[DC-887] RDR import script creates extra tables

Changed table names:
measurement_concept_sets => _measurement_concept_sets
measurement_concept_sets_descendants => _measurement_concept_sets_descendants
cdr_metadata => _cdr_metadata
operational_pii_fields =>_operational_pii_fields
Changed all string instances of table names in add_cer_metadata.py, remove_operational_pii_fields.py, required_labs.py
Renamed CSV files:
operational_pii_fields.csv => _operational_pii_fields
measurement_concept_sets.csv => _measurement_concept_sets.csv

Modified both add_cdr_metadata.py and add_cdr_metadata.py to use {metadata_table} as a parameter in both ADD_ETL_METADATA_QUERY and COPY_QUERY


AL-52-account-transfer-txn

https://mifosforge.jira.com/browse/AL-52

This PR points to a close source issue. The commit message is misleading. Please this should be reversed and the right commit message and issue created on Apache Fineract CN JIRA. cc @edcable .
Just rename the commit message.


Org icon broken

Private orgs used to have a lock icon. Now all orgs (both private and public) have a crossed-out eye (?)

In the screenshot, membership of the org on the left is private, while the one on the right is public. I've verified this by logging out (which makes the private org disappear).
Note that I have a personal token configured and haven't made any changes to it since it was working. I can also confirm that the token does have the correct permissions to see the orgs:
$ curl https://api.github.com/users/casperdcl/orgs
... # lists public orgs
$ curl -H "Authorization: token $GH_API_TOKEN" https://api.github.com/user/orgs
... # includes private orgs

That is because the URLs for organizations have changed on GitHub:

Here the URL is https://github.com/organizations/getferdi?type=source (previously https://github.com/getferdi?type=source, without organizations/).
The relevant part is:
https://github.com/sindresorhus/refined-github/blob/825f4fd51efaef62a5f131a83b2c7a2cc14116ee/source/features/mark-private-orgs.tsx#L27
publicOrganizations is publicOrganizationsNames, org is of type HTMLAnchorElement and org.pathname.slice(1) results in organizations/getferdi. We would need to get rid of the organizations/ there for the condition above to properly resolve.

This is great. There are a few features that would benefit from this piece of information being readily available (e.g. they no longer have to look for body.org to detect an organization profile for example)
Also page-detect needs to be updated


Allow switching to a window in a dwindle group via wlr-foreign-toplevel-management

Steps to reproduce:

Install rofi-lbonn-wayland-git
Make a dwindle group with two windows
Run rofi -show window
Try to switch to a window that is not currently active in the group.
Observe that the switch fails.

This also happens with misc:focus_on_activate = true on opening an URL from another app when firefox is in a dwindle group (in this case an urgency hint is set instead).
However, hyprctl dispatch focus is able to switch to the same window.

Maybe slightly unrelated, but does that mean that for "standard" windows, i.e., not in a group, rofi wayland works as a window switcher?

done in 0887e2ee6ec9c7ef106b71c906c6531730c8fdd4


/Help Command

Can you assign me this problem? I would like to help you with this or maybe also  with some other issues if you have already done this.

@DuchVladimir Welcome! Go ahead!

I made pull request. I hope you like it. :)

Implemented in #75


Add links and badges to the readme

Proposed Changes

Add links and badges to the readme

/cc @slinkydeveloper

/lgtm
/approve

/retest


fix(ui-canvas): Use screen scale for android canvas scaling

This PR decouples density property from android canvas scaling as they're two different things.
More specifically, density calls Canvas.setDensity internally so one may wish to increase bitmap density without affecting android scale.
Also, I performed a small cleanup of few unneeded any casts.

@CatchABus this does not break anything ? Nor does it change the rendering ?

@CatchABus this does not break anything ? Nor does it change the rendering ?

I haven't come across any case that breaks with this change. If you have anything specific in mind, I can test it.

