Random Memory Exceptions

Hi! I'm relatively new at SDL2, and needed a way to display text within my game loop that wouldn't slow it down by allocating new memory every frame. This library (so far) has worked out great!
However, I'm noticing that every once in a while, during a fast game-loop, your library throws an exception. These exceptions are always memory related. (Something like "Unable to access/read location 000DDDDD" or something like that) The confusing part is that they occur at random. Usually, when you get an exception there's a specific cause of the problem. (Like when I click a button, it throws an exception. Or when I move my mouse. Or something.) But in this case, my game loop will be happily speeding along, until out of the blue a memory exception is thrown.
My Theory: The game loop is running very fast, (~60FPS, according to my calculations) and sometimes memory is not properly managed properly. Like it gets corrupted or something. Is that a viable theory? Are there any other things that could be causing seemingly random memory exceptions? If this is the case, would having some kind of framerate capper in my game loop help?
Thanks for a great library!

Well, I figured it out! (Not sure how, but I did.)


Installing GoFreeTds in Windows 7 Error

I have Already install MinGW gcc is working ok, when I go get -u github.com/minus5/gofreetds I receive this error:
D:\GoLib\src\github.com\minus5\gofreetds\callbacks.go:8:22: fatal error: sybfront.h: No such file or directory
#include <sybfront.h>
^
compilation terminated.
Can you help me?

I have solved with an Environment variable C_INCLUDE_PATH pointing to freetds/include.
It seems that the problem is my version of Freetds for windows is 32 bit.
I am getting this error in conn.go:
warning: cast from pointer to integer of different size
This error occurs in three lines: 30:22, 36:22 and 50:10.
I cannot go get gofreetds because freetds cannot compile.
Can you tell where to find the correct freetds source.
Thanks

I don't have experience with using freetds on Windows.
On Mac I use homebrew, and on ubuntu apt packages.
According to the lines problem is cgo related.
Did you check the cgo doc about windows, https://github.com/golang/go/wiki/cgo:
In order to use cgo on Windows, you'll also need to first install a gcc compiler (for instance, mingw-w64) and have gcc.exe (etc.) in your PATH environment variable before compiling with cgo will work.


[BUG] Table with two file columns, Bread fails to upload second file

Version information

Laravel: v5.7.*
Voyager: v1.1
PHP: 7.2
Database: MySQL 5.4

Description
Having a table with 2 columns of type varchar(256) to upload 2 different files, fails when using insert new record form, there is no error displayed but the second file is never uploaded and the stored value in the table is an empty json
Steps To Reproduce
Steps to reproduce the behavior:

Create a simple table and add 2 varchar fields
Create BREAD for the table
Set input as File for both columns
Insert a new record
pick two files and save
check table info, one file gets correctly uploaded the other one doesn't upload and the stored value in the table is an empty json

Expected behavior
Both files should get uploaded and the info in the table should be complete.
Additional context
I don't need the multiple files option since I am creating an API that should retrieve each file as specific properties of the model

Happens only on localhost, probably for php artisan serve limitations?

Happened to me on production server too.

Duplicate of https://github.com/the-control-group/voyager/issues/3002


changed to use React.Component instead of RNative

For the dc-redux new branch.

updated requires to support 0.25+
also made sure the timeout is cleared on unmount.

closes #104 at the same time

+1

For the love of everything can this repo please be brought up to date?

Waiting for NPM maintainer status :(

@dozoisch I'd happily add you as an npm / github maintainer of https://github.com/maintained-repos/react-native-swipeout if you're interested.  I'd love to see this PR get merged.

@jsdevel hey thanks! I'll try to get this PR merged in DC-REDUX. I'll open an issue on the new repo to discuss that pr since it's not on the master branch and we would have to decide if we want to go forward with that other branch.

Looks like this will also close #138.


Not an issue: Updated to Openelec 5.0.1, it is working with Hyperion!!

Just updated my Raspberry Pi B to the latest Openelec 5.0.1. I had the latest Gotham build installed and decided to update. After I did the usual manual update the ambilight responded immediately! You can change the Install on Openelec bit.

I can confirm what @cmvac says, I just got my LED strip, and after I connected it to my OpenELEC 5.0.1 RPi, installed hyperion and rebooted the lights turned on!

@cmvac could you close this pleasE?

Closed due to age. Please open a new one, if problems still exists.
Thank you


update intensity measurement to account for no internal pixels

Codecov Report

Merging #17 (30bc990) into main (256cd8e) will increase coverage by 0.69%.
The diff coverage is 100.00%.

@@            Coverage Diff             @@
##             main      #17      +/-   ##
==========================================
+ Coverage   88.26%   88.96%   +0.69%
==========================================
  Files          24       24
  Lines         571      607      +36
==========================================
+ Hits          504      540      +36
  Misses         67       67

Impacted Files
Coverage Δ

src/morphometrics/measure/_tests/test_intensity.py
100.00% <100.00%> (ø)

src/morphometrics/measure/intensity.py
100.00% <100.00%> (ø)

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update 256cd8e...30bc990. Read the comment docs.


koji_parent checks label alias

Signed-off-by: Robert Cerven<EMAIL_ADDRESS>

Updated release notes draft:

koji_parent checks label alias


explore security job failure

NONE

@devinbinnie , saw this failing on master.
I've tried running it, the error is resolved, but the repo settings might only allow it to run on master
(ref: run : https://github.com/mattermost/desktop/actions/runs/5380780236/jobs/9763978916#step:4:231)

/e2e-test

/e2e-test OS=windows

Successfully triggered e2e desktop testing!
https://git.internal.mattermost.com/qa/desktop-e2e-testing/-/pipelines/348530

/e2e-test OS=mac

Successfully triggered e2e desktop testing!
https://git.internal.mattermost.com/qa/desktop-e2e-testing/-/pipelines/348532

@marianunez can you help me in knowing, who can I tag from security team to review ?

can you help me in knowing, who can I tag from security team to review ?

Adding @iyampaul for a sec-ops review on the ossf action update. Feel free to reassign as needed


MSSQL connection crash

Environment
Knex version: 0.95.4
Database + version: MSSQL 2016
OS: Windows Server 2016
Bug

Explain what kind of behaviour you are getting and how you think it should do
I created a basic mssql db connection with knexjs. Its was work great before but i getting this error right now.

Error message
Uncaught Error: No event 'socketConnect' in state 'SentPrelogin'

that error is coming from mssql drive so knex cant do much about it. Also cannot reproduce.


Make it clear how to configure the registry in AWS with IAM Users

The guide to configuring the Registry with IAM Users is confusing. It mentions IAM Roles, but we are not using roles

Is this specific to the latest version? If not, can we also do the change in older ones? (I don't think is necessary to do them all. Maybe just 1.0+?)

@rberrelleza yes, I was waiting for confirmation about the text and then applying it to all versions. All good with the text?


[Request for External] report 28 "Dimensions - Detail"

Dear ALAppExtensions team,
On behalf of 4PS I would like to request to make variable 'GLAcc' protected on report 28 "Dimensions - Detail".
protected var
    GLAcc: Record "G/L Account";

Thanks for reporting this. We agree, and we’ll publish a fix asap, either in an update for the current version or in the next major release. Please do not reply to this, as we do not monitor closed issues. If you have follow-up questions or requests, please create a new issue where you reference this one.


Allow any Machine Type when using Bring-Your-Own-Accont

Story
As a Gardener user I want to be able to use all the machine types that the cloud provider offers when I provide account details of my own infrastructure account, not just the ones provided by Gardener.
Motivation
In a completely managed environment we mainly would like to offer the machine types that are cost efficient for us, for which we have special discounts. When somebody brings their own resources (e.g. their own IaaS account) we really should not care and limit the user by the machine types he can use.

Well, we would have done that since long, would we have seen a reasonable way, but we don't. How would we do that? How should Gardener or the Dashboard even know? We are not the infrastructure provider, so we have a hard time getting to that information. Or do you imagine some "free-text" field that we then can't validate?

I can think of a way. Ultimately the one who brings the secret should determine the machine types; so there is an association between the secret and the machine types. How about this: the list of all possible machine types and the list of default machine types (more or less the list we do have today) shall be part of the cloud profile. When the list of machines is not specified when entering the secret then the default list of machine types is taken keeping compatibility to the current approach. When entering a secret the stakeholder could additionally select from the list of all types which is present in the cloud profile (or for UI simplicity) all machine types could be taken.

@vlerenc  please be careful with absolute statements like "this will never be implemented".
Not important, well my inbox is full of those kind of questions and requirements so I do not agree that this is not important. In case we want to offer this as a service we are in the driver seat and offer the machine types that we find appropriate and that suit us and our business needs. Others who pay the bill themselves are of course free to use whatever they need and want and for what they might have a special deal. Yes we can do that with different shoots and restrict projects on shoots but that sounds really awkward and wrong - at least for this use case.
Well I don't think that we need all of the above and that there is a reasonable way with reasonable effort to get this done. Here is a take:

the full list can be handled on demand, no need to write code to sync that with the cloud provider or keep looking every week.
I don't see an immediate need to maintain the data in the dashboard - if you bring your own secret you can just get everything, if not you get the default list.
I did not suggest put the machines into the secret but a config map beside (which is created based on the above rule
I don't see the need for an admission controller. Further to the above, if you have a secret (which means your brought it) the config map with the machine types is considered. If you have a secret-binding this one is ignored

I might be wrong but this does not sound like a huge project.

Lets discuss this with the team and decide, maybe I will be able to explain it. If not I will dump it.

In any case, can you please let us know wha

What will happen with this ticket? There is always the manual way to add all/many more machine types. Are you interested to file a PR @hummelo?


fix(tags): empty commit to trigger semantic

:tada: This PR is included in version 7.1.0 :tada:
The release is available on:

GitHub release
npm package (@latest dist-tag)

Your semantic-release bot :package::rocket:


Revise native API for consistency, usability, and api generation

To my unexpected delight, the native API has been surprisingly popular! It's time to do a pass on usage from the native side, and make that more consistent and pleasant to use.
A couple of objectives here

[ ] Consistency
API should follow consistent naming and argument conventions. It's generally good about this, but there's a few places where there's probably some holes. I'd also like for this to be more consistent with the C# API, I've spent a lot of time massaging the C# API, and those changes don't always make it back to the native side. This would make it easier to refer to the C# documentation when working in C++, and may also allow for procedural matching of native functions in the docs, to make that experience even better.
[ ] Usability
The C# API has a lot of convenience functionality built into it that make it nice to use. The C side never really got this sort of attention, so there's some holes here as well. Like for example: Matrix.TRS has a whole collection of variations, but matrix_trs is all the native side has. Doing math on native is the big item here, but this also applies to lines, and perhaps a few other things.
[ ] API binding generation
The PInvoke bindings for C# are currently written by hand, and this is a source of friction, and occasionally errors. Automating parts of this would make it more pleasant to add features, and worry less about binding bugs. This would also potentially allow generating bindings for other languages as well, like a proper OO C++ binding, Zig bindings, or whatever else. Early work can be found here, but additional modifications to the header file need to be made to disambiguate intent, especially around in/out/ref/array/array_of_arrays situations. Disambiguation is also great for dev readability, so win win here :)
[ ] & vs. *
Currently, StereoKit uses & for parameters in C++ and * for C. I promise it made sense to me when I first wrote it. Since StereoKit is now a fully C compatible header, it would probably be good to move this all over to *, or at the very minimum for out/ref parameters. The header is also not 100% consistent here with & vs *, and some functions (a lot of math!) probably should just use copy parameters instead of const&.

