Termination warning due to background task

Expected Behavior

Background task configured correctly
Current Behavior

I receive such message in console
Background Task 13 119 ("amplitude"), was created over 30 seconds ago.
In applications running in the background, this creates a risk of termination.
Remember to call UIApplication.endBackgroundTask(_:) for your task in a timely manner to avoid this.

Possible Solution

Not sure, probably call UIApplication.endBackgroundTask(_:) when task is completed
Steps to Reproduce

Just run the app, the logs will come soon. Sample log:
Amplitude.zip
I believe there was no such problem in Amplitude-iOS SDK, only in Swift one.
Environment

SDK Version:  Amplitude-Swift 1.1.0
OS Info:  iOS 17.1.1, iPhone 13

Hi @nezhyborets ,
Thanks for reporting this! I have done the following investigations:

The code does run endBackgroundTask in the expirationHandler and after task completion.
I am not able to see that log when running the UI example app with iPhone 15 and iso 17.0.1 locally.

Could you provide a sample app that can trigger these logs? Also, could you enable the debug mode or at least the log mode so we can get more useful output like this (I didn't see this info in the sample log above)?
Thanks

I've also been experiencing this issue, which appears to be part of an infinite loop that causes high CPU and memory leaking making the app unusable and eventually crashing.
I initialize with this code in a global singleton:
let amplitude = Amplitude(configuration: Configuration(
    apiKey: "<token here>",
    logLevel: .DEBUG,
    defaultTracking: .ALL
))

After running for about 30 seconds, I see a bunch of logs start to appear. First I see this:
Log: Start flushing 1 events
Log: Cleaned up 0 non-running uploads.
Log: Cleaned up 1 non-running uploads.
Log: Cleaned up 2 non-running uploads.
Log: Start flushing 30 events
1 log/signpost messages lost due to high rates in live mode recording. To guarantee delivery of all logs, set IDELogRedirectionPolicy to oslogToStdio in the environment of the executable.
2 log/signpost messages lost due to high rates in live mode recording. To guarantee delivery of all logs, set IDELogRedirectionPolicy to oslogToStdio in the environment of the executable.
1 log/signpost messages lost due to high rates in live mode recording. To guarantee delivery of all logs, set IDELogRedirectionPolicy to oslogToStdio in the environment of the executable.
1 log/signpost messages lost due to high rates in live mode recording. To guarantee delivery of all logs, set IDELogRedirectionPolicy to oslogToStdio in the environment of the executable.
1 log/signpost messages lost due to high rates in live mode recording. To guarantee delivery of all logs, set IDELogRedirectionPolicy to oslogToStdio in the environment of the executable.
1 log/signpost messages lost due to high rates in live mode recording. To guarantee delivery of all logs, set IDELogRedirectionPolicy to oslogToStdio in the environment of the executable.
1 log/signpost messages lost due to high rates in live mode recording. To guarantee delivery of all logs, set IDELogRedirectionPolicy to oslogToStdio in the environment of the executable.
1 log/signpost messages lost due to high rates in live mode recording. To guarantee delivery of all logs, set IDELogRedirectionPolicy to oslogToStdio in the environment of the executable.
1 log/signpost messages lost due to high rates in live mode recording. To guarantee delivery of all logs, set IDELogRedirectionPolicy to oslogToStdio in the environment of the executable.
1 log/signpost messages lost due to high rates in live mode recording. To guarantee delivery of all logs, set IDELogRedirectionPolicy to oslogToStdio in the environment of the executable.
2 log/signpost messages lost due to high rates in live mode recording. To guarantee delivery of all logs, set IDELogRedirectionPolicy to oslogToStdio in the environment of the executable.
1 log/signpost messages lost due to high rates in live mode recording. To guarantee delivery of all logs, set IDELogRedirectionPolicy to oslogToStdio in the environment of the executable.
2 log/signpost messages lost due to high rates in live mode recording. To guarantee delivery of all logs, set IDELogRedirectionPolicy to oslogToStdio in the environment of the executable.
Log: Cleaned up 15 non-running uploads.

This continues for a while printing at high frequency with different numbers of non-running uploads cleaned up each time.
It seems to change what logs are being printed every once in a while. Sometimes it stops the log/signpost messages and just shows the flushing/cleaning logs:
Log: Start flushing 30 events
Log: Cleaned up 0 non-running uploads.
Log: Cleaned up 0 non-running uploads.
Log: Start flushing 30 events
Log: Cleaned up 74 non-running uploads.
Log: Cleaned up 0 non-running uploads.
Log: Start flushing 30 events
Log: Cleaned up 5 non-running uploads.
Log: Cleaned up 0 non-running uploads.
Log: Start flushing 30 events
Log: Cleaned up 0 non-running uploads.
Log: Cleaned up 0 non-running uploads.
Log: Start flushing 30 events
Log: Cleaned up 49 non-running uploads.
Log: Cleaned up 0 non-running uploads.

Other times it begins showing the background task logs that are noted above:
Background Task 1,542 ("amplitude"), was created over 30 seconds ago. In applications running in the background, this creates a risk of termination. Remember to call UIApplication.endBackgroundTask(_:) for your task in a timely manner to avoid this.
Background Task 1,543 ("amplitude"), was created over 30 seconds ago. In applications running in the background, this creates a risk of termination. Remember to call UIApplication.endBackgroundTask(_:) for your task in a timely manner to avoid this.
Background Task 1,544 ("amplitude"), was created over 30 seconds ago. In applications running in the background, this creates a risk of termination. Remember to call UIApplication.endBackgroundTask(_:) for your task in a timely manner to avoid this.
Background Task 1,545 ("amplitude"), was created over 30 seconds ago. In applications running in the background, this creates a risk of termination. Remember to call UIApplication.endBackgroundTask(_:) for your task in a timely manner to avoid this.
Background Task 1,546 ("amplitude"), was created over 30 seconds ago. In applications running in the background, this creates a risk of termination. Remember to call UIApplication.endBackgroundTask(_:) for your task in a timely manner to avoid this.
Background Task 1,547 ("amplitude"), was created over 30 seconds ago. In applications running in the background, this creates a risk of termination. Remember to call UIApplication.endBackgroundTask(_:) for your task in a timely manner to avoid this.
Background Task 1,548 ("amplitude"), was created over 30 seconds ago. In applications running in the background, this creates a risk of termination. Remember to call UIApplication.endBackgroundTask(_:) for your task in a timely manner to avoid this.
Background Task 1,549 ("amplitude"), was created over 30 seconds ago. In applications running in the background, this creates a risk of termination. Remember to call UIApplication.endBackgroundTask(_:) for your task in a timely manner to avoid this.
Background Task 1,550 ("amplitude"), was created over 30 seconds ago. In applications running in the background, this creates a risk of termination. Remember to call UIApplication.endBackgroundTask(_:) for your task in a timely manner to avoid this.
Background Task 1,551 ("amplitude"), was created over 30 seconds ago. In applications running in the background, this creates a risk of termination. Remember to call UIApplication.endBackgroundTask(_:) for your task in a timely manner to avoid this.
Background Task 1,552 ("amplitude"), was created over 30 seconds ago. In applications running in the background, this creates a risk of termination. Remember to call UIApplication.endBackgroundTask(_:) for your task in a timely manner to avoid this.

In the device files (running on iPhone 15 Pro simulator) I can see a large number of files created:

These files appear to be full of events which are older than the files (2+ weeks). My hunch is that it is somehow trying and failing to upload these old events, which is causing an infinite loop. I do not see any logs that would indicate why the failures are occurring; I have confirmed that my token is correct, and the device is connected to the internet.
This is occurring both on the simulator mentioned above, as well as my physical device (iPhone 14 Pro).
Hopefully this is helpful in debugging! Please ping me if you need additional details 🙂

Hi @perrin-salus, thanks for sharing more information here!
I just tried with the exact same configure as you provided, running it in our test example app, and manually firing hundreds of events. The logs and app are still fine to me.
Since there are a lot of pending events in your app, I am curious how frequently does a track call happen in your app? It might be that your app is generating way more events than our default flush queue interval 30 event or 30 seconds. Could you also try to:

increase the flushQueueSize value?
use batch mode, with useBatch

Here are more detailed configurations: https://www.docs.developers.amplitude.com/data/sdks/ios-swift/#configuration.
As for the 400 error, I am not sure why you got that, it might not be from iOS source. I don't have more insights unfortunately. But the iOS SDK should not retry on this as 400 doesn’t belong to the retry bucket, so I think this is not related.
Thanks!

Hi @liuyang1520 thanks for the quick reply!
At the moment we don't call track manual events at all, aside from a couple times sending up a quick "Hello World" to make sure it was working when I was first setting it up. Typically I have been running it with defaultTracking: .ALL to get events for screen views, sessions, and app lifecycle.
After some more testing on my end, I've managed to partially resolved the issue and figured out why the requests have been failing. Searching through the configuration you linked, I noticed one particular setting that I hadn't previously given much thought: minIdLength. I've been testing so far on a development environment with only a few users - my usual test account is ID 3 (we are tracking users by their numeric ID rather than email address, which it seems is not the default expected use case). In the code, the default value for minIdLength is nil, which I assumed meant there is no minimum length. However, according to the documentation, the default value is actually 5 - I assume nil values get replaced with 5 somewhere internally. I updated my configuration to use 1 for the minimum length, and noticed improvements.
On a freshly-installed simulator, no errors occurred and all events were sent successfully. On the simulator which was previously having the looping issue, I noticed CPU spikes occurring every 30 seconds as it attempted to upload events, but calmed down again rather than staying high indefinitely. In the console I still noticed some of the log/signpost messages mixed in with the flush/cleanup logs, however the background task warnings no longer occurred. On the Ingestion Debugger I also noticed the errors changed from 400 to 429 - too many requests.

I then enabled useBatch as suggested and tried again, and after 1.5 CPU spikes it calmed down and finished uploading all the events. The device now runs fine again with no more issues.

Overall my observations are:

It can be fairly easy to misconfigure the settings, with the default value in the code being misleading compared to the documentation, and no clear indication that the configuration is wrong
Some unknown bug can cause a huge backlog of events to occur. Unfortunately I have no idea what could be causing this, though it happened multiple times on both the simulator and physical device
A combination of the above leads to infinite looping as it attempts to upload events unsuccessfully, causing high CPU, high memory usage, and the background task termination warning that this issue originally reported

Now that I have the configuration issue sorted, I'm hopeful that I should be able to continue deploying even without knowing the cause of the bug. It would still be great if any progress can be made on figuring that out, and maybe something could be done to avoid everything exploding in the case of misconfiguration.
One last thing, I did save a couple of the log files before everything was uploaded. I'm not sure if the events in them will be useful for debugging at all, but I'll provide them just in case:
9271
9260
Thanks again!

@perrinswanson , thank you very much for providing these details! Glad that it finally got sorted out!
In terms of the configuration, as the source code is open to read, also we have the configuration documentation, feel free to read the doc at first, as the default value in the source code might get overwritten in SDK pipeline/timeline/plugins. E.g., the minIdLength is 5 by default (mentioned in the doc), but this value is actually defined by our ingestion pipeline API.
As for debugging the payload, refer to this section, for example, you can add a simple callback like this to see the actual response:
configuration: Configuration(
    apiKey: "xxx",
    logLevel: LogLevelEnum.DEBUG,
    callback: { (event: BaseEvent, code: Int, message: String) -> Void in
        print("eventcallback: \(event), code: \(code), message: \(message)")
    },
    defaultTracking: .ALL
)

Thank you again for supporting Amplitude!


