Create device without model

Is your feature request related to a problem? Please describe.
Get the possibility to manage a device without its model
Describe the solution you'd like
When a device is present on the IoT Hub, and this has not any device model.
Get the possibility to manage it, and possibility to change its model.
Describe alternatives you've considered
N/A
Additional context
N/A

As discussed today, this feature doesn't need to be implemented anymore.
This could be achieved by creating a test device model and assign this device model to the device.


fix: ec label instead of enterprise-contract

Description
These tests were skipped when running tests in infra-deployments from[1]:
exec: ginkgo "-p" "--output-interceptor-mode=none" "--timeout=90m" "--output-dir=/logs/artifacts" "--junit-report=e2e-report.xml" "--label-filter=e2e-demo,rhtap-demo,spi-suite,integration-service,o11y,ec,byoc" "./cmd" "--" "--config-suites=/tmp/tmp.NlhpfTvYoJ/tests/e2e-demos/config/default.yaml" "--generate-rppreproc-report=true" "--rp-preproc-dir=/logs/artifacts"

Notice that enterprise-contract label is not included but ec label is.
This leads to issues like on [2] run going undetected.
So this changes the label to ec so the test suite runs on changes to infra-deployments.
[1] https://gcsweb-ci.apps.ci.l2s4.p1.openshiftapps.com/gcs/origin-ci-test/pr-logs/pull/redhat-appstudio_infra-deployments/2122/pull-ci-redhat-appstudio-infra-deployments-main-appstudio-e2e-tests/1681252356039118848/artifacts/appstudio-e2e-tests/redhat-appstudio-e2e/build-log.txt
[2] https://prow.ci.openshift.org/view/gs/origin-ci-test/pr-logs/pull/redhat-appstudio_e2e-tests/628/pull-ci-redhat-appstudio-e2e-tests-main-redhat-appstudio-e2e/1681556441929879552
Issue ticket number and link
Type of change

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

How Has This Been Tested?
Was not
Checklist:

[x] I have performed a self-review of my code
[ ] I have commented my code, particularly in hard-to-understand areas
[ ] I have made corresponding changes to the documentation
[ ] I have added meaningful description with JIRA/GitHub issue key(if applicable), for example HASSuiteDescribe("STONE-123456789 devfile source")
[ ] I have updated labels (if needed)

This requires either #636 or #638 (preferred) to be merged. So once one of those is merged I'll rebase.

/lgtm

/test redhat-appstudio-e2e

/retest

/test redhat-appstudio-e2e

/test redhat-appstudio-e2e

/test redhat-appstudio-e2e

/lgtm

unrelated test failure
merging...


!code command isn't working

What ever argument was given, it returns
You didn't provide any arguments, @yahu1031 !

Fixed in bd96e3f commit and fixed JRE link as a hotfix


Refactor RustSDK (#83)

[Refactor] Add #[form] for subtypes of WasmEdgeError.

[Refactor] fix memory leak

[Fix] Avoid the CPU 100% by tokio-wasi.

[Fix] Fix blocking during tokio-wasi TCP connect.

[Fix] async-wasi get envs & args

[Perf] Optimize the wasi-ctx

[Chore] Temporarily disable the serialize feature.

[feat] virtual file system

[Refactor] update the argument type of run_func_with_timeout and run_func_async_with_timeout (#76)

[Feat] run func timeout use std::time::Duration

[Doc]: update api docs

(cherry picked from commit 07e84b70ba7e9011ae6774e2ce28c968e25c576a)

[Refactor] delete useless code from validator

[Refactor] To pass the clippy

Disable timeout in musl libc (#71)

Disable timeout in musl libc

[Doc] update doc

[Fix] fix ffi::WasmEdge_String to String

[Fix] Fix unit test

[Fix] Modify the WasiModule.

[refactor] Introduce WasmEdge_FunctionInstanceGetData to drop host data (#84)

feat(rust-sys): drop host_data in Function::drop

chore(rust-sys): update drop of ImportModule and Function

chore(rust-sdk): update test code

chore(rust-sys): update drop of ImportModule and Function

refactor(rust-sys): update ImportModule::drop

chore(rust-sys): remove debug code

chore(rust-sdk): remove debug code

version(rust-sdk): bump to 0.13.0

chore(rust-sys): supress clippy warning

ci(ci-build): update rust version

ci(standalone): update rust version

chore(rust-sys): update rustdoc

chore(rust-sdk): update rustdoc and README

chore(rust-sys): update dependencies

ci: update to macos-13

doc: update CHANGELOG (#85)

[Chore] Update build script (#86)

chore(rust-sys): update build script

chore(rust-sys): update build script

[Fix] Fix sys test

Relax the version of wat dep  (#90)

chore(rust-sdk): update wat dep

chore(rust-sdk): update test code

chore(rust-sys): update test code

version(rust-sys): bump to 0.17.4

version(rust-sdk): bump to 0.13.1

doc: update CHANGELOG

[CI] skip test_vmbuilder on fedora

Fix static build to link agains zstd (#91)

Update doc for releasing v0.13.2 (#93)

ci: update workflows

chore(rust-sys): update versioning table

chore(rust-sdk): update versioning table

ci(rust-static-lib): fix typo

ci(rust-static-lib): update workflow

chore(rust-sys): update rustdoc

version(rust-sdk): bump to 0.13.2

chore(rust-sdk): update versioning table

ci(rust-static-lib): update workflow

[Version] Bump wasmedge-sys to v0.17.5 (#94)

version(rust-sys): bump to 0.17.5

chore(rust-sys): update versioning table

chore(rust-sys): update dependency

chore(rust-sdk): update versioning table

doc: update CHANGELOG (#95)

[CI] Update rust-static-lib workflow (#96)

ci(rust-static-lib): update workflow

ci(rust-static-lib): update workflow

ci(rust-static-lib): update workflow

ci(rust-static-lib): update workflow

ci(rust-static-lib): remove libboost-all-dev dep

[Fix] Temporary FuncRef extraction implementation.

[CI] fix test wat

Hello, I am a code review bot on flows.network.
It could take a few minutes for me to analyze this PR. Relax, grab a cup of coffee and check back later. Thanks!


Application details for Y Combinator

https://www.ycombinator.com/apply/

Reminder set in google calender. Application deadline 24.03.2022

Details listed here: https://docs.google.com/spreadsheets/d/1BtJrmX8b3SMdCD8ThohzLEWp5OKLzc60Chzfx14yqzM/edit#gid=1803223038


<EMAIL_ADDRESS>unmet dependency googlemaps and markerclustererplus

I Got this using yarn on my react project.
warning " ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning " ><EMAIL_ADDRESS>has unmet peer dependency
<EMAIL_ADDRESS>
This is a simple warning that means that I should have into my

<EMAIL_ADDRESS><EMAIL_ADDRESS>
or is there some dependencies missing from react-google-maps?

@GiovanniGiampaolo peer dependencies are dependencies which is not included into the package itself, but required for end user to install by himself next to the package requiring this peer dependencies.
The repo of this project is unmaintained more than a year, and we had build new version https://www.npmjs.com/package/@react-google-maps/api
We had rewrite it to TypeScript, and updating it frequently: https://github.com/JustFly1984/react-google-maps-api/tree/master/packages/react-google-maps-api
You can enjoy autocomplete.
You can see our docs: https://react-google-maps-api-docs.netlify.app
Also a lot of examples: https://react-google-maps-api-gatsby-demo.netlify.app/ https://github.com/JustFly1984/react-google-maps-api/tree/master/packages/react-google-maps-api-gatsby-example/src/examples
The bundle size is much smaller: https://bundlephobia.com/result?p=@react-google-maps/api
Our Spectrum community: https://spectrum.chat/react-google-maps
Our Slack channel: https://join.slack.com/t/react-google-maps-api/shared_invite/enQtODc5ODU1NTY5MzQ4LTBiNTYzZmY1YmVjYzJhZThkMGU0YzUwZjJkNGJmYjk4YjQyYjZhMDk2YThlZGEzNDc0M2RhNjBmMWE4ZTJiMjQ
Enjoy!

@GiovanniGiampaolo peer dependencies are dependencies which is not included into the package itself, but required for end user to install by himself next to the package requiring this peer dependencies.
The repo of this project is unmaintained more than a year, and we had build new version https://www.npmjs.com/package/@react-google-maps/api
We had rewrite it to TypeScript, and updating it frequently: https://github.com/JustFly1984/react-google-maps-api/tree/master/packages/react-google-maps-api
You can enjoy autocomplete.
You can see our docs: https://react-google-maps-api-docs.netlify.app
Also a lot of examples: https://react-google-maps-api-gatsby-demo.netlify.app/ https://github.com/JustFly1984/react-google-maps-api/tree/master/packages/react-google-maps-api-gatsby-example/src/examples
The bundle size is much smaller: https://bundlephobia.com/result?p=@react-google-maps/api
Our Spectrum community: https://spectrum.chat/react-google-maps
Our Slack channel: https://join.slack.com/t/react-google-maps-api/shared_invite/enQtODc5ODU1NTY5MzQ4LTBiNTYzZmY1YmVjYzJhZThkMGU0YzUwZjJkNGJmYjk4YjQyYjZhMDk2YThlZGEzNDc0M2RhNjBmMWE4ZTJiMjQ
Enjoy!

Thanks so much for ur detailed answer, I think I'm gonna migrating!


ARROW-15015: [R] Test / CI flag for ensuring all tests are run?

@jonkeane there are still a bunch of skips but the once relying on the "helpe-skip" functions should all be forced to run now.

Should be merge after https://github.com/apache/arrow/pull/12946


[spike] Investigate if we can use :* syntax in domain matching

One of the oldest issues we have on this project is related to how Envoy handles Host:  headers which include a :port suffix. #210, #398, #353 and many more.
A recent comment suggests that we may be able to specify
domains: []string{"host", "host:*"}

instead of tracking and hard coding the port the listener connected to this routing is using, can we use the :* syntax instead?
This is a spike to see if this is possible, if it will work for both HTTP and HTTPS, and if so scope the work to remove completely or partially the mechanisms for configuring and tracking the listening port's number throughout Contour.
nb: The resolution of this ticket is either closing this issue with a comment if it is not possible to use this syntax. Bonus points for replying to the original commenter. If this is something we can use, please scope the work necessary in one or more new issues -- we may choose to do it over several releases, especially if deprecating flags are involved.

Okay, I have a branch that will change the VirtualHost generation so that it will always add the hostname and hostname:* to the domains part of the config, instead of using the given port.
Things I've been able to confirm:

Envoy accepts the config
Traffic routes correctly to bare hostname requests (ie Host header is example.com)
Traffic routes correctly to hostnames with ports (ie Host header is example.com:80 or example.com:443, or even example.com:31273)
Kubernetes Services that are not available on whatever the value of --envoy-service-http-port or --envoy-service-https-port still work.

Changes that we'll need to make to actually make this a thing:

Fix all the tests to expect example.com:* instead of example.com:<port>
Remove the wiring that brings the ports down to the VIrtualHost. This will include deprecating some command line flags, like --envoy-service-http-port and --envoy-service-https-port, at least.
remove minikube.md and update documentation with minikube and kind examples.
Other stuff I don't know about.

Nice result. Make it so.


Avoid using Regexp to check for errDeniedHost

@rosa WDYT about this minor refactoring?

Trying to use an old API for backward compatbility with some go versions apparently...

Green now ✅


Added replies support and made use of new random color

Please do not install this 346 repo if you don't have Red 3.4.6.dev1, some of the cogs support the new replies and will not work until red 3.4.6. this will not be merged until then.

IT'S TIME. 🎉


Added a method to render QR Code as an Html table.

A useful addition - especially given the current lack of support for SVG in popular email clients like Gmail (link).
One issue I notice is that it's not possible to customize the table padding. This can be done later with CSS, but to remain consistent with the SVG parameters, I would suggest a couple of edits:
// new variable
var margin = options.padding;
...
// invisible margin added to outer table HTML
const aHTML = ['<table style="border:0;border-collapse:collapse;margin:' + margin + 'px">'];

One further improvement would allow users to customize the color of the padding, since the margin property inherits from the parent element.

thank you for the suggestion. you are certainly welcome to raise a PR with the suggested enhancement against Diophant/qrcode-svg-table (https://github.com/Diophant/qrcode-svg-table).


