text:
Italynorth as default_location

Community Note

Please vote on this issue by adding a 👍 reaction to the original issue to help the community and maintainers prioritize this request
Please do not leave "+1" or "me too" comments, they generate extra noise for issue followers and do not help prioritize the request
If you are interested in working on this issue or have submitted a pull request, please leave a comment

Versions

terraform: 1.3.9
azure provider:  "3.75.0"
module: 4.2.0
Description

module "enterprise_scale" {
source  = "Azure/caf-enterprise-scale/azurerm"
version = "4.2.0" # change this to your desired version, https://www.terraform.io/language/expressions/version-constraints
default_location = "italynorth"
providers = {
azurerm              = azurerm
azurerm.connectivity = azurerm
azurerm.management   = azurerm
}
### Using "italynorth" as default_location in main.tf, i get this error on plan
PS C:\Azure\ISP\SYS> terraform plan
data.azurerm_client_config.core: Reading...
data.azurerm_client_config.core: Read complete after 0s [id=Y2xpZW50Q29uZmlncy9jbGllbnRJZD1jMDgxNTQyMi02OTEwLTQ4OWMtYWNiNS00Mjc3MDAyMWZlNjE7b2JqZWN0SWQ9ZDAwY2IyOTItNTZlYy00ZGVlLWEwY2UtYzBkYTI1NTU3ODcyO3N1YnNjcmlwdGlvbklkPTVhM2U3YTQ1LThmMGEtNGNiMy1hZWYyLTM4ZDNjNmE5YmQwNzt0ZW5hbnRJZD1hMzYxYzE4NS1mZTFhLTQwZjYtODczNC03Njc5NjJlMGRkNWQ=]
╷
│ Error: Invalid index
│
│   on .terraform\modules\enterprise_scale\modules\connectivity\locals.tf line 1448, in locals:
│ 1448:       "privatelink.${local.lookup_azure_backup_geo_codes[location]}.backup.windowsazure.com"
│     ├────────────────
│     │ local.lookup_azure_backup_geo_codes is object with 134 attributes
│
│ The given key does not identify an element in this collection value.
**_Changing it on "westeurope" for example, the plan run correctly.
"Italynorth" seems to be missing on https://github.com/Azure/terraform-azurerm-caf-enterprise-scale/blob/main/modules/connectivity/locals.geo_codes.tf.json_**
-->
Additional context

This has been merged and will be included in the next release.

AB#31258

authors: emlain jaredfholgate


text:
export SdkClientAccessor

fix: https://github.com/Azure/typespec-azure/issues/969

All changed packages have been documented.

:white_check_mark: @azure-tools/typespec-client-generator-core

Show changes
@azure-tools/typespec-client-generator-core - fix ✏

export SdkClientAccessor

You can try these changes at https://cadlplayground.z22.web.core.windows.net/typespec-azure/prs/990/
Check the website changes at https://tspwebsitepr.z22.web.core.windows.net/typespec-azure/prs/990/

source: gharchive/pull-request

authors: azure-sdk tadelesh

repo: Azure/typespec-azure

license: MIT

license_type: permissive

license_source: github-api


text:
System.IdentityModel.Tokens.ISecurityTokenValidator could not be loaded

Visual Studio 2017 Web Site (not solution) VB.NET Webforms
Schweregrad Code Beschreibung Projekt Datei Zeile Unterdrückungszustand
Fehler Reference.svcmap: Der Typ "System.IdentityModel.Tokens.ISecurityTokenValidator" in der Assembly "System.IdentityModel.Tokens.Jwt, Version=5.3.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" konnte nicht geladen werden. App_WebReferences/CheckVatServiceReference1/
this comes after stuck in nuget packaged Versionen (removing, adding ….)
<?xml version="1.0" encoding="utf-8"?> <packages>   <package id="bootstrap" version="4.1.3" targetFramework="net462" />   <package id="EntityFramework" version="6.2.0" targetFramework="net462" />   <package id="EntityFramework.de" version="6.2.0" targetFramework="net462" />   <package id="FontAwesome" version="4.7.0" targetFramework="net462" />   <package id="jQuery" version="3.3.1" targetFramework="net462" />   <package id="Microsoft.AspNet.SignalR" version="2.3.0" targetFramework="net462" />   <package id="Microsoft.AspNet.SignalR.Core" version="2.3.0" targetFramework="net462" />   <package id="Microsoft.AspNet.SignalR.Core.de" version="2.3.0" targetFramework="net462" />   <package id="Microsoft.AspNet.SignalR.de" version="2.3.0" targetFramework="net462" />   <package id="Microsoft.AspNet.SignalR.JS" version="2.3.0" targetFramework="net462" />   <package id="Microsoft.AspNet.SignalR.SystemWeb" version="2.3.0" targetFramework="net462" />   <package id="Microsoft.AspNet.SignalR.SystemWeb.de" version="2.3.0" targetFramework="net462" />   <package id="Microsoft.AspNet.WebApi.Client" version="5.2.6" targetFramework="net462" />   <package id="Microsoft.Bcl" version="1.1.10" targetFramework="net462" />   <package id="Microsoft.Bcl.Build" version="1.0.21" targetFramework="net462" />   <package id="Microsoft.CodeDom.Providers.DotNetCompilerPlatform" version="2.0.1" targetFramework="net462" />   <package id="Microsoft.Graph" version="1.12.0" targetFramework="net462" />   <package id="Microsoft.Graph.Core" version="1.12.0" targetFramework="net462" />   <package id="Microsoft.Identity.Client" version="2.4.0-preview" targetFramework="net462" />   <package id="Microsoft.IdentityModel.JsonWebTokens" version="5.3.0" targetFramework="net462" />   <package id="Microsoft.IdentityModel.Logging" version="5.3.0" targetFramework="net462" />   <package id="Microsoft.IdentityModel.Protocol.Extensions" version="1.0.4.403061554" targetFramework="net462" />   <package id="Microsoft.IdentityModel.Protocols" version="5.3.0" targetFramework="net462" />   <package id="Microsoft.IdentityModel.Protocols.OpenIdConnect" version="5.3.0" targetFramework="net462" />   <package id="Microsoft.IdentityModel.Protocols.WsFederation" version="5.3.0" targetFramework="net462" />   <package id="Microsoft.IdentityModel.Tokens" version="5.3.0" targetFramework="net462" />   <package id="Microsoft.IdentityModel.Tokens.Saml" version="5.3.0" targetFramework="net462" />   <package id="Microsoft.IdentityModel.Xml" version="5.3.0" targetFramework="net462" />   <package id="Microsoft.Owin" version="4.0.0" targetFramework="net462" />   <package id="Microsoft.Owin.de" version="4.0.0" targetFramework="net462" />   <package id="Microsoft.Owin.Host.SystemWeb" version="4.0.0" targetFramework="net462" />   <package id="Microsoft.Owin.Host.SystemWeb.de" version="4.0.0" targetFramework="net462" />   <package id="Microsoft.Owin.Security" version="4.0.0" targetFramework="net462" />   <package id="Microsoft.Owin.Security.Cookies" version="4.0.0" targetFramework="net462" />   <package id="Microsoft.Owin.Security.OpenIdConnect" version="4.0.0" targetFramework="net462" />   <package id="Microsoft.ReportingServices.ReportViewerControl.WebForms" version="150.900.148" targetFramework="net462" />   <package id="Microsoft.SqlServer.Types" version="14.0.1016.290" targetFramework="net462" />   <package id="Microsoft.Web.Infrastructure" version="1.0.0.0" targetFramework="net462" />   <package id="Newtonsoft.Json" version="11.0.2" targetFramework="net462" />   <package id="Owin" version="1.0" targetFramework="net462" />   <package id="popper.js" version="1.14.3" targetFramework="net462" />   <package id="System.IdentityModel.Tokens.Jwt" version="5.3.0" targetFramework="net462" />   <package id="System.Net.Http" version="4.3.4" targetFramework="net462" />   <package id="System.Security.Cryptography.Algorithms" version="4.3.1" targetFramework="net462" />   <package id="System.Security.Cryptography.Encoding" version="4.3.0" targetFramework="net462" />   <package id="System.Security.Cryptography.Primitives" version="4.3.0" targetFramework="net462" />   <package id="System.Security.Cryptography.X509Certificates" version="4.3.2" targetFramework="net462" /> </packages>

Hi @hannespreishuber
<package id="Microsoft.IdentityModel.Protocol.Extensions" version="1.0.4.403061554" targetFramework="net462" />  looks out of place in that list. Try removing it.
For more information, check out Katana (OWIN) migration process on our wiki.

ok, thats sounds reasonable. I have setup a new blank identitcal website without any the logic, same packages (also extensions) and got no error. I see thats kind of mystical bug searching

source: gharchive/issue

authors: GeoK hannespreishuber


text:
Handling OS errors raised if input is empty and unrecognized for the first usage

Fixes #64
For Keychain:
Scenario:

Cache file already exists before using MSAL extensions for the very first time.
Gives an error as cannot find corresponding service name and account name entry in Keychain
Returned error is KeychainError : ITEM_NOT_FOUND

For Windows:

Empty cache file already exists before using MSAL extensions
Adds a check to not make call to DPAPI if input is an empty file.
Existing code returns an error which is OS error: WinError 87

Hey Ray,
Thanks for reviewing !

Added the description to the issue
Yes non empty cache file would cause the issue, I have added this in description, this fix however would not solve that error.
Also, after testing, using serialize of token_cache_previous and save in new extensions persistence in the same file(the migration scenario we talked about) does not work either. So the best way to handle this would be to either empty the already existing cache file or use a new one.
For having it in persistence layer, I did think  about that option, however since this is an macOS specific error, I think having it is osx.py makes more sense. Also, we already do such kind of handling in the same file here


text:
[TODO] Sample TODOs

Auth code sample here and here
Use flag package in samples main file instead of if...else chain here

Make the samples self contained. Refer:  https://github.com/AzureAD/microsoft-authentication-library-for-go/pull/228#issuecomment-865307266


text:
Clear hash only if it contains known properties

handleRedirectPromise clears the current hash before processing the redirect response. Today this logic doesn't care if the hash came from the current url or from the cache. This causes problems for hash routing scenarios where a custom (non-msal) hash may be removed, causing a route change before the hash can be processed.
This PR fixes this behavior by only clearing the current hash if it contains known response properties
Fixes #4400

Codecov Report

Merging #4415 (6808be4) into dev (7dd8b32) will not change coverage.
The diff coverage is 100.00%.

Flag
Coverage Δ

*Carryforward flag

msal-angular
96.39% <ø> (ø)

Carriedforward from 7dd8b32

msal-browser
87.50% <100.00%> (ø)

Carriedforward from 7dd8b32

msal-common
85.37% <ø> (ø)

Carriedforward from 7dd8b32

msal-core
82.65% <ø> (ø)

Carriedforward from 7dd8b32

msal-node
80.72% <ø> (ø)

Carriedforward from 7dd8b32

msal-node-extensions
76.03% <ø> (ø)

Carriedforward from 7dd8b32

msal-react
94.27% <ø> (ø)

*This pull request uses carry forward flags. Click here to find out more.

Impacted Files
Coverage Δ

...l-browser/src/interaction_client/RedirectClient.ts
100.00% <100.00%> (ø)

authors: codecov-commenter tnorling


text:
How to Visualize Detection Results from Coordinates in Quickstart?

Hello,
I encountered an issue while trying to follow the Quickstart guide provided in the README. When I use the question question = "What is liver in this image? Please output the box.", I only receive coordinates and am unable to visualize the detection results.
The specific returned result is: generated_texts Coordinates are [0.594, 0.34, 0.414, 0.938, 0.914, 0.895].
I would like to know how to use these coordinates to draw the detection box on the image for better visualization. Could you provide guidance or an example on how to achieve this?
Additionally, could you provide the ground truth for the example data used in Quickstart? This would greatly help in validating and comparing the results.
Thank you!

Hi,
You can find the visualization method here (https://github.com/BAAI-DCAI/M3D/blob/05bb41a29a9dcb5afe8dcafc2c0222b45e271c2a/Bench/eval/eval_pos.py#L148).
Best regards,
BAI Fan

authors: VinyehShaw baifanxxx


text:
The year plot lines are to wide

@baimingze the lines in the publication by year block are to wide in my opinion.

done

authors: baimingze ypriverol

license: mit

license_source: bigquery


text:
added some unittests for data_preparation.py, made some changes to da…

added some unittests for data_preparation.py, made some changes to data_preparation. added test_file folder, but it does not contain fast5 file and SA op due to space
shortage

Codecov Report

Merging #28 into master will decrease coverage by 0.24%.
The diff coverage is 0%.

