Bad response from application: 503 when sending a prediction request through ingress and IAP

When trying KFServing GCP/IAP example , I receive an error Bad response from application: 503
After some investigations (more details in issue 1199 ), we discovered that the prediction request cannot reach the KFServing endpoint because the Istio local gateway is missing

Quoted from #177

Hi @jal06, I think the PR is not the idiomatic way to install cluster-local-gateway with istio/asm (anthos service mesh).
Because looking at https://github.com/knative/docs/blob/master/docs/install/installing-istio.md#installing-istio-without-sidecar-injection (strange that it's different from https://knative.dev/docs/install/installing-istio/#installing-istio-without-sidecar-injection),
there's a declarative configuration in IstioOperator that allows installing cluster-local-gateway:
  components:
    ingressGateways:
      - name: istio-ingressgateway
        enabled: true
      - name: cluster-local-gateway
        enabled: true
        label:
          istio: cluster-local-gateway
          app: cluster-local-gateway
        k8s:
          service:
            type: ClusterIP
            ports:
            - port: 15020
              name: status-port
            - port: 80
              targetPort: 8080
              name: http2
            - port: 443
              targetPort: 8443
              name: https

This seems to be the correct approach when installing istio using operator. Similarly, we are installing asm using the operator per definition in https://github.com/kubeflow/manifests/blob/master/gcp/v2/asm/istio-operator.yaml.
Therefore, we should include a similar ingressGateway definition in that operator. This will ensure the added cluster-local-gateway work with the entire service mesh.
and I suspect that installing cluster-local-gateway using kubeflow/manifests installed a gateway with old istio versions not designed for ASM, that's why it fails IAP authentication in your later experimentation.

Quoted from #177

Hi @jal06, I think the PR is not the idiomatic way to install cluster-local-gateway with istio/asm (anthos service mesh).
Because looking at https://github.com/knative/docs/blob/master/docs/install/installing-istio.md#installing-istio-without-sidecar-injection (strange that it's different from https://knative.dev/docs/install/installing-istio/#installing-istio-without-sidecar-injection),
there's a declarative configuration in IstioOperator that allows installing cluster-local-gateway:
  components:
    ingressGateways:
      - name: istio-ingressgateway
        enabled: true
      - name: cluster-local-gateway
        enabled: true
        label:
          istio: cluster-local-gateway
          app: cluster-local-gateway
        k8s:
          service:
            type: ClusterIP
            ports:
            - port: 15020
              name: status-port
            - port: 80
              targetPort: 8080
              name: http2
            - port: 443
              targetPort: 8443
              name: https

This seems to be the correct approach when installing istio using operator. Similarly, we are installing asm using the operator per definition in https://github.com/kubeflow/manifests/blob/master/gcp/v2/asm/istio-operator.yaml.
Therefore, we should include a similar ingressGateway definition in that operator. This will ensure the added cluster-local-gateway work with the entire service mesh.
and I suspect that installing cluster-local-gateway using kubeflow/manifests installed a gateway with old istio versions not designed for ASM, that's why it fails IAP authentication in your later experimentation.

I think next steps would be

upgrade ASM to 1.6+ as per https://github.com/kubeflow/gcp-blueprints/issues/144
add the cluster-local-gateway definition during installation
verify whether kfserving works e2e

I think next steps would be

upgrade ASM to 1.6+ as per https://github.com/kubeflow/gcp-blueprints/issues/144
add the cluster-local-gateway definition during installation
verify whether kfserving works e2e


Numerous small change fixes

Moved flow diagram page to dynamic sections,

fixed flow diagram licence selector bug
partial fix for app selector bug (#4330)
fixed descriptive text on org unit list page
removed redundant (non-vararg) SetUtilities:minus
fixed layout for flow summary info view when client has
low horizontal resolution

#4328
#4330 (partial)
#4249
#4063

This pull request introduces 1 alert when merging d3b67ad97450c58d79c2d8e2eedcc260fea652d7 into 2c4cbdfc73711ff817453806fd4655f61d6f4b24 - view on LGTM.com
new alerts:

1 for Unused variable, import, function or class

Warning - Automated code review for khartec/waltz will be disabled on October 1, 2019. You can avoid this by installing the LGTM.com GitHub App. Read about the benefits of migrating to GitHub Apps in the blog.

Comment posted by LGTM.com


Update rJSmin and add option to keep bang comments

This is helpful to retain copyright notices which most libraries require.
Updated rJSmin is the latest upstream release.

Thanks.


Add fixtures and tests, fix core contracts

fixes https://github.com/raiden-network/raiden-contracts/issues/2
fixes https://github.com/raiden-network/raiden-contracts/issues/4

add more tests and fixtures
deploy script uses web3 directly; compiled contracts data must be provided

Closing this in favor of https://github.com/raiden-network/raiden-contracts/pull/15. The new PR contains the changes from this PR.


Obfuscator.io mirror?

Is there a working mirror of the online version? It appears https://obfuscator.io is down.
524 Timeout Error

Same - need to use it and offline. Eeek!

Looks like use uptimerobot.com to monitoring it could be a good idea if admin is still alive... It has been down for few days in a row.

try this website https://javascript-obfuscator.org/ it worked for me

Sorry for delay, i reloaded https://obfuscator.io/.
javascript-obfuscator.org - is not official

Is https://obfuscator.io down ?

Hiya @sanex3339 obfuscator.io seems to be down.

Hi, I'll look into this

Should work now

@sanex3339 it does, thank you 👍

down again it seems

Down again. Do you need help hosting it?

It's down again..

down.

Working for me

its down for me

down again..

We can offer you sponsored hosting for this tool. It is really important and cannot be down.
Please get it back online soon :)

If your work depends on the tool, you should self-host it locally (https://github.com/javascript-obfuscator/javascript-obfuscator-ui) or integrate it in your build process.

Should work now.
The problem that obfuscator server sometimes is stopped and currently the only way to rerun it is to restart instance manually.
I tried to enable pm2 (https://github.com/javascript-obfuscator/javascript-obfuscator-ui/blob/master/ecosystem.config.js), but got strange problem with Gandi simple hosting.
Will try again in the future.


Return error instead of panicking

Do you mind rewriting functions such as update_ip_addrs to return an error instead of panicking? I can do the work if you are willing to accept PRs.

I think that is reasonable. PRs are always welcome!


linalg.eigh fails for large matrix size unlike scikit-cuda

When diagonalizing a tridiagonal matrix on an Nvidia Fermi GT540M, CuPy fails for matrix size  4181 x 4181 (but is ok with 2584 x 2584) whereas scikit-cuda is able to deal with larger size (6765 x 6765). I've perfromed the installation pip install cupy-cuda80 .
Please add support for eigh_tridiagonal as in numpy which is more memory efficient.

Conditions CuPy Version          : 6.4.0
CUDA Root             : /usr
CUDA Build Version    : 8000
CUDA Driver Version   : 9010
CUDA Runtime Version  : 8000
cuDNN Build Version   : 7102
cuDNN Version         : 7102
NCCL Build Version    : 2213
NCCL Runtime Version  : (unknown)
Code to reproduce

import itertools
import numpy as np
import cupy as cp

def WordSeq(n): #desired order nesting sequence
    wordseq = ['A'];#starting word
    for i in range(1, n + 1):
        # definition of substitution rule for Fibonacci word sequence
        wordseq = list(map(lambda x: ['A','B'] if x is 'A' else 'A', wordseq));
        #flatten out resulting list after characters replacement
        wordseq = list(itertools.chain(*wordseq));
    return wordseq

niter = 17  # (size 2584 x 2584, limitation imposed by CUPY driver error, not occurring with scikit-cuda)

mylist_cpu = np.asarray(WordSeq(niter)); len(mylist_cpu)
myQClist_cpu = np.where(mylist_cpu == 'A', float(1.0), float(0.318));

myQClist_gpu = cp.asarray(myQClist_cpu);
d = cp.zeros(len(myQClist_gpu) + 1) # diagonal filled with zeros
e = myQClist_gpu # off-diagonal hopping matrix elements
a_gpu = cp.array(cp.diag(d) + cp.diag(e, k=1) + cp.diag(e, k=-1), cp.float64) # tridiagonal matrix
w_gpu, vr_gpu = cp.linalg.eigh(a_gpu, 'L')   #  vr are eigenvectors, w are eigenvalues

Error messages, stack traces, or logs

CUDADriverError                           Traceback (most recent call last)
<ipython-input-6-29b61d64cbc0> in <module>
      1 # Definition of a symmetric tridiagonal matrix A (version 1)
----> 2 a_gpu = cp.array(cp.diag(d) + cp.diag(e, k=1) + cp.diag(e, k=-1), cp.float64) # tridiagonal matrix
      3 w_gpu, vr_gpu = cp.linalg.eigh(a_gpu, 'L')   #  vr are eigenvectors, w are eigenvalues

cupy/core/core.pyx in cupy.core.core.ndarray.__add__()

cupy/core/_kernel.pyx in cupy.core._kernel.ufunc.__call__()

cupy/cuda/function.pyx in cupy.cuda.function.Function.linear_launch()

cupy/cuda/function.pyx in cupy.cuda.function._launch()

cupy/cuda/driver.pyx in cupy.cuda.driver.launchKernel()

cupy/cuda/driver.pyx in cupy.cuda.driver.check_status()

CUDADriverError: CUDA_ERROR_INVALID_VALUE: invalid argument

@jalfonsi Thanks for raising an issue.

Nvidia Fermi GT540M

Unfortunately CUDA GPU whose CC < 3.0 is no longer supported in CuPy v4.0 or later.
https://docs-cupy.chainer.org/en/stable/install.html

Compute Capability of the GPU must be at least 3.0.


Estimate model size before training

Description
For the training workflow, we first load all of the training data into native memory and then call the JNI train function. We precompute the amount of memory the training data will take up and fail if it won't fit in native memory. This works.
One thing we did not account for when training is the size of the model in native memory. I did some experiments and found that empty IVF, HNSW and PQ have the following memory footprint:

Method
Size Estimate

HNSW
0

IVF
4 * nlists * d /1024 + 1

PQ
4 * d * 2^code_size / 1024 + 1

A user may pass in a model configuration that takes up too much space. Because we do not have a way to check this before allocation, this will most likely result in a memory crash during training.
To prevent this, this PR adds an estimateOverheadInKb method to KNNMethodContext. This method, given a dimension as a parameter, returns an estimate in Kilobytes that the model will take up.
During training, a user will pass in a KNNMethodContext. To reserve estimateOverheadInKb for the model in the native memory cache, the TrainingJob.run method allocates an AnonymousAllocation in the cache. The AnonymousAllocation is added in this PR and is basically an Allocation we know nothing about on the Java side except its size. It should be used when a JNI function makes a big allocation and frees it before returning.
Issues Resolved
#129
Check List

[X] New functionality includes testing.

[X] All tests pass

[X] New functionality has been documented.

[X] New functionality has javadoc added

[X] Commits are signed as per the DCO using --signoff

By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license.
For more information on following Developer Certificate of Origin and signing off your commits, please check here.

