MRG: Fixes for flake

Will hopefully make the newer more strict flake8 happy.

[Current coverage][cc-pull] is 86.73% (diff: 80.00%)

Merging [#3456][cc-pull] into [master][cc-base-branch] will increase coverage by 0.10%

@@             master      #3456   diff @@
==========================================
  Files           339        339
  Lines         58537      58536     -1
  Methods           0          0
  Messages          0          0
  Branches       8911       8911
==========================================
+ Hits          50712      50770    +58
+ Misses         5145       5082    -63
- Partials       2680       2684     +4

[][cc-pull]

Powered by Codecov. Last update [6957b93...c85faaf][cc-compare]
[cc-base-branch]: https://codecov.io/gh/mne-tools/mne-python/branch/master?src=pr
[cc-compare]: https://codecov.io/gh/mne-tools/mne-python/compare/6957b937e15fab9768c9f0cfd235b549bfceced6...c85faaf6ab9cb91d84a0b31a8fd065cd2b5b34d2?src=pr
[cc-pull]: https://codecov.io/gh/mne-tools/mne-python/pull/3456?src=pr

All better, merging to make the CIs happy again


Ability to override pool member health monitor via AS3

Hello,
currently there is no way to override pool's member health monitor via AS3, yet this is possible to do via UI.
This setting:

It probably should be exposed via GSLB_Pool_Member_A, to add healthmonitor field to it that would allow to override pool's health monitors for that specific pool member.

Thank you for reaching out,k I have created AUTOTOOL-4119 and added to ourr internal product backlog.

EPIC AUTOTOOL-4141

No update on this issue. Closing out


just-type option for csharp

quicktype version 18.0.0
There is no just-type option for csharp, earlier version have --features just-types

Related to #2091?
Might be caused by #1997

I have checked and the options are still there. lines 1410 en 665 in the charp.ts  The only thing I can think of for this possible regression is that @dvdsgl  made some small changes to the PR before accepting it. One of them modified some export from the way they seem to have been. In this commit https://github.com/quicktype/quicktype/pull/1997/commits/9e8fd131e71ccdd59f0a4038d10fa3821e5a88f4
Maybe that causes the problem, making the option "undetected". The option that's missing is the same for both frameworks, so maybe it needs to be moved to a more common base option set,.

@heavenwing I am not really done yet, so if you don't need the system.text.json framework option then I would suggest sticking with an older quicktype version for now. Seems the safe option
However, if you are in dire need I made a PR #2104  that I think fixes the problem, but it's not properly tested. So use at your own risk. If you could see if it works that would be usefull to me.

thanks.
I have already used old version now.
获取 Outlook for iOShttps://aka.ms/o0ukef

发件人: bzuidgeest @.>
发送时间: Friday, January 13, 2023 6:02:59 AM
收件人: quicktype/quicktype @.>
抄送: Yongguang Zhu @.>; Mention @.>
主题: Re: [quicktype/quicktype] just-type option for csharp (Issue #2097)
@heavenwinghttps://github.com/heavenwing I am not really done yet, so if you don't need the system.text.json framework option then I would suggest sticking with an older quicktype version for now. Seems the safe option
However, if you are in dire need I made a PR #2104https://github.com/quicktype/quicktype/pull/2104 that I think fixes the problem, but it's not properly tested. So use at your own risk. If you could see if it works that would be usefull to me.
―
Reply to this email directly, view it on GitHubhttps://github.com/quicktype/quicktype/issues/2097#issuecomment-1381042726, or unsubscribehttps://github.com/notifications/unsubscribe-auth/AAWI47TXMFYVQVPTHPUR2RDWSB5RHANCNFSM6AAAAAATWN7YYA.
You are receiving this because you were mentioned.Message ID: @.***>


New home (GroupId) for raven-java artifacts, active maintenance has moved

About a year ago we moved the raven-java artifacts to a new home under the com.getsentry.raven GroupId. 🎆
All maintenance and future development is going on under the new GroupId. Note that the package names also changed, so imports and class names in configuration (such as logging integrations) will need to be renamed. There shouldn't be any other changes required unless you made customizations to Raven itself, and those should still be minor.
We've made a lot of improvements and bugfixes and have a lot more planned, so check it out when you have time. 😄
The documentation has been improved if you have any questions, or just ask me here!

Thanks. I think we don't use sentry and hence raven anymore though. But I suspect a bot wrote this for you? :)

Ah, nvm. We still support sentry.


HTTPS where available

As far as I can see, Grabber uses plain HTTP to access web. Please make it
possible to switch to HTTPS instead (incl. from command line). Danbooru at
least supports HTTPS access.

It is more private (what if I don't want my provider to know what I'm doing on
the net, with targeted ad companies buying the info) and there's less chance
password will be stolen (admitted, who cares about stealing Danbooru accounts,
but still).

Original issue reported on code.google.com by<EMAIL_ADDRESS>on 1 Feb 2015 at 7:24

May not be fully working (or I'm doing something wrong). Grabber 4.2.2, the only enabled source is "danbooru.donmai.us", default settings + "Use a secure connection (https)". No login or password.

Issue a search query: "rating:safe".
Captured by Wireshark:
HTTP GET /posts?login=&password_hash=&limit=50&page=1&tags=rating%3Asafe HTTP/1.1
HTTP GET /posts.xml?login=&password_hash=&limit=50&page=1&tags=rating%3Asafe HTTP/1.1
So it seems it would have sent login/pass in plaintext.
The querying of actual images goes through HTTPS like it should.

I think I've found the root of the problem.
When Site::getAllSites() in sites.cpp loads the URLs it prepends the protocol like this:
QString lineSsl = QString(settings.value("ssl", false).toBool() ? "https" : "http") + "://" + line;
_prependUrl(&details, lineSsl, "Urls/"+sr+"/Tags", "Urls/"+QString::number(j+1)+"/Tags");

(after that the url is passed unchanged to Page() which runs parseUrl() -> fixUrl() on it but it's too late, fixUrl() doesn't change explicitly set protocol)
The problem is that settings in this context is a global settings.ini, not this site's settings. There's no global ssl flag, so this is always false, and all site URLs (Home, Tags, Pools) end up being http:\\s.
There's sets slightly above, which is maybe what's needed:
QSettings sets(savePath("sites/"+dir[i]+"/"+line+"/settings.ini"), QSettings::IniFormat);

If so, the proposed fix is to change settings to sets.

There's another point where plain http is used: in Site::loadTags(). Perhaps it needs fixUrl() too instead of hardcoded http?

Login and loadTags aside, all page/pool/image requests seem to end up in get() or getAsync() and both in turn call makeRequest(). Maybe it's possible to just do something like this in makeRequest?
if (m_settings.value("ssl", false).toBool() && (url.scheme() == "http"))
{
  url.setScheme("https");
}

As a sort of catch-all net. This breaks the case where otherwise https-enabled source has one request that just has to be in plain http, but are there such cases?

Indeed, everything you say is right, so I replaced the settings (global) by sets (site-related), and forced SSL on all requests if set so.
Thanks :+1:

This breaks the case where otherwise https-enabled source has one request that just has to be in plain http, but are there such cases?

Since mixed content is now blocked by major browsers, I highly doubt so. Even so, I think it should be blocked, as it breaks the meaning of using SSL.

It works! Thank you for spending your time to implement this! :)


option to save columns as data series

Hello guys,
another useful function for the financial world will be to have time series for each column.
Let me explain:
date,open,close,adjusted
01-01-2017,10.0,12.0,12.0
02-01-2017,12.0,12.2,12.2
.....

Most libraries requires a JSON format like this:
[
dates:['01-01-2017','02-01-'2017'],
open:[10.0,12.0]
close:[10.0,12.2]
adjusted:[12.0,12.2]
]

It's basically a dictionary of the header with lists for each column.

Hi, As parser is stream based (which means reading data from 0 to end sequentially), it wont be able to read data vertically. Thus you will have to build code to match your needs.
const result={};
const csv=require('csvtojson');
csv()
.fromFile(csvFile)
.on('json',(data)=>{
    for (var key in data){
        if (!result[key]) result[key]=[];
        result[key].push(data[key]);
    }
})
.on('done',()=>{
    console.log(result);
})

Close due to no activity


RSA2 fingerprint TYPE WARNING

請問我使用tortoisegit 會有以下警告:
...

我匯入SSH Key 的格式都是...

請問這個有解嗎?

Please speak English, we have forum in Chinese.

The server has another host-key, ask the administrator if it's been replaced. If yes, press "yes", otherwise you should press "no" and inform whomever is in charge of security.
Closing because it's not related to Gogs.


Add releases on Chocolatey Package Manager

Is it possible to add official releases on Chocolatey?
This will help to always have the latest version of the server.

Soon ALVR will have an autoupdater. For now you can check for new releases with the button in the About tab.


Fix heap-use-after-free when using TileMap editor

My sanitizer build crashed when I'm playing around with the TileMap editor.
Same problem as in #69323.

Thanks!


Add retry loop to compactWorker

If compactWorker encounters an error while talking to etcd, it needs to
retry with backoff and never exit.
If the Retry function ever returns, we panic because compaction would no longer happen, and we should never be in that state.

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


Allow images to be piped in via stdin

Closes #9
If the image-file argument is "-", the program tries to read input from stdin
instead of a file.
The terminal cols function is modified to use stdout instead of stdin so that it produces correct output even if the program is being piped to.
NOTE: I have only tested this on Linux and not Windows. So, while I tried to lookup the differences and modify the program accordingly, it might not work properly.

That's pretty cool. Thanks for the PR. I'll test it on OS X
Did you test with all the test images?

Yes, I tested all the test images. The behavior is same for manually passing the filename, piping through cat and using input redirection.


[FEATURE] Support Spark 2.1

Code of Conduct

[X] I agree to follow this project's Code of Conduct

Search before asking

[X] I have searched in the issues and found no similar issues.

Describe the feature
Some companies are still using Spark 2.1, we should support this.
Motivation
No response
Describe the solution
No response
Additional context
No response
Are you willing to submit PR?

[X] Yes I am willing to submit a PR!

Spark 2.1 has been EOL'ed almost 6 years ago. Companies are missing out on features, performance and are subject to security issues. This should not be supported. Spark 4.0 will be released this summer.

Spark 2.1 has been EOL'ed almost 6 years ago. Companies are missing out on features, performance and are subject to security issues. This should not be supported. Spark 4.0 will be released this summer.

For some large companies, upgrading the Spark version is troublesome and complicated, as there are many historical burdens involved. We need to support these users to strengthen the activity of our community, and this will not bring any additional costs or burdens. I will try to implement this part. I think there is no need to worry about it.

@EnricoMi I will close this issue. Spark 2.1 is indeed too old. We only support Spark version 2.4 and above.


Add importance sampling

Add an importance parameter to materials. 50% of samples on interfacing with a surface will be directed towards a randomly selected primitive. This selection will be weighted by importance. This will allow tiny intense light sources to be reliably sampled.
This will require redesigning the materials to be able to:

Sample their own BRDF efficiently (current behaviour)
Return the BRDF for a specified direction where the Ray is externally selecting a direction based on importance.

To support this behaviour, a new customised world object will need to be made for the optical package. This object will cache a list of primitives with their importance.

Notes from thinking about architecture changes required to accommodate importance sampling.

Modify scenegraph change notifications to send arbitrary messages e.g. "Geometry", "Material", etc. This can then be used by the world to change multiple caches.

Modify material to hold a weak reference to the primitive it is attached to. This is to be able to inform the world via the scenegraph change notification that it has changed, e.g. importance.

Maintain a list of importances with links to BoundingBoxs of relevant primitives. This is to enable importance sampling.

Add method project_on_sphere() to BoundingBox3D to work out the direction and projected solid angle of the bounding box.

