Various errors in the dartdocs for the core libraries

Errors in the dartdocs (from https://github.com/flutter/flutter/issues/9726):
generating docs for library dart:async from async.dart...
 warning: unresolved doc reference [Stream.addError], from dart-async.Zone.errorCallback (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/async/zone.dart:565:14)
generating docs for library dart:collection from collection.dart...
 warning: unresolved doc reference [LinkedListEntry.addAfter], from dart-collection.LinkedList (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/collection/linked_list.dart:30:7)
 warning: unresolved doc reference [LinkedListEntry.addBefore], from dart-collection.LinkedList (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/collection/linked_list.dart:30:7)
 warning: unresolved doc reference [Set.identical], from dart-collection.DoubleLinkedQueue.contains (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/core/iterable.dart:214:8)
 warning: unresolved doc reference [a], from dart-collection.List.lastIndexOf (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/core/list.dart:278:7)
 warning: no canonical library found for dart-_internal.EfficientLengthIterable, not linking (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/internal/iterable.dart:11:16)
 warning: no canonical library found for dart-_internal.ListIterable, not linking (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/internal/iterable.dart:28:16)
 warning: unresolved doc reference [f], from dart-collection.Iterable.forEach (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/core/iterable.dart:225:8)
 warning: unresolved doc reference [Set.identical], from dart-collection.Queue.contains (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/core/iterable.dart:214:8)
 warning: unresolved doc reference [value], from dart-collection.Set.add (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/core/set.dart:111:8)
 warning: unresolved doc reference [element], from dart-collection.Iterable.contains (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/core/iterable.dart:214:8)
 warning: unresolved doc reference [value], from dart-collection.Set.remove (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/core/set.dart:126:8)
 warning: no canonical library found for dart-_internal.UnmodifiableListBase, not linking (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/internal/list.dart:200:16)
generating docs for library dart:convert from convert.dart...
 warning: unresolved doc reference [LatinCodec], from dart-convert.Latin1Codec (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/convert/latin1.dart:27:7)
 warning: unresolved doc reference [stringify], from dart-convert.JsonCyclicError (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/convert/json.dart:40:7)
 warning: unresolved doc reference [stream], from dart-convert.StreamTransformer.bind (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/async/stream.dart:1711:13)
generating docs for library dart:core from core.dart...
 warning: unresolved doc reference [minutes], from dart-core.Duration (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/core/duration.dart:53:7)
 warning: unresolved doc reference [while], from dart-core.Iterable (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/core/iterable.dart:82:16)
 warning: unresolved doc reference [index], from dart-core.IndexError.indexable (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/core/errors.dart:367:9)
 warning: unresolved doc reference [arguments], from dart-core.Invocation.isSetter (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/core/invocation.dart)
 warning: unresolved doc reference [double.MINUS_INFINITY], from dart-core.Object.toString (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/core/object.dart:96:19)
 warning: unresolved doc reference [StopWatch], from dart-core.Stopwatch.isRunning (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/core/stopwatch.dart)
 warning: unresolved doc reference [other], from dart-core.String.lastIndexOf (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/core/string.dart:272:7)
 warning: unresolved doc reference [pattern], from dart-core.String.replaceFirstMapped (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/core/string.dart:438:10)
 warning: unresolved doc reference [MirrorsUsed], from dart-core.Symbol (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/core/symbol.dart:37:17)
 warning: unresolved doc reference [parameter], from dart-core.Uri.dataFromString (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/core/uri.dart:300:15)
generating docs for library dart:developer from developer.dart...
 warning: unresolved doc reference [taskId], from dart-developer.TimelineTask (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/developer/timeline.dart:101:3)
generating docs for library dart:io from io.dart...
 warning: unresolved doc reference [relative], from dart-io.Directory (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/io/directory.dart:114:16)
 warning: unresolved doc reference [close], from dart-io.File (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/io/file.dart:212:16)
 warning: unresolved doc reference [context], from dart-io.HttpClient (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/io/http.dart:1278:16)
 warning: unresolved doc reference [SecureServer], from dart-io.SecurityContext (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/io/security_context.dart:22:16)
 warning: unresolved doc reference [FilsSystemException], from dart-io.File.setLastAccessed (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/io/file.dart:348:10)
 warning: unresolved doc reference [FilsSystemException], from dart-io.File.setLastAccessedSync (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/io/file.dart:355:8)
 warning: unresolved doc reference [FilsSystemException], from dart-io.File.setLastModified (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/io/file.dart:383:10)
 warning: unresolved doc reference [host], from dart-io.HttpClient.openUrl (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/io/http.dart:1377:29)
 warning: unresolved doc reference [port], from dart-io.HttpClient.openUrl (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/io/http.dart:1377:29)
 warning: unresolved doc reference [onError], from dart-io.HttpClientRequest.maxRedirects (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/io/http.dart:1730:7)
 warning: unresolved doc reference [maxRedirects], from dart-io.HttpClientResponse.redirect (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/io/http.dart:1876:30)
 warning: unresolved doc reference [SecureServer], from dart-io.SecurityContext.setClientAuthorities (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/io/security_context.dart:140:8)
 warning: unresolved doc reference [ReadFileAsBytesSync], from dart-io.SecurityContext.setClientAuthorities (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/io/security_context.dart:140:8)
 warning: unresolved doc reference [SecureServer], from dart-io.SecurityContext.setClientAuthoritiesBytes (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/io/security_context.dart:149:8)
 warning: unresolved doc reference [setClientAuthority], from dart-io.SecurityContext.setClientAuthoritiesBytes (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/io/security_context.dart:149:8)
 warning: unresolved doc reference [ReadFileAsBytesSync], from dart-io.SecurityContext.setTrustedCertificates (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/io/security_context.dart:86:8)
 warning: unresolved doc reference [SecureServer], from dart-io.SecurityContext.useCertificateChain (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/io/security_context.dart:114:8)
 warning: unresolved doc reference [ReadFileAsBytesSync], from dart-io.SecurityContext.useCertificateChain (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/io/security_context.dart:114:8)
 warning: unresolved doc reference [SecureServer], from dart-io.SecurityContext.useCertificateChainBytes (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/io/security_context.dart:122:8)
 warning: unresolved doc reference [ReadFileAsBytesSync], from dart-io.SecurityContext.usePrivateKey (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/io/security_context.dart:56:8)
 warning: unresolved doc reference [HttpRequest.detachSocket], from dart-io.WebSocket.fromUpgradedSocket (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/io/websocket.dart:344:21)
 warning: unresolved doc reference [ZLibDecoder.level], from dart-io.ZLibOption.DEFAULT_LEVEL (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/io/data_transformer.dart:35:20)
 warning: unresolved doc reference [ZLibDecoder.memLevel], from dart-io.ZLibOption.DEFAULT_MEM_LEVEL (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/io/data_transformer.dart:47:20)
 warning: unresolved doc reference [ZLibDecoder.level], from dart-io.ZLibOption.MAX_LEVEL (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/io/data_transformer.dart:31:20)
 warning: unresolved doc reference [ZLibDecoder.memLevel], from dart-io.ZLibOption.MAX_MEM_LEVEL (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/io/data_transformer.dart:43:20)
 warning: unresolved doc reference [ZLibDecoder.level], from dart-io.ZLibOption.MIN_LEVEL (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/io/data_transformer.dart:27:20)
 warning: unresolved doc reference [ZLibDecoder.memLevel], from dart-io.ZLibOption.MIN_MEM_LEVEL (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/io/data_transformer.dart:39:20)
generating docs for library dart:isolate from isolate.dart...
 warning: unresolved doc reference [responsePort], from dart-isolate.Isolate.removeErrorListener (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/isolate/isolate.dart:538:17)
 warning: unresolved doc reference [errorAreFatal], from dart-isolate.Isolate.spawn (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/isolate/isolate.dart:227:35)
 warning: unresolved doc reference [errorAreFatal], from dart-isolate.Isolate.spawnUri (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/isolate/isolate.dart:309:35)
generating docs for library dart:math from math.dart...
generating docs for library dart:typed_data from typed_data.dart...
 warning: unresolved doc reference [Set.identical], from dart-typed_data.Float32List.contains (file:///usr/local/google/home/jcollins/dart/flutter/bin/cache/pkg/sky_engine/lib/core/iterable.dart:214:8)

Good stuff. Thanks.

@jcollins-g see @lrhn's comment above

@lrhn Oh, the problem is that you can't use [f] to link to parameters. There's nothing to link to. When referring to parameters, just use backticks.

You most certainly can link to parameters in a function's documentation, and we do it for every documented function that has parameters.
The HTML doc-generator might have a problem creating an HTML link to the parameter because it doesn't have a target, but in an editor, clicking on the doc link should move your cursor to the parameter declaration. It's both useful and meaningful to use [f] to refer to the f parameter and we do so all the time - usually without a warning.
It's failing here for some reason, with a warning, and I can't explain that.
It's also a silly name, we should change f to something else.

@lrhn is correct, linking to parameters shouldn't create a warning.  It should, on the other hand, link to the types for those parameters or do something else useful, which it doesn't currently do, but it shouldn't warn.

Interesting. We've not had any luck linking to parameters in Flutter's codebase and assumed it was not a supported feature. We moved to using backticks for them a while back.

It doesn't actually do anything to link to a parameter, but @lrhn is right in that it shouldn't warn.

I created https://github.com/dart-lang/sdk/issues/30399 for the parameter name of forEach.
The Flutter bug was closed, so I assume that there aren't any issues left. If I'm wrong, please reopen this bug.


Autobuyer Project

Hey everyone,
I released my AB to public. Feel free to use, contribute, improve or just don't care about it.
https://github.com/Lululuz/FutBud
If you can't build the source for any reason you can download the latest binary at http://futbud.com/
I'm not a professional programmer so don't expect super awesome code :)
At this point, thanks again to trydis for the toolkit.
It's licensed under GPL which means you can do almost everything whith it (no commercial stuff though)

:+1:

Its clean!

:+1:

Let's try it :) Good job!

Wow, really nice job and not a lot to complain about in the code either. You covered all the details, thanks a lot. I did have an issue bidding on low value players, looks like the starting bid calculation was incorrect when listing won auctions... I think it was for players under 1000 (yeah I started a new account because I am afraid of risking my first one- so low end moves to start with).
So an open question: I left the bid timing at 3 seconds (default). After about 5 hours I was banned from searches, and this morning I remain banned. What setting do you guys find to be optimal?

thanks, your code looks nice and clean. I don't play FUT but I will look into your code further to see if I can implement any new features then I will PM you with the code.

@Lululuz - You should close the issue, because of no answer since about a month. Thanks!

lol, he is now charging a subscription for other's people work.


