text:
vscodium: Add 32bit architecture & disable in-app update and more

This PR includes various updates and refactoring to the VSCodium manifest, here are the details.

Description and Homepage: The description and homepage have been updated.
Architecture: The architecture now includes an entry for 32bit versions.
pre_install: and a new "pre_install" section has been added to prevent in-app updates.
Bin: A new "bin" section has been introduced, which allows users to type and employ the actual product name vscodium.
Checkver: The "checkver" field has been updated.
Autoupdate: The "autoupdate" field now includes an entry for 32bit versions.

Closes https://github.com/ScoopInstaller/Extras/issues/11529

[X] I have read the Contributing Guide.

Attribution
PR title and body provided by @StarsbySea .

/verify

I have committed the new homepage again, as the proposed value was reverted after your commit about the updated app version in the manifest. If the old value is intended to stay, please let me know.

If the old value is intended to stay, please let me know.

a mistake

authors: HUMORCE StarsbySea walpox

repo: ScoopInstaller/Extras

license: Unlicense


text:
riff: Add version 3.2.0

Add the riff program to the bucket
Closes #13783

[x] I have read the Contributing Guide.

/verify

/verify

source: gharchive/pull-request

authors: aliesbelik dstcruz niheaven

license_type: permissive

license_source: github-api


text:
Roll out the simplified approach to exposing properties

As introduced here: https://github.com/ScottLogic/d3-financial-components/pull/229/files#diff-a30185c4c94d14d4cf8dabce084e834fR6

Indicators

[ ] Bollinger Bands
[ ] Moving Avergage
[ ] RSI

Scale

[ ] Date Time
[ ] Linear
[ ] Gridlines

Series

[ ] Bar
[ ] Candlestick
[ ] Comparison
[ ] Line
[ ] OHLC

Tools

[ ] Annotation
[ ] Callouts
[ ] Crosshairs
[ ] Fibonacci Fan
[ ] Measure

Utilities

[ ] Chart Builder
[ ] Chart Layout
[ ] Data Generator

#244 #245 should fix fan and measure

This has been superseded by #275

source: gharchive/issue

authors: ColinEberhardt chrisprice jleft

license: MIT


text:
Feature/ternary axis added

Summary
This pull request introduces a new TernaryAxis class to support ternary plot capabilities within ScottPlot. It also includes two helper classes, TernaryAxisCornerLabel and TernaryAxisEdge, which allow for more customizable and flexible ternary plots.
Changes Introduced
TernaryAxis Class:
    Provides the core functionality for creating and rendering ternary plots.
    Includes options for customizable ticks, grid lines, background color, and minor grid settings.

TernaryAxisCornerLabel Class:
    Allows customization of corner labels, including text size, color, offset, alignment, and bolding options.

TernaryAxisEdge Class:
    Defines edges of the ternary triangle and enables customizable line styles, including color, thickness, and alpha (transparency).

Usage Example
var myPlot = new ScottPlot.Plot();
var ternaryAxis = new TernaryAxis();

// Customize corner labels
var componentALabel = ternaryAxis.CornerLabels[0];
componentALabel.LabelText = "Fuel (Methane)";
componentALabel.TextSize = 14;
componentALabel.Color = ScottPlot.Colors.Red;
componentALabel.IsBold = true;

// Add the ternary axis to the plot
myPlot.Add(ternaryAxis);
myPlot.SavePng("ternary_plot.png");

Issue Reference
This PR addresses and implements new ternary plotting features #4413

Hi @manaruto, wow that was super fast! 🚀
I'll probably be able to review this tomorrow and merge it in, then release it in the next NuGet package Sunday.
Note that the build is failing here because in pull requests it treats warnings as errors, so if you're able to resolve the warnings that would be helpful.
Thanks again!
Scott

I’ve addressed all the warnings and fixed the issues, so the build should pass now. Let me know if there's anything else you’d like me to adjust.
Thanks for the guidance!

Hi @manaruto, thanks again for this! I'm going to refine this a bit and make commits directly into this branch, so I recommend not making additional changes here because it may lead to merge conflicts. If you have additional suggestions after my changes, we can open up new pull requests to refine it based on your suggestions. I'm excited to merge this in soon!

Note that I'm going to rename TernaryAxis to TriangularAxis because my understanding is that ternary plots are for displaying points that must add to 100%, but a triangular axis is more flexible. Users could use a triangular axis to display ternary data if they want, but keeping it "triangular" makes it a little more flexible for other use cases.

Making progress!
Wow this is more complicated than I thought though. Classic problem for all programmers 🙃

Just finished adding cookbook examples and am about to merge this in. This is looking fantastic! 🚀
var ta = myPlot.Add.TriangularAxis();

ta.Bottom.Title("Methane", Colors.Red);
ta.Left.Title("Nitrogen", Colors.Green);
ta.Right.Title("Oxygen", Colors.Blue);

Coordinates[] points = [
    ta.GetCoordinates(0.50, 0.80, 0.20),
    ta.GetCoordinates(0.50, 0.60, 0.40),
    ta.GetCoordinates(0.65, 0.70, 0.30),
    ta.GetCoordinates(0.80, 0.80, 0.20),
];

myPlot.Add.Markers(points, MarkerShape.FilledCircle, 10, Colors.Red);

Hi @manaruto, I'd like your input on this. Should the units on the bottom extend from left to right or right to left? It seems they're not consistent in the screenshots shown in #4413

left to right
right to left

Typically, clockwise is more commonly used in general applications, but some ternary plots use counterclockwise labeling for geological applications, which is my field, so it will be good if we make an option for both settings, thanks.

Typically, clockwise is more commonly used in general applications, but some ternary plots use counterclockwise labeling for geological applications, which is my field, so it will be good if we make an option for both settings, thanks.

Done! Add.TriangularAxis() now has an optional argument to toggle clockwise vs. counter-clockwise (#4424)

authors: manaruto swharden

repo: ScottPlot/ScottPlot


text:
feat: make hero section responsive

Right now, the hero text overflows at a smaller screen width. Change the CSS  so that it is readable on all screens

I would like to work on this issue

Thanks @samuelsmith442!

repo: ScrimbaBootcamp/progress-tracker


text:
bar chart use of history

It would be great if the bar chart can be used with historic data history or sql. Or maybe the chart widget can have an options of bars. Bar can be usefull to show daily aggregated values over time.
regards Chris

You can use the JSON Chart in combination with a script to realize a bar chart based on history data


text:
Some Widgets disappear after a few seconds

I have insertet a View in 8 Widget
and implementet a picture.
After starting the runtime to see the widget in fullscreen the picture is gone after
32 seconds.

View in Widget 8 ist Not Part of thus Adapter.
Set the persistent checkbox in enabled. If the problem still exist pleased Report thus to the vis adapter.


text:
Missing primary_key attributes when generating from a DB with partitions

Description
When a postgres DB contains partitions of a partitioned table (if a partitioned table is present but doesn't have any partitions then this issue does not happen). If sea-orm-cli is used to generate entities none of the entities have the primary_key attribute (even non partitioned tables). This seems to have worked prior to 0.12.0.
Steps to Reproduce

Create a table
Create a partitioned table
Create a partition of the partitioned table
Generate entities using sea-orm-cli generate entity

Expected Behavior
Generated entities have primary_key attributes
Actual Behavior
Generated entities do not primary_key attributes
Reproduces How Often
Always
Workarounds
Manually add primary_key attributes after generating entities.
Reproducible Example
CREATE TABLE test_table (
	id serial,
	name text,
	PRIMARY KEY (id)
);

CREATE TABLE partitioned_table (
	partition_key int,
	id serial,
	name text,
	PRIMARY KEY (partition_key, id)
) PARTITION BY HASH (partition_key);

CREATE TABLE partitioned_table_0
PARTITION OF partitioned_table
FOR VALUES WITH (MODULUS 1, REMAINDER 0);

//! `SeaORM` Entity. Generated by sea-orm-codegen 0.12.14

use sea_orm::entity::prelude::*;

#[derive(Clone, Debug, PartialEq, DeriveEntityModel, Eq)]
#[sea_orm(table_name = "partitioned_table")]
pub struct Model {
    pub partition_key: i32,
    pub id: i64,
    #[sea_orm(column_type = "Text", nullable)]
    pub name: Option<String>,
}

#[derive(Copy, Clone, Debug, EnumIter, DeriveRelation)]
pub enum Relation {}

impl ActiveModelBehavior for ActiveModel {}

//! `SeaORM` Entity. Generated by sea-orm-codegen 0.12.14

use sea_orm::entity::prelude::*;

#[derive(Clone, Debug, PartialEq, DeriveEntityModel, Eq)]
#[sea_orm(table_name = "test_table")]
pub struct Model {
    pub id: i32,
    #[sea_orm(column_type = "Text", nullable)]
    pub name: Option<String>,
}

#[derive(Copy, Clone, Debug, EnumIter, DeriveRelation)]
pub enum Relation {}

impl ActiveModelBehavior for ActiveModel {}

Versions
Broken in 0.12.14. I have also tested 0.11.3 and when using it the primary_key attributes are correctly set.

I just compiled the latest main version of sea-orm-cli  and in my case it still failed to add that primary key attribute.

I just compiled the latest main branch version of sea-orm-cli and in my case it still failed to add that primary key attribute.

Looks like we are waiting on a new release of https://github.com/SeaQL/sea-schema

authors: gitmalong omjadas

repo: SeaQL/sea-orm


text:
Expected indentation of 4 spaces (indentation)

Codacy detected an issue:
Message: Expected indentation of 4 spaces (indentation)
Currently on:

Commit:  b5604cba1f4d4735fc9c166d7442ce0fb33bffdf
File:    doc/integrations/myDrive/src/styles/components/_MoverMenu.scss
LineNum: 33

For the convenience of the Seagate development team, this issue has been mirrored in a private Seagate Jira Server: https://jts.seagate.com/browse/CORTX-31451. Note that community members will not be able to access that Jira server but that is not a problem since all activity in that Jira mirror will be copied into this GitHub issue.

Amnuay Boottrakoat commented in Jira Server:

correct indent to match coding standard

repo: Seagate/cortx


text:
Deleting Prometheus logo

Deleting Prometheus logo before adding the resized logo.

Closing this ticket because I  need to upload multiple icons and resize them in the same PR


text:
Join VDI Machines to Domain

If I want to join the VDI machines to my Windows Domain, how would I go about doing this so that when they are re-populated each time restoring from initial snapshop they re-join the domain with same computer name? Is there a way to pull the machine name from KVM and use that to name it? Any suggestions are welcome...

When I use sysprep script, how can I have the VDI machines re-join the domain using a unique name? Like, if I name the initial machine machine01, then how can I have machine02, machine03, etc join the domain using a unique name so that there are no conflictions?
Thank you!

When I use sysprep script, how can I have the VDI machines re-join the domain using a unique name? Like, if I name the initial machine machine01, then how can I have machine02, machine03, etc join the domain using a unique name so that there are no conflictions?
Thank you!