@CatchABus I think there is a demo where you can change density. I guess if that works we are good


autoInstallBundle profile missing for Core Module

An established AEM convention is to use the profile autoInstallBundle to deploy just a project's OSGi bundles to a local AEM instance. Currently, this is done with the profile autoInstallPackage. In order to promote consistency across projects the autoInstallBundle profile should be used.

Closing as https://github.com/adobe/aem-spa-project-archetype/pull/45 has been released


NCCL WARN Failed to open libibverbs.so[.1]

I use the official example https://github.com/kubeflow/tf-operator/tree/master/examples/v1/distribution_strategy/keras-API
1.POD is  working but use only one GPU
and the second failed is
2. eval_fn is not passed in. The worker_fn will be used if an "evaluator" task exists in the cluster
eval_fn is not passed in. The worker_fn will be used if an "evaluator" task exists in the cluster
System information
Ubuntu 16.04
1Master   IP:14X.XXX.XXX.1
node1     IP:14X.XXX.XXX.8     GTX1060
node2     IP:14X.XXX.XXX.9     GTX1060
node3     IP:14X.XXX.XXX.10    GTX1070
docker18.09.7-3
cuda 10.0
nvidia-container-runtime=2.0.0
kubernetes 1.5.7
kubeflow 1.01

This is pod logs
2020-06-06 13:42:10.403622: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libnvinfer.so.6
2020-06-06 13:42:10.405097: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libnvinfer_plugin.so.6
2020-06-06 13:42:11.360954: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2020-06-06 13:42:11.392254: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties:
pciBusID: 0000:05:00.0 name: GeForce GTX 1070 computeCapability: 6.1
coreClock: 1.7715GHz coreCount: 15 deviceMemorySize: 7.93GiB deviceMemoryBandwidth: 238.66GiB/s
2020-06-06 13:42:11.392302: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-06-06 13:42:11.392337: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2020-06-06 13:42:11.394046: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2020-06-06 13:42:11.394338: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2020-06-06 13:42:11.396368: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2020-06-06 13:42:11.397503: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2020-06-06 13:42:11.397553: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-06-06 13:42:11.399221: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0
2020-06-06 13:42:11.399588: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2020-06-06 13:42:11.407640: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency:<PHONE_NUMBER> Hz
2020-06-06 13:42:11.409315: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x4216610 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-06-06 13:42:11.409351: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2020-06-06 13:42:11.506014: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x4a3ed20 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2020-06-06 13:42:11.506070: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): GeForce GTX 1070, Compute Capability 6.1
2020-06-06 13:42:11.507576: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties:
pciBusID: 0000:05:00.0 name: GeForce GTX 1070 computeCapability: 6.1
coreClock: 1.7715GHz coreCount: 15 deviceMemorySize: 7.93GiB deviceMemoryBandwidth: 238.66GiB/s
2020-06-06 13:42:11.507666: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-06-06 13:42:11.507688: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2020-06-06 13:42:11.507725: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2020-06-06 13:42:11.507748: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2020-06-06 13:42:11.507782: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2020-06-06 13:42:11.507812: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2020-06-06 13:42:11.507832: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-06-06 13:42:11.510127: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0
2020-06-06 13:42:11.510187: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-06-06 13:42:11.871917: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1096] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-06-06 13:42:11.871965: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102]      0
2020-06-06 13:42:11.871973: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] 0:   N
2020-06-06 13:42:11.873633: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1241] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 7169 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1070, pci bus id: 0000:05:00.0, compute capability: 6.1)
2020-06-06 13:42:11.877266: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties:
pciBusID: 0000:05:00.0 name: GeForce GTX 1070 computeCapability: 6.1
coreClock: 1.7715GHz coreCount: 15 deviceMemorySize: 7.93GiB deviceMemoryBandwidth: 238.66GiB/s
2020-06-06 13:42:11.877328: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-06-06 13:42:11.877352: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2020-06-06 13:42:11.877384: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2020-06-06 13:42:11.877416: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2020-06-06 13:42:11.877439: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2020-06-06 13:42:11.877466: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2020-06-06 13:42:11.877493: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-06-06 13:42:11.879798: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0
2020-06-06 13:42:11.879840: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1096] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-06-06 13:42:11.879856: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102]      0
2020-06-06 13:42:11.879866: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] 0:   N
2020-06-06 13:42:11.882065: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1241] Created TensorFlow device (/job:worker/replica:0/task:0/device:GPU:0 with 7169 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1070, pci bus id: 0000:05:00.0, compute capability: 6.1)
2020-06-06 13:42:11.890039: I tensorflow/core/distributed_runtime/rpc/grpc_channel.cc:300] Initialize GrpcChannelCache for job worker -> {0 -> localhost:2222, 1 -> multi-worker-worker-1.kubeflow.svc:2222, 2 -> multi-worker-worker-2.kubeflow.svc:2222}
2020-06-06 13:42:11.891220: I tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc:390] Started server with target: grpc://localhost:2222
WARNING:absl:Dataset mnist is hosted on GCS. It will automatically be downloaded to your
local data directory. If you'd instead prefer to read directly from our public
GCS bucket (recommended if you're running on GCP), you can instead pass
`try_gcs=True` to `tfds.load` or set `data_dir=gs://tfds-data/datasets`.