Codecov Report

Merging #134 (f397cf2) into feature/faiss-support (5809141) will decrease coverage by 0.51%.
The diff coverage is 79.54%.

:exclamation: Current head f397cf2 differs from pull request most recent head e13a74c. Consider uploading reports for the commit e13a74c to get more accurate results

@@                     Coverage Diff                     @@
##             feature/faiss-support     #134      +/-   ##
===========================================================
- Coverage                    83.13%   82.61%   -0.52%
- Complexity                     772      796      +24
===========================================================
  Files                          114      117       +3
  Lines                         3404     3567     +163
  Branches                       322      337      +15
===========================================================
+ Hits                          2830     2947     +117
- Misses                         433      468      +35
- Partials                       141      152      +11

Impacted Files
Coverage Δ

...java/org/opensearch/knn/index/util/KNNLibrary.java
85.27% <41.66%> (-7.53%)
:arrow_down:

.../java/org/opensearch/knn/training/TrainingJob.java
86.88% <77.77%> (-8.68%)
:arrow_down:

...java/org/opensearch/knn/index/MethodComponent.java
86.84% <85.18%> (+0.87%)
:arrow_up:

...earch/knn/index/memory/NativeMemoryAllocation.java
82.92% <88.00%> (+1.29%)
:arrow_up:

.../main/java/org/opensearch/knn/index/KNNMethod.java
100.00% <100.00%> (ø)

...ava/org/opensearch/knn/index/KNNMethodContext.java
93.10% <100.00%> (+0.08%)
:arrow_up:

...rch/knn/index/memory/NativeMemoryCacheManager.java
96.51% <100.00%> (ø)

...rch/knn/index/memory/NativeMemoryEntryContext.java
100.00% <100.00%> (ø)

...rch/knn/index/memory/NativeMemoryLoadStrategy.java
96.61% <100.00%> (+0.61%)
:arrow_up:

.../java/org/opensearch/knn/index/util/KNNEngine.java
100.00% <100.00%> (ø)

... and 12 more

Continue to review full report at Codecov.

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Powered by Codecov. Last update 5809141...e13a74c. Read the comment docs.


How to post more files?

I use the following method for posting more files to Gitlab: https://github.com/AntoninSlejska/php-gitlab-api/blob/8.0.1/lib/Gitlab/Api/Repositories.php#L369
The array $actions contains one or more objects: https://github.com/AntoninSlejska/php-gitlab-api/blob/8.0/docs/actions-array.txt
Is it possible to use for posting of more files some of the existing methods?

Not yet. There is an existing pull request but it has to be rebased.

Thanks @fbourigault
Is there any chance, that the pull request will be rebased and accepted in the near future?

I just notified the PR author about required rebase. As changes required are important (related to HTTPlug, feel free to open an other PR. I will be happy to review it!

If the author will not react, then I try to open other PR. Thanks.

It seams like you can open a PR. =)

OK. I have to finish a project at first. Then I will open the PR.

I try to send the following POST request to Gitlab:
PAYLOAD=$(cat << 'JSON'
{
  "branch": "master",
  "commit_message": "some commit message",
  "actions": [
    {
      "action": "create",
      "file_path": "foo/bar",
      "content": "some content"
    },
    {
      "action": "delete",
      "file_path": "foo/bar2"
    }
  ]
}
JSON
)
curl --request POST --header "PRIVATE-TOKEN: 9koXpg98eAheJpvBs5tK" --header "Content-Type: application/json" --data "$PAYLOAD" https://gitlab.example.com/api/v4/projects/1/repository/commits

See: https://docs.gitlab.com/ce/api/commits.html#create-a-commit-with-multiple-files-and-actions
I suppose the method 'actions' should look like this:
/**
 * @param int $project_id
 * @param array $actions
 * @param string $branch
 * @param string $commit_message
 * @param string $encoding
 * @return mixed
 */
public function actions($project_id, array $actions, $branch, $commit_message)
{
    return $this->post($this->getProjectPath($project_id, 'repository/commits'), array(
        'branch' => $branch,
        'commit_message' => $commit_message,
        'actions' => $actions,
    ));
}

But I get the following error message:

Gitlab\Exception\ErrorException: Unknown action ''
in /var/www/my-site/vendor/m4tthumphrey/php-gitlab-api/lib/Gitlab/HttpClient/Plugin/GitlabExceptionThrower.php:32

The array in the variable $actions looks like:
Array
(
[branch] => master
[commit_message] => some commit message
[actions] => Array
	(
	[0] => Array
		(
		[action] => create
		[file_path] => foo/bar
		[content] => some content
		)
	[1] => Array
		(
		[action] => delete
		[file_path] => foo/bar2
		)
	)
)

Have you any idea, what do I make wrong?


missing Harvest?

I get the following after starting worldHandler, reading the example wav file, and clicking F0 extraction:
Unrecognized function or variable 'Harvest'.
Error in pitchPlginHarvest (line 4)
output = Harvest(x, fs, options);
Error in pitchWrapper (line 2)
output = pitchExtractor(x, fs, options);
Error in worldHandler/FoextractionButtonPushed (line 675)
f0_parameter = pitchWrapper(x(span), fs, option_harvest, @pitchPlginHarvest);
Error in matlab.apps.AppBase>@(source,event)executeCallback(ams,app,callback,requiresEventData,event) (line 62)
newCallback = @(source, event)executeCallback(ams, ...
Error while evaluating Button PrivateButtonPushedFcn.

Please install WORLD package written in MATLAB.  (WORLD version 0.2.4 )
http://www.isc.meiji.ac.jp/~mmorise/world/english/index.html
Harvest is there.

That worked – thank you!
Denis Fitzpatrick
W:<PHONE_NUMBER>
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boystownhospital.orghttps://www.boystownhospital.org/
555 North 30th Street • Omaha, NE 68131
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This message is privileged and confidential and is intended only for the use of the addressee. Unauthorized use of this message is strictly prohibited. If you received this transmission in error, please immediately contact the sender and destroy the message. Thank you.
From: Hideki Kawahara @.>
Sent: Wednesday, August 7, 2024 11:16 AM
To: HidekiKawahara/worldGUItools @.>
Cc: Fitzpatrick, Denis F. @.>; Author @.>
Subject: Re: [HidekiKawahara/worldGUItools] missing Harvest? (Issue #2)
Please install WORLD package written in MATLAB. (WORLD version 0.2.4 )
http://www.isc.meiji.ac.jp/~mmorise/world/english/index.htmlhttps://urldefense.com/v3/__http:/www.isc.meiji.ac.jp/*mmorise/world/english/index.html__;fg!!CEjAKCqGPSl6!vBPHWjSdDSt5U1kAJzEW8akI_Am6xG76dmAN16oRT6TO4lqqKQW1hudt8zYDMJ_zWFHSlCH-5dJcKe57uDD9MBLeXvaUzetQTg$
Harvest is there.
—
Reply to this email directly, view it on GitHubhttps://urldefense.com/v3/__https:/github.com/HidekiKawahara/worldGUItools/issues/2*issuecomment-2273839260__;Iw!!CEjAKCqGPSl6!vBPHWjSdDSt5U1kAJzEW8akI_Am6xG76dmAN16oRT6TO4lqqKQW1hudt8zYDMJ_zWFHSlCH-5dJcKe57uDD9MBLeXvY39-OijQ$, or unsubscribehttps://urldefense.com/v3/__https:/github.com/notifications/unsubscribe-auth/AALEHNFA5YUVH3LRPVMNIOTZQJB3PAVCNFSM6AAAAABMEXBQLSVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDENZTHAZTSMRWGA__;!!CEjAKCqGPSl6!vBPHWjSdDSt5U1kAJzEW8akI_Am6xG76dmAN16oRT6TO4lqqKQW1hudt8zYDMJ_zWFHSlCH-5dJcKe57uDD9MBLeXvbZmY_wtA$.
You are receiving this because you authored the thread.Message ID<EMAIL_ADDRESS>

I will add notes to README. Thanx.


fix(minor): Fixed bug in Windows with incorrect slashes in paths

What is the purpose of this pull request?

[x] Bug fix
[ ] New Feature
[ ] Documentation
[ ] Other

Description
In Windows paths in publicDir is incorrect, so no images can be found to optimize.
Checked in Windows but didn't check in Linux/MacOS but should work :)
Checks

[ ] PR adheres to the code style of this project
[ ] Related issues linked using fixes #number
[x] Check that there isn't already a PR that solves the problem the same way to avoid creating a duplicate.
[x] Lint and build have passed locally by running pnpm lint && pnpm build
[x] Code changes have been verified in local
[ ] Documentation added/updated if necessary

Additional Context

Thanks for contribution! Please review the changes requested.

Done!


No multiselect controls for selection with the same uids

Problem:
When there are two remix scenes in the project, it is really easy to accidentally select the same element in both (by using the code editor). In this case we should a multiselect outline around those elements, which is kinda meaningless for theis special case.
Fix:
When the selected elements all share the same uid we don't show the multiselect outline and the strategy controls.

I did a very poor job following the remix track lately, could you link a test project here where I can play with this?

@balazsbajorics https://utopia.pizza/p/118b8b1e-topaz-mousepad/?branch_name=feature-all-uids-the-same-multiselection


[Gardening] Remove Dead Swift 3 Optional Migration Code

@swift-ci smoke test

It's been about four years since we last needed these diagnostics. Plus the tests for this codepath were nuked a long long time ago when we eliminated a lot of 2 -> 3 code.

⛵


Assistance using Tutorials

Include tutorial videos to provide step by step guidance for individuals to complete various tasks eg

Sign up Process.
Getting Started on Chimoney.
Contributing to Chimoney.

feedback


[ATLAS-3908] Update Kafka to 2.3.1 version

#128 also updates the version to 2.4.1 (the latest one for Scala 2.11)
But this PR is still useful with the change for SystemTime

@martin-g seems like SystemTime change was done in https://github.com/apache/atlas/commit/1907f6408c4c499a0cc7b4bf016571a4b752a7f8 already
But seems like it left an unused import, feel free to merge or close.

@medb I am not a member of Atlas team, so I don't have permissions.


sudo to avoid Permission denied.

If don't use sudo, you will get a Permission denied here, because /usr/local/bin is owned by root.

Docs Build status updates of commit 8eec5b7:
:white_check_mark: Validation status: passed

File
Status
Preview URL
Details

TerminalDocs/tutorials/powerline-setup.md
:white_check_mark:Succeeded
View

For more details, please refer to the build report.
Note: Broken links written as relative paths are included in the above build report. For broken links written as absolute paths or external URLs, see the broken link report.
For any questions, please:Try searching the docs.microsoft.com contributor guidesPost your question in the Docs support channel

Thanks for contributing this @Ruikuan. We decided a while back to eliminate this tutorial and redirect to this Custom Command Prompt tutorial. So going to close this PR, but please let us know if you have any feedback or contributions for the new tutorial. (I've got a few updates that I'm hoping to make to it later today).


feat: create bevy topic

Please confirm this pull request meets the following requirements:

[x] I followed the contributing guidelines: https://github.com/github/explore/blob/main/CONTRIBUTING.md.
[x] I have no affiliation with the project I am suggesting (as a maintainer, creator, contractor, or employee).

