(Using qwen2.5-coder-extra-large:7b (Ollama)) After Docker Build: Frontend Stuck on Loading

Describe the bug
After cloning the repository and using the Canary browser, I rebuilt the Docker container and encountered the following issues:
The terminal/CMD shows repeated warnings and errors related to dependencies and optimization.
The frontend on localhost:5173 is stuck on loading and does not generate any output, even after waiting for more than 10 minutes.
Link to the Bolt URL that caused the error
http://localhost:5173
Steps to reproduce

Clone the repository.
Build and run the Docker container using the provided Docker commands.
Open the application in the Canary browser (localhost:5173).
Observe the terminal/CMD output and frontend behavior.

Expected behavior
The app should load properly, generate responses, and function as intended without prolonged loading or errors.
Screen Recording / Screenshot

Platform
OS:Windows 10
Browser: Canary
Version: 132.0.6828.0
Additional context
This issue occurs despite several attempts, including rebuilding the Docker container and using different commands, the problem persists.

@darshanbuildx Did you found the solution?

@omarnahdi nope no luck.

@darshanbuildx what are your specs?

@omarnahdi Windows 10, Docker, Chrome Canary what else specs would you need to help?

is it possible that your pc just isn't fast enough? Btw with specs we mean your memory, processor etc.

thank you for responding @omarnahdi and @Intiiii here are specs:
I have 32gb RAM and
1TB SSD
16GB Intel R (UHD) Graphics 620  Graphics
CPU
Intel(R) Core(TM) i5-8350U CPU @ 1.70GHz
Base speed:	1.90 GHz
Sockets:	1
Cores:	4
Logical processors:	8
Virtualization:	Enabled
L1 cache:	256 KB
L2 cache:	1.0 MB
L3 cache:	6.0 MB

Utilization	100%
Speed	1.93 GHz
Up time	2:22:10:48
Processes	413
Threads	6063
Handles	203480

@darshanbuildx You have pretty much the same setup as me, I think you should try some smaller model, does that model run smoothly in the cmd terminal? if not then you should really shift to a smaller model mine runs as well but it is some sort of slow with bolt but it is lightning fast in the cmd and with open-webui as well. Don't know why it is slow with blot though, but I'll suggest try https://console.mistral.ai/api-keys/ as they are and has low restriction in free tier as well https://console.mistral.ai/usage/ or just use any model from github models as they all are OpenAI API compatible

Guys, don't post issues here if you don't know what models to use capable to your hardware. For integrated graphics, 7b qwen extra large just kills your machine
I have a Ryzen 5 5600X with 64gb of memory, RTX 4070 12gb vram and the only model that is decent enough is qwen-coder-2.5-7b with q6 quantization. This runs on my setup with around 10 tokens/s as response time. Not really a match to official bolt or how chatgpt outputs the text fast, but it can be used.
It's not all about VRAM it's also about how many Tensor cores you have on your GPU.  I tried 14b with q4 quantization and it's dead slow on my machine. (5tkn/s). And the actual CPU doesn't have at all Tensor cores, that's why all types of AIs must run on GPUs , stable diffusion, LLMs, etc.

Guys, don't post issues here if you don't know what models to use capable to your hardware. For integrated graphics, 7b qwen extra large just kills your machine
I have a Ryzen 5 5600X with 64gb of memory, RTX 4070 12gb vram and the only model that is decent enough is qwen-coder-2.5-7b with q6 quantization. This runs on my setup with around 10 tokens/s as response time. Not really a match to official bolt or how chatgpt outputs the text fast, but it can be used.
It's not all about VRAM it's also about how many Tensor cores you have on your GPU.  I tried 14b with q4 quantization and it's dead slow on my machine. (5tkn/s). And the actual CPU doesn't have at all Tensor cores, that's why all types of AIs must run on GPUs , stable diffusion, LLMs, etc.

@radumalica If that's the case the deepseek-coder-2V is running flawlessly on my system with open-webui through ollama but not with bolt. Can you elaborate, how and why? It runs on my i9-13900H CPU.

@radumalica If that's the case then the Qwenn 32B  is running flawlessly on my system with open-webui through ollama but not with bolt also :)


cudnn issue

HI, I can't solve this problem, I have already upgrade the cudnn to 8.1.0, but it don't work.
2021-10-13 17:19:29.385716: E tensorflow/stream_executor/cuda/cuda_dnn.cc:362] Loaded runtime CuDNN library: 8.0.4 but source was compiled with: 8.1.0.  CuDNN library needs to have matching major version and equal or higher minor version. If using a binary install, upgrade your CuDNN library.  If building from sources, make sure the library loaded at runtime is compatible with the version specified during compile configuration.
2021-10-13 17:19:29.390987: E tensorflow/stream_executor/cuda/cuda_dnn.cc:362] Loaded runtime CuDNN library: 8.0.4 but source was compiled with: 8.1.0.  CuDNN library needs to have matching major version and equal or higher minor version. If using a binary install, upgrade your CuDNN library.  If building from sources, make sure the library loaded at runtime is compatible with the version specified during compile configuration.
I would appreciate it very much  if someboay can slove this problem!!!

Hi @nqxjzm,
Thanks for the issue.
It seems something wrong with your CuDNN library.
It is hard for us to help you upgrade your CuDNN library.
I am closing the issue, but please feel free to open a new one if you have other questions.
Cheers,


[YUNIKORN-261] add request sorting policy

Add a request sorting policy per queue for requests in an application.
The policy is set on a leaf queue using "request.sort.policy". The policies
supported are FIFO (default) and priority.
FIFO sorts based on the create time of the request. Priority sorts requests
in a descending order. Requests that have the same priority are sorted FIFO
within the set of requests with the same priority.
Priorities range from 0 (lowest) to<PHONE_NUMBER> (maximum).
Internal change: application submit time is no longer stored as an integer
but as a time object equivalent to requests.

Hey @wilfred-s,
Your changes look good to me!
View build log
TravisBuddy Request Identifier: b31feba0-b531-11ea-aae7-0b05fcc524af

Hey @wilfred-s,
Your changes look good to me!
View build log
TravisBuddy Request Identifier: a3a3a480-b560-11ea-aae7-0b05fcc524af

Hey @wilfred-s,
Your changes look good to me!
View build log
TravisBuddy Request Identifier: e0d6d6e0-b562-11ea-aae7-0b05fcc524af

This is outdated and would a complete new implementation
closing for now


Typewiz

Maybe somehow reuse code from Typewiz for getting information about types for esmbly/transformer-v8?
https://github.com/urish/typewiz

Thanks for the tip, looks interesting. Will definitely check it out and see if it could be incorporated into this project somehow  ✨


Determine SERVER_CUSTOM data for BTR

Pretty much as the title says. SERVER_CUSTOM is used to send data about the end of the game. This data varies for each game type and is stored as JSON in the data field of the packet.
To get at this info, overriding Players.network and dumping all SERVER_CUSTOM packets should be sufficient.
For reference here is the packet definition:
[A.SERVER_CUSTOM]: [
    ["type", I.uint8],
    ["data", I.textbig]
]

The SERVER_CUSTOM packet appears to not get sent during BTR. Typing this in the console yields nothing. I could be wrong though.
const _PlayersNetwork = Players.network;
Players.network = (type, data) => {
  if (type === Network.SERVERPACKET.SERVER_CUSTOM) {
    console.log(data);
  }
  return _PlayersNetwork(type, data);
}

I'd have to look through the game source to confirm, but I'm not sure that the type parameter corresponds to the packet type. I ran a bot get some data from a BTR match and got the following packet
{
    "c":"SERVER_CUSTOM",
    "type":1,
    "data":"{\"p\":\"STEAMROLLER\",\"f\":122,\"b\":4000,\"k\":3,\"t\":13}"
}

The p fields appears to be the winner's name, and t is probably the amount of time that the notice should be displayed (as with CTF). b is most likely the amount of bounty awarded to the winner (same as CTF again).
That leaves k and f. I think k would be the number of kills, but I have no idea what frepresents. It might be worth looking further at what the game code does to fully determine what the fields mean.

f is the flag country code:
    Games.showBTRWin = function(e) {
        if (!$("#custom-msg").length) {
            var t = '

' + UI.escapeHTML(e.p) + '

' + e.k + " KILL" + (1 == e.k ? "" : "S") + "+" + e.b + " BOUNTY

";
            $("body").append(t),
            UI.showPanel("#custom-msg"),
            setTimeout(function() {
                UI.hidePanel("#custom-msg", !1, !0)
            }, 1e3 * e.t),
            Sound.gameComplete()
        }
    }

I've created serialization bindings for this here. Closing as all the work is done here.


Remove dep from django.test.TestCase

We are using pytest but in some cases we were still subclassing django.test.TestCase
This:

removes the dep from django.test.TestCase
replaces the login in setUp with python properties
renames LeelooTestCase to APITestMixin

Overall, the tests run faster now as we have less overhead.

Codecov Report

Merging #304 into develop will not change coverage.
The diff coverage is n/a.

@@           Coverage Diff            @@
##           develop     #304   +/-   ##
========================================
  Coverage    92.92%   92.92%
========================================
  Files           63       63
  Lines         2460     2460
  Branches       215      215
========================================
  Hits          2286     2286
  Misses         146      146
  Partials        28       28

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FIX: p0 parameter being ignored in celer_path()

p0 = 10 was hardcoded inside this function, so no matter which value was passed, p0 = 10 was used.

Codecov Report

Merging #45 into master will decrease coverage by 0.02%.
The diff coverage is 0%.

