rbd mount fails inside containerized kubelet

I'm using rke to setup a kubernetes cluster. kubelet is running in a docker container. When rbd_utils.go tries to check for the rbd-nbd utils it does a modprobe nbd. This fails inside the container because the kernel modules are not available. This error could be avoided if rbd_utils would do a lsmod | grep nbd first to check if the module is already loaded.
https://github.com/rancher/rke/issues/362 suggest another way by mounting /lib/modules in the container. But I think checking if the module is already loaded would be a cleaner solution.

Please create an issue upstream at kubernetes/kubernetes too, when kubelet is containerized it is supposed to bind mount everything needed
/area ceph/rbd


[FR]: Create option for cleanup without deleting underlying files

Is your feature request related to a problem? Please elaborate.
There are times when I'd like to remove a torrent from qBittorrent without actually deleting the underlying files. An example would be when I have multiple torrents running that point to a single file directory, but I have shorter seeding times defined for some trackers than others. If I enable the current cleanup implementation (which deletes the torrent files as well as the torrent), I run into the issue where the torrents I'd prefer to keep seeding longer have the files deleted from underneath them. If I keep cleanup disabled, the torrents never get cleaned up unless I do it manually and the remaining files eventually picked up by the remove_orphan function.
Describe the solution you'd like
Create a cleanup config option that doesn't force file deletion and can be placed into the config file the same way as the current cleanup function can. Alternatively, implement it as a command line command/docker environment variable to change the behavior of cleanup. The current one implements delete_files=True but the torrents_delete function supports (and defaults to) delete_files=False.
Does your solution involve any of the following?

[X] New config option
[X] New command option

Describe alternatives you've considered
N/A, solution section covers current idea.
Who will this benefit?
Users who want torrents cleaned up from the webUI once they've met seeding requirements, but preserve the torrent files in the event that other torrents with longer seed times may be using them.
Additional Information
No response

This already exists.  QBM will not delete data if torrents are already using it.


Update Thrift package

Resolves #394

This is a very simple change, could we get workflow approval for tests to run please?

Thanks for the review and approval! All tests fail on an seemingly unrelated error though, any advice?

@m1n0 interesting! I checked out your branchy and tried the unit tests and everything passed. Looks like something changed on github that results in these failures.

Looks like this is caused by a broader issue in virtualenv that is affecting many other developers: https://github.com/pypa/virtualenv/issues/2735
The solution is to force github actions to rebuild its virtualenvs (rather than caching them). I'm not quite clear how to force that since I can't see the GH Actions panel for this repo anymore. But this should be enough information to fix things, @kravets-levko

Yep, the tests are failing because actions/cache@v2 is pulling a cached version of the environment which was most recently rebuilt using a version of filelock that has since been yanked. Here are the instructions for blowing away this cache so that GH Actions can rebuild it. This will cause the tests to pass.

Thanks everyone!


Vector Store: Support for Cosine similarity and Dot Product when creating a FLOAT_VECTOR

Problem Statement
Today, when creating a FLOAT_VECTOR, it uses the default EUCLIDEAN_HNSW (L2) similarity.
During creation time, it should be possible to provide Cosine Similarity or Dot Product as similarity functions as available in lucene (https://lucene.apache.org/core/9_7_0/core/org/apache/lucene/document/KnnFloatVectorField.html). Default can remain Euclidian similarity.
Possible Solutions
Exposing options to choose from "cosine" or "dot" similarity function when not using the default "euclidian" function (https://lucene.apache.org/core/9_7_0/core/org/apache/lucene/index/VectorSimilarityFunction.html)
Considered Alternatives
No response

Today, when creating a FLOAT_VECTOR, it uses the default EUCLIDEAN_HNSW (L2) similarity.
During creation time, it should be possible to provide Cosine Similarity or Dot Product as similarity functions as available in lucene

"Feature I want is missing" is not a very good problem statement. Could you elaborate on the concrete use-case a bit more, to get a better idea about why cosine or dot product similarity would be beneficial there?

While euclidean distance focuses on the absolute magnitude difference between vectors, cosine similarity focuses on the angular difference, and dot product considers both the magnitude and angular difference. The choice of metric depends on the specific use case and the properties of the vector embeddings. The developer should be able to differentiate between these different options to create an optimal implementation for their use case - which might require to use a different similarity function.
Euclidean Distance

Measures the straight-line distance between two vectors in a multi-dimensional space.
Calculated as the square root of the sum of the squared differences between the corresponding vector components.
Sensitive to the magnitude of the vectors, so vectors with large values will have larger Euclidean distances even if they are otherwise similar.
Useful when the vector embeddings contain information about counts or measures, like in recommendation systems.

Cosine Similarity

Measures the cosine of the angle between two vectors.
Calculated as the dot product of the vectors divided by the product of their magnitudes.
Only considers the direction of the vectors, not their magnitude.
Commonly used in natural language processing to measure the similarity between documents.
Inverse relationship with cosine distance - as distance increases, similarity decreases.

Dot Product

Measures the projection of one vector onto another.
Calculated by summing the products of the corresponding vector components.
Proportional to both the vectors' magnitudes and the cosine of the angle between them.
Can be affected by the length of the vectors, so popular examples may skew the similarity metric.

The cosine variant is likely going to be removed from Lucene: https://github.com/apache/lucene/issues/13281

The proposal to remove cosine from Lucene has been removed https://github.com/apache/lucene/issues/13281#issuecomment-2248328086


Migrate from Groovy to Java 8

In addition to removing yet-another potential library dependency conflict (per #105), Grabbit would benefit from the additional performance and type-safety, per some of the changes made in grabbit-cli.

Can we get the same benefits by incorporating just the Java 8 runtime?
Would we gradually start incorporating Java 8 language features with new additions?

The runtime itself (i.e., JRE 1.8) is better performance and reliability, but that happens as a deployment concern for AEM. This is about taking advantage of the Java 8 language features and surrounding tooling (such as Checker Framework, static analysis, etc.).
The Groovy language is geared toward being dynamic, and even with the @CompileStatic it still doesn't match the Java in those aspect. Prior to Java 8, it was well worth it, but that's a much harder case to make now.

One obvious implication that should be made explicit is that it means that once the change is made it will only work on AEM 6.1+ running on JRE 1.8+. Since Grabbit v4+ is AEM 6.1 anyway, I don't think that's a problem in any way, but best to be very clear.

As far as strategy, I'll edit the description with a checklist. :-)

Hmm, okay. I'm still a bit confused on what problem we are trying to solve by doing this though. I'm not aware of any predominantly JVM-related performance issues we are currently running into.
PMD, and Checker might be nice; but introducing all these changes at once seems an unnecessarily high risk to value-add proposition.

It's far more a design/correctness thing than performance (which is primarily I/O-bound). While a LOT more static analysis isn't going to solve all problems, it certainly helps drive much better code.
 The more I can off-load to the computer to do for me, the better everyone's life is. Add to that this being open-source, the lower the barrier-to-entry is for others to contribute (more people know Java than Groovy) and the more guardrails we can put up for validating quality automatically before it even hits code-review... Well, again, the better everyone's life is.

As to "at once" --- that's why it's broken down into multiple stages. Fortunately, since Grabbit was written TDD, there's a lot of support to keep such mechanical transformations in line.
Now, once this is done, there's some much more ambitious things I'd like to see. But one thing at a time. 🚶


Add Hercules site config (ufs-weather-model only), fix macos-dom self-hosted runner tests, NOAA Parallelworks updates

Description
This PR adds a site config for Hercules. It is incomplete, because we are still missing several key packages for building the unified environment. The sysadmins are working on this, @climbfuji's ticket with them is [RDHPCS#2023030354000069] Request to install additional packages on hercules.
Therefore not adding it to the documentation yet. I used the site config to build the ufs-weather-model environment on Hercules (also added<EMAIL_ADDRESS>for testing).
From @ulmononian: NOAA Parallelworks updates: fix bug in module load commands (missing /), add mysql external dependencies.
Dependencies
Waiting on https://github.com/NOAA-EMC/spack/pull/237
Issues
None (adding the necessary zenhub items to this PR).
Testing
CI tests passed for:

macOS CI (clang+gfortran)
Ubuntu CI (Intel)
Dom's macOS (clang+gfortran)

See https://github.com/NOAA-EMC/spack-stack/pull/492/commits/ba2f97496f9bda5b14998471710de80cee403b79
Also: successfully installed ufs-weather-model environment on Hercules.

Approved once 'penmpi' is fixed

Fixed in https://github.com/NOAA-EMC/spack-stack/pull/492/commits/03505fbc8500c366f779329cad6ecd2d27a80fb9, thanks!


Deadlock scheduling finalizer task on failed build

Occurs in version range 5.5.1..5.7-20190721220031+0000.
Repro projects:

https://github.com/Triple-T/gradle-play-publisher/tree/96b63a8525b80ee4aa52a8072844e8783ad10be8
https://github.com/SUPERCILEX/issue-mcve/tree/52ae3dd651c054495bcd2f5e0f74e5390ec0fb55

Repro steps:

In gradle-play-publisher: ./gradlew publishToMavenLocal
In issue-mcve: ./gradlew pApk

publishReleaseApk will fail (as expected), but the build will now be frozen forever. This is because there's a finalizer task. Somehow, Gradle is deadlocking when trying to schedule the finalizer task.

@big-guy this is blocking the Gradle Play Publisher v2.3.1 release, would appreciate getting it into Gradle 5.6.

@lptr @wolfs could you look at this? I don't know of any specific finalizer changes?
@SUPERCILEX I haven't tried your repro yet.  Does this happen every time?  Could you provide a thread dump for the daemon when it's hung?  Thanks!

@big-guy yup, happens 100% of the time for me. Here's a thread dump. I feel like the culprit might be File lock request listener in dump.txt, but that's just a wild guess. Maybe someone never relinquished the lock?

Also, here's the full output of running ./gradlew pApk --debug. I've done some digging, but I don't see anything wrong.

@lptr @wolfs have you been able to repro?

@SUPERCILEX I can reproduce the problem. It seems like commitEditForComDotSupercilexDotTest destroys the output of :app:publishReleaseApk and can therefore not be scheduled since some other task still depends on the task generating the destroyed file, I guess generateEditForComDotSupercilexDotTest.
Currently, Gradle assumes that all task that depend on a task producing a file also consume it. Since :app:publishApk -> app:publishReleaseApk -> generateEditForComDotSupercilexDotTest, :app:publishApk blocks :commitEditForComDotSupercilexDotTest, since a finalizer is not considered as a dependency which can consume (and so destroy) the output.
Is the dependency/finalizer/destroys chain what you expect to happen?

