Center content on smaller widths

Centering content on larger devices is easy with the .center and center method. But on smaller widths the centering is lost.
Is there a way keep the centering on smaller widths?

:+1: thanks @brettdewoody !

Looks like this was confirmed that it's working. Let us know if you're running into this for v2 as well! Thanks!


feat: Filters for votes search

Issues
Fixes https://github.com/snapshot-labs/snapshot/issues/3677
Changes

Adds popover with filters in votes modal searchbar
Filter only votes with reason
Change voting power asc/desc
Refactor and bug fixes

How to test

Go to proposal with votes
Test asc/desc and filter with reason

Self-review checklist

[x] I have performed a full self-review of my changes
[x] I have tested my changes on a preview deployment
[x] I have tested my changes on a custom domain
[x] I have run end-to-end tests yarn cypress:test:e2e, and they have passed

@j0hnfl0w do you also want to implement the API changes?

yep added PR there https://github.com/snapshot-labs/snapshot-hub/pull/571

Infinite scroll seems broken
looks like loadMoreVotes() was not prepared for paginating votes, will fix it

I'll take this PR from here, and will implement without choice search for now

On this proposal votes, when sort ASC with reason
https://snapshot-git-j0hnfl0w-3677-snapshot.vercel.app/#/opcollective.eth/proposal/0x88583c43b196ec86cee45345611b582108f1d6933ab688a7cae992a6baa552a6
The reason have a weird new line before

Should we auto-strip new line fron start and end of reason ? New line on reason end are
common, we saw them when reviewing sidekick few days ago


chore: Removed ff4j configuration in favour of Flagsmith

Description
Removes ff4j configuration so that there is only one source of truth for ff configs for ME, ie, Flagsmith.
PR fixes following issue(s)
Fixes #25074
Fixes #25075
Type of change

Chore (housekeeping or task changes that don't impact user perception)

Testing

[x] Manual
[x] Cypress

/ok-to-test


Region properties middle key

What have you changed? (mandatory)
Add 2 middle_key in region properties response so that we can use the key to split region at approximate middle.
What are the type of the changes? (mandatory)
Improvement.
How has this PR been tested? (mandatory)
make dev.
Add a few positive/negative examples (optional)
tikv-ctl output changes to:
$ target/debug/tikv-ctl --host localhost:12347 region-properties -r 4
num_files: 1
num_entries: 3348
num_deletes: 2225
mvcc.min_ts:<PHONE_NUMBER>76902406
mvcc.max_ts:<PHONE_NUMBER>54816001
mvcc.num_rows: 1079
mvcc.num_puts: 1103
mvcc.num_versions: 1123
mvcc.max_row_versions: 21
middle_key: zmBootstr\377a\377pKey\000\000\377\000\000\373\000\000\000\000\000\377\000\000s\000\000\000\000\000\372\372n\347\2020\037\377\372

ping @huachaohuang PTAL.

LGTM


Change exec_module to load_module

I originally wrote this code using load_module and then changed it to exec_module because the documentation says that load_module is deprecated, but I didn't notice that exec_module requires an argument. Since it's not clear from the documentation how exec_module is supposed to be used, and load_module doesn't actually emit a DeprecationWarning (fixing the warning was the original motivation for changing this code), I think we should just use load_module for now.

Thanks for the quick update!
Is it possible to include tests for this code?
And do you have an idea why we only hit this on Python 3.9 and not 3.5-3.8?

@hugovk Unfortunately, I don't know the answer to either of those questions.

This issue hit me today on both 3.7.5 and 3.7.7.

Since it's not clear from the documentation how exec_module is supposed to be used, and load_module doesn't actually emit a DeprecationWarning (fixing the warning was the original motivation for changing this code), I think we should just use load_module for now.

By strange coincidence, I just happened to be looking at this same load_module vs exec_module usage for our setup.py in PyWavelets. I think in your case, the code would be something like the following (untested, adapated from an answer in this stack overflow post):
 import types
 from importlib.machinery import ExtensionFileLoader
 loader = ExtensionFileLoader(__name__, __file__)
 module = types.ModuleType(loader.name)
 loader.exec_module(module)

Can you please push a patch to PYPI? Many projects that don't pin the version are starting to get the bug.


chore(consensus): add documentation to consensus crate

This change is

#2626  👈 (View in Graphite)
main

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Merge activity

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Retapping a keg does not trigger an event

It appears that retapping a keg does not trigger a keg_tapped event -- is this an oversight in the event code or intended behavior?

There is no event for reactivating a keg, butkeg_tapped will be fired if/when the keg is attached to a tap.


Location for central documentation

We need to decide where we want our "central" eodash documentation to live.
The documentation should have all relevant information on setting up the eodash environment, so mainly, setting up of catalog and eodash client instance.
We need to investigate what we could do on organization level, first instincts say there is not much we can do there, so as an alternative the documentation would live in this repository (eodash repository of the eodash organization).
If that is the approach we take, we need to consider how to integrate static documentation with generated API documentation for the eodash client.

We have created a central docs repository to which also eodash.org points to. Each component of the ecosystem can have their own documentation page, this will be the central entry point.


Installation is failing on Ubuntu 20.04.2 Following Installation Guide In Official Documentation

Issue/Feature Description:
Installation is failing on Ubuntu 20.04.2 Following Installation Guide In Official Documentation.

Issue seems to be caused by usage of version-specific syntax of Ansible
Why this issue to fixed / feature is needed(give scenarios or use cases):
Ubuntu 20.04 is newer version so support is necessary.

Issues were noticed in 3 places :
During Installation

Error msg

root1@root1-Nitro-AN515-55:~/DME/installation/installer/ansible$ ansible-playbook site.yml -i local.hosts
[DEPRECATION WARNING]: The TRANSFORM_INVALID_GROUP_CHARS settings is set to allow bad characters in group names by default, this will change, but still be user configurable on deprecation. This feature
will be removed in version 2.10. Deprecation warnings can be disabled by setting deprecation_warnings=False in ansible.cfg.
[WARNING]: Invalid characters were found in group names but not replaced, use -vvvv to see details
ERROR! conflicting action statements: hosts, replace

The error appears to be in '/home/root1/DME/installation/installer/ansible/roles/ha-ip-update/tasks/main.yml': line 24, column 3, but may
be elsewhere in the file depending on the exact syntax problem.

The offending line appears to be:

# Same IP is used for both the services. Only Port is different (predefined)
- name: replace the gelato_ha_api_ip and gelato_ha_s3_ip in the gelato-ha.yml
^ here
root1@root1-Nitro-AN515-55:~/DME/installation/installer/ansible$

During Cleaning

ERROR! Syntax Error while loading YAML.
found unknown escape character

The error appears to be in '/home/root1/DME/installation/installer/ansible/roles/gelato-ha-uninstaller/scenarios/clean_gelato_ha.yml': line 35, column 47, but may
be elsewhere in the file depending on the exact syntax problem.

The offending line appears to be:

with_items:
 - "kubectl patch pvc mongo-0-pv-claim -p '\''{{patch_param }}'\'' --type=merge -n soda-multi-cloud"
                                           ^ here
We could be wrong, but this one looks like it might be an issue with
missing quotes. Always quote template expression brackets when they
start a value. For instance:

 with_items:
   - {{ foo }}

Should be written as:

 with_items:
   - "{{ foo }}"
root1@root1-Nitro-AN515-55:~/DME/installation/installer/ansible$

After resolving above

statically imported: /home/root1/DME/installation/installer/ansible/roles/gelato-ha-uninstaller/scenarios/clean_gelato_ha.yml
ERROR! conflicting action statements: file, ignore_error

The error appears to be in '/home/root1/DME/installation/installer/ansible/roles/gelato-ha-uninstaller/scenarios/clean_gelato_ha.yml': line 91, column 3, but may
be elsewhere in the file depending on the exact syntax problem.

The offending line appears to be:

# ---------delete the multi-cloud folder in /opt/opensds-gelato ---------
- name: Clean artifact path ub /opt/opensds-gelato...
 ^ here
root1@root1-Nitro-AN515-55:~/DME/installation/installer/ansible$

How to reproduce, in case of a bug:
Install per official documentation in ubuntu 20.04.2
** Environment Information: **
root1@root1-Nitro-AN515-55:~/DME/installation/installer/ansible$ ansible --version
ansible 2.9.6
  config file = /etc/ansible/ansible.cfg
  configured module search path = ['/home/root1/.ansible/plugins/modules', '/usr/share/ansible/plugins/modules']
  ansible python module location = /usr/lib/python3/dist-packages/ansible
  executable location = /usr/bin/ansible
  python version = 3.8.10 (default, Jun 22 2022, 20:18:18) [GCC 9.4.0]
root1@root1-Nitro-AN515-55:~/DME/installation/installer/ansible$ hostnamectl
   Static hostname: root1-Nitro-AN515-55
         Icon name: computer-laptop
           Chassis: laptop
        Machine ID: xxxxxxxxxxxxxxxxxxx
           Boot ID: xxxxxxxxxxxxxxxxxxx
  Operating System: Ubuntu 20.04.2 LTS
            Kernel: Linux 5.8.0-43-generic
      Architecture: x86-64
root1@root1-Nitro-AN515-55:~/DME/installation/installer/ansible$

I will take up this issue.

@rizwi33faizan okay. Add more founded issues related to syntax error of yaml files. And fix it.

fixed as part of release v1.8.0


GCP error when buld GPU docker

Hi,
when I run ./docker_build_gpu.sh, I meet the GCP error.
W: GPG error: https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  InRelease: The following signatures couldn't be verified because the public key is not available: NO_PUBKEY A4B469963BF863CC
E: The repository 'https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  InRelease' is not signed.
and how to solve it
Thanks,

same issue, have no idea what to do

Hi @zhzhqian，
this issue has been addressed in the latest commit of mater branch, please have a try and let us know if you still have any problem. I will close this issue for now. feel free to reopen it  if you have any concern.


Question

So im using the lua with memu since I don't have a android phone. How do I edit the luas to use nps and such

I suggestion downloading this app on the emulator to make the editing process easier:
https://play.google.com/store/apps/details?id=com.quseit.qlua5pro2
You will want to edit the FGO_XX_REGULAR file that suits your region. Follow the instructions listed in the readme file here on the site or that is downloaded for information on setting it up.


 No due date yet.
Pending Issues: #20, #24, #23

Might abandon LocalClient entirely, or just leave some barebones functionality. Better people use it minimally or use the valclient package by @colinhartigan

neat


Call import on proxy object

elasticsearch-model is very careful to not override any method names on
model classes if they already exist. When this happens you have to use
the elasticsearch proxy object directly. That's great but the
elasticsearch-rails import rake task does not respect that.
In our case there is a conflict with the activerecord-import gem which
defines an import method on ActiveRecord::Base. The way the rake task
was written it was ending up calling activerecord-import's version of
import instead of elasticsearch-model's version.
This change simply forces the rake task to use the proxy object.

@bdmac I'm terribly sorry, but apparently I have already fixed it in 4e1a9f3 :/ I must have forgot about this PR at that time. Sorry again, I probably didn't search Github issues properly when doing that...


WIP DO NOT MERGE - memory leak fixes

Resolves #1557

Just as a note, PR #1571 was an approach that solved the same problem with the same interface, but a different data structure behind the scenes.  I'm choosing to go forward with this implementation so I didn't have to work around limitations of the binary tree like not being able to delete while walking the tree.


