Counter Culture getting incorrect count with after_create hook

We are having a problem with our implementation of counter cache.  When we have an after_create method it ends up doubling up the count (returning 2 instead of 1).
Here is a simple setup
class Issue < ActiveRecord::Base
  belongs_to :order
  counter_culture :order
  after_create :set_ticket_number
  def set_ticket_number
    update_attribute(:ticket_number, 200000+id)
  end
end

class Order < ActiveRecord::Base
  has_many :issues
end

Then in the console
>> o = Order.first
=> #<Order id: 1, issues_count: 0>
>> o.issues_count
=> 0
>> o.issues
=> []
>> o.issues.create
   (0.1ms)  BEGIN
  SQL (0.5ms)  INSERT INTO `issues` (`order_id`, `ticket_number`) VALUES (1, NULL)
  Order Load (0.7ms)  SELECT `orders`.*  FROM `orders` WHERE `orders`.`id` = 1 LIMIT 1
=> #<Order id: 1, issues_count: 2>
>> o.issues_count
=> 2

Switching back to the Rails counter_cache: true fixes it, also commenting out the after_create fixes it.
Do you have any ideas why it might be doing that?  I took a look at the source shortly and thought I might have been interfering with your after_create hook.
Thanks for the awesome gem, it's stopped a lot of our dead lock issues.

Hey Magnusk, perhaps I can get some clarity on the issue i'm currently facing with counter culture gem. I've got a rails 4.2.4 url_shortening application which consists of both my user and link models. I am trying to use counter caching to ensure i can get a list of users sorted according the amount of links they have. After including counter_culture in my gem file, ran bundle and used its generator... i added <counter_culture :user>   to my link model  i keep getting this error in  my server  <UPDATE "users" SET "links_count" = COALESCE("links_count", 0) + 1 WHERE "users"."id" = ?  [["id", 6]]
SQLite3::SQLException: no such column: links_count: UPDATE "users" SET "links_count" = COALESCE("links_count", 0) + 1 WHERE "users"."id" = ?
Error in execute_after_commit block: #<ActiveRecord::StatementInvalid: SQLite3::SQLException: no such column: links_count: UPDATE "users" SET "links_count" = COALESCE("links_count", 0) + 1 WHERE "users"."id" = ?>
Completed 500 Internal Server Error in 1131ms (ActiveRecord: 369.5ms)
ActiveRecord::StatementInvalid (SQLite3::SQLException: no such column: links_count: UPDATE "users" SET "links_count" = COALESCE("links_count", 0) + 1 WHERE "users"."id" = ?):>


fix: [PIPE-22212]: update Policy As Code quickstart to clarify Free Tier limitations

Thanks for contributing to the Harness Developer Hub! Our code owners will review your submission.
Description

Please describe your changes: __________________________________
Jira/GitHub Issue numbers (if any): ______________________________
Preview links/images (Internal contributors only): __________________

PR lifecycle
We aim to merge PRs within one week or less, but delays happen sometimes.
If your PR is open longer than two weeks without any human activity, please tag a code owner in a comment.
PRs must meet these requirements to be merged:

[ ] Successful preview build.
[ ] Code owner review.
[ ] No merge conflicts.
[ ] Release notes/new features docs: Feature/version released to at least one prod environment.

 Thank you for your submission! We really appreciate it. Like many open source projects, we ask that you sign our Contributor License Agreement before we can accept your contribution.You have signed the CLA already but the status is still pending? Let us recheck it.

Please check the Execution Link of the Pipeline for the Website Draft URL. This is located in the Preview Step behind the Harness VPN and also is available in #hdh_alerts. E.g Website Draft URL: https://unique-id--harness-developer.netlify.app. Current Draft URL is: https://6703e9220695fd048d928118--harness-developer.netlify.app

Please check the Execution Link of the Pipeline for the Website Draft URL. This is located in the Preview Step behind the Harness VPN and also is available in #hdh_alerts. E.g Website Draft URL: https://unique-id--harness-developer.netlify.app. Current Draft URL is: https://6703e9e7db906d0cf7ba87cb--harness-developer.netlify.app


cannot import google.cloud.securitycenter_v1 in requirements

I'm trying to use this API in Cloud Function. When I use the import "from google.cloud.securitycenter_v1.proto.finding_pb2 import Finding" as shown in the code snippet, nothing I put in requirements.txt seems to satisfy the installer and it cannot find this module. When using the exact requirements.txt parameters in the example, it still cannot find the module. I am using the exact import of the examples and the same requirement, google-cloud-securitycenter==1.0.0
Any help would be appreciated.

That specific import looks like it's from the previous (<1.0.0) version of the library. What code snippet are you looking at? As far as I can tell the snippets on cloud.google.com (like the ones on https://cloud.google.com/security-command-center/docs/how-to-api-manage-notifications#python_2) are up to date with the latest release. You can also reference this directory: https://github.com/googleapis/python-securitycenter/tree/master/samples/snippets

I was trying to use code I had made a month or two ago, and had not noticed the changes. Thanks!


issue-15 patch and regression test.

Coverage remained the same at 100.0% when pulling 5fa64c8a24bb16c7de3fa56391d474e09cce6043 on issue-15-indefinite-job-timeout-broken into e0556f3e57fa38ec61aa6d3a6254fc4ddaba488e on master.


Add a C API so that non-Rust projects can use cpp_demangle

I am also thinking here of a daemon that receives requests through something like nanomsg(shm on the same machine).

That seems like something that would be best left to another crate/application that uses this one.


compilation error in typechain-runtime with noUnusedParameters

with noUnusedParameters compiler option enabled, I get the following error:
$ rm -rf dist && npx tsc -p .
build/ts_contracts/typechain-runtime.ts:98:42 - error TS6133: 'res2' is declared but its value is never read.

98         watchedEvent.stopWatching((err2, res2) => {
                                            ~~~~

Could you please rename the unused param to _res2 (or remove it)?  I'd submit a PR, but the change is really small, so let's not waste time for bureaucracy :)

If its unused, it should be removed. I will make sure to tweak compiler settings to cath these mistakes in future. Sorry about that.

Thanks!


customize theme and input field

i want customize theme.how do it?

i found it.
ng2-jalali-date-picker  ->  date-picker  ->  date-picker.component.metadata.json
found style there

Hi,
I am sorry I thought I have responded but I didn't
you could override its css as the following example
/deep/ dp-date-picker {
       .dp-calendar-nav-container{
                  background: #464646 !important;
                  color: #ffffff;
       }
}

thanks for your nice date picker, but I was wondering how we can change style when we use dp-material theme, which you offer to us. for example the width of date picker is 252px  by default , and I want to change it to 170px, so as I do something like that :
dp-date-picker.dp-material .dp-picker-input {
    -webkit-box-sizing: border-box;
    box-sizing: border-box;
    height: 30px;
    width: 170px // 252px;
    font-size: 13px;
    outline: none;
}

but After change and back to browser to see result, it is not changing and still the with is 252. I think it is not overriding the theme. or even adding the z-index .


mithproxyweb - Uncaught TypeError: Cannot read property 'scheme' of undefined

Problem Description
Using mithproxyweb
Screen get lost (blank) when using the rawtcp and such a packet is arriving.
In the browser console the the following error is displayed
Uncaught TypeError: Cannot read property 'scheme' of undefined
    at TLSColumn (app.js:50)
    at beginWork (vendor.js:122)
    at o (vendor.js:122)
    at u (vendor.js:122)
    at c (vendor.js:122)
    at batchedUpdates (vendor.js:122)
    at qb (vendor.js:122)
    at ob (vendor.js:122)
    at Object.batchedUpdates (vendor.js:122)
    at dispatchEvent (vendor.js:122

note, reloading the screen will resolve it, but when scrolling down to the screen disaprears again (total white browser screen)
Don't know if relevant, or even if that is causing this, but I was trying to look at a MQTT protocol with TLS
Steps to reproduce the behavior:

enable rawtcp (I did it in the through mithproxy web interface)
have a non http packet arrive (screen is fine until the packet is loaded)

System Information
Paste the output of "mitmproxy --version" here.
Mitmproxy: 6.0.2
Python:    3.8.5
OpenSSL:   OpenSSL 1.1.1i  8 Dec 2020
Platform:  Linux-5.4.0-72-generic-x86_64-with

Same problem here on OSX when capturing rawtcp or opening previously captured rawtcp flow
vendor.js:122 TypeError: Cannot read property 'scheme' of undefined
    at TLSColumn (app.js:50)
    at beginWork (vendor.js:122)
    at o (vendor.js:122)
    at u (vendor.js:122)
    at c (vendor.js:122)
    at f (vendor.js:122)
    at p (vendor.js:122)
    at Object.enqueueSetState (vendor.js:122)
    at i.v.setState (vendor.js:167)
    at i.onStateChange (vendor.js:128)

Output of "mitmweb --version":
➜  mitmweb --version
Mitmproxy: 6.0.2
Python:    3.9.4
OpenSSL:   OpenSSL 1.1.1k  25 Mar 2021
Platform:  macOS-10.15.7-x86_64-i386-64bit

Getting the same error on Brave:
Mitmproxy: 7.0.2 binary
Python:    3.9.5
OpenSSL:   OpenSSL 1.1.1k  25 Mar 2021
Platform:  Linux-5.10.52-antix.1-amd64-smp-x86_64-with-glibc2.28

and the following on Firefox:
Uncaught TypeError: t.request is undefined (app.js 50:177)
# This is the catch block

Similar behavior. On opening the page, it redirects to a blank http://localhost:8002/#/flows page almost immediately.

I just added basic TCP support today, this should be fixed with #4779 tomorrow. :)

This is resolved on the latest development snapshots now and will work in the next release. :)

Great this will be fixed in the next release! <3  Is there a workaround for this in the meantime?

You can use the development snapshots from https://mitmproxy.org/downloads :)

error still appears, I was following https://docs.mitmproxy.org/stable/howto-install-system-trusted-ca-android/ and it's appears after the last adb reboot
mitmweb.exe --version
Mitmproxy: 7.0.4 binary
Python:    3.9.7
OpenSSL:   OpenSSL 1.1.1l  24 Aug 2021
Platform:  Windows-10-10.0.22000-SP0

vendor.js:122 TypeError: Cannot read properties of undefined (reading 'scheme')
    at TLSColumn (app.js:50)
    at beginWork (vendor.js:122)
    at o (vendor.js:122)
    at u (vendor.js:122)
    at c (vendor.js:122)
    at f (vendor.js:122)
    at p (vendor.js:122)
    at Object.enqueueSetState (vendor.js:122)
    at i.v.setState (vendor.js:167)
    at i.onStateChange (vendor.js:128)

and
Uncaught (in promise) TypeError: Cannot read properties of undefined (reading 'scheme')
    at TLSColumn (app.js:50)
    at beginWork (vendor.js:122)
    at o (vendor.js:122)
    at u (vendor.js:122)
    at c (vendor.js:122)
    at f (vendor.js:122)
    at p (vendor.js:122)
    at Object.enqueueSetState (vendor.js:122)
    at i.v.setState (vendor.js:167)
    at i.onStateChange (vendor.js:128)

This should be fixed if you use the development snapshots (not yet in a release) from https://mitmproxy.org/downloads/#branches/main/.

This should be fixed if you use the development snapshots (not yet in a release) from mitmproxy.org/downloads/#branches/main.

