.MOV file portrait video displaying as landscape iOS 14

Bug
.MOV video recorded on iPhone 11 / iPhone 11 pro max device in portrait is displaying as a landscape video on iOS 14.
This screenshot of iPhone 11 simulator with resizeMode as cover - shows as landscape then zooms in to fit screen

This screenshot of iPhone 11 simulator with resizeMode as none - you can see how it's displaying as landscape and not portrait

This is screenshot of same video from iPhone 11 pro max iOS 13.7 - issue does not exist

If I convert to MP4 the video shows fine
I also see someone commented here with same issue for mov 4.4.5 https://github.com/react-native-community/react-native-video/pull/842#issuecomment-660703541
Platform

iOS

Environment info

React native info output:
    OS: macOS 10.15.5
    CPU: (12) x64 Intel(R) Core(TM) i7-8850H CPU @ 2.60GHz
    Memory: 396.60 MB / 16.00 GB
    Shell: 3.2.57 - /bin/bash
  Binaries:
    Node: 12.16.3 - /usr/local/bin/node
    Yarn: 1.19.1 - ~/.yarn/bin/yarn
    npm: 6.14.7 - /usr/local/bin/npm
    Watchman: 4.9.0 - /usr/local/bin/watchman
  Managers:
    CocoaPods: 1.9.1 - /usr/local/bin/pod
  SDKs:
    iOS SDK:
      Platforms: iOS 14.0, DriverKit 19.0, macOS 10.15, tvOS 14.0, watchOS 7.0
    Android SDK:
      API Levels: 22, 23, 25, 26, 27, 28, 29
      Build Tools: 26.0.3, 27.0.3, 28.0.3, 29.0.2
      System Images: android-26 | Google Play Intel x86 Atom, android-27 | Google Play Intel x86 Atom, android-28 | Google APIs Intel x86 Atom, android-28 | Google Play Intel x86 Atom
      Android NDK: 20.0.5594570
  IDEs:
    Android Studio: 3.5 AI-191.8<IP_ADDRESS>00203
    Xcode: 12.0/12A7209 - /usr/bin/xcodebuild
  Languages:
    Java: 1.8.0_181 - /usr/bin/javac
    Python: 2.7.16 - /usr/bin/python
  npmPackages:
    @react-native-community/cli: Not Found
    react: 16.13.1 => 16.13.1
    react-native: 0.63.2 => 0.63.2
    react-native-macos: Not Found
  npmGlobalPackages:
    *react-native*: Not Found

Library version: 4.4.5 and 5.1.0-alpha8
Steps To Reproduce

Record video on iphone 11 / iphone 11 pro max
Open with into app from camera roll (using library such as react-native-image-crop-picker)
Display video using react-native-video (video will be showing as landscape instead of portrait)
...

Expected behaviour

Video should display as portrait if recorded as portrait

Reproducible sample code
<Video
            ref={ videoUseRef }
            paused={ paused }
            onEnd={ () => {
              onEnd();
              setEnded(true);
            } }
            onLoad={ onLoad }
            onProgress={ onProgress }
            source={ from === 'local' ? profileVideoUrl : { uri: profileVideoUrl } }
            style={{
    position: 'absolute',
    top: 0,
    left: 0,
    bottom: 0,
    right: 0,
    backgroundColor: '#000'
  } }
            resizeMode={ 'cover' } //or 'none'
            repeat={ false }
            ignoreSilentSwitch="ignore"
          />

Video sample
https://drive.google.com/file/d/1c8qiVfRyhn4aKvY3wXd2u6Yt05qeCvX6/view?usp=sharing

the same for me :(

we face the same problem in our app, looking into that

Same here. :(

@leshaivanovsky @stefan-t
I followed the fix provided in https://github.com/react-native-community/react-native-video/issues/2159
Check where the ++++++ is (add those lines of code)

Cheers @dylan-westbury!
However, this fix introduces two new issues (as mentioned by @2xSamurai and @dackom in that issue.

@stefan-t
I see, for our use case we don't use controls.

Out of curiosity, how did you convert the .mov to .mp4? programatically or manually as part of troubleshooting?

@albmin manually outside of application

iOS 14.2 beta fixed the issue

@albmin manually outside of application

hi, how did you convert to mp4? I tried with moviepy, but its very slow process.

Just first result I found on google @sachithkadamba

yep same issue here

Yesterday iOS 14.1 released, the video is fixed now. Just update.

Fixed in iOS 14.1 indeed.

One work around I found was to use 'resizeMode: cover' but caveat with that is that you would have to compromise with the aspect ratio which can be a pretty big deal.

My (temporary) solution is to convert a .MOV-file to an .MP4-file with FFMPEG. I use react-native-fs (copyAssetsVideoIOS) to make a copy. Then I use the following FFMPEG command to conver the .MOV-file to the .MP4-file:
-i input.MOV  -preset ultrafast -vcodec h264 -acodec mp2 output.MP4

After that, I delete the copied input.MOV file (``RNFS.unlink('input.MOV')`. I use the path to utput.MP4 to show the video. This video is showed correctly. Extra is that I now only have MP4-files on my server, from IOS and from Android.

My (temporary) solution is to convert a .MOV-file to an .MP4-file with FFMPEG. I use react-native-fs (copyAssetsVideoIOS) to make a copy. Then I use the following FFMPEG command to conver the .MOV-file to the .MP4-file:
-i input.MOV  -preset ultrafast -vcodec h264 -acodec mp2 output.MP4

After that, I delete the copied input.MOV file (``RNFS.unlink('input.MOV')`. I use the path to utput.MP4 to show the video. This video is showed correctly. Extra is that I now only have MP4-files on my server, from IOS and from Android.

Closing as resolved in iOS 14.1+

Closing as resolved in iOS 14.1+

My (temporary) solution is to convert a .MOV-file to an .MP4-file with FFMPEG. I use react-native-fs (copyAssetsVideoIOS) to make a copy. Then I use the following FFMPEG command to conver the .MOV-file to the .MP4-file:
-i input.MOV  -preset ultrafast -vcodec h264 -acodec mp2 output.MP4

After that, I delete the copied input.MOV file (``RNFS.unlink('input.MOV')`. I use the path to utput.MP4 to show the video. This video is showed correctly. Extra is that I now only have MP4-files on my server, from IOS and from Android.

command don't active


Add Guidance & PowerShell Script for Cleaning Up An Enterprise-scale Deployment

Overview/Summary
As explained in the supporting documentation, see the snippet extracted from that documentation below:
"
We understand that some of you may be deploying Enterprise-scale multiple times within the same or multiple Azure AD Tenants for lab, demo or testing purposes for yourself or customers, but cleaning up an Enterprise-scale deployment can be a very time consuming and manual task.
With this in mind we have created a PowerShell script called Wipe-ESLZAzTenant.ps1 that will clean-up and remove the following resources from your Azure AD Tenant, based on the parameters you pass in to set its scope:

Service Principal (optional)
Management Groups (Only those in the hierarchy based on what you provide as an input for the parameter intermediateRootGroupID)
Move Susbcriptions, in above hierarchy, back to the Tenant Root Management Group
Remove all Resources and Resource Groups from the Subscriptions in scope
Remove all ARM deployments history from the following scopes:

Tenant
Management Groups (Only those in the hierarchy based on what you provide as an input for the parameter intermediateRootGroupID)
Subscriptions, in above Management Group hierarchy

"
This is much asked for from partners, customers and internal to assist in testing and usage of ESLZ. Also will be very useful for support/troubleshooting when an environment has entered a "failed" state and requires a complete clear down to be able to re-deploy successfully. 👍
This PR fixes/adds/changes/removes

Add Guidance & PowerShell (Wipe-ESLZAzTenant.ps1) Script for Cleaning Up An Enterprise-scale Deployment

Breaking Changes
None
Testing Evidence
Testing evidence is available in the screenshots in the documentation included in this PR: EnterpriseScale-Clean-Deployment.md
Also @JefferyMitchell has been kind enough to test this successfully 👍
As part of this Pull Request I have

[x] Checked for duplicate Pull Requests
[x] Associated it with relevant issues, for tracking and closure.

https://github.com/Azure/Enterprise-Scale/issues/747

[x] Ensured my code/branch is up-to-date with the latest changes in the main branch
[x] Performed testing and provided evidence.
[x] Updated relevant and associated documentation.
[x] Updated the "What's New?" wiki page (located: /docs/wiki/whats-new.md)

Thank you for addressing my comments, @jtracey93. We don't see any drawbacks in deleting all tenant level deployments?
@krnese, given the potential effects of running this in a production environment, can you please also take a look at this?

Thank you for addressing my comments, @jtracey93 Jack Tracey FTE. We don't see any drawbacks/risks in deleting all tenant level deployments? @krnese Kristian Nese FTE, given the potential effects of running this in a production environment, can you please also take a look at this?

Hey @daltondhcp, I don't see them as drawbacks/risks apart from the history would be deleted. However as Tenant level deployments, are not common for most and are only visible via hitting the APIs, rather than via the portal, I honestly doubt many use them anyway.
However, an option could be to create additional input boolean parameters that ask whether you want to delete each of the following:

Deployments at:

Tenant scope
Management Group scope (as selected in input today)
Subscription Scope (that are within the scope selected above)

Resource & Resource Group Deletion

Something like -deleteTenantDeployments 'true' etc..?
Although the risk is that it doesn't clean up enough, based on parameter values set, and when someone tries to re-deploy ESLZ they are unsuccessful due to a clashing deployment name or another conflict because it hasn't been cleared down fully.
Thoughts...?

Thank you for addressing my comments, @jtracey93 Jack Tracey FTE. We don't see any drawbacks/risks in deleting all tenant level deployments? @krnese Kristian Nese FTE, given the potential effects of running this in a production environment, can you please also take a look at this?

Hey @daltondhcp, I don't see them as drawbacks/risks apart from the history would be deleted. However as Tenant level deployments, are not common for most and are only visible via hitting the APIs, rather than via the portal, I honestly doubt many use them anyway.
However, an option could be to create additional input boolean parameters that ask whether you want to delete each of the following:

Deployments at:

Tenant scope
Management Group scope (as selected in input today)
Subscription Scope (that are within the scope selected above)

Resource & Resource Group Deletion

Something like -deleteTenantDeployments 'true' etc..?
Although the risk is that it doesn't clean up enough, based on parameter values set, and when someone tries to re-deploy ESLZ they are unsuccessful due to a clashing deployment name or another conflict because it hasn't been cleared down fully.
Thoughts...?

Thanks. Although we recognize that guidance for "clean-up" may be needed for scenarios like test iteration etc. , we decided earlier that we wouldn't provide automation for it due to many reasons. Having a structured, sequenced guidance with step-by-step instructions would be the preference if this is still needed.
Can you pls work on the step-by-step guidance, and we can link to it in the FAQ?
On the deletion of tenant deployment, we shouldn't delete anything more than we create in order to reset to the initial state.


Adding FieldValue.increment()

I am sending this out for early review, so I can get it merged by Friday. I haven't been able to run the integration tests (written on the bus to Santa Cruz), but I would appreciate some early comments.
Port of https://github.com/firebase/firebase-android-sdk/pull/105 and https://github.com/firebase/firebase-js-sdk/pull/1368

PR updated to use MaybeDocumentMap instead of FSTImmutableDocumentMap and to use new increment() Protobuf naming.

