ModuleNotFoundError: No module named 'keras.api'

When I ran  !/bin/bash convert-model.sh in Google Colab, it resulted in:
2022-03-22 09:32:42.586979: E tensorflow/stream_executor/cuda/cuda_driver.cc:271] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected
Traceback (most recent call last):
  File "keras_retinanet/keras_retinanet/bin/convert_model.py", line 103, in <module>
    main()
  File "keras_retinanet/keras_retinanet/bin/convert_model.py", line 79, in main
    model = models.load_model(args.model_in, backbone_name=args.backbone)
  File "keras_retinanet/keras_retinanet/bin/../../keras_retinanet/models/__init__.py", line 87, in load_model
    return keras.models.load_model(filepath, custom_objects=backbone(backbone_name).custom_objects, compile=compile)
  File "keras_retinanet/keras_retinanet/bin/../../keras_retinanet/models/__init__.py", line 57, in backbone
    from .resnet import ResNetBackbone as b
  File "keras_retinanet/keras_retinanet/bin/../../keras_retinanet/models/resnet.py", line 18, in <module>
    from keras.utils import get_file
ImportError: cannot import name 'get_file' from 'keras.utils' (/usr/local/lib/python3.7/dist-packages/keras/utils/__init__.py)

After some googling I found out that I should install kashgari, so I ran
pip install kashgari
And when I came back to  !/bin/bash convert-model.sh, I had:
Using TensorFlow backend.
Traceback (most recent call last):
  File "keras_retinanet/keras_retinanet/bin/convert_model.py", line 33, in <module>
    from ..utils.config import read_config_file, parse_anchor_parameters
  File "keras_retinanet/keras_retinanet/bin/../../keras_retinanet/utils/config.py", line 19, in <module>
    import keras
  File "/usr/local/lib/python3.7/dist-packages/keras/__init__.py", line 3, in <module>
    from . import utils
  File "/usr/local/lib/python3.7/dist-packages/keras/utils/__init__.py", line 26, in <module>
    from .vis_utils import model_to_dot
  File "/usr/local/lib/python3.7/dist-packages/keras/utils/vis_utils.py", line 7, in <module>
    from ..models import Model
  File "/usr/local/lib/python3.7/dist-packages/keras/models.py", line 12, in <module>
    from .engine.training import Model
  File "/usr/local/lib/python3.7/dist-packages/keras/engine/__init__.py", line 8, in <module>
    from .training import Model
  File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 14, in <module>
    from . import training_utils
  File "/usr/local/lib/python3.7/dist-packages/keras/engine/training_utils.py", line 17, in <module>
    from .. import metrics as metrics_module
  File "/usr/local/lib/python3.7/dist-packages/keras/metrics.py", line 1850, in <module>
    BaseMeanIoU = tf.keras.metrics.MeanIoU
  File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/lazy_loader.py", line 58, in __getattr__
    module = self._load()
  File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/lazy_loader.py", line 41, in _load
    module = importlib.import_module(self.__name__)
  File "/usr/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
ModuleNotFoundError: No module named 'keras.api'

Am I doing something wrong? Is there a way to address this error?

Hi,
I don't think kashgari (an NLP framework) has anything to do with the AIR detector, so I suggest you uninstall it from your current Python environment to avoid any module name conflicts.
The first error CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected suggests that you're either running on a CPU machine or your CUDA installation is somehow misconfigured. I recommend trying the CPU build/container of the AIR-detector and trying to run the convert-model.sh script again. Alternatively, you can play with your CUDA settings, but it can be difficult in a restricted Google Colab environment.
Lastly, maybe it's just about the CUDA_VISIBLE_DEVICES environment variable not being properly set, in which case running something like
CUDA_VISIBLE_DEVICES=0 /bin/bash convert-model.sh

might do the trick for you.


Write more instrumented tests

There should be one for each activity, but at the moment we only have tests for Conjugation and ConjInfo activities

Tests for Display and Main Activities have been written in PR #28

Remaining tests

[ ] Search Results
[ ] Search
[ ] Settings
[ ] Add Favorites
[ ] Conjugations (should be updated)
[ ] Conjugation Info (should be updated)
[ ] Conjugator
[ ] Launch

Tests for Search and Conjugator Activities in #56


Getting node data when node is deleted

Is there a way to get node info in node removed event
editor.on('nodeRemoved',function(id){     editor.getNodeFromId(id) })
i saw that getNodeFromId() returns an error when we try to access a removed node. Is there a way to get the info about the deleted node?

Hello  @iamnikhilrohan
You cannot access the information because it is already deleted.
Try this, I have changed the order of the event trigger before it has been deleted.
  editor.removeNodeId = function (id) {
    this.removeConnectionNodeId(id);
    var moduleName = this.getModuleFromNodeId(id.slice(5))
    if(this.module === moduleName) {
      this.container.querySelector(`#${id}`).remove();
    }
    this.dispatch('nodeRemoved', id.slice(5)); //Changed
    delete this.drawflow.drawflow[moduleName].data[id.slice(5)]; //Changed

  }

Jero

Thanks @jerosoler for the lighting quick reply!
and Thanks, it solved my problem.


Create troubleshooting install wizard article

Describe your pull request here: Create troubleshooting install wizard article
List the file(s) included in this PR: troubleshooting-zowe-server-install-wizrd.md, Installing-zowe-server-install-wizard.md
After creating the PR, follow the instructions in the comments.

Thanks @janan07. Looks good to me.


Netopeer2 cross compilation failed for ARM 32 bit, undefined reference for libssh

Hi Michal,
We are cross compiling sysrepo-netopeer2 and all dependencies for ARM 32 bit architecture.
Rest all compilation is done except netopeer-2.
Getting errors for
/usr/local/lib/libssh.so: undefined reference to OpenSSL_version_num@OPENSSL_1_1_0' /usr/local/lib/libssh.so: undefined reference to EVP_des_ede3_cbc@OPENSSL_1_1_0'
/usr/local/lib/libssh.so: undefined reference to EC_KEY_get0_group@OPENSSL_1_1_0' /usr/local/lib/libssh.so: undefined reference to EVP_PKEY_get_raw_private_key@OPENSSL_1_1_1'
Even though openssl and libssh are ARM compatible and found in respective path. Attaching Cmake and make logs for reference.
make_logs.txt
cmake_logs.txt
Libs:
/usr/local/lib# file ssh
libssh.so:        symbolic link to libssh.so.4
libssh.so.4:      symbolic link to libssh.so.4.8.6
libssh.so.4.8.6:  ELF 32-bit LSB shared object, ARM, EABI5 version 1 (SYSV), dynamically linked, BuildID[sha1]=1d1800c9dadd276ac65c7d6d8dc36dc9e13ac0bb, stripped
root@u1804cloudvm:/usr/local/lib# file ssl
libssl.a:     current ar archive
libssl.so:    symbolic link to libssl.so.3
libssl.so.3:  ELF 32-bit LSB shared object, ARM, EABI5 version 1 (SYSV), dynamically linked, BuildID[sha1]=403888c04aa273559a455633e74495367b80b41f, not stripped
root@u1804cloudvm:/usr/local/lib# file curl
libcurl.so:       ELF 32-bit LSB shared object, ARM, EABI5 version 1 (SYSV), dynamically linked, BuildID[sha1]=d57dacf727a07acb34b8cd5a5bc2cbf5e6c4ef83, not stripped
libcurl.so.4:     symbolic link to libcurl.so.4.7.0
libcurl.so.4.7.0: ELF 32-bit LSB shared object, ARM, EABI5 version 1 (SYSV), dynamically linked, BuildID[sha1]=deff417458b179feed2acb57c3b46a4e7da4706d, stripped
We are stuck at this point, please help, if we are doing something wrong or missing some step.
Regards,
Ankit

This is strictly a problem with linking libssh, not sure why you are asking me for help. In any case, I cannot help you because I have no experience with cross-compilation.

Hi Michal,
I have figured out the issue and found, Issue is with compatibility for LIBSSH and OPENSSL compatibility.
Even latest LIBSSH 0.9.5 only works with OPENSSL 1.0.2k version for ARM cross compilation.
Adding here as might be helpful to somebody with similar issue in future.
Thanks for your help anyways!
Regards,
Ankit

Fine, no problem.

Hi Michal,
I have figured out the issue and found, Issue is with compatibility for LIBSSH and OPENSSL compatibility. Even latest LIBSSH 0.9.5 only works with OPENSSL 1.0.2k version for ARM cross compilation.
Adding here as might be helpful to somebody with similar issue in future.
Thanks for your help anyways!
Regards, Ankit

Hi，
I'm sorry to bother you.I have completed the cross compilation of netopeer 2, but I don't know how to transplant it to the arm development board. Can you show me the next detailed steps？
Regards,
NTU

Hi Michal,
I have figured out the issue and found, Issue is with compatibility for LIBSSH and OPENSSL compatibility. Even latest LIBSSH 0.9.5 only works with OPENSSL 1.0.2k version for ARM cross compilation.
Adding here as might be helpful to somebody with similar issue in future.
Thanks for your help anyways!
Regards, Ankit

Hi， I'm sorry to bother you.I have completed the cross compilation of netopeer 2, but I don't know how to transplant it to the arm development board. Can you show me the next detailed steps？ Regards, NTU

Hi, I've also been working on cross-compiling netopeer2 recently, can I ask you some questions?

Like I said, I have no experience with cross-compilation so I am not able to tell you much. Except for the variables mentioned in the README, you should set it up for cross-compilation just like any other project.

I strongly suggest to use Buildroot or Yocto. Trying to do a DYI cross-compilation and/or "injecting" some random cross-built binaries to an ARM target is a recipe for failure.

Yes. Use Buildroot or Yocto, and don't invent a hand-crafted, DYI build. That's what I already suggested in my previous post above.


fix(telemetry): improvised the telemetry event

Improved the broadcasting event for telemetry.
Added a single event name for all the analytics tracking user events.

@invincibleJai @ia3andy please review

@jyasveer have you tested with https://github.com/fabric8-ui/fabric8-ui/pull/3147 ??

:tada: This PR is included in version 1.0.5 :tada:
The release is available on:

npm package (@latest dist-tag)
GitHub release

Your semantic-release bot :package::rocket:


feat: allow OOT platforms to use custom resolver.resolveRequest

Summary:
Currently, when we have an additional platform in react-native.config.js, users cannot use custom resolver.resolveRequest functions as they are overwritten by reactNativePlatformResolver. Goal of this PR is to allow OOT platforms to use additional custom resolvers besides remapping react native imports.
Changelog:
[GENERAL] [FIXED] - Allow Out Of Tree platforms to pass custom resolvers
Test Plan:

Add additional platform in react-native.config.js
Pass custom resolver to metro.config.js:

resolveRequest: (context, moduleName, platform) => {
      console.log('resolveRequest', moduleName, platform);
      return context.resolveRequest(context, moduleName, platform);
 }

Check if user's resolveRequest function is called.

cc: @huntie

This code used to be in RN CLI and the fix looks good from my side. cc @huntie


Update helm to 3.7.0

Fixes https://github.com/giantswarm/app-build-suite/issues/134
Needs testing.

