Debugging?

I'm having a lot of trouble trying to get the concepts on this page to work.
For instance, adding
await Task.Delay(1000); // 1 second delay
Before
CloudSpatialAnchor cloudAnchor = new CloudSpatialAnchor();
Made it work, partly.
Now my script hangs at
await cloudSession.CreateAnchorAsync(cloudAnchor);
And I get no feedback. I don't feel like plowing though the 10.000 lines of code in AzureSpatialAnchorsBridge.cs so if you have any tips, that would be welcome..

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

ID: 81ae8b39-161a-2042-8f61-a344cf115a05
Version Independent ID: a22f1476-b3be-37f0-a55e-398ca36a9d41
Content: Create and locate anchors using Azure Spatial Anchors in Unity
Content Source: articles/spatial-anchors/concepts/create-locate-anchors-unity.md
Service: azure-spatial-anchors
GitHub Login: @RamonArguelles
Microsoft Alias: ramonarguelles

@jelmerS2 You mentioned that adding a delay made  new CloudSpatialAnchor()  work. Were you getting any errors or feedback then?

Yes, so I create a new cloud session and subscribe to the events like so:
cloudSession = new CloudSpatialAnchorSession();             cloudSession.Configuration.AccountId = SpatialAnchorsAccountId.Trim();             cloudSession.Configuration.AccountKey = SpatialAnchorsAccountKey.Trim();             cloudSession.LogLevel = SessionLogLevel.Information;             cloudSession.OnLogDebug += CloudSpatialAnchorSession_OnLogDebug;             cloudSession.SessionUpdated += CloudSpatialAnchorSession_SessionUpdated;             cloudSession.AnchorLocated += CloudSpatialAnchorSession_AnchorLocated;             cloudSession.LocateAnchorsCompleted += CloudSpatialAnchorSession_LocateAnchorsCompleted;             cloudSession.Error += CloudSpatialAnchorSession_Error;
Then further down the line I have function to try to save an anchor, with the added 1 second delay between "localAnchor.AddARAnchor" and "localAnchor.GetNativeAnchorPointer" :
 Quaternion rotation = Quaternion.AngleAxis(0, Vector3.up);             localAnchor = Instantiate(anchorPrefab, hitPosition, rotation);             localAnchor.AddARAnchor();             // If the user is placing some application content in their environment,             // you might show content at this anchor for a while, then save when             // the user confirms placement.             await Task.Delay(1000); // 1 second delay             CloudSpatialAnchor cloudAnchor = new CloudSpatialAnchor();             cloudAnchor.LocalAnchor = localAnchor.GetNativeAnchorPointer();             await cloudSession.CreateAnchorAsync(cloudAnchor);             Debug.Log( $"Created a cloud anchor with ID={cloudAnchor.Identifier}");
After some debugging, I found out that my script "stops. / hangs" at
await cloudSession.CreateAnchorAsync(cloudAnchor);
I hope to see some sort of timeout / error / something, but I just nothing seems to happen?

@jelmerS2 Thanks again for the feedback! I have assigned the issue to the content author to evaluate and update as appropriate.

Hi @jelmerS2. Thanks for your feedback. The article you are following up (articles/spatial-anchors/concepts/create-locate-anchors-unity.md) is meant to explain, in depth and individually, all the different concepts that are required to use Azure Spatial Anchors. That said, it is not meant to be followed in order, top to bottom, to implement a fully working app. It is really meant to be used the other way around: once you have a fully working app (either by following one of the quickstarts, like https://docs.microsoft.com/en-us/azure/spatial-anchors/unity-overview, or one of the tutorials that guides you on writing an app from scratch, like https://docs.microsoft.com/en-us/azure/spatial-anchors/tutorials/tutorial-new-unity-hololens-app), then you can use this article to get a better understanding of what each of the areas of the code is doing.
Please, do give a try to one of the quickstarts or the tutorials to get a fully working app first, and then use this article as a reference to better understand the code in your working app. Thanks!

If there are no further issues, @Alberto-Vega-MSFT, can this now be closed?

@RamonArguelles Thanks for looking into this issue.
@jelmerS2 Please check the above suggestions.
We will now proceed to close this thread. If there are further questions regarding this matter, please tag me in your reply. We will gladly continue the discussion and we will reopen the issue.


Add examples to Docker image

Add examples/ into Docker image.
https://github.com/mitmproxy/mitmproxy/issues/1477#issuecomment-545007956
Checklist

[x] I have updated tests where applicable.
[x] I have added an entry to the CHANGELOG.

I don't see why CodeCov fails.

@mhils that's an easy one. )

Thanks! 🍰

This caused CI to fail on master. I've reverted the commit, feel free to send a new one. :)

@mhils no idea why it fails. Without tox everything works ok. Why use tox for Docker image?

https://github.com/mitmproxy/mitmproxy/blob/1b0fce6abaaf35e28b4f319e2fbc314dad210822/release/cibuild.py#L278-L279
I think the examples should be added here.

Building with tox locally fails.
/tmp/amend-pr-4447 (harfix)$ tox -e cibuild -- build
cibuild create: /tmp/amend-pr-4447/.tox/cibuild
cibuild installdeps: -e.[dev], pyinstaller==4.2, twine==3.4.1, awscli
...
cibuild run-test-pre: PYTHONHASHSEED='234653030'
cibuild run-test: commands[0] | mitmdump --version
Mitmproxy: 7.0.0.dev (+415, commit 8f6cd7c)
Python:    3.9.2
OpenSSL:   OpenSSL 1.1.1k  25 Mar 2021
Platform:  Linux-5.11.10-200.fc33.x86_64-x86_64-with-glibc2.32
cibuild run-test: commands[1] | python ./release/cibuild.py build
Traceback (most recent call last):
  File "/tmp/amend-pr-4447/./release/cibuild.py", line 512, in <module>
    cli()
  File "/tmp/amend-pr-4447/.tox/cibuild/lib/python3.9/site-packages/click/core.py", line 829, in __call__
    return self.main(*args, **kwargs)
  File "/tmp/amend-pr-4447/.tox/cibuild/lib/python3.9/site-packages/click/core.py", line 782, in main
    rv = self.invoke(ctx)
  File "/tmp/amend-pr-4447/.tox/cibuild/lib/python3.9/site-packages/click/core.py", line 1289, in invoke
    rv.append(sub_ctx.command.invoke(sub_ctx))
  File "/tmp/amend-pr-4447/.tox/cibuild/lib/python3.9/site-packages/click/core.py", line 1066, in invoke
    return ctx.invoke(self.callback, **ctx.params)
  File "/tmp/amend-pr-4447/.tox/cibuild/lib/python3.9/site-packages/click/core.py", line 610, in invoke
    return callback(*args, **kwargs)
  File "/tmp/amend-pr-4447/./release/cibuild.py", line 450, in build
    be.dump_info()
  File "/tmp/amend-pr-4447/./release/cibuild.py", line 156, in dump_info
    print(f"cibuild.{attr}={getattr(self, attr)}", file=fp)
  File "/tmp/amend-pr-4447/./release/cibuild.py", line 257, in version
    raise BuildError("We're on neither a tag nor a branch - could not establish version")
__main__.BuildError: We're on neither a tag nor a branch - could not establish version
ERROR: InvocationError for command /tmp/amend-pr-4447/.tox/cibuild/bin/python ./release/cibuild.py build (exited with code 1)
_______________________________________________________________________________ summary _______________________________________________________________________________
ERROR:   cibuild: commands failed
/tmp/amend-pr-4447 (harfix)$

@abitrolly: Can you remind me what's the purpose of having examples/ in the Docker image? I initially merged this because I don't have strong opinions here and it seemed straightforward. Thinking about this a bit longer, I don't see what's the purpose here. If you are concerned about HAR support, my reply would be that we should move it into the core anyhow. :)

@mhils I just want to play with contrib scripts without installing anything on my system.

I'd recommend you just expose a local copy of contrib to your Docker container. :)

Well, the container at least provides imaginary guarantee that examples within match the version shipped.


Tracing and core version alignment (Discussion)

Very interesting to see micrometer tracing initial release! Since it relies on micrometer-core v2. Would there be much sense to have the version parallel?

What do you mean by putting the versions in parallel? Like that we start with 2.0.0 ? That's not planned really.

Right. Similar to how all Jackson modules and even all Micrometer registries stay on the same version, even if that submodule hasn't been changed.
Alternatively providing a BOM that keeps the compatible versions in sync is needed. While Spring's BOM probably would be good enough (for me). Not everyone uses Spring and they might need another option.
I don't feel super strongly about this, but figured it would be good to discuss and be intentional if to try to avoid incompatible versions or confusion in the future.

We have a BOM for tracing https://github.com/micrometer-metrics/tracing/tree/main/micrometer-tracing-bom that takes in micrometer in https://github.com/micrometer-metrics/tracing/blob/main/dependencies.gradle#L29. So it's enough for you to know the micrometr-tracing version and you get the proper micrometer version

That sounds great! Thanks.


New functions recommended to add in the image2image mode.

Very nice plug-in.
I like this more interactive drawing experience.
It is suggested to add a preprocessing function of input image rotation in the image2image module, because the results generated from different angles for images such as faces vary greatly.
At the same time, an option is recommended to add in the image2image generation with mask to let the user decide whether to delete the content beyond the mask.
Thank you very much for your plug-in again, which avoids frequent image import and export.

Your second suggestion is great! Could you link me to a resource that explains the "preprocessing function of input image rotation" for image2img?

Your second suggestion is great! Could you link me to a resource that explains the "preprocessing function of input image rotation" for image2img?

When using image2image through this plugin, the incoming image is based on the orientation of the PS canvas, and it is inconvenient to adjust the orientation of the entire PS canvas. What I mean is that you can consider doing some simple preprocessing on the selected and incoming images, such as rotation and mirroring.
For example, when I use the WebUI to import images, I will use the screenshot tool to adjust the image to a more suitable angle for AI recognition, and then perform image2image. Specifically, I use Snipaste to take a screenshot, then paste it on the screen, rotate it, save it, and then import it into the WebUI.

Suggestion is very simple:
Mask for each generated image must be attached all the time

Strange why I need to paint specific area with white color, If I already selected specific area with lasso tool..


[otp_ctrl] Question on dif programming

The dif has an out of bounds check for dai programming that excludes the digest area.
based on my understanding however, for software partitions, the digest needs to be programmable.  So this address shouldn't be out or range for those partitions.
@msfschaffner let me know if i misunderstood.

