Packages conflict with first party

You have a dependency on cupertino_icons which is not supported for flutter_web tech preview. See https://flutter.dev/web for more details.
You have a dependency on flutter which is not supported for flutter_web tech preview. See https://flutter.dev/web for more details.
You have a dependency on flutter_test which is not supported for flutter_web tech preview. See https://flutter.dev/web for more details.
pub finished with exit code 78
This is showing in the terminal, while running on web, please help me.

Hi @prajwalprajwal
What command did you executed to have this messages? Can you please provide your flutter doctor -v, your pubspec.yaml?
Thank you

Hi @prajwalprajwal
What command did you executed to have this messages? Can you please provide your flutter doctor -v, your pubspec.yaml?
Thank you

I just try to run, first flutter web program. It is just a default program of clicking on button.

Hi @prajwalprajwal
It is till useful to me to try to reproduce the issue.

Without additional information, we are unfortunately not sure how to resolve this issue.
We are therefore reluctantly going to close this bug for now.
Please don't hesitate to comment on the bug if you have any more information for us; we will reopen it right away!
Thanks for your contribution.


Issue 1

Hello @see-why  Great job so far, you've done a very well-organized job and the javascript is working perfectly!
there are a few things that I could suggest :
-The timing of the slide-in animationof the homepage description should be less than 5s`.

Thanks for the reviews
Pull request has been raised and merged to resolve the concerns.


Use with source map loader

A previous version of this repo had a setup with source maps, I know the purs-loader went through some changes since then. I tried to enable source maps and set the right devtool/use source-map-loader as before but that didn't seem to work.
Do you know if it's possible to do this still or is there some blocker?

I admit I haven't tried this with the source-map-loader in a while, but I believe it should still work. One thing I can think of is to double check that pscArgs: { sourceMaps: true } is set as an option to the purs-loader. If you are able to provide your webpack config, I can dig into this a bit more.

Closing. Please open on ethul/purs-loader if there is still an issue with this. Thanks!

For the record I can't say one way or the other if there's any issue, I moved onto other things before getting anywhere, I've certainly lost all state by this point...

Thanks for the update.

I tried to get this working in this repo in a similar fashion to what was done in https://github.com/ethul/purescript-webpack-example/commit/14df7ec233fe7cb197c25bc01be7c135abc53726 but haven't been able to get source maps working that way yet.

Correction: https://github.com/ethul/purescript-webpack-example/commit/a015281de1251d067cec3df9c333613701820374

Some debug info:
DEBUG='purs-loader' npm run webpack:server

><EMAIL_ADDRESS>webpack:server /Users/py/projects/ethul/purescript-webpack-example
> webpack-dev-server --progress --inline --hot

 10% building modules 1/1 modules 0 active
Project is running at http://localhost:4008/
webpack output is served from /
Content not from webpack is served from .
 10% building modules 4/9 modules 5 active ...bpack-example/node_modules/url/url.js  purs-loader loading Example +0ms
  purs-loader compile psa [ '--output=output',
  purs-loader   '--source-maps',
  purs-loader   'bower_components/purescript-*/src/**/*.purs',
  purs-loader   'src/**/*.purs' ] +1ms
  purs-loader compiling PureScript... +3ms
  ...

FWIW, I see this:

Thanks for the information and following up on this. I will take a look at the example to see if we can get source maps going. In case it is an issue, I've opened up ethul/purs-loader#101

I've added source map support in the loader with version 3.1.0. The source-map-loader is not required for source map support. I have an example branch:
https://github.com/ethul/purescript-webpack-example/tree/source-maps
If anyone has a chance to give it a try, that would be great!
Closing this for now. We can reopen if there are more issues with source maps.

This worked for me, although the source maps weren't as interactive as I'd hoped. Thanks!

@paulyoung What do you mean interactive? If you can identify specific deficiencies it's probably something that needs to be fixed upstream in the compiler, but won't happen without such input

@nwolverson I meant that I wasn't able to hover over names of things and see their values, like I can when debugging JavaScript.
It also didn't seem possible to set a "nested" breakpoint like I can in JavaScript, which is useful for curried functions.


Docs: Mark all reference entries with Node, Link or Global

This should be a fix in the XSLT, not in each entry, ideally.

I do like how if you close something it's auto done 👍


added pydantic to guardrails

Adds a way to create the guardrails output parser from a pydantic model.

@smyja it looks like there's some typing errors in the from_pydantic method, there's some extra unexpected arguments being passed in.

Might be good to add an example notebook as well :)

@smyja it looks like there's some typing errors in the from_pydantic method, there's some extra unexpected arguments being passed in.
I could fix this, but just stuck on what api is for 🤔

It's supposed be for this. https://shreyar.github.io/guardrails/integrations/openai_functions/

@smyja Are you interested in fixing the implementation/linting? As of right now I don't think it really works. Would be great to have a notebook example of this as well

@smyja Are you interested in fixing the implementation/linting? As of right now I don't think it really works. Would be great to have a notebook example of this as well

I think I'll close the the pull request, guardrails team haven't really given me any feedback.


 Please, anyone, figure out my mistake.

I ran into the same problem. I trained the model for 75 epochs with batch normalization in the first phase, then for 25 epochs freezing the batch normalization. I noticed that the white patches appear only in the second phase.
Additional info:

size of dataset: 4800 images
batch size = 4
steps per epoch = 1000
validation steps = 100

Any suggestions about what may cause the problem?


Add support for the tiny stack

What happened?

What were you attempting to do?

I want to use the tiny stack with the nginx buildpack, because I want to have a very minimalist image.

What did you expect to happen?

nginx would happily run on tiny.

What was the actual behavior? Please provide log output, if possible.

'validating buildpack<EMAIL_ADDRESS>stack io.paketo.stacks.tiny is not supported'
Build Configuration

apiVersion: kpack.io/v1alpha1
kind: ClusterBuilder
metadata:
  name: node-script-builder
spec:
  order:
  - group:
    - id: paketo-buildpacks/nodejs
    - id: paketo-buildpacks/nginx
    - id: source-removal
  serviceAccountRef:
    name: default
    namespace: default
  stack:
    kind: ClusterStack
    name: tiny #! Make sure tiny is properly loaded from the dependency syncer.
  store:
    kind: ClusterStore
    name: node-cluster-store
  tag: #@ "{}/node-script-builder:0.0.1".format(data.values.docker_repository)

What platform (pack, kpack, tekton buildpacks plugin, etc.) are you
using? Please include a version.

kpack released with Tanzu Build Service 1.2.2

What buildpacks are you using? Please include versions.

gcr.io/paketo-buildpacks/nginx:0.3.1

What builder are you using? If custom, can you provide the output from pack inspect-builder <builder>?

Can you provide a sample app or relevant configuration (buildpack.yml,
nginx.conf, etc.)?

Checklist

[x] I have included log output.
[x] The log output includes an error message.
[x] I have included steps for reproduction.

Hey @paketo-buildpacks/web-servers-maintainers do we have a sense of whether the Tiny build and run images contain the necessary packages for NGINX to run? If not, which are missing?

It seems the only missing package is libpcre3. I was able to get nginx running on tiny after adding libpcre3 to the package list.

I'm willing to submit a PR to enable this support, but with the perceived low priority of this work, the stacks work already in flight and this ongoing investigation into the nginx dependency itself, it might make sense to hold off until things are more stable.


Program now visible in integration

I'm using latest version of integration, downloaded custom program to dishwasher (quick wash and dry), when I select the program in the Home Connect app, in Homeassistant I see the value: 'Dishcare.Dishwasher.Program.QuickD' under selected program entity. However, when I try to make that selection using 'Home Connect Alt: Select program' service, I get an error:
This service requires field device_id, please enter a valid value for device_id
I'd like to be able to select Dishcare.Dishwasher.Program.QuickD program via automation

The error is pretty self explanatory, you need to specify the device ID on which the service should operate. Please review the HA documentation on how to use services or consult other users in the discussion forum. Anyway, I'm not even sure if the API supports custom programs, but you;ll know when you get the service call correctly.

I pasted the wrong error message. I know I need to select the device and I am successfully running automation including via Siri. For some reason this new program I downloaded is not possible to be called by service call. My ask is to understand why and if it’s possible to use this program. I was able to download a different program called quick45 and it worked. Actual error message when device and program are selected below:
home_connect_alt.select_program. The selected program key is not available
I know it’s not the question is why..

As I expected, it's not supported by the API, you can bypass the local validation by setting validate: false on the service call but most likely you'll just get the error from the HC server.

Yup that’s what’s happening. So it’s on the manufacturer's side then to add this program to API?

yes. good luck with that :)


Add "replaceReducer" method to redux service.

This is the one method missing from the standard redux store: http://redux.js.org/docs/api/Store.html#store-methods. Needed if added reducers dynamically to the store (e.g. changing app state structure per route or dynamically loading component reducers).

@ssylivia this looks like something we can use to hot reload reducers that change also (still yet to expand the current hot loader I wrote beyond components but I think this method is what Dan used in the react ecosystem to inject reducers on the fly without a full page refresh if I remember right)

@ssylvia this is now included in the v2.6.0 build of ember-redux! thanks again!


new theme system leads to panic on selection

After updating to the latest commit, yazi panics on selecting an entry. In a debug build the application does not crash completely, I can move around and the preview does still work, but I can't see any entries in my current directory, see this screenshot:

I haven't managed to get any further debug info, but this regression came with the latest commit as a build from 15c34fed5c006c7b9b9152ffe6da32735ae77773 does work.
terminal: alacritty

Thanks for trying the new theme system in advance and reporting its issues!
Setting the parent layout width to 0 may be a reasonable guess as to what's causing the problem. I have pushed a new commit, please give it a try to see if it resolves your issue.

I can confirm that it was the 0 width for the parent directory column that caused the issue. After the latest commit, the issue is resolved for me, and it works as before.


Docker container doesn't work

Hello! I followed this instruction to start docker container with imgproxy:
docker pull darthsim/imgproxy:latest
docker run -p 8080:8080 -it darthsim/imgproxy

Then I try to open http://<IP_ADDRESS>/8080 I see this:

Can somebody help me to solve this problem?

