text:
Cylinder type Sample Auto-calibration Issue

I have checked your new v1.13 auto-cal function with my 4680 battery projection. And I can see that there is some issue at that case.

Center slice:

Bottom slice:

console log:

I will send my full test script code and my sample image location by your e-mail. I guess that the issue is related to modular-beam converting action because the if np.abs(res.x[2]) > 0.1: condition matched and go into below sub-routine, then this issue happens.
Best regards.

There is a typo in the demo script.  Line 214 should be: leapct.convert_to_modularbeam()
Anyway, this is just an estimation algorithm and the authors of the paper from which I got the algorithm say that the cost function is only locally convex, so if your initial guess is not close to the true solution, one may only converge to a local minimum.
Also for this code to work the projections must not be truncated.  It also helps to correct for flux modulation.  A flux modulation correction method can be found in the makeAttenuationRadiographs function in leap_preprocessing_algorithms.py
See the "ROI" argument in this function.

I found the bug point at your d12_geometric_calibration.py at the case of tilt angle bigger than 0.1.
Please check below picture.

I commented out the first step of  the estimation algorithm(costFcn_rc).  And run only the second costFcn_rct minimize for tilt angle. The first step minimizing is not effective currently owing the res.x[0] and res.x[1] issue.

@kylechampley  How about using the value from your leap.find_centerCol() 's result for the initial guess param for optimizing of your consistency-cost function. then you may focus on the narrow valley area for finding the min-values of res.x[0~2].

That sample script is a demonstration of a capability in LEAP, rather than a prescription of how to perform geometric calibration on an arbitrary dataset.  All CT systems are unique and may require different methods.  Thus, the best I can do is provide users with tools to use, but they will have to find the right way to use those tools to get the best results.

ok, test script usage method is user's side role, but I wonder there is no needs to check the res.x[0] or res.x[1] ?

I can see that your current GPU code of consistency_cost() is just for finding the tilt angle.  So it should not be used for center.

Those are the estimates provided by the optimization algorithm.  They might not be correct, but they are values that reduce the cost function.  I suggest that these be verified by reconstruction.  Single slice reconstructions are often sufficient for this verification.  You can also use the parameter_sweep function which I personally find very useful.
Regardless, the "proper" way to do CT is to scan some calibration phantom, like the so-called "ball phantom".  These scans usually provide good results.

Thanks for your recommendation. I will try it Today.^_^

Yes, parameter_sweep is useful, and I found that my cylinder type sample has a special position which is lower positioned from center line of row in detector.  So as you can see below if I use the centerRow+100 position, then your inconsistency recon shows very clear tilt angle which is best. But at the center of row. it shows broad area of cost values which may bring some incorrect res.x[0] or res.x[1].

@kylechampley I did check the deltaCol and deltaRow with my new centerRow position(+100). And I could see that this new row position can not cover the weakness of res.x[0] or res.x[1].  Anyway your new consistency-cost shows the robust finding of tilt angle of my cylinder sample. So I will use your new consistency-func for tilt and find_centerCol for centerCol.
If you there will be no more updating about this issue, please close this issue.

I did more same test with another row position rather than centerRow+100, then it shows also weak result in even res.x[2]. So I do better use parameter-sweep.

repo: LLNL/LEAP


text:
Compilation Error when enabling CUDA

Describe the bug
Compilation Error with CUDA enabled: "Unknown option pthread"
To Reproduce
mkdir build
cd build
cmake -DENABLE_CUDA=ON -DCMAKE_INSTALL_PREFIX=/home/crtrott/Software/umpire/install/cuda ..
make

Expected behavior
Compilation succeeds
Compilers & Libraries (please complete the following information):
GCC 6.4.0
CUDA 10.0
Additional context
Compile line with make VERBOSE=1
[ 37%] Linking CUDA device code CMakeFiles/version_tests.dir/cmake_device_link.o
cd /home/crtrott/Software/umpire/build/tests/unit && /projects-sems/sems/install/rhel6-x86_64/sems/utility/cmake/3.12.2/bin/cmake -E cmake_link_script CMakeFiles/version_tests.dir/dlink.txt --verbose=1
/home/projects/x86-64/cuda/10.0/bin/nvcc -ccbin=/projects/sems/install/rhel6-x86_64/sems/compiler/gcc/6.1.0/base/bin/g++  -Xcompiler=-fPIC -Wno-deprecated-gpu-targets -shared -dlink CMakeFiles/version_tests.dir/version_tests.cpp.o -o CMakeFiles/version_tests.dir/cmake_device_link.o  -L/home/projects/x86-64/cuda/10.0/lib64/stubs  -L/home/projects/x86-64/cuda/10.0/lib64 ../../lib/libumpire.a ../../lib/libgtest_main.a ../../lib/libgtest.a -pthread /home/projects/x86-64/cuda/10.0/lib64/libcudart_static.a -lpthread -ldl -Xnvlink /usr/lib64/librt.so -lcudadevrt -lcudart_static -lrt -lpthread -ldl
nvcc fatal   : Unknown option 'pthread'

The offending option is the "-pthread" in the compile line not the "-lpthread".

@crtrott - what version of CMake?

3.12.2

I want to try a random size allocation benchmark (as more discussion basis for the email you send).

This is a known issue with CMake, fixed in 3.12.4 - do you have a newer version available? (or older -  3.9.x works fine)

I think this is one of those cmake issues with nvcc ...

Jup cmake 3.10.3 works, feel free to close ..

source: gharchive/issue

authors: crtrott davidbeckingsale

repo: LLNL/Umpire

license: MIT

license_type: permissive

license_source: github-api


text:
Update Hypre and enable CUDA

This was originally buggy. We should see if the new versions are stable now.

We updated hypre to v2.26, but I think we still need to enable CUDA.

repo: LLNL/serac


text:
How can i help with translation ?

How can i help with translation ?

@giulnz we use Crowdin for translations. You can go here to translate for iOS and Desktop:
https://crowdin.com/project/zap-ios
https://crowdin.com/project/zap-desktop

authors: JimmyMow giulnz

repo: LN-Zap/zap-desktop

license: mit

license_source: bigquery


text:
Please integrate lnurl in Zap

Since withdrawal of funds from lapps is quiet inconvenient with all the copy/paste of invoices, how about implementing lnurl?
https://github.com/btcontract/lnurl-rfc/blob/master/spec.md

So far its not supported by many services, but I think its a chicken/egg problem, since this feature is really useful!
Services supporting lnurl:
microbet.fun
lightning.gifts
kriptode.com/satsforlikes/
lapps.co
lngames.net

We have to think about UX.
Lets say we add lnurl-withdraw. How can users scan the withdraw QR Code?
Right now the QR Code scanner only shows up in the send modal. that's not what you expect when you want to withdraw.
Maybe we need something like a higher-level QR Code scanner that understands all kind of codes. (i.e lets you open a channel if you scan a peer address, send if you scan an invoice and do all kinds of lnurl stuff if you scan an lnurl)
Or do we add a withdraw button? i don't think lnurl is big enough right now to add a specific button to zap iOS for it.
🤯 @ole1220 @JimmyMow

authors: Tigerix ottosuess

repo: LN-Zap/zap-iOS


text:
Chapter is empty

Source information
Asura Light Novel
Steps to reproduce

Expected behavior
The chapter should been showed by the last time i read
Actual behavior
The chapter only show blank page
LNReader version
1.1.19
Android version
Android 11
Other details
No response
Acknowledgements

[X] I have searched the existing issues and this is a new ticket, NOT a duplicate or related to another open issue.
[X] I have written a short but informative title.
[X] I have updated the app to version 1.1.19.
[X] I have updated all installed extensions.
[X] If this is an issue with the app itself, I should be opening an issue in the app repository.
[X] I will fill out all of the requested information in this form.

seems like asura closed and is now a redirect to https://srankmanga.com/

repo: LNReader/lnreader-plugins


text:
Performance improvement of very slow global search

Describe your suggested feature
https://github.com/LNReader/lnreader/assets/75091899/aff38a04-6c85-4476-a123-14954309d541
the video is 4x faster
As you can see the globak search has two problems that need performance improvement:

Almost impossible to open an entry mid loading with a global search due to it being very very slow;
Global search requests continue even after opening a search result. IF the opening of the novel triggers by miracle the loading of covers, plot, chapters etc.. will be very very slow too due to global search continuing background until the global search in background is 100% completed;
IF i click a lot of times(like in the video) on the entry while it is loading on global search the app will open the novel page in question multiple times once the global search is 100% completed like here:

https://github.com/LNReader/lnreader/assets/75091899/3cb36a61-f101-432e-9501-d884427b0982
So the limit shoukd be just one, not multiple.
Other details
OS: Android 8 Miui 8
Device: Huawei p8 lite 2017
Version: 1.1.15.1
I reported a similar issue on tachi with almost the same problem as the bug n°2:

EDIT: i just noticed i choose the "feature request" tag by mistake and idk how to change it to Bug.
Acknowledgements

[X] I have searched the existing issues and this is a new ticket, NOT a duplicate or related to another open or closed issue.
[X] I have written a short but informative title.
[X] If this is an issue with a source, I should be opening an issue in the sources repository.
[X] I have updated the app to version 1.1.14.
[X] I will fill out all of the requested information in this form.

Almost impossible to open an entry mid loading with a global search due to it being very very slow;

This is already mentioned in the app, searching a large number of sources is not recommended as all the concurrent network calls to different websites will freeze the app.

Global search requests continue even after opening a search result. IF the opening of the novel triggers by miracle the loading of covers, plot, chapters etc.. will be very very slow too due to global search continuing background until the global search in background is 100% completed;

This is the expected behaviour as the Global Search screen is still present in the navigation stack. We could implement a feature to pause searching if the screen is not on focus but if you aren't searching in a large number of sources, opening a novel isn't slow.

authors: Palloxin rajarsheechatterjee

repo: LNReader/lnreader


text:
Error that Desired structure doesn't exists

Dear Author,
I sincerely appreciate for your great work in Nature journal!
When I typed the command you instruct : ./data_extract_helix_one.sh 4QVF_B
I got the following error.
Could you please check this?
Thank you very much!

Same problem here.

Same problem here too

Hi,
In file masif/source/data_preparation/00-pdb_download.py
replace
pdbl = PDBList(server='http://ftp.wwpdb.org')
by
pdbl = PDBList()

Hi,
In file masif/source/data_preparation/00-pdb_download.py
replace
pdbl = PDBList(server='http://ftp.wwpdb.org')
by
pdbl = PDBList()

This worked for me

Hi,
In file masif/source/data_preparation/00-pdb_download.py
replace
pdbl = PDBList(server='http://ftp.wwpdb.org')
by
pdbl = PDBList()

The same question, it also worked for me. Thank you!

authors: Hylouis233 TobiasPol kskim-phd ugeshe yassermb zjlclimb


text:
scripts folder cannot be found when used from npm

Hi! 👋
Firstly, thanks for your work on this project! 🙂
Today I used patch-package to patch redis-streams-broker@0.0.11 for the project I'm working on.
When the module is loaded from npm the scripts dir cannot be found. This is because of different a different current working dir. Simple take any of the examples and try to run them from a clean npm init and you will see.
** reproduction steps **
yarn init
yarn add redist-stream-broker ioredis

...copy any of the examples in the index.js...

yarn .

** The fix **
The fix is to search from the directory where the file exists instead of the cwd.
Here is the diff that solved my problem:
diff --git a/node_modules/redis-streams-broker/index.js b/node_modules/redis-streams-broker/index.js
index 7c614b8..7f7981b 100644
--- a/node_modules/redis-streams-broker/index.js
+++ b/node_modules/redis-streams-broker/index.js
@@ -1,3 +1,4 @@
+const path = require("path");
 const shortid = require("shortid");
 const Scripto = require("redis-scripto");

@@ -19,7 +20,7 @@ class StreamChannelBroker {
         this._acknowledgeMessage = this._destroyingCheckWrapper(this._acknowledgeMessage.bind(this));
         this._unsubscribe = this._destroyingCheckWrapper(this._unsubscribe.bind(this), false);
         this._groupPendingSummary = this._destroyingCheckWrapper(this._groupPendingSummary.bind(this), false);
-        this._scriptManager.loadFromDir('./scripts');
+        this._scriptManager.loadFromDir(path.join(__dirname, 'scripts'));
     }

     _destroyingCheckWrapper(fn, async = true) {

This issue body was partially generated by patch-package.