@@            Coverage Diff             @@
##           master      #45      +/-   ##
==========================================
- Coverage   96.15%   96.13%   -0.03%
==========================================
  Files           4        4
  Lines         182      181       -1
  Branches       15       15
==========================================
- Hits          175      174       -1
  Misses          4        4
  Partials        3        3

Impacted Files
Coverage Δ

celer/homotopy.py
91.37% <0%> (-0.15%)
:arrow_down:

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Feature request: randomize TLS Let's Encrypt renewal logic

I propose changing the Let's Encrypt logic checking for TLS renewals instead of hard coding 12 hours to use a random value between 12 hours and 16 hours instead. This helps mitigate rate limit issues when running Caddy in a clustered environment and creating instances with automation (Terraform) all having nearly the same exact time for checking for TLS Let's Encrypt renewals.
The logic could be as simple as:
[0] - 12 hours
[1] - 12.5 hours
[2] - 13 hours
[3] - 13.5 hours
[4] - 14 hours
[5] - 14.5 hours
[6] - 15 hours
[7] - 15.5 hours
[8] - 16 hours

Then randomly select an integer between 0 and 8 and that's the duration to next check for Let's Encrypt renewals.

Hi -- thanks for the feature request.
I'm not sure that randomizing the timing of the renewal checks will really mitigate rate limit issues, since the typical relevant rate limit has a window of 7 days; varying it by a few hours won't help much.
Caddy is not currently designed to share certificates with other instances. However, when #2015 is merged in hopefully just a few days, Caddy will be more compatible with sharing certificates on disk used by other Caddy instances. One thing that PR does is cause Caddy to check on disk for an already-renewed certificate before trying to renew one itself. If the cert on disk is found to have a later expiry date, it simply loads it up and doesn't renew again. Even though this check is not (yet) synchronized between multiple Caddy instances, staggering the deployment of Caddy instances by a few seconds or minutes will mostly ensure in the vast majority of cases that rate limits aren't hit, and Caddy can nearly safely share the same certificates in the same ~/.caddy/acme folder. If timing of renewal checks is randomized, it is less certain that renewals won't step on each other. But with the PR as-is, I'm pretty confident in deploying many Caddy instances that share the same certificates on disk in step will work together well.
This is a stepping stone to truly modular, synchronized cert sharing, which is on the roadmap for future.

Thanks for reply @mholt. To confirm, does a check to determine if a cert is expired (basically the check that runs every 12 hours) count against any Let's Encrypt rate limits?

does a check to determine if a cert is expired (basically the check that runs every 12 hours) count against any Let's Encrypt rate limits?

Not at all. Caddy only iterates an in-memory map of certificates to determine if they need to be renewed.

Suppose we create 10 instances automatically with Terraform and they all boot within in the same couple of seconds.  They share a $CADDYPATH using Amazon Elastic Filesystem. Do all 10 instances fire off requests to create Let's Encrypt certificates at boot? That would cause a LE rate limit block right?

Suppose we create 10 Caddy instances automatically with Terraform and they all boot within in the same couple of seconds. They share a $CADDYPATH using Amazon Elastic Filesystem. Do all 10 instances fire off requests to create Let's Encrypt certificates at boot? That would cause a LE rate limit block right?

Depends. For the same hostname? And for certificates expiring soon? Certificates should be renewed unless Caddy hasn't been serving that hostname for some good amount of time (2+ months).
If you stagger the execution of Caddy instances by 5+ seconds, I think you should be good. (With that PR.)


Avoid creating base64 encodings with embedded newlines

Ruby's Base64.encode method adds newlines to the encoded string after
every 60 characters and also adds one at the end. Kubernetes documentation
clearly states that base64 encoded secrets must not contain newlines (see
https://kubernetes.io/docs/concepts/configuration/secret/#creating-a-secret-manually).
Also, Kubernetes 1.8 will now reject creating any secrets with embedded
newline characters.
The problem is solved by using Base64.strict_encode64, which does not embed
newline characters in the encoded string.

Thanks for the PR! I've merged https://github.com/Shopify/kubernetes-deploy/pull/196 instead since it is identical and was opened first. I'll take a look at the other PR you submitted shortly. 😄

Thx for addressing the issue so quickly and also for releasing a new gem version!
BTW, I only discovered the other pull request after I had created mine. Really doesn't matter which one got merged. It was a fairly straightforward.
Oh, and a big thank you for open sourcing this code in the first place. It has really helped a lot with our project.


Use warnings.simplefilter() to implement NUMBA_DISABLE_PERFORMANCE_WARNINGS env var

This PR implements the NUMBA_DISABLE_PERFORMANCE_WARNINGS environment variable using a warnings.simplefilter() in __init__.py, which provides better integration with the warnings stdlib module.
Currently, the NUMBA_DISABLE_PERFORMANCE_WARNINGS environment variable is implemented by reading it into config.DISABLE_PERFORMANCE_WARNINGS and checking this variable at each warning site. However, a few functions have crept into the code that lack this check, and therefore emit warnings even if the environment variable is set; see https://github.com/numba/numba/issues/9762.
This PR fixes that bug at the module level. This makes the code more maintainable because authors of future code that emits performance warnings don't need to remember to check the config variable.
To make test_warnings.py pass, I had to make a slight modification to the test code by removing the line warnings.simplefilter('always'). This appears to have been a "stress test" to ensure that warnings were actually being suppressed by the environment variable and not by some quirk of the test environment. But because the new implementation takes effect at the moment Numba is imported, this second filter overrides the filter under test.
Actually, the behavior of warnings.simplefilter('always') in this context highlights another benefit of this PR: With the new implementation, you can disable performance warnings globally using the env var, then selectively re-enable them in a local scope using with warnings.simplefilter('always'). This didn't work before.
Closes #9762.

Thanks for the patch and for contributing to Numba @maxkapur. This PR was discussed at the maintainer's meeting last week and the outcome was that there was a preference to fix the individual places where a warning is being raised, opposed to having a global/import level configuration, this so that it's easy to toggle the warning with the config variable during development (i.e. run some functions with it on and others with it off) and CI testing. Thanks for your understanding.

@stuartarchibald Revised PR: https://github.com/numba/numba/pull/9794


Speed data

Codecov Report

Merging #48 into develop will increase coverage by 0.06%.
The diff coverage is 90%.

@@             Coverage Diff             @@
##           develop      #48      +/-   ##
===========================================
+ Coverage    93.15%   93.21%   +0.06%
===========================================
  Files           23       23
  Lines          803      811       +8
===========================================
+ Hits           748      756       +8
  Misses          55       55

Impacted Files
Coverage Δ

Tests/NDT7TestTests.swift
87.05% <ø> (ø)
:arrow_up:

Sources/NDT7Test.swift
85.12% <90%> (+0.63%)
:arrow_up:

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Enhance listNeedUris REST Service

Update the listNeedUris method in the LinkedDataWebController:

add RequestParam "creationAfter"
implement sorting when using "modifiedafter" parameter -> newest first
implement sorting when using "creationAfter" parameter -> newest first
implement the "preferredSize" parameter in all the clause-branches -> so we can make sure that we do not get too many results on any call we make -> maybe refactor this to LIMIT so we do not have trouble figuring out what this actually means to begin with
refactor the method so that the "state" parameter is used in any of the options that list the needUris, this is not the case for all the clause-branches now

This is very useful for our new "Whats new" feature in the owner webapp

description and title changes pending


SR-10294: convertBoolToDarwinBool and friends should be inlined

Performance: -O

TEST
OLD
NEW
DELTA
RATIO

Regression

FlattenListLoop
3976
4333
+9.0%
0.92x

RemoveWhereMoveInts
34
37
+8.8%
0.92x (?)

Array2D
6928
7520
+8.5%
0.92x

RemoveWhereSwapInts
60
65
+8.3%
0.92x

ObjectiveCBridgeStubNSDateRefAccess
343
371
+8.2%
0.92x (?)

ObjectiveCBridgeStubFromNSDateRef
4060
4390
+8.1%
0.92x (?)

ObjectiveCBridgeStubFromNSDate
6650
7190
+8.1%
0.92x (?)

FlattenListFlatMap
6073
6532
+7.6%
0.93x (?)

Improvement

CharacterPropertiesStashed
1640
1230
-25.0%
1.33x

CharacterPropertiesStashedMemo
1550
1250
-19.4%
1.24x

CharacterLiteralsLarge
108
97
-10.2%
1.11x

Performance: -Osize

TEST
OLD
NEW
DELTA
RATIO

Improvement

CharacterPropertiesStashed
1840
1470
-20.1%
1.25x

CharacterPropertiesStashedMemo
1790
1500
-16.2%
1.19x

CharacterPropertiesFetch
4600
4150
-9.8%
1.11x (?)

ObjectiveCBridgeStubFromNSDateRef
4390
4000
-8.9%
1.10x (?)

Performance: -Onone

TEST
OLD
NEW
DELTA
RATIO

Improvement

CharacterPropertiesStashed
3250
2590
-20.3%
1.25x (?)

CharacterPropertiesStashedMemo
4290
3910
-8.9%
1.10x (?)

CharacterPropertiesFetch
6050
5540
-8.4%
1.09x (?)

Code size: -swiftlibs

TEST
OLD
NEW
DELTA
RATIO

Improvement

libswiftCloudKit.dylib
77824
73728
-5.3%
1.06x

How to read the data
The tables contain differences in performance which are larger than 8% and
differences in code size which are larger than 1%.
If you see any unexpected regressions, you should consider fixing the
regressions before you merge the PR.
Noise: Sometimes the performance results (not code size!) contain false
alarms. Unexpected regressions which are marked with '(?)' are probably noise.
If you see regressions which you cannot explain you can try to run the
benchmarks again. If regressions still show up, please consult with the
performance team (@eeckstein).