Just going to keep a running tab of stuff I bump into here:

model_get/set_* functions don't reflect the fact that they retrieve subset information, not model_t information.
log_* functions share the same prefix as the log_ enum. It's debatable.
sh_* functions are nice and short, but are not consistent with spherical_harmonics_t.
matrix_trs_out I'm not sure if I love this pattern. pose_matrix_out, matrix_mul, and matrix_inverse also do this. May need to profile to see if performance makes a difference here.
size_t should probably not be used in a public facing API. See tex_create_mem, tex_get_data, shader_create_mem, and model_create_mem


footer improved

Description of PR
Footer improvement

Hey, I think you can delete the old footer instead of commenting it out. No point in keeping the old code if we merge this

@shuklaayush @HarshSBhatt for further changes and improvements in the footer, it would be easy if we make a separate component for the footer. I made this (https://github.com/covid19india/covid19india-react/pull/1005). Please review that PR also.

@shuklaayush @HarshSBhatt for further changes and improvements in the footer, it would be easy if we make a separate component for the footer. I made this (#1005). Please review that PR also.

I made a separate component brother

Not working on Firefox with Adblock plus 🤔

Not working on Firefox with Adblock plus 🤔

Please explain what is wrong


Allow @CrossOrigin to appear at the class level (#5260)

Fixes #5260
Mini spec
Done:

Main logic
Some tests (to be discussed below)

Not Done:

examples/microprofile/cors  (I need some feedback about this draft)

Some questions:

How can I check logging? I can't see it in my tests. Test CrossOriginTest#testPreFlightMethodLevelAnnotationTakesPrecedenceOverClassLevel should log. Did I miss something?

Should I add some additional tests? For example testPreFlightConfigFileTakesPrecedenceOverMethodLevelAnnotation? I've done it only for class-level annotation. I'm not sure.

I can't run tests from my IDE. I get error

But I can run tests via maven. Should I do some specific actions with my IDE? It uses Java 17.

Error messages can be changed. Sorry for my English :) .

@tjquinno Any advice will be appreciated. Don't hesitate to write comments. It's my first important PR.

Hi @Captain1653
I will take a closer look at the changes soon.
About your questions...

Logging - Do you want the test to check the logging automatically, or are you just wanting to look at the logging yourself manually?

For automatic, take a look at webserver/cors MissingConfigTest. That test contains some code to capture log output so the test can look at it. The code in that test only looks for a single, hard-coded string but it could easily be generalized by, for example, adding a parameter to checkForLogMessage for the string to be searched for and using the parameter in the assertThat invocation.
For manual, look for target/surefire-reports. There will be one or two output files for each test. Look for the ones with names ending in -output.txtwhich will contain logging messages. You might also need to change or createtest/resources/logging.properties` in order to enable whatever logging you want to see or check for, depending on what logging level the messages have.

Additional tests - I do not remember if we check that configuration overrides annotations. It would be good to have a test like that if we do not already.
Testing from the IDE - I see the same error. I will try some things to resolve that and let you know if I find a solution.


optimize stale epoch handling when splitting.

E.,g [a, c) -> [a, b) + [b, c)
We may meet following cases:

Keys in the request are all in [a, b), we can still execute this request no matter the epoch is stale.
Keys are all in [b, c), ticlient now may retry, but later tikv may execute this too if the new region leader is in the same store.
Keys cover [a, c), we can combine 1 and 2.

/cc  @BusyJay @disksing

I'm closing this stale issue, please re-open it if it's still a problem and you'd like to revisit it.


Issues with long lines

When I print out a long line that wraps off the edge of the console more than once (i.e. a string that takes at least 3 lines to print out), single-line-log seems to miss the first few lines of the output when clearing what it previously printed. So those lines repeat over and over.
To reproduce, try this, with the console resized so that long line wraps several times:
var log = require('single-line-log');
var msg = "this line should repeat \n long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string ";

setInterval(() => log.stdout(msg), 500);

I've verified with a number of different terminal emulators, it seems to be an issue with how single-line-log calculates what it needs to overwrite.

I can verify that this happens. I guess you're right that it's to do with the calculation.
Do you want to give it a go at fixing it?

Sure


vnc password for kvm

Quite dirty hack, but we need to send { "execute": "change-vnc-password", "arguments": { "password": "%s" } } into QMP socket to set password for VNC. Without this one we cannot connect to VNC with password in KVM HV.
Maybe there is a more elegant way to get configuration parameters (VncPasswd) inside func (ctx kvmContext) Start(domainName string, domainID int) error?
Signed-off-by: Petr Fedchenkov<EMAIL_ADDRESS>

Hey @giggsoff -- I'm cleaning up our PRs and it would be great to get this in. However, as I pointed out -- we can't persist the VncPassword inside of pillar code itself -- we have to externalize it somehow. In case of Xen that is done in Xen's config. In case of KVM I suggest you look into using https://github.com/opencontainers/runtime-spec/blob/master/specs-go/config.go#L20
So... instead of doing https://github.com/lf-edge/eve/pull/1225/files#diff-81e96cd930b1704b4214d18d7ebc150d7f17d3622878aaade9b4e1f2daa4a674R363 you will simply stash that password into a tasks' annotation. And here -- https://github.com/lf-edge/eve/pull/1225/files#diff-81e96cd930b1704b4214d18d7ebc150d7f17d3622878aaade9b4e1f2daa4a674R589 you will simply read that same annotation from the task description and use it.
If you want to see how to use task annotations -- this will be very similar to what we do here https://github.com/lf-edge/eve/blob/master/pkg/pillar/containerd/oci.go#L154 (IOW -- you will do s.Annotations[] = ...
Does this make sense? Can you quickly change your PR that way?

Yes, I tested this variant with Eden. We have no test for VNC password, but --vnc-password in combination with --vnc-display flags for pod deploy.


Document Rc/Arc method receivers.

Stabilization PR: https://github.com/rust-lang/rust/pull/56805
Stabilization issue: https://github.com/rust-lang/rust/issues/55786
It sounds like this may still change (https://github.com/rust-lang/rust/issues/55786#issuecomment-450254059), but I figured it wouldn't hurt to get the docs started. Since Receiver and Pin are still unstable, they are not mentioned.

Pin is stable though: https://doc.rust-lang.org/nightly/src/core/pin.rs.html#117


Connection unsuccessful: SendMail: Failed to open connection, dial tcp: lookup smtp.google.com on <IP_ADDRESS>:53: no such host

Problem:
Connection unsuccessful: SendMail: Failed to open connection, dial tcp: lookup smtp.google.com on <IP_ADDRESS>:53: no such host
docker-compose.yml
version: "3.7"

services:

  db:
    build: db
    read_only: true
    restart: unless-stopped
    volumes:
      - ./volumes/db/var/lib/postgresql/data:/var/lib/postgresql/data
      - /etc/localtime:/etc/localtime:ro
    environment:
      - POSTGRES_USER=mmuser
      - POSTGRES_PASSWORD=mmuser_password
      - POSTGRES_DB=mattermost

  app:
    build:
      context: app
      # uncomment following lines for team edition or change UID/GID
      args:
        - edition=team
    restart: unless-stopped
    volumes:
      - ./volumes/app/mattermost/config:/mattermost/config:rw
      - ./volumes/app/mattermost/data:/mattermost/data:rw
      - ./volumes/app/mattermost/logs:/mattermost/logs:rw
      - ./volumes/app/mattermost/plugins:/mattermost/plugins:rw
      - ./volumes/app/mattermost/client-plugins:/mattermost/client/plugins:rw
      - /etc/localtime:/etc/localtime:ro
    depends_on:
      - db
    expose:
      - 8000
    environment:
      - MM_USERNAME=mmuser
      - MM_PASSWORD=mmuser_password
      - MM_DBNAME=mattermost

  caddy:
    build:
      context: .
      dockerfile: Dockerfile
    depends_on:
      - app
    volumes:
      - production_caddy:/root/.caddy
    environment:
      - DOMAIN_NAME=example.com
    ports:
      - "<IP_ADDRESS>:80:80"
      - "<IP_ADDRESS>:443:443"

volumes:
  production_caddy: {}

Mattermost Email configuration
Server :smtp.google.com
port : 587
Authen : True
user<EMAIL_ADDRESS>passwd: mypasswd
Conn Security: STARTTLS
Skip cert verification: false

I couldn't be able to figure out where am I wrong here. I did not put any hardcode to the DNS stuff in the yml file
issue

Are you sure you use the correct SMTP server ? Because it's seems to me that smtp.google.com do not exist :
$ dig +short smtp.google.com

but smtp.gmail.com does
$ dig +short smtp.gmail.com                                                                                gmail-smtp-msa.l.google.com.
<IP_ADDRESS>
<IP_ADDRESS>

That was embarrassing. Thank you very much.


No documentation

Need to use Sphinx to generate documentation for the package.

Sphinx has been implemented in v24.3 to generate documentation for the project.


VRAM usage randomly spikes. Continues fine after restart.

Hi,
I'm training on a single A100. I've tried multiple batch sizes, but the same thing keeps happening.
I usually use between 10-25% of all available GPU VRAM, and then all of the sudden it spikes to 100% and crashes.
See the latest 5 wandb runs.
https://wandb.ai/mhenrichsen/CFM-TTS?nw=nwusermhenrichsen

How is the dataset constructed.
Does it contain extremely few long samples?

@SWivid no. It's 600ish hours of 500k samples. Nothing longer than 25 seconds.

@SWivid no. It's 600ish hours of 500k samples. Nothing longer than 25 seconds.

It's weird. The training process with 38400 frames per gpu just goes well on A100 80G for us, and it usually use 60~80G mem...

