Character encoding issue

I just updated to the latest version (2.11.2) of this cookbook from 2.8.0. I now have a non-ascii character causing my tests to barf.
  No Chef resources found, skipping coverage calculation...
/var/lib/jenkins/.rvm/gems/ruby-2.1.5@cookbook-testing/gems/json-1.8.2/lib/json/common.rb:155:in `encode': "\xE2" on US-ASCII (Encoding::InvalidByteSequenceError)
	from /var/lib/jenkins/.rvm/gems/ruby-2.1.5@cookbook-testing/gems/json-1.8.2/lib/json/common.rb:155:in `initialize'
	from /var/lib/jenkins/.rvm/gems/ruby-2.1.5@cookbook-testing/gems/json-1.8.2/lib/json/common.rb:155:in `new'
	from /var/lib/jenkins/.rvm/gems/ruby-2.1.5@cookbook-testing/gems/json-1.8.2/lib/json/common.rb:155:in `parse'
	from /var/lib/jenkins/.rvm/gems/ruby-2.1.5@cookbook-testing/gems/ridley-4.1.2/lib/ridley/chef/cookbook/metadata.rb:473:in `from_json'
	from /var/lib/jenkins/.rvm/gems/ruby-2.1.5@cookbook-testing/gems/ridley-4.1.2/lib/ridley/chef/cookbook/metadata.rb:29:in `from_json'
	from /var/lib/jenkins/.rvm/gems/ruby-2.1.5@cookbook-testing/gems/ridley-4.1.2/lib/ridley/chef/cookbook.rb:36:in `from_path'
	from /var/lib/jenkins/.rvm/gems/ruby-2.1.5@cookbook-testing/gems/berkshelf-3.2.4/lib/berkshelf/cached_cookbook.rb:15:in `from_store_path'
	from /var/lib/jenkins/.rvm/gems/ruby-2.1.5@cookbook-testing/gems/berkshelf-3.2.4/lib/berkshelf/cookbook_store.rb:108:in `block in cookbooks'
	from /var/lib/jenkins/.rvm/gems/ruby-2.1.5@cookbook-testing/gems/berkshelf-3.2.4/lib/berkshelf/cookbook_store.rb:98:in `collect'
...

I can force my rspec run to use Encoding::UTF_8 and get past this but I can't easily force test-kitchen to do so.
It looks like the character causing the problem is just in a readme file:
 newrelic  % ag "[\x80-\xFF]"
test/fixtures/cookbooks/newrelic_lwrp_test/README.md
18:* ```config_file:``` The New Relic php agent config file, depends on your php external configuration directory; e.g. /etc/php5/conf.d/newrelic.ini, /etc/php5/mods-available/newrelic.ini, [0m¦ Defaults to nil

README.md
256:* `'config_file'` - The New Relic php agent config file, depends on your php external configuration directory; e.g. /etc/php5/conf.d/newrelic.ini, /etc/php5/mods-available/newrelic.ini, [0m¦ Defaults to nil

From 996a1d771789771f8e3dfc0f2dd88f0b940a8e80 and e33794738b9901d31cd7c9a828b866656bc53e0d
I'd be awfully grateful if my pull-request and merge are released quickly.

Ok well it's not that critical. After commit this fix on a local branch, I'm now having problems with the cookbooks getting pulled in from the apt dependency. They too have non-ascii characters. I've just reverted the version of this cookbook to 2.9.1 in my wrapper, which was the last version before apt was introduced, and everything is fine again.


Inline tooltip is too aggressive

IDE Information
Latest Idea
Bug Description
When editing text in IDE, trying to select almost full line is now prohibited by Cody aggressive tooltip. In some cases I want to select text from current cursor to the end of line, except last few characters. So I press Shift+Cmd+Right (not sure if this is default keystroke), then I typically press Left few times keeping Shift to maintain selection. But with the latest Cody update (6.0.12) it is impossible, cursor is stuck after the Cody tooltip and does not move left anymore.

Additional context
No response

As a matter of fact, that tooltip blinks hundred times per hour, when editing text. I'm finding it extremely annoying, would be great if it can be disabled completely, or at least delayed by a second or two.

I stopped using the nightly builds for JetBrain because of this. Looks like I have to figure out how to downgrade the plug-in or uninstall it until there's a way to disable this feature.

Too bad, that this feature was released together with Claude 3.5 support, so by downgrading you lose the model as well. Otherwise I'd downgrade, too.

Please, let users disable this tooltip. Please. It is extremely annoying. Every single time I do text selection, which often happens several time per minute, it shows up. And every single keystroke to extend the selection, the tooltip jumps over the screen. Every. Single. Keystroke. This is extremely distracting and reducing user productivity.
I installed your plugin long time ago. When inline edit came into life, I started using it. I learned the keystrokes. I know the plugin is there and I know can invoke it at any time. No need to remind it to me 100 times per hour (literally!). Then you changed the keystroke to invoke it. Fine. I learned new keystroke. Yet, every time I want to select 5 lines, you put tooltip jumping around my screen 5 times. Source code, which is usually static, you somehow made it animated, and it causes user eyes to look for the screen changes. It feels Iike I'm paying to have your advertisement jumping over my screen. And it is harmful, because it sometimes blocks cursor movement and I need to work it around. That hurts my productivity even further.

Thanks @Nopik
I will make sure the product team sees this in the weekly community feedback report.

It would be great if Cody did not interfere with "select to EOL".

I upgraded to v6.0.14 of the Jetbrains IDE plugin and the issue with selection is fixed. There's also an option to disable entirely.

Thanks for confirming @mdhender
@Nopik can you confirm. The lastest release should fix the issue: https://sourcegraph.com/blog/cody-jetbrains-6-0-14-release

@esafak can you screenshot it that way I can send it to the product team.

I've upgraded to 6.0.14 and basically immediately turned off the whole UI hints and never looked back. Minute ago I enabled it for a moment, and indeed, the tooltip was moved 1 line lower, so it doesn't interact with the cursor, so it is less 'aggressive', definitely. Still, as long as it appears immediately and jump around every time I move cursor, it is a no-go for me and the UI hints will get disabled.

@Nopik, thanks for the update; I'll let the team know. For now, I will close this.


Condenser

As token limits are always a hurdle, there's one trick to trade some short term for more long term benefits. Which is to use an AI call to condense text.
For instance, for my custom persona I wrote up it took 86 tokens. I then used the following custom persona:
You are a meta-tool for an AI model, your role is to convert human readable data into condensed and efficient text that is meant to be read by another AI with minimal token usage.
Then gave it the request "Convert the following: ..."
The resulting text from OpenChat 3.5 was 48 tokens, almost cutting it in half while keeping the same content.
So this could be used to drop token usage automatically in a number of places, especially places where the usage might be heavier (the persona because it's used on every request, longer conversations so that more can stay in the context window and less tokens are used on follow up requests)

I'd like to do this in a generic way. Where in the current app would it be a good place to start a condenser?

[ ] some sort of top level Tools > Condenser
[ ] an abstract "Prompts Store" > Condenser
[ ] in the Chat, on the "Composer" (where one types), to condense the text into the user message
[ ] in the Chat, on any Message (3 dots that appear when hovering a chat persona), to condense that message before sending it further
[ ] ...

Where else and what's the optimal workflow?

So I realized that some of this already exists in the form of the Flatten function in the menu, but still can be expanded on.
It should absolutely appear as an option on the custom personas (because that prompt it going to be in every single request made under that persona).
It could also appear as a dynamic button when the chat reaches a certain length in tokens a button could appear that basically says "this chat is reaching the limits of many models, would you like to condense/flatten it so that you can continue?".

Requirements:

[ ] Composer: new secondary button in desktop mode to condense the current composer
[ ] Composer: new secondary button to condense the chat when > 50% (or 25% larger models) of the context size


fix(spantest): use docker port to lookup port mapping

This also solves the problem that the host is empty if running with
Podman instead of Docker.

Codecov Report

Merging #361 (05b300b) into master (762bfd8) will increase coverage by 0.23%.
The diff coverage is 55.27%.

:exclamation: Current head 05b300b differs from pull request most recent head 67c802d. Consider uploading reports for the commit 67c802d to get more accurate results

@@            Coverage Diff             @@
##           master     #361      +/-   ##
==========================================
+ Coverage   44.66%   44.90%   +0.23%
==========================================
  Files          21       20       -1
  Lines        4267     4316      +49
==========================================
+ Hits         1906     1938      +32
- Misses       2249     2270      +21
+ Partials      112      108       -4

Impacted Files
Coverage Δ

internal/examples/freightdb/descriptor_gen.go
0.00% <0.00%> (ø)

internal/examples/musicdb/database_gen.go
20.81% <26.66%> (+0.05%)
:arrow_up:

internal/examples/freightdb/database_gen.go
23.28% <41.37%> (+0.07%)
:arrow_up:

...nternal/codegen/databasecodegen/readtransaction.go
100.00% <100.00%> (ø)


Test issue

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Test comment

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Test again

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Reopening

Autoclose this later

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Helloooo

Another comment for debugging

Yet another comment for debugging

Another comment, for debugging


Revert "templates: update Arch Linux to 20240315.221711" to avoid xz 5.6.1-1

This reverts commit 5416270f8b175734cbc69d5ded09700f7c12b676.
The source code archive (not the git repo) of xz v5.6.0 and v5.6.1 turned out to be compromised:

https://www.openwall.com/lists/oss-security/2024/03/29/4

While ArchLinux's xz package is very unlikely to be affected (see the == Affected Systems == section in the email), it is better to rollback the ArchLinux image to use an older version of xz.

ArchLinux v20240315.221711: xz v5.6.1-1
ArchLinux v20240115.207158: xz v5.4.5-1

After running pacman -Syu on ArchLinux v20240115.207158, a user will get xz v5.6.1-2. This package revision does not use the compromised v5.6.1 source archive:

https://gitlab.archlinux.org/archlinux/packaging/packages/xz/-/issues/2
https://gitlab.archlinux.org/archlinux/packaging/packages/xz/-/commit/881385757abdc39d3cfea1c3e34ec09f637424ad

Looks like they retracted v20240315.221711 and released v20240329.225538/

Probably we should just update the image to the latest revision


Add docker sec defaults

Closes https://github.com/docker/libentitlement/issues/33.
Adds reference tables for capabilities and syscalls as well as documentation on the default set of capabilities, seccomp profile and apparmor profile on Moby.

Updated accordingly with proper syscalls and capabilities types

Sure, I'll do that in a future PR!