Unfortunately, the issue still exists in 8.0.0.dev and the issue is random, sometimes it works fine and other times not.
This exception is thrown in the console and the web page is completely blank.
app.js:1 Uncaught Error: Cannot find module 'react'
    at s (app.js:1)
    at app.js:1
    at Object.1../backends/static (app.js:2)
    at s (app.js:1)
    at e (app.js:1)
    at app.js:1

Specs:
Mitmproxy: 8.0.0.dev binary
Python:    3.9.9
OpenSSL:   OpenSSL 1.1.1l  24 Aug 2021
Platform:  macOS-10.16-x86_64-i386-64bit

I couldn't for the life of me get this to work, I'd shutdown and restart the proxy to no avail - would still get this blank screen message. After grabbing mitmproxy-8.0.0.dev0-py3-none-any.whl everything seems to be working so far


Implement methods for moving a DriveItem to a new folder

Follows: Move a DriveItem to a new folder

Closing. This has been implemented:
https://github.com/sreeise/graph-rs/blob/d011b547f3b359fef62120216c7bbf2547a4ddc8/src/drive/request.rs#L448


Ship helpers for preparing common patterns of input

I noticed when writing the repeatability test suite that the examples had repeated patterns in the input:

f-plane Coriolis force field
beta-plane Coriolis force field
rectangular bordered pool geometry (i.e., the wetmask being all 1 except for a 1-cell border)

I think it's a good idea (in principle, no comment on priority) to ship helper functions that implement these patterns so that users doing basic things can get started more easily.
There is a choice about whether to implement those features in the Fortran core of MIM or in Python/Matlab/etc helper code that the project ships in conjunction with the Fortran.

Pro Fortran: Implemented just once in one place (whereas, if there come to be both Python and Matlab frontends, these helpers will be duplicated across them and will need to be kept in sync).
Pro Fortran: Feature is available to the hypothetical user that just compiles the Fortran and doesn't use any frontend.
Pro frontend: Programming in Fortran is tedious and error-prone.
Pro frontend: Helpers will be simple to unit-test (though, I don't know whether Matlab's unit testing framework(s) are any easier to use than Fortran's).
Pro frontend: Additional such patterns will be easier to add, if there aren't very many frontends to which they need to be added.
Pro frontend: Lower core maintenance burden and maintenance churn: If these helper functions change, the rest of the core does not need to be reverified, and does not even seem to need to be reverified.
Pro frontend: Research results are more reproducible because the computed inputs are always present to be saved, and loaded from.  We can presumably minimize this point by dumping the computed inputs even if they are computed in Fortran, but I think that having that computation in the frontend(s) emphasizes the distinction more.

If Issue #21 is implemented, sinusoidal and stochastic wind will become two more such input patterns.

Another pro frontend point: the parameters.in file won't become cluttered with so many unused options.
I think there may be some value to having a few options implemented as defaults in the Fortran code that the user must actively override - e.g. forcing the wetmask to have a boundary around the edge (at least until we've sorted out #15 ). This would be a way of making MIM kinder to inexperienced users. Though it should definitely throw a warning if the user doesn't specify something important.

Sensible defaults are definitely important.  For single numbers, I think it may be sufficient to implement them by providing a template parameters.in file with the defaults in it, and inviting users to copy it and modify to suit their needs.  And put the things they are most likely to want to modify near the top :)  Not sure offhand what to think about the automatic wet mask clobber you point out -- that looks like it will surprise someone who intentionally left water on the boundary.

For reference, the erstwhile sinusoidal and stochastic wind forcing features can come back as input generation helpers.

A fairly general class of helper is "Give me a function that computes this field at arbitrary points, and the resolution of your simulation, and I will construct the correct input field (by sampling your function)."  This should be directly useful for simulating idealized conditions, where the inputs are representable by simple mathematical curves.  This interface can also cover the use case "Here's a map, which is discretized differently than my simulation resolution; please interpolate."


FarmedList, farmedBlocks storage validations.

[ ] Check for duplicated farmedBlocks record.
[ ] Update farmer rewards from balance queries.
[ ] If user import a mnemonic with an already used account, regenerate farmedBlock list from network.

This may no longer be relevant after -> https://github.com/subspace/subspace-desktop/issues/238


moreover sentence isn't perfect, it doesn't quite follow

in particular, of 3rd paragraph of introduction.

made note in overleaf.


Docker port mapping instructions for MacOS

This PR explains how to run Indy SDK tests and samples on MacOS. It addresses the issues described in https://github.com/hyperledger/indy-sdk/issues/536.

@kazarena Thank you for the PR with clarification!
There was some refactoring in our documentation after your discussion with @vimmerru
I think it's better to move your update to root readme.md https://github.com/hyperledger/indy-sdk#how-to-start-local-nodes-pool-with-docker
and may be provide link to this section from macos readme.
Also please fix DCO for the first commit in your PR https://github.com/probot/dco#how-it-works

@jovfer All done


Multi-Dimensional HGP codes. / 3D Toric Code

Really nice library, and it seems very useful for one of my projects!
I was wondering if you plan to extend the HGP functionality to produce n-dimensional codes aswell?
This would be very helpful to construct a 3D Toric code for example.

Thank you! I would be happy to add additional functionality, particularly if it is helpful or useful to somebody :slightly_smiling_face:
I am not sure what an n-dimensional HGP code is, though, or how exactly that would work.  Do you have a reference I could use to understand it?  Is this the right place to look?

Assuming that I understand the request, I think Eq. 14 here gives me a concrete construction that I can implement for the product of n classical codes.  However, this product gives me an n-complex, in which any pair of adjacent boundary operators can be used to define a CSS code.  So in total, the n-complex gives me n-1 CSS codes.  Which of these codes is "the" hypergraph product of n classical codes?
Apologies in advance if the things I am saying do not make sense.  I am learning this stuff on the fly so I may be getting concepts or terminology wrong :sweat_smile:

Hey, thanks for looking at the issue! Im not aware of any generalizable construction method implemented yet. However the 3D toric is coded in panqec. Im struggeling myself to understand it as Im developing a decoder for my masters thesis. However the current libraries are pretty restricted in the type of codes they have.
I have read a paper that describes the construction a bit differently to the one you have, I will try to find kt again!

I see.  Are you simply after the n-dimensional toric code?  If so, perhaps I can add just that.  I would still be interested in adding a general hypergraph product code, though.

Yes im trying to validate my decoder concept on different type of codes. For now Im sticking to Toric codes, because the lack of generalizable construction. Also only Torica would be a nice to cross validate different libraries :)

I'll let @oliverweissl close this issue if+when it's addressed satisfactorily :slightly_smiling_face:

Hey i saw that the construction of the HGP  for 2 classical codes (based on [arXiv:2202.01702]) is quite elegant in code.
I have similar formulation for HGP of 3 classical codes [arXiv:2409.04300], maybe this is useful
if not please close this :D

Sorry for the delayed response!
Generally speaking, more code constructions sounds nice to have.  Based on the text between Eqs. (3) and (4) of your paper, it sounds like your 3D HGP code construction is equivalent to the strategy I used to construct the GeneralizedSurfaceCode (note that each Chain_complex.tensor_product here is equivalent to a hypergraph product).  Does that sound correct?  If so, maybe we rather than hard-coding the 3-fold HGP, it would be better to go ahead and inductively define the n-fold HGP.

Hey thanks for the reply! This sounds correct to me. And having n-fold construction would be quite elegant, also I think you would be the first library that does this ( at least that im aware of)


Use default build script names

@bot-gradle test this

OK, I've already triggered ReadyForMerge build for you.

@bot-gradle test this

OK, I've already triggered ReadyForMerge build for you.

@bot-gradle test this

OK, I've already triggered ReadyForMerge build for you.

Closed via d2b854b.


validation messaged  fixed

fix for wrong validation messaged displaying in the nationality page screen

CI: No security warnings found


About answer_ List modification

hello
 sorry to bother you, But I do have a problem I need your help with.

 if I use it to do my own VQA tasks, How do I modify answer_ List this file. Do I just need to save all my answers to a file in the same format？

 Hope to receive your reply as soon as possible.

 thank you very much

Yes you can do that, thanks.


Allow checkboxes to scroll

I'm trying to setup a dialog to allow uninstallation of installed Adobe apps, however the best option currently available to list the installed apps and provide a means of selecting one or several of the apps to "uninstall", is checkboxes, however with more than 20 possible Adobe apps that could be installed with the possibility of much more when someone leaves older versions installed where the year is attached to the name of the app, the window ends up quite large, and the checkbox section doesn't currently allow scrolling, which would help with managing the window size.
I will log a seperate version of this request to allow list items to be selectable as a better variation of this idea.

this is what --checkboxstyle switch is for.
Following apple guidelines, checkboxes remain fixed to the window surface whereas switches can be contained in a scrollable area.

I figured, but I'm not getting a scroll bar with the switch option. (The listitems version of this is issue #465.)

it scrolls for me
dialog --checkbox 1 --checkbox 2 --checkbox 3 --checkbox 4 --checkbox 4 --checkbox 5 --checkbox 6 --checkbox 7 --checkbox 8 --checkbox 9 --checkbox 10 --checkbox 11 --checkbox 12 --checkbox 13 --checkboxstyle switch

can you provide an example where it's not working?
Internally it determines that if the content of the switch view is going to be more than the available window space it will place them in a scrollview, otherwise it doesn't - e.g. 2 or 3 switches probably won'y scroll but more will. If you add a bunch of switches and include the --resizable flag you can see this in action as you change the height of the window.

grumble now it's working and I can't replicate when it wasn't working. Thanks, closed.

gotta love those ones 😛


<EMAIL_ADDRESS>is missing

I tried to upgrade to<EMAIL_ADDRESS>with go get -u all, but got such error message:
go get: inconsistent versions:
<EMAIL_ADDRESS>from all ended up at v0.1.1-0.20190729112526-ef68c9635478 unexpectedly (please report at golang.org/issue/new)

I checked with go mod graph:
go mod graph | grep "github.com/micro/mdns"
<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>
It appears that<EMAIL_ADDRESS>depends on<EMAIL_ADDRESS>And I checked at github.com/micro/mdns but found the v0.1.0 tag only. The v0.1.1 is missing.

I've created an updated point release which should fix the issue. Please close if fixed.

It's fixed. Thanks!


pmmenezes.md

Add scanner-results.md to Atlantis container image

Hey, pmmenezes, thanks for completing the final project! Just FYI, your scan might be stale. Our current grype scanner results show this:
✔ Scanned for vulnerabilities     [5 vulnerability matches]      ├── by severity: 0 critical, 2 high, 3 medium, 0 low, 0 negligible    └── by status:   5 fixed, 0 not-fixed, 0 ignored
Certificate coming your way shortly!


epkowa: add the GT-S650 plugin

This plugin adds support for the GT-S650, Perfection V19 and
Perfection V39 scanners. I've tested it personally with an Epson
Perfection V39.
This plugin uses the ISCAN_FIRMWARE_DIR environment variable, not
ISCAN_FW_DIR. A quick grep didn't find any use of ISCAN_FW_DIR in the
currently packaged plugins, but a quick Google suggests that this
variable does (or at least used to) exist, so I'm not touching it.

Motivation for this change
Things done

[ ] Tested using sandboxing (nix.useSandbox on NixOS, or option sandbox in nix.conf on non-NixOS linux)
Built on platform(s)

[x] NixOS
[ ] macOS
[ ] other Linux distributions

[ ] Tested via one or more NixOS test(s) if existing and applicable for the change (look inside nixos/tests)
[ ] Tested compilation of all pkgs that depend on this change using nix-shell -p nixpkgs-review --run "nixpkgs-review wip"
[ ] Tested execution of all binary files (usually in ./result/bin/)
[ ] Determined the impact on package closure size (by running nix path-info -S before and after)
[x] Ensured that relevant documentation is up to date
[x] Fits CONTRIBUTING.md.

Consider adding yourself as maintainer to be pinged when someone changes the package. This way you can ensure your device does not regress.

Consider adding yourself as maintainer to be pinged when someone changes the package. This way you can ensure your device does not regress.

Will do. Should I add a commit to this PR, or send a separate PR?

A separate commit in this PR is fine.

Done.

Thank you!


fatalError

Getting the error: fatalError("init(coder:) has not been implemented.  This UIBarButtonItem requires a custom view in order to use.  Please use the designated initializer -init(customView:value:)")
Implemented using Interface Builder.
What does this mean: Please use the designated initializer -init(customView:value:)?

If you installed this project using pod more likely you didn't get the most recent version of it. Check in your Podfile.lock, the latest version is 3.0.1 - at least according to the tags available at this repo.
You need to specify the version explicitly:
pod 'ENMBadgedBarButtonItem', '~> 3.0.1'
I don't think this is a proper fix, however as a workaround it's ok in my opinion.

