text:
[bug] [alpha.17] [windows] Cannot instantiate more than one window on some machines

Describe the bug
When I try to instantiate more than one window - either using WindowBuilder or by having more than one entry in the windows config array - on some Windows machine the app consistently dies and shows failed to create webview: WebView2 error: WindowsError(Error { code: HRESULT(0x8007139F), message: "The group or resource is not in the correct state to perform the requested operation." }) on the console.
This is only reproducible on some Windows machines (I managed to reproduce it on 1 out of 3 I tried), but on that one it is produced very consistently. I suspect it's linked to OneDrive (the machine where it fails is using OneDrive).
The workaround was to remove all the entries from the windows config array, and instantiate all the windows manually while setting different data_directory built from app_data_dir with different subdirectories attached to it. I suspect it might be a good idea to instantiate all windows with their own data_directory by default, at least on Windows.
Reproduction

Run npm create tauri-app@latest --alpha
Add a second entry to the windows array in the config
Run the app

Expected behavior
Two windows open
Platform and versions
[✔] Environment
    - OS: Windows 10.0.20348 X64
    ✔ WebView2: 118.0.2088.76
    ✔ MSVC: Visual Studio Community 2022
    ✔ rustc: 1.72.0 (5680fa18f 2023-08-23)
    ✔ cargo: 1.72.0 (103a7ff2e 2023-08-15)
    ✔ rustup: 1.26.0 (5af9b9484 2023-04-05)
    ✔ Rust toolchain: stable-x86_64-pc-windows-msvc (default)
    - node: 18.13.0
    - yarn: 1.22.19
    - npm: 8.19.3

[-] Packages
    - tauri [RUST]: 2.0.0-alpha.17
    - tauri-build [RUST]: 2.0.0-alpha.11
    - wry [RUST]: 0.34.2
    - tao [RUST]: 0.23.0
    - @tauri-apps/api [NPM]: 2.0.0-alpha.11
    - @tauri-apps/cli [NPM]: 2.0.0-alpha.17

[-] App
    - build-type: bundle
    - CSP: unset
    - distDir: ../dist
    - devPath: http://localhost:1420/
    - bundler: Vite

Stack trace
failed to create webview: WebView2 error: WindowsError(Error { code: HRESULT(0x8007139F), message: "The group or resource is not in the correct state to perform the requested operation." })

Additional context
No response

I can also reproduce this issue with the lastest alpha releases, while having 2 windows defined in the Tauri config. No matter how many times I run it no window(s) will ever be visibile, but only some of the time is that error explicitly printed.
This seems like a common problem as it was reported in issues #8143, #7897, #7826 and #8196 as well.

@FabianLars Any thoughts on timing? We switched from 1.x to 2.x for some useful Mac improvements, but are now blocked on Windows 11 on 3 different machines. It's alpha so at our own risk; that's fine. Just trying to decide whether we can afford to wait or have to go back to 1.x.

No idea. There are 2 bugs here though. one in webview2 itself (fixed in their canary builds) which makes it crash basically always, and something in tauri that crashes it sometimes (this is what this issue was about).
I hope microsoft pushes that fix to stable asap. We're close to their regular release date though so hopefully it'll be part of that.
For the tauri specific issue i'm not sure how to best handle the race condition yet (using seperate data dirs as proposed in OP's message is not a good solution imo, since those folders are already larger than we'd like), we're looking into it though.

Alrighty, so i spent the whole day on this and did not get much further.
All i know is that the crash happens here https://github.com/tauri-apps/wry/blob/dev/src/webview2/mod.rs#L304-L316. I tried to get more info with debuggers but just couldn't get more than the error we already had...
Also i think the issue is that something goes wrong with the first webview data dir registration which makes all subsequent ones fail because i tried to add some auto retry mechanism by either just retrying the linked call or including a new webview2 environment object as suggested in their docs. Both did not work, they just kept on failing with the same error.
If nobody in the team (or the community) has any more ideas i will open an issue in the webview2 repo over the weekend because i consider this a blocker for v2.
p.s. to reproduce this, just use the multiwebview example in wry, you may have to set the same user data dir for all 4 webviews but letting the webview use the default path also causes this error, just a bit less frequent i think. You can also reproduce this with the multiwindow example if you spam the button really fast, or easier by changing the code to spawn 2-4 windows in quick succession.
p.p.s. Until the next webview2 runtime release, don't forget to use the canary runtime to test this.

Thanks for digging in.

Good news!
I think Microsoft finally fixed the critical bug they had and released it into stable version!
To update it:

Open microsoft edge (stable, the regular) from search
Open edge://settings/help and check that version is 120.0.2210.133 or greater or wait for it to update
Rerun your app and ensure that it uses version 120.0.2210.133 or later using tauri::webview_version()

Nice, just tried and couldn't reproduce any crashes as before, will close this for now but we will keep monitoring the situation. If it crashes for anyone again, let us know.

I also had the same problem, and I installed only one webview2 runtime on all the latest dependencies.

Deleting the configuration list as you mentioned and manually creating all windows temporarily solved this problem.
Thank you very much
OS: Windows 10 LSTC 21H2 19044.3930 Windows Feature Experience Pack 1000.19053.1000.0
[build-dependencies]
tauri-build = { version = "2.0.0-alpha.14", features = [] }

[dependencies]
tauri = { version = "2.0.0-alpha.21", features = ["tray-icon"] }
tauri-plugin-process = "2.0.0-alpha.6"
tauri-plugin-single-instance = "2.0.0-alpha.6"

"dependencies": {
    "@solidjs/router": "^0.10.10",
    "@tauri-apps/api": "^2.0.0-alpha.13",
    "@tauri-apps/plugin-shell": "^2.0.0-alpha.3",
    "@tauri-apps/plugin-process": "^2.0.0-alpha.5",
    "solid-js": "^1.8.12"
  },
  "devDependencies": {
    "@tauri-apps/cli": "2.0.0-alpha.21",
    "@types/node": "^20.11.10",
    "@typescript-eslint/eslint-plugin": "^6.20.0",
    "@typescript-eslint/parser": "^6.20.0",
    "eslint": "^8.56.0",
    "eslint-plugin-simple-import-sort": "^10.0.0",
    "eslint-plugin-solid": "^0.13.1",
    "typescript": "^5.3.3",
    "vite": "^5.0.12",
    "vite-plugin-solid": "^2.9.1"
  }

I am still experiencing a crash when declaring multiple windows in tauri.conf.json. I have WebView2 121.0.2277.106. The error is "The group or resource is not in the correct state to perform the requested operation.".

I am still experiencing a crash when declaring multiple windows in tauri.conf.json. I have WebView2 121.0.2277.106. The error is "The group or resource is not in the correct state to perform the requested operation.".

I later chose to create it manually, and later, I directly adjusted it to a single-window application.

I experience this again.
Webview2 Version 121.0.2277.128 (Official build) (64-bit)
Windows 11
Latest wry #49d2
Multiwebview wry example
Reproduce:
git clone https://github.com/tauri-apps/wry
cd wry
cargo run --example multiwebview

wry issue: https://github.com/tauri-apps/wry/issues/1173

The argument passed to ICoreWebView2EnvironmentOptions.SetAdditionalBrowserArguments seems to be inconsistent in wry.
Default argument string is "--disable-features=msWebOOUI,msPdfOOUI,msSmartScreenProtection --autoplay-policy=no-user-gesture-required" and its code points should be
 [45, 45, 100, 105, 115, 97, 98, 108, 101, 45, 102, 101, 97, 116, 117, 114, 101, 115, 61, 109, 115, 87, 101, 98, 79, 79, 85, 73, 44, 109, 115, 80, 100, 102, 79, 79, 85, 73, 44, 109, 115, 83, 109, 97, 114, 116, 83, 99, 114, 101, 101, 110, 80, 114, 111, 116, 101, 99, 116, 105, 111, 110, 32, 45, 45, 97, 117, 116, 111, 112, 108, 97, 121, 45, 112, 111, 108, 105, 99, 121, 61, 110, 111, 45, 117, 115, 101, 114, 45, 103, 101, 115, 116, 117, 114, 101, 45, 114, 101, 113, 117, 105, 114, 101, 100]
But code points sometimes become different like [16560, 21260, 607], [26016, 48040, 354].
Error occurs when first WebView is created with invalid argument and the second WebView is created with valid argument.
Webviews are created successfully when both arguments are valid or invalid(ignorable).
I don't even know if this inconsistency is expected and irrelevant to the error, but this was the only difference I observed.

thank you @mrdkprj, that was indeed the problem. I am in the process of refactoring our code in WRY which touched this area of code and I didn't see this error in a while. Good to finally know what exactly caused the issue.
The refactor is being done on https://github.com/tauri-apps/wry/tree/refactor-backends if anyone wants to test it.

Rust version: Ok("129.0.2792.52") Same happens with me
[2024-09-26][05:38:59][tauri_runtime_wry][ERROR] failed to create webview: WebView2 error: WindowsError(Error { code: HRESULT(0x8007139F), message: "The group or resource is not in the correct state to perform the requested operation." })
My program is appbar with buttons, when user press button for internal web resource, js create new WebviewWindow for this entity with this error, and window flashes on create/destroy.
Moreover, I have been developing it for a couple of months (or rather, migrate from electron node ffi to tauri windows-rs), and this part of the code was made a long time ago, but now it suddenly stopped working. The program also creates some windows from rust backend - but there are no problems with them. Only with windows from the frontend.

Rust version: Ok("129.0.2792.52") Same happens with me [2024-09-26][05:38:59][tauri_runtime_wry][ERROR] failed to create webview: WebView2 error: WindowsError(Error { code: HRESULT(0x8007139F), message: "The group or resource is not in the correct state to perform the requested operation." }) My program is appbar with buttons, when user press button for internal web resource, js create new WebviewWindow for this entity with this error, and window flashes on create/destroy.
Moreover, I have been developing it for a couple of months (or rather, migrate from electron node ffi to tauri windows-rs), and this part of the code was made a long time ago, but now it suddenly stopped working. The program also creates some windows from rust backend - but there are no problems with them. Only with windows from the frontend.
UPD: This happened after i add additional_browser_args() to main window builder. Even it is empty string "", or "--enable-features=msWebView2EnableDraggableRegions --disable-features=ElasticOverscroll" - failed to create webview occures!

x2 when I try to add additional_browser_args the app start crashing with the same error.

if you open or find a issue/ticket for this tag me please.

I already created issue, and it is normal, but not documented behaviour. Specifying data_directory for builder with additional_browser_args fix this.

authors: Borber DexterHill0 Elendiar FabianLars ImageDeeply amrbashir eythaann gulbanana kris-ava mrdkprj thewh1teagle


text:
Use C11 standard atomics

Hi,
I notice some of the code uses instructions like GCC's __atomic_fetch_add. This seems unnecessary; these built-ins are primarily for standard C library implementers to implement atomics. If the only reason the stats have to be atomic is to avoid problems with multiple threads writing at the same time, making the struct members of type atomic_size_t and incrementing just using, say, stats.free++ should suffice. I could send a pull request if you could help me get my head around this.