@SWivid do you see anything wrong here?
accelerate launch finetune-cli.py --exp_name F5TTS_Base --learning_rate 0.00001 --batch_size_per_gpu 3500 --batch_size_type frame --max_samples 64 --grad_accumulation_steps 32 --max_grad_norm 1 --epochs 10 --num_warmup_updates 500 --save_per_updates 16000 --last_per_steps 5000 --dataset_name danish_dataset --finetune True

nothing wrong with your setting i think.
if the crash just have happened at the same training point, maybe we could see throught for data used at that specific point.
for if you are using the a100 your own or on a shared remote machine (check if conflicts of gpu resource using)

It's my own cloud vm. The strange thing is that it went through first epoch and then crashed in 2nd.

Just a theory... having this case:
https://github.com/SWivid/F5-TTS/blob/main/model/dataset.py#L51
means that get_frame_len (https://github.com/SWivid/F5-TTS/blob/main/model/dataset.py#L32) doesn't always match up. So it's possible for DynamicBatchSampler to sometimes exceed the frames_threshold.
Try adding the same logic to get_frame_len (if duration > 30 or duration < 0.3: self.get_frame_len((index + 1) % len(self.data)))?

So it's possible for DynamicBatchSampler to sometimes exceed the frames_threshold.

The self.get_frame_len() is intended to get duration exactly for a queried index, while __getitem__ do calling.
If __getitem__ encounters a sample exceeding 30s, it will only find a substitution <30s.

Here are the complete logs:
Epoch 1/10:  67%|█████████████████████████████████████████████████████████████████████████▎                                   | 5143/7653 [05:15<1:32:12,  2.20s/step, loss=0.557, step=5143]
Traceback (most recent call last):
  File "/home/mads_syv_ai/F5-TTS/finetune-cli.py", line 127, in <module>
    main()
  File "/home/mads_syv_ai/F5-TTS/finetune-cli.py", line 120, in main
    trainer.train(
  File "/home/mads_syv_ai/F5-TTS/model/trainer.py", line 270, in train
    loss, cond, pred = self.model(
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
    return forward_call(*args, **kwargs)
  File "/home/mads_syv_ai/F5-TTS/model/cfm.py", line 279, in forward
    pred = self.transformer(
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
    return forward_call(*args, **kwargs)
  File "/home/mads_syv_ai/F5-TTS/model/backbones/dit.py", line 155, in forward
    x = block(x, t, mask=mask, rope=rope)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
    return forward_call(*args, **kwargs)
  File "/home/mads_syv_ai/F5-TTS/model/modules.py", line 486, in forward
    attn_output = self.attn(x=norm, mask=mask, rope=rope)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
    return forward_call(*args, **kwargs)
  File "/home/mads_syv_ai/F5-TTS/model/modules.py", line 312, in forward
    return self.processor(self, x, mask=mask, rope=rope)
  File "/home/mads_syv_ai/F5-TTS/model/modules.py", line 366, in __call__
    x = attn.to_out[1](x)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
    return forward_call(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/dropout.py", line 70, in forward
    return F.dropout(input, self.p, self.training, self.inplace)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/functional.py", line 1425, in dropout
    _VF.dropout_(input, p, training) if inplace else _VF.dropout(input, p, training)
torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 338.00 MiB. GPU 0 has a total capacity of 79.14 GiB of which 29.62 MiB is free. Including non-PyTorch memory, this process has 79.10 GiB memory in use. Of the allocated memory 77.51 GiB is allocated by PyTorch, and 1.08 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
Traceback (most recent call last):
  File "/home/mads_syv_ai/F5-TTS/finetune-cli.py", line 127, in <module>
    main()
  File "/home/mads_syv_ai/F5-TTS/finetune-cli.py", line 120, in main
    trainer.train(
  File "/home/mads_syv_ai/F5-TTS/model/trainer.py", line 270, in train
    loss, cond, pred = self.model(
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
    return forward_call(*args, **kwargs)
  File "/home/mads_syv_ai/F5-TTS/model/cfm.py", line 279, in forward
    pred = self.transformer(
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
    return forward_call(*args, **kwargs)
  File "/home/mads_syv_ai/F5-TTS/model/backbones/dit.py", line 155, in forward
    x = block(x, t, mask=mask, rope=rope)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
    return forward_call(*args, **kwargs)
  File "/home/mads_syv_ai/F5-TTS/model/modules.py", line 486, in forward
    attn_output = self.attn(x=norm, mask=mask, rope=rope)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
    return forward_call(*args, **kwargs)
  File "/home/mads_syv_ai/F5-TTS/model/modules.py", line 312, in forward
    return self.processor(self, x, mask=mask, rope=rope)
  File "/home/mads_syv_ai/F5-TTS/model/modules.py", line 366, in __call__
    x = attn.to_out[1](x)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
    return forward_call(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/dropout.py", line 70, in forward
    return F.dropout(input, self.p, self.training, self.inplace)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/functional.py", line 1425, in dropout
    _VF.dropout_(input, p, training) if inplace else _VF.dropout(input, p, training)
torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 338.00 MiB. GPU 0 has a total capacity of 79.14 GiB of which 29.62 MiB is free. Including non-PyTorch memory, this process has 79.10 GiB memory in use. Of the allocated memory 77.51 GiB is allocated by PyTorch, and 1.08 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)

What I mean to suggest is, suppose we have these examples with these durations
ex1, 100
ex2, 1
ex2, 1
ex2, 1

Could the following happen? Suppose we have these examples with these frames lengths and durations.
ex1: 100, 2.0
ex2: 50, 1.0
ex3: 10, 0.2
ex4: 100, 2.0
Suppose frames_threshold is 101. Then if I understand correctly, DynamicBatchSchedule would create these batches [ex1], [ex4], [ex2, ex3]. When we actually process the [ex2, ex3] batch, because ex3 duration < 0.3, it will try the next item which is ex4. That means the batch becomes [ex2, ex4] which has a total frame length of 150, exceeding the limit of 101, and may cause CUDA oom?

@kmn1024 Ah yes, that makes sense.
Maybe the best way is to filter out null audio samples or we just remove the contraint <0.3?

Thanks for the feedback so far. I'll try to remove the constraint <0.3 and report back.

@kmn1024 @SWivid I ran it without the constraint and here are the result.
Epoch 1/10:  67%|█████████████████████████████████████████████████████████████████████████▏                                   | 5142/7653 [05:10<1:31:33,  2.19s/step, loss=0.558, step=5142]
Traceback (most recent call last):
  File "/home/mads_syv_ai/F5-TTS/finetune-cli.py", line 127, in <module>
    main()
  File "/home/mads_syv_ai/F5-TTS/finetune-cli.py", line 120, in main
    trainer.train(
  File "/home/mads_syv_ai/F5-TTS/model/trainer.py", line 259, in train
    for batch in progress_bar:
  File "/opt/conda/lib/python3.10/site-packages/tqdm/std.py", line 1181, in __iter__
    for obj in iterable:
  File "/opt/conda/lib/python3.10/site-packages/accelerate/data_loader.py", line 561, in __iter__
    next_batch = next(dataloader_iter)
  File "/opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 701, in __next__
    data = self._next_data()
  File "/opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1465, in _next_data
    return self._process_data(data)
  File "/opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1491, in _process_data
    data.reraise()
  File "/opt/conda/lib/python3.10/site-packages/torch/_utils.py", line 715, in reraise
    raise exception
RuntimeError: Caught RuntimeError in DataLoader worker process 15.
Original Traceback (most recent call last):
  File "/opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/worker.py", line 351, in _worker_loop
    data = fetcher.fetch(index)  # type: ignore[possibly-undefined]
  File "/opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py", line 52, in fetch
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py", line 52, in <listcomp>
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/home/mads_syv_ai/F5-TTS/model/dataset.py", line 131, in __getitem__
    mel_spec = self.mel_spectrogram(audio)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
    return forward_call(*args, **kwargs)
  File "/home/mads_syv_ai/F5-TTS/model/modules.py", line 64, in forward
    mel = self.mel_stft(inp)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
    return forward_call(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/torchaudio/transforms/_transforms.py", line 619, in forward
    specgram = self.spectrogram(waveform)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
    return forward_call(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/torchaudio/transforms/_transforms.py", line 110, in forward
    return F.spectrogram(
  File "/opt/conda/lib/python3.10/site-packages/torchaudio/functional/functional.py", line 126, in spectrogram
    spec_f = torch.stft(
  File "/opt/conda/lib/python3.10/site-packages/torch/functional.py", line 702, in stft
    input = F.pad(input.view(extended_shape), [pad, pad], pad_mode)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/functional.py", line 5096, in pad
    return torch._C._nn.pad(input, pad, mode, value)
RuntimeError: Argument #4: Padding size should be less than the corresponding input dimension, but got: padding (512, 512) at dimension 2 of input [1, 1, 96]

Traceback (most recent call last):
  File "/home/mads_syv_ai/F5-TTS/finetune-cli.py", line 127, in <module>
    main()
  File "/home/mads_syv_ai/F5-TTS/finetune-cli.py", line 120, in main
    trainer.train(
  File "/home/mads_syv_ai/F5-TTS/model/trainer.py", line 259, in train
    for batch in progress_bar:
  File "/opt/conda/lib/python3.10/site-packages/tqdm/std.py", line 1181, in __iter__
    for obj in iterable:
  File "/opt/conda/lib/python3.10/site-packages/accelerate/data_loader.py", line 561, in __iter__
    next_batch = next(dataloader_iter)
  File "/opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 701, in __next__
    data = self._next_data()
  File "/opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1465, in _next_data
    return self._process_data(data)
  File "/opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1491, in _process_data
    data.reraise()
  File "/opt/conda/lib/python3.10/site-packages/torch/_utils.py", line 715, in reraise
    raise exception
RuntimeError: Caught RuntimeError in DataLoader worker process 15.
Original Traceback (most recent call last):
  File "/opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/worker.py", line 351, in _worker_loop
    data = fetcher.fetch(index)  # type: ignore[possibly-undefined]
  File "/opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py", line 52, in fetch
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py", line 52, in <listcomp>
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/home/mads_syv_ai/F5-TTS/model/dataset.py", line 131, in __getitem__
    mel_spec = self.mel_spectrogram(audio)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
    return forward_call(*args, **kwargs)
  File "/home/mads_syv_ai/F5-TTS/model/modules.py", line 64, in forward
    mel = self.mel_stft(inp)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
    return forward_call(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/torchaudio/transforms/_transforms.py", line 619, in forward
    specgram = self.spectrogram(waveform)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
    return forward_call(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/torchaudio/transforms/_transforms.py", line 110, in forward
    return F.spectrogram(
  File "/opt/conda/lib/python3.10/site-packages/torchaudio/functional/functional.py", line 126, in spectrogram
    spec_f = torch.stft(
  File "/opt/conda/lib/python3.10/site-packages/torch/functional.py", line 702, in stft
    input = F.pad(input.view(extended_shape), [pad, pad], pad_mode)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/functional.py", line 5096, in pad
    return torch._C._nn.pad(input, pad, mode, value)
RuntimeError: Argument #4: Padding size should be less than the corresponding input dimension, but got: padding (512, 512) at dimension 2 of input [1, 1, 96]

Maybe I should reprocess the dataset and filter out audio with a duration less than 0.3 and more than 30?

