GridField and GridState_component bug

Hi!
I'm not sure if it's intentional but if you construct GridField through injector like in example bellow:
GridField::create('MyRelation')
	->setTitle('My Title')
	->setList($this->MyRelation())
	->setConfig( GridFieldConfig_Base::create() )

then GridfieldState_Component is not set on GridField config. GridState_Component is added to current GridFieldConfig in GridField constructor, but if we redefine GridFieldConfig with GridField::setConfig, GridState_Component is never set. That can cause JavaScript error while using GridFieldSortableRows and probably others.
Shouldn't you merge these lines
public function __construct($name, $title = null, SS_List $dataList = null, GridFieldConfig $config = null) {
	...
	$this->setConfig($config ?: GridFieldConfig_Base::create());

	$this->config->addComponent(new GridState_Component());
	$this->state = new GridState($this);
	...
}

and put it all in GridField::setConfig method?

Hey @t3hn0 - which version of SilverStripe are you running? Looks like 3.x, just want to confirm.

Hey @robbieaverill it was version 3.5.
It's not only GridFieldConfig_Base that's affected but as you guessed, every config which is set with GridField::setConfig is missing GridState_Component.
Suggested update should be ok.

Hey @t3hn0 - cool. Would you be willing to submit a patch for this?

👍 the original behaviour (assign state on constructor) is flawed, and as this issue demonstrates, isn't reliable.
I would also agree with moving to setConfig(), and having the constructor call this. Maybe have this code check to see if one such config already exists, and add it only conditionally.

@robbieaverill ok this is my first try, so please don't kill me if I got it wrong :)
I created pull request #6896


[JN-1151] adding easy demo refresh button

DESCRIPTION (include screenshots, and mobile screenshots for participant UX)
adds an easy "Refresh demo" button to the populate home page

This also makes some UX tweaks to the populate portal page -- hiding the overwrite text in an infopopup and reducing oversized button.  (originally I added the reset demo button to that page, but realized it would be easier to find on the default page.)

TO TEST:  (simple manual steps for confirming core behavior -- used for pre-release checks)

go to https://localhost:3000/populate
press the button
confirm demo repopulates

good call on adding it to the menu.

I couldn't reproduce the double-click on the confirm issue.  Agree we don't need to solve that yet...


[Vssue]Latest Threads

http://localhost:8080/

Hi near

This seems to be great library for developers, and daily issues faced by newcomers. Great work by team.


Inconsistent displacement in domain output when using the branch dmay/seas-checkpoint-greenfunc

Hi, there is an error in the domain output using the branch dmay/seas-checkpoint-greenfunc. The displacement u0 remains constant over time as Vpl, instead of Vpl*t, before any coseismic event.  While the calculation of fault slip rate and stresses are correct, compared to the main branch.

Can you give me more details about how I can reproduce the issue? (I even don't get a u0 output, only a u)

i put the setup here: /import/freenas-m-05-seissol/dli/Shared/inplane

I've tested the setup with green functions calculated, and with green functions loaded, and u0 and u1 vary over time as expected.


ImportError: libcudart.so.10.0: cannot open shared object file.

I am trying to run the code on this repository
Running ./eval.sh I get this error:

Configuration I am using:
torch.__version__ -> 0.4.1
torch.version.cuda -> 9.0.176
nvcc --version -> 9.2
conda list

I am able to locate libcudart.so.9.2 :

I don't see why it's trying to locate libcudart.so.10.0!

Hey i'm seeing the same bug, have you fix the issue?

Haven't fixed the issue but it's mostly related to the version of CUDA and corresponding version of pytorch. You could check which versions work well together. Maybe that'd fix issue.
Which OS do you use btw?
Sent from Yahoo Mail on Android
On Thu, Dec 26, 2019 at 6:52 AM<EMAIL_ADDRESS>wrote:
Hey i'm seeing the same bug, have you fix the issue?
—
You are receiving this because you authored the thread.
Reply to this email directly, view it on GitHub, or unsubscribe.


godep: error downloading dep (github.com/dwarvesf/glod): exit status 128

Seems we can restore the depencies :
$ godep restore
# cd $GOPATH/src/github.com/dwarvesf/glod; git checkout 391741190a8452537a65b3fca8b38370e5b33e9b
fatal: reference is not a tree: 391741190a8452537a65b3fca8b38370e5b33e9b
godep: error downloading dep (github.com/dwarvesf/glod): exit status 128
godep: Error downloading some deps. Aborting restore and check.

Hi Bencyril,
Give it a try
go get -u github.com/dwarvesf/glod
godep update github.com/dwarvesf/glod
godep restore

$ godep update github.com/dwarvesf/glod
godep: not in manifest: github.com/dwarvesf/glod
godep: Package (github.com/stretchr/testify/assert) not found

Seems I forgot something when I wanted to install.
go install github.com/dwarvesf/glod-cli
Instead I git cloned the repo, try to godep the depencies, but didn't works.
But now, it works exept when they're no arguments :
`$ glod-cli
panic: runtime error: index out of range
goroutine 1 [running]:
panic(0x42ae40, 0xc82000a0b0)
/Users/ben/.gvm/gos/go1.6/src/runtime/panic.go:464 +0x3e6
main.main.func1(0xc820088780)
/Users/ben/go/src/github.com/dwarvesf/glod-cli/main.go:42 +0xa8
github.com/codegangsta/cli.(*App).Run(0xc820088640, 0xc82000a260, 0x1, 0x1, 0x0, 0x0)
/Users/ben/go/src/github.com/codegangsta/cli/app.go:192 +0x117d
main.main()
/Users/ben/go/src/github.com/dwarvesf/glod-cli/main.go:45 +0x28a`

Yes, I have fixed this bug a few hours ago. It shows usage instead panic. Maybe next submission.
Thank you.


Support more HTTP methods.

Now we only got partial supports for 6 http methods, completion and improvement should be done later.

Support for HEAD method has been added in #6

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spotdl sync with m3u result in termination of updating synced path

System OS
Linux
Python Version
3.10 (CPython)
Install Source
Arch User Repository (Unofficial)
Install version / commit hash
v4.0.2
Expected Behavior vs Actual Behavior
sync is not carried out if .m3u file exist in paths to sync and possibly no file is updated removing m3u file resulted in successful updating of files in path
Steps to reproduce - Ensure to include actual links!
spotdl sync
Traceback
│ /usr/lib/python3.10/site-packages/spotdl/console/entry_point.py:19 │
│ 8 in entry_point                                                   │
│                                                                    │
│   197 │   │                                                        │
│ ❱ 198 │   │   OPERATIONS[arguments.operation](                     │
│   199 │   │   │   query=arguments.query,                           │
│                                                                    │
│ /usr/lib/python3.10/site-packages/spotdl/console/sync.py:140 in    │
│ sync                                                               │
│                                                                    │
│   139 │   │   if m3u_file:                                         │
│ ❱ 140 │   │   │   gen_m3u_files(                                   │
│   141 │   │   │   │   query,                                       │
│                                                                    │
│ /usr/lib/python3.10/site-packages/spotdl/utils/m3u.py:65 in        │
│ gen_m3u_files                                                      │
│                                                                    │
│    64 │   if (                                                     │
│ ❱  65 │   │   file_name.endswith("/")                              │
│    66 │   │   or file_name.endswith(r"\\")                         │
╰────────────────────────────────────────────────────────────────────╯
AttributeError: 'bool' object has no attribute 'endswith'

Other details
No response

what's the name of the m3u file that you want to use? it shouldn't be identified as bool

I have multiple playlist so dont know if I should include them in the config files at $HOME/.spotdl or in the another path maybe in the download path.
Changed the bool in the config file in $HOME/.spotdl.config to null but it is still throwing errors

Apparently changing the option for m3u under the config files removed all the error, however the m3u I included for the initial command was not updated for the general command below
spotdl sync "spotify playlist link" --output  --m3u  --save-file <path to save spotdl file, same as saved playlist path>
However, running spotdl sync <playlsit.spotdl> did not update the .m3u file while the playlist songs was updated (removal and addition of new songs)
so what should be included in the config for the m3u options (checked the official documentation no discussion about it there maybe I am missing it)
that is what are the options for m3u: null aside from null

M3U file will not be updated by default. You have to include it in you command if you want to update the m3u file.

Something like this ?
spotdl sync <playlist.spotdl> --m3u

Something like this ?
spotdl sync <playlist.spotdl> --m3u

Yeah this should work. Check the help message on how to use the m3u option by default it only creates only one file with the first playlist's name

I use spotdl to download multiple playlist
The below command can update all the files (delete and add new files) however the .m3u files is not updated tried different combination eve using --overwrite arguments also
sptdl sync playlist.spotdl --m3u --output

Check if you are on the latest version, there was some bugs related to m3u files

updating to the latest version fixed the issue thank you for your time and keep up the good efforts. I am closing the thread


Request for framemd5 option

Hi I’m just running through some workflows for exporting RAWcooked files to LTO and have decided to make framemd5 checksum manifests as part of long term storage sidecar files (and in line with vrecord capture and IFI copyit generated manifests).
I wonder if this could be an easy function to incorporate into the RAWcooking process?
Many thanks.

Just to clarify, copyit just uses whole file md5, not framemd5s.. we stopped using framemd5 with rawcooked due to whole file manifests being more useful due to rawcooked bring reversible.

Oops, my mistake! Apologies

Reversing my request, on the basis of RAWcooked being reversibly :)


feat: override dynamodb config

Description
This PR add the possibility to override dynamoDB configuration via storage_options:

AWS_ENDPOINT_URL_DYNAMODB overrides AWS_ENDPOINT_URL,
AWS_REGION_DYNAMODB overrides AWS_REGION,
AWS_ACCESS_KEY_ID_DYNAMODB overrides AWS_ACCESS_KEY_ID,
AWS_SECRET_ACCESS_KEY_DYNAMODB overrides AWS_SECRET_ACCESS_KEY.

It solves cases where we use an S3-compatible storage without mutual exclusion but we want to use DynamoDB anyway as a locking provider.
It also solves the case where we want to pass different credentials or region for S3 and dynamoDB.
Related Issue(s)

closes #2497
closes #2287

Documentation
Documentation has been updated to explain how to override configuration.

Issue with DCO, I'll re-open a PR ASAP.


Minor improve to assertion

improve error message, remove unnecessary check

Thanks for contributing to TVM!   Please refer to guideline https://docs.tvm.ai/contribute/ for useful information and tips. After the pull request is submitted, please request code reviews from Reviewers.

I am not sure about the motivation of this particular constructor. I can understand sometimes we need to downcast size_t into uint32_t, but wouldn't it be better to ask the dev to downcast explicitly? The most natural arguments about the constructor are the constructor itself

Why do we have uint32_t as index in the graph? Any particular reason? Memory savings?

The index node refers to the output index of the tensor. The number of outputs of an operator is not going to be bigger than 2^31. As a matter of fact,  there is no operator that has more than three outputs that I know of (except for split, which could results in 10 if the user specified so).
Given these considerations, using a 32bit integer is more than sufficient for this case.

Fair point. I will change it.

Fixed it.