You should run sysprep sript on Initial machine which renames machine/joins it into domain on next startup. After runing this script you should shutdown Initial machine and start VDI instances form that Initial image. This way VDI instances should name themselves and join domain. .

Ok, but my question is what will they use for the "name"? Something random or can I have it pull from the VM name?

No, you cannot get VM name into virtual machine.
You should find a way to generate your own unique names (like using VMIP, or MAC addres for that).

Ok, that works. Thank you for the clarification.

authors: Seitanas boguest

repo: Seitanas/kvm-vdi

license: mit

license_source: bigquery


text:
🛑 Sekai Viewer is down

In 411d413, Sekai Viewer (https://sekai.best) was down:

HTTP code: 525
Response time: 545 ms

Resolved: Sekai Viewer is back up in 5b0dc10.

repo: Sekai-World/uptime-monitor


text:
how to setup controls?

plyr.setup({'controls:["restart"]'})
this produces error unexpected token }
Is there an example of how to setup controls?

Yes because you're passing a JSON string as opposed to an object. Try this:
plyr.setup({ controls: ["restart"] });

tried it just now. now it says setAttributed of undefined.
Maybe because I have multiple players?

I've not tried such a bare bones UI so I'll have to get back to you on that.

Fixed in v1.5.0

repo: Selz/plyr


text:
removed vimeo id parsing because it is not necessary

This parser causes issues with different vimeo url formats such as:
https://vimeo.com/channels/staffpicks/194361952
I propose removing the id parser for Vimeo urls completely instead of fixing it. Vimeo's JS API accepts either an id or URL.

If you prefer I can do a better regex to pull the id but I don't think that is needed

This looks good - cheers 👍

Merged into develop so it'll go with v3.0.0

Ah looks like I'll have to revert this. It doesn't work passing a URL unfortunately. Would have been good to test before hand. I get this:

All good - it looks like the source video was removed. Whoops.

Haha alright. Got scared I broke it for a second. Thanks for the merge

authors: SamPotts stormrockwell


text:
Make all the components follow reactive programming's principles

To me, Ember's philosophy seems to put an emphasis on reactive programming: changing states instead of calling actions.
Proposition
For example, it seems more Emberish to write something like:
{{ui-modal visible=isModalVisible}}

and make the modal appear/disappear when the variable visible changes, and the variable visible change when the user opens the modal or close it another way (by clicking outside it for example)
than it is to call it the way you do in the README:
export default Ember.Controller.extend({
  actions: {
    openModal: function(name) {
      $('.ui.modal').modal('show');
    }
  }
});

It avoids making the controller aware of the DOM, and provides a beautiful way to have more features such as is the modal visible right now? (just read the variable isModalVisible).
Thus, the components should expose MORE properties corresponding to Semantic UI's behaviours, and internally change some of its properties depending on Semantic UI's events.
Partial implementation example
You can find here an example of a component I wrote to handle modals. It is not complete, because it just fills the need I had, but provides this kind of synchronisation between Ember properties and Semantic UI events:
and I can use it just like this:
HBS:
{{#my-modal isModalVisible=modalVisible}}

Title in the modal

{{/my-modal}}

{{#if modalVisible}}
Hide the modal
{{else}}
Show the modal
{{/if}}

Controller:
import Ember from 'ember';

export default Ember.Controller.extend({
    // "is/should the modal (be) currently visible"
    modalVisible: false,

    actions: {
        // Toggle modal visibility
        toggleModal: function () {
            // we can show or hide the modal, just by changing the property "modalVisible"
            this.set('modalVisible', !this.get('modalVisible'));
        }
    }
});

Note that here, if the user displays the modal and then click outside, modalVisible will know that the modal is now closed.
If you agree on this point I can try to implement this programming pattern for some Semantic UI's component in another branch.

Thanks for putting together this writeup @brendan-rius.
I've never been a fan of setting a property on your controller to control the invoking of the modal. I've seen it used in other examples and libraries. However, what I'd like to find is a way that's a bit closer to how ember's routing is setup that cleanly separates the logic and even transition into the modal.
Recently we've built a library that puts together some of the thoughts but I haven't had time to describe and document it and not sure whether we should merge it into this addon or create a separate one for it.
You can find the code here: https://github.com/unchartedcode/semantic-ui-ember-modal
Invoking modals is done through actions and fairly generically and allows you to pass arbitrary data into your modal.
Edit Project

It requires adding an outlet to your application.hbs
{{outlet}}
{{outlet modal}}

and then once the user clicks it the modal component in the path project.edit will get created and data loaded into it.
Here is an example with data loaded

  Generate

It also handles proxying something of the common modal events into ember actions such as mentioned in #80. Though I'd like to do that in a cleaner more generic way (instead of just naming them explicitly.)

In addition to @nathanpalmer last comment, we will be changing to follow a data down actions up methodology for the next major release. This will allow you to directly tie into semantics own methods better. For the modal we might come up with a dual way to handle them. One way through routes, and then another through showing them in a template. From personal experience, some modals are part of a workflow that should be tied to routes, and some are more of notifications that could be on any template when triggered.

Starting on a 2.0 release branch tonight. Closing for this ticket for now. When 2.0 comes out and the changes are available, if you have things on that branch you would like to go over, open another ticket then. Thanks

authors: aaronbhansen brendan-rius nathanpalmer

repo: Semantic-Org/Semantic-UI-Ember


text:
Tab grid props do not seem to take effect

Bug Report
Steps
Trying to create a two-tab Tab component with each taking 50% width.
Creating a Tab component and passing the prop grid={{ paneWidth: 16, tabWidth: 8 } seems to have no effect.
Expected Result
Each tab should have 50% width.
Actual Result
Each tab is smaller than 50% width.
Version
0.85.0
Testcase
https://codesandbox.io/s/jj2m2480o9

<Tab menu={{ pointing: true, fluid: true, widths: 2 }} panes={panes} />

As Tabs are the simple combination of Menu and Grid/Segment components, you need to use correct shorthands, in this case for Menu. This will do exactly that you want :)
https://react.semantic-ui.com/collections/menu/#variations-evenly-divided
https://codesandbox.io/s/9o0nnqzz5y

authors: alexerhardt layershifter

repo: Semantic-Org/Semantic-UI-React


text:
docs(TableExampleSortable): your description

Updated for react hooks

Codecov Report

Merging #3964 into master will decrease coverage by 0.00%.
The diff coverage is n/a.

@@            Coverage Diff             @@
##           master    #3964      +/-   ##
==========================================
- Coverage   99.84%   99.84%   -0.01%
==========================================
  Files         184      183       -1
  Lines        3220     3157      -63
==========================================
- Hits         3215     3152      -63
  Misses          5        5

Impacted Files
Coverage Δ

src/elements/Button/Button.js
95.45% <0.00%> (-0.11%)
:arrow_down:

src/modules/Popup/Popup.js
98.71% <0.00%> (-0.04%)
:arrow_down:

src/modules/Search/Search.js
99.53% <0.00%> (-0.03%)
:arrow_down:

src/modules/Sticky/Sticky.js
99.07% <0.00%> (-0.01%)
:arrow_down:

src/modules/Tab/Tab.js
100.00% <0.00%> (ø)

src/views/Card/Card.js
100.00% <0.00%> (ø)

src/elements/Flag/Flag.js
100.00% <0.00%> (ø)

src/elements/Icon/Icon.js
100.00% <0.00%> (ø)

src/elements/List/List.js
100.00% <0.00%> (ø)

src/elements/Step/Step.js
100.00% <0.00%> (ø)

... and 38 more

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update de506d5...74a7fde. Read the comment docs.

authors: codecov-commenter evcodes


text:
Video Module load over https

I tried using the video module on my website only to discover that the video could not load because semantic uses http to retrieve Youtube videos on my https website. Chrome then blocked this request because it was an insecure connection. Is there any way to force the video module to use https or is this a limitation?

That's a bug. Fixed in next

Sorry tagged wrong commit..

authors: fpayer jlukic

repo: Semantic-Org/Semantic-UI


text:
[Form validation] Wrong radio behavior

As mentioned in this comment.
The code reads:

if the result value is not set, set it to  (isChecked) ? true : false

Two problems here

since the code will set the value if it is not set, so everything is only relevant in the first iteration
the ternary operation is meaningless, isChecked is already the boolean

But the bigger problem is that, it doesn't tell which radio box is checked.
Before that particular commit, the field obj returned should hold the value (.val()) of the checked radio box. After that, however, the value is a boolean which is the checked state of the first radio in the group.

Sorry, duplicate of https://github.com/Semantic-Org/Semantic-UI/issues/5713


text:
Exclude list item link that are buttons from link color override.

Fixes the #2157 issue by excluding .ui.button from a override.

Closed with #2157, passed along credit in release notes

authors: ahtinurme jlukic


text:
Fixed punctuation and flow

Fixed a few punctuation errors and edited a sentence to make it flow better.

Thanks @samuelwight, I read through and it looks better.

authors: jlukic samuelwight


text:
[Popup] Respect the closable option for all kinds of popups, not just those with on='click'

The 'closable' option is useful for other kinds of popups, too, not just those automatically opened with on='click'. For example I use 'manual' popups in a React app, and having the popup code automatically deal with clicks anywhere on the page to close is a very useful thing. I was surprised it didn't work, and then I checked the source.
The "closable" option should IMHO be independent of all other popup options (this is what this pull request implements). If not, at the very least it should also work for 'manual'.
I have a corresponding (tiny) pull request for Semantic-UI-Docs, if this one is accepted.

I've implemented this in a slightly different way, similar to dimmer which uses an auto setting, which defaults to it only being used alongside on: click

authors: jlukic jwr


text:
C++: Support nested field flow

This is the C/C++ side of #1766.

The FPs introduced in this PR are an artifact of the test being a bit artificial. They are fixed by #1797, which is under consideration in Copenhagen.

There's now a conflict, other than this I'm ready to merge this PR.

repo: Semmle/ql


text:
multi delploy: component  not registered

weaver config:
[serviceweaver]
binary = "./hello"
colocate = [
    ["main"],
    ["com/bar/ReverserBar", "com/bar/ReverserFoo"],
]

code dir:
.
├── cmd
├── com
│   ├── bar
│   │   ├── bar.go
│   │   └── weaver_gen.go
│   └── foo
│       ├── foo.go
│       └── weaver_gen.go
├── go.mod
├── go.sum
├── hello
├── main.go
└── weaver.toml

multi delopy error:
error initializing Service Weaver: internal error creating weavelet: component "com.bar.ReverserBar" not registered
why? how to fixed it?

