text:
Unable to merge values from run config into Digma's injected ones

The user should be able to specify the following env variables:
OTEL_SERVICE = x
OTEL_RESOURCE_ATTRIBUTES= x=y,p=z
When activating the 'Observability' toggle button, these values should be merged with Digma's with the user's values taking precedence.
Need to also check the VM Options equivalent

OTEL_SERVICE is single value, and cannot be merged, I guess that if already exists, Digma should not override it.
regarding OTEL_RESOURCE_ATTRIBUTES - merge sounds a must

Exactly - Digma shouldn't override it if exists.

I manage to read the existing systemPropeties for Maven and regular java program, but for GradleRun, I cannot reach them - I opened a bug so we could track it:
https://youtrack.jetbrains.com/issue/IDEA-317118

authors: arik-dig doppleware

repo: digma-ai/digma-intellij-plugin


text:
no data yet view while loading insights

Private Zenhub Video

https://www.figma.com/proto/MxA95ZHK5NqGX4ptFQljRJ/⭐-Design-System-3.0?page-id=4193%3A89582&type=design&node-id=7082-121506&viewport=443%2C-11292%2C0.48&t=RbPF5GYpPaGxznCH-1&scaling=min-zoom&starting-point-node-id=7076%3A116087&mode=design - Design Prototype

source: gharchive/issue

authors: ionuiux shaykeren

repo: digma-ai/digma-ui

license: MIT

license_type: permissive

license_source: github-api


text:
Getting username using user id

I want to get user info such as username , full name using user id , this method
var user_info=ig.feed.user('USER ID');
just pass rankToken and ... , how can I get that ?
thanks

Use ig.user.info(id).

ig.user.info(id).

thanks but when I print it using console,log I see Promise { <pending> } I'm new to node js , why it doesn't return user info ?
thanks

ig.user.info(id).

thanks but when I print it using console,log I see Promise { <pending> } I'm new to node js , why it doesn't return user info ?
thanks

It's a Promise, you need to await it. It's similar to Futures in Rust and Dart and Tasks in C#.

ig.user.info(id).

thanks but when I print it using console,log I see Promise { <pending> } I'm new to node js , why it doesn't return user info ?
thanks

It's a Promise, you need to await it. It's similar to Futures in Rust and Dart and Tasks in C#.

Oh yes , I got it , thank you it's working prefect

how can i get instagram user id while user connect thier instagram profile in nodejs

how can i get instagram user id while user connect thier instagram profile in nodejs

I suppose you mean "how to get the currently logged-in user": ig.account.currentUser().

how can i get instagram user id while user connect thier instagram profile in nodejs

I suppose you mean "how to get the currently logged-in user": ig.account.currentUser().

yes.... explain this one also ig.account.currentUser(). is it package ??

authors: NaeemIntolap1 Nerixyz amirpdlll

repo: dilame/instagram-private-api


text:
License?

Hey there,
What is the license of this package? I found this very helpful and I really liked how you managed links here and was hoping to use something similar in my own package. Would this be alright?

Added the MIT license.. sounds good!

authors: codebykyle dillingham

repo: dillingham/nova-button


text:
🛑 kernel-android12-5.10-lts is down

In d22b187, kernel-android12-5.10-lts (http://aospxref.com/kernel-android12-5.10-lts/) was down:

HTTP code: 404
Response time: 5289 ms

Resolved: kernel-android12-5.10-lts is back up in 0f4d416 after 1 hour, 49 minutes.

repo: dimenspace/aosp-uptime


text:
Minor updates to the responsiveness of flat.html

Minor updates to the responsiveness of flat.html

Minor updates to the responsiveness of flat.html

source: gharchive/pull-request

repo: dimitriupe/VH-website


text:
Stuck at 'building MemoryEfficientAttnBlock with 512 in_channels...'

I'm using latest version of ComfyUI_windows_portable . I have installed ComfyUI-PixelArt-Detector and ComfyUI-Saveaswebp.
When I first time run the workflow.json, ComfyUI raise Exception like cv2 not found, so I installed opencv-python via pip. And it run successfully.
Then I changed prompt, but the workflow staucked at building MemoryEfficientAttnBlock with 512 in_channels....
I have restarted ComfyUI, it still not working event using the default workflow.json. Here is the full log:
D:\tools\ComfyUI_windows_portable>.\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build
Total VRAM 24564 MB, total RAM 65378 MB
xformers version: 0.0.21.dev581
Set vram state to: NORMAL_VRAM
Device: cuda:0 NVIDIA GeForce RTX 4090 : cudaMallocAsync
Using xformers cross attention
### Loading: PixelArtDetector
Copying PixelArtDetector JS files for Workflow loading
### Loading: Save as Webp
Copying JS files for Workflow loading

Import times for custom nodes:
   0.0 seconds: D:\tools\ComfyUI_windows_portable\ComfyUI\custom_nodes\ComfyUI-Saveaswebp
   0.0 seconds: D:\tools\ComfyUI_windows_portable\ComfyUI\custom_nodes\ComfyUI-PixelArt-Detector

Starting server

To see the GUI go to: http://127.0.0.1:8188
got prompt
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 2048 and using 10 heads.
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Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 2048 and using 10 heads.
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Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 2048 and using 10 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 2048 and using 10 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
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Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
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Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
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Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
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Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
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Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
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Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
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Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
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Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 2048 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 2048 and using 10 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 2048 and using 10 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 2048 and using 10 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 2048 and using 10 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 2048 and using 10 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 2048 and using 10 heads.
model_type EPS
adm 2816
making attention of type 'vanilla-xformers' with 512 in_channels
building MemoryEfficientAttnBlock with 512 in_channels...
Working with z of shape (1, 4, 32, 32) = 4096 dimensions.
making attention of type 'vanilla-xformers' with 512 in_channels
building MemoryEfficientAttnBlock with 512 in_channels...
left over keys: dict_keys(['cond_stage_model.clip_l.transformer.text_model.embeddings.position_ids'])
loading new
loading new
100%|██████████████████████████████████████████████████████████████████████████████████| 20/20 [00:12<00:00,  1.57it/s]
making attention of type 'vanilla-xformers' with 512 in_channels
building MemoryEfficientAttnBlock with 512 in_channels...
Working with z of shape (1, 4, 32, 32) = 4096 dimensions.
making attention of type 'vanilla-xformers' with 512 in_channels
building MemoryEfficientAttnBlock with 512 in_channels...

Sorted out, it caused by not enough RAM. After close some running software, workflow working again.

repo: dimtoneff/ComfyUI-PixelArt-Detector


text:
Browser width

Instead of forcing window.screen.width (for the breakpoints), add the possibility to drop this 3 options in when initializing the plugin:

window.screen.width - Complete screen width
document.body.offsetWidth - Width of browser window, as someone might have the browser resized and not at full width of the Monitor
element.parentElement.offsetWidth - As not all images might be at full width (for this you might also pass along the parent when iterating over the elements)

Hey @ivansieder
So you want the possibility to control what the breakpoints should adjust against instead of always window.screen.width?
The reason why I’m looking at the screen width and not the browser width is that I don’t want to load multiple image sources for the same image when you resize (multiple server request per image). And if you have a big screen but the start width of your browser window is something small I don’t want to upscale a low res image if you resize it up.

authors: dinbror ivansieder

repo: dinbror/blazy

license: mit

license_source: bigquery


text:
Fix incorrect filtering of no-op changes

Fixes issue #71
❯ ../tf-summarize --tree changes.tfplan
|---random_id
|       |---b(-/+)
|       |---c(+)

❯ ../tf-summarize  changes.tfplan
+----------+-------------+
|  CHANGE  |  RESOURCE   |
+----------+-------------+
| add      | random_id.c |
+----------+-------------+
| recreate | random_id.b |
+----------+-------------+

Thanks @QuintenBruynseraede for fixing this

authors: QuintenBruynseraede dineshba

repo: dineshba/tf-summarize


text:
How to get the visual results?

Hi,
Thanks for your code.
I don't know how to get the visual results such as :

Thank you very much for your reply!

Hi, we use  kitti_object_vis for visualization.
Thank you.

Feel free to reopen it if you have any further questions.

Hi, I have a question about the results of training. Here is the .txt result from training, such as image_2/002451.txt:

The first 4 values represent the upper-left and lower-right coordinates of the 2d box, is right?
But the 2d box coordinates in the label are(794.92, 171.12, 870.61, 235.39)(301.59, 184.71, 487.48, 262.98)...
The results are different. I don't know why. Thank you for your reply.

authors: YuqiHUO dingmyu study-ni


text:
Adding two ou more songs in Queue at the same time

If you try "play" command two times, and give the options to select the corresponding track, It will choose the two or more of the statements you made. Like the image:

Expected to happen
The previous command "play" be canceled

Corrected in commit https://github.com/diogoditorr/discord-bot-py/commit/6673940fbda8ee6ab26df4ea92b9c15e8a653beb

repo: diogoditorr/discord-bot-py


text:
[Unit Tests] Utils: getCached

Need to make a unit test for getCached utils

Moved to perm plugin

repo: discordeno/discordeno


text:
Typings interfere with those of SQLite

The (re)definition of the sqlite module in the deceleration causes TypeScript projects that also use sqlite to use the wrong declaration (the one provided rather than the one built into sqlite). This problem can be temporarally fixed through the deletion of these lines, but there should be a more permanent solution in place.
https://github.com/discordjs/Commando/blob/09f883317da6d7d8431367ffff827939dfbb2d74/typings/index.d.ts#L1-L4

Why is sqlite re-defined in the first place? I have to manually delete this to use sqlite at all

Can confirm this issue. Either have to comment out commando's definition or comment it out, let TypeScript discover the actual sqlite typings and the uncomment.
TypeScript is supposed to support module merging so not sure why it is interferring. Not sure why commando needs those types to be defined anyway. Sqlite has good typings already.