Maybe I should reprocess the dataset and filter out audio with a duration less than 0.3

Two possible reasons:

dataset contains null samples.
torchaudio just go crazy again, fail to load from audio file (i encountered once)

This happens when input to torch.stft is too short... it prevents you from computing stft over mostly zero-padding, which would be useless.
+1 to just filtering out samples with duration less than 0.3 and more than 30.

I can confirm that after removing all audio lower than 0.3 and higher than 30 it runs fine. Thanks for the help.

Hi guys, I have similar ploblems. Can you show some advice?
I am a Sound Engineer, very poor at computer engineering...
I tried various batch size ( 500~1000 ), and Epochs ( 50~500 ), it stops suddenly. With balckout of my display.
Here's a log.
Epoch 12/50:   0%|          | 0/65 [00:00<?, ?step/s]
Epoch 12/50:   0%|          | 0/65 [00:00<?, ?step/s, loss=1.02, step=716]
Epoch 12/50:   2%|1         | 1/65 [00:00<00:11,  5.41step/s, loss=1.02, step=716]
Epoch 12/50:   2%|1         | 1/65 [00:00<00:11,  5.41step/s, loss=0.745, step=717]
Epoch 12/50:   3%|3         | 2/65 [00:00<00:10,  6.28step/s, loss=0.745, step=717]
Epoch 12/50:   3%|3         | 2/65 [00:00<00:10,  6.28step/s, loss=1.28, step=718]
Epoch 12/50:   5%|4         | 3/65 [00:00<00:09,  6.63step/s, loss=1.28, step=718]
Epoch 12/50:   5%|4         | 3/65 [00:00<00:09,  6.63step/s, loss=0.712, step=719]
Epoch 12/50:   6%|6         | 4/65 [00:00<00:09,  6.74step/s, loss=0.712, step=719]
Epoch 12/50:   6%|6         | 4/65 [00:00<00:09,  6.74step/s, loss=0.503, step=720]
Epoch 12/50:   8%|7         | 5/65 [00:00<00:08,  6.88step/s, loss=0.503, step=720]
Epoch 12/50:   8%|7         | 5/65 [00:00<00:08,  6.88step/s, loss=0.38, step=721]
Epoch 12/50:   9%|9         | 6/65 [00:00<00:08,  6.75step/s, loss=0.38, step=721]
Epoch 12/50:   9%|9         | 6/65 [00:01<00:08,  6.75step/s, loss=0.466, step=722]
Epoch 12/50:  11%|#         | 7/65 [00:01<00:08,  6.86step/s, loss=0.466, step=722]
Epoch 12/50:  11%|#         | 7/65 [00:01<00:08,  6.86step/s, loss=5.56, step=723]
Epoch 12/50:  12%|#2        | 8/65 [00:01<00:08,  6.90step/s, loss=5.56, step=723]
Epoch 12/50:  12%|#2        | 8/65 [00:01<00:08,  6.90step/s, loss=1.36, step=724]
Epoch 12/50:  14%|#3        | 9/65 [00:01<00:08,  6.92step/s, loss=1.36, step=724]
Epoch 12/50:  14%|#3        | 9/65 [00:01<00:08,  6.92step/s, loss=0.509, step=725]
Epoch 12/50:  15%|#5        | 10/65 [00:01<00:07,  6.96step/s, loss=0.509, step=725]
Epoch 12/50:  15%|#5        | 10/65 [00:01<00:07,  6.96step/s, loss=0.392, step=726]
Epoch 12/50:  17%|#6        | 11/65 [00:01<00:07,  6.99step/s, loss=0.392, step=726]
Epoch 12/50:  17%|#6        | 11/65 [00:01<00:07,  6.99step/s, loss=0.469, step=727]
Epoch 12/50:  18%|#8        | 12/65 [00:01<00:07,  7.00step/s, loss=0.469, step=727]
Epoch 12/50:  18%|#8        | 12/65 [00:01<00:07,  7.00step/s, loss=0.482, step=728]
Epoch 12/50:  20%|##        | 13/65 [00:01<00:07,  7.10step/s, loss=0.482, step=728]
Epoch 12/50:  20%|##        | 13/65 [00:02<00:07,  7.10step/s, loss=0.958, step=729]
Epoch 12/50:  22%|##1       | 14/65 [00:02<00:07,  7.15step/s, loss=0.958, step=729]
Epoch 12/50:  22%|##1       | 14/65 [00:02<00:07,  7.15step/s, loss=0.526, step=730]
Epoch 12/50:  23%|##3       | 15/65 [00:02<00:06,  7.22step/s, loss=0.526, step=730]
Epoch 12/50:  23%|##3       | 15/65 [00:02<00:06,  7.22step/s, loss=0.638, step=731]
Epoch 12/50:  25%|##4       | 16/65 [00:02<00:06,  7.11step/s, loss=0.638, step=731]
Epoch 12/50:  25%|##4       | 16/65 [00:02<00:06,  7.11step/s, loss=0.968, step=732]
Epoch 12/50:  26%|##6       | 17/65 [00:02<00:06,  7.18step/s, loss=0.968, step=732]
Epoch 12/50:  26%|##6       | 17/65 [00:02<00:06,  7.18step/s, loss=2.71, step=733]
Epoch 12/50:  28%|##7       | 18/65 [00:02<00:06,  7.21step/s, loss=2.71, step=733]
Epoch 12/50:  28%|##7       | 18/65 [00:02<00:06,  7.21step/s, loss=0.73, step=734]
Epoch 12/50:  29%|##9       | 19/65 [00:02<00:06,  7.22step/s, loss=0.73, step=734]
Epoch 12/50:  29%|##9       | 19/65 [00:02<00:06,  7.22step/s, loss=0.66, step=735]
Epoch 12/50:  31%|###       | 20/65 [00:02<00:06,  7.24step/s, loss=0.66, step=735]
Epoch 12/50:  31%|###       | 20/65 [00:03<00:06,  7.24step/s, loss=0.84, step=736]
Epoch 12/50:  32%|###2      | 21/65 [00:03<00:06,  7.19step/s, loss=0.84, step=736]
Epoch 12/50:  32%|###2      | 21/65 [00:03<00:06,  7.19step/s, loss=0.443, step=737]
Epoch 12/50:  34%|###3      | 22/65 [00:03<00:05,  7.24step/s, loss=0.443, step=737]
Epoch 12/50:  34%|###3      | 22/65 [00:03<00:05,  7.24step/s, loss=0.655, step=738]
Epoch 12/50:  35%|###5      | 23/65 [00:03<00:05,  7.28step/s, loss=0.655, step=738]
Epoch 12/50:  35%|###5      | 23/65 [00:03<00:05,  7.28step/s, loss=0.551, step=739]
Epoch 12/50:  37%|###6      | 24/65 [00:03<00:05,  7.32step/s, loss=0.551, step=739]
Epoch 12/50:  37%|###6      | 24/65 [00:03<00:05,  7.32step/s, loss=2.87, step=740]
Epoch 12/50:  38%|###8      | 25/65 [00:03<00:05,  7.32step/s, loss=2.87, step=740]
Epoch 12/50:  38%|###8      | 25/65 [00:03<00:05,  7.32step/s, loss=0.413, step=741]
Epoch 12/50:  40%|####      | 26/65 [00:03<00:05,  7.17step/s, loss=0.413, step=741]
Epoch 12/50:  40%|####      | 26/65 [00:14<00:21,  1.83step/s, loss=0.413, step=741]
Traceback (most recent call last):
File "D:\AI_Projects\f5tts\F5-TTS\src\f5_tts\train\finetune_cli.py", line 161, in
main()
File "D:\AI_Projects\f5tts\F5-TTS\src\f5_tts\train\finetune_cli.py", line 154, in main
trainer.train(
File "D:\AI_Projects\f5tts\F5-TTS\src\f5_tts\model\trainer.py", line 311, in train
self.ema_model.update()
File "D:\AI_Projects\f5tts\F5-TTS\venv\Lib\site-packages\ema_pytorch\ema_pytorch.py", line 270, in update
step = self.step.item()
^^^^^^^^^^^^^^^^
RuntimeError: CUDA error: unknown error
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1
Compile with TORCH_USE_CUDA_DSA to enable device-side assertions.
Traceback (most recent call last):
File "", line 198, in _run_module_as_main
File "", line 88, in run_code
File "D:\AI_Projects\f5tts\F5-TTS\venv\Scripts\accelerate.exe_main.py", line 7, in
File "D:\AI_Projects\f5tts\F5-TTS\venv\Lib\site-packages\accelerate\commands\accelerate_cli.py", line 48, in main
args.func(args)
File "D:\AI_Projects\f5tts\F5-TTS\venv\Lib\site-packages\accelerate\commands\launch.py", line 1168, in launch_command
simple_launcher(args)
File "D:\AI_Projects\f5tts\F5-TTS\venv\Lib\site-packages\accelerate\commands\launch.py", line 763, in simple_launcher
raise subprocess.CalledProcessError(returncode=process.returncode, cmd=cmd)
subprocess.CalledProcessError: Command '['D:\AI_Projects\f5tts\F5-TTS\venv\Scripts\python.exe', 'src/f5_tts/train/finetune_cli.py', '--exp_name', 'F5TTS_Base', '--learning_rate', '1e-05', '--batch_size_per_gpu', '500', '--batch_size_type', 'frame', '--max_samples', '64', '--grad_accumulation_steps', '1', '--max_grad_norm', '1', '--epochs', '50', '--num_warmup_updates', '6', '--save_per_updates', '5000', '--last_per_steps', '1000', '--dataset_name', '1212_test02', '--finetune', 'True', '--tokenizer', 'pinyin', '--log_samples', 'True', '--logger', 'tensorboard']' returned non-zero exit status 1.

