[RDY] Fix tests for environment

While refactoring #3687 I discovered some missing asserts in the tests and also one test isn't testing at all.

Also, there are missing the same tests for DockerBuildEnvironment

@stsewd It's pretty hard for me sometimes to know when a PR is ready to be reviewed when you stagger commits like this. Can you staring including a [WIP] note in the title, or in the main comment, and then signify when it's ready for review in a comment?

@RichardLitt sorry, I add notes for my self sometimes. Sure, I will start to using the [wip] and [rdy] tags on my PRs. This one is ready for a review.

@stsewd No worries! I'm just trying to make it easier for other reviewers to know that you're ready to go or not. :)

@RichardLitt isn't @stsewd allowed to add the proper label to the PR that he opens?

@humitos That would make the most sense, but I haven't seen @stsewd label things yet and I don't know if he has access to that ability at the moment.

@RichardLitt @humitos I don't have the necessary permissions

@humitos please take a look at the latest messages from those commits https://github.com/rtfd/readthedocs.org/pull/3764#commits-pushed-4546db9, to be sure that this is really what we want (we are losing some information with the recent changes).

@stsewd want to rebase this and confirm it still passes. It looks like good stuff that we should have in master.

Rebased and checked, everything looks ready


Adding maps to gas/maintenance/paint shops

Added mapbox's map to Gas, Maintenance and Paint shops pages

Gas - Maintenance - Paint Shops pages now looks like this


[Feature] [Style] Add Spotless plugin to fix formatting and style errors automatically

Purpose of this pull request

Replaced Checkstyle and Scala Style with Spotless to fix formatting and style errors automatically
Gave examples on style fix of .java, .md, .scala files.
Updated related docs.
This PR closes: #2492

Check list

[x] Code changed are covered with tests, or it does not need tests for reason:
[x] If any new Jar binary package adding in your PR, please add License Notice according
New License Guide
[x] If necessary, please update the documentation to describe the new feature. https://github.com/apache/incubator-seatunnel/tree/dev/docs

This is a very helpful feature for us. @531651225  @legendtkl  @TyrantLucifer  @laglangyue  @getChan  @2013650523  @TaoZex  @liugddx  PTAL.

great

@ashulin Sorry for the late update, I add extra configurations for scala formatting configurations and rename the previous eclipse.importorder to seatunnel.importorder. BTW, I found there are lot of suppressions related to checkstyle, I suppose I need to remove them as well?
https://github.com/apache/incubator-seatunnel/blob/dev/tools/checkstyle/suppressions.xml
https://github.com/apache/incubator-seatunnel/blob/c29370a7b5bf6d70e4266b4b4b345e059a699148/seatunnel-connectors-v2/connector-elasticsearch/src/main/java/org/apache/seatunnel/connectors/seatunnel/elasticsearch/client/EsRestClient.java#L51

@ashulin  PTAL

Hi, @EricGao888 Is there any process here? If this is not convenient for you, I can continue to do this.

Hi, @EricGao888 Is there any process here? If this is not convenient for you, I can continue to do this.

Hello @FlechazoW , thanks for reaching out. Recently I've been quite busy with something else. I would appreciated it if you could help continue to complete this work. BTW,  the configuration of ratchetFrom in this PR may not function as expected. You may refer to https://github.com/diffplug/spotless/issues/710#issuecomment-1211495549 for a workaround. If possible, I suggest not using ratchetFrom and fix the legacy formatting errors once for all.

Hi, @EricGao888 Is there any process here? If this is not convenient for you, I can continue to do this.

Hello @FlechazoW , thanks for reaching out. Recently I've been quite busy with something else. I would appreciated it if you could help continue to complete this work. BTW, the configuration of ratchetFrom in this PR may not function as expected. You may refer to diffplug/spotless#710 (comment) for a workaround. If possible, I suggest not using ratchetFrom and fix the legacy formatting errors once for all.

Thanks for your advice, and I will perfect this as much as possible.


Add customization & integration examples

Basic

Default
Custom Theme
Custom close Nav
Custom styles
Hide drawer when click item #116
Secondary sidebar

Advanced

use with React router/Reach router
hide Sidebar in some route

#243


pixiecore: panic: runtime error: invalid memory address or nil pointer dereference

Hi!
I am running pixiecore in api mode, along with waitron as the api server.
Starting with pixiecore v2, I get this:
pixiecore_1  | panic: runtime error: invalid memory address or nil pointer dereference
pixiecore_1  | [signal SIGSEGV: segmentation violation code=0x1 addr=0x0 pc=0x7f2aaf7629cf]
pixiecore_1  | goroutine 38 [running]:
pixiecore_1  | panic(0x7f2aaff83ec0, 0xc420014050)
pixiecore_1  |  /usr/lib/go/src/runtime/panic.go:500 +0x1a5
pixiecore_1  | go.universe.tf/netboot/pixiecore.(*apibooter).BootSpec(0xc420042500, 0xc420152c38, 0x6, 0x5a4, 0x0, 0x0, 0x0, 0x0)
pixiecore_1  |  /tmp/go/src/go.universe.tf/netboot/pixiecore/booters.go:157 +0xcf
pixiecore_1  | go.universe.tf/netboot/pixiecore.(*Server).serveDHCP(0xc4200780e0, 0xc420015a00, 0x0, 0x0)
pixiecore_1  |  /tmp/go/src/go.universe.tf/netboot/pixiecore/dhcp.go:45 +0x3a1
pixiecore_1  | go.universe.tf/netboot/pixiecore.(*Server).Serve.func1(0xc4200780e0, 0xc420015a00)
pixiecore_1  |  /tmp/go/src/go.universe.tf/netboot/pixiecore/pixiecore.go:217 +0x37
pixiecore_1  | created by go.universe.tf/netboot/pixiecore.(*Server).Serve
pixiecore_1  |  /tmp/go/src/go.universe.tf/netboot/pixiecore/pixiecore.go:217 +0x6fc

whenever a box fires up its PXE firware and tries to obtain the boot files, when the box in question is not in build mode.
This event prompts pixiecore to query waitron about the mac address of the box requesting boot (simulated here with curl):
curl -v http://localhost:9090/v1/boot/70:10:9f:42:de:ad
* About to connect() to localhost port 9090 (#0)
*   Trying ::1...
* Connected to localhost (::1) port 9090 (#0)
> GET /v1/boot/70:10:6f:43:d3:08 HTTP/1.1
> User-Agent: curl/7.29.0
> Host: localhost:9090
> Accept: */*
>
< HTTP/1.1 404 Not Found
< Content-Type: text/plain; charset=utf-8
< X-Content-Type-Options: nosniff
< Date: Fri, 02 Sep 2016 10:33:27 GMT
< Content-Length: 18
<
Not in build mode
* Connection #0 to host localhost left intact

According to pixiecore's API specification:

Any non-200 response from the server will cause Pixieboot to ignore the requesting machine.

And yet, pixicore dies as shown in the previous dump.
Please note that when the box is in "build mode", pixiecore works as expected.
Here's the relevant section of the docker-compose.yml.
  pixiecore:
    image: danderson/pixiecore
    volumes:
      - ./pixiecore:/image
    command:
      - api
      - http://localhost:9090
      - --debug
      - --log-timestamps
      - --dhcp-no-bind
      - --port
      - "80"
    network_mode: host
    privileged: true
    restart: always

I work around this issue for now by setting restart: always, so pixiecore gets resucitated when it dies.
Also, kudos to the --dhcp-no-bind option, that allows running pixiecore alongside the dhcp server, yay!
N.B.: I'll probably transition from waitron to coreos' bootcfg soon.
If I manage to fix it, I'll do you a PR.
Cheers, and thanks again for pixiecore!

Doh, this is why we have unit test. I desperately need to add e2e tests :(.
I've pushed a fix, let me know if it works for you. I also have plans for how to unit test all this, but I don't have time to work through that right now :(.

The fix works nicely.  Thanks!


is-active class is applied to all the link of a dropdown

Using

  <o-dropdown dusk="agency-menu" ariaRole="list" v-if="hasRole(['agent', 'admin'])" >
    <template #trigger="{ active }">
      <o-button variant="primary">
        <o-icon icon="home" />
        <o-icon :icon="active ? 'menu-up' : 'menu-down'"></o-icon>
      </o-button>
    </template>

    <o-dropdown-item
      tag="router-link"
      :to="{name: 'agency.show', params: { agency_id: user.agency_id}}"
      aria-role="listitem"
      >
      My agency
    </o-dropdown-item>
    <o-dropdown-item
      tag="router-link"
      :to="{name: 'appointment.agency'}"
      aria-role="listitem"
      >
      Appointments pool
    </o-dropdown-item>
  </o-dropdown>

When you use a link from the dropdown, all the link are set has active.
You can put value on the link to avoid the bug.

As discussed on Discord, you should use active-class prop (related to router-link component and not Oruga).
The only issue is about missing value that select all items but the right place to add the issue is oruga main repository 😉


authentication fails for newly created bots

Version
4.11.0
Describe the bug
Hi guys, I'm creating a new bot using the Developer portal on ms-teams web app. (which by the way has a bug on Desktop client).
I'm getting the bot id, and the client secret (also known as app password) as I've done many times before.
I didn't make any code changes and my bot worked fine with other credentials just a couple of weeks ago, but in the last few days I'm getting
[Errno Unauthorized. Invalid AppId passed on token: ] <bot-id>.
when sending a post request using postman to: https://login.microsoftonline.com/botframework.com/oauth2/v2.0/token
with the same credentials it works fine I'm getting status code 200 and a token.
To Reproduce
in my code I'm creating a SimpleCredentialProvider with the bot-id and app password and an Activity from botbuilder.schema and the Authorization header from a flask Request
and sending it like this:
JwtTokenValidation.authenticate_request(
                    activity,    # Activity
                    current_request.headers.get("Authorization"),    # Request
                    self.credential_provider,    # SimpleCredentialProvider
                )

Expected behavior
expecting to be authenticated and get the token as usual

@DanielLevy0705 thanks for reporting this: Could you please provide a step-by-step of the repro, including the link to the ms-teams web app you are using?

Thanks for responding :)
not sure what you mean by ms-teams web app link, I'm using microsoft teams https://teams.microsoft.com/ and I've added a personal custom app, with a bot that was generated using "Developer Portal: https://teams.microsoft.com/_#/apps/14072831-8a2a-4f76-9294-057bf0b42a68/sections/57bb717d-fddc-4b1d-9bfd-3cb4ef4952a4?slug=28:0c5cfdbb-596f-4d39-b557-5d9516c94107
regarding repro steps:

create new bot using developer portal.
get the client secret and bot-id of the newly created bot
use the credentials to create a SimpleCredentialProvider
send this SimpleCredentialProvider to authenticate using JwtTokenValidation.authenticate_request

please note that I've sent also parameters that are coming from Flask
the Activity object was created from deserialized Request and the auth headers were also coming from the Request object.

Update: it's happening also on bots that were created and worked before.

Hi @DanielLevy0705, I'm investigating.

Hi guys, is there any progress with this? we're having many clients complaining about this issue.

update: tested today on a newly created bot and it looks like the issue is resolved!

@DanielLevy0705 Did you have to do something else to fix the issue? I am facing the same issue in my bots.

There's some update ou tips regards this?
I've similar issue on my server execution, while run normally executing on my machine with exactly the same configuration.

I'm facing the same issue on my part as well ? is there any update regarding it?

I'm facing the same issue on my part as well ? is there any update regarding it?

The problem we face was related to auth env not be properly passed to BotBuilder instance.

@flpms do you have a solution for our issue? is it something that we need to be doing on our part ?

I'm facing the same issue on my part as well ? is there any update regarding it?

