Bug: adding datasource plugin gives 'plugin unavailable'

Which package(s) does this bug affect?

[X] Create Plugin
[ ] Sign Plugin

Package versions
Need to install the following packages:
<EMAIL_ADDRESS>Ok to proceed? (y) y
npm WARN deprecated<EMAIL_ADDRESS>See https://github.com/lydell/source-map-url#deprecated
npm WARN deprecated<EMAIL_ADDRESS>Please see https://github.com/lydell/urix#deprecated
npm WARN deprecated<EMAIL_ADDRESS>https://github.com/lydell/resolve-url#deprecated
npm WARN deprecated<EMAIL_ADDRESS>See https://github.com/lydell/source-map-resolve#deprecated
1.9.0

What happened?
I am working with Grafana v7.0.0 for which I want to write a datasource plugin that connects data from an influxdb with data from an azure blob storage. Before writing the code for this, I wanted to test the template by adding it to grafana. When I do this, without adding code, I get the message 'plugin unavailable'. How do I fix this? The build was successful though with the same package warnings (which I feel are not the cause anyway).
What you expected to happen
I was hoping to add to the plugin so that as I write code, I can see the modifications.
How to reproduce it (as minimally and precisely as possible)
just download the template,
create the custom.ini file from grafana with the correct path to your plugin's folder and put it in development mode.
Environment
System:
    OS: Linux 5.10 Debian GNU/Linux 11 (bullseye) 11 (bullseye)
    CPU: (12) x64 Intel(R) Core(TM) i7-10750H CPU @ 2.60GHz
    Memory: 5.69 GB / 7.70 GB
    Container: Yes
    Shell: 5.1.4 - /bin/bash
  Binaries:
    Node: 18.17.0 - ~/.nvm/versions/node/v18.17.0/bin/node
    Yarn: 1.22.19 - ~/.nvm/versions/node/v18.17.0/bin/yarn
    npm: 9.6.7 - ~/.nvm/versions/node/v18.17.0/bin/npm
  npmPackages:
    @grafana/data: 9.5.3 => 9.5.3
    @grafana/e2e: 9.5.3 => 9.5.3
    @grafana/e2e-selectors: 9.5.3 => 9.5.3
    @grafana/eslint-config: ^6.0.0 => 6.0.0
    @grafana/runtime: 9.5.3 => 9.5.3
    @grafana/tsconfig: ^1.2.0-rc1 => 1.2.0-rc1
    @grafana/ui: 9.5.3 => 9.5.3

Additional context
No response

Hi @michiel2002v I tested a newly generated datasource plugin in Grafana 7.5.17 and managed to see the plugin available in the list. The plugin didn't work because of the many new API we use in our generated code that is intended for Grafana >9 so I modified it to have an absolute basic code and after that it  worked as expected.
Things to keep in consideration:

Run yarn dev or yarn build before you start your grafana server.  If you change anything in your plugin.json you also need to restart your grafana server.
Check your grafanaDependency field in your plugin.json. See that you are not setting a required version higher than the one you target.


Upgrade Katacodas to 20.2.5

Also remove the unused /environments directory.

This change is

Online preview: http://cockroach-docs-review.s3-website-us-east-1.amazonaws.com/5982e7284358eafeee92f689a98827ece14c621b/


Build fail on ubuntu 16.0.4 LTS

Get an error is following , when start test-node
[
throw in function chainbase::database::database(const bfs::path &, chainbase::database::open_flags, uint64_t)
Dynamic exception type: boost::exception_detail::clone_impl<boost::exception_detail::error_info_injectorstd::runtime_error >
std::exception::what: database dirty flag set (likely due to unclean shutdown) replay or resync required
]
start commend line:
./nodeos -e -p eosio --plugin eosio::chain_api_plugin --plugin eosio::history_api_plugin
I try to add --replay-blockchain.  the error still occur!
ubuntu is an instance in VMware, My operation is window 10

I resolved it
add  --resync-blockchain

It didn't work for me

--replay-blockchain doesn't do anything
--resync-blockchain is unrecognised option
Still getting: main.cpp:123                  main                 ] database dirty flag set (likely due to unclean shutdown): replay required
Any ideas?

I had this issue with no data dir present but @specialtrevor's comment about specifying a different data dir with that parameter worked. I just added a data dir and it worked.

@deba10106
the flags  --resync-blockchnain has been removed in new dawn version
new flags is
--replay-blockchain --hard-replay-blockchain --delete-all-blocks

nodeos --version is<PHONE_NUMBER>
rm -rf ~/.local
it worked for me.

I am getting this
1187219ms thread-0   main.cpp:113                  main                 ] 10 assert_exception: Assert Exception
db.revision() >= head->block_num: fork database is inconsistent with shared memory
    {"db":0,"head":1}
    thread-0  controller.cpp:243 init

    {}
    thread-0  chain_plugin.cpp:454 plugin_startup


【codegen】get_source("asm”) can generate assemble program.  How to get c++ code ?

Thanks @JKANG94 for asking the question, it would be great if you can open a new thread in https://discuss.tvm.apache.org/. You should be able to embed inline asm into llvm and call that from an extern function. The generated function can be access through TVM's runtime API


fix(config): Corrected style in Nortek PD300Z-2

Corrected to match evolve sister device.

@zwave-js-bot fix lint


Auto scroll not working well

First of all, I really like this component and your work! Thanks a lot!
I found one issue though.
The auto scrolling does not seem to work if you include, e.g. large images. Markdown content and preview get out of sync then and you see different parts of the document in the preview than in the editor.
I found a markdown editor that synchronizes the scrolling pretty well even with large images. Unfortunately, it seems unmaintained :-( But maybe you can get some inspiration from this project: https://github.com/code-farmer-i/vue-markdown-editor

This feature has been add in ^3.0.0. Have a try~


Adjusting CSS styling to fix a11y contrast errors

Draft PR testing out changes.
For #914

Switching this to ready for review, just to generate the branch rtd site.

@brdunfield hey can I trouble you to review the CSS changes for the handbook? I fixed the color contrast for the sidebar navigation and footer as reported by Wave and the results are visible here https://handbook.civicactions.com/en/color-contrast-fixes/010-welcome-to-civicactions/training/buddy-program/
compare that to the current live handbook: https://handbook.civicactions.com/en/latest/010-welcome-to-civicactions/training/buddy-program/
What to do you think?


Automatically add new issues to GitHub project

Possible actions to use:

https://github.com/actions/add-to-project

Looks like there is no way to add issues to a specific column atm: https://github.com/actions/add-to-project/issues/71
There is a workaround which I might get around to trying, but it's low priority since it's high effort. The workaround is adding the issue/PR to the project first, and then moving it to the desired column afterwards via the graphql API, though that might be limited to organizations only. More info on the workaround here: https://github.com/actions/add-to-project/issues/71#issuecomment-1152121720


Massive gaps in SPOT price

TP or SL not yet reached, not selling BTCUSDT for now 48868.49 - 48551.76 : -0.65%
TP or SL reached, selling 0.000307 BTCUSDT - 48868.49 - 9000.0 : -81.58%
Is binance just weird? 81.58% ??

That was on the testnet, right? Seen the same, it's very unreliable.

yes. testnet


swift_support branch fails to compile with cocoapods - can't find Classes/CocoaLumberjack.modulemap

My iOS project has cocoalumberjack configured to track the swift_support branch without locking to a specific commit (bad, I know). I discovered it was failing this morning, unable to find Classes/CocoaLumberjack.modulemap (you can also see this by running pod lib lint from within the CocoaLumberjack repo).
It looks like when #525 was merged into swift_support in fb70080, a couple of commits might have gotten lost along the way, specifically bd4d756 and ae92aea. Cherry-picking those seems to get things going farther.
Not sure if anything else got lost along the way or if something else is going on here, otherwise I'd just submit a PR.

+1 Just discovered that as well. I will stick to commit 9b31277c90d7c3968038af09a7bddd003aa28da9 meanwhile.

Yes please do submit a pull request while we find out why commits got lost.

@mattlilek sorry for this, there was an issue with the modulemap being moved and the podspec was not updated with this new path. Also, I fixed another issue that was causing pod lib lint to fail.
Please not that now you have to work with the Swift subspec. All the details are here: af40293


Order of Operations doesnt work as expected for specs with multiple classifications

Spec has various endpoints grouped under a separate folders, for such specs order of operations is not working when defined the order across folders.

Hi @yajiv789
Currently, we don't support order of operations across folders, mainly because that use-case was not really requested.
2 questions:

Could you share your OpenAPI file and Portman configuration, so we can reproduce the behaviour that you are experiencing?
How would you want to the folders to be organised if you would be mixing requests over multiple folders?

I am expecting the requests across folders to be ordered as per the portman config, under new folder or under contract tests folder.

I am not expecting to be organised in the folders.
I am trying to check the possibility if we could create a new folder and put the mixed requests under that new folder.

Eg.
Folder A -> R1 / R3 / R4
Folder B -> R2 / R5
Contract Tests ->  New Folder ->  R1 / R2 / R3 / R4 / R5

I have been blocked on this, any help much appreciated. Thanks in advance!!

@yajiv789 The folder ordering is complicated to manage in a generic way, so it will take quite a large development effort to figure this out.

hi @yajiv789
We created a https://github.com/apideck-libraries/portman/pull/641 to introduce the option to sort Postman folders based on the "orderOfFolders" configuration. See the PR for more details.
Would this solve your need?


.table-responsive inside .media-body problem

Hi, I am having problem in viewing on small devices like mobile, the table inside in media body is not responsive in small devices.
Thank you in advance.

