Fetch only active team lead staff and prevent lead duplication

https://app.zenhub.com/workspaces/epictrack-63891ea941d309001fa292cf/issues/gh/bcgov/epic.track/2004
Details:

Add a backend check to prevent team lead duplication
Only fetch active staff for work staff info in Work Cards

Codecov Report
Attention: Patch coverage is 92.85714% with 2 lines in your changes are missing coverage. Please review.

:exclamation: No coverage uploaded for pull request base (develop@bbd447e). Click here to learn what that means.

Files
Patch %
Lines

epictrack-api/src/api/services/work.py
89.47%
2 Missing :warning:

Additional details and impacted files
@@            Coverage Diff             @@
##             develop    #2032   +/-   ##
==========================================
  Coverage           ?   76.28%
==========================================
  Files              ?      283
  Lines              ?     9322
  Branches           ?        0
==========================================
  Hits               ?     7111
  Misses             ?     2211
  Partials           ?        0

Flag
Coverage Δ

epictrack-api
76.28% <92.85%> (?)

Flags with carried forward coverage won't be shown. Click here to find out more.

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


Questions about Table 1.

Hi, Ayaan, many thanks for your work! It's pretty awesome!
May I ask some questions:
(1) Table 1 is performed on which datasets?
(2) Is there a quantitative evaluation code? Just for fair comparisons with other works' results.

I will provide a folder in the code that contains the evaluation code and some more details on the evaluations performed in our paper!

https://github.com/ayaanzhaque/instruct-nerf2nerf/tree/main/metrics
Here's the folder with the metric calculations.
In terms of which datasets are used for the calculations in the table, we use 10 total edits between the bear and the face scene. Hopefully this answers your question!


[BUG] Password reset doesn't work

Browser Details: Chrome Version 85.0.4183.102 (Official Build) (64-bit)
Device Details: macOS 10.15.6 (MacBook Pro)
Note: Tried on cloud.
Steps to replicate:

Hit https://app.appsmith.com/
Click on Forgot Password
Enter Email ID and click on 'Reset'
From the received email, click on 'Reset Password'
Enter less than (or equal to) 5 characters in 'New Password' field.
click on 'Reset' button

Expected Result: 3 possibilities:

A message (Please provide a password with a minimum of 6 characters) which is displayed on the login screen, should be present even here.
An error message is thrown that minimum 6 characters are needed.
'Reset' Works

Actual Result: None of the three in expected result happens and user is stuck wondering what went wrong.
Attachment: https://drive.google.com/drive/folders/1BxeYZthBTd1Ix-kKEojSMNJSRYcsb89t?usp=sharing

Seems like a simple validation was missing here, lets just show the minimum characters error message as suggested

please assign this to me

@gagandeepp I've assigned the issue to you. Feel free to reach out if you have any trouble getting started!

@Nikhil-Nandagopal  Unable to install sentry on win 10,please help!

@gagandeepp not clear why do you need to install sentry?

It is a part of package.json file

Hey @gagandeepp, were you able to resolve this? Let me know if you need help in solving this hiccup


Setup Order History

Set up order history
Users should be able to access their order history when they are logged in, including details about the order status and purchased items.
All past order information should be made viewable to a logged-in user. Set up the pages needed for fetching and displaying the order history. Make sure to protect these resources from unauthorized access.


High Resolution Display using Retina Image Concept

Description 📜
Apple made a retina display for high resolution which is more pixelated and much cleared than normal one. Let's we go to study in detail.
Fixes  #7627

Type of change 📝

[ ] Au#dio (Should be in mp3 format Includes speech clarity, Concise ,Low distortion)
[ ] Vi#deo (Animations, screen-recordings, presentations and regular explanatory films are all possibilties etc)
[ ] Documentation (Content Creation in the form of codes or tutorials)
[ ] Other (If you choose other, Please mention changes below)

