How to upload multiple files within single call (using files.upload)?

Description
I'm using slackclient v1 (because I need compatibility with Python 2).
According to the files.upload documentation, when we uploading file using file field it will load file content as a multipart/form-data. However, I could not figure-out how to provide input for this parameter (through the api_call) when I want to upload multiple files at once?
Is this even possible? If yes, documentation should be updated... If no, are there any plans to add this feature?
What type of issue is this?

[ ] bug
[ ] enhancement (feature request)
[ ] question
[ ] documentation related
[ ] testing related
[x] discussion

Unfortunately at the moment, the https://api.slack.com/methods/files.upload endpoint only allows one file to be uploaded at once, meaning you have to iterate over your file list to make these multiple uploads through one api call for each.
files = ['file1.jpg', 'file2.jpg']

for my_file in my_files:
    response = client.files_upload(
        channels='#random',
        file=my_file)
    assert response["ok"]

This is just an example and not an Async one at that but it gives you an idea of how to implement this yourself. As for whether this is something Slack has available in the future, I don't know at the moment but absolutely something I'll let our team know of your interest in.

Understand, that's how I implemented currently, by iterating and uploading files one by one. However, I wanted to see if I can decrease pressure on Slack API.
Thank you very much for the quick response. I hope that this improvement will be on the Slack client roadmap (understand that it's low priority, but would be useful in some cases).

Worth writing a async upload for the library that iterates over a list of files? Ive done something similar with asyncio.gather(**) before, differen't API.

Worth writing a async upload for the library that iterates over a list of files?

This may be a good option but we're not planning to implement it as part of this library. The straight-forward way to enable more efficient and quick ways to upload multiple files would be having another path to do that. I'll share this feedback with the teams concerned. Let me close this issue now.

Any update on this, by chance? Is there any way to upload multiple files alongside one message?

Still nope :(

You cannot upload more than one file when the bot is called. Here is the tip: Just upload more than one files but you do not "display" it. Then take the response from the server with the URLs of the file uploaded, and "display" with markdown. I found this method in the link below:
https://stackoverflow.com/questions/59939261/send-multiple-files-to-slack-via-api

You cannot upload more than one file when the bot is called. Here is the tip: Just upload more than one files but you do not "display" it. Then take the response from the server with the URLs of the file uploaded, and "display" with markdown. I found this method in the link below:
https://stackoverflow.com/questions/59939261/send-multiple-files-to-slack-via-api

I've already accomplished that using the same stackoverflow document you used. But there should be some official way of doing it. Or atleast the documents should have some guidance about it.

You cannot upload more than one file when the bot is called. Here is the tip: Just upload more than one files but you do not "display" it. Then take the response from the server with the URLs of the file uploaded, and "display" with markdown. I found this method in the link below:
https://stackoverflow.com/questions/59939261/send-multiple-files-to-slack-via-api

This doesn't seem to work anymore - permalinks formatted this way do not display as file previews.
It's a pity, as there currently appears to be no way to construct a message sharing multiple images/other files unless they are public.

@zpincus Sharing permalinks still should work as long as you pick up the right URLs. But we recently released a new way to upload files onto Slack: https://github.com/slackapi/python-slack-sdk/releases/tag/v3.19.0 With the files_upload_v2 method, you can upload multiple files at a time like humans do!

Thanks! The new file upload method does the trick wonderfully.
Strangely, I was never able to get any of the permalinks produced by files.upload (even via the raw web api, much less the python wrappers) to render correctly.
I could make image galleries with links to public images just fine via posting a message consisting <url1...| ><url2...| > for various urls. But the same never worked for the permalinks (or any of the URLs provided by the file info data returned by files.upload), regardless of whether I shared the images to a channel in files.upload or just uploaded them with no channel destination at first.


Please confirm that my understanding of how to replicate state is correct.

I've been playing with this library today, and figuring out how to actually use it has been a bit tricky, so I just wanted to confirm my understanding is correct:
To replicate a new entry from the leader:

Append that entity to the log.
Call Raft.handle with an EHeartbeatTimeout event to generate the replication commands.
Process those commands.

This seems to work, but the use of an EHeartbeatTimeout was very non-obvious, so I want to check I haven't missed anything. Also, this method doesn't work if multiple entries have been appended, as the default handleHeartbeatTimeout only sends the latest entry.

This is how the initial Raft description worked as well, sort of: distributing entries was piggybacked on heartbeats, and could be 'sped up' by injecting a fake intermediate heartbeat. So yes, that's the way it's supposed to be done, though I admit it's non-obvious.
Wrt your last comment, this is also the way Raft was defined. I agree this approach is not the most efficient, but it is correct, the system will converge. My goal for Kontiki was to be as close to the Raft spec (in its draft version back then) as possible (but using uni-directional messages instead of RPC), have it correct, and then maybe improve efficiency where possible.


Help to configure WebRTC for Dahua doorbell

Hello,
I have a Dahua VTO3211D-P2-S2 doorbell. Could someone please help with the integration to HA?
HA has WebRTC Camera, go2rtc, Onvif and Generic camera installed, but I don't understand how to configure everything correctly for webrtc.
Generic camera and Onvif works, but webRTC doesn't even show the video:

Card Configuration
type: custom:webrtc-camera
url: rtsp://admin:pass!@<IP_ADDRESS>:554/cam/realmonitor?channel=1&subtype=0&unicast=true&proto=Onvif

Since I don't really understand it, I wrote only one in go2rtc.yaml:
stream:
  intercom_go2rtc: rtsp://admin:pass@<IP_ADDRESS>:554/cam/realmonitor?channel=1&subtype=0&unicast=true&proto=Onvif

I would really like to use the doorbell on a wall-mounted tablet and I really don't want to buy a VTH monitor, but I really lack the knowledge to implement it. I've seen audio issues resolved in other threads, but in my case, even the video on the WEBRTC card doesn't work.

You have very strange error - 'webrtc'
I don't know why it could happen.

@omenukas I've got an Amcrest AD410 (this company shares same company like Dahua and Hikvision) and I don't use the &unicast=true&proto=Onvif part.
Give it a try?

@omenukas I've got an Amcrest AD410 (this company shares same company like Dahua and Hikvision) and I don't use the &unicast=true&proto=Onvif part.
Give it a try?

I also tried this option:
type: custom:webrtc-camera
url: rtsp://admin:pass@<IP_ADDRESS>:554/cam/realmonitor?channel=1&subtype=1
Some...

Do you have some other settings in your card?
What is your browser? What is your OS?

Do you have some other settings in your card? What is your browser? What is your OS?

No, no other settings are on the card. The computer's OS is Windows 10. I tried with Crome, Edge, and Firefox browsers.

if I choose a stream from the go2rtc panel,

then the video shows:

go2rtc.yaml
stream:
intercom_go2rtc: rtsp://admin:pass@<IP_ADDRESS>:554/cam/realmonitor?channel=1&subtype=1
configuration.yaml
media_player:

platform: onkyo
sources:
video2: 'CBL/SAT'
video3: 'GAME'
video4: 'AUX'
video6: 'HTPC'
dvd: 'BD/DVD'
phono: 'PHONO'
cd: 'CD'
fm: 'FM'
usb: 'USB'
network: 'INTERNET'
strm-box: 'STRMBOX'
platform: webrtc
name: Dahua Camera
stream: dahua
audio: pcmu/48000
platform: webrtc
name: Tapo Camera
stream: tapo
audio: pcma
shell_command:
onkyo_command: onkyo {{ cmd }}

I didn't do anything else

Try adding "&subtype=0&authbasic=64" to the end of your URL.


项目无法启动

Describe the bug
装完依赖之后，始终无法启动项目，

Electron-Vite Version
1.0.19
Electron Version
21.4.2
Vite Version
4.1.4
Validations

[X] Follow the Code of Conduct.
[X] Read the Contributing Guidelines.
[X] Read the docs.
[X] Check that there isn't already an issue that reports the same bug to avoid creating a duplicate.

electron 没有安装成功，设置一下electron的下载代理： ELECTRON_MIRROR=https://npmmirror.com/mirrors/electron/

@mengdebiao 问题是否解决


how to run the demo

I am a Japanese university student and I am impressed by your work.
"Data preparation" has been completed. However, when I run "bash tools/code_for_demo/demo.sh" in the demo section, it asks for a pre-trained model. I downloaded "instblink_r50.pth" in the "Inference & Evaluation" section and tried to run the demo again, but it did not work. What is the problem?

Thanks for your appreciation. Can you provide the specific error message?

It seems that the path of the checkpoint file is not correct so the code can not find it. You should check "tools/code_for_demo/video_inference.py", line-22, and change it to the correct path on your side.

Normally, instblink_r50.pth is a file, not a directory. But in your case, it seems to be a directory because you can use the ls command.

I also thought that 'instblink_r50.pth' was a file, but when I downloaded it from Google Drive it was a directory.

I've tried several times and it is a folder. The contents are a data folder, a date.pkl file, and a version file.

What OS are you using?

When downloaded it becomes a directory.

I cannot "wget" command, because I cannot get URL.
Please tell me "wget" command. For example "wget https:// ~~~~~~".

You can try:
wget https://drive.google.com/file/d/1kRx_pPpOwAk9D6O3M5Ed7vyAqkZbCh83/view?usp=sharing

Is this correct?

![スクリーンショット 2024-06-07 18 35 11]

What about this?
wget https://drive.google.com/uc?export=download&id=1kRx_pPpOwAk9D6O3M5Ed7vyAqkZbCh83

error?

Maybe it is because the file is a little bit large. Sorry, currently I have no means to check the exact command for downloading. The URL is correct. You can look on the Internet to see if there is a good solution.

The problem was that the file was large.
I split the file, reduced the size, and downloaded it successfully.
Thank  you so much. If there are any other problems, please advise.