Merged master, including the changes to make MutationQueue a C++ interface. @schmidt-sebastian PTAL and this is ready for merging.

Thanks for the merge. PR looks good to me.


grid lines sometimes not showing up properly in orthographic camera

see https://forum.unity.com/threads/released-realtime-csg-level-design-for-unity.427601/page-9#post-3983161

Clarification needed - what are the gridlines?
I don't remember ever seeing gridlines apart from the default unity ones and the vertical and horizontal lines that show you where the current point is.
Doesn't seem like a relevant issue to me.


Problem with modal event load

When I click an event link after clicked other one, it loads the both url again. I'm using the modal and ajax configuration.
Must I have do any other configuration to work well?
Sorry about my english! It's not my first language.

Have same issue here.
Over time the page become slow, because with one click will request all previous link that call modal too.


Feature request: tmux kind of process in interactive window

Discussed in https://github.com/microsoft/vscode-python/discussions/16618

Originally posted by sergei3000 July  6, 2021
I often use the Python extension in a remote SSH window which I open using an SSH tunnel. Whenever the tunnel (or just a regular SSH session) breaks all my environment and its variables are lost. And among those variables there's often a connection to a remote cluster's session which cannot be serialized.
So it would be great if the Python interactive window, that is started by running cells marked by # %%, could be optionally opened with a kind of tmux process. Just so I could reconnect to it after breaking that ssh connection.

We handle new feature requests in Discussions now, once they get enough upvotes we open issues related to them. Closing this in favour of https://github.com/microsoft/vscode-python/discussions/16618.


Rewrite: Sending a Transactional Template Email over SMTP

Description of the change:
Purpose: Rewrite the Using Transactional Templates With the SMTP API.
Reason for the change:
Clarifying using the Transactional Templates with the SMTP API
Link to original source:

Closes #1755

Open to feedback!


Fix: Move the deleteScormFolder() method call to the deleteScorm() me…

When calling the method
public function uploadScormArchive(UploadedFile $file, $uuid = null)

It calls within it the method:
private function saveScorm($file, $filename, $uuid = null)

Which calls the method:
private function generateScorm($file)

Which calls the unzipper method on the scormDisk object. The scorm path is cleared within the method body before the new content is made available.
As deleteScormData is called further down in the uploadScormArchive method, all content is removed after a successful upload.

@devianl2 can someone do a pull?


fix qwen2 vl failure in intel cpu and xpu

Hi, I see your guys have added qwen2 vl into tgi, and I try the model in intel cpu, and find it does not work, failure like
data: {"error":"Request failed during generation: Server error: output with shape [1] doesn't match the broadcast shape [873]","error_type":"generation"}
I debug it and find following code is missing in modeling and cause prefill output shape is not as expected.
@drbh  @Narsil please help review it

hi @sywangyi thank you for sharing this fix! I've included these changes in another PR that includes a couple other tweaks/fixes to qwen2-vl. https://github.com/huggingface/text-generation-inference/pull/2708. Once that PR is merges I'll close this one. Thank you again for the fix! 🙏

2708 is merged. so close the pr


Convert server url to lowercase before connecting.

Fix:#345

Automated message from Dropbox CLA bot
@vishwesh3, it looks like you've already signed the Dropbox CLA. Thanks!

Why so much of code it could be easily achievable by changing here https://github.com/vishwesh3/zulip-android/blob/6b89888f82905f0055cd1127fb6f7beffdc59618/app/src/main/java/com/zulip/android/activities/LoginActivity.java#L278 and https://github.com/vishwesh3/zulip-android/blob/6b89888f82905f0055cd1127fb6f7beffdc59618/app/src/main/java/com/zulip/android/activities/LoginActivity.java#L287

Thanks @kunall17 Done :)

@timabbott LGTM!

Merged as<PHONE_NUMBER>7ed1f09a758e0fdeace9d43c691d0c, thanks @vishwesh3!


Update airmail-beta to <IP_ADDRESS>1,293

After making all changes to the cask:

[x] brew cask audit --download {{cask_file}} is error-free.
[x] brew cask style --fix {{cask_file}} left no offenses.
[x] The commit message includes the cask’s name and version.

Please update to the newest cask-repair. It fixes the issue you were seeing here.


Preview mode on Markdown editor

Package
filament/filament
Package Version
v3.0.68
Laravel Version
v10
Livewire Version
v3
PHP Version
PHP 8.1.0
Problem description
In Filament v2, the markdown editor has a preview mode, but in v3, the preview mode doesn't appear.
Expected behavior
A label to switch to preview mode.
Steps to reproduce
Adding a markdown editor.
Reproduction repository
https://github.com/filamentphp/demo.git
Relevant log output
No response

This is by design, since the new Markdown editor is kind of a WYSIWYG editor.

the Markdown Editor is based on EasyMDE wich supports preview button.
why not allowing this ?

https://github.com/filamentphp/filament/assets/36602392/c71c6738-ec5c-4a45-a200-38eb46adad93


Add standalone Bayesian optimization workflow

Issue #, if available:
Description of changes:
Added standalone bayesian optimization workflow to operate upon the core subset of dependencies.
For this change, additional changes in parts of the code have been made:

conditional import in backend
comment out metrics_df (this requires pandas, but after checking that this function is not yet used in other parts, commented out for now)

By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.

Hi @mseeger and @geoalgo, I've just updated a minor fix for importing. Thanks again for reviewing!


Update section "Backup only OS disk during modify protection with Azure CLI" to include True

[Enter feedback here]
Update section "Backup only OS disk during modify protection with Azure CLI" to include True on the end of the command.  If run with false, "Include disks" will be empty preventing parameter --disk-list-setting-include from working.
Example Scenario: Customer performs Daily VM Backups including the OS Disk only, however they need to include the Data Disks on-demand.  This requires running the following Selective Disk AZ commands:
(Command 1) Add Data disks to Associated Item in Enhanced Backup Policy
az backup protection update-for-vm --resource-group  --vault-name  -c  -i  --disk-list-setting include --diskslist 0 1 2 3 4 5 6 7 8
(Command 2) Set OS disk only in Associated Item in Enhanced Policy
az backup protection update-for-vm --resource-group  --vault-name  -c  -i  --exclude-all-data-disks true
Problem: In testing, Command 2 was run using "--exclude-all-data-disks false" thinking we could use command 2 (with True/False) to both add and remove the data disks.  However, once run with false, "Include disks" were empty and running command 1 no longer worked.
Resolution:  Run command 2 with "True" to reverse the changes.  Now the customer can toggle back and forth using command 1 and 2.

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

ID: c377efd1-001c-0c4f-cadb-0c60decc0c6d
Version Independent ID: ca9492ae-ca22-d786-b1a7-49f9b6ba9c8e
Content: Selective disk backup and restore for Azure virtual machines - Azure Backup
Content Source: articles/backup/selective-disk-backup-restore.md
Service: backup
GitHub Login: @AbhishekMallick-MS
Microsoft Alias: v-abhmallick

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

@amyholco - Thanks for your dedication to our documentation. Unfortunately, at this time we have been unable to review your issue in a timely manner and we sincerely apologize for the delayed response. We are closing this issue for now, but if you feel that it's still a concern, please respond and let us know. If you determine another possible update to our documentation, please don't hesitate to reach out again. #please-close


Login to subtitle sites

subliminal supports a few sites that require authentication.
How can I do this in the script.
Didn't find how to save configuration files in subliminal on Windows.

You could try this one https://github.com/davidde/mpv-autosub seems better than mine 👍


How to get back latitude and longitude from the browser to a view

I tried many things below is my latest attempt. The main issue is that the data is not serialized back ... possibly because is returning a function and not data. (When I hard code the object it returns the object) Any advise?

As you can see the Js method is being invoked

but I dont get the response serialized on: var result = await JSRuntime.Current.InvokeAsync("HuliGeolocation.GetLocation");
See full code example:

CSHTML

@page "/geo"

@Latitude

@Longitude

@functions {
    private string Latitude { get; set; }
    public string Longitude { get; set; }
    private class LocationVm {
        public string Latitude { get; set; }
        public string  Longitude { get; set; }

    }

    protected override async Task OnInitAsync()
    {
        var result = await JSRuntime.Current.InvokeAsync<LocationVm>("HuliGeolocation.GetLocation");
    }

    }
`

`
(window as any).HuliGeolocation =
    {

     GetLocation: () => {
            var Latitude = "";
            var Longitude = "";
            let myFunction = function () {
                navigator.geolocation.getCurrentPosition((position) => {
                    console.log("test");
                    console.log(position);
                    return {
                        Latitude : position.coords.latitude.toString(),
                        Longitude: position.coords.longitude.toString()
                    }
                });

            };
         return myFunction();
        },

};

Well the issue is that you're attempting to return from the callback function, but you return nothing from the myFunction function. As this API asynchronous / callback based, you need to return a Promise from GetLocation and resolve that promise from the callback where you're currently returning.

Look at the method "readUploadedFileAsText" here https://github.com/RemiBou/Toss.Blazor/blob/master/Toss/Toss.Client/wwwroot/site.js. You have to create a Promise and call resolve with the result you want to get from C#

Thanks for answering, @Suchiman and @RemiBou!

`(window as any).HuliGeolocation =
{
GetLocation: () => {
var myPromise= new Promise((resolve, reject) => {
            navigator.geolocation.getCurrentPosition(
                position => {

                    let geoLocation = {
                        Latitude: position.coords.latitude.toString(),
                        Longitude: position.coords.longitude.toString()
                    }
                    resolve(geoLocation);
                },
                error => { reject(error) }
            )
        }).catch(error => error);

        return myPromise;
    },
};`

@functions {
private double Latitude { get; set; }
public double Longitude { get; set; }

public class GeoLocation
{
    public double Latitude { get; set; }
    public double Longitude { get; set; }
}
protected override async Task OnInitAsync()
{
    var result = await JSRuntime.Current.InvokeAsync<GeoLocation>("HuliGeolocation.GetLocation");
    Latitude = result.Latitude;
    Longitude = result.Longitude;
}

}


Call to a member function delete() on null

Laravel Version:  5.6.38
PHP Version: 7.2.0
Laravel-admin: 1.6.3

Description:
If the record of table was deleted when the "only trashed" filter was turned on, when trying to permanently delete the record, we get an error:

Call to a member function delete() on null

exception: "Symfony\Component\Debug\Exception\FatalThrowableError"
file: "D:\OSPanel\domains\chronos\vendor\encore\laravel-admin\src\Form.php"
line: 284
message: "Call to a member function delete() on null"

Fixed, will be available in the next version


Fix 'Error loading shared library libssl.so.1.1' error

Fixes the error as per indicated solution found here: https://github.com/prisma/prisma/issues/16553#issuecomment-1348473683

As a side note, I also fixed this in my fork by updating prisma dependencies ("@prisma/client" & "prisma") to ^4.8 instead of 4.50 and changing line 4 of the Dockerfile to:
RUN apk add --no-cache libc6-compat openssl openssl-dev
I'm not sure openssl-dev is required but I haven't had time to look deeper into this.

I've opened another PR that addresses this issue with Prisma 4.8x #1728

I did resolve by removing the lock file and update the prisma and @prisma/client dep and in the Dockerfile
Dockerfile working on Railway.
# Install dependencies only when needed
FROM node:16-alpine AS deps
# Check https://github.com/nodejs/docker-node/tree/b4117f9333da4138b03a546ec926ef50a31506c3#nodealpine to understand why libc6-compat might be needed.
RUN apk add --no-cache libc6-compat openssl openssl-dev
WORKDIR /app
COPY package.json ./
RUN yarn install

# Rebuild the source code only when needed
FROM node:16-alpine AS builder
WORKDIR /app
COPY --from=deps /app/node_modules ./node_modules
COPY . .