Closing this issue due to the storyboard instantiation being addressed in v3.0.0.
@GalKeidar10 The latest release, v4.0.0, should resolve this error you are seeing.  If you want to use the minimum required version to resolve the error, you can use v3.0.1.  I'd recommend using the most recent version though.


Making the get_new_coins method a bit faster

This makes the time complexity of the get_new_coins method go from O(N^2) to O(N).

Can confirm this brings the iteration time down from 0.34 to 0.22s. Nicely done mate. I'll merge this.


Using a pageName attribute for a route element does not work

This is a (multiple allowed):

[x] bug

[ ] enhancement

[ ] feature-discussion (RFC)

Framework7 Version: 2.2.1

Platform and Target: iOS and md

Live Link or JSFiddle: https://jsfiddle.net/sy3pto8e/7/

What you did
Defining a route that loads a DOM element with a give 'data-name' attribute does not work. In the case of the Fiddle, I defined a new-page as part of the HTML with the attribute data-name="new-page" and added a route to that page. For contrast, I also added a route to the same page but using the 'el' attribute to show that the page would be possible to load.
In the fiddle, I expect "New page" and "New page by element" to work the same.
Expected Behavior
Per the documentation, defining a route with pageName: 'new-page' and placing the data-name='new-page' should work.
Actual Behavior
The referenced page is not loaded

This seems to be a non-issue now that I have restructured the HTML according to this form posting.


How to get only form style with dropdowns

I need only forms style. I copied form.css and dropdown.css with their js. but dropdowns not working
In console i can see this error

Transition: Element is no longer attached to DOM. Unable to animate.  Use silent setting to surpress this warning in production

To be able to use dropdown.js you also need transition.js

I had semanticui.js connected but there was such error, I tried to connect it separately but the error is the same

Can you create a JSFiddle  to replicate the issue?


Fix issue with download behavior

File and file derivative downloads have strange behavior after upgrading to CC1.5 and Rails 5. This PR resolves those issues.

Just force pushed an update. Turns out we don't need to override anything else in download_behavior, which is good.


Update os_family for kylin in enable-repo playbook

Signed-off-by: bo.jiang<EMAIL_ADDRESS>
What type of PR is this?
/kind feature
What this PR does / why we need it:
Update os_family for kylin in enable-repo playbook
Which issue(s) this PR fixes:

Fixes #
Special notes for your reviewer:

/lgtm


Timeout issue with Milvus

Getting consistent timeout issues when testing Milvus with standard Cohere 1M vectors, 768 dimensions test
On c5a.8xlarge EC2 instance 32vCPU and 64Gb RAM and 128Gb SSD

2024-06-05 10:59:09,978 | WARNING: VectorDB optimize timeout in 900 (task_runner.py:204) (197576)
2024-06-05 10:59:10,041 | WARNING: Failed to run performance case, reason = Performance case optimize timeout (task_runner.py:146) (197576)
Traceback (most recent call last):
File "/home/ubuntu/repos/vector-dbs/env/lib/python3.12/site-packages/vectordb_bench/backend/task_runner.py", line 202, in _optimize
return future.result(timeout=self.ca.optimize_timeout)[1]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.12/concurrent/futures/_base.py", line 458, in result
raise TimeoutError()
TimeoutError
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/ubuntu/repos/vector-dbs/env/lib/python3.12/site-packages/vectordb_bench/backend/task_runner.py", line 134, in _run_perf_case
build_dur = self._optimize()
^^^^^^^^^^^^^^^^
File "/home/ubuntu/repos/vector-dbs/env/lib/python3.12/site-packages/vectordb_bench/backend/task_runner.py", line 207, in _optimize
raise PerformanceTimeoutError("Performance case optimize timeout") from e
vectordb_bench.models.PerformanceTimeoutError: Performance case optimize timeout

PR: #332 increased the optimized time limit, you can update the vdbbench to v0.0.10.

I am also getting this timeout issue with v0.0.11 trying to run Search Performance Test (1M Dataset, 768 Dims), and 10M and 100M as well. Trying to run a test using DiskANN with search_list = 100.
Bare Metal Hardware:
Server: HPE ProLiant DL360 Gen11
BIOS: 2.20
CPU: Dual Socket Intel(R) Xeon(R) Gold 6426Y
DRAM: 128 GB
Milvus Version: 2.4.4 using Docker Image Latest
VectorDB Bench Version: 0.0.11 via pip install
Python Version: 3.11
`2024-07-02 14:31:06,433 | INFO: generated uuid for the tasks: 82c1b66f8ec24aad90dffc606128e746 (interface.py:66) (349602)
2024-07-02 14:31:06,565 | INFO | DB             | CaseType     Dataset               Filter | task_label (task_runner.py:338)
2024-07-02 14:31:06,565 | INFO | -----------    | ------------ -------------------- ------- | -------    (task_runner.py:338)
2024-07-02 14:31:06,565 | INFO | Milvus         | Performance  Cohere-MEDIUM-1M        None |<PHONE_NUMBER> (task_runner.py:338)
2024-07-02 14:31:06,565 | INFO: task submitted: id=82c1b66f8ec24aad90dffc606128e746,<PHONE_NUMBER>, case number: 1 (interface.py:231) (349602)
2024-07-02 14:31:07,086 | INFO: [1/1] start case: {'label': <CaseLabel.Performance: 2>, 'dataset': {'data': {'name': 'Cohere', 'size': 1000000, 'dim': 768, 'metric_type': <MetricType.COSINE: 'COSINE'>}}, 'db': 'Milvus'}, drop_old=True (interface.py:164) (349724)
2024-07-02 14:31:07,086 | INFO: Starting run (task_runner.py:100) (349724)
2024-07-02 14:31:07,336 | INFO: Milvus client drop_old collection: VectorDBBenchCollection (milvus.py:45) (349724)
2024-07-02 14:31:07,341 | INFO: Milvus create collection: VectorDBBenchCollection (milvus.py:55) (349724)
2024-07-02 14:31:08,347 | INFO: Read the entire file into memory: test.parquet (dataset.py:207) (349724)
2024-07-02 14:31:08,379 | INFO: Read the entire file into memory: neighbors.parquet (dataset.py:207) (349724)
2024-07-02 14:31:08,412 | INFO: Start performance case (task_runner.py:158) (349724)
2024-07-02 14:31:09,199 | INFO: (SpawnProcess-1:1) Start inserting embeddings in batch 5000 (serial_runner.py:35) (349889)
2024-07-02 14:31:09,199 | INFO: Get iterator for shuffle_train.parquet (dataset.py:225) (349889)
2024-07-02 14:31:22,801 | INFO: (SpawnProcess-1:1) Loaded 100000 embeddings into VectorDB (serial_runner.py:59) (349889)
2024-07-02 14:31:45,193 | INFO: (SpawnProcess-1:1) Loaded 200000 embeddings into VectorDB (serial_runner.py:59) (349889)
2024-07-02 14:32:09,818 | INFO: (SpawnProcess-1:1) Loaded 300000 embeddings into VectorDB (serial_runner.py:59) (349889)
2024-07-02 14:32:33,988 | INFO: (SpawnProcess-1:1) Loaded 400000 embeddings into VectorDB (serial_runner.py:59) (349889)
2024-07-02 14:32:58,992 | INFO: (SpawnProcess-1:1) Loaded 500000 embeddings into VectorDB (serial_runner.py:59) (349889)
2024-07-02 14:33:21,667 | INFO: (SpawnProcess-1:1) Loaded 600000 embeddings into VectorDB (serial_runner.py:59) (349889)
2024-07-02 14:33:45,791 | INFO: (SpawnProcess-1:1) Loaded 700000 embeddings into VectorDB (serial_runner.py:59) (349889)
2024-07-02 14:34:07,282 | INFO: (SpawnProcess-1:1) Loaded 800000 embeddings into VectorDB (serial_runner.py:59) (349889)
2024-07-02 14:34:31,609 | INFO: (SpawnProcess-1:1) Loaded 900000 embeddings into VectorDB (serial_runner.py:59) (349889)
2024-07-02 14:34:55,207 | INFO: (SpawnProcess-1:1) Loaded 1000000 embeddings into VectorDB (serial_runner.py:59) (349889)
2024-07-02 14:34:55,211 | INFO: (SpawnProcess-1:1) Finish loading all dataset into VectorDB, dur=226.0110656730103 (serial_runner.py:61) (349889)
2024-07-02 14:34:58,411 | INFO: Milvus post insert before optimize (milvus.py:101) (352315)
2024-07-02 15:04:56,111 | WARNING: VectorDB optimize timeout in 1800 (task_runner.py:249) (349724)
2024-07-02 15:04:56,185 | WARNING: Failed to run performance case, reason = Performance case optimize timeout (task_runner.py:191) (349724)
Traceback (most recent call last):
File "/home/labuser/.local/lib/python3.11/site-packages/vectordb_bench/backend/task_runner.py", line 247, in _optimize
return future.result(timeout=self.ca.optimize_timeout)[1]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.11/concurrent/futures/_base.py", line 458, in result
raise TimeoutError()
TimeoutError
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/labuser/.local/lib/python3.11/site-packages/vectordb_bench/backend/task_runner.py", line 165, in _run_perf_case
build_dur = self._optimize()
^^^^^^^^^^^^^^^^
File "/home/labuser/.local/lib/python3.11/site-packages/vectordb_bench/backend/task_runner.py", line 252, in _optimize
raise PerformanceTimeoutError("Performance case optimize timeout") from e
vectordb_bench.models.PerformanceTimeoutError: Performance case optimize timeout
2024-07-02 15:04:56,190 | WARNING: [1/1] case {'label': <CaseLabel.Performance: 2>, 'dataset': {'data': {'name': 'Cohere', 'size': 1000000, 'dim': 768, 'metric_type': <MetricType.COSINE: 'COSINE'>}}, 'db': 'Milvus'} failed to run, reason=Performance case optimize timeout (interface.py:179) (349724)
2024-07-02 15:04:56,192 | INFO |Task summary: run_id=82c1b, task_label=2024070214 (models.py:329)
2024-07-02 15:04:56,192 | INFO |DB     | db_label case              label      | load_dur    qps        latency(p99)    recall        max_load_count | label (models.py:329)
2024-07-02 15:04:56,192 | INFO |------ | -------- ----------------- ---------- | ----------- ---------- --------------- ------------- -------------- | ----- (models.py:329)
2024-07-02 15:04:56,192 | INFO |Milvus |          Performance768D1M<PHONE_NUMBER> | 0.0         0.0        0.0             0.0           0              | ?     (models.py:329)
2024-07-02 15:04:56,193 | INFO: write results to disk /home/labuser/.local/lib/python3.11/site-packages/vectordb_bench/results/Milvus/result_20240702_2024070214_milvus.json (models.py:187) (349724)
2024-07-02 15:04:56,197 | INFO: Success to finish task: label=2024070214, run_id=82c1b66f8ec24aad90dffc606128e746 (interface.py:203) (349724)`