Works for me for efk-stack-app, nginx-ingress-controller and cert-manager-app like this:
DABS_TAG="1.0.3-79d71fa6796c73770aee560fd16ab1b820b4c259" ../app-build-suite/dabs.sh \
  --chart-dir ./helm/efk-stack-app \
  --steps build


chore(biz-demo/easy_note): change etcd image

Signed-off-by: rogerogers<EMAIL_ADDRESS>What type of PR is this?

What this PR does / why we need it (en: English/zh: Chinese):

en: change biz-demo/easy_note docker-compose etcd image, fix run error on mac m1.
zh: 修改easy_note etcd镜像，适配苹果m1
Which issue(s) this PR fixes:

LGTM


ZK

To be summarized:

Reference:

https://www.youtube.com/watch?v=odaUPMNqJs8
https://zeroknowledge.fm/episodes/


Add possibility to retrieve signed URL from storage

The Storage library does not expose a way to fetch a signed URL at the moment.
To do this in our app, we currently need to deploy a copy of private PEM key that we then use to extract the rsaKey from, and use the RS256Signer to create the signature which looks like this:
GET\n\n\n' + '$expirationTime\n' + '/$bucketName/$storagePath

To do this, we need to import to source dart files from googleapis_auth:
// ignore: implementation_imports
import 'package:googleapis_auth/src/crypto/pem.dart';
// ignore: implementation_imports
import 'package:googleapis_auth/src/crypto/rsa_sign.dart';

The actual code that generates the signature:
final serviceAccount = JSON.decode(config.serviceAccountKey);
final pem = serviceAccount['private_key'];
final googleAccessId = serviceAccount['client_email'];
final rsaKey = keyFromString(pem);
final signer = new RS256Signer(rsaKey);
final signedRequestBytes = signer.sign(UTF8.encode(stringToSign));
final base64EncodedSignedRequest = Uri.encodeQueryComponent(BASE64.encode(signedRequestBytes));

The complete URL then looks like this:
https://storage.googleapis.com/$bucketName/$storagePath?GoogleAccessId=$googleAccessId&Expires=$expirationTime&Signature=$base64EncodedSignedRequest

Apart from the fact, that we would like to avoid deploying our service account key, it would be great if there was a function in Storage that would handle this properly.
Maybe there is also another, easier way to generate these URLs. If so, we are happy about any feedback.

Regarding the possibility of signing data: There is in fact a way to avoid having a service account key in the app (at least in the cloud): The IAM service provides an API to sign data. It can be used in Dart via package:googleapis/iam/v1.dart. The default oauth2 token available via the metadata service on Compute Engine has the cloud-platform scope -- though please note: you need to give the AppEngine service account an additional permission (namely iam.serviceAccounts.signBlob and maybe also storage.objects.{create,delete,get,update})
You can see this code as an example, how it can be used: This makes signed cloud storage upload URLs for the pub server.
Though of course, the metadata server is only available on the cloud -- for local testing one simply needs a service account.
(Another option is to store the service account in datastore/cloud-storage)

Still no official way to get a signed url ? 🥺

What should the stringToSign contain in @enyo's example? I keep on getting this error The request signature we calculated does not match the signature you provided. Check your Google secret key and signing method.

I need help with the following code that gives me a SignatureDoesNotMatch error.
Future<String> getWavSignedUrl(
      {required String bucketName, required String file}) async {
    const expiration = 172800; // 48 hours
    final canonicalUri = '$bucketName/$file';
    final dateTimeNow = DateTime.now().toUtc();
    final expirationDateStamp =
        '${dateTimeNow.year}${dateTimeNow.month.toString().padLeft(2, '0')}${dateTimeNow.day.toString().padLeft(2, '0')}';
    final expirationDateTimeStamp =
        '${dateTimeNow.toIso8601String().replaceAll('-', '').replaceAll(':', '').split('.')[0]}Z';
    final clientEmail = _accountCredentials.email;
    final credentialScope = '$expirationDateStamp/us/storage/goog4_request';
    final credential = '$clientEmail/$credentialScope';

    Map<String, String> headers = {};
    headers['host'] = '$bucketName.storage.googleapis.com';
    var sortedHeaderKeys = headers.keys.toList()..sort();
    var canonicalHeaders = '';
    var signedHeaders = '';
    for (var key in sortedHeaderKeys) {
      canonicalHeaders += '$key:${headers[key]?.toLowerCase()}\n';
      signedHeaders += '$key;';
    }
    signedHeaders = signedHeaders.substring(
        0, signedHeaders.length - 1); // remove trailing ';'

    Map<String, dynamic> queryParameters = {};
    queryParameters['X-Goog-Algorithm'] = 'GOOG4-RSA-SHA256';
    queryParameters['X-Goog-Credential'] = credential;
    queryParameters['X-Goog-Date'] = expirationDateTimeStamp;
    queryParameters['X-Goog-Expires'] = expiration;
    queryParameters['X-Goog-SignedHeaders'] = signedHeaders;

    var canonicalQueryString = '';
    var sortedQueryKeys = queryParameters.keys.toList()..sort();
    for (var key in sortedQueryKeys) {
      canonicalQueryString += '$key=${queryParameters[key].toString()}&';
    }
    canonicalQueryString = canonicalQueryString.substring(
        0, canonicalQueryString.length - 1); // remove trailing '&'

    String canonicalRequest = [
      'GET',
      canonicalUri,
      canonicalQueryString,
      canonicalHeaders,
      signedHeaders,
    ].join('\n');

    Digest canonicalRequestHash = sha256.convert(utf8.encode(canonicalRequest));
    String stringToSign = [
      'GOOG4-RSA-SHA256',
      expirationDateTimeStamp,
      credentialScope,
      '$canonicalRequestHash',
    ].join('\n');

    var pem = _accountCredentials.privateKey;
    var rsaKey = keyFromString(pem);
    var signer = RS256Signer(rsaKey);
    List<int> stringToSignList = utf8.encode(stringToSign);
    var signedRequestBytes = signer.sign(stringToSignList);
    var base64EncodedSignedRequest = HEX.encode(signedRequestBytes);
    const schemeAndHost = 'https://storage.googleapis.com/';
    var signedUrl =
        '$schemeAndHost$canonicalUri?$canonicalQueryString&X-Goog-Signature=$base64EncodedSignedRequest';
    return signedUrl;
  }

+1 to this issue – this would be super useful and would allow us to use this package in several places we currently use package:http


Remove unused kapt in API module

should be enforced next time

This PR was released with 0.22.1


Support JSON5 or JS configuration file

Feature request, as the title states.
Would be nice to support JSON5 or a JS file for configuration, like so:
// json5
{
  coverageDirectory: './coverage',
  coverageReporters: ['lcov'],
  globals: { __DEV__: true },
  roots: ['./src', './tests'],
  setupTestFrameworkScriptFile: './tests/setup.js',
  verbose: false
}

// js
module.exports = {
  coverageDirectory: './coverage',
  coverageReporters: ['lcov'],
  globals: { __DEV__: true },
  roots: ['./src', './tests'],
  setupTestFrameworkScriptFile: './tests/setup.js',
  verbose: false
};

Over the current:
{
  "coverageDirectory": "./coverage",
  "coverageReporters": ["lcov"],
  "globals": { "__DEV__": true },
  "roots": ["./src", "./tests"],
  "setupTestFrameworkScriptFile": "./tests/setup.js",
  "verbose": false
}

In Jest 20 you'll be able to define jest.config.js or pass a JS file to --config option.

Oh sweet. Is there an ETA on v20?

We target this week


Use dateModified as fallback for creation date

For reasons unknown, images under set_creation_date() may sometimes return None even when the original images are still valid. From personal observation, the likelihood of this occurring increases the older the saved generation gets.
This is also observed and pointed out in further detail under #22.
This commit would use dateModified from a collection item as fallback which, for all intents and purposes for saved generated images, would usually return the same date as the creation date. It should also be just as if not more persistent than the creation date because this date presumably applies to a collection item as a whole, and I've yet to see an item completely invalidated (as opposed to partially like missing thumbnail, original image etc.).

@rc-gr I've modified the code a little bit to prevent a KeyError when assigning images and I removed the declaration of creation_date_string, as there is no block level scope in Python.


Parameter in URL is not working

In current deployed release parameter in url has stopped working.
e.g.
https://kitware.github.io/paraview-glance/app/?name=diskout.vtp&url=https://data.kitware.com/api/v1/item/59de9de58d777f31ac641dc5/download

I think this is a result of caching, since that link works for me. I've updated the caching strategy, since I got it wrong the first time.

Thanks


[BUG] Aqara lumi.switch.l1aeu1 seen as end device instead of router and power source is unknow instead of mains

Describe the bug
The in-wall switch Aqara lumi.switch.l1aeu1 (link to the device on blakkader.com) is seen as an end device instead of a router and the power source is unkown instead of mains:
But if we look at Zigbee signature for the device, we got:
(...)
  "node_descriptor": (...) *is_end_device=False, *is_full_function_device=True, *is_mains_powered=True, *is_receiver_on_when_idle=True, *is_router=True, (...)",
(...)

To Reproduce
Steps to reproduce the behavior:

Pair a Aqara lumi.switch.l1aeu1
Go to 'Configuration' -> 'Devices and Services' -> 'Zigbee Home Automation'
Click on a Aqara lumi.switch.l1aeu1 switch
See the power source and device type as in the screenshot
Click on 'Zigbee Device Signature' to check the node inspector

Expected behavior
Aqara lumi.switch.l1aeu1 switches are supposed to be seen and act as router in the zigbee network
Aqara lumi.switch.l1aeu1 switches' source power is supposed to be 'Mains
Screenshots

Additional context
Device complete signature:
{
  "node_descriptor": "NodeDescriptor(logical_type=<LogicalType.EndDevice: 2>, complex_descriptor_available=0, user_descriptor_available=0, reserved=0, aps_flags=0, frequency_band=<FrequencyBand.Freq2400MHz: 8>, mac_capability_flags=<MACCapabilityFlags.AllocateAddress: 128>, manufacturer_code=4447, maximum_buffer_size=127, maximum_incoming_transfer_size=100, server_mask=11264, maximum_outgoing_transfer_size=100, descriptor_capability_field=<DescriptorCapability.NONE: 0>, *allocate_address=True, *is_alternate_pan_coordinator=False, *is_coordinator=False, *is_end_device=True, *is_full_function_device=False, *is_mains_powered=False, *is_receiver_on_when_idle=False, *is_router=False, *is_security_capable=False)",
  "endpoints": {
    "1": {
      "profile_id": 260,
      "device_type": "0x0100",
      "in_clusters": [
        "0x0000",
        "0x0002",
        "0x0003",
        "0x0004",
        "0x0005",
        "0x0006",
        "0x0009"
      ],
      "out_clusters": [
        "0x000a",
        "0x0019"
      ]
    },
    "242": {
      "profile_id": 41440,
      "device_type": "0x0061",
      "in_clusters": [],
      "out_clusters": [
        "0x0021"
      ]
    }
  },
  "manufacturer": "LUMI",
  "model": "lumi.switch.l1aeu1",
  "class": "zigpy.device.Device"
}

What am I missing… the data you posted doesn’t match what you are reporting. The node descriptor the device reported looks bad and matches the device screen screenshot.

But if we look at Zigbee signature for the device, we got:
(...)
  "node_descriptor": (...) *is_end_device=False, *is_full_function_device=True, *is_mains_powered=True, *is_receiver_on_when_idle=True, *is_router=True, (...)",
(...)

