Failures on [dds/dap] Don't sent breakpoint changed events until after the response to setBreakpoints....[cfe] Update test expectation

There are new test failures on [dds/dap] Don't sent breakpoint changed events until after the response to setBreakpoints....[cfe] Update test expectation.
The tests
pkg/dds/test/dap/integration/debug_breakpoints_test Timeout (expected Pass)

are failing on configurations
unittest-asserts-debug-linux

log
The only commit in that range related to DDS is:

b984c308beb4ba53af785aafa31d72f2815a76bd

Which happens to add that test.
cc @DanTup could you please take a look if this test is still doing the right thing after your change?

Can't see anything obviously wrong. Is this failing only on Linux, and 100% of the time?
Will try and get a Linux container set up and see if I can repro locally to debug.

Yes, consistently failing on https://ci.chromium.org/p/dart/g/be/console?limit=200 under pkg -> dl (now it's green because I approved the failing test with a reference to this issue). The first red was your commit.

Thanks! I'm struggling to reproduce this in an Ubuntu container in Docker. I'm using an SDK checkout at e1c21921101 with a pre-built bleeding edge SDK:
Dart SDK version: 3.0.0-edge.8d16c1dc871a42bb4f643d7c29a17148f0228935 (be) (Mon Apr 3 15:04:07 2023 +0000) on "linux_x64"
And I'm running the script like:
dart test/dap/integration/debug_breakpoints_test.dart

But everything is passing (and fairly quickly):
00:30 +28 ~2: All tests passed!

I tried running the version from the built SDK, as well as running the tests with an in-process debug adapter, and from source out-of-process
@dcharkes do you know anything more about the environment that might help me reproduce? Do the tests run in some container I can use directly?
And do you know what the timeout is? I'm not sure how to interpret the results.. the top of the results say "command VM took 16 minutes(?)" and then print a bunch of stacks, but this comes before any test output. Did this actually occur after the tests started (eg. it's some diagnostics trying to capture VM stacks after the tests took too long), and not something that occurred prior to the tests running?

--- Re-run this test:
python3 tools/test.py -n unittest-asserts-debug-linux pkg/dds/test/dap/integration/debug_breakpoints_test

This locally makes the test timeout for me:
$ tools/build.py -mdebug create_platform_sdk dart_precompiled_runtime
$ tools/test.py -n unittest-asserts-debug-linux pkg/dds/test/dap/integration/debug_breakpoints_test -v

This locally makes the test timeout for me:

Thanks - I'll try this out later. I would've thought using the pre-built SDK should behave the same, but maybe there's something different. Out of interest, what OS/distro are you using, so if I can't repro with Ubuntu I can try that?
btw, if you have a spare minute and still have this set up, running with DAP_TEST_VERBOSE=true in front of the command will cause all the DAP traffic to be printed. That might be useful to me if I can't repro locally here, as it might help understand what's happening.
Thanks!

DAP_TEST_VERBOSE=true : https://gist.github.com/dcharkes/4394b5c1c1bcc5124342feee3fba3b9b
$ cat /etc/os-release
PRETTY_NAME="Debian GNU/Linux rodete"
NAME="Debian GNU/Linux"
VERSION_ID="rodete"
VERSION="12 (rodete)"
VERSION_CODENAME=rodete
ID=debian

@dcharkes thanks! I'm still wrestling with setting it up locally, however I see what seems like a crash in your log:
../../runtime/vm/object.cc: 12832: error: expected: last_token_index->Serialize() <= source_length\n
version=3.0.0-edge.d758cf13921c208d7e8b1ac9409beb9b2a9d54f8 (be) (Mon Apr 3 16:41:54 2023 +0000) on \"linux_x64\"\n
os=linux, arch=x64, comp=no, sim=no
fp=7f8188efbf90, sp=7f8188efbe58, pc=5600108d164c\n
fp=7f8188efbf90,"
  pc 0x00005600108d164c fp 0x00007f8188efbf90 dart::Profiler::DumpStackTrace(void*)+0x7c\n
  pc 0x00005600104c4694 fp 0x00007f8188efc070 dart::Assert::Fail(char const*, ...) const+0x84\n
  pc 0x0000560010810368 fp 0x00007f8188efc0d0 /usr/local/google/home/dacoharkes/dart-sdk/sdk/out/DebugX64/dart-sdk/bin/dart+0x25bd368\n
  pc 0x000056001072e9c8 fp 0x00007f8188efc180 dart::Debugger::BreakpointLocationAtLineCol(dart::String const&, long, long)+0x1f8\n
  pc 0x000056001072ec6d fp 0x00007f8188efc1c0 dart::Debugger::SetBreakpointAtLineCol(dart::String const&, long, long)+0x2d\n
  pc 0x0000560010966236 fp 0x00007f8188efc220 /usr/local/google/home/dacoharkes/dart-sdk/sdk/out/DebugX64/dart-sdk/bin/dart+0x2713236\n
  pc 0x0000560010956568 fp 0x00007f8188efca30 dart::Service::InvokeMethod(dart::Isolate*, dart::Array const&, bool)+0x4d8\n
  pc 0x0000560010956fba fp 0x00007f8188efca60 dart::Service::HandleIsolateMessage(dart::Isolate*, dart::Array const&)+0x1a\n
  pc 0x0000560010753ff3 fp 0x00007f8188efd1f0 dart::IsolateMessageHandler::HandleMessage(std::__2::unique_ptr<dart::Message, std::__2::default_delete<dart::Message>>)+0x273\n
  pc 0x0000560010792f95 fp 0x00007f8188efd280 dart::MessageHandler::HandleMessages(dart::MonitorLocker*, bool, bool)+0x1d5\n
  pc 0x0000560010793619 fp 0x00007f8188efd2e0 dart::MessageHandler::HandleOOBMessages()+0x99\n
  pc 0x0000560010e3fee8 fp 0x00007f8188efd370 Dart_HandleServiceMessages+0x118\n
  pc 0x000056001075b22d fp 0x00007f8188efd3c0 dart::Isolate::PauseEventHandler()+0xad\n
  pc 0x000056001072306a fp 0x00007f8188efd570 dart::Debugger::Pause(dart::ServiceEvent*)+0xaa\n
  pc 0x000056001073082a fp 0x00007f8188efd6c0 dart::Debugger::SignalPausedEvent(dart::ActivationFrame*, dart::Breakpoint*)+0xaa\n
  pc 0x00005600107310eb fp 0x00007f8188efd760 dart::Debugger::PauseBreakpoint()+0x3fb\n
  pc 0x0000560010940098 fp 0x00007f8188efdf60 dart::DRT_BreakpointRuntimeHandler(dart::NativeArguments)+0x218\n
  pc 0x00007f8192982f22 fp 0x00007f8188efdfa8 Unknown symbol\n
  pc 0x00007f8192983da1 fp 0x00007f8188efdfd8 Unknown symbol\n
  pc 0x00007f8191c234b1 fp 0x00007f8188efe018 Unknown symbol\n
  pc 0x00007f8191c233f1 fp 0x00007f8188efe048 Unknown symbol\n
  pc 0x00007f8191c224a3 fp 0x00007f8188efe0b0 Unknown symbol\n
  pc 0x00007f8191c2273e fp 0x00007f8188efe0f8 Unknown symbol\n
  pc 0x00007f8191c224a3 fp 0x00007f8188efe160 Unknown symbol\n
  pc 0x00007f8191c214d9 fp 0x00007f8188efe1b8 Unknown symbol\n
  pc 0x00007f81929833aa fp 0x00007f8188efe230 Unknown symbol\n
  pc 0x000056001071c7e7 fp 0x00007f8188efe2b0 dart::DartEntry::InvokeCode(dart::Code const&, unsigned long, dart::Array const&, dart::Array const&, dart::Thread*)+0xf7\n
  pc 0x000056001071c45c fp 0x00007f8188efe320 dart::DartEntry::InvokeFunction(dart::Function const&, dart::Array const&, dart::Array const&, unsigned long)+0x13c\n
  pc 0x000056001071f595 fp 0x00007f8188efe380 dart::DartLibraryCalls::HandleMessage(long, dart::Instance const&)+0xf5\n
  pc 0x00005600107540fd fp 0x00007f8188efeb10 dart::IsolateMessageHandler::HandleMessage(std::__2::unique_ptr<dart::Message, std::__2::default_delete<dart::Message>>)+0x37d\n
  pc 0x0000560010792f95 fp 0x00007f8188efeba0 dart::MessageHandler::HandleMessages(dart::MonitorLocker*, bool, bool)+0x1d5\n
  pc 0x0000560010793c0f fp 0x00007f8188efec20 dart::MessageHandler::TaskCallback()+0x2ff\n
  pc 0x00005600109949c0 fp 0x00007f8188efecb0 dart::ThreadPool::WorkerLoop(dart::ThreadPool::Worker*)+0x180\n
  pc 0x00005600109952eb fp 0x00007f8188efed00 dart::ThreadPool::Worker::Main(unsigned long)+0x12b\n
  pc 0x00005600108ca153 fp 0x00007f8188efee50 /usr/local/google/home/dacoharkes/dart-sdk/sdk/out/DebugX64/dart-sdk/bin/dart+0x2677153\n
-- End of DumpStackTrace\n
  pc 0x0000000000000000 fp 0x00007f8188efdfa8 sp 0x0000000000000000 [Stub] CallToRuntime\n
  pc 0x00007f8192983da1 fp 0x00007f8188efdfd8 sp 0x00007f8188efdfb8 [Stub] UnoptStaticCallBreakpoint\n
  pc 0x00007f8191c234b1 fp 0x00007f8188efe018 sp 0x00007f8188efdfe8 [Unoptimized] main\n
  pc 0x00007f8191c233f1 fp 0x00007f8188efe048 sp 0x00007f8188efe028 [Unoptimized] main\n
  pc 0x00007f8191c224a3 fp 0x00007f8188efe0b0 sp 0x00007f8188efe058 [Unoptimized] _Closure@0150898.dyn:call\n
  pc 0x00007f8191c2273e fp 0x00007f8188efe0f8 sp 0x00007f8188efe0c0 [Unoptimized]<EMAIL_ADDRESS>closure>\n
  pc 0x00007f8191c224a3 fp 0x00007f8188efe160 sp 0x00007f8188efe108 [Unoptimized] _Closure@0150898.dyn:call\n
  pc 0x00007f8191c214d9 fp 0x00007f8188efe1b8 sp 0x00007f8188efe170 [Unoptimized] _RawReceivePort@1026248._handleMessage@1026248\n
  pc 0x00007f81929833aa fp 0x00007f8188efe230 sp 0x00007f8188efe1c8 [Stub] InvokeDartCode\n

@bkonyi I suspect this isn't related to my change, but perhaps my change is causing it. It seems to happen around the time I try to add breakpoints to lines 1-50 of a file (a file which doesn't actually have 50 lines).
I don't know why it would only occur on Linux (or why I can't repro locally yet). Is there enough info here to understand what's wrong?

