Added support for combined character classes (using ',')

I don't think this actually works yet.
For example, if I add the following test cases:
        [TestCase("minlength: 8; required: lower,upper; required: digit",
                  "aaaaa333")]
        [TestCase("minlength: 8; required: upper; required: digit",
                  "BBBBB444")]

to PasswordMatchesRuleTests.MatchesRule() and:
        [TestCase("minlength: 8; required: lower,upper; required: digit",
                  "11111111")]

to PasswordMatchesRuleTests.DoesNotMatchRule(), then this one fails:
Test | Duration | Traits | Error Message
-- | -- | -- | --
MatchesRule("minlength: 8; required: lower,upper; required: digit","aaaaa333") Failed | 24 ms |   | Expected: True   But was:  False

…and I think the other two only succeed incidentally.

Shouldn't the first two be one rule that is OR-combined?

(This relies on my assumption that ,-combining means OR. It's not 100% clear to me if that's Apple's intent.)


fix(Tile): re-enabled persistentMetadata and clean up transitions

Description

The inset persistent metadata was broken and after putting it back, the animation on/off the tile was happening on focus/unfocus even if the metadata should stay visible.
References

Testing

Go to Tile
In standard metadata position, test that it appears/disappears on focus/unfocus appropriately.
Repeat inset.
In standard metadata position, turn on persistentMetadata and test that it is visible in all modes.
Repeat with inset.
Repeat all of the above in the circle mode. The inset metadata should never be visible (regardless of persistency).
Reset back to the original args, turn on persistent metadata and confirm that in both the standard and inset positions, if you remove all of the text in title and description, they are not visible on the tile (this was an issue with the short circuit return in updateMetadata not actually alpha-ing anything out).

Automation

Checklist

[ ] all commented code has been removed
[ ] any new console issues have been resolved
[ ] code linter and formatter has been run
[ ] test coverage meets repo requirements
[ ] PR name matches the expected semantic-commit syntax

Sanity Tests Execution Failed.

Sanity Tests Execution Failed.

:tada: This PR is included in version @lightningjs/ui-components-v2.4.1 :tada:
The release is available on:

npm package (@latest dist-tag)
GitHub release

Your semantic-release bot :package::rocket:


Playbook SSH backend supports localhost

This PR makes it possible to run playbook with {{/* =% ssh %= */}} backends run locally as well
as remotely when --hostport slice contains remote host and/or localhost.  For example
infrakit use x do-y --hostport localhost,remote1:22 will execute the playbook using two go-routines, one against localhost (via sh) and one remotely (via ssh, on remote1:22).
Signed-off-by: David Chung<EMAIL_ADDRESS>

Please sign your commits following these rules:
https://github.com/moby/moby/blob/master/CONTRIBUTING.md#sign-your-work
The easiest way to do this is to amend the last commit:
$ git clone -b "0421-fixes" git@github.com:chungers/infrakit.git somewhere
$ cd somewhere
$ git rebase -i HEAD~842354290848
editor opens
change each 'pick' to 'edit'
save the file and quit
$ git commit --amend -s --no-edit
$ git rebase --continue # and repeat the amend for each commit
$ git push -f

Amending updates the existing PR. You DO NOT need to open a new one.


window doesn't have a document

is this by design? it breaks javascript written to be run client side in some cases

where is the code you tried?
import {parseHTML} from 'linkedom';
const {window} = parseHTML('

');
console.log(window.document);

It's there. If you mean that there is no global window, you are in charge of polluting the global, this module never touches the global scope/context.

I might be doing something wrong, lemme report back

if you use new DOMParser instead be sure you retrieve the defaultView from the returned document, which is what parseHTML exposes directly

so when I use window.eval to run a script that uses "document", I get a ReferenceError: document is not defined error, but if I add foo.window.document = foo.document the script runs flawlessly

again, there is no global document, neither a global window ... you have to set it to the globalThis object yourself, if you need it, but it won't be done here.

great, thanks, I have a really janky setup to ssr pages that are meant to be client side, I don't expect my use case to be supported haha

You can export anything you like via the document.defaultView and attach stuff to the global.document = ... or global.window = ... and so on, which might be necessary in some setup with FE libraries that (rightly) give a public document for granted


EventHubs Partition Creation inconsistency

In the documentation on the EventHubs concepts and scalability I can read:
"The number of partitions is specified at creation and must be between 2 and 32"
However, the number of partitions is not something I can edit or configure during the creation via the portal. This is either an inconsistency in the documentation or I'm missing something.

Document Details
⚠ Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.

ID: 3b3b4536-dac7-52f8-c469-c88c11039f4a
Version Independent ID: 32d2d122-5558-c091-0e20-693c98142783
Content: Scalability - Azure Event Hubs - Azure Event Hubs
Content Source: articles/event-hubs/event-hubs-scalability.md
Service: event-hubs
GitHub Login: @spelluru
Microsoft Alias: spelluru

Hi @bverhoeve , I see that you have closed this query. Hope you found the place where you select the partition count. In addition I'm sharing an image in case if anyone has similar query.

Hope this helps.


Accessing google cloud storage - UNABLE_TO_GET_ISSUER_CERT_LOCALLY

Hi,
I am behind a company firewall and I am trying to access Google cloud storage using the nodeJs Client API.
var gcloud = require('gcloud');

var gcs = gcloud.storage({
  keyFilename: './key-file.json',
  projectId: 'awesome-project'
});

gcs.getBuckets(function(err, result) {
  if (err) {
      console.log(err);
    } else {
      console.log(result);
    }
});

I get the following error which I try to run this.
$ node gs.js
{ [Error: unable to get local issuer certificate] code: 'UNABLE_TO_GET_ISSUER_CERT_LOCALLY' }

Can i connect to google cloud storage using the API with SSL certificate validation disabled ?
Thanks,
Abhishek

I did a quick search and found a possible solution https://github.com/npm/npm/issues/9580#issuecomment-166605021

This seems to be at the nodeJs level.
Setting the environment variable
$ export NODE_TLS_REJECT_UNAUTHORIZED=0
enables making calls with ssl certificate checking disabled.
However is may be a very insecure way of doing things :)

Any ideas for how/if we should try to support this @jgeewax @omaray?

I don't believe there is a way to do this currently, as SSL and the HTTPS endpoint is important for security. I'm going to close, but anyone can correct me and we'll do what we can to make it work.


Pass 'allow_global' to resolve_deps

Fixes #1002

Once I remember how to cause the problem

@techalchemy I think there is bug here.
Second invocation of resolve_deps doesn't use allow_global this line (L1114):
https://github.com/kennethreitz/pipenv/pull/1097/files#diff-e1e68886e05fef92bb74ed1699e5179eR1114
I think You should add it.

Yep, this is the same thing that wasn't addressed in the last attempted patch for this. Good spot @galuszkak, thanks!

OK, and now we pray to the cli gods.  That was a good catch, and I realized we aren't passing the --system flag through or picking it up in a majority of our CLI functions so I added it.  That should be helpful

@kennethreitz and this is the rest of why i needed --system

I don't think the title of this pull request encapsulates what's going on here, which is a lot.
What are you trying to accomplish, specifically?

This doesn't work, because now we still pipenv install --system for our CI pipeline, but then we pipenv run pytest in order to run the tests.  Since pipenv install --system doesnt make a virtualenv we can't use pipenv run.  I feel like the easiest solution and the best one for consistency is to keep the api call the same no matter what the user is doing, whether it just acts as a wrapper to a direct call or invokes something inside a virtualenv.
I don't know.  What is the best option here?

From a reviewing standpoint, can we get a few changes made
It's difficult to tell what this issue is about now, could we get a brief synopsis of the problem and the proposed solution in the original post?
There are now multiple sytlistic changes in the PR which make telling which changes are relevant and which are cosmetic difficult. Let's spin cleanup off to a new PR if we need to make those changes. We don't have a hard cap at 80 though, so I'm not sure they're needed.
The test also isn't very clear what it's trying to prove. Can we get a docstring on that?

Ok.  Let me describe the problem.
I am a user.  I am using pipenv with --system, inside of a docker container, but my pipenv install is not inside a virtualenv.  I make a folder directly off my filesystem root.  For some reason that is required to cause the bug.
When we resolve currently, we create a fake python version using which('python').  Under the circumstances I described, which('python') returns /bin/python because there is no project yet, there is no virtualenv, and --system doesn't get dropped through to the resolver.  That causes an error.
I would like to note that this error doesn't occur in master due to the use of a backup resolver:
 python_path = which('python', allow_global=allow_global)
 backup_python_path = shellquote(sys.executable)

But I don't really feel like that is a justification for leaving what is definitively a bug in the code.
I have now spent a significant amount of time explaining why we should pass a necessary argument to a function that expects it, trying to prove that the issue did exist, create a test for a thing that can't currently be reproduced, and this feels pretty silly at this point so I'll undo all of my changes and if that still isn't sufficient I'll just close the pull request and we can leave the bug in the code.

create a test for a thing that can't currently be reproduced

@techalchemy in original issue I attached how to reproduce it. If You are unable to do so I can create instruction or code that reproduce that issue from ground, and put it in repository.
Thanks for hard work on that @techalchemy

@galuszkak no worries I grabbed your instructions and included them in the docstring for the test. It just took me awhile to figure out what exactly caused it to error out, turns out the projects first parent has to be / as well. We’ll see if this one passes muster.

my inclination here is that when using install --system, lock and other
commands are not useful or needed, and that virtualenv creation is
neccessary for those features (e.g. the current implementation is just
fine).
On Tue, Nov 21, 2017 at 12:34 AM, Dan Ryan<EMAIL_ADDRESS>wrote:

@galuszkak https://github.com/galuszkak no worries I grabbed your
instructions and included them in the docstring for the test. It just took
me awhile to figure out what exactly caused it to error out, turns out the
projects first parent has to be / as well. We’ll see if this one passes
muster.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
https://github.com/kennethreitz/pipenv/pull/1097#issuecomment-345921895,
or mute the thread
https://github.com/notifications/unsubscribe-auth/AAHUVYhixDN7Y_kw2ZtSkubQQS4vrjHkks5s4mDdgaJpZM4QjfPM
.

@kennethreitz for me that contradicts purpose. So proposed solution with Docker workflow is using --system (per documentation), but when I want to pindown version through docker workflow You are saying it's not necessary. For me it doesn't matter if it's with --system flag or not, I expect that for example pipenv lock will work correctly with or without that flag, because people have different workflows, and if they are using docker they don't need virtualenv because they isolate on container level, and they still need Pipfile.lock for their peer workers when they will work on that codebase.