Hi,
This is exactly what you should see on the root page. Please read our guide about URL generation https://docs.imgproxy.net/#/generating_the_url_advanced

@SNPR, what do you expect to find at / of imgproxy service?

To check that docker container works you can:

Take any publicly available image, like this one: https://cdn.evilmartians.com/front/blocks/common/images/apple-touch-icon-28d6b28.png (180×180 pixels PNG)
Construct imgproxy url for it (manually or using a client library like imgproxy gem):  http://localhost:8080/unsafe/rs:fill:128:128/plain/https://cdn.evilmartians.com/front/blocks/common/images/apple-touch-icon-28d6b28.png (resize it to 128×128 pixels without url signature)
Open it in your browser (or embed it into your application's page)

If you see scaled image then it works. That is it.

@SNPR, what do you expect to find at / of imgproxy service?
To check that docker container works you can:

Take any publicly available image, like this one: https://cdn.evilmartians.com/front/blocks/common/images/apple-touch-icon-28d6b28.png (180×180 pixels PNG)
Construct imgproxy url for it (manually or using a client library like imgproxy gem):  http://localhost:8080/unsafe/rs:fill:128:128/plain/https://cdn.evilmartians.com/front/blocks/common/images/apple-touch-icon-28d6b28.png (resize it to 128×128 pixels without url signature)
Open it in your browser (or embed it into your application's page)

If you see scaled image then it works. That is it.

Thank you for your explanation!
I thought that docker container will give me web interface where I can change image size.
But after your answer I finally understood how to use imgproxy.


chore: update github token

:tada: This PR is included in version 3.1.16 :tada:
The release is available on:

npm package (@latest dist-tag)
GitHub release

Your semantic-release bot :package::rocket:


Link to 'ocm-backplane' binary instead of tarball

Fixes a bug where the backplane-cli tool logic would link to the tarball instead of the actual binary

/lgtm


64bits

Hi.
Has anyone been able to execute the code in MetaTrader 5 64 bits? I couldn't even compile the dll in 64 bits. What am I doing wrong? Thank you very much.

I tried hard but couldn't get it to work.
The reason is libwebsockets went through some big refactoring, changing names from libwebsockets_ to lws_ for example.
To understand how everything is connected probably exceeds my knowledge/skills. Any ideas/help is greatly appreciated.
Here is what I did:
I replaced the libwebsockets.dll with a 64bit version from december 2019 (lws-x64-29665442).
Then compiled as described in the intro by krisn.
First I got many 'undefined reference' errors.
So I edit lws2mql.c and libwebsockets.h to update the names:
libwebsocket_write -> lws_write
libwebsocket_create_context -> lws_create_context
libwebsocket_context_destroy -> lws_context_destroy
libwebsocket_client_connect -> lws_client_connect_via_info (not sure if this is correct)
libwebsocket_callback_on_writable -> lws_callback_on_writable
libwebsocket_service -> lws_service
Then I compiled again.
Now I still get one 'undefined reference' error for
libwebsocket_get_internal_extensions
I can't find any equivalent for this in libwebsockets...
I fear that not only the names may be changed, but also the logic/signatures/parameters etc.
Maybe we can combine powers to solve this and make it 64bit ready!
What do you think?

It seems that little is left to run perfectly on 64 bits. At the moment I am very busy with work and I cannot help with this mission.
My knowledge is also not very great in this area, since I didn't even compile it. However, as soon as I finish some work, I will dedicate myself to the conversion to 64 bits of lws2mql.

This is relevant - although I personally have not been able to build a project to date, can anyone have a normal instruction on how to build it?

Also wondering about this?


ipxe: unstable-2022-04-06 -> unstable-2023-01-20

Automatic update generated by nixpkgs-update tools. This update was made based on information from passthru.updateScript.
meta.description for ipxe is: Network boot firmware
meta.homepage for ipxe is: https://ipxe.org/
Updates performed

Ran passthru.UpdateScript

To inspect upstream changes

Compare changes on GitHub

Impact

Checks done (click to expand)

built on NixOS
The tests defined in passthru.tests, if any, passed
found unstable-2022-12-22 in filename of file in /nix/store/19x4536a2kwz7lp7f5qdc1sz2qkmlf1b-ipxe-unstable-2022-12-22

Rebuild report (if merged into master) (click to expand)

1 total rebuild path(s)

1 package rebuild(s)

First fifty rebuilds by attrpath
ipxe

Instructions to test this update (click to expand)

Either download from Cachix:
nix-store -r /nix/store/19x4536a2kwz7lp7f5qdc1sz2qkmlf1b-ipxe-unstable-2022-12-22 \
  --option binary-caches 'https://cache.nixos.org/ https://nix-community.cachix.org/' \
  --option trusted-public-keys '
  nix-community.cachix.org-1:mB9FSh9qf2dCimDSUo8Zy7bkq5CX+/rkCWyvRCYg3Fs=
  cache.nixos.org-1:6NCHdD59X431o0gWypbMrAURkbJ16ZPMQFGspcDShjY=
  '

(The Cachix cache is only trusted for this store-path realization.)
For the Cachix download to work, your user must be in the trusted-users list or you can use sudo since root is effectively trusted.
Or, build yourself:
nix-build -A ipxe https://github.com/r-ryantm/nixpkgs/archive/05e49f72804be84f3daec38d482e0f27101fc4a7.tar.gz

After you've downloaded or built it, look at the files and if there are any, run the binaries:
ls -la /nix/store/19x4536a2kwz7lp7f5qdc1sz2qkmlf1b-ipxe-unstable-2022-12-22
ls -la /nix/store/19x4536a2kwz7lp7f5qdc1sz2qkmlf1b-ipxe-unstable-2022-12-22/bin

Pre-merge build results
We have automatically built all packages that will get rebuilt due to
this change.
This gives evidence on whether the upgrade will break dependent packages.
Note sometimes packages show up as failed to build independent of the
change, simply because they are already broken on the target branch.
Result of nixpkgs-review run on x86_64-linux 1

1 package built:

ipxe

Maintainer pings
cc @ehmry for testing.

Automatic update generated by nixpkgs-update tools. This update was made based on information from passthru.updateScript.
meta.description for ipxe is: Network boot firmware
meta.homepage for ipxe is: https://ipxe.org/
Updates performed

Ran passthru.UpdateScript

To inspect upstream changes

Compare changes on GitHub

Impact

Checks done (click to expand)

built on NixOS
The tests defined in passthru.tests, if any, passed
found unstable-2023-01-20 in filename of file in /nix/store/igp0mv1rbqckl2mxqp52h37bh2604s6n-ipxe-unstable-2023-01-20

Rebuild report (if merged into master) (click to expand)

1 total rebuild path(s)

1 package rebuild(s)

First fifty rebuilds by attrpath
ipxe

Instructions to test this update (click to expand)

Either download from Cachix:
nix-store -r /nix/store/igp0mv1rbqckl2mxqp52h37bh2604s6n-ipxe-unstable-2023-01-20 \
  --option binary-caches 'https://cache.nixos.org/ https://nix-community.cachix.org/' \
  --option trusted-public-keys '
  nix-community.cachix.org-1:mB9FSh9qf2dCimDSUo8Zy7bkq5CX+/rkCWyvRCYg3Fs=
  cache.nixos.org-1:6NCHdD59X431o0gWypbMrAURkbJ16ZPMQFGspcDShjY=
  '

(The Cachix cache is only trusted for this store-path realization.)
For the Cachix download to work, your user must be in the trusted-users list or you can use sudo since root is effectively trusted.
Or, build yourself:
nix-build -A ipxe https://github.com/r-ryantm/nixpkgs/archive/9b1d27bd8d5905ba3400ba199ff58d684f6af4d3.tar.gz

After you've downloaded or built it, look at the files and if there are any, run the binaries:
ls -la /nix/store/igp0mv1rbqckl2mxqp52h37bh2604s6n-ipxe-unstable-2023-01-20
ls -la /nix/store/igp0mv1rbqckl2mxqp52h37bh2604s6n-ipxe-unstable-2023-01-20/bin

Pre-merge build results
We have automatically built all packages that will get rebuilt due to
this change.
This gives evidence on whether the upgrade will break dependent packages.
Note sometimes packages show up as failed to build independent of the
change, simply because they are already broken on the target branch.
Result of nixpkgs-review run on x86_64-linux 1

1 package built:

ipxe

Maintainer pings
cc @ehmry for testing.

Automatic update generated by nixpkgs-update tools. This update was made based on information from passthru.updateScript.
meta.description for ipxe is: Network boot firmware
meta.homepage for ipxe is: https://ipxe.org/
Updates performed

Ran passthru.UpdateScript

To inspect upstream changes

Compare changes on GitHub

Impact

Checks done (click to expand)

built on NixOS
The tests defined in passthru.tests, if any, passed
found unstable-2023-01-23 in filename of file in /nix/store/labffzm69phaq3bkrrxs7zmy6bfm2akp-ipxe-unstable-2023-01-23

Rebuild report (if merged into master) (click to expand)

1 total rebuild path(s)

1 package rebuild(s)

First fifty rebuilds by attrpath
ipxe

Instructions to test this update (click to expand)

Either download from Cachix:
nix-store -r /nix/store/labffzm69phaq3bkrrxs7zmy6bfm2akp-ipxe-unstable-2023-01-23 \
  --option binary-caches 'https://cache.nixos.org/ https://nix-community.cachix.org/' \
  --option trusted-public-keys '
  nix-community.cachix.org-1:mB9FSh9qf2dCimDSUo8Zy7bkq5CX+/rkCWyvRCYg3Fs=
  cache.nixos.org-1:6NCHdD59X431o0gWypbMrAURkbJ16ZPMQFGspcDShjY=
  '

(The Cachix cache is only trusted for this store-path realization.)
For the Cachix download to work, your user must be in the trusted-users list or you can use sudo since root is effectively trusted.
Or, build yourself:
nix-build -A ipxe https://github.com/r-ryantm/nixpkgs/archive/56fda271b1c1a9e4681ea454d3fa8572f8921b16.tar.gz

After you've downloaded or built it, look at the files and if there are any, run the binaries:
ls -la /nix/store/labffzm69phaq3bkrrxs7zmy6bfm2akp-ipxe-unstable-2023-01-23
ls -la /nix/store/labffzm69phaq3bkrrxs7zmy6bfm2akp-ipxe-unstable-2023-01-23/bin

Pre-merge build results
We have automatically built all packages that will get rebuilt due to
this change.
This gives evidence on whether the upgrade will break dependent packages.
Note sometimes packages show up as failed to build independent of the
change, simply because they are already broken on the target branch.
Result of nixpkgs-review run on x86_64-linux 1

1 package built:

ipxe

Maintainer pings
cc @ehmry for testing.