Got a change open at https://dart-review.googlesource.com/c/sdk/+/292784 that will print all stderr events that come via DAP when the TerminatedEvent is received. This should make this kind of issue easier to debug in future, since the stack above should be included in the stderr of the test (whether or not you're running verbose).
This doesn't solve the issue, I'll wait to here what @bkonyi thinks about the assertion failure (expected: last_token_index->Serialize() <= source_length) before making any changes to the test. It is trying to set breakpoints on invalid locations, but I don't think that should crash in this way (because it seems reasonable to want to test how DAP behaves when trying to set breakpoints in invalid locations, for example).

I've opened https://dart-review.googlesource.com/c/sdk/+/310880 to tweak this test so the lines are not invalid, but I think should fix this. The underlying issue (causing the assertion failure) should be handled via https://github.com/dart-lang/sdk/issues/51951.


animation rename bug

Issue title
Bug when renaming animations
When renaming an animation the default animation name is not removed/ replaced by the new one, instead the new name only gets added to the default name: Example (Animation0newname) not (newname)
Version

<0.98.30> <06/03/2022>

Platform

Web Version [x]
Windows [10]
Linux [X]

Hello
Thanks a lot for reporting this issue.
Cheers


Singleton category dictionary causing illegal memory access in searchPage

https://github.com/AHSAppDevTeam/Arcadia-High-Mobile-iOS/blob/master/AHS/AHS/UI/homePage/contentViewControllers/searchPage/searchPage.swift#L74
Seems like the usage of multiple DispatchQueues that are not the main thread is causing an issue listed here:
https://stackoverflow.com/questions/26515968/swift-access-to-dictionary-of-a-singleton-causes-exc-bad-access/28910283
Possible fix:
https://www.raywenderlich.com/5370-grand-central-dispatch-tutorial-for-swift-4-part-1-2

https://sachithrasiriwardhane.medium.com/thread-safe-singletons-and-their-usage-in-swift-c992d34d85dd

Still have some UI performance issues, however.


Fix formatting in Save controller for form generation

Currently super indented.
            use Magento\Backend\App\Action;
            use Pulsestorm2\Pestleform2\Model\Page;
            use Magento\Framework\App\Request\DataPersistorInterface;
            use Magento\Framework\Exception\LocalizedException;

Looks like we fixed this is a previous commit -- app/code/Pulsestorm/Checkingindent/Controller/Adminhtml/Thing/Save.php generates with non-indented use statements.


Training process

Hello. Can you share the details of neural model training? Did you train it yourself? Did you collect data for training from basenji dataset files? I am unable to reproduce the claimed results during training.

Has anyone looked into this yet? I am also interested in this, since training enformer from scratch using your implementation doesn't reproduce same Pearson correlation values (max I am getting is ~0.4).

@fransilvionGenomica @maksimallist i tried a while ago using TPUs (didn't have access to large cluster of GPU at the time) and didn't hit the mark (got around 0.5-0.6). this was before Ziga officially released their model over at deepmind
the training script i used is all open sourced here

@fransilvionGenomica are you planning on training it on proprietary data?

@lucidrains I am training your pytorch implementation using a single A100 GPU node with the original basenji dataset and gradient accumulation. I was using the following deepmind notebook as the reference: https://github.com/google-deepmind/deepmind-research/blob/master/enformer/enformer-training.ipynb. I do believe that it is possible to train the model on GPUs, since in the recent Borzoi paper from Enformer co-authors they did not use TPUs (https://www.biorxiv.org/content/10.1101/2<IP_ADDRESS>5582v1). Unfortunately, they don't provide any training script (https://github.com/calico/borzoi).

@fransilvionGenomica ahh, i have not checked out Borzoi yet, although someone else told me it is the successor to Enformer
why are you still using this repository if Borzoi is the new SOTA?

@fransilvionGenomica where do you work btw?

@fransilvionGenomica  it is possible to train on GPU, but you'll be training up to 2 months if it isn't distributed and well optimized. on TPUs, i was doing 3-4 days of training on 64 v3 TPUs

Oh I see. It makes sense. Even Borzoi mentioned it took them ~25 days on 2 GPUs. And I am training on a single GPU. I guess, I will just have to wait then. Thanks!

@fransilvionGenomica that is strange they waited that long. i thought calico had google level resources

@fransilvionGenomica i'll revisit genomics maybe end of the month. knee deep in other projects at the moment.

ahh ok, was told that Borzoi is nothing more than Enformer applied to RNA-seq data. ok then using this repository is fine in that case

Yes, architecture wise they are very similar. Borzoi is actually less complex.

@fransilvionGenomica ok, i'll just copy / paste the existing code and remove that complexity for Borzoi later this month after i read the paper. hopefully they got rid of the annoying gamma positions

Just curious, have you noticed anything about the batch size while training enformer from scratch? Like, does it have to be relatively big (like at least 32) or can you train decently even if batch size is 1 or 2?

@fransilvionGenomica it has to be big (32 or 64)

@fransilvionGenomica the code in this repository isn't even setup for training. i didn't set up synchronized batchnorm, which is required for it to train well.

@fransilvionGenomica actually let me just throw that in there for now

Have you tried to run your enformer implementation with pytorch lightning?

@fransilvionGenomica no i haven't, as i said above, my training was done in tensorflow sonnet with TPUs, as i had access to a large cluster of TPUs in collaboration with EleutherAI back then

@fransilvionGenomica if you ever wire up a working training script, always welcome a pull request, in the spirit of open source science.

@fransilvionGenomica ahh, i have not checked out Borzoi yet, although someone else told me it is the successor to Enformer
why are you still using this repository if Borzoi is the new SOTA? without reading the paper, did Borzoi set a new SOTA?

What the paper says:
"Performance is difficult to compare directly to Enformer due to differences in data processing"
(https://www.biorxiv.org/content/10.1101/2<IP_ADDRESS>5582v1.full)

@minjaf yea, whenever i see the word competitive my eyes glaze over; it is just a euphemism for 'close to SOTA but not quite'

David Kelley's name is on it though, so i guess i need to read it

@lucidrains do you have training/validation loss trends left by any chance? for your tensorflow training code I mean.

@fransilvionGenomica hey yes, actually still have it lying around (thanks wandb) https://api.wandb.ai/links/lucidrains/9ac4x106

@fransilvionGenomica the code in this repository isn't even setup for distributed training. i didn't set up synchronized batchnorm, which is required for it to train well.

hello，may i ask how to fix this? My training time is several times higher when I train with DDP than single GPU (with the same batch_size


chore: add packageManager field for github action

Thanks, I just fixed this. Thank you for your great work😘! I think I should also use pull request to submit the code later.

just open branch protected.

https://github.com/childrentime/reactuse/invitations would you linke to be a collaborator？

sure，I am very willing.


[Suggestion] Prevent breaking small, medium, and large amethyst bud

Is your feature request related to a problem? Please describe.
A clear and concise description of what the problem is. Ex. I'm always frustrated when I accidentally breaking non grown amethyst

Sure, sounds very doable and useful


Create arrays in the index document for new facet fields, update the fields in the f_subjects array

Add an array to the index such as f_genre_form to contain values from the index field "display_record.subjects.terms.type" genre_form
Add an array to the index such as f_geographic to contain values from the index field "display_record.subjects.terms.type" geographic
Update the f_subjects array to contain data from the following subject "term type" fields, and no other fields:
· Topical
· Occupation
· Names (People, Orgs, Buildings) – these come from “Agent” DB
· Function
· Temporal
· Style/Period
· Uniform Title
Refer to
https://github.com/dulibrarytech/digitaldu-frontend/issues/338

subjects.terms.type fields for the f_subjects array:
topical
occupation
function
uniform_title
temporal
style_period
subjects.title fields for the f_subjects array:
authority = naf
authority = viaf
subjects.terms.type field for the f_geographic array:
geographic
subjects.terms.type field for the f_genre_form array:
genre_form

In the frontend metadata display Topics bucket, I am capturing ALL subjects.title fields, IF the terms.type value in that field is NOT "geographic" or "genre_form"

Per Kate: Remove "Format" once enhancement is complete.


ssl support

Why the choice of tokio_openssl over the official tokio_native_ssl?

Also, this does not work.

I'm rejecting the PR do to incompleteness and low activity.


CLI argument --retireJsUrl fails for filenames other than jsrepository.json (using via Jenkins)

Describe the bug
When using DependencyChecker with the --retireJsUrl argument, it will fail if the obtained json file is using a different filename than 'jsrepository.json'.
I'm using the new DependencyChecker 5.2.0 via Jenkins, which is having the new 'decoupled' wrapper around the cli.
I'm passing the --retireJsUrl there, like;
dependencyCheck additionalArguments: 'https://somelocalurl/jsrepository-alt.json', odcInstallation: 'Dependency-Check5.2.0'.
When the local resource location is using the exact same file name as the standard one on;
https://raw.githubusercontent.com/Retirejs/retire.js/master/repository/jsrepository.json
e.g.;
https://somelocalurl/jsrepository.json
it works fine.
** Version of dependency-check used **
The problem occurs using version 5.2.0 of the cli, started by Jenkins using the new plugin structure.
** Log file **
I don't get much logging via Jenkins. Only get;
[DependencyCheck] [ERROR] Failed to initialize the RetireJS repo
To Reproduce
Steps to reproduce the behavior:

Set up a mirror of the jsrepository.json file, using an alternative filename
Configure DependencyChecker to use it, with the --retireJsUrl argument
Run DependencyCheck.
See error

Expected behavior
That the --retireJsUrl also allows for an alternative filename (since it's part of the full provide URL).
Additional context
What I'm thinking is that the code is just expecting to find the jsrepository.json file locally (after being downloaded from somewhere). It's likely a small glitch?

We do not support an alternate name for the retire JS repository and do not plan to support this.  Please use jsrepository.json.

Ok, thanks for responding. Good to know it's not intended to be working like this (and not considered for the future), which was not obvious to me from the documentation. Would you consider making this more clear on the documentation (https://github.com/jeremylong/DependencyCheck/blob/master/cli/src/site/markdown/arguments.md)?

An update is being included in the next release that will allow for custom JS repository names.  In the next release we will be using the filename as defined in the --retireJsURL (or comparable argument depending on the implementation used).


Add support for hashing sequences that contain None in hash_mutable on Python 3

What does this PR do?