Where did this come from? It doesn’t match what is below it

All of this said… they have done the reverse of this before so I wouldn’t be shocked if we need to override it to correct it.

But if we look at Zigbee signature for the device, we got:

(...)

"node_descriptor": (...) *is_end_device=False, *is_full_function_device=True, *is_mains_powered=True, *is_receiver_on_when_idle=True, *is_router=True, (...)",

(...)

Where did this come from? It doesn’t match what is below it

It comes from the device signature (se also at the end on my first message)

But if we look at Zigbee signature for the device, we got:
(...)
  "node_descriptor": (...) *is_end_device=False, *is_full_function_device=True, *is_mains_powered=True, *is_receiver_on_when_idle=True, *is_router=True, (...)",
(...)

Where did this come from? It doesn’t match what is below it

Please excuse me, I made a mistake on my copy-paste :(
But this device (WS-EUK01) is an in-wall switch, so the power source is mains.
And since this device is powered by mains, it should work as a router, right?

Not necessarily. Does it have a neutral wire?

No it doesn't

Then it’s probably an end device. I have a mix of the US Aqara switches. The ones w/ neutral wires are routers and the ones without are end devices.

Yes, it is very unlikely for a no-neutral switch to be a ZigBee router.
Works as designed.

All right, thanks for your indications.
Take care

You can looking on the network map if its only having one router as parent (= end device) or its have connections to more routers (= router) then its working as one router and having more connections to neighbors.

That's what I did, but since I believed that every mains powered devices where router capable, I thought something went wrong with my aqara switches


use python command

use python to fix heroku deployment issue

Firstly, #1646. Secondly, we won't be switching to mdillon/postgis by no means as having PostGIS around is an unwanted maintenance burden. Thirdly, I don't see any reason for us to revert our django image back to full python base.

Thanks for your response.
I was adding postgis to my project I thought it might be helpful to add it.
On Sun, May 20, 2018, 5:49 PM Nikita Shupeyko<EMAIL_ADDRESS>wrote:

Firstly, #1646
https://github.com/pydanny/cookiecutter-django/issues/1646. Secondly,
we won't be switching to mdillon/postgis
https://hub.docker.com/r/mdillon/postgis/ by no means as having PostGIS
around is an unwanted maintenance burden. Thirdly, I don't see any reason
for us to revert our django image back to full python base.
—
You are receiving this because you authored the thread.
Reply to this email directly, view it on GitHub
https://github.com/pydanny/cookiecutter-django/pull/1651#issuecomment-390491705,
or mute the thread
https://github.com/notifications/unsubscribe-auth/AVYSlIgXAUhikVPNbXj3I46H-zl274PZks5t0ZB6gaJpZM4UF5dd
.


:save() still creating duplicates on auto increment tables

Hey. Unfortunately #24 still persists. Tested on the latest version.

Actually this is my fault as I'm saving twice without making use of the asynchronous function
Is there any way to cache whether or not a certain model's "save" function has been called to prevent duplicate entries from being accidentally made?


[Spanish Phonemizer] Fixed transition issues & added new functions

Consonant transition issues got fixed.
Now supports VCV with more than one consonant if the voicebank has it.
Supports "consonant VCV", e.g. [n da], [l ba] etc. if the voicebank has it.
Consonant length adjustment.
"ny" is now a fallback for "nh" if the voicebank doesn't have the latter (CVVC-only).
Use seseo ("s" instead of "z") when a voicebank doesn't have "z" (might be CVVC-only).

TODO:

Make VCV with "ny" work automatically as a substitute for "nh" if the voicebank has it, instead of always going for CVVC.
Make ValidateAlias for using "i" instead of "y" and "u" instead of "w" work properly if the voicebank has them.

Found another error, apologies again.


[Bug]:  Missing lang lib methods in regexp langlib

Description
Consider the following code.
import ballerina/lang.regexp;

public function main() {
    regexp:RegExp reg = re ``;
    int a = 10;
}

The type symbol of reg is as follows.

As depicted below, the set of lang libs methods of reg type symbol does not have methods like find, findGroup(), etc.
https://github.com/ballerina-platform/ballerina-lang/blob/master/langlib/lang.regexp/src/main/ballerina/regexp.bal

Steps to Reproduce
No response
Affected Version(s)
2201.4.0-SNAPSHOT
OS, DB, other environment details and versions
No response
Related area
-> Editor
Related issue(s) (optional)
No response
Suggested label(s) (optional)
No response
Suggested assignee(s) (optional)
No response

@malinthar is it the same if we get the raw type and get langlibs?

@malinthar is it the same if we get the raw type and get langlibs?

Yes same behaviour

https://github.com/ballerina-platform/ballerina-lang/blob/2bff9487920ec2e28b243017bda6ceec836b9eb6/compiler/ballerina-lang/src/main/java/org/wso2/ballerinalang/compiler/semantics/model/types/BRegexpType.java#L49
why is this return anydata? don't we have to send it as REGEXP? @pcnfernando @lasinicl

https://github.com/ballerina-platform/ballerina-lang/blob/2bff9487920ec2e28b243017bda6ceec836b9eb6/compiler/ballerina-lang/src/main/java/org/wso2/ballerinalang/compiler/semantics/model/types/BRegexpType.java#L49
why is this return anydata? don't we have to send it as REGEXP? @pcnfernando @lasinicl

Seems to be a bug. Please update it. It won't have any semantic change anyways

Seems to be a bug. Please update it. It won't have any semantic change anyways

@malinthar Shall we correct this and handle regexp in lang-lib methods?


Create entry point for friendly: done but documentation needed

Instead of having to write
python -m friendly ...

try to set things up so that we can write
friendly ...

Implemented. However, the documentation needs to be done.

Closing this as I will not need this reminder when I work on improving/updating the documentation.


SwiftShims: unify assertion paths (NFC)

Use the C++ spelling for the static assertions.  This is a C11
extension, but GCC and MSVC both object to the reserved spelling
(_Static_assert).  Use the compatibility spelling of static_assert
on all targets instead.

Replace this paragraph with a description of your changes and rationale. Provide links to external references/discussions if appropriate.

Resolves SR-NNNN.

@swift-ci please test

Build failed
Swift Test OS X Platform
Git Sha - 95147bd67193ce801153f7965ac030d51f214358

Build failed
Swift Test Linux Platform
Git Sha - 95147bd67193ce801153f7965ac030d51f214358

@swift-ci please test

Build failed
Swift Test Linux Platform
Git Sha - 95147bd67193ce801153f7965ac030d51f214358

Build failed
Swift Test OS X Platform
Git Sha - 95147bd67193ce801153f7965ac030d51f214358

@swift-ci please test

@swift-ci please test

Build failed
Swift Test OS X Platform
Git Sha - d96b1ee6e35a5c74187272c01e55a736b1045739

Build failed
Swift Test Linux Platform
Git Sha - d96b1ee6e35a5c74187272c01e55a736b1045739

@swift-ci please test

Build failed
Swift Test Linux Platform
Git Sha - d96b1ee6e35a5c74187272c01e55a736b1045739

Build failed
Swift Test OS X Platform
Git Sha - d96b1ee6e35a5c74187272c01e55a736b1045739


Non Strict filter?

Is it possible to essentially do opposite of strict filter? Would seem essentially the same functionality on the service side but have use case for only returning answers that dont have certain metadata. If strict filter can only be used to return answers with specific meta data essentially have to have all qna pairs have all meta data values.
My use case is to filter answers for managers and regular employees. So would make more sense to only put a meta data field on questions that shouldnt be returned.

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

ID: cb1d6a8c-954c-9ce8-6101-c19b20724c91
Version Independent ID: 5c219f6c-260b-c17a-7f41-1bf4095aa521
Content: Metadata with GenerateAnswer API - QnA Maker - Azure Cognitive Services
Content Source: articles/cognitive-services/QnAMaker/How-To/metadata-generateanswer-usage.md
Service: cognitive-services
Sub-service: qna-maker
GitHub Login: @tulasim88
Microsoft Alias: tulasim

@01 Thanks for the feedback! We are currently investigating and will update you shortly.

@01 Based on the current functionality it does not look like there is an option to filter answers based on non strict filters. You can however use none to indicate response should have no metadata filters and get all values with no metadata.
@tulasim88 Could you please confirm if there is a possibility of using non strict filter while omitting some values and returning others instead of just using none to get all values with no metadata?

@RohitMungi-MSFT sorry just to be clear, thats an all or nothing situation. Either filter those that dont have the meta data or not. Im looking to accomplish a combination such as return answers that  has metadata filed value or doesnt have the meta data field at all.
Example use case have a specific QnA pair that should only be returned to Managers. From my understanding of filter functionality I would need every single QnA Pair to have a meta data field for isManager, what I am looking to accomplish is lets say 3 out 100 qna pairs are manager specific, have those three have isManager: true, and the filter would if user is not a manager, dont return questions with isManager: true metadata.

@01 Thanks for your detailed inputs. I think currently the requested functionality is not available to filter metadata values based on non strict filters. You can however pass on this request to the product team through our user voice forum, this will help our product team to take up features that users want to see. I hope this helps!!
@tulasim88 Please do let us know if there is any ongoing feature request for such functionality which we can expect in the future.

@01 thanks for the feedback. We have added this to our product backlog, for this example scenario- but don't have any immediate plans to support this. We will update this thread if we have any updates.
@diberry FYI

@tulasim88 Thanks for the update, Please do update the thread and documentation once the feature is complete. @01
We will now proceed to close this thread. If there are further questions regarding this matter, please tag @RohitMungi-MSFT in your reply. We will gladly continue the discussion and we will reopen the issue.


uberalls client 404

when i run a test ,the jenkins output is

[JENKINS] Archiving /var/jenkins_home/workspace/rhllor-coupon/pom.xml to com.ricebook.rhllor/coupon/1.0-SNAPSHOT/coupon-1.0-SNAPSHOT.pom
channel stopped
[phabricator:uberalls-client] Call failed: HTTP/1.1 404 Not Found
[phabricator:uberalls] unable to find coverage for parent commit
[phabricator:process-build-result] No unit results available.
[phabricator:harbormaster] Sending Harbormaster BUILD_URL via PHID: PHID-HMBT-d75u5zgi54dpeqjdcaii
[phabricator:send-harbormaster-uri] Harbormaster declined URI artifact: #1062: Duplicate entry 'uri-r6OsU.f9vK1Y' for key 'key_artifact'
[phabricator:process-build-result] Unable to send BUILD_URL to Harbormaster. This can be safely ignored, and is usually because it's already set.
[phabricator:process-build-result] Publishing coverage data to Harbormaster for 2 files.
[phabricator:process-build-result] Sending build result to Harbormaster with PHID PHID-HMBT-d75u5zgi54dpeqjdcaii, success: true
[phabricator:file-fetcher] no files found by path: '.phabricator-comment'
[Cobertura] Publishing Cobertura coverage report...
Publishing Cobertura coverage results...
Cobertura coverage report found.
Finished: SUCCESS

=.=, my uberalls is already run ,what should i do for this error ...

