[BUG] If Observer serializer is set, kwargs cause an unexpected argument error.

Describe the bug
if the Observer._serializer is not empty, the .serialize function will try to use it to generate the message_body. Using a standard ModelSerializer from DRF will cause a TypeError because of the arguments.
Removing self from the call will then create another TypeError unexpected keyword because the Field init does not support kwargs.
This part of the code is not covered in the test.
To Reproduce
Steps to reproduce the behavior:

Create a custom Observer extending ModelObserver (or similar).
Override the init method, while first calling the super() then assigning a Serializer using self.serializer(serializer)
Subscribe to the consumer using the custom Observer

Expected behavior
The serializer should be able to serialize the instance.
LOG
No Log
Additional context
Simply using self._serializer(instance).data will return the serialized instance under a ReturnDict format that can be converted into JSON.
Since this options is not covered in the test, I would guess that it is a very specific edge case or that it is a placeholder for something else but the fix seems to be simple.

This should be better documented, the _serializer should be a function not an instance of a DRF ModelSerializer.
The main reason is you likely need to include information such as the signal since the model observer will fire on all sorts of events include deleting a model.
Could consider detecting if it is an instance of ModelSerializer and calling it differently.
Im assuming your reason for changing Serialization on the observer is to reduce database load if you have many subscribers?

Thank you. I could not make sense from how the _serializer was set by the code and the tests, hence why I assumed it would use a standard ModelSerializer class.
I am generating dynamic observer with a single common consumer through a configuration file, having the message serialized directly in the Observer allows me to have a single end point for all of them that wrap around my standard DRF view directly.
A little like that:
WEBSOCKET_OBSERVER = {
    'observable_models': {
        'notifications': {
            'model': 'app.notifications.models.Notification',
            'function': 'general_observer',
            'route': '/api/notifications/',
            'serializer': 'app.notifications.serializers.NotificationSerializer'
        }
    }
}

oh ok, for this in the bast i have used a multiplexer so that from the client side it is one connection but on the server side i have a consumer for each type, i found this gives me more flexibility when it comes to things like permission checks etc.

This is the general idea. The generic consumer act as a multiplexer and redirect the signal to the right function and check the permission through the DRF Api route. This way I don't have to write a consumer for each model and can simply "activate" the consumer in the setting.

It is with noting that the serializer attached to the model observer is called on the thread were the object is changed. Then the result is sent over the channel layer to all the subscribe consumer instances.  This means you do not have access to the user during serialization.

Yes this was noted. Another permission check need to be done on the consumer side to ensure that the user has the permission to see the signal event.
Doing the serialization on the consumer side is also an option that I though of. The gain on the database use may be minor since the consumer still need to check the permission, depending on how the permission are checked, for example, stateless token will not incur a DB check.
In the end, using the serializer directly in a modified ModelObserver was simpler since I already needed to modify _connect to dispatch the signal content on pre_delete instead of post_delete for my use case.
By the way, thank you for this library.

One thing worth noting is depending on the channel layer you are using there might be a limit on the message size.
Just ensure the messages you encode are have predictable size you can be sure works with your selected channel layer.
If your using redis (most people are) then the size limit is quite large: 512 Megabytes (i would hope most peoples DRF sterilisers do not return this much data for a single object... ) but if you use something else Rabit the limit is 128MB however many cloud hosted rabbit systems might have much lower limits than these.


Keyboard binding to on_textinput closes as soon as any TextInput widget is focussed and unfocused in Kivy

Software Versions

Python: 3.10
OS: Windows 11
Kivy: master
Kivy installation method: pip git

Describe the bug
When binding to "on_textinput" to a Keyboard instance the keyboard gets closed as soon as you focus/click on any textinput and click out of it again. It should be noted, that the callback bound to "on_textinput" still receives inputs as long as you 1. haven't clicked any textinput 2. are still in the textinput you clicked. Just as soon as you leave the textinput the callbacks cease. The keyboard dispatches "keyboard_closed" as soon as you click into a textinput but still dispatches everything.
The callback works again as soon as you enter any textinput again.
Expected behavior
One of two things should happen

The keyboard never closes or always receives on_textinput callbacks.
The keyboard completely closes and instantly (not after you left  the textinput) stops all dispatches not just the "on_textinput"

Code and Logs and screenshots
from kivy.app import App
from kivy.core.window import Window
from kivy.uix.floatlayout import FloatLayout
from kivy.uix.textinput import TextInput

def text_input(keyboard, text):
    print(f"TI : {keyboard}, {text}")

class Root(FloatLayout):
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        keyboard = Window.request_keyboard(self.keyboard_closed, self, 'text')
        keyboard.bind(on_textinput=text_input)
        self.add_widget(TextInput(size_hint=(1,0.2)))

    def keyboard_closed(self):
        print(f"keyboard closed for {self}")

class MApp(App):
    def build(self):
        return Root()

MApp().run()

For anyone interested you can kid of avoid this by setting Config.set("kivy", "keyboard_mode", "systemandmulti")
This doesn't however solve the inconsistency with the even unbinding.

So the issue is caused by some odd SDL behaviour. Basically SDL_TEXTINPUT is enabled by default on Desktop platforms but disabled on mobile platforms. Now on desktop whenever anyone (here FocusBehaviour) requests a keyboard SDL_StartTextInput is called which on mobile brings up the onscreen keyboard but has no effect on Desktop (unless SDL_TEXTINPUT was disabled prior). Now if the anyone (FocusBehaviour here again) "releases" the keyboard SDL_StopTextInput is called which hides the keyboard on mobile and tells SDL to stop receiving/dispatching SDL_TEXTINPUT. (All this is inside kivy/core/window/_window_sdl2.pyx).
My proposed fix would be (I will open a PR soonish after some testing)
THIS IS THE CURRENT CODE
def hide_keyboard(self):
    if SDL_IsTextInputActive():
        SDL_StopTextInput()

This is my proposed change
def hide_keyboard(self):
    if (platform == 'android' or platform == 'Darwin') and SDL_IsTextInputActive():
        SDL_StopTextInput()

I am aware that this does not fix the issue for mac desktops. But since I cannot test if the issue actually occurs there I disregarded it as negligent until either I or someone else can look into this for macs.
Additionally, I assumed that it's better to fix it partly for other systems but keep functionality consistent with others.

How about https://kivy.org/doc/stable/api-kivy.uix.behaviors.html#kivy.uix.behaviors.FocusBehavior.keyboard_mode ?
TextInput:
    keyboard_mode: 'managed'

Cause here, you're actually requesting the keyboard on your own, (and you expect to receive events even outside of theTextInput)
#8292 seems hacky as in auto mode we expect to not receive events anymore.

Thought of that initially as well. Doesn't work, however.
First of

https://github.com/kivy/kivy/pull/8292 seems hacky as in auto mode we expect to not receive events anymore. (even on Desktop platforms)

I kinda disagree, cause on Desktop you always want to get the keyboard inputs for SDL_TEXTINPUT and even if you don't they are on by default so you have to disable them anyways manually.  So in auto mode we need to call SDL_StopTextInput otherwise its getting dispatched until you enter the text input. (Note: Unless the user bound to Window.on_text_input not stopping textinput has no effect on anything else since focus behaviour manages callback binding byitself and disconnects from SDL_TEXTINPUT).
I also believe that this should be the preferred way of doing SDL event callbacks as managing them privately (e.g. in your own class) instead of for everyone (by disabling it on the SDL layer.) causes less confusion and issues. But I am not the one to decide that so if you disagree that is totally fine with me.
I can see why one could call the fix hacky but in the end, it's not really violating any existing principles.
Maybe it could make use of an additional statement for the unlikely case that a user wants to disable text input on Desktop for some reason.
keyboard_mode:'managed' doesn't really work. Cause then you need to manually call show_keyboard and hide_keyboard which is not only very laborious but also causes the error again since hide_keyboard calls release_keyboard which then calls hide_keyboard as defined in _window_sdl2.pyx.
Tldr; keyboard_mode : 'managed' requires to manually toggle keyboard which not only is laborious but also doesn't prevent the error from happening.

@misl6 Do you mind running the checks - cause if they fail then I have to rethink this eitherway

@CoreTaxxe: Trying to see what is required to move this issue along. What does "running the checks" mean here?

I meant the PR git checks that are run before merging. (For the linked PR). Since misl6 and I didn't agree on the topic I at least wanted to know if my implementation would work.

@CorreTaxxe: oh, right. In GitHub, if you go to your fork you can select Actions and inherit them from the original, and then the checks are run against each of your commits.

Oh cool good to know Thanks!

@misl6 I implemented it a bit differently now with a config entry defaulting to false that controls this behaviour for desktop platforms


Go-to definition and renaming doesn't work for table's row type parameter

Description:
$title

Source code:
type Person record {|
    readonly int id;
    string name;
|};

type Student record {|
    *Person;
    int examYear?;
|};

function doThis() returns table<Student> {
    table<Person> tHuman = table [{id: 1, name: "Dulaj"}];

    table<Student> tStudent = tHuman.map(function(Person human) returns Student {
        return {
            id: human.id,
            name: human.name
        };
    });
    return tStudent;
}

Try renaming table<Person<cursor>>
Steps to reproduce:
Affected Versions:
OS, DB, other environment details and versions:
Related Issues (optional):

Suggested Labels (optional):

Suggested Assignees (optional):

This is happening due to the token capture logic. We consider the left inclusive and right exclusive approach to find a token. Also, this is only limited to this particular scenario and other symbols are working without issue. Hence considering as an improvement point.


Re-evaluate the need for supporting reachability

My feeling is that network reachability is different enough from accessing REST APIs that there isn’t much value in just building it into SCNetworkAPI. If we find we are writing a lot of per-app reachability code that is the same it might be a candidate for a separate shared library, however, I ALSO don’t feel like we are writing a lot of sharable code in that area, and that the reachability libraries that are our there already are probably enough. Looking at Spies as an example there’s only like 20 or 30 lines of reachability related code outside of the ReachabilitySwift pod, and the bulk of that is very app specific.

To be clear, we currently don't have any support for network reachability built into Netable, and this issue is proposing that we evaluate if we feel like we should add it or not.