Cannot create wrapped image in C API

Describe the question.
I am trying to test C API on Ubuntu 22.04 with CPU x86_64 architecture and CUDA 12.2 following this example. However, I faced an issue at step 5 as below:
nvimgcodecImage_t image;
nvimgcodecImageCreate(instance, &image, &image_info)

nvimgcodecImageCreate() function returned status of NVIMGCODEC_STATUS_INVALID_PARAMETER
Can anyone support me on this problem?
Thanks all.
Check for duplicates

[x] I have searched the open bugs/issues and have found no duplicates for this bug report

Hi @giapvn. To better help you with this, could you provided the whole code snippet you are compiling/running? Thanks

Thanks @jantonguirao. I fixed the errors in my code. It is a wrong instance in image_info propagated into.

Perfect. Thanks for letting us know. I will close the issue then.


Refactor trainer._log_device_info() method and warnings

Proposed refactor
Raised in discussion by @ananthsub and @justusschock in #11001 1/n Generalize internal checks for Accelerator in Trainer - remove trainer._device_type
Motivation
_log_device_info() in trainer is too verbose and message are not helpful
https://github.com/PyTorchLightning/pytorch-lightning/blob/master/pytorch_lightning/trainer/trainer.py#L1630-L1661
Accelerator/device selection Only happens in accelerator_connector, and related warning and logging should happen in accelerator_connector as well.
The warning and logic can be merged into select_accelerator_type()
Pitch
Simplify the log warning in trainer._log_device_info() and make it less verbose, remove unnecessary warnings, reduce log level from warning to debug
Move _log_device_info() to accelerator_connector and call at the end of the init(), or merge the logic into accelerator_connector

Additional context

If you enjoy Lightning, check out our other projects! ⚡

Metrics: Machine learning metrics for distributed, scalable PyTorch applications.

Lite: enables pure PyTorch users to scale their existing code on any kind of device while retaining full control over their own loops and optimization logic.

Flash: The fastest way to get a Lightning baseline! A collection of tasks for fast prototyping, baselining, fine-tuning, and solving problems with deep learning.

Bolts: Pretrained SOTA Deep Learning models, callbacks, and more for research and production with PyTorch Lightning and PyTorch.

Lightning Transformers: Flexible interface for high-performance research using SOTA Transformers leveraging Pytorch Lightning, Transformers, and Hydra.

cc @justusschock @awaelchli @akihironitta @carmocca @edward-io @ananthsub @kaushikb11 @ninginthecloud

related warning and logging should happen in accelerator_connector as well.

@four4fish It was initially part of the Accelerator Connector. But we had come across a use case where log_device_info needed to be overridden with custom logic. Hence, we introduced it as a method in Trainer for this purpose.

@kaushikb11  thank you for sharing the background! Could you share more detail about the use cases? Do you mean user want to override log_device_info()? There was no issue and I couldn't see any details from the PR
Is this still required?

Sure! We could have frameworks building on top of Lightning Trainer. For instance, they introduced a new IPEX accelerator and added modifications for it. _log_device_info would help them customize device info logging as well.

Interesting use case!
But seems user extended trainer class with Accelerator() and Plugins selection logic, then the accelerator, plugins and args been passed into super()init, which will call our accelerator_connector()? In that case, the log_device could be in either place? Am I missing something?


Upgrade myDIG ES to 5.4

Jason upgraded pinpoint to support ES 2.4 and 5.4, so it should be safe to update myDIG to ES 5.4

upgraded ELK components to 5.6, upgraded sandpaper and myDIG, DIGUI encounters errors with these updates but search function still works


fix: prompt sidebar not showing on small devices

Fixed the issue with Prompt sidebar not showing on small devices
Before

After

nice catch


Asset missing

Started GET "/groups/assets/loading.gif" for <IP_ADDRESS> at 2012-01-03 23:29:11 -0500
ActionController::RoutingError (No route matches [GET] "/groups/assets/loading.gif"):
when on the "New Group" page.

Edu, note that path may be different. Assets can even be deployed to its own server. It is better using asset_path
http://guides.rubyonrails.org/asset_pipeline.html#javascript-coffeescript-and-erb


AT commands gives no reply, jsut keep on repeating

I have tried to use the sample codes given in sample folder. none of them is working except 'sensor_test.py'
the AT commands repeat themselves on the terminal.
can i use some AT commands without using the sim?

Hello,
Yes, you can use some AT commands without sim. Your issue may reason this two things below:
1 - Did you enable RPi UART interface? If you didn't, these instructions below may lead to you the solution.
Enable serial_hw and I2C interfaces by following instructions below:
1 - Run sudo raspi-config
2 - Select 5 Interfacing Options
3 - Enable P5 I2C
4 - For P6 Serial
  * Disable Login shell to be accessible over serial (Attention: this is important!)
  * Enable Serial port hardware
5 - Finish
6 - Reboot RPi
It's done.

2 - What is your RPi version that you use. Example codes are suitable for direct use of RPi 3. UART serial port is default ttyS0 in the library. If you use an older version of RPi, your UART serial port should be ttyAMA0. In this situation, you must pass an extra parameter in init() method. For RPi 2, RPi A+, RPi B+ please change as below the initialization of the library class in the example you want to run:
node = cellulariot.CellularIoTApp("/dev/ttyAMA0")

Thanks for your reply, i am testing it shortly and then let you know the results

Got it working ow. I have raspberry pi 3b. The issue was with my uart actually. It was not working and hence not giving commands to the shield. My serial port was ttyS0. Thanks for your help


Was the end-user authentication of the Salesforce connector been deprecated?

I had used the Salesforce connector of deno-slack-hub with Slack Workflow.
import { Connectors } from "deno-slack-hub/mod.ts";

// ...

MyWorkflow.addStep(
  Connectors.Salesforce.functions.CreateRecord,
  {
    salesforce_access_token: { credential_source: "END_USER" },
    salesforce_object_name: "Task",
    metadata: {
      // ...
    },
  },
);

When I ran slack deploy recently, the following error message appeared and it seems not to work.

📝 This value has been deprecated. Please update. (end_user_oauth_requires_last_interactor)

Is there any solution of this problem?

Hey @hoyo 👋🏻 Thanks for using the Slack CLI and Deno SDK 🙂
Can you tell us whether the end_user_oauth_requires_last_interactor message is an error message or warning message?

Hello again @hoyo 👋🏻 I've been talking with a few backend engineers at Slack. If possible, would you mind adding user_context_source: "LAST_INTERACTOR" to your code sample to see if it removes the error/warning?
MyWorkflow.addStep(
  Connectors.Salesforce.functions.CreateRecord,
  {
    salesforce_access_token: { credential_source: "END_USER", user_context_source: "LAST_INTERACTOR" },
    salesforce_object_name: "Task",
    metadata: {
      // ...
    },
  },
);

The reasoning is that the Slack API defaults to user_context_source="CALLER_USER"  for backwards compatibility, but credential_source="END_USER" requires user_context_source="LAST_INTERACTOR". If this unblocks you, then I'll bring it back to the team so that we can improve our documentation and code samples. 🙇🏻

Thank you for your response.
I tried your advice, and the end_user_oauth_requires_last_interactor error no longer appears
However, the workflow is not being executed, so there may be an error in another part of the code
I have implemented a workflow that includes a Salesforce connector to be triggered via the workflow button. After running slack run or slack deploy, but the workflow that includes the Connectors.Salesforce.functions.CreateRecord step is not responding. If I comment out the Connectors.Salesforce.functions.CreateRecord step, the other steps work normally.

It appears theirs been a breaking change to the definition of Connectors.Salesforce.functions.CreateRecord on the backend 🤔
It now seems to be using a type that is not yet defined by the deno SDK, I opened up this PR to add it, reviews will soon be welcome
But this missing type caused the automated job generating the source code of deno-slack-hub to fail, I suspect this may be the underlying issue, I suspect Connectors.Salesforce.functions.CreateRecord is out of date

@hoyo<EMAIL_ADDRESS>is out and should resolve this issue 🚀
Let us know if we can close this issue!

@WilliamBergamin Thank you for your support.
Unfortunately, the problem was not solved when I have updated<EMAIL_ADDRESS>and<EMAIL_ADDRESS>The workflow including a step with Connectors.Salesforce.functions.CreateRecord, my shortcut trigger will not respond and there is no message in log. If I commenting out the step with Connectors.Salesforce.functions.CreateRecord, it will enable it to work normally.

I was able to reproduce this behavior, It seems this may be a backend issue related to the external authentication of the connector


jdk11 respin triage

Release Summary Report for openjdk11-pipeline
Report generated at: Thu, 18 Aug 2022 03:26:40 GMT
TRSS Build and TRSS Grid View
Jenkins Build URL https://ci.adoptopenjdk.net/job/build-scripts/job/openjdk11-pipeline/2046/
Started by user Wen Zhou at 8/17/2022, 5:07:17 AM

Test_openjdk11_hs_extended.openjdk_x86-64_mac ⚠ UNSTABLE ⚠
Test_openjdk11_hs_extended.openjdk_x86-64_mac_testList_0 ⚠ UNSTABLE ⚠
jdk_net_0 => deep history 3/7 passed | possible issues
jdk_security3_0 => deep history 5/7 passed | possible issues
jdk_security4_1 => deep history 5/7 passed | possible issues
jvm_compiler_1 => deep history 3/7 passed | possible issues
jdk_security4_0 => deep history 5/7 passed | possible issues
jdk_text_1 => deep history 5/7 passed | possible issues
jdk_jfr_1 => deep history 5/7 passed | possible issues
jdk_jdi_1 => deep history 5/7 passed | possible issues
Test_openjdk11_hs_extended.openjdk_x86-64_mac_testList_1 ⚠ UNSTABLE ⚠
jvm_compiler_0 => deep history 3/7 passed | possible issues
jdk_text_0 => deep history 5/7 passed | possible issues
jdk_net_1 => deep history 3/7 passed | possible issues
Test_openjdk11_hs_extended.openjdk_x86-64_mac_testList_2 ⚠ UNSTABLE ⚠
jdk_security2_0 => deep history 5/7 passed | possible issues
jdk_security2_1 => deep history 5/7 passed | possible issues
jdk_security3_1 => deep history 5/7 passed | possible issues
jdk_jdi_0 => deep history 5/7 passed | possible issues
jdk_jfr_0 => deep history 5/7 passed | possible issues
Test_openjdk11_hs_sanity.openjdk_x86-64_mac ⚠ UNSTABLE ⚠
Rerun all
java -version
openjdk version "<IP_ADDRESS>" 2022-08-12
OpenJDK Runtime Environment Temurin-<IP_ADDRESS>+1 (build <IP_ADDRESS>+1)
OpenJDK 64-Bit Server VM Temurin-<IP_ADDRESS>+1 (build <IP_ADDRESS>+1, mixed mode)

jdk_svc_sanity_0 => deep history 14/15 passed | possible issues

Release Summary Report for openjdk11-pipeline
Report generated at: Thu, 18 Aug 2022 03:29:14 GMT
TRSS Build and TRSS Grid View
Jenkins Build URL https://ci.adoptopenjdk.net/job/build-scripts/job/openjdk11-pipeline/2043/
Started by user Wen Zhou at 8/16/2022, 3:41:15 AM