Hmmmm, I don't get that last part: publish blocks commit since a finalizer is not considered as a dependency which can consume (and so destroy) the output. Why would commitEdit... be blocked by publishApk? And why publishApk be assumed to consume inputs if it has no task actions? (It can't do anything with those inputs anyway.) The other weird thing is that this scenario does work when nothing crashes:
> Task :app:publishReleaseBundleWrapper
> Task :app:publishReleaseBundle
> Task :app:publishRelease
> Task :app:publish
> Task :commitEditForComDotSupercilexDotTest

See for yourself by removing this line: https://github.com/Triple-T/gradle-play-publisher/blob/96b63a8525b80ee4aa52a8072844e8783ad10be8/plugin/src/main/kotlin/com/github/triplet/gradle/play/tasks/PublishApk.kt#L57. That's what I don't understand, why does crashing change the behavior?

Also, I should mention that what I'm actually trying to do here is just run a task after another. If there's a better way to do that than with finalizedBy, please let me know.

@SUPERCILEX I fixed the issue via https://github.com/gradle/gradle/pull/10232. The fix will be in Gradle 6.0. I'll ping you when I have a nightly for you to try out.

Thank you! 🙏 Is 6.0 coming after 5.6?

Yes.

@SUPERCILEX Can you try out 5.7-20190808220314+0000 and see if the problem is fixed for you?

Yaaaassss, it worked! Thank you!


Removed duplicate DISABLE_WRAPPER key in conf.py

Just a minor bit of cleanup of a duplicate config key.

Thanks!


ProfilingPlugin not rendering in Chrome

Bug report

What is the current behavior?
I'm getting an error when using the ProfilingPlugin. I get an error reporting that n.args.data.frames is not iterable when dropping the events.json into Chrome.
If the current behavior is a bug, please provide the steps to reproduce.

Create new project with npx create-react-app test-project.
Eject (cd test-project && npm run eject)
Add new webpack.debug.ProfilingPlugin() to plugins section at config/webpack.config.js
Start dev server with npm start then shutdown it (or just npm run build).
Drag and drop generated events.json into the Chrome DevTools Profiler.

The attached file gives me this experience: events.json.zip
These are the same steps defined in https://github.com/webpack/webpack/issues/11780 where @leimonio reported the same problem.

What is the expected behavior?
Chrome should render the profiler as per the instructions in the Usage: https://webpack.js.org/plugins/profiling-plugin/#usage-custom-outputpath

Other relevant information:
webpack version:  5.83.1
Node.js version: v16.19.0
Operating System: MacOSX
Additional tools:
Chrome version Version 113.0.5672.92

Can you try this https://github.com/webpack/webpack/issues/11780#issuecomment-763845922?

Due to this https://github.com/webpack/webpack/issues/11780#issuecomment-763840997

I've tried to reproduce this issue, and can confirm it occurs even after restarting the laptop. Just followed the steps @edhgoose mentioned above.

This is not inactive - it's just hasn't picked up yet as an issue to resolve.

Same issue for me on webpack 5.92.1 and Chrome 126.0.6478.127.
The same events.json does work in Firefox Profiler: https://profiler.firefox.com


[DUOS-2176][risk=no] Mark unused APIs as deprecated

Addresses
Partially addresses https://broadworkbench.atlassian.net/browse/DUOS-2176
Once these are deprecated for a release cycle, will remove them in a future PR.

Have you read CONTRIBUTING.md lately? If not, do that first.

Label PR with a Jira ticket number and include a link to the ticket
Label PR with a security risk modifier [no, low, medium, high]
PR describes scope of changes
Get a minimum of one thumbs worth of review, preferably two if enough team members are available
Get PO sign-off for all non-trivial UI or workflow changes
Verify all tests go green
Test this change deployed correctly and works on dev environment after deployment

I noticed that reprocessPurposeMatches is the only method in the class that doesn't seem to be mark deprecated.  I didn't see where the front end used that resource.  Should that one be deprecated as well?  If so, should the ref'ed model also be deprecated?

I noticed that reprocessPurposeMatches is the only method in the class that doesn't seem to be mark deprecated. I didn't see where the front end used that resource. Should that one be deprecated as well? If so, should the ref'ed model also be deprecated?

Great question. This is not used by the front end, only for administrative purposes. The case where we want to update a particular DAR's match status happens if a dataset has (in some non-standard way) changed its data use. For example, we currently have a group of datasets in prod that are incorrectly categorized (see https://broadworkbench.atlassian.net/browse/DUOS-2138). We will need to modify those and reprocess any DARs that have requested access to them.


Add an example of integrating with Rails

Looks good to me

I thought about adding a null producer for the test env, but I guess that'll just complicate things...


add HttpClientArc & TokenCredentialArc

This is to support http connection pooling #78. Using an Arc was recommended by @rylev in https://github.com/ctaggart/autorust/pull/86#issuecomment-725958207 to avoid lifetimes and it works. 🥳 This should work fine with an HttpClient interface once we have one, started by @MindFlavor in https://github.com/Azure/azure-sdk-for-rust/pull/79. The code generation changes are in https://github.com/ctaggart/autorust/pull/86 to make this work for mgmt services.

These changes are included in #81.


MAISTRA-816 Add a chapter explaining the move from cluster-scoped to …

…namespace-scoped CRs

I pushed the new changes. Also, I got kind of carried away writing a document on how to use RBAC in general; it was based on the bookinfo example. I then noticed that maistra/bookinfo does not create ServiceAccounts for the services, which makes it hard to base an example on it. I scrapped the longer example but am happy to push it in a separate PR once I figure out the ServiceAccount issue (maybe just base it on httpbin?)


Where is the mod modImplementation url?

Seriousy, I am so lost.

https://maven.terraformersmc.com/ is where our mods reside now

And new versions of ModMenu will be at https://maven.terraformersmc.com/releases/com/terraformersmc/modmenu.
~https://maven.terraformersmc.com/releases/io/github/prospector/modmenu~ is deprecated.


GDScript fail to call methods with variable arguments under certain conditions.

Godot version
4.0-dev
System information
Linux
Issue description
This is a generalized issue for #59939 .
After some digging I found out that the GDScript VM encounters an error when calling methods with variable arguments (e.g. rpc/rpc_id/call_group/etc) and certain conditions are met:
1 - No extra arguments are passed beside the minimum required.
2 - All arguments match the expected type (i.e. no type conversion is needed).
So:
get_tree().call_group("some_group", &"some_method") # Works.
get_tree().call_group(&"some_group", &"some_method") # Method/function failed.

See the "steps to reproduce below" for more examples.
Note that this does not appear to be an issue with StringName specifically, I've tried changing the type of rpc from StringName to String and the behavior ended up being swapped (failing when passing a String, working when passing a StringName).
Steps to reproduce
Run this script (or see the attached project):
func _ready():
	var g = &"some_group"
	var m = &"some_method"
	get_tree().call_group(g, m) # Works.
	get_tree().call_group("some_group", "some_method") # Works.
	get_tree().call_group("some_group", &"some_method") # Works.
	get_tree().call_group(&"some_group", &"some_method") # Method/function failed.

Minimal reproduction project
proj_vaargs_bug.zip

I did investigate that issue once and here are more details: https://github.com/godotengine/godot/issues/61975#issuecomment-1161070923


Statistics

a11a81c268 statistics: allow combobox theming
4a13836c6c statistics: make interface optional
02449e4e87 statistics: add missing dns types
940664242d statistics: move to status menu

I do not understand the reasoning for removing the interface module from the default selection. For the basic user, it is the main traffic counter / speed indicator in the LuCI stats. (Sure, there is nlbwmon, but that is a separate package.)
Your other changes are stylistic or adding missing types like DNS variants, but this is removal of a core feature ???

This pr in another feet belongs to this: https://github.com/openwrt/packages/pull/16319
@hnxman: With the other commit the package is still selected by default, but it could be deselected.
Current state is that there is now way to disable it, and after every sysupgrade it has to be removed again and again

Screenshot of "move to status menu"

I do not get your reasoning there. Moving the LuCI stats module selection partially to the packages side collectd config, and changing the modules selection logic there, causing changes to the case where somebody uses collectd independently of the LuCI statistics. So far NAK from me regarding that change.

As https://github.com/openwrt/packages/pull/16319 was closed i have to remove 1 commit


docs: reccomendation AOAI and kql

Overview/Summary
Recommend service is deployed in multiple regions for high availability/business continuity
Related Issues/Work Items
https://dev.azure.com/CSUSolEng/Accelerator - AI/_workitems/edit/38361/
As part of this pull request I have

[x ] Read the Contribution Guide and ensured this PR is compliant with the guide
[x ] Checked for duplicate Pull Requests
[x ] Associated it with relevant GitHub Issues or ADO Work Items (Internal Only)
[ x] Ensured my code/branch is up-to-date with the latest changes in the main branch
[ x] Ensured PR tests are passing
[x ] Performed testing and provided evidence (e.g. screenshot of output) for any changes associated to ARG queries
[x ] Updated relevant and associated documentation (e.g. Contribution Guide, Docs etc.)

Hi @lvlindv, please resolve the failed YAML test. Please let us know if you need any help.


Need more elaboration on "constexpr" and "const"

The following code in 2.02.constexpr.cpp:
    int len = 10;
    const int len_2 = len + 1;
    char arr_4[len_2];

seems non-standard, because the const integral type len_2 can be treated as a constant expression only if it is initialized by a constant expression, but len + 1 is not a constant expression, so len_2 is NOT a constant expression (despite the fact that it is a const variable), and thus it cannot be used to specify the size of an array. However, both gcc and clang allow arrays of variable length as an extension, but as for a tutorial for C++11/14/17 standard, this kind of behavior should not be encouraged. Also, if you add -pedantic option to clang, a warning should be generated.
I really love to see more elaborations on the relationship between const and constexpr as it would easily confuse anyone new to modern C++. Personally I like the top answer in this question https://stackoverflow.com/questions/14116003/difference-between-constexpr-and-const, so it would be really nice if you could incorporate some of the content in the tutorial.
By the way, I've learned a lot from your tutorial. So thank you and keep up the good work!

Hi @drelatgithub , I really appreciate you correct my misunderstanding regarding const and constexpr for so long. This is an unforgivable mistake that must be fixed soon, I feel guilty that provides a wrong explanation of them. I will rephrase this section ASAP.


Install webstr in qe-server role

Role qe-server should install webstr module, which will be used by our web test code.

When webstr module is published on PyPI, we could  just add line webstr into requirements.txt file of usmqe-tests. So no need to do any changes there.