Seemingly unsolvable state caused by wrong webhook configuration

Describe the bug:
I've reached an (seemingly) frozen state in my cert-manager deployment. At first I noticed that my ArgoCD applications went into an "Unknown" state due to a cert-manager webhook issue, causing me to investigate the situation further. During the debugging process I've made a mistake by applying the helm chart manually to my Kubernetes cluster. The error I made was that "RELEASE-NAME" was not yet replaced by the correct naming in the templates. I've been able to remove all cert-manager CRD's from the cluster by removing their finalizers and deleting them normally. However, the clusterissuers.cert-manager.io CRD does not allow me to edit it and remove its finalizers (after saving the updated file without the finalizer it does not do anything and the cli remains stuck untill using an escape).
I figured there may be some ClusterIssuer resource left on my cluster, but when querying for them it returns this message:
$ kubectl get clusterissuer -A
Error from server: conversion webhook for cert-manager.io/v1alpha2, Kind=ClusterIssuer failed: Post "https://RELEASE-NAME-cert-manager-webhook.default.svc:443/convert?timeout=30s": service "RELEASE-NAME-cert-manager-webhook" not found

Here it seems that the webhook now uses the wrong naming (with "RELEASE-NAME"). I get the same message when attempting to delete all current ClusterIssuers with kubectl delete clusterissuer --all -A.
Expected behaviour:
I expect to be able to edit the CRD, but this does not work.
Steps to reproduce the bug:
Template Helm chart using this values.yaml:
cert-manager:
  installCRDs: true
  prometheus:
    enabled: false

Deploy the Helm output manually without replacing the "RELEASE-NAME" properly (using  kubectl apply -f helm-output.yaml. The delete this deployment using kubectl delete -f helm-output.yaml. In my case the ClusterIssuer CRD remains and cannot be editted.
Environment details::

Kubernetes version: 1.20.8-do.0
Cloud-provider/provisioner: DigitalOcean
cert-manager version: 1.4.2 (helm chart version)
Install method: Helm

Could my assumption be right (about remaining ClusterIssuer resources)? Is this a known issue/state? And most importantly, are there solutions to fully remove cert-manager (so that I can re-install using the correct configuration)?
Kind regards,
Jacco
/kind bug

Update: On another system I was able to remove the finalizer and the CRD was immediately removed. Haven't figured out why it worked on this different machine but as the problem is resolved I will close this issue.

I just found this issue. It seems that when you don't properly uninstall it when creating it with another release name, it might left behind some ValidatingWebhooks.


Load models in finetune mode based on command line parameters.

Load models for incremental training.
Depends on https://github.com/RasaHQ/rasa/issues/7328.

[ ] load NLU pipeline in fine-tune mode as shown here
[ ] load Core policies in fine-tune mode as show here
[ ] pass in new number of epochs

@wochinge @joejuzl Do you folks know how the load method for each ML component will be called during finetuning? For example, would there be an extra parameter passed to load function of the components to load them in "finetune" mode and also pass other configurations parameters like number of epochs? Here's how I had done it in the working version branch but I am assuming it wouldn't be exactly the same. If we can plan this ahead, it would unblock me on some of the implementation due for my PR. 🙏

@dakshvar22 I haven' started looking at this area of the code yet (working on https://github.com/RasaHQ/rasa/issues/7330 first) so it's hard for me to say. @wochinge Any opinions/thoughts?

@joejuzl @wochinge Making a proposal to see if we can reach to a consensus quickly on the above question:

New number of epochs get changed inside the meta parameter that is passed to the load method of all components.
Add a boolean parameter to load method - finetune_mode which is set to True if the component is loaded in finetune mode.

What do you folks think?

@dakshvar22 So the solution we have come up with (today!) is as follows:
For NLU:

In Interpreter.load we pass in the new config along with the old model.
Interpreter.load updates the old metadata with the new epochs and calls Interpreter.create with a flag should_finetune.
 Interpreter.create sets should_finetune in the context which gets passed to each component.

@joejuzl Perfect! Small clarification:

Interpreter.create sets should_finetune in the context which gets passed to each component.

The context dictionary is what will be passed as part of the kwargs argument of load() methods of each component?

The context dictionary is what will be passed as part of the kwargs argument of load() methods of each component?

Yes exactly, via component_builder.load_component

For Core:

The new config is passed from Agent.load -> PolicyEnsemble.load.
Then the part of the new config for each policy is passed into its respective load which passes the new epochs into its constructor.
Still not clear exactly how the should_finetune flag will be passed around. I guess through the same path @wochinge ?

@dakshvar22 Our current approach would be to provide should_finetune through the constructor. The alternative would be to do so when calling train. Do you have preferences?

@wochinge Do you mean for Core specifically or NLU components also?
Either ways, we don't need it when calling train. It definitely has to be passed to the load() method of the ML components(DIETClassifier, TEDPolicy) so that the model weights can be instantiated in "training" mode and then load() would pass this parameter to the constructor anyways. If you want to see an example of what I mean, here is how I do it for ML components inside NLU.


list.inflate

We have list.reduce method, which turns an array+function into a scalar. However, we don't have a reverse operation, which would turn an array+function into a larger array:
inflate. > x
  QQ.collections.list
    *
  [a i]
    if. > @
      i.gt 5
      *
      * i

The object encapsulated by inflate has two free attributes: a is the entire array being inflated, i is the incrementing index of the iteration. The object must either dataize to an empty array, which means the end of inflation, or an array, which is appended to a.
The result here should be * 0 1 2 3 4.

@Graur what do you think?

@yegor256 I like the idea!
As far as I know list.reduce object was renamed to list.reduced. Maybe here we can call our new object list.inflated?

@Graur yes, agree, inflated is a better name


Use unified1 version of spirv.h

Output targets SPIR-V 1.0 always.

Did you look into the enum and string generation? Both of those in the cmake directory use the 1.0 specific headers.

I did not.  I'll add that.

Closed in favor of #534


[gstreamer] Need a usage like other ports

Is your feature request related to a problem? Please describe.
There is no usage in this port, please add one.
Proposed solution
like pkg_check_modules
Describe alternatives you've considered
No response
Additional context
No response

If you use the latest vcpkg-tool (git pull + ./bootstrap.sh) you get a pkgconfig usage

@Arlen-LT, Use the latest vcpkg looks gstreamer already provide pkgconfig usage:
gstreamer provides pkg-config modules:

    # Streaming media framework
    gstreamer-1.0

    # Allocators implementation
    gstreamer-allocators-1.0

    # Helper functions and base classes for application integration
    gstreamer-app-1.0

    # Audio helper functions and base classes
    gstreamer-audio-1.0

    # Base classes for GStreamer elements
    gstreamer-base-1.0

    # Dynamic parameter control for GStreamer elements
    gstreamer-controller-1.0

    # FFT implementation
    gstreamer-fft-1.0

    # Streaming media framework, OpenGL plugins libraries
    gstreamer-gl-1.0

    # Streaming media framework, OpenGL plugins libraries (OpenGL Prototypes)
    gstreamer-gl-prototypes-1.0

    # Network-enabled GStreamer plug-ins and clocking
    gstreamer-net-1.0

    # General utility functions
    gstreamer-pbutils-1.0

    # Streaming media framework, base plugins libraries
    gstreamer-plugins-base-1.0

    # RIFF helper functions
    gstreamer-riff-1.0

    # RTP base classes and helper functions
    gstreamer-rtp-1.0

    # RTSP base classes and helper functions
    gstreamer-rtsp-1.0

    # SDP helper functions
    gstreamer-sdp-1.0

    # Tag base classes and helper functions
    gstreamer-tag-1.0

    # Video base classes and helper functions
    gstreamer-video-1.0

@Arlen-LT, Use the latest vcpkg looks gstreamer already provide pkgconfig usage:
gstreamer provides pkg-config modules:

    # Streaming media framework
    gstreamer-1.0

    # Allocators implementation
    gstreamer-allocators-1.0

    # Helper functions and base classes for application integration
    gstreamer-app-1.0

    # Audio helper functions and base classes
    gstreamer-audio-1.0

    # Base classes for GStreamer elements
    gstreamer-base-1.0

    # Dynamic parameter control for GStreamer elements
    gstreamer-controller-1.0

    # FFT implementation
    gstreamer-fft-1.0

    # Streaming media framework, OpenGL plugins libraries
    gstreamer-gl-1.0

    # Streaming media framework, OpenGL plugins libraries (OpenGL Prototypes)
    gstreamer-gl-prototypes-1.0

    # Network-enabled GStreamer plug-ins and clocking
    gstreamer-net-1.0

    # General utility functions
    gstreamer-pbutils-1.0

    # Streaming media framework, base plugins libraries
    gstreamer-plugins-base-1.0

    # RIFF helper functions
    gstreamer-riff-1.0

    # RTP base classes and helper functions
    gstreamer-rtp-1.0

    # RTSP base classes and helper functions
    gstreamer-rtsp-1.0

    # SDP helper functions
    gstreamer-sdp-1.0

    # Tag base classes and helper functions
    gstreamer-tag-1.0

    # Video base classes and helper functions
    gstreamer-video-1.0

Thanks a lot, by updating 2023-12-12 vcpkg executable file I can see the usage

@Arlen-LT  Can anyone provide a real-world usable example? I've used the pkg_check_modules command and it compiles successfully, but at runtime it gives me an error due to missing DLLs!


MediaPipe use NNAPI

I want to use Android NNAPI in Hand landmarks detection.
How can i do?
Thanks you

Hey @Chris0215. We don't currently have support for that delegate with Hand Landmarker, and I don't actually have a timeline for it, unfortunately. Sorry!


Github publishing not working on Linux

When building on Linux, the GitHub publisher can't find the .deb files for some reason:
Unhandled rejection Error: ENOENT: no such file or directory, stat 'IRCCloud-0.1.2-i386.deb'
    at Error (native)
From previous event:
    at GitHubPublisher.<anonymous> (/vagrant/node_modules/electron-builder/src/gitHubPublisher.ts:77:32)
    at [object Generator].next (native)
From previous event:
    at tsAwaiter (/vagrant/node_modules/electron-builder/src/awaiter.ts:10:51)
    at GitHubPublisher.upload (/vagrant/node_modules/electron-builder/src/gitHubPublisher.ts:69:28)
    at /vagrant/node_modules/electron-builder/src/builder.ts:85:73
From previous event:
    at EventEmitter.<anonymous> (/vagrant/node_modules/electron-builder/src/builder.ts:85:19)
    at emitTwo (events.js:100:13)
    at EventEmitter.emit (events.js:185:7)
    at LinuxPackager.dispatchArtifactCreated (/vagrant/node_modules/electron-builder/src/platformPackager.ts:84:28)
    at /vagrant/node_modules/electron-builder/src/linuxPackager.ts:118:24
    at processImmediate [as _immediateCallback] (timers.js:383:17)
From previous event:
    at LinuxPackager.<anonymous> (/vagrant/node_modules/electron-builder/src/linuxPackager.ts:118:8)
    at [object Generator].next (native)
From previous event:
    at tsAwaiter (/vagrant/node_modules/electron-builder/src/awaiter.ts:10:51)
    at Object.build (/vagrant/node_modules/electron-builder/src/builder.ts:30:55)
    at Object.<anonymous> (/vagrant/node_modules/electron-builder/src/build-cli.ts:41:2)
    at Module._compile (module.js:413:34)
    at Object.Module._extensions..js (module.js:422:10)
    at Module.load (module.js:357:32)
    at Function.Module._load (module.js:314:12)
    at Function.Module.runMain (module.js:447:10)
    at startup (node.js:141:18)
    at node.js:933:3

The file IRCCloud-0.1.2-i386.deb definitely exists under dist/, so I suspect there's something specific to the deb building workflow which isn't sending the correct path to the publisher.

