Added whatsapp optin field

Targets #1113

@rathorevaibhav Can we provide the date and time field for the WhatsApp option as shown below? And can the date be other than the current data?

@GauravGusain98

The task was to capture the timestamp of opt-in, no? I didn't get your question
Yes, it can be some other date as well (we may require it specially when doing bulk upload)

@GauravGusain98

The task was to capture the timestamp of opt-in, no? I didn't get your question
Yes, it can be some other date as well (we may require it specially when doing bulk upload)

Initially there was only a time field, So I thought we only need a time field in UI.
Got it


GUVNOR-2585: In test scenario, expert contraint of type enum can be set but are always false

See https://issues.jboss.org/browse/GUVNOR-2585
Auto-import enums from the class in order to avoid this:

@manstis Could you review this? :-)

@Rikkola is king of Test Scenarios.. I'd prefer to defer review to him.

@karreiro just out of curiosity: what tool did you use to "record" the gif?

@jhrcek I'm using this one: https://github.com/GNOME/byzanz ;-)


feat(recipe): implement importing recipes using external csv files

Snackbar prompts tell user on status of recipe import. File picker dependency used is well liked on pub.dev but has limited functionality with restricting "all" file extensions - for our purposes, the package does not currently support limiting file selection to  .csv files. Hence, no file restriction is imposed.

New PR created to replace this PR.


feat: Use GOOGLE_APPLICATION_CREDENTIALS if json value

Modified Credential.fromApplicationDefaultCredentials to use GOOGLE_APPLICATION_CREDENTIALS from environmental variables if value is JSON.
Ideally, this should have been in googleapis_auth

What is this about already?

What is this about already?

@rrousselGit This modifies Credential.fromApplicationDefaultCredentials to be aware of instances where GOOGLE_APPLICATION_CREDENTIALS value is JSON and not a file path.

Could you add a test for this? That's quite important logic

@rrousselGit Please review when you can.

Thanks for the tests!


Planning Calendar onCellPress Issue

OpenUI5 version:  All version (you can use current one)
Browser/version (+device/version):  Chrome 122 on Windows 11
Any other tested browsers/devices(OK/FAIL):  Edge 122 on Windows 11 (Fail)
URL (minimal example if possible): https://sapui5.hana.ondemand.com/#/entity/sap.m.SinglePlanningCalendar/sample/sap.m.sample.SinglePlanningCalendar
User/password (if required and possible - do not post any confidential information here):
Steps to reproduce the problem:

In planning Calendar choose the view as Month View.

On the current date, create 4 timesheets at minimum (no limit for maximum)

Now for the current date, if you try to select the link or click the link, you are not able to click the link.

What is the expected result?
The link should be clickable and it should navigate to the other views just like on any another day if there exists more timesheets.
What happens instead?
We can see the link but we can't click it. Even if we click it, it doesn't fires the press event.
Any other information? (attach screenshot if possible)

Hello @SakshiVenom,
Thank you for sharing this finding. I've created an internal incident DINC0097731. The status of the issue will be updated here in GitHub.
Regards,
Teodor

Any update?

Any update on this one?

Hey @SakshiVenom,
Thank you for your report, the issue is now fixed with the linked commit coming to UI5 version 1.126.
Best,
Diana P.


FundingSource as a Role subclass

see https://github.com/scienceai/scholarly-article/issues/16

Thanks, applied!


Allow multiple tenants can report to a single VO

Currently a VO can only be mapped to a single tenant, this should be changed to allow that multiple tenants in OpenStack report usage for a single VO.

Fixed in c335ed06dd0b4316b635e1dfcfeee8bddca74593.


Newsletters: add 190 (2022-03-09)

[x] Lede, releases/RCs, topic links @harding
[x] PR review club summary @glozow
[x] Bitcoin Core 23542 @Xekyo
[x] BDK 522 @adamjonas

Made edits for feedback, thanks @adamjonas and @bitschmidty !  Reviewed contributions and made a few small edits, thanks @glozow and @adamjonas !  Added LDK release and topic links.
Thanks everyone!

Looks great, team! Thank you for an excellent newsletter @harding @glozow @adamjonas !


ImportError: cannot import name 'soft_unicode' from 'markupsafe'

Description
Markup has release a new version and now Jinja throws an error.
File "/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/dbt/utils.py", line 9, in <module>
    import jinja2
  File "/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/jinja2/__init__.py", line 12, in <module>
    from .environment import Environment
  File "/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/jinja2/environment.py", line [25](https://github.com/cloudfactory/edw-dbt/runs/5243038097?check_suite_focus=true#step:7:25), in <module>
    from .defaults import BLOCK_END_STRING
  File "/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/jinja2/defaults.py", line 3, in <module>
    from .filters import FILTERS as DEFAULT_FILTERS  # noqa: F401
  File "/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/jinja2/filters.py", line 13, in <module>
    from markupsafe import soft_unicode
ImportError: cannot import name 'soft_unicode' from 'markupsafe' (/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/markupsafe/__init__.py)
Error: Process completed with exit code 1.

Bug Replication

Install Jinja with MarkupSafe 2.1.0
Expected behavior

No errors when importing jinja.
Environment

Python version: 3.8.12
Jinja version: 2.11.3
MarkupSafe: 2.1.0

Jinja 2.x has not been supported for some time. Upgrade Jinja, and use something like pip-tools to pin your dependencies.

Jinja 2.x has not been supported for some time. Upgrade Jinja, and use something like pip-tools to pin your dependencies.

IMHO that is not reasonable at all. Many projects are still using Jinja 2 and the fix is easy.

You're supposed to ping your dependencies anyway, and if Jinja made a 2.11.x update you'd have to update your pins anyway (you did pin Jinja, right? Otherwise you'd get 3.x!), so why not just add a pin for markupsafe instead?

But in this case, it's an ImportError and it is necessary, and if you consider jinja2 deprecated, creating a patch shouldn't be annoying for anyone.

. IMHO it should be installed in an isolated venv where YOU control all the versions

Yes, we have that discussion often with our users but with Conan you write Python recipes and the user can plugin extension written in Python, so in that sense is a library and the could have other requirements.

Now that we know that it is breaking all other libraries that are dependent on Jinja 2.x, including Flask 1.1.4, could we please pin the max version of MarkupSafe?

Jinja 2.x has not been supported for some time. Upgrade Jinja, and use something like pip-tools to pin your dependencies.

This is a little bit awkward... Flask still uses Jinja2... 😯

Flask uses Jinja2 v3.x. Not v2.x

Please read https://hynek.me/articles/semver-will-not-save-you/, then use a tool like pip-tools to pin your dependencies and control when you get updates.

Flask uses Jinja2 v3.x. Not v2.x

Everything below Flask v2.x uses Jinja2 v2.x. Flask v1.x is still in wide use. A snippet below from Flask's setup.py.
    install_requires=[
        "Werkzeug >= 0.15, < 2.0",
        "Jinja2 >= 2.10.1, < 3.0",
        "itsdangerous >= 0.24, < 2.0",
        "click >= 5.1, < 8.0",
    ],

@jeremy-coulon https://github.com/conan-io/conan/issues/10611

Hi, since the jinja2==2.11.3 has dependency markupsafe>=0.23 without upper constraints, this led to break all library versions that transitively depends on jinja2<3 (notably, flask<2). That is a wrong constraints, and the most simplest fix would be releasing a patch release jinja2==2.11.4 with markupsafe>=0.23,<2.1.0 constraints.
Application developers can add their constraints.txt file to add an upper cap, but library developers have no way to provide extra constraint for non-direct dependency except for marking it as a direct dependency, which would add up more chaos in python library dependencies. This is not a good way to go.
For example, I'm maintaining tfx library which has extra dependency on airflow, which has a dependency flask<2. This breaks our library. Options that I can choose of:

Ask jinja2 library to release a 2.11.4 patch with markupsafe<2.1 constraints.
Ask flask library to release a 1.1.5 patch with adding markupsafe<2.1 dependency
Ask airflow library to upgrade their flask version to flask>=2.

Option 3 is probably what jinja2 maintainer is thinking about, but IMHO there will be so many other libraries which depends on flask<2, and the holistic effort for the migrations to flask v2 (I don't know whether there's a breaking changes introducedd) would be HUGE.
Option 2 is the worst idea because it is adding a wrong dependency for a workaround.
I think Option 1 is the easiest and the simplest solution to the current situation. I know it is hard to keep releasing a older version of the library (e.g. tensorflow maintains their 1.x version), but that would greatly help the python community.
I sincerely beg jinja2 team to do a patch release. 🙇

I don't understand the big deal, the fix with jinga2 is simple:
runtime.py and filters.py:
Change: from markupsafe import soft_unicode
To: from markupsafe import soft_str as soft_unicode
Although I get confused on this site where jinja2 is actually located, or if it's a dead package.  What brought me here is Spyder Python IDE uses this package, I traced the errors, I fixed them with the above very easily.

Jinja is not dead. But old versions are unsupported, and people should pin all their dependencies if they rely on older versions (which is perfectly fine, but not without pinning deps).

@chongkong the new airflow 2.2.4 doesn't have such issue, it has
Flask==1.1.2
Jinja2==3.0.3
MarkupSafe==2.0.1

in its constraints
well, at least for Python 3.8 :grinning:


Remove eventId from Event type (unused)

This field is not in use anywhere.

I don't think that removing the eventId is quite as simple as this - there are many places where we pattern match an event and the eventId and so all of these matches need updating.

I don't think that removing the eventId is quite as simple as this - there are many places where we pattern match an Event and the eventId and so all of these matches need updating.

You are right. I did a "git grep" for the field name but ignored that you can also write Event _ _ _…

I've done a first-pass to adjust all the Event constructor instances (including pattern matches) and simplify a few bits of code as a consequence. Further testing needed


Problematic behavior when trying to implement a Form

Hello, I am trying to implement a simple login form / view, here's the view
const Login = module.exports = (state, actions) =>

Login

    <FormInput label='Username' value={state.username} action={(value)=>actions.updateField({field: 'username', value}) } />
    <FormInput label='Password' value={state.password} action={(value)=>actions.updateField({field: 'password', value}) } />
    <button class='btn btn-primary' onclick={()=>actions.login()}>Ok</button>

and the FormInput component:
const FormInput = module.exports = ({label, value, action}) =>

    {label}
    <input class="form-input" type="text" id="{label}"
        placeholder={label} value={value}
        onkeyup={e => action(e.target.value)}
    />

(let's ignore for now the fact that the password is a simple text input, it doesn't matter for my question).
Now, I have the password and username on state and a corresponding action to update them, something like this:
var state = {
    auth: {
        token: null,
        username: null,
        password: null
    },
...

and
const reducers = module.exports = {
    location: location.actions,
    auth: {
        updateField: ({field, value}) => state => {
            return {
                [field]: value
            }
        },

        login: () => state => {
            console.log("LOGIN", state);
        }

    },
...