Add Auto Assign app

Add Auto Assign app to the list of apps.

Checklist

[x] If you're adding a listing for your app, be sure your app is in the /_apps/ folder before opening this Pull Request.
[x] All comments in frontmatter need to be removed.

Wooo! Thanks @kentaro-m!


Running behind reverse proxy

When running LiGet behind a reverse proxy, would be great if there was a way to specify the external URL to LiGet so it can be returned in the responses. In my case, I have LiGet running on internalserver:9011 and I have apache reverse proxying nuget.mycompany.com to internalserver:9011.
However in all the responses from Liget the internal (hidden) URL is used. e.g.:
{"version":"3.0.0","resources":[{"@id":"http://internalserver:9011/api/v2/package"...
Ideally should return:
{"version":"3.0.0","resources":[{"@id":"http://nuget.mycompany.com/api/v2/package"...

Probably fixed by accepting an environment variable for proxy endpoint, and using this to build the URL in CarterUrlExtensions.cs
https://github.com/ai-traders/liget/blob/e50c365a59a14499fd7cbb233f1533f514fe8c2e/src/LiGet/Extensions/CarterUrlExtensions.cs#L50
https://github.com/ai-traders/liget/blob/e50c365a59a14499fd7cbb233f1533f514fe8c2e/src/LiGet/Extensions/CarterUrlExtensions.cs#L55

@nivinj Im using it behind a traefik proxy, which is working fine. Maybe you have to check the passed HTTP headers

If you're using nginx, make sure you're passing the host header as well - since that's what LiGet uses to resolve the absolute URL
proxy_set_header Host            $host;
proxy_set_header X-Forwarded-For $remote_addr;

As i already mentioned in the baget repo, private feeds could be easily reached with a reverse proxy like nginx. I think this would be a good solution for now. In this issue i descriped my solution, could be useful for others: https://github.com/loic-sharma/BaGet/issues/515

Anyone know how to do this via a kubernetes ingress-nginx config and route a subpath to the service?
I tried using a generic solution I see in other articles using the rewrite-target annotation to rewrite the url, but all I get is 404 errors:
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: http-ingress
  annotations:
    nginx.ingress.kubernetes.io/enable-cors: "true"
    nginx.ingress.kubernetes.io/ssl-redirect: "true"
    nginx.ingress.kubernetes.io/rewrite-target: /$1/$3
spec:
  ingressClassName: public
  rules:
  - host: mydomain.com
    http:
      paths:
      - path: /(nuget)(/|$)(.*)
        pathType: Prefix
        backend:
          service:
            name: liget
            port:
              number: 9011


Fix test

Make tests more robust;

prevent tests from hanging when failing,
removed some ugly broken pipe errors,
added retry for a time sensitive test (getting pid from file not yet written)

I like this! tests need so much love! Thanks.

The CLI has been rewritten in feat/invalidations, so we need to make sure this is still relevant with the new code.

Well this touches the specs and

I don't think they have been changed in invalidations
If so, and if they are not relevant, then the person merging on rebasing invalidations is responsible for judging that and removing/adapting them as necessary.


Song duration is 0

I am try to play this song and 'duration' and 'formattedDuration' is zero.
https://music.apple.com/us/album/the-rebel-path/1544457960?i=1544457964

Duration is not available because it is not the streamed song, song.stream.song is available only when the song plays to reduce unnecessary requests and response time.
This is the nature of infoExtractor.

But Spotify plugin for Distube is InfoExtractor, and spotify plugin provide duration for songs. So this is not "nature of infoExtractor"

You're right, I'll fix this in next release


feature: decrese polling

Overview
We've been pulling events every 500 ms, which is obviously too much. The pull value should be less than frequency of pushed ethereum blocks (mainnet and rinkeby) + processing time
Changes

Increase
remove stale configuration

Testing
/

Is this global for each module?

I'm not sure what you mean @jbunce. It affects everything and everyone in the VM that uses this configuration.

Can we make this into a config provider? It seems to me a value that we would want to adjust per environment, and changes over time depending on the network's behaviour.

RE: config per module, that would be nice to have but I would say we don't need that level of granularity yet?

we can definitely make this into a config provider, but I would wait until we get a sufficient requirement

Coverage remained the same at ?% when pulling 498539f08386ffe3012656a4cd260c282290e531 on inomurko/events_check into f7fc35ab1735e69d5492f42e9f56020232160e03 on master.

@InoMurko thanks, that would be useful to write in the PR for reviewers

In resume-speak: Single-handedly reduced Ethereum calls by 1600%


Error when executing postgis image

I configured testcontainers on one of my projects that uses postgres with the postgis extension enabled. When I try to execute the tests, I receive this message:
23:10:57.225 [error] Task #PID<0.488.0> started from Testcontainers terminating
** (ArgumentError) Image postgis/postgis:16-3.4-alpine is not compatible with postgres
    (testcontainers 1.8.1) lib/container/postgres_container.ex:192: Testcontainers.ContainerBuilder.Testcontainers.PostgresContainer.build/1
    (testcontainers 1.8.1) lib/testcontainers.ex:226: Testcontainers.start_and_wait/2
    (testcontainers 1.8.1) lib/testcontainers.ex:132: anonymous fn/3 in Testcontainers.handle_call/3
    (elixir 1.16.3) lib/task/supervised.ex:101: Task.Supervised.invoke_mfa/2
    (elixir 1.16.3) lib/task/supervised.ex:36: Task.Supervised.reply/4
Function: #Function<1.102782053/0 in Testcontainers.handle_call/3>
    Args: []

As you can see, I'm using the image postgis/postgis on the tests. Howerver there is a validation that prevents executing images other than the official postgres.
In this case, what would be best solution:

Open a PR, allowing alternative images to be run
Implement my own container, based on Testcontainers.Postgrescontainer

I don't think there is much difference, in basic usage, between the two. So if you make a PR that either

Changes the validation of the postgres container to also include postgis (meh ...)
Make a method that disables image validation by setting a boolean field, similar to the other methods that sets config fields (maybe a good solution)
Then I could look into it.

Thanks for the suggestions. I'm looking into the best approach to turn off the image validation with a control field.

Thanks for all the help. With the release of 1.8.3, I can execute the postgis database using the Testcontainers.PostgresContainer implementation. I adjusted my application.ex with:
    if Application.get_env(:testcontainers, :enabled, false) do
      {:ok, _container} =
        Testcontainers.Ecto.postgres_container(
          app: :foundation,
          image: "postgis/postgis:16-3.4-alpine",
          check_image: "postgis/postgis"
        )
    end


Major Overhaul of the code, deprecation, refactoring, etc.

We now have a succinct mathematical representation of the code in our paper, and we want the code to reflect that simplicity.  We have deprecated a bunch of PHOENIX-specific code (#12 ), so we will architect two new classes:
LinearEmulator and LogEmulator vis-a-vis #24
We will update the docstrings to reflect the math in the paper #25
We may address other issues along the way.  Let's do this!

Closes #25
Begins to address items #12 and #24
We now have a better looking API documentation
Okay, this PR has made some great progress.  I am inclined to merge it and start a new one focused on the logEmulator.


Update go version to 1.21.13

Changes

updates go to version 1.21.13
updates dependencies to address a critical vulnerability found by govulncheck

Closed to follow our release branch naming conventions. The changes in this PR are now here


mnist example hangs

I got the same problem as #349. I have read all related issue and FAQs（set LIB_HDFS LIB_JVM ） but unfortunately, I still suck.
Environment:

Python version 2.7.12
Spark version 2.4.1
TensorFlow version 1.13.1
TensorFlowOnSpark version 1.4.3
Cluster version standalone cluster

Describe the bug:
After configuring the cluster, I run mnist example but sucked at "INFO python.PythonAccumulatorV2: Connected to AccumulatorServer at host: <IP_ADDRESS> port: 58645". After timeout, it gave following log(a little bit long)
19/05/31 15:18:59 WARN scheduler.TaskSetManager: Lost task 0.0 in stage 1.0 (TID 2, <IP_ADDRESS>, executor 0): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/worker.py", line 377, in main
    process()
  File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/worker.py", line 372, in process
    serializer.dump_stream(func(split_index, iterator), outfile)
  File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 2499, in pipeline_func
  File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 2499, in pipeline_func
  File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 2499, in pipeline_func
  File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 352, in func
  File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 801, in func
  File "/home/ghn/.local/lib/python2.7/site-packages/tensorflowonspark/TFSparkNode.py", line 420, in _train
Exception: Timeout while feeding partition

	at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:452)
	at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:588)
	at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:571)
	at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:406)
	at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
	at scala.collection.Iterator$class.foreach(Iterator.scala:891)
	at org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28)
	at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:59)
	at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:104)
	at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:48)
	at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:310)
	at org.apache.spark.InterruptibleIterator.to(InterruptibleIterator.scala:28)
	at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:302)
	at org.apache.spark.InterruptibleIterator.toBuffer(InterruptibleIterator.scala:28)
	at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:289)
	at org.apache.spark.InterruptibleIterator.toArray(InterruptibleIterator.scala:28)
	at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$13.apply(RDD.scala:945)
	at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$13.apply(RDD.scala:945)
	at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2101)
	at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2101)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
	at org.apache.spark.scheduler.Task.run(Task.scala:121)
	at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:403)
	at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:409)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
	at java.lang.Thread.run(Thread.java:748)

19/05/31 15:29:02 INFO scheduler.TaskSetManager: Starting task 0.1 in stage 1.0 (TID 4, <IP_ADDRESS>, executor 0, partition 0, ANY, 8537 bytes)
19/05/31 15:29:02 WARN scheduler.TaskSetManager: Lost task 1.0 in stage 1.0 (TID 3, <IP_ADDRESS>, executor 0): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/worker.py", line 377, in main
    process()
  File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/worker.py", line 372, in process
    serializer.dump_stream(func(split_index, iterator), outfile)
  File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 2499, in pipeline_func
  File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 2499, in pipeline_func
  File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 2499, in pipeline_func
  File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 352, in func
  File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 801, in func
  File "/home/ghn/.local/lib/python2.7/site-packages/tensorflowonspark/TFSparkNode.py", line 420, in _train
Exception: Timeout while feeding partition

	at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:452)
	at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:588)
	at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:571)
	at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:406)
	at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
	at scala.collection.Iterator$class.foreach(Iterator.scala:891)
	at org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28)
	at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:59)
	at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:104)
	at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:48)
	at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:310)
	at org.apache.spark.InterruptibleIterator.to(InterruptibleIterator.scala:28)
	at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:302)
	at org.apache.spark.InterruptibleIterator.toBuffer(InterruptibleIterator.scala:28)
	at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:289)
	at org.apache.spark.InterruptibleIterator.toArray(InterruptibleIterator.scala:28)
	at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$13.apply(RDD.scala:945)
	at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$13.apply(RDD.scala:945)
	at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2101)
	at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2101)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
	at org.apache.spark.scheduler.Task.run(Task.scala:121)
	at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:403)
	at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:409)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
	at java.lang.Thread.run(Thread.java:748)

