Import-XrmSolution -ConvertToManaged parameter not converting Unmanaged solution to Managed

Hi @WaelHamze,
When I import crm solution via Imort-XrmSolution command in PowerShell and set ConvertToManaged parameter to $true, it doesn't convert my solution to managed. I am importing unmanaged solution and as it says in documentation, it should convert solution to managed. Am I missing something?
Thank You
Mirko

@mpavlino I don't believe this would convert the solution to managed, I think this is used when importing a managed solution and certain unmanaged customizations are present in the system. In certain cases these should be converted to managed. I would recommend you try this out on different components and I believe most of them are not supported for this action.

I imagine this corresponds to the ImportSolutionRequest.ConvertToManaged Property.

Direct the system to convert any matching unmanaged customizations into your managed solution. Optional.

Important part there is matching customizations.
see also: Moving from unmanaged to managed solutions in your organization.


My first PR , I added Cole Kofoworola

I added my name

Well done for making your first contribution @kofodym 👏🏽

Thank you very much for this reply. I almost thought I was doing a wrong thing


Probably need to remove Type, as that functionality is handled via labels

https://github.com/konveyor/analyzer-lsp/blob/2fee76eb8e4f7bdc7a12546873f113395503f56c/output/v1/konveyor/violations.go#L99C4-L99C4

@RipulHandoo do you know if the Type field is set or used anywhere in the source code?

@RipulHandoo just search in the codebase where Type is used. Meanwhile, I will speak with other teams leveraging the API and check if they are using it and it'd break them. Will respond here in some time.

@pranavgaikwad
Ok. Let me know.

@pranavgaikwad
After going through the source code, I found that the usage of Type field was minimal. Its primary use seemed to involve passing it to the dependency struct of either the service client or server. Following your suggestion, if labels can fulfill its functionality, removing Type from these sections is achievable. However, as Type field was used in the gRPC server's dependency struct, removing it requires rebuilding the gRPC server after making required changes in the protobuf file and subsequently implementing these changes within the client and provider server.


feat: Add new rule [unregister-events]

Adds new rule unregister-events, including documentation and tests. Will track and make sure that all registered window events in React components are unregistered when the component is unmounted.

<3


UI updates to WorkflowFilterSearch

Description & motivation 💭
Screenshots (if applicable) 📸

Before
After

Design Considerations 🎨
Testing 🧪
How was this tested 👻

[x] Manual testing
[ ] E2E tests added
[ ] Unit tests added

Steps for others to test: 🚶🏽♂🚶🏽♀

[ ] Verify all types of filters have the close button and it works/looks as expected
[ ] Verify Relative and Absolute Datetime filters work as expected
[ ] Verify Start/End time Datetime filter works as expected

Checklists
Draft Checklist
Merge Checklist
Issue(s) closed
DT-1359
Docs
Any docs updates needed?

Couple of thoughts

Were you going to fix the issue where clicking away from the Workflow Status filter doesn't reset it in the dropdown?
Should we also use this PR to remove the labs mode check from the new filters?

Were you going to fix the issue where clicking away from the Workflow Status filter doesn't reset it in the dropdown?

@rossedfort I have another task for this (DT-1360), but we also may be removing this filter entirely with the addition of Workflow counts.

Should we also use this PR to remove the labs mode check from the new filters?

I'll create a separate task for this and get it approved by product.

Looks like there is some slightly inconsistent behavior here. If the user has selected "relative" but not specified a duration, should we disable the "Apply" button?

I've disabled Apply if relative is selected and there is an error or no specified duration (https://github.com/temporalio/ui/pull/1613/commits/b0067201716becadc595e2ac9244932d8979e466).

Similarly, should we disable it if they select "absolute" and clear the date input?

The existing behavior for the datetime filter is just to use the current date if there is none specified. I think it would be fine to keep that and allow apply even if the date input is empty.


chore: [perf optimisations for large data sets] optimised extract info binding code

Pull Request Template
Use this template to quickly create a well written pull request. Delete all quotes before creating the pull request.

Description

Add a TL;DR when description is extra long (helps content team)
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Links to Notion, Figma or any other documents that might be relevant to the PR

PR fixes following issue(s)
Fixes # (issue number)

if no issue exists, please create an issue and ask the maintainers about this first

Media

A video or a GIF is preferred. when using Loom, don’t embed because it looks like it’s a GIF. instead, just link to the video

Type of change

Please delete options that are not relevant.

Bug fix (non-breaking change which fixes an issue)
New feature (non-breaking change which adds functionality)
Breaking change (fix or feature that would cause existing functionality to not work as expected)
Chore (housekeeping or task changes that don't impact user perception)
This change requires a documentation update

Testing

How Has This Been Tested?

Please describe the tests that you ran to verify your changes. Also list any relevant details for your test configuration.
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[ ] Manual
[ ] Jest
[ ] Cypress

Test Plan

Add Testsmith test cases links that relate to this PR

Issues raised during DP testing

Link issues raised during DP testing for better visiblity and tracking (copy link from comments dropped on this PR)

Checklist:
Dev activity

[ ] My code follows the style guidelines of this project
[ ] I have performed a self-review of my own code
[ ] I have commented my code, particularly in hard-to-understand areas
[ ] I have made corresponding changes to the documentation
[ ] My changes generate no new warnings
[ ] I have added tests that prove my fix is effective or that my feature works
[ ] New and existing unit tests pass locally with my changes
[ ] PR is being merged under a feature flag

QA activity:

[ ] Speedbreak features have been covered
[ ] Test plan covers all impacted features and areas of interest
[ ] Test plan has been peer reviewed by project stakeholders and other QA members
[ ] Manually tested functionality on DP
[ ] We had an implementation alignment call with stakeholders post QA Round 2
[ ] Cypress test cases have been added and approved by SDET/manual QA
[ ] Added Test Plan Approved label after Cypress tests were reviewed
[ ] Added Test Plan Approved label after JUnit tests were reviewed

/ok-to-test sha=ee6b9e3ddff04348a702e18996a87d3895b516c3

/ok-to-test sha=352cefe7fbda63728774ec2fc00baa44725fc9ec

/ok-to-test sha=c59ccde48f2942f7650d26850b288e59b48c9f3e

/ok-to-test sha=7638ec3f694974b862fe400613e1557c26c7921e

/build-deploy-preview env=release

[ ] Was the actions endpoint affected? I'm seeing a 404 on older apps which I can't see on release.
new and old API and DB queries are failing

[ ] CRUD using sample DB fails in authentication

Unable to proceed since DP is not in a sane state for regression testing as required.
cc: @vsvamsi1

/build-deploy-preview skip-tests=true


Find a way to redefine \or

MediaWiki redefines \or as \lor. We should find a propper workaround so that there are no inconsistencies when copying from Wikis.

@physikerwelt You need to explain this much better than you did, please.

@HowardCohl I updated the description above.

On the texvc help page there are many different ways to referring to texvc, such as TexVC, and Texvc. texvc should probably be consistently written.


Unable to import the react-kanban components

I've got a Rails backend React frontend application going, and I needed a Kanban-like board for one of my views. I've got the setup using webpack and additionally am using Typescript to write my frontend code.
I'm trying to get started with this library. I've added styled-components as well as the react-kanban libraries to my project via yarn add styled-components @lourenci/react-kanban. They resolved to versions 5.1.1 and 1.1.1 respectively.
I'm now trying to use them in my application, however the line import Board, { moveCard } from '@lourenci/react-kanban'; generates the following error:
ERROR in ./app/javascript/components/auth_pages/app_pages/matches/MatchmakeForm.tsx 32:28-33
"export 'default' (imported as 'Board') was not found in '@lourenci/react-kanban'

I'm really not sure what's going on here, I've used other libraries in my frontend (such as the react-notifications-component library) before with no issues.
Any help would be appreciated.

Hi, i could not replicate this error in a tsx project. Have you check your tsconfig?
Maybe can you reproduce it on codesandbox?

Hello. Unfortunately, I'm not able to reproduce the error in a CodeSandbox. I'm really not sure what could be wrong here, other javascript libraries work nicely.
I'll share my TSConfig and the setup steps that I performed in case that's helpful.
tsconfig.json
{
    "compilerOptions": {
        "declaration": false,
        "emitDecoratorMetadata": true,
        "experimentalDecorators": true,
        "lib": ["es6", "dom"],
        "module": "es6",
        "moduleResolution": "node",
        "sourceMap": true,
        "target": "ES6",
        "jsx": "react",
        "allowSyntheticDefaultImports": true,
        "strict": true,
        "allowJs": true,
        "skipLibCheck": true,
        "forceConsistentCasingInFileNames": true,
        "noFallthroughCasesInSwitch": true,
        "noUnusedParameters": true,
        "noUnusedLocals": true
    },
    "exclude": ["**/*.spec.ts", "node_modules", "vendor", "public"],
    "include": ["app", "javascript", "decs.d.ts"],
    "compileOnSave": false
}

My typescript declaration file, which I use to tell typescript to ignore that there are no typings in the library:
decs.d.ts
declare module 'react-notifications-component'; // Another library that I'm using that works with this setup.
declare module '@lourenci/react-kanban';

The component in which I'm trying to use the Board
import React, { useState } from 'react';
import Board, { moveCard } from '@lourenci/react-kanban';
import MatchAPI from '../../../../api/matchAPI';
import { Button } from 'react-bootstrap';

type Props = {
    onSubmit?: (matchmakeParams: MatchAPI.MatchmakeParams) => void;
    participantProperties: string[];
};

type Card = {
    id: number | string;
    title: string;
    description?: string;
};

type DndColumn = {
    id: number | string;
    title: string;
    cards: Card[];
};

type Board = DndColumn[];

export default function MatchmakeForm(props: Props): JSX.Element {
    const propertiesCards: Card[] = props.participantProperties.map((property, index) => {
        return { id: index, title: property };
    });
    const initialBoard: Board = [
        { id: 1, title: 'Properties', cards: propertiesCards },
        { id: 2, title: 'Within', cards: [] },
        { id: 3, title: 'Between', cards: [] },
    ];

    const [board, setBoard] = useState<Board>(initialBoard);

    function onFormSubmit(): void {
        props.onSubmit?.(grabProperties());
        resetForm();
    }

    function resetForm(): void {
        setBoard(initialBoard);
    }

    function grabProperties(): MatchAPI.MatchmakeParams {
        const withinProperties: string[] = board[1].cards.map((card) => card.title);
        const betweenProperties: string[] = board[2].cards.map((card) => card.title);
        return { within: withinProperties, between: betweenProperties };
    }

    function handleCardMove(_card: any, source: any, destination: any) {
        const updatedBoard: Board = moveCard(board, source, destination);
        setBoard(updatedBoard);
    }

    return (
        <>
            <Board onCardDragEnd={handleCardMove} disableColumnDrag>
                {board}
            </Board>
            <Button onClick={() => onFormSubmit()}>Matchmake!</Button>
        </>
    );
}

Setup Steps

yarn add @lourenci/react-kanban styled-components
Added the library to the typescript declarations file decs.d.ts, which is in my tsconfig.
Made the component and tried compiling.