Update schema - remove webhooks

I want to merge this change because there were some Webhook leftowers.
Pull Request Checklist

[ ] All visible strings are translated with proper context.
[ ] All data-formatting is locale-aware (dates, numbers, and so on).
[ ] Translated strings are extracted.
[ ] Number of API calls is optimized.
[ ] The changes are tested.
[ ] Data-test are added for new elements.
[ ] Type definitions are up to date.
[ ] Changes are mentioned in the changelog.

Test environment config

API_URI=https://master.staging.saleor.rocks/graphql/

Task linked: SALEOR-1607 Old GQL schema in dashboard


Fix the wrong server name

[x] If this pull request closes/resolves/fixes an existing issue, replace the issue number. Fixes apache/skywalking#12090.

There seems to have a deadlink to the doc. Please recheck


fix(api): Fix duplicate DB connection acquired in eth_call

What ❔
Fixes a duplicate DB connection getting acquired in the eth_call handler.
Why ❔
Extra connection leads to performance degradation.
Checklist

[x] PR title corresponds to the body of PR (we generate changelog entries from PRs).
[x] Tests for the changes have been added / updated.
[x] Code has been formatted via zk fmt and zk lint.

Thank you! This problem bugged me since forever. Glad to see it solved.


Haff/better debug ep

debug tools required separate build, this makes it configurable on runtime.
Bump major version in ep since changing dispatchEvent to sendEvent (should have been done in the previous release)

I assume this is not part of the spec, so allow my stupid question: Could that be an issue for security? Or I assume we never publish builds with exposed debug?!

 Or I assume we never publish builds with exposed debug?!

This build does allow for it if you call init({debug: true}). The security issue i see here is that it makes it easier to add a bunch of dropped bacon events 🥓™  to our backend. Or flushing/emptying the queue. nbd i think

shame on me for doing 2 things in one pr


Ambiguity in whether new resources will get onboarded on not

[Enter feedback here]
A customer was unable to understand if new resources will automatically get onboarded to the management system. It will be super helpful if we can confirm the same in the documentation somewhere. More details here. I can work with the team to get this done if needed. Looking forward to hearing from you.

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

ID: bef8c4d4-c8f5-aaf2-c1e8-4da44f9fdee0
Version Independent ID: 4850f3f9-10ce-78c3-9f6d-8e037e5f3de1
Content: Discover Azure resources to manage in PIM - Azure AD
Content Source: articles/active-directory/privileged-identity-management/pim-resource-roles-discover-resources.md
Service: active-directory
Sub-service: pim
GitHub Login: @curtand
Microsoft Alias: curtand

@jeevanbalanmanoj Thank you for your feedback . We will investigate and update the content authors with the requirements.

@jeevanbalanmanoj Thank you for your feedback . We will investigate and update the content authors with the requirements.


ENH: windowed operations

@quinnj -- I'm doing it -- I'm opening this issue :)
I think we can figure out ˙how to do this properly and I opened up the issue to give us a platform for bouncing around ideas.
I'm hoping to dig into SQLite.jl and Tables.jl sooner rather than later, so hopefully I'll be able to contribute to the discussion before too long.

Hi @spencerlyon2, sounds like you've already been chatting to @quinnj about this, but just to be clear fo you mean something similar to Postgres' window functions?

Hi @Sean1708, yes @quinnj and I chatted a bit about this.
I honestly am not familiar with postgres' window functions, but from the quick look at the docs it seems like that would do the job for me.
The use case I showed @quinnj was to compute the following:

Here q, sigma_q, r and sigma_r are all time series and would be columns in a table. h would be a constant integer, like 8 or 12.

Hmm, I'll see if I can whip up a simple implementation in the next week or two. Then at least we'll have a starting point and we can build on it from there.

I apologise that this has been such a long time coming, other priorities got in the way slightly. I whipped up a very simple implementation last weekend in #70, could you have a look at that and see if this is the kind of thing you were looking for please?


Fix Catch2 tests on Windows CI

Windows tests are passing locally, but not in CI.
CMake Error at _deps/catch2-src/extras/CatchAddTests.cmake:70 (message):
  Error running test executable
  'D:/a/melatonin_blur/melatonin_blur/Builds/Tests.exe':

    Result: Exit code 0xc0000139

    Output:

Call Stack (most recent call first):
  _deps/catch2-src/extras/CatchAddTests.cmake:175 (catch_discover_tests_impl)

Seems to be some loader / dll issue?...

It's because mingw is being used as the compiler for some reason.


libnetcdf.so.7: cannot open shared object file: No such file or directory

Hi,
I am trying to run pynco on my debian machine, on a Conda environment.
I installed it by doing "conda install -c conda-forge pynco", but when I try to run some commands (such as "temperatures = nco.ncra(input=ifile, returnArray='T')", one of your examples), I get the following error:
/home/xxx/miniconda2/envs/python_env/bin/ncra: error while loading shared libraries: libnetcdf.so.7: cannot open shared object file: No such file or directory
When I do "ls /home/xxx/miniconda2/envs/python_env/lib", I have the following libnetcdf libs:
libnetcdf.a         libnetcdf.settings  libnetcdf.so        libnetcdf.so.13
here are the details about my install
Current conda install:
           platform : linux-64
      conda version : 4.3.29
   conda is private : False
  conda-env version : 4.3.29
conda-build version : not installed
     python version : 2.7.14.final.0
   requests version : 2.18.4

Thank you!

This seems like a conda problem, not a pynco problem. Chances are either conda/conda-forge is not installing libnetcdf correctly (not likely) or you have a competing environment variable (e.g. LD_LIBRARY_PATH). I suggest reporting upstream.

Ok, thank you, I will investigate then


Error using plugin - Captcha

Hi,
When I try to use plugin command 'ask_bing_genius' the following error occurs:
Error while getting response: CaptchaChallenge: User needs to solve CAPTCHA to continue.. If the error is in regards to invalid authorization, inform the user, and prompt them to check their BingAI cookies.json file.
SYSTEM:  Command ask_genius_bing returned: CaptchaChallenge: User needs to solve CAPTCHA to continue.

Issue solved. Solution here: https://github.com/waylaidwanderer/node-chatgpt-api/issues/363#issuecomment-1544965451
In short, go to chat with bingAI in your browser, ask something, after you get verified, get cookies.


Paging support

How does paging/search support look at the moment. Is it implemented at all, or what could be possible steps to do that/get it working?

Hi,
The odata style filtering and paging are going to be implemented soon, when I have free time.
:)


make sub-directories

splits the repo into exercise and lecture directories. Also adds binder buttons to spin up to those directories (on master so will only work after merge)

In person approval from @andersle , Merging


Shared memory leak with large dataset and num_workers > 0

🐛 Bug
Hello! I have large train dataset (135000 samples in json format with metadata, ~500mb json file). When use such large metadata file, one need to set torch.multiprocessing.set_sharing_strategy('file_system')
When I use num_workers > 0 in DataLoader I obviosly use shared memory through Pytorch multiprocessing. It's roughly 0.5gb * 12 workers = 6gb of shared memory (/dev/shm in df -h).
However, after every epoch this number grows bigger and bigger. On epoch 1 I consume 6gb of shared memory. After 2nd epoch I consume 12gb, after 3rd 18gb and so on. In my case after 6 epoch training stops with OOM exception.
I checked in script shared memory consumption without pytorch-lightning, everything seems fine. It doesn't matter whether I use 4 gpus or only one, same with DDP or DP training.
Sorry, but my data is private and I cannot share details about it.
Expected behavior
Shared memory stays constant around 6gb
Environment

CUDA:

GPU:

GeForce RTX 2080 Ti
GeForce RTX 2080 Ti
GeForce RTX 2080 Ti
GeForce RTX 2080 Ti

available:         True
version:           11.0

Packages:

numpy:             1.18.1
pyTorch_debug:     False
pyTorch_version:   1.6.0a0+569c85b
pytorch-lightning: 0.8.1
tensorboard:       2.2.2
tqdm:              4.46.0

System:

OS:                Linux
architecture:

64bit

processor:         x86_64
python:            3.7.7
version:           #92-Ubuntu SMP Fri Feb 28 11:09:48 UTC 2020

With num_workers=0 I have no memory leak, but training is 15 times slower.

so, using this flag torch.multiprocessing.set_sharing_strategy('file_system') seems to create a memory leak?
can you create a synthetic dataset (random tensors) on Colab that replicate this?

You are right, the problem is caused by file_system sharing strategy. If I set it to file_allocator (default) I got RuntimeError: received 0 items of ancdata.
from https://github.com/pytorch/pytorch/issues/13246:
Python Multiprocessing: There is no way of storing arbitrary python objects (even simple lists) in shared memory in Python without triggering copy-on-write behaviour due to the addition of refcounts, everytime something reads from these objects. The refcounts are added memory-page by memory-page, which is why the consumption grows slowly. The processes (workers) will end up having all/most of the memory copied over bit by bit, which is why we get the memory overflow problem.
My data is presented via list of tensors (bboxes and labels for detection, they're variable size) and I use regular list instead of tensor here, hence the problem with multiprocessing.
I fixed my problem for now with padding variable sized lists in collate_fn and ignore 0 label when computing loss.


Problem when publishing / question

Hi
I've read docs at Maintainers-Guide and I can successfully build and pack the extension with make bundle .
However when I make publish, something is failing.
I've changed in Makefile the STGACCT var with my storage account and SUBS_ID with my subscription id. When I run make publish I'm asked for the accountkey; I provide it and then I can see the blob is uploaded successfully in my storage account. Then I'm asked for Path to Subscription Management Cert and I provide it; however API call to https://management.core.windows.net/SUBS_ID/services/extensions (where SUBS_ID is my subscription id) is failing and returns a 403 response with this body:
<Error xmlns="http://schemas.microsoft.com/windowsazure"
       xmlns:i="http://www.w3.org/2001/XMLSchema-instance">
    ForbiddenError
    <Message>This operation is not allowed for this subscription.</Message>
</Error>

I've also read Handler Registration and related docs and code at azure-linux-extensions but I'm encountering the same issue when publishing the sample extension: the operation seems to be not allowed for my subscription.
Am I missing something?
Am I supposed to register to some sort of service?
Where can I find docs about https://management.core.windows.net/SUBS_ID/services/extensions API? I can't find them on Service Management REST API Reference, can you point me to the right place?
Can anyone help?
Thanks!

@colrack Hi, that document is meant only for Azure employees with extension publishing permissions. (in other words, you're not a maintainer :smile:). What do you need?