A 404 is a semantic "coverage not found".
Did you follow the instructions at https://github.com/uber/uberalls?
Specifically this part:

In order to have a baseline to compare against, you must also have jenkins build your project on your mainline branch ("master" by default). You can either create a separate job, or enable SCM polling under Build Triggers:

we  enable SCM  =.= ,but still have a 404
[JENKINS] Archiving /var/jenkins_home/workspace/rhllor-coupon/pom.xml to com.ricebook.rhllor/coupon/1.0-SNAPSHOT/coupon-1.0-SNAPSHOT.pom
[phabricator:non-differential] Sending coverage result for 55f687f28e17292e7890aaff867fb187da2f131e as package coverage = 0.0, files coverage = 0.0, classes coverage = 0.0, method coverage = 0.0, line coverage = 0.0, conditional coverage = 0.0
[phabricator:uberalls-client] Call failed: HTTP/1.1 404 Not Found
[Cobertura] Publishing Cobertura coverage report...
Publishing Cobertura coverage results...
Cobertura coverage report found.
Finished: SUCCESS

Sounds like you don't have jenkins configured to point at your uberalls
correctly. You shouldn't be getting a 404 trying to POST data.
What happens if you try to cURL the configured URL from your jenkins master?
On Tue, Sep 20, 2016 at 3:02 AM Reilost<EMAIL_ADDRESS>wrote:

we enable SCM =.= ,but still have a 404
[JENKINS] Archiving /var/jenkins_home/workspace/rhllor-coupon/pom.xml to com.ricebook.rhllor/coupon/1.0-SNAPSHOT/coupon-1.0-
SNAPSHOT.pom
[phabricator:non-differential] Sending coverage result for 55f687f28e17292e7890aaff867fb187da2f131e as package coverage = 0.0, files coverage = 0.0, classes coverage = 0.0, method coverage = 0.0, line coverage = 0.0, conditional coverage = 0.0
[phabricator:uberalls-client] Call failed: HTTP/1.1 404 Not Found
[Cobertura] Publishing Cobertura coverage report...
Publishing Cobertura coverage results...
Cobertura coverage report found.
Finished: SUCCESS
—
You are receiving this because you commented.
Reply to this email directly, view it on GitHub
https://github.com/uber/phabricator-jenkins-plugin/issues/179#issuecomment-248257972,
or mute the thread
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hmm...
I configured the url is http://<IP_ADDRESS>:14740
when i cURL this url is 404
and http://<IP_ADDRESS>:14740/health is  ;-)

oh,my mistake
configured shoud be  http://<IP_ADDRESS>:14740/metrics
Thanks~~~  @sectioneight

Do you have any more information available?

@sectioneight  my mistake, the uberall url should be   http://<IP_ADDRESS>:14740/metrics
my config is only the  http://<IP_ADDRESS>:14740


New Auto/Fast Installation with a .bat file for Windows users and .sh for Linux users

New Auto/Fast Installation with a .bat file for Windows users and .sh for Linux users
Description
New Auto/Fast Installation with a .bat file for Windows users and .sh for Linux users
Changes Made
New:
Whisper-TikTok-Linux.sh
Whisper-TikTok-Windows.bat
Changes:
Readme.md
Related Discussion

Addresses #26

Checklist
Please review and check the following items before submitting your pull request:

[x] I have tested my changes thoroughly.
[x] My code follows the project's coding style guidelines.
[x] I have updated documentation, if necessary.
[x] All existing tests pass successfully.
[x] I have added new tests, if applicable.
[x] I have reviewed my code to ensure it is clean and efficient.
[x] I have added any required comments or explanations for complex code changes.

Screenshots

Additional Notes
I have created these two files for a more friendly installation

Summary by CodeRabbit

Documentation

Updated the installation instructions with automated setup options for Linux and Windows users.
Enhanced the README to include a detailed description of the auto-installation scripts' actions.

New Features

Introduced auto-installation scripts for Linux and Windows to simplify the setup process.
Added prompts in the scripts for users to choose between running the web interface or the script directly.

Installation scripts are too much system dependent. For example depending on the linux distribution the package manager changes (debian based have apt while arch based have pacman). Also windows should have a spearate powershell script and command prompt script (not just one).
At this point maybe a Docker container will be better (something like a container which spins-up the Web-UI by installing all the required dependencies)


Updates for Xcode 9 and iOS 11

These updates can be merged to the project for up-to-date recommended settings for Xcode 9 and iOS 11 Core Data changes, when appropriate.

@drewmccormack can this be merged?

The problem with merging it as is is that everyone using Ensembles would have to move to Xcode 9, and I assume there are probably a lot of people not doing that just yet. The reason for that is the use of @available in ObjC. If someone wants to convert that to something that works with older compilers, I think I should be able to merge.

I think not only @available will be a problem but the symbol itself will be a problem too with the earlier Xcode versions.
@szotp just use my fork for the moment until @drewmccormack can merge this. I have no problems syncing between different iOS versions with it.


DAILY release-1.1-20181215-10-01

This is a generated PR that triggers a release, and will be automatically merged when all required tests have passed.
none

@hklai: The following test failed, say /retest to rerun them all:

Test name
Commit
Details
Rerun command

prow/istio-unit-tests.sh
be2cf301058962ccc899786734a72345ff4e2255
link
/test release-unit-tests

Instructions for interacting with me using PR comments are available here.  If you have questions or suggestions related to my behavior, please file an issue against the kubernetes/test-infra repository. I understand the commands that are listed here.


Add deploy preview links for GitLab backend

Is your feature request related to a problem? Please describe.
Deploy preview links are not supported with GitLab

Describe the solution you'd like
Add deploy preview links for GitLab

Describe alternatives you've considered
Going to GitHub.

Additional context
Related pull request : #2028
GitLab Statuses API : https://docs.gitlab.com/ee/api/commits.html#list-the-statuses-of-a-commit

Hi @maxerbox, this should be supported https://github.com/netlify/netlify-cms/blob/3025e5e8ff735fd1cfac686c2822c5b7d8b79935/packages/netlify-cms-backend-gitlab/src/API.ts#L853
Can you provide a reproduction detailing the steps you're taking and actual vs expected outcome?

The docs should be updated : https://www.netlifycms.org/docs/deploy-preview-links/
I'm closing the issue

https://github.com/netlify/netlify-cms/pull/3943


[BUG] GraphiQL explorer is not able to access import.meta.env variables

Describe the bug
We're setting storeDomain and storefrontToken in shopify.config.js via import.meta.env. It works fine for the Hydrogen frontend, but GraphiQL seems unable to access those variables resulting in a broken GraphiQL explorer that is unable to connect to the Storefront API.
To Reproduce
A fresh Stackblitz example with the issue can be found here: https://stackblitz.com/edit/shopify-hydrogen-t3qvjg?file=shopify.config.js
Expected behaviour
GraphiQL should get access to the same variables as the hydrogen frontend to avoid using hardcoded strings for store domain and storefront token which enables using the same codebase to connect to dev stores as well as the production store.
Additional context
Add any other context about the problem here. eg.

Hydrogen version: 0.13.2
Node version: 17.7.2

import.meta.env is not supported in shopify.config.js at the moment because this file is not parsed by Vite itself. Vite is not started when reading import shopifyConfig from './shopify.config in vite.config.js.
Workaround:
Use process.env instead, but beware that variables in .env files won't be available in shopify.config.js either.
Possible solution in Hydrogen:
Instead of importing shopify.config.js directly in vite.config.js, pass the path to the config file in the plugin and load it later using vite.ssrLoadModule.
This will be revisited once we make a decision in https://github.com/Shopify/hydrogen/issues/948

import.meta.env is not supported in shopify.config.js at the moment because this file is not parsed by Vite itself. Vite is not started when reading import shopifyConfig from './shopify.config in vite.config.js.
Workaround:
Use process.env instead, but beware that variables in .env files won't be available in shopify.config.js either.
Possible solution in Hydrogen:
Instead of importing shopify.config.js directly in vite.config.js, pass the path to the config file in the plugin and load it later using vite.ssrLoadModule.
This will be revisited once we make a decision in https://github.com/Shopify/hydrogen/issues/948

That's not my experience as import.meta.env works perfectly fine with the app itself, just not with GraphiQL. You just need to use optional chaining for the startup, i.e. import.meta.env?.PUBLIC_VAR. Otherwise the script crashes the dev server. Once past that stage it seems to inject the env variables and it works. Could it be that the file is imported at a later stage somewhere which causes the import.meta.env variables to then be available? Since this doesn't seem to be the expected behaviour from your side it makes me concerned it might break the app on some future update of hydrogen.
Using process.env is not the solution, nor a workaround, as you say the variables still won't be available in the file. Additionally as opposed to the above it never seems to update the file with the correct value so it makes the app unable to connect to the storefront api.
Supporting some type of .env is very important for us and I'd imagine most others to connect to different stores in different environments and regions.

Gotcha, thanks. We're happy as long as it's on the road map.


Script to compare two files and generate two files, one which has lines not in file 2, one which has lines in files 2 but not in file 1

Script to compare two files and generate two files, one which has lines not in file 2, one which has lines in files 2 but not in file 1

resolved by #2


fix: declare missing dependencies

Motivation
https://github.com/facebook/docusaurus/pull/6047 made Docusaurus PnP strict mode compatible but by default PnP still allows some fallbacks, this PR disables that fallback in the e2e test and adds the remaining undeclared dependencies.
Have you read the [Contributing Guidelines on pull requests]
Yes
Test Plan
The e2e test passes

Thanks, do you think it makes more sense to declare react as optional peer deps in @docusaurus/utils, since there's only one function that actually relies on it and it's hardly used?

It's currently always imported so no, if they were changed to use dynamic imports then sure.


Allow installerType to be updated with more precise, compatible installerType

Resolves #227
Currently, WingetCreate's Update command only checks if two installerTypes are compatible when matching installers. However, the manifest does not get updated with the more accurate/precise installerType. i.e. Msi -> Wix.
Changes:

Added logic that checks if an installerType is more precise. If so, the update will overwrite the existing InstallerType value with the more precise, compatible installerType value.

Tests:

Added unit test UpdateReplacesWithMorePreciseInstallerType() that verifies with a wix installer that the manifest gets updated from MSI -> WIX.

Microsoft Reviewers: Open in CodeFlow

/azp run

I am closing this PR for now as this will require further investigation regarding how installer switches are transferred between compatible installer types and whether some installer fields need to be reset in order to safely update the installer type.


User: part of the form was confusing

Question ID
there is another condition

What was confusing
In follow up to my other comment. about lighting this website should be updated because it does not, as of September 27, 2024, reflect any of the NEW effective 2023 revision of the Mass 105 CMR  requirements, including the numerous new 410 requirements; specifically but not limited to new lighting requirements for residences with more than 1 dwelling units (D) (3).

Other information
Please review the entire NEW Mass 105. 410 Sanitary Code because the references to code section numbers and requirement info are GREATLY incorrect.   Example is of Lighting:  New section and number 410.300  which combines the existing requirements in 410.253 and 254 into new number 410.300 Electricity Supply and Illuminations with numerous revision.  Otherwise, people will not cite the updated Code (correction) to be able to reference when they try to d0cument a violation .

Variable being sought
bad_conditions.there_is_another

Package version
1.12.1

This feedback is referencing the old MADEUptoCode interview, UptoCode sanitary code citations are currently up-to-date.

This feedback is referencing the old MADEUptoCode interview, UptoCode sanitary code citations are currently up-to-date.


reorder the project directory, make engine as the submodule

make things simple.

lack of iOS support.

finished on my own project. It's quite easy to make our project as submodule, only takes several minutes work.