Which change are you proposing?

[ ] Suggesting edits to an existing topic or collection
[x] Curating a new topic or collection
[ ] Something that does not neatly fit into the binary options above

Curating a new topic or collection

[x] I've formatted my changes as a new folder directory, named for the topic or collection as it appears in the URL on GitHub (e.g. https://github.com/topics/[NAME] or https://github.com/collections/[NAME])
[x] My folder contains a *.png image (if applicable) and index.md
[x] All required fields in my index.md conform to the Style Guide and API docs: https://github.com/github/explore/tree/main/docs

Bevy is a game engine in Rust that blew up in popularity. While only being released in 2020, it already shows signs of becoming a effective yet simple game engine.

Please note: we will close your PR without comment if you do not check the boxes above and provide ALL requested information.

Ok, I fixed the CI errors.

Any idea why the CI is still failing? I haven't used Rubocop before.

Any idea why the CI is still failing? I haven't used Rubocop before.

Maybe bot can't push to your forked repository.

Maybe bot can't push to your forked repository.

CC @kenyonj for help here

Yes, that is the case (the bot can't push to a fork). I am working on a fix now and should have this PR green shortly.

Thanks for the merge! :)


Problems importing react when running vite setup in node_modules

I want to ship a vite dev project in a npm package, which I start using the vite js api.

├── sketch.ts
├── node_modules
│   ├── entropretty-editor (my shipped vite setup)

It works fine in my monorepo where the entropretty-editor dependency comes from @workspace:*, but fails in this distributed npm package. The errors suggest to me it's related to this plugin as it first fails getting imports from react.
Uncaught SyntaxError:
The requested module '/node_modules/.pnpm/react@18.3.1/node_modules/react/index.js?v=abd2c501'
does not provide an export named 'useEffect'

I explained my setup a bit more in detail in this discussion: https://github.com/vitejs/vite/discussions/18000

Shipping a wrapper around the dev server via createServer is something that is Vite is made for, and that's what some meta frameworks do! But this doesn't mean that you should publish an App as is, this doens't make a lot of sense to me. Using TS/JSX inside node_modules is something that may works, but is not officially supported and not recommended. The issue probably comes here from the fact that React being CJS, the import fails because node_modules code is not pre-process like src code is done (or differently I don't remember exactly)
If you have a setup with a clean new version of the package bundle to be a npm lib et not just a "git push to npm" and still have trouble, please open a new issue with the new setup

Thanks for getting back to me. Makes sense. Can confirm running a bundled version of my app from node_module works just fine.
One reason I wanted to run a unmodified codebase is that I wanted to do a dynamic import in my shipped app. As far as I know, I lose this ability with a production bundled app.
I want to import a script form the users workspace into my shipped app. And it could and should be typescript. Doing a dynamic import to get the users code did all transpilation and gave me HMR for the users code.

I'm not sure I totally get it, but you could do something like:
import { WrapperApp } from "your-npm-lib";
Import UserApp from "./UserApp.tsx"

export const App() {
  return <WrapperApp UserApp={UserApp} />
}

And then either this is used inside a Vite project, or you ship a bin that do createDevServer with the react plugin so you don't need to add a vite config to your new app.


SEV 支持

(Write your question here.)

You mentioned SEV support in the above picture, so I am wondering what's the status of AMD platform support to run MesaTee. thanks!

Since AMD SEV isn't mature enough to support our current platform, we don't have plan to integrate it in this year. However, we'll keep eye on it and will release a short report of the feasibility of AMD SEV in the near future.
If you're interested in our 2021 plan, please read this blog (https://teaclave.apache.org/blog/2021-01-19-roadmap-in-2021-project-maturity-and-community-buildup/) and also documented in the issue #460 . Thanks.

Also, please read all documents around Teaclave instead. Others may cause some confusion. Thanks.

thanks for the quick answer.

You're welcome.


Base models and notebooks for general IPU embeddings model

[WIP] - more changes incoming
Support for multiple embeddings models (e5, mpnet, t5 (pending))
Idea is to create a general embeddings class which supports the pool and norm stage of embeddings model. These models use encoder stages from various base models (bert, t5, mpnet) which we don't support the direct use of with pipelining. I've added these in this folder for now, but they may need to be in the source lib if integrated.
Seemed like not a great idea to create new pipelined classes for all of these models, as they inherit from existing models we support and would just be an extra couple of lines which are common amongst them, so makes more sense to have a generic wrapper for embeddings, which instantiates the encoder stages through AutoModel as currently shown in HF.
The inference setup is manually outlined, but may be more user-friendly to create a pipeline for these eventually.
MPNet modeling file only includes the base model and maskedLM model, not tested for pretraining, only for embeddings.
notebook will likely be restructured

The docs for this PR live here. All of your documentation changes will be reflected on that endpoint.

relies on #437 being merged first


[ISSUE #653]🐛Fix Broker can not start

Which Issue(s) This PR Fixes(Closes)

Fixes #653
Brief Description

How Did You Test This Change?

🚀Thanks for your contribution🎉. CodeRabbit(AI) will review your code first🔥


Add Angad Gill to Contributors List

First contribution to first contributions.

Thank you for your contribution @angadgill92. Merging


Autoload the GOV.UK Frontend macros

Glob the file system for macro file within the @govuk-frontend node modules folder, parse each file and grab any macros they export, and then make them available as globals.
This allows us to remove the {% from “component/macro.njk" import govukComponent %} lines from the examples we create, which means that Prototype Kit users will not have to worry about what to do with that line.
Because we need a Nunjucks environment we can use to parse the templates, the config for the nunjucks engine has been moved into the config folder and required in both places.
We should be able to adopt this in the Prototype Kit as well (though we’ll probably need to swap the spread operators out for something that works in older versions of Node as well).
https://trello.com/c/lUJckE4D/614-spike-find-a-way-to-exclude-the-from-import-lines-from-the-macro-examples-in-the-design-system-1-hour

You can preview this change here:
Built with commit 570d6e9b74347be40aa1aedeab912417189f36c5
https://deploy-preview-164--govuk-design-system-preview.netlify.com

Are we sure this solves a problem?
I don't think adding more magic is a good idea if this is 'just in case', we should have evidence that this is a problem for people before straying off the default path for Nunjucks macros.

I agree it would be helpful to record more about the problem on the trello card.
A large proportion of design system users (possibly majority) will be using the design system with the prototype kit, in which case this import line is wrong.
As for production front-enders, I don't think they would be using the import line just above the component in this way either. I think we need to tell them how to do imports, but this might not be the best way. For example we could have a guide on how to use Frontend in Production, that would include telling people how to include macros. Alternatively we might bring back the Tech docs tab and tell people there.

Perhaps we should put this on hold and talk about this at the documentation session on Tuesday? It seems coupled to the way we present the examples in the Design System.

Closing this for now until we work out how we want to present the macros in the Design System.


Merge section Motivation into Abstract

The Motivation section was copied over from the initial proposal and needs to be merged into the abstract and other sections.

Done, PR has been merged.


Network not connect

Issue description
Network does not connect on profil 1, i add a second profil and it worked. On the first one it does still not connect.
Maybe it happend because i canceld before a profile. I´m using Ledger Nano S.
Error Log is empty
Firefly version
1.2.0
OS details
Windows 10 Pro_ 19043.1165
Can the issue be reliably reproduced?
No
Steps to reproduce the issue

...
.
Expected behaviour
.
Actual behaviour
.
Errors
No response

You can try to restore the profil with a freshly new created profile. To restore the profile just connect your ledger and choose the option restore hardware wallet and then Firefly ledger backup.

Yes i have done it. I wrote it just that you know that the problem does exist

I had the same issue. I solve it by creating a new profile, but i didnt create it from start. I create the new profile by restoring my previous wallet by the backup (stronghold, seed,etc). It worked and connected to the network correctly.

Hi @gasserf97, sorry for the delay. Can you remember if you changed your profiles pin before this occurred?

I've got the same problem. Every time I add a new profile for my Ledger Nano, the next time I open Firefly and check that profile it says "Network not connected", the wallet is gone ("Du hast keine Wallets. Bitte erstelle eine.") and I basically have to remove that profile and re-add it. But next time I open Firefly, the exact same problem occurs :(
I'm on macOS.

Hi @nicole-obrien  i´m not 100 % sure but i don´t think that i have changed my password

Do you currently have access to your accounts?

Yes i do


refactor: protocols const to an enum

I find it sad that we are using TypeScript yet most of our files are red because our protocol props are not (and cannot be) typed correctly with our current setup. This is because we are using an array of constsants PROTOCOLS. I think it very much makes sense to use a Protocols enum in this case as it simplifies things and makes them more straightforward.

In general LGTM.
I got some issues in testnet with names (was not able to make it default because of bad middleware work) and with multisig creation, but the same is on dev branch.


⚠ Bofrost.lu has degraded performance

In a718057, Bofrost.lu (https://www.bofrost.lu) experienced degraded performance:

HTTP code: 200
Response time: 1248 ms

Resolved: Bofrost.lu performance has improved in 04d0e3c after 6 minutes.


selectionId

Can anyone tell me what is selectionId parameter (Sending to selectionManager to appear drill through option in the context menu)?

Thanks for your comment, @HMLaozitech. This looks like a support request rather than documentation feedback. You might find a solution in the Power BI Community, found at https://community.powerbi.com. If not, you can get support by going to https://support.powerbi.com.


OpenSource : 1.4 Why Open Source?

Description :

Why you should open source your projects ?
Benefits of contributing to an open source softwares
Good practices while contributing to an open-source software

Note :

Changes should be made inside the Open_Source/ directory & Opensource branch.
Issue only for GWOC'21 contributors.
Issue will be assigned on a first come first serve basis, 1 Issue  ==  1 PR.

Must Follow : Contributing Guidelines & Code of Conduct before start Contributing.
Happy Contributing 🙌

Can I work on this? Kindly assign this to me.

Can I work on this? Kindly assign this to me.

Yea, Sure @shubhdholakiya , Assigning this to you ✅
Happy documenting 🙌

Can please tell me like I have to make a readme file and write all the answers to the given questions in the above description?

Can you please tell me like I have to make a readme file and write all the answers to the given questions in the above description?

Hey, You can contribute through documentation, audio & video. See: https://github.com/girlscript/winter-of-contributing/blob/main/.github/CONTRIBUTING.md
For documentation, It should be markdown format

Can you please tell me like I have to make a readme file and write all the answers to the given questions in the above description?

Hey, You can contribute through documentation, audio & video. See: https://github.com/girlscript/winter-of-contributing/blob/main/.github/CONTRIBUTING.md
For documentation, It should be markdown format

Okay Thank you very much.

@Aryamanz29  May I also work on this issue?