Thanks for the quick fix!


How can I save data of a user logged in with firebase auth play games in google firebase database?

Please fill in the following fields:
Unity editor version: 2017.3.1f1
Firebase Unity SDK version: 5.4.0
Firebase plugins in use (Auth, Database, etc.): Auth, database
Additional SDKs you are using (Facebook, AdMob, etc.): GoogleMobileAds, GooglePlayGames, Firebase
Platform you are using the Unity editor on (Mac, Windows, or Linux): Windows
Platform you are targeting (iOS, Android, and/or desktop): Android
So I am developing a game in unity3d similar to those asked, I use firebase to save all the data of the users that register with email and password, everything is going well, but I want new users to register and log in with play games or google, the question is: how can I save the tados of users logged in with google play games in firebase database, so where I understand firebase play games only works for a login,
In my current project I have a function of beginning of section and another of registration, the application only saves data in google firebase when it is registered, what happens in the case of google play games?

@patm1987 FYI
Unfortunately, we don't have Unity specific docs for these but this document describes how to associate a FirebaseUser with Play Games.  Once the account is associated with the FirebaseUser you can use the Firebase user ID to save user specific data.


Updated logo and added multiple requests feature

Added multiple requests feature

Fixes Issue

Changes proposed

Check List (Check all the applicable boxes)

[x] My code follows the code style of this project.
[x] My change requires changes to the documentation.
[ ] I have updated the documentation accordingly.
[x] All new and existing tests passed.
[x] This PR does not contain plagiarized content.
[x] The title of my pull request is a short description of the requested changes.

Screenshots

Note to reviewers
I'm still working on adding timestamp and url in each response.

How will the user input more than one URL?

Enter one URL
Click on "Analyze"
The results are shown
Re-enter different URL
Click on "Analyze" again

Ohhh, awesome
But can you also work on adding support to indicate the result of each request separately?

Addresses #40

Do you mean to say that I should add the URL and timestamp in each response? Because the response is shown individually for each request.

Yeah, you get it
But I was thinking, adding the whole URL won't be that good as it might look nice, what do you think we can do?

How about setting an ID for each request?

Yeah

Alright, I'll work on it

Hi, instead of generating a unique ID for each request on the client side, wouldn't it be a better option to do it on the server side and send the response back with the ID? This is the response from the backend URL, which is https://logiqapi.onrender.com/api/analyze:

As you can notice here, there is no ID property. Instead of setting a new unique ID on each request on the client size, wouldn't it be better if we handled it on the server side and sent a response like:
{
   "ID":"ccf7b488-9171-449f-aaca-5e36f94ef455",
   "URL":"https://google.com/",
   "isUp":true,
   "ipAddress":"<IP_ADDRESS>",
   "uptime":"99.00",
   "responseTime":"0.16"
}

Now, this would be for a successful response, when the URL is valid.
If the URL is invalid, your backend just sends the following response:

Instead of handling the invalid URL requests like this, you can send the response from the backend like:
{
   "ID":"ccf7b488-9171-449f-aaca-5e36f94ef455",
   "URL":"<the invalid url>",
   "isUp":false,
   "ipAddress":null,
   "uptime":null,
   "responseTime":null
}

What are your thoughts on this?

Yeah yeah, I want to said it ealier

Do you have time to work on it on the backend side?

Yeah, sure, let me know the details


[WIP] Integrate AHMC

Integrated the new AdvancedHMC.jl into Turing so we can start to test it.
Usage:
chn = sample(gdemo_default, ANUTS(10_000, 0.8), 1_000)

where 0.8 is the target accept ratio and 1_000 is the number of adaptation steps (happy to change the interface).
I feel we might want to play it with Turing for a while before replacing the current HMC codes completely.

NOTE: ANUTS doesn't support compositional interface yet.
Question: do we actually want dynamic HMC support that interface? I think @yebai and me had some discussion on the validation of the adaption phase of NUTS during Gibbs.

Question: do we actually want dynamic HMC support that interface? I think @yebai and me had some discussion on the validation of the adaption phase of NUTS during Gibbs.

Dual averaging should work fine within Gibbs, although might require more interactions before converging to a stable value. The no-U-turn criteria will be correct but might be suboptimal depending on the model structure.  I think we can try to integrate dual averaging to HMC within Gibbs as a first step (in a separate PR).

AHMC.jl is registered now so hopefully the tests shall pass. Also please don't merge this for now. I'm working with Jonas on his example of using DiffEq solvers together with Turing, which, works with Turing's NUTS but not AHMC's. Need to figure out why this happens.

I started an experimental interface; see https://github.com/TuringLang/Turing.jl/pull/739/commits/a32cec09d3acf5341d43a08c4c17e60ee62d8248#diff-4b1655bda3ebca1ef0e5a5a587d67152R42

CI should not pass until we tag the AHMC after https://github.com/TuringLang/AdvancedHMC.jl/pull/47 is merged.

Is this ready for merging? @xukai92

TODOs

[ ] tag a new release for AHMC
[ ] sort out dependency files on Turing for AHMC

We are almost here (see the TODOs in https://github.com/TuringLang/Turing.jl/pull/739#issuecomment-484893504).

@yebai @mohamed82008 This is ready for a review.

Thanks, @xukai92. This PR looks good. I left some comments above. Here are some further refactoring comments that I would consider:

merge support/hmc_core.jl, hmc.jl, hmcda.jl and nuts.jl into a single file, i.e. inference/hmc.jl
modify stan-interface.jl such that it returns a  StanNUTSAdaptor type. This would avoid passing around a CmdStan type in our code. https://github.com/TuringLang/Turing.jl/blob/kx/int-ahmc/src/utilities/stan-interface.jl
perhaps move NUTS HMCDA and HMC sampler type definition to AHMC? https://github.com/TuringLang/Turing.jl/blob/154069e41c6389a559b85de289f4605414a63e69/src/inference/nuts.jl#L36

After each adaption step, return a new sampler containing the full sampler configuration. This would create a fully immutable API design and enable us to remove Sampler.info safely.

TODO

[ ] disable SGLD and SGHMC for now

Create a PR in AdvancedHMC.jl to move them there

@yebai It's ready for another look. I don't think I can remove spl.info easily, without making huge RFC oh either Sampler or other stuff. I'd do this in another PR. I also discussed this with Mohamed  and he also has some plan on this. I will create an issue on this and let discuss over there.

I will review this today.

Thanks for the reviews. I will take a pass on fixing some of them tonight :)

Ready for another review - tests pass on my local machine but Travis CI might fail before https://github.com/TuringLang/AdvancedHMC.jl/pull/61 is merged.


error with grounded_sam_demo.py

admin123@ubuntu:~/grounded-Segment-Anything$ python3 grounded_sam_demo.py   --config GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py   --grounded_checkpoint groundingdino_swint_ogc.pth   --sam_checkpoint sam_vit_h_4b8939.pth   --input_image assets/demo1.jpg   --output_dir "outputs"   --box_threshold 0.3   --text_threshold 0.25   --text_prompt "bear"   --device "cuda"
/usr/local/lib/python3.8/dist-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  ../aten/src/ATen/native/TensorShape.cpp:2311.)
return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
final text_encoder_type: bert-base-uncased
Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.bias']

This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
_IncompatibleKeys(missing_keys=[], unexpected_keys=['label_enc.weight'])
/usr/local/lib/python3.8/dist-packages/transformers/modeling_utils.py:862: FutureWarning: The device argument is deprecated and will be removed in v5 of Transformers.
warnings.warn(
/usr/local/lib/python3.8/dist-packages/torch/utils/checkpoint.py:25: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
warnings.warn("None of the inputs have requires_grad=True. Gradients will be None")
Killed

same issue. it just randomly gets killed. did you figure out why?

the logs show it's an out of memory error. not sure what in the script is using so much RAM... will try to figure it out.


[FEATURE] Merge GPT-4 Turbo and Precise Mode

Checks

[x] I confirm that I have searched existing issues and couldn't find a similar proposal.
[x] I have read the documentation and test the software carefully and confirmed that this is not an existing feature.

Is your feature request related to a problem? Please describe.
When GPT-4 Turbo is enabled, Sydney will be forced to use Precise mode, which makes GPT-4 Turbo conflict with the selected conversation mode.
Describe the solution you'd like
We can have something like this:
optionsSetMap: map[string][]string{
	"Creative": append(basicOptionsSet, "h3imaginative"),
	"Balanced": append(basicOptionsSet, "galileo"),
	"Precise":  append(basicOptionsSet, "h3precise", "dlgpt4t"),
}

And it may require some other changes in frontend and webapi.

When GPT-4 Turbo is enabled, Sydney will be forced to use Precise mode, which makes GPT-4 Turbo conflict with the selected conversation mode.

How's this discovered (and confirmed). Is this from the Bing website etc?

How's this discovered (and confirmed). Is this from the Bing website etc?

You can ask Sydney "what's your mode".

I think we should wait a while.
Bing will surely officially launch turbo , and will launch turbo-based creative.

How's this discovered (and confirmed). Is this from the Bing website etc?

You can ask Sydney "what's your mode".

The model is not fully aware on which Tone it is.
Btw, on Copilot Pro,  GPT4 Turbo only works on creative.... the toggle switch uses gpt4tmnc OptionSet...
But as of today, dlgpt4t stopped working...

But as of today, dlgpt4t stopped working...

Mine is still working though. BTW, as you have Copilot Pro, would you please tell us more details about the option set? And it would be better if you can show us the initial message sent by client in websocket connection.

But as of today, dlgpt4t stopped working...

dlgpt4t stopped working for me just now. And gpt4tmnc is not working either. I'm still experimenting with different option sets.

Mine is still working though. BTW, as you have Copilot Pro, would you please tell us more details about the option set? And it would be better if you can show us the initial message sent by client in websocket connection.

dlgpt4t stopped working for me just now. And gpt4tmnc is not working either. I'm still experimenting with different option sets.

I don't have Copilot Pro, sadly. Just looked at the code, and the GPT4 Turbo switch enables gpt4tmnc...
At first when I tried to use gpt4tmnc, it would just refuse the connection. But now, just like dlgpt4t, both have just no effect at all.
I guess they probably switched to another OptionSet...

According to the latest findings on GPT-4 Turbo, it is not limited to precise mode. It can also operate without being set to any specific mode. Therefore, this issue is no longer valid.