Development complete and code merged.


Nifi 3093

Thank you for submitting a contribution to Apache NiFi.
In order to streamline the review of the contribution we ask you
to ensure the following steps have been taken:
For all changes:

[ ] Is there a JIRA ticket associated with this PR? Is it referenced
in the commit message?

[ ] Does your PR title start with NIFI-XXXX where XXXX is the JIRA number you are trying to resolve? Pay particular attention to the hyphen "-" character.

[ ] Has your PR been rebased against the latest commit within the target branch (typically master)?

[ ] Is your initial contribution a single, squashed commit?

For code changes:

[ ] Have you ensured that the full suite of tests is executed via mvn -Pcontrib-check clean install at the root nifi folder?
[ ] Have you written or updated unit tests to verify your changes?
[ ] If adding new dependencies to the code, are these dependencies licensed in a way that is compatible for inclusion under ASF 2.0?
[ ] If applicable, have you updated the LICENSE file, including the main LICENSE file under nifi-assembly?
[ ] If applicable, have you updated the NOTICE file, including the main NOTICE file found under nifi-assembly?
[ ] If adding new Properties, have you added .displayName in addition to .name (programmatic access) for each of the new properties?

For documentation related changes:

[ ] Have you ensured that format looks appropriate for the output in which it is rendered?

Note:
Please ensure that once the PR is submitted, you check travis-ci for build issues and submit an update to your PR as soon as possible.

+1 LGTM. Didn't break anything and the changes look good.

@markap14 @mattyb149 This looks like a simpler fix than this one.

I think for the setQueryTimeout() we should use the approach here. It was decided a while back that we should enforce setQueryTimeout() instead of just proceeding on if the driver doesn't support it. What the code in the other PR (#2138) does is to try to call setQueryTimeout on a Statement during customValidate, and will render the processor invalid if the user tries to set a Query Timeout to something other than zero when the driver doesn't support it.

I'm leery of adding "Hive support" when we know there's a defect, especially when we had to fix that processor for the exact same reason (other folks were running into issues using GenerateTableFetch once it accepted multiple tables). There's also a separate thread on the mailing lists about adding the database name to the table + column name, and I think it's probably a good idea. So we wouldn't be able to support an empty table name if we add a database name, and it's a bummer to default to an empty table name when we do know how to get the table name for the Hive case.

I agree that the code is helpful, but not "Hive Support". Why not cut a new PR, something like, "Some JDBC Drivers do not support Query Timeout", and put the current fixes in there. This existing ticket could be cancelled, or rescoped, to cover just the table name issue, and any other issues found if any.

@patricker I will create another ticket as you suggested. "Some JDBC Drivers do not support Query Timeout".


img-grid occasionally fails to find file -- seems like being resolved by wrong page

Scenario:
I use {{< bs/img-grid "attendees.yaml" >}} to build an image grid of photos of people attending an event on a page.
The images are stored in the page bundle, in a subdirectory e.g. attendess/foo.jpg.
Anomaly:
Page always builds correctly in production.
Sometimes, in development, e.g. if one edited that page bundle's index.md, the images are not found.
If one triggers a rebuild, they will often be found.
github.com/hugomods/images/layouts/partials/images/image.html reports that the images that were part of the page resources was not found.
Analysis:
By adding some debugging, I've determined that when things have failed, it has occurred because image.html's $page variable is Page(/_index.md) not Page(/events/2024-baja-mexico/index.md) (the proper page).
Strongly enough, the call to GitHub.com/hugomods/bootstrap/layouts/partials/bootstrap/img-grid.html (one level up in the call stack) is always called with the proper Page set.

Here is my debugging when it succeeded:
WARN  DATA: page=Page(/events/2024-baja-mexico/index.md) key=attendees data=[map[src:attendees/mug_mattgor.jpg title:Matt "Gecko" Gordon] map[src:attendees/mug_stuart.jpg title:Uncle Stu] map[src:attendees/mug_ian.jpg title:Ian] map[src:attendees/mug_hunt.jpg title:Huntley] map[src:attendees/mug_ich.jpg title:Ichabod] map[src:attendees/mug_raj.jpg title:Raj] map[src:attendees/mug_eminem.jpg title:Mike] map[src:attendees/mug_jake.jpg title:Jake] map[src:attendees/mug_milo.jpg title:Milo] map[src:attendees/mug_holland.jpg title:Holland Ja (unconfirmed)] map[src:attendees/mug_matthanley.jpg title:Matt Hanley (2 days)] map[src:attendees/mug_tuggle.jpg title:Steve Tuggle (2 days)] map[src:attendees/mug_charles.jpg title:Charles (unconfirmed)] map[src:attendees/mug_dan.jpg title:Dan G (unconfirmed)]]
WARN  looking for page resources getmatch path=attendees/mug_mattgor.jpg page=Page(/events/2024-baja-mexico/index.md) res=attendees/mug_mattgor.jpg
WARN  looking for page resources getmatch path=attendees/mug_stuart.jpg page=Page(/events/2024-baja-mexico/index.md) res=attendees/mug_stuart.jpg

My best guess is that something is partialCached when it shouldn't be, but this is really weird.. the page in question is just using a standard shortcut...
Full source code at https://github.com/goattrails/hb-goattrails/blob/main/content/events/2024-baja-mexico/index.md
My debugging diffs:
bootstrap.patch
images.patch

Update:
Happens when .Page for this short-cut is nil. Looks like we cover it with
$page = default page .Page in image.html  why is .Page nil when it was just fine when being called one step up by the bootstrap bs/img shortcut pulling in the data file?
WARN  page=Page(/_index.en.md) .Page=<nil> $page=Page(/_index.en.md)
WARN  looking for page resources getmatch path=attendees/mug_mattgor.jpg page=Page(/_index.en.md) res=<nil>
WARN  image not found: attendees/mug_mattgor.jpg


missing .pkl on first round of training

Working through the pokemon demo. I think there might be a step missing in the instructions to download pre-trained weights. Encountered the following error attempting to run train.py on first round of progressive training:
(sgxl) user@host:~/dmarx/stylegan_xl$ python train.py --outdir=./training-runs/pokemon --cfg=stylegan3-t --data=../../datasets/pokemon16.zip     --gpus=4 --batch=64 --mirror=1 --snap 10 --batch-gpu 16 --kimg 10000 --stem --syn_layers 10

Training options:
{
  "G_kwargs": {
    "class_name": "training.networks_stylegan3_resetting.Generator",
    "z_dim": 64,
    "w_dim": 512,
    "mapping_kwargs": {
      "num_layers": 2,
      "rand_embedding": false
    },
    "channel_base": 65536,
    "channel_max": 1024,
    "magnitude_ema_beta": 0.9977843871238888,
    "num_layers": 10
  },
  "D_kwargs": {
    "class_name": "pg_modules.discriminator.ProjectedDiscriminator",
    "backbones": [
      "deit_base_distilled_patch16_224",
      "tf_efficientnet_lite0"
    ],
    "diffaug": true,
    "interp224": true,
    "backbone_kwargs": {
      "cout": 64,
      "expand": true,
      "proj_type": 1,
      "num_discs": 4,
      "cond": false
    }
  },
  "G_opt_kwargs": {
    "class_name": "torch.optim.Adam",
    "betas": [
      0,
      0.99
    ],
    "eps": 1e-08,
    "lr": 0.0025
  },
  "D_opt_kwargs": {
    "class_name": "torch.optim.Adam",
    "betas": [
      0,
      0.99
    ],
    "eps": 1e-08,
    "lr": 0.002
  },
  "loss_kwargs": {
    "class_name": "training.loss.ProjectedGANLoss",
    "blur_init_sigma": 2,
    "blur_fade_kimg": 300,
    "pl_weight": 2.0,
    "pl_no_weight_grad": true,
    "style_mixing_prob": 0.0,
    "cls_weight": 0.0,
    "cls_model": "deit_small_distilled_patch16_224",
    "train_head_only": false
  },
  "data_loader_kwargs": {
    "pin_memory": true,
    "prefetch_factor": 2,
    "num_workers": 3
  },
  "training_set_kwargs": {
    "class_name": "training.dataset.ImageFolderDataset",
    "path": "../../datasets/pokemon16.zip",
    "use_labels": false,
    "max_size": 4729,
    "xflip": true,
    "resolution": 16,
    "random_seed": 0
  },
  "num_gpus": 4,
  "batch_size": 64,
  "batch_gpu": 16,
  "metrics": [
    "fid50k_full"
  ],
  "total_kimg": 10000,
  "kimg_per_tick": 4,
  "image_snapshot_ticks": 10,
  "network_snapshot_ticks": 10,
  "random_seed": 0,
  "ema_kimg": 20.0,
  "restart_every": 999999999,
  "run_dir": "./training-runs/pokemon/00000-stylegan3-t-pokemon16-gpus4-batch64"
}

Output directory:    ./training-runs/pokemon/00000-stylegan3-t-pokemon16-gpus4-batch64
Number of GPUs:      4
Batch size:          64 images
Training duration:   10000 kimg
Dataset path:        ../../datasets/pokemon16.zip
Dataset size:        4729 images
Dataset resolution:  16
Dataset labels:      False
Dataset x-flips:     True

Creating output directory...
Launching processes...
Loading training set...

Num images:  9458
Image shape: [3, 16, 16]
Label shape: [0]

Constructing networks...
Traceback (most recent call last):
  File "train.py", line 362, in <module>
    main()  # pylint: disable=no-value-for-parameter
  File "/home/user/miniconda/envs/sgxl/lib/python3.7/site-packages/click/core.py", line 829, in __call__
    return self.main(*args, **kwargs)
  File "/home/user/miniconda/envs/sgxl/lib/python3.7/site-packages/click/core.py", line 782, in main
    rv = self.invoke(ctx)
  File "/home/user/miniconda/envs/sgxl/lib/python3.7/site-packages/click/core.py", line 1066, in invoke
    return ctx.invoke(self.callback, **ctx.params)
  File "/home/user/miniconda/envs/sgxl/lib/python3.7/site-packages/click/core.py", line 610, in invoke
    return callback(*args, **kwargs)
  File "train.py", line 347, in main
    launch_training(c=c, desc=desc, outdir=opts.outdir, dry_run=opts.dry_run)
  File "train.py", line 106, in launch_training
    torch.multiprocessing.spawn(fn=subprocess_fn, args=(c, temp_dir), nprocs=c.num_gpus)
  File "/home/user/miniconda/envs/sgxl/lib/python3.7/site-packages/torch/multiprocessing/spawn.py", line 199, in spawn
    return start_processes(fn, args, nprocs, join, daemon, start_method='spawn')
  File "/home/user/miniconda/envs/sgxl/lib/python3.7/site-packages/torch/multiprocessing/spawn.py", line 157, in start_processes
    while not context.join():
  File "/home/user/miniconda/envs/sgxl/lib/python3.7/site-packages/torch/multiprocessing/spawn.py", line 118, in join
    raise Exception(msg)
Exception:

-- Process 1 terminated with the following error:
Traceback (most recent call last):
  File "/home/user/miniconda/envs/sgxl/lib/python3.7/site-packages/torch/multiprocessing/spawn.py", line 19, in _wrap
    fn(i, *args)
  File "/home/user/dmarx/stylegan_xl/train.py", line 49, in subprocess_fn
    training_loop.training_loop(rank=rank, **c)
  File "/home/user/dmarx/stylegan_xl/training/training_loop.py", line 170, in training_loop
    G = dnnlib.util.construct_class_by_name(**G_kwargs, **common_kwargs).train().requires_grad_(False).to(device) # subclass of torch.nn.Module
  File "/home/user/dmarx/stylegan_xl/dnnlib/util.py", line 303, in construct_class_by_name
    return call_func_by_name(*args, func_name=class_name, **kwargs)
  File "/home/user/dmarx/stylegan_xl/dnnlib/util.py", line 298, in call_func_by_name
    return func_obj(*args, **kwargs)
  File "/home/user/dmarx/stylegan_xl/torch_utils/persistence.py", line 104, in __init__
    super().__init__(*args, **kwargs)
  File "/home/user/dmarx/stylegan_xl/training/networks_stylegan3_resetting.py", line 583, in __init__
    self.mapping = MappingNetwork(z_dim=z_dim, c_dim=c_dim, w_dim=w_dim, num_ws=self.num_ws, **mapping_kwargs)
  File "/home/user/dmarx/stylegan_xl/torch_utils/persistence.py", line 104, in __init__
    super().__init__(*args, **kwargs)
  File "/home/user/dmarx/stylegan_xl/training/networks_stylegan3_resetting.py", line 138, in __init__
    with open(embed_path, 'rb') as f:
FileNotFoundError: [Errno 2] No such file or directory: 'in_embeddings/tf_efficientnet_lite0.pkl'

went missing thanks to .gitignore, thanks! :)

If you encounter this problem again, it's because you need your current working directory to by stylegan_xl, else it will fail with the error above.