Thanks, @larroy , this PR is now merged


Style charts

@daymos here are the colours from the wales.coop bars which I am using to colour-code the topics. Might be nice if you do the same, although feel free to develop a different look if you think its more appropriate since the users are not the same.
#623f99
#0ba6c8
#ecb826
#bb174c
#68859e
#23deaa
#3a4a86
#8764de
#e09334
#1d79c7
I will just take the first x for the no. of topics

done


Move "public" headers in zdw sub-directory

Description
This is so that I can create an RPM with libzdw.a and it's associate header files, and keep the header files together in their own directory, e.g. /usr/local/include/zdw
How Has This Been Tested?
Tested with provided movie_tickets test file.
Actually, since I've also now built RPMs from this codebase for internal Adobe use, I'm running convertDWfile -v + unconvertDWfile against all .sql files in the test-files directory (including comparing the .desc files) for both .gz and .xz (-J)
Types of changes

[ ] Bug fix (non-breaking change which fixes an issue)
[X] New feature (non-breaking change which adds functionality)
[X] Breaking change (fix or feature that would cause existing functionality to change)

Checklist:

[X] I have signed the Adobe Open Source CLA.
[X] My code follows the code style of this project.
[ ] My change requires a change to the documentation.
[ ] I have updated the documentation accordingly.
[X] I have read the CONTRIBUTING document.
[ ] I have added tests to cover my changes.
[X] All new and existing tests passed.

@mrimer  Thoughts on this?


Orchard 1.9 error if version number in ResourceManifest is single digit

ResourceManifest.cs file with script and style definitions works in Orchard 1.8 when the .SetVersion() attribute contains a single digit such as "1".
After upgrading to Orchard 1.9, an error is thrown:
"Version string portion was too short or too long."
Modifying the attribute value to "1.0" resolves the issue.
Since the value for .SetVersion() is a string, I would think any value would be acceptable.

:-1:
Although value you set is a string, this string gets later parsed into System.Version type which has this as a requirement.

Version numbers consist of two to four components: major, minor, build, and revision. The major and minor components are required; the build and revision components are optional, but the build component is required if the revision component is defined. All defined components must be integers greater than or equal to 0. The format of the version number is as follows (optional components are shown in square brackets ([ and ]):

major.minor[.build[.revision]]

Of course it would be possible to make it work with single digit by appending extra zero in the code, but I'd advise against such hacks. No versioning system I've ever encountere used less than 2 component versioning and that also applies to .NET. It's an industry standard.

:-1:  x :100:
I think it should be closed, it is not important and does not influence on Orchard.

I have similar problem with the following error:
Input string was not in a correct format.
src\Orchard\UI\Resources\ResourceManager.cs    Line: 199
I think this is because i had some non integer versions in my modules for Orchard 1.8.1 such as "1.4.0-rc.0".

@armanforghani Yes of course, that is not supported version format. You should use only digits in format major.minor[.build[.revision]] (bracketed being optional). To better understand why this is necessary. Unified System.Version provides reliable comparison of versions, so it knows that 1.11 is newer than 1.0 or <IP_ADDRESS>102 ...
Why this is a good thing. For example Orchard.jQuery module has resource called "jQuery" set to version 1.11.1, so lots of modules that depend on jQuery and include it with Script.Require("jQuery), will include jquery-1.11.1.min.json the page ... But what If you want to use the 2.x version of jQuery across your site? Thanks to version comparison you don't need to rewrite Orchard.jQuery module, but instead you can just define new resource in your theme or module and call it "jQuery" and if you give it higher version like 2.1.4, then entire site will be using this higher version. That way you can swap scripts and stylesheets without doing any modifications to original files. If you were allowed to input any string into version, comparisons would fail and it would be impossible to tell which resource should take priority.

@Xeevis, Yes, I read official document for System.Version. You are right. Thank you so much for your useful comment.

I always keep the latest version of jQuary, jQuaru-ui, bootstrap whatever. Why would anyone want to use version  in there modules. I had a recent issue where cookiecutter used version 1.4.0 of jquery.cookie.js
but it  used version 1.4.1 in Orchard.Jquary and caused me issues, same with several other modules calling a version of jQuary that I had updated to 2.1.3 not 1.1.1


Failed to resolve Dapr 1.9.0

If the issue is to do with Azure CLI 2.0 in-particular, create an issue here at Azure/azure-cli

Related command
Targeting a specific Dapr version (in this case, 1.9.0)
https://learn.microsoft.com/en-us/azure/aks/dapr#targeting-a-specific-dapr-version
az k8s-extension create --cluster-type managedClusters \
--cluster-name myAKSCluster \
--resource-group myResourceGroup \
--name myDaprExtension \
--extension-type Microsoft.Dapr \
--auto-upgrade-minor-version false \
--version 1.9.0

Extension name (the extension in question)
k8s-extension
Description of issue (in as much detail as possible)
Dapr 1.9.0 was released at Oct.13th and it is latest version.
https://docs.dapr.io/operations/support/support-release-policy/#supported-versions
https://github.com/dapr/dapr/releases/tag/v1.9.0
I try to install it with specific version parameter, but I got following error message. Could you please advise me how to install the latest version?
(ExtensionOperationFailed) The extension operation failed with the following error:  Failed to resolve the extension version from the given

@jyama4ta hello, Dapr 1.9.0 was released a few hours back, can you please try this command again?

@shubham1172 Thank you for your help. I have checked it is successeded now. Thanks again!


Eureka - ServiceInstance w/ health URLS?

Currently a ServiceInstance returned by the DiscoveryClient (and EurekaDiscoveryClient) has a limited amount of information. Would there be any interest in providing an extended interface of ServiceInstance, e.g. DiscoveryServiceInstance (or something more appropriate) that can return more information, such as the health and info URLs?
If so, this may be more appropriate for spring-cloud-commons unless it makes sense to only include this information with EurekaDiscoveryClient.
I understand that we could autowire the Netflix DiscoveryClient and use that, but it would be nice to keep the abstraction that Spring Cloud provides.
If there is interest, I am willing to contribute a proposed solution.

Similar to #419 as the different urls are meta-data.  Let's have a conversation about generic metadata at https://github.com/spring-cloud/spring-cloud-commons/issues/19


Export config into external config file

I choosed ini file for it's simplicity.
I'd like to put @actions into config also. But it will be for another PR

No movement for a long time now. closing this PR


New Formula: Dusty

Ping.

Closing as dead. Thanks for the submission! Feel free to submit a new PR if desired.


Centralize plugin info into info.js

This fixes two problems with the template:

A developer needed to go into two files to change the name of the plugin: once to register it to OpenRCT2, and again to change how it displays in the map dropdown
rollup.config.js needed to be edited for the exported filename to change, which broke the pattern of the src folder being the only folder to edit to make changes to the plugin

Now, a new file named info.js exports variables which the developer can change. This is for the most part a refactor, though a few (very minor) things distinct this from the previous version:

The README.md file has been updated to reflect the singular location where a developer would need to update information
The target API version is now 77 to match OpenRCT2's v0.4.5 release
By default, the name of the plugin registered to OpenRCT2 matches what displays in the map dropdown (I don't believe this will cause controversy, as it's unlikely someone would want these to be different from each other)
A developer can now set a minApiVersion from info.js (while this is technically different, it doesn't affect functionality given a developer could have added the parameter themselves; this just makes it so that all information is explicit in the info.js file)

Hey, thank you for your pull request. 😄 I really like the way you implemented the shared configuration, but I have mixed feelings about it compared to the goal of this template. I wanted to create a template that was as minimal as possible, with as few files, configuration and as little custom written "bloat" as possible.
Furthermore:

I don't think touching rollup.config.js is a bad thing, as it allows newcomers to get a little bit of familiarity with what this file is instead of having a directory full of unknown mystery files that mean nothing to a new developer.
Regarding the plugin name as map dropdown entry, it's not entirely uncommon to use different values for name and dropdown entry; my RideVehicleEditor and ParkRatingInspector have different dropdown entries vs. the name, SceneryManager uses a slugified version as the plugin name, and the OpenRCT2 dev tools registers 3 dropdown entries from a single plugin. I'd like to leave this up to the plugin creator's imagination instead of nudging the two fields to be similar.
Last of all, I'm not a fan of setting minApiVersion. When set, OpenRCT2 will just refuse to load the plugin without any notification to the player (apart from a behind-the-scenes log message). This can be very confusing for plugin users when they have a slightly older version than the target API.

And considering there are not really any other options that are shared between multiple files from the info.js, I'm afraid I'm not convinced yet whether I'd like it merged. My apologies for the time you already put into it!

No worries about the time I put into this request, as these changes are things I would implement myself to streamline my process anyway. Although, I wasn't aware of that last point with the minApiVersion so I'll go ahead and update that in the branch.
I can definitely see the benefits of how the template is currently, especially in terms of bloat. In fact, the main branch of the fork I created implements a minimal amount of code changes to fit the original goal I had when I was using the template: to only define the name of the plugin once so that the map dropdown entry wouldn't need to change separately. Though, as you stated in your second bullet, I can also see how it would make sense to define this as a different value. At the same time, I don't believe that this development would change the fact that you would need to change the name in two places, as a developer would simply remove the reference to the variable and put in their own string.
I'll keep this request open, since I do think that despite the extra file and other complications, it simplifies the process of specifying the information about a plugin, though mainly for those that require only one map dropdown entry. And no worries if you do decide to close this request, as I'll still be able to use my fork regardless.


Restore generator caching

See this dicussion: https://github.com/excalidraw/excalidraw/pull/233#issuecomment-573360445
This will help bring mouse move perf back to an acceptable level

Fixed by #316


emails from prod (not stage) to h2-administrators list aren't delivered

When a new collection is started, an email is supposed to be sent to<EMAIL_ADDRESS>
The code for this is done, including shared_configs
Turning on the feature flag in stage, Amy was able to generate an email to the list.
Emails in prod are being sent to individuals
rails logs show emails being sent

I, [2022-08-22T13:09:46.968615 #27871]  INFO -- : [f25eeb54-bfaa-456e-9552-a3683de5394b] [ActiveJob] Enqueued ActionMailer::MailDeliveryJob (Job ID: 9638f581-21cd-44f8-b29c-1d29e06e6f36) to Sidekiq(default) with argu
ments: "FirstDraftCollectionsMailer", "first_draft_created", "deliver_now", {:params=>{:collection_version=>#<GlobalID:0x0000000006a58448 @uri=#<URI::GID gid://happy-heron/CollectionVersion/324>>}, :args=>[]}
I, [2022-08-22T13:09:46.985020 #27871]  INFO -- : [f25eeb54-bfaa-456e-9552-a3683de5394b] method=POST path=/first_draft_collections format=turbo_stream controller=FirstDraftCollectionsController action=create status=302 duration=55.46 view=0.00 db=13.45 location=https://sdr.stanford.edu/collections/240
I, [2022-08-22T13:09:46.986954 #26619]  INFO -- : [ActiveJob] [ActionMailer::MailDeliveryJob] [9638f581-21cd-44f8-b29c-1d29e06e6f36] Performing ActionMailer::MailDeliveryJob (Job ID: 9638f581-21cd-44f8-b29c-1d29e06e6f36) from Sidekiq(default) enqueued at 2022-08-22T20:09:46Z with arguments: "FirstDraftCollectionsMailer", "first_draft_created", "deliver_now", {:params=>{:collection_version=>#<GlobalID:0x0000000004f517f8 @uri=#<URI::GID gid://happy-heron/CollectionVersion/324>>}, :args=>[]}
I, [2022-08-22T13:09:47.070248 #26619]  INFO -- : [ActiveJob] [ActionMailer::MailDeliveryJob] [9638f581-21cd-44f8-b29c-1d29e06e6f36] Performed ActionMailer::MailDeliveryJob (Job ID: 9638f581-21cd-44f8-b29c-1d29e06e6f36) from Sidekiq(default) in 100.69ms