Is there a function to obtain hotkeys implemented

Is there a function to obtain hotkeys implemented

That question does not make much sense. What do you mean by "obtain hotkeys implemented". Normally in Coherence a "hot key" refers to a key for an entry in a cache that is accessed far more often than the other entries in the cache, which then puts far more load on the cluster member that owns that key than the other members of the cluster. Is that what you are talking about? If you are then there is no API for this, Coherence does not keep access statistics for each entry.

There are APIs in Coherence that you could use to implement this yourself though. It depends what exactly you are looking for,

See https://medium.com/oracle-coherence/how-to-get-the-top-n-most-accessed-keys-in-a-coherence-cache-07113b591f4e


[TF-TRT]  Refactor Softmax converter to use OpConverterBase

This commit refactors the Softmax TF-TRT conversion to use OpConverterBase classes.

The dependent PR is merged, can you rebase?

Hi @pavanimajety Can you please resolve conflicts? Thank you!


RBF function

Hi, i right-clicked on RBF function on the TX waiting to be approved in Mp. But nothing happened. TX was approved while after that but I just wanted to try how rbf works. Are there any conditions when RBF works or not - or is it some way automatic ... How that works? Thanks a lot for reply.
@coinmasteroid

Can you check the log file (Help menu) to see if there is anything at the end that might indicate why it failed?

I will check with the next TX but it takes a while... to add info, i run own umbrel node in combo with sparrow + ledger HW. If that would have some influence ...

The information is still in the log file - you don't need to recreate the problem. Just look for any errors at the time of posting your original issue - each error has a timestamp.

This is the info from Log file on that day when tried to increase fee by RBF function:
2022-02-06 11:09:47,551 ERROR [JavaFX Application Thread] c.s.s.c.DevicePane [null:-1] Signing Error: read error
com.sparrowwallet.sparrow.io.SignTransactionException: read error
at com.sparrowwallet.sparrow@1.5.6/com.sparrowwallet.sparrow.io.Hwi.signPSBT(Unknown Source)
at com.sparrowwallet.sparrow@1.5.6/com.sparrowwallet.sparrow.io.Hwi$SignPSBTService$1.call(Unknown Source)
at com.sparrowwallet.sparrow@1.5.6/com.sparrowwallet.sparrow.io.Hwi$SignPSBTService$1.call(Unknown Source)
at javafx.graphics/javafx.concurrent.Task$TaskCallable.call(Unknown Source)
at java.base/java.util.concurrent.FutureTask.run(Unknown Source)
at javafx.graphics/javafx.concurrent.Service.lambda$executeTask$6(Unknown Source)
at java.base/java.security.AccessController.doPrivileged(Unknown Source)
at javafx.graphics/javafx.concurrent.Service.lambda$executeTask$7(Unknown Source)
at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source)
at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source)
at java.base/java.lang.Thread.run(Unknown Source)
2022-02-06 12:15:56,225 WARN [Thread-372] c.s.s.n.FeeRatesSource [null:-1] Error retrieving recommended fee rates from http://mempoolhqx4isw62xs7abwphsq7ldayuidyx2v2oethdhhj6mlo2r6ad.onion/api/v1/fees/recommended (SOCKS server general failure)

Thanks for this - unfortunately, it doesn't appear to be related to the RBF issue you experienced. It may be that there was no logging as a result of that error - if this the case then we have little to go on. Are there any other logs from 6 Feb?

This is all of the log. But let me ask this that came on my mind - while was clicking on RBF on the TX, my HW Ledger was disconnected - couldn't be this the cause? Some months ago i did try RBF same way already and didn't work either - nothing happened after click on RBF. So maybe it has something to do with the HW device.

There's nothing in the RBF function that needs the HWW, so I don't think it's related.
It's not possible to use RBF if there is insufficient change on the existing transaction and you don't have any additional UTXOs to add to the replacement. But that's an unlikely scenario, and would not result in "nothing happened" - you should still see the transaction that Sparrow attempted to create.
Just to be clear, when you say you "right-clicked on RBF function" do you mean you right-clicked on the "Unconfirmed" label and selected the Increase Fee (RBF) item from the context menu?

Exactly as you say with right click...
Plus you reminded me, i had no other utxo to take an additional fee to increase by the way 🤦♂.
But if i still would see some screen and some RBF action, i did not.
Next time i try i will send you log straight away... thanks.


重構 routes.js

as title
有考慮用 yarn 嗎？ 速度會比較快。 然後可能要調整一下npm start

yarn我沒接觸過喔，npm start還能調整成啥樣？o_O
是說我大多在直接用meteor指令，沒在用npm的。
2017-08-14 17:21 GMT+08:00 Yun<EMAIL_ADDRESS>
as title
有考慮用 yarn 嗎？ 速度會比較快。 然後可能要調整一下npm start
You can view, comment on, or merge this pull request online at:
https://github.com/mrbigmouth/acgn-stock/pull/2
Commit Summary

refactor route

File Changes

M routes.js
https://github.com/mrbigmouth/acgn-stock/pull/2/files#diff-0 (41)

Patch Links:

https://github.com/mrbigmouth/acgn-stock/pull/2.patch
https://github.com/mrbigmouth/acgn-stock/pull/2.diff

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weird bias figure

The bias looks weird. Any ideas? @micahwoodard

Huh that does look weird. I will investigate tomorrow

@XX-Yin what session was this?

@micahwoodard It was this session \allen\aind\scratch\ephys_rig_behavior_transfer\764769\behavior_764769_2024-12-12_16-05-00\behavior.
It seems to be related to the discontinuity of fitting for initial trials (more noisy).

Are you using the same smooth window for the average and the error shading?

Another case

Is the data from the previous session cleared?

Another case
Is the data from the previous session cleared?

Yes should be. I'll take a look at this


Veracode scan found CWE ID 331 and 201 in Firestore and Auth clients

We are using the Firestore library in an environment that requires static security scans. The scan tool (VeraCode) found vulnerability CWE 331 (Insufficient Entropy) in:
Firebase/Auth/Source/Utilities/FIRAuthWebUtils.m
Firestore/core/src/firebase/firestore/util/secure_random_arc4random.cc
VeraCode support says it is most probably related to the use of not completely random generators (arc4random).
And CWE 201 (Information Exposure Through Sent Data) in:
Firebase/Auth/Source/Backend/RPC/FIRSecureTokenRequest.m method requestURL
Since these could be security flaws and can block adoption of the library in regulated environments, please consider fixing.
Alternatively, if these use cases do not result in a security flaw, if you could provide a clarification on the intended usage of the random generator (for 331) and the url builder (for 201), it may be enough to conclude that it is acceptable for this use case or it is a false positive.

Firebase Component: Firestore
Installation method: CocoaPods

Veracode is wrong in this case. On Apple platforms, arc4random is a high quality source of random numbers and is recommended. Note that this does not actually use the RC4 cipher anymore.
From the man page:

The original version of this random number generator used the RC4 (also known as ARC4) algorithm.  In OS X 10.12 it was replaced with the NIST-approved AES cipher, and it may be replaced again in the future as cryptographic techniques advance.

Other platforms have also migrated away from actually using RC4: https://security.stackexchange.com/a/172905


Add --acpp-dryrun-noplugin flag to acpp executable

This PR add the --acpp-dryrun-noplugin to acpp which does the same as --acpp-dryrun but removes flags starting by -fplugin or -fpass-plugin from the output.
This is done in order to get the compile flags that should be passed to external tools such as clang-tidy. However, the plugin flags creates additional warning hence their removal from the output for clang-tidy or clangd.
Implementation
This is done by adding a new boolean to the run_or_print function which will remove those flags before printing. This new boolean is False by default to avoid modifying other cases.

Hm... I wonder if it might not be better to do the opposite and have a flag to print includes and a flag to print defines. Even if the -fplugin is gone, other compiler arguments like -mllvm stuff will still be in there which could trip up tooling. What do you think?

For clang-tidy and clangd removing only those two flags was sufficient to suppress any issue. But it is true that other flags should be removed for other (non clang based) tools.
Would you rather go with a whitelist or blacklist approach then ?

I think it should be "whitelist" in the sense of "give me all flags relating to includes/defines/etc"

Alright, I do agree that it is probably the best approach here. I'll make the changes soon

I've changed the implementation of the flag. Now it pass the arg list through a filter_cmd_args function that uses a whitelist to select the relevant arguments.

I've made the required changes :

The flag has been renamed to --acpp-dryrun-only-std-flags has it better reflect the actual behavior
-I <include> is also supported in the list


Doubts about the loss_cls and accuracy_cls calculation

Hi:
I have some doubts in the evaluation of loss_cls and accuracy_cls in  function of fast_rcnn_losses in lib/modling/fast_rcnn_heads.py.
Based on my understanding,  the following calculation seems assume cls_score and rois_label have the same length and matching order. Like pred [0,1,1], lable [0,1,2] (just the idea).
But the real is more like pred [0,1,2,3], label [0,1,2] (pred length may more or less And the order may not match).
Based on my experience in matterport's mask rcnn. Before calculate the class loss and accuracy, there is operation will matching the pred and label in order based on the nearest box. Basically, it found the nearest pred bbox as the 'right' pred for one label box (make sense).
I didnot found some operation in the code yet, i guess i ignore or misunderstand something (new to the mask rcnn/faster rcnn).
So my real question is how do you make sure the pred class and label class matching before calculate the loss/accuracy?
thanks.
def fast_rcnn_losses(cls_score, bbox_pred, label_int32, bbox_targets,
                     bbox_inside_weights, bbox_outside_weights):
    device_id = cls_score.get_device()
    rois_label = Variable(torch.from_numpy(label_int32.astype('int64'))).cuda(device_id)
    loss_cls = F.cross_entropy(cls_score, rois_label)
    ........
    # class accuracy
    cls_preds = cls_score.max(dim=1)[1].type_as(rois_label)
    accuracy_cls = cls_preds.eq(rois_label).float().mean(dim=0)

    return loss_cls, loss_bbox, accuracy_cls

Check out the function _sample_rois here

Thanks for the pointing.
One more silly/(time saver for me) question:
_sample_rois  in roi_data.fast_rcnn seems only imported by collect_and_distribute_fpn_rpn_proposals.py
But then collect_and_distribute_fpn_rpn_proposals.py or  fast_rcnn.py not imported by  fast_rcnn_heads.py or model_builder.py  or other palce(not found).
So How the _sample_rois been really connected in fast_rcnn_losses?
I am asking this in details because i finally want calculate the precision,recall,f1 for the each class on my dataset during training.

found it


Update google cloud storage docs

Make sure you're sending PR to dev branch
Give a short description what you have changed/added and why:
This is updating the google cloud example based on issue #292 and the resolution that was given there.