@@            Coverage Diff            @@
##           master     #28      +/-   ##
=========================================
- Coverage    5.51%   5.26%   -0.25%
=========================================
  Files          10      11       +1
  Lines        1070    1120      +50
=========================================
  Hits           59      59
- Misses       1011    1061      +50

Impacted Files
Coverage Δ

nanotensor/data_preparation.py
0% <0%> (ø)
:arrow_up:

nanotensor/unit-test.py
0% <0%> (ø)

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update b49237f...775fb79. Read the comment docs.

Changes needed for theses tests have been uploaded to another branch and a separate pull request will be created for those changes.

authors: adbailey4 codecov-io rojinsafavi


text:
Refactoring Linters

Refactored Flake8 and Black Linters and removed duplicate Linters.
Resolves #5 #7

@SKAUL05 Please review

@SKAUL05 Please review

@SKAUL05 Please review

@SKAUL05 Black run on whole master branch whenever there is a pull request.

Black Lint while running is showing no python files found.
Could u check that why ii is showing that.

@SKAUL05 Please review

@SKAUL05 I am closing this PR because there is a conflict with the file in my local system, sorry for the inconvenience. I will be making a new PR.

repo: BITSoC/livestream


text:
chore(main): release 0.61.3

:robot: I have created a release beep boop
0.61.3 (2023-12-12)
Bug Fixes

Workspace: fix multiple invitations to workspaces for a single user (#604) (8a1dfaa)

Miscellaneous

Catalog: keep only basic details for each datasource (#603) (1d63bf3)

This PR was generated with Release Please. See documentation.

:robot: Release is at https://github.com/BLSQ/openhexa-app/releases/tag/0.61.3 :sunflower:

repo: BLSQ/openhexa-app


text:
More detailed data api instruction ...

Hi there,
Now I have my directory BNN-ch21 like this
.
|-- README.md
|-- dataset
|   |-- gnnet-ch21-dataset-train
|   |   |-- 25
|   |   |-- 30
|   |   |-- 35
|   |   |-- 40
|   |   |-- 45
|   |   `-- 50
|   |-- gnnet-ch21-dataset-validation
|   |   |-- ch21-val-setting-1
|   |   |-- ch21-val-setting-2
|   |   `-- ch21-val-setting-3
|   |-- train.tar.gz
|   `-- validation.tar.gz
|-- dataset_prep.sh
|-- docker
|   |-- Dockerfile
|   |-- Dockerfile-cuda-11.1
|   |-- docker_start.sh
|   |-- environment.yml
|   `-- ssh_config
|-- model
|-- test.py
`-- util
    |-- __pycache__
    |   |-- datanetAPI.cpython-35.pyc
    |   `-- datanetAPI.cpython-38.pyc
    `-- datanetAPI.py

And I wrote in my test.py like this to test the data pipeline api,
from util import datanetAPI as api

reader = api.DatanetAPI(data_folder="/BNN-ch21/dataset/gnnet-ch21-dataset-train",
                        intensity_values=[25],
                        shuffle=False)

it = iter(reader)
for sample in it:
    print(sample)

And the output is always a stack of error loggings like
Error in the file: results_30_400-2000_9925_9949.tar.gz   iteration: 0
Error in the file: results_30_400-2000_9950_9974.tar.gz   iteration: 0
Error in the file: results_30_400-2000_9975_9999.tar.gz   iteration: 0
...

Could you help me by offering more detailed instructions?
Thanks in advance!

Hello,
Is it possible that you are using the wrong API version? For the GNNet challenge 2021 you should use: https://github.com/BNN-UPC/datanetAPI/tree/challenge2021. On the other hand, to filter topology sizes you should use the parameter TopologySizes . Intensity_values is used to filter the traffic intensity range that in this dataset goes from 400 to 2000.
Regards

repo: BNN-UPC/datanetAPI


text:
exiobase "energy products" in "standard international energy product classification"

This correspondance table could be potentially very useful. I think it has many users such as the IEA, the UN energy statistics database and I think national inventory reports to IPCC.
the classification is described on a pdf. To have it on csv format may require some manual work, but they sent me a docx version of some of the tables and that would speed up the process.
The downside is that the UN energy statistics database has a price, so I doubt we can use this database to improve the energy balances of Exiobase. what do you think @Stefano-MRC ?

I agree with Miguel, this classification can be useful.
If you send me the docx file I can easily convert the tables into csv file.

I was in contact with the UN statistical division and it seem they have dismantled the unit in charge of correspondance tables 1 year ago. The classification with the correspondance is in one pdf manual.
https://unstats.un.org/unsd/energy/ESCM_Whitecover_170323.pdf (from page 39)
I think this would be "nice to have" but proritary for the moment.

repo: BONSAMURAIS/Correspondence-tables


text:
Feature: Consider having the Deploy BizTalk Solution option in VS run a build before deploying

The Deploy BizTalk Solution menu item in the Visual Studio add-in currently does not run a build of the solution before deploying.  Would it be better to run a build first or not?
This work item was migrated from CodePlex
CodePlex work item ID: '5769'
Vote count: '1'

[NealWalters@1/21/2010]
What might be even better than that is a build before the "Build Server Deploy MSI".  For those that don't have a build-machine, this would make sure that build matches current code.  A couple of other nice features would be a TFS check-in and label right before building the MSI.

[UnknownUser@10/20/2011]

[UnknownUser@2/21/2013]

repo: BTDF/DeploymentFramework


text:
Jungle spiders attack without provocation in relaxed mode

BTW Version: CE 2.1.4
Multiplayer (yes/no): no
Issue (be as specific as possible): Jungle spiders attacked me after they found me after spawning in one of the Better Biomes tropical biomes.  Was just gathering materials.  Remade world and made sure was in relaxed mode and was attacked again.
Steps to Reproduce (even if obvious): go to a biome where they spawn and find one in relaxed survival mode
Addons (with versions):  Better Biomes addon v: 3.2.2
Better Terrain Addon v: 3.2.2
Craft Guide v: 3.0.1
Deco Addon v: 4.1.2
DynamicLight Mod v: 1.1.0
Crash Log (if there was a crash): N/A: no crash

My suspicion, along with #466, is that your world is not actually on relaxed difficulty. Better terrain used to have a bug where it did not respect the choice made at world creation, I believed that to be fixed but it is possible it is not.
Can you double check through the options screen with the world open, or by running the /difficulty command without arguments, to verify your current difficulty?

Confirmed that the worlds (plural) were on standard difficulty via the options menu despite making double sure they were on relaxed on creation.  I had double checked before (I thought) by checking if other relaxed features were active, (specifically jump placement of blocks, and mistakenly thought that block breaking wasn't startling animals) and assumed that since some relaxed world features were active that they all should be.
Just did another test and yes:
1-set relaxed on world creation
2-set the other better terrain/biomes settings to what I was using before
3-went back to confirm that it was still on relaxed
4-created the new world
5-immediately checked options and found difficulty on standard in options
6-tested jump placing of blocks (which worked on standard
7-tested animal reaction to placing blocks (panicked on standard, but didn't on relaxed)
Current workaround is sufficient for my needs, but I'm willing to do more testing on my end if it would help.

This was a known bug in BTA 3.2.1, fixed in 3.2.2. Can you test again without any addons installed?
Else this post is better suited for the respective addon's issue page.

Sorry for the delayed response, was busy with some stuff irl.
I did do some more testing and found a jungle biome without any addons.  The difficulty was not switching to hardcore survival and jungle spiders didn't attack.
That does mean that Arminias is correct that it should be in the respective addon's issue page.  IDK if there's a way to move this over there or if I should just create a new issue there.  (new to this stuff)

authors: Arminias DynamicWorlds ammoore00

repo: BTW-Community/BTW-Public


text:
2018-10-07 CAIC(Conversaional AI Club)第九次俱乐部沙龙活动纪要

<< 第八次沙龙纪要: #18  <<
第九次CAIC沙龙活动纪要

日期：2018-10-07 19:00-22:00
地点：北邮科研楼820会议室
轮值主席：李童俊 @TuringLee
会员：李卓桓、钱胜杰、朱正源、陈光、张崇宇、赵文骏
新人：段清华、徐子彭

轮值主席授蛋仪式

沙龙内容

李卓桓 @zixia A Mini Review of Word Embedding in Morpho
朱正源 @824zzy Auto Encoding Variational Bayes,2014
陈光 @cgpeter96 Adversarial Cross-Modal Retrieval
钱胜杰 @initc Large-scale Simple Question Answering with Memory Network
段清华 @qhduan A Simple But Tough-to-Beat Baseline for Sentence embeddings,2017
张崇宇 @High128Net CFO: Conditional Focused Neural Question Answering with Large-scale Knowledge Bases,2016
李童俊 @TuringLee context2vec : Learning Generic Context Embedding with Bidirectional LSTM
赵文骏 @zwj277567959 ELMo - Deep contextualized word representations

沙龙结论

第十次CAIC沙龙活动时间：10月14日（周日）19-22点

第十次CAIC沙龙活动

轮值主席：张崇宇 @High128Net
轮值副主席： 陈光 @cgpeter96

学术主席(Chief Academic Counselor)点评(李卓桓代)
Oral可以适当超时，但Poster的soft limit设置成5分钟，hard limit保留在15分钟，鼓励大家在更短的时间内将论文的核心思想阐述明白
鼓励新人发言

鼓励大家在提交PR的时候添加一句话来表述这片论文的核心思想或者要解决的问题。

本次主席(陈光)由于时间冲突，因此和张崇宇@High128Net，进行更换。本次轮值主席为张崇宇，下次副主席为陈光。

1、Oral
2、Maybe a new friend.

Poster, maybe new friends.

authors: TuringLee cgpeter96 zixia

repo: BUPT/awesome-chatbot


text:
Multi Event doesn't work?

For some reason multi event isn't working for the calendar, contacted issues page on the react-big-calendar page.

Fixed while Migrating to Firebase

repo: BUUPE/homepage


text:
RuntimeError: [json.exception.type_error.302] type must be string, but is array

pkts = (
    bmf.graph().decode({
        'input_path': stream,
        "loglevel": "quiet",
    })['video']
    .start()  # this will return a packet generator
)

for i, pkt in enumerate(pkts):
    # convert frame to a nd array
    if pkt.is_(bmf.VideoFrame):
        vf = pkt.get(bmf.VideoFrame)
        rgb = mp.PixelInfo(mp.kPF_RGB24)
        np_vf = vf.reformat(rgb).frame().plane(0).numpy()
        # we can add some more processing here, e.g. predicting
        print("frame", i, "shape", np_vf.shape)
    else:
        break

When I used the above code to read the stream, an error occurred. When I switched the video stream to a local video, the error disappeared. I don't know where the problem is, but my video stream is correct. I can use ffmpeg to read the stream and save it as mp4.
The following is the error message:

Is your video a live stream? What is the format of the stream url?

nohup ffmpeg -re -stream_loop -1 -i 00-10-00.mp4  -c:a aac -strict -2 -ar 44100 -ab 48k  -c:v libx264 -b:v 2000k -vf scale=1920:1080 -f rtsp -rtsp_transport tcp rtsp://*****:1508/ai/stream_1k >> nohup.out 2>&1 &

I push local videos to network streams through ffmpeg.

pkts = (
    bmf.graph().decode({
        'input_path': stream,
        "loglevel": "quiet",
    })['video']
    .start()  # this will return a packet generator
)

for i, pkt in enumerate(pkts):
    # convert frame to a nd array
    if pkt.is_(bmf.VideoFrame):
        vf = pkt.get(bmf.VideoFrame)
        rgb = mp.PixelInfo(mp.kPF_RGB24)
        np_vf = vf.reformat(rgb).frame().plane(0).numpy()
        # we can add some more processing here, e.g. predicting
        print("frame", i, "shape", np_vf.shape)
    else:
        break

When I used the above code to read the stream, an error occurred. When I switched the video stream to a local video, the error disappeared. I don't know where the problem is, but my video stream is correct. I can use ffmpeg to read the stream and save it as mp4. The following is the error message:

pls double check the "stream" object is a string but not array in decode config when video stream condition

I am certain that the stream object is a string.