Adding tip299

Hello Will, thanks for submitting your post. It looks great, we made some minor formatting changes and published it on our site. I'm closing this request as I was able to copy the file from this PR and put it into our private repo from where we push final content to this public site. https://microsoft.github.io/AzureTipsAndTricks/blog/tip300.html

Hello Will, thanks for submitting your post. It looks great, we made some minor formatting changes and published it on our site. I'm closing this request as I was able to copy the file from this PR and put it into our private repo from where we push final content to this public site. https://microsoft.github.io/AzureTipsAndTricks/blog/tip300.html


update ms.date

:white_check_mark: Validation status: passed

File
Status
Preview URL
Details

docs/checklist/availability.md
:white_check_mark:Succeeded
View

docs/checklist/resiliency-per-service.md
:white_check_mark:Succeeded
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docs/checklist/resiliency.md
:white_check_mark:Succeeded
View

docs/resiliency/index.md
:white_check_mark:Succeeded
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For more details, please refer to the build report.
Note: If you changed an existing file name or deleted a file, broken links in other files to the deleted or renamed file are listed only in the full build report.


feat: add infinite sp1 for testnet

Description

To test, make sure you have python3 installed.
Go to /batcher/client/send_infinite_sp1_tasks and run pip install -r requirements.txt.
Then run make batcher_send_infinite_sp1

I think https://github.com/yetanotherco/aligned_layer/pull/280 should be merged first, then call to batcher client should be with the installed version, since it recompiles ~6 crates each time it runs because of warning:
warning: skipping duplicate package `fibonacci-program`

I think #280 should be merged first, then call to batcher client should be with the installed version, since it recompiles ~6 crates each time it runs because of warning:
warning: skipping duplicate package `fibonacci-program`

I think #280 should be merged first, then call to batcher client should be with the installed version, since it recompiles ~6 crates each time it runs because of warning:
warning: skipping duplicate package `fibonacci-program`

Done 🫡


Request for reference to actuallymentor/sendy-docker-compose

Hey @bubbajames-docker!
If I read your commit and docker hub entries correctly you ran out of bandwidth to keep maintaining this. Earlier this year I made a more generic Docker compose setup (see actuallymentor/sendy-docker-compose) which downloads the latest source on docker compose up.
Would you be up for a notice in the readme that the above repo is available as an alternative?
I spent quite some time trying to find a good docker solution and would love to save others like me some time :)
If you're very busy I'm happy to draft a PR for it.


pip install doesn't work

Running python3 -m pip install git+https://github.com/gumyr/build123d resulted in
ERROR: Could not find a version that satisfies the requirement cadquery-ocp~=7.7.0a0 (from build123d) (from versions: none)

Also tried python3 -m pip install git+https://github.com/gumyr/build123d.git#egg=build123d, which had the same error

Update I tried the apple silicon workaround: https://github.com/gumyr/build123d/blob/3da1615b38302febf7182b14ff87b28b19937cdf/docs/installation.rst#special-notes-on-apple-silicon-installs
Now I'm running into this error:
(cad_stuff) ➜  cad_stuff python
Python 3.10.12 | packaged by conda-forge | (main, Jun 23 2023, 22:41:52) [Clang 15.0.7 ] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from build123d import *
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Users/petertao/homebrew/anaconda3/envs/cad_stuff/lib/python3.10/site-packages/build123d/__init__.py", line 10, in <module>
    from build123d.importers import *
  File "/Users/petertao/homebrew/anaconda3/envs/cad_stuff/lib/python3.10/site-packages/build123d/importers.py", line 34, in <module>
    from stl.mesh import Mesh
ModuleNotFoundError: No module named 'stl'

Running pip install numpy-stl fixed this error

The numpy-stl problem is strange as it's listed as a dependency in the pyproject.toml file:
dependencies = [
    "cadquery-ocp ~= 7.7.0a0",
    "OCP-stubs<EMAIL_ADDRESS>    "typing_extensions >= 4.4.0, <5",
    "numpy >= 1.24.1, <2",
    "svgpathtools >= 1.5.1, <2",
    "anytree >= 2.8.0, <3",
    "ezdxf >= 1.0.0, < 2",
    "numpy-stl >= 3.0.0, <4"
]

and therefore should be installed automatically.

I think it's because of the --no-deps. I just copy and pasted :P
pip install --no-deps git+https://github.com/gumyr/build123d svgwrite svgpathtools anytree scipy


allow arrow input in accessor

Currently we require awkward input for the .ak accessor to appear on a series. This is fine, since .astype("awkward") works. But do we want the accessor to also allow string[pyarrow] or arrow type inputs?

I think it makes sense, based on previous conversations I think we want to make it as easy as possible to use awkward as a compute engine on pyarrow types


FileNotFoundError: [Errno 2] No such file or directory: '.\output_models\llama_7b_lora\7B\params.json'

(lmflow) PS E:\LMFlow-main\LMFlow-main> python ./scripts/convert_llama_weights_to_hf.py --input_dir  .\output_models\llama_7b_lora\ --model_size 7B --output_dir .\output_models\llama-7b-hf
Traceback (most recent call last):
File "E:\LMFlow-main\LMFlow-main\scripts\convert_llama_weights_to_hf.py", line 279, in
main()
File "E:\LMFlow-main\LMFlow-main\scripts\convert_llama_weights_to_hf.py", line 267, in main
write_model(
File "E:\LMFlow-main\LMFlow-main\scripts\convert_llama_weights_to_hf.py", line 80, in write_model
params = read_json(os.path.join(input_base_path, "params.json"))
File "E:\LMFlow-main\LMFlow-main\scripts\convert_llama_weights_to_hf.py", line 66, in read_json
with open(path, "r") as f:
FileNotFoundError: [Errno 2] No such file or directory: '.\output_models\llama_7b_lora\7B\params.json'
(lmflow) PS E:\LMFlow-main\LMFlow-main>

To download the model, please go to the 'output_models' folder and execute 'bash download.sh all'.

下载之后出现的

Please refer to our Chinese version documentation for more information about setting up and tuning models. Thanks https://github.com/OptimalScale/LMFlow/blob/main/readme/README_zh-hans.md

This issue has been marked as stale because it has not had recent activity. If you think this still needs to be addressed please feel free to reopen this issue. Thanks


Special characters in text rendering as codes

Hi ,
I have a some special character in text like '£' or bullet points. These are values entered by users in a textarea field. I'm displaying that text in a label. Its displaying fine in the view but when exporting that to pdf its converting the special characters to some codes in the pdf.
Not sure if this is related to wkhtmltopdf or something related to encoding which am missing. Any suggestions please?
eg-
text  displayed in view:
1st supervisor x 1 @ £1000 Provisional 2nd supervisor x 2 @ £500 each 2nd Supervisor DBA x 1 @ £500
Text rendered inside pdf: (without spaces inbetween the codes)
1st supervisor x 1 @ & # 163 ;1000 Provisional 2nd supervisor x 2 @ & # 163 ;500 each 2nd Supervisor DBA x 1 @ & # 163 ;500
Thanks,
Sai

Can't reproduce. Perhaps was due to server env.


Have external links open in a new tab

Usually when linking to outside sources, I find myself wanting to do so in a new tab. Internal links will continue to route in the current tab.

Usually when linking to outside sources, I find myself wanting to do so in a new tab. Internal links will continue to route in the current tab.

Hey Brandon, going to keep this closed for now as current best-practice is to open links in new tab unless the user indicates otherwise (you can open any links in a new tab by holding a meta key like cmd or ctrl)
https://css-tricks.com/use-target_blank/#aa-a-bad-reason-because-you-like-it-that-way

Feel free to modify this in your own branch though :)

Could I make external links different color from internal links?

I get the logic of not forcing the UX of a new tab on people, but I still think it should be an option. Here's how I quick and dirty enabled it, for anyone interested:
Open quartz/components/Head.tsx. Add this before the </head> tag:
<script src="/static/custom.js" defer></script>
Put this in the static/custom.js file:
document.addEventListener('DOMContentLoaded', function() {
    var externalLinks = document.querySelectorAll('a.external');
    externalLinks.forEach(function(link) {
        link.addEventListener('click', function(event) {
            event.preventDefault();
            window.open(this.href, '_blank');
        });
    });
});


Remove pagination buttons on Homepage

Given it looks unlikely we are going to get this working before launch, we should comment out these buttons for the launch milestone.

I will open a separate issue to re-implement the design later.

Done @nlhkabu

After https://github.com/pypa/warehouse/commit/ead20ca0889f44f4806abe147870f3bce51bc245, this can be closed now.


First version of targeted concurrent safe strategy

Depends on:

[ ] https://github.com/ManageIQ/manageiq/pull/15198

First version of targeted concurrent safe strategy

@miq-bot assign @agrare

@miq-bot add_label enhancement


Suggestion: Release addon after primary mod

It causes unnecessary issues if the addon gets released before the main mod.
In this case EnderIO-1.7.10-<IP_ADDRESS>3 beta is still not officially published on curse/curseforge.
I for myself know how to get to the Jenkins server of EnderIO but others do not.
Please keep that in mind before releasing a new version of the addon that depends on a unreleased version of EnderIO.

http://ci.tterrag.com/job/EnderIO/423/


Remove private subnets from environment CloudFormation template

After removing the NAT gateways it seems the private subnets are still being created, but I don't think we're using them anywhere so we can consider removing them as well.

This was a conscious decision as they don't cost anything and should be in the same level as public subnets. Resolving it


New package: NAB v0.1.0

Registering package: NAB
Repository: https://github.com/akamai/NAB.jl
Created by: @bluesmoon
Version: v0.1.0
Commit: b93129f3db53ac6e6f018ae85177d6d99101436e
Reviewed by: @bluesmoon
Reference: https://github.com/akamai/NAB.jl/commit/b93129f3db53ac6e6f018ae85177d6d99101436e#commitcomment-127879821
Description: Julia implementation of the Numenta Anomaly Benchmark

[noblock]
This is a Julia port of the Python package by the same name found at https://github.com/numenta/NAB hence it makes sense to use the same name.

This can be closed. I'll rename the package


The query server does not give an xml output

No XML output yet, but it should.

Use html5lib parser instead of html.parser

I think this issue must be closed now...

@nikhilkumarsingh I don't see how this issue is related to PR #4 ..

@niranjan94 The earlier version used html.parser which was giving errors in parsing the HTML content.
So, I used html5lib as HTML parser. This resolved the issue. :)

@nikhilkumarsingh I think you misunderstood ... This issue is about the fact that the query server is not providing an XML Output. Not related to parsing of webpages.

@niranjan94 Did you mean that the xml output is not getting saved into files?
Check out PR #5. It works for me in all respects.

@nikhilkumarsingh XML output still not working. The issue is about XML output. If it works for you, please provide an XML sample output that we can see.

Please have a look at this file. This file gets generated using new version : https://github.com/nikhilkumarsingh/query-server/blob/9a735f14af371baf96166409fdf7ad3280225505/data/india.xml

@mariobehling The server provides the XML output in the console. Also a file which contains the XML output  is saved with the name of the 'query string', in query-server/data folder. I think you weren't able to find the stored files. Please explain the issue in case I misunderstood!