Test_openjdk11_hs_extended.openjdk_aarch64_linux ⚠ UNSTABLE ⚠
Rerun all
java -version
openjdk version "<IP_ADDRESS>" 2022-08-12
OpenJDK Runtime Environment Temurin-<IP_ADDRESS>+1 (build <IP_ADDRESS>+1)
OpenJDK 64-Bit Server VM Temurin-<IP_ADDRESS>+1 (build <IP_ADDRESS>+1, mixed mode)

jvm_compiler_0 => deep history 5/7 passed | possible issues
jvm_compiler_1 => deep history 5/7 passed | possible issues
Test_openjdk11_hs_extended.openjdk_aarch64_mac ⚠ UNSTABLE ⚠
Test_openjdk11_hs_extended.openjdk_aarch64_mac_testList_0 ⚠ UNSTABLE ⚠
jvm_compiler_2 => deep history 4/5 passed | possible issues
Test_openjdk11_hs_extended.openjdk_aarch64_mac_testList_1 ⚠ UNSTABLE ⚠
jvm_compiler_0 => deep history 4/5 passed | possible issues
jvm_compiler_1 => deep history 4/5 passed | possible issues
Test_openjdk11_hs_extended.openjdk_arm_linux ⚠ UNSTABLE ⚠
Test_openjdk11_hs_extended.openjdk_arm_linux_testList_0 ⚠ UNSTABLE ⚠
jvm_compiler_2 => deep history 5/6 passed | possible issues
Test_openjdk11_hs_extended.openjdk_ppc64_aix ⚠ UNSTABLE ⚠
Test_openjdk11_hs_extended.openjdk_ppc64_aix_testList_0 ⚠ UNSTABLE ⚠
jvm_compiler_1 => deep history 4/6 passed | possible issues
Test_openjdk11_hs_extended.openjdk_ppc64_aix_testList_1 ⚠ UNSTABLE ⚠
jvm_compiler_0 => deep history 5/6 passed | possible issues
Test_openjdk11_hs_extended.openjdk_ppc64le_linux ⚠ UNSTABLE ⚠
Test_openjdk11_hs_extended.openjdk_ppc64le_linux_testList_0 ⚠ UNSTABLE ⚠
jvm_compiler_1 => deep history 3/6 passed | possible issues
Test_openjdk11_hs_extended.openjdk_ppc64le_linux_testList_1 ⚠ UNSTABLE ⚠
jvm_compiler_0 => deep history 4/6 passed | possible issues
Test_openjdk11_hs_extended.openjdk_s390x_linux ⚠ UNSTABLE ⚠
Test_openjdk11_hs_extended.openjdk_s390x_linux_testList_0 ⚠ UNSTABLE ⚠
jvm_compiler_1 => deep history 4/6 passed | possible issues
Test_openjdk11_hs_extended.openjdk_s390x_linux_testList_1 ⚠ UNSTABLE ⚠
jvm_compiler_0 => deep history 5/6 passed | possible issues
Test_openjdk11_hs_extended.openjdk_x86-64_alpine-linux ⚠ UNSTABLE ⚠
Test_openjdk11_hs_extended.openjdk_x86-64_alpine-linux_testList_0 ⚠ UNSTABLE ⚠
jvm_compiler_1 => deep history 5/6 passed | possible issues
Test_openjdk11_hs_extended.openjdk_x86-64_alpine-linux_testList_1 ⚠ UNSTABLE ⚠
jvm_compiler_0 => deep history 5/6 passed | possible issues
Test_openjdk11_hs_extended.openjdk_x86-64_alpine-linux_testList_2 ⚠ UNSTABLE ⚠
jdk_security_infra_0 => deep history 5/6 passed | possible issues
Test_openjdk11_hs_extended.openjdk_x86-64_linux ⚠ UNSTABLE ⚠
Test_openjdk11_hs_extended.openjdk_x86-64_linux_testList_0 ⚠ UNSTABLE ⚠
jvm_compiler_1 => deep history 4/6 passed | possible issues
Test_openjdk11_hs_extended.openjdk_x86-64_linux_testList_1 ⚠ UNSTABLE ⚠
jvm_compiler_0 => deep history 5/6 passed | possible issues
Test_openjdk11_hs_extended.openjdk_x86-64_mac ⚠ UNSTABLE ⚠
Test_openjdk11_hs_extended.openjdk_x86-64_mac_testList_0 ⚠ UNSTABLE ⚠
jdk_net_0 => deep history 3/7 passed | possible issues
jdk_security4_1 => deep history 5/7 passed | possible issues
jdk_text_1 => deep history 5/7 passed | possible issues
jdk_security3_0 => deep history 5/7 passed | possible issues
jdk_security4_0 => deep history 5/7 passed | possible issues
jvm_compiler_1 => deep history 3/7 passed | possible issues
jdk_jdi_1 => deep history 5/7 passed | possible issues
jdk_jfr_1 => deep history 5/7 passed | possible issues
Test_openjdk11_hs_extended.openjdk_x86-64_mac_testList_1 ⚠ UNSTABLE ⚠
jvm_compiler_0 => deep history 3/7 passed | possible issues
jdk_net_1 => deep history 3/7 passed | possible issues
jdk_text_0 => deep history 5/7 passed | possible issues
Test_openjdk11_hs_extended.openjdk_x86-64_mac_testList_2 ⚠ UNSTABLE ⚠
jdk_nio_0 => deep history 6/7 passed | possible issues
jdk_security2_0 => deep history 5/7 passed | possible issues
jdk_security2_1 => deep history 5/7 passed | possible issues
jdk_jfr_0 => deep history 5/7 passed | possible issues
jdk_security3_1 => deep history 5/7 passed | possible issues
jdk_jdi_0 => deep history 5/7 passed | possible issues

Test_openjdk11_hs_extended.openjdk_x86-64_linux
jvm_compiler -> jdk/test/hotspot/jtreg/compiler/allocation/TestFailedAllocationBadGraph.java
[2022-08-16T08:14:55.654Z] ACTION: main -- Failed. Unexpected exit from test [exit code: 134]
[2022-08-16T08:14:55.654Z] REASON: User specified action: run main/othervm -ea -Xbootclasspath/a:. -XX:+UnlockDiagnosticVMOptions -XX:+WhiteBoxAPI -XX:-BackgroundCompilation TestFailedAllocationBadGraph
[2022-08-16T08:14:55.654Z] TIME:   2.508 seconds
[2022-08-16T08:14:55.654Z] messages:
[2022-08-16T08:14:55.654Z] command: main -ea -Xbootclasspath/a:. -XX:+UnlockDiagnosticVMOptions -XX:+WhiteBoxAPI -XX:-BackgroundCompilation TestFailedAllocationBadGraph
[2022-08-16T08:14:55.654Z] reason: User specified action: run main/othervm -ea -Xbootclasspath/a:. -XX:+UnlockDiagnosticVMOptions -XX:+WhiteBoxAPI -XX:-BackgroundCompilation TestFailedAllocationBadGraph
[2022-08-16T08:14:55.654Z] Mode: othervm [/othervm specified]
[2022-08-16T08:14:55.654Z] elapsed time (seconds): 2.508
[2022-08-16T08:14:55.654Z] configuration:
[2022-08-16T08:14:55.654Z] STDOUT:
[2022-08-16T08:14:55.654Z] #
[2022-08-16T08:14:55.654Z] # A fatal error has been detected by the Java Runtime Environment:
[2022-08-16T08:14:55.654Z] #
[2022-08-16T08:14:55.654Z] #  SIGSEGV (0xb) at pc=0x00007f914c5f5172, pid=3005093, tid=3005107
[2022-08-16T08:14:55.654Z] #
[2022-08-16T08:14:55.654Z] # JRE version: OpenJDK Runtime Environment Temurin-<IP_ADDRESS>+1 (<IP_ADDRESS>+1) (build <IP_ADDRESS>+1)
[2022-08-16T08:14:55.654Z] # Java VM: OpenJDK 64-Bit Server VM Temurin-<IP_ADDRESS>+1 (<IP_ADDRESS>+1, mixed mode, tiered, compressed oops, g1 gc, linux-amd64)
[2022-08-16T08:14:55.654Z] # Problematic frame:
[2022-08-16T08:14:55.654Z] # V  [libjvm.so+0xb04172]  PhaseIdealLoop::build_loop_late_post(Node*)+0x182
[2022-08-16T08:14:55.654Z] #
[2022-08-16T08:14:55.654Z] # Core dump will be written. Default location: /home/jenkins/workspace/Test_openjdk11_hs_extended.openjdk_x86-64_linux_testList_1/aqa-tests/TKG/output_16606360175379/jvm_compiler_0/work/scratch/2/core
[2022-08-16T08:14:55.654Z] #
[2022-08-16T08:14:55.654Z] Unsupported internal testing APIs have been used.

Appears that most platforms showing same testcase test/hotspot/jtreg/compiler/allocation/TestFailedAllocationBadGraph.java failing (with crash).  Need to check if that testcase is actually changed with the new openjdk tag, if so, update tags and rerun testcase.

Reruns of jvm_compiler target with <IP_ADDRESS>-release branch:
aarch64_linux - https://ci.adoptopenjdk.net/job/Grinder/5440/

@ShelleyLambert The jvm_compiler grinders are failing with
Use -nativepath to specify the location of native code
They should pass if you supply a link to the jdk binary and the test image in the CUSTOMIZED_SDK_URL parameter, one after the other separated by a single space. See https://ci.adoptopenjdk.net/job/Grinder/5448/parameters/

thanks @Haroon-Khel - yes forgot about that when I launched them... will update links in above comment shortly

Closing as completed


gene selection

Thanks for the great tool! I was wondering if you had any thoughts regarding the gene selection for input to scDist? I saw in the code for the simulation study you use the scale.data matrix as input for scDist. Would you suggest to loop through all celltypes in a dataset, recalculate HVGs and then running scDist on each celltype individually? Or to simply run scDist on an entire seurat object with a hard coded set of HVGs.
Thanks!

I would recommend the second option because the distance is only comparable between cell types when the same set of genes has been used. I.e., have one set of genes and define a normalized count matrix (across all cell types), and pass that in as the input to scDist.

thanks for the input! one last question, have you benchmarked how stable the distance calculation is across differing number of genes used?
Thanks again!

Not yet, although we did look at how varying the # of PCs used changed distance estimates (Figure S13 in the paper). Throughout the paper we typically used 2000 HVGs selected by Seurat. Because the first step is PCA, I would hope that it is not too sensitive adding or removing genes that contribute a low % of variance explained, but this is definitely an analysis we will consider when we revise the paper. Thanks!


Integrating EOS Libraries 1/2

🗃 Github Issue Or Explanation for this PR. (What is it supposed to do and Why is needed)
added dart_esr and eosdart packages to source code
Reasoning:

These packages are dead, no one is maintaining them
They depend on many crypto packages
Complicated dependency management
This will allow us to make them null safe in a future PR.
Keep your friends close, keep your enemies closer... this code is pretty rough with a lot of duplication. Over time we can improve this, especially when we have comprehensive automated tests.

Tests
I added unit tests to ensure that both encoding and decoding are still working.
I also added a unit test mode since firebase wasn't initialized and I needed hard coded node names.
Unit test mode is in addition to testnet mode, the tests check whether the flags are set.
These automated tests are the only way to ensure that ESR encoding and decoding are still working.
I verified both encoded and decoded ESR codes with external websites.
✅ Checklist

[x] Github issue details are up to date for people to QA.
[x] I have tested all my changes.

🕵♂ Notes for Code Reviewer
🙈 Screenshots
👯♀ Paired with

Checking to see if transactions till work...

@gguijarro-c-chwy if you approve will override the 500 line limit linter.
it's a little over 12,000 lines 😂
But - a lot better to have these inside our code base than in some fork of a branch of a dead library on github.

This is huge and impossible to code review.

We need to depend on manual testing for this one.

@gguij004  and @buildc0de  and @JGDoh  Can you guys run this branch and test, pretty much everything. This touches all major areas of our app.

Can you guys run this branch and test, pretty much everything.

I will run our Release Test Plan on this branch.

This is huge and impossible to code review.

We need to depend on manual testing for this one.

@gguij004 and @buildc0de and @JGDoh Can you guys run this branch and test, pretty much everything. This touches all major areas of our app.

I mean generally speaking all I did was move this huge code base out of its various repos and into our main repo.
I didn't change the code, except one place where I removed a TODO with an implementation (ability to create multi-action QR codes which we're not using, but I felt we might as well fix up this code and I tested it too).

Testing

Tested making a transaction with 1 action (send Seeds)
Did not yet test making multiple action transactions - should be the same but should be tested.

After that I can't think of anything else we could test then we have these tests

[ ] Create ESR code for single action
[ ] Create ESR for multi action transaction
[ ] Handle ESR for single action
[ ] Handle ESR for multiple actions
[ ] Send Seeds - single action transaction
[ ] Send multiple action transaction (could do by providing an ESR Code for that)

I think this is GTG - when can I merge this?

My vote would be to go ahead and merge this in 🤓 Nik already conducted comprehensive tests and everything will be double-checked via the Release Test Plan in the next release. @gguij004 @gguijarro-c-chwy @RaulUrtecho What do you, guys, think?

My vote would be to go ahead and merge this in 🤓 Nik already conducted comprehensive tests and everything will be double-checked via the Release Test Plan in the next release. @gguij004 @gguijarro-c-chwy @RaulUrtecho What do you, guys, think?

As we say in my beautiful Mexico:
Tu hazlo luego vemos que pedo ;)

Ok getting this in there!


Add ability to create Writer with config

This adds the ability to create a Writer with a config. The only supported config right now is Level for setting the block size.
I've also split up the reader and writer code as well as the tests.

Looks good! Thanks for contributing!


(maint) Release 0.18.1

This commit updates the changelog and package.json for the 0.18.1
release.

Pending #519 and #513

... and https://github.com/lingua-pupuli/puppet-vscode/pull/522

522 is merged.  Unblocking

I have to commit package-lock.json, it tracks version too. If i dont it is still on old version.