After restarting the container, this error disappeared. I think some services may not have started properly.
My other question is that when I want to terminate a task while reading the stream, ctrl+c will cause a "Segmentation fault (core dumped)" when terminating the task.
What other methods are there to stop the task?
The generated 'core. * * * *' file is too large.

that's a good question since the force quit without segfault or block for a while should be a feature need to be optmized

Looking forward to future updates

authors: HuHeng liujiachang sfeiwong

repo: BabitMF/bmf


text:
Broken Link at PBR tutorial

At
https://doc.babylonjs.com/how_to/physically_based_rendering_master#how-to-master-the-pbr-materials
Under Introduction section

After following the PBR Introduction, it is a good time to learn more about the PBRMaterial.

Deployment in progress thanks for reporting.

authors: ManhQLe sebavan

repo: BabylonJS/Documentation


text:
Add VER to the list of known LCov directives

Add VER directive to the list of known directives to support the LCov reports with it. VER is an optional directive that can be added by some tools. For example, gcovr is adding it right now.

IM sorry! I completely lost track of this one. Let me merge it in now. Ill release sometime soon.

Thank you! Finally sent this one out. Try out https://pypi.org/project/diff-cover/9.2.0/


text:
GriddlyJS: Tab in GDY editor goes 4 spaces

When I use tab (or shift-tab) in the GDY editor, it goes 4 spaces right or left. All the YAML examples are indented to 2 spaces. You should pick one and stick with it. I recommend 2 spaces but 4 will also be okay.

Think this should be fixed now :)

Confirmed, it's fixed.
On Thu, Nov 17, 2022 at 12:52 PM Chris Bamford @.***>
wrote:

Closed #256 https://github.com/Bam4d/Griddly/issues/256 as completed.
—
Reply to this email directly, view it on GitHub
https://github.com/Bam4d/Griddly/issues/256#event-7832638341, or
unsubscribe
https://github.com/notifications/unsubscribe-auth/AAAN3SX7E7YXFZYI6RYBVCDWIYE5RANCNFSM6AAAAAAR33GZ54
.
You are receiving this because you authored the thread.Message ID:
@.***>


text:
🛑 Health and Social Services is down

In 027290d, Health and Social Services (https://www.hss.gov.nt.ca) was down:

HTTP code: 403
Response time: 451 ms

Resolved: Health and Social Services is back up in 692eff7.

repo: Barctic/gnwt-monitor


text:
🛑 Health and Social Services is down

In 81752a9, Health and Social Services (https://www.hss.gov.nt.ca) was down:

HTTP code: 403
Response time: 440 ms

Resolved: Health and Social Services is back up in b3091ea.


text:
🛑 Education Culture and Employment is down

In 04fa941, Education Culture and Employment (https://www.ece.gov.nt.ca) was down:

HTTP code: 403
Response time: 359 ms

Resolved: Education Culture and Employment is back up in f4b28d3.


text:
🛑 Lands is down

In 1567e7c, Lands (https://www.lands.gov.nt.ca) was down:

HTTP code: 403
Response time: 318 ms

Resolved: Lands is back up in d9defdd.


text:
🛑 Lands is down

In 94ae2d7, Lands (https://www.lands.gov.nt.ca) was down:

HTTP code: 504
Response time: 565 ms

Resolved: Lands is back up in 8918fc2 after 20 minutes.


text:
🛑 Health and Social Services Authority is down

In 40a9dd7, Health and Social Services Authority (https://www.nthssa.ca) was down:

HTTP code: 504
Response time: 334 ms

Resolved: Health and Social Services Authority is back up in b55da39 after 7 minutes.


text:
🛑 QBitTorrent webUI is down

In cc8d9d5, QBitTorrent webUI (https://qbittorrent.jonthan.xyz) was down:

HTTP code: 404
Response time: 671 ms

Resolved: QBitTorrent webUI is back up in 6ac9d02 after 6 minutes.

repo: Baronhez/upptime


text:
🛑 QBitTorrent webUI is down

In 61463a2, QBitTorrent webUI (https://qbittorrent.local.jonthan.xyz) was down:

HTTP code: 404
Response time: 498 ms

Resolved: QBitTorrent webUI is back up in 5915b34 after 6 minutes.


text:
End-to-end encryption (E2EE) (iOS)

Same as #58, but for iOS

Implemented, at last!

authors: Bartalog Deleplace

repo: Bartalog/cool-maze


text:
get current positon work en France but not in Senegal

🐛 Bug Report

Expected behavior
Reproduction steps
Configuration
Version: 1.x
Platform:

[x] :iphone: iOS
[x] :robot: Android

Hey @tocks. Thanks for reporting your issue. Can you provide your flutter doctor -v and maybe your log files? Your code seems good to me.
I'm not sure whether the country is the problem, or the use of the geolocator plugin. It's rather confusing if that's the case.

Hi @florissmit1, thanks,
Send you the output of flutter doctor -v
boubacarkoita@MBP-de-Boubacar ~ % flutter doctor -v
boubacarkoita@MBP-de-Boubacar ~ % flutter doctor -v
[✓] Flutter (Channel stable, 3.0.1, on macOS 12.4 21F79 darwin-arm, locale fr-FR)
    • Flutter version 3.0.1 at /Applications/flutter
    • Upstream repository https://github.com/flutter/flutter.git
    • Framework revision fb57da5f94 (2 weeks ago), 2022-05-19 15:50:29 -0700
    • Engine revision caaafc5604
    • Dart version 2.17.1
    • DevTools version 2.12.2

[✓] Android toolchain - develop for Android devices (Android SDK version 32.1.0-rc1)
    • Android SDK at /Users/boubacarkoita/Library/Android/sdk
    • Platform android-32, build-tools 32.1.0-rc1
    • ANDROID_HOME = /Users/boubacarkoita/Library/Android/sdk
    • Java binary at: /Applications/Android Studio.app/Contents/jre/Contents/Home/bin/java
    • Java version OpenJDK Runtime Environment (build 11.0.11+0-b60-7772763)
    • All Android licenses accepted.

[✓] Xcode - develop for iOS and macOS (Xcode 13.4)
    • Xcode at /Applications/Xcode.app/Contents/Developer
    • CocoaPods version 1.11.3

[✓] Chrome - develop for the web
    • Chrome at /Applications/Google Chrome.app/Contents/MacOS/Google Chrome

[✓] Android Studio (version 2021.1)
    • Android Studio at /Applications/Android Studio.app/Contents
    • Flutter plugin can be installed from:
      🔨 https://plugins.jetbrains.com/plugin/9212-flutter
    • Dart plugin can be installed from:
      🔨 https://plugins.jetbrains.com/plugin/6351-dart
    • Java version OpenJDK Runtime Environment (build 11.0.11+0-b60-7772763)

[✓] VS Code (version 1.67.2)
    • VS Code at /Applications/Visual Studio Code.app/Contents
    • Flutter extension version 3.42.0

[✓] Connected device (2 available)
    • macOS (desktop) • macos  • darwin-arm64   • macOS 12.4 21F79 darwin-arm
    • Chrome (web)    • chrome • web-javascript • Google Chrome 102.0.5005.61

[✓] HTTP Host Availability
    • All required HTTP hosts are available

• No issues found!

![Capture d’écran 2022-06-03 à 12 10 24](https://user-images.githubusercontent.com/4772335/171834907-d57b4880-d97d-4e70-b717-66ab1b92c06a.png)

Hi @tocks,
Do you experience this on both Android and iOS devices?

authors: florissmit1 mvanbeusekom tocks

repo: Baseflow/flutter-geolocator


text:
fix: Stop building .egg files that PyPI rejects

Description

Removes the step from the release process that builds a .egg file. PyPI no longer accepts .egg files as of August 1 2023 - see this announcement

Testing required outside of automated testing?

[x] Not Applicable

Screenshots (if appropriate):

[x] Not Applicable

Rollback / Rollforward Procedure

[x] Roll Forward
[ ] Roll Back

Reviewer Checklist

[ ] Description of Change
[ ] Description of outside testing if applicable.
[ ] Description of Roll Forward / Backward Procedure
[ ] Documentation updated for Change

:tada: This PR is included in version 1.10.2 :tada:
The release is available on:

v1.10.2
GitHub release

Your semantic-release bot :package::rocket:

authors: bt-platform-eng dhudec

repo: Basis-Theory/basistheory-python


text:
【建议提交】【1.4.2】【pom中的scope感觉不太妥当】

这个包在编译和运行的时候都要用到，它的scope应该是默认才对，不应该是Provided：

io.github.BeardedManZhao
varFormatter
1.0.4
provided

回复
如果没有使用到 执行过程流程图 功能，此库可以省略，在编译期和运行期中将不会出现异常。
可以避免库在任何情况下都强制导入的问题

测试版本：1.4.2

测试效果
maven

在这里由于我们没有使用到 执行过程流程图 功能，因此也没有导入 varFormatter 库。

    <dependencies>
        <!-- Binding using the adapter of log4j2 -->
        <dependency>
            <groupId>org.apache.logging.log4j</groupId>
            <artifactId>log4j-slf4j-impl</artifactId>
            <version>2.20.0</version>
            <!--<scope>provided</scope>-->
        </dependency>

        <!-- Log4j2 log facade -->
        <dependency>
            <groupId>org.apache.logging.log4j</groupId>
            <artifactId>log4j-api</artifactId>
            <version>2.20.0</version>
            <!--<scope>provided</scope>-->
        </dependency>

        <!-- Log4j2 log real surface -->
        <dependency>
            <groupId>org.apache.logging.log4j</groupId>
            <artifactId>log4j-core</artifactId>
            <version>2.20.0</version>
            <!--<scope>provided</scope>-->
        </dependency>

        <dependency>
            <groupId>io.github.BeardedManZhao</groupId>
            <artifactId>mathematical-expression</artifactId>
            <version>1.4.2</version>
        </dependency>
    </dependencies>

代码
    public static void main(String[] args) {
        Mathematical_Expression.Options.setUseCache(true);
        final CompileCalculation instance = (CompileCalculation) Mathematical_Expression.getInstance(Mathematical_Expression.prefixExpressionOperation);
        final PrefixExpression compile1 = (PrefixExpression) instance.compile("1+30", true);
        final PrefixExpression compile2 = (PrefixExpression) instance.compileBigDecimal("1+30", true);
        System.out.println(compile1.calculationCache(false));
        System.out.println(compile2.calculationBigDecimalsCache(false));
    }

执行结果
CalculationNumberResults{result=31.0, source='prefixExpressionOperation'}
CalculationNumberResults{result=31.0, source='prefixExpressionOperation'}

新建一个项目，依赖于1.4.2，但是不配置依赖于varFormatter，使用项目说明中的代码做简单的修改：
public static void main(String[] args) throws WrongFormat {
        final Calculation instance = Mathematical_Expression.getInstance(
                Mathematical_Expression.bracketsCalculation2
        );
        System.out.println(instance.calculation("(1+2)*3"));
    }

编译没有问题，但是执行的时候会出现以下异常：
Exception in thread "main" java.lang.NoClassDefFoundError: top/lingyuzhao/varFormatter/utils/DataObj
	at io.github.beardedManZhao.mathematicalExpression.core.Mathematical_Expression$1.getInstance(Mathematical_Expression.java:31)
	at io.github.beardedManZhao.mathematicalExpression.core.Mathematical_Expression.getInstance(Mathematical_Expression.java:331)
	at io.github.beardedManZhao.mathematicalExpression.core.Mathematical_Expression.getInstance(Mathematical_Expression.java:86)
	at kain.mavenproject3.MAIN.main(MAIN.java:14)

从开发者的直觉逻辑，这个代码并没有使用执行过程流程图，而是直接进行了计算和结果输出，但是却会发生CNF的异常，此时的varFormatter变成了必选项而非可选项，与说明文档中的“可选”感觉是有所不一致。

更好的方式可能可以将执行过程的逻辑转移，比如这样：
public static void main(String[] args) throws WrongFormat {
        final Calculation instance = Mathematical_Expression.getInstance(
                Mathematical_Expression.bracketsCalculation2
        );
        System.out.println(instance.calculation("(1+2)*3"));
        //以上均不依赖varFormatter
        Explainer.explain(instance); //Explainer 在varFormatter包中
    }

确实复现了问题所在，支持 explain 操作的计算组件的获取时 若不包含 varFormatter 库都会出现此异常，这与日常的测试有出入，这并不是我们期望的，此问题一直被忽略，这算是一个 bug，感谢您的发现并反馈，我们决定将此问题的修复工作立刻开展，下一版本将修复此问题！感谢您的反馈与回复！

authors: BeardedManZhao iblader

repo: BeardedManZhao/mathematical-expression


text:
stm32

plans to STM32 L0 + SX1276 ?
If yes, i would like to test here!