multi-worker-worker-0:1:210 [0] NCCL INFO NET/Socket : Using [0]eth0:<IP_ADDRESS><0>
multi-worker-worker-0:1:210 [0] NCCL INFO NET/Plugin : No plugin found (libnccl-net.so).

multi-worker-worker-0:1:210 [0] external/nccl_archive/src/misc/ibvwrap.cc:63 NCCL WARN Failed to open libibverbs.so[.1]
WARNING:tensorflow:`eval_fn` is not passed in. The `worker_fn` will be used if an "evaluator" task exists in the cluster.
WARNING:tensorflow:`eval_fn` is not passed in. The `worker_fn` will be used if an "evaluator" task exists in the cluster.
WARNING:tensorflow:`eval_strategy` is not passed in. No distribution strategy will be used for evaluation.
WARNING:tensorflow:`eval_strategy` is not passed in. No distribution strategy will be used for evaluation.
2020-06-06 13:42:25.072418: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2020-06-06 13:42:25.854184: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
NCCL version 2.4.7+cudaCUDA_MAJOR.CUDA_MINOR
multi-worker-worker-0:1:292 [0] NCCL INFO Setting affinity for GPU 0 to 0fffff
multi-worker-worker-0:1:292 [0] NCCL INFO Could not find real path of /sys/class/net/eth0/device
multi-worker-worker-0:1:292 [0] NCCL INFO bazel-out/k8-py2-opt/bin/external/nccl_archive/_virtual_includes/include_hdrs/net.h:24 -> 2
multi-worker-worker-0:1:292 [0] NCCL INFO CUDA Dev 0[0], Socket NIC distance :  SYS
multi-worker-worker-0:1:292 [0] NCCL INFO Channel 00 :    0   1   2
multi-worker-worker-0:1:292 [0] NCCL INFO Could not find real path of /sys/class/net/eth0/device
multi-worker-worker-0:1:292 [0] NCCL INFO bazel-out/k8-py2-opt/bin/external/nccl_archive/_virtual_includes/include_hdrs/net.h:24 -> 2
multi-worker-worker-0:1:292 [0] NCCL INFO Ring 00 : 2 -> 0 [receive] via NET/Socket/0
multi-worker-worker-0:1:292 [0] NCCL INFO Ring 00 : 0 -> 1 [send] via NET/Socket/0
multi-worker-worker-0:1:292 [0] NCCL INFO Using 256 threads, Min Comp Cap 6, Trees disabled
multi-worker-worker-0:1:292 [0] NCCL INFO comm 0x7f5c38305e70 rank 0 nranks 3 cudaDev 0 nvmlDev 0 - Init COMPLETE
multi-worker-worker-0:1:291 [0] NCCL INFO Launch mode Parallel
2020-06-06 13:42:27.385638: I tensorflow/core/profiler/lib/profiler_session.cc:225] Profiler session started.
2020-06-06 13:42:27.385715: I tensorflow/core/profiler/internal/gpu/cupti_tracer.cc:1259] Profiler found 1 GPUs
2020-06-06 13:42:27.387084: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcupti.so.10.1
2020-06-06 13:42:27.487586: E tensorflow/core/profiler/internal/gpu/cupti_tracer.cc:1307] function cupti_interface_->Subscribe( &subscriber_, (CUpti_CallbackFunc)ApiCallback, this)failed with error CUPTI_ERROR_INSUFFICIENT_PRIVILEGES
2020-06-06 13:42:27.488406: E tensorflow/core/profiler/internal/gpu/cupti_tracer.cc:1346] function cupti_interface_->ActivityRegisterCallbacks( AllocCuptiActivityBuffer, FreeCuptiActivityBuffer)failed with error CUPTI_ERROR_INSUFFICIENT_PRIVILEGES
Downloading and preparing dataset mnist/3.0.1 (download: 11.06 MiB, generated: 21.00 MiB, total: 32.06 MiB) to /root/tensorflow_datasets/mnist/3.0.1...
Dataset mnist downloaded and prepared to /root/tensorflow_datasets/mnist/3.0.1. Subsequent calls will reuse this data.
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
conv2d (Conv2D)              (None, 26, 26, 32)        320
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 13, 13, 32)        0
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 11, 11, 64)        18496
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 5, 5, 64)          0
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 3, 3, 64)          36928
_________________________________________________________________
flatten (Flatten)            (None, 576)               0
_________________________________________________________________
dense (Dense)                (None, 64)                36928