@KyleRAnderson, i did just liek you said and worked fine for me.
I noticed a little mistake on the Board type i think it should me something like:
type Board = {     columns: DndColumn[] };
I did not tested any of the functions or the services you have, but the project builded correctly
Just to you know, i'm working on migrate the lib to typescript. I'm studying it yet, so i cant promisse anything in a short time :/

I'm facing the same issue with this lib and webpacker for rails project, @KyleRAnderson did you solve that issue?

I'm facing the same issue with this lib and webpacker for rails project, @KyleRAnderson did you solve that issue?

Unfortunately not, I worked around it in other ways.


Implement LayoutSettings

This issue is automatically created by @cobraz
Right now, the LayoutSettings component is empty. We need to implement it.
Path to the file is: settings/LayoutSettings.tsx

Could you please clarify issue?


A version of raylean without mathlib

The mathlib4 caching doesn't work for me. This makes builds very slow.
I'll keep this PR open as a version of raylean without the mathlib dependency and without proofs that require it to make it easier to work with.

Potentially the mathlib4 cache is broken at the moment? https://leanprover.zulipchat.com/#narrow/channel/287929-mathlib4/topic/cache.20rebuilding.20oleans

The pre-built cache only provides olean files. The project still needs to be compiled to native object files to be used in the executable target - this part is slower because we need to compile part of mathlib4.


Allow for ability to add tag components to popups

This is now possible using the Name component. There's a new name field for the popup events. The name component will be added to the root popup entity and can be used by the user to query and identity specific popups.
Ideally we could provide custom tag components, but this seems to be pretty involved when dealing with an event-based API vs a proper ECS-based solution.


Add a "Usage Guide" docs page

The individual API reference pages have some "how to use this" info, but we clearly need a specific page that gives guidance on how to use RSK correctly.
Wrote up the first chunk of this over the weekend.  It's not complete yet, but I figured it's worth getting the preview up in case anyone has feedback.

Just pushed a bunch of changes.  @denisw , I took most of your suggested edits into account, although I left some of the things like the "declaring functions in an object" section in place.  I agree there's some overlap between the API reference pages and the Usage Guide, but I don't think that's necessarily a bad thing.  It can be helpful to have the same info in multiple places, because people might be looking at the docs for different reasons.
In general, this page is intended to be more of a "how to".
We can always tweak it further later, but I want to get the current content merged in shortly.


specify encoding ascii

import grasp results in
---------------------------------------------------------------------------
UnicodeDecodeError                        Traceback (most recent call last)
<ipython-input-7-e0197a39b666> in <module>()
----> 1 import grasp
      2 from grasp import GrASP, CustomAttribute, remove_specialized_patterns
      3 from sklearn.model_selection import train_test_split
      4 from typing import Iterable, List, Set, Callable, Optional, Union, Sequence

/content/GrASP/grasp.py in <module>()
    267
    268 POSITIVE_LEXICON = [line.strip().lower() for line in open('resources/opinion-lexicon-English/positive-words.txt') if line.strip() != '' and line[0] != ';']
--> 269 NEGATIVE_LEXICON = [line.strip().lower() for line in open('resources/opinion-lexicon-English/negative-words.txt') if line.strip() != '' and line[0] != ';']
    270
    271 def _sentiment_extraction(text: str, tokens: List[str]) -> List[Set[str]]:

/content/GrASP/grasp.py in <listcomp>(.0)
    267
    268 POSITIVE_LEXICON = [line.strip().lower() for line in open('resources/opinion-lexicon-English/positive-words.txt') if line.strip() != '' and line[0] != ';']
--> 269 NEGATIVE_LEXICON = [line.strip().lower() for line in open('resources/opinion-lexicon-English/negative-words.txt') if line.strip() != '' and line[0] != ';']
    270
    271 def _sentiment_extraction(text: str, tokens: List[str]) -> List[Set[str]]:

/usr/lib/python3.7/codecs.py in decode(self, input, final)
    320         # decode input (taking the buffer into account)
    321         data = self.buffer + input
--> 322         (result, consumed) = self._buffer_decode(data, self.errors, final)
    323         # keep undecoded input until the next call
    324         self.buffer = data[consumed:]

UnicodeDecodeError: 'utf-8' codec can't decode byte 0xef in position 5273: invalid continuation byte

So I ran
from bs4 import UnicodeDammit
with open('resources/opinion-lexicon-English/positive-words.txt', 'rb') as file:
   content = file.read()

suggestion = UnicodeDammit(content)
suggestion.original_encoding

which outputs ascii
So I add the encoding when opening the positive and negative word files, which fixes the problem and allows import grasp

Hi @samhavens ,
Thank you for the pull request.
However, I tried specifying the encoding on my local repo to be ascii and it does not work.
Traceback (most recent call last):
  File "grasp.py", line 269, in <module>
    NEGATIVE_LEXICON = [line.strip().lower() for line in open('resources/opinion-lexicon-English/negative-words.txt', encoding='ascii') if line.strip() != '' and line[0] != ';']
  File "grasp.py", line 269, in <listcomp>
    NEGATIVE_LEXICON = [line.strip().lower() for line in open('resources/opinion-lexicon-English/negative-words.txt', encoding='ascii') if line.strip() != '' and line[0] != ';']
  File "C:\ProgramData\Anaconda3\lib\encodings\ascii.py", line 26, in decode
    return codecs.ascii_decode(input, self.errors)[0]
UnicodeDecodeError: 'ascii' codec can't decode byte 0xef in position 119: ordinal not in range(128)

Based on the error message, I think the problem is related to the negative-words.txt, not the positive one. So I try running UnicodeDammit on the negative-words.txt and it suggests iso-8859-1. After adding iso-8859-1 as the encoding to both the positive and the negative files, grasp can be run successfully on my machine. Would you mind testing if iso-8859-1 also works on yours? If it works, I think we should go with the iso-8859-1.

yes that encoding also worked, I will update the pr

ok @plkumjorn how does that look?

Thank you @samhavens. It looks good. I've merged it. :)


GEODE-4071: Stabilizes CLI integration tests, ThinClientFunctionExecutionTestsN

There are a couple of seemingly unrelated issues here but they relate. The unused code was just unused code and cleaning up make sense. The static lock around DS was left over form the app domain refcounting and since this test was creating and destroying lots of caches it made sense to eliminate it as a cause. Ultimately it came down to this test being long running and the windows stats collector being very buggy. Eventually the stats collector would cause a segfault and the test would either hang or fail.

So test splitting for good sanitary tests (do one thing) and runtime. Removing the lock for performance? In what scenario would a client be using a cache in a concurrent way? I guess I don't have context about the code to say "yeah, you don't need the lock there, so go ahead and remove it for performance"

@rhoughton-pivot The lock isn't being removed for performance but rather because it is unused. It used to synchronize a reference counter between .NET AppDomains and the C++ singleton DistributedSystem. Now that there is a one to one mapping and no more singletons the ref counting is gone and the lock is useless. I found the lock while looking at an area of code that blew up debugging this test. One could argue the change be moved to another ticket. I don't really care either way.

@pivotal-jbarrett I understand now. I think a second ticket is overkill for removal of dead code.


Question: Isn't MRTK supposed to handle level loads?

Overview
I was wondering how the MRTK vNext is supposed to handle level loads. When I load a scene non additively, the motion controller that was detected in a previous scene just goes away and does not come back. Seems it's not DontDestroyOnLoad. Shouldn't the toolkit be scene-load-proof?
Scenario:
The main reason for this the fact that the UNET NetworkManager uses LoadLevel
Mixed Reality Toolkit Release Version
mrtk_development

This issue has more info / discussion on scene loading: #3261

The main reason for this the fact that the UNET NetworkManager uses LoadLevel

In our projects we've dealt with the UNET limitation by writing a NetworkManager class which uses additive loading and enforces the active scene via SetActiveScene. As long as our synced objects either exist in the active scene on startup or are network-instantiated into that scene they should function normally.

Yeah I always found it odd that Unity would always promote additive level loading but then force people to use the network manager that didn't follow that guideline.
My personal workflow:

Have a base scene that includes the initial setup with the MixedRealityToolkit, and any persistent GameObjects.
When working in the editor you can have multiple scenes open at once. I generally open the base scene then find and drag my additive scene into the hierarchy then do what I need to.