I think this is a rather typical setup. The problem is that when I am writing on the textboxes I experience too much lag until the characters are actually displayed on the input - also, if I write too fast some characters are actually missing ... For example if I write<PHONE_NUMBER>23123 really quick I'll get something like this in the input:<PHONE_NUMBER>2. The problem, of course is that propagating the state for every keyup is too expensive and, in react is solved with local state. How can this problem be solved in hyperapp? Is there a technique I don't know -- the form is really not usable.
TIA

Have you tried using oninput instead of onkeyup?  I haven't had any issues with update speed or missing characters with that event.

Hello @SkaterDad thanks for the quick answer. Yes, oninput works much better (I don't notice any lag at all). Actually, to tell you the truth I wasn't aware (at all) about oninput - I always used onkeyup :)
Do you (or anybody else) have any idea why it works so much better with oninput vs with onkey?

Glad that solved your issue!
I'm not entirely sure why oninput is so much faster, but the browsers might be helping out behind the scenes.
I've read various comments on Stackoverflow implying that onkeyup sometimes fires before the input element value has been updated.  This could explain why it missed values for you sometimes.

@spapas onkeyup only tracks key releases; it doesn't know about text or values. onpinput tracks changes in the text / value of an input element.

onkeyup is fired when the user releases a key that's been pressed. It will fire for command, control, shift and other special keys.

oninput is fired when you (change) enter text in an input, but only if the text displayed would change, so it will not be called when you press a non-displayable key. For example, if you select the text, copy and overwrite it by pasting, you will not get any oninput events (but you will get a few onkeyup events for command and shift keys).

Thank you very much for all the answers !

I recommend trying a new library I wrote: hyperapp-forms


Article time zone issue

Articles from certain RSS feeds are being embedded with the wrong time due to a time zone issue.

Fixed with 6c749f692083c6d4acf932f36d5f04067d774a0e.


Merge pull request #430 from MicrosoftDocs/master

Deploy August 2021 publish

Docs Build status updates of commit 36f58ca:
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Note: Broken links written as relative paths are included in the above build report. For broken links written as absolute paths or external URLs, see the broken link report.
Note: Your PR may contain errors or warnings unrelated to the files you changed. This happens when external dependencies like GitHub alias, Microsoft alias, cross repo links are updated. Please use these instructions to resolve them.
For any questions, please:Try searching the docs.microsoft.com contributor guidesPost your question in the Docs support channel


Ssf format

This PR implements formatting by using the ssf module. Format codes are cached in the workbook object for better performance

cleans up that mess nicely, thanks!

may we release this on npm?

I always forget to do that..  all set now.


Issue: "project_name" @traceable or LANGCHAIN_PROJECT being ignored

Issue you'd like to raise.
Why trying to run an AI function with the following decorator:
@traceable(
    client=langsmith_client,
    project_name="Picture Categorization",
)
def my_ai_function(...)...

The langsmith expriment works perfectly by sending the data to langsmith UI, but unfortunately the project is incomprehensible: it sends all results to the "evaluators" project which I have never set in code.
Trying to set an env variable via LANGCHAIN_PROJECT does not work either, the results are still sent to the "evaluators" project.
No idea what is going wrong here...
Suggestion:
The documented methods to set a dynamic project name should work out of the box without complications.
Raising an exception would be more helpful than autogenerating a project name that has (to the best of my knowledge) never been specified in code.

Hi @majorgilles - evaluations are traced within experiments. Evaluator runs are assigned metadata based on the experiment, linked to the target run, but all located in the evaluators project to avoid cluttering your default or other projects with evaluator runs.


Ionicons close-circle not working

[X] Review the documentation: https://github.com/oblador/react-native-vector-icons
[X] Search for existing issues (including closed ones): https://github.com/oblador/react-native-vector-icons/issues

Environment
iOS 16.6
react-native-vector-icons 10.0.0
react-native 0.72.3

Description
I'm trying to use the icon close-circle from the Ionicons font, but it appears as a question mark even if it exists in the font, other icons like cloud-cirle or pause-circle are all working and I can't find a fix

It works for me with the same react-native-vector-icons and react-native versions.

same problem

I also apologize for omitting that I am currently testing the application only on iOS 16.6.
Anyway, here's a screenshot of the search bar with the missing icon

Apparently the icons names have been updated please check https://oblador.github.io/react-native-vector-icons/ for the latest icons name

Apparently the icons names have been updated please check https://oblador.github.io/react-native-vector-icons/ for the latest icons name

I'm sorry but... it's written the same also in the list you linked

@Takabrycheri were you able to solve your Ionicons issue?


Hotfix for Code / Line Lens

This commit provides last but important fixes for the code / line lens. See the commit message.
cc  @KevinRansom
There are only 2 known UI issues to me which luckily only appear on entering new lines.

Code Lens are misaligned if a new line is entered before a line of e.g. member definitions (They jump to the left). This is due to a visual studio bug and I'm going to figure out what's going wrong with a VS dev (hopefully).
Workaround:
a) Enter another line and remove the line afterwards. They now should be correctly aligned.
or
b) Use Line Lens
Code / Line Lens are misaligned after a newline is entered before a couple of lines containing them (I'm trying to find out what's wrong right now...)
Workaround:
a) from above
b) Enter a line between each definition, the bug shouldn't appear anymore.

PS:
I was surprised by the merge as I was working on the fix and didn't notice it! 😄
Thank you very much for merging!

Okay I've resolved the conflicts...

@realvictorprm I am going to move it to the code to the branch dev15.8, then we can stabilize it, without having to rush for 15.7

Good idea, many thanks @KevinRansom

Side note, performance etc. isn't a problem with the Code Lens :)

Going to put a new PR for the dev branch including a new branch on my side.

Okay.


Allocator (GPU_0_bfc) ran out of memory

Hello, I'm very sorry that I encountered some problems.
 Allocator (GPU_0_bfc) ran out of memory trying to allocate 6.36GiB.  Current allocation summary follows. ........  ................. Limit:<PHONE_NUMBER>9 InUse:<PHONE_NUMBER> MaxInUse:<PHONE_NUMBER> NumAllocs:                    1190 MaxAllocSize:<PHONE_NUMBER>
This may be insufficient memory（GPU）
situation:
I slightly modified your code so that it uses my own data ，several inputs have been added，and
subsampling_parameter=0
 batch_num=1,
The above error occurs when running here （trainer.py）
 _, L_out, L_reg, L_p, probs, labels, acc = self.sess.run(ops, {model.dropout_prob: 0.5})
My computer : RTX2080TI, Video Memory:11g.     CPU:intel i9 ,Memory:16g
TensorFlow 1.12.0, CUDA 9.0 and cuDNN 7.4
I don't want to buy any more graphics cards or reduce the depth of  network
Can you give me some suggestions to reduce the GPU memory usage of the program?
thank you!!

Hi @zhouhao957,
So if I understand correctly you do not subsample the input point cloud, this not very memory-safe because if your input have high density areas, then some of your convolution neighborhoods will be too big.
You can try to reduce the input radius, the convolution radius, or introduce some subsampling to reduce the number of points. Alternatively you can try to reduce the number of features the network use (first_features_dim) and use a smaller networks with fewer layers.
Hope this help!

Hi @HuguesTHOMAS,
I come across the OOM error every now and then during predicting. I am dealing with point clouds which have much higher density of points compared to NPM3D for example. Apart from lowering the batch size or increasing the first sub-sampling parameter, do you think lowering the percentile limit for "n_max" from 80 percentile (keep_ratio=0.8 in the calibrate_neighbors function in common.py) which you explained here in this comment on calibrate neighbors would help without any significant loss of accuracy.
I think I get these errors precisely because some regions in my dataset are way too dense so the prediction runs fine until it encounters such an area.
Thanks for sharing your awesome work btw!

Hi @vvaibhav08,
You are right that this parameter is the last one that could have an influence on the memory consumption after batch size and first-sub-sampling / input-radius. In my own experiments, I found that a keep_ratio=0.8 is very effective even on extremely uneven dataset like Semantic3D. You could try to lower it even further, but I don't think this would help a lot more that it does already. The reason is simple: you can see the distribution of the neighborhood sizes in your dataset and it will usually look like the right side of a Gaussian. Something with a shape like this:

The 20% largest neighborhoods are the area in red and the new n_max will be nearly divided by two. The next 20% biggest neighborhood are in green and as you can see the new value of n_max if you take the 60th percentile is very close to the 80th percentile. This is why in my opinion you wont gain much by lowering the keep_ratio parameters.
Anyway I have another question for you that might solve you problem. I understand you are using you own data. Are you subsampling it before feeding it to the network? Because the first subsampling ratio is not applied automatically. The first layer of the network assumes that the data was already subsampled before. So if you don't subsample your data, that could explain you OOM errors. This is the job of the function load_subsampled_clouds that I have in all my datasets. If you are curious, I also gave a link to my SemanticKitti implementation where I do this subsampling online on each input cloud before feeding it to the input generator.
I hope this helps.
Best,
Hugues

@HuguesTHOMAS I do subsample the data using the grid_subsampling function in the dataset script. It's just that my data is too dense in some cases which leads to memory issue.
It would be great if you could share the link anyways. It would help me understand subsampling better.
Thanks!

https://drive.google.com/open?id=12npkHHnqzhhl5i-2q_RD-Cw_urUdWC0J


added BadRequestHandler

Q
A

Is bugfix?
❌

New feature?
✔

Breaks BC?
❌

Fixed issues

Thanks!


CI: Isolate solana bins

Problem
CI installs solana bins in the user's home directory, clobbering any existing installation
Changes
Isolate the install to the local directory

https://github.com/solana-labs/solana-program-library/blob/81e86172e183ca4a350962258088c361f0cdee17/examples/c/makefile#L2
Oh hi

https://github.com/solana-labs/solana-program-library/blob/81e86172e183ca4a350962258088c361f0cdee17/examples/c/makefile#L2
Oh hi

@mvines mind taking another peek?  I "fixed" the makefile path and also added a check so we don't redownload bins if we already have the right version

@mvines mind taking another peek?  I "fixed" the makefile path and also added a check so we don't redownload bins if we already have the right version

But wait... there's more!

But wait... there's more!

Demoting to draft until I have some time to sort out the cached .d issue

Demoting to draft until I have some time to sort out the cached .d issue


Fix syscall warning in Bazel Build

This commit resolves #717 issue.

@ArthurBandaryk I'm not seeing a CLA signed for your GitHub username, is it possible you signed with a different username?


Add support for extra parameters (key, record)

Proposed changes

Types of changes

[ ] Bugfix (non-breaking change which fixes an issue)
[x] New feature (non-breaking change which adds functionality)
[ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
[ ] Documentation Update (if none of the other choices apply)

Checklist

[x] Lint and unit tests pass locally with my changes
[ ] I have added tests that prove my fix is effective or that my feature works
[x] I have added necessary documentation (if appropriate)

Further comments

@FriggaHel you're right about the record key. The "key" is the secret and "--ci-build-id" is what is used to identify a "build"


[EdgeTPU Backend for ONE-vscode] How to distinguish regular tflite model from edgeTPU-compiled tflite model?