I see that compiles!!!
Where is in your code make this config ?
#if defined (STM32L071xx)
#define RADIO_RESET                         PA_8
#define RADIO_MOSI                          PA_7
#define RADIO_MISO                          PA_6
#define RADIO_SCLK                          PA_5
#define RADIO_NSS                            PA_4
#define RADIO_DIO_0                         PA_12
#define RADIO_DIO_1                         PB_2
#define RADIO_DIO_2                         PB_5
#define RADIO_DIO_3                         PB_0
#define RADIO_DIO_4                         PA_11
#if defined (USE_TCXO)
#define ENABLE_TCXO							PB_1
#else
#define RADIO_DIO_5                         PB_1
#endif
#define RADIO_ANT_SWITCH_HF                 PA_15

Hi @tcpipchip
Check API in the README
sRFM_pins RFM_pins = {
  	.CS = SS,
  	.RST = RFM_RST,
  	.DIO0 = RFM_DIO0,
  	.DIO1 = RFM_DIO1,
  	.DIO2 = RFM_DIO2,
  	.DIO5 = RFM_DIO5
};

For config https://github.com/BeelanMX/Beelan-LoRaWAN/blob/master/src/arduino-rfm/Config.h

Thanks! I hope start the tests next week

Hi
Got the SX1276 today!
I see that i have
https://www.nicerf.com/Upload/ueditor/files/2020-03-05/LORA1276-100mW long range Spread Spectrum modulation wireless transceiver module V2.2-bdc587bf-32c2-413c-8e4f-a309a8a00abd.pdf
DIO0,DIO1,DIO2 ONLY, NOT DIO5
is that a problem to use with you LIB ?

Good news!!!! U-BLOX NINA B302 (NRF52840) is acessing the LoRaWAN :)

Good news!!!! Success using the NINA B302 to access the LoRaWAN :)

NINA B302 is NRF52840 core!

Nice thanks @tcpipchip !

thank you!

repo: BeelanMX/Beelan-LoRaWAN


text:
Breaks MM

Please update your module!  It's causing MM to not start because it needs the Request module.

YES, EXACTLY !
But with which request module can I still get it to work?

YES, EXACTLY !

See Issue #48 ...I believe it's called out there.

You can work around this by going to your MagicMirror/modules/MMM-SystemStats directory and running this command:
npm install --save request
The request library was widely used in Node and was deprecated, causing lots of things to break. As I'm sure the author of this module would say, feel free to update the module with the newer node dependencies and make a pull request (I don't plan to for now).

Unfortunately this didn't fix the issue for me.

repo: BenRoe/MMM-SystemStats


text:
Broadcast onDisconnect() & onNetworkChange() injected API events

Callbacks on Disconnect (aka remove from Whitelist) and on switching between Testnet & Mainnet would be very helpful, consider adding these?
Functionality should be similar to the onAccountChange() injected API call.

onNetworkChange is a good idea. Gonna add that one.
With onDisconnect I'm not sure, because as soon as you remove the page from the whitelist, in going and outgoing connections to the webpage are blocked. Also I don't think this would be a typical event or where do you see this as useful?

100% on the onNetworkChange(), thank you very much!
Regarding the onDisconnect() event, it's useful for updating the UI on apps that use the wallet during runtime.
Metamask also does this and they actually go the extra mile where you can actually trigger a disconnection from within the 3rd party website. LMKWYT

So onDisconnect would probably work best then if an endpoint cardano.disable() exists as well right?
I'll think about the onDisconnect event.

Agreed! Listening for disconnections is important as well as it enables more interaction via 3rd party apps and gets users closer to the classic 'account' user journey.

closer to the classic 'account' user journey.

This is the main point, user's should be able to interface with the wallet without necessarily opening the extension.

I get the idea, but the reason why you connect the wallet in first place is because you trust the website you visit. Why would you want to disconnect it again?

@alessandrokonrad For instance, I am building an interface that uses NFTs within your wallet to "login" to the website. If the user wants to log out (privacy or other), disconnecting your wallet could theoretically act as a catalyst or action to do so.
Also, what if you change your mind about trusting a website?

Perhaps your view changed of the website, or you only wanted to check it out but would prefer it not have automatic access from now on, depending on how you're implementing your whitelist. It is probably something good to have. Could we start the discussion in the cardano-foundation/CIPs#88 over which events we need to put in the standard? It was an oversight I think as I had included a disconnect event in the Ergo EIP-0012 standard we based the CIP on.

As the initial CIP-30 PR cardano-foundation/CIPs#88 was merged without an events API (since it wasn't finalized), we have put up a new PR cardano-foundation/CIPs#151 to amend it for events. Events-related discussion should take place in that PR.

authors: DEADPXLZ alessandrokonrad cjkoepke rooooooooob

repo: Berry-Pool/nami-wallet


text:
Support of registered file descriptors

IO Uring supports registration of file descriptors. Instead of maintaining reference counts for each IO operation Linux constructs user provided indirection array for each thread.
Registration is beneficial for long lived descriptors. Refcounting overhead is relatively significant for small submit batches.
I'm personally interested in driver-level support of registered file descriptors.
I see the following approaches:

define driver specific operations with registered fds. User either prefers a single platform or writes 2 IO data paths for regular and registered file descriptors. IO Uring could expose tokio-uring Fd or FixedFd, platform independent ops code wraps RawFd into Fd.
define common registration interface. For platforms that don't support registration driver could maintain indirection array in userspace.

separate operations for regular/registered fds, no runtime overhead (branching) at operation construction time, more duplicate code
generic interface with enum and 2 newtype wrappers like in tokio-uring - regular/registered enum, macro enum handling, macro param with implicit type that wraps either regular or registered fd - less duplication, but more complex macro-based code, branching runtime overhead

There's already a method attach which does nothing now. We can use that to register fd, maybe with a little change.

I don't have much attention to this feature now, and AFAIK, neither tokio-uring or monoio uses this feature, am I right? Welcome PRs to implement this. It you implement this, I'll review the PR and make sure the IOCP side will not break.

There's already a method attach which does nothing now. We can use that to register fd, maybe with a little change.

For efficiency we need registration/unregistration/update of fixed fd array. User calculates maximum number of fds at compile time. Like LISTEN_SOCKET + MAX_NUMBER_OF_CLIENTS + JOURNAL_FILE_FD.

For efficiency we need registration/unregistration/update of fixed fd array.

Yes, you're right.
If you want to work on this, I suggest making this feature as a must. In other words, all high-level File and Socket should use registered fd, and all low-level OpCode accept only registered fd. That will simplify the APIs. We can give a map between registered index and raw fd, or simply defining our own RawFd as the registered index.

I think I'll focus on building project-specific callback-based runtime using regular fds. If I find time for optimization I could try to use registered fds, registered buffers and measure gains if there will be any.
Anyway which approach would you prefer? I'm personally biased towards 2.i with implementation only for IO uring.

My preference is, TL;DR:
Redefining RawFd as the registered index. Don't use regular fds at all.
However, it's up to the implementation author to choose the approach. 2.i is also OK, I think, but it will make the API of Poller more complex. I would like to keep a simple API.

For efficiency we need registration/unregistration/update of fixed fd array.

Yes, you're right.
If you want to work on this, I suggest making this feature as a must. In other words, all high-level File and Socket should use registered fd, and all low-level OpCode accept only registered fd. That will simplify the APIs. We can give a map between registered index and raw fd, or simply defining our own RawFd as the registered index.

For thread-per-core runtime that makes sense. Newer kernels allow to pass data across thread-local rings, including registered descriptors or sets of descriptors - in case user will want coordinated cross-thread IO work.

I could try to implement indirection array to establish API and continue to use regular fds internally. Later it's possible to implement actual registration/deregistration for IO uring, potentially as a pluggable feature.
I think we can use 1024 fds as a default array size - it's a default limit of open fds for Linux.

compio aims at thread-per-core runtime. I prefer not considering multi-threading and multi-rings now. However, feel free to PR if you would like a thread safe driver.
For multiple drivers working together, I prefer that users don't register an fd everywhere, or move them quite often:). One reason is that on Windows, file handle could only attach one IOCP. (It's tricky to detach it because that needs undocumented API.)
I would like a Driver API that is (nearly) consistent on every supported platform, without requiring users writing #[cfg(target_os = "linux")] here and there. If we provide both regular and registered fd API, the best practice to use the driver is to use regular fd on Windows (because registering needs more space and time to lookup), and use registered fd on Linux (for ref-counting reasons). That will make it painful to use driver.

Yes, common interface is more user friendly. I'm not interested in cross-thread fds either but would like to offload blocking RocksDB IO work into separate thread. I'd prefer to use some notification mechanism like eventfd on Linux to wake up IO thread waiting for uring completions.

Well, if you want notifications, the current solution might be signals. Channels may not work because the driver might be waiting forever. And thank you for your remind, we still need a compatible channel for compio:)

The Poller::post for io-uring could not wake up the IO thread until now. I think we need a simple solution. Maybe eventfd.

What do you think about changing signal implementation to eventfd for Linux?
In this case sync_queue won't be needed for Linux target.

Well, if you want notifications, the current solution might be signals. Channels may not work because the driver might be waiting forever. And thank you for your remind, we still need a compatible channel for compio:)

This multi-thread channel is fast according to the benchmark.
It uses async-event for notifications. I'd prefer to make it configurable to support non async runtimes. For example, notify via eventfd.

What do you think about changing signal implementation to eventfd for Linux?

Yes, that's possible. Actually signal itself will interrupt io-uring and make it return EINTR.
However I'm thinking about Poller::post. It is complicated if we want it also interrupt the thread.
Channel is another problem. I think there should be an occassion that, when the runtime is waiting io-uring forever, a message comes from the channel. However, the thread is still stuck.

What do you think about changing signal implementation to eventfd for Linux?

Yes, that's possible. Actually signal itself will interrupt io-uring and make it return EINTR.
However I'm thinking about Poller::post. It is complicated if we want it also interrupt the thread.
Channel is another problem. I think there should be an occassion that, when the runtime is waiting io-uring forever, a message comes from the channel. However, the thread is still stuck.

It's possible to mask signals in the IO thread and have a dedicated thread for background work. In this case it's possible handle signals only when IO is driven.

Close due to lack of activity. Re-open if furthur progress is made.

authors: Berrysoft DXist George-Miao

repo: Berrysoft/compio


text:
Discord Server

https://discord.gg/hsmU2c7mVa
Join if any errors/help required

new link?

discord.gg/rone
On Sun, 5 May, 2024, 3:32 pm Nonstopgaming75, @.***>
wrote:

new link?
—
Reply to this email directly, view it on GitHub
https://github.com/BestGamersH/Multipurpose-Discord-Bot/issues/18#issuecomment-2094724356,
or unsubscribe
https://github.com/notifications/unsubscribe-auth/AY4XKL736EHIWZDCPND47Z3ZAX7SXAVCNFSM6AAAAAA6W7K7POVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDAOJUG4ZDIMZVGY
.
You are receiving this because you modified the open/close state.Message
ID: @.***>

Doesn't show any channels on your discord

repo: BestGamersH/Multipurpose-Discord-Bot


text:
Color of the bitmap changing from red to blue ?

Hi,
Thank you so much for the awesome library. Unfortunately when I tried to get bitmap from a tiff file using TiffLoader.getBitmap(int dir,int sampleSize) , but the color of the bitmap is changed from red to blue. I checked the config it is ARGB_8888. Can you please suggest a solution on this issue?

What version do you use? There is no class TiffLoader in library.

@ksyamkrishnan perhaps you are swapping red/blue channels, or your image has the channels reversed (BGR instead of RGB). In TiffBitmapFactory.Options there is one option named inSwapRedBlueColors that is usually set to false, but perhaps you need or are setting it the other way round.

