remote: Added support for stream remote read.

Design doc: https://docs.google.com/document/d/1JqrU3NjM9HoGLSTPYOvR217f5HBKBiJTqikEB9UiJL0
Changes:

Changed proto according to design (queryNum and responseType in request)
Commented to ensure the (Series/Query)Set interface contract is clear
Chagned remote read handler to support streamed and non streamed responses based on content negotiation (encoders,decoders)
Changed remote storage to support both types and prefer RAW_STREAMED.
Put sample limiting inside encoders

Potentially TODO in next PR: Make sure we do pure streaming in remote.Client.Read.  Right, now we buffer as we were initially been doing. This is to decrease risk in this PR.
Depends on: https://github.com/prometheus/prometheus/pull/4579
PTAL @brian-brazil @brancz @tomwilkie
Sorry for big PR, let me know if you want me to split it, but this is full swap to encoders/decoders that allows to negotiate stream/nonstream and it seems to work.

In general I feel like we didn't reach consensus on the design work yet.  I'm still not convinced we can't stream chunks, and that we've given enough thought to horizontal vs vertical "batching".  Perhaps best to do a call with interested parties?

Cool, we can schedule some call... @tomwilkie
In the meantime, regarding consensus:
a) stream chunks vs stream samples: The initial consensus was that we can always have both. We can extend this to chunks as well in next iterations. But let's discuss it more. Since I explored and implemented the non chunk version, maybe it's  good moment time to dig more the chunk bit https://docs.google.com/document/d/1JqrU3NjM9HoGLSTPYOvR217f5HBKBiJTqikEB9UiJL0/edit?disco=AAAACH4ldEU
b) horiontal / vertical I created some pros & cons specially for you @tomwilkie feel free to put your opinion on this (:   https://docs.google.com/document/d/1JqrU3NjM9HoGLSTPYOvR217f5HBKBiJTqikEB9UiJL0/edit?disco=AAAACH4ldEY


{ESP32] Added support for esp32c5 with latest idf master

Change Overview

Added the support for ESP32-C5 with latest idf master.

 Thank you for your submission! We really appreciate it. Like many open source projects, we ask that you sign our Contributor License Agreement before we can accept your contribution.esp seems not to be a GitHub user. You need a GitHub account to be able to sign the CLA. If you have already a GitHub account, please add the email address used for this commit to your account.You have signed the CLA already but the status is still pending? Let us recheck it.


Option for setting the log level at rascsi startup, improved device file support

With the -L/-l option the rascsi log level can now be set on startup. Note that this setting has no effect while the ProcessCmd() method is executed, because the log level is set later, after all command line options were evaluated.
This change also addresses https://github.com/akuker/RASCSI/issues/19.

I added a small change that helps with using device files as drive images. Device files have no extension, so they could not be directly used as image files. Now one can use a device file like this:
-ID 0 /dev/sda.hds
The change strips the extension from the device file but keeps the image file type ".hds", so that the setup above effectively uses "/dev/sda" as image file with type ".hds".


Openable refrigerator

Be able to open refrigerator so that food items may be displayed inside

~22kg CO2 emissions per kg beef -- https://ehp.niehs.nih.gov/doi/10.1289/ehp.11716

Above is from 2008 -- use this one instead https://interactive.carbonbrief.org/what-is-the-climate-impact-of-eating-meat-and-dairy/


Add data for hello

What this PR does:
给hello增加data字段，压测的时候控制包的大小可以

Codecov Report

Merging #195 (2afbe5d) into main (4bacab0) will decrease coverage by 0.20%.
The diff coverage is 100.00%.

@@            Coverage Diff             @@
##             main     #195      +/-   ##
==========================================
- Coverage   56.56%   56.36%   -0.21%
==========================================
  Files          51       51
  Lines        2215     2216       +1
==========================================
- Hits         1253     1249       -4
- Misses        841      845       +4
- Partials      121      122       +1

Impacted Files
Coverage Δ

pkg/grpc/api.go
77.33% <100.00%> (+0.04%)
:arrow_up:

...kg/filter/network/tcpcopy/persistence/work_pool.go
86.27% <0.00%> (-3.93%)
:arrow_down:

pkg/wasm/watcher.go
4.93% <0.00%> (-3.71%)
:arrow_down:

Continue to review full report at Codecov.

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Add (page for) first- and third-party packages/extensions

:information_source: If you're interested in this feature, please leave comment or add a :+1:

Kind of issue

[x] Feature
[ ] Bug
[ ] Design
[ ] Other, namely:

Description
Add something to the website (a page, modal, other suggestions?) that lists the first-party packages/extensions (npm package, packagist package, font) and the (known) third-party packages/extensions.
See also https://github.com/simple-icons/simple-icons/issues/1382

Maybe just some links at the top, like in the Github Readme?
Everything else ist just not really discoverable, I think.

Maybe just some links at the top, like in the Github Readme?
Everything else ist just not really discoverable, I think.

I'm mainly concerned that the (known) third-party packages/extensions won't fit 😅 I suppose the simple-icons font could be added to the header though.

Maybe just some links at the top, like in the Github Readme?

Do you mean a link to the GitHub README? If so, that is already there, it's the "About" link. Though that works, the idea behind this issue was to just integrate that into the website so it is "easier" to access.

I'm mainly concerned that the (known) third-party packages/extensions won't fit 😅 I suppose the simple-icons font could be added to the header though.

Maybe just some links at the top, like in the Github Readme?

Do you mean a link to the GitHub README? If so, that is already there, it's the "About" link. Though that works, the idea behind this issue was to just integrate that into the website so it is "easier" to access.

Maybe just some links at the top, like in the Github Readme?

Do you mean a link to the GitHub README?

No no 😅
I just meant that these links should be very clearly visible, and not be hidden inside a model or therelike.
Sorry for the confusion!

Maybe just some links at the top, like in the Github Readme?

Do you mean a link to the GitHub README?

No no 😅
I just meant that these links should be very clearly visible, and not be hidden inside a model or therelike.
Sorry for the confusion!

What's wrong with simply linking to the 3rd party table on Readme: https://github.com/simple-icons/simple-icons#third-party-extensions?
I think that's good enough.

I would expect either a modal or a separate page with a button or link (respectively) on the main page.
Also, I would also expect that we should be able to extract the third-party extensions from the README of the main project. In fact, if we're going to copy the content in this repository I would be against implementing this.

Essentially yes, as it's markdown we could even try to just convert it to HTML that way. I'd be okay trying it out as we control the README anyway.
Alternatively, we could change the main project to store the third (and first?) party extensions in a (new) data file and have a script generate that part of its README e.g. every release (similar to the slugs.md file). Then this project can use that data file to build a webpage.


Update CONTRIBUTING.md

Issue #, if available:
Description of changes:
By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.

AWS CodeBuild CI Report

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Commit ID: c4162ae97ef1f3241b917f05a362f2de4394f8a2
Result: SUCCEEDED
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CodeBuild project: codebuildgithubautobuildPro-QfXHf1ULVFb1
Commit ID: c4162ae97ef1f3241b917f05a362f2de4394f8a2
Result: SUCCEEDED
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Panic while formatting headers

My hyper use case is a reverse proxy between HTTP and binary protocol. Unfortunately sometimes while converting headers into a vector of strings, it panics:
thread 'worker 02' panicked at 'a Display implementation return an error unexpectedly: Error', /checkout/src/libcore/result.rs:859
note: Some details are omitted, run with `RUST_BACKTRACE=full` for a verbose backtrace.
stack backtrace:
   0: std::sys::imp::backtrace::tracing::imp::unwind_backtrace
             at /checkout/src/libstd/sys/unix/backtrace/tracing/gcc_s.rs:49
   1: std::sys_common::backtrace::_print
             at /checkout/src/libstd/sys_common/backtrace.rs:71
   2: std::panicking::default_hook::{{closure}}
             at /checkout/src/libstd/sys_common/backtrace.rs:60
             at /checkout/src/libstd/panicking.rs:355
   3: std::panicking::default_hook
             at /checkout/src/libstd/panicking.rs:371
   4: std::panicking::rust_panic_with_hook
             at /checkout/src/libstd/panicking.rs:549
   5: std::panicking::begin_panic
             at /checkout/src/libstd/panicking.rs:511
   6: std::panicking::begin_panic_fmt
             at /checkout/src/libstd/panicking.rs:495
   7: rust_begin_unwind
             at /checkout/src/libstd/panicking.rs:471
   8: core::panicking::panic_fmt
             at /checkout/src/libcore/panicking.rs:69
   9: core::result::unwrap_failed
             at /checkout/src/libcore/macros.rs:29

10: <core::iter::Map<I, F> as core::iter::iterator::Iterator>::next
             at /checkout/src/libcore/result.rs:761
             at /checkout/src/libcollections/string.rs:2027
             at /home/esafronov/.cargo/git/checkouts/hyper-817b53a166fc1c58/fc5b9cc/src/header/mod.rs:609
             at src/route/app.rs:278
             at /checkout/src/libcore/ops.rs:2711
             at /checkout/src/libcore/option.rs:392
             at /checkout/src/libcore/iter/mod.rs:1046
  11: cocaine_http_proxy::route::app::AppRequest::new
             at /checkout/src/libcollections/vec.rs:1899
             at /checkout/src/libcollections/vec.rs:1796
             at /checkout/src/libcollections/vec.rs:1791
             at /checkout/src/libcollections/vec.rs:1692
             at /checkout/src/libcore/iter/iterator.rs:1255
             at src/route/app.rs:276
  12: <cocaine_http_proxy::route::app::AppRoute<L> as cocaine_http_proxy::route::Route>::process
             at src/route/app.rs:173
             at src/route/app.rs:197
  13: cocaine_http_proxy::route::Router::process
             at src/route/mod.rs:90
  14: <cocaine_http_proxy::service::cocaine::ProxyService as tokio_service::Service>::call
             at src/service/cocaine.rs:41
  15: <tokio_proto::streaming::pipeline::server::Dispatch<S, T, P> as tokio_proto::streaming::pipeline::advanced::Dispatch>::dispatch
             at src/service/cocaine.rs:110
             at src/server/mod.rs:90
             at /home/esafronov/.cargo/git/checkouts/hyper-817b53a166fc1c58/fc5b9cc/src/server/mod.rs:335
             at /home/esafronov/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-proto-0.1.1/src/streaming/pipeline/server.rs:124
  16: <tokio_proto::streaming::pipeline::advanced::Pipeline<T> as futures::future::Future>::poll
             at /home/esafronov/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-proto-0.1.1/src/streaming/pipeline/advanced.rs:171
             at /home/esafronov/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-proto-0.1.1/src/streaming/pipeline/advanced.rs:121
             at /home/esafronov/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-proto-0.1.1/src/streaming/pipeline/advanced.rs:376
  17: <futures::future::map_err::MapErr<A, F> as futures::future::Future>::poll
             at /home/esafronov/.cargo/registry/src/github.com-1ecc6299db9ec823/futures-0.1.14/src/future/chain.rs:32
             at /home/esafronov/.cargo/registry/src/github.com-1ecc6299db9ec823/futures-0.1.14/src/future/and_then.rs:32
             at /home/esafronov/.cargo/registry/src/github.com-1ecc6299db9ec823/futures-0.1.14/src/future/map_err.rs:30
  18: tokio_core::reactor::Core::poll
             at /home/esafronov/.cargo/registry/src/github.com-1ecc6299db9ec823/futures-0.1.14/src/future/mod.rs:107
             at /home/esafronov/.cargo/registry/src/github.com-1ecc6299db9ec823/futures-0.1.14/src/task_impl/mod.rs:291
             at /home/esafronov/.cargo/registry/src/github.com-1ecc6299db9ec823/futures-0.1.14/src/task_impl/mod.rs:352
             at /home/esafronov/.cargo/registry/src/github.com-1ecc6299db9ec823/futures-0.1.14/src/task_impl/std/mod.rs:90
             at /home/esafronov/.cargo/registry/src/github.com-1ecc6299db9ec823/futures-0.1.14/src/task_impl/mod.rs:352
             at /home/esafronov/.cargo/registry/src/github.com-1ecc6299db9ec823/futures-0.1.14/src/task_impl/mod.rs:291
             at /home/esafronov/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-core-0.1.8/src/reactor/mod.rs:356
             at /home/esafronov/.cargo/registry/src/github.com-1ecc6299db9ec823/scoped-tls-0.1.0/src/lib.rs:135
             at /home/esafronov/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-core-0.1.8/src/reactor/mod.rs:355
             at /home/esafronov/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-core-0.1.8/src/reactor/mod.rs:316
             at /home/esafronov/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-core-0.1.8/src/reactor/mod.rs:304

What is done in src/route/app.rs:278?
It is the case that header values can be sent in encodings that are not UTF-8. In that case, the bytes would need to be decoded according to some other knowledge. Perhaps because of this, Headers should not itself implement Display, because it cannot always succeed...

let headers = req.headers()
    .iter()
    .map(|header| (header.name().to_string(), header.value_string()))
    .collect();

As an alternative I could pack them as Vec<u8>, but didn't find such method...