Windows PowerShell - Helm commands hanging

Output of helm version:
version.BuildInfo{Version:"v3.4.2", GitCommit:"23dd3af5e19a02d4f4baa5b2f242645a1a3af629", GitTreeState:"clean", GoVersion:"go1.14.13"}
Output of kubectl version:
Client Version: version.Info{Major:"1", Minor:"16+", GitVersion:"v1.16.6-beta.0", GitCommit:"e7f962ba86f4ce7033828210ca3556393c377bcc", GitTreeState:"clean", BuildDate:"2020-01-15T08:26:26Z", GoVersion:"go1.13.5", Compiler:"gc", Platform:"windows/amd64"}
Server Version: version.Info{Major:"1", Minor:"16+", GitVersion:"v1.16.6-beta.0", GitCommit:"e7f962ba86f4ce7033828210ca3556393c377bcc", GitTreeState:"clean", BuildDate:"2020-01-15T08:18:29Z", GoVersion:"go1.13.5", Compiler:"gc", Platform:"linux/amd64"}
Cloud Provider/Platform (AKS, GKE, Minikube etc.):
Windows 10 - 1909
Running Docker Desktop <IP_ADDRESS>
Helm was installed using https://chocolatey.org/packages/kubernetes-helm.  Using a Windows Powershell I having been using the helm commands, but every now and then they do not complete.  It just sits there.
I open another powershell command and sometimes it will work and other times again it will just sit there.. never returning.
Any thoughts or logs I should be looking at ?
Thanks

Hi @whgibbo. I'd first start by using both the --debug and -v 10 for more informative logging. The first enables debug logging from Helm's end. The latter adds verbose logging from the underlying kubernetes client connection.

Hi @whgibbo. I'd first start by using both the --debug and -v 10 for more informative logging. The first enables debug logging from Helm's end. The latter adds verbose logging from the underlying kubernetes client connection.


Allow user agent to pass in parameter X-REQUEST-DEBUG that logs a single HTTP request at DEBUG level. Also allow specifying a custom X-REQUEST-ID.

done


cd + tab does not change colors, setting via zsh does not work

Windows Version
Microsoft Windows [Version 10.0.19044.3448]
WSL Version
Invalid command line option: --version
Are you using WSL 1 or WSL 2?

[X] WSL 2
[ ] WSL 1

Kernel Version
<IP_ADDRESS>
Distro Version
Ubuntu 20.04
Other Software
No
Repro Steps
Since my bug report was closed for no good reason, I have to recreate the issue.
Go to a folder which has a subfolders, Run cd+TAB and you will see stupid folder highlighting:

Expected Behavior
No highlight, just different color like in ls after fix:

Actual Behavior
Highlighting that makes folders' names impossible to read:

I have added these lines at the end of the .zshrc:
zstyle ':completion:*' list-colors "${(@s.:.)LS_COLORS}"
autoload -Uz compinit
compinit
In normal linux distributions, this works and sets cd + TAB autocompletion to pre-set colors,. Here is the proof: https://github.com/ohmyzsh/ohmyzsh/issues/1563#issuecomment-26591369
However, this "fix" does not work in ubuntu WSL, so there is definitely something not correct going on.
PS: Please, do not close unsolved issues. I doubt this is by design.
Diagnostic Logs
No response

WSL does not control the terminal nor the shell.
In your case you need to investigate why your prompt is coloring folders and change your settings.
Based on your screenshots it looks like you're using zsh with oh-my-zsh. I'd recommend looking at themes and the rest of the oh-my-zsh documentation.
Closing since this is not a WSL issue.


Possibility to train custom model via REST API calls ?

Hello all, I am aware of training a new custom model in MS-AZURE through the portal but I would like to do the custom model training process ( upload the training datasets, train, test, deploy ) through REST API.
If not so, Please suggest me how can I do the custom model training programmatically (using Python). Thank you for your consideration.
Regards,
Manoj

I was just talked to our PM about programmatic access to our training portals yesterday.  He suggested we can use the SWAGER interfaces to do most things done manually in the Web Portal.
I was talking to him about the LUIS intent models, which can be found here, https://westus.dev.cognitive.microsoft.com/docs/services/luis-programmatic-apis-v3-0-preview/operations/5890b47c39e2bb052c5b9c2f
but I think we have the same for the "custom speech models" and I will try to find them and get back to you here.
For general Swagger info you can look here, https://docs.microsoft.com/en-us/azure/cognitive-services/speech-service/swagger-documentation#:~:text=The Speech service offers a Swagger specification to,portal can be completed programmatically using these APIs.
When the team gets back next week, I will see if they have any better links to Swagger and Rest documentation for custom speech models, but above is the info I have right now.

Our PM got back to me, and the REST/Swagger docs for the custom model building is here.
https://westeurope.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/CreateModel
Please take a look at these, and I will add a Work Item to our backlog to create SDK API surfaces for these REST APIs, similar to what I would requested for retrieving the list of supported TTS voice for the region and datacenter your application is connected to.

Thank you for the information. I will try this and get back to you if I face any issues.
Regards,
Manoj

Hi @BrianMouncer ,
I also used this option and now trying to use the services using a curl call in a script. I see the call is successful and see the changes ( eg: when I try to create a new custom model). But to do further actions inside the script, I want to get the response which includes the ID of the model it created (I mean in model creation case, I need the model ID). In swagger UI, I see the response headers include Location info and at the end of the path, I can access it. But for some reason, I do not get the response headers including Location info.
for Instance,
response  = requests.post(My_API_curl_call_here)
print(response.headers)

this contains only the below fields
{'Cache-Control': 'no-cache', 'Pragma': 'no-cache', 'Content-Length': '104', 'Content-Type': 'text/plain; charset=utf-8', 'Expires': '-1', 'Server': 'Microsoft-IIS/10.0', 'api-supported-versions': '2.0-apim, 2.0, 2.1, 3.0-beta1, 3.0-rc, 3.0', 'X-RateLimit-Limit': '50', 'X-RateLimit-Remaining': '49', 'X-RateLimit-Reset': '2020-07-13T11:51:09Z', 'X-Powered-By': 'ASP.NET', 'X-Content-Type-Options': 'nosniff', 'X-Frame-Options': 'SAMEORIGIN', 'Strict-Transport-Security': 'max-age=31536000; includeSubDomains', 'Date': 'Mon, 13 Jul 2020 11:51:04 GMT'}
Please suggest to me, how I can get a workaround for this.
Thank you,
Megha

Hi @BrianMouncer ,
I also used this option and now trying to use the services using a curl call in a script. I see the call is successful and see the changes ( eg: when I try to create a new custom model). But to do further actions inside the script, I want to get the response which includes the ID of the model it created (I mean in model creation case, I need the model ID). In swagger UI, I see the response headers include Location info and at the end of the path, I can access it. But for some reason, I do not get the response headers including Location info.
for Instance,
response  = requests.post(My_API_curl_call_here)
print(response.headers)

this contains only the below fields
{'Cache-Control': 'no-cache', 'Pragma': 'no-cache', 'Content-Length': '104', 'Content-Type': 'text/plain; charset=utf-8', 'Expires': '-1', 'Server': 'Microsoft-IIS/10.0', 'api-supported-versions': '2.0-apim, 2.0, 2.1, 3.0-beta1, 3.0-rc, 3.0', 'X-RateLimit-Limit': '50', 'X-RateLimit-Remaining': '49', 'X-RateLimit-Reset': '2020-07-13T11:51:09Z', 'X-Powered-By': 'ASP.NET', 'X-Content-Type-Options': 'nosniff', 'X-Frame-Options': 'SAMEORIGIN', 'Strict-Transport-Security': 'max-age=31536000; includeSubDomains', 'Date': 'Mon, 13 Jul 2020 11:51:04 GMT'}
Please suggest to me, how I can get a workaround for this.
Thank you,
Megha

I just figured it out. Whenever the POST call is not successful, the header will not contain Location info.

Closing as there hasn't been any activity for a while. Please reopen if you need to follow up.


Get list of subreddits?

Was hoping to find a method get_subreddits() which returned a list of all subreddit names, is this possible.
Side Note : Examples.md "r = reddit.Reddit" should be  "r = reddit.Reddit()"

Here's the code I use to generate a list of subreddits for inclusion in a select tag. It doesn't work very fast, but it does get done eventually
     ua = ''.join([random.choice(string.ascii_letters+string.digits) for i in xrange(0, 10)])
            r = requests.get('https://www.reddit.com/reddits.json?limit=100', headers={'user-agent': ua}).json()
            self.subs = [stanza['data']['display_name'] for stanza in r['data']['children']]
            while r['data']['after'] is not None:
                old_req = r
                ua = ''.join([random.choice(string.ascii_letters+string.digits) for i in xrange(0, 10)])
                r = requests.get('https://www.reddit.com/reddits.json?limit=100', headers={'user-agent': ua}, params={'after': old_req['data']['after']}).json()
                [self.subs.append(stanza['data']['display_name']) for stanza in r['data']['children']]
            subs = sorted(self.subs)
            return subs


Create production ready builds which align with Oracle Jan 2018 quarterly update

On 16th Jan 2018 Oracle released the latest quarterly update.  This means the AdoptOpenJDK can create builds at the same code level (jdk8u162-b12) and make them available on the main download page.
openjdk-hotspot builds should be able to be created right away, openjdk8-openj9 cannot be built just yet as we are waiting for an official eclipse/openj9 release level to combine with the openjdk level.
Once created the builds should be tested (including the JCK), and then marked as 'production ready' (see https://github.com/AdoptOpenJDK/openjdk-website/issues/209).

From @gdams Ref: https://adoptopenjdk.slack.com/archives/C09NW3L2J/p1518523866000151 - I've already started working on part 1 of this:

we need to update makejdk.sh to be able to run on a specific tag that we define in a job
we need to update https://github.com/AdoptOpenJDK/openjdk-website-backend/blob/master/sbin/Release.sh#L39 to auto fetch the VERSION var so that we don't have to set it twice (allowing it to fall out of sync)

2 isn't perhaps necessary for an actual release because we are setting the release that we want to build against but it is an issue for nightly builds because it doesn't update automatically as new tags appear (causing the issue Martijn linked to)

openj9 builds have been updated with the the quarterly release at HEAD and have been initiated at https://ci.adoptopenjdk.net/view/Pipelines/job/openjdk8_openj9_nightly_pipeline/ although xlinux is currently failing - see https://github.com/ibmruntimes/openj9-openjdk-jdk8/issues/44.