Yeah I don't know, it as added once because apparently it wasn't working correctly, but tbh I will just remove it again. It serves no real purpose.

authors: DusterTheFirst dhkatz headjoe3 iCrawl

repo: discordjs/Commando


text:
FEATURE: UI to update ai personas on admin page

Introduces a UI to manage customizable personas (admin only feature)
Part of the change was some extensive internal refactoring:

AIBot now has a persona set in the constructor, once set it never changes
Command now takes in bot as a constructor param, so it has the correct persona and is not generating AIBot objects on the fly
Added a .prettierignore file, due to the way ALE is configured in nvim it is a pre-req for prettier to work
Adds a bunch of validations on the AIPersona model, system personas (artist/creative etc...) are all seeded. We now ensure name uniqueness, and only allow certain properties to be touched for system personas.
(JS note) the client side design takes advantage of nested routes, the parent route for personas gets all the personas via this.store.findAll("ai-persona") then child routes simply reach into this model to find a particular persona.
(JS note) data is sideloaded into the ai-persona model the meta property supplied from the controller, resultSetMeta
This removes ai_bot_enabled_personas and ai_bot_enabled_chat_commands, both should be controlled from the UI on a per persona basis
Fixes a long standing bug in token accounting ... we were doing to_json.length instead of to_json.to_s.length
Amended it so {commands} are always inserted at the end unconditionally, no need to add it to the template of the system message as it just confuses things
Adds a concept of required_commands to stock personas, these are commands that must be configured for this stock persona to show up.
Refactored tests so we stop requiring inference_stubs, it was very confusing to need it, added to plugin.rb for now which at least is clearer
Migrates the persona selector to gjs

raised internally about the grey on names... it is our default styling...

I think we are all addressed now @martin-brennan except for 2 comments needing a tiny bit more clarification.
I think the biggest issue atm is that once a model is "dirty" we don't have any protection for you navigating away, so its super easy to just forget you are editing and click a different place and lose your work. Any idea how I would wire that up?
I worry about putting screenshots in cause they can not be translated, added help text for priority
overall this is getting very close!

@SamSaffron thanks for making all those changes -- I just sent a few more minor ones through, and sent comments for the ones that needed clarification.

Some help text or documentation around the enabled commands and what they are supposed to do as well would be good; maybe we can show a two-tiered select kit list like the category one, with name and description?

I still think this would be cool, but not a blocker ^^ Also agree with you about the screenshot thing...it's a shame because it's hard to know where these personas are used, but I definitely appreciate the issue with translations.
Also the JS code is looking much nicer now with async added, thanks for doing that :100:

ok @martin-brennan I think I am done with round 2 :)

authors: SamSaffron martin-brennan

repo: discourse/discourse-ai


text:
Maps workflow in 1.29_v1

Hi,
Is the map selection workflow working properly in 1.29?
We had to update to 1.29 for r20.3 (preview) and it doesn't looks like the plugin generates the maps .json. I tried manually creating it and while it works for the default map, additional maps just dont work at all - returning an "INVALID" argument in d3.

Hi, there is very little difference between plugin versions 1.28 and 1.29 so I would be surprised if updating the plugin caused an issue, but it's possible there is a bug in the r20.3 prerelease. I'll investigate internally and let you know.

Are you making sure to load each map at least once and hitting 'Save Current' as you do so?
The channel/schema for the map will only be written to the JSON if you load and save it.

I've just tested again on a blank project, created two maps and some channel definitions on each. Load + save.
No .json file created by renderstream plugin.

Do you have a d3 license on the machine? If d3 is unlicenced we won’t generate json files, since the renderstream dll file is encrypted.

I tested 2 different machines:
Machine A - regular computer, no d3 license - no json being generated and the following appears in log:
LogRenderStreamEditor: Warning: RenderStreamLink unavailable, skipped GenerateAssetMetadata
Machine B - RX render node with all licenses - json generated as expected after saving any map.

That sounds like d3 isn't installed on Machine A. It's a requirement to have d3 installed - it doesn't have to be licenced, just available - all the json saving code is built into d3's install.

Yep confirmed it works fine after installing  d3 - is this mentioned anywhere or simply assumed?

Hi. Yes, this is documented in the plugins section of the user guide - https://help.disguise.one/Content/Configuring/Render-engines/RenderStream-Unreal.htm but it could do with being clearer.

This is solved and can be closed :)

authors: chrisnashdisguise noxy-k twhittock

repo: disguise-one/RenderStream-UE


text:
Fix CVE-2022-28391 by bumping alpine from 3.15 to 3.16

addresses https://github.com/distribution/distribution/issues/3648

@milosgajdos @silvin-lubecki PTAL

Codecov Report

Merging #3649 (9f2bc25) into main (c202b9b) will not change coverage.
The diff coverage is n/a.

@@           Coverage Diff           @@
##             main    #3649   +/-   ##
=======================================
  Coverage   56.58%   56.58%
=======================================
  Files         103      103
  Lines        7520     7520
=======================================
  Hits         4255     4255
  Misses       2596     2596
  Partials      669      669

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
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authors: codecov-commenter thaJeztah

repo: distribution/distribution


text:
profile chages have some delays

https://www.mumble.dev/profile/dennis and go to https://www.mumble.dev/profile/mrspshuvo there is mill seconds  delay of change in profile pic

@pyanishjain there is a typo in the title of your PR

authors: MidouWebDev pyanishjain


text:
Very sorry

I'm very sorry because I don't know how to reach the project manager and find none emails.
This is not an issue but a question  I don't understand the "parsedXml" part ?  what it is? where does it come from ??   I think this project can help me to load x3d format 3d models and show it in Three.js. Do I wrong?
I converted an ".obj" model to ".x3d" and tried to load it use your example code but failed nothing shows  up. I'm very confused please help !

If you have an .obj model I think it is easier if you use the obj loader:
https://threejs.org/docs/#examples/loaders/OBJLoader

Do you know pop-buffer ? x3dom can load .x3d format 3d model step by step and looks great.
I think maybe there is a way to load .x3d model for Threejs and based on pop-buffer tech
https://x3dom.org/pop/

authors: dragonpoint jonaskello


text:
Add VFR_HUD info

I'd like to add the support for all the info you can find under VFR_HUD:

airspeed
groundspeed
heading
throttle
climb

I have a PR ready with a new class, HUD. If you think it's better to add these as vehicle's attributes, I will change my code and submit a PR.

Well, airspead, and groundspeed are already in the vehicle class. I think that velocity gives you the three elements for a heading (or were you thinking something else)?
What is the climb? Pitch angle? Is this something that you can get from Vehicle.attitude? Or again, is it an element from the velocity value?
For throttle, are you after the current channel value? This can be set using channel_override and default values (I think the throttle is channel 3). Are you just trying to get it, or set it? My concern here is that generally people shouldn't be setting throttle (or any channel_override), but instead using commands like takeoff and the change speed messages (in AUTO).
My general thoughts are:

These are vehicle-specific parameters, so I would expect them to be accessed through the Vehicle (what is the value of a new class here?)

@mrpollo - thoughts?

The heading is where the copter is looking: it's an integer between 0 and 360 where 0 is the north. I use it show the heading of the drone on google maps and it's very useful.
The throttle is the current throttle setting in integer percent (0 to 100), and it's useful to have a quick reference.
Since I wanted to add some attributes and they are all grouped under the same mavlink message, I thought would have been useful to have all of them under the same class like Attitude, Battery and Location, but maybe this is too much opinionated :)

Hi @maruina
Thank you for the explanation. I guess this is kind of a "philosophical" question - Python tends to promote just one way to do stuff, and having separate classes duplicates information. On the other hand, if the aim is to provide a nice wrapper for sending MAVLink commands, then it makes sense to map the MAVLink command structure.
Thinking about it I'm not sure which is better design. Having the information as vehicle attributes is only a little bit more obvious than having the vehicle own a HUD object to access the information.  Deciding that we'll always extend the vehicle by adding an attribute that maps to the command type (like HUD) would significantly reduce the work in thinking about how the architect changes in future.
I don't know which philosophy @mrpollo subscribes to :-) Lets see. Thanks for your patience.

authors: hamishwillee maruina

repo: diydrones/dronekit-python


text:
Added Location and Stock model

Added Location and Stock model
Location is a position in warehouse
and..
how to use same model in different bmf_model in  bmf_module.py.
eg. i want use stock model( in stock/model.py) to StockinModule and StockoutModule(in bmf_module.py).  this way let me use same table to differrent model and they have different stock_type ( a type point to stock in or stock out), here need two different view to control field and initial something
"stock in" is something into stock
"stock out" is something leave the stock
Can you understand what i am saying?

Hi and Thanks!
You can use one model and filter in the views via
    def filter_queryset(self, request, queryset, view):
        return queryset.filter(
            stock_type=A,
        )`

See: https://github.com/django-bmf/django-bmf/blob/develop/djangobmf/contrib/customer/bmf_module.py
And/or use the stock_type value from your instance to differencicate your DetailView.
I'll have to review your changes in detail and I'll be on vacation from July 2nd till July 10th. Merging your changes may take a couple of days.

Hi
I am a poor coder, but i want to learning more. so i hope you can help and teach me.  thanks.
About above question, how to automatic setting the stock_type is value in different bmf_model?

authors: glomium hellwen

repo: django-bmf/django-bmf


text:
Multiple Settings files

I'm wondering, if my app has more than one settings file, how can I add multiple Django-Shell kernels?
I didn't find this is docs and at the first glance in source code it is  impossible to do as well.
Am I miss something?
Thanks

@oduvan depending on what you're trying to do exactly my change #1509 allows you to generate more kernels. You would need to override the ENV or figure out another way to initialize your kernels. If all you need to do is specify multiple settings files I believe you would be able to use the ENV var DJANGO_SETTINGS_MODULE to point to the modules you want to be started as kernels.

authors: oduvan terencehonles

repo: django-extensions/django-extensions


text:
Some settings in shell_plus

I've made some changes to shell_plus to make it support autoload aliases and skipping modules autoloading.
This comes after being annoyed by all the models you never want to see in big projects. And some models having name collision with apps sharing the same names.. If this is not accepted, please give me some feedback of what is missing for going into the project.