The problem we face was related to auth env not be properly passed to BotBuilder instance.

Yes, the check the state of your .env when start botbuilder


Sritchie merge

Hi sritchie,
In this pull request, I made two major changes on carbonite:

Use kryo's build-in ObjectBuffer as default buffer;
Improve Keyword serializer. We should try to avoid using read-string and prn as serializer.

Hey, I'm really happy about this pull request, but i'm not actually the base fork. Can you submit this pull request to the revelytix branch?

Actually I have sent pull request to revelytix. But it seems they are not very active on this project. And I thought you might have token over the project ...
Anyway, I will send them a reminder about that pull request.
Thank you.

@sunng87, I'll write to the revelytix guys and see what I can do! We'll get this merged for you one way or another. I'll keep this open for now until we figure out how it'll get resolved.


quick and dirty fix for bandcamp scraper

may break "assorted artists" type functionality / might not work on albums with multiple artists

This should address issue #25


Compose Multiplatform support

Do you plan to support Compose Multiplatform?
I could help you with his support!

Yes, I am planning to support Compose Multiplatform!
Feel free to create a new issue to discuss your approach, or you can start working on a pull request directly.
Please make sure to pull request your branch to the develop branch. It would be great if you could follow the existing coding style and add comments if necessary. Let me know if you have any questions!


fix(Travis): yarn install with pure-lockfile

Issue:
What I did
How to test

Deploy preview ready!
Built with commit c4f497cb0d0c82688cf7fcf242d1dcc42d04337e
https://deploy-preview-472--mcs-lite-introduction.netlify.com

Deploy preview ready!
Built with commit c4f497cb0d0c82688cf7fcf242d1dcc42d04337e
https://deploy-preview-472--mcslite.netlify.com


response.ok is always true

👋 thank you for this great module.
I noticed, that an APIError is thrown if response.ok is falsy:
https://github.com/ajaishankar/openapi-typescript-fetch/blob/b62ee714e645c14b8e07c98aaaa53880e7d6c847/src/fetcher.ts#L163-L167
ok is omitted in ApiError constructor:
https://github.com/ajaishankar/openapi-typescript-fetch/blob/b62ee714e645c14b8e07c98aaaa53880e7d6c847/src/types.ts#L162
This means, that you always have true for ok, because it is not part of the error response.
It's not a real issue, but I expected that I could check for ok by myself to separate abnormal HTTP-statuses (4xx, 5xx) from network problems.
I suggest removing ok from the result, or do not throw an error if the response is not ok.

@lmachens ApiError does not have an ok field, so won't be true.
export class ApiError extends Error {
  readonly headers: Headers
  readonly url: string
  readonly status: number
  readonly statusText: string
  readonly data: any

  constructor(response: Omit<ApiResponse, 'ok'>) {
    super(response.statusText)
    Object.setPrototypeOf(this, new.target.prototype)

    this.headers = response.headers
    this.url = response.url
    this.status = response.status
    this.statusText = response.statusText
    this.data = response.data
  }
}

Also status is included in the response/error (4xx, 5xx)
Plus non ok responses are strongly typed as documented here

It is not entirely clear to me, where in the Typed Error Handling:
const findPetsByStatus = fetcher.path('/pet/findByStatus').method('get').create()
const addPet = fetcher.path('/pet').method('post').create()

try {
  await findPetsByStatus({ ... })
  await addPet({ ... })
} catch(e) {
  // check which operation threw the exception
  if (e instanceof addPet.Error) {
    // get discriminated union { status, data }
    const error = e.getActualType()
    if (error.status === 400) {
      error.data.validationErrors // only available for a 400 response
    } else if (error.status === 500) {
      error.data.errorMessage // only available for a 500 response
    } else {
      ...
    }
  }
}

can I detect if it is a network error (ok=false)?


expected [Error: Path not found ("nonce").] to not exist

Seems that https://github.com/ipld/js-ipld/pull/169#issuecomment-433479607 was not a hiccup. I get it to happen often on my machine.
  1) Browser,IPLD Resolver with eth-* (resolving across various Eth objects),resolver.get
       block-to-account resolve:
     Error: Uncaught AssertionError: expected [Error: Path not found ("nonce").] to not exist (webpack://ipld/node_modules/chai/lib/chai/assertion.js:141 <- node_modules/aegir/src/config/karma-entry.js:23107)

» node -v
v8.12.0

This issue was fixed by https://github.com/ipld/js-ipld-ethereum/pull/36.


Install capistrano and add QA environment

Connects to #6
Fixes #18
Why was this change made?
To install capistrano and begin testing passenger in standalone mode.
How was this change tested?
It was deployed to QA.
Which documentation and/or configurations were updated?
Shared_configs set up for QA.

@mjgiarlo there are some conflicts to resolve.  Feel free to merge when resolved.


Make a state machine design for dst software that is independant of perun contract versions.

dst project implements perun protocol to do offchain transactions. Current version of dst-go uses legacy contracts.
The contracts are being developed further and new versions are coming up. In near future, the dst-go project will have to be use these new versions. To provide a direction for this migration, a state machine design that can accommodate the workflows of both legacy and new versions needs to be created. The design should also be flexible to easily accommodate changes in contract that might come up in future.
The state machine design can be done in UML using plantuml tool. PlantUML tools allows one to write a UML diagram as simple text file (see here), from which PNG, JPEG and such image files can be created. This will also enable the changes to be easily tracked using version control systems like git.

First version of State machine diagrams have been added here

Issue closed, as it relates to the older implementation which is now archived on legacy/master


Revert texture max brightness changes

The brightness of textures was recently upped alongside color brightness without giving it too much extra thought, but this results in overly bright textures in some cases.
Here is what Ardougne rooftops look like currently:

Compared to what they looked like before:

I think it's safe to say the ideal end result is the bottom picture, but whether this PR is the best way to go about it is a different question. We should consider whether we want to change lighting strength for the overworld environment instead.
Note, this change doesn't affect infernal & fire capes.

Ceiling of 90 was obv chosen for some good reasons but it may be a bit low. Might wanna experiment with e.g. 100 rather than going all or nothing. Adjusted scene lighting may also make it look better in combination as some things are supposed to be more of a snowy white color when lit, but obviously not all of this stuff.

I've noticed the overbloom problem seems to only affect light colored and textures objects.
I believe what may be happening is for textured objects Jagex sets the object color to white and applies the texture.
Is the lighting calculation reading the color of the object? Perhaps it doesn't handle true white well?


Neofetch Interferes with Codeium Functionality in Neovim

I'm experiencing an issue where Codeium doesn't work in Neovim. The problem occurs only when Neofetch is enabled in my shell config. It seems that Neofetch's ASCII output interferes with Neovim's startup, causing Codeium to malfunction. Disabling Neofetch resolves the issue. Could you help identify why this is happening and suggest a solution?
The fact that i am using codeium.vim over codeium.nvim because of their completion style.

@amateur-hacker how are you doing with this? I have the same setup and it all works fine for me so maybe I can help out

@amateur-hacker how are you doing with this? I have the same setup and it all works fine for me so maybe I can help out

It's working properly, problem was from my side. Sorry :)


fetch (logical) replication slot definitions from the master [BF-1213]

Codecov Report

Merging #81 (d073ee2) into main (d568658) will increase coverage by 0.17%.
The diff coverage is 88.88%.

@@            Coverage Diff             @@
##             main      #81      +/-   ##
==========================================
+ Coverage   66.63%   66.80%   +0.17%
==========================================
  Files          10       10
  Lines         926      934       +8
  Branches      159      160       +1
==========================================
+ Hits          617      624       +7
  Misses        252      252
- Partials       57       58       +1

Impacted Files
Coverage Δ

pglookout/cluster_monitor.py
62.03% <88.88%> (+0.97%)
:arrow_up:

Help us with your feedback. Take ten seconds to tell us how you rate us. Have a feature suggestion? Share it here.


community: use correct id_key when deleting by id in LanceDB wrapper

Description: The current version of the delete method assumes that the id field will always be called id.
Issue: n/a
Dependencies: n/a
Twitter handle: ugh, Twitter :D

Thank you for contributing to LangChain!

[x] PR title: "package: description"

Where "package" is whichever of langchain, community, core, etc. is being modified. Use "docs: ..."  for CI changes.
Example: "community: add foobar LLM"

[x] PR message: Delete this entire checklist and replace with

Description: a description of the change
Issue: the issue # it fixes, if applicable
Dependencies: any dependencies required for this change
Twitter handle: if your PR gets announced, and you'd like a mention, we'll gladly shout you out!

[x] Add tests and docs: If you're adding a new integration, please include

a test for the integration, preferably unit tests that do not rely on network access,
an example notebook showing its use. It lives in docs/docs/integrations directory.

[x] Lint and test: Run make format, make lint and make test from the root of the package(s) you've modified. See contribution guidelines for more: https://python.langchain.com/docs/contributing/

Additional guidelines:

Make sure optional dependencies are imported within a function.
Please do not add dependencies to pyproject.toml files (even optional ones) unless they are required for unit tests.
Most PRs should not touch more than one package.
Changes should be backwards compatible.
If you are adding something to community, do not re-import it in langchain.

If no one reviews your PR within a few days, please @-mention one of baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.

Would also love to get this into a dedicated integration package in its own repo if you work at lancedb!
Here's the guide, and if you have questions, feel free to leave them in the comments on those pages so others can see them! https://python.langchain.com/docs/contributing/how_to/integrations/

Would also love to get this into a dedicated integration package in its own repo if you work at lancedb!
Here's the guide, and if you have questions, feel free to leave them in the comments on those pages so others can see them! https://python.langchain.com/docs/contributing/how_to/integrations/

Sadly I don’t work at lancedb - just doing work for a startup that’s started using it and we bumped into this bug!
Thanks for the merge and hopefully someone at lancedb will pick this integration up 😁


Infix precedence not respected

We added a custom hlint rule
error:
  lhs: \a b -> g (h a b)
  rhs: g .: h

using our infixr 9 .: function. Hlint suggested the following replacement
\x t -> (:&) <$> x <*>+ t
-- to:
(<$>) (:&) .: (<*>+)

Which, in addition to being messier, is wrong. Our custom operation <*>+ has the precedence infixl 4 <*>+ (the same as <$>), so the original expression is equivalent to
\x t -> ((:&) <$> x) <*>+ t

whereas the second one is equivalent to
\x t -> (:&) <$> (x <*>+ t)

The original expression is being falsely matched with the hlint rule because hlint seemed to match g = ((:&) <$>) and h = (x <*>+ t) which doesn't follow the infix precedence set.

In general HLint has little idea of the precedences of operators - if they aren't defined in the same file, or one of the standard ones in the Prelude, it just guesses. Normally those guesses are good enough for HLint purposes, but sometimes they go astray. See https://github.com/ndmitchell/hlint#why-doesnt-hlint-know-the-fixity-for-my-custom--operator. (I've improved the docs slightly to make it a bit clearer)

ah I have read that in the README before, but I didn't connect the dots. Thanks!


test_load_model_workaround fails when run outside of CircleCI

The location of the test files to read in are hard coded assuming CircleCi.

I need an environment variable to be set telling me I'm in circle ci, then I will know the path to load the test file from.

I've set $CIRCLECI==1 when it is running in CircleCI.

I have reverted my variable. CircleCI sets $CIRCLECI=true be default.
https://circleci.com/docs/1.0/environment-variables/

with the new docker container building things is this bug still open?

Indeed it does!
Fixed with #71.


Refactor FFI

What does this PR do?
A brief description of the change being made with this pull request.
Motivation
What inspired you to submit this pull request?
Additional Notes
Anything else we should know when reviewing?
How to test the change?
Describe here in detail how the change can be validated.