💩 to package-lock :-(


Switching from "dev" to "build"

Question
As we support 2 modes using the component operator dev (= inner) and build (= outer), do we want that when we switch from dev to build:

We create a new Deployment, update the selector of the service to pickup the new pod created from the built image within the existing namespace OR
That we offer the possibility to create a Build/Deployment/Service/Route within a different namespace ?

@tqvarnst @metacosm @aureamunoz

I would say the first one: within the existing namespace. I don't know if the other is a very demanded/missed/useful feature


Sign In component has unexpected behaviour when signing in with Enter key

Do you want to request a feature or report a bug?
bug
What is the current behavior?
We are using the withAuthenticator HOC to provide our app with authentication capabilities.
We changed the base HOC the following way:
export function createWithMyAuthenticator(awsConfig) {
  Amplify.default.configure(awsConfig);
  return (comp) =>
    withAuthenticator(comp, false, [
      <SignIn />,
      <ConfirmSignIn />,
      <VerifyContact />,
      <SignUp />,
      <ConfirmSignUp />,
      <ForgotPassword />,
    ]);
}

After we fill the username and password of the user the problem occurs when we try to sign in not by clicking the sign in button but by submitting the sign in form with the Enter key.
After pressing the Enter key a warning appears with the message Username cannot be empty even if it is actually not empty. This issue does not prevent login however and if the username and password are correct we can proceed to the application itself after a short delay.
The bug seems to be related to the arguments of the authenticatorComponents parameter i.e.
[
      <SignIn />,
      <ConfirmSignIn />,
      <VerifyContact />,
      <SignUp />,
      <ConfirmSignUp />,
      <ForgotPassword />,
]

since this issue disappears if we use withAuthenticator without providing any arguments to the authenticatorComponents parameter.
What is the expected behavior?
The warning saying Username cannot be empty should not appear unless the username is actually empty.
Which versions of Amplify, and which browser / OS are affected by this issue? Did this work in previous versions?
I am getting the bug with the following setup:
macOS 10.13.5
"electron": "2.0.4"
"aws-amplify": "0.4.8",
 "aws-amplify-react": "0.1.54",

Hello,
Were you able to reproduce this bug?
In any case thank you for your time

@powerful23 Thank you very much for looking into the problem and solving the issue! 👍

@stonecauldron My pleasure! Thanks for giving the feedback.


[PLAT-836] Partial Registrations backend

Purpose
Allow people to register a selection of components instead of an entire project.
Changes

Adds optional attribute children to payload with list of guids to be registered
Validates so those components contain no orphaned grandchildren.
Add tests

QA Notes
Make a POST request to  /nodes/{node_id}/registrations/ with a payload of JSON data like:
{
    "data":
{
    "type": "registrations",
    "attributes":
{
    "draft_registration": "{draft_registration_id}",
    "registration_choice": "embargo",
    "lift_embargo": "2017-05-10T20:44:03.185000",
    "children":
            [
                "fsd2s",
                "dwsa2"
            ]
        }
    }
}

Only the {node_id} node  and children whose's guids are listed should be registered. Sending invalid guids should cause descriptive 400s.
Documentation
https://github.com/CenterForOpenScience/developer.osf.io/pull/18
Ticket
https://openscience.atlassian.net/projects/PLAT/issues/PLAT-836

Closed while frontend is added.


Problem reading ACCESS1-3 and IPSL-CM5A-LR piControl data

This issue is related to porting the diagnostic tool for thermodynamics into version 2.0 of ESMValTool. Progress is tracked in this branch.
EDIT: the recipe has been ported, but there are remaining issues with preprocessing piControl data for experiments ACCESS1-3 and IPSL-CM5A-LR.

Thank you @ValerioLembo.
Can you also please update #503, adding your name and the link to this issue where appropriate?
:+1:

Done!

I have a few questions about the usage of the diagnostics in v. 2.0:
[ ]  what is the equivalent of configure-template.xml now? In other words, where do I put the paths to my input and output fields, and to the plots;
[ ] as for the CMIP5 datasets, do they have to be referenced with "mip" entry in any case? Can I use the generic flag "MIP" instead of "Amon", "Omon" etc? This would be a problem for me because I give both daily and monthly fields as input to the diagnostics.
[ ] I had a .conf script where I used to put some options to be specified by the user. Is that still possible?
[ ] is there an alternative usage instead of "python main.py nml/namelist_" in v. 2.0?
Thanks for helping!

The paths are specified in config-user.yml, in combination with config-developer.yml. The latter, however, should not be changed, unless you want to add a directory structure (aka drs) which is not already defined. Have a look at this presentation for more details.
The new recipe format is quite flexible and allows to specify mip at different levels (either for the  variable or for the dataset). Try to check the existing recipes and if you do not find a solution to your problem please open a dedicated issue.
The diagnostic specific settings can now be passed directly via the recipe, under the scripts dictionary. The presentation above shows some examples, or you can again check the existing recipes.
Feel free to ask again if you have more questions.

I managed to control the input/output directories through the config-user.xml.
As for the scripts dictionary, it seems to me from the examples that it does not allow to manage custom flags, such as local options (defined by the developer) e.g. those to include or exclude part of a code.
On top of everything, I do not understand why I cannot enter the main script; it reads the preamble, then goes directly to the end with no error. The only things that I find in the log.txt file are:

/home/zmaw/u234097/conda-envs/esmvaltool/lib/python2.7/site-packages/iris/fileformats/grib/init.py:59: IrisDeprecation: The module iris.fileformats.grib is deprecated since v1.10. Please install the package 'iris_grib' package instead.
"The module iris.fileformats.grib is deprecated since v1.10. "
/home/zmaw/u234097/conda-envs/esmvaltool/lib/python2.7/site-packages/matplotlib/cbook/deprecation.py:107: MatplotlibDeprecationWarning: The mpl_toolkits.axes_grid module was deprecated in version 2.1. Use mpl_toolkits.axes_grid1 and mpl_toolkits.axisartist provies the same functionality instead.
warnings.warn(message, mplDeprecation, stacklevel=1)
Could not load xarray

I am sure that I am lacking understanding of some very trivial issue...
One last thing: I call the log file to write my instructions like this:
logger = logging.getLogger(os.path.basename(__file__))
but it seems that I do not write anything, even if I call logger in the preamble...

@valeriupredoi can you help?

I had a *.conf script where I used to put some options to be specified by the user. Is that still possible?

Yes, but please use a yaml file instead.
Regarding the installation issues, did you follow the instructions here?
https://github.com/ESMValGroup/ESMValTool/blob/version2_development/README.md
Regarding the logging issue: by default the log level is set to info, so only messages that you write with logger.INFO("Some message"), logger.WARNING("Some message"), logger.ERROR("Some message") are logged, not logger.DEBUG("Some message").

As for the scripts dictionary, it seems to me from the examples that it does not allow to manage custom flags, such as local options (defined by the developer) e.g. those to include or exclude part of a code.

You can add any custom key/value pairs to the scripts section and they will be passed on to your diagnostic, see e.g. https://github.com/ESMValGroup/ESMValTool/blob/version2_development/esmvaltool/recipes/examples/recipe_python.yml
Only the script: my_diagnostic.py key/value pair is mandatory.
Make sure to start your diagnostic with the function esmvaltool.diag_scripts.shared.run_diagnostic so it sets up logging and picks up the configuration.

Regarding the installation issues, did you follow the instructions here?
https://github.com/ESMValGroup/ESMValTool/blob/version2_development/README.md

Actually I followed the instructions on the Technical Overview linked from the webpage for porting to Version 2.0.

Make sure to start your diagnostic with the function esmvaltool.diag_scripts.shared.run_diagnostic so it sets up logging and picks up the configuration.

Apparently, this what the reason why I was not entering the diagnostics. Now I am finally hands on the code.

Those installation instructions do not look very accurate, I have opened an issue for it https://github.com/ESMValGroup/ESMValTool/issues/653. The information in that install.rst document was somewhat inaccurate: I'm not sure what the current status is, but we have an open issue about that too https://github.com/ESMValGroup/ESMValTool/issues/446.

I have one more question and a possible bug in the preprocessor to report.
The question is: is there a way to upload one land-sea mask for every model, rather than masking each initial fields. I would rather use of the path to the land-sea masks for each model I analyse in the diagnostics.
In v. 1.0 I used the commands:
`for model in project_info['MODELS']:
currProject = getattr(projects, model.split_entries()[0])()
currProject.get_cf_lmaskfile(project_info,model)`

Is there something equivalent in v. 2.0?
As for the possible bug, I preprocess an MPI-ESM-LR fields of tas, just by subsetting 5 years from 150. This is what I get if I type cdo sinfov on the file:
`File format : NetCDF4
    -1 : Institut Source   T Steptype Levels Num    Points Num Dtype : Parameter name
     1 : MPIMET   MPI-ESM-LR v instant       1   1     18432   1  F32  : tas
     2 : MPIMET   MPI-ESM-LR v instant       1   1         1   2  F64  : year
   Grid coordinates :
     1 : gaussian                 : points=18432 (192x96)  np=48
                              lon : 0 to 358.125 by 1.875 degrees_east  circular
                              lat : -88.57217 to 88.57217 degrees_north
                        available : cellbounds
     2 : generic                  : points=1
   Vertical coordinates :
     1 : height                   : levels=1  scalar
                           height : 2 m
   Time coordinate :  72 steps
     RefTime =  1950-01-01 00:00:00  Units = days  Calendar = proleptic_gregorian  Bounds = true
  YYYY-MM-DD hh:mm:ss  YYYY-MM-DD hh:mm:ss  YYYY-MM-DD hh:mm:ss  YYYY-MM-DD hh:mm:ss
  1990-01-16 12:00:00  1990-02-15 00:00:00  1990-03-16 12:00:00  1990-04-16 00:00:00
  1990-05-16 12:00:00  1990-06-16 00:00:00  1990-07-16 12:00:00  1990-08-16 12:00:00
  1990-09-16 00:00:00  1990-10-16 12:00:00  1990-11-16 00:00:00  1990-12-16 12:00:00
  1991-01-16 12:00:00  1991-02-15 00:00:00  1991-03-16 12:00:00  1991-04-16 00:00:00
  1991-05-16 12:00:00  1991-06-16 00:00:00  1991-07-16 12:00:00  1991-08-16 12:00:00
  1991-09-16 00:00:00  1991-10-16 12:00:00  1991-11-16 00:00:00  1991-12-16 12:00:00
  1992-01-16 12:00:00  1992-02-15 12:00:00  1992-03-16 12:00:00  1992-04-16 00:00:00
  1992-05-16 12:00:00  1992-06-16 00:00:00  1992-07-16 12:00:00  1992-08-16 12:00:00
  1992-09-16 00:00:00  1992-10-16 12:00:00  1992-11-16 00:00:00  1992-12-16 12:00:00
  1993-01-16 12:00:00  1993-02-15 00:00:00  1993-03-16 12:00:00  1993-04-16 00:00:00
  1993-05-16 12:00:00  1993-06-16 00:00:00  1993-07-16 12:00:00  1993-08-16 12:00:00
  1993-09-16 00:00:00  1993-10-16 12:00:00  1993-11-16 00:00:00  1993-12-16 12:00:00
  1994-01-16 12:00:00  1994-02-15 00:00:00  1994-03-16 12:00:00  1994-04-16 00:00:00
  1994-05-16 12:00:00  1994-06-16 00:00:00  1994-07-16 12:00:00  1994-08-16 12:00:00
  1994-09-16 00:00:00  1994-10-16 12:00:00  1994-11-16 00:00:00  1994-12-16 12:00:00
  1995-01-16 12:00:00  1995-02-15 00:00:00  1995-03-16 12:00:00  1995-04-16 00:00:00
  1995-05-16 12:00:00  1995-06-16 00:00:00  1995-07-16 12:00:00  1995-08-16 12:00:00
  1995-09-16 00:00:00  1995-10-16 12:00:00  1995-11-16 00:00:00  1995-12-16 12:00:00