Honeybadger has no errors for this
sidekiq has no indication of failed deliveries or things in queue

The only thing I see different for our configuration for prod in shared_configs is:
# hostname for serving email images and links in emails:
host: sdr.stanford.edu

Ops pointed me to /var/log/maillog, where we see:
[root@sul-h2-prod log]# grep lists /var/log/maillog
Aug 22 13:09:47 sul-h2-prod postfix/smtp[31420]: 0680D10004A<EMAIL_ADDRESS>relay=smtp.stanford.edu[<IP_ADDRESS>]:25, delay=0.11, delays=0.05/0.02/0.02/0.02, dsn=2.0.0, status=sent (250 2.0.0 Ok: queued as 1DC7A40010)

Julian said "If you can send email there from your regular web mail, then it’s likely Proofpoint (the anti-spam thing) killing the emails from h2-prod."
And suggested I file a ticket with UIT.
I have done so;  Probably no one can see this but me:  https://stanford.service-now.com/services?sys_id=e49ff5b097a9155090e9bc171153afbe&id=ticket&table=incident
INC01723945 Created
"We have email to h2-administrators<EMAIL_ADDRESS>from sul-h2-prod.stanford.edu that didn't arrive.
"Our maillog on the prod server has this:
Aug 22 13:09:47 sul-h2-prod postfix/smtp[31420]: 0680D10004A<EMAIL_ADDRESS>relay=smtp.stanford.edu[<IP_ADDRESS>]:25, delay=0.11, delays=0.05/0.02/0.02/0.02, dsn=2.0.0, status=sent (250 2.0.0 Ok: queued as 1DC7A40010)

"Note that emails from sul-h2-prod.stanford.edu to individuals ARE arriving, and emails to the same list from sul-h2-stage.stanford.edu are also arriving.
"Can you determine what is happening to the emails from sul-h2-prod.stanford.edu to h2-administrators<EMAIL_ADDRESS>that look fine from our end?
"Thank you."

emails came through at 4:13am this morning

@amyehodge we're good now, yes?  If so, please close this ticket.


Roll Packages from 6328cfbd2a46 to 8febbba0c387 (21 revisions)

https://github.com/flutter/packages/compare/6328cfbd2a46...8febbba0c387
2023-05-15<EMAIL_ADDRESS>[ci] Enforce a minimum Kotlin version in examples (flutter/packages#3979)
2023-05-15<EMAIL_ADDRESS>[ci] Disable Windows repo tool tests (flutter/packages#3980)
2023-05-12<EMAIL_ADDRESS>[ci] Add LUCI repo tool tests (flutter/packages#3964)
2023-05-12<EMAIL_ADDRESS>[url_launcher] Set broadcast reciever visability as required by target api 34 (flutter/packages#3973)
2023-05-12<EMAIL_ADDRESS>Update build.gradle with a comment explaining version (flutter/packages#3972)
2023-05-12<EMAIL_ADDRESS>Roll Flutter from 8c5a1ea7281a to a76dbe44b99e (12 revisions) (flutter/packages#3958)
2023-05-12<EMAIL_ADDRESS>[image_picker] Fix Android lints (flutter/packages#3887)
2023-05-12<EMAIL_ADDRESS>[various] Update minimum Flutter version to 3.3 (flutter/packages#3967)
2023-05-12<EMAIL_ADDRESS>[tool] Target specific Android unit tests (flutter/packages#3955)
2023-05-12<EMAIL_ADDRESS>[camerax] Add a dependency on kotlin-bom to align versions of kotlin-stdlib (flutter/packages#3960)
2023-05-12<EMAIL_ADDRESS>[local_auth] Improve iOS test DI (flutter/packages#3959)
2023-05-12<EMAIL_ADDRESS>[ci] Ensure scripts fail if a command fails (flutter/packages#3963)
2023-05-11<EMAIL_ADDRESS>[ci] Pin the Flutter version for release (flutter/packages#3965)
2023-05-11<EMAIL_ADDRESS>[video_player] foundation - reduce seek accuracy to fix seek to end bug (flutter/packages#3784)
2023-05-11<EMAIL_ADDRESS>[image_picker] Fix use_build_context_synchronously violations (flutter/packages#3969)
2023-05-11<EMAIL_ADDRESS>[tool] Ensure that publish credential path is available (flutter/packages#3970)
2023-05-11<EMAIL_ADDRESS>[go_router] change fix_data.yaml uris to be absolute to fix lints (flutter/packages#3877)
2023-05-11<EMAIL_ADDRESS>[tool] Use new pub cache location for publish (flutter/packages#3962)
2023-05-10<EMAIL_ADDRESS>Roll Flutter (stable) from f72efea43c30 to 84a1e904f44f (1920 revisions) (flutter/packages#3961)
2023-05-10<EMAIL_ADDRESS>[image_picker_android] Improved Bitmap resize on Android (flutter/packages#3423)
2023-05-10<EMAIL_ADDRESS>[camerax] Add Kotlin duplicate classes known error to README.md (flutter/packages#3957)
If this roll has caused a breakage, revert this CL and stop the roller
using the controls here:
https://autoroll.skia.org/r/flutter-packages-flutter-autoroll
Please CC flutter-ecosystem<EMAIL_ADDRESS>on the revert to ensure that a human
is aware of the problem.
To file a bug in Flutter: https://github.com/flutter/flutter/issues/new/choose
To report a problem with the AutoRoller itself, please file a bug:
https://bugs.chromium.org/p/skia/issues/entry?template=Autoroller+Bug
Documentation for the AutoRoller is here:
https://skia.googlesource.com/buildbot/+doc/main/autoroll/README.md

Commit queue failed; closing this roll.


Remove version: Dreamtonics.SynthesizerVStudioBasicBeta version 1.11.0b2

[Automated] It returns code over 400 in all urls
Microsoft Reviewers: Open in CodeFlow

For moderators
This account is automated by Github Actions and the source code was created by CoolPlayLin. If you have any questions about any pull request, don't hesitate to ping Exorcism0666.

[!warning]
With the new version of Komac, numerous Pull Requests may contain bugs or unwanted elements. If such issues arise, please tag russellbanks so that these bugs can be addressed in future updates.

[!important]
Please carefully review these Pull Request before merging. If it is a Pull Request for removing incorrect content and the URLs are issue, free upon manual checking, please close this Pull Request directly. (It is best to inform Exorcism0666 of the closure.)

良い一日をお過ごしください！

[Policy] reset feedback

@wingetbot run

Automatic Validation ended with:

2024-02-09T17:33:47.4130584Z Processing manifest: manifests\d\Dreamtonics\SynthesizerVStudioBasicBeta\1.11.0b2
2024-02-09T17:33:47.4139080Z -----------------------------------------------------
2024-02-09T17:33:47.4147462Z Status: Started
2024-02-09T17:33:47.4155798Z Manifest file is Deleted
2024-02-09T17:33:47.7129238Z Status: Completed
2024-02-09T17:33:47.7136784Z ##[error] Result: Failed
2024-02-09T17:33:47.7333946Z ##[error] Sequence contains no elements
2024-02-09T17:33:47.7826686Z ##[error]Manifest Validation Failed

(Automated response - build 726.)

Sequence contains no elements

https://github.com/microsoft/winget-pkgs/issues/133371

(Automated response - build 726.)

Hi @Exorcism0666,
This manifest has the highest version number for this package. Is it available from another location?
(Automated response - build 726.)


Refactor and cleanup yaml MapToFields.

Avoid the use of MetaProviders, which was always kind of hacky. We may want to remove this infrastructure altogether as it does not play nicely with provider inference.

Split MapToFields into separate mapping, filtering, and exploding operations.

Allow MapToFields to act on non-schema'd PCollections.

The various langauge flavors of these UDFs are now handled by a preprocessing step.  This will make it easier to extend to other langauges, including in particular possible multiple (equivalent) implementations of javascript to minimize cross-langauge boundary crossings.

Thank you for your contribution! Follow this checklist to help us incorporate your contribution quickly and easily:

[ ] Mention the appropriate issue in your description (for example: addresses #123), if applicable. This will automatically add a link to the pull request in the issue. If you would like the issue to automatically close on merging the pull request, comment fixes #<ISSUE NUMBER> instead.
[ ] Update CHANGES.md with noteworthy changes.
[ ] If this contribution is large, please file an Apache Individual Contributor License Agreement.

See the Contributor Guide for more tips on how to make review process smoother.
To check the build health, please visit https://github.com/apache/beam/blob/master/.test-infra/BUILD_STATUS.md
GitHub Actions Tests Status (on master branch)

See CI.md for more information about GitHub Actions CI or the workflows README to see a list of phrases to trigger workflows.

R: @Polber


Add Windows build using meson

Checklist

[x] Used a fork of the feedstock to propose changes
[x] Bumped the build number (if the version is unchanged)
[ ] Reset the build number to 0 (if the version changed)
[x] Re-rendered with the latest conda-smithy (Use the phrase @conda-forge-admin, please rerender in a comment in this PR for automated rerendering)
[x] Ensured the license file is being packaged.

Required dependency for gtk3 on Windows.

Hi! This is the friendly automated conda-forge-linting service.
I just wanted to let you know that I linted all conda-recipes in your PR (recipe) and found it was in an excellent condition.

Waiting on build for fribidi (https://github.com/conda-forge/fribidi-feedstock/pull/8).

@conda-forge-admin, please restart ci
fribidi has been bumped.

@ryanvolz Windows failed here ... besides a gobject-introspection issue, note also the error a few lines above: ninja: fatal: ReadFile: The handle is invalid. — haven't seen that before.

@ryanvolz Windows failed here ... besides a gobject-introspection issue, note also the error a few lines above: ninja: fatal: ReadFile: The handle is invalid. — haven't seen that before.

Yes, it looks to be the same problem caused by the conda-build Windows prefix detection bug that's affecting gobject-introspection and causing build failures with atk and gdk-pixbuf (https://github.com/conda-forge/gobject-introspection-feedstock/issues/19).
I think the ninja: fatal: ReadFile: The handle is invalid. is related because it is present for all of them and isn't an issue building locally with conda-build=3.18.11.

Closes #18.


Feature Request: Adding Pokemon Stadium-inspired Portraits as an alternative to the Pokemon Sprites

I created a package of portraits using the Pokemon HOME renders as a base as an alternative to the Pokemon Party Preview Sprites used in the current build of the tracker. This currently replaces all 413 .gif images in the ...images/pokemon folder, and edits the Drawing.lua file to shift the images enough pixels to fit the blank space.
Ideally, this would just be a toggleable option, but as it currently stands, it just outright replaces the sprites. It's out of my coding scope to accomplish that, but maybe the gen 3 folks would like these?
Ironmon-Tracker-0.3.4 Home Portraits.ZIP
Examples of these look like the following:

This is due to them being inspired by Pokemon Stadium and Colosseum/XD, which also use black backgrounds for their portraits. It would require me re-exporting each and every pokemon icon, which at the moment I'm a little too busy to be able to do, but can see how long it'd take once I don't have a charity marathon planning phase taking up all my time. I'm not 100% sure if photoshop will be particularly co-operative with exporting them as .gifs with transparent backgrounds, but the pixel icons have a transparent background as it is so surely this shouldn't be a problem, in theory.