I agree, and if somebody has a real need for it there always free to make there own patch right :)
Also thanks for updating the docs.

authors: trbs xeor


text:
Release 3.0.0

Problem
This PR is a combination of other PRs which will form release 3.0.0
So far included:

Use 'create' flag instead of instance.pk (#1362)
Refactor admin import to include encoding param (#1306)
Check value of ManyToManyField in skip_row() (#1271)

Coverage increased (+0.07%) to 98.147% when pulling 11a1b6048f4113467955d572d8ecc40e7af0dc67 on rel-3-x-x into 0d4340f02582e86d4515159becb045cad1d53ee0 on main.

authors: coveralls matthewhegarty


text:
exists() for a QuerySet doesn't work

I want to use exists().
Here an example:
Entry.objects.filter(user='username').exists()
But it returns False.
But it returns something without exists()  like [<Entry: user01>]
Also if I use count(), it returns 1.
Entry.objects.filter(user='username').count()
Doesn't support django-ldapdb exists() or is there a bug?

Well, this looks like a bug indeed ;)
Probably some missing functions deep in the code.

repo: django-ldapdb/django-ldapdb


text:
Added Proxy for all Operations' state_forwards in ProjectState

ticket-29898

Thank You @charettes for the feedback.

In other words, operation: Operation instances should not be passed to the methods added on ProjectState otherwise you introduce bi-directional coupling between them.

This didn't strike my mind during the initial commit. I have made the necessary changes which are as follows:

I have passed the required parameters as arguments to the function in spite of the Operation instances.
I have merged alter_model_options.

@charettes I have committed the desired changes. Also, I have merged the django.db.migrations.operations.utils  with django.db.migrations.utils.
To be honest, my sincere apologies for some newbie mistakes. I will definitely try to minimize them as much as I can :)

Also, I was thinking to change double inverted commas with the single inverted commas in all 4 files. I want to ask whether I should change them for the changes committed by me or for the whole file?

Is there any need to write any new test? Also, Is there any documentation change required?

Also, Is there any documentation change required?

@manav014 Maybe we should clarify sth in the "Migration Operations -> Writing your own" :thinking:. Also, I merged moving django.db.migrations.operations.utils to django.db.migrations.utils in d718d99017b83987fe994fab968d6c3039f352b2 and rebased this PR.

The changes are indirectly tested through existing operations tests but this paves the way for direct testing of state alterations which will come handy when implementing the new registry.

@charettes I would be great to add direct tests. Do you have any specific scenarios in mind?

Maybe we should clarify sth in the "Migration Operations -> Writing your own" 🤔.

@felixxm I didn't find anything to clarify in "Migration Operations -> Writing your own". It would be great if you may specify what kind of clarification we are talking about.

While working on the central repository, I realized that using project_state.related_fields_registry[app_label, model_name]:[(from_field,app_label,model_name,to_field),...] kind of structure would make the central mapping or registry more bulky.
So I just came up with a solution. I would like to propose a new class for the same, in order to make it less bulky to store relations.
it would be something like this:
    class RelationValue:
        def __init__(self, from_field, to_app_label, to_model_name, to_field):
            self.from_field = from_field
            self.to_app_label = to_app_label
            self.to_model_name = to_model_name
            self.to_field = to_field
        #Methods
        def get_all_relations_by_model_name(self):
            # Something
            pass

The above mentioned class definition can be used in the central mapping or registry as follows:
project_state.related_fields_registry[app_label, model_name]:[(RelationValue instance 1), (RelationValue instance 2),...]

Maybe we should clarify sth in the "Migration Operations -> Writing your own" thinking.

@felixxm I didn't find anything to clarify in "Migration Operations -> Writing your own". It would be great if you may specify what kind of clarification we are talking about.

Agreed, there is probably nothing to clarify at the moment.

@manav014 I would like to limit this PR only to the proxy methods (i.e. chop the last three commits). If you're fine it, I will pushed final edits and merge these changes.

@felixxm Sure, I will create a separate PR for further changes. You may merge this PR with changes to proxy methods.

Hey, @felixxm I have chopped the last 3 commits. There are some conflicts in the imports of both the conflicting files. So I am not sure what to do with these. I know you will have a solution for the same.
Also, Please merge this PR so that I may create a new branch for further modifications.

Merged in 503ee41497f346de27843a4ea976c57303c76558.

repo: django/django


text:
Ticket 35321

ticket-[35321]
Branch description
Added CSS rule to make label elements font-size 1rem to address issue on mobile Safari where tapping on an input field or text area causes the screen to zoom slightly.
Checklist

[ ] This PR targets the main branch.
[ ] The commit message is written in past tense, mentions the ticket number, and ends with a period.
[ ] I have checked the "Has patch" ticket flag in the Trac system.
[ ] I have added or updated relevant tests.
[ ] I have added or updated relevant docs, including release notes if applicable.
[ ] For UI changes, I have attached screenshots in both light and dark modes.

This is again based on the stable/5.0.x branch instead of main.


text:
Fixed #25068 -- Added metaclassmaker to resolve metaclass conflicts in migrations state.

https://github.com/django/django/pull/4968
Now it's faster, or more like it's not slowing down ;)

@MarkusH - could you run your benchmarks on this or share how we can do it? I'll happily review the patch for style, etc. if it passes that stage of review.

I just use https://github.com/MarkusH/django-migrations-benchmark as some kind of benchmarking tool and uncomment the code in Django I don't want to have affect on the timing.

@MarkusH I used your benchmark, it seems my current solution doesn't slow down migrations. I have two thoughts after using your benchmark :) Please add __init__.py to your benchmark apps :) It will be easier to use by everyone that need to test it. And second one: this doesn't test my case, so after merging it would be nice to improve it and test also models with multiple metadatas.
Here are results, I ran it when working as I don't have spare computer so it's not best measurement, but I think is enough. As you can see it's almost the same, even better with fix, but difference is caused by IO.
With fix
real	7m7.687s
user	3m4.366s
sys	0m3.366s

real	7m15.488s
user	3m4.684s
sys	0m3.174s

Without fix
real	7m36.196s
user	3m16.878s
sys	0m3.293s

real	7m24.973s
user	3m11.605s
sys	0m3.414s

Can you make ItermidiateMetaClass and DummyMetaClass each add an attribute the object, please.

ItermidiateMetaClass shouldn't add special attr, I can add it to DummyMetaClass thought.

@timgraham if you want to merge, patch looks good to me.

Close in favor of #5334

authors: MarkusH kosz85 timgraham


text:
Fixed #27731 - Optimize AlterField related_name across AlterUniqueTogether/AlterIndexTogether

See for details: https://code.djangoproject.com/ticket/27731

Just like #7948 I don't think changing the signature of reduce() is required. I'll try to land the changes required to allow this kind of re-ordering optimizations to take place next week. Here's a sneak peak https://github.com/django/django/compare/master...charettes:ticket-27768.

@charettes thanks! I guess it's better for me to wait till your changes will be added to master.
Anyway, in general I am not sure that change requested in https://code.djangoproject.com/ticket/27731 should be done at all.

authors: andrewnester charettes


text:
Fixed #28584 -- Dropped support for SQLite < 3.8.

https://code.djangoproject.com/ticket/28584

Looks like RHEL 7 ships with sqlite 3.7.17 - is it worth supporting the current version of RHEL?

That version is unaffected by the removed code. The commit message could instead say "< 3.7.15" since that's the maximum version for which a workaround is removed.

Thanks. Would it be worth updating the gis doc to say that SQLite 3.7.17+ is supported, instead of 3.8+? Otherwise people on RHEL 7 might think the gis functionality isn't supported for them...

RHEL 7 doesn't ship with Python 3 AFAICT and Python 2 support was removed in Django 2.0. Also Django 2.1 will require Python 3.5 or later. So surely if you need a custom build of Python 3.5 anyway, then you may as well need a custom build of sqlite 3.8? (Granted, the commit message could be changed, but surely this is splitting hairs.)

Ok. So not being a RHEL user, I've seen that there is the EPEL, but the point still stands as it only provides Python 3.4, not 3.5 or later.

There's also Software Collections, which provide python 3.5 & 3.6.
But, it seems like in this case it would needlessly require the extra work, since SQLite 3.7.17 should work just fine. No point making users think they need to install a new version when they don't, right?

Nothing like providing everything in one place... Fair enough.

authors: bcail ngnpope timgraham


text:
Notify user that account hasn't been set up yet

Need to have a clear prompt to the user if the app hasn't been set up yet. Automatically enter the setup and guide them in some simple way.

Taking care of this by forcibly creating new profile if none exist. Profile will be set up and ready, and linked to the djjd47130 github user account.

In other words, no longer a reason.


text:
2.1.0 Extraneous panel attached to Topographic Table (MAPTABLE) object

MAPTABLE model/object has a rectangular panel attached to it, floating at an angle, some distance from the main object; otherwise, it appears to be okay.

Try this: https://github.com/djmonkeyuk/nms-base-builder/releases/tag/v2.1.1

authors: Voldrang djmonkeyuk

repo: djmonkeyuk/nms-base-builder


text:
Add Betcalsa by @emredurukn

[ ] Project URL:
[ ] Update contents.json instead of README
[ ] One project per pull request
[ ] Screenshot included
[ ] Avoid iOS or open-source in description as it is assumed
[ ] Use this commit title format if applicable: Add app-name by @github-username
[ ] Use approved format for your entry

@emredurukn thank you!

authors: emredurukn vpeschenkov

repo: dkhamsing/open-source-ios-apps


text:
High impact on compilation speed

import configy.read;

struct Config
{
}

void main()
{
	parseConfigFileSimple!Config("config.yaml");
}

The above program takes 3.5 seconds to compile on a Threadripper 3960X with DMD, which seems more than what would be reasonable to expect.

