text:
feat(STADTPULS-248): sensor page pagination

This PR adds pagination to the sensors overview page.
We are displaying 15 sensor's per page, which is more than what we currently have in our staging environment. So, in order to properly assess the pagination UI, you may want to reduce MAX_SENSORS_PER_PAGE in pages/sensors/index.tsx in your local setup; or simply check the Storybook entry.

Shouldn't we rather default to the first page? What do you think?

Good idea, thanks for pointing to this. I'm gonna change that.

@vogelino, thanks for the feedback. Can you take one more look at the latest commit? It ensures that we default to the first page if someone queries a sensorPage that exceeds our page count.

:tada: This PR is included in version 1.0.0 :tada:
The release is available on GitHub release
Your semantic-release bot :package::rocket:

authors: dnsos tsboter

repo: technologiestiftung/stadtpuls-frontend


text:
chore(STADTPULS-485): Deactivate signup

This PR removes the link to the signup page on the login page.

Cool! Thanks also for the TODO reminders. Can you also add this as a Jira ticket, just so it doesn't get lost?

There is one: https://citylab.atlassian.net/browse/STADTPULS-485

:tada: This PR is included in version 1.0.0 :tada:
The release is available on GitHub release
Your semantic-release bot :package::rocket:

source: gharchive/pull-request

authors: tsboter vogelino

license: MIT

license_type: permissive

license_source: github-api


text:
Add account project route

This PR adds the /account/projects/[id] route.

:tada: This PR is included in version 1.0.0 :tada:
The release is available on GitHub release
Your semantic-release bot :package::rocket:


text:
Error 404

I called it picture_labler as that seemed more descriptive. When running I get this.
Error: 404: Not Found
error: Uncaught (in promise) Error: Is a directory (os error 21): copy '/home/chris/Pictures/Screenshot from 2023-02-03 10-10-20.png' -> '/home/chris/Pictures/'
    at Object.copyFileSync (ext:deno_fs/30_fs.js:72:7)
    at file:///home/runner/work/airenamer/airenamer/main.ts:47:12
    at eventLoopTick (ext:core/01_core.js:183:11)

if this is still happening, let me know. will close .

source: gharchive/issue

authors: iplayfast technovangelist

repo: technovangelist/airenamer


text:
🛑 Cotizador Autos is down

In 7f458d3, Cotizador Autos (http://online.hispana.com.ve:8282/ords/ws_bleico/r/cotizador-publico-de-auto/home) was down:

HTTP code: 0
Response time: 0 ms

Resolved: Cotizador Autos is back up in 3b96e2a.

repo: tecnologia-hispana/monitoreo-web


text:
Karabiner just does nothing

Spent around 2 hours trying to get Karabiner 10.15.0 to do something... anything but it seems to have zero effect. I've tried both the UI and private.xml but nothing has any effect. Yes, Karabiner has been allowed the privileges in Security and Privacy.
Example config:
<?xml version="1.0"?>
<root>
  <item>
    <name>Swap Space and Tab</name>
    <identifier>private.swap_space_and_tab</identifier>
    <autogen>__KeyToKey__ KeyCode::SPACE, KeyCode::TAB</autogen>
    <autogen>__KeyToKey__ KeyCode::TAB, KeyCode::SPACE</autogen>
  </item>
</root>

My assumption is when I save private.xml my space and tab keys should immediately be swapped. This is not the case so I guess I am missing something really basic.
Interestingly in the UI I have 2 changes active which are not in the XML. When I click reload the XML nothing happens: the space/tab swap above does not appear.

The cause might be that your keyboard has own driver and it's not compatible with Karabiner.
Please tell me your hardware information.

Your Mac hardware:
Your keyboard hardware:

MacBookPro11,2 (Late 2013) - don't know much else. It's the built-in keyboard - Swiss layout.

Hmm, Karabiner should work on your environment.
Did you follow this instruction?
https://pqrs.org/osx/karabiner/document.html.en#privatexml

My assumption is when I save private.xml my space and tab keys should immediately be swapped.

No, you have to enable new setting.
See step 7 in the above instruction.

authors: cawoodm tekezo

repo: tekezo/Karabiner

license: unlicense

license_source: bigquery


text:
🛑 Tekhnee Cloud is down

In 30b9699, Tekhnee Cloud ($CLOUD_BENCHMARK_LINK) was down:

HTTP code: 403
Response time: 82 ms

Resolved: Tekhnee Cloud is back up in 2055331.

repo: tekhnee/status


text:
Update PipelineRun API documentation

Similar to #4601. We should update the PipelineRun API docs to reflect all existing fields.
/kind documentation
/help-wanted
/good-first-issue

Hi there,
Thank you so much for your reply and for giving me an opportunity to
contribute and learn here. Please give me some time to go through the
requirements and understand the process. I shall soon be coming up with my
list of questions. 🙂
However, to start with -

How can I communicate with the team - through this email or GitHub? I am
a novice in GitHub, so quite a lot to learn.
Is this a REST API that we have to document?

I'm ready and excited!
Thanks and Regards,
Sunita Arora
On Tue, Mar 15, 2022 at 12:05 PM Jerop Kipruto @.***>
wrote:

@ssunitaa https://github.com/ssunitaa welcome to the Tekton community
and thank you for volunteering to help! 😀
We need help updating the PipelineRun API definition in the docs
https://github.com/tektoncd/pipeline/blob/main/docs/pipelineruns.md#configuring-a-pipelinerun
to match the definition in the code
https://github.com/tektoncd/pipeline/blob/main/pkg/apis/pipeline/v1beta1/pipelinerun_types.go.
I updated the Pipeline API definition in #4601
https://github.com/tektoncd/pipeline/pull/4601 - hoping that this can
be a good example, but feel free to ask any questions.
We also have a bunch of prioritized documentation work -
https://github.com/orgs/tektoncd/projects/10/views/6 - that'd help us
work towards V1 release. We'd appreciate any help on these issues if you
have some time.
You may also be interested in joining the Tekton Documentation Working
Group
https://github.com/tektoncd/community/blob/main/working-groups.md#documentation

cc @geriom https://github.com/geriom.