I envision a dock or user generating a locked file before sending things to Docker, always
Sent from my iPad

On Nov 21, 2017, at 3:55 AM, Kamil Gałuszka<EMAIL_ADDRESS>wrote:
@kennethreitz for me that contradicts purpose. So proposed solution with Docker workflow is using --system (per documentation), but when I want to pindown version through docker workflow You are saying it's not necessary. For me it doesn't matter if it's with --system flag or not, I expect that for example pipenv lock will work correctly with or without that flag, because people have different workflows, and if they are using docker they don't need virtualenv because they isolate on container level, and they still need Pipfile.lock for their peer workers when they will work on that codebase.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub, or mute the thread.

In other words this functionality is offered for deployments not for development workflow
Sent from my iPad

On Nov 21, 2017, at 3:55 AM, Kamil Gałuszka<EMAIL_ADDRESS>wrote:
@kennethreitz for me that contradicts purpose. So proposed solution with Docker workflow is using --system (per documentation), but when I want to pindown version through docker workflow You are saying it's not necessary. For me it doesn't matter if it's with --system flag or not, I expect that for example pipenv lock will work correctly with or without that flag, because people have different workflows, and if they are using docker they don't need virtualenv because they isolate on container level, and they still need Pipfile.lock for their peer workers when they will work on that codebase.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub, or mute the thread.

@techalchemy I think this PR needs to be rebased.
Is this PR waiting still for review of @kennethreitz ?


when I change the model, form field not changes

Hi,
when I change the model, form field not changes.
I tried to update a field on change,
the model is changed and the form field not.
something like this:
{
type: 'input',
key: 'sumOfRows',
hide:false,
templateOptions: {
type: 'number',
maxlength: 15,
minlength: 3,
required: true,
change: (field, $event) => {
console.log('change',field);
this.func(field)
},
},
func(field:FormlyFieldConfig){
this.model.Amount=field.parent.model.sumOfRows
}
Thanks

Would  it be possible for you to setup a stackblitz?
Also from what you have here I think you want to use expressionProperties : { "model.fieldName" : "changeLogic" } instead

To trigger the model change you'll need to override the model value in order to let Angular detect changes, try with the following solution and it should work:
- this.model.Amount=field.parent.model.sumOfRows
+ this.model = {
+   ...this.model,
+   Amount: field.parent.model.sumOfRows
+ }

You may also try to use expressionProperties as pointed above which will update the field model.

when I do this:
this.model = {
...this.model,
Amount: field.parent.model.sumOfRows
}
the model is update , but not the form field.
I did this:
this.form.patchValue(this.model);
and the form was updated also.
Thanks

the model is update , but not the form field.

@chavilax working for me https://stackblitz.com/edit/ngx-formly-ui-bootstrap-dxcg44?file=src/app/app.component.ts

for me its not working,
maybe because my model contains some sections,
and I try to change a table in one of them:
func(field: FormlyFieldConfig) {
this.model.sectionA['table'] = {
... this.model.sectionA['table'][0],
amount: field.parent.model.sumOfRows
};
}

you should override the the entire model value not just model.sectionA['table'], another solution you may use is to set the desired field directly using form.get('sectionA.table.amount').patchValue('***');

Thanks,
that worked for me:
to change the model:
this.model.sectionA['table'][0]['amount']=field.parent.model.sumOfRows
and then to override the the entire model-that updated the form:
this.model = {
...this.model
};
when I tried this, it doesn't recognize the fields:
this.model = {
...this.model,
this.model.sectionA['table'][0]['amount']=field.parent.model.sumOfRows
};

when I tried this, it doesn't recognize the fields:
this.model = {
...this.model,
this.mod

please check https://stackoverflow.com/a/47103087/1406096


Detecting multiple GPUs

I'm trying to run this on a cluster with N_GPUS=3 in the config.py.I tried to fix it from the here but the error persists. Any feedback ?
Using TensorFlow backend.
[11/08/2020 19:35:24] Limited tf.compat.v2.summary API due to missing TensorBoard installation.
[11/08/2020 19:35:26] Running training.
[11/08/2020 19:35:26] Building mydata55_hien dataset
[11/08/2020 19:35:26] 	Applying tokenization function: "tokenize_none".
[11/08/2020 19:35:27] Creating vocabulary for data with data_id 'target_text'.
[11/08/2020 19:35:27] 	 Total: 34428 unique words in 80000 sentences with a total of 401199 words.
[11/08/2020 19:35:27] Creating dictionary of all words
[11/08/2020 19:35:27] Loaded "train" set outputs of data_type "text-features" with data_id "target_text" and length 80000.
[11/08/2020 19:35:27] 	Applying tokenization function: "tokenize_none".
[11/08/2020 19:35:27] Loaded "val" set outputs of data_type "text" with data_id "target_text" and length 1800.
[11/08/2020 19:35:27] 	Applying tokenization function: "tokenize_none".
[11/08/2020 19:35:27] Loaded "test" set outputs of data_type "text" with data_id "target_text" and length 1000.
[11/08/2020 19:35:28] 	Applying tokenization function: "tokenize_none".
[11/08/2020 19:35:28] Creating vocabulary for data with data_id 'source_text'.
[11/08/2020 19:35:28] 	 Total: 42963 unique words in 80000 sentences with a total of 466800 words.
[11/08/2020 19:35:28] Creating dictionary of all words
[11/08/2020 19:35:28] Loaded "train" set inputs of data_type "text-features" with data_id "source_text" and length 80000.
[11/08/2020 19:35:29] 	Applying tokenization function: "tokenize_none".
[11/08/2020 19:35:29] Creating vocabulary for data with data_id 'state_below'.
[11/08/2020 19:35:29] 	 Total: 34428 unique words in 80000 sentences with a total of 401199 words.
[11/08/2020 19:35:29] Creating dictionary of all words
[11/08/2020 19:35:30] Loaded "train" set inputs of data_type "text-features" with data_id "state_below" and length 80000.
[11/08/2020 19:35:30] Loaded "train" set inputs of type "file-name" with id "raw_source_text".
[11/08/2020 19:35:30] 	Applying tokenization function: "tokenize_none".
[11/08/2020 19:35:30] Loaded "test" set inputs of data_type "text-features" with data_id "source_text" and length 1000.
[11/08/2020 19:35:30] Loaded "test" set inputs of data_type "ghost" with data_id "state_below" and length 1000.
[11/08/2020 19:35:30] Loaded "test" set inputs of type "file-name" with id "raw_source_text".
[11/08/2020 19:35:30] 	Applying tokenization function: "tokenize_none".
[11/08/2020 19:35:30] Loaded "val" set inputs of data_type "text-features" with data_id "source_text" and length 1800.
[11/08/2020 19:35:30] Loaded "val" set inputs of data_type "ghost" with data_id "state_below" and length 1800.
[11/08/2020 19:35:30] Loaded "val" set inputs of type "file-name" with id "raw_source_text".
[11/08/2020 19:35:30] Keeping 1 captions per input on the val set.
[11/08/2020 19:35:30] Samples reduced to 1800 in val set.
[11/08/2020 19:35:30] <<< Saving Dataset instance to datasets/Dataset_mydata55_hien.pkl ... >>>
[11/08/2020 19:35:31] <<< Dataset instance saved >>>
[11/08/2020 19:35:31] <<< Building AttentionRNNEncoderDecoder Translation_Model >>>
[11/08/2020 19:35:31] From /home/pandramish.vinay/.local/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py:650: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.

[11/08/2020 19:35:31] From /home/pandramish.vinay/.local/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py:4786: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.

[11/08/2020 19:35:31] From /home/pandramish.vinay/.local/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py:157: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.

[11/08/2020 19:35:37] From /home/pandramish.vinay/.local/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py:3561: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where
2020-08-11 19:35:39.189366: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2020-08-11 19:35:39.216265: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency:<PHONE_NUMBER> Hz
2020-08-11 19:35:39.217710: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0xb6fd630 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-08-11 19:35:39.217731: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
Traceback (most recent call last):
  File "main.py", line 51, in <module>
    train_model(parameters, args.dataset)
  File "/home/pandramish.vinay/nmt-keras/nmt_keras/training.py", line 93, in train_model
    clear_dirs=clear_dirs)
  File "/home/pandramish.vinay/nmt-keras/nmt_keras/model_zoo.py", line 155, in __init__
    eval('self.' + model_type + '(params)')
  File "<string>", line 1, in <module>
  File "/home/pandramish.vinay/nmt-keras/nmt_keras/model_zoo.py", line 762, in AttentionRNNEncoderDecoder
    self.multi_gpu_model = multi_gpu_model(self.model, gpus=params['N_GPUS'])
  File "/home/pandramish.vinay/.local/lib/python3.5/site-packages/keras/utils/multi_gpu_utils.py", line 189, in multi_gpu_model
    available_devices))
ValueError: To call `multi_gpu_model` with `gpus=3`, we expect the following devices to be available: ['/cpu:0', '/gpu:0', '/gpu:1', '/gpu:2']. However this machine only has: ['/cpu:0']. Try reducing `gpus`.

It looks like Tensorflow doesn't detect any GPUs. Is tensorflow-gpu installed? Is the environment variable CUDA_VISIBLE_DEVICES properly set?

