Make sure our documentation is in a good state.

[x] Disable the obsolete CircleCI build for the docs repo.
[x] Replace readthedocs pages with a link to the new pages.
[x] Remove references to GPflow 1.x.
[ ] Revisit / rework front / landing page.
[x] Use sphinxcontrib-bibtex: https://github.com/GPflow/GPflow/issues/1821
[x] Fix all warnings in doc builds: https://github.com/GPflow/GPflow/issues/1254
[ ] Skim all notebooks and fix any obvious errors.
[ ] Skim entire API and fix any obvious erros.
[ ] Update gpflow.org to point to the new documentation (and any other updates...)

Was looking for a good place to put this, maybe here is appropriate:
The notebooks link (both from the frontpage and the top menu) simply lists all jupyter notebook files. This includes some, such as Likelihood Design that we meant to include, but no-one ever got around to write yet - it would seem better to keep track of such "to do" tasks within the issues (maybe we could pin it?), and to remove the stubs, because they're more confusing than helpful (e.g. it doesn't even link to the notebooks actually discussing heteroskedastic likelihood!).


Added Harry Potter sounds

They're free and there are quite a few of them to choose from.

Wrong repo. Disregard.


Bug in .startWith()

Hi there,
Was trying this package out today and ran into this error when using .startWith(). I know that the method successfully hit my action, but it threw the following error:
Uncaught Exception
TypeError: Cannot read property 'url' of undefined
actionResponse.items.forEach (node_modules/actions-on-google-testing/src/actions-on-google.ts:477:76)
ClientDuplexStream.conversation.on (node_modules/actions-on-google-testing/src/actions-on-google.ts:425:42)
Object.onReceiveMessage (node_modules/grpc/src/client_interceptors.js:1276:19)
InterceptingListener.recvMessageWithContext (node_modules/grpc/src/client_interceptors.js:603:19)
node_modules/grpc/src/client_interceptors.js:702:14

Looks like the error throws here:
} else if (i.mediaResponse) {
    assistResponse.mediaResponse = {
      type: i.mediaResponse.mediaType,
      name: i.mediaResponse.mediaObjects[0].name,
      description: i.mediaResponse.mediaObjects[0].description,
      sourceUrl: i.mediaResponse.mediaObjects[0].contentUrl,
      icon: i.mediaResponse.mediaObjects[0].icon.url,
    }
}

Are you returning a media response? Can you pull the content? It may be throwing because this library assumes that an icon is available, although that may not be the case and we may need to fix that.

@Fleker In the specification of the MediaObject structure, each MediaObject can have only one of icon or largeImage. It is possible that a MediaObject doesn't have the icon. That is, joecaine might had specified the largeImage. The current code of the latest version expects that the icon is specified. I guess that we need to fix the code so that both icon and largeImage can be specified.
I intend to confirm whether the thinking above is right or not, and if can confirm I will create a pull request to fix this issue.

Thanks for quick response. I hadn't actually specified either a largeImage or an icon. In the prototype I'd built I'd got it to run on a device just specifying a url.

@Fleker My pull request was merged into the master branch. This is a bug. I think that we should release a new version including the fix code for this issue soon.

Fixed in v0.2.2


Update sbt, scripted-plugin to 1.10.7

About this PR
📦 Updates

org.scala-sbt:sbt
org.scala-sbt:scripted-plugin

from 1.10.6 to 1.10.7
Usage
✅ Please merge!
I'll automatically update this PR to resolve conflicts as long as you don't change it yourself.
If you'd like to skip this version, you can just close this PR. If you have any feedback, just mention me in the comments below.
Configure Scala Steward for your repository with a .scala-steward.conf file.
Have a fantastic day writing Scala!

⚙ Adjust future updates
Add this to your .scala-steward.conf file to ignore future updates of this dependency:
updates.ignore = [ { groupId = "org.scala-sbt" } ]

Or, add this to slow down future updates of this dependency:
dependencyOverrides = [{
  pullRequests = { frequency = "30 days" },
  dependency = { groupId = "org.scala-sbt" }
}]

labels: library-update, early-semver-patch, semver-spec-patch, version-scheme:early-semver, commit-count:1

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useSlideLayout from root

I want to useSlideLayout from the root directory rather than the source presentation? I can hardcode the mapping; however, it would be more elegant to address root/Side Banner/Title Slide or root/No Side Banner/Title Only rather than 1 or 8
If this isn't possible I can work around it, just a nice to have

Did you try https://github.com/singerla/pptx-automizer/blob/f4ec662b94b4c01bca7ee5f9f088bd8b07a460d0/src/classes/slide.ts#L117
Please let me know if this already helps or if we should improve this more.

that's close. I was looking to be able to switch between templates. I'll work on generalizing my code and make a PR
https://github.com/jhaase1/presentation_creator/blob/d90595181aec86dd54b6db7c16ed0a47acf1af79/src/main/powerpoint/copySlidesToPPTX.js#L107-L127


Effect disabled after using light for baking

Hi,
first of all thanks very much for this great stuff!
Here´s my issue:
I use a directional light (Realtime mode) as a sun for my outdoor scene.
I setup the VoluemticLighting component on this light to get some nice godrays using realtime shadows.
I see everything is fine in runtime.
I switch this light to Baked mode for baking some lightmaps.
I switch this light back to realtime mode but the godrays are no longer present in runtime.
I clear baked data and godrays are present again.
Any ideas?
Kind regards,
Carsten

hi,
I'm afraid I don't have any experience with light baking in unity but I will take a look if I can find the time


The block remover TypeScript mutator doesn't remove lambda function bodies

export const isNumber: TypeGuard<number> = (n): n is number =>
  typeof n === "number";

Currently the only thing mutated about the above function is the equality. Considering there is a tslint rule to force all functions to be defined this way, which I use, this is a reasonably serious bug.

Further investigation has revealed this is only if you use the shorthand for return, the following will be elided correctly:
export const isNumber: TypeGuard<number> = (n): n is number => {
  return typeof n === "number";
}

Awesome, thanks for your investigation! Do you also want to implement the feature?
It would mainly be adding a test here:
https://github.com/stryker-mutator/stryker/tree/master/packages/stryker-typescript/test/unit/mutator
And implementing the mutator here:
https://github.com/stryker-mutator/stryker/tree/master/packages/stryker-typescript/src/mutator
You can look at the standard Block mutator as an example.

The SyntaxKind is an ArrowFunction.

I'll see if I can get a chance to take a look over the next couple days. I'm pretty busy at work, but worst case I should be able to find some time over the weekend. Thanks for the pointers!
For my info, what is the VSCode plugin you have that exposes the AST like that?

@mscharley this awesome plugin: https://marketplace.visualstudio.com/items?itemName=krizzdewizz.vscode-typescript-ast-explorer


⚠ Turnos 24x7 has degraded performance

In 710faf2, Turnos 24x7 (https://reservas-dev.alternativasinteligentes.com/) experienced degraded performance:

HTTP code: 200
Response time: 947 ms

Resolved: Turnos 24x7 performance has improved in 7bda888.


Since 1.8.3 IOS build fails

Hi,
just switched from 1.8.2 to 1.8.3, where the iOS build fails:
cordova build ios --release --device
result: https://paste.xinu.at/WTvPJ/

Looks like the build is failing only on El Capitan.
Fails with XCode 7.3.1
Works with XCode 8.0

@kaynz weird, because there was only one change in the iOS code from 1.8.2 to 1.8.3 and that was the removal of an unused line of code. Pretty sure this is just Apple messing with us. I should update the docs to tell everyone to use XCode 8 regardless.

Okay. So, how can I solve this? I can't update to Xcode 8 because of #1154.

It looks like it is unrelated to this plugin, thanks.


Updated custom build yaml to decrypt google-services.json.gpg

This is a quick fix to have @paolo's build success

So paolo can have his test build using mock api


App completely hangs if searching for Box.com

This started a few months back. I'm using the most current version of Quicksilver and have cleared out all caches.
Searching for "Box" to access box.com sync folder will immediately hang the app. 100% reproducible every single time. The only way to recover is to forcibly kill the app and restart.

Next time it hangs, take a sample, upload it somewhere (Box maybe?) and post a link here.


storage: put deadline in txn commands

What problem does this PR solve?
Per https://github.com/tikv/tikv/pull/10771#pullrequestreview-737000234, now we only save the max_execution_time_limit in the Context. But if one request is split into multiple txn commands, the deadline is refreshed. So such requests like ResolveLock may bypass deadline checks unexpectedly.
What is changed and how it works?
Based on #10771.
This PR puts deadline in Command. And if NextCommand is created, the deadline should be inherited from the last command.
Check List
Tests

Unit test
Integration test
Manual test (add detailed scripts or steps below)
No code

Release note
Check deadline correctly for resolve lock requests.

/merge

CI environment is unstable. Try again.
/merge

/test

/merge


UI: Optimize the backend calls

Affected module
UI
Describe the bug
We are making unnecessary and sometimes duplicated calls. This ticket is to track and resolve those.
Landing page

Users
We are making 2 calls one to fetch follows, owns and another one profile

https://sandbox.open-metadata.org/api/v1/users/0cd4048d-ce07-4699-a86f-98ac45dbdb4d?fields=follows,owns
https://sandbox.open-metadata.org/api/v1/users/name/harsha?fields=profile

These 2 calls can be merged into one with fields=profile,follow,owns
To Reproduce
Screenshots or steps to reproduce
Expected behavior
A clear and concise description of what you expected to happen.
Version:

OS: [e.g. iOS]
Python version:
OpenMetadata version: [e.g. 0.8]
OpenMetadata Ingestion package version: [e.g. openmetadata-ingestion[docker]==XYZ]

Additional context
Add any other context about the problem here.

@chirag-madlani @Sachin-chaurasiya @ShaileshParmar11 lets make some progress here

@chirag-madlani @Sachin-chaurasiya if this is resolved lets close it out

@harshach, this is not done yet.
we checked both API calls and both are in different components for different purposes.
This(https://sandbox.open-metadata.org/api/v1/users/0cd4048d-ce07-4699-a86f-98ac45dbdb4d?fields=follows,owns) one is to get the user's own and followed data on the landing page.
The other one (https://sandbox.open-metadata.org/api/v1/users/name/harsha?fields=profile) is from the activity feed card to get the user profile picture, we are making this separate call because, in the activity feed, we only get entity reference for the user.