Hardware Overview
  Model Name: Mac Pro
  Model Identifier: MacPro6,1
  Processor Name: 12-Core Intel Xeon E5
  Processor Speed: 2.7 GHz
  Number of Processors: 1
  Total Number of Cores: 12
  L2 Cache (per Core): 256 KB
  L3 Cache: 30 MB
  Memory: 64 GB

@swift-ci please smoke test

I think the benchmark regressions are noise. Most of them are ‘?’ and I would be surprised if most of them use DarwinBool at all; it’s not real common.

Yes, but you've got ObjCBool in there too, which is a little more common. (Still seems unlikely, but…)

@swift-ci please smoke test

@swift-ci please smoke benchmark

No performance and code size changes

Intriguing.

Looks like we don't get to do this until the compiler crash is fixed

I mean, that's not too surprising to me. Our benchmarks mostly aren't trafficking in ObjCBool anyway, and I'm sure none of them are testing DarwinBoolean.

Blocking bug was fixed

@swift-ci please smoke test

@swift-ci please smoke benchmark

Performance: -O

TEST
OLD
NEW
DELTA
RATIO

Regression

ObjectiveCBridgeStubFromNSDateRef
4060
4640
+14.3%
0.88x (?)

FlattenListLoop
4334
4709
+8.7%
0.92x

ObjectiveCBridgeStubNSDateRefAccess
343
371
+8.2%
0.92x (?)

Array2D
7520
8112
+7.9%
0.93x (?)

RemoveWhereSwapInts
65
70
+7.7%
0.93x

Improvement

CharacterPropertiesStashed
1660
1230
-25.9%
1.35x

DataSetCountSmall
160
125
-21.9%
1.28x

CharacterPropertiesStashedMemo
1540
1250
-18.8%
1.23x

CharacterLiteralsLarge
108
97
-10.2%
1.11x (?)

CharacterPropertiesFetch
4280
3970
-7.2%
1.08x (?)

Performance: -Osize

TEST
OLD
NEW
DELTA
RATIO

Improvement

DataSetCountSmall
160
125
-21.9%
1.28x

CharacterPropertiesStashed
1820
1460
-19.8%
1.25x

CharacterPropertiesStashedMemo
1780
1470
-17.4%
1.21x

CharacterPropertiesFetch
4480
4130
-7.8%
1.08x (?)

IterateData
1465
1360
-7.2%
1.08x (?)

Performance: -Onone

TEST
OLD
NEW
DELTA
RATIO

Improvement

CharacterPropertiesStashed
3250
2630
-19.1%
1.24x

DataSetCountSmall
214
180
-15.9%
1.19x

CharacterPropertiesStashedMemo
4290
3890
-9.3%
1.10x (?)

CharacterPropertiesFetch
6020
5590
-7.1%
1.08x (?)

Code size: -swiftlibs

TEST
OLD
NEW
DELTA
RATIO

Improvement

libswiftCloudKit.dylib
77824
73728
-5.3%
1.06x

How to read the data
The tables contain differences in performance which are larger than 8% and
differences in code size which are larger than 1%.
If you see any unexpected regressions, you should consider fixing the
regressions before you merge the PR.
Noise: Sometimes the performance results (not code size!) contain false
alarms. Unexpected regressions which are marked with '(?)' are probably noise.
If you see regressions which you cannot explain you can try to run the
benchmarks again. If regressions still show up, please consult with the
performance team (@eeckstein).

Hardware Overview
  Model Name: Mac Pro
  Model Identifier: MacPro6,1
  Processor Name: 12-Core Intel Xeon E5
  Processor Speed: 2.7 GHz
  Number of Processors: 1
  Total Number of Cores: 12
  L2 Cache (per Core): 256 KB
  L3 Cache: 30 MB
  Memory: 64 GB

@swift-ci please smoke test

Dammit, accidentally got another branch mixed in there >.<

@swift-ci please smoke test

@swift-ci please smoke test

Yaaay tests passed

@swift-ci please smoke test and merge

@swift-ci please smoke test and merge

@swift-ci please smoke test and merge

@swift-ci please smoke test and merge

@swift-ci please smoke test and merge


Using AAD Connect to assign Location

Could you please elaborate on how to use AAD Connect to set a user's location attribute, or provide a link to those instructions.
Thanks

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

ID: 7d1a3b25-8cbd-29f9-d205-31d320f6d315
Version Independent ID: 6711b6ba-154a-d078-d119-603d2c169453
Content: Assign licenses to a group - Azure Active Directory
Content Source: articles/active-directory/users-groups-roles/licensing-groups-assign.md
Service: active-directory
Sub-service: users-groups-roles
GitHub Login: @curtand
Microsoft Alias: curtand

@DeanGross Thanks for your feedback! We will investigate and update as appropriate.

@DeanGross Please refer to blog  for updating location in Azure AD connect. Also, refer to know more about configuration editor

@DeanGross We will now proceed to close this thread. If there are further questions regarding this matter, please reopen it and we will gladly continue the discussion.

Thanks for the info, it may be helpful to add a link to the content so that others can benefit from this in the future


use carbon relay blacklist to filter some of the hbase region metrics

This will roughly reduce the total footprint of metrics to about 30%,  if hbase_rs metrics dominates.

Have an updated one coming up.


[release-0.48] virt-launcher, hostdevice: Respect SR-IOV guest pciAddress and bootOrder

This is a manual cherry-pick of #7422.
It fixes Bugzilla 2070050.
Release note:

Fixed setting custom guest pciAddress and bootOrder parameter(s) to a list of SR-IOV NICs.

/uncc @stu-gott @zcahana

/cc @EdDev @ormergi

/retest-required

/retest-required

/test pull-kubevirt-e2e-kind-1.22-sriov

/retest-required

Opened PR #7489 in order to pass pull-kubevirt-generate.

/retest-required

https://github.com/kubevirt/kubevirt/pull/7480

/retest-required

/retest-required

/sig network
/retest

/approve


ISIC2018 dataset

Hello author, I was very efficient when I saw your team's code. I would like to try to run this code, take the trouble to bother you, and would like to ask you what kind of data storage structure should be for ISIC2018 data set in the./data directory, thank you very much!!

#3 You can refer to this dataset format.


Python: Add restricted and implicit address types

Description of change
Adds the new address types from TIP 50 to python. Does not add any functionality for the Capabilities flags.
Links to any relevant issues
Closes #1222
Open Questions
While looking at the code around Address I felt that some renames would be appropriate. I renamed the Address base class to BaseAddress and the AddressUnion to Address. If this is agreeable I can make the change to the other types that have the same pattern. Otherwise I will revert this.

Yea i like that base change, but lets do the rest in a new pr?

fine by me


Re export macros from submodules

Depends on https://github.com/async-rs/async-macros/pull/7. Re-exports various macros from their respective submodules. We were talking about this, and mostly wanted to test out how this feels to use.
Feedback would be great!
Screenshots

Let's keep this PR on hold. I'm having mixed feelings about it :)

I believe nobody's really pushing for this anymore, so I'm going to go ahead and close this. Thanks!


Consider switching to two-face for extra syntect syntaxes

Disclaimer: I'm two-face's author
Currently typst embeds bundled syntax definitions generated from the bat project. There's the crate two-face that does all the pre-generating and bundling for you while letting you interact with it as a normal crate instead of a weird differently versioned/generated thing. The only trick is that two-face's regex implementation has to match syntect
I believe this PR should cover updating everything. Neat test harness btw. The only change I can see with the updated file is likely just due to different points in time where the syntax snapshot get generated. I've highlighted the only part I noticed below

Could you say (a) which syntaxes this adds and (b) how this affects binary size?

(a) which syntaxes this adds

Most syntaxes should be the same, so I'm guessing you just want a brief diff. Let me know if you want a more exhaustive list instead. Here's the diff of the syntax_ref.name between main and this PR
31a32
>     "DMD Output",
48a50,58
>     "Git Attributes",
>     "Git Commit",
>     "Git Common",
>     "Git Config",
>     "Git Ignore",
>     "Git Link",
>     "Git Log",
>     "Git Mailmap",
>     "Git Rebase Todo",
51a62
>     "Groff/troff",
71d81
<     "JavaDoc",
73a84
>     "Javadoc",
89d99
<     "Man Page (groff/troff)",
91a102
>     "MediaWiki",
93d103
<     "Mediawiki NG",
125a136
>     "Regular Expressions (PHP)",

Just guessing from the names its
Added

git et. al
DMD Output (D-lang related thing)
PHP's regular expressions

Renamed

JavaDoc -> Javadoc
Man Page (groff/troff) -> Groff/troff
Mediawiki NG -> MediaWiki

Excluded due to fancy-regex
It's worth noting that the syntaxes that can fail with fancy-regex are still being excluded

02_Extra/Assembly (ARM).sublime-syntax
02_Extra/Elixir/Regular Expressions (Elixir).sublime-syntax
02_Extra/JavaScript (Babel).sublime-syntax
02_Extra/LiveScript.sublime-syntax
02_Extra/PowerShell.sublime-syntax
02_Extra/SCSS_Sass/Syntaxes/Sass.sublime-syntax
02_Extra/SLS/SLS.sublime-syntax
02_Extra/VimHelp.sublime-syntax
02_Extra/cmd-help/syntaxes/cmd-help.sublime-syntax

This exclusion happens automatically while generating the syntax set

(b) how this affects binary size?

two-face relies on the same trick that syntect does with embedded assets where you rely on the linker to remove the unused ones. two-face by itself is very lightweight, so the main difference should just be from the size difference in the embedded assets. Doing a quick check of the release binary size seems to confirm that this is the case too

What?
main (MiB)
This PR (MiB)
Delta (MiB)

Embedded syntax set
0.66
0.79
0.13

cargo b -r --bin typst
31.16
31.31
0.15

So the main benefit is not needing the syntax file here in the repository?

