HBASE-25164 Make ModifyTableProcedure support changing meta replica c…

…ount

This is the final piece of HBASE-25099, after this PR gets merged, we could use alter table to change the meta replica count.

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Any other concerns except the documentation? I've created a separated issue for documentation. @saintstack
Thanks.

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Searched hbase.meta.replica.count in code base and found a place where we set this option directly with 'hbase.meta.replica.count' literal in TestReplicaWithCluster. Removed.

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dupname asflicense spotbugs hadoopcheck hbaseanti checkstyle

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git revision
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bug: ion-select deafults values not getting set when multiple=true

I am using Ionic 4.
I am not sure if this is a bug, but can not manage to solve it, unless there is a solution.
I have created a  selection option , the default selection is set on page load up with values from an api. This works well when multiple is false.
If i set multiple to true, so that more then one option can be selected, then on page load , those values do not get selected. This my code. Please adv
`<div *ngIf="(outputportcon !='None') && ZoneMode !='Relayswitch' ">

Valve Sequencing Mode:

<ion-toggle color="primary" [(ngModel)]="Valve_Sequencing_Mode">

Irrigation Valve Switch:

<ion-select  multiple="{{Valve_Sequencing_Mode}}" [(ngModel)]="irrgationswitch" >
<div *ngIf="(outputportcon=='Standard')">
No Valve
Output Set 1
Output Set 2
Output Set 3

<div *ngIf="(outputportcon=='Relay Expansion 1')">
No Valve
Output Set 1
Output Set 2
Output Set 3
Output Set 4
Output Set 5
Output Set 6
Output Set 7
Output Set 8

`
Thank You

Thanks for the issue. Can you provide a repo with the code required to reproduce this issue? Additionally, please ensure that Valve_Sequencing_Mode and irrgationswitch are arrays that contain the values of the options that you would like to select.
For example:
<ion-select multiple [(ngModel)]="selectedValues">
  <ion-select-option value="a">a</ion-select-option>
  <ion-select-option value="b">b</ion-select-option>
  <ion-select-option value="c">c</ion-select-option>
</ion-select>

public selectedValues: string[] = ['a', 'c'];


make docserve fails with OSError

Can't find pip? I traced into fabricate, and the call arg is env/bin/pip and os.path.isfile is True. Not sure what's going on here ...
[aspen]$ make docserve
Passing through to 'python build.py docserve'...
env/bin/pip install -r requirements.txt
Traceback (most recent call last):
  File "build.py", line 300, in <module>
    , ignoreprefix="python"  # workaround for gh190
  File "/Users/whit537/personal/gratipay/aspen.py/fabricate.py", line 1578, in main
    this_status = eval(action, globals_dict)
  File "<string>", line 1, in <module>
  File "build.py", line 132, in docserve
    run(_virt('pip', envdir), 'install', '-r', 'requirements.txt')
  File "/Users/whit537/personal/gratipay/aspen.py/fabricate.py", line 1400, in run
    return default_builder.run(*args, **kwargs)
  File "/Users/whit537/personal/gratipay/aspen.py/fabricate.py", line 1142, in run
    return self._run(*args, **kwargs)
  File "/Users/whit537/personal/gratipay/aspen.py/fabricate.py", line 1125, in _run
    deps, outputs = self.runner(*arglist, **kwargs)
  File "/Users/whit537/personal/gratipay/aspen.py/fabricate.py", line 769, in __call__
    return self._runner(*args, **kwargs)
  File "/Users/whit537/personal/gratipay/aspen.py/fabricate.py", line 432, in __call__
    shell(*args, **shell_keywords)
  File "/Users/whit537/personal/gratipay/aspen.py/fabricate.py", line 163, in shell
    return _shell(args, **kwargs)
  File "/Users/whit537/personal/gratipay/aspen.py/fabricate.py", line 193, in _shell
    raise e
OSError: [Errno 2] No such file or directory

Cannot repro here.  Try 'make clean docserve' to make sure you've got a clean virtualenv

That did it.
!m @pjz


Personal branch

file to be merged after team's verification: test_common.py

Hi Varun, thanks for creating this pull request! However, the checks encountered two issues that led to failing:

Is there a specific reason why you're using the quit() command here? Could you remove this line?
It would be great if you could remove and replace all absolute paths to your local folders and files, such as here. The Github workflows are of course not able to locate these files. One possible solution would be to add your small test files to the repo in the tests's data folder.

Other than that, it looks great!


dockstore/dockstore#2604 created banner component

Can you please attach a screenshot of what it looks like?

This is meant to be an independent component that will be displayed at the top of certain pages when logged in, when Louise's layout is ready. I'm not sure which pages it will ultimately go on but for the purposes of this PR it's at the top of the home page so you can see it.

Can you please attach a screenshot of what it looks like?

Agreed that the placement of the background images isn't great, open to suggestions of where to move them. Although as you resize the screen, they move around relative to the text, so there's nowhere to put them where they would never overlap. Could also take them out, but I kind of like them

Didn't read other comments yet, but I would just remove the truck and ship.

https://github.com/angular/flex-layout for how to use flexbox with Angular, since we're obviously not going to resort to writing tons of CSS for no good reason.
In particular, https://tburleson-layouts-demos.firebaseapp.com/#/docs is useful to play around with

Before...

Everything centered:

Without background images:


Create 0000-01-02-jenna-inventables.md

Feel free to add a description of what you’ve accomplished so far. As a reminder, you have: created a branch, created a file and made a commit, and opened a pull request

All done


laravel 5.7 new resource folder structure

Expected Behavior
when we run ziggy:generate command, the js file is created in resource folder.
the resource path for laravel 5.7 have been changed and remove "assets" subfolder as written on
https://laravel.com/docs/5.7/upgrade#upgrade-5.7.0
Currently installed Laravel version:
5.7
I think this line https://github.com/tightenco/ziggy/blob/cb6baff5563cf719c6a3f194382da7bdc2c8f1db/src/CommandRouteGenerator.php#L13
should be changed to
protected $signature = 'ziggy:generate {path=./resources/js/ziggy.js} {--url=/}';
I'm sorry for my English

This needs to detect what version of Laravel and set the path accordingly.
Also, Laravel 5.7 does not force this flattened folder structure, so it would really need to do more detection than just version to be as accurate as possible (or allow a .env override?).

You can provide a path of the output file:
php artisan ziggy:generate --help
Description:
  Generate js file for including in build process

Usage:
  ziggy:generate [options] [--] [<path>]

Arguments:
  path                   [default: "./resources/assets/js/ziggy.js"]


use multi-stage Dockerfile for packaging

Use a multi-stage dockerfile to build the image and avoid using the binary built on the host directly.

The binary is not built on the host directly. We use a tool called dapper(https://github.com/rancher/dapper) to build the image in the container. You can run make directly which will trigger dapper to build the provisioner for you.

Ah well it wasn't obvious to me when I looked.  I deployed it to my microk8s using the Dockerfile I submitted.