We caching the above response on the UI side to avoid multiple calls for the same user.
I was thinking if we could have a separate API for just fetching profile picture data something like below:
/api/v1/user/{username}/profile-picture - Request
Response :

@harshach closing it since not any false API is being made.


worker metrics is empty

In version 0.10.3, use the command xinference-worker -e http://xinference-supervisor:9997 --worker-port 30001 --metrics-exporter-host xinference-worker --metrics-exporter-port 30002 to start the worker , access http://xinference-worker:30002/metrics through the browser, the results are as follows:

no indicator values are displayed. Please help guide me as to where the configuration problem is?

@amumu96 Could you help?

And @codingl2k1 could give some advice as well.

Your screen shot shows that the metrics works correctly. https://inference.readthedocs.io/en/latest/user_guide/metrics.html

This is my test, currently the metrics work well for the pytorch backend and vLLM backend.

 这是我的测试，目前这些指标对于 pytorch 后端和 vLLM 后端运行良好。

Then why are there no indicator values displayed?

 这是我的测试，目前这些指标对于 pytorch 后端和 vLLM 后端运行良好。

Then why are there no indicator values displayed?

Which method did you use to call the model for chatting？ /v1/chat/completions？


doc(compute): add create disk and delete disk samples

fixes #11806

This change is

Providing labels is completely optional so it's not an error if argc != 5. Used the more explicit label format example in the error message when it's in the wrong format.

D'oh. std::vector::at is not the same as std::vector::operator[]. My mistake :facepalm:.

Providing labels is completely optional so it's not an error if argc != 5.

Agreed.  But it still seems wrong to argv.at(4) when argv.size() == 4.


resizing window with docks doesn't resize proportionally

Prerequisites

[x] Put an X between the brackets on this line if you have done all of the following:

Reproduced the problem in Safe Mode: http://flight-manual.atom.io/hacking-atom/sections/debugging/#using-safe-mode
Followed all applicable steps in the debugging guide: http://flight-manual.atom.io/hacking-atom/sections/debugging/
Checked the FAQs on the message board for common solutions: https://discuss.atom.io/c/faq
Checked that your issue isn't already filed: https://github.com/issues?utf8=✓&q=is%3Aissue+user%3Aatom
Checked that there is not already an Atom package that provides the described functionality: https://atom.io/packages

Description
resizing window with docks doesn't resize proportionally
Steps to Reproduce (this is easier to repro on windows than mac)

Open a project in atom
Open two files
move one of the files to the bottom dock or the right dock
make the dock that you moved the file to fill half of the screen
Resize Atom to be smaller

Expected behavior: [What you expect to happen]
The docks are resize proportional to the window that is resized.
Actual behavior: [What actually happens]
The Dock takes up the entire window, hiding the center dock.
Reproduces how often: [What percentage of the time does it reproduce?]
100%
Versions
Atom    : 1.17.0-dev-3292e3b4d
Electron: 1.3.14
Chrome  : 52.0.2743.82
Node    : 6.5.0

You can get this information from copy and pasting the output of atom --version and apm --version from the command line. Also, please include the OS and what version of the OS you're running.
Additional Information
NOTE: will update screenshot

Any additional information, configuration or data that might be necessary to reproduce the issue.

hmm... looking back at this behavior this can be by design.  I was testing this out with just tree view on atom and sublime and they behavior similarly.  💭 .  I'm ok with closing this out as won't fix, but can be a bit confusing.


top-level legend option, and better types

This introduces a new top-level legend option which is inherited by scales that support legends (color, symbol, and opacity). This makes it slightly more concise to enable legends, and takes us a step closer to enabling them automatically if desired. For example:

Plot.plot({
  grid: true,
  inset: 12,
  legend: true,
  marks: [Plot.dot(decathlon, {x: "Long Jump", y: "100 Meters", symbol: "Country", stroke: "Country"})]
})

Note above that Plot is smart enough to realize that the symbol legend subsumes the color legend, and hence the color legend does not need to be displayed separately. Plot now suppresses the color legend automatically when the symbol legend also encodes color. You can force separate legends by using the stroke symbol legend option:

Plot.plot({
  grid: true,
  inset: 12,
  legend: true,
  symbol: {stroke: "currentColor"},
  marks: [Plot.dot(decathlon, {x: "Long Jump", y: "100 Meters", symbol: "Country", stroke: "Country"})]
})

I considered the alternative below, which we could have display a colorless symbol legend and no color legend, but it would require more finagling to allow the symbol legend to read the color legend options; let me know if you prefer it:
Plot.plot({
  grid: true,
  inset: 12,
  legend: true,
  color: {legend: false},
  marks: [Plot.dot(decathlon, {x: "Long Jump", y: "100 Meters", symbol: "Country", stroke: "Country"})]
})

I also wasn’t sure if we wanted a way to force the display of separate symbol and color legends, as this currently displays only a joint legend (which I think makes sense, since it’s equivalent to inheriting the top-level option):
Plot.plot({
  grid: true,
  inset: 12,
  color: {legend: true},
  symbol: {legend: true},
  marks: [Plot.dot(decathlon, {x: "Long Jump", y: "100 Meters", symbol: "Country", stroke: "Country"})]
})

Lastly, this fixes a types bug where legend options were not allowed on scale options. For example, setting the stroke symbol legend option previously errored with Object literal may only specify known properties, and 'stroke' does not exist in type 'ScaleOptions'. This also introduces more specific types for legend options for additional safety.

I love it. I don't see the need to have an option to force the separate legends when it's a double encoding. (Given that a user who really wants two legends can always create them with plot.legend.)
One caveat is that when we introduce radius legends (hopefully one day 😊 ), this will change the output (for the better…)?

Right, if you set the top-level legend option, you’re opting-in to potential future legends. I think that’s okay.


Support pagination in V2 engine, phase 1

Supersedes #1483 and https://github.com/Bit-Quill/opensearch-project-sql/pull/226
Description
https://github.com/Bit-Quill/opensearch-project-sql/blob/61767f2b200b2a7f8c8b2df32de209b3c30caa61/docs/dev/Pagination-v2.md
PHASE 1
Pagination supports queries like
select * from <index>;

For example:
curl -s -XPOST http://localhost:9200/_plugins/_sql -H 'Content-Type: application/json' -d '{"query": "SELECT * from calcs", "fetch_size": 5}'

Cursor request:
curl -s -XPOST http://localhost:9200/_plugins/_sql -H 'Content-Type: application/json' -d '{"cursor": "...." }'

All credits to @MaxKsyunz.
You can use attached script for testing as well. Command line: ./cursor_test.sh <table> <page size>. Requires jq. Rename it before use.
cursor_test.sh.txt
Scroll API usage doc:
https://www.elastic.co/guide/en/elasticsearch/client/java-api/current/java-search-scrolling.html
Please, see team review and discussion in https://github.com/Bit-Quill/opensearch-project-sql/pull/226
Pagination demo: https://user-images.githubusercontent.com/88679692/224208630-8d38d833-abf8-4035-8d15-d5fb4382deca.mp4
Further improvements are coming

Rework on serialization and deserialization in #252
Support queries with LIMIT, WHERE, HAVING and ORDER BY and queries without FROM into pagination. in #253 - Phase 2
Rework ComparisonIT to use pagination. in #254 - Phase 2

Issues Resolved
#656
Check List

[x] New functionality includes testing.

[x] All tests pass, including unit test, integration test and doctest

[x] New functionality has been documented.

[x] New functionality has javadoc added
[ ] New functionality has user manual doc added

[x] Commits are signed per the DCO using --signoff

By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license.
For more information on following Developer Certificate of Origin and signing off your commits, please check here.

Codecov Report

Merging #1497 (bc8c73f) into feature/pagination/integ (23cc0f6) will increase coverage by 0.04%.
The diff coverage is 100.00%.

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@@                      Coverage Diff                       @@
##             feature/pagination/integ    #1497      +/-   ##
==============================================================
+ Coverage                       98.46%   98.51%   +0.04%
- Complexity                       3869     4018     +149
==============================================================
  Files                             345      362      +17
  Lines                            9603     9911     +308
  Branches                          616      644      +28
==============================================================
+ Hits                             9456     9764     +308
  Misses                            142      142
  Partials                            5        5

Flag
Coverage Δ

sql-engine
98.51% <100.00%> (+0.04%)
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Impacted Files
Coverage Δ

...ion/serialization/DefaultExpressionSerializer.java
100.00% <ø> (ø)

...ch/sql/opensearch/client/OpenSearchRestClient.java
100.00% <ø> (ø)

...sql/opensearch/request/OpenSearchQueryRequest.java
100.00% <ø> (ø)

...ge/scan/OpenSearchIndexScanAggregationBuilder.java
100.00% <ø> (ø)

...earch/storage/scan/OpenSearchIndexScanBuilder.java
100.00% <ø> (ø)

...nsearch/storage/script/ExpressionScriptEngine.java
100.00% <ø> (ø)

...ge/script/aggregation/AggregationQueryBuilder.java
100.00% <ø> (ø)

...ript/aggregation/dsl/AggregationBuilderHelper.java
100.00% <ø> (ø)

...ript/aggregation/dsl/BucketAggregationBuilder.java
100.00% <ø> (ø)

...ript/aggregation/dsl/MetricAggregationBuilder.java
100.00% <ø> (ø)

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Lets we tag this for 2.8 and discuss the requested changes. we are a few hours away from code freeze as this is a huge PR with critical changes; lets move this to the next release train


Fixture parenthesis are corrected everywhere in the code - check can be re-enabled

Description
Fixture parenthesis are corrected everywhere in the code - check can be re-enabled
Type of change

[ ] Refactor
[ ] New feature
[ ] Bug fix
[ ] CVE fix
[ ] Optimization
[ ] Documentation Update
[x] Configuration Update
[ ] Bump-up dependent library

/lgtm

/approve

/retest

/retest


profile: long/ hyphenated names are pushing the "follow" button out of its position

not only is the name coming close to the follow button - the follow button appears to have moved further from its position (should have 40px padding to the right)
example here:

This is fixed in my most recent PR. Merge in a sec


Big-endian friendly uint16_t handling

Tested successfully on PowerPC via PQClean (https://github.com/PQClean/PQClean/runs/89571287)

AES matrix generation also big-endian safe now. Should be ready for review and merge, @patricklonga.  I've run a selection of consistency tests but not every combination that your run-all script does.

In config.h, is BYTE_ORDER expected to be defined by the OS?
Also, is _WIN32 expected to be set when either our macros X86 or AMD64 are set? if this is the case maybe use these two macros instead.

In config.h, is BYTE_ORDER expected to be defined by the OS?

__BYTE_ORDER__ is a GCC C preprocessor macro. It's not POSIX, so not all compilers define it (including Microsoft's compiler) which is why there's fall-through code when it's not defined.