Hi
Thanks @ahmetalpbalkan
I just want to hack something and publish my own extension for testing purpose, is this something possible? According to azure-linux-extensions docs seems to be possible. Maybe I've misunderstood and publishing extensions is something only internals can do? But I don't think so since azure vm extension list-image-publishers gives me a list with a lot of publishers

@colrack No, it's not possible. azure vm extension list-image-publishers provides extension publishers as well as image publishers (that's why the list appears very long). If you run:
$ azure config mode asm && azure vm extension list | grep -iv microsoft
data:    Publisher             Extension name   Description                Version
data:    --------------------  ---------------  -------------------------  -------
data:    AlertLogic.           AlertLogicLM     Alert Logic Log Manager    1.3
data:    AlertLogic.Extension  AlertLogicLM     Alert Logic Log Manager    1.9
data:    Chef.Bootstrap.Wi...  CentosChefCl...  Chef Extension that se...  11.14
data:    Chef.Bootstrap.Wi...  ChefClient       Chef Extension that se...  1210.12
data:    Chef.Bootstrap.Wi...  LinuxChefClient  Chef Extension that se...  1210.12
data:    CloudLinkEMC.Secu...  CloudLinkSec...  CloudLink SecureVM pro...  5.0
data:    CloudLinkEMC.Secu...  CloudLinkSec...  CloudLink SecureVM pro...  5.0
data:    Confer                ConferForAzure   Confer Advanced Threat...  1.0
data:    Datadog.Agent         DatadogLinux...  Datadog helps you moni...  0.4
data:    Datadog.Agent         DatadogWindo...  Datadog helps you moni...  0.5
data:    dynatrace.ruxit       ruxitAgentLinux  Ruxit Agent for Linux      1.81
data:    dynatrace.ruxit       ruxitAgentWi...  Ruxit Agent for Windows    1.81
data:    ESET                  FileSecurity     ESET File Security for...  6.2
data:    Gemalto.SafeNet.P...  ProtectVClie...  The ProtectV client en...  3.0
data:    Gemalto.SafeNet.P...  ProtectVClie...  The ProtectV client en...  3.0
data:    McAfee.EndpointSe...  McAfeeEndpoi...  McAfee Endpoint Protec...  6.0
data:    PuppetLabs            PuppetEnterp...  Puppet configuration r...  3.2
data:    Symantec              SymantecEndp...  Symantec Endpoint Prot...  12.1
data:    Test.TrendMicro.D...  TrendMicroDS...  Deep Security delivers...  9.6
data:    TrendMicro.DeepSe...  TrendMicroDSA    Deep Security delivers...  9.6
data:    TrendMicro.DeepSe...  TrendMicroDS...  Deep Security delivers...  9.6
data:    TrendMicro.Portal...  PortalProtec...  Trend Micro??PortalPro...  2.1
data:    Vormetric             VormetricTra...  Vormetric Transparent ...  5.2
info:    vm extension list command OK

you will see we only have a small number of external partners allowed to publish extensions. If you have an an ISV who can leverage extensions system to publish your software, feel free to contact us.


Upgrade guzzle to 6.*

Otherwise it's not possible to use latest guzzle in the project alongside vkapi:
  Problem 1
    - getjump/vk 0.4.0 requires guzzlehttp/guzzle 4.* -> no matching package found.

What is the problem upgrade to Guzzle 6?

+1

@thedotedge Mark this package as "Abandoned" please if u did not want supports him. It did not works on PHP 7.1 because you use Guzzle 4.*. This problem was be fixed in https://github.com/guzzle/guzzle/pull/1393 Guzzle 5.3


Turtlebot 3 Slam

I'm following along the TB3 tutorial wiki and experimenting with Slam. It is working ok. I am seeing the edge conditions and saw various odd warnings which are concerning:
$ ros_wtf
ERROR The following nodes should be connected but aren't:
 * /move_base->/move_base (/move_base/global_costmap/footprint)
 * /move_base->/move_base (/move_base/local_costmap/footprint)

Also from `roslaunch turtlebot3 navigation turtlebot3_navigation.launch map file:=... I got:
-- 4172.2622, global_pose stamp:<PHONE_NUMBER>.7426, tolerance: 0.5000
[ WARN] [1532714172.262382062]: Could not get robot pose, cancelling reconfiguration
[ WARN] [1532714173.270830690]: Costmap2DROS transform timeout. Current time:<PHONE_NUMBER>.2707, global_pose stamp:<PHONE_NUMBER>.7566, tolerance: 0.5000
[ WARN] [1532714173.362163917]: Could not get robot pose, cancelling reconfiguration
[ WARN] [1532714174.273314850]: Costmap2DROS transform timeout. Current time:<PHONE_NUMBER>.2732, global_pose stamp:<PHONE_NUMBER>.4053, tolerance: 0.5000
[ WARN] [1532714174.461652581]: Could not get robot pose, cancelling reconfiguration

and
[ WARN] [1532714689.861442860]: Could not get robot pose, cancelling reconfiguration
[ WARN] [1532714690.862292205]: Costmap2DROS transform timeout. Current time:<PHONE_NUMBER>.8622, global_pose stamp:<PHONE_NUMBER>.3163, tolerance: 0.5000
[ WARN] [1532714690.862362027]: Could not get robot pose, cancelling reconfiguration
[ WARN] [1532714691.873289155]: Costmap2DROS transform timeout. Current time:<PHONE_NUMBER>.8732, global_pose stamp:<PHONE_NUMBER>.3163, tolerance: 0.5000
[ WARN] [1532714691.961505715]: Could not get robot pose, cancelling reconfiguration
[ WARN] [1532714692.873500408]: Costmap2DROS transform timeout. Current time:<PHONE_NUMBER>.8733, global_pose stamp:<PHONE_NUMBER>.3163, tolerance: 0.5000
[ WARN] [1532714692.962005510]: Could not get robot pose, cancelling reconfiguration
[ WARN] [1532714693.961530347]: Costmap2DROS transform timeout. Current time:<PHONE_NUMBER>.9615, global_pose stamp:<PHONE_NUMBER>.5175, tolerance: 0.5000

Can you tell me what those mean and how to fix them? Thanks!

Hi :)
The problem can be occured because asynchronous time between SBC and remotePC
or bad condition of Wifi.
I recommended you are supposed to synchronize the time using ntpdate commands
http://emanual.robotis.com/docs/en/platform/turtlebot3/faq/#timesync-between-turtlebot3-and-remote-pc

This issue will be closed since there were no actions for a while. You can reopen this issue to show this issue to the users whenever. Thanks.

Of course. If you want to auto execute it, you could make shell file and copy and paste it into /etc/init.d folder.

Thanks!

But you could have given a little more detail. What do you mean by make a shell file? Shell file containing what? How do you mean "copy and paste it (the file) into /etc/init.d folder"?


Put win32 and datadir logic in it's own classes

Extracts all the Shell32 code into Win32Utils and introduces a DataDirFactory that contains OS-specific determination of which path the data directory should be in.

Looks good, thanks!


uart: Superfluous syscalls for driver API

Split from https://github.com/zephyrproject-rtos/zephyr/pull/10761#issuecomment-432311026
"Even more surprising was that bunch of IRQ-related functionality of UART API was made userspace syscalls. @andrewboie, how did we get to that? Even with reading the docstring, it's pretty clear that uart_irq_tx_enable() is IRQ related. What's the point of exporting it userspace, like, user calls uart_irq_tx_enable(), what happens next? It's not possible to define ISR callback from userspace anyway. And most of the UART API is strictly related to interrupt-driven mode. Only uart_poll_in()/uart_poll_out() could be exposed to userspace (and perhaps err_check()). They're also not too useful, but why not? IMHO, we should fix that ASAP."

@nashif : I don't see a good label for userspace-vs-kernel split and/or syscalls. I suggest to create one, as many already introduced syscalls are questionable, so might be a regular topic.

If you don't think those calls should be there, remove them.

The thought process for the current state of the code was that the interrupt callback could be setup at application initialization, but once everything is set up and the app drops most of its threads to user mode, enabling/disabling the interrupt could still be controlled from a user mode thread granted access to that uart device.

That's exactly the kind of feedback I'm looking for, thanks! And thanks for expanding beyond the original "If you don't think those calls should be there, remove them.". So, I definitely have a stereotype thinking that usermode would work as in Linux, where an app starts in user mode right away. This is both inflexible thinking and pretty far from Zephyr model, we "applications" don't "start", but rather a single one runs on boot.
I'm not closing this right away, let me think on this until release times. Even if there's a theoretic usecase, we may want to account of API status ("legacy" and being replaced by others) and pragmatic matters like keeping syscall set bounded.

I guess this should be closed after all. I definitely not up to speed with complete understanding of userspace and its APIs, and even if I'd think I am, I'd be reluctant to change something a more experience colleague set forth, given the good explanations given.
Thanks for guidance @andrewboie here and elsewhere.


IPP supports All Browsers

Tested to work and GUI is stable in Safari, Chrome, Firefox, iPad

UI responsiveness for multiple screen sizes

Only tested on Firefox
priority order

firefox
chrome
mac safari
ipad (reduced screen size)
screen autosizing
bug vs enhancement?


[Bug] Regression in v0.0.32+ of volume spec as only csi, emptyDir and nfs are now supported while previously all volumes were supported

Documentation

[x] I acknowledge that I have read the relevant documentation.

Describe the bug
Version v0.0.32 reduced the CRDs specs by 81% percent by removing many "unsupported" fields (see PR #869). However, this also removed many of the volumes in any of the volume specs. This is native functionality from K8s which was previously supported (and used by myself for example) by mariadb-operator.
Expected behaviour
Before version v0.0.32 I was happily using hostPath for my backups and even running the latest version (0.0.36) with older CRDS (yes I forgot to update the CRDS using the new crds helm release) it simply works (because it is supported by K8s). Hence, I would expect all supported volumes by K8s to also be supported by mariadb-operator CRDs (like storage.volume.hostPath).
Steps to reproduce the bug

Create any Backup/Restore CRD with storage.volume.hostPath or any of the volumes supported by K8s which are not explicitly listed in mariadb-operator spec (csi, emptyDir, nfs)

  storage:
    volume:
      hostPath:
        path: /storage/backups/mariadb/customers
        type: DirectoryOrCreate

Debug information
N/A
Environment details:

Kubernetes version: v1.31.4
Kubernetes distribution: k3s
mariadb-operator version: 0.36.0
Galera data-plane version (init and agent): -
Install method: helm via flux (HelmRelease)

Additional context
Please note that before v0.0.32 the API_REFERENCES.md simply linked to K8s volume spec https://github.com/mariadb-operator/mariadb-operator/blob/0bdf0b86d922bd738d3e4d64fb473a287ba7a596/docs/API_REFERENCE.md#backupstorage while nowadays it references its own volume spec https://github.com/mariadb-operator/mariadb-operator/blob/main/docs/API_REFERENCE.md#backupstorage
I wanted to use this, because I am writing these logical backups to ZFS backed (and mirrored offshore) disk.