Codecov Report
Attention: Patch coverage is 71.56463% with 209 lines in your changes missing coverage. Please review.

Project coverage is 70.80%. Comparing base (d339c64) to head (294f82a).
Report is 1 commits behind head on main.

Additional details and impacted files
@@            Coverage Diff             @@
##             main     #768      +/-   ##
==========================================
+ Coverage   70.53%   70.80%   +0.27%
==========================================
  Files         297      300       +3
  Lines       43484    44093     +609
==========================================
+ Hits        30673    31222     +549
- Misses      12811    12871      +60

Components
Coverage Δ

crashtracker
44.86% <ø> (ø)

crashtracker-ffi
8.41% <ø> (ø)

datadog-alloc
98.73% <ø> (ø)

data-pipeline
88.73% <71.38%> (-0.37%)
:arrow_down:

data-pipeline-ffi
77.33% <78.76%> (+77.33%)
:arrow_up:

ddcommon
83.80% <80.00%> (+0.34%)
:arrow_up:

ddcommon-ffi
70.20% <80.00%> (+1.07%)
:arrow_up:

ddtelemetry
59.05% <ø> (ø)

ddtelemetry-ffi
22.13% <ø> (ø)

dogstatsd
89.45% <ø> (ø)

dogstatsd-client
79.77% <ø> (ø)

ipc
82.76% <ø> (ø)

profiling
84.30% <ø> (ø)

profiling-ffi
77.46% <ø> (ø)

serverless
0.00% <ø> (ø)

sidecar
38.01% <ø> (ø)

sidecar-ffi
0.00% <ø> (ø)

spawn-worker
50.36% <ø> (ø)

tinybytes
93.47% <ø> (-1.30%)
:arrow_down:

trace-mini-agent
72.36% <ø> (ø)

trace-normalization
98.23% <ø> (ø)

trace-obfuscation
95.77% <ø> (ø)

trace-protobuf
77.67% <ø> (ø)

trace-utils
93.28% <100.00%> (-0.01%)
:arrow_down:


Dm/exception clean

📝 Summary
Previous Exceptions:

vs

Also clean up the name error for strict execution.
Your PR will be reviewed more quickly if you can figure out the right person to tag with @ -->
@akshayka OR @mscolnick

Also added the name error catching for normal mode:

With other regular NameErrors passing through:

MarimoRef NameErrors in Strict Mode are a bit more verbose, since they should not occur:

with a potentially detected NameError preventing execution:

& edgecase for self definition

Not the most dramatic change, but I think it will pair very nicely with #1799

Reran and it's passing. Maybe just flaky.


Emoji notification for author

Now that Zulip supports reactions to a message, would it make sense to notify authors about the reaction to their post? The notification is available on FB and Twitter. I am not aware of slack.

I don't believe Slack does it.  I think in Slack/GitHub/etc., the point of emoji reactions (vs. just sending a reply with an emoji in the reply) is to respond without triggering a notification to the other participants on the thread.
I guess I can see a case for wanting a notification about reactions to messages you yourself sent, though (but not have those notifications go to other folks).  I'd like to get more feedback on this question and the use cases involved; I can imagine wanting an easy way to narrow to your messages with emoji reactions on them being something folks want.

If Zulip is a chat tool for developers, I don't think the author would like to be notified about the reactions but if yeah a checkbox could be added to streams setting to enable or disable reaction notification
So I'd say that adding an extra feature would increase the number of features Zulip supports, therefore improving the product. On my opinion this may be be implemented using a checkbox

There a lot of product managers that would argue that sometimes adding new features can make a product worse :).
If someone wants to work on this direction, I'd probably started with adding a narrow, since I think that would be fairly easy and useful.  Maybe has:reaction as the keywords?

This issues looks like related to #14869.

Like @wolfsonchuang suggested:
I'd like to have a feature that the first emoji reaction should trigger a notification (limit to first, cause in streams there could be really many emoji reactions.
Cause in my zulip at work the emoji is always used when a yes/no question is raised.

I asked for something like this at https://chat.zulip.org/#narrow/stream/137-feedback/topic/I.20want.20to.20be.20able.20to.20see.20all.20reactions
For myself, the important part isn't notification so much as being able to see all the reactions in aggregate. This is how Slack does it (in response to @timabbott's comment above.)

My users have requested this too. An opt in feature to be able to get notified when someone reacts to your post would be great. Many people post something in our group, get lots of "likes" but the original poster never gets notified because there was no need to respond anything but a thumbs up or other confirming emoji..

Yes, this is needed. Otherwise, authors don't know people reacted to their messages.

Tim wrote above (back in 2017):

[…] the point of emoji reactions (vs. just sending a reply with an emoji in the reply) is to respond without triggering a notification to the other participants on the thread.
I guess I can see a case for wanting a notification about reactions to messages you yourself sent, though (but not have those notifications go to other folks).

Yeah, I think that last is the key point. A reaction still doesn't bump the thread for everyone else, but it gives feedback for the one person who sent the message you're reacting to.
I often feel like I miss signals from reactions — I happen across some thread a while later, and only then see that people reacted to one of my messages. Conversely, sometimes I find myself manually going and looking back at a thread to see if there are reactions. This feature would make sure those signals don't get dropped.

Fully agree...  much needed!

another  request
https://chat.zulip.org/#narrow/stream/137-feedback/topic/Emoji.20reactions.20notification

Hello!
This question is very relevant now. Please add this functionality. As previously discussed, push notifications should only come to the author of the message.

I have the same situation that has been written about before. I have to go to a topic to see how people reacted to my suggestion or idea. The fact that I am left in the dark makes me tense.


Can you show us how you made the render as you type example?

Thanks

Nvm, I figured out how you can do this. For example if you are using react and you have an input field you can have a function that updates the state to have the current value of the textfield, then you can simply pass text = { this.props.text} to the MathJax tag


App crash when opening scanner after previous scan

I am facing an issue where I complete an initial scan, OnScanResult is fired and I do my logic. Part of the logic is to execute await Navigation.PopModalAsync(); inside of a Device.BeginInvokeOnMainThread invocation (based on the custom scanner Xamarin Forms example).
When I open the scanner view a second time (via await Navigation.PushModalAsync(new QRPage()))  the scanner begins to initialize and I see the following output:
2018-06-05 16:24:26.933 Kobol.iOS[1592:283137] ZXingScannerView.Setup() took 0.1 ms.
2018-06-05 16:24:26.934 Kobol.iOS[1592:283137] StartScanning
Thread started:  #7
2018-06-05 16:24:28.145 Kobol.iOS[1592:283160] Too soon between frames
Thread started:  #8
2018-06-05 16:24:28.178 Kobol.iOS[1592:283211] Too soon between frames

, then the whole app just crashes. I don't get anything else from the debug output.
Any thoughts on what could be causing this issue?

I have determined that this is only happening AFTER I have navigated to my HyrbridWebView class that uses a custom DecidePolicy method.
[assembly: ExportRenderer(typeof(HybridWebView), typeof(HybridWebViewRenderer))]
namespace Kobol.iOS.Renderers
{
    public class HybridWebViewRenderer : ViewRenderer<HybridWebView, WKWebView>, IWKScriptMessageHandler, IWKNavigationDelegate
    {
        WKUserContentController userController;

        const string JavaScriptFunction = "function invokeSessionRefresh(data){window.webkit.messageHandlers.invokeAction.postMessage(data);}";

        protected override void OnElementChanged(ElementChangedEventArgs<HybridWebView> e)
        {
            base.OnElementChanged(e);

            if (Control == null)
            {
                userController = new WKUserContentController();

                var script = new WKUserScript(new NSString(JavaScriptFunction), WKUserScriptInjectionTime.AtDocumentStart, false);

                userController.AddUserScript(script);
                userController.AddScriptMessageHandler(this, "invokeAction");

                var config = new WKWebViewConfiguration
                {
                    UserContentController = userController
                };

                var webView = new WKWebView(Frame, config)
                {
                    NavigationDelegate = this
                };

                SetNativeControl(webView);
            }

            if (e.OldElement != null)
            {
                userController.RemoveAllUserScripts();
                userController.RemoveScriptMessageHandler("invokeAction");

                var hybridWebView = e.OldElement as HybridWebView;
                hybridWebView.Cleanup();
            }

            if (e.NewElement != null && Element.Uri != null)
            {

                var url = new NSUrl(Element.Uri);
                var request = new NSUrlRequest(url);

                Control.LoadRequest(request);
            }

        }

        protected override void OnElementPropertyChanged(object sender, PropertyChangedEventArgs e)
        {
            base.OnElementPropertyChanged(sender, e);

            var element = sender as HybridWebView;

            if (e.PropertyName.Equals(HybridWebView.UriProperty.PropertyName) && element.Uri != null)
            {
                Control?.StopLoading();

                var url = new NSUrl(element.Uri);
                var request = new NSUrlRequest(url);

                Control.LoadRequest(request);
            }
        }

        public void DidReceiveScriptMessage(WKUserContentController userContentController, WKScriptMessage message)
        {
            Element.InvokeAction(message.Body.ToString());
        }

        [Export("webView:decidePolicyForNavigationAction:decisionHandler:")]
        public void DecidePolicy(WKWebView webView, WKNavigationAction navigationAction, Action<WKNavigationActionPolicy> decisionHandler)
        {
            if (webView.Url.ToString().Contains("appname://"))
            {
                MessagingCenter.Send<SharedMessage, string>(new SharedMessage(), "login_completed", webView.Url.ToString());
            }

            decisionHandler(WKNavigationActionPolicy.Allow);
        }
    }
}

If I remove setting the NavigationDelegate to this, I no longer have this issue. I need to find a way to keep the Navigation delegate, though as it handles app logic for login.

I had similar problem, although using regular page, not modal. After navigate from scanner page, then navigate to it again, the app would crash. The solution was to clear the scanner page content.
public partial class ScannerPage : ContentPage
{
    protected override void OnDisappearing()
    {
        base.OnDisappearing();
        Content = null;
    }

Thanks for reporting this issue!  Unforunately it took me way too long to respond 😭.  Sorry, I apologize!  Recently the source code for this project was completely refactored to modernize it.  Many PR's were included in this effort, and many bugs were hopefully fixed.  Please try out the latest 3.x series of NuGet packages (currently in prerelease).  To try and make the project more maintainable in my spare time going forward, I've decided to close all existing issues to start with a clean slate.  If you're still experiencing this issue on the newest version, please open a new issue with as much detail as possible.  Thank you for your patience and understanding!  Happy scanning!


Frontmatter property does not work with emojis

I also feel like it would be nice if it could parse an icon property even if it has flanking colons like :LiThumbsUp:. Right now, it will only parse LiThumbsUp, i.e. without colons.
The reason I think this would be nice is for those of us who'd like to use this with Dataview, e.g. displaying an note-specific icon in  a table view, which is much easier with the colons present.

I also feel like it would be nice if it could parse an icon property even if it has flanking colons like :LiThumbsUp:. Right now, it will only parse LiThumbsUp, i.e. without colons.
The reason I think this would be nice is for those of us who'd like to use this with Dataview, e.g. displaying an note-specific icon in a table view, which is much easier with the colons present.

Feel free to create a new issue for that :)

Has been resolved with 2.7.1.

I took this to mean I could add 🙂 to icon: and it would work, but I still see "icon could not be found". What gives @FlorianWoelki?

I took this to mean I could add 🙂 to icon: and it would work, but I still see "icon could not be found". What gives @FlorianWoelki?
Edit: My bad. This is indeed fixed! I thought I hit update all in Obsidian but instead only updated a single different plugin (I was still on 2.7.0). Now it works!