Oh, found (https://hyper.rs/hyper/master/hyper/header/struct.Raw.html), sorry.

I think we can we assume value_string() method as dangerous, because it may crash your application depending on user input.

Well, technically it is possible (https://github.com/3Hren/cocaine-http-proxy/blob/master/src/route/app.rs#L285), but looks weird.
I can implement value_vec() -> Vec<u8> method if you like.

Woops, closed the wrong issue with that commit.

Yea, providing value_string and fmt::Display for Headers is a mistake. Fixing it is a breaking change, so will be taken care of likely in the 0.12 release.

For now, the new version of hyper won't be including the typed headers directly, so I'm closing this here as a won't fix.


Support sorting by dimension name

Dimension name sort should be alphanumeric - taking into account field values by letter or by number (or date)

Dimension names are now sortable within dimension tables.


URL encoded path mapping

As described in RFC3986, a path component might be percent-encoded. If a regex path mapping has any reserved characters like * in its pattern and a request path is percent-encoded, the request might not be mapped to the path mapping.

According to our documentation, we explicitly stated all path() and query() methods return the string in the form as defined in RFC3986, which means they are expected to return a percent-encoded form.
So, instead of changing the contract of the existing API, we could add PathMappingContext.decodedPath() and make our PathMapping implementations use it instead of PathMappingContext.path().
Similarly, how about adding decodedPath() to the following types?

RequestContext
HttpHeaders
HttpRequest
AggregatedHttpMessage
RequestLog
PathMappingResult (maybe not necessary?)

WRT annotated services, injected parameters should be decoded. :-)

Working on a fix


Add advanced_parquet_index.rs example of index in into parquet files

Which issue does this PR close?

Closes https://github.com/apache/datafusion/issues/10580
Rationale for this change
See https://github.com/apache/datafusion/issues/10580
This shows how to use the APIs in  https://github.com/apache/datafusion/issues/9929
Building and using external indexes in DataFusion is an important feature. Adding an example of how to do so will help drive the design and APIs and help other people discover it more easiy
Specifically, this example illustrates how to configure the ParquetExec to read  only some row groups / pages within a file and shows how to avoid reading the metadata each time. This is required for low latency parquet query access
What changes are included in this PR?
Add a new example: parquet_index_advanced
Are these changes tested?

Are there any user-facing changes?
This is an example

This PR is just waiting for a committer to approve it so I can merge it in. @crepererum  or @tustvold  might you have a moment to do so?

@crepererum  do you by any chance have a few minutes to review and approve this PR?

I can't merge this PR until a committer approves it:

@Dandandan or @thinkharderdev  any chance you have some time to review an example (mostly comments)?

Thank you everyone for the great comments and feedback


Error in training chatbot

Hi When I am trying to train my chatbot using corpuses I found a list of errors reporting and the chatbot is not trained. The error message is shown below:
Traceback (most recent call last):
  File "/Users/christinalai/anaconda/lib/python3.6/site-packages/sqlalchemy/engine/base.py", line 1182, in _execute_context
    context)
  File "/Users/christinalai/anaconda/lib/python3.6/site-packages/sqlalchemy/engine/default.py", line 470, in do_execute
    cursor.execute(statement, parameters)
sqlite3.OperationalError: database is locked

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/Users/christinalai/PycharmProjects/chatterbot/chatbot.py", line 17, in <module>
    bot.train(new)
  File "/Users/christinalai/anaconda/lib/python3.6/site-packages/chatterbot/trainers.py", line 91, in train
    self.storage.update(statement)
  File "/Users/christinalai/anaconda/lib/python3.6/site-packages/chatterbot/storage/sql_storage.py", line 277, in update
  File "/Users/christinalai/anaconda/lib/python3.6/site-packages/sqlalchemy/engine/default.py", line 470, in do_execute
    cursor.execute(statement, parameters)
sqlalchemy.exc.OperationalError: (sqlite3.OperationalError) database is locked [SQL: 'INSERT INTO "StatementTable" (text, extra_data) VALUES (?, ?)'] [parameters: ...]

My program is as below:
from chatterbot import ChatBot
from chatterbot.trainers import ListTrainer
import os

bot = ChatBot('ChatBot')
bot.set_trainer(ListTrainer)
new = []

for files in os.listdir('/Users/christinalai/Desktop/corpus'):
    print(files)
    data = open('/Users/christinalai/Desktop/corpus/' + files, mode='r', errors="ignore").readlines()
    for line in data:
        new.append(line)

bot.train(new)

while True:
    message = input("You: ")
    if message.strip() not in message_list:
        reply = bot.get_response(message).decode("utf-8")
        print("ChatBot: ", reply)

The program does not have the necessary access rights to access the db, try to run it as an administrator

Thanks I solved it now. And can I ask one more questions? How can we retrain the chatterbot when we get a new corpus, without having to train the entire corpus we had all over again? I just want to train the new corpus on my chatterbot that is already trained with my old corpuses.

See this issue https://github.com/gunthercox/ChatterBot/issues/1364#issuecomment-410578498


Create 0101-symmetric-tree.rs

File(s) Modified: 0101-symmetric-tree.rs
Language(s) Used: Rust
Submission URL: _https://leetcode.com/problems/symmetric-tree/submissions/914210675/_

@raul-sauco Thank you for your contributions 👍


[DOCUMENT] (#772, #535) Documentation for preference and terminal plugin

add JsDoc for public/private methods
clean up code and its dependencies
write TODOs
fix typo
update README.md

This will be merged after #867 merge.
Please wait.
Thanks.


depency on tumblr gem for creating a new agent?

Hi all, I managed to make huginn run on my raspberry pi2, however I commented out things I wouldn't use such as the tumblr gem.
It does run, but when I want to create new agent I recieve an error:
LoadError at /agents/new
cannot load such file -- tumblr_client
it furthermore shows a live shell and application frames and other things I don't understand.
Do I really need the tumblr gem? from

Hi, did you run bundle and then restarted the processes after modifying Gemfile?

yes I did, however I also changed some other things, I will let you know when it works again.  But this comfirms that I need the tumblr things right?

Hey @RMHogervorst. You should be able to comment out tumblr_client and omniauth-tumblr in your Gemfile, run bundle, and Huginn should work (except that you can't use the tumblr Agent then).

Would you mind giving #1615 a try?

I'm on it, I made the mistake to sudo bundle install. I'm still dealing
with the permission problems after that.
Op 3 aug. 2016 16:50 schreef "Andrew Cantino"<EMAIL_ADDRESS>
Would you mind giving #1615 https://github.com/cantino/huginn/pull/1615
a try?
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
https://github.com/cantino/huginn/issues/1614#issuecomment-237211983,
or mute the thread
https://github.com/notifications/unsubscribe-auth/AJGJ-FDd-EkypNS13NJjbc0RsFyR9Mjbks5qcHudgaJpZM4Ja9NS
.

@RMHogervorst sudo chown -R huginn:huginn /home/huginn or removing /home/huginn/huginn/vendor/bundle might help.

yes, this fixed the problem. I now can enter the new agent screen. Thanks! :+1:

I've merged the PR.  Thanks for letting us know!


Build files, Integration test, and Bugfixes

Added Dart support in build files.
Integration tests working with Python Server on buffered transport.
Fixed bug in generator with iterating over maps.
Fixed typo in Python test server.
Discovered bug in FramedTransport, and fixed it.
Make TClientSocketTransport simple and add TAsyncClientSocketTransport.

@evanweible-wf
@tylertreat-wf
@stevenosborne-wf

Ran the integration test and found another issue, addressing...

It was my problem with the context stack in the JSON protocol, and fixed in https://github.com/markerickson-wf/thrift/commit/c89d1d1fbbbe6747285ef22e7b0faf6623c74d1c .  This is ready for review now.
@evanweible-wf
@tylertreat-wf
@stevenosborne-wf

+1

+1

+1


44 refactor repository folder structure

Changed the project structure as follows:

├── data
├── misc
├── notebooks
└── src				<- all source code
    ├── app_data		<- runtime data for amvoc app
    ├── dash			<- runtime data for dash app
    └── models			<- autoencoder models etc.

Removed the following:

.idea folder			<- local configuration files
annotations.json		<- old file, not used by code

Changes in the following files for the new folder structure:

main.py
main_live.py
run_offline.sh
run_online.sh

Tested successfully offline and online app

@tyiannak


Add status in ProjectVulnAnalysisCompleteSubject

https://github.com/DependencyTrack/hyades-apiserver/pull/252

@sahibamittal Now that native images work again, could you please add the new Proto class to the notification-publisher's ProtoReflectionConfiguration? 🙏
https://github.com/DependencyTrack/hyades/blob/c1e3d9bec41497063391544f8b0f3faaf10843e3/notification-publisher/src/main/java/org/hyades/notification/config/ProtoReflectionConfiguration.java#L35-L65

The expected notification JSON for the e2e test will also need to be updated to include the new field:
https://github.com/DependencyTrack/hyades/blob/c1e3d9bec41497063391544f8b0f3faaf10843e3/e2e/src/test/java/org/hyades/e2e/BomUploadProcessingE2ET.java#L245-L284


Aggregation problem with 'cursor' option

What steps will reproduce the problem?
$collection->aggregate($pipeline, ['cursor' => ['batchSize' => 100]]);
What's expected?
Aggregation result
What do you get instead?
yii\base\ErrorException: Undefined index: result in .../vendor/yiisoft/yii2-mongodb/Command.php:734
Additional info
When aggregating with a large number of documents, mongodb produces an error "exception: aggregation result exceeds maximum document size (16MB)". In the mongodb manual, it is recommended that you use a cursor or temporary collection.
As I understand, yii2-mongodb uses a query variant, in which all results are returned as a single document. If this document large enough, mongodb will fail with BSON limit. When setting the 'cursor' option, mongodb returns the documents as an array (cursor). The element 'result' is missing in the results
I propose to enable the 'cursor' option by default and treat the result as a cursor.

Q
A

Yii version
2.0.12

Yii MongoDB version
2.1.4

MongoDB server version
v3.4.8-rc1

PHP version
7.1.1

Operating system
Linux Mint

Resolved by commit 9395f072c2bf220e4b62fb677f93542e9a53fffe


Import: fix issue involving flipped bone Z dir (#1396)

Fixes #1396.
Blender issue is confirmed, but don't think we need to around for a fix. This just avoids the issue by setting the bone Z axis after the length. This fixes the file from https://devtalk.blender.org/t/gltf-export-issue-upd-import-issue/18970.
This is also a good candidate for 2.93, either before the release next week, or after since its an LTS.