Update 7/17/2022: Due to the recent updates to the tracker (currently on version 0.5.1 as of writing this), the value needed to be changed in the Drawing.lua in order to render it in the correct location. The original request has been updated with a new download with the portraits and the new Drawing.lua, as well as the code needed to edit the Drawing.lua manually if desired.

Update 2. 7/17/2022: I've made transparent .pngs for these per UTDZac's request. They can be found here: GBA IronMON Stadium Portraits - Transparent


Display likes

In this feature, I:

Implemented Involvement API to show Likes : https://www.notion.so/Involvement-API-869e60b5ad104603aa6db59e08150270
Combined the API data with the Base API

Thanks for the review 🙏


"python" not defined on CentOS 8, code wants python2?

The scripts in defusion all start with
#! /usr/bin/python
but that is not defined on CentOS 8 (and probably other RH 8 derived distributions.)  Additionally it seems to require python 2 but that is EOL.  Python 3 does not work:
 python3 ./1_detect_fused_gene.py
  File "/usr/common/modules/el8/x86_64/software/defusion/1.0-CentOS-vanilla/bin/1_detect_fused_gene.py", line 169
    print seqFileL
                 ^
SyntaxError: Missing parentheses in call to 'print'. Did you mean print(seqFileL)?

I developed this tool under python 2.7.9, so python3 syntax is not supported.
Check the installation page here for dependencies. https://wjidea.github.io/defusion/installation.html#dependencies


CUDA illegal error access error when running distributed mixed precision

Whenever I try to train a model using MirroredStrategy and mixed precision, at an indeterminate time, I get the following error:
./tensorflow/core/kernels/conv_2d_gpu.h:970] Non-OK-status: GpuLaunchKernel( SwapDimension1And2InTensor3UsingTiles<T, kNumThreads, kTileSize, kTileSize, conjugate>, total_tiles_count, kNumThreads, 0, d.stream(), input, input_dims, output) status: Internal: an illegal memory access was encountered
2020-06-25 00:45:27.788127: E tensorflow/stream_executor/cuda/cuda_event.cc:29] Error polling for event status: failed to query event: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered
2020-06-25 00:45:27.788208: F tensorflow/core/common_runtime/gpu/gpu_event_mgr.cc:273] Unexpected Event status: 1

Unfortunately, I don't have a simply example to reproduce this and can't include my entire code. But maybe other people are having similar issues and can produce a better example.
I'm running tensorflow 2.2.0 on ubuntu 18.04. CUDA 10.1.243, CuDNN 7.6.5 using two RTX 2080 ti cards. I get the same error on a V100.

@lminer
Provide the exact sequence of commands / steps that you executed before running into the problem.Thanks!

I can't give you all the code, but I use the basic approach below:
policy = tf.keras.mixed_precision.experimental.Policy("mixed_float16")
tf.keras.mixed_precision.experimental.set_policy(policy)
strategy = tf.distribute.MirroredStrategy()
with strategy.scope():
    logits = get_logits()
    model = tf.keras.Model(inputs, logits)

model.fit(X, y)  # X and y are datasets read from tfrecords

I run this from the official tensorflow docker container.

Is there a flag I can use to get a more detailed stack trace?

I ran this with cuda-memcheck and the error occured at an earlier point:
========= Internal Memcheck Error: Initialization failed
=========     Saved host backtrace up to driver entry point at error
=========     Host Frame:/usr/lib/x86_64-linux-gnu/libcuda.so.1 [0x1403fc]
=========     Host Frame:/usr/local/cuda-10.1/targets/x86_64-linux/lib/libcufft.so.10 [0x3d7e4a]
=========     Host Frame:/usr/local/cuda-10.1/targets/x86_64-linux/lib/libcufft.so.10 [0x3caf70]
=========     Host Frame:/usr/local/cuda-10.1/targets/x86_64-linux/lib/libcufft.so.10 [0x3d719a]
=========     Host Frame:/usr/local/cuda-10.1/targets/x86_64-linux/lib/libcufft.so.10 [0x3dae9f]
=========     Host Frame:/usr/local/cuda-10.1/targets/x86_64-linux/lib/libcufft.so.10 [0x3db60a]
=========     Host Frame:/usr/local/cuda-10.1/targets/x86_64-linux/lib/libcufft.so.10 [0x3cec3c]
=========     Host Frame:/usr/local/cuda-10.1/targets/x86_64-linux/lib/libcufft.so.10 [0x3bed7e]
=========     Host Frame:/usr/local/cuda-10.1/targets/x86_64-linux/lib/libcufft.so.10 [0x3f022c]
=========     Host Frame:/usr/local/cuda-10.1/targets/x86_64-linux/lib/libcufft.so.10 [0x379a2]
=========     Host Frame:/usr/local/cuda-10.1/targets/x86_64-linux/lib/libcufft.so.10 [0x37fa6]
=========     Host Frame:/usr/local/cuda-10.1/targets/x86_64-linux/lib/libcufft.so.10 [0x39af2]
=========     Host Frame:/usr/local/cuda-10.1/targets/x86_64-linux/lib/libcufft.so.10 [0x37fa6][48/2973]=========     Host Frame:/usr/local/cuda-10.1/targets/x86_64-linux/lib/libcufft.so.10 [0x39af2]
=========     Host Frame:/usr/local/cuda-10.1/targets/x86_64-linux/lib/libcufft.so.10 (cufftXtMakePlanMany + 0x63a) [0x4d0ca]
=========     Host Frame:/usr/local/cuda-10.1/targets/x86_64-linux/lib/libcufft.so.10 (cufftMakePlanMany64 + 0x157) [0x4e087]
=========     Host Frame:/usr/local/cuda-10.1/targets/x86_64-linux/lib/libcufft.so.10 (cufftMakePlanMany + 0x193) [0x4aaf3]
=========     Host Frame:/home/lminer/anaconda3/envs/separate2/lib/python3.7/site-packages/tensorflow/python/../libtensorflow_framework.so.2 (_ZN15stream_executor3gpu11CUDAFftPlan10InitializeEPNS0_11GpuExecutorEPNS_6StreamEiPyS6_yyS6_yyNS_3fft4TypeEiPNS_16ScratchAllocatorE + 0x1e6) [0x15949b6]
=========     Host Frame:/home/lminer/anaconda3/envs/separate2/lib/python3.7/site-packages/tensorflow/python/../libtensorflow_framework.so.2 (_ZN15stream_executor3gpu7CUDAFft37CreateBatchedPlanWithScratchAllocatorEPNS_6StreamEiPyS4_yyS4_yyNS_3fft4TypeEbiPNS_16ScratchAllocatorE + 0xca) [0x159614a]
=========     Host Frame:/home/lminer/anaconda3/envs/separate2/lib/python3.7/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so (_ZN10tensorflow10FFTGPUBase5DoFFTEPNS_15OpKernelContextERKNS_6TensorEPyPS3_ + 0x3bf) [0x5157bff]
=========     Host Frame:/home/lminer/anaconda3/envs/separate2/lib/python3.7/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so (_ZN10tensorflow7FFTBase7ComputeEPNS_15OpKernelContextE + 0x453) [0x50f2fa3]
=========     Host Frame:/home/lminer/anaconda3/envs/separate2/lib/python3.7/site-packages/tensorflow/python/../libtensorflow_framework.so.2 (_ZN10tensorflow13BaseGPUDevice7ComputeEPNS_8OpKernelEPNS_15OpKernelContextE + 0xe6) [0xf385f6]
=========     Host Frame:/home/lminer/anaconda3/envs/separate2/lib/python3.7/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so (_ZN10tensorflow17KernelAndDeviceOp3RunEPNS_19ScopedStepContainerERKNS_15EagerKernelArgsEPSt6vectorINS_6TensorESaIS7_EEPNS_19CancellationManagerERKN4absl8optionalINS_25EagerRemoteFunctionParamsEEE + 0x64f) [0x378dd3f]
=========     Host Frame:/home/lminer/anaconda3/envs/separate2/lib/python3.7/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so (_ZN10tensorflow17KernelAndDeviceOp3RunERKNS_15EagerKernelArgsEPSt6vectorINS_6TensorESaIS5_EEPNS_19CancellationManagerERKN4absl8optionalINS_25EagerRemoteFunctionParamsEEE + 0x2f) [0x378e23f]
=========     Host Frame:/home/lminer/anaconda3/envs/separate2/lib/python3.7/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so (_ZN10tensorflow18EagerKernelExecuteEPNS_12EagerContextERKN4absl13InlinedVectorIPNS_12TensorHandleELm4ESaIS5_EEERKNS2_8optionalINS_25EagerRemoteFunctionParamsEEERKSt10unique_ptrINS_15KernelAndDeviceENS_4core15RefCountDeleterEEPNS_14GraphCollectorEPNS_19CancellationManagerENS2_4SpanIS5_EE + 0x60b) [0x376100b]
=========     Host Frame:/home/lminer/anaconda3/envs/separate2/lib/python3.7/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so (_ZN10tensorflow11ExecuteNode3RunEv + 0x170) [0x3761be0]
=========     Host Frame:/home/lminer/anaconda3/envs/separate2/lib/python3.7/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so (_ZN10tensorflow13EagerExecutor11SyncExecuteEPNS_9EagerNodeE + 0x1b$) [0x3789684]
=========     Host Frame:/home/lminer/anaconda3/envs/separate2/lib/python3.7/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so [0x375bb8c]
=========     Host Frame:/home/lminer/anaconda3/envs/separate2/lib/python3.7/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so (_ZN10tensorflow12EagerExecuteEPNS_14EagerOperationEPPNS_12TensorHandleEPi + 0x2d2) [0x375f1a2]
=========     Host Frame:/home/lminer/anaconda3/envs/separate2/lib/python3.7/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so (_ZN10tensorflow18OperationInterface7ExecuteEPN4absl10FixedArrayISt10unique_ptrI29AbstractTensorHandleInterfaceSt14default_deleteIS4_EELm18446744073709551615ESaIS7_EEEPi +
0x6b) [0x332917b]
=========     Host Frame:/home/lminer/anaconda3/envs/separate2/lib/python3.7/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so (TFE_Execute + 0x91) [0x3317e91]
=========     Host Frame:/home/lminer/anaconda3/envs/separate2/lib/python3.7/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so (_Z24TFE_Py_FastPathExecute_CP7_object + 0x1af1) [0x2fc9931]
=========     Host Frame:/home/lminer/anaconda3/envs/separate2/lib/python3.7/site-packages/tensorflow/python/_pywrap_tfe.so [0x20ef7]
=========     Host Frame:/home/lminer/anaconda3/envs/separate2/lib/python3.7/site-packages/tensorflow/python/_pywrap_tfe.so [0x42c09]
=========     Host Frame:python (_PyMethodDef_RawFastCallKeywords + 0x264) [0x163c34]
=========     Host Frame:python (_PyCFunction_FastCallKeywords + 0x21) [0x163d51]
=========     Host Frame:python (_PyEval_EvalFrameDefault + 0x4ebc) [0x1d00ac]
=========     Host Frame:python (_PyEval_EvalCodeWithName + 0x2f9) [0x1131b9]
=========     Host Frame:python (_PyFunction_FastCallKeywords + 0x387) [0x163437]
=========     Host Frame:python (_PyEval_EvalFrameDefault + 0x14eb) [0x1cc6db]
=========     Host Frame:python (_PyEval_EvalCodeWithName + 0xab8) [0x113978]
=========     Host Frame:python (_PyFunction_FastCallKeywords + 0x387) [0x163437]
=========     Host Frame:python (_PyEval_EvalFrameDefault + 0x4b29) [0x1cfd19]
=========     Host Frame:python (_PyEval_EvalCodeWithName + 0x2f9) [0x1131b9]
=========     Host Frame:python (_PyFunction_FastCallKeywords + 0x387) [0x163437]
=========     Host Frame:python (_PyEval_EvalFrameDefault + 0x14eb) [0x1cc6db]
=========     Host Frame:python (_PyFunction_FastCallKeywords + 0xfb) [0x1631ab]
=========     Host Frame:python (_PyEval_EvalFrameDefault + 0x416) [0x1cb606]
=========     Host Frame:python (_PyEval_EvalCodeWithName + 0x2f9) [0x1131b9]
=========     Host Frame:python (_PyFunction_FastCallKeywords + 0x387) [0x163437]
=========     Host Frame:python (_PyEval_EvalFrameDefault + 0x14eb) [0x1cc6db]
=========     Host Frame:python (_PyEval_EvalCodeWithName + 0x2f9) [0x1131b9]
=========     Host Frame:python (_PyFunction_FastCallKeywords + 0x325) [0x1633d5]
=========     Host Frame:python (_PyEval_EvalFrameDefault + 0x416) [0x1cb606]
=========     Host Frame:python (_PyFunction_FastCallKeywords + 0xfb) [0x1631ab]
=========     Host Frame:python (_PyEval_EvalFrameDefault + 0x6a3) [0x1cb893]
=========
2020-06-27 16:22:40.585959: E tensorflow/stream_executor/cuda/cuda_event.cc:29] Error polling for event
status: failed to query event: CUDA_ERROR_ILLEGAL_INSTRUCTION: an illegal instruction was encountered
2020-06-27 16:22:40.586021: F tensorflow/core/common_runtime/gpu/gpu_event_mgr.cc:273] Unexpected Event
status: 1