—
Reply to this email directly, view it on GitHub
https://github.com/tektoncd/pipeline/issues/4657#issuecomment-1068165150,
or unsubscribe
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You are receiving this because you were mentioned.Message ID:
@.***>

/assign @ssunitaa

/assign ssunitaa

authors: afrittoli lbernick ssunitaa

repo: tektoncd/pipeline


text:
Document Pipeline's use of Pod termination messages

Changes
Prior to this commit we primarily documented use of termination messages
through a lens of Task Results, but the mechanism is used more broadly
than this.
This commit adds notes to our developer docs explaining use of pod termination
messages, giving examples of the data included, how the data is distributed to
other parts of the Pipelines machinery and describing the lack of guarantees
around the message's content.
Submitter Checklist
As the author of this PR, please check off the items in this checklist:

[x] Docs included if any changes are user facing
[x] Follows the commit message standard
[x] Meets the Tekton contributor standards (including
functionality, content, code)
[x] Release notes block below has been filled in or deleted (only if no user facing changes)

Release Notes
Our developer docs now include more details on how Tekton Pipelines uses Pod termination messages to communicate information between Task pods and the Tekton Pipelines controller.

/lgtm

authors: pritidesai sbwsg


text:
Use apiVersion declarative naming in pipelineRef and taskRef test for…

Changes
This commit adds apiVersion to the unsigned Tasks and Pipelines ie. unsignedV1beta1Task so as to be declarative when we are swapping to v1 storage version. No function change.
/kind misc

Submitter Checklist
As the author of this PR, please check off the items in this checklist:

[n/a] Has Docs if any changes are user facing, including updates to minimum requirements e.g. Kubernetes version bumps
[x] Has Tests included if any functionality added or changed
[x] Follows the commit message standard
[x] Meets the Tekton contributor standards (including functionality, content, code)
[x] Has a kind label. You can add one by adding a comment on this PR that contains /kind <type>. Valid types are bug, cleanup, design, documentation, feature, flake, misc, question, tep
[n/a] Release notes block below has been updated with any user facing changes (API changes, bug fixes, changes requiring upgrade notices or deprecation warnings). See some examples of good release notes.
[n/a] Release notes contains the string "action required" if the change requires additional action from users switching to the new release

Release Notes
NONE

/hold
as https://github.com/tektoncd/pipeline/pull/6444#discussion_r1226607300 suggested, we could accept an apiVersion in the helper.

opened https://github.com/tektoncd/pipeline/pull/6815

@JeromeJu wondering why this is closed now, the corresponding PR is still open.

@JeromeJu wondering why this is closed now, the corresponding PR is still open.

Thanks @afrittoli , https://github.com/tektoncd/pipeline/pull/6815 is now opened to be a better alternative for this:) instead of renaming the structs, it allows us to accept apiVersions to the trustedResources helper functions.
I will update the reference in the parental PR.

authors: JeromeJu afrittoli


text:
Add option to enable prometheus histogram

Adds a config option PROMETHEUS_HISTOGRAM to enable prometheus histogram for the gRPC server. This records metrics to calculate request latency distributions.
resolves #510
Submitter Checklist
These are the criteria that every PR should meet, please check them off as you review them:

[x] Has Docs included if any changes are user facing
[ ] Has Tests included if any functionality added or changed
[x] Tested your changes locally (if this is a code change)
[x] Follows the commit message standard
[ ] Meets the Tekton contributor standards (including functionality, content, code)
[x] Has a kind label. You can add a comment on this PR that contains /kind <type>. Valid types are bug, cleanup, design, documentation, feature, flake, misc, question, tep
[x] Release notes block below has been updated with any user-facing changes (API changes, bug fixes, changes requiring upgrade notices or deprecation warnings)
[ ] Release notes contain the string "action required" if the change requires additional action from users switching to the new release

Release Notes
api: Config option `PROMETHEUS_HISTOGRAM` added to enable histograms (disabled by default).

/approve
Please add /kind label. Personally I'm considering this a feature, but I'm leaving it to you to decide.
My only question is if we really need the action required here? With the default option being disabled, users do not strictly require to do anything. They do need to set the configuration option to enable the functionality, but doing nothing changes/breaks nothing.

@enarha Removed "action required". Added kind label to miscellaneous as this change is not much of a feature, and more of enabling a feature which was already there.

/kind feature

/lgtm

authors: adambkaplan enarha sayan-biswas

repo: tektoncd/results


text:
Реализовать обработку дополнительных  типов сообщений

А именно (по приоритету):

Файл
Видео
Аудио
Стикер

@demidovakatya что думаешь? стоит ли вообще заморачиваться?

Может просто писать так? :
new sticker/file/audio/video in channel_name

Хотя, возможно стикер можно отослать как фото.

Заморачиваться так сильно я бы пока не стала, тбх.

оставим пока как есть

authors: REDNBLACK demidovakatya

repo: telegram-bots/telegram-channels-feed

license: mit


text:
BUG - Staked by others missing precision on Account Page

Describe the bug
Staked by others missing precision on Account Page
To Reproduce
Steps to reproduce the behavior:

Search for doublewabbit
Look at Staked by others

Expected behavior
Should render as 40.0000 and not 400000

@DestinyDevTeam   Please review and confirm fix. PR deployed here: https://app.netlify.com/sites/open-block-explorer-dev/deploys/6216ef1914c0a50008496ff5 Thanks.

Value is now showing correct.

Precision 2/4 TBD. Consistency across all numeral values on the same page / site.

Tested OK at https://deploy-preview-56--open-block-explorer-dev.netlify.app/

authors: DestinyDevTeam DonaldPeat

repo: telosnetwork/open-block-explorer


text:
🛑 Secret Site2 is down

In aa86555, Secret Site2 ($SECRET_SITE_2) was down:

HTTP code: 404
Response time: 125 ms

Resolved: Secret Site2 is back up in 102404d after 63 days, 8 hours, 9 minutes.

repo: templain/mywatcher


text:
🛑 Secret Site2 is down

In fd1c8af, Secret Site2 ($SECRET_SITE_2) was down:

HTTP code: 404
Response time: 115 ms

Resolved: Secret Site2 is back up in 625d29f after 34 minutes.


text:
🛑 Secret Site2 is down

In 05e59f9, Secret Site2 ($SECRET_SITE_2) was down:

HTTP code: 404
Response time: 99 ms

Resolved: Secret Site2 is back up in 7ff1fa6.


text:
🛑 Secret Site2 is down

In 80e19d6, Secret Site2 ($SECRET_SITE_2) was down:

HTTP code: 404
Response time: 43 ms

Resolved: Secret Site2 is back up in a178648.


text:
Web Error: The getter 'HttpHeaders' isn't defined for the class 'Deepgram'.