19/05/31 15:39:05 INFO scheduler.TaskSetManager: Starting task 1.1 in stage 1.0 (TID 5, <IP_ADDRESS>, executor 0, partition 1, ANY, 8537 bytes)
19/05/31 15:39:05 WARN scheduler.TaskSetManager: Lost task 0.1 in stage 1.0 (TID 4, <IP_ADDRESS>, executor 0): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/worker.py", line 377, in main
    process()
  File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/worker.py", line 372, in process
    serializer.dump_stream(func(split_index, iterator), outfile)
  File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 2499, in pipeline_func
  File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 2499, in pipeline_func
  File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 2499, in pipeline_func
  File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 352, in func
  File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 801, in func
  File "/home/ghn/.local/lib/python2.7/site-packages/tensorflowonspark/TFSparkNode.py", line 420, in _train
Exception: Timeout while feeding partition

	at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:452)
	at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:588)
	at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:571)
	at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:406)
	at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
	at scala.collection.Iterator$class.foreach(Iterator.scala:891)
	at org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28)
	at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:59)
	at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:104)
	at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:48)
	at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:310)
	at org.apache.spark.InterruptibleIterator.to(InterruptibleIterator.scala:28)
	at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:302)
	at org.apache.spark.InterruptibleIterator.toBuffer(InterruptibleIterator.scala:28)
	at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:289)
	at org.apache.spark.InterruptibleIterator.toArray(InterruptibleIterator.scala:28)
	at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$13.apply(RDD.scala:945)
	at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$13.apply(RDD.scala:945)
	at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2101)
	at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2101)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
	at org.apache.spark.scheduler.Task.run(Task.scala:121)
	at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:403)
	at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:409)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
	at java.lang.Thread.run(Thread.java:748)

19/05/31 15:49:08 INFO scheduler.TaskSetManager: Starting task 0.2 in stage 1.0 (TID 6, <IP_ADDRESS>, executor 0, partition 0, ANY, 8537 bytes)
19/05/31 15:49:08 WARN scheduler.TaskSetManager: Lost task 1.1 in stage 1.0 (TID 5, <IP_ADDRESS>, executor 0): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/worker.py", line 377, in main
    process()
  File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/worker.py", line 372, in process
    serializer.dump_stream(func(split_index, iterator), outfile)
  File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 2499, in pipeline_func
  File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 2499, in pipeline_func
  File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 2499, in pipeline_func
  File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 352, in func
  File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 801, in func
  File "/home/ghn/.local/lib/python2.7/site-packages/tensorflowonspark/TFSparkNode.py", line 420, in _train
Exception: Timeout while feeding partition

	at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:452)
	at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:588)
	at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:571)
	at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:406)
	at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
	at scala.collection.Iterator$class.foreach(Iterator.scala:891)
	at org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28)
	at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:59)
	at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:104)
	at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:48)
	at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:310)
	at org.apache.spark.InterruptibleIterator.to(InterruptibleIterator.scala:28)
	at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:302)
	at org.apache.spark.InterruptibleIterator.toBuffer(InterruptibleIterator.scala:28)
	at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:289)
	at org.apache.spark.InterruptibleIterator.toArray(InterruptibleIterator.scala:28)
	at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$13.apply(RDD.scala:945)
	at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$13.apply(RDD.scala:945)
	at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2101)
	at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2101)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
	at org.apache.spark.scheduler.Task.run(Task.scala:121)
	at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:403)
	at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:409)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
	at java.lang.Thread.run(Thread.java:748)

19/05/31 15:59:11 INFO scheduler.TaskSetManager: Starting task 1.2 in stage 1.0 (TID 7, <IP_ADDRESS>, executor 0, partition 1, ANY, 8537 bytes)
19/05/31 15:59:11 WARN scheduler.TaskSetManager: Lost task 0.2 in stage 1.0 (TID 6, <IP_ADDRESS>, executor 0): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/worker.py", line 377, in main
    process()
  File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/worker.py", line 372, in process
    serializer.dump_stream(func(split_index, iterator), outfile)
  File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 2499, in pipeline_func
  File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 2499, in pipeline_func
  File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 2499, in pipeline_func
  File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 352, in func
  File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 801, in func
  File "/home/ghn/.local/lib/python2.7/site-packages/tensorflowonspark/TFSparkNode.py", line 420, in _train
Exception: Timeout while feeding partition

	at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:452)
	at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:588)
	at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:571)
	at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:406)
	at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
	at scala.collection.Iterator$class.foreach(Iterator.scala:891)
	at org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28)
	at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:59)
	at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:104)
	at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:48)
	at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:310)
	at org.apache.spark.InterruptibleIterator.to(InterruptibleIterator.scala:28)
	at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:302)
	at org.apache.spark.InterruptibleIterator.toBuffer(InterruptibleIterator.scala:28)
	at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:289)
	at org.apache.spark.InterruptibleIterator.toArray(InterruptibleIterator.scala:28)
	at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$13.apply(RDD.scala:945)
	at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$13.apply(RDD.scala:945)
	at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2101)
	at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2101)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
	at org.apache.spark.scheduler.Task.run(Task.scala:121)
	at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:403)
	at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:409)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
	at java.lang.Thread.run(Thread.java:748)

19/05/31 16:09:14 INFO scheduler.TaskSetManager: Starting task 0.3 in stage 1.0 (TID 8, <IP_ADDRESS>, executor 0, partition 0, ANY, 8537 bytes)
19/05/31 16:09:14 WARN scheduler.TaskSetManager: Lost task 1.2 in stage 1.0 (TID 7, <IP_ADDRESS>, executor 0): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/worker.py", line 377, in main
    process()
  File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/worker.py", line 372, in process
    serializer.dump_stream(func(split_index, iterator), outfile)
  File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 2499, in pipeline_func
  File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 2499, in pipeline_func
  File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 2499, in pipeline_func
  File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 352, in func
  File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 801, in func
  File "/home/ghn/.local/lib/python2.7/site-packages/tensorflowonspark/TFSparkNode.py", line 420, in _train
Exception: Timeout while feeding partition

	at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:452)
	at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:588)
	at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:571)
	at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:406)
	at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
	at scala.collection.Iterator$class.foreach(Iterator.scala:891)
	at org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28)
	at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:59)
	at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:104)
	at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:48)
	at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:310)
	at org.apache.spark.InterruptibleIterator.to(InterruptibleIterator.scala:28)
	at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:302)
	at org.apache.spark.InterruptibleIterator.toBuffer(InterruptibleIterator.scala:28)
	at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:289)
	at org.apache.spark.InterruptibleIterator.toArray(InterruptibleIterator.scala:28)
	at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$13.apply(RDD.scala:945)
	at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$13.apply(RDD.scala:945)
	at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2101)
	at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2101)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
	at org.apache.spark.scheduler.Task.run(Task.scala:121)
	at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:403)
	at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:409)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
	at java.lang.Thread.run(Thread.java:748)

Logs:
Top of Log
Spark Executor Command: "/local/jdk1.8/bin/java" "-cp" "/local/spark-2.4.1-bin-hadoop2.7/conf/:/local/spark-2.4.1-bin-hadoop2.7/jars/*:/local/hadoop-2.7.1/etc/hadoop/" "-Xmx1024M" "-Dspark.driver.port=34771" "org.apache.spark.executor.CoarseGrainedExecutorBackend" "--driver-url" "spark://CoarseGrainedScheduler@hadoop222:34771" "--executor-id" "1" "--hostname" "<IP_ADDRESS>" "--cores" "1" "--app-id" "app-20190531150850-0054" "--worker-url" "spark://Worker@<IP_ADDRESS>:37957"
========================================