@jkosters Thanks for the issue report, will surely look into this in next 48 hrs and have a release for this is required...

authors: LRagji jkosters

repo: LRagji/redis-streams-broker

license: Unlicense


text:
add utility functions NmtBin.get_ell_min/max

This adds two minor functions that I've found useful in TXPipe.

The last round of tests failed due to some kind of build error before it got to the tests.  Do you have the power to just re-run it?  Otherwise I can just do a no-op commit.

Frustrating. Yep, just did. If it doesn't pass I can just clone your branch and fix

Crap, sorry, the changes in the setup file weren’t supposed to be included.

I've undone the changes to the setup.py file.  Let's see now if the tests run properly.

source: gharchive/pull-request

authors: damonge joezuntz

repo: LSSTDESC/NaMaster


text:
move seed to config params

missed a hardcoded seed

closed by #25


text:
sqlite-to-fdb: remove template fdb requirement

This now uses the extra type info contained in the SQLite DB to determine the target datatypes.
If desired we could keep the old version around as a separate tool to convert old SQLite files without type info, although I doubt this is necessary given most people have used lcdr's fdb_to_sqlite previously which already included this information.

Tool has been updated to keep support for conversion using a template, as discussed on Discord. The --template argument is optional and will be used if specified.
With #6 in mind, if the specified destination path is a directory, the output will be stored in a file called output.fdb in that directory, if this name is not yet in use. If it is, a descriptive error message will be returned.

Technically the overwrite check suffers from TOCTOU the way its currently implemented, but I've been nitpicking more than enough for one PR.

authors: Xiphoseer enteryournamehere

repo: LUDevNet/Assembly


text:
Test Windows

Verify that the bundles resolve and run on Windows x86/x86_64.

Test on Windows 10, x86_64 architecture
Test on Windows 7, x86 architecture
OSGI Implementation: Equinox from Eclipse Mars IDE
Packages org.lwjgl.lwjgl and org.lwjgl.opengl

Result: Plugin fails to load. Error output:

org.osgi.framework.BundleException: Could not resolve module: myBundle [488]
Unresolved requirement: Require-Bundle: org.lwjgl.lwjgl
-> Bundle-SymbolicName: org.lwjgl.lwjgl; bundle-version="3.1.2"
org.lwjgl.lwjgl [490]
Unresolved requirement: Import-Package: sun.misc; resolution:="optional"
Unresolved requirement: Require-Capability: osgi.native; native.paths.0:List="lwjgl32.dll,jemalloc32.dll"; native.paths.2:List="liblwjgl.so,libjemalloc.so"; native.paths.1:List="lwjgl.dll,jemalloc.dll"; native.paths.3:List="liblwjgl.dylib,libjemalloc.dylib"; filter:="(|(&(osgi.native.osname~=Windows)(osgi.native.processor~=x86))(&(osgi.native.osname~=Windows)(osgi.native.processor~=x86-64))(&(osgi.native.osname~=Linux)(osgi.native.processor~=x86-64))(&(osgi.native.osname~=MacOSX)(osgi.native.processor~=x86-64)))"
    at org.eclipse.osgi.container.Module.start(Module.java:434)
    at org.eclipse.osgi.container.ModuleContainer$ContainerStartLevel.incStartLevel(ModuleContainer.java:1582)
    at org.eclipse.osgi.container.ModuleContainer$ContainerStartLevel.incStartLevel(ModuleContainer.java:1561)
    at org.eclipse.osgi.container.ModuleContainer$ContainerStartLevel.doContainerStartLevel(ModuleContainer.java:1533)
    at org.eclipse.osgi.container.ModuleContainer$ContainerStartLevel.dispatchEvent(ModuleContainer.java:1476)
    at org.eclipse.osgi.container.ModuleContainer$ContainerStartLevel.dispatchEvent(ModuleContainer.java:1)
    at org.eclipse.osgi.framework.eventmgr.EventManager.dispatchEvent(EventManager.java:230)
    at org.eclipse.osgi.framework.eventmgr.EventManager$EventThread.run(EventManager.java:340)

Evaluation: The MANIFEST.MF contains "osname=Windows". According to the OSGI specification (core specification 6.0.0, pg 100-101) from osgi.org, the correct name would be "Windows 10" (similarly for other Windows). There is no alias "Windows". In order to target most Windows versions, the alias "Win32" can be used (excluding Windows Server 2008 and WindowsCE).
However, I could not verify that the name in the MANIFEST.MF must be the complete OS name and cannot be a partial name. If this was the case, the error would be on the side of equinox of course.
Proposed Fix: I rewrote the MANIFEST.MF in the above packages replacing "Windows" by "Win32" and everything worked fine. If you agree that this should be fixed, I can offer to make the change in all packages and propose a Pull Request if it helps.

Proposed Fix: I rewrote the MANIFEST.MF in the above packages replacing "Windows" by "Win32" and everything worked fine. If you agree that this should be fixed, I can offer to make the change in all packages and propose a Pull Request if it helps.

I agree. I'd certainly appreciate a PR!
Thanks again for testing these. I don't have access to a usable Windows or OS X environment right now, so it's always good to get reports from those that do.

I think we can assume that this is sufficiently tested now.

One last question for planning purposes: Do you know when the new version with the fix will be released on Maven Central?

I was thinking about this yesterday... For reasons of sanity, I'd like to keep the bundles' version numbers in lock step with the LWJGL libraries. This probably means that the fix will go out with the next LWJGL release.
@Spasi Any thoughts?

Not sure if there's a decent alternative to waiting for 3.1.3.

I'm not aware of one.

That's what I thought and I agree that this is probably the only decent way. Thanks for the quick response!

authors: Martin-Idel Spasi io7m


text:
🛑 Knjigocrvic is down

In dd59970, Knjigocrvic (https://knjigocrvic.com) was down:

HTTP code: 0
Response time: 0 ms

Resolved: Knjigocrvic is back up in 41d442d.

repo: LaMpiR/uptime


text:
[request] Ui changes and new features.

Edit novel info (cover , name , discription, tags , etc.) In library like tachiJ2K.

Incognito mode.

Option to show recent searches/extensions.

Better history info, like exact chapter read unlike now

It only shows total number of chapters instead it should show last chapter read.

Notification asking to update downloaded novels when new chapters are added.

Most of those features was planned, but I will get around to do them

authors: LagradOst Sidepunch

repo: LagradOst/QuickNovel


text:
Add e2e test for python version into CI

I think currently this part is not actually being tested in the CI, right?
Would also give an explanation of how to do it locally during development.

Originally everything was tested from both python and node, but as this caused double effort the tests were moved back to Rust. The plan was to make some simple tests for the bindings only (not for each prop and event etc.), but never had the time to.

Makes sense. Then i would add some tests that call each method with valid and invalid arguments and check the returned types.

authors: JanEricNitschke LaihoE

repo: LaihoE/demoparser


text:
Binary Search

Description
Given n sorted numbers(duplicates) and you have to implement binary search algo and gave output as the number is present or not?
If present then at what index? And if that element has duplicates then print that index at which it has first occurred.
like we have 7 elements
Input:
2 4 4 4 7 7 9
binary Search for : 4
Output:
4 is present at index 1
@Lakhankumawat Please consider the issue and assign it to me. Thank you.
I'm a GSSoC 2022 participant.
Domain
Algorithm
Type of Contribution
Addition
Code of Conduct

[X] I follow Contributing Guidelines & Code of conduct of LearnCPP.

please have look at this @n4i9kita

@lagwalsahil21821 go forth with the second part, i.e. find the index of first and the last occurrence of an element in a sorted array.
Make two separate functions : (1) 1st occurrence, (2) 2nd occurrence.
As an extension... you can write a third function to print the count of an element in a sorted array.
Take the input array and the element to be searched from the user.

@lagwalsahil21821, update the title, too.

Please also mention in which directory of project will your algorithm or problem-statement will go .

Depending on your response will decide to accept your issue or not .

@lagwalsahil21821 go forth with the second part, i.e. find the index of first and the last occurrence of an element in a sorted array.
Make two separate functions : (1) 1st occurrence, (2) 2nd occurrence.
As an extension... you can write a third function to print the count of an element in a sorted array.
Take the input array and the element to be searched from the user.

ok. I'll do it

hey!
please assigned me this issue under gssoc'22
thanks
sakshi

/assign

repo: Lakhankumawat/LearnCPP


text:
Array Implementation Of Circular Queue

Description
In this issue, I will implement circular queue using array.
I will show the different operations such as insertion, deletion and traversal in a circular queue.
Domain
Data Structure
Type of Contribution
Addition
Code of Conduct

[X] I follow Contributing Guidelines & Code of conduct of LearnCPP.

/assign


text:
Vertical traversal of a tree

Description
I want to add a code which prints the vertices of a tree in vertical order
Domain
Data Structure
Type of Contribution
Addition
Code of Conduct

[X] I follow Contributing Guidelines & Code of conduct of LearnCPP.

I want to work on this issue . Please assign it to me .

/assign

/assign

@Mrjoy832 @Lakhankumawat Why aren't you replying to this?

@Mrjoy832 @Lakhankumawat Why aren't you replying to this?

What's Your question??????


text:
sortedmerge

Related Issue #356
Write a SortedMerge() function that takes two lists, each of which is sorted in increasing order, and merges the two together into one list which is in increasing order. SortedMerge() should return the new list. The new list should be made by splicing together the nodes of the first two lists.
Proposed Changes
-Created SortedMerge() function to solve this issue
Checklist

[x] ✅ My code follows the code style of this project.
[x] 📝 My change requires a change to the README.
[ ] 🎀 I have updated the README accordingly.
[x] 👀 I have read the CONTRIBUTING document.
[ ] ✨ I have added tests to cover my changes.
[ ] 🚩 All new and existing tests passed.
[ ] 🌟 ed the repo

Wrote a SortedMerge function that takes two lists and merges them together in sequential order

I was able to successfully write a program function that would merge two lists in sequential order using recursion

To resolve the issue of writing a function of SortedMerge

https://github.com/Lakhankumawat/LearnCPP/issues/356

Compiled and tested on my own computer using Replit.

Used the online compiler of Replit to run tests.

[ ] Bug fix (non-breaking change which fixes an issue)
[ ] New feature (non-breaking change which adds functionality)
[ ] Breaking change (fix or feature that would cause existing functionality to change)

Output Screenshots
| Screenshot #1  |

//-----------------CheckList-------------------------///
Make sure your Pull Request is descriptive and complete the checklist .
I would recommend you to go through this tutorial video of the project here ,thanks.
///------------------Description -----------------------///
Let me show you how to check the check list , there should be no space inside brackets like [x]
[x]

[x] Checklist
[ ] Or you can just check them from outside without doing any of these
[x] âœ¨ I have added tests to cover my changes.
[x] ðŸš© All new and existing tests passed.
[ ] ðŸŒŸ ed the repo

Starr the repo if you haven't !
Have you watched this video ?

///----------------------PR README --------------------///
You need to follow particular guidelines for your README , please follow
these guidelines click here
also you can see how your README should look like see here
for more please go through these here


text:
Ceil Value in BST

Related Issue

Mention issue for which this pull request is a part of (please mention issue number # )
#711

Proposed Changes

i) Added Code and Output to find Ceil value in BST
ii) Updated readme file of LearnCPP/T-Tree/B-BinarySearchTree/Ceil Value BST

Additional Info

Added the code with minimal approach of O(h) time complexity.