 See 'git --help'.

 See 'git --help'.

 Unfortunately I'm not sure what my previous version was.

Closing because #733 was merged. Just found out by accident this was fixed in 3.25.

This issue is marked as closed while the problem still exists. All thefuck ever does is printing "usage: thefuck…".


S5: Include Recurrence tag to enable recurring transactions without CVV

@duff quick update to S5 to include the Recurrence tag and appropriate mode for future recurring transactions on a credit card that has been previously verified. Only question I have is whether or not it should also be included on the authorize action? I'm not 100% sure.
Also, as always with S5, the remote test is a bit "meh". I'm having to manually send what error I want back instead of S5 actually doing the validations in TEST mode. I've included it, but unfortunately it's very mildly helpful.

:+1: on including it in the authorization.


H3: drive quic in the same task as the connection

Not sure what the best way forward would be. But using two tasks seems potentially wasteful. @Ralith any thoughts?
Originally posted by @djc in https://github.com/djc/quinn/pull/697#discussion_r406143296

From a high level perspective, the simplest approach here would be to construct a future equivalent to what you'd get from join!(quinn_driver, h3_driver), or embed the quinn driver future inside the h3 driver future in an equivalent fashion. Does that make sense?

This is complicated by the fact that quinn implicitly spawns the driver future internally. That happens when a handshake begins, which is intentionally not synchronized with the application accepting new connections, as otherwise incoming connections could time out when the application is exerting backpressure.
A simple hack might be to expose a hidden interface or configuration option through which quinn-h3 can specify a Fn(impl Future) -> impl Future to wrap the driver. Would be nice to be more graceful than that, though.

It might be that hyper also would prefer to spawn its own tasks for this, so I think some kind of helper method would be useful indeed.


Custom mapping is broken

I use the following to configure nvim-comment. After I updated today my mappings broke. I think it was caused by this commit
https://github.com/terrortylor/nvim-comment/commit/22f81f96d75f5cf8eb2e19ebbb0c6f37f32cec2a

require('nvim_comment').setup({
    marker_padding = true,
    comment_empty = true
    create_mappings = true
    line_mapping = "<leader>cc",zzzzzzz
    operator_mapping = "<leader>c"
})

Hey is your leader mapped to space?
`:h normal
{commands} cannot start with a space.  Put a count of
1 (one) before it, "1 " is one space.`
Pushed a fix, FYI your config above has syntax issues, should be:
require('nvim_comment').setup({     marker_padding = true,     comment_empty = true,     create_mappings = true,     line_mapping = "<leader>cc",     operator_mapping = "<leader>c" })

Yup my leader is mapped to space and reported  this late last night so I was sleepy :)
thanks for the fix, will test it when I get back to my computer


Fix reconnect loop when reconnecting with bad secret

Right now clients get stuck in a reconnect loop when their secret is bad. This change makes it so they don't immediately reconnect. WebRTC connections will stay in tact, so players will be able to keep playing. But they won't be able to list or join other lobbies without reloading the game.

Let's send a "reconnect failed" packet so new clients close neatly and old clients ignore the same as returning nil.


[Bug]: run quantized model error

Your current environment

The output of `python collect_env.py`
PyTorch version: 2.4.0+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.4 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: version 3.30.2
Libc version: glibc-2.35

Python version: 3.10.14 (main, May  6 2024, 19:42:50) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-6.5.0-45-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.4.99
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3090
Nvidia driver version: 555.58.02
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
架构：                              x86_64
CPU 运行模式：                      32-bit, 64-bit
Address sizes:                      39 bits physical, 48 bits virtual
字节序：                            Little Endian
CPU:                                20
在线 CPU 列表：                     0-19
厂商 ID：                           GenuineIntel
型号名称：                          12th Gen Intel(R) Core(TM) i7-12700KF
CPU 系列：                          6
型号：                              151
每个核的线程数：                    2
每个座的核数：                      12
座：                                1
步进：                              2
CPU 最大 MHz：                      5000.0000
CPU 最小 MHz：                      800.0000
BogoMIPS：                          7219.20
标记：                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx est tm2 ssse3 sdbg fma cx16 xtpr pdcm sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb intel_pt sha_ni xsaveopt xsavec xgetbv1 xsaves split_lock_detect avx_vnni dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp hwp_pkg_req hfi vnmi umip pku ospke waitpkg gfni vaes vpclmulqdq rdpid movdiri movdir64b fsrm md_clear serialize arch_lbr ibt flush_l1d arch_capabilities
虚拟化：                            VT-x
L1d 缓存：                          512 KiB (12 instances)
L1i 缓存：                          512 KiB (12 instances)
L2 缓存：                           12 MiB (9 instances)
L3 缓存：                           25 MiB (1 instance)
NUMA 节点：                         1
NUMA 节点0 CPU：                    0-19
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit:        Not affected
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Not affected
Vulnerability Meltdown:             Not affected
Vulnerability Mmio stale data:      Not affected
Vulnerability Retbleed:             Not affected
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] nvidia-nccl-cu12==2.20.5
[pip3] onnxruntime==1.16.0
[pip3] pytorch-lightning==2.4.0
[pip3] pytorch-wpe==0.0.1
[pip3] pyzmq==26.1.0
[pip3] sentence-transformers==3.0.1
[pip3] torch==2.4.0
[pip3] torch-complex==0.4.4
[pip3] torchaudio==2.4.0
[pip3] torchmetrics==1.4.1
[pip3] torchvision==0.19.0
[pip3] transformers==4.43.4
[pip3] transformers-stream-generator==0.0.5
[pip3] triton==3.0.0
[pip3] vector-quantize-pytorch==1.15.6
[conda] numpy                     1.26.4                   pypi_0    pypi
[conda] nvidia-nccl-cu12          2.20.5                   pypi_0    pypi
[conda] pytorch-lightning         2.4.0                    pypi_0    pypi
[conda] pytorch-wpe               0.0.1                    pypi_0    pypi
[conda] pyzmq                     26.1.0                   pypi_0    pypi
[conda] sentence-transformers     3.0.1                    pypi_0    pypi
[conda] torch                     2.4.0                    pypi_0    pypi
[conda] torch-complex             0.4.4                    pypi_0    pypi
[conda] torchaudio                2.4.0                    pypi_0    pypi
[conda] torchmetrics              1.4.1                    pypi_0    pypi
[conda] torchvision               0.19.0                   pypi_0    pypi
[conda] transformers              4.43.4                   pypi_0    pypi
[conda] transformers-stream-generator 0.0.5                    pypi_0    pypi
[conda] triton                    3.0.0                    pypi_0    pypi
[conda] vector-quantize-pytorch   1.15.6                   pypi_0    pypi
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.5.4@4db5176d9758b720b05460c50ace3c01026eb158
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0	CPU Affinity	NUMA Affinity	GPU NUMA ID
GPU0	 X 	0-19	0		N/A

Legend:

  X    = Self
  SYS  = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)
  NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node
  PHB  = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)
  PXB  = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)
  PIX  = Connection traversing at most a single PCIe bridge
  NV#  = Connection traversing a bonded set of # NVLinks

🐛 Describe the bug
my codes:
from vllm import LLM, SamplingParams

# Sample prompts.
prompts = [
    "Hello, my name is",
    "The president of the United States is",
    "The capital of France is",
    "The future of AI is",
]
# Create a sampling params object.
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

# Create an LLM.
llm = LLM(model="/home/sky/.xinference/cache/internlm2_5-7b-chat-4bit",trust_remote_code=True,max_model_len=2048)
# Generate texts from the prompts. The output is a list of RequestOutput objects
# that contain the prompt, generated text, and other information.
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
    prompt = output.prompt
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

and i got this error:
python test.py
INFO 08-16 13:29:04 awq_marlin.py:89] The model is convertible to awq_marlin during runtime. Using awq_marlin kernel.
INFO 08-16 13:29:04 llm_engine.py:174] Initializing an LLM engine (v0.5.4) with config: model='/home/sky/.xinference/cache/internlm2_5-7b-chat-4bit', speculative_config=None, tokenizer='/home/sky/.xinference/cache/internlm2_5-7b-chat-4bit', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.float16, max_seq_len=2048, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=1, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=awq_marlin, enforce_eager=False, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), observability_config=ObservabilityConfig(otlp_traces_endpoint=None), seed=0, served_model_name=/home/sky/.xinference/cache/internlm2_5-7b-chat-4bit, use_v2_block_manager=False, enable_prefix_caching=False)
WARNING 08-16 13:29:04 tokenizer.py:129] Using a slow tokenizer. This might cause a significant slowdown. Consider using a fast tokenizer instead.
INFO 08-16 13:29:04 model_runner.py:720] Starting to load model /home/sky/.xinference/cache/internlm2_5-7b-chat-4bit...
Loading pt checkpoint shards:   0% Completed | 0/3 [00:00<?, ?it/s]
/home/sky/anaconda3/envs/Xinference/lib/python3.10/site-packages/vllm/model_executor/model_loader/weight_utils.py:405: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  state = torch.load(bin_file, map_location="cpu")
[rank0]: Traceback (most recent call last):
[rank0]:   File "/home/sky/Second/test.py", line 14, in <module>
[rank0]:     llm = LLM(model="/home/sky/.xinference/cache/internlm2_5-7b-chat-4bit",trust_remote_code=True,max_model_len=2048)
[rank0]:   File "/home/sky/anaconda3/envs/Xinference/lib/python3.10/site-packages/vllm/entrypoints/llm.py", line 158, in __init__
[rank0]:     self.llm_engine = LLMEngine.from_engine_args(
[rank0]:   File "/home/sky/anaconda3/envs/Xinference/lib/python3.10/site-packages/vllm/engine/llm_engine.py", line 445, in from_engine_args
[rank0]:     engine = cls(
[rank0]:   File "/home/sky/anaconda3/envs/Xinference/lib/python3.10/site-packages/vllm/engine/llm_engine.py", line 249, in __init__
[rank0]:     self.model_executor = executor_class(
[rank0]:   File "/home/sky/anaconda3/envs/Xinference/lib/python3.10/site-packages/vllm/executor/executor_base.py", line 47, in __init__
[rank0]:     self._init_executor()
[rank0]:   File "/home/sky/anaconda3/envs/Xinference/lib/python3.10/site-packages/vllm/executor/gpu_executor.py", line 36, in _init_executor
[rank0]:     self.driver_worker.load_model()
[rank0]:   File "/home/sky/anaconda3/envs/Xinference/lib/python3.10/site-packages/vllm/worker/worker.py", line 139, in load_model
[rank0]:     self.model_runner.load_model()
[rank0]:   File "/home/sky/anaconda3/envs/Xinference/lib/python3.10/site-packages/vllm/worker/model_runner.py", line 722, in load_model
[rank0]:     self.model = get_model(model_config=self.model_config,
[rank0]:   File "/home/sky/anaconda3/envs/Xinference/lib/python3.10/site-packages/vllm/model_executor/model_loader/__init__.py", line 21, in get_model
[rank0]:     return loader.load_model(model_config=model_config,
[rank0]:   File "/home/sky/anaconda3/envs/Xinference/lib/python3.10/site-packages/vllm/model_executor/model_loader/loader.py", line 327, in load_model
[rank0]:     model.load_weights(
[rank0]:   File "/home/sky/anaconda3/envs/Xinference/lib/python3.10/site-packages/vllm/model_executor/models/internlm2.py", line 327, in load_weights
[rank0]:     loaded_weight = loaded_weight.view(-1, 2 + kv_groups,
[rank0]: RuntimeError: shape '[-1, 6, 128, 768]' is invalid for input of size 3145728
Loading pt checkpoint shards:   0% Completed | 0/3 [00:00<?, ?it/s]

the mode i used : internlm2_5-7b-chat-4bit

Hi @soulzzz looking at the model you shared, it says it is in the "MLX format" which I guess is different from the traditional HF compatible AWQ format. Could you try a model that isn't in MLX? This one seems like a good candidate internlm/internlm2_5-7b-chat-4bit

ok i changed the model to ModelCloud/internlm-2.5-7b-chat-gptq-4bit and my code:
from vllm import LLM, SamplingParams