echo $CUDA_VISIBLE_DEVICES gives me 0,1,2  and after installing tensorflow-gpu I get
Using TensorFlow backend.
2020-08-11 20:15:00.601205: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
[11/08/2020 20:15:07] Running training.
[11/08/2020 20:15:07] Building mydata55_hien dataset
[11/08/2020 20:15:08] 	Applying tokenization function: "tokenize_none".
[11/08/2020 20:15:08] Creating vocabulary for data with data_id 'target_text'.
[11/08/2020 20:15:08] 	 Total: 34428 unique words in 80000 sentences with a total of 401199 words.
[11/08/2020 20:15:08] Creating dictionary of all words
[11/08/2020 20:15:08] Loaded "train" set outputs of data_type "text-features" with data_id "target_text" and length 80000.
[11/08/2020 20:15:08] 	Applying tokenization function: "tokenize_none".
[11/08/2020 20:15:08] Loaded "val" set outputs of data_type "text" with data_id "target_text" and length 1800.
[11/08/2020 20:15:08] 	Applying tokenization function: "tokenize_none".
[11/08/2020 20:15:08] Loaded "test" set outputs of data_type "text" with data_id "target_text" and length 1000.
[11/08/2020 20:15:09] 	Applying tokenization function: "tokenize_none".
[11/08/2020 20:15:09] Creating vocabulary for data with data_id 'source_text'.
[11/08/2020 20:15:09] 	 Total: 42963 unique words in 80000 sentences with a total of 466800 words.
[11/08/2020 20:15:09] Creating dictionary of all words
[11/08/2020 20:15:10] Loaded "train" set inputs of data_type "text-features" with data_id "source_text" and length 80000.
[11/08/2020 20:15:10] 	Applying tokenization function: "tokenize_none".
[11/08/2020 20:15:10] Creating vocabulary for data with data_id 'state_below'.
[11/08/2020 20:15:10] 	 Total: 34428 unique words in 80000 sentences with a total of 401199 words.
[11/08/2020 20:15:10] Creating dictionary of all words
[11/08/2020 20:15:11] Loaded "train" set inputs of data_type "text-features" with data_id "state_below" and length 80000.
[11/08/2020 20:15:11] Loaded "train" set inputs of type "file-name" with id "raw_source_text".
[11/08/2020 20:15:11] 	Applying tokenization function: "tokenize_none".
[11/08/2020 20:15:11] Loaded "val" set inputs of data_type "text-features" with data_id "source_text" and length 1800.
[11/08/2020 20:15:11] Loaded "val" set inputs of data_type "ghost" with data_id "state_below" and length 1800.
[11/08/2020 20:15:11] Loaded "val" set inputs of type "file-name" with id "raw_source_text".
[11/08/2020 20:15:11] 	Applying tokenization function: "tokenize_none".
[11/08/2020 20:15:11] Loaded "test" set inputs of data_type "text-features" with data_id "source_text" and length 1000.
[11/08/2020 20:15:11] Loaded "test" set inputs of data_type "ghost" with data_id "state_below" and length 1000.
[11/08/2020 20:15:11] Loaded "test" set inputs of type "file-name" with id "raw_source_text".
[11/08/2020 20:15:11] Keeping 1 captions per input on the val set.
[11/08/2020 20:15:11] Samples reduced to 1800 in val set.
[11/08/2020 20:15:11] <<< Saving Dataset instance to datasets/Dataset_mydata55_hien.pkl ... >>>
[11/08/2020 20:15:12] <<< Dataset instance saved >>>
[11/08/2020 20:15:12] <<< Building AttentionRNNEncoderDecoder Translation_Model >>>
Traceback (most recent call last):
  File "main.py", line 51, in <module>
    train_model(parameters, args.dataset)
  File "/home/pandramish.vinay/nmt-keras/nmt_keras/training.py", line 93, in train_model
    clear_dirs=clear_dirs)
  File "/home/pandramish.vinay/nmt-keras/nmt_keras/model_zoo.py", line 155, in __init__
    eval('self.' + model_type + '(params)')
  File "<string>", line 1, in <module>
  File "/home/pandramish.vinay/nmt-keras/nmt_keras/model_zoo.py", line 457, in AttentionRNNEncoderDecoder
    src_text = Input(name=self.ids_inputs[0], batch_shape=tuple([None, None]), dtype='int32')
  File "/home/pandramish.vinay/.local/lib/python3.5/site-packages/keras/engine/input_layer.py", line 178, in Input
    input_tensor=tensor)
  File "/home/pandramish.vinay/.local/lib/python3.5/site-packages/keras/legacy/interfaces.py", line 91, in wrapper
    return func(*args, **kwargs)
  File "/home/pandramish.vinay/.local/lib/python3.5/site-packages/keras/engine/input_layer.py", line 87, in __init__
    name=self.name)
  File "/home/pandramish.vinay/.local/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py", line 650, in placeholder
    x = tf.placeholder(dtype, shape=shape, name=name)
AttributeError: module 'tensorflow' has no attribute 'placeholder'

I think you installed tensorflow 2.X. You should install 1.x (e.g. pip install tensorflow-gpu==1.15)

This step resolves the errors, but I'm running out of memory as shown
python3 main.py
Using TensorFlow backend.
[11/08/2020 20:50:13] Running training.
[11/08/2020 20:50:13] Building mydata55_hien dataset
[11/08/2020 20:50:13] 	Applying tokenization function: "tokenize_none".
[11/08/2020 20:50:14] Creating vocabulary for data with data_id 'target_text'.
[11/08/2020 20:50:14] 	 Total: 34428 unique words in 80000 sentences with a total of 401199 words.
[11/08/2020 20:50:14] Creating dictionary of all words
[11/08/2020 20:50:14] Loaded "train" set outputs of data_type "text-features" with data_id "target_text" and length 80000.
[11/08/2020 20:50:14] 	Applying tokenization function: "tokenize_none".
[11/08/2020 20:50:14] Loaded "val" set outputs of data_type "text" with data_id "target_text" and length 1800.
[11/08/2020 20:50:14] 	Applying tokenization function: "tokenize_none".
[11/08/2020 20:50:14] Loaded "test" set outputs of data_type "text" with data_id "target_text" and length 1000.
[11/08/2020 20:50:15] 	Applying tokenization function: "tokenize_none".
[11/08/2020 20:50:15] Creating vocabulary for data with data_id 'source_text'.
[11/08/2020 20:50:15] 	 Total: 42963 unique words in 80000 sentences with a total of 466800 words.
[11/08/2020 20:50:15] Creating dictionary of all words
[11/08/2020 20:50:15] Loaded "train" set inputs of data_type "text-features" with data_id "source_text" and length 80000.
[11/08/2020 20:50:16] 	Applying tokenization function: "tokenize_none".
[11/08/2020 20:50:16] Creating vocabulary for data with data_id 'state_below'.
[11/08/2020 20:50:16] 	 Total: 34428 unique words in 80000 sentences with a total of 401199 words.
[11/08/2020 20:50:16] Creating dictionary of all words
[11/08/2020 20:50:17] Loaded "train" set inputs of data_type "text-features" with data_id "state_below" and length 80000.
[11/08/2020 20:50:17] Loaded "train" set inputs of type "file-name" with id "raw_source_text".
[11/08/2020 20:50:17] 	Applying tokenization function: "tokenize_none".
[11/08/2020 20:50:17] Loaded "val" set inputs of data_type "text-features" with data_id "source_text" and length 1800.
[11/08/2020 20:50:17] Loaded "val" set inputs of data_type "ghost" with data_id "state_below" and length 1800.
[11/08/2020 20:50:17] Loaded "val" set inputs of type "file-name" with id "raw_source_text".
[11/08/2020 20:50:17] 	Applying tokenization function: "tokenize_none".
[11/08/2020 20:50:17] Loaded "test" set inputs of data_type "text-features" with data_id "source_text" and length 1000.
[11/08/2020 20:50:17] Loaded "test" set inputs of data_type "ghost" with data_id "state_below" and length 1000.
[11/08/2020 20:50:17] Loaded "test" set inputs of type "file-name" with id "raw_source_text".
[11/08/2020 20:50:17] Keeping 1 captions per input on the val set.
[11/08/2020 20:50:17] Samples reduced to 1800 in val set.
[11/08/2020 20:50:17] <<< Saving Dataset instance to datasets/Dataset_mydata55_hien.pkl ... >>>
[11/08/2020 20:50:18] <<< Dataset instance saved >>>
[11/08/2020 20:50:18] <<< Building AttentionRNNEncoderDecoder Translation_Model >>>
[11/08/2020 20:50:18] From /home/pandramish.vinay/.local/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py:650: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.

[11/08/2020 20:50:18] From /home/pandramish.vinay/.local/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py:4786: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.

[11/08/2020 20:50:18] From /home/pandramish.vinay/.local/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py:157: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.

[11/08/2020 20:50:24] From /home/pandramish.vinay/.local/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py:3561: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where
[11/08/2020 20:50:26] From /home/pandramish.vinay/.local/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py:292: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.

[11/08/2020 20:50:26] From /home/pandramish.vinay/.local/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py:299: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.

[11/08/2020 20:50:26] From /home/pandramish.vinay/.local/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py:308: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.

2020-08-11 20:50:26.283764: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2020-08-11 20:50:26.311783: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency:<PHONE_NUMBER> Hz
2020-08-11 20:50:26.313223: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x9548a50 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-08-11 20:50:26.313241: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2020-08-11 20:50:26.314794: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2020-08-11 20:50:27.979868: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x9618b90 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2020-08-11 20:50:27.979910: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): GeForce GTX 1080 Ti, Compute Capability 6.1
2020-08-11 20:50:27.979920: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (1): GeForce GTX 1080 Ti, Compute Capability 6.1
2020-08-11 20:50:27.979927: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (2): GeForce GTX 1080 Ti, Compute Capability 6.1
2020-08-11 20:50:27.982474: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties:
name: GeForce GTX 1080 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.582
pciBusID: 0000:02:00.0
2020-08-11 20:50:27.983341: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 1 with properties:
name: GeForce GTX 1080 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.582
pciBusID: 0000:03:00.0
2020-08-11 20:50:27.985994: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 2 with properties:
name: GeForce GTX 1080 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.582
pciBusID: 0000:83:00.0
2020-08-11 20:50:27.991316: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0
2020-08-11 20:50:28.058570: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0
2020-08-11 20:50:28.091144: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10.0
2020-08-11 20:50:28.102065: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10.0
2020-08-11 20:50:28.178217: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10.0
2020-08-11 20:50:28.227447: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10.0
2020-08-11 20:50:28.227515: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-08-11 20:50:28.233741: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0, 1, 2
2020-08-11 20:50:28.233800: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0
2020-08-11 20:50:28.238665: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-08-11 20:50:28.238686: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165]      0 1 2
2020-08-11 20:50:28.238712: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0:   N Y N
2020-08-11 20:50:28.238721: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 1:   Y N N
2020-08-11 20:50:28.238732: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 2:   N N N
2020-08-11 20:50:28.243290: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10481 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:02:00.0, compute capability: 6.1)
2020-08-11 20:50:28.244710: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:1 with 10481 MB memory) -> physical GPU (device: 1, name: GeForce GTX 1080 Ti, pci bus id: 0000:03:00.0, compute capability: 6.1)
2020-08-11 20:50:28.245980: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:2 with 10481 MB memory) -> physical GPU (device: 2, name: GeForce GTX 1080 Ti, pci bus id: 0000:83:00.0, compute capability: 6.1)
[11/08/2020 20:50:28] From /home/pandramish.vinay/.local/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py:312: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.

[11/08/2020 20:50:28] From /home/pandramish.vinay/.local/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py:321: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.

[11/08/2020 20:50:28] From /home/pandramish.vinay/.local/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py:328: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.