Domain of Contribution 📊

[ ] Android Dev #(Flutter)
[ ] Android Dev #(Java)
[ ] Android Dev #(Kotlin)
[ ] Backend Dev #(Java)
[ ] Backend Dev #(.NET)
[ ] Backend Dev #(PHP)
[ ] Backend Dev #(Python)
[ ] C/#CPP
[ ] Competitive #Programming
[ ] Cyber #Security
[ ] DS#A
[ ] Data#base
[ ] Datascience with #Python
[ ] Datascience with #R
[ ] Frontend Dev HTML/CSS/JS
[ ] Frontend Dev #React/Angular/Vue
[ ] Go#lang
[ ] Interview #Prep
[ ] Java_#Domain
[ ] JavaScript_#Domain
[ ] ME#RN
[ ] Machine #Learning
[ ] Open #Source
[ ] Python_#Domain
[ ] Ru#st
[ ] Statis#tics
[ ] UI/#UX

Checklist ✅

[x] I follow Contributing Guidelines & Code of conduct of this project.
[x] I have performed a self-review of my own code or work.
[x] I have commented my code, particularly in hard-to-understand areas.
[x] My changes generates no new warnings.
[x] I'm GWOC'21 contributor

Screenshots / Gif (Optional) 📸

How much percentage of plagiarism will be considered. @2024-SANDHYA


Manually created input validation.

Hi, I have malually created input, with required attr and data-nette-rules. But as I see, LFV did not create span for errors. I dont know why. Input looks like
<input name="products[]" type="text" class="form-control" required data-nette-rules='[{"op":":filled","msg":"Názov je povinná položka."}]'>

If I open brovser debugger there are som changes which comes from LFV. But I dont understant what it means. It looks like

        <input name="products[]" type="text" class="form-control" required="" data-nette-rules="[{"op":":filled","msg":"Názov je povinná položka."}]" data-lfv-initialized="true" data-lfv-message-id="_message">

            <button class="btn btn-primary js-add-new" type="button" data-lfv-initialized="true" data-lfv-message-id="_message_1">+</button>

It seems that it is ok. But it is not.

Hi. What does mean manually and what is the problem exactly?
LFV created spans for errors, but because you didn't supplied "id" for your inputs, it used just common id "_message" (for input) and "_message_1" (for button). I recommend you set some id to your input.
And if you created that input element via JavaScript, make sure to call LiveForm.setupHandlers(element); on it.

Manually means - input is written in latte with required, data-nette-rules..., and is not generated by Nette framework. May be something is missing there.
<input name="products[]" type="text" class="form-control" required data-nette-rules='[{"op":":filled","msg":"Názov je povinná položka."}]'>

There is a rule :filled which means the field can not be empty. But it does not work. Other elements generated by framework works fine, but this one does not. It is the same with or without id.

So it seems the problem is in the brackets - [] in the input name. Hmmm what should I do?

Yes, I realized that too. Problem is with validation of these input arrays. Because it validates the result value - which is array of strings, in this case array of single empty string ([""]) is returned and successfully validated inside the "filled" validator because it is an array containing single string (so it's not null or empty anything).
Now I'm not sure on what place in code it should be fixed. Because it seems even classic netteForms.js couldn't validate it correctly (check it if you can).

Yes Nette also does not allow create text inputs with [] in the name. About 2 years ago I read the code of netteForms.js. It is quite complicate to solve all the situations in the validation. Hope you can do it. May be  :filled should not allow [""].

Oh, 2 years already?
I just commited fix, try latest master, please :-)

And here is working example: https://jsbin.com/kemijofolo/edit?html,js,output

It seems you fixed it. Hope there is no hidden bug. Thanks.


Adaptive optmizers do not work well with multi-head networks when some labels are missing

Use case: we have a multi-task network with many outputs. Each example in the dataset has only subset of the labels; non-existing loss components are masked out. A single optimizer is used, because the presence of particular labels in the example stream is statically unknown.
Problem: masked loss components give zero gradient estimates for the corresponding variables. That breaks adaptive optimizers (Momentum, Adam, ...) because steps are taken and slots are corrupted with the incoming zero gradient estimates. The desired behaviour is: do nothing for variables (and corresponding slots) that were effectively unused in the forward pass (like it is done for the embeddings using the IndexedSlices trick).
Related problem: tf.global_norm, tf.clip_by_global_norm are also affected by these stray zero gradient estimates.
Minimal example/demonstration:
In [14]: feature = tf.constant([1.0])
In [15]: output = feature * 42.0
In [16]: loss = tf.squared_difference(output, [0.0])
In [17]: mask = tf.constant([False])
In [18]: masked_loss = tf.boolean_mask(loss, mask)
In [19]: masked_loss_grad = tf.gradients(masked_loss, feature)
In [20]: masked_loss_grad
Out[20]: [<tf.Tensor 'gradients/mul_3_grad/Reshape:0' shape=(1,) dtype=float32>]
In [21]: masked_loss_grad[0].eval(session=session)
Out[21]: array([ 0.], dtype=float32)