Yup. The main benefit is that it's now just a regular crate to update

I see. We're considering to have more elaborate infrastructure for lazy loading of assets (like this or also CSL styles, hyphenation patterns, etc.), which would allow us to keep the WASM size down for our web app. However, in the meantime, I agree that this is a simplification, so I'm gonna go ahead and merge. Thanks!


Update version number

Codecov Report

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  Files         215      215
  Lines      124698   124698
  Branches    12032    12032
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  Misses     102016   102016
  Partials      864      864

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RN is unable to communicate with metro bundler when targetSdkVersion 28

Environment
React Native Environment Info:
System:
OS: macOS 10.14
CPU: x64 Intel(R) Core(TM) i7-4770HQ CPU @ 2.20GHz
Memory: 32.63 MB / 16.00 GB
Shell: 3.2.57 - /bin/bash
Binaries:
Node: 9.11.1 - ~/.nvm/versions/node/v9.11.1/bin/node
Yarn: 1.9.4 - /usr/local/bin/yarn
npm: 5.6.0 - ~/.nvm/versions/node/v9.11.1/bin/npm
Watchman: 4.9.0 - /usr/local/bin/watchman
SDKs:
iOS SDK:
Platforms: iOS 11.2, macOS 10.13, tvOS 11.2, watchOS 4.2
Android SDK:
Build Tools: 23.0.1, 23.0.2, 23.0.3, 25.0.2, 26.0.1, 26.0.2, 27.0.1, 27.0.3, 28.0.3
API Levels: 23, 24, 25, 26, 27, 28
IDEs:
Android Studio: 3.2 AI-181.55<IP_ADDRESS>56338
Xcode: 9.2/9C40b - /usr/bin/xcodebuild
npmPackages:
@types/react: ^16.4.7 => 16.4.7
@types/react-native: ^0.57.5 => 0.57.5
react: 16.5.1 => 16.5.1
react-native: 0.57 => 0.57.2
✨  Done in 5.26s.
Description
When running a RN app with targetSdkVersion 28 on Android Pie, the app is unable to communicate with metro bundler. If you type the expected url and port into the browser, you can see some stuff about metro bundler appear, so I don't think it is a proxy issue.
When I revert targetSdkVersion to 27, the issue does not reproduce. It seems likely that this is related to one of these Pie changes for apps that target 28.
Reproducible Demo
Set targetSdkVersion to 28 and run the app on Android Pie. Observe that the app won't be able to load the bundle from metro bundler.

I suspect isCleartextTrafficPermitted() being false by default may be the source of this, if the React Native app is trying to reach the Metro server over http. Have you tried whitelisting Metro to see if that resolves it?

Just to add to @luisnaranjo733 's answer, I managed to get the app to connect to the bundler by adding the security config as such to AndroidManifest.xml
...
<application
    ...
    android:networkSecurityConfig="@xml/network_security_config"
>
...

And then adding these config to res/xml/network_security_config.xml to whitelist localhost
<network-security-config>
    <domain-config cleartextTrafficPermitted="true">
        <domain includeSubdomains="true">localhost</domain>
    </domain-config>
</network-security-config>

I also had to add.. (on 0.55.3)
<domain includeSubdomains="true"><IP_ADDRESS></domain>

I can confirm that @kronosfere answer works (react-native version = "0.57.5"). Thank you!
Can anyone say whether there are any problem with this solution for publishing?

This should work in 0.59 (or 0.60 coming soon).


how to rename image file name

its store long name in database so i required to short name for image.

Uhhh.... the image comes across as a binary blob in the HTTP POST data.  It's basically the same as posting an HTML form with a <input type="file" name="webcam">.  It is up to you how to write the data to disk on your server.  Name the file however you like -- it's in your hands.  Webcam.js doesn't write any files.  Consult your HTTP library documentation for instructions.


Update st2-apply-rbac-definitions to remove assignments from database for users which don't exist in the database

This pull request updates st2-apply-rbac-definitions tools so it also removes role assignments from the database for users which don't exist in the database.
Before that change we already supported pre-creation of role assignments, meaning user could write role assignments for users which don't yet exist in the database and those assignments would get stored in the database (StackStorm user accounts are lazy created on first successful authentication with st2auth).
We didn't do that for removal (we only removed assignments for users which exist in the database) so the behavior was inconsistent (there are no negative security implications because assignments existing in the database for non-existent user is basically the same as if assignments wouldn't exist).

Still need a review.
/cc @lakshmi-kannan


"node is null" in reparentNodes

Currently getting the error which my source map traces back to src/lib/dom.ts:38.
I'm getting this when I'm setting a property that's used as the innerHTML of an element. Not entirely sure why reparentNodes is even being run.
I can try to create a minimal example, but this only seems to occur when I'm receiving the data via a WebSocket connection (which obviously shouldn't matter).

This is old and we'd need a reproduction anyway. We'll see if new reports occur with 2.0.


[3rd party integration] webhook sender

Add webhooks that can be subscribed to via the API and are added by plugins.

[ ] webhook sender based on senders / workers
[ ] use HMAC / keys to be accepted in high-security enviroments
[ ] API to add webhook to user
[ ] API to add webhook to part
[ ] API to add webhook to company
[ ] settings: limit webhooks

optional:

[ ] log send events -> warning if hook fails a lot

Part of #1421

This is not really needed for the MVP #1421 so I will do it at a later point.

A sample implementation will be / is available as ShopifyIntegrationPlugin. Until #2074 is merged you need to use that branch for testing. Follow instructions placed in the readme for installation.

would love to have this feature, thanks for adding it into the list!

@shierro this is a proposal with no milestone so no idea when it will be implemented. Without anybody assigned it is hard to say.


Add ability to save rules from memory to sigma

This PR adds the ability to marshal a Sigma rule from memory back to a compliant YAML-formatted Sigma rule on disk. This allows users of the library to in theory modify or fully create rules in memory and then save them back to disk.
For the most part, this is straightforward. The only semi-complicated piece was marshalling the condition expression back properly. For this, I added a toString method to the AST objects to do context-aware construction of the expression, and then put the search expression and optional aggregation expression back together in the a Marshal implementation for Condition.
I added a test, but it's not great. The marshalling process produces a logically identical rule, but some of the fields end up with their default empty values being marshalled back out to the file, so a one-to-one comparison doesn't work. I used the Google cmp package to compare the re-loaded Detection representation in the test to ensure the detection was the same, since that is arguably the most important part of the rule itself. Anectdotally, the other fields seem to be marshalled properly during my testing.
As far as a use-case/motivation here, I am building a sort of rule "bundler" which puts a library of rules together into a single file for uploading to an engine.  If the Rule struct can completely implement the yaml.Marshaler interface, then this is super simple.
If you have an ideas for better improving the marshalling or the test case, let me know. I'm open to opinions here. This is just a first stab that seems to be working for me. :)

I added appropriate yaml:",omitempty" tags to relevant fields in the rule itself, and now doing a direct comparison between the sigma.Rule objects in the unit test works (instead of comparing the Detection field only). This is a better test which actually validates that all the fields are marshalled and then unmarshalled correctly.

I realized I missed the implementation of toString for the Near aggregation, so I've added that and all tests are currently passing.


Uberv2 optimization

-All basic materials removed
-UberV2 generated on the fly on per-configuration basis

Regarding points 1 and 4 - we can't remove it since we don't know required material buffer size on UpdateMaterials step before we parse all materials.


Pkg 1680

Linter check found the following problems:
The following problems have been found:
ERROR: /tmp/abs_d9yabfpir6/clone/recipe/meta.yaml:15: pip_install_args: pip install should be run with --no-deps and --no-build-isolation.
ERROR: /tmp/abs_d9yabfpir6/clone/recipe/meta.yaml:22: missing_wheel: For pypi packages, wheel should be present in the host section
ERROR: /tmp/abs_d9yabfpir6/clone/recipe/meta.yaml:30: missing_pip_check: For pypi packages, pip check should be present in the test commands
WARNING: /tmp/abs_d9yabfpir6/clone/recipe/meta.yaml:38: missing_description: The recipe is missing a description
ERROR: /tmp/abs_d9yabfpir6/clone/recipe/meta.yaml:38: missing_documentation: The recipe is missing a doc_url or doc_source_url
Errors were found