I didn't get the time to talk to blender dev, so I think it's too late for 2.93. I am going to merge in master, and get it in 2.93.1 (LTS next release)


should show diff between override config vs default config

we should provide a way to display all the diff config of override vs default through out all the configs from config_paths.
This should help us a lot to detect any misconfiguration.
$ vagrant dev config diff

or something like that. The diff should be easier for fast checking (refer to git diff with colored)
We may need to create a dev vagrant plugin so let's use the "dev" namespace for teracy-dev.

closed for now, I don't see users need to see this. LOG_LEVEL=debug is enough for debugging on v0.6.0


Addin part to install VSCode on remote VM

**Replace issue name M14-LAB14a:
In the lab the students are required to use VSCode but
it's not installed, so I added a part to install it so
the students can follow the steps without getting stuck
Related Issue
Link related Github Issue 🢂 Fixes # . (Include issue number after #)
Checklist
Mark completed with "x" between brackets, "[x]", or checking the box once the PR is created:

[ ] Has related GitHub Issue 💥 Create Issue 📝
[X] Tested it
[X] Read the PR collaboration guide 👓 Collaboration Guide 📝

Changes proposed in this pull request:

Adding step to install VSCode on Ubuntu followed by this guide which is the must generic and worked on both Ubuntu 18 and 20

Thank you @kobi86!


More information on wraps

Looking at zlib in wrapdb there are 9 non-consecutive revisions: 1, 2, 3, 2, 4, 6, 7, 8 over two versions 1.2.8 and 1.2.11.
It is not clear to me which version is the one I should use. It seems like that the older zlib version has more iterations on the wrap and therefore a more mature wrap. However I guess I would like to use as late version as possible of zlib.
I think you should have only one row per zlib version. It could also show some additional information like how "complete" is the wrap (does it build on all OSs, does cross building work, are the tests also ported? does it produce bit-by-bit identical output as the projects "native" build system?). You could show creation date of when this wrap was made or uploaded.

The first is probably a bug that was fixed in #57 (wrong ordering), the usual practice is to install the latest wrap. Currently you should always use the latest one, as meson install does.
Other enhancements are a good idea, but I don't see this happening without extensive design. We need to modify upstream.wrap file to specify all this. The general idea of wraps is that there should be no meson.build file, it should be provided by upstream instead. wrapdb should only host upstream.wrap. However since Meson is not very popular we support simple patch system that allows us to import other projects.
Just to make it clear, I don't think extensive information about wraps is going to be supported anytime soon, but I don't see it as impossible in the distant future.


(non-issue)Where should I find a training instructions step by step?

Where should I find a training instructions step by step?
Thank you!

you can read a wonderful serial blog on Finding blocks of text in an image using Python, OpenCV and numpy
or you can try


Mj/gu cdk migration 2

This PR migrates the repo to a fully CDK-based CloudFormation stack.
Where possible, parameters local to the stack have been removed in the interest of portability, and are instead passed as arguments to the stack constructor, or are stored as SSM parameters for use across different CFN stacks.
I have attempted to use GuCDK constructs etc. as much as possible, working with their "out of the box" functionality. But some customisation has been necessary.
Because of the difficulty in "dual stacking" this repo, I have tested very thoroughly in CODE and I propose we deploy to PROD directly. Once deployed I will run further tests and also keep a close watch on reminders for the next week or so. I am confident that we won't have any downtime or problems.

Because of the difficulty in "dual stacking" this repo, I have tested very thoroughly in CODE and I propose we deploy to PROD directly. Once deployed I will run further tests and also keep a close watch on reminders for the next week or so. I am confident that we won't have any downtime or problems.

Although thorough testing gives us confidence in the new infrastructure I don't think we can be certain that this approach is going to avoid downtime.
In the past we have seen cases where clients are caching the old DNS entry for quite some time after the DNS change has been made (i.e. clients have continued making requests to the old infrastructure for longer than the TTL that is specified in the DNS record). If we use the dual stack migration path described here that isn't a big problem because the old infrastructure sticks around and deals with these requests until clients catch up. However, in this case the old DNS entry is pointing at an API Gateway instance that we're going to delete (i.e. the one that is defined in the CFN file), so any clients that don't pick up the DNS change quickly are going to start hitting errors.
I don't know exactly what the clients are in this case or how vital this service is, but it is worth considering this risk and whether it's acceptable in the context of this service before proceeding.


Add general purpose WheeledVehicle plugin + minor fixes

Adds general purpose WheeledVehicle plugin
Use OpenBot V1 versioned assets
Removes OpenBot plugin
Updates getting started example to work with SphereAgent and WheeledVehicleAgent
Minor fixes

Let's just call it OpenBot instead of OpenBot_V1.
Remove Master_Materials folder and just go with a flat structure inside Materials.
Make sure to "fix up redirectors" in the editor after renaming anything (https://docs.unrealengine.com/5.2/en-US/asset-redirectors-in-unreal-engine/).


feature bulking: added edition modes

Substitute this line for a meaningful title for your changes
Overview
This PR solves the casuistic of an user being able to perform unwanted changes when performing certain bulking actions.

Designs
Link to the related design prototypes (if applicable)
Testing instructions
Please explain how to test the PR: ID of a dataset, steps to reach the feature, etc.
Feature relevant tickets
Link to the related task manager tickets

Checklist before submitting

[ ] Meaningful commits and code rebased on develop.
[ ] If this PR adds feature that should be tested for regressions when
deploying to staging/production, please add brief testing instructions
to the deploy checklist (docs/deployment-checklist.md)
[ ] Update CHANGELOG file

@hotzevzl I think the solution (and the pic) are quite self-explanatory, but let me know if something comes up.

also in terms of UX here - maybe when bulk editing with > 1 feature selected, rather than showing some default values (50% and 1 respectively for target and fpf), should we leave the inputs blank? Really minor, and I don't think this will ever be a real cause of confusion for users (such as "whoops, I set all the targets to 50%, I didn't really want to do that, I just forgot to edit that value"), and it's also something that - if ever happens - is easy to recover from, by bulk editing again the relevant features, so I'm ok with us leaving things as they are (and have always been, IIRC) in terms of these defaults when the bulk edit modal is brought up.

and another small thing - at last in FF 129, if I bulk select all features by toggling the check box above the features, then edit the features and save the edits, once the processing finishes, I am back to the list of features, with none of the features selected, but the "select all" check box selected - again, super minor and I'd try to avoid cramming more things into this PR, but in case it's something really easy to change and regression-proof, please consider if it's worth changing:


devenv shell is not propertly cached

Describe the bug
devenv shell pauses and downloads and builds a derivation.
When devenv.nix is not changed, I expected a very fast startup that does not access the network.
Instant shell activation was advertised but I don't remember when I last saw that.
To reproduce
This seems to happen even after removing everything from my devenv.nix. It introduces at least a second of delay, but sometimes the network download is slower and it takes many seconds.
Version
Paste the output of $ devenv version here.
devenv: 0.6.3
Mac M1. However, a colleague on Mac Intel reported the same issue.

Does it happen like every hour?

I just tested this out on 2 other repos with different configurations- same result. I also just did a devenv init and devenv shell on a different repo. So devenv.nix is only what was generated by devenv init.
I don't see instant shell activation anywhere. I haven't seen instant shell activation for months, if ever (I can't remember if it ever worked at this point).

Perhaps we should clarify better that you need https://devenv.sh/automatic-shell-activation/ to have instant activation.
Although redownloading shouldn't happen into each shell activation, I'll look into it.

What does automatic shell activation have to do with Instant shell activation? The instant shell activation link explicitly mentions having to previously wait a few seconds for activation- which is my current experience. When I use devbox it takes < 2 seconds. When I use devenv it takes 5 seconds (provided my internet connection is good).

It's the same thing, although it's unfortunate it's titled differently in the blog post and in the documentation.
If you set up .envrc file, you'll get the promised speed.

Thanks, that does seem to work!
Would be great to not require direnv and have devenv shell be fast automatically and for the docs to be clearer here.

I fully agree!


bug fix: react complains about PropTypes.shape

Replaced invalid use of PropTypes.shape with PropTypes.object.
React prints this to console on every render call in development mode:

type specification of prop style is invalid; the type checker function must return null or an Error but returned a function. You may have forgotten to pass an argument to the type checker creator (arrayOf, instanceOf, objectOf, oneOf, oneOfType, and shape all require an argument).

Looks like this is failing the build because the React lint rule is forbidding object as a PropType. I would add an ignore line for this case:
/* eslint react/forbid-prop-types: "off" */


Add a DynamicallyAccessedMembers attribute to ViewBuilder.Create

(sort of) refs #281.
So... I've been using FuncUI to build an app that i've been publishing as a self contained, trimmed, exe (it's built with .NET 7, but you can run it without having to have an appropriate .NET version installed, and has fewer files to care about).
For various reasons (many things being broken basically), I've had it on partial trim mode so that it only trims libs that are explicitly marked as trimmable, whilst waiting for thins to get fixed or improve elsewhere. (e.g. the completion of some work in Avalonia to help it work better with trimming).
In Avalonia 11 Preview 6, parts of Avalonia are marked as trimmable, so they will be trimmed now when they weren't before. This should be quite nice as it will remove controls you aren't using and such, but alas it also causes this to happen with FuncUI:
Exception Info: System.MissingMethodException: Cannot dynamically create an instance of type 'Avalonia.Controls.TreeView'. Reason: No parameterless constructor defined.
   at System.RuntimeType.ActivatorCache..ctor(RuntimeType)
   at System.RuntimeType.CreateInstanceDefaultCtor(Boolean, Boolean)
   at System.Activator.CreateInstance(Type, Boolean, Boolean)
   at System.Activator.CreateInstance(Type , Boolean)
   at System.Activator.CreateInstance(Type )
   at Avalonia.FuncUI.VirtualDom.Patcher.create(ViewDelta viewElement)
   at Avalonia.FuncUI.VirtualDom.VirtualDom.updateBorderRoot(Border host, FSharpOption`1 last, FSharpOption`1 next)
   at <StartupCode$Avalonia-FuncUI>.$Component.Update@24.Invoke()
   at Avalonia.Threading.JobRunner.Job.Avalonia.Threading.JobRunner.IJob.Run() in /_/src/Avalonia.Base/Threading/JobRunner.cs:line 193
   at Avalonia.Threading.JobRunner.RunJobs(Nullable`1) in /_/src/Avalonia.Base/Threading/JobRunner.cs:line 38
   at Avalonia.Win32.Win32Platform.WndProc(IntPtr, UInt32, IntPtr, IntPtr) in /_/src/Windows/Avalonia.Win32/Win32Platform.cs:line 267
   at Avalonia.Win32.Interop.UnmanagedMethods.DispatchMessage(MSG&)
   at Avalonia.Win32.Win32Platform.RunLoop(CancellationToken) in /_/src/Windows/Avalonia.Win32/Win32Platform.cs:line 215
   at Avalonia.Threading.Dispatcher.MainLoop(CancellationToken) in /_/src/Avalonia.Base/Threading/Dispatcher.cs:line 61
   at Avalonia.Controls.ApplicationLifetimes.ClassicDesktopStyleApplicationLifetime.Start(String[]) in /_/src/Avalonia.Controls/ApplicationLifetimes/ClassicDesktopStyleApplicationLifetime.cs:line 122
   at Avalonia.ClassicDesktopStyleApplicationLifetimeExtensions.StartWithClassicDesktopLifetime(AppBuilder, String[], ShutdownMode ) in /_/src/Avalonia.Controls/ApplicationLifetimes/ClassicDesktopStyleApplicationLifetime.cs:line 214

because the trimmer can't find any references to said constructor and therefore bins it.
Activator.CreateInstance is annotated to explain this and in C# there is a roslyn analyzer that will try to tell you about that so that you can annotate your own code to be compatible, but no such help in F#.
Anyway, I'm sure there are other code paths that have the same issue (e.g. the ones that create instances of things and use a constructor with parameters), but just doing this one fixes the issues i'm getting by making it retain all the default constructors for used controls, so I'm opening this to at least open the question on how this situation should be handled.