Sum stats instead of last

What does this PR do?
Instead of showing last 1H/1D stat in overview, we sum all results.
Test Plan

[x] Manual QA

Related PRs and Issues
x
Have you read the Contributing Guidelines on issues?
Yes

Before:

After:


feature: modify defaut bridge mode.

Ⅰ. Describe what this PR did
Change the default nat bridge network from <IP_ADDRESS>/24 to <IP_ADDRESS>/24
Complete the bridge network configure.
Don't to change bridge device ip address.
Ⅱ. Does this pull request fix one issue?

Ⅲ. Describe how you did it
Ⅳ. Describe how to verify it
Ⅴ. Special notes for reviews
Signed-off-by: Rudy Zhang<EMAIL_ADDRESS>

Codecov Report

Merging #1424 into master will increase coverage by 20.39%.
The diff coverage is 58.33%.

@@             Coverage Diff             @@
##           master    #1424       +/-   ##
===========================================
+ Coverage   16.36%   36.75%   +20.39%
===========================================
  Files         207      250       +43
  Lines       13816    17895     +4079
===========================================
+ Hits         2261     6578     +4317
+ Misses      11399    10456      -943
- Partials      156      861      +705

Impacted Files
Coverage Δ

daemon/daemon.go
54.37% <ø> (ø)

daemon/config/config.go
19.73% <100%> (+11.84%)
:arrow_up:

main.go
62.25% <100%> (ø)

network/mode/bridge/bridge.go
56.86% <46.77%> (ø)

daemon/mgr/network.go
69.28% <83.33%> (+66.09%)
:arrow_up:

pkg/multierror/def.go
0% <0%> (-100%)
:arrow_down:

cri/v1alpha1/cri_utils.go
22.08% <0%> (-7.17%)
:arrow_down:

cri/v1alpha2/cri_utils.go
21.39% <0%> (-6.96%)
:arrow_down:

cli/upgrade.go
0% <0%> (ø)
:arrow_up:

cli/pull.go
0% <0%> (ø)
:arrow_up:

... and 134 more

LGTM


Modular data Structure

Hey guys,
this is supposed to be a discussion issue on the new modular data structure model.
Some stuff i noticed while working on it:

https://github.com/SteffenHeu/mzmine3/blob/4819a7fa401d742e61b3eab75c61208ab58cd39d/src/main/java/io/github/mzmine/datamodel/data/ModularFeatureListRow.java#L178
I think this will be misleading, because not every row necessarily contains a feature in every raw data file in an aligned feature list.
Additionally, I think to access all raw data files in a ModularFeatureListRow via the map returned by this https://github.com/SteffenHeu/mzmine3/blob/4819a7fa401d742e61b3eab75c61208ab58cd39d/src/main/java/io/github/mzmine/datamodel/data/ModularFeatureListRow.java#L148 is too complicated.
Thus, I would say it could be a good idea to add a RawDataFilesType which stores the raw data files in a list. Suggestion: https://github.com/SteffenHeu/mzmine3/blob/47187d2d8bb5b060118f136d2486bbddd22e7878/src/main/java/io/github/mzmine/datamodel/data/types/RawFilesType.java

Currently there is no DataType for the "best feature". Suggestion: https://github.com/SteffenHeu/mzmine3/blob/47187d2d8bb5b060118f136d2486bbddd22e7878/src/main/java/io/github/mzmine/datamodel/data/types/BestFeatureType.java

(There is probably more to come)
I will not be able to add everything discussed here myself, this is just meant as a discussion thread, so everyone who joins the work on the data model can leave some thoughts! :)

We should definitely talk about it. This is not the final structure, so we can still change many things.
General Note:

We want to use Properties to easily bind the data model to the JavaFX
To generate the cells in the feature table, every row relates to the same list of columns (DataTypes)
First we create a new ModularFeatureList and define all columns

You could basically add a filter to the method to return all raw files for detected features.
We only implemented some of the DataTypes. We could definitely have the "best feature". With the new data model you can add any new data types or statistics to features and rows.

I'm not exactly sure what you mean by that.

Just to add one thing @robinschmid explained to me on the data model, that might be useful to know for any collaborators:

DataType and it's extending classes are designed to behave like singletons. What that means is:

RTType a, b;
row.get(a) == row.get(b); // true
a.equals(b); // true

@tomas-pluskal / @robinschmid what would you consider the best way to make the columns in the new feature table distinguishable so we can specifically show/hide them? We could extend TreeTableColumn to store the datatype and raw/feature type it displays or use the property map and just put some properties in there.
The first option would make the information easy and more convenient to access, I think, as we would not have to introduce two new properties.

I will close this one and open a new issue with to-dos


Cookie of c2c handshake is set late.

The line(s) below sets the cookie of the c2c peer when receiving the auth message.
For initiator:
https://github.com/saltyrtc/saltyrtc-client-java/blob/d01e553c06a05c74a532104608c0dc57cb9a23f2/src/main/java/org/saltyrtc/client/signaling/InitiatorSignaling.java#L503
For responder:
https://github.com/saltyrtc/saltyrtc-client-java/blob/d01e553c06a05c74a532104608c0dc57cb9a23f2/src/main/java/org/saltyrtc/client/signaling/ResponderSignaling.java#L310
But as far as I understand  the  cookie should be set when first receiving a message from a client (independent of weather it's from/as the Server, Responder or Initiator). But before receiving the auth the Initiator/Responder do receive a Key and potential Token message, so I think the cookie should be set when receiving Token (or Key if no Token message is send/expected).

Details
Spec

In case this is the first message received from the sender, the peer:
[...]

MUST store cookie, overflow number and sequence number (or the combined sequence number) for checks on further messages.

This is clearly a bug and the security impact is actually quite hard to estimate. I'm not sure if the cookie is also checked elsewhere in the code.
It also clearly shows that always sending the cookie was a mistake in the spec, leading to such errors.

I couldn't really think of a plausible attack scenario so far but due to its complexity, we should fix this ASAP nevertheless.

I couldn't really think of a plausible attack scenario so far but due to its complexity, we should fix this ASAP nevertheless.

Same for me :+1:
I'm not a security expert so I didn't  add any speculations about security implications to the issue when I opened it :wink:

Uhm how :scream:
Sorry, I'm not sure what happened.

No worries. :slightly_smiling_face:


Implement cloud testing support

Implement support for Browserstack cloud testing support.

WebDriver remote configuration including credentials
VPN Tunnel

Middels de WbTstr configuration builder kan BrowserStack gebruikt worden.


feat: update to opentelemetry/api v0.20.0

Updating to latest opentelemetry versions, note the change back from an "rc" version to a dot version (see https://github.com/open-telemetry/opentelemetry-js-api/issues/74 for the history).  Hopefully the API will go 1.X final in a few weeks.
API changes including "span.context" renamed to "span.spanContext" (which does not impact this library), and the getSpan/setSpan moved to the trace object, which does impact this library.
I've added a version compatibility note to the README, but please check correct version reference when creating a release.

+1 i am also awaiting this merge


Why does the model get bigger after quantization(NAIVE,BNN)?