Thank you for contribution, and do not forget to get rid of this default message

Hello @jeremywrnr ,
Thank you for this PR.
Could you please describe changes.
Is API changed at Google Cloud end? Was this solution wrong from beginning?

Hi, and sure thing: based on what I found, the solution was wrong from the beginning: bucket.deleteFiles is meant to delete all the files in the bucket, so switching the method to be bucket.file(<name>).delete fixes the issue in the documentation.
Suggest getting added: https://github.com/GoogleCloudPlatform/google-cloud-node/issues/680
Adding deleteFiles: https://github.com/GoogleCloudPlatform/google-cloud-node/pull/698
Docs for deleteFiles: https://stephenplusplus.github.io/gcloud-node-gh-pages-again/#/docs/master/storage/bucket?method=deleteFiles


Serious performance related issue with _merge_prefix.

I'm getting a large payload via. AsyncHTTPClient and have been very surprised at how slow everything's going (only about 15mb). It takes about 2 minutes for my response callback to be called.
Whipping out cProfile, it looks like almost all the time is being spent in _merge_prefix. Switching this back to cStringIO (commit  6ac2c58db7 for anyone interested), takes my execution time down to about 4 seconds (and there aren't any hotspots in cProfile).
I'd be happy to submit a patch to fix this. Any suggestions on the right way to do it?

I'd like to understand this a little more before switching back - there really shouldn't be so big of a difference.  Can you either share the URL you're fetching or help collect a little data?  Can you find out which branch of the if at simple_httpclient.py:346 is being taken, and if it's chunked, how many chunks there are?  My hunch is that it has to do with many small chunks, and it could be improved by searching read_buffer[0] for the delimiter in IOStream.handle_read before trying to merge.
The commit you mentioned looks unrelated; did you mean https://github.com/facebook/tornado/commit/beee1c28ceec368b5c046fa2167abe5ca2a632c9#tornado/iostream.py instead?

beee1c28 is the correct commit. I'd just gone up to the parent.
I'm getting the data from a request to /db/_all_docs in couchdb. For this specific request, the number of chunks is 27468.


How to get paper_status command working

I am trying to check for the printer status but does not seem to be working. I have a printer I can connect to in my local network and all the commands below works except the last two printer.paper_status() and printer.is_online. Can anyone give me some guidance? Not a python guy by trade. I believe these commands should be available in the Network class as they are inherited from Escpos, but getting traceback messages like the one below.
AttributeError: 'Network' object has no attribute 'paper_status'
My script is provided here
#!/usr/bin/python

import sys

from escpos.printer import Network

""" Seiko Epson Corp. Receipt Printer M129 Definitions (EPSON TM-T88IV) """
printer = Network("<IP_ADDRESS>", port=9100, timeout=60)
# Print text
printer.text("Hello World\n")
# Print image
#Epson.image("logo.gif")
# Print QR Code
printer.qr("You can readme from your smartphone")
# Print barcode
printer.barcode('1324354657687','EAN13',64,2,'','')
# Cut paper
printer.cut()

# paper_status not working
printer.paper_status()

so im working on this with the DLE EOT commands using serial, im just getting an issue with how to read the data back in 8bit, but it some what works...
stackoverflow link

I have this working with bits not int which I feel is better anyway since it reads the whole reason its offline

Hello @jeffreyworsley I still can't get this working
can you please put a code snip showing us what you did before making a network pr usb object from escops and how did you call paper_status
Thank you
@bthai did you mange to get this command working ?!

Did anybody managed to get the ouput

I managed to query the printer status, using the version 3.0a4.
The problem is that the Network class does not implement the _read method, thus raising a NotYetImplementedError. By adding the following _read method I was able to correctly querying the paper and the printer status on a Citizen CTS310II-LAN:
class Network(Escpos):
  def _read(self):
    return self.device.recv(16)

I'm preparing a pull request so you can test if that works also for you.

Feel free to comment or reopen if it is not fixed yet

I’m working on a script using an Epson printer (TM-T88V). I’m able to print and all but I’m facing the same issue reported here.
‘Network’ object has no attribute ...


Label of permission "can csv on Superset " is not precise

Bug description
With permission "can csv on Superset ", a user can download a chart in CSV, Excel, Image format
How to reproduce the bug
Add "can csv on Superset " on a role
Contextual menu on Chart is :

Screenshots/recordings
No response
Superset version
master / latest-dev
Python version
3.9
Node version
16
Browser
Chrome
Additional context
No response
Checklist

[ ] I have searched Superset docs and Slack and didn't find a solution to my problem.
[ ] I have searched the GitHub issue tracker and didn't find a similar bug report.
[ ] I have checked Superset's logs for errors and if I found a relevant Python stacktrace, I included it here as text in the "additional context" section.

Label could be update but the permission seems to be deprecated... but used #30384

Closing this in favor of the other issue you've linked to... it is indeed a deprecation that didn't take place, and is on the proposals board for 5.0 https://github.com/orgs/apache/projects/345


[Bug] 最新版 lmdeploy 0.5.1 在v100以及rtx 2080 ti 上部署cogvlm2，推理过程会报错： ERROR - Engine loop failed with error: map::at

Checklist

[X] 1. I have searched related issues but cannot get the expected help.
[X] 2. The bug has not been fixed in the latest version.

Describe the bug
使用最新版的lmdeploy 0.5.1在多卡V100或者2080ti服务器上部署cogvlm2，可以正常启动，但在推理过程中会出现 ERROR - Engine loop failed with error: map::at 的错误，随后服务终止。试过internvl1.5-awq推理，没有问题。
Reproduction
使用v100部署lmdeploy0.5.1，使用openai方式进行推理就会出现。
Environment
2卡V100，或者4卡魔改版2080ti 22g

Error traceback
启动指令：
export CUDA_VISIBLE_DEVICES=0,1,2,3
lmdeploy serve api_server /root/autodl-tmp/model_zoo/cogvlm2-llama3-chinese-chat-19B/ --backend pytorch --tp 2 --server-name <IP_ADDRESS> --server-port 8000  --log-level INFO

loginfo:

2024-07-17 12:15:43,792 - lmdeploy - INFO - matching vision model: CogVLMVisionModel
2024-07-17 12:15:49,952 - lmdeploy - INFO - input backend=pytorch, backend_config=PytorchEngineConfig(model_name=None, tp=2, session_len=None, max_batch_size=128, cache_max_entry_count=0.8, eviction_type='recompute', prefill_interval=16, block_size=64, num_cpu_blocks=0, num_gpu_blocks=0, adapters=None, max_prefill_token_num=4096, thread_safe=False, enable_prefix_caching=False, device_type='cuda', download_dir=None, revision=None)
2024-07-17 12:15:49,952 - lmdeploy - INFO - input chat_template_config=None
2024-07-17 12:15:49,960 - lmdeploy - INFO - updated chat_template_onfig=ChatTemplateConfig(model_name='cogvlm2', system=None, meta_instruction=None, eosys=None, user=None, eoh=None, assistant=None, eoa=None, separator=None, capability=None, stop_words=None)
2024-07-17 12:15:49,990 - lmdeploy - INFO - Checking environment for PyTorch Engine.
2024-07-17 12:15:50,698 - lmdeploy - INFO - Checking model.
2024-07-17 12:15:50,699 - lmdeploy - WARNING - LMDeploy requires transformers version: [4.33.0 ~ 4.41.2], but found version: 4.42.3
2024-07-17 12:15:50,709 - lmdeploy - INFO - MASTER_ADDR=<IP_ADDRESS>, MASTER_PORT=29500
2024-07-17 12:15:53,705 - lmdeploy - INFO - Patching model.
2024-07-17 12:15:53,933 - lmdeploy - INFO - Loading model weights, please waiting.
2024-07-17 12:16:04,068 - lmdeploy - INFO - build CacheEngine with config:CacheConfig(block_size=64, num_cpu_blocks=1024, num_gpu_blocks=1699, window_size=-1, cache_max_entry_count=0.8, max_prefill_token_num=4096, enable_prefix_caching=False)
2024-07-17 12:16:06,814 - lmdeploy - INFO - updated backend_config=PytorchEngineConfig(model_name=None, tp=2, session_len=None, max_batch_size=128, cache_max_entry_count=0.8, eviction_type='recompute', prefill_interval=16, block_size=64, num_cpu_blocks=0, num_gpu_blocks=0, adapters=None, max_prefill_token_num=4096, thread_safe=False, enable_prefix_caching=False, device_type='cuda', download_dir=None, revision=None)
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
HINT:    Please open http://<IP_ADDRESS>:8000 in a browser for detailed api usage!!!
HINT:    Please open http://<IP_ADDRESS>:8000 in a browser for detailed api usage!!!
HINT:    Please open http://<IP_ADDRESS>:8000 in a browser for detailed api usage!!!
INFO:     Started server process [1073]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://<IP_ADDRESS>:8000 (Press CTRL+C to quit)

