primordic/IssueDupesLite huggingface dataset:
Repository: tenacityteam/tenacity-legacy

Issue #652: It is no longer possible to record computer audio starting with a particular Tenacity build
Created: 2021-10-02T09:50:50Z
Guidelines

 I have read the guidelines.

Version/Commit hash
eb774a4
Describe the bug.

Launch Tenacity.
Open Tenacity settings (e.g. by clicking: Edit > Preferences...), and go to "Devices" category.
Set the host interface to "Windows WASAPI".
Try to set the recording device to speakers/headphones to loop back computer audio for recording.
See what is described in the "Actual behavior" section.

Actual behavior
Only the microphone appears on the list, the speakers/headphones do not, as well as a few other devices, making it impossible to record computer audio using newer Tenacity builds.
Screenshot showing how it "works" (or actually does not) in newer Tenacity builds:

As you can see in the screenshot above, in newer Tenacity builds, only the microphone ("mikrofon" in my OS language) appears on the list, making it impossible to record computer audio.
Expected behavior
At least the speakers/headphones should also appear on the list as one of the available recording devices, as they did in older Tenacity builds.
Screenshot showing how it worked in older Tenacity builds, and how I expect it to work now as well:

As you can see in the screenshot above, in older Tenacity builds, the speakers ("głośniki" in my OS language), as well as a few other devices, all appeared on the list. This is how I expect it to work now as well.
OS
Windows
Additional context
This is something that broke between:

the Tenacity build based on commit 4e877b8 (this is the last good one)
the one based on ebd31bb (this is the first bad one).

This issue is not a duplicate

 I have confirmed this issue isn't a duplicate.

Duplicate issue #672: Logitech G933 (and possibly other wireless headsets) not being recognized for capturing computer audio
Created: 2021-10-28T20:10:18Z
Guidelines

 I have read the guidelines.

Version/Commit hash
None given
Describe the bug.

Open Tenacity
Select "Windows WASAPI"
Click on the input device

Expected behavior
The headset should appear in the list of audio input devices, but for some reason doesn't.
OS
Windows 7 Professional (Build 7601, SP1)
Additional context
For context, the headphones do show up if i use a wired set, but I can't do that with my G933 due to a lack of audio in one ear with an external cable. I'm pretty sure this happens in normal Audacity as well.
For the version, I used the one here from the nightly.link site.

This issue is not a duplicate

 I have confirmed this issue isn't a duplicate.

Closing comment (2021-11-02T17:36:08Z): Update:

it looks like this actually is a duplicate of #652, whoops.
It seems to do this for both of my sets of headphones I have. If it's alright, I guess I should close this now.
Repository: tenacityteam/tenacity-legacy

Issue #7: Create a chat room for real time communication
Created: 2021-07-04T21:36:20Z
Maybe on Matrix or on IRC

Duplicate issue #26: Discord Server
Created: 2021-07-05T20:45:47Z
I think it would be helpful to have a discord server for this audacity fork. Very much in favor of the fork and I think that a discord server would help with discussions and all.

Closing comment (2021-07-06T02:58:03Z): Closed as duplicate of #7.
Repository: tenacityteam/tenacity-legacy

Issue #12: Icon and Graphics Ideas
Created: 2021-07-05T11:04:44Z
I'm putting this here so it's visible and available to the new project:

unbranded_sound_program.zip
This image comes from your friendly Inkscape project. Forking is hard, but it is possible. Good luck.

Duplicate issue #37: Logo - offering to help.
Created: 2021-07-05T22:48:35Z
Hi, I'm a designer go would be glad to help, and contribute a logo to the project, free of cost, once a name has been decided.
My website: https://rolandixor.pro

Closing comment (2021-07-06T12:13:53Z): Closed as duplicate of #12.
Repository: tenacityteam/tenacity-legacy

Issue #53: Add to windows package managers
Created: 2021-07-06T08:04:00Z
Chocolatey is a package manager for windows like apt or pacman on Linux.
Chocolatey package after renaming and windows build is done?
https://community.chocolatey.org/packages/

Duplicate issue #307: Chocolatey package
Created: 2021-07-19T02:12:15Z
Thanks for doing this.  It was sorely needed.
Could you please create a Chocolatey package for easier installations and updates?  The instructions can be found here:
https://docs.chocolatey.org/en-us/create/create-packages

Closing comment (2021-07-20T09:10:22Z): This is a duplicate of #53
Repository: tenacityteam/tenacity-legacy

Issue #3: Possible trademark issue
Created: 2021-07-04T15:41:57Z
Audacity name and logo are registered trademarks of Muse Group now
Since the original logo, name, and other trademarks are now registered under Muse Group (MuseCY SM Ltd. to be more precise) (see https://en.wikipedia.org/wiki/Audacity_%28audio_editor%29#Audacity_trademarks for more information), it would be prudent to start thinking of a new brand identity for this project that is not going to collide with Muse Group’s assets.
Choose a new brand identity
If the trademark is indeed an issue, then everything that refers to Audacity’s trademarks needs to be properly expunged from this fork. I suggest we choose a new name, a new logo, and the replacement of every mention of Audacity in the PR materials in the early stages of this project (until we gradually remove them from the code). The sooner it happens the better, since it can cause serious issues down the road.
Alternative ideas
We might want to seek the legal advice of a lawyer or whomever is well-versed in copyright and trademark issue early on to avoid making costly or unnecessary moves.

Duplicate issue #85: New Name
Created: 2021-07-06T18:06:48Z
There was Open Office, now there is Libre Office
There was Audacity, not there is ... ?
Also a logo should be changed, since Muse Group trademarked it.