@timothytrippel FYI

o my mistake. it turns out there is another dif function specifically meant for the digetsts.


Major goal: improve sampler interface to support batching/async

Batching is added.
Async is still outstanding.

Updates:

We added duet as a dependency of cirq-core and have stubbed out async versions of run, run_sweep, and run_batch on cirq.Sampler.
We updated the gapic generated code for the quantum engine API which includes async support with asyncio (https://github.com/quantumlib/Cirq/pull/5139).
We have a prototype of adding async support to various Engine* classes (https://github.com/quantumlib/Cirq/pull/5219).

There are some additional things we want to add (proper async interface to Quantum Engine, "dynamic" batching that adapts based on request size, etc), but I think the sampler interface itself is in reasonable shape, so we can probably close this and address further improvements in separate issues/PRs.


Fix broken link in README.md

What does this PR do?
Improve Repo

A rather embarrassing mistake! Thanks, and welcome to free-programming-books.


multiple GPUs training on CMU datasets

when i trained the model on cmu datasets with multiple gpus the dataloader function encontered the followed problem. but it's worked with single gpu.
Traceback (most recent call last):
File "run/train_3d.py", line 163, in
main()
File "run/train_3d.py", line 136, in main
train_3d(config, model, optimizer, train_loader, epoch, final_output_dir, writer_dict)
File "/home/gw/Project/voxelpose/lib/core/function.py", line 37, in train_3d
for i, (inputs, targets_2d, weights_2d, targets_3d, meta, input_heatmap) in enumerate(loader):
File "/home/gw/anaconda3/envs/VIBE/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 435, in next
data = self._next_data()
File "/home/gw/anaconda3/envs/VIBE/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 1065, in _next_data
return self._process_data(data)
File "/home/gw/anaconda3/envs/VIBE/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 1111, in _process_data
data.reraise()
File "/home/gw/anaconda3/envs/VIBE/lib/python3.7/site-packages/torch/_utils.py", line 428, in reraise
raise self.exc_type(msg)
RuntimeError: Caught RuntimeError in DataLoader worker process 3.
Original Traceback (most recent call last):
File "/home/gw/anaconda3/envs/VIBE/lib/python3.7/site-packages/torch/utils/data/_utils/worker.py", line 198, in _worker_loop
data = fetcher.fetch(index)
File "/home/gw/anaconda3/envs/VIBE/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 47, in fetch
return self.collate_fn(data)
File "/home/gw/anaconda3/envs/VIBE/lib/python3.7/site-packages/torch/utils/data/_utils/collate.py", line 83, in default_collate
return [default_collate(samples) for samples in transposed]
File "/home/gw/anaconda3/envs/VIBE/lib/python3.7/site-packages/torch/utils/data/_utils/collate.py", line 83, in
return [default_collate(samples) for samples in transposed]
File "/home/gw/anaconda3/envs/VIBE/lib/python3.7/site-packages/torch/utils/data/_utils/collate.py", line 81, in default_collate
raise RuntimeError('each element in list of batch should be of equal size')
RuntimeError: each element in list of batch should be of equal size

Did you solve it ?

same question ; we need a multi-GPU version


Cap the input fields at a max length

Users can input infinite chars which could take up all of storage.

@loreina how should we display max char count

Unbold it and unmisspell characters 👀 Maybe right align too?
My only thing is to make it differentiable from the question
@ailishm might have more to say about this


Add better relation management

Possibility to save relations.
Do not automatically override relations while saving.

https://github.com/vlucas/spot2/pull/137


Sidebar not showing tests or debug options in code

Description
Installed the latest Mocha Sidebar, Node and VS Code; Sidebar doesn't show any test and debug/run test doesn't show in the test files. Mocha is throwing errors trying to render the sidebar. Everything is installed with default options.
I see these errors from Log (Window) also.
[2018-08-02 10:05:11.343] [renderer1] [error] Cannot read property 'passed' of undefined: TypeError: Cannot read property 'passed' of undefined
at _mochaProvider.onDidChangeTreeData (C:\Users\xxxxx\.vscode\extensions\maty.vscode-mocha-sidebar-0.20.17\extension.js:181:56)
at e.fire (c:\Program Files\Microsoft VS Code\resources\app\out\vs\workbench\node\extensionHostProcess.js:97:917)
at mochaProvider.refreshExplorer (C:\Users\xxxxx\.vscode\extensions\maty.vscode-mocha-sidebar-0.20.17\mochaProvider.js:99:35)
at subscriptions.push.vscode.commands.registerCommand (C:\Users\xxxxx\.vscode\extensions\maty.vscode-mocha-sidebar-0.20.17\extension.js:110:22)
at e._executeContributedCommand (c:\Program Files\Microsoft VS Code\resources\app\out\vs\workbench\node\extensionHostProcess.js:697:706)
at e.$executeContributedCommand (c:\Program Files\Microsoft VS Code\resources\app\out\vs\workbench\node\extensionHostProcess.js:698:212)
at e._doInvokeHandler (c:\Program Files\Microsoft VS Code\resources\app\out\vs\workbench\node\extensionHostProcess.js:658:87)
at e._invokeHandler (c:\Program Files\Microsoft VS Code\resources\app\out\vs\workbench\node\extensionHostProcess.js:657:779)
at e._receiveRequest (c:\Program Files\Microsoft VS Code\resources\app\out\vs\workbench\node\extensionHostProcess.js:656:851)
at e._receiveOneMessage (c:\Program Files\Microsoft VS Code\resources\app\out\vs\workbench\node\extensionHostProcess.js:656:620)
at c:\Program Files\Microsoft VS Code\resources\app\out\vs\workbench\node\extensionHostProcess.js:655:544
at c:\Program Files\Microsoft VS Code\resources\app\out\vs\workbench\node\extensionHostProcess.js:658:673
at c:\Program Files\Microsoft VS Code\resources\app\out\vs\workbench\node\extensionHostProcess.js:96:599
at e.fire (c:\Program Files\Microsoft VS Code\resources\app\out\vs\workbench\node\extensionHostProcess.js:97:917)
at u (c:\Program Files\Microsoft VS Code\resources\app\out\vs\workbench\node\extensionHostProcess.js:159:721)
at Socket. (c:\Program Files\Microsoft VS Code\resources\app\out\vs\workbench\node\extensionHostProcess.js:159:898)
at emitOne (events.js:96:13)
at Socket.emit (events.js:191:7)
at readableAddChunk (_stream_readable.js:178:18)
at Socket.Readable.push (_stream_readable.js:136:10)
at Pipe.onread (net.js:560:20)
[2018-08-02 10:05:13.867] [renderer1] [error] Cannot read property 'passed' of undefined: TypeError: Cannot read property 'passed' of undefined
at _mochaProvider.onDidChangeTreeData (C:\Users\xxxxx\.vscode\extensions\maty.vscode-mocha-sidebar-0.20.17\extension.js:181:56)
at e.fire (c:\Program Files\Microsoft VS Code\resources\app\out\vs\workbench\node\extensionHostProcess.js:97:917)
at mochaProvider.refreshExplorer (C:\Users\xxxxx\.vscode\extensions\maty.vscode-mocha-sidebar-0.20.17\mochaProvider.js:99:35)
at Timeout.setTimeout [as _onTimeout] (C:\Users\xxxxx\.vscode\extensions\maty.vscode-mocha-sidebar-0.20.17\changesNotification.js:93:57)
at ontimeout (timers.js:386:14)
at tryOnTimeout (timers.js:250:5)
at Timer.listOnTimeout (timers.js:214:5)
Steps to Reproduce

Install VS Code
Install Node
Install Mocha Sidebar
No test shown in sidebar

Expected Results
Test show in sidebar/have Debug/Run in test code
Actual Results
Test do not show in sidebar, no option to debug/run test from test file
Settings file
// code coverage with istanbul
"mocha.coverage": {
"enable": true,
"decoration": true,
"runWithInterval": false,
"autoUpdateInterval": 20000,
"activeOnStart": true,
"runAfterTest": false,
"runCoverageAfterFileSave": false,
"reporters": []
},
// Mocha: Environment variables to run your tests
"mocha.env": {},
// Mocha: Glob to search for test files
"mocha.files.glob": "test/**/*.js",
// Mocha: Globs to ignore when searching for test files
"mocha.files.ignore": [
"/.git//",
"/node_modules//"
],
// Mocha: mocha package path then the sideBar installed one for example ../node_modules/mocha
"mocha.logVerbose": true,
// Mocha: Options to pass to node executable
"mocha.node_options": [],
// Mocha: Options to run Mocha
"mocha.options": {},
// Mocha: set other mocha path then the built-in one for example ../node_modules/mocha (only path to the package)
"mocha.path": "",
// Array of module required to run Mocha. (for example babel-register, coffee-script/register or ts-node/register)
"mocha.requires": [],
// Mocha: run tests on each save
"mocha.runTestsOnSave": "false",
// Show popup when having any error while executing test
"mocha.showErrorPopup": "true",
// Show in explorer view or in test view(new feature since version 1.23)
"mocha.showInExplorer": false,
// Mocha: this option allows you to enable/disable  lens nad decorations and set update threshold
"mocha.sideBarOptions": {
"lens": true,
"decoration": true,
"autoUpdateTime": 2000,
"showDebugTestStatus": true
},
// Sets a subdirectory as the root for mocha-sidebar
"mocha.subdirectory": ""
Versions

VScode: v1.25.1
Mocha SideBar: v0.20.17
Node Version: v10.7.0

OS version

[ ] Linux
[ ] Mac
[X] Windows 10 Pro build 17134.191

Additional Info
I've tried uninstall and reinstall of VS Code and Mocha Sidebar, multiple times, no go.
I am only able to run tests via launch configuration (launch.json) and using debug window. I previously did not need this.
ex config:
{
"type": "node",
"request": "launch",
"name": "Mocha Tests",
"program": "${workspaceFolder}/node_modules/mocha/bin/_mocha",
"args": [
"-u",
"tdd",
"--timeout",
"999999",
"--colors",
"${workspaceFolder}/test"
],
"internalConsoleOptions": "openOnSessionStart"
}
Code Coverage is working from the sidebar.
Package.json includes:
"scripts": {
"test": "istanbul test node_modules/mocha/bin/_mocha"
},

I went back and installed Node 8.11 and mocha sidebar is working. I guess it's something to do with v10 of Node.