Wrong origin, with CORS error in browser but response is 200 with right body

Expected behavior
We do a request from browser localhost:8100 to http://localhost:3000/proxy/tun0/blabla which should be redirected to https://tun0.bla.bla.com/bla
The strange thing I use several routes and any  other is working expecting this one, and it is totally configured equals as the others.
The other strange thing is, the request is the only one request which has not 'localhost:8100' as Access-Control-Allow-Origin, but it has https://tun0.bla.bla.com, but in the response is OK 200 and has the response in it. But at the end I get a CORS error on the browser!!!
This is very very stranged, some ideas
Actual behavior
Setup

http-proxy-middleware: 0.17.4
server: express 4.15.4
cors 2.8.4

proxy middleware configuration
/* STUN */
var proxyMiddlewareStun0 = proxy({
    target: 'https://tun0.bla.bla.com/bla',
    changeOrigin: true,
    headers: {
        Origin: 'https://tun0.bla.bla.com'
    },
    cookieDomainRewrite: '',
    pathRewrite: {
        '/proxy/tun0': ''
    },
    logLevel: 'debug'
});```
#### server mounting
```javascript
var app = express();
    app.use(cors(
                    {
                        credentials: true,
                        origin: function (origin, callback) {
                                    callback(null, true)
                                }
                    }
                )
           );
    app.use('/proxy/tun0', proxyMiddlewareStun0);

console.log("listening on port 3000");
var server = app.listen(3000);

hello, do you fix this issure?

hello，What did you do about it？

Not sure about the impact but this works for me.
  onProxyRes: async (proxyRes: http.IncomingMessage) => {
    proxyRes.headers['access-control-allow-origin'] = '[ORIGIN HERE]';
  }

Not sure about the impact but this works for me.
  onProxyRes: async (proxyRes: http.IncomingMessage) => {
    proxyRes.headers['access-control-allow-origin'] = '[ORIGIN HERE]';
  }

This work for me. Thanks.


ProGuard file generated for android has the wrong name

Hi, I've noticed that the name of ProGuard file is not the same of the one listed in default gradle generated into android module.
In the gradle we have proguard-rules.pro
https://github.com/tommyettinger/gdx-liftoff/blob/4853024550a62fa677a54664749d2450f86cd971/src/main/kotlin/com/github/czyzby/setup/data/platforms/android.kt#L147
But the file generated is proguard-project.txt: https://github.com/tommyettinger/gdx-liftoff/blob/master/src/main/resources/generator/android/proguard-project.txt
Generated file should be renamed into proguard-rules.pro :)

Great, thanks, I'll rename the file now.


Dev

Fix Layout Page Tabs dragable
Type

[x] Bug fix (non-breaking change which fixes an issue)

Ant-design-vue 3.0 change Tabs componts CSS class name ~ant-tabs-nav~ to ant-tabs-nav-wrap , cause Tab Drag Selector Bug.
Is there a better way to get multipleTabsList element during useTabsDrag function without using ~document.querySelectorAll()~?


arch/tricore: add Bound Multi-Processing (BMP) support

Summary
arch/tricore: add Bound Multi-Processing (BMP) support
Add bmp support to tricore to enable all CPUs
Signed-off-by: chao an<EMAIL_ADDRESS>Impact
N/A
Testing
tc397/bmp

@anchao this PR depends on PR #12020 right?

@anchao this PR depends on PR #12020 right?

Yes, let's wait for the #12020 review completed


The problem about train

Hi, I want to ask about that when I try to run train.py, something is wrong. It shows the 'G has no attribute adjacency_list()'in the cora.py. Can you give me some advice for this

Hi~According to your feedback, the problem seems to be the version of networkx package. As is illustrated in README.md, please make sure your networkx version equals 1.11, since we only test our code under networkx==1.11. Hope this reply could help you.
Best Regards.


[Idea] Open an issue when a dependency has a conflict to be updated

Hello team 👋
First of all: thanks for this amazing project. Since we've merged dependabot/dependabot-core#1230, @usabilla have been using Dependabot form both frontend and backend teams, and it has saved us a lot of time 🎉❤
Last week, we were expecting some PRs from Dependabot that didn't arrive and we've figured that the problem was dependency conflicts.
I remember once that we had a misconfigured config.yml and Dependabot opened an issue reporting that.
So, my idea: Dependabot could open an issue saying: Hey, I'm trying to update dependency foo/bar but it has conflicts with foo/quox. Can you fix it so I can continue my work? 😄
If you think this is a nice idea and shouldn't be that difficult to implement, please let us know. We'd love to contribute back to you 😉
Keep up the good work 🤖

Similar to #2242


feat: Add Chrome Custom Tabs for browsing

Fixes #1300
Checklist:

[ ] I have checked for PMD and check-style issues
[x] I have read the Contribution & Best practices Guide and my PR follows them.
[x] My branch is up-to-date with the Upstream development branch.

Changes: Adds implementation for chrome custom tabs
Screenshots for the change:

@sridharjajoo @Masquerade0097 @srv-twry @iamareebjamal Please review

Verify that fdroid build is still working


Retrigger result from destination even if the same value.

 Thank you for your submission! We really appreciate it. Like many open source projects, we ask that you sign our Contributor License Agreement before we can accept your contribution.You have signed the CLA already but the status is still pending? Let us recheck it.


After 8 hours of super slow processing it announces maybe my processor won't work. Why not at beginning?

I tried to split a 1 hour wav file and just extract the drum. It took 8 hours and then said it had a problem. Is there a solution

Last Error Received:
Process: Demucs
The application was unable to allocate enough system memory to use this model.
Please do the following:

Restart this application.
Ensure any CPU intensive applications are closed.
Then try again.

Please Note: Intel Pentium and Intel Celeron processors do not work well with this application.
If the error persists, the system may not have enough RAM, or your CPU might not be supported.
Raw Error Details:
RuntimeError: "[enforce fail at C:\actions-runner_work\pytorch\pytorch\builder\windows\pytorch\c10\core\impl\alloc_cpu.cpp:72] data. DefaultCPUAllocator: not enough memory: you tried to allocate 155058176 bytes."
Traceback Error: "
File "UVR.py", line 4592, in process_start
File "separate.py", line 418, in seperate
These are the laptops specs:
Processor	Intel(R) Core(TM) i7-4710HQ CPU @ 2.50GHz, 2501 Mhz, 4 Core(s), 8 Logical Processor(s)
BIOS Version/Date	American Megatrends Inc. G551JM.205, 2/13/2015
SMBIOS Version	2.7
Embedded Controller Version	255.255
BIOS Mode	UEFI
BaseBoard Manufacturer	ASUSTeK COMPUTER INC.
BaseBoard Model	Not Available
BaseBoard Name	Base Board
Platform Role	Mobile
Secure Boot State	On
PCR7 Configuration	Binding Not Possible
Windows Directory	C:\Windows
System Directory	C:\Windows\system32
Boot Device	\Device\HarddiskVolume1
Locale	United States
Hardware Abstraction Layer	Version = "10.0.15063.909"
User Name	Asus\Asus
Time Zone	Central Standard Time
Installed Physical Memory (RAM)	16.0 GB
Total Physical Memory	15.9 GB
Available Physical Memory	11.1 GB

This is because you ran out of RAM. 16GB may be enough for most models, and most games, but 32GB is probably necessary for such a large file to process on that model.
It's likely it failed after 8 hours because there was a sudden jump in the usage of RAM that ended up going over your system budget, this could be because it has all of the chunks of the file it's processed in memory, and when it added another chunk to process, it just tipped it over the 16GB mark. It's possible if you closed all applications and then tried, it could work, but I'm not sure if I'd bet 8 more hours on it.
TLDR: Closing all applications, trying a different model and upgrading the amount of RAM you have I believe are the only solutions unfortunately.

Appreciate your reply. I doubt there's much chance to upgrade the memory
for this Asus Rog G551JM
Could you suggest a UVR5 model that's not so resource hungry? Thanks
On Thu, Feb 9, 2023 at 7:54 PM Derpy @.***> wrote:

This is because you ran out of RAM. 16GB may be enough for most models,
and most games, but 32GB is probably necessary for such a large file to
process on that model.
It's likely it failed after 8 hours because there was a sudden jump in the
usage of RAM that ended up going over your system budget, this could be
because it has all of the chunks of the file it's processed in memory, and
when it added another chunk to process, it just tipped it over the 16GB
mark. It's possible if you closed all applications and then tried, it could
work, but I'm not sure if I'd bet 8 more hours on it.
TLDR: Closing all applications, trying a different model and upgrading the
amount of RAM you have I believe are the only solutions unfortunately.
—
Reply to this email directly, view it on GitHub
https://github.com/Anjok07/ultimatevocalremovergui/issues/411#issuecomment-1425068122,
or unsubscribe
https://github.com/notifications/unsubscribe-auth/AWGBMLPZTUEPFHRSGPA6XOLWWWNUFANCNFSM6AAAAAAUUZCMJI
.
You are receiving this because you authored the thread.Message ID:
@.***>

Ah I see, I hadn't realised you were on a laptop!
I'm not very sure what model is most RAM efficient, but since you're trying to extract the drums, I think you're pretty constrained to the Demucs models. I've got even less experience with those, and I sort of doubt there's all that much of a difference between them anyway, when it comes to RAM.
I think your best option is to try to split the file you want to process in half, or in quarters, and process each of those individually, then compile them back together. As for how you'd go about doing that, I suggest FFMPEG, since it's the most lightweight, and most easily uninstall-able media editor I know of.
I'll go through the steps to cut a file up and recompile it using FFMPEG, as well as the installation, since it can be quite confusing if you've never used something like it before, here I'll show the steps (12 to be precise) to cut the audio file into two 30 minute parts:
DOWNLOADING FFMPEG:
1:
Go to this website: https://ffmpeg.org/download.html
Then you'll want to click on / hover over the blue Windows logo, and then click on the "Windows builds from gyan.dev" hyperlink.

2:
Next, when you're on the "gyan.dev" website, you'll want to look for the "ffmpeg-git-essentials-7z" file, and then click on that to download it. (I have all the text highlighted as I've got a dark mode extension for Chrome and it occasionally messes up colours, making the text invisible).

3:
When you've downloaded the zipped file, open it up and you should see a folder inside it called "ffmpeg-(date)-git-(version number?)-essentials_build", grab that folder and put it on your desktop, or somewhere you can easily access it temporarily.