Also, is _WIN32 expected to be set when either _X86_ or _AMD64_ are set? if this is the case maybe use these two macros instead.

Yes.  But macros for specific CPU architectures are annoyingly non-standardized across compilers, see https://sourceforge.net/p/predef/wiki/Architectures/.  I used _WIN32 to avoid that, but your choice if you prefer more specific macros.

Still in config.h. Is it necessary to check if __BYTE_ORDER__ is defined? apparently this is covered by the evaluations __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__ and __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__. But I could be wrong.

Still in config.h. Is it necessary to check if __BYTE_ORDER__ is defined? apparently this is implicitly covered by the evaluations __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__ and __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__. But I could be wrong.

I think it is necessary to still check if __BYTE_ORDER__ is defined.  As far as I can tell, equality of two undefined macros is true.  At least, the following program prints "if" on both clang and gcc for me:
#include <stdio.h>

int main() {
#if POTATO == FRENCH_FRIES
    printf("if\n");
#else
    printf("else\n");
#endif
}

It goes against intuition but yes, I just tried it too and we need those definition checks.


Don't allow valid users to request resend validation email

For issue #604

@gvilarino, @jfresco, have some questions regarding translations:
The new error message is only to be shown on signup process, is it ok to be at the "common." namespace?
How about the other languages? I only did the english one, I have to copy in english to the other languages, google translate it, or someone else will take care of it?

for translations keys we use some sort of tacit criterion: sort of like module-name.file-or-view-name.string-sort-of-name, but for some stuff like or or email that is used all over the place we just fit it under common.whatever.
And yes, if you add new keys you should provide translations for all languages based on google translate.

@mjlescano Is this ready for review & merge?

@jfresco i18n completion still missing

done!

Good work, thanks.


VM: Fix readonly disk directory shares

Use 9p virtfs-proxy-helper even when readonly=true (fixes #8809 by avoiding qemu apparmor profile).
Log why virtio-fs isn't being used - rather than a generic, and potentially inaccurate warning.
Don't use virtio-fs if disk device config has readonly=true (as doesn't appear to be supported). Fallback to 9p instead.
Improve lxd-agent startup process by waiting for /dev/vsock to appear and activating listener before setting up agent mounts. This avoid prematurely exiting (and getting systemd to restart lxd-agent) which was causing the virtiofs mount to be replaced (mounted over) by the fallback 9p mount.

@stgraber it looks like the issues with 9p not respecting the readonly property have been fixed (at least in the version of qemu in focal), here's my test inside a VM with a readonly disk share on /srv:
# cd /srv
# mount | grep /srv
# This shows lxd-agent has mounted /srv using 9p and readonly.
lxd_srv on /srv type 9p (ro,relatime,sync,dirsync,access=client,trans=virtio)
# touch test
touch: cannot touch 'test': Read-only file system
# As expected this fails, but check if we can work around that by remounting rw.
# mount -oremount,rw /srv
# mount | grep /srv
lxd_srv on /srv type 9p (rw,relatime,sync,dirsync,access=client,trans=virtio)
# Confirmed that /srv is remounted rw
# touch test
touch: cannot touch 'test': Read-only file system
# mkdir test
mkdir: cannot create directory ‘test’: Read-only file system
# chmod 777 .
chmod: changing permissions of '.': Read-only file system

@stgraber my test for the readonly block disk passthrough was using fdisk to open it inside the VM. When readonly=true the block device itself still shows as rw in /dev:
ls -la /dev/sdb
brw-rw---- 1 root disk 8, 16 May 25 13:18 /dev/sdb

But isn't openable as writable:
fdisk /dev/sdb
fdisk /dev/sb

Welcome to fdisk (util-linux 2.34).
Changes will remain in memory only, until you decide to write them.
Be careful before using the write command.

fdisk: cannot open /dev/sb: No such file or directory

So it looks like this works too.


Update URL to Adaptive Card designer push to live

:white_check_mark: Validation status: passed

File
Status
Preview URL
Details

docs/actionable-messages/toc.yml
:white_check_mark:Succeeded

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

:white_check_mark: Validation status: passed

File
Status
Preview URL
Details

docs/actionable-messages/toc.yml
:white_check_mark:Succeeded

docs/add-ins/toc.yml
:white_check_mark:Succeeded

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


Need to be able to search all windows sometimes

With the recent v2.1.0 release and support for virtual desktops it's nice to be able to search for windows only existing in the current desktop but sometimes I have the need to search across all desktops. Since the subtitle already shows which desktop it belongs to, let's:

add an option in settings to search for windows in all/current desktops. Or maybe a shortcut is better to activate search context- all/current desktops.
if search all desktops is selected then all windows result subtitle should also show which desktop they are from

Added in https://github.com/taooceros/Flow.Plugin.WindowWalker/pull/30


remove shopify's livedata-ktx dependency

Issue

close #143

Overview (Required)

remove Shopify's livedata-ktx that is in Readme, Dep,ktx , corecomponent\androidcomponent\build.gradle

👀


Msg box position fixed

Msg box is too bottom side of the screen, fixed it to normal or comfortable place.

Hi tvschaitanya,
Thanks for the pull request.
I previewed the changes and I think that the message box maybe a little too short. Currently, I haven't implemented a way to anchor the input box to the bottom of the webpage. It is something to take a look at.


Request: Plug and Play template

What would you guys think of putting together a more detailed auth server template? Something we could essentially clone down, add some env variables, change some branding, and safely deploy something to prod. The ones you have are good starts, but still require a lot of time to set up for practical use vs something like keycloak.
Obviously there’s no one solution fits all, but getting together a ready to go common starter would go a long way. The less work it takes to get something up the easier it is for teams to bring it on and the easier it is to sell to leadership teams compared to more plug and play solutions.

Note
At the end of the day, the faster and easier it is to get something up and running in production, the more customers you’ll be able to get.

👋 guys just thought of this again. made a couple minor tweaks to the above, but think it mostly still holds. hopefully this is still in the cards at some point. as always happy to answer any questions

hey i am just now coming in here .... way back i used identity server at one time.  now i am in a new project and need to setup a new one.  i think i have almost caught up on things and i do have one thing to add in here:
so i have been using the "quickstart" and the first parts i found helpful.
but now i came to the "Using Aspnet Core Identity" where it starts a new project and kind of dumps the prior one.
i feel like that is kind of a bad call.
i want to use ms sql for my data store. in the prior steps i had that started.
but now the current guide has made a sql lite db and now i feel like to get where i want i have to kind of merge things from the different quick starts and i am taking a step back.
i would rather the aspnet core identity start just let me keep what i had and just add the identity tables to the sql db i have.
let the user set the data storage before creating the migration and the database.
so that  quick start 5 uses what i did in # 4

also unless i am mistaken the quickstart pulled in an old reference to v4
i just found this line in the code for HostingExtensions.cs:
using IdentityServer4.EntityFramework.DbContexts;
have to see if that was me by accident....

FWIW for me, I'd definitely prefer something without identity to start and maybe there's an extension of it down the road that includes it if there's enough ask. It would just add a lot more work and delay and complexity to something that's already a massive lift.
And personally, I have no interest in maintaining identity info directly.

Ah and herein lies part of the complexity :) Everyone needs/wants something different based on different assumptions and requirements, and given that we're a framework we can be utilized in a variety of ways. Of course, I think that's well understood.
In any event, we have kept this issue open because it is still something we'd like to do something about as we have cycles available for it. Thanks!

@brockallen  and @pdevito3
yeah for sure there is no real "one size fits all" in this..... i do get that....
in my case i need "something" to hold the user login info and it needs to be not Microsoft/google etc....
and some of the other software i am working with has support for the Microsoft identity libraires.
so it may not be perfect but it fits in.

In any event, we have kept this issue open because it is still something we'd like to do something about as we have cycles available for it. Thanks!

That's exciting 🙂

Hmm, wasn't really thinking before but yeah an inherent part of this is user management, so theoretically identity actually saves some time?
So I guess up to the team on whatever's easiest I'd be open to. If that means having identity help with the heavy lifting then that's cool.
Sorry hadn't really thought this all the way through when I commented earlier lol
Regardless, yeah this is definitely a hard problem and everyone will have different thoughts, but at least having some kind of functional prof ready base to work from would be a major differentiator for y'all

also i have a limited time, if i am not able to make this work i have to use other means....
for example the system i am building will have a web app and a database in azure.
so one option is to use azure app registration and azure ad  for part of what i am creating that will work and its almost turn key.
there are also other id packages like auth0  but i am not sure what that costs .....
will have to check on options while i try to get a working proof of concept here....

i am reading the documentation for how to use the dotnet ef tool. i think i will get this working but it might be worth updating the quickstarts slightly.
just give new users an option for if you want to put the data in one database that is not sql lite then do this....
for example i want to store all my data in one mssql database.
it would help to have the templates and the quickstarts allow me to do that and not just assume that i want to have multiple sqllite databases.
also have quickstart 5 allow for using what was just done in quickstart 4 be used in 5
just helps for the folks who want to just get a very basic system working with a database and not in memory data.
yes we can figure it out .... but if the new developer is not familiar with how all the dotnet ef tools work it will help get started.


Rendering exceptions on DChartPieO

Getting rendering exceptions on the new chartpie. Seems to be a width problem, as i see a right overflow.
I simply updated the package from v2.3.0 to v2.6.2, and changed my code :
SizedBox(
                height: 300,
                child: DChartPie(
                  data: chartData,
                  animationDuration: const Duration(seconds: 1),
                  fillColor: (pieData, index) {
                    if (index == 0) {
                      return Colors.green;
                    } else {
                      return Colors.pink;
                    }
                  },
                  pieLabel: (pieData, index) => "${pieData['measure']}%",
                  labelPosition: PieLabelPosition.inside,
                  donutWidth: 70,
                  labelColor: Colors.white,
                  labelFontSize: 15,
                ),
              ),

to this :
SizedBox(
                height: 300,
                child: DChartPieO(
                  data: chartData,
                  animationDuration: const Duration(seconds: 1),
                  customLabel: (ordinalData, index) =>
                      "${ordinalData.measure}%",
                  configRenderPie: ConfigRenderPie(
                      arcRatio: 0.5,
                      //arcWidth: 70,
                      arcLabelDecorator: ArcLabelDecorator(
                          insideLabelStyle: const LabelStyle(
                              fontSize: 15, color: Colors.white),
                          labelPosition: ArcLabelPosition.inside)),
                ),
              ),

I didn't change anything else, and my SizedBox is still in a Scaffold > SingleChildScrollView > Column.
But now i'm getting a rendering exception :
══╡ EXCEPTION CAUGHT BY RENDERING LIBRARY ╞═════════════════════════════════════════════════════════
The following TypeErrorImpl was thrown during paint():
Unexpected null value.


double.NaN leads to errors

Steps to reproduce:
Hello together :)
When adding double.NaN or 0.0/0.0 anywhere in the I my Code (target framework 4.7.2) I get the error message below. I tried checking and unchecking the "Single + Double inf"- and "-nan(ind)" checkboxes in the DllExport GUI but it did not help.
I can get it to compile by using your IL assembler but when doing this I loose the ability to debug my code (no breakpoints are working anymore).
The same issue appears for +-Inf too.
Can you please help me how to fix this? Thanks!