Now, should the mrtk handle level loading? Theoretically it could be added as another service, and I could see this being beneficial, but also might be as annoying as the service locator to those who also question it's usefulness. Up to you as the developer I suppose.

This issue has more info / discussion on scene loading: #3261

The main reason for this the fact that the UNET NetworkManager uses LoadLevel

In our projects we've dealt with the UNET limitation by writing a NetworkManager class which uses additive loading and enforces the active scene via SetActiveScene. As long as our synced objects either exist in the active scene on startup or are network-instantiated into that scene they should function normally.

Do you mind sharing that NetworkManager?

looks like this is addressed as part of Lars' multiscene work.
Please reopen if it still repros with the latest Unity 2018 + MRTK release


Unsafe route that overlaps with existing route prevents Nebula from starting on MacOS

In 1.5.0 Nebula was changed to use system level route addition functions. If you have an unsafe_route defined that overlaps with an existing route on the device, a fatal error bubbles up via the tun .Activate function and prevents startup.
For instance you have a laptop on local ip <IP_ADDRESS>/<IP_ADDRESS>, but an unsafe route that defines <IP_ADDRESS>/24 via , nebula will fail to add the route and prevent startup.

@rawdigits What do you think is the appropriate resolution for this?
Does the install: false flag for unsafe_routes resolve this issue?


selected property in RadioGroup #label slot

Description
Right now, the docs show the following usage:
<template #label="{ option }">

    <UIcon :name="option.icon" />
    {{ option.label }}

</template>

In practice, it's common to need to apply some style to selected items, especially when hiding the ring.  This will look something like this:
<template #label="{ option }">

    <UIcon :name="option.icon" />
    {{ option.label }}

</template>

I'd much prefer to have a selected prop passed to the slot so I can do something like this:
<template #label="{ option, selected }">

    <UIcon :name="option.icon" />
    {{ option.label }}

</template>

Additional context
No response

Hello @cco3,
I'm working on it 🙂


TagBot trigger issue

This issue is used to trigger TagBot; feel free to unsubscribe.
If you haven't already, you should update your TagBot.yml to include issue comment triggers.
Please see this post on Discourse for instructions and more details.
If you'd like for me to do this for you, comment TagBot fix on this issue.
I'll open a PR within a few hours, please be patient!

Triggering TagBot for merged registry pull request: https://github.com/JuliaRegistries/General/pull/89119

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async.q and bypassing getDatalogQuery()

proof of concept, but still TODO would be

[ ] generate query / complementQuery on relation save
[ ] handle /context/ and /anchor/ if those are still used

.. and cleanup the code😁 I was testing a bunch of stuff and didn't revisit all of it.

Added a QueryTester.tsx with a bunch of results.
https://github.com/RoamJS/query-builder/pull/291/files#diff-3da15b5c2c39a89b7c2b7864e251037906a51f874ef11b71979279f9a4096c4b
The piece that is still evading me is exactly how a blocking function (getDatalogQuery()) inside of fireQuery which is wrapped in Promise.all(queries.map(fireQuery)) changes the query response times.


Migrate algorithms from AllocationOpt

https://github.com/graphprotocol/AllocationOpt.jl uses projected gradient descent, gssp, and halpern iteration to optimise. Migrate those algorithms here and make them more general.

#5 #6 #2


In code comments, single or double quote inside pair of brackets breaks bracket matching, showing brackets in red when near caret

Tested versions

Reproducible in v4.2.1.stable.official [b09f793f5], v4.3.rc2.official [3978628c6]

System information
Godot v4.2.1.stable - Ubuntu 22.04.4 LTS 22.04 - X11 - Vulkan (Mobile) - dedicated NVIDIA GeForce GTX 860M (nvidia; 535.161.07) - Intel(R) Core(TM) i7-4710HQ CPU @ 2.50GHz (8 Threads)
Issue description
If you write: # ('), or spanned on multiple lines:
# ('
# )

And you move the caret near the brackets, you'll see that they will appear highlighted in red, showing that they are no matching brackets, instead of underlining their match. This is because a closing quote is expected, as in code. In fact, closing the quote with a second quote of the same type will fix the issue.
However, this commonly happens in English and other languages using quotes:
# (We're unsure about this line)

and the quote themselves do not benefit from quote matching: placing the caret near them does not highlight any matching quote, e.g. in:
# (print 'hello')

so there is not much use is considering quotes as string delimiters in comments. We may just ignore them and focus on matching brackets instead.
This aslo applies to double quotes " and square brackets [], as well as export tooltip comments ##.
Steps to reproduce
Write comment:
# (')
or multiline:
# ('
# )

then move caret near a bracket, on its external side (left or opening bracket, right or closing bracket): instead of underlining matching bracket, only that bracket highlighted in red.
Add a closing quote and see how it fixes the issue.
Minimal reproduction project (MRP)
N/A

Can you test in 4.2.2? Just to make sure

Can reproduce in v4.3.beta.custom_build [da4f6e439]

Looks like ImmediateMesh should have a way to inform the mesh instance to be dirty, but it's mesh->instances is empty.

Reproduced in v4.3.rc2.official [3978628c6]


add mysql grammar support for function,procedure,server,view,tablespace,logfilegroup

Changes proposed in this pull request:

add mysql grammar support for function,procedure,server,view,tablespace,logfilegroup
fix createIndex and droupIndex

Pull Request Test Coverage Report for Build 9040

0 of 0   changed or added relevant lines in 0 files are covered.
No unchanged relevant lines lost coverage.
Overall coverage remained the same at 64.086%

Totals

Change from base Build 9039:
0.0%

Covered Lines:
9040

Relevant Lines:
14106

💛  - Coveralls


I cannot not connect to dropbox after upgrading to v1.1.4

Hello,
I am self-hosting the app using the gh-pages. Since I upgraded to v1.1.4 I cannot access the db on my dropbox apps folder anymore.
When I enter my app key and press enter the page reloads and prompts me to "click to open a file".
App version:
KeeWeb v1.1.4 (08087c3, 2016-04-21)
Environment: web
User-Agent: Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/49.0.2623.87 Safari/537.36
Any help is appreciated.
Thank you in advance

Hello!
Are you using custom Dropbox key?
In v1.1.4 replacing key in html file doesn't work anymore, you have to set it in app settings.

Hello,
Thank you for the reply.
I do not understand what you mean with custom dropbox key.
I have been using the key that was generated when the app folder was
created. I has worked very well.
Now I have downloaded a new html file an hour ago and tried without
replacing the key in html but it still not working.
I get the following error message:
"The file was not found. has it been remove from another computer"
Could you please tell me exactly what I have to do to get it working again?
Thank you in advance
On Tue, May 3, 2016 at 4:00 PM, antelle<EMAIL_ADDRESS>wrote:

Hello!
Are you using custom Dropbox key?
In v1.1.4 replacing key in html file doesn't work anymore, you have to set
it in app settings.
—
You are receiving this because you authored the thread.
Reply to this email directly or view it on GitHub
https://github.com/antelle/keeweb/issues/215#issuecomment-216536529

what you mean with custom dropbox key

I mean a custom Dropbox app. Previously this key was replaced in html. Are you using predefined app or your app? In other words, what's the name of the app folder in your Dropbox?

Hello,
I've got it working!
I just needed to leave the app folder field empty. I entered the folder
name in app folder field. It start working the moment I removed it.
Thank you for your help
On Tue, May 3, 2016 at 4:00 PM, antelle<EMAIL_ADDRESS>wrote:

Hello!
Are you using custom Dropbox key?
In v1.1.4 replacing key in html file doesn't work anymore, you have to set
it in app settings.
—
You are receiving this because you authored the thread.
Reply to this email directly or view it on GitHub
https://github.com/antelle/keeweb/issues/215#issuecomment-216536529