ValidateESGF Job implementation

The ValidateESGF job needs to be implemented.

Need to improve this, right now it just checks that the dataset exists on ESGF but it should check that all the files we were expecting to publish actually got publish.


Soft Placement Fails with Estimator Multi-worker CollectiveAllReduceStrategy

The cpu device appears to be ignored when SparseSoftmaxCrossEntropyWithLogits/assert_equal/Assert/Assert/data_0 is found to be incompatible with the GPU

Container: tensorflow/tensorflow:2.0.0-gpu-py3
Cuda: 10
Env: Kubeflow (tfjob)

    session_config = tf.compat.v1.ConfigProto(
        allow_soft_placement=True,
    )
    strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy()
    run_config = tf.estimator.RunConfig(
        model_dir=flags_obj.model_dir,
        save_checkpoints_steps=flags_obj.save_checkpoint_steps,
        log_step_count_steps=flags_obj.log_step_count_steps,
        save_summary_steps=flags_obj.save_summary_steps,
        train_distribute=strategy,
        eval_distribute=strategy,
        session_config=session_config
    )

Logs:
I1114 00:37:58.694322<PHONE_NUMBER>46848 main.py:254] {"cluster":{"chief":["**censor**-chief-0.kubeflow-jobs.svc:2222"],"worker":["**censor**-worker-0.kubeflow-jobs.svc:2222","**censor**-worker-1.kubeflow-jobs.svc:2222"]},"task":{"type":"chief","index":0},"environment":"cloud"}
INFO:tensorflow:Enabled multi-worker collective ops with available devices: ['/job:chief/replica:0/task:0/device:CPU:0', '/job:chief/replica:0/task:0/device:XLA_CPU:0', '/job:chief/replica:0/task:0/device:XLA_GPU:0', '/job:chief/replica:0/task:0/device:GPU:0']
I1114 00:37:58.906222<PHONE_NUMBER>46848 collective_all_reduce_strategy.py:269] Enabled multi-worker collective ops with available devices: ['/job:chief/replica:0/task:0/device:CPU:0', '/job:chief/replica:0/task:0/device:XLA_CPU:0', '/job:chief/replica:0/task:0/device:XLA_GPU:0', '/job:chief/replica:0/task:0/device:GPU:0']
INFO:tensorflow:Multi-worker CollectiveAllReduceStrategy with cluster_spec = {'chief': ['**censor**-chief-0.kubeflow-jobs.svc:2222'], 'worker': ['**censor**-worker-0.kubeflow-jobs.svc:2222', '**censor**-worker-1.kubeflow-jobs.svc:2222']}, task_type = 'chief', task_id = 0, num_workers = 3, local_devices = ('/job:chief/task:0/device:GPU:0',), communication = CollectiveCommunication.AUTO
I1114 00:37:58.908060<PHONE_NUMBER>46848 collective_all_reduce_strategy.py:310] Multi-worker CollectiveAllReduceStrategy with cluster_spec = {'chief': ['**censor**-chief-0.kubeflow-jobs.svc:2222'], 'worker': ['**censor**-worker-0.kubeflow-jobs.svc:2222', '**censor**-worker-1.kubeflow-jobs.svc:2222']}, task_type = 'chief', task_id = 0, num_workers = 3, local_devices = ('/job:chief/task:0/device:GPU:0',), communication = CollectiveCommunication.AUTO
INFO:tensorflow:TF_CONFIG environment variable: {'cluster': {'chief': ['**censor**-chief-0.kubeflow-jobs.svc:2222'], 'worker': ['**censor**-worker-0.kubeflow-jobs.svc:2222', '**censor**-worker-1.kubeflow-jobs.svc:2222']}, 'task': {'type': 'chief', 'index': 0}, 'environment': 'cloud'}
I1114 00:37:58.909245<PHONE_NUMBER>46848 run_config.py:535] TF_CONFIG environment variable: {'cluster': {'chief': ['**censor**-chief-0.kubeflow-jobs.svc:2222'], 'worker': ['**censor**-worker-0.kubeflow-jobs.svc:2222', '**censor**-worker-1.kubeflow-jobs.svc:2222']}, 'task': {'type': 'chief', 'index': 0}, 'environment': 'cloud'}
INFO:tensorflow:Initializing RunConfig with distribution strategies.
I1114 00:37:58.909479<PHONE_NUMBER>46848 run_config.py:566] Initializing RunConfig with distribution strategies.
INFO:tensorflow:RunConfig initialized for Distribute Coordinator with INDEPENDENT_WORKER mode
I1114 00:37:58.909686<PHONE_NUMBER>46848 estimator_training.py:177] RunConfig initialized for Distribute Coordinator with INDEPENDENT_WORKER mode
INFO:tensorflow:Using config: {'_model_dir': 's3://**censor**/**censor**', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': 1000, '_save_checkpoints_secs': None, '_session_config': allow_soft_placement: true
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 64, '_train_distribute': <tensorflow.python.distribute.collective_all_reduce_strategy.CollectiveAllReduceStrategy object at 0x7f649946f390>, '_device_fn': None, '_protocol': None, '_eval_distribute': <tensorflow.python.distribute.collective_all_reduce_strategy.CollectiveAllReduceStrategy object at 0x7f649946f390>, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f649946fe80>, '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1, '_distribute_coordinator_mode': 'independent_worker'}
I1114 00:37:58.909949<PHONE_NUMBER>46848 estimator.py:212] Using config: {'_model_dir': 's3://**censor**/**censor**', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': 1000, '_save_checkpoints_secs': None, '_session_config': allow_soft_placement: true
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 64, '_train_distribute': <tensorflow.python.distribute.collective_all_reduce_strategy.CollectiveAllReduceStrategy object at 0x7f649946f390>, '_device_fn': None, '_protocol': None, '_eval_distribute': <tensorflow.python.distribute.collective_all_reduce_strategy.CollectiveAllReduceStrategy object at 0x7f649946f390>, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f649946fe80>, '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1, '_distribute_coordinator_mode': 'independent_worker'}
INFO:tensorflow:Running `train_and_evaluate` with Distribute Coordinator.
I1114 00:37:58.912131<PHONE_NUMBER>46848 training.py:462] Running `train_and_evaluate` with Distribute Coordinator.
INFO:tensorflow:Running Distribute Coordinator with mode = 'independent_worker', cluster_spec = {'chief': ['**censor**-chief-0.kubeflow-jobs.svc:2222'], 'worker': ['**censor**-worker-0.kubeflow-jobs.svc:2222', '**censor**-worker-1.kubeflow-jobs.svc:2222']}, task_type = 'chief', task_id = 0, environment = 'cloud', rpc_layer = 'grpc'
I1114 00:37:58.912826<PHONE_NUMBER>46848 distribute_coordinator.py:776] Running Distribute Coordinator with mode = 'independent_worker', cluster_spec = {'chief': ['**censor**-chief-0.kubeflow-jobs.svc:2222'], 'worker': ['**censor**-worker-0.kubeflow-jobs.svc:2222', '**censor**-worker-1.kubeflow-jobs.svc:2222']}, task_type = 'chief', task_id = 0, environment = 'cloud', rpc_layer = 'grpc'
INFO:tensorflow:Multi-worker CollectiveAllReduceStrategy with cluster_spec = {'chief': ['**censor**-chief-0.kubeflow-jobs.svc:2222'], 'worker': ['**censor**-worker-0.kubeflow-jobs.svc:2222', '**censor**-worker-1.kubeflow-jobs.svc:2222']}, task_type = 'chief', task_id = 0, num_workers = 3, local_devices = ('/job:chief/task:0/device:GPU:0',), communication = CollectiveCommunication.AUTO
I1114 00:37:58.913650<PHONE_NUMBER>46848 collective_all_reduce_strategy.py:310] Multi-worker CollectiveAllReduceStrategy with cluster_spec = {'chief': ['**censor**-chief-0.kubeflow-jobs.svc:2222'], 'worker': ['**censor**-worker-0.kubeflow-jobs.svc:2222', '**censor**-worker-1.kubeflow-jobs.svc:2222']}, task_type = 'chief', task_id = 0, num_workers = 3, local_devices = ('/job:chief/task:0/device:GPU:0',), communication = CollectiveCommunication.AUTO
INFO:tensorflow:Multi-worker CollectiveAllReduceStrategy with cluster_spec = {'chief': ['**censor**-chief-0.kubeflow-jobs.svc:2222'], 'worker': ['**censor**-worker-0.kubeflow-jobs.svc:2222', '**censor**-worker-1.kubeflow-jobs.svc:2222']}, task_type = 'chief', task_id = 0, num_workers = 3, local_devices = ('/job:chief/task:0/device:GPU:0',), communication = CollectiveCommunication.AUTO
I1114 00:37:58.914542<PHONE_NUMBER>46848 collective_all_reduce_strategy.py:310] Multi-worker CollectiveAllReduceStrategy with cluster_spec = {'chief': ['**censor**-chief-0.kubeflow-jobs.svc:2222'], 'worker': ['**censor**-worker-0.kubeflow-jobs.svc:2222', '**censor**-worker-1.kubeflow-jobs.svc:2222']}, task_type = 'chief', task_id = 0, num_workers = 3, local_devices = ('/job:chief/task:0/device:GPU:0',), communication = CollectiveCommunication.AUTO
INFO:tensorflow:Updated config: {'_model_dir': 's3://**censor**/**censor**', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': 1000, '_save_checkpoints_secs': None, '_session_config': allow_soft_placement: true
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 64, '_train_distribute': <tensorflow.python.distribute.collective_all_reduce_strategy.CollectiveAllReduceStrategy object at 0x7f6499492908>, '_device_fn': None, '_protocol': None, '_eval_distribute': <tensorflow.python.distribute.collective_all_reduce_strategy.CollectiveAllReduceStrategy object at 0x7f64994943c8>, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f64994947f0>, '_task_type': 'chief', '_task_id': 0, '_global_id_in_cluster': 0, '_master': 'grpc://**censor**-chief-0.kubeflow-jobs.svc:2222', '_evaluation_master': 'grpc://**censor**-chief-0.kubeflow-jobs.svc:2222', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 3, '_distribute_coordinator_mode': 'independent_worker'}
I1114 00:37:58.915921<PHONE_NUMBER>46848 estimator_training.py:228] Updated config: {'_model_dir': 's3://**censor**/**censor**', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': 1000, '_save_checkpoints_secs': None, '_session_config': allow_soft_placement: true
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 64, '_train_distribute': <tensorflow.python.distribute.collective_all_reduce_strategy.CollectiveAllReduceStrategy object at 0x7f6499492908>, '_device_fn': None, '_protocol': None, '_eval_distribute': <tensorflow.python.distribute.collective_all_reduce_strategy.CollectiveAllReduceStrategy object at 0x7f64994943c8>, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f64994947f0>, '_task_type': 'chief', '_task_id': 0, '_global_id_in_cluster': 0, '_master': 'grpc://**censor**-chief-0.kubeflow-jobs.svc:2222', '_evaluation_master': 'grpc://**censor**-chief-0.kubeflow-jobs.svc:2222', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 3, '_distribute_coordinator_mode': 'independent_worker'}
INFO:tensorflow:The `input_fn` accepts an `input_context` which will be given by DistributionStrategy
I1114 00:37:59.079214<PHONE_NUMBER>46848 estimator.py:1111] The `input_fn` accepts an `input_context` which will be given by DistributionStrategy
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.
W1114 00:38:02.574301<PHONE_NUMBER>46848 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.
INFO:tensorflow:Calling model_fn.
I1114 00:38:02.581103<PHONE_NUMBER>45120 estimator.py:1147] Calling model_fn.
I1114 00:38:02.781386<PHONE_NUMBER>45120 model.py:348] tracing train forward: (3, 512, 512, 3) | (3, 65472, 4) | (3, None) | (3, None, 56, 56)
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/image_ops_impl.py:1518: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Deprecated in favor of operator or tf.math.divide.
W1114 00:38:02.793168<PHONE_NUMBER>45120 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/image_ops_impl.py:1518: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Deprecated in favor of operator or tf.math.divide.
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/indexed_slices.py:424: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.
  "Converting sparse IndexedSlices to a dense Tensor of unknown shape. "
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/indexed_slices.py:424: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.
  "Converting sparse IndexedSlices to a dense Tensor of unknown shape. "
INFO:tensorflow:Collective batch_all_reduce: 389 all-reduces, num_workers = 3
I1114 00:38:21.225312<PHONE_NUMBER>46848 cross_device_ops.py:1107] Collective batch_all_reduce: 389 all-reduces, num_workers = 3
INFO:tensorflow:Done calling model_fn.
I1114 00:38:26.023765<PHONE_NUMBER>45120 estimator.py:1149] Done calling model_fn.
INFO:tensorflow:Collective batch_all_reduce: 1 all-reduces, num_workers = 3
I1114 00:38:26.024793<PHONE_NUMBER>46848 cross_device_ops.py:1107] Collective batch_all_reduce: 1 all-reduces, num_workers = 3
INFO:tensorflow:Create CheckpointSaverHook.
I1114 00:38:27.469296<PHONE_NUMBER>46848 basic_session_run_hooks.py:541] Create CheckpointSaverHook.
INFO:tensorflow:all_hooks [<tensorflow_estimator.python.estimator.util.DistributedIteratorInitializerHook object at 0x7f6496df1358>, <tensorflow.python.training.basic_session_run_hooks.StopAtStepHook object at 0x7f6499494be0>, <tensorflow.python.training.basic_session_run_hooks.NanTensorHook object at 0x7f62f8076c88>, <tensorflow.python.training.basic_session_run_hooks.LoggingTensorHook object at 0x7f62d4072828>, <tensorflow.python.training.basic_session_run_hooks.StepCounterHook object at 0x7f62d424f0f0>, <tensorflow.python.training.basic_session_run_hooks.SummarySaverHook object at 0x7f62d424fcc0>, <tensorflow.python.training.basic_session_run_hooks.CheckpointSaverHook object at 0x7f62b46132b0>]
I1114 00:38:27.469497<PHONE_NUMBER>46848 monitored_session.py:408] all_hooks [<tensorflow_estimator.python.estimator.util.DistributedIteratorInitializerHook object at 0x7f6496df1358>, <tensorflow.python.training.basic_session_run_hooks.StopAtStepHook object at 0x7f6499494be0>, <tensorflow.python.training.basic_session_run_hooks.NanTensorHook object at 0x7f62f8076c88>, <tensorflow.python.training.basic_session_run_hooks.LoggingTensorHook object at 0x7f62d4072828>, <tensorflow.python.training.basic_session_run_hooks.StepCounterHook object at 0x7f62d424f0f0>, <tensorflow.python.training.basic_session_run_hooks.SummarySaverHook object at 0x7f62d424fcc0>, <tensorflow.python.training.basic_session_run_hooks.CheckpointSaverHook object at 0x7f62b46132b0>]
INFO:tensorflow:Creating chief session creator with config: device_filters: "/job:chief/task:0"
allow_soft_placement: true
graph_options {
  rewrite_options {
    scoped_allocator_optimization: ON
    scoped_allocator_opts {
      enable_op: "CollectiveReduce"
    }
  }
}
experimental {
  collective_group_leader: "/job:chief/replica:0/task:0"
}