To be honest I have no idea why I used the __atomic intrinsics instead of the standard ones.  I think originally I had written the code as part of a separate project that is C99 so I didn't rely on <stdatomic.h.>  But yeah I have no objections to using the standard ones.

source: gharchive/issue

repo: tavianator/oomify

license_type: permissive

license_source: github-api


text:
🛑 LG-Imports is down

In 9fca546, LG-Imports (https://www.lg-imports.be) was down:

HTTP code: 0
Response time: 0 ms

Resolved: LG-Imports is back up in 25e0b92.

license: MIT


text:
unnamed expressions

the current proposal does not set an optional Binding Identifier for AsyncGeneratorExpression.
Ref https://tc39.github.io/proposal-async-iteration/#prod-AsyncGeneratorExpression
Is this intended? (I'm writing tests accordingly the answer).

I'm sorry, I don't really understand. That section is mostly done with analogy to GeneratorExpression. Is this perhaps covered by https://tc39.github.io/proposal-async-iteration/#misc-patches , i.e. the general places that need to be patched to also consider AsyncGeneratorExpression?
Could you share what spec text you think might be missing, and how it affects a test result?

authors: domenic leobalter


text:
Update to macos-14 runner image

Let's see if (a) it works, and (b) it speeds up our Mac CI
https://github.blog/changelog/2024-01-30-github-actions-introducing-the-new-m1-macos-runner-available-to-open-source/

Filed https://github.com/actions/runner-images/issues/9273

Let's leave this for now

source: gharchive/pull-request

repo: tcbrindle/flux


text:
🛑 Twemoji Explorer is down

In 2d4f707, Twemoji Explorer (https://twemoji.tomichen.com/) was down:

HTTP code: 502
Response time: 2200 ms

Resolved: Twemoji Explorer is back up in e6cc8d4.


text:
Zlib errors when compiling.

I downloaded zlib-1.2.11 from its official website and compiled it. I referenced zlib with:
-DZLIB_LIBRARY=/usr/local/zlib/lib/ -DZLIB_INCLUDE_DIR=/usr/local/zlib/include/
but I'm getting this error:
[ 64%] Built target tdclient
[ 64%] Linking CXX executable generate_json
/usr/bin/ld: cannot find -lz
collect2: error: ld returned 1 exit status
td/generate/CMakeFiles/generate_json.dir/build.make:123: recipe for target 'td/generate/generate_json' failed
make[2]: *** [td/generate/generate_json] Error 1
CMakeFiles/Makefile2:916: recipe for target 'td/generate/CMakeFiles/generate_json.dir/all' failed
make[1]: *** [td/generate/CMakeFiles/generate_json.dir/all] Error 2
Makefile:138: recipe for target 'all' failed
make: *** [all] Error 2

If I reference source with -DZLIB_LIBRARY=/home/aex12/srcs/zlib-1.2.11/ I get a different error on same step
[ 64%] Linking CXX static library libtdclient.a
[ 64%] Built target tdclient
make[2]: *** No rule to make target '/home/aex12/srcs/zlib-1.2.11/', needed by 'td/generate/generate_json'.  Stop.
CMakeFiles/Makefile2:916: recipe for target 'td/generate/CMakeFiles/generate_json.dir/all' failed
make[1]: *** [td/generate/CMakeFiles/generate_json.dir/all] Error 2
Makefile:138: recipe for target 'all' failed
make: *** [all] Error 2

You should specify full path to the prebuilt library:
cmake -DZLIB_LIBRARY=/usr/local/zlib/lib/libz.so ..
If it will not help could you try to apply patch
zlib.txt
and recompile with it?

@Aex12 Have you succeeded in TDLib compilation?

Despite lack of feedback, the issue should be fixed in TDLib 1.2.0.

repo: tdlib/td


text:
Linting errors on master

I added SQF linting on the mission on #34, there are lots of errors :
https://github.com/team-gsri/Orion/runs/921044343?check_suite_focus=true
Files impacted are:

functions/dw_populate/fn_alertZone.sqf
functions/dw_populate/fn_populateZone.sqf

There is also ATM airdrop and EOS, which are going to be deprecated.
We may either disable linting until GOS is avilable or fix them.
What do you think ?

After discussion on discord, we will allow PR merging when linting fails.
The objective is to fix errors after we moved EOS to GOS and other major updates.

Problem actually caused by files being encoded as UTF-8+BOM.
Issue submitted to LordGolias/sqf#65
Most of this bug is resolved by #47.
ATM airdrop does not work since Arma 3 v2.0, removed in favor of legcs script.

repo: team-gsri/Orion


text:
Cypress I can't load it properly

Hello,
thank you first for the package.
I want to test the package with Cypress but I can't get it to start correctly.
Install the @vue/cli-plugin-e2e-cypress  package
And when executing
npm run test: e2e
I get the following error
  ERROR Failed to compile with 1 error 20:31:50

This relative module was not found:

* ./src/main.js in multi ./src/main.js

So I create the file vue.config.js with the following configuration
module.exports = {
     pages: {
         app: {
             entry: 'dev / serve.js',
         },
     },
};

I start it again and it performs the build but now when accessing I receive
Cannot GET /
I don't know that I'm doing the truth wrong.
Thanks for your time and help.

In a fresh project, I was able to get it to work just fine. Slightly different process, but you're pretty close:
After scaffolding, I ran:
vue add e2e-cypress

and that added the plugin with all the wiring that the vue cli needs.
Then, I added a very similar vue.config.js to what you have here:
module.exports = {
     pages: {
         index: {
             entry: 'dev/serve.js',
         },
     },
};

The difference is because the sample test generated by the cypress install looks for the root url - index instead of app.
Starting things up after that, cypress ran everything just fine. The test out of the box fails, because it's looking for the boilerplate markup from the cli. But editing the test to look for the scaffolded component was trivial.
Closing this issue since nothing is wrong with the scaffolded code, it was all custom configuration.

authors: GoldraK mgdodge

repo: team-innovation/vue-sfc-rollup


text:
github workflow 추가

github repository에서 release하면 npmjs에 자동으로 배포되게 workflow 추가하였습니다
repository secret에 npm 계정 토큰 추가하면 바로 사용할 수 있습니다

감사합니다 😍

repo: team-xquare/git-emoji-utils


text:
Condividi contenuti

La condivisione di contenuti non funziona con Telegram, mentre si potrebbe evitare, in quanto obsoleta, quella via e-mail.

Rimossi i link per la condivisione "dei contenuti" su Telegram e via Email.
Verifiche possibili su Develop. https://staging-mid-git-develop.vercel.app/

authors: matteovaba westcoast


text:
🛑 Appointments API is down

In c1a8149, Appointments API (https://app.greyfinch.com/rest/v1/public/divisions/981581/locations) was down:

HTTP code: 502
Response time: 3 ms

Resolved: Appointments API is back up in dfca7bc.

repo: teamgreyfinch/public-status


text:
Easier self-verification of the math

Improve the details on how the math works, so people can verify it with more ease.
Refs https://github.com/teamniteo/handbook/pull/343#discussion_r1479412496

repo: teamniteo/salarycalc


text:
L_PREFER_CANVAS support

With L_PREFER_CANVAS = true; the _path property doesn't exist on shapes like L.Circle, so L.Path._updatePath() throws an exception (invoked in L.Path.onAdd()).

+1

+1

+1

did the canvas support for the pluggin added?

@sargarsachin At Github you can easily see that kind of info yourself. Just check the date of the last commit, which is 3 years ago. All issues that are marked OPEN are not resolved. There are some forks of this repo that solve some of the issues.

authors: A1rPun atdiff kortos monking sainsb sargarsachin

license_source: bigquery


text:
Should tea name be willing to run /usr/bin/name?

I have some arbitrary command named /usr/local/bin/twhich installed. Should this work?
> /opt/tea/tea.xyz/v*/bin/tea twhich
Usage: twhich [ -aIrV ] [ -K command file ] file
Usage: twhich -s -K command [ -aIrV ] file
> twhich
Usage: twhich [ -aIrV ] [ -K command file ] file
Usage: twhich -s -K command [ -aIrV ] file

If it's supposed to work, I'm not sure why. I guess the point of tea is to set up an environment and run any command.  But I've bumped into some trouble with this because I was trying to switch from a brew installed tool to a tea installed tool and I couldn't tell which was running. In fact, it was running the brew version because the tea version didn't install like I thought it had.

It is working as intended. Consider adding libraries but using a system compiler. tea +zlib.net gcc main.c should work. tea package paths should be earlier in the path than anything else, which should be confirmable with tea +python.org which -a python.

Ok, I have to remember to do tea which .... I think maybe you told me to do this already. I need to write it down.

Ok, I the reason why I've cared about this is actually what is going on in https://github.com/teaxyz/cli/issues/170.

Closing this because #170 is really the problem.

I'm reopening this again because I think it is an issue. The usage says tea [-xdX] [flags] [+package~x.y] [file|URL|target|cmd|interpreter]. I still don't understand all of these options. Maybe we can get examples? The only usages I really understand are tea +pkg cmd and tea -X cmd.
I know tea can download markdown or scripts and execute them, but do you have to have -X? And do those work?
I have no idea what "interpreter" means. Is that tea -X python -c 'print("Hello, World")'? Isn't that really the same as tea -X cmd? Or does it really mean tea python.py or even a script that begins with /usr/local/bin/tea python?
I admit I haven't really gotten into the advanced usage of tea yet. I'm still trying to iron out the basic usage and incorporate it into my daily life. I've been thinking I might be ready to try tea URL.
Anyway, I'm opening this particular issue open because if tea gets extensions and one of them is a community package manager named "pm", then how would a user specify it? tea pm? Or, probably not a good idea, but just an example of a problem, but the package manager could be called brew. So if you called tea brew, then would it execute the extension or Homebrew's brew? Thus why I've reopened the issue.

The reason you might need pass through is this: tea +somelibrary cc -lsome file.c or tea +jq make. Since tea is concerned with more than executables, you might want to run something you already have in a modified environment.
-X is sugar to reduce the difficulty curve for someone to find a binary they might be looking for without knowing the package name.
Yes, interpreter is likely redundant but it's illustrative.

Ok. I am not sure what -x is supposed to mean though. It sounds like "exec" is always the behavior. Is there sometime it's not?
Also, how would you run an extension?
And I am really hoping for a -w for which. Should I open a new issue with it? I'm guessing yes, so I guess I'll do that now.

Yes, interpreter is likely redundant but it's illustrative.

I should take another pass over --help, since last I did it things have evolved somewhat.