                           Profile

Name	Jenny

Location	New york

Designation	Office staff

Status	Probitionary
.

@cvrebert , can I ask your help on this ?Thank you.

Duplicate of #15622.

@cvrebert , adding 1000px width to media body still doesn't work.

@cvrebert , I just put "overflow:auto" in media class and in media-body to have scrollbar.

The underlying issue has been closed as WontFix. For further assistance regarding this matter, please try one of the community forums, such as StackOverflow: https://github.com/twbs/bootstrap/blob/master/README.md#community


Added editor subcommand.

This PR adds editor subcommand, subcommad is just another proc triggered by keybinding which then parses user input and executes the procedure user specified, e.g. :open or :filter. All named procedures are accessible from subcommand.
Furthermore args can be provided on invocation like :open data/fixed.csv. Open and filter procedures were adapted to only prompt user for input if the argument wasn't provided.
Lexer for the subcommand is smart enough to recognise strings with spaces and escapes as single tokes so e.g. this is completely fine :open "data/insane \" : file \" name.csv"

@br1tney5pear5 sounds great. Could you please add some tests that cover this new feature, that run as part of the test-sheet target in app/test/Makefile? Please give the new tests a less generic prefix than the existing tests (e.g. test-sheet-subcommand-N rather than test-sheet-N), as the various sub-targets are starting to get lengthy and we'll want to make it easier for ourselves to move them into a separate app/test/sheet/Makefile (though that move can wait until after this is done).

@liquidaty 4 basic test-sheet-subcommand were added


Release v1.5.0

Prepared this at least for PC (and tested on Android) locally. Making PR for doc purposes before pushing to master and making a release on GitHub, then will attempt on Android and Steam next.

Hooray Jenkins reported success with all tests good!

Nice to see this project progressing! I bet it will become a very amazing game once more content is added.

Hooray Jenkins reported success with all tests good!


mysql server has gone away --> Token Validation succeeds

Hi Jordan,
the TokenValidator is calling getTokenList, which succeeds also if the PI server has no connection to its database, as piConnection:getTokenList does not call checkAPISuccess. This means, in case of an unstable database, that 2FA is bypassed. We see this as an security issue, the plugin should not let the flow succeed in this case.
Regards,
Martin

This is a design choice.  Currently this implementation is designed to fail open in the case you identified (missing database server) but also in other cases, such as Shibboleth being unable to securely contact the privacyIDEA server (due to bad certificate, etc).
I don't have any plans presently to fix this, but I am open to a pull request if someone else would like to try.


WebSocket handshake fails over WSS, if client uses TLS False Start

Describe the bug
If client uses TLS False Start, WebSocket handshake data sent from client gets thrown away in server.
Related to #466 and #665.
TLS False Start (https://tools.ietf.org/html/rfc7918) means the client can start sending application data immediately after sending ChangeCipherSpec and Finished messages during the TLS handshake, as opposed to having to wait for the server to respond with its ChangeCipherSpec and Finished messages.
The issue is similar to #665 in that any remaining data in the inCrypt buffer is dropped when processHandshake calls createBuffers(), but applying the fix proposed in #665 is only part of the solution, because in this case the data is received during the handshake and when handshake completes, no additional SSLSocketChannel2.read is attempted, which would restore the data. Not sure why, I haven't looked further than SSLSocketChannel2 in the code.
#466 was also caused by this issue.
https://github.com/TooTallNate/Java-WebSocket/issues/466#issuecomment-298877689
Java 8 does support TLS_ECDHE_RSA_WITH_AES_128_GCM_SHA256, the cipher suites mentioned in the SO post and which aren't supported by default with the limited crypto policy are AES_256 ciphers.
Disabling this cipher suite "fixed" the issue because TLS False Start can be used by the client when certain preconditions are met, one of which is a whitelisted cipher suite (TLS_ECDHE_RSA_WITH_AES_128_GCM_SHA256 is one of them). When this cipher suite is removed, TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA (in my case) gets negotiated, which isn't whitelisted.
To Reproduce
Use Firefox, has false start enabled by default (can be disabled in about:config security.ssl.enable_false_start). Can play around with this setting to make sure the issue is occurring because of TLS False Start.
Not reproducible with Chrome: did have TLS False Start enabled at some point, but is disabled by default now and only enabled if server supports ALPN (Application-Layer Protocol Negotiation): https://arstechnica.com/information-technology/2012/04/google-abandons-noble-experiment-to-make-ssl-less-painful/
Use CustomSSLWebSocketServerFactory with all cipher suites enabled (DefaultSSLWebSocketServerFactory disables TLS_ECDHE_RSA_WITH_AES_128_GCM_SHA256 as of v1.3.4).
Use some packet sniffing tool to make sure TLS False Start occurs. Won't occur when TLS session resumption gets used.
Environment:

Version used: 1.4.0
Java version: 1.8
Operating System and version: Win 7
Endpoint Name and version: Firefox 69.0

Hello @avojenk,
thx for looking into your issue.
If I understand you correctly, not supporting AES_X should help?
Or do you have some proposed changes?
Best regards,
Marcel

Why don't deploy your Java-WebSocket project as a Docker behind a NGINX gateway over proxy_pass.
If so, NGINX will help you manage SSL certificates.
So I often said that NGINX is our friend forever.
I did all projects like this.

We confirmed that this Also fix #958.


submit-queue: Display uncached request count to github API

We currently display number of total requests to github API, and number
of cached requests. Let's print number of uncached request as this is
the number that matters the most.

LGTM

You could change the default sort order, aka change self.StatOrder = "-Count"; in script.js to show what's most important. But this is good/fine/lgtm.

@eparis We should no longer need the retest-not-required label as https://github.com/kubernetes/contrib/pull/1474 is live on contrib.


Add splats to polars_util.rs

https://github.com/rerun-io/rerun/pull/736 added support for splats to re_query
We should go back and add comparable logic to polars_util for completeness, but the ergonomics of doing so is surprisingly painful. I think the answer is going to be to pull in polar-lazy so we can use https://pola-rs.github.io/polars/polars_lazy/dsl/enum.Expr.html#method.repeat_by
Trying to do the equivalent directly on top of raw series and dataframes was not working well.

We're moving away from polars.


Even if you take 0 amount of food you will still not be able to take any other amount of food in the same day

This is minor but I don't think this should happen since you haven't taken any food that day so you should still be able to take certain amount of food in that same day.
This is what the fix could be (unless other things also need to be changed). I can do this quickly if everyone agrees.
func (a *Base) TakeFood(amountOfFood float64) float64 {
	if a.floor == a.tower.currPlatFloor && !a.hasEaten && amountOfFood != 0 {
		foodTaken := math.Min(a.tower.currPlatFood, amountOfFood)
		a.updateHP(foodTaken)
		a.tower.currPlatFood -= foodTaken
		a.setHasEaten(true)
		a.Log("An agent has taken food", Fields{"floor": a.floor, "amount": foodTaken})
		return foodTaken
	}
	return 0.0
}

I think it's probably better to do a.setHasEaten(foodTaken > 0)

I think it's probably better to do a.setHasEaten(foodTaken > 0) rather than add the extra check for amountOfFood != 0

yeah that works too. Thanks!


Add Codespell to pre-commit

Adding codespell as part of our CI/pre-commit to prevent reoccurring waves of typo fixes.

Codecov Report

Merging #226 (325ec24) into develop (ccb71dd) will not change coverage.
The diff coverage is n/a.

@@           Coverage Diff            @@
##           develop     #226   +/-   ##
========================================
  Coverage    85.41%   85.41%
========================================
  Files           16       16
  Lines         2764     2764
========================================
  Hits          2361     2361
  Misses         403      403

Impacted Files
Coverage Δ

s3transfer/futures.py
95.13% <ø> (ø)

Continue to review full report at Codecov.

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Cursor / Iterator

Suggestion:
let users = prisma.user.iterator({
     where: ...
})

for( user in users ) {
         doSomething(user)
}

It can also have map and reduce function possible on whole query if big query. It's programmatically easy to do and if you're interested, I can code it too.

I am not sure I understand what you are suggesting. Right now, the code example just looks like a normal findMany with a weird name? Can you explain a bit more please?

You haven't noticed the for loop...
Definitely I am not thinking of normal findMany. If you wanted to go trough 99999 rows by findMany, you'll probably have node running out of memory. Imagine simple task, like you've got to export database into huge CSV, or you have pictures in database and you want to export a really many of those.
I suggest implementation of AsyncIterator or that'll enable following:

Chunking of request into smaller chunks, so you don't take up all available memory and don't crash node server by heapOutOfMemory with such request
Availability of result in for loop, so programmer can easily iterate trough even whole database

With iterator implemented, program that could otherwise endanger the application by enormous memory usage, can fit in memory size of one chunk.

A findMany would trigger one SELECT query to the database.
How would your iterator talk to the database to retrieve that data?

It would trigger select for every chunk of information.


Fix sending large requests

This fixes #810. The individual commit messages are:
Add a test capturing a bug in x11rb-async

We got a bug report about large requests not being written to the
stream. I managed to trace this down to a bug in WriteBuffer: Trying to
send something larger than the buffer size silently does nothing due to
a logic bug.

This commit adds a test that reproduces the problem. This test currently
fails with:

---- connection_sends_large_request stdout ----
thread 'connection_sends_large_request' panicked at 'assertion failed: `(left == right)`
  left: `0`,
 right: `32768`', x11rb-async/tests/connection_regression_tests.rs:84:5
note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace

Fix a logic error in WriteBuffer

The write buffer should continue writing as long as there are more bytes from
the input *or* a partial write caused data to be split out from the input.
Instead of the "or", the code contained an "and". Since "partial" is initially
empty, this caused the code to just not write anything at all.