Here is our project introduction
Project Introduction : #1621
Background : outputs after compiling tflite model with EdgeTPU
File before compiled with EdgeTPU and file after compiled with EdgeTPU are the same type, .tflite.
The image below illustrates the procedure of converting a .tflite model into an Edge TPU Model using the Edge TPU compiler. The red box located at the bottom represents the .tflite file intended for compilation, while the blue box symbolizes the resulting output of the compilation process.

It means that baseModel and Product are all tflite file. However, tflite file compiled with EdgeTPU doesn’t need to convert to circle file because edgetpu-custom-op cannot be inferenced in ONE-vscode. So this special tflite file is not a Base Model, it is a Product.
As a result, ONE explorer has to show the tflite file in Product type instead of  Base Model type after detecting tflite file and checks whether the tflite is a regular file or is from EdgeTPU Compiler.
The way to distinguish Base Model from Product in ONE-vscode

Find the Base Model with filename extension such as .pb, .tflite.
Read the Base Model’s .cfg file and finds the results that corresponding with Output Path.
Display the results.

Of course, it assumes that the user performs all works in ONE-vscode interface.
Problem
As I mentioned, because the file type of the EdgeTPU Compiler’s result is .tflite, ONE-vscode reads this result two times, while finding Base Model and finding Product with Output Path.
We considered about the way to distinguish those two types(regular tflite and tflite from EdgeTPU Compiler) of tflite files.
Process we discussed

After generating Base Models, set Products of each Models corresponding to Output Path in cfg files of each Models.
At this point, EdgeTPU Compiler’s outputs saved seperately
Exclude those files from Base Model and generate the rest files as the Base Model.

However, we believe that this straightforward method might incur a high time complexity. So we keep thinking about the better method.
If you have a better idea about it, please give us an innovative idea.

Process we discussed

After generating Base Models, set Products of each Models corresponding to Output Path in cfg files of each Models.
At this point, EdgeTPU Compiler’s outputs saved seperately
Exclude those files from Base Model and generate the rest files as the Base Model.

Could you explain more about this?
I agree that excluding edgetpu-compiled tflite files from BaseModel (data structure) is necessary in our view.
I suppose that we could define edgetpu-compiled files to have a certain postfix as *_edgetpu.tflite, like edgetpu-compiler do as default.

Before creating the TreeView component, we first utilize the getCfgList() function from OneStorage to fetch a list of cfg files.
Following this, we collect and save the output path generated by the EdgeTPU compiler into a variable.
Then, within the _buildChildren() code, we implement a conditional check to determine if the file is a .tflite file representing the baseModel.
In this check, we verify whether the model exists within the previously collected variables.
If we find the model in the collection, we've made the decision not to categorize it as a 'baseModel' because it originates from the EdgeTPU Compiler.

The method described above is what we have considered. We opted for this approach because we believed that if we differentiated them solely by postfix, it might limit our ability to customize the output path, and we would always need to include the postfix.
Initially, we will use postfix to differentiate them, and if we identify an additional method for distinguishing them, we will incorporate it as well.

We found a method
When view .tflite file compiled with edge tpu compiler using netron, we notice that description : Exported from Subgraph. We observed that this text appears in .tflite file compiled with edge tpu compiler, regardless of the original description.
Therefore, we concluded that if .tflite file has description : Exported from Subgraph, it is a edgetpu-compiled tflite file.
We attempted to open tflite file using flatbuffers library. We compiled schema.fbs file on Tensorflow's github using flatbuffers-compiler. And we success to read tflite file's description with schema_generated.ts that is result of flatbuffers-compiler.
Regarding this

We used Tensorflow's schema.fbs file that licensed under the Apache License V2. Is it ok?
When we open 6~7mb tflie file for the first time, it spend 20ms. After second times, it spend 3~4ms. Would this have significant impact on performace?

@Hyeon-Uk

Then, within the _buildChildren() code, we implement a conditional check to determine if the file is a .tflite file representing the baseModel.

How do you 'check to determine'? 🤔
Do you mean that you see whether the file is written as 'Product' in the cfgs?
(I am still curious about this approach even though you have suggested another flatbuffer way below. We may opt this one in case of latency problem arises)

The method described above is what we have considered. We opted for this approach because we believed that if we differentiated them solely by postfix, it might limit our ability to customize the output path, and we would always need to include the postfix.

I totally agree with the point.

@imsw0529

When view .tflite file compiled with edge tpu compiler using netron, we notice that description : Exported from Subgraph. We observed that this text appears in .tflite file compiled with edge tpu compiler, regardless of the original description.

That's cool :-D

We used Tensorflow's schema.fbs file that licensed under the Apache License V2. Is it ok?

Yes, Apache License is okay. :-D
It's the license we can use. (Apache 2.0)
Also the MIT License is fine to use.

When we open 6~7mb tflie file for the first time, it spend 20ms. After second times, it spend 3~4ms. Would this have significant impact on performace?

I think it matters... Imagine we have 500+ tflite files and latency gets 20ms * 500 = 10sec to load the view.
But it still sounds like a good thing that you discovered this one.
I think it's why we need to try modulization, to ensure the basic functionality of ONE-vscode while we try some optional functions for our backend.

@imsw0529

When view .tflite file compiled with edge tpu compiler using netron, we notice that description : Exported from Subgraph. We observed that this text appears in .tflite file compiled with edge tpu compiler, regardless of the original description.

That's cool :-D

We used Tensorflow's schema.fbs file that licensed under the Apache License V2. Is it ok?

Yes, Apache License is okay. :-D
It's the license we can use. (Apache 2.0)
Also the MIT License is fine to use.

When we open 6~7mb tflie file for the first time, it spend 20ms. After second times, it spend 3~4ms. Would this have significant impact on performace?

I think it matters... Imagine we have 500+ tflite files and latency gets 20ms * 500 = 10sec to load the view.
But it still sounds like a good thing that you discovered this one.
I think it's why we need to try modulization, to ensure the basic functionality of ONE-vscode while we try some optional functions for our backend.

@dayo09
#1630  This is a draft that solves the problem by referring to the attributes in the .cfg file without flatbuffer.
This is not the perfect way to solve this problem. so, we keep thinking about optimization of "flat buffers", or other methods,

To make long short; we concluded that using _edgetpu.tflite prefix for edgetpu compiled models.


MobiFlight > Help > Open help in web does not link to correct page

Currently the link from the menu takes you to an old website URL that doesn’t exist anymore.
It should link to the english documentation: https://www.mobiflight.com/en/documentation/software.html
or German: https://www.mobiflight.com/de/dokumentation/software.html
[Issue created by DocMoebiuz: 2020-12-04]
[Last updated on bitbucket: 2020-12-27]
[Comment created by DocMoebiuz: 2020-12-27]
Updated the links in the language files

https://www.mobiflight.com/en/documentation/software.html appears to be broken also now.


Fix container superceding

This matters for containers which take a while to start up, where
another request may come in before the first completes (such as on the
CSPS backend during testing). It's important to kill the old one before
trying to start the next one otherwise it may run out of memory.

This used to work but I think the functionality was lost during a refactor.


Fixed release Action semver

Current dependencies on/for this PR:

main

PR #462   👈

This comment was auto-generated by Graphite.


issue_template formatting, punctuation, and codesandox.io template

Added the codesandbox.io template.

@oliviertassinari feel free to edit, I have just experienced that a direct link to a starter template will get us many more good test cases than just the website.

It looks great like this.


[REFACTOR] Removed rogue print statement

There was a print statement executing upon sending a twin patch. No longer!

Fixed in #292


Trying to use Satellite net for 3 channel rgb images

I am utilising 3 channel rgb images and 1 channel mask images for training. So, how can I utilise them to train of satellite unet as I can see in the notebooks that the image is grayscale and mask also is of one channel. Should I make the mask also 3 channel ?Please suggest the solution.

Hi @shubhamsharma1609
It doesn't really matter if you're using 1-channel, 3-channel or 20-channels as an input. You can have any number of channels in your input image and at the same time your output mask can be of any different number of channels.
Number of channels in the output mask defines how many classes are you trying to recognize with your model and is defined by num_classes param in satellite_unet function.
Your input shape on the other hand is defined by input_shape param of satellite_unet function and should look something like this (height, width, num_channels).
Hope that helps

I am facing this issue while using the 3 channel training image and single channel mask :
import numpy as np
import matplotlib.pyplot as plt
#%matplotlib inline
import glob
import os
import sys
from PIL import Image
from skimage.transform import resize

masks = glob.glob("U:\\ALL\\masks_indb_255\\*.jpg")
orgs = glob.glob("U:\\train_indb\\*.png")

imgs_list = []
masks_list = []
for image, mask in zip(orgs, masks):
    imgs_list.append(np.array(Image.open(image)))#.convert('L')),(256,256)))#.resize((512,512))))

    im =np.array(Image.open(mask)) #resize(np.array(Image.open(mask)),(256,256))
    #imm=np.dstack([im,im,im])
    #width, height = im.size   # Get dimensions

    #left = (width - 388)/2
    #top = (height - 388)/2
    #right = (width + 388)/2
    #bottom = (height + 388)/2

    #im_cropped = im.crop((left, top, right, bottom))
    masks_list.append(im)

imgs_np = np.asarray(imgs_list)
masks_np = np.asarray(masks_list)

print(imgs_np.shape, masks_np.shape)

from keras_unet.utils import plot_imgs

plot_imgs(org_imgs=imgs_np, mask_imgs=masks_np, nm_img_to_plot=10, figsize=6)

print(imgs_np.max(), masks_np.max())

x = np.asarray(imgs_np, dtype=np.float32)/255
y = np.asarray(masks_np, dtype=np.float32)/255

from sklearn.model_selection import train_test_split

x_train, x_val, y_train, y_val = train_test_split(x, y, test_size=0.5, random_state=0)

print("x_train: ", x_train.shape)
print("y_train: ", y_train.shape)
print("x_val: ", x_val.shape)
print("y_val: ", y_val.shape)

from keras_unet.utils import get_augmented

train_gen = get_augmented(
    x_train, y_train, batch_size=30,
    data_gen_args = dict(
        rotation_range=15.,
        width_shift_range=0.05,
        height_shift_range=0.05,
        shear_range=50,
        zoom_range=0.2,
        horizontal_flip=True,
        vertical_flip=True,
        fill_mode='constant'
    ))

Got this error :
Traceback (most recent call last):

  File "<ipython-input-62-5b10d6cd4c26>", line 23, in <module>
    fill_mode='constant'

  File "c:\users\shubham\anaconda3\envs\kerasunet\lib\site-packages\keras_unet\utils.py", line 54, in get_augmented
    Y_datagen.fit(Y_train, augment=True, seed=seed)

  File "C:\Users\SHUBHAM\AppData\Roaming\Python\Python37\site-packages\keras_preprocessing\image\image_data_generator.py", line 936, in fit
    'Got array with shape: ' + str(x.shape))

ValueError: Input to `.fit()` should have rank 4. Got array with shape: (70, 512, 512)

Also, can I change the size to 256*256 and train.