whereas the same preprocessor on thets` field gives:
  File format : NetCDF4
    -1 : Institut Source   T Steptype Levels Num    Points Num Dtype : Parameter name
     1 : MPIMET   MPI-ESM-LR v instant       1   1     18432   1  F32  : ts
   Grid coordinates :
     1 : gaussian                 : points=18432 (192x96)  np=48
                              lon : 0 to 358.125 by 1.875 degrees_east  circular
                              lat : -88.57217 to 88.57217 degrees_north
                        available : cellbounds
   Vertical coordinates :
     1 : surface                  : levels=1
   Time coordinate :  72 steps
     RefTime =  1950-01-01 00:00:00  Units = days  Calendar = proleptic_gregorian  Bounds = true
  YYYY-MM-DD hh:mm:ss  YYYY-MM-DD hh:mm:ss  YYYY-MM-DD hh:mm:ss  YYYY-MM-DD hh:mm:ss
  1990-01-16 12:00:00  1990-02-15 00:00:00  1990-03-16 12:00:00  1990-04-16 00:00:00
  1990-05-16 12:00:00  1990-06-16 00:00:00  1990-07-16 12:00:00  1990-08-16 12:00:00
  1990-09-16 00:00:00  1990-10-16 12:00:00  1990-11-16 00:00:00  1990-12-16 12:00:00
  1991-01-16 12:00:00  1991-02-15 00:00:00  1991-03-16 12:00:00  1991-04-16 00:00:00
  1991-05-16 12:00:00  1991-06-16 00:00:00  1991-07-16 12:00:00  1991-08-16 12:00:00
  1991-09-16 00:00:00  1991-10-16 12:00:00  1991-11-16 00:00:00  1991-12-16 12:00:00
  1992-01-16 12:00:00  1992-02-15 12:00:00  1992-03-16 12:00:00  1992-04-16 00:00:00
  1992-05-16 12:00:00  1992-06-16 00:00:00  1992-07-16 12:00:00  1992-08-16 12:00:00
  1992-09-16 00:00:00  1992-10-16 12:00:00  1992-11-16 00:00:00  1992-12-16 12:00:00
  1993-01-16 12:00:00  1993-02-15 00:00:00  1993-03-16 12:00:00  1993-04-16 00:00:00
  1993-05-16 12:00:00  1993-06-16 00:00:00  1993-07-16 12:00:00  1993-08-16 12:00:00
  1993-09-16 00:00:00  1993-10-16 12:00:00  1993-11-16 00:00:00  1993-12-16 12:00:00
  1994-01-16 12:00:00  1994-02-15 00:00:00  1994-03-16 12:00:00  1994-04-16 00:00:00
  1994-05-16 12:00:00  1994-06-16 00:00:00  1994-07-16 12:00:00  1994-08-16 12:00:00
  1994-09-16 00:00:00  1994-10-16 12:00:00  1994-11-16 00:00:00  1994-12-16 12:00:00
  1995-01-16 12:00:00  1995-02-15 00:00:00  1995-03-16 12:00:00  1995-04-16 00:00:00
  1995-05-16 12:00:00  1995-06-16 00:00:00  1995-07-16 12:00:00  1995-08-16 12:00:00
  1995-09-16 00:00:00  1995-10-16 12:00:00  1995-11-16 00:00:00  1995-12-16 12:00:00
cdo sinfon: Processed 1 variable over 72 timesteps [0.05s 20MB]

So apparently a variable called year emerges, that was not included before preprocessing, appears in the file for tas. This is not the case for ts. It happens regardless of the model. Original fields come from DKRZ ESGF node.
When I try to combine the two files (tas and ts) with a cdo command, it consider the two files as having different grids.
Does anyone know why this happens?

Dear all,
I have some troubles testing the diagnostic with a few CMIP5 models in piControl scenario.
It seems those problems are related to the way time coordinates are treated.
One model is IPSL-CM5A-LR. I got this error in the preprocessing phase:
Traceback (most recent call last):   File "/home/zmaw/u234097/conda-envs/esmvaltool/lib/python2.7/site-packages/ESMValTool-2.0a1-py2.7.egg/esmvaltool/_main.py", line 215, in run     conf = main(args)   File "/home/zmaw/u234097/conda-envs/esmvaltool/lib/python2.7/site-packages/ESMValTool-2.0a1-py2.7.egg/esmvaltool/_main.py", line 143, in main     process_recipe(recipe_file=recipe, config_user=cfg)   File "/home/zmaw/u234097/conda-envs/esmvaltool/lib/python2.7/site-packages/ESMValTool-2.0a1-py2.7.egg/esmvaltool/_main.py", line 193, in process_recipe     recipe.run()   File "/home/zmaw/u234097/conda-envs/esmvaltool/lib/python2.7/site-packages/ESMValTool-2.0a1-py2.7.egg/esmvaltool/_recipe.py", line 1027, in run     self.tasks, max_parallel_tasks=self._cfg['max_parallel_tasks'])   File "/home/zmaw/u234097/conda-envs/esmvaltool/lib/python2.7/site-packages/ESMValTool-2.0a1-py2.7.egg/esmvaltool/_task.py", line 485, in run_tasks     _run_tasks_parallel(tasks, max_parallel_tasks)   File "/home/zmaw/u234097/conda-envs/esmvaltool/lib/python2.7/site-packages/ESMValTool-2.0a1-py2.7.egg/esmvaltool/_task.py", line 530, in _run_tasks_parallel     task.output_files = result.get()   File "/home/zmaw/u234097/conda-envs/esmvaltool/lib/python2.7/multiprocessing/pool.py", line 572, in get     raise self._value ValueError: Time units with interval of "months", "years" (or singular of these) cannot be processed, got 'months'.
The other models are ACCESS1-0 and ACCESS1-3, and specifically the preprocessing of their daily data. In this case I get this error:
2018-12-02 15:23:46,962 UTC [45168] ERROR   Program terminated abnormally, see stack trace below for more information Traceback (most recent call last):   File "/home/zmaw/u234097/conda-envs/esmvaltool/lib/python2.7/site-packages/ESMValTool-2.0a1-py2.7.egg/esmvaltool/_main.py", line 215, in run     conf = main(args)   File "/home/zmaw/u234097/conda-envs/esmvaltool/lib/python2.7/site-packages/ESMValTool-2.0a1-py2.7.egg/esmvaltool/_main.py", line 143, in main     process_recipe(recipe_file=recipe, config_user=cfg)   File "/home/zmaw/u234097/conda-envs/esmvaltool/lib/python2.7/site-packages/ESMValTool-2.0a1-py2.7.egg/esmvaltool/_main.py", line 193, in process_recipe     recipe.run()   File "/home/zmaw/u234097/conda-envs/esmvaltool/lib/python2.7/site-packages/ESMValTool-2.0a1-py2.7.egg/esmvaltool/_recipe.py", line 1027, in run     self.tasks, max_parallel_tasks=self._cfg['max_parallel_tasks'])   File "/home/zmaw/u234097/conda-envs/esmvaltool/lib/python2.7/site-packages/ESMValTool-2.0a1-py2.7.egg/esmvaltool/_task.py", line 485, in run_tasks     _run_tasks_parallel(tasks, max_parallel_tasks)   File "/home/zmaw/u234097/conda-envs/esmvaltool/lib/python2.7/site-packages/ESMValTool-2.0a1-py2.7.egg/esmvaltool/_task.py", line 530, in _run_tasks_parallel     task.output_files = result.get()   File "/home/zmaw/u234097/conda-envs/esmvaltool/lib/python2.7/multiprocessing/pool.py", line 572, in get     raise self._value ValueError: day is out of range for month
As far as I understood from other issues, some fix has to be applied, which has to be specific of a model (but does it depend on the experiment as well? I am using ACCESS1-x and IPSL-CM5A-LR in historical and rcp85 settings). Is there a protocol for applying these changes? I guess these should be made shared through all branches, once applied and effective...

@jvegasbsc can maybe help in solving this issue?

@ValerioLembo I am a bit confused about these errors: one thing I can tell you is that daily data is not fully supported (I have never tried to run with daily data myself) - as to monthly data issues they shouldn't happen if you use the latest version of CMIP5 files, I don't have access to the DKRZ ESGF node but it should be fully mirrored to the BADC one so if you can post or link the recipe then I can have a quick test using BADC data on CEDA-Jasmin

@valeriupredoi here is the recipe. I commented all the models and scenarios except the ones giving me troubles... Thanks for trying!

@valeriupredoi so IPSL-CM5A-LR is preprocessed correctly, in your case?

@ValerioLembo Now that your pull request has been merged, can this issue be closed?

I do not know if @valeriupredoi and @jvegasbsc have finally addressed that problem with ACCESS1-3 and IPSL-CM5A-LR. it completely fell off my radar... Anyway, it is not necessarily related to my diagnostic tool, it is more an issue of the preprocessor...

I've changed the issue title so it is more clear what needs to be fixed.


qt.qpa.plugin: Could not load the Qt platform plugin "xcb"

Cannot launch the viewer.
Error:
QObject::moveToThread: Current thread (0x55963f7b4e10) is not the object's thread (0x55963fcbcfd0).
Cannot move to target thread (0x55963f7b4e10)

qt.qpa.plugin: Could not load the Qt platform plugin "xcb" in "/home/zfan/miniconda3/envs/aitviewer/lib/python3.8/site-packages/cv2/qt/plugins" even though it was found.
This application failed to start because no Qt platform plugin could be initialized. Reinstalling the application may fix this problem.

Available platform plugins are: xcb, eglfs, linuxfb, minimal, minimalegl, offscreen, vnc, wayland-egl, wayland, wayland-xcomposite-egl, wayland-xcomposite-glx, webgl.

This error is due to opencv.
Solution:
pip uninstall opencv-python
pip uninstall opencv-python-headless
pip install opencv-python-headless

i encountered the same error, but i had never installed opencv in this environment.


[entropy_src/dv] Changed sanity to smoke

Signed-off-by: Steve Nelson<EMAIL_ADDRESS>

@senelson7 I think the private CI failure is due to private CI run script issue. It is trying to support both sanity and smoke. I will try to fix that soon.


Hi, I'm a Chinese player, I don't know much about python, I have a problem after following your instructions, I hope to get your answer, I will be very grateful

Hi.
following the install instruction for this project will help. However i am unsure if this project still works but good luck!
https://github.com/anycaptcha/anycaptcha-python


Choose postgresql PVC size?

I am deploying this helm chart on a self-managed cluster. The data-retool-postgresql-0 defaults to 8Gi. Is there any way to increase this value from the helm chart values?
I realize that this postgres instance isn't recommended for production, but it is sufficient for my current needs. I could manually increase the PVC with a kubectl edit, but I am deploying this chart in multiple locations so it would be nice to include the PVC spec in the initial deploy.

Postgresql is deployed with a sub chart based on the Bitnami chart, which can be found at : https://github.com/bitnami/charts/tree/main/bitnami/postgresql
All values contained within the .Values.postgresql are injected into this sub chart. Therefore, if you want to customize the size of the PVC for the postgres primary, you can do the following :
postgresql:
  enabled: true
  primary:
    persistence:
      size: 50Gi


Amend wrong title

Amend wrong title

Docs Build status updates of commit aa8ef44:
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[Warning] Author Xiangyu Luo cannot be recognized in GitHub.
[Warning] Author Xiangyu Luo cannot be recognized in GitHub.

For more details, please refer to the build report.
Note: If you changed an existing file name or deleted a file, broken links in other files to the deleted or renamed file are listed only in the full build report.


fix: update physical package

:tada: This PR is included in version 2.11.2 :tada:
The release is available on GitHub release
Your semantic-release bot :package::rocket:


Unused attributes prefixed with _ don't work

For example:
#[self_referencing]
pub struct RentalStream {
    lifetime: (),
    #[borrows(lifetime)]
    #[covariant]
    context: uvc::Context<'this>,
    #[borrows(context)]
    #[covariant]
    device: uvc::Device<'this>,
    #[borrows(device)]
    #[covariant]
    device_handle: uvc::DeviceHandle<'this>,
    #[borrows(device_handle)]
    #[covariant]
    stream_handle: uvc::StreamHandle<'this>,
    #[borrows(mut stream_handle)]
    #[covariant]
    active_stream: uvc::ActiveStream<'this, ()>,
}

warning: field is never read: `active_stream`
  --> examples/demo-viz/uvc_camera.rs:41:9
   |
41 |     pub active_stream: uvc::ActiveStream<'this, ()>,
   |         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
   |
   = note: `#[warn(dead_code)]` on by default

but if I change it to _active_stream, I now get:
warning: method `with__active_stream` should have a snake case name
  --> examples/demo-viz/uvc_camera.rs:41:5
   |
41 |     _active_stream: uvc::ActiveStream<'this, ()>,
   |     ^^^^^^^^^^^^^^ help: convert the identifier to snake case: `with_active_stream`
   |
   = note: `#[warn(non_snake_case)]` on by default

warning: method `borrow__active_stream` should have a snake case name
  --> examples/demo-viz/uvc_camera.rs:41:5
   |
41 |     _active_stream: uvc::ActiveStream<'this, ()>,
   |     ^^^^^^^^^^^^^^ help: convert the identifier to snake case: `borrow_active_stream`

warning: method `with__active_stream_mut` should have a snake case name
  --> examples/demo-viz/uvc_camera.rs:41:5
   |
41 |     _active_stream: uvc::ActiveStream<'this, ()>,
   |     ^^^^^^^^^^^^^^ help: convert the identifier to snake case: `with_active_stream_mut`

So I had to end up putting a #[allow(dead_code)] on top of my struct RentalStream, a crude hack.