I1114 00:38:27.469661<PHONE_NUMBER>46848 distribute_coordinator.py:251] Creating chief session creator with config: device_filters: "/job:chief/task:0"
allow_soft_placement: true
graph_options {
  rewrite_options {
    scoped_allocator_optimization: ON
    scoped_allocator_opts {
      enable_op: "CollectiveReduce"
    }
  }
}
experimental {
  collective_group_leader: "/job:chief/replica:0/task:0"
}

INFO:tensorflow:Collective batch_all_reduce: 1 all-reduces, num_workers = 3
I1114 00:38:33.972837<PHONE_NUMBER>46848 cross_device_ops.py:1107] Collective batch_all_reduce: 1 all-reduces, num_workers = 3
INFO:tensorflow:Collective batch_all_reduce: 1 all-reduces, num_workers = 3
I1114 00:38:33.976987<PHONE_NUMBER>46848 cross_device_ops.py:1107] Collective batch_all_reduce: 1 all-reduces, num_workers = 3
INFO:tensorflow:Collective batch_all_reduce: 1 all-reduces, num_workers = 3
I1114 00:38:33.988512<PHONE_NUMBER>46848 cross_device_ops.py:1107] Collective batch_all_reduce: 1 all-reduces, num_workers = 3
INFO:tensorflow:Collective batch_all_reduce: 1 all-reduces, num_workers = 3
I1114 00:38:33.992657<PHONE_NUMBER>46848 cross_device_ops.py:1107] Collective batch_all_reduce: 1 all-reduces, num_workers = 3
INFO:tensorflow:Collective batch_all_reduce: 1 all-reduces, num_workers = 3
I1114 00:38:34.086326<PHONE_NUMBER>46848 cross_device_ops.py:1107] Collective batch_all_reduce: 1 all-reduces, num_workers = 3
INFO:tensorflow:Collective batch_all_reduce: 1 all-reduces, num_workers = 3
I1114 00:38:34.090441<PHONE_NUMBER>46848 cross_device_ops.py:1107] Collective batch_all_reduce: 1 all-reduces, num_workers = 3
INFO:tensorflow:Collective batch_all_reduce: 1 all-reduces, num_workers = 3
I1114 00:38:34.102018<PHONE_NUMBER>46848 cross_device_ops.py:1107] Collective batch_all_reduce: 1 all-reduces, num_workers = 3
INFO:tensorflow:Collective batch_all_reduce: 1 all-reduces, num_workers = 3
I1114 00:38:34.106123<PHONE_NUMBER>46848 cross_device_ops.py:1107] Collective batch_all_reduce: 1 all-reduces, num_workers = 3
INFO:tensorflow:Collective batch_all_reduce: 1 all-reduces, num_workers = 3
I1114 00:38:34.117756<PHONE_NUMBER>46848 cross_device_ops.py:1107] Collective batch_all_reduce: 1 all-reduces, num_workers = 3
INFO:tensorflow:Graph was finalized.
I1114 00:38:40.463714<PHONE_NUMBER>46848 monitored_session.py:240] Graph was finalized.
INFO:tensorflow:Running local_init_op.
I1114 00:38:55.357866<PHONE_NUMBER>46848 session_manager.py:500] Running local_init_op.
INFO:tensorflow:Done running local_init_op.
I1114 00:38:56.174259<PHONE_NUMBER>46848 session_manager.py:502] Done running local_init_op.
INFO:tensorflow:Saving checkpoints for 0 into s3://**censor**/**censor**/model.ckpt.
I1114 00:39:17.262061<PHONE_NUMBER>46848 basic_session_run_hooks.py:606] Saving checkpoints for 0 into s3://**censor**/**censor**/model.ckpt.
Traceback (most recent call last):
  File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py", line 1365, in _do_call
    return fn(*args)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py", line 1350, in _run_fn
    target_list, run_metadata)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py", line 1443, in _call_tf_sessionrun
    run_metadata)
tensorflow.python.framework.errors_impl.InvalidArgumentError: From /job:chief/replica:0/task:0:
Cannot assign a device for operation detection_class_loss/SparseSoftmaxCrossEntropyWithLogits/assert_equal/Assert/Assert/data_0: Could not satisfy explicit device specification '/job:chief/replica:0/task:0/device:GPU:0' because no supported kernel for GPU devices is available.
Colocation Debug Info:
Colocation group had the following types and supported devices:
Root Member(assigned_device_name_index_=-1 requested_device_name_='/job:chief/replica:0/task:0/device:GPU:0' assigned_device_name_='' resource_device_name_='' supported_device_types_=[CPU] possible_devices_=[]
Const: CPU

Colocation members, user-requested devices, and framework assigned devices, if any:
  detection_class_loss/SparseSoftmaxCrossEntropyWithLogits/assert_equal/Assert/Assert/data_0 (Const) /job:chief/replica:0/task:0/device:GPU:0

Op: Const
Node attrs: dtype=DT_STRING, value=Tensor<type: string shape: [] values: >
Registered kernels:
  device='XLA_CPU_JIT'; dtype in [DT_FLOAT, DT_DOUBLE, DT_INT32, DT_UINT8, DT_INT8, ..., DT_COMPLEX128, DT_HALF, DT_UINT32, DT_UINT64, DT_STRING]
  device='GPU'; dtype in [DT_VARIANT]
  device='GPU'; dtype in [DT_BOOL]
  device='GPU'; dtype in [DT_COMPLEX128]
  device='GPU'; dtype in [DT_COMPLEX64]
  device='GPU'; dtype in [DT_UINT64]
  device='GPU'; dtype in [DT_INT64]
  device='GPU'; dtype in [DT_QINT32]
  device='GPU'; dtype in [DT_UINT32]
  device='GPU'; dtype in [DT_QUINT16]
  device='GPU'; dtype in [DT_QINT16]
  device='GPU'; dtype in [DT_INT16]
  device='GPU'; dtype in [DT_UINT16]
  device='GPU'; dtype in [DT_QINT8]
  device='GPU'; dtype in [DT_INT8]
  device='GPU'; dtype in [DT_UINT8]
  device='GPU'; dtype in [DT_DOUBLE]
  device='GPU'; dtype in [DT_FLOAT]
  device='GPU'; dtype in [DT_BFLOAT16]
  device='GPU'; dtype in [DT_HALF]
  device='GPU'; dtype in [DT_INT32]
  device='CPU'
  device='XLA_CPU'; dtype in [DT_UINT8, DT_QUINT8, DT_INT8, DT_QINT8, DT_INT32, DT_QINT32, DT_INT64, DT_HALF, DT_FLOAT, DT_DOUBLE, DT_COMPLEX64, DT_COMPLEX128, DT_BOOL, DT_BFLOAT16]
  device='XLA_GPU'; dtype in [DT_UINT8, DT_QUINT8, DT_INT8, DT_QINT8, DT_INT32, DT_QINT32, DT_INT64, DT_HALF, DT_FLOAT, DT_DOUBLE, DT_COMPLEX64, DT_COMPLEX128, DT_BOOL, DT_BFLOAT16]
  device='XLA_GPU_JIT'; dtype in [DT_FLOAT, DT_DOUBLE, DT_INT32, DT_UINT8, DT_INT8, ..., DT_COMPLEX128, DT_HALF, DT_UINT32, DT_UINT64, DT_STRING]

	 [[{{node detection_class_loss/SparseSoftmaxCrossEntropyWithLogits/assert_equal/Assert/Assert/data_0}}]]
	 [[loss_ops/detection_loss/StatefulPartitionedCall]]