Nice to hear :)

@FlorianWoelki just wanted to let you know that I did the mistake to add the icon first with the new properties-interface, which added the icon as a list... so just a hints up :D
icon:
  - 😄

which got me this error:
plugin:obsidian-icon-folder:5545 TypeError: str.match is not a function
    at Object.isEmoji (plugin:obsidian-icon-folder:2527:13)
    at IconFolderPlugin.eval (plugin:obsidian-icon-folder:5540:36)
    at Generator.next (<anonymous>)
    at eval (plugin:obsidian-icon-folder:31:71)
    at new Promise (<anonymous>)
    at __awaiter (plugin:obsidian-icon-folder:27:12)
    at eval (plugin:obsidian-icon-folder:5523:79)
    at e.tryTrigger (app.js:1:695499)
    at e.trigger (app.js:1:695432)
    at t.trigger (app.js:1:1800309)
    at t.<anonymous> (app.js:1:1792684)
    at app.js:1:237027
    at Object.next (app.js:1:237132)
    at a (app.js:1:235850)
eval @ plugin:obsidian-icon-folder:5545
eval @ plugin:obsidian-icon-folder:31
__awaiter @ plugin:obsidian-icon-folder:27
eval @ plugin:obsidian-icon-folder:5523
e.tryTrigger @ app.js:1
e.trigger @ app.js:1
t.trigger @ app.js:1
(anonymous) @ app.js:1
(anonymous) @ app.js:1
(anonymous) @ app.js:1
a @ app.js:1

of course changing it to a usual icon: 😄 fixed it.
and thanks a lot for the great work!!!

@FlorianWoelki just wanted to let you know that I did the mistake to add the icon first with the new properties-interface, which added the icon as a list... so just a heads up :D
icon:
  - 😄

which got me this error:
plugin:obsidian-icon-folder:5545 TypeError: str.match is not a function
    at Object.isEmoji (plugin:obsidian-icon-folder:2527:13)
    at IconFolderPlugin.eval (plugin:obsidian-icon-folder:5540:36)
    at Generator.next (<anonymous>)
    at eval (plugin:obsidian-icon-folder:31:71)
    at new Promise (<anonymous>)
    at __awaiter (plugin:obsidian-icon-folder:27:12)
    at eval (plugin:obsidian-icon-folder:5523:79)
    at e.tryTrigger (app.js:1:695499)
    at e.trigger (app.js:1:695432)
    at t.trigger (app.js:1:1800309)
    at t.<anonymous> (app.js:1:1792684)
    at app.js:1:237027
    at Object.next (app.js:1:237132)
    at a (app.js:1:235850)
eval @ plugin:obsidian-icon-folder:5545
eval @ plugin:obsidian-icon-folder:31
__awaiter @ plugin:obsidian-icon-folder:27
eval @ plugin:obsidian-icon-folder:5523
e.tryTrigger @ app.js:1
e.trigger @ app.js:1
t.trigger @ app.js:1
(anonymous) @ app.js:1
(anonymous) @ app.js:1
(anonymous) @ app.js:1
a @ app.js:1

of course changing it to a usual icon: 😄 fixed it.
and thanks a lot for the great work!!!

Yeah so the type has to be a text (so not as a list or number) :D
I'll fix that soon and keep track of it at #290!


Fix path problem for windows

As title, after introducing youtube-api, it makes the hierarchy too complicated and when unzip Kaku on Windows, this will make the path exceed the MAX_LENGTH and will break the application.

Noooo ...
@EragonJ
you need to unziped (while) on desktop. After that you could move it where ever you want.

I think that is quite the same because it hits the native limitation. To be honest, there is no good way to fix this because normal users can rename their folders to a REALLY_LONG_NAME and no matter how tried, this problem would happen sometimes somewhere, but this is an edge case and we can just ignore it.
Debugged from the log I got, there is only one 3rd party library which hits the limitation and if I can find a better alternative, this should be fixed. (Well, to be honest, that's the best 3rd party I found ...) Never mind, i'll keep this issue open and maybe rewrite a new one library by my own to make it work.
again, windows sucks !!!!!


Allow tracking of lifecycle events involving multiple activities

Android apps can be separated into different paradigms of structure. The LifecycleHandler has traditionally only accommodated single activity apps, but it would be useful to allow automatic setting of background/foreground based on the lifecycle events of multiple activities.

Closing due to inactivity.


Modify put function to support a time that exceeds setTimeout's maximum time

setTimeout can support a maximum of<PHONE_NUMBER> milliseconds (about 24.9 days), which limits the time that the put function can support. Now it can support the maximum safe integer supported by the javascript number primitive<PHONE_NUMBER>740991).<PHONE_NUMBER>740991 milliseconds is about 285616.41 years.

@ptarjan please check this out please

@ptarjan I fixed the merge conflict


Change maxUnavailable parameter in machineDeployment to 0.

Reason: During rolling update, the number of available replicas should not go below the desired number of replicas

This would increase the time, which the mcm needs to perform the rolling update, because it has to wait before draining a node until a replacing node has joined the cluster (and it ready), right?
With the current configuration, the process would be faster, but with the possibility that there are not enough nodes to schedule the entire workload. Especially one node clusters would have temporarily no nodes, right?
Am I right with my assumption?
@vlerenc: What do you mean?

Yes @dkistner
your assumptions are correct.

Yes, we must avoid causing downtimes to the workload. maxUnavailable should therefore be 0 and maxSurge 1 until we can make it configurable (via the shoot resource (Dashboard) or via a CRD inside the shoot cluster itself (this time shoot cluster, not shoot cluster control plane in the seed cluster)).

@ggaurav10: Could you please rebase?

@ggaurav10 you have included many commits which should not be part of this PR can you remove those?

Rebased.


feat: track urlref

Fixes #14

Codecov Report

Merging #21 (f8c7ef3) into main (6470efd) will increase coverage by 0.27%.
The diff coverage is 100.00%.

:exclamation: Your organization is not using the GitHub App Integration. As a result you may experience degraded service beginning May 15th. Please install the Github App Integration for your organization. Read more.
@@            Coverage Diff             @@
##             main      #21      +/-   ##
==========================================
+ Coverage   88.55%   88.83%   +0.27%
==========================================
  Files           3        3
  Lines         201      206       +5
==========================================
+ Hits          178      183       +5
  Misses         23       23

Impacted Files
Coverage Δ

asgi_matomo/middleware.py
88.39% <100.00%> (+0.32%)
:arrow_up:

:mega: We’re building smart automated test selection to slash your CI/CD build times. Learn more


Use gradle to run from intellij and add vm options to bootRun

Closes #1443 and #1546

I advise against using Gradle to run because it takes way more time to launch.

In what situation does it take more time to launch? I did not experience this in my testing

Running on my machine after all the tasks are run once they seem comparable.
BootRun:

Details
`6:41:24 AM: Executing task 'bootRun'...

Configure project :
Platform is: win

Task :downloadIceAdapter UP-TO-DATE
Download https://github.com/FAForever/java-ice-adapter/releases/download/v3.1.1/faf-ice-adapter.jar

Task :downloadUnixUid
Download https://github.com/FAForever/uid/releases/download/v4.0.4/faf-uid

Task :downloadWindowsUid UP-TO-DATE
Download https://github.com/FAForever/uid/releases/download/v4.0.4/faf-uid.exe

Task :downloadNativeDependencies
Task :processResources UP-TO-DATE
Task :webview-patch:compileJava UP-TO-DATE
Task :webview-patch:processResources NO-SOURCE
Task :webview-patch:classes UP-TO-DATE
Task :webview-patch:jar UP-TO-DATE
Task :webview-patch:assemble UP-TO-DATE
Task :webview-patch:compileTestJava NO-SOURCE
Task :webview-patch:processTestResources NO-SOURCE
Task :webview-patch:testClasses UP-TO-DATE
Task :webview-patch:test NO-SOURCE
Task :webview-patch:check UP-TO-DATE
Task :webview-patch:build UP-TO-DATE
Task :compileJava UP-TO-DATE
Task :classes UP-TO-DATE

Task :bootRun
06:41:27.835 [main] DEBUG com.faforever.client.preferences.PreferencesService - Logger initialized`

Main Task:

Details
`6:41:37 AM: Executing task 'Main.main()'...

Configure project :
Platform is: win

Task :downloadIceAdapter UP-TO-DATE
Download https://github.com/FAForever/java-ice-adapter/releases/download/v3.1.1/faf-ice-adapter.jar

Task :downloadUnixUid
Download https://github.com/FAForever/uid/releases/download/v4.0.4/faf-uid

Task :downloadWindowsUid UP-TO-DATE
Download https://github.com/FAForever/uid/releases/download/v4.0.4/faf-uid.exe

Task :downloadNativeDependencies
Task :processResources UP-TO-DATE
Task :webview-patch:compileJava UP-TO-DATE
Task :webview-patch:processResources NO-SOURCE
Task :webview-patch:classes UP-TO-DATE
Task :webview-patch:jar UP-TO-DATE
Task :webview-patch:assemble UP-TO-DATE
Task :webview-patch:compileTestJava NO-SOURCE
Task :webview-patch:processTestResources NO-SOURCE
Task :webview-patch:testClasses UP-TO-DATE
Task :webview-patch:test NO-SOURCE
Task :webview-patch:check UP-TO-DATE
Task :webview-patch:build UP-TO-DATE
Task :compileJava UP-TO-DATE
Task :classes UP-TO-DATE

Task :Main.main()
06:41:41.466 [main] DEBUG com.faforever.client.preferences.PreferencesService - Logger initialized`

You don't see the difference because you didn't set this:

Therefore, your IDE always started via Gradle - mine did not. But with the changed run configuration it would :)

Ah yes there are also issues for some people when they build and run using intellij IDEA. We have only been able to get consistent builds using gradle and I believe the current setup docs say to use gradle as well. At least that is what Alex told me when I started.

But we can have the run configurations use the application route since they are in the idea specific folder anyway

I had only changed that to try and reduce duplication of the jvm arguments as I wasn't aware of a way to reference the ones added to the bootRun

Also it seems like if you use the spring based start it always bypasses gradle anyway. But I am not sure if that is only available in Intellij Ultimate


[Bug]: Linting does not work on actions selector subfields

Is there an existing issue for this?

[X] I have searched the existing issues

Description
Lint errors don't show up on bindings. Executing such code returns an error but does not get logged in the debugger - (verify if this is needed)
Steps To Reproduce

Public Sample App
No response
Environment
Production
Issue video log
No response
Version
Cloud

@ohansFavour could you investigate the reason for this behaviour?

@bharath31 we never supported lint errors in non-js mode (see attached images). However, I think this is a great enhancement that needs to be introduced for better error mitigation.


Release of 2.14.2

We have discovered and fixed some more bugs in 2.14.x
https://github.com/mojohaus/versions/milestone/11?closed=1
I will release 2.14.2 in a few days, about 21-22 of December.
If some other issue should be consider please let me know.

#867

I hope we have everything

done

Now waiting for bug reports 😂


CYF-London I Saeid Heidari Orojloo I Model User Focused Data - Module Wireframe I Week 1

Learners, PR Template
Self checklist

[x] I have committed my files one by one, on purpose, and for a reason
[x] I have titled my PR with COHORT_NAME | FIRST_NAME LAST_NAME | REPO_NAME | WEEK
[x] I have tested my changes
[x] My changes follow the style guide
[x] My changes meet the requirements of this task

Changelist
Briefly explain your PR.
Questions
Ask any questions you have for your reviewer.

Page looks great.

There is another pseudo-class selector which can be used to select the first <article> element in the page.
You can consider using that if :first-child does not suit your need.

The regions of the <footer> and the <main> elements overlap; part of <main> seems to appear beneath the footer.
See if you can do something about it. (This is an optional change).


