Fix required package version

lock.requirements.txt:

REPO_ID_SEPARATOR is deprecated in transformer version 4.20.0 so transformers and
sentence-transformers need to be upgraded.

Reference: https://github.com/UKPLab/sentence-transformers/issues/1599
Type of change

[x] Bug fix (non-breaking change which fixes an issue)

How Has This Been Tested?
Please describe the tests that you ran to verify your changes. Provide instructions so we can reproduce. Please also list any relevant details for your test configuration

[x] Local

Checklist:

[ ] My code follows the style guidelines of this project

[ ] I have performed a self-review of my own code

[ ] I have commented my code, particularly in hard-to-understand areas

[ ] I have made corresponding changes to the documentation

[ ] My changes generate no new warnings

[ ] I have added tests that prove my fix is effective or that my feature works

[ ] New and existing unit tests pass locally with my changes

[ ] Any dependent changes have been merged and published in downstream modules

[ ] I have used an autoformatter like lint or blackc

[ ] This requires a code review

Pending dependency mayhem to be resolved.


gedit segfaults with gtk+-3.20.5 on nixos-unstable

Upstream has already fixed this and I can confirm that the issue goes away when compiling against gtk+-3.20.6.
What is the process for changes involving mass rebuilds? Should I raise a PR with the gtk version/hash under nixpkgs-master?
Steps to reproduce

launch gedit
press the save button
type in a filename and press save in the dialog
notice that gedit crashed

Technical details

System: (NixOS: nixos-version, Ubuntu/Fedora: lsb_release -a, ...)
16.09pre85639.453086a (Flounder)

Nix version: (run nix-env --version)
nix-env (Nix) 1.11.2

Nixpkgs version: (run nix-instantiate --eval '<nixpkgs>' -A lib.nixpkgsVersion)
"16.09pre85639.453086a"

The process is to commit mass rebuilds to the staging branch. You can create a PR against that.

Thanks @vcunat just raised a PR.

this is fixed in master now, just check that gedit works


Add dropdown plugin support

Coverage remained the same at 100.0% when pulling 1aa491c67f405ba4fbe27148a72ca88bdb394a47 on dropdown into 286b9d09b3662baf9ff4a2e3777ed83a7e93253c on master.


Visual Studio Code cannot run programs from within VSCode starting with version 1.62

Godot version
3.4 stable
System information
Windows 10
Issue description
Visual Studio Code recently released version 1.62.  Unfortunately, with this version it is no longer possible to run or debug your projects from within Visual Studio Code.  I had to downgrade to version 1.61 to be able to debug again.
https://godotforums.org/discussion/comment/56303#Comment_56303
Steps to reproduce
Create a simple scene in Godot and attach a c# script to it.  Open the script in VSCode 1.62 or higher.  Try to run the game from within VSCode.  The process will hang without launching the game.
Minimal reproduction project
No response

The same issue also happens on Windows 11, I tried everything and it does not appear to be compatible any longer.

I wonder if this is due to the same cause as https://github.com/godotengine/godot/issues/54076.

Where would I find the patch they're talking about, and how would I apply it?

Version 0.2.1 of the extension has been released with the patch applied.

It works perfectly now! Play in editor and debugging seems fully supported. Thanks for your hard work.

Closing as resolved in the extension. Make sure to update it to the latest version if you still run into this issue locally.

looks like downgrading also works for godot-tools extenstion, see how to downgrade


Update cloudwatch, sdk-core, ses, sns, sqs, ssm to 2.20.134

About this PR
📦 Updates

software.amazon.awssdk:cloudwatch
software.amazon.awssdk:sdk-core
software.amazon.awssdk:ses
software.amazon.awssdk:sns
software.amazon.awssdk:sqs
software.amazon.awssdk:ssm

from 2.20.130 to 2.20.134
Usage
✅ Please merge!
I'll automatically update this PR to resolve conflicts as long as you don't change it yourself.
If you'd like to skip this version, you can just close this PR. If you have any feedback, just mention me in the comments below.
Configure Scala Steward for your repository with a .scala-steward.conf file.
Have a fantastic day writing Scala!

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

Or, add this to slow down future updates of this dependency:
dependencyOverrides = [{
  pullRequests = { frequency = "30 days" },
  dependency = { groupId = "software.amazon.awssdk" }
}]

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

Superseded by #2867.


Need to add rules to entity

We need to add rules to every entity id needed to validate entity before save them to tables.
the idea is:

add rules to entity mapping yml file.
we need to create function called like 'validate'.
we can use Validator php to validate them, create array from mapping yml for validation then validate it.
when the entity manager will persist them, we call the 'validate' function, we could throw them as error so we can rollback to any db transaction exist

already exist in main


Language selector is broken

See https://bugs.chromium.org/p/chromium/issues/detail?id=601672 for explanation


crash in stacker.copyBuiltTypeBaseToOutput

panic: runtime error: invalid memory address or nil pointer dereference
[signal SIGSEGV: segmentation violation code=0x1 addr=0x0 pc=0xbfcbed]
goroutine 1 [running]:
github.com/anuvu/stacker.copyBuiltTypeBaseToOutput(0x7ffd77c19516, 0x24, 0x7ffd77c19545, 0x20, 0x7ffd77c19572, 0x22, 0x0, 0xe012e9, 0x5, 0xc0003abd70, ...)
/root/go/src/github.com/anuvu/stacker/base.go:248+0x7ad
github.com/anuvu/stacker.SetupRootfs(0x7ffd77c19516, 0x24, 0x7ffd77c19545, 0x20, 0x7ffd77c19572, 0x22, 0x0, 0xe012e9, 0x5, 0xc0003abd70, ...)
/root/go/src/github.com/anuvu/stacker/base.go:86+0x561
github.com/anuvu/stacker.(*Builder).Build(0xc000b2f3e0, 0xc0003d7c20, 0x29, 0x0, 0x0)
/root/go/src/github.com/anuvu/stacker/build.go:199+0xe2a
github.com/anuvu/stacker.(*Builder).BuildMultiple(0xc000b2f3e0, 0xc00026b3f0, 0x1, 0x1, 0x12, 0x7ffd77c19572)
/root/go/src/github.com/anuvu/stacker/build.go:539+0x502
main.doBuild(0xc00020e160, 0x0, 0x0)
/root/go/src/github.com/anuvu/stacker/cmd/build.go:99+0x148
github.com/urfave/cli.HandleAction(0xcc3380, 0xe48870, 0xc00020e160, 0xc00020e160, 0x0)
/root/go/pkg/mod/github.com/urfave/cli@v1.22.4/app.go:526+0xbe
github.com/urfave/cli.Command.Run(0xe012ee, 0x5, 0x0, 0x0, 0x0, 0x0, 0x0, 0xe2b611, 0x2f, 0x0, ...)
/root/go/pkg/mod/github.com/urfave/cli@v1.22.4/command.go:174+0x528
github.com/urfave/cli.(*App).Run(0xc0001fc000, 0xc0000f4000, 0xf, 0xf, 0x0, 0x0)
/root/go/pkg/mod/github.com/urfave/cli@v1.22.4/app.go:279+0x731
main.main()
/root/go/src/github.com/anuvu/stacker/cmd/main.go:204+0xb8e

This issue is due to following stacker file, It is definitely wrong. First timewhen godev is not built it throws an unrelated error message.  Then I tried building godev layer and after it is finished, run the this stacker again. It will crash.
config:
prerequisites:
- ../godev/stacker.yaml
build-minio:
from:
type: built
tag: godev
run: |
cd /root
export GO111MODULE=on        # force use of go modules
export GOCACHE=/root/go/.cache # enabling caching modules
export GOPATH=/root/go         # also needed for caching
git clone https://github.com/minio/minio.git /root/go/src/github.com/minio/minio
cd /root/go/src/github.com/minio/minio
git checkout RELEASE.2020-07-20T02-25-16Z
make
cp minio /root/minio.bin
build_only: true
config:
prerequisites:
- ../base/stacker.yaml
from:
type: built
tag: base
import:
- stacker://build-minio/root/minio.bin
run: |
cp /stacker/minio.bin /bin/minio
# clean unnecesary data
/usr/local/bin/oci-clean

What version is this? The line numbers don't match up with current git master, unfortunately :(

Can you post a complete example that fails? I can't seem to reproduce this.

So I think the underlying problem here was a typo in the file (it sounds like) combined with an assumption we made in how prerequisites work. I've just pushed a fix that closes this with a better error message, but let's re-open if there's a real bug in the resolution of things somehow.


fix: Json(Object) is not correct when f32 type in Object, when project use serde_json's "arbitrary_precision" feature.

cargo.toml：
...
serde_json = { version = "1.0.104", features = ["arbitrary_precision"] }

test code：
use poem::{listener::TcpListener, Route, Server};
use poem::Result;
use poem_openapi::{payload::Json, ApiResponse, OpenApi, OpenApiService};

struct Api;

#[derive(ApiResponse)]
enum TestResponse {
    #[oai(status = 200)]
    Ok(Json<Vec<f32>>),
}

#[OpenApi]
impl Api {
    #[oai(path = "/hello", method = "get")]
    async fn index(&self) -> Result<TestResponse> {
        let test_arr = vec![32.1, 32.2];
        Ok(TestResponse::Ok(Json(test_arr)))
    }
}

#[tokio::main]
async fn main() -> Result<(), std::io::Error> {
    if std::env::var_os("RUST_LOG").is_none() {
        std::env::set_var("RUST_LOG", "poem=debug");
    }
    tracing_subscriber::fmt::init();

    let api_service =
        OpenApiService::new(Api, "Hello World", "1.0").server("http://localhost:3000/api");
    let ui = api_service.swagger_ui();

    Server::new(TcpListener::bind("<IP_ADDRESS>:3000"))
        .run(Route::new().nest("/api", api_service).nest("/", ui))
        .await
}

I want got:
[
  32.1,
  32.2
]

But I got response:
[
  32.099998474121094,
  32.20000076293945
]

the commit is fixed the problem.

Thanks for the PR! 🙂
I think we can add a new feature called arbitrary_precision to the Poem-openapi,  and then enable serde_json/arbitrary_precision when the user enables it on Poem-openapi, what do you think?
[features]
arbitrary_precision = ["serde_json/arbitrary_precision"]

@sunli829 I think it's good.

If it's convenient, can you help me add a description of this feature to README.md and lib.rs?  🙂

I added a description of the feature to README.md and lib.rs under the poem-openapi folder.

Thanks😄

arbitrary_precision caused some other problems, which I will look into again.

Ok, I'll try to use f64 . Thank


update code comment docs for kubemark machine

This change adds documentation to the API fields that were added for the
extended resource update.
Also updates the generated files.

/lgtm


Broken link in the README.md

Output of docker version:
Git version
(paste your output here)
Docker began as an open-source implementation of the deployment engine which powers dotCloud, a popular Platform-as-a-Service. It benefits directly from the experience accumulated over several years of large-scale operation and support of hundreds of thousands of applications and databases.
Output of docker info:
broken link about dotCloud

Additional environment details (AWS, VirtualBox, physical, etc.):
Steps to reproduce the issue:

click the link
get the https to http
that bring to the right page

Describe the results you received:
Broken link
Describe the results you expected:
Right page: https://cloud.docker.com/
Additional information you deem important (e.g. issue happens only occasionally):

hm, actually, since this is referring to the original "dotCloud" (not the new cloud.docker.com), perhaps we should change it to the wikipedia page https://en.wikipedia.org/wiki/DotCloud, or an archive link http://web.archive.org/web/20130530031104/https://www.dotcloud.com/

opened a pull request; https://github.com/docker/docker/issues/22468


Add a command to get detailed Informations about what a server type does.