This error is in fact related to your masks shape.
ValueError: Input to `.fit()` should have rank 4. Got array with shape: (70, 512, 512)

Your y_train is of rank 3 shape: (70, 512, 512) but it should be of rank 4 (include num_channels) like this (70, 512, 512, 1).
So to fix your code you need to expand dims of your y_train like this:
y_train = np.expand_dims(y_train, axis=-1)

Do it before passing it into get_augmented function

Ok. I reshaped y to y = y.reshape(y.shape[0], y.shape[1], y.shape[2], 1) before augmenting.

I trained the model using satellite_unet and in prediction images ..I am getting complete black images. not able to make out what is going wrong. This is my code.
`import numpy as np
import matplotlib.pyplot as plt
#%matplotlib inline
import glob
import os
import sys
from PIL import Image
from skimage.transform import resize
masks = glob.glob("U:\indapur_baramati\ALL\masks_indb_255\.jpg")
orgs = glob.glob("U:\indapur_baramati\train_indb\.png")
imgs_list = []
masks_list = []
for image, mask in zip(orgs, masks):
imgs_list.append(np.array(Image.open(image)))#.convert('L')),(256,256)))#.resize((512,512))))
im =np.array(Image.open(mask)) #resize(np.array(Image.open(mask)),(256,256))
#imm=np.dstack([im,im,im])
#width, height = im.size   # Get dimensions

#left = (width - 388)/2
#top = (height - 388)/2
#right = (width + 388)/2
#bottom = (height + 388)/2

#im_cropped = im.crop((left, top, right, bottom))
masks_list.append(im)

imgs_np = np.asarray(imgs_list)
masks_np = np.asarray(masks_list)
print(imgs_np.shape, masks_np.shape)
from keras_unet.utils import plot_imgs
plot_imgs(org_imgs=imgs_np, mask_imgs=masks_np, nm_img_to_plot=10, figsize=6)
print(imgs_np.max(), masks_np.max())
x=np.array(Image.open("U:\indapur_baramati\ALL\masks_indb\17.jpg"))
np.count_nonzero(x == 255)
x = np.asarray(imgs_np, dtype=np.float32)/255
y = np.asarray(masks_np, dtype=np.float32)/255
print(x.max(), y.max())
y = y.reshape(y.shape[0], y.shape[1], y.shape[2], 1)
print(x.shape, y.shape)
x = x.reshape(x.shape[0], x.shape[1], x.shape[2], 1)
print(x.shape, y.shape)
from sklearn.model_selection import train_test_split
x_train, x_val, y_train, y_val = train_test_split(x, y, test_size=0.5, random_state=0)
print("x_train: ", x_train.shape)
print("y_train: ", y_train.shape)
print("x_val: ", x_val.shape)
print("y_val: ", y_val.shape)
from keras_unet.utils import get_augmented
train_gen = get_augmented(
x_train, y_train, batch_size=10,
data_gen_args = dict(
rotation_range=15.,
width_shift_range=0.05,
height_shift_range=0.05,
shear_range=50,
zoom_range=0.2,
horizontal_flip=True,
vertical_flip=True,
fill_mode='constant'
))
sample_batch = next(train_gen)
xx, yy = sample_batch
print(xx.shape, yy.shape)
from keras_unet.utils import plot_imgs
plot_imgs(org_imgs=xx, mask_imgs=yy, nm_img_to_plot=2, figsize=6)
from keras_unet.models import satellite_unet
input_shape = x_train[0].shape
model = satellite_unet(
input_shape,
#use_batch_norm=False,
num_classes=1,
#filters=64,
#dropout=0.2,
output_activation='relu',
num_layers=4
)
from keras.callbacks import ModelCheckpoint
model_filename = 'U:\model_v0.h5'
callback_checkpoint = ModelCheckpoint(
model_filename,
verbose=1,
monitor='val_loss',
save_best_only=True,
)
from keras.optimizers import Adam, SGD
from keras_unet.metrics import iou, iou_thresholded
from keras_unet.losses import jaccard_distance
model.compile(
#optimizer=Adam(),
optimizer=SGD(lr=0.01, momentum=0.99),
loss='binary_crossentropy',
#loss=jaccard_distance,
metrics=[iou, iou_thresholded]
)
history = model.fit_generator(
train_gen,
steps_per_epoch=5,
epochs=2,
validation_data=(x_val, y_val),
callbacks=[callback_checkpoint]

)
model.load_weights(model_filename)
y_pred = model.predict(x_val)
from keras_unet.utils import plot_imgs
plot_imgs(org_imgs=x_val, mask_imgs=y_val, pred_imgs=y_pred, nm_img_to_plot=9)

Well, there might be a lot of different issues. I don't know what your images and masks look like and how hard it is for the model to learn anything.
Dont expect results right away. I would start from training for longer than just 2 epochs and monitor loss value over epochs. If loss value is not decreasing then something is wrong.
Did you inspect your images before and after the augmentations? Do they look ok?

I am using tensorflow cpu for training...and here the batch size is small which is 10.The resultant images :

How good is your labelling? From the 2 examples you showed it seems like there's nothing easily distinguishable from the background where your masks are labelled.
The general rule of thumb is: if human eye can't tell the difference - deep learning model definitely can't do it. In your case I can't tell what you're trying to recognize in those images (maybe they are just to small for me to tell).
My first opinion based on what you showed is that model simply can't learn (or its hard for it to learn) what you're trying to detect because it's not easily distinguishable from the other stuff in the image.
But again I would add more data, increase steps_per_epoch and train for longer than just 2 epochs. Monitor the loss value in the process and let it run for a while - then check the results and see if it's learning anything.

Ok.When,I am trying to train on cpu, I am getting memory error.I will increase the batch size and steps per epoch  and run this on gpu to get some meaningful output.

I tried to run the above mentiioned code in AWS sagemaker instance having keras =2.3.1 and tensorflow=2.0.2. But I am getting the following error after running this :
history = model.fit_generator(
    train_gen,
    steps_per_epoch=5,
    epochs=3,

    validation_data=(x_val, y_val),
    callbacks=[callback_checkpoint]
)

The error :
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-23-a832c74cfe86> in <module>
      5
      6     validation_data=(x_val, y_val),
----> 7     callbacks=[callback_checkpoint]
      8 )

~/anaconda3/envs/amazonei_tensorflow2_p36/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
   1295         shuffle=shuffle,
   1296         initial_epoch=initial_epoch,
-> 1297         steps_name='steps_per_epoch')
   1298
   1299   def evaluate_generator(self,

~/anaconda3/envs/amazonei_tensorflow2_p36/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_generator.py in model_iteration(model, data, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch, mode, batch_size, steps_name, **kwargs)
    142       batch_size=batch_size,
    143       epochs=epochs - initial_epoch,
--> 144       shuffle=shuffle)
    145
    146   do_validation = validation_data is not None

~/anaconda3/envs/amazonei_tensorflow2_p36/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_generator.py in convert_to_generator_like(data, batch_size, steps_per_epoch, epochs, shuffle)
    475
    476   # Create generator from NumPy or EagerTensor Input.
--> 477   num_samples = int(nest.flatten(data)[0].shape[0])
    478   if batch_size is None:
    479     raise ValueError(

AttributeError: 'zip' object has no attribute 'shape'


How do PINS differ from Passwords

So if a PIN becomes more complex is it not just a password? Doesn't this make computers more vulnerable since people are familiar with complex passwords buy they associate a PIN with a 4-digit code?
Importantly if I login to computer A and B with the same Microsoft account; will A and B have 2 seperate,  locally stored PINS or can I login to computer B with Computer A's PIN

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

ID: c34dbafa-df3c-a5a5-32d5-2ce152893534
Version Independent ID: cebdd9a6-45d7-6306-373c-7d31121f9e16
Content: Why a PIN is better than a password (Windows 10) - Microsoft 365 Security
Content Source: windows/security/identity-protection/hello-for-business/hello-why-pin-is-better-than-password.md
Product: w10
GitHub Login: @mapalko
Microsoft Alias: mapalko

PIN is stored in local machine and does not stored in Microsoft account.
Moreover  PIN can create only after enabling of Fingerprint reader by scanning of Fingerprint.
I think both fingerprint and PIN are both stored in TPM module.

I don't have a fingerprint reader on the laptop in question and I don't
have a fingerprint even associated with that Microsoft Business Account
On Wed, Mar 24, 2021, 10:06 PM VARADHARAJAN K @.***>
wrote:

PIN is stored in local machine and does not stored in Microsoft account.
Moreover PIN can create only after enabling of Fingerprint reader by
scanning of Fingerprint.
I think both fingerprint and PIN are both stored in TPM module.
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Release process

Use the github action from #3 or similar to auto-create a tag from the version number when the version number changes.
Maintain a whats_new file as per core.

My preference is to use the GitHub Release process, which has a wonderful option to auto-generate release notes.
Simple, more intuitive, doesn't require a commit.


Can't get Consumers information..

Cluster info include zk status , kafka topic is nomal.

but can't get consumer info.

Installation

So sad, even I set
mycluster1.kafka.eagle.offset.storage=kafka

or set this:
mycluster1.kafka.eagle.offset.storage=zk

Still not work...


Rephrased a sentence

Looked like a placeholder was left. Removed it and updated the wording of the following sentence.

@ranieuwe,
Thanks for your contribution as a Microsoft full-time employee or intern. You do not need to sign a CLA.
Thanks,
Microsoft Pull Request Bot

@ronortloff Please review these proposed changes. Thanks.

looks good


/searchtop: suppres google embed

Because

Google has added an embed to the search links, which adds nothing but takes up a large amount of chat space

This PR

suppresses the embed from the link

Pull Request Requirements

[x] I have thoroughly read and understand The Odin Project Contributing Guide
[x] The title of this PR follows the location of change: brief description of change format, e.g. Callbacks command: Update verbiage
[x] The Because section summarizes the reason for this PR
[x] The This PR section has a bullet point list describing the changes in this PR
[x] If applicable, I have ensured all tests related to any command files included in this PR pass, and/or all snapshots are up to date

thank you!


added note before the first lab to start the ElasticSearch cluster cr…

The start of Lab 3 requires creating an ElasticSearch cluster which takes 10 minutes or so and delays working on the lab. It might be useful to start the cluster creation before the first lab so that it saves time later.

I'll look at the CFN template. My original comment was on changing the instructions to suggest that the cluster be started at the beginning of the lab. I will see if it can be done automatically with a template mod.

Not all participants will want to do this lab and therefore we don't launch it in the stack. Also we want to provide experience in navigating the ES service console pages so they get familiar with the console. Asking them to launch it in the beginning may lead to a cluster unnecessarily being launched for a lab they may not do or may not get to.
Alternatively, in v2.0.0 release, I have provided an instruction in the guide after the cluster launch suggesting that participants move on to the next lab and complete that while the cluster is launching. We will consider implementing this suggestion in a future release as we revisit the ES lab and consider additional steps to this lab.
Thanks for the detailed feedback guys!