Remove np from dtype

First step of #2649. This commit removes np from dtype and uses ctype instead. Also adjust the logic to keep a translation from ctypes to dtype until numpty is fully removed
Update: Sorry for the failing tests. I'm checking now and will update once they are fixed

Way too many lines changed, and this still has numpy!

got it! Will convert to draft and see how to do it better

Closing for now


Add bindings to knative sync functions

Description
Currently, OpenFuncAsync functions can not be triggered by HTTP, we need to add HTTP trigger for Async functions.
The idea is:

Use Knative sync functions to receive an event in plain JSON or CloudEvent format
Send the received event to a dapr output binding or pubsub component, most likely a MQ such as Kafka or NATS
Trigger an async function from the MQ

So we need to add dapr output bindings or pubsub to knative sync functions like below:
  serving:
    runtime: "Knative"
    bindings:
      dapr:
        annotations:
          dapr.io/log-level: "debug"
        components:
          kafka-receiver:
            type: bindings.kafka
            version: v1
            metadata:
              - name: brokers
                value: "kafka-logs-receiver-kafka-brokers:9092"
              - name: authRequired
                value: "false"
              - name: publishTopic
                value: "logs"
              - name: topics
                value: "logs"
              - name: consumerGroup
                value: "logs-handler"
    scaleOptions:
      minReplicaCount: 1
      maxReplicaCount:  10
    knative:
      outputs:
        - name: kafka
          component: kafka-receiver
          type: bindings

Knative with dapr enabled sample can be found here: https://github.com/dapr/samples/tree/master/knative-distributed-calculator

I have made some adjustments to spec.serving.scaleOptions and spec.serving.runtime:
apiVersion: core.openfunction.io/v1beta1
kind: Function
metadata:
  name: function-sample
spec:
  serving:
    scaleOptions:
      min: 1
      max: 10
      keda:
        scaledJob:
          pollingInterval: 30
          maxReplicaCount: 10
        scaledObject:
          pollingInterval: 30
          cooldownPeriod: 30
          minReplicaCount: 0
          maxReplicaCount: 10
      knative:
        autoscaling:
          target: 200
          max-scale: 3
          min-scale: 1
    runtime:
      async:
        inputs:
          - name: kafka
            component: kafka-receiver
            type: bindings
        outputs:
          - name: nats
            component: nats-eventbus
            type: pubsub
      sync:
        outputs:
          - name: kafka
            component: kafka-receiver
            type: bindings
      knative:
        outputs:
          - name: kafka
            component: kafka-receiver
            type: bindings
    bindings:
      annotations:
        dapr.io/log-level: "debug"
      components:
        kafka-receiver:
          type: bindings.kafka
          version: v1
          metadata:
            - name: brokers
              value: "kafka-logs-receiver-kafka-brokers:9092"
            - name: authRequired
              value: "false"
            - name: publishTopic
              value: "logs"
            - name: topics
              value: "logs"
            - name: consumerGroup
              value: "logs-handler"
    pubsub:
      annotations:
        dapr.io/log-level: "debug"
      components:
        kafka-receiver:
          type: pubsub.natsstreaming
          version: v1
          metadata:
            - name: brokers
              value: "nats:8222"
            - name: authRequired
              value: "false"
            - name: publishTopic
              value: "eventbus"
            - name: topics
              value: "eventbus"
            - name: consumerGroup
              value: "eventbus"
    template:
      containers:
        - name: function
          imagePullPolicy: Always


angular 18: registerQuery in layout.mjs crashes in Production bundles.

Bug report

What is the current behavior?
My angular app using angular 18 and angular Material is crashing with the following error in release/prod bundles.
TypeError: Cannot read properties of undefined (reading 'U')
    at T._registerQuery (layout.mjs:168:33)
    at layout.mjs:133:55
    at Array.map (<anonymous>)
    at T.observe (layout.mjs:133:37)
    at new xe (a11y.mjs:2333:14)
    at Object.xe_Factory [as factory] (ɵfac.js:1:1)
    at Cr.hydrate (core.mjs:3239:43)
    at Cr.get (core.mjs:3096:33)
    at qn (core.mjs:1107:40)
    at Zn (core.mjs:1113:42)

If the current behavior is a bug, please provide the steps to reproduce.
I tried reproducing this behavior by creating a test app but I couldn't get it to happen, however I discovered the following facts along the way
It occurs because my app is using angular material
My app uses only one component of material called popups
Material has some built-in high contrast mode detector which is causing this
The real underlying issue is that _webpack_require is unable to fulfill 'map' of rxjs/operators which is required by HighContrastModeDetector.ts
This is not happening for development bundles.
What is the expected behavior?
The 'map' symbol must not be missing and must be fulfilled by _webpack_require()
Other relevant information:
webpack version:  5.1.4
Node.js version: 20.x
Operating System: Windows 10
Additional tools: terser 5.3.10, zone 0.14.7, typescript 5.4.2, ts-loader 9.5.1

I solved this issue by setting sideEffects property of optimization object to false in the config.


Middleware fields, or really any pre-defined field, should be overridable.

I was looking at using the middlware portion of the SDK to log and capture incoming HTTP requests. Unfortunately, it doesn't allow you to override the fields use in the logger. While this isn't a huge deal, it does make the logs harder to work with and search on in a system that already has pre-defined criteria. With DataDog as an example if I provide log messages with these attributes they'll automatically be searchable:
https://docs.datadoghq.com/logs/log_configuration/attributes_naming_convention/#http-requests
In the best case, you have a callable function you can use to overwrite or extend the logging message based on the incoming request. At a minimum though just being able to override the existing fields would be fine.

This is something I noticed: that DD is using different field names than we are here. I feel like it may make sense to switch to using those as the default:
ReqDuration   = "request_duration"
ReqPath       = "http_path"
ReqMethod     = "http_method"
ReqRemoteAddr = "http_remote_addr"

However, I can't see an obvious to replace them without making the options and interface for setting up the middleware cludgey and too numerous with a system better than what we currently have:
// This in effect changes what field name we use for request method, eg:
mware := middleware.NewHTTPRequestMiddleware(logger, log.INFO, &middleware.HTTPRequestLogAttributes{
    SkipMethod: true,
    Synthetics: map[string]func(*http.Request) string{
        "X-Alt-Method-Header": func(r *http.Request) string { return r.Method },
    },
})

In fact, using the "Skip..." boolean fields, as opposed to like Skip []string // List of field names. was meant to make it more obvious and simpler to do such a thing.

OK, going to come up with a whole new Middleware configuration interface for v2, and will handle this accordingly.


Allow overriding browser entry points ignore list

Description
Summary: We would like to be able to override the following list: https://github.com/brillout/vite-plugin-ssr/blob/547e5ebaf76b2300457ec85bd027956abba31ef4/vite-plugin-ssr/node/plugin/build.ts#L85
Background:
We are developing an abstraction layer over vite/vite-plugin-ssr. Users can install our npm module, which handles the rendering for certain use cases with minimal code from the user's side. The entry point to the project and build options are also handled by the npm module. This means in the user's project, the entry point will be in node_modules, in our module. The line of code linked above is causing us issues as it ignores files in node_modules.
We would like to be able to provide a config option to vite-plugin-ssr to override this ignore list with custom entries.
Implementation:
I wanted to implement this and make a PR but wasn't sure which approach to take from a design perspective, as the plugin as it is now doesn't take any options that I could look at for guidance.

developing an abstraction layer

Which is?
Vps 0.4 is designed to enable folks to build on top of vps.

developing an abstraction layer

Which is?

We are developing a tool to create websites with minimal user code. The tool (npm dependency) uses vite and vite-plugin-ssr internally. I use "abstraction" because the tool handles all the vite config, as well as the vite entry point.

Vps 0.4 is designed to enable folks to build on top of vps.

I was not aware. What is the recommended way to get started with this (do I need to build the docs myself?), and do you have a planned release date?
Thanks!

Your use case is the kind of thing vps 0.4 is aiming at enabling.
There are already a couple of folks using it in production and building frameworks with it. So release is coming soon.
Is your current workaround satisfactory or do you need something sooner rather than later?

We're currently shipping a git patch file and applying it on npm install. For internal demos it's fine but we will need a more permanent fix within the next 6 weeks or so. Sounds like this would align with the release timeline for 0.4?

I do not mean to impose a deadline (implied or otherwise) - sorry.
We're looking forward to the 0.4 release. Until then our patch will be fine. If we end up forking and making changes that we think would benefit general public interest, we would obviously create a PR.
Thank you for your help and for your work!

I guess we close this.
Let me know if you have questions.

Btw. feel free to elaborate your use case if you believe that you may need something from vps beyond it's current state.
Being able to build frameworks on top of vps is going to be my primary focus in the next coming weeks.

@brillout thanks. Is there already a document where you describe the features for framework development you plan to add to 0.4?

Not yet but there will be. There will be a recommended way to develop, build, and release frameworks to npm.

I can try to summarize some of the issues we've run into so far while developing our framework if that would be helpful for your development.

I'm probably already aware of most of them. But what would help are high-level feature requests.

@hannessolo Any news on your side? I'm seeing that https://github.com/adobe-rnd/franklin doesn't exist anymore. Feel free to let me know if you have any questions.

An update: Thank you for the release of 0.4, we've updated our code to make use of the new features. We had to make our repo private for now, but are still using VPS.

👍 Let me know if you need something. I'm keen on supporting use cases like yours. Note that, as the author of vps, I may find solutions to problems you think are not solvable; so feel free to keep me updated.

No offense taken :-).
I'm looking forward to enabling easy framework building :-).
On Mon, Jun 13, 2022 at 5:38 PM Hannes Hertach @.***>
wrote:

I do not mean to impose a deadline (implied or otherwise) - sorry.
We're looking forward to the 0.4 release. Until then our patch will be
fine. If we end up forking and making changes that we think would benefit
general public interest, we would obviously create a PR.
Thank you for your help and for your work!
—
Reply to this email directly, view it on GitHub
https://github.com/brillout/vite-plugin-ssr/issues/339#issuecomment-1154077909,
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Versification file for 'base' scheme?

I suspect we need a versification file that describes the full scheme from which other versification schemes are derived.  We probably need documentation for that as well, including some justification for the pragmatically derived choices that were made.

@jonathanrobie I should be able to generate the JSON expression of org, lxx etc fairly easily. Obviously we can't include the documention within that JSON because, well, it's JSON.

@jonathanrobie I generated and added JSON representations of the 7 .vrs files in my PT8Projects directory. Fun fact of the day is that there's verse mapping in org.vrs which is a strange definition of "original". Compounding fun fact is that you won't find out why by looking at the JSON, because there's no way to preserve the comments in the .vrs in JSON.


#26 single order route

Assignee Tasks

[ ] added unit tests (or none needed)
[ ] written relevant docs (or none needed)
[ ] referenced any relevant issues (or none exist)

Guidelines
Please add a description of this Pull Request's motivation, scope, outstanding issues or potential alternatives, reasoning behind the current solution, and any other relevant information for posterity.