Yay, It's pull request #300

useless imo

I removed the source for community docs. I wrote this myself, I hope it is right

I know that it may is not perfect, but I think it will help the users. And the people explaining the 10000th time what aio is.. Hehe

i dont think users even gonna notice this command

DBH!server count is also used. So why not


Not working on Firefox 106

Ubuntu 18.04.6, Firefox 106.0.2, flatpak 1.14.0 from the official flatpak PPA
gnome-software 3.28.1, gnome-software-plugin-flatpak 3.28.1-0ubuntu<IP_ADDRESS>
flathub repo set up and working
I have verified all of the above as working by installing flathub packages, both from cli and gnome-software.
The firefox extension seemed to install without issues, but when I click on an install button on a flathub page - nothing happens.  No process starts.  Nothing at all.  Even if I pre-launch gnome-software, nothing happens.
I don't know much at all about firefox's plugin architecture, javascript or json, but if you tell me what you need I'll be happy to try to help troubleshoot this.

Hello, thanks for reporting.
After installing the extension, am you restarting Firefox and/or recharged the page? (override cache - Ctrl + Shift + R)?
Did you give the site permission to link with System Handler?

In Settings > Genera l> Applications shows that appstream uses System Handler?

I apologize; I forgot to mention that not only had I restarted FIrefox, I rebooted before reporting this.
I have never been presented with the above permission dialog.  I've tried clicking several different install buttons at flathub.  Appstream does not appear in the list of content handlers.

I'm closing for inactivity.


An error when saving/opening model and weights (babi_memnn)!

I just used the example script examples/babi_memnn.py and added the saving code at the end of the file.
json_string = answer.to_json()
open('my_model_architecture.json', 'w').write(json_string)
answer.save_weights('my_model_weights.h5', overwrite=True)

And after the training is finished I try to load the model with
from keras.models import model_from_json
model = model_from_json(open('my_model_architecture.json').read())
model.load_weights('my_model_weights.h5')

and then get the Exception: You are trying to load a weight file containing 11 layers into a model with 13 layers.
Here is the gist https://gist.github.com/anonymous/f433fa0b79d251b77edf8d862071841a

Yes, there appears to be an issue with model serialization for deeply nested Sequential models. I'm looking into it.
Note that a simple fix would be to use the functional API (recommended for complex models) instead of deeply nested Sequential models.

@fchollet When I am using the functional API to create the model and try to save it using the following line, It gives me the following error saying:
  File "trainer.py", line 48, in create_training_features
json_string = model.to_json()

File "/usr/local/lib/python2.7/site-packages/keras/engine/topology.py", line 2368, in to_json
config = self.get_config()
File "/usr/local/lib/python2.7/site-packages/keras/engine/topology.py", line 2163, in get_config
new_node_index = node_conversion_map[node_key]
KeyError: 'input_1_ib-0'

Please provide the code to reproduce your error.

input1 = Input(shape=(maxlen,), dtype='int32',name='input1')
input2 = Input(shape=(maxlen,), dtype='int32',name='input2')
embedding = Embedding(input_dim=2000,
                         output_dim=300,
                         weights=weights)
e1=embedding(input1)
e2=embedding(input2)
dropout = Dropout(0.25)

 f_rnn = LSTM(259, return_sequences=True, dropout_U=0.2, consume_less='mem')
 b_rnn = LSTM(259, return_sequences=True, dropout_U=0.2, consume_less='mem',go_backwards=True)

  input1_f_rnn = f_rnn(e1)
  input1_b_rnn = b_rnn(e1)
  input1_f_dropout = dropout(input1_f_rnn)
  input1_b_dropout = dropout(input1_b_rnn)

    # maxpooling
    maxpool = Lambda(lambda x: K.max(x, axis=1, keepdims=False), output_shape=lambda x: (x[0], x[2]))
    input1_pool = merge([maxpool(input1_f_dropout), maxpool(input1_b_dropout)], mode='concat', concat_axis=-1)

   #couple of more dense layers
   activation = Activation('tanh')
   activated= activation(input1_pool)

   dense_layer=Dense(15, activation='softmax',name='output')
   output_final=dense_layer(activated)

    model=Model(input=[input1,input2],output=[output_final])
    model.compile(loss='categorical_crossentropy',optimizer='adam')

   model.fit({'input1': X_train1, 'input2': X_train2},{'output': y_train},nb_epoch=1)

   model.to_json()

@akmahaja Is it your complete code? Your input2 is never used in the model, and remove input2 from Model(input=[input1, input2], ...) can solve the error.

@fchollet @joelthchao I am using the input2, I hadn't included that in the earlier version. I have updated the code with the latest version that I am working on, still the same issue.
AttentionLSTM that I am using in the above piece of code is taken from here:
https://github.com/codekansas/keras-language-modeling/blob/master/attention_lstm.py

Please provide the code to reproduce your error.

The keyword here is "reproduce", if you share some code snippet that I can't run and thus doesn't allow me to reproduce your error, that's not very helpful...

@fchollet this is the entire code here that I am using to reproduce the error. Are you referring to X_train1 and X_train2, y_train values?
After experimentation issue is mainly because the "input1" is not being fed all the way to the output layer. It stops at "input1_pool" in the above code, hence when the overall network diagram is also created it does not show up there.

I have fixed the issue from OP.
@akmahaja please open a thread for your own issue, which is different from this one. It should have a code snippet which I can copy and run (i.e. it should be self-contained, include imports and data. Use randomly generated data).


Add airqalibrate subdomain

WHAT DOES THIS PR DO?
Configures https://airqalibrate.airqo.net/ and https://staging-airqalibrate.airqo.net/ subdomains

Jira cards

PLAT-1156

HOW DO I TEST OUT THIS PR?
You should be able to securely access the calibrate app via https://airqalibrate.airqo.net/ and https://staging-airqalibrate.airqo.net/

Hi @NoahNsimbe, there's also this frontend PR: airqo-platform/AirQo-frontend#628
Yes, @123MwanjeMike. The ingress resource on both yaml files have same name and hosts but the front end ingress resource has less configurations than the backend ingress resource that is why all APIs are inaccessible when we apply the frontend ingress resource. We need to delete one.


Improve NPE message in LookupDimensionSpec when lookup does not exist.

The message used to be empty, which made things hard to debug.

👍

remember to backport


bug when opening a note in neovim

When I open notes through neovim, the application terminates with the error: "Error: error reading input: read /dev/stdin: resource temporarily unavailableexit status 1". If I use regular vim to open notes, there is no such error and everything works as it should.

Thanks for opening this issue. Note now properly supports multiple editors through a fallback chain:

Your configured editor in ~/.config/note/config.yaml
NOTE_EDITOR environment variable
VISUAL environment variable
EDITOR environment variable
/usr/bin/vi if it exists
/bin/ed as a final fallback

You can now:

Check your current editor configuration with note --config
Set your preferred editor (including neovim) in the config file:

Fixed in #16


Tableau ingesting only default site

Even if other sites are configured:
{
   "sink":{
      "type":"metadata-rest",
      "config":{

      }
   },
   "workflowConfig":{
      "openMetadataServerConfig":{
         "hostPort":"[https://openmetadata##/api](https://openmetadata/##/api)",
         "authProvider":"azure",
         "securityConfig":{
            "clientSecret":"####",
            "authority":"https://login.microsoftonline.com/####",
            "clientId":"####",
            "scopes":[
               "api://####/.default"
            ]
         }
      }
   },
   "source":{
      "type":"tableau",
      "serviceName":"tableau_internal_people",
      "serviceConnection":{
         "config":{
            "type":"Tableau",
            "username":"admin",
            "password":"***",
            "hostPort":"https://tableau/.####.com",
            "siteName":"EBANXPeople",
            "apiVersion":"3.14",
            "env":"tableau_prod"
         }
      },
      "sourceConfig":{
         "config":{
            "chartFilterPattern":{

            },
            "dashboardFilterPattern":{

            }
         }
      }
   }
}

From support https://openmetadata.slack.com/archives/C02B6955S4S/p1662991128194019?thread_ts=1660313254.390619&cid=C02B6955S4S

the user confirmed it was an issue about adding siteUrl. @OnkarVO7 let's doublecheck if we need to update the docs to make this more explicit as we are getting a few questions around the same.
Closing this. Thanks


feature - adds dockerfile, helper script, build action

Adds a dockerimage to build t-route, a helper script to execute it, and a github action to build and test it.
breakout from #660
Dependencies
#668
#667
Additions
Dockerfile.troute

uses rockylinux 9.2
installs latest depenencies from epel-release
clones latest version of t-route
installs requirements.txt
runs ./compile.sh

docker/troute.sh
Helper script that allows you to execute the docker container like
it was a local install of t-route e.g.
 ~ python -m nwm_routing -V3 -f test/LowerColorado_TX/test_AnA.yaml
Can be executed as
 ~ docker/troute.sh -V3 -f test/LowerColorado_TX/test_AnA.yaml
docker.yml

Github action to auto build the docker image from the master branch on push
builds from incoming changes on pull request
Uses helper script to run the LowerColorado_TX test using the freshly built docker image

Testing

This pr should trigger it 🤞

Notes

The build should be separate from the test jobs (this requires an image artifact to be saved or an image upload to dockerhub)
While versions aren't locked it's possible this will fail for reasons unrelated to incoming pull requests. (may want a schedulled build of this to those issues early)

Checklist

[ ] PR has an informative and human-readable title
[ ] Changes are limited to a single goal (no scope creep)
[ ] Code can be automatically merged (no conflicts)
[ ] Code follows project standards (link if applicable)
[ ] Passes all existing automated tests
[ ] Any change in functionality is tested
[ ] New functions are documented (with a description, list of inputs, and expected output)
[ ] Placeholder code is flagged / future todos are captured in comments
[ ] Visually tested in supported browsers and devices (see checklist below :point_down:)
[ ] Project documentation has been updated (including the "Unreleased" section of the CHANGELOG)
[ ] Reviewers requested with the Reviewers tool :arrow_right:

Testing checklist
Target Environment support

[ ] Windows
[ ] Linux
[ ] Browser

@JoshCu Is this PR ready for review/merge?

tested recently here -> https://github.com/JoshCu/t-route/actions/runs/6829885833/job/18576872962


add checks for correct use of use_inline_resources

recommend use_inline_resource in all 'LWRPBase' providers
catch the use of use_inline_resources when users are also declaring
def action_whatever instead of action :whatever which defeats
use_inline_resources

:+1: as it is restricted to LWRPBase-based things.

Yeah, I'm certainly intending to only target the bodies of classes that inherit from Providers::LWRPBase, we'll have to see how buggy my Ruby AST XPath parsing foo is -- but it does have a test that it avoids the simple case of a library file with only a resource in it, which does not declare use_inline_resources and ignores that.  I'm not sure I could actually do any 'better' than that, since we're doing static analysis.
There might be edge cases where an abstract base class inherits from LWRPBase but doesn't declare use_inline_resources, even though all the subclasses do, but I think the solution there is to either move use_inline_resources up to the baseclass or else just comment out the FC rule.