Enable exporting jax graphs to onnx format

I would like to be able to export jax graphs to onnx format for inference. Please note that for many practitioners a new framework is no go unless the results of training can be exported in a standard format for fine tuning/inference. Would the developers be open to this idea? I could also contribute if someone can guide me on where to get started.

This is possible by first converting JAX->TF and then converting to ONNX. Here is an example: https://colab.research.google.com/gist/tomhennigan/8075058b6e36e2573610dcff6efaa14d/example-of-jax-tf-onnx.ipynb
Depending on your setup, you may want to export from TF differently (e.g. convert model parameters to tf.Variables such that they are stored in the "external tensor storage" dict).  This should be possible using jax2tf. Here is a short guide where we define a model in JAX, train it in TF and then save the result with saved model. https://dm-haiku.readthedocs.io/en/latest/notebooks/jax2tf.html

Thanks for the prompt response. This solves the issue for now. If I face problems in exporting more complex graphs than the examples provide, I might reopen the issue.

can i convert dalle-mini model using this process ?
https://github.com/borisdayma/dalle-mini


fix(raw_vehicle_cmd_converter): fix parameter files to parse path to csv files

Description
According to https://github.com/autowarefoundation/autoware.universe/pull/5134, some parameters are moved into yaml files.
However, if we launch raw_vehicle_cmd_converter from autoware_launch, some parameters defined in launch files does not appear.
In this change, I fixed above problems.
accel/brake/steer map path should change at launch, so I removed csv_path_**_map from yaml file and add them into xml file.

Tests performed

Not applicable.
Effects on system behavior

Not applicable.
Pre-review checklist for the PR author
The PR author must check the checkboxes below when creating the PR.

[x] I've confirmed the contribution guidelines.
[x] The PR follows the pull request guidelines.

In-review checklist for the PR reviewers
The PR reviewers must check the checkboxes below before approval.

[x] The PR follows the pull request guidelines.

Post-review checklist for the PR author
The PR author must check the checkboxes below before merging.

[ ] There are no open discussions or they are tracked via tickets.

After all checkboxes are checked, anyone who has write access can merge the PR.

@PhoebeWu21 Hello,
I changed the parameter file in raw_vehicle_cmd_converter to change path to csv files dynamically. Do you have any comments for this change?

Looks good to me.
But, @ambroise-arm I'm not sure if this is fine in ROS Node Configuration layout rule.

@PhoebeWu21 All parameters defined in the C++ code should be listed in the schema and parameter files according to the ROS Node Configuration page. If we want both that and being able to pass the parameters from external launch file, I think this leads to the same discussion about parameter override as in https://github.com/autowarefoundation/autoware.universe/pull/5773. I don't think there was a clear conclusion on what should be done.

@PhoebeWu21 @ambroise-arm Thank you for your comments!
We want to fix the problem for parameter overriding for now, is it okay to merge this PR?
Let us discuss the problem again separately.

It seems the same issue is discussed in https://github.com/autowarefoundation/autoware-github-actions/issues/232


Invalid DNS identifier

Trying to run JF-secure v3.1 and caddy.exe keeps complaining that the DNS domain name I entered includes an invalid character. I've made sure that all my DNS settings and port settings on both my router and pc are correct, just cant wrap my head around this one.

im having exactly the same problem T_T

Hi, Thanks for making a Issue 👋
I have to ask a few questions so I can debug this issue.
What Version are you using?
Can you share your Config file (Caddyfile from your install location).
There should also be a log file in the ./log folder in the install location, could you also upload that as well.

Out of curiosity, I decided to investigate and this is what I found.
Using the domain from your Screen Shot I pinged it to get your IP address

And then I checked if the ports were open using a online tool and This is what I found:
Both Port 80 and 443 are closed.

Opening the ports securely is an important step for Reverse Proxy, You cannot expect it to work without port 80 and 443 open and forwarded to your Server.
I'll keep this issue open just in case something else comes up when you post the logs or config.
Now if you are sure you opened the ports, you can check if your ISP is blocking the port, many are notorious for doing so.

Hi, forgot to reopen ports 80 and 443 so I did so as demonstrated here:

Here's the contents of the caddyfile config file:
lilredpanda9.duckdns.org {
	encode gzip
		}
    reverse_proxy localhost:8096
}

I couldn't find the ./log file you were referencing, not sure if this one is in regards to windows or linux. Here's what my installation folder looks like (I'm using windows):

Ports 80 and 443 should be open and are hopefully directing towards my server, I have made sure to add the exemption to both ports in my firewall as well

Try changing the config file to this:
jellyfin.duckdns.org {
	encode gzip
		log {
			output file C:\install-location-path\logs\Jellyfin.log {
				roll true
				roll_size_mb 5
				roll_gzip true
				roll_local_time true
				roll_keep 2
				roll_keep_days 7
			}
		}
    reverse_proxy localhost:8096
}

And create a log folder and jellyfin.log file in that folder

Explanation for above changes

I can't accurately trace the problem without logs, so this will provide us log and hopefully I can fix the issue

Surprise, Surprise!
It's working. Your domain is registering and showing up with Lets Encrypt SSL Certs.

The only problem the page is blank.
This means that Ports are open, and that JF-Secure and Caddy are working just fine.
The problem likely is the JF server address, is that correct?
I can work on it more when you upload logs

Okay did some further research, We have a few possibilities in front of us.

AT&T, who I guess is your ISP is blocking your Incoming traffic. This Reddit comment perfectly describes there position on blocking traffic is

"Does AT&T block ports for incoming traffic?"
Are you an AT&T home customer? Meaning you purchased a service intended for homes and consumers. The answer is probably yes, or yes, but unpredictably.

So such a preposition is not out of the question.
This is supported by the fact that your IP address or your domain do not respond to ping requests. You may have to Talk to AT&T. You may have to change your NAT type.
Try Navigating to your domain on a local network as well as accessing it from the server it is running on and see what happens. Make sure that the localhost:8096 in reverse_proxy localhost:8096   is replaced by whatever address or port the JF server is running on. Make sure JF is running.

You have a over aggressive firewall, either put in place by your self or by your ISP.
Upload the logs, and we'll scan through them to make sure everything is in order.

@cybergeek2000 just alerted me to a important factor in #9. Your ISP could be using CGNAT or Blocking ports.
And AT&T had a History of doing so.
Plus I am now 100% sure that Caddy is working as Analyzing HTTP headers to check what server is serving data, gives us this result

Don't worry about leaking Domain and IP as I'll edit all posts later to remove those

Figured out how to run caddy.exe from the command line, replacing the caddyfile with the debug stuff creates an argument error for me

Yes, the problem seems to lie with the server address. localhost:8096 does point to the JF server address but entering the actual domain name in a web browser, whether it be on the server client or on another client on the same network, fails to load anything.
I'm not sure if CGNAT is preventing me from connecting to the domain name because, unless it can cancel out the pinholes I set in my at&t router, it should be able to bridge http connections.
There shouldn't be anything else running that uses ports 80 and 443, this is the first time I've ever opened a port on my computer lol and I couldn't find any other services that required in/outbound http(s) connections

Ok so Here's the thing: Caddy is not running as the file path is incorrect here's the correct one to use
lilredpanda9.duckdns.org {
	encode gzip
		log {
			output file D:\"Program files (x86)"\JF-Secure\logs\Jellyfin.log {
				roll true
				roll_size_mb 5
				roll_gzip true
				roll_local_time true
				roll_keep 2
				roll_keep_days 7
			}
		}
    reverse_proxy localhost:8096
}

Hopefully I added all the information correctly.
For File Paths with spaces you have to use "" marks

Ok, I don't know how this happened, but after fixing the caddyfile by quoting the entire file path everything decided to work perfectly???
lilredpanda9.duckdns.org {
	encode gzip
		log {
			output file "D:\Program files (x86)\JF-Secure\logs\Jellyfin.log" {
				roll true
				roll_size_mb 5
				roll_gzip true
				roll_local_time true
				roll_keep 2
				roll_keep_days 7
			}
		}
    reverse_proxy localhost:8096
}

Here's the output that I got in the windows terminal:

Here's what jellyfin.log looked like after closing caddy.exe:
https://pastebin.com/hKRWSfU0
Everything seems to be working now I guess. Thankfully I didn't have to contact at&t lol
lilredpanda9.duckdns.org

Ok, I don't know how this happened, but after fixing the caddyfile by quoting the entire file path everything decided to work perfectly???

Oh Wow! This is a surprise. Must have been a glitch within Caddy.

Here's what jellyfin.log looked like after closing caddy.exe:

The logs appear normal there is nothing in there that indicates anything out of the ordinary.

Everything seems to be working now I guess. Thankfully I didn't have to contact at&t lol

In my years with AT&T I came to the conclusion that they have the shittiest Customer Service.

Also, the server only seems to run when I go into windows powershell, cd into the JF-Secure directory, and run .\caddy.exe run and not when I try to run the JF Secure.exe nor the caddy.exe executables from the file manager

Thats coz they are console apps. Press Ctrl+Shift+Esc to open Task Manager, and click on more details and then navigate to the services tab. From there Scroll down and find JF-Secure, if its running you don't have to start it manually.


Are DirectMerge and CartesianMerge implemented in outlines?

They are mentioned in this blog https://vivien000.github.io/blog/journal/llm-decoding-with-regex-constraints.html , and they look very helpful.

@youkaichao I think it might not be very relevant with popular sampling strategy? Suppose logits is x, the new probability of token i is exp(x_i)/(sum(exp(x_j))-sum(exp(x_k))) where x_j's are all tokens in a given vocabulary and x_k's are filtered tokens. While the new probability for improper tokens will increase, the new probability for proper token will increase as well. This means with top-p sampling, improper tokens will still be filtered out eventually and does not affect the ultimate result.


adapt to zeus gunicorn_config

Coverage remained the same at 44.58% when pulling 993aacdfdb8b7ae661c91fb90b819b3dfdb7dee1 on wangxiangyu:service_register into abb8dca4e0b8677e4464da6bb2b391d2d40054cd on eleme:master.


[BUG] TestClient crashes on websocket endpoints with dependencies that require fastapi.Request

First check

[x] I added a very descriptive title to this issue.
[x] I used the GitHub search to find a similar issue and didn't find it.
[x] I searched the FastAPI documentation, with the integrated search.
[x] I already searched in Google "How to X in FastAPI" and didn't find any information.
[x] I already read and followed all the tutorial in the docs and didn't find an answer.
[x] I already checked if it is not related to FastAPI but to Pydantic.
[x] I already checked if it is not related to FastAPI but to Swagger UI.
[x] I already checked if it is not related to FastAPI but to ReDoc.
[x] After submitting this, I commit to one of:

Read open issues with questions until I find 2 issues where I can help someone and add a comment to help there.
I already hit the "watch" button in this repository to receive notifications and I commit to help at least 2 people that ask questions in the future.
Implement a Pull Request for a confirmed bug.