I am also getting this timeout issue with v0.0.11 trying to run Search Performance Test (1M Dataset, 768 Dims), and 10M and 100M as well. Trying to run a test using DiskANN with search_list = 100.
Bare Metal Hardware: Server: HPE ProLiant DL360 Gen11 BIOS: 2.20 CPU: Dual Socket Intel(R) Xeon(R) Gold 6426Y DRAM: 128 GB Milvus Version: 2.4.4 using Docker Image Latest VectorDB Bench Version: 0.0.11 via pip install Python Version: 3.11
2024-07-02 14:31:06,433 | INFO: generated uuid for the tasks: 82c1b66f8ec24aad90dffc606128e746 (interface.py:66) (349602)
2024-07-02 14:31:06,565 | INFO | DB             | CaseType     Dataset               Filter | task_label (task_runner.py:338)
2024-07-02 14:31:06,565 | INFO | -----------    | ------------ -------------------- ------- | -------    (task_runner.py:338)
2024-07-02 14:31:06,565 | INFO | Milvus         | Performance  Cohere-MEDIUM-1M        None |<PHONE_NUMBER> (task_runner.py:338)
2024-07-02 14:31:06,565 | INFO: task submitted: id=82c1b66f8ec24aad90dffc606128e746,<PHONE_NUMBER>, case number: 1 (interface.py:231) (349602)
2024-07-02 14:31:07,086 | INFO: [1/1] start case: {'label': <CaseLabel.Performance: 2>, 'dataset': {'data': {'name': 'Cohere', 'size': 1000000, 'dim': 768, 'metric_type': <MetricType.COSINE: 'COSINE'>}}, 'db': 'Milvus'}, drop_old=True (interface.py:164) (349724)
2024-07-02 14:31:07,086 | INFO: Starting run (task_runner.py:100) (349724)
2024-07-02 14:31:07,336 | INFO: Milvus client drop_old collection: VectorDBBenchCollection (milvus.py:45) (349724)
2024-07-02 14:31:07,341 | INFO: Milvus create collection: VectorDBBenchCollection (milvus.py:55) (349724)
2024-07-02 14:31:08,347 | INFO: Read the entire file into memory: test.parquet (dataset.py:207) (349724)
2024-07-02 14:31:08,379 | INFO: Read the entire file into memory: neighbors.parquet (dataset.py:207) (349724)
2024-07-02 14:31:08,412 | INFO: Start performance case (task_runner.py:158) (349724)
2024-07-02 14:31:09,199 | INFO: (SpawnProcess-1:1) Start inserting embeddings in batch 5000 (serial_runner.py:35) (349889)
2024-07-02 14:31:09,199 | INFO: Get iterator for shuffle_train.parquet (dataset.py:225) (349889)
2024-07-02 14:31:22,801 | INFO: (SpawnProcess-1:1) Loaded 100000 embeddings into VectorDB (serial_runner.py:59) (349889)
2024-07-02 14:31:45,193 | INFO: (SpawnProcess-1:1) Loaded 200000 embeddings into VectorDB (serial_runner.py:59) (349889)
2024-07-02 14:32:09,818 | INFO: (SpawnProcess-1:1) Loaded 300000 embeddings into VectorDB (serial_runner.py:59) (349889)
2024-07-02 14:32:33,988 | INFO: (SpawnProcess-1:1) Loaded 400000 embeddings into VectorDB (serial_runner.py:59) (349889)
2024-07-02 14:32:58,992 | INFO: (SpawnProcess-1:1) Loaded 500000 embeddings into VectorDB (serial_runner.py:59) (349889)
2024-07-02 14:33:21,667 | INFO: (SpawnProcess-1:1) Loaded 600000 embeddings into VectorDB (serial_runner.py:59) (349889)
2024-07-02 14:33:45,791 | INFO: (SpawnProcess-1:1) Loaded 700000 embeddings into VectorDB (serial_runner.py:59) (349889)
2024-07-02 14:34:07,282 | INFO: (SpawnProcess-1:1) Loaded 800000 embeddings into VectorDB (serial_runner.py:59) (349889)
2024-07-02 14:34:31,609 | INFO: (SpawnProcess-1:1) Loaded 900000 embeddings into VectorDB (serial_runner.py:59) (349889)
2024-07-02 14:34:55,207 | INFO: (SpawnProcess-1:1) Loaded 1000000 embeddings into VectorDB (serial_runner.py:59) (349889)
2024-07-02 14:34:55,211 | INFO: (SpawnProcess-1:1) Finish loading all dataset into VectorDB, dur=226.0110656730103 (serial_runner.py:61) (349889)
2024-07-02 14:34:58,411 | INFO: Milvus post insert before optimize (milvus.py:101) (352315)
2024-07-02 15:04:56,111 | WARNING: VectorDB optimize timeout in 1800 (task_runner.py:249) (349724)
2024-07-02 15:04:56,185 | WARNING: Failed to run performance case, reason = Performance case optimize timeout (task_runner.py:191) (349724)
Traceback (most recent call last):
  File "/home/labuser/.local/lib/python3.11/site-packages/vectordb_bench/backend/task_runner.py", line 247, in _optimize
    return future.result(timeout=self.ca.optimize_timeout)[1]
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.11/concurrent/futures/_base.py", line 458, in result
    raise TimeoutError()
TimeoutError

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/home/labuser/.local/lib/python3.11/site-packages/vectordb_bench/backend/task_runner.py", line 165, in _run_perf_case
    build_dur = self._optimize()
                ^^^^^^^^^^^^^^^^
  File "/home/labuser/.local/lib/python3.11/site-packages/vectordb_bench/backend/task_runner.py", line 252, in _optimize
    raise PerformanceTimeoutError("Performance case optimize timeout") from e
vectordb_bench.models.PerformanceTimeoutError: Performance case optimize timeout
2024-07-02 15:04:56,190 | WARNING: [1/1] case {'label': <CaseLabel.Performance: 2>, 'dataset': {'data': {'name': 'Cohere', 'size': 1000000, 'dim': 768, 'metric_type': <MetricType.COSINE: 'COSINE'>}}, 'db': 'Milvus'} failed to run, reason=Performance case optimize timeout (interface.py:179) (349724)
2024-07-02 15:04:56,192 | INFO |Task summary: run_id=82c1b, task_label=2024070214 (models.py:329)
2024-07-02 15:04:56,192 | INFO |DB     | db_label case              label      | load_dur    qps        latency(p99)    recall        max_load_count | label (models.py:329)
2024-07-02 15:04:56,192 | INFO |------ | -------- ----------------- ---------- | ----------- ---------- --------------- ------------- -------------- | ----- (models.py:329)
2024-07-02 15:04:56,192 | INFO |Milvus |          Performance768D1M<PHONE_NUMBER> | 0.0         0.0        0.0             0.0           0              | ?     (models.py:329)
2024-07-02 15:04:56,193 | INFO: write results to disk /home/labuser/.local/lib/python3.11/site-packages/vectordb_bench/results/Milvus/result_20240702_2024070214_milvus.json (models.py:187) (349724)
2024-07-02 15:04:56,197 | INFO: Success to finish task: label=2024070214, run_id=82c1b66f8ec24aad90dffc606128e746 (interface.py:203) (349724)

I also have noticed that I do not exhibit this behavior when switching back to v0.0.7 of vectordb-bench on the exact same hardware / OS:
2024-07-03 09:32:25,935 | INFO: generated uuid for the tasks: 16b30ded9e904e4d97e77d59a56c89a3 (interface.py:69) (725651)
2024-07-03 09:32:26,116 | INFO | DB             | CaseType     Dataset               Filter | task_label (task_runner.py:288)
2024-07-03 09:32:26,116 | INFO | -----------    | ------------ -------------------- ------- | -------    (task_runner.py:288)
2024-07-03 09:32:26,116 | INFO | Milvus         | Performance  Cohere-MEDIUM-1M        None |<PHONE_NUMBER> (task_runner.py:288)
2024-07-03 09:32:26,116 | INFO: task submitted: id=16b30ded9e904e4d97e77d59a56c89a3,<PHONE_NUMBER>, case number: 1 (interface.py:235) (725651)
2024-07-03 09:32:26,640 | INFO: [1/1] start case: {'label': <CaseLabel.Performance: 2>, 'dataset': {'data': {'name': 'Cohere', 'size': 1000000, 'dim': 768, 'metric_type': <MetricType.COSINE: 'COSINE'>}}, 'db': 'Milvus'}, drop_old=True (interface.py:167) (725751)
2024-07-03 09:32:26,896 | INFO: Milvus client drop_old collection: VectorDBBenchCollection (milvus.py:45) (725751)
2024-07-03 09:32:26,901 | INFO: Milvus create collection: VectorDBBenchCollection (milvus.py:55) (725751)
2024-07-03 09:32:28,136 | INFO: Read the entire file into memory: test.parquet (dataset.py:207) (725751)
2024-07-03 09:32:28,186 | INFO: Read the entire file into memory: neighbors.parquet (dataset.py:207) (725751)
2024-07-03 09:32:29,015 | INFO: (SpawnProcess-1:1) Start inserting embeddings in batch 5000 (serial_runner.py:35) (725918)
2024-07-03 09:32:29,016 | INFO: Get iterator for shuffle_train.parquet (dataset.py:225) (725918)
2024-07-03 09:32:43,467 | INFO: (SpawnProcess-1:1) Loaded 100000 embeddings into VectorDB (serial_runner.py:61) (725918)
2024-07-03 09:33:08,294 | INFO: (SpawnProcess-1:1) Loaded 200000 embeddings into VectorDB (serial_runner.py:61) (725918)
2024-07-03 09:33:30,597 | INFO: (SpawnProcess-1:1) Loaded 300000 embeddings into VectorDB (serial_runner.py:61) (725918)
2024-07-03 09:33:58,060 | INFO: (SpawnProcess-1:1) Loaded 400000 embeddings into VectorDB (serial_runner.py:61) (725918)
2024-07-03 09:34:23,968 | INFO: (SpawnProcess-1:1) Loaded 500000 embeddings into VectorDB (serial_runner.py:61) (725918)
2024-07-03 09:34:49,068 | INFO: (SpawnProcess-1:1) Loaded 600000 embeddings into VectorDB (serial_runner.py:61) (725918)
2024-07-03 09:35:12,599 | INFO: (SpawnProcess-1:1) Loaded 700000 embeddings into VectorDB (serial_runner.py:61) (725918)
2024-07-03 09:35:36,397 | INFO: (SpawnProcess-1:1) Loaded 800000 embeddings into VectorDB (serial_runner.py:61) (725918)
2024-07-03 09:35:58,726 | INFO: (SpawnProcess-1:1) Loaded 900000 embeddings into VectorDB (serial_runner.py:61) (725918)
2024-07-03 09:36:21,494 | INFO: Milvus post insert before optimize (milvus.py:100) (725918)