DllExport -version: 1.7.3
Used Visual Studio 2019 v16.7.5

Information from Data tab or log data:
1>C:\test\packages\DllExport.1.7.3\tools\net.r_eg.DllExport.targets(76,5): error : C:\test\test_project\test.cs(123) : error : syntax error at token '-' in:     IL_0029:  ldc.r8     -nan(ind)
Demo Project files / Samples / etc.:
. . .

@kcl93 Please follow the minimal information from the issue template:

Attach information from Data tab
DllExport [args if used] > cfg.log do not change anything, just Apply.
Build project msbuild [your.sln] /t:Rebuild /m:4 /v:diag > build.log

Attach cfg.log and build.log
Thanks.

Hello @3F
thanks for the quick reply. I have added the required files (including a whole small test project to trigger the issue) below:
Test.zip
Installed: True; 1.7.3+9a4bc51; invoked: 1.7.3
Project type: Cs
Storage: ProjectFiles
Compiler.Platform: Auto
Compiler.ordinalsBase: 1
Compiler.rSysObj: False
Compiler.ourILAsm: False
Compiler.customILAsm:
Compiler.genExpLib: False
Compiler.peCheck: PeIl
Compiler.patches: 3
PreProc.Type: None
PreProc.Cmd:
PostProc.Type: None
PostProc.ProcEnv: $(SolutionPath);$(MSBuildThisFileFullPath)
PostProc.Cmd:
SignAssembly:
Identifier: 21E8B94B-4558-4AEA-848D-9D6D56785116
Instance: C:\Program Files (x86)\Microsoft Visual Studio\2019\Professional\MSBuild\Current\Bin\amd64\MSBuild.exe
Project path: C:\Test\Test\Test.csproj
Action: Configure
PlatformTarget:
TargetFramework:
TargetFrameworks:
TargetFrameworkVersion: v4.7.2
RootNamespace: Test
AssemblyName: Test
MgrArgs:
MetaLib: tools\raw\lib\net20\DllExport.dll
MetaCor: tools\raw\lib\netstd\DllExport.dll
Proxy:
StoragePath: .net.dllexport.targets
ddNS: Test
ddNS max buffer: 500
UseCecil: True
intermediateFiles: False
timeout: 30000
RootPath: C:\Test\
PkgPath: C:\Test\packages\\DllExport.1.7.3\
SlnFile:
SlnDir: C:\Test\.\
DxpTarget: tools\net.r_eg.DllExport.targets
MsgGuiLevel: -1
LockIfError:

Looks like a bug in processing scopes for Compiler.patches.
@kcl93 Thanks for the detailed information and project sample!
Temporarily either encapsulate problematic definitions inside some export methods OR try to use my IL assembler.
Unfortunately I still can't talk any specific about fixes/releases at least from me due to my health problems and more; thus just follow the news. Thanks.

Hello @3F,
thanks for the quick reply! Sadly I can not use your IL assembler as this prevents me from debugging my code (breakpoints do not trigger). However I will try your suggestion to use an exported function that creates NaN values or stay with my current workaround where I just use -666.6 instead of NaN as this will suit me fine to visually see invalid values.
Don't hurry and get well soon! :) Thanks for this great piece of software! I do not know how I could create my programs without it!

Don't hurry and get well soon! :) Thanks for this great piece of software! I do not know how I could create my programs without it!

@kcl93 🤝 Thank you! I hope for it 🖐

Sadly I can not use your IL assembler as this prevents me from debugging my code (breakpoints do not trigger).

This is because of disabled .line https://github.com/3F/coreclr/issues/3

I just use -666.6 instead of NaN as this will suit me fine to visually see invalid values.

Try like this:
double inf = 1e+280;
inf *= 1e+210;
inf *= 1e+110;

inf * 0.0F;

where 0.0F finally must align infinity to -nan(ind)

Please test the fix before merge:

DllExport -force -pkg-link https://ci.appveyor.com/api/buildjobs/wmeu0q8ufdthsi4v/artifacts/bin/Release/DllExport.1.7.3.nupkg

Hello @3F
I have tested your fix as requested and my project now compiles perfectly fine. Thank you very much!
I hope, that you had some nice christmas celebration and are going to have a good start in a better year 2021 :)

Thank you!
I crossed my fingers for a good next year 🤞🤞21
And thanks for using my projects,
https://github.com/3F
-- Denis


chore(dal): use alternative to JoinSet Id to drop unstable requirement

This change tracks an alternate "task ID" (in this case a random Ulid) which is used in the same task_id_to_av_id hash map logic, thus removing the need for the unstable task Id type. In this way it's a little easier to setup a development environment with editors that use cargo clippy and rust-analyzer underneath.

bors try

bors merge

@fnichol what happens here if the task panics?


read memory from 0xc0 failed

@asmello
While debugging something unrelated, I ran into this after a call to Pointer::as_str. This pointer should have been root/default. This is using vs code. I assume the debugger is not throwing bogus data but I suppose that is also a possibility?

The behavior is consistent on each run of the test. It requires getting to the end and encountering my faulty logic before it manifests.
https://github.com/chanced/grill/blob/f48725f562b8972a92156a29b620d7406e4a0ce3/grill-core/src/source/walk/to.rs

Hmm, interesting, Does it behave in some abnormal way otherwise? Or is it just the debugger that's throwing a fit?

My only guess, assuming this is indeed a bug, is that it's a lifetime bug, and the &str is outliving the original Pointer.

Hmm, interesting, Does it behave in some abnormal way otherwise? Or is it just the debugger that's throwing a fit?

It behaves normally


[NuGet Symbol Server] Use the ValidatingType on the Orchestrator side to filter symbols by the packages.

Review the Orchestrator services and update them to use the ValidatingType in APIs that query the Validation DB and when symbols will be ingested could return data for both symbols and packages
There are at least two types that need to have the APIs reviewed:
src\Validation.Common.Job\Storage\ValidatorStateService.cs ( method IsRevalidationRequestAsync )
\src\NuGet.Services.Validation.Orchestrator\ValidationStorageService.cs

Not sure this one either why has to be done for MVP.


Make Slack notification's mrkdwn_in configurable

Slack's webhook API allows passing the mrkdwn_in attribute for specifying which fields that contain markdown. In Alertmanager's current implementation this attribute value is hard-coded to {"fallback", "pretext", "text"}. This is somewhat limiting as Slack supports markdown rendering for more attributes than these – for example in title and fields.
I believe this attribute should be configurable via SlackConfig like the other ones (it can still default to the current value).

This is the link to API docs: https://api.slack.com/docs/message-formatting#message_formatting

I gave this a try:
https://github.com/prometheus/alertmanager/pull/1967


ETH support planned?

Hi,
great project!
Any plans for a revision with ethernet mac support for fast communication with companion computer?
Peter

Hello,
Thank you for your advice, current version provides IMU message at 500Hz using maximum baudrate. The maximum control rate and message rate are limited by MCU frequncy and also PX4 firmware. I don't think there is a need for ethernet mac in Nxt, because of the PCB size and also the task of our research. But if there is a need for that, I may seek for some cooperation to achive that.


Allow user to pass in custom date parsing functionn

This changes the option auto_parse_date to also accept function.
ex:
options = {
  ...,
  auto_parse_date: function(value) { return value+' 00:05:00'; }
}

Alternatively we could add auto_parse_custom using a similar approach to this PR and keep auto_parse_date as is.
Thoughts?

This make sense, thanks

Note: I didn't update the docs, wasn't sure where to do that to let people know they could pass in a fct.

Docs are here. I've already updated them.


Error with custom composite type as parameter in function

I'm getting this error when I use a custom composite type as a parameter in an sql function.
malformed record literal: "somefilter"
Here is the custom type and the function in my database schema:
CREATE TYPE search_recipes_condition as (
    title_like text
);

create or replace function ivtracker.search_recipes(condition search_recipes_condition) returns setof ivtracker.recipe as $$
    select recipe.*
    from ivtracker.recipe as recipe
    where (
      (condition.title_like) is null OR
      recipe.title ilike ('%' || (condition.title_like) || '%')
    )
$$ language sql stable;

Here is the graphql query I'm sending that causes the error:
query {
  searchRecipes(condition: {titleLike: "somefilter"}) {
    nodes {
      nodeId
      title
    }
  }
}

The really weird thing is if I modify the query to add parenthesis to the filter, the error goes away and the query works as expected:
query {
  searchRecipes(condition: {titleLike: "(somefilter)"}) { # <--- note the change here
    nodes {
      nodeId
      title
    }
  }
}

I'm trying to set up the function like this to mirror the autogenerated functions that postgraphql provides for each table.  This will allow me to share some code between my components.
I could change my components to send the query with the parenthesis but it seems like it might be a signal of another issue.  Any ideas?

Please confirm “postgraphql --version” and whether this still happens with the latest version.

Original error occurred with 3.5.0.
I just upgraded to 3.5.1 and got the same error.

I think this is fixed in postgraphql@next; would you mind confirming? See #506

Upgraded to 4.0.0-alpha2.22, issue still present.

Please provide a minimal test case that reproduces this (an SQL file I can import into a clean database and a postgraphql command line) and I'll look into it.

Here's a minimal schema to recreate the issue:
DROP TABLE if exists recipe cascade;
create table recipe
(
    recipe_id                    serial primary key,
    title                        varchar(100)
);
drop type if exists search_recipes_condition;
CREATE TYPE search_recipes_condition as (
    title_like text
);
create or replace function search_recipes(condition search_recipes_condition) returns setof recipe as $$
    select recipe.*
    from recipe as recipe
    where (
      (condition.title_like) is null OR
      recipe.title ilike ('%' || (condition.title_like) || '%')
    )
$$ language sql stable;

insert into recipe (title) values
('firsttitle'),
('secondtitle'),
('thirdtitle'),
('fourthtitle');

Here's a graphql query that throws the error:
query {
  searchRecipes(condition: {titleLike: "title"}) {
    nodes {
      nodeId
      title
    }
  }
}

Thank you for your work and stewardship on this awesome tool!

v3.5.1 definitely does:

I'm afraid I'm unlikely to solve this issue in v3 as my work is focussed on getting v4 ready for release. If you want to raise a PR to fix this in v3 that'd be welcome, but failing that I recommend you use v4 for now 👍

Ah yes, you are right.  Looks like I had it installed globally and locally (woops!) so the upgrade didn't take when I tested.
Don't worry about fixing v3, definitely not worth it.