推理过程loginfo:
INFO:     <IP_ADDRESS>:56100 - "GET /v1/models HTTP/1.1" 200 OK
INFO:     <IP_ADDRESS>:56112 - "POST /v1/chat/completions HTTP/1.1" 200 OK
2024-07-17 12:16:32,555 - lmdeploy - INFO - start ImageEncoder._forward_loop
2024-07-17 12:16:32,555 - lmdeploy - INFO - ImageEncoder received 1 images, left 1 images.
2024-07-17 12:16:32,555 - lmdeploy - INFO - ImageEncoder process 1 images, left 0 images.
2024-07-17 12:16:36,615 - lmdeploy - INFO - ImageEncoder forward 1 images, cost 4.060s
2024-07-17 12:16:36,616 - lmdeploy - INFO - ImageEncoder done 1 images, left 0 images.
2024-07-17 12:16:36,618 - lmdeploy - INFO - prompt='<IMAGE_TOKEN>Question: (\'你是一个表单审核人员，需要判断图片中表格的类型。请判断图片是哪一种表格：[0."其他类型",1."活动记录表",2."登记表"]，如果是"其他类型"，输出"type":0；如果是"活动记录表"，输出"type":1；如果是"登记表"，则输出"type":2。结果只按照如下json格式输出:{ "type":}\',) Answer:', gen_config=EngineGenerationConfig(n=1, max_new_tokens=2048, top_p=0.7, top_k=40, temperature=0.0, repetition_penalty=1.0, ignore_eos=False, random_seed=11971543465637689405, stop_words=[128001], bad_words=None, min_new_tokens=None, skip_special_tokens=True, logprobs=None), prompt_token_id=[128000, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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2024-07-17 12:16:36,618 - lmdeploy - INFO - session_id=1, history_tokens=0, input_tokens=2408, max_new_tokens=2048, seq_start=True, seq_end=True, step=0, prep=True
2024-07-17 12:16:36,618 - lmdeploy - WARNING - `temperature` is 0, set to 1e-6
2024-07-17 12:16:38,315 - lmdeploy - ERROR - Engine loop failed with error: map::at
Traceback (most recent call last):
  File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/lmdeploy/pytorch/engine/request.py", line 17, in _raise_exception_on_finish
    task.result()
  File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/lmdeploy/pytorch/engine/engine.py", line 857, in async_loop
    await self._async_loop()
  File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/lmdeploy/pytorch/engine/engine.py", line 847, in _async_loop
    await __step(True)
  File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/lmdeploy/pytorch/engine/engine.py", line 833, in __step
    raise e
  File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/lmdeploy/pytorch/engine/engine.py", line 825, in __step
    raise out
  File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/lmdeploy/pytorch/engine/engine.py", line 774, in _async_loop_background
    await self._async_step_background(
  File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/lmdeploy/pytorch/engine/engine.py", line 683, in _async_step_background
    output = await self._async_model_forward(inputs,
  File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/lmdeploy/utils.py", line 253, in __tmp
    return (await func(*args, **kwargs))
  File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/lmdeploy/pytorch/engine/engine.py", line 590, in _async_model_forward
    return await __forward(inputs)
  File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/lmdeploy/pytorch/engine/engine.py", line 568, in __forward
    return await self.model_agent.async_forward(
  File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/lmdeploy/pytorch/engine/model_agent.py", line 1241, in async_forward
    output = self._forward_impl(inputs,
  File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/lmdeploy/pytorch/engine/model_agent.py", line 1208, in _forward_impl
    output = model_forward(
  File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/lmdeploy/pytorch/engine/model_agent.py", line 497, in model_forward
    output = patched_model.patched_forward(
  File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/lmdeploy/pytorch/models/patch.py", line 210, in __call__
    output = self._model(*args, **kwargs)
  File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
    return forward_call(*args, **kwargs)
  File "/root/.cache/huggingface/modules/transformers_modules/modeling_cogvlm.py", line 649, in forward
    outputs = self.model(
  File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
    return forward_call(*args, **kwargs)
  File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/lmdeploy/pytorch/models/cogvlm.py", line 268, in forward
    layer_outputs = decoder_layer(
  File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
    return forward_call(*args, **kwargs)
  File "/root/.cache/huggingface/modules/transformers_modules/modeling_cogvlm.py", line 262, in forward
    hidden_states, self_attn_weights, present_key_value = self.self_attn(
  File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1561, in _call_impl
    result = forward_call(*args, **kwargs)
  File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/lmdeploy/pytorch/models/cogvlm.py", line 233, in forward
    return self._contiguous_batching_forward_impl(
  File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/lmdeploy/pytorch/models/cogvlm.py", line 191, in _contiguous_batching_forward_impl
    paged_attention_fwd(
  File "<string>", line 3, in paged_attention_fwd
  File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/lmdeploy/pytorch/kernels/dispatcher.py", line 95, in load_and_call
    return self.dispatched_func(*args, **kwargs)
  File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/lmdeploy/pytorch/kernels/cuda/pagedattention.py", line 759, in paged_attention_fwd
    _fwd_kernel[grid](q,
  File "<string>", line 41, in __fwd_kernel_launcher
  File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/triton/runtime/jit.py", line 532, in run
    self.cache[device][key] = compile(
  File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/triton/compiler/compiler.py", line 543, in compile
    next_module = compile_kernel(module)
  File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/triton/compiler/compiler.py", line 441, in <lambda>
    lambda src: ttgir_to_llir(src, extern_libs, target, tma_infos))
  File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/triton/compiler/compiler.py", line 167, in ttgir_to_llir
    return translate_triton_gpu_to_llvmir(mod, target.capability, tma_infos, runtime.TARGET.NVVM)
IndexError: map::at
ERROR:    Exception in ASGI application
Traceback (most recent call last):
  File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/starlette/responses.py", line 265, in __call__
    await wrap(partial(self.listen_for_disconnect, receive))
  File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/starlette/responses.py", line 261, in wrap
    await func()
  File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/starlette/responses.py", line 238, in listen_for_disconnect
    message = await receive()
  File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/uvicorn/protocols/http/httptools_impl.py", line 553, in receive
    await self.message_event.wait()
  File "/root/miniconda3/envs/lmdeploy/lib/python3.9/asyncio/locks.py", line 226, in wait
    await fut
asyncio.exceptions.CancelledError: Cancelled by cancel scope 7f15eddfc130

During handling of the above exception, another exception occurred:

  + Exception Group Traceback (most recent call last):
  |   File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/uvicorn/protocols/http/httptools_impl.py", line 399, in run_asgi
  |     result = await app(  # type: ignore[func-returns-value]
  |   File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/uvicorn/middleware/proxy_headers.py", line 70, in __call__
  |     return await self.app(scope, receive, send)
  |   File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/fastapi/applications.py", line 1054, in __call__
  |     await super().__call__(scope, receive, send)
  |   File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/starlette/applications.py", line 123, in __call__
  |     await self.middleware_stack(scope, receive, send)
  |   File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/starlette/middleware/errors.py", line 164, in __call__
  |     await self.app(scope, receive, _send)
  |   File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/starlette/middleware/cors.py", line 85, in __call__
  |     await self.app(scope, receive, send)
  |   File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/starlette/middleware/exceptions.py", line 65, in __call__
  |     await wrap_app_handling_exceptions(self.app, conn)(scope, receive, send)
  |   File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/starlette/_exception_handler.py", line 53, in wrapped_app
  |     await app(scope, receive, sender)
  |   File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/starlette/routing.py", line 756, in __call__
  |     await self.middleware_stack(scope, receive, send)
  |   File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/starlette/routing.py", line 776, in app
  |     await route.handle(scope, receive, send)
  |   File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/starlette/routing.py", line 297, in handle
  |     await self.app(scope, receive, send)
  |   File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/starlette/routing.py", line 77, in app
  |     await wrap_app_handling_exceptions(app, request)(scope, receive, send)
  |   File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/starlette/_exception_handler.py", line 53, in wrapped_app
  |     await app(scope, receive, sender)
  |   File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/starlette/routing.py", line 75, in app
  |     await response(scope, receive, send)
  |   File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/starlette/responses.py", line 265, in __call__
  |     await wrap(partial(self.listen_for_disconnect, receive))
  |   File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/anyio/_backends/_asyncio.py", line 680, in __aexit__
  |     raise BaseExceptionGroup(
  | exceptiongroup.BaseExceptionGroup: unhandled errors in a TaskGroup (1 sub-exception)
  +-+---------------- 1 ----------------
    | Traceback (most recent call last):
    |   File "/root/miniconda3/envs/lmdeploy/lib/python3.9/asyncio/queues.py", line 166, in get
    |     await getter
    | asyncio.exceptions.CancelledError
    |
    | During handling of the above exception, another exception occurred:
    |
    | Traceback (most recent call last):
    |   File "/root/miniconda3/envs/lmdeploy/lib/python3.9/asyncio/tasks.py", line 490, in wait_for
    |     return fut.result()
    | asyncio.exceptions.CancelledError
    |
    | The above exception was the direct cause of the following exception:
    |
    | Traceback (most recent call last):
    |   File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/lmdeploy/pytorch/engine/request.py", line 171, in __no_threadsafe_get
    |     return await asyncio.wait_for(self.resp_que.get(), timeout)
    |   File "/root/miniconda3/envs/lmdeploy/lib/python3.9/asyncio/tasks.py", line 492, in wait_for
    |     raise exceptions.TimeoutError() from exc
    | asyncio.exceptions.TimeoutError
    |
    | During handling of the above exception, another exception occurred:
    |
    | Traceback (most recent call last):
    |   File "/root/miniconda3/envs/lmdeploy/lib/python3.9/asyncio/runners.py", line 44, in run
    |     return loop.run_until_complete(main)
    |   File "uvloop/loop.pyx", line 1511, in uvloop.loop.Loop.run_until_complete
    |   File "uvloop/loop.pyx", line 1504, in uvloop.loop.Loop.run_until_complete
    |   File "uvloop/loop.pyx", line 1377, in uvloop.loop.Loop.run_forever
    |   File "uvloop/loop.pyx", line 555, in uvloop.loop.Loop._run
    |   File "uvloop/loop.pyx", line 474, in uvloop.loop.Loop._on_idle
    |   File "uvloop/cbhandles.pyx", line 83, in uvloop.loop.Handle._run
    |   File "uvloop/cbhandles.pyx", line 63, in uvloop.loop.Handle._run
    |   File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/starlette/responses.py", line 261, in wrap
    |     await func()
    |   File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/starlette/responses.py", line 250, in stream_response
    |     async for chunk in self.body_iterator:
    |   File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/lmdeploy/serve/openai/api_server.py", line 504, in completion_stream_generator
    |     async for res in result_generator:
    |   File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/lmdeploy/serve/async_engine.py", line 615, in generate
    |     async for outputs in generator.async_stream_infer(
    |   File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/lmdeploy/pytorch/engine/engine_instance.py", line 177, in async_stream_infer
    |     resp = await self.req_sender.async_recv(req_id)
    |   File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/lmdeploy/pytorch/engine/request.py", line 314, in async_recv
    |     resp: Response = await self._async_resp_get()
    |   File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/lmdeploy/pytorch/engine/request.py", line 187, in _async_resp_get
    |     return await __no_threadsafe_get()
    |   File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/lmdeploy/pytorch/engine/request.py", line 175, in __no_threadsafe_get
    |     exit(1)
    |   File "/root/miniconda3/envs/lmdeploy/lib/python3.9/_sitebuiltins.py", line 26, in __call__
    |     raise SystemExit(code)
    | SystemExit: 1
    +------------------------------------
ERROR:    Traceback (most recent call last):
  File "/root/miniconda3/envs/lmdeploy/lib/python3.9/asyncio/queues.py", line 166, in get
    await getter
asyncio.exceptions.CancelledError