Sounds good, thanks. I just wanted to make sure I understood, besides -X, is there any time it's not in -x or --exec mode?

k, was shower-thoughtsing this this morning and I'm leaning towards:
tea --dump becomes a tea/cmd (when tea/cmd implemented).
Then we have:
tea foo  # magic! installs command or executes it from `PATH` (implicitly is `tea -M foo`)
tea -n foo  # dry run, shows what it will do
tea --PATH=no foo  # only run with tea
tea -m foo   # no magic, if `foo` is not in `PATH` fails

We now have -n (alias for --dry-run)

Thanks!

@magnusviri LMK what you think ☺

authors: jhheider magnusviri mxcl

repo: teaxyz/cli


text:
Add support for writing avro files

Add support for writing codecs
Add test to re-write avro files and re-read to verify
Add support for re-used named record schemas

Closes #2

Can you check again - There's already write support, only the github repo (which is mirror of https://bitbucket.org/tebeka/fastavro) was a bit messed up. It's fixed now.

Hey thanks for the feedback, I missed the bitbucket changes.  Glad to have write support!

No worries thank for helping out.
On Thu, May 14, 2015 at 9:08 PM, rockandroll notifications@github.com
wrote:

Hey thanks for the feedback, I missed the bitbucket changes. Glad to have
write support!
—
Reply to this email directly or view it on GitHub
https://github.com/tebeka/fastavro/pull/4#issuecomment-102121809.

authors: rodcarroll tebeka

repo: tebeka/fastavro


text:
Make this a library, addresses #11

[x] Move non-CLI specific code into lib.rs
[x] replace codegen script in tests with build.rs using the library
[ ] replace anyhow with thiserror in library part
[x] Bonus: #![deny(missing_docs)]
[x] add fn for reading from/to buffer rather than files

This is excellent, just what I need.

Nice! Can we still keep a small main.rs around? Maybe in a dbc-codegen-cli crate to not have the cli dependencies. I'd rather check in the generated code myself than doing the build.rs dance :)
Want me to contribute to this branch? I have some time to fiddle with this :)

Sure feel free to change everything! I think moving the bin part to a separate crate is certainly an option.

Done! Based on your stuff, there's now a -cli crate, and I did everything from your checklist except adding convenience functions for buffer/path handling.

Awesome! Actually I think the thiserror checkpoint can be ignored. Thanks for also publishing it to crates.io really happy to see that. That should make using it quite convenient. cargo install dbc-codegen-cli FTW.

authors: andresv killercup marcelbuesing

repo: technocreatives/dbc-codegen


text:
Rename of primary branch to "main"

Solution for https://github.com/techservicesillinois/SecOps-Powershell-CISDSC/issues/101
Actions updated
Module manifests updated
Branch protection policy and default branch have been updated on the repository settings (please double check me!)

Consider adding "Require linear history" to the branch policy. It squashes all your branch commits into one when you merge to main and makes the commit history a bit cleaner.
We also enable "Include administrators" so the configured policy is enforced for repo admins as well.

Consider adding "Require linear history" to the branch policy. It squashes all your branch commits into one when you merge to main and makes the commit history a bit cleaner.
We also enable "Include administrators" so the configured policy is enforced for repo admins as well.

Done

Also please let me know if there's a better way to link to the license and changelog. I didn't see anyway to link to a file without specifying branch in the URL.

authors: MKletz zcarrington


text:
Events Created By/Interacted with By UserID

The controller takes in the userID and displays the events they created, or interacted with in three different json objects.

I have updated the following things:

sends on json object for all events related to the user.
sends an empty object if user has either created no events or if user is not interested in any events.
only added "interested" status to the participation status.
adjusted the seed script for testing.

repo: techstartucalgary/CampusBuddy


text:
Blobbies for borders

It would be nice to have some blobbies straddling the border between the light and dark sections of the pages.
We will need to change them to be opaque (probably via a prop that you can trigger on and off so this doesn't change the other blobbies)
Be sure to test their positioning and sizing extensively to make sure they don't interfere with page different screen sizes!

Can you please elaborate what is to be done.  and what type of styling do you want.
Please share image/video.

Won't do

repo: techstartucalgary/tsu-website


text:
can't get real webull account information or place order

I logged in successfully in my webull account and got the trading token(return true).
But I can not do anything about my real account. (everything is ok with my paper account)
eg.
wb.get_history_orders('Filled')
it returns like this:

{'msg': 'Failed to convert value of type 'java.lang.String' to required type 'java.lang.Long'; nested exception is java.lang.NumberFormatException: For input string: "None"',
'traceId': '5079290334bd4e3f92ba9921510d6b69',
'code': '400',
'success': False}

wb.get_portfolio()
it returns like this:

KeyError                                  Traceback (most recent call last)
 in
----> 1 wb.get_portfolio()

e:\code\trade\venv\lib\site-packages\webull\webull.py in get_portfolio(self)
221         data = self.get_account()
222         output = {}
--> 223         for item in data['accountMembers']:
224             output[item['key']] = item['value']
225         return output
KeyError: 'accountMembers'
I've tried to reinstall webull, python and even tried another two computer. It still shows like this.
could anyone help me solve this problem?
thank you so much!

Interesting... I see Java type error in your code:
{'msg': 'Failed to convert value of type 'java.lang.String' to required type 'java.lang.Long'; nested exception is java.lang.NumberFormatException: For input string: "None"',
'traceId': '5079290334bd4e3f92ba9921510d6b69',
'code': '400',
'success': False}

can you print(data)
what do you see? make sure you sensor out some sensitive data like your account ID.

Interesting... I see Java type error in your code:
{'msg': 'Failed to convert value of type 'java.lang.String' to required type 'java.lang.Long'; nested exception is java.lang.NumberFormatException: For input string: "None"',
'traceId': '5079290334bd4e3f92ba9921510d6b69',
'code': '400',
'success': False}

can you print(data)
what do you see? make sure you sensor out some sensitive data like your account ID.

yes, I tried to print data as you told.
it returns like this:
{'msg': 'Failed to convert value of type \'java.lang.String\' to required type \'java.lang.Long\'; nested exception is java.lang.NumberFormatException: For input string: "None"', 'traceId': 'ee67504c4c614b0080a51d08d7b22978', 'code': '400', 'success': False}

Thanks,
I think that is the reason you are not seeing any information.
There is still java type error in your code...
could you post your entire code? Including how you initaited the wb variable and how you login? (Just remove your email and password and trade pin)

Thanks,
I think that is the reason you are not seeing any information.
There is still java type error in your code...
could you post your entire code? Including how you initaited the wb variable and how you login? (Just remove your email and password and trade pin)

sure, only type these code will return the error, I'm sure I logged in successfully, cuz 'wb.get_detail()' works. Perhaps this might be the only function I can use related to my account.
I tried to use 'pip install webull' or clone codes from github. It returns the same error.
and I tried to log in through MFA. It still didn't work.
I've been using my paper_account for quite a long time. Everyting goes fine. This is the first time I use this project in my real account.
`from webull import webull
wb = webull()
data = wb.login('xxx', 'xxx')
wb.get_trade_token('xxx')
print(wb.get_account())`

{'msg': 'Failed to convert value of type 'java.lang.String' to required type 'java.lang.Long'; nested exception is java.lang.NumberFormatException: For input string: "None"', 'traceId': '5c60e1eb4470400497ff13a20e9402d0', 'code': '400', 'success': False}

Thanks, I am quite curious where the java type came in.
Thanks for posting the code, it looks exactly the same as mine.
I am guessing you might have some python3 package mismatch on your environment. Unfortunately the information here is not enough.
you mentioned that paper_account works well. So I think there should still be some way to get it to work.
Could you try this and give me the output or the error?
data = self.get_account()
print(type(data))

Let's see what the return is.

Thanks, I am quite curious where the java type came in.
Thanks for posting the code, it looks exactly the same as mine.
I am guessing you might have some python3 package mismatch on your environment. Unfortunately the information here is not enough.
you mentioned that paper_account works well. So I think there should still be some way to get it to work.
Could you try this and give me the output or the error?
data = self.get_account()
print(type(data))

Let's see what the return is.

yes, I tried different computers(windows 10 and ubuntu 18.4 server) and it returns the same thing.
I tried your code and it returns a dict and here is the contents in the dict:
{'msg': 'Failed to convert value of type \'java.lang.String\' to required type \'java.lang.Long\'; nested exception is java.lang.NumberFormatException: For input string: "None"', 'traceId': '6c7b92e7b54c48f1b015f9edf1f162d4', 'code': '400', 'success': False}
Thank you so much for trying to help me out.

Thanks for testing the type. Dict looks alright, but the content is entirely off. Since you tried on both computers and the output is the same, I am suspecting this is not the issue of the code or your environment, but related to your account...
Just by the way, are you able to access and login via here?
http://app.webull.com/
Using the web version of webull?

Thanks for testing the type. Dict looks alright, but the content is entirely off. Since you tried on both computers and the output is the same, I am suspecting this is not the issue of the code or your environment, but related to your account...
Just by the way, are you able to access and login via here?
http://app.webull.com/
Using the web version of webull?

yes, I can log in to use the web version of webull.
well. I might found the reason.
I tried my friend's account. everything works fine. but my account doesn't.
it seems like there are something wrong with my account.
is it beacuse I opened webull HK account several days ago? otherwise, there should be no difference between mine and my friend's account.
so could you tell me what should I do to make it work?
thanks so much!

I see..
Sorry, I have no experience with an HK account.
This is quite an isolated issue, so the help I can offer is limited. I know they have API support for various regions (you can find places to specify region code in the code).
If you want, I can take a look at it remotely personally. But from the status of the HK account, I suspect I can do much about it.

I see..
Sorry, I have no experience with an HK account.
This is quite an isolated issue, so the help I can offer is limited. I know they have API support for various regions (you can find places to specify region code in the code).
If you want, I can take a look at it remotely personally. But from the status of the HK account, I suspect I can do much about it.

thanks, I really appreciate your help! If you are willing to remotely help me check it, my wechat is ivorytower123, my skype is niuyulin@live.com

changing
def account_id(self):
        return f'{self.base_trade_url}/account/getSecAccountList/v4'

to
def account_id(self):
        return f'{self.base_trade_url}/account/getSecAccountList/v5'

worked.

Also having this problem when placing orders

even with the new account_id endpoint

Hello Ted/Others , I recently opened the account and I see same error for my real account. I even added following in webull.py file.
def account_id(self):
return f'{self.base_trade_url}/account/getSecAccountList/v5'
wb.get_portfolio returns KeyError: 'accountMembers'. Unable to pull out information for account. Any suggestion?
Thanks in advance,

@shailesh9r can you please check my comment in defect 157 and see if it resolves the issue.

@shailesh9r can you please check my comment in defect 157 and see if it resolves the issue.

No it does not solve it. I have same comment as cliu (reposting below). Any other suggestion to fix this for new account?
@shamssheikh Thanks for sharing it. I tested it with my new account but it still return 500 when I call account() function. It is so weird that these APIs can only work on an old account rather than a new account

@shailesh9r not sure what's the new endpoint. Did you try to inspect the new endpoint via browser.