Checklist

[X] ✅ My code follows the code style of this project.
[X] 📝 My change requires a change to the README.
[X] 🎀 I have updated the README accordingly.
[X] 👀 I have read the CONTRIBUTING document.
[X] ✨ I have added tests to cover my changes.
[X] 🚩 All new and existing tests passed.
[X] 🌟 ed the repo

[ ] Bug fix (non-breaking change which fixes an issue)
[ ] New feature (non-breaking change which adds functionality)
[ ] Breaking change (fix or feature that would cause existing functionality to change)

Output Screenshots

1st Merged PR-> https://github.com/Lakhankumawat/LearnCPP/pull/517
2ndMerged PR-> https://github.com/Lakhankumawat/LearnCPP/pull/726

Work on your README's you can still modify them , look how are other README's inside the REPO , next time i won't allow minor mistakes in README of your PR

Issues

unusual horizontal lines
unusual side rules
and big size for headings infact i mentioned ealier to use ### instead of ##

Work on your next README's you can still modify them , look how are other README's inside the REPO , next time i won't allow minor mistakes in README of your PR

Ok I will try to modify from next time


text:
Having a bow is not enough

Gear and rope gates can only be opened with a bow, but you can't use or even equip a bow until you've had the bow tutorial. Yes, even if you buy one first.
So now the bow tutorial is part of progression logic.

Yes I am aware of that, that's why the first bow pickup is randomized but still in logic. You should be able to find a bow pickup near the start of the seed

authors: Lakifume SenseiLeRoof

repo: Lakifume/Ladybug-Randomizer


text:
Scene Graph

Is your feature request related to a problem? Please describe.
N/A
Describe the solution you'd like
Translate scene graph code from old uTernion library
Describe alternatives you've considered
N/A
Additional context
N/A

Consider Unity-like implementation.

repo: Lakuna/Umbra


text:
The Development process

I would like to set a process for developing features and pushing to master.
Feel free to voice out your concerns and opinions! This is an open source project, everyone should have a say in this.
I propose the following

All development must be done on separate branches/forks
Any change must not be merged unless it has a pull request that has been approved
Each PR must have at least two people approving it
No PR can be merged unless 24hrs have  passed (give enough time for people who are on different timezones to take a look and contribute)

There are no exceptions for how commits get on master, all commits must go through a pull request. However pull requests can have exceptions where they can be expedited if it is a hotfix (critical issue, fixes merge conflict incidents and so on..)
Your feedback is much appreciated

Please mention everyone involved and allow time for review. If no one replies with any objections within 24 hours from the time of mention, the rules will be enforced as-is.

@fadeenk
can you update README.md with these details?

Sure, will do so when i get the chance

Since there are a lot of likes I will suggest the change to the last paragraph to be a bit more clear
All commits must go through a pull request to be merged into master branch. PR can be expedited if it is a hotfix (critical issue, fixes merge conflict incidents and so on..
Also in regard to forking, I would think that it should not be used unless we are building a completely different version of the code, Why don't we make sure that any feature must always be coded inside a feature branch using git flow

@lnyousif That would require us giving write access to everyone for the main repo and can lead to lots of problems. It is best practice to have people do things on personal forks and PR into main repo.
Was thinking of taking things from react's contributing guide and implementing it for us. I just dont have the time right now to type it up

authors: AlKass fadeenk lnyousif

repo: Lamassau/PiTrol


text:
refactored FormModalComp to be more of a static component

Description
PR description: what did you do?

refactored the modal to more dev friendly. more of a static component now as opposed to being fully reusable.

This decision was made due to unnecessary complexities for a reusable modal that we really don't need for the scope of this project*

Type of change
Please delete options that are not relevant.

[x] Bug fix (non-breaking change which fixes an issue)
[ ] New feature (non-breaking change which adds functionality)
[ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
[ ] This change requires a documentation update

Change Status

[x] Complete, tested, ready to review and merge
[ ] Complete, but not tested (may need new tests)
[ ] Incomplete/work-in-progress, PR is for discussion/feedback

40% test coverage is required for main branch
Has This Been Tested

[x] Yes
[ ] No
[ ] Not necessary

Checklist

[x] My code follows the style guidelines of this project
[x] I have performed a self-review of my own code
[ ] My code has been reviewed by at least two peers
[x] I have commented my code, particularly in hard-to-understand areas
[ ] I have made corresponding changes to the documentation
[ ] My changes generate no new warnings
[ ] I have added tests that prove my fix is effective or that my feature works
[x] New and existing unit tests pass locally with my changes
[ ] There are no merge conflicts
[ ] I have included test coverage as much as possible as this stage

Checks are failing due to the FormModalComp.test.js failing. Tests probably need a refactor based on the changes you did to the components.


text:
new test added to conveyancer adds address feature file to allow for …

…non standard characters in property address
new scenario added  @bug_fix_characters_in_property_address to conveyancer_provides_property_address feature file

👍 looks good.

authors: MandyNimmo nigelpain

repo: LandRegistry/dm-acceptance-tests


text:
记忆补嵌包使用识别问题

1.5版本试用了下，还会出现这个问题，应该是偶发性的，就很迷惑。不过我的补给包今天试--第三个速战不能正常识别的问题（1.5貌似解决了，旧日志的原因是速战位置是500，但是限制了只能450以内的）的时候用完了，看看有没有其他人有这个情况复现了。
问题现象：点击使用记忆补嵌包后，概率出现不点击确定的情况。
下面是日志：

11:12:08 - DEBUG - OCR识别结果: ['0/12']
记忆嵌片更新成功：0
11:12:09 - DEBUG - OCR识别结果: ['个人故事', '画', '0/12', '+', '次数：', '0/2', '次数：', '0/2', '次数：', '0/2', '次数', 'ija', '幽潮', '龙舌兰', '朝翼', '豹豹', '辉耀', '茉莉安', '薇蒂雅', '凯茜娅', '伊切尔', '芬妮', '主线故事', '常规行动', '特别派遣', '个人故事', '超元链接', '@', 'o']
11:12:09 - DEBUG - 目标文字：超元链接 相似度：1.00
11:12:10 - DEBUG - 鼠标移动 (1158, 826)
11:12:10 - DEBUG - 滚轮滚动 49700 次
尝试使用2个记忆嵌片
11:12:10 - DEBUG - 目标图片：app/resource/images/person/add_power.png 相似度：1.00
11:12:11 - DEBUG - 鼠标移动后点击 (1568, 52)
11:12:11 - DEBUG - 未识别出任何文字
11:12:11 - DEBUG - 目标文字：暂时没有该类道具 未找到匹配文字
11:12:11 - DEBUG - 目标图片：app/resource/images/person/add_num.png 相似度：1.00
11:12:12 - DEBUG - 鼠标移动后点击 (1221, 626)
11:12:13 - DEBUG - OCR识别结果: ['A提示', '是否将2记忆补嵌包兑换成6记忆嵌片', '6', '®', '最大', 'α取消', '2', '?确定', '主线故事', '黑\u3000常规行动', '。特别派遣', '个人故事', '超元链接']
11:12:13 - DEBUG - 目标文字：确定 未找到匹配文字
11:12:14 - DEBUG - OCR识别结果: ['A提示', '是否将2记忆补嵌包兑换成6记忆嵌片', '6', '+', '最大', 'α取消', '2', '?确定', '主线故事', '黑\u3000常规行动', '。特别派遣', '个人故事', '超元链接']
11:12:14 - DEBUG - 目标文字：确定 未找到匹配文字
11:12:16 - DEBUG - OCR识别结果: ['A提示', '是否将2记忆补嵌包兑换成6记忆嵌片', '6', '+', '最大', 'α取消', '2', '?确定', '主线故事', '黑\u3000常规行动', '。特别派遣', '个人故事', '超元链接']
11:12:16 - DEBUG - 目标文字：确定 未找到匹配文字
11:12:17 - DEBUG - 键盘按下 esc
11:12:18 - DEBUG - OCR识别结果: ['个人故事', '国', '0/12', '+', '0/2', '次数：0/2\u3000', '次数：', '次数：', '0/2', '久委', '龙舌兰', '豹豹', '朝翼', '幽潮', '辉耀', '茉莉安', '薇蒂雅', '凯茜娅', '伊切尔', '芬妮', '主线故事', '常规行动', '特别派遣', '个人故事', '超元链接']
11:12:18 - DEBUG - 目标文字：豹豹 相似度：1.00
11:12:18 - DEBUG - 目标文字：['速战'] 未找到匹配文字
没找到速战position：(None, None)
豹豹未达成速战条件或无刷取次数
豹豹速战未成功
11:12:18 - DEBUG - OCR识别结果: ['0/12']
记忆嵌片更新成功：0

这个问题在于确定识别到“?确定”去了，代码很容易改，感谢反馈，描述的很详细

对了，提个建议：把日志文件单独放个文件夹吧，这样有历史日志的时候也好找。
之前按停止停不下来的时候，想拿历史日志都不行。
不过这样又要有清除历史日志的操作了，看你的选择吧。

对了，提个建议：把日志文件单独放个文件夹吧，这样有历史日志的时候也好找。
之前按停止停不下来的时候，想拿历史日志都不行。
不过这样又要有清除历史日志的操作了，看你的选择吧。

已经写好了，下个版本会加上

repo: LaoZhuJackson/SnowbreakAutoAssistant


text:
How to close an issue

kannst Du ja mal mit dem hier üben: Sobald ein issue ganz abgearbeitet ist, kannst Du mit den letzten Kommentar oder auch nur so ein issue mit dem entsprechenden Button unten schließen.

Okay


text:
unpkg.com and cloudflare

Apparently CloudFlare today does not allow additional parameters in the URL, for example:
https://unpkg.com/select2@4.0.13/dist/css/select2.min.css?f87fe3cd3b0b
While it works https://unpkg.com/select2@4.0.13/dist/css/select2.min.css
This is blocking the work.

Hey @FabioGuin
Thanks for the report.
We are tracking this issue here: https://github.com/Laravel-Backpack/community-forum/issues/928

This affects anyone using Backpack. If you are using backpack in a production environment don't do a php artisan basset:clear as you'll break your production site.
You can set dev_mode to false in your config/backpack/basset.php to load the assets from the local file system but this of course only works if you have previously downloaded assets successfully.

the problem is always here with 1.3.1

@dauvray can you do php artisan basset:clear && php artisan basset:cache ?
Let me know the output.
Cheers

To avoid having duplicate conversations about the same subject, I am closing this issue in favor of https://github.com/Laravel-Backpack/community-forum/issues/928
Please post any feedback you need there.
@dauvray I opened an issue for your problem. Please let's move the conversation over there.

it works if i put BASSET_DEV_MODE=false in my .env.
if i remote it, it fails

authors: FabioGuin dauvray pxpm rich-williamson

repo: Laravel-Backpack/basset


text:
AWS Polly 연동 시 채팅 로그 미표시 문제

안녕하세요 저는 트위치에서 방송을 하고 있는 스트리머 워나힐입니다. 먼저
좋은 툴 만들어주셔서 감사합니다. 리듬게임 방송하면서 항상 TTS 감사히 사용하고 있습니다.
오늘 Polly 연동을 시도해 보았는데, 연동 자체는 잘 되어 채팅이 읽히는 것은 확인하였으나 Polly를 사용하지 않을 때와는 다르게 페이지 내에 채팅 로그가 뜨지 않습니다.
기존에 블랙리스트 추가를 커맨드 사용 없이 채팅 로그 단축 버튼을 통해 이용하는 입장에서, 채팅 로그가 뜨지 않는 점이 살짝 아쉽습니다. 혹시 Polly 사용 시에도 채팅 로그를 조회할 수 있도록 수정이 가능할까요?