I am also having that problem
when I do a query, not matter what the string is, it turns to 'undefined'
Search for >> fossasia
        querying -> undefined

also it says this  saved to file : query-server/data/undefined.xml but there is no such file in the data folder

the XML file for google search is getting created successfully, but it is not creating when its doing the query with duckduckgo (or bing). I think thats what @mariobehling meant in this issue @enigmaeth

sorry, its working, xml files are created

I think this was problem was with duckduckgo query since it didnt happen properly because of the AttributeError, mentioned in https://github.com/fossasia/query-server/issues/11
The problem should be fixed with https://github.com/fossasia/query-server/pull/13


If Webpack is not being used, user is not asked if they are using Stimulus; no index.js files are created by custom generator

What did you do?
Created a Rails app using the template.
What did you expect to happen?
I should have been asked if I used Stimulus, and the custom generator should have created the relevant .js files.
What actually happened?
I was not asked if I used Stimulus, because all the USE_STIMULUS code is wrapped in an if USE_WEBPACK block; I instead got a reference to #14.
Additional context
It would be better to ask the user if they're using Stimulus in all cases, then install the relevant config if Webpack is being used, otherwise give the reference to #14.
As a result, the custom generator does not have an option to create index.js, and the user must do it themselves each time.
Environment
Ruby Version:
ruby 3.1.2p20 (2022-04-12 revision 4491bb740a) [aarch64-linux]
Framework Version (Rails, whatever):
Rails <IP_ADDRESS>
View Component Contrib Version:
view_component-contrib (0.1.1)

Thanks for the report!
Agree, we should ask a user if they want to add Stimulus-related stuff to generators independently of whether Webpack is being used. Would you like to prepare a PR?

Would you like me to tackle this?

Hey @helpotters! Yes, please (if you're still interested)

I'll check it out this weekend!


Broken URL Portuguese Java Course

Here, we have a broken link for a java course in Youtube. The owner account is closed
https://github.com/vhf/free-programming-books/commit/937ab8f0986121ff26eb096ca8b9f104bde5c28f

Could you submit a PR to remove these entries please?
Thanks a lot.

@vhf PR created :)
https://github.com/vhf/free-programming-books/pull/1420


Append thread to body and back to its place, due to positioning

Some text containers have relative positioning, so taking the commenting link offset().left does not really work, because the left for comments thread is not 0, but the text container position.

Closing as outdated


Slides display

fix #119 {#Display} Change Animation order
add observable slideease$

Need merge before PR !

New PR should be submited


Azure Machine Learning integration with Synapse Workspaces --> data exfiltration protection NOT supported because of missing Managed Private Endpoints  -->  discrepancy between PREREQUISITES and CONFIGURATION GUIDE

Good morning,
I found a discrepancy in this doc in this paragraph [https://learn.microsoft.com/en-us/azure/machine-learning/how-to-private-endpoint-integration-synapse#prerequisites] where it is said that:
"The Azure Machine Learning integration is not currently supported in Synapse Workspaces with data exfiltration protection"

Moreover there are the details after [https://learn.microsoft.com/en-us/azure/machine-learning/how-to-private-endpoint-integration-synapse#understanding-the-network-communication]:
"Azure Machine Learning doesn't provide managed private endpoints or virtual networks"

In contrast, in the configuration guide, in this paragraph [https://learn.microsoft.com/en-us/azure/machine-learning/how-to-private-endpoint-integration-synapse#configure-azure-synapse] it seems possibile to configure:
"New managed private endpoint" --> "for Azure Machine Learning"

Could you please check the prerequisites and the configuration guide details?
Which is the right one?
An explanation is needed to clarify the inconsistency.
I look forward to hearing from you.
Thanks for your support,
Luca Bovo - beanTech

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

ID: 1de6a01c-9a3c-b835-3eab-65571426394a
Version Independent ID: 5372a6cf-257b-3ae7-1692-7ab4ca5caedd
Content: Securely integrate with Azure Synapse - Azure Machine Learning
Content Source: articles/machine-learning/how-to-private-endpoint-integration-synapse.md
Service: machine-learning
Sub-service: enterprise-readiness
GitHub Login: @jhirono
Microsoft Alias: jhirono

@lucabovo
Thanks for your feedback! We will investigate and update as appropriate.

@jhirono
Could you please review this and update as appropriate.

@lucabovo
Thank you for bringing this to our attention. I've assigned this issue to the author who will investigate and update as appropriate.

Hi @jhirono,
any news about this issue?
Please let me know.
Thanks a lot.

@lucabovo sorry for my delayed response. In our synapse azureml configuration guide, we explain how synapse access azureml worksapce privately using managed private endpoint. It is supported.
What we want to do is, AzureML itself does not have AzureML managed virtual network or AzureML managed private endpoints like synapse.

@Naveenommi-MSFT i think we can close this issue

Hi @jhirono
We can close this issue.
Hi @lucabovo
We are going to close this thread, if there are any further questions regarding the documentation, please tag us in your reply and we will be happy to continue the conversation.


silent refresh getting error ( frame because it set 'X-Frame-Options' to 'deny error )

My token is 1 hrs validation. I call the silentRefresh method in 55th min. I am getting (frame because it set 'X-Frame-Options' to 'deny error). I need guidance for this error.

We need far more details (towards a "way to reproduce the issue") to be able to definitively and reliably help.
In general terms though, this is likely a problem on your server (Identity Server) side: it's probably somehow trying to show HTML even though the iframe loads a /connect/authorize?.....prompt=none page, with headers that prevent the browser from loading that page in an iframe. A typical case is a developer exception page from that endpoint.

this is need to handle in server side or client side auth configuration problem. We have the two factor authentication also. this is my config file.
export const authConfig: AuthConfig = {
redirectUri: window.location.origin + '/index.html',
silentRefreshRedirectUri: window.location.origin + '/silent-refresh.html',
responseType: 'token id_token',
strictDiscoveryDocumentValidation: false,
skipIssuerCheck: true,
clearHashAfterLogin: true,
oidc: true,
issuer: environment.issuer,
tokenEndpoint: environment.tokenEndpoint,
loginUrl: environment.loginUrl,
clientId: environment.clientId,
scope: environment.scope,
}

I'm sorry, I'm not entirely following, and also unsure if my previous comment came across entirely? In addition, I noticed you crossposted to a different client side library, did you try both libs and have the same issue with both? Because as I mentioned, I recommend investigating your server side configuration first.

We are created custom policy in identify server. That's why getting this issue.

Did we get any workaround for this? I get similar issues with azure b2c. The call to b2c instance returns back x-frame-options deny header.


scripting-addition failed to inject

scripting-addition failed to inject
$ yabai -m space --focus next
cannot focus space due to an error with the scripting-addition.
$ sudo yabai --load-sa
Password:
yabai: scripting-addition failed to inject payload into Dock.app!

version
yabai-v4.0.0
macOS 12.01
M1 apple silicon
SIP is disabled
same problem from others
this is migrated from comments on koekeishiya/yabai#787
@alephpt and @rublev has the problem, but on macOS 12.2

Hey! I had this very problem and just solved it. When you add the line to the visudo yabai file, replace the path from yabai to the output of which yabai as it appear that they can be different. After that just restart!

Hey! I had this very problem and just solved it. When you add the line to the visudo yabai file, replace the path from yabai to the output of which yabai as it appear that they can be different. After that just restart!

I am afraid that we must have different problems.
Your problem seems to be that you were calling the wrong yabai.
My problem is that I called the right yabai and failed.

@SichangHe Have you taken a look at the #1054 issue yet? I had the same issues and error messages (I'm on M1 12.2.1) but managed to solve it with some commands. Check out this code block. Hopefully that can solve your issue (this command sudo nvram boot-args=-arm64e_preview_abi specifically might help).

@SichangHe Have you taken a look at the #1054 issue yet? I had the same issues and error messages (I'm on M1 12.2.1) but managed to solve it with some commands. Check out this code block. Hopefully that can solve your issue (this command sudo nvram boot-args=-arm64e_preview_abi specifically might help).

After executing the code in the code block you mentioned (copied below), I was able to focus next window ✌
sudo nvram boot-args=-arm64e_preview_abi
# reboot
sudo yabai --uninstall-sa
sudo yabai --install-sa
sudo yabai --load-sa

It seems that, for M1,
sudo nvram boot-args=-arm64e_preview_abi

is needed for the script addition to work.
which is not mentioned in the installation part of the wiki.
I will suggest adding the code to the wiki

I tried Automatically load scripting addition on startup and it does not work upon reboot
$ yabai -m space --focus next
yabai-msg: failed to connect to socket..

After quitting Finder and opening it again, which supposedly trigger the script addition to load again, it becomes
$ yabai -m space --focus next
cannot focus space due to an error with the scripting-addition.

It works, however, if I manually do
sudo yabai --load-sa

what I did according to Automatically load scripting addition on startup
sudo visudo -f /private/etc/sudoers.d/yabai

add this line to the opened file
sichanghe ALL = (root) NOPASSWD: /opt/homebrew/bin/yabai --load-sa

add these two lines to yabairc
sudo /opt/homebrew/bin/yabai --load-sa
yabai -m signal --add event=dock_did_restart action="sudo /opt/homebrew/bin/yabai --load-sa"

Hi @SichangHe

It seems that, for M1,
sudo nvram boot-args=-arm64e_preview_abi

is needed for the script addition to work.
which is not mentioned in the installation part of the wiki.

It is in fact mentioned in the wiki, though a bit lower down. Check this page.

 yabai -m space --focus next

yabai-msg: failed to connect to socket..

Are you running yabai manually or via brew services start yabai? If the former, then you need to make sure to start yabai in the command line before trying to focus spaces. I believe the latter is recommended (definitely more hands off).
Your yabairc addition looks about right, you might have to reboot your computer for the visudo to take effect. I assume that you're using the path of yabai from which yabai in there (I have a different path /usr/local/bin/yabai)?

@Tweoss

It is in fact mentioned in the wiki, though a bit lower down.

I see it now. I missed it because I already disabled SIP before I started following the wiki and I skipped the SIP part.
I would say that this configuration and SIP are usually considered two different aspects. So, the way the wiki is managed is confusing.

Are you running yabai manually or via brew services start yabai?

I use brew to manage both yabai and skhd

Your yabairc addition looks about right, you might have to reboot your computer for the visudo to take effect.

I rebooted twice.

I assume that you're using the path of yabai from which yabai in there.

You are right about this.

I see it now. I missed it because I already disabled SIP before I started following the wiki and I skipped the SIP part.
I would say that this configuration and SIP are usually considered two different aspects. So, the way the wiki is managed is confusing.
I agree, that little snippet is pretty hard to locate.

I use brew to manage both yabai and skhd

Interesting, I figured that brew ought to have started yabai, so you wouldn't need to quit Finder (or pkill Dock). Does yabai work now without manually killing Finder?

Interesting, I figured that brew ought to have started yabai, so you wouldn't need to quit Finder (or pkill Dock). Does yabai work now without manually killing Finder?

I killed and restarted Finder to trigger this in the yabairc to see if it works
yabai -m signal --add event=dock_did_restart action="sudo /opt/homebrew/bin/yabai --load-sa"

yabai is started by brew no problem, and yabai works after manually running sudo yabai --load-sa.
(It causes some buggy window behaviors though)

Since the topic of this issue, scripting-addition failed to inject, is solved by this.
I am closing this issue.

Since the topic of this issue, scripting-addition failed to inject, is solved by this. I am closing this issue.

On Macos Ventura, I try all the ways but it still failed.

@palteksolution, the Ventura scripting addition was just fixed in this commit, but it's not released yet: https://github.com/koekeishiya/yabai/commit/206c0d6968210f48363f206526760e5750810a61
brew unlink yabai
brew install yabai --head
sudo yabai --load-sa

Don't forget to update your sudoers config with the new sha hash of yabai binary, though.


upgrade the local node version of the repo to support HIP-904 and ensure the feature flag is turned on.