Your PR Notes Here

Hey, once Cassandra's admin-checking branch gets pulled in, I think you can change your routes to use her code. So they don't need to be commented out anymore.
Let's wait for her to be done with that and for it to be pulled


ecrecover implementation

I'm trying to implement ecrecover to get the public key from a signed message: https://github.com/ethereum/wiki/wiki/JavaScript-API#web3ethsign
I know you mentioned implementing libsecp256k1 support here https://github.com/se3000/ruby-eth/issues/1
Looks like ecrecover isn't possible right now with your current implementation since the signature in your recover_compact method is not the same as the signature generated by web3.  Let me know if this is incorrect.  Great work on the gem btw.

Ecrecover is supported; you wouldn't be able to determine the transaction signer/sender if it wasn't.
The signature is different because OpenSSL generates it instead of libsecp. Specifically, OpenSSL uses a nondeterministic K value. This doesn't make it invalid, just that each time you run the signing function you get a different output. It is still a recoverable signature.

You can see the signature being recovered in ruby here.
Is there anything else you're looking for with this issue?

Just to clarify, it sounds like because go-ethereum and web3 use libsecp to generate signatures, it's not possible to use this gem (which uses openssl instead) to recover keys from a message signed on the front-end of a web application using web3?
jbgh, assuming this is accurate, did you find any alternative approaches to recovering keys generated by libsecp in ruby?

That is not accurate. You can still recover the public key, the k value has nothing to do with recoverability, just in how the signature is generated. You should be able to recover the signature. If you can post some examples, I can try to point you in the right direction.

@jbgh I may have misunderstood your comment last time. You should be able to recover a signature generated by libsecp. Right now the gem does not support generating signatures with libsecp, but it can recover signatures generated by libsecp. Similarly, libsecp can recover signatures generated by this gem.

In rails I'm using
yarn install  eth-sig-util 'ethereumjs-util
bundle install schmooze
require 'schmooze'

class BabelSchmoozer < Schmooze::Base
  dependencies ethUtil: 'ethereumjs-util'
  dependencies sigUtil: 'eth-sig-util'

  method :bufferToHex, 'ethUtil.bufferToHex'
  method :recoverPersonalSignature, 'sigUtil.recoverPersonalSignature'
end

 it 'matches public address' do
    babel = BabelSchmoozer.new(__dir__)
    msgBufferHex = babel.bufferToHex('Hello')

    address = babel.recoverPersonalSignature({data: msgBufferHex, sig: signature})
    expect(address.downcase).to eq(auth_service.eth_address.downcase)
  end


OpenGL.CoreUI does not exist .NET Core 2.0

When trying using OpenGL.CoreUI on .NET Core 2.0 after installing the nuget package I get the error saying something along the lines of namespace CoreUI does not exist. Help would be much appreciated (I'm using Ubuntu 17.10).

The CoreUI namespace is not included in nuget package (it will be in a separated package, anyway).
Moreover, the CoreUI currently support only Windows (altought it should be straightforward to support Linux too).

How do you create a window then without the NativeWindow class? (I'm new to OpenGL programming)

The NativeWindow can be created with the factory pattern, just extend NativeWindow with a class NativeWindowX11 (or NativeWindowGLX), which implements windowing using XLib/X11 methods.
You can start from scratch using an authoritative sample, just translating calls using P/Invoke (Glx has the necessary definitions, they could made public). A faster method could be copy and replicate the internal implementation of the native window.
At the moment in CoreUI there is only the strictly necessary for running .NET Core 1/2 samples on Windows, and I don't require anymore; your can PR the Linux implementation, if you wish; however, be warned that recently I've noticed instabilities when using my GLX implementation.


No Error but no Message received and no Id is found?

Hello im using the project in a C# Console Test (.NET 7.0), i have configurated the app in the developer tool and Access Token but when i send a message to my number the message isn't receied and also the BusinessId is not found from the NumberId ?
Can you help me to find the error? There is not Exception.

Hi, the get business profile just returns the information about the business since it does not return businessid and phone number id. The business id is only available when you go live with a production phone number. To send a message you will only need phone number id and whatsapp business account id which you can find them from the developer portal.
If you are testing with the test phone number from meta you need to send a sample template message from its portal and reply with any text then you can send a message to your phone number.


The problem with using Yolov8 as a detector.(TensorFlow)

Hi, I'm trying to connect Yolov8 as a detector, obviously there was a mistake - Too many users have recently viewed or downloaded this file. I managed to download it from the link and put it in /.deepface/weights/yolov8n-face.pt. But after the launch, an error related to TensorFlow comes out.
Problem:2023-12-26 23:25:45.753053: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: SSE SSE2 SSE3 SSE4.1 SSE4.2 AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
OMP: Error #15: Initializing libiomp5md.dll, but found libiomp5md.dll already initialized.
OMP: Hint This means that multiple copies of the OpenMP runtime have been linked into the program. That is dangerous, since it can degrade performance or cause incorrect results. The best thing to do is to ensure that only a single OpenMP runtime is linked into the process, e.g. by avoiding static linking of the OpenMP runtime in any library. As an unsafe, unsupported, undocumented workaround you can set the environment variable KMP_DUPLICATE_LIB_OK=TRUE to allow the program to continue to execute, but that may cause crashes or silently produce incorrect results. For more information, please see http://www.intel.com/software/products/support/.

this is an external dependency. you may consider to raise this issue in the original repo - https://github.com/derronqi/yolov8-face


clarify onProgress documentation

The docs for progressUpdateInterval say that the value is in milliseconds, but the docs for onProgress said that the value was in seconds. This fixes the latter documentation to be consistent with the former.
This is a documentation-only update with no code changes.

Hi @mlc is this issue is open can I please take it


Custom ctx error

I have made a sharded bot and I have made custom dpy context class Context(commands.Context): but this thing takes overrides created in this module and then so I can't use await ctx.send("hi!.", view=view) plz fix it

Views are part of discord.py 2.x.
We aren't supporting them. If you want to use views, use discord.py


Create a Build Framework

I will take this task up and would be using maven. My changes and progress is on branch: asteriod_mining


SPARK SQL does not work

Describe the bug
in AWS EMR SPARK SQL does not work. The error thrown is  cannot run on GPU because no GPU enabled version of operator class org.apache.spark.sql.execution.datasources.v2.SetCatalogAndNamespaceExec could be found
Steps/Code to reproduce bug
NOTE that the https://github.com/apache/spark/blob/master/examples/src/main/scripts/getGpusResources.sh was downloaded into all the nodes as a part of bootstrap actions.
from pyspark.sql import SparkSession
from pyspark import SparkConf
conf = SparkConf().setAppName("MortgageETL")
conf.set("spark.jars", "s3://gourav-bucket/gourav/gpu/cudf-0.9.2.jar,s3://gourav-bucket/gourav/gpu/rapids-4-spark_2.12-0.1.0.jar,s3://gourav-bucket/gourav/gpu/cudf-0.14-cuda10-1.jar")
conf.set('spark.rapids.sql.explain', 'ALL')
conf.set("spark.executor.instances", "20")
conf.set("spark.executor.cores", "2")
conf.set("spark.task.cpus", "1")
conf.set("spark.rapids.sql.concurrentGpuTasks", "1")
conf.set("spark.executor.memory", "4g")
conf.set("spark.rapids.memory.pinnedPool.size", "1G")
conf.set("spark.executor.memoryOverhead", "2G")
conf.set("spark.executor.extraJavaOptions", "-Dai.rapids.cudf.prefer-pinned=true")
conf.set("spark.locality.wait", "0s")
conf.set("spark.sql.files.maxPartitionBytes", "512m")
conf.set("spark.executor.resource.gpu.amount", "1")
conf.set("spark.task.resource.gpu.amount", "0.25")
conf.set("spark.plugins", "com.nvidia.spark.SQLPlugin")
conf.set("spark.rapids.sql.hasNans", "false")
conf.set('spark.rapids.sql.batchSizeBytes', '512M')
conf.set('spark.rapids.sql.reader.batchSizeBytes', '768M')
conf.set('spark.rapids.sql.variableFloatAgg.enabled', 'true')
conf.set("spark.plugins", "com.nvidia.spark.SQLPlugin")
conf.set("spark.sql.adaptive.enabled", False)
conf.set("spark.executor.resource.gpu.discoveryScript","/mnt/mapred/getGpusResources.sh")
spark = SparkSession.builder.enableHiveSupport()
.config(conf=conf)
.master("yarn").getOrCreate()
Expected behavior
The code should work
Environment details (please complete the following information)

Environment location: YARN, Cloud(AWS EMR 6.1.0)
Spark configuration settings related to the issue

Additional context
the code does not work with and without hive support enabled

Is AWS EMR 6.1.0 using Apache Spark 3.0? If not, Spark SQL on GPU wont work

Is AWS EMR 6.1.0 using Apache Spark 3.0? If not, Spark SQL on GPU wont work

Yes it is indeed

SetCatalogAndNamespaceExec is a metadata operation that we will never put on the GPU.  We have plans to stop reporting it as a warning (#499).  You can ignore it for now.  Are there other warnings that you are seeing?

SetCatalogAndNamespaceExec is a metadata operation that we will never put on the GPU. We have plans to stop reporting it as a warning (#499). You can ignore it for now. Are there other warnings that you are seeing?

The cluster does not even start without hive enabled. the containers do not get allocated at all. The following is from the YARN logs:
20/08/28 18:03:06 ERROR RapidsExecutorPlugin: Exception in the executor plugin java.lang.NoSuchMethodError: ai.rapids.cudf.Cuda.setDevice(I)V 	at com.nvidia.spark.rapids.GpuDeviceManager$.setGpuDeviceAndAcquire(GpuDeviceManager.scala:90) 	at com.nvidia.spark.rapids.GpuDeviceManager$.$anonfun$initializeGpu$1(GpuDeviceManager.scala:117) 	at scala.runtime.java8.JFunction1$mcII$sp.apply(JFunction1$mcII$sp.java:23) 	at scala.Option.map(Option.scala:230) 	at com.nvidia.spark.rapids.GpuDeviceManager$.initializeGpu(GpuDeviceManager.scala:117) 	at com.nvidia.spark.rapids.GpuDeviceManager$.initializeGpuAndMemory(GpuDeviceManager.scala:125) 	at com.nvidia.spark.rapids.RapidsExecutorPlugin.init(Plugin.scala:230) 	at org.apache.spark.internal.plugin.ExecutorPluginContainer.$anonfun$executorPlugins$1(PluginContainer.scala:111) 	at scala.collection.TraversableLike.$anonfun$flatMap$1(TraversableLike.scala:245) 	at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62) 	at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55) 	at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49) 	at scala.collection.TraversableLike.flatMap(TraversableLike.scala:245) 	at scala.collection.TraversableLike.flatMap$(TraversableLike.scala:242) 	at scala.collection.AbstractTraversable.flatMap(Traversable.scala:108) 	at org.apache.spark.internal.plugin.ExecutorPluginContainer.<init>(PluginContainer.scala:99) 	at org.apache.spark.internal.plugin.PluginContainer$.apply(PluginContainer.scala:164) 	at org.apache.spark.internal.plugin.PluginContainer$.apply(PluginContainer.scala:152) 	at org.apache.spark.executor.Executor.$anonfun$plugins$1(Executor.scala:158) 	at org.apache.spark.util.Utils$.withContextClassLoader(Utils.scala:221) 	at org.apache.spark.executor.Executor.<init>(Executor.scala:158) 	at org.apache.spark.executor.CoarseGrainedExecutorBackend$$anonfun$receive$1.applyOrElse(CoarseGrainedExecutorBackend.scala:168) 	at org.apache.spark.rpc.netty.Inbox.$anonfun$process$1(Inbox.scala:115) 	at org.apache.spark.rpc.netty.Inbox.safelyCall(Inbox.scala:203) 	at org.apache.spark.rpc.netty.Inbox.process(Inbox.scala:100) 	at org.apache.spark.rpc.netty.MessageLoop.org$apache$spark$rpc$netty$MessageLoop$$receiveLoop(MessageLoop.scala:75) 	at org.apache.spark.rpc.netty.MessageLoop$$anon$1.run(MessageLoop.scala:41) 	at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511) 	at java.util.concurrent.FutureTask.run(FutureTask.java:266) 	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) 	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) 	at java.lang.Thread.run(Thread.java:748)

There is something odd happening.  The API that is throwing the NoSuchMethodError has been in the cudf jar since 0.12. So unless you have a a 0.9.x release of the cudf jar in your classpath you should not get that error.

