TextField doesn't support 'ref' ?

I'm defining a TextField component (within a card) which I pass a ref
but the ref isn't defined ...
<div ref='lessonDetailContainer'>
...
            <TextField
              defaultValue={lessonData.vocab}
              multiLine={true}
              floatingLabelText='vocab'
              ref='lessonVocab'
            />
</div>

How are people making forms with material-ui? Just using the onChange events on every part of the form?
FWIW the TextField is inside a Card/Form
the ref on the wrapper div works fine.
like the very second issue in this repo...
https://github.com/callemall/material-ui/issues/2

I'm working on this as well. The problem is that the ref is passed down to the input. something that doesn't work with React. I'm addressing this in the following PR I'll make.

OK great, let me know if i can help! when do you expect the PR to land?

In 20 minute at most.

i'm not sure if your PR will address this, but in trying to workaround,
none of the events send info in the parameters either:
            <TextField
              defaultValue={this.data.topic.inherits}
              floatingLabelText='inherits'
              onChange={this.editTopic}
            />

  editTopic(evt, p1, p2, p3) {
    evt.preventDefault();
    Logger.loga(['editTopic', evt, p1, p2, p3], logfile);

with onChange and onEnterKey there are no other values sent apart from the synthetic event.
just p1, p2 etc are undefined. I think this is a regression?
so we can't read via ref, or by using the component methods.
that makes it impossible to use these fields for anything other than display, afai can see?
Is there another workaround to use these text fields for forms, or one event that does send the fields data along?
thanks.

It does get passed into that callback, via the first argument evt.target.value. It's a lot messed up, granted. we have to address a lot of issues regarding these components

@dcsan - Just a little plug for https://github.com/mbrookes/formsy-material-ui - even if you don't want to use formsy for form validation, you it might help you with ideas for using the components in forms.

@mbrookes Good job on your project :+1: :+1: I liked it. might not be bad to open an issue requesting a mention in the README.

Thanks @alitaheri  - maybe a "related projects" section? I'll have a dig around github and see if there are others. I don't think it could be justified for mine alone. :)

@mbrookes That would be great :+1: :+1: Thanks a lot

@alitaheri I searched guthub for material-ui sorted by popularity, and while there are lots of Material Design related projects projects that use material-ui in their name, most are not related to material-ui at all. (It says something about this project that it's name is now synonymous with Material Design!). There may well be material-ui related projects that don't use material-ui in their name, but github's search is going to make them hard to find. I tried NPM as well, but nothing further there, If anything pops up in the future we can revisit this. @dcsan, sorry to hijack your issue!

@mbrookes Thanks for the effort :+1: :+1: Feel free to open another issue requesting that page. I think is good pattern to promote these related project as they might fulfill some needs our users have. :+1:

So the solution for being able to use a ref in the TextField is to use another library? :-1:

@schuchowsky: this issue is from December 30th, this is working since a long time ago, man.

I'm trying to use a ref on a TextField, to attach an onfocus event on it. On componentDidMount the ref's value is null... Are you sure it is working?

@schuchowsky: yes, 2 apps running in production and using a few ref props here and there.
I strongly suggest using https://github.com/mbrookes/formsy-material-ui though, it's an awesome formsy-react wrapper to match Material UI style.
If you are indeed having an issue with ref on <TextField />, please open another issue following the ISSUE_TEMPLATE, commenting on issues from 1 year ago is definitely not gonna help you.

@schuchowsky check this way it works for me.
http://stackoverflow.com/questions/43960183/material-ui-component-reference-does-not-work

Not working in 1.0

What worked for me, in this case and others using Material UI components, was to wrap a regular component (i.e. div or span) around my Material UI component.
Once I got the wrapper reference, using the querySelector, I could query for any needed element.
Getting the wrapper reference:
<div
     ref={(div) => { this.inputWrapper = div; }}
     style={{ flex:1, marginRight: '7px' }}
>
     <TextField
               onChange={this.onChange.bind(this)}
               value={this.state.value}
               floatingLabelFixed={true}
               multiLine={multiLine}
               fullWidth={true}
               rowsMax={30}
               underlineShow={true}
               textareaStyle={{minHeight: '80px'}}
               style={{paddingRight, transition:'none', boxSizing:'border-box'}}
               floatingLabelText={field.title} />
</div>

Using it:
fixHeight(){
      if(this.inputWrapper){
            let textArea = this.inputWrapper.querySelector('textarea[id]');
            if(textArea){
                let maxHeight = textArea.clientHeight + 48;
                this.setState({maxHeight});
            }
      }
}

Code extracted from my open source CMS for Hugo sites.

@jankalfus: How is RootRef supposed to work? It seems to be incompatible with the default React-ref, since, e.g here.:
      <RootRef rootRef={inputs.name}>
        <TextField
          id="name"
          label={t("x")}
          placeholder={t("y")}
        />
      </RootRef>

inputs.name.current is defined, but input.ref.current.value is undefined!
To be compatible to the react-ref-hook-standard, input.ref.current.value should contain the value of the text field!

What's wrong?
Is this a bug?
What can I do as workaround?

@mwaeckerlin
That's because you're trying to access a value of the wrapper div, not the input. See this fiddle for an example: https://jsfiddle.net/8ojwcvyd/
The key part is this: this.myRef.current.querySelector("input").value

I found a solution that is compatible with the normal html elements:
https://stackoverflow.com/questions/51310609/unable-to-retrieve-the-input-field-of-material-ui-using-refs-in-react-js/55195196#55195196

BTW, @jankalfus, I was looking for hours for a tutorial or example on how to use ref=… in material-ui and only found that nearly undocumented RootRef plus some tickets and some questions in Stack Overflow, but none with a good and working solution.
That means, I was not successful in finding a good comprehensive documentation on how to use React.createRef with material-ui. In any case: Please add some comprehensive documentation on how to properly use ref=… from React.createRef() in material-ui, including access to the value!
What I really want, is to use ref in exactly the same way, as I would use it for a standard <input> element. The solution above is the best I found.
I suggest to make this the default behaviour (so that no explicit onChange is necessary) and to forget about <RootRef>, which seems to me to be a dirty hack only.

BTW: I'll use it in conjunction with an addition to Drizzle-React-Components that I am working on.

In case anyone comes across this looking for answer like I did, take a look at the recent TextField API documentation (https://material-ui.com/api/text-field/#textfield-api). It has an inputRef property to pass a ref to the input element and access the value like a normal <input>
// Create ref
const myInputRef = React.useRef()

// Get value from input ref
myInputRef.current.value

// Create component that uses the ref
<TextField inputRef={myInputRef} />

I think this isn´t working with multiline prop true. Using inputRef returns current as null


Documenting the reason for kubectl top showing > 100% usage

This is a Feature Request

So a few months back I discovered an issue which might be related to kubectl top . This is related to this issue https://github.com/kubernetes-sigs/metrics-server/issues/1060
What would you like to be added

I would like to add information that I made in this and I made a PR related to this.
Basically explains why kubectl top command might show above 100% usage.
Why is this needed
Because this seems like more of a kubectl top documentation rather than metrics-server, since for new users it might not be obvious. More details https://github.com/kubernetes-sigs/metrics-server/pull/1186#issuecomment-1415510739
Comments

I just wanted to know if this repo is the right place for me to write documentation for this. The above mentioned PR depends on this. Let me know if there is more information needed.

/language en

hey I want to fixed this issue


Recognize package names according to OPAM's convention.

See https://github.com/ocaml/opam/issues/2683

fde3300 does the basic checks. Supporting case insensitiveness will entail more work.

According to https://github.com/ocaml/opam/issues/2683#issuecomment-252792433 opam package names are case sensitive, it's just that opam won't install two packages that differ only by case and that you can specify them on the cli in a case insensitive manner. The latter is currently not the case; we may want to this in the future but in any case there is no bug here.


Add dataset: [arabic_htr_groundtruth]

@adikeinan, thanks for suggesting thi!  I just realised the dataset suggestion template was broken (it should be fixed now). Is it this dataset: https://bl.iro.bl.uk/collections/ffab19f8-e9b7-485c-a7c3-64aba143be9c?locale=en. I'll open a new issue for this using the template if os.

Yes it is! It's well described there, but let me know if you need any additional details. Thanks!


Swipe between images in same comment

Closes #123
In it's current implementation it puts the tapped image first and then swipe through the other ones. Could maybe make it simpler by keeping them in original order and changing the initial image with NYT - The downside of this is if you get put in the middle of a gallery, might be a bit confusing to the user?
Was just toying around not precious about this change!

Gonna go ahead and close this, think there are bigger issues around the photo library we use and it's limitations


Refactor: Replace Common-Values References in Verbose and Non-Verbose Test Objects

This issue aims to refactor the codebase by replacing common-values references in both verbose and non-verbose test objects for the "POOL" and "GATE" modules. This refactoring effort will improve code maintainability and set the foundation for managing test templates efficiently in the future.
Scope

Identify common-values references in test objects.
Replace these references with more maintainable and reusable code.
Ensure that both verbose and non-verbose test objects are updated as needed.

Tasks

[ ] Create/Refactor Values-Object for Pool
[ ] Create/Refactor Values-Object for Gate

We should update the description to make it more clear what the issue is about

Done


Container capacity limited, when using charged smaller containers in recipe

Flux Networks <IP_ADDRESS>, MC 1.19.2, Forge 43.2.14
When upgrading a Flux Container using smaller containers with a stored amount, the new container will have it's storage limited to the total stored power when it was crafted. It refuses to charge beyond that limit.
Craft the next tier of container, using any amount of existing smaller containers with charge in them.
It should give you a container with the correct maximum size that can charge past the amount stored in it.

Do you keep your config as mod default? The capacity of Flux Storage is guaranteed to be 6 times that of lower tier, not related to its stored energy.

I have multiple of the gargantuans at 128mil with no issues. It only happens when crafting with non-empty tanks

I don't get what you mean. Flux Storage capacity is only determined by your server config, not what you're crafting with.

That's why it's a bug report lmao. This isn't supposed to be happening

bruh


Computing likelihood

Hello. Is there any option to compute the likelihood for the given graph in this model?

Yes, the NLL for validation and test graphs is reported, you just have to replace the val/test datasets.


Add projectid to project details page

Display the project ID on the details view of a project.

LGTM, @pdion891 can we have a screenshot to confirm regression testing?

Packaging result: ✖centos6 ✔centos7 ✔debian repo: http://packages.shapeblue.com/cloudstack/pr/1630
Job ID-69

Packaging result: ✔centos6 ✔centos7 ✔debian repo: http://packages.shapeblue.com/cloudstack/pr/1630
Job ID-71

I've added some more information in the project detail page as on following screenshot:

LGTM.

LGTM

Manually tested, LGTM. We can merge this, the failing Travis tests are not related to the change (which is purely UI).