Azure Automation Windows Updates - Support Operating Systems (Windows 10/11 Multi-session)

Hi Team, please clarify if Windows 10 / 11 Ent Multi-session is supported for Update automation when running as a multi-session host in Azure Virtual Desktop.
The statement "Client operating systems (such as Windows 7 and Windows 10) aren't supported.
For Azure Virtual Desktop, the recommended method to manage updates is Microsoft Endpoint Configuration Manager for Windows 10 client machine patch management."  is not clear as this may indicate personal Windows 10 VM's are not supported but doesn't clearly identify the multi-session OS.
Please also verify that Server 2022 is now supported?
Thanks
Andrew

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

ID: e4515d4f-2f9b-26dc-d7ab-6a2eae42b9b0
Version Independent ID: deb378f1-9b68-9c24-d64b-18f633e9e4f9
Content: Azure Automation Update Management Supported Clients
Content Source: articles/automation/update-management/operating-system-requirements.md
Service: automation
Sub-service: update-management
GitHub Login: @SnehaSudhirG
Microsoft Alias: sudhirsneha

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


Publishing the JTORCTL jar into maven

You can use the following repository to fetch jtorctl from maven:
<repository>             <id>spring-plugins</id>             <url>http://repo.spring.io/plugins-release/</url>         </repository>


MHA Fails with Secondary mailboxes

We have many users who access their primary mailbox in Outlook but also use Shared mailboxes. We use the latest version of MHA add-in (v3.1). The add-in works perfectly for messages sent to the primary mailbox but it fails when looking at message headers sent to those shared mailboxes. It simply displays summary diagnostics as shown below.
The users have disabled/re-enabled the add-in to no avail. I also tried disabling all other add-ins in Outlook but that made no difference. Any hints for us? This has stumped me so far.

Thank you!

So is there there no way for the MHA add-in to support emails in additional IMAP accounts that have been added to the Outlook desktop client?
Wunderlist add-on is lit up, but MHA is not for emails in these accounts.
Otherwise, love how easy this add-in is to use! Appreciate the authors' work and publishing it freely!


Make timeline refresh before being shown on app activation

When leaving app open over night, the timeline only refreshes once the previous day is already visible. This causes the user to see the old data (and probably the wrong heading for it) for maybe half a second, before the new day is shown.

Is this issue related to the screenshot that iOS takes when the app enters background and shows as the app is entering foreground?

Oh, that is a good idea. That might well be the case. I take it there is little we can do about that?

From what I see the screenshot is taken after applicationWillEnterForeground returns and is used when applicationDidBecomeActive. One idea would be to show a full screen viewController in applicationWillEnterForeground and remove it in applicationDidBecomeActive. This vc can be either just white or something like the splash screen. We might need to put a fade animation just to make it look nice.

There seems to be ignoreSnapshotOnNextApplicationLaunch() as well, which I believe would cause it to use the splash screen instead of the screenshot.
It this means that every time the app resumes we see the splash screen, that might be too jarring to be worth it. Ideally we only want to change the screenshot behaviour when the app is resumed on a new day (causing the timeline to switch days). Could you investigate if this is possible at all? And if not, let's try how it looks with disabled screenshots/an overlay VC (I especially want to know if it will then always show this, even if the app was only in the background for a minute for example).

ignoreSnapshotOnNextApplicationLaunch() would have been a good solution but unfortunately it can only be called when the the app is preparing to go in background and not as the app becomes active again. Further more it looks like it is related to app launch not app reactivation from background state. I'm not sure the method works at all. I have been testing different configurations but I can't see any difference between using it and not using it. If the method worked correctly we might use it with Background Fetch enabled where we would see if the date has changed and call it (just a thought). At the moment I can only see solutions that run every time and not just when the date changes.

Might not get around to it anytime soon, but the best solution might be to use snapshotView(afterScreenUpdates:) to update screenshots in the background such that they are always close enough to the current state of the app (assuming this is possible?).
In fact from the documentation it sounds like iOS should create new screenshots anytime the app is woken up in the background.
This might need some thinking and further investigation though, to make sure we actually handle the day-switching case (which is the most, if not only, problematic one).

Let's investigate this briefly, but as thoroughly as possible, and either implement a clean fix, or close as wont-fix.


STAT-101 ⁃ Testnet Sprint One - Transfer fails because of transaction expiration timeout

Transfer from inita to initb fails sometimes due to the low limit on transaction expiration time.
Steps to reproduce:

start a local eosd-walletd
Import relevant keys
Transfer 1000 eos from inita to initb

Output:
MacBook01:build pravin$ programs/eosc/eosc --host <IP_ADDRESS> --port 8889 --wallet-port 8899 transfer inita initb 1000
An error occurred while submitting the transaction for this command!

reopened by jira, closing.

➤ Bezwada Satyapravin commented:
Please see related: https://blockone.atlassian.net/browse/STAT-128


The result seems to be no change

Hi, I am very impressed with the results you have achieved in this project. So I want to reproduce it but have some problem with the result.
I followed the instructions of "[Tutorial] Infer your own data" to get GOPT features from Kaldi and then Inferent with pretrained weight.
I tried on many experiments with my own voice and voices of speechocean762 but the results are almost the same (Ex: u1:accuracy always from 1.7 -1.8 (tensor), ...).
I also test with incorrect item (voice different with transcript) and the voice with the bad score in speechocean762. But the results are the same as I described above. Hope you can give me some advice.
And one more thing that I do tensor*5 to get the score on ten scale cause tensor_value in range[0-2]. Is it the right way??
Ex: speaker 9604 of speechocean762, this voice have "accuracy": 3 in scores.json -> bad voice but the result is high:

text-phone:
096040025.0	DH_B EH0_I R_E
096040025.1	W_B AH0_I Z_E
096040025.2	N_B AH1_I TH_I IH0_I NG_E
096040025.3	T_B UW0_E
096040025.4	B_B IY0_E
096040025.5	G_B EY0_I N_I D_E
096040025.6	B_B AY0_E
096040025.7	IH0_B T_E

spk2age       9604	21

spk2gender  9604	f

spk2utt         9604 096040025

text               096040025	THERE WAS NOTHING TO BE GAINED BY IT

utt2spk         096040025 9604

wav              096040025	WAVE/SPEAKER9604/096040025.WAV

=> GOPT features  (zip file): feature.zip
=> Result:
u1 = tensor([1.7430])
u2 = tensor([1.5640])
...

Hi @dohuuphu i am also testing the infer your own voice example, and i am getting the similar results like this. BTW how did you find that the u1....u5 results are from 0-2??
Also do you know how can i use 'p' or 'w1'...'w3' to get the scores of individual words spoken??

Hi @dohuuphu i am also testing the infer your own voice example, and i am getting the similar results like this.?? Also do you know how can i use 'w3' (i.e total for word score) to get the scores of individual words spoken?? because whenever i print it it gives me 50 lines data which i am confused how to convert to word level???

Please check: https://github.com/YuanGongND/gopt/issues/16#issuecomment-1478085343

I think there are some problems with Kaldi when trying to generate GOP features. Because I generated feature for speechocean762 dataset and took it to training. All scores at validation phase is nan. If I use public features (dropbox link) then train normally.
Strange that I used public Kaldi ASR model and there is no error when creating feature. So I don't understand why my features are different from theirs

I think there are some problems with Kaldi when trying to generate GOP features. Because I generated feature for speechocean762 dataset and took it to training. All scores at validation phase is nan. If I use public features (dropbox link) then train normally. Strange that I used public Kaldi ASR model and there is no error when creating feature. So I don't understand why my features are different from theirs

Hi @dohuuphu,
You use the same ASR model with us. I assume the GOP feature should be similar at least in statistics - so, could you check the mean/varience/median of your feature and ours? In an extreme case, if your features are all nan, then the output will be nan.
Also - this work was done around September 2021, there might be some change for the Kaldi recipe itself, I think we used this commit - https://github.com/kaldi-asr/kaldi/tree/d6198906fbb0e3cfa5fae313c7126a78d8321801/egs/gop_speechocean762.
-Yuan

Hi all, I tried to read gop.ark after I ran the gop_speechocean762 recipe on a single file. The gop it is giving is fine i.e. it can clearly tell pronunciation missed words.
Same feats are being brought to gopt but it gives same output for every wav. I can use gop_speechocean gop output for pronunciation but to get other features like fluency or completeness I was taking a look at GOPT.

@amandeepbaberwal Thanks for sharing this. You are very correct in that GOP feature itself contains mispronounciation performance. However, in our paper, Table I, column 2&3, we compared the phone-level MSE and CORR between GOP w/ basic classifier and GOPT. GOPT is noticably better.
-Yuan

@amandeepbaberwal And also please be aware of the completeness score almost gets 0 correlation (very bad) due to many reasons. Please see the paper for details.

@YuanGongND Hi can you tell me what #15 is talking about. I am very new to kaldi and GOPT, it is surely a complex tool, Can you explain how did he solve this problem. I don't understand when he talks about

In gen_seq_data_phn.py, tr_label_phn or te_label_phn is generated by the phn_dict that is specific to the dataset that we want to use. However, the pretrain model is based on SpeechOcean762. When trying to inference any other dataset, the model will receive these labels specific to the inference dataset not the SpeechOcean dataset, causing inconsistent inference results.

also what he meant when he said

The correct method is to always generate the phn_dict from the one generated when training the SpeechOcean762.
I will update the inference tutorial if you think it is necessary.

This means you need to use same phn_dict as the pretrained model for single wav inference.
For example:
/a/ in our model training maps to 3
But when you pass a single wav to Kaldi, it will map /a/ => another value, e.g., 5. Then the trained GOPT model will get confused. The result will be totally wrong.
I apologize, but the main purpose of this repo is research - we want to help other people to reproduce our result. For engineering problems, especially not mentioned in the paper, we can not provide much support. So I cannot provide further explaination on this.
-Yuan

phn_dict means text-phone file in dataset?

phn_dict means text-phone file in dataset?

I think he mentioned the "phn_dict" variable at gen_seq_data_phn.py

Hi @dohuuphu i am also testing the infer your own voice example, and i am getting the similar results like this.?? Also do you know how can i use 'w3' (i.e total for word score) to get the scores of individual words spoken?? because whenever i print it it gives me 50 lines data which i am confused how to convert to word level???

Have you fixed this, i am also testing the same wav file the the result is similar, still return high score even though it's a bad one

Hi, I am very impressed with the results you have achieved in this project. So I want to reproduce it but have some problem with the result.
I followed the instructions of "[Tutorial] Infer your own data" to get GOPT features from Kaldi and then Inferent with pretrained weight. I tried on many experiments with my own voice and voices of speechocean762 but the results are almost the same (Ex: u1:accuracy always from 1.7 -1.8 (tensor), ...). I also test with incorrect item (voice different with transcript) and the voice with the bad score in speechocean762. But the results are the same as I described above. Hope you can give me some advice.
And one more thing that I do tensor*5 to get the score on ten scale cause tensor_value in range[0-2]. Is it the right way??
Ex: speaker 9604 of speechocean762, this voice have "accuracy": 3 in scores.json -> bad voice but the result is high:

text-phone:
096040025.0	DH_B EH0_I R_E
096040025.1	W_B AH0_I Z_E
096040025.2	N_B AH1_I TH_I IH0_I NG_E
096040025.3	T_B UW0_E
096040025.4	B_B IY0_E
096040025.5	G_B EY0_I N_I D_E
096040025.6	B_B AY0_E
096040025.7	IH0_B T_E
spk2age       9604	21
spk2gender  9604	f
spk2utt         9604 096040025
text               096040025	THERE WAS NOTHING TO BE GAINED BY IT
utt2spk         096040025 9604
wav              096040025	WAVE/SPEAKER9604/096040025.WAV

=> GOPT features (zip file): feature.zip => Result: u1 = tensor([1.7430]) u2 = tensor([1.5640]) ...