Sounds convincing. Should I do that in this PR or should that be done in an extra PR?

Sounds convincing. Should I do that in this PR or should that be done in an extra PR?

I'd say to do it in this PR. Just squash it into the second commit, IMO

Just squash it into the second commit, IMO

Do you know me? I am writing a test! ;-)


Documentation v30

Hi,
I’m updating my pipeline for variant prioritization and I was wondering about upgrading to Gemini v30. Are there any updates to the manual for this new version? Latest docs I can find go to v20.1.
Thanks,
Phil

No, the manual is up to date with respect to functionality.


Should default access level be 'public' ?

[Storage] Should default access level be 'public'
Capturing some of the TODO's in an Issue. In JS, it is by default public: https://aws-amplify.github.io/docs/js/storage#file-access-levels
I find the 'public' directory kind of odd use case, i'm not sure when we would ever upload to a public directory since anyone can delete from there. I could extract someone's amplifyconfiguration.json, reconstruct my own app using those configuration values, and call remove on all of the keys in the public folder

this has been resolved to guest


Train Tensorflow Model task failed with error code -9

problem
I was recently at a NVIDIA course where we got an introcution to Deep Learning, and I really like DIGITS and I would like to explore it a bit more. So I wanted to try to run it in our local kubernetes cluster which has 10 GTX 1080 Ti cards, and I'm running into a few issues, mainly I cannot run training either on Tensorflow or Caffe (I'm not a ML guy but I prefer TF due to it being standard in our group). I sort of managed to run the mnist example, but only using DIGITS container build 17.10, and now I wanted to try something more custom. Currently I'm hosting a pod of the nvcr.io/nvidia/digits:19.09-tensorflow image but I tried caffe as well, with similar results.
I'm seeing a lot of warnings in the embedded examples as well, it seems a  lot of things are deprecated and it does seem like not a lot has been changed in 2 years looking through the repository. I would really love to be able to utilize DIGITS though, it is an awesome tool!
details
platform: kubernetes
version: 1.14.6
host os: ubuntu 16.04.4
NVIDIA drivers
nvidia-smi: NVIDIA-SMI 418.87.00    Driver Version: 418.87.00    CUDA Version: 10.1
docker image: nvcr.io/nvidia/digits:19.09-tensorflow
os in docker image: Ubuntu 18.04.2
docker version
17.03.2-ce
nvidia-container-runtime verson:
nvidia-container-runtime --version
runc version 1.0.0-rc2
commit: 54296cf40ad8143b62dbcaa1d90e520a2136ddfe-dirty
spec: 1.0.0-rc2-dev

Not sure what else to dig up, looking at this I'm thinking it coooould be the nvidia container runtime, but as mentioned Tensorflow complains about a lot of stuff being deprecated. Any help is appreciated!
job specifics
image size 256x256 (dataset from kaggle, creating dataset works fine)
only change from default was 5 epochs, more details from log:
Task subprocess args: "/usr/bin/python /opt/digits/digits/tools/tensorflow/main.py --network=network.py --epoch=5 --networkDirectory=/workspace/jobs/20190927-193801-dcf2 --save=/workspace/jobs/20190927-193801-dcf2 --snapshotPrefix=snapshot --snapshotInterval=1.0 --lr_base_rate=0.01 --lr_policy=step --mean=/workspace/jobs/20190927-192950-64cf/mean.binaryproto --labels_list=/workspace/jobs/20190927-192950-64cf/labels.txt --train_db=/workspace/jobs/20190927-192950-64cf/train_db --validation_db=/workspace/jobs/20190927-192950-64cf/val_db --lr_gamma=0.1 --lr_stepvalues=33.0 --shuffle=1 --subtractMean=image --optimization=sgd --validation_interval=1 --log_runtime_stats_per_step=0 --augFlip=none --augHSVh=0 --augHSVs=0 --augHSVv=0"

Network.py:
# Preferred settings for this model is:
# Base Learning Rate = 0.001
# Crop Size = 224

from model import Tower
from utils import model_property
import tensorflow as tf
import tensorflow.contrib.slim as slim
import utils as digits

class UserModel(Tower):

    @model_property
    def inference(self):
        x = tf.reshape(self.x, shape=[-1, self.input_shape[0], self.input_shape[1], self.input_shape[2]])
        with slim.arg_scope([slim.conv2d, slim.fully_connected],
                            weights_initializer=tf.contrib.layers.xavier_initializer(),
                            weights_regularizer=slim.l2_regularizer(1e-6)):
            model = slim.conv2d(x, 96, [11, 11], 4, padding='VALID', scope='conv1')
            model = slim.max_pool2d(model, [3, 3], 2, scope='pool1')
            model = slim.conv2d(model, 256, [5, 5], 1, scope='conv2')
            model = slim.max_pool2d(model, [3, 3], 2, scope='pool2')
            model = slim.conv2d(model, 384, [3, 3], 1, scope='conv3')
            model = slim.conv2d(model, 384, [3, 3], 1, scope='conv4')
            model = slim.conv2d(model, 256, [3, 3], 1, scope='conv5')
            model = slim.max_pool2d(model, [3, 3], 2, scope='pool5')
            model = slim.flatten(model)
            model = slim.fully_connected(model, 4096, activation_fn=None, scope='fc1')
            model = slim.dropout(model, 0.5, is_training=self.is_training, scope='do1')
            model = slim.fully_connected(model, 4096, activation_fn=None, scope='fc2')
            model = slim.dropout(model, 0.5, is_training=self.is_training, scope='do2')
            model = slim.fully_connected(model, self.nclasses, activation_fn=None, scope='fc3')
        return model

    @model_property
    def loss(self):
        model = self.inference
        loss = digits.classification_loss(model, self.y)
        accuracy = digits.classification_accuracy(model, self.y)
        self.summaries.append(tf.summary.scalar(accuracy.op.name, accuracy))
        return loss

logs
Logs from submitting the job to last message after failing job:
2019-09-27 19:38:03 [20190927-193801-dcf2] [INFO ] Task subprocess args: "/usr/bin/python /opt/digits/digits/tools/tensorflow/main.py --network=network.py --epoch=5 --networkDirectory=/workspace/jobs/20190927-193801-dcf2 --save=/workspace/jobs/20190927-193801-dcf2 --snapshotPrefix=snapshot --snapshotInterval=1.0 --lr_base_rate=0.01 --lr_policy=step --mean=/workspace/jobs/20190927-192950-64cf/mean.binaryproto --labels_list=/workspace/jobs/20190927-192950-64cf/labels.txt --train_db=/workspace/jobs/20190927-192950-64cf/train_db --validation_db=/workspace/jobs/20190927-192950-64cf/val_db --lr_gamma=0.1 --lr_stepvalues=33.0 --shuffle=1 --subtractMean=image --optimization=sgd --validation_interval=1 --log_runtime_stats_per_step=0 --augFlip=none --augHSVh=0 --augHSVs=0 --augHSVv=0"

I0927 19:38:03.321882<PHONE_NUMBER>60544 task.py:213] Task subprocess args: "/usr/bin/python /opt/digits/digits/tools/tensorflow/main.py --network=network.py --epoch=5 --networkDirectory=/workspace/jobs/20190927-193801-dcf2 --save=/workspace/jobs/20190927-193801-dcf2 --snapshotPrefix=snapshot --snapshotInterval=1.0 --lr_base_rate=0.01 --lr_policy=step --mean=/workspace/jobs/20190927-192950-64cf/mean.binaryproto --labels_list=/workspace/jobs/20190927-192950-64cf/labels.txt --train_db=/workspace/jobs/20190927-192950-64cf/train_db --validation_db=/workspace/jobs/20190927-192950-64cf/val_db --lr_gamma=0.1 --lr_stepvalues=33.0 --shuffle=1 --subtractMean=image --optimization=sgd --validation_interval=1 --log_runtime_stats_per_step=0 --augFlip=none --augHSVh=0 --augHSVs=0 --augHSVv=0"

2019-09-27 19:38:03 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: 2019-09-27 19:38:03.745362: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.1

W0927 19:38:03.773541<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: 2019-09-27 19:38:03.745362: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.1

2019-09-27 19:38:05 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: I0927 19:38:05.819633<PHONE_NUMBER>73760 main.py:425] Train batch size is 16 and validation batch size is 16

W0927 19:38:05.905564<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: I0927 19:38:05.819633<PHONE_NUMBER>73760 main.py:425] Train batch size is 16 and validation batch size is 16

2019-09-27 19:38:05 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: I0927 19:38:05.819813<PHONE_NUMBER>73760 main.py:429] Training epochs to be completed for each validation : 1

W0927 19:38:05.906068<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: I0927 19:38:05.819813<PHONE_NUMBER>73760 main.py:429] Training epochs to be completed for each validation : 1

2019-09-27 19:38:05 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: I0927 19:38:05.820301<PHONE_NUMBER>73760 main.py:433] Training epochs to be completed before taking a snapshot : 1.0

W0927 19:38:05.906439<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: I0927 19:38:05.820301<PHONE_NUMBER>73760 main.py:433] Training epochs to be completed before taking a snapshot : 1.0

2019-09-27 19:38:05 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: I0927 19:38:05.820651<PHONE_NUMBER>73760 main.py:437] Model weights will be saved as snapshot_<EPOCH>_Model.ckpt