The first thing in your list of jars is indeed cudf-0.9.2 which explains the issue.
conf.set("spark.jars", "s3://gourav-bucket/gourav/gpu/cudf-0.9.2.jar,s3://gourav-bucket/gourav/gpu/rapids-4-spark_2.12-0.1.0.jar,s3://gourav-bucket/gourav/gpu/cudf-0.14-cuda10-1.jar")

The first thing in your list of jars is indeed cudf-0.9.2 which explains the issue.
conf.set("spark.jars", "s3://gourav-bucket/gourav/gpu/cudf-0.9.2.jar,s3://gourav-bucket/gourav/gpu/rapids-4-spark_2.12-0.1.0.jar,s3://gourav-bucket/gourav/gpu/cudf-0.14-cuda10-1.jar")

Yeah tried it using the below, still the same issue
conf.set("spark.jars", "s3://gourav-bucket/gourav/gpu/cudf-0.14-cuda10-1.jar,s3://gourav-bucket/gourav/gpu/rapids-4-spark_2.12-0.1.0.jar")
the issue reported now is:
`
20/08/28 20:25:21 WARN ResourceRequestHelper: YARN doesn't know about resource yarn.io/gpu, your resource discovery has to handle properly discovering and isolating the resource! Error: The resource manager encountered a problem that should not occur under normal circumstances. Please report this error to the Hadoop community by opening a JIRA ticket at http://issues.apache.org/jira and including the following information:

Resource type requested: yarn.io/gpu
`

That implies the YARN cluster has not been configured to schedule for GPUs.  Please check the YARN configuration files and verify it is configured to support GPU scheduling.  https://hadoop.apache.org/docs/r3.2.1/hadoop-yarn/hadoop-yarn-site/UsingGpus.html

https://hadoop.apache.org/docs/r3.2.1/hadoop-yarn/hadoop-yarn-site/UsingGpus.html

done that now I am getting the error "Could not load cudf jni library.." this has been mentioned in https://github.com/NVIDIA/spark-rapids/issues/149 which is referring to cudf-0.15 but I cannot see it here: https://repo1.maven.org/maven2/ai/rapids/cudf/

If this message is occurring only on the driver node then it should be a benign message.  The driver does not require a GPU or the cudf code to load in order to function.

which is referring to cudf-0.15 but I cannot see it here: https://repo1.maven.org/maven2/ai/rapids/cudf/

cudf-0.15 has not yet released.  Once it has the jar will be posted there.  Note that cudf-0.15 is likely not compatible with version 0.1.0 of the plugin jar, so you should stick with cudf-0.14 as long as you are using plugin version 0.1.0.

If this message is occurring only on the driver node then it should be a benign message. The driver does not require a GPU or the cudf code to load in order to function.

which is referring to cudf-0.15 but I cannot see it here: https://repo1.maven.org/maven2/ai/rapids/cudf/

cudf-0.15 has not yet released. Once it has the jar will be posted there. Note that cudf-0.15 is likely not compatible with version 0.1.0 of the plugin jar, so you should stick with cudf-0.14 as long as you are using plugin version 0.1.0.

the current error:
20/08/28 20:46:43 ERROR NativeDepsLoader: Could not load cudf jni library... java.lang.UnsatisfiedLinkError: /mnt3/yarn/usercache/hadoop/appcache/application_1598647482677_0001/container_1598647482677_0001_01_000005/tmp/rmm3831790456313469993.so: libcudart.so.10.1: cannot open shared object file: No such file or directory 	at java.lang.ClassLoader$NativeLibrary.load(Native Method) 	at java.lang.ClassLoader.loadLibrary0(ClassLoader.java:1934) 	at java.lang.ClassLoader.loadLibrary(ClassLoader.java:1817) 	at java.lang.Runtime.load0(Runtime.java:809) 	at java.lang.System.load(System.java:1088) 	at ai.rapids.cudf.NativeDepsLoader.loadDep(NativeDepsLoader.java:81) 	at ai.rapids.cudf.NativeDepsLoader.loadNativeDeps(NativeDepsLoader.java:49) 	at ai.rapids.cudf.Cuda.<clinit>(Cuda.java:28) 	at com.nvidia.spark.rapids.GpuDeviceManager$.setGpuDeviceAndAcquire(GpuDeviceManager.scala:90) 	at com.nvidia.spark.rapids.GpuDeviceManager$.$anonfun$initializeGpu$1(GpuDeviceManager.scala:117) 	at scala.runtime.java8.JFunction1$mcII$sp.apply(JFunction1$mcII$sp.java:23) 	at scala.Option.map(Option.scala:230) 	at com.nvidia.spark.rapids.GpuDeviceManager$.initializeGpu(GpuDeviceManager.scala:117) 	at com.nvidia.spark.rapids.GpuDeviceManager$.initializeGpuAndMemory(GpuDeviceManager.scala:125) 	at com.nvidia.spark.rapids.RapidsExecutorPlugin.init(Plugin.scala:230) 	at org.apache.spark.internal.plugin.ExecutorPluginContainer.$anonfun$executorPlugins$1(PluginContainer.scala:111) 	at scala.collection.TraversableLike.$anonfun$flatMap$1(TraversableLike.scala:245) 	at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62) 	at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55) 	at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49) 	at scala.collection.TraversableLike.flatMap(TraversableLike.scala:245) 	at scala.collection.TraversableLike.flatMap$(TraversableLike.scala:242) 	at scala.collection.AbstractTraversable.flatMap(Traversable.scala:108) 	at org.apache.spark.internal.plugin.ExecutorPluginContainer.<init>(PluginContainer.scala:99) 	at org.apache.spark.internal.plugin.PluginContainer$.apply(PluginContainer.scala:164) 	at org.apache.spark.internal.plugin.PluginContainer$.apply(PluginContainer.scala:152) 	at org.apache.spark.executor.Executor.$anonfun$plugins$1(Executor.scala:158) 	at org.apache.spark.util.Utils$.withContextClassLoader(Utils.scala:221) 	at org.apache.spark.executor.Executor.<init>(Executor.scala:158) 	at org.apache.spark.executor.CoarseGrainedExecutorBackend$$anonfun$receive$1.applyOrElse(CoarseGrainedExecutorBackend.scala:168) 	at org.apache.spark.rpc.netty.Inbox.$anonfun$process$1(Inbox.scala:115) 	at org.apache.spark.rpc.netty.Inbox.safelyCall(Inbox.scala:203) 	at org.apache.spark.rpc.netty.Inbox.process(Inbox.scala:100) 	at org.apache.spark.rpc.netty.MessageLoop.org$apache$spark$rpc$netty$MessageLoop$$receiveLoop(MessageLoop.scala:75) 	at org.apache.spark.rpc.netty.MessageLoop$$anon$1.run(MessageLoop.scala:41) 	at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511) 	at java.util.concurrent.FutureTask.run(FutureTask.java:266) 	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) 	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) 	at java.lang.Thread.run(Thread.java:748) 20/08/28 20:46:43 ERROR RapidsExecutorPlugin: Exception in the executor plugin java.lang.UnsatisfiedLinkError: ai.rapids.cudf.Cuda.setDevice(I)V 	at ai.rapids.cudf.Cuda.setDevice(Native Method) 	at com.nvidia.spark.rapids.GpuDeviceManager$.setGpuDeviceAndAcquire(GpuDeviceManager.scala:90) 	at com.nvidia.spark.rapids.GpuDeviceManager$.$anonfun$initializeGpu$1(GpuDeviceManager.scala:117) 	at scala.runtime.java8.JFunction1$mcII$sp.apply(JFunction1$mcII$sp.java:23) 	at scala.Option.map(Option.scala:230) 	at com.nvidia.spark.rapids.GpuDeviceManager$.initializeGpu(GpuDeviceManager.scala:117) 	at com.nvidia.spark.rapids.GpuDeviceManager$.initializeGpuAndMemory(GpuDeviceManager.scala:125) 	at com.nvidia.spark.rapids.RapidsExecutorPlugin.init(Plugin.scala:230) 	at org.apache.spark.internal.plugin.ExecutorPluginContainer.$anonfun$executorPlugins$1(PluginContainer.scala:111) 	at scala.collection.TraversableLike.$anonfun$flatMap$1(TraversableLike.scala:245) 	at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62) 	at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55) 	at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49) 	at scala.collection.TraversableLike.flatMap(TraversableLike.scala:245) 	at scala.collection.TraversableLike.flatMap$(TraversableLike.scala:242) 	at scala.collection.AbstractTraversable.flatMap(Traversable.scala:108) 	at org.apache.spark.internal.plugin.ExecutorPluginContainer.<init>(PluginContainer.scala:99) 	at org.apache.spark.internal.plugin.PluginContainer$.apply(PluginContainer.scala:164) 	at org.apache.spark.internal.plugin.PluginContainer$.apply(PluginContainer.scala:152) 	at org.apache.spark.executor.Executor.$anonfun$plugins$1(Executor.scala:158) 	at org.apache.spark.util.Utils$.withContextClassLoader(Utils.scala:221) 	at org.apache.spark.executor.Executor.<init>(Executor.scala:158) 	at org.apache.spark.executor.CoarseGrainedExecutorBackend$$anonfun$receive$1.applyOrElse(CoarseGrainedExecutorBackend.scala:168) 	at org.apache.spark.rpc.netty.Inbox.$anonfun$process$1(Inbox.scala:115) 	at org.apache.spark.rpc.netty.Inbox.safelyCall(Inbox.scala:203) 	at org.apache.spark.rpc.netty.Inbox.process(Inbox.scala:100) 	at org.apache.spark.rpc.netty.MessageLoop.org$apache$spark$rpc$netty$MessageLoop$$receiveLoop(MessageLoop.scala:75) 	at org.apache.spark.rpc.netty.MessageLoop$$anon$1.run(MessageLoop.scala:41) 	at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511) 	at java.util.concurrent.FutureTask.run(FutureTask.java:266) 	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) 	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) 	at java.lang.Thread.run(Thread.java:748)