Maybe you should add the package name to the path？

go mod:
module git-biz.qianxin-inc.cn/test/hello
weaver generate code
//components  git-biz.qianxin-inc.cn/test/hello/com/bar/ReverserBar
func init() {
	codegen.Register(codegen.Registration{
		Name:  "git-biz.qianxin-inc.cn/test/hello/com/bar/ReverserBar",
		Iface: reflect.TypeOf((*ReverserBar)(nil)).Elem(),
		New:   func() any { return &reverser{} },
		LocalStubFn: func(impl any, tracer trace.Tracer) any {
			return reverserBar_local_stub{impl: impl.(ReverserBar), tracer: tracer}
		},
		ClientStubFn: func(stub codegen.Stub, caller string) any {
			return reverserBar_client_stub{stub: stub, reverseMetrics: codegen.MethodMetricsFor(codegen.MethodLabels{Caller: caller, Component: "git-biz.qianxin-inc.cn/test/hello/com/bar/ReverserBar", Method: "Reverse"})}
		},
		ServerStubFn: func(impl any, addLoad func(uint64, float64)) codegen.Server {
			return reverserBar_server_stub{impl: impl.(ReverserBar), addLoad: addLoad}
		},
	})
}
//components git-biz.qianxin-inc.cn/test/hello/com/foo/ReverserFoo
func init() {
	codegen.Register(codegen.Registration{
		Name:  "git-biz.qianxin-inc.cn/test/hello/com/foo/ReverserFoo",
		Iface: reflect.TypeOf((*ReverserFoo)(nil)).Elem(),
		New:   func() any { return &reverser{} },
		LocalStubFn: func(impl any, tracer trace.Tracer) any {
			return reverserFoo_local_stub{impl: impl.(ReverserFoo), tracer: tracer}
		},
		ClientStubFn: func(stub codegen.Stub, caller string) any {
			return reverserFoo_client_stub{stub: stub, reverseMetrics: codegen.MethodMetricsFor(codegen.MethodLabels{Caller: caller, Component: "git-biz.qianxin-inc.cn/test/hello/com/foo/ReverserFoo", Method: "Reverse"})}
		},
		ServerStubFn: func(impl any, addLoad func(uint64, float64)) codegen.Server {
			return reverserFoo_server_stub{impl: impl.(ReverserFoo), addLoad: addLoad}
		},
	})
}

weaver config:
[serviceweaver]
binary = "./hello"
colocate = [
    ["main"],
    ["git-biz.qianxin-inc.cn/test/hello/com/bar/ReverserBar", "git-biz.qianxin-inc.cn/test/hello/com/bar/ReverserFoo"],
]

main code
.......
reverser_bar, err := weaver.Get[bar.ReverserBar](root)
    if err != nil {
        log.Fatal(err)
    }
    // Get a client to the Reverser component.
    reverser_foo, err := weaver.Get[foo.ReverserFoo](root)
    if err != nil {
        log.Fatal(err)
    }
    // Serve the /hello endpoint.
    http.HandleFunc("/hello_bar", func(w http.ResponseWriter, r *http.Request) {
        reversed, err := reverser_bar.Reverse(r.Context(), r.URL.Query().Get("name"))
        if err != nil {
            http.Error(w, err.Error(), http.StatusInternalServerError)
        }
        fmt.Fprintf(w, "Bar, %s!\n", reversed)
    })
    // Serve the /hello endpoint.
    http.HandleFunc("/hello_foo", func(w http.ResponseWriter, r *http.Request) {
        reversed, err := reverser_foo.Reverse(r.Context(), r.URL.Query().Get("name"))
        if err != nil {
            http.Error(w, err.Error(), http.StatusInternalServerError)
        }
        fmt.Fprintf(w, "Foo, %s!\n", reversed)
    })
....

when use command weaver multi deploy weaver.toml , got error logs:
error initializing Service Weaver: internal error creating weavelet: component "git-biz.qianxin-inc.cn/test/hello/com/bar/ReverserFoo" not registered

thanks a lot for you.

update config git-biz.qianxin-inc.cn/test/hello/com/bar/ReverserFoo => git-biz.qianxin-inc.cn/test/hello/com/foo/ReverserFoo

update config git-biz.qianxin-inc.cn/test/hello/com/bar/ReverserFoo => git-biz.qianxin-inc.cn/test/hello/com/foo/ReverserFoo

😅

my config is invalid

authors: lemon-1997 luxixing youyuanrsq

repo: ServiceWeaver/weaver


text:
Using 02_recon just for subspace reconstruction

Hi there,
I'm currently working on subspace reconstruction. I'm wondering would the code in 02_recon work for subspace reconstruction or is it specific to MRF?
To be more specific, I have a

kspace data with shape [NCoils, Nreadouts, Ninterleaves, Ntimespoints]

kspace trajectory of shape [ndim=3, Nreadouts, Ninterleaves, Ntimepoints], scaled to [-1/2,1/2]

subspace basis $\phi$ of shape [Ntimepoints, Nbasis]

I could do adjoint reconstruction in MATLAB. But when I tried to pass these data into the main.py, the reconstructed output doesn't make sense.
May I ask is there any advice for making the code run for my data? Or does the code only work for MRF data?
Cannot be more grateful for the help!

Hi Qijia, sorry for the delay in getting back to you, I've not been monitoring this repo for a while.
There is no inherent reason this code wouldn't work for non-MRF data but it has never been tested on anything else. It also has some specific quirks that are worth being aware of, e.g.

In load_data.py (https://github.com/SetsompopLab/MRF/blob/f034fcdcc89881e104abeb7725d451e1da4cbe4c/src/02_recon/load_data.py#L61) you can see some things like by default it will throw away rewinder points in the trajectory as well as some points from the beginning of the trajectory and data (this made sense for our use case, but that might not be the case in general).
load_data.py  also uses .mat files for the trajectory and phi, but the raw data is imported as numpy arrays. There are different conventions with regards to how the data are stored in these different formats which this function takes care of, but if you're importing your data in a different way you might run into trouble.
Many of the optional parameters (https://github.com/SetsompopLab/MRF/blob/f034fcdcc89881e104abeb7725d451e1da4cbe4c/src/02_recon/main.py#L313) can affect your result and their defaults are set MRF data in our pipeline. Worth going through and checking that it's not something there that is messing you up.

I'm glad you are trying out this code, and hope you can get it to work. If you want me to have a closer look at your specific data and call to main.py, please send more details (in particular the full call with all arguments, and what data types and inherent shapes you use).
Cheers!

authors: Michaelsqj SophieSchau

repo: SetsompopLab/MRF


text:
Saves the songs the users queue in the backup list.

One requested feature on the server I'm using this bot on
(TKbreezy/ConeyZZ subscriber Discord) was that the bot play music that
was previously requested when there's nothing on the queue. This
accomplishes that by writing to the backup list when a user !plays a
song and erasing it from the backup list when that song is !skipped.
This seems to work, even though the bot complains that "the file is
locked" whenever a skip occurs.

This should be an option or a command, or both.  I'll implement this at some point since its really quick to do, but PRs go to review, not develop.

authors: imayhaveborkedit johnroot

repo: SexualRhinoceros/MusicBot


text:
I cant import my character

Describe your bug here.
When i try to lead my character it crashes and shows this message

Command Prompt/Terminal logs (if existing)
No response
Are you modding a build from source or with Lua?
Lua
What is your build target?
Windows
Did you edit anything in this build? If so, mention or summarize your changes.
No, just added mods.

this is the sprite sheet btw

Wrong path?

authors: Chidubemmo CollerGIT

repo: ShadowMario/FNF-PsychEngine


text:
non playable characters turn into playable characters when saved

Describe your bug here.
I'm trying to save a character as a nonplayable character but it keeps reverting to a playable character.
Command Prompt/Terminal logs (if existing)
No response
Are you modding a build from source or with Lua?
Lua
What is your build target?
Windows
Did you edit anything in this build? If so, mention or summarize your changes.
no

That checkbox is only visual and it does not impact gameplay.

authors: ShadowMario baja-snick


text:
Apotheosis Logging correction please.

Can you please correct your logging to follow the standard forge log4j logging standard please?
forge-1.16.4-35.1.36
Apotheosis-1.16.3-4.4.1
[13:01:55] [main/INFO] [ne.mi.co.Co.astralsorcery/COREMODLOG]: Adding 'set_player_field' ASM patch...
[13:01:55] [main/INFO] [ne.mi.co.Co.astralsorcery/COREMODLOG]: Added 'set_player_field' ASM patch!

[ApotheosisCore]: Patching LivingEntity#blockUsingShield
[ApotheosisCore]: Patching LivingEntity#applyPotionDamageCalculations

[13:01:55] [main/INFO] [ne.mi.co.Co.astralsorcery/COREMODLOG]: Adding 'water_movement_slowdown_prevention' ASM patch...
[13:01:55] [main/INFO] [ne.mi.co.Co.astralsorcery/COREMODLOG]: Added 'water_movement_slowdown_prevention' ASM patch!

[ApotheosisCore]: Patching SharedMonsterAttributes#readAttributeModifier

[FastWorkbench]: Patching PlayerContainer#onCraftMatrixChanged
[FastWorkbench]: Patching PlayerContainer#transferStackInSlot

[ApotheosisCore]: Patching RepairContainer#updateRepairOutput
[ApotheosisCore]: Successfully removed the anvil level cap.
[ApotheosisCore]: Replaced ContainerRepair Enchantment#getMaxLevel #1.
[ApotheosisCore]: Replaced ContainerRepair Enchantment#getMaxLevel #2.
[ApotheosisCore]: Patching TemptGoal#isTempting

[13:01:58] [main/INFO] [ne.mi.co.Co.astralsorcery/COREMODLOG]: Adding 'add_enchantment_helper_levels' ASM patch...

done for next build, not sure when ASMAPI was given a log method

done for next build, not sure when ASMAPI was given a log method

repo: Shadows-of-Fire/Apotheosis


text:
小姐姐有个手机兼容问题

1.手机上查看时会有左右可以拉的现象,点击文章才能恢复正常,
2.第一次点击更多文章时会出现直接跳到顶部的现象
小姐姐可以解决下吗

这周抽空看看

主题的便签什么的怎么做的，在那个文件夹

@1373246212 手写的，简单的css样式

@xiaokar 你根据自己的需求在body上设置
body {
   max-width: 1000px;
   margin: 0 auto;
}

1000px根据你的值修改

设置后貌似没什么效果，能不能设置禁止缩放和禁止左右滑动，hexo搞不来

authors: 1373246212 ShanaMaid xiaokar

repo: ShanaMaid/hexo-theme-shana


text:
Por Favor Add Config & Other Stuff

Thank you for not removing sprinting mechanic.
& rotten flesh gives 4 hp for what its worth.
Also pls add config for;
Foods health multiplier
Consume instantly
thank you

This mod involves the simplest mechanics for implementing instant food consumption and health point restoration, as it was in the old alpha versions of Minecraft, without adding additional functions and their settings. Moreover, since I'm only a beginner developer, I probably won't be able to implement settings for health multipliers and instant food consumption.
I apologize if I've disappointed you.🙏

okay fair enough ty

authors: Rhaptein SharaGGa

repo: SharaGGa/RetroHungerMod


text:
🛑 Sharafat's Notes is down

In 0f1902c, Sharafat's Notes (https://sharafat.is-a.dev/notes/) was down:

HTTP code: 0
Response time: 0 ms

Resolved: Sharafat's Notes is back up in 2f38061 after 30 minutes.

repo: SharafatKarim/uptime


text:
Get-PnPProvisioningTemplate generates wrong pnp:Security section

As I can guess, Get-PnPProvisioningTemplate cmdlet generates output XML with <pnp:AdditionalAdministrators> after <pnp:AdditionalOwners> in <pnp:Security> section, while XSD desribes them in opposite order in the sequence. So, Apply-PnPProvisioningTemplate fails with error “The provided template is not valid”.