After reading https://github.com/kubernetes/kubernetes/issues/62872 I see the problem... Namely, you cannot use $refs to custom types nor to K8s native types.
Going through https://kubernetes.io/docs/concepts/storage/volumes/#volume-types you could argue  that local and hostPath are "usable" ways to write data for backups. I have a real world (and previously working great) use-case for hostPath so could at least that be added in for StorageVolumeSource spec?


request_users with max_quantity and user_is_bot doesn't work across all platforms

https://core.telegram.org/bots/api#keyboardbuttonrequestusers
--
set max_quantity: 10 and user_is_bot: true
windows desktop & webK - it works: it shows bots multi-select screen
android/ios - it doesn't work: it shows only contacts selection screen
--
set max_quantity: 1 and user_is_bot: true
windows desktop & webK - it works: it shows bots single-select screen
android/ios app - it works: it shows bot selection screen, but I can select only 1 bot
--
expected result: to select more than 1 bot-user at once on android/ios

https://github.com/tdlib/telegram-bot-api/blob/master/telegram-bot-api/Client.cpp#L7785
@levlam

I've reported the issue on other "channels" - but I got no response

Those are client-side bugs and nothing can be done on the bot side. You need to create a separate issue for Android at https://bugs.telegram.org and wait for them to be fixed.

sure, I've just done it for Android too.
though it wasn't fixed for almost a year already... would be nice if you coud "ping" your mobile colleagues, thanks!


Improve CLI user management

Private server can now run when there are no users
Add a couple warning to help users understand what's going on
Make sure server is running before loading users to avoid some potentially weird race conditions, and also to produce a nice output (otherwise it's all mangled)
Make The Lounge private by default
Fix a couple bugs

This is meant to help #1350. I'll be happy to fix conflicts in #1350 after this PR lands if that helps.

Forgot to mention, but I recommend reviewing with ?w=1.


Created Emojibubble Script for One-Click

Created the Emojibubble script for One-Click, a way for GMs and players to declare and express more options than token markers, with less permanence and more expressive purpose.

Nice! You could add a link to the Forum thread for the script.json description.
For the Repo Maintainer, I'd categorize this API under "Maps & Drawing" when it's added to approved.yaml

I'll do that - I'm planning a 1.1 release that keeps bubbles adjacent until they are dismissed (moves with the token), and a mathematical adjustment for larger and smaller tokens.

Decided to move 1.1 changes into 1.0, since there is time before release - all good - now bubbles follow you until they are dismissed, and bubbles are positioned relative to token size instead of an absolute offset. .


TestNg 6.9.5 do not behave as 6.8.x do.

When we did a uplift of TestNg the we saw a that expectedException behaves differently.
@Test(expectedExceptions = SkipException.class)

Duplicate of #773 and fixed with #774.
It will be available in the next TestNG release.


Improve exception detection / handling

Fixes #70
Use subprocess.check_call instead of subprocess.Popen, to raise when an exception occurs in one of the steps.

This PR content will be merged into PR #89 .

Superseded by #89


A little change in readme & BodyMassController class

README file improvement
Removed redundant class package import in [BodyMassController.java]

@marcogara you didn't close the #1 issue


chore(overview): Add Rust edition to overview

Building the entire workspace gives a warning without an edition

Looks good. One question, though, the Direct minimal versions fails again, and looks like it's something to do with workflow in policy-reasoner not compiling? Are git repo's minimal version'ed as well?

Okay, I found the problem. Well, I did something we should have, and it solved the problem, I think.
We did not pin the git dependencies in the policy reasoner, doing so resolves this issue.
Why this did not break in the PR for the git version pinning itself is still a mystery to me, but I think we should just fix it and move on. I will create a PR somewhere today.

Are git repo's minimal version'ed as well?

I don't think that is possible. Tags are just tags and have no implication of being semver to begin with. It could also be calver, or anything else.

Looking into this even more, I'm getting more confused by the second. On my computer, cargo insists on updating the lockfile because the enum-debug dependency has been pinned to a specific tag. I am not sure why it didn't do that last week, but I can live with that. It gets stranger however, CI still thinks updating the lockfile is not necessary and compiles fine, however locally, my normal build runs into the same issues as the minimal version build.
So the big question is, why does the same rust/cargo version, compiling the same commit, lead to drastically different results on CI and my machine.

Ah right xD registry caches sound like they would do the trick. Good catch!


https://jihulab.com/xihan123/QDReadHook.git

？

Unable to understand, the issue is invalid and closed.


Protobuf Codec: Fails to decode lists of lists

Currently, we fail to decode empty lists of lists. Issue can replicated with the following test case
  case class WithPiggyback(
    conversationId: Long,
    message: Chunk[Byte],
    gossip: List[Chunk[Byte]]
  )

  object WithPiggyback {
    implicit val schema: Schema[WithPiggyback] = DeriveSchema.gen

    implicit val gen: Gen[Random with Sized, WithPiggyback] =
      for {
        conversationId <- Gen.anyLong
        message        <- Gen.chunkOf(Gen.anyByte)
        gossip         <- Gen.listOf(Gen.chunkOf(Gen.anyByte))
      } yield WithPiggyback(conversationId, message, gossip)
  }

      testM("record with array fields") {
        checkM(WithPiggyback.gen) { value =>
          encodeAndDecodeNS(Schema[WithPiggyback], value, print = true).map { ed =>
            assert(ed)(equalTo(value))
          }
        }
      }

The issue seems to be in composing Decoders where flatMap errors out if there are no bytes remaining in the chunk after running the "first" decoder. This causes an issue when the final field in a Record is a collection. In that case, the key will be decoded as a length delimited field with zero length. As expected there are no more bytes to decode since the list is empty but when flatMaping the Decoder will error out.

The problem occurs with a single empty list as well:
testM("record with array fields") {
  val value = WithPiggyback(List(), 0)
  encodeAndDecodeNS(Schema[WithPiggyback], value, print = true).map { ed =>
    assert(ed)(equalTo(value))
    }
}

case class WithPiggyback(
  gossip: List[String],
  conversationId: Long
)

Even without the conversationId field.

I'll work on this.


Godot Physics collisions and solver processed on threads

Use ThreadWorkPool to process physics step tasks in multiple threads.
Collisions are all processed in parallel and solving impulses is processed in parallel for rigid body islands.
Additional changes:

Proper islands for soft bodies linked to active bodies
All moving areas are on separate islands (can be parallelized)
Fix inconsistencies with body islands (Kinematic bodies could link bodies together or not depending on the processing order)
Completely prevent static bodies to be active (it could cause islands to be wrongly created and cause dangerous multi-threading operations as well as inconsistencies in created islands)
Apply impulses only on dynamic bodies to avoid unsafe multi-threaded operations (static bodies can be on multiple islands)
Removed inverted iterations when populating body islands, it's now faster in regular order (maybe after fixing inconsistencies) - see discussion https://github.com/godotengine/godot/pull/47846#discussion_r612548318 (CC @AndreaCatania)

Test configuration
CPU: AMD Ryzen 9 4900HS with Radeon Graphics, 3000 Mhz, 8 Core(s), 16 Logical Processor(s)
RAM: 16.0 GB
Display: GeForce RTX 2060 with Max-Q Design/PCIe/SSE2
Test scenes
2D - Simple contacts (1 island - 500 rigid bodies)
https://github.com/godotengine/godot-demo-projects/tree/master/2d/physics_tests/tests/performance/test_perf_contacts.tscn
~15% improvement (physics ticks)
2D - Advanced contacts (multiple islands - 9 x 300 rigid bodies)
https://github.com/godotengine/godot-demo-projects/tree/master/2d/physics_tests/tests/performance/test_perf_contact_islands.tscn
~25% improvement (physics ticks)
3D - Simple contacts (1 island - 500 rigid bodies)
https://github.com/godotengine/godot-demo-projects/tree/master/3d/physics_tests/tests/performance/test_perf_contacts.tscn
~30% improvement (physics ticks)
3D - Advanced contacts (multiple islands - 16 x 200 rigid bodies)
https://github.com/godotengine/godot-demo-projects/tree/master/3d/physics_tests/tests/performance/test_perf_contact_islands.tscn
~65% improvement (physics ticks)
These results are measured with a 3.x port of this PR.
The lower results in 2D can be explained by a bottleneck in the broadphase, which takes more than half of the physics tick time. It would be worth checking if replacing the Hash Grid with a 2D dynamic BVH would help with that.
Edit: This is done on the 3.x branch with #48314.

Thanks!


The pumped (hydro) storage slider disappears sometimes for a UK scenario (LIVE)

For a UK scenario (LIVE), the slider to set the capacity of pumped storage sometimes disappears. Not sure what causes the slider to disappear though..

vs.

@antw / @noracato Would you have any idea what's going on here?

@noracato @sfp-kemp Would you mind looking into this? It seems like the area dependencies on input elements (maybe other YModels?) might not be working as expected.
I loaded an NL scenario on production and the "Reservoirs" slider was shown alongside "OPAC", even though "Reservoirs" is dependent on "has_mountains". "has_mountains" is false for NL. "OPAC" has no dependency and seems to be correctly shown always.
Then I loaded a UK scenario, which showed both "Reservoirs" and "OPAC" sliders as expected, then loaded NL again and "Reservoirs" disappeared. It doesn't seem to be predictable, but refreshing the page causes the slider to sometimes appear and sometimes not.

Should be fixed by #3277. Thanks @noracato!


Can't build typescript nodejs app when algoliasearch is included

I have been able to install the npm module and also wrote code for the functions. But I keep getting this when i try to run my code/build with tsc. Not sure if this is a bug

Algolia version - 4.4.0
Typescript version - 3.3.3

So it turns out I had to upgrade my typescript version. I upgraded to 4.0.2 and this error disappeared. Cheers


Document meta-components

Ones I know about: :Self and :Window

https://svelte.technology/guide#-self-tags


no template named 'two_div_root_pi' in namespace

On my Mac, both with C++ and clang++ I get:
rob@Rob-M1 redding-stan % make check
* bin/stanc ok
* Stan submodule ok
* Stan Math submodule ok

Check: All checks pass
rob@Rob-M1 redding-stan % bin/stanc --version
stanc3 v2.29.1 (Unix)
rob@Rob-M1 redding-stan % make examples/bernoulli/bernoulli
c++ -I src -isystem stan/src -isystem stan/lib/stan_math -isystem stan/lib/stan_math/lib/eigen_3.3.9 -isystem stan/lib/stan_math/lib/boost_1.75.0 -isystem stan/lib/stan_math/lib/sundials_6.0.0/include -isystem stan/lib/stan_math/lib/tbb_2020.3/include -std=c++14 -D_REENTRANT -O3   -c -o src/main.o src/main.cpp
In file included from src/main.cpp:7:
In file included from stan/src/stan/model/model_header.hpp:4:
In file included from stan/lib/stan_math/stan/math.hpp:19:
In file included from stan/lib/stan_math/stan/math/rev.hpp:8:
In file included from stan/lib/stan_math/stan/math/rev/core.hpp:28:
In file included from stan/lib/stan_math/stan/math/rev/core/operator_addition.hpp:10:
stan/lib/stan_math/stan/math/prim/fun/constants.hpp:169:31: error: no template named 'two_div_root_pi' in namespace 'boost::math::constants'; did you mean 'one_div_root_pi'?
    = boost::math::constants::two_div_root_pi<double>();
      ~~~~~~~~~~~~~~~~~~~~~~~~^
/usr/local/include/boost/math/constants/constants.hpp:300:30: note: 'one_div_root_pi' declared here
  BOOST_DEFINE_MATH_CONSTANT(one_div_root_pi, 5.641895835477562869480794515607725858e-01, "5.64189583547756286948079451560772585844050629328998856844085721710642468441493414486743660202107363443028347906e-01")
                             ^
1 error generated.
make: *** [src/main.o] Error 1

Thanks for reporting the issue! I'll try to piece together what went wrong based on the output... I may need a little more info.
I'll try to loop back around tonight.