Seems like most of it is coming from D-YAML... would it be perhaps possible to use D-YAML via an opaque interface so that the implementation isn't imported when compiling programs that use configy?

repo: dlang-community/configy


text:
正規表現の利用でescaperの結果を文字列として再利用する例を追加

使うことがあったので追記してみました

Codecov Report

Merging #96 into master will increase coverage by 0.01%.
The diff coverage is 100.00%.

@@            Coverage Diff             @@
##           master      #96      +/-   ##
==========================================
+ Coverage   96.98%   96.99%   +0.01%
==========================================
  Files          21       21
  Lines         862      866       +4
==========================================
+ Hits          836      840       +4
  Misses         26       26

Impacted Files
Coverage Δ

source/regex_example.d
94.94% <100.00%> (+0.21%)
:arrow_up:

Continue to review full report at Codecov.

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authors: codecov-commenter lempiji

repo: dlang-jp/Cookbook

license: Unlicense


text:
Fix < and > in C_HEADER_DESCRIPTION

E.g. at top of https://dlang.org/library/core/stdc/stdlib.html:
from the standard C header <stdlib.h>.

The change here used to be wrong in ddoc, I don't know if that's changed recently but make sure it generates correctly in regular ddoc too.
(or deprecate ddoc and only use good generators but since this is the official website thing it gotta work there for now)

Something weird is happening, the original actually looks OK the way I generate the HTML. My change is wrong. In the online page linked in the description, the link text has this HTML (at least in Firefox):
&lt;stdlib<wbr>.h&gt;

So the entity characters are getting broken up into span'd punctuation characters.

Filed: https://issues.dlang.org/show_bug.cgi?id=23659

authors: adamdruppe ntrel


text:
Zip.back is wrong

monarchdodra reported this on 2014-03-18T07:31:34Z
Transfered from https://issues.dlang.org/show_bug.cgi?id=12404
CC List

bearophile_hugs
n8sh.secondary
peter.alexander.au (@Poita)

Description
Because it processes the back of each range, rather than an index. EG:

//----
import std.range, std.stdio;

void main()
{
    auto a = [1];
    auto b = [1, 2];
    auto c = [1, 2, 3];
    zip(a, b, c).back.writeln();
}
//----

This writes "Tuple!(int, int, int)(1, 2, 3)".

For "shortest", it should print "1, 1, 1".
For "longest", it should print "0, 0, 3".

For "same length", it's an error.

monarchdodra commented on 2014-03-20T04:20:02Z
(In reply to comment #3)
> (In reply to comment #2)
>
> > Yeah. I think the best thing to do at this point is to just let it be.
>
> Really?

As Peter said, fixing it would break this:

`zip("abc", "123").back`

The fix would be silent breakage, because instead of static asserting, we'd be run-time asserting.

And even then, if the user wrote:
`zip(StoppingPolicy.requireSameLength, "abc", "123").back`

We'd add *yet* more run-time overhead to something that is meant to be fast to begin with.
And it would *still* not be completely correct, since it isn't an *Error* to give it ranges with different lengths...

At best, we could Band-Aid it with a message:
//----
"back/popBack will only function correctly on the `requireSameLength` stopping scheme, and provided the ranges actually do have the same length."
//----

So I'm all ears, but I don't see how we can do much about it at this point.

//---------

Related, opIndex/opSlice has the same issue for "longuest":

import std.range, std.stdio;

void main()
{
    auto a = [1];
    auto b = [1, 2];
    auto c = [1, 2, 3];
    zip(StoppingPolicy.longest, a, b, c)[2];
}

That said, *that's* fixable, but there'd still be a definite run-time overhead.

//--------

My stance is I don't want to waste any more time/effort on something that can't be fixed.

n8sh.secondary commented on 2018-10-16T19:11:37Z
The code from OP currently prints "Tuple!(int, int, int)(1, 1, 1)" because of changes in https://github.com/dlang/phobos/pull/6307. This only applies to `zip` without an runtime StoppingPolicy argument, which implicitly uses StoppingPolicy.shortest, and only when the argument ranges support random access and/or slicing. (So it doesn't fix the problem with strings.)

bearophile_hugs commented on 2014-03-20T03:40:39Z
(In reply to comment #2)

> Yeah. I think the best thing to do at this point is to just let it be.

Really?

peter.alexander.au (@Poita) commented on 2014-03-19T16:23:52Z
Urgh, fixing this is going to be a breaking change, but not necessarily fix bugs.

assert(zip("abc", "123").back == tuple('c', '3'));

That's correct, and works, but your fix requires length and indexing, which narrow strings don't have.

If indexing and length aren't available, back should assert if the stopping policy is not requireSameLength.

repo: dlang/phobos


text:
Issue #14403 - Change all SUB(MODULE/REF) macros to use the new globa…

…l (M)REF(_ALTTEXT) macros.
This fixes cross references in the Ddox based library documentation (apart from bad formatting, which needs to be fixed in a separate PR).

LGTM - any ddoc expert around?

The table on the std.experimental.ndslice page has become unreadable, and it generally looks ugly. But broken links are worse, I guess.

it generally looks ugly

A nicer REF_ALTTEXT could be done with the new DDOX_ROOT_DIR, but for some reason DDOX replaces CamelCase with underscores in URLs. As far as I can tell, that's a game breaker. I think there's no way to write a macro that does that.

We need a way to express a named cross-reference in DDOX to fix the formatting. The easiest way would be to simply recognize REF_ALTTEXT as a special macro (or maybe rather something like $(DDOX_XREF name, qualified.symbol.name)). But that needs to be done separately in DDOX/dlang.org.

Accidentally #4303 contains the same modifications, however leaving (TT $2))$(NBSP) seems to keep the table layout.

I still think we should have the REF macros generally output a <code> tag, though, as they always reference a D symbol and it doesn't make sense to format them differently anyway. Probably the   should be more something like a <ul> that gets properly reformatted using CSS, but I can add that back here, too (depending on which if the PRs gets merged).

#4303 made it in :), so I guess we can close this PR?

This one also fixes the SUBREF macros. I'll rebase.

Okay, scratch that. Also seems to be done already.

authors: aG0aep6G s-ludwig wilzbach


text:
[WIP] Experiment with generating a header file the unicode_tables

There's a bug in the header generation with return - will look into that one shortly
It's stil failing with another D header file generation bug :/

> echo "import std.net.curl;" | dmddev -c -o- -
std/uni.d(7002): Error: function std.internal.unicode_tables.isHangL is not accessible from module uni
std/uni.d(10335): Deprecation: std.internal.unicode_tables.TrieEntry(T...) is not visible from module uni
std/uni.d(10335): Deprecation: std.internal.unicode_tables.TrieEntry(T...) is not visible from module uni
std/uni.d(10335): Deprecation: std.internal.unicode_tables.TrieEntry(T...) is not visible from module uni
std/uni.d(7004): Error: function std.internal.unicode_tables.isHangV is not accessible from module uni
std/uni.d(10335): Deprecation: std.internal.unicode_tables.TrieEntry(T...) is not visible from module uni
std/uni.d(10335): Deprecation: std.internal.unicode_tables.TrieEntry(T...) is not visible from module uni
std/uni.d(7008): Error: function std.internal.unicode_tables.isHangT is not accessible from module uni
std/uni.d(7032): Error: function std.internal.unicode_tables.isHangL is not accessible from module uni
std/uni.d(7034): Error: function std.internal.unicode_tables.isHangV is not accessible from module uni
std/uni.d(7048): Error: function std.internal.unicode_tables.isHangV is not accessible from module uni
std/uni.d(7050): Error: function std.internal.unicode_tables.isHangT is not accessible from module uni
std/uni.d(7059): Error: function std.internal.unicode_tables.isHangT is not accessible from module uni
std/uni.d(10335): Deprecation: std.internal.unicode_tables.TrieEntry(T...) is not visible from module uni
std/uni.d(10335): Deprecation: std.internal.unicode_tables.TrieEntry(T...) is not visible from module uni
std/uni.d(10335): Deprecation: std.internal.unicode_tables.TrieEntry(T...) is not visible from module uni
std/uni.d(10335): Deprecation: std.internal.unicode_tables.TrieEntry(T...) is not visible from module uni
std/uni.d(7104): Error: template instance std.uni.genericDecodeGrapheme!false.genericDecodeGrapheme!(const(char)[]) error instantiating
std/format.d(3123):        instantiated from here: graphemeStride!char
std/format.d(2807):        instantiated from here: formatRange!(Appender!string, string, char)
std/format.d(1777):        instantiated from here: formatValueImpl!(Appender!string, string, char)
std/format.d(567):        ... (1 instantiations, -v to show) ...
std/format.d(6115):        instantiated from here: formattedWrite!(Appender!string, char, const(ushort), string, const(ushort), const(ushort))
std/net/curl.d(3295):        instantiated from here: format!(char, const(ushort), string, const(ushort), const(ushort))

It's stil failing with another D header file generation bug :/

Here's the first PR: https://github.com/dlang/dmd/pull/7835

and another DMD PR is required: https://github.com/dlang/dmd/pull/7836
However, with both PRs this compiles, but doesn't lead to any significant improvement, because even though the tables are private, they are still templated and thus included in the header file :/

Yeah, I guess there's also that they won't be usable in CTFE when hidden this way...

authors: CyberShadow wilzbach


text:
{news_title} не везде работает.

здравствуйте. некоторые новости,не все а только некоторые приходит вместо названия {news_title}
https://yadi.sk/i/R2OmiLI03GhNXM можно с этим что то сделать? модуль версии 1.4.0
@pafnuty не забудь посмотреть этот тиккет :ticket:

@yandex55 нужно понять при каких условиях это происходит.

при каких условиях, когда человеку материал не понравился и он на него жалуется! а если серьезно как понять? скиньте мне вашу почту я ее пропишу в модуль и скину вам скину адрес сайта и поищу конкретную страницу,так как не во всех не работает.