This is not an issue in v4


arro3 version in osx-arm

@keewis are any workarounds to install arro3-cor from conda-forge in osx-arm? I get the following error under a conda environment:
Channels:
 - conda-forge
 - defaults
Platform: osx-arm64
Collecting package metadata (repodata.json): done
Solving environment: - warning  libmamba Added empty dependency for problem type SOLVER_RULE_UPDATE                                                                                failed

LibMambaUnsatisfiableError: Encountered problems while solving:
  - package arro3-core-0.4.2-py310hc06a964_1 requires python >=3.10,<3.11.0a0 *_cpython, but none of the providers can be installed

Could not solve for environment specs
The following packages are incompatible
├─ arro3-core is installable with the potential options
│  ├─ arro3-core 0.4.2 would require
│  │  └─ python >=3.10,<3.11.0a0 *_cpython, which can be installed;
│  ├─ arro3-core 0.4.2 would require
│  │  └─ python >=3.11,<3.12.0a0 *_cpython, which can be installed;
│  ├─ arro3-core 0.4.2 would require
│  │  └─ python >=3.12,<3.13.0a0 *_cpython, which can be installed;
│  └─ arro3-core 0.4.2 would require
│     └─ python >=3.9,<3.10.0a0 *_cpython, which can be installed;
└─ pin-1 is not installable because it requires
   └─ python 3.13.* , which conflicts with any installable versions previously reported.

When I type pip install -e ., it returns:
ERROR: Could not find a version that satisfies the requirement arro3-core>=0.4.0 (from xdggs) (from versions: 0.1.0b1, 0.1.0, 0.2.0b1, 0.2.0b2, 0.2.0b3, 0.2.0, 0.2.1, 0.3.0b1, 0.3.0b2)
ERROR: No matching distribution found for arro3-core>=0.4.0

Related to #77

I believe it just isn't available on python=3.13, yet.

right - what's then the best developer practice in this case? should we declare the supported python versions in pyproject.toml?

I think that's what the classifiers are for: indicate which python versions are tested. We can't use upper bounds in requires-python because that leads to all sorts of issues.
I personally would develop on the most recent supported version of python (in this case, python=3.12).

That's fine - I just updated the title to specify the issue is with Python 3.13. Hope it's useful for others facing similar issues.


Failed to load opencv.js when using Segment Everything

My actions before raising this issue

[x] Read/searched the docs
[x] Searched past issues

Steps to Reproduce (for bugs)

For the version v2.4.5, deploy the Segment Everything model
Go to annotation page
Interact with the AI Tools and choose Segment Everything, click Interact
Console prints error: GET http://localhost:8/opencv/opencv.js net::ERR_CONNECTION_REFUSED   opencv-wrapper.ts:68
Added console.log and rebuild cvat/ui image, it shows baseURL= http://localhost:8 at opencv-wrapper.ts:68

Expected Behaviour

Expect the opencv.js loaded successfully,
the baseURL should be http://localhost:8080/api instead of http://localhost:8
Current Behaviour

opencv.js load failure, baseUrl is wrong at here:
https://github.com/opencv/cvat/blob/release-2.4.5/cvat-ui/src/utils/opencv-wrapper/opencv-wrapper.ts#L13
const baseURL = core.config.backendAPI.slice(0, -7);

Possible Solution

const baseURL = core.config.backendAPI.replace("/api","");
Context

Your Environment

Git hash commit (git log -1):
Docker version docker version (e.g. Docker 17.0.05):
Are you using Docker Swarm or Kubernetes?
Operating System and version (e.g. Linux, Windows, MacOS):
Code example or link to GitHub repo or gist to reproduce problem:
Other diagnostic information / logs:
Logs from `cvat` container

I had this randomly occur as well. I restarting my computer and the containers a few times and it went away, but it was quite weird... Hopefully it doesn't come back

@FrankUSC
Why slice does not work for you? Are you using not default configuration?


Additional title logic fixes

:sob: @erengy reported more issues. This should finally be the end of it.
/cc @NuckChorris let's get this merged.

:+1: :fire: :watermelon: :8ball:


No shadows cast on a background when below a Material in a view

As in:
https://gist.github.com/HansMuller/7d7dd73728164adbfa8388ebfa97a493
When the background alpha goes to 1.0, the shadow cast by the above material widget becomes invisible.

I don't think this is a bug. A Material widget is only defined to cast a shadow on the Material widget below it.
See http://docs.flutter.io/flutter/material/Material-class.html

I have been convinced that this is indeed a bug.

I don't understand what this bug means.


Add health check grpc command

Codecov Report

Merging #99 (fdcdf51) into main (7d1f054) will decrease coverage by 1.30%.
The diff coverage is 0.00%.

@@            Coverage Diff             @@
##             main      #99      +/-   ##
==========================================
- Coverage   70.93%   69.62%   -1.31%
==========================================
  Files         145      146       +1
  Lines        2697     2749      +52
  Branches       69       69
==========================================
+ Hits         1913     1914       +1
- Misses        784      835      +51

Impacted Files
Coverage Δ

...ukg/surge/multilanguage/scalasdk/sample/Main.scala
0.00% <0.00%> (ø)

...e/multilanguage/scalasdk/BusinessServiceImpl.scala
0.00% <0.00%> (ø)

...a/com/ukg/surge/multilanguage/scalasdk/Model.scala
0.00% <ø> (ø)

.../ukg/surge/multilanguage/scalasdk/ScalaSurge.scala
0.00% <0.00%> (ø)

...tilanguage/GenericAsyncAggregateCommandModel.scala
0.00% <0.00%> (ø)

...rge/multilanguage/MultilanguageGatewayServer.scala
0.00% <0.00%> (ø)

...ultilanguage/MultilanguageGatewayServiceImpl.scala
0.00% <0.00%> (ø)

...health/windows/actor/HealthSignalWindowActor.scala
93.10% <0.00%> (ø)

...a/surge/kafka/KafkaPartitionShardRouterActor.scala
62.77% <0.00%> (+0.72%)
:arrow_up:

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
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Is this one ready to go?

Is this one ready to go?

Not yet @garyKeorkunian


View Custom Option without scroll on RF builder

Jenny's comment on #1331.  Minor usability request, only if it's easy: Any simple adjustments you could make to the templates menu, such that the Custom option is more likely to be visible without needing to scroll?
Here's what I see now:

@jennyhavah What sort of solution would you prefer?  We could make the dropdown box droppier downer, or font smaller, etc...  Or should I just hand this off to @axoplasm .

I'm inclined to adjust the length of the menu so that more items can be seen, rather than do anything non-standard with the fonts. (I tried not to be too prescriptive, since I don't have a sense of what adjustments would be straightforward versus what would require hackery.)


flotr2 contains its dependencies

core/src/vendor/flotr2/flotr2.js
contains an old copy of underscore, and bean.
we should a version without libs, or that works with our module system.
or, just get rid of it, what is it used for anyway?

Used in: ./core/src/main/web/outputdisplay/bko-chart.js


Mount an FSx for Lustre file system to a Notebook Instance.

Issue #, if available:
N/A.
Description of changes:
Added new on-start script to mount an FSx for Lustre file system to a Notebook Instance.
Testing Done

[x] Notebook Instance created successfully with the Lifecycle Configuration
[x] Notebook Instance stopped and started successfully
[x] Documentation in the script around any network access requirements
[x] Documentation in the script around any IAM permission requirements
[x] CLI commands used to validate functionality on the instance
[x] New script link and description added to README.md

# Provide your commands here

# Here is a list of CLI commands I used to test the script:

SCRIPT_NAME=mount-fsx-lustre-file-system
ROLE_ARN=my-role-arn
SUBNET_ID=my-subnet-id
SECURITY_GROUP_ID=my-security-group-id

RESOURCE_NAME="$SCRIPT_NAME-$RANDOM"

aws2 sagemaker create-notebook-instance-lifecycle-config \
    --notebook-instance-lifecycle-config-name "$RESOURCE_NAME" \
    --on-start Content=$((cat scripts/$SCRIPT_NAME/on-start.sh || echo "")| base64)

# At this point, we need to update the script that was created to provide
# the appropriate values for the FSX_DNS_NAME and FSX_MOUNT_NAME parameters.

aws2 sagemaker create-notebook-instance \
    --notebook-instance-name "$RESOURCE_NAME" \
    --instance-type ml.t2.medium \
    --role-arn "$ROLE_ARN" \
    --lifecycle-config-name "$RESOURCE_NAME" \
    --subnet-id "$SUBNET_ID" \
    --security-group-ids "$SECURITY_GROUP_ID"

aws2 sagemaker wait \
    notebook-instance-in-service \
    --notebook-instance-name "$RESOURCE_NAME"

aws2 sagemaker create-presigned-notebook-instance-url \
    --notebook-instance-name "$RESOURCE_NAME"

# At this point, we can open the notebook, go to a terminal and
# make sure the file system is mounted on the /fsx directory.

aws2 sagemaker stop-notebook-instance \
    --notebook-instance-name "$RESOURCE_NAME"

aws2 sagemaker wait \
    notebook-instance-stopped \
    --notebook-instance-name "$RESOURCE_NAME"

aws2 sagemaker start-notebook-instance \
    --notebook-instance-name "$RESOURCE_NAME"

aws2 sagemaker wait \
    notebook-instance-in-service \
    --notebook-instance-name "$RESOURCE_NAME"

By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.

Thanks for contributing this script!  I'll merge it in!


Convert workbox-routing to Typescript

R: @jeffposnick
This PR continues the migration to Typescript (started in #2058) and adds type definitions for workbox-routing.

Coverage remained the same at 80.794% when pulling cac9560d5bb848bb4bb4310ae25b2dbba1d0fc32 on ts-workbox-routing into 5a57742bb739f0e390a748ae88e0848fa44edf38 on master.


[Patch] Fix UTC timezone issue with timestamptz

Fix UTC time zone in Annon.Requests.Analytics

timestamptz resulted in datetime with additional UTC correction added. Replaced by timestamp.