2024-07-03 10:12:29,141 | INFO: (SpawnProcess-1:1) Loaded 1000000 embeddings into VectorDB (serial_runner.py:61) (725918)
2024-07-03 10:12:29,142 | INFO: (SpawnProcess-1:1) Finish loading all dataset into VectorDB, dur=2400.126074722997 (serial_runner.py:63) (725918)
2024-07-03 10:12:30,164 | INFO: Milvus optimizing before search (milvus.py:92) (732543)
2024-07-03 10:12:34,820 | INFO: Finish loading the entire dataset into VectorDB, insert_duration=2401.1505225989968, optimize_duration=4.540166485996451 load_duration(insert + optimize) = 2405.6907 (task_runner.py:136) (725751)
2024-07-03 10:12:35,638 | INFO: SpawnProcess-1:3 start search the entire test_data to get recall and latency (serial_runner.py:173) (732670)
2024-07-03 10:12:46,387 | INFO: SpawnProcess-1:3 search entire test_data: cost=4.3488s, queries=1000, avg_recall=0.977, avg_latency=0.0043, p99=0.0054 (serial_runner.py:208) (732670)
2024-07-03 10:12:47,106 | INFO: Start search 300s in concurrency 60, filters: None (mp_runner.py:93) (725751)
2024-07-03 10:12:57,267 | INFO: Syncing all process and start concurrency search, concurrency=60 (mp_runner.py:102) (725751)
2024-07-03 10:17:57,305 | INFO: SpawnProcess-1:36 search 300s: actual_dur=300.0066s, count=28500, qps in this process: 94.9979 (mp_runner.py:73) (732938)
2024-07-03 10:17:57,308 | INFO: SpawnProcess-1:57 search 300s: actual_dur=300.0074s, count=28074, qps in this process: 93.5777 (mp_runner.py:73) (732959)
2024-07-03 10:17:57,309 | INFO: SpawnProcess-1:10 search 300s: actual_dur=300.0155s, count=28016, qps in this process: 93.3818 (mp_runner.py:73) (732912)
2024-07-03 10:17:57,310 | INFO: SpawnProcess-1:38 search 300s: actual_dur=300.008s, count=28444, qps in this process: 94.8108 (mp_runner.py:73) (732940)
2024-07-03 10:17:57,314 | INFO: SpawnProcess-1:62 search 300s: actual_dur=300.0124s, count=28597, qps in this process: 95.3194 (mp_runner.py:73) (732964)
2024-07-03 10:17:57,314 | INFO: SpawnProcess-1:15 search 300s: actual_dur=300.0097s, count=28575, qps in this process: 95.2469 (mp_runner.py:73) (732917)
2024-07-03 10:17:57,316 | INFO: SpawnProcess-1:56 search 300s: actual_dur=300.0132s, count=27957, qps in this process: 93.1859 (mp_runner.py:73) (732958)
2024-07-03 10:17:57,317 | INFO: SpawnProcess-1:45 search 300s: actual_dur=300.0049s, count=28597, qps in this process: 95.3218 (mp_runner.py:73) (732947)
2024-07-03 10:17:57,319 | INFO: SpawnProcess-1:11 search 300s: actual_dur=300.01s, count=28065, qps in this process: 93.5469 (mp_runner.py:73) (732913)
2024-07-03 10:17:57,321 | INFO: SpawnProcess-1:9 search 300s: actual_dur=300.0064s, count=28598, qps in this process: 95.3246 (mp_runner.py:73) (732911)
2024-07-03 10:17:57,324 | INFO: SpawnProcess-1:34 search 300s: actual_dur=300.0053s, count=28407, qps in this process: 94.6883 (mp_runner.py:73) (732936)
2024-07-03 10:17:57,325 | INFO: SpawnProcess-1:31 search 300s: actual_dur=300.0053s, count=28573, qps in this process: 95.2417 (mp_runner.py:73) (732933)
2024-07-03 10:17:57,324 | INFO: SpawnProcess-1:63 search 300s: actual_dur=300.007s, count=28012, qps in this process: 93.3712 (mp_runner.py:73) (732965)
2024-07-03 10:17:57,326 | INFO: SpawnProcess-1:20 search 300s: actual_dur=300.0051s, count=28461, qps in this process: 94.8684 (mp_runner.py:73) (732922)
2024-07-03 10:17:57,327 | INFO: SpawnProcess-1:52 search 300s: actual_dur=300.0047s, count=28378, qps in this process: 94.5919 (mp_runner.py:73) (732954)
2024-07-03 10:17:57,328 | INFO: SpawnProcess-1:5 search 300s: actual_dur=300.0069s, count=28629, qps in this process: 95.4278 (mp_runner.py:73) (732905)
2024-07-03 10:17:57,328 | INFO: SpawnProcess-1:19 search 300s: actual_dur=300.01s, count=27979, qps in this process: 93.2602 (mp_runner.py:73) (732921)
2024-07-03 10:17:57,329 | INFO: SpawnProcess-1:64 search 300s: actual_dur=300.0107s, count=28614, qps in this process: 95.3766 (mp_runner.py:73) (732966)
2024-07-03 10:17:57,329 | INFO: SpawnProcess-1:44 search 300s: actual_dur=300.0088s, count=28569, qps in this process: 95.2272 (mp_runner.py:73) (732946)
2024-07-03 10:17:57,329 | INFO: SpawnProcess-1:42 search 300s: actual_dur=300.0106s, count=28414, qps in this process: 94.71 (mp_runner.py:73) (732944)
2024-07-03 10:17:57,330 | INFO: SpawnProcess-1:49 search 300s: actual_dur=300.007s, count=28649, qps in this process: 95.4944 (mp_runner.py:73) (732951)
2024-07-03 10:17:57,329 | INFO: SpawnProcess-1:6 search 300s: actual_dur=300.0131s, count=28053, qps in this process: 93.5059 (mp_runner.py:73) (732908)
2024-07-03 10:17:57,329 | INFO: SpawnProcess-1:16 search 300s: actual_dur=300.0117s, count=28277, qps in this process: 94.253 (mp_runner.py:73) (732918)
2024-07-03 10:17:57,330 | INFO: SpawnProcess-1:33 search 300s: actual_dur=300.0108s, count=28267, qps in this process: 94.2199 (mp_runner.py:73) (732935)
2024-07-03 10:17:57,330 | INFO: SpawnProcess-1:48 search 300s: actual_dur=300.0069s, count=28427, qps in this process: 94.7545 (mp_runner.py:73) (732950)
2024-07-03 10:17:57,330 | INFO: SpawnProcess-1:46 search 300s: actual_dur=300.0079s, count=27985, qps in this process: 93.2809 (mp_runner.py:73) (732948)
2024-07-03 10:17:57,331 | INFO: SpawnProcess-1:30 search 300s: actual_dur=300.009s, count=28427, qps in this process: 94.7538 (mp_runner.py:73) (732932)
2024-07-03 10:17:57,331 | INFO: SpawnProcess-1:29 search 300s: actual_dur=300.005s, count=28379, qps in this process: 94.5951 (mp_runner.py:73) (732931)
2024-07-03 10:17:57,331 | INFO: SpawnProcess-1:26 search 300s: actual_dur=300.013s, count=27931, qps in this process: 93.0993 (mp_runner.py:73) (732928)
2024-07-03 10:17:57,331 | INFO: SpawnProcess-1:22 search 300s: actual_dur=300.0065s, count=28648, qps in this process: 95.4913 (mp_runner.py:73) (732924)
2024-07-03 10:17:57,332 | INFO: SpawnProcess-1:25 search 300s: actual_dur=300.0046s, count=28342, qps in this process: 94.4719 (mp_runner.py:73) (732927)
2024-07-03 10:17:57,332 | INFO: SpawnProcess-1:18 search 300s: actual_dur=300.0112s, count=28221, qps in this process: 94.0665 (mp_runner.py:73) (732920)
2024-07-03 10:17:57,332 | INFO: SpawnProcess-1:53 search 300s: actual_dur=300.0027s, count=28641, qps in this process: 95.4691 (mp_runner.py:73) (732955)
2024-07-03 10:17:57,333 | INFO: SpawnProcess-1:43 search 300s: actual_dur=300.0052s, count=28211, qps in this process: 94.035 (mp_runner.py:73) (732945)
2024-07-03 10:17:57,333 | INFO: SpawnProcess-1:7 search 300s: actual_dur=300.0079s, count=28281, qps in this process: 94.2675 (mp_runner.py:73) (732909)
2024-07-03 10:17:57,333 | INFO: SpawnProcess-1:47 search 300s: actual_dur=300.0053s, count=28149, qps in this process: 93.8283 (mp_runner.py:73) (732949)
2024-07-03 10:17:57,335 | INFO: SpawnProcess-1:23 search 300s: actual_dur=300.0107s, count=28526, qps in this process: 95.0833 (mp_runner.py:73) (732925)
2024-07-03 10:17:57,335 | INFO: SpawnProcess-1:39 search 300s: actual_dur=300.0064s, count=28483, qps in this process: 94.9413 (mp_runner.py:73) (732941)
2024-07-03 10:17:57,336 | INFO: SpawnProcess-1:60 search 300s: actual_dur=300.0048s, count=28462, qps in this process: 94.8718 (mp_runner.py:73) (732962)
2024-07-03 10:17:57,337 | INFO: SpawnProcess-1:28 search 300s: actual_dur=300.0071s, count=28591, qps in this process: 95.3011 (mp_runner.py:73) (732930)
2024-07-03 10:17:57,336 | INFO: SpawnProcess-1:14 search 300s: actual_dur=300.0039s, count=27943, qps in this process: 93.1421 (mp_runner.py:73) (732916)
2024-07-03 10:17:57,337 | INFO: SpawnProcess-1:51 search 300s: actual_dur=300.0053s, count=28490, qps in this process: 94.965 (mp_runner.py:73) (732953)
2024-07-03 10:17:57,337 | INFO: SpawnProcess-1:61 search 300s: actual_dur=300.0059s, count=28229, qps in this process: 94.0948 (mp_runner.py:73) (732963)
2024-07-03 10:17:57,338 | INFO: SpawnProcess-1:8 search 300s: actual_dur=300.0024s, count=28498, qps in this process: 94.9926 (mp_runner.py:73) (732910)
2024-07-03 10:17:57,337 | INFO: SpawnProcess-1:50 search 300s: actual_dur=300.0103s, count=28284, qps in this process: 94.2768 (mp_runner.py:73) (732952)
2024-07-03 10:17:57,337 | INFO: SpawnProcess-1:13 search 300s: actual_dur=300.0058s, count=28375, qps in this process: 94.5815 (mp_runner.py:73) (732915)
2024-07-03 10:17:57,338 | INFO: SpawnProcess-1:27 search 300s: actual_dur=300.0016s, count=28323, qps in this process: 94.4095 (mp_runner.py:73) (732929)
2024-07-03 10:17:57,339 | INFO: SpawnProcess-1:59 search 300s: actual_dur=300.0082s, count=28012, qps in this process: 93.3708 (mp_runner.py:73) (732961)
2024-07-03 10:17:57,339 | INFO: SpawnProcess-1:40 search 300s: actual_dur=300.006s, count=28612, qps in this process: 95.3714 (mp_runner.py:73) (732942)
2024-07-03 10:17:57,340 | INFO: SpawnProcess-1:21 search 300s: actual_dur=300.0056s, count=28086, qps in this process: 93.6183 (mp_runner.py:73) (732923)
2024-07-03 10:17:57,340 | INFO: SpawnProcess-1:35 search 300s: actual_dur=300.0054s, count=28016, qps in this process: 93.385 (mp_runner.py:73) (732937)
2024-07-03 10:17:57,341 | INFO: SpawnProcess-1:17 search 300s: actual_dur=300.0014s, count=28462, qps in this process: 94.8729 (mp_runner.py:73) (732919)
2024-07-03 10:17:57,341 | INFO: SpawnProcess-1:55 search 300s: actual_dur=300.0039s, count=28632, qps in this process: 95.4388 (mp_runner.py:73) (732957)
2024-07-03 10:17:57,341 | INFO: SpawnProcess-1:24 search 300s: actual_dur=300.003s, count=27957, qps in this process: 93.1891 (mp_runner.py:73) (732926)
2024-07-03 10:17:57,342 | INFO: SpawnProcess-1:12 search 300s: actual_dur=300.0051s, count=28514, qps in this process: 95.0451 (mp_runner.py:73) (732914)
2024-07-03 10:17:57,342 | INFO: SpawnProcess-1:37 search 300s: actual_dur=300.0012s, count=27994, qps in this process: 93.313 (mp_runner.py:73) (732939)
2024-07-03 10:17:57,342 | INFO: SpawnProcess-1:41 search 300s: actual_dur=300.0026s, count=28504, qps in this process: 95.0125 (mp_runner.py:73) (732943)
2024-07-03 10:17:57,343 | INFO: SpawnProcess-1:58 search 300s: actual_dur=300.0057s, count=28552, qps in this process: 95.1715 (mp_runner.py:73) (732960)
2024-07-03 10:17:57,344 | INFO: SpawnProcess-1:32 search 300s: actual_dur=300.0031s, count=28428, qps in this process: 94.759 (mp_runner.py:73) (732934)
2024-07-03 10:17:57,346 | INFO: SpawnProcess-1:54 search 300s: actual_dur=300.0052s, count=28526, qps in this process: 95.085 (mp_runner.py:73) (732956)
2024-07-03 10:17:57,348 | INFO: End search in concurrency 60: dur=300.07863932699547s, total_count=1700846, qps=5668.0009 (mp_runner.py:109) (725751)
2024-07-03 10:17:58,041 | INFO: Update largest qps with concurrency 60: current max_qps=5668.0009 (mp_runner.py:113) (725751)
2024-07-03 10:17:58,042 | INFO: Performance case got result: Metric(max_load_count=0, load_duration=2405.6907, qps=5668.0009, serial_latency_p99=0.0054, recall=0.977) (task_runner.py:150) (725751)
2024-07-03 10:17:58,042 | INFO: [1/1] finish case: {'label': <CaseLabel.Performance: 2>, 'dataset': {'data': {'name': 'Cohere', 'size': 1000000, 'dim': 768, 'metric_type': <MetricType.COSINE: 'COSINE'>}}, 'db': 'Milvus'}, result=Metric(max_load_count=0, load_duration=2405.6907, qps=5668.0009, serial_latency_p99=0.0054, recall=0.977), label=ResultLabel.NORMAL (interface.py:169) (725751)
2024-07-03 10:17:58,042 | INFO |Task summary: run_id=16b30, task_label=2024070309 (models.py:286)
2024-07-03 10:17:58,042 | INFO |DB     | db_label case              label      | load_dur     qps          latency(p99)    recall        max_load_count | label (models.py:286)
2024-07-03 10:17:58,042 | INFO |------ | -------- ----------------- ---------- | ------------ ------------ --------------- ------------- -------------- | ----- (models.py:286)
2024-07-03 10:17:58,042 | INFO |Milvus |          Performance768D1M<PHONE_NUMBER> | 2405.6907    5668.0009    0.0054          0.977         0              | :)    (models.py:286)
2024-07-03 10:17:58,042 | INFO: write results to disk /home/labuser/.local/lib/python3.11/site-packages/vectordb_bench/results/Milvus/result_20240703_2024070309_milvus.json (models.py:144) (725751)
2024-07-03 10:17:58,042 | INFO: Succes to finish task: label=2024070309, run_id=16b30ded9e904e4d97e77d59a56c89a3 (interface.py:207) (725751)

@mattbloke99 Your machine needs 2400.126074722997  seconds to load + index the dataset in 0.0.7.
We changed the process in the latest vdbbench, the index part is moved into optimize.
Since it only costs you dur=226.0110656730103s to load data into milvus, you'll probablly need 3000s timeout config for optimize.
Just change this OPTIMIZE_TIMEOUT_768D_1M    =  30 * 60   # 30min into 3000 in vectordb_bench/init.py and you'll be fine.
We'll check if the older timeout config still works for the latest milvus cases and ajust it accordingly. AFAIK, in our 16c64G tests it costs around 1500s.