I'm prototyping a tag option using the jdk10 arm32 builds (since we don't ship these as releases it seemed a safe option!) at https://ci.adoptopenjdk.net/job/openjdk10_build_arm32_linux/
The --tag option to makejdk.sh was added in https://github.com/AdoptOpenJDK/openjdk-build/pull/220

Ref the OpenJ9 build - AIX build machine ran out of space - now fixed https://github.com/AdoptOpenJDK/openjdk-build/issues/222
xLinux build is failing https://github.com/ibmruntimes/openj9-openjdk-jdk8/issues/44
WIndows OpenJ9 has a known issue: http://github.com/eclipse/openj9/issues/845 (Start at http://github.com/eclipse/openj9/issues/845#issuecomment-363406782)

Using the jdk10 arm32 builds wasn't the smart idea I thought it was as they appear to be fundamentally broken even without the tag parameter being specified. Have therefore integrated the option on the xlinux jdk10 build jobs instead ...

sbin/build.sh will also need updated as it has it's own detection of the latest tag that it uses for the top level directory inside the tarball

@sxa555 left some comments  - https://github.com/AdoptOpenJDK/openjdk-build/pull/220

Archiving our conversation for posterity https://github.com/AdoptOpenJDK/openjdk-build/pull/220#discussion_r168516626

openjdk-openj9 "Release Candidate" builds are these ones which I'll tell jenkins to save in the absence of us having an official RC process just now:
https://ci.adoptopenjdk.net/job/openjdk8_openj9_build_ppc64_aix/77/artifact/OpenJDK8_ppc64_AIX_201822020500.tar.gz
https://ci.adoptopenjdk.net/job/openjdk8_openj9_build_ppc64le_linux/74/artifact/OpenJDK8_ppc64le_Linux_201822021300.tar.gz
https://ci.adoptopenjdk.net/job/openjdk8_openj9_build_s390x_linux/73/artifact/OpenJDK8_s390x_Linux_201822020800.tar.gz
xLinux: TBD

OpenJ9 branch updated to openj9-0.8 in https://github.com/AdoptOpenJDK/openjdk-build/commit/d0e331732423bcb8d9086975b7191dd4d04badb6
Builds were initially coming out on the nightly site still labelled on the web site as jdk8u152 but were actually 162 (as you can see inside the tarballs) I'm re-running the nightly pipeline to resolve that with the second build on March 1.
JCK runs are in progress

We now have the builds for the openjdk8-openj9 variant which maps to the Jan 18 oracle update. What is the status for the openjdk8-hotspot builds?

I think I'm reasonably close to having a reasonable mirror in https://github.com/sxa555/openjdk8-temp but need to do some sanity checks first. Also I haven't done anything to mimic the dev branch we build from. I've been test building it from various tags at https://ci.adoptopenjdk.net/view/work in progress/job/sxa_openjdk8_xlinux/.
I've attempted to get mirrors of all tags from 8u144 up to the latest 8u172 - only taking the latest -b revision of each one since those correspond to the GA levels.

Hopefully this update makes sense - I'm in a bit of a daze today ...
So https://github.com/sxa555/openjdk8-temp now finally has (I believe!) the same HEAD as https://github.com/AdoptOpenJDK/openjdk-jdk8u (other than it missing get_source.sh and README but crucially includes the tags for earlier builds.
The questions outstanding now to produce production read HotSpot buids are:

Can someone else give me a sanity check on the codebase to see if it looks sane. That repo was populated using a modified - and still a little "fluid" - version of the script currently sitting on my laptop that is used to populate the OpenJDK+Eclipse OpenJ9 repository under http://github.com/ibmruntimes
We'll need to get this new script in place to populate the repository at adoptopenjdk once we'rte happy it's done the right thing (Obviously, under sxa555 is not the sane place for this) - if we have to remirror this could take a couple of days (machine time)
I have no real idea of what we have in the dev branch (the one we build from) or how to populate it so that will be a prereq to the following steps
Build from the jdk8u162-b12 tag (or will it need an equivalent on the dev branch?)
Run all the tests against it
Ship it! (Subject to the tests not indicating a problem, obvs!)

While I admit the chances of getting all that done before the next quarterly Oracle release comes out is fairly slim, it gives us something to work towards and streamline for next time round ;-)
FYA @neomatrix369 @karianna @lumpfish @gdams @irinarada (feel free to assign yourself to this item along with me if you so wish, but I suspect tagging you is enough to get it on your notifications list now)

Hi @sxa555
1.) It looks sane and you can probably check using a git or local linux diff tool between your repo and openjdk-jdk8u (dev branch)
2.) I'm not sure which new script you are referring to here?
3.) The dev branch is almost identical to master except it contains the 1-2 patches we added for supporting some platforms that OpenJDK upstream refused the patches for.  I'd suggest running a diff between dev and master which should give you what those small patches are...
4.) Yes build from the tag on the dev branch
5.) & 6.) :-)
7.) Profit!  err wait...

Yep it seemed ok to me from diffing the directores other than the couple of other files mentioned
It's one that I got from @yongja79 (I think that's his ID) and I don't think it's on github anywhere, but had support for doing clever stuff with git filter-branch to mirror the tags accurately. It might have been based on the adoptopenjdk one. I've tweaked it a bit to mirror a fixed set of historic tags (The last one for each u level which means it just gets the GA ones. Obviously if we're happy with this I'll chuck it back up into the repo :-)
3+4) My only concern with the patches is how we handle the tags. Should the tags in the master branch or the dev branch have the quarterly level. Possibly master, with a suffix -ao or something for the equivalents in dev. Will need some work to get such tags in the dev branch set up.
5,6,7) Ker-ching! (Hahahaahaaahahahaa)

I thought the hg-git scripts pulled in the tags for both master and dev?  If not that's perhaps where we should fix that.

It put tags in but they weren't correct - didn't seem to be taking in account all the subrepos so they weren't all there when you checked out the tag. I'll look into it again if there's stuff to deal with the dev branch too there already though.

openjdk9-openj9 builds aligning with the Jan18 were created and tested, but the issues around building the correct level of openjdk8-hotspot were still ongoing at the time of the Oracle Apr 2018 quarterly update.
The build issues would now be better continued by building the new update level.
See https://github.com/AdoptOpenJDK/openjdk-build/issues/303 and https://github.com/AdoptOpenJDK/openjdk-build/issues/304.

I'm going to close this as we will not be completing a release of the Jan 18 quarterly update as it has been superceded by the April releases which we have done under https://github.com/AdoptOpenJDK/openjdk-build/issues/303


How to get the value of the key, and access its value pair

Here is what to include in your request to make sure we implement a solution as quickly as possible.
1. Description
Ok so I have a List(CustomObject) that I have successfully filtered and grouped. This returns a List(dynamic). (Key as Key, "it"(is List(CustomObject))
I can iterate through it but failed to get the value of the key as string, then the Keys value pair w/c is a List(CustomObject)
2. Exception
If you are seeing an exception, include the full exception details (message and stack trace).
Exception message:
Stack trace:

3. Fiddle or Project
If you are able,
Provide a Fiddle that reproduce the issue: https://dotnetfiddle.net/25Vjsn
Or provide a project/solution that we can run to reproduce the issue.

Make sure the project compile
Make sure to provide only the code that is required to reproduce the issue, not the whole project
You can send private code here<EMAIL_ADDRESS>
Otherwise, make sure to include as much information as possible to help our team to reproduce the issue.
4. Any further technical details
Add any relevant detail that can help us.

@3ggerhappy
The dotnetfiddle links to different code, can you please use the correct link?

@3ggerhappy
Can you please update the dotnet fiddle?

@3ggerhappy
The dotnetfiddle links to different code, can you please use the correct link?

@3ggerhappy
I'll close this question now. In case you have still questions, reply here.


Document plots are static

Due to #842 , I opened this PR #843 to temporarily show a screenshot of the plots, while we don't find the error. This should be reverted as soon as we find out the error or when https://github.com/ansys-internal/kitware-ansys/issues/20 is fixed.

This is a duplicate of #697

Let's close this one then


NWM2.1 reanalysis question

Hi, I'm new to kerchunk. I followed the example (see below) to convert the NWM S3Files to Zarr.
It worked well for files before 2007.
After 2007, I got an error saying file signature not found,
File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
File "h5py/h5f.pyx", line 96, in h5py.h5f.open
OSError: Unable to open file (file signature not found)
It turns out after 2007 NWM2.1 reanalysis data are not stored as h5 files (they are netCDF files). I wonder if you know of a way of getting around that.
Thanks,
Alex
#my code starts here =============================================
from kerchunk.hdf import SingleHdf5ToZarr
from kerchunk.combine import MultiZarrToZarr
import fsspec

def testme():
    #u='s3://noaa-nwm-retrospective-2-1-pds/forcing/1996/199602182000.LDASIN_DOMAIN1' #this works
    u='s3://noaa-nwm-retrospective-2-1-pds/forcing/2007/2007010100.LDASIN_DOMAIN1'   # but this does not work
    so = dict(
        mode="rb", anon=True, default_fill_cache=False, default_cache_type="none"
    )
    with fsspec.open(u, **so) as inf:
        print (u)
        h5chunks = SingleHdf5ToZarr(inf, u, inline_threshold=300)

if __name__ == '__main__':
    testme()

In [14]: fs = fsspec.filesystem("s3", anon=True)

In [17]: fs.head("s3://noaa-nwm-retrospective-2-1-pds/forcing/2007/2007010100.LDASIN_DOMAIN1", 8)
Out[17]: b'CDF\x01\x00\x00\x00\x00'

It appears to be "classic netCDF CDF-1 format" (see here). That would need a separate conversion class; the file format looks simpler, but I don't know if the old CDF libraries will be as convenient. If the chunking remains the same, there would be, in principle, no problem combining the different file formats into a global kerchunked dataset.
@rsignell-usgs , any idea why it looks like the data format became older in 2007? Or was this some sort of HDF5 (not CDF) -> CDF (not HDF) evolution?

Just add some extra observations: All post-2007 NWM2.1 files are not only bigger in size (540mb each), but also have slightly different naming conventions (e.g., 10-digit<PHONE_NUMBER> vs. 12-digit<PHONE_NUMBER>00).
In any case, I'd appreciate if the people in this forum can help me find a temporary solution. I've spent several days converting pre-2007 files.

I don't anticipate having time to implement a netCDF<4 scanner in the near term, but perhaps someone else has? At a guess, the files are much larger because there is no compression; but maybe the chunking is still the same.

@dialuser, also note that the NWM2.1 data is already available in Zarr format from
https://registry.opendata.aws/nwm-archive/
Specifically:
aws s3 ls s3://noaa-nwm-retrospective-2-1-zarr-pds/ --no-sign-request

The rechunking-and-conversion-to-zarr was done by @jmccreight who would likely be able to answer these questions if necessary.

@rsignell-usgs Thanks for pinging me here.
@dialuser I changed jobs and had covid, your email fell through the cracks. i was looking for your email recently but could not find it. I had several inquiries on this exact topic, which also confused me.
Thanks for these questions. The answer is that no single conversion process or person produced all the LDASIN files here. I'm not fully up on what was done, but I had some similar (but different) myself when processing the data on NCAR systems.
https://github.com/NCAR/rechunk_retro_nwm_v21/blob/da170bf2af462a4a117ceebc39f751d3ba91ea74/precip/symlink_aorc.py#L18
You can see there are essentially 3 different periods of data with different conventions. (at least it's finite, right?)
I can anecdotally confirm what @martindurant uncovered above
jamesmcc@casper-login2[1017]:/glade/p/cisl/nwc/nwm_forcings/AORC> for ff in $f1 $f2 $f3; do echo $ff: `ncdump -k $ff`;  done
/glade/campaign/ral/hap/zhangyx/AORC.Forcing/2007/200702010000.LDASIN_DOMAIN1: netCDF-4
/glade/p/cisl/nwc/nwm_forcings/AORC/2007020101.LDASIN_DOMAIN1: classic
/glade/p/cisl/nwc/nwm_forcings/AORC/202002010100.LDASIN_DOMAIN1: netCDF-4

I believe that the file size difference is because no compression ("deflate level") is available for classic (against @martindurant pointed out), while _DeflateLevel = 2 is applied for the other, netCDF-4 files (that I looked at). I was surprised to see that there is chunking in the netCDF-4 files: for (time, y, x): _ChunkSizes = 1, 768, 922. It appears there is no chunking in the classic (as far as I can tell).
I dont expect much can be done on the NCAR/NOAA end at this point except to take note that this is a problem. I will connect you with at least one other user who is interested in this data. Perhaps you can collaborate on a solution (I may point them here). It would be nice to see. I honestly did not know that all this forcing data was part of the release, i thought that only the Zarr precip field that I processed was what was released.