Hello, @ybod! This is your first Pull Request that will be reviewed by Ebert, an automatic Code Review service. It will leave comments on this diff with potential issues and style violations found in the code as you push new commits. You can also see all the issues found on this Pull Request on its review page. Please check our documentation for more information.

Ebert has finished reviewing this Pull Request and has found:

2 possible new issues (including those that may have been commented here).
3 fixed issues! 🎉

But beware that this branch is 24 commits behind the Nebo15:master branch, and a review of an up to date branch would produce more accurate results.
You can see more details about this review at https://ebertapp.io/github/Nebo15/annon.api/pulls/270.

sorry, wrong repo


Implemented feature for issue #39 & #36.

As title says, it's mentioned in Issue #39 & Issue #36.

Also fix for issue mentioned by "Barns#1757".


Initial support for column-split cpu predictor

When data is split by column, produce predictions in two passes by leveraging bit vectors.
Part of #8424

@trivialfis @hcho3

merging the master branch should fix the CI error.


Document minimum system requirements for uProxy on Android

High-end Android phones seem to be able to run uProxy with acceptable behavior.  Low-end phones do not.  We'll need to figure out where the boundary is, and document it for users.

This probably is related to deciding the minimum android API level we're
targeting.  There are many fewer Low-end phones running Android 5.0, if we
need that anyway.
On Wed, Nov 25, 2015 at 12:05 PM, Benjamin M. Schwartz <
<EMAIL_ADDRESS>wrote:

High-end Android phones seem to be able to run uProxy with acceptable
behavior. Low-end phones do not. We'll need to figure out where the
boundary is, and document it for users.
—
Reply to this email directly or view it on GitHub
https://github.com/uProxy/uproxy/issues/2097.

Yes, Android API version matters, and this is tied to the choice of whether to use Crosswalk or native webview.  In my testing, uProxy works with the native webview on Android L and later, whereas with Crosswalk it works with K (and probably several previous releases) too.
Without crosswalk, our .apk is about 4 MB.  With Crosswalk, it's an order of magnitude larger.


feat: Make async session statuses describe a session transition inste…

…ad of a session

The terms "canceled session" and "created session" did not work for the use case where an app was preloaded into the inactive state and then session is called to bring it to foreground but is gated by a user grant. The session was already returned in a previous session call, so it does not make sense to call onSessionCreated for a session that was already created. Also, if the user grant is deferred, then onSessionCanceled does not make sense in this case. The session will still exist in the inactive state, so the session was not "canceled".
This change is so that the event describes a "transition" of a session instead of the session itself. For the use case above, when the session API is called, a transition is started from the inactive status to the active status. This transition can then "completed" or be "canceled". If a transition is canceled, then it just remains in the current status (inactive).
The "state" field was also dropped and replaced with a boolean "transitionPending". This makes it more of a transitive property instead of a final state. A transition can be pending for when the status is going to "not-exists" to "active" or from "inactive" to "active". What the caller cares about is if a transition is happening as a result of the session call so they know to check the events to get the updated session asynchronously.

:tada: This PR is included in version 1.1.0-next.1 :tada:
The release is available on GitHub release
Your semantic-release bot :package::rocket:


Beam Search 4 Translate.py

Hi,
I'm wondering which steps are necessary to move from the greedy decoder currently implemented to an actual beam search decoder. Is this enhancement already in someone's roadmap? If not, could anyone tell me which is the right point in the code where to add this functionality?
Thanks a lot!
Marcello

FYI, I have a variation of a beam search for TF here:
https://github.com/wchan/tensorflow/blob/master/speech4/models/las_decoder.py

I wouldn't mind helping out either. I currently use sampling with temperature to generate different outputs given the same input.

I like the first code a lot, I think it's advantegous when the beam-search is done in the graph, so we can just feed the graph once and get the whole sequence. I think the only thing missing was pulling out hypotheses, right? But we have the top-k op in TensorFlow, woudn't that suffice?

Hi,
let me first say that my knowledge of tensorflow is still quite limited, hence forgive me if I'll write something wrong. From what I read I understood that (1) it is not good getting back and forth between the computations on the graph (on GPU) and computations in python (on CPU), and (2) it is advisable
to also exploit as much as possibly parallel computations on the graph (on GPU), which means in our case computing expansions of alternative hypotheses in parallel.
I have put down the following figure to explain how the beam search could work.

The figure focuses on the decoding step and shows on the top outputs of decoding steps that can be computed in parallel, and the best K=2 output words for each step. As no recombination is possible with RNN, because each output word depends on its whole history, we have to select the output words (with possible repetitions) that result in the top B=3 cumulative scores.  (Notice that K should ideally be equal to B to have beams search with beam B ) These B words become the input for the following step. To allow backtracking of the best translation we only need to store the provenance of each input with respect to the input of the previous step (see dotted arrows). A numeric index corresponding to a position should work. Once we have finished with all computations, we can start backtracking the input trellis
(on the bottom) starting from the input word </s> with the best global score. This could be performed with a linear pass over all columns, from right to left.

@lukaszkaiser In the Seq2Seq paper they say, "As soon as the “”
symbol is appended to a hypothesis, it is removed from the beam and is added to the set of complete
hypotheses". The problem with the implementation I did above is that once an EOS token is appended, the hypothesis remains on the beam. This means that the effective beam size is reduced by one. The easy way to avoid this is to extract 2k hypothesis, which guarantees that we will follow the Seq2Seq approach. Of course, it has the downside of doubling computation.
@mfederico In TF, Python is just a front end language -- no operations are actually run in Python. Whether an operation runs on GPU depends on whether the operation has a GPU kernel (e.g. .cu file) and other scheduling heuristics. Computing hypotheses in parallel is equivalent to decreasing the batch size for each model replica (actually faster because data transfer between GPUs doesn't have to happen).
I like the idea of computing top k on each hypothesis, then computing top k on the combination of remaining words. I think this would be faster if the top k operation is computed in parallel for each row (O(n + k^2) vs. O(nk), where n = vocab size, k = beam size, k << n). @lukaszkaiser would this be faster?

@PrajitR, i really like the approach as well ... question, how would you stop the graph early? i.e., if u know all future partial hypothesis will be worse than the best completed partial hypothesis.

I like the idea of computing top k on each hypothesis, then computing top k on the combination of remaining words.

I like this idea too, but isn't computing top k on the combination of remaining words expensive?

If the k top-k lists are sorted, they will remain sorted if we add the cumulative score of the input hypotheses they were generated from.  The finding the global top-k takes O(k log k) in the worst case with k space (see algorithm below adapted from  http://stackoverflow.com/a/21051271).
With a beam size of 500 this should be rather
initialize a MIN heap
for each array
for each item in the array
item.score+=cum_score[array]
if (heap.count < k)
heap.insert(item)
else if (item.score > heap.peek())
{
// item is larger than the smallest item on the heap
// remove the smallest item and replace with this one
heap.remove_root()
heap.insert(item)
}
else
break;  // go to next array

@PrajitR, I'm trying to implement your suggestion into my experiments, using the Seq2seq interfaces. I think your code is very clever and make lots of sense. Nevertheless, I'm still confused by one thing you mentioned:
"(...) extracts the correct recurrent state for each hypothesis. This can be done with an embedding lookup of prev_state using beam_parent multiplied by each example's index in batch * beam_size."
If I'm using a batch of 1 (i.e., one sentence at the time - am I right with this assertion?), wouldn't  just the lookup of prev_state suffice? If not, I think I didn't get that.
In addition, I think that if we combine the predicted symbols with their parents right after producing them, we could keep a list of complete hypotheses to add those which reach the EOS symbol and we could try to remove them from the beam_path. This would not stop the graph earlier but I think would solve the problem of reducing the effectiveness of the beam size.

Just as a comment: there is some experimental support for session.partial_run() in TensorFlow in the 0.7 release. Since partial_run does not deallocate the tensors, it should make step-by-step decoding from seq2seq models much easier. And since we can have a few of them in parallel, it could also greatly simplify beam_search. But it's experimental for now, so beware - I'm just testing it.

Finally, I'm interested to know if there is a version of translate.py which support beam search in decoding?

I learning translation currently, and found many papers that describe beam seach for Statistical Machine Translation. The main idea is that for the translations that cover the same number of source sentence words, select at most k best candidates, which may combined with Estimating Future Cost method. Are there any papers  describe beam search for neural machine translation (NMT)? At each step,  is selecting k best candidates that have same length is enough? thanks!

@giancds Is it possible to get the code of your implementation?

Hi tilneyyang,
I have declined of implementing the solution suggested in here. I am currently trying some new options on the decoder and have found out that this particular solution does not give me the flexibility I need.
However, I am still doing the beam search but running one step at the time, so part of the beam search is done outside the graph (at the cost of decoding speed to get that freedom).
My code is largely based on the code of @kyunghyuncho (you can find his code here), but he uses Theano in the implementation.
Mine you can find here under the nmt_models.py file.

I'd like to share my in-graph beam search implementation that uses the loop_function approach
https://gist.github.com/nikitakit/6ab61a73b86c50ad88d409bac3c3d09f
I believe it correctly implements length-bucketed beam search. So far I've only tested that the outputs look reasonable (as opposed to comparing with a known-good beam search implementation).
The loop_function API is really nice, but I'm not sure if bending over backwards to avoid writing a custom op was the best decision here.

For those interested in a dynamic_rnn() based beam search, here is an implementation that is working well for us - https://github.com/sdlg/nlc/commit/b5088d102752a9c2aa4a923abb17abfb35026d82

@avati It appears that your implementation only works on a single example at a time batch_size=1. Is this by accident or by design?
For example, compare your code with mine. Note that your call to top_k is on a flat vector (so if there is a batch size >1, the examples compete with each other), while mine is on a 2D matrix. My code has hacks for allowing batch_size>1, though I have now discovered that it's flawed in a different way.

As of now batch_size=1 is by design. I suppose it is possible to support batch_size>1 with dynamic_rnn(), but the code got unreadable quickly the first time i tried. dynamic_rnn() based code is in general less readable already compared to the time-step unfolded code (which enjoys use of native python loops etc). I plan to revisit batch_size>1 support sometime in the future.

FYI my gist is now updated with fixes and a new API.
I'd love to get feedback from others on whether this API would suit their needs!
I think I've mostly converged on something that works well with typical uses of tensorflow. I'm curious if there is interest in getting something like this added to tf.contrib (maybe @lukaszkaiser can comment?) If that is the case, I'm willing to add in some more optimizations (e.g. dynamic early-stopping like the code by @avati) and do general profiling/cleanup on the code.