-----------------------------------------------------------------------------------
		TranslationModel instance
-----------------------------------------------------------------------------------
_model_type: AttentionRNNEncoderDecoder
name: mydata55_hien_AttentionRNNEncoderDecoder_src_emb_500_bidir_True_enc_LSTM_756_dec_ConditionalLSTM_756_deepout_linear_trg_emb_500_Adam_0.001
model_path: /scratch/trained_models/mydata55_hien_AttentionRNNEncoderDecoder_src_emb_500_bidir_True_enc_LSTM_756_dec_ConditionalLSTM_756_deepout_linear_trg_emb_500_Adam_0.001/
verbose: 1

Params:
	ACCUMULATE_GRADIENTS: 1
	ADDITIONAL_OUTPUT_MERGE_MODE: Add
	ALIGN_FROM_RAW: True
	ALPHA_FACTOR: 0.6
	AMSGRAD: False
	APPLY_DETOKENIZATION: False
	ATTENTION_DROPOUT_P: 0.0
	ATTENTION_MODE: add
	ATTENTION_SIZE: 756
	BATCH_NORMALIZATION_MODE: 1
	BATCH_SIZE: 412
	BEAM_SEARCH: True
	BEAM_SIZE: 3
	BETA_1: 0.9
	BETA_2: 0.999
	BIDIRECTIONAL_DEEP_ENCODER: True
	BIDIRECTIONAL_ENCODER: True
	BIDIRECTIONAL_MERGE_MODE: concat
	BPE_CODES_PATH: examples/mydata55//training_codes.joint
	CLASSIFIER_ACTIVATION: softmax
	CLIP_C: 5.0
	CLIP_V: 0.0
	COVERAGE_NORM_FACTOR: 0.2
	COVERAGE_PENALTY: False
	DATASET_NAME: mydata55
	DATASET_STORE_PATH: datasets/
	DATA_AUGMENTATION: False
	DATA_ROOT_PATH: examples/mydata55/
	DECODER_HIDDEN_SIZE: 756
	DECODER_RNN_TYPE: ConditionalLSTM
	DEEP_OUTPUT_LAYERS: [('linear', 500)]
	DETOKENIZATION_METHOD: detokenize_none
	DOUBLE_STOCHASTIC_ATTENTION_REG: 0.0
	DROPOUT_P: 0.0
	EARLY_STOP: True
	EMBEDDINGS_FREQ: 1
	ENCODER_HIDDEN_SIZE: 756
	ENCODER_RNN_TYPE: LSTM
	EPOCHS_FOR_SAVE: 1
	EPSILON: 1e-08
	EVAL_EACH: 1
	EVAL_EACH_EPOCHS: True
	EVAL_ON_SETS: ['val']
	EXTRA_NAME:
	FF_SIZE: 128
	FILL: end
	FORCE_RELOAD_VOCABULARY: False
	GLOSSARY: None
	GRU_RESET_AFTER: True
	HEURISTIC: 0
	HOMOGENEOUS_BATCHES: False
	INIT_ATT: glorot_uniform
	INIT_FUNCTION: glorot_uniform
	INIT_LAYERS: ['tanh']
	INNER_INIT: orthogonal
	INPUTS_IDS_DATASET: ['source_text', 'state_below']
	INPUTS_IDS_MODEL: ['source_text', 'state_below']
	INPUTS_TYPES_DATASET: ['text-features', 'text-features']
	INPUT_VOCABULARY_SIZE: 42966
	JOINT_BATCHES: 4
	KERAS_METRICS: ['perplexity']
	LABEL_SMOOTHING: 0.0
	LENGTH_NORM_FACTOR: 0.2
	LENGTH_PENALTY: False
	LOG_DIR: tensorboard_logs
	LOSS: categorical_crossentropy
	LR: 0.001
	LR_DECAY: None
	LR_GAMMA: 0.8
	LR_HALF_LIFE: 100
	LR_REDUCER_EXP_BASE: -0.5
	LR_REDUCER_TYPE: exponential
	LR_REDUCE_EACH_EPOCHS: False
	LR_START_REDUCTION_ON_EPOCH: 0
	MAPPING: examples/mydata55//mapping.hi_en.pkl
	MAXLEN_GIVEN_X: True
	MAXLEN_GIVEN_X_FACTOR: 2
	MAX_EPOCH: 15
	MAX_INPUT_TEXT_LEN: 150
	MAX_OUTPUT_TEXT_LEN: 150
	MAX_OUTPUT_TEXT_LEN_TEST: 450
	MAX_PLOT_Y: 100.0
	METRICS: ['perplexity']
	MINLEN_GIVEN_X: True
	MINLEN_GIVEN_X_FACTOR: 3
	MIN_DELTA: 0.0
	MIN_LR: 1e-09
	MIN_OCCURRENCES_INPUT_VOCAB: 0
	MIN_OCCURRENCES_OUTPUT_VOCAB: 0
	MODE: training
	MODEL_NAME: mydata55_hien_AttentionRNNEncoderDecoder_src_emb_500_bidir_True_enc_LSTM_756_dec_ConditionalLSTM_756_deepout_linear_trg_emb_500_Adam_0.001
	MODEL_SIZE: 32
	MODEL_TYPE: AttentionRNNEncoderDecoder
	MOMENTUM: 0.0
	MULTIHEAD_ATTENTION_ACTIVATION: linear
	NESTEROV_MOMENTUM: False
	NOISE_AMOUNT: 0.01
	NORMALIZE_SAMPLING: False
	N_GPUS: 3
	N_HEADS: 8
	N_LAYERS_DECODER: 2
	N_LAYERS_ENCODER: 2
	N_SAMPLES: 5
	OPTIMIZED_SEARCH: True
	OPTIMIZER: Adam
	OUTPUTS_IDS_DATASET: ['target_text']
	OUTPUTS_IDS_MODEL: ['target_text']
	OUTPUTS_TYPES_DATASET: ['text-features']
	OUTPUT_VOCABULARY_SIZE: 34431
	PAD_ON_BATCH: True
	PARALLEL_LOADERS: 1
	PATIENCE: 5
	PLOT_EVALUATION: False
	POS_UNK: True
	REBUILD_DATASET: True
	RECURRENT_DROPOUT_P: 0.0
	RECURRENT_INPUT_DROPOUT_P: 0.0
	RECURRENT_WEIGHT_DECAY: 0.0
	REGULARIZATION_FN: L2
	RELOAD: 0
	RELOAD_EPOCH: False
	RHO: 0.9
	SAMPLE_EACH_UPDATES: 300
	SAMPLE_ON_SETS: ['train', 'val']
	SAMPLE_WEIGHTS: True
	SAMPLING: max_likelihood
	SAMPLING_SAVE_MODE: list
	SAVE_EACH_EVALUATION: True
	SCALE_SOURCE_WORD_EMBEDDINGS: False
	SCALE_TARGET_WORD_EMBEDDINGS: False
	SEARCH_PRUNING: False
	SKIP_VECTORS_HIDDEN_SIZE: 500
	SKIP_VECTORS_SHARED_ACTIVATION: tanh
	SOURCE_TEXT_EMBEDDING_SIZE: 500
	SRC_LAN: hi
	SRC_PRETRAINED_VECTORS: None
	SRC_PRETRAINED_VECTORS_TRAINABLE: True
	START_EVAL_ON_EPOCH: 1
	START_SAMPLING_ON_EPOCH: 1
	STOP_METRIC: Bleu_4
	STORE_PATH: /scratch/trained_models/mydata55_hien_AttentionRNNEncoderDecoder_src_emb_500_bidir_True_enc_LSTM_756_dec_ConditionalLSTM_756_deepout_linear_trg_emb_500_Adam_0.001/
	TARGET_TEXT_EMBEDDING_SIZE: 500
	TASK_NAME: mydata55
	TEMPERATURE: 1
	TENSORBOARD: True
	TEXT_FILES: {'train': 'train.', 'val': 'val.', 'test': 'TEST.'}
	TIE_EMBEDDINGS: False
	TOKENIZATION_METHOD: tokenize_none
	TOKENIZE_HYPOTHESES: True
	TOKENIZE_REFERENCES: True
	TRAINABLE_DECODER: True
	TRAINABLE_ENCODER: True
	TRAIN_ON_TRAINVAL: False
	TRG_LAN: en
	TRG_PRETRAINED_VECTORS: None
	TRG_PRETRAINED_VECTORS_TRAINABLE: True
	USE_BATCH_NORMALIZATION: True
	USE_CUDNN: False
	USE_L1: False
	USE_L2: False
	USE_NOISE: False
	USE_PRELU: False
	USE_TF_OPTIMIZER: True
	VERBOSE: 1
	WARMUP_EXP: -1.5
	WEIGHT_DECAY: 0.0001
	WRITE_VALID_SAMPLES: True
-----------------------------------------------------------------------------------
Model: "mydata55_hien_AttentionRNNEncoderDecoder_src_emb_500_bidir_True_enc_LSTM_756_dec_ConditionalLSTM_756_deepout_linear_trg_emb_500_Adam_0.001_training"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to
==================================================================================================
source_text (InputLayer)        (None, None)         0
__________________________________________________________________________________________________
source_word_embedding (Embeddin (None, None,<PHONE_NUMBER>0    source_text[0][0]
__________________________________________________________________________________________________
src_embedding_batch_normalizati (None, None, 500)    2000        source_word_embedding[0][0]
__________________________________________________________________________________________________
remove_mask_1 (RemoveMask)      (None, None, 500)    0           src_embedding_batch_normalization
__________________________________________________________________________________________________
bidirectional_encoder_LSTM (Bid (None, None, 1512)   7602336     remove_mask_1[0][0]
__________________________________________________________________________________________________
annotations_batch_normalization (None, None, 1512)   6048        bidirectional_encoder_LSTM[0][0]
__________________________________________________________________________________________________
bidirectional_encoder_1 (Bidire (None, None, 1512)   13722912    annotations_batch_normalization[0
__________________________________________________________________________________________________
annotations_1_batch_normalizati (None, None, 1512)   6048        bidirectional_encoder_1[0][0]
__________________________________________________________________________________________________
add_1 (Add)                     (None, None, 1512)   0           annotations_batch_normalization[0
                                                                 annotations_1_batch_normalization
__________________________________________________________________________________________________
source_text_mask (GetMask)      (None, None, 500)    0           src_embedding_batch_normalization
__________________________________________________________________________________________________
annotations (ApplyMask)         (None, None, 1512)   0           add_1[0][0]
                                                                 source_text_mask[0][0]
__________________________________________________________________________________________________
state_below (InputLayer)        (None, None)         0
__________________________________________________________________________________________________
ctx_mean (MaskedMean)           (None, 1512)         0           annotations[0][0]
__________________________________________________________________________________________________
target_word_embedding (Embeddin (None, None,<PHONE_NUMBER>0    state_below[0][0]
__________________________________________________________________________________________________
initial_state (Dense)           (None,<PHONE_NUMBER>     ctx_mean[0][0]
__________________________________________________________________________________________________
initial_memory (Dense)          (None,<PHONE_NUMBER>     ctx_mean[0][0]
__________________________________________________________________________________________________
state_below_batch_normalization (None, None, 500)    2000        target_word_embedding[0][0]
__________________________________________________________________________________________________
initial_state_batch_normalizati (None, 756)          3024        initial_state[0][0]
__________________________________________________________________________________________________
initial_memory_batch_normalizat (None, 756)          3024        initial_memory[0][0]
__________________________________________________________________________________________________
decoder_AttConditionalLSTMCond  [(None, None, 756),  12378745    state_below_batch_normalization[0
                                                                 annotations[0][0]
                                                                 initial_state_batch_normalization
                                                                 initial_memory_batch_normalizatio
__________________________________________________________________________________________________
proj_h0_batch_normalization (Ba (None, None, 756)    3024        decoder_AttConditionalLSTMCond[0]
__________________________________________________________________________________________________
permute_general_1 (PermuteGener multiple             0           decoder_AttConditionalLSTMCond[0]
                                                                 logit_ctx[0][0]
__________________________________________________________________________________________________
decoder_LSTMCond1 (LSTMCond)    [(None, None, 756),  9147600     proj_h0_batch_normalization[0][0]
                                                                 permute_general_1[0][0]
                                                                 initial_state_batch_normalization
                                                                 initial_memory_batch_normalizatio
__________________________________________________________________________________________________
proj_h1_batch_normalization (Ba (None, None, 756)    3024        decoder_LSTMCond1[0][0]
__________________________________________________________________________________________________
add_2 (Add)                     (None, None, 756)    0           proj_h0_batch_normalization[0][0]
                                                                 proj_h1_batch_normalization[0][0]
__________________________________________________________________________________________________
logit_ctx (TimeDistributed)     (None, None, 500)    756500      decoder_AttConditionalLSTMCond[0]
__________________________________________________________________________________________________
logit_lstm (TimeDistributed)    (None, None, 500)    378500      add_2[0][0]
__________________________________________________________________________________________________
logit_emb (TimeDistributed)     (None, None, 500)    250500      state_below_batch_normalization[0
__________________________________________________________________________________________________
out_layer_mlp_batch_normalizati (None, None, 500)    2000        logit_lstm[0][0]
__________________________________________________________________________________________________
out_layer_ctx_batch_normalizati (None, None, 500)    2000        permute_general_1[1][0]
__________________________________________________________________________________________________
out_layer_emb_batch_normalizati (None, None, 500)    2000        logit_emb[0][0]
__________________________________________________________________________________________________
additional_input (Add)          (None, None, 500)    0           out_layer_mlp_batch_normalization
                                                                 out_layer_ctx_batch_normalization
                                                                 out_layer_emb_batch_normalization
__________________________________________________________________________________________________
activation_1 (Activation)       (None, None, 500)    0           additional_input[0][0]
__________________________________________________________________________________________________
linear_0 (TimeDistributed)      (None, None, 500)    250500      activation_1[0][0]
__________________________________________________________________________________________________
out_layer_linear_0_batch_normal (None, None, 500)    2000        linear_0[0][0]
__________________________________________________________________________________________________
target_text (TimeDistributed)   (None, None, 34431)  17249931    out_layer_linear_0_batch_normaliz
==================================================================================================
Total params: 102,759,872
Trainable params: 102,741,776
Non-trainable params: 18,096
__________________________________________________________________________________________________
[11/08/2020 20:50:40] From /home/pandramish.vinay/nmt-keras/nmt_keras/model_zoo.py:213: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead.