Closing comment (2021-07-06T18:09:12Z): Dupe. #3, #5, #33
Repository: tenacityteam/tenacity-legacy

Issue #55: Create a space for holding general discussions about the project other than GitHub Issues
Created: 2021-07-06T08:34:58Z
As the title said, please enable the Discussion feature, it is more suitable for discussion.
This will enable people to ask questions without creating a new issue.
Github offical link

Duplicate issue #68: Discussion Panel
Created: 2021-07-06T10:43:33Z
Is your feature request related to a problem? Please describe.
The issues tab is often plagued with topics like a new logo, or a new interface. It would be nice to have a dedicated discussion tab for topics like this.
Describe the solution you'd like

Enable discussion tab in the repository settings tab.

Closing comment (2021-07-06T10:57:38Z): Closed as a duplicate of #55.
Repository: tenacityteam/tenacity-legacy

Issue #12: Icon and Graphics Ideas
Created: 2021-07-05T11:04:44Z
I'm putting this here so it's visible and available to the new project:

unbranded_sound_program.zip
This image comes from your friendly Inkscape project. Forking is hard, but it is possible. Good luck.

Duplicate issue #82: Logo Idea (Temporary)
Created: 2021-07-06T17:23:55Z
I need some opinions, does this look 💩 or alright for now?
I will attach files if it is usable.

Closing comment (2021-07-06T17:25:30Z): Closed as duplicate of #12.
Repository: tenacityteam/tenacity-legacy

Issue #196: Only English language after install
Created: 2021-07-10T03:27:16Z
After build & make, test of the binary shows GUI with es_MX (my system language) and interface preferences do offer many different languages to choose. Then, after doing make install and launching GUI, this only show English language without option to change it on preferences. I guess it can be modified with cmake options.
Tested on Toshiba and Dell laptops and generic desktop.
EDIT: Linux Mint 19.3 OS

Duplicate issue #310: BUG: The language cannot be changed in the settings. 
Created: 2021-07-19T11:15:21Z
Describe the bug
The language cannot be changed in the settings.
To Reproduce
Steps to reproduce the behavior:

Go to 'Settings'
Click on ' Language'
And you can only choose English or System Independent of what is selected, the language does not change.

Closing comment (2021-07-19T11:17:46Z): Oh, I see it's a duplicate of , #196 I'll close it here.
Repository: tenacityteam/tenacity-legacy

Issue #435: On Linux, "audio device selector" toolbars is too small and dropdowns are cut off
Created: 2021-08-08T03:01:55Z
I have read the specified guidelines for issues

Describe the bug
On Linux, the "audio device selector" toolbar is too small and cuts off the left side of the combo boxes/dropdowns.
To Reproduce
Steps to reproduce the behavior:

Install and run Tenacity (or Audacity) on Linux.

Expected behavior
The toolbars are expanded, or the dropdowns are shrunken, so the audio device selector dropdowns don't have their left sides cut off.
As a workaround, you can drag the right side of the toolbar to expand it until the dropdowns are fully visible.
Screenshots
If applicable, add screenshots to help explain your problem.
https://twitter.com/TenacityAudio/status/1424146535386865668

Additional information (please complete the following information):

OS: I'm using Arch Linux and KDE.
Version: long-standing issue since Audacity 2.4.x.

Additional context
Looking at the tweet linked above, apparently the issue also happens on GNOME. I think this issue happens on all Linux DEs where WxWidgets is backed by wxGTK and GTK 3, on both low and high DPI desktops, but I don't encounter it on Windows.

Duplicate issue #623: Broken UI
Created: 2021-09-14T21:14:10Z
Guidelines

 I have read the guidelines.

Version/Commit hash
Audacity-2.4.1-1947-gdce03ba
Describe the bug.
if you look at the device selection you'll notice it looks weird, even when selected they're still blank, plus their size is not well divided (selection of channels is partially covered by the other options)

Expected behavior
it should show which devices are selected and widgets should be better separated from each other
OS
Zorin OS 16
Additional context
No response
This issue is not a duplicate

 I have confirmed this issue isn't a duplicate.

Closing comment (2021-09-14T23:12:14Z): Duplicate of #435
Repository: tenex/rails-assets

Issue #153: bignumbers.js component is lacking main file
Created: 2014-05-14T12:48:58Z
There should be a manifest in component, but it's not there.
Now one had to use require bignumber.js/bignumber.js, which is quite unelegant and annoying.

Duplicate issue #169: Missing manifest for retina.js gem
Created: 2014-08-11T01:15:56Z
Gemfile:
gem 'rails-assets-retina.js'
application.js.coffee:
#= require retina.js
I get this error: couldn't find file 'retina.js'
It seems the bundled gem does not have a generated manifest (only a directory containing the bower files):
louissimoneau@simoneau: ~/.rbenv/versions/2.1.1/lib/ruby/gems/2.1.0/gems/rails-assets-retina.js-1.3.0/vendor/assets/javascripts
$ ls -al                                                                                                                                                                [9:06:02]
total 0
drwxr-xr-x  3 louissimoneau  staff  102 18 Jul 09:52 .
drwxr-xr-x  4 louissimoneau  staff  136 18 Jul 09:52 ..
drwxr-xr-x  7 louissimoneau  staff  238 18 Jul 09:52 retina.js

Maybe the fact that the library name contains a . is causing a problem?

Closing comment (2014-08-11T01:20:19Z): Looks like this is related to #153, probably the same issue.
Repository: tenex/rails-assets

Issue #121: Add support for Bowerfile
Created: 2014-02-28T04:08:23Z
I've used bower-rails and rails-assets and both have their advantages.
rails-assets
pros:

Easy to setup and deploy
Makes continuous integration simple (like with circle)
Uses a familiar format for installing gems

cons:

Unscalable syntax  (#94)
Proxy site can be slow (But I won't dwell on that now)

bower-rails
pros:

Uses the mainline bower code and repositories
Creates logical separation between assets and other gems
Implements Bowerfile, a DSL that mimics the Gemfile format

cons:

Requires more setup time, since it requires node environment setup
Pushing to Heroku requires using multi-buildback

Suggestion
Add support for Bowerfile and merge the two efforts into creating a common file format.
What will this do?