@jaymzh  @tas50 etc got any thoughts to add about this one?

I approve AND I prefer on rule per Pull Request.
:+1:

rebased, squashed, fixed the merge conflicts over the numbering...


The Container Apps Environment failed to provision

Please provide us with the following information:

This issue is a: (mark with an x)

[ x] bug report -> please search issues before submitting
[ ] documentation issue or request
[ ] regression (a behavior that used to work and stopped in a new release)

Issue description
I have an environment with 9 container apps that has been running for almost a year. Last week I went to deploy a container and it showed that the container app environment failed to provision. The only solution provided was to delete the environment and redeploy all of containers.
I was not able to find a cause in any of the health reports or logs.
Steps to reproduce
I can't reproduce as this happened automatically on the Azure backend on a Sunday when no one was working.
Additional context
This issue was seen in the portal and the CLI.

Thank you for reaching out @mquile1209. When you say the environment failed to provision did you mean the container app within the environment. Usually once an environment runs it doesn't get recreated when you deploy a new container or app. Also would you be able to share the resource URL or name, location and resource group for the environment. That'll allow us to take a look at the backend logs to see if we find the cause of your issue.

Thank you for reaching out @mquile1209. When you say the environment failed to provision did you mean the container app within the environment. Usually once an environment runs it doesn't get recreated when you deploy a new container or app. Also would you be able to share the resource URL or name, location and resource group for the environment. That'll allow us to take a look at the backend logs to see if we find the cause of your issue.

No the container environment was somehow reprovisioned. I saw it on the monitoring dashboard. I deleted the environment as I had to recreate it since it was needed by our devs. This screenshot was the only indicator that something had happened to the environment.

Thank you for this detail. Was the environment re-provisioned by you? Or are you suggesting a control plane process did so? Could it be that the app was scaled to zero and came back after some delay? We will need something to help find the app could you please email your details to "Azure Container Apps Support"<EMAIL_ADDRESS>

I would like to add that this also happened for us a few days ago. In our case the location is Australia East. I have opened a support ticket with Microsoft.

Hi,
I would like to add that I am experiencing the same issue in Australia East as @chriswue is above. I can create container applications through the UI after although since I generate everything via terraform it blocks our deployment pipeline.
This tends to only happen to me when I specify internal/subnet with the consumption plan. It works when the following has been enabled.

This is exactly the problem I reported in https://github.com/microsoft/azure-container-apps/issues/868. MS support claimed that the VNET for the environment was moved out of the subscription but it was not. Like @mquile1209 my environment was running for over a year when one day I started experiencing this problem. Then again, out of nowhere, after about a month of no resolution from support the error went away on its own. I did nothing to fix it. In fact, I had created a new environment and started to deploy containers to it while the original environment wouldn't let me update even existing containers.

My environment is the same as @praneetloke. Consumption only, internal only using a VNET. I have sent an email as requested.

Our container app environment just uses consumption only tier with no explicit VNET attached (i.e. fully default configuration).

@praneetloke do you still see issues? If yes, can you help to send env information to<EMAIL_ADDRESS>so I can check more.
@chriswue I have replied your email, let's work via email.

We also started having the same issue today. Our container app is in West Europe Region. Container environment is attached to a VNET with external ingress enabled. We have 12 apps running inside that environment, although they are working as expected but our release process is blocked because of this issue.

We have been seeing the same issue but the 'Quick' solution is to add a tag to the Container App Environment
This cause the CA Env to return to a Provisioned State and then you should be able to proceed.
See #1113

@simonjj -
I'm seeing similar problems today trying to create ACA environments and subsequently apps in eastus:
Sorry you don't get the pretty colors, but there is the Terminal output:

{
  "id": "/subscriptions/2554f64d-0419-4e39-8121-5e01270578ea/resourceGroups/charris-mb-2",
  "location": "eastus",
  "managedBy": null,
  "name": "charris-mb-2",
  "properties": {
    "provisioningState": "Succeeded"
  },
  "tags": null,
  "type": "Microsoft.Resources/resourceGroups"
}
PS C:\git\ai\metabase> ^C
PS C:\git\ai\metabase> az account show --query "{subscriptionId:id, subscriptionName:name}"
{
  "subscriptionId": "2554f64d-0419-4e39-8121-5e01270578ea",
  "subscriptionName": "ScottHunter_TeamDemoSub"
}
PS C:\git\ai\metabase> ^C
PS C:\git\ai\metabase> az containerapp env create --resource-group charris-mb-2 --name myEnvironment --location eastus
No Log Analytics workspace provided.
Generating a Log Analytics workspace with name "workspace-charrismb2YIbL"
\ Running ..The command failed with an unexpected error. Here is the traceback:
Expecting property name enclosed in double quotes: line 1 column 2 (char 1)
Traceback (most recent call last):
  File "D:\a\_work\1\s\build_scripts\windows\artifacts\cli\Lib\site-packages\azure/cli/command_modules/containerapp/containerapp_env_decorator.py", line 146, in create
  File "D:\a\_work\1\s\build_scripts\windows\artifacts\cli\Lib\site-packages\azure/cli/command_modules/containerapp/_clients.py", line 474, in create
  File "D:\a\_work\1\s\build_scripts\windows\artifacts\cli\Lib\site-packages\azure/cli/command_modules/containerapp/_clients.py", line 62, in poll_status
  File "D:\a\_work\1\s\build_scripts\windows\artifacts\cli\Lib\site-packages\azure/cli/core/util.py", line 1004, in send_raw_request
azure.cli.core.azclierror.HTTPError: Service Unavailable(<!DOCTYPE html PUBLIC '-//W3C//DTD XHTML 1.0 Transitional//EN' 'http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd'><meta content='text/html; charset=utf-8' http-equiv='content-type'/><title>AzureResourceManager</title>

Our services aren't available right now

We're working to restore all services as soon as possible. Please check back soon.

Ref A: 7D5B3E992F1F44B6B08BF90447C2406F Ref B: CO6AA3150217045 Ref C: 2024-03-08T18:49:26Z

)

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "D:\a\_work\1\s\build_scripts\windows\artifacts\cli\Lib\site-packages\knack/cli.py", line 233, in invoke
  File "D:\a\_work\1\s\build_scripts\windows\artifacts\cli\Lib\site-packages\azure/cli/core/commands/__init__.py", line 664, in execute
  File "D:\a\_work\1\s\build_scripts\windows\artifacts\cli\Lib\site-packages\azure/cli/core/commands/__init__.py", line 729, in _run_jobs_serially
  File "D:\a\_work\1\s\build_scripts\windows\artifacts\cli\Lib\site-packages\azure/cli/core/commands/__init__.py", line 721, in _run_job
  File "D:\a\_work\1\s\build_scripts\windows\artifacts\cli\Lib\site-packages\azure/cli/command_modules/containerapp/_client_factory.py", line 28, in _polish_bad_errors
  File "D:\a\_work\1\s\build_scripts\windows\artifacts\cli\Lib\site-packages\azure/cli/core/commands/__init__.py", line 698, in _run_job
  File "D:\a\_work\1\s\build_scripts\windows\artifacts\cli\Lib\site-packages\azure/cli/core/commands/__init__.py", line 334, in __call__
  File "D:\a\_work\1\s\build_scripts\windows\artifacts\cli\Lib\site-packages\azure/cli/core/commands/command_operation.py", line 121, in handler
  File "D:\a\_work\1\s\build_scripts\windows\artifacts\cli\Lib\site-packages\azure/cli/command_modules/containerapp/custom.py", line 836, in create_managed_environment
  File "D:\a\_work\1\s\build_scripts\windows\artifacts\cli\Lib\site-packages\azure/cli/command_modules/containerapp/containerapp_env_decorator.py", line 149, in create
  File "D:\a\_work\1\s\build_scripts\windows\artifacts\cli\Lib\site-packages\azure/cli/command_modules/containerapp/_client_factory.py", line 45, in handle_raw_exception
  File "json\__init__.py", line 346, in loads
  File "json\decoder.py", line 337, in decode
  File "json\decoder.py", line 353, in raw_decode
json.decoder.JSONDecodeError: Expecting property name enclosed in double quotes: line 1 column 2 (char 1)
To check existing issues, please visit: https://github.com/Azure/azure-cli/issues
PS C:\git\ai\metabase> az containerapp env create --resource-group charris-mb-2 --name myEnvironment --location eastus
No Log Analytics workspace provided.
Generating a Log Analytics workspace with name "workspace-charrismb2HeP3"
(ManagedEnvironmentOperationInProgress) Cannot modify Container Apps environment myEnvironment because another operation is in progress.
PS C:\git\ai\metabase> ^C
PS C:\git\ai\metabase> az containerapp show --resource-group charris-mb-2 --name myMetabase --query 'data.properties.address.fqdn' -o tsv
(ResourceNotFound) The Resource 'Microsoft.App/containerApps/myMetabase' under resource group 'charris-mb-2' was not found. For more details please go to https://aka.ms/ARMResourceNotFoundFix
PS C:\git\ai\metabase> az containerappenv show --resource-group charris-mb-2 --name myMetabase
'containerappenv' is misspelled or not recognized by the system.
Did you mean 'containerapp' ?

Examples from AI knowledge base:
https://aka.ms/cli_ref
Read more about the command in reference docs
PS C:\git\ai\metabase> az containerapp env show --resource-group charris-mb-2 --name myMetabase
(ResourceNotFound) The Resource 'Microsoft.App/managedEnvironments/myMetabase' under resource group 'charris-mb-2' was not found. For more details please go to https://aka.ms/ARMResourceNotFoundFix
PS C:\git\ai\metabase> az containerapp env create --resource-group charris-mb-2 --name myEnvironment --location eastus
No Log Analytics workspace provided.
Generating a Log Analytics workspace with name "workspace-charrismb2c3ZU"
(ManagedEnvironmentOperationInProgress) Cannot modify Container Apps environment myEnvironment because another operation is in progress.
PS C:\git\ai\metabase> az containerapp env create --resource-group charris-mb-2 --name myEnvironment --location eastus
No Log Analytics workspace provided.
Generating a Log Analytics workspace with name "workspace-charrismb2uVjh"
(ManagedEnvironmentOperationInProgress) Cannot modify Container Apps environment myEnvironment because another operation is in progress.