I see what's going on; I'll try to figure out how to get around it.
This part of the error message clued me in: /usr/local/include/boost/math/constants/constants.hpp
Somehow, you're compilation instructions are including a different boost version than the one included in math.
I'm looking at the compiler invocation and I don't see anything obvious. I think it's some default compiler option and we've got to disable it.

Just ask!
I'm also trying to compare this with using generate_quantities(). But that I guess is definitely more limited.

I'm also trying to compare this with using generate_quantities(). But that I guess is definitely more limited.

@goedman what are you comparing with generate_quantities()? Are you trying to call the generated quantities block from ReddingStan?

Not in redding-stan, using cmdstan:
stan5_1 = "
data {
    int < lower = 1 > N; // Sample size
    vector[N] D; // Outcome
    vector[N] A; // Predictor
}
parameters {
    real a; // Intercept
    real bA; // Slope (regression coefficients)
    real < lower = 0 > sigma;    // Error SD
}
transformed parameters {
    vector[N] mu;               // mu is a vector
    for (i in 1:N)
        mu[i] = a + bA * A[i];
}
model {
    a ~ normal(0, 0.2);         //Priors
    bA ~ normal(0, 0.5);
    sigma ~ exponential(1);
    D ~ normal(mu , sigma);     // Likelihood
}
generated quantities {
    vector[N] log_lik;
    for (i in 1:N)
        log_lik[i] = normal_lpdf(D[i] | mu[i], sigma);
}
";

@goedman, I think I found it. I had forgotten about this problem and I had written it up a few years ago:
https://discourse.mc-stan.org/t/clang-include-path-order/2787
For the time being, can you try these two steps in order?

Build src/main.o. I replaced -isystem with -I:

c++ -I src -I stan/src -I stan/lib/stan_math -I stan/lib/stan_math/lib/eigen_3.3.9 -I stan/lib/stan_math/lib/boost_1.75.0 -I stan/lib/stan_math/lib/sundials_6.0.0/include -I stan/lib/stan_math/lib/tbb_2020.3/include -std=c++14 -D_REENTRANT -O3   -c -o src/main.o src/main.cpp

Build the example you were trying:

make examples/bernoulli/bernoulli

If this works, you should be able to continue building any executable since you don't have to rebuild src/main.o. We can patch the build process to change the include flag to -I.
If it doesn't work, we'll have to keep looking for a solution.

I updated the makefile as follows:
STAN_VERSION = v2.29.1
STANCFLAGS ?= --warn-pedantic

CXXFLAGS = -I src
CXXFLAGS += -I stan/src -I stan/lib/stan_math
CXXFLAGS += -I stan/lib/stan_math/lib/eigen_3.3.9
CXXFLAGS += -I stan/lib/stan_math/lib/boost_1.75.0
CXXFLAGS += -I stan/lib/stan_math/lib/sundials_6.0.0/include
CXXFLAGS += -I stan/lib/stan_math/lib/tbb_2020.3/include
CXXFLAGS += -std=c++14
CXXFLAGS += -D_REENTRANT
CXXFLAGS += -O3

and then I get:
rob@Rob-M1 redding-stan % make examples/bernoulli/bernoulli
bin/stanc examples/bernoulli/bernoulli.stan --o examples/bernoulli/bernoulli.cpp
c++ -I src -I stan/src -I stan/lib/stan_math -I stan/lib/stan_math/lib/eigen_3.3.9  -I stan/lib/stan_math/lib/boost_1.75.0 -I stan/lib/stan_math/lib/sundials_6.0.0/include -I stan/lib/stan_math/lib/tbb_2020.3/include -std=c++14 -D_REENTRANT -O3   -c -o examples/bernoulli/bernoulli.o examples/bernoulli/bernoulli.cpp
c++ -I src -I stan/src -I stan/lib/stan_math -I stan/lib/stan_math/lib/eigen_3.3.9  -I stan/lib/stan_math/lib/boost_1.75.0 -I stan/lib/stan_math/lib/sundials_6.0.0/include -I stan/lib/stan_math/lib/tbb_2020.3/include -std=c++14 -D_REENTRANT -O3    examples/bernoulli/bernoulli.o  src/main.o stan/lib/stan_math/lib/tbb/libtbb.dylib stan/lib/stan_math/lib/tbb/libtbbmalloc.dylib stan/lib/stan_math/lib/tbb/libtbbmalloc_proxy.dylib -Wl,-L,stan/lib/stan_math/lib/tbb -Wl,-rpath,stan/lib/stan_math/lib/tbb -o examples/bernoulli/bernoulli
rm examples/bernoulli/bernoulli.o examples/bernoulli/bernoulli.cpp
rob@Rob-M1 redding-stan %

So I think that fixed it!!!! Will continue with the README steps.
Thanks for your help!

Yip, it works:
rob@Rob-M1 redding-stan % ./examples/bernoulli/bernoulli

ReddingStan version 1.0

ReddingStan is free software and comes with ABSOLUTELY NO WARRANTY.

Type 'help' for some help, 'list' a list of commands.

[redding]$ list

List of all commands:
  list           list all commands
  help           prints basic usage
  status         status of the current run
  N              number of parameters in the model
  load           loads data
  unload         unloads data
  eval           evaluate log probability of the model at an unconstrained parameter value
                 dropping constants; equivalent to `eval_J_true`
  eval_J_true    evaluate log probability of the model at an unconstrained parameter value
                 with Jacobian adjustment, dropping constants. Output:
                   Line 1:  log prob
                   Line 2:  gradient; length N
                   Line 3:  evaluation time (μs)
                   Line 4+: messages from the program
  eval_J_false   evaluate log probability of the model at an unconstrained parameter value
                 excluding Jacobian adjustment, dropping constants. Output format is the same
                 as `eval_J_true`.
  eval_J_only    evaluate the log absolute determinant of the Jacobian; does not include
                 a gradient. Output:
                   Line 1:  log prob
                   Line 2:  evaluation time (μs)
                   Line 3+: messages from the program
  history        prints history
  quit           quit

[redding]$ load examples/bernoulli/bernoulli.data.R
[INFO] model initialized with data from "examples/bernoulli/bernoulli.data.R"

[redding]$ eval_J_true 0.2
-8.97767
-3.59801
34
""
[redding]$

Same results with clang++ (I added cXX=clang++ early on in makefile above CXXFLAGS lines).
Thanks a lot Daniel!

Thanks for reporting back! If you're feeling generous, want to put those changes in a PR? If not, I'm happy to make those changes when I have a minute.

Yip, will make a PR tomorrow.

PR done. Shall I close this?


Add command to open file relative to active workspace

This PR adds a second command (no default key binding) that starts the file picker at the root of the current workspace rather than the directory of the current file.

Thank you for the PR! Looks Good 👍
Could you run yarn fmt to fix the CI build?

Done! Didn't trigger Node CI though 🤔

Thank you for fixing!

Didn't trigger Node CI though 🤔

Oh...this is due to my having changed base branch from master to main yesterday 😓 (I forgot how the CI is triggered 🙃)
As for the format, I've confirmed it's fine locally!

will you release a new version soon that contains the new command?

I'm going to release it on this weekend 😄

This has been released! 🎉
https://github.com/jit-y/vscode-advanced-open-file/releases/tag/v0.3.0


Various fixes for check_firewall task

The check to see if nmap is available was being run on the remote host, whereas the checks to see if the applicable Ceph ports were open were being run as local_actions.
Also included are various related fixes now that the above functionality has been corrected (tested on Ansible 2.x only).
Closes: https://github.com/ceph/ceph-ansible/issues/754

test this please

test this please

I'm getting the following error (cntl0 is one of my mon hosts)
TASK [ceph.ceph-common : check if monitor port is not filtered] ****************
fatal: [cntl0]: FAILED! => {"failed": true, "msg": "'dict object' has no attribute u'ansible_interface'"}

My environment:
% ansible-playbook --version
ansible-playbook <IP_ADDRESS>

@ASBishop What is the value of your "monitor_interface" variable, as it looks like it is set to "interface" based on the output you provided, ie: the attribute it is referring to should look look something like "ansible_eth0" as opposed to "ansible_interface"

@matthewrees I define monitor_address and do not define monitor_interface.

@ASBishop would you mind giving it another try with the latest commit?

@ASBishop Bearing in mind that you might need to set check_firewall to true manually now as we have disabled it by default. Thanks!

@matthewrees Sorry, but now I get a similar error, but with the monitor_address.
TASK [ceph.ceph-common : check if monitor port is not filtered] ****************
fatal: [cntl0]: FAILED! => {"failed": true, "msg": "'dict object' has no attribute u'A.B.C.D'"}
where A.B.C.D is the actual address of the host.

@ASBishop Sorry, silly mistake, hopefully this will be the last time!

@matthewrees OK, the syntax problem is fixed, but it exposed two new problems.
I have 3 mons, and it looks like the with_items:  "{{ groups[mon_group_name] }}" causes it to generate a local_action command string that always references the host name and address of the first mon. In my test environment, I manually closed the first mon's port, and left it open on my other two mons. I expected the first mon host to fail and the second two to pass.
For example, if I my mons are moe, larry and curly,  the "failed" output message will report this:
failed: [moe] ...
failed:[larry] ...
failed:[curly] ...
But the command string will show that nmap was always testing moe and moe's IP address.
The second problem is I think nmap needs to run as root. I use a non-root local account, and if I manually run nmap the command will fail ("Host seems down" but the error message does not contain the word "filtered". This will cause a false positive. The test will always fail, but will never be detected as a failure.

Sorry, ignore my comment about the second problem (nmap needs to run as root). While that appears to be true when I run it manually, it does not causes a false positive or else the tasks would succeed.

@ASBishop I've made some fixes to how nmap is called and filtered, and have also tested the firewall checks against monitor_address instead of monitor_interface. Could you test it again and let me know?
PS. If you are an IRC user you are welcome to reach me in the Ceph IRC channel as the user "secate" if you'd like to debug this in real time.