[11/08/2020 20:50:40] Preparing optimizer and compiling. Optimizer configuration:
	 LR: 0.001
	 LOSS: categorical_crossentropy
	 BETA_1: 0.9
	 BETA_2: 0.999
	 EPSILON: 1e-08
[11/08/2020 20:50:40] From /home/pandramish.vinay/.local/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py:1192: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.

[11/08/2020 20:50:40] <<< Training model >>>
[11/08/2020 20:50:40] Training parameters: {
	batch_size: 412
	class_weights: None
	da_enhance_list: []
	da_patch_type: resize_and_rndcrop
	data_augmentation: False
	each_n_epochs: 1
	epoch_offset: 0
	epochs_for_save: 1
	eval_on_epochs: True
	eval_on_sets: None
	extra_callbacks: [<keras_wrapper.extra.callbacks.EvalPerformance object at 0x14d0400de320>, <keras_wrapper.extra.callbacks.Sample object at 0x14d0400e6860>]
	homogeneous_batches: False
	initial_lr: 0.001
	joint_batches: 4
	lr_decay: None
	lr_gamma: 0.8
	lr_half_life: 100
	lr_reducer_exp_base: -0.5
	lr_reducer_type: exponential
	lr_warmup_exp: -1.5
	maxlen: 150
	mean_substraction: False
	metric_check: Bleu_4
	min_delta: 0.0
	min_lr: 1e-09
	n_epochs: 15
	n_gpus: 3
	n_parallel_loaders: 1
	normalization_type: None
	normalize: False
	num_iterations_val: None
	patience: 5
	patience_check_split: val
	reduce_each_epochs: False
	reload_epoch: 0
	shuffle: True
	start_eval_on_epoch: 1
	start_reduction_on_epoch: 0
	tensorboard: True
	tensorboard_params: {'write_grads': False, 'batch_size': 412, 'update_freq': 'epoch', 'embeddings_layer_names': None, 'histogram_freq': 0, 'write_images': False, 'word_embeddings_labels': None, 'write_graph': True, 'log_dir': 'tensorboard_logs', 'embeddings_freq': None, 'embeddings_metadata': None}
	verbose: 1
	wo_da_patch_type: whole
}
[11/08/2020 20:50:40] <<< creating directory /scratch/trained_models/mydata55_hien_AttentionRNNEncoderDecoder_src_emb_500_bidir_True_enc_LSTM_756_dec_ConditionalLSTM_756_deepout_linear_trg_emb_500_Adam_0.001/tensorboard_logs ... >>>
[11/08/2020 20:50:44] From /home/pandramish.vinay/.local/lib/python3.5/site-packages/keras/callbacks/tensorboard_v1.py:200: The name tf.summary.merge_all is deprecated. Please use tf.compat.v1.summary.merge_all instead.

[11/08/2020 20:50:44] From /home/pandramish.vinay/.local/lib/python3.5/site-packages/keras/callbacks/tensorboard_v1.py:203: The name tf.summary.FileWriter is deprecated. Please use tf.compat.v1.summary.FileWriter instead.

Epoch 1/15
2020-08-11 20:51:21.244817: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0
2020-08-11 20:51:36.044298: W tensorflow/core/common_runtime/bfc_allocator.cc:419] Allocator (GPU_0_bfc) ran out of memory trying to allocate 27.4KiB (rounded to 28160).  Current allocation summary follows.
2020-08-11 20:51:36.044578: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (256): 	Total Chunks: 243, Chunks in use: 239. 60.8KiB allocated for chunks. 59.8KiB in use in bin. 2.5KiB client-requested in use in bin.
2020-08-11 20:51:36.044605: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (512): 	Total Chunks: 13, Chunks in use: 11. 9.2KiB allocated for chunks. 8.2KiB in use in bin. 5.9KiB client-requested in use in bin.
2020-08-11 20:51:36.044629: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (1024): 	Total Chunks: 2, Chunks in use: 2. 2.2KiB allocated for chunks. 2.2KiB in use in bin. 1.5KiB client-requested in use in bin.
2020-08-11 20:51:36.044640: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (2048): 	Total Chunks: 252, Chunks in use: 251. 632.2KiB allocated for chunks. 630.2KiB in use in bin. 606.3KiB client-requested in use in bin.
2020-08-11 20:51:36.044665: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (4096): 	Total Chunks: 58, Chunks in use: 58. 310.2KiB allocated for chunks. 310.2KiB in use in bin. 275.8KiB client-requested in use in bin.
2020-08-11 20:51:36.044674: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (8192): 	Total Chunks: 70, Chunks in use: 70. 740.5KiB allocated for chunks. 740.5KiB in use in bin. 719.1KiB client-requested in use in bin.
2020-08-11 20:51:36.044682: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (16384): 	Total Chunks: 211, Chunks in use: 211. 3.44MiB allocated for chunks. 3.44MiB in use in bin. 3.30MiB client-requested in use in bin.
2020-08-11 20:51:36.044691: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (32768): 	Total Chunks: 1, Chunks in use: 1. 32.2KiB allocated for chunks. 32.2KiB in use in bin. 16.1KiB client-requested in use in bin.
2020-08-11 20:51:36.044700: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (65536): 	Total Chunks: 195, Chunks in use: 195. 19.33MiB allocated for chunks. 19.33MiB in use in bin. 19.29MiB client-requested in use in bin.
2020-08-11 20:51:36.044708: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (131072): 	Total Chunks: 62, Chunks in use: 62. 11.79MiB allocated for chunks. 11.79MiB in use in bin. 9.23MiB client-requested in use in bin.
2020-08-11 20:51:36.044717: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (262144): 	Total Chunks: 2601, Chunks in use: 2601. 1015.97MiB allocated for chunks. 1015.97MiB in use in bin. 1012.12MiB client-requested in use in bin.
2020-08-11 20:51:36.044726: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (524288): 	Total Chunks: 422, Chunks in use: 422. 332.47MiB allocated for chunks. 332.47MiB in use in bin. 256.36MiB client-requested in use in bin.
2020-08-11 20:51:36.044750: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (1048576): 	Total Chunks: 9, Chunks in use: 9. 13.00MiB allocated for chunks. 13.00MiB in use in bin. 11.03MiB client-requested in use in bin.
2020-08-11 20:51:36.044759: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (2097152): 	Total Chunks: 56, Chunks in use: 56. 127.48MiB allocated for chunks. 127.48MiB in use in bin. 126.31MiB client-requested in use in bin.
2020-08-11 20:51:36.044767: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (4194304): 	Total Chunks: 122, Chunks in use: 122. 648.19MiB allocated for chunks. 648.19MiB in use in bin. 639.69MiB client-requested in use in bin.
2020-08-11 20:51:36.044775: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (8388608): 	Total Chunks: 112, Chunks in use: 112. 1.21GiB allocated for chunks. 1.21GiB in use in bin. 1.18GiB client-requested in use in bin.
2020-08-11 20:51:36.044782: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (16777216): 	Total Chunks: 99, Chunks in use: 99. 2.11GiB allocated for chunks. 2.11GiB in use in bin. 2.02GiB client-requested in use in bin.
2020-08-11 20:51:36.044790: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (33554432): 	Total Chunks: 2, Chunks in use: 2. 69.81MiB allocated for chunks. 69.81MiB in use in bin. 47.76MiB client-requested in use in bin.
2020-08-11 20:51:36.044798: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (67108864): 	Total Chunks: 15, Chunks in use: 15. 1.09GiB allocated for chunks. 1.09GiB in use in bin. 1.04GiB client-requested in use in bin.
2020-08-11 20:51:36.044805: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (134217728): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-08-11 20:51:36.044813: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (268435456): 	Total Chunks: 6, Chunks in use: 6. 3.63GiB allocated for chunks. 3.63GiB in use in bin. 3.59GiB client-requested in use in bin.
2020-08-11 20:51:36.044821: I tensorflow/core/common_runtime/bfc_allocator.cc:885] Bin for 27.5KiB was 16.0KiB, Chunk State:
2020-08-11 20:51:36.044827: I tensorflow/core/common_runtime/bfc_allocator.cc:898] Next region of size<PHONE_NUMBER>
2020-08-11 20:51:36.044843: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8b0000000 next 3716 of size 964619008
2020-08-11 20:51:36.044849: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8e97eeb00 next 4149 of size 414464
2020-08-11 20:51:36.044855: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8e9853e00 next 4150 of size 414464
2020-08-11 20:51:36.044861: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8e98b9100 next 4151 of size 828672
2020-08-11 20:51:36.044867: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8e9983600 next 4152 of size 24857344
2020-08-11 20:51:36.044873: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8eb138100 next 4153 of size 16640
2020-08-11 20:51:36.044879: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8eb13c200 next 4154 of size 414464
2020-08-11 20:51:36.044884: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8eb1a1500 next 4155 of size 417536
2020-08-11 20:51:36.044890: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8eb207400 next 4156 of size 417536
2020-08-11 20:51:36.044896: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8eb26d300 next 4157 of size 417536
2020-08-11 20:51:36.044902: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8eb2d3200 next 4158 of size 208896
2020-08-11 20:51:36.044908: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8eb306200 next 4159 of size 104448
2020-08-11 20:51:36.044914: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8eb31fa00 next 4160 of size 104448
2020-08-11 20:51:36.044919: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8eb339200 next 4161 of size 104448
2020-08-11 20:51:36.044942: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8eb352a00 next 4162 of size 417536
2020-08-11 20:51:36.044947: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8eb3b8900 next 4163 of size 834816
2020-08-11 20:51:36.044953: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8eb484600 next 4164 of size 25038848
2020-08-11 20:51:36.044959: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8ecc65600 next 4165 of size 16640
2020-08-11 20:51:36.044964: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8ecc69700 next 4166 of size 16640
2020-08-11 20:51:36.044970: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8ecc6d800 next 4167 of size 16640
2020-08-11 20:51:36.044975: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8ecc71900 next 4168 of size 834816
2020-08-11 20:51:36.044981: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8ecd3d600 next 4169 of size 417536
2020-08-11 20:51:36.044986: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8ecda3500 next 4170 of size 417536
2020-08-11 20:51:36.044992: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8ece09400 next 4171 of size 417536
2020-08-11 20:51:36.044998: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8ece6f300 next 4172 of size 417536
2020-08-11 20:51:36.045003: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8eced5200 next 4173 of size 417536
2020-08-11 20:51:36.045009: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8ecf3b100 next 4174 of size 16640
2020-08-11 20:51:36.045014: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8ecf3f200 next 4175 of size 12519424
2020-08-11 20:51:36.045020: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8edb2fa00 next 4176 of size 417536
2020-08-11 20:51:36.045026: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8edb95900 next 4177 of size 2286336
2020-08-11 20:51:36.045031: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8eddc3c00 next 4178 of size 417536
2020-08-11 20:51:36.045037: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8ede29b00 next 4180 of size 417536
2020-08-11 20:51:36.045042: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8ede8fa00 next 4181 of size 12519424
2020-08-11 20:51:36.045048: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8eea80200 next 4182 of size 417536
2020-08-11 20:51:36.045054: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8eeae6100 next 4183 of size 417536
2020-08-11 20:51:36.045059: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8eeb4c000 next 4184 of size 16640
2020-08-11 20:51:36.045065: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8eeb50100 next 4185 of size 417536
2020-08-11 20:51:36.045070: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8eebb6000 next 4186 of size 417536
2020-08-11 20:51:36.045076: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8eec1bf00 next 4187 of size 417536
2020-08-11 20:51:36.045082: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8eec81e00 next 4188 of size 828672
2020-08-11 20:51:36.045087: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8eed4c300 next 4189 of size 414464
2020-08-11 20:51:36.045093: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8eedb1600 next 4190 of size 414464
2020-08-11 20:51:36.045098: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8eee16900 next 4191 of size 414464
2020-08-11 20:51:36.045104: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8eee7bc00 next 4192 of size 414464
2020-08-11 20:51:36.045109: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8eeee0f00 next 4193 of size 414464
2020-08-11 20:51:36.045115: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8eef46200 next 4194 of size 414464
2020-08-11 20:51:36.045121: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8eefab500 next 4195 of size 274176
2020-08-11 20:51:36.045126: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8eefee400 next 4196 of size 414464
2020-08-11 20:51:36.045131: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8ef053700 next 4197 of size 414464
2020-08-11 20:51:36.045153: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0x14c8ef0b8a00 next 4198 of size 414464