Can confirm this worked with v0.0.11, thank you very much!

Can not confirm this worked with v0.0.12,  why create this question,the timeout was set 8 hours

@1191510903 8hours is a really long time. Could you please upload logs of Milvus so that I can know why this is taking so much time?
Also I'd like to ask some quick questions:

What's your Milvus version?
Distributed or standalone?
What's the cpu and memory resources for Milvus? Especially IndexNode?


tests: run softhsm-using tests in serial with each other

Ping?

@mathstuf I apologize, I thought I merged this a long time ago


(HTTP code 500) server error - {"message":"Get https://registry-1.docker.io/v2/: dial tcp: lookup registry-1.docker.io on <IP_ADDRESS>:53: read udp <IP_ADDRESS>:33821-><IP_ADDRESS>:53: i/o timeout"}

Expected behavior
Actual behavior
Information about the Issue
Steps to reproduce the behavior

...
...

Please make sure that you're on a stable connection and use a solid DNS such as Google DNS.


Functioning devcontainer

At this point the containers are still bundled with packages that should be extracted out and live in their respective containers.


feat(release): v1.0.11 release

Reason for This PR

1.0.11 release.

Description of Changes

Update deps, changelog.

License Acceptance
By submitting this pull request, I confirm that my contribution is made under
the terms of the MIT license.
PR Checklist
[Author TODO: Meet these criteria.]
[Reviewer TODO: Verify that these criteria are met. Request changes if not]

[x] All commits in this PR are signed (git commit -s).
[x] The reason for this PR is clearly provided (issue no. or explanation).
[x] The description of changes is clear and encompassing.
[x] Any required documentation changes (code and docs) are included in this PR.
[x] Any user-facing changes are mentioned in CHANGELOG.md.
[x] All added/changed functionality is tested.

Codecov Report

Merging #122 (6844cab) into master (824fc2a) will not change coverage.
The diff coverage is n/a.

@@           Coverage Diff           @@
##           master     #122   +/-   ##
=======================================
  Coverage   65.38%   65.38%
=======================================
  Files          18       18
  Lines        1043     1043
=======================================
  Hits          682      682
  Misses        268      268
  Partials       93       93

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update 824fc2a...6844cab. Read the comment docs.


Add explanatory comments of a more likely architectural approach for production.

I understand that this document provides a simplified sample to teach the concepts of using the Video Indexer API.  The section that starts right after submitting a video and starts polling could include a comment about using a webhook notification to invoke an Azure Function, rather than polling which wouldn't be the best approach at scale.  The comments could include a link to Azure Functions docs or samples that use webhooks.

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

ID: 079621d5-cf9b-3a9c-c1ed-77e29e43b2be
Version Independent ID: a8e46e38-c7e4-5f47-b306-482f2e2f30fb
Content: Use Azure Video Indexer API
Content Source: articles/cognitive-services/video-indexer/video-indexer-use-apis.md
Service: cognitive-services
GitHub Login: @Juliako
Microsoft Alias: juliako

@DarinShapiroMS Thanks for the feedback! I have assigned the issue to the content author to investigate further and update the document as appropriate.
@Juliako Hi, this is a doc-enhancement issue, can you please check it and update the document as necessary? Thanks a lot!

Webhooks are currently not supported by VI.
There are 2 options at the moment in VI – either polling the state or callback upon indexing completion to a callback URL.

#please-close

@DarinShapiroMS , thank you.


update lava

It's about time 😉

I just wanted to ask you when you have time for this - but please no stress. Thank you

Thank you! 🍒 Appreciate it a lot. And sorry for the delay - very little time for freetime coding these days 👽

No worries, just wanted to know if you are still there :wink:
Time is tight, I can well understand. :+1: Thanks again for looking in here.


ci(fmt): time examples-checker and do CI checks

Description
Checklist

[ ] I have linked to any relevant issues.
[ ] I have commented my code, particularly in hard-to-understand areas.
[ ] I have updated the documentation where relevant (API docs, the reference, and the Sway book).
[ ] I have added tests that prove my fix is effective or that my feature works.
[ ] I have added (or requested a maintainer to add) the necessary Breaking* or New Feature labels where relevant.
[ ] I have done my best to ensure that my PR adheres to the Fuel Labs Code Review Standards.
[ ] I have requested a review from the relevant team or maintainers.

This adds about ~5 minutes to install a master version of forc-fmt vs the current version of forc-fmt, runs it through the examples and prints some diagnostics for it: https://github.com/FuelLabs/sway/actions/runs/4190149757/jobs/7263258070
I don't think there's a way to auto-run this, so it seems like if we want to use this, we would have to manually go into the job to see the results :thinking:

Going to close this for now because I don't think the added CI time is really worth it for this.


multinode: Add adoption vars for osp repos

Add vars for osp 17 repos into the base multinode CI job for adoption testing. Those become shared by the multinode single-stack and multinode multi-stack (novacells) jobs.
Required-by: https://review.rdoproject.org/r/c/rdo-jobs/+/55528
Jira: #OSPRH-6548

recheck cifmw-crc-podified-edpm-baremetal

/lgtm

/approve

/retest

/test images


Wrong time shown

Checklist:

[x ] I updated to the latest version available
[ ] I cleared the cache of my browser

Release with the issue:
Browser and Operating System:
Win 11, Chrome
Description of problem:
It shows different time when it runs. Like it has ~6 seconds late in running state than if it paused

It's because of time mismatch between ha server and client. The default timer card will show same result.

OK, got it.


Expected input[32, 768, 3, 576] to have 3 channels, but got 768 channels instead.

Hi, I just set the correct paths to the annotations and to your datasets, I run the 'training.py' locally and I got the following error:

RuntimeError: Given groups=1, weight of size 32 3 3 3, expected input[32, 768, 3, 576] to have 3 channels, but got 768 channels instead

Also a warning:

UserWarning: nn.init.xavier_normal is now deprecated in favor of nn.init.xavier_normal_.

For the runtime error, you may have to transpose the images such that the dimensions are (32, 3, 768, 576).

For the runtime error, you may have to transpose the images such that the dimensions are (32, 3, 768, 576).

Hi, If I transpose the images and execute 'training.py' using net = LYTNetV2 on line 30, I find this other error: RuntimeError: Given input size: (960x9x12). Calculated output size: (960x0x1). Output size is too small

Is this issue only for LytNetV2, or does it also happen in LytNetV1?

From: Ronaldo da Silva Moura<EMAIL_ADDRESS>Sent: Monday, March 16, 2020 10:34 PM
To: samuelyu2002/ImVisible<EMAIL_ADDRESS>Cc: Yu; Samuel<EMAIL_ADDRESS>Comment<EMAIL_ADDRESS>Subject: Re: [samuelyu2002/ImVisible] Expected input[32, 768, 3, 576] to have 3 channels, but got 768 channels instead. (#2)
For the runtime error, you may have to transpose the images such that the dimensions are (32, 3, 768, 576).
Hi, If I transpose the images and execute 'training.py' using net = LYTNetV2 on line 30, I find this other error: RuntimeError: Given input size: (960x9x12). Calculated output size: (960x0x1). Output size is too small
—
You are receiving this because you commented.
Reply to this email directly, view it on GitHubhttps://apc01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Fsamuelyu2002%2FImVisible%2Fissues%2F2%23issuecomment-599570429&data=02|01|samuel01px2020%40saschina.org|8b35a311e12645fb92a408d7c9b721ee|d4c1f548d2494e72a7cb733b702e48d3|0|0|637199661321184921&sdata=vI0rYKhnyaaM3US8BWp6WNSXIels%2FtVcLAchBm8PVqA%3D&reserved=0, or unsubscribehttps://apc01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Fnotifications%2Funsubscribe-auth%2FAIMINVBE4LUGDMMP54KU65LRHY2HDANCNFSM4LD6B2FQ&data=02|01|samuel01px2020%40saschina.org|8b35a311e12645fb92a408d7c9b721ee|d4c1f548d2494e72a7cb733b702e48d3|0|0|637199661321184921&sdata=S9%2FrSAJjeXF3U63E7A%2FZlK9%2BS%2F02RyxHdn8hiKLj2I0%3D&reserved=0.

Is this issue only for LytNetV2, or does it also happen in LytNetV1?
…
________________________________ From: Ronaldo da Silva Moura<EMAIL_ADDRESS>Sent: Monday, March 16, 2020 10:34 PM To: samuelyu2002/ImVisible<EMAIL_ADDRESS>Cc: Yu; Samuel<EMAIL_ADDRESS>Comment<EMAIL_ADDRESS>Subject: Re: [samuelyu2002/ImVisible] Expected input[32, 768, 3, 576] to have 3 channels, but got 768 channels instead. (#2) For the runtime error, you may have to transpose the images such that the dimensions are (32, 3, 768, 576). Hi, If I transpose the images and execute 'training.py' using net = LYTNetV2 on line 30, I find this other error: RuntimeError: Given input size: (960x9x12). Calculated output size: (960x0x1). Output size is too small — You are receiving this because you commented. Reply to this email directly, view it on GitHubhttps://apc01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Fsamuelyu2002%2FImVisible%2Fissues%2F2%23issuecomment-599570429&data=02|01|samuel01px2020%40saschina.org|8b35a311e12645fb92a408d7c9b721ee|d4c1f548d2494e72a7cb733b702e48d3|0|0|637199661321184921&sdata=vI0rYKhnyaaM3US8BWp6WNSXIels%2FtVcLAchBm8PVqA%3D&reserved=0, or unsubscribehttps://apc01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Fnotifications%2Funsubscribe-auth%2FAIMINVBE4LUGDMMP54KU65LRHY2HDANCNFSM4LD6B2FQ&data=02|01|samuel01px2020%40saschina.org|8b35a311e12645fb92a408d7c9b721ee|d4c1f548d2494e72a7cb733b702e48d3|0|0|637199661321184921&sdata=S9%2FrSAJjeXF3U63E7A%2FZlK9%2BS%2F02RyxHdn8hiKLj2I0%3D&reserved=0.
I have this error: RuntimeError: Given input size: (960x12x9). Calculated output size: (960x1x0). Output size is too small
only for LytNETV2

Can you show me the full error message - ie. what line the error is from?

Thank you @samuelyu2002 transposing using images = images.permute(0, 2, 3, 1) does the trick, but now I'm getting another error:
RuntimeError: Given input size: (960x9x12). Calculated output size: (960x0x1). Output size is too small
But is strange that it just not run without errors, shouldn't it just work? I'm trying to train the model using your dataset, not custom images that might have diferent shapes. And changed net = LYTNetV2() for net = LYTNetV2() so it uses LYTNetV2. I'm doing something wrong? I just set the right paths to the files and call 'training.py'. Is LYTNetV2 not ready?