Here is the link for extension of  tensorflow seq2seq model for conversation models and has the option of beam search and setting beam size .
https://github.com/pbhatia243/Neural_Conversation_Models

@pbhatia243 Hi
I have read your code https://github.com/pbhatia243/Neural_Conversation_Models/blob/master/my_seq2seq.py#L729 for beam_search, and I got several questions maybe you can shed some light on that
1)line 119
    emb_prev  = tf.reshape(emb_prev,[beam_size,embedding_size]), it seams that emb_prev has a shape of [batch_size * beam_size, embedding_size] before reshape, which i thought could lead some error?

line 100
probs = tf.reshape(probs + log_beam_probs[-1],                             [-1, beam_size * num_symbols])

As i considered, only when the probs matrix is in beam-major order could this reshape work well,
like:
batch0_beam0_symbol_vec
batch0_beam1_symbol_vec
batch0_beam2_symbol_vec
....
But i found that in line  668 - 670
for kk in range(beam_size):
      states.append(state)
state = tf.reshape(tf.concat(0, states), [-1, state_size])

the  state is constructed in a seemly batch-major manner, which can't be align to the probs (which come from last cell output that takes the concatenation of state and x as input)
is there anything i missed?

the problem are soluved, the batch_size is 1, so all the reshape works well, all clear

I see that the raw_rnn API has been updated a few weeks ago to allow hidden state reordering, as required by beam search. This means that using it will likely yield the cleanest and most computationally efficient way to integrate beam search with tensorflow.

@nikitakit could you elaborate what you mean with "hidden state reordering" and how you believe this can solve the requirements by beam search?
Thanks

Jumping in on this -- has beam search been added to the seq2seq models, or has someone implemented a neat way of incorporating it into existing models?

Now that my research has once again touched on beam search, I've taken another look at the latest comments here. It seems that my previous remarks about raw_rnn were greeted with some confusion, though I thought the meaning was sufficiently clear if you understand the math behind beam search. I hope that in the meantime everyone found a working beam search solution among those posted in this thread.
I've now also updated my gist to take advantage of the latest changes to raw_rnn. The API it provides has changed slightly, and the code passes all of the test cases I have (though fair warning: I have yet to apply it to my own full-scale models).
This new version is more efficient in that it can stop early if the highest-scoring solution has already been found. I've also now figured out how to transparently handle all cells (including those with attention) without requiring a custom wrapper class, and have updated the API to take advantage of that. The new API is just a single beam_decoder function.
It should be straightforward to slot in anywhere greedy search is currently used. (If anyone has code that can adapt it to the seq2seq primitives, I think that may be helpful for others. I myself tend to use the base rnn functions instead of anything higher-level).
Last time my beam search implementation was suggesting for merging into tf.contrib, it was determined that the old API was not ideal. I'd be happy to try again with this new API if others are interested.
To conclude, a note on back-propagation: a few people have asked me whether it's possible to use my code during training, and back-prop through the beam search. I've come to conclude that it is fine to just run the beam search to find the top-scoring sequences, and then do a second forced decode under those sequences (where the second decode is back-propagated through). Beam search has to consider a very large number of possible candidates, so duplicating this work shouldn't be too much of an issue. On the other hand, the way the beam search couples multiple candidate sequence means that any naive application of automatic differentiation may instantiate backwards-pass tensors for all of the candidates, rather than just the sequence you care about. That would be an even bigger performance hit than duplicating a small fraction of the forwards pass computations.

Can you send me the Pull Request with the changes

@pbhatia243 Could you clarify what you meant by your last comment?
To be clear, my implementation self-contained and independent of what's in your repo, so surely you were referring to something else? (Feel free to use my code if you need it, though)
One thing I want to point out, having taken a look at your Neural Conversion Models repo, is that it uses a loop function approach. I therefore suspect that the implementation is actually subtly broken -- I believe it contains the same mistake that I made when I first tried to implement in-graph beam search. (It's really hard to notice without directly looking for it or having good tests.) If you look at the code here, you'll see that the cell produces both outputs and hidden states. The outputs are eventually reordered in a top-k step. But when the outputs are re-ordered, the states need to be re-ordered as well! Yet, I have not found a re-ordering step anywhere (i.e. states are never passed to a function like tf.gather). This is the reason why I had to abandon the original loop_function based approach and waited for hidden state reordering to be added to raw_rnn before I could take advantage of it.

Can't we use the newly added dynamic_rnn_decoder?
According to the documentation, we can use context_state parameter of decoder_fn to implement beam search, however I am not sure how to use that.
I think I could see if we can implement beam search using the function in a couple of weeks, but is there anyone interested on this?

@jihunchoi Thanks for bringing this to my attention. As far as I can tell dynamic_rnn_decoder is just a thin wrapper around raw_rnn. That is, the raw_rnn based implementation I have would be functionally identical to a dynamic_rnn_decoder one, so no point in modifying something that already works.

The dynamic_rnn_decoder was built to, eventually, support beam search. We just havn't gotten around to implement it yet.

In the original Google's SmartReply paper:

Our search is conducted as follows. First, the elements of R (the set of all possible responses) are organized into a trie. Then, we conduct a left-to-right beam search, but only retain hypotheses that appear in the trie. This search process has complexity O(bl) for beam size b and maximum response length l. Both b and l are typically in the range of 10-30, so this method dramatically reduces the time to find the top responses and is a critical element of making this system deployable.

I want to implement something similar. Some questions:

How to represent the set of all possible responses (R) in a trie data structure in TF?
Does the current implementation of beam search support retaining only hypotheses that appear in the trie?

Hi, @pbhatia243
As @nikitakit says, it seems that your code in Neural Conversion Models repo is not the complete Beam Search. It may be a solution between the absolutely greedy method and Beam Search.
As the normal Beam Search method, it need to reset the state of RNN during decoding.
Could you add a step to reset the state by the beam_parent in your code?

@Syndrome777 : I am about to give a second pass on beam search. Could you elaborate on reseting the state. Do you mean reorder ?

@nikitakit : How has the performance been on your large dataset. Using my approach, even though its an approximation, I do get realistic answers and lower perplexity. I am about to give a second pass on beam search. Could you elaborate on reseting the state. Do you mean reorder ?

@pbhatia243 I think you must have figured this out already, but, I am leaving this comment, hoping to help people reading this thread. I believe what Syndrome777 and nikitakit meant to point out was "reordering" the states after you reorder the outputs sequences.
If you do not re-order the states along with the outputs(beams), then you could end up trying to generate next token of sequence A with a state used to generate sequences B in the previous step, which most likely would not know of the context of sequence A.

Agree with junskang, I have used nikitakit doing in graph beam search and face this problem. I you use attention you also need to re-order attention. Also for someone trying to use beam search I suggest refering to https://github.com/google/seq2seq/tree/master/seq2seq which has a dynamic implementaion of ingraph beam search. For out graph beam search you can refer to  models/im2txt

@chenghuige Thanks! Would give us some example of the google implementation? Does it cover mini-batches? If we can only process a sequence at a time, does time matter?

I'd recommend using the google/seq2seq github repo. It's a very good, complete and maintained seq2seq implementation. Beam search is here: https://github.com/google/seq2seq/blob/master/seq2seq/inference/beam_search.py

@lukaszkaiser any plans on adding beam search to tensorflow from google/seq2seq?
The google/seq2seq repo seems very good for experimentation but it does a lot of things different than TF (e.g., conf files to define classes).

I think we always welcome contributions and stable things moving to tf.contrib, but I'll not be working on it personally.

thanks @lukaszkaiser. If I end up implementing it i'll send a pull request.

Great! I think tensorflow/contrib/seq2seq/ would be a good place for it.

Hi All,
I have problem with NMT all out put is unknown. my parallel data is small. is this the problem and how can i solve it

Hello, I am currently trying to use nikitakit's beam search code in conjunction with an attention mechanism. @nikitakit mentioned in a previous post that the code now works with attention but later @chenghuige said that it doesn't reorder attention -- does this mean it is not possible to currently use nikitakit's code with an attention mechanism? Thanks!

My code works fine with attention. All of my own models that i used it for relied on attention. However, you do need to make sure that cell_transform is set correctly (instructions in the docstring).
That said, the code still uses the tensor flow 0.12 API and I haven't updated it for tensorflow 1.0

Great, thanks! Out of curiosity, when you've integrated attention have you been using AttentionCellWrapper, or some other means? Also, does the code in general support custom attention cell wrappers?

@classicCoder16 I used custom cells, but AttentionCellWrapper will work too. Anything that implements the RNNCell interface is compatible -- the interface specifies how the cells are supposed to store their state, and the beam search has access to that for state reordering.

Hi, @nikitakit , I am currently working on an application in which I would like to incorporate attention with your implementation of beam search. So far I have been struggling to implement my own custom attention wrapper and I wanted to ask if you would be willing to share your code for the custom attention cells which you referenced in your most recent post? Thanks for the help in advance.

Hi, @nikitakit, Does your implementation works in tensorflow version >=1.0?  For example, if I use TrainingHelper during the training? how should I use your implementation during the testing? Thanks.

Following some implementations of the beam search decoding, I should say it becomes difficult and impossible to modify the codes implemented within more than 500 lines. Here, I provide a concise implementation of the beam search decoding. This implements a hard attention over encoder hidden units and applies beam search to find the most probable output.  Please comment if there are any technical errors.  Basically, this code benefits from the tf.nn.top_k function.
(This may not be computationally efficient)
def beamsearch_decoding(self, H, beamsize):

        #batch size
        b_size = tf.shape(H)[0]

        """Reload softmax prediction layer"""
        with tf.variable_scope("softmax", reuse=True):
            U_softmax = tf.get_variable("U_softmax")
            b_softmax = tf.get_variable("b_softmax")

        #we need to reload the tag embedding layer.
        with tf.variable_scope("tag_embedding_layer", reuse=True):
            tag_lookup_table = tf.get_variable("tag_lookup_table")

        GO_symbol = tf.zeros((b_size, self.tag_size), dtype=tf.float32)
        initial_state = self.decoder_lstm_cell.zero_state(b_size, tf.float32)
        H_t = tf.transpose(H, [1,0,2])

        """ we will need index to select top ranked beamsize stuff"""
        #batch index
        b_index = tf.reshape(tf.range(0, b_size),(b_size, 1))