수정되었습니다.

authors: FelisCatusKR Lastorder-DC

repo: Lastorder-DC/chatreader-kor


text:
Gpay icon is small in size

Gpay icon size is smaller than others

Please ignore this was related to a specific icon pack

repo: LawnchairLauncher/lawnicons


text:
unbindLabel followed by bindLabel doesn't re-render labels until I zoom out and in

I've got a markercluster with labels that are noHide: true (always visible).  Works really well so that when the markers are de-clustered the labels show up.
I added a toggle to show/hide labels (they're always visible by default - either always visible or off).
When I do
geojson.eachLayer(function(layer) {
	layer.unbindLabel();
});

...
few seconds later
...
geojson.eachLayer(function(layer) {
	layer.bindLabel(layer.feature.properties.labelName, {noHide: true, clickable: true});
});

The labels don't show up until I zoom out and back in (until the markers are redrawn).  Any help fixing this is appreciated! Is there an update method maybe?

I encounter this issue too.

For my use case, I need to use updateLabelContent instead of unbind and bind label again.
For your use case, you might try to set the css of label to hide and unhide it instead.

authors: TheSisb khuevu


text:
Doors - Trigger on Click

I'd like a door trap to trigger when clicked on a locked door, however if I specify a door location with @Door it only works with open/close door triggers, not on click.

This would be a really useful feature!
One thing I'm trying to do is have players have keys as an item for doors. Through a macro, we could prompt the player to attempt to use the key for the locked door and unlock the door if the correct key is in their inventory.

The locked/door key is possible using a couple of other modules (innocenti open lock; pick up sticks; 5e loot sheet npc). I haven’t set it up myself,  it watched a video on how to implement.


text:
connection.readyState returns incorrect value when using auto_reconnect

While testing error handling when connection drops I found that sometimes connection.readyState returns 1 event when MongoDB is down, in fact when this happens the 'disconnected' event is not emitted. After a while I found that this only happens when auto_reconnect is set to true, although this may happen after an arbitrary number of connection drops (sometimes 2, sometimes 10)
I was using this code while having this issue:
mongoose.connection.on('disconnected', function() {
	console.log(chalk.red('Connection to MongoDB is down'))
})
mongoose.connection.on('error', function(err) {
	console.log(chalk.red(err))
	if(mongoose.connection.readyState) mongoose.connection.disconnect()
})
db = mongoose.connect(config.db)

Then I changed the code to:
mongoose.connection.on('disconnected', function() {
	console.log(chalk.red('Connection to MongoDB is down'))
	setTimeout(function() {
		mongoose.connect(config.db,{server:{auto_reconnect:false,socketOption:{keepAlive:true}}})
	},2000)
})
mongoose.connection.on('error', function(err) {
	console.log(chalk.red(err))
	if(mongoose.connection.readyState) mongoose.connection.disconnect()
})
db = mongoose.connect(config.db,{server:{auto_reconnect:false,socketOption:{keepAlive:true}}})

And everything works as it should. I'm using Mongoose 3.8.21, Node 0.10.25, MongoDB 2.6.7
(I guess it's a driver issue)

Hmm are you on OSX? I can't repro this on my linux mint box, but on OSX it seems to be losing an event handler somewhere.

Waiting for mongodb@2.0.23 for this fix

repo: LearnBoost/mongoose


text:
to_xml joint type

if I write a "revolute" joint type in SDF+, it becomes "hinge" in to_xml(), which causes an error when loading into pybullet
if I write a "fixed" joint in SDF+, parsing gives me error:
Traceback (most recent call last):
  File "/Users/z/Documents/simulators/PyBullet/kitchen-worlds/pybullet_lisdf.py", line 65, in <module>
    world = load_lisdf_pybullet(sdf_path)
  File "/Users/z/Documents/simulators/PyBullet/kitchen-worlds/pybullet_lisdf.py", line 39, in load_lisdf_pybullet
    world = load_sdf(sdf_path).worlds[0]
  File "/Users/z/Documents/simulators/PyBullet/kitchen-worlds/lisdf/parsing/sdf_j.py", line 378, in load_sdf
    return visitor.load_file(filename).data
  File "/Users/z/Documents/simulators/PyBullet/kitchen-worlds/lisdf/parsing/xml_j/visitor.py", line 39, in load_file
    return self.visit(filename, node)
  File "/Users/z/Documents/simulators/PyBullet/kitchen-worlds/lisdf/parsing/xml_j/visitor.py", line 75, in visit
    return _inner(root)
  File "/Users/z/Documents/simulators/PyBullet/kitchen-worlds/lisdf/parsing/xml_j/visitor.py", line 58, in _inner
    node.children[i] = _inner(c)
  File "/Users/z/Documents/simulators/PyBullet/kitchen-worlds/lisdf/parsing/xml_j/visitor.py", line 58, in _inner
    node.children[i] = _inner(c)
  File "/Users/z/Documents/simulators/PyBullet/kitchen-worlds/lisdf/parsing/xml_j/visitor.py", line 58, in _inner
    node.children[i] = _inner(c)
  File "/Users/z/Documents/simulators/PyBullet/kitchen-worlds/lisdf/parsing/xml_j/visitor.py", line 66, in _inner
    return proc(node)
  File "/Users/z/Documents/simulators/PyBullet/kitchen-worlds/lisdf/parsing/xml_j/visitor.py", line 132, in wrapped
    assert len(rv.children) == 0
AssertionError
numActiveThreads = 0
stopping threads
destroy semaphore
semaphore destroyed
Thread with taskId 0 exiting
Thread TERMINATED
destroy main semaphore
main semaphore destroyed

Process finished with exit code 1

Already fixed in the latest commits in branch Jiayuan

authors: vacancy zt-yang

repo: Learning-and-Intelligent-Systems/lisdf


text:
Rename app

Checklist

[x] App update process has been followed
[x] Target branch is develop
[x] Application version has been bumped

PR to App database open here: https://github.com/LedgerHQ/ledger-app-database/pull/178
This PR changes the app name to "Lisk Legacy" and the coin to "LSK_LEGACY".
The method claim_message was also added, so the user is able to sign directly the msg content and not only the message hash (sign_message method that already existed).
Claim message will show the message hash to the user, because the message can have non-printable characters.

PR to App database open here: https://github.com/LedgerHQ/ledger-app-database/pull/178

Hello,
Just a friendly reminder to LedgerHQ that people have coins stuck on their device because of this unmerged PR :)
Best,
hirish

@chcmedeiros could you please target develop branch instead of main

authors: chcmedeiros hirishh tdejoigny-ledger

repo: LedgerHQ/app-lisk-new


text:
Europa:Use partial wf for suggestion buttons swipe

Changes include

[x] Bugfix (non-breaking change that solves an issue)
[ ] New feature (non-breaking change that adds functionality)
[ ] Breaking change (change that is not backwards-compatible and/or changes current functionality)
[ ] Tests
[ ] Documentation
[ ] Other (for changes that might not fit in any category)

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

Project coverage is 59.65%. Comparing base (86010d8) to head (0d249b9).

Additional details and impacted files
@@           Coverage Diff           @@
##           master     #571   +/-   ##
=======================================
  Coverage   59.65%   59.65%
=======================================
  Files          12       12
  Lines        1678     1678
=======================================
  Hits         1001     1001
  Misses        677      677

Flag
Coverage Δ

unittests
59.65% <ø> (ø)

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

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

authors: abonnaudet-ledger codecov-commenter

repo: LedgerHQ/ledger-secure-sdk


text:
Ripple Wallet App Not Working with Windows 10

Greetings,
I found out I'm not alone with this issue, as several online discussions address problems running the native program (ledger wallet for ripple) on Windows (I'm running Windows 10).  Basically, the app doesn't work.
I've tried several software installs, including both the "setup_ledger_wallet_ripple_win_x64" and "setup_ledger_wallet_ripple_win_x86" versions.  For the x64 version, which downloads via the applicable Ledger webpage link, the app doesn't come up at all.  For the x86 version, the app display a window, but it doesn't sync/communicate with the Ledger Nano S after starting Ripple on the Ledger Nano S device.
I've turned off anti-virus, uninstalled/reinstalled, rebooted, etc., but nothing.  It doesn't work and there doesn't appear to be anything online to help people address the problem.
Hoping you can provide assistance.  Thank you for your time.
Scott

Hi,
The x64 version of the ledger wallet ripple has indeed some compatibility issues but there is no report of issue about the x86 yet.
If you see the window of the app displaying, this means the launch was successfull and now you have another of the following issues:

You have the Ledger Manager, Bitcoin wallet, Ethereum Wallet or Electrum running in the background
The ripple app on your device has the setting "Browser support" turned on (which should be turned off in order for the app to be able to work)
Try another USB port and cable

If you have issue with the synchronization (incorrect balance, missing transactions and so), make sure you clicked on the "two arrows" on the top right corner of the app after launch. However, because of the recent events, the Ripple node wss://s2.ripple.com (which we do not control but use by default) has difficulties responding and thus, prevent our wallet from synchronizing. You can change the node you want to use, by unplugging your device and clicking on "change node" at the bottom of the opening screen.

Greetings,
Thanks for getting back with me.
I’ve tried all three of your suggestions below…prior to sending in the trouble report…but no luck.  I’ve even tried running the app and device on two other machines…all Windows 10, as that is all I have…but with no luck.
At this point, I have my XRP sitting on an exchange and can’t get it to a safe wallet.  May have to set up and use an online digital wallet as a temporary solution (to move XRP off the exchange)…until I can use my two Ledger Nano S devices.
Please let me know what other options I have.  Can you do a remote login and help?  Would appreciate any help you can provide…I’m feeling a bit exposed and at a dead end.
Scott Weber
928-853-5006
From: amougel [mailto:notifications@github.com]
Sent: Sunday, December 17, 2017 3:31 AM
To: LedgerHQ/ledger-wallet-ripple ledger-wallet-ripple@noreply.github.com
Cc: Scott Weber scott@weberorbit.com; Author author@noreply.github.com
Subject: Re: [LedgerHQ/ledger-wallet-ripple] Ripple Wallet App Not Working with Windows 10 (#14)
Hi,
The x64 version of the ledger wallet ripple has indeed some compatibility issues but there is no report of issue about the x86 yet.
If you see the window of the app displaying, this means the launch was successfull and now you have another of the following issues:

You have the Ledger Manager, Bitcoin wallet, Ethereum Wallet or Electrum running in the background
The ripple app on your device has the setting "Browser support" turned on (which should be turned off in order for the app to be able to work)
Try another USB port and cable

If you have issue with the synchronization (incorrect balance, missing transactions and so), make sure you clicked on the "two arrows" on the top right corner of the app after launch. However, because of the recent events, the Ripple node wss://s2.ripple.com (which we do not control but use by default) has difficulties responding and thus, prevent our wallet from synchronizing. You can change the node you want to use, by unplugging your device and clicking on "change node" at the bottom of the opening screen.
—
You are receiving this because you authored the thread.
Reply to this email directly, view it on GitHubhttps://github.com/LedgerHQ/ledger-wallet-ripple/issues/14#issuecomment-352245811, or mute the threadhttps://github.com/notifications/unsubscribe-auth/AhAcQpv_Sg833iHntE5LWYtybhQ5tnYOks5tBO1jgaJpZM4REfo-.

authors: amougel sdweber

repo: LedgerHQ/ledger-wallet-ripple


text:
[ISSUE-3] Apply Annotation processor structure

:bell: Related issues
https://github.com/LeeKyoungIl/phobos-satellite/issues/3
:pencil: Changes and Reasons

add sample project for check annotation processor
develop sample Aspect class
convert annotation processor
apply to sample project

:sparkles: PR Point
N/A
:gift: Notes
N/A

Can one of the admins verify this patch?

build please

build please

build please

build please

build please

build please

repo: LeeKyoungIl/phobos-satellite


text:
🛑 MobileCompanyGlobal2 is down

In cff612b, MobileCompanyGlobal2 (https://mglobal2.wisereport.co.kr/Home/ServerCheck) was down:

HTTP code: 0
Response time: 0 ms

Resolved: MobileCompanyGlobal2 is back up in 5a5dd9c.