With net = LYTNet() I get the same errors.

I checked the code and both LytNet and LytNetV2 should be correct. I suspect the issue was with the normalization in dataset.py, which I have commented out. Maybe try redownloading all of the code and running again, I think it should work now.

Thank you @samuelyu2002
Removed my changes pulled the new code and run again.
The original error about Expected input is fixed, but I still get the second error:
RuntimeError: Given input size: (960x9x12). Calculated output size: (960x0x1). Output size is too small

Is that error caused by the AvgPool2d on line 153? Maybe try using nn.AdaptiveAvgPool2d(1) instead. Or, try changing the nn.AvgPool2d(12,9) into nn.AvgPool2d(9,12).

Is that error caused by the AvgPool2d on line 153? Maybe try using nn.AdaptiveAvgPool2d(1) instead. Or, try changing the nn.AvgPool2d(12,9) into nn.AvgPool2d(9,12).

Using "nn.AvgPool2d (9,12)" fixed the error for me. Thank you!

Yes, nn.AvgPool2d (9,12) fixes the error.
Now there is just a warning about a deprecation:
UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.
But it apparently works, I say apparently because after around 50 iterations on the first epoch the process dies without any error: [1]    15411 killed     Model/training.py
I'm using Ubuntu and python 3 to run it, I'll try to debug what it is, might be something about my local environment, I don't know.
Thank you very much for your help @samuelyu2002

Apparently the process is slowly eating all my resources filling the 16GB of RAM and the 16GB of Swap until the kernel kills it. I never seen a neural network using so many resources, so there might be something wrong with the code.

The code does use a lot of RAM - maybe it is because the lists from line 41 to 51 in training.py are storing too many torch tensors. Try changing all of the values stored from torch tensors to normal floats - eg. on line 107 append running_loss_MSE.item() instead of running_loss_MSE.

I don't think so, the ram increments on each step inside the epoch, and you append that data after every epoch, I run out of RAM and Swap before anything is stored on those arrays.
Until the moment I was trying it in the laptop and now I tried in my desktop and I have the same problem with the RAM. The thing is that in the desktop I did not have to change the nn.AvgPool2d (9,12), it just worked, which is weird.
Anyway, I think the problem with the RAM might be related to the fact that I'm using CPU not GPU to train, because I don't have any GPU with CUDA. It should use more RAM, but not as much as it does.

Seem to be a similar problem to this one: https://discuss.pytorch.org/t/cpu-ram-usage-increasing-for-every-epoch/24475
But there is no solution there.
Also from here: https://github.com/pytorch/pytorch/issues/27971 I tried to use LRU_CACHE_CAPACITY=1 and from here: https://github.com/pytorch/pytorch/issues/13246 I tried to use num_workers = 0 and none of them worked.

Try changing all of the places with += loss into += loss.item(). For example, change lines 97-99 into:
running_loss += loss.item()
running_loss_MSE += MSE.item()
running_loss_cross_entropy += cross_entropy.item()
Also, change lines 165-167 into:
val_running_loss += loss.item()
val_mse_loss += MSE.item()
val_ce_loss += cross_entropy.item()
The problem might be because the computational graphs were also being stored, taking up large amounts of RAM.

Yes, that was it, now it works fine.
Thank you very much @samuelyu2002 !

I see that you committed the changes, thank you!


Support APIKEY

Proposed changes
This PR adds apikey as IDM type.
Types of changes
What types of changes does your code introduce to the project: Put an x in the boxes that apply

[ ] Bugfix (non-breaking change which fixes an issue)
[X] New feature (non-breaking change which adds functionality)
[ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
[ ] Update only documentation, not any source code.

Checklist
Put an x in the boxes that apply. You can also fill these out after creating the PR. If you're unsure about any of
them, don't hesitate to ask. We're here to help! This is simply a reminder of what we are going to look for before
merging your code.

[X] I have read the CONTRIBUTING doc
[X] I have signed the CLA
[X] I have added tests that prove my fix is effective or that my feature works
[X] I have added necessary documentation (if appropriate)
[ ] Any dependent changes have been merged and published in downstream modules

Further comments
N/A

Coverage remained the same at 100.0% when pulling 540dea8fb156dd1fcda484a0a7072838f3a6af66 on fisuda:task/support_apikey into 334f022e62402e097d6b7636e52b71e8f86e8de0 on lets-fiware:main.


Update fs2-reactive-streams to 3.9.1

About this PR
📦 Updates co.fs2:fs2-reactive-streams from 3.7.0 to 3.9.1
📜 GitHub Release Notes - Version Diff
Usage
✅ Please merge!
I'll automatically update this PR to resolve conflicts as long as you don't change it yourself.
If you'd like to skip this version, you can just close this PR. If you have any feedback, just mention me in the comments below.
Configure Scala Steward for your repository with a .scala-steward.conf file.
Have a fantastic day writing Scala!

⚙ Adjust future updates
Add this to your .scala-steward.conf file to ignore future updates of this dependency:
updates.ignore = [ { groupId = "co.fs2", artifactId = "fs2-reactive-streams" } ]

Or, add this to slow down future updates of this dependency:
dependencyOverrides = [{
  pullRequests = { frequency = "30 days" },
  dependency = { groupId = "co.fs2", artifactId = "fs2-reactive-streams" }
}]

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

Superseded by #1235.


Bad Request error

Steps to reproduce:

git clone https://github.com/passport/todos-express-email.git
cd todos-express-email
npm install
vim .env
Added this line:

SENDGRID_API_KEY=<API key copied from https://app.sendgrid.com/settings/api_keys>

npm start
Navigated to http://localhost:3000/login and entered my email address
Clicked "Sign in with email" button

Got this error page:
Bad Request
Error: Bad Request
    at C:\path\to\todos-express-email\node_modules\@sendgrid\client\src\classes\client.js:146:29
    at processTicksAndRejections (node:internal/process/task_queues:96:5)

Same error. @jaredhanson

same error

yeah same error... . It should be fixed or removed. It's a bad look for passport.js

Pretty much same. THe line causing the error in sendgrid is different.
" Error: Bad Request
at C:\Users\James\Desktop\New folder\todos-express-email\node_modules@sendgrid\client\src\classes\client.js:167:29 "
--I the error is from @sendgrid/mail's newest version? I got the message:
node_modules/axios
@sendgrid/client  7.0.0 - 7.7.0
Depends on vulnerable versions of axios
node_modules/@sendgrid/client
@sendgrid/mail  7.0.0 - 7.7.0
Depends on vulnerable versions of @sendgrid/client
node_modules/@sendgrid/mail
--so I ran:
npm audit fix --force
-- which gave:
npm WARN audit Updating @sendgrid/mail to 8.1.3, which is a SemVer major change.


Add new_with to Value

Hi, Just wrote this for my own convenience. Submitting in case you find it useful.

I'd rather these bindings were redesigned in a safe manner.

Hi @pablocouto. Unfortunately, we can't merge this because it isn't basically in GTK and doesn't bring something gtk-rs would really need (I mean, you can absolutely do this in your code). I'll close this PR.
Don't hesitate to come back if you want to help and contribute! :)


T265 and D455 not enumerating after USB power reset

Required Info

Camera Model
T265, D455

Firmware Version
T265: <IP_ADDRESS>1, D455: <IP_ADDRESS>

Operating System & Version
Ubuntu 18

Kernel Version (Linux Only)
Jetpack 4.5 (L4T 32.5.1)

Platform
NVIDIA Jetson Xavier NX

SDK Version
2.43.0

ROS Version
ROS Melodic, realsense-ros 2.2.20

Issue Description
We are trying to simultaneously use the T265 camera for odometry and the D455 for depth / point cloud measurements. We have the problem described in #4681, where the T265 does not enumerate properly during system boot. The suggested solution was to reset the root USB hub power using uhubctl, which we are doing before we launch any of our ROS nodes. Previously we were using the D435 camera, which worked fine and was not affected by this workaround.
The problem occurred now when we wanted to upgrade to the D455 camera. It behaves exactly opposite of the T265: it enumerates fine during system boot, but fails to re-enumerate after the USB power reset.
After initial system boot the D455 works, the T265 does not:
$ rs-enumerate-devices -s
 20/08 08:20:47,399 ERROR [548517545024] (tm-boot.h:39) Error booting T265
 20/08 08:20:50,451 ERROR [548517545024] (tm-boot.h:39) Error booting T265
Device Name                   Serial Number       Firmware Version
Intel RealSense D455<PHONE_NUMBER>11        <IP_ADDRESS>

$ sudo uhubctl
Current status for hub 2-3 [0bda:0411 Generic 4-Port USB 3.1 Hub, USB 3.10, 4 ports, ppps]
  Port 1: 02a0 power 5gbps Rx.Detect
  Port 2: 02a0 power 5gbps Rx.Detect
  Port 3: 0203 power 5gbps U0 enable connect [8086:0b5c Intel(R) RealSense(TM) Depth Camera 455 Intel(R) RealSense(TM) Depth Camera<PHONE_NUMBER>52589]
  Port 4: 02a0 power 5gbps Rx.Detect
Current status for hub 1-2 [0bda:5411 Generic 4-Port USB 2.1 Hub, USB 2.10, 4 ports, ppps]
  Port 1: 0100 power
  Port 2: 0503 power highspeed enable connect [03e7:2150 Movidius Ltd. Movidius MA2X5X 03e72150]
  Port 3: 0100 power
  Port 4: 0100 power

To fix the T265, we perform the USB power reset:
$ sudo uhubctl -l 1-2 -a cycle -d 5
Current status for hub 2-3 [0bda:0411 Generic 4-Port USB 3.1 Hub, USB 3.10, 4 ports, ppps]
  Port 1: 02a0 power 5gbps Rx.Detect
  Port 2: 02a0 power 5gbps Rx.Detect
  Port 3: 0203 power 5gbps U0 enable connect [8086:0b5c Intel(R) RealSense(TM) Depth Camera 455 Intel(R) RealSense(TM) Depth Camera<PHONE_NUMBER>52589]
  Port 4: 02a0 power 5gbps Rx.Detect
Sent power off request
New status for hub 2-3 [0bda:0411 Generic 4-Port USB 3.1 Hub, USB 3.10, 4 ports, ppps]
  Port 1: 00a0 off
  Port 2: 00a0 off
  Port 3: 00a0 off
  Port 4: 00a0 off
Current status for hub 1-2 [0bda:5411 Generic 4-Port USB 2.1 Hub, USB 2.10, 4 ports, ppps]
  Port 1: 0100 power
  Port 2: 0503 power highspeed enable connect [03e7:2150 Movidius Ltd. Movidius MA2X5X 03e72150]
  Port 3: 0100 power
  Port 4: 0100 power
Sent power off request
New status for hub 1-2 [0bda:5411 Generic 4-Port USB 2.1 Hub, USB 2.10, 4 ports, ppps]
  Port 1: 0000 off
  Port 2: 0000 off
  Port 3: 0008 off
  Port 4: 0008 off
Current status for hub 2-3 [0bda:0411 Generic 4-Port USB 3.1 Hub, USB 3.10, 4 ports, ppps]
  Port 1: 00a8 off
  Port 2: 00a8 off
  Port 3: 00a8 off
  Port 4: 00a8 off
Sent power on request

dmesg output during reset:
[  121.693642] usb 1-2.2: USB disconnect, device number 4
[  121.815907] usb 1-2: reset high-speed USB device number 2 using tegra-xusb
[  121.991395] usb 1-2: usb_suspend_both: status 0
[  122.503385] usb 1-2.2: new high-speed USB device number 5 using tegra-xusb
[  122.524523] usb 1-2.2: New USB device found, idVendor=03e7, idProduct=2150
[  122.524542] usb 1-2.2: New USB device strings: Mfr=1, Product=2, SerialNumber=3
[  122.524551] usb 1-2.2: Product: Movidius MA2X5X
[  122.524557] usb 1-2.2: Manufacturer: Movidius Ltd.
[  122.524563] usb 1-2.2: SerialNumber: 03e72150