Use jsonaf instead of ezjsonm

@nmtake How would you feel about this? How annoying would it be to update code that gets raw JSON out of this library?

Hi! I haven't used OCaml for a long time, so can't say anything useful for the PR. How about asking on /r/ocaml?


"delivery-messages" section is duplicated after saving the quest

What is your Quests version / build number (do not say "latest")?
3.9.5
Describe the problem and how to replicate it. Any console errors to post using Pastebin.com?
Whenever I save a quest using the in-game editor, the "delivery-messages" section is being duplicated in the quests.yml file.
Steps to reproduce:

Create any quest with delivery-messages, save it and make sure it is loaded in game.
Enter the in-game editor and edit the quest you made before. Don't make any changes, but make sure to save it.
Observe the "delivery-messages" in quests.yml - all messages are duplicated.

If applicable, can you provide an example quest from quests.yml using Pastebin.com?
https://pastebin.com/Xk2X3GUp

@piotrskibapl This fix is now available in https://ci.codemc.io/job/PikaMug/job/Quests/142/
Note that QuestsGUI may not be compatible with this development build.


Huge icons when searching for podcast

Hi,
not sure if problem on my side as in the screenshots from you it looks different, but the icons I see when I am searching for a podcast are huge :)

Yes I noticed the same thing. I am using default Obsidian theme and on Windows.


[Python Template] PubSubLiteToBigQuery

ref. [Spike] Explore the usage of PubSub Lite Spark Connector for Python template

Template development completed. Writes to BQ are failing, debugging in-progress.


tritonclientutils.utils.InferenceServerException: [StatusCode.UNIMPLEMENTED]

Description
Hello, okay so I am trying to do object detection inference on the GPU, RTX 2070 Super, of my own custom trained TensorFlow neural network based on the Faster Rcnn Inception Resnet v2 COCO (pre-trained) network provided in the Model Zoo. (here is the direct download link)
Triton Information
What version of Triton are you using?
nvcr.io/nvidia/tritonserver   20.03.1-py3
AND
nvcr.io/nvidia/tritonserver   20.03.1-py3-clientsdk
Nvidia driver version
nvidia-driver-440 |    440.100-0ubuntu<IP_ADDRESS>
Are you using the Triton container or did you build it yourself?
I am using the container. versions 20.03.1-py3  and 20.03.1-py3-clientsdk
To Reproduce
To walk you step by step on what I did (to replicate this):

Downloaded the pre-trained network from above.
You can skip the following two steps (STEP 2 & 3) to save time since it does the same thing without custom re-training.
Used this config file "pipeline_tf1.15.config" to re-train ("transfer-learning" for about 4000 steps on GPU) by running cmd:

$ python ${OBJ_DET_DIR}/model_main.py --pipeline_config_path="${CKPT_DIR}/pipeline_tf1.15.config" \
--model_dir="${TRAIN_DIR}" --num_train_steps=${i} --num_eval_steps=400

Here is the config file "pipeline_tf1.15.config":
# Faster R-CNN with Inception Resnet v2, Atrous version;
# Configured for MSCOCO Dataset.
# Users should configure the fine_tune_checkpoint field in the train config as
# well as the label_map_path and input_path fields in the train_input_reader and
# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
# should be configured.

model {
  faster_rcnn {
    num_classes: 3
    image_resizer {
      fixed_shape_resizer {
        height: 640
        width: 960
	resize_method: BICUBIC
      }
    }
    feature_extractor {
      type: 'faster_rcnn_inception_resnet_v2'
      first_stage_features_stride: 8 # Must be 8 or 16
    }
    first_stage_anchor_generator {
      grid_anchor_generator {
        scales: [0.25, 0.5, 1.0, 2.0]
        aspect_ratios: [0.5, 1.0, 2.0]
        height_stride: 2 #8
        width_stride: 2 #8
      }
    }
    first_stage_atrous_rate: 2
    first_stage_box_predictor_conv_hyperparams {
      op: CONV
      regularizer {
        l2_regularizer {
          weight: 0.0
        }
      }
      initializer {
        truncated_normal_initializer {
          stddev: 0.01
        }
      }
    }
    first_stage_nms_score_threshold: 0.0
    first_stage_nms_iou_threshold: 0.7
    first_stage_max_proposals: 9 #18
    first_stage_localization_loss_weight: 2.0
    first_stage_objectness_loss_weight: 1.0
    initial_crop_size: 34 #17
    maxpool_kernel_size: 8 #4
    maxpool_stride: 1
    second_stage_box_predictor {
      mask_rcnn_box_predictor {
        use_dropout: true #false
        dropout_keep_probability: 0.8
        fc_hyperparams {
          op: FC
          regularizer {
            l2_regularizer {
              weight: 0.0
            }
          }
          initializer {
            variance_scaling_initializer {
              factor: 1.0
              uniform: true
              mode: FAN_AVG
            }
          }
        }
      }
    }
    second_stage_post_processing {
      batch_non_max_suppression {
        score_threshold: 0.0
        iou_threshold: 0.6
        max_detections_per_class: 3
        max_total_detections: 3 #6
      }
      score_converter: SOFTMAX
    }
    second_stage_localization_loss_weight: 2.0
    second_stage_classification_loss_weight: 1.0
    second_stage_batch_size: 64
  }
}
train_config {
  batch_size: 1
  data_augmentation_options {
    random_rotation90 {
    }
    random_rgb_to_gray {
      probability: 0.05
    }
    random_pixel_value_scale {
      minval: 0.9
      maxval: 1.1
    }
    random_patch_gaussian {
      random_coef: 0.80
      min_patch_size: 1
      max_patch_size: 50
    }
    random_black_patches {
      max_black_patches: 10
      probability: 0.40
    }
    random_adjust_brightness {
    }
    random_adjust_contrast {
    }
    random_adjust_hue {
      max_delta: 0.06
    }
    random_adjust_saturation {
      min_delta: 0.8
      min_delta: 1.25
    }
    random_jitter_boxes {
      ratio: 0.08
    }
  }
  sync_replicas: true
  optimizer {
    adam_optimizer: {
      learning_rate: {
        exponential_decay_learning_rate {
          initial_learning_rate: 0.0003
          decay_steps: 2000
          decay_factor: 0.85
        }
      }
    }
    use_moving_average: true
  }
  gradient_clipping_by_norm: 10.0
  fine_tune_checkpoint: "/tensorflow/learn_pet/ckpt/model.ckpt"
  num_steps: 200000
  from_detection_checkpoint: true
  load_all_detection_checkpoint_vars: true
  batch_queue_capacity: 1
  num_batch_queue_threads: 1
  prefetch_queue_capacity: 1
}
train_input_reader {
  label_map_path: "/tensorflow/learn_pet/dataset/ag_label_map.pbtxt"
  shuffle: true
  tf_record_input_reader {
    input_path: "/tensorflow/learn_pet/dataset/train_dataset_bigger_bb.record"
  }
  max_number_of_boxes: 1
}
eval_config {
  metrics_set: "coco_detection_metrics"
  num_examples: 400
}
eval_input_reader {
  label_map_path: "/tensorflow/learn_pet/dataset/ag_label_map.pbtxt"
  shuffle: true
  num_readers: 1
  tf_record_input_reader {
    input_path: "/tensorflow/learn_pet/dataset/validate_dataset_bigger_bb.record"
  }
  max_number_of_boxes: 1
}

Exported the model into a frozen inference graph (savedmodel.pb) by running cmd:

$ python ${OBJ_DET_DIR}/export_inference_graph.py --pipeline_config_path="${CKPT_DIR}/pipeline_tf1.15.config" \
--trained_checkpoint_prefix="${TRAIN_DIR}/model.ckpt-$i" --output_directory="${OUTPUT_DIR)"

Ran Nvidia Triton inference server container version nvcr.io/nvidia/tritonserver   20.03.1-py3 NGC docker pull command here by cmd:

$ docker run --gpus all --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 -p8000:8000 -p8001:8001 -p8002:8002 -v/full/path/to/example/model/repository:/models <docker image ID>

saved_model_cli shows this:
MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs:

signature_def['serving_default']:
  The given SavedModel SignatureDef contains the following input(s):
    inputs['inputs'] tensor_info:
        dtype: DT_UINT8
        shape: (-1, -1, -1, 3)
        name: image_tensor:0
  The given SavedModel SignatureDef contains the following output(s):
    outputs['detection_boxes'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 3, 4)
        name: detection_boxes:0
    outputs['detection_classes'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 3)
        name: detection_classes:0
    outputs['detection_features'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, -1, -1, -1, -1)
        name: detection_features:0
    outputs['detection_multiclass_scores'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 3, 4)
        name: detection_multiclass_scores:0
    outputs['detection_scores'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 3)
        name: detection_scores:0
    outputs['num_detections'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1)
        name: num_detections:0
    outputs['raw_detection_boxes'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 9, 4)
        name: raw_detection_boxes:0
    outputs['raw_detection_scores'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 9, 4)
        name: raw_detection_scores:0
  Method name is: tensorflow/serving/predict

Converted Tensorflow model into Tensorflow TensorRT model by running cmd: (Inside the docker container 20.03.1-py3 container)

$ python3 trt_model_converter.py

Here is the code in script file "trt_model_converter.py":
from tensorflow.python.compiler.tensorrt import trt_convert as trt

converter = trt.TrtGraphConverter(input_saved_model_dir='/triton-inference-server/docs/examples/my_models/frcnn_incep_resnet_v2/5000/model.savedmodel',
	minimum_segment_size=3,is_dynamic_op=True,maximum_cached_engines=5,precision_mode="FP16") # max_workspace_size_bytes=4000000000

converted = converter.convert()

converter.save(output_saved_model_dir='/triton-inference-server/docs/examples/my_models/frcnn_incep_resnet_v2/5000_FP16')

Once model was converted successfully, here is the repository structure:

$ export MODEL_REPO="/triton-inference-server/docs/examples/my_models"

root@aa94996e622f:/triton-inference-server/docs/examples/my_models# $ lsar
.:
total 4
drwxr-xr-x 3 root root 4096 Jul  3 23:45 frcnn_incep_resnet_v2

./frcnn_incep_resnet_v2:
total 20
drwxr-xr-x 3 root root 4096 Jun 24 21:25 5001
-rw-r--r-- 1 root root  110 Jun 26 23:38 ag_label_map.txt
-rw-r--r-- 1 root root 1630 Jul  3 23:45 config.pbtxt
-rw-r--r-- 1 root root 4599 Jun 19 05:09 pipeline_tf1.15.config

./frcnn_incep_resnet_v2/5001:
total 4
drwxr-xr-x 2 root root 4096 Jun 24 21:25 model.savedmodel

./frcnn_incep_resnet_v2/5001/model.savedmodel:
total 233560
-rw-r--r-- 1 root root 239161986 Jun 24 19:51 saved_model.pb