# Sample prompts.
prompts = [
    "Hello, my name is",
    "The president of the United States is",
    "The capital of France is",
    "The future of AI is",
]
# Create a sampling params object.
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

# Create an LLM.
llm = LLM(model="/home/sky/.xinference/cache/internlm2_5-chat-gptq-7b",trust_remote_code=True,max_model_len=2048)
# Generate texts from the prompts. The output is a list of RequestOutput objects
# that contain the prompt, generated text, and other information.
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
    prompt = output.prompt
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

and the output error:
INFO 08-16 23:44:28 gptq_marlin.py:98] The model is convertible to gptq_marlin during runtime. Using gptq_marlin kernel.
INFO 08-16 23:44:28 llm_engine.py:174] Initializing an LLM engine (v0.5.4) with config: model='/home/sky/.xinference/cache/internlm2_5-chat-gptq-7b', speculative_config=None, tokenizer='/home/sky/.xinference/cache/internlm2_5-chat-gptq-7b', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=2048, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=1, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=gptq_marlin, enforce_eager=False, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), observability_config=ObservabilityConfig(otlp_traces_endpoint=None), seed=0, served_model_name=/home/sky/.xinference/cache/internlm2_5-chat-gptq-7b, use_v2_block_manager=False, enable_prefix_caching=False)
INFO 08-16 23:44:28 model_runner.py:720] Starting to load model /home/sky/.xinference/cache/internlm2_5-chat-gptq-7b...
Loading safetensors checkpoint shards:   0% Completed | 0/1 [00:00<?, ?it/s]
[rank0]: Traceback (most recent call last):
[rank0]:   File "/home/sky/Second/test.py", line 14, in <module>
[rank0]:     llm = LLM(model="/home/sky/.xinference/cache/internlm2_5-chat-gptq-7b",trust_remote_code=True,max_model_len=2048)
[rank0]:   File "/home/sky/anaconda3/envs/Xinference/lib/python3.10/site-packages/vllm/entrypoints/llm.py", line 158, in __init__
[rank0]:     self.llm_engine = LLMEngine.from_engine_args(
[rank0]:   File "/home/sky/anaconda3/envs/Xinference/lib/python3.10/site-packages/vllm/engine/llm_engine.py", line 445, in from_engine_args
[rank0]:     engine = cls(
[rank0]:   File "/home/sky/anaconda3/envs/Xinference/lib/python3.10/site-packages/vllm/engine/llm_engine.py", line 249, in __init__
[rank0]:     self.model_executor = executor_class(
[rank0]:   File "/home/sky/anaconda3/envs/Xinference/lib/python3.10/site-packages/vllm/executor/executor_base.py", line 47, in __init__
[rank0]:     self._init_executor()
[rank0]:   File "/home/sky/anaconda3/envs/Xinference/lib/python3.10/site-packages/vllm/executor/gpu_executor.py", line 36, in _init_executor
[rank0]:     self.driver_worker.load_model()
[rank0]:   File "/home/sky/anaconda3/envs/Xinference/lib/python3.10/site-packages/vllm/worker/worker.py", line 139, in load_model
[rank0]:     self.model_runner.load_model()
[rank0]:   File "/home/sky/anaconda3/envs/Xinference/lib/python3.10/site-packages/vllm/worker/model_runner.py", line 722, in load_model
[rank0]:     self.model = get_model(model_config=self.model_config,
[rank0]:   File "/home/sky/anaconda3/envs/Xinference/lib/python3.10/site-packages/vllm/model_executor/model_loader/__init__.py", line 21, in get_model
[rank0]:     return loader.load_model(model_config=model_config,
[rank0]:   File "/home/sky/anaconda3/envs/Xinference/lib/python3.10/site-packages/vllm/model_executor/model_loader/loader.py", line 327, in load_model
[rank0]:     model.load_weights(
[rank0]:   File "/home/sky/anaconda3/envs/Xinference/lib/python3.10/site-packages/vllm/model_executor/models/internlm2.py", line 327, in load_weights
[rank0]:     loaded_weight = loaded_weight.view(-1, 2 + kv_groups,
[rank0]: RuntimeError: shape '[-1, 6, 128, 4096]' is invalid for input of size 4096
Loading safetensors checkpoint shards:   0% Completed | 0/1 [00:00<?, ?it/s]

next model internlm/internlm2_5-7b-chat-4bit:
my code:
from vllm import LLM, SamplingParams

# Sample prompts.
prompts = [
    "Hello, my name is",
    "The president of the United States is",
    "The capital of France is",
    "The future of AI is",
]
# Create a sampling params object.
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

# Create an LLM.
llm = LLM(model="/home/sky/.xinference/cache/internlm2_5-7b-chat-4bit",trust_remote_code=True,max_model_len=2048)
# Generate texts from the prompts. The output is a list of RequestOutput objects
# that contain the prompt, generated text, and other information.
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
    prompt = output.prompt
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

the error output:
INFO 08-17 00:04:33 awq_marlin.py:89] The model is convertible to awq_marlin during runtime. Using awq_marlin kernel.
INFO 08-17 00:04:33 llm_engine.py:174] Initializing an LLM engine (v0.5.4) with config: model='/home/sky/.xinference/cache/internlm2_5-7b-chat-4bit', speculative_config=None, tokenizer='/home/sky/.xinference/cache/internlm2_5-7b-chat-4bit', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.float16, max_seq_len=2048, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=1, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=awq_marlin, enforce_eager=False, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), observability_config=ObservabilityConfig(otlp_traces_endpoint=None), seed=0, served_model_name=/home/sky/.xinference/cache/internlm2_5-7b-chat-4bit, use_v2_block_manager=False, enable_prefix_caching=False)
WARNING 08-17 00:04:33 tokenizer.py:129] Using a slow tokenizer. This might cause a significant slowdown. Consider using a fast tokenizer instead.
INFO 08-17 00:04:33 model_runner.py:720] Starting to load model /home/sky/.xinference/cache/internlm2_5-7b-chat-4bit...
Loading pt checkpoint shards:   0% Completed | 0/3 [00:00<?, ?it/s]
/home/sky/anaconda3/envs/Xinference/lib/python3.10/site-packages/vllm/model_executor/model_loader/weight_utils.py:405: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  state = torch.load(bin_file, map_location="cpu")
[rank0]: Traceback (most recent call last):
[rank0]:   File "/home/sky/Second/test.py", line 14, in <module>
[rank0]:     llm = LLM(model="/home/sky/.xinference/cache/internlm2_5-7b-chat-4bit",trust_remote_code=True,max_model_len=2048)
[rank0]:   File "/home/sky/anaconda3/envs/Xinference/lib/python3.10/site-packages/vllm/entrypoints/llm.py", line 158, in __init__
[rank0]:     self.llm_engine = LLMEngine.from_engine_args(
[rank0]:   File "/home/sky/anaconda3/envs/Xinference/lib/python3.10/site-packages/vllm/engine/llm_engine.py", line 445, in from_engine_args
[rank0]:     engine = cls(
[rank0]:   File "/home/sky/anaconda3/envs/Xinference/lib/python3.10/site-packages/vllm/engine/llm_engine.py", line 249, in __init__
[rank0]:     self.model_executor = executor_class(
[rank0]:   File "/home/sky/anaconda3/envs/Xinference/lib/python3.10/site-packages/vllm/executor/executor_base.py", line 47, in __init__
[rank0]:     self._init_executor()
[rank0]:   File "/home/sky/anaconda3/envs/Xinference/lib/python3.10/site-packages/vllm/executor/gpu_executor.py", line 36, in _init_executor
[rank0]:     self.driver_worker.load_model()
[rank0]:   File "/home/sky/anaconda3/envs/Xinference/lib/python3.10/site-packages/vllm/worker/worker.py", line 139, in load_model
[rank0]:     self.model_runner.load_model()
[rank0]:   File "/home/sky/anaconda3/envs/Xinference/lib/python3.10/site-packages/vllm/worker/model_runner.py", line 722, in load_model
[rank0]:     self.model = get_model(model_config=self.model_config,
[rank0]:   File "/home/sky/anaconda3/envs/Xinference/lib/python3.10/site-packages/vllm/model_executor/model_loader/__init__.py", line 21, in get_model
[rank0]:     return loader.load_model(model_config=model_config,
[rank0]:   File "/home/sky/anaconda3/envs/Xinference/lib/python3.10/site-packages/vllm/model_executor/model_loader/loader.py", line 327, in load_model
[rank0]:     model.load_weights(
[rank0]:   File "/home/sky/anaconda3/envs/Xinference/lib/python3.10/site-packages/vllm/model_executor/models/internlm2.py", line 327, in load_weights
[rank0]:     loaded_weight = loaded_weight.view(-1, 2 + kv_groups,
[rank0]: RuntimeError: shape '[-1, 6, 128, 768]' is invalid for input of size 3145728
Loading pt checkpoint shards:   0% Completed | 0/3 [00:00<?, ?it/s]

is there any plan to fix the bug?


improvementSupport get oracle jdbc row count. (#45214)

backport: https://github.com/apache/doris/pull/45214

Thank you for your contribution to Apache Doris.
Don't know what should be done next? See How to process your PR.
Please clearly describe your PR:

What problem was fixed (it's best to include specific error reporting information). How it was fixed.
Which behaviors were modified. What was the previous behavior, what is it now, why was it modified, and what possible impacts might there be.
What features were added. Why was this function added?
Which code was refactored and why was this part of the code refactored?
Which functions were optimized and what is the difference before and after the optimization?

run buildall


Two-fer rewrite - draft

Here's an attempt at adding more context around the term two-fer, and adding a touch of story.

I really like the explanation. As per my gigasecond comments I think it should be slightly shorter though.

Do you think the instructions should be shorter as well?

No, I think that adds clarity, which is basically always good in my mind.
I think the aim for the introduction should be fun and context without noise. And for the instructions, the aim should be clarity.

Cool, that's useful.

I've shorted the intro a bit more. @iHiD would you take another look?

Opened PR upstream.


Update README.md

Removed leftover text from python readme

Please sign the CLA and then I can merge.