cudnn failed to initialyze

Hello i tried to install the docker-gpu images, i've download the weights and the data, but when i launch /bin/bash infer.sh i get this response :
root@rbena-GF63-Thin-10SCSR:/home# /bin/bash infer.sh
Using TensorFlow backend.
2022-03-01 23:46:15.554674: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2022-03-01 23:46:15.578708: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency:<PHONE_NUMBER> Hz
2022-03-01 23:46:15.579340: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x561a790 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2022-03-01 23:46:15.579364: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2022-03-01 23:46:15.580946: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2022-03-01 23:46:15.636357: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-03-01 23:46:15.636553: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x53a4460 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2022-03-01 23:46:15.636569: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): NVIDIA GeForce GTX 1650 Ti with Max-Q Design, Compute Capability 7.5
2022-03-01 23:46:15.636762: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-03-01 23:46:15.636888: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties:
name: NVIDIA GeForce GTX 1650 Ti with Max-Q Design major: 7 minor: 5 memoryClockRate(GHz): 1.2
pciBusID: 0000:01:00.0
2022-03-01 23:46:15.637043: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0
2022-03-01 23:46:15.637856: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0
2022-03-01 23:46:15.638563: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10.0
2022-03-01 23:46:15.638766: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10.0
2022-03-01 23:46:15.639774: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10.0
2022-03-01 23:46:15.640478: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10.0
2022-03-01 23:46:15.642728: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2022-03-01 23:46:15.642815: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-03-01 23:46:15.642967: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-03-01 23:46:15.643040: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0
2022-03-01 23:46:15.643072: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0
2022-03-01 23:46:15.643622: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:
2022-03-01 23:46:15.643634: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165]      0
2022-03-01 23:46:15.643639: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0:   N
2022-03-01 23:46:15.643714: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-03-01 23:46:15.643833: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-03-01 23:46:15.643934: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 2857 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce GTX 1650 Ti with Max-Q Design, pci bus id: 0000:01:00.0, compute capability: 7.5)
Loading model, this may take a second...
tracking <tf.Variable 'Variable:0' shape=(15, 4) dtype=float32> anchors
tracking <tf.Variable 'Variable_1:0' shape=(15, 4) dtype=float32> anchors
tracking <tf.Variable 'Variable_2:0' shape=(15, 4) dtype=float32> anchors
tracking <tf.Variable 'Variable_3:0' shape=(15, 4) dtype=float32> anchors
tracking <tf.Variable 'Variable_4:0' shape=(15, 4) dtype=float32> anchors
Running inference on image folder: /home/data/images/test
Running network: N/A% (0 of 4) |                                                                                | Elapsed Time: 0:00:00 ETA:  --:--:--2022-03-01 23:46:40.231228: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2022-03-01 23:46:40.548887: E tensorflow/stream_executor/cuda/cuda_dnn.cc:329] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR
2022-03-01 23:46:40.554431: E tensorflow/stream_executor/cuda/cuda_dnn.cc:329] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR
Traceback (most recent call last):
  File "keras_retinanet/keras_retinanet/bin/infer.py", line 215, in <module>
    main()
  File "keras_retinanet/keras_retinanet/bin/infer.py", line 192, in main
    profile=args.profile
  File "keras_retinanet/keras_retinanet/bin/../../keras_retinanet/utils/eval.py", line 250, in get_detections
    max_inflation_factor=max_inflation_factor
  File "keras_retinanet/keras_retinanet/bin/../../keras_retinanet/utils/eval.py", line 134, in run_inference_on_image
    boxes, scores, labels = model.predict_on_batch(np.expand_dims(image, axis=0))[:3]
  File "/usr/local/lib/python3.6/dist-packages/keras/engine/training.py", line 1580, in predict_on_batch
    outputs = self.predict_function(ins)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/backend.py", line 3476, in __call__
    run_metadata=self.run_metadata)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py", line 1472, in __call__
    run_metadata_ptr)
tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
  (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
	 [[{{node conv1/convolution}}]]
	 [[filtered_detections/map/while/LoopCond/_9551]]
  (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
	 [[{{node conv1/convolution}}]]
0 successful operations.
0 derived errors ignored.

inside the container I checked under the cuda repository and noticed that the cudnn librairies where not inside , so i downloaded it and copy it inside cuda/include and cuda/lib64 but i've got the same result.
I am on ubuntu 18.04 i have a GeForce GTX 1650 , nvidia-smi return cuda 11.6 nvida-driver 510
Can you help me on that ?
Thank you

Hi, thanks for reporting the issue.
I think I know the reason for this, but I don't currently have time to test it properly.
Could you try editing your infer.sh like this
...
python3 keras_retinanet/keras_retinanet/bin/infer.py \
    --gpu 0 \
    --backbone resnet152 \
...

and report if the problem persists? So just add that --gpu 0 flag to the python script call.
I'll push a fix later if this worked.

Thanks for your fast reply , so i added the --gpu 0 option, but know i have memory error
Using TensorFlow backend.
2022-03-03 14:58:30.312397: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2022-03-03 14:58:30.343955: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency:<PHONE_NUMBER> Hz
2022-03-03 14:58:30.344621: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x595f530 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2022-03-03 14:58:30.344634: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2022-03-03 14:58:30.346912: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2022-03-03 14:58:30.445422: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-03-03 14:58:30.445649: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x56ec400 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2022-03-03 14:58:30.445689: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): NVIDIA GeForce GTX 1650 Ti with Max-Q Design, Compute Capability 7.5
2022-03-03 14:58:30.445993: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-03-03 14:58:30.446098: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties:
name: NVIDIA GeForce GTX 1650 Ti with Max-Q Design major: 7 minor: 5 memoryClockRate(GHz): 1.2
pciBusID: 0000:01:00.0
2022-03-03 14:58:30.447122: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0
2022-03-03 14:58:30.478695: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0
2022-03-03 14:58:30.493179: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10.0
2022-03-03 14:58:30.497231: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10.0
2022-03-03 14:58:30.528241: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10.0
2022-03-03 14:58:30.548327: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10.0
2022-03-03 14:58:30.619992: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2022-03-03 14:58:30.620400: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-03-03 14:58:30.621051: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-03-03 14:58:30.621384: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0
2022-03-03 14:58:30.622065: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0
2022-03-03 14:58:30.624780: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:
2022-03-03 14:58:30.624791: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165]      0
2022-03-03 14:58:30.624796: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0:   N
2022-03-03 14:58:30.625270: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-03-03 14:58:30.625397: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-03-03 14:58:30.625530: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3121 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce GTX 1650 Ti with Max-Q Design, pci bus id: 0000:01:00.0, compute capability: 7.5)
Loading model, this may take a second...
tracking <tf.Variable 'Variable:0' shape=(15, 4) dtype=float32> anchors
tracking <tf.Variable 'Variable_1:0' shape=(15, 4) dtype=float32> anchors
tracking <tf.Variable 'Variable_2:0' shape=(15, 4) dtype=float32> anchors
tracking <tf.Variable 'Variable_3:0' shape=(15, 4) dtype=float32> anchors
tracking <tf.Variable 'Variable_4:0' shape=(15, 4) dtype=float32> anchors
Running inference on image folder: /home/data/images/test
Running network: N/A% (0 of 4) |         | Elapsed Time: 0:00:00 ETA:  --:--:--2022-03-03 14:58:57.547785: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2022-03-03 14:58:59.637530: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0
2022-03-03 14:59:00.303684: W tensorflow/core/common_runtime/bfc_allocator.cc:239] Allocator (GPU_0_bfc) ran out of memory trying to allocate 3.34GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2022-03-03 14:59:00.303723: W tensorflow/core/common_runtime/bfc_allocator.cc:239] Allocator (GPU_0_bfc) ran out of memory trying to allocate 3.34GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2022-03-03 14:59:00.703934: W tensorflow/core/common_runtime/bfc_allocator.cc:239] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.68GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2022-03-03 14:59:00.703967: W tensorflow/core/common_runtime/bfc_allocator.cc:239] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.68GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2022-03-03 14:59:01.966572: W tensorflow/core/common_runtime/bfc_allocator.cc:239] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.09GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2022-03-03 14:59:01.966603: W tensorflow/core/common_runtime/bfc_allocator.cc:239] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.09GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2022-03-03 14:59:02.188302: W tensorflow/core/common_runtime/bfc_allocator.cc:239] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.16GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2022-03-03 14:59:02.188326: W tensorflow/core/common_runtime/bfc_allocator.cc:239] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.16GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2022-03-03 14:59:02.234087: W tensorflow/core/common_runtime/bfc_allocator.cc:239] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.09GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2022-03-03 14:59:02.234108: W tensorflow/core/common_runtime/bfc_allocator.cc:239] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.09GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2022-03-03 14:59:04.119234: I tensorflow/stream_executor/cuda/cuda_driver.cc:831] failed to allocate 1.05G<PHONE_NUMBER> bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2022-03-03 14:59:04.119585: I tensorflow/stream_executor/cuda/cuda_driver.cc:831] failed to allocate 967.39M<PHONE_NUMBER> bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2022-03-03 14:59:04.120012: I tensorflow/stream_executor/cuda/cuda_driver.cc:831] failed to allocate 1.05G<PHONE_NUMBER> bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2022-03-03 14:59:09.894982: I tensorflow/stream_executor/cuda/cuda_driver.cc:831] failed to allocate 1.05G<PHONE_NUMBER> bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2022-03-03 14:59:09.895344: I tensorflow/stream_executor/cuda/cuda_driver.cc:831] failed to allocate 1.05G<PHONE_NUMBER> bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2022-03-03 14:59:24.072675: I tensorflow/stream_executor/cuda/cuda_driver.cc:831] failed to allocate 1.05G<PHONE_NUMBER> bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2022-03-03 14:59:24.073058: I tensorflow/stream_executor/cuda/cuda_driver.cc:831] failed to allocate 1.05G<PHONE_NUMBER> bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
Running network:  50% (2 of 4) |####     | Elapsed Time: 0:00:58 ETA:   0:00:322022-03-03 14:59:59.489396: W tensorflow/core/common_runtime/bfc_allocator.cc:305] Garbage collection: deallocate free memory regions (i.e., allocations) so that we can re-allocate a larger region to avoid OOM due to memory fragmentation. If you see this message frequently, you are running near the threshold of the available device memory and re-allocation may incur great performance overhead. You may try smaller batch sizes to observe the performance impact. Set TF_ENABLE_GPU_GARBAGE_COLLECTION=false if you'd like to disable this feature.
2022-03-03 14:59:59.520640: I tensorflow/stream_executor/cuda/cuda_driver.cc:831] failed to allocate 2.00G<PHONE_NUMBER> bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2022-03-03 14:59:59.521148: I tensorflow/stream_executor/cuda/cuda_driver.cc:831] failed to allocate 2.00G<PHONE_NUMBER> bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2022-03-03 14:59:59.521577: I tensorflow/stream_executor/cuda/cuda_driver.cc:831] failed to allocate 2.00G<PHONE_NUMBER> bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2022-03-03 14:59:59.522053: I tensorflow/stream_executor/cuda/cuda_driver.cc:831] failed to allocate 2.00G<PHONE_NUMBER> bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2022-03-03 14:59:59.522465: I tensorflow/stream_executor/cuda/cuda_driver.cc:831] failed to allocate 2.00G<PHONE_NUMBER> bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2022-03-03 14:59:59.533667: I tensorflow/stream_executor/cuda/cuda_driver.cc:831] failed to allocate 2.00G<PHONE_NUMBER> bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2022-03-03 14:59:59.533990: I tensorflow/stream_executor/cuda/cuda_driver.cc:831] failed to allocate 2.00G<PHONE_NUMBER> bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2022-03-03 15:00:09.535761: I tensorflow/stream_executor/cuda/cuda_driver.cc:831] failed to allocate 2.00G<PHONE_NUMBER> bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2022-03-03 15:00:09.537261: I tensorflow/stream_executor/cuda/cuda_driver.cc:831] failed to allocate 2.00G<PHONE_NUMBER> bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2022-03-03 15:00:09.537327: W tensorflow/core/common_runtime/bfc_allocator.cc:419] Allocator (GPU_0_bfc) ran out of memory trying to allocate 4.00MiB (rounded to 4194304).  Current allocation summary follows.
2022-03-03 15:00:09.537427: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (256): 	Total Chunks: 294, Chunks in use: 294. 73.5KiB allocated for chunks. 73.5KiB in use in bin. 10.9KiB client-requested in use in bin.
2022-03-03 15:00:09.537461: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (512): 	Total Chunks: 66, Chunks in use: 66. 33.0KiB allocated for chunks. 33.0KiB in use in bin. 33.0KiB client-requested in use in bin.
2022-03-03 15:00:09.537490: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (1024): 	Total Chunks: 361, Chunks in use: 361. 363.2KiB allocated for chunks. 363.2KiB in use in bin. 361.9KiB client-requested in use in bin.
2022-03-03 15:00:09.537518: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (2048): 	Total Chunks: 62, Chunks in use: 62. 124.0KiB allocated for chunks. 124.0KiB in use in bin. 124.0KiB client-requested in use in bin.
2022-03-03 15:00:09.537548: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (4096): 	Total Chunks: 150, Chunks in use: 150. 606.5KiB allocated for chunks. 606.5KiB in use in bin. 600.0KiB client-requested in use in bin.
2022-03-03 15:00:09.537579: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (8192): 	Total Chunks: 18, Chunks in use: 18. 147.8KiB allocated for chunks. 147.8KiB in use in bin. 144.0KiB client-requested in use in bin.
2022-03-03 15:00:09.537609: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (16384): 	Total Chunks: 4, Chunks in use: 4. 64.0KiB allocated for chunks. 64.0KiB in use in bin. 64.0KiB client-requested in use in bin.
2022-03-03 15:00:09.537638: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (32768): 	Total Chunks: 4, Chunks in use: 4. 170.2KiB allocated for chunks. 170.2KiB in use in bin. 147.0KiB client-requested in use in bin.
2022-03-03 15:00:09.537666: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (65536): 	Total Chunks: 20, Chunks in use: 20. 1.33MiB allocated for chunks. 1.33MiB in use in bin. 1.25MiB client-requested in use in bin.
2022-03-03 15:00:09.537693: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (131072): 	Total Chunks: 13, Chunks in use: 13. 1.84MiB allocated for chunks. 1.84MiB in use in bin. 1.77MiB client-requested in use in bin.
2022-03-03 15:00:09.537724: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (262144): 	Total Chunks: 48, Chunks in use: 48. 12.51MiB allocated for chunks. 12.51MiB in use in bin. 11.89MiB client-requested in use in bin.
2022-03-03 15:00:09.537760: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (524288): 	Total Chunks: 37, Chunks in use: 37. 20.46MiB allocated for chunks. 20.46MiB in use in bin. 19.98MiB client-requested in use in bin.
2022-03-03 15:00:09.537817: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (1048576): 	Total Chunks: 221, Chunks in use: 221. 223.49MiB allocated for chunks. 223.49MiB in use in bin. 219.69MiB client-requested in use in bin.
2022-03-03 15:00:09.537874: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (2097152): 	Total Chunks: 159, Chunks in use: 159. 362.77MiB allocated for chunks. 362.77MiB in use in bin. 356.30MiB client-requested in use in bin.
2022-03-03 15:00:09.537914: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (4194304): 	Total Chunks: 20, Chunks in use: 20. 85.85MiB allocated for chunks. 85.85MiB in use in bin. 77.00MiB client-requested in use in bin.
2022-03-03 15:00:09.537953: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (8388608): 	Total Chunks: 15, Chunks in use: 15. 136.06MiB allocated for chunks. 136.06MiB in use in bin. 130.81MiB client-requested in use in bin.
2022-03-03 15:00:09.538000: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (16777216): 	Total Chunks: 4, Chunks in use: 4. 72.00MiB allocated for chunks. 72.00MiB in use in bin. 72.00MiB client-requested in use in bin.
2022-03-03 15:00:09.538058: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (33554432): 	Total Chunks: 3, Chunks in use: 3. 105.15MiB allocated for chunks. 105.15MiB in use in bin. 70.97MiB client-requested in use in bin.
2022-03-03 15:00:09.538093: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (67108864): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2022-03-03 15:00:09.538121: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (134217728): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2022-03-03 15:00:09.538155: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (268435456): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2022-03-03 15:00:09.538186: I tensorflow/core/common_runtime/bfc_allocator.cc:885] Bin for 4.00MiB was 4.00MiB, Chunk State:
2022-03-03 15:00:09.538220: I tensorflow/core/common_runtime/bfc_allocator.cc:898] Next region of size 536870912
2022-03-03 15:00:09.538257: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x7f0e00000000 next 1194 of size 524288
2022-03-03 15:00:09.538296: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x7f0e00080000 next 1195 of size 256
2022-03-03 15:00:09.538337: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x7f0e00080100 next 1196 of size 256
2022-03-03 15:00:09.538378: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x7f0e00080200 next 1197 of size 1024
2022-03-03 15:00:09.538416: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x7f0e00080600 next 1198 of size 589824
2022-03-03 15:00:09.538454: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x7f0e00110600 next 1199 of size 419430