I'm picking this up. You can assign me


[Bug]: If the amount of data is large, the query is disabled and becomes invalid

ODC version
odc 4.3.1
OB version
no related
What happened?
If you use a black screen to query a table with a large amount of data, close the query and the session ID will be invalid

What did you expect to happen?
You can gracefully close a session that is being queried
How can we reproduce it (as minimally and precisely as possible)?
If you use a black screen to query a table with a large amount of data, close the query and the session ID will be invalid
Anything else we need to know?
No response

Non-defective, the actual black screen window is small, the interface is constantly refreshing data, and the actual query session has been closed


Could not play on Android Pausing after 1 second everytime I plays

Info :
"react-native-youtube": "^1.0.0-alpha.3"
"react": "~15.4.0-rc.4",
"react-native": "0.42.3"
Usage :
<YouTube
youtubeVideoId={'myYoutubeVideoId'}
play
hidden={false}
playsInline
loop={false}
/>
Working in Full screen.

What version? Plus there is no youtubeVideoId property

sorry.It is videoId only. Which package version you are asking. react-native-youtube ? You can find it in above comment.
"react-native-youtube": "^1.0.0-alpha.3"

try to log errors onError={e => console.log(e.error)}

When I Worked on it again. This time I got error like playsInline prop is dropped like that.So I Removed that prop and now it is playing continuously.

When I Worked on it again. This time I got error like playsInline prop is dropped like that.So I Removed that prop and now it is playing continuously.


asko_import: Make 1st queue position reserved (ASU-1430)

Make the first position in the queue as reserved as this
was discussed in the review of the previous asko_import PR.
Also includes some related fixes and tooling improvements.

Codecov Report

Merging #376 (5128557) into main (eb2f144) will decrease coverage by 0.21%.
Report is 34 commits behind head on main.
The diff coverage is 11.62%.

@@            Coverage Diff             @@
##             main     #376      +/-   ##
==========================================
- Coverage   90.37%   90.17%   -0.21%
==========================================
  Files         336      336
  Lines       11932    11965      +33
  Branches      847      852       +5
==========================================
+ Hits        10784    10789       +5
- Misses       1013     1041      +28
  Partials      135      135

Files
Coverage Δ

...sko_import/management/commands/import_from_asko.py
37.50% <0.00%> (-2.50%)
:arrow_down:

asko_import/importer.py
14.08% <11.90%> (+0.15%)
:arrow_up:

:mega: We’re building smart automated test selection to slash your CI/CD build times. Learn more


Fix virtualizer persisted keys with drag and drop

Depends on #6640
This fixes the virtualizer persistedKeys during drag and drop so that keyboard navigation works correctly and focus isn't lost. Since we only render one of the drop positions (before or after), we need to normalize the key we persist accordingly so the correct one is in the DOM and able to get focus.
I've also changed the API on Virtualizer and collection renderers in general to accept persistedKeys as a prop rather than a single focusedKey. This enables multiple keys to be persisted and is more extensible for future use cases.
Finally, based on feedback in https://github.com/adobe/react-spectrum/pull/6631#discussion_r1657916779, I've renamed the Layout validate method to update to make it easier to understand. This was originally pulled from v2 CollectionView but since we are doing a breaking change we can rename it now.

Hmm yeah that seems to be an issue with persisted keys in TableLayout...

Ok fixed 3 issues:

TableLayout was using the wrong indices to calculate persisted keys. When we perform an incremental layout, the index should be a count of the items that we actually have built, not all of the nodes in the collection. For example, if we start layout from the middle, there will be items before that we didn't layout. This matches how firstVisibleRow and lastVisibleRow are computed.
In Spectrum, revert back to only persisting either the focused key or the drop target, not both. In RAC we need to persist both to avoid the dragged element going out of view, getting removed from the DOM, and triggering the end of the drag session due to this layout effect. This doesn't happen in Spectrum for some reason. On the flip side, we can't persist both in Spectrum because when dragging ends, there seems to be a race where the focusedKey isn't updated to the dropped item right away and we scroll back to the original focused item from before the drop. I can't reproduce this in RAC. Not quite sure what the difference is...
@LFDanLu noticed that the async loading story was scrolling to the top, because we were placing the loading indicator in the wrong spot.

Brain dump before break:
Actually I did manage to reproduce (2) in RSP as well, so we should persist the drag target as well as the drop target. It only occurs when the drag target element is removed from the dom and not reused by another view, most commonly with variable row heights.
The other issue with scrolling to the top on drop also only seems to occur in the non-util handlers stories, I think because the onDrop is async there and we refocus in DragManager's end method prior to the items being moved therefore scrolling to the wrong position. If you delay that by a micro task it works, but this breaks a lot of tests.

In all versions, /story/listview-drag-and-drop-util-handlers--drop-onto-root dragging via mouse does not scroll to the dropped item. Dragging via the keyboard does.
/story/listview-drag-and-drop--drag-within-scroll Item overlaps/spacing can be wrong after dnd. Select item 1 via keyboard, hit 'end' key, drop the item. Note that there's a big gap between the last two items and two drop indicators will appear if you try to drop in that area.
It works fine if you use mouse, I assume because then everything will have been laid out during the scrolling.
Those are the only issues I've found so far, and they aren't that big of issues.
Tested on Chrome and Safari, keyboard and mouse, RAC and RSP.

dragging via mouse does not scroll to the dropped item

Looks like this was the case before as well. Probably due to scroll into view only occurring in keyboard modality...

Item overlaps/spacing can be wrong after dnd

This is strange, but the behavior is better than before where it didn't scroll at all. 😂


Missing sponsors

Would be cool to be able to sponsor x11docker

See https://github.com/mviereck/x11docker#support:

If you like to make a donation: Thank you! :)
I don't take the money myself, but please spend some to Galsan Tschinag in Mongolia (Wikipedia). One of his great projects is the afforestation of Mongolia. A donation account in Germany is provided by Förderverein Mongolei e.V..
Förderverein Mongolei e.V.
IBAN DE7261290120 0394 3660 00
BIC GENODES1NUE
Volksbank Kirchheim-Nürtingen
I personally know some of the people behind this. I assure that they are trustworthy and have a great heart and soul and do a good thing.

Thank you!
I've asked github today to add a sponsor account. They'll review it.
Likely that is easier than to spend to the Mongolia project directly. And to admit, I could use a bit of money for myself, too.
:-)

@mviereck you do deserve some money for yourself as well! Take some coffee/beer/soda or buy some hardware :wink:

github has approved my account; now I am part of Github Sponsors. :-)
The button at my profile and at some projects (notable x11docker) leads to
https://github.com/sponsors/mviereck
Thank you for the suggestion!


Pretext accuracy of RotNet with AlexNet on ImageNet?

What is the rotation prediction accuracy of RotNet using AlexNet trained on ImageNet

Also, does better rotation prediction accuracy correlate with better accuracy on the downstream task? In Figure 5(a) in the paper, the downstream accuracy did not improve after 40 epochs, although the rotation accuracy did improve.

Hi Jason,
The rotation prediction accuracy of a AlexNet-based RotNet on the validation set of ImageNet is around 89.0%.
Regarding the correlation question, in general (and I believe this is a common observation among all self-supervised methods) training for longer on the pretext task (which usually leads to improved  accuracy on the pretext task) leads to improved accuracy on the downstream task.


Split closures test and add regression tests for SR-5202.

@swift-ci Please smoke test and merge

Awesome! 🎉


Migrating wex cert/generate

Move it form /project/extend/v2/bash/default/cert/generate to /project/bash/default
No more [0]=, [1]=, needed in args
Now you have a "default value" for arguments, placed at the end (after true / false for required)
Try to sole all the TODO
No more $(wex text/color -c=lightblue -t='Folders '), use ${WEX_COLOR_BLUE}Folders${WEX_COLOR_RESET} instead (performance)
Use [ ... ] instead of [[ ... ]] when possible (compatibility)
Use simple = instead of == for comparison (yes it works, compatibility)
use _wexLog "Text" for log or _wexMessage, see wex demo/... for more info
Thanks to create a unit test for all scripts, see in project/tests/bash/default/, and in README.

These tasks are dispatched in different other tasks and dev policy rules.


Temperature lower bound is 0 degrees C

Screwed up again. The sketch does a lot of work to display negative temperatures correctly, but it then transmits using the UINT32 API, which is wrong; negative numbers won't get represented.
Problem is here:
https://github.com/mcci-catena/MCCI_Catena_SDP/blob/3707f99518aeeb65af16b3c95b77bf4bd21a50b8/examples/sdp_lorawan/cMeasurementLoop.cpp#L325
This should certainly not be uint32_t. I think it should be int32_t, but this needs to be tested to confirm; it's odd that the uint32_t applied to a negative int16_t is causing 0 to be transmitted rather than positive full scale.

@dhineshkumarmcci @sjpark-mcci  please hard-code a substitute value for testing and confirm that negative values are properly transmitted (after changing uint32_t to int32_t in the indicated line). Since the units are 1/200 of a degree, suggest hard-coding -5 * 200, and confirm that you get 0xFC,0x18 uplinked for temperature.


hostingBundle

Can change hostingBundle at runtime?

No, it's not possible at this moment. Please feel free to open a new issue with a little more reasoning about why you would want this.


Need A4 English version

English language version is only available in "Letter" which is a weird US domestic sizing only used there. For the rest of the English speaking world, we use metric so print predominantly with A4. Thanks.

concept ack


can not upgrade a chart when appVersion contains semVer build metadata

Output of helm version:
version.BuildInfo{Version:"v3.4.0", GitCommit:"7090a89efc8a18f3d8178bf47d2462450349a004", GitTreeState:"clean", GoVersion:"go1.15.3"}

Output of kubectl version:
Client Version: version.Info{Major:"1", Minor:"19", GitVersion:"v1.19.3", GitCommit:"1e11e4a2108024935ecfcb2912226cedeafd99df", GitTreeState:"archive", BuildDate:"2020-10-16T20:58:56Z", GoVersion:"go1.15.3", Compiler:"gc", Platform:"linux/amd64"}
Server Version: version.Info{Major:"1", Minor:"17", GitVersion:"v1.17.9", GitCommit:"4fb7ed12476d57b8437ada90b4f93b17ffaeed99", GitTreeState:"clean", BuildDate:"2020-07-15T16:10:45Z", GoVersion:"go1.13.9", Compiler:"gc", Platform:"linux/amd64"}

Cloud Provider/Platform (AKS, GKE, Minikube etc.): Kubernetes 1.17.9 on AWS
When upgrading a helm chart where appVersion has build metadata in it:
ARGS:
  0: helm (4 bytes)
  1: upgrade (7 bytes)
  2: --install (9 bytes)
  3: --reset-values (14 bytes)
  4: citadel (7 bytes)
  5: development/citadel (30 bytes)
  6: --version (9 bytes)
  7: 99.99.99+bug-DIE-235-use-redis-sentinel (39 bytes)
  8: --create-namespace (18 bytes)
  9: --namespace (11 bytes)
  10: brandtegrity (12 bytes)
  11: --values (8 bytes)
  12: /tmp/values622990503 (20 bytes)
  13: --history-max (13 bytes)
  14: 10 (2 bytes)
ERROR:
  exit status 1
EXIT STATUS
  1
STDERR:
  Error: UPGRADE FAILED: cannot patch "citadel" with kind Service: Service "citadel" is invalid: metadata.labels: Invalid value: "99.99.99+bug-DIE-235-use-redis-sentinel": a valid label must be an empty string or consist of alphanumeric characters, '-', '_' or '.', and must start and end with an alphanumeric character (e.g. 'MyValue',  or 'my_value',  or '12345', regex used for validation is '(([A-Za-z0-9][-A-Za-z0-9_.]*)?[A-Za-z0-9])?') && cannot patch "citadel" with kind Deployment: Deployment.apps "citadel" is invalid: metadata.labels: Invalid value: "99.99.99+bug-DIE-235-use-redis-sentinel": a valid label must be an empty string or consist of alphanumeric characters, '-', '_' or '.', and must start and end with an alphanumeric character (e.g. 'MyValue',  or 'my_value',  or '12345', regex used for validation is '(([A-Za-z0-9][-A-Za-z0-9_.]*)?[A-Za-z0-9])?') && cannot patch "citadel" with kind VirtualService: VirtualService.networking.istio.io "citadel" is invalid: metadata.labels: Invalid value: "99.99.99+bug-DIE-235-use-redis-sentinel": a valid label must be an empty string or consist of alphanumeric characters, '-', '_' or '.', and must start and end with an alphanumeric character (e.g. 'MyValue',  or 'my_value',  or '12345', regex used for validation is '(([A-Za-z0-9][-A-Za-z0-9_.]*)?[A-Za-z0-9])?')