I just signed it :)


Model just stops when denoising

Everytime I run the model with the regular inputs from the readme I get this.
Denoising: p232_001.wav
0%|                                                                                | 0/1 [00:00<?, ?it/s]
(wavenet) C:\Users\JSai\Documents\Cochlear_Implants\speech-denoising-wavenet>
ANd it terminates.
Any help would be great.

@JSai23 The same with you

the same with you

Everytime I run the model with the regular inputs from the readme I get this.
Denoising: p232_001.wav
0%| | 0/1 [00:00<?, ?it/s]
(wavenet) C:\Users\JSai\Documents\Cochlear_Implants\speech-denoising-wavenet>
ANd it terminates.
Any help would be great.

hello ,now have you solved that problem? i meet the same problem.

Same problem I have also faced. Is anyone solved it? @JSai23  @czh-learn @hedes1992


If you do /wild and then move to cancel it and then do /wild again you'll be teleported twice

When you do /wild and then move to cancel that action and then do /wild a second time and wait for it to work, you'll first be teleported to one spot and then to another spot.
How to reproduce:
(need to have a wait time) /wild move to cancel the command /wild wait while you get tp ed

I've known about this for a while. I just haven't had time to try to fix it yet I will try to fix it soon.


(#527) Add masteruser and auth_user parameters

Pull Request (PR) description
Enable setting the masteruser and auth_user parameter which was introduced in Redis 6+ to be able to connect using the new ACL rules. Without the sensitive deferred types.
This Pull Request (PR) fixes the following issues
Fixes #527
Fixes #528

This is a duplicate PR of #528, but it does not treat masteruser or auth_user as secrets (sensitive/deferred types)


renamed from aurora to nanoleaf_aurora

Description:
Renamed aurora platform to nanoleaf_aurora as to not clash with the existing binary sensor aurora after review comments from @MartinHjelmare
Related issue (if applicable): None
Pull request in home-assistant.github.io with documentation (if applicable): home-assistant/home-assistant.github.io#5158
Example entry for configuration.yaml (if applicable):
# Example configuration.yaml entry
light:
  - platform: nanoleaf_aurora
    host: <IP_ADDRESS>
    token: xxxxxxxxxxxxxxxxxxxxx

Checklist:

[X] The code change is tested and works locally.
[X] Local tests pass with tox. Your PR cannot be merged unless tests pass

If user exposed functionality or configuration variables are added/changed:

[X] Documentation added/updated in home-assistant.github.io

If the code communicates with devices, web services, or third-party tools:

[X] New dependencies have been added to the REQUIREMENTS variable (example).
[X] New dependencies are only imported inside functions that use them (example).
[X] New dependencies have been added to requirements_all.txt by running script/gen_requirements_all.py.
[X] New files were added to .coveragerc.

Hmm I'm not sure if the original PR is targeted for 0.67 or not. I'll look into it.


Performance related I/O issues

Description of problem
We are seeing less than desirable numbers when running the Cassandra stress test on a 2 node Scylla cluster running on the same machine.  The baseline is the same cluster but running on straight Docker containers.
We are also seeing the same behavior if using Storage Driver: devicemapper instead of Storage Driver: overlay2
More details here: http://blog.serverbooter.com/post/scylla-in-kata-containers/
Expected result
Better performance number. There's an overhead by running in VM, but the numbers show up to 80% degradation on reads for the test.  Something like around like 30% degradation would be nice. Or we can take advantage of VMs talking directly to the hardware it would be great, to possible better performance(?)
Actual result
Details here:
http://blog.serverbooter.com/post/scylla-in-kata-containers/

Output of kata-collect-data.sh
Meta details
Running kata-collect-data.sh version 0.1.0 (commit bc83bf052de7157c3e36afa24d7f0f54e91b5ddf) at 2018-04-10.13:55:38.980643542+0000.

Runtime is /usr/local/bin/kata-runtime.
kata-env
Output of "/usr/local/bin/kata-runtime kata-env":
[Meta]
  Version = "1.0.10"

[Runtime]
  Debug = false
  [Runtime.Version]
    Semver = "0.1.0"
    Commit = "bc83bf052de7157c3e36afa24d7f0f54e91b5ddf"
    OCI = "1.0.1"
  [Runtime.Config]
    Path = "/usr/share/defaults/kata-containers/configuration.toml"

[Hypervisor]
  MachineType = "pc"
  Version = "QEMU emulator version 2.7.1(2.7.1+git.d4a337fe91-11.cc), Copyright (c) 2003-2016 Fabrice Bellard and the QEMU Project developers"
  Path = "/usr/bin/qemu-lite-system-x86_64"
  Debug = false
  BlockDeviceDriver = "virtio-scsi"

[Image]
  Path = "/usr/share/kata-containers/kata-containers-2018-04-06-03:29:24.440175437+0000-2b2b68d"

[Kernel]
  Path = "/usr/share/clear-containers/vmlinuz-4.14.22-86.container"
  Parameters = "agent.log=debug"

[Initrd]
  Path = ""

[Proxy]
  Type = "kataProxy"
  Version = "kata-proxy version 0.0.1-de18929631091cb1f079b2e6e1caa3e4625f4930"
  Path = "/usr/libexec/kata-containers/kata-proxy"
  Debug = false

[Shim]
  Type = "kataShim"
  Version = "kata-shim version 0.0.1-2bdea2e41109b6b71c600899e1bf5794b125200d"
  Path = "/usr/libexec/kata-containers/kata-shim"
  Debug = false

[Agent]
  Type = "kata"
  Version = "<<unknown>>"

[Host]
  Kernel = "4.4.122-mainline-rev1"
  Architecture = "amd64"
  VMContainerCapable = true
  [Host.Distro]
    Name = "Ubuntu"
    Version = "16.04"
  [Host.CPU]
    Vendor = "GenuineIntel"
    Model = "Intel(R) Atom(TM) CPU  C2550  @ 2.40GHz"

Runtime config files
Runtime default config files
/etc/kata-containers/configuration.toml
/usr/share/defaults/kata-containers/configuration.toml

Runtime config file contents
Config file /etc/kata-containers/configuration.toml not found
Output of "cat "/usr/share/defaults/kata-containers/configuration.toml"":
# XXX: WARNING: this file is auto-generated.
# XXX:
# XXX: Source file: "cli/config/configuration.toml.in"
# XXX: Project:
# XXX:   Name: Kata Containers
# XXX:   Type: kata

[hypervisor.qemu]
#path = "/usr/bin/qemu-system-x86_64"
path = "/usr/bin/qemu-lite-system-x86_64"
kernel = "/usr/share/kata-containers/vmlinuz.container"
# initrd = "/usr/share/kata-containers/kata-containers-initrd.img"
image = "/usr/share/kata-containers/kata-containers.img"
machine_type = "pc"

# Optional space-separated list of options to pass to the guest kernel.
# For example, use `kernel_params = "vsyscall=emulate"` if you are having
# trouble running pre-2.15 glibc.
#
# WARNING: - any parameter specified here will take priority over the default
# parameter value of the same name used to start the virtual machine.
# Do not set values here unless you understand the impact of doing so as you
# may stop the virtual machine from booting.
# To see the list of default parameters, enable hypervisor debug, create a
# container and look for 'default-kernel-parameters' log entries.
kernel_params = " agent.log=debug"

# Path to the firmware.
# If you want that qemu uses the default firmware leave this option empty
firmware = ""

# Machine accelerators
# comma-separated list of machine accelerators to pass to the hypervisor.
# For example, `machine_accelerators = "nosmm,nosmbus,nosata,nopit,static-prt,nofw"`
machine_accelerators=""

# Default number of vCPUs per POD/VM:
# unspecified or 0                --> will be set to 1
# < 0                             --> will be set to the actual number of physical cores
# > 0 <= number of physical cores --> will be set to the specified number
# > number of physical cores      --> will be set to the actual number of physical cores
default_vcpus = 1

# Bridges can be used to hot plug devices.
# Limitations:
# * Currently only pci bridges are supported
# * Until 30 devices per bridge can be hot plugged.
# * Until 5 PCI bridges can be cold plugged per VM.
#   This limitation could be a bug in qemu or in the kernel
# Default number of bridges per POD/VM:
# unspecified or 0   --> will be set to 1
# > 1 <= 5           --> will be set to the specified number
# > 5                --> will be set to 5
default_bridges = 1

# Default memory size in MiB for POD/VM.
# If unspecified then it will be set 2048 MiB.
#default_memory = 2048

# Disable block device from being used for a container's rootfs.
# In case of a storage driver like devicemapper where a container's
# root file system is backed by a block device, the block device is passed
# directly to the hypervisor for performance reasons.
# This flag prevents the block device from being passed to the hypervisor,
# 9pfs is used instead to pass the rootfs.
disable_block_device_use = false

# Block storage driver to be used for the hypervisor in case the container
# rootfs is backed by a block device. This is either virtio-scsi or
# virtio-blk.
block_device_driver = "virtio-scsi"

# Enable iothreads (data-plane) to be used. This causes IO to be
# handled in a separate IO thread. This is currently only implemented
# for SCSI.
#
enable_iothreads = false

# Enable pre allocation of VM RAM, default false
# Enabling this will result in lower container density
# as all of the memory will be allocated and locked
# This is useful when you want to reserve all the memory
# upfront or in the cases where you want memory latencies
# to be very predictable
# Default false
#enable_mem_prealloc = true

# Enable huge pages for VM RAM, default false
# Enabling this will result in the VM memory
# being allocated using huge pages.
# This is useful when you want to use vhost-user network
# stacks within the container. This will automatically
# result in memory pre allocation
#enable_hugepages = true

# Enable swap of vm memory. Default false.
# The behaviour is undefined if mem_prealloc is also set to true
#enable_swap = true

# This option changes the default hypervisor and kernel parameters
# to enable debug output where available. This extra output is added
# to the proxy logs, but only when proxy debug is also enabled.
#
# Default false
enable_debug = true

# Disable the customizations done in the runtime when it detects
# that it is running on top a VMM. This will result in the runtime
# behaving as it would when running on bare metal.
#
#disable_nesting_checks = true

[proxy.kata]
path = "/usr/libexec/kata-containers/kata-proxy"

# If enabled, proxy messages will be sent to the system log
# (default: disabled)
#enable_debug = true

[shim.kata]
path = "/usr/libexec/kata-containers/kata-shim"

# If enabled, shim messages will be sent to the system log
# (default: disabled)
#enable_debug = true

[agent.kata]
# There is no field for this section. The goal is only to be able to
# specify which type of agent the user wants to use.

[runtime]
# If enabled, the runtime will log additional debug messages to the
# system log
# (default: disabled)
#enable_debug = true
#
# Internetworking model
# Determines how the VM should be connected to the
# the container network interface
# Options:
#
#   - bridged
#     Uses a linux bridge to interconnect the container interface to
#     the VM. Works for most cases except macvlan and ipvlan.
#
#   - macvtap
#     Used when the Container network interface can be bridged using
#     macvtap.
internetworking_model="macvtap"

Agent
version:
kata-agent version 0.0.1-c77567216cf0b9c7df4851592b25956b6f21dce1

Logfiles
Runtime logs
No recent runtime problems found in system journal.
Proxy logs
No recent proxy problems found in system journal.
Shim logs
No recent shim problems found in system journal.