I see that you are using a batch size of 412. This is probably too large. Try reducing it.

Works for as low as 64. Thank You !

The same error persists even now (batch size: 16/32/64 too)... Maybe update  the keras_wrapper to limit gpu growth from here

keras_wrapper should not modify the behavior of GPU memory growth. In case you want to change the behavior of it, you can set an environment variable (export TF_FORCE_GPU_ALLOW_GROWTH=true).
Regarding you Killed error, it seems a problem with the main memory, rather than the GPU one.

Hey, I filtered out very long sentences from the dataset and it gave no issues (In a ideal scenario, the system should work for longer sentences).
Yeah will set the environment variables if I run into errors !


[REQ] .m3u playlist support for inserting multiple disks automatically

libretro MSX, PS1 and X68 cores support .m3u playlist to load multiple disks at once. https://www.reddit.com/r/miniSNESmods/comments/7tccrf/list_of_cores_from_kmfdmanic_set_their_command/
Game.m3u

Game_Disk_1.fdd
Game_Disk_2.fdd
Game_Disk_3.fdd

First two lines disks are loaded into drives FDD1 and FDD2.
Also with this it would be useful for "disk swap" function to cycle through the discs in the playlist.
Please consider these features for inclusion in NP2kai.
Thank you!

Could be useful as long as there aren't games that expect two floppies at once (like x68k). I have a easy way to create m3u files (a script i made) so i'd probably use this for the retroarch version.

PX68k already supports m3u. It loads the first two disks into floppy 0 and 1. The rest of the playlist can be accessed through the swap function.

I'm mind about follow 2 issues.

Retroarch's Swap Disc API uses .m3u file.
PX68k reads .m3u file when start.
I like second issue. I think I want to implement that.
But there may be people who are confused.

It can be explained in documentation with examples. Some other cores already work this way: MSX, PS1, PX68K

I could apply for Disk Swap Interface.
Please try it.

I'll try

I opened a issue request on beetle for a 'autoswap' idea that might (or might not)  interest you  because it's related to this:
https://github.com/libretro/beetle-psx-libretro/issues/305

There seems to be a problem with loading the m3u file, and it is under investigation.
Libretro's image swap interface can swap on only one slot.
So I implemented a floppy drive selection for 'Option'.

See also,
https://forums.libretro.com/t/m3u-file-loading/15716


docs: Automated Changelog Update for v5.0.0-beta6.2

copilot:all
Automated changes by create-pull-request GitHub action

1


Seems like crop is adding the x and y offsets twice?

I t seems that in Grafika/Imagick/Editor.php lines 179 and 180 X and Y offsets are added to the $x and $y params.
But it seems that the offsets are already added at Grafika/Position.php on lines 119 and 120 right? Those values are used to initialize $x and $y later in the first place.
So Grafika/Imagick/Editor.php lines 179 and 180 can be just removed?

Thank you for reporting. You are right. Fixed


some trouble with Express 4

I have some problem to run the last build of django_react with express 4. The source of the problem seems to come from render.js. django_node send post request and thus request.query.var return nothing. To solve it, i changed every occurence of request.query.--- to request.body.--- in the "var service = function(request, response)" of render.js.  Have a nice day !

Yeah, master's pretty crazily unstable at the moment. I hit some issues with passing large data sets along, so I've been testing a couple of different solutions.
If you roll django-node back a day or two, it should work.
I'll try to update django-react's master branch when I have some time in the next day or two.
Otherwise, if you're happy to hack on stuff, you can check https://github.com/markfinger/django-frontend-tools/tree/master/django_frontend_tools/services for some samples that'll work with django-node's master branch. The changes mostly boil down to the request object no longer being passed in, rather the server will pass the data in as the first argument.

Should be fixed now.

Thanks. I just succeed to make it work. I also modified webpack the same way to recover the data from django-node to build the bundle. Now the app start on the client side too. \o/


"user project is inactive" error when using the default provided Infura API key

I used the example script provided in the docs but it didn't work:

Perhaps the issue is that you closed/ deleted the Infura project associated to that API key?
At any rate, I think instead of actual value the example script should contain a placeholder and a comment to let the user know that they have to sign up on Infura.
Related: How does getDefaultProvider work in ethers.js?

I didnt delete it I need help making a new one I got hacked
On Sat, Mar 12, 2022, 2:04 PM Paul Razvan Berg @.***>
wrote:

I used the example script
https://usedapp.readthedocs.io/en/latest/getting-started.html#example
provided in the docs but it didn't work:
[image: Screen Shot 2022-03-12 at 9 52 37 PM]
https://user-images.githubusercontent.com/8782666/158036597-efe45a7a-56e8-4eea-9138-86ddc234c939.png
Perhaps the issue is that you closed/ deleted the Infura project
associated to that API key?
At any rate, I think instead of actual value the example script should
contain a placeholder and a comment to let the user know that they have to
sign up on Infura.
—
Reply to this email directly, view it on GitHub
https://github.com/TrueFiEng/useDApp/issues/582, or unsubscribe
https://github.com/notifications/unsubscribe-auth/AXUTFEDODPJXEKPIWJ4O2EDU7UIGXANCNFSM5QSNZL4Q
.
Triage notifications on the go with GitHub Mobile for iOS
https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675
or Android
https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub.
You are receiving this because you are subscribed to this thread.Message
ID: @.***>

Good idea!


fix: interrupted server

When the server is stopped,  the sockets throw a IOException, not SocketException,  so catch that and check if we got interrupted instead of reporting an errorr

 Here is an overview of what got changed by this pull request:

Complexity increasing per file
==============================
- Assets/Mirror/Runtime/Transport/Tcp/Server.cs  2

See the complete overview on Codacy


can we have a means of not generating fields for certain columns?

The created_at, updated_at and deleted_at all should never have a field in the create/edit view. Those are handled by Laravel and aren't meant to be added or modified manually.
Maybe we could have something in the config/generator.php where we can specify which columns to not generate fields for for the create/edit views? I've been find/replacing them manually for now.

With the latest code, created_at and updated_at are not added in forms anymore. And we are going give the option to specify if a field should be added in index and form.
Check the branch https://github.com/InfyOmLabs/laravel-generator/tree/35_include_in_index_form
duplicate of #35

I'm using latest code with datatables and created_at, updated_at and deleted_at are still added to the edit form!!
What's worse, when you edit an entry it automatically populates deleted_at with zeros which then causes it not to show up in the list!!!!!
I've asked for a means to have those 3 fields not appear in forms, your answer to this was to add the ability to have fields not show up in form when using the schema.json file.
But what about those of us that don't use that file? I always generate my schemas from existing table.
I would like to be able to configure the laravel_generator.php configuration file in which I could say which fields to never show up in forms.
I still want my model to use these timestamp fields, they just have no business appearing in the create/edit forms!