W0927 19:38:05.906802<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: I0927 19:38:05.820651<PHONE_NUMBER>73760 main.py:437] Model weights will be saved as snapshot_<EPOCH>_Model.ckpt

2019-09-27 19:38:05 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: I0927 19:38:05.820871<PHONE_NUMBER>73760 main.py:450] Loading mean tensor from /workspace/jobs/20190927-192950-64cf/mean.binaryproto file

W0927 19:38:05.907147<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: I0927 19:38:05.820871<PHONE_NUMBER>73760 main.py:450] Loading mean tensor from /workspace/jobs/20190927-192950-64cf/mean.binaryproto file

2019-09-27 19:38:05 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: I0927 19:38:05.865480<PHONE_NUMBER>73760 main.py:456] Loading label definitions from /workspace/jobs/20190927-192950-64cf/labels.txt file

W0927 19:38:05.907476<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: I0927 19:38:05.865480<PHONE_NUMBER>73760 main.py:456] Loading label definitions from /workspace/jobs/20190927-192950-64cf/labels.txt file

2019-09-27 19:38:05 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: I0927 19:38:05.865655<PHONE_NUMBER>73760 main.py:462] Found 2 classes

W0927 19:38:05.907784<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: I0927 19:38:05.865655<PHONE_NUMBER>73760 main.py:462] Found 2 classes

W0927 19:38:06.009073<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: I0927 19:38:05.995457<PHONE_NUMBER>73760 tf_data.py:221] Found 5416 images in db /workspace/jobs/20190927-192950-64cf/train_db

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: I0927 19:38:05.995457<PHONE_NUMBER>73760 tf_data.py:221] Found 5416 images in db /workspace/jobs/20190927-192950-64cf/train_db

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: Instructions for updating:

W0927 19:38:06.009669<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: Instructions for updating:

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.from_tensor_slices(string_tensor).shuffle(tf.shape(input_tensor, out_type=tf.int64)[0]).repeat(num_epochs)`. If `shuffle=False`, omit the `.shuffle(...)`.

W0927 19:38:06.010023<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.from_tensor_slices(string_tensor).shuffle(tf.shape(input_tensor, out_type=tf.int64)[0]).repeat(num_epochs)`. If `shuffle=False`, omit the `.shuffle(...)`.

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: W0927 19:38:05.996030<PHONE_NUMBER>73760 deprecation.py:323] From /opt/digits/digits/tools/tensorflow/tf_data.py:472: string_input_producer (from tensorflow.python.training.input) is deprecated and will be removed in a future version.

W0927 19:38:06.010365<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: W0927 19:38:05.996030<PHONE_NUMBER>73760 deprecation.py:323] From /opt/digits/digits/tools/tensorflow/tf_data.py:472: string_input_producer (from tensorflow.python.training.input) is deprecated and will be removed in a future version.

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: Instructions for updating:

W0927 19:38:06.010723<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: Instructions for updating:

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.from_tensor_slices(string_tensor).shuffle(tf.shape(input_tensor, out_type=tf.int64)[0]).repeat(num_epochs)`. If `shuffle=False`, omit the `.shuffle(...)`.

W0927 19:38:06.011027<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.from_tensor_slices(string_tensor).shuffle(tf.shape(input_tensor, out_type=tf.int64)[0]).repeat(num_epochs)`. If `shuffle=False`, omit the `.shuffle(...)`.

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: Instructions for updating:

W0927 19:38:06.061865<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: Instructions for updating:

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.from_tensor_slices(input_tensor).shuffle(tf.shape(input_tensor, out_type=tf.int64)[0]).repeat(num_epochs)`. If `shuffle=False`, omit the `.shuffle(...)`.

W0927 19:38:06.062249<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.from_tensor_slices(input_tensor).shuffle(tf.shape(input_tensor, out_type=tf.int64)[0]).repeat(num_epochs)`. If `shuffle=False`, omit the `.shuffle(...)`.

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: W0927 19:38:06.031169<PHONE_NUMBER>73760 deprecation.py:323] From /usr/local/lib/python2.7/dist-packages/tensorflow/python/training/input.py:278: input_producer (from tensorflow.python.training.input) is deprecated and will be removed in a future version.

W0927 19:38:06.062603<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: W0927 19:38:06.031169<PHONE_NUMBER>73760 deprecation.py:323] From /usr/local/lib/python2.7/dist-packages/tensorflow/python/training/input.py:278: input_producer (from tensorflow.python.training.input) is deprecated and will be removed in a future version.

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: Instructions for updating:

W0927 19:38:06.062947<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: Instructions for updating:

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.from_tensor_slices(input_tensor).shuffle(tf.shape(input_tensor, out_type=tf.int64)[0]).repeat(num_epochs)`. If `shuffle=False`, omit the `.shuffle(...)`.

W0927 19:38:06.063240<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.from_tensor_slices(input_tensor).shuffle(tf.shape(input_tensor, out_type=tf.int64)[0]).repeat(num_epochs)`. If `shuffle=False`, omit the `.shuffle(...)`.

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: Instructions for updating:

W0927 19:38:06.063683<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: Instructions for updating:

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.from_tensors(tensor).repeat(num_epochs)`.

W0927 19:38:06.063982<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.from_tensors(tensor).repeat(num_epochs)`.

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: W0927 19:38:06.032020<PHONE_NUMBER>73760 deprecation.py:323] From /usr/local/lib/python2.7/dist-packages/tensorflow/python/training/input.py:190: limit_epochs (from tensorflow.python.training.input) is deprecated and will be removed in a future version.

W0927 19:38:06.064273<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: W0927 19:38:06.032020<PHONE_NUMBER>73760 deprecation.py:323] From /usr/local/lib/python2.7/dist-packages/tensorflow/python/training/input.py:190: limit_epochs (from tensorflow.python.training.input) is deprecated and will be removed in a future version.

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: Instructions for updating:

W0927 19:38:06.064568<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: Instructions for updating:

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.from_tensors(tensor).repeat(num_epochs)`.

W0927 19:38:06.064855<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.from_tensors(tensor).repeat(num_epochs)`.

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: Instructions for updating:

W0927 19:38:06.065270<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: Instructions for updating:

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: Prefer Dataset.range instead.

W0927 19:38:06.065560<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: Prefer Dataset.range instead.

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: W0927 19:38:06.035306<PHONE_NUMBER>73760 deprecation.py:323] From /usr/local/lib/python2.7/dist-packages/tensorflow/python/training/input.py:113: count_up_to (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.

W0927 19:38:06.065850<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: W0927 19:38:06.035306<PHONE_NUMBER>73760 deprecation.py:323] From /usr/local/lib/python2.7/dist-packages/tensorflow/python/training/input.py:113: count_up_to (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: Instructions for updating:

W0927 19:38:06.066134<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: Instructions for updating:

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: Prefer Dataset.range instead.

W0927 19:38:06.066406<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: Prefer Dataset.range instead.

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: Instructions for updating:

W0927 19:38:06.066852<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: Instructions for updating:

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: Prefer Dataset.range instead.

W0927 19:38:06.067118<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: Prefer Dataset.range instead.

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: W0927 19:38:06.035453<PHONE_NUMBER>73760 deprecation.py:323] From /usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variables.py:2322: count_up_to (from tensorflow.python.ops.state_ops) is deprecated and will be removed in a future version.

W0927 19:38:06.067378<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: W0927 19:38:06.035453<PHONE_NUMBER>73760 deprecation.py:323] From /usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variables.py:2322: count_up_to (from tensorflow.python.ops.state_ops) is deprecated and will be removed in a future version.

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: Instructions for updating:

W0927 19:38:06.067642<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: Instructions for updating:

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: Prefer Dataset.range instead.

W0927 19:38:06.067893<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: Prefer Dataset.range instead.

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: Instructions for updating:

W0927 19:38:06.068263<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: Instructions for updating:

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: To construct input pipelines, use the `tf.data` module.

W0927 19:38:06.068528<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: To construct input pipelines, use the `tf.data` module.

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: W0927 19:38:06.038276<PHONE_NUMBER>73760 deprecation.py:323] From /usr/local/lib/python2.7/dist-packages/tensorflow/python/training/input.py:199: __init__ (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.

W0927 19:38:06.068794<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: W0927 19:38:06.038276<PHONE_NUMBER>73760 deprecation.py:323] From /usr/local/lib/python2.7/dist-packages/tensorflow/python/training/input.py:199: __init__ (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: Instructions for updating:

W0927 19:38:06.069062<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: Instructions for updating:

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: To construct input pipelines, use the `tf.data` module.

W0927 19:38:06.069329<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: To construct input pipelines, use the `tf.data` module.

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: Instructions for updating:

W0927 19:38:06.069700<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: Instructions for updating:

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: To construct input pipelines, use the `tf.data` module.

W0927 19:38:06.069962<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: To construct input pipelines, use the `tf.data` module.

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: W0927 19:38:06.039386<PHONE_NUMBER>73760 deprecation.py:323] From /usr/local/lib/python2.7/dist-packages/tensorflow/python/training/input.py:199: add_queue_runner (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.

W0927 19:38:06.070219<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: W0927 19:38:06.039386<PHONE_NUMBER>73760 deprecation.py:323] From /usr/local/lib/python2.7/dist-packages/tensorflow/python/training/input.py:199: add_queue_runner (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: Instructions for updating:

W0927 19:38:06.070478<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: Instructions for updating:

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: To construct input pipelines, use the `tf.data` module.

W0927 19:38:06.070760<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: To construct input pipelines, use the `tf.data` module.