I hadn't tried it before, but fwiw this change also allows a self-contained/trimmed version of the control catalog to run, whereas without that it just crashed on startup because of missing constructors


Coldstart... how to make faster?

Hi there :)
First of all thanks a lot! Finally managed to install successfully, run and generate speech
my current code is:
from MeloTTS.melo.api import TTS

from pydub import AudioSegment
from pydub.playback import play

# Speed is adjustable
speed = 1.0

# CPU is sufficient for real-time inference.
# You can set it manually to 'cpu' or 'cuda' or 'cuda:0' or 'mps'
device = 'auto' # Will automatically use GPU if available

# English
text = "Did you ever hear a folk tale about a giant turtle?"
model = TTS(language='EN', device=device)
speaker_ids = model.hps.data.spk2id
output_path = 'en-default.wav'

while True:
    print("Enter message:")
    text = input()
    model.tts_to_file(text, speaker_ids['EN-Default'], output_path, speed=speed)
    print("________")
    sound = AudioSegment.from_wav(output_path)
    play(sound)

the thing is.. sometimes the generation is INSTANT but MOST times, theres a short lag...
I've noticed that this lag happens for both one-word-generations
and long-text-generations they seem to take around the same time!
Hence! A cold start!

meaning that something is loaded over and over in the code...
and perhaps theres away to keep it loaded, for example setting a local/global variable to save most of the things loaded
and try to redo as less as possible when getting a new text to generate

My assumption is that somewhere in this code, you could skip a few fetches that were already been done before:
def tts_to_file(self, text, speaker_id, output_path=None, sdp_ratio=0.2, noise_scale=0.6, noise_scale_w=0.8, speed=1.0, pbar=None, format=None, position=None, quiet=False,):
       ...
        for t in tx:
            if language in ['EN', 'ZH_MIX_EN']:
                t = re.sub(r'([a-z])([A-Z])', r'\1 \2', t)
            device = self.device
            bert, ja_bert, phones, tones, lang_ids = utils.get_text_for_tts_infer(t, language, self.hps, device, self.symbol_to_id)
            with torch.no_grad():
                x_tst = phones.to(device).unsqueeze(0)
                tones = tones.to(device).unsqueeze(0)
                lang_ids = lang_ids.to(device).unsqueeze(0)
                bert = bert.to(device).unsqueeze(0)
                ja_bert = ja_bert.to(device).unsqueeze(0)
                x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
                del phones
                speakers = torch.LongTensor([speaker_id]).to(device)
                audio = self.model.infer(
                        x_tst,
                        x_tst_lengths,
                        speakers,
                        tones,
                        lang_ids,
                        bert,
                        ja_bert,
                        sdp_ratio=sdp_ratio,
                        noise_scale=noise_scale,
                        noise_scale_w=noise_scale_w,
                        length_scale=1. / speed,
                    )[0][0, 0].data.cpu().float().numpy()
                del x_tst, tones, lang_ids, bert, ja_bert, x_tst_lengths, speakers
                #
            audio_list.append(audio)
        torch.cuda.empty_cache()
        audio = self.audio_numpy_concat(audio_list, sr=self.hps.data.sampling_rate, speed=speed)

{my thoughts}:
like maybe you can skip [torch.cuda.empty_cache() or torch.LongTensor([phones.size(0)]).to(device)  or something ] somehow.
maybe one of those vars are the same, or theres a way to make part of them beforehand, or reuse previously, etc

I wouldnt complain, but i have a feeling you can get this to generate much faster
please let me know if you get what i'm suggesting, and or if theres a better/recommended way to generate faster
and avoid the noticeable coldstart of generations
Thanks a lot and all the best!

I am also interested in this. It doesn't seem like any activity lately - is the unok commit irrelevant?


feat(v-model): add normalize modifier

What kind of change does this PR introduce? (check at least one)

[ ] Bugfix
[x] Feature
[ ] Code style update
[ ] Refactor
[ ] Build-related changes
[ ] Other, please describe:

Does this PR introduce a breaking change? (check one)

[ ] Yes
[x] No

The PR fulfills these requirements:

[x] It's submitted to the dev branch for v2.x (or to a previous version branch), not the master branch
[ ] All tests are passing (I'm not 100% sure; please see note below)
[x] New/updated tests are included

If adding a new feature, the PR's description includes:

[x] A convincing reason for adding this feature

Other information:
Using String.normalize() is desirable when dealing with user-facing text inputs, considering that unicode strings that look the same on screen can sometimes be encoded differently and thus bring a bunch of problems later on (i.e. when trying to find duplicate strings).
Moreover, having to add String.normalize() every time is pretty much just as annoying as having to add String.trim() every time, so by that rationale I believe that adding this modifier to v-model makes just as much sense as having trim in there (which I very much appreciate!).
Notes:

Sorry for not filing an issue first! I decided to just go for it since it's a pretty small modification. Hopefully this explanation is enough, but I can always add an issue later if needed.
I'm pretty sure that I added all relevant tests and they all seem to have passed. I did experience an issue trying to run the ChromeHeadless instance on my PC, however, although I'm not sure if that test suite applies to this PR anyway (if it does please let me know and I'll try to get it fixed asap).
I'm using the default NFC form of unicode normalization. It might be worth considering adding the other forms as well (NFD,NFKC,NFKD).

More informaton on String.normalize() and its usefulness can be found in the following links:

Why you need to normalize unicode strings
String.normalize() — MDN docs

String.prototype.normalize is not supported by IE. Vue.js is compatible down to IE9.

Right. Totally forgot to check for IE compatibility. Bummer. :-1:
There's a polyfill out there, but I guess it's not worth the added weight, huh?
Thanks for your time regardless!

Thank you for your contribution anyway. 👍


Implement exporting models to glTF

https://github.com/vpenades/SharpGLTF looks like a good lib to use.
Some thoughts are here: https://docs.google.com/document/d/12QWpRG5hnRU4pw0XLuWLj_SGH7BlaqAWIwqj0mhnYd4/view

Work is being done here: https://github.com/SteamDatabase/ValveResourceFormat/tree/gltf


Missing form widget for field type decimal

RuntimeError: Missing form widget for field my_field of type decimal(18,4)
I tried with this:
@staticmethod
def widget_decimal(attr, field, value, _class='float', _id=None):
    return FormStyle.widget_string(attr, field, value, _class, _id)

But expects the name of the property to be "widget_decimal(18,4)"
Also with this:
  form_widgets = {
       'my_field': FormStyle.widget_float
   }

TypeError: widget_float() missing 1 required positional argument: 'value'

@josejachuf yup this is a bug. I should patch the FormStyle class. Will keep you updated on this.

For now I solved it with a custom widget. Thanks


Handle Long Characters Better

Characters like "Ŕ" do not represented as single char in a std::string, so we need to come up with a way to handle these characters better.

It looks like the appropriate thing to do is to use a std::wstring in place of std::string and wchar_t in place of char.


Add recipe for polish bigos

I love this recipe! I'm not from Poland, but for me are tomato paste and garlic essential for the good taste. @kKondratek what do you think?

I love this recipe! I'm not from Poland, but for me are tomato paste and garlic essential for the good taste. @kKondratek what do you think?

I heard about this variation :) never tried before but I'm sure it's great too

I would simplify the name to just "bigos".

I would simplify the name to just "bigos".
@m-chrzan I agree, just renamed it


prepare sequence diagrams for each user action

Sequence diagrams show interactions between the components of the systems in detail. After the main system components are decided, sequence diagrams should be ready.

https://github.com/muatik/Swe573HW2016F/wiki/sequence-diagrams


Improvement make free static for more flexible recycle

Proposed changes
At present, the free method of allocator is non-static, which is not very convenient for scenarios that need to be transferred for recycling management. Converting to static can be more flexible to use.

Further comments
If this is a relatively large or complex change, kick off the discussion at<EMAIL_ADDRESS>by explaining why you chose the solution you did and what alternatives you considered, etc...

run buildall

run buildall

TeamCity pipeline, clickbench performance test result:
the sum of best hot time: 38.28 seconds
stream load tsv:          452 seconds loaded<PHONE_NUMBER>9 Bytes, about 157 MB/s
stream load json:         21 seconds loaded<PHONE_NUMBER> Bytes, about 107 MB/s
stream load orc:          57 seconds loaded<PHONE_NUMBER> Bytes, about 18 MB/s
stream load parquet:          28 seconds loaded 861443392 Bytes, about 29 MB/s
insert into select:          69.1 seconds inserted 10000000 Rows, about 144K ops/s
https://doris-community-test-1308700295.cos.ap-hongkong.myqcloud.com/tmp/20230704095951_clickbench_pr_171960.html

TeamCity pipeline, clickbench performance test result:
the sum of best hot time: 40.35 seconds
stream load tsv:          454 seconds loaded<PHONE_NUMBER>9 Bytes, about 157 MB/s
stream load json:         19 seconds loaded<PHONE_NUMBER> Bytes, about 118 MB/s
stream load orc:          58 seconds loaded<PHONE_NUMBER> Bytes, about 18 MB/s
stream load parquet:          29 seconds loaded 861443392 Bytes, about 28 MB/s
insert into select:          67.7 seconds inserted 10000000 Rows, about 147K ops/s
https://doris-community-test-1308700295.cos.ap-hongkong.myqcloud.com/tmp/20230704101243_clickbench_pr_171966.html


release date isn't formatted to timtezone in cart

Description:
the release date of a product isn't formatted correctly in cart to the chosen date format if its as "Wesentliches Merkmal" displayes
Steps to reproduce:

create product
add release date in the future
create "Wesentliches Merkmal"
add release date to Wesentliches Merkmal
add Wesentliches Merkmal to product
watch product in cart

Expected result:
Date in the format dd.mm.yy
Actual result:
Date is in the format YYYY-mm-dd

[public] Automated response: This issue is linked to the internal issue https://shopware.atlassian.net/browse/NEXT-33161.
[created from NEXT-33161, comment 486115]

[public] Hello,
Within our company roadmap and work capacity, we try to address each bug or improvement request but admit that not each one will be resolved. To continue our culture of honesty and openness, we are closing this issue to focus on our roadmap on behalf of all Shopware users.
If you feel like this issue is still impacting you, please create a new issue and let us know. Thank you again for your valuable feedback.
[created from NEXT-33161, comment 492304]


Apply To Be a Member in footer not working

footer links should be absolute instead of relative
way to reproduce:
when clicking apply to member in footer it redirects to
https://kucc.ku.edu.np/https://forms.gle/Y5vHobrCDVx2fPMaA
instead of
https://forms.gle/Y5vHobrCDVx2fPMaA

This issue has been fixed and merged into main branch.


Configure scrape-uri via environment variable

Support configuration of --haproxy.scrape-uri via an environment variable. The URI can contain username:password combination and might be visible on Linux using a simple ps(1) command. This commit allows to use haproxy_exporter with docker as follows:
docker -e HAPROXY_EXPORTER_SCRAPE_URI=https://username:password@haproxy.my.corp:9001/statistics;csv

As a matter of policy the official project does not allow configuration via any sources other than command line flags and configuration files. This serves as a temporary solution until a configuration file has been added to the exporter.