@shailesh9r not sure what's the new endpoint. Did you try to inspect the new endpoint via browser.

yes, that is what I am going through...sucks that nothing works for real account :(...

changed self.base_trade_url = 'https://tradeapi.webullbroker.com/api/trade' to self.base_trade_url = 'https://ustrade.webullfinance.com/api/trade' and to v5 for    def account(self, account_id):
return f'{self.base_trade_url}/v5/home/{account_id}', but now I get following error : {'msg': 'Your session has expired, please login again.', 'traceId': 'XXX', 'code': 'trade.token.expire', 'data': {}, 'success': False} . Any idea how to fix this now?

changed self.base_trade_url = 'https://tradeapi.webullbroker.com/api/trade' to self.base_trade_url = 'https://ustrade.webullfinance.com/api/trade' and to v5 for def account(self, account_id): return f'{self.base_trade_url}/v5/home/{account_id}', but now I get following error : {'msg': 'Your session has expired, please login again.', 'traceId': 'XXX', 'code': 'trade.token.expire', 'data': {}, 'success': False} . Any idea how to fix this now?

hey @shailesh9r , im getting exactly same error, did you find a way to fix it?
does anyone know how to fix that? im using access_token to login


text:
support encoding path segments

When there are path segments that contain characters that are not encoded correctly, bad things happen.
For example if you have a route /:name and name's value contains a / the route never matches correctly.
This fixes that!

Hi @tedsuo this is the PR just for the CreateURL method.

Sorry, I missed the alerts for these. Looking now.

LGTM @chendrix

authors: chendrix mhuangpivotal tedsuo

repo: tedsuo/rata

license: mit


text:
Hostedpage new subscription?

I may be missing something, but how can one get the hosted page for a new subscription?
There is HostedPage.UpdateSubscriptionAsync and HostedPage.UpdateCardAsync, but nothing for new subscription (or buy one time addon).
Thanks!

Hello @orsing
You spot it right, as I mentioned in README.md - this library is not complete and WIP at the moment. I have only imported or implemented the API fragments that I need for immediate use for myself.
But again, I wish it can be useful for everyone like me seeking a simple .NET SDK to access Zoho Sub. API. The HostedPage section is missing two fragments - "Create a subscription" & "Buy one-time addon for a subscription".
I will work on it this week and push an update one done. Hope that helps, glad to know you consuming this little side effort I made :)
Feel free to open issue if you find any bug or need more missing fragments from API.

@orsing
Pushed what you asked for:
https://github.com/teference/zoho-dotnet/releases/tag/v0.6.13
https://www.nuget.org/packages/Zoho.Api/0.6.13
Only ran few primary tests, but should work. Ping if that is not the case with some sample data that fails!
Hope that helps, cheers!

authors: jsinh orsing

repo: teference/zoho-dotnet


text:
Request: Support ~/.config/ ($XDG_CONFIG_HOME)

A home folder gets very crowded if every program creates its own config files/folders there.
A standard alternative that is gaining traction is to place config files under ~/.config instead.
(At least, if you're not an OSX-only app that uses ~/Library/Application Support.)
For example, the fish shell uses ~/.config/fish/ and git looks for its config files under ~/.config/git/ out of the box.
It would be nice if we could place config files under ~/.config/karabiner/ instead of ~/.karabiner.d/. Karabiner-Elements could either do this by changing its default config folder, or by searching there if a config is not found at ~/.karabiner.d/.

Looking at this a bit more, this standard comes from a project called XDG.

$XDG_DATA_HOME defines the base directory relative to which user specific data files should be stored. If $XDG_DATA_HOME is either not set or empty, a default equal to $HOME/.local/share should be used.
$XDG_CONFIG_HOME defines the base directory relative to which user specific configuration files should be stored. If $XDG_CONFIG_HOME is either not set or empty, a default equal to $HOME/.config should be used.

Looking at the git implementation, a simple heuristic in constants.cpp should do (assuming you're willing to support it:
https://github.com/git/git/blob/35f6318d44379452d8d33e880d8df0267b4a0cd0/path.c#L1254
if (!once) {
  once = true;
  if (auto x = std::getenv("XDG_CONFIG_HOME")) {
    directory = x;
    directory += "/karabiner";
  } else if (auto p = std::getenv("HOME")) {
    directory = p;
    directory += "/.karabiner.d";
  }
}

However, that doesn't cover src/apps/PreferencesWindow/PreferencesWindow/Base.lproj/MainMenu.xib or src/apps/PreferencesWindow/PreferencesWindow/PreferencesWindowController.m

Great idea. Besides git and fish, neovim also uses XDG convention.

+1

I opened PR https://github.com/tekezo/Karabiner-Elements/pull/114 – this should do it.

+1

This seems to be the current standard for modern unixoid software, to unclutter the home directory. More info here and here.

Is there any chance that Karabiner-Elements will be ported to other Unices? I thought it was highly Mac specific.

@alanshutko this was not my suggestion (I think there is no reason to port it to other Unices), but macOS is a unixoid OS after all and many similar tools use the $XDG_CONFIG_HOME nowadays.

@lgarron the title should be corrected, "Request: Support ~/.config/ ($XDG_CONFIG_HOME)" should be changed to "Request: Support $XDG_CONFIG_HOME". Because $XDG_CONFIG_HOME should default to $HOME/.config.
https://wiki.archlinux.org/index.php/XDG_Base_Directory_support

Quite some time ago I submit a similar request to hammerspoon. Their solution is here: https://github.com/Hammerspoon/hammerspoon/pull/582 (the default path is set in the in the app plist in ~/Library, allowing users to change the value).

The default path has changed, so this bug should be considered fixed. :-D
Others may like to be able to configure the path using $XDG_CONFIG_HOME from default shell, but that's a follow-up feature as far as my request was concerned.

Very nice, bc of this feature one is able to cleanly backup the Karabiner-Elements config with his/her other Dotfiles.
Thanks for the great work!

authors: Suprc alanshutko cseelus lgarron nfarrar sunapi386 talha131

repo: tekezo/Karabiner-Elements

license: unlicense


text:
Setting key repeat smaller than 30 ms is ignored

El Capitan. I can set any key repeat delay, even 10 ms, but in fact when I press and hold a key I sill get ~30 keystrokes per minute.

Please enable this setting.

Thank you! It worked. You should write this on ‘Key Repeat’ page in small font.

Thank you.
I decided to delete the limitation.
The setting has been added a long time ago.
The setting UI did not support mouse and we had to set key repeat rate by keyboard input.
So the limitation was necessary.
But I think this limitation is no more necessary and it confuses people.

That will be funny. For a long time I had these settings set to 20 ms delay and 10 ms repeat and thought that this is how it feels like. Imagine a person with such settings getting the next update without the limit :)

The limitation has been removed at Karabiner-10.14.16.

authors: danilcha tekezo

repo: tekezo/Karabiner


text:
Add check for ES primary shard errors

Signed-off-by: Will Fisher wfisher@chef.io

Maybe also needs to be retry_failed=true ? https://discuss.elastic.co/t/allocate-stale-primary-primary-is-already-assigned/142995/7

I'm pretty sure that we don't need the =true part https://www.elastic.co/guide/en/elasticsearch/reference/current/cluster-reroute.html#_retrying_failed_allocations
I believe the user was probably just mistaken as it looks like the reroute api prints the current state of the system before it's done any work.

authors: nrgetik teknofire

repo: teknofire/gatherlogs-inspec-profiles


text:
fix link to vendor docs in README

Changes
Updated the link to the vendor docs to point towards the vendor directory in the Tekton Chains repository, previously this would redirect to https://github.com/docs/vendor which led to a 404 error.
Submitter Checklist
As the author of this PR, please check off the items in this checklist:

[x] Has Docs included if any changes are user facing
[ ] Has Tests included if any functionality added or changed
[x] Follows the commit message standard
[x] Meets the Tekton contributor standards (including
functionality, content, code)
[x] Release notes block below has been updated with any user facing changes (API changes, bug fixes, changes requiring upgrade notices or deprecation warnings)
[ ] Release notes contains the string "action required" if the change requires additional action from users switching to the new release

Release Notes
NONE

ready for review!

repo: tektoncd/chains


text:
Enum Parameter Type

Some of the template sections are not feature-request oriented, so I'll just give a trimmed down version. Today this does not exist as far as I can tell from the docs. There is a suggestion in https://github.com/tektoncd/pipeline/issues/1393#issuecomment-558833526 but it's asking for a lot of other flexibility as well. Which might be a larger and slower to get ask.
Expected Behavior
params:
  # Existing today
  - name: cat-name
    description: Your cat name
    type: string
  - name: nicknames
    description: Nicknames for your cat
    type: array

  # New
  - name: breed
    description: The breed of your cat
    type: enum
    default: not-sure
    values:
      - persian
      - siamese
      - himalayan
      - not-sure

I believe this format works for Pipeline params, Task params, and maybe even worth going to TriggerTemplates on the Trigger package if viable.
Additional Info
Expectation would be for the logic to match 1:1 each values option. Not sure if it needs to be only strings but since we don't really have non-string types today not sure there is a concern here. Either way, I think it can just be a 1:1 equality check against the list of items and if the types grow, it should "just strong equality match". So if support of Booleans or Numbers come down the line, it won't be a problem.

@skaegi probably has some thoughts here, he's been working on adding something like a "schema" to params to allow for many different types beyond just the array and string we currently have

/kind feature

I'll add this to the agenda of our API working group on Monday to get some more eyes on it and see where the schema work (https://github.com/tektoncd/pipeline/issues/1393) is at.

For validation of params it's a bit of a balancing act as we want to retain backwards-compatibility at the same time as improving validation. For the validation in particular I'm hesitant to re-invent syntax here so what I'm thinking of borrows a lot from json schema. (See https://tools.ietf.org/html/draft-handrews-json-schema-validation-02#section-6.1.2)
For enum I'd suggest something like...
params:
  # New
  - name: breed
    description: The breed of your cat
    type: string
    default: not-sure
    schema:
      enum:
        - persian
        - siamese
        - himalayan
        - not-sure

(e.g. leave all schema aspects in a schema property)
or
params:
  # New
  - name: breed
    description: The breed of your cat
    type: string
    default: not-sure
    enum:
      - persian
      - siamese
      - himalayan
      - not-sure

(e.g. add peer schema aspects directly to the param [might be hard to support long term])

@andrewballantyne there is still a ton of work to actually support multiple types as per #1393 however we could still make progress on the validation aspect independently. e.g. schema could be implemented right now I believe.

params:
  # New
  - name: breed
    description: The breed of your cat
    type: string
    default: not-sure
    schema:
      enum:
        - persian
        - siamese
        - himalayan
        - not-sure

This is definitely more open-ended and can evolve in the future to support anything. I think this is probably the better of the two solutions (and for the support of #1393 and it's goals).

params:
  # New
  - name: breed
    description: The breed of your cat
    type: string
    default: not-sure
    enum:
      - persian
      - siamese
      - himalayan
      - not-sure

This definitely aligns more with what I was thinking, but I wonder if it's a little less long-lasting.