I just success the training chainging logger type: wandb ( 50 epochs, 500 batch size ) , but failed while testing model.
Can I get some advice? Should I change specific version of Pytorch or CUDA?
Thanks.
Fetching 4 files: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<?, ?it/s]
Traceback (most recent call last):
File "D:\AI_Projects\f5tts\F5-TTS\venv\Lib\site-packages\gradio\queueing.py", line 624, in process_events
response = await route_utils.call_process_api(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\AI_Projects\f5tts\F5-TTS\venv\Lib\site-packages\gradio\route_utils.py", line 323, in call_process_api
output = await app.get_blocks().process_api(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\AI_Projects\f5tts\F5-TTS\venv\Lib\site-packages\gradio\blocks.py", line 2043, in process_api
result = await self.call_function(
^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\AI_Projects\f5tts\F5-TTS\venv\Lib\site-packages\gradio\blocks.py", line 1590, in call_function
prediction = await anyio.to_thread.run_sync(  # type: ignore
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\AI_Projects\f5tts\F5-TTS\venv\Lib\site-packages\anyio\to_thread.py", line 56, in run_sync
return await get_async_backend().run_sync_in_worker_thread(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\AI_Projects\f5tts\F5-TTS\venv\Lib\site-packages\anyio_backends_asyncio.py", line 2505, in run_sync_in_worker_thread
return await future
^^^^^^^^^^^^
File "D:\AI_Projects\f5tts\F5-TTS\venv\Lib\site-packages\anyio_backends_asyncio.py", line 1005, in run
result = context.run(func, *args)
^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\AI_Projects\f5tts\F5-TTS\venv\Lib\site-packages\gradio\utils.py", line 865, in wrapper
response = f(*args, **kwargs)
^^^^^^^^^^^^^^^^^^
File "D:\AI_Projects\f5tts\F5-TTS\src\f5_tts\train\finetune_gradio.py", line 1234, in infer
tts_api = F5TTS(
^^^^^^
File "D:\AI_Projects\f5tts\F5-TTS\src\f5_tts\api.py", line 51, in init
self.duration_model = load_duration_model()
^^^^^^^^^^^^^^^^^^^^^
File "D:\AI_Projects\f5tts\F5-TTS\src\f5_tts\infer\utils_infer.py", line 245, in load_duration_model
transformer=DurationTransformer(
^^^^^^^^^^^^^^^^^^^^
File "D:\AI_Projects\f5tts\F5-TTS\src\f5_tts\model\modules.py", line 814, in init
self.input_embed = DurationInputEmbedding(mel_dim, text_dim, dim).to(self.device)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\AI_Projects\f5tts\F5-TTS\venv\Lib\site-packages\torch\nn\modules\module.py", line 1174, in to
return self._apply(convert)
^^^^^^^^^^^^^^^^^^^^
File "D:\AI_Projects\f5tts\F5-TTS\venv\Lib\site-packages\torch\nn\modules\module.py", line 780, in _apply
module._apply(fn)
File "D:\AI_Projects\f5tts\F5-TTS\venv\Lib\site-packages\torch\nn\modules\module.py", line 805, in _apply
param_applied = fn(param)
^^^^^^^^^
File "D:\AI_Projects\f5tts\F5-TTS\venv\Lib\site-packages\torch\nn\modules\module.py", line 1160, in convert
return t.to(
^^^^^
RuntimeError: CUDA error: unknown error
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1
Compile with TORCH_USE_CUDA_DSA to enable device-side assertions.


fill() and stroke() don't do anything inside draw()

When either fill() or stroke() are run from inside draw, they do not change the colors at all. They work as intended inside setup().
Below is an example:
from pyprocessing import (
    size, stroke, frame_rate, fill,
    background, square,
)

def setup():
    size(640, 360)
    stroke(0)
    fill(255)
    frame_rate(60)
    background(50)

def draw():
    square(10, 10, 50)  # Draws white box as expected
    fill(127)
    square(70, 10, 50)  # Should draw grey box, instead draws white box

Some attributes in window.Window seems to not be updating while draw() is running, only when its initialized it seems.
I made a spaghetti fix, no idea of how it was supposed to be but seems to work.
diff --git a/pyprocessing/renderer/tkinter/window.py b/pyprocessing/renderer/tkinter/window.py
index e8871b4..bcb8ec2 100644
--- a/pyprocessing/renderer/tkinter/window.py
+++ b/pyprocessing/renderer/tkinter/window.py
@@ -13,9 +13,6 @@ class Window(tk.Frame):

         width = namespace.get('width', 640)
         height = namespace.get('height', 480)
-        self.frame_rate = namespace.get('framerate', 60)
-        self.stroke = namespace.get('stroke', '#000000')
-        self.fill = namespace.get('fill', '#ffffff')

         self.canvas = tk.Canvas(self, width=width, height=height)
         self.canvas.pack(fill=tk.BOTH, expand=True)
@@ -23,13 +20,22 @@ class Window(tk.Frame):
         self.queued_actions = deque()
         self.canvas_objs = []

+    @property
+    def frame_rate(self):
+        return self.pyprocessing.namespace.get('framerate', 60)
+
+    @property
+    def stroke(self):
+        return self.pyprocessing.namespace.get('stroke', '#000000')
+
+    @property
+    def fill(self):
+        return self.pyprocessing.namespace.get('fill', '#ffffff')
+
     def setup(self):
         namespace = self.pyprocessing.namespace
         width = namespace.get('width', 640)
         height = namespace.get('height', 480)
-        self.frame_rate = namespace.get('framerate', 60)
-        self.stroke = namespace.get('stroke', '#000000')
-        self.fill = namespace.get('fill', '#ffffff')

         self.config(width=width, height=height)
         self.canvas.config(width=width, height=height)

Using properties is a good idea. I may refactor that to move those onto the TkRenderer object.
There is also some optimisation that can be done, as accessing a dictionary is quite slow.
I'll probably make the optimisation at the PyProcessing.namespace object level though, by caching the values (until they change)


Bad Request on https://icebreaker-6apmwcttcisg4.azurewebsites.net/api/processnow/

<Error> <Message>An error has occurred.</Message> <ExceptionMessage>Bad Request</ExceptionMessage> <ExceptionType> Microsoft.Bot.Connector.MicrosoftAppCredentials+OAuthException </ExceptionType> <StackTrace> at Microsoft.Bot.Connector.MicrosoftAppCredentials.<RefreshTokenAsync>d__34.MoveNext() --- End of stack trace from previous location where exception was thrown --- at System.Runtime.CompilerServices.TaskAwaiter.ThrowForNonSuccess(Task task) at System.Runtime.CompilerServices.TaskAwaiter.HandleNonSuccessAndDebuggerNotification(Task task) at Microsoft.Bot.Connector.MicrosoftAppCredentials.<GetTokenAsync>d__27.MoveNext() --- End of stack trace from previous location where exception was thrown --- at System.Runtime.CompilerServices.TaskAwaiter.ThrowForNonSuccess(Task task) at System.Runtime.CompilerServices.TaskAwaiter.HandleNonSuccessAndDebuggerNotification(Task task) at Icebreaker.Controllers.ProcessNowController.<Get>d__3.MoveNext() --- End of stack trace from previous location where exception was thrown --- at System.Runtime.CompilerServices.TaskAwaiter.ThrowForNonSuccess(Task task) at System.Runtime.CompilerServices.TaskAwaiter.HandleNonSuccessAndDebuggerNotification(Task task) at System.Threading.Tasks.TaskHelpersExtensions.<CastToObject>d__3'1.MoveNext() --- End of stack trace from previous location where exception was thrown --- at System.Runtime.CompilerServices.TaskAwaiter.ThrowForNonSuccess(Task task) at System.Runtime.CompilerServices.TaskAwaiter.HandleNonSuccessAndDebuggerNotification(Task task) at System.Web.Http.Controllers.ApiControllerActionInvoker.<InvokeActionAsyncCore>d__0.MoveNext() --- End of stack trace from previous location where exception was thrown --- at System.Runtime.CompilerServices.TaskAwaiter.ThrowForNonSuccess(Task task) at System.Runtime.CompilerServices.TaskAwaiter.HandleNonSuccessAndDebuggerNotification(Task task) at System.Web.Http.Controllers.ActionFilterResult.<ExecuteAsync>d__2.MoveNext() --- End of stack trace from previous location where exception was thrown --- at System.Runtime.CompilerServices.TaskAwaiter.ThrowForNonSuccess(Task task) at System.Runtime.CompilerServices.TaskAwaiter.HandleNonSuccessAndDebuggerNotification(Task task) at System.Web.Http.Dispatcher.HttpControllerDispatcher.<SendAsync>d__1.MoveNext() </StackTrace> <InnerException> <Message>An error has occurred.</Message> <ExceptionMessage> Response status code does not indicate success: 400 (Bad Request). </ExceptionMessage> <ExceptionType>System.Net.Http.HttpRequestException</ExceptionType> <StackTrace> at System.Net.Http.HttpResponseMessage.EnsureSuccessStatusCode() at Microsoft.Bot.Connector.MicrosoftAppCredentials.<RefreshTokenAsync>d__34.MoveNext() </StackTrace> </InnerException> </Error>

Fixed the issue by redeploying

@yashrajmungale Can you please help me with this ? Not able to find any support regarding this

Thank you for submitting this issue, as we work to ensure consistent responses with items logged in our issues list, we are working to address these items that did not get responses. Please let us know if you are still facing this issue and require assistance and we will be happy to discuss. If no further action is needed at moment and no response is received, we will close out the issue in 3 business days. You may also always open a new issue or respond here if something does come back up. Thank you!


CNV-46603: add CUDN kind

Followup on https://github.com/openshift/networking-console-plugin/pull/134 to add ClusterUserDefinedNetwork to list / forms

Rebased and addressed feedback

/unhold

/lgtm

/ok-to-test


Loading the model on VM from attached volumes is extremely slow

What is the issue?
When pulling the model and running it the first time everything works fine.
However, after deallocating the VM and starting it again (attaching a permanent disk with Ollama models downloaded) it takes more than 20 minutes to load any large model.
It seems it's loading it to the CPU first with a speed of 100 MB per second.
It doesn't happen when I download a new model with "ollama pull" && "ollama run", only with models that were attached.
What did you expect to see?
Same loading time as after downloading the model
Steps to reproduce

Install Ollama on VM Ubuntu 22.04
ollama pull llama2:70b
ollama run llama2:70b --> loads fast
restart VM (deallocate)
ollama run llama2:70b --> takes 20x longer to start

Are there any recent changes that introduced the issue?
No response
OS
Linux
Architecture
x86
Platform
No response
Ollama version
0.1.28
GPU
Nvidia
GPU info
Nvidia A100
CPU
No response
Other software
No response

Is it a cloud platform, such as AWS, GCP, or self-hosted? If self-hosted, what hypervisor? What kind of disk is attached to the VM? Please provide as much detail as you can

x86? Hoping that you just typed a wrong, I thought it should be x86-64, because the memory on x86 is too low to run ollama on any models.
And what disk type you have? Local SSD or iSCSI?
It is easy to test the disk performance on Ubuntu. You can try any test software on first time before starting up ollama and second time before ollama.
Thanks,

@levy42 if you're still having performance problems, can you share more information about your setup and I'll reopen.
This feels more like a hypervisor/virtualization platform performance issue and less likely to be Ollama causing the problem, we just do a lot of I/O on large files.  For example, if your virtualization layer is doing a disk replication from one server to another when you restart/recreate the VM, that could be causing slow I/O.  Share more information about what hypervisor you're using, where the virtual disks are being stored, is there a storage array, what the mechanism is for re-creating the VM, are multiple hosts involved or is this all on a single host, etc.  Anything that can help us understand how I/O work, as that feels like the most likely cause of the poor performance.