_________________________________________________________________
dense_1 (Dense)              (None, 10)                650
=================================================================
Total params: 93,322
Trainable params: 93,322
Non-trainable params: 0
_________________________________________________________________
Train for 70 steps
Epoch 1/10
2020-06-06 13:42:27.521448: E tensorflow/core/profiler/internal/gpu/cupti_tracer.cc:1329] function cupti_interface_->EnableCallback( 0 , subscriber_, CUPTI_CB_DOMAIN_DRIVER_API, cbid)failed with error CUPTI_ERROR_INVALID_PARAMETER
2020-06-06 13:42:27.521483: I tensorflow/core/profiler/internal/gpu/device_tracer.cc:88]  GpuTracer has collected 0 callback api events and 0 activity events.
67/70 [===========================>..] - ETA: 0s - loss: 0.8933 - accuracy: 0.7327
Learning rate for epoch 1 is 0.0010000000474974513
70/70 [==============================] - 9s 123ms/step - loss: 0.8707 - accuracy: 0.7398
Epoch 2/10
66/70 [===========================>..] - ETA: 0s - loss: 0.2171 - accuracy: 0.9326
Learning rate for epoch 2 is 0.0010000000474974513
70/70 [==============================] - 3s 44ms/step - loss: 0.2138 - accuracy: 0.9333
Epoch 3/10
67/70 [===========================>..] - ETA: 0s - loss: 0.1507 - accuracy: 0.9527
Learning rate for epoch 3 is 0.0010000000474974513
70/70 [==============================] - 3s 49ms/step - loss: 0.1503 - accuracy: 0.9530
Epoch 4/10
65/70 [==========================>...] - ETA: 0s - loss: 0.1092 - accuracy: 0.9683
Learning rate for epoch 4 is 9.999999747378752e-05
70/70 [==============================] - 3s 48ms/step - loss: 0.1098 - accuracy: 0.9685
Epoch 5/10
67/70 [===========================>..] - ETA: 0s - loss: 0.1067 - accuracy: 0.9702
Learning rate for epoch 5 is 9.999999747378752e-05
70/70 [==============================] - 4s 52ms/step - loss: 0.1068 - accuracy: 0.9702
Epoch 6/10
69/70 [============================>.] - ETA: 0s - loss: 0.0914 - accuracy: 0.9709
Learning rate for epoch 6 is 9.999999747378752e-05
70/70 [==============================] - 4s 52ms/step - loss: 0.0909 - accuracy: 0.9711
Epoch 7/10
67/70 [===========================>..] - ETA: 0s - loss: 0.0889 - accuracy: 0.9722
Learning rate for epoch 7 is 9.999999747378752e-05
70/70 [==============================] - 4s 52ms/step - loss: 0.0882 - accuracy: 0.9724
Epoch 8/10
67/70 [===========================>..] - ETA: 0s - loss: 0.0907 - accuracy: 0.9732
Learning rate for epoch 8 is 9.999999747378752e-06
70/70 [==============================] - 4s 52ms/step - loss: 0.0908 - accuracy: 0.9730
Epoch 9/10
66/70 [===========================>..] - ETA: 0s - loss: 0.0968 - accuracy: 0.9704
Learning rate for epoch 9 is 9.999999747378752e-06
70/70 [==============================] - 4s 54ms/step - loss: 0.0955 - accuracy: 0.9710
Epoch 10/10
69/70 [============================>.] - ETA: 0s - loss: 0.0858 - accuracy: 0.9736
Learning rate for epoch 10 is 9.999999747378752e-06
70/70 [==============================] - 4s 53ms/step - loss: 0.0860 - accuracy: 0.9736
2020-06-06 13:43:03.252370: W tensorflow/core/kernels/data/generator_dataset_op.cc:103] Error occurred when finalizing GeneratorDataset iterator: Cancelled: Operation was cancelled
2020-06-06 13:43:03.625375: W tensorflow/python/util/util.cc:319] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1786: 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.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1786: 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.
2020-06-06 13:43:05.762394: W tensorflow/core/common_runtime/eager/context.cc:349] Unable to destroy server_ object, so releasing instead. Servers don't support clean shutdown.