Example
Here's a self-contained, minimal, reproducible, example with my use case:

from fastapi import WebSocket, Depends, FastAPI
from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
from starlette.testclient import TestClient

app = FastAPI()

@app.websocket('/')
async def socket(
    websocket: WebSocket,
    bearer_token: HTTPAuthorizationCredentials = Depends(HTTPBearer())
):
    await websocket.accept()
    await websocket.send_text("hello there")
    await websocket.close()

def test_socket():
    client = TestClient(app)
    with client.websocket_connect("/") as ws:
        ws.read_text()
        ws.close()

Description
Adding any dependency that requires additional parameters makes this endpoint crash with a TypeError  __call__() missing 1 required positional argument: 'request' when used from the TestClient.
It works fine when running with uvicorn.

Temporary workaround:
Make a custom Auth scheme and use the Header param instead of the Request.
That's weird.

from typing import Optional

from fastapi import WebSocket, Depends, FastAPI
from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
from starlette.requests import Request
from starlette.testclient import TestClient

app = FastAPI()

class JWTAuth(HTTPBearer):

    async def __call__(self, request: Request=None, websocket: WebSocket=None) -> Optional[HTTPAuthorizationCredentials]:
        request = request or websocket
        return await super().__call__(request)

@app.websocket('/')
async def socket(
    websocket: WebSocket,
    bearer_token: HTTPAuthorizationCredentials = Depends(JWTAuth())
):
    await websocket.accept()
    await websocket.send_text("hello there")
    await websocket.close()

def test_socket():
    client = TestClient(app)
    with client.websocket_connect("/", headers={'authorization': 'Bearer this works'}) as ws:
        ws.receive_text()
        ws.close()

A better workaround (doesn't duplicate the authorization header in the openapi schema)

I was using OAuth2PasswordBearer in my dependency chain for authentication/authorization, and I did something similar to the above:
from fastapi import Request, WebSocket
from fastapi.security import OAuth2PasswordBearer

class CustomOAuth2PasswordBearer(OAuth2PasswordBearer):
    async def __call__(self, request: Request = None, websocket: WebSocket = None):
        return await super().__call__(request or websocket)

oauth2_scheme = CustomOAuth2PasswordBearer(tokenUrl="login")

I was using OAuth2PasswordBearer in my dependency chain for authentication/authorization, and I did something similar to the above:
from fastapi import Request, WebSocket
from fastapi.security import OAuth2PasswordBearer

class CustomOAuth2PasswordBearer(OAuth2PasswordBearer):
    async def __call__(self, request: Request = None, websocket: WebSocket = None):
        return await super().__call__(request or websocket)

oauth2_scheme = CustomOAuth2PasswordBearer(tokenUrl="login")

Thank you..  I have no idea why this works..  but it does ;).
it is because Oauth2 in fastapi doesn't accept websocket requests?  -> https://github.com/tiangolo/fastapi/blob/58ab733f19846b4875c5b79bfb1f4d1cb7f4823f/fastapi/security/oauth2.py#L128

@yeus I haven't looked at this (or even used fastapi) in a while. But if I understand correctly, fastapi is injecting the request to OAuth2PasswordBearer.__call__ (based on the type Request, etc.). This is problematic because we need to check the websocket object, not the request object (which doesn't even exist for websocket endpoints). So the solution is to get both objects optionally injected, and use the one that exists. Does that make sense?

Well, if I recall correctly, the weird thing about it is that it breaks only when using TestClient.

I believe it broke for me when attempting to connect with a regular client, not just when testing with TestClient.

I believe it broke for me when attempting to connect with a regular client, not just when testing with TestClient.

i am using strawberry with fastapi and an authrization dependency (https://strawberry.rocks/docs/integrations/fastapi#context_getter) when I am getting this error. It looks like this:
async def get_context(
        request: Request = None,
        ws: WebSocket = None,
        user: cognito.CognitoUser = Depends(optional_user_verifier)
):
    return {
        # "custom_value": custom_value,
        "request": request or ws,
        # "background_tasks": background_tasks
        "user": user
    }

It works if I comment out the fastapi dependency ;).
where optional_use_verifier is a normal fastapi dependency for optional user authorization:
def optional_user_verifier(token: str = Depends(oauth2_scheme)) -> Optional[CognitoUser]
   (...)

But https://github.com/tiangolo/fastapi/issues/2031#issuecomment-784651961 helped with that as stated in the previous comment..
@mosheduminer yes, does kind of make sense..   I guess I am a bit too lazy to check how fastapi does dependency injection in order to understand it completly ;).


Charmcraft init and added install functionality

Did a charmcraft init and modified it to match structure of a machine charm
Removed actions.yaml & config.yaml since these are not implemented yet
Implemented on Install hook

LGTM, once we resolve @jnsgruk 's conversations we can merge it.


[$1000] Web - Cursor gets half hidden on Welcome message page

If you haven’t already, check out our contributing guidelines for onboarding and email<EMAIL_ADDRESS>to request to join our Slack channel!

Action Performed:

Create a workspace > go to announce > click on header
Click on Settings > welcome message
Type something and hit enter key

Expected Result:
Cursor should be fully visible
Actual Result:
Cursor gets half hidden when a new line is created
Workaround:
Unknown
Platforms:

Which of our officially supported platforms is this issue occurring on?

[ ] Android / native
[ ] Android / Chrome
[ ] iOS / native
[ ] iOS / Safari
[x] MacOS / Chrome / Safari
[ ] MacOS / Desktop

Version Number: 1.3.34-1
Reproducible in staging?: y
Reproducible in production?: y
If this was caught during regression testing, add the test name, ID and link from TestRail:
Email or phone of affected tester (no customers):
Logs: https://stackoverflow.com/c/expensify/questions/4856
Notes/Photos/Videos: Any additional supporting documentation
https://github.com/Expensify/App/assets/93399543/fecd84dd-f59f-49cb-8bcb-7f7097600ab3
https://github.com/Expensify/App/assets/93399543/d9a41e21-5d65-4426-961d-47c35cd2a234
Expensify/Expensify Issue URL:
Issue reported by: @Nathan-Mulugeta
Slack conversation: https://expensify.slack.com/archives/C049HHMV9SM/p1687982750576449
View all open jobs on GitHub
Upwork Automation - Do Not Edit

Upwork Job URL: https://www.upwork.com/jobs/~01e84d49c89d9b9d8e
Upwork Job ID:<PHONE_NUMBER>722532864
Last Price Increase: 2023-06-30

Proposal
Please re-state the problem that we are trying to solve in this issue.
What is the root cause of that problem?
We get the required height of the TextInput here
https://github.com/Expensify/App/blob/3b721c4cbf1714720d529a9ef5b81f14e1cb699c/src/components/TextInput/BaseTextInput.js#L384-L394
We use Text component and its height doesn't increase when the text is ended with line break.
This is the root cause
What changes do you think we should make in order to solve the problem?
We can add space character to the end of the text if text is ended with line-break.
    {this.state.value ? this.state.value + (this.state.value.endsWith('\n') ? ' ' : '') : this.props.placeholder}

This works as expected.

Result

What alternative solutions did you explore? (Optional)
In stead of above code, we can use add '\n' character to the text.
    {this.state.value ? this.state.value + '\n' : this.props.placeholder}

replicated!
https://github.com/Expensify/App/assets/42940078/20397591-a9ba-4944-8515-9488764d781a

Isn't that issue on task description but this one is on welcome message page @aimane-chnaif

My proposal is a general solution for auto-grow TextInput component

I which case, I'll put this issue on hold for https://github.com/Expensify/App/issues/21271 and @Nathan-Mulugeta you can still get the reporting bonus.

Thanks @sophiepintoraetz that'll be great. Have you noticed that #21271 is closed?

I was working on this issue during the weekend. I posted my proposal at https://github.com/Expensify/App/issues/21271#issuecomment-1617399591. Thanks

Ah, apologies, I've been turned around by the closed issue. Seeing as https://github.com/Expensify/App/issues/21205 did not fix this issue, and the timeline says Applause (22 June) reported this bug before Nathan (29 June), @Nathan-Mulugeta, you are not eligible for the reporting bonus, I'm afraid.
@fabriziobertoglio1987 and @s-alves10 - I see you have dropped proposals in #21271 so let's continue the conversation there.


Fastify implementation

Have a demo to implement this in fastify framework?

Hi! You mean, with the fastify-socket.io plugin?

Yes for use socket.io are ok but not integrate the ADMIN UI control page.

this is an example implementation
import fastify from "fastify";
import fastifyIO from "fastify-socket.io";

const server = fastify();

server.register(fastifyIO, {
  cors: {
    origin: ["https://admin.socket.io"],
    credentials: true,
  },
});

server.ready().then(() => {
  instrument(server.io, { auth: false, development: true });
});

server.listen({ port: 3000 });

and go to https://admin.socket.io/
write on server url http://localhost:3000 and click connect


Fixed not dimming/hiding watched items on channel page

This fixes #113.

FYI, YT Premium accounts not experiencing the updated CSS (yet?).  See discussion on #113

Maybe supporting both variants might be the best option for now?
I.e. adding item.closest('.ytd-grid-renderer') to
watchedItem = (
    item.closest('ytd-rich-item-renderer') ||
    item.closest('ytd-video-renderer') ||
    item.closest('ytd-grid-video-renderer')
);

I just saw that adding item.closest('.ytd-grid-renderer') is not required as it is already found by item.closest('ytd-grid-video-renderer')

Works well in my testing. Thanks for the contribution!

YT Premium still working for me, too.  Thanks @Programie!


[10/30 Hack] 'pwsh' is not recognized as an internal or external command

When creating the Azure Resources from the Settings > Publish page, the command that I need to run is "cd C:/code/BotFramework-Composer/SampleBots/WeatherBot2
pwsh ./Scripts/create.ps1 -name WeatherBot2 -environment Production -location WestUS2 -appPassword 'A12345678912345*'".
When I run it I get this error

Put some link to install the right powershell in the dialog as a prerequisite.

Closing as by design


Upgrade ctranslate2 to version 4

I am not a maintainer, but I've been running with CTranslate2-4.0.0 for a while and just upgraded to CTranslate2-4.1.0 and didn't run into any issues

I am not a maintainer, but I've been running with CTranslate2-4.0.0 for a while and just upgraded to CTranslate2-4.1.0 and didn't run into any issues

Good to know thanks.
Does anyone know what's new in CTranslate2 v4? I'm not seeing anything on the OpenNMT forum, Github, or the docs. They had a post on the forum for v2 and v3.
I generally take a somewhat slow approach to updating unless the new version has something we need just to let other people find all the bugs first. I'll probably update eventually though.
Also I updated another line in requirements.txt so this commit has a merge conflict now.

I upgraded because of CUDA 12 support. I rent GPUs from various providers, and CUDA12 is now the norm.

I upgraded because of CUDA 12 support. I rent GPUs from various providers, and CUDA12 is now the norm.

That makes sense. I'll upgrade now


Highbury Multicall3 Deployment

Chain Name
Highbury EVM
Chain ID
710
RPC URL
https://highbury.furya.io
Block Explorer URL
https://explorer.furya.io
Deployment Funds
No

Done, thanks!