[4.2] [Serialization] Follow compatibility typealiases for non-generic types

Cherry-pick of #16618 to the 4.2 branch. Reviewed by @DougGregor.
rdar://problem/39661212

@swift-ci Please test


Need to add temperature sensor in bridge-app

Issue :
Need to add temperature sensor in bridge-app when chip-tool read DeviceTypeList with EP3, 4, 5, 6, 10,11, 12, 13.
When DeviceTypeList is read with above EP, the device-list should contain Type 770(Temperature sensor)
PFA Logs:
BR.txt

@sowmyassp Does https://github.com/project-chip/connectedhomeip/pull/21287 resolve this?

@bzbarsky-apple Yes 21287 will resolve this issue so closing this issue here will follow up with 21287, thank you


Dealing with HTML characters

In some selects I have characters like <.
The old select2 used to successfully handle these but the new version  of select2 seems to just HTML entity encode them < and leave them there to display like that without brining them back to <

It sounds like Select2 might be double-encoding the data, as it should only be doing it once (when it displays it).
Can you create a jsbin/jsfiddle that reproduces the issue?

Here's one: https://jsfiddle.net/jLz8mjob/ it shows how it is seemingly doubel encoding

Oh correction it does, it just can't do code highlighting right, so if you force a < instead of < you will see it

For comparison Version 3 works as expected:
Version 4: http://jsfiddle.net/ok0c5bdm/1/
Version 3: http://jsfiddle.net/drv777h4/2/

Making https://github.com/select2/select2/issues/3115 the issue for dealing with this.


Implement Direct3D9 Frontend

It's finally here!~
This PR merges D9VK into DXVK, which is a Direct3D9 implementation using the DXVK backend.
The reason being is D9VK is usable and mostly feature complete now, even having some additional features not exposed by other implementations, and it would be nice to have all our D3D implementations in one place/project! 😄
It's finally time to merge!
What's next?
Once this gets merged, the d3d9 frontend will then be developed inside this repository.
What's to-do feature wise to be properly complete as of this PR:

Fixed function bumpmapping
Fixed function premodulation
Software cursor (hardware cursor is currently implemented)
ComposeRects (from D3D9Ex, however I have no test case for this)

and of course...
Lots of bug fixing!
Have fun!
Love from Josh 🐸❤~

:frog:

My prayers have been answered! Praise the frog! :frog:

Do you know what would be even nicer? To have a D12VK, so that we can play DX12 game in Linux.

@IvoAndonov well... Technically there's vkd3d for that.


add AdCellerant

Adds or updates the following companies, events, or universities:

AdCellerant

Checklist
Company updates

[x] I have put the most recent relevant date in the "Last Update" column
[ ] I have linked to the article about the change, not the company's homepage (Please put N/A if there is no public post)

Event updates

[ ] I have linked to the article about the change, not the event's homepage

University updates

[ ] I have linked to the article about the change, not the university's homepage

I have no article, but I work there.


Warned even though did enter comment, when using keyboard Tab

URL
No response
How to reproduce the issue?

Enter a changeset comment. Here I typed "Paths."
Now use the "Tab" key on your keyboard several times to advance to the Upload button.

We still get the warning that we need to enter a comment.
Screenshot(s) or anything else?

Which deployed environments do you see the issue in?
Released version at openstreetmap.org/edit
What version numbers does this issue effect?
Latest live as of this posting.
Which browsers are you seeing this problem on?
Chrome

hi @jidanni , I would like to work on this issue, can you provide me url for the issue


zgui: Add bindings for tables API

This is a really good first issue, I've been PR'ing things to zgui as I need them for my own projects.

Implemented by PR #167


Main screen appears for a brief moment when clicking the logo from Login Screen

Side issue from #29

Fixed with edfd6a7539e2d3b6686a719596cf0c07717d37d5


Node Node.js 8.10 jumping up due to end of life questions.

Hi All:
Just using Node .JS. 8.10 for a minor lambda function.  Can I keep the existing cost to upgrade to 10.x or even 12.x?
I read the functionality changes and it does look like it is code related.  So based on my quickly scan, no code changes would be impacted, but I will test either way.
Regards,
-Mike Franklin

Moving to help repo as I think it fits better there.


Create a new /column-data endpoint

Create a new endpoint which takes as its argument a list of column names, and returns a list of dictionaries, each representing those columns' value for each row in the table.

Each of the provided column names must exist in the table. If any columns are not present, the entire query should return a 400.


Render vehicle only

Thanks for your great work!
I noticed that NeuRAD supports rendering only static background in https://github.com/georghess/neurad-studio/blob/b7012ac5ac41c8ac86a63ba7ba799bd27a57d2f0/nerfstudio/scripts/render.py#L738, I want to know if there is a function or boolean to render moving objects only. If not, some advice to change the code will help me a lot！

Never mind !
I found the final render video has no problem!


Foliage instancer fixed/random_angle doesn't work

Description
Both fixed_angle and random_angle only tilt at 0 or ~85 degrees in some circumstances such as in Kyra's testing and in Out of the Ashes. However it appears to work fine in the demo.
Also fixed/random_tilt is a better name

I was not able to replicate this issue.

I cannot in the demo, but it's a definite problem in Out of the Ashes, and the user's project. I haven't figured out the difference yet.

The previous build we used from only a week or two ago, tested today, still demonstrated this issue in Out of the Ashes. After renaming the variables in #519 and rebuilding this now works. I reverted the name change and it still works. So perhaps the cause is in cached Godot data. I don't see any issues from the code or testing the data, so will close this.


Readme not rendering for std/testing

When I visit<EMAIL_ADDRESS>the readme is not visible, even though README.md is bold.
Other folders with a readme seem to work fine.

Seems like std/testing is not the only page with this issue:

https://deno.land/std
<EMAIL_ADDRESS><EMAIL_ADDRESS>
Others are still fine<EMAIL_ADDRESS>for instance

Closed by #2067


FS-928 Optimize bom cmd according to thread discussion.

This PR optimized the bom cmd format according to discussion in slack thread https://equinix.slack.com/archives/C056JN319PG/p1695317935291199
example:
mctl % ./mctl create bom --from-xlsx-file ~/Downloads/test-alva1.xlsx
 &{0 0 0 0 0 {0x1400061a1f0 <nil> <nil> <nil> <nil>} resource created   <nil> <nil>}
mctl % ./mctl get bom --aoc-mac B5
[
  {
    "serial_num": "A5",
    "aoc_mac_address": "B5,B6",
    "bmc_mac_address": "bmc1,bmc2",
    "num_defi_pmi": "3CECEFCF0087",
    "num_def_pwd": "FIWUZMZZGQ",
    "metro": ""
  }
]
mctl % ./mctl get bom --bmc-mac bmc1
[
  {
    "serial_num": "A5",
    "aoc_mac_address": "B5,B6",
    "bmc_mac_address": "bmc1,bmc2",
    "num_defi_pmi": "3CECEFCF0087",
    "num_def_pwd": "FIWUZMZZGQ",
    "metro": ""
  }
]

error example:
mctl % ./mctl get bom
 --aoc-mac and --bmc-mac not set

mctl % ./mctl get bom --aoc-mac B5 --bmc-mac b2
Error: if any flags in the group [aoc-mac bmc-mac] are set none of the others can be; [aoc-mac bmc-mac] were all set

Tested both locally and in sandbox.

I know it's kind of a hassle, but if you could remove the test that will never come back true that would be best.

Thanks Vince,
I am lost here. Can you please help explain more about "remove the test"? I didn't add test to this PR.
Do you suggest I paste logs that's sending invalid requests and getting failures?

I know it's kind of a hassle, but if you could remove the test that will never come back true that would be best.

Thanks Vince, I am lost here. Can you please help explain more about "remove the test"? I didn't add test to this PR. Do you suggest I paste logs that's sending invalid requests and getting failures?

I meant “remove the if statement”. You got it.


Tutorial "Using kubectl to Create a Deployment" not working on ARM architecture cluster

The tutorial found at https://kubernetes.io/docs/tutorials/kubernetes-basics/deploy-app/deploy-intro/ leads to error on ARM cluster.
The deployment state is CrashLoopBackOff and logs mention exec /bin/sh: exec format error.
After checking https://cloud.google.com/kubernetes-engine/docs/troubleshooting/troubleshooting-arm-workloads the error indicates the deployment has a container image which does not support ARM architecture.

A quick fix: document that the tutorial required AMD64 architecture.
Then we can track a longer-term issue to support a more diverse set of CPU instruction sets.

/kind bug
It's a bug because the page doesn't tell you about this prerequisite.

/triage accepted

This is correct that the image will not work on ARM.
However, the links to create a first cluster mention to use Minikube.
Using Minikube to Create a Cluster
Hello Minikube
If Minikube is used with the Docker Desktop as the driver, then Docker Desktop has the emulation in place for running AMD64 images on ARM architecture and the deployment will work just fine.
Maybe it just needs to be highlighted clearly to use Minikube with Docker Desktop driver as a pre-requisite.

/assign @pegasas

Thanks for the suggestions all. I was indeed not using docker desktop as a driver but colima.


Fix #87 - make expDesign computing the design of both MTplus and MT/MST loc

This PR make possible to have MTplus and MT/MST localziers' design computed by the refactored function expDeisgn. To my knowledge, the MT/MST loc was not working at v0.3.1 .
I am pushing to master cause, as said, we do not have an official release working with MT/MST and so this will be a minor bump as v0.4.0
What do you think @Remi-Gau ?