We closed this issue as it had not activity within last 180 days. This is a generic process we have decided to perform for issues, which have not been explicitly marked still to be "work in progress" based on tags. We are performing this cleaning to make sure that old issues that have already been solved (but not closed) or are no longer relevant are cleaned out and make the issues more manageable. If this issue still valid, we would ask you to open a new issue and follow the guidance in the issue template related on the recommended location. We do apologize any inconveniences this might cause. Please do remember that issues in the issue lists are also messages for others in the community, so you can also check if you can assist on any of them. “Sharing is caring!”

authors: msgx spdevdocs

repo: SharePoint/PnP-Provisioning-Schema


text:
[Question] What is the template elements sequence to apply?

Anyone knows where I can find some information of the sequence used by PnP to apply each root elements?
For example the <pnp:WebSettings />is one of the first elements on the xml template but is the last to be applied to the site.
The root element of the SharePoint Provisioning Template.
https://github.com/SharePoint/PnP-Provisioning-Schema/blob/master/ProvisioningSchema-2019-03.md#provisioningtemplate
Thank you

You can have a look at the engine itself: https://github.com/SharePoint/PnP-Sites-Core/blob/master/Core/OfficeDevPnP.Core/Framework/Provisioning/ObjectHandlers/SiteToTemplateConversion.cs#L77

authors: PaoloPia PedroMordeP


text:
Error with "Use custom dialog boxes with SharePoint Framework Extensions" tutorial

When testing the dialog and clicking "Load debug scripts" as prompted am getting the below error.
***Error loading debug manifests.
***Error loading debug script. Ensure the server is running and the "debugManifestsFile" parameter URL is correct.
Error: Script error for "https://localhost:4321/temp/manifests.js" http://requirejs.org/docs/errors.html#scripterror

That seems to imply that your localhost is not running and that your serve.json file is not correct, so would double check those. Can you share the content of those files as currently there's really not enough information to assist in the root cause?

Thanks for the response.  There was an incorrect reference in the json file

I am getting the same error. Could you please point out where was the incorrect reference?

Further info - the error is occurring on Edge browser. Works fine on IE and chrome.

In the serve.json file I had to remove the $schema reference at the top.
{
"$schema": "https://dev.office.com/json-schemas/core-build/serve.schema.json",
"port": 4321,
"https": true,
"serveConfigurations": {
"default": {
"pageUrl": "https://abfoods.sharepoint.com/sites/ABMauri/publishingsite/Documents/Forms/AllItems.aspx",
"customActions": {
"7d00d36d-6d8b-4efa-b850-d597e7a05ad0": {
"location": "ClientSideExtension.ListViewCommandSet.CommandBar",
"properties": {
"sampleTextOne": "One item is selected in the list",
"sampleTextTwo": "This command is always visible."
}
}
}
}
}
}

Hi,
I'm also getting this error when I use Gulp Serve which opens IE Edge browser as default one.
However, when I copy the URL and try it on Firefox, it works perfectly fine.
Something messing up in Edge only I believe.

In order to resolve this issue, I have just opened the (https://localhost:4321/temp/manifests.js) in chrome and made it not secure for test. During this I have kept my command prompt open with build option i.e. gulp serve --nobrowser . You will able to see all the request made during this test in command prompt.

@abashutosh / @MeenaMakwana If you're still having an issue, can you open a new issue with additional detail? The issue that @sfmp originally posted is resolved according to his comment so your issue, if still present, is different and needs different context. Please make sure to include as much detail as possible (don't just reference this issue). Thanks!

authors: MeenaMakwana VesaJuvonen abashutosh andrewconnell mubash7 sfmp

repo: SharePoint/sp-dev-docs


text:
Error loading debug manifests - SPFx Extensions 2

Hello,
I posted question here, but i am not sure if the issue is closed, that someone will receive notification or something. - https://github.com/SharePoint/sp-dev-docs/issues/1175

I have the issue with Error loading debug manifests. I can run debug only in Chrome . When i try to do it on other browser, I get the error.
SPFx 1.7.0
Node - v8.9.4
I ran gulp trust-dev-cert
It is Application Customize Extension
I scaffold new App Customizer with latest version of yeoman and the issue is still in
Nothing. Sad.
Best Regards,
Yavor

Hey all,
just wanted to let you guys know I also have this issue, it works fine if I use IE but not Edge when I develop Extensions.
IE: 11.523.17134
Edge: 42.17134.10
Node: v8.15.0
Thanks,
David

Can you provide the following?

verify you are testing this against latest SPFx v1.7.1
what environment (SPO / SP2019)
URL that's generated & used to load the extension on the page
screenshot of the command prompt used when running gulp serve to run the command
and ideally, screenshot of the error in the browser's NETWORK tab

authors: AntyfrizZz andrewconnell regularroutine


text:
Requires Premium Flow License

From my (limited) understanding of the Flow licensing, calling Flow from a site design will require a Premium License (P1/P2) because the flow uses the HTTP trigger.    I feel that this should be called out in this document so users are aware that they are going to have a per/user charge in order to use this feature.
Actually the right answer is to exempt this scenario from requiring a premium connector since this is really just communicating between two O365 applications; but that is another battle.

Document Details
⚠ Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.

ID: 1afac891-6cf5-1746-6392-8cb61a441770
Version Independent ID: b5d1f95b-f40b-8d0c-7334-7608a3add9ff
Content: Using site designs and Microsoft Flow to track site creation requests
Content Source: docs/declarative-customization/site-design-trigger-flow-tutorial.md
Product: sharepoint
GitHub Login: @spdevdocs
Microsoft Alias: spdevdocs

Dupe of #4467

authors: andrewconnell kylepdx


text:
Consume the Microsoft Graph in the SharePoint Framework tutorial errors

Category

[ ] Question
[ ] Typo
[X] Bug
[ ] Additional article idea

Expected or Desired Behavior
To complete this tutorial successfully https://docs.microsoft.com/en-us/sharepoint/dev/spfx/use-aad-tutorial
Observed Behavior
query any /groups or /me/memberOf
The method of approving API Permissions in sharepoint is not clearly documented or linked to.
Further when you finally work out how to do it, it can't be done. Attempts to approve Microsoft Graph access for Group.Read.All fails with a 400 message. There is no way to view what the error is without raising a support ticket with Microsoft as you are only given a Correlation ID.
Steps to Reproduce
I am a Site Administrator on our Sharepoint Instance (i.e. I have checked permissions and I have them)
in a react spfx app
const client: MSGraphClient;
// ...
client
  .api('/me/joinedGroups')
  .get((error, res: any) => {
    console.log(res.value);
  });

"webApiPermissionRequests": [ { "resource": "Microsoft Graph", "scope": "Group.Read.All" } ]

Related to these I think #2121 , #2243 , #2390 , #1477 , #3247

I was getting this same issue.  Once I gave permissions to read the directory, i was able to get the /me/memberOf.
https://docs.microsoft.com/en-us/graph/api/user-list-memberof?view=graph-rest-1.0&tabs=http
I ran the follow command in the office365 cli powershell and we able to get me solution to call to /memberOf
spo serviceprincipal grant add --resource 'Microsoft Graph' --scope 'Directory.AccessAsUser.All'

authors: jonesron petersgiles


text:
pageContext does not refresh in app customizers on inline navigation

Category
[x ] Question
Question
We assume that based on inline navigation between modern pages and lists/libs, that there is a possible issue with refreshing the page context. This is likely not only a problem with SPFX, perhaps more a general logic bug in SPO itself ?!
This has been tested using the following app customizer template from github:
https://github.com/estruyf/appcustomizer-navigation-issues-spfx
We extended the information the app customizer delivers within the header section with additional properties like "pageItemId, listTitle..."

When a user does an inline navigation between a modern UI page and e.g. the SitePages library, the header displays empty properties, even if you use the standard refresh events in SPFX like:
this.context.application.navigatedEvent
This is the default output if you directly load a doc lib.

This is the output if you do an inline navigation from a page to the lib

As you can see, also the url of the lib is incorrect.
We also figured out that inline navigation to doc libs/lists causes some additional issues which may be related to this behavior, like:

Header placeholder element in DOM is contained twice (one from the page you navigated from, the other from the lib). Perhaps the Modern Page Header is simply outdated?

If you navigate inline from a modern page to SitePages lib, you are not able to open a modern news link in edit mode anymore (an url parameter is not set correctly - ?stay=true) > Fully reload SitePage lib (F5) and it works as expected.

Any help or tipps on how to address this issues to the right people? We allready opened support tickets for these issues on our tenant, still at first level support, and it is not easy to explain the direct user impact because the app customizer is somehow "custom development".
Thanks
Bjoern
Environment details

Your Developer Environment:Windows 10
Target Environment: SharePoint Online
Framework: Node.js v8
Browser(s): any
Tooling: VS Code | SPFx 1.10

Tags
#spfx-tooling #spfx
Possible related issues
#6237

We have noticed this too. Could this be a general problem with query parameters when using inline navigation?

There are indeed issues with partial page transitions, check the following posts for a couple of techniques to overcome this: https://www.eliostruyf.com/things-to-check-in-your-spfx-application-customizer-after-page-transitions/

@estruyf - Thanks for your answer..
Actually we are allready using the same techniques you mentioned in your blog post to workaround this issues. If nothing works, pageContext somehow did not refresh, the only thing we can do (like in your code) is to trigger a full page reload.

The big difference is, that this actually happens if a user navigates between a modern page and a doc lib and not like in your code, only when the user navigates between different hubs or site collections. Means: the overall inline navigation concept between pages and libs can not be used and always needs a full page reload in order to provide the correct pageContext.

BTW, the double placeholders #4945 was already reported a long time ago. Issue is still open.

Can confirm that we are also experiencing this issue.
We are making use of an ApplicationExtension to run some code on all pages, which is now only running on full page loads/reloads.
@estruyf Will explore the options in your article, thanks for that.

We are experiencing this as well.
It happens with the simplest test:
Scaffold a new extension and deploy it as-is.
Navigate between pages using the SharePoint navigation and the dialog only appears when the same navigation link is clicked twice.