Hi,
Thank you for making this amazing package.
I am trying to use the live stream speech to text, and it works perfectly on android and iOS. But I am getting the below error when testing on web and fail to compile. I've read the previous thread and believe web support is added since version 1.0.4, but I am stilling getting the below error.
Your help will be highly appreciated!
Error Message---:
../../.pub-cache/hosted/pub.dev/deepgram_speech_to_text-2.1.1/lib/src/deepgram.dart:112:9: Error: The getter 'HttpHeaders' isn't defined for the class 'Deepgram'.

'Deepgram' is from 'package:deepgram_speech_to_text/src/deepgram.dart' ('../../.pub-cache/hosted/pub.dev/deepgram_speech_to_text-2.1.1/lib/src/deepgram.dart').
Try correcting the name to the name of an existing getter, or defining a getter or field named 'HttpHeaders'.
HttpHeaders.authorizationHeader: 'Token $apiKey',
^^^^^^^^^^^
../../.pub-cache/hosted/pub.dev/deepgram_speech_to_text-2.1.1/lib/src/deepgram.dart:139:9: Error: The getter 'HttpHeaders' isn't defined for the class 'Deepgram'.
'Deepgram' is from 'package:deepgram_speech_to_text/src/deepgram.dart' ('../../.pub-cache/hosted/pub.dev/deepgram_speech_to_text-2.1.1/lib/src/deepgram.dart').
Try correcting the name to the name of an existing getter, or defining a getter or field named 'HttpHeaders'.
HttpHeaders.authorizationHeader: 'Token $apiKey',
^^^^^^^^^^^
../../.pub-cache/hosted/pub.dev/deepgram_speech_to_text-2.1.1/lib/src/deepgram.dart:140:9: Error: The getter 'HttpHeaders' isn't defined for the class 'Deepgram'.
'Deepgram' is from 'package:deepgram_speech_to_text/src/deepgram.dart' ('../../.pub-cache/hosted/pub.dev/deepgram_speech_to_text-2.1.1/lib/src/deepgram.dart').
Try correcting the name to the name of an existing getter, or defining a getter or field named 'HttpHeaders'.
HttpHeaders.contentTypeHeader: 'application/json',
^^^^^^^^^^^
../../.pub-cache/hosted/pub.dev/deepgram_speech_to_text-2.1.1/lib/src/deepgram.dart:141:9: Error: The getter 'HttpHeaders' isn't defined for the class 'Deepgram'.
'Deepgram' is from 'package:deepgram_speech_to_text/src/deepgram.dart' ('../../.pub-cache/hosted/pub.dev/deepgram_speech_to_text-2.1.1/lib/src/deepgram.dart').
Try correcting the name to the name of an existing getter, or defining a getter or field named 'HttpHeaders'.
HttpHeaders.acceptHeader: 'application/json',
^^^^^^^^^^^
../../.pub-cache/hosted/pub.dev/deepgram_speech_to_text-2.1.1/lib/src/deepgram.dart:186:9: Error: The getter 'HttpHeaders' isn't defined for the class 'Deepgram'.
'Deepgram' is from 'package:deepgram_speech_to_text/src/deepgram.dart' ('../../.pub-cache/hosted/pub.dev/deepgram_speech_to_text-2.1.1/lib/src/deepgram.dart').
Try correcting the name to the name of an existing getter, or defining a getter or field named 'HttpHeaders'.
HttpHeaders.authorizationHeader: 'Token $apiKey',
^^^^^^^^^^^
../../.pub-cache/hosted/pub.dev/deepgram_speech_to_text-2.1.1/lib/src/deepgram.dart:187:9: Error: The getter 'HttpHeaders' isn't defined for the class 'Deepgram'.
'Deepgram' is from 'package:deepgram_speech_to_text/src/deepgram.dart' ('../../.pub-cache/hosted/pub.dev/deepgram_speech_to_text-2.1.1/lib/src/deepgram.dart').
Try correcting the name to the name of an existing getter, or defining a getter or field named 'HttpHeaders'.
HttpHeaders.contentTypeHeader: 'audio/*',
^^^^^^^^^^^
../../.pub-cache/hosted/pub.dev/deepgram_speech_to_text-2.1.1/lib/src/deepgram.dart:203:9: Error: The getter 'HttpHeaders' isn't defined for the class 'Deepgram'.
'Deepgram' is from 'package:deepgram_speech_to_text/src/deepgram.dart' ('../../.pub-cache/hosted/pub.dev/deepgram_speech_to_text-2.1.1/lib/src/deepgram.dart').
Try correcting the name to the name of an existing getter, or defining a getter or field named 'HttpHeaders'.
HttpHeaders.authorizationHeader: 'Token $apiKey',
^^^^^^^^^^^
../../.pub-cache/hosted/pub.dev/deepgram_speech_to_text-2.1.1/lib/src/deepgram.dart:204:9: Error: The getter 'HttpHeaders' isn't defined for the class 'Deepgram'.
'Deepgram' is from 'package:deepgram_speech_to_text/src/deepgram.dart' ('../../.pub-cache/hosted/pub.dev/deepgram_speech_to_text-2.1.1/lib/src/deepgram.dart').
Try correcting the name to the name of an existing getter, or defining a getter or field named 'HttpHeaders'.
HttpHeaders.contentTypeHeader: 'application/json',
^^^^^^^^^^^
Failed to compile application.

Flutter Doctor ---
Doctor summary (to see all details, run flutter doctor -v):
[✓] Flutter (Channel stable, 3.22.1, on macOS 14.5 23F79 darwin-arm64, locale en-CA)
[✓] Android toolchain - develop for Android devices (Android SDK version 34.0.0)
[✓] Xcode - develop for iOS and macOS (Xcode 15.4)
[✓] Chrome - develop for the web
[✓] Android Studio (version 2024.1)
[✓] Connected device (4 available)
[✓] Network resources
• No issues found!
Thank you!

Hey thank you for reporting the issue I will look into it asap :)

Thank you so much!!