Update hash_mutable to allow hash_mutable(['test', None]) to succeed, whereas right now it fails with a TypeError on Python 3:

>>> from datadog_checks.base.utils.containers import hash_mutable
>>> hash_mutable(['test', None])
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Users/florimond.manca/go/src/github.com/DataDog/integrations-core/datadog_checks_base/datadog_checks/base/utils/containers.py", line 25, in hash_mutable
    return hash(freeze(m))
  File "/Users/florimond.manca/go/src/github.com/DataDog/integrations-core/datadog_checks_base/datadog_checks/base/utils/containers.py", line 13, in freeze
    return tuple(sorted(freeze(e) for e in o))
TypeError: '<' not supported between instances of 'NoneType' and 'str'

Add unit tests for this function, highlighting what cases are not supported yet (because we don't need them).

Motivation

We need this in the PDH check for accepting additional_metrics that contain None as the inst_name, see: https://github.com/DataDog/integrations-core/pull/7752#issuecomment-706207004
Additional Notes

Review checklist (to be filled by reviewers)

[ ] Feature or bugfix MUST have appropriate tests (unit, integration, e2e)
[ ] PR title must be written as a CHANGELOG entry (see why)
[ ] Files changes must correspond to the primary purpose of the PR as described in the title (small unrelated changes should have their own PR)
[ ] PR must have changelog/ and integration/ labels attached

Not sure about all the places where we use hash_mutable, but that means that we'll find hash collisions between ['test', None] and ['test', ""] which I'm not sure would be what we want.
That also means that [1, 2, None] can't go into hash_mutable

we'll find hash collisions between ['test', None] and ['test', ""]

I don't think that's correct: None is treated as '' for the purposes of sorting (comparing items during timsort), but the frozen value is still None:
>>> from datadog_checks.base.utils.containers import freeze
>>> freeze(['test', None])
(None, 'test')
>>> freeze(['test', ''])
('', 'test')

And so the hashes are still different:
>>> from datadog_checks.base.utils.containers import hash_mutable
>>> hash_mutable(['test', '']) == hash_mutable(['test', None])

However, you're right about this:

That also means that [1, 2, None] can't go into hash_mutable

>>> hash_mutable([1, 2, None])
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Users/florimond.manca/go/src/github.com/DataDog/integrations-core/datadog_checks_base/datadog_checks/base/utils/containers.py", line 33, in hash_mutable
    return hash(freeze(m))
  File "/Users/florimond.manca/go/src/github.com/DataDog/integrations-core/datadog_checks_base/datadog_checks/base/utils/containers.py", line 21, in freeze
    return tuple(sorted((freeze(e) for e in o), key=key_none_safe))
TypeError: '<' not supported between instances of 'str' and 'int'

FWIW, it was still not supported before:
>>> hash_mutable([1, 2, None])
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Users/florimond.manca/go/src/github.com/DataDog/integrations-core/datadog_checks_base/datadog_checks/base/utils/containers.py", line 25, in hash_mutable
    return hash(freeze(m))
  File "/Users/florimond.manca/go/src/github.com/DataDog/integrations-core/datadog_checks_base/datadog_checks/base/utils/containers.py", line 13, in freeze
    return tuple(sorted(freeze(e) for e in o))
TypeError: '<' not supported between instances of 'NoneType' and 'int'

I think we might need to use a cmp function here, because we can't deal with those TypeError's from a key function (since we only have access to a single item…).
Or alternatively, we should make sure that the key function returns a homogenous type, e.g. always an int (eg using key=hash). The purpose of sorting values is just so that order of items in the containers/sequences doesn't matter, so we can sort by any key that's a 1:1 mapping ("bijection") with the value.

@FlorianVeaux Tentatively switched to just key=hash and added a comment to the code. Let me know if you think this is right or if I'm missing something (it's Friday afternoon, after all 😛).

Moved to "no-changelog" since this was reverted by #7768.


[Frontend] Scroll Feature Not Working Properly

Deliverable -

[ ] The scroll feature of view broadcast doesn't seem to work as expected.

GIF of the Bug -

i would like to work on this issue

i would like to work on this issue

Sure @nlok5923 please go ahead with this issue. We look forward to your PR

Hello @Kajol-Kumari . I'm a GSSOC'21 participant and I would really like to contribute to this issue. I think the position attribute should be switched to 'sticky'. Could you please assign the problem to me?

Hey @pragati-kumar as this issue has already been assigned to someone we can't assign this to you. Please look for some other issue

Hey @pragati-kumar as this issue has already been assigned to someone we can't assign this to you. Please look for some other issue

Sure. Thanks for responding!

Hey @nlok5923 can I please get an update on this issue?

Yeah I am working on it  still getting little distortion with social icons.

Yeah I am working on it still getting little distortion with social icons.

This issue is not about the footer fix.(Please read it's description once)

Sorry my bad currently I am working on footer issue so you can unassign me to this issue.

Hey @Kajol-Kumari can I take on this issue?

Hey @Kajol-Kumari , apologies for the delay but have a look again it doesn't seems necessary to add a scroll-bar.
If it is still required let me know.
Screenshot Reference:

Hey @satcasm it's required only when the texts go beyond the window height. You can check the same for the Private AI Series broadcast


Unable to add activity (client-side)

Hi there,
I can't add an activity from the client side.
According to https://getstream.io/docs/js/#adding-activities, I must pass a "userToken" to the feed constructor; but every time I get a 403.
Isn't it the same token as the one generated with createUserToken ? Or have I to generate one for follow/unfollow and one for adding activities to the user feed ?
Thanks for your help

@Djiit you don't need to pass the token, just initialize the client with it; can you share the user_id and the feed id that you are using?

Yes, I use it with the client. But I have to instanciate one client for follow/unfollow actions, and one client for adding activities. Here is my code (user_id is "xY4mbWw3pwMlrrQO9NAI2e9t4yF2":
var stream = require('getstream');
var client = stream.connect('changed', 'userToken', 'github'); // here 'userToken' is a token generated by createUserToken('xY4mbWw3pwMlrrQO9NAI2e9t4yF2')

var userFeed = client.feed('user', 'xY4mbWw3pwMlrrQO9NAI2e9t4yF2')
var res = userFeed.addActivity({ actor: "xY4mbWw3pwMlrrQO9NAI2e9t4yF2", verb: "act", object: "test"}) // this doesn't work
// res => 403
var userFeed = client.feed('user', 'xY4mbWw3pwMlrrQO9NAI2e9t4yF2', feedToken) //  Here 'userToken' is a token generated by printing feed.token server-side.

res = userFeed.addActivity({ actor: "xY4mbWw3pwMlrrQO9NAI2e9t4yF2", verb: "act", object: "test"}) // this works

I hope it helps ;)
Thx

On  v4.0.9 I get a 403 when leaving out actor. I did get it working by prepending "SU:" to the actor after finding this issue.


Add Snips NLU python library

Description
Snips (snips.ai) recently open sourced their NLU ecosystem. As part of it is Snips NLU, a Python library useful to perform an information extraction task named Intent Parsing.

Thanks for the PR @adrienball! Thanks for creating snips and sharing with us!


Bring back the separate step for deps in GHA

Description
This step was deleted because of the dependency on ouroboros-consensus but as cardano-client no longer depends on it, we should be able to bring it back.

bors merge


device error: DOMException: Could not start video source

Description
Complete error output
device error: DOMException: Could not start video source
(anonymous) @ applicationVideo.js:376
i.dispatcher.i.dispatcher @ video.es.js:1935
en @ video.es.js:2073
trigger @ video.es.js:2965
onDeviceError @ videojs.record.js:638
Promise.catch (async)
getDevice @ videojs.record.js:460
l @ applicationVideo.js:425
dispatch @ jquery.js:4670
v.handle @ jquery.js:4490
Steps to reproduce
Not able to record  video in windows 10 chrome browser getting above error when started recording
Expected
Same code working on Mac chrome browser
version:
"videojs-record": "^4.0.0"

@ishwarid this usually means the webcam/video device is not connected or being used by a different application or webpage. Can you doublecheck this?

@ishwarid this usually means the webcam/video device is not connected or being used by a different application or webpage. Can you doublecheck this?

no feedback, closing.


HAWQ-275. Add connection check when dispatcher wants to reuse the QE forked by former queries, and when dispather dispatches the detailed query command to QE

Add connection check when dispatcher wants to reuse the QE forked by former queries, and when dispather dispatches the detailed query command to QE

+1 and it's better to reuse dispatcherAYT, because executormgr_validate_conn  and dispatcherAYT have a lot of repeated code.

One more thing, is there a need to consider errno == EWOULDBLOCK in your executormgr_validate_conn()  if connection is nonblocking

Yes, function executormgr_validate_conn and dispatcherAYT have repeated code, but I'm not sure whether we can merge the two functions into one since dispatcherAYT is in ic_udp.c and interconnect seems a independent part. Shall we merge that? @changleicn

Yes, I'll add that errno check errno == EWOULDBLOCK in the code. And seems that EAGAIN and EWOULDBLOCK often return same values.

+1


1.19.2 - Velocity?

is there already 1.19.2 - Velocity?

Velocity for 1.19.2 does not support chat API functions yet. The Velocity team are still working on this and it is taking awhile due to the changes in the update and the need to accommodate secure (signed) chat and chat reporting.


How to upgrade controller-runtime in project generated by Operator-SDK

Feature Request
I have a project generated by Operator-sdk v0.10.0, I noticed the version of controller-runtime is v0.1.12. it's a very old version, some useful features is missing, such as:

webhook
MultiNamespacedCach

So I wonder to know, if we have a official document to guide user to upgrade these package?

Hi @vincent-pli,
The latest version released of SDK, which is 0.11.0, is using the version sigs.k8s.io/controller-runtime v0.2.2. So, to upgrade controller-runtime you need to upgrade your project to use an upper version of SDK.
See version upgrade guide to help you achieve it. Also, may you would like to check here the PR to upgrade the Memcached sample from 0.10 to 0.11 as well.

@camilamacedo86
Thanks, that's exactly what I need.