Sao mình infer cả tập data ocean thì kết quả của u1,u2,u3,u4,u5 lúc nào cũng cao vút nhỉ, trong khi trong tập data có cả các mẫu chất luợng kém nữa. Mình dùng cả dữ liệu output của nhóm này nhưng kết quả vẫn vậy, hay là code inference mẫu có vấn đề nhỉ? Không biết bạn fix đuợc lỗi này chưa

Now I'm temporarily not doing this task so I haven't fixed it yet.
But as I remember, the problem is come from GOPT feature generation process of Kaldi especially lable_phn.
I calculated mean of 2 generated feature, one is given by the repo (right) and the rest is mine by using Kaldi (left).
As you can see, there are some different that highlighted and that is the reason I can't reproduce the result of repo.
I think you can debug on Kaldi process to find out what makes it different

Now I'm temporarily not doing this task so I haven't fixed it yet. But as I remember, the problem is come from GOPT feature generation process of Kaldi especially lable_phn. I calculated mean of 2 generated feature, one is given by the repo (public) and the rest is mine by using Kaldi (own). As you can see, there are some different that highlighted and that is the reason I can't reproduce the result of repo.
I think you can debug on Kaldi process to find out what makes it different

But I tried to infer speechocean dataset with this repo’s output and code from inference instruction but the result I got is weird. Values of u1,u2,u3,u4,u5 are always high which is unexpected because there are multiple “bad” examples in the speechocean dataset.

I didn't see any problem with this code If I use the given data. So I have no idea for your case. Sorry about that

I didn't see any problem with this code If I use the given data. So I have no idea for your case. Sorry about that

Thanks for responding. Which python version did you use? And do you install the exact same module versions from requirements.txt?

I used python 3.8.16 and the same lib in requirements

I found that inference code missing norm part for features. So the result is totally error.
Need add norm part like valid code so it can work well.
need add
def norm_valid(feat, norm_mean=3.203, norm_std=4.045):     norm_feat = torch.zeros_like(feat)     for i in range(feat.shape[0]):         for j in range(feat.shape[1]):             if feat[i, j, 0] != 0:                 norm_feat[i, j, :] = (feat[i, j, :] - norm_mean) / norm_std             else:                 break     return norm_feat
andt_input_norm_feat = norm_valid(t_input_feat)before input to model

I found that inference code missing norm part for features. So the result is totally error. Need add norm part like valid code so it can work well.
need adddef norm_valid(feat, norm_mean=3.203, norm_std=4.045):  like
https://github.com/YuanGongND/gopt/blob/bed909daf8eca035095871e51642525acc5b9b55/src/traintest.py#L351
andt_input_norm_feat = norm_valid(t_input_feat)before input to model

Have you addressed it?


IOS - Search - App is crashed when taping something on search results

If you haven’t already, check out our contributing guidelines for onboarding and email<EMAIL_ADDRESS>to request to join our Slack channel!

Action Performed:

Launch the app
Create a new gmail account
Tap on magnify glass
Search for user

Expected Result:
App is not crashed
Actual Result:
App is crashed when taping something on search results
Workaround:
Unknown
Platforms:

Which of our officially supported platforms is this issue occurring on?

[ ] Android / native
[ ] Android / Chrome
[x] iOS / native
[ ] iOS / Safari
[ ] MacOS / Chrome / Safari
[ ] MacOS / Desktop

Version Number: <IP_ADDRESS>
Reproducible in staging?: Yes
Reproducible in production?: No
If this was caught during regression testing, add the test name, ID and link from TestRail:
Email or phone of affected tester (no customers):
Logs: https://stackoverflow.com/c/expensify/questions/4856
Notes/Photos/Videos: Any additional supporting documentation
https://github.com/Expensify/App/assets/93399543/a961a18d-3a2b-4f10-a4f2-ed928764ec59
https://github.com/Expensify/App/assets/93399543/c6050cfb-cfe9-4576-b0e4-68ee760d6650
Expensify/Expensify Issue URL:
Issue reported by: Applause - Internal Team/ @Puneet-here
Slack conversation: https://expensify.slack.com/archives/C049HHMV9SM/p1686988343621529
View all open jobs on GitHub

:wave: Friendly reminder that deploy blockers are time-sensitive ⏱ issues! Check out the open StagingDeployCash deploy checklist to see the list of PRs included in this release, then work quickly to do one of the following:

Identify the pull request that introduced this issue and revert it.
Find someone who can quickly fix the issue.
Fix the issue yourself.

I believe this was related to the missing personalDetails onyx data. Also I'm no longer able to reproduce this:
https://github.com/Expensify/App/assets/10736861/1136c1f9-c845-47aa-b04f-b90a3e7e7947


util: fix install.sh mn_deps step on Debian 11

For python 2, the ubuntu 20 package is still called pep8

@lantz is there any other change needed before this can be merged?

Replaced by #1118.


Frequent Coredumps in NGINX

Describe your environment
Ubuntu 18.04
NGINX 1.21.6 (built using a modified version of https://launchpad.net/~ondrej/+archive/ubuntu/nginx-mainline -- NGINX backported to Ubuntu 18)
GRPC Version v1.36.4
OpenTelemetry-cpp 1.4.0
OpenTelemetry-cpp-contrib main
See https://gist.github.com/dgolombek/0800c77d4a4dd17369fba13ae49d2cd2 for the Dockerfile used to build nginx plugin shared library (taken from NGINX's build scripts, but customized slightly).
Steps to reproduce
We have OpenTelemetry enabled on one route:
opentelemetry on;
opentelemetry_config /etc/proxy/otel-nginx.toml;
opentelemetry_propagate b3;
opentelemetry_operation_name upstream1;
access_log /var/log/nginx/upstream_logging.log upstream_b3_logging;

What is the expected behavior?
OpenTelemetry headers are added and traces are emitted to collector.
What is the actual behavior?
After a variable number of requests, NGINX coredumps. I haven't isolated exact traffic that causes the crash, this happens in a low volume environment (1 request/second) but with a lot of different callers.
# gdb /usr/sbin/nginx CoreDump
GNU gdb (Ubuntu 8.1.1-0ubuntu1) 8.1.1
This GDB was configured as "x86_64-linux-gnu".
Reading symbols from /usr/sbin/nginx...(no debugging symbols found)...done.

Core was generated by `nginx: worker process                           '.
Program terminated with signal SIGSEGV, Segmentation fault.
#0  0x00007f08b9a35530 in std::_Hashtable<ngx_http_request_s*, std::pair<ngx_http_request_s* const, TraceContext*>, std::allocator<std::pair<ngx_http_request_s* const, TraceContext*> >, std::__detail::_Select1st, std::equal_to<ngx_http_request_s*>, std::hash<ngx_http_request_s*>, std::__detail::_Mod_range_hashing, std::__detail::_Default_ranged_hash, std::__detail::_Prime_rehash_policy, std::__detail::_Hashtable_traits<false, false, true> >::_M_bucket_index (this=0x0, __k=@0x7ffd2b9ba058: 0x555f852e7da0, __c=93868744670624) at /usr/include/c++/7/bits/hashtable.h:631
631	      { return __hash_code_base::_M_bucket_index(__k, __c, _M_bucket_count); }
[Current thread is 1 (Thread 0x7f08c40f3740 (LWP 28612))]
(gdb) bt
#0  0x00007f08b9a35530 in std::_Hashtable<ngx_http_request_s*, std::pair<ngx_http_request_s* const, TraceContext*>, std::allocator<std::pair<ngx_http_request_s* const, TraceContext*> >, std::__detail::_Select1st, std::equal_to<ngx_http_request_s*>, std::hash<ngx_http_request_s*>, std::__detail::_Mod_range_hashing, std::__detail::_Default_ranged_hash, std::__detail::_Prime_rehash_policy, std::__detail::_Hashtable_traits<false, false, true> >::_M_bucket_index (this=0x0, __k=@0x7ffd2b9ba058: 0x555f852e7da0, __c=93868744670624) at /usr/include/c++/7/bits/hashtable.h:631
#1  0x00007f08b9a2cd57 in std::_Hashtable<ngx_http_request_s*, std::pair<ngx_http_request_s* const, TraceContext*>, std::allocator<std::pair<ngx_http_request_s* const, TraceContext*> >, std::__detail::_Select1st, std::equal_to<ngx_http_request_s*>, std::hash<ngx_http_request_s*>, std::__detail::_Mod_range_hashing, std::__detail::_Default_ranged_hash, std::__detail::_Prime_rehash_policy, std::__detail::_Hashtable_traits<false, false, true> >::find (this=0x0, __k=@0x7ffd2b9ba058: 0x555f852e7da0) at /usr/include/c++/7/bits/hashtable.h:1424
#2  0x00007f08b9a2620f in std::unordered_map<ngx_http_request_s*, TraceContext*, std::hash<ngx_http_request_s*>, std::equal_to<ngx_http_request_s*>, std::allocator<std::pair<ngx_http_request_s* const, TraceContext*> > >::find (this=0x0, __x=@0x7ffd2b9ba058: 0x555f852e7da0) at /usr/include/c++/7/bits/unordered_map.h:920
#3  0x00007f08b9a18769 in GetTraceContext (req=0x555f852e7da0) at /build/opentelemetry-cpp-contrib/instrumentation/nginx/src/otel_ngx_module.cpp:198
#4  0x00007f08b9a19ebe in FinishNgxSpan (req=0x555f852e7da0) at /build/opentelemetry-cpp-contrib/instrumentation/nginx/src/otel_ngx_module.cpp:525
#5  0x0000555f82c905e5 in ?? ()
#6  0x0000555f82c92404 in ngx_http_free_request ()
#7  0x0000555f82c936ef in ?? ()
#8  0x0000555f82c8db34 in ngx_http_core_find_config_phase ()
#9  0x0000555f82c886ed in ngx_http_core_run_phases ()
#10 0x0000555f82c94d2e in ngx_http_process_request ()
#11 0x0000555f82c951d8 in ?? ()
#12 0x0000555f82c957dc in ?? ()
#13 0x0000555f82c7820f in ?? ()
#14 0x0000555f82c6c906 in ngx_process_events_and_timers ()
#15 0x0000555f82c75f15 in ?? ()
#16 0x0000555f82c74741 in ngx_spawn_process ()
#17 0x0000555f82c76204 in ?? ()
#18 0x0000555f82c76f9e in ngx_master_process_cycle ()
#19 0x0000555f82c49569 in main ()

I've seen other backtraces too though, which suggests a use-after-free issue. When I saw this previously (with a Release build), teh second to last stack frame was
0x00007f2fe24df076 in opentelemetry::v1::exporter::otlp::OtlpGrpcExporter::Export(opentelemetry::v1::nostd::span<std::unique_ptr<opentelemetry::v1::sdk::trace::Recordable, std::default_delete<opentelemetry::v1::sdk::trace::Recordable> >,<PHONE_NUMBER>3709551615ul> const&) () from /usr/share/nginx/modules/otel_ngx_module.so
Unfortunately I lost some of the other stack traces I saw (built with RelWithDbgInfo, which still was missing a lot of frames). My NGINX is built with --with-debug so I'm not sure why all the NGINX frames don't have symbols -- possibly the build is missing -g in it.
This appears every time I enabled opentelemetry in NGINX, but it varies in how long it takes. Once it was 10 seconds, another time it was 30 minutes.  I can't leave it on for long, since it causes requests to be dropped (sometimes the same one repeatedly), breaking other testing.
Additional context
I can try to run nginx with error logs at debug level, but the logs get really big really fast and sometimes it takes 15-30 minutes for a coredump to occur.