I've just had this same issue, and it appears that #[allow(dead_code)] is no longer working.

This is still an issue for me, neither _ nor using #[allow(dead_code)] will disable warnings


Basic template - .SignedUser.Theme

i copied the templates files from the repository folder to the custom/templates folder on my machine but now gitea throw me an error:

template: base/head:152:21: executing "base/head" at <.SignedUser.Theme>: can't evaluate field Theme in type interface {}

https://github.com/go-gitea/gitea/blob/master/templates/base/head.tmpl#L152

User themes are part of 1.8+
For 1.7.x you will need to download the template from the release/1.7 branch.


Set intellisense defaults

Set the following IntelliSense defaults for Bicep language in the VS code extension:

Default to expanded completion UX
Choose first selection in completion list
Allow completions to work inside an active snippet
Disables text-based completions.

This fixes #882

Codecov Report

Merging #1019 (3b5667c) into main (a0a38b2) will increase coverage by 0.47%.
The diff coverage is n/a.

@@            Coverage Diff             @@
##             main    #1019      +/-   ##
==========================================
+ Coverage   94.26%   94.73%   +0.47%
==========================================
  Files         328      324       -4
  Lines       15810    15659     -151
  Branches       12        0      -12
==========================================
- Hits        14903    14835      -68
+ Misses        907      824      -83

Flag
Coverage Δ

dotnet
94.73% <ø> (-0.09%)
:arrow_down:

typescript
?

Flags with carried forward coverage won't be shown. Click here to find out more.

Impacted Files
Coverage Δ

...cep.Core.UnitTests/FileSystem/FileResolverTests.cs
79.31% <0.00%> (-20.69%)
:arrow_down:

.../Bicep.Core.UnitTests/Assertions/BaselineHelper.cs
63.63% <0.00%> (-15.91%)
:arrow_down:

src/Bicep.Core/FileSystem/PathHelper.cs
96.15% <0.00%> (-3.85%)
:arrow_down:

src/Bicep.Core.UnitTests/Utils/CompilerFlags.cs
33.33% <0.00%> (ø)

...Bicep.Core.UnitTests/FileSystem/PathHelperTests.cs
100.00% <0.00%> (ø)

src/vscode-bicep/src/extension.ts

src/vscode-bicep/src/language/client.ts

src/vscode-bicep/src/utils/telemetry.ts

src/vscode-bicep/src/utils/logger.ts


Node.js

https://sendgrid.com/docs/Integrate/Code_Examples/nodejs.html
I have  succeeded to implement sendgrid  in azure  with code as described in this example.
https://chriztalk.wordpress.com/2014/05/22/email-reports-from-sql-azure/
In this example the content in the  parameter  "MailBody"  is  a variable retrived from a store procedure in a azure sql database.  I  hope you can  extend the example and show how  the other parameters in this example (to, from, and subject)  can be retrived as variables in the store procedure too.

Hello @ej500,Thanks for your suggestion or question! In an attempt to clear out our backlog,                                              we are closing out issues over 90 days old.                                             If this is still an issue, please feel free to reopen it.                                             Team SendGrid DX-Docs


Performance Degradation in YOLOv8s Model Exported to ONNX via SparseML's Exporter

Describe the bug
When exporting the YOLOv8s (pruned50-quant, model.pt from sparsezoo) model via the ONNX exporter (sparseml.ultralytics.export_onnx), its performance noticeably decreases compared to the ONNX model available in SparseZoo
Expected behavior
Perfomance of the two ONNX files should be the same, as it is the same model.
Environment
Include all relevant environment information:

OS: Ubuntu 22.04
Python version: 3.9.19
SparseML version or commit hash: sparseml==1.7.0
ML framework version(s): torch==2.1.2
Other Python package versions: deepsparse==1.7.1, sparsezoo==1.7.0, ultralytics==8.0.124
Other relevant environment information: CPU: i9-12900KS

To Reproduce
Exact steps to reproduce the behavior:
Download model.onnx for yolov8s-pruned50-quant from SparseZoo (https://sparsezoo.neuralmagic.com/models/yolov8-s-coco-pruned50_quantized). Benchmark it using deepsparse.benchmark:
> deepsparse.benchmark yolov8s-coco-pruned50_quantized.onnx
2024-05-10 13:56:31 deepsparse.benchmark.helpers INFO     Thread pinning to cores enabled
DeepSparse, Copyright 2021-present / Neuralmagic, Inc. version: 1.7.1 COMMUNITY | (3904e8ec) (release) (optimized) (system=avx2_vnni, binary=avx2)
2024-05-10 13:56:31 deepsparse.benchmark.benchmark_model INFO     deepsparse.engine.Engine:
	onnx_file_path: yolov8s-coco-pruned50_quantized.onnx
	batch_size: 1
	num_cores: 8
	num_streams: 1
	scheduler: Scheduler.default
	fraction_of_supported_ops: 1.0
	cpu_avx_type: avx2
	cpu_vnni: True
2024-05-10 13:56:31 deepsparse.utils.onnx INFO     Generating input 'images', type = uint8, shape = [1, 3, 640, 640]
2024-05-10 13:56:31 deepsparse.benchmark.benchmark_model INFO     Starting 'singlestream' performance measurements for 10 seconds
Original Model Path: yolov8s-coco-pruned50_quantized.onnx
Batch Size: 1
Scenario: sync
Throughput (items/sec): 87.1154
Latency Mean (ms/batch): 11.4735
Latency Median (ms/batch): 11.4148
Latency Std (ms/batch): 0.2300
Iterations: 872

Notice fraction_of_supported_ops: 1.0 and Throughput (items/sec): 87.1154.
Now download model.pt from the same page and export it to ONNX using the provided tool:
> sparseml.ultralytics.export_onnx --model yolov8s-coco-pruned50_quantized.pt

                   from  n    params  module                                       arguments
  0                  -1  1       928  ultralytics.nn.modules.conv.Conv             [3, 32, 3, 2]
  1                  -1  1     18560  ultralytics.nn.modules.conv.Conv             [32, 64, 3, 2]
  2                  -1  1     29056  ultralytics.nn.modules.block.C2f             [64, 64, 1, True]
  3                  -1  1     73984  ultralytics.nn.modules.conv.Conv             [64, 128, 3, 2]
  4                  -1  2    197632  ultralytics.nn.modules.block.C2f             [128, 128, 2, True]
  5                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]
  6                  -1  2    788480  ultralytics.nn.modules.block.C2f             [256, 256, 2, True]
  7                  -1  1   1180672  ultralytics.nn.modules.conv.Conv             [256, 512, 3, 2]
  8                  -1  1   1838080  ultralytics.nn.modules.block.C2f             [512, 512, 1, True]
  9                  -1  1    656896  ultralytics.nn.modules.block.SPPF            [512, 512, 5]
 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']
 11             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]
 12                  -1  1    591360  ultralytics.nn.modules.block.C2f             [768, 256, 1]
 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']
 14             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]
 15                  -1  1    148224  ultralytics.nn.modules.block.C2f             [384, 128, 1]
 16                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]
 17            [-1, 12]  1         0  ultralytics.nn.modules.conv.Concat           [1]
 18                  -1  1    493056  ultralytics.nn.modules.block.C2f             [384, 256, 1]
 19                  -1  1    590336  ultralytics.nn.modules.conv.Conv             [256, 256, 3, 2]
 20             [-1, 9]  1         0  ultralytics.nn.modules.conv.Concat           [1]
 21                  -1  1   1969152  ultralytics.nn.modules.block.C2f             [768, 512, 1]
 22        [15, 18, 21]  1   2147008  ultralytics.nn.modules.head.Detect           [80, [128, 256, 512]]
Model summary: 225 layers, 11166560 parameters, 11166544 gradients

Applying structure from sparseml checkpoint at epoch -1
2024-05-10 13:58:11 sparseml.pytorch.utils.logger INFO     Logging all SparseML modifier-level logs to sparse_logs/10-05-2024_13.58.11.log
Loaded previous weights from checkpoint
Source: 'sparseml' detected; Exporting model from SparseML checkpoint...
/home/user/anaconda3/envs/sparse_issue_env/lib/python3.9/site-packages/torch/onnx/utils.py:823: UserWarning: It is recommended that constant folding be turned off ('do_constant_folding=False') when exporting the model in training-amenable mode, i.e. with 'training=TrainingMode.TRAIN' or 'training=TrainingMode.PRESERVE' (when model is in training mode). Otherwise, some learnable model parameters may not translate correctly in the exported ONNX model because constant folding mutates model parameters. Please consider turning off constant folding or setting the training=TrainingMode.EVAL.
  warnings.warn(
/home/user/anaconda3/envs/sparse_issue_env/lib/python3.9/site-packages/ultralytics/nn/modules/head.py:50: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  elif self.dynamic or self.shape != shape:
2024-05-10 13:58:15 sparseml.exporters.transforms.onnx_transform INFO     [ConstantsToInitializers] Transformed 92 matches
2024-05-10 13:58:16 sparseml.exporters.transforms.onnx_transform INFO     [FoldIdentityInitializers] Transformed 0 matches
2024-05-10 13:58:16 sparseml.exporters.transforms.onnx_transform INFO     [InitializersToUint8] Transformed 54 matches
2024-05-10 13:58:16 sparseml.exporters.transforms.onnx_transform INFO     [FlattenQParams] Transformed 0 matches
2024-05-10 13:58:16 sparseml.exporters.transforms.onnx_transform INFO     [FoldConvDivBn] Transformed 57 matches
2024-05-10 13:58:16 sparseml.exporters.transforms.onnx_transform INFO     [DeleteRepeatedQdq] Transformed 2 matches
2024-05-10 13:58:16 sparseml.exporters.transforms.onnx_transform INFO     [QuantizeQATEmbedding] Transformed 0 matches
2024-05-10 13:58:16 sparseml.exporters.transforms.onnx_transform INFO     [PropagateEmbeddingQuantization] Transformed 0 matches
2024-05-10 13:58:16 sparseml.exporters.transforms.onnx_transform INFO     [PropagateDequantThroughSplit] Transformed 0 matches
2024-05-10 13:58:16 sparseml.exporters.transforms.onnx_transform INFO     [MatMulAddToMatMulIntegerAddCastMul] Transformed 0 matches
2024-05-10 13:58:16 sparseml.exporters.transforms.onnx_transform INFO     [MatMulToMatMulIntegerCastMul] Transformed 0 matches
2024-05-10 13:58:16 sparseml.exporters.transforms.onnx_transform INFO     [FoldReLUQuants] Transformed 0 matches
2024-05-10 13:58:16 sparseml.exporters.transforms.onnx_transform INFO     [ConvToConvIntegerAddCastMul] Transformed 55 matches
2024-05-10 13:58:16 sparseml.exporters.transforms.onnx_transform INFO     [GemmToQLinearMatMul] Transformed 0 matches
2024-05-10 13:58:16 sparseml.exporters.transforms.onnx_transform INFO     [GemmToMatMulIntegerAddCastMul] Transformed 0 matches
2024-05-10 13:58:16 sparseml.exporters.transforms.onnx_transform INFO     [QuantizeResiduals] Transformed 0 matches
2024-05-10 13:58:16 sparseml.exporters.transforms.onnx_transform INFO     [RemoveDuplicateQConvWeights] Transformed 0 matches
2024-05-10 13:58:17 sparseml.exporters.transforms.onnx_transform INFO     [RemoveDuplicateQuantizeOps] Transformed 0 matches
2024-05-10 13:58:17 sparseml.pytorch.sparsification.quantization.quantize_qat_export INFO     Model initial QuantizeLinear node(s) deleted and inputs set to uint8
2024-05-10 13:58:17 sparseml.pytorch.utils.exporter INFO     Created deployment folder at /home/user/Desktop/projects/sparse/issue/exported/deployment
2024-05-10 13:58:17 sparseml.pytorch.utils.exporter INFO     Saved model.onnx in the deployment folder at /home/user/Desktop/projects/sparse/issue/exported/deployment/model.onnx
2024-05-10 13:58:17 sparseml.pytorch.utils.exporter INFO     Created config.json file at /home/user/Desktop/projects/sparse/issue/exported/deployment
Recipe checkpoint detected, saving the recipe to the deployment directory /home/user/Desktop/projects/sparse/issue/exported/deployment

Conversion is successful. Now benchmark exported onnx model:
> deepsparse.benchmark exported/model.onnx
2024-05-10 13:59:27 deepsparse.benchmark.helpers INFO     Thread pinning to cores enabled
DeepSparse, Copyright 2021-present / Neuralmagic, Inc. version: 1.7.1 COMMUNITY | (3904e8ec) (release) (optimized) (system=avx2_vnni, binary=avx2)
2024-05-10 13:59:27 deepsparse.benchmark.benchmark_model INFO     deepsparse.engine.Engine:
	onnx_file_path: exported/model.onnx
	batch_size: 1
	num_cores: 8
	num_streams: 1
	scheduler: Scheduler.default
	fraction_of_supported_ops: 0.0
	cpu_avx_type: avx2
	cpu_vnni: True
2024-05-10 13:59:27 deepsparse.utils.onnx INFO     Generating input 'images', type = uint8, shape = [1, 3, 640, 640]
2024-05-10 13:59:27 deepsparse.benchmark.benchmark_model INFO     Starting 'singlestream' performance measurements for 10 seconds
Original Model Path: exported/model.onnx
Batch Size: 1
Scenario: sync
Throughput (items/sec): 20.2886
Latency Mean (ms/batch): 49.2855
Latency Median (ms/batch): 49.0293
Latency Std (ms/batch): 2.1290
Iterations: 203

Notice fraction_of_supported_ops: 0.0 and Throughput (items/sec): 20.2886.
Throughput decreased from ~88 down to ~20 for the same model.