....

2022-03-03 15:00:09.543402: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x7f0e78a48d00 next 379 of size 1024
2022-03-03 15:00:09.543412: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x7f0e78a49100 next 380 of size 65536
2022-03-03 15:00:09.543422: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x7f0e78a59100 next 382 of size 4096
2022-03-03 15:00:09.543430: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x7f0e78a5a100 next 383 of size 4096size 4096
2022-03-03 15:00:09.545968: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x7f0e7ac0bc00 next 791 of size 1024
2022-03-03 15:00:09.545972: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x7f0e7ac0c000 next 792 of size 1024
2022-03-03 15:00:09.545976: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x7f0e7ac0c400 next 794 of size 4096
2022-03-03 15:00:09.545980: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x7f0e7ac0d400 next 795 of size 1024
2022-03-03 15:00:09.545984: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x7f0e7ac0d800 next 796 of
2022-03-03 15:00:09.548304: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 214 Chunks of size 1048576 totalling 214.00MiB
2022-03-03 15:00:09.548312: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 1 Chunks of size 1309696 totalling 1.25MiB
2022-03-03 15:00:09.548319: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 4 Chunks of size 1310720 totalling 5.00MiB
2022-03-03 15:00:09.548326: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 1 Chunks of size 1507328 totalling 1.44MiB
2022-03-03 15:00:09.548333: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 1 Chunks of size 1887488 totalling 1.80MiB
2022-03-03 15:00:09.548340: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 11 Chunks of size 2097152 totalling 22.00MiB
2022-03-03 15:00:09.548347: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 138 Chunks of size 2359296 totalling 310.50MiB
2022-03-03 15:00:09.548355: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 4 Chunks of size 2764800 totalling 10.55MiB
2022-03-03 15:00:09.548362: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 4 Chunks of size 3145728 totalling 12.00MiB
2022-03-03 15:00:09.548370: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 1 Chunks of size 3970816 totalling 3.79MiB
2022-03-03 15:00:09.548376: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 1 Chunks of size 4128768 totalling 3.94MiB
2022-03-03 15:00:09.548384: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 15 Chunks of size 4194304 totalling 60.00MiB
2022-03-03 15:00:09.548391: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 1 Chunks of size 4562688 totalling 4.35MiB
2022-03-03 15:00:09.548398: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 2 Chunks of size 4718592 totalling 9.00MiB
2022-03-03 15:00:09.548406: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 1 Chunks of size 6291456 totalling 6.00MiB
2022-03-03 15:00:09.548412: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 1 Chunks of size 6815744 totalling 6.50MiB
2022-03-03 15:00:09.548419: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 3 Chunks of size 8388608 totalling 24.00MiB
2022-03-03 15:00:09.548427: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 1 Chunks of size 9175040 totalling 8.75MiB
2022-03-03 15:00:09.548432: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 1 Chunks of size 9233408 totalling 8.81MiB
2022-03-03 15:00:09.548440: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 9 Chunks of size 9437184 totalling 81.00MiB
2022-03-03 15:00:09.548447: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 1 Chunks of size 14155776 totalling 13.50MiB
2022-03-03 15:00:09.548453: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 4 Chunks of size 18874368 totalling 72.00MiB
2022-03-03 15:00:09.548461: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 1 Chunks of size 36673536 totalling 34.97MiB
2022-03-03 15:00:09.548467: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 1 Chunks of size 36744704 totalling 35.04MiB
2022-03-03 15:00:09.548473: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 1 Chunks of size 36841984 totalling 35.13MiB
2022-03-03 15:00:09.548480: I tensorflow/core/common_runtime/bfc_allocator.cc:921] Sum Total of in-use chunks: 1023.00MiB
2022-03-03 15:00:09.548486: I tensorflow/core/common_runtime/bfc_allocator.cc:923] total_region_allocated_bytes_:<PHONE_NUMBER> memory_limit_:<PHONE_NUMBER> available bytes:<PHONE_NUMBER> curr_region_allocation_bytes_:<PHONE_NUMBER>
2022-03-03 15:00:09.548495: I tensorflow/core/common_runtime/bfc_allocator.cc:929] Stats:
Limit:<PHONE_NUMBER>
InUse:<PHONE_NUMBER>
MaxInUse:<PHONE_NUMBER>
NumAllocs:                   61535
MaxAllocSize:<PHONE_NUMBER>