Automatic update generated by nixpkgs-update tools. This update was made based on information from passthru.updateScript.
meta.description for ipxe is: Network boot firmware
meta.homepage for ipxe is: https://ipxe.org/
Updates performed

Ran passthru.UpdateScript

To inspect upstream changes

Compare changes on GitHub

Impact

Checks done (click to expand)

built on NixOS
The tests defined in passthru.tests, if any, passed
found unstable-2023-01-25 in filename of file in /nix/store/imp1zmbl9xnrbj4ispgp92dqwnpc54ld-ipxe-unstable-2023-01-25

Rebuild report (if merged into master) (click to expand)

1 total rebuild path(s)

1 package rebuild(s)

First fifty rebuilds by attrpath
ipxe

Instructions to test this update (click to expand)

Either download from Cachix:
nix-store -r /nix/store/imp1zmbl9xnrbj4ispgp92dqwnpc54ld-ipxe-unstable-2023-01-25 \
  --option binary-caches 'https://cache.nixos.org/ https://nix-community.cachix.org/' \
  --option trusted-public-keys '
  nix-community.cachix.org-1:mB9FSh9qf2dCimDSUo8Zy7bkq5CX+/rkCWyvRCYg3Fs=
  cache.nixos.org-1:6NCHdD59X431o0gWypbMrAURkbJ16ZPMQFGspcDShjY=
  '

(The Cachix cache is only trusted for this store-path realization.)
For the Cachix download to work, your user must be in the trusted-users list or you can use sudo since root is effectively trusted.
Or, build yourself:
nix-build -A ipxe https://github.com/r-ryantm/nixpkgs/archive/cb12769f62a868d3e12bbc59f14752145f2bda45.tar.gz

After you've downloaded or built it, look at the files and if there are any, run the binaries:
ls -la /nix/store/imp1zmbl9xnrbj4ispgp92dqwnpc54ld-ipxe-unstable-2023-01-25
ls -la /nix/store/imp1zmbl9xnrbj4ispgp92dqwnpc54ld-ipxe-unstable-2023-01-25/bin

Pre-merge build results
We have automatically built all packages that will get rebuilt due to
this change.
This gives evidence on whether the upgrade will break dependent packages.
Note sometimes packages show up as failed to build independent of the
change, simply because they are already broken on the target branch.
Result of nixpkgs-review run on x86_64-linux 1

1 package built:

ipxe

Maintainer pings
cc @ehmry for testing.


License?

What license is this code under? I'm not sure about contributing under the possibility that this could become paid/closed-source later.

Sorry, I have no experience with this. I want to keep it open to everybody to contribute and I am not interested in closing it down or getting paid. Is there a good/ reliable resource to learn about different licenses?

@MMEnter See GitHub's https://choosealicense.com/ . It covers all the bases when it comes to choosing a license.

Thanks @rogersachan I read this: https://opensource.guide/legal/#which-open-source-license-is-appropriate-for-my-project and think that the MIT License is the right choice.

I think that's a good fit for the project.


Lost user input

Sometimes users input are lost permanently. We need to figure out why it happened.

For the first step, we want to add a debug page to show information we want users to report.


Demangler: further speed improvements.

Avoid using std::string and std::vector in the demangler. Instead use vectors/strings with storage allocated by the NodeFactory’s bump pointer allocator.
This brings another 35% speedup. Especially in the case the Demangle::Context is not reused for subsequent demanglings.

@swift-ci Please smoke test and merge

@eeckstein Can you commit a performance test harness for the demangler? Or something like that? That way we can be sure that we do not regress.

@gottesmm I'm not sure this is worth the effort. But let me think about it.

We do already have a command-line utility for testing the demangler; if we have an existing C++ harness, it should be easy to hook that up.


Need support http_proxy and set Ansible.var.http_proxy, when install zstack

Description:
If install zstack in company network, which need use http_proxy and https_proxy to connect internet, zstack-install.sh should be able to correctly use system http_proxy variable or accept http_proxy parameter. When doing installation, the http_proxy should also be able to set in zstack.properties for later adding host and other resource.

https://github.com/zstackorg/zstack-utility/commit/3911a32d804f684bf1fa5705ffac008fc4866548


feat:  XML Validator prevent parsing XXE

prevent the XmlValidation from XXE parsing - prevent XML external entity (XXE) injection
This is considered a security measure.
fixes #1061 as described here: https://github.com/CycloneDX/cyclonedx-javascript-library/pull/1063#issuecomment-2098335774

Great work @jkowalleck! I found this when I was researching on how to prevent the parsing vulnerabilities following up on CVE-2024-34393.
This looks great and I agree that the nonet and noent options should solve the vulnerabilities.
I was wondering the reason behind adding the compact option. Was it only for the performance improvements for your specific use case or can it also prevent some kind of vulnerability?
According to the official documentation, all it does is:

Compact small text nodes; no modification of the tree allowed afterwards (will possibly crash if you try to modify the tree)

yes, the compact was added for my specific purposes, it is not related to the actual workaround for the thing.

this fix will be resulting an security advisory: https://github.com/CycloneDX/cyclonedx-javascript-library/security/advisories/GHSA-38gf-rh2w-gmj7

!!! REVERTED !!!
had to revert this merge/patch.
And had to yank v6.7.0
!!! REVERTED !!!
instead of preventing XXE, it actually enabled it.

I was wrong. This library was not affected by XXE in the first place.
see a hardening and POC/tests https://github.com/CycloneDX/cyclonedx-javascript-library/pull/1064


Load saved quant to continue training

Thanks for your efforts , looking forward to new updates.
I'm experimenting with ternary quant and 2 bits, I'm using the fine tuning example file and saved the quant successfully into these files:
qmodel.pt
config.json
###########################################################
trainer = SFTTrainer(
model=WrappedModel(model),
tokenizer=tokenizer,
max_seq_length=max_tokens,
train_dataset=dataset,
eval_dataset=None,
peft_config=None,
args=training_args,
packing=True,
#neftune_noise_alpha=5,
#data_collator=data_collator,
)
model.is_parallelizable       = False
trainer.is_model_parallel     = False
trainer.place_model_on_device = False
#print('perplexity', compute_perplexity_batched(model=model, tokenizer=tokenizer, predictions=[s['text'] for s in dataset_val], batch_size=1, max_length=max_tokens))
model.train()
trainer.train()
save_dir = './quantized_models/mistral7b02instruct'
print(f"Saving into {save_dir}")
model.save_quantized(save_dir)
###########################################################
However, when I reload the quant to continue the fine tuning but when I reload it and train it with the same arguments and SFTTrainer I get this error:
Traceback (most recent call last):
File "/home/abc/workspace/distill.py", line 29, in
model     = HQQModelForCausalLM.from_quantized(save_dir)
File "/home/abc/miniconda3/envs/unsloth_env/lib/python3.10/site-packages/hqq/engine/base.py", line 86, in from_quantized
model = cls._get_hqq_class(arch_key).from_quantized(
File "/home/abc/miniconda3/envs/unsloth_env/lib/python3.10/site-packages/hqq/models/base.py", line 364, in from_quantized
cls.patch_model(
File "/home/abc/miniconda3/envs/unsloth_env/lib/python3.10/site-packages/hqq/models/base.py", line 160, in patch_model
cls.patch_linearlayers(model, patch_linear_fct, patch_params, verbose=verbose)
File "/home/abc/miniconda3/envs/unsloth_env/lib/python3.10/site-packages/hqq/models/hf/mistral.py", line 44, in patch_linearlayers
layers[i].self_attn.q_proj = patch_fct(
File "/home/abc/miniconda3/envs/unsloth_env/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/home/abc/miniconda3/envs/unsloth_env/lib/python3.10/site-packages/hqq/models/base.py", line 337, in _load_module
return module.to(device=device, dtype=compute_dtype, non_blocking=True)
File "/home/abc/miniconda3/envs/unsloth_env/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1160, in to
return self._apply(convert)
File "/home/abc/miniconda3/envs/unsloth_env/lib/python3.10/site-packages/torch/nn/modules/module.py", line 833, in _apply
param_applied = fn(param)
File "/home/abc/miniconda3/envs/unsloth_env/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1158, in convert
return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking)
NotImplementedError: Cannot copy out of meta tensor; no data!
###########################################################
Here's the code I modified in your example, you can see how I load the model:
###########################################################
hf_auth    = None #HuggingFace token
cache_path = 'cache'   #cache directory to store data
device        = 'cuda:0'
#Chose a model
model_id  = "mistralai/Mistral-7B-Instruct-v0.2"
save_dir = './quantized_models/mistral7b02instruct'
#model_id  = "meta-llama/Llama-2-13b-hf"
#model_id  = "meta-llama/Llama-2-70b-hf"
#HQQ Quantize
######################################################################################
######################################################################################
from hqq.engine.hf import HQQModelForCausalLM, AutoTokenizer
import torch
from hqq.core.quantize import *
import gc
#model     = HQQModelForCausalLM.from_quantized(save_dir, device='cuda')
#tokenizer = AutoTokenizer.from_pretrained(model_id,       use_auth_token=hf_auth, cache_dir=cache_path)
train_dtype      = torch.float32
#model     = HQQModelForCausalLM.from_pretrained(model_id, torch_dtype=train_dtype)
model     = HQQModelForCausalLM.from_quantized(save_dir)
tokenizer = AutoTokenizer.from_pretrained(model_id)
#quant_config = BaseQuantizeConfig(nbits=2, group_size=16, quant_scale=False, quant_zero=False)
#model.quantize_model(quant_config=quant_config)
model.to('cuda')

Hi @Sneakr
I thought the SFTTrainer was broken with HQQ, good to see that it works now!
save_quantized / from_quantized only save/load the quantized model. The low-rank adapters are saved and loaded separately:
https://github.com/mobiusml/hqq/?tab=readme-ov-file#peft-training

Yes, it seems to work well!
Thank you! Looking forward if you could possible implement ternary bitnet into the quant options, I assume we could use the 2 bit to hold the values but using ternary values to make it compatible with inference and hardware. I'm relatively new to training LLM and trying to test some theories of mine. Appreciate your efforts, thanks again!