Hey @SimpleTracker the issue is fixed in 2.2.1, I also updated the flutter example to support web
In case you need it : https://github.com/tempo-riz/deepgram_speech_to_text/tree/main/example/flutter_example

That's amazing! I just tested it, and it's working like a charm. Thank you so much for the quick fix!

authors: SimpleTracker tempo-riz


text:
Update metrics config to global

What was changed

Per-service metrics config is deprecated. Use global config section instead.
Why?

Checklist

Closes

How was this tested:

manual helm template run

Any docs updates needed?

I'll close this pr for now. Will update after some more discussion.

repo: temporalio/helm-charts


text:
Fix possible stalling issues when polling for activities

Do so by removing outstanding activities from tracking when issuing
cancellations for them to lang.

What was changed:
Do so by removing outstanding activities from tracking when issuing
cancellations for them to lang.
Why?
I wrote a new test that does a lot of concurrent heartbeating. It revealed that it's very easy to hang polling by not completing activities because you thought those activities were cancelled. It makes sense that any outstanding tasks should just be dropped once we issue a cancel to lang.
Checklist

Closes issue:

How was this tested:

Any docs updates needed?

This is not right - lang should confirm the cancel with status canceled, but there is still interesting weird behavior to dig into here.

repo: temporalio/sdk-core


text:
Unexpected Heartbeat errors when activities are queued up

While evaluating temporal for our use cases, we ran into something that seemed like a bug. To isolate any issues that could be happening with our possibly buggy implementation, we reproduced the issue by modifying one of the Java SDK examples.
Our setup is simple:

We have a bunch of workflows being started in one process. The workflows get started much faster than they finish.
Each activity task gets completed in a different process.
Each process can only work on one activity task at a time.
We set a heartbeat timeout of 15s, and send heartbeats every 5s.
We run just one activity worker, so one activity at a time.

Expected Behaviour
When the worker or workers pick up the activity, they run the activities to completion with no errors.
Actual Behaviour
Sometimes, but at least once in our reproduction, the workers fail to heartbeat because the workflow was already completed. See the stack trace below.
In the Temporal web UI, their history shows multiple attempts at starting an ActivityTask with "activity timeout" failures.
It seems like something about the activity expired before any worker has had a chance to start it, hence the attempts going up. Our startToClose timeout is long enough to not be relevant here. We are setting .setMaxConcurrentActivityExecutionSize(1) and .setActivityPollThreadCount(1).
11:53:25.025 [Activity Executor taskQueue="MONEY_TRANSFER_TASK_QUEUE", namespace="default": 1] ERROR i.t.internal.worker.PollerOptions - uncaught exception
java.lang.RuntimeException: Failure processing activity task. WorkflowId=25c41288-f9a3-4587-9f42-a0df2d811c51, RunId=e2ecab8d-30b0-4060-b61e-a82eedeb4d16, ActivityType=Deposit, ActivityId=2345852d-742f-3007-8b4d-c4fc51486faa
        at io.temporal.internal.worker.ActivityWorker$TaskHandlerImpl.wrapFailure(ActivityWorker.java:254)
        at io.temporal.internal.worker.ActivityWorker$TaskHandlerImpl.wrapFailure(ActivityWorker.java:154)
        at io.temporal.internal.worker.PollTaskExecutor.lambda$process$0(PollTaskExecutor.java:79)
        at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128)
        at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628)
        at java.base/java.lang.Thread.run(Thread.java:834)
Caused by: io.grpc.StatusRuntimeException: NOT_FOUND: invalid activityID or activity already timed out or invoking workflow is completed
        at io.grpc.stub.ClientCalls.toStatusRuntimeException(ClientCalls.java:262)
        at io.grpc.stub.ClientCalls.getUnchecked(ClientCalls.java:243)
        at io.grpc.stub.ClientCalls.blockingUnaryCall(ClientCalls.java:156)
        at io.temporal.api.workflowservice.v1.WorkflowServiceGrpc$WorkflowServiceBlockingStub.respondActivityTaskCompleted(WorkflowServiceGrpc.java:2746)
        at io.temporal.internal.worker.ActivityWorker$TaskHandlerImpl.lambda$sendReply$0(ActivityWorker.java:278)
        at io.temporal.internal.common.GrpcRetryer.lambda$retry$0(GrpcRetryer.java:109)
        at io.temporal.internal.common.GrpcRetryer.retryWithResult(GrpcRetryer.java:127)
        at io.temporal.internal.common.GrpcRetryer.retry(GrpcRetryer.java:106)
        at io.temporal.internal.worker.ActivityWorker$TaskHandlerImpl.sendReply(ActivityWorker.java:272)
        at io.temporal.internal.worker.ActivityWorker$TaskHandlerImpl.handle(ActivityWorker.java:198)
        at io.temporal.internal.worker.ActivityWorker$TaskHandlerImpl.handle(ActivityWorker.java:154)
        at io.temporal.internal.worker.PollTaskExecutor.lambda$process$0(PollTaskExecutor.java:73)
        ... 3 common frames omitted

Steps to Reproduce the Problem

Clone/Download my fork https://github.com/cmlenius/money-transfer-project-template-java (has a single commit with the changes to reproduce the bug)
Run ./gradlew startWorker
Run ./gradlew initiateTransfer and wait

Specifications

Version: 1.03
Platform: OSX - 10.14.6

What does the workflow history for the WorkflowId=25c41288-f9a3-4587-9f42-a0df2d811c51, RunId=e2ecab8d-30b0-4060-b61e-a82eedeb4d16 says about the Deposit activity? Or just post the whole history of this workflow here.

Here is the whole history exported as JSON. I had to reproduce the error so the workflowID and RunID are different.