2022-03-03 15:00:09.548546: W tensorflow/core/common_runtime/bfc_allocator.cc:424] *********************************************x***x**************************************************
2022-03-03 15:00:09.548581: W tensorflow/core/framework/op_kernel.cc:1651] OP_REQUIRES failed at conv_ops.cc:886 : Resource exhausted: OOM when allocating tensor with shape[2048,512,1,1] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
Traceback (most recent call last):
  File "keras_retinanet/keras_retinanet/bin/infer.py", line 218, in <module>
    main()
  File "keras_retinanet/keras_retinanet/bin/infer.py", line 195, in main
    profile=args.profile
  File "keras_retinanet/keras_retinanet/bin/../../keras_retinanet/utils/eval.py", line 250, in get_detections
    max_inflation_factor=max_inflation_factor
  File "keras_retinanet/keras_retinanet/bin/../../keras_retinanet/utils/eval.py", line 134, in run_inference_on_image
    boxes, scores, labels = model.predict_on_batch(np.expand_dims(image, axis=0))[:3]
  File "/usr/local/lib/python3.6/dist-packages/keras/engine/training.py", line 1580, in predict_on_batch
    outputs = self.predict_function(ins)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/backend.py", line 3476, in __call__
    run_metadata=self.run_metadata)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py", line 1472, in __call__
    run_metadata_ptr)
tensorflow.python.framework.errors_impl.ResourceExhaustedError: 2 root error(s) found.
  (0) Resource exhausted: OOM when allocating tensor with shape[2048,512,1,1] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
	 [[{{node res5a_branch2c/convolution}}]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.

	 [[filtered_detections/map/while/LoopCond/_9551]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.

  (1) Resource exhausted: OOM when allocating tensor with shape[2048,512,1,1] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
	 [[{{node res5a_branch2c/convolution}}]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.

0 successful operations.
0 derived errors ignored.

I have a GeForce gtx 1650 4Gb ,is  my grahic card enough ?

It would seems like it. As a workaround, you can play with these parameters in infer.sh
--image_min_side 1525 \
--image_max_side 2025 \

Setting them lower (e.g. halving both) might fix the OOM problem, but this will naturally have a performance impact  (detector accuracy goes down). I recommend setting them as high as your GPU can handle and keeping the same "aspect ratio".

Hello! I tried to run infer.sh in Docker on CPU and got the following:
root@docker-desktop:/home# /bin/bash infer.sh
Using TensorFlow backend.
2022-03-06 16:23:57.704343: F tensorflow/core/platform/cpu_feature_guard.cc:37] The TensorFlow library was compiled to use AVX instructions, but these aren't available on your machine.
qemu: uncaught target signal 6 (Aborted) - core dumped
infer.sh: line 32:    74 Aborted                 python3 keras_retinanet/keras_retinanet/bin/infer.py --backbone resnet152 --image_min_side 1525 --image_max_side 2025 --score_threshold 0.05 --max_detections 100000 --nms_threshold 0.25 --config $PWD/keras_retinanet/config.ini --anchor_scale 0.965 --convert_model true --image_tiling_dim 4 --nms_mode $BBA_MODE --model $PWD/models/$MODEL $IMAGE_FOLDER $PWD/data/predictions/${MODEL/.h5/}-$BBA_MODE-inference

Turned out, this was because I was using Apple M1 processor, and TensorFlow binary crashes on Apple M1 in x86_64 Docker container.
Is there any chance I could compile a Tensorflow from the source that does not use the AVX instruction set? Or maybe there is an already compiled one?
Would deeply appreciate any help.

Thank you for your reply.
Reducing the the min and max side worked for me

@dinarakhay Unfortunately I haven't yet tried to install the AIR detector on ARM64 architecture so I can't give you any exact advice. Although, I think it should be possible if you compile everything from source or build the docker container on the host machine. It might be possible to get the Dockerfile out of the keras-retinanet-gpu image provided by using alpine/dfimage and feeding that to docker build command on the ARM64 machine. Another way could be using the buildx command. Anyhow, you need to test these methods out. Also, I'd be eager to hear the results.
Although, maybe you need to be picky about the Tensorflow installation inside the container, not sure if Docker can resolve what instructions it will be compiled to use. You can try replacing the TF installation with tensorflow-aarch64. I'd try tensorflow-1.15.5-cp36-cp36m-manylinux_2_24_aarch64.whl from the releases page.
@mrba59 No problem, good to hear it worked out.

Closing this due to inactivity.


add domain Korea National Sport University

add domain Korea National Sport University

https://www.knsu.ac.kr/knsu/index.do

@micble Thank you for your patience while waiting for a review and processing your request. Pull request merged. Please start applying for JetBrains educational subscription at https://www.jetbrains.com/shop/eform/students in an hour, to allow the changes to propagate through JetBrains systems.


Log spam if date time api not available

If there is a problem with the date-time skill (API) the logs get spammed with;
2022-10-28 06:20:30.159 - skills - skill_ovos_homescreen:update_dt:187 - WARNING - Requested update before datetime API loaded
2022-10-28 06:20:36.163 - skills - skill_ovos_homescreen:update_dt:197 - WARNING - No datetime_api, skipping update
2022-10-28 06:20:40.172 - skills - skill_ovos_homescreen:update_dt:187 - WARNING - Requested update before datetime API loaded
2022-10-28 06:20:46.176 - skills - skill_ovos_homescreen:update_dt:197 - WARNING - No datetime_api, skipping update
2022-10-28 06:20:50.186 - skills - skill_ovos_homescreen:update_dt:187 - WARNING - Requested update before datetime API loaded
2022-10-28 06:20:56.190 - skills - skill_ovos_homescreen:update_dt:197 - WARNING - No datetime_api, skipping update
2022-10-28 06:21:00.199 - skills - skill_ovos_homescreen:update_dt:187 - WARNING - Requested update before datetime API loaded
2022-10-28 06:21:06.203 - skills - skill_ovos_homescreen:update_dt:197 - WARNING - No datetime_api, skipping update
2022-10-28 06:21:10.212 - skills - skill_ovos_homescreen:update_dt:187 - WARNING - Requested update before datetime API loaded
2022-10-28 06:21:16.216 - skills - skill_ovos_homescreen:update_dt:197 - WARNING - No datetime_api, skipping update
2022-10-28 06:21:20.226 - skills - skill_ovos_homescreen:update_dt:187 - WARNING - Requested update before datetime API loaded
2022-10-28 06:21:26.230 - skills - skill_ovos_homescreen:update_dt:197 - WARNING - No datetime_api, skipping update
2022-10-28 06:21:30.238 - skills - skill_ovos_homescreen:update_dt:187 - WARNING - Requested update before datetime API loaded
2022-10-28 06:21:36.242 - skills - skill_ovos_homescreen:update_dt:197 - WARNING - No datetime_api, skipping update

I keep saying this should use língua franca directly, but everyone seems to think a skill is better.... at very least i think by default no skill should be used unless configured.  The only reason for this to be a skill is if you want to replace it wirh a stardate skill because you are a nerd

In this case, i screwed up a dependency with the date-time skill preventing it from loading.
besides what is the cause, i hate logs that keep stating the same every so few seconds.
btw ovos-core still does not honour the log level but will report an issue after i migrated it to use our new logger instead of stdout

What's the workaround to get this to stop, short of using lingua franca directly?

https://github.com/OpenVoiceOS/skill-ovos-homescreen/pull/72 this should fix the spam


Documentation for Billing Deferral

This PR provides documentation for the new optional billing deferral functionality as outlined here: https://github.com/prebid/Prebid.js/issues/4494#issuecomment-1412487621
🏷 Type of documentation

[x] new feature

📋 Checklist

[x] Related pull requests in prebid.js or server are linked -> Paste link in this list or reference it on the PR itself

Related PR: https://github.com/prebid/Prebid.js/pull/9640

Hi @bretg ,
The code changes related to this PR are merged and released.
Can you please review this documentation PR?

Thanks. Fixed the other 3 places where {adId} appeared.

Ah, thanks!


jit ifp lecture

Still need to update lecture text regarding bellman vs. coleman iterator and their efficiency

@shizejin thanks for your help on this - sorry it's taken a while to merge
@jstac I removed references to the speed from time iteration because this wasn't present anymore after jitting, as discussed here: https://github.com/jstac/jitting_py_lectures/pull/8

@jstac this is ready to merge


& in AppName causes apk build failure

If Screen1.AppName contains &, the apk build fails with:
WARNING: YAIL compiler - AAPT execution failed.
Should we prevent users to put characters like & in AppName? Are there other characters not allowed?

Could you send a specific example of how to create this error?   App Inventor already has a check to remove ampersands in project names, but I guess we need more checks.  I'd like to see where they are missing.

Create a new project. In Designer, type (or copy/paste) in Screen1.AppName "foo & bar".
The ampersand is not removed.

I am looking into that.

I can confirm the symptom - build failed when the app name contains the &, ampersand.
After several intense debugging session, I found out there is no problem in the implementation, but rather a bug in the android aapt.  An ampersand in the name string breaks AAPT and other places in Android.
This bug is also described in this Java file, line 300
https://github.com/mit-cml/appinventor-sources/blob/master/appinventor/buildserver/src/com/google/appinventor/buildserver/Compiler.java
I am going to follow the same style to fix this problem - by replacing the & with the word and.
@halatmit

You can use & also

#464 was closed


Grades Calculator

Python programs to calculate your cgpa and grade points.

@Dhanush2612 Make it a GUI application

Is GUI enough or is web.app required?

Is GUI enough or is web.app required?