This is technically correct -- the gradient is indeed zero -- but if feature was a Variable, we don't really want to apply this gradient in an adaptive optimizer, because the training example didn't have useful information about the loss function.
Feature request: better way to deal with that (probably a special value for the gradients that would signify "nonexisting" gradients; e.g. tensorflow uses None, when this information is available statically).
For now here is a simple and ugly workaround:
def _is_all_zeros(grad):
    all_zeros = tf.equal(tf.count_nonzero(grad), 0)
    return all_zeros

class HackedMomentumOptimizer(tf.train.MomentumOptimizer):
    def __init__(self, *args, **kwargs):
        super(HackedMomentumOptimizer, self).__init__(*args, **kwargs)

    def _apply_dense(self, grad, var):
        all_zeros = _is_all_zeros(grad)
        return tf.cond(all_zeros,
                       lambda: tf.no_op(),
                       lambda: super(HackedMomentumOptimizer, self).
		       _apply_dense(grad, var))

    def _resource_apply_dense(self, grad, var):
        all_zeros = _is_all_zeros(grad)
        return tf.cond(all_zeros,
                       lambda: tf.no_op(),
                       lambda: super(HackedMomentumOptimizer, self).
                       _resource_apply_dense(grad, var))

    def _apply_sparse(self, grad, var):
        all_zeros = _is_all_zeros(grad)
        return tf.cond(all_zeros,
                       lambda: tf.no_op(),
                       lambda: super(HackedMomentumOptimizer, self).
                       _apply_sparse(grad, var))

    def _resource_apply_sparse(self, grad, var, indices):
        all_zeros = _is_all_zeros(grad)
        return tf.cond(all_zeros,
                       lambda: tf.no_op(),
                       lambda: super(HackedMomentumOptimizer, self).
                       _resource_apply_sparse(grad, var, indices))

def _clip_gradients(gradients_variables, clip_norm=20.):
    gradients, variables = six.moves.zip(*gradients_variables)

    def _replace_nonexisting_grad(grad):
        all_zeros = _is_all_zeros(grad)
        return tf.cond(all_zeros,
                       lambda: tf.zeros([], dtype=tf.float32),
                       lambda: grad)
    gradients_filtered = [_replace_nonexisting_grad(g) for g in gradients]
    fixed_global_norm = tf.global_norm(gradients_filtered)
    gradients, global_norm = tf.clip_by_global_norm(gradients, clip_norm,
	                                            use_norm=fixed_global_norm)
    tf.summary.scalar('global_norm', global_norm)
    return six.moves.zip(gradients, variables)

@alextp You may have context on gradients as well as optimizers -- I don't know how much trouble we'd be causing if we added a special value for gradients.

I think special-casing the optimizers is the right answer here. This code you wrote can be moved into C++ kernels (added initially in contrib) if you see a performance regression.
TensorFlow distinguishes between not differentiable and differentiable but 0 gradient. Trying to further refine this distinction feels like a losing battle to me with a static computation graph.

Same situation here: a multi-task NN with 5-6 outputs (tasks), where each task has ≈ 80% subset of overall labels of the same dataset.
What is the best current solution for this problem in TensorFlow?
Will directly using upper-mentioned HackedMomentumOptimizer for a combined loss work well?
Do we need to weigh/mask the losses?
Have you resolved this problem @ilya-edrenkin?
Currently in production, I use alternate multi-task learning where I have separate Optimizers for each loss and filter tf.contrib.Datasets accordingly available labels.
This has it's downsides:

Optimizers are not being able to effectively communicate (e.g. share internal states - velocity, etc), so they do not know about each other.
This is wasting a lot of computation as single forwards/backward passes could use information from multiple losses/tasks instead of only one.

Will directly using upper-mentioned HackedMomentumOptimizer for a combined loss work well?

It works for me at least.

Do we need to weigh/mask the losses?