libcudart.so.10.1: cannot open shared object file: No such file or directory

This is the relevant portion of the error.  The cudf jar is built for a specific CUDA runtime, 10.1 in this case.  There is a version built for the CUDA 10.2 runtime at https://repo1.maven.org/maven2/ai/rapids/cudf/0.14/cudf-0.14-cuda10-2.jar.  Typically the CUDA runtimes are installed under /usr/local/cuda, see which version you have.  The nvidia-smi will show you which version of the driver you have installed (which is NOT necessarily the same as the CUDA runtime version!) but the driver version will at least tell you whether it's capable of supporting either the CUDA 10.1 or CUDA 10.2 runtimes, one of which is required to run the RAPIDS Accelerator plugin.  See https://docs.nvidia.com/deploy/cuda-compatibility/index.html for a description of the CUDA environment and how the driver and runtime versions interact.  That page also has a compatibility table that shows the minimum driver versions required to support the various CUDA runtimes.  If you have a recent enough driver version for CUDA 10.1 or CUDA 10.2 you should be able to install the corresponding CUDA runtime package if it's missing from your system.

your team is absolutely brilliant, I am infact surprised by your cooperation and support.
We have the following file in EMR:
0 lrwxrwxrwx 1 root root 16 Aug 28 20:11 /usr/local/cuda/lib64/libcudart.so -> libcudart.so.9.2
Can this person, who is causing all this confusion having not done proper diligence, please be asked to update this article: https://aws.amazon.com/blogs/big-data/improving-rapids-xgboost-performance-and-reducing-costs-with-amazon-emr-running-amazon-ec2-g4-instances/?nc1=b_rp?
The article is clearly misleading, people who are using something like xgboost will try running SQL first to prepare their data. And this article is just frustratingly incomplete.

I am infact surprised by your cooperation and support.

Just trying to help, glad you are trying out the software and are willing to work through issues!
I took a quick look at the article, and it appears to be not using the RAPIDS Accelerator for Apache Spark (this project) but rather a custom solution for xgboost that was built earlier.  The notebook referred to in that article uses an old GpuDatasetReader class, and it was based on cudf-0.9.2 which would work with the CUDA 9.2 runtime.  I suspect it works if you follow the exact directions in the article, but it won't be using the RAPIDS Accelerator.
It appears the updated getting started guide is now at https://github.com/NVIDIA/spark-xgboost-examples/blob/spark-3/getting-started-guides/csp/aws/ec2.md which shows running with the RAPIDS Accelerator plugin and xgboost, but it does so under EC2 rather than EMR, running Spark in standalone mode rather than with Spark-on-YARN.
There probably needs to be a getting started guide for EMR for those that would rather work in that environment.  Would you be willing to file an issue in the https://github.com/NVIDIA/spark-xgboost-examples repo requesting an AWS EMR getting started guide?

Hi @jlowe  I am currently working on it, is there a way I could contribute and write the notebook and check it in?

is there a way I could contribute and write the notebook and check it in?

You can definitely write up a notebook and submit a pull request against https://github.com/NVIDIA/spark-xgboost-examples, that would be great!  I can't guarantee it would be accepted verbatim or at all, as it's up to the committers in that repository to review and ultimately decide to accept the contribution.  However in general contributions of all types (issues reported, features requested, pull requests posted, etc.) are welcome!
I would recommend filing the issue first and post a followup comment to the issue stating you are interested in working on it and plan on posting a pull request.  Then you can fork the repo in Github, put your notebook changes on a branch off of the spark-3 branch in your fork, then post the pull request after you push your changes to that branch of your fork.
I'm going to close this issue since it's a documentation issue in the spark-xgboost-examples repo rather than a bug in the RAPIDS Accelerator plugin.


Fix to TensorFlow-Slim README

Same TF Slim README fix as earlier today.
This is a resubmit with my email added to git config to pass your CLA check.

Can one of the admins verify this patch?

I'm not sure why @googlebot can't verify my CLA (I am both the commit author and sender of the pull request). I set user.email in my git config before resubmitting this pull request.
Please let me know if I need to update a config elsewhere.

I think you have to say something like "I signed it!". It's somewhere in the instructions.

Thanks @drpngx , I'll try that and see if it resolves the issue.

I signed it!

OK, I'm able to see the CLA with your gmail address. Not clear why the bot is confused.

Jenkins, test this please.

Ignoring unrelated failure.
==================== Test output for //bazel_pip/tensorflow/python/kernel_tests:sparse_reshape_op_test:
2017-06-28 18:29:35.102138: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2017-06-28 18:29:35.102177: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-06-28 18:29:35.102184: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2017-06-28 18:29:35.102187: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2017-06-28 18:29:35.102190: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
F..................
======================================================================
FAIL: testFeedDenseReshapeSemantics (__main__.SparseReshapeTest)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/var/lib/jenkins/workspace/tensorflow-pull-requests-cpu-python3/bazel-ci_build-cache/.cache/bazel/_bazel_jenkins/eab0d61a99b6696edb3d2aff87b585e8/execroot/workspace/bazel-out/local-py3-opt/bin/bazel_pip/tensorflow/python/kernel_tests/sparse_reshape_op_test.runfiles/org_tensorflow/bazel_pip/tensorflow/python/kernel_tests/sparse_reshape_op_test.py", line 277, in testFeedDenseReshapeSemantics
    self.assertAllEqual(output_val.indices, new_indices)
  File "/workspace/pip_test/venv/lib/python3.4/site-packages/tensorflow/python/framework/test_util.py", line 725, in assertAllEqual
    np.testing.assert_array_equal(a, b)
  File "/workspace/pip_test/venv/lib/python3.4/site-packages/numpy/testing/utils.py", line 854, in assert_array_equal
    verbose=verbose, header='Arrays are not equal')
  File "/workspace/pip_test/venv/lib/python3.4/site-packages/numpy/testing/utils.py", line 778, in assert_array_compare
    raise AssertionError(msg)
AssertionError:
Arrays are not equal

(mismatch 66.95152019231006%)
 x: array([[  0,   0,   0,   0],
       [  0,   0,   0,   0],
       [  0,   0,   0,   0],...
 y: array([[  0,   0,   0,   2],
       [  0,   0,   0,   3],
       [  0,   0,   0,   9],...

----------------------------------------------------------------------
Ran 19 tests in 0.774s

FAILED (failures=1)
not equal where =  (array([     0,      1,      2, ..., 405178, 405178, 405178]), array([3, 3, 3, ..., 1, 2, 3]))
not equal lhs =  [  0   0   0 ...,   7  47 413]
not equal rhs =  [  2   3   9 ...,   9  59 448]
================================================================================


Search form

Currently, the search form only search in the current directory. Is this possible that the search form can search inside subdirectory of the current directoy ?
How to do this ?

Hi @Tilkeii nope.. The idea is to find a file in the current folder.
Actually we not prioritize that functionality
If you want to add finding-recursive module you should write it in frontend and backend.


Changes to Modbus and sam0 in order to fix issue 38264.

…ow the de/re pin to be toggled correctly after all data has been transmitted. Bug was that the toggle was happening after the buffer was empty this lead to early toggle of the pin(s)
Signed-off-by: Gustav Karlsson<EMAIL_ADDRESS>

I am closing this as it appears there is a better solution.


Add some benchmarks

At least to track any performance regression introduced during development

I can create one using benchmark.js, I'm the micro optimization freak (*hint hint https://github.com/pocesar/js-bettercurry *)

Sounds good :)

Implemented in 0.10


sling.com header

develop the header for sling.com pages
desktop

mobile

Most was done in PR #88 , needs some style/font adjustments which I can do with this ticket.
Addition of the zip code finder can be done via another ticket.


The cards in the "About Us Section"

The cards in the about us section has the icons overlapped on the images. It will be better if the social media icons can be a little lower

Yes,I have read the code of conduct.
I would like to work on this issue.

Hey @Suvojit-roy Thank you for taking time to report this issue. This issue has already been worked on via https://github.com/HITK-TECH-Community/Community-Website/issues/490 So, I am closing this one, Please feel free to claim any  unassigned issue to work on
Happy Learning :)


Update Docs For CalcBEDROC

Reference Issue

What does this implement/fix? Explain your changes.
Update documentation on Scoring#CalcBEDROC
Any other comments?
I'm not sure if I formatted it properly.  Is there a CONTRIBUTING.md which goes through code format, docstring format or linting?

Also side note way out of scope.  The sklearn metrics API is pretty clean.
method_name(y_true, y_pred, **kwargs)

Thanks for this.
We don't (yet) have a CONTRIBUTING.md... would be great, but we just haven't gotten there yet.


sensor.mixergy_electric_heat_energy unexpected device class

I added sensor.mixergy_electric_heat_energy to Individual devices on a day the mixergy had used electricity itself.. It showed fine in the graph.
After midnight, and before any electricity had been used, it was the showing as:

I'm not using it for anything specifically (other than in this case), mostly just keeping track/for awareness of energy usage of a few high energy usage devices.
So yeah, mostly a case of it shouldn't probably happen :)

It seems (checking again, just before midnight) it's.. still showing like that.
It's showing energy used correctly on "Monitor individual devices" though for the Mixergy...

I'm going to close this ticket. Some changes were made this entity a few versions ago and I haven't had anyone else report the issue.
If you're still experiencing a problem, let me know!


#57 fixed

done with "create a simple stopwatch"

@Xlgd its working fine.
Add gui snapshot to readme file


Very long and redundant file-path

Describe the bug
When uploading a file with the standard configuration (you provide) the resulting file location in the configured bucket is very long: Every image is located in its own subdirectory and the file name includes the filename + the filehash, whereas the filehash would be just fine.
Example (See the image below):
An image DSC2737.jpeg with the hash DSC2737_2cab11f170 gets stored under <bucket-name>/DSC2737_2cab11f170/_DSC2737_DSC2737_2cab11f170.jpeg. Why not just <bucket-name>/DSC2737_2cab11f170.jpeg?
Screenshots

I think the problem is focuses in the part where the developer can do a upload referenced media to strapi as described in documentation: https://strapi.io/documentation/3.0.0-beta.x/plugins/upload.html#upload-files-related-to-an-entry
With this approach the upload file name goes well formatted. But in normal way, using admin panel or simple upload form, the upload file name really goes so trick: it repeats the file hash in path, including size (thumbnail, large...) as parent path and the first part is "undefined_".