text:
🛑 MobileCompanyGlobal3 is down

In e48fa6b, MobileCompanyGlobal3 (https://mglobal3.wisereport.co.kr/Home/ServerCheck) was down:

HTTP code: 0
Response time: 0 ms

Resolved: MobileCompanyGlobal3 is back up in f2bb2fa.


text:
🛑 CompanyGlobal2 is down

In 78895f4, CompanyGlobal2 (https://compglobal2.wisereport.co.kr/Home/ServerCheck) was down:

HTTP code: 0
Response time: 0 ms

Resolved: CompanyGlobal2 is back up in 8b3e96f.


text:
🛑 MobileETFGlobal2 is down

In 0be23a1, MobileETFGlobal2 (https://mglobaletf2.wisereport.co.kr/Home/ServerCheck) was down:

HTTP code: 0
Response time: 0 ms

Resolved: MobileETFGlobal2 is back up in 3384a1a.


text:
🛑 Naver3 is down

In bd8cdf7, Naver3 (https://navercomp3.wisereport.co.kr/v2/servercheck.aspx) was down:

HTTP code: 0
Response time: 0 ms

Resolved: Naver3 is back up in e7097e5 after 10 minutes.


text:
Long server names looks bad - Guild leaderboards page

Bugreport:
Bug reported by Jecsham
Is it a bugreport for the website or for the bot?: website
What is the title of your bugreport?: Long server names looks bad - Guild leaderboards page
How can you reproduce the bug? (Explain in steps): Create a server with a long name and got to their leaderboard page
https://i.imgur.com/y9yNIZ4.png
Which result would normally have to be?: Short name (with ellipsis)
What happens if you do the action now?: same
ReportID: 80

➤ LenoxBot commented:
Deny:
---> <#497392981419098112>
Profxy

➤ LenoxBot commented:
Approve:
could be reproduced (pls next time make a proposal but you can assign these Reports to you on github anyway)
Dadi

➤ LenoxBot commented:
Approve:
Could be reproduced
Nickkel

➤ LenoxBot commented:
Approve:
could be reproduced
Monkeyyy11

repo: LenoxBot/LenoxBot


text:
Attribute Load Order issue

Originally found in https://github.com/Noaaan/MythicMetals/issues/259
The method which adds attributes for LivingEntities is cancellable, meaning depending on what order mods load in, it could cause attributes from other mods to be misisng.
https://github.com/LeoMinecraftModding/eternal-starlight/blob/adf37aab706cf92812cd5961a29b8d741914cc44/common/src/main/java/cn/leolezury/eternalstarlight/common/mixin/LivingEntityMixin.java#L69-L71
The fix for this is quite simple - adding to the return value simply works, as the map is mutable in this state.
See https://github.com/Noaaan/MythicMetals/blob/5673317bb5401a631db4c960b103e9e71339761e/src/main/java/nourl/mythicmetals/mixin/LivingEntityMixin.java#L98-L101

Thanks for reporting! But it seems to be already fixed here. It should be fixed in 0.1.6.

authors: LeoLezury Noaaan

repo: LeoMinecraftModding/eternal-starlight


text:
How to calculate the comprehensive results of the three streams

Hello, for the papers,“For all the reported results of three streams, we use the weights of [0.6, 0.6, 0.4] for weighted fusion like other multi-stream GCN methods.”
But I still don’t understand, can you teach me how to calculate the comprehensive results of the three streams?
For example, how did the 83.8 in the last line of the fourth column in Table II calculated?

Just simply ensemble the results. See 'ensemble_ntu_cs.py'. Recently we are exploring more efficient aggregation methods.

Thank you for your prompt reply！
------------------ 原始邮件 ------------------
发件人:                                                                                "Levigty/AimCLR"                                                                                @.>;
发送时间: 2022年11月24日(星期四) 晚上8:38
@.>;
@.@.>;
主题: Re: [Levigty/AimCLR] How to calculate the comprehensive results of the three streams (Issue #11)
Just simply ensemble the results. See 'ensemble_ntu_cs.py'. Recently we are exploring more efficient aggregation methods.
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Reply to this email directly, view it on GitHub, or unsubscribe.
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repo: Levigty/AimCLR


text:
Checksum should be CRCCCITT

While Carl was testing today we found that the checksum used by VESC appears to be CCITT. This should be an easy CRTL-H fix.

Fixed in commit: 1cace665c220fbeafcec38e213d879379299910b

repo: LiamBindle/vesc


text:
Difference Similarity Graph Attention Module，DSGAT

I would like to ask the author about your Difference Similarity Graph Attention Module (DSGAT) in that py file, looking forward to your reply thanks!

Begin in this (Line 15),  feel free to cite us if this helps!


text:
About  validation results  of client

Hello,
Thanks for the interesting work!
Train Model:
python train_FedAVG.py --FL_platform ViT-FedAVG --net_name ViT-small --dataset cifar10 --E_epocmax_communication_rounds 100 --num_local_clients -1 --split_type split_3 --save_model_flag
Then ,  I find that every client has same Valid metric after first train and have tried many times in different ways with the same result. I'm not sure that is a normal result.
Calculate the model avg----
Update each client model parameters----
++++++ Running Validation of client train_1 ++++++
Valid Loss: 1.59348 Valid metric: 0.62360
The updated best metric of client train_1 0.6236
++++++ Running Validation of client train_1 ++++++
We also update the test acc of client train_1 as 0.628
++++++ Running Validation of client train_2 ++++++
Valid Loss: 1.59348 Valid metric: 0.62360
The updated best metric of client train_2 0.6236
++++++ Running Validation of client train_2 ++++++
We also update the test acc of client train_2 as 0.628
++++++ Running Validation of client train_3 ++++++
Valid Loss: 1.59348 Valid metric: 0.62360
The updated best metric of client train_3 0.6236
++++++ Running Validation of client train_3 ++++++
We also update the test acc of client train_3 as 0.628
++++++ Running Validation of client train_4 ++++++
Valid Loss: 1.59348 Valid metric: 0.62360
The updated best metric of client train_4 0.6236
++++++ Running Validation of client train_4 ++++++
We also update the test acc of client train_4 as 0.628
++++++ Running Validation of client train_5 ++++++
Valid Loss: 1.59348 Valid metric: 0.62360
The updated best metric of client train_5 0.6236
++++++ Running Validation of client train_5 ++++++
We also update the test acc of client train_5 as 0.628
train_FedAVG.py:140: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  args.record_val_acc = args.record_val_acc.append(args.current_acc, ignore_index=True)
train_FedAVG.py:142: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  args.record_test_acc = args.record_test_acc.append(args.current_test_acc, ignore_index=True)
train_FedAVG.py:147: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.
  tmp_round_acc = [val for val in args.current_test_acc.values() if not val == []]
Train the client train_1 of communication round 1

Thanks!

If the vision transformer is applied as baseline, and if the validation dataset is a union validation dataset, then it is normal that the results of difference clients are the same.
For Retina and Cifar-10, we use the union validation dataset, thus it is normal that each client has the same validation acc.

repo: Liangqiong/ViT-FL-main


text:
Misspell in seed tasks

line 172 of BELLE/1.5M/zh_seed_tasks.json
"给定一个主题，基于这个主题写一篇作为，要求立意清晰，思想积极向上。。"
It should be "作文", not "作为".

感谢，可否提一个pr？

authors: Merlininice tjadamlee

repo: LianjiaTech/BELLE


text:
Update README.md

fork from https://github.com/deep-diver/Alpaca-LoRA-Serve

if possible, you can try the quantized models and llama models. thanks

authors: GoooIce tjadamlee


text:
Audio Files are Distorted when imported to Ableton Live

When attempting to import any audio sample into Ableton Live 10, the wavelengths are completely distorted like above. This might be caused by some frequency compression either on the site or the original export of the files. I attempted this on multiple samples from different audio collections. You can see the exact sample for the image above in the bottom left corner.
Acceptance Criteria:

Downloaded audio files can be properly played back in Ableton Live.

thank you, @cozma!  this is the type of technical user testing i needed.
if it's possible, can you send me two files:
(1) the wav file that you linked above in the screenshot
(2) any wav file that does work in your Ableton Live 10... it could be anything.  i just would like to compare the two on my machine
You can upload them to my dropbox:
https://www.dropbox.com/request/agxFc8AQ0JYOdlf121yE
And lastly, what OS are you using?

thank you, @cozma! this is the type of technical user testing i needed.
if it's possible, can you send me two files:
(1) the wav file that you linked above in the screenshot
(2) any wav file that does work in your Ableton Live 10... it could be anything. i just would like to compare the two on my machine
You can upload them to my dropbox:
https://www.dropbox.com/request/agxFc8AQ0JYOdlf121yE
And lastly, what OS are you using?

No problem, happy to help!
I uploaded a random sample I've used KSHMR_Cello_17_Melody_120_Dm.wav and the file from the screenshot to the Dropbox. My OS is OSX 10.11.6

hey @cozma, just found Citizen DJ today.
I've downloaded a few songs from there this morning and have imported them into Ableton Live 10.1.3 with no issues or distortion.
I'm on OS X 10.14.6

@cozma I have a feeling this might be because the wav format is 32-bit, can you try out this file that's 16-bit?
https://www.dropbox.com/s/fdtyf20pxzmepk5/A-better-America_2001642615_004_00-00-30.wav?dl=1
If that works, I should probably just export these at 16-bit since the source material is 16-bit (i.e. no quality gain for increasing bit depth).  Thanks again for helping troubleshoot this.

@cozma I have a feeling this might be because the wav format is 32-bit, can you try out this file that's 16-bit?
https://www.dropbox.com/s/fdtyf20pxzmepk5/A-better-America_2001642615_004_00-00-30.wav?dl=1
If that works, I should probably just export these at 16-bit since the source material is 16-bit (i.e. no quality gain for increasing bit depth). Thanks again for helping troubleshoot this.

Yes, this file works! Thanks for looking into it

that's great to hear!   I've updated the .wav files on the website, so hopefully things will work now.  Apologies for having to re-download everything

authors: beefoo cozma ttruiett

repo: LibraryOfCongress/citizen-dj


text:
Add icon or other emphasis to group listings in search results

It has been suggested that the groups need a design element to make them more prominent in the search result lists. A couple of suggestions follow

Add an icon along the lines of the following example  [dead link]

Make the arrow at the end more distinct by making it the same color as the title text instead of white.

Priority: 3

Can you provide an example link to a groups page? A screenshot would also be helpful

Can you provide an example link to a groups page? A screenshot would also be helpful

For the record, a "group" in this context is a set of books/projects, such as a multi-volume work, a series of books, or a set of magazines. Books of the Bible are grouped by translation; National Geographic Magazines are all in a group; etc. They show as a single line item in some of the search results.
One example, searching on "geographical":
https://librivox.org/search?q=geographical&search_form=advanced

The arrow is not well contrasted to the background. I can see making it the same gray as the "in progress" text above it, or the blue that the titles and other darker blue graphics are.
This second example shows it in a list that returns other result types:
https://librivox.org/search?q=king james&search_form=advanced

If one were to go further with it, an option would be to line it up with the others and put a graphic indicating "group" (such as we have for author, reader, and title) on the left side.

For the record, a "group" in this context is a set of books/projects, such as a multi-volume work, a series of books, or a set of magazines. Books of the Bible are grouped by translation; National Geographic Magazines are all in a group; etc. They show as a single line item in some of the search results.
One example, searching on "geographical":
https://librivox.org/search?q=geographical&search_form=advanced

The arrow is not well contrasted to the background. I can see making it the same gray as the "in progress" text above it, or the blue that the titles and other darker blue graphics are.
This second example shows it in a list that returns other result types:
https://librivox.org/search?q=king james&search_form=advanced

If one were to go further with it, an option would be to line it up with the others and put a graphic indicating "group" (such as we have for author, reader, and title) on the left side.