19/05/31 15:08:51 INFO executor.CoarseGrainedExecutorBackend: Started daemon with process name: 19542@hadoop222
19/05/31 15:08:51 INFO util.SignalUtils: Registered signal handler for TERM
19/05/31 15:08:51 INFO util.SignalUtils: Registered signal handler for HUP
19/05/31 15:08:51 INFO util.SignalUtils: Registered signal handler for INT
19/05/31 15:08:51 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
19/05/31 15:08:51 INFO spark.SecurityManager: Changing view acls to: ghn
19/05/31 15:08:51 INFO spark.SecurityManager: Changing modify acls to: ghn
19/05/31 15:08:51 INFO spark.SecurityManager: Changing view acls groups to:
19/05/31 15:08:51 INFO spark.SecurityManager: Changing modify acls groups to:
19/05/31 15:08:51 INFO spark.SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users  with view permissions: Set(ghn); groups with view permissions: Set(); users  with modify permissions: Set(ghn); groups with modify permissions: Set()
19/05/31 15:08:52 INFO client.TransportClientFactory: Successfully created connection to hadoop222/<IP_ADDRESS>:34771 after 78 ms (0 ms spent in bootstraps)
19/05/31 15:08:52 INFO spark.SecurityManager: Changing view acls to: ghn
19/05/31 15:08:52 INFO spark.SecurityManager: Changing modify acls to: ghn
19/05/31 15:08:52 INFO spark.SecurityManager: Changing view acls groups to:
19/05/31 15:08:52 INFO spark.SecurityManager: Changing modify acls groups to:
19/05/31 15:08:52 INFO spark.SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users  with view permissions: Set(ghn); groups with view permissions: Set(); users  with modify permissions: Set(ghn); groups with modify permissions: Set()
19/05/31 15:08:52 INFO client.TransportClientFactory: Successfully created connection to hadoop222/<IP_ADDRESS>:34771 after 1 ms (0 ms spent in bootstraps)
19/05/31 15:08:52 INFO storage.DiskBlockManager: Created local directory at /tmp/spark-2276f4d2-5eba-4e5b-ac21-5c7e5952fcad/executor-3ece4c79-9a4a-4a88-9cfa-bd101e7f9aa9/blockmgr-3399348d-98ba-4022-aa1c-e55925267063
19/05/31 15:08:52 INFO memory.MemoryStore: MemoryStore started with capacity 366.3 MB
19/05/31 15:08:52 INFO executor.CoarseGrainedExecutorBackend: Connecting to driver: spark://CoarseGrainedScheduler@hadoop222:34771
19/05/31 15:08:52 INFO worker.WorkerWatcher: Connecting to worker spark://Worker@<IP_ADDRESS>:37957
19/05/31 15:08:52 INFO client.TransportClientFactory: Successfully created connection to /<IP_ADDRESS>:37957 after 2 ms (0 ms spent in bootstraps)
19/05/31 15:08:52 INFO worker.WorkerWatcher: Successfully connected to spark://Worker@<IP_ADDRESS>:37957
19/05/31 15:08:52 INFO executor.CoarseGrainedExecutorBackend: Successfully registered with driver
19/05/31 15:08:52 INFO executor.Executor: Starting executor ID 1 on host <IP_ADDRESS>
19/05/31 15:08:52 INFO util.Utils: Successfully started service 'org.apache.spark.network.netty.NettyBlockTransferService' on port 42153.
19/05/31 15:08:52 INFO netty.NettyBlockTransferService: Server created on <IP_ADDRESS>:42153
19/05/31 15:08:52 INFO storage.BlockManager: Using org.apache.spark.storage.RandomBlockReplicationPolicy for block replication policy
19/05/31 15:08:52 INFO storage.BlockManagerMaster: Registering BlockManager BlockManagerId(1, <IP_ADDRESS>, 42153, None)
19/05/31 15:08:52 INFO storage.BlockManagerMaster: Registered BlockManager BlockManagerId(1, <IP_ADDRESS>, 42153, None)
19/05/31 15:08:52 INFO storage.BlockManager: Initialized BlockManager: BlockManagerId(1, <IP_ADDRESS>, 42153, None)
19/05/31 15:08:52 INFO executor.CoarseGrainedExecutorBackend: Got assigned task 0
19/05/31 15:08:52 INFO executor.Executor: Running task 0.0 in stage 0.0 (TID 0)
19/05/31 15:08:52 INFO executor.Executor: Fetching spark://hadoop222:34771/files/mnist_dist.py with timestamp<PHONE_NUMBER>739
19/05/31 15:08:52 INFO client.TransportClientFactory: Successfully created connection to hadoop222/<IP_ADDRESS>:34771 after 1 ms (0 ms spent in bootstraps)
19/05/31 15:08:52 INFO util.Utils: Fetching spark://hadoop222:34771/files/mnist_dist.py to /tmp/spark-2276f4d2-5eba-4e5b-ac21-5c7e5952fcad/executor-3ece4c79-9a4a-4a88-9cfa-bd101e7f9aa9/spark-f6cea8b7-46c1-4a8e-bc15-9a9bd6968207/fetchFileTemp9187758738095291929.tmp
19/05/31 15:08:52 INFO util.Utils: Copying /tmp/spark-2276f4d2-5eba-4e5b-ac21-5c7e5952fcad/executor-3ece4c79-9a4a-4a88-9cfa-bd101e7f9aa9/spark-f6cea8b7-46c1-4a8e-bc15-9a9bd6968207/2542979141559286530739_cache to /local/spark-2.4.1-bin-hadoop2.7/work/app-20190531150850-0054/1/./mnist_dist.py
19/05/31 15:08:52 INFO broadcast.TorrentBroadcast: Started reading broadcast variable 2
19/05/31 15:08:52 INFO client.TransportClientFactory: Successfully created connection to hadoop222/<IP_ADDRESS>:33769 after 1 ms (0 ms spent in bootstraps)
19/05/31 15:08:53 INFO memory.MemoryStore: Block broadcast_2_piece0 stored as bytes in memory (estimated size 12.4 KB, free 366.3 MB)
19/05/31 15:08:53 INFO broadcast.TorrentBroadcast: Reading broadcast variable 2 took 134 ms
19/05/31 15:08:53 INFO memory.MemoryStore: Block broadcast_2 stored as values in memory (estimated size 26.0 KB, free 366.3 MB)
2019-05-31 15:08:54,232 INFO (MainThread-19713) connected to server at ('<IP_ADDRESS>', 35631)
2019-05-31 15:08:54,234 INFO (MainThread-19713) TFSparkNode.reserve: {'port': 36833, 'authkey': '\x9d\xe76\x8dpkMD\xbe}\xfe\xf00\x99\xe0b', 'executor_id': 0, 'addr': ('<IP_ADDRESS>', 44841), 'tb_port': 0, 'task_index': 0, 'host': '<IP_ADDRESS>', 'tb_pid': 0, 'job_name': 'ps'}
2019-05-31 15:08:55,238 INFO (MainThread-19713) node: {'executor_id': 0, 'addr': ('<IP_ADDRESS>', 44841), 'task_index': 0, 'port': 36833, 'authkey': '\x9d\xe76\x8dpkMD\xbe}\xfe\xf00\x99\xe0b', 'host': '<IP_ADDRESS>', 'job_name': 'ps', 'tb_pid': 0, 'tb_port': 0}
2019-05-31 15:08:55,238 INFO (MainThread-19713) node: {'executor_id': 1, 'addr': '/tmp/pymp-4PhZsB/listener-jIlI2K', 'task_index': 0, 'port': 36745, 'authkey': '\x99\x977\xe0\x99UL\xe2\xa9\xdf\xfa\xe4\xc8\xd9\x02|', 'host': '<IP_ADDRESS>', 'job_name': 'worker', 'tb_pid': 0, 'tb_port': 0}
2019-05-31 15:08:55,238 INFO (MainThread-19713) Starting TensorFlow ps:0 as ps on cluster node 0 on background process
2019-05-31 15:08:55,376 INFO (MainThread-19727) 0: ======== ps:0 ========
2019-05-31 15:08:55,376 INFO (MainThread-19727) 0: Cluster spec: {'ps': ['<IP_ADDRESS>:36833'], 'worker': ['<IP_ADDRESS>:36745']}
2019-05-31 15:08:55,377 INFO (MainThread-19727) 0: Using CPU
2019-05-31 15:08:55.378921: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 AVX512F FMA
2019-05-31 15:08:55.432072: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency:<PHONE_NUMBER> Hz
2019-05-31 15:08:55.435059: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x36e1d50 executing computations on platform Host. Devices:
2019-05-31 15:08:55.435106: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): <undefined>, <undefined>
2019-05-31 15:08:55.438044: I tensorflow/core/distributed_runtime/rpc/grpc_channel.cc:252] Initialize GrpcChannelCache for job ps -> {0 -> localhost:36833}
2019-05-31 15:08:55.438093: I tensorflow/core/distributed_runtime/rpc/grpc_channel.cc:252] Initialize GrpcChannelCache for job worker -> {0 -> <IP_ADDRESS>:36745}
2019-05-31 15:08:55.443546: I tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc:391] Started server with target: grpc://localhost:36833

Spark Submit Command Line:
${SPARK_HOME}/bin/spark-submit
--master ${MASTER}
--py-files ${TFoS_HOME}/examples/mnist/spark/mnist_dist.py
--conf spark.cores.max=${TOTAL_CORES}
--conf spark.task.cpus=${CORES_PER_WORKER}
--conf spark.executorEnv.JAVA_HOME="$JAVA_HOME"
--conf spark.executorENV.LD_LIBRARY_PATH=$LIB_HDFS:$LIB_JVM
--conf spark.executorEnv.CLASSPATH=$(hadoop classpath --glob)
${TFoS_HOME}/examples/mnist/spark/mnist_spark.py
--cluster_size ${SPARK_WORKER_INSTANCES}
--images examples/mnist/csv/train/images
--labels examples/mnist/csv/train/labels
--format csv
--mode train
--model mnist_model
I

Are there any errors in the executor logs for the worker node?  You just posted the ps node?