If you want to use this hack, the masked loss's gradient must be zero.

I think special-casing the optimizers is the right answer here. This code you wrote can be moved into C++ kernels (added initially in contrib) if you see a performance regression.
TensorFlow distinguishes between not differentiable and differentiable but 0 gradient. Trying to further refine this distinction feels like a losing battle to me with a static computation graph.

Dear @alextp: I agree that adding special values for nonexisting gradients would cause a mess. Is my understanding correct that you are fine with using all-zero-gradient-tensor as a special case signifying that this gradient tensor should be ignored?
If yes, I can add this behaviour to tensorflow. What would be the best way to do it?

Add a separate optimizer to contrib?
Add it as an optional behaviour to the existing optimizers?
Add it as the default behaviour to the existing optimizers?

Thank you!

Add a separate optimizer to contrib.
On Mon, Sep 11, 2017 at 2:22 AM, Ilya Edrenkin<EMAIL_ADDRESS>wrote:

I think special-casing the optimizers is the right answer here. This code
you wrote can be moved into C++ kernels (added initially in contrib) if you
see a performance regression.
TensorFlow distinguishes between not differentiable and differentiable but
0 gradient. Trying to further refine this distinction feels like a losing
battle to me with a static computation graph.
Dear @alextp https://github.com/alextp: I agree that adding special
values for nonexisting gradients would cause a mess. Is my understanding
correct that you are fine with using all-zero-gradient-tensor as a special
case signifying that this gradient tensor should be ignored?
If yes, I can add this behaviour to tensorflow. What would be the best way
to do it?

Add a separate optimizer to contrib?
Add it as an optional behaviour to the existing optimizers?
Add it as the default behaviour to the existing optimizers?

Thank you!
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
https://github.com/tensorflow/tensorflow/issues/12453#issuecomment-328470835,
or mute the thread
https://github.com/notifications/unsubscribe-auth/AAATxctbl7V4Nv7rbhMWsypOKa9heREYks5shPvDgaJpZM4O9lnu
.

--

Alex

@ilya-edrenkin I run into the same problem and I can't find a proper solution... Could you please show an example of how to use your HackedMomentumOptimizer please, I am not sure to understand how I can use it in my code..
Thanks a lot!

Dear @Threynaud : simply copy the code I have provided and use it as a drop-in replacement for the MomentumOptimizer.
If you use gradient clipping with global norm, you can also use the provided function.
@alextp : will submit a PR this weekend or most likely next weekend. Thanks!

@ilya-edrenkin Thanks a lot for your quick response!! Does it work the same way with Adam?

@Threynaud I haven't tried it myself yet, but in my understanding it should work. To try you can simply replace the superclass (i.e. inherit from tf.train.AdamOptimizer instead of tf.train.MomentumOptimizer).

@ily-edrenkin I am so sorry but I think I am still missing something... The code works like a charm but I still get the erratic behavior related to the missing labels for one of the tasks! I think the problem is due to how I combine my 3 losses!
What I am doing now is simply summing the 3 losses of the 3 heads of my network and then applying the optimizer one the resulting loss but I realized that it might not be the good way to do it because the HackedOptimizer can't detect when an example has missing labels for one of the task..
I am clearly missing something here, sorry for that :/

To make it work as intended, you should mask out the "nonexisting" components of the loss function. This will make the gradients specific to this loss component equal to zero, which will be detected by the optimizer.
Btw, if you are taking tf.reduce_mean of an empty tensor you are likely to get an 'inf'.
The assertion error you are getting is likely a different problem -- looks like a type mismatch in the input of the gradient clipping function. For now, you can try make it work without gradient clipping, and solve this problem later.

@ilya-edrenkin Thanks a lot! So if I understand correctly, it doesn't work for batch training, does it!?Also I will try with tf.reduce_sum instead!

Why? It will work for batch training seamlessly.

I guess I misunderstood when you talked about masking the loss components. What I tried at first was setting a weight to zero for each sample with missing label, ie a target vector full of zeros:
target_skills = tf.cast(labels['skills'], tf.float32)
weights_skills = tf.count_nonzero(target_skills, 1)
weights_skills = tf.clip_by_value(weights_skills, 0, 1)

loss_skills = tf.reduce_sum(
    tf.losses.sigmoid_cross_entropy(
        target_skills, logits_skills, weights=weights_skills))

Is it how you wouId mask each loss? I was a bit reluctant to use tf.boolean_mask since it "reduces" the dimension of the original tensor and I am unsure how the gradients are computed in this case  but maybe this is the way to go?