Update, I'm not sure you need to verify, but I did some testing, it seems that when quantizing and evaluate the model after quantization directly I have a good precision from the higher quant, but when adding the lora adapter, it loses alot of precision in the evals. PeftUtils.add_lora(model, lora_params)
I'm not sure why this could be as I'm new to this, but a guess is that the lora adapter is in 'train_dtype': torch.float32 and might not be compatible with with the quant. Just heads up.

Great, happy to help!
Yeah we could use 2-bit packing for ternary but as [0,1,2] values not [-1,0,1],, the issue is that bit-packing negative values with int8/uint8 is a bit tricky, but using [0,1,2] and adapting the zero-point is equivalent to [-1,0,1] since it's just a shift by -1.

Sorry for bumping this up, but trying to get som clarification if possible any pointer in any direction so I can get some clarify would be appreciated.
When I evaluate the 2 bits (as well as 4 bits) I get great results using HQQ quantization. I have 8-bit quant config for the attention headers.
However, If I add the Lora parameters and run 1 small training set, the eval numbers significantly decrease, regardless of my dataset and training.
I noticed however when I fine tune using math dataset, the evaluation numbers goes back up slightly for gsm8k for example, but still far beyond the pre-fine tuning process.
I think this would have something to do with the precision loss during fine tuning a quant (?) but I'm only guessing, do you have any pointers or hint what the issue could be? Or do you simply suggest avoid fine tuning a quant HQQ? The same goes for 4 bit and so on altough starting with higher evaluation results compared to the 2-bit the drop is significant too as soon as training starts.
I tested with the SFTTrainer without Lora , the drop was not as high but it still dropped significantly too. Adjusting Lora parameters did have some different impact. Here's the tests I did for one eval and a dataset with 1000 entries:
wandb:      winogrande/alias winogrande
Quantized model to 2 bit quantization with 8 bit header, no fine tuning:
wandb:        winogrande/acc 0.69771
wandb: winogrande/acc_stderr 0.01291
self_attn.q_proj, after fine tuning the Quantized model with only this layer active with LORA parameters:
wandb:                 winogrande/acc 0.61089
wandb:          winogrande/acc_stderr 0.0137
self_attn.k_proj, after fine tuning the Quantized model with only this layer active with LORA parameters:
wandb:                 winogrande/acc 0.64009
wandb:          winogrande/acc_stderr 0.01349
self_self_attn.v_proj, after fine tuning the Quantized model with only this layer active with LORA parameters:
wandb:                 winogrande/acc 0.49961
wandb:          winogrande/acc_stderr 0.01405
wandb:               winogrande/alias winogrande
self_attn.o_proj, after fine tuning the Quantized model with only this layer active with LORA parameters:
wandb:                 winogrande/acc 0.57301
wandb:          winogrande/acc_stderr 0.0139
mlp.gate_proj, after fine tuning the Quantized model with only this layer active with LORA parameters:
wandb:                 winogrande/acc 0.65272
wandb:          winogrande/acc_stderr 0.01338
wandb:               winogrande/alias winogrande
mlp.up_proj, after fine tuning the Quantized model with only this layer active with LORA parameters:
wandb:                 winogrande/acc 0.64088
wandb:          winogrande/acc_stderr 0.01348
mlp.down_proj, after fine tuning the Quantized model with only this layer active with LORA parameters:
wandb:                 winogrande/acc 0.5438
wandb:          winogrande/acc_stderr 0.014

Hey, sure no problem, happy to help:

What learning rate and scheduler do you use?
If your data is not diverse enough, it would probably overfit. Is the same thing happening with the mix of data we shared in the blogpost? For faster experimentation, can you try:
OpenAssistant (10K) + MathQA (10K) with random.seed(100), ALL 2-bit group-size=8, quant_zero/quant_scale=False, you should get something like:

HellaSwag (10-shot): 73.82
MMLU (5-shot): 43.13
TruthfulQA-MC2 (0-shot): 41.5
Winogrande (5-shot): 70.56
GSM8K (5-shot): 29.11
Average: 50.88

"I tested with the SFTTrainer without Lora": What are the trainable parameters in this case? Other than the LoRA weights, nothing is trainable.
Did you make sure that the LoRA weights are either bfp16 or fp32?
if you use 8-bit, you don't need LoRA for those (pass None in the peft config), unless you actually want to fine-tune them to be aligned with your data.

Otherwise, can you share a snippet of the training code (you can hide the data and model), just to check if everything looks ok.
I am using my own custom training code (no SFTTrainer), so maybe SFTTrainer is doing something to the model. I know that for example, it casts normalization layers to fp32 and some other things, to be confirmed.

I got it working now following your tips, getting good results!
One sidenote, I installed https://github.com/bigcode-project/bigcode-evaluation-harness to be able to run Humaneval and so on, it crashed on HQQ due to the class HQQModelForCausalLM returned the function instead of the model, I refactored it to use self.hqq_quantized instead of quantized and it works now. Hope I didn't break anything, I'm not a python coder but this works so far. Thanks!
	@classmethod
	def _make_quantizable(cls, model, quantized: bool) -> None:
		model.hqq_quantized = quantized
		model.arch_key = model.config.architectures[0]
		model.quantize_model = lambda quant_config, compute_dtype=torch.float16, device="cuda": cls.quantize_model_(
			model=model, quant_config=quant_config, compute_dtype=compute_dtype, device=device
		)
		model.save_quantized = lambda save_dir: cls.save_quantized_(model=model, save_dir=save_dir)
		model.base_class = cls._get_hqq_class(model)

		def _quantized_method(method_name):
			def _wrapper(self, *args, **kwargs):
				if self.hqq_quantized:
					return self
				else:
					return getattr(super(type(self), self), method_name)(*args, **kwargs)
			return _wrapper

		model.cuda = _quantized_method('cuda').__get__(model)
		model.to = _quantized_method('to').__get__(model)
		model.float = _quantized_method('float').__get__(model)
		model.half = _quantized_method('half').__get__(model)

Thanks for the tip! That HQQModelForCausalLM class is a bit of a mess, needs some refactoring. Better use https://github.com/mobiusml/hqq/?tab=readme-ov-file#auto-mode-1 for Hugging Face models. The reason why it exists is to have different engines (HF, timm, VLLM), but now that everyone is using just HF and VLLM would need a separate branch, it's just redundant messy code.

I'm sorry for taking your time but I have one final question, I have managed to quantize mixtral to 2bit with 4bit attention header, and I got slightly better results than your quantized mixtral due to using fine tuned one.
I'm trying to save the quant as a safetensors file , but it does not seem possible? The file output does not contain the weights and only around 500mb big only 1 file. Is it not possible to save it as safetensors file if its quantized?
I looked at AWQ and tried to implement their method into your pack:

class MixtralHQQ(MixtralPatch, BaseHQQHFModel):
	def save_quantized(self, save_dir: str, safetensors: bool = True, shard_size: str = "5GB"):
		save_dir = save_dir[:-1] if save_dir[-1] == "/" else save_dir

		# Save model
		class EmptyModule(nn.Module):
			def __init__(self):
				super(EmptyModule, self).__init__()

			def forward(self, x):
				return x

		# Save model and config files with empty state dict
		self.config.save_pretrained(save_dir, state_dict=EmptyModule().state_dict())

		# Remove empty state dict
		default_paths = [
			f"{save_dir}/model.safetensors",
			f"{save_dir}/pytorch_model.bin",
		]
		for path in default_paths:
			if os.path.exists(path):
				os.remove(path)

		# model_name has no extension, add it when saving state_dict
		model_name = "model.safetensors" if safetensors else "pytorch_model.bin"

		logging.info(f"Total model size before saving: {sum([param.nelement() for param in self.parameters()])} elements")

		# shard checkpoint into chunks (10GB default)
		shards, index = shard_checkpoint(
			self.state_dict(), max_shard_size=shard_size, weights_name=model_name
		)
		print(f"Saving model to {save_dir} in {len(shards)} shards")

		logging.info(f"Saving model to {save_dir} in {len(shards)} shards")
		for shard_file, shard in shards.items():
			if safetensors:
				original_shard_size = {k: v.nelement() for k, v in shard.items()}
				# safetensors must be in the same memory, so we duplicate and use contiguous memory
				shard = {k: v.clone().contiguous() for k, v in shard.items()}
				save_file(
					shard, os.path.join(save_dir, shard_file), metadata={"format": "pt"}
				)
				print(f"Saved {shard_file} with safetensors")
				reloaded_shard = load_file(os.path.join(save_dir, shard_file))  # Use appropriate SafeTensors load function
				reloaded_shard_size = {k: v.nelement() for k, v in reloaded_shard.items()}
				assert original_shard_size == reloaded_shard_size, "Mismatch in shard sizes before and after saving with SafeTensors"
			else:
				torch.save(shard, os.path.join(save_dir, shard_file))
				print(f"Saved {shard_file}")

		# save shard index
		if index is not None:
			with open(f"{save_dir}/{model_name}.index.json", "w+") as file:
				file.write(json.dumps(index, indent=4))
	pass

This did not make any difference , I still see only 1 file at the same size. It does not contain the weights, neither for mistral regular model. Do you have any pointer to direct me in the right direction? I'm feeling so close now :) Cheers!