@matthewrees Progress! It now correctly probes all of the hosts (not just the first). However, the combination of using with_items to loop over the hosts and run_once to only perform the loop once causes any errors to be reported against the first host (the lucky run_once host). Using my previous example, let's say moe's and curly's port is open but larry's port is not. The test will properly fail, but it will claim it was moe who failed.
With this in mind, I humbly offer the following alternative that uses inventory_hostname. It eliminates the need for with_items and run_once.

name: check if monitor port is not filtered
local_action: shell set -o pipefail && nmap -p 6789 {{ hostvars[inventory_hostname]['ansible_' + monitor_interface]['ipv4']['address'] if hostvars[inventory_hostname]['ansible_' + monitor_interface] is defined else hostvars[inventory_hostname]['monitor_address'] }} | grep -sq -e filtered -e '0 hosts up'
changed_when: false
failed_when: false
register: monportstate
when:

check_firewall
mon_group_name in group_names
nmapexist.rc == 0

name: fail if monitor port is filtered
fail:
msg: "Please allow port 6789 on your firewall"
when:

check_firewall
mon_group_name in group_names
nmapexist.rc == 0
monportstate.rc == 0

Obviously the same pattern would need to be used for the other port tests.
I currently do not have IRC access, but will look for you when I get it back.

@ASBishop Thanks! I should have noticed a cleanup was possible after all the changes, done.

@matthewrees Whoops, there's a typo in the shell command that probes the monitor port (6689 should be 6789).

Huzzah! Everything works for me now. I did just a quick test on mons, osds and rgws (sorry, no mds) and things behave as I expected. No false positives or false negatives.

test this please

Thanks guys, nice collaboration! Let's see what the CI has to say about this and then we can merge this :)


Point to private GPT instance

In order to support private development, it would be necessary to point to a custom API endpoint such as GPT4all, or even Azure's OpenAI as a service:  https://azure.microsoft.com/en-in/products/cognitive-services/openai-service/.  Having a 'Custom', a few standard configuration paramenters should do the trick based on Azure's docs:
import os
import openai
openai.api_type = "azure"
openai.api_base = "https://docs-test-001.openai.azure.com/"
openai.api_version = "2023-03-15-preview"
openai.api_key = os.getenv("OPENAI_API_KEY")

Yes, you can connect to custom API endpoints like Azure's OpenAI service in CodeGPT. Here are the steps to do so:
Create an account in Microsoft Azure.
Get access to Azure OpenAI Services.
Go to Azure OpenAI Studio.
Get your Secret Key and the Custom Link.
Go to VSCode and choose Azure as Provider.
Set connection on 🔑 button.
Paste API Key here, and click on Connect.
If you want to remove your API Key from CodeGPT, select Edit Connection on 🔑, and click on Disconnect.
Please note that you will need to have the necessary permissions and credentials to access the API. If you encounter any issues, you can refer to the Microsoft Azure Documentation.


snippet-bot full scan

Life is too short to manually check unmatched region tags.
snippet-bot scan result

[ ] appengine/flexible/RedisCache/Controllers/HomeController.cs:58, tag redis_cache doesn't have a matching start tag
[ ] appengine/flexible/RedisCache/Startup.cs:58, tag redis_startup doesn't have a matching start tag
[ ] bigtable/api/Snippets/Filters.cs:23, tag bigtable_filters_imports doesn't have a matching start tag
[ ] bigtable/api/Snippets/Reads.cs:21, tag bigtable_reads_imports doesn't have a matching start tag
[ ] iot/api/CloudIotSample/Program.cs:1434, tag iot_attach_device doesn't have a matching end tag
[ ] texttospeech/api/TextToSpeech/TextToSpeech.cs:257, tag tts_synthesize_text_file has already started
[ ] texttospeech/api/TextToSpeech/TextToSpeech.cs:297, tag tts_synthesize_ssml_file has already started
[ ] texttospeech/api/TextToSpeech/TextToSpeech.cs:221, tag tts_synthesize_text_file doesn't have a matching end tag
[ ] texttospeech/api/TextToSpeech/TextToSpeech.cs:259, tag tts_synthesize_ssml_file doesn't have a matching end tag

I've fixed them all and activated snippet-bot on the .NET samples repo.
Thanks!


Support Linux DRM backend

Currently, using the DRM legacy interface is suitable for Mado's backend because we only need basic window display without advanced features.

Currently, using the DRM legacy interface is suitable for Mado's backend because we only need basic window display without advanced features.

What compatibility risks exist when using DRM legacy interfaces with newer Linux kernels? Could you verify the compatibility status of libdrm across different kernel versions?
What prevents the DRM backend from rendering content across the full screen resolution?

@a1091150, You might check DRM backend for your environment as well.

Currently, using the DRM legacy interface is suitable for Mado's backend because we only need basic window display without advanced features.

What compatibility risks exist when using DRM legacy interfaces with newer Linux kernels? Could you verify the compatibility status of libdrm across different kernel versions?
What prevents the DRM backend from rendering content across the full screen resolution?

I need more time to research and adjust.

Check the DRM implementation for reference: https://github.com/zlgopen/awtk-linux-fb/blob/master/awtk-port/lcd_linux/lcd_linux_drm.c


Discussion Forum Tab

Describe the solution you'd like
Discussion forum for people to share questions, ideas, or any problems with building deep learning model. Think "friendly stack overflow"
Setup Instructions (what branch to work off of)
Create branch discussion-forum off of latest version of dev.
Instructions:
git checkout dev
git pull origin dev
git checkout -b discussion-forum

@noah-iversen status on this?


Performance issues with Range on DocSite

Please verify the version of auro-datepicker you have installed
3.1.8
Please describe the bug

Visit the DocSite: https://auro.alaskaair.com/components/auro/datepicker
Interact with the "Range" example
Click around and note performance begins to degrade before entirely stopping

Video:
https://github.com/user-attachments/assets/76df1bb2-8426-4441-a8a2-d3fd3616c4fa
Expected behavior
Component should be performant and continue to function, regards of interaction.
What browsers are you seeing the problem on?
Reproduced in Safari & Chrome

I think this might not actually be a bug.  The Date Picker doesn't let you select an earlier date after you have selected an initial date.  It locks selection to only be same day or later.  I wasn't able to reproduce this bug on Edge or Chrome.


Fundamentals incorrectly state only cdn-cgi is added

Which Cloudflare product does this pertain to?
Fundamentals
Existing documentation URL(s)
https://developers.cloudflare.com/fundamentals/reference/policies-compliances/content-security-policies/#using-a-csp-with-cloudflare
What changes are you suggesting?
The section states: Cloudflare does not: [...] Modify URLs (besides adding the /cdn-cgi/ endpoint).
This is no longer correct, as Cloudflare Fonts is adding an additional /cf-fonts/ endpoint.
Additional information
No response

@Le0Developer Thank you for bringing this up. We addressed the issue in this: PR https://github.com/cloudflare/cloudflare-docs/pull/12313


Add configuration for x-maturity of services

Add in configuration parameters to specify which x-maturity of services we would like to use for a given deployment. Logically this can be done similarly to how it is currently being done in https://github.com/ranking-agent/kp-registry

Here's the PR on the kp registry where I did this: https://github.com/ranking-agent/kp-registry/pull/26


Update catsrc items

Update build_catsrc.sh and catsrc action to work with new structure.
Change Makefile target images to build image with git version hash in tag.

https://github.ibm.com/IBMPrivateCloud/roadmap/issues/48268


Making querying method configurable

This long coming PR should hopefully address #10 and #14
// plainto_tsquery()
$results = App\Post::search('cat rat')->usingPlainQuery()->get()

// plainto_tsquery()
$results = App\Post::search('cat rat')->usingPhraseQuery()->get()

// to_tsquery()
$results = App\Post::search('fat & (cat | rat)')->usingTsQuery()->get()

// DYI using a callback
use ScoutEngines\Postgres\TsQuery\ToTsQuery;

$results = App\Post::search('fat & (cat | rat)', function ($builder, $config) {
    return new ToTsQuery($builder->query, $config);
})->get();

It's possible to configure the default querying method in scout.php
    'pgsql' => [
        'connection' => 'pgsql',
        'maintain_index' => true,
        'search_config' => 'english',
        // Possible values are plainquery|phrasequery|tsquery. Default - plainquery
        'search_using' => 'tsquery',
    ],

@pmatseykanets I'm going to test this pull-request.
Thank you for this new feature!

If I understand well this pull request, it only allows to customize the text search part of the SQL query, but not the entire query.

@tortuetorche Yes, in its current form it only allows you to pick the function that produces the tsquery, but I should've been clearer in my comment to #19 if we pass $query and $bindings into the closure it's becomes possible to customize (to some extent) the overall query.


Still maintained?

In case this module is no longer maintained (last commit was ~3 years ago), here are some more actively maintained alternatives:

exit-hook
async-exit-hook - with async support, which this module doesn't have (it was released before ES2017).

I haven't worked with node for the past 1.5 years or so. Does anyone see reason to continue maintaining this module in light of the other available options?

After spending some time looking into the other modules, they aren't without issues:

exit hook hasn't added async support since it was requested in 2014, and it looks like that might be waiting on a core Node feature
async-exit-hook needs native promise support and to handle errors in the exitHook

If there's no time to maintain this module, and exit-hook offers a superset of its functionality (neither have async support), then I agree that npm deprecation and archiving the repo would be a good idea, hopefully funneling developer resources towards the other two modules.

@dandv it looks like the issue with exit-hooks had been solved.

Just to keep this updated, exit-hook added async support but also went "pure ESM" which is very frustrating for those still stuck on CJS.


Use config_context in sklearn interface.

Related https://github.com/dmlc/xgboost/issues/7308

Test for nested config_context.
Test for thread safety of global config.
Use config context in sklearn interface.
Format the sklearn module.

I ran formatter on the sklearn module since the change is already spanning through the module anyway.

I ran formatter on the sklearn module since the change is already spanning through the module anyway.


When renaming an object, fill in current name

Is your feature request related to a problem? Please describe.
When renaming an GameObject the current name is not populated.
Describe the solution you'd like
When renaming a GameObject it would be nice if it filled in the objects current name.
Additional context

Hello @JamesTKhan , i would like to work on this

Hello @JamesTKhan , i would like to work on this

Sure, let me know if you have any questions we have also a Wiki page: https://github.com/JamesTKhan/Mundus/wiki/Working-from-source

just one question, in this context current name mean the name in the Inspector?

just one question, in this context current name mean the name in the Inspector?

Yeah, the name of the GameObject. Here is an example of how renaming works


input not focus when files update in git

Issue Type: Bug
in workspace

open terminal
build your code in react or patch update files
in the meantime. if you are input commit text in first git project. then it will onblur and sometime the text will show in another input item.
hope files change not influence on input which i am working on.