Add parameterized supertype support

Closes #122
Changes:

Binding.supertypeClassName is now a TypeName instead of a ClassName to allow parameterized supertype

Note:

To allow support with BoundTo annotation, we would need to introduce something like val typeArguments: List<KClass<*>> in the annotation. But this is not perfect either, since it only accounts for 1 level of nesting

Example:
@Bound
class Foo @Inject constructor() : Converter<String, Int>

@Bound
class Bar @Inject constructor() : Converter<List<String>, List<Int>>

will generate:
@Binds
@NonNull
Converter<List<? extends String>, List<? extends Integer>> bindBar(@NonNull Bar implementation);

@Binds
@NonNull
Converter<String, Integer> bindFoo(@NonNull Foo implementation);

and can be injected like this:
class TestInject @Inject constructor(
    val foo: Converter<String, Int>,
    val bar: Converter<List<String>, List<Int>>
)

Nice one. Thanks.


FeatureNotFound: Couldn't find a tree builder with the features you requested: lxml. Do you need to install a parser library?

Bug
I receive the following error:
FeatureNotFound: Couldn't find a tree builder with the features you requested: lxml. Do you need to install a parser library?
To Reproduce
This happens when running the .get_posts() function from Profiles
Expected behavior
I expected to receive an object containing my posts.
Desktop (please complete the following information):

OS
Browser: chrome

This was an earlier issue in #43.
Try pip install lxml to resolve your issue.

I've the same issue, even though I have lxml installed. Not really sure how to resolve this.

Is there any solution for this?  I'm having the same issue, as well.  Installing LXML doesn't appear to resolve the issue (same error message).

Same here. Installed lxml library but the error still pops up.

I also got this error today,
but it resolved when I upgraded my packages:
pip install -U lxml
pip install -U beautifulsoup4
Does it help?


chore: support validate apache release automatically

Test First, mark as draft PR

and why we install jdk twice in ci.yml (action)
https://github.com/apache/incubator-hugegraph/blob/b3949997fd25594a51a45d12fe65d88899f502f8/.github/workflows/ci.yml#L33
then
https://github.com/apache/incubator-hugegraph/blob/b3949997fd25594a51a45d12fe65d88899f502f8/.github/workflows/ci.yml#L55


fixed toggle and set default unchecked

yeah, the background color of ended is wrong, and need to fix it out for now, and will merge.


Bump go eigensdk to 0.1.12, solve bug reported by LeastAuthority

Changes
Updates go eigensdk to version 0.1.12 which fixes the double signature err. All files using the SDK have been updated accordingly to the new version. Finally, our check has been removed since the SDK handles it now.
Test
When modifying the operator and sending two signed responses after verifying one batch, the aggregator should ignore the second one and the system should continue working as usual. See here for a more detailed testing #1097.

Default config of operator not registering

Fixed! We needed to wait for the receipt when depositing into the strategy contract. Tbh, I don't know why it was working with the rest of the configurations.


Fix getting user data from SSL, fix calloc warning

The same user data issue is not present in listen_dnsport.c because all the SSL_get_app_data() calls are behind
#ifndef HAVE_NGTCP2_CRYPTO_QUICTLS_CONFIGURE_SERVER_CONTEXT

Thanks, I'll merge and continue.


Fix: dynamic cards list, cards not rendering straight

fixes: #86, #84, #31
Explanation:
#31 - cards were not rendering straight at some point because resetTopCard method didn't reset pan VALUE, but OFFSET only. Every other place where  this.state.pan.setOffset was called, there was a call of this.state.pan.setValue
#84, #86 - cards were not updated because swiper used cards from state, not from props.

Hi, when can we expect this to be merged? @webraptor @nofacez
The swiper isn't usable now, because the app fails whenever I run out of the cards or move from the screen with Swiper. #84

Merged, released under 2.0.11

Hi, so how can I install with npm this? Npm install command is same for old unmantained repo


Running Server Doesn't get side panel

This is what you get when you run yarn start in the server.

Is there some setup to get the side panel?

If you're talking about the side panel to explore your schema it should come up if you click docs on the right.

I'm talking about getting this screen:https://camo.githubusercontent.com/93d8f4ab24d8453f0bd9df94965b4f1eb806ab76/68747470733a2f2f696d6775722e636f6d2f563168703449442e706e67 from the docs.

I am guessing that is preventing me from running the mutations since later it says "Copy the following two mutations into the editor pane - make sure to have the default Playground from the app project selected in the left side-menu:" and that panel is not there to be selected.

The mutations are now fixed on the current master, about the left-side panel that's a feature of the desktop application seemingly.

Thanks. I hacked around it with,
test.js
const { prisma } = require('./generated/prisma-client')

// A `main` function so that we can use async/await
async function main() {
  // Create a new link
  const newLink = await prisma.createLink({ description: "Prisma turns your database into a GraphQL API 😎",
  url: "https://www.prismagraphql.com" })
  console.log(`Created new link: ${newLink.url} (ID: ${newLink.id})`)
  const urqlLink = await prisma.createLink({ description: "The best GraphQL client for React",
  url: "https://github.com/FormidableLabs/urql"})
  console.log(`Created new link: ${urqlLink.url} (ID: ${urqlLink.id})`)
}

main().catch(e => console.error(e))

showed in app and graphiql


macOS

Code：
import ssl

ssl._create_default_https_context = ssl._create_unverified_context
import torch

model, _ = torch.hub.load(repo_or_dir="snakers4/silero-vad", model="silero_vad", verbose=True)

if __name__ == '__main__':
    recorder = AudioToTextRecorder(spinner=False)

    print("Say something...")
    while (True):
        print(recorder.text(), end=" ", flush=True)

Error:
RealTimeSTT: root - ERROR - Unhandled exeption in _recording_worker:
Exception in thread Thread-1 (_recording_worker):
Traceback (most recent call last):
  File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/threading.py", line 1038, in _bootstrap_inner
    self.run()
  File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/threading.py", line 975, in run
    self._target(*self._args, **self._kwargs)
  File "/Users/hanxirui/workspace/python/DataScience/venv/lib/python3.11/site-packages/RealtimeSTT/audio_recorder.py", line 667, in _recording_worker
    while self.audio_queue.qsize() > self.allowed_latency_limit:
          ^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/multiprocessing/queues.py", line 126, in qsize
    return self._maxsize - self._sem._semlock._get_value()
                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
NotImplementedError```

Damn. Researched it and yes, MacOS does not support queue.qsize() method from the multiprocessing module.
Need to find a workaround for this. Sorry for this issue.

Updated audio_recorder.py to a new version which hopefully fixes this (not available with pip install yet).
Would be great to hear feedback, if that works.

Fix now available also with pip install (untested though, unfortunately I have no Mac):
pip install --upgrade realtimestt==0.1.7

Any idea this error?
Say something...
RealTimeSTT: root - WARNING - Audio queue size exceeds latency limit. Current size: 84. Discarding old audio chunks.
zsh: segmentation fault  PYTHONPATH=. python tests/simple_test.py
Process Process-2:
Traceback (most recent call last):
  File "/Users/xiaopel/opt/anaconda3/envs/torch2/lib/python3.9/multiprocessing/process.py", line 315, in _bootstrap
    self.run()
  File "/Users/xiaopel/opt/anaconda3/envs/torch2/lib/python3.9/multiprocessing/process.py", line 108, in run
    self._target(*self._args, **self._kwargs)
  File "/Users/xiaopel/Github/Startup/RealtimeSTT/RealtimeSTT/audio_recorder.py", line 369, in _transcription_worker
    audio, language = conn.recv()
  File "/Users/xiaopel/opt/anaconda3/envs/torch2/lib/python3.9/multiprocessing/connection.py", line 255, in recv
    buf = self._recv_bytes()
  File "/Users/xiaopel/opt/anaconda3/envs/torch2/lib/python3.9/multiprocessing/connection.py", line 419, in _recv_bytes
    buf = self._recv(4)
  File "/Users/xiaopel/opt/anaconda3/envs/torch2/lib/python3.9/multiprocessing/connection.py", line 388, in _recv
    raise EOFError
EOFError

I'm sorry, I can't really tell what's going wrong here. After bit of research it seems Mac does have some issues with pythons multiprocessing. Maybe it's worth a try with a newer python version, python 3.9 is already two years old.

I get around this by use a transcribe function, instead of sending data to transcribe via Pipe. From the design, it seems that there is no need for starting the process of transcription_worker. Anything I missed?

If you run recorder.text() in a loop the transcription of the last sentence pulls so much resources, that the voice activity detection is not reliable in the time the transcription runs. This is a problem if the transcription needs some time (long last sentence) and the next sentence is very short (depends on VAD). Then the short next sentence would not be detected.
So basically it is a fix for a quite specialized problem. I did not realize that multiprocessing would introduce that many new problems as it did, especially for non-windows platforms.

@eelxpeng what changes did you make in the audio_recorder.py, when replacing transcription_worker with transcribe, can you post the diff?

Maybe this checkpoint  helps, that was before introducing multiprocessing.

Thanks but that causes the https://github.com/KoljaB/RealtimeSTT/issues/3 issue with stream closed

Still the same issue - tried on python 3.11 and 3.12. Will take a look later.

Great work. KoljaB! I found the fix after breaking my head for a while in macOSX. replace the multiprocessing Queue with Manager.Queue. It works perfectly.
I don't want the wake words and other aspects. So i had to strip out certain aspects of the code. Still it serves my purpose.
Another thing I noticed was the device index. It works without passing, and in my case the mic was on the device 1. took me a while to list channels & identify the right value.
from multiprocessing import Manager
manager = Manager()
queue = manager.Queue()
... use the queue ...
if queue.qsize() > 0:  # Check for elements
print("Queue has elements.")

Thanks a lot for this hint. I recently switched RealtimeSTT (and RealtimeTTS) from multiprocessing to torch.multiprocessing. Is the problem still existing with v1.9.0? (I hope to be lucky and the switch to torch.multiprocessing does the same for macOSX).
For the device_index I prob need to add an option to list the devices.

Unfortunately the switch to torch.multiprocessing reintroduced the qsize issue on macOS.

is this using pytorch MPS acceleration?

Unfortunately the switch to torch.multiprocessing reintroduced the qsize issue on macOS.

I'll make a fix for this.

is this using pytorch MPS acceleration?

RealtimeSTT depends on the faster-whisper library, which in turn uses CTranslate2. This issue discussion from faster-whisper gh repo says there's no built-in support for AMD, MPS etc accelerations but it's possible to enable these backends by compiling CTranslate2 from the source with the desired backend before installing faster-whisper.
So - if I got this right - this would mean for MPS acceleration you would first compile CTranslate2 with the necessary backend support (MPS enabled). Then proceed with the installation of RealtimeSTT - which installs faster-whisper, but this should not override the manually compiled version of CTranslate2.

Unfortunately the switch to torch.multiprocessing reintroduced the qsize issue on macOS.

Should be fixed with v0.1.12 now.