Here is the "config.pbtxt" file:
name: "frcnn_incep_resnet_v2"
platform: "tensorflow_savedmodel"
max_batch_size: 0
version_policy: { all { }} # "latest" Only the latest 'n' versions of the model in the repository
# are available for inferencing. Or "specific" Only the specifically listed versions of the model are
# available for inferencing.
input [
  {
    name: "inputs"
    data_type: TYPE_UINT8
    dims: [-1, -1, -1, 3 ]
  }
]
output [
  {
    name: "detection_boxes"
    data_type: TYPE_FP32
    dims: [-1, 3, 4] # the 3 here represents max_total_detections in pipeline.config
  },
  {
    name: "detection_classes"
    data_type: TYPE_FP32
    dims: [-1, 3] # the 3 here represents max_total_detections in pipeline.config
  },
  {
    name: "detection_features"
    data_type: TYPE_FP32
    dims: [-1, -1, -1, -1, -1]
  },
  {
    name: "detection_multiclass_scores"
    data_type: TYPE_FP32
    dims: [-1, 3, 4] # the 3 here represents max_total_detections in pipeline.config
  },
  {
    name: "detection_scores"
    data_type: TYPE_FP32
    dims: [-1, 3] # the 3 here represents max_total_detections in pipeline.config
  },
  {
    name: "num_detections"
    data_type: TYPE_FP32
    dims: [-1]
  },
  {
    name: "raw_detection_boxes"
    data_type: TYPE_FP32
    dims: [-1, 9, 4] # the 9 here represents first_stage_max_proposals in pipeline.config
  },
  {
    name: "raw_detection_scores"
    data_type: TYPE_FP32
    dims: [-1, 9, 4] # the 9 here represents first_stage_max_proposals in pipeline.config
  }
]
instance_group [
  {
    count: 5
    kind: KIND_GPU
    gpus: [ 0 ]
  }
]

Loaded converted model into tritonserver by running cmd: (Inside the docker container 20.03.1-p3)

$ tritonserver --strict-model-config=True --log-verbose=1 --model-repository=$(MODEL_REPO) \
--api-version=1 --allow-http=True --http-port=8080 --allow-grpc=True --grpc-port=8082 \
--grpc-infer-allocation-pool-size=0  --trace-file=/tmp/tritonserver_trace.json --trace-rate=1000 \
--trace-level=MAX --tf-allow-soft-placement=True

Ran nvidia triton inference clientsdk container version nvcr.io/nvidia/tritonserver   20.03.1-py3-clientsdk NGC docker pull command here by cmd:

$ sudo docker run -it --name=tritonserver_client --net=host nvcr.io/nvidia/tritonserver:20.03.1-py3-clientsdk

In the docker container (20.03.1-py3-clientsdk) run command:
$ python3 frcnn_grpc_v2_triton_infer_client.py

Here is the code in script file "frcnn_grpc_v2_triton_infer_client.py":
import argparse
import numpy as np
import sys
import cv2
from PIL import Image
import PIL.Image
import PIL.ImageDraw
import PIL.ImageFont
import pdb

import tritongrpcclient

REDU_WIDTH_FRCNN = 960
REDU_HEIGHT_FRCNN = 640
REDU_WIDTH_SSD = 960
REDU_HEIGHT_SSD = 640
ORIG_WIDTH = 1920
ORIG_HEIGHT = 1280
ORIG_IMAGE_SIZE = (ORIG_WIDTH, ORIG_HEIGHT)
REDU_IMAGE_SIZE_FRCNN = (REDU_WIDTH_FRCNN, REDU_HEIGHT_FRCNN)
REDU_IMAGE_SIZE_SSD = (REDU_WIDTH_SSD, REDU_HEIGHT_SSD)
MIN_SCORE_THRESHOLD = 0.10

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('-v',
                        '--verbose',
                        action="store_true",
                        required=False,
                        default=False,
                        help='Enable verbose output')
    parser.add_argument('-u',
                        '--url',
                        type=str,
                        required=False,
                        default='localhost:8082',
                        help='Inference server URL. Default is localhost:8001.')

    FLAGS = parser.parse_args()
    try:
        triton_client = tritongrpcclient.InferenceServerClient(url=FLAGS.url,
                                                         verbose=FLAGS.verbose)
    except Exception as e:
        print("channel creation failed: " + str(e))
        sys.exit()

    model_name = 'frcnn_incep_resnet_v2'

    # Infer
    inputs = []
    outputs = []
    #inputs.append(tritongrpcclient.InferInput('inputs', [-1, -1, -1, 3], "UINT8"))
    inputs.append(tritongrpcclient.InferInput('inputs', [640, 960, 3], "UINT8"))

    # Obtain the data for the input tensors.
    infer_image = cv2.imread('/workspace/install/python/IMG_4678.JPG')
    reduced_infer_image_frcnn = cv2.resize(infer_image, REDU_IMAGE_SIZE_FRCNN, interpolation=cv2.INTER_AREA)
    cv2.imwrite('/workspace/install/python/IMG_4678_resized.JPG', reduced_infer_image_frcnn)

    im = np.array(Image.open('/workspace/install/python/IMG_4678_resized.JPG'))

    # Initialize the data
    inputs[0].set_data_from_numpy(im)

    outputs.append(tritongrpcclient.InferRequestedOutput('detection_boxes'))
    outputs.append(tritongrpcclient.InferRequestedOutput('detection_classes'))
    outputs.append(tritongrpcclient.InferRequestedOutput('detection_features'))
    outputs.append(tritongrpcclient.InferRequestedOutput('detection_multiclass_scores'))
    outputs.append(tritongrpcclient.InferRequestedOutput('detection_scores'))
    outputs.append(tritongrpcclient.InferRequestedOutput('num_detections'))
    outputs.append(tritongrpcclient.InferRequestedOutput('raw_detection_boxes'))
    outputs.append(tritongrpcclient.InferRequestedOutput('raw_detection_scores'))

    # Test with outputs
    results = triton_client.infer(model_name=model_name,
                                  inputs=inputs,
                                  outputs=outputs,
                                  headers={'test': '1'})

    statistics = triton_client.get_inference_statistics(model_name=model_name)
    print(statistics)
    if len(statistics.model_stats) != 1:
        print("FAILED: Inference Statistics")
        sys.exit(1)

    # Get the output arrays from the results
    detection_boxes_output = results.as_numpy('detection_boxes')
    detection_classes_output = results.as_numpy('detection_classes')
    detection_features_output = results.as_numpy('detection_features')
    detection_multiclass_scores_output = results.as_numpy('detection_multiclass_scores')
    detection_scores_output = results.as_numpy('detection_scores')
    num_detections_output = results.as_numpy('num_detections')
    raw_detection_boxes_output = results.as_numpy('raw_detection_boxes')
    raw_detection_scores_output = results.as_numpy('raw_detection_scores')

    # Test with no outputs
    results = triton_client.infer(model_name=model_name,
                                  inputs=inputs,
                                  outputs=None)

    # Get the output arrays from the results
    output0_data = results.as_numpy('OUTPUT0')
    output1_data = results.as_numpy('OUTPUT1')

Expected behavior
I expect the client to send inference request and receive back inference results of Object detected Class name, bounding boxes, confidence and so on.

Unless I missed it I don't see where you say what went wrong. Your config.pbtxt is a little strange in that it specifies 5 instances of the model... nothing wrong with that but there is usually not a need to have 5 model instances. Also, the model.savedmodel directory usually (always?) has a variables sub-directory.

@deadeyegoodwin My model.savedmodel, "faster_rcnn_inception_resnet_v2", custom-retrained does not generate any files in its variables sub-directory so I omitted it. The 5 instances was just to test max inference throughput.
You are right, my mistake, I did not specify the error I'm getting. Here it is:
With the 20.03.1-py3 container server running on this command (ingesting that config.pbtxt):
tritonserver --strict-model-config=True --log-verbose=1 --model-repository=$(MODEL_REPO) \
--api-version=1 --allow-http=True --http-port=8080 --allow-grpc=True --grpc-port=8082 \
--grpc-infer-allocation-pool-size=0  --trace-file=/tmp/tritonserver_trace.json --trace-rate=1000 \
--trace-level=MAX --tf-allow-soft-placement=True

I run frcnn_grpc_v2_triton_infer_client.py in the 20.03.1-py3-clientsdk container:
python3 /workspace/install/python/frcnn_grpc_v2_triton_infer_client.py

I get:
Traceback (most recent call last):
File "/workspace/install/python/frcnn_grpc_v2_triton_infer_client.py", line 115, in <module>
headers={'test': '1'})
File "/usr/local/lib/python3.6/dist-packages/tritongrpcclient/__init__.py", line 940, in infer
raise_error_grpc(rpc_error)
File "/usr/local/lib/python3.6/dist-packages/tritongrpcclient/__init__.py", line 49, in raise_error
grpc
 raise get_error_grpc(rpc_error) from None
tritonclientutils.utils.InferenceServerException: [StatusCode.UNIMPLEMENTED]

If I change the --grpc-port I get:
tritonclientutils.utils.InferenceServerException: [StatusCode.UNAVAILABLE] Connection reset by peer

So obviously some form of communication is happening but I am not getting my (inferred results) message back from the server to the client.

From the naming it looks like you are running a V2 client (that is a client that is using the V2 grpc/http protocols) but you launched the server with --api-version=1. Try --api-version=2.

@deadeyegoodwin
Good catch! The 20.03 has both the V1 and V2 protocol.
Trying --api-version=2 I get an input mismatch:
tritonclientutils.utils.InferenceServerException: [StatusCode.INVALID_ARGUMENT] unexpected
shape for input 'inputs' for model 'frcnn_incep_resnet_v2'. Expected [-1,-1,-1,3], got [640,960,3]

even though that is the input reported from saved_model_cli (as you can see in the main issue details above).
I train with images resized down by TensorFlow to [640, 960] and then I resize using cv2 to make the client inference request.

The batch dimension is handled specially in the config file. Since you have a saved-model I would let triton generate the initial config.pbtxt for you and then you can copy it an add whatever enhancements you want. See https://docs.nvidia.com/deeplearning/triton-inference-server/master-user-guide/docs/model_configuration.html#generated-model-configuration

@deadeyegoodwin
Got it. Allowed triton to generate its own config.pbtxt by setting --strict-model-config=false and then read the config file by using the endpoint metadata port, here it is:
name: "inference:0"
version: "1.13.0"
extensions: "classification"
extensions: "sequence"
extensions: "model_repository"
extensions: "schedule_policy"
extensions: "model_configuration"
extensions: "system_shared_memory"
extensions: "cuda_shared_memory"
extensions: "binary_tensor_data"
extensions: "statistics"

name: "frcnn_incep_resnet_v2"
versions: "5001"
platform: "tensorflow_savedmodel"
inputs {
  name: "inputs"
  datatype: "UINT8"
  shape: -1
  shape: -1
  shape: 3
}
outputs {
  name: "detection_boxes"
  datatype: "FP32"
  shape: 3
  shape: 4
}
outputs {
  name: "num_detections"
  datatype: "FP32"
  shape: 1
}
outputs {
  name: "detection_classes"
  datatype: "FP32"
  shape: 3
}
outputs {
  name: "detection_multiclass_scores"
  datatype: "FP32"
  shape: 3
  shape: 4
}
outputs {
  name: "raw_detection_scores"
  datatype: "FP32"
  shape: 9
  shape: 4
}
outputs {
  name: "detection_scores"
  datatype: "FP32"
  shape: 3
}
outputs {
  name: "detection_features"
  datatype: "FP32"
  shape: -1
  shape: -1
  shape: -1
  shape: -1
}
outputs {
  name: "raw_detection_boxes"
  datatype: "FP32"
  shape: 9
  shape: 4
}

I then to tweaked the frcnn_grpc_v2_triton_infer_client.py script you saw above to use the triton auto generated config file's input dims of [-1, -1, 3]
Here is the new frcnn_grpc_v2_triton_infer_client.py (for completeness sake):
#!/usr/bin/env python

import argparse
import numpy as np
import sys
import cv2
from PIL import Image
import PIL.Image
import PIL.ImageDraw
import PIL.ImageFont
import pdb

import tritongrpcclient

REDU_WIDTH_FRCNN = 960
REDU_HEIGHT_FRCNN = 640
REDU_WIDTH_SSD = 960
REDU_HEIGHT_SSD = 640
ORIG_WIDTH = 1920
ORIG_HEIGHT = 1280
ORIG_IMAGE_SIZE = (ORIG_WIDTH, ORIG_HEIGHT)
REDU_IMAGE_SIZE_FRCNN = (REDU_WIDTH_FRCNN, REDU_HEIGHT_FRCNN)
REDU_IMAGE_SIZE_SSD = (REDU_WIDTH_SSD, REDU_HEIGHT_SSD)
MIN_SCORE_THRESHOLD = 0.10

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('-v',
                        '--verbose',
                        action="store_true",
                        required=False,
                        default=False,
                        help='Enable verbose output')
    parser.add_argument('-u',
                        '--url',
                        type=str,
                        required=False,
                        default='localhost:8082',
                        help='Inference server URL. Default is localhost:8001.')