Here's a quick demo, using that same King James search query:

For now, I've reused the 'Genre' icon, rather than create a new one.  If it ought to have an icon of its own, we'd tack it onto the bottom of this icon file.
Perhaps one of our cover-makers might like to give it a shot?
As for the arrow, we could turn JUST the group arrows black/blue, or we could change the author arrows too.
One last thing: the background colors for groups (and for genres, though I've never seen a genre as a search result) are a little darker than for projects or authors.  We could make that a bit more pronounced as well.
Saving what I have so far, but I'll refrain from opening a PR until we've discussed more about what it ought to look like.

Markup take 2, now with a black arrow for groups:

If a proper icon is the only other thing this needs, then we can put up a request for that.

One more mockup for today:

authors: RayBB redrun45 twinkietoes-on

repo: LibriVox/librivox-catalog


text:
RFC: Future of init context managers in Fabric

Description & Motivation
Recently, #17488 was merged with the following changes in summary:

Fabric.init(): managing the device and precision part that Fabric.init_module() does
Undeprecated fabric.sharded_model()
Renamed the Strategy.init_module and Precision.init_module to just init_context()
Renamed Strategy.init_sharded_context without backward compatibility

These points work against the preparation work done in #17462 to provide a unified, strategy-agnostic way of initializing the model, and the PR didn't provide much motivation to for this overhaul. The main point was given in the documentation: "Using these separately can provide more control for expert users".
Here are my concerns and why #17488 should be reverted, or partially reverted.
Concern 1: There are multiple ways of doing the same thing now.
Fabric.init(), Fabric.init_module() have overlapping functionality. They can both be used to initialize the module and control how tensors are created. "Which one should be used?" is what the user could ask. The ambiguous name "init" doesn't help here. Furthermore, Fabric.init()'s implementation is tied to the strategy because it calls Strategy.init_context(), but why should the strategy decide how tensors are created outside the model? There is a lack of motivation through examples and use cases. From a documentation perspective, personally, I would find myself struggling to explain the differences to a newcomer.
Concern 2: Fabric.init() is redundant.
PyTorch accepts device and dtype attributes everywhere and has plenty of flexibility when it comes to tensor creation this way. With the fabric.device property, you can already create tensors directly on the device. In my opinion, this is an unneeded PyTorch abstraction layer. The cost is that it increases the API surface of Fabric, and makes the user study the differences between the overlapping features of the context managers. It is unclear to me why this feature was promoted as "more flexibility for expert users" when in reality the choice of dtype and device is tied to the strategy.
Concern 3: Fabric.init is a misleading name
Generally speaking, method names should be communicating the action they perform on the object or what they are meant to return. Here, without knowing any further context, Fabric.init() communicates that it initializes Fabric itself, which is of course wrong. The previous name Fabric.init_module() was better, because the user is forced to study its return type to understand the intentions. You can't pass in the model as an argument, because that would lead to an error and thus the user will understand that this is actually a context manager.
Concern 4: The sharded_model API is misleading and misunderstood.
One motivation behind the original PR #17462 was to provide a better-named API for instantiating modules, where both the name and the functionality would be agnostic to the strategy/device choice. This meant the Fabric.sharded_model() context had to be discouraged in favor of the more generalized Fabric.init_module(). Confusions like https://github.com/Lightning-AI/lightning/pull/17488#pullrequestreview-1414249897 are the prime example that there exists a misunderstanding of what Fabric.sharded_model() does. Hint: It does NOT shard the model!
Concern 5: It is unclear where empty weight initialization /  deferred init should be implemented now.
The motivation in #17462 was to have a unified model instantiation context that would take care of efficient initialization, and potentially deferred/lazy instantiation if it made sense given the strategy. Where should this feature be added now? To only some of the context managers, to all of them? It is very unclear to me what the future of this feature is now.
Pitch
My proposal to address the concerns is:

Discuss the above concerns and align on the future to provide a more focused API.
Discourage the use of the sharded_model() by reverting that part in #17488.
Either revert the addition of Fabric.init() or keep it at least undocumented/protected until there is a strong demand for it.

Alternatives
A) An alternative would be to find better-fitting names for the context managers discussed here, but it would only address the concerns partially. There would still be a lot of open questions.
B) Instead of Fabric.init(), provide a standalone function/context manager (pseudo code):
@contextmanager
def init(device, dtype):
    torch.set_default_tensor_dtype(dtype)
    with device:
        yield
    ...

Additional context
Requesting review from @carmocca @lantiga @justusschock
We should align on this before 2.1, because after that it will be too late to change any of these APIs.

Renamed Strategy.init_sharded_context without backward compatibility

This is safe because this hook comes from a protected interface: _Sharded

There are multiple ways of doing the same thing now.

is redundant.

init and init_module have overlapping functionality by design: one is just a wrapper over the other. We recommend init_module as the plug-n-go option in the docs: https://lightning.ai/docs/fabric/latest/api/fabric_methods.html#init-module.
The strategy decides this because it controls the accelerator (device) and precision (dtype).

the choice of dtype and device is tied to the strategy.

.init() has a few advantages over recommending fabric.device.

discoverability: having a fabric method for it allows the user to learn they can do this, it's not well known for most pytorch users that you can use torch.device as a context manager, especially non-experts.
compatibility: using torch.device as a context manager requires checking your torch version. Init can do it for you
dtype support: by going through the strategy, the dtype is also set. currently there's no with torch.float16 support.
future-proofing: if in the future there's new enhancements to initialization that depend on the strategy, we can simply add them to .init() (via .strategy.init_context()) and both this method and .init_module() get the benefit.

is a misleading name

Sounds good! Let's change it with a clearer alternative.

The previous name

.init_module() is unchanged and still our recommended solution. #17488 doesn't intend to change it functionally.

The sharded_model API is misleading and misunderstood.

Sounds good, we can add back the deprecation for sharded_model. It's true that it's hard to see any users enabling that context manager exclusively.

unclear where empty weight initialization / deferred init should be implemented now.

I don't see how its (possible) implementation changes based on #17488. At the level of the strategy the changes are just in the method names. Would you mind clarifying this?
Happy to address your concerns!

@carmocca Thanks for looking at this feedback. I opened #17607 which adds back the deprecation and reverts a few things while keeping Fabric.init() separated.

The strategy decides this because it controls the accelerator (device) and precision (dtype).

The strategy should decide how to init the module yes, but this decision (device/precision) may not be the right decision for just arbitrary tensor instantiation outside the module. The functionality overlaps for something that should be kept separate, this is my main complaint here. While the introduction of init_module() was fairly motivated with clear example usages (e.g. in lit-llama), the introduction and design for Fabric.init() was not motivated at all by practical use cases.

This is safe because this hook comes from a protected interface: _Sharded

The class is protected, but it's added to the public interface of every strategy, and the method itself is public. I personally would prefer to just remove it, but after 2.0 we have strict rules and to make an exception here would be very arbitrary.

The strategy should decide how to init the module yes, but this decision (device/precision) may not be the right decision for just arbitrary tensor instantiation outside the module. The functionality overlaps for something that should be kept separate, this is my main complaint here

I don't fully grasp this argument. The practical use would be to replace Lit-LLaMa's EmptyInitOnDevice (currently -quantization and -empty) for non-modules where you don't care about the sharded logic, or modules if you don't want to run the sharded logic for whatever reason.

The class is protected, but it's added to the public interface of every strategy, and the method itself is public. I personally would prefer to just remove it, but after 2.0 we have strict rules and to make an exception here would be very arbitrary.

These methods are not meant to be called externally. The only breakage would be in somebody extending our strategies and overriding the method. These are very experimental APIs and we should prefer future UX and clarity over a very slim chance of breakage which would only impact super expert users who are very deep into the framework.

For an alternative name for Fabric.init, I propose Fabric.init_tensor.

authors: awaelchli carmocca

repo: Lightning-AI/lightning


text:
Pytorch lighting Fabric + lit data + DDP hangs when finishing epoch

🐛 Bug
I am training CLIP using pytorch lighing fabric + litdata on a distributed set up (4 nodes each 4 GPUs). I noticed that when finishing the 1st epoch the training dataloaders hang for some nodes.
The image bellow shows fabric.print()doing the logging on 4 nodes before finishing an epoch (I print every 25 steps). Only one rank successfully finishes the rest hang, otherwise the message ++++ Epoch: 0 completed ++++ will appear 4 times, once in each node). I shared relevant parts of the code bellow, any help would be appreciated

Additional context + Parts of Training code

The data chunks are downloaded in each node before fabric launches via popen the subprocesses for all the devices (so the dataloading is local) not streamed
I tested mosaicml-streaming on the same fabric training code and it works without issues.
I put here in another issue the VertexAI cluster environment I use to configure Fabric

StreamingDataset setup
I am using the following script to load the litdata:
class ImageCaptionDataset(StreamingDataset):
    """
    Main dataset to retrieve imager-caption optimized dataset
    """

    @staticmethod
    def get_zero_shot_one_hot(zero_shot_attributes: List[int]):
        one_hot_encoded = torch.zeros(len(CLASSES), dtype=torch.float)
        one_hot_encoded[zero_shot_attributes] = 1.0
        return one_hot_encoded

    def __getitem__(self, idx: int) -> Any:
        _, image_bytes, text_ids, mask, zero_shot_attr = super().__getitem__(idx)
        image = Image.open(io.BytesIO(image_bytes))
        input_ids = torch.tensor(np.frombuffer(text_ids, dtype=np.int64))
        attention_mask = torch.tensor(np.frombuffer(mask, dtype=np.int64))
        zero_shot_attr = self.get_zero_shot_one_hot(literal_eval(zero_shot_attr))
        return image, input_ids, attention_mask, zero_shot_attr

def collate_fn(batch, processor):
    """Arrange the batch into a dictionary of tensors for HF
    """
    images = processor(images=[ex[0] for ex in batch], return_tensors="pt")
    return {
        "pixel_values": images["pixel_values"],
        "input_ids": torch.stack([ex[1] for ex in batch]),
        "attention_mask": torch.stack([ex[2] for ex in batch]),
        "labels": torch.stack([ex[3] for ex in batch])
    }

def get_dataloader(split: str, config: configs.ExperimentConfig):
    dataset = ImageCaptionDataset(
        input_dir=os.path.join(config.local_data_path, split),
        shuffle=True if split == "train" else False,
    )
    # Image transformation function
    processor = CLIPProcessor.from_pretrained(
        config.training_config.pre_trained_backbone
    )
    return StreamingDataLoader(
        dataset,
        batch_size=config.dataset_config.batch_size,
        shuffle=True if split == "train" else False,
        num_workers=config.dataset_config.num_workers,
        collate_fn=partial(
            collate_fn,
            processor=processor
        ),
        drop_last=True,
        pin_memory=config.dataset_config.pin_memory,
    )

Am I setting properly the dataloader here?, I checked and litGPT uses torch DataLoader instead of StreamingDataloader
Here I show what I managed to monitor on how CPU and RAM looks like for an entire epoch