Yay it's working! Thank you 🫶🏼

Great work. KoljaB! I found the fix after breaking my head for a while in macOSX. replace the multiprocessing Queue with Manager.Queue. It works perfectly.

I had to settle on python 3.11 for faster-whisper and other dependencies to work

I don't want the wake words and other aspects. So i had to strip out certain aspects of the code. Still it serves my purpose.
Another thing I noticed was the device index. It works without passing, and in my case the mic was on the device 1. took me a while to list channels & identify the right value.
from multiprocessing import Manager

manager = Manager()
queue = manager.Queue()

# ... use the queue ...

if queue.qsize() > 0:  # Check for elements
    print("Queue has elements.")

Thank you, this Manager Queue works for me as well


[Input+Tooltip] useImperativeHandle removes access to inner dom element, breaking Tooltip

Description
Input component uses useImperativeHandle to give parent access to custom features.
Tooltip radix primitive requires to call e.getBoundingClientRect on wrapped element to properly display around it.
Therefore when it is wrapping an input, its display is broken

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

npm package (@latest dist-tag)
GitHub release

Your semantic-release bot :package::rocket:


Prefix summary table shows locations count in table. Previous 1:1 behaviour displayed actual location name

As ...
Ozzie - Operator
I want ...
An easy way of seeing at a glance which prefixes are assigned to which location in a summarised table. New behaviour now that prefixes can be assigned to many locations is to display the count in the "locations" column.
https://demo.nautobot.com/ipam/prefixes/ for example.
This change was made to make the table cope with prefixes assigned to very many locations but has made the table less useful for the majority of cases where prefixes are assigned to a single location. Suggest a compromise:

if the prefix is assigned to one location display the location name
if the prefix is assigned to multiple locations display the count

So that ...
The current summary table displays a more useful view showing which prefixes are assigned to which location
I know this is done when...
The prefix summary table as per https://demo.nautobot.com/ipam/prefixes/ behaves like the previous behaviour unless a prefix is assigned to more than one location
Optional - Feature groups this request pertains to.

[ ] Automation
[ ] Circuits
[ ] DCIM
[X] IPAM
[ ] Misc (including Data Sources)
[ ] Organization
[ ] Apps (and other Extensibility)
[ ] Security (Secrets, etc)
[ ] Image Management
[ ] UI/UX
[ ] Documentation
[ ] Other (not directly a platform feature)

Database Changes
UI change only - no database changes required
External Dependencies
No response

See also #5773

Yes that issue addresses multiple shortcomings in the UI to avoid multiple click-throughs to see the data. The changes to the IPAM prefix view is very pressing as it has significantly reduced the usability of this view from previous behaviour.

Team , will you have a solution for this anytime soon?

If I'm understanding https://github.com/django/django/commit/242499f2dc2bf24a9a5c855690a2e13d3303581a correctly, it should be possible to efficiently get the first N (say, 1 or 2) associated Locations for each Prefix in the table view with an appropriate prefetch_related(Prefetch...) call, which would make it acceptable performance-wise to implement this behavior. (Doing an unbounded prefetch_related("locations") would be problematic since it could potentially fetch a huge number of Locations for each Prefix in the table)

Developers do not understand the difficulty of not having the location view. At least if prefixes are only on a single site, display it please.

If I'm understanding django/django@242499f correctly, it should be possible to efficiently get the first N (say, 1 or 2)

First couple would be fine. In 99% of uses cases a prefix is assigned to one location.

Looks like when this was implemented, it is using the location display attribute rather than the name attribute.  The display attribute contains the full location hierarchy path e.g. Americas → United States → ATL01.  The name attribute is just ATL01.
This results in an excessively long name to try and fit into a table view that has many columns. This makes line wrapping very likely and makes the data hard to read.  Can this be adjusted to use location.name instead of location.display?  I believe the previous functionality this change is replicating used the location name and not the full location hierarchy.

Thanks for the feedback! The requested change is a bit tricky to do because of the generic/"automagical" way that #6450 does things. In most cases display is the correct attribute to use for related objects, it's just a few cases (such as Location and VRF -- as you've pointed out on #6637) where the display is more verbose than strictly necessary.
IMHO the generic display attribute is overloaded for too many purposes at present, as it's used for all of the following:

the title of each object's "detail" page
the default display of a reference to that object in various table views
the default display of a reference to that object in create/edit forms (see also #2668)
any number of one-off special cases as well

In 3.0 we should really think about separating these into distinct use cases and/or providing a way in each of these cases to explicitly override the display where it's either too brief or too verbose for a specific situation.


many: make progress when possible for *0 parsers

This change attempts to resolve #982.

Coverage increased (+0.06%) to 87.182% when pulling e7f3c772b77aa04e832209d99a6c120ece63200f on tones111:issue_982 into f40e4a74bd25abea2d2de306476c143dd0e9a181 on Geal:master.

accepting this behaviour would hide potential bugs, I'd rather document clearly that many0 and many1 have this protection against infinite loops


Refresh Dockerfile For Greater Usability

Expected Behavior
Too many people are confused with the application docker file. We could make it just have the foundation -- and a simple script to use that foundation ("docker run") and mount the user's code directories.
Given the confusion on the current code directory requirement maybe it's best to make the script work with relative pathing to the output of the launcher and ask for the directories.  i.e.
docker-run.sh
echo "Enter path to your LEAN binaries [default ./Launcher/bin/Debug]:"
read $binaries
echo "Enter path to your Data Folder [default ./Data]:"
read $datafolder
 docker run -it quantconnect/lean:foundation -v $binaries:/root/Lean -v $datafolder:/Data

docker-run.bat etc

Actual Behavior
Two docker files and no documentation.
Potential Solution
As above
Checklist

[x] I have completely filled out this template
[x] I have confirmed that this issue exists on the current master branch
[x] I have confirmed that this is not a duplicate issue by searching issues

I create my own container here based on the mono container. It works well. It would be ideal to have a container which is available that already has Lean installed (the git clone, etc).
My request: you can't get around a build, but it would be great if the csproj format can be updated to the .NET Core spec, which would eliminate my hack of creating a copy of the csproj locally in order to include the files needed to run the algorithm.
I haven't tried this with Python, but the base image likely can just be changed from the mono one I'm using to your foundation.
It's important that there's no local building done outside of the container, which isn't the case now. Your docker-run scripts should be replaced with docker-compose file. (also the example I'm using for myself is in the repo). It would be extremely difficult/impossible to run this on Heroku or within a K8 cluster without this.
My repo is here, along with notes: https://github.com/nabeelio/QuantConnectDockerExample
BTW, why was tagging stopped in 2017? I have to checkout a specific commit of the master branch, which is less than ideal.

I create my own container here based on the mono container. It works well. It would be ideal to have a container which is available that already has Lean extracted (the git clone, etc).
My request: you can't get around a build, but it would be great if the csproj format can be updated to the .NET Core spec, which would eliminate my hack of creating a copy of the csproj locally in order to include the files needed to run the algorithm.
I haven't tried this with Python, but the base image likely can just be changed from the mono one I'm using to your foundation.
It's important that there's no local building done outside of the container, which isn't the case now. IMHO, the docker-run scripts should be replaced with docker-compose (also a stripped down example I'm using for myself is in the repo below). It would be extremely difficult/impossible to run this on Heroku or within a K8 cluster without this.
My repo is here, along with notes: https://github.com/nabeelio/QuantConnectDockerExample
I'm using a VS project with the nuget packages (thanks for that!), I want to keep the algorithms as separate from the core engine as possible. This allows that. BTW, why was tagging stopped in 2017? I have to checkout a specific commit of the master branch, which is less than ideal.

I create my own container here based on the mono container. It works well. It would be ideal to have a container which is available that already has Lean extracted (the git clone, etc).
My request: you can't get around a build, but it would be great if the csproj format can be updated to the .NET Core spec, which would eliminate my hack of creating a copy of the csproj locally in order to include the files needed to run the algorithm.
I haven't tried this with Python, but the base image likely can just be changed from the mono one I'm using to your foundation.
It's important that there's no local building done outside of the container, which isn't the case now. IMHO, the docker-run scripts should be replaced with docker-compose (also a stripped down example I'm using for myself is in the repo below). It would be extremely difficult/impossible to run this on Heroku or within a K8 cluster without this. The scripts assume a build environment that is identical to the runtime environment (I've seen some notes about hosts being on CentOS while containers are on Debian). This completely eliminates that issue. My host machine OSX 10.15, and I can run everything containerized, with a startup time of about a minute, including a complete build with an updated algo script. I've organized the Dockerfile so that the cache for the least-modified steps does not get invalidated.
My repo is here, along with notes: https://github.com/nabeelio/QuantConnectDockerExample
I'm using a VS project with the nuget packages (thanks for that!), I want to keep the algorithms as separate from the core engine as possible. This allows that. BTW, why was tagging stopped in 2017? I have to checkout a specific commit of the master branch, which is less than ideal.

I create my own container here based on the mono container. It works well. It would be ideal to have a container which is available that already has Lean extracted (the git clone, etc).
My request: you can't get around a build, but it would be great if the csproj format can be updated to the .NET Core spec, which would eliminate my hack of creating a copy of the csproj locally in order to include the files needed to run the algorithm. By default, the Core csproj files include all source-files that are found in the directory into the build.
I haven't tried this with Python, but the base image likely can just be changed from the mono one using your foundation.
It's extremely important that there's no building done outside of the container, which isn't the case now. IMHO, the docker-run scripts should be replaced with docker-compose (also a stripped down example I'm using for myself is in the repo below). It would be extremely difficult/impossible to run this on Heroku or within a K8 cluster without this. The scripts assume a build environment that is identical to the runtime environment (I've seen some notes about hosts being on CentOS while containers are on Debian). This completely eliminates that issue. My host machine is OSX 10.15, and I can run everything containerized, with a startup time of about a minute (at most), which includes a complete build with an updated algo file. I've organized the Dockerfile so that the cache for the least-modified steps does not get invalidated. docker-compose will also auto-restart any downed containers, either in case of a crash or system reboot. It's a Docker-native tool, so there's nothing to worry about users having to install separately.
I run pylivetrader in a similar way, inside of a K8 cluster, with a hot/cold standby, with docker-compose. My plan is to run QuantConnect in a similar manner (I prefer the C# type-safety but that's an aside). Without this, QC is a no-go.
My repo is here, along with notes: https://github.com/nabeelio/QuantConnectDockerExample
I'm using a VS project with the nuget packages (thanks for that!), I want to keep the algorithms as separate from the core engine as possible. This allows that. BTW, why was tagging stopped in 2017? I have to checkout a specific commit of the master branch, which is less than ideal.

Thanks for the notes @nabeelio; I've summarized them and posted here.
Closing #4305 as superseded by #4425


[Feature]Integrate swagger

Add this feature to ensure the sync of code and api documents.