Short summary about the issue/question:
Brief what process you are following:

How to reproduce it:

nni Environment:

nni version:
nni mode(local|pai|remote):
OS:
python version:
is conda or virtualenv used?:
is running in docker?:

need to update document(yes/no):
Anything else we need to know:

Short summary about the issue/question:
Brief what process you are following:
How to reproduce it:
nni Environment:

nni version:
nni mode(local|pai|remote):
OS:
python version:
is conda or virtualenv used?:
is running in docker?:

need to update document(yes/no):
Anything else we need to know:

@Greyman-Seu - could you please enter the above info so that we know your environment and issue better?

Hi @Greyman-Seu, could you tell us how to reproduce your case? Maybe with some code you are using if it is possible?

duplicate to #2097 , close


Probe_success value for non exisisting URL

What did you do?
I installed prometheus on my pc and tried to scrape Google.com but the probe status is 0 .
What did you expect to see?
It should give an error message as non existing url
What did you see instead? Under which circumstances?
Iam seeing probe success  = 0 message for google.com
Environment
windows

System information:
insert output of uname -srm here
Microsoft Windows [Version 6.1.7601]

Prometheus version:
insert output of prometheus -1.3.1 windows amd64

Alertmanager version:
insert output of alertmanager -version here (if relevant to the issue)

Prometheus configuration file:
global:
external_labels:
monitor: 'hostname'

rule_files:
- 'C:\Users\prasanta1.sahoo\go\bin\alert.rules'
scrape_configs:

job_name: 'bb'
scrape_interval: 30s
scrape_timeout: 10s
metrics_path: /probe
params:
module: [http_2xx]  # Look for a HTTP 200 response.
URL: google.com
relabel_configs:

source_labels: [address]
regex: (.*)(:80)?
target_label: __param_target
replacement: ${1}
source_labels: [__param_target]
regex: (.*)
target_label: instance
replacement: ${1}
source_labels: []
regex: .*
target_label: address
replacement: <IP_ADDRESS>:9115  # Blackbox exporter.
Please let us know for any changes in the above config file.

Alertmanager configuration file:

insert configuration here (if relevant to the issue)

Logs:

insert Prometheus and Alertmanager logs relevant to the issue here

my global config.docx

Dupe of #2256


Feat: To add middlewares to the IIITs specifc page

To add the middleware to  IIITs specific page  in order to restrict users to w/o sign up

Thanks @tyler-007  for showing interest, assigning you.


Type hinting on Datasources and Workbooks

This PR enables the mypy type checking, as well as adds type hints to datasource and workbook endpoints and items. Through testing, I noticed deprecation errors on python 3.5, and that new releases of pip no longer support it, so that was removed as well.

Workbook and datasource endpoints still need more work to figure out how to get mypy to not complain about accepting a path or file object.

Not sure why the tests don't seem to show up over here on my PRs. They definitely are running in my fork. https://github.com/jorwoods/server-client-python/runs/3937331545

@t8y8 Added the missing pull_request trigger so the CI tests will run on PRs.


EMD crash when the vectors are all zeros

When one or more of the input vectors are [0,0,...0], the Earth Mover's Distance function crashes.
Using matlab & mexopenCV, on windows8
cv.EMD(single([0 0]),single([0 0]))
Error using cv.EMD
Unexpected Standard exception from MEX file.
What() is:C:\builds\2_4_PackSlave-win64-vc12-shared\opencv\modules\imgproc\src\emd.cpp:408: error: (-211)  in function icvInitEMD
cv.EMD(single([0 0]'),single([0 1]'),'DistType','User','Cost',single([0 1; 1 0]))
Error using cv.EMD
Unexpected Standard exception from MEX file.
What() is:C:\builds\2_4_PackSlave-win64-vc12-shared\opencv\modules\imgproc\src\emd.cpp:408: error: (-211)  in function icvInitEMD

reproduced from c++:
Mat_<float> a(1,4); a << 1,2,3,4;
Mat_<float> b(1,4,0.0f); // all 0 initially

//comment below line to reproduce issue
b << 4,5,6,7;

float dist = EMD(a,b,DIST_L2);
cerr << dist << endl;

The assertion in the OpenCV code is correct, although without a helpful error message...
It is not really the fact that the signature is all zeros, but that the total weight of points is zero (i.e sum of the first column of a signature cannot be zero)`. In MATLAB notation that would be:
a = [...];  % whether this one point 1x(dims+1) or more Nx(dims+1)
b = [...];
assert(sum(a(:,1))>0 && sum(b(:,1))>0);
dist = cv.EMD(a, b);

Obviously if all points in a signature have zero weight, there's nothing to compare and no distance to compute! You gotta have at least one non-zero-weighted point.


Track underlying cloud resources for Services

SIG CP backlog/tracking issue for https://github.com/kubernetes/kubernetes/issues/70159
Following Tim's comment we also need to have a discussion on whether we want to document this as a best practice or enforce this across existing providers.

cc @aoxn

@andrewsykim this issue and the originating one in k/k seem to concern the very point I raised the other day on Slack about attaching DigitalOcean load-balancer UUIDs as annotations. I'm currently PoC'ing if this is possible to do with the latest cloud provider framework (i.e., the Kubernetes 1.15 dependencies), especially with regards to Service objects being modified not only by the service controller. (https://github.com/kubernetes/kubernetes/issues/70159#issuecomment-434145533 touches on the same topic, albeit at a time when PATCH semantics were not yet in use.)

especially with regards to Service objects being modified not only by the service controller.

especially with regards to Service objects being modified not only by the service controller.

(edit: Sorry for hitting enter too soon :/)
I was thinking about this as well. With PATCH now being used for updating Service status / metadata, we shouldn't be hitting conflict issue if underlying cloud provider implementation updates the annotations in between. Though it seems to oppose what the interface originally defines:
https://github.com/kubernetes/cloud-provider/blob/402566916174f020983cb0bd467daeae6206ae02/cloud.go#L117-L121
Maybe relaxing what the interface defines is the easiest way, however it feels somehow fragile without a generic mechanism to enforce the behavior.

/remove-lifecycle stale

My PoC to validate that patching the Service object does not interfere with the service controller's own operations and patching was successful -- I couldn't spot any error logs or changing behaviors as the LBs came up. I think the patching of mine triggers another service update, but that's not too much of a concern.
One issue I'm still somewhat struggling with is that I'd like the patching to occur as my cloud provider methods are executed (i.e., during EnsureLoadBalancer) which means I need to inject a Kubernetes clientset. The Initialize interface method already passes on a cloudprovider.ControllerClientBuilder to derive the client from. However, the client should ideally (at least that's what I think) go into the cloud implementation that's being returned as a callback to cloudprovider.RegisterCloudProvider. I currently manage to do this by setting a field on my cloud struct during Initialize, but that feels kinda dirty and relies on the current cloud provider initialization order (that is, Initialize is called prior to the service controllers starting).
I could also set up a controller of my own, but that seems like a lot of extra work I don't really need.
Curious if anyone has any ideas. (@MrHohn, did you possibly explore a similar direction?)

@aoxn do we want to work on this for v1.16?