VS Code version: Code 1.54.2 (fd6f3bce6709b121a895d042d343d71f317d74e7, 2021-03-11T00:56:09.783Z)
OS version: Darwin x64 19.6.0
Extensions (17)

Extension
Author (truncated)
Version

vue-peek
dar
1.0.2

dart-code
Dar
3.20.1

flutter
Dar
3.20.0

vscode-eslint
dba
2.1.19

gitlens
eam
11.3.0

vue-snippets
hol
1.0.4

vue
jcb
0.1.5

formate
Mik
1.1.6

vscode-docker
ms-
1.11.0

vscode-language-pack-zh-hans
MS-
1.54.2

remote-containers
ms-
0.163.2

korofileheader
OBK
4.8.3

vetur
oct
0.33.1

markdown-preview-enhanced
shd
0.5.16

vue-helper
she
2.4.7

tabnine-vscode
Tab
3.2.8

markdown-all-in-one
yzh
3.4.0

A/B Experiments
vsliv368cf:30146710
vsreu685:30147344
python383cf:30185419
vspor879:30202332
vspor708:30202333
vspor363:30204092
pythonvsdeb440:30248342
pythonvsded773:30248341
pythonvspyt875:30259475
vspre833:30267464
vsfjg287:30275553

Can confirm this is happening. I start typing on a project's commit input, then after the diff reloads the project on the bottom gets focused, interrupting what I was doing.

Duplicate of https://github.com/microsoft/vscode/issues/120162


Numpy version pinned to <1.23 causes install to fail in combination with newer pandas and numpy versions

I tried leveraging h-nne together with newer pandas and numpy versions and this makes install fail because of the numpy version being pinned to <1.23.
Could you publish a new release with the numpy version unpinned? I tested locally and at least the getting started worked with numpy 1.24.4.

ah, yeah it was because pynndescent internally requires numba which at the time was not supporting numpy version >1.23. @koulakis , I think numba now works with numpy >1.23 , could you please check and if this is the case then remove this numpy version restriction.

@dani2112, thank you for bringing this up! The incompatibility of numba with the latest numpy versions has been an issue in this project, @ssarfraz can attest to that :sweat_smile:
I published hnne vesion 0.1.10 which should now have no upper bound on the numpy version, but installs numba first to make sure it has no issues. Indeed, when installing the new package it picks now version 1.24.4 of numpy which is enforced by numba.
Give it a try and let me know if there is any further issue. Cheers!

@ssarfraz @koulakis thanks for the quick fix, works like a charm now!


Feature Request: Persistent Clipboard History Between Seessions

I'm a big fan of Clipman, and I find it incredibly useful. However, I have a feature request that would make it even better. Currently, the clipboard history is lost upon reboot or logout, and I think it would be a valuable improvement to make the clipboard history persist across sessions.
I kindly request the implementation of a feature that allows the clipboard history to be saved and restored even after system reboots or user logouts. This would greatly enhance the usability of the extension and make it more convenient for users who rely on it for their daily tasks.
One possible implementation suggestion is to create a dedicated data file that stores clipboard history entries and is automatically loaded upon Clipman startup. This way, the clipboard history can be restored each time the user logs in or the system reboots.
If there are any technical constraints, considerations, or challenges associated with this feature request, please let me know. I'd be happy to assist or test any proposed changes to help make this feature a reality.
Thank you for your work on the Clipman extension, and I hope you will consider this feature request. If you have any questions or need further clarification, please feel free to ask.

Fixed in version 55.


Godot editor spawning zombie processes

Operating system or device - Godot version:
debian 8 x64 ( stable )
godot 2.1 stable
Issue description (what happened, and what was expected):
On being instructed to use external editor, Godot spawns zombie processes of that editor and itself.
Steps to reproduce:

Set external editor in the Settings -> Editor Settings -> External editor ( click "On" and write the path to executable )

Open up 2-3 scenes with scripts so that external text editor will fire up showing .gd files

close external text editor

see zombie processes crawling around:

ps -A | grep defunct
or
top
Tested and confirmed with geany and gvim.
Observed 2-3 copies of zombie "Godot_v2.1-stab" process and a dozen of gvim or geany zombie instances.

still happening in 3.2 by the way


client.py is hanging

The client.py code seems to run to some extent but now there seems to be a problem.
There has been no movement after I ran the code and input my user goal for several minutes.  Here is the terminal output:
(venv) Dazzas-iMac:test-client dazzagreenwood$ python client.py
Enter your project goal: Create a reusable process for LLMs to generate many diverse, useful, and creative ideas expressed as project goals.
2024-12-09 23:18:26,665 - INFO - Processing request of type CallToolRequest
INFO:mcp.server:Processing request of type ListToolsRequest

o1
What’s Happening?
The client appears to be stuck after you enter the goal. The logs show a ListToolsRequest processed, and then presumably a CallToolRequest is sent to the Module 1 server, which then talks to the Gemini server. Since you’re not seeing further progress, it’s likely the Gemini server is hanging while trying to generate content.
A common cause is that the code calling the Gemini API (model.generate_content(prompt)) might be blocking, waiting indefinitely for a response. This can happen if:

Invalid or Missing API Key: If the GEMINI_API_KEY is incorrect or expired, the call may hang or fail silently.
Network or API Issues: The model.generate_content() call communicates with the external Gemini API. If the network is slow, the API is down, or the request is malformed, it might never return.
Code Mismatch with the Current Library Version: The google.generativeai library you’re using might have changed its methods or require additional parameters. Check the library’s current documentation to ensure model.generate_content(prompt) is the correct method and syntax.
Lack of Async/Await Compatibility: If model.generate_content() is synchronous and blocking, and you’re mixing it with async code, it might appear to hang until the external call finishes (or times out, if it ever does).

What to Do:

Verify Your API Key:

Double-check that GEMINI_API_KEY is correctly set in gemini-server/.env.
Ensure that the API key is still valid and has the necessary permissions.

Check the Gemini API Documentation:

Review the google.generativeai documentation to confirm that model.generate_content(prompt) is the correct method and that it should return promptly.
If the method has changed or requires additional parameters, update your code accordingly.

Test a Simple Call:

Try a simple Python script outside of the MCP framework that just calls model.generate_content(prompt) with a known prompt and prints the result.
If it hangs there too, the problem is likely with the API call or your API key, not the MCP infrastructure.

Add Timeouts or Logging:

Add a print statement before and after the model.generate_content(prompt) call in generate_text():print("About to call generate_content...")
response = model.generate_content(prompt)
print("Received response from generate_content.")

If you see "About to call generate_content..." but never "Received response...", it confirms the call is stuck.
Consider adding a timeout or using a version of the call that can fail fast if it doesn’t return in a certain timeframe.

Check Network and Firewall:

Ensure your machine can access the Gemini API endpoint.
A firewall or network setting might be blocking outgoing requests.

Next Steps:

Double-check .env and API key validity.
Consult the google.generativeai documentation or try a minimal example script.
Add debugging prints and consider timeouts.
If the call still hangs, investigate network issues or consider reaching out to the Gemini API provider for support.

By diagnosing these points, you can isolate why model.generate_content() isn’t returning and fix the issue so the code will proceed and print the generated plan.

Gemini 1.5 Experimental 1206
The output shows that the client successfully sent the call_tool request to the module1-server, and that the module1-server successfully received the list_tools request from the Gemini server. However, there are no further log messages indicating progress or completion. This suggests a hang or block somewhere in the communication flow between the module1-server and the gemini-server, or within the servers themselves after these final messages.
Here's a structured debugging approach:

Check Gemini Server Logs: The Gemini server might be encountering errors during text generation. Examine the stderr output of the Gemini server's terminal. Look for error messages, stack traces, or any clues about what might be causing a hang.

Check Module 1 Server Logs: Similarly, check the Module 1 server's stderr output. This server logs events related to connecting to the Gemini server, sending prompts, receiving responses, and handling errors. Look for logs indicating whether the connection to the Gemini server was successful, whether the prompt was sent, and whether a response was received. Any errors during JSON processing or validation will also be logged here.  Specifically, look for log messages after “Processing request of type ListToolsRequest.”

Verify Gemini Server Responsiveness: In a new terminal, try sending a direct request to the Gemini server to confirm it's responsive.  Create a simple test script (e.g., gemini_test.py) that connects to the Gemini server using ClientSession and stdio_client, sends a simple generate request, and prints the response.  This isolates the Gemini server and checks if it functions correctly in isolation. (Make sure to activate your virtual environment in the new terminal before running the test client.) This will also confirm that the server paths you are using are correct.

import asyncio
import os

from mcp.client.session import ClientSession
from mcp.client.stdio import StdioServerParameters, stdio_client
from mcp.shared.exceptions import McpError

async def test_gemini():
    gemini_server_path = os.environ.get("GEMINI_SERVER_PATH") or "../gemini-server/server.py"
    server_params = StdioServerParameters(command="python", args=[gemini_server_path])
    async with stdio_client(server_params) as (read, write):
        async with ClientSession(read, write) as session:
            try:
                await session.initialize()
                response = await session.call_tool("generate", arguments={"prompt": "Hello, Gemini!"})
                print(response)

            except McpError as e:
                print(f"MCP Error: {e}")

if __name__ == "__main__":
    asyncio.run(test_gemini())

If this test client works and returns the expected output, you know your Gemini server is configured correctly, including its path, and that there are not any errors in how the model is being used or in the environment. This also means that there are no errors related to the API key. Therefore the problem is located in module1-server or in how the client (test-client) is interacting with it. If the test client does not work correctly, recheck the steps we went through to ensure correctness before.  If these are perfectly correct, then retry the steps we went through to fix any potential issues with mcp or the virtual environments.

Use the Inspector: If the above steps don't reveal the issue, use the MCP Inspector. Run:
cd /path/to/your/mcp-project/gemini-server
npx @modelcontextprotocol/inspector python server.py

This launches the inspector in one terminal. Launch the module1-server and test-client in other terminals as before.
Now open the inspector in your browser. The inspector provides a more detailed view of the communication happening between servers and clients which is probably more informative than the limited stderr output from VS Code.

Check Connection Status: Look for connection errors or unexpected disconnections in the Inspector's connection pane.

Monitor Message Flow: Observe the messages being exchanged between the test-client, module1-server, and gemini-server. Are requests being sent and received? Are responses being returned? Are there any unexpected delays? The message content itself will also probably be informative, by showing the exact content of the message payloads, and might provide clues on how to solve the problem.

Inspect Logs: Review the logs from the stderr output in the Inspector’s history panel to confirm they match what you’re seeing in VS Code’s terminal. This ensures that your logging setup is correct.

Simplify Module 1 Server: If the hang occurs specifically within the module1-server, try temporarily simplifying it to rule out issues in the generate_plan_structure or call_tool functions. For example, have the call_tool function return a hardcoded JSON plan without calling the Gemini server. If this works, the problem is likely within the generate_plan_structure function or its interaction with the Gemini client.