Hi! You can't use safetensors with HQQ-quantized models, because safetensors doesn't support storing certain types which are necessary for the meta-data in HQQ. There are so many things that are done when we load the quantized model, so better use the functions provided in the documentation:
If the architecture is exactly like the original Mixtral, you can do this:
#Save the quantized model
model.save_quantized(model, save_dir=save_dir)

#Load from local directory or Hugging Face Hub on a specific device
model = HQQModelForCausalLM.from_quantized(save_dir_or_hfhub, device='cuda')

Otherwise, you can use Auto-Mode: https://github.com/mobiusml/hqq/?tab=readme-ov-file#auto-mode-1

@mobicham Thanks for reply! Oh, I see now and the meta data. What would it take for me to work around this and be able to provide support for safetensors? Is it even possible with the HQQ if I try to refactor things or is it completely hopeless to try?
Where can I start if possible, any pointers in the right direction would be nice. Thanks for your time!
Btw, I saw that Intel has taken your HQQ code into their github compressor pack, good work and gratz! :)

Update:
I managed to refactor and create something new but based on your HQQ . I made my first ever GGUF which is only  1.9gb big in file size - a Mistral 7B
"mistralai/Mistral-7B-Instruct-v0.2"
I ran winogrande on your 2-bit quant using HQQ:  (0 shot for both model evals)
q2_config    = BaseQuantizeConfig(nbits=2, group_size=8)
qA_config    = BaseQuantizeConfig(nbits=4, group_size=64)
linear_tags  = HQQModelForCausalLM.get_linear_tags(model)
quant_config = {k: q2_config for k in linear_tags}
quant_config['self_attn.v_proj'] = qA_config

wandb: Run summary:
wandb:        winogrande/acc 0.71823
wandb: winogrande/acc_stderr 0.01264
wandb:      winogrande/alias winogrande
Here's my results from the same mistral quant into 2 bits using the GGUF directly to evaluate:

Great work! =)

@Sneakr that's great, really appreciate your work here!
I think it would be great to have a util tool to port HQQ into GGUF. Maybe we can add it in https://github.com/mobiusml/hqq/blob/master/hqq/models/base.py
Regarding safetenors, it's a bit tricky but possible, there are different ways of doing it in a way that wouldn't break other things.
I can tell you more in details on Discord: https://discord.com/invite/VJcFz5TR


Multiple PDF Files

What would it take to support Multiple PDF File Import and providing search capability?

Yes ofcourse that can be done within pdfGPT. Have added this item to the backlog for version 2.

+1 for multiple PDF files. Would be also amazing if it would be possible to add other file formats as well .docx, HTML etc.

Yes ofcourse that can be done within pdfGPT. Have added this item to the backlog for version 2.

Nice, I am curious to hear the plan to add [PDF name along with line number]

+1

+1

have created a very crude version of multiple-pdf support but it may not be the best code as I know some known issues.
In the mean while you can check the sandbox environment here:
https://huggingface.co/spaces/bhaskartripathi/pdfChatterSandbox

+1. I need it badly.

Hi, great tool! Is it possible to upload a collection of articles on a particular subject ONCE and instruct the tool to always return answers from the given set of articles with references?

I think multiple PDF support could be an outstanding feature. I can imagine this for software release notes published only as PDF by vendors or "old school" eLearning material. Imagine you could turn all that into an interactive knowledge base! This could really be a huge deal for some companies.

I am working on it. Will soon release this feature.


List program probes when -l is also provided

Extend listing functionality to also list all the probes a program would attach to when '-l' arg is provided.
e.g.
$ sudo bpftrace -l -e 'kfunc:vmlinux:vfs_* { exit(); }'
kfunc:vmlinux:vfs_cancel_lock
kfunc:vmlinux:vfs_clean_context
kfunc:vmlinux:vfs_cleanup_quota_inode
kfunc:vmlinux:vfs_clone_file_range
kfunc:vmlinux:vfs_cmd_create
kfunc:vmlinux:vfs_copy_file_range
kfunc:vmlinux:vfs_create
kfunc:vmlinux:vfs_create_mount
kfunc:vmlinux:vfs_dedupe_file_range
kfunc:vmlinux:vfs_dedupe_file_range_one
kfunc:vmlinux:vfs_dentry_acceptable
kfunc:vmlinux:vfs_dup_fs_context
kfunc:vmlinux:vfs_fadvise
...

Checklist

[ ] Language changes are updated in man/adoc/bpftrace.adoc and if needed in docs/reference_guide.md
[ ] User-visible and non-trivial changes updated in CHANGELOG.md
[ ] The new behaviour is covered by tests

(still in a draft because I need to do some more testing - and add more tests)

Although this looks more like a bug fix than a new feature, right?

I think that can probably be debated since there is nowhere in the docs saying they can be used together. It's probably a bug that passing both didn't produce an error. That being said, I don't feel strongly either way.

Although this looks more like a bug fix than a new feature, right?

I think that can probably be debated since there is nowhere in the docs saying they can be used together. It's probably a bug that passing both didn't produce an error. That being said, I don't feel strongly either way.

Fair enough :) As-is is fine then


generate the debugger-specific port forward command from squashctl

rather than assemble this in vscode, just pass it from squashctl

closed by https://github.com/solo-io/squash/pull/92


test(components): add custom cypress workflow

Preview environments are getting posted here, as soon as they are ready!


Wordcloud

Changed the image issues. Please review it #113 .

Add this whole thing in the folder I have mentioned in the issue

Add this whole thing in the folder I have mentioned in the issue

Add this whole thing in the folder I have mentioned in the issue

I had created a folder

Add this whole thing in the folder I have mentioned in the issue

I had created a folder

You need to add this word cloud folder in to the visualisation folder and for that you need to fork the latest repo

You need to add this word cloud folder in to the visualisation folder and for that you need to fork the latest repo


We often use a string instead of a PipelineSelection in Stream

See original issue on GitLab
In GitLab by [Gitlab user @jennis] on Sep 12, 2019, 11:06
Summary
In Stream, we often load a target and retrieve a specific set of dependencies using a PipelineSelection.
Many of our methods within Stream require a PipelineSelection (or set a default). However, we often pass in the string associated with the --deps option if it has been provided. for example:

load_selection() uses it here but may be set as a string here
build() uses it here, but may be passed in as a string here
fetch() uses it here but may be passed in as a string here

These three examples are not the only offenders.
Although it's not detrimental, from a type-safety perspective, we should try and use the enums as soon as possible (i.e. from the moment the translation from string makes sense)
See the related discussion: https://gitlab.com/BuildStream/buildstream/merge_requests/1599#note_216085324

In GitLab by [Gitlab user @jennis] on Sep 12, 2019, 11:06
changed the description

In GitLab by [Gitlab user @jennis] on Sep 12, 2019, 16:08
mentioned in merge request !1599

In GitLab by [Gitlab user @tlater] on Dec 31, 2019, 16:20
mentioned in merge request !1787

In GitLab by [Gitlab user @tlater] on Jan 8, 2020, 18:24
closed via merge request !1787

In GitLab by [Gitlab user @tlater] on Jan 8, 2020, 18:24
mentioned in commit 5960a90e03b1e5115f6e5912b126529846e589f5


Bugfix/talamini/thermomechanics with state

Note: this PR contains a breaking change. I'm leaning towards not merging. Please help in evaluating.
Our thermomechanics physics module did not account for self-heating due to thermomechanical coupling. The material model interface does return the internal source term, but it was not being integrated in the thermal residual. This PR adds in this missing effect.
The breaking change is that the heat transfer module is now imbued with internal state variables (that is, quadrature point data). The mechanics module has this design, but the heat transfer currently does not. Should the this facet of design be symmetric between physics modules? If so, can it be moved into the base class, to avoid manual repetition?

/style


readsettings.py : AttributeError: 'list' object has no attribute 'copy

In readsettings.py:
line 25: return default.copy()
causes python2 and python3 to throw an error:
AttributeError: 'list' object has no attribute 'copy'
Is there a particular reason you're using copy here?
I tried changing to return default and it seems to be running ok now...?

Plagued by more errors now:
Input and output extensions are the same so passing back the original file [process-same-extensions: False].
Bypassing conversion and setting outputfile to inputfile.
Relocating MOOV atom to start of file.
Traceback (most recent call last):
  File "manual.py", line 410, in <module>
    main()
  File "manual.py", line 386, in main
    walkDir(path, silent=silent, tmdbid=args.get('tmdbid'), tvdbid=args.get('tvdbid'), imdbid=args.get('imdbid'), preserveRelative=args['preserverelative'], tag=settings.tagfile, optionsOnly=args['optionsonly'])
  File "manual.py", line 276, in walkDir
    processFile(filepath, None, mp, info=info, relativePath=relative)
  File "manual.py", line 239, in processFile
    mp.QTFS(output['output'])
  File "/home/pi/sickbeard_mp4_automator/resources/mediaprocessor.py", line 1582, in QTFS
    processor.process(inputfile, outputfile)
  File "/home/pi/.local/lib/python2.7/site-packages/qtfaststart/processor.py", line 197, in process
    moov = _patch_moov(datastream, moov_atom, offset)
  File "/home/pi/.local/lib/python2.7/site-packages/qtfaststart/processor.py", line 263, in _patch_moov
    moov.write(struct.pack(struct_fmt, *offset_entries))
struct.error: integer out of range for 'L' format code

Thanks for any suggestions.

https://www.programiz.com/python-programming/methods/list/copy
That sort of explains why it was done, weird variables were transferring over because the default value was being modified (it manifested in parts of the hwaccel settings getting blended together).
Unfortunately it looks like list.copy() was only added in python3.3 so that's not backwards compatible for python2
Confirmation that it is in python3 documentation though so not sure why you say python3 doesn't work
https://docs.python.org/3/tutorial/datastructures.html
This should be a python2 compatible solution
6ba662170e2da759d24d9b3d6ee3e35bdf8e65e9
For your second error, that appears to be an error with qtfaststart and it's not one I've ever seen before. I can't seem to find any documentation on it but its specifically with that package. Is it an error with every file? Sometimes you see out of range errors are large files using 32 bit versions of python which may be the case here. If it's a persistent error you can offload the moov atom relocation to FFMPEG and disable qtfaststart in autoProcess.ini potentially