ARG DATABASE_TYPE
ARG BASE_PATH

ENV DATABASE_TYPE $DATABASE_TYPE
ENV BASE_PATH $BASE_PATH

ENV NEXT_TELEMETRY_DISABLED 1

RUN yarn build-docker

# Production image, copy all the files and run next
FROM node:16-alpine AS runner
WORKDIR /app

ENV NODE_ENV production
ENV NEXT_TELEMETRY_DISABLED 1

RUN addgroup --system --gid 1001 nodejs
RUN adduser --system --uid 1001 nextjs

RUN yarn add npm-run-all dotenv<EMAIL_ADDRESS>
# You only need to copy next.config.js if you are NOT using the default configuration
COPY --from=builder /app/next.config.js .
COPY --from=builder --chown=nextjs:nodejs /app/public ./public
COPY --from=builder /app/package.json ./package.json
COPY --from=builder /app/prisma ./prisma
COPY --from=builder /app/scripts ./scripts

# Automatically leverage output traces to reduce image size
# https://nextjs.org/docs/advanced-features/output-file-tracing
COPY --from=builder --chown=nextjs:nodejs /app/.next/standalone ./
COPY --from=builder --chown=nextjs:nodejs /app/.next/static ./.next/static

USER nextjs

EXPOSE 3000

ENV PORT 3000

CMD ["yarn", "start-docker"]

I did resolve by removing the lock file and update the prisma and @prisma/client dep and in the Dockerfile
Dockerfile working on Railway with release v1.39.5
# Install dependencies only when needed
FROM node:16-alpine AS deps
# Check https://github.com/nodejs/docker-node/tree/b4117f9333da4138b03a546ec926ef50a31506c3#nodealpine to understand why libc6-compat might be needed.
RUN apk add --no-cache libc6-compat openssl openssl-dev
WORKDIR /app
COPY package.json ./
RUN yarn install

# Rebuild the source code only when needed
FROM node:16-alpine AS builder
WORKDIR /app
COPY --from=deps /app/node_modules ./node_modules
COPY . .

ARG DATABASE_TYPE
ARG BASE_PATH

ENV DATABASE_TYPE $DATABASE_TYPE
ENV BASE_PATH $BASE_PATH

ENV NEXT_TELEMETRY_DISABLED 1

RUN yarn build-docker

# Production image, copy all the files and run next
FROM node:16-alpine AS runner
WORKDIR /app

ENV NODE_ENV production
ENV NEXT_TELEMETRY_DISABLED 1

RUN addgroup --system --gid 1001 nodejs
RUN adduser --system --uid 1001 nextjs

RUN yarn add npm-run-all dotenv<EMAIL_ADDRESS>
# You only need to copy next.config.js if you are NOT using the default configuration
COPY --from=builder /app/next.config.js .
COPY --from=builder --chown=nextjs:nodejs /app/public ./public
COPY --from=builder /app/package.json ./package.json
COPY --from=builder /app/prisma ./prisma
COPY --from=builder /app/scripts ./scripts

# Automatically leverage output traces to reduce image size
# https://nextjs.org/docs/advanced-features/output-file-tracing
COPY --from=builder --chown=nextjs:nodejs /app/.next/standalone ./
COPY --from=builder --chown=nextjs:nodejs /app/.next/static ./.next/static

USER nextjs

EXPOSE 3000

ENV PORT 3000

CMD ["yarn", "start-docker"]

for me the your Docker and  updating prisima in package.json to 4.8.0  did the job.
This should be updated in main repo


Make the event callback configurable or consider changing the convention

One of the key benefits of the Pusher PubSub implementation is that it adds an event abstraction. django-pusherable currently forces all events to go through a single callback pusherable_notify(eventName, data). In doing this we lose the evented benefit.
We should consider:

Make the convention function pusherable_notify_{event}(data){ ... }
Keep the current ultra-simplified version, but make the function name configurable `{% pusherable_subscribe 'update' object 'myCallbackFunction' %}
Something even nicer :)

For 1. it would be good to know if using underscore is a Django convention. Else it would probably be best to go with snake/camel case for the function name to match the general JS naming convention e.g. pusherableNotifyUpdate.

I think option 2 is a good idea, if no-one is assigned by time I get back from DjangoCon I'll create a PR. Also the underscore is a Django convention, but I shouldn't be letting it bleed into my JS. I should be following the conventions of the language I'm working in, that was just an oversight on my part.


Add footer column with "report a bug/error"

Are you thinking of this being something on the whole site template, or on the DE?

Just the home page, as a new card in the same row as "About Us"

Maybe we just modify the Github card to "Report a bug"

Aaron's idea was that this could be a form that sends us an email, so that people who don't use github can send us bugs

Ah, gotcha.

I'm going to close this as inactive right now. It's in the "Data Explorer/Site Enhancements" bit in our project board and document, and I want to keep the Issues list focused on active things.
This is one of those things we can bundle into site template updates to test, like the search btn location too.


Define table schema for processing settings

vpts files can have a column processing with an identifier to information about the vol2bird (or other) processing settings used. This settings can be described in a processing.csv, where many vpts rows can point to a single row in processing.csv (i.e. same settings). As a result, processing.csv will often contain just a single row, but expressing it as a csv offers the flexibility to record more processing settings and relate it to the primary data.
The expected fields in processing.csv should be recorded in a table schema (cf. the one for vpts)
beamwidth
wavelength
...

@adokter can you tackle this?

Will not implement this, in favour of a simple flat format as described in #25.


Missing hability to modify original parameter name

Hello,
I might have missed something but it looks like we can't modify the original parameters names by using generators.
For example: What I would like to automate:
hxxps://test.com/index.php?test1=aaa&test2=aaaa -> hxxps://test.com/index.php?test1[]=aaa&test2[]=aaaa
Did I missed something or this would be an awesome functionnality to add ? 😁
Regards,

Currently, Jaeles doesn't have a direct func to replace that but you can work around with this one https://jaeles-project.github.io/signatures/generator/#replace-fuzz

I did something a bit different, it works fine :) :
variables:
  - name: InputCmd('echo "{{.RawQuery}}" | tr "&" "\n" | sed -E "s/=.*//"')

requests:
  - generators:
      - Query("[[.original]]","{{.name}}[]")

Thanks !


Read/write gzip compressed files

I wrapped CNPY into RcppCNPy, an R package to read/write NumPy files. I put one extension in: the ability to read and write gzip-compressed files (but I only do npy files, not npz files).
If there was interest, I'd be happy to provide a pull request.
My code is now in this github repo.

When trying to open a compressed npz/npy (generated by np.savez_compressed()) with cnpy I indeed got an exception:
cnpy.cpp:92: void cnpy::parse_npy_header(FILE*, unsigned int&, unsigned int*&, unsigned int&, bool&): Assertion `littleEndian' failed.
@eddelbuettel: do you mean you have some patch that would allow cnpy to open/read compressed npz/npy files ?
Kind

Not npz but we handle npy -- as we can just rely on libz to read from a compressed stream.  It's all over in the repo I linked to, including some test cases.  But it is set up for R users so you may not be able to read everything easily but there is eg tests tests/ directory.
You could call R (ie via Rscript) to have RcppCNPy read the compressed file and write it uncompressed...

Hi Dirk, thanks. Carl has found an easy way to support compressed npz/npy and should patch cnpy soon.
To be continued.
Kind

I pushed some code to read npz files using libzip, which is now a dependency. William do you mind giving it a go and see if it works for you?
Itd be preferable to use either zlib or libzip, not both, I'll have to look into this a little later

I don't get to vote on what you do with CNPy ... but I have a weak preference for zlib because R already uses it.  Else users of RcppCNPy will have to get libzip-dev to build.  Which is probably only a minor nuisance as that should be common, but still "zlib is free" in our use case.

Dirk, you totally get a vote :) universal preference seems in favor of no extra dependencies, so I'll see if I can retool this to work with zlib

Out of curiousity, is one of the two "easier" or "cheaper" for you?   On Linux it doesn't really matter ...

Hi Carl, thank you.
I ve just tested and it seems to work.
Now, like @eddelbuettel, I would also have a preference for removing the dependency to libzip.
Have you considered using some of these pure zlib solutions as for example "miniz": single self contained header, no library, no dependency:
https://github.com/richgel999/miniz
Cheers
W.

@Dirk: I thought libzip would be much easier, but zlib wasn't too bad bc there was already code to manually parse the zip headers/footers. I'd agree fewer/more common deps are more preferable for the reasons you outlined.
I put up some new code up that will load using zlib, no more dependencies on libzip. I tried a few examples with npy_compressed and it seemed ok. inefficient in how it allocates memory but I will fix that later.

@rogersce  Hi Carl, I ve just tested and seems to work. Thank you.
In order to track upcoming changes/enhancements, what about adding a version in the sources: for example:
const versionMajor = 0;
const versionMinor = 1;
or creating a 1.0 branch  ?
Kind

@wolfv That's great! Glad it was useful. Writing out compressed NPY zips from C++ would be an awesome feature, when I get a chance I'll take a look at your code in depth and try to add that back into cnpy. Of course, if you think it could be done easily and want to submit a pull request, I wouldn't say no to that, either, haha :)


Not able to access filtered ng-repeat array in controller

So i have a ng-repeat that is set up like this in my Ionic application
<ion-slide ng-repeat="item in ( filtered = (products | filter:{ category: filters.category}))"
    ng-style="{ 'background-image': 'url({{site_path}}{{item.feat_image}})' }">

and in my controller, I'm watching for when the $scope.filtered gets updated/changed with the following code, but it's as if $scope.filtered does not exist in my controller.
$scope.$watch('filtered', function(newValue, oldValue) {
	console.log(newValue);
	console.log(oldValue);
}, true);

I've also read up that there was a major change in 1.3.0 that this doesn't work but instead Angular came up with a alternative where it will look a little bit like this.
<ion-slide ng-repeat="item in products | filter:{ category: filters.category} as filtered"
    ng-style="{ 'background-image': 'url({{site_path}}{{item.feat_image}})' }">

but this also doesn't work. Am I missing out something or is this a bug of some sort?

And to add, I actually am able to display $scope.filtered in my application when put together with the template file with {{ filtered }}

It's possible that your controller is in one scope, and the repeat inside a new scope. In that case, filtered is not accessible in your controller scope. http://plnkr.co/edit/n7zrum5ub6IzW3kkEw4w?p=preview

Hey Narretz.
How will I be able to tell if the controller is in one scope and the repeat is inside a new scope?

Have a look at the plunkr. The ng-repeat is inside a directive that creates a new scope with scope: true. You should look for something like that.

Anyway, this is more of a support question. You should ask them on one of these channels in the future: https://github.com/angular/angular.js/blob/master/CONTRIBUTING.md#-got-a-question-or-problem

@xavianaxw Did you find a solution to this? I have a similar issue where I define a filtered array in ng-repeat, but $scope.watch doesn't pick up changes as they happen.