 This is what it looks like in one the Scylla containers.
This is how the container is started.
docker run --runtime kata-runtime --name my-scylla -p 9042:9042 -d scylladb/scylla

# docker exec -it d052f8473bf4 /bin/bash

   _____            _ _       _____  ____
  / ____|          | | |     |  __ \|  _ \
 | (___   ___ _   _| | | __ _| |  | | |_) |
  \___ \ / __| | | | | |/ _` | |  | |  _ <
  ____) | (__| |_| | | | (_| | |__| | |_) |
 |_____/ \___|\__, |_|_|\__,_|_____/|____/
               __/ |
              |___/

Nodetool:
	nodetool help
CQL Shell:
	cqlsh
More documentation available at:
	http://www.scylladb.com/doc/

[root@d052f8473bf4 ~]# df
Filesystem     1K-blocks    Used Available Use% Mounted on
/dev/sda        10190136  656464   8993000   7% /
tmpfs              65536       0     65536   0% /dev
kataShared      47929956 5545460  39926708  13% /etc/hosts
shm                65536       0     65536   0% /dev/shm
tmpfs            1001948       0   1001948   0% /proc/scsi
tmpfs            1001948       0   1001948   0% /sys/firmware
[root@d052f8473bf4 ~]# mount
/dev/sda on / type ext4 (rw,relatime,stripe=128,data=ordered)
proc on /proc type proc (rw,nosuid,nodev,noexec,relatime)
tmpfs on /dev type tmpfs (rw,nosuid,size=65536k,mode=755)
devpts on /dev/pts type devpts (rw,nosuid,noexec,relatime,gid=5,mode=620,ptmxmode=666)
sysfs on /sys type sysfs (ro,nosuid,nodev,noexec,relatime)
mqueue on /dev/mqueue type mqueue (rw,nosuid,nodev,noexec,relatime)
kataShared on /etc/resolv.conf type 9p (rw,nodev,relatime,sync,dirsync,access=client,trans=virtio)
kataShared on /etc/hostname type 9p (rw,nodev,relatime,sync,dirsync,access=client,trans=virtio)
kataShared on /etc/hosts type 9p (rw,nodev,relatime,sync,dirsync,access=client,trans=virtio)
shm on /dev/shm type tmpfs (rw,nosuid,nodev,noexec,relatime,size=65536k)
kataShared on /var/lib/scylla type 9p (rw,nodev,relatime,sync,dirsync,access=client,trans=virtio)
kataShared on /sys/fs/cgroup type 9p (rw,nodev,relatime,sync,dirsync,access=client,trans=virtio)
proc on /proc/bus type proc (ro,relatime)
proc on /proc/fs type proc (ro,relatime)
proc on /proc/irq type proc (ro,relatime)
proc on /proc/sys type proc (ro,relatime)
tmpfs on /proc/timer_list type tmpfs (rw,nosuid,size=65536k,mode=755)
tmpfs on /proc/sched_debug type tmpfs (rw,nosuid,size=65536k,mode=755)
tmpfs on /proc/scsi type tmpfs (ro,relatime)
tmpfs on /sys/firmware type tmpfs (ro,relatime)

Here's the output of docker inspect
 docker inspect d052f8473bf4
[
    {
        "Id": "d052f8473bf43c4325a372839e23d66071e94a6f322c124183fb07b22399dca3",
        "Created": "2018-04-10T03:28:28.638042603Z",
        "Path": "/docker-entrypoint.py",
        "Args": [
            "--seeds=<IP_ADDRESS>"
        ],
        "State": {
            "Status": "running",
            "Running": true,
            "Paused": false,
            "Restarting": false,
            "OOMKilled": false,
            "Dead": false,
            "Pid": 7551,
            "ExitCode": 0,
            "Error": "",
            "StartedAt": "2018-04-10T03:28:34.767123631Z",
            "FinishedAt": "0001-01-01T00:00:00Z"
        },
        "Image": "sha256:d02433971564e2b8d2df0191278e979f3dc98360a7b7dff1da2cf570d0e4c533",
        "ResolvConfPath": "/var/lib/docker/containers/d052f8473bf43c4325a372839e23d66071e94a6f322c124183fb07b22399dca3/resolv.conf",
        "HostnamePath": "/var/lib/docker/containers/d052f8473bf43c4325a372839e23d66071e94a6f322c124183fb07b22399dca3/hostname",
        "HostsPath": "/var/lib/docker/containers/d052f8473bf43c4325a372839e23d66071e94a6f322c124183fb07b22399dca3/hosts",
        "LogPath": "/var/lib/docker/containers/d052f8473bf43c4325a372839e23d66071e94a6f322c124183fb07b22399dca3/d052f8473bf43c4325a372839e23d66071e94a6f322c124183fb07b22399dca3-json.log",
        "Name": "/my-scylla2",
        "RestartCount": 0,
        "Driver": "devicemapper",
        "Platform": "linux",
        "MountLabel": "",
        "ProcessLabel": "",
        "AppArmorProfile": "docker-default",
        "ExecIDs": null,
        "HostConfig": {
            "Binds": null,
            "ContainerIDFile": "",
            "LogConfig": {
                "Type": "json-file",
                "Config": {}
            },
            "NetworkMode": "default",
            "PortBindings": {},
            "RestartPolicy": {
                "Name": "no",
                "MaximumRetryCount": 0
            },
            "AutoRemove": false,
            "VolumeDriver": "",
            "VolumesFrom": null,
            "CapAdd": null,
            "CapDrop": null,
            "Dns": [],
            "DnsOptions": [],
            "DnsSearch": [],
            "ExtraHosts": null,
            "GroupAdd": null,
            "IpcMode": "shareable",
            "Cgroup": "",
            "Links": null,
            "OomScoreAdj": 0,
            "PidMode": "",
            "Privileged": false,
            "PublishAllPorts": false,
            "ReadonlyRootfs": false,
            "SecurityOpt": null,
            "UTSMode": "",
            "UsernsMode": "",
            "ShmSize": 67108864,
            "Runtime": "kata-runtime",
            "ConsoleSize": [
                0,
                0
            ],
            "Isolation": "",
            "CpuShares": 0,
            "Memory": 0,
            "NanoCpus": 0,
            "CgroupParent": "",
            "BlkioWeight": 0,
            "BlkioWeightDevice": [],
            "BlkioDeviceReadBps": null,
            "BlkioDeviceWriteBps": null,
            "BlkioDeviceReadIOps": null,
            "BlkioDeviceWriteIOps": null,
            "CpuPeriod": 0,
            "CpuQuota": 0,
            "CpuRealtimePeriod": 0,
            "CpuRealtimeRuntime": 0,
            "CpusetCpus": "",
            "CpusetMems": "",
            "Devices": [],
            "DeviceCgroupRules": null,
            "DiskQuota": 0,
            "KernelMemory": 0,
            "MemoryReservation": 0,
            "MemorySwap": 0,
            "MemorySwappiness": null,
            "OomKillDisable": false,
            "PidsLimit": 0,
            "Ulimits": null,
            "CpuCount": 0,
            "CpuPercent": 0,
            "IOMaximumIOps": 0,
            "IOMaximumBandwidth": 0
        },
        "GraphDriver": {
            "Data": {
                "DeviceId": "23",
                "DeviceName": "docker-43:0-1836972-0f44df23dab874cc64069cc67cb59d6e909752af37feffa9d1095e638bff32bb",
                "DeviceSize": "10737418240"
            },
            "Name": "devicemapper"
        },
        "Mounts": [
            {
                "Type": "volume",
                "Name": "442144ed7fde3714920e8c2be0ba20a6777439252e72b9f37b3fb921eba231bf",
                "Source": "/var/lib/docker/volumes/442144ed7fde3714920e8c2be0ba20a6777439252e72b9f37b3fb921eba231bf/_data",
                "Destination": "/sys/fs/cgroup",
                "Driver": "local",
                "Mode": "",
                "RW": true,
                "Propagation": ""
            },
            {
                "Type": "volume",
                "Name": "d78ad1d0c2a2b91738153c8c4ee1d6cb087b800f75668c4bda28b0f3ca4ce3f7",
                "Source": "/var/lib/docker/volumes/d78ad1d0c2a2b91738153c8c4ee1d6cb087b800f75668c4bda28b0f3ca4ce3f7/_data",
                "Destination": "/var/lib/scylla",
                "Driver": "local",
                "Mode": "",
                "RW": true,
                "Propagation": ""
            }
        ],
        "Config": {
            "Hostname": "d052f8473bf4",
            "Domainname": "",
            "User": "",
            "AttachStdin": false,
            "AttachStdout": false,
            "AttachStderr": false,
            "ExposedPorts": {
                "10000/tcp": {},
                "7000/tcp": {},
                "7001/tcp": {},
                "9042/tcp": {},
                "9160/tcp": {},
                "9180/tcp": {}
            },
            "Tty": false,
            "OpenStdin": false,
            "StdinOnce": false,
            "Env": [
                "PATH=/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin",
                "container=docker"
            ],
            "Cmd": [
                "--seeds=<IP_ADDRESS>"
            ],
            "Image": "scylladb/scylla",
            "Volumes": {
                "/sys/fs/cgroup": {},
                "/var/lib/scylla": {}
            },
            "WorkingDir": "",
            "Entrypoint": [
                "/docker-entrypoint.py"
            ],
            "OnBuild": null,
            "Labels": {
                "build-date": "20180302",
                "license": "GPLv2",
                "name": "CentOS Base Image",
                "vendor": "CentOS"
            }
        },
        "NetworkSettings": {
            "Bridge": "",
            "SandboxID": "32ba07dcd67c2e2037e0e45240a24e5da1f0bb92b57558cf21bedca291cd7ebe",
            "HairpinMode": false,
            "LinkLocalIPv6Address": "",
            "LinkLocalIPv6PrefixLen": 0,
            "Ports": {
                "10000/tcp": null,
                "7000/tcp": null,
                "7001/tcp": null,
                "9042/tcp": null,
                "9160/tcp": null,
                "9180/tcp": null
            },
            "SandboxKey": "/var/run/docker/netns/32ba07dcd67c",
            "SecondaryIPAddresses": null,
            "SecondaryIPv6Addresses": null,
            "EndpointID": "a9268beeb573a12f5feab3336f1cf57f8904e596d9b5ab0d43a9c3aa51bcc644",
            "Gateway": "<IP_ADDRESS>",
            "GlobalIPv6Address": "",
            "GlobalIPv6PrefixLen": 0,
            "IPAddress": "<IP_ADDRESS>",
            "IPPrefixLen": 16,
            "IPv6Gateway": "",
            "MacAddress": "02:42:ac:11:00:03",
            "Networks": {
                "bridge": {
                    "IPAMConfig": null,
                    "Links": null,
                    "Aliases": null,
                    "NetworkID": "a1f9340c191a98e5901ed9bfabeaf66e932b57d9a88b8a17892b5e82ce210954",
                    "EndpointID": "a9268beeb573a12f5feab3336f1cf57f8904e596d9b5ab0d43a9c3aa51bcc644",
                    "Gateway": "<IP_ADDRESS>",
                    "IPAddress": "<IP_ADDRESS>",
                    "IPPrefixLen": 16,
                    "IPv6Gateway": "",
                    "GlobalIPv6Address": "",
                    "GlobalIPv6PrefixLen": 0,
                    "MacAddress": "02:42:ac:11:00:03",
                    "DriverOpts": null
                }
            }
        }
    }
]

/cc @grahamwhaley @amshinde - FYI.

@egernst yep, I asked @raravena80 for the mount info. @raravena80 - is the scylla DB on /var/lib/scylla? - as we have
kataShared on /var/lib/scylla type 9p (rw,nodev,relatime,sync,dirsync,access=client,trans=virtio)

being a 9p mount, rather than a direct block mapping.
I'll let @amshinde comment on how, why etc., presuming that is they key volume mount we'd like 'optimised'

@grahamwhaley yes. All the DB items including data, and commit logs are under /var/lib/scylla

@raravena80 @egernst @grahamwhaley Since the actual database is on a shared 9pfs mount, the performance measured is over 9p. Using devicemapper here would not make much of a difference since the database files are read/written on a 9p mount(/var/lib/scylla)
@raravena80 I think you would see better numbers if you had your database on the rootfs itself, since in case of devicemapper this would be a block based device and show better numbers.