@lminer Can you please provide us the full reproducible code for us to reproduce this issue. We can't reproduce the issue currently as the code you provided is not enough. Thanks!

@lminer did you figure what causes this error?

@ben0it8 no, and I'm having trouble creating a reproducible example that isn't just my entire code base. Do you have one?

@gowthamkpr, I have a reproducible example. This will crash if I run: TF_FORCE_GPU_ALLOW_GROWTH=true python fail.py.
Where fail.py is as below. Interestingly, it does not crash with I don't set TF_FORCE_GPU_ALLOW_GROWTH to true. I'm running tensorflow 2.2.0 on ubuntu 18.04. CUDA 10.1.243, CuDNN 7.6.5 using two RTX 2080 ti cards. I get the same error on a V100. This only happens if I enable mixed precision and Mirrored distribute strategy.
import numpy as np
import tensorflow as tf

class Conv2dLayer(tf.keras.layers.Layer):
    def __init__(self, filters, kernel_size, strides=1, **kwargs):
        super().__init__(**kwargs)
        self.activation = tf.keras.layers.LeakyReLU()
        self.conv = tf.keras.layers.Conv2D(
            filters, kernel_size, strides=strides, padding="same", kernel_initializer="he_normal",
        )
        self.batch_norm = tf.keras.layers.BatchNormalization()
        self.filters = filters
        self.kernel_size = kernel_size
        self.strides = strides

    def call(self, inputs, **kwargs):
        x = self.conv(inputs)
        x = self.activation(x)
        x = self.batch_norm(x)
        return x

    def get_config(self):
        config = super().get_config()
        config["filters"] = self.filters
        config["kernel_size"] = self.kernel_size
        config["strides"] = self.strides
        return config

    def compute_output_shape(self, input_shape):
        return self.conv.compute_output_shape(input_shape)

class UpSampleLayer(tf.keras.layers.Layer):
    def __init__(self, filters, strides=2, **kwargs):
        super().__init__(**kwargs)
        self.dropout = tf.keras.layers.Dropout(0.5)
        self.activation = tf.keras.layers.LeakyReLU()
        self.upconv = tf.keras.layers.Conv2DTranspose(
            filters, 4, strides=strides, padding="same", kernel_initializer="he_normal"
        )
        self.batch_norm = tf.keras.layers.BatchNormalization()
        self.filters = filters
        self.strides = strides

    def call(self, inputs, **kwargs):
        x = self.upconv(inputs)
        x = self.batch_norm(x)
        x = self.dropout(x)
        return self.activation(x)

    def get_config(self):
        config = super().get_config()
        config["filters"] = self.filters
        config["strides"] = self.strides
        return config

class DownsampleBlock(tf.keras.layers.Layer):
    def __init__(self, filters, **kwargs):
        super().__init__(**kwargs)
        self.filters = filters

        self.conv1 = Conv2dLayer(filters, 4)
        self.conv2 = Conv2dLayer(filters, 4)
        self.downsample_conv = Conv2dLayer(filters, 4, strides=2)
        self.dropout = tf.keras.layers.Dropout(0.5)

    def call(self, inputs, **kwargs):
        x = self.conv1(inputs)
        x = self.conv2(x)
        x = self.downsample_conv(x)
        x = self.dropout(x)
        return x

    def get_config(self):
        config = super().get_config()
        config["filters"] = self.filters
        return config

class Unet(tf.keras.models.Model):
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.mask = tf.keras.layers.Activation("relu")
        self.axis = -1
        self.downsample_blocks = []
        self.upsample_blocks = []

        n_maps_list = []

        for i in range(6):
            n_maps = 16 * 2 ** i
            n_maps_list.insert(0, n_maps)
            self.downsample_blocks.append(DownsampleBlock(n_maps))

        for i, n_maps in enumerate(n_maps_list[1:]):
            self.upsample_blocks.append(UpSampleLayer(n_maps, strides=2))
        self.upsample_blocks.append(UpSampleLayer(2, strides=2))

    def call(self, inputs, training=None, mask=None):
        skip_connections = []
        x = inputs
        for downsample_block in self.downsample_blocks:
            x = downsample_block(x)
            skip_connections.insert(0, x)

        x = self.upsample_blocks[0](x)  # no skip connection used for first block
        for upsample_block, h in zip(self.upsample_blocks[1:], skip_connections[1:]):
            x = upsample_block(tf.keras.layers.concatenate([x, h], axis=self.axis))
        return self.mask(x)

def train():
    BATCH_SIZE = 16
    WIDTH = 256
    HEIGHT = 512
    CHANNELS = 2
    policy = tf.keras.mixed_precision.experimental.Policy("mixed_float16")
    tf.keras.mixed_precision.experimental.set_policy(policy)
    strategy = tf.distribute.MirroredStrategy()
    with strategy.scope():
        model = Unet()
        model.build(input_shape=(None, WIDTH, HEIGHT, CHANNELS))
        model.compile(optimizer="adam", loss="mean_absolute_error")

    examples = np.random.rand(BATCH_SIZE * 20, WIDTH, HEIGHT, CHANNELS)
    target = np.random.rand(BATCH_SIZE * 20, WIDTH, HEIGHT, CHANNELS)

    ds = tf.data.Dataset.from_tensor_slices((examples, target))
    ds = ds.repeat()
    ds = ds.batch(BATCH_SIZE)
    model.fit(ds, steps_per_epoch=1875, epochs=10)

train()