There was a problem with swapping red and blue colors. It was fixed but for some reason I decided to keep inSwapRedBlueColors option.
Any way in the latest release 0.9.8.7 this problem is solved

authors: Beyka GrayCygnus ksyamkrishnan

repo: Beyka/Android-TiffBitmapFactory


text:
Assess whether ecospat and flexsdm packages still needed

If we decide that the only SDM evaluation metric we need for all 5 SDMs is TSS, then we may not need ecospat and flexsdm packages anymore.

@jcoliver flexsdm package is needed to calculate TSS values and thresholds for CV models as part of evaluate_sdm() function. ecospat is also used in evaluate_sdm(), but only to calculate CBI, which we're not using to evaluate or weight models - we're using TSS instead.  We could change the evaluate_sdm() function so we're no longer including a column for CBI values in table with evaluation metrics (and remove ecospat from the list of dependencies), but maybe best to keep in for now in case?
That said, I did remove both from the list of required packages in templates 2 and 3 because they're not used in those scripts (3fc121db)

I think we are good where we are at - flexsdm and ecospat no longer depend on all those retired geospatial packages, so it seems like something we don't have to worry about anymore.

authors: ezylstra jcoliver


text:
Add more engines, and a simple way to make new ones

...what should I say?

Wow just noticed that merge commits get included in the commit list of PRs. ...that seems like too many commits :/

repo: BigBastard/Valkyrien-Warfare-Revamped


text:
Applied filters bar + changes in simple/advanced filters

See - https://app.asana.com/0/1202525679663009/1202838947527265/f

I would like to arrange a meeting between me, @RomanMahola and you about the used data structure to save the filters. We believe that it can be simplified and be more developers-friendly

@elador13
May I close this PR now?


text:
UASTC to RGBA32 decoding precision

While UASTC to ASTC transcoding is lossless, decoding UASTC to RGBA32 directly could produce slightly different results that going via UASTC->ASTC->RGBA32 route with the last step done in hardware.
The reason is that by default ASTC yields interpolation results as 16-bit values divided by 65536.0 and converted to a half-float with round-to-zero semantics.
Applying this process to all possible UASTC configurations, we get 16219 possible 16-bit values that are rounded to 5061 unique half-float values. About 30% of the latter do not match 8-bit values decoded directly from UASTC.
To avoid bringing in all that complexity into Basis, the UASTC spec could simply say that UASTC to RGBA32 decoding follows GL_RGBA8 decode mode as defined by the GL_EXT_texture_compression_astc_decode_mode OpenGL extension.

Another confusing point comes from this recently-added spec note:

For decoding UASTC to RGBA, or transcoding UASTC to other texture formats, srgb will be false.

Most texture assets are authored with sRGB encoding, i.e. sampling from 8-bit 0x7F yields 0.212 rather than 0.498. Performing such conversion on ASTC hardware requires the texture to be uploaded as GL_COMPRESSED_SRGB8_ALPHA8_ASTC_4x4_KHR. This, in turn, enables ASTC sRGB output that affects the first step (8->16 bit extension) before interpolation.
Assuming that an application decompresses the texture in software but still intends to use sRGB hardware sampling (e.g. by uploading the uncompressed data with GL_SRGB_ALPHA8), why shouldn't decoder's srgb be set to true to match expected ASTC's behavior?

Without cross-checking, I assume the GL_RGBA8 decode mode of GL_EXT_texture_compression_astc_decode_mode corresponds to decode_unorm8 in the ASTC chapter. I'm a little wary of this because it is an extension not in the original ASTC specification, although it's not the end of the world for there to be a documented difference here.
Agreed that ruling out sRGB seems to be pretty restrictive. Given how ubiquitous sRGB support is, it does seem very odd to restrict it unless there's a very good reason.

Another confusing point comes from this recently-added spec note:
For decoding UASTC to RGBA, or transcoding UASTC to other texture formats, srgb will be false.

I put this here because other developers (working on writing purely software decoder/transcoder libs in other languages) were getting confused (about how UASTC data is decoded in software to 32-bit RGBA). I'll clarify it. (This is somewhat tricky/complex!)

To avoid bringing in all that complexity into Basis, the UASTC spec could simply say that UASTC to RGBA32 decoding follows GL_RGBA8 decode mode as defined by the GL_EXT_texture_compression_astc_decode_mode OpenGL extension.

OK. I'll think this over. Thanks a bunch for pointing this out.

say that UASTC to RGBA32 decoding follows GL_RGBA8 decode mode as defined by the GL_EXT_texture_compression_astc_decode_mode OpenGL extension

If Rich decides to use this decode mode, the reference should be to decode_unorm8 mode as described in the Khronos Data Format Specification, section 23.19 as KDFS is now the canonical source for compressed format specifications. @fluppeteer, decode_unorm8 is the same as the extension's GL_RGBA8 decode mode.

After some thinking about it, this is probably (hopefully) the last major issue to focus on with UASTC. Any input is appreciated, because it's tricky.
For this question:

Assuming that an application decompresses the texture in software but still intends to use sRGB hardware sampling (e.g. by uploading the uncompressed data with GL_SRGB_ALPHA8), why shouldn't decoder's srgb be set to true to match expected ASTC's behavior?

This is a good point. The encoder assumes "srgb" is false in its astc_interpolate() function, so it optimizes for lowest PSNR assuming this flag is always false. I considered allowing this to be an encoder parameter, but decided against it at the time for three reasons:

From experience, many users wouldn't set it correctly. The encoder needs a decent default. We can't automatically determine if the input will be sampled using sRGB reads.
Some users use BasisU for just plain images/sprites, and don't care at all about sRGB reads.
It adds more complexity.

If the user unpacks UASTC->RGBA32 with srgb set to true, the resulting RGBA32 texture data will have slightly more error.

For now the UASTC spec in the wiki states:
"Note: The "srgb" parameter is for hardware texture sampling implementations. It'll be true if sRGB conversion ("sRGB reads") are enabled when sampling the texture. Currently, the encoder always optimizes for lowest PSNR assuming the srgb flag in the interpolation function is false."

For now the UASTC spec in the wiki states:...

Thanks!

From experience, many users wouldn't set it correctly. The encoder needs a decent default. We can't automatically determine if the input will be sampled using sRGB reads.

KTX file format preserves color space information and the ecosystem around it (libktx atm) aims to be color-correct. For example, it understands PNG's color information chunks and provides a way to use them or override if needed. I'd expect coming tools rely on that even more.
It would be great to have the flag exposed at least on the API level (to not affect current Basis users).

FWIW, I would expect the majority of content, even "for just plain images/sprites", to have a non-linear transfer function (typically sRGB). Someone merely copying data to the framebuffer doesn't need to be aware of this, but nonetheless the framebuffer itself typically represents an approximate sRGB transfer function, reversed by the monitor's nonlinearity. Explicit management of sRGB in graphics allows, for example, filter functions to be performed correctly in linear space. It's perfectly reasonably to want to store linear data in textures for other reasons (bump maps, inputs to other functions, etc.) - but if what you're storing is an image intended for relatively direct human consumption, nonlinearity is your friend.
If Mark has been converting PNG files then I assume he has more direct knowledge than I, but I would expect the majority of PNG and JPEG files to be sRGB. (PPM is conventionally BT.709 gamma, but sRGB is close.) I can't vouch for how many users are describing their ASTC or BC7 textures correctly.
If the intent is to minimise PSNR, I'd have thought assuming that the input was sRGB would be more accurate than assuming it was linear - presumably it also involves not applying a transfer function, depending on how you're calculating PSNR, which might be faster. If you can support both, I'd claim it was valuable.

I would expect the majority of PNG and JPEG files to be sRGB

PNG uses sRGB by default. Some files may include custom gamma, primaries, or a full ICC profile.
JPEG uses BT.601 by default but nowadays many files embed an sRGB ICC profile.

@fluppeteer is correct to say the majority of images/sprites are sRGB.
KTX has a field that says whether the encoded data is sRGB or not. Applications are expected to, and libktx does, use an sRGB texture format for the transcoded data when the encoded data is sRGB. The flag is based on the input file and toktx does color transforms of the input to the sRGB OETF if necessary. For sRGB data it always sets m_perceptual true in the basis_compressor params.  This issue seems to be a big hole in our attempt to be fully color correct. We are providing the encoder the correct information.

I've been thinking about the sRGB issue more. I'm leaning on saying "to get 32-bpp data from a UASTC texture, it's recommended to first transcode to ASTC, then decode that by following the ASTC specification". If you want sRGB, set the "sRGB" flag in the ASTC interpolation function to true or false, then apply the sRGB transfer function.
My reasoning: UASTC is an interchange format, not (yet) an actual GPU texture format. Its an intermediate format designed to be simple to transcode to ASTC or BC7. It's also a lossy format that can be transcoded to different formats (notably ASTC or BC7).
To unpack a UASTC texture to 32-bpp RGBA texels, the encoder assumes the UASTC texture is first transcoded to ASTC, then it unpacks the ASTC data to 32-bpp with the "srgb" flag in the ASTC interpolation function set to false. It does this even if the input is sRGB, for reasons I pointed out above. We just don't know how exactly the ASTC texture will be sampled. It could even be sampled using both sRGB and non-sRGB settings at runtime. I've seen engines do this. Nevertheless, I'll add support for setting the "sRGB" flag in the interpolation function (should be very easy).
However, you can also transcode the UASTC texture data to BC7, then unpack the BC7 data to 32-bit RGBA. The encoder does this as well, and it tries to balance the error between the ASTC decode and the BC7 decode. (In other words, it doesn't just focus on ASTC quality. It also tries to choose an encoding that minimizes BC7 error too.) This way would lose a little quality vs. the ASTC route. Notably, transcoding to BC7 then decoding that data is independent of sRGB, because the BC7 endpoint color interpolation is always done in the same way.

I've added this to the UASTC spec:
"Decoding UASTC texture data to 32-bpp RGBA texels
UASTC is purely a subset of 4x4 LDR ASTC. To unpack a UASTC block to 32-bpp RGBA texels, first transcode the UASTC texture data to ASTC, then unpack the ASTC block by following the ASTC specification and any relevant extensions."

Nevertheless, I'll add support for setting the "sRGB" flag in the interpolation function (should be very easy).

In the encoder, would it be possible to have two error modes like this:

regular ASTC interpolation (c = (c << 8) | c, UNORM16 path) + linear BC7 (0x7F -> 0.498);
sRGB ASTC interpolation (c = (c << 8) | 0x80, truncating path) + sRGB BC7 (0x7F -> 0.212)?

In the encoder, would it be possible to have two error modes like this:

Yes! That's exactly what I'm adding. (I always knew this was needed, but it fell between the cracks.)

@richgel999 I've been staring at this for some time now for the purposes of KDFS documentation. And I'm still confused. Slightly less so having composed this, but still...
Currently (?) the sRGB path performs a round to nearest, as documented for the RGB channels of sRGB mode in ASTC, and the non-sRGB path bit replicates, as for ASTC. I assume astc_interpolate() should not set the sRGB mode for the alpha channel, since the alpha channel goes through the default (non-sRGB) expansion path in ASTC, although I can't spot this happening in the code. The spec provided for the KDFS says that the alpha endpoints are bit-replicated in sRGB mode, but does not apply the float division by 65536. The documented UASTC decode follows the decode_unorm8 behaviour (selecting the top 8 bits rather than dividing by 65536) irrespective of sRGB mode - correctly for RGB channels for sRGB mode (and everything if we assume decode_unorm8, but explicitly not for the alpha channel in the sRGB case.
Sorry it's taken four years to get this question semi-coherent (when I kept thinking I could make sense of it if I stared at it for long enough, and paging it back in has been painful), but what exactly do you want the UASTC spec to say here?