Yep it's maybe it related although I can't think of any reason for causing it can you try run the test from mocha side bar workspace folder with node 10

sure, I'll close this for now. Maybe when my company needs to upgrade to v10+ I'll revisit this issue. It could of been something local to my machine, but I don't recalling changing anything that would affect this.

BTW, I seem to be having the same issue on node v10+.

Hi I'll try my best to figure out what is the problem . I have a lot of pressure right now at the work but I'll find time soon for debugging it sry for the delay

Hame same problem.

I only got to see tests after pointing mocha.files.glob to my test files in the workspace settings, (settings.json). In usersettings mocha.files.glob seem to have no effect.
Node 10 LTS, I never saw errors. In fact no test output in output nor debug window. I created compile error in my test. I did not see it in vscode, but the test stayed red in side bar, same as if it failed.
Is it possible to see similar test output in vscode as when running mocha from cmd?

+1

+1


Liquid in JSON string values

Description
Liquid contained in JSON strings and likely JavaScript/TypeScript strings is being indented. Such structures should be preserved. Take the following example:
Current Result
The following is a current output result when beautification is being applied to an embedded region:
<script type="application/ld+json">
  {
      "availability": "http://schema.org/ {%- if product.available %}InStock
      {%- else -%}
        OutOfStock{%- endif -%}",
  }
</script>

Expected Result
The {% if %} and {% else %} conditional should be preserved.
<script type="application/ld+json">
  {
      "availability": "http://schema.org/ {%- if product.available %}InStock{%- else -%}OutOfStock{%- endif -%}",
  }
</script>

Partially solved in latest release (v0.3.0.beta) but single spaced between values persist here, eg:
{ "prop": "foo{{ xx }}"  }

inserts a single space:
{ "prop": "foo {{ xx }}"  }

I'll address this in later releases


Fix tracker closing on follow and checking jankiness

@keybase/react-hackers
This fixes https://keybase.atlassian.net/browse/DESKTOP-1378
Which was caused by a race condition where we get a userUpdated action before we get a on(Re)Follow action.
This causes the app to think that you tracked from somewhere else and closes that window.
(We might want to discuss if that logic even makes sense, removing it would mean the only way to close a tracker is to manually close it)
This also fixes the jankiness you get with mutliple identifies and flashing windows.
The problem is we defaulted to checking if we couldn't parse the proofstate, that meant that if we knew something was broken, if we got another proof update that didn't make sense we would change it to checking. Now we keep the old proof state until told otherwise. A much smoother experience.

:+1: but FYI it doesn't look like Jenkins ran on this, to make sure tests pass before merging

retest this please

we need to have trackers close automatically as we have a situation where if you have multiple desktops you'll get a broken proof popup, but if its resolved / retracked on one you want the other ones to close automatically

Another option:
Trackers get updated and not close. So if you resolve an issue in one tracker on a different device, other devices will update to showing it as resolved too.

:mag_right: The commits d2ac63bfe8c1...916ac3cd6576 introduced some visual changes on win32:

Changed: unlock_folders-Success (before) (after)


Must be able to grab Pycrypto, miniupnpc on its own

Pip installs as a wheel.
Using a local version of pip, one can then call pip install requirements.txt -t pkgs to get all requirements locally.
This must not include miniupnpc for windows, as the pypi version will not compile under Windows. Alternate solution must be found.

Will be solved as part of #35. Closing.


Use a stable set for schema directories

There is one more thing we should consider:
With the current design, the user has no way to give a higher priority to one of the predefined directories.
Consider vast --schema-paths=[/usr/share/vast/schema]. The given path would still get lower priority than the xdg ones.

That is not true, we still have --no-default-schema to disable the default paths.
We can also change the insertion order such that user dirs are put first in the order. I'll gladly make that change, because I think that makes more sense anyways.

That is not true, we still have --no-default-schema to disable the default paths.

I forgot about that.

We can also change the insertion order such that user dirs are put first in the order. I'll gladly make that change, because I think that makes more sense anyways.

I prefer it the way it is now, we should assume that the user knows what they're doing.


fatal: No url found for submodule path 'libraries/python-single-thread' in .gitmodules

Hi,
I input 'git submodule update --init --recursive', and it output 'fatal: No url found for submodule path 'libraries/python-single-thread' in .gitmodules';
what happened with this issue?
Thank you

Fixed, try "git pull" to update to the latest source code.


Remove signal handling from Gedis server

Description
In js-sdk, the SIGTERM signal is handled by the 3Bot server and then the stop method of Gedis and several other components are called. But the Gedis server also handles the signal and calls its stop method, this might cause trouble as there are no guarantees on the idempotency of this method.

There are a couple of suggestion that was proposed to solve this issue:

Remove the stop handler from the rack server.
Remove the stop handler from the gedis server.
Make a global signal handler that guarantees the sequential execution of handlers to avoid concurrency issues.
Created a PR with this second option for now.

There are a couple of suggestion that was proposed to solve this issue:

Remove the stop handler from the rack server.
Remove the stop handler from the gedis server.
Make a global signal handler that guarantees the sequential execution of handlers to avoid concurrency issues.
Created a PR with this second option for now.

Resolved in #559.


error when I run "cmake .."

A library with BLAS API not found. Please specify library location.
-- LAPACK requires BLAS
-- A library with LAPACK API not found. Please specify library location.
-- Could NOT find GLPK (missing:  GLPK_LIBRARY GLPK_INCLUDE_DIR GLPK_PROPER_VERSION_FOUND)
-- Could NOT find CPLEX (missing:  CPLEX_LIBRARY CPLEX_INCLUDE_DIR)
-- Could NOT find Eigen3 (missing:  EIGEN_INCLUDE_DIR) (Required is at least version "3.1.2")
-- Could NOT find OPENCL (missing:  OPENCL_LIBRARY OPENCL_INCLUDE_DIR)
-- Could NOT find ViennaCL (missing:  VIENNACL_INCLUDE_DIR VIENNACL_ENCODED_VERSION OPENCL_INCLUDE_DIRS OPENCL_LIBRARIES) (Required is at least version "1.5.0")
-- Checking for one of the modules 'libColPack>=1.0.9;ColPack>=1.0.9'
-- Could NOT find NLOPT (missing:  NLOPT_LIBRARY NLOPT_INCLUDE_DIR)
-- Could NOT find LPSOLVE (missing:  LPSOLVE_LIBRARIES LPSOLVE_INCLUDE_DIR)
-- Could NOT find ColPack (missing:  COLPACK_LIBRARIES COLPACK_LIBRARY_DIR COLPACK_INCLUDE_DIR)
-- Could NOT find Doxygen (missing:  DOXYGEN_EXECUTABLE) (Required is at least version "1.8.6")
-- Found PythonInterp: /usr/bin/python (found version "2.7.12")
-- Could NOT find Ctags (missing:  CTAGS_EXECUTABLE)
CMake Error at CMakeLists.txt:739 (message):
Ctags required for meta examples.
-- Configuring incomplete, errors occurred!
See also "/home/hao/Documents/gsoc/shogun/shogun-shogun_5.0.0/build/CMakeFiles/CMakeOutput.log".
See also "/home/hao/Documents/gsoc/shogun/shogun-shogun_5.0.0/build/CMakeFiles/CMakeError.log".
os: ubuntu 16.04
Is it the reason that wrong method of cmake?

@hao555sky, you need to install lapack or blas to make shogun work
Try running this
sudo apt-get install liblapack-dev libblas-dev

Or you could even consult this Dockerfile
in case you need help for installing shogun

Sorry for error again, same error.
I want to ask several questions:

Should I cd shogun and run the cmd git submodule update --init
what is the specific cmd about cmake when I cd build? I always think I use the cmake wrong.
Sorry again for these tedious quesions.

There are detailed instructions in the installation readme.
The error message says to install ctags (or disable meta examples)
Closing as the issues here are not related to Shogun or cmake itself.
If you have trouble using cmake and related, please consider using the precompiled ppa

I added a more informative error message in 1a3b9d71c2daf48818e4ce1466a83aca5735b07a


config: add reana-server as component

Fix images version to v0.0.1.

Signed-off-by: Diego Rodriguez<EMAIL_ADDRESS>

(on behalf of @diegodelemos)
What:
In case there is some trouble related to reana-server, when starting the Kubernetes cluster (or minikube) reana-server -pod should be killed with kubectl. Reana-server should restart itself automatically.
Why:
Reana-server is accessing "Kubernetes provided metadata" (IP-address of the pod is used as the host) when forming URL for reana-workflow-controller and because Kubernetes starts reana-server before reana-workflow-controller, the necessary metadata is not available.
When reana-server -pod is killed, it restarts itself, but this time metadata for reana-workflow-controller -pod is available and reana-server can successfully form a URL that points to reana-workflow-controller.
In the future, the way URLs for different components are defined will change and this problem should be solved.


pnpx support

Describe the feature
Installing swc via pnpm then running pnpx swc ./myfile.ts results in a Binary not found error but running npx results in swc running fine. Not sure why this is the case but feels weird to be using pnpm for everything else and then having to use npx just for swc.
Babel plugin or link to the feature description
No response
Additional context
No response

I think this is a bug of pnpm


[iOS] How do I still get notifications with setBackgroundMessageHandler commented out?

I have an issue on iOS where my notifee custom sounds are not playing.
I narrowed down the bug to this case, even with setBackgroundMessageHandler() commented out, I still receive a push notifications.
Which means the notifee functions within my messageHandler() is not handling my notifications.
So firstly, how am I still receiving push notifications even when "displayNotification" is not called. And secondly, how do I force notifee to handle the notification so I may get my custom sound
Thanks so much

Hi there! I think we need to agree on terms first.
In technical terms used by google and device APIs, there are these things:

Firebase Cloud Messages, FCM, these are chunks of JSON that are sent from google firebase to APNS (the Apple Push Notification Service) and then to your device. For our purposes we should call these FCM, and the JSON you can send to the FCM REST API should be published here as part of any troubleshooting effort, minus the device token.