@NoelBaron since this was a year ago i'm not too sure what my exact codes were like. If I was not mistaken, I filtered out the array in my controller instead. That way I was able to access it from my controller. So it was something like this.
$scope.filtered = $filter('filter')(data);

Hope that helps.
Cheers


A way to run one action when focus is in source tab

I have a couple of actions I can use on general members, WBUILD is one that I would use while I have the source open in a tab.  Another action is ACMSCHKOUT command built by Rocket Aldon.  I only want this action to appear when I selected it from the member list of the object browser.
I would want WBUILD to be selectable when I choose to run an action when the selection focus is in the member list of the object browser as well as ACMSCHKOUT.
Is there a piece of information about where the focus is at so that a setting can be used to control when actions appear for selection?
Thanks, Matt
This is Low PTY for me.

Correct. I don't want my check-out command to appear when I initiate the actions within the source tab but I do want the WBUILD command to be active.

Had a look into this, and it will actually take a lot of work:

Separate commands needed when opening from source view or tree view.

It used to be this way about a year ago but was removed

New configuration options on actions.
New UI on Action editor to support this new configuration
Documentation updated to explain the use case.

I will leave this open, but before I work on I'd need to hear from others to make sure it's worth the time.

Yep, not a problem.  If its costs too much then I say it can be left alone.  I understand that others might not want the same ask as I have.


meaning of checkbox in the blacklist

Hi,
Because of a bit of a lack of context, what's the meaning of the checkbox nin the list of bloatware? Is it to add it to the whitelist or to the blocklist? Because you did write blacklist on the main menu but then up the new popup you writ white and black list which confuses me a lot.
So when something is checked will it be removed or kept?

I have this exact same issue!

Please anyone to shed some lights about it ?

As far as i know the checked boxes will be removed

What do you mean ? Why would it be removed ?

A little sub question would be that: is it still possible to use some windows store apps after having used the script or not ?
Because I would like to use it on a tablet to unload some of the windows tasks but still being able to use Nebo to handwriting on it

I have the same question, can't tell if the apps that I check are going to be removed or not.

Checked mean = Remove/disable it

Thank you @WoodyPampa


Add a Java library for a better CLI

The CLI of the application is pretty simple, but not very user friendly. (https://github.com/dodie/hgt-map-renderer/blob/master/src/main/java/hu/awm/srtm/cli/CliApplication.java)
Integrate a Java library to handle CLI arguments better, for example Airline

Hello there! I would like to give it a try, please!


Upgrade to Nextjs 13.4.4

Issue #, if available:
Description of changes:
This pull request upgrades NextJS in order to complify with Vercel's requirement in order for this repo to be listed as a template in their marketplace.
Two fixes are also addressed as a result of two new errors that appear as a result of the upgrade

Multiple children in title element (See https://github.com/vercel/next.js/discussions/38256)
Client side rendering error mismatch error (See https://github.com/vercel/next.js/discussions/17443)

By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.

LGTM. Will merge and close.


Properties with the unknown type should use LSPAny.

There are several places where the type of a property is unknown:

Diagnostic data property -- data?: unknown;
CallHierarchyItem data property -- data?: unknown;

Additionally, should the other places that use array and object use the LSPArray and LSPObject types, with some places like ResponseError.data use LSPAny?

It also looks like ResponseMessage.result should be LSPAny even though it is currently specified to disallow array values. The JSON-RPC specification says that the type is determined by the method, so it can be anything at this stage (defining the generic protocol).


Warning persists after upgrading to Mac OS 12- Could not identify NUMA node of platform GPU ID 0

This warning was supposed to be resolved in MacOs 12 . How to fix this?
from tensorflow.keras.datasets import mnist
from tensorflow.keras.utils import to_categorical

from tensorflow.keras import layers
from tensorflow.keras import models
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))
model.summary()

Metal device set to: Apple M1
2021-10-26 01:42:59.638063: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.
2021-10-26 01:42:59.638241: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)

yes exactly this issue persists but the TensorFlow team on GitHub is unable to catch this issue and they keep directing us to different threads .They dont even check the MacOs version people are using .

@rayjennings3rd @xiangpeng2008
I recommend you to check this installation guide for proper installation on M1 Macs.I am not sure about the intel Macs .

@par1hsharma ,
Can you please confirm the tensorflow version you are using.Thanks

2.6.0

This fixed this issue for me:

Removing my previous kernel
jupyter kernelspec remove tensorflow

Installing the TensorFlow Metal plugin (Skip the Miniforge installation if you already have it installed, also installed it globally)
conda install -c apple tensorflow-deps
python -m pip uninstall tensorflow-macos python -m pip uninstall tensorflow-metal
conda install -c apple tensorflow-deps --force-reinstall
conda install -c apple tensorflow-deps==2.6.0
python -m pip install tensorflow-macos
python -m pip install tensorflow-metal

Creating a new kernel
python -m ipykernel install --user --name tensorflow --display-name "Python 3.9 (tensorflow)"

@par1hsharma what you listing is from the past. I've always been using Intel based Macs. Again, this problem happened on exactly the day (and immediately right after) upgrading to MacOS Monterey. This is not an issue of not being able to install it. I can still install it, it just fails on a keras model fit() call when it never did before.
I can run something like:
import tensorflow as tf
tf.config.list_physical_devices()

And see my CPU and GPU but doing something more substantial will not work.

@par1hsharma
Please refer tot he above comments and let us know if this is still an issue.

I just encountered the same issue insalled all the deps of tensorflow and running macos 12.
using keras.
message:
Metal device set to: Apple M1 Pro
systemMemory: 32.00 GB
maxCacheSize: 10.67 GB
2021-11-10 18:12:48.892303: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.
2021-11-10 18:12:48.892954: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: )

I encountered the same error everything works perfectly fine but error while running epochs, running macos 12
Metal device set to: m1 pro
Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.Layer lstm_2 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.

@Arsham1024 @ssd31  - did you notebook/program die after that or was it just downgraded. My kernel completely dies.

@Arsham1024 @ssd31 - did you notebook/program die after that or was it just downgraded. My kernel completely dies.

mine runs completely fine but while running epochs CPU and GPU running at 0 frequency I don't know why! i am fedup searching for the solution as nobody has it as of now

I am also getting this issue after following the install instructions here:  https://developer.apple.com/metal/tensorflow-plugin/ which references this project
`Metal device set to: Apple M1 Pro
systemMemory: 32.00 GB
maxCacheSize: 10.67 GB
2021-11-22 08:20:43.007615: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.
2021-11-22 08:20:43.007998: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: )
Traceback (most recent call last):
File "/Users/frush/GIT/TensorFlow-Examples/examples/1_Introduction/helloworld.py", line 22, in
sess = tf.Session()
`

I get those warnings too (at the start of training) but I also get a message:
I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.
Training then continues using the GPU.

I also get the same warning.
I am using a M1 macbook pro with Monterey OS (12.0.1).
When I create a virtual environment using miniforge3 and run the following python script in the virtual environment, the warning occurs.
Python 3.9.7 | packaged by conda-forge | (default, Sep 29 2021, 19:24:02)
[Clang 11.1.0 ] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from tensorflow.python.client import device_lib
>>> device_lib.list_local_devices()
Metal device set to: Apple M1 Pro

systemMemory: 16.00 GB
maxCacheSize: 5.33 GB

2021-11-26 20:35:32.907078: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.
2021-11-26 20:35:32.908054: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)
[name: "/device:CPU:0"
device_type: "CPU"
memory_limit: 268435456
locality {
}
incarnation:<PHONE_NUMBER>0387806521
, name: "/device:GPU:0"
device_type: "GPU"
locality {
  bus_id: 1
}
incarnation:<PHONE_NUMBER>964346266
physical_device_desc: "device: 0, name: METAL, pci bus id: <undefined>"
]

Had the exact same response as @Okabe-Junya.

Same, tried every "workaround" which one can found on the internet at the moment but still:
import tensorflow as tf
>>> tf.constant(1)
Metal device set to: Apple M1

systemMemory: 16.00 GB
maxCacheSize: 5.33 GB

2021-11-29 11:31:17.682907: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.
2021-11-29 11:31:17.683127: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)
<tf.Tensor: shape=(), dtype=int32, numpy=1>

Got M1 and currently OS 12.0.1
I'm using pyenv instead of of Conda, installed tendorflow-macos and tensorflow-metal
Python 3.8.10 (default, Nov 29 2021, 09:43:34)
[Clang 13.0.0 (clang-13<IP_ADDRESS>)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> tf.config.list_physical_devices()
[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'), PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]

>>> with tf.device('/GPU'):
...     a = tf.random.normal(shape=(2,), dtype=tf.float32)
...     b = tf.nn.relu(a)
...
2021-11-29 11:44:15.755838: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE4.2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
Metal device set to: Apple M1

systemMemory: 16.00 GB
maxCacheSize: 5.33 GB

2021-11-29 11:44:15.758586: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.
2021-11-29 11:44:15.761337: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)

Guys i've had the same problem while using M1 pro:
Metal device set to: Apple M1 Pro 2021-12-01 16:46:02.449022: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support. 2021-12-01 16:46:02.449505: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)
sorry to see that we all stuck here.

I followed the installing guidance here, it's for M1 not M1 pro, I thought it would be the same but I failed.
And I haven't tried the Apple developer's instructions here, don't know if the document is up to date.

I followed the installing guidance here, it's for M1 not M1 pro, I thought it would be the same but I failed.
And I haven't tried the Apple developer's instructions here, don't know if the document is up to date.

That is up to date install it from apple developer instruction
@KejianCui99

Hey I've finally solved this problem by completely following this install guidance. Although it's for M1, it works well on my M1 pro.
There will still be warnings but the code works, and I can see from the GPU monitor that it is actually using my GPU.
However I found the training speed is quite slow, it may due to the lack of adaptation, so I decided to disable GPU and use CPU only and it works much better.
To disable GPU you can use: tf.config.experimental.set_visible_devices([], 'GPU') at the beginning of your code.
This is how my device works:

I've also confirmed that a fresh re-install of Miniforge using the instructions referenced by @KejianCui99 seem to clear up this issue. (though others related to jupyter that are unrelated remain...)
Thanks for the find!
I ran both with and without GPU support (and confirmed GPU usage or lack thereof) and got similar times...
2021-12-04 10:15:35.681524: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz Epoch 1/5 2021-12-04 10:15:36.097538: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled. 938/938 [==============================] - 12s 9ms/step - loss: 0.2221 - accuracy: 0.9314 Epoch 2/5 938/938 [==============================] - 9s 9ms/step - loss: 0.0758 - accuracy: 0.9762 Epoch 3/5 938/938 [==============================] - 9s 9ms/step - loss: 0.0556 - accuracy: 0.9824 Epoch 4/5 938/938 [==============================] - 9s 10ms/step - loss: 0.0442 - accuracy: 0.9860 Epoch 5/5 938/938 [==============================] - 9s 10ms/step - loss: 0.0365 - accuracy: 0.9883 <keras.callbacks.History object at 0x15c0c8f40>
vs no GPU, which was slightly (5s) faster:
>>> model.fit(train_images, train_labels, epochs=5, batch_size=64) 2021-12-04 10:28:04.884353: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz Epoch 1/5 938/938 [==============================] - 8s 8ms/step - loss: 0.1684 - accuracy: 0.9484 Epoch 2/5 938/938 [==============================] - 8s 9ms/step - loss: 0.0452 - accuracy: 0.9855 Epoch 3/5 938/938 [==============================] - 9s 9ms/step - loss: 0.0312 - accuracy: 0.9902 Epoch 4/5 938/938 [==============================] - 9s 9ms/step - loss: 0.0243 - accuracy: 0.9924 Epoch 5/5 938/938 [==============================] - 9s 10ms/step - loss: 0.0187 - accuracy: 0.9940 <keras.callbacks.History object at 0x14bcd9ac0>

Maybe we are using different network structures, mine was extremely slow when using GPU (about 40s per epoch).