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/usr/lib/python3.6/runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "/usr/lib/python3.6/runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "/opt/model/main.py", line 307, in <module>
    absl_app.run(main)
  File "/usr/local/lib/python3.6/dist-packages/absl/app.py", line 299, in run
    _run_main(main, args)
  File "/usr/local/lib/python3.6/dist-packages/absl/app.py", line 250, in _run_main
    sys.exit(main(argv))
  File "/opt/model/main.py", line 299, in main
    run_train_and_eval(flags.FLAGS, flags.FLAGS.flag_values_dict())
  File "/opt/model/main.py", line 287, in run_train_and_eval
    tf.estimator.train_and_evaluate(est, train_spec, eval_spec)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow_estimator/python/estimator/training.py", line 464, in train_and_evaluate
    estimator, train_spec, eval_spec, _TrainingExecutor)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/distribute/estimator_training.py", line 290, in train_and_evaluate
    session_config=run_config.session_config)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/distribute/distribute_coordinator.py", line 853, in run_distribute_coordinator
    task_id, session_config, rpc_layer)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/distribute/distribute_coordinator.py", line 360, in _run_single_worker
    return worker_fn(strategy)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/distribute/estimator_training.py", line 252, in _worker_fn
    hooks=hooks)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow_estimator/python/estimator/estimator.py", line 370, in train
    loss = self._train_model(input_fn, hooks, saving_listeners)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow_estimator/python/estimator/estimator.py", line 1158, in _train_model
    return self._train_model_distributed(input_fn, hooks, saving_listeners)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow_estimator/python/estimator/estimator.py", line 1221, in _train_model_distributed
    self._config._train_distribute, input_fn, hooks, saving_listeners)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow_estimator/python/estimator/estimator.py", line 1332, in _actual_train_model_distributed
    saving_listeners)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow_estimator/python/estimator/estimator.py", line 1410, in _train_with_estimator_spec
    estimator_spec, worker_hooks, saving_listeners)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow_estimator/python/estimator/estimator.py", line 1356, in _train_with_estimator_spec_distributed
    _, loss = mon_sess.run([estimator_spec.train_op, estimator_spec.loss])
  File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/training/monitored_session.py", line 754, in run
    run_metadata=run_metadata)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/training/monitored_session.py", line 1259, in run
    run_metadata=run_metadata)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/training/monitored_session.py", line 1360, in run
    raise six.reraise(*original_exc_info)
  File "/usr/lib/python3/dist-packages/six.py", line 693, in reraise
    raise value
  File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/training/monitored_session.py", line 1345, in run
    return self._sess.run(*args, **kwargs)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/training/monitored_session.py", line 1418, in run
    run_metadata=run_metadata)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/training/monitored_session.py", line 1176, in run
    return self._sess.run(*args, **kwargs)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py", line 956, in run
    run_metadata_ptr)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py", line 1180, in _run
    feed_dict_tensor, options, run_metadata)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py", line 1359, in _do_run
    run_metadata)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py", line 1384, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: From /job:chief/replica:0/task:0:
Cannot assign a device for operation detection_class_loss/SparseSoftmaxCrossEntropyWithLogits/assert_equal/Assert/Assert/data_0: Could not satisfy explicit device specification '/job:chief/replica:0/task:0/device:GPU:0' because no supported kernel for GPU devices is available.
Colocation Debug Info:
Colocation group had the following types and supported devices:
Root Member(assigned_device_name_index_=-1 requested_device_name_='/job:chief/replica:0/task:0/device:GPU:0' assigned_device_name_='' resource_device_name_='' supported_device_types_=[CPU] possible_devices_=[]
Const: CPU

Colocation members, user-requested devices, and framework assigned devices, if any:
  detection_class_loss/SparseSoftmaxCrossEntropyWithLogits/assert_equal/Assert/Assert/data_0 (Const) /job:chief/replica:0/task:0/device:GPU:0

Op: Const
Node attrs: dtype=DT_STRING, value=Tensor<type: string shape: [] values: >
Registered kernels:
  device='XLA_CPU_JIT'; dtype in [DT_FLOAT, DT_DOUBLE, DT_INT32, DT_UINT8, DT_INT8, ..., DT_COMPLEX128, DT_HALF, DT_UINT32, DT_UINT64, DT_STRING]
  device='GPU'; dtype in [DT_VARIANT]
  device='GPU'; dtype in [DT_BOOL]
  device='GPU'; dtype in [DT_COMPLEX128]
  device='GPU'; dtype in [DT_COMPLEX64]
  device='GPU'; dtype in [DT_UINT64]
  device='GPU'; dtype in [DT_INT64]
  device='GPU'; dtype in [DT_QINT32]
  device='GPU'; dtype in [DT_UINT32]
  device='GPU'; dtype in [DT_QUINT16]
  device='GPU'; dtype in [DT_QINT16]
  device='GPU'; dtype in [DT_INT16]
  device='GPU'; dtype in [DT_UINT16]
  device='GPU'; dtype in [DT_QINT8]
  device='GPU'; dtype in [DT_INT8]
  device='GPU'; dtype in [DT_UINT8]
  device='GPU'; dtype in [DT_DOUBLE]
  device='GPU'; dtype in [DT_FLOAT]
  device='GPU'; dtype in [DT_BFLOAT16]
  device='GPU'; dtype in [DT_HALF]
  device='GPU'; dtype in [DT_INT32]
  device='CPU'
  device='XLA_CPU'; dtype in [DT_UINT8, DT_QUINT8, DT_INT8, DT_QINT8, DT_INT32, DT_QINT32, DT_INT64, DT_HALF, DT_FLOAT, DT_DOUBLE, DT_COMPLEX64, DT_COMPLEX128, DT_BOOL, DT_BFLOAT16]
  device='XLA_GPU'; dtype in [DT_UINT8, DT_QUINT8, DT_INT8, DT_QINT8, DT_INT32, DT_QINT32, DT_INT64, DT_HALF, DT_FLOAT, DT_DOUBLE, DT_COMPLEX64, DT_COMPLEX128, DT_BOOL, DT_BFLOAT16]
  device='XLA_GPU_JIT'; dtype in [DT_FLOAT, DT_DOUBLE, DT_INT32, DT_UINT8, DT_INT8, ..., DT_COMPLEX128, DT_HALF, DT_UINT32, DT_UINT64, DT_STRING]

	 [[{{node detection_class_loss/SparseSoftmaxCrossEntropyWithLogits/assert_equal/Assert/Assert/data_0}}]]
	 [[loss_ops/detection_loss/StatefulPartitionedCall]]

@gdj0nes
Will it be possible to share related complete code snippet to reproduce the issue in our environment,it helps us localizing the issue faster. Thanks!

@gdj0nes
Any update on this issue please. Thanks!

Automatically closing due to lack of recent activity. Please update the issue when new information becomes available, and we will reopen the issue. Thanks!


Add Amazon Simple Notification Service Agent user-agent (#343)

Fixes #343

[x] Tests passes.
[x] Added 1 user-agent

[x] Added "Amazon Simple Notification Service Agent" user-agent

♥


fix graph-gen tests in microns branch

All 4 tests fail because of 403 error. I think that this has something to do with the fact that we are not using tokens in tests.

@Aeusman Fix the latest push to microns and open a pull request.

@Aeusman This is a TODO after statuspage.

@perlman @randalburns Do we care about this service or should be deprecate this? AFAIK no one has used it.


Download Queue

There's a new API call, /renter/downloadqueue, which gives a list of all downloads that have been requested, are in progress, or have finished. I believe API.md documents what each of the values indicates.
The user should have an idea of what downloads are in progress, so that after they click on a file to download it, there's more than a vague notification that it's started working.

I think the best way to solve this is to have the download notification linger with a progress bar, then be optionally dismissed.

For the immediate future, there's not going to be a progress bar, but it's something that Luke knows how to implement. We would just add two fields to the downloadqueue responses: bytesDownloaded and bytesTotal, from which you could derive a %, and potentially a velocity. For the time being though we're working on other things.
If you want to fake a progress bar for the time being, that's fine by me.


Keeps asking to install ide-php

I've said Never to the question to install ide-php package, and I even see ide-php in the package settings in the disabled prompt list, but php-debug does not seem to take no for an answer and still keeps asking me to install ide-php on every startup :smile: .

Should be fixed in 966d988 which should land in v0.3.1 later today. Thanks

Should be fixed in v0.3.1. Please let me know if you experience continued issues around this.

Seems to be fixed, thanks!


Sounddevice isnt detecting portaudio on my MacBook | OSError: PortAudio library not found |

When i run this code
import sounddevice

i get this error
Traceback (most recent call last):
  File "/Users/faizrahim/PycharmProjects/voice-lag-simul/venv/lib/python3.8/site-packages/sounddevice.py", line 71, in <module>
    raise OSError('PortAudio library not found')
OSError: PortAudio library not found

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/Users/faizrahim/PycharmProjects/voice-lag-simul/writejson.py", line 1, in <module>
    import sounddevice
  File "/Users/faizrahim/PycharmProjects/voice-lag-simul/venv/lib/python3.8/site-packages/sounddevice.py", line 80, in <module>
    import _sounddevice_data
ModuleNotFoundError: No module named '_sounddevice_data'

This is how i installed sounddevice
pip3 install sounddevice

But I'm sure i installed portaudio on my mac. Even Pyaudio seems to work fine but sounddevice is not working, What can i do now?

I'm seeing the same as OP and am running on an M1 Mac.
I do have portaudio installed via brew and have tried running
pip3 install --no-cache-dir --global-option='build_ext' --global-option='-I/usr/local/opt/portaudio/include' --global-option='-L/usr/local/opt/portaudio/lib' sounddevice but no success thus far

I'm still getting the error, on an Intel Macbook w/ 11.4. I tried the brew install portaudio --HEAD trick mentioned in various places without luck. My sounddevice version seems to be 0.4.3.

I never saw any permissions pop up, is there a way to force this? And also, fyi, I'm using Python 3.10

Update: fixed by downgrading to Python 3.8. Maybe some other issue was present. I did now create a few conda env, and then used pip to install sounddevice.


Error: listen EADDRINUSE :::8080

Hi everyone :) Quick question, how can I add more tests that need to connect to DB similar to the model/post.spec.js?
I keep getting a
 1. Uncaught Exception
   Error: listen EADDRINUSE :::8080
    Object.exports._errnoException (util.js:1026:11)
    exports._exceptionWithHostPort (util.js:1049:20)

every time I try to add a new spec file that has the code below in it.
test.beforeEach('connect to database', t => {
  connectDB(t, () => {
    console.log('connected to DB for account tests');
  });
});

test.afterEach.always(t => {
  dropDB(t);
});

Any suggestions/ideas? Thank you :)

Anyone?

EADDRINUSE means that the port number which listen() tries to bind the server to is already in use.
So, in your case, there must be running a server on port 8080 already.

anyone fixed this?