another executor log as follows
`Spark Executor Command: "/local/jdk1.8/bin/java" "-cp" "/local/spark-2.4.1-bin-hadoop2.7/conf/:/local/spark-2.4.1-bin-hadoop2.7/jars/*:/local/hadoop-2.7.1/etc/hadoop/:/etc/hadoop" "-Xmx1024M" "-Dspark.driver.port=35945" "org.apache.spark.executor.CoarseGrainedExecutorBackend" "--driver-url" "spark://CoarseGrainedScheduler@hadoop222:35945" "--executor-id" "1" "--hostname" "<IP_ADDRESS>" "--cores" "1" "--app-id" "app-20190622192712-0007" "--worker-url" "spark://Worker@<IP_ADDRESS>:40367"
19/06/22 19:27:13 INFO executor.CoarseGrainedExecutorBackend: Started daemon with process name: 26732@hadoop222
19/06/22 19:27:13 INFO util.SignalUtils: Registered signal handler for TERM
19/06/22 19:27:13 INFO util.SignalUtils: Registered signal handler for HUP
19/06/22 19:27:13 INFO util.SignalUtils: Registered signal handler for INT
19/06/22 19:27:13 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
19/06/22 19:27:13 INFO spark.SecurityManager: Changing view acls to: ghn
19/06/22 19:27:13 INFO spark.SecurityManager: Changing modify acls to: ghn
19/06/22 19:27:13 INFO spark.SecurityManager: Changing view acls groups to:
19/06/22 19:27:13 INFO spark.SecurityManager: Changing modify acls groups to:
19/06/22 19:27:13 INFO spark.SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users  with view permissions: Set(ghn); groups with view permissions: Set(); users  with modify permissions: Set(ghn); groups with modify permissions: Set()
19/06/22 19:27:13 INFO client.TransportClientFactory: Successfully created connection to hadoop222/<IP_ADDRESS>:35945 after 83 ms (0 ms spent in bootstraps)
19/06/22 19:27:13 INFO spark.SecurityManager: Changing view acls to: ghn
19/06/22 19:27:13 INFO spark.SecurityManager: Changing modify acls to: ghn
19/06/22 19:27:13 INFO spark.SecurityManager: Changing view acls groups to:
19/06/22 19:27:13 INFO spark.SecurityManager: Changing modify acls groups to:
19/06/22 19:27:13 INFO spark.SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users  with view permissions: Set(ghn); groups with view permissions: Set(); users  with modify permissions: Set(ghn); groups with modify permissions: Set()
19/06/22 19:27:14 INFO client.TransportClientFactory: Successfully created connection to hadoop222/<IP_ADDRESS>:35945 after 1 ms (0 ms spent in bootstraps)
19/06/22 19:27:14 INFO storage.DiskBlockManager: Created local directory at /local/spark-2.4.1-bin-hadoop2.7/spark-9d2e57ba-faba-453b-9e13-e0a2bbdea87b/executor-85f7be59-feed-40d4-9d9b-d180772294cb/blockmgr-dc7d77b0-69ab-4694-8a21-9138bc0627b7
19/06/22 19:27:14 INFO memory.MemoryStore: MemoryStore started with capacity 366.3 MB
19/06/22 19:27:14 INFO executor.CoarseGrainedExecutorBackend: Connecting to driver: spark://CoarseGrainedScheduler@hadoop222:35945
19/06/22 19:27:14 INFO worker.WorkerWatcher: Connecting to worker spark://Worker@<IP_ADDRESS>:40367
19/06/22 19:27:14 INFO client.TransportClientFactory: Successfully created connection to /<IP_ADDRESS>:40367 after 23 ms (0 ms spent in bootstraps)
19/06/22 19:27:14 INFO worker.WorkerWatcher: Successfully connected to spark://Worker@<IP_ADDRESS>:40367
19/06/22 19:27:14 INFO executor.CoarseGrainedExecutorBackend: Successfully registered with driver
19/06/22 19:27:14 INFO executor.Executor: Starting executor ID 1 on host <IP_ADDRESS>
19/06/22 19:27:14 INFO util.Utils: Successfully started service 'org.apache.spark.network.netty.NettyBlockTransferService' on port 42253.
19/06/22 19:27:14 INFO netty.NettyBlockTransferService: Server created on <IP_ADDRESS>:42253
19/06/22 19:27:14 INFO storage.BlockManager: Using org.apache.spark.storage.RandomBlockReplicationPolicy for block replication policy
19/06/22 19:27:14 INFO storage.BlockManagerMaster: Registering BlockManager BlockManagerId(1, <IP_ADDRESS>, 42253, None)
19/06/22 19:27:14 INFO storage.BlockManagerMaster: Registered BlockManager BlockManagerId(1, <IP_ADDRESS>, 42253, None)
19/06/22 19:27:14 INFO storage.BlockManager: Initialized BlockManager: BlockManagerId(1, <IP_ADDRESS>, 42253, None)
19/06/22 19:27:14 INFO executor.CoarseGrainedExecutorBackend: Got assigned task 1
19/06/22 19:27:14 INFO executor.Executor: Running task 1.0 in stage 0.0 (TID 1)
19/06/22 19:27:14 INFO executor.Executor: Fetching spark://hadoop222:35945/files/mnist_dist.py with timestamp<PHONE_NUMBER>335
19/06/22 19:27:14 INFO client.TransportClientFactory: Successfully created connection to hadoop222/<IP_ADDRESS>:35945 after 1 ms (0 ms spent in bootstraps)
19/06/22 19:27:14 INFO util.Utils: Fetching spark://hadoop222:35945/files/mnist_dist.py to /local/spark-2.4.1-bin-hadoop2.7/spark-9d2e57ba-faba-453b-9e13-e0a2bbdea87b/executor-85f7be59-feed-40d4-9d9b-d180772294cb/spark-047c742c-b659-4084-a8c0-6907bdcc6f75/fetchFileTemp2069986072485490944.tmp
19/06/22 19:27:14 INFO util.Utils: Copying /local/spark-2.4.1-bin-hadoop2.7/spark-9d2e57ba-faba-453b-9e13-e0a2bbdea87b/executor-85f7be59-feed-40d4-9d9b-d180772294cb/spark-047c742c-b659-4084-a8c0-6907bdcc6f75/10464580501561202832335_cache to /local/spark-2.4.1-bin-hadoop2.7/work/app-20190622192712-0007/1/./mnist_dist.py
19/06/22 19:27:14 INFO broadcast.TorrentBroadcast: Started reading broadcast variable 2
19/06/22 19:27:14 INFO client.TransportClientFactory: Successfully created connection to hadoop222/<IP_ADDRESS>:44701 after 1 ms (0 ms spent in bootstraps)
19/06/22 19:27:14 INFO memory.MemoryStore: Block broadcast_2_piece0 stored as bytes in memory (estimated size 10.2 KB, free 366.3 MB)
19/06/22 19:27:14 INFO broadcast.TorrentBroadcast: Reading broadcast variable 2 took 87 ms
19/06/22 19:27:14 INFO memory.MemoryStore: Block broadcast_2 stored as values in memory (estimated size 14.3 KB, free 366.3 MB)
2019-06-22 19:27:15,636 INFO (MainThread-26908) connected to server at ('<IP_ADDRESS>', 44749)
2019-06-22 19:27:15,637 INFO (MainThread-26908) TFSparkNode.reserve: {'port': 38709, 'authkey': 'h\xe8.1\x85\x16I\xbf\xb1\xce\x8f\xc4:\xa8\xb1\xfc', 'executor_id': 1, 'addr': '/tmp/pymp-O0qNTh/listener-HSou74', 'tb_port': 0, 'task_index': 0, 'host': '<IP_ADDRESS>', 'tb_pid': 0, 'job_name': 'worker'}
2019-06-22 19:27:17,643 INFO (MainThread-26908) node: {'executor_id': 0, 'addr': ('<IP_ADDRESS>', 39897), 'task_index': 0, 'port': 37507, 'authkey': 'E2\xaf\xe3\x10\xb0H\xcd\x80\xcc@\xe0G\xdd#\x13', 'host': '<IP_ADDRESS>', 'job_name': 'ps', 'tb_pid': 0, 'tb_port': 0}
2019-06-22 19:27:17,643 INFO (MainThread-26908) node: {'executor_id': 1, 'addr': '/tmp/pymp-O0qNTh/listener-HSou74', 'task_index': 0, 'port': 38709, 'authkey': 'h\xe8.1\x85\x16I\xbf\xb1\xce\x8f\xc4:\xa8\xb1\xfc', 'host': '<IP_ADDRESS>', 'job_name': 'worker', 'tb_pid': 0, 'tb_port': 0}
2019-06-22 19:27:17,643 INFO (MainThread-26908) Starting TensorFlow worker:0 as worker on cluster node 1 on background process
19/06/22 19:27:17 INFO python.PythonRunner: Times: total = 2936, boot = 318, init = 93, finish = 2525
2019-06-22 19:27:17,661 INFO (MainThread-26921) 1: ======== worker:0 ========
2019-06-22 19:27:17,661 INFO (MainThread-26921) 1: Cluster spec: {'ps': ['<IP_ADDRESS>:37507'], 'worker': ['<IP_ADDRESS>:38709']}
2019-06-22 19:27:17,662 INFO (MainThread-26921) 1: Using CPU
2019-06-22 19:27:17.663308: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 AVX512F FMA
19/06/22 19:27:17 INFO executor.Executor: Finished task 1.0 in stage 0.0 (TID 1). 1418 bytes result sent to driver
19/06/22 19:27:17 INFO executor.CoarseGrainedExecutorBackend: Got assigned task 2
19/06/22 19:27:17 INFO executor.Executor: Running task 0.0 in stage 1.0 (TID 2)
2019-06-22 19:27:17.712017: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency:<PHONE_NUMBER> Hz
2019-06-22 19:27:17.713863: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x4073d50 executing computations on platform Host. Devices:
2019-06-22 19:27:17.713895: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): ,
2019-06-22 19:27:17.715411: I tensorflow/core/distributed_runtime/rpc/grpc_channel.cc:252] Initialize GrpcChannelCache for job ps -> {0 -> <IP_ADDRESS>:37507}
2019-06-22 19:27:17.715430: I tensorflow/core/distributed_runtime/rpc/grpc_channel.cc:252] Initialize GrpcChannelCache for job worker -> {0 -> localhost:38709}
2019-06-22 19:27:17.718672: I tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc:391] Started server with target: grpc://localhost:38709
19/06/22 19:27:17 INFO broadcast.TorrentBroadcast: Started reading broadcast variable 3
19/06/22 19:27:17 INFO memory.MemoryStore: Block broadcast_3_piece0 stored as bytes in memory (estimated size 8.2 KB, free 366.3 MB)
19/06/22 19:27:17 INFO broadcast.TorrentBroadcast: Reading broadcast variable 3 took 12 ms
19/06/22 19:27:17 INFO memory.MemoryStore: Block broadcast_3 stored as values in memory (estimated size 15.1 KB, free 366.3 MB)
19/06/22 19:27:17 INFO rdd.HadoopRDD: Input split: hdfs://hadoop222:9000/user/ghn/examples/mnist/csv/train/images/part-00000:0+9338236
19/06/22 19:27:17 INFO broadcast.TorrentBroadcast: Started reading broadcast variable 0
19/06/22 19:27:17 INFO memory.MemoryStore: Block broadcast_0_piece0 stored as bytes in memory (estimated size 23.2 KB, free 366.2 MB)
19/06/22 19:27:17 INFO broadcast.TorrentBroadcast: Reading broadcast variable 0 took 12 ms
2019-06-22 19:27:17,825 WARNING (MainThread-26921) From /home/ghn/.local/lib/python2.7/site-packages/tensorflow/python/data/ops/dataset_ops.py:429: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version.
Instructions for updating:
tf.py_func is deprecated in TF V2. Instead, use
tf.py_function, which takes a python function which manipulates tf eager
tensors instead of numpy arrays. It's easy to convert a tf eager tensor to
an ndarray (just call tensor.numpy()) but having access to eager tensors
means tf.py_functions can use accelerators such as GPUs as well as
being differentiable using a gradient tape.
19/06/22 19:27:17 INFO memory.MemoryStore: Block broadcast_0 stored as values in memory (estimated size 328.1 KB, free 365.9 MB)
2019-06-22 19:27:17,851 WARNING (MainThread-26921) From /home/ghn/.local/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
tensorflow model path: hdfs://hadoop222:9000/user/ghn/mnist_model
2019-06-22 19:27:17,982 INFO (MainThread-26921) Create CheckpointSaverHook.
19/06/22 19:27:18 INFO rdd.HadoopRDD: Input split: hdfs://hadoop222:9000/user/ghn/examples/mnist/csv/train/labels/part-00000:0+204800
19/06/22 19:27:18 INFO broadcast.TorrentBroadcast: Started reading broadcast variable 1
19/06/22 19:27:18 INFO memory.MemoryStore: Block broadcast_1_piece0 stored as bytes in memory (estimated size 23.2 KB, free 365.9 MB)
19/06/22 19:27:18 INFO broadcast.TorrentBroadcast: Reading broadcast variable 1 took 12 ms
19/06/22 19:27:18 INFO memory.MemoryStore: Block broadcast_1 stored as values in memory (estimated size 328.1 KB, free 365.6 MB)
2019-06-22 19:27:18,674 INFO (MainThread-27090) Connected to TFSparkNode.mgr on <IP_ADDRESS>, executor=1, state='running'
2019-06-22 19:27:18,680 INFO (MainThread-27090) mgr.state='running'
2019-06-22 19:27:18,680 INFO (MainThread-27090) Feeding partition <itertools.chain object at 0x7f3941ddde90> into input queue <multiprocessing.queues.JoinableQueue object at 0x7f397bb46f50>
19/06/22 19:27:20 INFO python.PythonRunner: Times: total = 1817, boot = -770, init = 838, finish = 1749
19/06/22 19:27:20 INFO python.PythonRunner: Times: total = 101, boot = 4, init = 43, finish = 54
19/06/22 19:37:21 ERROR executor.Executor: Exception in task 0.0 in stage 1.0 (TID 2)
org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/worker.py", line 377, in main
process()
File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/worker.py", line 372, in process
serializer.dump_stream(func(split_index, iterator), outfile)
File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 2499, in pipeline_func
File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 2499, in pipeline_func
File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 2499, in pipeline_func
File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 352, in func
File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 801, in func
File "/home/ghn/.local/lib/python2.7/site-packages/tensorflowonspark/TFSparkNode.py", line 420, in _train
raise Exception("Timeout while feeding partition")
Exception: Timeout while feeding partition
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:452)
at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:588)
at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:571)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:406)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at scala.collection.Iterator$class.foreach(Iterator.scala:891)
at org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28)
at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:59)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:104)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:48)
at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:310)
at org.apache.spark.InterruptibleIterator.to(InterruptibleIterator.scala:28)
at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:302)
at org.apache.spark.InterruptibleIterator.toBuffer(InterruptibleIterator.scala:28)
at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:289)
at org.apache.spark.InterruptibleIterator.toArray(InterruptibleIterator.scala:28)
at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$13.apply(RDD.scala:945)
at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$13.apply(RDD.scala:945)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2101)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2101)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:121)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:403)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:409)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)