Remove need for hacky ' gem "rails-assets-someone--some-package" ' syntax
Make it easy to easily switch between different asset management tools which can be proposed to be standardized
The folks at 42dev has done the hard work of getting started. Here is the conversation that led to the Bowerfile name: rharriso/bower-rails#29

And check out it's current documentation. https://github.com/42dev/bower-rails#ruby-dsl-configuration
Thoughts?
cc: @SergeyKishenin @rharriso

Duplicate issue #55: Extraction of gem conversion process
Created: 2013-12-09T16:17:01Z
Gem conversion needs to be extracted to separate gem (rails_assets), to allow offloading gem conversion  of components to many servers (currently done in background process). After successful conversion .gem file can be downloaded and index updated in fast manner.
Also it'll allow to generate .gem files for components locally and not depending on rails-assets.org service.

Closing comment (2014-03-06T03:55:48Z): It's now duplicate of #121
Repository: tenex/rails-assets

Issue #195: rails-assets.org is down
Created: 2014-11-11T15:49:25Z
Amazing service! Currently blocking deploy since service is down.
Not sure if this is the proper contact method, but couldn't find another with the site down.

Duplicate issue #274: Service down?
Created: 2015-08-14T17:18:05Z
I'm getting 500 errors willy-nilly from the service.

PS: Also getting timeouts from the dependency endpoint.
Anything, that I can help out figure/resolve this issue?

Closing comment (2015-11-19T11:21:12Z): Duplicate #195
Repository: tenex/rails-assets

Issue #132: How to handle assets conflict (bootstrap)
Created: 2014-03-31T11:09:50Z
I use bootstrap-sass gem because I like to use variable and mixins form it:
gem 'bootstrap-sass'
I've installed bootstrap-multiselect library form rails-assets:
gem 'rails-assets-bootstrap-multiselect'`
But bootstrap-multiselect depends on rails-assets-bootstrap and it has been installed by dependency. Now, when I include bootstrap in my sass file
@import "bootstrap";
version from rails-assets-bootstrap gem is included which is not sass version and doesn't have any variables and mixins.
How can I handle this conflict and load bootstrap version from bootstrap-sass gem?

Duplicate issue #347: Cannot add component that is made up of only dependencies
Created: 2016-05-22T19:48:00Z
Cloning issue:
#264 (comment) [Issue automatically imported by https://github.com/controlgroup/CSV-GitHub-import-export]

Closing comment (2016-05-26T20:17:14Z): This is actually a duplicate of #132
Repository: tenex/rails-assets

Issue #132: How to handle assets conflict (bootstrap)
Created: 2014-03-31T11:09:50Z
I use bootstrap-sass gem because I like to use variable and mixins form it:
gem 'bootstrap-sass'
I've installed bootstrap-multiselect library form rails-assets:
gem 'rails-assets-bootstrap-multiselect'`
But bootstrap-multiselect depends on rails-assets-bootstrap and it has been installed by dependency. Now, when I include bootstrap in my sass file
@import "bootstrap";
version from rails-assets-bootstrap gem is included which is not sass version and doesn't have any variables and mixins.
How can I handle this conflict and load bootstrap version from bootstrap-sass gem?

Duplicate issue #180: Sprockets::FileNotFound (couldn't find file 'bootstrap')
Created: 2014-09-16T21:02:24Z
Having trouble with rails-assets-bootstrap. Bundle updated successfully
Error: http://i.imgur.com/T5ZUL9W.png
Gemfile:
gem 'rails-assets-bootstrap'
application.css:
*= require_tree .
*= require_self
*= require bootstrap
//= require jquery
//= require jquery_ujs
//= require turbolinks
//= require_tree .
//= require bootstrap
//= require.js
"Using rails 4.1.5"
"Using rails-assets-bootstrap 3.2.0"
Processing by WelcomeController#index as HTML
Rendered welcome/index.html.erb within layouts/application (0.1ms)
Completed 500 Internal Server Error in 173ms
ActionView::Template::Error (couldn't find file 'bootstrap'
(in /vagrant/app/assets/stylesheets/application.css:15)):
2: 
3: 
4:   <title>Vmdevdeploy</title>
5:   <%= stylesheet_link_tag    'application', media: 'all', 'data-turbolinks-track' => true %>
6:   <%= javascript_include_tag 'application', 'data-turbolinks-track' => true %>
7:   <%= csrf_meta_tags %>
8: 
app/views/layouts/application.html.erb:5:in `_app_views_layouts_application_html_erb__555197690486104126_39065400'
Rendered /home.rvm/gems/ruby-2.1.2@vmdev/gems/actionpack-4.1.5/lib/action_dispatch/middleware/templates/rescues/_trace.html.erb (2.2ms)
Rendered /home.rvm/gems/ruby-2.1.2@vmdev/gems/actionpack-4.1.5/lib/action_dispatch/middleware/templates/rescues/_request_and_response.html.erb (1.3ms)
Rendered /home.rvm/gems/ruby-2.1.2@vmdev/gems/actionpack-4.1.5/lib/action_dispatch/middleware/templates/rescues/template_error.html.erb within rescues/layout (15.4ms)

Closing comment (2014-09-26T18:06:48Z): Duplicate of #132
Repository: tenex/rails-assets