With respect to CPU vs GPU performance, I see two possibilities:

There is a tensorflow-metal implementation issue that makes using the GPU slow (possibly CPU-bound).
The M1 architecture itself is such that tensorflow operations are as fast or faster via the CPU vs the GPU. There could be various reasons for this.

It would be nice to get an answer for this; as it is tensorflow-metal is worse than useless. Side question: tensorflow-metal allows us to use the GPU, but is it using the GPU or the neural engine or what?

FYI there's a lot of discussion of installation and performance issues in the apple developer forums : https://developer.apple.com/forums/tags/tensorflow-metal
In particular this thread suggests that the CPU simply works better for small models/batch/data, but the GPU works better for large models/batch/data.

I've also confirmed that a fresh re-install of Miniforge using the instructions referenced by @KejianCui99 seem to clear up this issue. (though others related to jupyter that are unrelated remain...)

@rfrush do you know why a "fresh re-install of Miniforge" meant you stopped getting the message about Could not identify NUMA node of platform GPU ID 0? e.g. had you previously installed Miniforge via Homebrew rather than directly from the bash script?

@ollie-bell  I had followed the instructions at https://developer.apple.com/metal/tensorflow-plugin/, which are very similar to https://makeoptim.com/en/deep-learning/tensorflow-metal.    It does appear that between the two installs the TensorFlow version had also updated, so there could be multiple factors leading to the fix.

I've tried TensorFlow 2.6.0 and 2.7.0 on python versions 3.8 and 3.9. Unfortunately none above solves Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.

deadend here, as well after following both the Apple and MakeOptim instructions on a new MBA M1
model = Sequential([
    Dense(32, activation='relu', input_shape=(10,)),
    Dense(32, activation='relu'),
    Dense(1, activation='sigmoid'),
])

Metal device set to: Apple M1
2021-12-19 12:18:04.851761: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.
2021-12-19 12:18:04.858068: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)

here is my mambaforge environment
name: tensorflow
channels:
  - apple
  - conda-forge
dependencies:
  - absl-py=0.10.0=pyhd8ed1ab_1
  - aiohttp=3.8.1=py39h5161555_0
  - aiosignal=1.2.0=pyhd8ed1ab_0
  - anyio=3.4.0=py39h2804cbe_0
  - appnope=0.1.2=py39h2804cbe_2
  - argon2-cffi=21.1.0=py39h5161555_2
  - astunparse=1.6.3=pyhd8ed1ab_0
  - async-timeout=4.0.1=pyhd8ed1ab_0
  - async_generator=1.10=py_0
  - attrs=21.2.0=pyhd8ed1ab_0
  - babel=2.9.1=pyh44b312d_0
  - backcall=0.2.0=pyh9f0ad1d_0
  - backports=1.0=py_2
  - backports.functools_lru_cache=1.6.4=pyhd8ed1ab_0
  - bleach=4.1.0=pyhd8ed1ab_0
  - blinker=1.4=py_1
  - brotli=1.0.9=h3422bc3_6
  - brotli-bin=1.0.9=h3422bc3_6
  - brotlipy=0.7.0=py39h5161555_1003
  - c-ares=1.18.1=h3422bc3_0
  - ca-certificates=2021.10.8=h4653dfc_0
  - cached-property=1.5.2=hd8ed1ab_1
  - cached_property=1.5.2=pyha770c72_1
  - cachetools=4.2.4=pyhd8ed1ab_0
  - certifi=2021.10.8=py39h2804cbe_1
  - cffi=1.15.0=py39h52b1de0_0
  - charset-normalizer=2.0.9=pyhd8ed1ab_0
  - click=8.0.3=py39h2804cbe_1
  - cryptography=36.0.1=py39hfb8cd70_0
  - cycler=0.11.0=pyhd8ed1ab_0
  - dataclasses=0.8=pyhc8e2a94_3
  - debugpy=1.5.1=py39hfb83b0d_0
  - decorator=5.1.0=pyhd8ed1ab_0
  - defusedxml=0.7.1=pyhd8ed1ab_0
  - entrypoints=0.3=pyhd8ed1ab_1003
  - fonttools=4.28.5=py39h5161555_0
  - freetype=2.10.4=h17b34a0_1
  - frozenlist=1.2.0=py39h5161555_1
  - gast=0.4.0=pyh9f0ad1d_0
  - google-auth=1.35.0=pyh6c4a22f_0
  - google-auth-oauthlib=0.4.6=pyhd8ed1ab_0
  - google-pasta=0.2.0=pyh8c360ce_0
  - grpcio=1.43.0=py39h9e1b6db_0
  - h5py=3.1.0=nompi_py39h99babb8_100
  - hdf5=1.10.6=nompi_h0fc092c_1114
  - idna=3.1=pyhd3deb0d_0
  - importlib-metadata=4.9.0=py39h2804cbe_0
  - importlib_resources=5.4.0=pyhd8ed1ab_0
  - ipykernel=6.6.0=py39h32adebf_0
  - ipython=7.30.1=py39h2804cbe_0
  - ipython_genutils=0.2.0=py_1
  - jbig=2.1=h3422bc3_2003
  - jedi=0.18.1=py39h2804cbe_0
  - jinja2=3.0.3=pyhd8ed1ab_0
  - joblib=1.1.0=pyhd8ed1ab_0
  - jpeg=9d=h27ca646_0
  - json5=0.9.5=pyh9f0ad1d_0
  - jsonschema=4.3.1=pyhd8ed1ab_0
  - jupyter_client=7.1.0=pyhd8ed1ab_0
  - jupyter_core=4.9.1=py39h2804cbe_1
  - jupyter_server=1.13.1=pyhd8ed1ab_0
  - jupyterlab=3.2.5=pyhd8ed1ab_0
  - jupyterlab_pygments=0.1.2=pyh9f0ad1d_0
  - jupyterlab_server=2.9.0=pyhd8ed1ab_0
  - keras=2.7.0=pyhd8ed1ab_0
  - keras-preprocessing=1.1.2=pyhd8ed1ab_0
  - kiwisolver=1.3.2=py39h4d2d688_1
  - krb5=1.19.2=hd92b7a7_3
  - lcms2=2.12=had6a04f_0
  - lerc=3.0=hbdafb3b_0
  - libblas=3.9.0=12_osxarm64_openblas
  - libbrotlicommon=1.0.9=h3422bc3_6
  - libbrotlidec=1.0.9=h3422bc3_6
  - libbrotlienc=1.0.9=h3422bc3_6
  - libcblas=3.9.0=12_osxarm64_openblas
  - libcurl=7.80.0=h8fe1914_1
  - libcxx=12.0.1=h168391b_0
  - libdeflate=1.8=h3422bc3_0
  - libedit=3.1.20191231=hc8eb9b7_2
  - libev=4.33=h642e427_1
  - libffi=3.4.2=h3422bc3_5
  - libgfortran=5.0.0.dev0=11_0_1_hf114ba7_23
  - libgfortran5=11.0.1.dev0=hf114ba7_23
  - liblapack=3.9.0=12_osxarm64_openblas
  - libllvm11=11.1.0=h93073aa_2
  - libnghttp2=1.43.0=he4cd7f6_1
  - libopenblas=0.3.18=openmp_h5dd58f0_0
  - libpng=1.6.37=hf7e6567_2
  - libprotobuf=3.19.1=hccf11d3_0
  - libsodium=1.0.18=h27ca646_1
  - libssh2=1.10.0=hb80f160_2
  - libtiff=4.3.0=h74060c4_2
  - libwebp-base=1.2.1=h3422bc3_0
  - libzlib=1.2.11=hee7b306_1013
  - llvm-openmp=12.0.1=hf3c4609_1
  - lz4-c=1.9.3=hbdafb3b_1
  - markdown=3.3.6=pyhd8ed1ab_0
  - markupsafe=2.0.1=py39h5161555_1
  - matplotlib=3.5.1=py39hdf13c20_0
  - matplotlib-base=3.5.1=py39h5aa4fe7_0
  - matplotlib-inline=0.1.3=pyhd8ed1ab_0
  - mistune=0.8.4=py39h5161555_1005
  - multidict=5.2.0=py39h5161555_1
  - munkres=1.1.4=pyh9f0ad1d_0
  - nbclassic=0.3.4=pyhd8ed1ab_0
  - nbclient=0.5.9=pyhd8ed1ab_0
  - nbconvert=6.3.0=py39h2804cbe_1
  - nbformat=5.1.3=pyhd8ed1ab_0
  - ncurses=6.2=h9aa5885_4
  - nest-asyncio=1.5.4=pyhd8ed1ab_0
  - notebook=6.4.6=pyha770c72_0
  - numpy=1.19.5=py39h1f3b974_2
  - oauthlib=3.1.1=pyhd8ed1ab_0
  - olefile=0.46=pyh9f0ad1d_1
  - openjpeg=2.4.0=h062765e_1
  - openssl=1.1.1l=h3422bc3_0
  - opt_einsum=3.3.0=pyhd8ed1ab_1
  - packaging=21.3=pyhd8ed1ab_0
  - pandas=1.3.5=py39h7f752ed_0
  - pandocfilters=1.5.0=pyhd8ed1ab_0
  - parso=0.8.3=pyhd8ed1ab_0
  - pexpect=4.8.0=pyh9f0ad1d_2
  - pickleshare=0.7.5=py_1003
  - pillow=8.4.0=py39ha74c66e_0
  - pip=21.3.1=pyhd8ed1ab_0
  - prometheus_client=0.12.0=pyhd8ed1ab_0
  - prompt-toolkit=3.0.24=pyha770c72_0
  - protobuf=3.19.1=py39hfb83b0d_1
  - ptyprocess=0.7.0=pyhd3deb0d_0
  - pyasn1=0.4.8=py_0
  - pyasn1-modules=0.2.7=py_0
  - pycparser=2.21=pyhd8ed1ab_0
  - pygments=2.10.0=pyhd8ed1ab_0
  - pyjwt=2.3.0=pyhd8ed1ab_1
  - pyopenssl=21.0.0=pyhd8ed1ab_0
  - pyparsing=3.0.6=pyhd8ed1ab_0
  - pyrsistent=0.18.0=py39h5161555_0
  - pysocks=1.7.1=py39h2804cbe_4
  - python=3.9.7=h54d631c_3_cpython
  - python-dateutil=2.8.2=pyhd8ed1ab_0
  - python_abi=3.9=2_cp39
  - pytz=2021.3=pyhd8ed1ab_0
  - pyu2f=0.1.5=pyhd8ed1ab_0
  - pyzmq=22.3.0=py39h02c6a76_1
  - readline=8.1=hedafd6a_0
  - requests=2.26.0=pyhd8ed1ab_1
  - requests-oauthlib=1.3.0=pyh9f0ad1d_0
  - rsa=4.8=pyhd8ed1ab_0
  - scikit-learn=1.0.1=py39hef7049f_3
  - scipy=1.7.3=py39h5060c3b_0
  - send2trash=1.8.0=pyhd8ed1ab_0
  - setuptools=59.6.0=py39h2804cbe_0
  - six=1.15.0=pyh9f0ad1d_0
  - sniffio=1.2.0=py39h2804cbe_2
  - sqlite=3.37.0=h72a2b83_0
  - tensorboard=2.6.0=pyhd8ed1ab_1
  - tensorboard-data-server=0.6.0=py39hfb8cd70_1
  - tensorboard-plugin-wit=1.8.0=pyh44b312d_0
  - tensorflow-deps=2.7.0=0
  - termcolor=1.1.0=py_2
  - terminado=0.12.1=py39h2804cbe_1
  - testpath=0.5.0=pyhd8ed1ab_0
  - threadpoolctl=3.0.0=pyh8a188c0_0
  - tk=8.6.11=he1e0b03_1
  - tornado=6.1=py39h5161555_2
  - traitlets=5.1.1=pyhd8ed1ab_0
  - typing-extensions=<IP_ADDRESS>=0
  - typing_extensions=<IP_ADDRESS>=py_0
  - tzdata=2021e=he74cb21_0
  - urllib3=1.26.7=pyhd8ed1ab_0
  - wcwidth=0.2.5=pyh9f0ad1d_2
  - webencodings=0.5.1=py_1
  - websocket-client=1.2.3=pyhd8ed1ab_0
  - werkzeug=2.0.1=pyhd8ed1ab_0
  - wheel=0.35.1=pyh9f0ad1d_0
  - wrapt=1.12.1=py39h5161555_3
  - xz=5.2.5=h642e427_1
  - yarl=1.7.2=py39h5161555_1
  - zeromq=4.3.4=hbdafb3b_1
  - zipp=3.6.0=pyhd8ed1ab_0
  - zlib=1.2.11=hee7b306_1013
  - zstd=1.5.0=h861e0a7_0
  - pip:
    - flatbuffers==2.0
    - libclang==12.0.0
    - tensorflow-estimator==2.7.0
    - tensorflow-macos==2.7.0
    - tensorflow-metal==0.3.0