4:
Once you've put the folder from the zipped file somewhere, open it up, and navigate into the "bin" sub-folder.

5:
Now that you're in the "bin" sub-folder, you should see "ffmpeg.exe", "ffplay.exe" and "ffprobe.exe". The "ffmpeg.exe" file is what we're looking for, if the others aren't there then that's no problem.

PRE-PROCESSING THE AUDIO FILES:
6:
Now you'll want to drag in the file you want to process with UVR (the 1 hour long one). Here I've named my one "example.mp3", but it doesn't matter what the name is, or if it is a .wav file (you can just replace the .mp3 with .wav, as well as the name).

7:
To use FFMPEG you need a prompt window open, I prefer Windows PowerShell, as it's easier to open. While in the "bin" folder, SHIFT + Right Click in an empty space, and it should bring up a prompt menu with "Open PowerShell window here" in it. If you're on Windows 11, you may have to click "Show more options" to see it.

8:
Now you can use FFMPEG to cut the first 30 minutes of the audio file using this command:
ffmpeg -i "example.mp3 [swap .mp3 with .wav if applicable]" -c:a copy -to 00:30:00 "first30Minutes.mp3 [swap .mp3 with .wav if applicable]". You should remove the comments I added in the [].

9:
Like the last step, we'll use a very similar command to split off the 30 minutes at the end of the audio file:
ffmpeg -i "example.mp3 [swap .mp3 with .wav if applicable]" -c:a copy -ss 00:30:00 "second30Minutes.mp3 [swap .mp3 with .wav if applicable]". And much like the previous step you should remove the commented parts in the [].

PROCESSING THE SPLIT AUDIO FILES:
10:
After those two commands, if you check back in the folder, you should see that two more files were created, in my case "first30Minutes.mp3", and "second30Minutes.mp3".

You'll want to take each of those two files and process them through UVR individually with the model you tried to use before. Since these are half the size, hopefully they'll take up half the amount of RAM, and take half the time.
COMBINING THE TWO PROCESSED AUDIO FILES:
11:
Finally, once you've processed both of the files (you may want to rename them so you don't get them muddled up, but in this example I'll keep the same names), put both of them back in the "bin" folder with the "ffmpeg.exe" file in it, then, use this command in the PowerShell console:
ffmpeg -i "concat:first30Minutes.mp3|second30Minutes.mp3" -i second30Minutes.mp3 -c:a copy combined_output.mp3

12:
Now, you should have a full hour long audio file named "combined_output.mp3" (or ".wav" if you changed it), this should have combined both the files you processed.

And to finish off, once you're happy with the "combined_output" file, move that, and the original hour long file to a different folder, then you can delete the zipped file, and the ffmpeg folder you downloaded. So it'll be like you never downloaded it in the first place, then nothing will get cluttered up!
Let me know if you need help with anything else; and if this fails you can try and send the audio file to me through something like Google Drive and I can see if I can process it for you.

Hi
That's kind of you to outline those steps. Clearly you're way over my
technical level. Even though you laid them out well it would still be a
challenging process. So it's the length of this thing that makes it so
difficult? I didn't know that. I checked my laptop's ability to be upgraded
and see 16 gb memory is the best it can do. By the way, I barely have used
this but through my Reaper forum I did install a program called AVConvert.
https://www.audioworld.de/AudioWorld AVConvert_e.php  I think it does
what you're talking about in a way that a person not so familiar with some
of the steps can grasp more easily. Actually the file that I've been
fooling with is simply a little bit of entertainment that I wanted to try
for fun. I find announcers in tennis to be distracting as they often talk
over the points so I wanted to try and separate them. I've been trying a
number of the AI programs after learning about them a few weeks ago. RipX,
Stemroller, LALAL.AI and UVR5. I'm afraid I can't remember which one I used
on this file the only time it worked but it only did half of it. It worked
as I hoped it would after I realized that the AI interpreted the racquet
hitting the ball as drums. I cut the file into around 1/3 the size and put
the file in my google docs. I don't use Google Docs much..  If you were
interested in breaking it down to stems (at least the drum part,which is
all I think I need) how would I share it with you?  Thanks!
On Fri, Feb 10, 2023 at 3:11 PM Derpy @.***> wrote:

Ah I see, I hadn't realised you were on a laptop!
I'm not very sure what model is most RAM efficient, but since you're
trying to extract the drums, I think you're pretty constrained to the
Demucs models. I've got even less experience with those, and I sort of
doubt there's all that much of a difference between them anyway, when it
comes to RAM.
I think your best option is to try to split the file you want to process
in half, or in quarters, and process each of those individually, then
compile them back together. As for how you'd go about doing that, I suggest
FFMPEG, since it's the most lightweight, and most easily uninstall-able
media editor I know of.
I'll go through the steps to cut a file up and recompile it using FFMPEG,
as well as the installation, since it can be quite confusing if you've
never used something like it before, here I'll show the steps (12 to be
precise) to cut the audio file into two 30 minute parts:
DOWNLOADING FFMPEG: 1:
Go to this website: https://ffmpeg.org/download.html
Then you'll want to click on / hover over the blue Windows logo, and then
click on the "Windows builds from gyan.dev" hyperlink.
[image: 1]
https://user-images.githubusercontent.com/38544173/218192689-4b70d7db-3d09-4a1e-aeaf-83fd391374a6.png
2:
Next, when you're on the "gyan.dev" website, you'll want to look for the
"ffmpeg-git-essentials-7z" file, and then click on that to download it. (I
have all the text highlighted as I've got a dark mode extension for Chrome
and it occasionally messes up colours, making the text invisible).
[image: 2]
https://user-images.githubusercontent.com/38544173/218192911-f157a8ab-d516-4993-93a3-9996e987853c.png
3:
When you've downloaded the zipped file, open it up and you should see a
folder inside it called "ffmpeg-(date)-git-(version
number?)-essentials_build", grab that folder and put it on your desktop, or
somewhere you can easily access it temporarily.
[image: 3]
https://user-images.githubusercontent.com/38544173/218193023-3045167a-a43f-4ca2-b497-70ab882595ee.png
4:
Once you've put the folder from the zipped file somewhere, open it up, and
navigate into the "bin" sub-folder.
[image: 4]
https://user-images.githubusercontent.com/38544173/218193383-f03bff04-56de-4f15-9daf-44d42bc442eb.png
5:
Now that you're in the "bin" sub-folder, you should see "ffmpeg.exe",
"ffplay.exe" and "ffprobe.exe". The "ffmpeg.exe" file is what we're looking
for, if the others aren't there then that's no problem.
[image: 5]
https://user-images.githubusercontent.com/38544173/218193511-bf0afa29-bd92-4c03-8161-74f5dcabd632.png
PRE-PROCESSING THE AUDIO FILES: 6:
Now you'll want to drag in the file you want to process with UVR (the 1
hour long one). Here I've named my one "example.mp3", but it doesn't matter
what the name is, or if it is a .wav file (you can just replace the .mp3
with .wav, as well as the name).
[image: 6]
https://user-images.githubusercontent.com/38544173/218193678-480006ff-4b18-4cb1-87ec-acaf8bc4c1c0.png
7:
To use FFMPEG you need a prompt window open, I prefer Windows PowerShell,
as it's easier to open. While in the "bin" folder, SHIFT + Right Click in
an empty space, and it should bring up a prompt menu with "Open PowerShell
window here" in it. If you're on Windows 11, you may have to click "Show
more options" to see it.
[image: 7]
https://user-images.githubusercontent.com/38544173/218194027-75e9cdf0-3c1c-4379-81ae-14c24f1c6245.png
8:
Now you can use FFMPEG to cut the first 30 minutes of the audio file using
this command:
ffmpeg -i "example.mp3 [swap .mp3 with .wav if applicable]" -c:a copy -to
00:30:00 "first30Minutes.mp3 [swap .mp3 with .wav if applicable]". You
should remove the comments I added in the [].
[image: 8]
https://user-images.githubusercontent.com/38544173/218194303-6c44b087-918c-4f63-81d3-b0fc015ab709.png
9:
Like the last step, we'll use a very similar command to split off the 30
minutes at the end of the audio file:
ffmpeg -i "example.mp3 [swap .mp3 with .wav if applicable]" -c:a copy -ss
00:30:00 "second30Minutes.mp3 [swap .mp3 with .wav if applicable]". And
much like the previous step you should remove the commented parts in the
[].
[image: 9]
https://user-images.githubusercontent.com/38544173/218194739-c8cd2216-47f9-4a03-9555-016f69d215f2.png
PROCESSING THE SPLIT AUDIO FILES: 10:
After those two commands, if you check back in the folder, you should see
that two more files were created, in my case "first30Minutes.mp3", and
"second30Minutes.mp3".
[image: 10]
https://user-images.githubusercontent.com/38544173/218195056-ce45c55d-9af2-42a4-9804-5ba267b68b74.png
You'll want to take each of those two files and process them through UVR
individually with the model you tried to use before. Since these are half
the size, hopefully they'll take up half the amount of RAM, and take half
the time.
COMBINING THE TWO PROCESSED AUDIO FILES: 11:
Finally, once you've processed both of the files (you may want to rename
them so you don't get them muddled up, but in this example I'll keep the
same names), put both of them back in the "bin" folder with the
"ffmpeg.exe" file in it, then, use this command in the PowerShell console:
ffmpeg -i "concat:first30Minutes.mp3|second30Minutes.mp3" -i
second30Minutes.mp3 -c:a copy combined_output.mp3
[image: 11]
https://user-images.githubusercontent.com/38544173/218195433-a559a511-4225-494a-ae4f-216d1322a4d0.png
12:
Now, you should have a full hour long audio file named
"combined_output.mp3" (or ".wav" if you changed it), this should have
combined both the files you processed.
[image: 12]
https://user-images.githubusercontent.com/38544173/218195564-75894c0e-f020-4925-ba81-7a4a5aa482f1.png
And to finish off, once you're happy with the "combined_output" file, move
that, and the original hour long file to a different folder, then you can
delete the zipped file, and the ffmpeg folder you downloaded. So it'll be
like you never downloaded it in the first place, then nothing will get
cluttered up!
Let me know if you need help with anything else; and if this fails you can
try and send the audio file to me through something like Google Drive and I
can see if I can process it for you.
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@.***>