@react-native-firebase/messaging (and transitively firebase-ios-sdk or firebase-android-sdk) these are the underlying SDKs that listen for / receive the FCM when it comes from the cloud. You should specify all your versions here and the execution environment (device make/model/version, whether it is plugged in or not, etc) as you ask for help

@notifee/react-native this is responsible for handling device-local notification presentation and management. It is like a relay race where the firebase SDKs and react-native-firebase get the baton from firebase/APNS servers, and then hand it to Notifee. You should specify an index.js and App.js that can be dropped in the reference app here for testing when you ask for help

Without knowing the details on the above items, I am not sure anyone can speculate on what is going on.

Hi @mikehardy thank you so much for your reply.
I will try to answer your questions one point at a time
RE: 1.
I send my push notifications via expo-server which handles all the FCM stuff for me.
Here is my expo code
const assembledChunks: any[] = [];
for (let chunk of notificationChunks) {
    if (!Expo.isExpoPushToken(chunk.token)) {
        console.error(
            `Push token ${chunk.token} is not a valid Expo push token`
        );
    } else {
        assembledChunks.push({
            to: chunk.token,
            // sound: chunk.sound,
            sound: 'default',
            title: chunk.message,
            body: 'Visit them!',
            priority: 'high',
            data: { vendorId: chunk.vendorId },
        });
    }
}
console.log({ assembledChunks });
let chunks = expo.chunkPushNotifications(assembledChunks);
const sendNotifications = async () => {
    console.log('TRY TO SEND', chunks);
    for (let chunk of chunks) {
        let ticketChunk = await expo.sendPushNotificationsAsync(chunk);
        console.log('Send Partial -> ', chunk, 'Ticket Chunk -> ', ticketChunk);
    }
};

RE 2.
"@react-native-firebase/messaging": "^14.9.1",
Test on both iPhone 7+ and iPhone 13 physical device run using expo run:ios -d command
RE 3.
My issue is that
messaging().setBackgroundMessageHandler(messageHandler)
Does not even get called, so I don't think the notifee code here is relevant as it doesn't even get to it. But if needed I can include it
Thanks so much!

I don't see content available key in that server message there :thinking: - not sure how to set that with expo, but if you don't set content-available (the FCM key, sometimes called 'contentAvailable' in other SDKs, take great care in naming it for you context...) then you will not get delivery https://rnfirebase.io/messaging/usage#data-only-messages

Thanks for the feedback. expo-notifications isn't really working out for me due to these issues so i'm switching back to react-native-firebase. Seems to be doing the trick mostly.


Remove Misleading Sentence About StorageDoubleMap

StorageDoubleMap has no magically efficient storage removal operation compared to an iterator which will remove all the storage items in the double map.
Users are getting confused like here: https://substrate.stackexchange.com/questions/1280/complexity-of-storagedoublemapremove-prefix/1285#1285

bot merge


sign 2 batches of tx groups in a roll

Hi there,
I have a large size array of txns that I would like to sign and I've divided them into smaller arrays with a length of 16 (due to the tx group size limitation). It's something like this:

and what I wanna do next is to sign this array of tx group in sequence:
let signedTxg = new Array(txgg.length); // txgg here is the chunked array above
for (let i = 0; i < txgg.length; i++) {
  signedTxg[i] = await walletContext.myAlgoConnect.signTransaction(
    txgg[i].map((txn) => txn.toByte())
  );
  console.log(txgg[i]);
}
console.log(signedTxg);

The expected behavior is when I close the first popup, the second popup can show up.
However, the first popup did work and I did get the signed txgroup while the second one doesn't show up and I got an error message like this: PopupOpenError: Can not open popup window - blocked
I'm using nextjs and the code is triggered by a button onclick.
I tried to setTimeout after the signTransaction function returns but it didn't work.
I got the myAlgoConnect instance from the contextAPI and I'm wondering if this is an issue?
Should I use a new instance everytime I signTx?
Thanks in advance.

ah I see it's blocked by my browser.
I should do error handling.
Thanks!

Hello @Mine77 ! Yes, every MyAlgoConnect method must be triggered by an onClick event and this event cannot be reused.


The mod browser server is under heavy load please try again..

For some reason, Everytime I try to download a mod it says The mod browser server is under heavy load please try again.. Please fix this its very annoying

It's not a bug, it's because it's under heavy load. In the meantime, you can try to download mods from their homepages.

Consider leaving this open both as a reminder until we get a better server, and as reference for future people tempted to post the same issue?

That sounds like a good idea.

Close for now.


Update task reserved resource logic to handle PRNs

We introduced PRNs in 3.50 and now they are the default for listings in the reserved_resources_record. Also updated task translation for all the known tasks in the all-in-one container.

Not seeing any issues in the tasks I have now, merging :)


Puppet class for CreamCE

The simple_grid_puppet_env defines a CreamCE role and relevant profiles for installing docker/git on nodes that will deploy cream compute element containers. One of these profiles will refer to the cream_ce class defined in this puppet module. The task of the puppet class is to

Fetch the wlcg_lightweight_site_cream_ce repo
Parse the site-level-configuration-file
Run the pre-configuration script

Closing as the new design described in #10 takes care of it.


Plot data with respect to time

Hello,
I would like to plot data with respect to time in real time. The x-axis should be a time step and it increases over time. The y-axis is the data at the specific time step.
I tried to use updateTrace( ) function to achieve that. How do I update the X with time steps?

I think the easiest way to achieve this is by repeatedly calling line() with the new data X as input, and setting the update argument to 'append'.

In other words, you need to maintain your own X timestep data.


Ada_Aku

Week 2: Your first repository

[x] 1. Install GitHub desktop and connect it to your GitHub account
[x] 2. Create a public repository and add a readme file
[x] 3. Publish the repository to GitHub
[x] 4. Add a new file in your local repository, commit the file addition, and push it to the remote repository on GitHub
[x] 5. Modify the new file (or the Readme file), commit the change, and push it to the remote repository on GitHub
[x] 6. Comment on this issue with a link to your repository

Week 4: Rmarkdown

[x] 1. Create a new public repository on GitHub Desktop and add a readme file.
[x] 2. Publish the repository to GitHub
[x] 3. Open RStudio and create a new project in an existing directory (the directory of your repository)
[x] 4. Download the file "R_Rstudio_Rmarkdown.Rmd" from the week 4 "Practical: R markdown" section on MyAberdeen and save it in your project
[x] 5. Work through the Rmarkdown file, remembering to commit and push your changes to the remote repository on GitHub
[x] 6. Optional: if you have questions you'd like to discuss with your group members, create issues on GitHub and let them know
[x] 7. Comment on this issue with a link to your new repository

Week 5: Practical: reading data in R

[x] 1. Create a new public repository on GitHub Desktop and add a readme file. This will be the main repository for the R material from this course, so give it a good name :smiley:
[x] 2. Publish the repository to GitHub
[x] 3. Open RStudio and create a new project in an existing directory (the directory of your repository)
[x] 4. Create an appropriate folder structure according to the good coding practices discussed in week 5
[x] 5. Download the data files and Rmarkdown file from the week 5 "Practical: reading data in R" section on MyAberdeen and save them in the appropriate folders in your project
[x] 6. Work through the Rmarkdown file, remembering to commit and push your changes to the remote repository on GitHub
[x] 7. Optional: if you have questions you'd like to discuss with your collaborators, create issues on GitHub and let them know
[x] 8. Comment on this issue with a link to your new repository

Week 6-10: Your project
Now that you have become familiar with GitHub, you can start using it to prepare your report, and get help and feedback from your group members.

[ ] Comment on this issue with a link to your report repository

Hi
Here is a link to my repository
https://github.com/Ada-aku/SchoolRepo

Well done @Ada-aku! 🥳

Thank you
Here is a link to my R project repo
https://github.com/Ada-aku/Rmarkdown_assign/blob/main/README.md

My week 5 practical
https://github.com/Ada-aku/R_Practical

How do I knit directly to Result folder or do I have to manually move the file

Hi @Ada-aku, please post questions on MyAberdeen, so other students can see them too 😃


build(v2): update v2 dependencies

split from #4

:tada: This PR is included in version 2.1.0 :tada:
The release is available on GitHub release
Your semantic-release bot :package::rocket:


ignore or include

have a pattern where branches with certain names are either included or ignored
ingore/baldur/foo
or
content/baldur/amazingpost

Do you mean, ignored for the purposes of the displayed list, e.g. at https://precog.mapzen.com/mapzen/blog/ ?

yeah ... @burritojustice was asking me to delete branches and I assume it was for him to have a better view of what was in flight

also generally useful to know what is what...
I've been going back and forth between
/blog/user/short-meaningful-name
and
/user/blog/short-meaningful name
any feelings?

No particular thoughts or feelings from me. These meaningful branch names would be specific to only the mapzen/blog repo.

the only question is whether they are inclusive or
exclusive @burritojustice suggested the latter which is probably right so
if the name has precog-ignore? The alternative is just to let long live
branches live ... there shouldn't ever be more than a handful anyways.
On Wed, Feb 24, 2016 at 6:05 PM, migurski<EMAIL_ADDRESS>wrote:

No particular thoughts or feelings from me. These meaningful branch names
would be specific to only the mapzen/blog repo.
—
Reply to this email directly or view it on GitHub
https://github.com/mapzen/precog/issues/25#issuecomment-188503027.

--
Baldur Gudbjornsson
VP Engineering
Mapzen -<PHONE_NUMBER>

I’m going to close this—we’ve been letting long branches live, and it hasn’t seemed to be an issue.


License

Could you add a license? Thank you.