None of the suggestions written previously prevent the Could not identify NUMA node of platform GPU ID 0, defaulting to 0 message. I have tried TensorFlow 2.5.0, 2.6.0 and 2.7.0 with tensorflow-metal plugins 0.1.2, 0.2.0 and 0.3.0 respectively. This was the first thing I tried after doing a complete reinstall of MacOS (which I did for unrelated reasons).
The simplest way for me to encounter the issue is to tf.config.list_logical_devices(). Note that tf.config.list_physical_devices() doesn't trigger the message.
>>> import tensorflow as tf
Init Plugin
Init Graph Optimizer
Init Kernel
>>> tf.config.list_physical_devices()
[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'), PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
>>> tf.config.list_logical_devices()
Metal device set to: Apple M1

systemMemory: 8.00 GB
maxCacheSize: 2.67 GB

2022-01-09 15:57:38.705551: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.
2022-01-09 15:57:38.706074: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)
[LogicalDevice(name='/device:CPU:0', device_type='CPU'), LogicalDevice(name='/device:GPU:0', device_type='GPU')]

@par1hsharma I can reproduce the issue. I ran a simple model (from TF tutorials page) on Apple M1 mini. Here is the code for reference.
The trace is as shown below
(base) vishnu@vishnuvhansmini ~ % /Users/vishnu/miniforge3/bin/python3 /Users/vishnu/Desktop/Codes/TF_Keras/test.py

2.7.0
[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'), PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz
11493376/11490434 [==============================] - 3s 0us/step
11501568/11490434 [==============================] - 3s 0us/step
Metal device set to: Apple M1

systemMemory: 8.00 GB
maxCacheSize: 2.67 GB

2022-01-21 11:08:28.160120: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.
2022-01-21 11:08:28.160641: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)
2022-01-21 11:08:28.778421: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz
Epoch 1/5
2022-01-21 11:08:28.909028: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.
1875/1875 [==============================] - 11s 5ms/step - loss: 0.2919 - accuracy: 0.9158
Epoch 2/5
1875/1875 [==============================] - 9s 5ms/step - loss: 0.1383 - accuracy: 0.9591
Epoch 3/5
1875/1875 [==============================] - 9s 5ms/step - loss: 0.1013 - accuracy: 0.9689
Epoch 4/5
1875/1875 [==============================] - 9s 5ms/step - loss: 0.0818 - accuracy: 0.9749
Epoch 5/5
1875/1875 [==============================] - 9s 5ms/step - loss: 0.0687 - accuracy: 0.9782
2022-01-21 11:09:14.423644: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.
313/313 [==============================] - 1s 4ms/step - loss: 0.0789 - accuracy: 0.9743

However, this is more related to Apple. Community is trying to build TF for Apple M1 here. Please follow that thread for more discussion. Thanks!

Got the same message, but checked after msg  ...import sys
import tensorflow.keras
import pandas as pd
import sklearn as sk
-----------
print(f"Tensor Flow Version: {tf.version}")
print(f"Keras Version: {tensorflow.keras.version}")
print()
print(f"Python {sys.version}")
print(f"Pandas {pd.version}")
print(f"Scikit-Learn {sk.version}")
gpu = len(tf.config.list_physical_devices('GPU'))>0
print("GPU is", "available" if gpu else "NOT AVAILABLE")
and received :
Tensor Flow Version: 2.5.0
Keras Version: 2.5.0
Python 3.9.7 (default, Sep 16 2021, 23:53:23)
[Clang 12.0.0 ]
Pandas 1.3.5
Scikit-Learn 1.0.2
GPU is available.
So, is the msg spurious?

I tried on macOS 12.5.1, with Tensorflow 2.9.2. I didn’t see any warning messages.
>>> import tensorflow as tf
>>> tf.__version__
'2.9.2'
>>> from tensorflow.keras.datasets import mnist
>>> from tensorflow.keras.utils import to_categorical
>>> from tensorflow.keras import layers
>>> from tensorflow.keras import models
>>> model = models.Sequential()
>>> model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
>>> model.add(layers.MaxPooling2D((2, 2)))
>>> model.add(layers.Conv2D(64, (3, 3), activation='relu'))
>>> model.add(layers.MaxPooling2D((2, 2)))
>>> model.add(layers.Conv2D(64, (3, 3), activation='relu'))
>>> model.add(layers.Flatten())
>>> model.add(layers.Dense(64, activation='relu'))
>>> model.add(layers.Dense(10, activation='softmax'))
>>> model.summary()
Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #
=================================================================
 conv2d (Conv2D)             (None, 26, 26, 32)        320

 max_pooling2d (MaxPooling2D  (None, 13, 13, 32)       0
 )

 conv2d_1 (Conv2D)           (None, 11, 11, 64)        18496

 max_pooling2d_1 (MaxPooling  (None, 5, 5, 64)         0
 2D)

 conv2d_2 (Conv2D)           (None, 3, 3, 64)          36928

 flatten (Flatten)           (None, 576)               0

 dense (Dense)               (None, 64)                36928

 dense_1 (Dense)             (None, 10)                650

=================================================================
Total params: 93,322
Trainable params: 93,322
Non-trainable params: 0
_________________________________________________________________

@gadagashwini
If i remember correctly, i saw in another thread, that your GPU is Radeon, which means you are not using M1.  It happened to me too today. I have spent whole day trying to solve this issue, but it is impossible to solve it on M1. I am on latest version of macos operating system on Macbook max 16. I get NUMA 0 error just like everyone else and model.fit gets STUCK, it doesn't even start. I left it for 30 mins, just in case it's that slow, but of course this was not the issue. IF i switch to CPU, then it works properly, but of course, the whole point is to use the GPU so we could speed up the training process. M1 tensorflow does NOT work on GPU. And it seems like noone is taking it seriously, not Apple, not Tensorflow / Keras team. If at least there was a confirmed BUG, i would be happy to wait for a solution. But noone even confirmed there is a bug, yet if u search the internet... it is FLOODED with frustrating people that paid 4000 USD for their Macs to do some machine learning on Tensorflow... and it doesn't work on GPU at all. I am very frustrated as well. Can someone at least mark this is a BUG, because only then someone will probably look into it in the future. Until it's properly classified, i have no hope that this will be solved anytime soon. I guess back to PC until then.

I am on MacOS 12.6, MAC-STUDIO, 20c CPU, 64cGPU, 128GB RAM. Getting the same message (while running on CPU):
2022-09-23 19:29:10.329587: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz
2022-09-23 19:29:10.400489: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.
Moreover, I am seeing some strange thing as well:
^M1/1 [==============================] - ETA: 0s^H^H^H^H^H^H^H^H^H^H^H^H^H^H^H^H^H^H^H^H^H^H^H^H^H^H^H^H^H^H^H^H^H^H^H^H^H^H^H^H^H^H^H^H^H^H^M1/1 [==============================] - 1s 1s/step
I am running my code on latest TF-MACOS == 2.9.2 and TF-METAL=0.5.1 with python = 3.9.13 (miniforge was installed using the process of bash scripting as mentioned in https://developer.apple.com/metal/tensorflow-plugin/).
My observations so far:

GPUs sucks (huge memory leak issue) and dead slow than CPU
CPU only shows relatively less memory leak and bit faster than GPU (~50% less)
training stops when memory usage reaches ~95%

@karbapi Did you solve your issue? I had exactly the same issue as yours.

I solved it,
by disabling gpu and training the model only with cpus.

for m1 chip you must neeed to check the python version because stable version with gpu is necessary !
and I use this method
Removing my previous kernel
jupyter kernelspec remove tensorflow
Installing the TensorFlow Metal plugin (Skip the Miniforge installation if you already have it installed, I also have it installed globally)
conda install -c apple tensorflow-deps
python -m pip uninstall tensorflow-macos
python -m pip uninstall tensorflow-metal
conda install -c apple tensorflow-deps --force-reinstall
conda install -c apple tensorflow-deps==2.6.0
python -m pip install tensorflow-macos
python -m pip install tensorflow-metal

Creating a new kernel
python -m ipykernel install --user --name tensorflow --display-name "Python 3.9 (tensorflow)"


Update CAfile path for Debian-based OS.

This fixes issue 96. I also updated the ChefSpec test to reflect the changes.

@someara after you get your feet back on the ground, this could use your attention.

LGTM
On Wed, Feb 4, 2015 at 3:32 PM, Steven Murawski<EMAIL_ADDRESS>wrote:

@someara https://github.com/someara after you get your feet back on the
ground, this could use your attention.
—
Reply to this email directly or view it on GitHub
https://github.com/opscode-cookbooks/postfix/pull/97#issuecomment-72933041
.


document.querySelector   set  attribute   (id /style)  exception

Hello @kensoh i hope you are fine.
all  is  error
document.querySelector('#toAreaCtrl .addr_text').style.width="100px";
document.querySelector('#toAreaCtrl .addr_text').setAttribute("id","wwww")

Hi @cousin-wang
Seems like your method of identifying the element is not working. The following works for me on this sample website and identifying the element with queryselector.
https://www.google.com/doodles
dom begin
document.querySelector('#latest-title').style.backgroundColor = "blue";
dom finish
wait 15

Do you have the website for us to inspect?

thanks @ruthtxh
the  flellowing code  is ths same  element.  the  first is not working.
dom  document.querySelector('#s_btn_wr .btn').style.color="#ab1313";
document.querySelector('#su').style.color="#ab1313"
i  use querySelector('#s_btn_wr .btn').id that  is is not working/exception . but I have no [id] ,How to do?
http://www.baidu.com/
dom  document.querySelector('#su').id="12344";
wait 10

because input   donot have [id]
thranks  @ruthtxh

Hi @cousin-wang thanks for using TagUI, and of course @kangyiwen for promoting TagUI in China!
I'm good, I've moved to London and started work there. @ruthtxh is taking over my work with TagUI.
And I trust that she is ready to bring the project to the next level! Beyond what I can achieve.