Generating a Log Analytics workspace with name "workspace-charrismb2He7U"
(ManagedEnvironmentScheduledForDelete) The environment 'myEnvironment' is under deletion. Please retry the creation with new name or wait for the deletion completed.
PS C:\git\ai\metabase> az containerapp env create --resource-group charris-mb-2 --name myEnvironment --location eastus
No Log Analytics workspace provided.
Generating a Log Analytics workspace with name "workspace-charrismb2vtuo"
(ManagedEnvironmentScheduledForDelete) The environment 'myEnvironment' is under deletion. Please retry the creation with new name or wait for the deletion completed.
PS C:\git\ai\metabase> az containerapp env create --resource-group charris-mb-2 --name myEnvironment --location eastus
No Log Analytics workspace provided.
Generating a Log Analytics workspace with name "workspace-charrismb24gB3"
(ManagedEnvironmentScheduledForDelete) The environment 'myEnvironment' is under deletion. Please retry the creation with new name or wait for the deletion completed.
PS C:\git\ai\metabase> az containerapp env create --resource-group charris-mb-2 --name myEnvironment --location eastus
No Log Analytics workspace provided.
Generating a Log Analytics workspace with name "workspace-charrismb2sbNy"
(ManagedEnvironmentScheduledForDelete) The environment 'myEnvironment' is under deletion. Please retry the creation with new name or wait for the deletion completed.
PS C:\git\ai\metabase> ^C
PS C:\git\ai\metabase> az containerapp env create --resource-group charris-mb-2 --name myEnvironment2 --location eastus
No Log Analytics workspace provided.
Generating a Log Analytics workspace with name "workspace-charrismb2v1Uw"
Containerapp environment creation in progress. Please monitor the creation using `az containerapp env show -n myEnvironment2 -g charris-mb-2`
{
  "id": "/subscriptions/2554f64d-0419-4e39-8121-5e01270578ea/resourceGroups/charris-mb-2/providers/Microsoft.App/managedEnvironments/myEnvironment2",
  "location": "East US",
  "name": "myEnvironment2",
  "properties": {
    "appLogsConfiguration": {
      "destination": "log-analytics",
      "logAnalyticsConfiguration": {
        "customerId": "7a259451-9d13-4954-84aa-71e308611f6b",
        "sharedKey": null
      }
    },
    "customDomainConfiguration": {
      "certificatePassword": null,
      "certificateValue": null,
      "customDomainVerificationId": "F4E3370D922718D36A215A0CAD8775EC78DAE97CF1B32E122308FFCCA3C28F7E",
      "dnsSuffix": null,
      "expirationDate": null,
      "subjectName": null,
      "thumbprint": null
    },
    "daprAIConnectionString": null,
    "daprAIInstrumentationKey": null,
    "daprConfiguration": {
      "version": "1.11.6"
    },
    "defaultDomain": "mangopebble-19c68396.eastus.azurecontainerapps.io",
    "eventStreamEndpoint": "https://eastus.azurecontainerapps.dev/subscriptions/2554f64d-0419-4e39-8121-5e01270578ea/resourceGroups/charris-mb-2/managedEnvironments/myEnvironment2/eventstream",
    "infrastructureResourceGroup": null,
    "kedaConfiguration": {
      "version": "2.12.0"
    },
    "peerAuthentication": {
      "mtls": {
        "enabled": false
      }
    },
    "provisioningState": "Waiting",
    "staticIp": "<IP_ADDRESS>",
    "vnetConfiguration": null,
    "workloadProfiles": [
      {
        "name": "Consumption",
        "workloadProfileType": "Consumption"
      }
    ],
    "zoneRedundant": false
  },
  "resourceGroup": "charris-mb-2",
  "systemData": {
    "createdAt": "2024-03-08T19:35:30.4222606",
    "createdBy"<EMAIL_ADDRESS>    "createdByType": "User",
    "lastModifiedAt": "2024-03-08T19:35:30.4222606",
    "lastModifiedBy"<EMAIL_ADDRESS>    "lastModifiedByType": "User"
  },
  "type": "Microsoft.App/managedEnvironments"
}
PS C:\git\ai\metabase> ^C
PS C:\git\ai\metabase> az containerapp create --resource-group charris-mb-2 --name myMetabase --environment myEnvironment2 --image metabase/metabase --target-port 3000
Usage error: must specify --ingress with --target-port
PS C:\git\ai\metabase> ^C
PS C:\git\ai\metabase> az containerapp create --resource-group charris-mb-2 --name myMetabase --environment myEnvironment2 --image metabase/metabase --target-port 3000 --ingress External
(ManagedEnvironmentNotProvisioned) The environment '/subscriptions/2554f64d-0419-4e39-8121-5e01270578ea/resourceGroups/charris-mb-2/providers/Microsoft.App/managedEnvironments/myEnvironment2' has not been provisioned successfully.
PS C:\git\ai\metabase> az containerapp create --resource-group charris-mb-2 --name myMetabase --environment myEnvironment2 --image metabase/metabase --target-port 3000 --ingress External
(ManagedEnvironmentNotProvisioned) The environment '/subscriptions/2554f64d-0419-4e39-8121-5e01270578ea/resourceGroups/charris-mb-2/providers/Microsoft.App/managedEnvironments/myEnvironment2' has not been provisioned successfully.
PS C:\git\ai\metabase> ^C
PS C:\git\ai\metabase> az containerapp up --resource-group charris-mb-3 --name myMetabase3 --environment myEnvironment3 --image metabase/metabase --target-port 3000 --ingress External
Creating resource group 'charris-mb-3'
Creating ContainerAppEnvironment 'myEnvironment3' in resource group charris-mb-3
No Log Analytics workspace provided.
Generating a Log Analytics workspace with name "workspace-charrismb3ITuV"
Creating Containerapp myMetabase3 in resource group charris-mb-3
(ManagedEnvironmentNotProvisioned) The environment '/subscriptions/2554f64d-0419-4e39-8121-5e01270578ea/resourceGroups/charris-mb-3/providers/Microsoft.App/managedEnvironments/myEnvironment3' has not been provisioned successfully.

@sridraj  do you still see issues? If yes, can you help to send env information to<EMAIL_ADDRESS>so I can check more.
@charris-msft  I checked your env, it is deployed to canary region central us euap which has an outage at that time due to dependent service unavailable, you can retry the deployment.

We experienced the same issue on Friday 8th of March when we did an incremental deployment with only tag changes (some properties was reset, but they always gets reset to default since it is not assigned any value in our template). I have tried the "quick fix" and changed a tag from the portal, but it is stuck failed to provision. Also tried redeploy with the Bicep template.
It is a managed environment with internal vnet integration in west Europe.
Even if it is a test environment, I do not want to delete and redeploy. (Which would have been the quick fix.) The containers are running fine (except we can't do any changes to them since the environment is in failed state) and have a crucial role internally in the company.
I have created a Microsoft support ticket, but is anyone has a tips or solution to solve this, it would be much appreciated.

I have investigated several issues reported via email, I have not seen common issue yet. Several notes I want to share,

If you are creating new container app consumption only environment, and failed, it is normal due to vNet configuration issues, refer to https://learn.microsoft.com/en-us/azure/container-apps/firewall-integration?tabs=consumption-only for detail network requirements.
We also show the detail error in the deployment error property of the environment.
In the meanwhile, I strongly recommend to create workload profile environment (you can only use consumption workload profile), it will give you more flexibility on network configuration.

If you fail to update existing environment, you can send your env information to<EMAIL_ADDRESS>so we can check more. And in the meanwhile, you can retry the update to see whether it is transient issue or persistent issue, also note update existing environment failed will not impact existing workload.

If you fail to create or update container apps in the environment, this is normal related to container app configuration, the log always have the details about the error, and let us know if you do not see the error.

@StianSaeten  can you help to send you enviroment information and ticket information to<EMAIL_ADDRESS>so we can check more.

Yes.. the issue got resolved.. I added a temporary tag via portal.. and then the container environment went back to successful provisioned state.

Same here, just adding a tag got it out of the bad state. Seems a bit random

Just wanted to say I am facing this today with WESTUS only. My other region, WESTUS2, is running fine.

I managed to reproduce this on my end. I had a container app environment deployed with bicep. It was set to point to a subnet in our own vnet and I also had a workload profile defined for the environment.
I then recently changed the container app environment, so no workload profiles were defined (meaning it would run Consumption plan only), deleted the container app environment, and redeployed the template again.
Now the container app fails to provision. If I add the workload profile again, it does provision.
So it seems that I cannot provision a container app environment, integrating it into an existing vnet, if it is a Consumption plan only environment.
According to the documentation here https://learn.microsoft.com/en-us/azure/container-apps/vnet-custom?tabs=bash%2Cazure-cli&pivots=azure-portal I should be able to do so.

Same here, just adding a tag got it out of the bad state. Seems a bit random

For whom it may concern, I just ran into a similar issue.
Context
Container app environment was running happily with 12 apps. I had to change the access mode on one of the file shares (I'm using Terraform), but it was reporting that it was still in use (which was correct). So I did a targeted destroy of the container app that was using it and tried to re-run the entire Terraform plan which would reprovision the destroyed container app and also included the change in file share access mode. This is what resulted in the error ManagedEnvironmentNotProvisioned: The environment '/subscriptions/***/resourceGroups/***/providers/Microsoft.App/managedEnvironments/cae-prd' has not been provisioned successfully.
Solution
Following suggestions by @sridraj and @chriswue, I edited the tags (added one), which reset the provisioning state back to Success. A subsequent Terraform reprovisioning of the destroyed container app and change in the file share access mode worked correctly.

@chinadragon0515
I had the same issue 2 days ago. My app still works fine, the last revision I created in 26/08/2024. But after that, but after that, I was unable to create new revisions anymore.
I emailed you my subscription error already. Wait for your response, thank you very much.

Updated:
@chinadragon0515
Thanks, ACA support team, my ACA app is back to work again.
The problem is Subnet setting (which use for Container environment)
Emailed from Vincent He of ACA support team:
`For consumption only environment, you should NOT set delegation for the subnet.
For workload profile environment, you must set delegation to Microsoft.app/environments.
The difference is how the IP of subnet is managed. Consumption only environment and workload profile environment use different way to manage the subnet IPs.`
So, if your ACA meet the issue suddenly, you should check your Subnet config, and set delegation to null your your subnet ( for consumption only environment)

I am facing this issue:

Created a new container app (by default Environment type: Workload profiles and no option to change it to Consumption)

Networking

Use your own virtual network: yes
Created a new subnet in an existing VNet and Virtual IP: Internal

Tried deploying

 az containerapp update `
  --name <container-app> `
  --resource-group rg-xxx-xxxxx-xxx-001 `
  --image '<image>' `
  --min-replicas 1 `
  --max-replicas 1

Output
The behavior of this command has been altered by the following extension: containerapp
{
  "id": "/subscriptions/<subscriptionId>/resourceGroups/rg-xxx-xxxxx-xxx-001/providers/Microsoft.App/containerapps/<container-app>",
  "identity": {
    "type": "None"
  },
  "kind": "containerapps",
  "location": "East US 2",
  "name": "<container-app>",
  "properties": {
    "configuration": {
      "activeRevisionsMode": "Single",
      "dapr": null,
      "identitySettings": [],
      "ingress": null,
      "maxInactiveRevisions": 100,
      "registries": null,
      "runtime": null,
      "secrets": null,
      "service": null
    },
    "customDomainVerificationId": "6D856BC35D493EED9148F0118DEDE32FC8AAC8B4530A7919A8436444AF907AEC",
    "delegatedIdentities": [],
    "environmentId": "/subscriptions/<subscriptionId>/resourceGroups/rg-xxx-xxxxx-xxx-001/providers/Microsoft.App/managedEnvironments/<container-app-environment>",
    "eventStreamEndpoint": "https://eastus2.azurecontainerapps.dev/subscriptions/<subscriptionId>/resourceGroups/rg-xxx-xxxxx-xxx-001/containerApps/<container-app>/eventstream",
    "latestReadyRevisionName": "<container-app>--qrivu4c",
    "latestRevisionFqdn": "",
    "latestRevisionName": "<container-app>--qrivu4c",
    "managedEnvironmentId": "/subscriptions/<subscriptionId>/resourceGroups/rg-xxx-xxxxx-xxx-001/providers/Microsoft.App/managedEnvironments/<container-app-environment>",
    "outboundIpAddresses": null,
    "patchingMode": "Automatic",
    "provisioningState": "Failed",
    "runningStatus": "Running",
    "template": {
      "containers": [
        {
          "image": "mcr.microsoft.com/k8se/quickstart:latest",
          "imageType": "ContainerImage",
          "name": "simple-hello-world-container",
          "resources": {
            "cpu": 0.25,
            "ephemeralStorage": "1Gi",
            "memory": "0.5Gi"
          }
        }
      ],
      "initContainers": null,
      "revisionSuffix": "",
      "scale": {
        "maxReplicas": 10,
        "minReplicas": 0,
        "rules": null
      },
      "serviceBinds": null,
      "terminationGracePeriodSeconds": null,
      "volumes": null
    },
    "workloadProfileName": "Consumption"
  },
  "resourceGroup": "rg-xxx-xxxxx-xxx-001",
  "type": "Microsoft.App/containerApps"
}
'replicaSet' is not recognized as an internal or external command,
operable program or batch file.
'retrywrites' is not recognized as an internal or external command,
operable program or batch file.
'maxIdleTimeMS' is not recognized as an internal or external command,
operable program or batch file.
'appName' is not recognized as an internal or external command,
operable program or batch file.

