing, updating the user does not cause getAccessTokenSilently to be redefined -- which makes sense since useCallback is used. I think it would be safe to remove that check entirely.

@AndreasA - thanks for raising this
I wonder if we can use the raw ID Token, or Access Token since they always change, even in the Refresh Grant - rather than the updated_at claim which clearly doesn't. Will investigate.

I did a little research and it looks like the updated_at check was added to resolve this https://github.com/auth0/auth0-react/issues/210.

@inv8der The updated_at check was added before that issue to prevent unintentional renders when using the SDK with localStorage enabled and is required

@adamjmcgrath Checking the raw tokens - probably both to be safe- sounds like a good bet. It avoids unnecessary re-renderings but re-renders things if anything changes..

FYI, this update is waiting on some changes in the upstream spa js sdk

Hey @AndreasA and @inv8der, we just shipped the first beta for Auth0-React v2 that includes the fix for this issue.
You can install it using npm i @auth0/auth0-react@beta and the migration guide can be found here, if you'd like to try it out and provide any feedback that would be great!

Thanks. I will probably not have time to test it in the immediate future. However, it is good to know that there is a solution available, even if it is only beta for now.


variable names in hdf5

Currently the hdf5 file calls the x-axis/y-axis columns from the excel (.csv) as 'x' and 'y' but they should be properly named as (for x) 'wavelength' and (for y) 'counts'
Think this is under @ParkerSteichen realm, but also not sure how much it impacts @kernb2
Note: Sometimes the y is also called 'intensity' or 'arb. units' in literature because the counts / amount of resonance that the raman probe and cell emitted can be a function of the set up.

I updated this in the the .py to make it more clear when reading the code directly. I don't believe those variable names actually got saved to the hdf5 and were just internal to the function but it never hurts to make the code more clear.


feat: fixed for the issue (#305)

1. Description

1.1. Checklist

[ ] The title of my PR starts with a Conventional Commit prefix (fix:, feat:, docs: etc).
[ ] I have read the Contributor Guide and followed the process outlined for submitting PRs.
[ ] I have updated/added tests for ALL new/updated/fixed functionality.
[ ] I have updated/added relevant documentation in docs and added dartdoc comments with ///.
[ ] I have updated/added relevant examples in examples.

1.2. Breaking Change

[ ] Yes, this is a breaking change.
[ ] No, this is not a breaking change.

1.3. Related Issues

Codecov Report

Merging #508 (bcf1f38) into main (206225c) will increase coverage by 0.08%.
The diff coverage is 100.00%.

:exclamation: Current head bcf1f38 differs from pull request most recent head 47199d4. Consider uploading reports for the commit 47199d4 to get more accurate results

:exclamation: Your organization is not using the GitHub App Integration. As a result you may experience degraded service beginning May 15th. Please install the Github App Integration for your organization. Read more.
@@            Coverage Diff             @@
##             main     #508      +/-   ##
==========================================
+ Coverage   68.37%   68.45%   +0.08%
==========================================
  Files         202      203       +1
  Lines        1894     1899       +5
==========================================
+ Hits         1295     1300       +5
  Misses        599      599

Impacted Files
Coverage Δ

packages/atproto/lib/src/entities/records.dart
100.00% <100.00%> (ø)

...oto/lib/src/repositories/repositories_service.dart
94.82% <100.00%> (+0.28%)
:arrow_up:

:mega: We’re building smart automated test selection to slash your CI/CD build times. Learn more


tf-mlir-translate is not coming in binaries

I built mlir using : bazel build --config opt tensorflow/compiler/mlir/...
But tf-mlir-translate is not there in the tensorflow/bazel-bin/tensorflow/compiler/mlir/tensorflow/
Thank You

@ArunaKote,
Could you please fill in the below details
System information

OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device:
TensorFlow installed from (source or binary):
TensorFlow version:
Python version:
Installed using virtualenv? pip? conda?:
Bazel version (if compiling from source):
GCC/Compiler version (if compiling from source):
CUDA/cuDNN version:
GPU model and memory:

and also provide the exact sequence of commands / steps that you executed before running into the problem? Thanks!

OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Linux Ubuntu 20.04
Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device:
TensorFlow installed from (source or binary):https://github.com/tensorflow/tensorflow.git
TensorFlow version:2.0
Python version:3.8
Installed using virtualenv? pip? conda?:pip
Bazel version (if compiling from source):3.1.0
GCC/Compiler version (if compiling from source):9.3.0
I am installing only for CPU.
Thank You

@ArunaKote translate is present in tensorflow/tensorflow/compiler/mlir/tensorflow/ Please take a look at here in tensorflow 2.0


add avatar for mushroomsfi.eth

add avatar for mushroomsfi.eth

The images file size should not exceed 50KB. Please reduce their weight.

reduced. Thanks for taking time to review this PR.

The images file size should not exceed 50KB. Please reduce their weight.

reduced. Thanks for taking time to review this PR.


chore: bump nixpkgs to newest stable NixOS release

Hmm, looks like the linting rules in cppcheck have become a bit stricter with the update, causing the CI to fail.