Note on this point:

It appears there is no chunking in the classic (as far as I can tell)

if the blocks are not compressed, then, from a kerchunk point of view, we can pick any chunking we like on the biggest dimension (and second-biggest, if we choose a chunksize of 1 for the biggest), so it may still be possible to get consistency across the different file species.


svg images on page are rasterized

Which version of PhantomJS are you using? Tip: run phantomjs --version.

2.1.1

What steps will reproduce the problem?

run phantomjs.exe rasterize.js https://commons.wikimedia.org/wiki/File:Test.svg out.pdf
or phantomjs.exe rasterize.js https://commons.wikimedia.org/wiki/File:Wikipedia_logo_%28svg%29.svg out.pdf

open out.pdf and zoom to the image too see it's pixelated

Which operating system are you using?
any: windows, debian

Did you use binary PhantomJS or did you compile it from source?
both

relates to ariya/phantomjs#10634, though in this case opacity is not used

https://bugreports.qt.io/browse/QTBUG-43452 Rendering SVG using WebKit, non-opaque elements are rasterized

proposed (and abandoned) patch by Allan Sandfeld Jensen https://codereview.qt-project.org/#/c/102903/ fixes the issue

works fine in 2.1


Tell if tweet is retweet

Is there any way to tell if a tweet is a retweet? The "retweeted" bool doesn't seem to be changing.

IIRC the retweeted value returns if the user has retweeted that tweet.
If you want to check if a tweet is a RT, then check for the existence of retweeted_status
If retweeted_status has a value, then it is a retweet, or else it's null.

I tried this, but every time it checks if retweeted_status has a value it returns true, yet the retweeted_status object is "invalid". Should I be checking if the JSON object is equal to Invalid? Also, retweeted doesn't seem to ever be returning true, even when a tweet has a value for retweeted status.

Note that retweeted and retweeted_status are completely different things.
retweeted is only true if the authenticated user has retweeted that tweet (regardless if it was a retweet or not).
retweeted_status is null (aka Invalid in Swifter.JSON) if it is not a retweet, and is a JSON object if it is a retweet.

This issue has been answered and is now 2+ months old. It will now be closed.

Hi @meteochu ! It seems retweeted_status is not returning Invalid if it's not a retweet.

It does return .invalid but couldn't check. #286 fixes it


Update legal recipe from 3.0 to 3.1

https://github.com/ibm-granite-community/granite-legal-cookbook/pull/25


Add the ability to register assertions after the main code

Hello!
Your project has impressed me with its usefulness! And I have a suggestion which functionality would be very good to add.
It would be very useful for me to be able to add assertions after the main block with the sample code, and not before it, as I see it in the documentation for markdown-pytest. Something like this:
<!-- name: test_name -->
```
import function

result = function()
```
<!-- name: test_name
```python
assert result == 5
```
-->

I have a lot of projects where this functionality will be very appropriate.

https://github.com/mosquito/markdown-pytest/pull/3 should be works as well

I tried it right now. The comment block located after the code is recognized as belonging to the next block of code.

this blocks must has the same name. Example https://github.com/mosquito/markdown-pytest?tab=readme-ov-file#code-split

I'm talking about invisible code after the visible code block.


Display 5-day forecast with daily 3-hour forecast view

We have implemented a 5-day forecast with detailed daily view that fixes #1 . We decided to display the 5-day view instead of letting the user pick a day and hour because it's a better user experience (when clicking one of the days, the 3-hourly forecast for that day is displayed). We used the 5 day API because it's the one included in the free tier, but this could more or less easily be extended to use the 16 day (paid) API if we finally can use it.
We have implemented it as a Forecast component independent from the current weather display on App.js to avoid merge conflicts, as there is a lot of people already relying on the current weather API. It probably will make sense to fuse both into a single component in the future, after the yet-to-come PRs that rely on the current weather API have been merged.
@akshgpt7 this is ready for review and merging.

Damn! This looks awesome!
Can you just fix the merge conflict and I'll merge it.

@akshgpt7 it should be good now


BLE: With BT_SETTINGS and BT_SMP, second connection blocks the system in connection event notification

Describe the bug
A second BLE connection event makes a circular reference on 'gatt_conn_cb' in linked list callback_list (conn.c), blocking the system in event notification (infinite loop).
This is due to the fact that the function bt_gatt_connected (gatt.c) can register multiple times the gatt_conn_cb object with bt_conn_cb_register here if CONFIG_BT_SETTINGS and CONFIG_BT_SMP are enabled.
To Reproduce
Steps to reproduce the behavior:

Enable CONFIG_BT_SETTINGS  and CONFIG_BT_SMP
Make a first BLE connection and disconnect
Make a second BLE connection

Expected behavior
The gatt_conn_cb callback should not be registered twice
Impact
The system is blocked in infinite loop.
Logs and console output
Environment (please complete the following information):
Additional context

I've just hit this very bug when working on the 3.0 update for https://github.com/zmkfirmware/zmk/
We use multiple connections for our split keyboards, and our central side if getting into this infinite loop.


mac-Typora-filepath

Hi, I follow the step until to cp the inject node to the file of typora but I can get the right file path , because typora is an img, I use the path /Applications/Typora.app/Contents, it does not help. Can you give me some advices?

hi,friend!
The generated binary execution program is designed to inject some code into the app. asar to hijack the verification process.
As long as an activation code that meets Typora's local judgment is entered at the input location, it can enter the network verification process and be returned with the correct verification data by the hook code.
So, you can try unzipping the AppImage package and check if the app. sar file exists in the unzipped directory. If so, you can try the following:

Please copy the generated binary execution program into this directory and execute the binary program
Repackage into AppImage program

生成的二进制执行程序是为了给app.asar注入部分代码，以实现对验证流程的劫持，只要在输入激活码的地方输入能够满足typora本地判断的激活码即可。
进入网络验证流程并被hook代码返回正确的验证数据。
所以，你可以尝试解压AppImage包，检查解压目录下是否存在app.asar文件。
若有，你可以进行以下尝试:

请复制生成的二进制执行程序到这个目录下，并执行该二进制程序
2.重新打包成AppImage程序

Hi, I follow the step until to cp the inject node to the file of typora but I can get the right file path , because typora is an img, I use the path /Applications/Typora.app/Contents, it does not help. Can you give me some advices?

I do not test this on mac platform. if  the file app.asar has found  in the installed path of typora, this way would work correctly.
So you can make a try:

check if app.asar exists in the typora directory.
if so, just copy the executable binary into that directory and execute it.

./node_inject
// You will see some outputs on  screen

remove the binary,  go back to this project and use another binary to generate the codes for activation
open typora software, and input the codes to activate.

no matter your software format is .exe or .deb,.AppImage, just make sure app.asar exists in some directory, and this way will be work theoretically.
test passed platform:
[x] linux ubuntu
[x] windows

Typora can not activated by  this way on mac os.


@media gets overridden by default styles

This code
Button {
  display: block;

  @media screen and (min-width: 48rem) {
    display: none;
  }
}

gets parsed into
@media screen and (min-width: 48rem) {
    .__Button__hash__ {
        display: none;
    }
}

.__Button__hash__ {
    display: block;
}

which results in display: block on any screen, because @media gets overridden by .__Button__hash__

sorry for long replay.
will fix this and a lot of other bugs in next release (~ 8 - 12 Oct)


Buff autoloaders

Autoloaders are easily one of the worst guns at the moment. This should make them significantly more viable in comparison to other options (but hopefully not too overpowered; if this seems too strong I'll fix it lol):

Decreased mass by 47%
Increased mag size by 2
Increased overall RPM by 3

I'm not so sure about the mass buff, I remember changing it on all guns so it was somehow consistent with the model and the density of steel. I think we could honestly review that as a whole and use a better method to get the weapon's volume, since it's using the entity's volume and it's not really correct to assume we're dealing with a solid chunk of steel.

Imo the mass buff here is probably the most important part. Autoloaders weigh many times more than any of the other guns as they are now; for the mass that a single 170mm autoloader takes up, you could instead have 4 203mm howitzers and still have a couple tons to spare. Even if all weights are based off the model size and the density of steel, their weight still seems inconsistent, since a 170mm autoloader would weigh ~17 tons more than a 170mm cannon, which is relatively comparable in model size.


Sticky Element inside Sidebar.Pushable doesn't work because transform: translateZ

Steps

Add a Sidebar.Pushable with a Sidebar and Sidebar.Pusher

Try and add a Sticky inside either element

Expected Result
Sticky items will stick AND be visible
Actual Result
Sticky Items don't stick and are in fact hidden as the css:
.pushable:not(body) {
transform: translateZ(0);
}
or depending on version
.pushable:not(body){
-webkit-transform: translate3d(0,0,0);
transform: translate3d(0,0,0);
}
breaks this.
Version
x.y.z
Testcase
https://codesandbox.io/s/k3vjyzl6nv

Any update on this?
its being long enough

Please can anyone at least offer a workaround for this issue

<Sidebar.Pushable style={{transform:"none"}}>
after adding this its working.. please try it..


Remove rust-toolchain before running builds

This avoids running with the wrong version of the toolchain.
I had the same experience as in https://github.com/rust-lang/docs.rs/issues/555#issuecomment-624631379 - the test succeeds even if I don't change rustwide, so it's not actually testing anything.

Thanks! Could you also add a changelog line?

Sure thing, done.

The windows-latest job doesn't seem to have logs :/

That seems like a bug in GHA, also because another windows-latest build was executed successfully. Merging this!


Feature/extra zoom manager

Description
Add animation into zoom in/out process.
Type of change

[ ] Bug fix (non-breaking change which fixes an issue)
[x] New feature (non-breaking change which adds functionality)
[ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
[ ] This change requires a documentation update

How Has This Been Tested?
Please describe the tests that you ran to verify your changes. Provide instructions so we can reproduce. Please also list any relevant details for your test configuration

[x] Zoom in/out with the wheel.
[x] Zoom in/out with side buttons.