In the output:

Subnet is delegated to Microsoft.App/environments.

Updated: @chinadragon0515 Thanks, ACA support team, my ACA app is back to work again.
The problem is Subnet setting (which use for Container environment)
Emailed from Vincent He of ACA support team:
For **consumption only** environment, you should **NOT** set delegation for the subnet.
For **workload** profile environment, you **must** set delegation to Microsoft.app/environments.

The difference is how the IP of subnet is managed. Consumption only environment and workload profile environment use different way to manage the subnet IPs.

So, if your ACA meets the issue suddenly, you should check your Subnet config, and set delegation to null for your subnet ( for consumption-only environment). Then "restart" the Container Apps Environment by changing any dummy config of the Container Apps Environment like adding or removing a Tags.
Then try to create a new revision again.

what's the root cause?


Rebasing Service Catalog onto API machinery 1.7

According to the roadmap, Kubernetes 1.7 is planned to be released on June 28.
As of now, Service Catalog depends on API machinery 1.5.5.
In the meantime, we can discuss the implications of rebasing onto 1.7, see comment https://github.com/kubernetes-incubator/service-catalog/pull/944#issuecomment-308722052, and prepare for the migration starting to rebase onto master branch of API machinery, see PR https://github.com/kubernetes-incubator/service-catalog/pull/944.
We could also define the process of regular rebasing onto the latest release of API machinery, see https://groups.google.com/forum/#!topic/kubernetes-sig-service-catalog/Jo5eseslMaI

I do agree with you that we need to do this on a regular basis.  I will say
from our experience in this repo and my own in other repositories that you
have to be very strategic about when you choose to rebase onto the API
machinery and what version to rebase onto.  If you do it too often, it can
burn an entire person's day-job time.  Given that we have only a handful of
people that actively make code contributions to the repository, we do not
have a lot of time-resources to work with currently.
I will say, I am really happy the 944 is started and to have the pool of
people who know how to perform the rebase growing :-)
One other thing that I have made an observation on in another thread today
is that we need to establish a communication channel between the API
machinery SIG and this SIG.  As an example, when PR 944 was started, the
author could not possibly have known what type of changes would be
waiting.  We need to work together to have some guidance for anyone writing
user API servers about what type of changes to expect.
Thoughts?
P
On Mon, Jun 19, 2017 at 5:36 PM, Nail Islamov<EMAIL_ADDRESS>wrote:

According to the roadmap
https://github.com/kubernetes/kubernetes/milestones/, Kubernetes 1.7 is
planned to be released on June 28.
As of now, Service Catalog depends on API machinery 1.5.5.
In the meantime, we can discuss the implications of rebasing onto 1.7, see
comment #944 (comment)
https://github.com/kubernetes-incubator/service-catalog/pull/944#issuecomment-308722052,
and prepare for the migration starting to rebase onto master branch of API
machinery, see PR #944
https://github.com/kubernetes-incubator/service-catalog/pull/944.
We could also define the process of regular rebasing onto the latest
release of API machinery, see https://groups.google.com/
forum/#!topic/kubernetes-sig-service-catalog/Jo5eseslMaI
—
You are receiving this because you are subscribed to this thread.
Reply to this email directly, view it on GitHub
https://github.com/kubernetes-incubator/service-catalog/issues/949, or mute
the thread
https://github.com/notifications/unsubscribe-auth/AAWXmBmJL5hSs7g98qaXoCtjC444lpLxks5sFunrgaJpZM4N-0mQ
.

Do we want a 1.6 intermediate state or wait for 1.7 final?

I think there are bugs present in the last 1.6 point release that are fixed in 1.7, so I'd prefer to wait until 1.7

Closed by #944


Mutex lock initialization call

Description
Please provide a paragraph or two giving a summary of the change, including relevant motivation and context.
Checklist:

[ ] I have reviewed my diff in github, line by line.
[ ] Every change is related to the PR description.
[ ] I have linked this pull request to the issue(s) that it resolves.
[ ] There are no unexpected formatting changes, superfluous debug logs, or commented-out code.
[ ] There are no circuit changes, OR specifications in /markdown/specs have been updated.
[ ] There are no circuit changes, OR a cryptographer has been assigned for review.
[ ] I've updated any terraform that needs updating (e.g. environment variables) for deployment.
[ ] The branch has been rebased against the head of its merge target.
[ ] I'm happy for the PR to be merged at the reviewer's next convenience.
[ ] New functions, classes, etc. have been documented according to the doxygen comment format. Classes and structs must have @brief describing the intended functionality.
[ ] If existing code has been modified, such documentation has been added or updated.

Tests don't seem to be running probably because this is your first contribution or because you are not a member of the organisation. I'll copy your changes onto a separate branch to run CI.

LGTM, thanks for this!


[TF2.0] Gradients become None when using a SparseTensor kernel in a custom layer.

System information

Have I written custom code: Yes
OS Platform and Distribution (e.g., Linux Ubuntu 16.04): MacOSX 10.14.5
TensorFlow installed from (source or binary): binary
TensorFlow version (use command below): v1.12.0-9492-g2c319fb415 2.0.0-alpha0
Python version: 3.6
CUDA/cuDNN version: running on mac/cpu
GPU model and memory: running on mac/cpu

Describe the current behavior
In a DeepRL setting, using a DQN implementation in TF2. After a certain number of calls, the custom training loop with gradient tape is not able to compute gradient for the SparseMatrices used in the model (weights inside the sparse matrice), the gradient for biases is computed without any issue.
The model can still be called, the weights are not zero. The gradients in _SparseTensorDenseMatMulGrad() are not None, and seem ok. That is, a_values_grad and b_grad are not None (also not None in _gradient_function)
(None, a_values_grad, None, b_grad)
But the output of pywrap_tensorflow.TFE_Py_TapeGradient() in imperative_grad has None for the weights in the SparseTensor.
It is worth noting that if I do not call the model before the first training loop, the gradients are OK. Next loop they become None.
Describe the expected behavior
Valid gradient for the weights in the SparseTensor whatever the number of calls done previously to the model.
Code to reproduce the issue
The model is quite complex. But the issue seem to come from one of the custom Layer which has a kernel defined like so:
kernel_weights = self.add_weight('kernel_weights',
                                         shape=[self.indices.shape[0]],
                                         initializer=self.kernel_initializer,
                                         regularizer=self.kernel_regularizer,
                                         constraint=self.kernel_constraint,
                                         dtype=self.dtype,
                                         trainable=self.trainable)

self.kernel = tf.SparseTensor(self.indices,
                                      kernel_weights,
                                      self.dense_shape)

And a call function defined as so:
def call(self, x, **kwargs):
        x = tf.transpose(tf.sparse.sparse_dense_matmul(self.kernel, x, adjoint_b=True))
        if self.use_bias:
            x += self.bias
        x = self.activation(x)
        return x

I tried to optimize the layer outside of the RL setting, with a dummy loss, and the issue remains. Here is the code for the custom loop:
optimizer = Adam(learning_rate=1e-3)
x = tf.Variable(tf.ones((160, 41)))
data = Dataset.from_tensor_slices((x)).batch(20)
for obs0 in data:
    with tf.GradientTape() as tape:
        x = model(obs0)

        loss_value = tf.reduce_mean(x, name='dummy_loss')

        grads = tape.gradient(loss_value, model.trainable_weights)
        # Here the gradient for the sparse kernel is None on the second loop
        #The gradient for the bias is fine

        optimizer.apply_gradients(zip(grads, model.trainable_weights))

Other info / logs
W0523 16:11:28.381706<PHONE_NUMBER> optimizer_v2.py:928] Gradients does not exist for variables ['custom_layer/kernel_weights:0'] when minimizing the loss.

Ok so I solved the issue, at least the gradients are not None. I'm not sure if its a bug or not.
But if instead of storing the SparseTensor as a kernel i store the weights and build the sparse tensor at each call it work (albeit quite slow..):
build()

        self.kernel_weights = self.add_weight('kernel_weights',
                                         shape=[self.indices.shape[0]],
                                         initializer=self.kernel_initializer,
                                         regularizer=self.kernel_regularizer,
                                         constraint=self.kernel_constraint,
                                         dtype=self.dtype,
                                         trainable=self.trainable)

And call()
kernel = tf.SparseTensor(self.indices, self.kernel_weights, self.dense_shape)
x = tf.transpose(tf.sparse.sparse_dense_matmul(kernel, x, adjoint_b=True))

Happy to close the issue, although im not sure if was the intended behaviour.

Closing this issue since its resolved. Feel free to reopen if have any further questions. Thanks!