This is definitely still an issue for me and according to my logs, it started happening more and more starting in May for me.  I see this happen across tenants thousands of times per week.  I've used some of the workarounds but it's not guaranteed.

Sorry @netti01 for the long delay.
We've fixed this issue, and the fix should be available WW in the next couple of weeks.

This should be WW now.

authors: AJIXuMuK MartinWhiteDAZN MrTantum Peter-Ross-C coreyroth estruyf netti01 patmill


text:
For videos uploaded to SharePoint, Transcription / Closed caption are not auto generated and MediaTranscriptionAutomaticFeatures missing

What type of issue is this?
other
What SharePoint development model, framework, SDK or API is this about?
other (enter in the "Additional environment details" area below)
Target SharePoint environment
SharePoint Online
What browser(s) / client(s) have you tested

[ ] 💥 Internet Explorer
[ ] 💥 Microsoft Edge
[X] 💥 Google Chrome
[ ] 💥 FireFox
[ ] 💥 Safari
[ ] mobile (iOS/iPadOS)
[ ] mobile (Android)
[ ] not applicable
[ ] other (enter in the "Additional environment details" area below)

Additional environment details
Browser version: Chrome (Version 121.0.6167.185 (Official Build) (64-bit))
Issue description
I have users uploading videos to SharePoint environment. They want transcript/captions generated for all videos. Looking more I found a way to turn this feature on by default using powershell script. However I could never read or set the value for property MediaTranscriptionAutomaticFeatues. Also auto captioning is not happening upon uploading videos. Created a support ticket and support analyst could not see it either. I was asked to create an issue here and hence I am creating it here. Please help.
I found help doc at https://learn.microsoft.com/en-us/powershell/module/sharepoint-online/set-spotenant?view=sharepoint-ps which talks about MediaTranscriptionAutomaticFeatures.

Hello, Is there an update to this issue analysis? Please let me know.


text:
Minor changes - ensure correct usage of EnsureProperty method

Minor changes to the code, changed from EnsureProperties to EnsureProperty  extension method where necessary.
Saw a lot of places where we are not using it correctly, am also planning to change it in the Core repository as well, hope that's ok :)
Minor performance improvements since we bypass the unnecessary foreach loop (used internally in the EnsureProperties method.

Thx @gautamdsheth, that indeed makes sense when there's only one property retrieved.

authors: gautamdsheth jansenbe

repo: SharePoint/sp-dev-modernization


text:
the Multi-GPU training acutally duplicates data in each GPU ?

Hello.
I find that the Dataloader constructed in diffuseq/text_datasets.py not used pytorch's DistributedSampler
https://github.com/Shark-NLP/DiffuSeq/blob/bea43e1fd0a954486bc36ad62f2a71dcb2bd300a/diffuseq/text_datasets.py#L47
, which makes the data is actually duplicated in each GPU, e.g., in func:forward_backward in train_util.py
https://github.com/Shark-NLP/DiffuSeq/blob/bea43e1fd0a954486bc36ad62f2a71dcb2bd300a/train_util.py#L235
i.e., each GPU is processing the same data, which makes distributed training pointless.
Is my conjecture correct?
just FYI, the training script in Diffusion-LM's repo train_run.py uses transformers's training script run_clm.py, in which DistributedSampler is used in the Trainer

Another info is that in the multi-gpu sampling script (which is recently updated) "sample_seq2seq.py"
https://github.com/Shark-NLP/DiffuSeq/blob/bea43e1fd0a954486bc36ad62f2a71dcb2bd300a/sample_seq2seq.py#L120-L121
It has an operation to "split data per gpu".
Howerver, the training scripts ("diffuseq/text_datasets.py" or "train_util.py")  do not have this operation

Hi,
Good question!
We follow the training script in Diffusion-LM's repo script/run_train.py. The train_run.py you mentioned uses run_clm.py to train the classifier instead of LM itself.
It's true that we "split data per gpu" when we do sampling. That's because we only want to iterate each test case once and in order. However, when training, we set shuffle=True, which means each GPU gets a different batch of data. It functions in the same way as using DistributedSampler.

Thank you for your reply!  But I am still confused.

the script/run_train.py in Diffusion-LM's repo  also doesn't use DistributedSampler, which means they also have the problem of "data is actually duplicated in each gpu"
set shuffle=True has nothing to do with "each GPU gets a different batch of data".

To verfiy my point, we can turn infinite_loader off and see how many batch iteration it actually runs.
say, if a signle GPU training script has a dataloader of 800 interations.
Then for 4 GPUs training, the dataloader (with DistributedSampler) will run 200 iterations (for the same batchsize) Note that for  DistributedSampler & DistributedDataParallel, the the batchsize of dataloader  is directly the batchsize on each GPU.
But with the existing multi-gpu training script, the data is  duplicated in each gpu, an it will still run 800 iterations for  4 GPU training

Another information:
The the script/run_train.py in Diffusion-LM's repo is modified based on OpenAI's  improved-diffusion.
We can observe that in OpenAI's  improved-diffusion, the dataloader is constructed in improved-diffusion/image_datasets.py. Alghouth they also not use DistributedSampler in dataloader, they directly split the dataset into each gpu in load_data by set (before construct dataloader).
shard=MPI.COMM_WORLD.Get_rank(),
num_shards=MPI.COMM_WORLD.Get_size()

refer to openai/improved-diffusion/improved_diffusion/image_datasets.py
In contrast, in Diffusion-LM's  load_data_text function, there is no such operation (the MPI from mpi4py is even not used at all).
refer to
Diffusion-LM/improved-diffusion/improved_diffusion/text_datasets.py

@summmeer
I will be very appreciate that if you can point out the "split data per gpu" operation (exactly which lines of code) in  existing multi-gpu training script

@Dawn-LX I experienced the same problem, and solved it by implementing custom seeding function.
def seed_all(seed: "Any", deterministic: "bool" = False) -> "None":
    import random
    import numpy as np
    import torch
    from basic_utils.dist_util import get_rank
    if deterministic:
        seed = hash(seed)
        torch.backends.cudnn.deterministic = True  # NOQA
        torch.backends.cudnn.benchmark = False  # NOQA
    else:
        seed = hash(seed) + get_rank()  # Make seed differ by node rank
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)  # contains torch.cuda.manual_seed_all
    generator.seed(seed)

Even though each seed per node differs, model parameters would be synchronized in initialization process refer to [this].(https://discuss.pytorch.org/t/setting-seed-in-torch-ddp/126638) (by this line

@Dawn-LX I experienced the same problem, and solved it by implementing custom seeding function.
def seed_all(seed: "Any", deterministic: "bool" = False) -> "None":
    import random
    import numpy as np
    import torch
    if deterministic:  # False in training, True in sampling
        seed = hash(seed)
        torch.backends.cudnn.deterministic = True  # NOQA
        torch.backends.cudnn.benchmark = False  # NOQA
    else:
        seed = hash(seed) + int(os.environ.get("LOCAL_RANK", "0"))  # Make seed differ by node rank
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)  # contains torch.cuda.manual_seed_all

Even though each seed per node differs, model parameters would be synchronized in initialization process refer to this. (by this line)

@Dawn-LX I experienced the same problem, and solved it by implementing custom seeding function.
def seed_all(seed: "Any", deterministic: "bool" = False) -> "None":
    import random
    import numpy as np
    import torch
    if deterministic:  # False in training, True in sampling
        seed = hash(seed)
        torch.backends.cudnn.deterministic = True  # NOQA
        torch.backends.cudnn.benchmark = False  # NOQA
    else:
        seed = hash(seed) + int(os.environ.get("LOCAL_RANK", "0"))  # Make seed differ by node rank
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)  # contains torch.cuda.manual_seed_all

If you want to use same seed per nodes, you can consider alternative code like below:
Step 1. Change this function to my code below.
https://github.com/Shark-NLP/DiffuSeq/blob/bea43e1fd0a954486bc36ad62f2a71dcb2bd300a/diffuseq/text_datasets.py#L11
def load_data_text(
    batch_size,
    seq_len,
    deterministic=False,
    data_args=None,
    model_emb=None,
    split='train',
    loaded_vocab=None,
    loop=True,
    seed=None,  # ADD THIS
):
    training_data = get_corpus(data_args, seq_len, split=split, loaded_vocab=loaded_vocab)

    dataset = TextDataset(
        training_data,
        data_args,
        model_emb=model_emb
    )

    if seed is not None:
        batch_generator = torch.Generator()
        batch_generator.manual_seed(hash(seed) + int(os.environ.get("LOCAL_RANK", "0")))
    else:
        batch_generator = None

    data_loader = DataLoader(
        dataset,
        batch_size=batch_size,  # 20,
        # drop_last=True,
        shuffle=not deterministic,
        num_workers=0,
        generator=batch_generator,  # ADDED
    )
    if loop:
        return infinite_loader(data_loader)
    else:
        # print(data_loader)
        return iter(data_loader)

Step 2. Add seed argument in training script.
https://github.com/Shark-NLP/DiffuSeq/blob/bea43e1fd0a954486bc36ad62f2a71dcb2bd300a/train.py#L44
line 44~63
    data = load_data_text(
        batch_size=args.batch_size,
        seq_len=args.seq_len,
        data_args = args,
        loaded_vocab=tokenizer,
        model_emb=model_weight,  # use model's weights as init
        seed=args.seed
    )
    next(data)

    data_valid = load_data_text(
        batch_size=args.batch_size,
        seq_len=args.seq_len,
        data_args=args,
        split='valid',
        deterministic=True,
        loaded_vocab=tokenizer,
        model_emb=model_weight,  # using the same embedding wight with tranining data
        seed=args.seed
    )

Hi,
@Dawn-LX In our code, if each GPU loaded different batches, for current gradiant update, it is the same with larger batch size (benefit from multi-GPU).

But with the existing multi-gpu training script, the data is duplicated in each gpu, an it will still run 800 iterations for 4 GPU training

If you want to train total 200 iters on 4 GPU, you can set iters as 50 so that 4 GPU will load 50*4 iters of the data. It maybe ture that this way is not strictly the same as DistrubutedSampler, but on each GPU, the data is not duplicated for each batch.
@kdha0727 Thanks for your clarification, but in code, each node loads different batch of data is we wanted and this fuction is already implemented. I'm a little bit confused by your PR.

Hi,
Good question!
We follow the training script in Diffusion-LM's repo script/run_train.py. The train_run.py you mentioned uses run_clm.py to train the classifier instead of LM itself.
It's true that we "split data per gpu" when we do sampling. That's because we only want to iterate each test case once and in order. However, when training, we set shuffle=True, which means each GPU gets a different batch of data. It functions in the same way as using DistributedSampler.