я сейчас сделал так к вашей новости {current_page} было со странице и ссылка активная. но если начнем проверять все верну обратно

repo: dle-modules/DLE-UniForm


text:
Vectorization

This function was optimized in its GNU Octave version. I am porting the changes to matGeom.
Would you like to add a GNU Octave branch to your repo?

Hi,
I have created the branch and merged modifs.

Perfect, I will be committing the GNU Octave code to that branch asap.

btw, these two branches will be unmergeable.

Hmm, in this case it would make sense to have two distinct projects, no ? I had the impression that branches were for developments that had to be integrated when they're ready. Another solution could be to create sub projects, it seems to be possible for large projects.

Yes, I think you are right. Another option is to have a subfolder "MatGeom_Octave" or something of the sort.
I am not familiar with subprojects, if you think that's better then go ahead.
Let me know what you decide. If you think another repository is the best option, then I could make one (we already have one still in sf, but a github backup is not a bad idea)

authors: dlegland kakila

repo: dlegland/matGeom


text:
fixing issue with locale accessors in Mobility

I realized that we were still getting an error on assignment; the method arguments have to be converted to a hash and double splatted for mobility to accept it.  This makes it compatible with the locale_accessor https://github.com/shioyama/mobility/blob/master/lib/mobility/plugins/locale_accessors.rb

Hello, do you need anything for this pull request to be merged in?

repo: dmeremyanin/normalizr


text:
New constraints and defaults (forbid DUE=START)

[x] CustomizedDefaultFieldAdapter: Introduce customizable default values (new interface Default) in order to provide different defaults for DUE (should be after START: new class DefaultAfter) and START (should be before DUE: new class DefaultBefore).
[x] New constraint After: requires that a value is after another value (e.g. DUE must be after START); if constraint is violated, set value to the default (DefaultAfter); similar to the old NotBefore.
[x] New constraint BeforeOrShiftTime: requires that a value is before another value (e.g. START must be before DUE); if constraint is violated, then the reference value (e.g. DUE) is shifted with respect to the duration of the task.

With the two new constraints, we prohibit DUE=START and therefore fix #262. The constraint BeforeOrShiftTime is a tradeoff between shifting always (cp. constraint ShiftTime) and shifting never (cp. constraint NotAfter); maybe this is sufficient in order to close #197.

@dmfs Please review.
It looks like you have currently no time for this project. But particularly in this case, it would be cool to merge all open pull requests and then publish a new version of the app. :smiley:

I agree, some of them solve long-standing issues...

@lemonboston please help to review this PR.

I started to check this out, but as I am not very much familiar with the details involved here, all my comments should be considered more just as questions and inputs rather than recommendations.
FieldAdapter has now getDefault() and getCustomizableDefault() - it's not clear for me why is the customization version needed. I don't know the contract and use-cases of getDefault() in detail, but if getCustomizableDefault() is only added to support more dynamic, strategy-based value, then I suppose getDefault() could accommodate both under the hood.
AbstractDefault could simply be an interface - I know there are types like this in the app, but I checked with @dmfs , he said we can abandon that abstract class style.
The idea to represent a default value calculation looks nice to me. I haven't checked the actual requirements and implementation logic for these relative Times, but from the first look the genericDefault parameter is not clear for me. For example in After, the value of DTSTART as referenceAdapter is both passed in as constructor param to DefaultAfter and as method param, too.
Couldn't any reference value / adapter that's needed to calculate the default be passed in as constructor parameter? So could this method param be removed?
I am thinking what would be needed in general to calculate any default.. wouldn't this suffice?:
interface Default
{
   T getDefault(ContentSet currentValue, FieldAdapter fieldAdapter);
}

fieldAdapter is the current field, so implementation can check current value. Isn't it only the current field value and other field values that are needed to calculate a default? Anything else could go into constructor, to reference other fields for example.
Instead of FieldAdapter the actual value could go in as well, but I am also wondering if Default could apply the Constraints of a type if it wants to. (a getter would be needed)

Thanks for your feedback!

FieldAdapter has now getDefault() and getCustomizableDefault() - it's not clear for me why is the customization version needed. I don't know the contract and use-cases of getDefault() in detail, but if getCustomizableDefault() is only added to support more dynamic, strategy-based value, then I suppose getDefault() could accommodate both under the hood.

I can do that, if favored. However, this would require to extend TimeFieldAdapter (e.g.) with StartTimeFieldAdapter and DueTimeFieldAdapter, each implementing the respective default-strategy. We would then have two field adapters which depend on the usage (StartTimeFieldAdapter can be used for a time field, which is used as start field). Since all other existing field adapters only depend on a specific type, I thought it would be better to keep this structure. But I'm fine with realizing the other variant (= introduce two subclasses from TimeFieldAdapter and remove the default package). That's a strategic decision. So please give an advise.

AbstractDefault could simply be an interface - I know there are types like this in the app, but I checked with @dmfs , he said we can abandon that abstract class style.

Okay, I will change this (only relevant if the general approach is kept, see "strategic decission" above).

[...] but from the first look the genericDefault parameter is not clear for me. For example in After, the value of DTSTART as referenceAdapter is both passed in as constructor param to DefaultAfter and as method param, too.
Couldn't any reference value / adapter that's needed to calculate the default be passed in as constructor parameter? So could this method param be removed?
I am thinking what would be needed in general to calculate any default.. wouldn't this suffice?:
[...] T getDefault(ContentSet currentValue, FieldAdapter fieldAdapter);
fieldAdapter is the current field, so implementation can check current value. Isn't it only the current field value and other field values that are needed to calculate a default? Anything else could go into constructor, to reference other fields for example.

I think it should be possible to replace the param genericDefault by fieldAdapter (and then call fieldAdapter.getDefault(currentValues) to get the default). But what do we win? If we want to eliminate that parameter and move it into the constructor, then the instantiation in TaskFieldAdapters would be much more complex.

but I am also wondering if Default could apply the Constraints of a type if it wants to. (a getter would be needed)

I don't understand what do you mean with this. What do you want to achieve with this?

I think keeping one TimeFieldAdapter is right and applying default value calculation to it generally. We shortly discussed it with @dmfs , one way might be to use a FieldAdapter decorator that sets up the Default strategy. Something like this:
FieldAdapter fieldAdapter2 = new CustomizedDefault(fieldAdapter1, customDefault)

concretely (may not be correct, just for indication):
TimeFieldAdapter DUE = new CustomizedDefault(
new TimeFieldAdapter(Tasks.DUE, Tasks.TZ, Tasks.IS_ALLDAY).addConstraint(new After(_DTSTART)),
new DefaultAfter(_DTSTART));

Would it make sense? So no getCustomizedDefault() and setDefault(), just the decorator which delegates everything else, except getDefault().
It's still a question for me though whether DUE or DTSTART need different way of calculating the default value at different places of the app or not.

Would it make sense?

I think it's just a matter of programming style. I will adapt the style of this PR to whatever style you want ;-)

It's still a question for me though whether DUE or DTSTART need different way of calculating the default value at different places of the app or not.

As far as I know, the default value calculation of TimeFieldAdapter is only used by the TimeFieldEditor. So I think no: they do not need different ways of calculating the default value at different places of the app.

As far as I know, the default value calculation of TimeFieldAdapter is only used by the TimeFieldEditor. So I think no: they do not need different ways of calculating the default value at different places of the app.

I've also double-checked it now and I didn't see any other usage either, so it's good news, I think it would be better then to use getDefault() for the customized version as well, setting that 'strategy' with that decorator. Since FieldAdapter is kind of central type, I think it's worth keeping it simple if we can.
Could you please try to update it in that way?
dmfs will probably join as well later to check out the actual logic around these date-time defaults.

I began the refactoring, but I have the problem, that the new CustomizedDefaultFieldAdapter<Type> should be generic (extends FieldAdapter<Type>) but when be used for DUE and DTSTART, the adapter should extend also TimeFieldAdapter which is not possible in Java. So I have at least three options:

Don't implement a generic CustomizedDefaultFieldAdapter<Type> but instead only a CustomizedDefaultTimeFieldAdapter which extends TimeFieldAdapter, or
change at several places in the code TimeFieldAdapter to FieldAdapter<Time> (I don't even know if this is always possible, since TimeFieldAdapter implements new methods), or
extract an interface from TimeFieldAdapter and let a new AbstractTimeFieldAdapter as well as a new CustomizedDefaultTimeFieldAdapter (which extends the generic CustomizedDefaultFieldAdapter<Type>) implement that interface.

I think the third one is too complicated. But what would you think is the best solution?
I've made a new commit which should show the problem (currently, it has compile errors due to the problem).

You're right TimeFieldAdapter shouldn't implement two new methods that don't event implement an interface. We should get rid of these two methods and use option 2.
TimeFieldAdapter.hasTimeZoneField() was a bad idea. No other code should depend on that.
It's only used once in TimeFieldView.onContentChanged(ContentSet):
if (!taskTimeZone.equals(mDefaultTimeZone) && mAdapter.hasTimeZoneField() && mTimeZoneText != null)

I think we should just check taskTimeZone for UTC in that case. If the time zone of a task or field is UTC it should just be shown in local time. UTC is not a real time zone and no country in the world uses UTC as its time zone.
So I guess the check above can be replaced by
if (!taskTimeZone.equals(mDefaultTimeZone) && !Time.UTC.equals(newValue.timezone) && mTimeZoneText != null)

TimeZoneAdapter.isAllDay() is a bit harder to replace, because that field might be of interest even the get(...) methods return null. A better solution would be an AllDayFieldAdapter which extends FieldAdapter<Boolean>. So all calls to TimeZoneAdapter.isAllDay(values) could be replaced by AllDayFieldAdapter.get(values).
FYI: we're trying to get away from inheritance and move towards composition, hence the change request. In our recent projects we don't use inheritance at all, which worked out pretty well.