Review Ticket #10

Big Idea 2 Quiz

Individual CPT

Future Implementations

GitHub Commits

CPT Outline

2.7- Contributed ideas to group project, college board quiz is thorough, has detailed CPT outline


Remove references to actions from API docs

Fix #1713

@desa Can you create the docs PR to update the API with this new version?


Properly handle matrix.to links for Rooms #87

Run Robrix without entering any other rooms, only entering the room with a message that contains OwnedRoomId link sent by someone, click on the link to enter and this will work well.
Otherwise, robrix will panic if you enter any other rooms before that.
thread 'main' panicked at src/home/room_screen.rs:1804:18:
BUG: couldn't get timeline state for first-viewed room.
note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace
thread 'main' panicked at src/home/room_screen.rs:1907:32:
called `Result::unwrap()` on an `Err` value: PoisonError { .. }

At the moment I have not dealt with alias links yet.
This will work:
2024-11-11_14-35-47.webm
This will panic:
2024-11-11_14-36-27.webm

lol, well yes, this issue is not nearly as simple as just inserting a call to set_displayed_room(). I would definitely expect that to panic. Take a look at how show and hide timeline functions work, specifically how we handle the TimelineUiState.
Not to mention that there are other very complex cases here:

the client may not know about a room yet, meaning it hasn't been synced from the homeserver yet.
the room may not be one that the user has joined, so we cannot display it at all.

In this case we need a completely new UI element that fetches the room info, prompts the user to join the room, and (optionally) shows a preview of the room if that room supports previews.

The room may no longer exist, or the current logged-in user may not have sufficient permissions to view it, join it, or even knock (request access to it). So those errors must also be handled properly and displayed to the user with a nice modal.

Once you understand the requirements for this, and how timeline state is managed, you may be in a better position to tackle part of this issue, e.g., the simple case where we already know about the room and the user has joined it.

Closing for now; feel free to open a new PR that properly implements a smaller subset of the above-listed features.


Starter next-corporate with TypeScript: backend server not able to start due to TypeError

Description
Running the starter command  yarn create strapi-starter my-site next-corporate and picking the options "Custom" and "TypeScript", leads to error: Cannot read properties of undefined (reading 'routes'), so the backend server isn't able to start.
Option "Quickstart" (JavaScript) does work.
Steps to reproduce + outputs

Use latest node 16 LTS~/strapi-next-corporate-starter-test$ nvm use 16
Now using node v16.17.0 (npm v8.15.0)

Run Starter command

> Custom (manual settings)
> TypeScript
> sqlite

  ~/strapi-next-corporate-starter-test$ yarn create strapi-starter my-site next-corporate
  yarn create v1.22.15
  [1/4] Resolving packages...
  [2/4] Fetching packages...
  [3/4] Linking dependencies...
  [4/4] Building fresh packages...
  success Installed<EMAIL_ADDRESS>with binaries:
        - create-strapi-starter
  ? Choose your installation type Custom (manual settings)
  Installing @strapi/starter-next-corporate starter.
  ? Choose your preferred language TypeScript
  ? Choose your default database client sqlite
  ? Filename: .tmp/data.db

  Creating a project with custom database options.
  Creating a new Strapi application at /home/falk/strapi-next-corporate-starter-test/my-site/backend.
  Creating files.
  Installing @strapi/template-corporate template.
  Dependencies installed successfully.

  Your application was created at /home/falk/strapi-next-corporate-starter-test/my-site/backend.

  Available commands in your project:

    yarn develop
    Start Strapi in watch mode. (Changes in Strapi project files will trigger a server restart)

    yarn start
    Start Strapi without watch mode.

    yarn build
    Build Strapi admin panel.

    yarn strapi
    Display all available commands.

  You can start by doing:

    cd /home/falk/strapi-next-corporate-starter-test/my-site/backend
    yarn develop

  Creating Strapi starter frontend at /home/falk/strapi-next-corporate-starter-test/my-site/frontend.
  Installing frontend dependencies
  Dependencies installed successfully.
  Dependencies installed successfully.
  Starting the app
  yarn run v1.22.15
  $ cross-env FORCE_COLOR=1 npm-run-all -l -p develop:*
  $ wait-on http://localhost:1337/admin && yarn --cwd frontend develop
  $ yarn --cwd backend develop
  $ strapi develop
  [develop:backend ] Starting the compilation for TypeScript files in /home/falk/strapi-next-corporate-starter-test/my-site/backend
  [develop:backend ] Building your admin UI with development configuration...
  [develop:backend ] ℹ Compiling Webpack
  [develop:backend ] ✔ Webpack: Compiled successfully in 11.23s
  [develop:backend ] Admin UI built successfully
  [develop:backend ] [2022-08-31 11:27:38.773] debug: ⛔ Server wasn't able to start properly.
  [develop:backend ] [2022-08-31 11:27:38.774] error: Cannot read properties of undefined (reading 'routes')
  [develop:backend ] TypeError: Cannot read properties of undefined (reading 'routes')
  [develop:backend ]     at Object.addCreateLocalizationAction (/home/falk/strapi-next-corporate-starter-test/my-site/backend/node_modules/@strapi/plugin-i18n/server/services/core-api.js:184:41)
  [develop:backend ]     at /home/falk/strapi-next-corporate-starter-test/my-site/backend/node_modules/@strapi/plugin-i18n/server/register.js:78:22
  [develop:backend ]     at Array.forEach (<anonymous>)
  [develop:backend ]     at extendLocalizedContentTypes (/home/falk/strapi-next-corporate-starter-test/my-site/backend/node_modules/@strapi/plugin-i18n/server/register.js:56:38)
  [develop:backend ]     at Object.module.exports [as register] (/home/falk/strapi-next-corporate-starter-test/my-site/backend/node_modules/@strapi/plugin-i18n/server/register.js:12:3)
  [develop:backend ]     at Object.register (/home/falk/strapi-next-corporate-starter-test/my-site/backend/node_modules/@strapi/strapi/lib/core/domain/module/index.js:47:46)
  [develop:backend ]     at Object.register (/home/falk/strapi-next-corporate-starter-test/my-site/backend/node_modules/@strapi/strapi/lib/core/registries/modules.js:33:19)
  [develop:backend ]     at async Strapi.runLifecyclesFunctions (/home/falk/strapi-next-corporate-starter-test/my-site/backend/node_modules/@strapi/strapi/lib/Strapi.js:510:5)
  [develop:backend ]     at async Strapi.register (/home/falk/strapi-next-corporate-starter-test/my-site/backend/node_modules/@strapi/strapi/lib/Strapi.js:379:5)
  [develop:backend ]     at async Strapi.load (/home/falk/strapi-next-corporate-starter-test/my-site/backend/node_modules/@strapi/strapi/lib/Strapi.js:456:5)
  error Command failed with exit code 1.
  info Visit https://yarnpkg.com/en/docs/cli/run for documentation about this command.
  error Command failed with exit code 1.
  info Visit https://yarnpkg.com/en/docs/cli/run for documentation about this command.
  ERROR: "develop:backend" exited with 1.
  error Command failed with exit code 1.
  info Visit https://yarnpkg.com/en/docs/cli/run for documentation about this command.
  This error originated either by throwing inside of an async function without a catch block, or by rejecting a promise which was not handled with .catch(). The promise rejected with the reason:
  Error: Command failed with exit code 1: yarn develop
      at makeError (/home/falk/.config/yarn/global/node_modules/execa/lib/error.js:60:11)
      at handlePromise (/home/falk/.config/yarn/global/node_modules/execa/index.js:118:26)
      at processTicksAndRejections (node:internal/process/task_queues:96:5)
      at async buildStarter (/home/falk/.config/yarn/global/node_modules/create-strapi-starter/utils/build-starter.js:194:3)
  Done in 200.17s.

Did you get a resolution to this? I'd like to use typescript and jsx if possible with the template

Looking for a solution / workaround as well.

Should the next-corporate template perhaps be removed? It's been non-functional for over a year now...


Encoding non alphabetic characters

Hi.
I am using spring-cloud Hoxton.SR4 with spring boot (spring-boot-starter-parent) 2.2.5.
I am facing an issue with spring cloud gateway which encoding non alphabetic characters incoming.
For instance, the query param "test=! 1." Is send "%211." to the backend!
By default, non alphabet characters receive are urlencode by SCG.
How can I preserve the integrity of query parameters treated by the spring cloud gateway?
Thank you in advance

Sorry, but my previoius description is bad. I am going to create a more accurate issue
best regards


Missing project from Amplify Apps Dashboard

I am experiencing some issues that I just noticed today where my Application is no longer available on the Amplify dashboard.
I will say that when I deployed there seemed to be some changes to the website as I was pushing out. But long story short I am unable to access the files that are still being hosted (and are still accessible via my url) on aws and when i go to my domain settings the url is different for the missing application than what is created for new applications. My new applications have a domain of amplifyapp.com where as my missing project is cloudfront.net.
Doing my due diligence I have checked the cloundfront dashboard to make sure that there isn't anything there, but my cloudfront dashboard does not show anything.

Sorry this chalks up to user error. I was looking in the wrong region. Thanks for the assistance


[2.x] Error while building the app

Describe the bug
After updating to v2.0.2 and running nuxt build this happens (while building Nitro server):
RollupError: Could not resolve "../../page-utils" from "node_modules/nuxt-simple-sitemap/dist/runtime/util/entries.mjs"

Reproduction
No response
System / Nuxt Info
Nuxt: v3.1.3
Nitro: v2.3.1
Sitemap: v2.0.2

Yes, same error here

Here a reproduction to view the error, juste start the project with npm run dev and the error appear
https://stackblitz.com/edit/github-rp5jz9?file=package-lock.json

I have a same issue.
 ERROR  RollupError: Could not resolve "../../page-utils" from "node_modules/nuxt-simple-sitemap/dist/runtime/util/entries.mjs"

Nuxt 3.3.1 version

Thank you for the issue. I should probably avoid releasing things at 3am in the morning :facepalm:
Please use the latest version and it should be fixed


Change cosmos sample from track1 to track2

Change cosmos sample from track1 to track2

 Thank you for your submission, we really appreciate it. Like many open source projects, we ask that you sign our Contributor License Agreement before we can accept your contribution.:x: Melanie-Ma sign nowYou have signed the CLA already but the status is still pending? Let us recheck it.

@anuchandy - Please review. Thanks, Jon

@conniey - please review, thank you.