As explained in #102 we do not allow configuration via environment variables. In addition environment variables are not a secure way to handle secrets.


module source reported: Could not read from resource

HI, I really don't know if this is a bug or is a bad configuration that I did, but I searched a lot for days and I can't solve it (or understand what I have to do)
I can't get face recognition working and I have some warnings  when trying to authenticate or when running sudo howdy snapshot.
[<EMAIL_ADDRESS>global /build/opencv/src/opencv-4.5.5/modules/videoio/src/cap_gstreamer.cpp (2401) handleMessage OpenCV | GStreamer warning: Embedded video playback halted; module source reported: Could not read from resource. [<EMAIL_ADDRESS>global /build/opencv/src/opencv-4.5.5/modules/videoio/src/cap_gstreamer.cpp (1356) open OpenCV | GStreamer warning: unable to start pipeline [<EMAIL_ADDRESS>global /build/opencv/src/opencv-4.5.5/modules/videoio/src/cap_gstreamer.cpp (862) isPipelinePlaying OpenCV | GStreamer warning: GStreamer: pipeline have not been created Face detection timeout reached
What I did to install it was:
yay -S --editmenu howdy    //to install it
mpv /dev/video2       //to test camera IR, Here the camera works it's blink in dark and white and the red sensor turn on.
sudo vim /lib/security/howdy/config.ini           //Here I edited device_path = /dev/video2
sudo howdy add        //Here I get the warnings and  -> Please look straight into the camera /Scan complete/Added a new model to ...
sudo vim /etc/pam.d/sudo      //Here I added pam.py
Linux distribution: Arch LInux
Howdy version 2.6.1

Your camera can not be read, is another program accessing it at the same time?

Your camera can not be read, is another program accessing it at the same time?

No, I just installed Arch a few days ago, So I don't have many apps, Maybe mpv? Is a video player that I downloaded to test if my IR was on /dev/video2. I will unistall it and reboot and see waht happens

When removed MPV I am getting this:
[<EMAIL_ADDRESS>global /build/opencv/src/opencv-4.5.5/modules/videoio/src/cap_gstreamer.cpp (2401) handleMessage OpenCV | GStreamer warning: Embedded video playback halted; module source reported: Could not read from resource. [<EMAIL_ADDRESS>global /build/opencv/src/opencv-4.5.5/modules/videoio/src/cap_gstreamer.cpp (1356) open OpenCV | GStreamer warning: unable to start pipeline [<EMAIL_ADDRESS>global /build/opencv/src/opencv-4.5.5/modules/videoio/src/cap_gstreamer.cpp (862) isPipelinePlaying OpenCV | GStreamer warning: GStreamer: pipeline have not been created All frames were too dark, please check dark_threshold in config Average darkness: [86.16612], Threshold: 50.0 Face detection image too dark

What does a sudo howdy snapshot look like?

What does a sudo howdy snapshot look like? (please upload the file)

This was with lights turned off
[pelli@PelliArch ~]$ sudo howdy snapshot [<EMAIL_ADDRESS>global /build/opencv/src/opencv-4.5.5/modules/videoio/src/cap_gstreamer.cpp (2401) handleMessage OpenCV | GStreamer warning: Embedded video playback halted; module source reported: Could not read from resource. [<EMAIL_ADDRESS>global /build/opencv/src/opencv-4.5.5/modules/videoio/src/cap_gstreamer.cpp (1356) open OpenCV | GStreamer warning: unable to start pipeline [<EMAIL_ADDRESS>global /build/opencv/src/opencv-4.5.5/modules/videoio/src/cap_gstreamer.cpp (862) isPipelinePlaying OpenCV | GStreamer warning: GStreamer: pipeline have not been created Generated snapshot saved as /usr/lib/security/howdy/snapshots/20220421T101229.jpg
Snapshot

Hm, looks great, please add export OPENCV_LOG_LEVEL=ERROR at the bottom of nano ~/.bashrc and set dark_threshold to 100 in sudo howdy config

Hm, looks great, please add export OPENCV_LOG_LEVEL=ERROR at the bottom of nano ~/.bashrc and set dark_threshold to 100 in sudo howdy config

Done. Now getting timeout reached
[pelli@PelliArch ~]$ sudo howdy snapshot Face detection timeout reached [sudo] password for pelli:  [<EMAIL_ADDRESS>global /build/opencv/src/opencv-4.5.5/modules/videoio/src/cap_gstreamer.cpp (2401) handleMessage OpenCV | GStreamer warning: Embedded video playback halted; module source reported: Could not read from resource. [<EMAIL_ADDRESS>global /build/opencv/src/opencv-4.5.5/modules/videoio/src/cap_gstreamer.cpp (1356) open OpenCV | GStreamer warning: unable to start pipeline [<EMAIL_ADDRESS>global /build/opencv/src/opencv-4.5.5/modules/videoio/src/cap_gstreamer.cpp (862) isPipelinePlaying OpenCV | GStreamer warning: GStreamer: pipeline have not been created Generated snapshot saved as /usr/lib/security/howdy/snapshots/20220421T110003.jpg

Strange, if you run sudo howdy test is there a circle around your face and does the top right say "scan frame" a lot?

Yes there is a circle around my face but It says SCAN FRAME one time, the IR is blinking, same for camera screen

sudo howdy test
Identified face as pelli
[<EMAIL_ADDRESS>global /build/opencv/src/opencv-4.5.5/modules/videoio/src/cap_gstreamer.cpp (2401) handleMessage OpenCV | GStreamer warning: Embedded video playback halted; module source reported: Could not read from resource.
[<EMAIL_ADDRESS>global /build/opencv/src/opencv-4.5.5/modules/videoio/src/cap_gstreamer.cpp (1356) open OpenCV | GStreamer warning: unable to start pipeline
[<EMAIL_ADDRESS>global /build/opencv/src/opencv-4.5.5/modules/videoio/src/cap_gstreamer.cpp (862) isPipelinePlaying OpenCV | GStreamer warning: GStreamer: pipeline have not been created
Opening a window with a test feed
Press ctrl+C in this terminal to quit
Click on the image to enable or disable slow mode
QStandardPaths: XDG_RUNTIME_DIR not set, defaulting to '/tmp/runtime-root'
^C
Closing window

Well sounds like everything is working perfectly then now, your log even says "Identified face as pelli"

Well sounds like everything is working perfectly then now, your log even says "Identified face as pelli"

Yes!! but before executing howdy test it wasn't working, Now tried sudo pacman ... and it worked thanks!!

Happy to help!

Happy to help!

Sorry, but it's possible to add it to gnome? How I cand do it?  Maybe adding to sudo vim /etc/pam.d/sudo but I don't have any ideda of what to put in the line

So this wiki page explains part of adding Howdy authentication to specific auth types. It seems that you have added it to sudo specifically. If you add
auth       sufficient   pam_python.so  /lib/security/howdy/pam.py

to /etc/pam.d/common-auth you should be good to go anywhere. Be sure to remove any lines you've added to pam files before this so Howdy does not get triggered twice

It didn't work in /etc/pam.d/common-auth but I did the same thing in /etc/pam.d/system-auth
and now is working everywere! Again, thanks!!


tf.saved_model.save() excessive function tracing

Click to expand!
Issue Type
Performance
Source
binary
Tensorflow Version
tf 2.8
Custom Code
No
OS Platform and Distribution
Ubuntu 20.04
Mobile device
No response
Python version
3,8.10
Bazel version
No response
GCC/Compiler version
No response
CUDA/cuDNN version
No response
GPU model and memory
No response
Current Behaviour?
This is filed against TF 2.8 but the behavior described appeared when upgrading from TF 2.3 to TF 2.4.

Create a tf.Module subclass containing a tf.keras.models.Model subclass.
Add tf.functions to the tf.Module that call the model.
Saved the tf.Module with tf.saved_model.save() in order to reload and use it in a language other than python.

With TF2.3, the functions are traced only once.
With TF2.4, the functions are traced twice.

With large models, this can almost double the model save time (e.g. up to 40s for a distilbert model with 6 layers).

Standalone code to reproduce the issue
Execute this same code with TF2.3.2 and TF2.4.4 (output with TF2.8 is the same as for 2.4.4)

import tensorflow as tf
print('tf version', tf.__version__)

import os

F1_SIG=[tf.TensorSpec([None, None], tf.float32, name='inputs_1')]
F2_SIG=[tf.TensorSpec([None], tf.float32, name='inputs_2')]

class CustomModel(tf.keras.models.Model):
    def __init__(self, **kwargs):
        super().__init__(**kwargs) # handles standard args (e.g., name)

    def call(self, x):
        _x = tf.random.normal((2,3,4)) + x
        return _x

class CustomModule(tf.Module):
    def __init__(self):
        self.model = CustomModel()

    @tf.function(input_signature=F1_SIG)
    def f1(self, x):
        print('tracing f1')
        return self.model(x[0])

    @tf.function(input_signature=F2_SIG)
    def f2(self, x):
        print('tracing f2')
        return self.model(x)

x = tf.random.uniform((2,4))*10

saved_model_dir='saved_model'
os.makedirs(saved_model_dir, exist_ok=True)

module = CustomModule()
signatures = {"serving_default": module.f1.get_concrete_function(*F1_SIG),
              "serve_sig_v2"   : module.f2.get_concrete_function(*F2_SIG),
              }
tf.saved_model.save(module, saved_model_dir, signatures=signatures)

Output with tensorflow 2.3.2:
tf version 2.3.2
tracing f1
tracing f2
INFO:tensorflow:Assets written to: saved_model/assets

Output with tensorflow 2.4.4:
tf version 2.4.4
tracing f1
tracing f2
tracing f1
tracing f2
INFO:tensorflow:Assets written to: saved_model/assets

Why are the functions traced a second time with TF>=2.4 ?
TF2.3 colab:
https://colab.research.google.com/drive/19iniJKEcFmhkNDVe6sbseOyASWb3oCYC?usp=sharing
TF2.4 colab:
https://colab.research.google.com/drive/1NVCSiSednJnnZqk_s1VmOsIMSUluaLP7?usp=sharing
Relevant log output
No response

@thierryherrmann tf.function has a mechanism, that lets you control this function tracing, by defining a method tf_trace_type. Please have a look at this link for more details and let us know if it helps?
Thank you!

Thanks but unfortunately that link only covers retracing when calling the function several times with different arguments.
In this issue, tracing (with TF2.4) is done several times for a single invocation (or by calling pretty_printed_concrete_signatures() or get_concrete_function() like in the colabs).
Things become weirder when we add a Embedding layer in the custom model.
This is shown by this simple code, pastable in any colab:
import tensorflow as tf
print('tf version', tf.__version__)
import os

SIG=[tf.TensorSpec([None], tf.int32)]

class CustomModel(tf.keras.models.Model):
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.embedding = tf.keras.layers.Embedding(input_dim=3, output_dim=4, mask_zero=True)

    def call(self, x):
        _x = tf.reduce_sum(self.embedding(x))   # <<<< new!
        _x = tf.random.normal((3,)) + _x
        return _x

class CustomModule(tf.Module):
    def __init__(self):
        self.model = CustomModel()

    @tf.function(input_signature=SIG)
    def f1(self, x):
        print('tracing f1')
        return self.model(x)

module = CustomModule()
# added CustomModel.embedding ==> module.f1 now traced 2 times instead of once (same in TF2.3 and TF2.4)
print('======== pretty_printed_concrete_signatures() ========\n', module.f1.pretty_printed_concrete_signatures())

# with tf.saved_model.save(), TF2.4 does an extra tracing
print('======= tf.saved_model.save() =======')
module = CustomModule()
os.makedirs('saved_model', exist_ok=True)
signatures = {"serving_default": module.f1.get_concrete_function(*SIG)}
tf.saved_model.save(module, 'saved_model', signatures=signatures) # module.f1() traced 3 times with TF2.4, 2 times with TF2.3

Output with TF2.3.2:
tf version 2.3.2
tracing f1
tracing f1
======== pretty_printed_concrete_signatures() ========
 f1(x)
  Args:
    x: int32 Tensor, shape=(None,)
  Returns:
    float32 Tensor, shape=(3,)

f1(x)
  Args:
    x: int32 Tensor, shape=(None,)
  Returns:
    float32 Tensor, shape=(3,)
======= tf.saved_model.save() =======
tracing f1
tracing f1
INFO:tensorflow:Assets written to: saved_model/assets

Output with TF2.4.4 (same with TF2.8.x):
tf version 2.4.4
tracing f1
tracing f1
======== pretty_printed_concrete_signatures() ========
 f1(x)
  Args:
    x: int32 Tensor, shape=(None,)
  Returns:
    float32 Tensor, shape=(3,)

f1(x)
  Args:
    x: int32 Tensor, shape=(None,)
  Returns:
    float32 Tensor, shape=(3,)
======= tf.saved_model.save() =======
tracing f1
tracing f1
tracing f1
INFO:tensorflow:Assets written to: saved_model/assets

@chunduriv  I was able to replicate the issue on colab, please find the gist here for reference. Thank you!