You're right, I misunderstood what you meant by "loss components" my bad..
Does the following looks ok to you:
target_skills = tf.cast(labels['skills'], tf.float32)
# Compute the mask
mask_skills = tf.count_nonzero(target_skills, 1)
mask_skills = tf.cast(mask_skills, tf.bool)

#Compute all the losses components
losses_skills = tf.losses.sigmoid_cross_entropy(
    target_skills, logits_skills)

# Apply the mask
masked_losses = tf.boolean_mask(losses_skills, mask_skills)
# Reduce
loss_skills = tf.reduce_sum(masked_losses)

and that for each loss corresponding to each head of the network. And then apply the optimizer.
I have troubles understanding how the optimizer handles the mask but it is because of my limited knowledge about gradient computation in tf...

@Threynaud This workaround has a bug when all of the loss values are masked in a batch.

@lipixun Do you refer to the fact that the gradient clipping helper routine does not handle the case when the global norm is zero?

@ilya-edrenkin Ooops, no. My fault, I didn't read the above codes carefully. Applyingreduce_sum instead of reduce_mean to masked loss values can avoid nan error.

Fixed in #13274.


[core] Run prettier

Basically resolving an outdated merge.

Thanks for the follow-up on #26610. Hopefully, it will be enough for me to remember now.
For the others in the team that hasn't been hit by this issue yet: Would it make sense to force prettier to run on all the files of the yarn.lock changes? For the.git-blame-ignore-revs file, would it make sense to remove it?

For the.git-blame-ignore-revs file, not sure what we can do to avoid this mistake in the future.

That's not a mistake. Just nice to have

Would it make sense to force prettier to run on all the files of the yarn.lock/prettier config file changes?

I don't think this is an issue worth discussing.


Updated Smol Multimodal RAGinstallation instructions - new colpali package version released

What does this PR do?

Fixes #252
Who can review?
@merveenoyan and @stevhliu.

The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.


Refactor cursor

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

Project coverage is 100.00%. Comparing base (b20586b) to head (c236598).

Additional details and impacted files
@@            Coverage Diff            @@
##              main      #476   +/-   ##
=========================================
  Coverage   100.00%   100.00%
=========================================
  Files           30        30
  Lines          302       313   +11
  Branches        76        82    +6
=========================================
+ Hits           302       313   +11

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


Support ignoring specific lines

It would be like cut blocks but with open-closed boundaries and support multiples of them within a file. The use case would be,
// the console.log line is the API the author wants to put the spotlight on
// the types annotation of JSX's div and style are not needed and
// can be distracting based on the author's preferences
//
export const C: React.FC = () => (
	// ---ignore-start---
	<div style={{ color: 'red' }}
	// ---ignore-end---
		{console.log('very important API to highlight');
	// ---ignore-start---
	</div>
	/// ---ignore-end---
)

Not sure whether it could be done here or have to be done in TS-Twoslasher. We can just mark the ignore lines, and not add the <data-lsp> tags when encounter these line numbers in ranges??

Hrm, do you think this could be more of a user-choice on how to handle the CSS for highlighting?
```ts twoslash {1,3,5}
export const C: React.FC = () => (
	<div style={{ color: 'red' }}
		{console.log('very important API to highlight');
	</div>
)
```

Would mark lines 1, 3, 5 with the css as highlight, and the others as dim

dim lines could have display: none?

No no. I don't want to hide the lines. I just want those lines to not have type annotations.

Yeah, makes sense, same concept could apply for LSP queries too then:
pre .dim  data-lsp  {
  border-bottom: none;
}

.twoslash .dim data-lsp:hover::before {
  display: none;
}


Allow framerate override

This adds functionality to override the framerate of a video stream. Since there is no native way in FFmpeg to do this, I am patching timestamp and duration of the video samples (this is also how ffmpeg.exe does it).
@mcosmin222 What do you think of my approach of allowing to set the fps override in VideoStreamInfo? I thought that per-stream modifications are more flexible than having this in the config class. Maybe this is not so relevant for video. But for future modifications of audio output formats, one might decide to override the output format of one stream but leave it untouched for a different stream, depending on stream codec or whatever.