@andrewballantyne there is still a ton of work to actually support multiple types as per #1393 however we could still make progress on the validation aspect independently. e.g. schema could be implemented right now I believe.

This feature request was something that spawned out of a conversation around a future use-case. So in short, it's not something that needs to be done today as it won't immediately solve anything. After a discussion about general usefulness of enum strings, this issue was created.
I found #1393 when I was looking for this request already, and I was concerned around the talk of beta and how it may not be easily to support this kind of feature quickly. So I logged this in the hopes to pair-down to the request I was after.

how about just having the default existing data types and then add a spec field called choices which can list what values it need to be e.g.
  - name: colors
      description: >-
        The context directory within the source repository
      choices:
         - blue
         - green

and if the parameter type is array then we can do do mutli from the chocies
@vdemeester  ^^

/remove-lifecycle stale

Does it make sense to keep this issue open, or should we focus on our attention specifically on #1393 and tackling param types as whole?

Agreed @bobcatfish, #1393 will be able to cover this. This was not adopted at the rate that I had hoped it would be so we can lean into #1393 as the solution that will cover this scenario as well.

authors: andrewballantyne bobcatfish kameshsampath skaegi vdemeester

repo: tektoncd/pipeline


text:
"tkn pipelinerun logs hello-goodbye-run -f -n default" command shows no output

Expected Behavior
$ tkn pipelinerun logs hello-goodbye-run -f -n default should return pipeline output as described Getting Started
[hello : hello] Hello World!

[goodbye : goodbye] Goodbye Tekton!

I tried running task,pipeline,pipelineRun yaml file with apiVersion: tekton.dev/v1 and apiVersion: tekton.dev/v1beta1, both results same output
Actual Behavior
Return nothing on CLI
Steps to Reproduce the Problem

Create task and run
Create pipeline and run
Create Pipeline, PipelineRun and run

Additional Info

Kubernetes version:
Output of kubectl version:

Client Version: version.Info{Major:"1", Minor:"26", GitVersion:"v1.26.1", GitCommit:"8f94681cd294aa8cfd3407b8191f6c70214973a4", GitTreeState:"clean", BuildDate:"2023-01-18T15:51:24Z", GoVersion:"go1.19.5", Compiler:"gc", Platform:"darwin/arm64"}
Kustomize Version: v4.5.7
Server Version: version.Info{Major:"1", Minor:"25", GitVersion:"v1.25.3", GitCommit:"434bfd82814af038ad94d62ebe59b133fcb50506", GitTreeState:"clean", BuildDate:"2022-10-12T10:49:09Z", GoVersion:"go1.19.2", Compiler:"gc", Platform:"linux/arm64"}

Tekton Pipeline version:
Output of tkn version or kubectl get pods -n tekton-pipelines -l app=tekton-pipelines-controller -o=jsonpath='{.items[0].metadata.labels.version}'

Client version: 0.19.1
Pipeline version: v0.44.0

Minikube version

minikube version: v1.28.0

mitul@Mituls-MacBook-Air tekton_files % kubectl apply --filename hello-world.yaml
task.tekton.dev/hello created
mitul@Mituls-MacBook-Air tekton_files % kubectl apply --filename hello-world-run.yaml
taskrun.tekton.dev/hello-task-run created
mitul@Mituls-MacBook-Air tekton_files % kubectl get taskrun hello-task-run
NAME             SUCCEEDED   REASON      STARTTIME   COMPLETIONTIME
hello-task-run   True        Succeeded   8s          2s
mitul@Mituls-MacBook-Air tekton_files % kubectl logs --selector=tekton.dev/taskRun=hello-task-run
Defaulted container "step-echo" out of: step-echo, prepare (init), place-scripts (init)
Hello World
mitul@Mituls-MacBook-Air tekton_files % kubectl apply --filename goodbye-world.yaml
error: the path "goodbye-world.yaml" does not exist
mitul@Mituls-MacBook-Air tekton_files % ls
good-bye-world.yaml		hello-goodbye-pipeline.yaml	hello-world.yaml
hello-goodbye-pipeline-run.yaml	hello-world-run.yaml
mitul@Mituls-MacBook-Air tekton_files % kubectl apply --filename goodbye-world.yaml
task.tekton.dev/goodbye created
mitul@Mituls-MacBook-Air tekton_files % kubectl apply --filename hello-goodbye-pipeline.yaml
pipeline.tekton.dev/hello-goodbye created
mitul@Mituls-MacBook-Air tekton_files % kubectl apply --filename hello-goodbye-pipeline-run.yaml
pipelinerun.tekton.dev/hello-goodbye-run created
mitul@Mituls-MacBook-Air tekton_files % tkn pipelinerun logs hello-goodbye-run -f -n default
mitul@Mituls-MacBook-Air tekton_files % kubectl get pipeline
NAME            AGE
hello-goodbye   4m9s
mitul@Mituls-MacBook-Air tekton_files % kubectl get task
NAME      AGE
goodbye   5m19s
hello     7m50s
mitul@Mituls-MacBook-Air tekton_files % kubectl get pipelinerun
NAME                SUCCEEDED   REASON      STARTTIME   COMPLETIONTIME
hello-goodbye-run   True        Succeeded   3m54s       3m42s

[update]
kubectl logs generate the similar output as expected in Getting-Started Guide, but something seems wrong with tkn commands
$ kubectl logs --selector=tekton.dev/pipeline=hello-goodbye
Defaulted container "step-goodbye" out of: step-goodbye, prepare (init), place-scripts (init)
Defaulted container "step-echo" out of: step-echo, prepare (init), place-scripts (init)
Goodbye Tekton!
Hello World

Hi @Mitulsharma123, thanks for reporting the issue! Tekton CLI is not compatible with Tekton versions that bigger than v0.43.0 https://github.com/tektoncd/cli/issues/1837 tracks the fix.

Thanks! Will track this

v0.30.0 of tkn has fixed this. If not, please reopen.
/close

/close

authors: Mitulsharma123 XinruZhang Yongxuanzhang khrm vdemeester


text:
Add handling of working directory

Changes
Tekton currently overwrites the working directory in task containers if a specific working directory is not specified. This behaviour is problematic when an image explicitly sets a working directory and expects it to be set to that value upon running.
This PR introduces a feature-flags ConfigMap with a single flag - disable-working-directory-overwrite - that, when set to "true" will prevent Tekton from overriding the working directory in Task containers. This behaviour will be kept for one release which must include a deprecation warning that the behaviour will be changing soon. In the release after that the flag will be flipped so that the default behaviour becomes for Tekton to NOT override the working directory.
Contributes to issue #1836
Submitter Checklist
These are the criteria that every PR should meet, please check them off as you
review them:

[x] Includes tests (if functionality changed/added)
[x] Includes docs (if user facing)
[x] Commit messages follow commit message best practices

Reviewer Notes
If API changes are included, additive changes must be approved by at least two OWNERS and backwards incompatible changes must be approved by more than 50% of the OWNERS, and they must first be added in a backwards compatible way.
Release Notes
Tekton currently overwrites the working directory in Step containers if not explicitly specified by the user. This behaviour is problematic when an image explicitly sets the working directory and expects it to be set to that value upon running. You can now disable this behaviour in Tekton by using the feature-flags ConfigMap. See docs/install.md for further details on disabling this behaviour.

/lgtm
I just tested with my kube-hunter image and now it works without explicitly setting workingDir

@vdemeester good point!  I took a very quick look at the configmap watcher and got :fearful: because I couldn't easily trace where the actual configmaps to watch were being configured.  I can take another look at this.  Issue to track it: https://github.com/tektoncd/pipeline/issues/2117

/lgtm

authors: othomann sbwsg skaegi


text:
Add debug with breakpoint onFailure to PipelineRun Spec

Changes

Add debug with breakpoint onFailure to PipelineRun Spec

spec:
  taskRunSpecs:
    - pipelineTaskName: clone
      debug:
        breakpoint: ["onFailure"]

/kind feature
Submitter Checklist
As the author of this PR, please check off the items in this checklist:

[ ] Docs included if any changes are user facing
[ ] Tests included if any functionality added or changed
[ ] Follows the commit message standard
[ ] Meets the Tekton contributor standards (including
functionality, content, code)
[ ] Release notes block below has been filled in
(if there are no user facing changes, use release note "NONE")

Release Notes

Add debug with breakpoint onFailure to PipelineRun Spec

/ok-to-test

@du2016 Thank you for your PR! Looking forward to this feature :)
A couple of questions / notes:

Is this associated with TEP-0097?

If so, I just wanted to make sure that you discussed this with the TEP author @waveywaves (to avoid double work).
The TEP is still in proposed state. It's not a big deal, and it's great to start experimenting, the only thing is that the TEP will have to be marked as implementable before this can be merged

Could you add to the release notes that this adds the API spec but no functionality yet? It may be worth adding this to the docs as well, to make it clear to users that this cannot be used it (unless this is released together with the implementation).
Could you check the "submitted checklist" in the PR description, and tick any relevant item?

Thank you!!

@du2016谢谢你的公关！期待这个功能：）
几个问题/注意事项：

这与TEP-0097有关吗？

如果是这样，我只是想确保您与 TEP 作者讨论过这个问题@waveywaves（避免双重工作）。
TEP 仍处于提议状态。这没什么大不了的，开始试验很好，唯一的事情是 TEP 必须标记为可实施才能合并

您能否在发行说明中添加这会添加 API 规范但还没有功能？也可能值得将此添加到文档中，以使用户清楚地知道它不能使用它（除非它与实现一起发布）。

您能否检查 PR 描述中的“已提交清单”，并勾选任何相关项目？

谢谢！！
thanks it's the same,i'll close this pr


text:
fix: the params in step replace other fields in step that are not in stepaction

Fixes: #7753
Prior to this PR, the step contains params, these params are
passed to stepaction, but it may be passed to other fields in step,
such as: StdoutConfig.
This commit will only pass the fields in stepaction and replace
these fields with params in step.
/kind bug
/assign @chitrangpatel
Submitter Checklist
As the author of this PR, please check off the items in this checklist:

[x] Has Docs if any changes are user facing, including updates to minimum requirements e.g. Kubernetes version bumps
[x] Has Tests included if any functionality added or changed
[x] pre-commit Passed
[x] Follows the commit message standard
[x] Meets the Tekton contributor standards (including functionality, content, code)
[x] Has a kind label. You can add one by adding a comment on this PR that contains /kind <type>. Valid types are bug, cleanup, design, documentation, feature, flake, misc, question, tep
[x] Release notes block below has been updated with any user facing changes (API changes, bug fixes, changes requiring upgrade notices or deprecation warnings). See some examples of good release notes.
[ ] Release notes contains the string "action required" if the change requires additional action from users switching to the new release

Release Notes
Pass only the fields in `stepaction` and replace these fields with the `params` in step.