Model exported: https://drive.google.com/file/d/1ZDlRd6c1X05lrnxRThUo8FxuapS5Kgm7/view?usp=sharing
You can see that this style of Conv is not being folded to a ConvInteger correctly - @bfineran

@mgoin we'll need to take a look at the recipe and its application - conv integer requires two quantized inputs (weight and act) to the Conv, here we see a quantize (weight) input and the output being quantized (although this may be the input quantization to another layer)

I exactly am having the same performance degradation as @rsazizov on yolov8n from Throughput (items/sec): 110.0278 (on sparsezoo-yolov8n) to Throughput (items/sec): 15.5770 after converting the sparsezoo-yolov8n .pt model using sparseml onnx exporter. Is there any known bug or update on the issue?

Hi @imAhmadAsghar we're aware of the issue and are looking into it internally - it doesn't seem to be a version compatibility issue, but you could potentially try rolling back your sparseml/pytorch versions.  The issue seems to be that the model exports differently now at the beginning (a simple split node is not a few slices).

@bfineran Thank you for your response.
I actually did not get the last part of your response which is "The issue seems to be that the model exports differently now at the beginning (a simple split node is not a few slices)." Can you please explain what do you mean by that in detail, if possible? I am not a performance/optimization engineer and I just want to use sparseml/deepsparse to speed up the inference on CPU. However, the whole library is inconvenient and super foggy.
I have tested the following:

I exported the base yolov8 model (without any recipes) via onnx sparseml.
I exported the pruned yolov8 model (trained with the provided pruning recipe on sparsezoo) via onnx sparseml.
I exported the pruned and quantized yolov8 model (trained with the provided recipe on sparsezoo) via onnx sparseml.

And here are the results:
Performance test between pruned and default model:

As you can see in the above plot that the prunning does nothing.
Performance test between pruned vs pruned and quantized model:

I just don't get this plot. Nothing makes sense at all. The quantization does not work and it is getting super slow by a high margin.
Right now, I am super confused and it does not make any sense to use your library at all. I think I am lacking a lot of information regarding the whole process. Can you please provide me with the proper reference where to start because the one that is provided on the homepage is not leading me anywhere as you can see from the results.
I would really love to get it run and achieve the results you promised.

@imAhmadAsghar Hi, could you find a fix to this? What is going wrong with the exports?

@yoloyash Hi, no I could not unfortunately.

@rsazizov @imAhmadAsghar
Hi I exactly had the same problem too, I was training yolov8-n-coco-pruned49_quantized from official sparseml zoo, and export with sparseml.ultralytics.export_onnx, then when benchmarking, it shows fraction_of_supported_ops: 0.0, and onnx graph is not same to the downloaded official onnx yolov8-n-coco-pruned49_quantized

when Analyze it, has error of no weights

@bfineran can you help with it? I guess it's either the recipe or the export that caused this problem

@mydhui you could try exporting a non quantized FP32 model to see if the problematic slice node is still there around this conv.  Additionally, you could skip this conv during quantization to export a runnable model

Hi has anyone managed to find versions of libraries where quantization does not break the model trained on custom dataset?


docs(config): "coverage" preprocessor uses a deprecated version

The current config uses "karma-config" which uses a two years old
deprecated version of Istanbul.
In order to encourage users to upgrade to the new & maintained
version of IstanbulJS (vs. Istanbul which is deprecated),
a notice has been added and link to the package which comes in
Angular.
Closes #3204
Check that your description matches the automatic change-log format:
http://karma-runner.github.io/latest/dev/git-commit-msg.html
then delete this reminder.

Sorry this did not work out. I'm going to close a lot of older pull requests as the source code has changed a lot over the last year.
Thanks!


Add RingProgress component

This PR adds the RingProgress component. Here is a small example,
import dash_mantine_components as dmc
from dash import Dash, html

app = Dash()
app.layout = html.Div([
    dmc.RingProgress(
        sections = [
            dict(value=50, color="red"),
            dict(value=50, color="blue"),
        ],
        label=[dmc.Center("Label", style=dict(width="100%"))]
    ),
])

if __name__ == '__main__':
    app.run_server(port=7777)

I was actually a little in doubt about the color property. As I read the docs, the available colors depends on the theme. So maybe the PropType should just be Proptypes.string, and there should be some comment that elaborates a bit (e.g. with the colors available in the default theme, a link, or so). What do you think @snehilvj ?

Two things here:

color prop: A lot of other components only support colors from Mantine theme. This is one of the exceptions. I think, specifying it like this would form the correct docstring:

color: PropTypes.oneOfType([
        PropTypes.string,
        PropTypes.oneOf([
            "dark",
            "gray",
            "red",
            "pink",
            "grape",
            "violet",
            "indigo",
            "blue",
            "cyan",
            "teal",
            "green",
            "lime",
            "yellow",
            "orange",
        ]),
    ]),

Can you please help me understand the use of renderDashComponent from dash-extensions-js?

Also, going forward I'd like to follow some kind of system for new contributions (maybe like dev-master) instead of merging all changes directly to master. Any inputs, you'd like to provide?

Ah, interesting. I didn't notice that. In that case, I guess your proposed solution is better. However, I can't keep thinking that it feels not-so-nice to keep repeating the list of theme colors. Couldn't we figure out something better?

One option could be just to use PropTypes.string and then create an Enum (in Python) with the color values supported as part of the dmc library. It could be named Color (or maybe ThemeColor), e.g. something like
class Color(Enum):
    RED = "red"
    BLUE = "blue"
    ...

in the library, so that the syntax would be along the lines of,
import dash_mantine_components as dmc
from dash import Dash, html

app = Dash()
app.layout = html.Div([
    dmc.RingProgress(
        sections = [
            dict(value=50, color=dmc.Color.RED),
            dict(value=50, color=dmc.Color.BLUE),
        ],
        label=[dmc.Center("Label", style=dict(width="100%"))]
    ),
])

if __name__ == '__main__':
    app.run_server(port=7777)

What do you think?

Per default, Dash only renders Dash objects passed via the children property. Hence passing a Dash object via a non-children property like label,

label=[dmc.Center("Label", style=dict(width="100%"))]
will not work per default. The renderDashComponent function turns the json structure received in the js layer into the desired React node, similar to the internals of the dash renderer itself, thus allowing (with a few line of code) to omit this limitation.

@snehilvj This is also a discussion we are currently having at my workplace. At the moment, we are leaning towards not using a development branch, but rather only merge in feature branches when they are mature enough to be released. I am interested in hearing your thoughs as well.

will not work per default. The renderDashComponent function turns the json structure received in the js layer into the desired React node, similar to the internals of the dash renderer itself, thus allowing (with a few lines of code) to overcome this limitation. The limitation of this approach that i know of is that you can't attach callbacks to Dash objects that are rendered this way.

Wow 🤯 this exists.. I need to go through all the existing components and refactor them.
On the issue of colors, we need to think more about it. The solutions needs to account for the entire theme not just colors, see: https://mantine.dev/theming/mantine-provider/
Here you can also see that this theme can be customised. In that case, hardcoded enum might not be the way to go.
The solution needs to be more dynamic. I'd think more about this and might create a new issue on this.
Regarding the discussion on development branch, keeping master stable is not a requirement here as such, but I think it will be easier to track the changes between different releases.

It exists! With emphasis on presence ;). I made it recently, so it probably didn't exist when you made the initial implementation. But yes, I agree that it would be really nice to refactor a number of components to use the strategy. It will improve flexibility and keep the dmc better in sync with mantine.
About tracking changes, I have been really happy about simply creating a CHANGELOG.md file, e.g. this one
https://github.com/thedirtyfew/dash-leaflet/blob/master/CHANGELOG.md
A pull request that e.g. adds a component (like this one) must then add an appropriate statement to this file prior to merging. That's also how it works making pull requests to the main Dash repo. If you are thinking more along the lines of "code changes", you can simply do a compare between tags on the master branch,
https://github.com/snehilvj/dash-mantine-components/compare/0.2.1...0.2.2

yeah I think this will be fine. I'll start a changelog starting next release.

I think renderDashComponents needs more work at this stage. I frequently get this error:
Warning: li: 'key' is not a prop. Trying to access it will result in 'undefined' being returned. If you need to access the same value within the child component, you should pass it as a different prop.
Lets hold adding dash-extensions-js to dmc for now. I'll also create an issue in the dash-extensions-js repo with some sample code.


Add Dockerfile for container deployment

Sadly, this is not that simple. I gave it a quick shot and ran into multiple problems.
Many thinks don't work from scratch, when putting this web-application into a docker container.
For example displaying of bitcoin daemon uptime won't work. From inside the container, you are unable to get information about processes running outside the container (and that's where bitcoind is running).
This is one of the reasons why you dockerize applications. Your application is in some kind of sandbox here.
I know, this doesn't solve your request. But I hope this explanation helps a bit to understand why there isn't a dockerfile (yet?).

Hmm...maybe I'll make an all-in-one container that runs this and bitcoind. If I do I'll update you.
Thanks for trying!

Hi all,
I'm closing this issue for now - if anyone does end up with a Dockerised version, please do feel free to reopen this issue or create a PR :)
Thanks,
Craig


Specify return type of Class.new using RBI sigs

Our goal with this PR is for Sorbet to automatically know the type of Class.new(Parent). In #6162, we originally tried to accomplish in the rewriter, but @jez pointed out that T.cast is not allowed in all of the locations that a rewrite could occur. We've attempted a different approach using only RBI sigs to properly type the return of this method.
Motivation
Right now, the result of Class.new is always typed as Class:
x = Class.new(Parent)
T.reveal_type(x) # => Class

This forces users to cast the results whenever they use Class.new.
By using generics to specify the correct type in the RBI signature, we can ensure that the correct type is returned by default.
x = Class.new(Parent)
T.reveal_type(x) # => T.class_of(Parent)

Test plan
See included automated tests.
cc: @Morriar

We have a policy of testing changes to Sorbet against Stripe's codebase before
merging them. I've kicked off a test run for the current PR.  When the build
finishes, I'll share with you whether or how it failed. Thanks!
Stripe employees can see the build results here:
→ https://go/builds/bui_MuUcNWhuT5Syc7
→ https://go/builds/bui_MuUc3hqow63m3m

You have one CFG snapshot test that fails because one of the inferred intermediate types becomes more specific. You can use tools/scripts/update_testdata_exp.sh to fix the failure.

@jez I ran that command and pushed the results, so hopefully things should be passing now.


[Feature] (json)add json_contains function

Proposed changes
Issue Number: close #17216

Further comments
If this is a relatively large or complex change, kick off the discussion at<EMAIL_ADDRESS>by explaining why you chose the solution you did and what alternatives you considered, etc...

run buildall

run buildall

run buildall

run buildall


bazel: update rules_docker

Hmm, I cannot reproduce this error. I'm going to assume that it has to do with a different bazel version (0.29.1) vs the one in Prow.
As this isn't really a blocker for anything, I'll close this PR for now.