Test Configuration:

Firmware version:
Hardware version:

Checklist:

[x] My code follows the style guidelines of this project
[x] I have performed a self-review of my own code
[x] I have commented my code, particularly in hard-to-understand areas
[x] I have made corresponding changes to the documentation
[x] My changes generate no new warnings
[x] I have added tests that prove my fix is effective or that my feature works
[x] New and existing unit tests pass locally with my changes
[x] Any dependent changes have been merged and published in downstream modules

Deprecated.


gulp watch crashes after restart on Windows 10

The gulp watch crashes after restart on Windows 10. The issue seems  to be caused by a problem with uninstalling the theme's OSGI bundle.
How to reproduce:

Install liferay-7.1-b3 on Windows 10
Set include-and-override=portal-developer.properties in portal-ext.properties
Startup liferay with startup.bat
Install node, yeoman, gulp, and generator-liferay-theme as documented at https://dev.liferay.com/de/develop/tutorials/-/knowledge_base/7-1/creating-themes
Create a new theme with yo liferay-theme
Deploy the newly generated theme with gulp watch (will launch live preview with chrome on port 9089)
Login in to Liferay and select the generated theme as theme for the default site
Modify the theme (e.g. set background-color: red for body tag in my-liferay-theme/src/css/_custom.scss)
Reload the browser at localhost:8080 (if its not automatically reloaded at port 9080) - body background is red
Modify the theme (e.g. set background-color: cyan for body tag in my-liferay-theme/src/css/_custom.scss)
Reload the browser at localhost:8080 (if its not automatically reloaded at port 9080) - body background is cyan
Stop gulp watch task in cmd window with CTRL-C
Restart gulp watch in the same cmd window
[22:18:31] Starting 'watch:clean'...
gulp crashes with the message below:

[22:19:24] 'watch:clean' errored after 53 s
[22:19:24] Error: EBUSY: resource busy or locked, unlink 'C:\liferay\my-liferay-theme\.web_bundle_build\WEB-INF\lib\util-bridges.jar'
C:\liferay\my-liferay-theme\node_modules\liferay-theme-tasks\tasks\watch.js:50
                                        throw err;
                                        ^
Error: EBUSY: resource busy or locked, unlink 'C:\liferay\my-liferay-theme\.web_bundle_build\WEB-INF\lib\util-bridges.jar'

Reload browser at localhost:8080 - screen remains blank
Connect to gogo shell with: telnet localhost 11311
Uninstall the broken theme-bundle with: uninstall bundle-id
Relaunch gulp watch in cmd window
Reload browser at localhost:8080 - theme is working again
Modify _custom.scss
Reload browser at localhost:8080 - modification is visible in browser

Expected behaviour:
A frontend developer using Windows 10 should be able to stop and restart the gulp watch task without having to uninstall the theme manually via the gogo shell.
Additional note:
If you use both, gulp deploy and gulp watch, you end up with two themes deployed with the same name, since one is installed via fileinstall and the other via the deploy process.
Since this is a common mistake which must be expected to be made from users (the current training material for 7.1 Frontend Development suggests this usage pattern, too), the gulp watch task should take care of uninstalling themes of the same name and version installed via the deploy process before installing them via fileinstall / gogo shell.

Thanks @chrberndt just for anyone who is watching the original filing on https://github.com/liferay/liferay-theme-tasks/issues/85 We are looking at this.
Since many of us don't use Window's, it's been a little tough to track down, but my thinking is that it could possibly be an issue with a mix of Window's file locking behavior in general, and perhaps an issue in the gogo-shell-helper package that's not properly releasing the lock on the util-bridges.jar file.
It could also just be a weird quirk of the Windows file system (but my gut tells me that even if so, there should be a workaround), but as soon as we know, we'll push in a fix (or update this comment to reflect if we're not able to fix it from our end).
@jbalsas have you guys experienced this issue at all?

Hey @natecavanaugh, we haven't had the chance to do extensive testing on Windows and were hoping  to put in some time soon.
I'll reach out to some people to see if they've run into this.

Hi @natecavanaugh , Hi @jbalsas , thank you very much for looking into this! I understand that Windows compatibility might not be your first priority in testing but for us in Training it's in fact the most relevant platform. The majority of my trainees attends our trainings with Windows laptops and for a lot of them it's their first contact with Liferay Tooling.

Hey @chrberndt, windows compatibility is indeed one of our priorities. We'll get to it as soon as we can, but provisioning and setting up windows machines takes some time.
As I said, we'll look into it and we're aware of the importance of this for training. Of course, we'd also appreciate any additional help we could get :)

The PR doesn't fix the Additional note problem stated in this comment. It will be addressed in the future.


Handle gracefully null list items

As Arrays are just wrappers around other types and other types are nullable by default, a type of [Person] can include null values. To avoid this you can write [Person!]. If you want to ensure the array exists, you'd add [Person!]!. You can keep wrapping wrapped types in this fashion, if you want to return an array of arrays for instance.

@martijnwalraven on iOS side are you skip items in the list that nil or you add them to array?

@digitalbuddha @BenSchwab
there are 2 ways to solve this: the easiest way just ignore null items or more effort to correctly handle them and null values to the list.

I vote ignore. What else will you do an app code differently? Wouldn't you just ignore null anyway?

I see only one case when the order of items matter and you have some specific logic that depends on this condition list[3] == null

Relay gives you the nulls. Our web code didn't crash. @sav007 can we make configurable (skip nulls directive)

Ok I will skip nulls for now, if someone later really need this we will implement

@sav007: I would say the nulls are as much a part of the response as other values, so I wouldn't skip them. If you don't want to deal with null elements, you should define the list element type as non-null (e.g. [Person!]).


Codelab not working with Current version of WebVR Chrome Emulator Extension

Problem:
Manipulating the controls of the WebVR  extension in Devtools has no effect on the running WebVR Codelab Application.
I have tried reloading both the WebVR application via the browser, as well as restarting the Web Server but it has no effect in resolving the issue stated above.  The manipulating the WebVR controls in emulator do not change orientation or position of objects in Codelab WebVR application.
You may want to update the Codelab to state it is not currently supported by the WebVR Emulator extension.  This issue has been reported via the Extension Support section via Chrome Web Store.
Environment Info:
Win 7 SP 1
WebVR API Emulation 1.1.4
Chrome Version: 59.0.3071.86 (Official Build) (64-bit)

Update:  I was using  Chrome Dev Tools in undocked mode for the issuer reported above.
The WebVR Emulator Extension works if it is Docked to the page you are emulating.
You may want to add a note to the codelab stating that user should have Devtools in Docked mode in order to make use of emulator.
You can close this issue accordingly.


Banned By No Reason :/

Hey,
Me and other 3 people were banned for Lua Executor found and 1 for GodMod but not a single person was using any kind of godmod and lua executor.
I have changed my all trigger events properly and re-checked all of them.
Please Help

It's probably some kind of script that is bringing players to life, check all your scripts and see if there are any scripts that are bringing players to life ...
Priority to esx_ambulancejob

Can I get the ban reasons, do not need the identifiers (only the ban reason it self)

Reason: Lua executor found on player

Can I get the ban reasons, do not need the identifiers (only the ban reason it self)

This is because the player tells the server for a second time that they are loaded TigoAntiCheat.
This may be due to someone restarting their resource using restart TigoAntiCheat or an external tool.
A player would never be able to invoke this function a second time without using a restart command or external tools.

This is because the player tells the server for a second time that they are loaded TigoAntiCheat.
This may be due to someone restarting their resource using restart TigoAntiCheat or an external tool.
A player would never be able to invoke this function a second time without using a restart command or external tools.

Alright!


Admin app

Hey can you help me with the admin app I'm not able to run the code for admin app

Sure


UIFont familyNames not implemented

I am using UIFont's familyNames method to retrieve list of the fonts available on Windows. Without this, there is no way to check if specific fonts are available or not on user's system.
Code:
NSArray *familyNames = [[NSArray alloc] initWithArray:[UIFont familyNames]];
fontNames = [[NSMutableArray alloc] initWithCapacity:0];
_objects = [familyNames sortedArrayUsingSelector:@selector(localizedCaseInsensitiveCompare:)];

for (int i = 0; i < [familyNames count]; i++)
{
    NSArray *fontNamesData = [UIFont fontNamesForFamilyName:[familyNames objectAtIndex:i]];
    [fontNames addObject:fontNamesData];
}

Error:
C:\WinObjC\Frameworks\UIKit\UIFont.mm(147)(null)!56BBA2A2: (caller: 00AD144D) LogHr(26) tid(33b8) 80004001 Not implemented
Msg:[Stubbed function called!] [+UIFont familyNames]

This issue has been fixed and it is available in the latest SDK.


Swift 4.2 (Xcode 10) compatibility

Would be great if some one could make a PR to upgrade this great framework for Swift 4.2 (Xcode 10).
Thanks!

This project already supported Xcode 10, no need additional modification. @saormart


Can't get scrcpy to control android (macOS Big Sur)

[x] I have read the FAQ.
[x] I have searched in existing issues.

Describe the bug
Unable to control my android from my mac, got ADB installed too. The screenshare is fine. Please help

https://github.com/Genymobile/scrcpy/blob/master/FAQ.md#control-issues

Hello guys, I am a newbie and don't know much about GitHub but I am very interested in this project and would like to contribute as much as possible. (I am good at C programming and JAVA). Please help me get started.

Awesome! Thank you 😄


It has to increment and decrement on every clone. Still a lot slower than I would expect.

It's slower than I expected too. In addition to the increment for clones, here's some possible reasons that I haven't verified:

the Rc version is using RunTimeEndian, and the slice version is using LittleEndian
EndianRcSlice<RunTimeEndian> is 40 bytes, and EndianSlice<LittleEndian> is 16 bytes, so the slice is cheaper to pass around


Clean up the public API

Cervus is built with modularity in mind.  This change codifies this design principle by making it easy to only use select parts of Cervus. This is a toned down alternative to #51 and #52.
Currently the source modules are stored in the src directory. A set of top-level files like core.js are also present and they re-export the API from /src/.
The problem with this approach is that nowhere do we check if those top-level re-exporters are up-to-date. This results in errors like this one: https://github.com/michalbe/cervus/blob/b36992b6c238e4ba1b1f3694eb59f1f869b7f8c7/core.js. This file is currently broken on master: ./src/core/gam is misspelled and the ./src/core/math module doesn't have a named math export.
This PR renames src to modules to encourage consumers of the API to deep-import only things they need. The primary way of using Cervus is by importing modules directly from cervus/modules:
import { Game } from 'cervus/modules/core';
import { Box, Plane } from 'cervus/modules/shapes';
import { basic } from 'cervus/modules/materials';
import { vec3, mat4 } from 'cervus/modules/math';

When using the built versions of Cervus from the dist/ folder, the same namespace hierarchy as found under cervus/modules is exported on the Cervus global:
const game = new Cervus.core.Game();
const box = new Cervus.shapes.Box({
  material: Cervus.materials.phong
});

For the record I would prefer to move the individual modules out of the dedicated modules/ directory which would simplify the imports even further: import { Game } from 'cervus/core;.


Build analyzers in source build

We need to build analyzer packages in source build.

cc @dseefeld

We should be fine. These only have a dependency on "Microsoft.CodeAnalysis.CSharp.Workspaces".