I actually think it's okay to close this now. Service w/ Type=LB now have finalizers so cloud providers should be able to find and delete any associated resource without worrying about the service resource no longer existing.

@andrewsykim my understanding was that the tracking issue kubernetes/kubernetes#70159 had a different focus, which is to track a non-ambiguous ID for the cloud LB. Right now the only identifier that the Cloud Provider interface provides is the name; however, the name can be subject to change (even on purpose to support LB renaming), so a different means seems necessary.
The finalizer seems to ensure that resources get deleted eventually, but does not guarantee that I am able to reference the cloud LB easily / efficiently / non-ambiguously at any point during its lifetime. That's why we started to track the LB ID as an annotation on the Service at DigitalOcean but that comes with a few restrictions. (See my comment https://github.com/kubernetes/cloud-provider/issues/27#issuecomment-510058223 above.)

that makes sense
/reopen

/remove-lifecycle rotten
/lifecycle frozen
@timoreimann let me know if you can work on this

@andrewsykim I can put together a PR as a basis for further discussion. We've been "violating" the do-not-modify-services-directly advice mentioned in the GoDoc for quite some time in DigitalOcean's CCM in order to associate the LB ID with the Service. Having a "blessed" approach for doing this would be beneficial from my point of view.
I'm not sure if I can make it for 1.19 though.


Sheet floating for embedded WebView with small amount of content

I'm using the BottomSheetLayout to display a WebView. Everything works fine if the WebView's content fills the height of the screen. But if the content is very small the sheet is "floating" the first time it is called (also notice that the background is dimmed nearly to black -- much darker than on the second screenshot below):

After I dismiss it and bringing it up again it is displayed correctly:

I assume this is due to the WebView calculating the correct height the first time it is displayed.
Any ideas for a fix or a workaround?

This seems to be the same thing as https://github.com/Flipboard/bottomsheet/issues/81, which should be fixed by https://github.com/Flipboard/bottomsheet/commit/c3b2e5a254a20889d2f011f3fe9922a75d023c68. I'll try to get a release out today

Thanks for the lightning fast response. But the problem still occurs. I was able to track it down to the layout_height of my WebView set to wrap_content. Setting it to match_parent generates a clean result but a bottom sheet which too large for its content.
I've build a minimum working example which I could provide you if required (I'm not allowed to upload a ZIP-file here). Here are the most important parts:
The layout:
<RelativeLayout xmlns:android="http://schemas.android.com/apk/res/android"
                xmlns:tools="http://schemas.android.com/tools"
                android:layout_width="match_parent"
                android:layout_height="match_parent"
                tools:context=".MainActivity"
                android:background="#ffaa00">

    <com.flipboard.bottomsheet.BottomSheetLayout
        android:id="@+id/bottom_sheet_layout"
        android:layout_width="match_parent"
        android:layout_height="match_parent">

        <FrameLayout
            android:layout_width="match_parent"
            android:layout_height="match_parent">
            <Button
                android:id="@+id/button"
                android:layout_width="wrap_content"
                android:layout_height="wrap_content"
                android:layout_gravity="center"
                android:text="Show Bottom Sheet"/>
        </FrameLayout>
    </com.flipboard.bottomsheet.BottomSheetLayout>
</RelativeLayout>

The Activity:
package com.example.bootomsheet.app;

import android.app.Activity;
import android.os.Bundle;
import android.view.Menu;
import android.view.MenuItem;
import android.view.View;
import android.view.ViewGroup;
import android.webkit.WebView;
import android.widget.FrameLayout;
import com.flipboard.bottomsheet.BottomSheetLayout;

public class MainActivity extends Activity {

    @Override
    protected void onCreate(Bundle savedInstanceState) {
        super.onCreate(savedInstanceState);
        setContentView(R.layout.activity_main);

        final BottomSheet bottomSheet = new BottomSheet();
        findViewById(R.id.button).setOnClickListener(new View.OnClickListener() {
            @Override
            public void onClick(View v) {
                ((BottomSheetLayout) findViewById(R.id.bottom_sheet_layout)).showWithSheetView(bottomSheet);
            }
        });
    }

    private class BottomSheet extends WebView {

        public BottomSheet() {
            super(MainActivity.this);
            final FrameLayout.LayoutParams l = new FrameLayout.LayoutParams(ViewGroup.LayoutParams.MATCH_PARENT, ViewGroup.LayoutParams.WRAP_CONTENT);
            setLayoutParams(l);
            loadData(
                    "
Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua.
",
                    null, "utf-8");
        }
    }
}

I plugged this code into the sample and was able to reproduce it too. Thanks!
It seems to be because the WebView first reports its size as 420 pixels (on my device), then as 70 pixels, and then as 283 pixels. The animation of the sheet is started after the first time everything is measured, which means the sheet will be animated to 420 pixels, even though it ends up being only 283 pixels. The dim is similarly affected.
We can fix the bad position by clamping the translation to the max translation, and we should. That won't completely fix the issue with views that change size rapidly, like webview. Even with clamping, there is flickering and other jarring behavior. We need to either come up with a way of somehow waiting for a view's size to be stable, or say that these views are not supported with dynamic sizing. Please let me know if you have suggestions. I'll also think about it some more.
In the mean time I think the best thing you can do is call setSheetHeight with a value from your dimens.xml before calling showWithSheet with a WebView

I've found a way that works, if it is possible to inflate the view to be shown in the bottom sheet some time before it is actually shown (like in my example). In this case one could add the view somewhere to the layout with visibility set to INVISIBLE. The view then has some time to calculate its actual height and when the bottom sheet is shown everything works fine.
Here a slightly adjusted version of the activity's onCreate() method from the example above which demonstrates the behavior:
    @Override
    protected void onCreate(Bundle savedInstanceState) {
        super.onCreate(savedInstanceState);
        setContentView(R.layout.activity_main);

        final ViewGroup frameLayout = ((ViewGroup) findViewById(R.id.frame_layout));
        final BottomSheet bottomSheet = new BottomSheet();
        frameLayout.addView(bottomSheet);
        bottomSheet.setVisibility(View.INVISIBLE);
        findViewById(R.id.button).setOnClickListener(new View.OnClickListener() {
            @Override
            public void onClick(View v) {
                frameLayout.removeView(bottomSheet);
                bottomSheet.setVisibility(View.VISIBLE);
                ((BottomSheetLayout) findViewById(R.id.bottom_sheet_layout)).showWithSheetView(bottomSheet);
            }
        });
    }

If the BottomSheetLayout would provide an option to pre-add views that will be used later in showWithSheetView(), this would be a big help for these scenarios. It would require the possibility to pre-add multiple views, if I am displaying different bottom sheets in my activity.
But I totally understand if you don't like this solution as it is ... well ... a bit special. A workaround would be a method on the BottomSheetLayout to query the final display width (as I understood it is smaller that the screen width on tablets in landscape mode). With this helper method I could reliably setup the mechanism described above on my own...


Feature request: X11 core profile support

I would like to create a core profile under Mesa/X11 which doesn't seem possible currently.

Right now, contexts created with glutin are always of the core profile.