I've already added  a MIT license

Thank you very much.


windows-latest (20211122.1) Has not latest dotnet 5 version 12

Description
We are using the vm windows-latest in our Azure DevOps pipelines and have currently an issue with the dotnet runtime installed on the vm.
We run a dotnet tool that requires dotnet 5.0.12 runtime
but we get an error that this is not installed, but dotnet 5.0.11 is installed.
Expecting from the readme for windows-latest (20211122.1), I would expect that the dotnet runtime of 5.0.12 is installed!
reference: https://github.com/actions/virtual-environments/blob/win19/20211122.1/images/win/Windows2019-Readme.md
Here is the initializing log of our pipeline:
Starting: Initialize job
Agent name: 'Azure Pipelines 5'
Agent machine name: 'WIN-1NESLPMR11N'
Current agent version: '2.195.0'
Operating System
Microsoft Windows Server 2019
10.0.17763
Datacenter
Virtual Environment
Environment: windows-2019
Version: 20211122.1
Included Software: https://github.com/actions/virtual-environments/blob/win19/20211122.1/images/win/Windows2019-Readme.md
Image Release: https://github.com/actions/virtual-environments/releases/tag/win19%2F20211122.1
Virtual Environment Provisioner
<IP_ADDRESS>-master-20211123-1
Current image version: '20211122.1'
Agent running as: 'VssAdministrator'
Prepare build directory.
Set build variables.
Download all required tasks.
Downloading task: UseDotNet (2.195.0)
Downloading task: CredScan (2.332.0)
Downloading task: PublishSecurityAnalysisLogs (2.316.0)
Downloading task: SdtReport (1.232.0)
Downloading task: PublishBuildArtifacts (1.192.0)
Downloading task: AzureKeyVault (1.182.1)
Downloading task: PowerShell (2.194.0)
Downloading task: CopyFiles (2.190.1)
Downloading task: NuGetAuthenticate (0.194.0)
Downloading task: npmAuthenticate (0.179.0)
Downloading task: CmdLine (2.182.0)
Checking job knob settings.
   Knob: AgentToolsDirectory = C:\hostedtoolcache\windows Source: ${AGENT_TOOLSDIRECTORY}
   Knob: AgentPerflog = C:\agents\perflog Source: ${VSTS_AGENT_PERFLOG}
Finished checking job knob settings.
Start tracking orphan processes.
Finishing: Initialize job

And here the error message:
Starting: Execute Bomfilter
==============================================================================
Task         : PowerShell
Description  : Run a PowerShell script on Linux, macOS, or Windows
Version      : 2.194.0
Author       : Microsoft Corporation
Help         : https://docs.microsoft.com/azure/devops/pipelines/tasks/utility/powershell
==============================================================================
Generating script.
========================== Starting Command Output ===========================
"C:\Program Files\PowerShell\7\pwsh.exe" -NoLogo -NoProfile -NonInteractive -ExecutionPolicy Unrestricted -Command ". 'D:\a\_temp\ad41a16f-5652-497f-aac3-debe6b5c7b2a.ps1'"
It was not possible to find any compatible framework version
The framework 'Microsoft.NETCore.App', version '5.0.12' was not found.
  - The following frameworks were found:
      5.0.11 at [C:\hostedtoolcache\windows\dotnet\shared\Microsoft.NETCore.App]

You can resolve the problem by installing the specified framework and/or SDK.

The specified framework can be found at:
  - https://aka.ms/dotnet-core-applaunch?framework=Microsoft.NETCore.App&framework_version=5.0.12&arch=x64&rid=win10-x64
##[error]PowerShell exited with code '1'.
Finishing: Execute Bomfilter

Virtual environments affected

[ ] Ubuntu 18.04
[ ] Ubuntu 20.04
[ ] macOS 10.15
[ ] macOS 11
[ ] Windows Server 2016
[X] Windows Server 2019
[ ] Windows Server 2022

Image version and build link
20211122.1
Is it regression?
No response
Expected behavior
dotnet runtime 5.0.12 is installed
Actual behavior
dotnet runtime 5.0.11 is installed (not 5.0.12)
Repro steps
Use a dotnet tool that needs dotnet runtime 5.0.12
let it run on the windwos-latest (20211122.1)
Error message will occur:
It was not possible to find any compatible framework version
The framework 'Microsoft.NETCore.App', version '5.0.12' was not found.

The following frameworks were found:
5.0.11 at [C:\hostedtoolcache\windows\dotnet\shared\Microsoft.NETCore.App]

Hello @patspaeth. Thank you for your report. We will take a look.

I want to give some more information:
We use also a Use dotnet taks:
Starting: Use global dotnet sdk
==============================================================================
Task         : Use .NET Core
Description  : Acquires a specific version of the .NET Core SDK from the internet or the local cache and adds it to the PATH. Use this task to change the version of .NET Core used in subsequent tasks. Additionally provides proxy support.
Version      : 2.195.0
Author       : Microsoft Corporation
Help         : https://aka.ms/AA4xgy0
==============================================================================
Found version 5.0.402 in channel 5.0 for user specified version spec: 5.0.402
Getting URL to download .NET Core sdk version: 5.0.402.
Detecting OS platform to find correct download package for the OS.
C:\Windows\System32\WindowsPowerShell\v1.0\powershell.exe -NoLogo -Sta -NoProfile -NonInteractive -ExecutionPolicy Unrestricted -Command "& 'D:\a\_tasks\UseDotNet_b0ce7256-7898-45d3-9cb5-176b752bfea6\2.195.0\externals\get-os-platform.ps1'"
Primary:win-x64
Detected platform (Primary): win-x64
Version 5.0.402 was not found in cache.
Downloading: https://download.visualstudio.microsoft.com/download/pr/c826dbd3-e72f-46f7-a60a-8edde0e086de/aced282e985e941bfdd222781c722fe2/dotnet-sdk-5.0.402-win-x64.zip
Extracting downloaded package D:\a\_temp\e487332a-136e-4e86-b310-ec23ab471d51.
Extracting archive
C:\Windows\system32\chcp.com 65001
Active code page: 65001
C:\Windows\System32\WindowsPowerShell\v1.0\powershell.exe -NoLogo -Sta -NoProfile -NonInteractive -ExecutionPolicy Unrestricted -Command "$ErrorActionPreference = 'Stop' ; try { Add-Type -AssemblyName System.IO.Compression.FileSystem } catch { } ; [System.IO.Compression.ZipFile]::ExtractToDirectory('D:\a\_temp\e487332a-136e-4e86-b310-ec23ab471d51', 'D:\a\_temp\bc4e')"
Successfully installed .NET Core sdk version 5.0.402.
Creating global tool path and pre-pending to PATH.
Finishing: Use global dotnet sdk

Hello @patspaeth. We were unable to reproduce your problem, could you please provide a minimal example in a public repo so we could reproduce the issue on our side?

@patspaeth I'm going to close the issue since we cannot proceed with our investigation due to the lack of repro steps.
Please feel free to contact us if you have any concerns.
Thank you!


improvement: Improve SettingsDialog

Changelog

Change black background to colored background (backlog-blue in light-mode & planning-pink in dark-mode)
Add transition to SettingsDialog
Use style constants introduced in mb/leave-board-dialog branch

Visual Changes
Before

After

personally, I'd speed up the animation a little so the dialog can be interacted with sooner, but that's just my preference. Also, on mobile the slide in looks kinda weird imo

Personally, I don't think the transition duration is too long.
I've added an additional transition config which will be used for screens smaller than 450px (when the dialog is fullscreen).


typo improvement SQL manual database administration doc

Proposed changes
Issue Number: close #xxx

Further comments
If this is a relatively large or complex change, kick off the discussion at<EMAIL_ADDRESS>by explaining why you chose the solution you did and what alternatives you considered, etc...

run buildall


Non-OK-status: CudaLaunchKernel Internal: invalid configuration argument

Please make sure that this is a bug. As per our GitHub Policy, we only address code/doc bugs, performance issues, feature requests and build/installation issues on GitHub. tag:bug_template
System information

Have I written custom code (as opposed to using a stock example script provided in TensorFlow):No
OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Linux Ubuntu 16.04
Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device:
TensorFlow installed from (source or binary): Binary
TensorFlow version (use command below): 1.14.0
Python version: 3.6
Bazel version (if compiling from source):
GCC/Compiler version (if compiling from source):
CUDA/cuDNN version: 9.2, V9.2.148
GPU model and memory: two gpus, GTX 1080 Ti, each 11178MiB

You can collect some of this information using our environment capture
script
You can also obtain the TensorFlow version with: 1. TF 1.0: python -c "import tensorflow as tf; print(tf.GIT_VERSION, tf.VERSION)" 2. TF 2.0: python -c "import tensorflow as tf; print(tf.version.GIT_VERSION, tf.version.VERSION)"
Describe the current behavior
I am trying to run the follow sample code(keras tensorflow backend) my my GPU
left = Input(shape=(128, 3072), dtype='float32', name='Input-Left')
right = Input(shape=(128, 3072), dtype='float32', name='Input-Right')
lstm = Bidirectional(LSTM(units=768,
                          activation='tanh'),
                      name='Bidirectional-LSTM')
l_lstm = lstm(left)
r_lstm = lstm(right)
subtracted = Subtract(name='Subtract')([l_lstm, r_lstm])
abs_subtracted = Lambda(function=backend.abs)(subtracted)
mul = Multiply(name='multiplication')([l_lstm, r_lstm])
concat = concatenate([abs_subtracted, mul])
output = Dense(units=1)(concat)
model = Model(inputs=[left, right],
              outputs=output)
model = multi_gpu_model(model, gpus=2)
model.compile(loss='mean_squared_error',
              optimizer='Adam',
              metrics=['acc'])
import numpy as np
x1 = np.random.rand(100, 128, 3072)
x2 = np.random.rand(100, 128, 3072)
y = np.random.rand(100)
model.fit(x = [x1, x2],
         y=y,
         epochs=10)

Describe the expected behavior
expected it run normally without error.
Code to reproduce the issue
Provide a reproducible test case that is the bare minimum necessary to generate the problem.
Other info / logs
Include any logs or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached.
When I ran the code on my GPUs, I got the following error, However, if I uninstall tensorflow-gpu, there was no error, but I can only use cpu.
Using TensorFlow backend.
2019-07-12 15:38:28,543 From /home/.local/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.

2019-07-12 15:38:28,552 From /home/.local/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.

2019-07-12 15:38:28,552 From /home/.local/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.

2019-07-12 15:38:29,759 From /home/.local/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4185: The name tf.truncated_normal is deprecated. Please use tf.random.truncated_normal instead.

2019-07-12 15:38:29,837 From /home/.local/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.