Wanted to bump this up because I am running into the same issue. I have a couple of layers I have written with
tf.sparse.sparse_dense_matmul that are not producing a gradient.
and also produce this warning:
WARNING:tensorflow:Gradients do not exist for variables ['rgcn_autencoder/RGCN_Input0/graph_order_0_PcP_W_1:0', 'rgcn_autencoder/RGCN_Input0/graph_order_0_PbA_W_1:0',...when minimizing the loss.
`
class RGCN_Input(Layer):
def init(self,out_dim,graph_index,
relations= ['PcP','PbA','PiV','PuK','PbP','AwP','VpP','KpP','self'],
name="RGCN_Input",
activation=None,
use_bias=False,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
**kwargs
):
super(RGCN_Input, self).init(name=name+str(graph_index),**kwargs)
self.out_dim = out_dim
self.relation = relations
self.support = len(self.relation)
self.graph_index = graph_index
self.use_bias = use_bias
self.activation = tf.keras.activations.get(activation)
self.kernel_initializer = tf.keras.initializers.get(kernel_initializer)
self.bias_initializer = tf.keras.initializers.get(bias_initializer)
self.kernel_regularizer = tf.keras.regularizers.get(kernel_regularizer)
self.bias_regularizer = tf.keras.regularizers.get(bias_regularizer)
def build(self,input_shape):# define w & b
    self.kernel = tf.concat([
        self.add_weight(shape=(input_shape[1][1],self.out_dim),
                        name='graph_order_{}_{}_W_1'.format(self.graph_index,self.relation[i]),
                        trainable=True,
                        initializer=self.kernel_initializer,
                        regularizer=self.kernel_regularizer,
                       )
                        for i in range(self.support)]
        ,0)

    if self.use_bias:
        self.bias = self.add_weight(shape=(self.units,),
                                    initializer=self.bias_initializer,
                                    name='graph_order_{}_B_1'.format(self.graph_index),
                                    regularizer=self.bias_regularizer,
                                    )
    else: self.bias = None

    super(RGCN_Input, self).build(input_shape)

def call(self,x): # define one r-gcn architecture
    assert isinstance(x, (list,tuple))
    adj_list,feature = x
    supports=[]
    if isinstance(adj_list[0], tf.SparseTensor):
        for i in range(self.support):
            supports.append(tf.sparse.sparse_dense_matmul(adj_list[i],feature))
    else:
        for i in range(self.support):
            supports.append(tf.matmul(adj_list[i],feature))
    support = tf.concat(supports,1) #squish them all together
    H = tf.matmul(support,self.kernel)

    if self.use_bias:
        H+=self.bias

    if self.activation is not None:
        H = self.activation(H)

    return H

def compute_output_shape(self, input_shape):
    return (input_shape[1][0], self.output_dim)

`


Cannot dockerize v 1.5

Hi j3ssie. I'm trying to dockerize the newly released v 1.5 but I've found that at the end of the install.sh script you invoke Osmedeus like this:
python3 osmedeus.py --reset

This instruction prevents the docker build process to finish and, therefore, to build the image, as the Flask API stays running in the foreground. Therefore, I suggest that the install.sh script just finishes the installation of software without launching the process.
It seems that the --reset argument does some kind of initialization. Could it be invoked so it just does its tasks and then finishes the process?
Regards.

Just remove that line on install file. Will add it to first time run later.
Thank you for feedback.

I had already done that in a test Dockerfile and it seemed to work. Now I've seen you've already committed that change. Great!


44kb of uncompressed code

Hello!
I found this lib in npm, hoping to use it as a simple number checker for slider component.
When I came up to standalone build, I found that it compiles to too much of code. Digging into details, I found out that simple require('is-number') with nothing else but self, browserified results in 44kb of uncompressed code. That is because is-number requires kind-of, which requires Buffer and other platform stuff.
So I think, is it really reasonable to keep such a stack of code just for simple ponyfill-like number checker? In that case one could require jQuery or underscore, sorry.
So as a temporary solution I’ve taken mutype/is-number, or 101/is-number, but I leave this note here, just in case :)

with nothing else but self, browserified results in 44kb of uncompressed code.

That's insane. I agree, that needs to be reduced thanks for letting me know.

is it really reasonable to keep such a stack of code just for simple ponyfill-like number checker?

Let me try to rephrase:
is it really reasonable to [try to be correct] for simple ponyfill-like number checker?

Yes.
is it really [pragmatic] to keep such a stack of code [now that you know the footprint] just for simple ponyfill-like number checker?

No.
based on conversations with @alexcjohnson I was planning to make changes to this anyway, I can also make changes to kind-of, thanks for bringing this to my attention.
@alexcjohnson any thoughts?

@jonschlinkert just for a bit of background on my own use case, Plotly found that for certain large plots, up to three quarters of the rendering time was spent calling $.isNumeric :scream: and we're trying to get rid of jQuery from our main plotting library anyway. So my main criterion was speed, and entirely in a browser context (almost - we call it from node ourselves but in that case we know all the input is coming from json so it's easier).
What we're using now is this one: https://gist.github.com/alexcjohnson/74902c646f08f456dd56 which is quite fast (and pretty small, though that doesn't matter too much to me), and as far as I can tell gets all the answers right EXCEPT that I've explicitly chosen to drop support for number and string constructors (do those have a legitimate use case? Please say no :hear_no_evil: ) If you see anything else I've missed with this I'd love to hear about it!
If one wanted to extend what I started with to support these oddballs, I expect it wouldn't be hard at all (and probably a minimal performance hit) to wedge something in right before else if(type !== 'number'), something like:
else if(type === 'object') {
    if(n instanceof String) {
        /* do the original string block */
    }
    else if(!(n instanceof Number)) return false;
}

Isn't this whole isNumber just typeof value === "number" && isFinite(value); ?

@vcekov the goal is to support numeric strings as well - check out the tests
and just FYI, the code I pointed to above now has its own home and npm package: https://github.com/plotly/fast-isnumeric

Isn't this whole isNumber just typeof value === "number" && isFinite(value); ?

no

I'm just wondering what the advantage of using kind-of is in this case (over using a standard typeof)?

Hm. Why not get just the number and string checks from kind-of?
Something like that?
function isValid(val) {
  if (typeof val === 'string' || val instanceof String) {
    return val.length > 0; // we dont want empty strings
  }
  if (typeof val === 'number' || val instanceof Number) {
    return true;
  }
  return false;
}

module.exports = function isNumber(num) {
  if (!isValid(num)) {
    return false;
  }
  var n = +num;
  return (n - n + 1) >= 0;
};

It seems it works locally.

@tunnckoCore demonstrates this in a different way in his code:
var five = new Number(5);
console.log(typeof five);
//=> object

console.log(five + 10);
//=> 15

Unlikely? Yes. But that's only one edge case. I like consistent, reliable, fast code. Given that this library's job is to check for numbers, I think it's reasonable to be more comprehensive the you might be in the average code project.
That said, my suggestion is that you try running your assumptions against the unit tests instead of asking here (since it's more likely to draw debate about things that are already answered in the code).

Tests are passing and benchmarks are still the fastest way.

Ok, feel free to keep discussing. I'm always open to optimizations - so I'll check back from time to time to see if a conclusion is drawn that there is a definite optimization that is consistent and reliable. But I probably won't be active in the discussion.

Yea, no problems. I'll PR with just added new way to the benchmark/code/ and we can see what to do, okey?

@jonschlinkert the painpoint is that such a good and natural name in npm is taken by such a shockingly large dependency, it breaks any thinkable expectations. I would not expect checking anything more than 'typeof' and 'instanceof', otherwise it is 'is-numeric' or else. That just undermines trust in npm.
Just an opinion.

"shockingly large" wtf are you talking about?
locking.

@dfcreative your last comment was deleted as it was pejorative and antagonistic.


layers: GH177 Fix deadlocks

Fix some early return cases where lock wasn't correctly released.

+1

+1, pushed to Master.  Thanks Tobin.


Confusion between classes and structs regarding fields

So currently we choose to use either the field_accessors or fields partial based on if we determine a type to be a class or a struct. Unfortunately this doesnt always line up. git_time has standard fields on it, that can be set, but are treated as access functions only, because it is determined to be a "class".
I'm starting to think that maybe the whole class/struct thing needs to fade together, but short term, we should at least try to get this fixed. I can take a look at this, since it's kind of making some date-time stuff tricky.

With how memory management is going, this is a no go I think.


[P1] Possible to do batch inference?

I'm doing this atm
for q_ in tqdm(rando):
    #print('quote:',q_)
    quotes_fol = []
    quotes_nodes_edges = []
    sentences = sent_tokenize(q_)
    for q in sentences:
        # tokenize and prepare the input
        prompt = prompt_no_input_template % q
        prompt = tokenizer(prompt, return_tensors="pt").to(device)

        unit_locations = torch.IntTensor([pyreft.get_intervention_locations(
            last_position=prompt["input_ids"].shape[-1],
            first_n=first_n,
            last_n=last_n,
            pad_mode="last",
            num_interventions=len(reft_config.representations),
            share_weights=share_weights
        )]).permute(1, 0, 2).tolist()

        # Generate with beam search
        _, reft_response = reft_model.generate(
            prompt,
            unit_locations={"sources->base": (None, unit_locations)},
            intervene_on_prompt=True,
            max_new_tokens=537,
            do_sample=True,
            top_k=50,
            temperature=0.7,
            num_beams=5,  # Using beam search with 5 beams
            eos_token_id=terminators,
            early_stopping=True
        )
        response = tokenizer.decode(reft_response[0], skip_special_tokens=True)
        #print(response)
        #out = lcel_chain.invoke({"input": response})
        #print('node/csv:',out)
        quotes_fol.append(response)

        #quotes_nodes_edges.append(out)
    quotes_fol_.append(quotes_fol)
    #quotes_nodes_edges_.append(quotes_nodes_edges)

but i'd like to escape the iteration, and I'm not sure how to format unit_locations.  Normally one would do something like model.generate(**inputs), but this being pyreft, I'm not sure if that is supported as it's a custom class (I haven't delved into the class for this specific feature).
Thought I'd ask first as well as for visibility for others who might be interested.

@thistleknot Yes, it supports batched inference calls.
You can take a look at this function for batching:
https://github.com/stanfordnlp/pyreft/blob/main/examples/loreft/compute_metrics.py#L111
In a nutshell, you need to apply left padding to your tokenizer and calculate the batched intervention locations accordingly.

'calculate the batched intervention locations accordingly.'
that doesn't sound easy.
I'm not sure if I can use the same -1 position as I was before for each prompt... or if it's expecting it to be where it is within the batch tensor.

you able to help a brother out?

dataset = load_dataset("Abirate/english_quotes")
quotes = [q for q in dataset['train']['quote'] if (len(q) > 23 and len(q) < 140)]
#for q in quotes[0:10]:
    #print(q)

#rando = np.random.choice(quotes, 100, replace=False)
cleaned_quotes = [q.replace('“','').replace('”','') for q in quotes]

rando = random.choices(cleaned_quotes,k=100)

# Define constants
max_tokens = 115
desired_token_limit = 8192
batch_size = desired_token_limit // max_tokens

# Define the tokenizer with left-side padding
tokenizer = transformers.AutoTokenizer.from_pretrained(
    model_name_or_path, model_max_length=115,
    padding_side="left", use_fast=True,
    attn_implementation=attn_implementation
    # , add_eos_token=True, add_bos_token=True
)
tokenizer.pad_token = tokenizer.eos_token

# Position info about the interventions
share_weights = True # Whether the prefix and suffix interventions share weights.
positions = "f3+l3"  # The intervening positions of prefix tokens (f[irst]1) and suffix tokens (l[ast]1).
first_n, last_n = pyreft.parse_positions(positions)

terminators = [tokenizer.eos_token_id]

def get_intervention_locations(last_position, first_n, last_n, pad_mode, num_interventions, share_weights):
    # Placeholder function for getting intervention locations, replace with actual logic
    return [[0] * last_position for _ in range(num_interventions)]

tokenized_prompts = []
# Preprocess: Split into sentences and tokenize
for q_ in range(len(rando)):
    sentences = sent_tokenize(rando[q_])
    for s_ in sentences:
        original_prompt = prompt_no_input_template % s_
        last_position = len(tokenizer.encode(original_prompt))  # Get actual length before padding
        tokenized_prompt = tokenizer(original_prompt, return_tensors="pt", padding="max_length", truncation=True, max_length=max_tokens)
        tokenized_prompts.append((q_, tokenized_prompt.to(device), last_position))