/ok-to-test

Thanks for the catch and the fix @cugykw!
/approve

/cc @vdemeester

authors: chitrangpatel cugykw


text:
Add pod output to build logs

Fixes #944

Changes
Add the output of pods attached to taskruns used by failed E2E
tests. We do this today for YAML tests but not for other E2E tests.
Submitter Checklist
These are the criteria that every PR should meet, please check them off as you
review them:

[ ] Includes tests (if functionality changed/added)
[ ] Includes docs (if user facing)
[ ] Commit messages follow commit message best practices

See the contribution guide
for more details.
Release Notes

release-note

/ok-to-test

/test pull-tekton-pipeline-integration-tests

/approve

authors: afrittoli dlorenc vdemeester


text:
REST access for Logs RPCs need to be accessed by GetRecord input pattern

https://github.com/tektoncd/results/blob/357145ba7395933d9fbe9a5c3534db1d281c2885/proto/v1alpha2/api.proto#L99
In the above code, the REST handler for the API GetLog will set the input Name = <parent>/results/<result-id>/logs/<record-id>. But the GRPC handler expects the parameter Name in the format <parent>/results/<result-id>/records/<record-id>.

/kind bug

authors: adambkaplan sayan-biswas

repo: tektoncd/results


text:
add items in order to disable cipher suites

@flopezag

LGTM

authors: flopezag jesuspg

repo: telefonicaid/fiware-paas


text:
fix: Converts Approved Review Count to string

Fixes #229

@rickardl @rorymalcolm

@isaacsanders Great work

authors: isaacsanders rickardl

repo: telia-oss/github-pr-resource


text:
Transaction View

[ ] As a user, I want to be able to view my transaction history, including the type of transaction (e.g., send, receive, swap), the addresses involved, the transaction hash with a link to Telos Scan, the amounts involved in USD and tokens (when available), the date and time of the transaction, and the gas fee.
[ ] As a user, I want the Telos EVM Wallet to display transaction types using icons, to make them more easily recognizable.
[ ] As a user, I want to see the relative time (e.g. 10 minutes ago) of my transaction when I hover over the date and time it was executed
[ ] As a user, I want to be able to paginate through my transactions

Figma
https://www.figma.com/file/eIpbnt6vqwqptEbZWtwO8Y/v0.3?node-id=223%3A1853&t=JzCFOo3I7ogrKlS5-1

duplicate of https://github.com/telosnetwork/telos-wallet/issues/167

repo: telosnetwork/telos-wallet


text:
Add pgmq

Add pgmq extension Dockerfile and Trunk.toml

Closing because this is published in its own project repo.

repo: tembo-io/trunk

license: PostgreSQL


text:
Allow for custom playground live view size

The opening lines of the sample playground provided creates a live view.
There does not seem to be a way, when using the playground live view, to set custom dimensions.
For teaching purposes, I'm often having students work inside of a playground using Xcode on macOS – being able to set custom view dimensions would be helpful.

@lcs-rgordon
I added new initializer init(size: CGSize) to PlaygroundCanvasLiveView.
let size = CGSize(width: 300, height: 300)
let liveView = PlaygroundCanvasLiveView(size: size)
PlaygroundPage.current.liveView = liveView

Please check the master branch or 1.0.0-beta.2.
Note that you need to use the latest version 11.1 of Xcode.

@temoki Works great in testing at my end. Thank-you!

authors: lcs-rgordon temoki

repo: temoki/TortoiseGraphics


text:
🛑 Secret Site2 is down

In 7203527, Secret Site2 ($SECRET_SITE_2) was down:

HTTP code: 404
Response time: 119 ms

Resolved: Secret Site2 is back up in 5848807 after 20 minutes.

repo: templain/mywatcher


text:
🛑 QRCODE VIEWER is down

In f94c3ab, QRCODE VIEWER (https://qrcode-viewer.onrender.com/qrcode-viewer/) was down:

HTTP code: 0
Response time: 0 ms

Resolved: QRCODE VIEWER is back up in 12768ff after 26 minutes.


text:
[Bug] Unable to start with only one worker (for xdebuging)

Describe the bug
When using this .rr.yaml configuration:
temporal:
  address: ${TEMPORAL_CLI_ADDRESS}
  namespace: ${TEMPORAL_NAMESPACE}
  activities:
    num_workers: 1

I'm seeing 2 php worker processes running. That is quite unfortunate when I'm trying to debug activities execution, since the two are competing for tasks and I'm only able to debug one worker at a time.
To Reproduce
Steps to reproduce the behavior:

set num_workers to 1 in your .rr.yaml
run rr serve -c .rr.yaml
Look in the process list (top or ps -aux) and see two workers running

Expected behavior
One worker should be running soI would be able to connect to the one which does the processing.
Screenshots/Terminal ouput
If applicable, add screenshots or code blocks to help explain your problem. You can also use Loom to do short, free video bug reports.
Versions (please complete the following information where relevant):

OS: Linux (Ubuntu from Docker)
Temporal Version: 1.10.2
are you using Docker or Kubernetes or building Temporal from source? Docker

Hey @Rastusik. Could you please show me the output from the ./rr workers -i command? I suspect, that you are referring to the workflow worker. Because initially, you should have an activity worker per task queue and 1 workflow worker.

Hey @rustatian I don't see any output from tat command, only blank screen... Is there something I should configure to .rr.yaml? I tried the mentioned command a few times but it always gave me blank output.
So is that by design, that there is always one worflow and one activity worker?
Maybe that is not a bug then ... But it is really complicated to debug workflows and activities this way

It is by design, and in future releases, it will be even more workers (1-2 by each task queue).
The reason of that - activity workers works as classic blocking pipeline, while the workflow is an async worker. We can not keep the code of both in a single worker in modern PHP (maybe it will change in the future).

The way to debug:
Set number of max connections > number of workers and simply do not put the interception to the workflow worker. I was able to debug activities without many issues. There are workshop on YouTube how to do so.

ok, thanks, I'll try to find the video

Hey @Rastusik, feel free to close the issue if you have no any questions. Keep in mind, that 2 workers are a minimum for the temporal. 1 - workflow worker and 1 for the activities (by design).

@rustatian sorry, I didn't know that I should be the one resolving this. Closing now.

authors: Rastusik rustatian wolfy-j

repo: temporalio/roadrunner-temporal


text:
How to calculate similarity between the two graphs?

I have to learn similarity between graphs using deep learning. I have many samples (~500k) of graphs. Graphs have ~5000 nodes and ~4000 egges in the average.
How can I compute similarity score between two graphs? I am thinking:

convert graphs into vectors using Graph2Vec embedding
then compare them using various similarity calculating techniques like cosine similarity.

I would really appreciate if I can get some feedback whether this is the correct way to approach this problem or not.

hi smith-co@,
I wonder what types of samples you have. Do you have labeled pairs of things that should be "similar" and things that should not be similar (positives and negative pairs) ? Or do you simply have unsupervised graphs ?
I ask because while things like Graph2Vec work, it has an specific induction bias, and its embedding may or may not be useful for you, you will have to try. Usually any unsupervised (or self-supervised in some way) training method, you will have to try if it works well for your application.
It is usually better to have labeled data of what should be similar (and not) to your application, feed those to two tower models (aka. dual-encoders) and use that to learn a good embedding. You can also start with an unsupervised training technique and then finetune.
Notice these comments on similarity (metric) training are generic and orthogonal to GNNs. Meaning you use dual-encoder, similarity metrics on top of a GNN on top of your graph data. (I'm making some assumptions on your type of data here, but I hope you get the picture).
An interesting related read: "Deep Metric Learning: A (Long) Survey". And a bunch of interesting links in github.com/qdrant/awesome-metric-learning.
I hope this helps!

authors: janpfeifer smith-co

repo: tensorflow/gnn


text:
Use OP_NAME constant instead of hard coding

Simple fix for op generation, results in lots of changed files.

Ok @rnett , I would really like to simplify the review process to speed up PR merging. In this PR, it looks like 99% of the changes are reformatting again. So here's what I propose:

you push your changes without reformatting (disable the plugin and the IDE auto-reformat)
I approve (eventually) the PR
you just run the formatter plugin goal and push a new version
We merge it

Let's experiment this out, wdyt?

Ok, I pushed format-less changes.  I'll do the formatting and op generation once you approve it.

Op generation is done.

@rnett sorry but I think you'll need to rebase again, since we've merged TF2.5 not long ago

@karllessard it's rebased.

authors: karllessard rnett

repo: tensorflow/java


text:
Tensorflow: Troubles with BeamSearchDecoder - dynamic_decode

Hi,
I am implementing a seq2seq model with bidirectional multilstm layers with attention and beamsearch. (Only posted the necessary code to keep it simple)
# helper to create the layers
def make_lstm(rnn_size, keep_prob):
lstm = tf.nn.rnn_cell.LSTMCell(rnn_size, initializer = tf.random_uniform_initializer(-0.1, 0.1, seed=2))
lstm_dropout = tf.nn.rnn_cell.DropoutWrapper(lstm, input_keep_prob = keep_prob)
return lstm_dropout

# helper to create the attention cell with
def decoder_cell(dec_cell, rnn_size, enc_output,  lengths):
    attention_mechanism = tf.contrib.seq2seq.BahdanauAttention(
            num_units              = rnn_size,
            memory                 = enc_output,
            memory_sequence_length = lengths,
            normalize                  = True,
            name  = 'BahdanauAttention')

    return tf.contrib.seq2seq.AttentionWrapper(
            cell                 = dec_cell,
            attention_mechanism  = attention_mechanism,
            attention_layer_size = rnn_size)

Encoder:

# foward
cell_fw = tf.nn.rnn_cell.MultiRNNCell([make_lstm(rnn_size, keep_prob) for _ in range(n_layers)])

# backward
cell_bw = tf.nn.rnn_cell.MultiRNNCell([make_lstm(rnn_size, keep_prob) for _ in range(n_layers)])

enc_output, enc_state = tf.nn.bidirectional_dynamic_rnn(cell_fw,
                                                        cell_bw,
                                                        rnn_inputs,
                                                        sequence_length=sequence_length,
                                                        dtype=tf.float32,
                                                        )

enc_output = tf.concat(enc_output,-1)