Are you using internal requests? From my testing that is still broken. See https://github.com/open-telemetry/opentelemetry-cpp-contrib/issues/105#issuecomment-1126002534.


Installation instructions?

How do you actually apply this for use with sigrok/pulseview?

It looks like you have to install pulseview, install libsigrokdecode to your system (presumably to /usr/local/...), then copy the sigrok-rvswd folder to /usr/local/share/libsigrokdecode/decoders

I honestly don't remember, and I can't find any resources on it quickly
What you wrote sounds about write, there is probably a way to do this locally, but I just don't remember, sorry :disappointed:
When correctly installed it should just appear on pulseview like every other decoder

Ah, I found it... see Protocol_decoder_HOWTO
Somewhere in there you have:

You don't need to reinstall the whole libsigrokdecode project every time you make a change on your decoder. Instead, you can use the environment variable SIGROKDECODE_DIR to point the software to your development directory:

$ SIGROKDECODE_DIR=/path/to/libsigrokdecode/decoders/ sigrok-cli … -P

I definitely don't want to have to worry about setting it.  Setting env vars to run a regular app the normal way is not a palatable way to live.
I was able to install it once, and then it defaults to /usr/local/share/libsigrokdecode/decoders.

Well, of course, I mentioned this because it is likely someone might want to tinker with this decoder, and modifying it locally makes sense then.


Add BindTarget to Bindable for use in object initializers

Just allowing the following syntactic sugar, which will tidy up "glue" classes (like Player in osu!) which are responsible for connecting various children together.
before:
var component = new SomeComponent()
{
    Colour = Color4.Rainbows
};

Add(component);

component.OurBindable.BindTo(config.Target);

after:
Add(new SomeComponent
{
    Colour = Color4.Rainbows,
	OurBindable = { BindTarget = config.Target }
});

Example usage for critique. Not sure if more or less readable in extreme nesting cases:


Syntax colouring error on of when out of a for ... of statement

From @fguitton on June 10, 2018 11:30

VSCode Version: 1.24.0
OS Version: Windows 10

Hello everybody !
The following issue is also present on a vanilla fresh install of VSCode Insider.
While using something like the RxJS library which exports an of function, the syntax colouring misses on it. I imagine is believes it part of a for … of structure.

Best day to you all 👍
Copied from original issue: Microsoft/vscode#51544

Example code:
of(1);
af(1);


Enable time offset

Hello,
We are currently using several Nanoscan3 safety lidars on AGVs on ROS2 Galactic.
To make the lidar time stamp fit the robot motion, I implemented the time offset of the stamp.
It was a TODO, I just migrated the implementation from ROS1 to ROS2.

Hi thanks for the contribution.
It will take me a couple of days until I can test it, but looks good to me. Will be merged after testing.


Ansible deployment issues

Referring #2
Ansible version 2.2.0 gives an error when deploying the ote.yml playbook:
failed: [localhost] (item=[u'packages']) => {"failed": true, "item": ["packages"], "msg": "No package matching 'packages' is available"}

Fixed with PR #3


⚠ Bofrost.be has degraded performance

In 05cf201, Bofrost.be (https://www.bofrost.be) experienced degraded performance:

HTTP code: 200
Response time: 1325 ms

Resolved: Bofrost.be performance has improved in 7c14f71.


Faster metrics calculations + Fix warnings added by the new version of torchmetrics

What does this PR do?
Newer version of torchmetrics can be faster if we update our custom metrics.
What issue(s) does this change relate to?
Composer generating extra warning
TEST:
No warning with this change.
import torch
from torchmetrics.utilities import check_forward_full_state_property

from composer.metrics import MaskedAccuracy

batch_size = 16
num_classes = 10
generated_preds = torch.rand((batch_size, num_classes))
true_labels = torch.randint(low=0, high=num_classes - 1, size=(batch_size,))

m = MaskedAccuracy(ignore_index=0)
m.update(generated_preds.float(), true_labels.float())

import torch
from torchmetrics.utilities import check_forward_full_state_property

from composer.metrics import MaskedAccuracy

batch_size = 16
num_classes = 10
generated_preds = torch.rand((batch_size, num_classes))
true_labels = torch.randint(low=0, high=num_classes - 1, size=(batch_size,))

check_forward_full_state_property(
    MaskedAccuracy,
    init_args={"ignore_index": 0},
    input_args={"preds": generated_preds.float(), "target": true_labels.float()},
)

@mvpatel2000 Removed the link.


How to parse output video as a stream instead of saving it as a .avi file?

Hey!,
Till now I was able to connect to an IP camera and get live feed input, which saves it as an .avi file.
This saved file is not efficient enough. Is there a way to get a output video as a stream like the input stream?

Anybody with the same question please, perhaps with a solution? :)

You can use the sample code linked below to display the detected video stream.
https://gist.github.com/OlafenwaMoses/a943de4a9c4125d9b2580061b726a6fa
Also study extensively the sample codes provided in the documentation linked below.
https://imageai.readthedocs.io/en/latest/video/index.html


cross-compile toolchain variant doesn't working properly with multilib toolchain

Describe the bug
In case of ZEPHYR_TOOLCHAIN_VARIANT=cross-compile we don't set SYSROOT_DIR cmake variable, so we don't execute
block of code which sets LIBC_LIBRARY_DIR variable based on the toolchain C flags (including mcpu flag):
https://github.com/zephyrproject-rtos/zephyr/blob/88aa873fbc544b99c1e48e37d5c74662f90b3de0/cmake/compiler/gcc/target.cmake#L74-L88
So, in case of multilib cross-compile toolchain we always use default libraries set no mater which mcpu value we've provided. Obviously it's incorrect.
Impact
Implicit & unexpected usage of libraries which are build for different mcpu than was specified.
If we are lucky we may get compile error - for example if we got something not linkable, like 64bit libraries from toolchain and 32 bit object files from rest of zephyr. However we may get successful linkage and use some not optimal libraries (i.e built for processor which use smaller instruction set) or get runtime issues.
possible solutions

add additional environment variable for cross-compile toolchain variant, which will be pointing to correct SYSROOT_DIR path.
try to extract SYSROOT_DIR path from the CROSS_COMPILE variable. I've checked the value of CROSS_COMPILE I've previously used - it doesn't seems to be feasible for these toolchains I.e:

CROSS_COMPILE=/home/user/dev/arc64-unknown-elf-1205/arc64-unknown-elf/bin/arc64-snps-elf-
SYSROOT_DIR should be /home/user/dev/arc64-unknown-elf-1205/arc64-unknown-elf/arc64-unknown-elf/

CROSS_COMPILE=/global/tools/Linux/arc_gnu_2021.09_prebuilt_elf32_le/bin/arc-elf32-
SYSROOT_DIR should be /global/tools/Linux/arc_gnu_2021.09_prebuilt_elf32_le/arc-snps-elf/

BTW, gcc has option with promising name - -print-sysroot - Display the target libraries directory.. However it always doesn't print anything for all toolchains I've tried (no matter ARC ones or host toolchains, like X86_64)

add additional environment variable for cross-compile toolchain variant, which will be pointing to correct SYSROOT_DIR path.

@evgeniy-paltsev it's already possible today to use -DSYSROOT_DIR=<sysroot-dir>

@evgeniy-paltsev FYI, https://github.com/zephyrproject-rtos/zephyr/pull/50584#pullrequestreview-1118344285. There likely is a configuration issue with your toolchain build.


CIP-5 compliance

1c061c5f86123111f2d4d95a1563bf6defcfaafb
:round_pushpin: expose all CIP-5 compliant prefixes in a dedicated module.

6b28bf142f484cf8af00cb4e10315f0d6f7312aa
:round_pushpin: remove encoding choice for all key commands and force bech32 with adequate prefixes.
It is still very easy to use raw hexadecimal binaries by using an
intermediate tool like bech32 to convert from hex to bech32. Yet,
the human-readable part are crucial in making the implementation work
well with multiple derivation levels, they also provide slightly
stronger guarantee and a safer API.
BREAKING-CHANGE: no more --encoding options available on any of the
key sub-commands. Commands now expect only bech32
inputs with specific prefixes (listed in case of
error).
BREAKING-CHANGE: no more --legacy option available on the key                  child command. The derivation scheme is now inferred
from the path and the key type.
BREAKING-CHANGE: key child can now only derive from root to account,
root to address or account to address instead of any
arbitrary path.

48483f6a188d0bca6621d8db831f892d820f7929
:round_pushpin: remove encoding choice for all address commands and force bech32 with adequate prefixes.
BREAKING-CHANGE: commands no longer accept arbitrary encoding for keys
but require that all keys are bech32 encoded and have
matching prefixes.
BREAKING-CHANGE: credentials are now inferred from their bech32
prefixes (instead of --from-key or --from-script
options).

I just noticed I forgot to remove the Encoding option on the script hash command too. Also, the script parser there seems to still expect base16 strings, which is not right.


uploading file consumes a lot of memory (20 Mb file -> 2 to 3 GB memory)

Empire 1.4, 1.5 and dev
Client OS : Windows 7 and 10
Hi,
I've found a strange behavior but i did not found any bug in the code explaining that.
Maybe it's a bad usage of PowerShell logic or PowerShell is not a good language to process binary data ;-).
TL;DR : uploading files bigger than 25MB consumes so much memory that it do not work on "normal" computer with Windows 7 and 10.
The problem appears in agent.ps1, when uploading a file, it consumes a lot of RAM and finally explodes. More precisely, it happens at several places:

at the end of the function Get-Task, during the $result return
during UTF8.GetString($data)
during the call of Decrypt-Bytes in Process-Tasking
during the call of Decode-Packet in Process-Tasking

For a 10MB file, the average memory consumption is :

$result => 700 MB
UTF8.GetString($data) => 700 MB
Decrypt-Bytes => 2,5 GB
Decode-Packet => 2,1 GB

For a 20 MB file, the average memory consumption is :

$result => 1,3 GB
UTF8.GetString($data) => 1,3 GB
Decrypt-Bytes => 3,1 GB
Decode-Packet => 2,4 GB

For files bigger than 25MB on a computer (Windows 7 and 10) with 4GB of RAM, it explodes.
Does anybody have the same behavior ?
Any idea about the reason and how to solve that ?

@rvrsh3ll Will start trying to debug this issue. Thanks for the report, I will respond back with my findings!

Confirmed, 12Mb upload spiked to 1GB of RAM usage.