#Incorrect batch
if(False):
    #attempted batch

    # Process: Generate responses in batches
    quotes_fol_ = []

    for r in tqdm(range(0, len(tokenized_prompts), batch_size)):
        batch_prompts = tokenized_prompts[r: r + batch_size]

        input_ids = torch.cat([bp[1]['input_ids'] for bp in batch_prompts], dim=0)
        attention_masks = torch.cat([bp[1]['attention_mask'] for bp in batch_prompts], dim=0)

        unit_locations = torch.IntTensor([get_intervention_locations(
            last_position=max_tokens,
            first_n=first_n,
            last_n=last_n,
            pad_mode="last",
            num_interventions=len(reft_config.representations),
            share_weights=share_weights
        )]).repeat(input_ids.shape[0] // len(batch_prompts), 1, 1).permute(1, 0, 2).tolist()

        # Generate with beam search
        generation_args = {
            "base": {"input_ids": input_ids, "attention_mask": attention_masks},
            "unit_locations": {"sources->base": (None, unit_locations)},
            "intervene_on_prompt": True,
            "max_new_tokens": max_tokens,
            "do_sample": True,
            "top_k": 50,
            "temperature": 0.7,
            "num_beams": 5,
            "eos_token_id": terminators,
            "early_stopping": True
        }

        _, reft_response = reft_model.generate(**generation_args)

        responses = tokenizer.batch_decode(reft_response, skip_special_tokens=True)

        quotes_fol = []
        for i, response in enumerate(responses):
            quotes_fol.append(response)
            original_index = batch_prompts[i][0]
            quotes_fol_.append([original_index, quotes_fol])

    # Output the final results
    print(quotes_fol_)

that's what I got atm, but it's not applying the control vector appropriately


Option to enable automatic SSO login

Is it possible to make a option to enable Automatic SSO login when enabled in the config? As example a option like SECURITY_OAUTH2_AUTO_LOGIN=true.
If this setting is true the login screen will be skipped and redirected automatically to the SSO service.

What would the default provider be?

It would be the provider you've setup in the SECURITY_OAUTH2_ISSUER. In my case it is Authentik.
The local login would not be usable unless you set the new environment variable to false so no automatic redirect would happen.

If you are using a proxy you can add a rewrite rule to do this, nginx example below.
rewrite ^/login$ /oauth2/authorization/oidc;

Is there any progress here? The silent login-option is a perfect way to prevent strangers of using the service, without causing any friction. Now it takes two clicks every day - one to select SSO and one to select the only SSO-provider configured.
The rewrite-rule is elegant, but I found that my knowledge on docker compose and nginx-proxy is limited to configure that.
I would suggest using the name of the OAUTH-provider, such that a backup can silently be configured. In case of Murphy, the fall-back SSO-provider can be enabled.
SECURITY_OAUTH2_AUTOLOGINPROVIDER=keycloak


Boot2docker fails to start

I'm running OS X 10.10.2.
boot2docker init creates the virtual machine.
boot2docker start starts the virtual machine, but it fails to connect to it with autodetection of the VM docker socket failing.
These are the last few lines of the verbose output:
Started.
Trying to get Docker socket one more time
2015/04/05 20:09:58 executing: /usr/bin/ssh ssh -o IdentitiesOnly=yes -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o LogLevel=quiet -p 2022 -i /Users/sarvagnan/.ssh/id_boot2docker docker@localhost grep tcp:// /proc/$(cat /var/run/docker.pid)/cmdline
Error requesting socket: exit status 255
2015/04/05 20:09:58 executing: /usr/bin/ssh ssh -o IdentitiesOnly=yes -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o LogLevel=quiet -p 2022 -i /Users/sarvagnan/.ssh/id_boot2docker docker@localhost tar c /home/docker/.docker/*.pem
Auto detection of the VM's Docker socket failed.
Please run boot2docker -v up to diagnose.
How do I fix this? Any help would be greatly appreciated.
Thanks!

Maybe you should check your ~/.ssh/config


Terraform provider or docs on recommended restapi usage

Looking to use IaC to manage openziti and so far have seen guides using the terraform restapi provider to craft api requests, wondering if openziti is looking at creating a terraform provider or if there is a "right" way of using the restapi using terraform (docs).

Clint linked you to the references for the two edge APIs: client, management. It sounds like you're looking for the management API, i.e. managing an OpenZiti network's resources with IaC.
The restapi provider that I adapted to work with OpenZiti is a thin wrapper around the OpenZiti management API, so it does the bare minimum you'd expect from a TF provider, essentially translating a resource declaration into the necessary CRUD operations. It was an experiment, so not a lot of polish has gone into it.
Are you partial to Terraform, CDK, Ansible, Pulumi for IaC? The Terraform path is the best-blazed trail so far, but the Python library for the management API is a great start toward an Ansible Collection of modules for managing OpenZiti network resources.

no longer seems relevant to the project
other projects are in progress to provide similar functionality


Add Rust action to actions.lua

Hi Oli,
I'm a big fan of onedarkpro.nvim and I'm excited about this project and the level of polish to it.
This PR is a small tweak to extend the Chat as actions. I hope it might provide some value. I understand your involvement in further development may be minimal, but if your circumstances permit, I'm looking forward to making more meaningful contributions as I integrate this plugin into my work and personal workflow.
Thanks,
Yomi

Thanks for the PR...Rust is a very worthy addition.
Also, I've recently added a RECIPES.md file showing how you can expand the plugin further. You'll probably think of some much better use cases than I have.


pythonPackages.pyrr: 0.10.3 -> 2022-07-22

Description of changes
It stopped building on python39Packages.pyrr, which is needed for pkgs.friture to work, due to some other old dependencies. There are no new releases on PyPI.
Tested build of friture, python39Packages.pyrr and python3Packages.pyrr on x86_64-linux: all three previously failed builds work.

Things done

Built on platform(s)

[x] x86_64-linux
[ ] aarch64-linux
[ ] x86_64-darwin
[ ] aarch64-darwin

[ ] For non-Linux: Is sandbox = true set in nix.conf? (See Nix manual)
[ ] Tested, as applicable:

NixOS test(s) (look inside nixos/tests)
and/or package tests
or, for functions and "core" functionality, tests in lib/tests or pkgs/test
made sure NixOS tests are linked to the relevant packages

[ ] Tested compilation of all packages that depend on this change using nix-shell -p nixpkgs-review --run "nixpkgs-review rev HEAD". Note: all changes have to be committed, also see nixpkgs-review usage
[x] (friture) Tested basic functionality of all binary files (usually in ./result/bin/)
23.05 Release Notes (or backporting 22.11 Release notes)

[ ] (Package updates) Added a release notes entry if the change is major or breaking
[ ] (Module updates) Added a release notes entry if the change is significant
[ ] (Module addition) Added a release notes entry if adding a new NixOS module

[x] Fits CONTRIBUTING.md.

This pull request has been mentioned on NixOS Discourse. There might be relevant details there:
https://discourse.nixos.org/t/prs-already-reviewed/2617/875

Thank you for maintaining friture, I use it like, at least once a week, very fun. :3

Kinda feel obligated to clarify that I just maintain the NixOS package for friture, I'm only an occasional contributer to upstream :sweat_smile:

Result of nixpkgs-review pr 221879 run on x86_64-linux 1

1 package failed to build:

manim

5 packages built:

friture
python310Packages.moderngl-window
python310Packages.pyrr
python311Packages.moderngl-window
python311Packages.pyrr

Friture seems to explode on runtime on https://github.com/NixOS/nixpkgs/commit/9af5c719fa2653e40201bd63e9b158c7669e934b (very recent master) now:

Unhandled exception: Traceback (most recent call last):
  File "/nix/store/hwrvr28a6h6b22sghfd27hns45yk015y-python3.11-numpy-1.24.2/lib/python3.11/site-packages/numpy/core/__init__.py", line 23, in
    from . import multiarray
  File "/nix/store/hwrvr28a6h6b22sghfd27hns45yk015y-python3.11-numpy-1.24.2/lib/python3.11/site-packages/numpy/core/multiarray.py", line 10, in
    from . import overrides
  File "/nix/store/hwrvr28a6h6b22sghfd27hns45yk015y-python3.11-numpy-1.24.2/lib/python3.11/site-packages/numpy/core/overrides.py", line 6, in
    from numpy.core._multiarray_umath import (
ModuleNotFoundError: No module named 'numpy.core._multiarray_umath'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/nix/store/xpw3in80j6x3v20a7ilfrs6srfx5qjfi-friture-0.49/bin/.friture-wrapped", line 9, in
from friture.analyzer import main
File "/nix/store/xpw3in80j6x3v20a7ilfrs6srfx5qjfi-friture-0.49/lib/python3.9/site-packages/friture/analyzer.py", line 39, in
from friture.about import About_Dialog  # About dialog
File "/nix/store/xpw3in80j6x3v20a7ilfrs6srfx5qjfi-friture-0.49/lib/python3.9/site-packages/friture/about.py", line 23, in
import numpy
File "/nix/store/hwrvr28a6h6b22sghfd27hns45yk015y-python3.11-numpy-1.24.2/lib/python3.11/site-packages/numpy/init.py", line 141, in
from . import core
File "/nix/store/hwrvr28a6h6b22sghfd27hns45yk015y-python3.11-numpy-1.24.2/lib/python3.11/site-packages/numpy/core/init.py", line 49, in
raise ImportError(msg)
ImportError:
IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE!
Importing the numpy C-extensions failed. This error can happen for
many reasons, often due to issues with your setup or how NumPy was
installed.
We have compiled some common reasons and troubleshooting tips at:
https://numpy.org/devdocs/user/troubleshooting-importerror.html

Please note and check the following:

The Python version is: Python3.9 from "/nix/store/93x6crjd9rcdi12cqv1am8mym3iyq424-python3-3.9.16/bin/python3.9"
The NumPy version is: "1.24.2"

and make sure that they are the versions you expect.
Please carefully study the documentation linked above for further help.
Original error was: No module named 'numpy.core._multiarray_umath'

The stack trace doesn't indicate this is related to pyrr though.

Oof, another issue. I'll try to bisect it


Crash upon loading a world

Crash upon loading an existing or new world.
fml-client-latest.txt: https://gist.github.com/yamikaitou/c9584ccda5f759ca061c
Using Java 1.8_60, ATLauncher <IP_ADDRESS> with Xisuminati Mod Pack, Minecraft 1.7.10, Forge <IP_ADDRESS>2

Duplicate of #96. It's up to the Gilded Games Utils author to backport the fix.
You won't be able to use Schematica until that fix is backported to 1.7.10.