Remove max-submission-delay

I don't think there's any real use for this anymore once we're sure the max_hmm_cost approach works @szhan?

I think so! And since the max HMM cost approach can be used even if one has no access to submission dates.


Support running with URL as project path

One of the requirements for deployment is being able to run ambient project (run/serve) with an URL.

So if I've understood it this allows using an already running url as the "file server" for the project files, while the local command only does the computing?
Perhaps we should mention this in the book that it is possible to point the project to an url, similar to how vim allows ssh paths.

So if I've understood it this allows using an already running url as the "file server" for the project files, while the local command only does the computing?
Perhaps we should mention this in the book that it is possible to point the project to an url, similar to how vim allows ssh paths.

Yes, like a read-only file server for the built project files.


How to reduce logger to only errors?

Checks

[X] I understand project setup issues should be asked on StackOverflow or in GitHub Discussions.
[X] I updated to latest http-proxy-middleware.

Describe the bug (be clear and concise)
Before v3 we could use logLevel: 'error' to only log errors.
With v3 we now use logger: console but this output lots of info logs.
What's the best way to filter only error logs, keeping HPM's log format?
Step-by-step reproduction instructions
1. ...
2. ...

Expected behavior (be clear and concise)
A way to reduce logs to only errors.
How is http-proxy-middleware used in your project?
=> Found<EMAIL_ADDRESS>info Has been hoisted to "http-proxy-middleware"
info This module exists because it's specified in "dependencies".
info Disk size without dependencies: "376KB"
info Disk size with unique dependencies: "896KB"
info Disk size with transitive dependencies: "2.21MB"
info Number of shared dependencies: 7

What http-proxy-middleware configuration are you using?
app.use('/api', createProxyMiddleware({
  target: 'https://example.com/api',
  changeOrigin: true,
  logger: console,
}));

What OS/version and node/version are you seeing the problem?
MacOS 14.4.1 and Node 20.12.0

Additional context (optional)
No response

Is the solution to use the on error event?
https://github.com/chimurai/http-proxy-middleware/blob/32a5b233a1ba488083821cfd1c356deb56792d27/src/plugins/default/logger-plugin.ts#L22-L31

You can use a logger library and set the log level there:
https://github.com/chimurai/http-proxy-middleware/blob/master/recipes/logger.md
Or create a custom logger object to only log errors:
app.use('/api', createProxyMiddleware({
  target: 'https://example.com/api',
  changeOrigin: true,
  logger: {
    info: () => {},
    warn: () => {},
    error: console.error,
  },
}));

That's exactly what I wanted, thank you!


Text Area multi editor not working properly!!!!

When i have use 5-6 Editor in same page its not working correctly its automatic select all editor component when we select one of those and only first editor is working others are not . so please suggest me to update those things

Hi socialinfotech,
First of all, we're glad that you're using our theme and about editor we already took care of it so will be available in our feature update.
Regards,
Andrew.

There´s an issue with the editor himself too, try to write any words, mark it and click A for coloring this word, and you will see, the menu for colors try to opening but its container ul is empty :(
... only for your information
Greetz Sascha


[Feature]: modal params

Is there an existing issue for this?

[X] I have searched the existing issues

Summary
The ability to pass params while opening a modal.
https://docs.appsmith.com/framework-reference/show-modal#signature
This way the modal window can become stateless.
Similar to action params.
https://docs.appsmith.com/framework-reference/run#signature
Why should this be worked on?
use-cases:
Instead of referencing other widgets in a modal, pass an object based on which a form is pre-populated.
Users could also use this to set edit/show mode.

@eco-monk this can be achieved by storing the current params as
{{ storeValue('modalParams', { mode: "edit" }).then(() => showModal('Modal1')) }}


The editor of one adls gen2 blob container is blank

Storage Explorer Version: 1.10.1
Build: 20191010.3
Branch: master
Platform/OS: Windows 10/ Linux Ubuntu 19.04/macOS High Sierra
Architecture: ia32/x64
Regression From: Previous release (1.10.1)
Steps to reproduce:

Expand one ADLS Gen2 account -> Blob Containers.
Select one blob container and check its editor.

Expect Experience:
The editor shows well.

Actual Experience:
The editor is blank.

Ah, this is because we're looking for a new string resource that's not in the current version of the ADLS Gen2 package. We need to update it.

This issue has been fixed on build 20191012.1. So we close it.


Travis fail if vcxproj is out of date

@nbougalis

(camping on this since I'm "Mr. Build" these days...)

Well, that was easy.  :+1: - some tiny quibbles which I really don't care about, and I learned something new too!

:+1:


Gateway refactor

Made functions static where possible
Fixed various memory leaks
Added response size limit for HTTP parser
Use a single buffer for a http response

Anyone merging this one?


Running Multiple instances.

Operating System: Ubuntu 16.10
Installed version of CUDA and cuDNN: 8.0 and cuDNN 5.2
Tensorflow Version : 1.0.0-rc2
I have a website that will take an image of a number plate, the website passes the numberplate to tensor for to be detected, sensor flow then returns the detected text back to the website to be inserted into the database.
I found this issue when using a dropzone to detect many number plates at once when I upload an image one by one, the system is quite reliable. When using the dropzone the system is very unreliable.
I tested this by opening 6 terminal sessions and running the following command in each instance:
/usr/bin/python3 /home/chad/Documents/anpr-python/detect.py /var/www/Honours/public/number_plate/fec5e719-ee8f-4b7c-a259-0cf8ddc5a28f.jpeg /home/chad/Documents/anpr-python/weights.npz out.jpg

I need to be able to fix this as the website needs to allow for mass upload of images to be detected.
Could you help me figure out the best way to fix this?
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcublas.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcudnn.so.5 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcufft.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcurand.so.8.0 locally
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations.
I tensorflow/core/common_runtime/gpu/gpu_device.cc:885] Found device 0 with properties:
name: GeForce GTX 1070
major: 6 minor: 1 memoryClockRate (GHz) 1.683
pciBusID 0000:01:00.0
Total memory: 7.92GiB
Free memory: 126.19MiB
I tensorflow/core/common_runtime/gpu/gpu_device.cc:906] DMA: 0
I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 0:   Y
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1070, pci bus id: 0000:01:00.0)
E tensorflow/stream_executor/cuda/cuda_driver.cc:1002] failed to allocate 126.19M (132317184 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
E tensorflow/stream_executor/cuda/cuda_dnn.cc:397] could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR
E tensorflow/stream_executor/cuda/cuda_dnn.cc:364] could not destroy cudnn handle: CUDNN_STATUS_BAD_PARAM
F tensorflow/core/kernels/conv_ops.cc:605] Check failed: stream->parent()->GetConvolveAlgorithms(&algorithms)
Aborted (core dumped)
chad@chad-GA-990XA-UD3:/var/www/Honours/public/number_plate$