Okay. I'm fine with that. Should we integrate that in this Pull Request or make a new one? (It's also possible to outsource the spell changes (contraint to constraint) into a separate PR in order to keep this one simple.)
However, I can only do anything in a week, at least. Maybe you want to realize your proposed changes in the meanwhile ...

I've just opened PR #335 which removes the stale methods. Would you do the review?
Regarding the typo in constraint, yes please fix that in a separate PR. That makes it easier to review.

I've just merged #335 into staging. You can rebase now.

I've outsourced the typo fix for constraint into #338. Since this PR is also working on constraints, I'm waiting for #338 to be merged in order to reduce merge-conflicts.

I've rebased this PR and it's ready for a detailed review (again)!

@dmfs @lemonboston Any news on this? Do my changes comply with your requirements, now?

Doesn't seem to work for me:
06-08 00:15:41.560  3100  3100 E AndroidRuntime: Process: org.dmfs.tasks, PID: 3100
06-08 00:15:41.560  3100  3100 E AndroidRuntime: java.lang.ClassCastException: org.dmfs.tasks.model.adapters.CustomizedDefaultFieldAdapter cannot be cast to org.dmfs.tasks.model.adapters.TimeFieldAdapter
06-08 00:15:41.560  3100  3100 E AndroidRuntime: 	at org.dmfs.tasks.widget.TimeFieldView.setFieldDescription(TimeFieldView.java:136)
06-08 00:15:41.560  3100  3100 E AndroidRuntime: 	at org.dmfs.tasks.model.FieldDescriptor.getDetailView(FieldDescriptor.java:348)
06-08 00:15:41.560  3100  3100 E AndroidRuntime: 	at org.dmfs.tasks.widget.TaskView.setModel(TaskView.java:85)
06-08 00:15:41.560  3100  3100 E AndroidRuntime: 	at org.dmfs.tasks.ViewTaskFragment.updateView(ViewTaskFragment.java:433)
06-08 00:15:41.560  3100  3100 E AndroidRuntime: 	at org.dmfs.tasks.ViewTaskFragment.onModelLoaded(ViewTaskFragment.java:484)
06-08 00:15:41.560  3100  3100 E AndroidRuntime: 	at org.dmfs.tasks.model.Sources.loadModelAsync(Sources.java:111)
06-08 00:15:41.560  3100  3100 E AndroidRuntime: 	at org.dmfs.tasks.ViewTaskFragment.onContentLoaded(ViewTaskFragment.java:691)
06-08 00:15:41.560  3100  3100 E AndroidRuntime: 	at org.dmfs.tasks.model.ContentSet.notifyLoadedListeners(ContentSet.java:615)
06-08 00:15:41.560  3100  3100 E AndroidRuntime: 	at org.dmfs.tasks.model.ContentSet.onContentLoaded(ContentSet.java:185)
06-08 00:15:41.560  3100  3100 E AndroidRuntime: 	at org.dmfs.tasks.utils.AsyncContentLoader.onPostExecute(AsyncContentLoader.java:113)
06-08 00:15:41.560  3100  3100 E AndroidRuntime: 	at org.dmfs.tasks.utils.AsyncContentLoader.onPostExecute(AsyncContentLoader.java:35)
06-08 00:15:41.560  3100  3100 E AndroidRuntime: 	at android.os.AsyncTask.finish(AsyncTask.java:651)
06-08 00:15:41.560  3100  3100 E AndroidRuntime: 	at android.os.AsyncTask.-wrap1(AsyncTask.java)
06-08 00:15:41.560  3100  3100 E AndroidRuntime: 	at android.os.AsyncTask$InternalHandler.handleMessage(AsyncTask.java:668)
06-08 00:15:41.560  3100  3100 E AndroidRuntime: 	at android.os.Handler.dispatchMessage(Handler.java:102)
06-08 00:15:41.560  3100  3100 E AndroidRuntime: 	at android.os.Looper.loop(Looper.java:148)
06-08 00:15:41.560  3100  3100 E AndroidRuntime: 	at android.app.ActivityThread.main(ActivityThread.java:5417)
06-08 00:15:41.560  3100  3100 E AndroidRuntime: 	at java.lang.reflect.Method.invoke(Native Method)
06-08 00:15:41.560  3100  3100 E AndroidRuntime: 	at com.android.internal.os.ZygoteInit$MethodAndArgsCaller.run(ZygoteInit.java:726)
06-08 00:15:41.560  3100  3100 E AndroidRuntime: 	at com.android.internal.os.ZygoteInit.main(ZygoteInit.java:616)
06-08 00:15:41.569  1636  1727 W ActivityManager:   Force finishing activity org.dmfs.tasks/.ViewTaskActivity
06-08 00:15:41.571  1636  1727 W ActivityManager:   Force finishing activity org.dmfs.tasks/.TaskListActivity

I got this when I tried to open the details of a task. Looks like there are still some places where TimeFieldAdapter is used rather than FieldAdapter<Time>

From a code style and design perspective I have no objections.

Hmm :-(
When does this happen?

Sorry, found your description. Will have a look at this ...

Strange, that those were still there. I've fixed this, now.
Please test again!

This is also ready for testing 😉
Let's get this finished, soon -- it has been started more than seven months ago, too.

Looks fine, thx.

authors: dmfs korelstar lemonboston simonspa

repo: dmfs/opentasks


text:
Battery % always wrong

The reported battery % is always wrong. When I install brand new batteries, it shows 36% and then over days goes down to 25% and stays at that level. Any ideas?

You are probably using rechargable NiMH batteries. Those have a nominal voltage of 1.2V compared to alkaline batteries with 1.5V. That's why you are seeing a much lower battery charge percentage even at full charge. The discharge curve of NiMH batteries is much less steep than Alkaline batteries though, so you should be able to use them for a long time at about 25% charge.

Image Source

Ah, you are absolutely right, I did not think of NiMH causing this! Thanks a lot for the context.

authors: ckoca reinisb

repo: dmitry-cherkas/esphome-danfoss-eco


text:
TextCNN rand model downloads pretrained vectors even if not needed

TextCNN rand model downloads pretrained word vectors even if not needed. rand model initializes word vectors randomly and updates in the training process. So, I think there is not need to download word vectors for this model.

There should be a flag passed to _build_vocab  function representing the type of model. If the model is other than rand, then pretrained vectors should be downloaded.

@leezu @eric-haibin-lin your thoughts on this?

@leezu I have code ready for this and iam able to reproduce the results as well. What do you think?

Yes, your proposal is sensible.

Sure, will submit PR to this

Fixed via #1222

authors: avinashsai leezu

repo: dmlc/gluon-nlp


text:
npx.save -> npx.savez

Update for https://github.com/apache/incubator-mxnet/commit/5dc404dfb0cb5e9a034c9db5d58721e15791ad33

We can merge after the nightly has been updated.

@leezu Do you know what's the status of this PR?

Rerunning the timed-out build to see if 20201127 wheel includes the miniz fix.

The speed is still slow I think. For the previous run, it took 1hour for completing the GPU test.

I think the timeout issue due to serialization has been fixed (thanks @leezu). @sxjscience any idea about the name errors in the failed tests?

Does the 20201127 build include the miniz fix?

@szha The error message should be related to wikiextractors: Seems that the 3.0.0 version contains this bug. We will need to force the dependency to wikiextractors==0.1.

authors: leezu sxjscience szha


text:
Save and Evaluate MXNet Model Between Full Epochs

Hi all,
Currently for the dataset I am working with, epochs contain a very large number of images --
I would like to decrease the number of images my model sees each epoch in order to
a) save the model more frequently and
b) call eval_metric to generate metric results on the validation data iterator more frequently
How should I approach this while still iterating through the full data iterator over multiple epochs? Thanks!

use epoch_size

Just to confirm, so if I define epoch size to be smaller than the number of examples I have in the entire training recorditerator, then the iterator rolls over at the start of the next epoch , and it only resets when all batches in the iterator have been processed, regardless of the end of an epoch? Thanks for the prompt response.

Just to confirm, so if I define epoch size to be smaller than the number of examples I have in the entire training recorditerator, then the iterator rolls over at the start of the next epoch , and it only resets when all batches in the iterator have been processed, regardless of the end of an epoch? Thanks for the prompt response.

If you are using the most recent code, yes.

Thanks for the help!

@piiswrong For some reason this solution is not working for me, even when I am using the latest commit of mxnet. The issue is as follows: epoch size is defined below
epoch_size = args.num_examples / args.batch_size

Yet, regardless of what I choose for num_examples, the epoch still only finishes when the data iterator makes a full pass through the data. For example, if I set num_examples = 32, and batch_size = 32, each batch should correspond to a single epoch correct? However, when calling fit, the model does not trigger epoch_callback until after the data iterator completes a full cycle. Is this a bug? or am I making a mistake somewhere? Thanks!

It's working for me. You can go into model.py and see why it's not breaking at model.py:260L

I finally figured out what wasn't working. I am using train_model.py from the image_classifications examples directory in my pipeline, but calling mx.FeedForward with the model_args keyword argument does not initialize epoch_size correctly for some odd reason. I added in the epoch_size parameter to the FeedForward function manually, and the model worked correctly. Is this issue working on your end?

The culprit line seems to be:
https://github.com/dmlc/mxnet/blob/master/example/image-classification/train_model.py#L50
It's missing something like this after the epoch_size = args.num_examples / args.batch_size
model_args['epoch_size'] = epoch_size

@leehonglak Thanks for catching this.
It's also weird that num_exmaples is not declared anywhere.
@mli Could you look into this?

authors: RishabGargeya leehonglak piiswrong

repo: dmlc/mxnet


text:
Inception model on Android

Hi,
I am also working on deep learning on mobile device. , I implemented Caffe, FastRCNN, and FasterRCNN to Android platform. But I would like to remove some layers in different models to speed up and reduce memory usage. May I know which layers you remove? What's your inception model? What's the accuracy? Thanks
https://github.com/tzutalin/Android-Object-Detection

In my thesis there is a tutorial of make a 0.4 time complexity compared to
AlexNet, and performance is same to ZF (83% in top 5). But as it is still
private now, you may find Kaiming He's paper in constrain complexity
helpful.
On Wed, Nov 11, 2015 at 06:41 darrenl notifications@github.com wrote:

Hi,
I am also working on deep learning on mobile device. , I implemented
Caffe, FastRCNN, and FasterRCNN to Android platform. But I would like to
remove some layers in different models to speed up and reduce memory usage.
May I know which layers you remove ? How's the accuracy?
https://github.com/tzutalin/Android-Object-Detection
—
Reply to this email directly or view it on GitHub
https://github.com/dmlc/mxnet/issues/542.