"model_fn should return an EstimatorSpec." Error when running "predicting_movie_reviews_with_bert_on_tf_hub.ipynb" for fine-tuning BERT-Chinese Model

I tried to run "predicting_movie_reviews_with_bert_on_tf_hub.ipynb", but fine-tuning on a Chinese-text csv with corresponding labels. As such I loaded 'https://tfhub.dev/google/bert_chinese_L-12_H-768_A-12/1' model instead of English-model in the original code
I only made minimum changes from the original jpynb for data preparation, which was running OK. But during training as in
estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)
there was an error of "ValueError: model_fn should return an EstimatorSpec." - Note that I didn't make any modification in model_fn_builder(num_labels, learning_rate, num_train_steps, num_warmup_steps)
The full error log is as follows - it was running on CPU (8GB RAM) with Windows 10 environment:
Traceback (most recent call last):
File "bert_classify.py", line 352, in
estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)
File "C:\Miniconda_python\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py", line 370, in train
loss = self._train_model(input_fn, hooks, saving_listeners)
File "C:\Miniconda_python\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py", line 1161, in _train_model
return self._train_model_default(input_fn, hooks, saving_listeners)
File "C:\Miniconda_python\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py", line 1191, in _train_model_default
features, labels, ModeKeys.TRAIN, self.config)
File "C:\Miniconda_python\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py", line 1153, in _call_model_fn
raise ValueError('model_fn should return an EstimatorSpec.')
ValueError: model_fn should return an EstimatorSpec.
Just wondering what went wrong and needs to be done when loading BERT-Chinese model to fine-tune it for text classification task? Much appreciated

I tried to run "predicting_movie_reviews_with_bert_on_tf_hub.ipynb", but fine-tuning on a Chinese-text csv with corresponding labels. As such I loaded 'https://tfhub.dev/google/bert_chinese_L-12_H-768_A-12/1' model instead of English-model in the original code
I only made minimum changes from the original jpynb for data preparation, which was running OK. But during training as in
estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)
there was an error of "ValueError: model_fn should return an EstimatorSpec." - Note that I didn't make any modification in model_fn_builder(num_labels, learning_rate, num_train_steps, num_warmup_steps)
The full error log is as follows - it was running on CPU (8GB RAM) with Windows 10 environment:
Traceback (most recent call last):
File "bert_classify.py", line 352, in
estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)
File "C:\Miniconda_python\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py", line 370, in train
loss = self._train_model(input_fn, hooks, saving_listeners)
File "C:\Miniconda_python\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py", line 1161, in _train_model
return self._train_model_default(input_fn, hooks, saving_listeners)
File "C:\Miniconda_python\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py", line 1191, in _train_model_default
features, labels, ModeKeys.TRAIN, self.config)
File "C:\Miniconda_python\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py", line 1153, in _call_model_fn
raise ValueError('model_fn should return an EstimatorSpec.')
ValueError: model_fn should return an EstimatorSpec.
Just wondering what went wrong and needs to be done when loading BERT-Chinese model to fine-tune it for text classification task? Much appreciated

Can you please let me know how you are able to fix the issue

There is no issue on the model, just my mistake


Update BE synonyms

@nicolevasilevsky can you eyeball before merging the other one? This adds the BE synonyms.

this looks good!


email tracking

https://github.com/dukangxu/google-api

general multi channel poor mans system. Rosie could use it.

add interpreter so that biz person can modify email template in google drive, and etc etc.
good old CLI for now...


RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn

Hi, I have a problem here.  My codes are as follows:
import os

from kogito.core.knowledge import KnowledgeGraph
from kogito.models.bart.comet import COMETBART, COMETBARTConfig, AutoTokenizer

path = 'data/csv'

config = COMETBARTConfig(
    output_dir="bart",
    num_workers=16,
    learning_rate=1e-5,
    gpus=1,
    sortish_sampler=True,
    atomic=True,
    max_epochs=56,
    pretrained_model="facebook/bart-large",
)
model = COMETBART(config)
model.tokenizer = AutoTokenizer.from_pretrained("mismayil/comet-bart-ai2")

train_graph = KnowledgeGraph.from_csv(os.path.join(path, 'train.csv'))
val_graph = KnowledgeGraph.from_csv(os.path.join(path, 'val.csv'))
test_graph = KnowledgeGraph.from_csv(os.path.join(path, 'test.csv'))

model.train(train_graph=train_graph, val_graph=val_graph, test_graph=test_graph)

# Save as a pretrained model
model.save_pretrained("comet-bart/v1")

However, i got following errors when i run the code:
  File "/home/wzh/work/comet_ft/cometbart_train.py", line 25, in <module>
    model.train(train_graph=train_graph, val_graph=val_graph, test_graph=test_graph)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/kogito/models/bart/comet.py", line 109, in train
    trainer: pl.Trainer = generic_train(
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/kogito/models/bart/utils.py", line 262, in generic_train
    trainer.fit(model)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 740, in fit
    self._call_and_handle_interrupt(
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 685, in _call_and_handle_interrupt
    return trainer_fn(*args, **kwargs)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 777, in _fit_impl
    self._run(model, ckpt_path=ckpt_path)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 1199, in _run
    self._dispatch()
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 1279, in _dispatch
    self.training_type_plugin.start_training(self)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py", line 202, in start_training
    self._results = trainer.run_stage()
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 1289, in run_stage
    return self._run_train()
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 1319, in _run_train
    self.fit_loop.run()
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/loops/base.py", line 145, in run
    self.advance(*args, **kwargs)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py", line 234, in advance
    self.epoch_loop.run(data_fetcher)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/loops/base.py", line 145, in run
    self.advance(*args, **kwargs)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py", line 193, in advance
    batch_output = self.batch_loop.run(batch, batch_idx)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/loops/base.py", line 145, in run
    self.advance(*args, **kwargs)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/loops/batch/training_batch_loop.py", line 88, in advance
    outputs = self.optimizer_loop.run(split_batch, optimizers, batch_idx)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/loops/base.py", line 145, in run
    self.advance(*args, **kwargs)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 215, in advance
    result = self._run_optimization(
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 266, in _run_optimization
    self._optimizer_step(optimizer, opt_idx, batch_idx, closure)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 378, in _optimizer_step
    lightning_module.optimizer_step(
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/core/lightning.py", line 1652, in optimizer_step
    optimizer.step(closure=optimizer_closure)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/core/optimizer.py", line 164, in step
    trainer.accelerator.optimizer_step(self._optimizer, self._optimizer_idx, closure, **kwargs)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/accelerators/accelerator.py", line 339, in optimizer_step
    self.precision_plugin.optimizer_step(model, optimizer, opt_idx, closure, **kwargs)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/plugins/precision/precision_plugin.py", line 163, in optimizer_step
    optimizer.step(closure=closure, **kwargs)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/torch/optim/lr_scheduler.py", line 68, in wrapper
    return wrapped(*args, **kwargs)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/torch/optim/optimizer.py", line 140, in wrapper
    out = func(*args, **kwargs)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
    return func(*args, **kwargs)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/transformers/optimization.py", line 457, in step
    loss = closure()
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/plugins/precision/precision_plugin.py", line 148, in _wrap_closure
    closure_result = closure()
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 160, in __call__
    self._result = self.closure(*args, **kwargs)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 155, in closure
    self._backward_fn(step_output.closure_loss)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 327, in backward_fn
    self.trainer.accelerator.backward(loss, optimizer, opt_idx)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/accelerators/accelerator.py", line 314, in backward
    self.precision_plugin.backward(self.lightning_module, closure_loss, *args, **kwargs)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/plugins/precision/precision_plugin.py", line 91, in backward
    model.backward(closure_loss, optimizer, *args, **kwargs)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/pytorch_lightning/core/lightning.py", line 1434, in backward
    loss.backward(*args, **kwargs)
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/torch/_tensor.py", line 488, in backward
    torch.autograd.backward(
  File "/home/wzh/anaconda3/envs/kogito/lib/python3.10/site-packages/torch/autograd/__init__.py", line 197, in backward
    Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn

I do not kown why, any reply will be appreciated.
Thanks.

BTW, I can't run the examples 'examples/train_comet_bart.py' directly, it will report the following error:
ValueError: Usecols do not match columns, columns expected but not found: ['relation', 'tails', 'head']

It seems like kogito or source code of COMET use the old version of pytorch which is not support the Ampere architecture GPUs, the code can run on V100 which belongs to Volta architecture.
But it still has some problem with numpy==1.24.1, it will got following errors:
ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2026,) + inhomogeneous part.

Replace numpy==1.24.1 it with older version like numpy==1.21.2 can solve this problem but still have warrings like this:
<__array_function__ internals>:5: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.

Still, it works.

It seems like kogito or source code of COMET use the old version of pytorch which is not support the Ampere architecture GPUs, the code can run on V100 which belongs to Volta architecture. But it still has some problem with numpy==1.24.1, it will got following errors:
ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2026,) + inhomogeneous part.

Replace numpy==1.24.1 it with older version like numpy==1.21.2 can solve this problem but still have warrings like this:
<__array_function__ internals>:5: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.

Still, it works.

Regarding the other errors, unfortunately, since we do not actively maintain this repo, it might not support dependency updates. Please, try to use the original dependencies as much as possible and in case you can't and you find a solution, feel free to open a pull request.

It seems like kogito or source code of COMET use the old version of pytorch which is not support the Ampere architecture GPUs, the code can run on V100 which belongs to Volta architecture. But it still has some problem with numpy==1.24.1, it will got following errors:
ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2026,) + inhomogeneous part.

Replace numpy==1.24.1 it with older version like numpy==1.21.2 can solve this problem but still have warrings like this:
<__array_function__ internals>:5: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.

Still, it works.

Regarding the other errors, unfortunately, since we do not actively maintain this repo, it might not support dependency updates. Please, try to use the original dependencies as much as possible and in case you can't and you find a solution, feel free to open a pull request.

Very grateful for your reply, btw, may i ask for a requirements with specific package version list?

It seems like kogito or source code of COMET use the old version of pytorch which is not support the Ampere architecture GPUs, the code can run on V100 which belongs to Volta architecture. But it still has some problem with numpy==1.24.1, it will got following errors:
ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2026,) + inhomogeneous part.

Replace numpy==1.24.1 it with older version like numpy==1.21.2 can solve this problem but still have warrings like this:
<__array_function__ internals>:5: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.

Still, it works.

Regarding the other errors, unfortunately, since we do not actively maintain this repo, it might not support dependency updates. Please, try to use the original dependencies as much as possible and in case you can't and you find a solution, feel free to open a pull request.

Very grateful for your reply, btw, may i ask for a requirements with specific python version and package version list? Use pip install kogito can't work directly, it will automatic install numpy==1.24.1 when python==3.10

I see, in that case it seems like we need to resolve the issue so that new numpy version is supported on this architecture. Could you maybe share the full stack trace of the error you get with numpy==1.24.1?

I see, in that case it seems like we need to resolve the issue so that new numpy version is supported on this architecture. Could you maybe share the full stack trace of the error you get with numpy==1.24.1?