Do you want the spec to reflect the actual endpoint interpolation for ASTC (with the float16 intermediary for the sRGB alpha) and assume that the BASIS encoder is (currently) a buggy implementation?
Do we want to note that the behaviour as documented corresponds to ASTC with unorm8 decode mode (as described, in GL terms) and remove the sRGB divergent behaviour path entirely? (It seemed as though this was desired for KTX, but I'm not sure whether the supposed lack of support in the transcoder is still the case.)
Do we want to keep the specification as is (if this is what the existing BASIS transcoder does with sRGB input) and moderate the statement that "Transcoding UASTC to ASTC is always a 100% lossless operation" (assuming the ASTC is then interpreted with unorm8), for the alpha channel of sRGB?
When you said "it's recommended to first transcode to ASTC, then decode that by following the ASTC specification and any applicable extensions", are "any applicable extensions" unorm8 and a note that sRGB diverges from the ASTC spec, or was there something else intended? The most recent answer you gave on the subject seemed to be that the BASIS transcoder will convert directly to (s)RGB - which makes me think that the ASTC path is not so canonical? Or should the ASTC version be authoritative and the rest informative, noting the difference in interpolation behaviour?

Currently the specification states:

UASTC is a subset of 4x4 LDR ASTC. UASTC can be decoded directly to a 4x4 block of 32-bit RGBA texels (with no intermediate transcode step). Alternately, it can be transcoded to ASTC and then unpacked from ASTC to RGBA using the EXT_texture_compression_astc_decode_mode extension with the decode mode set to GL_RGBA8. Both approaches are conceptually identical and should result in the same final output.

...but it then follows it with:

UASTC endpoints are interpolated exactly like ASTC endpoints

...and then the divergent code (for the sRGB case). The reference encoder presumably gets around this because the specification says:

the Basis Universal reference encoder always sets this value to false

I don't know whether that's still true, or how to interpret it from a spec perspective, and haven't walked through the code enough to confirm it.
Please advise.
@lexaknyazev, @MarkCallow, heads-up if this has been resolved somewhere else and I'm failing to see it. I'm finishing merging any other minor fixes I can find, and I'll share that for review ASAP (likely when I wake up at this point, although I'll keep going for a bit), but I believe this needs resolving before release.

The principle assertion is that decoding UASTC directly to RGBA must yield identical results as transcoding it to ASTC and decoding the latter in hardware. Any cases for which it's not the case are either spec ambiguities or software bugs.

Thanks, @lexaknyazev - then I'll make the correction to match the sRGB ASTC case (and assume that the transcoder will eventually support this). Churning.

Eugh. Having fixed that, I realised this trips over an internal change that says the alpha channel for sRGB should just take the top 8 bits, not use decode_float16, in which case the transcoder is correct (subject to unorm8 decode mode for non-sRGB). Reverting.

Okay, I see Rich's other thread on this subject. I'll have this all merged later today.

authors: MarkCallow fluppeteer lexaknyazev richgel999


text:
Autoadjust "Spacer between genomes in the linear Layout" wastes space

The calculation of the genome distance wastes vertical space due to the way it is calculated:
var genomeDistance = this.getCanvasHeight() * 1 / this.filters.karyo.genome_order.length;

Because there is one less spacer required than there are genomes, the denominator can be decreased by one. However there is one additional karyoHeight required otherwise the last genome is just outside of the canvas (substract this from the numerator):
var genomeDistance = (this.getCanvasHeight() - this.getKaryoHeight) / (this.filters.karyo.genome_order.length - 1);

Tests have to be updated.

Add the better calculation of the genomeSpacer and updated the tests for it.

authors: iimog sonjahohlfeld

repo: BioInf-Wuerzburg/AliTV


text:
Think about VS Code's "alt-enter"

When code is executed in VS Code with "ALT-Enter", it shows the full stack trace. "CTRL-Enter" which works by copying code into the REPL, abbreviates it.
Is there a way to make it work for both that doesn't also prevent full errors from being shown in non-interactive contexts?
Could just say that the VSCode REPL is always interactive. But may need to make sure any worker processes spawned by VSCode still show full errors I think?

Seems to work now.

Nope! Instead, it's eliding extra frames, presumably because they are assigned to the file being run. However, it shouldn't be considered internal.

Got it


text:
PAT token title way to the right

Move it to the left so it's aligned with the rest of the text.

Closing via c1f4483

authors: DeveloppSoft xsttx


text:
Can not compile the repo

rust version: 1.79.0-nightly
os: macos 13.2.1
After running uint test test_groth16_verifier, some error happened.
error[E0599]: no method named `modinv` found for struct `num_bigint::BigUint` in the current scope
   --> src/fflonk/compute_c_wi.rs:131:23
    |
131 |         let r_inv = r.modinv(&h).unwrap();
    |                       ^^^^^^
    |
help: there is a method `div` with a similar name
    |
131 |         let r_inv = r.div(&h).unwrap();
    |                       ~~~

It seems that we can not use modinv with num-bigint 0.4.4. After I update to 0.4.6, it works well.
But there are some more errors.

error[E0277]: the trait bound `bitcoin::XOnlyPublicKey: treepp::pushable::Pushable` is not satisfied
   --> src/bridge/transactions.rs:281:9
    |
281 |         { n_of_n_pubkey }
    |         ^^^^^^^^^^^^^^^^^ the trait `treepp::pushable::NotU8Pushable` is not implemented for `bitcoin::XOnlyPublicKey`, which is required by `bitcoin::XOnlyPublicKey: treepp::pushable::Pushable`
    |
    = help: the trait `treepp::pushable::Pushable` is implemented for `u8`
note: required for `bitcoin::XOnlyPublicKey` to implement `treepp::pushable::Pushable`
   --> src/lib.rs:7:5
    |
7   |     define_pushable!();
    |     ^^^^^^^^^^^^^^^^^^
note: required by a bound in `generate_pre_sign_script::{closure#0}::push`
   --> src/bridge/transactions.rs:281:9
    |
281 |         { n_of_n_pubkey }
    |         ^^^^^^^^^^^^^^^^^ required by this bound in `push`
    = note: this error originates in the macro `define_pushable` (in Nightly builds, run with -Z macro-backtrace for more info)

error[E0609]: no field `enforce_stack_limit` on type `bitcoin_scriptexec::Options`
   --> src/lib.rs:142:10
    |
142 |     opts.enforce_stack_limit = false;
    |          ^^^^^^^^^^^^^^^^^^^ unknown field

Hey, I cannot reproduce this with the latest commit. Make sure to run cargo update

Hey, I cannot reproduce this with the latest commit. Make sure to run cargo update

Thanks! Need to update dependency manually.

repo: BitVM/BitVM


text:
Question about 256: absurdly-high-fee

Hello! First of all thx for your work!
I have an error 256: absurdly-high-fee when I send not all transaction amount to another wallet.
For example I have a test address bchtest:qp4xte74xs7xyt9d6njz9j299ctyqk0xtvd3qkrykv UTXO for it is
amount: 2.59979565
cashAddress: "bchtest:qp4xte74xs7xyt9d6njz9j299ctyqk0xtvd3qkrykv"
confirmations: 141
height: 1252157
legacyAddress: "mqDXzuumTK9f2cc16v7jHEV3HhQxV7vdhJ"
satoshis: 259979565
scriptPubKey: "76a9146a65e7d5343c622cadd4e422c9452e164059e65b88ac"
txid: "e641caf50a6f0791d5d0322f3caf2aac62e9ff52d65c4063fc833d08484346b1"
vout: 1

I'am using testnet and I try to send 5800 satoshi to another wallet bchtest:qrhf7n4nqz6k44lhyyv6lzwq50e9up2ujue2kgfpc2.
let BITBOXCli = require('bitbox-cli/lib/bitbox-cli').default
let BITBOX = new BITBOXCli({ restURL: 'https://trest.bitcoin.com/v1/' })

const mnemonic = 'my mnemonic' // I can provide it if you needed

let rootSeed = BITBOX.Mnemonic.toSeed(mnemonic)
let masterHDNode = BITBOX.HDNode.fromSeed(rootSeed, 'testnet')
let account = BITBOX.HDNode.derivePath(masterHDNode, "m/44'/1'/0'")
let change = BITBOX.HDNode.derivePath(account, '0/0')

let cashAddress = 'qp4xte74xs7xyt9d6njz9j299ctyqk0xtvd3qkrykv'

let transactionBuilder = new BITBOX.TransactionBuilder('testnet')
let originalAmount = 5800
let vout = 1
let txid = 'e641caf50a6f0791d5d0322f3caf2aac62e9ff52d65c4063fc833d08484346b1'
transactionBuilder.addInput(txid, vout)

let byteCount = BITBOX.BitcoinCash.getByteCount({P2PKH: 1}, {P2PKH: 1})
let sendAmount = originalAmount - byteCount
transactionBuilder.addOutput('qrhf7n4nqz6k44lhyyv6lzwq50e9up2ujue2kgfpc2', sendAmount)

let keyPair = BITBOX.HDNode.toKeyPair(change)
let redeemScript
transactionBuilder.sign(0, keyPair, redeemScript, transactionBuilder.hashTypes.SIGHASH_ALL, originalAmount)

let tx = transactionBuilder.build()
let hex = tx.toHex()

BITBOX.RawTransactions.sendRawTransaction(hex, true).then((result) => { console.log('Success:', result) }, (err) => { console.log('Error', err) })

Transaction HEX is
0200000001b1464348083d83fc63405cd652ffe962ac2aaf3c2f32d0d591076f0af5ca41e6010000006a473044022038ea20126ae3292cb0216a44692206b4fa3bbd60a288c0952dcabab99c26f399022034b0149c5ae7ff8ccfab26a4404a8c91173426c0ac5d27fa3190e2990e5d67a74121025ba60c1ea64f64e2c5564e6e9872d8e2a4f549a07d205cb6650f175eef2c0cfeffffffff0194160000000000001976a914ee9f4eb300b56ad7f72119af89c0a3f25e055c9788ac00000000

And I get an error 256: absurdly-high-fee.
Seem it try to send all amount of the transaction and that's why I get this error. If I send all amount to  another wallet all works perfect.
Could you please tell me what I'm doing wrong?

Try setting let originalAmount = 259979565
And update the getByteCount to have 2 P2PKH outputs so you can send yourself the change.
Add a 2nd output w/ the change.
let byteCount = BITBOX.BitcoinCash.getByteCount({P2PKH: 1}, {P2PKH: 2})
let sendAmount1 = 5800
let sendAmount2 = originalAmount - byteCount - sendAmount1;
transactionBuilder.addOutput('qrhf7n4nqz6k44lhyyv6lzwq50e9up2ujue2kgfpc2', sendAmount1)
transactionBuilder.addOutput('bchtest:qp4xte74xs7xyt9d6njz9j299ctyqk0xtvd3qkrykv', sendAmount2)

@cgcardona Gabriel thanks! I was used your advice and it works fine

authors: cgcardona dmitry-udod

repo: Bitcoin-com/bitbox-javascript-sdk


text:
Refactor Token/Get to returns drop and token objects separately

Is your feature request related to a problem? Please describe.
the result of this method is not very suitable, we should return tokens and drops data in different objects separately as follows:
{
drop:{},
token:{}
}

returns drop and token objects separately in develop branch

repo: BlackBoot-io/Api


text:
why raised cuda error?

i used tiiuae/falcon-40b
and want to doing full fine-tuning by lima instruction dataset
model = tp.tensor_parallel(model, sharded=True)
just use like this and i have 1) A100 80GB * 2 and another server 2) A100 80GB * 4
but when i running code on 1) device or 2) device
raised error like this
Model parameters were moved to incorrect devices, did call on model.cuda() or model.to(device)? If so, please avoid doing that
why?