Decoder:

beam_width = 10
dec_cell = tf.nn.rnn_cell.MultiRNNCell([make_lstm(rnn_size, keep_prob) for _ in range(num_layers)])
output_layer = Dense(vocab_size, kernel_initializer = tf.truncated_normal_initializer(mean = 0.0, stddev=0.1))
dec_cell = decoder_cell(dec_cell, rnn_size, enc_output, text_length)

with tf.variable_scope("decode"):
    # (dec_embed_input comes from another function but should not be
    #   relevant in this context. )
    helper = tf.contrib.seq2seq.TrainingHelper(inputs = dec_embed_input,
                                               sequence_length = summary_length,
                                               time_major = False)

    decoder = tf.contrib.seq2seq.BasicDecoder(cell = dec_cell,
                                              helper = helper,
                                              initial_state = dec_cell.zero_state(batch_size, tf.float32),
                                              output_layer = output_layer)

    logits = tf.contrib.seq2seq.dynamic_decode(decoder=decoder,
                                           output_time_major=False,
                                           impute_finished=True,
                                           maximum_iterations=max_summary_length)

enc_output = tf.contrib.seq2seq.tile_batch(enc_output, multiplier=beam_width)
enc_state = tf.contrib.seq2seq.tile_batch(enc_state, multiplier=beam_width)
text_length = tf.contrib.seq2seq.tile_batch(text_length, multiplier=beam_width)

dec_cell = tf.nn.rnn_cell.MultiRNNCell([make_lstm(rnn_size, keep_prob) for _ in range(num_layers)])
dec_cell = decoder_cell(dec_cell, rnn_size, enc_output, text_length)

start_tokens = tf.tile(tf.constant([word2ind['<GO>']], dtype = tf.int32), [batch_size], name = 'start_tokens')

with tf.variable_scope("decode", reuse = True):

    decoder = tf.contrib.seq2seq.BeamSearchDecoder( cell=dec_cell,
                                                    embedding=embeddings,
                                                    start_tokens=start_tokens,
                                                    end_token=end_token,
                                                    initial_state=dec_cell.zero_state(batch_size = batch_size*beam_width , dtype = tf.float32),
                                                    beam_width=beam_width,
                                                    output_layer=output_layer,
                                                    length_penalty_weight=0.0)

    logits = tf.contrib.seq2seq.dynamic_decode(decoder=decoder,
                                           output_time_major=False,
                                           impute_finished=True,
                                           maximum_iterations=max_summary_length)`

In this line:
logits = tf.contrib.seq2seq.dynamic_decode(decoder=decoder,
                                               output_time_major=False,
                                               impute_finished=True,
                                               maximum_iterations=max_summary_length)

I get following error:
ValueError: Shapes must be equal rank, but are 3 and 2 for 'decode_1/decoder/while/Select_4' (op: 'Select') with input shapes: [64,10], [64,10,256], [64,10,256].
Tensorflow: 1.6.0,
batch_size = 64,
rnn_size = 256
I cannot figure out why the fist one from different shape.
Has anyone got some advice on how to fix this?
Thanks a lot in advance!

Hi, I had exactly the same problem. Would you mind sharing how you fixed it?

^ also running into this issue as well. Any help would be appreciated!

Set the  impute_finished=False, I don't know why, but it work for me.

It seems that tf.where doesn't support dimension extension. If impute_finished is True, the helper will check if the decoding is finished and perform different inputs, that is tf.where(is_finished, x, y). But is_finished is [batch_size, beam_size], and x y are [batch_size, beam_size, num_input_units].
So I think this is a bug

Set the impute_finished=False, I don't know why, but it work for me.

It seems that tf.where doesn't support dimension extension. If impute_finished is True, the helper will check if the decoding is finished and perform different inputs, that is tf.where(is_finished, x, y). But is_finished is [batch_size, beam_size], and x y are [batch_size, beam_size, num_input_units].
So I think this is a bug

Thank you so much, you save my weekend !

authors: MeteorsHub kev1876 nlp4whp randomsgs thomasschmied zackkhan

repo: tensorflow/nmt


text:
Augmentor for Barlow Twins

This fork includes an implementation of the augmenters used in Barlow Twins. This is an adaptation of the paper Barlow Twins: Self-Supervised Learning via Redundancy Reduction, and much of the code has been borrowed from this.
Apologies if there are any mistakes in this pull request, this is my first to this repository.

Hmm, I'm a little confused about these tests, because for Barlow Twins a lot of these parameters aren't needed(ex. num_augmentations_per_image, y, etc.). Would it be okay if it didn't extend the Augmenter class?

Hi @dewball345, thanks for the PR. I think it's beneficial to provide these architecture specific augmenters for reproducing the paper results.
On note, we're looking at removing a lot of the image functions from the augmenter module and instead moving to using keras-cv. One concern is that we are adding a lot of image specific code that is not core to the similarity package. Having said that, as I mentioned, I think it's good to provided the augmenters so we can reproduce the results.
Let's add this PR to the development branch instead of the master branch, and I left some comments on the module.
Thanks.

Interesting, i've never heard of keras-cv before, so will be interesting to try and implement that

@owenvallis Okay, looks like all the checks have worked and have placed augmentation utils in a shared module

Thanks again for the PR. Refactoring the image augmentation functions into their own module is a huge help.

I'll chat with @ebursztein and see if we can move the changes to the master branch. Until then, they are available on the development branch and in tfsim-nightly.

Thanks! Seems great :)

@dewball345 can we add test coverage for the augmenters and the individual augmentation functions? Once we have that we can merge into master. Thanks.

@dewball345 can we add test coverage for the augmenters and the individual augmentation functions? Once we have that we can merge into master. Thanks.

Apologies if this is obvious, but how would I do this?

Generally, we want to write tests for the invariants like flags in the params and conditional branches in the function, although it can also be good to write a basic check that the output is what we expect.
There are some good examples in the tests directory, but tests/test_layers.py shows the use a test fixture and the tests are pretty focused. You can also run the coverage report locally using coverage run -m pytest tests/. The coverage report will help show which lines of the code still need test coverage.

@owenvallis I've added test coverage, seems like everything works out as expected

repo: tensorflow/similarity


text:
Decode issue at inference

Hi all,
I'm working on the languagemodel_wiki_noref_v8k_l1k problem.
the configuration file for datagen, train and decode is:
`PROBLEM=languagemodel_wiki_noref_v8k_l1k
MODEL=transformer
HPARAMS=transformer_base_v1
DATA_DIR=/home/razor22/PycharmProjects/TED_AI/t2t_wiki
TMP_DIR=/home/razor22/PycharmProjects/TED_AI/tmp/t2t_wiki
TRAIN_DIR=/home/razor22/PycharmProjects/TED_AI/t2t_train/$PROBLEM/$MODEL-$HPARAMS
mkdir -p $DATA_DIR $TMP_DIR $TRAIN_DIR

Generate data
t2t-datagen
--data_dir=$DATA_DIR
--tmp_dir=$TMP_DIR
--problem=$PROBLEM

Train
t2t-trainer
--data_dir=$DATA_DIR
--problems=$PROBLEM
--model=$MODEL
--hparams_set=$HPARAMS
--hparams='batch_size=1024'
--output_dir=$TRAIN_DIR

Decode
BEAM_SIZE=4
ALPHA=0.6

t2t-decoder
--data_dir=$DATA_DIR
--problems=$PROBLEM
--model=$MODEL
--hparams_set=$HPARAMS
--output_dir=$TRAIN_DIR
--decode_hparams="beam_size=$BEAM_SIZE,alpha=$ALPHA"
`

I trained the model, then, when decoding I get the following error:
INFO:tensorflow:schedule=continuous_train_and_eval INFO:tensorflow:worker_gpu=1 INFO:tensorflow:sync=False WARNING:tensorflow:Schedule=continuous_train_and_eval. Assuming that training is running on a single machine. INFO:tensorflow:datashard_devices: ['gpu:0'] INFO:tensorflow:caching_devices: None INFO:tensorflow:ps_devices: ['gpu:0'] INFO:tensorflow:Using config: {'_save_checkpoints_secs': None, '_keep_checkpoint_max': 20, '_task_type': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f4c8c613090>, '_keep_checkpoint_every_n_hours': 10000, '_session_config': gpu_options {   per_process_gpu_memory_fraction: 0.95 } allow_soft_placement: true graph_options {   optimizer_options {   } } , 'use_tpu': False, '_tf_random_seed': 1234, '_num_worker_replicas': 0, '_task_id': 0, 't2t_device_info': {'num_async_replicas': 1}, '_evaluation_master': '', '_log_step_count_steps': 100, '_num_ps_replicas': 0, '_is_chief': True, '_tf_config': gpu_options {   per_process_gpu_memory_fraction: 1.0 } , '_save_checkpoints_steps': 1000, '_environment': 'local', '_master': '', '_model_dir': '/home/razor22/PycharmProjects/TED_AI/t2t_train/languagemodel_wiki_noref_v8k_l16k/transformer-transformer_base_v1', 'data_parallelism': <tensor2tensor.utils.expert_utils.Parallelism object at 0x7f4c9784ad50>, '_save_summary_steps': 100} WARNING:tensorflow:Estimator's model_fn (<function wrapping_model_fn at 0x7f4c922d0c80>) includes params argument, but params are not passed to Estimator. INFO:tensorflow:Performing local inference from dataset for ['languagemodel_wiki_noref_v8k_l1k']. INFO:tensorflow:Reading data files from /home/razor22/PycharmProjects/TED_AI/t2t_wiki/languagemodel_wiki_noref_v8k_l1k-dev* INFO:tensorflow:partition: 0 num_data_files: 1 INFO:tensorflow:Setting T2TModel mode to 'infer' INFO:tensorflow:Setting hparams.layer_prepostprocess_dropout to 0.0 INFO:tensorflow:Setting hparams.symbol_dropout to 0.0 INFO:tensorflow:Setting hparams.attention_dropout to 0.0 INFO:tensorflow:Setting hparams.dropout to 0.0 INFO:tensorflow:Setting hparams.relu_dropout to 0.0 WARNING:tensorflow:Beam searching for a model with no inputs. WARNING:tensorflow:Non-random sampling for a model with no inputs. INFO:tensorflow:Beam Decoding with beam size 4 Traceback (most recent call last):   File "/usr/local/bin/t2t-decoder", line 16, in <module>     tf.app.run()   File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/platform/app.py", line 124, in run     _sys.exit(main(argv))   File "/usr/local/bin/t2t-decoder", line 12, in main     t2t_decoder.main(argv)   File "/usr/local/lib/python2.7/dist-packages/tensor2tensor/bin/t2t_decoder.py", line 114, in main     decode(estimator, hp, decode_hp)   File "/usr/local/lib/python2.7/dist-packages/tensor2tensor/bin/t2t_decoder.py", line 96, in decode     dataset_split="test" if FLAGS.eval_use_test_set else None)   File "/usr/local/lib/python2.7/dist-packages/tensor2tensor/utils/decoding.py", line 158, in decode_from_dataset     for num_predictions, prediction in enumerate(predictions):   File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/estimator/estimator.py", line 423, in predict     features, None, model_fn_lib.ModeKeys.PREDICT, self.config)   File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/estimator/estimator.py", line 725, in _call_model_fn     model_fn_results = self._model_fn(features=features, **kwargs)   File "/usr/local/lib/python2.7/dist-packages/tensor2tensor/utils/t2t_model.py", line 788, in wrapping_model_fn     use_tpu=use_tpu)   File "/usr/local/lib/python2.7/dist-packages/tensor2tensor/utils/t2t_model.py", line 833, in estimator_model_fn     return model.estimator_spec_predict(features)   File "/usr/local/lib/python2.7/dist-packages/tensor2tensor/utils/t2t_model.py", line 919, in estimator_spec_predict     decode_length=decode_hparams.extra_length)   File "/usr/local/lib/python2.7/dist-packages/tensor2tensor/utils/t2t_model.py", line 437, in infer     features, decode_length, beam_size, top_beams, alpha)   File "/usr/local/lib/python2.7/dist-packages/tensor2tensor/models/transformer.py", line 227, in _beam_decode     features, decode_length, beam_size, top_beams, alpha)   File "/usr/local/lib/python2.7/dist-packages/tensor2tensor/models/transformer.py", line 298, in _fast_decode     partial_targets = tf.squeeze(tf.to_int64(features["inputs"]), [2, 3]) KeyError: 'inputs'

I've no clue why I get this error.
Can anyone help me to solve this issue?