I understood that you implemented different data loader with shuffle=True keyword in load_data_text function, in diffuseq/text_datset.py. However, this works improperly, due to reasons below.
in torch.utils.data.DataLoader, when using shuffle=True, DataLoader objects makes torch.utils.data.RandomSampler instead of torch.utils.data.SequentialSampler. In this case, without generator keyword, RandomSampler makes new torch.Generator() instance, and its seed is set by torch._C._default_generator. (see torch source code.) which is dependent on transformer.set_seed(args.seed) function in train.py. As a consequence, we will get same data per each process, even though using shuffle=True keyword.
My PR intended to fix this issues. By setting only generator's seed different, other processes will go on with same seed per each processes!

I tested the outputs, and checked that current code returns same data outputs.
python -m torch.distributed.launch --nproc_per_node=4 --master_port=12233 --use_env run_train.py --diff_steps 2000 --lr 0.0001 --learning_steps 140000 --save_interval 20000 --seed 102 --noise_schedule sqrt --hidden_dim 128 --bsz 2048 --microbatch 64 --dataset dialogue --data_dir datasets/CommonsenseConversation --vocab bert --seq_len 128 --schedule_sampler lossaware --notes dialogue

modified train.py

"""
Train a diffusion model on images.
"""

import argparse
import json, torch, os
import numpy as np
from diffuseq.utils import dist_util, logger
from diffuseq.text_datasets import load_data_text
from diffuseq.step_sample import create_named_schedule_sampler
from basic_utils import (
    load_defaults_config,
    create_model_and_diffusion,
    args_to_dict,
    add_dict_to_argparser,
    load_model_emb,
    load_tokenizer
)
from train_util import TrainLoop
from transformers import set_seed
import wandb

### custom your wandb setting here ###
# os.environ["WANDB_API_KEY"] = ""
os.environ["WANDB_MODE"] = "offline"

def create_argparser():
    defaults = dict()
    defaults.update(load_defaults_config())
    parser = argparse.ArgumentParser()
    add_dict_to_argparser(parser, defaults) # update latest args according to argparse
    return parser

def main():
    args = create_argparser().parse_args()
    set_seed(args.seed)
    dist_util.setup_dist()
    logger.configure()
    logger.log("### Creating data loader...")

    tokenizer = load_tokenizer(args)
    model_weight, tokenizer = load_model_emb(args, tokenizer)

    data = load_data_text(
        batch_size=args.batch_size,
        seq_len=args.seq_len,
        data_args = args,
        loaded_vocab=tokenizer,
        model_emb=model_weight # use model's weights as init
    )
    from torch.distributed import barrier
    barrier()
    print(next(data)[1]['input_ids'][0])
    import sys
    sys.exit(0)

Outputs

tensor([  101,  1045,  2219,  2019,  5641, 14007, 16419,  1012,   102,   102,
          101,  1018,  7787,  1029,  4638,  1012,  3194,  1999,  2392,  1029,
         4638,  1012,  8134,  1037, 18851,  1012,  2039, 22994,  2063,  1012,
          102,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0])
tensor([  101,  1045,  2219,  2019,  5641, 14007, 16419,  1012,   102,   102,
          101,  1018,  7787,  1029,  4638,  1012,  3194,  1999,  2392,  1029,
         4638,  1012,  8134,  1037, 18851,  1012,  2039, 22994,  2063,  1012,
          102,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0])
tensor([  101, 25742,  5092,  2497,  2034,  4836,  2385,  2086,  3283,  1010,
         2021,  4268,  2024,  3652,  2039,  2007,  1996,  9476,  2157,  2085,
         1012,  4268,  3065,  2031,  1037,  3109,  1997,  1037, 11142,  2166,
         1012,   102,   102,   101,  2026,  2048,  2095,  2214,  2365,  7425,
         2015, 25742,  5092,  2497,  1012,   102,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0])
tensor([  101,  1045,  2219,  2019,  5641, 14007, 16419,  1012,   102,   102,
          101,  1018,  7787,  1029,  4638,  1012,  3194,  1999,  2392,  1029,
         4638,  1012,  8134,  1037, 18851,  1012,  2039, 22994,  2063,  1012,
          102,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0])

I tested the outputs, and checked that current code returns same data outputs.
Since process 0 uses random generator while initializing random embedding (load_model_emb), data of process 0 is different, however, other processes' datum were same, in output.
python -m torch.distributed.launch --nproc_per_node=4 --master_port=12233 --use_env run_train.py --diff_steps 2000 --lr 0.0001 --learning_steps 140000 --save_interval 20000 --seed 102 --noise_schedule sqrt --hidden_dim 128 --bsz 2048 --microbatch 64 --dataset dialogue --data_dir datasets/CommonsenseConversation --vocab bert --seq_len 128 --schedule_sampler lossaware --notes dialogue

modified train.py

"""
Train a diffusion model on images.
"""

import argparse
import json, torch, os
import numpy as np
from diffuseq.utils import dist_util, logger
from diffuseq.text_datasets import load_data_text
from diffuseq.step_sample import create_named_schedule_sampler
from basic_utils import (
    load_defaults_config,
    create_model_and_diffusion,
    args_to_dict,
    add_dict_to_argparser,
    load_model_emb,
    load_tokenizer
)
from train_util import TrainLoop
from transformers import set_seed
import wandb

### custom your wandb setting here ###
# os.environ["WANDB_API_KEY"] = ""
os.environ["WANDB_MODE"] = "offline"

def create_argparser():
    defaults = dict()
    defaults.update(load_defaults_config())
    parser = argparse.ArgumentParser()
    add_dict_to_argparser(parser, defaults) # update latest args according to argparse
    return parser

def main():
    args = create_argparser().parse_args()
    set_seed(args.seed)
    dist_util.setup_dist()
    logger.configure()
    logger.log("### Creating data loader...")

    tokenizer = load_tokenizer(args)
    model_weight, tokenizer = load_model_emb(args, tokenizer)

    data = load_data_text(
        batch_size=args.batch_size,
        seq_len=args.seq_len,
        data_args = args,
        loaded_vocab=tokenizer,
        model_emb=model_weight # use model's weights as init
    )
    from torch.distributed import barrier
    barrier()
    print(get_rank(), next(data)[1]['input_ids'][0])
    import sys
    sys.exit(0)

Outputs

1 tensor([  101,  1045,  2219,  2019,  5641, 14007, 16419,  1012,   102,   102,
          101,  1018,  7787,  1029,  4638,  1012,  3194,  1999,  2392,  1029,
         4638,  1012,  8134,  1037, 18851,  1012,  2039, 22994,  2063,  1012,
          102,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0])
2 tensor([  101,  1045,  2219,  2019,  5641, 14007, 16419,  1012,   102,   102,
          101,  1018,  7787,  1029,  4638,  1012,  3194,  1999,  2392,  1029,
         4638,  1012,  8134,  1037, 18851,  1012,  2039, 22994,  2063,  1012,
          102,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0])
0 tensor([  101, 25742,  5092,  2497,  2034,  4836,  2385,  2086,  3283,  1010,
         2021,  4268,  2024,  3652,  2039,  2007,  1996,  9476,  2157,  2085,
         1012,  4268,  3065,  2031,  1037,  3109,  1997,  1037, 11142,  2166,
         1012,   102,   102,   101,  2026,  2048,  2095,  2214,  2365,  7425,
         2015, 25742,  5092,  2497,  1012,   102,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0])
3 tensor([  101,  1045,  2219,  2019,  5641, 14007, 16419,  1012,   102,   102,
          101,  1018,  7787,  1029,  4638,  1012,  3194,  1999,  2392,  1029,
         4638,  1012,  8134,  1037, 18851,  1012,  2039, 22994,  2063,  1012,
          102,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
            0,     0,     0,     0,     0,     0,     0,     0])

Hi
@kdha0727 You're right. I just tested the case when --nproc_per_node=2. When the number of nodes is greater than 2, the duplication problem does exist. In this situation, using DistrubutedSampler is a more convenient implementation solution, instead of manually setting random seed? I will fix this soon.

Hi
@kdha0727 You're right. I just tested the case when --nproc_per_node=2. When the number of nodes is greater than 2, the duplication problem does exist. In this situation, using DistrubutedSampler is a more convenient implementation solution, instead of manually setting random seed? I will fix this soon.

Yes. my method is fine for infinite loops, however, considering more general cases, DistributedSampler would be more compact solution. Thank you for reviewing!

@summmeer @kdha0727
Thank you for both of your replies and contributions !

By the explanation in https://github.com/Shark-NLP/DiffuSeq/issues/37#issuecomment-1484040068, I'm now undertstand why the original code use shuffle=True to achieve "each node loads different batch of data", and not need to explicitly split data to each GPU.
I am also very appreciate the explanation by @kdha0727  at https://github.com/Shark-NLP/DiffuSeq/issues/37#issuecomment-1484060303 ,  which shows simply set shuffle=True can not  implement different data loader for each gpu, due to the random seed issue.

Thank both of your contributions again!, from which I learned a lot about torch's DataLoader and random seed.

@kdha0727
Another small question (although might not necessary now),
In load_model_emb, how (and where) does the process 0 uses (another?) random generator, another random seed different from process ,1,2,3 ?

@Dawn-LX
In default, torch random operation such as torch.randn shares default random generator torch._C._default_genetator (it's for CPU and genrrator for GPU also exists), unless we don't add generator keyword. My intend (for PR) was to make dataloader use another generator instead of default one.
Meanwhile, in funcction load_model_emb, when local rank equals 0 function calls torch.nn.init.normal_(model.weight), and this is random operation which uses default generator.

@Dawn-LX In default, torch random operation such as torch.randn uses default random generator torch._C._default_genetator (it's for CPU and genrrator for GPU also exists), unless we don't add generator keyword. My intend (for PR) was to make dataloader use another generator instead of default one. Meanwhile, in funcction load_model_emb, when local rank equals 0 function calls torch.nn.init.normal_(model.weight), and this is random operation which uses default generator. (Model initialization is also random process too but it's same in all processes and doesn't make gap)

Thank you very much !
I get it !

wait, I still confused.
a) I understand that we construct Dataloader with generator=None, and all processed use RandomSampler, which build new torch.Generator() instance, of which the seed is set by seed = int(torch.empty((), dtype=torch.int64).random_().item()), where the .random_() uses torch._C._default_genetator, which is dependent on transformer.set_seed(args.seed).
b) On the other hand, process 0 calls torch.nn.init.normal_(model.weight), which is also uses torch._C._default_genetator ( and is  dependent on transformer.set_seed(args.seed). )
So why process 0 has different data batch in dataloader's interation (uses different random seed) ?, In my view,  since all processes use the same seed according to the  description in a). Why process-0 do things in b) makes it's dataloader uses another seed ? I mean, why does the seed in RandomSampler has something to do with ``torch.nn.init.normal_(model.weight)` ?

@Dawn-LX It doesn't mean that process 0 use another seed. In original code all processes use same seed, however, process 0 has ONLY 1 MORE RANDOM OPERATION before RandomSampler being initialized, and this ONLY 1 MORE RANDOM OPERATION makes different output even with same seed.