WorkflowId=1f6be884-69c5-4336-8a73-dbae380331a3
RunId=380f5dd3-afbe-43c9-9dbc-048887e35fea,
ActivityId=7efb95eb-c445-3929-a78f-97d95482588b

Workflow History

[
  {
    "eventTime": "2020-11-17T17:26:13.000Z",
    "eventType": "WorkflowExecutionStarted",
    "eventId": "1",
    "details": {
      "workflowType": {
        "name": "MoneyTransferWorkflow"
      },
      "parentWorkflowNamespace": "",
      "parentWorkflowExecution": null,
      "parentInitiatedEventId": "0",
      "taskQueue": {
        "name": "MONEY_TRANSFER_TASK_QUEUE",
        "kind": "Normal"
      },
      "input": {
        "payloads": [
          "001-001",
          "002-002",
          "532ee256-d095-4e40-b53e-c5e9e972c8a5",
          "18.74"
        ]
      },
      "workflowExecutionTimeout": {
        "duration": 315360000
      },
      "workflowRunTimeout": {
        "duration": 315360000
      },
      "workflowTaskTimeout": {
        "duration": 10
      },
      "continuedExecutionRunId": "",
      "initiator": "Unspecified",
      "continuedFailure": null,
      "lastCompletionResult": null,
      "originalExecutionRunId": "380f5dd3-afbe-43c9-9dbc-048887e35fea",
      "identity": "82797@Cameron-Lenius",
      "firstExecutionRunId": "380f5dd3-afbe-43c9-9dbc-048887e35fea",
      "retryPolicy": null,
      "attempt": 1,
      "workflowExecutionExpirationTime": "2030-11-15T17:26:13.000Z",
      "cronSchedule": "",
      "firstWorkflowTaskBackoff": {
        "duration": 0
      },
      "memo": null,
      "searchAttributes": null,
      "prevAutoResetPoints": null,
      "header": null,
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We did some more digging (I work with @cmlenius) and we found this issue which seems to describe what we're seeing: https://github.com/uber/cadence/issues/3475, particularly this comment: https://github.com/uber/cadence/issues/3475#issuecomment-696930088

Since activity is considered "started" when activity poller polls the task from Cadence, activities with shorter start to close timeouts will timeout in the buffer when task executor finishes working on long running activities. This issue amplifies especially when: ... Task execution thread pool is more likely to be blocked (e.g. due to max concurrency limit)

Cadence is tracking the issue in their java client now in https://github.com/uber/cadence-java-client/issues/554, but the original ticket describes the issue in good detail.
If Cadence provides a fix for their java client will Temporal be able to pick up the fix as well?

Resubmitted this issue as a specific feature request: #760

Temporal activity pollers don't prefetch activities if there is no executors available. It's achieved by this semaphore: https://github.com/temporalio/sdk-java/blob/07a10cfe86ac95e3a0363551d9a11f19fa37b0ac/temporal-sdk/src/main/java/io/temporal/internal/worker/ActivityPollTask.java#L63
It's implemented in #286

authors: Gibstick Spikhalskiy cmlenius mfateev

repo: temporalio/sdk-java


text:
Timezone bug in Timeline view

Describe the bug
A clear and concise description of what the bug is.
When viewing the timeline for a workflow run in temporal-ui, the timeline displays the user's local time as the x-axis labels, even when the UI is configured to display times in UTC.
To Reproduce
Steps to reproduce the behavior:

Go to any workflow run

Ensure the time setting is set to UTC:

Scroll down to the timeline view

See error where the timeline labels are local time instead of UTC (even though the hover-tooltip displays UTC).

Expected behavior
I expect the timeline x-axis labels to be display in UTC instead of local-time.
Screenshots

Desktop (please complete the following information):

OS: macOS
Browser chrome
Version 2.22.2

Additional context
Temporal-ui version: 2.22.2

Fixed

authors: Alex-Tideman harsh-stripe

repo: temporalio/ui


text:
Check for first group event id before setting path

What was changed

Why?
If the first item in the grouped events does not have an id, the UI redirects to /compact/undefined, which then causes an error with getGroupForEvent
Checklist

Closes 357

How was this tested:

Any docs updates needed?

 Thank you for your submission! We really appreciate it. Like many open source projects, we ask that you sign our Contributor License Agreement before we can accept your contribution.Alex Tideman seems not to be a GitHub user. You need a GitHub account to be able to sign the CLA. If you have already a GitHub account, please add the email address used for this commit to your account.You have signed the CLA already but the status is still pending? Let us recheck it.

authors: Alex-Tideman CLAassistant


text:
cmd/baseserver, */rest: allow baseserver to choose which handlers to use

Make handlers easily configurable to use in cmd/baseserver/main.go.
This way client users can trivially change what functionality they'd
like.
It involves moving ServeCmd out of client/rest to */main.go
and lets client/rest become a bazaar for available mux.Router
registrars.
Updates #200

/cc @ethanfrey

Okay, maybe serve in main.go is the best approach, since there is little logic there, and it lets each app create it's own server stack - set up https, middleware, etc.. as needed.
I am convinced.

Thanks for the review and merge @ethanfrey!

authors: ethanfrey odeke-em

repo: tendermint/basecoin


text:
Small fixes here and there

[x] Prevent user from deleting last token. This shouldn't happen:

[x] Add a shadow to the hamburger circle button
[x] Try sending tokens when the backend is not available (or becomes unavailable). Send tokens button spins forever, should somehow revert to original state if send was unsuccessful.

https://github.com/tendermint/vue/pull/64/commits/284fc671b05d637cb18830705dd580833f31ce9f
https://github.com/tendermint/vue/pull/64/commits/b98317c4eef65bc739a25e684736076c662ae02e
https://github.com/tendermint/vue/pull/64/commits/ca296d0ce64d78ba031e400e22fbc5eb5c6f4411
:)

[ ] prevent users from adding the same denom endlessly (in this case a user only has 10token in total)

[ ] disable button, unless the form looks like it can be submitted (basic validation): address should be there, token values should be numbers, etc.
[ ] make 0 in input as a placeholder. Right now a user has to manually remove the zero, and input a value.

1 and 3 done in https://github.com/tendermint/vue/pull/64/commits/d47309cd3eeb3c6b2152b7017bbd07d35572e900

2 done on https://github.com/tendermint/vue/pull/64/commits/1dd36a3ee499f43972fac572d113402ba0241cea
(if this is ok as well I'll go ahead and merge that PR)

@clockworkgr For some reason, it prevents me from sending tokens. On master, on GP.

Maybe it's just me, but could you try going through the process yourself to see if it works properly? Creating a chain, signing in with Alice's account, sending tokens to Bob, etc.

Does sending fail? Or is the button actually disabled?

fixed in https://github.com/tendermint/vue/pull/64/commits/d781745ab98a4d52ae793706a608bc7aba6ac835

authors: clockworkgr fadeev

repo: tendermint/vue


text:
WIP: add cleanup after quick-start-test

https://github.com/tensorchord/envd/issues/802
Signed-off-by: nullday aseaday@hotmail.com

I think it was caused by quick-start test, But ir.DefaultGraph=new Graph seems a little wired and ugly. Could we have a better way?