I will be making a PR by tonight!

@shubhdholakiya  Any updates on this ?

May i contribute in this?

May i contribute in this?

@paras-saxena11  Can you open a new issue for which you would like to contribute to the open-source domain ? by following the above issue convention.

@shubhdholakiya Any updates on this ?

Yes, will be posting PR by tonight.

@Aryamanz29 Hey brother, while making PR do I need to add any other document, or do I just need to add the documentation I made?

@Aryamanz29 Hey brother, while making PR do I need to add any other document, or do I just need to add the documentation I made?

Only doc


Revert self-hosted AppID validation and only disallow dots

Signed-off-by: Shubham Sharma<EMAIL_ADDRESS>Description
This PR reverts the logic introduced in #5556 to validate self-hosted app-ids with RFC-1123. It only throws an error if the app-id contains a period (.).
A major reason is that upper-case in app-ids was no longer allowed after #5556, which broke the case-sensitive implementations in state store (app-id is prefixed) and pub/sub (consumer groups use app-id).
From #5556, we introduced stricter rules for appID validation in self-hosted mode. This PR relaxes the case restrictions by transforming the appID into a lowercase string. This will prevent breaking existing Dapr integrations where users are using appIDs with uppercase characters.
Issue reference
Please reference the issue this PR will close: #5552
Checklist
Please make sure you've  completed the relevant tasks for this PR, out of the following list:

[ ] Code compiles correctly
[ ] Created/updated tests
[ ] Unit tests passing
[ ] End-to-end tests passing
[ ] Extended the documentation / Created issue in the https://github.com/dapr/docs/ repo: dapr/docs#[issue number]
[ ] Specification has been updated / Created issue in the https://github.com/dapr/docs/ repo: dapr/docs#[issue number]
[ ] Provided sample for the feature / Created issue in the https://github.com/dapr/docs/ repo: dapr/docs#[issue number]

We should add new E2E test scenarios to make sure things break on breaking changes.

We do not have E2E scenarios for self-hosted currently :(

We should add new E2E test scenarios to make sure things break on breaking changes.

We do not have E2E scenarios for self-hosted currently :(

We don't, but that's a point in time situation. It actually would be easier to orchestrate self hosted tests than our k8s e2e tests. I'm just wondering if the kind of test to catch something like this should actually be done on the components side or on the daprd side.


Can not download file, bacause of the ob_flush() called without ob_start() called first

Describe the bug
I installed filegator v7.8.1, and I uploaded files to it, but when I download files, the downloaded file is damaged. and  I got this message in log file:
Notice: ob_flush(): failed to flush buffer. No buffer to flush
Environment :

Debian GNU/Linux 11 (bullseye)
PHP 7.4.29
nginx 1.18.0

How I fixed the bug
I add this line :
ob_start();
before
echo fread($file['stream'], 1024 * 8);
in backend/Controller/DownloadController.php on line 60.
Additional context
After I added ob_start() before echo statement, I can download file correctly.
So, What puzzles me is why your Filegator instance don't have this bug.

Hi,
It could be some server-side buffering or compression.
Can you try replacing: ob_flush(); with:  if (ob_get_level() > 0) {ob_flush();} and removing the ob_start(); part completely?

It works.
Thank you ver much. Have a nice day!


Segmentation fault when GPUs are already used

When I set the nvidia driver in exclusive mode and one of the GPU is already used by another process, I get a segmentation fault:
$ python -c "import tensorflow as tf;tf.InteractiveSession()"
I tensorflow/core/common_runtime/local_device.cc:25] Local device intra op parallelism threads: 12
Segmentation fault (core dumped)

If I limit the visible GPUs to only GPUs that have nothing running on them, it don't segfault:
$CUDA_VISIBLE_DEVICES=1 python -c "import tensorflow as tf;tf.InteractiveSession()"
I tensorflow/core/common_runtime/local_device.cc:25] Local device intra op parallelism threads: 12
I tensorflow/core/common_runtime/gpu/gpu_init.cc:88] Found device 0 with properties:
name: GeForce GTX TITAN X
major: 5 minor: 2 memoryClockRate (GHz) 1.076
pciBusID 0000:09:00.0
Total memory: 12.00GiB
Free memory: 11.87GiB
I tensorflow/core/common_runtime/gpu/gpu_init.cc:112] DMA: 0
I tensorflow/core/common_runtime/gpu/gpu_init.cc:122] 0:   Y
I tensorflow/core/common_runtime/gpu/gpu_device.cc:643] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX TITAN X, pci bus id: 0000:09:00.0)
I tensorflow/core/common_runtime/gpu/gpu_region_allocator.cc:47] Setting region size to<PHONE_NUMBER>3
I tensorflow/core/common_runtime/local_session.cc:45] Local session inter op parallelism threads: 12

Here is my output of nvidia-smi:
$ nvidia-smi
Wed Nov 11 16:48:27 2015
+------------------------------------------------------+
| NVIDIA-SMI 352.39     Driver Version: 352.39         |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  GeForce GTX 750     Off  | 0000:05:00.0      On |                  N/A |
| N/A   48C    P8     0W /  38W |     25MiB /  2047MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
|   1  GeForce GTX TIT...  Off  | 0000:06:00.0     Off |                  N/A |
| 42%   82C    P2   127W / 250W |    262MiB / 12287MiB |     39%   E. Process |
+-------------------------------+----------------------+----------------------+
|   2  GeForce GTX TIT...  Off  | 0000:09:00.0     Off |                  N/A |
| 22%   46C    P8    17W / 250W |     23MiB / 12287MiB |      0%   E. Process |
+-------------------------------+----------------------+----------------------+
|   3  GeForce GTX TIT...  Off  | 0000:0A:00.0     Off |                  N/A |
| 22%   37C    P8    15W / 250W |    361MiB / 12287MiB |      0%   E. Process |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID  Type  Process name                               Usage      |
|=============================================================================|
|    0      1171    G   /usr/bin/X                                      17MiB |
|    1     32740    C   python                                         209MiB |
|    3      9429    C   python                                         336MiB |
+-----------------------------------------------------------------------------+

I suppose that in the code that check the available GPUs, it don't handle correctly a case when one of the GPUs can't be used.

@nouiz, you are correct in your analysis. TensorFlow tries to grab all the GPU it sees from the system that passes its criteria. CUDA_VISIBLE_DEVICES is the solution I would suggest. Please let us know if that is not enough.

@zheng-xq +1 on CUDA_VISIBLE_DEVICES; this fixes the issue for me.

Re-open if CUDA_VISIBLE_DEVICES isn't good enough

@vrv This is still a problem when there are multiple GPUs (in exclusive mode) on the machine and some of them have processes running on them. Setting CUDA_VISIBLE_DEVICES is not ideal if you do not actually know which GPU is available. nvidia-smi will only show the current state of the GPUs and there's no way of ensuring that while you spawn your process, no other process occupied the GPU you set for use. Is there a way of polling GPUs and assigning the process to the first available, compatible GPU on the machine instead of having to manually specify the GPU id?
PS: I cannot reopen this issue.

@zheng-xq: is it possible to query the status of the exclusive mode bit?

@noisychannel, even if we can query which GPU is available, and by the time we actually try to assign the GPU, it could be taken by other processes. This is not very different from having a script query GPU status through nvidia-smi, and set the GPU id accordingly.
The only way I can think of that makes your case easier is actually to create the context, and if it fails, ignore that failure and move on to the next. However, this is undesirable for many more common cases, where such context creation errors are signs of bigger problems.

The established way to do this seems to be:

Set the GPUs to operate in exclusive mode.
Do not call cudaSetDevice().
When you run your program, it will try and create the context on the first GPU and fail if the GPU is busy.  Assuming you're using CUDART, it should silently try and create the context on the next GPUs and fail if all GPUs are busy.

As for exception handling while creating the context, it seems like the this scenario can be handled with the exceptions returned by cuCtxCreate().
A solution like this is optimal when you delegate the scheduling of your jobs to something like SGE and cannot trust the script based querying approach which I think is common for large clusters.

This would work fine if the framework only wants to use a single GPU. TensorFlow is designed to use multiple GPU seamlessly at the same time. So at the initialization stage, it grabs all the visible devices that are compatible, since the client program might use all of them. The actual device used is only known at the graph construction/execution time, which is at a much later stage.
For most of our existing users, the GPUs that are available to a particular job is known when it starts. So it is okay for TensorFlow to take all of them. A few options to think about:

Is it possible for the job scheduler to reserve the GPUs in your clusters? That would work best with TensorFlow.
If you only know the list of candidate GPUs, and the number of GPUs to use, we can add a special mode where we try to create context in the candidate list, and stops if desired number of GPUs are taken. This requires more plumbing to both TensorFlow and the underlying Stream-Executor that actually manages GPUs. We would much prefer #1 if that is possible for you.

For 1: AFAIK, the only responsibility of most job schedulers is to ensure that you will have access to the number of GPUs you have asked for on the machine which has been assigned to you. It leaves the actual CPU/GPU scheduling of the processes on that machine to the OS/Cuda driver. Also, hypothetically, if the scheduler was to attempt something of this sort, it would not be able to block the GPU without creating something like a context.
For 2 : I understand that this is probably painful. However, it does add a more general set of features. The case where I want k out of n GPUs on the machine is most prominent. Eg,, it would be able to handle the case where the candidate list is the set of all GPUs on the machine and I want only 2 available and compatible GPUs from that list. Having the ability to specify how many GPUs you want to use will be a great addition and this will tie in perfectly with most job schedulers. So to tensorflow, I specify, I want to run this thing on 2 GPUs and I tell the job scheduler that I need 2 GPUs.

Drive-by comment: the sysadmin for my local SGE cluster was able to have an environment variable set that tells me which GPUs my jobs have been assigned. I then use this variable to set CUDA_VISIBLE_DEVICES. I would guess that most common schedulers have similar capabilities, although they may require root-level tweaking.

This is useful to know. However, something like this would require all jobs on the cluster to respect that environment variable when using GPUs. And it takes away some of the scheduling responsibility that should ideally belong to the CUDA driver, adding another layer of complexity. In fact, most other toolkits that are GPU capable will not require something like this and as long as the GPUs are set in exclusive mode, they work just fine.

Another drive-by comment: when the allocation fails, the app actually segfaults on our machines. That does not provide any possibility for the user to recover from it at the python level.
A good-enough solution would be
a) throw an exception so that user can it catch and try (for example) another device
b) ideally, adding /gpu:auto which would just iterate through the devices and allocate the one that is available.

@noisychannel, please see if you can work around this issue at the moment. Meanwhile, I will investigate if we can officially support the soft-try-and-device-limit approach in TensorFlow.

Okay. Thanks. Keep me posted.

@zheng-xq: Is this still an issue?

I'm not aware of a "soft-try-and-assign-device" implementation in Tensorflow.

Closing this automatically due to lack of recent activity. Please reopen when new information becomes available. @zheng-xq, please re-open if you plan to be still working on this.

@aselle I'm on a shared multi GPU machine and this doesn't appear to have been fixed.