@majian159 To help others better understand the feature, please elaborate the feature description. Thanks.
On my side, I support this feature. I spent much time on the old restful api doc and it will be easy to become out-of-date.

fixed by #764


Doesn't work by default with JetBrains Rider (due to a issue in Rider)

As you may know, JetBrains announced a .NET IDE of their own called Rider, which is based on the IntelliJ platform.
By default, when launching console applications, Rider redirects the output to its own Output pane, rather than launching the actual console.
This causes Colorful.Console to throw:
Unhandled Exception: Colorful.ColorMappingException: Color conversion failed with system error code 6!
 at Colorful.ColorMapper.MapColor(ConsoleColor color, UInt32 r, UInt32 g, UInt32 b)
 at Colorful.ColorMapper.MapColor(ConsoleColor oldColor, Color newColor)
 at Colorful.ColorManager.GetConsoleColor(Color color)
 at Colorful.Console.MapToScreen(StyledString styledString, String trailer)
 at Colorful.Console.WriteAsciiInColorStyled(String trailer, StyledString target, StyleSheet styleSheet)
 at Colorful.Console.WriteLineStyled(StyledString value, StyleSheet styleSheet)
 at Colorful.Console.WriteAsciiStyled(String value, FigletFont font, StyleSheet styleSheet)
 at Program.Main(String[] args)

The "6" comes from Marshal.GetLastWin32Error(), and it's the value for invalid handle. Looking at the library's source, I see the source of the error here: https://github.com/tomakita/Colorful.Console/blob/master/src/Colorful.Console/ColorMapper.cs#L107
My best guess, is since Rider's Output window is not a real console, it doesn't have a handle, and the library fails because of it.
I'm not sure what can be done about this, but I wanted to give you heads up about it, and let you know that I've also submitted this bug to JetBrains here: https://youtrack.jetbrains.com/issue/RIDER-389

Thanks for the heads-up.  I'll keep an eye on the thread that you linked to on the JetBrains forum!

I have the same issue running a console application in docker (Windows Server 2016 / base image dotnet-framework:latest). Any plan to fix it?

Thanks for letting me know.  I don't think I'm going to be able to fix this entirely, i.e. I don't think I can support all of the Colorful.Console functionality in Rider and Docker.  However, would it be helpful if Colorful.Console could detect when it isn't running in a Windows console (e.g. in Rider or Docker), and simply turn off its enhanced color palette?  This would make Colorful.Console less colorful, but the other styling functionality would remain.  If this would be useful, I can look into it.

Thanks for replying.
Detecting "unusual" environments like docker and disabling advanced functionalies is an acceptable compromise in my opinion.

Sounds good.  I'm reopening this, and will start looking into it.  I'll update this thread when there's something to report!

This should be fixed in 1.0.7.  You should be able to use Colorful.Console from Rider and Docker now (and from any other application, for that matter), but without support for the full RGB color palette.
I don't have Rider or Docker, so I wasn't able to test this fix for the two specific use cases mentioned in this thread, but it does seem to have fixed the issue in my test setup (calling Colorful.Console from a WPF application).  Let me know how this works.

Version 1.0.7 for me under Docker. Thank you!

Cool!


Start with go-platform

This is a work in progress. If you want, you can merge this to get started.
Next step is to define Request and Response. This has to be as simple as possible, since I have to define it in roc, go and c (host.h).
We should start simple and can make them complex later

Ahh. That merge was easy :)


Intellisense does not work w/ VS Code + Vetur

[bug] What is the current behavior?
Intellisense (go to definition, etc.) does not work w/ VS Code + Vetur in .vue files

[bug] What is the expected behavior?
These should work out of the box

I attempted tweaking the jsconfig.json w/o luck

[bug] Please provide the steps to reproduce and if possible a minimal demo of the problem via

Pull master
go to src/themes/default/core/components/ProductsSlider.vue and attempt to go to definition on both the ProductSlider and ProductTile imports

[bug] Please tell us about your environment:

Version: MacOS (latest VS code and Vetur)
Browser: [all | Chrome XX | Firefox XX | IE XX | Safari XX | Mobile Chrome XX | Android X.X Web Browser | iOS XX Safari | iOS XX UIWebView | iOS XX WKWebView ]
Language: [all ]

What is the motivation / use case for changing the behavior or adding new feature?
Going to definition / autocomplete is important to anyone who doesn't have intimate knowledge of the underlying framework and should be available out of the box.

Acceptance criteria

[ ] Using .vue files should have the equivalent development experience as other projects

Not sure if it's related to https://github.com/DivanteLtd/vue-storefront/issues/1719
@filrak ?

Yes it's related to lerma  and @vue-storefront  alias that is not recognized by InteliSense

looks like this is already an issue logged against Vetur:
https://github.com/vuejs/vetur/issues/890


Add week-number-and-progress-bar extension

Description

Screencast

Checklist

[x] I read the extension guidelines
[ ] I read the documentation about publishing
[x] I ran npm run build and tested this distribution build in Raycast
[x] I checked that files in the assets folder are used by the extension itself
[x] I checked that assets used by the README are placed outside of the metadata folder

Hi @aesadde The Week Number extension is already available in the shop

Thanks @koinzhang, I may submit a PR to that then. Would be cool to customize the entire date string to show the year, month, etc.


VirtioFS wants to create a /host_mnt folder into the root of the disc, but MacOS say is read only FS

I try to use VirtioFS, but it crashes on volume creation with error:
Error response from daemon: error while creating mount source path '/host_mnt/private/tmp/com.apple.launchd.wxmkUy2zBy/Listeners': mkdir /host_mnt/private/tmp/com.apple.launchd.wxmkUy2zBy/Listeners: operation not supported
Docker tries to create the /host_mnt/... folders into the root of the disc, which is now allowed by MacOS.
MacBook Pro M1 Pro, MacOS v12.3.1, Docker v4.7.0 (77141) .

This looks like a bug not a feature request, so moving to for-mac repo


Automated Package Upgrade

:tada: This PR is included in version 2.2.269 :tada:
The release is available on:

npm package (@latest dist-tag)
GitHub release

Your semantic-release bot :package::rocket:


GTK3: fix preedit string style

For whatever reason, it isn't a "widget", but the name should be specific enough to use alone.

Hm, changing the css means, this might causes issues for people, that use css to style it already? I am not sure if this is the case or if this is a problem however. I have a feeling that styling the gtk UI using css is not used much, but not sure

This patch loosens the rule selector, which means its precedence may only become lower. Any overriding rules should still work as before. This rule is meant to override anything more general (e.g. a rule that applies to any GtkLabel) so any more general rule may stop working because this one starts working, but it's intended behavior anyway.

okay thanks


editing singly linked list descriptions

Pre-Submission Checklist

[x] Your pull request targets the staging branch of freeCodeCamp.
[x] Branch starts with either fix/, feature/, or translate/ (e.g. fix/signin-issue)
[x] You have only one commit (if not, squash them into one commit).
[x] All new and existing tests pass the command npm test. Use git commit --amend to amend any fixes.

Type of Change

[x] Small bug fix (non-breaking change which fixes an issue)
[ ] New feature (non-breaking change which adds new functionality)
[ ] Breaking change (fix or feature that would change existing functionality)
[ ] Add new translation (feature adding new translations)

Checklist:

[x] Tested changes locally.
[ ] Closes currently open issue (replace XXXX with an issue no): Closes #XXXX

Description

These changes target the Singly Linked List Data Structure challenge.
Specifically, they deal with enhancing the descriptions to improve readability and aid in comprehension, as the instructions and descriptions were unclear. Grammar errors were corrected, and word choice improved.

@TheeMattOliver looks like you have merged in 1486 files. I am closing this. Please ping me when you are available.

Weird, ok.
On Tue, Feb 28, 2017 at 10:00 AM, mrugesh mohapatra <
<EMAIL_ADDRESS>wrote:

Closed #13643 https://github.com/freeCodeCamp/freeCodeCamp/pull/13643.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
https://github.com/freeCodeCamp/freeCodeCamp/pull/13643#event-980384819,
or mute the thread
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First attempt at adding a mobilenet backbone

As discussed, this is a very rough attempt at putting in a mobilenet backbone.

It uses @tonylins Pytorch-Mobilenet (so the licensing / CLA needs to be checked with him)
Errors in the modifications to that are all my own of course
I just grab the last 5 things from the model.features (which I made into a ModuleList instead of a sequential, you could probably leave it as sequential and it would work, too, but I don't intend to call it, so this seems cleaner),
I attempted to do one of the todos in the FPN to not make a size-doubling assumption for the layers.
I'm using the pretrained weights @tonylins links to, but I'm not sure how to link them directly into the file, so I download that to the working directory,
Similar to the feature extraction, the layer freezing is pretty arbitrary, I freeze 14 of 19 bits in the model.features list.

I must say that I absolutely loved how the training works with the stock mscoco out of the box and is set up to be extensible!

Just received an email about this thread. I put an Apache License 2.0 in my repo. Feel free to use my repo and contact me if you meet any problem :).

Finished training, box AP is at 26.5. I'll try it without the FPN to see if it gets any faster. The RPN seems to be dominating the runtime of the whole net right now.

I think dropping the FPN and amending the Backbone for the feature layers would be good next steps.

@t-vi: do you already have an idea how you would do it?
For a first attempt based off of the ResNetC4 structure, I took the last of the MobileNet layers with 96 channels for my output. I have not completely understood the differences/reasons for choosing the ROI_BOX_HEAD, so I just continued using the FPN2MLPFeatureExtractor. My config-file looks like this
MODEL:
  META_ARCHITECTURE: "GeneralizedRCNN"
  WEIGHT: "./mobilenet_v2.pth.tar"
  BACKBONE:
    CONV_BODY: "MobileNetV2-FPN"
    OUT_CHANNELS: 96
    FREEZE_CONV_BODY_AT: 3
  RPN:
    PRE_NMS_TOP_N_TEST: 6000
    POST_NMS_TOP_N_TEST: 1000
  ROI_BOX_HEAD:
    POOLER_RESOLUTION: 14
    POOLER_SCALES: (0.0625, )
    POOLER_SAMPLING_RATIO: 0
    FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
    PREDICTOR: "FPNPredictor"
DATASETS:
  TRAIN: ("coco_2014_train", "coco_2014_valminusminival")
  TEST: ("coco_2014_minival",)
SOLVER:
  BASE_LR: 0.02
  CHECKPOINT_PERIOD: 10000
  WEIGHT_DECAY: 0.0001
  STEPS: (180000, 270000)
  MAX_ITER: 360000
  IMS_PER_BATCH: 12

It's still training, currently at a box mAP at 19.9 at 0.049s/image. Classifier and box_reg losses look good, however objectness and rpn_box_reg losses are rather high. If anyone has any ideas for a better structure, let me know.
Also, I retrained the MobileNet with FPN with a larger batch-size and slightly different schedule, got it up to a box mAP of 29.8 (still 0.095s/image).