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: Instructions for updating:

W0927 19:38:06.071424<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: Instructions for updating:

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: tf.py_func is deprecated in TF V2. Instead, there are two

W0927 19:38:06.071897<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: tf.py_func is deprecated in TF V2. Instead, there are two

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: options available in V2.

W0927 19:38:06.072227<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: options available in V2.

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: - tf.py_function takes a python function which manipulates tf eager

W0927 19:38:06.072547<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: - tf.py_function takes a python function which manipulates tf eager

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: tensors instead of numpy arrays. It's easy to convert a tf eager tensor to

W0927 19:38:06.072848<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: tensors instead of numpy arrays. It's easy to convert a tf eager tensor to

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: an ndarray (just call tensor.numpy()) but having access to eager tensors

W0927 19:38:06.073157<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: an ndarray (just call tensor.numpy()) but having access to eager tensors

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: means `tf.py_function`s can use accelerators such as GPUs as well as

W0927 19:38:06.073462<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: means `tf.py_function`s can use accelerators such as GPUs as well as

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: being differentiable using a gradient tape.

W0927 19:38:06.073765<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: being differentiable using a gradient tape.

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: - tf.numpy_function maintains the semantics of the deprecated tf.py_func

W0927 19:38:06.074062<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: - tf.numpy_function maintains the semantics of the deprecated tf.py_func

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: (it is not differentiable, and manipulates numpy arrays). It drops the

W0927 19:38:06.074354<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: (it is not differentiable, and manipulates numpy arrays). It drops the

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: stateful argument making all functions stateful.

W0927 19:38:06.074678<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: stateful argument making all functions stateful.

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: W0927 19:38:06.043739<PHONE_NUMBER>73760 deprecation.py:323] From /opt/digits/digits/tools/tensorflow/tf_data.py:547: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version.

W0927 19:38:06.125327<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: W0927 19:38:06.043739<PHONE_NUMBER>73760 deprecation.py:323] From /opt/digits/digits/tools/tensorflow/tf_data.py:547: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version.

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: Instructions for updating:

W0927 19:38:06.125744<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: Instructions for updating:

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: tf.py_func is deprecated in TF V2. Instead, there are two

W0927 19:38:06.126051<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: tf.py_func is deprecated in TF V2. Instead, there are two

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: options available in V2.

W0927 19:38:06.126352<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: options available in V2.

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: - tf.py_function takes a python function which manipulates tf eager

W0927 19:38:06.126682<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: - tf.py_function takes a python function which manipulates tf eager

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: tensors instead of numpy arrays. It's easy to convert a tf eager tensor to

W0927 19:38:06.126966<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: tensors instead of numpy arrays. It's easy to convert a tf eager tensor to

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: an ndarray (just call tensor.numpy()) but having access to eager tensors

W0927 19:38:06.127243<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: an ndarray (just call tensor.numpy()) but having access to eager tensors

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: means `tf.py_function`s can use accelerators such as GPUs as well as

W0927 19:38:06.127531<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: means `tf.py_function`s can use accelerators such as GPUs as well as

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: being differentiable using a gradient tape.

W0927 19:38:06.127805<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: being differentiable using a gradient tape.

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: - tf.numpy_function maintains the semantics of the deprecated tf.py_func

W0927 19:38:06.128091<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: - tf.numpy_function maintains the semantics of the deprecated tf.py_func

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: (it is not differentiable, and manipulates numpy arrays). It drops the

W0927 19:38:06.128364<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: (it is not differentiable, and manipulates numpy arrays). It drops the

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: stateful argument making all functions stateful.

W0927 19:38:06.128633<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: stateful argument making all functions stateful.

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: Instructions for updating:

W0927 19:38:06.179543<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: Instructions for updating:

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: Use `tf.cast` instead.

W0927 19:38:06.180011<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: Use `tf.cast` instead.

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: W0927 19:38:06.046334<PHONE_NUMBER>73760 deprecation.py:323] From /opt/digits/digits/tools/tensorflow/tf_data.py:274: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.

W0927 19:38:06.180298<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: W0927 19:38:06.046334<PHONE_NUMBER>73760 deprecation.py:323] From /opt/digits/digits/tools/tensorflow/tf_data.py:274: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: Instructions for updating:

W0927 19:38:06.180577<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: Instructions for updating:

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: Use `tf.cast` instead.

W0927 19:38:06.180838<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: Use `tf.cast` instead.

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: Instructions for updating:

W0927 19:38:06.181229<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: Instructions for updating:

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.batch(batch_size)` (or `padded_batch(...)` if `dynamic_pad=True`).

W0927 19:38:06.181505<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.batch(batch_size)` (or `padded_batch(...)` if `dynamic_pad=True`).

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: W0927 19:38:06.049055<PHONE_NUMBER>73760 deprecation.py:323] From /opt/digits/digits/tools/tensorflow/tf_data.py:410: batch (from tensorflow.python.training.input) is deprecated and will be removed in a future version.

W0927 19:38:06.181839<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: W0927 19:38:06.049055<PHONE_NUMBER>73760 deprecation.py:323] From /opt/digits/digits/tools/tensorflow/tf_data.py:410: batch (from tensorflow.python.training.input) is deprecated and will be removed in a future version.

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: Instructions for updating:

W0927 19:38:06.182107<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: Instructions for updating:

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.batch(batch_size)` (or `padded_batch(...)` if `dynamic_pad=True`).

W0927 19:38:06.182368<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.batch(batch_size)` (or `padded_batch(...)` if `dynamic_pad=True`).

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: 2019-09-27 19:38:06.070614: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency:<PHONE_NUMBER> Hz

W0927 19:38:06.182688<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: 2019-09-27 19:38:06.070614: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency:<PHONE_NUMBER> Hz

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: 2019-09-27 19:38:06.073639: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x5615a16948b0 executing computations on platform Host. Devices:

W0927 19:38:06.186625<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: 2019-09-27 19:38:06.073639: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x5615a16948b0 executing computations on platform Host. Devices:

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: 2019-09-27 19:38:06.073671: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): <undefined>, <undefined>

W0927 19:38:06.187032<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: 2019-09-27 19:38:06.073671: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): <undefined>, <undefined>

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: 2019-09-27 19:38:06.077832: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcuda.so.1

W0927 19:38:06.187335<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: 2019-09-27 19:38:06.077832: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcuda.so.1

W0927 19:38:06.691137<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: 2019-09-27 19:38:06.654802: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x5615a3d3b730 executing computations on platform CUDA. Devices:

2019-09-27 19:38:06 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: 2019-09-27 19:38:06.654802: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x5615a3d3b730 executing computations on platform CUDA.  Please use tf.compat.v1.variable_scope instead.

W0927 19:38:07.960506<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: W0927 19:38:07.853491<PHONE_NUMBER>73760 deprecation_wrapper.py:119] From /opt/digits/digits/tools/tensorflow/model.py:156: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.

2019-09-27 19:38:08 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: Instructions for updating:

W0927 19:38:08.363987<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: Instructions for updating:

2019-09-27 19:38:08 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: Use keras.layers.flatten instead.

W0927 19:38:08.364383<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: Use keras.layers.flatten instead.

2019-09-27 19:38:08 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: W0927 19:38:08.316798<PHONE_NUMBER>73760 deprecation.py:323] From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/layers/python/layers/layers.py:1637: flatten (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.

W0927 19:38:08.364715<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: W0927 19:38:08.316798<PHONE_NUMBER>73760 deprecation.py:323] From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/layers/python/layers/layers.py:1637: flatten (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.

2019-09-27 19:38:08 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: Instructions for updating:

W0927 19:38:08.365031<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: Instructions for updating:

2019-09-27 19:38:08 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: Use keras.layers.flatten instead.

W0927 19:38:08.365331<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: Use keras.layers.flatten instead.

2019-09-27 19:38:09 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: W0927 19:38:09.128760<PHONE_NUMBER>73760 deprecation_wrapper.py:119] From /opt/digits/digits/tools/tensorflow/model.py:160: The name tf.get_variable_scope is deprecated. Please use tf.compat.v1.get_variable_scope instead.

W0927 19:38:09.221976<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: W0927 19:38:09.128760<PHONE_NUMBER>73760 deprecation_wrapper.py:119] From /opt/digits/digits/tools/tensorflow/model.py:160: The name tf.get_variable_scope is deprecated. Please use tf.compat.v1.get_variable_scope instead.

W0927 19:38:09.323100<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: W0927 19:38:09.136358<PHONE_NUMBER>73760 deprecation_wrapper.py:119] From /opt/digits/digits/tools/tensorflow/model.py:300: The name tf.get_collection is deprecated. Please use tf.compat.v1.get_collection instead.

2019-09-27 19:38:09 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: W0927 19:38:09.136358<PHONE_NUMBER>73760 deprecation_wrapper.py:119] From /opt/digits/digits/tools/tensorflow/model.py:300: The name tf.get_collection is deprecated. Please use tf.compat.v1.get_collection instead.

2019-09-27 19:38:09 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: W0927 19:38:09.136517<PHONE_NUMBER>73760 deprecation_wrapper.py:119] From /opt/digits/digits/tools/tensorflow/model.py:300: The name tf.GraphKeys is deprecated. Please use tf.compat.v1.GraphKeys instead.

W0927 19:38:09.424246<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: W0927 19:38:09.136517<PHONE_NUMBER>73760 deprecation_wrapper.py:119] From /opt/digits/digits/tools/tensorflow/model.py:300: The name tf.GraphKeys is deprecated. Please use tf.compat.v1.GraphKeys instead.