PS - Btw @ruthtxh, if AI Singapore wants to collapse TagUI repo into aimakerspace, that is theoretically doable. By right, GitHub will map and redirect old URLs pointing to kelaberetiv to aimakerspace. There is probably going to be some rough unexpected situations for migrating over when you will only know when you do it. But just want to highlight that it is doable. I notice that kelaberetiv org isn't being actively developed but instead aimakerspace, so I thought to flag this out to you.

Hi @cousin-wang
I'm not sure which element you're looking for on the page, because when I do a search for that id/class in the dev tools elements it does not point to a particular element.
Here's an example of changing the background color of the search button on baidu.
To get the class of the button, I inspected the element by right clicking. For instance:

http://www.baidu.com/
dom document.querySelector('.bg .s_btn').style.backgroundColor = "#ab1313";

FYI when using querySelector if there are more than one elements with the same css identifier, it will only pick the first one on in the list if you are using it this way.

Hi @kensoh, thanks for the suggestions! We are actually migrating from both kelaberativ and aimakerspace to a new github org soon.

Oh sounds exciting!!
For my side, I have a links to this repo from my website tebel.org, and from Python rpa package readme.
If you remember to, just ping or @me when you have migrated. I'll update the links if they break.

Hi @kensoh FYI we have migrated our repo to the new org https://github.com/aisingapore/TagUI/. The old link should redirect to the new one too.

Thanks @ruthtxh I happen to see, and yes the links from RPA for Python GitHub page redirect correctly to the new repo =)

(I've updated the links there and on tebel.org anyway, and verified the links to aisingapore's org work)

Btw @ruthtxh, I notice that all AISG folks show their membership in aisinapore org as private. This is the default GitHub setting (private). That means anyone visiting aisingapore org GitHub page will not see its members. It also means that people viewing the staff's GitHub profiles will not see the aisingapore logo there.
Not sure if this is intended or not being aware. Fyi, in case it is due to unawareness so this marketing opportunity is missed.

You can test by visiting this link in private tab of browser - https://github.com/aisingapore
Private/Public setting can be changed individually at - https://github.com/orgs/aisingapore/people

Closing but please ping back if this is still relevant or an issue.


Update client version in readme

LGTM, but I have noticed that we still have the page_size in the examples. Will open a separate PR to take it out.

Good point, thank you!


make docker fails in a clean environment without manually generated keys

make docker fails in a clean environment.
Simple solution is to run:
security/bin/gen-keys.sh

Since this mucks with security, I have not yet had a proper chance to evaluate the impact of adding this 1 liner to he documentation (or the make system).

For now addressed by PR #2831


Dead link http://whatis.cmmiinstitute.com/get-started

Found in docs/work/work-items/guidance/cmmi/guidance-background-to-cmmi.md

@nschonni - - Thank you for reporting this issue. I'm fixing the content and it should be live later today.


ios Mutable Content

I am trying to enable mutable content in pushed and am using this data:
[
                    "badge": "0",
                    "sound": "default",
                    "mutable_content": "true",
                    "priority": "high",
                    // other stuff
                ]

why won't it run my code on the service extension?

@MihaelIsaev ideas?

even using apns specific stuff doesn't work

but the firebase console does

Hi @Mcrich23, sorry for the delayed response. Have you found a way to use mutable_content yet?

Yes

@Mcrich23 is any fix needed in the lib for that?

@MihaelIsaev Sorry, I didn't see your response, but I did! I added "mutable_content" : "true" to the end of it


Hydrogen: Cart events not shown on Live View or Analytics dashboard

What is the location of your example repository?
No response
Which package or tool is having this issue?
Hydrogen
What version of that package or tool are you using?
2023.4.0
What version of Remix are you using?
1.16.0
Steps to Reproduce
I have a store built with Remix and Hydrogen. It's deployed on Vercel.

The store is a Remix website deployed on Vercel. The setup is almost identical to the Hydrogen demo store except that the store is hosted on Vercel so I'm creating the storefront client a bit differently.
I'm using config data (PUBLIC_STOREFRONT_API_TOKEN, PRIVATE_STOREFRONT_API_TOKEN, and PUBLIC_STOREFRONT_ID). from the Hydrogen Sales channel for the storefrontClient:

createStorefrontClient({
    i18n: {
      language: language_code,
      country: country_code,
    },
    publicStorefrontToken: env.PUBLIC_STOREFRONT_API_TOKEN,
    privateStorefrontToken: env.PRIVATE_STOREFRONT_API_TOKEN,
    storeDomain,
    storefrontApiVersion: STOREFRONT_API_VERSION,
    storefrontId: env.PUBLIC_STOREFRONT_ID,
  }).storefront

I'm using a useAnalytics hook that is identical to the one in the demo Hydrogen store and it's using the Hydrogen sales channel.

The store is deployed on a test vercel domain: https://shopify-template-test.vercel.app/

Expected Behavior
I should be able to see "Active carts" in Live View or cart sessions in the analytics dashboard.
Actual Behavior
No cart events are shown on the Live View or Analytics dashboard.

Any updates on this issue? Happy to provide any more context if needed.

I don't see an add to cart event being fired when an item is added to cart on your vercel app.
The LiveView event relies on both the add to cart mutation and an add to cart event using sendShopifyAnalytics function

@wizardlyhel maybe you missed it because the first add to cart analytics event take a bit of time to get triggered but it's definitely there and it has this payload:
    "events": [
        {
            "schema_id": "custom_storefront_customer_tracking/1.0",
            "payload": {
                "source": "hydrogen",
                "hydrogenSubchannelId": "0",
                "is_persistent_cookie": true,
                "ccpa_enforced": false,
                "gdpr_enforced": false,
                "unique_token": "19a46833-C076-4691-A2E1-677A4DD7770F",
                "event_time":<PHONE_NUMBER>681,
                "event_id": "67509cf9-EB6A-4DDC-D2F9-A876EA224B28",
                "event_source_url": "https://shopify-template-test.vercel.app/?country=ZZ",
                "referrer": "https://shopify-template-test.vercel.app/",
                "user_agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/<IP_ADDRESS> Safari/537.36",
                "navigation_type": "navigate",
                "navigation_api": "PerformanceNavigationTiming",
                "shop_id":<PHONE_NUMBER>5,
                "currency": "USD",
                "event_name": "product_added_to_cart",
                "cart_token": "c1-db7c64b3aee172cece8ff55f44c3667c",
                "total_value": 5,
                "products": [
                    "{\"product_gid\":\"gid://shopify/Product/7989631385833\",\"name\":\"Tinloof Tote Bag\",\"variant\":\"Black\",\"brand\":\"\",\"price\":\"5.0\",\"quantity\":1,\"variant_gid\":\"gid://shopify/ProductVariant/44046250213609\",\"product_id\":7989631385833,\"variant_id\":44046250213609}"
                ]
            },
            "metadata": {
                "event_created_at_ms":<PHONE_NUMBER>681
            }
        }
    ],
    "metadata": {
        "event_sent_at_ms":<PHONE_NUMBER>681
    }
}`

[Here's a screencast too](https://res.cloudinary.com/https-tinloof-com/video/upload/v1685360665/add_to_cart_event_qe4arw.mp4).

I'm having the same issue :)

@siffogh The only thing I can notice from event payload is the currency mismatch. Not sure if that is where the issue is, but could you give it a try?

@siffogh The only thing I can notice from event payload is the currency mismatch. Not sure if that is where the issue is, but could you give it a try?

@wizardlyhel I'm hardcoding the currency to USD just to test but I have the same issue even when you switch to USD on the store: https://shopify-template-test.vercel.app/?country=US

@siffogh I tried it again and also access the live view for your store. I see the Top Locations from the live view working when I add to cart from your test vercel app. However, the active cart is not working.
I did some search to see if I can find anything that would prevent it from working and I did find a potential one that could fix your issue.
When you pass the product data to sendShopifyAnalytics, could you make sure the product variant price is a float type? Right now, it is a string.

If that works for you, then this is a bug in our library for sendShopifyAnalytics and we can get that fix.

Hmm .. I can also see active cart chart is not working for hydrogen.shop anymore.
This is gonna take some time for internal investigation.

@wizardlyhel thanks for checking. Would it be possible to share an ETA for a possible update from your end?

No ETA on this. It's a different Shopify team that will be looking into this.

No ETA on this. It's a different Shopify team that will be looking into this.

@wizardlyhel Noted, look forward to your update.

@wizardlyhel curious to know if there's any update from the Shopify team looking into this.

@siffogh Is it possible to assign your online store domain and your vercel app under the same top level domain?
Online store: checkout.example.com
Vercel app: staging.example.com
Make sure to add the domain prop to the useShopifyCookies({domain: 'example.com'})

@siffogh Is it possible to assign your online store domain and your vercel app under the same top level domain?
Online store: checkout.example.com Vercel app: staging.example.com
Make sure to add the domain prop to the useShopifyCookies({domain: 'example.com'})

@wizardlyhel I just made it so that:

The Vercel app is now running on shopify-test.tinloof.com
Checkout is on checkout-shopify-test.tinloof.com
useShopifyCookies({domain: 'tinloof.com'})

The Add to cart events are still not shown. Let me know if I misunderstood any of your hints.

Awesome! If you don't mind, would you share what you have for your server.ts so that it works on Vercel?

@wizardlyhel I published a version of the demo store that is deployable on Vercel:

Source code: https://github.com/tinloof/vercel-hydrogen-demo-store
Deployed version: https://vercel-hydrogen.tinloof.com/


Add social links to the collection metadata to offchain metadata

I added social links to the collection metadata, i did not include it into the onchain metadata as i don't have enough understanding about arrays in ton.

@samyarkd why did you remove the /dist folder from .env?

@tuminzee  I think i did it because i needed the social links in my project and no one reviewed this issue so gave up on this and i was using my fork in my project and thus i wanted the build of the project too, i should have closed this issue.

@samyarkd I understand why you raised the PR regarding the need for social links.
However, I'd like to understand why the build folder was removed from .gitignore.
Regardless, here are a few things you can do to get your PR merged faster in the future:

Check for linting/Prettier rules. If the project doesn't explicitly define these rules, it's generally assumed that you shouldn't change them unless specifically requested.
Build folder exclusion. The build folder typically contains compiled JavaScript files generated from your TypeScript code. Since these are derived files, there's no need to include them in the repository.

@tuminzee are you one of the maintainers? I don't think they accept pr at all. The first two commits were my actual commits in this pr. But they did not review this pr at all nor give me anything.
So I started using my own build in this package, (that's why I removed the dist folder) I made my own build and deployed it directly on GitHub and pulled it from GitHub and used it in my project.
If you are a maintainer I willa make necessary changes to this pr to be reviewed and accepted.

@tuminzee are you one of the maintainers? I don't think they accept pr at all. The first two commits were my actual commits in this pr. But they did not review this pr at all nor give me anything.
So I started using my own build in this package, (that's why I removed the dist folder) I made my own build and deployed it directly on GitHub and pulled it from GitHub and used it in my project.
If you are a maintainer I willa make necessary changes to this pr to be reviewed and accepted.