@k-w-w , Any inputs on the above comment. Thanks!

Hi,
Thank you for opening this issue. Since this issue has been open for a long time, the code/debug information for this issue may not be relevant with the current state of the code base.
The Tensorflow team is constantly improving the framework by fixing bugs and adding new features. We suggest you try the latest TensorFlow version with the latest compatible hardware configuration which could potentially resolve the issue. If you are still facing the issue, please create a new GitHub issue with your latest findings, with all the debugging information which could help us investigate.
Please follow the release notes to stay up to date with the latest developments which are happening in the Tensorflow space.


When sending XYM on the offline wallet from a non-multisig wallet, a person has to type absolute units?

It seems like sending from an offline wallet doesn't work. When trying to enter a XYM amount instead of entering a whole number with decimal points a person has to enter absolute units.  Even then, it seems like the tx doesn't go through for some reason.

Hi Jeff.

When sending XYM on the offline wallet from a non-multisig wallet

Are you mean to create  Offline transactions and announce it via CLI?

@AnthonyLaw did we get any more info about this issue? or reproduction? Thanks.

Nope, waiting for more input.

when entering "10 XYM" in the offline tx mode and announcing it, the tx goes through as 0.000010 XYM.  Meaning if I really want to send 10 XEM, then I have to type in 10,000,000 which is quite scary if somebody had that much and made that mistake.
I would duplicate this but I can't get any offline tx to work from a private key wallet.
Not just this build, but every build since launch, I've not ever been able to make an offline tx work correctly from a single account.

Thanks for the input @jabo38
I will take a look :)

awesome!!

@AnthonyLaw it still doesn't work for me
Steps to reproduce:

Turn off your internet connection, clear from local storage networkCurrencyCache, networkCache, mosaicCache in your browser.
Generate transaction QR

The first problem is that there is Mosaic (1/1) Expired. Expired should not be presented.

Turn on WiFi, login into wallet and try announce transaction. Example QR:

Result: Transaction is not announced
Expected result: Transaction is announced
If I do entire process online generated QR allow me announce transaction.

Alright,  thanks for the test.
I will take a look on it

Fixed. @AnthonyLaw @bassemmagdy please merge into integration.

Fixed in integration branch.


Update for nvim 0.10

On its 0.10 version nvim does not need comment plugin and has changed some stuff. Thanks in advance!

resolved by upstream merge.


custom_data --> Adding a try

Adding a try block, I had originally tested with the try. I am unsure how the change got merged without it.

@LaurentLesle

thanks @abdulrabbani00, I think @LaurentLesle has been faster at fixing it ;)
I'll put you as reviewer on patch.5.4.4 so you can validate :)

thanks @abdulrabbani00, I think @LaurentLesle has been faster at fixing it ;)
I'll put you as reviewer on patch.5.4.4 so you can validate :)

Thank you Arnaud, that would make me very happy, and flattered.


[BUG] Output is always undefined ⚠

Describe the bug

Whatever you try to run - output is always 'undefined'
Also, the previous output is not cleared when "run code" is clicked.

How are you going to fix it?

Ensure your code actually returns a value, and within the "run code" function, update the displayed output with the returned value.
Clear the existing output area before displaying the new result.

Hi @meet244 I am excited to work on this issue. Could you please assign it to me?

@LoayTarek5 I am already working on this.

@meet244 Oh, okay, I did not notice. I thought that you wanted someone to work on this.
I am sorry

@LoayTarek5 No Problem!


[BUG] CoreDNS NodeHosts lost after adding a new node

What did you do

How was the cluster created?

k3d cluster create

INFO[0016] Injecting records for hostAliases (incl. host.k3d.internal) and for 2 network members into CoreDNS configmap...

What did you do afterwards?

Check configmap for coredns

The host host.k3d.internal is present on the cluster creation
  NodeHosts: |
    <IP_ADDRESS> host.k3d.internal
    <IP_ADDRESS> k3d-local-serverlb
    <IP_ADDRESS> k3d-local-server-0
kind: ConfigMap

Add a new node with k3d node create newserver --cluster local --role server

INFO[0000] Adding 1 node(s) to the runtime local cluster 'local'...
INFO[0000] Using the k3d-tools node to gather environment information
INFO[0000] Starting new tools node...
INFO[0000] Starting Node 'k3d-local-tools'
INFO[0000] HostIP: using network gateway <IP_ADDRESS> address
INFO[0001] Starting Node 'k3d-newserver-0'
INFO[0015] Updating loadbalancer config to include new server node(s)
INFO[0015] Successfully configured loadbalancer k3d-local-serverlb!
INFO[0016] Successfully created 1 node(s)!

Check the coreDNS configmap

  NodeHosts: |
    <IP_ADDRESS> k3d-newserver-0
kind: ConfigMap

What did you expect to happen
The new host is added but others NodeHosts are not lost
Screenshots or terminal output
Which OS & Architecture

Linux,  Fedora 35

Which version of k3d

output of k3d version v5.3.0     k3s version v1.22.6-k3s1 (default)

Which version of docker

output of

 Version:           20.10.12
 API version:       1.41
 Go version:        go1.16.12
 Git commit:        e91ed57
 Built:             Mon Dec 13 11:46:03 2021
 OS/Arch:           linux/amd64
 Context:           default
 Experimental:      true

Server: Docker Engine - Community
 Engine:
  Version:          20.10.12
  API version:      1.41 (minimum version 1.12)
  Go version:       go1.16.12
  Git commit:       459d0df
  Built:            Mon Dec 13 11:43:48 2021
  OS/Arch:          linux/amd64
  Experimental:     false
 containerd:
  Version:          1.4.12
  GitCommit:        7b11cfaabd73bb80907dd23182b9347b4245eb5d
 runc:
  Version:          1.0.2
  GitCommit:        v1.0.2-0-g52b36a2
 docker-init:
  Version:          0.19.0
  GitCommit:        de40ad0

Hi  @lerminou , thanks for opening this issue!
This is indeed pretty weird and I haven't seen this before.
This would in fact mean, that K3s even deletes its own entries :thinking:
I put this on the list to be checked before the next release :+1:

Hi @iwilltry42
Thanks for your time.
One more precision, My cluster was created with a 1.21 k3s image..
My second node was created with a 1.22 version  ( I submitted another bug about that)
If a check k3s changelog. There are some changes about the coredns config.
I will try to reproduce it in a full 1.22 cluster

Hey @lerminou in context of #1032 I just did several node adds and my CoreDNS configmap now looks like this:
NodeHosts: |
    <IP_ADDRESS> k3d-test-server-0
    <IP_ADDRESS> k3d-testnode2-0
    <IP_ADDRESS> host.k3d.internal
    <IP_ADDRESS> k3d-testnode3-0
    <IP_ADDRESS> k3d-test-agent-0
    <IP_ADDRESS> k3d-test-serverlb

So I assume that the issue is limited to creating a new server node.
Which after taking another look at your original post raises a question:
You just ran k3d cluster create without any additional flags (Meaning: 1 server and no --cluster-init flag), right? :thinking:
That would mean, that the new server node shouldn't even be able to join the existing cluster (or induce some split-brain mode), since the original server-0 does not use etcd and thus cannot add another server node.

Hi @iwilltry42
I didn't fill the config file but the cluster was created with the correct flag to use etcd.
I agree it only appears when adding a new node to an existing cluster

I didn't fill the yaml config file but the cluster was created with the correct flag to use etcd.

Huh? The --cluster-init flag should only be set when creating more than 1 server at cluster creation or when you set it manually :thinking:
Can you share the output of docker inspect <server-0-container>? (inspecting the initial server container created by k3d cluster create)

I agree it only appears when adding a new node to an existing cluster

Also when adding an agent node?

Here is the relevant informations from my config.yaml used when I create the cluster
apiVersion: k3d.io/v1alpha4
kind: Simple
metadata:
  name: local
servers: 1
agents: 0
image: rancher/k3s:v1.21.10-k3s1
options:
  k3s: # options passed on to K3s itself
    extraArgs: # additional arguments passed to the `k3s server|agent` command; same as `--k3s-arg`
      - arg: --tls-san=back-local.kube.com
        nodeFilters:
          - all
      - arg: --cluster-init
        nodeFilters:
          - server:0