Issue #203: Exception on build latest marionette assets( Rubygems does not support || in version string '^1.8.0 || ^2.0.0' )
Created: 2014-12-21T13:57:16Z
I checked it happens in jquery lib, because that version is on the website http://marionettejs.com/
I installed it with bower install manually and It was 2.1.3 version.
What if It will be as next expression instead exception?
if version.include?('||')
   version = 'latest'    
end

bin/rake 'component:convert[marionette,2.3.0]'                                                                                                           maxim@matrix
I, [2014-12-21T15:13:47.563571 #28371]  INFO -- : ** [Raven] Raven 0.6.0 ready to catch errors
[fyi] /home/maxim/dev/rails-assets/node_modules/.bin/bower install -p -F marionette#2.3.0 --json --config.tmp=/tmp/bower20141221-28371-14jjs6r/tmp --config.storage.packages=/tmp/bower20141221-28371-14jjs6r/cache --config.interactive=false
rake aborted!
Rubygems does not support || in version string '^1.8.0 || ^2.0.0'
/home/maxim/dev/rails-assets/app/models/build/utils.rb:61:in `fix_version_string'
/home/maxim/dev/rails-assets/app/models/build/utils.rb:172:in `block in fix_dependencies'
/home/maxim/dev/rails-assets/app/models/build/utils.rb:169:in `each'
/home/maxim/dev/rails-assets/app/models/build/utils.rb:169:in `map'
/home/maxim/dev/rails-assets/app/models/build/utils.rb:169:in `fix_dependencies'

Hm, I've found rails-assets-marionette-gem latest version. But It's a mess with this one: 3 rails-assets  gem to one bower lib

Duplicate issue #209: Rubygems does not support || in version string '^1.8.0 || ^2.0.0'
Created: 2015-01-14T16:16:44Z
Any thoughts on how to handle an issue like this from a bower package? (source is backbone.marionette v 2.3.0)

Closing comment (2015-02-10T19:46:31Z): Dup of #203
Repository: tenex/rails-assets

Issue #218: rails-assets-fontawesome is missing woff2 font
Created: 2015-02-02T22:43:37Z
Getting this error:
http://localhost:3000/fonts/fontawesome/fonts/fontawesome-webfont.woff2?v=4.3.0
Failed to load resource: the server responded with a status of 404 (Not Found)

Duplicate issue #222: rails-assets-bootstrap-sass missing woff2 fonts
Created: 2015-02-11T16:26:38Z
the bootstrap-sass gem has woff2 fonts:
https://github.com/twbs/bootstrap-sass/tree/v3.3.3/assets/fonts/bootstrap
there are not present in the rails-assets-bootstrap-sass gem

Closing comment (2015-02-11T16:39:33Z): Duplicate of #218
Repository: tenex/rails-assets

Issue #195: rails-assets.org is down
Created: 2014-11-11T15:49:25Z
Amazing service! Currently blocking deploy since service is down.
Not sure if this is the proper contact method, but couldn't find another with the site down.

Duplicate issue #289: rails-assets.org is down
Created: 2015-11-19T03:48:41Z
https://rails-assets.org is currently throwing a 504

Closing comment (2015-11-19T10:47:42Z): Duplicate #195
Repository: tensorflow/serving

Issue #700: Publish python 3 package
Created: 2017-12-21T17:59:06Z
The tensorflow-serving-api package is only published for python 2. It should also be published for python 3.
This makes following the tutorial impossible using python 3 - and the tutorial makes no note of the python 2 requirement.

Duplicate issue #570: serving not supported python3?
Created: 2017-08-21T09:57:54Z
i'm using python3 to complie serving ,everything is ok but when i followed the tutorial to test mnist bazel-bin/tensorflow_serving/example/mnist_saved_model /tmp/mnist_model
i got imported error! no mock module found,but i'm sure i have installed mock,
this confused me a long time!
finnally i found i'm using python3 not python2,after i installed pip2 packege under python2,and pip2 installed mock, my problem solved! what a joy!!
when i run python tensorflow_serving/example/mnist_client.py --num_tests=1000 --server=localhost:9000
error again! then i use python2 tensorflow_serving/example/mnist_client.py --num_tests=1000 --server=localhost:9000
error solved again!
now i  summarize that most tutorial codes or examples are run under python2 not supported python3(may be partially supported!)
then i hope that serving or it's examples or tutorials can fully supported python3,may be it's a huge hard work!
thank you !!

Closing comment (2018-07-24T21:50:55Z): Duplicate of #700
Repository: tensorflow/serving

Issue #287: Inception Model fails to run on Kubernetes
Created: 2017-01-07T21:59:34Z
From the second step from the following guide: https://tensorflow.github.io/serving/serving_inception, tensorflow_serving is failing to run after creating the deployment with kubectl create -f tensorflow_serving/example/inception_k8s.yaml.
Here is the log output I get.
/bin/sh: 1: /serving/bazel-bin/tensorflow_serving/model_servers/tensorflow_model_server: not found

Duplicate issue #390: Kubernetes deployments fails
Created: 2017-03-31T13:29:47Z
the Kubernetes deployment manifest at:
https://github.com/tensorflow/serving/blob/master/tensorflow_serving/example/inception_k8s.yaml
Fails to run.
The server is not there in the image.
$ kubectl logs inception-deployment-1543703507-042r7
/bin/sh: 1: /serving/bazel-bin/tensorflow_serving/model_servers/tensorflow_model_server: not found

The image on gcr has no model_servers directory:
docker run -ti --rm gcr.io/tensorflow-serving/inception /bin/bash
root@cd0d1569d74c:/# cd /serving/bazel-bin/tensorflow_serving/
root@cd0d1569d74c:/serving/bazel-bin/tensorflow_serving# ls -l
total 36
drwxr-xr-x  7 root root 4096 Mar 21  2016 batching
drwxr-xr-x 19 root root 4096 Mar 21  2016 core
drwxr-xr-x 10 root root 4096 Mar 21  2016 example
drwxr-xr-x  5 root root 4096 Mar 21  2016 resources
drwxr-xr-x  4 root root 4096 Mar 21  2016 servables
drwxr-xr-x  8 root root 4096 Mar 21  2016 session_bundle
drwxr-xr-x  3 root root 4096 Mar 21  2016 sources
drwxr-xr-x  3 root root 4096 Mar 21  2016 test_util
drwxr-xr-x 12 root root 4096 Mar 21  2016 util

Is there an image tag we can use to make sure we got a working image ?
thanks, just trying to follow this guide https://tensorflow.github.io/serving/serving_inception

Closing comment (2017-03-31T13:59:50Z): fwiw, this is a dupe of #287
it would be great to have a tag of a working image.
Repository: tensorflow/serving

Issue #700: Publish python 3 package
Created: 2017-12-21T17:59:06Z
The tensorflow-serving-api package is only published for python 2. It should also be published for python 3.
This makes following the tutorial impossible using python 3 - and the tutorial makes no note of the python 2 requirement.