[Converter]
postopts = -movflags, faststart

[Metadata]
relocate-moov = False

The reason this isn't the default solution and the python-based moov atom relocation is performed is that there was some errors with tagging that were seen (although very rare) when applying the tag metadata after the moov atom had already been moved

I downloaded the latest commit probably an hour ago from the time of making this post and I'm also getting this error as well.
I've looked at the code in question, and it looks like it's trying to convert an array into a string possibly, and may not be able to if the array only has one item in the split (no array)...
But not like I know anything about Python. Just what I gleamed from it...
Here's the error in question.
20-10-10 10:43:18.7|Debug|CustomScript|Executing external script: D:\Python3\python.exe
20-10-10 10:43:18.7|Debug|python.exe|Starting D:\Python3\python.exe D:\sickbeard_mp4_automator\postRadarr.py
20-10-10 10:43:19.9|Debug|python.exe|2020-10-10 10:43:19 - RadarrPostProcess - INFO - Radarr extra script post processing started.
20-10-10 10:43:19.9|Debug|python.exe|2020-10-10 10:43:19 - resources.readsettings - INFO - D:\Python3\python.exe
20-10-10 10:43:20.0|Debug|python.exe|2020-10-10 10:43:19 - resources.readsettings - INFO - Loading config file D:\sickbeard_mp4_automator\config\autoProcess.ini.
20-10-10 10:43:19.9|Error|python.exe|Traceback (most recent call last):
20-10-10 10:43:20.0|Error|python.exe|  File "D:\sickbeard_mp4_automator\postRadarr.py", line 86, in <module>
20-10-10 10:43:20.0|Error|python.exe|    settings = ReadSettings()
20-10-10 10:43:20.0|Error|python.exe|  File "D:\sickbeard_mp4_automator\resources\readsettings.py", line 585, in __init__
20-10-10 10:43:20.0|Error|python.exe|    self.readConfig(config)
20-10-10 10:43:20.0|Error|python.exe|  File "D:\sickbeard_mp4_automator\resources\readsettings.py", line 595, in readConfig
20-10-10 10:43:20.0|Error|python.exe|    self.hwdevices = config.getdict(section, "hwdevices", lower=False, replace=[])
20-10-10 10:43:20.1|Error|python.exe|  File "D:\sickbeard_mp4_automator\resources\readsettings.py", line 43, in getdict
20-10-10 10:43:20.1|Error|python.exe|    output[split[0]] = split[1]
20-10-10 10:43:20.1|Error|python.exe|TypeError: list indices must be integers or slices, not str
20-10-10 10:43:20.1|Debug|CustomScript|Executed external script: D:\Python3\python.exe - Status: 1
20-10-10 10:43:20.1|Debug|CustomScript|Script Output:
10/10/2020 5:43:19 PM - Standard - 2020-10-10 10:43:19 - RadarrPostProcess - INFO - Radarr extra script post processing started.
10/10/2020 5:43:20 PM - Standard - 2020-10-10 10:43:19 - resources.readsettings - INFO - D:\Python3\python.exe
10/10/2020 5:43:20 PM - Standard - 2020-10-10 10:43:19 - resources.readsettings - INFO - Loading config file D:\sickbeard_mp4_automator\config\autoProcess.ini.
10/10/2020 5:43:20 PM - Error - Traceback (most recent call last):
10/10/2020 5:43:20 PM - Error -   File "D:\sickbeard_mp4_automator\postRadarr.py", line 86, in <module>
10/10/2020 5:43:20 PM - Error -     settings = ReadSettings()
10/10/2020 5:43:20 PM - Error -   File "D:\sickbeard_mp4_automator\resources\readsettings.py", line 585, in __init__
10/10/2020 5:43:20 PM - Error -     self.readConfig(config)
10/10/2020 5:43:20 PM - Error -   File "D:\sickbeard_mp4_automator\resources\readsettings.py", line 595, in readConfig
10/10/2020 5:43:20 PM - Error -     self.hwdevices = config.getdict(section, "hwdevices", lower=False, replace=[])
10/10/2020 5:43:20 PM - Error -   File "D:\sickbeard_mp4_automator\resources\readsettings.py", line 43, in getdict
10/10/2020 5:43:20 PM - Error -     output[split[0]] = split[1]
10/10/2020 5:43:20 PM - Error - TypeError: list indices must be integers or slices, not str
20-10-10 10:43:23.7|Debug|Api|[GET] /api/queue?sort_by=timeleft&order=asc: 200.OK (0 ms)
20-10-10 10:44:41.0|Debug|Api|[GET] /api/queue?sort_by=timeleft&order=asc: 200.OK (15 ms)

Radarr is calling PostRadarr, but the movie is just moved, not encoded.

Whoops looks like I introduced a type error
ff28285100ac9666a47c6b8c9ea881284e840bc1
That update should fix it sorry about that

Hi @mdhiggins ,
Good idea! And thanks for your quick reply!
So I want to try offloading MOOV atom to FFmpeg like you suggested.
I disabled relocate-moov = True in my config file.
So now I assume I need to pass post-processing options -movflags faststart to FFmpeg. Where's the best place to add these? Here?

I included that in the sample above, use postopts

also, am I right in saying that telling FFmpeg to relocate moov will essentially cause it process everything again?  Thanks! =)

Doing it through FFMPEG I believe is relatively fast, faster than qtfaststart which I believe is limited by your HD write speed.


chore(package): update standard to version 9.0.0

https://greenkeeper.io/

Coverage remained the same at 89.286% when pulling f675e7a6945873224e55657ca6e5f2bd545faee9 on greenkeeper/standard-9.0.0 into 22d4a4685a7d5f6341d39d650849563a08c0f768 on master.


Listen Port 22

is there a way or is there a -e option to set the listener? I need to listen on port 22 for a server_name So what I am looking for is something like:
$ docker run -e VIRTUAL_HOST=foo.bar.com -e LISTEN_PORT=22 ...
for a result of
    server {
        server_name foo.bar.com;
	listen 22 ;
	access_log /var/log/nginx/access.log vhost;
	location / {
		proxy_pass ssh://foo.bar.com ;
	}
}

@Awatatah did You managed to configure listen port?

@metyl No, I gave up on it. After doing research I found that it is not possible.  What you will have to do is expose addition ports like p- 222:22
Example: docker run --name foo -p 8080:80 -p 222:22 -d  image
Then you can just ssh using that port ssh<EMAIL_ADDRESS>-p 222


Generative AI Studio API Vision QA

In the current notebooks custom models are used. Is it possible to access the generative ai vision qa via api?

Found it in the vertexai source code


Vulnerability roundup 111: gradle-7.3.3: 1 advisory [7.5]

search, files

[ ] CVE-2022-23630 CVSSv3=7.5 (nixos-unstable)

CVE details
CVE-2022-23630
Gradle is a build tool with a focus on build automation and support for multi-language development. In some cases, Gradle may skip that verification and accept a dependency that would otherwise fail the build as an untrusted external artifact. This occurs when dependency verification is disabled on one or more configurations and those configurations have common dependencies with other configurations that have dependency verification enabled. If the configuration that has dependency verification disabled is resolved first, Gradle does not verify the common dependencies for the configuration that has dependency verification enabled. Gradle 7.4 fixes that issue by validating artifacts at least once if they are present in a resolved configuration that has dependency verification active. For users who cannot update either do not use ResolutionStrategy.disableDependencyVerification() and do not use plugins that use that method to disable dependency verification for a single configuration or make sure resolution of configuration that disable that feature do not happen in builds that resolve configuration where the feature is enabled.

Scanned versions: nixos-unstable: 5aaed40d22f.
Cc @liff
Cc @lorenzleutgeb

Fixed in 555e71335641d3613f19ca0dc266996e5b33b793.


My Solutions To The Exercises Of Last Few Chapters

The solutions  written by Mooophy and others are really helpful to our study of CPP. The pity is that solutions to the exercises of last few chapters are not complete, and the formats of file names are not consistent. I wrote my own solutions to the last few chapters. All of the exercises that should be programmed are completed. Others are still being processed. Might be of some help to you.

awesome! :+1:


Replace peerInfo addresses with listen addresses

@dirkmc fyi I needed these changes in another PR, and as I think you are ooo today, I just fixed the linting 😛


This plugin uses symfony console, but does not require it so a newer version can be included breaking build processes.

Module version(s) affected
2.0.3
Description
As far as I can tell this project expects symfony/console 6 (recipe plugin too)
but there is no dependency, so if you update you get console 7 and a bunch of errors.
This is the root cause of #82
How to reproduce
Check the composer.json for this project.
Possible Solution
Add a dependency for the symfony console version this supports.
Additional Context
No response
Validations

[X] Check that there isn't already an issue that reports the same bug
[X] Double check that your reproduction steps work in a fresh installation of silverstripe/installer (with any code examples you've provided)

This is the root cause of #82

Closing as a duplicate of #82


Update http4s-dsl, http4s-ember-client, ... to 0.23.28

About this PR
📦 Updates

org.http4s:http4s-dsl
org.http4s:http4s-ember-client
org.http4s:http4s-ember-server

from 0.23.18 to 0.23.28
📜 GitHub Release Notes - Version Diff
Usage
✅ Please merge!
I'll automatically update this PR to resolve conflicts as long as you don't change it yourself.
If you'd like to skip this version, you can just close this PR. If you have any feedback, just mention me in the comments below.
Configure Scala Steward for your repository with a .scala-steward.conf file.
Have a fantastic day writing Scala!

⚙ Adjust future updates
Add this to your .scala-steward.conf file to ignore future updates of this dependency:
updates.ignore = [ { groupId = "org.http4s" } ]

Or, add this to slow down future updates of this dependency:
dependencyOverrides = [{
  pullRequests = { frequency = "30 days" },
  dependency = { groupId = "org.http4s" }
}]

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

Superseded by #818.