@Dawn-LX It doesn't mean that process 0 use another seed. In original code all processes use same seed, however, process 0 has ONLY 1 MORE RANDOM OPERATION before RandomSampler being initialized, and this ONLY 1 MORE RANDOM OPERATION makes different random output (for dataloader's seed) even with same seed.

@kdha0727
Thank you! Now it all makes sense to me.
That means in contrast to process 1,2,3,  process-0 has different results of this line seed = int(torch.empty((), dtype=torch.int64).random_().item()) when build the generator RandomSampler. Because process-0 did ONLY 1 MORE RANDOM OPERATION by torch.nn.init.normal_(model.weight).

authors: Dawn-LX kdha0727 summmeer

repo: Shark-NLP/DiffuSeq


text:
npm installed node-xlsx does not provide xlsx.utils. Use the npm 'xlsx' package instead.

Hi - Writing for a friend.. hope it helps someone else.
I was (and am still) mixing up libraries when I found myself banging my head against all examples pointing to npm install node-xlsx and then simply cite xlsx.utils.XYZ, but for me, .utils was not an object.  Elsewhere I found this debug snippet:
...
xlsx = require('node-xlsx'),
...
console.log(Object.keys(xlsx));  // not even down to .utils, just main xlsx

Output of this is:
[ 'parse', 'build', 'default' ]
Ah hah.  So I was in fact using the wrong npm.  Instead of node-xlsx I needed just node install --save xlsx and all works as expected.  console.log(Object.keys(xlsx)); gives this and .utils gives that.
This:
  'stream',
  'parse_xlscfb',
  'parse_ods',
  'parse_fods',
  'write_ods',
  'parse_zip',
  'read',
  'readFile',
  'readFileSync',
  'write',
  'writeFile',
  'writeFileSync',
  'writeFileAsync',
  'utils',
  'SSF',
  'CFB' ]

That:
  'encode_row',
  'encode_cell',
  'encode_range',
  'decode_col',
  'decode_row',
  'split_cell',
  'decode_cell',
  'decode_range',
  'format_cell',
  'get_formulae',
  'make_csv',
  'make_json',
  'make_formulae',
  'sheet_add_aoa',
  'sheet_add_json',
  'aoa_to_sheet',
  'json_to_sheet',
  'table_to_sheet',
  'table_to_book',
  'sheet_to_csv',
  'sheet_to_txt',
  'sheet_to_json',
  'sheet_to_html',
  'sheet_to_dif',
  'sheet_to_slk',
  'sheet_to_eth',
  'sheet_to_formulae',
  'sheet_to_row_object_array',
  'consts',
  'book_new',
  'book_append_sheet',
  'book_set_sheet_visibility',
  'cell_set_number_format',
  'cell_set_hyperlink',
  'cell_set_internal_link',
  'cell_add_comment',
  'sheet_set_array_formula' ]```
Thanks for listening.

@bronius tell your friend that node-xlsx and xlsx are in fact different projects. It's confusing.  Someone else also published js-xlsx on npm so it gets even more confusing.

Ok thanks for validating my observation, psychological framework, and even including a little humor. :D I assure you my friend feels better.

authors: bronius reviewher

repo: SheetJS/js-xlsx


text:
Install fails - linux - compile issues - numpy import_array()

Hello,
I was unable to install on Linux. I tried pip and from source. There did not seem to be any error, just warnings relating to numpy import_array(). The console output and conda env list is copied below.
I have done a bit of searching and found a solution. I will make a PR of the proposed fix just now.
Console output
(bayesopt) agp1@imapspc0049 ~/GitHub/ajgpitch/GPy $ python setup.py develop
running develop
running egg_info
writing GPy.egg-info/PKG-INFO
writing dependency_links to GPy.egg-info/dependency_links.txt
writing requirements to GPy.egg-info/requires.txt
writing top-level names to GPy.egg-info/top_level.txt
reading manifest file 'GPy.egg-info/SOURCES.txt'
reading manifest template 'MANIFEST.in'
warning: no files found matching 'README.rst'
writing manifest file 'GPy.egg-info/SOURCES.txt'
running build_ext
building 'GPy.kern.src.stationary_cython' extension
gcc -pthread -B /home/agp1/anaconda3/envs/bayesopt/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I/home/agp1/anaconda3/envs/bayesopt/lib/python3.7/site-packages/numpy/core/include -I. -I/home/agp1/anaconda3/envs/bayesopt/include/python3.7m -c GPy/kern/src/stationary_cython.c -o build/temp.linux-x86_64-3.7/GPy/kern/src/stationary_cython.o -fopenmp -O3
In file included from /home/agp1/anaconda3/envs/bayesopt/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1823:0,
                 from /home/agp1/anaconda3/envs/bayesopt/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
                 from /home/agp1/anaconda3/envs/bayesopt/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
                 from GPy/kern/src/stationary_cython.c:242:
/home/agp1/anaconda3/envs/bayesopt/lib/python3.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
 #warning "Using deprecated NumPy API, disable it by " \
  ^
GPy/kern/src/stationary_cython.c: In function ‘__Pyx_ExceptionSave’:
GPy/kern/src/stationary_cython.c:19389:19: error: ‘PyThreadState’ has no member named ‘exc_type’
     *type = tstate->exc_type;
                   ^
GPy/kern/src/stationary_cython.c:19390:20: error: ‘PyThreadState’ has no member named ‘exc_value’
     *value = tstate->exc_value;
                    ^
GPy/kern/src/stationary_cython.c:19391:17: error: ‘PyThreadState’ has no member named ‘exc_traceback’
     *tb = tstate->exc_traceback;
                 ^
GPy/kern/src/stationary_cython.c: In function ‘__Pyx_ExceptionReset’:
GPy/kern/src/stationary_cython.c:19403:22: error: ‘PyThreadState’ has no member named ‘exc_type’
     tmp_type = tstate->exc_type;
                      ^
GPy/kern/src/stationary_cython.c:19404:23: error: ‘PyThreadState’ has no member named ‘exc_value’
     tmp_value = tstate->exc_value;
                       ^
GPy/kern/src/stationary_cython.c:19405:20: error: ‘PyThreadState’ has no member named ‘exc_traceback’
     tmp_tb = tstate->exc_traceback;
                    ^
GPy/kern/src/stationary_cython.c:19406:11: error: ‘PyThreadState’ has no member named ‘exc_type’
     tstate->exc_type = type;
           ^
GPy/kern/src/stationary_cython.c:19407:11: error: ‘PyThreadState’ has no member named ‘exc_value’
     tstate->exc_value = value;
           ^
GPy/kern/src/stationary_cython.c:19408:11: error: ‘PyThreadState’ has no member named ‘exc_traceback’
     tstate->exc_traceback = tb;
           ^
GPy/kern/src/stationary_cython.c: In function ‘__Pyx_GetException’:
GPy/kern/src/stationary_cython.c:19451:22: error: ‘PyThreadState’ has no member named ‘exc_type’
     tmp_type = tstate->exc_type;
                      ^
GPy/kern/src/stationary_cython.c:19452:23: error: ‘PyThreadState’ has no member named ‘exc_value’
     tmp_value = tstate->exc_value;
                       ^
GPy/kern/src/stationary_cython.c:19453:20: error: ‘PyThreadState’ has no member named ‘exc_traceback’
     tmp_tb = tstate->exc_traceback;
                    ^
GPy/kern/src/stationary_cython.c:19454:11: error: ‘PyThreadState’ has no member named ‘exc_type’
     tstate->exc_type = local_type;
           ^
GPy/kern/src/stationary_cython.c:19455:11: error: ‘PyThreadState’ has no member named ‘exc_value’
     tstate->exc_value = local_value;
           ^
GPy/kern/src/stationary_cython.c:19456:11: error: ‘PyThreadState’ has no member named ‘exc_traceback’
     tstate->exc_traceback = local_tb;
           ^
GPy/kern/src/stationary_cython.c: In function ‘__Pyx_ExceptionSwap’:
GPy/kern/src/stationary_cython.c:19478:22: error: ‘PyThreadState’ has no member named ‘exc_type’
     tmp_type = tstate->exc_type;
                      ^
GPy/kern/src/stationary_cython.c:19479:23: error: ‘PyThreadState’ has no member named ‘exc_value’
     tmp_value = tstate->exc_value;
                       ^
GPy/kern/src/stationary_cython.c:19480:20: error: ‘PyThreadState’ has no member named ‘exc_traceback’
     tmp_tb = tstate->exc_traceback;
                    ^
GPy/kern/src/stationary_cython.c:19481:11: error: ‘PyThreadState’ has no member named ‘exc_type’
     tstate->exc_type = *type;
           ^
GPy/kern/src/stationary_cython.c:19482:11: error: ‘PyThreadState’ has no member named ‘exc_value’
     tstate->exc_value = *value;
           ^
GPy/kern/src/stationary_cython.c:19483:11: error: ‘PyThreadState’ has no member named ‘exc_traceback’
     tstate->exc_traceback = *tb;
           ^
In file included from /home/agp1/anaconda3/envs/bayesopt/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:27:0,
                 from /home/agp1/anaconda3/envs/bayesopt/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
                 from GPy/kern/src/stationary_cython.c:242:
GPy/kern/src/stationary_cython.c: At top level:
/home/agp1/anaconda3/envs/bayesopt/lib/python3.7/site-packages/numpy/core/include/numpy/__multiarray_api.h:1463:1: warning: ‘_import_array’ defined but not used [-Wunused-function]
 _import_array(void)
 ^
error: command 'gcc' failed with exit status 1