This error is raised during forward pass when model parameters are found on a device they are not expected to be on. This usually happens when parameters are moved after making model tensor_parallel.
Can you provide a little more code to specify how you deploy the model and what happens between model = tp.tensor_parallel(model, sharded=True) and model(...)?

this is my all train code.... i don't know why moving parameter to another device...😅
import copy
import logging
import os

import torch
import tensor_parallel as tp
import torch._dynamo
from datasets import load_dataset
from setproctitle import setproctitle
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, Trainer
from transformers.trainer_utils import is_main_process

from arguments import DatasetsArguments, ModelArguments, MyTrainingArguments
from utils import DataCollatorForSupervisedDataset

torch._dynamo.config.verbose = True
UNUSED0 = ">>QUESTION<<"
UNUSED1 = ">>ANSWER<<"
IGNORE_INDEX = -100
os.environ["TORCHDYNAMO_DISABLE"] = "1"

def main(model_args: ModelArguments, dataset_args: DatasetsArguments, training_args: MyTrainingArguments):
    setproctitle(model_args.model_name_or_path + "/" + dataset_args.data_path + "/finetuning")

    tokenizer = AutoTokenizer.from_pretrained(
        model_args.model_name_or_path, model_max_length=model_args.max_length, use_fast=False
    )
    tokenizer.pad_token_id = tokenizer.pad_token_id or tokenizer.eos_token_id

    model = AutoModelForCausalLM.from_pretrained(
        model_args.model_name_or_path,
        low_cpu_mem_usage=True,
        use_cache=False,
        torch_dtype=torch.bfloat16,
        trust_remote_code=True,
        offload_state_dict=True,
    )
    model = tp.tensor_parallel(model, sharded=True)
    dataset = load_dataset(dataset_args.data_path, split="train")

    def preprocess(raw):
        # text = raw["prompt"].replace(USER, UNUSED0).replace(SYSTEM, UNUSED1) + raw['instruction'] + tokenizer.eos_token
        input_text = UNUSED0 + raw["conversations"][0] + tokenizer.eos_token + UNUSED1
        label_text = raw["conversations"][1] + tokenizer.eos_token
        total_text = input_text + label_text
        input_seq_token_len = len(tokenizer(input_text)["input_ids"])
        tokenized_text = tokenizer(total_text, return_token_type_ids=False, return_tensors="pt")
        raw["input_ids"] = tokenized_text["input_ids"][0]
        raw["attention_mask"] = tokenized_text["attention_mask"][0]

        labels_ids = copy.deepcopy(raw["input_ids"])
        labels_ids[:input_seq_token_len] = IGNORE_INDEX
        raw["labels"] = labels_ids
        return raw

    dataset = dataset.map(preprocess, remove_columns=dataset.column_names)
    dataset = dataset.filter(lambda x: len(x["input_ids"]) <= model_args.max_length)
    dataset.set_format("torch")

    data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer)
    if training_args.local_rank == 0:
        import wandb

        wandb.init(
            project=training_args.wandb_project,
            entity=training_args.wandb_entity,
            name=training_args.wandb_name,
        )

    trainer = Trainer(
        model=model.cuda(),
        data_collator=data_collator,
        train_dataset=dataset,
        args=training_args,
    )
    trainer.train()
    with tp.save_tensor_parallel(model):
        trainer.save_model(training_args.output_dir)
        tokenizer.save_pretrained(training_args.output_dir)

if __name__ == "__main__":
    parser = HfArgumentParser((ModelArguments, DatasetsArguments, MyTrainingArguments))
    model_args, dataset_args, training_args = parser.parse_args_into_dataclasses()
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO if is_main_process(training_args.local_rank) else logging.WARN,
    )
    main(model_args=model_args, dataset_args=dataset_args, training_args=training_args)

and another question, i want to training 1model to shard 2 or 4 EA multi-gpus, how can i script in?
i put it in like this,
model_name_or_path="tiiuae/falcon-40b"
TENSOR_PARALLEL_USE_NATIVE=1 \
python3 train_falcon_single_turn.py \
    --output_dir "falcon-7b-test" \
    --model_name_or_path "${model_name_or_path}" \
    --data_path "GAIR/lima" \
    --max_length 2048 \
    --num_train_epochs 1 \
    --per_device_train_batch_size 1 \
    --learning_rate 2e-5 \
    --gradient_accumulation_steps 64 \
    --evaluation_strategy "no" \
    --save_strategy "epoch" \
    --logging_strategy "steps" \
    --logging_steps 10 \
    --save_total_limit 5 \
    --wandb_name "falcon-40b-lima-single-turn" \
    --wandb_entity "test_by_me" \
    --wandb_project "LLM-finetune-test" \
    --dataloader_num_workers 0 \
    --weight_decay 0. \
    --warmup_ratio 0.03 \
    --lr_scheduler_type "cosine" \
    --remove_unused_columns False \
    --optim adafactor \
    --torch_compile True \
    --bf16 True \
    --tf32 True

i think, 1 model to multi gpu, so don't launch torchrun.
if i run that shell script, it is correctly 1model to multi-gpus shard learning?

Firstly, don't call model.cuda(). This is what's causing the issue.
Secondly, yes. The script above should split one model among all available GPUs.

when if i don't call model.cuda(), but error is raise samely
  0%|                                                                                | 0/15 [00:00<?, ?it/s](device(type='cuda', index=0), device(type='cuda', index=1))
Traceback (most recent call last):
  File "/ssd/data01/bart/LLM42/train/train_falcon_single_turn.py", line 90, in <module>
    main(model_args=model_args, dataset_args=dataset_args, training_args=training_args)
  File "/ssd/data01/bart/LLM42/train/train_falcon_single_turn.py", line 76, in main
    trainer.train()
  File "/ssd/data01/bart/LLM42/.venv/lib/python3.10/site-packages/transformers/trainer.py", line 1645, in train
    return inner_training_loop(
  File "/ssd/data01/bart/LLM42/.venv/lib/python3.10/site-packages/transformers/trainer.py", line 1938, in _inner_training_loop
    tr_loss_step = self.training_step(model, inputs)
  File "/ssd/data01/bart/LLM42/.venv/lib/python3.10/site-packages/transformers/trainer.py", line 2759, in training_step
    loss = self.compute_loss(model, inputs)
  File "/ssd/data01/bart/LLM42/.venv/lib/python3.10/site-packages/transformers/trainer.py", line 2784, in compute_loss
    outputs = model(**inputs)
  File "/ssd/data01/bart/LLM42/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
    return forward_call(*args, **kwargs)
  File "/ssd/data01/bart/LLM42/.venv/lib/python3.10/site-packages/accelerate/utils/operations.py", line 553, in forward
    return model_forward(*args, **kwargs)
  File "/ssd/data01/bart/LLM42/.venv/lib/python3.10/site-packages/accelerate/utils/operations.py", line 541, in __call__
    return convert_to_fp32(self.model_forward(*args, **kwargs))
  File "/ssd/data01/bart/LLM42/.venv/lib/python3.10/site-packages/torch/amp/autocast_mode.py", line 14, in decorate_autocast
    return func(*args, **kwargs)
  File "/ssd/data01/bart/LLM42/.venv/lib/python3.10/site-packages/tensor_parallel/pretrained_model.py", line 78, in forward
    return self.wrapped_model(*args, **kwargs)
  File "/ssd/data01/bart/LLM42/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
    return forward_call(*args, **kwargs)
  File "/ssd/data01/bart/LLM42/.venv/lib/python3.10/site-packages/tensor_parallel/sharding.py", line 95, in forward
    return self.module(*args, **kwargs)
  File "/ssd/data01/bart/LLM42/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
    return forward_call(*args, **kwargs)
  File "/ssd/data01/bart/LLM42/.venv/lib/python3.10/site-packages/tensor_parallel/tensor_parallel.py", line 124, in forward
    raise ValueError(
ValueError: Model parameters were moved to incorrect devices, did call on model.cuda() or model.to(device)? If so, please avoid doing that

i am monitoring my gpu memory on nvidia-smi, if gpu memory is not enough, error raise like that?
because my gpu 0's A100 is going to 79000MiB/81920MiB and error is raised

@BlackSamorez um... when i debugged, i can find something
input_ids is on cuda:0, but model.device is cuda:1 how can i make same it?

my code is changed
import copy
import json
import logging
import os

import accelerate
import tensor_parallel as tp
import torch
import torch._dynamo
from arguments import DatasetsArguments, ModelArguments, MyTrainingArguments
from datasets import load_dataset
from peft import LoraConfig, get_peft_model
from setproctitle import setproctitle
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, LlamaTokenizer, Trainer
from transformers.trainer_utils import is_main_process
from transformers.utils.bitsandbytes import replace_with_bnb_linear
from transformers.utils.quantization_config import BitsAndBytesConfig
from utils import DataCollatorForSupervisedDataset

torch._dynamo.config.verbose = True
os.environ["TOKENIZERS_PARALLELISM"] = "false"
os.environ["TORCHDYNAMO_DISABLE"] = "1"

UNUSED0 = ">>QUESTION<<"
UNUSED1 = ">>ANSWER<<"
IGNORE_INDEX = -100

def main(model_args: ModelArguments, dataset_args: DatasetsArguments, training_args: MyTrainingArguments):
    setproctitle("train_qlora_tp")
    assert (
        training_args.tp_gpu_num is not None
    ), "Maybe recommended running ddp or something, this is TENSOR PARALLEL running!"
    device_ids = list()
    for gpu_num in training_args.tp_gpu_num:
        device_ids.append("cuda:" + gpu_num)

    if model_args.model_name_or_path == "decapoda-research/llama-30b-hf":
        tokenizer = LlamaTokenizer.from_pretrained(model_args.model_name_or_path, trust_remote_code=True)
    else:
        tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path, trust_remote_code=True)
        tokenizer.pad_token = tokenizer.eos_token

    if training_args.bf16:
        torch_dtype = torch.bfloat16
    elif training_args.fp16:
        torch_dtype = torch.float16
    else:
        torch_dtype = torch.float32

    with accelerate.init_empty_weights():
        model = AutoModelForCausalLM.from_config(
            AutoConfig.from_pretrained(model_args.model_name_or_path, trust_remote_code=True, torch_dtype=torch_dtype),
            trust_remote_code=True,
        )

    model = tp.TensorParallelPreTrainedModel(  # <- tensor parallelism starts here
        model,
        device_ids=device_ids,
    )

    model = replace_with_bnb_linear(
        model,
        quantization_config=BitsAndBytesConfig(
            load_in_4bit=True,
            llm_int8_threshold=6.0,
            llm_int8_has_fp16_weight=False,
            bnb_4bit_compute_dtype=torch_dtype,
            bnb_4bit_use_double_quant=True,
            bnb_4bit_quant_type="nf4",
        ),
    )
    model.is_loaded_in_4bit = True

    device_map = tp.infer_sharded_device_map(model)  # <- The model is on meta device but we can sill deduce
    #    the target devices for each weight using this helper function

    # Get nums parts
    with open(os.path.join(model_args.model_name_or_path, "pytorch_model.bin.index.json"), "r") as index_file:
        shard_filenames = set(json.load(index_file)["weight_map"].values())

    for shard_filename in sorted(shard_filenames):
        # Download a shard
        shard_path = os.path.join(model_args.model_name_or_path, shard_filename)

        # Convert model shard
        converted_state_dict = tp.convert_state_dict(  # <- tensor_parallel helper function.
            torch.load(shard_path),
            model.tensor_parallel_config,
            world_size=2,
            for_pretrained=True,
        )

        # Dispatch the shard
        for param_name, param in converted_state_dict.items():
            module_name = param_name

            while len(module_name) > 0 and module_name not in device_map:
                module_name = ".".join(module_name.split(".")[:-1])
            param_device = device_map[module_name]

            accelerate.utils.set_module_tensor_to_device(model, param_name, param_device, value=param)
            converted_state_dict[param_name] = None
        del converted_state_dict

    # def get_num_layers(model):
    #     numbers = set()
    #     for name, _ in model.named_parameters():
    #         for number in re.findall(r"\d+", name):
    #             numbers.add(int(number))
    #     return max(numbers)