@kimnik6 I believe that you might be using only the first layer of the MobileNetV2-FPN model to do the training, which have very small boxes. This could explain why you are seeing the objectness and rpn_box_losses as being high.
I'd recommend taking the config from R50-C4, and modify slightly the backbone to not return the 4 feature levels, only returning the last feature map.

I advise to strictly prefer @fmassa's advice over anything I say as he has a lot more insight into this.
With that in mind, I looked a bit at TF's mobile detection model and what it does is it amends the Mobilenet (v1 in their case) with four blocks that each half the number of channels and the resolution. So there, you start with 786 channels and the first Resblock is with 768 input channels, 384 output channels, a bottleneck of 192 channels and halfs the input resolution.
Then the last output of the mobilenet and the additional block's outputs are used as outputs fo the backbone.

@fmassa That was my initial guess as well, but now I think it just simply is higher than the other models, as I checked the model multiple times and the results aren't actually too bad (right now at 26.8mAP with 0.049s/image, waiting for the learning rate to be dropped one last time).
@t-vi Could you post the link to the model/script your referring to?

I looked at the ssd_mobilenet_v1_0.75_depth_coco linked from here: https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md

Alright, training finished at 27.8 mAP (without FPN). I will leave it at that for now.
So overall, my (or rather mostly @t-vi's ) changes were:

maskrcnn_benchmark/config/defaults.py: same as @t-vi
maskrcnn_benchmark/modeling/backbone/backbone.py: add this after line 10

from . import mobilenet

@registry.BACKBONES.register("MobileNetV2-FPN")
def build_mobilenet_backbone(cfg):
    body = mobilenet.MobileNetV2(cfg)
    in_channels_stage2 = cfg.MODEL.MOBILENET.OUT_CHANNELS
    out_channels = cfg.MODEL.BACKBONE.OUT_CHANNELS
    if cfg.MODEL.RPN.USE_FPN:
        fpn = fpn_module.FPN(
            in_channels_list=in_channels_stage2,
            out_channels=out_channels,
            conv_block=conv_with_kaiming_uniform(cfg.MODEL.FPN.USE_GN, cfg.MODEL.FPN.USE_RELU)
            top_blocks=fpn_module.LastLevelMaxPool()
        )
        model = nn.Sequential(OrderedDict([("body", body), ("fpn", fpn)]))
    else:
        model = nn.Sequential(OrderedDict([("body", body)]))
    return model

new file maskrcnn_benchmark/modeling/backbone/mobilenet.py (some slight changes from @t-vi's commit)

# taken from https://github.com/tonylins/pytorch-mobilenet-v2/
# Published by Ji Lin, tonylins
# licensed under the  Apache License, Version 2.0, January 2004

from torch import nn

def conv_bn(inp, oup, stride):
    return nn.Sequential(
        nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
        nn.BatchNorm2d(oup),
        nn.ReLU6(inplace=True)
    )

def conv_1x1_bn(inp, oup):
    return nn.Sequential(
        nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
        nn.BatchNorm2d(oup),
        nn.ReLU6(inplace=True)
    )

class InvertedResidual(nn.Module):
    def __init__(self, inp, oup, stride, expand_ratio):
        super(InvertedResidual, self).__init__()
        self.stride = stride
        assert stride in [1, 2]

        hidden_dim = round(inp * expand_ratio)
        self.use_res_connect = self.stride == 1 and inp == oup

        if expand_ratio == 1:
            self.conv = nn.Sequential(
                # dw
                nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False),
                nn.BatchNorm2d(hidden_dim),
                nn.ReLU6(inplace=True),
                # pw-linear
                nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
                nn.BatchNorm2d(oup),
            )
        else:
            self.conv = nn.Sequential(
                # pw
                nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False),
                nn.BatchNorm2d(hidden_dim),
                nn.ReLU6(inplace=True),
                # dw
                nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False),
                nn.BatchNorm2d(hidden_dim),
                nn.ReLU6(inplace=True),
                # pw-linear
                nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
                nn.BatchNorm2d(oup),
            )

    def forward(self, x):
        if self.use_res_connect:
            return x + self.conv(x)
        else:
            return self.conv(x)

class MobileNetV2(nn.Module):
    def __init__(self, cfg, n_class=1000, input_size=224, width_mult=1.):
        super(MobileNetV2, self).__init__()
        block = InvertedResidual
        input_channel = 32
        if cfg.MODEL.RPN.USE_FPN:
            last_channel = 1280
            interverted_residual_setting = [
                # t, c, n, s
                [1, 16, 1, 1],
                [6, 24, 2, 2],
                [6, 32, 3, 2],
                [6, 64, 4, 2],
                [6, 96, 3, 1],
                [6, 160, 3, 2],
                [6, 320, 1, 1],
            ]
        else:
            last_channel = 96
            interverted_residual_setting = [
                # t, c, n, s
                [1, 16, 1, 1],
                [6, 24, 2, 2],
                [6, 32, 3, 2],
                [6, 64, 4, 2],
                [6, 96, 3, 1],
            ]

        # building first layer
        assert input_size % 32 == 0
        input_channel = int(input_channel * width_mult)
        self.last_channel = int(last_channel * width_mult) if width_mult > 1.0 else last_channel
        self.features = nn.ModuleList([conv_bn(3, input_channel, 2)])
        # building inverted residual blocks
        for t, c, n, s in interverted_residual_setting:
            output_channel = int(c * width_mult)
            for i in range(n):
                if i == 0:
                    self.features.append(block(input_channel, output_channel, s, expand_ratio=t))
                else:
                    self.features.append(block(input_channel, output_channel, 1, expand_ratio=t))
                input_channel = output_channel
        # building last several layers
        self.features.append(conv_1x1_bn(input_channel, self.last_channel))
        self.rpn_layers = [3, 6, 10, 18] if cfg.MODEL.RPN.USE_FPN else [13]
        # make it nn.Sequential
        #self.features = nn.Sequential(*self.features)

        self._initialize_weights()
        self._freeze_backbone(cfg.MODEL.BACKBONE.FREEZE_CONV_BODY_AT)

    def _freeze_backbone(self, freeze_at):
        for layer_index in range(freeze_at):
            for p in self.features[layer_index].parameters():
                p.requires_grad = False

    def forward(self, x):
        res = []
        for idx, m in enumerate(self.features):
            x = m(x)
            if idx in self.rpn_layers:
                res.append(x)
        return res

    def _initialize_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, (2. / n) ** 0.5)
                if m.bias is not None:
                    m.bias.data.zero_()
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()
            elif isinstance(m, nn.Linear):
                n = m.weight.size(1)
                m.weight.data.normal_(0, 0.01)
                m.bias.data.zero_()

new config files:
with FPN

MODEL:
  META_ARCHITECTURE: "GeneralizedRCNN"
  WEIGHT: "./mobilenet_v2.pth.tar"
  BACKBONE:
    CONV_BODY: "MobileNetV2-FPN"
    OUT_CHANNELS: 256
    FREEZE_CONV_BODY_AT: 3
  MOBILENET:
    # OUT_CHANNELS: (160, 160, 160, 320, 1280)
    OUT_CHANNELS: (24, 32, 64, 1280)
  RPN:
    USE_FPN: True
    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
    PRE_NMS_TOP_N_TRAIN: 2000
    PRE_NMS_TOP_N_TEST: 1000
    POST_NMS_TOP_N_TEST: 1000
    FPN_POST_NMS_TOP_N_TEST: 1000
  ROI_HEADS:
    USE_FPN: True
  ROI_BOX_HEAD:
    POOLER_RESOLUTION: 7
    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
    POOLER_SAMPLING_RATIO: 2
    FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
    PREDICTOR: "FPNPredictor"
DATASETS:
  TRAIN: ("coco_2014_train", "coco_2014_valminusminival")
  TEST: ("coco_2014_minival",)
DATALOADER:
  SIZE_DIVISIBILITY: 32
SOLVER:
  BASE_LR: 0.02
  CHECKPOINT_PERIOD: 10000
  WEIGHT_DECAY: 0.0001
  STEPS: (180000, 270000)
  MAX_ITER: 360000
  IMS_PER_BATCH: 4

without FPN:
MODEL:
  META_ARCHITECTURE: "GeneralizedRCNN"
  WEIGHT: "./mobilenet_v2.pth.tar"
  BACKBONE:
    CONV_BODY: "MobileNetV2-FPN"
    OUT_CHANNELS: 96
    FREEZE_CONV_BODY_AT: 3
  RPN:
    PRE_NMS_TOP_N_TEST: 6000
    POST_NMS_TOP_N_TEST: 1000
  ROI_BOX_HEAD:
    POOLER_RESOLUTION: 14
    POOLER_SCALES: (0.0625,)
    POOLER_SAMPLING_RATIO: 0
    FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
    PREDICTOR: "FPNPredictor"
DATALOADER:
  SIZE_DIVISIBILITY: 32
DATASETS:
  TRAIN: ("coco_2014_train", "coco_2014_valminusminival")
  TEST: ("coco_2014_minival",)
SOLVER:
  BASE_LR: 0.02
  CHECKPOINT_PERIOD: 10000
  WEIGHT_DECAY: 0.0001
  STEPS: (180000, 270000)
  MAX_ITER: 360000
  IMS_PER_BATCH: 12

I'll try to upload the weights soon

Awesome work @kimnik6! Do you have a github branch I can update this PR with or should I copy the files?
I tend to use the github release mechanism for releasing weights (e.g. https://github.com/t-vi/pytorch-tvmisc/releases).

You can just copy the code. Thanks for the suggestion @t-vi, I uploaded them under https://github.com/kimnik6/maskrcnn-benchmark-mobilenet/releases.

hi @kimnik6
how many gpus do u use when train the model without FPN?

Without the FPN, multi-GPU didn't work, so I only trained it on one GPU. The initial weights I used are from https://github.com/tonylins/pytorch-mobilenet-v2, you can find my trained models at  https://github.com/kimnik6/maskrcnn-benchmark-mobilenet/releases.

hi @kimnik6
i train the mobile-net with fpn and get 0.306 with these configurations:

2x lr
fix bn
4 gpus with bacthsize=16
and train the mobile-net with retinanet and get 0.330 with these configurations:
2x lr
fix bn
4 gpus with bacthsize=16
also i train it from scratch using GN and get 0.35.
but the speed is not fast.

Concerning the speed, the rest of the network really slows down the process. The fastest I got the MobileNet to work was ~20FPS without the FPN (without changing the image dimensions)

Note that FBNet PR has just been merged https://github.com/facebookresearch/maskrcnn-benchmark/pull/463
and it seems to be faster than MobileNet. @newstzpz will be uploading pre-trained models and numbers soon.

hi @fmassa
i run configs/e2e_faster_rcnn_fbnet.yaml and get 24.6, and the speed is 0.04926319899559021s/img using single gpu GeForce GTX 1080 (11G). it's great to improve the speed.

@zimenglan-sysu-512 did you change anything in the training config?
I believe results are expected to be better, and to run faster as well.
cc @newstzpz who will be uploading trained models and statistics

hi @fmassa
i don't change any settings in the training config.