You can see how instead of jumping again to load the samples of the test set, it hangs ...
Training using Fabric
I put here some parts of my training script which basically follows open-clip implementation but using Lighting Fabric, maybe I am doing something wrong when the epoch is finishing? I noticed state is not saved at the end of the epoch on the last iteration (just in the middle of training on checkpoint_step):
def set_fabric(logger: CSVLogger, config: configs.ExperimentConfig):
    """Set fabric using VertexAI cluster environment
    """
    strategy = DDPStrategy(
        static_graph=True,
        cluster_environment=utils.infrastructure.VertexAICluster()
    )
    fabric = Fabric(
        accelerator=config.training_config.accelerator,
        strategy=strategy,
        devices=config.training_config.num_gpus,
        num_nodes=config.training_config.num_nodes,
        precision=config.training_config.precision,
        loggers=logger,
    )
    fabric.launch()
    # utils.data_streaming.set_streaming_env_vars(fabric) <- necessary for mosaicml-streaming
    return fabric

def save_model_weights(model: nn.Module, fabric: Fabric, experiment_config: configs.ExperimentConfig):
    """Save only model weights to remote storage,
     these are the ones that need to be called at inference time
    """
    if fabric.global_rank == 0:
        model.backbone.save_pretrained(experiment_config.model_path)

def save_state(
        model: nn.Module,
        state: dict,
        fabric: Fabric,
        experiment_config: configs.ExperimentConfig
):
    """
    Save state of the training session every checkpoint_step or at the end of the epoch
    """
    if state["current_step"] != 0 and (
            state["current_step"] % experiment_config.checkpoint_step == 0
            or state["iteration"] == state["num_batches_per_epoch"] - 1
    ):
        fabric.save(
            os.path.join(
                experiment_config.checkpoint_path,
                f"{state['current_epoch']:04d}-{state['current_step']:04d}-state.ckpt"
            ),
            state
        )
        save_model_weights(model, fabric, experiment_config)

def train_epoch(
        train_loader: torch.utils.data.DataLoader,
        model: nn.Module,
        state: dict,
        zero_shot_attributes: dict,
        optimizer: torch.optim.Optimizer,
        criterion: nn.Module,
        scheduler: torch.optim.lr_scheduler,
        fabric: Fabric,
        metrics_dict: dict,
        experiment_config: configs.ExperimentConfig
):
    """Train the model for one epoch (an entire training cycle)
    """
    model.train()

    accum_samples, accum_features = [], {}
    num_accumulated = 0

    for idx, batch in enumerate(train_loader, start=state["start_iteration"]):
        time_start = time.perf_counter()
        batch = fabric.to_device(batch)

        optimizer.zero_grad()

        with torch.no_grad():
            output = model(batch)
            output.pop("logit_scale", None)

            for key, value in output.items():
                if key not in accum_features:
                    accum_features[key] = [value]
                else:
                    accum_features[key].append(value)

            accum_samples.append(batch)

        num_accumulated += 1
        state["iteration"] += 1

        if (idx + 1) % experiment_config.training_config.accum_freq > 0:
            continue

        # compute embeddings on zero shot attributes
        with torch.no_grad():
            zero_shot_embeddings = model(zero_shot_attributes=zero_shot_attributes)

        # compute loss aggregating the embeddings accumulated
        optimizer.zero_grad()
        for j in range(num_accumulated):
            batch = accum_samples[j]
            output = model(batch)

            # keep scale partially
            inputs = {"logit_scale": output.pop("logit_scale")}
            for name, features in accum_features.items():
                accumulated = accum_features[name]
                inputs[name] = torch.cat(accumulated[:j] + [output[name]] + accumulated[j + 1:])

            loss = criterion(**inputs, fabric=fabric)

            del inputs
            fabric.backward(loss)

        optimizer.step()
        scheduler.step()

        # update metrics
        if check_if_is_log_step(state, experiment_config):
            update_metrics(
                "train", loss, accum_features, accum_samples, zero_shot_embeddings, metrics_dict, fabric
            )

        # reset accumulation
        accum_samples, accum_features = [], {}
        num_accumulated = 0

        with torch.no_grad():
            model.backbone.logit_scale.clamp_(0, math.log(100))

        timing = time.perf_counter() - time_start

        # log results
        if check_if_is_log_step(state, experiment_config):
            log_batch_metrics(
                timing, metrics_dict, fabric, state, scheduler.get_last_lr(), experiment_config
            )

        # save state if needed
        save_state(model, state, fabric, experiment_config)

        state["current_step"] += 1

    fabric.print(f"++++ Epoch {state['current_epoch']} completed ++++")

@torch.no_grad()
def validate_epoch(
        test_loader: torch.utils.data.DataLoader,
        model: nn.Module,
        state: dict,
        zero_shot_attributes: dict,
        criterion: nn.Module,
        metrics_dict: dict,
        fabric: Fabric,
        experiment_config: configs.ExperimentConfig
):
    fabric.barrier()
    fabric.print(f"++++ Validating epoch {state['current_epoch']} ++++")
    model.eval()
    for idx, batch in enumerate(test_loader):
        batch = fabric.to_device(batch)
        output = model(batch)
        zero_shot_embeddings = model(zero_shot_attributes=zero_shot_attributes)
        loss = criterion(**output, fabric=fabric)
        update_metrics("test", loss, output, batch, zero_shot_embeddings, metrics_dict, fabric)

        if idx % (experiment_config.verbose_step * 10) == 0 or idx == len(test_loader) - 1:
            fabric.print(
                f"+++ Epoch: {state['current_epoch']:04d} "
                f"| Test Step: {idx}/{len(test_loader)}"
                f"| Test Loss: {metrics_dict['test_loss'].compute().item()}"
                f"| Test Label Hit Rate {metrics_dict['test_label_hit_rate'].compute().item()}"
                f"| Test Precision@k {metrics_dict['test_precision_at_k'].compute().item()}"
                f"| Test Cosine Similarity {metrics_dict['test_cosine_similarity'].compute().item()}"
                f" +++"
            )

def fit(
        train_loader: torch.utils.data.DataLoader,
        test_loader: torch.utils.data.DataLoader,
        model: nn.Module,
        state: dict,
        zero_shot_attributes: dict,
        optimizer: torch.optim.Optimizer,
        criterion: nn.Module,
        scheduler: torch.optim.lr_scheduler,
        fabric: Fabric,
        metrics_dict: dict,
        experiment_config: configs.ExperimentConfig
):
    """Fit the model"""
    fabric.print(f"+++ Number of raw training batches: {len(train_loader)}")
    fabric.print(f"+++ Number of raw test batches: {len(test_loader)}")

    for epoch in range(state["current_epoch"], experiment_config.dataset_config.num_epochs):
        train_epoch(
            train_loader,
            model,
            state,
            zero_shot_attributes,
            optimizer,
            criterion,
            scheduler,
            fabric,
            metrics_dict,
            experiment_config
        )
        validate_epoch(
            test_loader,
            model,
            state,
            zero_shot_attributes,
            criterion,
            metrics_dict,
            fabric,
            experiment_config
        )

        log_epoch_metrics(metrics_dict, fabric)
        state["start_iteration"] = 0
        state["current_epoch"] += 1

    # save final version of the mode & metadata
    save_model_weights(model, fabric, experiment_config)
    save_metadata(fabric, experiment_config)
    fabric.logger.finalize("success")

def get_current_epoch_iteration(
        train_loader: torch.utils.data.DataLoader,
        fabric: Fabric,
        experiment_config: configs.ExperimentConfig
) -> int:
    """
    Get epoch iteration on resume for enumerate to start
    """
    state_dict = train_loader.state_dict()
    num_train_samples = experiment_config.dataset_config.num_train_samples

    if "num_samples_yielded" in state_dict:
        # litdata format
        samples_seen = state_dict["num_samples_yielded"]
        current_epoch = state_dict["current_epoch"]
    else:
        # mosaic-ml format
        samples_seen = state_dict["sample_in_epoch"]
        current_epoch = state_dict["epoch"]

    iteration = (samples_seen * len(train_loader)) // num_train_samples
    fabric.print(f"++++ Resuming epoch {current_epoch} from iteration: {iteration} ++++")
    return iteration

def get_initial_state(model, optimizer, train_loader, scheduler):
    """Get the state of the session"""
    return {
        "current_epoch": 0,
        "current_step": 0,  # model update
        "iteration": 0,  # dataset-wise
        "model": model.state_dict(),
        "optimizer": optimizer.state_dict(),
        "train_loader": train_loader.state_dict(),
        "scheduler": scheduler.state_dict()
    }

def get_state(
        model: nn.Module,
        optimizer: torch.optim.Optimizer,
        train_loader: torch.utils.data.DataLoader,
        scheduler: torch.optim.lr_scheduler,
        fabric: Fabric,
        experiment_config: configs.ExperimentConfig
):
    """Loads a checkpoint from a given file into state from remote storage
    """
    current_checkpoints = sorted(utils.io.glob_gcs(
        os.path.join(experiment_config.checkpoint_path, "*-state.ckpt")
    ), reverse=True)
    if current_checkpoints:
        fabric.print(
            f"++++ Resuming training using state from: {os.path.basename(current_checkpoints[0])} ++++"
        )
        state = fabric.load(current_checkpoints[0].replace("gs://", "/gcs/"))
        model.load_state_dict(state["model"])
        optimizer.load_state_dict(state["optimizer"])
        train_loader.load_state_dict(state["train_loader"])
        scheduler.load_state_dict(state["scheduler"])
        # modify start iteration state in case of resuming training
        state["start_iteration"] = get_current_epoch_iteration(train_loader, fabric, experiment_config)
    else:
        fabric.print(f"++++ Starting Training from epoch: 0 ++++")
        state = get_initial_state(model, optimizer, train_loader, scheduler)
        state["start_iteration"] = 0

    # extra metadata
    state["num_batches_per_epoch"] = len(train_loader)
    state["num_steps_per_epoch"] = len(train_loader) // experiment_config.training_config.accum_freq
    return state

def launch_training(experiment_config: configs.ExperimentConfig):
    """Set up the training environment, launcher and data loaders
    """
    logger = CSVLogger(
        root_dir=experiment_config.artifacts_path,
        name="logs", version="clip"
    )
    fabric = set_fabric(logger, experiment_config)

    fabric.print("++++ Setting up model and optimizer ++++")
    model = models.get_model(experiment_config)
    optimizer = optimization.get_optimizer(model, experiment_config)
    model, optimizer = fabric.setup(model, optimizer)
    lr_scheduler = optimization.get_learning_rate(optimizer, experiment_config)

    fabric.print("++++ Setting up dataloaders ++++")
    train_loader = data.get_dataloader("train", experiment_config)
    test_loader = data.get_dataloader("test", experiment_config)
    fabric.barrier()

    # get and resume state if available
    state = get_state(
        model, optimizer, train_loader, lr_scheduler, fabric, experiment_config
    )

    # get losses
    criterion = losses.get_loss(experiment_config, fabric)
    models.print_trainable_parameters(model, fabric, experiment_config.training_config)

    # get zero shot tokens
    fabric.print("++++ Loading zero shot attribute tokens ++++")
    zero_shot_attributes = fabric.to_device(
        torch.load(experiment_config.zero_shot_tokens_fn)
    )

    # define metrics to track during training/validation
    metrics_dict = metrics.get_metrics(fabric.device)

    # sync before starting training
    fabric.barrier()

    fit(
        train_loader,
        test_loader,
        model,
        state,
        zero_shot_attributes,
        optimizer,
        criterion,
        lr_scheduler,
        fabric,
        metrics_dict,
        experiment_config
    )

Environment

GPU models and configuration: 4 n1-standard-32 nodes in GCP each with 4 NVIDIA _L4 GPUs
Any other relevant information:
I use the container image (pytorch) from GCP:
europe-docker.pkg.dev/vertex-ai/training/pytorch-gpu.1-13.py310:latest

Expected behavior
The training dataloader finish the epoch and the rest of the code continues its execution

Hey @miguelalba96. Thanks for reporting this issue.
Would you mind printing the length of each dataset, dataloader on each rank. Usually it hangs when a rank have more data than others. It shouldn't happen but I want to exclude this eventuallity.
Do you think you could share a tiny reproducible example with dummy data for me to debug ?
Best,
T.C

when printing the ranks per node and on each: len(dataset), len(dataloader)` I get homogeneous number of samples on each:

Not sure how to reproduce this problem, I will check. I also noticed that when I load the state to resume training using the function I wrote above get_state, the dataloader doesn't seem to resume properly and iterates all over again through the data until it hangs 🤔:

repo: Lightning-AI/litdata


text:
Test CI

I think the jsonargparse release yesterday broke Python 3.9 support. This is a PR to debug it.