The error is as follows:
2020-07-09 12:29:38.319163: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
2020-07-09 12:29:39.314182: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcuda.so.1
[96/108]2020-07-09 12:29:39.348856: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties:
pciBusID: 0000:0a:00.0 name: GeForce RTX 2080 Ti computeCapability: 7.5
coreClock: 1.635GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s
2020-07-09 12:29:39.349533: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 1 with properties:
pciBusID: 0000:42:00.0 name: GeForce RTX 2080 Ti computeCapability: 7.5
coreClock: 1.635GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s
2020-07-09 12:29:39.349555: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
2020-07-09 12:29:39.350837: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10
2020-07-09 12:29:39.351923: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10
2020-07-09 12:29:39.352101: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10
2020-07-09 12:29:39.353272: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10
2020-07-09 12:29:39.353937: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10
2020-07-09 12:29:39.356280: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7
2020-07-09 12:29:39.358938: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu
devices: 0, 1
2020-07-09 12:29:39.359275: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary
is optimized with oneAPI Deep Neural Network Library (oneDNN)to use the following CPU instructions in performance-critical operations:  AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2020-07-09 12:29:39.383097: I tensorflow/core/platform/profile_utils/cpu_utils.cc:104] CPU Frequency:<PHONE_NUMBER> Hz
2020-07-09 12:29:39.384148: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55d32e968bc0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-07-09 12:29:39.384186: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2020-07-09 12:29:39.566332: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties:
pciBusID: 0000:0a:00.0 name: GeForce RTX 2080 Ti computeCapability: 7.5
coreClock: 1.635GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s
2020-07-09 12:29:39.566949: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 1 with properties:
pciBusID: 0000:42:00.0 name: GeForce RTX 2080 Ti computeCapability: 7.5
coreClock: 1.635GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s
2020-07-09 12:29:39.566982: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
2020-07-09 12:29:39.567005: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10
2020-07-09 12:29:39.567016: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully
 opened dynamic library libcufft.so.10                                                          [48/108]2020-07-09 12:29:39.567027: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10
2020-07-09 12:29:39.567036: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10
2020-07-09 12:29:39.567045: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10
2020-07-09 12:29:39.567057: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7
2020-07-09 12:29:39.569326: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu
devices: 0, 1
2020-07-09 12:29:39.569507: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
2020-07-09 12:29:40.324951: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1257] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-07-09 12:29:40.324995: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1263]      0 1
2020-07-09 12:29:40.325002: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1276] 0:   N N
2020-07-09 12:29:40.325007: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1276] 1:   N N
2020-07-09 12:29:40.327546: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.
2020-07-09 12:29:40.327582: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1402] Created TensorFlow
device (/job:localhost/replica:0/task:0/device:GPU:0 with 10066 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2080 Ti, pci bus id: 0000:0a:00.0, compute capability: 7.5)
2020-07-09 12:29:40.329152: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.
2020-07-09 12:29:40.329169: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1402] Created TensorFlow
device (/job:localhost/replica:0/task:0/device:GPU:1 with 10064 MB memory) -> physical GPU (device: 1, name: GeForce RTX 2080 Ti, pci bus id: 0000:42:00.0, compute capability: 7.5)
2020-07-09 12:29:40.330787: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55d35311c4c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2020-07-09 12:29:40.330807: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): GeForce RTX 2080 Ti, Compute Capability 7.5
2020-07-09 12:29:40.330814: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (1): GeForce RTX 2080 Ti, Compute Capability 7.5
Epoch 1/10
WARNING:tensorflow:From /home/lminer/anaconda3/envs/separate2/lib/python3.7/site-packages/tensorflow/python/data/ops/multi_device_iterator_ops.py:601: get_next_as_optional (from tensorflow.python.data.ops.iterator_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Iterator.get_next_as_optional()` instead.
2020-07-09 12:30:03.105866: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7
2020-07-09 12:30:04.367518: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10
tensor1:73142:73386 [1] NCCL INFO Bootstrap : Using [0]enp8s0:<IP_ADDRESS><0>
tensor1:73142:73386 [1] NCCL INFO NET/Plugin : No plugin found (libnccl-net.so), using internal implementation
tensor1:73142:73386 [1] NCCL INFO NET/IB : No device found.
tensor1:73142:73386 [1] NCCL INFO NET/Socket : Using [0]enp8s0:<IP_ADDRESS><0>
tensor1:73142:73386 [1] NCCL INFO Using network Socket
NCCL version 2.7.3+cudaCUDA_MAJOR.CUDA_MINOR
tensor1:73142:73601 [0] NCCL INFO Channel 00/02 :    0   1
tensor1:73142:73601 [0] NCCL INFO Channel 01/02 :    0   1
tensor1:73142:73601 [0] NCCL INFO threadThresholds 8/8/64 | 16/8/64 | 8/8/64
tensor1:73142:73601 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1|-1->0->1/-1/-1 [1] 1/-1/-1->0->-1|-1->0->1/-1/-1
tensor1:73142:73601 [0] NCCL INFO Setting affinity for GPU 0 to ffff
tensor1:73142:73602 [1] NCCL INFO threadThresholds 8/8/64 | 16/8/64 | 8/8/64
tensor1:73142:73602 [1] NCCL INFO Trees [0] -1/-1/-1->1->0|0->1->-1/-1/-1 [1] -1/-1/-1->1->0|0->1->-1/-1/-1
tensor1:73142:73602 [1] NCCL INFO Setting affinity for GPU 1 to ffff0000
tensor1:73142:73602 [1] NCCL INFO Could not enable P2P between dev 1(=42000) and dev 0(=a000)
tensor1:73142:73601 [0] NCCL INFO Could not enable P2P between dev 0(=a000) and dev 1(=42000)
tensor1:73142:73601 [0] NCCL INFO Could not enable P2P between dev 0(=a000) and dev 1(=42000)
tensor1:73142:73602 [1] NCCL INFO Could not enable P2P between dev 1(=42000) and dev 0(=a000)
tensor1:73142:73601 [0] NCCL INFO Channel 00 : 0[a000] -> 1[42000] via direct shared memory
tensor1:73142:73602 [1] NCCL INFO Channel 00 : 1[42000] -> 0[a000] via direct shared memory
tensor1:73142:73601 [0] NCCL INFO Could not enable P2P between dev 0(=a000) and dev 1(=42000)
tensor1:73142:73602 [1] NCCL INFO Could not enable P2P between dev 1(=42000) and dev 0(=a000)
tensor1:73142:73601 [0] NCCL INFO Could not enable P2P between dev 0(=a000) and dev 1(=42000)
tensor1:73142:73602 [1] NCCL INFO Could not enable P2P between dev 1(=42000) and dev 0(=a000)
tensor1:73142:73601 [0] NCCL INFO Channel 01 : 0[a000] -> 1[42000] via direct shared memory
tensor1:73142:73602 [1] NCCL INFO Channel 01 : 1[42000] -> 0[a000] via direct shared memory
tensor1:73142:73601 [0] NCCL INFO 2 coll channels, 2 p2p channels, 2 p2p channels per peer
tensor1:73142:73601 [0] NCCL INFO comm 0x55d409f64000 rank 0 nranks 2 cudaDev 0 busId a000 - Init COMPLETE
tensor1:73142:73602 [1] NCCL INFO 2 coll channels, 2 p2p channels, 2 p2p channels per peer
tensor1:73142:73602 [1] NCCL INFO comm 0x55d3fee0e000 rank 1 nranks 2 cudaDev 1 busId 42000 - Init COMPLETE
tensor1:73142:73598 [0] NCCL INFO Launch mode Group/CGMD
 256/1875 [===>..........................] - ETA: 2:30 - loss: 0.45892020-07-09 12:30:29.803542: E tensorflow/stream_executor/cuda/cuda_event.cc:29] Error polling for event status: failed to query event: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered
2020-07-09 12:30:29.803584: F tensorflow/core/common_runtime/gpu/gpu_event_mgr.cc:220] Unexpected Event
status: 1
2020-07-09 12:30:29.803545: E tensorflow/stream_executor/cuda/cuda_event.cc:29] Error polling for event
status: failed to query event: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered
2020-07-09 12:30:29.803665: F tensorflow/core/common_runtime/gpu/gpu_event_mgr.cc:220] Unexpected Event
status: 1
./fail.sh: line 7: 73142 Aborted                 (core dumped) NCCL_DEBUG=INFO TF_FORCE_GPU_ALLOW_GROWTH=true LD_LIBRARY_PATH=/usr/local/cuda-10.1/extras/CUPTI/lib64/:$LD_LIBRARY_PATH TCMALLOC_LARGE_ALLOC_REPORT_THRESHOLD=99999999999999999999999999999999 LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libtcmalloc.so.4 /home/lminer/anaconda3/envs/separate2/bin/python fail.py

Unfortunately I was not able to reproduce this on P100 or Titan-V.  Can you try running with CUDA_LAUNCH_BLOCKING=1?  That will tell us which kernel cause the crash.

@sanjoy when I run it with that option, the model is loaded into the memory of both GPUs, but only one GPU actually sees any utilization and there is no crash.

@dubey Have you seen similar issues before?

I get the same error when running multi-gpu training with 2 or 3 RTX 2080Tis. My code is very similar to yours, with the exception that I do not use mixed precision.

No I haven't seen this issue before.

Ok guys, I think I've found a solution, which seems to work for me.
I followed the instructions here: https://github.com/NVIDIA/framework-determinism - I enabled os.environ['TF_CUDNN_DETERMINISTIC']='1'
Then I fixed all the random seeds:
random.seed(42)
np.random.seed(42)
tf.random.set_seed(42)

The model has been running without a hitch for many epochs now. Seems that the non-determinism of some operations might cause these multi-gpu issues. Keep in mind - I don't fully understand WHY this works, just know that it does work for a similar problem. Do let me know if this helps.
Also, keep in mind the instructions here: https://github.com/NVIDIA/framework-determinism are a bit different from the ones I originally used (here; https://stackoverflow.com/questions/50744565/how-to-handle-non-determinism-when-training-on-a-gpu/62712389#62712389). Might be worth trying both sets.

I get the same error when running multi-gpu mixed precision training with 2  RTX 2080Ti . Any solution ?

@dolhasz can you confirm that  enforcing determinism indeed solved issue? also, what layer/op do you suspect causes the error?

I have a very similar setup as @lminer, and Luke's solution of NOT setting TF_FORCE_GPU_ALLOW_GROWTH works for me too. Thanks, @lminer!

Ok guys, I think I've found a solution, which seems to work for me.
I followed the instructions here: https://github.com/NVIDIA/framework-determinism - I enabled os.environ['TF_CUDNN_DETERMINISTIC']='1'
Then I fixed all the random seeds:
random.seed(42)
np.random.seed(42)
tf.random.set_seed(42)

The model has been running without a hitch for many epochs now. Seems that the non-determinism of some operations might cause these multi-gpu issues. Keep in mind - I don't fully understand WHY this works, just know that it does work for a similar problem. Do let me know if this helps.
Also, keep in mind the instructions here: https://github.com/NVIDIA/framework-determinism are a bit different from the ones I originally used (here; https://stackoverflow.com/questions/50744565/how-to-handle-non-determinism-when-training-on-a-gpu/62712389#62712389). Might be worth trying both sets.

This works for me. Thanks @dolhasz !
My set up is CUDA10.1, tensorflow 2.4.1, GTX1080Ti x2. Previously, I ran into this error whenever I use my newly added GPU to train. Interestingly, the newly added GPU has GPU id 0

I faced a very similar issue.
I was loading the Tensorflow pretrained model at every function call.
I am not sure but for me this was the problem as the GPU was running out of memory.
Loading the model just once solved the issue.

@lminer ,
Can you please try this comment with the latest stable version v2.7 and let us know the issue still persists.Thanks!


TUM RGBD Point Cloud Issues

I tried to run the TUM RGBD Dataset using the following config file
{
  "name": "Pointcloud Creation",
  "n_gpu": 8,
  "output_dir": "saved/pointclouds/monorec",
  "file_name": "tmrgbd.ply",
  "roi": [
    160,
    480,
    40,
    600
  ],
  "start": 0,
  "end": 300,
  "max_d": 20,
  "use_mask": true,
  "arch": {
    "type": "MonoRecModel",
    "args": {
      "pretrain_mode": 0,
      "checkpoint_location": [
          "saved/checkpoints/monorec_depth_ref.pth"
      ]
    }
  },
  "data_set": {
    "type": "TUMRGBDDataset",
    "args": {
      "dataset_dir": "data/tumrgbd/",
      "frame_count": 2,
      "target_image_size": [
        480,
        640
      ],
      "dilation": 1
    }
  }
}

Dataset: https://vision.in.tum.de/rgbd/dataset/freiburg3/rgbd_dataset_freiburg3_long_office_household.tgz
I'm using FR3 dataset since the intrinsic parameters were set already for this in the module
https://github.com/Brummi/MonoRec/blob/1d05d2483a270fb3b9a7f06e8dc8259760c0305d/data_loader/tum_rgbd_dataset.py#L25-L30
But these are the results i get from the point cloud