        #beam index
        be_index = tf.constant(
                                beamsize*beamsize,
                                dtype=tf.int32,
                                shape=(1, beamsize)
                                )

        with tf.variable_scope("decoder_rnn", reuse=True) as scope:
            for time_index in range(self.max_sentence_length):
                if time_index==0:
                    output, (c_state, m_state) = self.decoder_lstm_cell(GO_symbol, initial_state, scope)
                    H_and_output = tf.concat([H_t[time_index], output], axis=1)
                    pred = tf.add(tf.matmul(H_and_output, U_softmax), b_softmax)
                    predictions = tf.nn.softmax(pred)
                    probs, indices = tf.nn.top_k(predictions, k=beamsize, sorted=True)
                    prev_indices = indices
                    beam = tf.expand_dims(indices, axis=2)
                    prev_probs = tf.log(probs)
                    prev_c_states = [c_state for i in range(beamsize)]
                    prev_c_states = tf.stack(prev_c_states, axis=1)
                    prev_m_states = [m_state for i in range(beamsize)]
                    prev_m_states = tf.stack(prev_m_states, axis=1)

                else:
                    prev_indices_t = tf.transpose(prev_indices, [1,0])
                    prev_probs_t = tf.transpose(prev_probs, [1,0])
                    prev_c_states_t = tf.transpose(prev_c_states, [1,0,2])
                    prev_m_states_t = tf.transpose(prev_m_states, [1,0,2])
                    beam_t = tf.transpose(beam, [1,0,2])

                    probs_candidates = []
                    indices_candidates = []
                    beam_candidates = []
                    c_state_candidates = []
                    m_state_candidates = []
                    for b in range(beamsize):
                        prev_output = tf.nn.embedding_lookup(tag_lookup_table, prev_indices_t[b])
                        output, (c_state, m_state) = self.decoder_lstm_cell(
                                                        prev_output,
                                                        (prev_c_states_t[b],prev_m_states_t[b]),
                                                        scope
                                                        )

                        H_and_output = tf.concat([H_t[time_index], output], axis=1)
                        pred = tf.add(tf.matmul(H_and_output, U_softmax), b_softmax)
                        predictions = tf.nn.softmax(pred)
                        probs, indices = tf.nn.top_k(predictions, k=beamsize, sorted=True)
                        probs_t = tf.transpose(probs, [1,0])
                        indices_t = tf.transpose(indices, [1,0])
                        for bb in range(beamsize):
                            probs_candidates.append(tf.add(prev_probs_t[b], tf.log(probs_t[bb])))
                            indices_candidates.append(indices_t[bb])
                            beam_candidates.append(tf.concat(
                                                        [beam_t[b],
                                                         tf.expand_dims(indices_t[bb], axis=1)
                                                         ], axis=1
                                                         )
                                                    )
                            c_state_candidates.append(c_state)
                            m_state_candidates.append(m_state)

                    temp_probs = tf.stack(probs_candidates, axis=1)
                    temp_indices = tf.stack(indices_candidates, axis=1)
                    temp_beam = tf.stack(beam_candidates, axis=1)
                    temp_c_states = tf.stack(c_state_candidates, axis=1)
                    temp_m_states = tf.stack(m_state_candidates, axis=1)
                    _, max_indices = tf.nn.top_k(temp_probs, k=beamsize, sorted=True)

                    #index
                    index = tf.add(
                                tf.matmul(b_index, be_index),
                                max_indices
                                )
                    prev_probs = tf.gather(tf.reshape(temp_probs, [-1]), index)
                    prev_indices = tf.gather(tf.reshape(temp_indices, [-1]), index)
                    beam = tf.gather(tf.reshape(temp_beam, [-1, time_index+1]), index)
                    prev_c_states = tf.gather(
                                            tf.reshape(
                                                temp_c_states,
                                                [-1, self.tag_size]
                                            ),
                                            index
                                        )
                    prev_m_states = tf.gather(
                                            tf.reshape(
                                                temp_m_states,
                                                [-1, self.tag_size]
                                            ),
                                            index
                                        )

            beam_t = tf.transpose(beam, [1,0,2])
            self.outputs = beam_t[0]

        return

@PrajitR I extended your version and verified it. Hope it helps.
https://stackoverflow.com/a/50304227/3552975


Add torch weight conversion

Why do we need numpy as the intermediate format?

intermediate

I'm not sure if you can import mindspore and torch at the same time

I think the current .py files are not straightforward enough to use. My suggestion is to use just two .py files:

pt2ms.py: accepting version (1 or 2) and pt_weight_path as arguments;
ms2pt.py: accepting version (1 or 2) and ms_weight_path as arguments.

.pkl files may be saved as intermediate states, or deleted after weights translation.

done

I think the current .py files are not straightforward enough to use. My suggestion is to use just two .py files:

pt2ms.py: accepting version (1 or 2) and pt_weight_path as arguments;

ms2pt.py: accepting version (1 or 2) and ms_weight_path as arguments.

.pkl files may be saved as intermediate states, or deleted after weights translation.

please move tools/* to https://github.com/mindspore-lab/mindone/tree/master/examples/stable_diffusion_v2/tools/*
conversion is only for stable diffusion models.
examples/stable_diffusion_v2/tools/

pt2ms.py
xxxx.py
mv readme.md examples/stable_diffusion_v2/pt2ms.md

please move tools/* to https://github.com/mindspore-lab/mindone/tree/master/examples/stable_diffusion_v2/tools/* conversion is only for stable diffusion models. examples/stable_diffusion_v2/tools/

pt2ms.py
xxxx.py
mv readme.md examples/stable_diffusion_v2/pt2ms.md

done


Add a way to retrieve all types of Comment node (not only documentation)

Hi 👋
I was looking for a way to add a LintRule on comments. I figured out that the Comment nodes retrieved with LintRuleNodeRegistry.addComment were only documentation comments.
After investigating and opening an issue on the Dart repo, it seems that there is a wrong documentation of what a Comment node is.
I would like to have an easy way to retrieve all types of Comment in the LintRule.run method.
An other solution may be to use StringScanner(source).tokenize with the String source of the file retrieved with resolver.source.contents.data, but this would be better for users to have an easier way of retrieving all comments.

:wave:
I'm not sure why this would be the responsibility of custom_lint. Could you expand on why something should be done here instead of in the analyzer package?

The registry is a one-to-one match with GeneralizedAstVisitor though.
Yes that's what I saw. I was thinking of an extension method to not edit the lint_visitor_generator.

Maybe if it picks up a lot of 👍 but for now I'd say no.
Ok with this. I let you decide if you want to close it.


Documentation: No mention of records in object

Does hegel support objects with arbitary keys ?
Like
const headers: { [key: string]: string } = req.headers
const headers: Record<string, string> = req.headers

An object with an arbitary key ( string or number I guess ... ) and a known value T, in the example above string but could by anything.
This was not found on https://hegel.js.org/docs/object-types

Does hegel support objects with arbitary keys ?
No. It supports it only inside the d.ts typing (via $Collection, but you can't create value for the type).
Our position is: if you need key-value storage - use Map.
But, we try to implement it inside inner typing to have the ability do like this

Using {} as a dictionary instead of Map is important for using hegelJS on existing codebases.
I want to look at taking an existing JS library like https://github.com/Raynos/error/blob/master/index.js#L62-L76 and porting it to hegel.
Here is another example ( https://github.com/Raynos/fake-ses/blob/master/index.js#L112-L120 ).
I understand {} or Object.create(null) might not be as nice as Map but it should be supported.

@JSMonk well objects with arbitrary keys sounds like a bad idea but Array is a object and a collection and we are okay with it
both flow and typescript supports this and it's common pattern in js so you really need to consider supporting it

Okay, I will add the ability to use $Collection with objects soon. Thank you for the arguments and issue ^_^


Fe mongo with feature toggles

MongoDB experiment was run in branch fe-mongo-with-feature-toggles but given the results, it was discarded.


Update accconfirm.md

Fixes #7954
@MSIH  please review

@barry-schneider  thanks for review and pointing this out.


added jobs for drlx-parser

Is this the same as https://github.com/kiegroup/kie-jenkins-scripts/pull/73?

@psiroky yes you right. it is the same. so #73 can be closed and this one merged


Support self signed certificates for proxy's

Motivation
I am using the proxy feature to talk securely with my secure kentico kontent repository via somee middleware. My middleware is created in .net and running with a self signed certificate. The SDK is throwing a reject error because of the fact that the development endpoint is sucered with a self signed certificate. This makes debugging harder.
Proposed solution
Support the axios rejectUnauthorized option
Additional context
The error that is being thown right now:

Note: this only happends when call's are made from the (node) server.

Thanks for submitting the issue!
I've exposed AxiosRequestConfig in the HttpService so you can now fully configure it as per Axios' documentation. For your scenario you could use it like this:
const client = new DeliveryClient({
    projectId: 'xxx',
    httpService: new HttpService({
        axiosRequestConfig: {
          httpsAgent: new Agent({
              rejectUnauthorized: false
          })
        }
    })
});

This commit is not yet released, but will be soon.

Great! Looks promising!
Btw: You happen to be at the Kentico Usergroup Meeting in Breda (The Netherlands) on 30 oktober?


VM: Implementing Resource class

Tracking issue for the VM's implementation of Resource class.
See meta issue https://github.com/dart-lang/sdk/issues/23823

Blocking roll-out of dartdoc in SDK, hence High pri.

Landed in https://github.com/dart-lang/sdk/commit/f63db4960cf66bc0f19ea1eceb4549efead30dfc
Thanks!

I'm also curious where the Resource tests live.


Need to add better logging

I think we need better logging to know:

when app decides not to make no prediction at all b/c not confident enough
anytime a user installs the app.

Let me know if you can think of any other items to log.
cc: @jlewi @inc0

This has been addressed


[HUDI-3189] Fallback to full table scan with incremental query when files are cleaned up or achived for MOR table

Tips

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Please review https://hudi.apache.org/contribute/how-to-contribute before opening a pull request.

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(For example: This pull request adds quick-start document.)
Brief change log
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Modify AnnotationLocation checkstyle rule in checkstyle.xml

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(Please pick either of the following options)
This pull request is a trivial rework / code cleanup without any test coverage.
(or)
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This change added tests and can be verified as follows:
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Added integration tests for end-to-end.
Added HoodieClientWriteTest to verify the change.
Manually verified the change by running a job locally.

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[ ] Has a corresponding JIRA in PR title & commit

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Another thinking should we also let IncrementalRelation extend HoodieBaseRelation and HoodieIncrementalRelationTrait?  There are many common codes exists in these classes.

@danny0405 @nsivabalan could you plz review this?

@danny0405 the CI pass~


Update Chinese translation for docs

Summary
Update Chinese translation for docs

Thanks for sending a PR @edwardwang0302! However, these files are auto generated - you wanna make your change on crowdin here: https://crowdin.com/project/jest. The changes are then synced when we roll out a new version of the website