Duplicate issue #661: No python3 tensorflow_serving_api package.
Created: 2017-11-16T17:14:03Z
Our entire codebase is written in py3 and tensorflow proper supports py3 heavily. I was wondering if there is any plan to add this for py3 or should I just build from bazel.

Closing comment (2018-07-24T21:18:10Z): Duplicate of #700
Repository: tensorflow/serving

Issue #422: Best practice: loading new models during runtime (not versions)
Created: 2017-04-26T14:05:55Z
I'm curious to see/discuss what are the best options to deploy new models during runtime. I learned from the tutorials and examples how new versions of a model are loaded as well as how to load multiple models (at the start of the server). Unfortunately I could not find the functionality of loading new models during runtime. (I'm aware that parts where discussed in this issue #380.) I guess my first question is if there exists already any type of dynamic loading of new models during runtime? (I think technically all components should be there, however I could not find a 'trigger' which starts the process of loading the model.)
In general I can think of several ways to serve multiple models during runtime:

Use a separate server for each model. A small manager module could start a new server or even docker container for each model. (Question: How to best forward the prediction request to the right server?)
Similar to watching multiple versions on the file system, we could watch the parent directory if a new folder for a new model.
If we use the 'custom_model_config' we periodically check it for changes and see if a new model is in there (or one is missing).
Have a gRPC call which sends the required information for the new model.

It would be great if some of the developers could post their opinion on how such a case should be handled or people share their opinion on best practices related to deploying new models.

Duplicate issue #537: How do I swap between modules during runtime?
Created: 2017-07-27T05:24:19Z
I understand that Tensorflow-serving will reload automatically when a new model version is added (0, 1, 2, 3, ...) but AFAIK, there's no way to do that for a new model.
I could only find documentation to change models either by specifying all models beforehand via a static configuration file (then changing between them somehow) or via the command line but is it possible to change to a model during runtime without Tensorflow-serving knowing about it upfront?

Closing comment (2018-02-26T17:29:10Z): FYI this thread is closely related to #422
Repository: tensorflow/tensor2tensor

Issue #142: unicode error when using python 3
Created: 2017-07-12T11:14:54Z
Encountered an error related to using 'unicode' in python 3. My environment uses python 3 anaconda with tensorflow installed using virtualenv.
File "...tensorflow-gpu/lib/python3.6/site-packages/tensor2tensor/data_generators/text_encoder.py", line 476, in _escape_token
assert isinstance(token, unicode)
NameError: name 'unicode' is not defined
Seems to be fixed by replacing the line with:
if PY2 :
assert isinstance(token, unicode)
else :
assert isinstance(token, str)

Duplicate issue #760: *bug* ImageCelebA generator expects non-binary raw image data?
Created: 2018-04-30T20:59:47Z
Description
Stock celeba_problem.generate_data() fails with UnicodeDecodeError.
TensorFlow and tensor2tensor versions
tensor2tensor==1.6.1
tensorboard==1.8.0
tensorflow==1.8.0
Steps to reproduce and error log
See https://gist.github.com/cwbeitel/1cd9331dfb23d239a8f2ce692ee59055

Closing comment (2018-05-01T14:53:54Z): Both of these run to completion with Python 2.7 runtime.
Duplicate or related to #142, #102
Should use tf.gfile.Open in place of built-in Open?
Repository: tensorflow/tensor2tensor

Issue #415: How to measure the number of training epochs
Created: 2017-11-13T16:45:04Z
In order to compare with other NMT frameworks, I would like to know how many training epochs (i.e. passes over the whole training data) are done at the moment.
I can see the number of training (global) steps and I guess epochs = steps * batch_size / training_subwords.
So the questions boils down to: How to make T2T report (e.g. in the log) the number of subwords in the training data?

Duplicate issue #733: How to set the number of epoch while training a machine translation？
Created: 2018-04-20T14:14:53Z
Description

…

TensorFlow and tensor2tensor versions


…How to set the number of epoch while training a machine translation？

In case of bug report: Steps to reproduce the problem

…

In case of bug report: Error log



…

Closing comment (2018-04-21T11:40:15Z): No need to modify the code, just use --train_steps=123456. I suggest to close this issue: specifying training steps is possible and the rest is a duplicate of #415.
Repository: tensorflow/tensor2tensor

Issue #266: Evaluation failed when training with multiple work gpus
Created: 2017-08-30T23:53:29Z
I set worker_gpu to 2 and use 2 gpus in the same node. training is completely fine. But evaluation fails with this error
"
tensorflow.python.framework.errors_impl.InvalidArgumentError: Number of ways to split should evenly divide the split dimension, but got split_dim 0 (size = 47) and num_split 2
[[Node: split_2 = Split[T=DT_INT32, num_split=2, _device="/job:localhost/replica:0/task:0/cpu:0"](split_2/split_dim, input_reader/ExpandDims_3/_1823)]]
[[Node: split_2/_1825 = _HostRecvclient_terminated=false, recv_device="/job:localhost/replica:0/task:0/gpu:1", send_device="/job:localhost/replica:0/task:0/cpu:0", send_device_incarnation=1, tensor_name="edge_1113_split_2", tensor_type=DT_INT32, _device="/job:localhost/replica:0/task:0/gpu:1"]]
Caused by op u'split_2', defined at:
File "/raid/skyw/venv/tensorflow-pip-py27/bin/t2t-trainer", line 5, in 
pkg_resources.run_script('tensor2tensor==1.2.1', 't2t-trainer')
"
It wasn't very clear what the error is. But single GPU training/evaluation is fine, the problem comes with 2 worker GPUs.