@panvourtsis  literally read this https://github.com/Hashnode/mern-starter/issues/279#issuecomment-287099746

@Fallenstedt yes i know what it means. The thing is that when you got more than one test in place for the models this happens in whatever port you define. It just starting the next test without closing the usage of the port. Tried 8080, 4350, 6565 etc etc always the same


Revise the datapath context and access interfaces

From discussions with @karlcz, we have the following idea...
The datapath context is identified by the last entity in a datapath like entity/A/B/C where C is the context or entity/a:=A/B/C/$a where A is the context, both of the entity access interface.
The proposed change would drop context from the DataPath instance and move the access interfaces (entity, attribute, attributegroup,...) to the PathTable instance which therefore implicitly identifies the context of the invocation.
See also #65 as a corequisite of this change.
Revised interface
PathTable
 .entity
 .attribute
 ...

Entity
 .get()
 .delete()
 ...

Example usage:
var dp = DataPath(A);
var a = dp.root;
a.join(B);
var promise = a.entity.get(); /* gets all entities for ...a:=A/B/$a */

@robes is this still relevant?

no
From: Hongsuda<EMAIL_ADDRESS>Reply-To: informatics-isi-edu/ermrestjs<EMAIL_ADDRESS>Date: Wednesday, July 13, 2016 at 10:44 AM
To: informatics-isi-edu/ermrestjs<EMAIL_ADDRESS>Cc: Robert Schuler<EMAIL_ADDRESS>Mention<EMAIL_ADDRESS>Subject: Re: [informatics-isi-edu/ermrestjs] Revise the datapath context and access interfaces (#70)
@robeshttps://github.com/robes is this still relevant?
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHubhttps://github.com/informatics-isi-edu/ermrestjs/issues/70#issuecomment-232432174, or mute the threadhttps://github.com/notifications/unsubscribe/AEAhWqCuVh-iCrPCMzuBNhSu34HQbu_Rks5qVSP8gaJpZM4JAX8L.

I think this issue is obviated by the revised reference api


Error when submit NFT minting tx

Discussed in https://github.com/Python-Cardano/pycardano/discussions/164

Originally posted by reitcircles February 14, 2023
When submitting tx for the NFT minting of this example: https://github.com/Python-Cardano/pycardano/blob/main/examples/native_token.py
blockfrost.utils.ApiError: {'error': 'Bad Request', 'message': '"transaction read error RawCborDecodeError [DecoderErrorDeserialiseFailure \\"Byron Tx\\" (DeserialiseFailure 1 \\"Size mismatch when decoding TxAux.\\\\nExpected 2, but found 4.\\"),DecoderErrorDeserialiseFailure \\"Shelley Tx\\" (DeserialiseFailure 1 \\"Size mismatch when decoding \\\\nRecord RecD.\\\\nExpected 4, but found 3.\\"),DecoderErrorDeserialiseFailure \\"Shelley Tx\\" (DeserialiseFailure 1 \\"Size mismatch when decoding \\\\nRecord RecD.\\\\nExpected 4, but found 3.\\"),DecoderErrorDeserialiseFailure \\"Shelley Tx\\" (DeserialiseFailure 1 \\"Size mismatch when decoding \\\\nRecord RecD.\\\\nExpected 4, but found 3.\\"),DecoderErrorDeserialiseFailure \\"Shelley Tx\\" (DeserialiseFailure 43 \\"expected string\\"),DecoderErrorDeserialiseFailure \\"Shelley Tx\\" (DeserialiseFailure 43 \\"expected string\\")]"', 'status_code': 400}
Does anyone have a clue how to solve this ?

This looks a lot like the issue discussed here and actually an issue of cardano-submit-api and not pycardano https://github.com/ImperatorLang/eopsin/discussions/46

Please read the attached discussion. Blockfrost is internally using the cardano-submit-api

This looks a lot like the issue discussed here and actually an issue of cardano-submit-api and not pycardano ImperatorLang/eopsin#46

The basic point is. this. In your own pycardano repo there is an example which does not seem to work. if now you say it is a problem of cardano-submit-api it sends the message that this repo example is wrong. So it must be a problem of this library than theirs. What is the proof that it is the problem of submit api ?

Hi @reitcircles , could you confirm you are using exactly the same script? I just reran the script and it succeeded. Here is the test account, and you can see it just minted two NFTs.

Hi @reitcircles , could you confirm you are using exactly the same script? I just reran the script and it succeeded. Here is the test account, and you can see it just minted two NFTs.

Got it. Why it coming out as an error while it should not. I have a perfect working example that mints using cardano-cli. here is the problem.
`
Calculate the minimum amount of lovelace that need to hold the NFT we are going to mint
min_val = min_lovelace(
chain_context, output=TransactionOutput(clientAddress, Value(0, my_nft))
)
Send the NFT to our own address
builder.add_output(TransactionOutput(clientAddress, Value(min_val, my_nft)))
`
I am trying to send the NFT to a different address than the payment address. A perfectly valid address. And then it comes up with the above error. No clue why.

clientAddr = Address.from_primitive(clientAddress)

thanks ... i was just looking into this already ...let me try.

tagging @while0x1 who had a similar issue

Auto correction is added in this commit: https://github.com/Python-Cardano/pycardano/commit/4bf6a626f6f6093ae368b5bd980b1271d9cae4e4.

This looks a lot like the issue discussed here and actually an issue of cardano-submit-api and not pycardano ImperatorLang/eopsin#46

Can I submit this somehow to the local cardano node running on my server ? Say using cardano-cli ? In other words can i use the cbor generated and use cardano-cli ? Are the format compatible ...any clue ? Because frankly neither can i change submit api and nor blockfrost. We need to find a solution rather than putting the blame on others ...
And i am taking here an example (that should work), as it is in this repository examples. Right ?

I discovered you can take CBOR generated from a cardano-cli build command and then just pass it to a CIP30 wallet for signing after a couple of transformations through pycardano:
Procedure:
take cbor from cardano cli remove first byte and last 2 bytes
create a transaction body with the parsed CBOR
restored_tx_body = TransactionBody.from_cbor(<remove first byte and last 2 bytes>)
#import into py cardano transaction and set empty witness
tx = Transaction(restored_tx_body, TransactionWitnessSet())
tx.to_cbor()
#take cbor from to_cbor and sign with CIP30 wallet
#get signed cbor witness hex output from the signTx() call and set witness in pycardano
witness = TransactionWitnessSet.from_cbor()
#import witness into transaction
tx.transaction_witness_set = witness
#export cbor for tx submission
tx.to_cbor
#submitTx() in your cip30 Wallet

Do you have any idea why one has to remove the bytes?


Invoke shellHook

Context
cached-nix-shell doesn't currently invoke shellHook after the first run.
Intent
Invoke shellHook on interactive mode and shebang mode.
Discussion
How do I make this less ugly?
        exec_string.push(nix_shell_args.interpreter);
        exec_string.push(" ");

Sorry, weird. I'll create the PR on canva's fork.


Convert attributes to empty object if there are no attributes

If you transform a resource without any attributes using the JsonApiSerializer the resulting JSON currently looks like "attributes": []. This does not satisfy the JSON API spec: The value of the attributes key MUST be an object (an “attributes object”)..
It would also be possible to remove the entire attributes key from the resource object, since JSON API does not require it. However, I think many clients expect there to be an attributes key, so it is better to set it to an empty object. It just can't be an array.
If you would like me to make any changes to the pull request, don't hesitate to ask.

@greydnls can I ping you on this? This is required for an issue I'm working on :)

This looks great. Thank you, @BastianHofmann


Labels on left side and chart on right - IE Only

Currently am solving the problem in IE11, where the chart is displayed but all the x labels are on left side and the line chart itself is on the right.
The chart is normally displaying in Chrome and Firefox.
Could you help me how could I resolve this problem?

Am using:
Chart.js 2.7.1
polyfill.io/v2/polyfill.min.js

@Nasatya do you have a fiddle that reproduces this?

@etimberg Thank you for your response. After a long testing it turned out that the problem was caused by wrongly generated generated dates (needs ISO 8601) for labels with moment.js.


Remove usage of dart:js & package:js and therefore remove usage of allowInterop

Issue https://github.com/dart-lang/sdk/issues/54908

This was originally extracted from Flutter, is Flutter also using package:web for the same code now?

Yes flutter itself is using package:web. Flute contains old copy of flutter and doesn't have these changes yet. Maybe benchmark results will be affected, but we should do this anyway and we'll see any changes attributed to the roll of flute into Dart SDK.

The commit landed (via gerrit), but somehow it wasn't synced here yet (and PR stays open) /cc @athomas
Will manually close.


feat: add support for bridging gaps between different languages

Description
I have used Translation with lang.Merge to bridge the gap between different languages when a specific language version does not exist. For example, if you have the following blogs:

blog.1.en
blog.1.ch
blog.2.en

When you check the English blogs, you will see:

blog.1.en
blog.2.en

When you check the Chinese blogs, you will see:

blog.1.ch
blog.2.en

@horw Sorry for my slow reply and thank you for implementing the solution to use lang.Merge to merge content from other languages (i.e. missing content translations).
@georgik PTAL at my answer as well.
This PR only partially solves the problem.  We have the following issues:

Issue 1: absence of translated content

sequenceDiagram
    participant User
    participant Website
    User->>Website: Switch to non-default target language
    Website-->>User: Display target language content
    User->>Website: Navigate to content category (Blog, Workflows, Events)
    alt At least one page in target language
        Website-->>User: Link on nav bar works, navigate to folder
    else No pages in target language
        Website-->>User: Link on nav bar doesn't work
    end

Issue 2: Opening pages with missing translation (default language) resets the website language setting.

sequenceDiagram
    participant User
    participant Website
    User->>Website: Switch to non-default target language
    Website->>User: Display target language content
    User->>Website: Navigate to content category (Blog, Workflows, Events)
    Website->>User: Fill missing translations with default-language content
    User->>Website: Open default-language page
    Website-->>User: Language resets to default

Probably, issue 2 appears because the language setting is only stored in the URL. Our idea was to try and preserve the chosen target language setting by changing defaultContentLanguageInSubdir to true. I tested it, the string en/gets added to the URL indeed, but both issue 1 and issue 2 are still present.
I also believe that using default-language content for missing translations is misleading for the target language speakers who are uncomfortable with the default language. They have no way to easily find what content is actually translated into their target language. Their only option would be only identifying the translations visually, which is suboptimal.
There is a discussion about possible ways of addressing the issue of missing content. If we decide to continue working on this issue, we have to decide on the best strategy first.

I will close this PR because I think it will create a lot of overhead.


Render extended symbol pages with their extended module

Issue: rdar://107729534
Summary
For bundles with extension block symbols, it can be a bit confusing to understand which module is actually needed to use an extension symbol. This PR adds context to these symbols by adding the extended module as a "related module" to the RenderNode, which renders both the owning module and the extended module to be shown in the rendered page.
(Note that this doesn't work for bundles without extension block symbols, due to inconsistencies in the Swift symbol graph. This is noted in the symbol graph loader as being connected to rdar://63200368. An interim fix will likely need to land in SymbolKit, since the module name processing code mainly happens there now.)
Dependencies
None
Testing
Steps:

Convert docs for the Tests/SwiftDocCTests/Test Bundles/BundleWithRelativePathAmbiguity.docc bundle.
Ensure that the page for Dependency.AmbiguousType.foo() has both BundleWithRelativePathAmbiguity and Dependency in its page header.

Checklist

[x] Added tests
[x] Ran the ./bin/test script and it succeeded
[ n/a ] Updated documentation if necessary

@swift-ci Please test

I've dropped the commit that modified extension symbols' titles and declarations, pending further design discussion.

@swift-ci please test