19/06/22 19:37:21 INFO executor.CoarseGrainedExecutorBackend: Got assigned task 3
19/06/22 19:37:21 INFO executor.Executor: Running task 1.0 in stage 1.0 (TID 3)
19/06/22 19:37:21 INFO rdd.HadoopRDD: Input split: hdfs://hadoop222:9000/user/ghn/examples/mnist/csv/train/images/part-00001:0+11231804
19/06/22 19:37:21 INFO rdd.HadoopRDD: Input split: hdfs://hadoop222:9000/user/ghn/examples/mnist/csv/train/labels/part-00001:0+245760
2019-06-22 19:37:21,257 INFO (MainThread-27241) Connected to TFSparkNode.mgr on <IP_ADDRESS>, executor=1, state='running'
2019-06-22 19:37:21,262 INFO (MainThread-27241) mgr.state='running'
2019-06-22 19:37:21,262 INFO (MainThread-27241) Feeding partition <itertools.chain object at 0x7f3941ddde90> into input queue <multiprocessing.queues.JoinableQueue object at 0x7f397bb46f50>
19/06/22 19:37:23 INFO python.PythonRunner: Times: total = 2113, boot = 2, init = 5, finish = 2106
19/06/22 19:37:23 INFO python.PythonRunner: Times: total = 63, boot = 4, init = 7, finish = 52
19/06/22 19:47:24 ERROR executor.Executor: Exception in task 1.0 in stage 1.0 (TID 3)
org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/worker.py", line 377, in main
process()
File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/worker.py", line 372, in process
serializer.dump_stream(func(split_index, iterator), outfile)
File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 2499, in pipeline_func
File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 2499, in pipeline_func
File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 2499, in pipeline_func
File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 352, in func
File "/local/spark-2.4.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/rdd.py", line 801, in func
File "/home/ghn/.local/lib/python2.7/site-packages/tensorflowonspark/TFSparkNode.py", line 420, in _train
raise Exception("Timeout while feeding partition")
Exception: Timeout while feeding partition
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:452)
at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:588)
at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:571)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:406)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at scala.collection.Iterator$class.foreach(Iterator.scala:891)
at org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28)
at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:59)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:104)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:48)
at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:310)
at org.apache.spark.InterruptibleIterator.to(InterruptibleIterator.scala:28)
at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:302)
at org.apache.spark.InterruptibleIterator.toBuffer(InterruptibleIterator.scala:28)
at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:289)
at org.apache.spark.InterruptibleIterator.toArray(InterruptibleIterator.scala:28)
at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$13.apply(RDD.scala:945)
at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$13.apply(RDD.scala:945)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2101)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2101)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:121)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:403)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:409)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)

19/06/22 19:47:24 INFO executor.CoarseGrainedExecutorBackend: Got assigned task 4
19/06/22 19:47:24 INFO executor.Executor: Running task 0.1 in stage 1.0 (TID 4)
19/06/22 19:47:24 INFO rdd.HadoopRDD: Input split: hdfs://hadoop222:9000/user/ghn/examples/mnist/csv/train/images/part-00000:0+9338236
19/06/22 19:47:24 INFO rdd.HadoopRDD: Input split: hdfs://hadoop222:9000/user/ghn/examples/mnist/csv/train/labels/part-00000:0+204800
2019-06-22 19:47:24,262 INFO (MainThread-27486) Connected to TFSparkNode.mgr on <IP_ADDRESS>, executor=1, state='running'
2019-06-22 19:47:24,270 INFO (MainThread-27486) mgr.state='running'
2019-06-22 19:47:24,270 INFO (MainThread-27486) Feeding partition <itertools.chain object at 0x7f3941ddde90> into input queue <multiprocessing.queues.JoinableQueue object at 0x7f397bb46f50>
19/06/22 19:47:25 INFO python.PythonRunner: Times: total = 1755, boot = 4, init = 7, finish = 1744
19/06/22 19:47:25 INFO python.PythonRunner: Times: total = 57, boot = 3, init = 7, finish = 47
19/06/22 19:51:54 INFO executor.Executor: Executor is trying to kill task 0.1 in stage 1.0 (TID 4), reason: Stage cancelled
19/06/22 19:51:54 INFO executor.CoarseGrainedExecutorBackend: Driver commanded a shutdown
19/06/22 19:51:54 ERROR executor.CoarseGrainedExecutorBackend: RECEIVED SIGNAL TERM
tdown`

by the way, does 'standalone cluster' of single host read/write all result  from local file system rather than hdfs? why i found operation of that involves hdfs?

yes, the spark standalone example just uses a single node to host multiple spark executors.  In that case, the local file system acts as the "distributed file system", i.e. all nodes in the cluster are able to access the same files.  If you're using multiple physical hosts in spark standalone mode, you will need to use an actual distributed file system, e.g. HDFS... otherwise, each node will read/write on it's own local disk (which is most likely not what you want and can lead to issues like you're seeing).  Have you been able to run the example as-is on a single host?

Also can you try using hdfs://default/user/ghn/mnist_model as the path to the model dir instead of hdfs://hadoop222:9000/user/ghn/mnist_model in your spark-submit command line?

During my trying fixing bug, I found another problem.
TensorflowonSpark requires me to execute ${SPARK_HOME}/sbin/start-slave.sh -c $CORES_PER_WORKER -m 3G ${MASTER}. I have a close look at /sbin/start-slave.sh. look here
line 43 gives echo "Usage: ./sbin/start-slave.sh [options] <master>"
line 59 gives MASTER=$1
Is this a conflict?

This is in Spark's codebase, so if anything, it's a conflict between their documentation on line 43 vs. their implementation on line 59.  That all said, these lines haven't changed in 4 years, and if you trace the code further, it actually just ends up reconstructing the command line arguments in order again... and if you trace that further, you eventually end up at this parsing code, which does the right thing.

Actually, I can run the script correctly. However, I still cannot run the example of standalone mode with single host. It is stuck with the same problem.

Can you try something like this on one of your grid nodes (in a python shell)?
# write a simple text file on HDFS (from TF)
import tensorflow as tf
f = tf.gfile.Open("hdfs://default/user/ghn/foo.txt")
f.write("hello, world")
f.close()

f = tf.gfile.Open("hdfs://default/user/ghn/foo.txt")  does not raise error. But f.write("hello, world")  raises error tensorflow.python.framework.errors_impl.PermissionDeniedError: File isn't open for writing. Can't my tensorflow use hdfs?  And I found step "Convert the MNIST zip files using Spark" will put result on hdfs.  Does that mean if I install hdfs, i will not run example of standalone mode of single node ?

The "Convert MNIST" step is actually using Spark RDDs to save the data to HDFS, so this should mean that your Spark and HDFS setup is working.  That said, it looks like your TF setup isn't able to read from HDFS (per the simple write example above).
So, this is usually due to the LIB_HDFS and LIB_JVM setup again, so before you run that example, try:
export LIB_HDFS=/path/to/your/libhdfs.so
export LIB_JVM=/path/to/your/libjvm.so
export LD_LIBRARY_PATH=$LIB_HDFS:$LIB_JVM

You will need to find where the libhdfs.so and libjvm.so files are located on that box.
If that doesn't work, then I don't know what else could be wrong... (possibly try setting up a new environment?)

Really thank you for your help. I re-configure the environment and it works on single node without hdfs. I think former problem may be caused by I mistake different mode of cluster.

@HannanKan After you used hdfs://default/user/ghn/mnist_model as the path to the model dir instead of hdfs://hadoop222:9000/user/ghn/mnist_model, have you run successfully on the multiple hosts?
I tried use "default", but got hang yet.