This was the output on console

<THE MODEL ARCH OUTPUT>

        (1): ConvReLU2(
          (pad_0): PadSameConv2d()
          (conv_y): Conv2d(64, 64, kernel_size=(3, 1), stride=(1, 1))
          (leaky_relu): LeakyReLU(negative_slope=0.1)
          (pad_1): PadSameConv2d()
          (conv_x): Conv2d(64, 64, kernel_size=(1, 3), stride=(1, 1))
        )
      )
      (3): Refine(
        (conv2d_t): ConvTranspose2d(192, 48, kernel_size=(4, 4), stride=(2, 2))
        (pad): PadSameConv2dTransposed()
        (leaky_relu): LeakyReLU(negative_slope=0.1)
      )
      (4): Sequential(
        (0): ConvReLU2(
          (pad_0): PadSameConv2d()
          (conv_y): Conv2d(96, 32, kernel_size=(3, 1), stride=(1, 1))
          (leaky_relu): LeakyReLU(negative_slope=0.1)
          (pad_1): PadSameConv2d()
          (conv_x): Conv2d(32, 32, kernel_size=(1, 3), stride=(1, 1))
        )
        (1): PadSameConv2d()
        (2): Conv2d(32, 24, kernel_size=(3, 3), stride=(1, 1))
        (3): LeakyReLU(negative_slope=0.1)
      )
    )
    (predictors): ModuleList(
      (0): Sequential(
        (0): PadSameConv2d()
        (1): Conv2d(256, 1, kernel_size=(3, 3), stride=(1, 1))
      )
      (1): Sequential(
        (0): PadSameConv2d()
        (1): Conv2d(128, 1, kernel_size=(3, 3), stride=(1, 1))
      )
      (2): Sequential(
        (0): PadSameConv2d()
        (1): Conv2d(64, 1, kernel_size=(3, 3), stride=(1, 1))
      )
      (3): Sequential(
        (0): PadSameConv2d()
        (1): Conv2d(24, 1, kernel_size=(3, 3), stride=(1, 1))
      )
    )
  )
)
  0%|                                                                   | 0/300 [00:00<?, ?it/s]/opt/conda/conda-bld/pytorch_1587428398394/work/torch/csrc/utils/tensor_numpy.cpp:141: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program.
/opt/conda/conda-bld/pytorch_1587428398394/work/torch/csrc/utils/tensor_numpy.cpp:141: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program.
/opt/conda/conda-bld/pytorch_1587428398394/work/torch/csrc/utils/tensor_numpy.cpp:141: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program.
/opt/conda/conda-bld/pytorch_1587428398394/work/torch/csrc/utils/tensor_numpy.cpp:141: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program.
/opt/conda/conda-bld/pytorch_1587428398394/work/torch/csrc/utils/tensor_numpy.cpp:141: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program.
/opt/conda/conda-bld/pytorch_1587428398394/work/torch/csrc/utils/tensor_numpy.cpp:141: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program.
/opt/conda/conda-bld/pytorch_1587428398394/work/torch/csrc/utils/tensor_numpy.cpp:141: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program.
/opt/conda/conda-bld/pytorch_1587428398394/work/torch/csrc/utils/tensor_numpy.cpp:141: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program.
/opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:3226: UserWarning: Default grid_sample and affine_grid behavior has changed to align_corners=False since 1.3.0. Please specify align_corners=True if the old behavior is desired. See the documentation of grid_sample for details.
  warnings.warn("Default grid_sample and affine_grid behavior has changed "
/opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:1558: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.
  warnings.warn("nn.functional.tanh is deprecated. Use torch.tanh instead.")
100%|█████████████████████████████████████████████████████████| 300/300 [02:03<00:00,  2.42it/s]

These are my dependencies
torchvision==0.6.0
jupyterlab
tensorboard
tensorboardX
opencv-python==<IP_ADDRESS>
scipy==1.7.0
kornia==0.3.2
scikit-image==0.18.2
tqdm==4.61.2
pykitti==0.3.1
colour-demosaicing==0.1.6

and i'm using Pytorch 1.5.0 from docker pytorch/pytorch:1.5-cuda10.1-cudnn7-devel

Hi, thanks for being interested in the project and experimenting with the code!
Firstly, I think that you might have to reconfigure the network to use a depth range that is suitable for the given dataset.
The default inverse depth range for outdoor environments is 0.33 to 0.0025 (3m to 400m). This can be defined in the config file when specifying the architecture ('arch'). I would suggest to use 2.0 to 0.1 (.5m to 10m) or similar.
Secondly, the pointcloud script has cut off values for distances, which need to be set correctly.
I would like to note that while we evaluated tum rgbd to demonstrate that the method can generalize better than other methods to vastly different datasets, it is still not trained for these kind of tasks. Therefore, the predictions will likely be pretty noisy, which is especially noticeable in the pointclouds.

Thanks ! I will experiment with some values of depth and get back with results.

@satyajitghana How is your experiment results? I have the same probem, could you share your configuration file? Thanks.


Deserialization not using DataMember attribute when property is interface

I have a JSON string e.g.
{
  "first_name": "Paul"
  "company": {
    "company_name": "Microsoft"
  }
}

and my class is in the following structure
[DataContract]
class User {
  [DataMember(Name = "first_name")]
  public string FirstName { get; set; }

  [DataMember(Name = "company")]
  public ICompany Company { get; set; }
}

interface ICompany {
  string CompanyName { get; set; }
}

[DataContract]
class Company : ICompany {
  [DataMember(Name = "company_name")]
  public string CompanyName { get; set; }
}

I use a custom auto resolver which just finds a implementation of a interface using autofac (pulled from json.net website)
public class AutofacContractResolver : DefaultContractResolver
    {
        private readonly IContainer _container;

        public AutofacContractResolver(IContainer container)
        {
            _container = container;
        }

        protected override JsonObjectContract CreateObjectContract(Type objectType)
        {
            JsonObjectContract contract = base.CreateObjectContract(objectType);

            // use Autofac to create types that have been registered with it
            if (_container.IsRegistered(objectType))
            {
                contract.DefaultCreator = () => _container.Resolve(objectType);
            }

            return contract;
        }
    }

I then deserialize the json string using
User user = JsonConvert.DeserializeObject<User>(json, new JsonSerializerSettings
                {
                    ContractResolver = _resolver,

                });

Now when I deserialize the json the first_name gets serialized into the FirstName property but the company_name does not, it just returns null. When I use the TraceWriter it states that it cannot find a member called company_name on the ICompany interface (which it obviously cannot as that is set on the Company object class)
Why is JSON.NET not using the DataMember name from the Company object to deserialize against? As it knows about the Compmany object from the custom resolver and it creates that object but just with no values inside.
How can I get it to use the implementation of the interface and not the interface itself?
FYI, this is not the whole object and I cannot just change my property to a class instead of an interface. This is just a slimmed down example of what is happening to all the interface objects.

I've improved the sample to resolve the contract using the resolved type rather than the interface
https://github.com/JamesNK/Newtonsoft.Json/commit/c42d46fb4cf59fbb749bb54dca153059659cc0b5
Note that this will only work with some registrations.


Remove the filenames-ignored-by-default list

Previously, "." and ".." were looked up using strcmp() in
a builtin-ignores list.
Compare them manually char-by-char instead.
This should avoid unneeded calls, returns, array traversing,
and string dereferencing.
Hardcoding .pyc, etc. by default hardly is worth it,
as without them in .gitignore already, user experience
is improper anyway.

Did you actually benchmark/profile this? It seems to have no difference in performance for me.

No I did not.

Hardcoding .pyc, etc. by default hardly is worth it,
as without them in .gitignore already, user experience
is improper anyway.

@ggreer the idea was to move attention from built-in rules to sharing-with-git-or-anything-else.
I.e. force (in good faith) the user to maintain proper .gitignore and ~/.config/git/ignore.
Accordingly, do not confuse ag source reader with builtin-ignores idea.

"Micro-optimization" was the term.


SpecRunner precompiles assets?

Hi. I am having an issue with SpecRunner loading angular-mocks twice when running tests from the terminal (and this is causing infinite loop). Everything works very nicely when running through the mounted engine though.
In a spec file I have sprocket command #= require angular-mocks. When I remove it, I have no access to angular mocks, however when it is there it's causing this file to be loaded twice. After some digging, I managed to find a difference between runner.html and /specs pages - runner.html is using precompiled version of Spec file (with a angular-mocks file content included within it) together with angular-mock file itself. How can I stop it from doing this?
I have created a simple application to reproduce the issue: https://github.com/BroiSatse/angular-jasmine-error. localhost:3000/specs works as charm, rake spec:javascript gets an infinite loop.

Try RAILS_ENV=test rake spec:javascript. I believe this is a dupe of #121. Could you look at helping finish #162 to resolve this?

Changing rails environment works. Thank you very much. I'll have a look at #162 in the evening. Again, thanks for help. :+1:


:rocket: Add simple performance test

This is a very simple test that will currently only work when run on a dev machine due to the value of target in the test script. Target can be overridden on the command line so it could be changed based on the environment it need to run against. Or, different environments can be specified in the script and chosen at runtime. More information available at https://artillery.io/docs/index.html
Things to think about are how this would be work in practice as part of the CI pipeline. Atm Heroku review apps are used but they are deployed by Heroku. That information (the URL) is not available in Travis (but could be calculated). However, the deployments could be moved to become Travis's responsibility, but, I don't think review apps are able to be used taking that approach.
It might be the case that the tests are run against persistent environments and the deployment pipeline is changed e.g. use pre-release branch for all working feature merges and only once everything is working on that branch does it get merged into master (which is still used as the main site's deployment source).

I'm going to close this PR as I've found an easier to way to generate the information I wanted to get out of it. I didn't really want to have performance tests running what I really wanted was to be able to get an idea of whether changes made had made any discernible difference to the execution time of the existing tests.
In order to do that I have been running the tests with the xunit reporter, saving a copy of the report and comparing it between runs: npm run test -- --reporter xunit --grep <filter-word> > <file-name>.xml


[build] Avoid symbol collision with manylinux1

This PR changes build option on unix to avoid possible symbol collision with manylinux build.
It is a copy-paste from pyFAI setup.py
closes #1070

In pyFAI we had to use different options for python 2 & 3 ... how did you manage this ?

LGTM


[BULK] DocuTune - Fix build validation issues: docs-link-absolute

Global effort to fix build validation errors
The Microsoft Skilling team is fixing build validation errors and specific brand inconsistencies in Microsoft technical documentation for the rest of FY23 Q1. This will help address various accessibility, security, and usability issues. This PR was generated using DocuTune. It includes only targeted fixes and minor formatting adjustments.
DocuTune PRs improve quality of technical content by finding and automatically correcting a broad set of issues only where the correction is suitable. Please review within five business days and merge or comment in the PR with any changes you'd like to see. Thank you!
CorrelationId: 0df9c2d6-63f8-4d1e-8b57-b33611a06514
InitiativeId: docutune-build-validation-issues-2022-05
PointOfContact: Alex Buck

#docutune-draft

#docutune-ready

@gewarren @chlowell @mateoatr
Please merge the changes in this PR. For more information, see the description at the top of this PR.
Any pre-existing build validation issues or insufficient Acrolinx scores should be addressed in a separate PR.
#docutune-request-approval


Timber 1.9.5 breaks my site "Ce site rencontre des difficultés techniques."

Hi,
I just updated my website with Timber 1.9.5 and I get this message 'Ce site rencontre des difficultés techniques.' (in english : The site is experiencing technical difficulties. equivalent to the White Screen of Death since WP5.2) on all the pages that use Timber + Twig templates.
My setup (with a custom theme) :

WP last version (5.2.1)
ACF Pro (last version)
CPT UI (last version)
Woocommerce (last version)
WPML (last version)

I tried to desactivate them individually but the issue remains.
In advance, thanks for your help.

This message is just more user friendly 500 in WP 5.2
If you would downgrade timber to earlier version will the problem still
exists?
Also try to turn on WP_DEBUG it should show you the real problem
pon., 3 cze 2019, 12:17 użytkownik AL20<EMAIL_ADDRESS>napisał:

Hi,
I just updated my website with Timber 1.9.5 and I get this message 'Ce
site rencontre des difficultés techniques.' (in french : this website
encountered technical issue.) on all the pages that use Timber + Twig
templates.
I tried to find the meaning of this error message but can't find anything.
Does anyone know it ?
My setup (with a custom theme) :

WP last version (5.2.1)
ACF Pro (last version)
CPT UI (last version)
Woocommerce (last version)
WPML (last version)

I tried to desactivate them individually but the issue remains.
In advance, thanks for your help.
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When I rollback to Timber 1.9.4, the issue is gone.
I set :
define('WP_DEBUG', true);
define('WP_DEBUG_LOG', true);
define('WP_DEBUG_DISPLAY', true);

but I don't get more informations.