What dependencies does that have? (@jaredpar / @jasonmalinowski )?

@JunTaoLuo - guessing the helix failures in an OpenAPI test are not related.  Should we go ahead and merge?

Right. I think I'll retarget this to 3.1 since source build can just apply this as a patch for the 3.0 release. There's no rush or need to get this in to 3.0.

Based on a mail thread I'm on, people are still interested in getting these into the 3.0 branch directly.


InvalidArgumentError: assertion failed: [0] [Op:Assert] name: EagerVariableNameReuse

when i use tensorflow2.1 .I trained my model custom,yesterday，the code can run correctly. But torday is error, I debug my code , I found this code is error ,
self.train_accuracy = tf.keras.metrics.CategoricalAccuracy('train_accuracy')

but this code is actually correct,
Now I assert a variable using this in jupyter notebook, it's wrong !
the error is :
InvalidArgumentError: assertion failed: [0] [Op:Assert] name: EagerVariableNameReuse

who can tell me the reason and some solutions,thanks.
the whole code is:
import os
import numpy as np
import cv2
import tensorflow as tf

class ModelTrain():
    def __init__(self):
        self.loss_object = tf.keras.losses.CategoricalCrossentropy()
        self.optimizer = tf.keras.optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999)
        self.train_loss = tf.keras.metrics.CategoricalCrossentropy('train_loss', dtype=tf.float32)
        self.train_accuracy = tf.keras.metrics.CategoricalAccuracy('train_accuracy')
        self.validation_loss = tf.keras.metrics.CategoricalCrossentropy('validation_loss', dtype=tf.float32)
        self.validation_accuracy = tf.keras.metrics.CategoricalAccuracy('validation_accuracy')

if __name__ == "__main__":
    model_train = ModelTrain()

the error is :
Traceback (most recent call last):
  File "/media/huaxin/tcl3/facepro/hand-gesture-recognition/jester-data-preprocessing_v0.2/test.py", line 18, in <module>
    model_train = ModelTrain()
  File "/media/huaxin/tcl3/facepro/hand-gesture-recognition/jester-data-preprocessing_v0.2/test.py", line 12, in __init__
    self.train_loss = tf.keras.metrics.CategoricalCrossentropy('train_loss', dtype=tf.float32)
  File "/media/huaxin/tcl3/facepro/anaconda3/envs/python3.7.4/lib/python3.7/site-packages/tensorflow_core/python/keras/metrics.py", line 2818, in __init__
    label_smoothing=label_smoothing)
  File "/media/huaxin/tcl3/facepro/anaconda3/envs/python3.7.4/lib/python3.7/site-packages/tensorflow_core/python/keras/metrics.py", line 560, in __init__
    super(MeanMetricWrapper, self).__init__(name=name, dtype=dtype)
  File "/media/huaxin/tcl3/facepro/anaconda3/envs/python3.7.4/lib/python3.7/site-packages/tensorflow_core/python/keras/metrics.py", line 460, in __init__
    reduction=metrics_utils.Reduction.WEIGHTED_MEAN, name=name, dtype=dtype)
  File "/media/huaxin/tcl3/facepro/anaconda3/envs/python3.7.4/lib/python3.7/site-packages/tensorflow_core/python/keras/metrics.py", line 296, in __init__
    'total', initializer=init_ops.zeros_initializer)
  File "/media/huaxin/tcl3/facepro/anaconda3/envs/python3.7.4/lib/python3.7/site-packages/tensorflow_core/python/keras/metrics.py", line 276, in add_weight
    aggregation=aggregation)
  File "/media/huaxin/tcl3/facepro/anaconda3/envs/python3.7.4/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/base_layer.py", line 446, in add_weight
    caching_device=caching_device)
  File "/media/huaxin/tcl3/facepro/anaconda3/envs/python3.7.4/lib/python3.7/site-packages/tensorflow_core/python/training/tracking/base.py", line 744, in _add_variable_with_custom_getter
    **kwargs_for_getter)
  File "/media/huaxin/tcl3/facepro/anaconda3/envs/python3.7.4/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/base_layer_utils.py", line 142, in make_variable
    shape=variable_shape if variable_shape else None)
  File "/media/huaxin/tcl3/facepro/anaconda3/envs/python3.7.4/lib/python3.7/site-packages/tensorflow_core/python/ops/variables.py", line 258, in __call__
    return cls._variable_v1_call(*args, **kwargs)
  File "/media/huaxin/tcl3/facepro/anaconda3/envs/python3.7.4/lib/python3.7/site-packages/tensorflow_core/python/ops/variables.py", line 219, in _variable_v1_call
    shape=shape)
  File "/media/huaxin/tcl3/facepro/anaconda3/envs/python3.7.4/lib/python3.7/site-packages/tensorflow_core/python/ops/variables.py", line 197, in <lambda>
    previous_getter = lambda **kwargs: default_variable_creator(None, **kwargs)
  File "/media/huaxin/tcl3/facepro/anaconda3/envs/python3.7.4/lib/python3.7/site-packages/tensorflow_core/python/ops/variable_scope.py", line 2596, in default_variable_creator
    shape=shape)
  File "/media/huaxin/tcl3/facepro/anaconda3/envs/python3.7.4/lib/python3.7/site-packages/tensorflow_core/python/ops/variables.py", line 262, in __call__
    return super(VariableMetaclass, cls).__call__(*args, **kwargs)
  File "/media/huaxin/tcl3/facepro/anaconda3/envs/python3.7.4/lib/python3.7/site-packages/tensorflow_core/python/ops/resource_variable_ops.py", line 1411, in __init__
    distribute_strategy=distribute_strategy)
  File "/media/huaxin/tcl3/facepro/anaconda3/envs/python3.7.4/lib/python3.7/site-packages/tensorflow_core/python/ops/resource_variable_ops.py", line 1557, in _init_from_args
    graph_mode=self._in_graph_mode)
  File "/media/huaxin/tcl3/facepro/anaconda3/envs/python3.7.4/lib/python3.7/site-packages/tensorflow_core/python/ops/resource_variable_ops.py", line 232, in eager_safe_variable_handle
    shape, dtype, shared_name, name, graph_mode, initial_value)
  File "/media/huaxin/tcl3/facepro/anaconda3/envs/python3.7.4/lib/python3.7/site-packages/tensorflow_core/python/ops/resource_variable_ops.py", line 164, in _variable_handle_from_shape_and_dtype
    math_ops.logical_not(exists), [exists], name="EagerVariableNameReuse")
  File "/media/huaxin/tcl3/facepro/anaconda3/envs/python3.7.4/lib/python3.7/site-packages/tensorflow_core/python/ops/gen_logging_ops.py", line 55, in _assert
    _ops.raise_from_not_ok_status(e, name)
  File "/media/huaxin/tcl3/facepro/anaconda3/envs/python3.7.4/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py", line 6606, in raise_from_not_ok_status
    six.raise_from(core._status_to_exception(e.code, message), None)
  File "<string>", line 3, in raise_from
tensorflow.python.framework.errors_impl.InvalidArgumentError: assertion failed: [0] [Op:Assert] name: EagerVariableNameReuse

the same situation existed yesterday,I uninstall tensorflow2.1 and re-install,the problemis solved,but today same problem exist again,what's the reason, and how can solve this.

@LoveMIssY
please share standalone code for us to replicate the issue faced

Facing the same issue when trying to define a tf variable.
learning_rate = tf.Variable(initial_value=0.0001, trainable=False, dtype=tf.float32)

@matheushent
please share standalone code for us to replicate the issue faced

@Saduf2019  standalone code available on the gist.
After instantiating ConvDiscriminator class the same error raises.

@matheushent
i have replicate the code shared, please find the gist here

@Saduf2019 my bad, sorry. I fixed the error. Here follows the fixed gist.
But this is a very strange problem since sometimes it happens and sometimes everything goes very well.
Note you need install tensorflow addons.

@matheushent
please let us know as the error is fixed can we move this to closed status

@matheushent
please let us know as the error is fixed can we move this to closed status

I am not sure the error is fixed. As I said, sometimes it happens and sometimes everything goes very well.
Actually I made a test and I realized that the error just appears for me if I use GPU. When using CPU the error doesn't appear.
My GPU setup is a RTX 2070 8GB GDDR6.

@matheushent
as this is one of a kind and not able to replicate it, can we move this to closed status, in case it appears again please share the error and code for us to replicate the same

@matheushent
as this is one of a kind and not able to replicate it, can we move this to closed status, in case it appears again please share the error and code for us to replicate the same

Right.

In case somebody runs into this issue, I had a very similar error and it ended up that I had another background process running that was using Tensorflow (I was running a model serving process while trying to a run a model training process). It looks like trying to run two pythong programs that use TF at the same time triggers this error.

i have similar error and fixed it by close other python session using tensorflow. Use 'nvidia-smi' to find running process.

same here, another process running in background triggered this error.

@osushkov Yes, I got the same error.
In the jupyter lab, I used two notebook.
Finally with closing all notebooks, I solved the issue after open with one notebook.

this problem have been solved，this problem happens when there are many GPU in your server，but some GPU have been used by others，so when you create some variables or objects, there is no GPU memory for you,the solution is that you shuold  choose one GPU that have not been used.

this problem have been solved，this problem happens when there are many GPU in your server，but some GPU have been used by others，so when you create some variables or objects, there is no GPU memory for you,the solution is that you shuold choose one GPU that have not been used.

Um, this problem certainly has not been solved. Many of us only have one GPU.

Ran into this issue as well. 1 GPU. This problem goes away if I disable GPU.

For me, the issue was caused by the Chrome GPU process.  Apparently, something in Chrome is using the name "train_loss".  By encapsulating all tensors into a variable scope (like '__chrome' or something), Chrome would do the ML community a huge favor.
I've just filed a bug with Chrome to fix that. https://bugs.chromium.org/p/chromium/issues/detail?id=1086032

Restarting and clearing the output of jupyter notebook worked for me.

Regardless of other underlying circumstances (running on 0/1/many gpus, et.c.) this problem hasn't been solved as long as the error message is totally opaque and unhelpful to anyone not actually developing tensorflow. An addition of 'different gpu processes might be in conflict' would already be a lot better and some more "forensic" information wouldn't come amiss either.

Two or more processes running tensorflow (2.2.0), one of them running a code as simple as var = tf.Variable([3, 3]) will trigger this error.
Solution: kill the other processes.

Honestly, killing other processes is not a solution it self. In my opinion this issue must be open since the problem wasn't solved yet.

Honestly, killing other processes is not a solution it self. In my opinion this issue must be open since the problem isn't solved yet.

Completly agree..  I just wrote "solution" for those googling it..