By systematically investigating each component and using the MCP Inspector for detailed monitoring, you should be able to isolate the source of the hang and take appropriate corrective action. If you continue to have issues after checking all of the above steps and making corrections, including confirming the versions of all of your dependencies, checking the logs carefully in the inspector and in VS Code, and setting up new virtual environments from scratch, and still cannot find the error, then please include the server and client code and we can check that again.  Also include the exact error messages, stack traces, and log outputs, as those will have more information to help me understand what might be causing the issue, and if you can use the inspector as well and observe the message flow there, then please also include screenshots of that as these will also contain helpful information. If possible, provide a minimal reproducible example that isolates the issue.

o1
Analysis of the New Information:

Your Gemini API code works outside of MCP:
This confirms that your Gemini API key and prompt logic are correct. The stand-alone script you provided successfully produces output. This suggests the issue isn't with the API key itself or the Gemini SDK’s basic usage.

The MCP code hangs when calling model.generate_content(prompt):
Since the same code works outside the MCP context, the difference is how the code runs inside the MCP server’s async event loop. The generate_content call is synchronous and may be blocking the async loop indefinitely if the network request never returns or if the MCP environment alters how the code is run.

Inspector Behavior:
The inspector repeatedly shows "Spawned stdio transport" and similar messages, which can happen if the server never fully initializes or responds. The inspector tries to connect, but since the Gemini server code is stuck waiting on generate_content, it never completes the initialization. This doesn't give a direct clue, except that the server is not responding as expected.

Likely Cause of the Hang:
The model.generate_content(prompt) function is synchronous and blocking. In a simple script, this just runs and returns. In your async MCP server environment, this call is made inside an async function without using an async-friendly approach. If model.generate_content takes a long time or something about the environment causes it to not return, the entire async loop is blocked, and no further progress is made.
Potential Reasons for the Block:

Async vs. Sync Mismatch: The MCP server.run loop expects async operations. Blocking calls should be run in a separate thread or via asyncio.to_thread() to avoid blocking the event loop.
Network or SSL Issues Under the MCP Environment: Perhaps the way Python’s SSL or network stack is initialized in the MCP environment differs. If model.generate_content tries to make an HTTP request and it’s hanging due to SSL validation or DNS issues, that would cause no output.
Environment Variable or Auth Not Loading Properly: In theory, if the Gemini API call fails but doesn’t raise an exception, it might hang. Double-check that GEMINI_API_KEY is printed correctly from within gemini-server/server.py after load_dotenv() to confirm it’s loading as expected.

Suggested Fixes:

Run model.generate_content in a Non-Blocking Way:
Wrap the call in a thread executor so it doesn’t block the async loop:
import asyncio
from functools import partial

async def generate_text(prompt: str):
    if verbose:
        print(f"\nPrompt sent to Gemini:\n{prompt}\n")

    model = genai.GenerativeModel(GEMINI_MODEL)
    loop = asyncio.get_running_loop()
    try:
        # Run the blocking call in a thread:
        response = await loop.run_in_executor(None, partial(model.generate_content, prompt))
        return response.text
    except Exception as e:
        raise McpError(INTERNAL_ERROR, f"Error generating text: {e}")

This ensures the async event loop isn’t blocked by a synchronous network call.

Verify API Key and Networking:

Just before calling model.generate_content, print out the GEMINI_API_KEY to confirm it’s loaded:print("Using GEMINI_API_KEY:", GEMINI_API_KEY)

If GEMINI_API_KEY is missing or incorrect, correct it in .env.
Check network connectivity: ensure your system can reach the Gemini API endpoint from within the MCP environment.

Test a Simple Network Call Inside MCP:

Try making a simple requests.get("https://www.google.com") call inside the gemini-server/server.py (wrapped in await loop.run_in_executor) to confirm networking works.
If a simple network request works but model.generate_content does not, consider logging at multiple points to identify where it’s stuck.

Consult Gemini API Docs for Non-blocking Calls (if available):

If the Gemini library has an async version of generate_content, prefer that.
If not, the thread executor approach is a common solution.

Conclusion:

The new information shows that the standalone Gemini code works and that the inspector fails because the server never responds. This strongly suggests that the blocking model.generate_content call under the async MCP environment is causing the hang.
The recommended solution is to run the blocking call in a separate thread using run_in_executor() or asyncio.to_thread() so that it doesn’t block the event loop.
After implementing these changes, try again. If it still hangs, verify environment variables, network connectivity, and add more logging to isolate the issue.

Gemini
The ModuleNotFoundError: No module named 'google' you encountered earlier in the Gemini server and the repeated SSE connection attempts shown in the Inspector output point to the same root problem:  the MCP Inspector is not using the Python virtual environment you've set up for your servers.
The Inspector, when launched with npx ... python server.py, uses Node.js's own process spawning mechanisms (via cross-spawn within its code).  It does not automatically inherit or use the virtual environment you activated in VS Code's terminal.  This is why it can't find the google module (installed in your venv) and why it repeatedly tries to establish SSE connections, as the Gemini server isn't starting correctly.
You have a few options to address this:
1. Explicitly Activate venv Inside Inspector (Simplest):
This is the easiest and most direct way. The MCP Inspector lets you configure and pass custom environment variables.

Edit Command: When starting the inspector from gemini-server, include a modified env object for the Python process that adds the bin directory of your virtual environment to PATH.  You’ll also need to set PYTHONPATH if it's necessary for your local mcp installation:

First, run which python3 in the virtual environment to locate its python command.

Next, if applicable, use pip show mcp to get the location of the mcp package directory.  Add this to PYTHONPATH as explained before.  If mcp was installed via pip or uv then it is usually within the virtual environment and thus included already and you don't need to set it.

Now include these paths when invoking the inspector:

# ... after activating venv ...
cd gemini-server

PATH=$(which python3):$PATH npx @modelcontextprotocol/inspector python server.py
#or set both path and pythonpath
PYTHONPATH=/path/to/your/mcp/package/:$PYTHONPATH PATH=$(which python3):$PATH npx @modelcontextprotocol/inspector python server.py

If you have errors related to not being able to find dotenv or other required dependencies when testing your project, use pip show <packagename> to get the location of the package and add the location to the PYTHONPATH variable by prepending the path, for example:
PYTHONPATH=/full/path/to/site-packages/:$PYTHONPATH

Mac users will need to add PYTHONPATH=<path>:$PYTHONPATH to their .zshrc file and uncomment it if necessary, where <path> is the path output by pip show <packagename>.
2. Use uv for Servers (Recommended Long Term):
Packaging your servers (gemini-server and module1-server) and managing dependencies with uv (or similar tools like poetry) is the cleaner long-term solution. This ensures environment consistency and facilitates distribution.
With this approach:
a. Create pyproject.toml in each server directory to define the packages.
b. Run uv sync --dev --all-extras to capture the dependencies for each server in a uv.lock file.
c. Add an __init__.py and a __main__.py file to each of these server directories, as you will be converting them into packages now.  Ensure they contain a main function as well, called by the entry point in the pyproject.toml file.
d. Create a single pyproject.toml at the project root, including all server dependencies there, and listing each subdirectory in the tool.uv.workspace.members field as follows:
# Root pyproject.toml
# (mcp-agento/pyproject.toml)

[tool.uv.workspace]
members = ["gemini-server", "module1-server"]

e. Run uv sync --dev --all-extras in the project root directory, to install all dependencies according to your pyproject.toml file. This also generates lock files for reproducibility.
Your inspector invocation would then be:
npx @modelcontextprotocol/inspector uvx mcp-server-gemini  # Assumes gemini-server is a package

3. Global mcp Installation (Least Recommended):
You could install mcp globally, but this is generally not recommended due to potential conflicts with other projects. However, if you choose this route, the Inspector should be able to find it. However, this would not necessarily solve the problem of the google package not being available in the inspector's PATH or PYTHONPATH, or of other environment variables or dependencies not being available to the inspector, so you would still need to take the appropriate corrective action as described above.
I strongly recommend option 2. It aligns better with packaging best practices and makes your servers more portable. However, option 1 is the quickest fix for testing if you want to avoid setting up server packages for now.

o1
(upon showing o1 Gemini's analysis and recommendation and also stressing that the MCP team specifically recommended using uv, they changed their tune.  Now there is a clearer path to try to fix this issue, I think)
Do I Disagree with Trying uv?
No, I do not disagree. Given the complexity of MCP environment handling and the fact that the MCP team themselves advised using uv, it’s reasonable to follow their recommendation. uv provides a standardized, project-level configuration and dependency management system, which can resolve many non-obvious issues related to environment inheritance, packaging, and consistent execution that are causing trouble here.
Why uv Makes Sense:

Consistent Environment Management:
With uv, you define all dependencies in pyproject.toml files. uv sync --dev --all-extras then creates locked, reproducible environments. This ensures that whenever you start servers or run the Inspector, they run in the exact same environment with all dependencies accessible.

Easier Inspector Integration:
Because uv knows about your project’s dependencies and entry points, starting your servers via uvx <package> ensures that the Inspector runs with the correct environment. You won’t have to manually tweak PATH, PYTHONPATH, or environment variables to get it working.

Recommended by MCP Team:
The MCP team’s knowledge of the ecosystem and tools is a strong indicator. They’ve likely encountered similar issues before and found that uv solves them well. Their advice is based on experience with MCP’s quirks and best practices.

What to Do Next:

Adopt uv for Your Project:

Add a pyproject.toml file at the root of your project and in each server directory (gemini-server and module1-server).
Specify your dependencies in these pyproject.toml files.
Add a [tool.uv.workspace] section in the root pyproject.toml listing your members:[tool.uv.workspace]
members = ["gemini-server", "module1-server"]

Run uv sync --dev --all-extras in the project root to set up the environment.

Restructure Your Servers as Packages:
This may mean adding __init__.py and __main__.py to gemini-server and module1-server, and configuring the pyproject.toml to declare an entry point for each server. For example:
# Inside gemini-server/pyproject.toml
[project]
name = "mcp-server-gemini"
version = "0.1.0"
dependencies = [
    "mcp",
    "google-generativeai",
    "python-dotenv",
    "pydantic"
]

[project.scripts]
mcp-server-gemini = "gemini_server:main"

Here, gemini_server:main should refer to a main function defined in gemini-server/__main__.py that starts the server. This way, uvx mcp-server-gemini starts your Gemini server with the correct environment and paths.

Use uvx With the Inspector:
Once configured, you can run:
npx @modelcontextprotocol/inspector uvx mcp-server-gemini

The Inspector will launch the server in the environment defined by uv, ensuring all dependencies are found, and no manual environment variable manipulation is needed.

Conclusion:
I agree with the suggestion to use uv. It’s a well-supported approach that aligns with MCP best practices and should resolve the environment-related issues you’re experiencing. By standardizing your project’s configuration and dependency management with uv, you’ll create a smoother development experience, make the Inspector easier to use, and avoid the non-obvious pitfalls of manual environment management.