Will check it tomorrow
There is a dev branch on this repo but it seems behind master... No?
I should probably fix those failing CI too.

Yes, dev is behind but not so match. Looks like main has been patched with a couple of things (update of the submod). Do you suggest as good practice to always pr to dev? In this case I can delete this one and open a new one (after rebasing dev to main) and then work like that. Though I would release the new version after this pr cause it is important it is out.

Yes, dev is behind but not so match. Looks like main has been patched with a couple of things (update of the submod). Do you suggest as good practice to always pr to dev? In this case I can delete this one and open a new one (after rebasing dev to main) and then work like that. Though I would release the new version after this pr cause it is important it is out.

So in general dev should not be behind main or master so any change we apply to main, should be applied to dev: the easiest is to merge main into dev.
Also there is no need to open a new PR to change the "base" (the branch you target in a PR): if you go at the top of that PR page and click edit (right of the PR title) you can then alter the base branch. VERY useful.
I will update dev by merging main into it and will let you change the base.

I will update dev by merging main into it

done

ok will try to see why the tests are failing

Adding a reminder in getMockConfig that if the parameters for nbRep and nbEvents are different than 12 for both the tests will fail

Adding a reminder in getMockConfig that if the parameters for nbRep and nbEvents are different than 12 for both the tests will fail, correct?

Better would be to make the tests more flexible and read the "correct" value from the cfg itself.
I won't read a comment somewhere to know why a test fails: I think I would assume that the test is failing because I change something in the code and not in the mock.

fixes #91

Great! Thank you very much :) I will give it a round of try with all the task etc to check if something is broken and merge it. Will also create a release name v0.4.0. LMK if there is something I am missing

Great! Thank you very much :) I will give it a round of try with all the task etc to check if something is broken and merge it. Will also create a release name v0.4.0. LMK if there is something I am missing

happy to have a new release. :-)
I had made a checklist for new releases:
https://github.com/cpp-lln-lab/.github/blob/main/release_protocol.md

Ps: so nice to see all the checks green :)

I know, right?!


how to work with network with loop in connection with pox controller

Hi sir
I use mininet with pox controller.
In mininet terminal when I give the command 'sudo mn --topo=torus,3,3 --controller=remote --switch=lxbr,stp=1 '
the pingall is working successfully, but in pox terminal 'connection aborted' is displayed
I want to know how to make the network with loop work (ping) and at the same time connect to the controller

This is a general problem with learning switches.  You'll want to run it with the spanning_tree or spanning_forest component.  (Or write a better routing component.)


intel_adsp_cavs18: Test cases failed on SMP related test cases (when CONFIG_MP_NUM_CPUS > 1)

Describe the bug
Many test suites failed in intel_adsp_cavs18 hardware board, this issue is likely to be SMP related:
tests/kernel/smp/ (no output)
tests/kernel/spinlock/ (no output)
tests/kernel/mp/ (no output)
tests/kernel/workq/work/ (no output, but with -DCONFIG_MP_NUM_CPUS=1 only test_smp_simple_queue, test_1cpu_sync_queue, test_smp_running_cancel)
And bellow test suites failed without any output log, but they can pass with -DCONFIG_MP_NUM_CPUS=1:
tests/kernel/profiling/profiling_api/
tests/kernel/mem_protect/sys_sem/
tests/kernel/msgq/msgq_api/
tests/kernel/msgq/msgq_usage/
tests/kernel/queue/
tests/kernel/obj_tracking/
tests/kernel/events/event_api/
tests/kernel/fatal/message_capture/
tests/kernel/mbox/mbox_usage/
tests/kernel/threads/thread_stack/
tests/kernel/lifo/lifo_api/
tests/kernel/mutex/sys_mutex/
tests/kernel/mem_slab/mslab_concept/
tests/kernel/fifo/fifo_api/
tests/kernel/timer/timer_monotonic/
tests/kernel/mem_heap/k_heap_api/
tests/kernel/mem_heap/mheap_api_concept/
tests/kernel/condvar/condvar_api/
tests/kernel/semaphore/smaphore/
tests/kernel/device/
To Reproduce
Steps to reproduce the behavior:

west build -b intel_adsp_cavs18 -p always -t tests/kernel/smp/
west sign -d build -t rimage -p /home/ztest/zephyrproject/modules/audio/sof/rimage/build/rimage -D /home/ztest/zephyrproject/modules/audio/sof/rimage/config -- -k /home/ztest/zephyrproject/modules/audio/sof/keys/otc_private_key.pem --no-manifest
ssh root@localhost "boards/xtensa/intel_adsp_cavs15/tools/cavs-fw-v15.py build/zephyr/zephyr.ri"
python3 -u ${ZEPHYR_BASE}/boards/xtensa/intel_adsp_cavs15/tools/adsplog.py
You can see there is no output from ADSP.

Expected behavior
There should be testing messaged output. No matter it passed or failed
Impact
The whole kernel test pass rate down to 50%.
Logs and console output
Firmware completing manifest v1.8
 meta: completing ADSP manifest
 meta: limit is 0x16b80
 auth: completing authentication manifest
 cse: ri_css_v1_8_hdr_create completing CSS manifest
 css: set build date to 2022:00:20
 cse: completing CSE V1.8 manifest
Firmware file size 0x1b000 page count 21
 rimage_read_key: read key '/home/ztest/zephyrproject/modules/audio/sof/keys/otc_private_key.pem'
 pkcs: RSA private key is valid.
 pkcs: digest for manifest is a237faea7ee4b0e8a2efff1091e9a3983f44cab3013548d78106613a9d187ed0
Firmware manifest and signing completed !
INFO:cavs-fw:cAVS firmware loader v15
INFO:cavs-fw:Mapped PCI bar 0 of length 16384 bytes.
INFO:cavs-fw:Selected output stream 7 (GCAP = 0x1009701)
INFO:cavs-fw:Mapped PCI bar 4 of length 1048576 bytes.
INFO:cavs-fw:Detected cAVS 1.8+ hardware
INFO:cavs-fw:Obtained the physical address of the mapped huge page.
INFO:cavs-fw:Mapped 2M huge page at 0x15a200000 to contain 86016 bytes of firmware
INFO:cavs-fw:Filled the buffer descriptor list (BDL) for DMA.
INFO:cavs-fw:ADSPCS = 0xf0f0000

(hang up and no output here .....)

Environment (please complete the following information):

OS:  Linux
Toolchain: Zephyr SDK 0.13.2
Commit SHA or Version used: f08c44def5fc

Additional context

I don't have this hardware currently, so I'm going to take myself off as assignee while we find someone else to look at this.  Most likely this is a kconfig mistake or accidental regression with recent work.  The MP mechanisms here are almost the same as 1.5 and 2.5.  And IIRC SOF has 1.8 zephyr multiprocessor setups that don't seem to be failing.

Indeed, secondary core startup wasn't working (but amusingly in a way that SOF was insulated from by an existing workaround, so they never noticed).  Fix up at https://github.com/zephyrproject-rtos/zephyr/pull/42063 should unblock things.  I haven't done a full twister run yet though.


Add extra informations to @example

Why not adding language and description to examples annotations?

Thanks @HugoGiraudel, I forgot to close this issue.


Skip to PyThaiNLP 3.0

Hello! After we were release PyThaiNLP 2.4.0-dev0. In Thai NLP group, We have feedback that advice release PyThaiNLP 3.0 instead of PyThaiNLP 2.4.
What are you thinking? @bact @heytitle @cstorm125 @korakot @Gorlph

post https://web.facebook.com/groups/thainlp/posts/1390690147979138

Skipping to v3.0 seems reasonable given the list of changes we have introduced in the main branch; I personally don't have any opposing opinion to stay at v2.4.x. Given several changes (in different components) have been introduced, we might also consider extending the pre-release testing to be longer.
For the future, we might have a road map indicating on which (major) features we would introduce in each release. (maybe we already have the roadmap and I'm not aware of).

I have read the changes. I don't know much about how advanced these new features are, but there is a lot of new features and a few of breaking changes. Most importantly, as Ekapol commented, the main tokenizer's behavior has already been changed.
So, I think skipping to v3.0 is reasonable.

If we want to skip to 3.0, I have a change to newmm I want to add.
Now, we use 1 trie for both searching for normal tokens and OOV words. I want to use 2 different tries. The OOV can use a smaller trie so it won't match too easily and split the OOV words.

OK. I think we may skip tp PyThaiNLP 3.0 and we want to write the roadmap for PyThaiNLP 3.0.

The roadmap of PyThaiNLP 3.0 #605


Microsoft

Type of issue
Missing information
Feedback
Opening
Page URL
https://learn.microsoft.com/en-us/windows/apps/develop/security/design
Content source URL
https://github.com/MicrosoftDocs/windows-dev-docs/blob/docs/hub/apps/develop/security/design.md
Author
@alvinashcraft
Document Id
b62e3f4d-6141-9051-1d51-dc484a86cd53

It looks like there's no actionable feedback here, so I'll go ahead and close the issue. Please open a new issue if you have feedback about the documentation that you'd like us to address.


how to get refund

Please add guide on how to get refund.
You are breaking uk consumer law.
Your devices are not bespoke/ custom made. This is a lie!

@ripplethor Sorry this isn't a support channel but for documentation only. If you have an issue please open a support ticket here https://support.nebra.com/support/home/


Adding my name in the list

What type of PR is this? (check all applicable)

[x] 🚀 Added Name
[ ] ✨ Feature
[ ] 🌟 stared the repo
[ ] 🐛 Grammatical Error
[ ] 📝 Documentation Update
[ ] 🚩 Other

Description
Hello, i am a new dev. This is my first hacktobefest.
I added my name and corrected the spacing of the previous name.
Add Link of GitHub Profile
https://github.com/Gangorra

Is something missing to be approved?


libmupdf initial port

-For some reason the linting process kept nagging at me until I set both VCPKG_LIBRARY_LINKAGE and VCPKG_CRT_LINKAGE to static.
-While creating the port, I saw that sumatrapdf actually builds a non-static version of this library.  The source provided by libmupdf.org are all geared towards a static build.  The non-static sumatrapdf build of libmupdf seems pretty intricate and involves a custom module-definition file for the linker and something I don't quite grasp yet having to do with a resource file.  I guess what I'm asking is "how hard should I try to provide a non-static build" for this port?