Ledger Claim Unstaked Balance Error

I'm getting this error and am unable to claim refund, any idea why, thanks?  Invalid data received (0x6a80)
Update: got it to work, make sure you have Greymass 0.6.1 and eos ledger app version 1.1.0 installed on device and enable arbitrary data in menu, thanks greymass for solving this 👍

I should probably have added that to the release notes :)


Callback Events : onOpening, onOpened, onClosing, onClosed

I used reveal on a recent project and required needed some callback events to hide/unhide other elements on the page based on the state of the reveal modal.
Here is what I added:

onOpening - Callback made when the modal is preparing to open, visibility is hidden.
onOpened - Callback made when modal is fully opened, visibility is set to visible.
onClosing - Callback made when modal is preparing to close, either esc, close button or modal background are clicked.
onClosed - Callback made when modal has fully closed, visibility is hidden.

Hey alettieri, thanks for this pull request. I took your code and used it as the basis for the most recent commits to Reveal that add this functionality.
After doing so I realized that I didn't start from your commit, so you were not added to the contributor list. If you want to send over a white space pull request I'm happy to merge it in so you're on the list. Also if you send your mailing address to<EMAIL_ADDRESS>we'll mail you a yeti sticker. Thanks again.

Cool. Will do.


Register

@JuliaRegistrator register()
@JuliaRegistrator register subdir=lib/QuadratureCuba
@JuliaRegistrator register subdir=lib/QuadratureCubature

Registration pull request created: JuliaRegistries/General/26213
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:
git tag -a v1.6.0 -m "<description of version>" 52ebeecf9464ae7f28804629c3ff2668c07f6932
git push origin v1.6.0

@JuliaRegistrator register()

Registration pull request created: JuliaRegistries/General/26715
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:
git tag -a v1.7.0 -m "<description of version>" 23de4486f774be74a51f46ed08be6f653a8bb0e4
git push origin v1.7.0

@JuliaRegistrator register()

Registration pull request created: JuliaRegistries/General/26912
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:
git tag -a v1.8.0 -m "<description of version>" a1def7ab1658426075307c4ba2658af375c722cf
git push origin v1.8.0

@JuliaRegistrator register()

Registration pull request created: JuliaRegistries/General/39358
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:
git tag -a v1.9.0 -m "<description of version>" 7af32908a8264f351d7751e3683beb5db7cd19d6
git push origin v1.9.0

@JuliaRegistrator register()

Registration pull request created: JuliaRegistries/General/42705
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:
git tag -a v1.10.0 -m "<description of version>" b167fa35adaca6a869b8c18f2607e5d49cf45c90
git push origin v1.10.0

@JuliaRegistrator register()

Registration pull request created: JuliaRegistries/General/45016
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:
git tag -a v1.11.0 -m "<description of version>" f913f39d9766a5d15f3829c51155ab3b655269d3
git push origin v1.11.0

@JuliaRegistrator register()

Registration pull request created: JuliaRegistries/General/46410
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:
git tag -a v1.12.0 -m "<description of version>" 6743dd66d0fae3226e0fb269acd57d76f92c222e
git push origin v1.12.0

@JuliaRegistrator register()
@JuliaRegistrator register subdir=lib/QuadratureCuba
@JuliaRegistrator register subdir=lib/QuadratureCubature

@JuliaRegistrator register subdir=lib/QuadratureCuba
@JuliaRegistrator register subdir=lib/QuadratureCubature

Registration pull request created: JuliaRegistries/General/60549
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:
git tag -a v2.0.0 -m "<description of version>" d36ea4e0dbe1e8720e726d983c2d2e9a89bcaec8
git push origin v2.0.0

@JuliaRegistrator register subdir=lib/QuadratureCubature

Registration pull request created: JuliaRegistries/General/60550
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:
git tag -a QuadratureCuba-v0.1.0 -m "<description of version>" d36ea4e0dbe1e8720e726d983c2d2e9a89bcaec8
git push origin QuadratureCuba-v0.1.0

Registration pull request created: JuliaRegistries/General/60551
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:
git tag -a QuadratureCubature-v0.1.0 -m "<description of version>" d36ea4e0dbe1e8720e726d983c2d2e9a89bcaec8
git push origin QuadratureCubature-v0.1.0

@JuliaRegistrator register subdir=lib/QuadratureCubature

Registration pull request updated: JuliaRegistries/General/60550
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:
git tag -a QuadratureCuba-v0.1.0 -m "<description of version>" a267d2d1b6053c9cbf7f49419fb788331241f784
git push origin QuadratureCuba-v0.1.0

Registration pull request updated: JuliaRegistries/General/60551
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:
git tag -a QuadratureCubature-v0.1.0 -m "<description of version>" a267d2d1b6053c9cbf7f49419fb788331241f784
git push origin QuadratureCubature-v0.1.0

@JuliaRegistrator register subdir=lib/QuadratureCuba

@JuliaRegistrator register subdir=lib/QuadratureCubature

Registration pull request updated: JuliaRegistries/General/60550
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:
git tag -a QuadratureCuba-v0.1.0 -m "<description of version>" a267d2d1b6053c9cbf7f49419fb788331241f784
git push origin QuadratureCuba-v0.1.0

Registration pull request updated: JuliaRegistries/General/60551
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:
git tag -a QuadratureCubature-v0.1.0 -m "<description of version>" a267d2d1b6053c9cbf7f49419fb788331241f784
git push origin QuadratureCubature-v0.1.0

@JuliaRegistrator register()
@JuliaRegistrator register subdir=lib/IntegralsCuba
@JuliaRegistrator register subdir=lib/IntegralsCubature

Error while trying to register: "Tag with name v2.0.0 already exists and points to a different commit"

@JuliaRegistrator register()

Error while trying to register: Changing package names not supported yet

@JuliaRegistrator register()

Error while trying to register: Changing package names not supported yet

@JuliaRegistrator register()

Registration pull request created: JuliaRegistries/General/61333
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:
git tag -a v2.1.0 -m "<description of version>" e5a98e8927ed30a3b3bc96da3e17068930a3c580
git push origin v2.1.0

Also, note the warning: This looks like a new registration that registers version 2.1.0.
Ideally, you should register an initial release with 0.0.1, 0.1.0 or 1.0.0 version numbers
This can be safely ignored. However, if you want to fix this you can do so. Call register() again after making the fix. This will update the Pull request.

@JuliaRegistrator register()

Error while trying to register: "Tag with name v1.0.0 already exists and points to a different commit"

@JuliaRegistrator register()
@JuliaRegistrator register subdir=lib/IntegralsCuba
@JuliaRegistrator register subdir=lib/IntegralsCubature

Registration pull request created: JuliaRegistries/General/61334
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:
git tag -a v3.0.0 -m "<description of version>" 0af11ce37b54418559fdefc5ebf592c0811b5e35
git push origin v3.0.0

Also, note the warning: This looks like a new registration that registers version 3.0.0.
Ideally, you should register an initial release with 0.0.1, 0.1.0 or 1.0.0 version numbers
This can be safely ignored. However, if you want to fix this you can do so. Call register() again after making the fix. This will update the Pull request.

@JuliaRegistrator register subdir=lib/IntegralsCuba

@JuliaRegistrator register subdir=lib/IntegralsCubature

Error while trying to register: Error in (Julia)Project.toml: Package 'Integrals' with UUID: de52edbc-65ea-441a-8357-d3a637375a31 not found in registry or stdlib

Error while trying to register: Error in (Julia)Project.toml: Package 'Integrals' with UUID: de52edbc-65ea-441a-8357-d3a637375a31 not found in registry or stdlib

@JuliaRegistrator register branch=quad_backport

Error while trying to register: Changing package repo URL not allowed, please submit a pull request with the URL change to the target registry and retry.

@JuliaRegistrator register branch=quad_backport

Registration pull request created: JuliaRegistries/General/61360
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:
git tag -a v2.1.0 -m "<description of version>" c42a7f32f7f78bc0ab927b3a824d26c8195d52c1
git push origin v2.1.0

@JuliaRegistrator register branch=quad_backport subdir=lib/QuadratureCuba

@JuliaRegistrator register branch=quad_backport subdir=lib/QuadratureCubature

Registration pull request created: JuliaRegistries/General/61361
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:
git tag -a QuadratureCuba-v0.1.1 -m "<description of version>" c42a7f32f7f78bc0ab927b3a824d26c8195d52c1
git push origin QuadratureCuba-v0.1.1

Registration pull request created: JuliaRegistries/General/61362
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:
git tag -a QuadratureCubature-v0.1.1 -m "<description of version>" c42a7f32f7f78bc0ab927b3a824d26c8195d52c1
git push origin QuadratureCubature-v0.1.1

@JuliaRegistrator register branch=quad_backport subdir=lib/QuadratureCubature

Registration pull request updated: JuliaRegistries/General/61362
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:
git tag -a QuadratureCubature-v0.1.1 -m "<description of version>" 3828070a45482aa1b1b22c90fed032cc871590aa
git push origin QuadratureCubature-v0.1.1

@JuliaRegistrator register branch=quad_backport subdir=lib/QuadratureCuba

Registration pull request updated: JuliaRegistries/General/61361
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:
git tag -a QuadratureCuba-v0.1.1 -m "<description of version>" 3828070a45482aa1b1b22c90fed032cc871590aa
git push origin QuadratureCuba-v0.1.1

@JuliaRegistrator register subdir=lib/IntegralsCuba
@JuliaRegistrator register subdir=lib/IntegralsCubature

@JuliaRegistrator register subdir=lib/IntegralsCubature

Registration pull request created: JuliaRegistries/General/61562
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:
git tag -a IntegralsCuba-v0.2.0 -m "<description of version>" fdce1e8a978f5cdfbcd41ed312fcfa6f07b7e24d
git push origin IntegralsCuba-v0.2.0

Also, note the warning: This looks like a new registration that registers version 0.2.0.
Ideally, you should register an initial release with 0.0.1, 0.1.0 or 1.0.0 version numbers
This can be safely ignored. However, if you want to fix this you can do so. Call register() again after making the fix. This will update the Pull request.

Registration pull request created: JuliaRegistries/General/61563
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:
git tag -a IntegralsCubature-v0.2.0 -m "<description of version>" fdce1e8a978f5cdfbcd41ed312fcfa6f07b7e24d
git push origin IntegralsCubature-v0.2.0

Also, note the warning: This looks like a new registration that registers version 0.2.0.
Ideally, you should register an initial release with 0.0.1, 0.1.0 or 1.0.0 version numbers
This can be safely ignored. However, if you want to fix this you can do so. Call register() again after making the fix. This will update the Pull request.