@AndreasMadsen, what is your exact hardware/software configuration and steps to reproduce (see the new issue template).

The steps are the same @nouiz describe. python -c "import tensorflow as tf;tf.InteractiveSession()" fails with a Segmentation fault. Adding CUDA_VISIBLE_DEVICES=1 fixes it.
I have a hunch that it might be related to mixing GPUs with diffrent CUDA capabilities. But I have no way to test that.
Sat Oct 22 16:07:37 2016
+------------------------------------------------------+
| NVIDIA-SMI 352.39     Driver Version: 352.39         |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  Tesla K40c          On   | 0000:02:00.0     Off |                    0 |
| 23%   23C    P8    18W / 235W |     22MiB / 11519MiB |      0%   E. Process |
+-------------------------------+----------------------+----------------------+
|   1  Tesla K20c          On   | 0000:03:00.0     Off |                    0 |
| 30%   20C    P8    16W / 225W |     13MiB /  4799MiB |      0%   E. Process |
+-------------------------------+----------------------+----------------------+
|   2  Tesla K20c          On   | 0000:83:00.0     Off |                    0 |
| 30%   22C    P8    16W / 225W |     13MiB /  4799MiB |      0%   E. Process |
+-------------------------------+----------------------+----------------------+
|   3  GeForce GTX TIT...  On   | 0000:84:00.0     Off |                  N/A |
| 22%   31C    P8    29W / 250W |    161MiB / 12287MiB |      0%   E. Process |
+-------------------------------+----------------------+----------------------+

A backtrace can be useful here. Either run under pdb directly, or examine
core file under pdb (ie, set ulimit -c unlimited, get core file, open core
file with pdb and using pywraptensorflow.so as binary)
On Sat, Oct 22, 2016 at 7:10 AM, Andreas Madsen<EMAIL_ADDRESS>wrote:

The steps are the same @nouiz https://github.com/nouiz describe. python
-c "import tensorflow as tf;tf.InteractiveSession()" fails with a
Segmentation fault. Adding CUDA_VISIBLE_DEVICES=1 fixes it.
I have a hunch that it might be related to mixing GPUs with diffrent CUDA
capabilities. But I have no way to test that.
Sat Oct 22 16:07:37 2016
+------------------------------------------------------+
| NVIDIA-SMI 352.39     Driver Version: 352.39         |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  Tesla K40c          On   | 0000:02:00.0     Off |                    0 |
| 23%   23C    P8    18W / 235W |     22MiB / 11519MiB |      0%   E. Process |
+-------------------------------+----------------------+----------------------+
|   1  Tesla K20c          On   | 0000:03:00.0     Off |                    0 |
| 30%   20C    P8    16W / 225W |     13MiB /  4799MiB |      0%   E. Process |
+-------------------------------+----------------------+----------------------+
|   2  Tesla K20c          On   | 0000:83:00.0     Off |                    0 |
| 30%   22C    P8    16W / 225W |     13MiB /  4799MiB |      0%   E. Process |
+-------------------------------+----------------------+----------------------+
|   3  GeForce GTX TIT...  On   | 0000:84:00.0     Off |                  N/A |
| 22%   31C    P8    29W / 250W |    161MiB / 12287MiB |      0%   E. Process |
+-------------------------------+----------------------+----------------------+
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Hi,
I am still observing problems w/ GPU reservation in a 16 GPU node. For some GPUs, when reserving the GPU with CUDA_VISIBLE_DEVICES, it fails to reserver the node. In TF 0.10 it generated a coredump. In TF 0.12 it complains about not being able to start the tf.Session.
The node status is this:
+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID  Type  Process name                               Usage      |
|=============================================================================|
|    0     46690    C   /usr/bin/python2.7                            2326MiB |
|    2     13781    C   python                                        3456MiB |
|    3     26767    C   python                                         282MiB |
|    4     53525    C   /usr/bin/python2.7                             740MiB |
|    5     13782    C   python                                        3456MiB |
|    6      9427    C   /usr/bin/python2.7                            3455MiB |
|    9     53527    C   /usr/bin/python2.7                             740MiB |
|   10     53222    C   /usr/bin/python2.7                             740MiB |
|   11     53526    C   /usr/bin/python2.7                             740MiB |
|   12     54930    C   /usr/bin/python2.7                            1085MiB |
|   13     54926    C   /usr/bin/python2.7                             785MiB |
|   14     54927    C   /usr/bin/python2.7                             785MiB |
+-----------------------------------------------------------------------------+

And when I use CUDA_VISIBLE_DEVICES=1 , which is free:
Before reservation the free GPUs  [1, 7, 8, 15]
Setting CUDA_VISIBLE_DEVICES=1
E tensorflow/core/common_runtime/direct_session.cc:135] Internal: failed initializing StreamExecutor for CUDA device ordinal 0: Internal: failed call to cuDevicePrimaryCtxRetain: CUDA_ERROR_INVALID_DEVICE
Traceback (most recent call last):
...
 File "PATH/run.py", line 138, in reserveGPUs
    with  tf.Session() as session:
  File "PATH/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1186, in __init__
    super(Session, self).__init__(target, graph, config=config)
  File "PATH/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 551, in __init__
    self._session = tf_session.TF_NewDeprecatedSession(opts, status)
  File "PATH/lib/python2.7/contextlib.py", line 24, in __exit__
    self.gen.next()
  File "/amr/tools/tensorflow/0.12-cuda7.5/lib/python2.7/site-packages/tensorflow/python/framework/errors_impl.py", line 469, in raise_exception_on_not_ok_status
    pywrap_tensorflow.TF_GetCode(status))
tensorflow.python.framework.errors_impl.InternalError: Failed to create session.

Actually, this also fails in the above example:
 CUDA_VISIBLE_DEVICES=1 python2.7 -c "import tensorflow as tf;tf.InteractiveSession()"
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcublas.so.7.5 locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcudnn.so.5 locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcufft.so.7.5 locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcurand.so.7.5 locally
E tensorflow/core/common_runtime/direct_session.cc:135] Internal: failed initializing StreamExecutor for CUDA device ordinal 0: Internal: failed call to cuDevicePrimaryCtxRetain: CUDA_ERROR_INVALID_DEVICE
...
tensorflow.python.framework.errors_impl.InternalError: Failed to create session.

but
CUDA_VISIBLE_DEVICES=8 python2.7 -c "import tensorflow as tf;tf.InteractiveSession()"

and
CUDA_VISIBLE_DEVICES=15 python2.7 -c "import tensorflow as tf;tf.InteractiveSession()"

work.
Note that the same happens with the new ConfigProto options: https://github.com/tensorflow/tensorflow/issues/1888
python2.7 -c "import tensorflow as tf; config_proto = tf.ConfigProto(); config_proto.gpu_options.visible_device_list=\"1\"; tf.Session(config=config_proto)"

doesn't work but
python2.7 -c "import tensorflow as tf; config_proto = tf.ConfigProto(); config_proto.gpu_options.visible_device_list=\"8\"; tf.Session(config=config_proto)"

works.

One problem that I ran into with using both CUDA_VISIBLE_DEVICES and gpu_options.visible_device_list is that GPU numbering can be different between Nvidia NVML (and nvidia-smi for example) and between what you get from cuda if you do cuDeviceGet().  On some systems, NVML and nvidia-smi apparently order the card numbers by sorting the PCI id.  Cuda doesn't necessarily use the same order.  This can happen on a machine where all the GPU cards are identical.
The result is a crash when CUDA_VISIBILE_DEVICES is used to select a GPU.  With gpu_options.visible_device_list you get a failure opening the session for a mismatched GPU that isn't actually available.
Here's the mapping I get on a particular system at work:
NVML id 0 maps to Cuda index 4
NVML id 1 maps to Cuda index 5
NVML id 2 maps to Cuda index 6
NVML id 3 maps to Cuda index 7
NVML id 4 maps to Cuda index 0
NVML id 5 maps to Cuda index 1
NVML id 6 maps to Cuda index 2
NVML id 7 maps to Cuda index 3
NVML id 8 maps to Cuda index 8
NVML id 9 maps to Cuda index 9
NVML id 10 maps to Cuda index 10
NVML id 11 maps to Cuda index 11
NVML id 12 maps to Cuda index 12
NVML id 13 maps to Cuda index 13
NVML id 14 maps to Cuda index 14
NVML id 15 maps to Cuda index 15
Here is the python that generated this:
#!/usr/bin/env python

import pynvml
import os
import ctypes

cuda = ctypes.CDLL('libcuda.so')
cuda_device = ctypes.c_int32(0)
cuda_pci_bus = ctypes.c_int32(0)
cuda_pci_slot = ctypes.c_int32(0)

if cuda.cuInit(ctypes.c_int32(0)) != 0:
    raise Exception("Cuda failure")
total_gpus = ctypes.c_int32(0)
if cuda.cuDeviceGetCount(ctypes.byref(total_gpus)) != 0:
    raise Exception("Cuda failure")

pci_map = {}
for gpu in range(total_gpus.value):
    if cuda.cuDeviceGet(ctypes.byref(cuda_device), ctypes.c_int32(gpu)) != 0:
        raise Exception("Cuda failure")
    if cuda.cuDeviceGetAttribute(ctypes.byref(cuda_pci_bus), ctypes.c_int32(33), cuda_device) != 0:
        raise Exception("Cuda failure")
    if cuda.cuDeviceGetAttribute(ctypes.byref(cuda_pci_slot), ctypes.c_int32(34), cuda_device) !=0:
        raise Exception("Cuda failure")
    # The below is correct on my system but...
    pci_string = "0000:%02X:%02X.0" % (cuda_pci_bus.value, cuda_pci_slot.value)
    pci_map[pci_string] = gpu

pynvml.nvmlInit()
assert pynvml.nvmlDeviceGetCount() == total_gpus.value

for gpu in xrange(total_gpus.value):
    h = pynvml.nvmlDeviceGetHandleByIndex(gpu)
    bus_id = pynvml.nvmlDeviceGetPciInfo(h).busId
    print "NVML id {} maps to Cuda index {}".format(gpu, pci_map[bus_id])

@cbquillen do I understand it correctly that the problem arises only when you use CUDA_VISIBLE_DEVICES mechanism to select the GPU device used by TensorFlow, and some other mechanism (which?) to select GPU device used by another program?

Indeed, visible_device_list deals with what is provided at runtime via the cuGetDevice API -- how external drivers and processes remap the physical ID to the 'visible id' is out of TensorFlow's control, and it sounds like you agree, that visible_device_list must use f(N), not N, since f is not something TF controls.
I believe our documentation is pretty clear on this point: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/protobuf/config.proto#L48 . -- let us know if there's any further documentation that might help?

@yaroslavvb
I am running into this in the case where exclusive GPU access is turned on and there are other processes using some GPUs on the machine.  If I find a free GPU subset using nvidia-smi (or the NVML library) and try to limit Tensorflow to using this subset via CUDA_VISIBLE_DEVICES or gpu_options.visible_device_list in the Tensorflow config_proto, the result is a request for busy GPUs unless I use the mapping derived by looking at PCI-ids.