Thanks for the PR!
If the upstream sources seem totally incompatible with a non-static build, then it's fine to simply use[1]
if (VCPKG_LIBRARY_LINKAGE STREQUAL dynamic)
    message(STATUS "Warning: Dynamic building not supported yet. Building static.")
    set(VCPKG_LIBRARY_LINKAGE static)
endif()

However, I am somewhat concerned about the linting issue. What was the message, specifically?
[1] Example from: https://github.com/Microsoft/vcpkg/blob/83464ec5b70ac881972600dfcea1a02644c80b03/ports/fcl/portfile.cmake#L3-L6

Since you're providing a replacement buildsystem anyway (the vcxproj)
I get that vcxproj at very little cost, by running vcupgrade (or devenv /upgrade) on the provided, made-for-older-msbuild vcproj.  I would script it rather than provide the upgraded project manually I could find a reliable way to do so.  Even better would be the ability to force msbuild to build vcproj (not the newer vcprojs, the old ones) project files but no combination of switches have ever allowed me to do so without first upgrading to vcXproj project file first.

generally portfiles shouldn't be switching the CRT linkage because it will likely result in unusable binaries
Let me double check on that one.

About having to set VCPKG_CRT_LINKAGE:

What I wrote needs some clarification: I only get linting problems when vcpkg expects a dynamic library but I provide a static one instead of failing.  For example the command vcpkg install libmupdf:x64-windows should fail, however it does not by virtue of the port file adjusting the build to static (set VCPKG_LIBRARY_LINKAGE static).
Asking for (and getting) a static build works without linting issues every time.  vcpkg install libmupdf:x64-windows-static
The resulting packages vcpkg/packages/libmupdf-x64-static and vcpkg/packages/libmupdf-x64 folders and byte-to-byte compare identical.

If I only set VCPKG_LIBRARY_LINKAGE static, the linting process complains that it

Expected Debug,Dynamic crt linkage but [...] got [...] Debug,Static

To me the linking process is as close as you can get to real magic.  Any advice on how to proceed further?

Maybe this article https://www.codeproject.com/Articles/1190061/Compiling-MuPDF-DLL-to-Render-and-Edit-PDF-Documen can help make a DLL build.

I won't make any further modifications on this pull request because I don't have the expertise to maintain a cmake build system for this (or any) library.  I also didn't want to close the branch because that would likely mean losing what Robert commited.

Thanks!


allow zoomAtPointBy to take SVGPoint as parameter

Fixes #163

I did cherry-pick only last commit. It is here ef06336559409a86fbcad4e97329b337b591021f.
Thank you!


Laravel form submit - select don't pass

Very simple select:
<form action="laravel-request" method="post">
<select name="qq[]" multiple class="selectpicker">
  <option value="1">1</option>
  <option value="2">2</option>
</select>
<button type="submit">Send</button>

If no selectpicker class - form submit select values successfully. If selectpicker is active - form send NULL
What am I doing wrong?
bootstrap-select last ver.
laravel 5.2

Duplicate of #1342.


HAWQ-1357. Super user also need to check create privilege of public schema from Ranger.

HAWQ-1318 add create|usage privilege of public schema to superuser to fix hawq stop BUG caused by Resource Manager.
But RM only need the usage privilege of public schema to query HAWQ, So create privilege need to be removed.

+1


fix PrimitiveActions drag of WhiskActivations

the akka after implementation creates an actor, but opaquely. thus, we have no opportunity to cancel any timeout futures. this PR adjust the after impl to expose and utilize the Cancellables
the consequence of this drag is that WhiskActivations, and a bunch of related artifacts, hang around until various timeouts expire.
this is a descendent of #3118
the relevant change for this PR is in ExecutionContextFactory.scala, i.e. https://github.com/apache/incubator-openwhisk/pull/3127/files#diff-37b2c2f6b7bfbdbd928baaa6696ba64d

closed in favor of cleaner #3129


[SPARK-25262][DOC][FOLLOWUP] Fix missing markup tag

What changes were proposed in this pull request?
This adds a missing end markup tag. This should go master branch only.
How was this patch tested?
This is a doc-only change. Manual via SKIP_API=1 jekyll build.

Merged build finished. Test PASSed.

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Test build #96773 has started for PR 22584 at commit 14bf049.

LGTM

Thank you for review, @mgaido91 !

Test build #96773 has finished for PR 22584 at commit 14bf049.

This patch passes all tests.
This patch merges cleanly.
This patch adds no public classes.

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Thank you, @HyukjinKwon and @mgaido91 !


chore: Make the pngs smaller

find . -iname '*.png' | xargs oxipng -o max --trip safe

Nice, the golden files should not be edited like this though.

I wonder why the golden tests fail though, since this is supposed to be a lossless operation :thinking:

Tried just going w/ the doc, example, media directories


Apple music Control

For Some reasons Apple music Downloaded form Snap store can not be seen by Media control. I know it's a web version, but it seem that the control do not see the app open.

Then the software does not support the interface (MPRIS) used by the extension to interact with the player I guess


Upgrade 'node' (16.14 -> 16.15); Upgrade 'pnpm' (6 --> 7)

Also pulled in the latest version of the recipes submodule.

Should we also upgrade on other repos?

There was a PR raised by my colleague on the recipes and the main app. So, these 3 repos are done - is there any repo that I am missing @SpecialAro ?

Should we also upgrade on other repos?

There was a PR raised by my colleague on the recipes and the main app. So, these 3 repos are done - is there any repo that I am missing @SpecialAro ?

I saw the other PRs after commenting, sorry!


[SPARK-18352][DOCS] wholeFile JSON update doc and programming guide

What changes were proposed in this pull request?
Update doc for R, programming guide. Clarify default behavior for all languages.
How was this patch tested?
manually

Test build #73708 has started for PR 17128 at commit d542802.

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This patch merges cleanly.
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merged to master


Memory leak when using osmobject.timestamp

I'm trying to parse OSM Planet file and export it into the BigQuery dataset.
After several tries, I've realized that memory usage is increasing for every processed object when I'm using the timestamp field of the OSMObject.
Here is sample code to reproduce this problem:
import sys
import osmium
import logging
import psutil

def bytes_to_mb(size_in_bytes):
    return size_in_bytes / (1024 ** 2)

class OsmHandler(osmium.SimpleHandler):

    def __init__(self):
        osmium.SimpleHandler.__init__(self)
        self.processing_counter = 0

    def node(self, node):
        self.processing_counter = self.processing_counter + 1
        node_timestamp = node.timestamp
        if self.processing_counter % 10000000 == 0:
            used_memory = "{:.3f}".format(bytes_to_mb(psutil.virtual_memory().used))
            free_memory = "{:.3f}".format(bytes_to_mb(psutil.virtual_memory().free))
            logging.info("Processed: {}. Memory: used - {} MB, free {} MB".format(self.processing_counter,
                                                                                used_memory, free_memory))

    def way(self, way):
        pass

    def relation(self, relation):
        pass

if __name__ == "__main__":
    logging.getLogger().setLevel(logging.INFO)

    simple_handler = OsmHandler()
    simple_handler.apply_file(sys.argv[1])

Running with:
python3 main.py planet-latest.pbf
Link to the latest Planet file: download
Output:
INFO:root:Processed: 10000000. Memory: used - 7234.180 MB, free 3765.926 MB
INFO:root:Processed: 20000000. Memory: used - 8102.402 MB, free 2885.051 MB
INFO:root:Processed: 30000000. Memory: used - 8475.586 MB, free 2507.949 MB
INFO:root:Processed: 40000000. Memory: used - 8957.559 MB, free 1938.234 MB
INFO:root:Processed: 50000000. Memory: used - 9407.355 MB, free 1427.562 MB
INFO:root:Processed: 60000000. Memory: used - 9884.508 MB, free 874.156 MB
INFO:root:Processed: 70000000. Memory: used - 10322.539 MB, free 375.598 MB

Is there any way to access node.timestamp without increasing of used memory?
Python 3.8
osmium 3.0.0

Are you sure this is related to your use of timestamp. Osmium gobbling up a certain amount of memory for buffers is expected behaviour, it should level out at some point.

@joto yes, it happens with timestamp. For example, here is the output when we get the whole osm object:
    def node(self, node):
        self.processing_counter = self.processing_counter + 1
        node_obj = node

INFO:root:Processed: 10000000. Memory: used - 6485.578 MB, free 4393.523 MB
INFO:root:Processed: 20000000. Memory: used - 6446.410 MB, free 4433.418 MB
INFO:root:Processed: 30000000. Memory: used - 6501.168 MB, free 4377.680 MB
INFO:root:Processed: 40000000. Memory: used - 6482.125 MB, free 4404.516 MB
INFO:root:Processed: 50000000. Memory: used - 6475.480 MB, free 4410.633 MB
INFO:root:Processed: 60000000. Memory: used - 6475.918 MB, free 4409.676 MB
INFO:root:Processed: 70000000. Memory: used - 6477.867 MB, free 4407.344 MB

Fixed on master. Thank you for the example script. That was very helpful.

@lonvia Thank you!


No global variables, pass all the tests and not working.

Challenge Title Case a Sentence has an issue.
User Agent is: Mozilla/5.0 (Windows NT 6.3; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36.
Please describe how to reproduce this issue, and include links to screenshots if possible.
My code:

function titleCase(str) {
  var oldArrayLetters = str.split('');
  var oldArrayWords = str.split(' ');
  var newArray = [];
  for(var i = 0; i < oldArrayWords.length; i++){
    newArray.push(oldArrayWords[i].split('')[0].toUpperCase());

    for(var j = 1; j<oldArrayWords[i].split('').length; j++){
      newArray.push(oldArrayWords[i].split('')[j].toLowerCase());
    }
    newArray.push(' ');

  }

  return newArray.join("");
}

titleCase("sHoRt AnD sToUt");

Hey @GabrielAmazonas, there seems to be an issue with your code. It's a bit subtle.
Look at this test for example:
titleCase("sHoRt AnD sToUt")

which expects
"Short And Stout"

Your implementation returns something similar, but not exactly the same. If you highlight the output of your code, it looks something like this:
"Short And Stout " // <--- Notice the extra space.

That extra space character originates from this line: newArray.push(' ');.

@suhussai Thanks for the fast reply!
Sorry for the opened issue without needing.
Spoiler Alert:
Here's the entire solution :
function titleCase(str) {
  var oldArrayLetters = str.split('');
  var oldArrayWords = str.split(' ');
  var newArray = [];
  for(var i = 0; i < oldArrayWords.length; i++){
    newArray.push(oldArrayWords[i].split('')[0].toUpperCase());

    for(var j = 1; j<oldArrayWords[i].split('').length; j++){
      newArray.push(oldArrayWords[i].split('')[j].toLowerCase());
    }

    if((oldArrayWords.length - 1) !== i)
    newArray.push(' ');

  }

  return newArray.join("");
}

titleCase("I'm a little tea pot");