Yep, the longer the file is, the more data it has to store in RAM.
I'm happy to have a go at extracting the drum stem from the file. I presume you mean Google Drive when you're referring to Google Docs? To share a file you can right-click on it, click on "Share", and I believe you should be able to click on copy link, and just send that?
I remember I had some trouble with permissions last time I tried Google Drive a year or so ago, so in that case, https://www.mediafire.com/ hasn't let me down so far, as an alternative (although I haven't checked that in a while either). It might be worth sending both for redundancy, but one will likely work anyway if that's easier.
No worries about the method I suggested, I've never really made a guide before so it was fun either way!

Thanks. I'm going to do that with a different one. I already watched the
one before. This one is quite long but if it's possible, great. I joined
Mediafire for it. As I said all I really need would traditionally be the
drums (where the racquet hits the ball) but as long as the voice is out I'm
good with any other sounds that might be there. Thanks!
https://www.mediafire.com/file/qp2r5zoqohqnt6q/tenn.mp3/file
On Sat, Feb 11, 2023, 3:23 AM Derpy @.***> wrote:

Yep, the longer the file is, the more data it has to store in RAM.
I'm happy to have a go at extracting the drum stem from the file. I
presume you mean Google Drive when you're referring to Google Docs? To
share a file you can right-click on it, click on "Share", and I believe you
should be able to click on copy link, and just send that?
I remember I had some trouble with permissions last time I tried Google
Drive a year or so ago, so in that case, https://www.mediafire.com/
hasn't let me down so far, as an alternative (although I haven't checked
that in a while either). It might be worth sending both for redundancy, but
one will likely work anyway if that's easier.
No worries about the method I suggested, I've never really made a guide
before so it was fun either way!
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Hey,
I managed to do about 7 hours of the total ~9h30m. I was running into some "Assertion" errors towards the end and I wasn't able to get past them.
I split it up into hour long bits and each bit took about 25GB of RAM in case you were wondering!
Here's a Google Drive link to the combined version. It's 8 hours long, which I don't know if it should be, since I only think I processed 7 hours, so there may be a duplicate near the end. Combining that many files with FFMPEG had me a little confused, so hopefully not, but possible: https://drive.google.com/file/d/12Ee6RtIJdEUSf2W5YC5D56aC9VrlCzVG/view?usp=sharing
I'm also going to include this mediafire link to a .ZIP file of all the 1 hour sections I processed, so you can go through those instead, to avoid any duplicates. It's also a smaller download than the combined one: https://www.mediafire.com/file/m0ew3otb3vaje65/1h-7h_Drum_Stem.zip/file

Great! Very nice of you to give it a try. I'm downloading it now and will
let you know how it goes. Thanks!
On Sun, Feb 12, 2023 at 3:35 PM Derpy @.***> wrote:

Hey,
I managed to do about 7 hours of the total ~9h30m. I was running into some
"Assertion" errors towards the end and I wasn't able to get past them.
I split it up into hour long bits and each bit took about 25GB of RAM in
case you were wondering!
Here's a Google Drive link to the combined version. It's 8 hours long,
which I don't know if it should be, since I only think I processed 7 hours,
so there may be a duplicate near the end. Combining that many files with
FFMPEG had me a little confused, so hopefully not, but possible:
https://drive.google.com/file/d/12Ee6RtIJdEUSf2W5YC5D56aC9VrlCzVG/view?usp=sharing
I'm also going to include this mediafire link to a .ZIP file of all the 1
hour sections I processed, so you can go through those instead, to avoid
any duplicates. It's also a smaller download than the combined one:
https://www.mediafire.com/file/m0ew3otb3vaje65/1h-7h_Drum_Stem.zip/file
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So far it's working well! I can only look at it from the beginning since I
don't want to see the score or who won before I look at the match. It will
take me quite a while (maybe some days) to get through it (especially as
The Super Bowl is just about to start) but the sound is in sync at the
beginning and not hearing the announcers so far. This will be fun.Good
job!  I'll keep you posted. Thanks!
On Sun, Feb 12, 2023 at 5:04 PM @.***> wrote:

Great! Very nice of you to give it a try. I'm downloading it now and will
let you know how it goes. Thanks!
On Sun, Feb 12, 2023 at 3:35 PM Derpy @.***> wrote:

Hey,
I managed to do about 7 hours of the total ~9h30m. I was running into
some "Assertion" errors towards the end and I wasn't able to get past them.
I split it up into hour long bits and each bit took about 25GB of RAM in
case you were wondering!
Here's a Google Drive link to the combined version. It's 8 hours long,
which I don't know if it should be, since I only think I processed 7 hours,
so there may be a duplicate near the end. Combining that many files with
FFMPEG had me a little confused, so hopefully not, but possible:
https://drive.google.com/file/d/12Ee6RtIJdEUSf2W5YC5D56aC9VrlCzVG/view?usp=sharing
I'm also going to include this mediafire link to a .ZIP file of all the 1
hour sections I processed, so you can go through those instead, to avoid
any duplicates. It's also a smaller download than the combined one:
https://www.mediafire.com/file/m0ew3otb3vaje65/1h-7h_Drum_Stem.zip/file
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This is because you ran out of RAM. 16GB may be enough for most models, and most games, but 32GB is probably necessary for such a large file to process on that model.
EDIT: Although it's possible you could split the file you're processing in half (two 30 minute files) and then process those individually.

Well, I have the same issue with a 44,1 khz 16 bits  audio that's under 3 minutes long. It just crashes miserably.


Source declaration conflicts with itself

This page states
> You specify additional feeds by creating your own nuget.config file in the project directory.  You also specify additional feeds per invocation at a command prompt.
But in the -s documentation it states:
> This setting overrides all of the sources specified in the nuget.config files
The first makes it sound like it will add another feed when supplied at the command line.  The second makes it clear that it replaces all the ones found in the nuget.config.
This document should be updated to clarify which is correct.

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

ID: 0b256dc4-c4ad-c115-f4e2-40310bc3505a
Version Independent ID: 0b993779-65a2-6092-4047-2fc2ec16170b
Content: dotnet restore command - .NET Core CLI
Content Source: docs/core/tools/dotnet-restore.md
Product: dotnet-core
Technology: dotnet-cli
GitHub Login: @mairaw
Microsoft Alias: mairaw

Hello!
I see where the confusion is. It's with this statement specifically:

You also specify additional feeds per invocation at a command prompt.

Really this should state that you can override the feeds in a nuget.config file with the -s command prompt parameter.
I'm going to close this issue. If your problem is not solved, feel free to reopen or comment. Thanks!


Updated cloning address

Corrected the cloning address to "trade-motors" instead of "trademotors"

Thank you for the PR. This is already Fixed in #39


Does RTMDet instance segmentation support onnx conversion?

What's the feature?
As the title says, does the RTMDet instance segmentation models support conversion to the .onnx format?
When reading the documentation for the 3.x branch the section regarding tutorials and onnx conversion is removed (https://mmdetection.readthedocs.io/en/3.x/overview.html).
Additionally, the master branch documentation does not mention RTMDet as supported models for onnx conversion.
(https://mmdetection.readthedocs.io/en/latest/tutorials/pytorch2onnx.html#results-and-models)
If no: Is there any plans to support onnx conversion for the RTMDet instance segmentation models?
Any other context?
No response

We will support RTMDet-Ins ONNXRuntime and TensorRT support soon. If the PR is ready, I'll inform you.

Hi, I opened a PR to support RTMDet-Ins in MMDeploy: https://github.com/open-mmlab/mmdeploy/pull/1662.
We will merge it soon.

That's great news!
Do you want any help to test it out?

I would also be willing to help out here if needed.


[h2] Add a generic Trailers constructor

Currently there's no easy way to build a Trailers frame from application code.
(There is a netty-specific builder, but that is and should only be visible
within the netty codec.)
This change introduces a generic Frame.Trailers(hdrs: Seq[(String, String)])
constructor that's usable from application code (or a gRPC iplementation ;)

https://github.com/BuoyantIO/linkerd/commit/b4a1ae3807d855b792e0680c133d3de7723f9a99


Remove "my drafts" from post-sorting menu

Instead we probably want to replace it with a section on a user's profile page that lists their drafts

Make sure that "all draft" is only shown to admins


Only run publish workflow on release

Description
This PR updates the publish.yml workflow to run only when a new release is published. I've removed the triggers for pull requests and pushes to the main branch, focusing the workflow solely on the release process.
Note: With this change, someone with the necessary permissions will need to create a new tag or release to trigger the publish workflow. This ensures that we only publish new versions of Giraffe.OpenApi when explicitly releasing a new version.

I'm going to give this a shot. As far as I'm understanding it I need to push a 0.0.1 tag and the action will do the rest.
This is fine for now, but in the future we should really automate this and publish/release once the changelog in the main branch has changed. (example of how we did that for the ionide analyzers, the tl;dr is that we extract the changelog version via https://github.com/ionide/KeepAChangelog and just compare with previous version)

Sounds like a good idea. I'll give it a try next weekend. :+1:

Cool, tremendous thanks for all your work on this and for letting us have this repository in this organization!
The NuGet package was published to https://www.nuget.org/packages/Giraffe.OpenApi
//cc @dungpa


Request to make pipermerriam (me) a contributor

I put a lot of work into getting this project backed by a test suite as well as improving many parts of the codebase.  I would like to request being added as a contributor to this project.

Definitely! You are already in credits list.
Feel free to send pull request if you like others.

Sorry, I think my intention may have been mis-understood.  I'm hoping
you'll add me as a contributor on the github project which gives privileges