Hm, that's kind of annoying actually, I've also been parsing nvidia-smi generate CUDA_VISIBLE_DEVICES on shared computers (along the lines of setup_one_gpu. Haven't run into problems yet, but it would be nice to have a work-around that works reliably.

@vrv
The problem is that users will see the ordering provided by nvidia-smi (sorted by PCI-Id) and try to use it, not realizing it actually has nothing to do with what CUDA_VISIBLE_DEVICES actually selects.  Now a lot of people think this is an NVidia bug, and it's possible that they agree and fix it in newer drivers.  However it might be worth adding a prominent warning in the documentation to the effect that this problem exists.  Otherwise you will continue to get queries about it.

I agree that this is a problem.  I'm not sure if NVidia would prefer people to not use the cuGetDevice API in favor of whatever nvidia-smi is using.  Perhaps @benbarsdell or @cliffwoolley might know?
In lieu of that, would you like to add the warning to the documentation somewhere?  You probably know best where others like you would go looking for it :)

@vrv
A short warning in tensorflow.Session about using CUDA_VISIBLE_DEVICES with a link would probably help.  The visible_device_list doc config.proto would be a place where the a complete  problem is described and where you would link to.
stracing nvidia-smi reveals that it loads libnvidia-ml.so.  So it is using NVML and so could Tensorflow.

I was thinking documentation rather than source code might be better: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/g3doc/how_tos/using_gpu/index.md#using-gpus as a possible area to write a short section on it?
cc @zheng-xq: I know he had reservations about adding visible_device_list for this exact reason of confusion -- perhaps he can weigh in if he wants.

This may be helpful to some users who are on a cluster. Often, you don't have information about which GPU has been allocated to you or which one to occupy if free, with CUDA_VISIBLE_DEVICES.
I use this script to (https://gist.github.com/noisychannel/cdf57e2f177e98ae653230323a093d1e) to return the first free GPU and then use something like :
CUDA_VISIBLE_DEVICES=`scripts/free-gpu` python

@yaroslavvb
Looking at the NVIDIA docs more, I think you can get your setup_one_gpu script to work reliably if you set the CUDA_DEVICE_ORDER environment variable to PCI_BUS_ID.

It has been 14 days with no activity and this issue has an assignee.Please update the label and/or status accordingly.

If someone is interested in adding CUDA_DEVICE_ORDER='PCI_BUS_ID' support in TensorFlow, a contribution is welcome. But note that we can only override it if it is not already set. And it has to been before any potential Cuda calls are made.

You can certainly set environment variables within an application before the first call to tf.Session().

Of course it's possible.  Just as in C you'd use setenv().  I was just saying that for those particular environment variables, the choice is a bit specific to the combination of application and machine the application is run on.  So usually the user (or script) invoking the application would set those env vars rather than having the application set them unilaterally.  I'd argue for example that it's unwise for TF itself to set CUDA_DEVICE_ORDER, because it would undo the CUDA feature that this variable sits on top of, which is to, by default, have the "best" device be device 0; this is handy when you don't know much about the system you're running on and are just trying things out.  More sophisticated users just pick a GPU or, if really fancy, pick one based on NVML output as we're discussing here.  But we shouldn't, IMO, assume all users are so sophisticated.

export CUDA_VISIBLE_DEVICES=0  solves my issue!

Closing as this is resolved, free to reopen if problem persists.

Python 3.6.9
Tensorflow 2.1.0 gpu supported
Cuda 10.0
TensorRT 6.0
GPU - 1
Is still get the error even after export CUDA_VISIBLE_DEVICES=0  or export CUDA_VISIBLE_DEVICES=1
Error log :
Can't identify the cuda device. Running on device 0
Segmentation fault (core dumped)


Lint the VR bits recently moved from separate repo.

Follow-up to https://github.com/PolymerLabs/arcs/commit/22cb45f7eb3ad011d19092bea2ca4388b58b7a00
Generated via lint sigh --fix.

👍 Thank you!!!


[Onboarding] Fix issue when searching for artists to scroll to top

Fix #2270

Code looks good, but maybe add a CHANGELOG entry?

@alloy done ^_^

Yay!


Parameters of all commands under "az ml service" should have long forms in addition to short forms

Is your feature request related to a problem? Please describe.
Some commands under "az ml service" have parameters which only have short (one letter) forms. This makes scripts using these commands less readable and breaks user expectations, because the same parameters in other commands do accept long forms.

command
parameter
short form
long form in other command(s)

az ml service create aci
execution_script
-s
--execution-script (ml image create container)

az ml service create aci
runtime
-r
--runtime (ml image create container)

az ml service create aks
service_name
-n
--name (ml service create aci)

az ml service create aks
num_replicas
-z
no existing long form, suggestion: --num-replicas

az ml service create aks
execution_script
-s
--execution-script (ml image create container)

az ml service create aks
runtime
-r
--runtime (ml image create container)

az ml service update aks
service_name
-n
--name (ml service create aci)

az ml service update aks
num_replicas
-z
no existing long form, suggestion: --num-replicas

az ml service run
input_data
-d
--input-data (ml image run)

Describe the solution you'd like
Please add long forms to these parameters.
Additional context
Azure ML CLI extension version: 1.0.10

ml


Updated PUT endpoint for devices in core command.

#1244.
Requires changes from https://github.com/edgexfoundry/go-mod-core-contracts/pull/73
Signed-off-by: Brandon Forster<EMAIL_ADDRESS>

FYI -- @brandonforster You will want to update the go.mod now that https://github.com/edgexfoundry/go-mod-core-contracts/pull/73 has been merged.

FYI -- @brandonforster You will want to update the go.mod now that edgexfoundry/go-mod-core-contracts#73 has been merged.

Updated.

recheck


SHA mismatch while trying to install tensorflow

Describe the bug
While trying to install tensorflow==2.1.0 in a docker container, an SHA mismatch happens
I'd created another container (small unrelated differences) without such an issue about a week ago, so might be someone messing with the codebase.
Expected behavior
Tensorflow installs without such an error.
My Platform
Docker community edition - 19.03.8

Did not provide correct labels. Will reopen with them in a separate issue


availableSharedCapacity will be slowly exhausted

Without this line of code(Line390):this.head.reclaimSpace(LINK_CAPACITY);
availableSharedCapacity will gradually be exhausted, eventually leading to reserveSpace (int space) returning false and Recycler will completely invalid.

@YuanHuCoding would it be possible to add a unit test ?

My test scenario is as follows:
1)Construct Recycler without discarding any objects:
new Recycler < T > (4096, 1, 1, Integer. MAX_VALUE)

2)Submit the recovery operation through a single thread pool:
RECYCLE_THREAD_POOL.submit(() -> {
    xxx.recycle();
});

3)Statistics of all the creation of new objects, objects recycled, objects discarded. You'll find out availableSharedCapacity will gradually be exhausted, eventually leading to reserveSpace (int space) returning false and Recycler will completely invalid.

@netty-bot test this please

@YuanHuCoding can you also sign our icla and let me know once done:
https://netty.io/s/icla

`
public class Test_Recycler_availableSharedCapacity {
public static void  main(String[] args) {
    WorkTask workTask = new WorkTask();
    for (int i=0;i<10000;i++) {
        WORK_THREAD_POOL.submit(workTask);
    }
}

private static final org.slf4j.Logger logger = org.slf4j.LoggerFactory.getLogger(Test_Recycler_availableSharedCapacity.class);
private static final ThreadPoolExecutor WORK_THREAD_POOL = new ThreadPoolExecutor(1, 1,
        0L, TimeUnit.MILLISECONDS,
        new LinkedBlockingQueue<Runnable>(102400),new DefaultThreadFactory("WorkThreadPool",false,Thread.NORM_PRIORITY));

private static final ThreadPoolExecutor RECYCLE_THREAD_POOL = new ThreadPoolExecutor(1, 1,
        0L, TimeUnit.MILLISECONDS,
        new LinkedBlockingQueue<Runnable>(102400),new DefaultThreadFactory("RecycleThreadPool",false,Thread.NORM_PRIORITY));

private static LongAdder NEW_OBJECT_NUM=new LongAdder();

//If you can't reproduce it, you can try it several times more, or you can reduce the parameter maxCapacityPerThread to make it easier to reproduce.
private static final Recycler<Cycler> CyclerRecycler = new Recycler<Cycler>(256,1,1,Integer.MAX_VALUE) {
    @Override
    protected Cycler newObject(Handle<Cycler> handle) {
        NEW_OBJECT_NUM.increment();
        logger.info("NEW_OBJECT_NUM:"+NEW_OBJECT_NUM.longValue());
        return new Cycler(handle);
    }
};
static final class Cycler {
    private String value;
    public void setValue(String value) {
        this.value = value;
    }
    private Recycler.Handle<Cycler> handle;
    public Cycler(Recycler.Handle<Cycler> handle) {
        this.handle = handle;
    }
    public void recycle() {
        handle.recycle(this);
    }
}

static class WorkTask implements Runnable{
    public WorkTask(){}
    @Override
    public void run() {
        Cycler cycler1 = CyclerRecycler.get();
        cycler1.setValue("hello,netty");
        RECYCLE_THREAD_POOL.submit(() -> {
            cycler1.recycle();
        });
    }
}}