2019-09-27 19:38:09 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: W0927 19:38:09.136657<PHONE_NUMBER>73760 deprecation_wrapper.py:119] From /opt/digits/digits/tools/tensorflow/model.py:168: The name tf.add_to_collection is deprecated. Please use tf.compat.v1.add_to_collection instead.

W0927 19:38:09.525564<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: W0927 19:38:09.136657<PHONE_NUMBER>73760 deprecation_wrapper.py:119] From /opt/digits/digits/tools/tensorflow/model.py:168: The name tf.add_to_collection is deprecated. Please use tf.compat.v1.add_to_collection instead.

2019-09-27 19:38:09 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: W0927 19:38:09.137783<PHONE_NUMBER>73760 deprecation_wrapper.py:119] From /opt/digits/digits/tools/tensorflow/model.py:176: The name tf.summary.scalar is deprecated. Please use tf.compat.v1.summary.scalar instead.

W0927 19:38:09.626677<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: W0927 19:38:09.137783<PHONE_NUMBER>73760 deprecation_wrapper.py:119] From /opt/digits/digits/tools/tensorflow/model.py:176: The name tf.summary.scalar is deprecated. Please use tf.compat.v1.summary.scalar instead.

2019-09-27 19:38:09 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: I0927 19:38:09.139045<PHONE_NUMBER>73760 model.py:265] Optimizer:sgd

W0927 19:38:09.677603<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: I0927 19:38:09.139045<PHONE_NUMBER>73760 model.py:265] Optimizer:sgd

2019-09-27 19:38:09 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: W0927 19:38:09.139211<PHONE_NUMBER>73760 deprecation_wrapper.py:119] From /opt/digits/digits/tools/tensorflow/model.py:267: The name tf.train.GradientDescentOptimizer is deprecated. Please use tf.compat.v1.train.GradientDescentOptimizer instead.

W0927 19:38:09.728379<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: W0927 19:38:09.139211<PHONE_NUMBER>73760 deprecation_wrapper.py:119] From /opt/digits/digits/tools/tensorflow/model.py:267: The name tf.train.GradientDescentOptimizer is deprecated.  Please use tf.compat.v1.train.Saver instead.

W0927 19:38:10.034274<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: W0927 19:38:09.905936<PHONE_NUMBER>73760 deprecation_wrapper.py:119] From /opt/digits/digits/tools/tensorflow/main.py:548: The name tf.train.Saver is deprecated. Please use tf.compat.v1.train.Saver instead.

2019-09-27 19:38:10 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: 2019-09-27 19:38:10.056024: W tensorflow/compiler/jit/mark_for_compilation_pass.cc:1412] (One-time warning): Not using XLA:CPU for cluster because envvar TF_XLA_FLAGS=--tf_xla_cpu_global_jit was not set.  If you want XLA:CPU, either set that envvar, or use experimental_jit_scope to enable XLA:CPU.  To confirm that XLA is active, pass --vmodule=xla_compilation_cache=1 (as a proper command-line flag, not via TF_XLA_FLAGS) or set the envvar XLA_FLAGS=--xla_hlo_profile.

W0927 19:38:10.084955<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: 2019-09-27 19:38:10.056024: W tensorflow/compiler/jit/mark_for_compilation_pass.cc:1412] (One-time warning): Not using XLA:CPU for cluster because envvar TF_XLA_FLAGS=--tf_xla_cpu_global_jit was not set.  If you want XLA:CPU, either set that envvar, or use experimental_jit_scope to enable XLA:CPU.  To confirm that XLA is active, pass --vmodule=xla_compilation_cache=1 (as a proper command-line flag, not via TF_XLA_FLAGS) or set the envvar XLA_FLAGS=--xla_hlo_profile.

2019-09-27 19:38:12 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: W0927 19:38:12.469755<PHONE_NUMBER>73760 deprecation_wrapper.py:119] From /opt/digits/digits/tools/tensorflow/main.py:559: The name tf.summary.FileWriter is deprecated. Please use tf.compat.v1.summary.FileWriter instead.

W0927 19:38:12.538279<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: W0927 19:38:12.469755<PHONE_NUMBER>73760 deprecation_wrapper.py:119] From /opt/digits/digits/tools/tensorflow/main.py:559: The name tf.summary.FileWriter is deprecated. Please use tf.compat.v1.summary.FileWriter instead.

2019-09-27 19:38:12 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: I0927 19:38:12.765419<PHONE_NUMBER>73760 model.py:203] Starting queue runners (val)

W0927 19:38:12.791570<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: I0927 19:38:12.765419<PHONE_NUMBER>73760 model.py:203] Starting queue runners (val)

2019-09-27 19:38:12 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: Instructions for updating:

W0927 19:38:12.792138<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: Instructions for updating:

2019-09-27 19:38:12 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: To construct input pipelines, use the `tf.data` module.

W0927 19:38:12.792433<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: To construct input pipelines, use the `tf.data` module.

2019-09-27 19:38:12 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: W0927 19:38:12.766309<PHONE_NUMBER>73760 deprecation.py:323] From /opt/digits/digits/tools/tensorflow/model.py:212: start_queue_runners (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.

W0927 19:38:12.792709<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: W0927 19:38:12.766309<PHONE_NUMBER>73760 deprecation.py:323] From /opt/digits/digits/tools/tensorflow/model.py:212: start_queue_runners (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.

2019-09-27 19:38:12 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: Instructions for updating:

W0927 19:38:12.792974<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: Instructions for updating:

2019-09-27 19:38:12 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: To construct input pipelines, use the `tf.data` module.

W0927 19:38:12.793224<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: To construct input pipelines, use the `tf.data` module.

2019-09-27 19:38:12 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: I0927 19:38:12.785586<PHONE_NUMBER>73760 model.py:213] Queue runners started (val)

W0927 19:38:12.793592<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: I0927 19:38:12.785586<PHONE_NUMBER>73760 model.py:213] Queue runners started (val)

2019-09-27 19:38:12 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: W0927 19:38:12.785973<PHONE_NUMBER>73760 deprecation_wrapper.py:119] From /opt/digits/digits/tools/tensorflow/model.py:245: The name tf.summary.merge is deprecated. Please use tf.compat.v1.summary.merge instead.

W0927 19:38:12.844299<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: W0927 19:38:12.785973<PHONE_NUMBER>73760 deprecation_wrapper.py:119] From /opt/digits/digits/tools/tensorflow/model.py:245: The name tf.summary.merge is deprecated. Please use tf.compat.v1.summary.merge instead.

2019-09-27 19:38:13 [20190927-193801-dcf2] [WARNING] Train Tensorflow Model unrecognized output: 2019-09-27 19:38:13.393811: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10

W0927 19:38:13.397892<PHONE_NUMBER>60544 task.py:242] Train Tensorflow Model unrecognized output: 2019-09-27 19:38:13.393811: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10

Traceback (most recent call last):

  File "src/gevent/greenlet.py", line 766, in gevent._greenlet.Greenlet.run

  File "/opt/digits/digits/model/tasks/train.py", line 220, in hw_socketio_updater

    nvml_info = device_query.get_nvml_info(index)

  File "/opt/digits/digits/device_query.py", line 252, in get_nvml_info

    raise RuntimeError('nvmlInit() failed with error #%s' % rc)

RuntimeError: nvmlInit() failed with error #999

2019-09-27T19:38:21Z <Greenlet at 0x7fdf54f1b578: <bound method TensorflowTrainTask.hw_socketio_updater of <digits.model.tasks.tensorflow_train.TensorflowTrainTask object at 0x7fdf54a29790>>(['0', '1'])> failed with RuntimeError

2019-09-27 19:38:21 [20190927-193801-dcf2] [ERROR] Train Tensorflow Model task failed with error code -9

E0927 19:38:21.842375<PHONE_NUMBER>60544 task.py:261] Train Tensorflow Model task failed with error code -9

So after running some more tests, I figured out that part of the problem was me not giving enough ram to the pod. I guess trying to run AlexNet on 10k images with 2GB of ram was rather ambitious... However, even with about 200 images it crashes unless I give the pods about 8GB ram, is this normal behaviour? This was again just my custom test, I did not yet test the included mnist example with more ram, I'll report back on that tomorrow.


Add "UTF-8" encoding to fix Arabic text issue

Solved for this issue: https://github.com/streamsync-cloud/streamsync/issues/193

could you edit your PR to set it on the branch streamsync-could:dev instead of streamsync-could:master ?

I am really sorry for that.
I made a new one to the streamsync-cloud:dev branch


Add service registry to application guides

Add service registry commands to guides

We cannot add those until registry is in the production

@smccarthy-ie Would that be done by your team? Do you have any draft for this?

Done. Pinged team for eventual feedback.
@PaulRMellor and @smccarthy-ie taking over that for docs reviews/perspective.


[Enhancement] Add ability to use domain names for client connections

The Issue
Currently, client connections can only be specified as IP address strings. It would be handy if applications could also specify a domain name to connect to.
As an alternative, this library could let applications specify separate connection IP addresses and certificate domains, allowing them to do their own DNS resolution.

I have to think a bit more about adding DNS resolution inside quinnet.
I am not sure that I understand the alternative you mention. Do you mean: the user resolves DNS on its own, but still provides the library with IP and Domain name (if any) ? (this is what might come out of #9)

#9  was solved with #14

Do you mean: the user resolves DNS on its own, but still provides the library with IP and Domain name (if any) ?

Yes, this is what I meant.

I will leave DNS resolution out of Quinnet scope for now. Since #14 was implemented, I will close this.