Fixing 400 bad request for Edm.Decimal

While fuzzing any endpoint, there were many 400 Bad Requests given as response from the endpoint. One of the Major reasons being Type conversion errors for Edm.Decimal.
Edm.Decimal: The error was regarding the scale given for the Decimal Digit, this scale has been parsed from the metadata file for the endpoint which we have to fuzz and so scale decides how many digits will be there after the decimal point. When the scale was 0 the Decimal number had no digits after the decimal point due to which the endpoint was not considering the digit as a Decimal number due to which the endpoint was giving a Data Type Error.
This is the code which was used earlier to decide the scale range which was taken between a random of 0 and the scale parsed from the metadata.
scale_range = random.randint(0, scaleValue)

Made changes to the code to take a min of 2 as the starting range value for deciding the scale_range as there must be atleast 2 digits after a decimal number to consider it a valid Decimal Number.
scaleValue = self.scale
      if self.scale < 2:
          scaleValue = 2
      scale_range = random.randint(2, scaleValue)

Example:
Earliar:<PHONE_NUMBER>4682734682736482374
Now:<PHONE_NUMBER>4682734682736482374.00
Subsequently added a testcase for this particular scenario where the scale value should be 0 in test_generators.py file
def test_decimal_precision_scale_zero():
    random.seed(10)

    mckdecimal = DecimalMock(3,0)
    generated_decimal = EdmDecimal.generate(mckdecimal)

    assert generated_decimal == '495m'

Codecov Report
Base: 62.72% // Head: 62.75% // Increases project coverage by +0.03% :tada:

Coverage data is based on head (f150da6) compared to base (23d23d8).
Patch coverage: 100.00% of modified lines in pull request are covered.

Additional details and impacted files
@@            Coverage Diff             @@
##           master     #119      +/-   ##
==========================================
+ Coverage   62.72%   62.75%   +0.03%
==========================================
  Files          18       18
  Lines        3694     3697       +3
==========================================
+ Hits         2317     2320       +3
  Misses       1377     1377

Impacted Files
Coverage Δ

odfuzz/generators.py
64.89% <100.00%> (+0.43%)
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Note - the PR does not change encodings in the test.. only additional needed call to random module ends in different .choice.
The " 'Order_Details(OrderID=1227227588,ProductID=-652437806)?sap-client=500'," are generated same, but this time is chosen Url for string properties, that are in this setup urlencoded.


Remove workaround for drag area on Windows 10

Resolved / Related Issues
Items resolved / related issues by this PR.
Latest WinAppSdk appears to fix the titlebar issue on windows 10. This PR removes the workaround we had.
Also tries to solve #9919 aligning the code to what we do in MainPage (not sure if it works)
Validation
How did you test these changes?

[x] Built and ran the app

Screenshots (optional)
Add screenshots here.

This fixes #9919 as well


Dynamically changing color of arrows based on background

As of now default color of arrow is white.
$slick-arrow-color: white !default;

If someone is trying slick for the first time and if the background is white they will not be able to see arrows.
Can we dynamically change the arrow color based on background with javascript i.e if background is dark arrows will be bright and vice-versa. ?
We can also provide an option to override color.

I would love to see the code to detect the background-color hahahahahaha.. :joy: - seems like a very bad idea for performance.
I think anyone with half a brain will realise why they cannot see the arrows had they even glanced at the demo/docs page :smile:

This demo even went a step ahead and dynamically changing arrow color based on image color.
http://www.kennethcachia.com/background-check/slider.html
I am not proposing we should do that.
I thought it is an interesting usecase relevant to this discussion.

There are some interesting tricks we can do with sass to dynamically change color.
http://thesassway.com/intermediate/dynamically-change-text-color-based-on-its-background-with-sass
But this project need to support pure css version also. So this may not be applicable

:) I admire your desire to help the developers, Akiran :+1:
That plugin you show for changing the arrow color is almost 900 lines of code unminified :cry: - it would pretty much double the weight of Slick. - also Slick's arrows by default are outside of the slider.
Generally detecting the color of the space behind an element is incredibly difficult without taking a screenshot (<canvas>) purely because there could be a thousand parents and absolutely positioned elements behind that element.
Again, the sass solution is useful for elements inside a parent with an explicit BG color, if it's the parent's parent's parent or an absolutely position element it's completely useless :cry:
Very sorry!
Simon.

I dont think this functionality merits inclusion in the core codebase.


fix: package name should match

package.json should match the npm module name.

Note: NPM package-name has been changed from proton-js to proton-engine
In fact, this is the case, I am upgrading proton v4.0, so I also migrated the npm package, I prefer this name.

I hope that v4.0 can support typescript, smaller file sizes, improved rendering performance, etc.

Oh, I wasn't aware of that change. I'll close this :)


Handle rotated, skewed or flipped GeoTIFF tile grids

Fixes #15294
Fixes #15370
Fixes #15428
Replaces #15402
Replaces #15962
New branch with commits cherry picked from #15962 to cleanly resolve conflicts

Replaced by #16227


ion-input of type number allows non-numeric input on iOS

Ionic version:  (check one with "x")
[ ] 1.x (For Ionic 1.x issues, please use https://github.com/ionic-team/ionic-v1)
[ ] 2.x
[X ] 3.x
I'm submitting a ...  (check one with "x")
[X ] bug report
[ ] feature request
[ ] support request => Please do not submit support requests here, use one of these channels: https://forum.ionicframework.com/ or http://ionicworldwide.herokuapp.com/
Current behavior:
Using a ion-input of type number allows to type non-numeric values on iOS.
Expected behavior:
Expecting to be able to enter only [0-9] values
Steps to reproduce:

Related code:
<!-- Limit -->
  <ion-item>
    <ion-label stacked>Limiter le nombre de participants</ion-label>
    <ion-toggle formControlName="isLimited" color="secondary" checked="{{isLimited}}"></ion-toggle>
  </ion-item>

  <ion-item>
    <ion-label stacked>Nombre de participants maximum</ion-label>
    <ion-input [disabled]="!newEventForm.controls['isLimited'].value" formControlName="limitNumber" type="number" placeholder='Nombre limite de participants' tappable></ion-input>
  </ion-item>

// Creating the newEventForm and specifying controls
    this.newEventForm = this.formBuilder.group({
      title: ['', Validators.required],
      sport: ['', Validators.required],
      date: ['', Validators.required],
      duration: ['', Validators.required],
      location: ['', Validators.required],
      isLimited: [{ value: false, disabled: (this.id ? true : false) }, Validators.required],
      limitNumber: [{ value: '', disabled: (this.id ? true : false) }, Validators.pattern("^[1-9][0-9]*")],
      additionalInfo: ['']
    });

Other information:
Screenshot:

Ionic info: (run ionic info from a terminal/cmd prompt and paste output below):
cli packages:

    @ionic/cli-plugin-ionic-angular : 1.4.1
    @ionic/cli-utils                : 1.6.0
    ionic (Ionic CLI)               : 3.6.0

global packages:

    Cordova CLI : 7.0.1

local packages:

    @ionic/app-scripts        : 1.3.12
    @ionic/cli-plugin-cordova : 1.4.1
    Cordova Platforms         : android 6.2.3 ios 4.4.0
    Ionic Framework           : ionic-angular 3.2.1

System:

    Android SDK Tools : 25.2.3
    Node              : v6.9.2
    OS                : macOS Sierra
    Xcode             : Xcode 8.3.2 Build version 8E2002
    ios-deploy        : 1.9.0
    ios-sim           : 5.0.13
    npm               : 3.10.9

Hello, thanks for using Ionic! Would you mind installing our wkwebview plugin and giving this another try?

Hi @jgw96 I surely can. I am working on a production app. Any risk installing your plugin?

@jgw96 I just installed the wkwebview plugin and the problem persists...

Hmm interesting. @royalcheeseburger thanks for giving that a try. If you inspect that ion-input do you see the type=number on the real input that it wraps?

@jgw96 yes that's, by the way, the most incomprehensible part... see the screenshot

@jgw96 yes that's, by the way, the most incomprehensible part... see the screenshot

Hmm, so it looks like type=number is being added. After further research https://github.com/angular/material/issues/8502#issuecomment-220329325 this looks like a safari issue. This article looks like a good workaround for it. Thanks!

@jgw96 alright. Thanks for the research and the help !


Electrode selection in Extracellular electrophysiology Recording

Hi,
I'm trying to convert electrophysiological data from Axona to NWB using the code from the Neuroconv Examples Gallery.
Currently in the lab we're using channels 17-32 for recording, so tetrodes 5-8.
When I extract metadata from the .set file I can see that the correct groups are extracted.
If I print metadata["Ecephys"]["ElectrodeGroup"] , I get:
[{'name': '8',
  'location': '',
  'device': 'Axona',
  'description': 'Group 8 electrodes.'},
 {'name': '5',
  'location': '',
  'device': 'Axona',
  'description': 'Group 5 electrodes.'},
 {'name': '6',
  'location': '',
  'device': 'Axona',
  'description': 'Group 6 electrodes.'},
 {'name': '7',
  'location': '',
  'device': 'Axona',
  'description': 'Group 7 electrodes.'}]

But then after running the conversion and plotting the extracted signals, I don't get the correct channels (I can see it's only noise in the output).
If instead, I try to extract the first 32 channels (so tetrodes 1-8) I'm getting the correct signals in channels 17-32.
I wonder if I'm selecting the groups correctly.
Thanks

@letiziasignorelli Thanks for the email - I guess I'm still not sure how you are creating the _all.nwb file
In general, it appears as if upstream control over Axona groups/streams is not exposed as my  minimal attempt to get the RecordingExtractor results in only 16 channels (not 32) and only a single group (well, None implying only a single group anyway)
from spikeinterface.extractors import AxonaRecordingExtractor

recording = AxonaRecordingExtractor(file_path=".../2509202301.bin")

recording.channel_ids
> array(['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15'], dtype='<U64')

recording.get_channel_groups()
> None

As such, could you open issues upstream at https://github.com/NeuralEnsemble/python-neo/issues and https://github.com/SpikeInterface/spikeinterface/issues and share that same data with them? The metadata you saw extracted from the .set files by NeuroConv is only used properly if the underlying readers actually have the same group structure

There is a PR open for this:
https://github.com/NeuralEnsemble/python-neo/pull/1520/files


DBZ-778 Make the tests resilient from raise condition

https://issues.jboss.org/browse/DBZ-778

Applied; thx!


Error: buffer is not large enough. in Apple M2

Hi OmegaFold team,
Thank you for your previous kind teaching.
Now, I try to run OmegaFold in M2 Macbook Pro, and clone repository using gh repo clone HeliXonProtein/OmegaFold.
I think that It run normally just before prediction. However, during or after prediction step, the program showed error and was aborted.
Log is below(I'm sorry to hide residues' number, it is less than 300.).