I think it is OK. If the application wants to override the settings on all streams, it can do so with a foreach.
I will take a run at those changes later on.

The only confusing thing about it is that the API looks as if it allows you to modify the framerate during playback, when in fact it doesn't. I am not sure if that's even possible with the current winRT API.
The winRT property does seem to allow setting at arbitrary points, not sure if it would actually register
https://docs.microsoft.com/en-us/uwp/api/windows.media.mediaproperties.videoencodingproperties.framerate?view=winrt-19041#Windows_Media_MediaProperties_VideoEncodingProperties_FrameRate

It does not even matter what frame rate we store in VideoEncodingProperties. I think it is only used when doing encoding. The only thing that matters during playback is the timestamps we assign to the samples. So theoretically, we could even support dynamically changing the frame rate during playback.
But it would cause some weird side effects, when you think it through: The current algorithm just applies a multiplicator to timestamp and duration. If we'd change that multiplicator during playback, the algorithm would kind of "assume" that we played the whole file with that frame rate, and calculate a totally different timestamp for the next frame. Instead, we'd have to compensate for that with an offset, so the next frame correctly follows the previous frame - just with a different duration. But if we apply an offset, and then seek the file, we'd get very weird behavior. It might even lead to negative timestamps. So I don't think that this would be a good idea.
I put a comment in the XML docs that the value must be changed before creating the PlaybackItem. People will probably see that in the IDE when they type the method name. And also, they would quickly notice that setting it during playback just does not have an effect. So I don't see that as a problem.

I guess we will sort that out later. Happy merge.


Update: BTC ledger

Integrate the new ledger feature from xchainjs
Related to #72  and requires #151

Tested and working


Can not ref object in different yaml file.

Hi ,
I have three yaml file ,one named api-description.yaml ,one named paramters.yaml ,and one named paths.yaml . In paths.yaml ,I can not reference entity defined in api-description.yaml, my reference is 🥇  $ref: '../api-description.yaml#/components/responses/Created'
But the swagger can only find $ref: '../api-description.yaml#/components ,How can I solve this?

Please file tool-specific issues with their respective project. This repository is about the spec itself.


Generate multiple tests using TLC

As discussed previously, we want to generate multiple tests from one test assertion. This is possible already for TLC using the --continue option; for Apalache this will become possible when https://github.com/informalsystems/apalache/issues/542 is resolved (to be tracked separately).

Get multiple traces from a single test using Apalache and TLC #97


"Tătârlaua" is listed in the drop-down menu

Description:
"Tătârlaua" is listed in the drop-down menu alongside its corresponding county and commune, according to the law
Precondition:
The website is up an running.

Step 1
Type "Tătârlaua" in the search bar.

Expected results
The location is listed in the drop-down menu as "Tătârlaua, Alba (Cetatea de Baltă)".


merge master to live-based branch

:x: Validation status: errors

File
Status
Preview URL
Details

:x:Error

Details

[Error] Cannot sync git repo to specified commit because branch live-based has been deleted or has been force pushed: fatal: Could not parse object '2ab0d0eadee19d580cac2d343adb0e5e071ae0fb'.

For more details, please refer to the build report.
Note: If you changed an existing file name or deleted a file, broken links in other files to the deleted or renamed file are listed only in the full build report.

:x: Validation status: errors

File
Status
Preview URL
Details

:x:Error

Details

[Error] Cannot sync git repo to specified commit because branch live-based has been deleted or has been force pushed: fatal: Could not parse object '2ab0d0eadee19d580cac2d343adb0e5e071ae0fb'.

For more details, please refer to the build report.
Note: If you changed an existing file name or deleted a file, broken links in other files to the deleted or renamed file are listed only in the full build report.


WL-4641: HTML tables in user-created content ignore cellpadding/spacing

The general problem here is that bootstrap/morpheus code is setting the cellspacing and cellpadding to 0 and overwriting/ignoring any cellspacing/cellpadding attributes that are set (by the user or elsewhere in the code).  The reason I'm guessing this is in the code is for responsiveness, so that there isn't any unneeded whitespace on tables on eg phones.
This change applies this overwriting of the attributes only to phones etc.