Add reason in help menu

Resolves #423
QA for 423

@Draeieg  would you please merge ubiquity:development into your development branch to resolve conflicts?


error building with Apple clang 8.1.0

I currently can not build latest xtensor trunk with Apple clang 8.1.0 (AFAIK based on clang 3.9.1) with tests enabled. I get the following error:
FAILED: test/CMakeFiles/test_xtensor_lib.dir/test_xreducer.cpp.o
/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/c++  -DXTENSOR_ENABLE_ASSERT -DXTENSOR_ENABLE_CHECK_DIMENSION -DXTENSOR_USE_XSIMD -I/Users/bdaci01/builds/DaPSmLFR/0/xtensor/include -isystem /data/Debug/include -ggdb3 -fno-omit-frame-pointer -O1 -march=core2 -msse4.1 -mtune=sandybridge -mmacosx-version-min=10.12  -DDEBUG -std=c++14 -march=native -Wunused-parameter -Wextra -Wreorder -Wconversion -Wsign-conversion -Wold-style-cast -Wunused-variable -O3 -DNDEBUG -fPIE   -std=gnu++14 -MD -MT test/CMakeFiles/test_xtensor_lib.dir/test_xreducer.cpp.o -MF test/CMakeFiles/test_xtensor_lib.dir/test_xreducer.cpp.o.d -o test/CMakeFiles/test_xtensor_lib.dir/test_xreducer.cpp.o -c /Users/bdaci01/builds/DaPSmLFR/0/xtensor/test/test_xreducer.cpp
In file included from /Users/bdaci01/builds/DaPSmLFR/0/xtensor/test/test_xreducer.cpp:10:
In file included from /Users/bdaci01/builds/DaPSmLFR/0/xtensor/include/xtensor/xarray.hpp:19:
In file included from /Users/bdaci01/builds/DaPSmLFR/0/xtensor/include/xtensor/xcontainer.hpp:21:
In file included from /Users/bdaci01/builds/DaPSmLFR/0/xtensor/include/xtensor/xiterable.hpp:12:
/Users/bdaci01/builds/DaPSmLFR/0/xtensor/include/xtensor/xiterator.hpp:1062:38: error: invalid operands to binary expression ('const xt::const_array<unsigned long, 3>' and 'const xt::const_array<unsigned long, 3>')
        XTENSOR_ASSERT(this->shape() == rhs.shape());
                       ~~~~~~~~~~~~~ ^  ~~~~~~~~~~~

Does this mean anything to you? Is my compiler too old? Anything I can do to help debug this issue?

The class const_array does not provide operator==  and operator!=, this is definitely a bug ;)

Thanks @JohanMabille . Its not urgent for us right now, we can disable MacOSX tests and everything else works for now.

I'm gonna make a quick fix before releasing xtensor.

Dear @JohanMabille , sorry for my slow reply! I've tested the fix and it works for us. We can now build with Apple clang 8.1.0. Thanks!


Decode URL to utf-8 before joining.

To avoid UnicodeDecodeError's like on http://blip.fm/~1abvfu
Note: This doesn't have a test, which sucks, because for the life of me I can't seem to figure out how to trigger this error with httpbin. Maybe flask has better handling of UTF-8 characters than the server used at blip.fm and others? Really not sure.
Here's an ad-hoc test for you:
Before:
>>> import requests
>>> requests.head('http://blip.fm/~1abvfu', allow_redirects=True)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "requests/api.py", line 73, in head
    return request('head', url, **kwargs)
  File "requests/api.py", line 39, in request
    return s.request(method=method, url=url, **kwargs)
  File "requests/sessions.py", line 200, in request
    r.send(prefetch=prefetch)
  File "requests/models.py", line 559, in send
    self._build_response(r)
  File "requests/models.py", line 232, in _build_response
    url = urljoin(r.url, url)
  File "/System/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/urlparse.py", line 219, in urljoin
    params, query, fragment))
  File "/System/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/urlparse.py", line 184, in urlunparse
    return urlunsplit((scheme, netloc, url, query, fragment))
  File "/System/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/urlparse.py", line 190, in urlunsplit
    url = '//' + (netloc or '') + url
UnicodeDecodeError: 'ascii' codec can't decode byte 0xe2 in position 62: ordinal not in range(128)

After:
>>> import requests
>>> requests.head('http://blip.fm/~1abvfu', allow_redirects=True)
<Response [200]>

Note that you can't assume that the Location header will be UTF-8 (as opposed to another encoding), so the correct fix is actually to quote the string (the commit in #472 gets it right)

Glad to hear someone took care of it.


venv location

Adopt the uv default location.

Things I used to believe:

Virt env should go under homedir, similar to conda.
uv pip ... behaves like pip ....
The documented uv venv /some/where behaved like python -m venv /some/where.

background

conda puts virt env under home dir, rather than within project folder.
Several standard tools recurse: du, grep -r, find ., pyright ., etc.
A tool may be less useful when asked to recurse through a few GiB of deps rather than through a few dozen project files.

Things I now believe:

New users should be supported, across diverse (posix) configurations.
New users may arrive with old interpreters, e.g. cPython 3.9.
uv is fast, so we should use it.
uv doesn't really support locations other than .venv/
The blackboard project needs to bootstrap to 3.12 from random interpreter version.
We need to put the virt env in the default .venv/ location, for speed and for uniform 3.12 interpreter.

The 57-venv-location branch resolves these issues.


Violin Plot Scalability

User story
From https://www.covid19cellatlas.org/index.patient.html (Bronchoalveolar lavage fluid) the violin plot can cause the page to become completely unresponsive it seems.  I'm not sure exactly what is causing this but this issue is more to investigate than anything.

2 possible pain point:
https://github.com/vitessce/vitessce/blob/737c8bb9c1a5a3d84fbfa2f15135b008048945fc/src/components/sets/hooks.js#L42
https://github.com/vitessce/vitessce/blob/master/src/components/sets/CellSetExpressionPlot.js#L77


fix: tanh scaler nan bug

Explain what this PR does.

Codecov Report

Merging #112 (89ec866) into main (b9a4aef) will not change coverage.
The diff coverage is n/a.

@@           Coverage Diff           @@
##             main     #112   +/-   ##
=======================================
  Coverage   86.87%   86.87%
=======================================
  Files          18       18
  Lines         975      975
  Branches      107      107
=======================================
  Hits          847      847
  Misses        106      106
  Partials       22       22

:mega: We’re building smart automated test selection to slash your CI/CD build times. Learn more


net/usrsock: fix get/setsockopt issue about usrsock protocol

Summary
net/usrsock: fix get/setsockopt issue about usrsock protocol
Signed-off-by: dongjiuzhu1<EMAIL_ADDRESS>Impact
fix issue about https://github.com/apache/incubator-nuttx/pull/7651
Testing
djz:nuttx$ ./nuttx
NuttShell (NSH) NuttX-10.3.0
nsh> us
usleep
usrsocktest
nsh> usrsocktest
Starting unit-tests...
Testing group "char_dev" =>
Group "char_dev": [OK]
Testing group "no_daemon" =>
Group "no_daemon": [OK]
Testing group "basic_daemon" =>
Group "basic_daemon": [OK]
Testing group "basic_connect" =>
Group "basic_connect": [OK]
Testing group "basic_connect_delay" =>
Group "basic_connect_delay": [OK]
Testing group "no_block_connect" =>
Group "no_block_connect": [OK]
Testing group "basic_send" =>
Group "basic_send": [OK]
Testing group "no_block_send" =>
Group "no_block_send": [OK]
Testing group "block_send" =>
Group "block_send": [OK]
Testing group "no_block_recv" =>
Group "no_block_recv": [OK]
Testing group "block_recv" =>
Group "block_recv": [OK]
Testing group "remote_disconnect" =>
Group "remote_disconnect": [OK]
Testing group "basic_setsockopt" =>
Group "basic_setsockopt": [OK]
Testing group "basic_getsockopt" =>
Group "basic_getsockopt": [OK]
Testing group "basic_getsockname" =>
Group "basic_getsockname": [OK]
Testing group "wake_with_signal" =>
Group "wake_with_signal": [OK]
Testing group "multithread" =>
Group "multithread": [OK]
Unit-test groups done... OK:17, FAILED:0, TOTAL:17
-- number of checks made: 3589
HEAP BEFORE TESTS:
total       used       free    largest
Mem:      67108224     284448   66823776   66823712
HEAP AFTER TESTS:
total       used       free    largest
Mem:      67108224     624768   66483456   66481408
nsh> poweroff

@masayuki2009 @jerpelea Hello, please try this patch to fix usrsock issue, i do usrsock test, it'is OK.


Fix logger for error response case

What this PR does / why we need it:
There is a regression to propagate the error response when we consolidate the logger to the agent.
Which issue(s) this PR fixes (optional, in fixes #<issue number>(, fixes #<issue_number>, ...) format, will close the issue(s) when PR gets merged):
Fixes #1530
Release note:

/assign @theofpa

I created a new e2e test and the current change doesn't write the error response to the cloudevent, see below and theofpa/kfserving@fa0caf6. Can I edit the PR to add the test?

Do we need to capture the error response?

I created a new e2e test and the current change doesn't write the error response to the cloudevent, see below and theofpa/kfserving@fa0caf6. Can I edit the PR to add the test?

Do we need to capture the error response?

Yes, when producing statistics about the failures, based on logs, that would be useful.

I created a new e2e test and the current change doesn't write the error response to the cloudevent, see below and theofpa/kfserving@fa0caf6. Can I edit the PR to add the test?

Do we need to capture the error response?

Yes, when producing statistics about the failures, based on logs, that would be useful.