2019-07-12 15:38:29,840 From /home/.local/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.
Instructions for updating:
Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.
2019-07-12 15:38:29.856975: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-07-12 15:38:29.860863: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcuda.so.1
2019-07-12 15:38:29.974060: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-07-12 15:38:29.976417: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-07-12 15:38:29.977100: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x97d2a40 executing computations on platform CUDA. Devices:
2019-07-12 15:38:29.977112: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): GeForce GTX 1080 Ti, Compute Capability 6.1
2019-07-12 15:38:29.977117: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (1): GeForce GTX 1080 Ti, Compute Capability 6.1
2019-07-12 15:38:29.978574: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency:<PHONE_NUMBER> Hz
2019-07-12 15:38:29.979175: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x984ed80 executing computations on platform Host. Devices:
2019-07-12 15:38:29.979185: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): <undefined>, <undefined>
2019-07-12 15:38:29.980287: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-07-12 15:38:29.980881: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties:
name: GeForce GTX 1080 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.683
pciBusID: 0000:01:00.0
2019-07-12 15:38:29.980907: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-07-12 15:38:29.981642: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 1 with properties:
name: GeForce GTX 1080 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.683
pciBusID: 0000:02:00.0
2019-07-12 15:38:29.981842: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0
2019-07-12 15:38:29.982531: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0
2019-07-12 15:38:29.983173: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0
2019-07-12 15:38:29.983363: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0
2019-07-12 15:38:29.984149: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0
2019-07-12 15:38:29.984728: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0
2019-07-12 15:38:29.985920: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7
2019-07-12 15:38:29.985961: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-07-12 15:38:29.986728: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-07-12 15:38:29.987487: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-07-12 15:38:29.988224: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-07-12 15:38:29.988948: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0, 1
2019-07-12 15:38:33.252621: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-07-12 15:38:33.252666: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]      0 1
2019-07-12 15:38:33.252672: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0:   N Y
2019-07-12 15:38:33.252677: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 1:   Y N
2019-07-12 15:38:33.253003: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-07-12 15:38:33.253769: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-07-12 15:38:33.254223: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-07-12 15:38:33.254958: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10054 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1)
2019-07-12 15:38:33.255392: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-07-12 15:38:33.255840: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:1 with 10054 MB memory) -> physical GPU (device: 1, name: GeForce GTX 1080 Ti, pci bus id: 0000:02:00.0, compute capability: 6.1)
2019-07-12 15:38:33.714112: F ./tensorflow/core/kernels/random_op_gpu.h:227] Non-OK-status: CudaLaunchKernel(FillPhiloxRandomKernelLaunch<Distribution>, num_blocks, block_size, 0, d.stream(), gen, data, size, dist) status: Internal: invalid configuration argument
Aborted (core dumped)

I tried reproducing issue with the provided code ,but i am unable to reproduce .Will it be possible to provide minimal code snippet which can reproduce the reported issue.Thanks

@ravikyram Thanks. I was able to solve the issue. I think it is compatibility issue between tensorflow-gpu and cuda.

CUDA 10.1 and Tensorflow-gpu 1.14: No Error
CUDA 9.2 and Tensorflow-gpu 1.14: CudaLaunchKernel error.
CUDA 9.2 and Tensorflow-gpu 1.12: No Error.

@xinsu626
I am closing this issue as it was resolved.Thanks!

Sorry for commenting on a closed issue, but reg.

CUDA 10.1 and Tensorflow-gpu 1.14: No Error

Did you compile 1.14 manually against CUDA 10.1? As per the documentation and from experience
2019-07-19 21:26:32.698745: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcudart.so.10.0'; dlerror: libcudart.so.10.0: cannot open shared object file: No such file or directory
2019-07-19 21:26:32.698819: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcublas.so.10.0'; dlerror: libcublas.so.10.0: cannot open shared object file: No such file or directory
2019-07-19 21:26:32.698888: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcufft.so.10.0'; dlerror: libcufft.so.10.0: cannot open shared object file: No such file or directory
2019-07-19 21:26:32.698928: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcurand.so.10.0'; dlerror: libcurand.so.10.0: cannot open shared object file: No such file or directory
2019-07-19 21:26:32.698996: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcusolver.so.10.0'; dlerror: libcusolver.so.10.0: cannot open shared object file: No such file or directory
2019-07-19 21:26:32.699039: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcusparse.so.10.0'; dlerror: libcusparse.so.10.0: cannot open shared object file: No such file or directory
2019-07-19 21:26:32.701819: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2019-07-19 21:26:32.701833: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1641] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...

neither 1.14 nor the nightly, nor the nightly-tf2-preview, work with CUDA 10.1...
@ravikram could you please reopen this?
Many thanks!

@skeydan Hi,
Sorry I just checked my server and the CUDA version is 10.0(Cuda compilation tools, release 10.0, V10.0.130) instead of 10.1.
I didn't compile 1.14 manually. I used pip to install it. python3.6 -m pip install tensorflow-gpu. I also ran the code below with CUDA 10.0 and tensorflow 1.14, and there was no error.
import numpy as np
from keras.layers import Input, Bidirectional, LSTM, Subtract, Lambda, Multiply, concatenate, Dense
from keras.utils import multi_gpu_model
from keras.models import Model
import keras.backend as backend

x1 = np.random.rand(100, 128, 3072)
x2 = np.random.rand(100, 128, 3072)
y = np.random.rand(100)

left = Input(shape=(128, 3072), dtype='float32', name='Input-Left')
right = Input(shape=(128, 3072), dtype='float32', name='Input-Right')
lstm = Bidirectional(LSTM(units=768,
                          activation='tanh'),
                     name='Bidirectional-LSTM')
l_lstm = lstm(left)
r_lstm = lstm(right)
subtracted = Subtract(name='Subtract')([l_lstm, r_lstm])
abs_subtracted = Lambda(function=backend.abs)(subtracted)
mul = Multiply(name='multiplication')([l_lstm, r_lstm])
concat = concatenate([abs_subtracted, mul])
output = Dense(units=1)(concat)
model = Model(inputs=[left, right],
              outputs=output)
model = multi_gpu_model(model, gpus=2)
model.compile(loss='mean_squared_error',
              optimizer='Adam',
              metrics=['acc'])
model.fit(x=[x1, x2],
          y=y,
          epochs=10)

@skeydan
Can you please go through the comments of xinsu626 and let us know still if you want us to reopen this issue. Thanks!

@ravikyram thanks for asking, @xinsu626 thanks for clarifying!
We have a similar-looking issue here https://github.com/rstudio/keras/issues/843 and I'm now passing on the recommendation to use TF 1.14 with CUDA 10.0 to evade the problem.
If  it's okay with you, I might ask to reopen at a later time in case that issue persists even in  the recommended configuration. Thanks!

Had the same issue with 4 workers, each with 2 Tesla K80s, on the AI Platform. Not sure whether this is a CUDA problem, since it is not reproducible: launching another job worked well.

To use w/ CUDA10.1, please use the tf-nightly version.
Command to install: pip install tf-nightly.

请确保这是一个错误。根据我们的GitHub政策，我们仅解决GitHub上的代码/文档错误，性能问题，功能请求和构建/安装问题。标记：bug_template
系统信息

我是否编写了自定义代码（与使用TensorFlow提供的股票示例脚本相反）：
操作系统平台和发行版（例如，Linux Ubuntu 16.04）：Linux Ubuntu 16.04
如果问题发生在行动装置上，则行动装置（例如iPhone 8，Pixel 2，Samsung Galaxy）：
从（源或二进制）安装TensorFlow：二进制
TensorFlow版本（使用下面的命令）：1.14.0
Python版本：3.6
Bazel版本（如果从源代码编译）：
GCC /编译器版本（如果从源代码编译）：
CUDA / cuDNN版本：9.2，V9.2.148
GPU型号和内存：两个GPU，GTX 1080 Ti，每个11178MiB

您可以使用我们的环境捕获
脚本收集一些此类信息。
您还可以通过以下方式获取TensorFlow版本：1. TF 1.0：python -c "import tensorflow as tf; print(tf.GIT_VERSION, tf.VERSION)"2. TF 2.0：python -c "import tensorflow as tf; print(tf.version.GIT_VERSION, tf.version.VERSION)"
描述
我正在尝试在我的GPU中运行以下示例代码（keras tensorflow backend）的当前行为
left = Input(shape=(128, 3072), dtype='float32', name='Input-Left')
right = Input(shape=(128, 3072), dtype='float32', name='Input-Right')
lstm = Bidirectional(LSTM(units=768,
                          activation='tanh'),
                      name='Bidirectional-LSTM')
l_lstm = lstm(left)
r_lstm = lstm(right)
subtracted = Subtract(name='Subtract')([l_lstm, r_lstm])
abs_subtracted = Lambda(function=backend.abs)(subtracted)
mul = Multiply(name='multiplication')([l_lstm, r_lstm])
concat = concatenate([abs_subtracted, mul])
output = Dense(units=1)(concat)
model = Model(inputs=[left, right],
              outputs=output)
model = multi_gpu_model(model, gpus=2)
model.compile(loss='mean_squared_error',
              optimizer='Adam',
              metrics=['acc'])
import numpy as np
x1 = np.random.rand(100, 128, 3072)
x2 = np.random.rand(100, 128, 3072)
y = np.random.rand(100)
model.fit(x = [x1, x2],
         y=y,
         epochs=10)

描述预期的行为
预期它可以正常运行而不会出现错误。
重现问题的代码
提供可重现的测试用例，这是产生问题所必需的最低要求。
其他信息/日志
包括有助于诊断问题的任何日志或源代码。如果包括回溯，请包括完整的回溯。大型日志和文件应附加。
当我在GPU上运行代码时，出现以下错误，但是，如果我卸载tensorflow-gpu，则没有错误，但是我只能使用cpu。
Using TensorFlow backend.
2019-07-12 15:38:28,543 From /home/.local/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.

2019-07-12 15:38:28,552 From /home/.local/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.

2019-07-12 15:38:28,552 From /home/.local/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.

2019-07-12 15:38:29,759 From /home/.local/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4185: The name tf.truncated_normal is deprecated. Please use tf.random.truncated_normal instead.

2019-07-12 15:38:29,837 From /home/.local/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.