@amshinde the numbers are even lower using the bare metal rootfs as the data storage.  I think the scylla docker workload itself is very much tied to Docker volumes, as it creates the /var/lib/scylla directory as a volume.
I havent found the exact Dockerfile, but it should be similar to
https://github.com/scylladb/scylla/blob/master/dist/docker/ubuntu/Dockerfile#L16
Had the VOLUME /var/lib/scylla been replaced by RUN mkdir /var/lib/scylla, that would have created the data directory on the rootfs.
One way to improve performance would be to use a volume that is block based, but using block-based volumes is something that is still missing in kata. I have opened an issue here related to this: https://github.com/kata-containers/runtime/issues/198

Ran the tests with /var/lib/scylla inside the container and got almost the same results. (Slightly better) I believe the performance penalty might not just be necessarily related to 9pfs
Modified the last couple of lines of the Dockerfile like this:
#VOLUME [ "/var/lib/scylla" ]
RUN mkdir -p /var/lib/scylla

Then built the container:
docker build -t localscylla:latest .
   Are you and @amshinde happy that we now understand where the disparity (at least the read/write disparity) was coming from (small RAM for kata by default). If both @raravena80 and @amshinde are happy, then I think we can close the Issue?
Otherwise, please detail any more configs we wan to try here.
thanks!

@amshinde you ok with this? Performance overall should be better if we have block device support in the VM.  Overall it's hard to match raw container performance unless the VM has more components directly talking to the hardware.

Closing this. Tweaking the VM parameters improves performance. There are more improvements coming in the form of https://github.com/kata-containers/runtime/issues/198


WARN HttpParser - URI is too large >8192

The server doesn't serve big text and constantly returns this message: WARN HttpParser - URI is too large >8192

@dnanhkhoa could you please fix the server to switch from Query Parameter to POST payload? The current implementation cannot handle a long query parameter, hence this error happens.

Hi @alg0s , thank you very much for raising this issue,
Unfortunately I cannot sit on this problem this time. I will try to look into it.

@dnanhkhoa could you please fix the server to switch from Query Parameter to POST payload? The current implementation cannot handle a long query parameter, hence this error happens.

hi, I wrote R wrapper for this and it can handle long text. You can also run it as docker service and use any clients you want:
https://github.com/vega-ai/vietr


Created About Us page and Contact us page alongwith navbar and footer

Description
Created About Us and Contact Us Page
Created Navbar and Footer
Added blur, transparency effects and interactive animations
Added Dark mode for all pages
Fixes #47
Type of change
Please delete options that are not relevant.

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

Checklist:

[x] My code follows the style guidelines of this project
[x] I have performed a self-review of my own code
[ ] I have commented my code, particularly in hard-to-understand areas
[ ] I have made corresponding changes to the documentation
[x] My changes generate no new warnings
[ ] I have added tests that prove my fix is effective or that my feature works
[ ] New and existing unit tests pass locally with my changes
[x] Any dependent changes have been merged and published in downstream modules

Screenshots (if appropriate):
https://github.com/user-attachments/assets/d052889b-3746-4cf4-97bb-4c18290c34c2
Additional context
Add any other context or screenshots about the pull request here.

Summary by Sourcery
This pull request introduces new 'About Us' and 'Contact Us' pages, along with a new Navbar and Footer. It also adds dark mode support and various UI enhancements, including blur and transparency effects. Additionally, it fixes a minor issue in the mode toggle button and updates the Next.js configuration for image loading.

New Features:

Created 'About Us' and 'Contact Us' pages with detailed content and animations.
Implemented a new Navbar and Footer for site-wide navigation and information.
Added dark mode support across all pages for better user experience.

Bug Fixes:

Fixed a minor issue in the mode toggle button's Moon icon class.

Enhancements:

Introduced blur, transparency effects, and interactive animations to improve UI aesthetics.
Updated the 'Home' page to integrate the new Navbar and Footer components.

Build:

Configured Next.js to allow loading images from 'dummyimage.com'.

If there's a problem while merging this pr, then inform me, I will fix these errors and raise a new pr

@avinashkranjan kindly look into my pr and merge it if it aligns with your expectations

@r7projects-shayan Will Review the PR in 1 - 2 Days

@avinashkranjan Sir, kindly review and merge the PR


065-gtp-common-pips: fix overlapping piplist project names

Signed-off-by: Alessandro Comodi<EMAIL_ADDRESS>This fixes an issue discovered in this master CI build.
065- and 065b-gtp-common-pips share the piplist directory and the vivado project needs to be located in separate directories.

Good catch


App get crash when user taps on device back button after navigating to  "Licenses" page

Frequency: 100%
** Repro App version:** Play store v1.8.2
Repro on device: redmi note 6 pro Android (9.0), Samsung Galaxy M30s  (9.0)
Does not repro on Google Pixel XL (8.1),
Tested Account:<PHONE_NUMBER> / Backup key
Repro Steps:

Launch app and login with tested account
Tap on cog icon click on Licenses option
Tap on device back button once or twice and observe

Current Behavior: App is getting crashed
Expected Behavior: Should get back to Celo home screen
Attachment:

Suddencrashinlicensesbackbutton.mp4 (Bug observe at 30 sec)
celo_logs.txt

@i1skn please look out for this while finishing the Settings redesign


fix: only garbage collection empty channels

I randomly noticed this issue while running all tests locally. It's possible that the below code returns null:
$channel = $member->channel;
$this->socket->removeMemberFromChannel($member, $channel);

removeMemberFromChannel() expects Channel and does not permit null so this results in a type error.
Mitigations:

Moved deleting channels so it's done last
Added check if $member->channel is null
Added transaction to ensure two garbage collecting transactions don't conflict - one details channels while another one tries to read a channel

Transactions in InnoDB are row level and the rows that we're locking are expected to be garbage collected so I don't think this should be become a performance issue...

Codecov Report

Merging #6 (71f5dbc) into master (94e0c72) will increase coverage by 0.00%.
The diff coverage is 100.00%.

@@            Coverage Diff            @@
##             master       #6   +/-   ##
=========================================
  Coverage     99.61%   99.61%
  Complexity       65       65
=========================================
  Files            14       14
  Lines           258      260    +2
=========================================
+ Hits            257      259    +2
  Misses            1        1

Impacted Files
Coverage Δ

src/PollcastBroadcaster.php
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 94e0c72...71f5dbc. Read the comment docs.


Mad Writer does not handle duplicated elements correctly

A Madx sequence like this
mb: sbend, l:=1, angle:=0.1, k0:=0.2, k1=0.1;
ss: sequence, l:=2, refer=entry;
  mb, at=0;
  mb, at=1;
endsequence;

when loaded into Xsuite, and dumped back into mad format, gives an output like this:
ss: sequence, l=2.0;
  ss$start: marker, at=0.0;
  mb: sbend, l = 1.0, angle = 0.1, k0 = 0.2, e1 = 0.0, fint = 0.0, hgap = 0.0, e2 = 0.0, at=0.5;
  mb_entry:0: marker, at=1.0;
  mb:0: sbend, l = 1.0, angle = 0.1, k0 = 0.2, at=1.5;
  mb_exit:0: marker, at=2.0;
  ss$end: marker, at=2.0;
endsequence;

Which is not correct, as colons cannot be part of the element name in mad. We should decide how to handle these situations. I can see the following options:

Replace the colon with something else – an underscore?
Just repeat the name?

Regardless of madx, how do we want to handle repeated names? A typical case arises when identical cells are repeated.
Could we support keys in the element_dict in the for (string,int)?


How can we use a tab character in a spec file?

Problem
The HardTabs pre-commit hook prevents spec files from having hard tabs in example output used to test parsing.
Background
I'm preparing a new PR that introduces support for PHP Mess Detector, and the spec test for parsing the output of the command includes hard tabs. The spec file I'm working with looks like this with the tabs highlighted.

Question
What's the best approach to including this tabbed example output in a spec file?

Hey @craig-davis, anything holding you back from using the \t escape sequence to represent a tab?
Just switch from single quotes to double quotes in your strings and replace a literal tab with \t. Hope that helps!


Update use-microsoft-365-cdn-with-spo.md

@kelleyvice-msft  Please hold and review with me

Merging contributions from Msft internal contributor.


How does this work with play's default compile time DI

Guice seems to not be preferred anymore https://www.playframework.com/documentation/2.5.x/ScalaCompileTimeDependencyInjection. There are rumours that it is goto's little brother and it has been seen playing with var.
How do we get around it?

Hi @i-am-the-slime -- has someone answered your question yet (maybe on HipChat or in person)?

Nope, not yet. But thanks for getting back to me, @LappleApple !

@i-am-the-slime , in our play projects we use runtime DI, so we have no needs in compile-time DI. From other side if this will increase adoption of the library then PR is more than welcome!

@i-am-the-slime , compile-time DI makes app startup time faster. A web app is rarely restarted unlike a mobile app. Use of runtime DI has no performance impact on the web service.
On the other hand, if runtime DI get widely adopted , then a PR is welcome.


Support schema 1 image.

Now containerd only supports schema 2 manifest.
However, a lot of images are still using schema 1 manifest.
We should at least support pulling schema 1 image.

@Random-Liu I just took a look at the code here and it looks more than doable. The only missing aspect is that schema1 has BlobSums instead of descriptors, but we can generate them.

@stevvooe Cool~ It would be very helpful if we could support schema 1 before 1.0, if it's not that complex. :)
But if we don't have bandwidth, it's also fine for us to leave it to next release. However, we need to do some work around on our side in that case.

@dmcgowan @stevvooe This becomes one of biggest blockers for our integration test.
Do we have plan to prioritize this? Does this need much effort? If not, maybe I could try it out. :)

@dmcgowan Thanks! Given that I've got no experience on the image format, it would be super great if you could get to this.
I'll work on the other 2 smaller blockers listed in https://github.com/kubernetes-incubator/cri-containerd/issues/35, so that we could have a better test result for the alpha integration.

@crosbymichael Thanks for including this in alpha release 1.


Feature/portoaj/connect fosters page

Administrative Info
Issue #62
Make sure your branch name conforms to: <feature/staging/hotfix/...>/[username]/[3-4 word description separated by dashes]. Otherwise, please rename your branch and create a new PR.
Changes
-Connected page to backend to load users and the relevant administrators and coordinators
Testing
Looked at site vs figma
Confirmation of Change

Note
Still need to make 'view profile' button work, waiting on PR #94 to be be merged as it contains the "FosterProfile" component to display

Added modal that opens up when you click the view profile button:


Adding ctstate.edu as email domain

Gateway Community College
https://gatewayct.edu/
https://catalog.ctstate.edu/preview_program.php?catoid=19&poid=7701&returnto=2075 https://ctstate.edu/locations/gateway
Cheers!

@pgaulin Thank you for your patience while waiting for a review and processing your request. Pull request merged. Please start applying for JetBrains educational subscription at https://www.jetbrains.com/shop/eform/students in an hour, to allow the changes to propagate through JetBrains systems.


blueman: use python3Packages

Motivation for this change
https://github.com/NixOS/nixpkgs/issues/18185
Things done

[x] Tested using sandboxing (nix.useSandbox on NixOS, or option sandbox in nix.conf on non-NixOS)
Built on platform(s)

[x] NixOS
[ ] macOS
[ ] other Linux distributions

[ ] Tested via one or more NixOS test(s) if existing and applicable for the change (look inside nixos/tests)
[ ] Tested compilation of all pkgs that depend on this change using nix-shell -p nox --run "nox-review wip"
[ ] Tested execution of all binary files (usually in ./result/bin/)
[ ] Determined the impact on package closure size (by running nix path-info -S before and after)
[x] Fits CONTRIBUTING.md.

cc @abbradar
Note that this was not tested.