@mynameisv Still working this, I have free time today so Im going to dive into the agent side. Will report back soon

@killswitch-GUI  nice :-).
But don't you think it's a bug in powershell or a missuse of data type ?
$wc.DownloadData() returns an Array that's not a flexible data type ;-).

So @killswitch-GUI and I have looked into the issue, it doesn't appear there is a concrete fix for this problem. Currently, all of the data in a file upload is passed to the agent in one exchange with the server. Uploads cannot be chunked like download jobs as there is no guarantee in the order of the data. Thus you could have issues with corrupted files. Going forward, the best solution would be to impose a file upload limit between 2-5 mb. I will do some testing and see what works best.

Added a 1MB file upload limit.

What is the best alternative to upload large size file? One way is to split the zip file into multiple files and pack them back on the server side. Any other ways?


Bump

@duncanwp hopefully this one will work as expected.

Hi! This is the friendly automated conda-forge-linting service.
I just wanted to let you know that I linted all conda-recipes in your PR (recipe) and found it was in an excellent condition.

Yep. No double CIs! Sorry for the confusion in #11 not sure why that happened. I usually do a fork -> branch -> PR from CLI. Did you do something different?

Great, thanks.
Since I had a branch on here, I did a fork, fetch, checkout and push. I didn't think it should make a difference. Perhaps it was your rerender that did it?

Perhaps it was your rerender that did it?

Probably not as the re-render should not affect how github behaves. It is only another "change" to commit.

Since I had a branch on here, I did a fork, fetch, checkout and push.

you can try git remote -v and check if the fork origin and upstream are correct.
Feel free to merge this when the CIs passes. Anything you need ping me.


Lint rules for Camunda Cloud 1.0, 1.1, 1.2, and 1.3 exist

Rules for Camunda Cloud versions are defined and implemented based on

docs (cf. 1.0, 1.1, 1.2, 1.3)
Zeebe test diagrams (cf. https://github.com/camunda-cloud/zeebe/tree/develop/bpmn-model/src/test/resources/io/camunda/zeebe/model/bpmn)

Rules are implemented as bpmnlint plugin: https://github.com/camunda/bpmnlint-plugin-camunda-compat

Exists, and integrated via https://github.com/camunda/camunda-modeler/pull/2610/commits/8633666276635e116988e44fcabcaeb059da1bdc.

Provided as a re-usable building block (https://github.com/camunda/bpmnlint-plugin-camunda-compat).


Update config location in README

Fix to issue #356

Thank you for submitting this pull request, however I do not see a valid CLA on file for you. Before we can merge this request please visit https://yahoocla.herokuapp.com/ and agree to the terms. Thanks! :smile:

Okay, agreed to the Yahoo CLA.

Thanks!


BUG when trainning with multiple ps-server

I post a issue previous https://github.com/tensorflow/tensorflow/issues/6326
The original problem is solved by @yaroslavvb  with add sharded=True to Saver's init .
BUT , One more problem occurs:

when I training with 5 workers and m (m=1) ps-server, then training process works well.
BUT when training with 5 workers and m (m>1) ps-servers, then training process crash with error NotFoundError (see above for traceback): ./supervisor/model.ckpt-0_temp_994ae96906954e838fc2f3481ce8f296/part-00001-of-00002.data-00000-of-00001

It seems that when have multiple ps-servers , the checkpoint-save has some weird bugs.
The detail error:
Traceback (most recent call last):
  File "distributed_deepcake.py", line 441, in <module>
    exit(1)
  File "/home/serving/anaconda2/lib/python2.7/contextlib.py", line 24, in __exit__
    self.gen.next()
  File "/home/serving/anaconda2/lib/python2.7/site-packages/tensorflow/python/training/supervisor.py", line 974, in managed_session
    self.stop(close_summary_writer=close_summary_writer)
  File "/home/serving/anaconda2/lib/python2.7/site-packages/tensorflow/python/training/supervisor.py", line 802, in stop
    stop_grace_period_secs=self._stop_grace_secs)
  File "/home/serving/anaconda2/lib/python2.7/site-packages/tensorflow/python/training/coordinator.py", line 386, in join
    six.reraise(*self._exc_info_to_raise)
  File "/home/serving/anaconda2/lib/python2.7/site-packages/tensorflow/python/training/coordinator.py", line 296, in stop_on_exception
    yield
  File "/home/serving/anaconda2/lib/python2.7/site-packages/tensorflow/python/training/coordinator.py", line 487, in run
    self.run_loop()
  File "/home/serving/anaconda2/lib/python2.7/site-packages/tensorflow/python/training/supervisor.py", line 1069, in run_loop
    global_step=self._sv.global_step)
  File "/home/serving/anaconda2/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 1323, in save
    {self.saver_def.filename_tensor_name: checkpoint_file})
  File "/home/serving/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 766, in run
    run_metadata_ptr)
  File "/home/serving/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 964, in _run
    feed_dict_string, options, run_metadata)
  File "/home/serving/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1014, in _do_run
    target_list, options, run_metadata)
  File "/home/serving/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1034, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.NotFoundError: ./supervisor/distributed_img0_uid_extra-ftrl-lr0.01-fs30000000-b1000-u<IP_ADDRESS>/model.ckpt-0_temp_cde50a9b175b4ca596e757677ce5d86c/part-00004-of-00006.data-00000-of-00001
         [[Node: save/SaveV2_4 = SaveV2[dtypes=[DT_INT32], _device="/job:ps/replica:0/task:2/cpu:0"](save/ShardedFilename_4, save/SaveV2_4/tensor_names, save/SaveV2_4/shape_and_slices, global_step)]]
         [[Node: save/Identity_S197 = _Recv[client_terminated=false, recv_device="/job:worker/replica:0/task:0/cpu:0", send_device="/job:ps/replica:0/task:2/cpu:0", send_device_incarnation=-6527834651342755590, tensor_name="edge_107_save/Identity", tensor_type=DT_STRING, _device="/job:worker/replica:0/task:0/cpu:0"]()]]

Caused by op u'save/SaveV2_4', defined at:
  File "distributed_deepcake.py", line 293, in <module>
    saver = tf.train.Saver(max_to_keep = 2, sharded=True)
  File "/home/serving/anaconda2/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 1000, in __init__
    self.build()
  File "/home/serving/anaconda2/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 1030, in build
    restore_sequentially=self._restore_sequentially)
  File "/home/serving/anaconda2/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 618, in build
    save_tensor = self._AddShardedSaveOps(filename_tensor, per_device)
  File "/home/serving/anaconda2/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 314, in _AddShardedSaveOps
    return self._AddShardedSaveOpsForV2(filename_tensor, per_device)
  File "/home/serving/anaconda2/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 288, in _AddShardedSaveOpsForV2
    sharded_saves.append(self._AddSaveOps(sharded_filename, saveables))
  File "/home/serving/anaconda2/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 229, in _AddSaveOps
    save = self.save_op(filename_tensor, saveables)
  File "/home/serving/anaconda2/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 172, in save_op
    tensors)
  File "/home/serving/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/gen_io_ops.py", line 552, in save_v2
    tensors=tensors, name=name)
  File "/home/serving/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 759, in apply_op
    op_def=op_def)
  File "/home/serving/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2240, in create_op
    original_op=self._default_original_op, op_def=op_def)
  File "/home/serving/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1128, in __init__
    self._traceback = _extract_stack()

NotFoundError (see above for traceback): ./supervisor/distributed_img0_uid_extra-ftrl-lr0.01-fs30000000-b1000-u<IP_ADDRESS>/model.ckpt-0_temp_cde50a9b175b4ca596e757677ce5d86c/part-00004-of-00006.data-00000-of-00001
         [[Node: save/SaveV2_4 = SaveV2[dtypes=[DT_INT32], _device="/job:ps/replica:0/task:2/cpu:0"](save/ShardedFilename_4, save/SaveV2_4/tensor_names, save/SaveV2_4/shape_and_slices, global_step)]]
         [[Node: save/Identity_S197 = _Recv[client_terminated=false, recv_device="/job:worker/replica:0/task:0/cpu:0", send_device="/job:ps/replica:0/task:2/cpu:0", send_device_incarnation=-6527834651342755590, tensor_name="edge_107_save/Identity", tensor_type=DT_STRING, _device="/job:worker/replica:0/task:0/cpu:0"]()]]

What related GitHub issues or StackOverflow threads have you found by searching the web for your problem?
Only find one issue and it's myself . https://github.com/tensorflow/tensorflow/issues/6326
Environment info
Operating System:
centos 6.5 ,glibc 2.17, gcc6.2, Python 2.7.12, tensorflow newest version 0.12rc1 with cpu
If possible, provide a minimal reproducible example (We usually don't have time to read hundreds of lines of your code)
with tf.device(tf.train.replica_device_setter(
        worker_device="/job:worker/task:%d" % FLAGS.task_index,
        cluster=cluster)):
    # Read TFRecords files for training
    filename_queue = tf.train.string_input_producer(
        tf.train.match_filenames_once(FLAGS.train),
        num_epochs=epoch_number)
    serialized_example = read_and_decode(filename_queue)
    batch_serialized_example = tf.train.shuffle_batch(
        [serialized_example],
        batch_size=batch_size,
        num_threads=thread_number,
        capacity=capacity,
        min_after_dequeue=min_after_dequeue)
    features = tf.parse_example(
        batch_serialized_example,
        features={
            "label": tf.FixedLenFeature([], tf.float32),
            "ids": tf.VarLenFeature(tf.int64),
            "values": tf.VarLenFeature(tf.float32),
        })
    batch_labels = features["label"]
    batch_ids = features["ids"]
    batch_values = features["values"]

    # Read TFRecords file for validatioin
    validate_filename_queue = tf.train.string_input_producer(
        tf.train.match_filenames_once(FLAGS.eval),
        num_epochs=epoch_number)
    validate_serialized_example = read_and_decode(validate_filename_queue)
    validate_batch_serialized_example = tf.train.shuffle_batch(
        [validate_serialized_example],
        batch_size=validate_batch_size,
        num_threads=thread_number,
        capacity=capacity,
        min_after_dequeue=min_after_dequeue)
    validate_features = tf.parse_example(
        validate_batch_serialized_example,
        features={
            "label": tf.FixedLenFeature([], tf.float32),
            "ids": tf.VarLenFeature(tf.int64),
            "values": tf.VarLenFeature(tf.float32),
        })
    validate_batch_labels = features["label"]
    validate_batch_ids = features["ids"]
    validate_batch_values = features["values"]
    logits = inference(batch_ids, batch_values)
    batch_labels = tf.to_int64(batch_labels)
    cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits,
                                                                   batch_labels)
    loss = tf.reduce_mean(cross_entropy, name='loss')

    print("Use the optimizer: {}".format(FLAGS.optimizer))

    optimizer = tf.train.FtrlOptimizer(learning_rate)

    global_step = tf.Variable(0, name='global_step', trainable=False)
    train_op = optimizer.minimize(loss, global_step=global_step)

    # Initialize saver and summary
    steps_to_validate = FLAGS.steps_to_validate
    init_op = tf.initialize_all_variables()

    saver = tf.train.Saver(max_to_keep = 2)
    keys_placeholder = tf.placeholder("float")
    keys = tf.identity(keys_placeholder)
    tf.add_to_collection("inputs", json.dumps({'key': keys_placeholder.name}))
    tf.add_to_collection("outputs", json.dumps({'key': keys.name,
                                                'softmax': inference_softmax.name,
                                                'prediction': inference_op.name}))

    summary_op = tf.merge_all_summaries()

sv = tf.train.Supervisor(is_chief=(FLAGS.task_index == 0),
                         logdir="./supervisor/",
                         init_op=init_op,
                         summary_op=summary_op,
                         saver=saver,
                         global_step=global_step,
                         save_model_secs=60)

with sv.managed_session(server.target) as sess:

    while not sv.should_stop():
        # Get coordinator and run queues to read data
        coord = tf.train.Coordinator()
        threads = tf.train.start_queue_runners(coord=coord, sess=sess)

        try:
            while not coord.should_stop():
                _, loss_value, step = sess.run([train_op, loss, global_step])

        except tf.errors.OutOfRangeError:
            print("Done training after reading all data")
        finally:
            coord.request_stop()

        coord.join(threads)

this is a bug....when use distributed training,,,,if ps server restart,,,you must copy worker0 checkpoint dir to all ps server Same directory。。。。。then restart ps server

@guoying1030  thanks, but my ps server doesn't restart.  It happens at the begining of training.
My start order is:
1、start ps0
2、start ps1
3、start worker0
4、start worker1
when every server started , it report error like that.