I try two versions of python on my device, and unfortunately, the subject of this issue is occurred, here is the problem of run the scripts examples/train_comet_bart.py:
Here is my hardware environment:
Ubuntu 20.04
Nvidia Tesla V100
Driver Version: 535.54.03
Cuda 12.2

Just use conda create -n env python=3.10 and pip install kogito to configure the environment.
The cuda versoon of torch which is automatically installed :

Python 3.10.13 (main, Sep 11 2023, 13:44:35) [GCC 11.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import torch
>>> torch.version.cuda
'11.7'

Here is the output when run pip list:
absl-py                  2.0.0
aiohttp                  3.9.1
aiosignal                1.3.1
annotated-types          0.6.0
async-timeout            4.0.3
attrs                    23.1.0
bert-score               0.3.13
blis                     0.7.11
cachetools               5.3.2
catalogue                2.0.10
certifi                  2023.11.17
charset-normalizer       3.3.2
click                    8.1.7
cloudpathlib             0.16.0
colorama                 0.4.6
confection               0.1.4
contourpy                1.2.0
cycler                   0.12.1
cymem                    2.0.8
docker-pycreds           0.4.0
et-xmlfile               1.1.0
filelock                 3.13.1
fonttools                4.46.0
frozenlist               1.4.0
fsspec                   2023.12.2
future                   0.18.3
gitdb                    4.0.11
GitPython                3.1.40
google-auth              2.25.2
google-auth-oauthlib     1.2.0
grpcio                   1.51.3
huggingface-hub          0.19.4
idna                     3.6
inflect                  5.6.2
Jinja2                   3.1.2
joblib                   1.3.2
kiwisolver               1.4.5
kogito                   0.6.2
langcodes                3.3.0
lightning-utilities      0.10.0
lxml                     4.9.3
Markdown                 3.5.1
MarkupSafe               2.1.3
matplotlib               3.8.2
multidict                6.0.4
murmurhash               1.0.10
nltk                     3.8.1
numpy                    1.26.2
nvidia-cublas-cu11       <IP_ADDRESS>
nvidia-cuda-nvrtc-cu11   11.7.99
nvidia-cuda-runtime-cu11 11.7.99
nvidia-cudnn-cu11        <IP_ADDRESS>
oauthlib                 3.2.2
openai                   0.18.1
openpyxl                 3.1.2
packaging                23.2
pandas                   1.5.3
pandas-stubs             <IP_ADDRESS>928
pathtools                0.1.2
Pillow                   10.1.0
pip                      23.3.1
portalocker              2.8.2
preshed                  3.0.9
promise                  2.3
protobuf                 3.20.3
psutil                   5.9.6
pyasn1                   0.5.1
pyasn1-modules           0.3.0
pydantic                 2.5.2
pydantic_core            2.14.5
pyDeprecate              0.3.1
pyparsing                3.1.1
python-dateutil          2.8.2
pytorch-lightning        1.5.10
pytz                     2023.3.post1
PyYAML                   6.0.1
regex                    2023.10.3
requests                 2.31.0
requests-oauthlib        1.3.1
rouge-score              0.0.4
rsa                      4.9
sacrebleu                2.4.0
safetensors              0.4.1
sentencepiece            0.1.99
sentry-sdk               1.39.1
setproctitle             1.3.3
setuptools               59.5.0
shortuuid                1.0.11
six                      1.16.0
smart-open               6.4.0
smmap                    5.0.1
spacy                    3.7.2
spacy-legacy             3.0.12
spacy-loggers            1.0.5
srsly                    2.4.8
tabulate                 0.9.0
tensorboard              2.15.1
tensorboard-data-server  0.7.2
thinc                    8.2.1
tokenizers               0.15.0
torch                    1.13.1
torchmetrics             1.2.1
tqdm                     4.66.1
transformers             4.36.1
typer                    0.9.0
types-pytz               2<IP_ADDRESS>
typing_extensions        4.9.0
urllib3                  2.1.0
wandb                    0.12.21
wasabi                   1.1.2
weasel                   0.3.4
Werkzeug                 3.0.1
wheel                    0.41.2
yarl                     1.9.4

And here is the full stack trace:
Global seed set to 42
/root/miniconda3/envs/peak/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/callback_connector.py:147: LightningDeprecationWarning: Setting `Trainer(checkpoint_callback=<pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint object at 0x7f1634d0ff40>)` is deprecated in v1.5 and will be removed in v1.7. Please consider using `Trainer(enable_checkpointing=<pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint object at 0x7f1634d0ff40>)`.
  rank_zero_deprecation(
/root/miniconda3/envs/peak/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/callback_connector.py:167: LightningDeprecationWarning: Setting `Trainer(weights_summary=None)` is deprecated in v1.5 and will be removed in v1.7. Please set `Trainer(enable_model_summary=False)` instead.
  rank_zero_deprecation(
GPU available: True, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
/root/miniconda3/envs/peak/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py:1584: UserWarning: GPU available but not used. Set the gpus flag in your trainer `Trainer(gpus=1)` or script `--gpus=1`.
  rank_zero_warn(
/root/miniconda3/envs/peak/lib/python3.10/site-packages/transformers/optimization.py:429: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning
  warnings.warn(
Validation sanity check: 0it [00:00, ?it/s]/root/miniconda3/envs/peak/lib/python3.10/site-packages/pytorch_lightning/trainer/data_loading.py:132: UserWarning: The dataloader, val_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 52 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.
  rank_zero_warn(
Validation sanity check:   0%|                                                                                | 0/2 [00:00<?, ?it/s]/root/miniconda3/envs/peak/lib/python3.10/site-packages/transformers/generation/utils.py:1355: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.
  warnings.warn(
Global seed set to 42
/root/miniconda3/envs/peak/lib/python3.10/site-packages/kogito/models/bart/utils.py:490: UserWarning: All learning rates are 0
  warnings.warn("All learning rates are 0")
/root/miniconda3/envs/peak/lib/python3.10/site-packages/pytorch_lightning/trainer/data_loading.py:132: UserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 52 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.
  rank_zero_warn(
Epoch 0:   0%|                                                                                | 0/3126 [00:00<?, ?it/s]Traceback (most recent call last):
  File "/root/autodl-tmp/kogito-main/examples/train_comet_bart.py", line 39, in <module>
    model.train(
  File "/root/miniconda3/envs/peak/lib/python3.10/site-packages/kogito/models/bart/comet.py", line 109, in train
    trainer: pl.Trainer = generic_train(
  File "/root/miniconda3/envs/peak/lib/python3.10/site-packages/kogito/models/bart/utils.py", line 262, in generic_train
    trainer.fit(model)
  File "/root/miniconda3/envs/peak/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 740, in fit
    self._call_and_handle_interrupt(
  File "/root/miniconda3/envs/peak/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 685, in _call_and_handle_interrupt
    return trainer_fn(*args, **kwargs)
  File "/root/miniconda3/envs/peak/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 777, in _fit_impl
    self._run(model, ckpt_path=ckpt_path)
  File "/root/miniconda3/envs/peak/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 1199, in _run
    self._dispatch()
  File "/root/miniconda3/envs/peak/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 1279, in _dispatch
    self.training_type_plugin.start_training(self)
  File "/root/miniconda3/envs/peak/lib/python3.10/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py", line 202, in start_training
    self._results = trainer.run_stage()
  File "/root/miniconda3/envs/peak/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 1289, in run_stage
    return self._run_train()
  File "/root/miniconda3/envs/peak/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 1319, in _run_train
    self.fit_loop.run()
  File "/root/miniconda3/envs/peak/lib/python3.10/site-packages/pytorch_lightning/loops/base.py", line 145, in run
    self.advance(*args, **kwargs)
  File "/root/miniconda3/envs/peak/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py", line 234, in advance
    self.epoch_loop.run(data_fetcher)
  File "/root/miniconda3/envs/peak/lib/python3.10/site-packages/pytorch_lightning/loops/base.py", line 145, in run
    self.advance(*args, **kwargs)
  File "/root/miniconda3/envs/peak/lib/python3.10/site-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py", line 193, in advance
    batch_output = self.batch_loop.run(batch, batch_idx)
  File "/root/miniconda3/envs/peak/lib/python3.10/site-packages/pytorch_lightning/loops/base.py", line 145, in run
    self.advance(*args, **kwargs)
  File "/root/miniconda3/envs/peak/lib/python3.10/site-packages/pytorch_lightning/loops/batch/training_batch_loop.py", line 88, in advance
    outputs = self.optimizer_loop.run(split_batch, optimizers, batch_idx)
  File "/root/miniconda3/envs/peak/lib/python3.10/site-packages/pytorch_lightning/loops/base.py", line 145, in run
    self.advance(*args, **kwargs)
  File "/root/miniconda3/envs/peak/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 215, in advance
    result = self._run_optimization(
  File "/root/miniconda3/envs/peak/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 266, in _run_optimization
    self._optimizer_step(optimizer, opt_idx, batch_idx, closure)
  File "/root/miniconda3/envs/peak/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 378, in _optimizer_step
    lightning_module.optimizer_step(
  File "/root/miniconda3/envs/peak/lib/python3.10/site-packages/pytorch_lightning/core/lightning.py", line 1652, in optimizer_step
    optimizer.step(closure=optimizer_closure)
  File "/root/miniconda3/envs/peak/lib/python3.10/site-packages/pytorch_lightning/core/optimizer.py", line 164, in step
    trainer.accelerator.optimizer_step(self._optimizer, self._optimizer_idx, closure, **kwargs)
  File "/root/miniconda3/envs/peak/lib/python3.10/site-packages/pytorch_lightning/accelerators/accelerator.py", line 339, in optimizer_step
    self.precision_plugin.optimizer_step(model, optimizer, opt_idx, closure, **kwargs)
  File "/root/miniconda3/envs/peak/lib/python3.10/site-packages/pytorch_lightning/plugins/precision/precision_plugin.py", line 163, in optimizer_step
    optimizer.step(closure=closure, **kwargs)
  File "/root/miniconda3/envs/peak/lib/python3.10/site-packages/torch/optim/lr_scheduler.py", line 68, in wrapper
    return wrapped(*args, **kwargs)
  File "/root/miniconda3/envs/peak/lib/python3.10/site-packages/torch/optim/optimizer.py", line 140, in wrapper
    out = func(*args, **kwargs)
  File "/root/miniconda3/envs/peak/lib/python3.10/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
    return func(*args, **kwargs)
  File "/root/miniconda3/envs/peak/lib/python3.10/site-packages/transformers/optimization.py", line 457, in step
    loss = closure()
  File "/root/miniconda3/envs/peak/lib/python3.10/site-packages/pytorch_lightning/plugins/precision/precision_plugin.py", line 148, in _wrap_closure
    closure_result = closure()
  File "/root/miniconda3/envs/peak/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 160, in __call__
    self._result = self.closure(*args, **kwargs)
  File "/root/miniconda3/envs/peak/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 155, in closure
    self._backward_fn(step_output.closure_loss)
  File "/root/miniconda3/envs/peak/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 327, in backward_fn
    self.trainer.accelerator.backward(loss, optimizer, opt_idx)
  File "/root/miniconda3/envs/peak/lib/python3.10/site-packages/pytorch_lightning/accelerators/accelerator.py", line 314, in backward
    self.precision_plugin.backward(self.lightning_module, closure_loss, *args, **kwargs)
  File "/root/miniconda3/envs/peak/lib/python3.10/site-packages/pytorch_lightning/plugins/precision/precision_plugin.py", line 91, in backward
    model.backward(closure_loss, optimizer, *args, **kwargs)
  File "/root/miniconda3/envs/peak/lib/python3.10/site-packages/pytorch_lightning/core/lightning.py", line 1434, in backward
    loss.backward(*args, **kwargs)
  File "/root/miniconda3/envs/peak/lib/python3.10/site-packages/torch/_tensor.py", line 488, in backward
    torch.autograd.backward(
  File "/root/miniconda3/envs/peak/lib/python3.10/site-packages/torch/autograd/__init__.py", line 197, in backward
    Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn

Just use conda create -n env python=3.8 and pip install kogito to configure the environment.
The cuda versoon of torch which is automatically installed is also 11.7
Here is the output when run pip list:

absl-py                  2.0.0
aiohttp                  3.9.1
aiosignal                1.3.1
annotated-types          0.6.0
async-timeout            4.0.3
attrs                    23.1.0
bert-score               0.3.13
blis                     0.7.11
cachetools               5.3.2
catalogue                2.0.10
certifi                  2023.11.17
charset-normalizer       3.3.2
click                    8.1.7
cloudpathlib             0.16.0
colorama                 0.4.6
confection               0.1.4
contourpy                1.1.1
cycler                   0.12.1
cymem                    2.0.8
docker-pycreds           0.4.0
et-xmlfile               1.1.0
filelock                 3.13.1
fonttools                4.46.0
frozenlist               1.4.0
fsspec                   2023.12.2
future                   0.18.3
gitdb                    4.0.11
GitPython                3.1.40
google-auth              2.25.2
google-auth-oauthlib     1.0.0
grpcio                   1.51.3
huggingface-hub          0.19.4
idna                     3.6
importlib-metadata       7.0.0
importlib-resources      6.1.1
inflect                  5.6.2
Jinja2                   3.1.2
joblib                   1.3.2
kiwisolver               1.4.5
kogito                   0.6.2
langcodes                3.3.0
lightning-utilities      0.10.0
lxml                     4.9.3
Markdown                 3.5.1
MarkupSafe               2.1.3
matplotlib               3.7.4
multidict                6.0.4
murmurhash               1.0.10
nltk                     3.8.1
numpy                    1.24.4
nvidia-cublas-cu11       <IP_ADDRESS>
nvidia-cuda-nvrtc-cu11   11.7.99
nvidia-cuda-runtime-cu11 11.7.99
nvidia-cudnn-cu11        <IP_ADDRESS>
oauthlib                 3.2.2
openai                   0.18.1
openpyxl                 3.1.2
packaging                23.2
pandas                   1.5.3
pandas-stubs             <IP_ADDRESS>814
pathtools                0.1.2
Pillow                   10.1.0
pip                      23.3.1
portalocker              2.8.2
preshed                  3.0.9
promise                  2.3
protobuf                 3.20.3
psutil                   5.9.6
pyasn1                   0.5.1
pyasn1-modules           0.3.0
pydantic                 2.5.2
pydantic_core            2.14.5
pyDeprecate              0.3.1
pyparsing                3.1.1
python-dateutil          2.8.2
pytorch-lightning        1.5.10
pytz                     2023.3.post1
PyYAML                   6.0.1
regex                    2023.10.3
requests                 2.31.0
requests-oauthlib        1.3.1
rouge-score              0.0.4
rsa                      4.9
sacrebleu                2.4.0
safetensors              0.4.1
sentencepiece            0.1.99
sentry-sdk               1.39.1
setproctitle             1.3.3
setuptools               59.5.0
shortuuid                1.0.11
six                      1.16.0
smart-open               6.4.0
smmap                    5.0.1
spacy                    3.7.2
spacy-legacy             3.0.12
spacy-loggers            1.0.5
srsly                    2.4.8
tabulate                 0.9.0
tensorboard              2.14.0
tensorboard-data-server  0.7.2
thinc                    8.2.2
tokenizers               0.15.0
torch                    1.13.1
torchmetrics             1.2.1
tqdm                     4.66.1
transformers             4.36.1
typer                    0.9.0
types-pytz               2<IP_ADDRESS>
typing_extensions        4.9.0
urllib3                  2.1.0
wandb                    0.12.21
wasabi                   1.1.2
weasel                   0.3.4
Werkzeug                 3.0.1
wheel                    0.41.2
yarl                     1.9.4
zipp                     3.17.0

And the problem is as same as the first situation
Actually, I'm trying to reproduce PeaCoK, follow the guide I recreate an environment use python=3.10 and install the requirements. This time when I use this environment to run examples/train_comet_bart.py, the problem is like this:
Global seed set to 42
Traceback (most recent call last):
  File "/root/autodl-tmp/kogito-main/examples/train_comet_bart.py", line 39, in <module>
    model.train(
  File "/root/miniconda3/envs/kgto/lib/python3.10/site-packages/kogito/models/bart/comet.py", line 108, in train
    trainer: pl.Trainer = generic_train(
  File "/root/miniconda3/envs/kgto/lib/python3.10/site-packages/kogito/models/bart/utils.py", line 249, in generic_train
    trainer = pl.Trainer(
  File "/root/miniconda3/envs/kgto/lib/python3.10/site-packages/pytorch_lightning/utilities/argparse.py", line 348, in insert_env_defaults
    return fn(self, **kwargs)
TypeError: Trainer.__init__() got an unexpected keyword argument 'weights_summary'

Then I replace the pytorch-lightning==1.9.5 with pytorch-lightning==1.6.5, this error which due to numpy==1.24.1 occurred:
Global seed set to 42
/root/miniconda3/envs/kgto/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/callback_connector.py:151: LightningDeprecationWarning: Setting `Trainer(checkpoint_callback=<pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint object at 0x7f13358ebee0>)` is deprecated in v1.5 and will be removed in v1.7. Please consider using `Trainer(enable_checkpointing=<pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint object at 0x7f13358ebee0>)`.
  rank_zero_deprecation(
/root/miniconda3/envs/kgto/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/callback_connector.py:171: LightningDeprecationWarning: Setting `Trainer(weights_summary=None)` is deprecated in v1.5 and will be removed in v1.7. Please set `Trainer(enable_model_summary=False)` instead.
  rank_zero_deprecation(
GPU available: True, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
/root/miniconda3/envs/kgto/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py:1814: PossibleUserWarning: GPU available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='gpu', devices=1)`.
  rank_zero_warn(
/root/miniconda3/envs/kgto/lib/python3.10/site-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning
  warnings.warn(
Sanity Checking: 0it [00:00, ?it/s]/root/miniconda3/envs/kgto/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:240: PossibleUserWarning: The dataloader, val_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 52 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.
  rank_zero_warn(
Sanity Checking DataLoader 0:   0%|                                                                                | 0/2 [00:00<?, ?it/s]/root/miniconda3/envs/kgto/lib/python3.10/site-packages/transformers/generation/utils.py:1387: UserWarning: Neither `max_length` nor `max_new_tokens` has been set, `max_length` will default to 20 (`self.config.max_length`). Controlling `max_length` via the config is deprecated and `max_length` will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.
  warnings.warn(
/root/miniconda3/envs/kgto/lib/python3.10/site-packages/kogito/models/bart/utils.py:487: UserWarning: All learning rates are 0
  warnings.warn("All learning rates are 0")
/root/miniconda3/envs/kgto/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:240: PossibleUserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 52 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.
  rank_zero_warn(
Epoch 0:   0%|                                                                                | 0/3126 [00:00<?, ?it/s]Traceback (most recent call last):
  File "/root/autodl-tmp/kogito-main/examples/train_comet_bart.py", line 39, in <module>
    model.train(
  File "/root/miniconda3/envs/kgto/lib/python3.10/site-packages/kogito/models/bart/comet.py", line 108, in train
    trainer: pl.Trainer = generic_train(
  File "/root/miniconda3/envs/kgto/lib/python3.10/site-packages/kogito/models/bart/utils.py", line 262, in generic_train
    trainer.fit(model)
  File "/root/miniconda3/envs/kgto/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 770, in fit
    self._call_and_handle_interrupt(
  File "/root/miniconda3/envs/kgto/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 723, in _call_and_handle_interrupt
    return trainer_fn(*args, **kwargs)
  File "/root/miniconda3/envs/kgto/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 811, in _fit_impl
    results = self._run(model, ckpt_path=self.ckpt_path)
  File "/root/miniconda3/envs/kgto/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 1236, in _run
    results = self._run_stage()
  File "/root/miniconda3/envs/kgto/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 1323, in _run_stage
    return self._run_train()
  File "/root/miniconda3/envs/kgto/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 1353, in _run_train
    self.fit_loop.run()
  File "/root/miniconda3/envs/kgto/lib/python3.10/site-packages/pytorch_lightning/loops/base.py", line 204, in run
    self.advance(*args, **kwargs)
  File "/root/miniconda3/envs/kgto/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py", line 266, in advance
    self._outputs = self.epoch_loop.run(self._data_fetcher)
  File "/root/miniconda3/envs/kgto/lib/python3.10/site-packages/pytorch_lightning/loops/base.py", line 199, in run
    self.on_run_start(*args, **kwargs)
  File "/root/miniconda3/envs/kgto/lib/python3.10/site-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py", line 153, in on_run_start
    _ = iter(data_fetcher)  # creates the iterator inside the fetcher
  File "/root/miniconda3/envs/kgto/lib/python3.10/site-packages/pytorch_lightning/utilities/fetching.py", line 179, in __iter__
    self._apply_patch()
  File "/root/miniconda3/envs/kgto/lib/python3.10/site-packages/pytorch_lightning/utilities/fetching.py", line 115, in _apply_patch
    apply_to_collections(self.loaders, self.loader_iters, (Iterator, DataLoader), _apply_patch_fn)
  File "/root/miniconda3/envs/kgto/lib/python3.10/site-packages/pytorch_lightning/utilities/fetching.py", line 154, in loader_iters
    loader_iters = self.dataloader_iter.loader_iters
  File "/root/miniconda3/envs/kgto/lib/python3.10/site-packages/pytorch_lightning/trainer/supporters.py", line 545, in loader_iters
    self._loader_iters = self.create_loader_iters(self.loaders)
  File "/root/miniconda3/envs/kgto/lib/python3.10/site-packages/pytorch_lightning/trainer/supporters.py", line 585, in create_loader_iters
    return apply_to_collection(loaders, Iterable, iter, wrong_dtype=(Sequence, Mapping))
  File "/root/miniconda3/envs/kgto/lib/python3.10/site-packages/pytorch_lightning/utilities/apply_func.py", line 99, in apply_to_collection
    return function(data, *args, **kwargs)
  File "/root/miniconda3/envs/kgto/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 435, in __iter__
    return self._get_iterator()
  File "/root/miniconda3/envs/kgto/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 381, in _get_iterator
    return _MultiProcessingDataLoaderIter(self)
  File "/root/miniconda3/envs/kgto/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1072, in __init__
    self._reset(loader, first_iter=True)
  File "/root/miniconda3/envs/kgto/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1105, in _reset
    self._try_put_index()
  File "/root/miniconda3/envs/kgto/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1339, in _try_put_index
    index = self._next_index()
  File "/root/miniconda3/envs/kgto/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 618, in _next_index
    return next(self._sampler_iter)  # may raise StopIteration
  File "/root/miniconda3/envs/kgto/lib/python3.10/site-packages/torch/utils/data/sampler.py", line 254, in __iter__
    for idx in self.sampler:
  File "/root/miniconda3/envs/kgto/lib/python3.10/site-packages/kogito/core/utils.py", line 125, in __iter__
    np.concatenate(np.random.permutation(ck_idx[1:]))
  File "mtrand.pyx", line 4703, in numpy.random.mtrand.RandomState.permutation
ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (3121,) + inhomogeneous part.
Epoch 0:   0%|          | 0/3126 [00:01<?, ?it/s]