I tried to add it to https://github.com/tensorchord/envd/blob/5c4971b2f5fa6b32c2c247e18cf6c6a178d28f57/pkg/lang/ir/compile.go#L88-L90 but sometimes it doesn't work as expected.

BTW, you can search "pip install" in the e2e test log. You will find "pip install numpy numpy numpy".

BTW, you can search "pip install" in the e2e test log. You will find "pip install numpy numpy numpy".

Yeah, That what I want to find out a clean way rather than dirty work one by one to fix it. Let me check the interpret logic because I refact it last week

authors: aseaday kemingy

repo: tensorchord/envd


text:
An operation has None for gradient for triplet_hard_loss

System information

OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Linux Ubuntu 16.04
TensorFlow version and how it was installed (source or binary): 2.1.0 binary
TensorFlow-Addons version and how it was installed (source or binary): 0.9.1 binary
Python version: 3.6
Is GPU used? (yes/no): yes

Describe the bug
I get An operation has None for gradient. when I use tfa.losses.triplet_hard_loss in non-eager mode. categorical_crossentropy works fine as loss
Code to reproduce the issue
   base_model = InceptionResNetV2(weights='imagenet', include_top=True)
    model = Model(inputs=base_model.input, outputs=predictions)
   loss=tfa.losses.triplet_hard_loss(y, z, margin=margin, soft=False)

    model.compile(optimizer=RMSprop(lr=0.0001, rho=0.9, epsilon=1e-08, decay=0.0), loss=loss, metrics=['accuracy'])

model.fit_generator(train_iterator, steps_per_epoch, epochs=num_epochs,
                                callbacks=[plot_loss_callback, reduce_learningrate_callback, checkpointer],  # , tensorboard_callback],
                                validation_data=test_iterator,
                                validation_steps=np.ceil(len(label_validation) / float(batch_size)),
                                workers=4)

Other info / logs
  File "/home/laurens/anaconda2/envs/naturalis3c/lib/python3.6/site-packages/keras/legacy/interfaces.py", line 91, in wrapper
    return func(*args, **kwargs)
  File "/home/laurens/anaconda2/envs/naturalis3c/lib/python3.6/site-packages/keras/engine/training.py", line 1732, in fit_generator
    initial_epoch=initial_epoch)
  File "/home/laurens/anaconda2/envs/naturalis3c/lib/python3.6/site-packages/keras/engine/training_generator.py", line 42, in fit_generator
    model._make_train_function()
  File "/home/laurens/anaconda2/envs/naturalis3c/lib/python3.6/site-packages/keras/engine/training.py", line 316, in _make_train_function
    loss=self.total_loss)
  File "/home/laurens/anaconda2/envs/naturalis3c/lib/python3.6/site-packages/keras/legacy/interfaces.py", line 91, in wrapper
    return func(*args, **kwargs)
  File "/home/laurens/anaconda2/envs/naturalis3c/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py", line 75, in symbolic_fn_wrapper
    return func(*args, **kwargs)
  File "/home/laurens/anaconda2/envs/naturalis3c/lib/python3.6/site-packages/keras/optimizers.py", line 259, in get_updates
    grads = self.get_gradients(loss, params)
  File "/home/laurens/anaconda2/envs/naturalis3c/lib/python3.6/site-packages/keras/optimizers.py", line 93, in get_gradients
    raise ValueError('An operation has `None` for gradient. '
ValueError: An operation has `None` for gradient. Please make sure that all of your ops have a gradient defined (i.e. are differentiable). Common ops without gradient: K.argmax, K.round, K.eval.

Hi @laurensnaturalis, could you provide the minimal runnable code snippet so that we could diagnose where the problem is easily? Thank you!

authors: WindQAQ laurensnaturalis

repo: tensorflow/addons


text:
Linear warmup learning rate schedule

Describe the feature and the current behavior/state.
A learning rate schedule implementing linear warmup with step decay, currently unavailable in tensorflow
Relevant information

Are you willing to contribute it (yes/no): Yes
Are you willing to maintain it going forward? (yes/no): Yes
Is there a relevant academic paper? (if so, where): https://arxiv.org/abs/1706.02677
Is there already an implementation in another framework? (if so, where): Yes. tensorflow model garden, google auto ml
Was it part of tf.contrib? (if so, where):

Which API type would this fall under (layer, metric, optimizer, etc.)
Learning rate schedule
Who will benefit with this feature?

Considerable number of papers use warmup strategies, retinanet, efficientdet,
Users training on cloud tpus which need a high learning rate due to the large batch size, starting with a linear warmup is often helpful to achieve convergence sooner

Any other info.
Here is a prelimary code implementation, please let me know if this is something that can find a place in tf-addons.
class PiecewiseConstantDecayWithLinearWarmup(
        tf.keras.optimizers.schedules.PiecewiseConstantDecay):

    def __init__(self, warmup_learning_rate, warmup_steps, boundaries, values,
                 **kwargs):
        super(PiecewiseConstantDecayWithLinearWarmup,
              self).__init__(boundaries=boundaries, values=values, **kwargs)

        self.warmup_learning_rate = warmup_learning_rate
        self.warmup_steps = warmup_steps
        self._step_size = self.values[0] - self.warmup_learning_rate

    def __call__(self, step):
        with tf.name_scope(self.name or
                           'PiecewiseConstantDecayWithLinearWarmup'):
            learning_rate = tf.cond(
                pred=tf.less(step, self.warmup_steps),
                true_fn=lambda:
                (self.warmup_learning_rate + tf.cast(step, dtype=tf.float32) /
                 self.warmup_steps * self._step_size),
                false_fn=lambda: (super(PiecewiseConstantDecayWithLinearWarmup,
                                        self).__call__(step)))
        return learning_rate

    def get_config(self):
        config = {
            "warmup_learning_rate": self.warmup_learning_rate,
            "warmup_steps": self.warmup_steps,
        }
        base_config = super(PiecewiseConstantDecayWithLinearWarmup,
                            self).get_config()
        return dict(list(base_config.items()) + list(config.items()))

Possible extension
We could convert this into a generalized wrapper for all the existing lr schedules, hence adding the warmup functionality to them.

Hi @srihari-humbarwadi thank for bringing this up. Yes, warmup schedules are pretty common nowadays. Please feel free to open a PR.