`
Test result:
2019-07-20 23:17:22,256[INFO ]me.yuanhu.core.io.test.Unit_Recycler_availableSharedCapacity-NEW_OBJECT_NUM:5501
2019-07-20 23:17:22,256[INFO ]me.yuanhu.core.io.test.Unit_Recycler_availableSharedCapacity-NEW_OBJECT_NUM:5502
2019-07-20 23:17:22,256[INFO ]me.yuanhu.core.io.test.Unit_Recycler_availableSharedCapacity-NEW_OBJECT_NUM:5503
2019-07-20 23:17:22,256[INFO ]me.yuanhu.core.io.test.Unit_Recycler_availableSharedCapacity-NEW_OBJECT_NUM:5504
If you add that line of code:this.head.reclaimSpace(LINK_CAPACITY);，The number of new objects created has been greatly reduced.
Test result:
2019-07-20 23:17:42,982[INFO ]me.yuanhu.core.io.test.Unit_Recycler_availableSharedCapacity-NEW_OBJECT_NUM:30
2019-07-20 23:17:42,986[INFO ]me.yuanhu.core.io.test.Unit_Recycler_availableSharedCapacity-NEW_OBJECT_NUM:31
2019-07-20 23:17:43,004[INFO ]me.yuanhu.core.io.test.Unit_Recycler_availableSharedCapacity-NEW_OBJECT_NUM:32
2019-07-20 23:17:43,014[INFO ]me.yuanhu.core.io.test.Unit_Recycler_availableSharedCapacity-NEW_OBJECT_NUM:33
**You can also use the version I added statistics to make it easier to see the problem. **
Recycler_statistics.txt
The test results are as follows:
2019-07-20 23:14:15,980[INFO ]io.netty.util.Recycler-RECYCLER_STATISTICS_NEW_OBJECT：{class me.yuanhu.core.io.test.Unit_Recycler_availableSharedCapacity$Cycler=9472}
2019-07-20 23:14:15,980[INFO ]io.netty.util.Recycler-RECYCLER_STATISTICS_RECYCLE：{class me.yuanhu.core.io.test.Unit_Recycler_availableSharedCapacity$Cycler=528}
2019-07-20 23:14:15,980[INFO ]io.netty.util.Recycler-RECYCLER_STATISTICS_DROP：{class me.yuanhu.core.io.test.Unit_Recycler_availableSharedCapacity$Cycler=9471}
2019-07-20 23:14:15,980[INFO ]io.netty.util.Recycler-reclaimSpaceNum：17
2019-07-20 23:14:15,980[INFO ]io.netty.util.Recycler-reserveSpaceNum：32
2019-07-20 23:13:58,711[INFO ]io.netty.util.Recycler-RECYCLER_STATISTICS_NEW_OBJECT：{class me.yuanhu.core.io.test.Unit_Recycler_availableSharedCapacity$Cycler=30}
2019-07-20 23:13:58,711[INFO ]io.netty.util.Recycler-RECYCLER_STATISTICS_RECYCLE：{class me.yuanhu.core.io.test.Unit_Recycler_availableSharedCapacity$Cycler=9999}
2019-07-20 23:13:58,711[INFO ]io.netty.util.Recycler-RECYCLER_STATISTICS_DROP：{class me.yuanhu.core.io.test.Unit_Recycler_availableSharedCapacity$Cycler=0}
2019-07-20 23:13:58,711[INFO ]io.netty.util.Recycler-reclaimSpaceNum：624
2019-07-20 23:13:58,711[INFO ]io.netty.util.Recycler-reserveSpaceNum：624

@YuanHuCoding thanks a lot...great catch!


EDPM Configuration Generation

Commits:

Add service Secret data to ExtraMounts
Add service ConfigMap data to ExtraMounts
Add ConfigMaps and Secrets OpenStackDataPlaneService CRD

2 of the kuttl tests will fail until https://github.com/openstack-k8s-operators/edpm-ansible/pull/217 is merged.

I've split the commit that migrates nova and libvirt to the composable service interface into a seperate PR: https://github.com/openstack-k8s-operators/dataplane-operator/pull/337

/test dataplane-operator-build-deploy-kuttl

recheck

/test dataplane-operator-build-deploy-kuttl


New varible for SiteContents to determine build type

This gives a cleaner way to test whether the refresh js socket should be injected or not

Hey,
I’ve been thinking about something like that for a while and I think I’d prefer to use #WATCH define  when it watch-mode rather than passing bool around.

I think this would be easier to implement:
#if WATCH
#endif

Does that look okay?

Oh, that's exactly what I've meant.  I shouldn't write messages on GitHub before first tea ;-)

I've implemented this in b8fffa974d43a99744a65e75a1ff44a89bbc83e4


Feature/witherable

Part #485
Adds:

Witherable typeclass
default implementations for witherDefault and wiltDefault (?) should we add them?
withered and wilted functions
default implementation for Traversable's traverse via Witherable (?) should we add it?

Updates:

Traversable signature

default implementations for witherDefault and wiltDefault (?) should we add them?

I'd say no (same as with Filterable)

default implementation for Traversable's traverse via Witherable (?) should we add it?

Idem, I'd say no

withered and wilted functions

Each of them has 35 (!) overloadings and they are a trivial application of wilt/wither, I think we should avoid them.

Witherable typeclass
Traversable signature

Interesting, you are using a new technique which I'd like to talk about further. I'm a bit concerned by the number of overloadings, I have no idea of its possible impact on compilation times / quick info service

Other observations

if your your technique works well with Witherable we could apply it to Traversable too, and we could deprecate Travsersable.traverse
some overloadings in Wither{1,2,3,2C,3C}<W> / Wilt{1,2,3,2C,3C}<T> are defined twice
if I understand how it works, Wither<W> / Wilt<T> look useless and Witherable<T> should define only one version

export interface Witherable<T> extends Traversable<T>, Filterable<T> {
  /**
   * Partition a structure with effects
   */
  wilt: <F>(F: Applicative<F>): <RL, RR, A>(
    wa: HKT<W, A>,
    f: (a: A) => HKT<F, Either<RL, RR>>
  ) => HKT<F, Separated<HKT<W, RL>, HKT<W, RR>>>

  /**
   * Filter a structure  with effects
   */
  wither: <F>(F: Applicative<F>): <A, B>(ta: HKT<W, A>, f: (a: A) => HKT<F, Option<B>>) => HKT<F, HKT<W, B>>
}

Well, removing all default implementations together with wilted and withered makes this PR useless because I can't think of any other possible usage of Witherable typeclass (yet) :)
New signature for Wilt/Wither (actually now new, similar was used for sequenceT in Apply) was introduced to reuse it in Witherable methods and witherDefault/wiltDefault return types, so it's also redundant.
Traversable signature was only extended to be polymophic for Applicative as well (previous implementation always used HKT<F, A>, and now it works with all kinds: Type, Type2 and Type3).
Summing up, looks like the only thing changed is Traversable which is handy IMO. Should I push it in another PR or it's ok to leave it here and clean the rest up?

I can't think of any other possible usage of Witherable typeclass (yet)

purescript-filterable defines instances for

Array
StrMap
Option
Either

which are interesting (at least for Array, StrMap)

actually not new, similar was used for sequenceT in Apply

kind of, in sequenceT was used in the return type, here is in a type class definition (actually I'm a bit surprised that it works...)

Traversable signature was only extended to be fully polymorphic for Applicative as well

Yes and that's pretty nice, you didn't use the same encoding though, I would expect something like
export interface Traverse1<T extends URIS> {
  <F extends URIS3, FU, FL>(F: Applicative3C<F, FU, FL>): <A, B>(
    ta: Type<T, A>,
    f: (a: A) => Type3<F, FU, FL, B>
  ) => Type3<F, FU, FL, Type<T, B>>
  <F extends URIS3>(F: Applicative3<F>): <FU, FL, A, B>(
    ta: Type<T, A>,
    f: (a: A) => Type3<F, FU, FL, B>
  ) => Type3<F, FU, FL, Type<T, B>>
  <F extends URIS2, FL>(F: Applicative2C<F, FL>): <A, B>(
    ta: Type<T, A>,
    f: (a: A) => Type2<F, FL, B>
  ) => Type2<F, FL, Type<T, B>>
  <F extends URIS2>(F: Applicative2<F>): <FL, A, B>(
    ta: Type<T, A>,
    f: (a: A) => Type2<F, FL, B>
  ) => Type2<F, FL, Type<T, B>>
  <F extends URIS>(F: Applicative1<F>): <A, B>(ta: Type<T, A>, f: (a: A) => Type<F, B>) => Type<F, Type<T, B>>
  <F>(F: Applicative<F>): <A, B>(ta: Type<T, A>, f: (a: A) => HKT<F, B>) => HKT<F, Type<T, B>>
  <F>(F: Applicative<F>): <A, B>(ta: Type<T, A>, f: (a: A) => HKT<F, B>) => HKT<F, Type<T, B>>
}

export interface Traversable1<T extends URIS> extends Functor1<T>, Foldable1<T> {
  traverse: Traverse1<T>
}


Rotate with Mindsensors AbsoluteIMU

It is necessary to know how to get the gyro rate from AbsoluteIMU

Blocked, it is necessary to know how to get the Rate of a Gyro

Reading the documentation exist a way to get the angle:

There is a Trigonometric relation between the tilt readings and tilt angle of the sensor. As follows:
angle = sin-1 (tilt reading/128)
where tilt-reading is from Accelerometer at Sensitivity 2G.
also as a alternative to sin-1, you can use: angle = 2 * arctan(t / (1 + sqrt(1-t^2))),
where t ≡ tilt_reading / 128 to avoid a negative sqrt() operand when tilt_reading = 128.

2 * arctan(t / (1 + sqrt(1- (t^2))))
2 * atan   (a / (1 + sqrt(1- (a^2))))

The calculus is for angle, but I don´t know how get Gyro Rate

Doubt on forum:
https://www.mindsensors.com/forum/topic/223_how-to-get-gyro-rate-with-absoluteimu

I will continue the development using compass features


Make inline equations breakable

Title. Currently a very long equation will overflow, but it would be better to automatically break up the equation.

I think I would prefer an overflow warning. You typeset with a specific base font size for a legibility and consistency reasons. Equations often contain subscript or superscript already set with a very small font size. Decreasing that automatically is not a good default IMO.
Maybe an autoscale option with min-font-size would be good for this purpose.

Title. Currently a very long equation will overflow, but it would be better to auto shrink the math font size.

If it overflows you need to modify your equation in such a way that it fits. Changing the font size would break every convention.

modify your equation in such a way that it fits

What if you can't or don't want to? ~Is it that unreasonable to shrink-to-fit?~ Hmmm, the internet seems to agree with you. According to this, you're supposed to break up equations. So then I'll change this feature request to auto break up a long equation.

I've thought about this some more and I don't think you can design an algorithm that will break up the equation in the right places. For example, suppose your equation includes a series of summations, some of which are related. The human would know which ones and break the line so as to split the sums with each line keeping related stuff together, but the computer can't know this. So just do it manually.

If I understood it correctly, #1826 would solve this.

i'm not sure if this is related but if i force a linebreak inside inline equation the order of rendered input lines is wrong:
is $tilde$ symmetric? $(a,b) tilde (c,d) = (a-c, b-d) = (t,t) =\ = (t,t) = (c-a, d-b) = (c,d) tilde (a,b)$
produces:

i'm not sure if this is related but if i force a linebreak inside inline equation the order of rendered input lines is wrong:
is $tilde$ symmetric? $(a,b) tilde (c,d) = (a-c, b-d) = (t,t) =\ = (t,t) = (c-a, d-b) = (c,d) tilde (a,b)$
produces:

It is somewhat related. Inline equations consist of a single box currently, so line breaking will just give you a box with two lines in it.

should i open a new ticket for this? if i change this to a not-inline-equation it renders as expected below the preceding text.

should i open a new ticket for this? if i change this to a not-inline-equation it renders as expected below the preceding text.

If inline equations become breakable, then it would be natural for an explicit linebreak to do what you want. I don't think a separate issue is required.

Fixed by https://github.com/typst/typst/pull/2938


The file path is on on \wsl$

Describe the issue
The file path is on on \wsl$ as it is in other linux distros
To Reproduce
Open Run (Windows Key + R) and type \wsl$
That will sned you to all the file systems of the wsl distros, Arch is not there
Expected behavior
To be able to see Arch filesystem from there
Screenshots

Enviroment:

Windows build number: 10.0.18363.1198
Security Software: NA
WSL version 1/2: 2

Works for me...