Replace numpy==1.24.1 it with older version like numpy==1.21.2 can solve this error, but with another warnings like this:
Global seed set to 42
/root/miniconda3/envs/kgto/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/callback_connector.py:151: LightningDeprecationWarning: Setting `Trainer(checkpoint_callback=<pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint object at 0x7f5b436913c0>)` is deprecated in v1.5 and will be removed in v1.7. Please consider using `Trainer(enable_checkpointing=<pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint object at 0x7f5b436913c0>)`.
  rank_zero_deprecation(
/root/miniconda3/envs/kgto/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/callback_connector.py:171: LightningDeprecationWarning: Setting `Trainer(weights_summary=None)` is deprecated in v1.5 and will be removed in v1.7. Please set `Trainer(enable_model_summary=False)` instead.
  rank_zero_deprecation(
GPU available: True, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
/root/miniconda3/envs/kgto/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py:1814: PossibleUserWarning: GPU available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='gpu', devices=1)`.
  rank_zero_warn(
/root/miniconda3/envs/kgto/lib/python3.10/site-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning
  warnings.warn(
Sanity Checking: 0it [00:00, ?it/s]/root/miniconda3/envs/kgto/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:240: PossibleUserWarning: The dataloader, val_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 52 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.
  rank_zero_warn(
Sanity Checking DataLoader 0:   0%|                                                                                | 0/2 [00:00<?, ?it/s]/root/miniconda3/envs/kgto/lib/python3.10/site-packages/transformers/generation/utils.py:1387: UserWarning: Neither `max_length` nor `max_new_tokens` has been set, `max_length` will default to 20 (`self.config.max_length`). Controlling `max_length` via the config is deprecated and `max_length` will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.
  warnings.warn(
/root/miniconda3/envs/kgto/lib/python3.10/site-packages/kogito/models/bart/utils.py:487: UserWarning: All learning rates are 0
  warnings.warn("All learning rates are 0")
/root/miniconda3/envs/kgto/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:240: PossibleUserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 52 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.
  rank_zero_warn(
Epoch 0:   0%|                                                                                | 0/3126 [00:00<?, ?it/s]/root/miniconda3/envs/kgto/lib/python3.10/site-packages/kogito/core/utils.py:125: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
  np.concatenate(np.random.permutation(ck_idx[1:]))
<__array_function__ internals>:5: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.


Implement "modify and resend"

Like in the Network panel, the "modify and resend" feature would be quite useful in order to interact with the server directly (and to debug it). The feature would be available for the outgoing messages only.
Florent

Hey.
I had a look at this issue, but after creating the gui, I failed to figure out how to hook into an active web socket connection. We have a webSocketSerialID on a frame, but I find no API that lets us connect using this ID. If you have any suggestions on how one could do this, I'm all ears.

I failed to figure out how to hook into an active web socket connection

Actually, I don't even know if Firefox provides such an API, there might be some requirements to fulfill. @bakulf Is it already possible? How hard would it be to implement that?
Would you be interested in sharing your work in progress?
Florent

Of course, @fflorent. This is what I got so far: https://github.com/firebug/websocket-monitor/compare/master...esphen:issue21
It looks like this:

Thanks @esphen and nice patch!
As this bug sounds to be blocked until we have a clear answer for your question, would you be interested in contributing on other issues? (this one for example)
If so and when you need some assistance, don't hesitate to ping me on IRC if I can help (my nickname is fflorent on #firebug or #devtools ; server: irc.mozilla.org). You can also ping Honza on these channels (he's more active during the work week).
Florent

We don't have such functionality and I don't know if we really want to have it.
'modify and resend' for http request makes totally sense because they are somehow "state-less": a completely new request is sent, disconnected from the website logic. But resending a frame can break the logic of the website because, I guess, we want to use the same WebSocket object, that is alive somewhere and it could be in the middle of some kind of its own logic state...
But if we decide to go for this functionality, we must implement quite few things:
a WebSocket can run in any thread (any worker thread and main-thread) and the sending must happen in the correct thread as well. This means that we must modify the frame, then deliver it to some kind of service that knows where the WebSocket channel lives (on which thread, let's say) and send it via such WebSocket channel.
And of course we want it also for e10s and b2g :)
@janodvarko can you give a feedback about this?


Edited documentation for ReactionParser and its tests.

Cleaned up documentation for ReactionParser object and its tests. Removed test about not handling reversible reactions because we'll be adding that soon (so expecting coverage to decrease).

Coverage decreased (-0.5%) to 96.857% when pulling 22dadf2658bf54b4f63ec2d2401099df59e37516 on hsim13372:parser_cleanup into b98e3d21a46533ff3189bb93216386485e28ba21 on cs207-group11:master.

Coverage decreased (-0.5%) to 96.857% when pulling 81f1f9416722c205731334bec9f84a99e68a9063 on hsim13372:parser_cleanup into b98e3d21a46533ff3189bb93216386485e28ba21 on cs207-group11:master.


Missing Driver

I have tried to follow the doc. But none of docker's maria/postgres driver listed in https://atlasgo.io/concepts/dev-database seems to work.
I got Error: sql/sqlclient: missing driver. See: https://atlasgo.io/url
Did I miss something?

Other info that might be helpful
Docker version 20.10.17, build 100c701
OSX Apple Silicon installed via brew

my schema.sql
CREATE TABLE "Shoes" (
  "id" int PRIMARY KEY,
  "brand" varchar,
  "model" varchar,
  "size" varchar,
  "current_price" float
);

CREATE TABLE "Customers" (
  "id" int PRIMARY KEY,
  "name" varchar,
  "address" varchar
);

CREATE TABLE "Orders" (
  "id" int PRIMARY KEY,
  "customer_id" int
);

CREATE TABLE "OrderItems" (
  "id" int PRIMARY KEY,
  "order_id" int,
  "shoe_id" int,
  "quantity" int,
  "price" float
);

CREATE UNIQUE INDEX ON "Shoes" ("brand", "model", "size");

ALTER TABLE "Orders" ADD FOREIGN KEY ("customer_id") REFERENCES "Customers" ("id");

ALTER TABLE "OrderItems" ADD FOREIGN KEY ("order_id") REFERENCES "Orders" ("id");

ALTER TABLE "OrderItems" ADD FOREIGN KEY ("shoe_id") REFERENCES "Shoes" ("id");

Hey @piti118 👋🏻
Please, add the file:// scheme to the desired state as defined in the doc: https://atlasgo.io/versioned/diff#generate-migrations-from-sql-schemas
—-to file://schema.sql

Thank you very much that works.

Great, closing 😃


404 text : Not Found is Printed in the Back-end When You Create a Tenant When you have configured Sales-force outbound provisioning in super tenant

Move from https://wso2.org/jira/browse/IDENTITY-7059
Steps to reproduce:

Configure Salesforce outbound provisioning connector and enable it.
Configure it in resident SP
Create a tenant

Below error can be seen in logs.
[2017-12-06 20:43:16,507]  INFO {org.wso2.carbon.tenant.mgt.services.TenantMgtAdminService} -  Added the tenant 'test.com [3]' by<EMAIL_ADDRESS>[2017-12-06 20:43:18,861] ERROR {org.wso2.carbon.identity.provisioning.connector.salesforce.SalesforceProvisioningConnector} -  recieved response status code :404 text : Not Found [2017-12-06 20:43:18,864] ERROR {org.wso2.carbon.identity.provisioning.connector.salesforce.SalesforceProvisioningConnector} -  recieved response status code :404 text : Not Found

This issue is being closed due to extended inactivity. Please feel free to reopen it if further attention is needed. Thank you for helping us keep the issue list relevant and focused!


Doesn’t support ReactNative 0.63

Hello!
There’s a problem in React Native 0.63 because it is not compatible with<EMAIL_ADDRESS>warning "@haskkor/react-native-recaptchav3 ><EMAIL_ADDRESS>has incorrect peer dependency<EMAIL_ADDRESS><0.62". warning "@haskkor/react-native-recaptchav3 ><EMAIL_ADDRESS>has unmet peer dependency<EMAIL_ADDRESS>Is there any posibility to fix it?

Any updates?

This might be caused by having another version of react-native-webview in your project.
Try to change it to "^9.4.0"

This might be caused by having another version of react-native-webview in your project.
Try to change it to "^9.4.0"


Open in Browser is not working

Fails with the following error.
Unable to open 'knative-tutorial-greeter:quarkus': Unable to read file '/quay.io/rhdevelopers/knative-tutorial-greeter:quarkus' (Error: Unable to resolve non-existing file '/quay.io/rhdevelopers/knative-tutorial-greeter:quarkus').

fixed in #37


Feat/credentials licenses rollback sumo

Forward-port of Anton's credentials work, backport of license fixes, forward- and backport of RPi rollback, and bump aktualizr to latest to match the rocko PR.
Note that the RPi rollback is untested; I don't really know how to test it.

@lbonn Finally got around to backporting all the necessary commits. oe-selftest still passes.

Cool, thanks!