@mrry could you take a look when you get a chance?

I don't think this is a bug, but it might be poor documentation, and/or grounds for a feature request.
It looks like the problem arises because the chief worker and the parameter server do not share the same file system (because the client performs some file operations on the checkpoint data locally, but the checkpoint data is loaded by a Restore op that runs on the parameter server). This has been a requirement since time immemorial, but I don't think it's well documented.
You can work around this problem by using a shared filesystem (e.g. HDFS, GCS, or an NFS mount at the same mount point) on the workers and the parameter servers.

HDFS support is built as part of the standard PIP package, and it supports reading and writing checkpoints (as well as input data, summaries, etc.). You'll need to set the HADOOP_HDFS_HOME environment variable to use it.

thanks, I will try it and give my feedback.

Thank you @mrry for the advice. The conclusion I'm drawing is to use a distributed file system when using distributed TensorFlow. So I'm going to close this one out. If you're still experiencing this issue @ericyue, let me know and I'll re-open.

I'll also add that I will gladly review a pull request updating our documentation to make this more clear to users. We've done a lot of work recently getting GCS to be available for everyone by default in our build, which should hopefully make this very simple to do.

Hit this issue with even only one parameter server with distributed workers. Looks exactly as what @mrry described. Can we update the subject of this issue accordingly?


dev-cmd/bump-formula-pr: Fix fetch_resource_and_forced_version types

[x] Have you followed the guidelines in our Contributing document?
[x] Have you checked to ensure there aren't other open Pull Requests for the same change?
[x] Have you added an explanation of what your changes do and why you'd like us to include them?
[ ] Have you written new tests for your changes? Here's an example.
[ ] Have you successfully run brew style with your changes locally?
[ ] Have you successfully run brew typecheck with your changes locally?
[ ] Have you successfully run brew tests with your changes locally?

This should fix https://github.com/Homebrew/homebrew-core/actions/runs/9839505520, that is:

Parameter 'tag': Expected type Float, got type String with value "2024-07-05"
Caller: /home/linuxbrew/.linuxbrew/Homebrew/Library/Homebrew/vendor/bundle/ruby/3.3.0/gems/sorbet-runtime-0.5.11473/lib/types/private/methods/call_validation.rb:215
Definition: /home/linuxbrew/.linuxbrew/Homebrew/Library/Homebrew/dev-cmd/bump-formula-pr.rb:445 (Homebrew::DevCmd::BumpFormulaPr#fetch_resource_and_forced_version)

I think I read https://sorbet.org/docs/sigs#rest-parameters too literally, yes Sorbet expands the kwargs into a hash type, but it's the value type that needs specifying in the sig (which for us is String).

Thanks @issyl0!


refactor(podman-extension): uses api env.isWindows

What does this PR do?
Replace the utils#isWindows with the api env.isWindows.
Screenshot / video of UI

What issues does this PR fix or reference?
Part 1/3 of https://github.com/podman-desktop/podman-desktop/issues/9617
How to test this PR?

[x] Tests are covering the bug fix or the new feature

This is very weird, on windows src/plugin/filesystem-monitoring.spec.ts:81:28 is failing 🤔 (not on my local machine only CI)

@axel7083 yes the test is flaky (since some days I saw it on Windows only)


Align and test handling of directories in S3_File

All of our file APIs have a create_directory method, except for S3_File.
This is not surprising, because S3 does not really have a concept of directories like most filesystems. Just creating a file with path a/b/c/f.txt makes directories a/, a/b/ and a/b/c/ existent.
The AWS Console is using a 'trick' to be able to create an empty directory. To create a directory a/b/c/ it creates an empty object with key a/b/c/ (with the trailing slash) to indicate the directory exists. We can likely replicate this behaviour as well.
All operations should still work without having to explicitly create the parent directories, like one would need to in regular filesystems. The user should not need to care about these with S3, but at the same time if the user wants to explicitly create an empty directory - that should also be possible with the trick mentioned above.

[ ] Add create_directory method to S3_File.

It should only work if self.is_directory is true. For path a/b/c/ it should only create one key a/b/c/, it should not create additional keys along the path - e.g. a/b/ - these directories will start to exist 'virtually' by the fact of the child key existing.

[ ] Add tests for handling directory logic.

[ ] After explicitly creating a directory with create_directory, it should yield exists == True.
[ ] It still should be possible to write a nested file without explicitly creating all parent directories.
[ ] Such parent directories should also yield exists == True even if not explicitly created.
[ ] Test that both explicitly and implicitly created directories show up in the same way in S3_File.list.
[ ] Test that if the directory was created explicitly, it still exists after all files created inside of it are deleted.
[ ] Test that if the directory was created implicitly, it automatically disappears once all files inside of it are deleted.
[ ] Add tests for delete with recursive flag, consistent with local and Cloud variants.

[ ] delete recursive=False should fail if there are any files or subdirectories inside of a given directory, but should succeed if the directory is empty and exists only because it was explicitly created with create_directory.
[ ] delete recursive=True should always delete the directory entry (if it exists) and any files inside of it.

As reference, a worthwhile discussion of the edge cases around S3 virtual 'directory' can be found here: https://github.com/fsspec/s3fs/issues/401


Default to 0 ballot if no duration set

What does this PR do and why?
People set 0 fc duration, expecting their project to be reconfigurable instantly. Because we default to a 3-day delay, users skim over this. They end up with a 0fc duration but a 3-day delay.
This PR defaults projects to a 0x00 delay (ballot). If they set an FC duration, we'll auto-set a 3-day delay.
Screenshots or screen recordings

Acceptance checklist

[x] I have evaluated the Approval Guidelines for this PR.
[x] I have tested this PR in all supported browsers.
[x] I have tested this PR in dark mode and light mode (if applicable).

Great call, this tripped me up too. LGTM

Great call, this tripped me up too. LGTM

Praise: switching between 0 and 3-day depending on duration is amazing


nixos-rebuild should treat any build/target host as an SSH target

Describe the bug
nixos-rebuild treats the string build/target host "localhost" as special, using SSH to run the build job if the host is anything but this specific value. This is problematic in two ways:

There are quite a few ways to refer to "this host". For example, IPv4 addresses like "<IP_ADDRESS>", "<IP_ADDRESS>" or "<IP_ADDRESS>" (depending on setup), IPv6 addresses like "::1", other local addresses like "localhost.localdomain", and probably more that I'm forgetting.
On the flip side, it is at least conceivable that someone has mapped "localhost" to some other IP. I realise this isn't likely (although I've seen setups where "localhost" is unmapped), but it's a possibility.

Steps To Reproduce
Steps to reproduce the behavior:

Run nixos-rebuild with the --build-host option and a loopback reference other than "localhost"

Expected behavior
Rather than second guess the user nixos-rebuild should use the build and target host values if non-empty, no matter their value, to SSH into the relevant host. This way there's no magic value, and the script ends up simpler because of not having to treat "localhost" as special.
Notify maintainers
@Artturin
Metadata
$ nix-shell -p nix-info --run "nix-info -m"
 - system: `"x86_64-linux"`
 - host os: `Linux 5.10.81, NixOS, 21.11 (Porcupine)`
 - multi-user?: `yes`
 - sandbox: `yes`
 - version: `nix-env (Nix) 2.3.16`
 - channels(root): `"nixos-21.11.334027.e34c5379866, home-manager-21.05"`
 - channels(victor): `""`
 - nixpkgs: `/nix/var/nix/profiles/per-user/root/channels/nixos`

Maintainer information:
# a list of nixpkgs attributes affected by the problem
attribute:
# a list of nixos modules affected by the problem
module:

On the flip side, it is at least conceivable that someone has mapped "localhost" to some other IP.

I ran into this where I had port-forwarding from a VM configured, so localhost:2221 forwarded to port 22 on the VM. Using user@localhost seems to work around for now.


Added onBeforeSave hook

I think was usefull if you want to save all fields and control when to save (with a button, for example).
Saving is triggered automatically on protect(), so if you want to save data manually, you will need to initialize onBeforeSave to return false. Before manual save, onBeforeSave should be set to return true.

Thanks a lot for the contribution! However, I am not sure how I feel about the idea of saving forms' data on button clicks as the original idea was to save it instantly or, at least, periodically, as users may not even get to click "save" button.

I have used your library not with automatic saving, but with manually saving, to hold values in case connection droeed.
It was used for a survey. The values I intended to save were only ready when user hit the submit button. Before that values may not be the final user wanted to save.

Thanks for the contribution, @gabrieljenik!


Move or clone add inputs to top of page

Inputs for adding new record are at bottom of the page when editing zone.
With small zones its ok but if you have hundreds of records in one zone, it isn't comfortable to scroll down every time you want to add something.
Move the element to top is probably not best option. Cloning it so you can add new record from top and also from bottom would be probably better.

Okay yes, i can understand this issue. Moving to the top would indeed be very easy. I try to get it to work duplicated.

Doublicating would be possible with the 2.0 architecture but I do not like the look an layout. But with paging support the add inputs are not that low.


hivemq > improve "temp-extensions" workaround

To support the withoutPrepackagedExtensions methods of the HiveMQContainer, we (HiveMQ) initially built in a workaround that copies extensions into /opt/hivemq/temp-extensions/ instead of /opt/hivemq/extensions/ and modifies the container entrypoint and command to move the extensions into their right place.
We have some upcoming changes to the HiveMQ container images that will remove the need for the withoutPrepackagedExtensions methods in most cases. This change applies the workaround only if any of the withoutPrepackagedExtensions methods are called.
This change also makes it easier to use the withCopyToContainer method with a Transferable argument to copy files into an HiveMQ extension's folder by just specifying /opt/hivemq/extensions/ as containerPath without needing to know about any implementation details. Currently, one is limited to methods like withFileInExtensionHomeFolder that apply the "temp-extensions" workaround internally, but these methods only support MountableFile arguments.

Is there any way we can get merge rights if we just touch the hivemq module?

@eddumelendez Who should we talk to if we want to become code owners/maintainers of the hivemq module?

HiveMQ module is provided in Java and NodeJS

This change tries to remove a workaround that is only present in the Java implementation. I don't see how the NodeJS implementation is affected because it does not have this workaround at all.

copyFileToContainer must not be overridden.