No, GUI will work

Reply
On Fri, Mar 12, 2021, 6:20 PM Kaustubh Gupta @.***>
wrote:

Is GUI enough or is web.app required?
No, GUI will work
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
https://github.com/avinashkranjan/Amazing-Python-Scripts/issues/505#issuecomment-797470157,
or unsubscribe
https://github.com/notifications/unsubscribe-auth/AMQSUDBCMPN4MMW5OM2MJDLTDIEZ7ANCNFSM4ZAQ2I5Q
.

Hello, I am a GSSOC'21 participant and would like to contribute to this project.

@Dhanush2612 updates?

I am working on it


HB-7367: update the demo with the new PreinitializationConfiguration

Mediation.start() will be gone soon, but the new setPreinitializationConfiguration() will stay.

I wonder if we should wait until we move the demo into the monorepo before making these changes.
This is not going to compile until we release 5.0.0, so we shouldn't merge it to main until then. But by then everything should be in the monorepo already.

I wonder if we should wait until we move the demo into the monorepo before making these changes. This is not going to compile until we release 5.0.0, so we shouldn't merge it to main until then. But by then everything should be in the monorepo already.

Good call, I was under the impression that this demo repo is private but actually it's not. Will put a hold on this PR. Thanks!

I wonder if we should wait until we move the demo into the monorepo before making these changes. This is not going to compile until we release 5.0.0, so we shouldn't merge it to main until then. But by then everything should be in the monorepo already.

Actually we can put these changes on a release/5.0.0 branch. I just created one and changed the base commit of this PR.


Opensearch cluster node health wrong reporting

We have recently started using Opensearch 2.1.0 version in our environment as a replacement of Opendistro. AFAIU elasticsearch_exporter is not ES version specific hence it should work without any issue with opensearch.
Broady, facing two issues.

Prometheus intermittenly couldn't scrape the metrics from ES nodes. We are running exporter on each ES node. Faced this issue 1.3 version updated to 1.5 version but not of much help.

prometh+ 11495  0.1  0.2<PHONE_NUMBER>6 ?        Ssl  08:28   0:01 /usr/bin/elasticsearch_exporter --es.uri=https://prometheus:prometheus@localhost:9200 --es.indices --es.ssl-skip-verify --web.listen-address=<IP_ADDRESS>:9108

elasticsearch_cluster_health_up reported as 0 on few of the nodes in cluster (sometime one node) in cluster while checking https://NODE_URL:9108/metrics . At same time other reports the state as 1 .. checking the ES cluster and node health from ES API returns everything in healthy state. Could it be a operator issue.

Exporter keep on giving these messages with opensearch with opendistro it was never an issue. 500 indicates the server error but ES API itself works fine. 403 is permissioning after seeing this error prometheus user mapped to monitoring role but still error keep on coming.

Aug 10 08:42:48 elasticsearch-master-1 elasticsearch_exporter[11495]: level=warn ts=2022-08-10T08:42:48.930801946Z caller=cluster_health.go:286 msg="failed to fetch and decode cluster health" err="HTTP Request failed with code 500"
Aug 10 08:43:10 elasticsearch-master-1 elasticsearch_exporter[11495]: level=warn ts=2022-08-10T08:43:10.96161973Z caller=cluster_health.go:286 msg="failed to fetch and decode cluster health" err="HTTP Request failed with code 500"

Aug 10 08:52:18 elasticsearch-master-1 elasticsearch_exporter[11495]: level=warn ts=2022-08-10T08:52:18.928680081Z caller=indices.go:1207 msg="failed to fetch and decode index stats" err="HTTP Request failed with code 403"
Aug 10 08:52:40 elasticsearch-master-1 elasticsearch_exporter[11495]: level=error ts=2022-08-10T08:52:40.919025987Z caller=indices.go:1126 err="HTTP Request failed with code 403"
Aug 10 08:52:40 elasticsearch-master-1 elasticsearch_exporter[11495]: level=warn ts=2022-08-10T08:52:40.919084396Z caller=indices.go:1207 msg="failed to fetch and decode index stats" err="HTTP Request failed with code 403"
Aug 10 08:52:48 elasticsearch-master-1 elasticsearch_exporter[11495]: level=error ts=2022-08-10T08:52:48.932423852Z caller=indices.go:1126 err="HTTP Request failed with code 403"
Aug 10 08:52:48 elasticsearch-master-1 elasticsearch_exporter[11495]: level=warn ts=2022-08-10T08:52:48.932494568Z caller=indices.go:1207 msg="failed to fetch and decode index stats" err="HTTP Request failed with code 403"

These two issues seems to be inter-related but couldn't find why sometime it start failing to decode cluster health.

elasticsearch_clusterinfo_up is with result 1 and elasticsearch_cluster_health_up with result 0. No Json parse failure.
# HELP elasticsearch_cluster_health_json_parse_failures Number of errors while parsing JSON.
# TYPE elasticsearch_cluster_health_json_parse_failures counter
elasticsearch_cluster_health_json_parse_failures 0
# HELP elasticsearch_cluster_health_total_scrapes Current total ElasticSearch cluster health scrapes.
# TYPE elasticsearch_cluster_health_total_scrapes counter
elasticsearch_cluster_health_total_scrapes 44917
# HELP elasticsearch_cluster_health_up Was the last scrape of the ElasticSearch cluster health endpoint successful.
# TYPE elasticsearch_cluster_health_up gauge
elasticsearch_cluster_health_up 0
# HELP elasticsearch_clusterinfo_last_retrieval_failure_ts Timestamp of the last failed cluster info retrieval
# TYPE elasticsearch_clusterinfo_last_retrieval_failure_ts gauge
elasticsearch_clusterinfo_last_retrieval_failure_ts{url="https://localhost:9200"} 1.659449338e+09
# HELP elasticsearch_clusterinfo_last_retrieval_success_ts Timestamp of the last successful cluster info retrieval
# TYPE elasticsearch_clusterinfo_last_retrieval_success_ts gauge
elasticsearch_clusterinfo_last_retrieval_success_ts{url="https://localhost:9200"} 1.660122238e+09
# HELP elasticsearch_clusterinfo_up Up metric for the cluster info collector
# TYPE elasticsearch_clusterinfo_up gauge
elasticsearch_clusterinfo_up{url="https://localhost:9200"} 1


keep-alive can not cache two router-view

Version
2.5.1
Reproduction link
https://github.com/xuzpeng/vue-sell.git
Steps to reproduce
The order page and home page switch pages are not cached, or the home page -> find page -> order page -> home page still occurs.
What is expected?
Order page and home page are cached
What is actually happening?
not cached

Without having run the reproduction, it seems like you should set a key.
<keep-alive>
  <router-view v-if="$route.meta.keepAlive" key="A"/>
</keep-alive>
<router-view v-if="!$route.meta.keepAlive" :key="B"/>

I have tried it, but it has the same question.

@LinusBorg  I have tried it, but it has the same question.

Please boil down the repro to the minimal when reporting a bug, this is your whole project. You can use codesandbox or jsfiddle.net to easily share something


Mini Map not showing and none of the buttons above mini map working

Describe the bug
Mini map not visible and mini map buttons not working
To Reproduce
Steps to reproduce the behavior:

Only CQUI mod installed
start new game
explore and see no progress in mini map or any buttons working

Expected behavior
A working mini map
General informations

OS: macOS 10.14.6
Version: v1.2.3
Expansion All (Rise & Fall, Gathering Store, call civs)
No other mods

Save game and logs
Civ VI Logs.zip
VICTORIA 73 1120 BC.Civ6Save.zip

I fixed a different issue in the mini-map with the PR #415 that was merged into the expansion2 branch today.  Does it happen with that set of changes?


inline ellipsis location?

Hi, can anybody help me to figure out how to move the ellipsis (...) to output inline next to the truncated text? I need to have the 'more' link on its own line which I have done using css but i can't see how to move the '...' to append inline at the end of the truncated text?
Here is an example of how I would like my finished text block to look (notice the different placement of the '...' :
Example text lorem ipsum dolor ...
Read More
but all I can get is:
Example text lorem ipsum dolor
... Read More
Looking at the plugin code, I can't see where in the code I can change where the ellipsis is output?
Many Thanks for any help..

Thanks.  I merged the patch.


Not obvious where the documentation for one variable ends and the other begins

See for example the Variables section in http://godoc.org/github.com/kisielk/sqlstruct
In the code itself it's obvious because the variables are clearly separated by blank lines, and in this case some other code.
This actually affects the godoc command's -http switch as well, but not the terminal version because there's some indentation to add some separation.

This example is difficult to read because the indented text in the doc comment uses the same style as the declarations. The  style is usually the visual cue for the start of a variable, but not here.
Indenting is one way to fix this issue. Another option is to lighten the background and border for indented text in a comment.

This looks like it should be pretty easy to do. I think indenting makes the most sense.
If you guys are okay with this. I can try my hand at this one. It will allow me to get my hands dirty with gddo code; and get an understanding of how the code is laid out.

Indenting hurts readability on small screens. Changing the background and border color does not.

I agree that a color change is probably the right approach.
On 25 September 2015 at 13:58, Gary Burd<EMAIL_ADDRESS>wrote:

Indenting hurts readability on small screens. Changing the background and
border color does not.
—
Reply to this email directly or view it on GitHub
https://github.com/golang/gddo/issues/315#issuecomment-143117868.

I'm dealing with the limitations of GTMarkdown. But, yes I see what you guys mean.