    # def get_last_layer_linears(model):
    #     names = []

    #     num_layers = get_num_layers(model)
    #     for name, module in model.named_modules():
    #         print(name)
    #         if str(num_layers) in name and not "encoder" in name:
    #             if isinstance(module, torch.nn.Linear):
    #                 names.append(name)
    #     return names

    lora_config = LoraConfig(
        r=16, lora_alpha=32, target_modules=["query_key_value", "dense"], lora_dropout=0.05, bias="none"
    )
    model = get_peft_model(model, lora_config)
    model.config.use_cache = False
    dataset = load_dataset(dataset_args.data_path, split="train")

    def preprocess(raw):
        # text = raw["prompt"].replace(USER, UNUSED0).replace(SYSTEM, UNUSED1) + raw['instruction'] + tokenizer.eos_token
        input_text = UNUSED0 + raw["conversations"][0] + tokenizer.eos_token + UNUSED1
        label_text = raw["conversations"][1] + tokenizer.eos_token
        total_text = input_text + label_text
        input_seq_token_len = len(tokenizer(input_text)["input_ids"])
        tokenized_text = tokenizer(total_text, return_token_type_ids=False, return_tensors="pt")
        raw["input_ids"] = tokenized_text["input_ids"][0]
        raw["attention_mask"] = tokenized_text["attention_mask"][0]

        labels_ids = copy.deepcopy(raw["input_ids"])
        labels_ids[:input_seq_token_len] = IGNORE_INDEX
        raw["labels"] = labels_ids
        return raw

    dataset = dataset.map(preprocess, remove_columns=dataset.column_names)
    dataset = dataset.filter(lambda x: len(x["input_ids"]) <= model_args.max_length)
    dataset.set_format("torch")

    data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer)

    if training_args.local_rank == 0:
        import wandb

        wandb.init(
            project=training_args.wandb_project,
            entity=training_args.wandb_entity,
            name=training_args.wandb_name,
        )

    if training_args.gradient_checkpointing:
        logging.warning("TensorParallelPreTrainedModel does not support gradient checkpointing.")
        training_args.gradient_checkpointing = False

    trainer = Trainer(
        model=model,
        data_collator=data_collator,
        train_dataset=dataset,
        # eval_dataset=dataset["test"],
        args=training_args,
    )
    trainer.train()
    with tp.save_tensor_parallel(model):
        trainer.save_model(training_args.output_dir)
        tokenizer.save_pretrained(training_args.output_dir)

if __name__ == "__main__":
    parser = HfArgumentParser((ModelArguments, DatasetsArguments, MyTrainingArguments))
    model_args, dataset_args, training_args = parser.parse_args_into_dataclasses()
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO if is_main_process(training_args.local_rank) else logging.WARN,
    )
    main(model_args=model_args, dataset_args=dataset_args, training_args=training_args)

falcon-40B tensor parallel is only work commit hash: c47b371b31a68349c233104050ac76680b8485db

@BlackSamorez
transformers.trainer._inner_training_loop.user_accelerator_prepare make _sanity_check_params's cuda device same....
i will find some more....

this make cuda device same

https://github.com/BlackSamorez/tensor_parallel/blob/main/examples/training_flan-t5-xl.ipynb
i think that example is accured same error too...🤣
what kind of transformers did you use?

upper transformers v4.30.0, i think this error is accured

i've override my trainer, but training is not good...
my dataset is lima...
i don't know how can i fix it.
change learning rate and optimizer, but my training is not change dynamically

I have the same problem with transformers version 4.30.0 or greater (seems to work with 4.29, but without fp16 enabled for some reason...).

i think, my override trainer is good.
lima training is done successfully....
@dragosconst
maybe, you have to override transformers trainer...

first of all, i'm success training to using Tensor Parallel, and now, i use deepspeed ZeRO3.
so, this issues is not necessary any more. so, i closed.

authors: BlackSamorez YooSungHyun dragosconst


text:
.NET 6 migration

migrate projects to .NET 6

done

repo: Blackbird-tech/Stark


text:
Improve ASC Docs in Readme

What I want to do is to improve the ASC commands documentation in the Readme.md.
What I need is a script that runs each subcommand of ASC with the -h option and generates a MD file from it. Similar to the one the readme shows currently (but that one is outdated).
Sine I don't want to update this file each time I add a new sub command I want to auto-generate the readme.
Please just ignore the other commands the Assist tool contains. I only need a good documentation for ASC at the moment.

Duplicate of https://github.com/Blackjacx/Assist/issues/27

repo: Blackjacx/Assist


text:
关于加密类EncryptUtils的建议

private static final char hexDigits[] = {'0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D', 'E', 'F'};

有时项目加密需要hexDigits中的字母需要是小写的，建议最好可以更换大小写字母！辛苦了

得到的结果不会String.toLowerCase()吗，我这么多函数都加个你说的标志何必呢

authors: Blankj chunca

repo: Blankj/AndroidUtilCode


text:
Can we update Next and React versions?

When I update, I get all sort of problems

can you make this issue clearer. its hard to understand what you mean by update. what exactly did you update?

repo: Blazity/next-saas-starter


text:
Upstream PRs 1391, 1290, 1389, 1397, 1399, 1400, 1348, 1402, 1274, 1394, 1404, 1062, 1401, 1373, 1403, 1398, 1405, 1396, 1406, 1410, 1409, 1411, 1412, 1414, 1413, 1415, 1417, 1390, 1416, 1422, 1424, 1395

[bitcoin-core/secp256k1#1391]: refactor: take use of secp256k1_scalar_{zero,one} constants (part 2)
[bitcoin-core/secp256k1#1290]: cmake: Set ENVIRONMENT property for examples on Windows
[bitcoin-core/secp256k1#1389]: ci: Run "Windows (VS 2022)" job on GitHub Actions
[bitcoin-core/secp256k1#1397]: ci: Remove "Windows (VS 2022)" task from Cirrus CI
[bitcoin-core/secp256k1#1399]: ci, gha: Run "SageMath prover" job on GitHub Actions
[bitcoin-core/secp256k1#1400]: ctimetests: Use new SECP256K1_CHECKMEM macros also for ellswift
[bitcoin-core/secp256k1#1348]: tighten group magnitude limits, save normalize_weak calls in group add methods (revival of #1032)
[bitcoin-core/secp256k1#1402]: ci: Use Homebrew'''s gcc in native macOS task
[bitcoin-core/secp256k1#1274]: test: Silent noisy clang warnings about Valgrind code on macOS x86_64
[bitcoin-core/secp256k1#1394]: ci, gha: Run "x86_64: macOS Ventura" job on GitHub Actions
[bitcoin-core/secp256k1#1404]: ci: Remove "arm64: macOS Ventura" task from Cirrus CI
[bitcoin-core/secp256k1#1062]: Removes _fe_equal_var, and unwanted _fe_normalize_weak calls (in tests)
[bitcoin-core/secp256k1#1401]: ci, gha: Run all MSVC tests on Windows natively
[bitcoin-core/secp256k1#1373]: Add invariant checking for scalars
[bitcoin-core/secp256k1#1403]: ci, gha: Ensure only a single workflow processes github.ref at a time
[bitcoin-core/secp256k1#1398]: ci, gha: Add Windows jobs based on Linux image
[bitcoin-core/secp256k1#1405]: ci: Drop no longer needed workaround
[bitcoin-core/secp256k1#1396]: ci, gha: Add "x86_64: Linux (Debian stable)" GitHub Actions job
[bitcoin-core/secp256k1#1406]: ci, gha: Move more non-x86_64 tasks from Cirrus CI to GitHub Actions
[bitcoin-core/secp256k1#1410]: ci: Use concurrency for pull requests only
[bitcoin-core/secp256k1#1409]: ci: Move remained task from Cirrus to GitHub Actions
[bitcoin-core/secp256k1#1411]: ci: Make repetitive command the default one
[bitcoin-core/secp256k1#1412]: ci: Switch macOS from Ventura to Monterey and add Valgrind
[bitcoin-core/secp256k1#1414]: ci/gha: Add ARM64 QEMU jobs for clang and clang-snapshot
[bitcoin-core/secp256k1#1413]: ci: Add release job
[bitcoin-core/secp256k1#1415]: release: Prepare for 0.4.0
[bitcoin-core/secp256k1#1417]: release cleanup: bump version after 0.4.0
[bitcoin-core/secp256k1#1390]: tests: Replace counting_illegal_callbacks with CHECK_ILLEGAL_VOID
[bitcoin-core/secp256k1#1416]: doc: Align documented scripts with CI ones
[bitcoin-core/secp256k1#1422]: cmake: Install libsecp256k1.pc file
[bitcoin-core/secp256k1#1424]: ci: Bump major versions for docker actions
[bitcoin-core/secp256k1#1395]: tests: simplify random_fe_non_zero (remove loop limit and unneeded normalize)
This PR can be recreated with ./contrib/sync-upstream.sh -b master range ee7aaf213ea3eb42fc8960c7d178b5ffb286440f.
Tip: Use git show --remerge-diff to show the changes manually added to the merge commit.' --web

Replace fe_equal_var with fe_equal
Use CHECK_ILLEGAL instead of CHECK/ecount
Turn on secp256k1-zkp specific modules in CI

My fork shows that the branch passes GH Actions CI: https://github.com/jonasnick/secp256k1-zkp/actions

git grep ecount gives me a few remaining cases.
want to add -zkp modules also for "macos-native" and "win64-native"?

The remaining occurances of ecount are intentional. They occur whenever the callback is called more than once.

Oh sure

authors: jonasnick real-or-random


text:
Prevent nrnivmodl running on every build

Description
This changes how dependencies are declared in CMake to ensure that nrnivmodl is not called to recompile halfgap.mod ever time the project is built.
How to test this?
I tested with CoreNEURON built as a submodule of NEURON. After running
cmake ..
make -j
make -j # this should barely do anything, but without it this PR it was rerunning nrnivmodl.

Use certain branches for the SimulationStack CI
CI_BRANCHES:NEURON_BRANCH=master,

Good spot! I agree the implicit dependency on the builtin modfiles was not captured.
I pushed two fixes, unfortunately in two repositories:

in this PR, I added a rule expressing that {build}/share/modfile/foo.mod depends on {source}/coreneuron/mechanism/mech/modfile/foo.mod, and I now export a cache variable CORENEURON_BUILTIN_MODFILES containing all the {build}/share/modfile/*.mod paths. The explicit invocation of nrnivmodl-core in CoreNEURON now depends on these files.
in https://github.com/neuronsimulator/nrn/pull/1011 the nrnivmodl rules generated by nrn_add_test now depend on CoreNEURON's builtin modfiles if -coreneuron is passed to nrnivmodl

repo: BlueBrain/CoreNeuron


text:
Ability to fetch and save metadata around the server (OS version)

This is so we can determine if there are some features that we shouldn't show on the app

This should be fairly easy to do. We should have a page that you can go to that will fetch the info from the server. The endpoint already exists.

repo: BlueBubblesApp/BlueBubbles-Android-App


text:
Live Photos

It would be awesome to have the ability to view Live Photos in a chat (if technically possible)

So live photos are stored in the attachments directory (same one as the jpeg) just with the mov extension.
Transferring this issue to the server so an API can be made to grab that mov

authors: dacmcbibs tneotia

repo: BlueBubblesApp/bluebubbles-app


text:
Android: Unable to navigate away from BB using back button

Hi,
Pixel 8 user here on BB app build 1.14.0-b.1.
The back button/gesture doesn't return to the home screen or previous app, in the case of sharing content to BlueBubbles.
It works to return from a chat to the chat list.

So to summarize:

Share to the app
Send the shared attachment
Use back button to return to original app
**Fails to because it just goes back to the chat list

Correct.

Well, this might be changed in the latest beta. In the latest beta, something with the SDK changed, and now it's harder to back out to the chat list after entering thru a notification. Maybe that's intentional and android/Google wants us to have the back button leave the app
@tneotia thoughts on this? Maybe what we thought was a bug is a feature now?


text:
🛑 hosted-PalmettoGBA is down

In 32124b5, hosted-PalmettoGBA (https://palmettogba.com/) was down:

HTTP code: 0
Response time: 0 ms

Resolved: hosted-PalmettoGBA is back up in 2b131bc.


text:
🛑 hosted-PalmettoGBA is down

In 0c2024b, hosted-PalmettoGBA (https://palmettogba.com/) was down:

HTTP code: 0
Response time: 0 ms

Resolved: hosted-PalmettoGBA is back up in 09d6ef8.


text:
🛑 hosted-PalmettoGBA is down

In c42f0b5, hosted-PalmettoGBA (https://palmettogba.com/) was down:

HTTP code: 0
Response time: 0 ms

Resolved: hosted-PalmettoGBA is back up in 0d0e968 after 13 minutes.


text:
Sample Grafana Dashboard available on Grafana

Hi,
maybe the README should include a reference to a sample Dashboard on Grafana, #2525.
And the metric implemented are most likely not "node" metrics as stated in the README.
Compiled and works, thanks a lot!
Andreas

Hi @ipsecguy!
Interesting, I'll check the dashboard out, hadn't seen that one!
Regarding the node metrics, I think this was named after the path in the logstash endpoint: node_info. Makes sense?

authors: carlpett ipsecguy


text:
Update lore for Vagrant deploys to point to lore

Previously, README.rst included information on testing hoist in Vagrant.
Now that lore has been moved to BonnyCI/lore.
Signed-off-by: Matt Langbehn matthew.langbehn@gmail.com

This is a duplicate of #78

Thanks, @eggshell.

authors: eggshell mlangbehn

repo: BonnyCI/hoist