Make HPC groupname and foldername editable suggestions during approval

Currently, the HPC groupname and foldername are generated from the users name. Include two fields in the HPC admins approve dialogue that populate the values in the fields but let the user edit the fields.

Enforce same naming convention as for projects done with #180


Bugfix missing title pattern navigator page

Description
Closes #224
Add svlete:head and title to research-pattern-navigator
Title is 'Patterns - Research Methods for Design-Oriented Research in ICT'
Type of change
Please select the option that is relevant

[x] Bug fix (non-breaking change which fixes an issue)
[ ] New feature (non-breaking change which adds functionality)
[ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
[ ] Documentation

Checklist

[x] I have commented my code, particularly in hard-to-understand areas.
[x] All the required documentation is added.
[x] My code follows the style guidelines of this project.
[x] I have performed a self-review of my own code.

Thanks @Elmar-Wiese !


Created workshop item returns the wrong ID

Okay so this is a weird issue and I will try and look further into it, but the gist of it is the following:
I'm using steamworks-rs to create and upload workshop items. In general, this works alright, except for a glaring issue: If you create a workshop item with ugc.create_item, it will return the wrong ID for the created workshop item.
Example:
I created an item, I get the following from my callback handler:

Okay, looks sane, even though the ID is a little bit... high, but whatever. Now, checking out the newly created workshop item in Steam shows the following ID:

I have no idea if this only happens on my machine or if something else is going on. I will run some tests on other machines and add some logging...
Maybe someone already encountered this and can tell me what's going on.

Okay, so Steam creates it with the correct ID, at least according to the log at Steam/logs/workshop_log.txt, leaving me to believe there's either something wrong with the bindings themselves or it's a communication issue between the Steam API and my application.

The current bindings.rs runs the last steamworks sdk version 153a since https://github.com/Noxime/steamworks-rs/pull/79
I'm a little bit confused about what you did to fix. Can you explain better? Feel free to open a PR too.

To fix, I literally just cloned the repo, cded into steamworks_sys and ran cargo build --features rebuild-bindings.

That is weird. But anyway, if it fixed the problem, you can open a PR :)
@james7132 any thoughts on that?

If updating the bindings fixes it, I see no reason to question the change. We should be regularly keeping the bindings and SDK binaries up to date.

So this bug is likely a result of not using platform-specific bindings, meaning it should resolved with #106 .


[Backport - Newton] MaaS: Move MaaS plugins into MaaS role

Consolidates MaaS plugins into rpc_maas roles where they belong.
connects rcbops/u-suk-dev#1106

recheck_ceph


more helpful missing component error message - in case the component is not needed

Currently, after #139 - the error message for missing component is more helpful but it assumes you want this dependency. This PR updates the message to hint with how to deal with the situation where you don't want this component.
I understand that error messages are not meant to give instructions for proper use, but in this case even after the more helpful message added in #139 it is not clear why wiredep is failing when you uninstall a component.
Reproduce steps:

Given you have a bower.json file with some dependencies
You then uninstall a dependency using bower uninstall or manually remove from bower_components
But - you forgot to update your bower.json (with --save or manually)
You then run wiredep

Cool, thanks!


Config fails to load if Interface name does no match config file name

It is not clear what to type in List of VPN clients to handle.
I added an interface vpn_blabla, then uploaded the config file vpn.conf, vpn fails to start :
wg-quick: The config file must be a valid interface name, followed by .conf
Then I added a new interface vpn and removed the interface vpn_blabla. After restart I get:
vpn_blabla | not running | enabled | x.y.z.u:v
vpn | running | enabled | x.y.z.w:v

So interface name and config file name seem to be coupled.
If possible, the UX would be better if we would just ask for a config file to load, and the corresponding interface would be created accordingly. So basically, a list of config files with:

a field to change the file
another field to enable/disable.

The configuration panel should automatically rename the file to $interface_name.conf. Can you confirm it still happens?
It is not possible to do it the other way around with the config panels, as far as I know.


ci: Use setup-node to cache node.js environment in CI

This is a partial revert of a06ac87382095f882a89e37794b4303a6b8a4caf. It turns out that the caching that setup-node does is too important to lose: without it, dodgy/slow downloads from node.js dependency servers cause spurious CI failures for us.

See https://github.com/endlessm/kolibri-explore-plugin/pull/782#issuecomment-1723508031 for some discussion about the cause/symptoms of this.

That looks good, but you can add a commit that moves the -e in the shebang line to an explicit set -e command so that the script will reliably fail no matter how it's executed?

oops, I completely forgot about that part of your comment in the other issue. Fixed!


The diff coverage is n/a.

@@            Coverage Diff             @@
##           master     #587      +/-   ##
==========================================
- Coverage   87.11%   86.87%   -0.24%
==========================================
  Files          77       77
  Lines        7332     7332
  Branches     1476     1476
==========================================
- Hits         6387     6370      -17
- Misses        754      772      +18
+ Partials      191      190       -1

Impacted Files
Coverage Δ

devito/data.py
71.71% <0%> (-5.56%)
:arrow_down:

devito/yask/wrappers.py
81.96% <0%> (-1.1%)
:arrow_down:

devito/tools.py
72.39% <0%> (-1.05%)
:arrow_down:

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update 8681d15...c24fea4. Read the comment docs.


Add metaphor understanding task

Metaphoric usage requires understanding not only the surface form or the literal meaning of text but modelling how concepts mentioned in the text possess properties of other concepts of the world. The task is trying to measure the ability for models to understand metaphoric usage of text. To perform high on this task, models should be able to understand metaphoric references to objects and ideas as well as avoid falling into the trap of picking up the meaning of the surface form.

@kaustubhdhole Thank you for submitting your task! We are in the process of pairing submissions with task reviewers and will be in touch soon.

@kaustubhdhole We are now requesting that task authors please include an explicit section in the README.md file called "data source", listing exactly where you got your data from (or explicitly state that you made it up yourself). Many thanks!

@TalLinzen are one of the assigned reviewers for this task.  Please provide feedback and be sure to notify me with a formal accept or does not meet criteria by Tuesday June 22 (if you have not already done so).

@gdemelo are one of the assigned reviewers for this task.  Please provide feedback and be sure to notify me with a formal accept or does not meet criteria by Tuesday June 22 (if you have not already done so).

The amount of tasks we received this round is tremendous.  With so many tasks to review we have decided to extend the review period to Tuesday, June 29.
Reviewers: if the submitter has made revisions please be sure to notify me with a formal ”accept” or ”does not meet criteria” by Tuesday, June 29  (if you haven't already done so).
this is an automated message

@TalLinzen @gdemelo could you please mark a formal accept if the PR meets the official criteria.

We are going to push your task through the initial review stage as accepted. The next stage of this process is for us to assign your task a meta-reviewer for final review and merge. An assigned meta-reviewer will follow-up by commenting on the PR should it need further revisions.

Meta-reviewer here. Since the task did not receive a review, I'll review it as well.
@kaustubhdhole Thanks for your submission! Can you please add a Data Sources section that explains how the data was collected?
Task review
This is a thorough submission that fits well in BIG-bench.
Specificity, Thoroughness, and Justification: The task measures a specific ability of language models: comparing and contrasting metaphoric sentences. As the README clearly lays out, this is a basic natural language understanding skill and is interesting to measure how well language models perform on it. The task contains several hundred examples, which is more than enough for the benchmark.
Difficulty: The task is not solvable by GPT-2, and I suspect it will be challenging for other existing language models.
Correctness and Formatting: The JSON file is easy to read, and the automatic tests pass. The choice to separate example sentences with <--> is unusual, but I don't see an issue with it.

@kaustubhdhole Can you please add a Data Sources section to the README? This is the only blocker before we can accept the task.

Hi @kaustubhdhole, (cc meta-reviewers @guygurari and @gauravmishra), I noticed that this task is quite similar to one that was recently accepted in PR #384, which is by @RedHilarious.  Given the similarity, and the fact that task #384 has two subtasks already, I wonder how you both would feel about making this task a subtask of #384.  Please let me know if you are open to this change, as I think it should be relatively painless and also make the repo cleaner.

Thanks @ramasesh - yes they are similar. :) They have different data though and the other one is a multi-choice question type. This one is a boolean test. To avoid clashes, I have renamed this one too metaphor_boolean.

Thank you @kaustubhdhole ! Since some of the data was derived from published work, I suggest inviting the authors of the paper to be included as task authors. Have you reached out to them yet?

@kaustubhdhole Friendly ping on the question above: Have you discussed task authorship with the authors of the published work this task builds on?

Merging the task now. If the authors would like to be added we can do that later. Thank you @kaustubhdhole for your submission!


allow to customize keyword

Will be great to allow setup keyword on different names, in webpack we need support assert and with together for compatibility, yes we can use old acorn-import-asse package, but there is a problem - no bugs fixes in acorn-import-assertions, because it was renamed
/cc @xtuc

Try to find time on PR, but a lot of issues, right now... Also will be great to undestand assert or with keyword were used, because we need to keep code as is for external requests

Published in<EMAIL_ADDRESS>


Allow Pulling Contract ABIs Directly From NPM

Load a JSON file directly from NPM registry!

Implemented with #248


Mark certain refs as implicit [phab:D2166]

Created by zarko at 2018-02-02 23:28:48:
This diff calls out refs from non-physical ranges and
from underneath template instantiations as implicit,
using the new edge kinds ref/implicit, ref/init/implicit,
and ref/call/implicit. This should clean up
the results of cross-reference queries, where users are
sometimes surprised to find references to their definitions
located inside (for example) standard library templates
(as those definitions were used there during instantiation).
It may be possible to mark refs from macro expansions as
implicit as well, but the situations in which this is a
good idea are currently unclear.

fromberger commented inline (context lost during migration) at 2018-02-03 01:25:08:
Perhaps just to be extra-clear: The expression spanned by A

fromberger commented inline (context lost during migration) at 2018-02-03 01:25:08:
Can you please add these kinds to the schema packages in kythe/go/util/schema/... as well?

fromberger commented inline (context lost during migration) at 2018-02-03 01:25:08:
I'm not wedded to this, but what do you think about calling the enumerators Implicit and Explicit and naming the type for that to make sense. Some strawbeings perhaps: Implicitness, Plicitness, Explicitness, Implicity, Denotation, Variety, Actuality, …
I feel like "yes" and "no" don't have much advantage over the built-in bool constants at the call site.

fromberger commented inline (context lost during migration) at 2018-02-03 19:49:25:
(On the other hand if we're going to use them fully-qualified anyway, this might be pointless advice)

zarko wrote at 2018-02-05 18:39:32:

Address comments.

fromberger accepted this revision at 2018-02-05 20:15:20

zarko closed via commit d0826adf426d29c9f6f245f099ba6cabab7c5ff2 at 2018-02-05 20:29:51

shahms wrote at 2018-02-06 18:09:04:
LGTM