The tests passing now confirms that jsonargparse 4.33.1 broke Python 3.9 compatibility. We can either deprecate 3.19 or just fix the jsoargparse version for now.
Since there is no jsonargparse feature we really need, we can probably have a upper limit for jsonargparse via #1748

I think

repo: Lightning-AI/litgpt


text:
🛑 Limbo Yggdrasil Multiplayer Session Service is down

In d15c312, Limbo Yggdrasil Multiplayer Session Service (https://auth.lsmp.site/session) was down:

HTTP code: 0
Response time: 0 ms

Resolved: Limbo Yggdrasil Multiplayer Session Service is back up in fd58ce0 after 10 hours, 48 minutes.

repo: Limbo-Studios/limbo-status-page


text:
🛑 Siyntra Minecraft Profile is down

In 693579d, Siyntra Minecraft Profile (https://auth.siyntrastudios.com/session/minecraft/profile) was down:

HTTP code: 502
Response time: 158 ms

Resolved: Siyntra Minecraft Profile is back up in 0da2b6e after 17 hours, 35 minutes.

repo: Limbo-Studios/siyntra-status-page


text:
Use createMockedFunction to mock a function returning a tuple

Hi,
I'm trying to use the createMockedFunction method to mock a contract call that returns a tuple, but I keep hitting errors.
Here is a short extract of my test file:
// Create an array of values
const tupleArray: Array<ethereum.Value> = [
    ethereum.Value.fromAddress(Address.fromString("f75be6f9418710fd516fa82afb3aad07e11a0f1b")),
    ethereum.Value.fromAddressArray([Address.zero()]),
    ethereum.Value.fromBoolean(true),
    ethereum.Value.fromString("string"),
  ];

// Convert it to the Tuple type
const tuple = changetype<ethereum.Tuple>(tupleArray);

// Create a tuple Value
const tupleValue = ethereum.Value.fromTuple(tuple as ethereum.Tuple);

// Mock the call to the contract
createMockedFunction(
  Address.fromString("506f88a5Ca8D5F001f2909b029738A40042e42a6"),
  "getTuple",
  "getTuple(address):((address,address[],bool,string))",
)
  .withArgs([ethereum.Value.fromAddress(Address.fromString("f75be6f9418710fd516fa82afb3aad07e11a0f1b"))])
  .returns([tupleValue]);

test("Should mock the call to TupleContract", () => {
  const contract = TupleContract.bind(Address.fromString("506f88a5Ca8D5F001f2909b029738A40042e42a6"));
  const result = contract.getTuple(Address.fromString("f75be6f9418710fd516fa82afb3aad07e11a0f1b"));

  assert.tupleEquals(tuple, result);
});

But when I run this test, I get the following error:
ERROR AS100: Not implemented: Closures
       .returns([tupleValue]);
                 ~~~~~~~~~~~~~~~~
 in tests/tuple-contract.test.ts(32,17)

What am I missing? Is there any issue with my code or is this a limitation of the framework?
Many thanks in advance for your help! 🙏

Try removing the as Tuple part from const tupleValue = ethereum.Value.fromTuple(tuple as ethereum.Tuple);
You have already casted it as tuple here const tuple = changetype<ethereum.Tuple>(tupleArray);

Good catch! This simplified my code a bit.
To solve my issue, I finally understood that I had to wrap my mock setup in a beforeAll block!
In the end, my code looks like that and works smoothly:
import { beforeAll, createMockedFunction, describe } from "matchstick-as";
import { Address, ethereum } from "@graphprotocol/graph-ts";

describe("handleTupleCall()", () => {
  beforeAll(() => {
    // Create an array of values
    const tupleArray: Array<ethereum.Value> = [
      ethereum.Value.fromAddress(Address.fromString("f75be6f9418710fd516fa82afb3aad07e11a0f1b")),
      ethereum.Value.fromAddressArray([Address.zero()]),
      ethereum.Value.fromBoolean(true),
      ethereum.Value.fromString("string"),
    ];

    // Convert it to the Tuple type
    const tuple = changetype<ethereum.Tuple>(tupleArray);

    // Create a tuple Value
    const tupleValue = ethereum.Value.fromTuple(tuple);

    // Mock the call to the contract
    createMockedFunction(
      Address.fromString("506f88a5Ca8D5F001f2909b029738A40042e42a6"),
      "getTuple",
      "getTuple(address):((address,address[],bool,string))",
    )
      .withArgs([ethereum.Value.fromAddress(Address.fromString("f75be6f9418710fd516fa82afb3aad07e11a0f1b"))])
      .returns([tupleValue]);
  });

  test("Should mock the call to TupleContract", () => {
    const contract = TupleContract.bind(Address.fromString("506f88a5Ca8D5F001f2909b029738A40042e42a6"));
    const result = contract.getTuple(Address.fromString("f75be6f9418710fd516fa82afb3aad07e11a0f1b"));

    assert.tupleEquals(tuple, result);
  });
});

Thanks a lot for your help @dimitrovmaksim !

authors: alainncls dimitrovmaksim

repo: LimeChain/matchstick


text:
[1.18.x] Known Issues

Breaks with Apotheosis in version 1.18.1-1.0.1- and 1.18.2-1.1.0-, partially fixed in 1.18.1-1.0.2+ and 1.18.2-1.1.1+ (sacrificing blacksmith's power on anvil when Apotheosis loaded). Will be completely fixed if Apotheosis#552 is closed properly.
Currently (1.18.1-1.0.2.1- & 1.18.2-1.1.1-) depending specifically on Origins(Forge) 1.3.1.14, breaks with lower version. Origins(Forge) 1.18.2-1.3.1.14 (Alpha) could break with Forge >40.0.46.
Currently (1.18.1-1.0.3+ & 1.18.2-1.1.2+) depending on Origins(Forge) 1.18.1-1.3.2+ & 1.18.2-1.4.1+, 1.18.2 needs Forge >= 40.0.47.

Yep, all fixed :P

repo: LimonBlaze/origins-classes-forge


text:
Step 5.5 Error "name 'sample_every_n_type' is not defined"

Hi, this afternoon i just train some dataset and it went well, but suddenly it's error when i tried to trained another one. Can someone help me? I followed the tutorial from here https://www.youtube.com/watch?v=ddpaECgRCHg, it worked well at first, but i dont know what happened now it's said error.
First image is the error and second one is the setting i used. Thanks in advance.

Hi, i think you're forgot to run 5.3. Sample Prompt Config

Yes! Forgive me for my clueless and careless, it's working now. You can close this issue now, thanks!

repo: Linaqruf/kohya-trainer


text:
README: markdown syntax fix for links

Improved the markdown syntax in README for links and code blocks. Further, fixed the syntax of hyperlink for Yocto Project Quick Build.

I have created another PR #692 to address syntax improvements.

repo: Linaro/meta-qcom


text:
🛑 SearXNG is down

In de543fe, SearXNG (https://searxng.linerly.tk) was down:

HTTP code: 0
Response time: 0 ms

Resolved: SearXNG is back up in 1dee93a.

repo: Linerly/status


text:
🛑 SearXNG is down

In e31c1a4, SearXNG (https://searxng.linerly.repl.co) was down:

HTTP code: 0
Response time: 0 ms

Resolved: SearXNG is back up in aa662bb.


text:
🛑 SearXNG is down

In c395c43, SearXNG (https://searxng.linerly.repl.co) was down:

HTTP code: 0
Response time: 0 ms

Resolved: SearXNG is back up in e72c7ca.


text:
🛑 FUNC is down

In ec36ecd, FUNC (https://func.flitswallet.app/api) was down:

HTTP code: 0
Response time: 0 ms

Resolved: FUNC is back up in c016a01.


text:
first Commit Test

test

hello

authors: Lirodek SJ-J

repo: Lirodek/WipiaJavaStudy


text:
Fix arm64v8 Dockerfile

Category:
Bugfix
Overview
Fixes Dockerfile-arm64v8.
Issue Number  #117
Code Quality Checklist

[x] All changes are backwards compatible
[x] All lint checks and tests are passing
[x] There are no (new) build warnings or errors
[x] Bumps version, if new feature added

Thanks so much, fingers crossed this will work :)

If it doesn't work, I think I've just managed to get a version running using node:lts-alpine as the base and multiarch/qemu-user-static for the ARM 64 part. Looks like it's working on DockerHub, and the build time wasn't too long, but haven't yet tested it on my Pi

Ah the image is now on DockerHub.

Awesome, your fix is working!
This is amazing, I'm a proper noob at Docker, and spent ages yesterday trying to figure this out, so I'm super happy it's working now, you're really good at this, thanks so much for the PR's :) Your container is now live on Docker Hub, and all green

Haha no problem, I'm glad it's working. I wish I knew as much as you do about Vue.js etc. ;)


text:
save（）保存不了

Gate gate = new Gate();
gate.setAction("face_add");
gate.setId("1");
gate.setType("permanent");
gate.setName("dimen");
gate.setSn("A123");
gate.setStart_time("2018-09-09  08:00");
gate.setEnd_time("2018-09-09  18:00");
gate.setNote("Abc");
gate.setData("1234567890");
boolean save = gate.save();
在3.0.0中我用save（）保存数据在数据库，但是返回一直都是false的。去查看数据库，发现是表没有创建，导致保存失败。不是说用save（）会自动创建表的吗？

如果有字段为string id，导致保存失败。要把id字段替换掉。

我们后台给返回的数据也是string类型的Id，这里提供一种思路解决这个问题：如果你们解析数据用的gson，那么可以在
bean中不用id字段去接收后台数据，改为任意其他字段，然后上面写上注解@SerializedName("Id") 这样就可以解决了。具体gson的这种用法请搜索：如何使用@SerializedName更改字段命名

楼上是最标准的解决方案，感谢解答。

authors: ChloeDimen guolindev zhq217217

repo: LitePalFramework/LitePal


text:
插件机制的完善

[ ] 插件系统的自定义鉴权设置支持
[ ] 插件管理系统 + 相关 cli + plugin repo

此目标被完成

repo: LlmKira/LLMBot


text:
🛑 invest is down

In 64ca778, invest (https://blog-pos-17/) was down:

HTTP code: 0
Response time: 0 ms

svs

repo: LloydGain/UppTIME


text:
Исправления опечаток в "МатАн - Коллок-3".

Если что, то я не всё по первоисточнику проверял, но вроде всё разумно.

Исправил, спасибо :+1:

authors: AIWMUS LoDThe

repo: LoDThe/hse-tex


text:
Rdio synclet (against imthemap)

Still contains a few commits I merged in from master that aren't in imthemap (not really sure why...), but should be clean enough to evaluate the Rdio-specific bits.

Woop woop shutting this down for yet another pull request against master. THANKS @smurthas.

repo: LockerProject/Locker


text:
🛑 SentinelDB is down

In 34417d7, SentinelDB (https://db.logsentinel.com) was down:

HTTP code: 502
Response time: 314 ms

Resolved: SentinelDB is back up in b62b335.

repo: LogSentinel/status


text:
Add core spelling highlight groups

Related to #5
This is what I'm using locally to address this issue. Figured I'd put it up for review if useful.

Fixed in 6b811e619110b4a51dd243a4818c514fe75d84d1

@Lokaltog  - Could I see a screenshot of what that looks like for you? Locally your commit using the ALE settings isn't very readable.
Example:

Huh, that's strange. I'll take a look at it. In supported terminal emulators it should display a curly underline but it should fallback to something that looks a bit better than this. :)

authors: Lokaltog mcritchlow

repo: Lokaltog/vim-monotone


text:
🛑 gadget-lab.fr is down

In a6b6705, gadget-lab.fr (https://gadget-lab.fr) was down:

HTTP code: 0
Response time: 0 ms

Resolved: gadget-lab.fr is back up in a85dd6e after 10 minutes.


text:
🛑 eu2 is down

In 15250d3, eu2 ($EU2) was down:

HTTP code: 0
Response time: 0 ms

Resolved: eu2 is back up in d1502ad.

repo: LonelyJupiter/UPPTIME


text:
🛑 jpvir is down

In 1abe241, jpvir ($JPVIR) was down:

HTTP code: 0
Response time: 0 ms

Resolved: jpvir is back up in 4f19c9c.