    FLAGS = parser.parse_args()
    try:
        triton_client = tritongrpcclient.InferenceServerClient(url=FLAGS.url,
                                                         verbose=FLAGS.verbose)
    except Exception as e:
        print("channel creation failed: " + str(e))
        sys.exit()

    model_name = 'frcnn_incep_resnet_v2'

    # Infer
    inputs = []
    outputs = []
    inputs.append(tritongrpcclient.InferInput('inputs', [-1, -1, 3], "UINT8"))
    #inputs.append(tritongrpcclient.InferInput('inputs', [640, 960, 3], "UINT8"))

    # Obtain the data for the input tensors.
    infer_image = cv2.imread('/workspace/install/python/IMG_4678.JPG')
    reduced_infer_image_frcnn = cv2.resize(infer_image, REDU_IMAGE_SIZE_FRCNN, interpolation=cv2.INTER_AREA)
    cv2.imwrite('/workspace/install/python/IMG_4678_resized.JPG', reduced_infer_image_frcnn)

    im = np.array(Image.open('/workspace/install/python/IMG_4678_resized.JPG'))

    # Initialize the data
    inputs[0].set_data_from_numpy(im)

    outputs.append(tritongrpcclient.InferRequestedOutput('detection_boxes'))
    outputs.append(tritongrpcclient.InferRequestedOutput('detection_classes'))
    outputs.append(tritongrpcclient.InferRequestedOutput('detection_features'))
    outputs.append(tritongrpcclient.InferRequestedOutput('detection_multiclass_scores'))
    outputs.append(tritongrpcclient.InferRequestedOutput('detection_scores'))
    outputs.append(tritongrpcclient.InferRequestedOutput('num_detections'))
    outputs.append(tritongrpcclient.InferRequestedOutput('raw_detection_boxes'))
    outputs.append(tritongrpcclient.InferRequestedOutput('raw_detection_scores'))

    # Test with outputs
    results = triton_client.infer(model_name=model_name,
                                  inputs=inputs,
                                  outputs=outputs,
                                  headers={'test': '1'})

    statistics = triton_client.get_inference_statistics(model_name=model_name)
    print(statistics)
    if len(statistics.model_stats) != 1:
        print("FAILED: Inference Statistics")
        sys.exit(1)

    # Get the output arrays from the results
    detection_boxes_output = results.as_numpy('detection_boxes')
    detection_classes_output = results.as_numpy('detection_classes')
    detection_features_output = results.as_numpy('detection_features')
    detection_multiclass_scores_output = results.as_numpy('detection_multiclass_scores')
    detection_scores_output = results.as_numpy('detection_scores')
    num_detections_output = results.as_numpy('num_detections')
    raw_detection_boxes_output = results.as_numpy('raw_detection_boxes')
    raw_detection_scores_output = results.as_numpy('raw_detection_scores')

    # Test with no outputs
    results = triton_client.infer(model_name=model_name,
                                  inputs=inputs,
                                  outputs=None)

    # Get the output arrays from the results
    output0_data = results.as_numpy('OUTPUT0')
    output1_data = results.as_numpy('OUTPUT1')

The error code I get when sending the grpc message for inference is:
tritonclientutils.utils.InferenceServerException: got unexpected
numpy array shape [640, 960, 3], expected [-1, -1, 3]

If I copy the Triton auto generated config.pbtxt and deploy it as is like you suggested, I started getting errors along the lines of:
failed to load 'frcnn_incep_resnet_v2' version 5001: Invalid argument: model 'frcnn_incep_resnet_v2',
tensor 'inputs': the model expects 4 dimensions (shape [-1,-1,-1,3]) but the
model configuration specifies 3 dimensions (shape [-1,-1,3])

Since the model retrained on TensorFlow is not of that shape that Triton is inferring.
Or if I tweak only the input back to dims: [-1, -1, -1, 3] I get:
failed to load 'frcnn_incep_resnet_v2' version 5001: Invalid argument: model 'frcnn_incep_resnet_v2',
tensor 'detection_boxes': the model expects 3 dimensions (shape [-1,3,4]) but the
model configuration specifies 2 dimensions (shape [3,4])

One by one the outputs will trigger dimension mismatch errors.

In the model configuration you can use dimensions of -1 to indicate that the dimension can take on any value. But for a given inference request the input has a specific size, and in the client you must indicate that specific size for the input (and the data you provide must match that specific size). You should also read this section carefully: https://docs.nvidia.com/deeplearning/triton-inference-server/master-user-guide/docs/model_configuration.html#inputs-and-outputs

Closing, reopen if issue continues.

Yes @deadeyegoodwin I missed essential details on configuration vs client outbound message formats. The gRPC message was sent successfully. Still need to figure out how to batch messages together and/or enable Dynamic batching for video inference (continuous stream of frames). If you can link me to the right direction.
Thanks

You could look at deepstream, which is designed for processing video. The latest release has some Triton integration for inferencing. https://docs.nvidia.com/metropolis/deepstream/dev-guide/index.html

@adfayed Hi good day, I'm having exactly the same problems that you had with the dimension mismatch errors; I have read about the configuration vs client outbound message formats but I still have errors, could you please post how the inference client and / or config was left to see what was changed to be able to send the grpc message? Thanks


trivial: link from readme to builder's guide [skip ci]

This PR changes a link in the section Developer Resources from dev.lightning.community (unmaintained) to docs.lightning.engineering.
It also specifies more clearly what readers can expect to find there.

Cool, you may want to change the commit message as suggested to skip the CI checks.
old: trivial: link from readme to builder's guide
new: trivial: link from readme to builder's guide [skip ci]


default values for the parameters in the API documentation

Currently, the mandatory parameters in the API doc page have no default values. I suggest to provide some default values, so that a developer can just click "try it out" and get results.
E.g. for e-entity that is the dataset name and input text.

I have added default values wherever it seemed to be useful. I didn't put any in all USER calls for instance, neither in PUT and DELETE calls. Where examples are given in the description, I did not copy them to the body or input field. I provided examples where I thought it was useful. Can we close this?

I propose we should prefer defaults for the body then for the input parameter.

This looks alos interesting for body defaults.

Also remove the input parameter from the documentation.

Thanks @ArneBinder . The input parameters are removed from the documentation. I've checked the required parameters, they should now set to true everywhere where it is needed. I think we can close this.


Questions on usuage

Hello!
I have just found your repository and it seems that this is exactly what I am looking for.
Is there a GUI available for complete beginners like me. Reading the instructions make is sound quite complicated for me.
I have some ebooks in epub format and I have an audiobook of these ebooks, where each mp3 file equals one chapter of the book.
Would your tool be able to create an ebook with synchronized text and audio when I have the files like described above ?
If yes how good/accurate is the alignment?
(Does your tool work the same way as aeneas  or is it using completly different method to do the syncing?)

Hello! Unfortunately, the GUI for syncabook is not available. Synchronized EPUB ebook cannot be produced directly from another EPUB file, you need to prepare a list XHTML files containing fragments (units of synchronization), one file per chapter. This is done using to_xhtml command which converts plain text files to XHTMLs. So you may copy the contents of the EPUB ebook to the plain text files and follow the instructions.
I recommend you to follow the example in the readme. Just copy line by line and see the result. This is much easier than trying to understand the process just by reading my tangled prose.
I haven't measured the alignment accuracy quantitatively. It feels good. It should be at least as good as aeneas – the alignment algorithm is the same except the syncabook can handle some non-matching content and works faster.

Hi thank you for your quick response.
I have googled a bit and what I have found out is that this lib will convert an epub to I think xhtml chapter by chapter:
This is the library
https://github.com/aerkalov/ebooklib
The code to generate the xhtml chapter by chapter I have found here:
import ebooklib
from ebooklib import epub
def epub2thtml(epub_path):
    book = epub.read_epub(epub_path)
    chapters = []
    for item in book.get_items():
        if item.get_type() == ebooklib.ITEM_DOCUMENT:
            chapters.append(item.get_content())
    return chapters

Since I am a complete beginner I would like to ask if you maybe (if it is not to much to ask) could take a quick look if the xhtml files which this library would generate would work with your tool directly (so no need to convert them to txt first and then to xhtml).

You can extract xhtml contents from the EPUB file using this tool but you cannot use any xhtml as an input for syncabook. The reason is that the synchronization is done by mapping text fragments to audio intervals and these text fragments have to be explicitly marked. Here is an example. You can see that each fragment is  tag with its own id.
So it's not possible to skip to_xhtml step. You can avoid extracting the text manually using, as an option, the method showed in the article.

Will your script work  if I have a whole ebook as epub file and one single mp3 which covers the whole book ?
If yes I will give it a try because it seems to be easy to handle when it is just 1 single file each.
One more question, all the formating which was present in the original epub will get lost right ?
Is there anyway that you could maybe modify your script to work directly with epubs ? (I would even pay for such a functionality to be added)

I decided not to modify existing epubs. It requires much more work. So I'm not going to introduce this functionality.
You can synchronize a single mp3.


Incapable of manipulating VpnPacketBufferList

Hi there!
According to this page, most of the methods are marked as Not supported.. Please point out the alternative methods to those unsupported ones :).
IMHO a VpnPacketBufferList object shouldn't be  immutable.
https://github.com/MicrosoftDocs/winrt-api/blob/docs/windows.networking.vpn/vpnpacketbufferlist_append_1519252907.md
https://github.com/MicrosoftDocs/winrt-api/blob/docs/windows.networking.vpn/vpnpacketbufferlist_addatbegin_136422118.md
https://github.com/MicrosoftDocs/winrt-api/blob/docs/windows.networking.vpn/vpnpacketbufferlist_append_1519252907.md
https://github.com/MicrosoftDocs/winrt-api/blob/docs/windows.networking.vpn/vpnpacketbufferlist_clear_556466.md

Hey @mcleblanc -- I'm having trouble finding an owner for this API Ref - could you help with finding the right person to address this issue?

Thanks for the heads up, @bdbai. I've removed the "Not supported", since these APIs are, in fact, supported. That change will go live tomorrow (Oct 26 2017).

@stevewhims hooray! Thanks for tracking and fixing.

Ok, the fix is now live. To verify, see links in previous comment.

@bdbai, this is fixed now. Feel free to close the issue if you're ok with the resolution.


[Chef manage global objects] Leave Organization API

User Story

[Chef manage global objects] Leave Organization API

https://docs.chef.io/server/api_chef_server/#delete-15
DELETE /organizations/NAME/users/NAME - disassociate a user from an organization (leave)
Definition of Done
Demo Script / Repro Steps

We will handle it differently


Please HELP. How works this jitsi-keycloack?