Should i just do it for PiecewiseConstantDecay or implement a generic wrapper?

A generic wrapper makes more sense but if you want to keep the first PR simple, you can just implement PiecewiseConstantDecay first.

TensorFlow Addons is transitioning to a minimal maintenance and release mode. New features will not be added to this repository. For more information, please see our public messaging on this decision:
TensorFlow Addons Wind Down
Please consider sending feature requests / contributions to other repositories in the TF community with a similar charters to TFA:
Keras
Keras-CV
Keras-NLP


text:
Package 0.5.1 against TF2-RC1

Looks like RC1 was released. We were already planning the next minor release so should probably build against this version.
https://groups.google.com/a/tensorflow.org/forum/#!topic/discuss/9kOKxqVsv8Q
cc @facaiy

Thanks for reminding me, Sean. Totally agree.
I suggest that we should make correlation_cost private, see https://github.com/tensorflow/addons/issues/509 . I'm happy to release 0.5.1 after finishing it. What do you think?

+@tensorflow/model-maintainers for visibility

Release looks great... thanks Yan!

@guillaumekln reported the bug in https://github.com/tensorflow/addons/issues/532

@seanpmorgan Is it good to drop 0.5.1 release, and create 0.5.2 package built on rc0? Perhaps we'd better to support rc1 or newer tf version after https://github.com/tensorflow/addons/issues/532

I think that's a good idea. I don't see a reason to not use rc1 and just not pin it using setup.py though. However, seeing as rc2 is coming soon whatever is easiest to rollback and release is good with me

Think we'll have to fix this on the next minor release. Closing.

authors: facaiy seanpmorgan


text:
1.1.0 crash on MacOS (M2)

Hi, in my Mac (M2) VSCode crashes always when run "import tensorflow_decision_forests as tfdf". I already reinstalled tensorflow (2.11.0), tensorflow-macos (2.11.0), tensorflow-metal (0.7.0) and tensorflow_decision_forests (1.1.0). Any clue?
"Canceled future for execute_request message before replies were done
The Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click here for more info. View Jupyter log for further details."
Originally posted by @mtcdultra in https://github.com/tensorflow/decision-forests/issues/16#issuecomment-1373905354

Hi Marcos, thank you for your report. Can you please provide more information about the python environment you're using? It sounds like you're in VSCode with an IPython / Jupyter environment, but I want to make sure in order to reproduce this

Hi Richard, yes, I´m using VSCode with Jupyter environment. And on Mac terminal, tried to use iPtyhon directly and crashed again: "zsh: illegal hardware instruction  ipython".
Below is the code that I've tried run:
import pandas as pd
import tensorflow as tf
import tensorflow_decision_forests as tfdf
Load a dataset into a Pandas Dataframe.
dataset_df = pd.read_csv("SP202102.csv", encoding='ISO-8859-1')
Show the first three examples.
print(dataset_df.head(3))
Convert the pandas dataframe into a tf dataset.
dataset_tf = tfdf.keras.pd_dataframe_to_tf_dataset(dataset_df, label="species")
Train the Random Forest
model = tfdf.keras.RandomForestModel(compute_oob_variable_importances=True)
model.fit(x=dataset_tf)

Hi @mtcdultra,
Can you try downgrading the tensorflow-metal to ver 0.5.0 or lower?

Hi, sorry for taking a bit of time to get to this. It looks like tensorflow-metal is incompatible with TF-DF at this time. This is on our Todo list, but we will need additional investigation on our side.
@mtcdultra Can you confirm that it works if you uninstall tensorflow-metal?

does this also apply to m1?

Yes, TF-DF is not compatible with tensorflow-metal on M1 processors. This is a tricky issue to solve AFAICT: Normal tensorflow and tensorflow-metal have different interfaces, but TF-DF only ships a single binary per OS / processor / python version on Pypi. Any ideas are very welcome!

authors: Arnold1 arnavrneo mtcdultra rstz

repo: tensorflow/decision-forests


text:
Fixes Windows build

Windows build is broken since upgrading to TF2.10.1, the Windows TF build of libtensorflow_cc is broken itself, this adds a patch that solves the issue.
One problem remains with the CustomGradientTest though, which is only failing on Windows. The JNI code generated by JavaCPP crashes the JVM, so I've temporarily disable it (which also means that custom gradient creation is not supported in Windows until we find a fix for that).

I'll merge this out right now as it fixes a broken build.
About the CustomGradientTest crashing in the JNI code, @saudet if you have chance to reenable it and check this out, that would be great.
Here's some traces:
---------------  T H R E A D  ---------------

Current thread (0x000002382b662000):  JavaThread "main" [_thread_in_native, id=5220, stack(0x00000045fcd00000,0x00000045fce00000)]

Stack: [0x00000045fcd00000,0x00000045fce00000],  sp=0x00000045fcdfac90,  free space=1003k
Native frames: (J=compiled Java code, j=interpreted, Vv=VM code, C=native code)
C  [jnitensorflow.dll+0x4c030]