Stability of generated csv

Bug Report
Logic has been added to generate our bundle https://github.com/bf2fc6cc711aee1a0c2a/kas-fleetshard/tree/main/bundle and we're seeing that the generated csv is not stable.
What did you expect to see?
Stable csv
What did you see instead? Under which circumstances?
We have 2 crds, the owned crds may be listed in either order.  We are creating the Deployments via quarkus integration and providing env variables via the application.properties.  The env variable ordering also seems to not be stable.
Environment
Quarkus operator extension 4.0.7

I think that this issue belongs to: https://github.com/quarkiverse/quarkus-operator-sdk

Yes.


ghostscript: link against "systemwide" liblcms2

Motivation for this change
GS ships with a fork of lcms2 ("lcms2mt"), but the ABI separation
between the fork and the original seems insufficient. If libgs is linked
alongside liblcms2 (for example, this is the case with imagemagick) then
it will call into the original library instead of the fork, causing
segfaults.
Follow the example of both Arch and Debian in this regard -- they both
use the systemwide lib instead of the fork.
Fixes #55118. cc @Mic92 who merged #55634
Things done

[x] Tested using sandboxing (nix.useSandbox on NixOS, or option sandbox in nix.conf on non-NixOS)
Built on platform(s)

[x] NixOS
[ ] macOS
[ ] other Linux distributions

[ ] Tested via one or more NixOS test(s) if existing and applicable for the change (look inside nixos/tests)
[x] Tested compilation of all pkgs that depend on this change using nix-shell -p nox --run "nox-review wip"
[x] Tested execution of all binary files (usually in ./result/bin/)
[x] Determined the impact on package closure size (by running nix path-info -S before and after)
[x] Assured whether relevant documentation is up to date
[x] Fits CONTRIBUTING.md.

I suppose we also should backport this?

@Mic92 :+1:


Adding albumentations to conda-forge

Hi @albu et al.,
I'm a big fan of this project and use it as the primary source of augmentations in our new geospatial ML pipeline, solaris. I'm setting up a conda install for solaris, and would like to help add a conda-forge feedstock for albumentations to make that possible.
Justification
I need to get a conda package up for solaris, which necessitates access to all dependencies on the conda cloud. I know from #273 and other previous issues that you have it in a private conda channel, but that makes it hard to use albumentations as a dependency to other conda packages: for example, if I had solaris on conda-forge, a downstream user would need to run
conda install solaris -c conda-forge -c albumentations

I can of course give this instruction for installation, but it complicates things. Furthermore, the conda-forge maintainers frown on using package dependencies outside of defaults or conda-forge channels, as that channel name would need to be propagated through installs down the dependency tree. For example, if a package uses solaris as a dependency, they'd need to do
conda install [package requiring solaris] -c conda-forge -c albumentations
...when it becomes increasingly opaque why the albumentations channel is necessary.
The request
I'd like to help set up a conda-forge feedstock for albumentations. Given that you already have the conda build files in the project, this will be very easy. I would also be happy (if desired) to list myself as a maintainer on that conda-forge feedstock to make sure version bumps, etc. go smoothly.
The advantages of using conda-forge instead of a private channel:

Automatic re-builds anytime you update albumentations on PyPI so you don't need to update albumentations on the conda cloud manually
Easier-to-search repository for conda projects, increasing likelihood that albumentations will be used by other packages

Disadvantages:

Some (minimal) maintenance can be required (pretty much just approving pull requests that are automatically triggered by updates to albumentations) - I'm happy to help monitor this if desired
If you ever add a dependency that isn't available on conda, future versions of the conda install won't work until it's added (though this would be true for any conda channel)

Let me know what you think, and thanks for building a great augmentation pipeline.

Hey @nrweir
Your help with creating and maintaining a conda-forge feedstock would be much appreciated. Do you need any assistance with this task from our side?

@creafz thanks for the response! I don't think I need any help from you all to get it up and running. The only question I have is who I should list as maintainers: along with myself, I assume I should also include @albu, @ternaus, @BloodAxe, and you - let me know if you don't want any of those 5 users listed as maintainers on the conda-forge repo.

@nrweir Yes, you are right, those are the correct people to include.

OK great, I'll get that up and running this week.
On Mon, Jul 15, 2019 at 11:59 AM Alex Parinov<EMAIL_ADDRESS>wrote:

@nrweir https://github.com/nrweir Yes, you are right, those are the
correct people to include.
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You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
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As I went to create the feedstock, I discovered that another user had created one on June 5: https://github.com/conda-forge/albumentations-feedstock
Looks like this isn't necessary, I'll close.

I'm the creator of the conda-forge feedstock and I just discovered this discussion. I'll add the people listed in https://github.com/albu/albumentations/issues/291#issuecomment-511430166 to the feedstock. If anyone shouldn't be included anymore, please tell me.


[Bug]: Brightness change makes the brightness change to what value you attempted to set it to, then instantly reverts back to where you started, then finally settles to what you attempted to set it to

Steps for reproducing the issue
Title basically.
More details: https://github.com/maximbaz/wluma/pull/127#issuecomment-2561017868
What is the buggy behavior?

What is the expected behavior?

Logs
No response
Version
main
Environment
-

Sorry for not fleshing this out properly.


Warn using #joins('left outer join ...') or #joins('lef join ...') in Rails 5.0 and later

This is either a question or a feature request.
Is it possible to make a cop that warns to use #left_outer_joins or #left_joins if use #joins('left outer join ...') or #joins('lef join ...') in Rails 5.0 and later?
I tried searching but I could not find it, but if you already have such a cop please let me know.

WIP PR is #5895

Is this still an issue?


[Bug] Can't move Player/Unit/target frames

Addon Version
1.3.4
World of Warcraft Version
Retail
Describe the bug
I cannot remove any of the Unit frames.  I am sorry if its a dumb question.  I've looked all over for a option and tried different modifiers on the window.
Steps to reproduce
.
BugSack
.

You press this button to open up Edit Mode.
I'm workshopping some changes to make it more apparent.

First thank you.  I looked everywhere and it was right in front of me. My suggestion is rename it to :Unlock Unit frames anchor" and change the color to red for it to stand out.  Everything else was clear on my end.  I attached Mua portraits to mine via weak aura.  Not a big deal but since the target portrait is on the right the a "Reverse" grow direction on health/mana would be handy but no big.  Appreciate this and love the addon.  I loved cell but wanted it to just replace them all.


Replace check_arg methods with check_arg util

Summary

Adds a new util function check_arg
Replace check_arg methods in cogs/general.py & cogs/wiki.py with check_arg util (no more duplication)
Sets require_var_positional to False for guide. This should give us the expected behavior of giving the user a list of consoles to use for the guides argument

Huh, removed the license info from the head of the file? I guess that works, since until now the whole util page was not any different from Kurisu's utils.py

Haven't taken a look at Kurisu in a long time so I just assumed
command_signature and send_dm_message were from Kurisu and marked it as
such.
On Wednesday, August 18, 2021, lifehackerhansol @.***>
wrote:

Merged #11 into main.
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Server keeps on disconnecting

I always get this error around a few hours/days after I host my bot:
Error: connection refused: localtunnel.me:38261 (check your firewall settings)

Is there a way to fix this problem? I did some research and saw that it was because of bad connectivity of the localtunnel servers. Is there anyway I can listen to this error or catch this error and reconnect to localtunnel after this? It has been an hassle trying to sort this out and have caused a lot of problems. Thanks.

I'm using this https://github.com/kirillshevch/localtunnel-restarter. It automatically reconnects when there are connection issues.

If this happens sporadically, while already running, his may just be a connection hickup.
There's a PR open with a retry policy: #311.
Maybe you can upvote it to bring it to the maintainers attention?

I'm also experiencing occasional connection refused - I establish the tunnel fine, everything works for a couple of hours, suddenly a wild connection refused error appears and then it hangs. I found that it tries to start a new tunnel, but it waits forever on the axios request to get the assigned url. My solution is adding a 5s timeout to that axios call: #646

Hmm, by now I've built my own implementation that doesn't rely on axios.
I just tried adding the timeout for the fun of it but it fails.
I think the issue may actually reside in either the docker image, nginx, or the dev warning page.
When I run the same code against a locally hosted<EMAIL_ADDRESS>it works fine.


[BUG] - https://www.nebari.dev/docs/how-tos/nebari-azure bugs

Describe the bug
https://www.nebari.dev/docs/how-tos/nebari-azure has a line "Refer to [Conceptual guides] for more information on how Nebari's components are secured."  Conceptual guides is supposed to be a link but it's not right now.
Additionally, the screenshot under guided-init shows project-name even though it'll throw an error if users use dashes.

Expected behavior
link
OS and architecture in which you are running Nebari
h
How to Reproduce the problem?
h
Command output
No response
Versions and dependencies used.
No response
Compute environment
None
Integrations
No response
Anything else?
No response

Thanks for sharing! I've made a note of the second-half (about project name) in https://github.com/nebari-dev/nebari/issues/1478, so I'll strike it from this issue.


Cannot create new project with react native cli

Description
When attempting to create a fresh react native project I'm unable to run the project from either Xcode or with the react native cli. The error I receive when running npx react-native run-ios is:
The following build commands failed: 	CompileC /Users/rondyck/Library/Developer/Xcode/DerivedData/client-budrlbueixzfnqhhsjuyaeanzjlx/Build/Intermediates.noindex/Pods.build/Debug-iphonesimulator/Flipper-RSocket.build/Objects-normal/x86_64/TcpDuplexConnection.o /Users/rondyck/Sites/Universal/appv3.2/client/ios/Pods/Flipper-RSocket/rsocket/transports/tcp/TcpDuplexConnection.cpp normal x86_64 c++ com.apple.compilers.llvm.clang.1_0.compiler (1 failure)
React Native version:
System:
OS: macOS 11.2
CPU: (16) x64 Intel(R) Core(TM) i9-9980HK CPU @ 2.40GHz
Memory: 4.56 GB / 64.00 GB
Shell: 5.8 - /bin/zsh
Binaries:
Node: 12.5.0 - ~/.nvm/versions/node/v12.5.0/bin/node
Yarn: 1.22.10 - /usr/local/bin/yarn
npm: 6.9.0 - ~/.nvm/versions/node/v12.5.0/bin/npm
Watchman: 4.9.0 - /usr/local/bin/watchman
Managers:
CocoaPods: 1.9.3 - /usr/local/bin/pod
SDKs:
iOS SDK:
Platforms: iOS 14.4, DriverKit 20.2, macOS 11.1, tvOS 14.3, watchOS 7.2
Android SDK: Not Found
IDEs:
Android Studio: 4.0 AI-193.69<IP_ADDRESS>21437
Xcode: 12.4/12D4e - /usr/bin/xcodebuild
Languages:
Java: Not Found
Python: 2.7.16 - /usr/bin/python
npmPackages:
@react-native-community/cli: Not Found
react: 16.13.1 => 16.13.1
react-native: 0.63.4 => 0.63.4
react-native-macos: Not Found
npmGlobalPackages:
react-native: Not Found
Steps To Reproduce

npx react-native init AwesomeProject
cd AwesomeProject && npx react-native start

Expected Results
I've run the pod install command in the ios directory with the following results
cd ios ➜  ios pod install Ignoring ffi-1.12.2 because its extensions are not built. Try: gem pristine ffi --version 1.12.2 Analyzing dependencies Downloading dependencies Generating Pods project Integrating client project Pod installation complete! There are 47 dependencies from the Podfile and 38 total pods installed.
I'm running xcode v12.4 on OSX 11.2
This seems very unusual, I'm able to run my other RN apps created from previous versions of RN successfully and so I'm assuming my environment is properly setup.
Any help is appreciated.
Thanks,
Ron

I solved this by removing the flipper section from the Podfile in the starter project. I don't use Flipper, so worked perfectly for me after that.

Thanks @rachelnabors, I did discover that later, as well as installing (or re-installing) the pod gem. Unfortunately the typescript template still doesn’t compile, but I suppose I can work around that for now and will close this ticket.

Thank you this worked for me, however, might need to reopen this issue.
Anyone creating a brand new react-native app with npx is encountering this issue.