Can you explain me why?


fix shiftVisibleRangeOnNewBar behaviour

Type of PR: bugfix
PR checklist:

[x] Addresses an existing issue: fixes #1443
[x] Includes tests
N/A ~Documentation update~

Overview of change:
shiftVisibleRangeOnNewBar behaviour was broken in the 4.1 release. This PR attempts to fix the broken behaviour while still maintaining the new behaviour that the timescale shouldn't shift when replacing whitespace.

PR which broke the behaviour: https://github.com/tradingview/lightweight-charts/pull/1431
Which was for this issue: https://github.com/tradingview/lightweight-charts/issues/1201

Added allowShiftVisibleRangeOnWhitespaceReplacement option to the time scale options which can be enabled if the old behaviour is required. As an example, our one plugin example works better with this behaviour since we are using whitespace for being able to draw shapes into the future.

The d.ts changes are expected 👍


Istio. request. latency bucket customization

Is this the right place to submit this?

[X] This is not a security vulnerability or a crashing bug
[X] This is not a question about how to use Istio

Bug Description
istio_request_duration_milliseconds_bucket metrics are emitting the le values bucket with some set of finite values
is there any way to customise this bucket values ?
Version
Istio version :istio/proxyv2:1.16.1
Kubernetes version : 1.24

Additional Information
No response
Affected product area

[ ] Ambient
[ ] Docs
[ ] Dual Stack
[ ] Installation
[ ] Networking
[ ] Performance and Scalability
[ ] Extensions and Telemetry
[ ] Security
[ ] Test and Release
[ ] User Experience
[X] Developer Infrastructure
[ ] Upgrade
[ ] Multi Cluster
[ ] Virtual Machine
[ ] Control Plane Revisions

i recall there is a annotation can do this, should be sidecar.istio.io/statsHistogramBuckets


Many mitmweb filter expressions aren't working

Steps to reproduce the problem:

Run mitmweb
In one of the "Start" boxes, start to type each of the filter expressions.   e.g. "~b asdf"

Any other comments? What have you tried so far?
I realize that mitmweb is beta, but the documentation indicates "We consider it stable for all features currently exposed in the UI", so maybe this is an oversight.  Filter expressions that cause the error: "SyntaxError: Expected filter expression but "~" found" include:
~b
~bq
~bs
~dst
~http
~marked
~src
System information
Mitmproxy version: 1.0.1
Python version: 3.5.2
Platform: Linux-3.13.0-100-generic-i686-with-debian-jessie-sid
SSL version: OpenSSL 1.0.2g  1 Mar 2016
Linux distro: debian jessie/sid

Thanks! It seems like these got out of sync, this needs to be fixed.

Adding the missing filters as reference:

[ ] ~b
[ ] ~bq
[ ] ~bs
[ ] ~dst
[ ] ~http
[x] ~marked
[ ] ~src
[ ] ~tcp

Thanks. As mentioned on Slack, let's keep the body filters out for now and do the others first. :)

Gents, so no chance I could get ~bs filter working still?

Unfortunately it's not really trivial as we do not transmit body contents to the front end - see https://github.com/mitmproxy/mitmweb/issues/10.
I'm closing this here as this issue is actually tracked in https://github.com/mitmproxy/mitmweb/issues/10!

I am not sure whether I should open a new issue here, in mitmproxy/mitmweb or do not open it at all (because of the comment above).
Anyway, right now an attempt to use a body filter will navigate browser to another location with an empty page.
To rephrase,

Open some flow
Click on Highlight field, type ~b (you will get an error about an expected filter expression)
Try to type something, e.g. ~b body. You will immediately be redirected to http://<IP_ADDRESS>:8081/#/flows?h=~b%20s which is completely empty [*].
The back button (in browser) does not work,

[*] Well, not really empty but it looks empty. The page source is
<!DOCTYPE html>

<meta charset="UTF-8">
<title>mitmproxy</title>
<meta name="viewport" content="width=device-width, initial-scale=1">
<link rel="stylesheet" href="/static/vendor.css"/>
<link rel="stylesheet" href="/static/app.css"/>
<link rel="icon" href="/static/images/favicon.ico" type="image/x-icon"/>

And there was an error which can be seen in the browser console:

ReferenceError: True is not defined
at t (app.js:206)
at app.js:47
at Array.map ()
at e.value (app.js:47)
at d (vendor.js:123)
at c (vendor.js:123)
at x (vendor.js:123)
at u (vendor.js:123)
at p (vendor.js:123)
at h (vendor.js:123)

https://github.com/mitmproxy/mitmweb/issues/10 has been archived. Are there still plans to add the filter?


U2++ lost word problem

Why U2++ lose words at the end of sentences compared to non-streaming conformer?
non-streaming conformer
Overall -> 15.21 % N=263 C=223 S=40 D=0 I=0

U2++
Overall -> 29.66 % N=263 C=185 S=33 D=45 I=0

It's unnormal. The WER should be close.


mongorestore 10gb database insertion error EOF

version: 3.2.11
mongorestore --host <IP_ADDRESS> --db databasename --drop --numParallelCollections 16 --port 33005 path/to/dump
05:27:49 2017-02-28T05:27:49.444+0000	[#################.......]  database1.collection1  1.39GB/1.91GB  (73.0%)
05:27:49 2017-02-28T05:27:49.449+0000	[################........]  database1.collection2  1.22GB/1.82GB  (67.2%)
05:27:49 2017-02-28T05:27:49.449+0000	[#####################...]  database1.collection3  949MB/1.03GB  (90.1%)
05:27:49 2017-02-28T05:27:49.449+0000	Failed: database.collection1: error restoring from backup_2017-02-28T03:57:16/database1/collection1.bson: insertion error: EOF
05:27:49
05:27:49 Fatal error: local() encountered an error (return code 1) while executing 'mongorestore --host <IP_ADDRESS> --db database --drop --numParallelCollections 16 --port 33043 backup_2017-02-28T03:57:16/database1'
05:27:49
05:27:49 Aborting.

Similar setup as in https://github.com/docker-library/mongo/issues/140
        mongo_container = docker_client.containers.run(
            image='mongo:{}'.format('3.2.11'),
            detach=True,
            name=db_name,
            network_mode='bridge',
            ports={
                '{}/tcp'.format(mongo.MONGO_DEFAULT_PORT): (localhost, None)
            },
            publish_all_ports=False,
            shm_size='1G'
        )

Is it just increase shm_size? Is there anything else to tune?

tried on aufs and still happening. any idea @tianon ? must be a limit im unware of that it's hitting. definitely related to the size of the workload

Hmm, is it running in a VM? (Docker for Windows, Docker for Mac, and Docker Toolbox all start a Linux VM to run your containers in)
Does it happen on other versions of MongoDB as well? (mongo:3.4, for example?)

Hi @tianon. Yes there is running on EC2 Ubuntu. Does that matter?
Haven't tried 3.4. Could try it out.

What does the system's storage/RAM look like?  Can you reproduce with
mongodb not running in Docker?

When you hit "localhost" -- is that going through "docker-proxy", or are
you running this in the same container?  Can you try hitting the
container's external IP instead?
For context, when you do something like "-p 80:80", traffic to "localhost"
is routed through a "docker-proxy" process, which is doing "io.Copy" on all
the data, and could cause issues (although EOF still seems odd -- grasping
at straws here to try and help debug what's going on).

are you saying the local ip the bridge network assigns the container? i can try that. i'll have to see if it's exposed via https://github.com/docker/docker-py currently it's using the port exposed to localhost. i was not aware of the docker-proxy process.
Btw, this only happens when we are mongorestoring a large database 10gb. less than that it seems to work fine.

Sorry @jamesongithub -- yes, I meant the IP that Docker assigns the container on the bridge network.
Did you ever get a chance to try that and verify whether docker-proxy was related to your issue?

i think the problem was devicemapper and the 10gb base storage size.
i recall increasing the storage size with dm.basesize and i think it might have solved the issue.
i wished the docker docs were more clear on this
haven't seen it since, but we also switched to overlay2 recently.

Ahh, that would make a ton of sense -- devicemapper is kind of a PITA in my experience (and difficult to configure in a performant way).
Thanks for confirming that this appears to be fixed (I'm going to close)!
Future travelers running into this issue: check your dm.basesize if you're using devicemapper :+1:


[Feature] Add the search functions to search bar

Currently, if you try and use search to find a book, it doesnt work.
Needs to have the functions so that the search bar works.

Hey gbowne1,
I would like to take up this issue. Please assign this issue to me.

Hi, I think I just fixed this issue and I also eliminated the need for a search button to automate the search functionality.

Ok, thanks.  You can PR if you like @Mayor-Rhymes.  I will review and merge if its good.

I just sent a PR. Thanks for the opportunity @gbowne1.

Thanks for the opportunity to review. @Mayor-Rhymes


Add Squirrel support

From @GoogleCodeExporter on May 31, 2015 11:30
I found a Squirrel package for SL4A.
How about supporting this officially.

http://forum.squirrel-lang.org/mainsite/forums/default.aspx?g=posts&t=2098

Original issue reported on code.google.com by tomcatalbino on 26 Jan 2012 at 12:58
Copied from original issue: damonkohler/android-scripting#594

From @GoogleCodeExporter on May 31, 2015 11:30
>It gets some rpc error message but it seems the output works !
It wasn't an error. It was only print messages for debug.
I tried these kind of codes and it worked nice !

dofile("/sdcard/com.googlecode.squirrelforandroid/init.nut");
android.makeToast("Toast using Squirrel");
android.webViewShow("http://www.google.com");
android.launch("com.android.calculator2.Calculator");
android.notify("Notify using Squirrel","Message");
android.ttsSpeak("Speak using Squirrel");
android.vibrate();

By the way, What's the difference with the Issues list and the forum site ?
It seems that some messages are hide (or maybe post failed or maybe not
permitted).

Original comment by tomcatalbino on 4 Feb 2012 at 4:33

From @GoogleCodeExporter on May 31, 2015 11:30
I installed it into my android phone.
It seems the install succeed.
But when I selected the "Add" menu it falls.
So I wrote a Squireel code from an editor and named it "Hello.nut"
Write this code.

print("Hello\n");

It gets some rpc error message but it seems the output works !

Original comment by tomcatalbino on 26 Jan 2012 at 4:32

From @GoogleCodeExporter on May 31, 2015 11:30
[deleted comment]

From @GoogleCodeExporter on May 31, 2015 11:30
I investigated what is required to support Squirrel.

1. Add SquirrelLanguage.java to
http://code.google.com/p/android-scripting/source/browse/android/Common/src/com/
googlecode/android_scripting/language/

It needs at least these codes.

///////////////////////////////
package com.googlecode.android_scripting.language;
public class SquirrelLanguage extends Language {}
///////////////////////////////

2. Fix the class SupportedLanguages at enum KnownLanugage in
http://code.google.com/p/android-scripting/source/browse/android/Common/src/com/
googlecode/android_scripting/language/SupportedLanguages.java

It needs this code below between [PHP(".php", PhpLanguage.class)] and [;].

///////////////////////////////
SQUIRREL(".nut", SquirrelLanguage.class)
///////////////////////////////

3. Add an icon named "nut_icon.png" in
http://code.google.com/p/android-scripting/source/browse/android/ScriptingLayerF
orAndroid/res/drawable/
(It means all support languages icon have to be in the SL4A's apk.)
The name must be "nut_icon.png". It seems to be a rule.
See more information in
http://code.google.com/p/android-scripting/source/browse/android/Common/src/com/
googlecode/android_scripting/FeaturedInterpreters.java
"getInterpreterIcon" decides the icon name.