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/root/miniconda3/envs/lmdeploy/lib/python3.9/asyncio/tasks.py", line 490, in wait_for
    return fut.result()
asyncio.exceptions.CancelledError

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/lmdeploy/pytorch/engine/request.py", line 171, in __no_threadsafe_get
    return await asyncio.wait_for(self.resp_que.get(), timeout)
  File "/root/miniconda3/envs/lmdeploy/lib/python3.9/asyncio/tasks.py", line 492, in wait_for
    raise exceptions.TimeoutError() from exc
asyncio.exceptions.TimeoutError

During handling of the above exception, another exception occurred:

@kklots hi, pls. try with triton==2.1.0

@kklots hi, pls. try with triton==2.1.0
thanks, it worked. but now I have a new "out of GPU memory" promble on a server with 4 V100 32g cards. i tried the cogvlm2 model on an A800 80g server and it worked fine. it seems that the GRAM consumption on the 4 V100 cards is not very balanced.

log:
2024-07-17 13:52:26,995 - lmdeploy - INFO - session_id=1, history_tokens=0, input_tokens=2408, max_new_tokens=2048, seq_start=True, seq_end=True, step=0, prep=True
2024-07-17 13:52:26,995 - lmdeploy - WARNING - temperature is 0, set to 1e-6
2024-07-17 13:52:34,618 - lmdeploy - ERROR - Rank[2] failed.
Traceback (most recent call last):
File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/lmdeploy/pytorch/engine/model_agent.py", line 1016, in _start_tp_process
func(rank, *args, **kwargs)
File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/lmdeploy/pytorch/engine/model_agent.py", line 974, in _tp_model_loop
inputs, swap_in_map, swap_out_map = _broadcast_inputs(
File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/lmdeploy/pytorch/engine/model_agent.py", line 893, in _broadcast_inputs
dist.broadcast_object_list(inputs)
File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/torch/distributed/c10d_logger.py", line 72, in wrapper
return func(*args, **kwargs)
File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/torch/distributed/distributed_c10d.py", line 2438, in broadcast_object_list
object_list[i] = _tensor_to_object(obj_view, obj_size)
File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/torch/distributed/distributed_c10d.py", line 2123, in _tensor_to_object
return _unpickler(io.BytesIO(buf)).load()
File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/torch/storage.py", line 371, in _load_from_bytes
return torch.load(io.BytesIO(b))
File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/torch/serialization.py", line 1040, in load
return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args)
File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/torch/serialization.py", line 1268, in _legacy_load
result = unpickler.load()
File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/torch/serialization.py", line 1205, in persistent_load
wrap_storage=restore_location(obj, location),
File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/torch/serialization.py", line 391, in default_restore_location
result = fn(storage, location)
File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/torch/serialization.py", line 268, in _cuda_deserialize
with torch.cuda.device(device):
File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/torch/cuda/init.py", line 374, in enter
self.prev_idx = torch.cuda._exchange_device(self.idx)
RuntimeError: CUDA error: out of memory
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
Compile with TORCH_USE_CUDA_DSA to enable device-side assertions.
2024-07-17 13:52:34,623 - lmdeploy - ERROR - Rank[1] failed.
Traceback (most recent call last):
File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/lmdeploy/pytorch/engine/model_agent.py", line 1016, in _start_tp_process
func(rank, *args, **kwargs)
File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/lmdeploy/pytorch/engine/model_agent.py", line 974, in _tp_model_loop
inputs, swap_in_map, swap_out_map = _broadcast_inputs(
File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/lmdeploy/pytorch/engine/model_agent.py", line 893, in _broadcast_inputs
dist.broadcast_object_list(inputs)
File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/torch/distributed/c10d_logger.py", line 72, in wrapper
return func(*args, **kwargs)
File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/torch/distributed/distributed_c10d.py", line 2438, in broadcast_object_list
object_list[i] = _tensor_to_object(obj_view, obj_size)
File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/torch/distributed/distributed_c10d.py", line 2123, in _tensor_to_object
return _unpickler(io.BytesIO(buf)).load()
File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/torch/storage.py", line 371, in _load_from_bytes
return torch.load(io.BytesIO(b))
File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/torch/serialization.py", line 1040, in load
return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args)
File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/torch/serialization.py", line 1268, in _legacy_load
result = unpickler.load()
File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/torch/serialization.py", line 1205, in persistent_load
wrap_storage=restore_location(obj, location),
File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/torch/serialization.py", line 391, in default_restore_location
result = fn(storage, location)
File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/torch/serialization.py", line 268, in _cuda_deserialize
with torch.cuda.device(device):
File "/root/miniconda3/envs/lmdeploy/lib/python3.9/site-packages/torch/cuda/init.py", line 374, in enter
self.prev_idx = torch.cuda._exchange_device(self.idx)
RuntimeError: CUDA error: out of memory
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
Compile with TORCH_USE_CUDA_DSA to enable device-side assertions.

you can reduce --cache-max-entry-count to reduce pre-allocated kv cache mem
https://lmdeploy.readthedocs.io/en/latest/faq.html#inference

you can reduce --cache-max-entry-count to reduce pre-allocated kv cache mem https://lmdeploy.readthedocs.io/en/latest/faq.html#inference

thx, the model can inference now, but the result is incorrect, there are a lot of garbled characters, and the model can't give the correct content according to the prompts. I tried lmdeploy and cogvlm2 on A800 and 4090, and their results are both correct.
image:

prompt:
"你是一个表单审核人员，需要判断图片中表格的类型。请判断图片是哪一种表格：[0."其他类型",1."活动记录表",2."登记表"]，如果是"其他类型"，输出"type":0；如果是"活动记录表"，输出"type":1；如果是"登记表"，则输出"type":2。结果只按照如下json格式输出:{ "type":}"
result:
"2023"类型":"2023"类型":"2023.11","2023.2.11","2023.11","2023.11","2023.2.11","2023","2023.2.11","2023.11","2023.11","2023.11","2023.11","2023.11","2023.11","2023.11","2023.11","2023.11","2023.11","2023.11","2023.11","2023.11","2023.11","2023.11","2023.11","2023.11","2023.11","2023.11","2023.11","2023.11","2023","2023","2023","2023","2023","2023","202","2023","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202","202"
result on A800：
{ "type": 1}

hi, seems your image is not feeded into lmdeploy. Can you share a sample code and post your env info by running lmdeploy check_env to reproduce the issue?

here is code and log. thx!
[
test.zip
](url)

here is code and log. thx!
test.zip

@kklots sorry, no 2080ti resources. But tested and it works good on A100. Maybe you can try with pipeline to see if it works
from lmdeploy import pipeline, PytorchEngineConfig
from PIL import Image

img_path = 'test.jpg'
image = Image.open(img_path)

if __name__ == "__main__":
    pipe = pipeline('THUDM/cogvlm2-llama3-chinese-chat-19B', backend_config=PytorchEngineConfig(tp=4, cache_max_entry_count=0.2), log_level='INFO')
    response = pipe(('describe this image', image))
    print(response)

@kklots sorry, no 2080ti resources. But tested and it works good on A100. Maybe you can try with pipeline to see if it works
from lmdeploy import pipeline, PytorchEngineConfig
from PIL import Image

img_path = 'test.jpg'
image = Image.open(img_path)

if __name__ == "__main__":
    pipe = pipeline('THUDM/cogvlm2-llama3-chinese-chat-19B', backend_config=PytorchEngineConfig(tp=4, cache_max_entry_count=0.2), log_level='INFO')
    response = pipe(('describe this image', image))
    print(response)

ok, I will try it, thx.