@samyarkd I am not a maintainer of this repo. sorry to bother you.


Add FAQ entry on new release notifications, & link on project detail page

This would include the ability to upload a requirements.txt adn get notified for everything in it.

note by @brainwane 2018-03-22: see update

+1

Thanks for your note and sorry for the slow response!
The folks working on Warehouse have gotten funding to concentrate on improving and deploying Warehouse, and have kicked off work towards our development roadmap -- the most urgent task is to improve Warehouse to the point where we can redirect pypi.python.org to pypi.org so the site is more sustainable and reliable. Since the email notification feature isn't something that the legacy site has, I've moved it to a future milestone.
Thanks and sorry again for the wait.

https://libraries.io/about Libraries.io now provides a service very similar to what is requested here.

@alex @bjornpost have you had a chance to check out libraries.io to see whether their subscription service does what you want? As I said in https://github.com/pypa/warehouse/issues/991#issuecomment-375079848 and elsewhere in that thread, I am leaning towards pointing people who want this feature to libraries.io, at least for the near future.

I'm not familiar with libraries.io; nowadays I use pyup (like warehouse!)

OK, so how about we turn this issue into the following, @alex?

Add an FAQ entry about notifications (or add this to the existing APIs answer), linking to pyup and libraries.io
Add a "get notifications" link/button/something to project detail pages that links to that FAQ answer

Those seem reasonable. At this point I'm not sure I'm the best reference on
"how do we make sure people are informed about new releases".
On Thu, Mar 22, 2018 at 9:25 PM, Sumana Harihareswara <
<EMAIL_ADDRESS>wrote:

OK, so how about we turn this issue into the following, @alex
https://github.com/alex?

Add an FAQ entry about notifications (or add this to the existing APIs
answer https://pypi.org/help/#APIs), linking to pyup
https://pyup.io/ and libraries.io
Add a "get notifications" link/button/something to project detail
pages that links to that FAQ answer

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@jonparrott if you feel like helping with Warehouse a bit, the first half of this issue (adding an answer to the FAQ) would be up your alley, I bet. For directions for getting set up, see our Getting Started Guide. And git blame and commit history on the /help template page will help you see how to approach this task in particular.

PR for the first part is at https://github.com/pypa/warehouse/pull/3388

Sorry, we need the second part of it now, the link on detail pages.

@brainwane where would we put that?

I defer here to @nlhkabu on whether/where to put this link on the project detail page.

PR opened here: https://github.com/pypa/warehouse/pull/3418
@jonparrott - could you please review? Thx


v1.0.0 (b9)

Additional Details

Version: v1.0.0 (b9)
Last updated: 11 March 2023 05:40 (UTC)

~ Add

Firebase AppCheck
Ads

~ Fix

Main page for mobile renderer

✓ This pull request has been approved ✓
Additional Details
Pull number: #11


Many Changes

remove eclipse profiles (we should either support everything or none)
add index.yaml to 2-structured-data
Use new plugins
Fix SQL
Fix list by user
Propagate all fixes to next version.
change paths (made shorter, removed managedvms)

Still to do - remove personalized data; remove flex keywords;  Add some
tags for docs;  Lots of docs fixes.

rename setters / getters for updated google-cloud-java

@tswast PTAL - I hate reviewing things this long -- apologies for giving it to you.  I'll probably need to update travis.sh for this as well.

That get/set/new change to the Google Cloud libraries sure made for a lot of changes. Are we sure it was worth it? (I know it's done so it's not like we'd switch back)

ADMIN override for this.  I've built it a lot.

Actually, I want to prep a CL to match this PR.


Packaging fails when appImage build property is set

I'm exploring if I can set --type app-image for modifying images built for my macOS app (docs). Doing this for specifying an app image directory doesn't seem to work:
tasks.withType<AbstractJPackageTask>().all {
  appImage.set(file("foo"))
}

This fails with a jpackage error:
> External tool execution failed:
  * Command: [/Users/saket/.sdkman/candidates/java/16.0.1.j9-adpt/bin/jpackage, @/Users/saket/projects/teleport/desktop/build/compose/tmp/packageDmg.args.txt]
  * Working dir: []
  * Exit code: 1
  * Standard output log: .../build/compose/logs/packageDmg/jpackage-2021-06-17-17-41-19-out.txt
  * Error log: .../build/compose/logs/packageDmg/jpackage-2021-06-17-17-41-19-err.txt

The error log file contains this message:
Error: Option [--java-options] is not valid with type [dmg]

What am I doing wrong?

Please check the following ticket on YouTrack for follow-ups to this issue. GitHub issues will be closed in the coming weeks.


Removed reference to Android Developer as the link is not valid

Removed reference to Android Developer as the link (https://grow.google/certificates/android-developer/) provided is no longer valid
Attaching the error page screenshot for reference

@karentamrazyan @Pavan-Gunda @jbampton
The changes are not merged to master though it was approved. Can you please check and confirm.
Thanks

@karentamrazyan has write access to the repo, so his approval is needed to merge.

Sure thanks.Will wait for @karentamrazyan approval to merge the changes.


Improve custom toolchain to support unknown compiler/linker.

Is your feature request related to a problem? Please describe.
But I haven't figured out how to design the API yet.
Describe the solution you'd like
.
Describe alternatives you've considered
No response
Additional context
No response

One idea I had would be to separate tools from the toolchain, giving essentially full control over the entire pipeline
tool("mycxx")
    set_bindir("path/to/bin") -- "mycxx.exe" is assumed
    set_kind("file")          -- run this per file
    set_filetypes("cpp")      -- run this only on .cpp files
    set_extension("o")        -- generate .o file for linker/archiver
    set_format("gcc")         -- use default gcc format
                                --> mycxx -c -o file.o main.cpp -Wall

    -- alternatively, for custom formats
    set_format({ "-key $(key)", "-c -o $(file)", "$(flags)", "$(file)" })
        --> mycxx -key P -o main.o -Wall main.cpp
tool_end()

key thing to note here would be the set_kind, which would specify if the tool is run once per file, like compilers, or once per output, like the linker
it would determine what is input into the $(file) or $(files) keys.
creating the toolchain would then be more like
toolchain("MyToolchain") -- tools are run in order they are added
    add_tool("mycxx")
    add_tool("myar", { kind = "static" }) -- not sure how kinds should be handeled
    add_tool("myld", { kind = "binary" })
toolchain_end()

target("project")
    set_kind("binary")
    add_files("src/main.cpp")

    set_toolchain("MyToolchain")
    set_toolvar("key", "P") -- user variable
    set_flag("-Wall")
target_end()

In some aspects it's similar to the rule API.
This would at least satisfy the needs for some Sony platforms I've worked with.
This tool api could then be extended to other areas, like custom debuggers or specific platform tools
debugger("MyDebugger")
    set_filetypes("elf")
    add_tool("mydbg")
debugger_end()

target("project")
    set_kind("binary")
    add_files("src/main.cpp")

    set_toolchain("MyToolchain")
    set_toolchain("MyDebugger")
    set_toolvar("key", "P") -- user variable
    set_flag("-Wall")
target_end()


Playlist Title doesnt appear

Checklist

[x ] I'm reporting a broken site support issue
[x ] I've verified that I'm running youtube-dl version 2020.11.17
[x ] I've checked that all provided URLs are alive and playable in a browser
[x ] I've checked that all URLs and arguments with special characters are properly quoted or escaped
[x ] I've searched the bugtracker for similar bug reports including closed ones
[x ] I've read bugs section in FAQ

Verbose log

[debug] Encodings: locale cp1252, fs utf-8, out utf-8, pref cp1252
[debug] youtube-dl version 2020.11.17
[debug] Python version 3.8.3 (CPython) - Windows-10-10.0.19041-SP0
[debug] exe versions: ffmpeg 4.3.1-2020-10-01-full_build-www.gyan.dev, ffprobe 4.3.1-2020-10-01-full_build-www.gyan.dev
[debug] Proxy map: {}

Description

If i extract the playlist info, is "title" and "playlist" the same as "id".
Ex.:
If i want the playlist info of: https://www.youtube.com/list=PLetg744TF10BrdPjaEXf4EsJ1wz6fyf95
title: "PLetg744TF10BrdPjaEXf4EsJ1wz6fyf95"
id: "PLetg744TF10BrdPjaEXf4EsJ1wz6fyf95"
playlist: "PLetg744TF10BrdPjaEXf4EsJ1wz6fyf95"
url: "https://www.youtube.com/playlist?list=PLetg744TF10BrdPjaEXf4EsJ1wz6fyf95"
webpage_url: "PLetg744TF10BrdPjaEXf4EsJ1wz6fyf95"
etc...
If i print "youtube_dl.YoutubeDL({'extract_flat': True, 'quiet': True}).extract_info("PLetg744TF10BrdPjaEXf4EsJ1wz6fyf95", download=False)" i'll get this:
{'_type': 'url', 'url': 'https://www.youtube.com/playlist?list=PLetg744TF10BrdPjaEXf4EsJ1wz6fyf95', 'ie_key': 'YoutubeTab', 'id': 'PLetg744TF10BrdPjaEXf4EsJ1wz6fyf95', 'extractor': 'youtube:playlist', 'webpage_url': 'PLetg744TF10BrdPjaEXf4EsJ1wz6fyf95', 'webpage_url_basename': 'PLetg744TF10BrdPjaEXf4EsJ1wz6fyf95', 'extractor_key': 'YoutubePlaylist'}

(In this dict. there is no title or something like that, but before 2020.11.17, which is i think now irrelevant, there was title and it was actually the same like the playlist id. My English is not the best now, so I would apologize for any mistakes that may arise.)

I made also a post on the r/youtubedl subreddit: https://www.reddit.com/r/youtubedl/comments/juafr6/playlist_title_doesnt_appear/ If i am not allowed to post links like that, just delete it :)


prefix doesn't work

Command doesn't work with prefix, only slash command seems to function :-? Any ideas what is going on? Much appreciated

does it have the message intent enabled?

prefix is only for custom commands.


handling with different input/output shapes

Hi,
I would like to fit the algorithm so it will be able to handle different input/output shapes. does the algorithm support such a thing? if not, do you have an idea how to do so?
Thanks again

Hi, you can change the argument enc_in, dec_in and c_out.

Oh i understand you point now. You mean one certain model predict different future lengths? I think it is a great idea, but nobody has tested this strategy.
However, Autoformer support this! You can just load the model and change the seq_len, pred_len as you want.


If the remaining channel for a layer is zero, it reports zero division error

I trained vgg with cifar100 dataset and there are a few layers with zero remaining channels. As a result I got a zerodivisionerror in vgg.py part when initializing weights. Can someone help me plz?

i think you are using a large prune ratio, try smaller prune ratio e.g. 0.1, 0.2 to see if it still exists?

Hi Mingjie,
Many thanks for your reply. I will try with smaller ratio again later. Informative suggestion.
Regards
Haoran Geng
---Original---
From: @.>
Date: Wed, Dec 15, 2021 07:33 AM
To: @.>;
Cc<EMAIL_ADDRESS>Subject: Re: [Eric-mingjie/network-slimming] If the remaining channel for a layer is zero, it reports zero division error (Issue #80)
i think you are using a large prune ratio, try smaller prune ratio e.g. 0.1, 0.2 to see if it still exists?
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