Ok. I'll let @newstzpz comment on the expected numbers for those models

Hi @zimenglan-sysu-512 , thanks for experimenting with our model. The number you got is expected as it was target for mobile and not GPU friendly. We have models, although still not very GPU friendly, runs at 0.188ms with mAP 33.5 measuring in Caffe2 using V100. Please see here for more details.
Our model supports various efficient building blocks and architecture, including the one in your PR. In addition, we also support using efficient building blocks in RPN and roi heads (bbox, mask etc.). This is important for fast speed on mobile. Here is an example of how to define the architecture:
    "cham_v1a": {
        "block_op_type": [
            # stage 0
            ["ir_k3"],
            # stage 1
            ["ir_k7"] * 2,
            # stage 2
            ["ir_k3"] * 5,
            # stage 3
            ["ir_k5"] * 7 + ["ir_k3"] * 5,
            # stage 4, bbox head
            ["ir_k3"] * 5,
            # stage 5, rpn
            ["ir_k3"] * 3,
        ],
        "block_cfg": {
            "first": [32, 2],
            "stages": [
                # [t, c, n, s]
                # stage 0
                [[1, 24, 1, 1]],
                # stage 1
                [[4, 48, 2, 2]],
                # stage 2
                [[7, 64, 5, 2]],
                # stage 3
                [[12, 56, 7, 2], [8, 88, 5, 1]],
                # stage 4, bbox head
                [[7, 152, 4, 2], [10, 104, 1, 1]],
                # stage 5, rpn head
                [[8, 88, 3, 1]],
            ],
            # [c, channel_scale]
            "last": [0, 0.0],
            "backbone": [0, 1, 2, 3],
            "rpn": [5],
            "bbox": [4],
        },
    },

We provide a few baseline architectures, please feel free to check them out here and all the supported building blocks here.

This is obsolete now.

Note that FBNet PR has just been merged #463
and it seems to be faster than MobileNet. @newstzpz will be uploading pre-trained models and numbers soon.

FBNet is a searched architecture. In my experience these have bad performance when changing domain.
I would still merge this MobileNet PR.


Remove Sunrise prototype.

Removes the Sunrise prototype as the map file isn't even on master currently.
@LittleBuilderJane

Please do, I keep removing it from my local and it always finds a way back into the file...I had no idea it got sneaked into the master.


[AVM Question/Feedback]: template does not respect TFNFR1

Check for previous/existing GitHub issues

[X] I have checked for previous/existing GitHub issues

Description
I was checking TFNFR1
https://azure.github.io/Azure-Verified-Modules/specs/terraform/#id-tfnfr1---category-documentation---descriptions
And I believe the customer_managed_key variable in the template does not respect TFNFR1
https://github.com/Azure/terraform-azurerm-avm-template/blob/fa17004c828890160442a24dfd21d5c21db933ce/variables.tf#L19-L31

Also lock is missing the example in the description:
https://github.com/Azure/terraform-azurerm-avm-template/blob/fa17004c828890160442a24dfd21d5c21db933ce/variables.tf#L94-L107

@matt-FFFFFF

I think the NFR needs changing to MAY


xib2nib: clean up

The package xib2nib has a commit version packaged that is not even in the master branch, but a branch called "test"!
https://github.com/NixOS/nixpkgs/blob/master/pkgs/development/tools/xib2nib/default.nix
https://github.com/matthewbauer/xib2nib/commit/97c6a53aab83d919805efcae33cf80690e953d1e
@matthewbauer please get the repo in a clean state (merge commit to master branch, tag version e.g. 0.1) and update the nix package and add a version.
or remove the nix package if it is not relevant anymore

no reaction and still same state. the project don't even has a description or readme and seems abandoned. i suggest to remove it from nixpkgs


refactor(source): ensure source_ctl_opts.chunk_size is under rate_limit

I hereby agree to the terms of the RisingWave Labs, Inc. Contributor License Agreement.
What's changed and what's your intention?
In source, we have some reader implementations use source_ctl_opts.chunk_size as max chunk size, some other impls use source_ctl_opts.rate_limit as max chunk size. The most intuitive way IMO is to only use source_ctl_opts.chunk_size as max chunk size, and remove rate_limit from SourceCtlOps. This is because the semantics of rate_limit is unclear to source reader impls (they don't know the "clock" and current available quota), they simply shouldn't know anything about rate limit.
Towards that goal (only use source_ctl_opts.chunk_size as max chunk size, and remove rate_limit from SourceCtlOps), let's first make sure source_ctl_opts.chunk_size be the limited size in this PR, which is less than or equal to the rate limit value (max burst per sec).
This PR also fixes the issue mentioned by @StrikeW in https://github.com/risingwavelabs/risingwave/pull/15948/files#r1572060693.
Checklist

[ ] I have written necessary rustdoc comments
[ ] I have added necessary unit tests and integration tests
[ ] I have added test labels as necessary. See details.
[ ] I have added fuzzing tests or opened an issue to track them. (Optional, recommended for new SQL features #7934).
[ ] My PR contains breaking changes. (If it deprecates some features, please create a tracking issue to remove them in the future).
[ ] All checks passed in ./risedev check (or alias, ./risedev c)
[ ] My PR changes performance-critical code. (Please run macro/micro-benchmarks and show the results.)

[ ] My PR contains critical fixes that are necessary to be merged into the latest release. (Please check out the details)

Documentation

[ ] My PR needs documentation updates. (Please use the Release note section below to summarize the impact on users)

Release note
If this PR includes changes that directly affect users or other significant modifications relevant to the community, kindly draft a release note to provide a concise summary of these changes. Please prioritize highlighting the impact these changes will have on users.

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In source, we have some reader implementations use source_ctl_opts.chunk_size as max chunk size, some other impls use source_ctl_opts.rate_limit as max chunk size. The most intuitive way IMO is to only use source_ctl_opts.chunk_size as max chunk size, and remove rate_limit from SourceCtlOps.

Could you clarify the exact semantics of max_chunk_size after this PR?
Is it just min(opts.chunk_size, rate_limit), if rate_limit is Some, and opts.chunk_size otherwise?

In source, we have some reader implementations use source_ctl_opts.chunk_size as max chunk size, some other impls use source_ctl_opts.rate_limit as max chunk size. The most intuitive way IMO is to only use source_ctl_opts.chunk_size as max chunk size, and remove rate_limit from SourceCtlOps.

Could you clarify the exact semantics of max_chunk_size after this PR?
Is it just min(opts.chunk_size, rate_limit), if rate_limit is Some, and opts.chunk_size otherwise?

As an option in SourceCtlOps that are passed in when creating readers, the max_chunk_size only means max size of each StreamChunk produced (while atm this is not guaranteed, will fix in next PR #16440), no other semantics.
When SourceCtlOps constructed by source executors, executors should set the option to be min(config.chunk_size, rate_limit) if rate_limit else config.chunk_size. This isn't because the semantics of max_chunk_size should be the min, but rather because we don't want to split chunks read by readers again in executors.

Seems like various tests are still failing. Take a look again after the ci passes.


refs gh-200

Coverage increased (+0.03%) to 33.426% when pulling b9fbdea68296cfa10d86329ac5f4e52bde91c1c0 on fedys:issue-200 into f8d06d67db6a3172ef25583cf17c999f8e24d45d on cross-solution:develop.

Coverage increased (+0.03%) to 33.426% when pulling b9fbdea68296cfa10d86329ac5f4e52bde91c1c0 on fedys:issue-200 into f8d06d67db6a3172ef25583cf17c999f8e24d45d on cross-solution:develop.

Please add @author tags to all classes you modified (Testcases also)
If you don't mind, please use your full name.

Coverage increased (+0.03%) to 33.426% when pulling 58e11304e9c81fb9b9fec2e1d79374d3f0d56883 on fedys:issue-200 into f8d06d67db6a3172ef25583cf17c999f8e24d45d on cross-solution:develop.

Thank you.


Corrected error in hasViewComponents() docs...

The docs state that one should pass an array of view component classes to hasViewComponents() when, in fact, it simply requires a list of one or more view component class arguments...after the prefix, of course.
I noticed this when passing an array, per the docs, and the package would attempt to iterate over the ...$viewComponentNames arguments, but there was only one, which was an array, which then caused an exception when trying to use it as an array index.
So...easy-peasy change....unless I've missed something. 🤓

I should add...I know this is Hacktoberfest time and there are more and more complaints each year about people spamming for t-shirts. I'm a fan of Hacktoberfest and love the t-shirts, but this isn't spam. I ran into the problem today while working on a package and the (documentation) fix appears to be quite simple. I hope you'll give me the benefit of the doubt and let me know if you're otherwise worried. Thx. 🤓


[DS-2616] oai_dc crosswalk does not export format of bitstreams

@tdonohue cherry-picked these from #963, this PR is against master
https://jira.duraspace.org/browse/DS-2616

+1 Corrects a broken XPATH query which attempts to fill out dc:format.  This looks good to me.

+1 by inspection


[KIECLOUD-663] Create pipeline for rhpam-kogito operator

Signed-off-by: desmax74<EMAIL_ADDRESS>Many thanks for submitting your Pull Request :heart:!
Please make sure your PR meets the following requirements:

[ ] You have read the contributors' guide
[ ] Pull Request title is properly formatted: [KOGITO-XYZ] Subject
[ ] Pull Request contains a link to the JIRA issue
[ ] Pull Request contains a description of the issue
[ ] Pull Request does not include fixes for issues other than the main ticket
[ ] Your feature/bug fix has a unit test that verifies it
[ ] You've ran make before-pr and everything is working accordingly
[ ] You've tested the new feature/bug fix in an actual OpenShift cluster
[ ] You've added a RELEASE_NOTES.md entry regarding this change

How to retest this PR or trigger a specific build:

Run operator BDD testing
Please add comment: /jenkins test

Run RHPAM operator BDD testing
Please add comment: /jenkins RHPAM test

How to backport a pull request to a different branch?

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

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

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

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

hi @desmax74, thanks for sending this.
New we will need to call this script on Jenkins, we need to make sure that this image is added on the osbs-seed descriptor


Fix model download for ONNX embedder

Description of changes
The current function is looking for the tar.gz file instead of checking if the folder already exists, so if the tar.gz gets deleted after extraction, it downloads it again..  This PR resolves this and checks for the model in the extracted folder before attempting to download or extract again.
Test plan
By using it
Documentation Changes
I didn't find any documentation about how this does the download.

For more context on the problem this solves:
In my software, I am using the ONNX embedder, but we don't want to make it download every time someone launches the container, so we changed the DOWNLOAD_PATH to our software folder and downloaded the tar.gz and extracted it in the folder in order to make it work.  This PR will make it to where if you have either the tar.gz or the onnx folder in the DOWNLOAD_PATH location, it will continue on to initializing the model.  Currently you need to have both for it to work properly.