I had the same problem when working with PyCharm. Tensorflow was working with all my python files but I couldn't run any Ipynb files from Pycharm. I always got that error when defining and TF model.
My tensorflow version in the Jupyter notebook was 2.3.0b, I uninstalled it and reinstalled Tensorflow 2.2.0 and it became normal.
Another factor that might have affected was that initially, the notebook kernel didn't match project interpreter. I had a python3.6 notebook kernel while I used to run all my Tensorflow python codes using python 3.7.7. So I configured the Python 3 kernel to python3.7.7 in jupyter notebook.

This issue should probably be re-opened, as it exists with only one GPU, with all other services killed (excluding Windows service processes). The issue is resolved by adding something like
tf.config.experimental.set_visible_devices([], 'GPU')
to disable the GPU, but that means that you can't train networks using your GPU.

For me, running unitests while also using my library from jupyter on the same machine also triggers the same error, unless I fall back to tensorflow-cpu or use @AndrewKhans 's solution. This can't be the intended behavior - this should be reopened.

I got the the same issue when I run the code at https://www.tensorflow.org/xla/tutorials/compile. I run the code on databricks/GPU (p2.xlarge) with one driver and one worker.
I installed Tensorflow 2.2.0 from
https://docs.databricks.com/applications/deep-learning/single-node-training/tensorflow.html#install-tensorflow-22-on-databricks-runtime-66-ml&language-GPU.
My databricks environment:
6.6 ML, spark 2.4.5, GPU, Scala 2.11
Keras version : 2.2.5
     nvidia-smi
    NVIDIA-SMI 440.64.00    Driver Version: 440.64.00    CUDA Version: 10.2

The error popped at:
  for images, labels in train_ds:
  **if optimizer.iterations > TRAIN_STEPS:  # error popped here !**
      break
   train_mnist(images, labels)

I do not have other python process running the same GPU.

I had the same problem. Please reopen the bug. It's a shame to put this under the carpet...
The issue happens when another process is already using - even partially - the same GPU.
Looks like Tensorflow tries to occupy the same namespace across processes - the type of bug that gets people fired on most companies and promoted at Google Brain...

I am having the same issue with no other processes running. I started to get this error after updating my driver to 418.87. In another node with same GPU series, but earlier driver (396.82), the code works just fine..
After analyzing my code, I discovered the problem and it is funny. In my code, I am using torch and tensorflow and if I import torch first tensorflow will break
import torch
import tensorflow as tf
train_loss = tf.keras.metrics.Mean(name=“train_loss”)
The above code will give InvalidArgumentError assertion failed: [0] [Op:Assert] name: EagerVariableNameReuse

So, basically you're saying that is not possible to run torch and tf models at the same time, right? If it is, this is completely insane and just reaffirms the need of reopening the issue

Any news on this issue?
I'm training multiple models at the same time. I should be able to maximize the usage of my GPUs by training concurrently.

I have the same problem. The only way it works is by disabling GPU, but I want to train the neural network using the GPU. Any update on how to solve this issue? This issue should be reopened

I have the same problem. The only way it works is by disabling GPU, but I want to train the neural network using the GPU. Any update on how to solve this issue? This issue should be reopened

What fixed it for me was switching my TensorFlow version (I moved to some kind of older release). You can do this by uninstalling and reinstalling a specific version.

Just got this error when trying to do
tensorflow.keras.models.load_model(path_to_file)
Already tried to close google and all the things mentioned before, but nothing worked

I just had a similar problem with model.compile() without a using metric while running multiple processes. As soon as I replaced it with model.compile(metrics=['mean_squared_error']) there was now problem anymore, as I suspect a new name was given which is not "train_loss".

I have same issue too. I have 2 laptop. One has rtx2060 mobile and other gtx860M.
I installed linux a while of time ago. before that it is worked smoothly but after i install windows 10 back its doesn't working anymore
I m facing with this error on gtx860m.
Cuda: 10.1 update2
Cudnn: 7.6.5
Windows10
( two laptop has same installation versions)
I tried all possiblities.
I tried python 3.7, python 3.8, anaconda versions, i tried driver version wich comes with cuda, i tried updating driver version to 451. I tried tensorflow 2.2.0 and 2.3.0.
and always i installed windows 10 from zero.
import tensorflow as tf train_loss = tf.keras.metrics.Mean(name=“train_loss”)
or trying to train a model it s not working and giving this error
 File "C:\Users\mehmet\Anaconda3\lib\site-packages\tensorflow\python\ops\resource_variable_ops.py", line 1507, in __init__ 	self._init_from_args(   File "C:\Users\mehmet\Anaconda3\lib\site-packages\tensorflow\python\ops\resource_variable_ops.py", line 1661, in _init_from_args 	handle = eager_safe_variable_handle(   File "C:\Users\mehmet\Anaconda3\lib\site-packages\tensorflow\python\ops\resource_variable_ops.py", line 242, in eager_safe_variable_handle 	return _variable_handle_from_shape_and_dtype(   File "C:\Users\mehmet\Anaconda3\lib\site-packages\tensorflow\python\ops\resource_variable_ops.py", line 174, in _variable_handle_from_shape_and_dtype 	gen_logging_ops._assert(  # pylint: disable=protected-access   File "C:\Users\mehmet\Anaconda3\lib\site-packages\tensorflow\python\ops\gen_logging_ops.py", line 49, in _assert 	_ops.raise_from_not_ok_status(e, name)   File "C:\Users\mehmet\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 6843, in raise_from_not_ok_status 	six.raise_from(core._status_to_exception(e.code, message), None)   File "<string>", line 3, in raise_from InvalidArgumentError: assertion failed: [0] [Op:Assert] name: EagerVariableNameReuse
or stoping like this.
2020-08-05 17:36:46.242249: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudart64_101.dll 2020-08-05 17:36:46.242249: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudart64_101.dll 2020-08-05 17:36:48.741475: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library nvcuda.dll 2020-08-05 17:36:46.242249: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudart64_101.dll 2020-08-05 17:36:48.741475: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library nvcuda.dll 2020-08-05 17:36:49.608631: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties: pciBusID: 0000:01:00.0 name: GeForce GTX 860M computeCapability: 5.0 coreClock: 1.0195GHz coreCount: 5 deviceMemorySize: 4.00GiB deviceMemoryBandwidth: 74.65GiB/s 2020-08-05 17:36:49.608692: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudart64_101.dll 2020-08-05 17:36:49.613753: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cublas64_10.dll 2020-08-05 17:36:49.618772: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cufft64_10.dll 2020-08-05 17:36:49.620362: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library curand64_10.dll 2020-08-05 17:36:49.626329: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cusolver64_10.dll 2020-08-05 17:36:49.629463: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cusparse64_10.dll 2020-08-05 17:36:49.641028: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudnn64_7.dll 2020-08-05 17:36:49.641171: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0 2020-08-05 17:36:49.641781: 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 To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. 2020-08-05 17:36:49.659086: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x18a99c5aff0 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2020-08-05 17:36:49.659142: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version 2020-08-05 17:36:49.659427: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties: pciBusID: 0000:01:00.0 name: GeForce GTX 860M computeCapability: 5.0 coreClock: 1.0195GHz coreCount: 5 deviceMemorySize: 4.00GiB deviceMemoryBandwidth: 74.65GiB/s 2020-08-05 17:36:49.659465: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudart64_101.dll 2020-08-05 17:36:49.659487: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cublas64_10.dll 2020-08-05 17:36:49.659506: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cufft64_10.dll 2020-08-05 17:36:49.659523: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library curand64_10.dll 2020-08-05 17:36:49.659539: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cusolver64_10.dll 2020-08-05 17:36:49.659555: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cusparse64_10.dll 2020-08-05 17:36:49.659576: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudnn64_7.dll 2020-08-05 17:36:49.659646: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0 2020-08-05 17:36:49.751107: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1257] Device interconnect StreamExecutor with strength 1 edge matrix: 2020-08-05 17:36:49.751144: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1263]  	0 2020-08-05 17:36:49.751154: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1276] 0:   N 2020-08-05 17:36:49.751380: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1402] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3121 MB memory) -> physical GPU (device: 0, name: GeForce GTX 860M, pci bus id: 0000:01:00.0, compute capability: 5.0) 2020-08-05 17:36:49.755399: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x18a9c483ba0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2020-08-05 17:36:49.755435: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): GeForce GTX 860M, Compute Capability 5.0

I got this error when I upgraded from tf 2.2 to 2.3. I downgraded back to 2.2 and I am no longer getting the error.

I am training a model with tf 2.3 and is the only python running in cpu and I am getting the same error every time.

I got this error message in a new 2.3 environment (both tensorflow and tensorflow-gpu). When I created a new environment with version 2.2 for both, the problem went away. So this bug shouldn't be closed.

And just that quickly, when I tried to install it on another computer with 2.2.0, the error message came back. Tried it both with and without tensorflow-gpu, and had the same problem.

I'm getting the same error. If I force it to use CPU, code executes fine, when using GPU I get the error:
"InvalidArgumentError: assertion failed: [0] [Op:Assert] name: EagerVariableNameReuse"
Running Win10 Conda setup with GF 930M (Cc 5)
All tests indicate that the GPU is available and tests okay.

I am stuck with the same problem from days, no other processes are running on my GPU. Did someone find a solution?

I am stuck with the same problem from days, no other processes are running on my GPU. Did someone find a solution?

try to downgrade to 2.2.0 if you havent alredy, for me the problem was Paint 3D, which is running in a background process

Yes, thank you. I actually tried to downgrade to 2.2.0 after posting my reply and it resolved the problem for me.

This issue is still persistent and there is no proper cause and solution relationship. Please re-open this issue.

Same problem here! Downgrading to 2.2.0 didn't solve the problem for me ...

It seems that the problem is in Python versions.
I'm trying to repeat tutorial https://www.tensorflow.org/tutorials/text/nmt_with_attention
In py -3.6, tf.2.30 (Windows 7, 64 bit) line
encoder = Encoder(vocab_inp_size, embedding_dim, units, BATCH_SIZE)
creates the same error.
I switched to Windows 10, py -3.7 - no problems.
In both computers tf  uses GPU, cudart64_101.dll
This kind of problems are with tf very common, thus I have installed Pyhton 3.5 (works best with tf 1.*), 3.6, 3.7 (currently seems the best) and 3.8 (already 64b)

It seems that the problem is in Python versions.
I'm trying to repeat tutorial https://www.tensorflow.org/tutorials/text/nmt_with_attention
In py -3.6, tf.2.30 (Windows 7, 64 bit) line
encoder = Encoder(vocab_inp_size, embedding_dim, units, BATCH_SIZE)
creates the same error.
I switched to Windows 10, py -3.7 - no problems.
In both computers tf uses GPU, cudart64_101.dll
This kind of problems are with tf very common, thus I have installed Pyhton 3.5 (works best with tf 1.*), 3.6, 3.7 (currently seems the best) and 3.8 (already 64b)

I don't think that Pyton version is the problem. I am running tf in Win10 with Python 3.7.
I would like to point out that there isn't a single specific line of code that generates the problem.
Even a simple line like model = tf.keras.Sequential() causes the assertion to fail.