2019-07-12 15:38:29,840 From /home/.local/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.
Instructions for updating:
Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.
2019-07-12 15:38:29.856975: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-07-12 15:38:29.860863: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcuda.so.1
2019-07-12 15:38:29.974060: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-07-12 15:38:29.976417: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-07-12 15:38:29.977100: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x97d2a40 executing computations on platform CUDA. Devices:
2019-07-12 15:38:29.977112: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): GeForce GTX 1080 Ti, Compute Capability 6.1
2019-07-12 15:38:29.977117: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (1): GeForce GTX 1080 Ti, Compute Capability 6.1
2019-07-12 15:38:29.978574: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency:<PHONE_NUMBER> Hz
2019-07-12 15:38:29.979175: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x984ed80 executing computations on platform Host. Devices:
2019-07-12 15:38:29.979185: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): <undefined>, <undefined>
2019-07-12 15:38:29.980287: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-07-12 15:38:29.980881: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties:
name: GeForce GTX 1080 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.683
pciBusID: 0000:01:00.0
2019-07-12 15:38:29.980907: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-07-12 15:38:29.981642: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 1 with properties:
name: GeForce GTX 1080 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.683
pciBusID: 0000:02:00.0
2019-07-12 15:38:29.981842: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0
2019-07-12 15:38:29.982531: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0
2019-07-12 15:38:29.983173: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0
2019-07-12 15:38:29.983363: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0
2019-07-12 15:38:29.984149: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0
2019-07-12 15:38:29.984728: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0
2019-07-12 15:38:29.985920: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7
2019-07-12 15:38:29.985961: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-07-12 15:38:29.986728: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-07-12 15:38:29.987487: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-07-12 15:38:29.988224: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-07-12 15:38:29.988948: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0, 1
2019-07-12 15:38:33.252621: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-07-12 15:38:33.252666: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]      0 1
2019-07-12 15:38:33.252672: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0:   N Y
2019-07-12 15:38:33.252677: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 1:   Y N
2019-07-12 15:38:33.253003: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-07-12 15:38:33.253769: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-07-12 15:38:33.254223: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-07-12 15:38:33.254958: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10054 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1)
2019-07-12 15:38:33.255392: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-07-12 15:38:33.255840: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:1 with 10054 MB memory) -> physical GPU (device: 1, name: GeForce GTX 1080 Ti, pci bus id: 0000:02:00.0, compute capability: 6.1)
2019-07-12 15:38:33.714112: F ./tensorflow/core/kernels/random_op_gpu.h:227] Non-OK-status: CudaLaunchKernel(FillPhiloxRandomKernelLaunch<Distribution>, num_blocks, block_size, 0, d.stream(), gen, data, size, dist) status: Internal: invalid configuration argument
Aborted (core dumped)
``when I explore multi_gpu_model in keras, I have a same problem, can you tell me how to settle this .

i am also getting the same error and not able to resolve it if I m running the following code:
import tensorflow as tf
import numpy as np
model=tf.keras.Sequential()
model.add(tf.keras.layers.Dense(64,activation='relu'))
model.add(tf.keras.layers.Dense(64,activation='relu'))
model.add(tf.keras.layers.Dense(10,activation='softmax'))
model.compile(optimzer='Adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
data=np.random.random((1000,32))
labels=np.random.random((1000,10))
model.fit(data,labels,batch_size=32,epochs=3)<-----------
""   after this I m getting error::"""
2019-11-28 11:04:06.964863: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties:
name: GeForce GTX 1650 major: 7 minor: 5 memoryClockRate(GHz): 1.56
pciBusID: 0000:01:00.0
2019-11-28 11:04:06.971063: I tensorflow/stream_executor/platform/default/dlopen_checker_stub.cc:25] GPU libraries are statically linked, skip dlopen check.
2019-11-28 11:04:06.976492: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0
2019-11-28 11:04:06.979274: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-11-28 11:04:06.984074: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165]      0
2019-11-28 11:04:06.986746: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0:   N
2019-11-28 11:04:06.992165: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/device:GPU:0 with 3023 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1650, pci bus id: 0000:01:00.0, compute capability: 7.5)
2019-11-28 11:04:08.220421: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties:
name: GeForce GTX 1650 major: 7 minor: 5 memoryClockRate(GHz): 1.56
pciBusID: 0000:01:00.0
2019-11-28 11:04:08.227433: I tensorflow/stream_executor/platform/default/dlopen_checker_stub.cc:25] GPU libraries are statically linked, skip dlopen check.
2019-11-28 11:04:08.233568: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0
2019-11-28 11:04:08.235936: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-11-28 11:04:08.241661: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165]      0
2019-11-28 11:04:08.250926: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0:   N
2019-11-28 11:04:08.256356: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/device:GPU:0 with 3023 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1650, pci bus id: 0000:01:00.0, compute capability: 7.5)
2019-11-28 11:04:16.908225: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties:
name: GeForce GTX 1650 major: 7 minor: 5 memoryClockRate(GHz): 1.56
pciBusID: 0000:01:00.0
2019-11-28 11:04:16.915702: I tensorflow/stream_executor/platform/default/dlopen_checker_stub.cc:25] GPU libraries are statically linked, skip dlopen check.
2019-11-28 11:04:16.921282: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0
2019-11-28 11:04:16.927152: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties:
name: GeForce GTX 1650 major: 7 minor: 5 memoryClockRate(GHz): 1.56
pciBusID: 0000:01:00.0
2019-11-28 11:04:16.947245: I tensorflow/stream_executor/platform/default/dlopen_checker_stub.cc:25] GPU libraries are statically linked, skip dlopen check.
2019-11-28 11:04:16.952123: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0
2019-11-28 11:04:16.954761: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-11-28 11:04:16.962555: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165]      0
2019-11-28 11:04:16.977982: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0:   N
2019-11-28 11:04:16.996614: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3023 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1650, pci bus id: 0000:01:00.0, compute capability: 7.5)
2019-11-28 11:04:17.374983: F .\tensorflow/core/kernels/random_op_gpu.h:227] Non-OK-status: GpuLaunchKernel(FillPhiloxRandomKernelLaunch, num_blocks, block_size, 0, d.stream(), gen, data, size, dist) status: Internal: invalid device function

please help if anyone can resolve this
i  using Tensorflow version =2.0
have installed tensorfloe-gpu with pip
CUDA version is 10.0
python version is 3.6x

Same eoor here, my environment:
tensorfloe-gpu 2.0 installed with pip
CUDA version is 9.2/10.0
python version is 3.6.9

tf-nightly-gpu - install. they fixed that.

After reinstall tensorflow-gpu==2.0.0 in a clean environment, the issue disappears.


Add types to class implementations

Since PHP has now started allowing explicit type definitions, this PR starts adding those to all classes in this library.

Oops! Sorry! I didn't meant to pass this one upstream.


No products in WCFM in secondary language

Can you reproduce this issue on default Wordpress theme (eg Storefront)?
Can you reproduce this issue when all other plugins are disabled except WooCommerce, Polylang and Hyyan WooCommerce Polylang Integration?
What product versions and settings are you using when this issue occurs?

PHP: 5.4.17 (USB webserver)
WordPress: 4.9.8
WooCommerce: 3.4.4
Polylang: [state if using Polylang PRO] 2.3.10
Hyyan WooCommerce Polylang Integration: 1.2.0
Browser: Chrome

Steps to Reproduce

What I Expected
After installing the woo-poly-integration plugin I expected to be able to edit products in the secondary language. But they just don't show up in the WCFM product page. In the WP-admin page they are visible though in Dutch (primary language) as well as Englisch (secondary language).
What Happened Instead
Secondary language products don't show up in WCFM product page.
WordPress Environment

```
Copy and paste the system status report from **WooCommerce > System Status** in WordPress admin here.
```

Problem solved. Just forgot to create an English WCFM shop manager page.

ok, closing issue then


Allow custom babel config for content plugins

🚀 Feature
(A clear and concise description of what the feature is.)
Have you read the Contributing Guidelines on issues?
Yes
Motivation
I want to use tailwindcss in my docs/blog/pages.
You can see here the createMDXLoaderRule hardcodes the babel loader and doesn't allow configuring the babelOptions.
  function createMDXLoaderRule(): RuleSetRule {
        return {
          test: /(\.mdx?)$/,
          include: flatten(versionsMetadata.map(getDocsDirPaths))
            // Trailing slash is important, see https://github.com/facebook/docusaurus/pull/3970
            .map(addTrailingPathSeparator),
          use: compact([
            getCacheLoader(isServer),
            getBabelLoader(isServer),
            {

export function getBabelLoader(
  isServer: boolean,
  babelOptions?: TransformOptions | string,
): Loader {
  return {
    loader: require.resolve('babel-loader'),
    options: getBabelOptions({isServer, babelOptions}),
  };
}

export function getBabelOptions({
  isServer,
  babelOptions,
}: {
  isServer?: boolean;
  babelOptions?: TransformOptions | string;
} = {}): TransformOptions {
  if (typeof babelOptions === 'string') {
    return {
      babelrc: false,
      configFile: babelOptions,
      caller: {name: isServer ? 'server' : 'client'},
    };
  } else {
    return Object.assign(
      babelOptions ?? {presets: [require.resolve('../babel/preset')]},
      {
        babelrc: false,
        configFile: false,
        caller: {name: isServer ? 'server' : 'client'},
      },
    );
  }
}

Pitch
The easy fix is to add babelOptions to the plugin config and pass it through.

Workaround
module.exports = function customWebpackPlugin(context, options) {
  return {
    name: 'custom-webpack-plugin',
    configureWebpack(config, isServer, utils) {
      modifyBabelConfigForDocusaurusContentDocsPlugins(config, isServer, utils)
	}
  }
}

function modifyBabelConfigForDocusaurusContentDocsPlugins(config, isServer, utils) {
  const mdxRegex = /(\.mdx?)$/
  const matchingRules = config.module.rules.filter(
    rule => rule.test.toString() === mdxRegex.toString(),
  )

  //const {getBabelLoader} = utils

  matchingRules.map(rule => {
    const babelLoaders = rule.use.filter(use => use.loader.match('babel-loader'))
    babelLoaders.map(loader => {
      loader.options.configFile = join(process.cwd(), 'babel.config.js')
      delete loader.options.presets
      delete loader.options.babelrc
    })
  })
}

Workaround
module.exports = function customWebpackPlugin(context, options) {
  return {
    name: 'custom-webpack-plugin',
    configureWebpack(config, isServer, utils) {
      modifyBabelConfigForDocusaurusContentDocsPlugins(config, isServer, utils)
	}
  }
}

function modifyBabelConfigForDocusaurusContentDocsPlugins(config, isServer, utils) {
  const mdxRegex = /(\.mdx?)$/
  const matchingRules = config.module.rules.filter(
    rule => rule.test.toString() === mdxRegex.toString(),
  )

  //const {getBabelLoader} = utils

  matchingRules.map(rule => {
    const babelLoaders = rule.use.filter(use => use.loader.match('babel-loader'))
    babelLoaders.map(loader => {
      loader.options.configFile = join(process.cwd(), 'babel.config.js')
      delete loader.options.presets
      delete loader.options.babelrc
    })
  })
}

Hi,
We already support the babel.config.js file out of the box:
https://v2.docusaurus.io/docs/next/configuration/#customizing-babel-configuration
Have you tried it?
You are right that it seems it's not used by the mdx loaders of the content plugins, leading to your custom config not being applied when loading mdx files.
Your custom babel config would only work when loading .js/jsx components, not .md/.mdx files.
Can you confirm it's the case?
If we add support for the babel config to the mdx loaders, is it good enough for you? Or do you see usecases where the babel config should be different per plugin, or between .js/mdx?