The chart was generated with helm 3.3.4
The culprit is in _helpers.tpl:
{{/*
Common labels
*/}}
{{- define "citadel.labels" -}}
helm.sh/chart: {{ include "citadel.chart" . }}
{{ include "citadel.selectorLabels" . }}
{{- if .Chart.AppVersion }}
app.kubernetes.io/version: {{ .Chart.AppVersion | quote }}
{{- end }}
app.kubernetes.io/managed-by: {{ .Release.Service }}
{{- end }}

This is how Chart.yaml looks like:
apiVersion: v2
appVersion: 99.99.99+bug-DIE-235-use-redis-sentinel
description: A Helm chart for Kubernetes
name: citadel
type: application
version: 99.99.99+bug-DIE-235-use-redis-sentinel

According to the docs: appVersion: The version of the app that this contains (optional). This needn't be SemVer., but it is even semVer as it is the same as the chart version.

This is a difficult one to handle. It might be worth putting together some documentation on it.
First, the app.kubernetes.io/version label is recommended by the community. But, it is not required. So, the helm create command has it built in.
A version with build metadata that has uppercase letters is valid for a version but it is not valid as a value for a Kubernetes label.
So, there is a mismatch and no perfect solution.
If Helm changes the default chart to force lowercase letters it's changing the version. I'm not sure if we should do that by default. A chart author could stick a lower call in there and change it themselves if they are comfortable with it. Then it would be opt-in.
app.kubernetes.io/version: {{ .Chart.AppVersion | lower | quote }}

We want end-users of Helm to have a simple experience but we are willing to put more expectations on to chart creators. Is this one of those times?
I'm curious of others opinions and reasons.

The problem is not the capital letter but the '+' sign. As stated in the error the regex used for validation is: (([A-Za-z0-9][-A-Za-z0-9_.]*)?[A-Za-z0-9])?

Good catch. Suggestions are welcome.

app.kubernetes.io/version: {{ regexReplaceAll "[^-A-Za-z0-9_.]" .Chart.AppVersion "-" | quote }}

I would argue that the above although would change the version, but the original version could not be deployed anyways so I wonder why should it be opt-in?

Hi,
I hit the same issue today. I think this bug should be re-opened... using semver (and build metadata) for Chart versioning is an important use case in my opinion...


Clarify the payload for the commit API call

It's not clear from reading https://gandalf.readthedocs.org/en/latest/api.html#commit what the format should be for the zipfile payload.  I'm hoping it's not the entire repo that we want to see diffed/pushed.

I added a hopefully better description in ea16af71eb9476e518d32473adf180b666856499. In short, you don't need to send the entire repo in the zipfile. Only files to be added or modified should be sent in the zipfile. A downside of this approach is that it's not currently possible to remove files from the repository using this API call.


Possible to change display in left-hand menu?

The add-in displays some options on the left-hand menu under your Inbox in Outlook corresponding to the various apps and solutions, such as Field Service, Community, Services, Portals, Marketing and so on.
Is it possible to change which of these are displayed?

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

ID: 13e03a42-8e14-e95f-a6dc-998cf9f99e94
Version Independent ID: 80a356ef-6cc7-5538-93ee-42b7ccde3dd8
Content: Which fields can be synchronized between Dynamics 365 and Outlook?
Content Source: ce/outlook-addin/admin-guide/which-fields-synchronized.md
Service: crm-online
GitHub Login: @mduelae
Microsoft Alias: mkaur

@sriharibs - Can you help answer this question?

@sriharibs - Can you help answer this question?

Thanks @mduelae, forgot to check back here. The customer is currently testing if this can be done through the client display settings in the Site Map. I'll close for now to remove this from your items and check back again if this approach doesn't work out.


Create an SAP Fiori App Using SAP Business Application Studio

Tutorials: https://developers.sap.com/tutorials/appstudio-fioriapps-create.html
Step 3: Generate an app based on SAPUI5 Application template
Issue:
The data source and Screenshot should be changed. The data source Connect to an SAP system is now Connect to a system as below.

Best Regards,
Priyanka

Thanks, @PriyankaRShivakumar .
Updated and pushed to production.
Regards - Raz


Removing broken links

There is still an issue with dups

@vlio20 note that some urls give 301, so those will still fail the build. We can merge the PR with your things though, even if the build fails as I plan to take care of these url redirects later on, unless you'd like to fix those too.

@rolandjitsu , I about to make another pull request.


Include documentation and test against create-react-native-app

Hello! You are going to have lots of people try to use react-native-elements with create-react-native-app, so you might want to include some documentation on how to do it. Additionally, you may want to work it into your testing workflow to make sure that react-native-elements works as expected in CRNA.
The process for using it with CRNA is the same as with Expo: https://blog.expo.io/using-react-native-ui-toolkits-with-exponent-3993434caf66#.zhsqwy51v

@brentvatne I'll put this on our immediate roadmap. Thanks for pointing it out!


Update ConsensusFilecoinMeetup2024.md

@WinsCodes Added host and logo

Update to the event completed using the Pull request - https://github.com/memser-spaceport/pln-events/pull/471

Update to the event completed using the Pull request - #471


Unable to locate metadata for 'passport-saml'

Hi all
I like to ask if anyone is getting an error with openidp.feide.no?
I followed the example app by Gerard
Set up the app have it running
but when I click login, I ran into the following
SimpleSAML_Error_MetadataNotFound: METADATANOTFOUND('%ENTITYID%' => '\'passport-saml\'')
Backtrace:
3 /www/openidp.feide.no/simplesamlphp/lib/SimpleSAML/Metadata/MetaDataStorageHandler.php:293 (SimpleSAML_Metadata_MetaDataStorageHandler::getMetaData)
2 /www/openidp.feide.no/simplesamlphp/lib/SimpleSAML/Metadata/MetaDataStorageHandler.php:310 (SimpleSAML_Metadata_MetaDataStorageHandler::getMetaDataConfig)
1 /www/openidp.feide.no/simplesamlphp/modules/saml/lib/IdP/SAML2.php:296 (sspmod_saml_IdP_SAML2::receiveAuthnRequest)
0 /www/openidp.feide.no/simplesamlphp/www/saml2/idp/SSOService.php:19 (N/A)

So like to ask if I'm missing out anything here? or could someone give me a hint to get the example app working?
or is there any other testing site that I can leverage on?
Thank you very much
I have registered for an account openidp.feide.no, but I do not see anywhere to upload metadata?

Ran into the same issue while trying the sample to work.
Also I cant seem to "register the site as a service provider".
Did you get the sample / any other way for testing to work?

Having the same problem. Did you resolve this @bosslee ?

Bump. I'm having this issue as well. Some help is appreciated.

@thomashzhang Apparently, their service is not open to the general public. You have to be building for the Norwegian edu sector.


Endpoint types in the documentation are incorrect

In docs/resources/endpoint.md, the endpoint types are documented as being written in snake case:

endpoint_type - (Required) Specifies the endpoint resource type: custom, slack, pager_duty, big_panda, data_dog, victorops. Use the appropriate parameters for your selected endpoint type.

However, trying to use these for vendors will generate the following error message:
Error: value for endpoint type is unknown

Peeking at the source of the client https://github.com/logzio/logzio_terraform_client/blob/master/endpoints/client_endpoints.go#L36-L41 reveals that the strings expected are in one word:
const (
    EndpointTypeSlack     endpointType = "slack"
    EndpointTypeCustom    endpointType = "custom"
    EndpointTypePagerDuty endpointType = "pagerduty"
    EndpointTypeBigPanda  endpointType = "bigpanda"
    EndpointTypeDataDog   endpointType = "datadog"
    EndpointTypeVictorOps endpointType = "victorops"
)

Hi @Tenchi2xh thank you for bringing it up to our attention! PR #72 fixed this mistake :)


Nhpa 34 search by alias

Change name search to search KNOWN_AS instead of LOSS_OF_LIBERTY
In alias section, click an alias to search for other records matching that alias
See https://dsdmoj.atlassian.net/browse/NHPA-34

Not ready yet...


[14.0] ISPN-14451 Parse Hot Rod "AUTO" version

https://issues.redhat.com/browse/ISPN-14451

Integrated into 14.0.x, thanks @tristantarrant !