Please HELP. How works this jitsi-keycloack?
One host has Jitsi and Jitsi Keycloak installed in Docer. When there is a redirect from Jitsi to Jitsi-Keycloak on port 3000 error ERR_SSL_PROTOCOL_ERROR If you move Jitsi Keycloak to a separate host, http://kck.urd-jitsi-vks1.jitsimeethost.com:3000 authorization on keycloack passes but then an error Access to XMLHttpRequest at 'https://keycloak-int-test.jitsimeethost.com/auth/realms/office/account' from origin 'http://kck.urd-jitsi-vks1.jitsimeethost.com:3000/' has been blocked by CORS policy: No 'Access-Control-Allow-Origin' header is present on the requested resource

Hi, to fix the CORS error you have to add http://kck.urd-jitsi-vks1.jitsimeethost.com:3000/ to the Web Origins list in keycloak


[WC-1535]: Configure dependabot to ignore local packages

Description
This change update config generator. Now generated config includes ignore rules for local (workspace) packages.
Pull request type

[x] No code changes (changes to documentation, CI, metadata, etc)
[ ] Dependency changes (any modification to dependencies in package.json)
[ ] Bug fix (non-breaking change which fixes an issue)
[ ] New feature (non-breaking change which adds functionality)
[ ] Breaking change (fix or feature that would cause existing functionality to change)
[ ] Test related change (New E2E test, test automation, etc.)

What should be covered while testing?

cc: @leonardomendix


Update run mypy instructions in contributing doc

Description

Types of Changes

[ ] Core
[ ] Bugfix
[ ] New feature
[ ] Enhancement/optimization
[x] Documentation

Issues Fixed or Closed by This PR

Checklist

[x] My code follows the code style of this project.
[x] My change requires a change to the documentation.
[x] I have updated the documentation accordingly.
[x] I have read the CONTRIBUTING document.
[x] I have added tests to cover my changes.
[x] I have tested the changes and verified that they work and don't break anything (as well as I can manage).

I'll close this, @alexauritt let me know if we should reopen this 😊


Replace deprecated fs-promise with mz

#700 Upgrade deprecated fs-promise with mz

Hi @jackytck !
Thanks for you contribution ! And it looks ok for me, we can merge this for next release I think


Update README.md

There was a copy-paste mention that Saferpay offers plugins to various shop systems - it's pointless as we focus only on Sylius here.
Also, I've made a few changes according to Grammarly. ;)

Thank you, @zamaskowany-szachista 🏅


[Brew] Unable to perform Brew Search

Extension – Brew
Author: @nhojb
Description
Unable to perform Brew Search command.
https://user-images.githubusercontent.com/4593816/182888363-0c6b351c-2e24-4c0b-9916-c40de19ef95e.mov
Steps To Reproduce

Launch Raycast
Type Brew Search, return
Observe Brew search failed error message

Note: It appears I'm unable to capture the error logs. ⌘ T followed by return results in an empty clipboard.

The current behavior
Brew Search fails, with no logs to capture.
The expected behavior
Brew Search should allow searching formulae by name.
Raycast version
Version: 1.38.3
System information
OS: macOS Monterey
Version: 12.3.1
Model: MacBook Pro (16-inch, 2021)
Chip: Apple M1 Pro
Memory: 16 GB

Hi @kanemontreuil thanks for the bug report.
Could you run brew doctor on your Mac and post the output? Thanks.

Sure thing @nhojb!

@nhojb it turns out my employer's proxy (Zscaler) was causing this issue. I realized after closing #2485 that they might be related. Works like charm now.

Great, thanks for the update. Glad you are good now.


Yarn package just broke

info No lockfile found.
warning prezola.com: No license field
[1/4] Resolving packages...
warning gulp > vinyl-fs > glob-stream ><EMAIL_ADDRESS>Please update to minimatch 3.0.2 or higher to avoid a RegExp DoS issue
warning gulp > vinyl-fs > glob-watcher > gaze > globule ><EMAIL_ADDRESS>Please update to minimatch 3.0.2 or higher to avoid a RegExp DoS issue
warning gulp > vinyl-fs > glob-watcher > gaze > globule > glob ><EMAIL_ADDRESS>graceful-fs v3.0.0 and before will fail on node releases >= v7.0. Please update to<EMAIL_ADDRESS>as soon as possible. Use 'npm ls graceful-fs' to find it in the tree. 28/03/2017, 11:08:08
warning browser-sync > localtunnel > request ><EMAIL_ADDRESS>Use uuid module instead
[2/4] Fetching packages...
error An unexpected error occurred: "https://registry.yarnpkg.com/gulp-uglifycss/-/gulp-uglifycss-1.0.7.tgz: invalid tar file".
info If you think this is a bug, please open a bug report with the information provided in "/srv/src/yarn-error.log".
info Visit https://yarnpkg.com/en/docs/cli/install for documentation about this command.
Removing intermediate container ab2294a90d76```

Same issue applies to npm
`npm install
npm ERR! tar.unpack untar error /Users/mb/.npm/gulp-uglifycss/1.0.7/package.tgz
npm ERR! Darwin 15.6.0
npm ERR! argv "/usr/local/bin/node" "/usr/local/bin/npm" "install"
npm ERR! node v4.4.1
npm ERR! npm  v2.14.20
npm ERR! invalid tar file
`

Here too

my npm install is broken too

I've publish a maintenance version yesterday (v1.0.7). I've used yarn publish to publish it. I think this could be an explanation of this.
I just publish a new version (v1.0.8) of the package using npm publish as before. Please tell me if this solve the issue.

The problem of corrupted package seems to already be reported on yarn site: https://github.com/yarnpkg/yarn/issues/2764#issuecomment-290008862
Wait and see!

Hi @Spy-Seth, v1.0.8 works just fine over here where 1.0.7 did not, thanks!


Frozen column in slick grid v2.3

Frozen column does not support in slick grid v2.3. How to use it. Thanks

This is the latest intel on frozen columns: https://github.com/6pac/SlickGrid/issues/26


feat(sumstat qc): adding methods for QC of summary statistics

Adding three methods of the QC for GWAS summary statistics.

Codecov Report
Attention: 198 lines in your changes are missing coverage. Please review.

Comparison is base (42b366c) 85.67% compared to head (90a97a8) 85.44%.
Report is 97 commits behind head on dev.

Additional details and impacted files

@@            Coverage Diff             @@
##              dev     #455      +/-   ##
==========================================
- Coverage   85.67%   85.44%   -0.24%
==========================================
  Files          89       96       +7
  Lines        2101     2665     +564
==========================================
+ Hits         1800     2277     +477
- Misses        301      388      +87

Files
Coverage Δ

src/airflow/dags/common_airflow.py
90.38% <100.00%> (ø)

src/airflow/dags/dag_preprocess.py
100.00% <ø> (ø)

src/airflow/dags/finngen_preprocess.py
100.00% <100.00%> (ø)

src/airflow/dags/gwas_curation_update.py
100.00% <100.00%> (ø)

src/gentropy/__init__.py
100.00% <ø> (ø)

src/gentropy/assets/__init__.py
100.00% <ø> (ø)

src/gentropy/assets/data/__init__.py
100.00% <ø> (ø)

src/gentropy/assets/schemas/__init__.py
100.00% <ø> (ø)

src/gentropy/cli.py
91.66% <100.00%> (ø)

src/gentropy/common/Liftover.py
80.64% <ø> (ø)

... and 81 more

... and 11 files with indirect coverage changes

My comments at this point is purely design related. I'm happy to discuss them further and happy to chip in with coding as well.
Moving code to a separate module
I would recommend moving all qc related code under /methods/summary_statistics_quality_controls.py, because I feel this logic, although tightly linked to sumstats, is big enough, and complex enough to separete into its own module. Similar to what we do with window based clumping. Also would provide space to expand the documenation on quality controls. Then the main qc method can be wired into SummaryStatistics and the usage could be something like this in the summary_statistics.py file (assuming all the time we want to run all metrics):
from gentropy.methods.summary_statistics_quality_controls import quality_metrics

class SummaryStatistics:
    def get_quality_control_metrics(self: SummaryStatistics) -> Dataframe:
        return quality_metrics(self._df)

Any check has to be windowed
SummaryStatistics datasets might contain data from multiple studies. Not accounting for this, could lead to nonsense output. So input summary statistics needs to be aggregated by studyId and each metrics should return a (set of) values for each study in the dataset.
The main QC function can call all separate metrics and then join the resulting columns together like this:
|--------|--------|--------|--------|
|studyId | QC_1   | QC_2   | QC_3   |
|--------|--------|--------|--------|
| S1     | Passed | Passed | Passed |
| S2     | Passed | Passed | Failed |
|--------|--------|--------|--------|

Making sure the methods can handle multiple studies at the same time is quite important in my opinion even if these functions are meant to be called on data from a single study.
Run metrics - judge later
I think the QC methods should just return the metrics, I don't really like that the same functions decide if these values are OK or not. I believe there should be a central QC configuration (in a yaml) telling which values are tolerated in which metrics. It would be more transparent what criteria needs to be met. Also I think it would be easier to test and change configuration. I'm not overly concerned about this issue if the thresholds are super solid in the scientific field.
Thinking about the process
My big concern here is how the results of the QC would be picked up, how it would impact the ingestion process... I see it as a major complexity. Even if the study fails the QC, it is not enough to not promote the dataset downstream, we need to propagate this information to the study index so the users of the released data would know why certain summary statistics are missing. It means the QC needs to yield some sort of manifest that needs to be picked up by the release- or the pre-process.

Thank you for comments Daniel, lets discuss when on campus. I think, I can address 3 out of 4.

@DSuveges I have corrected the code according to your comments. It is now a separate class with several separate functions, including the wrapper that performs all the checks and returns a Pyspark dataframe with QC metrics for each studyId. I have also added the documentation. I left sanity_filter in SummryStats because it belongs there.

Thanks @addramir, I was thinking about these modifications exactly. I'm looking into the code in details now.

@DSuveges I updated PZ check and removed linear regression, should be a bit faster now. And made small correction in neff check. All should work now.

@DSuveges there is no more actions from my side. Please have a look, when have time.


[6.0.1] Guard parse_version

The most recent versions of parse_version throw an exception if the version is empty. The version passed in is only set on Darwin (call to mac_ver(), so it's causing test failures on newer versions of Linux since the test suite can't even start.
Now, the only reason for the version parse is because the tests are looking at whether or not concurrency is available on the OS. This is only a limitation if we're working with Darwin. Swift 5.10 on Windows and Linux always have a Swift 5.10 concurrency runtime, so we don't even need to check for a version.
rdar://128502662
(cherry picked from commit 65e6ecdce29101129911c49760b0f85ac18d21e5) (cherry picked from commit f26bc2138b8cd8f73317d7b7c019bf016ca5bc33)

@swift-ci test


Quote Generator extension

some kind of quote generator and to give the thought for the day on each day login

Hey @vaibhavmehro, I would like to work on this issue.

ok go for it and do give leads.

ok thanks @ridsuteri .


Unable to deploy saleor on heroku trough deploy button.

What I'm trying to achieve
Deploy on the heroku server
Steps to reproduce the problem

Click on the deploy button

What I expected to happen
Deployment
Screenshots

System information
Operating system: Heroku
Browser: Chrome

Hey, thanks for reporting. It's likely that the Heroku deploy is broken on master since we're doing a lot of changes in the core now, but we'll try to look at it as soon as possible.


feat(shipyard-controller): Add getBlockingSequences endpoint to the Debug-UI

Signed-off-by: Andreas Samonik<EMAIL_ADDRESS>This PR

adds the Blocking Sequence endpoint to the Debug-UI

the shipyard controller has a debug ui? Fantastic! Is this documented?

the shipyard controller has a debug ui? Fantastic! Is this documented?

Soon ™  shipyard controller will have a Debug UI 😝 At the moment it is not yet exposed and is still in the "experimental" phase. As soon as we have a good enough prototype, we will provide some documentation for it :)

reviewed the backend and looks good to me


Add support to the fastpath for archival of measurements (canning)

Implement some form of canning to use the current collector and/or the new API to receive measurements without relying on batch.

Implemented in https://github.com/ooni/api/pull/192