Those sound like great ideas for another PR.  This fix satisfies the need to download the model properly, additional features would make more sense in another PR, right?

I don't think another PR 'makes more sense' but sure if you do not want to make those changes I am happy to land this as is.

@HammadB , @Josh-XT , do you think it is reasonable to do the following steps in the respective order of "difficulty"

Attempt to load the model from the expected path
If the above fails, checksum the file and extract
If the above fails, download the file, checksum, and extract it

I think you have to checksum the individual files in addition to the tar because the extraction can be partial

I think you have to checksum the individual files in addition to the tar because the extraction can be partial.

As a more "brute-force" approach, one can simply checksum tarball and extract every time. (possibly remove any model dir that may exists).

I just added an iteration for checking if each file exists in _download_model_if_not_exists.  I didn't add anything for checksum checking.  I wasn't saying that it should be another PR out of laziness, just that it isn't functionality that exists currently or would make this PR any more useful (in my opinion at least) to do the checksum part.  As someone using this embedder, I'd personally prefer to not have to worry about that hash changing and me needing to scramble to update my software to accomodate changes made elsewhere.

Thanks @Josh-XT this change makes sense to me incrementally. We can add checksumming as an extra step in a subsequent PR. Much appreciated!


When put .abi and .wasm file to contract, some wrong occurs!(Maybe .abi file format is wrong ,but why?)

cleos set contract debug /Users/changlingyi/eos/build/tools/debug/debug.wast /Users/changlingyi/eos/build/tools/debug/debug.abi
Reading WAST...
Assembling WASM...
340819ms thread-0   main.cpp:1158                 main                 ] Failed with error: Assert Exception (10)
!"error parsing wast": Error parsing WebAssembly text file:
:1:1: expected '('
{
^

I meet the same problem, my abi file is same as in the document:
debug.abi
 {   "structs": [{       "name": "foo",       "base": "",       "fields": {         "from": "account_name",         "to": "account_name",         "amount": "uint64"       }     }   ],   "actions": [{       "action_name": "foo",       "type": "foo"     }   ] }

Can I have a look on your debug.hpp and debug.cpp files?

this is my debug.abi content
{
"____comment": "This file was generated by eosio-abigen. DO NOT EDIT - 2018-04-24T02:02:17",
"types": [],
"structs": [{
"name": "foo",
"base": "",
"fields": [{
"name": "from",
"type": "account_name"
},{
"name": "to",
"type": "account_name"
},{
"name": "amount",
"type": "uint64"
}
]
}
],
"actions": [{
"name": "foo",
"type": "foo",
"ricardian_contract": ""
}
],
"tables": [],
"clauses": []
}

Hi guys, I got the same errors, but I finally try it out, run the command like this:
./cleos set contract currency ../../contracts/currency
It mean that you just need pass the directory path, not .apiwast and .api, it is different from the tutorial, but it works.

when I commit deploy a contract on blockchain, "Error 3030002: signatures do not satisfy declared authorizations" occurs!
Details are as follows：
cleos set contract debug /Users/changlingyi/eos/build/tools/debug/
Reading WAST...
Assembling WASM...
Publishing contract...
Error 3030002: signatures do not satisfy declared authorizations
Ensure that you have the related private keys inside your wallet and you wallet is unlocked.
Error Details:
transaction declares authority '{"actor":"debug","permission":"active"}', but does not have signatures for it.
I have checked that have the related private keys inside your wallet and you wallet is unlocked.
what should I do?give me some suggestion!

For now the branch is so unstable, so I suggest you wait for the master branch release.

two suggestion to resolve it:

create a new account to publish the contract, maybe you have publish one use eosio account.
remember import new key into your wallet, and add the option -p ***@active, alike that.
hope to useful.

Yes, the new command looks like this
cleos set contract debug /Users/changlingyi/eos/build/tools/debug/
instead of
cleos set contract debug /Users/changlingyi/eos/build/tools/debug/debug.wast /Users/changlingyi/eos/build/tools/debug/debug.abi
For the next error, it's very likely that you didn't import debug's private key to your wallet (or you didn't unlock your wallet)

Thank you.It's error message. I have runing the contract，but I want to replace the contract. how to do
account.code_version != code_id: contract is already running this version of code.


Opening command palette doesn't work when go to file palette is open

VSCode Version: 1.9.0-insiders
OS Version: mac 10.12.1

Steps to Reproduce:

open vscode
open go to file palette with cmd+p
type cmd+shift+p to open the command palette
expect opens command pallete
err opens the last file in the go-to file palette

https://github.com/Microsoft/vscode/issues/10957


Android driver timeout

I work with a custom made device which has some fairly modest hardware specs which keeps timing out whenever I try to run a test.
java.util.concurrent.TimeoutException: Maestro Android driver did not start up in time
	at maestro.drivers.AndroidDriver.awaitLaunch(AndroidDriver.kt:119)
	at maestro.drivers.AndroidDriver.open(AndroidDriver.kt:83)

Changing the timeout fixed this issue for us. Is there any specific reason why SERVER_LAUNCH_TIMEOUT_MS  is kept at 5s? Could we bump it up to 10s instead? Another option would be to accept the timeout as a command argument and fallback to the built-in one if not specified by the user.

I think this is a duplicate of this closed (but still ongoing) issue - https://github.com/mobile-dev-inc/maestro/issues/144

Also encountering this issue trying to get Android tests up and running on CI.

@carlosmuvi-stripe @simon-gilmurray @ebarrenechea we have tweaked the timeout to be 15 seconds which should give enough time to start the maestro server. Let me know if this works for you 🙌
Feel free to reopen if this is still an issue!

Thanks @amanjeetsingh150 unfortunately this still seems to be causing the same issue on our CI using a Genymotion emulator. I need to do some more investigating in to why as I can see from a screenshot that the emulator seems to be displaying a system popup that could potentially be interfering.

@amanjeetsingh150 version 1.11.4 works great with our custom device, thanks! 🙌


Replication issue between CVE and Vulnrichment on CVE-2024-40711

The Automatable SSVC decision point in this repo, and upstream in CVE, disagree.
In the Vulnrichment repo, we see:
        "metrics": [
          {
            "other": {
              "type": "ssvc",
              "content": {
                "id": "CVE-2024-40711",
                "role": "CISA Coordinator",
                "options": [
                  {
                    "Exploitation": "none"
                  },
                  {
                    "Automatable": "yes"
                  },
                  {
                    "Technical Impact": "total"
                  }
                ],

So, Automatable: yes
But on CVE.org, we see this rendered:

So, Automatable: no.
(For what it's worth, I believe "yes" is correct.)
Looks like there's a disconnect between the two. The timestamp on the Vulnrichment record is newer, but hasn't been replicated up.

Interestingly, the reference has a CVSS statement that disagrees with the CVE text from the issuing CNA.

I looked into this and I think I see what happened here. For what it's worth, the CVE Services data is "correct," inasmuch as it is actually the most recent score. The timestamps were skewed here a little due to a CVE record update that caused the Vulnrichment record to appear with the same date as the CVE record update.
There are a few issues here that will need to look into more closely to determine what the right path forward is to resolve the kind of conflict that caused this discrepancy.
Vulnrichment repo data should update within the hour and should then match the CVE Services data.
Leaving this open for tracking.

fwiw, the CVE Program's cvelistV5 repo has blame history
https://github.com/CVEProject/cvelistV5/blame/main/cves/2024/40xxx/CVE-2024-40711.json


Getting compiler output from a mid compiler phase for doc generation

Description:
Currently the BLangPackage used for doc generation in standard library goes through all the compiler phases(up to codegen).
https://github.com/ballerina-platform/ballerina-lang/blob/a2756b9b7c65eb84acaaa2227272835e80353698/misc/lib-creator/src/main/java/org/ballerinalang/stdlib/utils/GenerateBalo.java#L117
Some unnecessary variables for doc generation are added in the desugar compiler phase. So it is required to get a compiler output before running this phase.
Steps to reproduce:
Affected Versions:
Swan lake
OS, DB, other environment details and versions:
Related Issues (optional):

Suggested Labels (optional):

Suggested Assignees (optional):

Example of some unnecessary variables.

Seems like these init functions in the http module are also a result of this.

This issue is because API docs are generated after finishing up the compilation process. I think we'll have to rethink this. In the compiler FE, we were planning on allowing desugaring at any phase. I guess that'll complicate things further for doc generation. Will raise with the rest of the team and see.

Closing as no longer relevant.

Closing as no longer relevant.


Update for Windows 10 21H1 (build 19043)

Hi,
It appears syscall structures have been changed in the latest Windows 10 build (21H1 / build 19043), as tools using Syswhispers fail on this build. Verification of this and an update would be much appreciated! :)
Many thanks in advance.

For people stumbling apon this: I have just verified syscalls generated with Syswhispers2 to work on 21H1, so go look at that :)

(Edit: Will actually leave this issue open since updating will still have it's uses. I'm depending on e.g. Nimline Whispers which depends on version 1 of Syswhispers as well)

Closed by https://github.com/jthuraisamy/SysWhispers/commit/0787fa4ca08f93dca6b2a2d9a9f19be6f1d31b3a


Client Interface and Implementation

This PR will be for the first stage of implementing the discord client class. Functionality will be the user can instantiate the discord class, and run the main loop which in turn will send heartbeat payloads and receive heartbeat ACK payloads from the discord gateway.
Next step for PR#3 is setting up the identity payload and receiving the ready payload.

For commit https://github.com/BleuGamer/Discorded/pull/2/commits/f021b6a7154d1ed1fcd48366432bc080102bce53 this is where the unit tests will eventually go, for now I am doing basic tests for each phase just to make sure things actually work, once the whole discord client is finished, then the full unit tests will be written!

For commit https://github.com/BleuGamer/Discorded/pull/2/commits/8fedb56b8825e2780df632524fbd2cf542e04f41 now if a user enters in a token (demonstrated in discord_client_test.cpp) they will be able to fully connect to discord. This means that you can see your bot come online, and events are being sent back to the client.
This will be the farthest the functionality will go for this pull request. Next up is syncing back up with upstream and making sure everything is fine with general building and DLL loading. After that we can work on handling all the events being sent back!


Implemented Form Data to Email

Hey @SauravMukherjee44 Implemented this feature I am attaching one video of Testing on loaclhost I have Implemented this using phpmailer so except github it work on any server u can also check the documentation of phpmailer To get emails on your email just change the email in send.php file located in public folder that's it.
Testing.zip

Share links and screenshots to view your change.

Share links and screenshots to view your change.

Plz check the above comment I have attached a zip File Namely (Testing.zip) click on it will download automatically Which contains a video of Testing form Kindly check and let me know if you have any doubt.

Great, How could i add my mail, to that?
add this mail<EMAIL_ADDRESS>

Great, How could i add my mail, to that? add this mail<EMAIL_ADDRESS>
Okay I am adding This email in code I am sending one test mail on your mail check your email for testing!