chad@chad-GA-990XA-UD3:/var/www/Honours/public/number_plate$ /usr/bin/python3 /home/chad/Documents/anpr-python/detect.py /var/www/Honours/public/number_plate/fec5e719-ee8f-4b7c-a259-0cf8ddc5a28f.jpeg /home/chad/Documents/anpr-python/weights.npz out.jpg
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcublas.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcudnn.so.5 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcufft.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcurand.so.8.0 locally
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations.
I tensorflow/core/common_runtime/gpu/gpu_device.cc:885] Found device 0 with properties:
name: GeForce GTX 1070
major: 6 minor: 1 memoryClockRate (GHz) 1.683
pciBusID 0000:01:00.0
Total memory: 7.92GiB
Free memory: 82.19MiB
I tensorflow/core/common_runtime/gpu/gpu_device.cc:906] DMA: 0
I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 0:   Y
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1070, pci bus id: 0000:01:00.0)
E tensorflow/stream_executor/cuda/cuda_driver.cc:1002] failed to allocate 82.19M (86179840 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
E tensorflow/stream_executor/cuda/cuda_driver.cc:1002] failed to allocate 15.62M (16374016 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
E tensorflow/stream_executor/cuda/cuda_driver.cc:1002] failed to allocate 15.62M (16374016 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (256):       Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (512):       Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (1024):      Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (2048):      Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (4096):      Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (8192):      Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (16384):     Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (32768):     Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (65536):     Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (131072):    Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (262144):    Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (524288):    Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (1048576):   Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (2097152):   Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (4194304):   Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (8388608):   Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (16777216):  Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (33554432):  Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (67108864):  Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (134217728):         Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (268435456):         Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:660] Bin for 256.00MiB was 256.00MiB, Chunk State:
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x1030ec00000 of size 1280
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x1030ec00500 of size 4864
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x1030ec01800 of size 1039104
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x1030ecff300 of size 307200
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x1030ed4a300 of size 1024
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x1030ed4a700 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x1030ed4a800 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x1030ed4a900 of size 819200
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x1030ee12900 of size 512
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x1030ee12b00 of size 8192
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x1030ee14b00 of size 2072576
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x1030f00eb00 of size 65551360
I tensorflow/core/common_runtime/bfc_allocator.cc:693]      Summary of in-use Chunks by size:
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 2 Chunks of size 256 totalling 512B
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 512 totalling 512B
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 1024 totalling 1.0KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 1280 totalling 1.2KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 4864 totalling 4.8KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 8192 totalling 8.0KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 307200 totalling 300.0KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 819200 totalling 800.0KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 1039104 totalling 1014.8KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 2072576 totalling 1.98MiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 65551360 totalling 62.51MiB
I tensorflow/core/common_runtime/bfc_allocator.cc:700] Sum Total of in-use chunks: 66.57MiB
I tensorflow/core/common_runtime/bfc_allocator.cc:702] Stats:
Limit:                    86179840
InUse:                    69805824
MaxInUse:                 69805824
NumAllocs:                      12
MaxAllocSize:             65551360

W tensorflow/core/common_runtime/bfc_allocator.cc:274] ******************************************************************************xxxxxxxxxxxxxxxxxxxxxx
W tensorflow/core/common_runtime/bfc_allocator.cc:275] Ran out of memory trying to allocate 256.00MiB.  See logs for memory state.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (256):       Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (512):       Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (1024):      Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (2048):      Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (4096):      Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (8192):      Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (16384):     Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (32768):     Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (65536):     Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (131072):    Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (262144):    Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (524288):    Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (1048576):   Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (2097152):   Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (4194304):   Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (8388608):   Total Chunks: 1, Chunks in use: 0 15.61MiB allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (16777216):  Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (33554432):  Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (67108864):  Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (134217728):         Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (268435456):         Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:660] Bin for 47.56MiB was 32.00MiB, Chunk State:
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x1030ec00000 of size 1280
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x1030ec00500 of size 4864
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x1030ec01800 of size 1039104
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x1030ecff300 of size 307200
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x1030ed4a300 of size 1024
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x1030ed4a700 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x1030ed4a800 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x1030ed4a900 of size 819200
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x1030ee12900 of size 512
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x1030ee12b00 of size 8192
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x1030ee14b00 of size 2072576
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x1030f00eb00 of size 65551360
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x10313800000 of size 4864
I tensorflow/core/common_runtime/bfc_allocator.cc:687] Free at 0x10313801300 of size 16369152
I tensorflow/core/common_runtime/bfc_allocator.cc:693]      Summary of in-use Chunks by size:
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 2 Chunks of size 256 totalling 512B
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 512 totalling 512B
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 1024 totalling 1.0KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 1280 totalling 1.2KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 2 Chunks of size 4864 totalling 9.5KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 8192 totalling 8.0KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 307200 totalling 300.0KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 819200 totalling 800.0KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 1039104 totalling 1014.8KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 2072576 totalling 1.98MiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 65551360 totalling 62.51MiB
I tensorflow/core/common_runtime/bfc_allocator.cc:700] Sum Total of in-use chunks: 66.58MiB

Taking a shot in the dark.
I do not think you want start TensorFlow each time.  You might get away with it by setting Allow GPU Memory To Grow.  I think a likely better solution would be to leave TensorFlow running, e.g. the python script, and have it pick up the images to process from a directory queue or whatever.  Even better might be to check out TensorFlow Serving.  I would put the time into setting up TensorFlow Serving if possible that would give you a robust solution.

The errors are clearly coming from running out of GPU memory by trying to run multiple TF computations simultaneously.  As @tfboyd says, it looks like you are creating a separate TF process and runtime for each, and they are all trying to grab some GPU memory until you run out.  Being separate processes, the TF instances have no way of coordinating with each other (other than maybe blocking than waiting to allocate GPU memory).  If you are wanting to make this reliable you should probably be running some kind of service with concurrency control (i.e. limit the number of concurrent operations in flight).
@nfiedel Any suggestions here wrt tf.serving?
Failing that, I'd suggest posting a question on StackOverflow, since this is more of a generic programming question.

Automatically closing due to lack of recent activity. Please update the issue when new information becomes available, and we will reopen the issue. Thanks!


Implementing a metric is getting complicated

In PL 0.9.0 I had the Metric implementation bellow:
class MRRMetric(TensorMetric):

    def similarities(self, x1, x2):
        """
        Calculates the cosine similarity matrix for every pair (i, j),
        where i is an embedding from x1 and j is another embedding from x2.

        :param x1: a tensors with shape [batch_size, hidden_size].
        :param x2: a tensors with shape [batch_size, hidden_size].
        :return: the cosine similarity matrix with shape [batch_size, batch_size].
        """
        x1 = x1 / torch.norm(x1, dim=1, p=2, keepdim=True, dtype=torch.float32)
        x2 = x2 / torch.norm(x2, dim=1, p=2, keepdim=True, dtype=torch.float32)
        return torch.matmul(x1, x2.t())

    def forward(self, r1, r2):
        distances = 1 - self.similarities(r1, r2)
        correct_elements = torch.unsqueeze(torch.diag(distances), dim=-1)
        batch_ranks = torch.sum(distances < correct_elements, dim=-1) + 1.0

        return torch.mean(1.0 / batch_ranks)

In PL 1.0.8 the same metric is implemented with the same logic as:
class MRRMetric(Metric):
    def __init__(self):
        super().__init__()
        self.add_state("mrrs", default=[])

    def similarities(self, x1, x2):
        """
        Calculates the cosine similarity matrix for every pair (i, j),
        where i is an embedding from x1 and j is another embedding from x2.