Correction to my earlier:
problem seems to be Microsoft:
under Windows 10 both python 3.6 and python 3.7 do not create error;
under Windows 7 (64 bit) 3.6 creates error (python 3.7 is not installed)

Hi! I have 2 laptops with Windows 10, CUDA 10.1, cudnn 7.6.5:

I tested on version 1909, then update it to version 2004 and repeated the test

on Windows 10 Pro, Python 3.7, Tensorflow 2.3 raises the error.
on Windows 10 Pro, Python 3.7, Tensorflow 2.2 everything is working.

On corporate version 1809 with Python 3.7 and Tensorflow 2.3 no this error

May be the problem is in Windows?

Like many comments above, I confirm the problem on tensorflow 2.3. Downgrading to tensorflow 2.2 is working. But it is a workaround but it is definitly not solving the problem on 2.3.
@Saduf2019, we should reopen the issue.

I'm a newb here.  First time trying to get TF working with my Quadro M500M card.  Not sure if it's even relevant, but considering any bit of information could be useful information...
TF 2.3 + CUDA 10.1 + cudnn 10.1 - <IP_ADDRESS> + python 3.8 kept telling me it couldn't find cudnn64_7.dll.  After looking around I found with cudnn 10.1 came cudnn64_8.dll.  I'm new to this so I don't know much, but that just struck me as odd that it was asking for a file from a version of cudnn not meant for CUDA 10.1.  That aside, after pointing my path at cudnn 10.0 - <IP_ADDRESS>, which had the correct file, I got past the previous stumbling block, but then to this error.
tensorflow.python.framework.errors_impl.InvalidArgumentError: assertion failed: [0] [Op:Assert] name: EagerVariableNameReuse
From this line (from the beginner tutorial)
model = keras.Sequential([
keras.layers.Flatten(input_shape=(28,28)),
keras.layers.Dense(128, activation='relu'),
keras.layers.Dense(10)
])

I am having same problem. I can downgrade to 2.2 but then i cant use tf.keras.preprocessing.text_dataset_from_directory like tensorflow tutorial says, and "solution" to that is to use 2.3 but i cant because of problem above. Any suggestions?

I got this error when I upgraded from tf 2.2 to 2.3. I downgraded back to 2.2 and I am no longer getting the error.

i am having same problem. it's really work for me. i uninstall tensorflow2.3 and  i install tensorflow 2.2 .

Hey everyone. After struggling with this issue I finally got tensor flow to work with windows and python. I was getting the eager variable name reuse error. Really was annoying because if you downgrade you can't use functions to import image sets. The other alternative is to not use the GPU...which defeats the purpose if you want train models and use them to make meaningful predictions.
Step 1: Uninstall tensorflow with pip
Step 2: Go into your site-packages folder within your python installation. Delete ever folder having to do with tensorflow. This is very important. Even after you uninstall a python package it still leaves the tensor flow folders. You must remove these. This is the thing that made it work for me.
Step3: Use pip to install the tf-nightly package
Step4: Now tensorflow complains about tensorboard not being present. Go ahead and pip install tensorboard
Step5: You will see it working but tensorflow complains about cudnn64_8.dll not being available.
Step6: Go to nvdia, create an account and download cuDNN8 for v10.1
Step7: Extract the contents of the zip files into your CUDA directory.
Step8: Attempt to run and it should work now 👍
Hope this helps anyone who has been stuck! Onward to your AI dreams!!!

Any other ideas to make it works with tensorflow 2.3.1 please ?

Any other ideas to make it works with tensorflow 2.3.1 please ?

Use Google Colab if you can.

Same issue, any suggestions?

You guys need to use the tf-nightly build package....not the official TF one...and before you install it you need to remove all TF stuff from your python site packages directory. If you don't delete TF out of site packages, tf-nightly install won't work like you think.

You guys need to use the tf-nightly build package....not the official TF one...and before you install it you need to remove all TF stuff from your python site packages directory. If you don't delete TF out of site packages, tf-nightly install won't work like you think.

Thanks, I will try it.

You guys need to use the tf-nightly build package....not the official TF one...and before you install it you need to remove all TF stuff from your python site packages directory. If you don't delete TF out of site packages, tf-nightly install won't work like you think.

Now tf-nightly is 2.5.0.dev20201030, it seems not supports GPU?

@jsudlow, Thank you, indeed, the bug does not seem to appear with tf-nightly. But in my case, I don't have the choise to use tensorflow 2.3.1 because autokeras is only compatible with this version... With tf-nightly, I have another problem (certainly because it is not compatible)

Same problem here with TF 2.3.1:
>>> var = tf.Variable([3, 3])
(...)
2020-11-06 13:28:09.651195: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1402] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 1750 MB memory) -> physical GPU (device: 0, name: GeForce GTX 960M, pci bus id: 0000:01:00.0, compute capability: 5.0)
2020-11-06 13:28:09.651826: I tensorflow/core/common_runtime/process_util.cc:146] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/lib/python3.8/site-packages/tensorflow/python/ops/variables.py", line 262, in __call__
return cls._variable_v2_call(*args, **kwargs)
File "/usr/lib/python3.8/site-packages/tensorflow/python/ops/variables.py", line 244, in _variable_v2_call
return previous_getter(
File "/usr/lib/python3.8/site-packages/tensorflow/python/ops/variables.py", line 237, in <lambda>
previous_getter = lambda **kws: default_variable_creator_v2(None, **kws)
File "/usr/lib/python3.8/site-packages/tensorflow/python/ops/variable_scope.py", line 2633, in default_variable_creator_v2
return resource_variable_ops.ResourceVariable(
File "/usr/lib/python3.8/site-packages/tensorflow/python/ops/variables.py", line 264, in __call__
return super(VariableMetaclass, cls).__call__(*args, **kwargs)
File "/usr/lib/python3.8/site-packages/tensorflow/python/ops/resource_variable_ops.py", line 1507, in __init__
self._init_from_args(
File "/usr/lib/python3.8/site-packages/tensorflow/python/ops/resource_variable_ops.py", line 1661, in _init_from_args
handle = eager_safe_variable_handle(
File "/usr/lib/python3.8/site-packages/tensorflow/python/ops/resource_variable_ops.py", line 242, in eager_safe_variable_handle
return _variable_handle_from_shape_and_dtype(
File "/usr/lib/python3.8/site-packages/tensorflow/python/ops/resource_variable_ops.py", line 174, in _variable_handle_from_shape_and_dtype
gen_logging_ops._assert(  # pylint: disable=protected-access
File "/usr/lib/python3.8/site-packages/tensorflow/python/ops/gen_logging_ops.py", line 49, in _assert
_ops.raise_from_not_ok_status(e, name)
File "/usr/lib/python3.8/site-packages/tensorflow/python/framework/ops.py", line 6843, in raise_from_not_ok_status
six.raise_from(core._status_to_exception(e.code, message), None)
File "<string>", line 3, in raise_from
tensorflow.python.framework.errors_impl.InvalidArgumentError: assertion failed: [0] [Op:Assert] name: EagerVariableNameReuse

I had the same issue today. Using
pip install tensorflow-gpu==2.2.0

"fixed" the problem.
However, possibly unrelated, it now takes a long time (like a minute or more) to go from attaching GPU 0 to start training.  Unclear what is taking so long.

I had the same issue today. Using
pip install tensorflow-gpu==2.2.0

"fixed" the problem.
However, possibly unrelated, it now takes a long time (like a minute or more) to go from attaching GPU 0 to start training. Unclear what is taking so long.

Hi eafpres, I got this error first on tf-gpu-2.3.0 then I switched to tf-gpu-2.2.0 and had the same issue with you.

I switched to tf-gpu-2.2.0 and had the same issue with you.

Today sometimes it’s very fast to start. I cannot find any background process.  But it’s working so I can continue my work!

Hey everyone. After struggling with this issue I finally got tensor flow to work with windows and python. I was getting the eager variable name reuse error. Really was annoying because if you downgrade you can't use functions to import image sets. The other alternative is to not use the GPU...which defeats the purpose if you want train models and use them to make meaningful predictions.
Step 1: Uninstall tensorflow with pip
Step 2: Go into your site-packages folder within your python installation. Delete ever folder having to do with tensorflow. This is very important. Even after you uninstall a python package it still leaves the tensor flow folders. You must remove these. This is the thing that made it work for me.
Step3: Use pip to install the tf-nightly package
Step4: Now tensorflow complains about tensorboard not being present. Go ahead and pip install tensorboard
Step5: You will see it working but tensorflow complains about cudnn64_8.dll not being available.
Step6: Go to nvdia, create an account and download cuDNN8 for v10.1
Step7: Extract the contents of the zip files into your CUDA directory.
Step8: Attempt to run and it should work now 👍
Hope this helps anyone who has been stuck! Onward to your AI dreams!!!

Did until step3 and now getting RuntimeError: The current Numpy installation fails to pass a sanity check due to a bug in the windows runtime error :(

Was having this same issue with Tensorflow v2.3 on a Windows 10 pc with the latest python, conda and pycharm versions, just downgraded to v2.2 and it did the trick. I only needed to go to my Conda prompt, activate my Pycharm environment, uninstall, install and voila! Basically this:
(base) conda activate Pycharm
(Pycharm) conda uninstall tensorflow
...
(Pycharm) conda install tensorflow==2.2

Uninstalling tf 2.3.1 and installing tensorflow==2.4.0rc0 solved my problem.

Just wanted to drop a line to say I faced the same issue today - trying to run the https://www.tensorflow.org/tutorials/text/text_classification_rnn tutorial.
OS: Windows 10 Pro x64
GPU: GeForce 940M
Drivers:

CUDA 10.1
cudNN 7
Nvidia 457.30

Would crash if tensorflow==2.3
Downgrading to tensorflow==2.2 'fixed' the issue but causes other bugs within above tutorial (such as inability to evaluate encoder e.g. encoded_example = encoder(example)[:3].numpy() as it is missing patch a92ff929b818c7dbca2d0c2648ae17e8d6ae3a40.

ror afte

place torch's import behind tf's import works for me.
import tensorflow as tf
import torch
...


Errors when using Example Notebooks

Errors occurred when running the following notebooks. See screenshots for more details.

cube_insights.ipynb

gapfilling_process.ipynb

transfer_learning.ipynb

cube_insights module  (now insights): fix integrated into the get_insights function
helper function is now used correcly to create the according file.
No loss was assigned within the trainer class. The default loss value was update to None. Now this notebook contains the corresponding loss

Thanks for looking into the Issues.
In the notebook gapfilling_process.ipynb there's an issue with the import of ml4xcube.xr_plots. I edited it and wrote a PR (#19).


Create CODE_OF_CONDUCT.md

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fix typo

Thanks, submitted to CQ here: https://dart-review.googlesource.com/c/sdk/+/177520

Thanks, submitted to CQ here: https://dart-review.googlesource.com/c/sdk/+/177520