@tzutalin this might also be helpful mxnet.readthedocs.org/en/latest/developer-guide/note_memory.html As the arch was used to reduce memory usage

@tqchen
Thanks. I will take a detailed look at the website.
@antinucleon,
Hope you can release your model someday.

You may use this model for now;
https://github.com/Leliana/WhatsThis/tree/master/app/src/main/res/raw
and we will release it later
On Wed, Nov 11, 2015 at 18:00 darrenl notifications@github.com wrote:

@tqchen https://github.com/tqchen
Thanks. I will take a detailed look at the website.
@antinucleon https://github.com/antinucleon,
Hope you can release your model someday.
—
Reply to this email directly or view it on GitHub
https://github.com/dmlc/mxnet/issues/542#issuecomment-155973189.

closing for now, please feel free to open it. @tzutalin if you are interested, we would also love to have your contribution to mxnet

authors: antinucleon tqchen tzutalin


text:
fix batch norm

@winstywang @antinucleon @tornadomeet
Current master doesn't set gamma to 1 during testing, in order to accommodate old models introduced before fix_gamma was introduced and set to true by default.
However this creates another problem as kvstore_local keeps a internal copy of all the weights so resetting to 1 inside bn is ignored and overwritten by kv's internal copy. As a result weight decay decreases gamma to 0 during training.
This PR fix this by resetting gamma whenever BN::Forward/Backward is called. However this invalidates all models trained before Dec 5 2015.
My suggestion is to merge this when we release 1.0 and explain this to the users in transition guide as well as provide a converter that set fix_gamma to false in .json symbols.

agree!
i think we should reserve fix_gamma(fix_gamma=True is very useful instead of data mean-val normalization ), and set fix_gamma=False by default.

Can you add an entry to NEWS under the ongoing v1.0 milestone and list it as breaking changes?

authors: piiswrong pluskid tornadomeet


text:
Do type checking for the input and kernel in the qnn conv2d

The privous commit #3580 did not have the data type checks needed for qnn conv2d. Existing test cases are passing.

@anijain2305 can you verify the checks.

@zhiics @vinx13 can you take a look at this PR.

@vinx13 can this be merged?

@zhiics can this be merged?

repo: dmlc/tvm


text:
What is the mechanism of using param 'scale_pos_weight'

My dataset has 90% negative samples and 10% positive samples which is very imbalanced.
I try to use the parameter of scale_pos_weight and set it as 9.
What is the mechanism of this param do.
I am curious about what it actually means: does it mean repeat the positive samples 9 times? Or everytime pull out 1/9 samples of negative samples and train the model in many times.
besides, if I have a dataset whose negative samples just a little more than the positive ones, should I need to specify the parameter again?

Hi,
It doesn't modify your data but change the weights of the positive obeservations.
The approache is called cost sensitive, the idea is to force the model to take care of the rare event by increasing the loss if it fails to correctly predict them.
It works much better than subsampling and co.
https://github.com/dmlc/xgboost/blob/d581a3d0e7fbc8e73dbeb6f81fdc1e101606737e/src/objective/regression_obj.cc#L69-L76
Kind regards,
Michaël

I came here looking for the documentation of scale_pos_weight:

Control the balance of positive and negative weights, useful for unbalanced classes. A typical value to consider: sum(negative cases) / sum(positive cases) See Parameters Tuning for more discussion. Also see Higgs Kaggle competition demo for examples: R, py1, py2, py3

I found it via What is the proper usage of scale_pos_weight in xgboost for imbalanced datasets?.

repo: dmlc/xgboost


text:
question about cover metric

Can anyone give some insights into cover metric?
Just don't find any information about this one.

This is the sum of the second order gradient in the branch, mainly can be viewed as how many training instances goes into this branch

authors: orenov tqchen


text:
Trouble while cloning the repository.

Hi! There seems to be a problem while cloning the repository. Im trying to build with multiple GPU support and this seems to be the root of the problem. Any ideas on how to fix this?
fatal: reference is not a tree: e1c8056f6a0ee1c42fd00430b74176e67db66a9f Unable to checkout 'e1c8056f6a0ee1c42fd00430b74176e67db66a9f' in submodule path 'rabit'

What is your Git version? I cannot reproduce the error:
ubuntu@ip-172-31-1-97:~$ git --version
git version 2.17.1
ubuntu@ip-172-31-1-97:~$ git clone --recursive https://github.com/dmlc/xgboost
Cloning into 'xgboost'...
remote: Enumerating objects: 15, done.
remote: Counting objects: 100% (15/15), done.
remote: Compressing objects: 100% (13/13), done.
remote: Total 29649 (delta 3), reused 8 (delta 2), pack-reused 29634
Receiving objects: 100% (29649/29649), 12.15 MiB | 37.80 MiB/s, done.
Resolving deltas: 100% (17171/17171), done.
Submodule 'cub' (https://github.com/NVlabs/cub) registered for path 'cub'
Submodule 'dmlc-core' (https://github.com/dmlc/dmlc-core) registered for path 'dmlc-core'
Submodule 'rabit' (https://github.com/dmlc/rabit) registered for path 'rabit'
Cloning into '/home/ubuntu/xgboost/cub'...
remote: Enumerating objects: 4, done.
remote: Counting objects: 100% (4/4), done.
remote: Compressing objects: 100% (4/4), done.
remote: Total 32675 (delta 0), reused 4 (delta 0), pack-reused 32671
Receiving objects: 100% (32675/32675), 16.52 MiB | 45.71 MiB/s, done.
Resolving deltas: 100% (28643/28643), done.
Cloning into '/home/ubuntu/xgboost/dmlc-core'...
remote: Enumerating objects: 29, done.
remote: Counting objects: 100% (29/29), done.
remote: Compressing objects: 100% (22/22), done.
remote: Total 5677 (delta 6), reused 20 (delta 5), pack-reused 5648
Receiving objects: 100% (5677/5677), 1.43 MiB | 27.67 MiB/s, done.
Resolving deltas: 100% (3429/3429), done.
Cloning into '/home/ubuntu/xgboost/rabit'...
remote: Enumerating objects: 3345, done.
remote: Total 3345 (delta 0), reused 0 (delta 0), pack-reused 3345
Receiving objects: 100% (3345/3345), 980.42 KiB | 21.31 MiB/s, done.
Resolving deltas: 100% (2178/2178), done.
Submodule path 'cub': checked out 'b20808b1b04ec3d6a625e51fbc1eb76f337754ad'
Submodule path 'dmlc-core': checked out '7245c9d42e0baf3b030ae580c125f403dbe57c9f'
remote: Enumerating objects: 1, done.
remote: Total 1 (delta 0), reused 0 (delta 0), pack-reused 1
Unpacking objects: 100% (1/1), done.
From https://github.com/dmlc/rabit
 * branch            e1c8056f6a0ee1c42fd00430b74176e67db66a9f -> FETCH_HEAD
Submodule path 'rabit': checked out 'e1c8056f6a0ee1c42fd00430b74176e67db66a9f'

With git 2.22.0 I cannot reproduce.

Hi!
Аналогично:
Cloning into 'rabit'...
fatal: reference is not a tree: e1c8056f6a0ee1c42fd00430b74176e67db66a9f
Unable to checkout 'e1c8056f6a0ee1c42fd00430b74176e67db66a9f' in submodule path 'rabit'

i use Docker:
##########################################################
# Dockerfile to build Data Science Python Toolbox
# Based on latest LTS release ubuntu:xenial
##########################################################

## Set the base image to debian
FROM ubuntu:16.04

## File author
LABEL maintainer="user"

## Version
ENV REFRESHED_AT 2019-01-06

## Specify user
USER root

## No tty
ENV DEBIAN_FRONTEND noninteractive

## Configure locale and environment variables
RUN apt-get update && apt-get install -y apt-utils locales && locale-gen en_US.UTF-8 && dpkg-reconfigure locales

ENV HOME /root
ENV WORKDIR /root/shared

## Setup work directory
RUN mkdir $WORKDIR

## Set current dir
WORKDIR $HOME

## Install compilers and essential tools
RUN apt-get update && apt-get install --no-install-recommends -y \
    gcc \
    g++ \
    gfortran \
    build-essential \
    software-properties-common \
    pkg-config \
    cmake \
    vim \
    nano \
    tar \
    unzip \
    git \
    curl \
    wget \
    python3 \
    python3-dev \
    python3-pip \
    net-tools \
    && apt-get autoremove -y && apt-get clean && rm -rf /var/lib/apt/lists/*

## Install dependencies
RUN apt-get update && apt-get install --no-install-recommends -y \
    python3-tk \
    libpng-dev \
    libfreetype6-dev \
    libxft-dev \
    libjpeg-dev \
    libhdf5-dev \
    libffi-dev \
    libssl-dev \
    libatlas-dev \
    libatlas-base-dev \
    liblapack-dev \
    && apt-get autoremove -y && apt-get clean && rm -rf /var/lib/apt/lists/*

## Install scipy stack
RUN pip3 install --upgrade --ignore-installed pip setuptools wheel
RUN pip3 install \
    numpy \
    scipy \
    sympy \
    matplotlib \
    seaborn \
    pandas \
    hyperopt \
    shap \
    dask \
    slacker \
    tqdm \
    ipyparallel \
    jupyter_contrib_nbextensions \
    telepyth \
    statsmodels \
    nose \
    cython \
    h5py \
    pillow \
    scikit-learn \
    glmnet_py \
    cvxopt \
    pystan \
    ipykernel \
    ipython \
    jupyter \
    notebook \
    jupyterlab \
    bokeh \
    holoviews \
    ipywidgets \
    ipyvolume \
    joblib \
    arrow \
    numba \
    pytz \
    graphviz \
    lightgbm \
    tensorflow \
    pyarrow \
    feather-format \
    ptvsd \
    && apt-get autoremove -y && apt-get clean && rm -rf /var/lib/apt/lists/* $HOME/.cache/pip/*

##install --upgrade tables
RUN pip3 install --upgrade --ignore-installed pip setuptools wheel
RUN pip3 install \
    dask[dataframe] --upgrade \
    --upgrade tables \
    && apt-get autoremove -y && apt-get clean && rm -rf /var/lib/apt/lists/* $HOME/.cache/pip/*

## Install XGBoost library
RUN git clone --recursive https://github.com/dmlc/xgboost && cd xgboost && make -j4 && cd python-package && python3 setup.py install

Could you specify git version?