Camera Shake Sustain Getting Progressively Stronger

Good catch! Thankfully it's a simple fix

This is now fixed in v0.1.1

Perfect thank you


triton reduce_sum is much slower than torch native kernel

I am testing some case in torch inductor, for example, a reduce_sum operation with
input shape = [256, 14, 14, 256],  reduce_axis = [0,1,2]
The code bellow is generator by torch inductor.
Torch native kernel is about 2x faster than triton kernel.
I trying to analysis the triton code; In each program, the loaded data is not contiguous； Is there any way to speedup the triton code
from ctypes import c_void_p, c_long
import torch
import random
from torch import empty_strided, as_strided, device
from torch._inductor.codecache import AsyncCompile

aten = torch.ops.aten
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
async_compile = AsyncCompile()

import triton
import triton.language as tl
from torch._inductor.triton_ops.autotune import grid
from torch._C import _cuda_getCurrentRawStream as get_cuda_stream

triton_fused_sum_1_0 = async_compile.triton('''
import triton
import triton.language as tl
from torch._inductor.ir import ReductionHint
from torch._inductor.ir import TileHint
from torch._inductor.triton_ops.autotune import reduction
from torch._inductor.utils import instance_descriptor

@reduction(size_hints=[256, 65536],
              reduction_hint=ReductionHint.INNER,
              filename=__file__,
              meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': 0, 'constants': {}, 'mutated_arg_names': set(), 'configs': [instance_descriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]})
@triton.jit
def triton_(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr, RBLOCK : tl.constexpr):
    xnumel = 256
    rnumel = 50176
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.reshape(tl.arange(0, XBLOCK), [XBLOCK, 1])
    xmask = xindex < xnumel
    rbase = tl.reshape(tl.arange(0, RBLOCK), [1, RBLOCK])
    x0 = xindex
    _tmp1 = tl.zeros([XBLOCK, RBLOCK], tl.float32) + 0
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp0 = tl.load(in_ptr0 + (x0 + (256*r1)), xmask & rmask, eviction_policy='evict_last')
        _tmp1 = tl.where(xmask & rmask, _tmp1 + tmp0, _tmp1)
    tmp1 = tl.reshape(tl.sum(_tmp1, 1), [XBLOCK, 1])
    tl.store(out_ptr0 + x0, tmp1, xmask)
''')

async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, = args
    args.clear()
    buf0 = empty_strided((1, 1, 1, 256), (256, 256, 256, 1), device='cuda', dtype=torch.float32)
    stream0 = get_cuda_stream(0)
    triton_fused_sum_1_0.run(arg0_1, buf0, 256, 50176, grid=grid(256), stream=stream0)
    del arg0_1
    return (buf0, )

if __name__ == "__main__":
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((256, 14, 14, 256), (50176, 3584, 256, 1), device='cuda', dtype=torch.float32)
    print_performance(lambda: call([arg0_1]))                                  #  result is 0.002817
    print_performance( lambda : torch.sum( arg0_1, axis = [0, 1,2], keepdim=True))        #  result is 0.001500

I do think we can optimize triton_ internally, but I'm confused about the testing result.
Are they the same shape?
    print(call([arg0_1])[0].shape)
    print(torch.sum(arg0_1, axis = 0).shape)

The slowness might be caused by the fact that reduce isn't performed on the fast changing dimension.
Before deriving a conclusion, I think we should have a valid test first.

I do think we can optimize triton_ internally, but I'm confused about the testing result.
Are they the same shape?
    print(call([arg0_1])[0].shape)
    print(torch.sum(arg0_1, axis = 0).shape)

My mistake.  The right code is
torch.sum( arg0_1, axis = [0, 1, 2], keepdim=True)

It's about 2x times faster than triton  kernel.

Thanks, will take a look

@phlrain May I know how triton_fused_sum_1_0 is generated? I assume you compiled a pytorch model and extract the code here, right? I asked because I want to have an end-to-end test.

@Jokeren  The code is here:
import torch
import numpy as np

def test_f( x ):
    return torch.sum( x, axis = [0, 1, 2], keepdim=False)

x = torch.randn( [256, 14, 14, 256]).cuda()

compile_f = torch.compile( test_f )
out = compile_f( x )

But after update pytorch from 1.14.0.dev20221204+cu117 to 2.0.0.dev20230201+cu117; Triton is Faster than torch native kernel.
triton             0.000960
torch native   0.001460

I compare the code generator by torch inductor.  The new version using two kernel to speedup.
It shows powerfull calculate by triton.
Actually I am testing batch_norm in NHWC memory format. I decomposite batch norm by myself (not including running mean and variance update)
import torch
import numpy as np
import time

def composite_batchnorm(
    x,
    run_mean,
    variance,
    scale,
    bias,
    is_test = False,
    momentum = 0.9,
    epsilon=1e-5,
    data_layout="NCHW",
    use_global_stats=None,
    trainable_statistics=None,
):
    n, c, h, w = x.shape

    mean = torch.mean( x, [0,1, 2], keepdim=True )
    pow_mean = torch.mean( x*x, [0, 1, 2], keepdim=True )

    var2 = x - mean
    var1 = pow_mean - mean*mean

    var1 = torch.sqrt( var1 + epsilon)

    t1 = var2 / var1

    y = t1 * scale + bias
    return y

input = torch.randn( [256, 256, 7, 7]).cuda().to(memory_format=torch.channels_last)   # we need test nhwc

x = torch.randn( [256, 7, 7, 256]).cuda()
running_mean = torch.ones( [256] ).cuda()
running_variance = torch.ones( [256] ).cuda()
weight = torch.ones( [256] ).cuda()
bias = torch.ones( [256] ).cuda()

compile_batchnorm = torch.compile( composite_batchnorm )

out = compile_batchnorm( x,running_mean, running_variance, weight, bias  )

from torch._inductor.utils import print_performance

print_performance( lambda : compile_batchnorm( x,running_mean, running_variance, weight, bias  ) )
print_performance( lambda : torch.nn.functional.batch_norm( input, running_mean, running_variance, weight, bias) )

The result is:
triton            0.001317
torch native  0.000485

It seems the calculate of mean and variance is slower than the torch native kernel named "batch_norm_collect_statistics_channels_last_kernel".

Thanks for the code. It will be useful

Note that a comparable test in master using the decompositions in core yields the same performance as the eager function:
import torch
import numpy as np
import time

def composite_batchnorm(input, running_mean, running_variance, weight, bias):
    return torch.nn.functional.batch_norm( input, running_mean, running_variance, weight, bias)

input = torch.randn( [256, 256, 14, 14]).cuda().to(memory_format=torch.channels_last)   # we need test nhwc

x = torch.randn( [256, 14, 14, 256]).cuda()
running_mean = torch.ones( [256] ).cuda()
running_variance = torch.ones( [256] ).cuda()
weight = torch.ones( [256] ).cuda()
bias = torch.ones( [256] ).cuda()

compile_batchnorm = torch.compile( composite_batchnorm )
print(compile_batchnorm)

from torch._inductor.utils import print_performance

print_performance( lambda : compile_batchnorm( input, running_mean, running_variance, weight, bias) )
print_performance( lambda : torch.nn.functional.batch_norm( input, running_mean, running_variance, weight, bias) )

returns
0.003762
0.003755

Note that a comparable test in master using the decompositions in core yields the same performance as the eager function:
import torch
import numpy as np
import time

def composite_batchnorm(input, running_mean, running_variance, weight, bias):
    return torch.nn.functional.batch_norm( input, running_mean, running_variance, weight, bias)

input = torch.randn( [256, 256, 14, 14]).cuda().to(memory_format=torch.channels_last)   # we need test nhwc

x = torch.randn( [256, 14, 14, 256]).cuda()
running_mean = torch.ones( [256] ).cuda()
running_variance = torch.ones( [256] ).cuda()
weight = torch.ones( [256] ).cuda()
bias = torch.ones( [256] ).cuda()

compile_batchnorm = torch.compile( composite_batchnorm )
print(compile_batchnorm)

from torch._inductor.utils import print_performance

print_performance( lambda : compile_batchnorm( input, running_mean, running_variance, weight, bias) )
print_performance( lambda : torch.nn.functional.batch_norm( input, running_mean, running_variance, weight, bias) )

returns
0.003762
0.003755

Also, I think the issue the repro in https://github.com/openai/triton/issues/1172#issuecomment-1429335406 was that one implementation was being tested with a contiguous input input and the other one was being tested with a channels_last input x.


[usbdev] Document data_toggle_clear as only for hardware debug

Closes #2364.

Sorry, I should have marked this as blocked - that discussion does not seem to be settled yet. I'll put this back into "draft" mode.

Closing this PR due to inactivity. It can be re-opened if needed based on the outcome of lowrisc/opentitan#2364


RefCOCO+ Result

RefCOCO + experiment result is 4 M or 14 M in Table5?

It is from 14M model

OK! Thanks!


Feat/submission form modal

Resolves #700
🔧 What changed
A modal was added that only triggers on use of the back button in the browser.
🧪 Testing instructions
No tests were added.
📸 Screenshots

Before

It didn't exist before.

After

https://github.com/user-attachments/assets/62b0dcf1-436b-45ec-a8b5-77c3f0afb8f5

Got a lot of support from @ProgressChris88 in the initial stage and an enormous amount of help in solving the mystery of the maddeningly inconsistent modal from @NabbeunNabi. Big thanks guys.

Great work getting the modal working! First thought that comes to mind is that we probably only want it to appear if there are actual unsaved changes. I noticed in the demo video that the modal opened if a user navigated to the edit page and hit the back button without making any changes.

Nit: As a good practice, commit message titles should be limited to about 50 characters. Anything longer than that could be added to the message body. You may want to think about how you could write this message more concisely:

@all-contributors
please add @ShizuokaTerrier for coding.
please add @ProgressChris88 for coding.

@all-contributors please add @ProgressChris88 for coding

@all-contributors please add @ShizuokaTerrier for coding


live share not loading for guest

The live share starts on the host but when the guest tries to connect
it is stuck loading the workspace. Running win 10. Live share logs from host attached.
ls-logs.zip
Version: 1.32.0-insider (system setup)
Commit: 0a4a3b98282175a82a819b70d2bbe46475e1ebd0
Date: 2019-02-28T06:18:12.644Z
Electron: 3.1.3
Chrome: 66.0.3359.181
Node.js: 10.2.0
V8: 6.6.346.32
OS: Windows_NT x64 10.0.17763

#1750 could be related

Same problem I have as well.
Here is the log:
LiveShare-logs.zip

That's a regression in the VSCode Insiders and we track it here: https://github.com/Microsoft/vscode/issues/68546. Sorry for inconvinience.
Thanks!

@legomushroom Thanks, but your state your issue states "it happens with any workspace file that has a virtual file system provider scheme in their root folder". - I do not have a virtual file system provider scheme in the root folder, so this is a non-related issue.

@pjninnim that's just a technical detail, Live Share will use the virtual file system provider when you join a session. I'm pretty sure that that's the same issue you have. If you are blocked, you can use a stable VSCode release for now.

@legomushroom Thanks, yes it does still working using stable VS Code. Let's hope it gets resolved soon in the Insiders version.

This should be fixed in the latest VSCode Insiders, please verify.

@legomushroom Yes, just tried and I can confirm it's now been resolved in the latest version.

@pjninnim thanks for verifying!


Wrong documentation about installation

Package
filament/tables
Package Version
v3.1.35
Laravel Version
v10
Livewire Version
v3.0
PHP Version
irrelevant
Problem description
Trying to add custom tailwind classes, I followed the installation guide here https://filamentphp.com/docs/3.x/tables/installation#configuring-your-layout and saw that the documentation is not accurate.

You should not create a new resources/views/components/layouts/app.blade.php file, but a resources/views/vendor/filament-panels/components/layout/base.blade.php file.
You should not paste in it the content shown in the documentation, but the content shown in the original file (vendor/filament-panels/components/layout/base.blade.php and add the line @vite('resources/css/app.css') underneath @filamentStyles.

Expected behavior
You should be able to add your new tailwind classes that are not currently compiled.
Steps to reproduce
If you follow the instructions in the current documentation, new tailwind classes are not read neither from app.css nor from any ->recordClasses() methods you may add.
Reproduction repository

Relevant log output
No response

How should I send a wrong documentation issue?

@yannisc (and anyone else who finds themselves having this issue) it seems the fix is to follow the custom theme instructions at https://filamentphp.com/docs/3.x/panels/themes. It's not intuitive, because the filament docs talk about the livewire layout, and that includes vite directives, but that's not used to render a panel. Hope this helps!


Make your first Contributions!

Mark your beginning to this year's Hacktoberfest with this Repo!
Happy Hacking :tada:

Sure Thanks ! 👍