It appears there only in wsl2. You probably might be using wsl1

@rromanv Do you still have this issue? If not, consider closing this issue. I can't reproduce it myself in my Windows 11 (build number 10.0.22000), WSL 2, ArchWSL <IP_ADDRESS>.


Update to tensorflow 2.6.1 in Rasa 2.8.x

Rasa Open Source version
2.8.13
Rasa SDK version
No response
Rasa X version
No response
Python version
3.8
What operating system are you using?
Linux
What happened?
Tensorflow 2.6.0 has the following security vulnerabilities:

CRIT Vulnerability found in non-os package type (python) - /opt/venv/lib/python3.8/site-packages/tensorflow (fixed in: 2.6.1)(GHSA-57wx-m983-2f88 - https://github.com/advisories/GHSA-57wx-m983-2f88)
HIGH Vulnerability found in non-os package type (python) - /opt/venv/lib/python3.8/site-packages/tensorflow (CVE-2021-37635 - https://nvd.nist.gov/vuln/detail/CVE-2021-37635)
HIGH Vulnerability found in non-os package type (python) - /opt/venv/lib/python3.8/site-packages/tensorflow (CVE-2021-37638 - https://nvd.nist.gov/vuln/detail/CVE-2021-37638)
HIGH Vulnerability found in non-os package type (python) - /opt/venv/lib/python3.8/site-packages/tensorflow (CVE-2021-37639 - https://nvd.nist.gov/vuln/detail/CVE-2021-37639)
HIGH Vulnerability found in non-os package type (python) - /opt/venv/lib/python3.8/site-packages/tensorflow (CVE-2021-37641 - https://nvd.nist.gov/vuln/detail/CVE-2021-37641)
HIGH Vulnerability found in non-os package type (python) - /opt/venv/lib/python3.8/site-packages/tensorflow (CVE-2021-37643 - https://nvd.nist.gov/vuln/detail/CVE-2021-37643)
HIGH Vulnerability found in non-os package type (python) - /opt/venv/lib/python3.8/site-packages/tensorflow (CVE-2021-37648 - https://nvd.nist.gov/vuln/detail/CVE-2021-37648)
HIGH Vulnerability found in non-os package type (python) - /opt/venv/lib/python3.8/site-packages/tensorflow (CVE-2021-37650 - https://nvd.nist.gov/vuln/detail/CVE-2021-37650)
HIGH Vulnerability found in non-os package type (python) - /opt/venv/lib/python3.8/site-packages/tensorflow (CVE-2021-37651 - https://nvd.nist.gov/vuln/detail/CVE-2021-37651)
HIGH Vulnerability found in non-os package type (python) - /opt/venv/lib/python3.8/site-packages/tensorflow (CVE-2021-37652 - https://nvd.nist.gov/vuln/detail/CVE-2021-37652)
HIGH Vulnerability found in non-os package type (python) - /opt/venv/lib/python3.8/site-packages/tensorflow (CVE-2021-37654 - https://nvd.nist.gov/vuln/detail/CVE-2021-37654)
HIGH Vulnerability found in non-os package type (python) - /opt/venv/lib/python3.8/site-packages/tensorflow (CVE-2021-37655 - https://nvd.nist.gov/vuln/detail/CVE-2021-37655)
HIGH Vulnerability found in non-os package type (python) - /opt/venv/lib/python3.8/site-packages/tensorflow (CVE-2021-37656 - https://nvd.nist.gov/vuln/detail/CVE-2021-37656)
HIGH Vulnerability found in non-os package type (python) - /opt/venv/lib/python3.8/site-packages/tensorflow (CVE-2021-37657 - https://nvd.nist.gov/vuln/detail/CVE-2021-37657)
HIGH Vulnerability found in non-os package type (python) - /opt/venv/lib/python3.8/site-packages/tensorflow (CVE-2021-37658 - https://nvd.nist.gov/vuln/detail/CVE-2021-37658)
HIGH Vulnerability found in non-os package type (python) - /opt/venv/lib/python3.8/site-packages/tensorflow (CVE-2021-37659 - https://nvd.nist.gov/vuln/detail/CVE-2021-37659)
HIGH Vulnerability found in non-os package type (python) - /opt/venv/lib/python3.8/site-packages/tensorflow (CVE-2021-37662 - https://nvd.nist.gov/vuln/detail/CVE-2021-37662)
HIGH Vulnerability found in non-os package type (python) - /opt/venv/lib/python3.8/site-packages/tensorflow (CVE-2021-37663 - https://nvd.nist.gov/vuln/detail/CVE-2021-37663)
HIGH Vulnerability found in non-os package type (python) - /opt/venv/lib/python3.8/site-packages/tensorflow (CVE-2021-37664 - https://nvd.nist.gov/vuln/detail/CVE-2021-37664)
HIGH Vulnerability found in non-os package type (python) - /opt/venv/lib/python3.8/site-packages/tensorflow (CVE-2021-37665 - https://nvd.nist.gov/vuln/detail/CVE-2021-37665)
HIGH Vulnerability found in non-os package type (python) - /opt/venv/lib/python3.8/site-packages/tensorflow (CVE-2021-37666 - https://nvd.nist.gov/vuln/detail/CVE-2021-37666)
HIGH Vulnerability found in non-os package type (python) - /opt/venv/lib/python3.8/site-packages/tensorflow (CVE-2021-37667 - https://nvd.nist.gov/vuln/detail/CVE-2021-37667)
HIGH Vulnerability found in non-os package type (python) - /opt/venv/lib/python3.8/site-packages/tensorflow (CVE-2021-37671 - https://nvd.nist.gov/vuln/detail/CVE-2021-37671)
HIGH Vulnerability found in non-os package type (python) - /opt/venv/lib/python3.8/site-packages/tensorflow (CVE-2021-37676 - https://nvd.nist.gov/vuln/detail/CVE-2021-37676)
HIGH Vulnerability found in non-os package type (python) - /opt/venv/lib/python3.8/site-packages/tensorflow (CVE-2021-37678 - https://nvd.nist.gov/vuln/detail/CVE-2021-37678)
HIGH Vulnerability found in non-os package type (python) - /opt/venv/lib/python3.8/site-packages/tensorflow (CVE-2021-37679 - https://nvd.nist.gov/vuln/detail/CVE-2021-37679)
HIGH Vulnerability found in non-os package type (python) - /opt/venv/lib/python3.8/site-packages/tensorflow (CVE-2021-37681 - https://nvd.nist.gov/vuln/detail/CVE-2021-37681)
HIGH Vulnerability found in non-os package type (python) - /opt/venv/lib/python3.8/site-packages/tensorflow (CVE-2021-37682 - https://nvd.nist.gov/vuln/detail/CVE-2021-37682)
HIGH Vulnerability found in non-os package type (python) - /opt/venv/lib/python3.8/site-packages/tensorflow (fixed in: 2.6.1)(GHSA-374m-jm66-3vj8 - https://github.com/advisories/GHSA-374m-jm66-3vj8)
HIGH Vulnerability found in non-os package type (python) - /opt/venv/lib/python3.8/site-packages/tensorflow (fixed in: 2.6.1)(GHSA-3rcw-9p9x-582v - https://github.com/advisories/GHSA-3rcw-9p9x-582v)
HIGH Vulnerability found in non-os package type (python) - /opt/venv/lib/python3.8/site-packages/tensorflow (fixed in: 2.6.1)(GHSA-49rx-x2rw-pc6f - https://github.com/advisories/GHSA-49rx-x2rw-pc6f)
HIGH Vulnerability found in non-os package type (python) - /opt/venv/lib/python3.8/site-packages/tensorflow (fixed in: 2.6.1)(GHSA-4f99-p9c2-3j8x - https://github.com/advisories/GHSA-4f99-p9c2-3j8x)
HIGH Vulnerability found in non-os package type (python) - /opt/venv/lib/python3.8/site-packages/tensorflow (fixed in: 2.6.1)(GHSA-7pxj-m4jf-r6h2 - https://github.com/advisories/GHSA-7pxj-m4jf-r6h2)
HIGH Vulnerability found in non-os package type (python) - /opt/venv/lib/python3.8/site-packages/tensorflow (fixed in: 2.6.1)(GHSA-cqv6-3phm-hcwx - https://github.com/advisories/GHSA-cqv6-3phm-hcwx)
HIGH Vulnerability found in non-os package type (python) - /opt/venv/lib/python3.8/site-packages/tensorflow (fixed in: 2.6.1)(GHSA-cvgx-3v3q-m36c - https://github.com/advisories/GHSA-cvgx-3v3q-m36c)
HIGH Vulnerability found in non-os package type (python) - /opt/venv/lib/python3.8/site-packages/tensorflow (fixed in: 2.6.1)(GHSA-f54p-f6jp-4rhr - https://github.com/advisories/GHSA-f54p-f6jp-4rhr)
HIGH Vulnerability found in non-os package type (python) - /opt/venv/lib/python3.8/site-packages/tensorflow (fixed in: 2.6.1)(GHSA-fr77-rrx3-cp7g - https://github.com/advisories/GHSA-fr77-rrx3-cp7g)
HIGH Vulnerability found in non-os package type (python) - /opt/venv/lib/python3.8/site-packages/tensorflow (fixed in: 2.6.1)(GHSA-gpfh-jvf9-7wg5 - https://github.com/advisories/GHSA-gpfh-jvf9-7wg5)
HIGH Vulnerability found in non-os package type (python) - /opt/venv/lib/python3.8/site-packages/tensorflow (fixed in: 2.6.1)(GHSA-j86v-p27c-73fm - https://github.com/advisories/GHSA-j86v-p27c-73fm)
HIGH Vulnerability found in non-os package type (python) - /opt/venv/lib/python3.8/site-packages/tensorflow (fixed in: 2.6.1)(GHSA-m342-ff57-4jcc - https://github.com/advisories/GHSA-m342-ff57-4jcc)
HIGH Vulnerability found in non-os package type (python) - /opt/venv/lib/python3.8/site-packages/tensorflow (fixed in: 2.6.1)(GHSA-pgcq-h79j-2f69 - https://github.com/advisories/GHSA-pgcq-h79j-2f69)
HIGH Vulnerability found in non-os package type (python) - /opt/venv/lib/python3.8/site-packages/tensorflow (fixed in: 2.6.1)(GHSA-rg3m-hqc5-344v - https://github.com/advisories/GHSA-rg3m-hqc5-344v)
HIGH Vulnerability found in non-os package type (python) - /opt/venv/lib/python3.8/site-packages/tensorflow (fixed in: 2.6.1)(GHSA-vwhq-49r4-gj9v - https://github.com/advisories/GHSA-vwhq-49r4-gj9v)

Command / Request
No response
Relevant log output
No response

Is there anything preventing us from upgrading to 2.7?

@koaning Micro updates are easier to do because they are bug/security fixes. Minor updates would require more thought in case thought it case they introduce regressions.


Update cloudflare to 1.2.2

Please update my adapter ioBroker.cloudflare to version 1.2.2.
This pull request was created by https://www.iobroker.dev c0726ff.

RE-CHECK!