This will change the padding on tables in Sakai as well that are part of tools that are used on a mobile. I'm not sure this is a good direction to go in as we may well continue to have this tussle between the layout desires of user's content and the layout requirements of the tool authors.
It seems if we start doing this we lose the benefits that http://nicolasgallagher.com/about-normalize-css/ is trying to bring to web development.

Yeah agreed.  This change will please our users though, assuming that the Sakai tables still look okay.
i'll let Adam wade in here .. I don't mind really what we do.. I asked Adam if this change was okay and he said it was.

In a nutshell, users are complaining because the tables they create lose the cell padding so in a left aligned cell, the first letter of any text is hard up against the LHS table border. The tables that Sakai tool display on screens do not suffer from this, ie, somehow they DONT have cellpadding = 0. This could be because these sorts of tables always have an id or class assigned where as user tables dont.
An example from polls is .
Short of checking the source of all tables, I don't know whether we can conclusively know whether this is the case. It may be that the only tables without any "class" attribute are the ones created by users in the CK Editor. If this is the case then the only tables that Nicks modification will impact upon atre the user generated ones - this of course is exactly what we are aiming for.
I dont have any solutions other than suggesting that we get Nick's change onto DEV and see what happens. If it ruins everything then we back it out.


[UI DESIGN] Implement nav navigation

Need new navigation ux since links are getting cluttered.
Designs: https://www.figma.com/design/UKdLtH0isKn0jagjrqaVN3/Anoncast?node-id=465-8563&t=L23dYS1e7WuV4cgK-1

is it still open I would like to work on this

hey man I worked on this today and this i show it looks :

I have added the dropdown options and made it responsive with sidebar


Do you plan to release the Camera Motion LoRA model and code in the future ?

Good work!
I have been exploring the field of video diffusion models, and your recent paper, "Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets," caught my attention. In Section 4.3.1, specifically discussing Camera Motion LoRA, you mentioned, "We train these additional parameters on a small dataset with rich camera-motion metadata." I am interested in understanding if there are any plans to publicly release the specific training code and dataset mentioned in this section.
If you have any plans or considerations regarding the release of the code and data associated with the mentioned section, I would greatly appreciate any information you could share.

Thanks for your great work! I would also be very interested in the camera motion LoRA checkpoints. Looking forward :)

me too

+1

+10086

+1

+1


Automated Package Upgrade

:tada: This PR is included in version 7.0.226 :tada:
The release is available on:

npm package (@latest dist-tag)
GitHub release

Your semantic-release bot :package::rocket:


Change the timestamp on userpage thumbnails to a human readable format

Now it says:
Thu Feb 4 2016 15:15:15 etc.
Better:
2 minutes ago

done in #1415


[bug]Error invalid device ordinal

i used 4090 now and The previous 0.38 (in 3070ti) did not encounter such error
when i update installing 0.39.0 and 0.39.1
Then use Paged AdamW8bit
Error invalid device ordinal at line 359 in file D:\a\bitsandbytes-windows-webui\bitsandbytes-windows-webui\csrc\pythonInterface.c
Traceback (most recent call last):
File "E:\Python310\lib\runpy.py", line 196, in _run_module_as_main
return _run_code(code, main_globals, None,
File "E:\Python310\lib\runpy.py", line 86, in run_code
exec(code, run_globals)
File "F:\lora-scripts\venv\Scripts\accelerate.exe_main.py", line 7, in
File "F:\lora-scripts\venv\lib\site-packages\accelerate\commands\accelerate_cli.py", line 45, in main
args.func(args)
File "F:\lora-scripts\venv\lib\site-packages\accelerate\commands\launch.py", line 1104, in launch_command
simple_launcher(args)
File "F:\lora-scripts\venv\lib\site-packages\accelerate\commands\launch.py", line 567, in simple_launcher
raise subprocess.CalledProcessError(returncode=process.returncode, cmd=cmd)
no Paged AdamW8bit is OK....

I find this,
https://github.com/artidoro/qlora/issues/73
if we can use paged optim?

https://github.com/stoperro/bitsandbytes_windows/commit/e02f078000ed19fe57321c464dd16d60f18d6803
i try to use build with changes
whl↓
https://github.com/stoperro/bitsandbytes_windows/releases/tag/pre-v0.39.0-win0
it works well.
Can you help me  build lastest version for qlora？thank u for great works!