There was an issue and we did not log error response intentionally, I think this needs to be an option.
Some use cases for the logger are to store the core request, responses and allow them to be further analyzed for outlier detection, drift detection, adversarial detection etc. If we allow error payloads then this creates a different and unexpected payloads and is a different use case I think.
We could add error_response as a new type and it would need to be given a different cloud event type so downstream processes like above could ignore them.

/retest

/lgtm


Project setting Rendering/Node name casing not work while custom class node instantiate in scene

Godot version
3.5 stable
System information
Win10, Core I7-9700,RTX2080
Issue description

Steps to reproduce
see description
Minimal reproduction project
No response

I've just worked on another name casing issue, keen to give this one a go!


DS-3584 when editing an eperson, trying to change its email address is ignored if another user already has that email address

https://jira.duraspace.org/browse/DS-3584

also committed to dspace 6 branch:
https://github.com/DSpace/DSpace/pull/1732
also committed to dspace 4 branch:
https://github.com/DSpace/DSpace/pull/1734

👍 Looks good. Obvious one-liner, and already tested/merged into 6.x


Cyclical dependency requires a clean build

A cyclical dependency where a reference to the target is totally removed does not result in the build "fixing" itself. The  cyclical dependency check continues outputs the deleted target, necessitating a clean build.

Yeah that's because there are two cycle detectors ... one that works, and one that works sometimes, and the former isn't enabled by default.
It should be. cc @cjhopman

Oh, actually, it looks like it is the default... So no, that's not it.

Nothing that's public unfortunately, I've had this happen a few times now though, so I'll see if I can put together a small repro.

yeah this is probably eager deps, which will be fixed in a few months.

Nothing that's public unfortunately, I've had this happen a few times now though, so I'll see if I can put together a small repro when it happens again.

It's probably not too important, I think we have repros of eager deps causing spurious cycles already (but I'll let Bob correct me if not).
i'll close the issue for now so we don't clutter our issue tracker too much, though we realize it's not "fixed" yet.


Adding diameter & radius for Cylinder

Is your feature request related to a problem? Please describe.

Describe the solution you'd like

I would like to add the calculation of radius and diameter also for the cylinder shape calculator.
And I want to change the image since it is too small and labelling are barely visible. I'll create an image for the same.

Please assign this to me under GSSoC.

Go ahead
Deadline 29 April,2021


Docker build: TCP and RDMA tier

Docker build files for the TCP and RDMA tier.
User can extend configuration to use NVMf or other tiers.
Signed-off-by: Jonas Pfefferle<EMAIL_ADDRESS>

Just to give some background: If we want our docker images to be publish as the Apache user on the Docker Hub the docker build files need to be part of the Apache source repository.


Unbound break point problem ( vs code version 1.55.02,chrome: 87.0.4280).give a solution to this problem

Hi team,
when i try to debug angular code in vs code. Before  debugging beak point show as red. after debug start debug point
show as  unbound
i installed extension for debug for chrome in visular studio.
.vscode folder launch.json files content.
{
// Use IntelliSense to learn about possible attributes.
// Hover to view descriptions of existing attributes.
// For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
"version": "0.2.0",
"configurations": [
{
"type": "pwa-chrome",
"request": "launch",
"name": "Launch Chrome against localhost",
"url": "http://localhost:4200",
"webRoot": "${workspaceFolder}"
}
]
}

/jsDebugLogs


1794-dev-ontosealevel

There a few issues/concerns that require attention. @sm453 , would appreicate your inputs, thanks.

Currently, there is a SeaLevel concept and a SeaLevelMeasurement concept. The intention is that a sea level (say global mean sea level or Singapore mean sea level) can have different measurements at different date and time. Instead of instantiating different instances of SeaLevel each time we measure the global mean sea level or Singapore mean sea level, we have one instance of SeaLevel to represent the concept of the global mean sea level or Singapore mean sea level, then different instances of SeaLevelMeasurement to represent the sea level measured at different time. And then om:Height is linked to SeaLevelMeasurement. @sm453, would appreciate your thoughts on whether this would be the good practice or om:Height is to be linked to SeaLevel and SeaLevelMeasurement to be removed?

Currently, the concept of Damage due to rising sea level is incorporated into OntoSeaLevel, and is linked directly to SeaLevelMeasurement via hasImpact. I feel that there is a better way to represent this.

For example, we could have a concept called SeaLevelRise, perhaps in OntoSeaLevel or a new ontology called OntoSeaLevelRise. Then, one could have SeaLevelRise link to a SeaLevel, and have a starting SeaLevelMeasurment and an ending SeaLevelMeasurement. Then, the Damage concept can be linked to the SeaLevelRise concept via hasImpact and will no longer link to SeaLevel.
Please see the Miro board for a visual illustration. See the frame ‘2nd draft’ for the current OntoSeaLevel draft in this pull request. See the frame ‘potential 3rd draft’ for the illustration of the above comment in 2) regarding a possible SeaLevelRise concept.

Currently, there is a SeaLevel concept and a SeaLevelMeasurement concept. The intention is that a sea level (say global mean sea level or Singapore mean sea level) can have different measurements at different date and time. Instead of instantiating different instances of SeaLevel each time we measure the global mean sea level or Singapore mean sea level, we have one instance of SeaLevel to represent the concept of the global mean sea level or Singapore mean sea level, then different instances of SeaLevelMeasurement to represent the sea level measured at different time. And then om:Height is linked to SeaLevelMeasurement. @sm453, would appreciate your thoughts on whether this would be the good practice or om:Height is to be linked to SeaLevel and SeaLevelMeasurement to be removed?

Sounds to me like any sea level should be a standard time series, but the way you talk about it suggests that isn't currently your plan? I'm guessing this would solve all the issues you mention. Also, this would have the added advantage that you could use the Forecasting Agent to create forecasts, and you wouldn't need to reinvent any wheels how to represent this ontologically (because this has already been done).

Currently, the concept of Damage due to rising sea level is incorporated into OntoSeaLevel, and is linked directly to SeaLevelMeasurement via hasImpact. I feel that there is a better way to represent this.

For example, we could have a concept called SeaLevelRise, perhaps in OntoSeaLevel or a new ontology called OntoSeaLevelRise. Then, one could have SeaLevelRise link to a SeaLevel, and have a starting SeaLevelMeasurment and an ending SeaLevelMeasurement. Then, the Damage concept can be linked to the SeaLevelRise concept via hasImpact and will no longer link to SeaLevel.

For starters, I'd call it "change" rather than "rise", for generality. I believe it should simply point to two entries in a sea-level time series (the "start" and "end" of the delta). This would then contain all necessary information (the times over which the change is considered, and the start and end values of the sea level). You could then associate any damage to an instance of such a SeaLevelChange.
Whether such a design is politically a good idea is a different question... I would expect that this will expose a large attack surface, e.g. people will question whether any particular damage is really attributable to a certain change, and over what period this should or should not have been considered...

Sounds to me like any sea level should be a standard time series

Yeah. I have updated accordingly.

believe it should simply point to two entries in a sea-level time series (the "start" and "end" of the delta).

Makes sense. Only thing is that I am not sure how this should be represented in the ontology. As the time series is actually store in PostgreSQL, and OntoTimeSeries does not have any concept that represents an entry in the time series.

If sea level is represented as time series, does the measuredIn relationship it still make sense? Is this not already captured by the time series?

believe it should simply point to two entries in a sea-level time series (the "start" and "end" of the delta).

Makes sense. Only thing is that I am not sure how this should be represented in the ontology. As the time series is actually stored in PostgreSQL, and OntoTimeSeries does not have any concept that represents an entry in the time series.

Use time stamps (with which you can then look up in the time series)?


RSS feeds are no longer following the RSS specification

I'm submitting a bug report
Brief Description
RSS feeds are reported as invalid by W3C RSS feed checker
What is the motivation / use case for this feature?

What  is the current behaviour, (attach relevant screenshots) ?
see https://validator.w3.org/feed/check.cgi?url=https://osmcha.org/api/v1/aoi/9c5e20c2-f8c3-46d3-8ed4-4bd0040d8b9a/changesets/feed/
What  is the expected behaviour ?
RSS feeds are valid

It's fixed now!


Pagination is available with empty tables

Even though my users table is empty I'm still able to paginate through the links provided in the listing.

We probably still want to return a 200 status code and an empty array for data even if you exceed the maximum page but null values for prev and next.
{
  data: [],
  links: {
    self: 'http://localhost:4000/users?page=1',
    first: 'http://localhost:4000/users?page=1',
    last: 'http://localhost:4000/users?page=1',
    prev: null,
    next: null
  }
}

Agreed.


404 at: /engine/reference/builder/

URL: /engine/reference/builder/
Heads up: Dockerfile on the main site is inaccessible.
When I Click on the Dockerfile reference, I get a Big 404.
I guess I'll look into the TheWayBackMachine for now:
https://web.archive.org/web/20170107004921/https://docs.docker.com/engine/reference/builder/

Fixed in #1286


begin moving reducers into logically correct place

Refactoring reducers to a single folder
-no logical changes, just moving dispatch reducers for camera_settings into its own file
-will make this adjustment for all components, after this first one is merged

Not quite sure how this will move all the reducers to a single folder, when your new reducer.ts is still inside the camera_settings folder? Or are you going to move it again?

Single reducer / feature. No other application has the reducers inside of the actual component, it just isn't good style. The alternate approaches are having a reducer/ folder with all the reducers or have a reducer/feature. The reducer/feature is the widely accepted best practice for redux at the moment.