How to use middleware on routes

Hey @cretueusebiu,
can you show a simple example on how to use the auth middleware on vue js routes? Is it possible to use it on a parent route so all children are also protected?
Thank you!

@molerat619 just add middleware to your parent component.
Example:
File - router/router.js
...
{ path: '/settings',
    component: Settings,
    children: [
      { path: '', redirect: { name: 'settings.profile' } },
      { path: 'profile', name: 'settings.profile', component: SettingsProfile },
      { path: 'password', name: 'settings.password', component: SettingsPassword }
    ] },
...

File - settings/index (parent component)

export default {
  middleware: 'auth',
}

That would explain, why I couldn't find any middleware logic in the routes-file :) Thank you!

@molerat619 np
@cretueusebiu close


Add rendering enums as API

Since #3476 renders most of #3309 obsolete, this is the remaining parts, revealing the bits of API necessary for a custom implementation of BlockMeshGenerator, such as FlowingLiquids uses for making meshes for liquid blocks.
Although this is not currently necessary due to the module access restrictions being disabled, it still seems desirable to have this marked as API as it's actually used in a module (and harmless).
The things revealed do relate to some rather ad-hoc internals of rendering which may be changed at some point, but if that's a problem, it indicates that changes need to be made elsewhere so that access to these isn't necessary.
Contains
Reveals ChunkVertexFlag and ChunkMesh.RenderType as API

I've been looking more at how separation of chunk rendering into phases is done, and I think that it might make sense to change the ChunkMesh.RenderTypes enum, so it might not be a good idea to add it to the API yet.


ci: Semantic PRs

What does this change?

lints PRs for conventional commit compliance, rather than individual commits

Why?

we should get the benefit of automatic semantic release without getting too involved with individual developer's ways of working

:tada: This PR is included in version 0.7.0 :tada:
The release is available on:

npm package (@latest dist-tag)
GitHub release

Your semantic-release bot :package::rocket:


[perf] optimize ValueAtPercentile(s): introduce nextCountAtIdx() and reduce by 25.6% the on-cpu usage

This PR optimizes ValueAtPercentile(s) by introducing nextCountAtIdx() and only updating the iterator highestEquivalentValue when required in order to reduce wasteful cpu usage. If we look at ValueAtPercentile numbers, It reduced by 25.6% the on-cpu usage.
master branch benchmark percentiles calculation:
% go test -bench="BenchmarkHistogramValueAtPercentile" -benchtime=10s -cpuprofile profile.out
goos: darwin
goarch: arm64
pkg: github.com/HdrHistogram/hdrhistogram-go
BenchmarkHistogramValueAtPercentile-8                        	  264561	     42905 ns/op	       0 B/op	       0 allocs/op
BenchmarkHistogramValueAtPercentileGivenPercentileSlice-8    	   58002	    214438 ns/op	       0 B/op	       0 allocs/op
BenchmarkHistogramValueAtPercentilesGivenPercentileSlice-8   	  173206	     69352 ns/op	     248 B/op	       4 allocs/op

cpu.optimize.nextCountAtIdx branch benchmark percentiles calculation:
% go test -bench="BenchmarkHistogramValueAtPercentile" -benchtime=10s -cpuprofile profile.out
goos: darwin
goarch: arm64
pkg: github.com/HdrHistogram/hdrhistogram-go
BenchmarkHistogramValueAtPercentile-8                        	  369895	     31900 ns/op	       0 B/op	       0 allocs/op
BenchmarkHistogramValueAtPercentileGivenPercentileSlice-8    	   83106	    147954 ns/op	       0 B/op	       0 allocs/op
BenchmarkHistogramValueAtPercentilesGivenPercentileSlice-8   	  232894	     52160 ns/op	     248 B/op	       4 allocs/op

Codecov Report

Merging #46 (164c49c) into master (35c7773) will increase coverage by 0.10%.
The diff coverage is 100.00%.

@@            Coverage Diff             @@
##           master      #46      +/-   ##
==========================================
+ Coverage   76.81%   76.91%   +0.10%
==========================================
  Files           6        6
  Lines         690      693       +3
==========================================
+ Hits          530      533       +3
  Misses         95       95
  Partials       65       65

Impacted Files
Coverage Δ

hdr.go
89.93% <100.00%> (+0.10%)
:arrow_up:

Continue to review full report at Codecov.

Legend - Click here to learn more
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Powered by Codecov. Last update 35c7773...164c49c. Read the comment docs.


Kong is crashing when the L1 cache miss happens

NOTE: GitHub issues are reserved for bug reports only.
Please read the CONTRIBUTING.md guidelines to learn on which channels you can
seek for help and ask general questions:
https://github.com/Kong/kong/blob/master/CONTRIBUTING.md#where-to-seek-for-help
Summary
Kong is crashing when the L1 cache miss happens and start sending 500 for all the requests
Steps To Reproduce

edit the api config when the kong is under load
make cassandra latency go high
kong will hit a cache miss and crash and start sending 500 to all the requests

Additional Details & Logs

Kong version 0.11.1-0
Kong debug-level startup logs

==================
false
2017/11/07 01:11:32 [verbose] Kong: 0.11.1
2017/11/07 01:11:32 [debug] ngx_lua: 10008
2017/11/07 01:11:32 [debug] nginx: 1011002
2017/11/07 01:11:32 [debug] Lua: LuaJIT 2.1.0-beta2
2017/11/07 01:11:32 [verbose] no config file found at /etc/kong/kong.conf
2017/11/07 01:11:32 [verbose] reading config file at /etc/kong.conf
2017/11/07 01:11:32 [debug] KONG_NGINX_DAEMON ENV found with "off"
2017/11/07 01:11:32 [debug] admin_access_log = "logs/admin_access.log"
2017/11/07 01:11:32 [debug] admin_error_log = "logs/error.log"
2017/11/07 01:11:32 [debug] admin_http2 = false
2017/11/07 01:11:32 [debug] admin_listen = "<IP_ADDRESS>:8001"
2017/11/07 01:11:32 [debug] admin_listen_ssl = "<IP_ADDRESS>:8444"
2017/11/07 01:11:32 [debug] admin_ssl = false
2017/11/07 01:11:32 [debug] anonymous_reports = true
2017/11/07 01:11:32 [debug] cassandra_consistency = "ONE"
2017/11/07 01:11:32 [debug] cassandra_contact_points = {""}
2017/11/07 01:11:32 [debug] cassandra_data_centers = {"dc1:2","dc2:3"}
2017/11/07 01:11:32 [debug] cassandra_keyspace = ""
2017/11/07 01:11:32 [debug] cassandra_lb_policy = "RoundRobin"
2017/11/07 01:11:32 [debug] cassandra_local_datacenter = "# When using the DCAwareRoundRobin load"
2017/11/07 01:11:32 [debug] cassandra_password = "******"
2017/11/07 01:11:32 [debug] cassandra_port = 9042
2017/11/07 01:11:32 [debug] cassandra_repl_factor = 1
2017/11/07 01:11:32 [debug] cassandra_repl_strategy = "SimpleStrategy"
2017/11/07 01:11:32 [debug] cassandra_schema_consensus_timeout = 10000
2017/11/07 01:11:32 [debug] cassandra_ssl = false
2017/11/07 01:11:32 [debug] cassandra_ssl_verify = false
2017/11/07 01:11:32 [debug] cassandra_timeout = 5000
2017/11/07 01:11:32 [debug] cassandra_username = ""
2017/11/07 01:11:32 [debug] client_body_buffer_size = "8k"
2017/11/07 01:11:32 [debug] client_max_body_size = "0"
2017/11/07 01:11:32 [debug] client_ssl = false
2017/11/07 01:11:32 [debug] custom_plugins =
2017/11/07 01:11:32 [debug] database = ""
2017/11/07 01:11:32 [debug] db_cache_ttl = 3600
2017/11/07 01:11:32 [debug] db_update_frequency = 5
2017/11/07 01:11:32 [debug] db_update_propagation = 0
2017/11/07 01:11:32 [debug] dns_error_ttl = 1
2017/11/07 01:11:32 [debug] dns_hostsfile = "/etc/hosts"
2017/11/07 01:11:32 [debug] dns_no_sync = false
2017/11/07 01:11:32 [debug] dns_not_found_ttl = 30
2017/11/07 01:11:32 [debug] dns_order = {"LAST","SRV","A","CNAME"}
2017/11/07 01:11:32 [debug] dns_resolver = {}
2017/11/07 01:11:32 [debug] dns_stale_ttl = 4
2017/11/07 01:11:32 [debug] error_default_type = "text/plain"
2017/11/07 01:11:32 [debug] http2 = false
2017/11/07 01:11:32 [debug] latency_tokens = true
2017/11/07 01:11:32 [debug] log_level = "debug"
2017/11/07 01:11:32 [debug] lua_package_cpath = ""
2017/11/07 01:11:32 [debug] lua_package_path = "./?.lua;./?/init.lua;"
2017/11/07 01:11:32 [debug] lua_socket_pool_size = 30
2017/11/07 01:11:32 [debug] lua_ssl_verify_depth = 1
2017/11/07 01:11:32 [debug] mem_cache_size = "128m"
2017/11/07 01:11:32 [debug] nginx_daemon = "off"
2017/11/07 01:11:32 [debug] nginx_optimizations = true
2017/11/07 01:11:32 [debug] nginx_user = "nobody nobody"
2017/11/07 01:11:32 [debug] nginx_worker_processes = "auto"
2017/11/07 01:11:32 [debug] pg_database = "kong"
2017/11/07 01:11:32 [debug] pg_host = "<IP_ADDRESS>"
2017/11/07 01:11:32 [debug] pg_port = 5432
2017/11/07 01:11:32 [debug] pg_ssl = false
2017/11/07 01:11:32 [debug] pg_ssl_verify = false
2017/11/07 01:11:32 [debug] pg_user = "kong"
2017/11/07 01:11:32 [debug] prefix = "/usr/local/kong/"
2017/11/07 01:11:32 [debug] proxy_access_log = "logs/access.log"
2017/11/07 01:11:32 [debug] proxy_error_log = "logs/error.log"
2017/11/07 01:11:32 [debug] proxy_listen = "<IP_ADDRESS>:8000"
2017/11/07 01:11:32 [debug] proxy_listen_ssl = "<IP_ADDRESS>:8443"

Kong error logs 2017/11/07 01:12:28 [error] 64#0: *8276 lua entry thread aborted: runtime error: /usr/local/share/lua/5.1/kong/core/balancer.lua:166: attempt to index local 'upstream' (a nil value)
stack traceback:
coroutine 0:
/usr/local/share/lua/5.1/kong/core/balancer.lua: in function 'get_balancer'
/usr/local/share/lua/5.1/kong/core/balancer.lua:274: in function 'execute'
/usr/local/share/lua/5.1/kong/core/handler.lua:391: in function 'after'

Operating System : centos

Hi,
Thank you for reporting. I have a few questions about things I would like to clarify first, before investigating this, if you don't mind.

when the L1 cache miss happens

What do you call the L1 cache?
Also, could you clarify a little bit your "steps to reproduce" section?

edit the api config when the kong is under load

What did you edit exactly? What type of load is Kong being put under at this point? Do you have numbers and/or can reproduce this load with tools such as ab or wrk for us to reproduce similar traffic?

make cassandra latency go high

How exactly did you proceed to do this? We are interested not only to know how to reproduce the issue, but also to ensure this reproducible step is acceptable and allows for this to be qualified as a bug or not.

Kong error logs

I suppose you are seeing many of these errors due to traffic on the proxy port, and not just one?

I do have a strong hunch as to what is happening here, but to confirm it, I would very much like to clarify the last point about the error logs that you are seeing. How frequent are those errors? Do they stop after a few seconds have passed? That'd be helpful to know.

we have observed this when we put the load to the kong instances the load is 200K RPM
By L1 Cache i meant the in memory cache of kong.
Increasing cassandra latency i dont think we will be able to simulate but this we observed in our prod instances.
The Issue happened and errored out all the traffic went to the instances at that time and it was completely every request went 500
The issue got rectified with a restart

What we have observed is that the local cache is not having the upstream and the checks are on the table  in balancer.lua
return singletons.cache:get(upstream_cache_key, nil,
load_upstream_into_memory, upstream_id)

Hello @rajagopalvreghunath, are you still experiencing this problem? Did you try 0.12?

I believe this was fixed in more recent work on the balancer in 0.12 and 0.13. Closing this then, thank you for the report.