And the output of the container
[
    {
        "Id": "f03c57557f5e0e74efc7d9ac6c6bf672af1a9b35dbbb0e009e288cc8d1e2a153",
        "Created": "2022-03-26T14:35:51.512776582Z",
        "Path": "/bin/k3s",
        "Args": [
            "server",
            "--tls-san=back-local.kube.com",
            "--cluster-init",
            "--cluster-domain=kube.com",
            "--disable=traefik",
            "--resolv-conf=/var/resolv.conf",
            "--tls-san",
            "back-local.kube.com"
        ],
        "State": {
            "Status": "running",
            "Running": true,
            "Paused": false,
            "Restarting": false,
            "OOMKilled": false,
            "Dead": false,
            "Pid": 45282,
            "ExitCode": 0,
            "Error": "",
            "StartedAt": "2022-03-26T14:35:52.169325626Z",
            "FinishedAt": "0001-01-01T00:00:00Z"
        },
        "Image": "sha256:ce2cc3439feeb2a54dc01daf414754a64897f815e4c9a5c59c02b6d9d5b8d38c",
        "ResolvConfPath": "/home/docker-images/containers/f03c57557f5e0e74efc7d9ac6c6bf672af1a9b35dbbb0e009e288cc8d1e2a153/resolv.conf",
        "HostnamePath": "/home/docker-images/containers/f03c57557f5e0e74efc7d9ac6c6bf672af1a9b35dbbb0e009e288cc8d1e2a153/hostname",
        "HostsPath": "/home/docker-images/containers/f03c57557f5e0e74efc7d9ac6c6bf672af1a9b35dbbb0e009e288cc8d1e2a153/hosts",
        "LogPath": "/home/docker-images/containers/f03c57557f5e0e74efc7d9ac6c6bf672af1a9b35dbbb0e009e288cc8d1e2a153/f03c57557f5e0e74efc7d9ac6c6bf672af1a9b35dbbb0e009e288cc8d1e2a153-json.log",
        "Name": "/k3d-local-server-0",
        "RestartCount": 0,
        "Driver": "overlay2",
        "Platform": "linux",
        "MountLabel": "",
        "ProcessLabel": "",
        "AppArmorProfile": "",
        "ExecIDs": null,
        "HostConfig": {
            "Binds": [
                "/home/projects/k3d/manifests:/var/lib/rancher/k3s/server/manifests/",
                "/home/projects/k3d/CA-kube.crt:/etc/ssl/certs/ca.pem",
                "/home/projects/k3d/storage:/var/lib/rancher/k3s/storage",
                "/home/projects/k3d/resolv.conf:/var/resolv.conf",
                "/home:/projects",
                "k3d-local-images:/k3d/images"
            ],
            "ContainerIDFile": "",
            "LogConfig": {
                "Type": "json-file",
                "Config": {}
            },
            "NetworkMode": "default",
            "PortBindings": null,
            "RestartPolicy": {
                "Name": "unless-stopped",
                "MaximumRetryCount": 0
            },
            "AutoRemove": false,
            "VolumeDriver": "",
            "VolumesFrom": null,
            "CapAdd": null,
            "CapDrop": null,
            "CgroupnsMode": "host",
            "Dns": null,
            "DnsOptions": null,
            "DnsSearch": null,
            "ExtraHosts": null,
            "GroupAdd": null,
            "IpcMode": "private",
            "Cgroup": "",
            "Links": null,
            "OomScoreAdj": 0,
            "PidMode": "",
            "Privileged": true,
            "PublishAllPorts": false,
            "ReadonlyRootfs": false,
            "SecurityOpt": [
                "label=disable"
            ],
            "Tmpfs": {
                "/run": "",
                "/var/run": ""
            },
            "UTSMode": "",
            "UsernsMode": "",
            "ShmSize": 67108864,
            "Runtime": "runc",
            "ConsoleSize": [
                0,
                0
            ],
            "Isolation": "",
            "CpuShares": 0,
            "Memory": 0,
            "NanoCpus": 0,
            "CgroupParent": "",
            "BlkioWeight": 0,
            "BlkioWeightDevice": null,
            "BlkioDeviceReadBps": null,
            "BlkioDeviceWriteBps": null,
            "BlkioDeviceReadIOps": null,
            "BlkioDeviceWriteIOps": null,
            "CpuPeriod": 0,
            "CpuQuota": 0,
            "CpuRealtimePeriod": 0,
            "CpuRealtimeRuntime": 0,
            "CpusetCpus": "",
            "CpusetMems": "",
            "Devices": null,
            "DeviceCgroupRules": null,
            "DeviceRequests": null,
            "KernelMemory": 0,
            "KernelMemoryTCP": 0,
            "MemoryReservation": 0,
            "MemorySwap": 0,
            "MemorySwappiness": null,
            "OomKillDisable": false,
            "PidsLimit": null,
            "Ulimits": null,
            "CpuCount": 0,
            "CpuPercent": 0,
            "IOMaximumIOps": 0,
            "IOMaximumBandwidth": 0,
            "MaskedPaths": null,
            "ReadonlyPaths": null,
            "Init": true
        },
        "GraphDriver": {
            "Data": {
                "LowerDir": "/home/docker-images/overlay2/b99aeab69d19a2a6c3837ac92b6fb0b56724d203459a9f4de47a75b636a7e2d2-init/diff:/home/nicolasdubut/docker-images/overlay2/2a7d00212eac6ae53ab10ddee65285eb88f69c80f1b4fbc539787da3719a7001/diff:/home/nicolasdubut/docker-images/overlay2/5ac2e683a098eb7db25da3239d0b11620528e0cc7f208a9cb0c63f96392d1c80/diff:/home/nicolasdubut/docker-images/overlay2/a781511d2cea6e430e927f862a3b443862af1b4ef74b12029b03d499760cf968/diff",
                "MergedDir": "/home/docker-images/overlay2/b99aeab69d19a2a6c3837ac92b6fb0b56724d203459a9f4de47a75b636a7e2d2/merged",
                "UpperDir": "/home/docker-images/overlay2/b99aeab69d19a2a6c3837ac92b6fb0b56724d203459a9f4de47a75b636a7e2d2/diff",
                "WorkDir": "/home/docker-images/overlay2/b99aeab69d19a2a6c3837ac92b6fb0b56724d203459a9f4de47a75b636a7e2d2/work"
            },
            "Name": "overlay2"
        },
        "Mounts": [
            {
                "Type": "bind",
                "Source": "/home/projects/k3d/storage",
                "Destination": "/var/lib/rancher/k3s/storage",
                "Mode": "",
                "RW": true,
                "Propagation": "rprivate"
            },
            {
                "Type": "volume",
                "Name": "3c31da469f55de07251b661d5f1c42a88ee6a479cb08cc35397037894eabe302",
                "Source": "/home/docker-images/volumes/3c31da469f55de07251b661d5f1c42a88ee6a479cb08cc35397037894eabe302/_data",
                "Destination": "/var/lib/cni",
                "Driver": "local",
                "Mode": "",
                "RW": true,
                "Propagation": ""
            },
            {
                "Type": "volume",
                "Name": "678124af0cdcc59b2714c59b91e94a8ac9a3bdd1042ca25dda118742b3aa2a39",
                "Source": "/home/docker-images/volumes/678124af0cdcc59b2714c59b91e94a8ac9a3bdd1042ca25dda118742b3aa2a39/_data",
                "Destination": "/var/lib/rancher/k3s",
                "Driver": "local",
                "Mode": "",
                "RW": true,
                "Propagation": ""
            },
            {
                "Type": "bind",
                "Source": "/home/projects/k3d/manifests",
                "Destination": "/var/lib/rancher/k3s/server/manifests",
                "Mode": "",
                "RW": true,
                "Propagation": "rprivate"
            },
            {
                "Type": "bind",
                "Source": "/home/projects/k3d/resolv.conf",
                "Destination": "/var/resolv.conf",
                "Mode": "",
                "RW": true,
                "Propagation": "rprivate"
            },
            {
                "Type": "bind",
                "Source": "/home",
                "Destination": "/projects",
                "Mode": "",
                "RW": true,
                "Propagation": "rslave"
            },
            {
                "Type": "volume",
                "Name": "k3d-local-images",
                "Source": "/home/docker-images/volumes/k3d-local-images/_data",
                "Destination": "/k3d/images",
                "Driver": "local",
                "Mode": "z",
                "RW": true,
                "Propagation": ""
            },
            {
                "Type": "volume",
                "Name": "6554fec529be600aebc4219f07891c27ca6a12d1d51d36566b104ed65afd4996",
                "Source": "/home/docker-images/volumes/6554fec529be600aebc4219f07891c27ca6a12d1d51d36566b104ed65afd4996/_data",
                "Destination": "/var/lib/kubelet",
                "Driver": "local",
                "Mode": "",
                "RW": true,
                "Propagation": ""
            },
            {
                "Type": "volume",
                "Name": "e86a826c0420a7033f933d396b60ed6948c1d4a28b897b6a3e64dc47871827db",
                "Source": "/home/docker-images/volumes/e86a826c0420a7033f933d396b60ed6948c1d4a28b897b6a3e64dc47871827db/_data",
                "Destination": "/var/log",
                "Driver": "local",
                "Mode": "",
                "RW": true,
                "Propagation": ""
            },
            {
                "Type": "bind",
                "Source": "/home/projects/k3d/CA-kube.crt",
                "Destination": "/etc/ssl/certs/ca.pem",
                "Mode": "",
                "RW": true,
                "Propagation": "rprivate"
            }
        ],
        "Config": {
            "Hostname": "k3d-local-server-0",
            "Domainname": "",
            "User": "",
            "AttachStdin": false,
            "AttachStdout": false,
            "AttachStderr": false,
            "Tty": false,
            "OpenStdin": false,
            "StdinOnce": false,
            "Env": [
                "bar=baz",
                "K3S_TOKEN=superSecretToken",
                "K3S_KUBECONFIG_OUTPUT=/output/kubeconfig.yaml",
                "PATH=/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/bin/aux",
                "CRI_CONFIG_FILE=/var/lib/rancher/k3s/agent/etc/crictl.yaml"
            ],
            "Cmd": [
                "server",
                "--tls-san=back-local.kube.com",
                "--cluster-init",
                "--cluster-domain=kube.com",
                "--disable=traefik",
                "--resolv-conf=/var/resolv.conf",
                "--tls-san",
                "back-local.kube.com"
            ],
            "Image": "rancher/k3s:v1.21.10-k3s1",
            "Volumes": {
                "/var/lib/cni": {},
                "/var/lib/kubelet": {},
                "/var/lib/rancher/k3s": {},
                "/var/log": {}
            },
            "WorkingDir": "",
            "Entrypoint": [
                "/bin/k3s"
            ],
            "OnBuild": null,
            "Labels": {
                "app": "k3d",
                "k3d.cluster": "local",
                "k3d.cluster.imageVolume": "k3d-local-images",
                "k3d.cluster.network": "k3d-local",
                "k3d.cluster.network.external": "false",
                "k3d.cluster.network.id": "d29ad6e8de48162f149e07fd956c5deb59f335dea88ccd6c49475ecb368e6507",
                "k3d.cluster.network.iprange": "<IP_ADDRESS>/16",
                "k3d.cluster.token": "superSecretToken",
                "k3d.cluster.url": "https://k3d-local-server-0:6443",
                "k3d.node.staticIP": "<IP_ADDRESS>",
                "k3d.role": "server",
                "k3d.server.api.host": "back-local.kube.com",
                "k3d.server.api.hostIP": "<IP_ADDRESS>",
                "k3d.server.api.port": "36145",
                "k3d.version": "v5.3.0",
                "org.opencontainers.image.created": "2022-02-24T23:35:30Z",
                "org.opencontainers.image.revision": "471f5eb3dbfeaee6b6dd6ed9ab4037c10cc39680",
                "org.opencontainers.image.source": "https://github.com/k3s-io/k3s.git",
                "org.opencontainers.image.url": "https://github.com/k3s-io/k3s"
            }
        },
        "NetworkSettings": {
            "Bridge": "",
            "SandboxID": "e0e8f724abe0e51bcf123e5b21e07b40f64c28ec2cc7294eae16668f1b9b5367",
            "HairpinMode": false,
            "LinkLocalIPv6Address": "",
            "LinkLocalIPv6PrefixLen": 0,
            "Ports": {},
            "SandboxKey": "/var/run/docker/netns/e0e8f724abe0",
            "SecondaryIPAddresses": null,
            "SecondaryIPv6Addresses": null,
            "EndpointID": "",
            "Gateway": "",
            "GlobalIPv6Address": "",
            "GlobalIPv6PrefixLen": 0,
            "IPAddress": "",
            "IPPrefixLen": 0,
            "IPv6Gateway": "",
            "MacAddress": "",
            "Networks": {
                "k3d-local": {
                    "IPAMConfig": {
                        "IPv4Address": "<IP_ADDRESS>"
                    },
                    "Links": null,
                    "Aliases": [
                        "f03c57557f5e",
                        "k3d-local-server-0"
                    ],
                    "NetworkID": "d29ad6e8de48162f149e07fd956c5deb59f335dea88ccd6c49475ecb368e6507",
                    "EndpointID": "95d2c18a81c178b1beac39b6be429755409c9bb7c9c76a05301a1c63a805664e",
                    "Gateway": "<IP_ADDRESS>",
                    "IPAddress": "<IP_ADDRESS>",
                    "IPPrefixLen": 16,
                    "IPv6Gateway": "",
                    "GlobalIPv6Address": "",
                    "GlobalIPv6PrefixLen": 0,
                    "MacAddress": "02:42:ac:28:00:03",
                    "DriverOpts": null
                }
            }
        }
    }
]

When I create a new AGENT node in this cluster, the nodeHost is not modified:
 projects  k3d  k3d node create newserver --cluster local --role agent
INFO[0000] Adding 1 node(s) to the runtime local cluster 'local'...
INFO[0000] Using the k3d-tools node to gather environment information
INFO[0000] Starting new tools node...
INFO[0000] Starting Node 'k3d-local-tools'
INFO[0000] HostIP: using network gateway <IP_ADDRESS> address
INFO[0000] Starting Node 'k3d-newserver-0'
INFO[0009] Successfully created 1 node(s)!

when I create a new SERVER node, the configMap is broken

Okay, now that makes more sense. From your initial post I couldn't see that you were using a config file.
I can verify that that's an issue. It's quite weird though that K3s overwrites the already deployed CoreDNS config :thinking:
Maybe that's worth an upstream issue.