Not sure next release will fix that with basePath.
I reopened it, but this "problem" provide from Strapi Upload plugin their self.

Looking at how the official providers work, I realized that the files are clustered in the root folder, without adding any type of change.
AWS S3:
          const path = file.path ? `${file.path}/` : '';
          S3.upload(
            {
              Key: `${path}${file.hash}${file.ext}`,
              Body: Buffer.from(file.buffer, 'binary'),

Cloudinary:
        const upload_stream = cloudinary.uploader.upload_stream(
            { resource_type: 'auto', public_id: file.hash, ...customConfig },
            (err, image) => {

Local file:
          // write file in public/assets folder
          fs.writeFile(
            path.join(strapi.config.paths.static, `/uploads/${file.hash}${file.ext}`),
            file.buffer,

Rackspace:
        const writeStream = client.upload({
          ...options,
          remote: file.hash,
          contentType: file.mime,
        });

All of then uses file.hash and in some cases file.ext attributes to store the files. Maybe it could be a good approach to use.

@Lith @dostuffthatmatters What's your opinion about that? :point_up_2: :

@dgmike Thank you for looking this up! Looks like Cloudinary and Rackspace get the extension implicitly from the uploaded file itself.
I will try to modify the generateUploadFileName method (https://github.com/Lith/strapi-provider-upload-google-cloud-storage/blob/master/lib/provider.js#L118) tonight.

Impact on current users
The impact of file path changes for old users is definitely something we'd have to look out for!
We could maybe use an environment variable GOOGLE_CLOUD_STORAGE_SHORT_PATHS (or something similar) and only if that one exists and is set to true then the mentioned method uses the new shorter path.
I could write a script that turns the old urls into the new urls (database and bucket), I will use one for MongoDB anyway. However a simple "Replace media" in the strapi dashboard would also do it.

@dostuffthatmatters I preffer to use configurations over environment variables because is the default way to configure providers. And, to configure it on strapi could be using environment variable.
I don't really know about the possible problems with script and interfere on strapi dashboard for a provider. Maybe two PR could be done with each solutions for avail it...
PS: now we have a contribution guide. Is good to take a look before start. :wink:

@dgmike Yes, you are right: Config vars would be more elegant!
The scripting thing is not something I would recommend to a regular user 😅 But I already did that in a project and the media collection in the database as well as the actual files are the only things you'd have to modify.
I guess with a proper way to set this new method via the configuration with the default config being the old way there is no need for two PR right?

Hi team,
Any update/timeline for the above?

I have cloned the library locally and use:
const generateUploadFileName = (basePath, file) => {
  const path = file.path ? `${file.path}/` : '';
  return `${basePath}${path}${file.hash}${file.ext}`;
};

And it is working exactly as I would expect it to. i.e. if uploading via admin it goes straight into basePath and if uploading via multipart I can set the sub path of base like aws plugin.

I've miss to check for this feature 😱
Sorry guys, and thanks @ScottEAdams for your code improvements.
Send me a PR, I will check it asap.
Strapi Admin panel do not use path configuration, so... this great feature is only available for custom client side application.
I will add it on next Strapi release.
Thanks

Fixed in release 3.1.6


chore: bdd-ghfjxy-import-1582319700 to 0.0.1

chore: Promote bdd-ghfjxy-import-1582319700 to version 0.0.1

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Cosmos DB CheckNameExists failing with 500

Bug Report

import path of package in question, e.g. .../services/compute/mgmt/2018-06-01/compute
"github.com/Azure/azure-sdk-for-go/services/cosmos-db/mgmt/2015-04-08/documentdb"
SDK version e.g. master, latest, 18.1.0
github.com/Azure/azure-sdk-for-go v30.0.0+incompatible
output of go version
go version go1.12.6 darwin/amd64

With no code changes all our cosmos DB tests started failing sometime in the last week with:
``
------- Stdout: -------
=== RUN   TestAccAzureRMCosmosDBAccount_strong
=== PAUSE TestAccAzureRMCosmosDBAccount_strong
=== CONT  TestAccAzureRMCosmosDBAccount_strong
--- FAIL: TestAccAzureRMCosmosDBAccount_strong (516.37s)
testing.go:568: Step 0 error: errors during apply:
    Error: Error checking if CosmosDB Account "acctest-190628022825260962" already exists (Resource Group "acctestRG-190628022825260962"): documentdb.DatabaseAccountsClient#CheckNameExists: Failure responding to request: StatusCode=500 -- Original Error: autorest/azure: error response cannot be parsed: "" error: EOF

      on /opt/teamcity-agent/temp/buildTmp/tf-test027250150/main.tf line 7:
      (source code not available)

<!--
Thanks!
-->

Thanks I can repro this, it also takes a really long time before the 500 is returned.

Known issue, RP is working on a fix.  Will update when I have more info.

thanks for the update @jhendrixMSFT, we are going to release 1.31 today with an unideal fix (ignore 500s) but it'll unblock users

@katbyte RP has fixed this and I confirmed it's working now.  Please let me know if you still have issues.


Rename parameter_name to field_name

With this rename, {}__id_exact is added to parameter name and AutocompleteAdminFilter will act like django's default filter for ForeignKey.
This may be useful if you use ForeignKey.get_forward_related_filter for constructing filters

@ron8mcr : This change broke my project; I do now have an error after changing parameter_name to field_name:

DisallowedModelAdminLookup at /admin/test
Filtering by test__id__exact not allowed

Any idea how to solve this on my side or yours!?


Almanac: Knowledge-Grounded Language Models for Clinical Medicine

Hey @lucidrains, just wanted to bring your attention to our preprint: https://arxiv.org/abs/2303.01229 where we ground an LLM in various medical websites, calculators etc, resulting in more accurate outputs. We're working on a full manuscript and will release more results soon!

oh hey Cyril, glad to see that you've finished medical school and even found your way to the bay area
yea, would be happy to check out your model. do you plan on putting it on huggingface?

yea, shoot me an email with the deets once you are ready
i have my own plans for this repo


Is there a config to change from dark theme to light theme?

I prefer light theme as it looks like I'm coding on GUI, I don't see any config in default-config.ini to change this.

Got it. I changed the default properties of my terminal application to black over white blackground and it changed in Slap too.
However, there is header.style in
https://github.com/slap-editor/slap/blob/master/slap.ini
but no style for the margin which shows line numbers.


Base64 encoding the icon font

@himedlooff @Scotchester Back in the day did you consider Base64 encoding the icon font? It would make cf-icons a whole lot easier to work with because you wouldn't have to ensure the font files are in the proper directory relative to your stylesheet.
I tried it and it seems to work well with a couple caveats:

The encoded font is ~ 15% larger, is this performance hit acceptable?
IE < 9 doesn't support Data URLS greater than 32KB so we still need an EOT fallback for IE8.

Here's the demo: http://contolini.github.io/cf-import-test/
The demo compiles all Less in the browser using less.js by pulling in a single entry point for CF (see #151). Here's the encoded font that's being used. And here's a version of the demo with the Less pre-compiled.

What's with the vertical alignment of the icons? (Look at one in a button.) Is that an artifact of the base64 encoding?

It's possible that the performance could offset by the single http request. It'd be interesting to measure the difference. Do we have a similar demo without the encoded fonts to measure against?

@Scotchester Good eye on the alignment. I don't want to debug why that's happening so I'm going to move this idea to the backburner.
@ascott1 Great point. If you're interested in pursuing that experiment, let me know if I can help.

We've switched to inline SVG icons.


AVRO-2908: Skip enum using in.readEnum instead of in.readInt

Make sure you have checked all steps below.
Jira

[x] My PR addresses the following Avro Jira issues and references them in the PR title. For example, "AVRO-1234: My Avro PR"

https://issues.apache.org/jira/browse/AVRO-2908
In case you are adding a dependency, check if the license complies with the ASF 3rd Party License Policy.

Tests

[x] My PR adds the following unit tests OR does not need testing for this extremely good reason:

TestSkipEnumSchema

Commits

[x] My commits all reference Jira issues in their subject lines. In addition, my commits follow the guidelines from "How to write a good git commit message":

Subject is separated from body by a blank line
Subject is limited to 50 characters (not including Jira issue reference)
Subject does not end with a period
Subject uses the imperative mood ("add", not "adding")
Body wraps at 72 characters
Body explains "what" and "why", not "how"

Documentation

[x] In case of new functionality, my PR adds documentation that describes how to use it.

All the public functions and the classes in the PR contain Javadoc that explain what it does

Hey, a trivial fix I'd like to see (with my apologies -- I should have seen it with the patch!)  Would it be possible to move the test to the org.apache.avro.generic package instead of the core package?  It seems like this would be a better place for someone looking for this test, or to complete the missing unit tests on skip!

Hey, a trivial fix I'd like to see (with my apologies -- I should have seen it with the patch!) Would it be possible to move the test to the org.apache.avro.generic package instead of the core package? It seems like this would be a better place for someone looking for this test, or to complete the missing unit tests on skip!

Moved the test to org.apache.avro.generic. I kept the name though.

Thanks for the fix!  I cherry-picked manually into branch-1.10 so this fix will appear in the next 1.10 release as well.


Exporting Out of Memory (OOM) events from Linux Guest UVM to Host

Added ability to publish Out of Memory (OOM) events (TaskOOM) to containerd's events pub/sub (to TaskOOMEventTopic) that originated in Linux containers on a guest UVM.
Created notification queue that containers can publish events on; added OOM monitoring on the Linux UVM (via containerd's OOM watcher, which leverages cgroup notification API and Linux epolls); updated Linux guest bridge to publish OOM events to host; and added monitoring to host shim to forward notifications to containderd.
Signed-off-by: Hamza El-Saawy<EMAIL_ADDRESS>

panic: Fail in goroutine after TestBridgeRPCResponseTimeout has completed

goroutine 13 [running]:
testing.(*common).Fail(0xc0001c0b60)
	C:/hostedtoolcache/windows/go/1.17.2/x64/src/testing/testing.go:710 +0xe5
testing.(*common).Error(0xc000042620, {0xc0001c5ec8, 0x103b92a, 0x1309760})
	C:/hostedtoolcache/windows/go/1.17.2/x64/src/testing/testing.go:812 +0x4f
github.com/Microsoft/hcsshim/internal/gcs.sendMessage(0xc0001c0b60, {0x13c0760, 0xc000042620}, 0x20100101, 0x0, {0xc000018d20, 0x0, 0x53})
	D:/a/hcsshim/hcsshim/internal/gcs/bridge_test.go:41 +0xc5
github.com/Microsoft/hcsshim/internal/gcs.reflector(0xc0001c0b60, {0x13c4720, 0xc000042620}, 0x0)
	D:/a/hcsshim/hcsshim/internal/gcs/bridge_test.go:63 +0x11b
created by github.com/Microsoft/hcsshim/internal/gcs.startReflectedBridge
	D:/a/hcsshim/hcsshim/internal/gcs/bridge_test.go:81 +0x205

gcs tests are failing after this, could you take a look?