conda env
# packages in environment at /home/agp1/anaconda3/envs/bayesopt:
#
# Name                    Version                   Build  Channel
alabaster                 0.7.12                   py37_0
asn1crypto                0.24.0                   py37_0
astroid                   2.0.4                    py37_0
babel                     2.6.0                    py37_0
backcall                  0.1.0                    py37_0
blas                      1.0                         mkl
bleach                    3.0.2                    py37_0
ca-certificates           2018.03.07                    0
certifi                   2018.10.15               py37_0
cffi                      1.11.5           py37he75722e_1
chardet                   3.0.4                    py37_1
cloudpickle               0.6.1                    py37_0
cryptography              2.3.1            py37hc365091_0
cycler                    0.10.0                   py37_0
cython                    0.29             py37he6710b0_0
dbus                      1.13.2               h714fa37_1
decorator                 4.3.0                    py37_0
docutils                  0.14                     py37_0
entrypoints               0.2.3                    py37_2
expat                     2.2.6                he6710b0_0
fontconfig                2.13.0               h9420a91_0
freetype                  2.9.1                h8a8886c_1
glib                      2.56.2               hd408876_0
gmp                       6.1.2                h6c8ec71_1
GPy                       1.9.5                     <pip>
GPyOpt                    1.2.5                     <pip>
gst-plugins-base          1.14.0               hbbd80ab_1
gstreamer                 1.14.0               hb453b48_1
icu                       58.2                 h9c2bf20_1
idna                      2.7                      py37_0
imagesize                 1.1.0                    py37_0
intel-openmp              2019.0                      118
ipykernel                 5.0.0            py37h39e3cac_0
ipython                   7.0.1            py37h39e3cac_0
ipython_genutils          0.2.0                    py37_0
isort                     4.3.4                    py37_0
jedi                      0.13.1                   py37_0
jeepney                   0.4                      py37_0
jinja2                    2.10                     py37_0
jpeg                      9b                   h024ee3a_2
jsonschema                2.6.0                    py37_0
jupyter_client            5.2.3                    py37_0
jupyter_core              4.4.0                    py37_0
keyring                   15.1.0                   py37_0
kiwisolver                1.0.1            py37hf484d3e_0
lazy-object-proxy         1.3.1            py37h14c3975_2
libedit                   3.1.20170329         h6b74fdf_2
libffi                    3.2.1                hd88cf55_4
libgcc-ng                 8.2.0                hdf63c60_1
libgfortran-ng            7.3.0                hdf63c60_0
libpng                    1.6.35               hbc83047_0
libsodium                 1.0.16               h1bed415_0
libstdcxx-ng              8.2.0                hdf63c60_1
libuuid                   1.0.3                h1bed415_2
libxcb                    1.13                 h1bed415_1
libxml2                   2.9.8                h26e45fe_1
markupsafe                1.0              py37h14c3975_1
matplotlib                3.0.0            py37h5429711_0
mccabe                    0.6.1                    py37_1
mistune                   0.8.4            py37h7b6447c_0
mkl                       2019.0                      118
mkl_fft                   1.0.6            py37h7dd41cf_0
mkl_random                1.0.1            py37h4414c95_1
nbconvert                 5.3.1                    py37_0
nbformat                  4.4.0                    py37_0
ncurses                   6.1                  hf484d3e_0
nose                      1.3.7                    py37_2
numpy                     1.15.2           py37h1d66e8a_1
numpy-base                1.15.2           py37h81de0dd_1
numpydoc                  0.8.0                    py37_0
openssl                   1.0.2p               h14c3975_0
packaging                 18.0                     py37_0
pandoc                    2.2.3.2                       0
pandocfilters             1.4.2                    py37_1
paramz                    0.9.4                     <pip>
parso                     0.3.1                    py37_0
pcre                      8.42                 h439df22_0
pexpect                   4.6.0                    py37_0
pickleshare               0.7.5                    py37_0
pip                       18.1                      <pip>
pip                       10.0.1                   py37_0
prompt_toolkit            2.0.6                    py37_0
psutil                    5.4.7            py37h14c3975_0
ptyprocess                0.6.0                    py37_0
pycodestyle               2.4.0                    py37_0
pycparser                 2.19                     py37_0
pyflakes                  2.0.0                    py37_0
pygments                  2.2.0                    py37_0
pylint                    2.1.1                    py37_0
pyopenssl                 18.0.0                   py37_0
pyparsing                 2.2.2                    py37_0
pyqt                      5.9.2            py37h05f1152_2
pysocks                   1.6.8                    py37_0
python                    3.7.0                h6e4f718_3
python-dateutil           2.7.3                    py37_0
pytz                      2018.5                   py37_0
pyzmq                     17.1.2           py37h14c3975_0
qt                        5.9.6                h8703b6f_2
qtawesome                 0.5.1                    py37_1
qtconsole                 4.4.1                    py37_0
qtpy                      1.5.1                    py37_0
qutip                     4.3.1                     <pip>
readline                  7.0                  h7b6447c_5
requests                  2.19.1                   py37_0
rope                      0.11.0                   py37_0
scipy                     1.1.0            py37hfa4b5c9_1
secretstorage             3.1.0                    py37_0
setuptools                40.4.3                   py37_0
simplegeneric             0.8.1                    py37_2
sip                       4.19.8           py37hf484d3e_0
six                       1.11.0                   py37_1
snowballstemmer           1.2.1                    py37_0
sphinx                    1.8.1                    py37_0
sphinxcontrib             1.0                      py37_1
sphinxcontrib-websupport  1.1.0                    py37_1
spyder                    3.3.1                    py37_1
spyder-kernels            0.2.6                    py37_0
sqlite                    3.25.2               h7b6447c_0
testpath                  0.4.2                    py37_0
tk                        8.6.8                hbc83047_0
tornado                   5.1.1            py37h7b6447c_0
traitlets                 4.3.2                    py37_0
urllib3                   1.23                     py37_0
wcwidth                   0.1.7                    py37_0
webencodings              0.5.1                    py37_1
wheel                     0.32.1                   py37_0
wrapt                     1.10.11          py37h14c3975_2
xz                        5.2.4                h14c3975_4
zeromq                    4.2.5                hf484d3e_1
zlib                      1.2.11               ha838bed_2

Actually I found that what actually fixed the the problem was the re cythonize of the source files. Works even with the fix in #693

I don't know if this is the same problem but make sure the necessary libraries are installed and up to date with...
(copying from other issue):...
I'm not sure which of these are necessary, sorry, but some combination of the below will probably fix this (on AWS Ubuntu 18.04 anyway)
sudo apt-get install python3-dev
sudo apt-get update
sudo apt-get install build-essential

or maybe:
conda update anaconda
(please let us know if that solves your problems)

repo: SheffieldML/GPy


text:
More bugs with all discrete values

For example, I set domain = {["type":"discrete", "domain":(0,1)]}
Now the freedom of the parameter space is only 2. If I set max_iter =3, the GpyBayesianOptimizer will crash after two iterations, saying that it covers the entire parameter space.
However, that's limited the use cases. If the entire para-space has been covered, you should return the best rather than just crush....

We have done some work on this that has been pushed to the devel branch. This problems should be fixed now. And thanks for mentioning it and using the library!

repo: SheffieldML/GPyOpt


text:
Fix the bug with respect to scipy 1.5

The LP optimization fails when scipy upgrades to 1.5 (#329).  The optimization code is changed to support the interface of Scipy 1.5 while keeping backward compatibility.

Codecov Report

Merging #330 into master will increase coverage by 0.00%.
The diff coverage is 100.00%.

@@           Coverage Diff           @@
##           master     #330   +/-   ##
=======================================
  Coverage   77.44%   77.45%
=======================================
  Files          97       97
  Lines        4668     4670    +2
=======================================
+ Hits         3615     3617    +2
  Misses       1053     1053

Impacted Files
Coverage Δ

GPyOpt/core/evaluators/batch_local_penalization.py
100.00% <100.00%> (ø)

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update 43dd7ae...c1c03b1. Read the comment docs.

Thanks for a quick fix! Just a sanity check: have you tried it with 1.4 and 1.5 to make sure it works for both versions?

Yes, it works for both versions.

authors: apaleyes codecov-commenter zhenwendai


text:
Implement theme switcher

Implement the Skeleton UI theme switcher seen on their page: https://www.skeleton.dev/.
If this is possible, please save this setting in local storage as well, so I don't have to change it everytime.

I already reached out to Team Skeleton in Discord and they told me to wait a bit longer because many people are asking for that and they're currently working on a solid and official solution.

authors: Shackless TimoKorinth

repo: ShipBit/slickgpt


text:
Eco system with/without considering wind force and moment in MMG 3DOF simulation.

Even if wind force and moment are not considered, we should input some information of wind for simulating MMG.
This is really bothersome for users.

#59 #60 #61


text:
adding db instance

https://github.com/Shippable/api/issues/17999

Merging as this is purely to test provisioning. The credentials will be converted to secure values in a subsequent PR.

repo: Shippable/infra


text:
🛑 ShippersEdge Rates API is down

In 4e795a3, ShippersEdge Rates API (https://api.shippersedge.com/v2/rates/shipment/365186086) was down:

HTTP code: 0
Response time: 0 ms

Resolved: ShippersEdge Rates API is back up in 9220532.

repo: ShippersEdge/upptime


text:
Responsive Layout Fix

Inventory Page, User Page needs their responsiveness fixed

fixed inventory page
user page sidebar is buggy

fixed


text:
[Bug]: shopify app deploy automatically selects client-id, if not provided

Please confirm that you have:

[x] Searched existing issues to see if your issue is a duplicate. (If you’ve found a duplicate issue, feel free to add additional information in a comment on it.)
[x] Reproduced the issue in the latest CLI version.

In which of these areas are you experiencing a problem?
App
Expected behavior
shopify app deploy should deploy the config specified via shopify app config use or --config=<path to file>
Actual behavior
I have two app .toml files for dev and prod. I previously was swapping between them with shopify app config use and was able to deploy both apps separately just with shopify app deploy. Currently, shopify app dev and shopify app deploy seem "locked" to the dev version.
Both dev and deploy always use the api key from the dev version and overwrite .../shopify-cli-app-nodejs/config.json with the incorrect app id, even if I set it again with shopify app config use.

dev shows the warning (The flag api-keyhas been deprecated in favor ofclient-id), deploy` does not
explicitly setting --config does not work
explicitly setting --client-id does work
changing .env files did not work
clearing all shopify npm cache directories did not work

These commands work fine:

shopify app versions list
shopify app info

I'm assuming it's a local setup thing, but as far as I know, I didn't change anything except upgrading the cli, so others might run into this.
Verbose output
Sorry, I don't have time to redact the output right now! All I've got is:

FindApp runs with the wrong apiKey value.

Reproduction steps
I'll add exact steps later if I can but so far all I know is:

try to run shopify app deploy in a project with multiple deployed .toml configurations, switching between them with shopify app config use

Operating System
Mac 14.6 (23G80)
Shopify CLI version (shopify --version)
@shopify/cli/3.71.4 darwin-arm64 node-v20.16.0
Shell
zsh
Node version (run node -v if you're not sure)
20.16.0
What language and version are you using in your application?
node v20.16.0

Hi @Goondrious!
So this is likely because you have some api key defined in your ENV, this line is the hint:

The flag api-key has been deprecated in favor of client-id

Look for SHOPIFY_API_KEY in your terminal env (echo $SHOPIFY_API_KEY) or in any process that might be setting this value (SHOPIFY_API_KEY). This ENV value takes precedence over any other defined via config use.
Let me know if this works!

With all the random testing I did, I somehow did not think of directly checking system ENV. I have no idea how it go there. Thank you!!!

authors: Goondrious isaacroldan

repo: Shopify/cli


text:
dashing start

when i tried dashing start
alfr@M299 MINGW32 ~/Documents/MMA-project/dashboard_project
$ dashing start
Le chemin d'accès spécifié est introuvable.
(path is not found)
how can i resolve that ? i work on windows 7 may be this is a windows probleme but
$ gem install dashing
...
$ bundle
are working

i have solve my probleme with this tuto http://3man.co.za/?p=6
and particulary with this line :
gem install bundler

repo: Shopify/dashing