As your usecase seems to be using Tailwind with Twin macros (https://github.com/facebook/docusaurus/issues/3994#issuecomment-759141647), I must say I'm not sure you would be able to use the tw or css prop working in MDX, as these features rely on using a JSX pragma. You can only use one JSX pragma at the same time, and MDX already adds its pragma, so it's likely to conflict with other css-in-js libs that relies on pragma to add non-std props (emotion/styled-components/twin/theme-ui... ).
Let me know if it works, I'd be interested to understand how 😅

Hi,
We already support the babel.config.js file out of the box:
https://v2.docusaurus.io/docs/next/configuration/#customizing-babel-configuration
Have you tried it?
You are right that it seems it's not used by the mdx loaders of the content plugins, leading to your custom config not being applied when loading mdx files.
Your custom babel config would only work when loading .js/jsx components, not .md/.mdx files.
Can you confirm it's the case?
If we add support for the babel config to the mdx loaders, is it good enough for you? Or do you see usecases where the babel config should be different per plugin, or between .js/mdx?

As your usecase seems to be using Tailwind with Twin macros (https://github.com/facebook/docusaurus/issues/3994#issuecomment-759141647), I must say I'm not sure you would be able to use the tw or css prop working in MDX, as these features rely on using a JSX pragma. You can only use one JSX pragma at the same time, and MDX already adds its pragma, so it's likely to conflict with other css-in-js libs that relies on pragma to add non-std props (emotion/styled-components/twin/theme-ui... ).
Let me know if it works, I'd be interested to understand how 😅

you would be able to use the tw or css prop working in MDX,

It does seem to work for me, doing something like:
import 'twin.macro'

Your custom babel config would only work when loading .js/jsx components, not .md/.mdx files.

Yep. I mistakenly thought .mdx could handle JS code, but it only supports import and JSX elements.
So the custom babel config is useless for much else...but still it does seem to work for mdx.

you would be able to use the tw or css prop working in MDX,

It does seem to work for me, doing something like:
import 'twin.macro'

Your custom babel config would only work when loading .js/jsx components, not .md/.mdx files.

Yep. I mistakenly thought .mdx could handle JS code, but it only supports import and JSX elements.
So the custom babel config is useless for much else...but still it does seem to work for mdx.

Sorry but I'm a bit confused 😅
what does work and what does not? What do you expect me to do?
We have a brand new http://new.docusaurus.io/ to easily create repro cases if you. can show me exactly what I should add support for with a failing sandbox, that would help me implement the missing bits needed

Sorry but I'm a bit confused 😅
what does work and what does not? What do you expect me to do?
We have a brand new http://new.docusaurus.io/ to easily create repro cases if you. can show me exactly what I should add support for with a failing sandbox, that would help me implement the missing bits needed

Ideally I would like to be able to pass in a custom createMDXLoaderRule function, and therefore have every function used by this function to be exported...so I can swizzle it.

Ideally I would like to be able to pass in a custom createMDXLoaderRule function, and therefore have every function used by this function to be exported...so I can swizzle it.

One of the most pressing configs I need actually in a monorepo context is the ability to exclude custom paths from each plugin-content-docs loader.
Here is a dump of my webpack config for my root docs plugin. I need to be able to modify the exclude so that it ignores paths where other plugin-content-docs plugins can be found to avoid mdx files being processed twice.
      {
        test: /(\.mdx?)$/,
        include: [ '/xxx' ],
        use: [
          {
            loader: '/xxx/node_modules/.pnpm/cache-loader<EMAIL_ADDRESS>            options: { cacheIdentifier: 'cache-loader:4.1.0false' }
          },
          {
            loader<EMAIL_ADDRESS>            options: {
              configFile: '/xxx/repos/live/packages/apps/docs/babel.config.js',
              caller: { name: 'client' }
            }
          },
          {
            loader: '/xxx/node_modules/.pnpm/@docusaurus/mdx-loader@2.0.0-alpha.69_react-dom@16.14.0+react<EMAIL_ADDRESS>            options: {
              staticDir: '/xxx/repos/live/packages/apps/docs/static'
            }
          },
          {
            loader: '/xxx/node_modules/.pnpm/@docusaurus/plugin-content-docs@2.0.0-alpha.69_react-dom@16.14.0+react<EMAIL_ADDRESS>            options: {
              siteDir: '/xxx/repos/live/packages/apps/docs',
              sourceToPermalink: {
<EMAIL_ADDRESS>'/xxx/docs/_readme',
              },
              versionsMetadata: [
                {
                  versionName: 'current',
                  versionLabel: 'Next',
                  versionPath: '/thirtyfive',
                  isLast: true,
                  routePriority: -1,
                  sidebarFilePath: '/xxx/docs/sidebar.js',
                  docsDirPath: '/xxx'
                }
              ]
            }
          }
        ],
        exclude: [ /node_modules/, /\/repos/, /\/.dev/, /\/tmp/ ] <----- I had to manually modify the webpack config to add these excludes.
      },

This doesn't seem to be set in the plugin-content-docs. It must be set in @docusaurus/core perhaps?
      function createMDXLoaderRule(): RuleSetRule {
        return {
          test: /(\.mdx?)$/,
          include: flatten(versionsMetadata.map(getDocsDirPaths)),
          use: compact([
            getCacheLoader(isServer),
            getBabelLoader(isServer),
            {
              loader: require.resolve('@docusaurus/mdx-loader'),
              options: {
                remarkPlugins,
                rehypePlugins,
                beforeDefaultRehypePlugins,
                beforeDefaultRemarkPlugins,
                staticDir: path.join(siteDir, STATIC_DIR_NAME),
                metadataPath: (mdxPath: string) => {
                  // Note that metadataPath must be the same/in-sync as
                  // the path from createData for each MDX.
                  const aliasedPath = aliasedSitePath(mdxPath, siteDir);
                  return path.join(dataDir, `${docuHash(aliasedPath)}.json`);
                },
              },
            },
            {
              loader: path.resolve(__dirname, './markdown/index.js'),
              options: docsMarkdownOptions,
            },
          ]),
        };
      }

      // Suppress warnings about non-existing of versions file.
      const stats = {
        warningsFilter: [VERSIONS_JSON_FILE],
      };

      return {
        stats,
        devServer: {
          stats,
        },
        resolve: {
          alias: {
            '~docs': pluginDataDirRoot,
          },
        },
        module: {
          rules: [createMDXLoaderRule()],
        },
      };
    },
  };
}

One of the most pressing configs I need actually in a monorepo context is the ability to exclude custom paths from each plugin-content-docs loader.
Here is a dump of my webpack config for my root docs plugin. I need to be able to modify the exclude so that it ignores paths where other plugin-content-docs plugins can be found to avoid mdx files being processed twice.
      {
        test: /(\.mdx?)$/,
        include: [ '/xxx' ],
        use: [
          {
            loader: '/xxx/node_modules/.pnpm/cache-loader<EMAIL_ADDRESS>            options: { cacheIdentifier: 'cache-loader:4.1.0false' }
          },
          {
            loader<EMAIL_ADDRESS>            options: {
              configFile: '/xxx/repos/live/packages/apps/docs/babel.config.js',
              caller: { name: 'client' }
            }
          },
          {
            loader: '/xxx/node_modules/.pnpm/@docusaurus/mdx-loader@2.0.0-alpha.69_react-dom@16.14.0+react<EMAIL_ADDRESS>            options: {
              staticDir: '/xxx/repos/live/packages/apps/docs/static'
            }
          },
          {
            loader: '/xxx/node_modules/.pnpm/@docusaurus/plugin-content-docs@2.0.0-alpha.69_react-dom@16.14.0+react<EMAIL_ADDRESS>            options: {
              siteDir: '/xxx/repos/live/packages/apps/docs',
              sourceToPermalink: {
<EMAIL_ADDRESS>'/xxx/docs/_readme',
              },
              versionsMetadata: [
                {
                  versionName: 'current',
                  versionLabel: 'Next',
                  versionPath: '/thirtyfive',
                  isLast: true,
                  routePriority: -1,
                  sidebarFilePath: '/xxx/docs/sidebar.js',
                  docsDirPath: '/xxx'
                }
              ]
            }
          }
        ],
        exclude: [ /node_modules/, /\/repos/, /\/.dev/, /\/tmp/ ] <----- I had to manually modify the webpack config to add these excludes.
      },

This doesn't seem to be set in the plugin-content-docs. It must be set in @docusaurus/core perhaps?
      function createMDXLoaderRule(): RuleSetRule {
        return {
          test: /(\.mdx?)$/,
          include: flatten(versionsMetadata.map(getDocsDirPaths)),
          use: compact([
            getCacheLoader(isServer),
            getBabelLoader(isServer),
            {
              loader: require.resolve('@docusaurus/mdx-loader'),
              options: {
                remarkPlugins,
                rehypePlugins,
                beforeDefaultRehypePlugins,
                beforeDefaultRemarkPlugins,
                staticDir: path.join(siteDir, STATIC_DIR_NAME),
                metadataPath: (mdxPath: string) => {
                  // Note that metadataPath must be the same/in-sync as
                  // the path from createData for each MDX.
                  const aliasedPath = aliasedSitePath(mdxPath, siteDir);
                  return path.join(dataDir, `${docuHash(aliasedPath)}.json`);
                },
              },
            },
            {
              loader: path.resolve(__dirname, './markdown/index.js'),
              options: docsMarkdownOptions,
            },
          ]),
        };
      }

      // Suppress warnings about non-existing of versions file.
      const stats = {
        warningsFilter: [VERSIONS_JSON_FILE],
      };

      return {
        stats,
        devServer: {
          stats,
        },
        resolve: {
          alias: {
            '~docs': pluginDataDirRoot,
          },
        },
        module: {
          rules: [createMDXLoaderRule()],
        },
      };
    },
  };
}