from lmdeploy import pipeline, PytorchEngineConfig

i used this, but got some error
2024-09-06 02:32:09,895 - lmdeploy - INFO - Using pytorch engine
2024-09-06 02:32:11,594 - lmdeploy - INFO - matching vision model: CogVLMVisionModel
WARNING[XFORMERS]: xFormers can't load C++/CUDA extensions. xFormers was built for:
PyTorch 2.3.0+cu121 with CUDA 1201 (you have 2.3.0+cu118)
Python  3.10.14 (you have 3.10.5)
Please reinstall xformers (see https://github.com/facebookresearch/xformers#installing-xformers)
Memory-efficient attention, SwiGLU, sparse and more won't be available.
Set XFORMERS_MORE_DETAILS=1 for more details
2024-09-06 02:32:13,636 - lmdeploy - ERROR - matching vision model: CogVLMVisionModel failed
Traceback (most recent call last):
File "/root/openedai-vision/lmdploy_cogvlm2.py", line 4, in
pipe = pipeline('THUDM/cogvlm2-llama3-chinese-chat-19B', backend_config=PytorchEngineConfig(tp=4, cache_max_entry_count=0.2), log_level='INFO')
File "/usr/lib/python3.10/site-packages/lmdeploy/api.py", line 89, in pipeline
return pipeline_class(model_path,
File "/usr/lib/python3.10/site-packages/lmdeploy/serve/vl_async_engine.py", line 21, in init
self.vl_encoder = ImageEncoder(model_path,
File "/usr/lib/python3.10/site-packages/lmdeploy/vl/engine.py", line 85, in init
self.model = load_vl_model(model_path, backend_config=backend_config)
File "/usr/lib/python3.10/site-packages/lmdeploy/vl/model/builder.py", line 55, in load_vl_model
return module(**kwargs)
File "/usr/lib/python3.10/site-packages/lmdeploy/vl/model/base.py", line 31, in init
self.build_model()
File "/usr/lib/python3.10/site-packages/lmdeploy/vl/model/cogvlm.py", line 48, in build_model
device_map = infer_auto_device_map(
File "/usr/lib/python3.10/site-packages/accelerate/utils/modeling.py", line 1238, in infer_auto_device_map
tied_parameters = find_tied_parameters(model)
File "/usr/lib/python3.10/site-packages/accelerate/utils/modeling.py", line 708, in find_tied_parameters
all_named_parameters = {name: param for name, param in _get_named_parameters(model, remove_duplicate=False)}
File "/usr/lib/python3.10/site-packages/accelerate/utils/modeling.py", line 708, in
all_named_parameters = {name: param for name, param in _get_named_parameters(model, remove_duplicate=False)}
File "/usr/lib/python3.10/site-packages/accelerate/utils/modeling.py", line 667, in _get_named_parameters
members = module._parameters.items()
AttributeError: 'NoneType' object has no attribute '_parameters'

@tdf1995 hi, pls install xformers built for cuda118 and try again. You can refer to https://lmdeploy--2428.org.readthedocs.build/en/2428/multi_modal/cogvlm.html#install

xFormers can't load C++/CUDA extensions. xFormers was built for:
PyTorch 2.3.0+cu121 with CUDA 1201 (you have 2.3.0+cu118)
Python 3.10.14 (you have 3.10.5)
Please reinstall xformers (see https://github.com/facebookresearch/xformers#installing-xformers)
Memory-efficient attention, SwiGLU, sparse and more won't be available.


Creative crash

When going in creative and scrolling to the 14th page of items, the game crashes
crash-2022-04-17_13.11.25-client.txt

This is a known issue and it will be fixed in the next update. It's being caused by the Oculus mod.

Fixed in version 0.20.


[BUG] The VM Web VNC console connection is disconnected after around 10 minutes open usage

Describe the bug

Open VM in Web VNC console, after 10 minutes usage it suddenly  display error "Web VNC console connection is disconnected"

To Reproduce
Steps to reproduce the behavior:

Create a Ubuntu desktop image
Enable virtual network with harvester-mgmt
Create a virtual network vlan806 with id 806
Create a virtual machine with Ubuntu desktop and vlan 806
Finish the config and installation steps
Open VM from WebVNC
Install chrome and docker CE
Create rancher container from command
sudo docker run -d --restart=unless-stopped -p 80:80 -p 443:443 --privileged rancher/rancher:v2.6.5-rc6
Use VM for around 10 minutes
Check the Web VNC connection status

Expected behavior

Web VNC should not disconnect while the VM is in usage
Support bundle

supportbundle_2175eb63-d1a2-4efa-9174-7b9cae83c3d7_2022-05-09T09-58-25Z.zip
Environment:

Harvester ISO version: v1.0.2-rc1
Underlying Infrastructure (e.g. Baremetal with Dell PowerEdge R630): 3 nodes harvester on Provo bare machines

Results of the survey：
VNC disconnection is caused by a close event initiated by the backend.
case 1:
If the user is doing something in VNC like typing continuously, the websocket link will not be broken, otherwise the websocket will be broken.
case 2:
I found that this may be related to the environment, some environments may keep the websocket link for a long time, more than half an hour, and some environments will automatically disconnect after 2 minutes。
Need help from back-end engineers to continue to troubleshoot the problem。

@jeff-radick-suse is noticing this on v1.1.2 as well in Firefox - I'm not sure if this has been seen on v1.2.0-rc5 yet

I'm seeing this issue on v1.2.1 using Chrome on MacOS

This looks good on master-head, thanks for the fix @Yu-Jack :smile: :+1:
https://github.com/harvester/harvester/assets/5370752/3005fe7b-6824-4882-b52a-abfd22653f55


Set up the development environment

As a developer
I need to set up the development environment for the customer accounts microservice
So that I can start working on the microservice implementation.
Details and Assumptions: Set up Python, Flask, and other required dependencies for development.
Acceptance Criteria: The development environment is successfully set up.

As a developer
I need to set up the development environment for the customer accounts microservice
So that I can start working on the microservice implementation.
Details and Assumptions: Set up Python, Flask, and other required dependencies for development.
Acceptance Criteria: The development environment is successfully set up.


Support kyc verification on the imported wallets

Is your feature request related to a problem? Please describe
the user might have multiple wallets used on tfchain and wants them verified.
Describe the solution you'd like
in the wallet details add a button for kyc verification.

Work in progress :

All ui done and functionality are done, but trying to make all kyc steps as a separate component.  To not have duplicates between identity page and wallet info page.


HCO: Use a labelselector and manage kube-control-plane-signer CM

This change does a couple of things in the HCO:

Changes the global cache to use a labelselector, so that it can watch
namespaced objects without ending up creating a huge cache
Adds a wrapper around its client that enforces the label of the caches
labelselector to be present
Adds a CreateOrUpdate wrapper that can deal with the case that an
external actor removed the label
Moves the management from the kubelet-serving-ca into the resources
reconciler
Make the resources reconciler manage the
openshift-kube-apiserver-operator/kube-control-plane-signer secret
with the cluster signer cert. This fixes the OAuth server has the correct token and certificate fallback semantics
conformance test

There will be more followups to remove the existing one-object
reconcilers and move them into the resources reconciler.
Ref https://issues.redhat.com/browse/HOSTEDCP-257
/cc @csrwng

failure seems unrelated, we timed out connecting to the guest api but the hosted-cluster-controller was able to do that
/retest

/retest

/lgtm
Thx! I want to merge it with the bootstraper removal stuff I'm working on sooner than later.


State Management

Let move to actual Redux library and use  Redux toolkit

Hi @takumade, Can you assign this ticket to me ?
Can you also attach hackoctoberfest labe with this issue ?

Hello @Ravijhabit
I did add redux about 3 hours ago, but let update the issues when I wake up, assign labels to them then you can choose what you like to contribute


[Failing Test]: Python PostCommit xlang jdbc tests failing due to breaking change introduced in #24851

What happened?
      with ExternalTransform.service(self._expansion_service) as service:
        response = service.Expand(request)
        if response.error:
>         raise RuntimeError(response.error)
E         RuntimeError: java.lang.IllegalArgumentException: Cannot find field maxConnections in schema Fields:
E         Field{name=driverClassName, description=, type=STRING NOT NULL, options={{}}}
E         Field{name=jdbcUrl, description=, type=STRING NOT NULL, options={{}}}
E         Field{name=username, description=, type=STRING NOT NULL, options={{}}}
E         Field{name=password, description=, type=STRING NOT NULL, options={{}}}
E         Field{name=connectionProperties, description=, type=STRING, options={{}}}
E         Field{name=connectionInitSqls, description=, type=ITERABLE, options={{}}}
E         Field{name=readQuery, description=, type=STRING, options={{}}}
E         Field{name=writeStatement, description=, type=STRING, options={{}}}
E         Field{name=fetchSize, description=, type=INT16, options={{}}}
E         Field{name=outputParallelization, description=, type=BOOLEAN, options={{}}}
E         Field{name=autosharding, description=, type=BOOLEAN, options={{}}}
E         Encoding positions:
E         {connectionProperties=4, autosharding=10, password=3, writeStatement=7, outputParallelization=9, fetchSize=8, driverClassName=0, jdbcUrl=1, connectionInitSqls=5, username=2, readQuery=6}
E         Options:{{}}UUID: 49358cda-6f26-4080-86fc-36fcc5d52b44
E         	at org.apache.beam.vendor.guava.v26_0_jre.com.google.common.base.Preconditions.checkArgument(Preconditions.java:440)
E         	at org.apache.beam.sdk.schemas.Schema.indexOf(Schema.java:1365)
E         	at org.apache.beam.sdk.values.Row.getInt32(Row.java:154)
E         	at org.apache.beam.sdk.io.jdbc.JdbcSchemaIOProvider$JdbcSchemaIO.getDataSourceConfiguration(JdbcSchemaIOProvider.java:178)
E         	at org.apache.beam.sdk.io.jdbc.JdbcSchemaIOProvider$JdbcSchemaIO$2.expand(JdbcSchemaIOProvider.java:144)
E         	at org.apache.beam.sdk.io.jdbc.JdbcSchemaIOProvider$JdbcSchemaIO$2.expand(JdbcSchemaIOProvider.java:139)

Issue Failure
Failure: Test is continually failing
Issue Priority
Priority: 1 (unhealthy code / failing or flaky postcommit so we cannot be sure the product is healthy)
Issue Components

[X] Component: Python SDK
[X] Component: Java SDK
[ ] Component: Go SDK
[ ] Component: Typescript SDK
[X] Component: IO connector
[ ] Component: Beam examples
[ ] Component: Beam playground
[ ] Component: Beam katas
[ ] Component: Website
[ ] Component: Spark Runner
[ ] Component: Flink Runner
[ ] Component: Samza Runner
[ ] Component: Twister2 Runner
[ ] Component: Hazelcast Jet Runner
[ ] Component: Google Cloud Dataflow Runner

@Abacn is this issue closed?