This is indeed the intended behavior on K3s' end (thanks @brandond for the explanation :pray: )
However, there are many ways this can be tackled on k3d's side, so let's see what nice solution we can come up with for the next release :+1:

Hi, I found this issue while investigating why when K3s containers created via K3d would not maintain the CoreDNS NodeHosts ConfigMap on host machine reboot. Could it be the same issue?
From my testing the issues comes down to the K3s starting by themselves without K3d orchestraction and it rewrites CoreDNS ConfigMap to only contain k3d-test-server-0 for my 1-server cluster.

and i think i've this issue, too, in a different scenario: just a k3s container restart, or docker restart, triggers the configmap to be emptied... issue here: 1112


Can not register micro when different namespace

I deploy micro-api, micro-web, micro-proxy with namespace "micro" and rbac following:
rbac.yaml:
apiVersion: rbac.authorization.k8s.io/v1beta1
kind: Role
metadata:
  name: pod-viewer-role
  namespace: micro
rules:
- apiGroups: [""]
  resources: ["pods"]
  verbs: ["get", "patch", "list", "watch"]

---

apiVersion: rbac.authorization.k8s.io/v1beta1
kind: RoleBinding
metadata:
  name: "pod-viewer-role-binding"
  namespace: micro
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: Role
  name: "pod-viewer-role"
subjects:
- apiGroup: ""
  kind: ServiceAccount
  name: "default"
  namespace: micro

Then I try to deploy a micro service.
If this micro is on the same namespace: "micro" . It's work ( this service can be list in registry page of micro-web)
Then I try to change the deployment of this service in another namespace ( also add rbac for new namespace). I can not found it in registry page.
Is it mean that all micro must be the same namespace with namespace of micro-api , micro-web, micro-proxy?

What you're setting is kubernetes namespace which provides a way to logically separate services on kubernetes. Micro can only see the services in whatever kubernetes namespace you put it in. It does not have access to all k8s namespaces.

Can we close the issue if you're happy with the response?

Thank you for your quick response.


Edit Alias fix

When the Edit panel is opened to edit an existing Dynamic IPv6 Alias, the proto type is visible. If you select another type of Alias and then return to the Dynamic IPv6 type the proto has gone. This PR just adds a hide proto selectpicker.

@marjohn56 thanks!


Can we donate to improve development speed?

I was wondering how we could improve the development speed. Maybe we could motivate you with monthly donations?

I do not accept donations. We can improve development speed by having more people work on this project. As stated in the README, I always welcome contributors.

People with the necessary skill set to do so, seem to be very rare though. May I ask why you don't want to accept donations?

Because I want the freedom to choose how I spend my time.

I don't know if you already knew, but it seems that Inox accepts donations. ungoogled-chromium uses most of Inox's patches (especially the Safe Browsing patch, which can take considerable time to update). Having Inox be up-to-date will speed up the updating process for ungoogled-chromium. Also, donating may encourage Inox to grow and include more patches that will reduce the work to update ungoogled-chromium.


[TEVA-1948] - Remove job alert link ab test

Jira ticket URL
https://dfedigital.atlassian.net/browse/TEVA-1948
Changes in this PR

🔥

Review app deployed to https://teaching-vacancies-review-pr-2740.london.cloudapps.digital

Review app deployed to https://teaching-vacancies-review-pr-2740.london.cloudapps.digital


angular 13+ compilation :  legend+* -> Cannot read properties of undefined (reading 'type') v5.1.3

Prerequisites
I have searched for duplicate or closed issues
I have validated any HTML to avoid common problems
I have read the contributing guidelines
Describe the issue
Hi Team,
I am getting that watning when i compile my angular 13.2.1 project :
Warning: 2 rules skipped due to selector errors:
legend+* -> Cannot read properties of undefined (reading 'type')
legend+* -> Cannot read properties of undefined (reading 'type')
Reduced test cases
If i comment the import of bootstrap i have no longer that warning.
Looking forward for your valuable response.
What operating system(s) are you seeing the problem on?
windows 10
What browser(s) are you seeing the problem on?
Chrome
What version of Bootstrap are you using?
v5.1.3

no matters, Angular abandoned

Angular is not abandoned 😅
I suspect an issue with your compiler or something. This CSS compiles fine everywhere so that's very likely your tooling (compiler, linter, loader...) which struggles.
This CSS selector is old enough to not be an issue anywhere (that's CSS 2.1, 20 years ago 😄) so there can't be an issue.
Moreover that's only a warning, so I'm closing this for now — but feel free to comment again with any information you'd grab, if needed.

@ffoodd https://docs.angularjs.org/misc/version-support-status

AngularJS is not the same as Angular. 🙂

I have the problem after updating angular/cli 13.3.2->13.3.7.
You are receiving this because you commented.Message ID: @.***>

For reference, this is the root cause: https://github.com/GoogleChromeLabs/critters/issues/103


v0.16 release tagged code is 0.17-DEV

Hi there,
We have had a problem with go get -u -v github.com/spf13/hugo on the latest version of hugo when trying to run it through Codeship.
We've been beating our heads over the issue of it always installing 0.17-DEV, not 0.16.
After doing testing of the following:

Install from go get -u -v github.com/spf13/hugo, output of hugo version is 0.17-DEV
Download from https://github.com/spf13/hugo/releases/download/v0.16/hugo_0.16_linux-64bit.tgz and run ./hugo, we get version 0.16
Download source from https://github.com/spf13/hugo/archive/v0.16.tar.gz, build using go build -o hugo main.go and run ./hugo, we get version 0.17-DEV

Even if this issue is still existent I don't think that it will be fixed nor that it affects a lot of users anymore. Hence I propose to close this issue.


WIP: Add wminkowski

Partially addresses issue ( https://github.com/jakirkham/dask-distance/issues/54 ).
Implements a Dask array function for computing the weighted Minkowski distance between two 1-D arrays. Also tests it by comparing to the SciPy implementation of the same name.

Tests are incomplete, which will need to be addressed. However this worked ok locally on some test data.


Add marshal_strict feature

Hello! We in gowaves use this plug-in to marshal messages for producing signatures futher and compare them to signed messages which would be marshalled by other languages implementations. The problem is that many other implementations of protobuf (for e.g. Java) marshal fields in the strict order by their numbers declared in .proto file. Consequently, the most rational way to use such marshal scheme in all languages we use in our project.
marshal feature doesn't satisfy this requirement and mustn't do as it should behave indentically to proto.Marshal() which doesn't satisfy respectively. To be more precise the difference is that oneOfs fields are marshalled before others(#22)  and in the example below ethereum_transaction would be marshalled before proofs:
message SignedTransaction {
    oneof transaction {
        Transaction waves_transaction = 1;
        bytes ethereum_transaction = 3;
    }
    repeated bytes proofs = 2;
}

But we would like ethereum_transaction to be marshalled after proofs.
We came with implementation of the new feature marshal_strict that solves this problem and can be useful in similar cases.

How about replacing the default marshal feature with this one? Does this take more space on the wire or is less optimized in time or space somehow? Thanks

I'm afraid it can break something in projects which are using current order.

How about replacing the default marshal feature with this one? Does this take more space on the wire or is less optimized in time or space somehow? Thanks

I'm afraid it can break something in projects which are using current order.

Looking at this PR today!


tf.dynamic_stitch gives wrong shape and outputs raw memory

System information

Linux Ubuntu 20.04
TensorFlow 2.3.0 installed from binary
Python version 3.8.2 (default, Jul 16 2020, 14:00:26) [GCC 9.3.0]
no GPU available

Describe the current behavior
When stitching empty partitions, the output shape is wrong.
>>> tf.dynamic_stitch([1], [1.2])
<tf.Tensor: shape=(2,), dtype=float32, numpy=array([2.4e-44, 1.2e+00], dtype=float32)>

The 2.4e-44 is from uninitialized memory and will typically vary per execution. There is nothing special about 1.2
Describe the expected behavior
From documentation: merged[indices[m], ...] = data[m][...] so we would expect:
>>> tf.dynamic_stitch([1], [1.2])
<tf.Tensor: shape=(1,), dtype=float32, numpy=array([1.2], dtype=float32)>

Extra
I don't know if it's a security concern, but the amount of data read can be arbitrarily large:
size = 8
>>> tf.dynamic_stitch([1], [tf.zeros([1, size])])[0]
<tf.Tensor: shape=(8,), dtype=float32, numpy=
array([-2.8330522e-19,  4.5886920e-41, -2.8330522e-19,  4.5886920e-41,
        6.6383240e-07,  1.6802996e-04,  1.7299214e-04,  4.3915941e-05],
      dtype=float32)>

Edit: simplified the code

@shlapfish
I ran the code shared, please find the gist here, and let us know if it confirms your issue.

@shlapfish
I ran the code shared, please find the gist here, and let us know if it confirms your issue.

That does confirm my issue.

It took me a while to figure out a workaround. If it's any help:
def my_stitch(indices, data):
    shapes = [tf.shape(idx) for idx in indices]
    tmp = tf.concat([tf.reshape(idx, [-1]) for idx in indices], axis=0)
    args = tf.argsort(tmp, direction='ASCENDING')
    tmp = tf.scatter_nd(tf.expand_dims(args, axis=-1), tf.range(tmp.shape[0]), shape=[tmp.shape[0]])

    indices = tf.split(tmp, [prod(s) for s in shapes])
    indices = [tf.reshape(idx, s) for idx, s in zip(indices, shapes)]
    return tf.dynamic_stitch(indices, data)

I'm so sorry. I misread the documentation :facepalm:


[x64 ubuntu 16.04] CoreRT build script fails on ubuntu 16.04

CoreRT build script cannot load libobjwriter on ubuntu 16.04 and fails. I need to fix it by this patch
.diff --git a/buildscripts/buildvars-setup.sh b/buildscripts/buildvars-setup.sh
index 178a4391..343dc59f 100755
--- a/buildscripts/buildvars-setup.sh
+++ b/buildscripts/buildvars-setup.sh
@@ -134,7 +134,8 @@ case $OSName in

     Linux)
         export __BuildOS=Linux
-        export __NugetRuntimeId=$(get_current_linux_distro)-x64
+        export __NugetRuntimeId=ubuntu.14.04-x64
+        ## export __NugetRuntimeId=$(get_current_linux_distro)-x64
         ;;

     NetBSD)

Another problem is that ilc fails with the following message
"/share/CORERT/corert/tests/../bin/Linux.x64.Debug/tools/ilc" @"/share/CORERT/corert/tests/src/Simple/Hello/obj/Debug/x64/native/Hello.ilc.rsp"
Failed to initialize CoreCLR, HRESULT: 0x80004005

CC: @Dmitri-Botcharnikov @BredPet @mskvortsov

The workaround for this should be to override the objectwriter NuPkgRid when restoring. Add this line <RuntimeIdentifiers Condition="$(NuPkgRid.StartsWith('ubuntu.'))">ubuntu.14.04-x64</RuntimeIdentifiers> here: https://github.com/dotnet/corert/blob/master/src/ILCompiler/ObjectWriter/ObjectWriter.depproj#L10  . Could you please give it a try and submit PR if it works? Several other projects are using the same workaround, e.g.: https://github.com/dotnet/corert/blob/master/src/ILCompiler/RyuJIT/RyuJIT.depproj
The proper way to fix is would be to build objectwriter for "portable Linux" like other .NET Core projects. cc @sandreenko

Another problem is that ilc fails with the following message

I think this may be caused by the buildvars-setup.sh change.

@jkotas Yes, you're right. It's working.

https://github.com/dotnet/corert/pull/4082