        :param x1: a tensors with shape [batch_size, hidden_size].
        :param x2: a tensors with shape [batch_size, hidden_size].
        :return: the cosine similarity matrix with shape [batch_size, batch_size].
        """
        x1 = x1 / torch.norm(x1, dim=1, p=2, keepdim=True, dtype=torch.float32)
        x2 = x2 / torch.norm(x2, dim=1, p=2, keepdim=True, dtype=torch.float32)
        return torch.matmul(x1, x2.t())

    def update(self, r1, r2):
        distances = 1 - self.similarities(r1, r2)
        correct_elements = torch.unsqueeze(torch.diag(distances), dim=-1)
        batch_ranks = torch.sum(distances < correct_elements, dim=-1) + 1.0
        mrr = torch.mean(1.0 / batch_ranks)
        self.mrrs.append(mrr )

    def compute(self):
        return torch.mean(torch.tensor(self.mrrs))

However I'm getting differents outputs when calling this metrics:

PL 0.9.0

s=torch.tensor([[0.0023, 0.8380, 0.2920, 0.2263, 0.3362, 0.7074, 0.5290, 0.5974, 0.8176,
         0.5816],
        [0.6414, 0.9122, 0.0162, 0.3203, 0.5081, 0.9035, 0.3201, 0.2870, 0.0693,
         0.3736],
        [0.4570, 0.2787, 0.0140, 0.5623, 0.7226, 0.3223, 0.3566, 0.6783, 0.8384,
         0.1610],
        [0.1334, 0.7529, 0.5574, 0.1949, 0.9526, 0.9542, 0.2212, 0.1759, 0.1354,
         0.8739],
        [0.3224, 0.2001, 0.2776, 0.4203, 0.0720, 0.2702, 0.0114, 0.1538, 0.0090,
         0.4954]])
r=torch.tensor([[1.7271e-01, 6.2802e-01, 1.9287e-01, 3.7181e-01, 9.8512e-01, 3.3500e-01,
         7.8245e-01, 8.2903e-01, 3.8158e-01, 9.0951e-01],
        [1.6962e-01, 1.8394e-01, 8.1776e-01, 1.5577e-01, 2.6253e-01, 8.9279e-02,
         4.3836e-01, 9.3631e-01, 9.9012e-01, 7.0277e-01],
        [6.0912e-01, 3.9780e-04, 2.9773e-02, 3.0968e-01, 9.5504e-01, 5.9606e-01,
         7.1733e-01, 5.7158e-01, 7.0587e-01, 2.0964e-01],
        [5.1019e-01, 3.9333e-01, 8.7822e-02, 8.1885e-01, 3.9182e-01, 6.6586e-02,
         1.6968e-01, 1.6206e-01, 6.1676e-01, 1.8388e-01],
        [8.3266e-01, 5.5918e-01, 6.3568e-01, 5.4674e-01, 3.4784e-01, 6.0922e-01,
         1.3884e-01, 2.3742e-01, 9.0644e-01, 2.3266e-01]])
mrr = MRRMetric()
mrr(s, r)
#tensor(0.5300)