Java frames: (J=compiled Java code, j=interpreted, Vv=VM code)
j  org.tensorflow.internal.c_api.NameMap.erase(Lorg/bytedeco/javacpp/BytePointer;)J+0
j  org.tensorflow.GraphOperationBuilder.finishDangerousGradient(Lorg/tensorflow/internal/c_api/TF_Graph;Lorg/tensorflow/internal/c_api/TF_OperationDescription;)Lorg/tensorflow/internal/c_api/TF_Operation;+36
j  org.tensorflow.GraphOperationBuilder.build()Lorg/tensorflow/GraphOperation;+34
j  org.tensorflow.GraphOperationBuilder.build()Lorg/tensorflow/Operation;+1
j  org.tensorflow.op.core.Constant.create(Lorg/tensorflow/op/Scope;Lorg/tensorflow/types/family/TType;)Lorg/tensorflow/op/core/Constant;+37
j  org.tensorflow.op.core.Constant.scalarOf(Lorg/tensorflow/op/Scope;F)Lorg/tensorflow/op/core/Constant;+7
j  org.tensorflow.op.Ops.constant(F)Lorg/tensorflow/op/core/Constant;+5
j  org.tensorflow.CustomGradientTest.lambda$testCustomGradient$1(Lorg/tensorflow/op/Ops;Lorg/tensorflow/op/nn/NthElement$Inputs;Ljava/util/List;)Ljava/util/List;+13
j  org.tensorflow.CustomGradientTest$$Lambda$394.call(Lorg/tensorflow/op/Ops;Lorg/tensorflow/op/RawOpInputs;Ljava/util/List;)Ljava/util/List;+6
j  org.tensorflow.op.TypedGradientAdapter.call(Lorg/tensorflow/internal/c_api/TF_Scope;Lorg/tensorflow/internal/c_api/NativeOperation;Lorg/tensorflow/internal/c_api/NativeOutputVector;Lorg/tensorflow/internal/c_api/NativeOutputVector;)Lorg/tensorflow/internal/c_api/NativeStatus;+125
v  ~StubRoutines::call_stub
j  org.tensorflow.internal.c_api.global.tensorflow.TF_AddGradientsWithPrefix(Lorg/tensorflow/internal/c_api/TF_Graph;Ljava/lang/String;Lorg/tensorflow/internal/c_api/TF_Output;ILorg/tensorflow/internal/c_api/TF_Output;ILorg/tensorflow/internal/c_api/TF_Output;Lorg/tensorflow/internal/c_api/TF_Status;Lorg/tensorflow/internal/c_api/TF_Output;)V+0
j  org.tensorflow.Graph.addGradients(Lorg/tensorflow/internal/c_api/TF_Graph;Ljava/lang/String;[Lorg/tensorflow/internal/c_api/TF_Operation;[I[Lorg/tensorflow/internal/c_api/TF_Operation;[I[Lorg/tensorflow/internal/c_api/TF_Operation;[I)[Ljava/lang/Object;+162
j  org.tensorflow.Graph.addGradients(Ljava/lang/String;[Lorg/tensorflow/Output;[Lorg/tensorflow/Output;[Lorg/tensorflow/Output;)[Lorg/tensorflow/Output;+221
j  org.tensorflow.Graph.addGradients(Lorg/tensorflow/Output;[Lorg/tensorflow/Output;)[Lorg/tensorflow/Output;+12
j  org.tensorflow.CustomGradientTest.testCustomGradient()V+101

The generated JNI code:
JNIEXPORT jlong JNICALL Java_org_tensorflow_internal_c_1api_NameMap_erase__Lorg_bytedeco_javacpp_BytePointer_2(JNIEnv* env, jobject obj, jobject arg0) {
    std::unordered_map<tensorflow::string,tensorflow::Node*>* ptr = (std::unordered_map<tensorflow::string,tensorflow::Node*>*)jlong_to_ptr(env->GetLongField(obj, JavaCPP_addressFID));
    if (ptr == NULL) {
        env->ThrowNew(JavaCPP_getClass(env, 7), "This pointer address is NULL.");
        return 0;
    }
    jlong position = env->GetLongField(obj, JavaCPP_positionFID);
    ptr += position;
    signed char* ptr0 = arg0 == NULL ? NULL : (signed char*)jlong_to_ptr(env->GetLongField(arg0, JavaCPP_addressFID));
    jlong size0 = arg0 == NULL ? 0 : env->GetLongField(arg0, JavaCPP_limitFID);
    void* owner0 = JavaCPP_getPointerOwner(env, arg0);
    jlong position0 = arg0 == NULL ? 0 : env->GetLongField(arg0, JavaCPP_positionFID);
    ptr0 += position0;
    size0 -= position0;
    StringAdapter< char > adapter0(ptr0, size0, owner0);
    jlong rarg = 0;
    jlong rval = ptr->erase((std::basic_string< char >&)adapter0);
    rarg = (jlong)rval;
    signed char* rptr0 = adapter0;
    jlong rsize0 = (jlong)adapter0.size;
    void* rowner0 = adapter0.owner;
    if (rptr0 != ptr0) {
        JavaCPP_initPointer(env, arg0, rptr0, rsize0, rowner0, &StringAdapter< char >::deallocate);
    } else {
        env->SetLongField(arg0, JavaCPP_limitFID, rsize0 + position0);
    }
    return rarg;
}

Also, this binding seems to receive a special treatment from our end: https://github.com/tensorflow/java/blob/455fc731ece7aba751e7ec7ee1cd06dff3a1bfe0/tensorflow-core/tensorflow-core-api/src/main/java/org/tensorflow/internal/c_api/presets/tensorflow.java#L391

I'm guessing TF_OperationName() returns an invalid pointer for some reason...

authors: karllessard saudet

repo: tensorflow/java


text:
Question: How to set the encoding to prevent unexpected conversion?

While following the tutorial, i met two issues as below. Would you please share me your comments and suggestions? thanks in advance!

Encoding?
The target strings were converted into unicode such as "B\xc2\xa0..." I notice that in the tutorial, text should be displayed as they are (for example, Vietnamese text). I use ubuntu x64 + python 3.5. The default encoding of system is UTF-8. Any idea for what I might do wrong here?

Errors as below.
There's no error during the training, however we cannot use the trained module and get errors as below. I guess it might relate to the first issue but i cannot figure out where exactly goes wrong. Any suggestion would be welcome

我在pycharm上输出时加了decode（’utf-8‘）解决了，但是看其它人都没遇到这个问题很奇怪

For 1, I think it is because the tutorial logs are generated by python2. I will investigate later to make the logging works correctly for both python2 and 3.
For 2, We recently updated the flags. Change model_dir to out_dir should fix the problem.  For example

python -m nmt.nmt \
    --out_dir=/tmp/nmt_model \
    --inference_input_file=/tmp/my_infer_file.vi \
    --inference_output_file=/tmp/nmt_model/output_infer

Thank you @chuangfortytwo @oahziur! I would try today and share you the result later.

I tried the vietnamese to english translation in the tutorial and get decoder output all in byte string format, i.e., string with a 'b' in front of it. Is this the expected behavior? Should I manually convert the output strings into utf-8? Thanks!

@chuangfortytwo ,那能不能在输出到文件的时候就给转码了（抱歉，pycharm没有接触过），还有如果加载到一个服务上，是否可以输出转码的内容

@sdlmw 最后计算bleu值的时候就是从文件读取的，可以在输出到文件时转码。服务这个没试过

sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')

Thank you All for the supports! Confirmed all issues had been fixed now.

authors: anglil chuangfortytwo luosmart oahziur sdlmw yanl10n

repo: tensorflow/nmt