Error using a custom component

Hi,
Really liking this admin dashboard!
I am using the last beta (1.6.3.beta)
I am trying to get a custom component to work. It literally does nothing.
component:
import React from 'react'
import { PropertyInShow } from 'admin-bro'

export default function Component({property, record}) {
  const value = record.params[property.name]
  return (<PropertyInShow property={property}>
    {value}
  </PropertyInShow>)
}

which is transpiled to
"use strict";
var __importDefault = (this && this.__importDefault) || function (mod) {
    return (mod && mod.__esModule) ? mod : { "default": mod };
};
Object.defineProperty(exports, "__esModule", { value: true });
const react_1 = __importDefault(require("react"));
const admin_bro_1 = require("admin-bro");
function Component({ property, record }) {
    const value = record.params[property.name];
    return (<admin_bro_1.PropertyInShow property={property}>
    {value}
  </admin_bro_1.PropertyInShow>);
}
exports.default = Component;

.entry.js content:
AdminBro.UserComponents = {}
import Component1 from '../build/adminDashboard/components/simple-custom-component'
AdminBro.UserComponents.Component1 = Component1

Error
The 'this' keyword is equivalent to 'undefined' at the top level of an ES module, and has been rewritten
The 'this' keyword is equivalent to 'undefined' at the top level of an ES module, and has been rewritten
The 'this' keyword is equivalent to 'undefined' at the top level of an ES module, and has been rewritten
The 'this' keyword is equivalent to 'undefined' at the top level of an ES module, and has been rewritten
{ Error: 'default' is not exported by build/adminDashboard/components/simple-custom-component.jsx
    at error (/Users/haytham/Documents/projects/dancehub.io/backend/node_modules/rollup/dist/rollup.js:5365:30)
    at Module.error (/Users/haytham/Documents/projects/dancehub.io/backend/node_modules/rollup/dist/rollup.js:9708:9)
    at handleMissingExport (/Users/haytham/Documents/projects/dancehub.io/backend/node_modules/rollup/dist/rollup.js:9625:21)
    at Module.traceVariable (/Users/haytham/Documents/projects/dancehub.io/backend/node_modules/rollup/dist/rollup.js:10018:17)
    at ModuleScope.findVariable (/Users/haytham/Documents/projects/dancehub.io/backend/node_modules/rollup/dist/rollup.js:8685:39)
    at Identifier$1.bind (/Users/haytham/Documents/projects/dancehub.io/backend/node_modules/rollup/dist/rollup.js:4394:40)
    at AssignmentExpression.bind (/Users/haytham/Documents/projects/dancehub.io/backend/node_modules/rollup/dist/rollup.js:3143:23)
    at AssignmentExpression.bind (/Users/haytham/Documents/projects/dancehub.io/backend/node_modules/rollup/dist/rollup.js:6180:15)
    at ExpressionStatement$1.bind (/Users/haytham/Documents/projects/dancehub.io/backend/node_modules/rollup/dist/rollup.js:3143:23)
    at Program$1.bind (/Users/haytham/Documents/projects/dancehub.io/backend/node_modules/rollup/dist/rollup.js:3139:31)
    at Module.bindReferences (/Users/haytham/Documents/projects/dancehub.io/backend/node_modules/rollup/dist/rollup.js:9680:18)
    at Graph.link (/Users/haytham/Documents/projects/dancehub.io/backend/node_modules/rollup/dist/rollup.js:13216:20)
    at Promise.all.then (/Users/haytham/Documents/projects/dancehub.io/backend/node_modules/rollup/dist/rollup.js:13073:18)
  code: 'MISSING_EXPORT',
  url:
   'https://rollupjs.org/guide/en/#error-name-is-not-exported-by-module',
  pos: 37,
  loc:
   { column: 7,
     file:
      '/Users/haytham/Documents/projects/dancehub.io/backend/.adminbro/.entry.js',
     line: 2 },
  frame:
   '1: AdminBro.UserComponents = {}\n2: import Component1 from \'../build/adminDashboard/components/simple-custom-component\'\n          ^\n3: AdminBro.UserComponents.Component1 = Component1\n4: import Component2 from \'../build/adminDashboard/components/show-list-string-component\'',
  watchFiles:
   [ '/Users/haytham/Documents/projects/dancehub.io/backend/.adminbro/.entry.js',
     '/Users/haytham/Documents/projects/dancehub.io/backend/build/adminDashboard/components/simple-custom-component.jsx',
     '/Users/haytham/Documents/projects/dancehub.io/backend/build/adminDashboard/components/show-list-string-component.jsx' ] }

Do you have any idea where to start debugging?
It seems to be working using node-ts. I does build using tsc, but whenever I try to access the resource (login into the dashboard), it just hangs.

it might be a rollup issue (we use it for bundling). Please try to rewrite your component like this
import React from 'react'
import { PropertyInShow } from 'admin-bro'

const Component = ({property, record}) => {
  const value = record.params[property.name]
  return (<PropertyInShow property={property}>
    {value}
  </PropertyInShow>)
}

export default Component

and let me know.

aaa. and point AdminBro.bundle to your src folder instead of build.

Thanks!
The file change did not change it, I was doing that at the beginning.
How would I point the AdminBro.bundle to src?

in the AdminBroOptions (where you create AdminBro instance) you are passing the component by using AdminBro.bundle method like this (I assume):
new AdminBro({
  resources: [{
      resource: YourResource,
      options: {
        ...,
        properties: {
           yourProperty: {
              components: {
                  list: AdminBro.bundle(pathToYourComponent)
              }
           }
        }
      }
  }]
})

so in pathToYourComponent just set the path to src/.... instead of 'build/..'

Hey!
I am using the relative path, and the "npm run start" rewrites the .entry.js file.
show: AdminBro.bundle('../../components/simple-custom-component'),

Does it work on your side though? Would you be able to try something like this?

hehe - this will be a fix:
show: AdminBro.bundle('../../../src/components/simple-custom-component')
or whatever number of ../ you have to add in front to get to the root scope of your app where is a src dir.

Sneaky but Good call!

I still have this problem with rollup
Any insights?
My version
adminJs: 5.2.1

I still have this problem with rollup
Any insights?
My version adminJs: 5.2.1
I am using typescript. This problem appeares just on build mode.

same here. adminJs version "6.1.1" locally all works perfect, but in prod mode see error like component is undefined.
AdminJS.UserComponents always empty object on prod and request to /assets/components.bundle.js throw 404. looks like it not generated during production build


Maestral not starting - unable to create the socket?

Describe the bug

Maestral doesn't seem to be starting. In doing an strace, it shows:
newfstatat(AT_FDCWD, "/run/user/1001", {st_mode=S_IFDIR|0700, st_size=180, ...}, 0) = 0
mkdirat(AT_FDCWD, "/run/user/1001/maestral", 0777) = -1 EEXIST (File exists)
newfstatat(AT_FDCWD, "/run/user/1001/maestral", {st_mode=S_IFDIR|0775, st_size=60, ...}, 0) = 0
socket(AF_UNIX, SOCK_STREAM|SOCK_CLOEXEC, 0) = 3
setsockopt(3, SOL_SOCKET, SO_REUSEADDR, [1], 4) = 0
connect(3, {sa_family=AF_UNIX, sun_path="/run/user/1001/maestral/maestral.sock"}, 39) = -1 ENOENT (No such file or directory)
close(3)                                = 0
pselect6(0, NULL, NULL, NULL, {tv_sec=0, tv_nsec=200000000}, NULL) = 0 (Timeout)
socket(AF_UNIX, SOCK_STREAM|SOCK_CLOEXEC, 0) = 3
setsockopt(3, SOL_SOCKET, SO_REUSEADDR, [1], 4) = 0
connect(3, {sa_family=AF_UNIX, sun_path="/run/user/1001/maestral/maestral.sock"}, 39) = -1 ENOENT (No such file or directory)
close(3)                                = 0
pselect6(0, NULL, NULL, NULL, {tv_sec=0, tv_nsec=200000000}, NULL) = 0 (Timeout)
socket(AF_UNIX, SOCK_STREAM|SOCK_CLOEXEC, 0) = 3
setsockopt(3, SOL_SOCKET, SO_REUSEADDR, [1], 4) = 0
connect(3, {sa_family=AF_UNIX, sun_path="/run/user/1001/maestral/maestral.sock"}, 39) = -1 ENOENT (No such file or directory)
close(3)                                = 0

To Reproduce

I'm running Ubuntu 20.10 Groovy Gorilla on ARM. Python 3.8.6
Expected behaviour

System:

Maestral version: 1.4.2
OS: [e.g. Ubuntu] Ubuntu 20.10
Desktop environment: [e.g. Gnome 3.32] headless
PyQt version (for Linux GUI):

Additional context

strace output attached.
Thanks!
maestral.strace.out.gz

maestral log show
gives me:
2021-02-19 13:24:12 maestral.sync INFO: Connected
2021-02-19 13:40:01 maestral.sync DEBUG: Could not load mignore rules from /home/Colossus/Dropbox/.mignore: [Errno 2] No such file or directory: '/home/Colossus/Dropbox/.mignore'
2021-02-19 13:40:01 maestral.sync INFO: Connected
2021-02-19 13:51:55 maestral.sync INFO: Connected
2021-02-19 13:56:48 maestral.sync INFO: Connected
alton@ubuntu:~$ cat ~/.config/maestral/maestral.ini
[main]
path = /home/Colossus/Dropbox
excluded_items = []
version = 15.0.0
[account]
account_id = dbid:AAAyzz13xxxxxxxLhy-jr7xRxKxX38E
[app]
notification_level = 15
log_level = 20
update_notification_interval = 604800
keyring = keyrings.alt.file.PlaintextKeyring
[sync]
reindex_interval = 1209600
max_cpu_percent = 20.0
keep_history = 604800
upload = True
download = True
Thanks!

strace -fa if it helps. :)
maestral.strace.out.gz

It look like the daemon is starting but the CLI cannot communicate with it properly over the created socket. Could try starting the process in the foreground and in verbose mode with maestral start -f -v? Does this raise any errors? If it doesn't, what does running maestral status in other terminal return?

It works!
alton@ubuntu:~$ /usr/local/bin/maestral start -f -v
2021-02-19 15:22:59 maestral.sync INFO: Connected
2021-02-19 15:22:59 maestral.sync INFO: Connected
2021-02-19 15:23:02 maestral.sync INFO: Indexing remote Dropbox
2021-02-19 15:23:56 maestral.sync INFO: Indexing 500...
2021-02-19 15:23:56 maestral.sync INFO: Syncing ↓ 1/500

alton@ubuntu:/home/Colossus/Dropbox$ maestral status
Account<EMAIL_ADDRESS>(Basic)
Usage:        30.2% of 23.5 GB used
Status:       Creating folders...
Sync errors:  0

Nice! I'm still confused as to why the regular command didn't work. Is it still failing to connect to the socket?