Bond removal and readdition changes isomer

Description:

RDKit Version: 2018.09.1
Platform: Pop OS 18.04 LTS (Ubuntu 18.04 LTS)

When removing and adding the same bond the chirality sometimes changes if the new bond has a higher idx than before.
I am not a chemist but from my understand rdkit is using the order of the bonds to determine order in which different R groups added to the chiral center. Perhaps due to this when I remove a bond to make fragments, and later need to combine then, the same isomer is not created. Any help is appreciated.
(Code from python API)

from rdkit import Chem

Initmol = Chem.MolFromSmiles("[4*][C@H](c1cccs1)[C@H]([4*])C")
smiles = Chem.MolToSmiles(Initmol, isomericSmiles=True)
print(smiles)
mol = Chem.MolFromSmiles(smiles)
# C[C@H](c1cccs1)[C@H](C)C
emol = Chem.EditableMol(mol)
emol.RemoveBond(1, 2)

emol.AddBond(1, 2, Chem.rdchem.BondType.SINGLE)
mol2 = emol.GetMol()
new_smiles = Chem.MolToSmiles(mol2, isomericSmiles=True)
print(new_smiles)

# Outputs:
# [4*][C@H](c1cccs1)[C@H]([4*])C
# [4*][C@@H](c1cccs1)[C@H]([4*])C

This does not make sense as I should be recreating the original molecule, not a isomer of it.

If there was a way to change Bond Index or ordering of bonds on an atom then it would allow developers to create the isomer they want.

This is exactly what is happening.  You are changing the order of the bonds around the atom.
>>> mol = Chem.MolFromSmiles(smiles)
>>> emol = Chem.RWMol(mol)
>>> order = [bond.GetIdx() for bond in emol.GetAtomWithIdx(1).GetBonds()]
>>> order
[0, 1, 6]
>>> emol.RemoveBond(1, 2)
>>> emol.AddBond(1, 2, Chem.rdchem.BondType.SINGLE)
10
>>> order = [bond.GetIdx() for bond in emol.GetAtomWithIdx(1).GetBonds()]
# note atom #6 has become #5
>>> order
[0, 5, 9]

In this case as you've changed the clockwise/counter clockwise ordering, you need to invert chirality:
>>> Chem.MolToSmiles(emol)
'[4*][C@@H](c1cccs1)[C@H]([4*])C'

>>> emol.GetAtomWithIdx(1).InvertChirality()
>>> Chem.MolToSmiles(emol)
'[4*][C@H](c1cccs1)[C@H]([4*])C'

Thank you for your response.
Right, however there are times where I do not need to invert chirality.
I trying breaking and reconnecting thousands of bonds around hundreds of molecules, and I cannot tell which one needs their chirality inverted to make it the same as the original molecule.
Would there be any good way to track this chirality issue and only invert as needed?

Currently, I store the chiralTag of the molecule where I break the bond and restore it when I recombine it, however because fragments are rendered different than the original molecule, I may be running into some issues there. When I added the tracking of the chiralTag less False Fragments were generated.

To clarify I want to order the bonds for a given atom, so the chirality is exactly as I want it to be.
Any help would be appreciated.

There's probably a way to do this, but I want to understand what exactly you're trying to do.
You say "I want to order the bonds for a given atom, so the chirality is exactly as I want it to be"
Given that the RDKit understands atomic chirality based on the order of the bonds I'm not quite sure what that means or how you would tell the RDKit what chirality you want so that it can either give you the appropriate order of the bonds or, more likely, the correct chiral tag.
Do you want to replace a bond in an molecule without changing the stereochemistry?

Sorry for the lack of clarity, its my first time doing chemistry related coding.
Do you want to replace a bond in an molecule without changing the stereochemistry?
Yes, but I cannot use the replace substruct or replace bond methods as I want both the broken parts and later on rebuild some of these broken bonds.
I break molecules into fragments (replacing the brokes ends of both new ends with 0 atomic number, and different isotopes as IDs). The breakdown works fine, I get only valid sub fragments.
Valid: being a proper substructure of the original molecule.
However when I rebuild the fragments into different substructures, I need to remove that placeholder atomic number 0 on both fragments and reattach them. Here is where the issue happens.
example:
CCC(NO)CC breaks into CCC([1*])CC and [2*]NO
When I recombine them potentially get CCC(CC)NO.
The ordering of the R groups around C is changed, making the rebuild not the same as the original.
I was wondering if there was anyway for me to indicate, when adding a bond, that NO should be ordered before CC.

ok, I think I'm starting to get it.

Original mol: COc1ccc(N2CCN([C@H](c3cccs3)[C@@H](C)NC(=O)C(=O)NCc3ccco3)CC2)cc1
frag 1: [106*][C@H]([116*])[C@H]([112*])C
frag 2: [117*]c1cccs1
frag 1 and 2 from BRICS.Decompose and from my code(i'ved just changed the isotope on the pseudo atoms)
desired: [4*][C@H](c1cccs1)[C@H]([4*])C (from BRICS.Decompose())
Getting: [4*][C@H](C)[C@@H]([4*])c1cccs1 (from my code)
I'm trying to build up different sub structures as opposed to breaking down to them.
Subfragments from BRICS.Decompose() I do cannot build. (2nd one is the given example)

Subfragments BRICS.Decompose() cannot built, but I can, some have this bond order issue. (4th column, 5th row is given example)

@harsh6646 did the gist help out?

To carry on for @harsh6646  here (I am now also working on this issue a little bit), unfortunately the gist does not help (or I am misunderstanding it).
Specifically there is one bond being deleted (a-b), two atoms being added (x,y), and then two new bonds being added ((a-x), (y-b)).
So originally the bonds for atom a might look like this:
[(2, 3), (3, 4), (3, 5)]
then afterwards they look like this:
[(3, 4), (3, 5), (3, 30)]
What I need is to have them look like this:
[(3, 30), (3, 4), (3, 5)]
Similarly, for atom b:
old b bonds: [(1, 2), (2, 3)]
new b bonds: [(1, 2), (2, 31)]
In this case there is no issue (but not always.)
However, as far as I can tell, adding a bond to an atom always adds it at the end of this list, and the order of this list is being used to define the chirality? If I delete those other bonds and add all of them back in the correct order, this error propagates to all the atoms that were connected to those atoms as well, ending only when we get to atoms with only one bond or where the removed bond was last in the list of bonds.
the eMol.ReplaceBond() function is also not going to work here, as even if I replace one bond, I will still need to add another for the other atom.

The gist doesn’t preserve ordering, it preserves chirality, I.e. it knows when to flip the chirality in the output molecule.

Does it flip the chirality of all stereocenters? Or only the incorrect ones?

The incorrect ones.  The “trick” here is that the attachment points in all fragments need to be dummy atoms so we know the original bond orders.

Brian Kelley

On Oct 8, 2019, at 5:41 PM, Andrew E Brereton<EMAIL_ADDRESS>wrote:

Does it flip the chirality of all stereocenters? Or only the incorrect ones?
—
You are receiving this because you commented.
Reply to this email directly, view it on GitHub, or mute the thread.

I do have dummy atoms, and I am storing the chiral tags, but I'm not sure I understand how to apply this gist to this issue.
The issue is not just with zipping molecules together, but also in breaking them down. In the process of breaking apart a molecule we’re destroying one bond and adding two. This sometimes borks the stereochemistry, long before we attach molecule fragments back together.
Ideally we should see that even just the frag_a -> dummy_atom_a bond and the frag_b -> dummy_atom_b bond should have the same stereochemistry as before fragmentation. However, upon adding the bond to the dummy atom, the stereochemistry often changes (because the bond order changes).

@bp-kelley Okay so we have managed to get something working by sidestepping this entirely, and simply creating a different non rdchem.Mol object, editing the bonds in that object, and then using that object to build each fragment from scratch as an editableMol object.
Thanks all for your help and feel free to close this issue if you want. Though I still think it would be really nice to add a feature to the editableMol class that allows you to reorder the bonds for a given atom.

The main idea of Brian's code snippet is to look at the parity of the permutation order of the bonds, which is either even or odd. If it's changed, then swap the chirality from CW to CCW or back.
He mentioned some writings I did on the topic. The most specific is at http://www.dalkescientific.com/writings/diary/archive/2016/08/14/fragment_chiral_molecules.html .
The work I did then lead to some fixes in the RDKit, which should have improved some of the errors I found.
My general view these days is to manipulate things as SMILES strings, rather than as molecule objects, because getting chirality (and double-bond stereochemistry) correct, while editing a molecule object, ended up being difficult and confusing.

Wow! Thanks for sharing the link, that’s a remarkably clear analysis of this issue! Since the consensus seems to be that it’s better to deal with this outside of the Chem.Mol format anyway, I feel a little better about the approach we ended up taking, which only converts back to Mol at the end, when finished.


Pretrained Inception V4 to Keras Application Folder

Would it be possible to add Inception V4 to the Keras Applications folder?
System/User Specifics: Tensorflow 2.0 with tf.keras module
Due to recent discoveries made in neural networks and also according to many benchmarking standards, Inception V4 is one of the best networks available with very high accuracies in both Top 5 and Top 1.
Inception V4 should be included because it has been consistently proven to be one of the most accurate networks for Image Recognition. It will benefit many users who want a network with very high accuracy and also have the resources to run it.

It has been 3 months, do we have any info about this request?

keras applications is actually in the merging process with tf model garden. Maybe we should have a formal discussion of where this should go.

I think we should make this contributions welcome. @Alpheron Would you be interested?

So I think tensorflow-slim has an Inception V4 model which has been written which can be found here. However, according to the documentation saying that Slim is not supported with Tensorflow 2.0, I think it would be fair to say that this model must be converted sometime in the future to be able to work with Keras. In fairness, the models found [here](https://github.com/tensorflow/models/blob/master/research/slim/README.md#pre-trained-models are all very capable models not all of which are supported. I am not 100% sure about if the Tensorflow Model Garden was also merged recently. In that case, it may not make sense to only implement Inception V4 to Keras Applications, but it may make sense to port the entire Tensorflow Slim model library to Keras Applications. That might sound a bit ambitious but I think the models all are very capable in different use cases and may be of use to people. As far as Inception V4 goes, I think that I will be able to port that to Keras and will be able to issue a PR sometime in the very near future (around a week or so).

Hi @amangalampalli !
Could you consider this duplicate  to #1153 and put your thoughts in that issue.
Thank you!

Yeah, sorry for the long delays, I will close this issue and will speak on the other issue.


remove dated IPVS warning for k8s v1.10

Description
Remove a dated warning about locally-assigned ExternalIP for Kubernetes v1.10 in releases that no longer support K8s v1.10.
Related issues/PRs

Todos

[ ] Tests
[ ] Documentation
[ ] Release note

Release Note

None required

Deploy preview for calico ready!