Hoping to give this another look in a couple hours!

Oop needs a rebase now after Amit's chronyd changes I believe

Containerds pkg/oom package is pretty much exactly what we need but it really sucks how it has to take a shim.Publisher 😔 Be interesting if we could contribute a change to just return the events over a chan or similar. Or possibly change the function signature really, not sure how much of a high bar there is for not changing APIs in pkg packages.

@katiewasnothere ptal

It would be great if you can expand a bit on the architecture of this change in the PR description. Especially knowing what pieces of this exist in which packages, and how they interact with each other to send a notification would be helpful.

Closing due lack of customer need


Implementation of precision and recall

Implement precision and recall for all experiments
Plot PR curve
Compute confusion matrix

Also constructed some data about the dataset


Typo in function line

https://github.com/ZetaNumbers/wasm4-rs/blob/main/wasm4/src/draw.rs#L69
Should be end[0], end[1]

Oh shoot! Thanks! Comment if you need another crates.io release. There's #3 which is also a bug to be fixed tho.


Feature/configure auth strategy

Coverage decreased (-1.04%) to 98.835% when pulling b7c917c8dd7edc07f215d34a2a5c991d9ca970f5 on feature/configure-auth-strategy into 3adeb294f77ad692fb2016110c417305bb07ce41 on feature/#129-config-file-support.


Add annotation layer to plot to indicate average of averages

Would like some indication on the plot of an average number of rrn copies by taxonomic ranks that indicates the mean for that taxonomic rank, ideas...

box and whisker plot
point
line plot
segment making each mean

closes dd01407

closes

A bit concerned that the strip chart does not adequately show the data well, let's look at alternatives...

box plot
histogram
density plot
ridgeline plot - using density or histogram


Core refactoring

#22 Make user-friendly configuration of microservice

Coverage remained the same at 100.0% when pulling 0cdbf71882d26753ae4208cbcdd0188ab6708652 on core-refactoring into 53fde61bf75b2b3915c897915021b126498f3835 on master.


AutoConfigureStubRunner doesn't work with Ribbon listOfServers configuration

Hi,
I'm having an issue with stub runner since upgrading from Edgware to Finchley. It appears as if the enabling AutoConfigureStubRunner clears out any other Ribbon configuration, attempts to use my Feign client results in the following error:
Load balancer does not have available server for client: cool-service
Here's how to reproduce the issue
mock profile for configuring ribbon for use by a feign client
cool-service:
  ribbon:
    NIWSServerListClassName: com.netflix.loadbalancer.ConfigurationBasedServerList
    listOfServers: localhost:${local.server.port}

stubrunner:
  idsToServiceIds:
    nifty-spec: "nifty-service"
  ids: "com.foo:nifty-spec"

eureka:
  client:
    enabled: true
    register-with-eureka: false
    fetch-registry: false

feign:
  hystrix:
    enabled: false

Test code
@SpringBootTest(webEnvironment = SpringBootTest.WebEnvironment.RANDOM_PORT)
@ActiveProfiles({ "mock" })
@EnableFeignClients(clients = { GreetingApiClient.class })
@DirtiesContext
@AutoConfigureStubRunner
@RunWith(SpringRunner.class)
public class StubRunnerIssue {

	@Autowired
	private CoolApiClient client;

	@Test
	public void stubRunnerIssueExample() {

		this.client.getCool();
	}
}

Feign client and service interface/implementation
@FeignClient(name="cool-service", configuration = CoolClientConfiguration.class)
public interface CoolServiceApiClient extends CoolApi {
}

public interface CoolApi {

    @RequestMapping(value = "/cool",
        produces = "application/json",
        consumes = "",
        method = RequestMethod.GET)
    ResponseEntity<CoolResponse> getCool();

}

@RestController
public class CoolController implements CoolApi {

	@Autowired
	private NiftyClient niftyClient;

	@Override
	public ResponseEntity<CoolResponse> getCool() {

		try {

		    // call service that should be stubbed by stub runner
			ResponseEntity<NiftyResponse> niftyResponse = this.niftyClient.getSomethingNifty();
		}
		catch (NullPointerException e) {
			throw e;
		}
		return ResponseEntity.ok();
	}
}

In the unit test code, if you remove @AutoConfigureStubRunner then ribbon is able to find the configured server and the rest service is called, the test will eventually fail because the service then tries to call the client that was supposed to be stubbed out by the stub runner, but we disabled it.
Please note that this configuration worked in the Edgware release train, but since upgrading to Finchley.RELEASE this problem presents.

Your cool-service is not mapped to ant stub id. Your ids mapping invalid

I don't understand what you're trying to achieve. So don't use stub runner  for this test.
I remember the issue that we fixed which was related to not delegating to ribbon cause it doesn't make any sense really.
You should have separate tests that test the integration of contract related stuff separately to other things.

These are acceptance tests that run against virtualized dependencies. The example here is overly simplified, but the gist is that in many circumstances the stubs generated by Spring Cloud Contract work well for this purpose.
Regardless of the test strategy being employed, the question is should I be able to use Stub Runner and Ribbon at the same time? It looks like we cannot and it sounds like you are saying it was intentionally 'fixed'.

Yes it was fixed.
You can run stub runner in a mode that disables the service discovery stubbing. It will try to register the stubs in a service discovery tool.

I've tried every permutation of configuration options that I can find, none of them enable the use of StubRunner with pre-existing Ribbon configuration.
It looks like the StubRunnerRibbonBeanPostProcessor is overriding the ConfigurationBasedServerList bean with the StubRunnerRibbonServerList bean.
class StubRunnerRibbonBeanPostProcessor implements BeanPostProcessor {
...
	@Override
	public Object postProcessBeforeInitialization(Object bean, String beanName) throws BeansException {
		if (bean instanceof ServerList && !(bean instanceof StubRunnerRibbonServerList)) {
			return new StubRunnerRibbonServerList(stubFinder(), stubMapperProperties(), clientConfig());
		}
		return bean;
	}
...
}

Would an acceptable enhancement be to merge the Servers from ConfigurationBasedServerList (or whatever other implementation of ServerList<Server> comes in) into the StubRunnerRibbonServerList? This could be made configurable, default behavior is how it works today, optionally turn on the 'merging' similar to what stubrunner.cloud.delegate.enabled=true would do for discovery clients.

Actually, that looks like how it used to work in 1.2.x release. Removing the delegate must be the fix you referred to.
Bean post processing code from the 1.2.5.RELEASE tag
@Override
	public Object postProcessBeforeInitialization(Object bean, String beanName) throws BeansException {
		if (bean instanceof ServerList && !(bean instanceof StubRunnerRibbonServerList)) {
			return new StubRunnerRibbonServerList(stubFinder(), stubMapperProperties(), clientConfig(), (ServerList<?>) bean);
		}
		return bean;
	}

If this is the expected behavior then this issue can be closed. I would like to make the final argument that this behavior limits the usage of SCC, specifically stub runner, when the consumer is writing integration tests. If my consuming service has non-Spring Cloud Contract integration points (services that do not have contracts/stubs) and we are using Ribbon in any capacity to integrate with them then using the stub runner is not possible. This is a very real scenario, many services we build must integrate with legacy services as well as nice new services that use SCC.
For anyone else who has this issue a work around would be to remove use of stub runner and replace it with programatic configuration of wiremock from your test code. You should be able to load the SCC wiremock stubs from the classpath.

Let's continue this converation :)
I think that you're trying to fix a wrong problem.
If you have onion layered architecture, you can test the contract pieces in one test (with spring cloud contract), and in other tests you can create a mock that will replace the client calling the endpoints that you tested with contract tests. WDYT?

Yes, I'd started a similar conversation with the team. Thinking about this more it seems like we may be stretching the purpose/intent of stub runner by using it in our more advanced behavioral testing where we need to virtualize/mock multiple external dependencies (the outer layer of onion). What you are suggesting lines up with some of our other test strategy conversations.


[docs] Introduce HowTo and Glossary documents

Let's introduce HowTo and Glossary documents.
ONE-vscode-DCO-1.0-Signed-off-by: Yongseop Kim<EMAIL_ADDRESS>

While not touching README and OVERVIEW, let's start introducing HowTo documents.

PTAL @Samsung/one-vscode

LGTM


PlatformChecks emits error when compiling for MacCatalyst

Description
When attempting to compile the Swift OpenAPI Generator on MacCatalyst, code that compiles perfectly on iOS will not run as it will fail on the PlatformChecks. This is unexpected.
Reproduction
Use the same instructions as used in https://developer.apple.com/wwdc23/10171 but enable the MacCatalyst target in the project settings. The project will compile fine for iOS but when targeting mac, the following error will be emitted:
OpenAPIGeneratorCore is only to be used by swift-openapi-generator itself—your target should not link this library or the command line tool directly.
Package version(s)
1.2.0
Expected behavior
I would have expected this to work fine without extra adjusments.
Environment
macOS Sonoma
Xcode 15.2
Additional information
I suspect that it has to do with the behaviour of MacCatalyst. When compiling the app normally targetEnvironment(macCatalyst) will be true while os(macOS) will be false which is already confusing by itself but with good reason. Perhaps the build tool plug-in is ran in a similar environment?
It looked like from my limited testing that when disabling this check it will not include the runtime, so the setup should be correct.

Hi @lennartkerkvliet,
yeah that's possible, I don't believe we've tested this with macCatalyst. Would you be willing to open a PR fixing the issue? 🙏

@lennartkerkvliet thanks for filing the issue and for the contribution!
It should now be available in the 1.2.1 release: https://github.com/apple/swift-openapi-generator/releases/tag/1.2.1


[JS][Hydrogen] Image zoom

When clicking on an image in a discussio there is no option to zoom in the image (either single click to 1:1 followed by panning, nor pinch to zoom).
This is probably to be implemented in the actual hydrogen (fork)

I found out that commenting this line disable hydrogen's image viewer.
Unfortunately, as there aren't link nothing happens inside onRecvAsyncMessage.


Multiple Like condition

How can i add multiple LIKE conditions on fields?

Maybe aggregation can help you please check on mongodb document
https://docs.mongodb.com/manual/reference/operator/aggregation/match/

Can you please share one example with this library?

$info = $this->mongo_db->aggregate(
'col_users',[
[
'$match' => [
'$or' => [
[ 'user_email'=> [ '$regex'=> 'mytext','$options'=>'gi' ] ],
[ 'user_name'=> 'mytext'  ]
]
],
],[
'$project' => [
'user_email' => 1,
]
]
],[
'cursor' => [ 'batchSize' => 0 ]
]
);