It still fails to socket, but works fine in the foreground. :)

I'm running v1.5.1 and the maestral start -f -v did not work for me.. It just sat there "Starting daemon.."

Running with log_level = 1, I see this in the log:
2021-11-23 11:58:19 daemon DEBUG: Acquired daemon lock: '/home/turbo/.cache/maestral/maestral.lock'
2021-11-23 11:58:19 daemon DEBUG: Socket path: '/home/turbo/.cache/maestral/maestral.sock'
2021-11-23 11:58:19 daemon DEBUG: Creating Pyro daemon
2021-11-23 11:58:19 sync DEBUG: Could not load mignore rules: No such file or directory
2021-11-23 11:58:36 daemon ERROR: Could not communicate with daemon
[because it can't find the socket file]

Comparing that with the code:
        dlogger.debug("Creating Pyro daemon")

        maestral_daemon = expose(Maestral)(config_name, log_to_stderr=log_to_stderr)

        dlogger.debug("Starting event loop")

So it's not going into the event loop. My Python is extremely rusty (and it wasn't that good to begine with :), so I'm not sure what the problem is..

So running with maestral start -f -v (which didn't work initially, but apparently works now!??), it does create the socket and I see:
2021-11-23 12:33:38 daemon DEBUG: Acquired daemon lock: '/home/turbo/.cache/maestral/maestral.lock'
2021-11-23 12:33:38 daemon DEBUG: Socket path: '/home/turbo/.cache/maestral/maestral.sock'
2021-11-23 12:33:38 daemon DEBUG: Creating Pyro daemon
2021-11-23 12:33:38 sync DEBUG: Could not load mignore rules: No such file or directory
2021-11-23 12:33:38 daemon DEBUG: Starting event loop
2021-11-23 12:33:38 oauth DEBUG: Using keyring: keyrings.alt.file.PlaintextKeyring (priority: 0.5)
2021-11-23 12:33:38 manager INFO: Connected

Closing since this (1) seems to be resolved now and (2) daemon communication reliability saw many improvements since back then.


[Next13 starter kit with CSS modules] - Add Kit on the main site

Background
The website has attempted to include all the kits appropriately but may need some added configuration to display correctly.
Acceptance

[ ] Ensure the kit displays on the website
[ ] Add any missing logos for technologies as needed

Manually closing this issue. It is merged to next13-zustand-bulma - will be merged to main all at once.


terminology.rst: fixing 1 typo

Hi there,
As per title.
Didier

@didier-durand looks like you will need to re-open the PR against master add needs-backport/1.9 label instead of targeting v1.9 branch directly. Please also make sure to sign-off you commit! Thanks a lot :)

@didier-durand I spoke too soon, GitHub allows changing base-branch now, looks like a new feature... Could you please rebase the commit on master and make sure sign-off? No need to re-open :)

re-opened as https://github.com/cilium/cilium/pull/14181

Thank you, well spotted!

@errordeveloper : you're very welcome. If I see more, I'll open other PRs


Current cores vs dendroband stem sample sizes by species

Hey @RHelcoski @jenajordan the following info might help prioritize which trees to core this fall. Here is a Venn Diagram illustrating all possible combinations of cores vs dendroband stem sample sizes that I'm using for the Bayesian data fusion project (note all 1289 stems here have census measurements).

Example: D is the number of stems that have biannual but not biweekly dendroband measures as well as core measures. So values C & D are most of interest to you. Below are the values of A-E broken down by species. Let me know if you need more info

sp
A
B
C
D
E
total_stems

litu
186
41
2
8
99
336

quru
27
28
7
2
62
126

qual
23
33
4
3
59
122

quve
34
3
0
9
75
121

fram
35
0
0
6
63
104

fagr
1
10
3
0
77
91

qupr
12
0
0
2
65
79

cagl
28
3
0
0
38
69

juni
8
1
0
3
28
40

pist
3
0
0
3
33
39

caco
9
2
0
1
17
29

cato
12
0
0
0
17
29

caovl
3
0
0
2
22
27

caca
3
15
0
0
0
18

frni
0
0
0
1
15
16

nysy
9
1
0
0
0
10

acru
9
0
0
0
0
9

ulru
9
0
0
0
0
9

ploc
5
0
0
0
0
5

ceca
4
0
0
0
0
4

tiam
1
1
0
0
0
2

cofl
0
1
0
0
0
1

prse
1
0
0
0
0
1

rops
1
0
0
0
0
1

saal
1
0
0
0
0
1

total
424
139
16
40
670
1289

Hey @rudeboybert , I think something got lost in translation or I just missed something. You mention that C and D are the most valuable, but if groups C,D, and E already have core measures... then are you requesting we core them a second time?
Is this in addition to the list that @teixeirak developed here? https://github.com/SCBI-ForestGEO/SCBImortality/issues/50. My plan was to first finish that list and then get every oak over 50 cm dbh. Should I change our plans for this Saturday?

@teixeirak got it. Let me know if we plan on grabbing them later.


Laravel 10.x Compatibility

This is an automated pull request from Shift to update your package code and dependencies to be compatible with Laravel 9.x.
Before merging, you need to:

Checkout the l10-compatibility branch
Review all comments for additional changes
Thoroughly test your package

If you do find an issue, please report it by commenting on this PR to help improve future automation.

:warning: Shift updated your build matrix for Laravel 10. However, since you are supporting older versions of Laravel, you need to add the following exclusions:
exclude:
  - laravel: 10.*
    php: 8.0

:warning: Shift detected GitHub Actions which run jobs using a version matrix. Shift attempted to update your configuration for Laravel 10. However, you should review these changes to ensure the desired combination of versions are built for your package.

:alembic: Using this package? If you would like to help test these changes or believe them to be compatible, you may update your project to reference this branch.
To do so, temporarily add Shift's fork to the repositories property of your composer.json:
{
    "repositories": [
        {
            "type": "vcs",
            "url": "https://github.com/laravel-shift/laravel-typescript.git"
        }
    ]
}

Then update your dependency constraint to reference this branch:
{
    "require": {
        "based/laravel-typescript": "dev-l10-compatibility",
    }
}

Finally, run: composer update

Hi, will this be merged?

I have also just upgraded an app to 10.x and would be interested in still using this.


Display total solar is missing - Adding SMA Inverter

Hello
I have a question about the integration of a SMA inverter.
It is a Tripower X 20-50, which is unfortunately not automatically detected by Venus OS. That is why I have inserted the values via mqtt.
This also works so far only the solar yield is not displayed in the VRM. The following (red marked) parts are missing:

First of all: I did not make any adjustments in the services.yml.
Does anyone have an idea how I can include solar generation in the VRM portal?
Without the solar generation, unfortunately, the consumption values are not displayed correctly:

Is this the topic you are looking for?: W/.../pvinverter/6/Ac/Energy/Forward

i hope this topic is correct.
The current problem is that the firmware of the Tripower X does not yet provide this value via Modbus. Here a firmware update must take place first. let's see when this happens...

Maybe you can calculate it by yourself or use a different source?
Everything would be better than nothing.

As this isn't directly related to the driver I am going to close this issue now.


Possible data type discrepancy for gcp.compute.BackendService

What happened?
While using our doc examples, it seems like there's a data type discrepancy for the healthChecks property.
Right now, the healthChecks property is of type pulumi.Input<string> but this doesn't seem to be right according to upstream TF provider doc or the Google API resource definition.
Steps to reproduce

Use the code below

import * as pulumi from "@pulumi/pulumi";
import * as gcp from "@pulumi/gcp";

export = async () => {

    const defaultHttpHealthCheck = new gcp.compute.HttpHealthCheck("defaulthttphealthcheck", {
        requestPath: "/",
        checkIntervalSec: 1,
        timeoutSec: 1,
    });

    const defaultBackendService = new gcp.compute.BackendService("defaultBackendService", {
        healthChecks: [defaultHttpHealthCheck.id],
        enableCdn: true,
        cdnPolicy: {
            cacheMode: "CACHE_ALL_STATIC",
            defaultTtl: 3600,
            clientTtl: 7200,
            maxTtl: 10800,
            negativeCaching: true,
            signedUrlCacheMaxAgeSec: 7200,
        },
    });

    return {

    }
}

Expected Behavior

The code should pass vscode linting
The code should deploy correctly

Actual Behavior

The code fails to pass linting with the error Type 'Output<string>[]' is not assignable to type 'Input<string> | undefined'.ts(2322)
The code fails to deploy
I'm unable to provide more than one health check if I remove the []

Versions used
$ pulumi about
CLI
Version      3.32.1
Go Version   go1.17.9
Go Compiler  gc

Plugins
NAME    VERSION
gcp     6.24.0
nodejs  unknown

Host
OS       debian
Version  11.3
Arch     x86_64

This project is written in nodejs (/usr/local/bin/node v16.15.0)

Current Stack: dev

TYPE                                         URN
pulumi:pulumi:Stack                          urn:pulumi:dev::gcp-ts-backend-service::pulumi:pulumi:Stack::gcp-ts-backend-service-dev
pulumi:providers:gcp                         urn:pulumi:dev::gcp-ts-backend-service::pulumi:providers:gcp::default_6_24_0
gcp:compute/httpHealthCheck:HttpHealthCheck  urn:pulumi:dev::gcp-ts-backend-service::gcp:compute/httpHealthCheck:HttpHealthCheck::defaulthttphealthcheck
gcp:compute/backendService:BackendService    urn:pulumi:dev::gcp-ts-backend-service::gcp:compute/backendService:BackendService::defaultbackendservice

Found no pending operations associated with dev

Backend
Name           pulumi.com
URL            https://app.pulumi.com/aureq
User           aureq
Organizations  aureq, team-ce, menfin, demo, pulumi

NAME            VERSION
@pulumi/gcp     6.24.0
@pulumi/pulumi  3.32.1
@types/node     14.18.18

Pulumi locates its logs in /tmp by default

Additional context
Removing the [] as shown below allows the deployment to complete.
...
    const defaultBackendService = new gcp.compute.BackendService("defaultbackendservice", {
        healthChecks: defaultHttpHealthCheck.id,
        enableCdn: true,
        cdnPolicy: {
            cacheMode: "CACHE_ALL_STATIC",
            defaultTtl: 3600,
            clientTtl: 7200,
            maxTtl: 10800,
            negativeCaching: true,
            signedUrlCacheMaxAgeSec: 7200,
        },
    });

Contributing
Vote on this issue by adding a 👍 reaction.
To contribute a fix for this issue, leave a comment (and link to your pull request, if you've opened one already).

Hi @aureq Sorry we never responded to you here.
The upstream provider says

Currently at most one health check can be specified.

The code also defines it as MaxItems:1: https://github.com/hashicorp/terraform-provider-google-beta/blob/b3ebec364de8e2daf7afc0006fa5b0a86093ff83/google-beta/services/compute/resource_compute_backend_service.go#L642-L651
Because of that, TF Bridge maps the property to a simple string, since multiple values aren't accepted anyway.
I'll go ahead and close this issue as by-design.