(4.Optional) If SL4A supports "Squirrel" officially it should have an apk path
to download.
It's also in FeaturedInterpreters.java.

Perhaps only these procedures. If there is some other things to be done please
reply to this.

Original comment by tomcatalbino on 7 Feb 2012 at 11:42

From @GoogleCodeExporter on May 31, 2015 11:30
A new Squirrel for Android is released; see
http://forum.squirrel-lang.org/default.aspx?g=posts&t=2225

Will see how to make it part of the offical SL4A release...

Original comment by<EMAIL_ADDRESS>on 6 Apr 2012 at 5:43

From @GoogleCodeExporter on May 31, 2015 11:30
working on the changes you suggested, in order to make Squirrel officially
supported

Original comment by<EMAIL_ADDRESS>on 2 Apr 2012 at 2:28


changing direction of anchor node or sceneform in ARcore

i want to make sceneform parallel to ground instead of vertical how can i fix it

Anchors are oriented to the normal of the plane.  You can add nodes to the AnchorNode and apply a 90 degree rotation to be parallel to the ground.


Adds the new rinkeby options with correct dai address.

:tada: This PR is included in version 0.5.0 :tada:
The release is available on:

npm package (@latest dist-tag)
GitHub release

Your semantic-release bot :package::rocket:


Adding CAT23001 commands to CVMFS CI

As it says in the title, now adding commands from CAT23001
For significance calculation, have reduced N toys to 1000 for speed (mostly this is just to just the method works)

Thanks Nick! This is great to add.
As a next step, we should add something which checks the actual output values of some of these commands and compares them to the expected values. But this is a nice step in that direction.


[Python] daily request issue release

https://github.com/msyyc/file-storage/blob/release-issue-status/published_issues_python.csv
https://github.com/msyyc/file-storage/blob/release-issue-status/release_python_status.md

https://gitter.im/Azure/azure-sdk-for-python?utm_source=notification&utm_medium=email&utm_campaign=unread-notifications

move to another issue : https://github.com/Azure/azure-sdk-for-python/issues/21637


[WIP] PP-3039 Upgrades Node.js version in package.json as well as test/prod Dockerfiles

WHAT
Upgrades Node.js in both the package.json 'engines' (if deployed to Heroku) as well as the build/test and production Dockerfiles to use the 6.12.2 release of Node.js which fixes an Open SSL vulnerability released on the 8th December.

Also worth changing .nvmrc


Allow attachments to be collapsible

Perhaps a small thumbnail should be displayed? Should attachments be shown in a lightbox/popup, or should we just expand in-place?

I believe to expand them in place is the right thing.
If we have multiple popups opened in the same time, we may not know to each step they correspond.


user guide service img - [closed]

In GitLab by @luotian-github on Sep 25, 2017, 09:34
Merges doc2 -> dev
user guide service img

In GitLab by @wknet123 on Nov 13, 2019, 10:03
closed


Resolve serving of static assets

Includes all static items such as images, css, js, etc.

Here's an idea: https://devcenter.heroku.com/articles/django-assets

+1 Seems reasonable.

We'll use WhiteNoise to serve static files on Heroku.


load crate.yml explicitly and do not allow any other extension than yml

this is a combination of commits https://github.com/crate/crate/commit/e11f532bb5c367174cf33b4a76ee7158c7857f97 and https://github.com/crate/crate/commit/1f9e67a3e16101b6ef10e5ab7992bfa3eca1ea90 that have been applied to 0.54 and adopted to the recent ES 2.2 upgrade

ack


Some test cases failed to pass in the docker image.

Bug Report
Please answer these questions before submitting your issue. Thanks!
1. Minimal reproduce step (Required)
Restore the "kclvm/runner/test.rs/fn test_kclvm_runner_execute()" method that has been commented out.
In the KCLVM
make sh-in-docker
cd kclvm/runner
cargo test -- --nocapture

2. What did you expect to see? (Required)
running 1 test
test tests::test_kclvm_runner_execute ... ok

test result: ok. 1 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 7.10s

3. What did you see instead (Required)
running 1 test
thread '<unnamed>' panicked at 'called `Result::unwrap()` on an `Err` value: Os { code: 2, kind: NotFound, message: "No such file or directory" }', src/command.rs:391:66
note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace

src/command.rs:391:66
let txt_path = std::path::Path::new(&executable_root)
      .join(if Self::is_windows() { "libs" } else { "lib" })
      .join("rust-libstd-name.txt");
391 ------>  let rust_libstd_name = std::fs::read_to_string(txt_path).unwrap();
                                                                     ^ 66

Cannot found the file "rust-libstd-name.txt".
4. What is your KusionStack components version? (Required)
kclvm version is 0.4.2; checksum: e07ed7af0d9bd1e86a3131714e4bd20c

Method “test.rs/fn test_kclvm_runner_execute()" is in branch "refactor/zong-zhe/add_eval_to_kclvm_runner".

I add “println!()” to show the value of "txt_path" at runtime.

The directory where file "rust-libstd-name.txt" is located is “_build/dist/ubuntu/kclvm/lib/rust-libstd-name.txt”.


Simulate Dell Smart Card Reader Keyboard in tests

Add the second device into our small simulator used for testing Smart Card Connector - Dell Smart Card Reader Keyboard.
This allows us to verify the behavior of CCID and PC/SC-Lite for composite USB devices, in particular for those that have a non-smart-card interface#0 and the smart-interface#1. Follow-up PR will use this for regression-testing https://github.com/GoogleChromeLabs/chromeos_smart_card_connector/issues/849.

Have to re-publish this again. Sorry! blush:


3.7: forward headers that are treated special into foxx as well. (#13068)

Backport of https://github.com/arangodb/arangodb/pull/13068#event-4052110924

http://jenkins01.arangodb.biz:8080/view/PR/job/arangodb-matrix-pr/12915/

tests blue.


Unable to use Gruntfile.coffee as the gruntfile?

When Gruntfile.coffee is used instead of Gruntfile.js as the gruntfile, play-yeoman will throw an ERROR:
Reading "Gruntfile.js" Gruntfile...ERROR
Fatal error: Unable to find "/Users/hanfeisun/Documents/waycanon/ui/Gruntfile.js" Gruntfile.
I think Grunt support to use coffeescript as the gruntfille natively, is it possible for play-yeoman to support this..

This is configurable using the setting yeomanGruntfile


.gitmodules should specify the OZ version as v4.

The OZ master branch is currently at version 5.0.0, which requires Solidity version ^0.8.20. This conflicts with the project's current version of 0.8.17.
https://github.com/OpenZeppelin/openzeppelin-contracts/blob/master/contracts/utils/cryptography/ECDSA.sol
Therefore, the following error will be reported during the build:
Found incompatible Solidity versions:
    src/diamonds/nayms/AppStorage.sol (0.8.17)
    lib/ozv4/contracts/utils/cryptography/ECDSA.sol (^0.8.20)

This can be fixed by specifying the branch in the .gitmodules file:
[submodule "lib/ozv4"]
	path = lib/ozv4
	url = https://github.com/openzeppelin/openzeppelin-contracts
	branch = release-v4.8

Hi imp0wd3r,
Thanks for opening an issue. I just bumped our contracts to use 0.8.20 and refactored the code to use openzeppelin branch merge/release-v5.0. Let me know if you spot anymore issues, thanks!

Thank you for your prompt response.
I heard that OZ 4.x and 5.x have made many updates. I haven't looked into the details, but I hope you can understand them before upgrading to avoid compatibility issues.
Additionally, I've submitted a new report on Immunefi, number 24944, I discovered this issue while writing the report. I hope you can review it to determine its validity.


Issue #394: created CHANGELOG.adoc on master

@tradej @zrhoads WDYT?

Formatted as .adoc instead of  .md
Messages have been edited to provide more context for each issue.
The formatting is intentionally simple. Is this sufficiently detailed? Do we need to include any other info?
If this gets approved and merged I'll file a PR to include a similar log (sans the Current Development section) for the production branch, too.

resolves #394

thanks @tradej, typo has been removed.

merged: 62d847f2f3daadacc217219a783fbdd8d0422223


Empty pull request.

Remove empty line from README.

Codecov Report

Merging #1 into master will not change coverage.
The diff coverage is n/a.

@@            Coverage Diff            @@
##             master       #1   +/-   ##
=========================================
  Coverage     72.87%   72.87%
  Complexity      175      175
=========================================
  Files            16       16
  Lines           778      778
  Branches         84       84
=========================================
  Hits            567      567
  Misses          167      167
  Partials         44       44

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 5fb3f03...e13c581. Read the comment docs.

To avoid confusion change to use reference to 'this' object instead of using super to access parent properties. Use super only when required.


Don't use an unnecessary iterator

Please check if the PR fulfills these requirements

[x] The commit message follows our guidelines
[x] Tests for the changes have been added (for bug fixes / features)
[x] Docs have been added / updated (for bug fixes / features)

What kind of change does this PR introduce? (Bug fix, feature, docs update, ...)
Simplifies logic for copying an array

What is the current behavior? (You can also link to an open issue here)
unchanged

What is the new behavior (if this is a feature change)?
unchanged

Does this PR introduce a breaking change? (What changes might users need to make in their application due to this PR?)
no

Other information:

Thanks!
(cc @igoroctaviano)


Removed restangular from CacheGroupParameterService

What does this PR (Pull Request) do?
Removes a dependency on Restangular from the "CacheGroupParameterService"

[x] This PR partially addresses #3571

Which Traffic Control components are affected by this PR?

Traffic Portal

Traffic Portal dependencies are not individually documented, and so no documentation changes are necessary.
What is the best way to verify this PR?
No functionality should have changed (except that errors will now be logged instead of ignored in many cases), so the existing tests should all pass.
Jeremy: actually, the only way to truly verify that the functionality did not change is to:

run the UI tests (which is a very small subset of all TP functionality)
manually verify the functionality associated with ALL the services functions that changed just to be sure all is well.

The following criteria are ALL met by this PR

[x] This PR includes tests OR I have explained why tests are unnecessary
[x] I have explained why documentation is unnecessary
[x] This PR includes an update to CHANGELOG.md OR such an update is not necessary
[x] This PR includes any and all required license headers
[x] This PR does not include a database migration
[x] This PR DOES NOT FIX A SERIOUS SECURITY VULNERABILITY

Refer to this link for build results (access rights to CI server needed):
https://builds.apache.org/job/trafficcontrol-PR/3745/
Test PASSed.

Refer to this link for build results (access rights to CI server needed):
https://builds.apache.org/job/trafficcontrol-PR/3810/
Test PASSed.

Refer to this link for build results (access rights to CI server needed):
https://builds.apache.org/job/trafficcontrol-PR/4733/


Error: Could not find Chrome (ver. 119.0.6045.105). This can occur if either

I'm using Laravel Sail
"puppeteer": "^21.7.0"
I got the error when try to render the image
Error: Could not find Chrome (ver. 119.0.6045.105). This can occur if either
I already check the .cache/puppeteer/ and see the Chrome has been downloaded but not sure why still got the error

I made it works now, hope it help you https://stackoverflow.com/a/77784336/6108378
Close this issue