This is an issue with the language model code. If you modify tensor2tensor/models/transformer.py to have this line at the start of the else statement around line 295:
if 'inputs' not in features:
    features['inputs'] = features['targets']

It should work. Though this may have unintended consequences elsewhere in the code. @nshazeer, @rsepassi, is this an okay hack or is it better handled elsewhere?

Hi Rasmi,
I have same issue with @rafaelbou. When I tried your solution of adding 2 lines here:
    else:
      if 'inputs' not in features:
        features['inputs'] = features['targets']
      # The problem has no inputs.
      # In this case, features["inputs"] contains partial targets.
      # We force the outputs to begin with these sequences.
      encoder_output = None
      encoder_decoder_attention_bias = None

It stop complain about missing 'inputs' but it complains about another issue.
Traceback (most recent call last):
  File "/home/xiaonzha/.local/bin/t2t-decoder", line 16, in <module>
    tf.app.run()
  File "/home/xiaonzha/.local/lib/python2.7/site-packages/tensorflow/python/platform/app.py", line 48, in run
    _sys.exit(main(_sys.argv[:1] + flags_passthrough))
  File "/home/xiaonzha/.local/bin/t2t-decoder", line 12, in main
    t2t_decoder.main(argv)
  File "/home/xiaonzha/xzefs/repos/t2t-official/tensor2tensor/bin/t2t_decoder.py", line 187, in main
    decode(estimator, hp, decode_hp)
  File "/home/xiaonzha/xzefs/repos/t2t-official/tensor2tensor/bin/t2t_decoder.py", line 89, in decode
    checkpoint_path=FLAGS.checkpoint_path)
  File "/home/xiaonzha/xzefs/repos/t2t-official/tensor2tensor/utils/decoding.py", line 267, in decode_from_file
    for result in result_iter:
  File "/home/xiaonzha/.local/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.py", line 411, in predict
    features, None, model_fn_lib.ModeKeys.PREDICT, self.config)
  File "/home/xiaonzha/.local/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.py", line 694, in _call_model_fn
    model_fn_results = self._model_fn(features=features, **kwargs)
  File "/home/xiaonzha/xzefs/repos/t2t-official/tensor2tensor/utils/t2t_model.py", line 886, in wrapping_model_fn
    use_tpu=use_tpu)
  File "/home/xiaonzha/xzefs/repos/t2t-official/tensor2tensor/utils/t2t_model.py", line 931, in estimator_model_fn
    return model.estimator_spec_predict(features)
  File "/home/xiaonzha/xzefs/repos/t2t-official/tensor2tensor/utils/t2t_model.py", line 1052, in estimator_spec_predict
    decode_length=decode_hparams.extra_length)
  File "/home/xiaonzha/xzefs/repos/t2t-official/tensor2tensor/utils/t2t_model.py", line 537, in infer
    top_beams, alpha)
  File "/home/xiaonzha/xzefs/repos/t2t-official/tensor2tensor/models/transformer.py", line 237, in _beam_decode
    features, decode_length, beam_size, top_beams, alpha)
  File "/home/xiaonzha/xzefs/repos/t2t-official/tensor2tensor/models/transformer.py", line 405, in _fast_decode
    ret["outputs"] = ret["outputs"][:, :, partial_targets_length:]
  File "/home/xiaonzha/.local/lib/python2.7/site-packages/tensorflow/python/ops/array_ops.py", line 538, in _SliceHelper
    name=name)
  File "/home/xiaonzha/.local/lib/python2.7/site-packages/tensorflow/python/ops/array_ops.py", line 706, in strided_slice
    shrink_axis_mask=shrink_axis_mask)
  File "/home/xiaonzha/.local/lib/python2.7/site-packages/tensorflow/python/ops/gen_array_ops.py", line 5430, in strided_slice
    name=name)
  File "/home/xiaonzha/.local/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
    op_def=op_def)
  File "/home/xiaonzha/.local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2958, in create_op
    set_shapes_for_outputs(ret)
  File "/home/xiaonzha/.local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2209, in set_shapes_for_outputs
    shapes = shape_func(op)
  File "/home/xiaonzha/.local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2159, in call_with_requiring
    return call_cpp_shape_fn(op, require_shape_fn=True)
  File "/home/xiaonzha/.local/lib/python2.7/site-packages/tensorflow/python/framework/common_shapes.py", line 627, in call_cpp_shape_fn
    require_shape_fn)
  File "/home/xiaonzha/.local/lib/python2.7/site-packages/tensorflow/python/framework/common_shapes.py", line 691, in _call_cpp_shape_fn_impl
    raise ValueError(err.message)
ValueError: Index out of range using input dim 2; input has only 2 dims for 'transformer/strided_slice_6' (op: 'StridedSlice') with input shapes: [?,?], [3], [3], [3] and with computed input tensors: input[3] = <1 1 1>.

I'm trying to decode one of held-out data of languagemodel_lm1b32k
ROBLEM=languagemodel_lm1b32k
MODEL=transformer
HPARAMS=transformer_base

efs=$HOME/xzefs
DATA_DIR=$efs/t2t-ws/data
TMP_DIR=$efs/tmp/t2t_datagen
TRAIN_DIR=$efs/t2t-ws/lm-3

mkdir -p $DATA_DIR $TMP_DIR $TRAIN_DIR

# Generate data
# t2t-datagen \
#  --data_dir=$DATA_DIR \
#  --tmp_dir=$TMP_DIR \
#  --problem=$PROBLEM

# Train
# *  If you run out of memory, add --hparams='batch_size=1024'.
export CUDA_VISIBLE_DEVICES=7
t2t-decoder \
  --data_dir=$DATA_DIR \
  --tmp_dir=$TMP_DIR \
  --problems=$PROBLEM \
  --model=$MODEL \
  --hparams_set=$HPARAMS \
  --output_dir=$TRAIN_DIR \
  --decode_from_file="tmp/1-billion-word-language-modeling-benchmark-r13output/heldout-monolingual.tokenized.shuffled/news.en.heldout-00000-of-00050" \
  # --decode_to_file test2.txt \

Hi @FreeAlex, I think that error is addressed in #655. That PR has not been merged -- you can modify your source directly using the changes there as a guide in the meantime.

@rafaelbou Is this issue fixed with #679?

authors: FreeAlex rafaelbou rasmi stefan-it


text:
Error when serializing LAYER_NAME_UIDS

System information

Have I written custom code: yes
OS Platform and Distribution: Linux Ubuntu 16.04
TensorFlow installed from: source
TensorFlow version: v1.2.0-rc0-312-g0b72359 1.2.0-rc0
Bazel version: 0.4.5
CUDA/cuDNN version: 8.0/5.1
GPU model and memory: NVIDIA GeForce GTX 750 Ti

After recompiling TensorFlow I got this strange log message.
I doesn't seem to affect any of the computation.
All of the other tools behave as expected.
WARNING:tensorflow:Error encountered when serializing LAYER_NAME_UIDS.
Type is unsupported, or the types of the items don't match field type in CollectionDef.
'dict' object has no attribute 'name'

This was triggered here:
tf.train.export_meta_graph("model.ckpt.meta")

Just found this related issue: #9939

Closing out duplicate. Thanks for the report.

authors: Androbin jart

repo: tensorflow/tensorflow


text:
AttributeError: module 'tensorflow' has no attribute 'test'

import tensorflow as tf
device_name = tf.test.gpu_device_name()
if device_name != '/device:GPU:0':
raise SystemError('GPU device not found')
print('Found GPU at: {}'.format(device_name))

AttributeError                            Traceback (most recent call last)
 in ()
1 import tensorflow as tf
----> 2 device_name = tf.test.gpu_device_name()
3 if device_name != '/device:GPU:0':
4   raise SystemError('GPU device not found')
5 print('Found GPU at: {}'.format(device_name))
AttributeError: module 'tensorflow' has no attribute 'test'

Thank you for your post. We noticed you have not filled out the following field in the issue template. Could you update them if they are relevant in your case, or leave them as N/A? Thanks.
Have I written custom code
OS Platform and Distribution
TensorFlow installed from
TensorFlow version
Bazel version
CUDA/cuDNN version
GPU model and memory
Exact command to reproduce

Tnanks for response
TensorFlow 1.8
Was running on collab
The issue solved already,
probably caused by "failed to assign a backend GPU"
I've tried as well
!pip uninstall tf-nightly
!pip uninstall  tensorflow
and then
!pip install --upgrade tensorflow
a magic happened and it works

Nagging Assignee @jart: It has been 29 days with no activity and this issue has an assignee. Please update the label and/or status accordingly.

Nagging Assignee @jart: It has been 45 days with no activity and this issue has an assignee. Please update the label and/or status accordingly.

authors: ZackPashkin tensorflowbutler


text:
Keras Save / Load with Eager

https://github.com/tensorflow/tensorflow/blob/ec357afc00c8fb0e9c99754afd78d47fc0ab2116/tensorflow/python/keras/engine/network.py#L1357-L1361
Since TensorFlow uses more and more of Eager execution.
Would it make sense to add a bit more verbose mention on how to save a non-graph model?
Just mentioning more explicitly the reason and linking tfe.Checkpoint docs would be already of great help.
Any objections?

agree, actually, why we limit to graph network at the beginning?

cc @fchollet and @alextp

@allenlavoie , assigning to you

I am not sure, if we need to adapt save method for Eager, but I would at least add an explicit reference to tfe.Checkpoint and docs, so once a person sees it she or he can figure out what to do next.
Please let me know if I can help

There is an effort to make Model.save work with TensorFlow formats (SavedModel). Right now it requires get_config() and from_config() to be implemented for subclassed Models, but that could change (@k-w-w has been iterating on tf.contrib.saved_model.save_keras_model).
On the doc issue, maybe we should reference save_weights/load_weights from save for now? Those work fine with subclassed Models (+eager). It won't save the Model, but neither does tf.train.Checkpoint. Happy to review a PR for that @lc0 , or I can get to it (but not super soon).

@allenlavoie just added a small reference.
Any better way to do it?

Since the fix is merged, I am closing the issue. Looking forward to the second stage. Thanks @allenlavoie for support!

authors: alextp allenlavoie lc0 zerocurve