I was going to suggest changing line 192 of functional.py to this:
def elementwise_func(func_name, A, B, value, prefetch=True if platform.system()!="Windows" else False):

As well as adding import platform to the top of the script.

As far as I can tell, line 359 of \csrc\pythonInterface.c isn't used if this is done.

Windows does not support the prefetch code, which I believe to be the source of the issue.

 I'll build the latest versions with that change for you.

 I'll build the latest versions with that change for you.

Thanks for your quick and efficient replying!
I just tested the previously changed part and I was able to optimize the memory by 75% using Paged optim ....
This is very useful!!!

0.39.1 build is completed:
https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.39.1-py3-none-win_amd64.whl
0.39.0 build will be done shortly.
Can you test that this works for you?

0.39.1 build is completed: https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.39.1-py3-none-win_amd64.whl
0.39.0 build will be done shortly.
Can you test that this works for you?

thank u ! it works well on lora training!

bf16 is also works，thank u for hard work again~


38 add logger detail

#38

I fixed the get_contract_state response format to match what the frontend is expecting. @bradleySuira, please take a look since I can't ask for your review here because you are the owner of the PR.


anquangou

Please give some context in this is about.

请提供有关此内容的背景信息。

I installed safdog in the range, but waff00f didn't detect it

请提供有关此内容的背景信息。

I installed safdog in the range, but waff00f didn't detect it

closing for now. If there's further information about how it should be detected, please add a comment

closing for now. If there's further information about how it should be detected, please add a comment


Consider more efficient lookup of partial parses to advance

Auxilliary hashtables probably only become efficient when there are many partial parses active in each Earleme, but may be necessary to guarantee a strict O(N) upper bound on parsing.
Also possible: sort the elements of an Earleme so we can use binary search.  Likely also only a win when there are many partial parses in each Earleme.  Slightly complicated by the fact that the current earleme can't be sorted (though #6 would make that a non-issue).
Small linear searches are extremely hard to beat—this suggests that the break-even point for int-sized data is around 60 elements, and this suggests 22 elements for int triples.
Probably Aycock&Horspool optimization into a pushdown automaton would reduce the number of partial parses in each Earleme, so in the end this may never become a real win for any practical grammar.
Need to measure, obviously

We are now doing binary search in each Earleme.


💡 [REQUEST] - Questions Regarding the Release Date of RLHF and the Application of DPO

起始日期 | Start Date
No response
实现PR | Implementation PR
No response
相关Issues | Reference Issues
No response
摘要 | Summary
Hello,
I have been reviewing the excellent projects by the Qwen team. Thank you for your work.

As mentioned in the README, I am curious about the scheduled release date of the Qwen team's RLHF model.

Also, do the Qwen general model and the Qwen-chat model use the same template? I am wondering if the same template was applied to the training as well.

Lastly, given the processed data by Qwen, it seems quite feasible to quickly apply DPO (Directed Preference Optimization). Are you not considering the application of DPO? If not, I am curious about the reasons.

Is there any information or additional technical reports on the tokenizers used in Qwen chat and Qwen coder? It would be very helpful to have detailed information on how the tokenizers differ for each model.

Thank you in advance for your response.
基本示例 | Basic Example
N/A
缺陷 | Drawbacks
N/A
未解决问题 | Unresolved questions
No response

According to available information, the Qwen-Chat-RLHF models mentioned in the technical report is not currently planned for public release.
The Qwen base/general models, by design, does not incorporate templates in its training.
We have recently made available Qwen1.5, featuring chat models that has been developed with DPO. It's ready to be interacted with; give it a try!
In the technical report, the Code-Qwen models utilize the same tokenizer as those used in the standard Qwen models. For more details on the tokenization process, you can refer to the documentation found at https://github.com/QwenLM/Qwen/blob/main/tokenization_note.md.

jklj077
Thank you for your kind response!


Packaging and deploying apps: Deploying NuGet packages

Issue tracking new topic at http://dotnet.github.io/docs/deployment/nuget-packages.html

nuget.org packages
not for Build: packages not on nuget.org

Closed by #508