Duplicate issue #260: when I use new version tensor2tensor 1.2.0, only use cpu instead of GPU
Created: 2017-08-29T06:04:15Z
I doing ende translation problem use transfomer model. and I have 8 GPU on my machine, I used them all.
during model building, program give imformation as follows:

INFO:tensorflow:Unsetting shared_embedding_and_softmax_weights.
INFO:tensorflow:Doing model_fn_body took 2.946 sec.
INFO:tensorflow:Doing model_fn_body took 1.755 sec.
INFO:tensorflow:Doing model_fn_body took 1.756 sec.
INFO:tensorflow:Doing model_fn_body took 1.876 sec.
INFO:tensorflow:Doing model_fn_body took 1.828 sec.
INFO:tensorflow:Doing model_fn_body took 1.573 sec.
INFO:tensorflow:Doing model_fn_body took 1.906 sec.
INFO:tensorflow:Doing model_fn_body took 1.711 sec.

but, when I see : nvidia-smi, I found no GPU used, Could you help me why ?

Closing comment (2017-09-05T12:58:51Z): I suggest to close this as a duplicate of #266.
Repository: tensorflow/tensor2tensor

Issue #333: Tokenization issue after upgrading to T2T 1.2.4
Created: 2017-10-03T09:28:16Z
My t2t-decoder started to insert a blank symbol after each character after upgrading to T2T 1.2.4:

t2t-decoder 
--data_dir=t2t_data/ 
--problems=translate_ende_wmt32k 
--model=transformer 
--hparams_set=transformer_base
(...)
INFO:tensorflow:Inference results INPUT: Airport building evacuated
INFO:tensorflow:Inference results OUTPUT: F l u g h a f e n g e b <C3> <A4> u d e   e v a k u i e r t

If I remove the blanks afterwards with

cat decodes | sed 's/   //g' | tr -d ' ' | sed 's// /g'

the output looks okay again. I have generated the data and trained the model with T2T 1.2.3.

Duplicate issue #357: How do I generate plain decoded text?
Created: 2017-10-12T22:48:47Z
It generates something like this, spaces and delimiters.
H a l l o   W e l t
G � � t e r v e r k e h r s w e l t
I remember decode used to generate plain text. How could I do that now?

Closing comment (2017-10-13T08:13:21Z): This is a duplicate of #333.
Repository: tensorflow/tensor2tensor

Issue #581: OOM Error after long training
Created: 2018-02-13T18:05:05Z
If I set batch_size too high, the training fails with Out Of Memory error. This is expected. The annoying point is that the failure can happen e.g. after 3 hours of training (15k steps) or even after two and half days of training (150k steps) . Thus it is difficult to find the highest possible batch size (for a given model, optimizer and max_length, which are other hyperparameters influencing the memory consumption I am aware of).
When I set batch_size much too high, the training fails immediately (i.e. in two minutes, after building the graph on GPU and saving the first checkpoint). However, when I set batch_size just a bit too high, the failure comes usually in the first few thousand steps, but sometimes much later (the 3 hours, 15000 steps). Moreover, it seems the number of steps before OOM failure is non-deterministic and it is not always true that higher batch_size fails earlier. Also it is not true that the maximal batch size can be approximated as the maximal batch size that fails immediately minus the max_length (which was my original hypothesis) - it can be be much lower. BTW: In #444 (comment) I report the empirical maximum batch size for a given model, optimizer and max_length.
My guess is that there is something wrong with the batching and bucketing. I was lazy to check the source code carefully. Maybe it is allowed to exceed the batch_size (the first sentence which exceeds the given number of subwords is kept in the batch, although it should be excluded). Maybe the memory is consumed by the queue of sentences for bucketing (although I would say, these are in the CPU RAM, not GPU memory). Anyway, it would be nice if the training fails either immediately or never.

Duplicate issue #637: out of gpu memory at the end closed to converge...
Created: 2018-03-05T04:40:35Z
I used transformer in Chinese to English translation task..
all the time it was running normally.. but after about 300000 iters, it always ran out of gpu memory when running several iters.
my configuration was all the same..
except this, all is perfect..
Does anybody has some ideas?
the machine is 4 * K80..
and tensor2tensor version is 1.4.1..
my training configuration is like below:
@registry.register_hparams
def translate_any_c2e_hparams():
hparams = transformer.transformer_big_single_gpu()
hparams.optimizer_adam_beta2 = 0.999
hparams.batch_size = 2000
hparams.learning_rate_warmup_steps = 2000
hparams.learning_rate = 0.05
return hparams

Closing comment (2018-03-05T09:27:04Z): I think this is a duplicate of #581
Repository: tensorflow/tfx

Issue #362: Parameterized pipelines using Kubeflow? 
Created: 2019-07-19T07:16:16Z
hi again! As you can see im a new and very excited user of the technology and have so many questions as I want to implement TFX in our company's workflows and want to convince everyone of it as well! (P.S. if theres a way for me not to bombard this board and just ask questions on another format, I would be do that).
This time I wanted to ask about Kubeflow pipelines. In Kubeflow, it is relatively easy to just specify parameters that can be changed on the UI (see image below). This lets you run the same pipeline with different configurations (i.e. different feature transforms, different hyper parameters, different model types even)
However, I am not sure how TFX solves this problem. It is surely an important point - If pipelines are not parameterized, how can we re-use them and run them again and again? Do we always have to edit our codebase (i.e. tf.transform preprocessing_fn, training_fn etc) and upload a new pipelines each time?  If that is the case, is the ML metadata shared across pipelines or is there a ML metadata store per pipeline? Specifically, if cache is enabled and we have two pipelines (A and B) with the same first examplegen, schemagen and statisticsgen code and then different transform and train code, and we run pipeline A, and then we run pipeline B on the same inputs, would pipeline B be 'aware' enough to skip the first three component steps?
I hope the example illustrates what im asking. If not, i can clarify. Thanks in advance!