~/Desktop/omega fold/OmegaFold $python main.py --device mps INPUT.fasta OUTPUT_DIR
INFO:root:Loading weights from ~/.cache/omegafold_ckpt/model.pt
INFO:root:Constructing OmegaFold
INFO:root:Reading INPUT.fasta
INFO:root:Predicting 1th chain in INPUT.fasta
INFO:root:--- residues in this chain.
/AppleInternal/Library/BuildRoots/20d6c351-ee94-11ec-bcaf-7247572f23b4/Library/Caches/com.apple.xbs/Sources/MetalPerformanceShaders/MPSCore/Types/MPSNDArray.mm:782: failed assertion `[MPSNDArray, initWithBuffer:descriptor:] Error: buffer is not large enough. Must be 5448 bytes
'
zsh: abort      python main.py --device mps INPUT.fasta OUTPUT_DIR

Versions:
Python 3.10.5
biopython          1.79
certifi            2022.6.15
charset-normalizer 2.1.0
idna               3.3
numpy              1.23.1
Pillow             9.2.0
pip                22.2.2
requests           2.28.1
setuptools         63.4.2
torch              1.13.0.dev20220809
torchaudio         0.14.0.dev20220603
torchvision        0.14.0.dev20220809
typing_extensions  4.3.0
urllib3            1.26.11
I can't distinguish whether this error originate from Pytorch or from Omegafold itself.　So this is rather just a report.
Best,
Shimo

Hi,
Thanks for your interest!
Update the code by git clone and try with smaller --subbatch_size, the default is the number of residues in the sequence.

Hi Rui Wang,
Thanks a lot.
When I run command with --subbatch_size 50, it normally run, and result PDB file was generated!
However, I can't see anything when I opened PDB file either in PyMOL or in ChimeraX.
I checked the PDB and found that all alpha Carbons are situated at the same position(0.000 0.000 0.000).
I think this problem is caused by abnormal environment such as Apple silicon(GPU, some packages not optimized for Apple silicon, etc...). I have other machines(Linax, Intel mac, Windows) to use this interesting structure prediction tool, and almost all persons who want to use some machine learning may have normal Windows machine. So You should solve other issues first, because this issue is very rare case and not important I think.
Finally, thank you very much for your kind help.
Best,
Shimo

Hi,
Thanks so much for your understanding!
And yes we are seeing the same issue that you found and it turned out to be more complicated than we thought. Guess that's our dose of warning for using a nightly built.


port mapping: move the pod level whitelist rules to pod setup

move the pod level whitelist rules to pod setup from container setup
Note: hyperhq/hyperd#652 doesn't depend on this PR, this PR is just a request for comments/review, NOT urgent.
Signed-off-by: Wang Xu<EMAIL_ADDRESS>

updated based on the comments from @laijs
could we merge it before 1.1 release? pending here for too long time

What about hyper_cleanup_container_portmapping? If we set up at the pod level, we should clean up at the pod level as well.

LGTM!


Issue #1 - Migration from pages router to app router

Migration to App Router
This pull request addresses issue #1 by migrating the Nex music player application from the "pages router" to the "app router." This change helps us take advantage of the latest Next.js features and improves the overall maintainability and performance of Nex.
In this process, _app.tsx and _document.tsx have been merged into layout.tsx file in the root directory of the app folder, and 404.tsx has been replaced with the new convention - not-found.tsx. Some components have been added with use client; directive in order to use the new router() router instance and usePathame() function from next/navigation instead of the old router instance from next/router, both of which can only be used in the client side.
In addition to the migration, the SEO page has been replaced with the new built-in Metadata API to export metadata easily inside the app router.
Every page inside the app router has been split into two files - layout.tsx and page.tsx. layout.tsx is where every client side operation takes place and page.tsx is where server side operations take place. For now, the only server side operation in page.tsx files is the export of metadata using Metadata API. A void function has been written in order to bypass the error of not exporting a react component in the file. This void function can be used for future server side features as well.
Screenshots
The improved not-found page

The new commits have made all the pages to be server-side and components to be server-side, except the sidebar and the carousel as it needs the user interactivity in order for it to work. All the above commented required changes have been made in a proper manner too.


Preview Named Locations do not show up in old view

Adding a note to make it clear that the Named Locations created through PowerShell only show up in the Named Locations (preview)

@Demywil : Thanks for your contribution! The author(s) have been notified to review your proposed change.

#sign-off

@ktoliver : Thanks for your contribution! The author(s) have been notified to review your proposed change.


Kevin

This can't be merged because it contains many project-specific additions and reorganizations.
If you do want to open PRs and merge them, you can specify a branch in your own fork as the base branch.


TestNonLocalTimezone / cron_test.go seems to be racy, fail intermittently

Some of the tests in cron_test.go seem to fail intermittently due to race conditions. The test schedules a job for 1 second and 2 seconds in the future (in absolute time), waits 2 seconds and checks it's been called twice, which sometimes doesn't occur reliably. For further details, see
https://bugzilla.redhat.com/show_bug.cgi?id=1714432#c7

I'll have a look at making that less flaky. Thanks for the report

I was able to reproduce it using go test -count=100 and combined with some extra logging solved the problem. This should be fixed at head. I'm planning on tagging another release today or this weekend that includes it. Thanks


fix: Add typescript definitions

Because:

There was an issue opened asking for Typescript definitions for this package.

This commit:

Adds an index.d.ts file to the src folder, as well as jsdoc documentation.
Adds a typings entry on the package.json file

👍

Any updates or intentions to move this forward? Can we iterate this PR or create a new one?

Any updates or intentions to move this forward? Can we iterate this PR or create a new one?

I've just pushed a commit with a fix on the types definition file.

@bahmutov Is there anything else needed here to merge this?

Looks good, merging it, thank you

Why did you switch from the default import to named one?

It is because type definition is misleading. In the type definition there is a declare export const which shouldn’t be. The 2 const shouldn’t be exported.

Ok, I will be open to a pull request to fix this

Ok, I will be open to a pull request to fix this

Please check my open PR:)


Fareedkot Teeka in SGGS has Khanda Symbols

jkmjmk example shabad

I am aware of this, the khanda is supposed to represent something.
Will need to check proof.

The Khandey are supposed to separate the Arth of the actual line and other info like the Uthaanaka.
For example, in the Shabad ਪਹਿਲਪੁਰਸਾਬਿਰਾ॥, it starts off with the Uthaanaka (Bhagat Namdev Ji asking his Guru Gian Dev Ji about how the world was created, etc). Afterwards, you get the Arth of the verses. But in the Arth, there are a few places that need clarification as to who is talking (Namdev or Giandev), so those are also separated with Khandey.
I don't believe the original Faridkot Teeka has these Khandey and assume they were added in the data somewhere along the way to show this information.


kube-apiserver: wait for the 8443 port to be released by old apiserver

suggestion: try ss -Htan state listening '( sport = 6443 or sport = 6080 )' instead.
I wouldn't trust the return code from curl. Too fragile; it depends exactly on how the connection fails, and can change.

/hold

closing in favor of https://github.com/openshift/cluster-kube-apiserver-operator/pull/1191 because I believe that is better solution


Vendor in changes from kube master

Moving cri-o master to track kube master branch.
Vendored in the changes from kube master branch.
Updated the integration tests to use the kube master branch.
Signed-off-by: umohnani8<EMAIL_ADDRESS>

@mrunalp @runcom PTAL

Looks fine. We can merge after tests pas..

LGTM

/test all

/retest

/test all

/test all

/test all

/test integration

Skipping the two "NodePort service" tests for now. The tests are basically trying to hit node-internal-ip:node-port after setting up a NodePort service, but are getting connection refused. Seems to be happening due to AWS having 2 different IPs, which might be confusing the test. Opening an issue to track that so we can enable the tests once we figure out the networking issue.

iptables rules?

iptables rules?

that may be as well, let's skip the tests for now (they are new anyway https://github.com/kubernetes/kubernetes/commit/4655f52329b7102cde72be2986844650d5c5c378). Let's figure them out with the networking team as well.

The image pull with signature test seems to be failing for the other PRs as well with "signature not found". Did we get rid of the signature?

/test all

The image pull with signature test seems to be failing for the other PRs as well with "signature not found". Did we get rid of the signature?

that may be, not sure, need to check

@runcom integration tests are still broken?

/test all

/test all

/test all

/test e2e_rhel

/test critest_fedora

Ok this finally passes tests, I am going to merge.


errorCode":473

{"itemData":[{"id":274721998472,"reason":"Destination Full","pile":"trade","success":false,"errorCode":473}]}
response from server;
catch this error when try make a bid /fifa17/trade/191959237803/bid
whats i make wrong?

The problem is that you're trade pile is full. You need to sell some of you're items before you can buy new ones.

Thanks


Default to not requiring a single-value on dict

This fixes some cases of plot_benefit.
Reviewed by @arahuja over my shoulder, will merge on 🍏

Coverage remained the same at 58.418% when pulling 78ce133a87967d436048d529528cc31276ddaae3 on fix_plot_benefit into 301048ee7c1e77c464c1dcb801559fbcddb980ae on master.


feat: Change Time fields to List instead of Set

Closes: #40

Change type of bySeconds, byMinutes, byHours to List<int> Since when there are times that has the same seconds, minutes or hours, after parsing they are remove since the type is Set

@JonasWanke Can't seem to add labels. This is a Breaking Change.

Thanks for creating this PR! I added some comments, but, in general, the changes look good.
However, have you customized the formatter settings? The usual line limit is at 80 columns and this PR changes some code to longer lines, which my local formatter then reverts again

Yeah sorry mine was set to 120. Adjusted it to 80

@JonasWanke I'm having a hard time figuring out though what causes the monthly_test.dart to fail 🤔

@hbdejesus Sorry for the delay! Code looks good now, and I just found the reason for the failing test. The following lines also have to be updated to use a list instead of a set:
https://github.com/JonasWanke/rrule/blob/a861fc93b0f3f46c1de1a781608c0074e5630790/lib/src/codecs/text/encoder.dart#L68-L71

@JonasWanke Updated. Thanks!

Thanks for your contribution! I'll comment here when this change gets released, but that will still take some time as I have other breaking changes planned as well (most notably, the switch to https://github.com/JonasWanke/chrono)


Added select method to Page Class

This PR adds the Page.select method to interact with select elements on the page.
The accompanying tests and docs demonstrate the basic usage.

Hi @aslushnikov! Nightmare's page.select only covers the most trivial selection scenario. My implementation is actually highly based on GhostJS's fill method, with a few changes, namely:

more reliable selector fallback for elements with multiple property
checking for standard label attribute of <option> (according to MDN)

Hey @aslushnikov, I pushed the fixes you requested - should I make any other changes?

@aslushnikov Sorry for the delay in pushing all the requested changes, but I've had a very busy week. :100:
Please let me know if anything else is needed.

@aslushnikov Fixed typo. :)