At this point the T265 enumerates, but the D455 does not enumerate:
$ rs-enumerate-devices -s
Could not create device - set_xu(ctrl=1) failed! Last Error: Input/output error . Check SDK logs for details
Device Name                   Serial Number       Firmware Version
Intel RealSense T265<PHONE_NUMBER>06        <IP_ADDRESS>1

dmesg during rs-enumerate-devices:
[  131.684303] usb 1-2.2: USB disconnect, device number 5
[  131.875371] usb 1-2: usb_suspend_both: status 0
[  131.891812] usb 2-3.2: new SuperSpeed USB device number 4 using tegra-xusb
[  131.913175] usb 2-3.2: New USB device found, idVendor=8087, idProduct=0b37
[  131.913223] usb 2-3.2: New USB device strings: Mfr=1, Product=2, SerialNumber=3
[  131.913232] usb 2-3.2: Product: Intel(R) RealSense(TM) Tracking Camera T265
[  131.913238] usb 2-3.2: Manufacturer: Intel(R) Corporation
[  131.913291] usb 2-3.2: SerialNumber:<PHONE_NUMBER>06
[  133.479181] uvcvideo: Failed to query (SET_CUR) UVC control 1 on unit 3: -71 (exp. 1024).
[  133.530607] uvcvideo: Failed to query (SET_CUR) UVC control 1 on unit 3: -71 (exp. 1024).
[  133.582013] uvcvideo: Failed to query (SET_CUR) UVC control 1 on unit 3: -71 (exp. 1024).
# Many more errors like this...
[  138.489756] uvcvideo: Failed to query (SET_CUR) UVC control 1 on unit 3: -71 (exp. 1024).
[  138.541441] uvcvideo: Failed to query (SET_CUR) UVC control 1 on unit 3: -71 (exp. 1024).
[  138.593152] uvcvideo: Failed to query (SET_CUR) UVC control 1 on unit 3: -71 (exp. 1024).
# The errors stop when rs-enumerate-devices exits

This error has been reported in the past (#5302, #7060), however I wasn't able to find any definite solution to it.
If I perform another USB reset as an attempt to fix the D455, both cameras no longer enumerate:
$ rs-enumerate-devices -s
Could not create device - set_xu(ctrl=1) failed! Last Error: Input/output error . Check SDK logs for details
Could not create device - Unable to communicate with device . Check SDK logs for details
Device Name                   Serial Number       Firmware Version
 20/08 08:49:34,671 ERROR [548268663872] (tm-device.cpp:2038) Bulk request error RS2_USB_STATUS_IO

At this point the only way to fix it is to manually re-connect both cameras. In this case they both enumerate correctly. If I power the system off and on, it goes back to the state where the D455 works and T265 does not enumerate.

I think I found the issue. It seems that our root USB hub was somehow ending up in an inconsistent state after the power cycle, which interfered with the cameras and caused them not to enumerate. The solution was to use uhubctl with the argument -R - reset hub after each power-on action, causing all devices to reassociate.
sudo uhubctl -l 1-2 -a cycle -d 5 -R

can not work on jetson-xavier :-(
after restart linux system, can not find realsense t265

This command worked for me on a Raspberry Pi 4 using a single T265. The issue was that the T265 would not be able to boot after a system reboot.

sudo uhubctl -l 1-1 -a cycle -d 5 -R

After that, booting the T265 works. However, I must only run this command once after the system reboot, and NOT before every T265 launch. Otherwise the T265 won't boot anymore.


Integration tests should use <IP_ADDRESS> consistently

Including the local etcd (for hack/local-up-cluster.sh)

+cc @thockin (unsure to whom I should assign this)

Actually this is small enough I'm comfortable reviewing.

Originally (way back) everything in the test environment bound to <IP_ADDRESS>.  When we switched to etcd 2.0, the behavior of the "-addr" flag changed - originally -addr <IP_ADDRESS> meant bind and expose on <IP_ADDRESS>, but after 2.0 it now means "advertise to clients on <IP_ADDRESS>, but listen on the first public address".  However, that doesn't work (because the first public address isn't going to be the loopback address).  So we needed to use -bind-addr instead of -addr to preserve the old behavior, and the old behavior used <IP_ADDRESS> everywhere.

but shy s/localhost/<IP_ADDRESS>/ all over?
On Mon, Feb 9, 2015 at 1:45 PM, Clayton Coleman<EMAIL_ADDRESS>wrote:

Originally (way back) everything in the test environment bound to
<IP_ADDRESS>. When we switched to etcd 2.0, the behavior of the "-addr" flag
changed - originally -addr <IP_ADDRESS> meant bind and expose on <IP_ADDRESS>,
but after 2.0 it now means "advertise to clients on <IP_ADDRESS>, but listen
on the first public address". However, that doesn't work (because the first
public address isn't going to be the loopback address). So we needed to use
-bind-addr instead of -addr to preserve the old behavior, and the old
behavior used <IP_ADDRESS> everywhere.
--
Reply to this email directly or view it on GitHub
https://github.com/GoogleCloudPlatform/kubernetes/pull/4254#issuecomment-73596858
.

Yeah - for instance in integration "localhost" is the hostname (which has to resolve, because NodeController at one point didn't allow IPs)
----- Original Message -----

but shy s/localhost/<IP_ADDRESS>/ all over?
On Mon, Feb 9, 2015 at 1:45 PM, Clayton Coleman<EMAIL_ADDRESS>wrote:

Originally (way back) everything in the test environment bound to
<IP_ADDRESS>. When we switched to etcd 2.0, the behavior of the "-addr" flag
changed - originally -addr <IP_ADDRESS> meant bind and expose on <IP_ADDRESS>,
but after 2.0 it now means "advertise to clients on <IP_ADDRESS>, but listen
on the first public address". However, that doesn't work (because the first
public address isn't going to be the loopback address). So we needed to use
-bind-addr instead of -addr to preserve the old behavior, and the old
behavior used <IP_ADDRESS> everywhere.
--
Reply to this email directly or view it on GitHub
https://github.com/GoogleCloudPlatform/kubernetes/pull/4254#issuecomment-73596858
.

Reply to this email directly or view it on GitHub:
https://github.com/GoogleCloudPlatform/kubernetes/pull/4254#issuecomment-73597995

Wait, are you saying 'localhost' does not resolve?  I'd just call that
"broken"
On Mon, Feb 9, 2015 at 2:00 PM, Clayton Coleman<EMAIL_ADDRESS>wrote:

Yeah - for instance in integration "localhost" is the hostname (which has
to resolve, because NodeController at one point didn't allow IPs)
----- Original Message -----

but shy s/localhost/<IP_ADDRESS>/ all over?
On Mon, Feb 9, 2015 at 1:45 PM, Clayton Coleman <
<EMAIL_ADDRESS>wrote:

Originally (way back) everything in the test environment bound to
<IP_ADDRESS>. When we switched to etcd 2.0, the behavior of the "-addr"
flag
changed - originally -addr <IP_ADDRESS> meant bind and expose on
<IP_ADDRESS>,
but after 2.0 it now means "advertise to clients on <IP_ADDRESS>, but
listen
on the first public address". However, that doesn't work (because the
first
public address isn't going to be the loopback address). So we needed
to use
-bind-addr instead of -addr to preserve the old behavior, and the old
behavior used <IP_ADDRESS> everywhere.
--
Reply to this email directly or view it on GitHub
<
https://github.com/GoogleCloudPlatform/kubernetes/pull/4254#issuecomment-73596858

.

Reply to this email directly or view it on GitHub:

https://github.com/GoogleCloudPlatform/kubernetes/pull/4254#issuecomment-73597995
--
Reply to this email directly or view it on GitHub
https://github.com/GoogleCloudPlatform/kubernetes/pull/4254#issuecomment-73599472
.

I mean I didn't s/localhost/<IP_ADDRESS>/ specifically because of that in cmd/integration.go (where one node name is <IP_ADDRESS>, and the other node name is localhost), and there are probably other areas like that.
----- Original Message -----

Wait, are you saying 'localhost' does not resolve?  I'd just call that
"broken"
On Mon, Feb 9, 2015 at 2:00 PM, Clayton Coleman<EMAIL_ADDRESS>wrote:

Yeah - for instance in integration "localhost" is the hostname (which has
to resolve, because NodeController at one point didn't allow IPs)
----- Original Message -----

but shy s/localhost/<IP_ADDRESS>/ all over?
On Mon, Feb 9, 2015 at 1:45 PM, Clayton Coleman <
<EMAIL_ADDRESS>wrote:

Originally (way back) everything in the test environment bound to
<IP_ADDRESS>. When we switched to etcd 2.0, the behavior of the "-addr"
flag
changed - originally -addr <IP_ADDRESS> meant bind and expose on
<IP_ADDRESS>,
but after 2.0 it now means "advertise to clients on <IP_ADDRESS>, but
listen
on the first public address". However, that doesn't work (because the
first
public address isn't going to be the loopback address). So we needed
to use
-bind-addr instead of -addr to preserve the old behavior, and the old
behavior used <IP_ADDRESS> everywhere.
--
Reply to this email directly or view it on GitHub
<
https://github.com/GoogleCloudPlatform/kubernetes/pull/4254#issuecomment-73596858

.

Reply to this email directly or view it on GitHub:

https://github.com/GoogleCloudPlatform/kubernetes/pull/4254#issuecomment-73597995
--
Reply to this email directly or view it on GitHub
https://github.com/GoogleCloudPlatform/kubernetes/pull/4254#issuecomment-73599472
.

Reply to this email directly or view it on GitHub:
https://github.com/GoogleCloudPlatform/kubernetes/pull/4254#issuecomment-73599950

I'm confused.  Let me start over :)
Why did you change "localhost" to <IP_ADDRESS> in places that SEEM to have no effect?
What is consistency or something more concrete

Comments inline

I'll keep reading but Tim seems to have a handle on the right questions to ask, so reassigning.

There's nothing in here that is contentious, just curious.
On Mon, Feb 9, 2015 at 2:29 PM, Maxwell Forbes<EMAIL_ADDRESS>wrote:

I'll keep reading but Tim seems to have a handle on the right questions to
ask, so reassigning.
--
Reply to this email directly or view it on GitHub
https://github.com/GoogleCloudPlatform/kubernetes/pull/4254#issuecomment-73604486
.


pjsip fails to compile with "undefined reference to `__dso_handle"

Issue description
account.cpp:(.text+0x8c76): undefined reference to `__dso_handle'
/nix/store/z470j6lybdsy4ql972k392490bprhd2g-binutils-2.28.1/bin/ld: ../bin/pjsua2-test-x86_64-unknown-linux-gnu: hidden symbol `__dso_handle' isn't defined
/nix/store/z470j6lybdsy4ql972k392490bprhd2g-binutils-2.28.1/bin/ld: final link failed: Bad value
make[2]: *** [/tmp/nix-build-pjsip-2.6.drv-0/pjproject-2.6/build/rules.mak:125: ../bin/pjsua2-test-x86_64-unknown-linux-gnu] Error 1
make[2]: Leaving directory '/tmp/nix-build-pjsip-2.6.drv-0/pjproject-2.6/pjsip/build'
make[1]: *** [Makefile:280: pjsua2-test-x86_64-unknown-linux-gnu] Error 2
make[1]: Leaving directory '/tmp/nix-build-pjsip-2.6.drv-0/pjproject-2.6/pjsip/build'
make: *** [Makefile:14: all] Error 1
builder for ‘/nix/store/7xsljaz2vw5ygzf60ixkjhb0wh6hmhjr-pjsip-2.6.drv’ failed with exit code 2
e

Cc: @viric
Steps to reproduce

check out unstable
nix-build -A pjsip $(pwd)

Fails with the same error for version 2.7 (not currently in nixpkgs).
Technical details

NixOS: 18.03.git.9205014c52M (Impala)
Nix: nix-env (Nix) 1.11.15
nixpkgs: 18.03pre-git
Sandboxing enabled:  true

Works with 2.8