... ahem, but not yet to the formatter, apparently 😅


[Bug]: AxiosError: Request failed with status code 408

Contact Details
<EMAIL_ADDRESS>VRM portal ID
c0619ab33f21
Country / region
Germany (de)
B max
10
TB max
5
FB max
3.2
TG max
0
FG max
2.5
Battery costs
0.025
Buy price
(p+0.139)*1.19
Sell price
(p+0.139)*1.19
feed-in possible
no
feed-in control
yes
Version
0.1.14
What happened?
I'm always getting "AxiosError: Request failed with status code 408" and Dynamic ESS tells me "Could not create a feasible schedule". It was working, i just changed some parameters and deployed again, it stopped. Reverting the changes does not help, restart does not help, even reimporting the flow does not help.
When did it happen?
28/02/2023 12:00
Relevant log output
29.2.2024, 10:40:24node: Dynamic ESS
msg : Object
{ url: "https://vrm-dynamic-ess-api.vi…", options: object, headers: object }
29.2.2024, 10:40:27node: Dynamic ESS
msg : error
"AxiosError: Request failed with status code 408"

Screenshots
![DESCRIPTION](LINK.png)

A http 408 code means that the connection timed out. The only thing I can think of that might cause this is if your DNS is unable to resolve vrm-dynamic-ess-api.victronenergy.com or that your firewall does not allow the https connection to that host.


path parsing optimization and readability improvement to  parse_prepared_path

This PR will slightly increase the probability that a file-type path avoids the slow path. It also improves code readability.
This improves the performance (check).

Merging because it is should quite uncontroversial.


Add link to apispec

Add link to api spec

Related microservices-demo/microservices-demo#181

Coverage remained the same at 23.252% when pulling a354f3313fbf59d8b3031e554ac6297815ff8041 on api-spec-link into 691561b71e499cd9efb094afa132e7cdf705aa7d on master.


page reload issue

Hello! Faced a problem, when reloading a page in google chrome, the hover zone moves somewhere to the top of the page. The effect starts working normally again if you scroll to the top of the page and reload. I don’t see any point in attaching the code, I used your files and connected the demo4.e22d7780.js file to the project. I checked for conflicts with other files - I found nothing. In all other browsers, everything works as it should. Please tell me what this problem may be related to, what I did wrong.

Thanks for the heads up! I think we've fixed the problem. Could you try with the new version?

Thanks, but unfortunately the problem persists. I think I am doing something wrong. Should I include something other than demo4.e22d7780.js and styles for the button?

Thanks for the heads up! I think we've fixed the problem. Could you try with the new version?

A bit hard to help like this. Maybe you could share a live link?


'format document' produces a very ugly document, say html document.

VSCode Version: Code 1.8.1 (ee428b0eead68bf0fb99ab5fdc4439be227b6281, 2016-12-19T14:49:23.350Z)
OS Version: Windows_NT ia32 10.0.10586
Extensions:

Extension
Author
Version

speech
bierner
0.0.1

scala
daltonjorge
0.0.4

vscode-markdownlint
DavidAnson
0.6.0

vscode-instant-markdown
dbankier
0.2.1

glslx-vscode
evanw
0.0.8

Theme-HotDogStand
gerane
0.0.2

theme-microsoft-graph
martellaj
0.0.2

easy-less
mrcrowl
1.3.4

Theme-MarkdownKit
ms-vscode
0.1.1

vshaxe
nadako
1.0.1

sass-indented
robinbentley
1.3.0

preview-vscode
searKing
1.1.1

shader
slevesque
0.2.2

shader-toy
stevensona
0.1.8

ActionScript3
yopsolo
0.0.1

Material-theme
zhuangtongfa
1.0.3

Steps to Reproduce:

new a html document segment
contextmenu, format document

@aeschli I'll provide an example:
If I enter the following (and format myself):
<!doctype html>

</body>

And then right-click anywhere in the editor and select Format Document, it then mangles all of the text to make it look like this:
<!doctype html>

Which is absolutely horrible.
I don't have any extensions installed other than the ones that come with VS Code. My version is 1.9.0 and I'm on Windows 10.

duplicate of #17849

@aeschli I don't believe this to be a duplicate. The Issue you linked to relies on the HTML having style tags in it. If not then might I suggest including extra information in the original issue stating that style tags are not required to reproduce this issue?

@jaseonline Maybe not a exact duplicate but both issues are cause by the same bug. I fixed it an and you can try out the fix in the latest insider build.

@jaseonline Sorry, you are absolutly right, not a duplicate. What you see is the way how the jsbeautify formatter works. But, you can control this behaviour by using the following setting
"html.format.indentInnerHtml": true'.


Feat updated dependency

FYI

error npm ci can only install packages when your package.json and package-lock.json or npm-shrinkwrap.json are in sync. Please update your lock file with npm install before continuing.

Already fixed


[5th Edition OGL by Roll20] Spellcard convenience addition

The spellcard contains now the spell save DC and save type. E.g. "DC15, Wisdom save".
It would be very useful for the GM if they could select a token or multiple tokens, and just click on the "Wisdom save" text, and it would roll that save for selected token(s). No need to open character pages.

Let me think about that and review the code. That would be useful though I'm not sure off the top of head if our current functionality would support it. Its still an interesting suggestion. :)

Moving this to an internal ticket.


0.4.1 release does not contain TaskManager

0.4.1 release notes have

A new TaskManager object and the deprecation of lm_eval.tasks.initialize_tasks(), for achieving the easier registration of many tasks and configuration of new groups of tasks

But neither code attached to the release notes, nor the pypi package contain TaskManager.
Could you please either release a fix or verify, that it is present in the new release?

Closed: TaskManager was announced to be in the release after 0.4.1