Tag modules for api-version

Description

[ ] Query bala for updated sdk versions used in AzureStack
[ ] Tag existing Nuget packageswith :, for example Microsoft.Compute:2017-05-01
[ ] Add the corresponding tag into each existing csproj for ARM packages, and add checking the value to the PR review checklist

Cost: 2

This work will benefit from the below issue
https://github.com/Azure/autorest/issues/2434

This is no longer the case, we are not doing any work for AzureStack.
Rather we have a different workItem for addering profiles for the entire SDK and not just AzureStack


Make necessary columns search/ filterable over

Country? might shift this to metadata displayed in the results
Spanish language translation, no jurisdiction translation
Function (not yet in Airtable)
GIC
NZ Scope 1, 2, 3
Versions: "climate clause" and version 2 = aligned w/ net zero principles
cop 26 priority
timeline, in two columns

to be added: turkish, chinese (mandarin? simplified)

Still need:

[ ] Function
[ ] Spanish Language Translation (no jurisdiction translation)
[ ] country


change audiopitch to the normal

Describe the solution you'd like
On Button press, change audiopitch to the normal.
Describe alternatives you've considered
Dont change the pitch at all

added.


AnimatedSprite.playing and .frame returns Nil.

Godot version
3.3.2.stable.oficial
System information
Windows 10, GLES3, NVIDIA GeForce GT 540M, graphics driver version <IP_ADDRESS>35, Intel(R) Core(TM) i7-2670QM CPU @ 2.20GHz
Issue description
Accessing AnimatedSprite.playing and AnimatedSprite.playing can return Nil. In a certain situation changing AnimatedSprite animation suspends the program.

Steps to reproduce
I have included a minimal reproduction project. When running project, it breaks, and shows error: cannot convert Nil to String, at line: push_warning($AnimatedSprite.playing).
After deleting the line, project breaks, and shows the same error at line: push_warning($AnimatedSprite.frame)
After deleting both lines, project suspends while executing.
Minimal reproduction project
AnimatedSprite_returns_Nil.zip

Possible connection with bug described hehre: https://www.reddit.com/r/godot/comments/gzk19l/end_of_animation_function_called_twice/

Possible conection with the bug described here: https://www.reddit.com/r/godot/comments/gzk19l/end_of_animation_function_called_twice/

It doesn't hang when you change the last line to $AnimatedSprite.set_deferred("animation", "a"). Seems like the animation finished signal is emitted immediately when switching animation, because it has 0 length, which causes the program to infinite-loop or something.

I tried to use print(), but it printed nothing.

I tried to use print(), but it printed nothing.

It prints when you use the call_deferred() workaround I mentioned.

That's right. However, regarding the infinite loop, by adding line:
if $AnimatedSprite.animation != "a":
$AnimatedSprite.animation = "a"
I made sure that the animation would only be switched to "a" once(not infinitely), but if I don't use set_deferred(), the program still hangs.

That's right. However, regarding the infinite loop, by adding line:
if $AnimatedSprite.animation != "a":
$AnimatedSprite.animation = "a"
I made sure that the animation would only be switched to "a" once(not infinitely), but if I don't use set_deferred(), the program still hangs.

I looked at the source and the problem is changing animation during finished signal (it modifies some variables that are used after emitting the signal and it causes invalid state of AnimatedSprite). We either should disallow doing it or make sure that it doesn't hang.

It seems to me that there is also a problem with print(), because as the first part of the function, it should have printed before the program suspended, which did not happen (unlike push_warning() in the console)

Yeah if the program hangs, the print doesn't flush. You can still see the values in the external console.

Is this normal or a known issue?

Is this normal or a known issue? Or does print just execute at the end of the function?

I don't know the internals of print(), but it stops working when you cause infinite loop in C++ code. It rarely happens, because we should have safeguards against this (e.g. not being able to call a method with invalid arguments etc).

This is still a problem for debugging, if you want the function that crashes the program to output something before it does.

I looked at the source and the problem is changing animation during finished signal (it modifies some variables that are used after emitting the signal and it causes invalid state of AnimatedSprite). We either should disallow doing it or make sure that it doesn't hang.
EDIT:
Although this is probably unrelated to the null issue. It doesn't make sense that you can print the value, but push_warning fails.

If it is about not being able to set FPS to zero, I will explain, that (in my other project) I used this technique to take advantage of the second animation as a texture when some object is not clicked by a user for a long time. It is easier than adding separate Nodes for Sprite and AnimatedSprite, and setting FPS to zero is to avoid updating the object unnecessarily.

I looked at the source and the problem is changing animation during finished signal (it modifies some variables that are used after emitting the signal and it causes invalid state of AnimatedSprite). We either should disallow doing it or make sure that it doesn't hang.
EDIT:
Although this is probably unrelated to the null issue. It doesn't make sense that you can print the value, but push_warning fails.

If it is about not being able to set FPS to zero, I will explain, that (in my other project) I used this technique, to take advantage of the second animation as a texture; when some object is not clicked by a user for a long time. It is easier than adding separate Nodes for Sprite and AnimatedSprite, and setting FPS to zero is to avoid updating the object animation unnecessarily.

The null part is weird though. When you use print() instead, it correctly prints true.

push_warning expects its argument to be a String. It works fine if written as push_warning(str($AnimatedSprite.playing)).
Maybe it's the error message that reports the wrong type.

The bug still exists in Godot version 3.3.4.stable.official.

I have made a new, analogous 4.1 reproduction project, and it seems that the issue is gone.
AnimatedSpriteNullTest4.1.zip
The only change that may be needed is adding information about setting fps to 0 or using set_animation_speed("name", 0) in the SpriteFrames' documentation, something like the one AnimatedSprite2D's speed_scale property already has.
Version: v4.1.stable.official.970459615
Graphics: OpenGL API 3.3.0 NVIDIA 391.35 - Compatibility - Using Device: NVIDIA - GeForce GT 540M


LB instances not coming up on EKS

Describe the bug
On EKS LB instances for kubecf-router-public = 0/3, kubecf-ssh-proxy-public=3/3 and kubecf-tcp-router-public=0/3
This looks similar to the issue we had with scf on EKS
https://documentation.suse.com/suse-cap/1.5.1/html/cap-guides/cha-cap-depl-eks.html#sec-cap-install-scf-eks
Before traffic is routed to EKS load balancers, health checks are performed on them. These health checks require access to ports on the tcp-router service, which are only opened by request via cf map-route and other associated commands.
Use kubectl patch to patch the tcp-router service to expose a port to the EKS load balancer so the health checks can be completed.

https://github.com/SUSE/cloudfoundry/wiki/Load-balancers-in-AWS-EKS#aws-eks-specific-issue-with-scfuaa-deployment-and-solution
To Reproduce
Deploy kubecf on EKS. Check LBs on EKS UI
Expected behavior
Post kubecf deployment check instances of LBs on AWS. All instances won't come up
Environment

cf-operator-v0.4.2-201.gc4ad274e.tgz
kubecf-3.0.0-1f48733.tgz

just to me clear, this is a CAP2 blocker

Will be fixed by https://github.com/SUSE/kubecf/pull/300 and https://github.com/SUSE/kubecf/pull/301.

Fixed on https://github.com/SUSE/kubecf/commit/58399a370a4bc809a2f7c603c2922fbc054dd292 and https://github.com/SUSE/kubecf/commit/f82059dc86cd9d7700f34919dfc7999bba09eadd.


Campus Branding Update

Time Tracking: MA-42
This is done when:

[x] App icon replaced with campus branding update icon
[x] Remove app loading/onboarding animation
[x] Verify no other uses of UCSD french fry icon/artwork in campus mobile
[ ]  Test kit scanner replace with new logo

app icon replaced, currently focused on replacing animation with new branding idea

Hi @victor-thai - I have updated the requirements to remove the loading animation.
@woznaut Please add a new issue for the creation of a new branded loading animation.

implemented removal of old french fry animations

@victor-thai updated scanner icon attached


Indexing of Nodes

The input file of this model should be continuous series of integers starting from 0? Like 0,1,2,3,4,5...
What if the node numbers are like 0,2,5,8,7,13,3. Should I reindex the node numbers?
Also, for the npy dump files generated by the python implementation, there is no index for each line. So can I assume that the first line embeds node 0, the second line embeds node1, and so on?

Yes. For now, the node numbers should be integers starting from 0. In your case, reindexing is required.  For output embedding, the k-th line embeds node k.

Cool. Thanks for your response!


IE11 getusermedia

Is there an example of how quaggajs works with IE11?

With platform are you using? In iOS, only Safari will work.
Other browsers don't have the getUserMedia


Add CTH.ExtendedMediaPicker

Could you guys add the 'CTH.ExtendedMediaPicker' to the 'getNativeLabel' method, like this?
function getNativeLabel(datatype, value, scope) {

    switch (datatype.selectedEditor) {
    	case "Imulus.UrlPicker":
    		return imulusUrlPicker(value, scope, {});
    	case "Umbraco.TinyMCEv3":
    		return coreTinyMce(value, scope, {});
        case "Umbraco.MultiNodeTreePicker":
            return coreMntp(value, scope, datatype);
    	case "Umbraco.MediaPicker":
    	case "CTH.ExtendedMediaPicker":
            return coreMediaPicker(value, scope, datatype);
        case "Umbraco.DropDown":
            return coreDropdown(value, scope, datatype);
    	default:
    		return "";
    }
}

@kipusoep, we can likely do this. Can you point me to what package belongs to CTH.ExtendedMediaPicker?
Also, if you send me a PR, this can happen pretty fast.

It is an extended media picker with direct edit link, but since Umbraco 7.4 already includes an improved Media Picker, it's not necessary anymore.

Cool no worries, if you stumble upon any other datatypes that need included let us know.


Provide Readmes NPM

The npm pages all say something like "Unable to find a readme for<EMAIL_ADDRESS>which looks a bit concerning when browsing potential packages:
https://www.npmjs.com/package/redux-dynamic-modules
I think there should at least be a minimal readme per package included, which gives a short overview and points to the github page and/or homepage for more details.
I really think this project deserves more attention and the missing readme might be something that makes people turn away.

Thanks for the suggestion!


$stateParams not behaving as expected

Bug Report
My version of UI-Router is: 1.0.0-beta.1
When passing objects through $stateParams, attributes on arrays disappear. See plunkr.
I like to add functionality to arrays to be able to do stuff like Blog.articles.add( {..}). Currently this breaks.
https://plnkr.co/edit/tkGHjpXlwiHUSswpyDMw?p=preview

@maurei Why aren't you using 1.0.0-rc.1 ?

@mattbrunetti actually I am, bad copy paste. My bad.

Just wondering, as there has been no reply; am I misunderstanding this part of UI router?


Develop

Coverage increased (+0.003%) to 83.297% when pulling 470486ac3f0af2eb94f2a0302a7f7d54e086ab8a on develop into dead7b10eb37ab3b0eb9f71d832d94fd14af6d73 on qa.