I see you are using WPML. From what I remember Wpml also uses twig and there might be some conflict here. If I would have to guess I think it might be related with this update - https://github.com/timber/timber/pull/1963 .
Is there anything in an error_log?

Maybe, I know WPML brings a lot of issue in general…
Here's what I got from the log :
Warning: Cannot declare interface Twig_LoaderInterface, because the name is already in use in /Users/vinsan/Dropbox/DEV/Sites/site_skyfarms_boutique/wp-content/plugins/timber-library/vendor/twig/twig/src/Loader/LoaderInterface.php on line 61

Warning: Cannot declare interface Twig_ExistsLoaderInterface, because the name is already in use in /Users/vinsan/Dropbox/DEV/Sites/site_skyfarms_boutique/wp-content/plugins/timber-library/vendor/twig/twig/src/Loader/ExistsLoaderInterface.php on line 33

Warning: Cannot declare interface Twig_SourceContextLoaderInterface, because the name is already in use in /Users/vinsan/Dropbox/DEV/Sites/site_skyfarms_boutique/wp-content/plugins/timber-library/vendor/twig/twig/src/Loader/SourceContextLoaderInterface.php on line 38

Warning: Cannot declare class Twig_Loader_Filesystem, because the name is already in use in /Users/vinsan/Dropbox/DEV/Sites/site_skyfarms_boutique/wp-content/plugins/timber-library/vendor/twig/twig/src/Loader/FilesystemLoader.php on line 324

Warning: Cannot declare class Twig_Environment, because the name is already in use in /Users/vinsan/Dropbox/DEV/Sites/site_skyfarms_boutique/wp-content/plugins/timber-library/vendor/twig/twig/src/Environment.php on line 1638

Warning: Cannot declare interface Twig_CacheInterface, because the name is already in use in /Users/vinsan/Dropbox/DEV/Sites/site_skyfarms_boutique/wp-content/plugins/timber-library/vendor/twig/twig/src/Cache/CacheInterface.php on line 60

Warning: Cannot declare class Twig_Cache_Null, because the name is already in use in /Users/vinsan/Dropbox/DEV/Sites/site_skyfarms_boutique/wp-content/plugins/timber-library/vendor/twig/twig/src/Cache/NullCache.php on line 42

Fatal error: Cannot redeclare twig_cycle() (previously declared in /Users/vinsan/Dropbox/DEV/Sites/site_skyfarms_boutique/wp-content/plugins/woocommerce-multilingual/vendor/twig/twig/lib/Twig/Extension/Core.php:288) in /Users/vinsan/Dropbox/DEV/Sites/site_skyfarms_boutique/wp-content/plugins/timber-library/vendor/twig/twig/src/Extension/CoreExtension.php on line 332

Yeah, it looks like it could be because of a conflict with WPML's version of Twig. We upgraded the Twig version that 1.9.5 ships with — let me see if there's a way I can investigate that

@AL20 I'm not able to trigger the error you reported, but I'm pretty confident this is an issue with a collision of Twig versions. Can you replace your wp-content/plugins/timber-library directory with this test version and see if that resolves the issue?
https://www.zipshare.com/download/eyJhcmNoaXZlSWQiOiI3YzVhMjY0NS0yNGQwLTQ3NjQtYmQyZC1mMWQxNjBkNTYzMzYiLCJlbWFpbCI6ImphcmVkQHVwc3RhdGVtZW50LmNvbSJ9

I use your test version and ahve a new error :
Fatal error: Uncaught Error: Class 'Twig\Loader\FilesystemLoader' not found in /Users/vinsan/Dropbox/DEV/Sites/site_skyfarms_boutique/wp-content/plugins/timber-library/lib/Loader.php:144 Stack trace: #0 /Users/vinsan/Dropbox/DEV/Sites/site_skyfarms_boutique/wp-content/plugins/timber-library/lib/Loader.php(102): Timber\Loader->get_loader() #1 /Users/vinsan/Dropbox/DEV/Sites/site_skyfarms_boutique/wp-content/plugins/timber-library/lib/Timber.php(309): Timber\Loader->choose_template(Array) #2 /Users/vinsan/Dropbox/DEV/Sites/site_skyfarms_boutique/wp-content/plugins/timber-library/lib/Timber.php(377): Timber\Timber::compile('pages/page-home...', Array, false, 'default', true) #3 /Users/vinsan/Dropbox/DEV/Sites/site_skyfarms_boutique/wp-content/plugins/timber-library/lib/Timber.php(404): Timber\Timber::fetch('pages/page-home...', Array, false, 'default') #4 /Users/vinsan/Dropbox/DEV/Sites/site_skyfarms_boutique/wp-content/themes/skyfarms/home.php(40): Timber\Timber::render('pages/page-home...', Array) #5 /Users/vinsan/Dropbox/ in /Users/vinsan/Dropbox/DEV/Sites/site_skyfarms_boutique/wp-content/plugins/timber-library/lib/Loader.php on line 144

Crap. Well at least it's new. Can you deactivate plugins and isolate which one causes the issue? For example, if it's just WPML that'd be helpful so I can try to simulate the issue on my end

Just thought I'd chime in on this one...
Ran core & plugin updates last night on 14 dev sites, all of the ones using Timber are WSOD right now.
Running  WP 5.2.1
For this site, all plugins disabled except for Timber.
I roll back to Timber 1.9.4 all is fine....Error makes more sense if Twig got updated.
Fatal error: Uncaught Twig\Error\LoaderError: Unable to find template<EMAIL_ADDRESS>(looked into: /app/wp-content/themes/gesso/pattern-lab/source/_patterns/03-components) in<EMAIL_ADDRESS>at line 3. in /app/wp-content/plugins/timber-library/vendor/twig/twig/src/Loader/FilesystemLoader.php:266 Stack trace: #0 /app/wp-content/plugins/timber-library/vendor/twig/twig/src/Loader/FilesystemLoader.php(161<EMAIL_ADDRESS>#1 /app/wp-content/plugins/timber-library/vendor/twig/twig/src/Environment.php(351<EMAIL_ADDRESS>#2 /app/wp-content/plugins/timber-library/vendor/twig/twig/src/Environment.php(445<EMAIL_ADDRESS>#3 /app/wp-content/plugins/timber-library/vendor/twig/twig/src/Template.php(369<EMAIL_ADDRESS>NULL) #4 /app/wp-content/plugins/timber-library/vendor/twig/twig/src/Environment.php(497) : eval()'d code in /app/wp-content/plugins/timber-library/vendor/twig/twig/src/Loader/FilesystemLoader.php on line 266

Hey @jarednova - any plans to release a hotfix to the one-click version of this on WP, or should we just be rolling back and holding on for a 1.9.6 version.

https://github.com/timber/timber/issues/2016#issuecomment-498838477 here is your answer

Not Sure if this should be a new issue or If I can tack onto this one, But I've also found that 1.9.5 Breaks a part of my site. it has a conflict with Paid Membership pro directory.
I looked through the other plugin and don't see the use of any twig files so I don't think it could be that.
What makes this error hard for me to debug is that WP debug spits out no error, just a white screen.
For now, I will just roll back to fix the issues

Hi and can you check your error logs? There should be some info there.
pt., 7 cze 2019, 18:31 użytkownik Ivan Villa<EMAIL_ADDRESS>napisał:

Not Sure if this should be a new issue or If I can tack onto this one, But
I've also found that 1.9.5 Breaks a part of my site. it has a conflict with
Paid Membership pro directory.
I looked through the other plugin and don't see the use of any twig files
so I don't think it could be that.
What makes this error hard for me to debug is that WP debug spits out no
error, just a white screen.
For now, I will just roll back to fix the issues
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@ivillacreative thanks for the heads-up on that. Yes, please post the logs so we can see what's triggering that. I'm also in the midst of a release to pull 1.9.5 and hold 1.9.x users at an earlier version while we smooth out upgrade issues

I wish I could, In this case, both the logs and page come out completely empty with no errors. It's a very odd Issue, I've been chatting with support at Paid Membership pro and they say they don't use twig so They don't believe its that for them.
sorry I can't be more help.

I've this error on a site as well (running on Timber version 1.10.0)
PHP Warning:  Cannot declare interface Twig_CacheInterface, because the name is already in use
I've done a grep Twig_CacheInterface ./* -R and found that it's running on two locations.
./vendor/twig/twig/lib/Twig/CacheInterface.php
./public/wp-content/plugins/timber-library/vendor/twig/twig/lib/Twig/CacheInterface.php

The twig libary in the vendor directory is installed using composer for Themosis Framework v1.3.3
After downgrading Timber to v1.9.4 the issues went away.

Thanks @tomasvanrijsse — good to know about that conflict.
We've pulled 1.9.5 due to the compatibility issues like this one. I'm considering pulling 1.10.* from WordPress.org too. I'm wondering (out loud here) if the conflict risk outweighs the benefit from 1.10 vs. 1.9. Perhaps its better to leave things at a lower version for plugin/wp.org users — whereas Composer users can continue to upgrade (since composer can better manage versions/conflicts)

I posted this in other places, but just to make sure it's clear:

So after further investigation, it seems that the issues here are overlapping but down to a central problem: WPML's Twig conflicts with Timber's Twig. From my testing, the issue only manifests itself with the WooCommerce and WooCommerce Multilingual plugin running.
Unfortunately, attempting to rollback our Twig version (which seems to solve the issue under some limited circumstances) creates a chain of other issues that need to be resolved.
I'm going to release 1.10.0 as a version that does NOT work with these plugins (at least, for the time being). I'll note in the release notes to hopefully prevent developers from upgrading and then getting frustrated. So what should you/they do? I recommend using 1.9.2 — this seems to be stable before these various WPML / WooCommerce issues started. Once 1.10 is released I'm going to pull 1.9.5, 1.9.4 and 1.9.3. The only thing they seem to have done is introduce a raft of compatibility issues for sites.
We're in talks via email with the WPML team to hopefully figure out a long-term solution to this for 1.10 and beyond

A new release of WPML is coming soon which will resolve the conflicts. I'm in touch with their team and will post an update once I've verified the fix

Great news to anyone following this thread! As of 4.2.8 WPML no longer ships with Twig — which means no more version conflicts!


feat: add a test with notablescan for MongoDB

Closes https://github.com/prisma/team-orm/issues/1121

Note: to be merged after the release only (happening very soon)


Check for full path in FC004

FC004: Use a service resource to start and stop services
http://acrmp.github.io/foodcritic/#FC004
Foodcritic reports FC004 for this resource:
execute 'start the apache2 service' do
  command 'service apache2 start'
  not_if "/usr/sbin/service apache2 status | grep 'apache2 is running'"
end

But not this one:
execute 'start the apache2 service' do
  command '/usr/sbin/service apache2 start'
  not_if "/usr/sbin/service apache2 status | grep 'apache2 is running'"
end

If this behavior is by design, or I'm mistaken, please help clarify for me!
Thanks!

Ah, false negative rather than positive.  Yeah both of those should alert for FC004