Success on x86_64-linux (full log)
Attempted: blueman
Partial log (click to expand)
wrapping `/nix/store/vq06c17a74mifizah860cawnzhsp0ccs-blueman-2.0.5/bin/.blueman-applet-wrapped'...
wrapping `/nix/store/vq06c17a74mifizah860cawnzhsp0ccs-blueman-2.0.5/bin/.blueman-assistant-wrapped'...
wrapping `/nix/store/vq06c17a74mifizah860cawnzhsp0ccs-blueman-2.0.5/bin/.blueman-browse-wrapped'...
wrapping `/nix/store/vq06c17a74mifizah860cawnzhsp0ccs-blueman-2.0.5/bin/.blueman-report-wrapped'...
wrapping `/nix/store/vq06c17a74mifizah860cawnzhsp0ccs-blueman-2.0.5/bin/.blueman-sendto-wrapped'...
wrapping `/nix/store/vq06c17a74mifizah860cawnzhsp0ccs-blueman-2.0.5/bin/.blueman-adapters-wrapped'...
wrapping `/nix/store/vq06c17a74mifizah860cawnzhsp0ccs-blueman-2.0.5/bin/.blueman-manager-wrapped'...
wrapping `/nix/store/vq06c17a74mifizah860cawnzhsp0ccs-blueman-2.0.5/bin/.blueman-services-wrapped'...
wrapping `/nix/store/vq06c17a74mifizah860cawnzhsp0ccs-blueman-2.0.5/libexec/.blueman-mechanism-wrapped'...
wrapping `/nix/store/vq06c17a74mifizah860cawnzhsp0ccs-blueman-2.0.5/libexec/.blueman-rfcomm-watcher-wrapped'...

Success on aarch64-linux (full log)
Attempted: blueman
Partial log (click to expand)
wrapping `/nix/store/zxi0b38vz6pcavmzxaln35qw8f6l105l-blueman-2.0.5/bin/.blueman-browse-wrapped'...
wrapping `/nix/store/zxi0b38vz6pcavmzxaln35qw8f6l105l-blueman-2.0.5/bin/.blueman-assistant-wrapped'...
wrapping `/nix/store/zxi0b38vz6pcavmzxaln35qw8f6l105l-blueman-2.0.5/bin/.blueman-sendto-wrapped'...
wrapping `/nix/store/zxi0b38vz6pcavmzxaln35qw8f6l105l-blueman-2.0.5/bin/.blueman-services-wrapped'...
wrapping `/nix/store/zxi0b38vz6pcavmzxaln35qw8f6l105l-blueman-2.0.5/bin/.blueman-manager-wrapped'...
wrapping `/nix/store/zxi0b38vz6pcavmzxaln35qw8f6l105l-blueman-2.0.5/bin/.blueman-applet-wrapped'...
wrapping `/nix/store/zxi0b38vz6pcavmzxaln35qw8f6l105l-blueman-2.0.5/bin/.blueman-adapters-wrapped'...
wrapping `/nix/store/zxi0b38vz6pcavmzxaln35qw8f6l105l-blueman-2.0.5/libexec/.blueman-mechanism-wrapped'...
wrapping `/nix/store/zxi0b38vz6pcavmzxaln35qw8f6l105l-blueman-2.0.5/libexec/.blueman-rfcomm-watcher-wrapped'...
/nix/store/zxi0b38vz6pcavmzxaln35qw8f6l105l-blueman-2.0.5

Python 2 still gets pulled in because of the bluez dependency. Can you make bluez use Python 3 as well?

Tested it quickly, works without problems

I'm not sure whether this should have been merged into master.

Failure on x86_64-linux (full log)
Attempted: blueman, bluez
Partial log (click to expand)
cannot build derivation '/nix/store/y4c8f0hmf3dn42ch28avw9ivsfyrfrz6-bluez-5.50.drv': 3 dependencies couldn't be built
cannot build derivation '/nix/store/7452ni8m8kzi698sq8xx5176rn7yx30g-gtk+3-3.22.30.drv': 10 dependencies couldn't be built
cannot build derivation '/nix/store/d77gqy360z5j0l84laaa3b483cwdvyj2-libgsf-1.14.42.drv': 1 dependencies couldn't be built
cannot build derivation '/nix/store/f12pzxr8q93rz2qlxqnvh2h9zzqjwkcz-libnotify-0.7.7.drv': 2 dependencies couldn't be built
cannot build derivation '/nix/store/h0825d7cbhyl5545zfb32p6xmqqvgcds-librsvg-2.42.4.drv': 8 dependencies couldn't be built
cannot build derivation '/nix/store/x217rjniw7bf63dai77qkb67nh4djh64-openobex-1.7.2.drv': 1 dependencies couldn't be built
cannot build derivation '/nix/store/0amzrqm02navy6lwflw74hfg28gs1fy3-hook.drv': 3 dependencies couldn't be built
cannot build derivation '/nix/store/62cawnsaqp9rs6hkyxd56cn0xly3m7gr-obex-data-server-0.4.6.drv': 2 dependencies couldn't be built
cannot build derivation '/nix/store/pp1vywkyiwsz0hjw77b323qw08lhyhp7-blueman-2.0.5.drv': 10 dependencies couldn't be built
error: build of '/nix/store/pp1vywkyiwsz0hjw77b323qw08lhyhp7-blueman-2.0.5.drv', '/nix/store/y4c8f0hmf3dn42ch28avw9ivsfyrfrz6-bluez-5.50.drv' failed

@dotlambda I think just a big number of rebuilds doesn't automatically mean staging, staging is mainly thought for stdenv big things afaik. So I think this is okay.
Also, the above failure seems to be from Rust not building again, not related to this PR (I didn't notice because I didn't test off the merge, ofborg does).

Failure on aarch64-linux (full log)
Attempted: blueman, bluez
Partial log (click to expand)
post-installation fixup
shrinking RPATHs of ELF executables and libraries in /nix/store/81yv134wfram6hy6li2sxkiymmgbld2p-obex-data-server-0.4.6
shrinking /nix/store/81yv134wfram6hy6li2sxkiymmgbld2p-obex-data-server-0.4.6/bin/obex-data-server
gzipping man pages under /nix/store/81yv134wfram6hy6li2sxkiymmgbld2p-obex-data-server-0.4.6/share/man/
strip is /nix/store/zrs21zqcchgyabjf4xfimncdq16njizc-binutils-2.30/bin/strip
stripping (with command strip and flags -S) in /nix/store/81yv134wfram6hy6li2sxkiymmgbld2p-obex-data-server-0.4.6/bin
patching script interpreter paths in /nix/store/81yv134wfram6hy6li2sxkiymmgbld2p-obex-data-server-0.4.6
checking for references to /build in /nix/store/81yv134wfram6hy6li2sxkiymmgbld2p-obex-data-server-0.4.6...
cannot build derivation '/nix/store/mh368r5cwxcxb1jklzmcwfqn9xlzv06g-blueman-2.0.5.drv': 2 dependencies couldn't be built
error: build of '/nix/store/mh368r5cwxcxb1jklzmcwfqn9xlzv06g-blueman-2.0.5.drv' failed


Day One (Light)

This tries to imitate the Day One editor (or at least as close as I could get to it with the available styles).
It supports:

H1-H6
Flagged tasks (! -> !! -> !!!)
Highlights (::text::)
Strikethrough (text)
Minor edits to initial bullets for distinction
Different colors for task statuses.
Hack-y block quote citation (~ Author Name) for emphasis

Some additional styles I'd love to see available:

Padding
Borders
Customizable horizontal lines for different "types" (straight line, double line, centered dots, etc...)

Nice! Thanks @kennonb!! I'll leave the preview in the folder for now and add an abbreviated one for the readme.


how to disable seekbar for my case video should not move position in video

pls help if its possible for this library. thanks

Hi @putrarifin
SeekBar mSeeker = (SeekBar) rootview.findViewById(R.id.seeker);
mSeeker.setVisibility(View.GONE);
can you try this one


Bundle zip should contain DOCX files if the originals are DOCX

Perhaps only if the format="docx" option is specified for the download_list_html method

Fix after #226 due to slight refactoring in that PR


Join Posts

Is there a functionality for creating join posts with other accounts?

This exists, I will show you where in a brief moment.

"In extra_data, you can pass additional media settings, for example:
Method	Type	Description
custom_accessibility_caption	String	Set alternative text {"custom_accessibility_caption": "ALT TEXT HERE"}
like_and_view_counts_disabled	Int	Disable like and view counts {"like_and_view_counts_disabled": 1}
disable_comments	Int	Disable comments {"disable_comments": 1}
invite_coauthor_user_id	Int	Add a coauthor to the post {"invite_coauthor_user_id": "USER ID OF COAUTHOR HERE"}. You also need to add this user to usertags"

"photo_upload(path: Path, caption: str, upload_id: str, usertags: List[Usertag], location: Location, extra_data: Dict = {})	Media	Upload photo (Support JPG files)"

Read documentation

As "collab" or coauthor currently can be multiple accounts, could this be changed into an array instead of int?

@JaapBroeders Hello! Thank you, could you send me your PR with this changes?

I am sorry, I was not clear. In the app it is possible to add multiple accounts as collaborators. I have no idea how to sniff out that request to figure out how the extra_data key should look like. Can anyone help with that?

As far as I've understood, at the moment it's only possible to send invites to collab posts, what about accepting the collab request? Is there any way to do it as of right now?

Seconding @JaapBroeders's question: Is there a way to add multiple collaborators on a single post? The api docs indicates this should be possible, but I'm not seeing how to do it through this package https://developers.facebook.com/docs/instagram-platform/instagram-graph-api/reference/ig-user/media
@adw0rd @subzeroid

Seconding @JaapBroeders's question: Is there a way to add multiple collaborators on a single post? The api docs indicates this should be possible, but I'm not seeing how to do it through this package developers.facebook.com/docs/instagram-platform/instagram-graph-api/reference/ig-user/media @adw0rd @subzeroid

Answered my own question. invite_coauthor_user_ids (plural) accepts a list of ids. Also, it does not seem to require adding usertags for the users
cl = Client()
cl.login(INSTAGRAM_USERNAME, INSTAGRAM_PASSWORD)

user_id_1 = cl.user_id_from_username("username1")
user_id_2 = cl.user_id_from_username("username2")

cl.photo_upload(
    path=IMAGE_PATH,
    caption="Testing...",
    extra_data={"invite_coauthor_user_ids": [user_id_1, user_id_2]},
)


Automatically generate regular images from retina (@2x) ones?

Can spritesmith generate the simple images from the retina 2x ones that are provided? I tend to only have a retina version of an icon and would don't want to have to track all of my icons twice.

For scenarios like this, we recommend using a secondary grunt task to generate the normal sized images. Here are some plugins which can help with that:

https://www.npmjs.com/package/grunt-image-resize
https://www.npmjs.com/package/grunt-imagemagick
https://www.npmjs.com/package/grunt-gm

@twolfson thanks for the response. Would it make sense to write a backend for grunt-spritesmith that resizes images, or do you think it's better to have it outside of grunt-spritesmith?

I'd prefer to keep them separate is practical for purposes outside of spritesmith. Someone could definitely wrap spritesmith with something that does resizing. However, that's more to maintain and a less frequent use case


compiling dependencies installer script (prebuild-dep.sh)

There's also already a prebuild.sh script. This looks like its 80% a duplicate of that. Can the two be combined?

yea i guess the two can be combined i just know the pre-build didnt get me everything i needed, thats more if you already have most of the common tools there

ill see about touching up the pre-build with missing deps that we should check for