Yes it should be closed


Metadata is loading in after the script tags, discussion

Im not using your package however i have done pretty much the same thing - a component that you pass metadata info into and then it loads it into svelte head.
The issue I have noticed when using w3 validator is that the metadata component has no load time specificity or priority - this means that when rollup bundles the project it could load/run the metadata component last which can cause the metadata to show underneath the scripts and not be first in the head as recommended.
Which looks like this:

W3 Validator is saying that it does not appreciate the meta tags being loaded in last and having all the scripts above it - whether or not this impacts anything im not sure but it is interesting to know.
The error W3 is giving me:

Its also important to note this component gets bundled and will require the browser to download its javascript so it can run in the browser which i guess is unavoidable but also unnecessary as the component should not be included or run on the client-side.

Thanks for your help!
So far I haven't had any major problems using it to create my company's website, but I think it would be good if I could improve the loading order.
I don't have any ideas, but do you have any advice?

I came to post here because im not sure yet how to solve this for myself, I just wanted to raise it because I know its something that exists for both of us. As you say it doesnt seem to have any issues as far as I can tell but the W3 Validator does flag it:
https://validator.w3.org/nu/?doc=https://www.utickets.co.za/events/soundland-festival-durban-july-after-party-by-dlala-thukzin
I think we would have to change our current implementation of SEO from a component that sets the metadata to using load functions that return the metadata and it gets set in the main layout.svelte.
Its hard to say if a different implementation will put the metadata above the script tags because there's no way to specify the importance of the svelte head you are setting.
Let me check this and feedback.

After trying what is recommended in the svelte documentation, an ssr approach, it is clear the issue is with svelte not having the ability to specify where the svelte head contents go in the head tag.
Even with this ssr approach, which i kind of prefer anyways (it still needs work), the head is jumbled and the meta tags are showing up underneath the script tags
Here we setup svelte:head in the layout component. We then set the default metadata in the layout load function and in each page we overwrite that with page specific metadata:
https://codesandbox.io/p/devbox/jolly-wave-kmp4mt?file=%2Fsrc%2Flib%2Fmetadata%2Fmetadata.ts%3A8%2C17&layout=%257B%2522sidebarPanel%2522%253A%2522EXPLORER%2522%252C%2522rootPanelGroup%2522%253A%257B%2522direction%2522%253A%2522horizontal%2522%252C%2522contentType%2522%253A%2522UNKNOWN%2522%252C%2522type%2522%253A%2522PANEL_GROUP%2522%252C%2522id%2522%253A%2522ROOT_LAYOUT%2522%252C%2522panels%2522%253A%255B%257B%2522type%2522%253A%2522PANEL_GROUP%2522%252C%2522contentType%2522%253A%2522UNKNOWN%2522%252C%2522direction%2522%253A%2522vertical%2522%252C%2522id%2522%253A%2522clxiqctar0007356kb11vznn8%2522%252C%2522sizes%2522%253A%255B100%252C0%255D%252C%2522panels%2522%253A%255B%257B%2522type%2522%253A%2522PANEL_GROUP%2522%252C%2522contentType%2522%253A%2522EDITOR%2522%252C%2522direction%2522%253A%2522horizontal%2522%252C%2522id%2522%253A%2522EDITOR%2522%252C%2522panels%2522%253A%255B%257B%2522type%2522%253A%2522PANEL%2522%252C%2522contentType%2522%253A%2522EDITOR%2522%252C%2522id%2522%253A%2522clxiqctar0002356kqjvunw0q%2522%257D%255D%257D%252C%257B%2522type%2522%253A%2522PANEL_GROUP%2522%252C%2522contentType%2522%253A%2522SHELLS%2522%252C%2522direction%2522%253A%2522horizontal%2522%252C%2522id%2522%253A%2522SHELLS%2522%252C%2522panels%2522%253A%255B%257B%2522type%2522%253A%2522PANEL%2522%252C%2522contentType%2522%253A%2522SHELLS%2522%252C%2522id%2522%253A%2522clxiqctar0004356kpx6ecfbf%2522%257D%255D%252C%2522sizes%2522%253A%255B100%255D%257D%255D%257D%252C%257B%2522type%2522%253A%2522PANEL_GROUP%2522%252C%2522contentType%2522%253A%2522DEVTOOLS%2522%252C%2522direction%2522%253A%2522vertical%2522%252C%2522id%2522%253A%2522DEVTOOLS%2522%252C%2522panels%2522%253A%255B%257B%2522type%2522%253A%2522PANEL%2522%252C%2522contentType%2522%253A%2522DEVTOOLS%2522%252C%2522id%2522%253A%2522clxiqctar0006356koriww7qe%2522%257D%255D%252C%2522sizes%2522%253A%255B100%255D%257D%255D%252C%2522sizes%2522%253A%255B100%252C0%255D%257D%252C%2522tabbedPanels%2522%253A%257B%2522clxiqctar0002356kqjvunw0q%2522%253A%257B%2522tabs%2522%253A%255B%257B%2522id%2522%253A%2522clxiqctar0001356kktp57umw%2522%252C%2522mode%2522%253A%2522permanent%2522%252C%2522type%2522%253A%2522FILE%2522%252C%2522filepath%2522%253A%2522%252FREADME.md%2522%252C%2522state%2522%253A%2522IDLE%2522%257D%252C%257B%2522id%2522%253A%2522clxirxs9m0002356jhzwxppi9%2522%252C%2522mode%2522%253A%2522permanent%2522%252C%2522type%2522%253A%2522FILE%2522%252C%2522initialSelections%2522%253A%255B%257B%2522startLineNumber%2522%253A8%252C%2522startColumn%2522%253A17%252C%2522endLineNumber%2522%253A8%252C%2522endColumn%2522%253A17%257D%255D%252C%2522filepath%2522%253A%2522%252Fsrc%252Flib%252Fmetadata%252Fmetadata.ts%2522%252C%2522state%2522%253A%2522IDLE%2522%257D%255D%252C%2522id%2522%253A%2522clxiqctar0002356kqjvunw0q%2522%252C%2522activeTabId%2522%253A%2522clxirxs9m0002356jhzwxppi9%2522%257D%252C%2522clxiqctar0006356koriww7qe%2522%253A%257B%2522id%2522%253A%2522clxiqctar0006356koriww7qe%2522%252C%2522tabs%2522%253A%255B%255D%257D%252C%2522clxiqctar0004356kpx6ecfbf%2522%253A%257B%2522tabs%2522%253A%255B%257B%2522id%2522%253A%2522clxiqctar0003356k6iriam9y%2522%252C%2522mode%2522%253A%2522permanent%2522%252C%2522type%2522%253A%2522TASK_LOG%2522%252C%2522taskId%2522%253A%2522dev%2522%257D%255D%252C%2522id%2522%253A%2522clxiqctar0004356kpx6ecfbf%2522%252C%2522activeTabId%2522%253A%2522clxiqctar0003356k6iriam9y%2522%257D%257D%252C%2522showDevtools%2522%253Afalse%252C%2522showShells%2522%253Afalse%252C%2522showSidebar%2522%253Atrue%252C%2522sidebarPanelSize%2522%253A15%257D7

I raised an issue with svelte here:
https://github.com/sveltejs/svelte/issues/12055

I see, that's an interesting result.
Thanks for raising the issue on the Svelte side.


Auto discover column to data-type mapping from origin

Auto discover column to data-type mapping from origin (& remove the applicable config)

Done & released as part of 3.4.0 release. 3.x stable branch has the changes


ASN detection is broken, see comment

Need to check if it starts with as and is followed by a number.
https://github.com/Lookyloo/uwhoisd/commit/c39b97f27f96351679b8fe364b413bb6eb974ea1#r50231598

Fixed by https://github.com/Lookyloo/uwhoisd/commit/6861ec749188f9aa84ed38c4a35c163f13ca7e0e


some changes from the code review

Added building and bundling for both cjs and esm formats
Added package.json exports
Moved ethers to peer dependency
Converted abi files to json and added TypeChain for typing and factories
Added .editorconfig and eslint with some basic config
Extensive refactor for typing
Various other small refactors
Moved the example into this repo and refactored it to make it more sveltey

Closing. Won't fix. These changes got moved elsewhere.


New animation conditions: minimum/maximum seconds passed since starting level

These animation conditions would prohibit an animation from playing unless a certain amount of time has passed, or has not passed. For example, you could force an animation to not play unless 30 seconds have passed since starting the level, or prevent an animation from playing if 60 seconds have passed in a level.
There is already a condition to force an animation to only play once the in-game timer is below a certain number, but that only works for levels with a timer to begin with, and can be interfered with by changing the timer itself, either through powerups like the clock item, or animations that change the amount of time left.

Isn't there the "after seconds" trigger for that? You can then shut it off later using a number check. If after seconds doesn't work you could just have another animation that sleeps for x amount of time then does the actions.

This literally already exists why is the issue still open


chore: Add test coverage to events utils

Follow on to #1120 for events.

Awesome @SCasarotto!
I wonder if we should use ariakit-test-utils' render function in these tests to avoid having to clean up the DOM.

Looking at the render function, I noticed that it is just @testing-library's function. Looks like it does automatically clean up. We could do that in both of these testing PRs. Do you think that is better? If so I can update it on both.
I do wonder if I switch to render if I should start to use the @testing-library's selectors as well. Thoughts?

I think it's worth using ariakit-test-utils on these tests and the selectors since they will simplify the code a lot.
I'll merge this PR as it's in a good state. But feel free to open a follow-up PR if you think the change above makes sense.


Passenger gem is rebuilt on chef runs.