like merge permission.  Not the list of people in the credits list.
On Tue, May 19, 2015 at 4:58 PM Ming Chen<EMAIL_ADDRESS>wrote:

Definitely! You are already in credits list.
Feel free to send pull request if you like others.
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I have added you to Collaborators. You are very welcome to contribute this project.


Feature: Display OneDrive options in main context menu

What feature or improvement do you think would benefit Files?
The shell extensions for OneDrive files/folders are shown in the overflow flyout. It would be easier to access them if they were shown in the main menu.
Requirements

OneDrive options should get a placeholder in the context menu until the shell extensions finish loading
This should apply for other cloud storage locations as well
Apply these changes to the sidebar, file views and home page widgets

Files Version
v2.4.40
Windows Version
Windows 11
Comments
No response

I believe those are rarely if ever used, please keep them tucked away like they currently are.
I would just promote to the root of context menu the "Edit" (in Paint/Photos app) when right clicking an image file.
Thanks

@carlosvsilva this only makes a difference for OneDrive files. The "open" and "open with" options are already in the main menu so I'm not sure how relevant the "edit" option really is.


add AttentionPool1D

Codecov Report

Merging #63 (61bd880) into main (33a174e) will decrease coverage by 0.00%.
The diff coverage is 100.00%.

@@            Coverage Diff             @@
##             main      #63      +/-   ##
==========================================
- Coverage   94.04%   94.03%   -0.01%
==========================================
  Files          45       46       +1
  Lines        1999     2013      +14
==========================================
+ Hits         1880     1893      +13
- Misses        119      120       +1

Impacted Files
Coverage Δ

src/hearth/modules/__init__.py
100.00% <100.00%> (ø)

src/hearth/modules/pooling.py
100.00% <100.00%> (ø)

src/hearth/vision/ops.py
93.33% <0.00%> (-6.67%)
:arrow_down:

Continue to review full report at Codecov.

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Clean up all warnings in Java client test

Issue #, if available:
Description of changes:
Cleans up all compiler warnings in the Java/Client/src/test folder.  Most of the changes are like this:
        CompletableFuture<Object> testResponse = mock(CompletableFuture.class);
        when(testResponse.get()).thenReturn(value);

changed to:
        CompletableFuture<Object> testResponse = new CompletableFuture<>();
        testResponse.complete(value);

The above tests clean up the type error when converting from CompletableFuture to CompletableFuture<Object>.
By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.

Please add @Timeout for client UTs

Please add @Timeout for client UTs

It's just ConnectionWithGlideMockTests.java.  Do we need it for that test?

I hanged once UTs by using incorrect mockito's mock.

Please send a ping once CI is passing

@barshaul


Getting different output from the PyTorch model using the same input

@sayakpaul @gustheman
The PyTorch model generates different outputs with the same input.
I have compared the outputs of two attempts (attempt 0 and attempt 2) with the same input using numpy.testing.assert_allclose and got the following-

But there are times when out of 10 attempts I got the same Final Output for multiple attempts. Here attempt 0, 3, 7, and 8 has the same Final Output.

To understand the issue, I have inspected the layer-wise outputs. The output of the PatchEmbed3D layer, Basic Layer 1, and Basic Layer 2 produces similar output at each attempt. Though the second basic layer sometimes gives different output.

[ Here I have passed the same input through the PyTorch model 4 times, extracted the output of each layer at every attempt, and then compared them]
The deviation starts occurring after the Basic layer2. The basic layer3 provides different outputs most of the time. Here for attempts 2, 3 Basic Layer3 has provided the same output but different output for attempts 0 and 1.  The following layers also do the same.

About the TensorFlow model,
The output of the PatchEmbed3D layer of both of the models matches.

[Here I have passed the same input through both of the models after assigning the PyTorch model's weights to the TF model and extracted the output of each layer. Then I compared them]
In the Basic layer 1 &  2 there is a slight deviation in the output values-

This variation increases in the subsequent layers-

And when it comes to the final output they seem totally different.
But sometimes I have got similar Final Output from them-

Here you can see the final output of the two models has only slight variation.
But if I run these two models again with the same input as this, I will get a different output for the PyTorch model.

NB: The TF model's outputs are the same for each attempt with the same input
I think the reason behind this could be floating point calculation.
Here is the colab's link for your reference -
https://colab.research.google.com/drive/1N6ZJKBz_Ha4MYi05Sv_OgujL3GHQoKk5?usp=sharing
Please have a look and suggest to me what I should do about it.

@shoaib6174 some thoughts:

Could it be because of the wrong transposition format for the Conv3D params?
I would start with the first layer from the PT and TF model and see if the outputs match. If they don't, we have a problem. You could extend this to the individual layers and see if that's a problem. Of course, the layers need to be having the same params ported inside of them, otherwise, the comparison will be meaningless.
Please ensure you're following the exact same defaults the PT model is using for the TF model.
Also, get_x() returns outputs that have channels_first memory layout. For TF models, we would want to have channels_last memory layout. This impacts how the layer params are initialized inside the TF model.

@sayakpaul
Here I have printed the layerwise output of both models-

The output of the patch_embed layer matches exactly. So the Conv3D layer is not the problem.
About the input format- I have transposed the input before passing through the Conv3D layer. Here is the code-

The basic layer1 of both models gets the same input from the patch_embed layer and creates different outputs.  If you look at the output values of basic layer1- the variation in the values is very small.
I get this comparing the output of "basic layer1" of both models

@sayakpaul
Our main issue is that the PyTorch model gives different outputs for the same input.
Here you can see that I am passing the same input through the PyTorch Model for 10 attempts and storing the outputs of each layer in outputs.

All attempts give the same result for the patch_embed layer.

The outputs of all attempts are also the same for "basic layer1"

For "basic layer2" the output of attempt 5 is differnt from others though it received the same input as others-

The situation gets worse at "basic layer3". The output of attempts 5,6,8,9 are different-

And when we go to the final_output the output of only attempt 0,1,3,4 are the same. The rest is different.

So we get different final outputs for the same input in the PyTorch model. And we can't say it's because of any random number initialization as the final outputs of four attempts have matched.
What could be the reason behind this?

@sayakpaul
In the case of TensorFlow, the same input always creates the same output. The output of the PyTorch model is inconsistent.

What happens if the random generator seed is fixed for PyTorch?

@sayakpaul  I have solved the issue of PyTorch Model creating different output with same input values. I had to use model.eval() after loading the model. Now each attempt producing same result.
This result has some some difference with the TF output. np.testing.assert_allclose() with atol and rtol set to 1e-1 asserts the following-

If I set atol and rtol to 1e-4 then it asserts the following-

And here is the layerwise output of both model-

We can' settle with 1e-1, it's too high of a tolerance here.
Also, when comparing outputs, set the gradient computation for PT to False torch.set_grad_enabled(False). With this, you won't have to do with torch.no_grad().

@sayakpaul
Now the situation is basic layer1 of both models gets the same input and it produces different output. For testing purpose I'm using 1e-2 .

Here 33% elements are mismatched. In the subsequent layers it creases and reaches 72% for the final layer.
Could the floating point calculation be the reason behind this?

Please keep the tolerance to a minimum of 1e-4.

Could the floating point calculation be the reason behind this?

Not to this extent. Now that we know where the mismatch stems from (BasicLayer), I guess you can look into the implementation and correct what's missing?

Okay, Dada. I will look into the basic layer. Thank you so much.
On Mon, Aug 22, 2022 at 5:19 PM Sayak Paul @.***> wrote:

Please keep the tolerance to a minimum of 1e-4.
Could the floating point calculation be the reason behind this?
Not to this extent. Now that we know where the mismatch stems from
(BasicLayer), I guess you can look into the implementation and correct
what's missing?
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Support other boards

It would be great if it support other boards (orange pi, banana pi, etc)

Please also add Omega2 🙏


WebGL error displaying bounding volumes when using regions

Problem:
I am trying to debug a tileset.json I generated. I am using regions instead of spheres or boxes and it works fine, but when I try to enable displaying of bounding boxes I am getting the following error:
[.Offscreen-For-WebGL-0x30d53fdee00]GL ERROR :GL_INVALID_OPERATION : glDrawElements: attempt to access out of range vertices in attribute 4
WebGL: too many errors, no more errors will be reported to the console for this context.

Example region:
The tileset.json is very big (>10MB), but a n example region looks like this:
{
    "region": [-1.680111719624372, 0.53360849538126576, -1.680111352014404, 0.53360812210272279, 0, 1000]
}

Could this be a bug with cesium or did I do something wrong with my tileset.json?
Please let me know if you need any additional information from me.
Information about my system:
$ chromium --version
Chromium 59.0.3071.86
$ uname -a
Linux auenland 4.11.4-1-ARCH #1 SMP PREEMPT Fri Jun 9 07:46:48 CEST 2017 x86_64 GNU/Linux
$ lspci -v | grep VGA
01:00.0 VGA compatible controller: Advanced Micro Devices, Inc. [AMD/ATI] Tahiti XT [Radeon HD 7970/8970 OEM / R9 280X] (prog-if 00 [VGA controller])

With region being [west, south, east, north, minHeight, maxHeight], your south is greater than your north. After flipping the two the region is drawn correctly (though very small).

Thank you. That fixed it.