Duplicate issue #38: Add example with hyperparameters for Kubeflow
Created: 2019-04-10T07:28:27Z
It would be great if there would be an example on how to run TFX pipelines in Kubeflow with hyperparameters. Right now all the parameters are hard-coded, but having these exposed in the Kubeflow Pipelines UI (with defaults) would help with setting up a professional ML pipeline.
It's not entirely clear how to do this, so that @PipelineDecorator can pick these up. I presume they need to live in input_dict as kfp.dsl.PipelineParams but a simple example would make that a lot easier to do.

Closing comment (2019-09-27T17:46:18Z): Duplicate of #362
Repository: tensorflow/tfx

Issue #3326: Support Secrets for Kubeflow serialized components
Created: 2021-03-03T18:11:08Z
I currently have a presto_example_gen like component for reading from an SQL database which takes database credentials.

  
    
      tfx/tfx/examples/custom_components/presto_example_gen/presto_component/executor.py
    
    
         Line 123
      in
      5f78c24
    
  
  
    

        
          
           return prestodb.auth.BasicAuthentication(conn_config.basic_auth.username, 
        
    
  


Since the kubeflow_dag_runner serializes the components the password is available in plain text in Kubeflow even if I were to add it through the CI instead of committing it to the pipeline code.
While there is support to mount secrets as env variables through operator functions, I don't think there is a way to use variables in the TFX container. Would be great to be able to support substituting in secrets when de-serializing, or if there is already a way, add an example.

Duplicate issue #5049: How to set environment variables for pipeline stages
Created: 2022-07-18T17:47:09Z
I'm trying to figure out how to set environment variables when creating a pipeline, the use case here is that I want to use S3 and I need to push down the credentials to tensorflow-io via environment variables, in previous releases of TFX when using reusable kubeflow components, I could simply add the environment variables, but if I try to use the built-in components and then compile them via DSL KubeflowDagRunnerConfig to a package, I don't see how I can set environment variables for individual stages.
Is the only option to wrap TFX native components in function components?

Closing comment (2022-07-22T02:24:34Z): Thank you for the question and it seems like a duplicate of #3326. I believe that @ConverJens is working on this in #4861.
Repository: tensorflow/tfx

Issue #2545: Support RuntimeParams in Airflow
Created: 2020-09-23T00:01:06Z
With the addition of RangeConfig for ExampleGen components, it would be nice to parametrize the pipeline to load data based on execution_date Airflow context variable to perform backfills. This variable is only resolvable at component/operator execution time so we need some way to tell TFX we want to parametrize the pipeline using that. This also opens a way to use Airflow native parametrization interface for parametrizing TFX pipelines.
Related to apache/airflow#11054

Duplicate issue #3010: Pass in runtime args to Airflow orchestrator
Created: 2020-12-27T19:26:55Z
With TFX LocalDagRunner I can do something like this:
    dag = LocalDagRunner()
    dag.run(create_pipeline(
        pipeline_name="eval_pipeline",
        pipeline_root=cur_dir,
        beam_pipeline_args=_beam_pipeline_args,
        metadata_path=_metadata_path,
        my_custom_args=args
    ))

Where my_custom_args are new args that I provide to my __main__ function each time I run the TFX program.
but with the airflow runner, doing the same:
    DAG = AirflowDagRunner(AirflowPipelineConfig(_airflow_config)).run(
        create_pipeline(
            pipeline_name="eval_pipeline",
            pipeline_root=cur_dir,
            beam_pipeline_args=_beam_pipeline_args,
            metadata_path=_metadata_path,
            my_custom_args=args
        )
    )

wouldn't work since DAG is supposed to be discovered when the scheduler is started, so I need to give these parameters when the airflow task is run:
airflow dags trigger --conf '{"conf1": "value1"}' my_dag

So now my question is: How do you manage to have a consistent code between the two runners where create_pipeline can receive the configuration the same way?
Thanks!

Closing comment (2021-01-21T17:49:45Z): Related to: #2545
Repository: tensorflow/tfx

Issue #21: Support split config across the pipeline platform
Created: 2019-03-22T16:35:07Z
We need to support specifying a list of splits in various example_gen implementations and corresponding distribution, and make sure all components can select which splits they process.

Duplicate issue #352: ExampleGen should support custom time series splits
Created: 2019-07-17T19:34:10Z
Is there a way to specify a split for the ExampleGen component that splits across a 'timestamp' field? This cannot work currently as the raw data might not be sorted according to time.

Closing comment (2019-07-22T17:53:42Z): @htahir1 Currently many components only work on hard-coded splits 'train' and 'eval', although we plan to support more splits in the future (tracked by #21).
Repository: tensorflow/tfx

Issue #2223: TensorFlow-extended: no interactive HTML results generated on databricks
Created: 2020-07-26T21:11:27Z
Hi, I am trying to run the TensorFlow-extended example code
https://www.tensorflow.org/tfx/tutorials/tfx/components_keras
on databricks with env:
   7.1 ML Spark 3.0.0 Scala 2.12 GPU
   tfx==0.22.0
   TensorFlow version: 2.2.0
   py: 3.7
   Unbuntu: 4.4.0

When I run
   context.show(statistics_gen.outputs['statistics'])

I got:
   <IPython.core.display.HTML object>
   <IPython.core.display.HTML object>
   <IPython.core.display.HTML object>
   <IPython.core.display.HTML object>
   <IPython.core.display.HTML object>