@JuliaRegistrator register()

Registration pull request created: JuliaRegistries/General/61566
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:
git tag -a v3.0.1 -m "<description of version>" 6f1f63d7ff526b042cc7856b6a44e21cc6461040
git push origin v3.0.1

@JuliaRegistrator register()

Registration pull request created: JuliaRegistries/General/61769
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:
git tag -a v3.0.2 -m "<description of version>" dc9ed4e1f6f1be30dbf290c130700abca64c3416
git push origin v3.0.2

@JuliaRegistrator register()

Registration pull request created: JuliaRegistries/General/63085
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:
git tag -a v3.1.0 -m "<description of version>" 25361e64f0671b7039cd0bb84886297c95b50e55
git push origin v3.1.0

@JuliaRegistrator register()

Registration pull request created: JuliaRegistries/General/65337
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:
git tag -a v3.1.1 -m "<description of version>" e12b7b94363f252ccd027e65c25edd7f52384075
git push origin v3.1.1

@JuliaRegistrator register()

Registration pull request created: JuliaRegistries/General/66400
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:
git tag -a v3.1.2 -m "<description of version>" 0f36d68e7769e3be0361e0f350fdc44c72fbe96c
git push origin v3.1.2

@JuliaRegistrator register()

@JuliaRegistrator register subdir=lib/QuadratureCuba
@JuliaRegistrator register subdir=lib/QuadratureCubature

Error while trying to register: "File (Julia)Project.toml not found"

@JuliaRegistrator register subdir=lib/QuadratureCuba

Error while trying to register: "File (Julia)Project.toml not found"

@JuliaRegistrator register subdir=lib/QuadratureCubature

Error while trying to register: "File (Julia)Project.toml not found"

@JuliaRegistrator register subdir=lib/IntegralsCuba

Registration pull request created: JuliaRegistries/General/72020
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:
git tag -a IntegralsCuba-v0.2.1 -m "<description of version>" 6727a03e0b97dc918c2d8e8faae1134de045bddb
git push origin IntegralsCuba-v0.2.1

@JuliaRegistrator register subdir=lib/IntegralsCubature

Registration pull request created: JuliaRegistries/General/72021
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:
git tag -a IntegralsCubature-v0.2.1 -m "<description of version>" 6727a03e0b97dc918c2d8e8faae1134de045bddb
git push origin IntegralsCubature-v0.2.1

@JuliaRegistrator register()
@JuliaRegistrator register subdir=lib/IntegralsCuba
@JuliaRegistrator register subdir=lib/IntegralsCubature

@JuliaRegistrator register subdir=lib/IntegralsCuba

@JuliaRegistrator register subdir=lib/IntegralsCubature

Registration pull request created: JuliaRegistries/General/72295
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:
git tag -a IntegralsCuba-v0.2.2 -m "<description of version>" c0b6b60e2ccac63e0130a1bfdad156082ef45962
git push origin IntegralsCuba-v0.2.2

Registration pull request created: JuliaRegistries/General/72296
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:
git tag -a IntegralsCubature-v0.2.2 -m "<description of version>" c0b6b60e2ccac63e0130a1bfdad156082ef45962
git push origin IntegralsCubature-v0.2.2

@JuliaRegistrator register()

Registration pull request created: JuliaRegistries/General/74502
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:
git tag -a v3.4.0 -m "<description of version>" c1b518044f3043eb3072805f819de87503920509
git push origin v3.4.0

Registration pull request created: JuliaRegistries/General/77186
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:
git tag -a v3.5.0 -m "<description of version>" 2500ba02bd76fd4bf72d9a202b754b00a282152b
git push origin v3.5.0

@JuliaRegistrator register()

Registration pull request created: JuliaRegistries/General/77666
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:
git tag -a v3.6.0 -m "<description of version>" ee89c14363ba4f35b767c70e6617f22c9ffa3ef4
git push origin v3.6.0

@JuliaRegistrator register()

Registration pull request created: JuliaRegistries/General/78047
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:
git tag -a v3.7.0 -m "<description of version>" 7558b18d4c6a88f627c7dec3c0975b4e8ab63091
git push origin v3.7.0

@JuliaRegistrator register()

Registration pull request created: JuliaRegistries/General/81962
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:
git tag -a v3.8.0 -m "<description of version>" 646abf1e787b8675ae3db2e581276ee31f42ca0e
git push origin v3.8.0

@JuliaRegistrator register()

Registration pull request created: JuliaRegistries/General/89503
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:
git tag -a v3.9.0 -m "<description of version>" bb6a2a976adb6facd7cbebc68c9628f7c8df9039
git push origin v3.9.0

@JuliaRegistrator register subdir=lib/IntegralsCuba

Registration pull request created: JuliaRegistries/General/92190
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:
git tag -a IntegralsCuba-v0.3.0 -m "<description of version>" 49e38ad7f9432f64f81506ff5c1b5d2cf24b1a23
git push origin IntegralsCuba-v0.3.0

@JuliaRegistrator register subdir=lib/IntegralsCubature

Registration pull request created: JuliaRegistries/General/92289
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:
git tag -a IntegralsCubature-v0.2.3 -m "<description of version>" 51108f1020018d464ff10e5f312362c8da6e4b4d
git push origin IntegralsCubature-v0.2.3

@JuliaRegistrator register()

Registration pull request created: JuliaRegistries/General/94681
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:
git tag -a v4.0.0 -m "<description of version>" 91bdca90012657abd069c9a800770d4aefba3a71
git push origin v4.0.0

@JuliaRegistrator register()

Registration pull request created: JuliaRegistries/General/95389
Tip: Release Notes
Did you know you can add release notes too? Just add markdown formatted text underneath the comment after the text
"Release notes:" and it will be added to the registry PR, and if TagBot is installed it will also be added to the
release that TagBot creates. i.e.
@JuliaRegistrator register

Release notes:

## Breaking changes

- blah

To add them here just re-invoke and the PR will be updated.
Tagging
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:
git tag -a v4.1.0 -m "<description of version>" b9dc38531e8346fc8e635a7240e9da50aa018f58
git push origin v4.1.0

Error while trying to register: Version 4.1.0 already exists

@JuliaRegistrator register()

Registration pull request created: JuliaRegistries/General/100627
Tip: Release Notes
Did you know you can add release notes too? Just add markdown formatted text underneath the comment after the text
"Release notes:" and it will be added to the registry PR, and if TagBot is installed it will also be added to the
release that TagBot creates. i.e.
@JuliaRegistrator register

Release notes:

## Breaking changes

- blah

To add them here just re-invoke and the PR will be updated.
Tagging
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:
git tag -a v4.3.0 -m "<description of version>" e8684ebb6f9b57266aebdec555b64fe91c8645c2
git push origin v4.3.0

Also, note the warning: Version 4.3.0 skips over 4.2.0
This can be safely ignored. However, if you want to fix this you can do so. Call register() again after making the fix. This will update the Pull request.

@JuliaRegistrator register()

Registration pull request created: JuliaRegistries/General/100628
Tip: Release Notes
Did you know you can add release notes too? Just add markdown formatted text underneath the comment after the text
"Release notes:" and it will be added to the registry PR, and if TagBot is installed it will also be added to the
release that TagBot creates. i.e.
@JuliaRegistrator register

Release notes:

## Breaking changes

- blah

To add them here just re-invoke and the PR will be updated.
Tagging
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:
git tag -a v4.2.0 -m "<description of version>" 81dec33b4d81a0c5586d68c30df4cf6b05e32090
git push origin v4.2.0

@JuliaRegistrator register()

Registration pull request created: JuliaRegistries/General/100664
Tip: Release Notes
Did you know you can add release notes too? Just add markdown formatted text underneath the comment after the text
"Release notes:" and it will be added to the registry PR, and if TagBot is installed it will also be added to the
release that TagBot creates. i.e.
@JuliaRegistrator register

Release notes:

## Breaking changes

- blah

To add them here just re-invoke and the PR will be updated.
Tagging
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:
git tag -a v4.3.0 -m "<description of version>" 10d9e901554c95d045605c873aaa6449bc1f940c
git push origin v4.3.0

@JuliaRegistrator register()

Registration pull request created: JuliaRegistries/General/101407
Tip: Release Notes
Did you know you can add release notes too? Just add markdown formatted text underneath the comment after the text
"Release notes:" and it will be added to the registry PR, and if TagBot is installed it will also be added to the
release that TagBot creates. i.e.
@JuliaRegistrator register

Release notes:

## Breaking changes

- blah

To add them here just re-invoke and the PR will be updated.
Tagging
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:
git tag -a v4.4.0 -m "<description of version>" a1ca91dfe8ba98141767973e75eccf05d8cc939a
git push origin v4.4.0

Registration pull request created: JuliaRegistries/General/102097
Tip: Release Notes
Did you know you can add release notes too? Just add markdown formatted text underneath the comment after the text
"Release notes:" and it will be added to the registry PR, and if TagBot is installed it will also be added to the
release that TagBot creates. i.e.
@JuliaRegistrator register

Release notes:

## Breaking changes

- blah

To add them here just re-invoke and the PR will be updated.
Tagging
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:
git tag -a v4.4.1 -m "<description of version>" c2f94295d0110f85a2593c8cc03d84f8587d5f85
git push origin v4.4.1

@JuliaRegistrator register()

Registration pull request created: JuliaRegistries/General/114182
Tip: Release Notes
Did you know you can add release notes too? Just add markdown formatted text underneath the comment after the text
"Release notes:" and it will be added to the registry PR, and if TagBot is installed it will also be added to the
release that TagBot creates. i.e.
@JuliaRegistrator register

Release notes:

## Breaking changes

- blah

To add them here just re-invoke and the PR will be updated.
Tagging
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:
git tag -a v4.5.0 -m "<description of version>" 33d598596df330d225c2b614fdbdfe86f6e1c7a7
git push origin v4.5.0