PL 1.0.8

```python
s=torch.tensor([[0.0023, 0.8380, 0.2920, 0.2263, 0.3362, 0.7074, 0.5290, 0.5974, 0.8176,
         0.5816],
        [0.6414, 0.9122, 0.0162, 0.3203, 0.5081, 0.9035, 0.3201, 0.2870, 0.0693,
         0.3736],
        [0.4570, 0.2787, 0.0140, 0.5623, 0.7226, 0.3223, 0.3566, 0.6783, 0.8384,
         0.1610],
        [0.1334, 0.7529, 0.5574, 0.1949, 0.9526, 0.9542, 0.2212, 0.1759, 0.1354,
         0.8739],
        [0.3224, 0.2001, 0.2776, 0.4203, 0.0720, 0.2702, 0.0114, 0.1538, 0.0090,
         0.4954]])
r=torch.tensor([[1.7271e-01, 6.2802e-01, 1.9287e-01, 3.7181e-01, 9.8512e-01, 3.3500e-01,
         7.8245e-01, 8.2903e-01, 3.8158e-01, 9.0951e-01],
        [1.6962e-01, 1.8394e-01, 8.1776e-01, 1.5577e-01, 2.6253e-01, 8.9279e-02,
         4.3836e-01, 9.3631e-01, 9.9012e-01, 7.0277e-01],
        [6.0912e-01, 3.9780e-04, 2.9773e-02, 3.0968e-01, 9.5504e-01, 5.9606e-01,
         7.1733e-01, 5.7158e-01, 7.0587e-01, 2.0964e-01],
        [5.1019e-01, 3.9333e-01, 8.7822e-02, 8.1885e-01, 3.9182e-01, 6.6586e-02,
         1.6968e-01, 1.6206e-01, 6.1676e-01, 1.8388e-01],
        [8.3266e-01, 5.5918e-01, 6.3568e-01, 5.4674e-01, 3.4784e-01, 6.0922e-01,
         1.3884e-01, 2.3742e-01, 9.0644e-01, 2.3266e-01]])
mrr = MRRMetric()
mrr(r, s)
#tensor(0.6800)

This was my fault, it seems that in this metric order matters and the difference is in the way I called the methods.


csv_export() to data_export() with csv as a return type

CSV export works, but I need to have the option to switch between writing the row to a file and just returning a list of lists that the user can use for something else.

Created markup()


feat: change variable definition msi_auth_for_monitoring_enabled

Changing the value of the msi_auth_for_monitoring_enabled boolean seemed complicated to figure out, changed it to a variable in the cluster.workspace.enable variable to make it more straightforward

Incoprorated into another feature branch


Ozzie reversions and reinstatement

I am killing the noise about the state_class, and also the draft v4.0.33 release.
It is what it is. And long-term statistics for forecasts are meaningless to anyone but Solcast themselves.
This pull is draft because it's likely that another Oziee change that is impacting dampening may need to come out. That and I don't want to forget to reinstate the state_class.

Targting this PR for a 4.0.34 release (with no pre-release), soon after 4.0.33 which I think would suit @isorin's performance improvements (which I love), after that goes GA.
I do not believe the reversion of Oziee's state_class has wormed its way into that proposed merge. From what I can see it shows state_class= SensorStateClass.TOTAL, which it should.


python: kll_floats_sketch happily ingests numpy.NaN

From the dev list: https://lists.apache.org/thread.html/rd35d7f43b54b6c818ad260304e4369d6fa71b1cd552e4836d66eda28%40<dev.datasketches.apache.org>
Easy to reproduce:
sk = kll_floats_sketch(10)
sk.update(numpy.nan)
sk.get_min_value()

Expected behavior is to ignore NaNs.

Yup, it doesn't check in C++, only in Java. Will work on a patch shortly.


Solve “500 Internal Server Error”

Solve
I tried to post articles via python but there are errors why
Can help me, thank you
Error message:
Traceback (most recent call last):
File "/Users/apple/PycharmProjects/爬虫学习/wordpress.py", line 17, in
client.call(NewPost(post))
File "/Users/apple/anaconda3/lib/python3.6/site-packages/wordpress_xmlrpc/base.py", line 37, in call
raw_result = server_method(*args)
File "/Users/apple/anaconda3/lib/python3.6/xmlrpc/client.py", line 1112, in call
return self.__send(self.__name, args)
File "/Users/apple/anaconda3/lib/python3.6/xmlrpc/client.py", line 1452, in __request
verbose=self.__verbose
File "/Users/apple/anaconda3/lib/python3.6/xmlrpc/client.py", line 1154, in request
return self.single_request(host, handler, request_body, verbose)
File "/Users/apple/anaconda3/lib/python3.6/xmlrpc/client.py", line 1187, in single_request
dict(resp.getheaders())
xmlrpc.client.ProtocolError: <ProtocolError for www.地址.com/xmlrpc.php: 500 Internal Server Error>

A 500 error is being returned from your server. You'll have to check your server logs to understand what the issue is, it's impossible to diagnose from the client library.


service endpoint parameter is missing in create subnet step

Service endpoint parameter is missing in create subnet step
[Enter feedback here]

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

ID: 916ac90f-8a94-5f3d-eb99-fe06c39eb6cf
Version Independent ID: 2aad2e47-7fde-8d24-f824-236a26e5d887
Content: Create virtual nodes using Azure CLI - Azure Kubernetes Service
Content Source: articles/aks/virtual-nodes-cli.md
Service: container-service
GitHub Login: @MGoedtel
Microsoft Alias: magoedte

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

@deeepakmhaskar
Could you please elaborate the query for better understanding. if possible, please help me with the screenshot So we can assist you further.

@ManoharLakkoju-MSFT , The information is missing during the "Enable virtual nodes addon". You can either add parameter --delegations Microsoft.ContainerInstance/containerGroups while creating subnet for virtual node or add parameter --addons virtual-node while running virtual node addon command.
It took me some time to understand that it adds subnet with delegation "Microsoft.ContainerInstance/containerGroups". I think , this information should be added.

@deeepakmhaskar
Thanks for bringing this to our attention.
I'm going to assign this to the document author so they can take a look at it accordingly
@MGoedtel
Can you please check and add your comments on this doc update request as applicable.

#reassign:schaffererin

Hi, @deeepakmhaskar, thank you for your feedback. I apologize for the delay in responding. I tested the deployment using Azure CLI and received an error when only passing the --delegations parameter. The error stated that the --addons parameter is required. Therefore, no doc updates are needed. I will now close this GitHub issue. Thank you again!
#please-close


Using accounts that aren't linked to FB

Hi,
I would like to know if it was possible to use the API with accounts that have SMS auth instead of FB auth. I don't see it right now in the docs, so I guess it isn't yet available. I'm unable to sniff my phone's traffic because I've got an iPhone and no Mac, so I can't really dig into it. If someone can give me a clue on where to start, I'd gladly code something for it and push it forward.
Thanks in advance,
Regards

Are you sure that not having a Mac disallows you from monitoring traffic?
I am sure it is possible and has a different auth flow -- but I have not seen it.

I thought so because native tools are unavailable on Windows, but I've managed to set up a proxy just fine with Fiddler. I've got all the connection workflow decrypted, so I'll be able to push a pull request soon that adds SMS auth.
I've rarely seen people use FB to connect to Tinder in Europe, which I kinda understand considering all the controversy there has been recently.

Support was added. Thanks to @fbessez for reviewing my commits and merging them !


Optimize master query taskInstance's new state from cache.

What is the purpose of the pull request
In my understanding, the worker updates the status of the task through the message after the master has distributed the task, but the new task status in the master is directly obtained from the database without the use of TaskInstanceCacheManager. So I optimized the code, If there is any problem, please correct it.😊

after master submit the task to worker and db, cache it.
when receive the task state update response from worker, update the cache in addition to synchronizing to the db.
query new state from cache, reduce the pressure on the db.

This change added tests and can be verified as follows:

Manually verified the change by testing locally.

Pull the latest code

Please add ut

great
1，pid meaningless, no need to add，can remove
2，taskInstance cache no problem，but there are many places involving master code，please commit dev and test

thanks,

save process id  in zk is to solve this issue. I solve it on another PR, but checkout new branch with this commit, so it can be see here.
i will add the UT. BTW, could you give me some advices?

Unit Test not running . please retry commit . Thx

Unit Test not running . please retry commit . Thx

I check the log detail, it is blocked by some connect problem, such as hadoop or http. Do need  modify some configurations? or Can you give some advice?

Hi,
You can change one blank line to rerun the ut.

Hi, please follow the reviewer's suggestion to change it. If there is no update after 7 days, the PR will be closed, and you can reopen it when your problem is resolved.


Expose event for completion item selected

monaco-editor version: 0.12.0
Browser: Any
OS: Any
Monaco Editor should expose an event that notifies hosts when a user selects a completion item. This would likely be implemented as a callback property on a CompletionItem:
export interface CompletionItem {
        /**
         * The callback to invoke when this CompletionItem is selected.
         */
        label: () => void;

or as a new API on CompletionItemProvider:
export interface CompletionItemProvider {
        triggerCharacters?: string[];

        /**
         * Notification that the user accepted a CompletionItem
         */
        resolveCompletionItem?(item: CompletionItem): void;

        /**
         * Given a completion item fill in more data, like [doc-comment](#CompletionItem.documentation)
         * or [details](#CompletionItem.detail).
         *
         * The editor will only resolve a completion item once.
         */
        resolveCompletionItem?(item: CompletionItem, token: CancellationToken): CompletionItem | Thenable<CompletionItem>;
    }

Justification
Some extensions are able to improve user experience over time by adapting to a user's coding style. One opportunity for such improvement is via the styling and sort order of a completion list. In order to understand the effectiveness of the improvement, these extensions need to be notified regarding the user's eventual choice of a completion item.

@rebornix we had chatted about this some time back. Opening this issue to track progress.

Related issue https://github.com/Microsoft/vscode/issues/46451


Any data structures and algorithms left for me to complete?

Hi, I'm looking to make my first ever contribution to a project on github and to practice my C++ skills. Are there any more data structures and algorithms left for me to complete on this project?

@jdouglass Yes there are a lot of data structures yet to be implemented. You can explore the repository and open a PR once done.

i have a problem that should be included here . IF interested please allow me to contribute

@himanshu007-creator Yes you may.

So i just add the file right?

@himanshu007-creator yes add it, then it will be reviewed

i have submitted a pull request, please check it, thx!

i have submitted a pull request, please check it, thx!

@TermKnive Done.

@TermKnive Done.

Aight thx

Aight thx

HI I have two more algorithm that should be included here .IF interested please allow me to contribute

Moore's voting algorithm [code to find majority of element in less time complexity ]
jungling algorithm [to rotate the array according to given no ].

@amritansh22 check my own pull request