I can run
   html="""
  <html lang="en">
  <head>
  <b>'hello'</b>
  </head>
  """
   displayHTML(html)

and got the exptected HTML.
How to get them for the example TFX code ?
I have tried IPython.core.display and import html, but it does not work.
thanks

Duplicate issue #2221: TensorFlow-extended: How to generate interactive HTML outputs on databricks notebook
Created: 2020-07-25T21:12:14Z
I am trying to run some example codes at https://www.tensorflow.org/tfx/tutorials/model_analysis/tfma_basic on a databricks notebook.
I would like to use TensorFlow-extended to do ML model development and production on databricks GPU clusters.
The expected results from the code should be an interactive HTML file. There will be some tabs and buttons on the HTML. The users can manipulate the results and visualize them in multiple layouts.
I have run the code by Colab and it works well. You can try it by clicking the button "Run in google Colab" at the top of the webpage and run the notebook on Colab and see the interactive results.
But, when I run the same code on databricks notebook, no interactive results are generated. Only plain txt messages are dumped to the output cells in the notebook.
The databricks env :
7.1 ML Spark 3.0.0 Scala 2.12 GPU
Could you please help us about this ?

Closing comment (2020-07-27T16:51:01Z): Closing this issue as it is been tracked in #2223. Thanks!
Repository: tensorflow/tfx

Issue #4902: TFX 1.8.0 Issues
Created: 2022-05-26T18:52:01Z
Please comment or link any issues you find with TFX 1.8.0
Thanks.

Duplicate issue #5051: TFX 1.9.0 Issues
Created: 2022-07-18T19:22:31Z
Please comment or link any issues you find with TFX 1.9.0
Thanks.

Closing comment (2022-08-05T00:10:59Z): Still running into problems with Dataflow jobs being stuck and killed after 1 hour. TFX 1.9.1 with Apache Beam 2.40.0, using the TFX docker image tensorflow/tfx:1.9.1
Error message from Dataflow:

Workflow failed. Causes: The Dataflow job appears to be stuck because no worker activity has been seen in the last 1h. Please check the worker logs in Stackdriver Logging. You can also get help with Cloud Dataflow at https://cloud.google.com/dataflow/support.

Related to #4902 (comment)
Edit: Found the solution. It turns out it is related to this known issue. Setting the flag --experiments=disable_worker_container_image_prepull helped resolve the issue. TFX image has been getting larger and larger (TFX 1.9.1 around 17GB uncompressed), which means we would need to keep using this workaround in the future releases.
Repository: termux/termux-app

Issue #2503: [Bug]: Can't switch input method correctly
Created: 2022-01-02T07:38:18Z
Problem description
Termux cannot switch input mode correctly, The keyboard used is Gboard
Steps to reproduce the behavior.

  
    
    

    SVID_20220102_153256_1.mp4
    
  

  

  


What is the expected behavior?
ls
System information
Application version:
0.117
Packages CPU architecture:
aarch64
Subscribed repositories:
sources.list
deb https://packages.termux.org/apt/termux-main/ stable main
Updatable packages:
All packages up to date
Android version:
9
Kernel build information:
Linux localhost 4.9.148 #1 SMP PREEMPT Thu Dec 9 02:49:28 CST 2021 aarch64 Android
Device manufacturer:
HUAWEI
Device model:
VTR-AL00

Duplicate issue #2743: [Bug]: Cannot switch keyboards normally
Created: 2022-04-28T05:22:23Z
Problem description

  
    
    

    Screenrecorder-2022-04-28-13-15-17-721.mp4
    
  

  

  


Steps to reproduce the behavior.
Just update to the latest version
What is the expected behavior?
No response
System information

Termux application version: 0.118.0
Android OS version: 12
Device model: xiaomi k40

Closing comment (2022-04-28T06:03:49Z): Duplicate of #2503
Repository: termux/termux-app

Issue #2077: Native text input box hides the keyboard
Created: 2021-05-21T18:13:54Z
Problem description

When you swipe the extra keys row to get to the native input text box for typing, the soft keyboard hides itself.
In addition, when you tap the terminal, if you tap the text input box again, the keyboard hides and returns really quickly, resizing the terminal twice in the process. (Which can be really noticeable in ncurses programs)
Steps to reproduce


Open a new Termux window with an extra keys row while using a soft keyboard
Swipe the extra keys row to get access to the native input box

And for bonus points, after the above:

Tap the terminal window
Tap the native input box as if you were going to type


Expected behavior

Neither situation should hide the keyboard and cause terminal resizes.
Additional information

Termux application version: Version 0.113
Android OS version: 11
Device model: Pixel 5

Duplicate issue #2110: The keyboard automatically exits
Created: 2021-06-05T10:55:10Z
Termux Version：0.103
But when I swipe to the left, the keyboard automatically exits. How can I disable it so that the keyboard is always displayed?

Closing comment (2021-06-05T11:24:04Z): Duplicate of #2077. Will be fixed in next release.
Repository: termux/termux-app

Issue #309: Support for Samsung DeX (Desktop Experience)
Created: 2017-05-04T04:33:30Z
As discussed in this Google+ thread, I would like to ask if changes could be made to Termux in order to work w/ the new Samsung DeX docking station thingy. All Android Apps work w/ DeX out of the box, however, if re-sizing or fullscreen should be supported, some adjustments must be made to the app, according to Samsung's website:
http://developer.samsung.com/samsung-dex/modify
In Google+ has been mentioned, that Termux should potentially be re-sizable right now. I've tested it with a DeX-Station and found out, that it does not work (see attached Screenshot).
Improvements that could be made

Add support for fullscreen
Add support for re-sizing of the App

I am happy to help with this by testing the App! Just let me know :)

Duplicate issue #454: termux doesn't support samsung dex resolution resize
Created: 2017-10-17T21:39:34Z
it's a fixed size window in samsung dex.

Closing comment (2017-10-18T00:15:15Z): This is a duplicate of #309 which includes the screenshot and additional info.