Could you specify git version?

Step 12/18 : RUN git --version
 ---> Running in 34fece459459
git version 2.7.4

@trivialfis you can run my script in terminal:
user@ubuntu:~/mlbox$ ./build.sh
Where build.sh:
docker build -t user:cpu -f Dockerfile.cpu .
And  Dockerfile.cpu:
##########################################################
# Dockerfile to build Data Science Python Toolbox
# Based on latest LTS release ubuntu:xenial
##########################################################

## Set the base image to debian
FROM ubuntu:16.04

## File author
LABEL maintainer="user"

## Version
ENV REFRESHED_AT 2019-07-26

## Specify user
USER root

## No tty
ENV DEBIAN_FRONTEND noninteractive

## Configure locale and environment variables
RUN apt-get update && apt-get install -y apt-utils locales && locale-gen en_US.UTF-8 && dpkg-reconfigure locales

ENV HOME /root
ENV WORKDIR /root/shared

## Setup work directory
RUN mkdir $WORKDIR

## Set current dir
WORKDIR $HOME

## Install compilers and essential tools
RUN apt-get update && apt-get install --no-install-recommends -y \
    gcc \
    g++ \
    gfortran \
    build-essential \
    software-properties-common \
    pkg-config \
    cmake \
    vim \
    nano \
    tar \
    unzip \
    git \
    curl \
    wget \
    python3 \
    python3-dev \
    python3-pip \
    net-tools \
    && apt-get autoremove -y && apt-get clean && rm -rf /var/lib/apt/lists/*

## check git vers
RUN git --version

## Install dependencies
RUN apt-get update && apt-get install --no-install-recommends -y \
    python3-tk \
    libpng-dev \
    libfreetype6-dev \
    libxft-dev \
    libjpeg-dev \
    libhdf5-dev \
    libffi-dev \
    libssl-dev \
    libatlas-dev \
    libatlas-base-dev \
    liblapack-dev \
    && apt-get autoremove -y && apt-get clean && rm -rf /var/lib/apt/lists/*

## Install scipy stack
RUN pip3 install --upgrade --ignore-installed pip setuptools wheel
RUN pip3 install \
    numpy \
    scipy \
    sympy \
    matplotlib \
    seaborn \
    pandas \
    hyperopt \
    shap \
    dask \
    slacker \
    tqdm \
    ipyparallel \
    jupyter_contrib_nbextensions \
    telepyth \
    statsmodels \
    nose \
    cython \
    h5py \
    pillow \
    scikit-learn \
    glmnet_py \
    cvxopt \
    pystan \
    ipykernel \
    ipython \
    jupyter \
    notebook \
    jupyterlab \
    bokeh \
    holoviews \
    ipywidgets \
    ipyvolume \
    joblib \
    arrow \
    numba \
    pytz \
    graphviz \
    lightgbm \
    tensorflow \
    pyarrow \
    feather-format \
    ptvsd \
    && apt-get autoremove -y && apt-get clean && rm -rf /var/lib/apt/lists/* $HOME/.cache/pip/*

##install --upgrade tables
RUN pip3 install --upgrade --ignore-installed pip setuptools wheel
RUN pip3 install \
    dask[dataframe] --upgrade \
    --upgrade tables \
    && apt-get autoremove -y && apt-get clean && rm -rf /var/lib/apt/lists/* $HOME/.cache/pip/*

## Install XGBoost library
RUN git clone --recursive https://github.com/dmlc/xgboost && cd xgboost && make -j4 && cd python-package && python3 setup.py install

This error was due to Azure's Linux machines. By default they seem to have an really old git version. Updating git solved the problem.

@anthonymakela but i don't use Azure's Linux.
something else?

Can you try updating your git version to >= 2.20? Im pretty confident that it will fix the issue. @paantya

Joining in this thread as I have the same problem... unfortunately, I don't have the ability to update git - 2.7.4 is the latest version on Ubuntu 16.04, which is what I'm running, and I'm not the sysadmin (a work machine.)   Any other thoughts?

For now you can just use a release source from github release page.  I'm not sure about the cause of this.

Could you please help testing the latest master branch? @jpbowman01 @anthonymakela @paantya

@anthonymakela updating and it work!
Add in Docker before install git:
##Get last git
RUN apt-get update

RUN apt-get install software-properties-common -y

RUN add-apt-repository ppa:git-core/ppa -y

@jpbowman01 u can try:
sudo apt-get update
sudo apt-get install software-properties-common -y
sudo add-apt-repository ppa:git-core/ppa -y
sudo apt-get install git

Could you please help testing the latest master branch? @jpbowman01 @anthonymakela @paantya

You about at git < 2.20 testing the latest master branch?

@paantya Right.  See if the problem persists with older version of git (the one you used before).

@trivialfis all ok!
i run Docker file (look up), and it finished ok done:
`

Finished processing dependencies for xgboost===1.0.0-SNAPSHOT

 git 2.7.4`

@paantya Great news!


text:
compliance-level-3/0103-feel-is-function

diff --git a/TestCases/compliance-level-3/0103-feel-is-function/0103-feel-is-function.dmn b/TestCases/compliance-level-3/0103-feel-is-function/0103-feel-is-function.dmn
index a4f83a7..3a0f148 100644
--- a/TestCases/compliance-level-3/0103-feel-is-function/0103-feel-is-function.dmn
+++ b/TestCases/compliance-level-3/0103-feel-is-function/0103-feel-is-function.dmn
@@ -170,12 +170,12 @@
     </decision>

     <decision name="time_006" id="_time_006">
-        <description>Tests FEEL expression: 'is(@"23:00:50@Australia/Melbourne", @"23:00:50+10:00")' and expects result: 'false (boolean)'</description>
-        <question>Result of FEEL expression 'is(@"23:00:50@Australia/Melbourne", @"23:00:50+10:00")'?</question>
+        <description>Tests FEEL expression: 'is(@"23:00:50@Australia/Melbourne", @"23:00:50+11:00")' and expects result: 'false (boolean)'</description>
+        <question>Result of FEEL expression 'is(@"23:00:50@Australia/Melbourne", @"23:00:50+11:00")'?</question>
         <allowedAnswers>false (boolean)</allowedAnswers>
         <variable name="time_006"/>
         <literalExpression>
-            <text>is(@"23:00:50@Australia/Melbourne", @"23:00:50+10:00")</text>
+            <text>is(@"23:00:50@Australia/Melbourne", @"23:00:50+11:00")</text>
         </literalExpression>
     </decision>

This test is valid only between 1.October and 1.April when DST time is active.


text:
SSH authentication now fails on SSH repos in the GUI

... because I hardcoded HTTP authentication nearly everywhere to make all of that work. I need to integrate SSH authentication back in, specifically with regards to push, fetch,e tc.

SSH seems to be working in many cases. The problem I saw here was really a manifestation of #209. Closing this bug in lieu of more specific issues (like #209).

repo: dmusican/Elegit


text:
Bundle patch for 1.3.3.patch3

wmagent branch version of:
Added "project" metadata for block and container objects #9667
Properly deal with MSS/Tape container rule creation #9666
Always create block-level rules for blocks inserted by RucioInjector #9672

 Fix #9673 - Bundle patch for 1.3.3.patch3

repo: dmwm/WMCore


text:
Nuget packages for 9.8.1 not available?

I'm using DNN 9.8.1 but when trying to develop a custom module I only find the 9.8.0 dnn nuget package, when I publish my module it replaces the 9.8.1 dnn dll's 😭

We had an issue with those getting unlisted, I'll see if I can grab them and push back out.
BUT - Please note you should NEVER publish dotnetnuke.dll with a module installation

Sure, I'm not publishing dotnetnuke*.dlls to the production website, but on my development environment when I update some code on the module I use the VS publish to my local IIS so I can test my changes. I will only commit my modules dll changes anyway. Tks for the help!

For the part about them being included when you build (deploy) locally, for each package you can go in it's properties and change "CopyLocal" to false and they should stop being copied over

For those looking at and/or more comfortable, the equivalent of what Daniel said is found in the csproj/vbproj as <Private>False</Private> (meaning, that's what CopyLocal changes).

For the part about them being included when you build (deploy) locally, for each package you can go in it's properties and change "CopyLocal" to false and they should stop being copied over

Tks! Good idea. It will help!!

Just a tad more background for anyone reading this in the future...
If CopyLocal is set to True (<Private>True</Private>), it indicates to most build processes that you intend to distribute that DLL as part of your module.  The build script(s) may then put the DLL(s) into the resulting extension package you've been working on.  When you install that package, you could accidentally break DNN and/or other installed extensions if you're not deliberate about which DLL(s) need to be installed, and declare them correctly in your manifest.

authors: hismightiness mitchelsellers rodrigoratan valadas