I have had a major (so far transient) problem with this cookbook. The goal is to install nginx with passenger by building everything from source. This will mean nginx has to compile with passenger.
this installation works fine at the beginning and passenger is installed. when it works there are 3447 files under the passenger gem:
find /usr/local/lib/ruby/gems/2.1.0/gems/passenger-4.0.59 | wc -l
3447
but after couple of chef runs i see passenger is not working
➜  cornice_frontend  passenger-status
ERROR: Phusion Passenger doesn't seem to be running.
and after a lot of investigation i noticed PassengerWatchdog is missing.
➜  passenger-4.0.59  locate PassengerWatchdog
➜
when the passenger watchdog is not there passenger wouldn't work
and
➜  passenger-4.0.59  find /usr/local/lib/ruby/gems/2.1.0/gems/passenger-4.0.59 | wc -l
3349
which shows some of the files are not there any more.
when passenger is properly installed:
➜  passenger-4.0.59  locate PassengerWatchdog
/usr/local/lib/ruby/gems/2.1.0/gems/passenger-4.0.59/buildout/agents/PassengerWatchdog
/usr/local/lib/ruby/gems/2.1.0/gems/passenger-4.0.59/buildout/agents/PassengerWatchdog.o
These all lead to the fact that the gem has been reinstalled! the time stamp of the gem files confirms this. I dont know what is causing the gem to be reinstalled. I am using the cookbook out of the box with no modifications. not even a wrapper cookbook. all i have done was to set the attributes for install method and few other passenger.conf stuff.

Distro? Version? Run List? Attributes?

Thanks for opening this issue. Just today we merged the changes from the chef_nginx fork of this cookbook back to master here. It’s a pretty huge change set and includes over a year of active development that resulted in several major releases. I think there’s a very high chance that this issue has been resolved with that work and at this point I’m going to close this issue out. I’d encourage you to checkout the master branch and please open this issue back up if you’re still having the problem there.
Here’s the changes that were just merged in: https://github.com/chef-cookbooks/nginx/pull/435


Add manual token mount and release 2.10

What this PR does / why we need it:
Adds the manual token mount with fixes from https://github.com/Kong/kubernetes-ingress-controller/pull/2612 on top.
Releases 2.10.
Checklist
[Place an '[x]' (no spaces) in all applicable fields. Please remove unrelated fields.]

[x] PR is based off the current tip of the main branch.
[x] Changes are documented under the "Unreleased" header in CHANGELOG.md
[x] New or modified sections of values.yaml are documented in the README.md
[x] Commits follow the Kong commit message guidelines

 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.


tls: require RSA certificates with 2048-bit or larger keys.

RSA certificates with keys smaller than 2048-bits are disallowed by NIST,
Internet migrated to 2048-bit keys in 2013, and no CAs issue certificates
with smaller keys, so let's prevent users from accidentally using such
certificates.
Signed-off-by: Piotr Sikora<EMAIL_ADDRESS>

@agl @davidben I originally started this as FIPS-only requirement, but it seems that noone should be using 1024-bit RSA anymore, and since Envoy got a bit opinionated recently (i.e. allowing only P-256 ECDSA certificates), I think that it's sane to enforce this as well. Let me know if you disagree!
cc @mattklein123 @htuch

What are the chances that someone is using a 1024-bit key in production? I don't have any sense of the likelihood of this. If it's basically 0% this is fine with me. If higher perhaps we should config guard this and default to on? Thoughts?

@agl this PR is specifically about rejecting RSA certificates with keys smaller than 2048 bits in all builds (FIPS and non-FIPS). We can (should?) add more requirements the FIPS PR and guard them with #ifdef BORINGSSL_FIPS. I agree that enforcing all FIPS requirements in non-FIPS builds is a bad idea.
@mattklein123 there is 0% chance of this happening on the edge / public Internet, since no browsers accept such certificates and no CAs issue them... However, if there are people using self-signed certificates (i.e. produced using openssl genrsa and not any CA-like tool) inside their service mesh, then this might affect them. IMHO, this should affect close to 0% of production deployments, but it's impossible to tell, really...


Clean and Build targets do not properly clean previous build products.

If the Id or Version properties change between builds, the previous build output is not deleted.

I've made a pull-request for this issue. Not sure about the procedure, is there anything else I need to do?

:+1:  for this issue.  :)

Hmm, that's almost by-design. The ID is conceptually part of the name. Regular C# projects have the same behavior. If you change the assembly name, the old file isn't deleted on rebuild either. We need to think about how this plays nicely with incremental builds.

The C# build does delete old assemblies if you change the assembly name. The build keeps track of all files that it writes during the build in a "FileListAbsolute.txt" file. On subsequent builds, it subtracts the set of written files from the previous list and removes the resulting set of files.
I've replicated this functionality into the pull request and I believe it is what the majority of users would expect.

Considering that now we don't handle incremental builds at all, I think this is an improvement over current situation, and should be merged in.


Update fin-ops-connector.md

:x: Organization membership check failed
Members of the MicrosoftDocs organization should use the private repo. Please move your changes to https://github.com/MicrosoftDocs/Dynamics-365-Operations, and close this pull request.


Update test.yml

I believe the CI is not working due to this issue https://github.com/ionrock/cachecontrol/issues/292.

I attempted to pin urllib4 to 1.26.15, however I get the same error:
$ poetry add urllib3==1.26.15

  AttributeError

  'HTTPResponse' object has no attribute 'strict'

  at ~/Library/Application Support/pypoetry/venv/lib/python3.11/site-packages/cachecontrol/serialize
.py:54 in dumps
       50│                 ),
       51│                 u"status": response.status,
       52│                 u"version": response.version,
       53│                 u"reason": text_type(response.reason),
    →  54│                 u"strict": response.strict,
       55│                 u"decode_content": response.decode_content,
       56│             }
       57│         }
       58│

I believe we need to bump poetry to at least version 1.3.2 as that seems to work locally for me.

@sbdchd I've bumped poetry to 1.3.2 in https://github.com/sbdchd/celery-types/pull/116.


Value passed to parameter 'input_sizes' has DataType float32 not in list of allowed values: int32, int64

When I run you item, I get this error.
Traceback (most recent call last):
File "E:/GAN/7-3D-IWGAN-master/3D-IWGAN-master/3D-Generation/32-3D-Gan.py", line 49, in
net_g , G_train     = generator_32(z, is_train=True, reuse = False, sig= True, batch_size=args.batchsize)
File "E:\GAN\7-3D-IWGAN-master\3D-IWGAN-master\scripts\models.py", line 74, in generator_32
net_2 = Deconv(net_1, gf_dim, eighth, '2', batch_size)
File "E:\GAN\7-3D-IWGAN-master\3D-IWGAN-master\scripts\models.py", line 20, in Deconv
act=tf.identity, name='g/net_' + net + '/deconv')
File "D:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorlayer\layers.py", line 1512, in init
self.outputs = act( tf.nn.conv3d_transpose(self.inputs, W, output_shape=output_shape, strides=strides, padding=padding) + b )
File "D:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 2473, in conv3d_transpose
name=name)
File "D:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 2539, in conv3d_transpose_v2
name=name)
File "D:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\ops\gen_nn_ops.py", line 2237, in conv3d_backprop_input_v2
dilations=dilations, name=name)
File "D:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 627, in _apply_op_helper
param_name=input_name)
File "D:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 61, in _SatisfiesTypeConstraint
", ".join(dtypes.as_dtype(x).name for x in allowed_list)))
TypeError: Value passed to parameter 'input_sizes' has DataType float32 not in list of allowed values: int32, int64
I find this error raise becaues 'Conv3DBackpropInputV2' not has float32 dtype, I don't konw why.
Do you have some ideas for this error.
By the why, what tensorflow and tensorlayer version do you used?

Hi, try tensorflow==1.13.2 and tensorlayer==1.5.0

Hi, try tensorflow==1.13.2 and tensorlayer==1.5.0

After replacing with tensorflow==1.13.2 and tensorlayer==1.5.0, the error still exists. Is there any other solution?

Hi, which python version are you using?

I found that the issue is in the Deconv function in ./scripts/models.py, where the out_shape should be of type int instead of float. So I modified it like this:
def Deconv(inputs, f_dim_in, dim, net, batch_size, f_dim_out=None, stride=2):
    if f_dim_out is None:
        f_dim_out = f_dim_in / 2
    output_shape = [batch_size, dim, dim, dim, f_dim_out]
    output_shape = tf.cast(output_shape, tf.int32)
    return tl.layers.DeConv3dLayer(inputs,
                                   shape=[4, 4, 4, f_dim_out, f_dim_in],
                                   output_shape=output_shape,
                                   strides=[1, stride, stride, stride, 1],
                                   W_init=tf.random_normal_initializer(stddev=0.02),
                                   act=tf.identity, name='g/net_' + net + '/deconv')

But this might not be the best solution, as it could lose some precision.


chore(lists): shutdown notice on website

:-(

After talking with @antoniomika we are going to pause this for now.  We are thinking about merging lists.sh and prose.sh together but want to think through the ramifications

@neurosnap @antoniomika might be worth while to implement it as a goldmark extension

Great idea!  We are definitely thinking about folding lists.sh into prose.sh since they are so similar, this could be one way to do it.


No Twitter login due to REST API v1 is no longer active

[error] => Array
    (
        [provider] => Twitter
        [code] => 410
        [raw] => {"errors":[{"message":"The Twitter REST API v1 is no longer active. Please migrate to API v1.1. https://dev.twitter.com/docs/api/1.1/overview.","code":64}]}
    )

[timestamp] => 2015-01-20T15:13:37+01:00

Ps. i just downloaded it and gave it a whirl. But out of the box returning 404?

That is an issue of opauth/twitter, although current master does use api
v1.1, looks like you used an incorrect version of opauth/twitter
2015-01-20 15:19 GMT+01:00 joland<EMAIL_ADDRESS>
[error] => Array
(
[provider] => Twitter
[code] => 410
[raw] => {"errors":[{"message":"The Twitter REST API v1 is no longer active. Please migrate to API v1.1. https://dev.twitter.com/docs/api/1.1/overview.","code":64}]}
)
[timestamp] => 2015-01-20T15:13:37+01:00
Ps. i just downloaded it and gave it a whirl. But out of the box returning
404?
—
Reply to this email directly or view it on GitHub
https://github.com/opauth/opauth/issues/107.


Update insertion_sort.c

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