Indent

Variables
var NameMapper func(string) string = strings.ToLower

NameMapper is the function used to convert struct fields which do not have sql tags into database column names.
The default mapper converts field names to lower case. If instead you would prefer field names converted to snake case, simply assign sqlstruct.ToSnakeCase to the variable:
    sqlstruct.NameMapper = sqlstruct.ToSnakeCase

Alternatively for a custom mapping, any func(string) string can be used instead.
var TagName = "sql"

TagName is the name of the tag to use on struct fields

V.S. Color Change

Variables
var NameMapper func(string) string = strings.ToLower

NameMapper is the function used to convert struct fields which do not have sql tags into database column names.
The default mapper converts field names to lower case. If instead you would prefer field names converted to snake case, simply assign sqlstruct.ToSnakeCase to the variable:

sqlstruct.NameMapper = sqlstruct.ToSnakeCase

Alternatively for a custom mapping, any func(string) string can be used instead.
var TagName = "sql"

TagName is the name of the tag to use on struct fields

I don't know if the vertical gray bar is the result of markdown limitations or not, but like it.
This feature requires CSS change only.  A pre with class decl should use the current style. A pre without class decl should use the new style.

You should be able to achieve this in CSS by setting the background to
'none' and setting something like "border-left: 5px solid #999".
On 25 September 2015 at 14:22, Gary Burd<EMAIL_ADDRESS>wrote:

I don't know if the vertical gray bar is the result of markdown
limitations or not, but like it.
This feature requires CSS change only. A pre with class decl should use
the current style. A pre without class decl should use the new style.
—
Reply to this email directly or view it on GitHub
https://github.com/golang/gddo/issues/315#issuecomment-143120883.

Here's what you get by adding this rule:
.decl pre { border-left: 10px solid #ccc; }

Maybe the gray bar should be on the text in the example, not the declaration? Because it does have the side-effect of indenting the text some more.
Speaking of which, the declarations themselves are indented relative to their documentation which seems a bit weird.

Yeah, there are lots of ways you could cut it.
You could remove the border on plain pre elements and only show a border on .decl pre elements (headings), and also make the .decl pre elements have a slightly darker background. That would probably be enough to disambiguate them.

Would it help to bold the first word on the next line?

NameMapper is the function...


[ButtonBase] Simpler global focus visible style override

Add the global override back:
  const theme = createMuiTheme({
    overrides: {
      MuiButtonBase: {
        focusVisible: {
          outline: 'thick double green',
        },
      },
    },
  });

Solves @z-ax concern: https://github.com/mui-org/material-ui/issues/10976#issuecomment-383915833.

Nice, thank you!

@oliviertassinari You forgot to change the corresponding type definition file :-D

@franklixuefei This is not the first time 👼


Just thank you !!!

Hi !
I just want to thank @pburtchaell for redux-promise-middleware !
It's a so great package ! It saves me ! It simplify my code and I can easily do chaining actions.
Thank you !
(sorry for opening an issue for that, but I just wanted so much to thank you !)
You can close this issue :)

You're welcome! Also thank you to @tomatau and everyone who has made contributions over time. 😄


Add "var goog;" to testGenerateExports() to avoid an exception

GenerateExports expects base.js to be included. Specifically, the
variable "goog" should exist in the global scope.

This is related to #1393

Why was this passing before this change?

Because a test() method in CommandLineRunnerTest wasn't checking the exit code.

@MatrixFrog This is ready.


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Higher level model / training loop

Hi,
After writing some models by hand, I am missing having higher-level building blocks akin to Keras's Model or TF's Estimator/Head.
Head is particularly interesting as a bite-sized building block that saves a decent amount of boiler plate without locking in too many assumptions.
An imaginary API could look something like:
init_fn, train_step_fn = binary_classification_head(
logits=stax.serial(...),
optimizer=optim.momentum(...),
metrics={'metric_1': (metric_1_init_fn, metric_1_step_fn), ... }
)
initial_state = init_fn(input_shape)
trained = reduce(train_step_fn, initial_state, dataset_generator)
Where binary_classification_head composes all the init_fns and step_fns of its constituent components, and returns a single init_fn and train_step_fn. train_step_fn(state, batch) -> next_state
Nice thing is that init_fn, train_step_fn are subject to further JAX transforms, obviously jit but also vmap and others could be interesting. This is property that doesn't easily hold if one were to simply import designs from other frameworks without modification.

Adding a breadcrumb here for other users who might be interested:
https://github.com/tensorflow/tensor2tensor/tree/master/tensor2tensor/trax
contains some interesting higher-level constructs and useful utility functions.


Fintuning

Is there any instruction on how to fine tune it for my dataset?

Hi
does anyone successfully train from metric checkpoint and loaded the fine tuned model ?

@rick-dn  did it work ?

Hi! Yes and No. It did work in the sense that  I trained it on my own dataset and got some results, but the results are not as good as expected, but that's my research work, maybe it will work for other datasets.
I just needed to modify the dataset/hypersim.py for my dataset and change the train.py accordingly. Also, the model does not load for inference after training. It says
Missing key(s) in state_dict: "pretrained.cls_token"......
Unexpected key(s) in state_dict: "model", "optimizer", "epoch", "previous_best".__
I had to  write the following:
my_state_dict = {} for key in state_dict['model'].keys():     my_state_dict[key.replace('module.', '')] = state_dict['model'][key]
Let me know if this is the correct approach or am I missing something

Same problem here. I managed to train it on my own dataset by replacing the filename paths of the vkitti2 in the /splits folder
The inference did not work and am also having the Missing key problem.

Where exactly did you write these lines ?
my_state_dict = {} for key in state_dict['model'].keys(): my_state_dict[key.replace('module.', '')] = state_dict['model'][key]
and what changes did you do for the train.py ?

Hi @rick-dn
I think you are correct, i did the same too by replacing the keys in the map

train.py changes were to just load my dataloader instead of hyperism data loader. For inference if you follow the instructions
on the main page, the Use our models bit, you need to load the more like:
model.load_state_dict(torch.load(f'checkpoints/depth_anything_v2_{encoder}.pth', map_location='cpu'))
so just before this line I had to write the missing key workaround.
Cheers
Rick

@rick-dn  thank you so much !
The results are not good enough though, I'll update you once I find a way to improve the depth map.

Hi, I confronted with the same problems while performing the inference of my own trained checkpoints, it showed the missking keys error as you guys. Sorry to bother you again, but do you know how to fix this problem or what did I miss while fine-tuning? @LiheYoung

@rick-dn Hi，I'm sorry to bother you could you tell me why I got the following mistakes by adding your codes? I really wanted to fix this problem
Traceback (most recent call last):
  File "/home/edric/Depth-Anything-V2/metric_depth/depth_to_pointcloud_edric.py", line 54, in <module>
    for key in state_dict['model'].keys():
KeyError: 'model'

My fine-tuned results are horrible as well, I'm not sure about 3 aspects:

where should I pay special attention besides ignoring the depth gt where there is no value(I used sparse gt)
2.the image resizes as 518*518 in training, since the resolution of my own datasets are different, should I modify its resize?
3.all of us in this issues seemed to  have confronted with the problems of the missing keys, even though I got the codes running by adding the codes as @rick-dn did, still not sure about some probable misktakes

hi i also meet the same error

and my infer code according to run.py
`model_configs = {
'vits': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]},
'vitb': {'encoder': 'vitb', 'features': 128, 'out_channels': [96, 192, 384, 768]},
'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]}
}
encoder = 'vitl' # or 'vits', 'vitb'
dataset = 'vkitti' # 'hypersim' for indoor model, 'vkitti' for outdoor model
max_depth = 80 # 20 for indoor model, 80 for outdoor model
finetune_custom_model_path =osp.join(f'metric_depth/exp/custom-'+f'{encoder}', 'latest.pth')
model = DepthAnythingV2(**{**model_configs[encoder], 'max_depth': max_depth})
model.load_state_dict(torch.load(f'checkpoints/depth_anything_v2_metric_{dataset}_{encoder}.pth', map_location='cpu'))
model.load_state_dict(torch.load(f'{finetune_custom_model_path}', map_location='cpu'))`
how to correct my code? thank u

May I know how you guys prepared your own custom dataset? What format I need to follow? Is there a guideline?

hi i also meet the same error
and my infer code according to run.py `**model_configs = { 'vits': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]}, 'vitb': {'encoder': 'vitb', 'features': 128, 'out_channels': [96, 192, 384, 768]}, 'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]} }
encoder = 'vitl' # or 'vits', 'vitb' dataset = 'vkitti' # 'hypersim' for indoor model, 'vkitti' for outdoor model max_depth = 80 # 20 for indoor model, 80 for outdoor model finetune_custom_model_path =osp.join(f'metric_depth/exp/custom-'+f'{encoder}', 'latest.pth') model = DepthAnythingV2(**{model_configs[encoder], 'max_depth': max_depth}) model.load_state_dict(torch.load(f'{finetune_custom_model_path}', map_location='cpu'))`
how to correct my code? thank u

Please see my comments earlier

depth_anything = DepthAnythingV2(**{**model_configs[encoder], 'max_depth': max_depth})
old_dict = torch.load(model_path, map_location='cpu')
if "model" in old_dict:
old_dict = old_dict["model"]
new_dict = {key.replace('module.', ''): value for key, value in old_dict.items()}
depth_anything.load_state_dict(new_dict)
depth_anything = depth_anything.to(device).eval()
Could use this code too, please adjust the code to your variable


New workflow submission - Cleanup Lambda versions using Step Functions

Issue #, if available:
Description of changes:
This workflow shows how to manage/cleanup the number of available versions of an AWS Lambda function. The workflow uses the ListFunctions, ListVersionsByFunction and DeleteFunction SDK calls to determine which functions should be processed as well as to get to know the available number of versions and to reduce them to a specified amount. The workflow takes Lambda aliases into account as well as provides the possibility to filter which functions should be taken into account and how many versions - starting from the most recent one - should be kept available.
By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.

https://serverlessland.com/workflows/step-functions-lambda-version-cleanup