are you using nvidia-runtime?

IB is not tested with tf-operator. Maybe you should use ethernet

Yes  I using nvidia-container-runtime=2.0.0+docker18.09.7-3
Sorry, where show IB is not tested with tf-operator
My Kubernetes is built locally
What should I set to use the Internet?

Can you show us the code?

office code
"""An example of multi-worker training with Keras model using Strategy API."""

from __future__ import absolute_import, division, print_function

import argparse
import json
import os

import tensorflow as tf
import tensorflow_datasets as tfds
from tensorflow.keras import datasets, layers, models

def make_datasets_unbatched():
  BUFFER_SIZE = 10000

  # Scaling MNIST data from (0, 255] to (0., 1.]
  def scale(image, label):
    image = tf.cast(image, tf.float32)
    image /= 255
    return image, label

  datasets, info = tfds.load(name='mnist', with_info=True, as_supervised=True)

  return datasets['train'].map(scale).cache().shuffle(BUFFER_SIZE)

def build_and_compile_cnn_model():
  model = models.Sequential()
  model.add(
    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
  model.add(layers.MaxPooling2D((2, 2)))
  model.add(layers.Conv2D(64, (3, 3), activation='relu'))
  model.add(layers.MaxPooling2D((2, 2)))
  model.add(layers.Conv2D(64, (3, 3), activation='relu'))
  model.add(layers.Flatten())
  model.add(layers.Dense(64, activation='relu'))
  model.add(layers.Dense(10, activation='softmax'))

  model.summary()

  model.compile(optimizer='adam',
                loss='sparse_categorical_crossentropy',
                metrics=['accuracy'])

  return model

def decay(epoch):
  if epoch < 3:
    return 1e-3
  elif epoch >= 3 and epoch < 7:
    return 1e-4
  else:
    return 1e-5

def main(args):

  # MultiWorkerMirroredStrategy creates copies of all variables in the model's
  # layers on each device across all workers
  # if your GPUs don't support NCCL, replace "communication" with another
  strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy(
    communication=tf.distribute.experimental.CollectiveCommunication.NCCL)

  BATCH_SIZE_PER_REPLICA = 64
  BATCH_SIZE = BATCH_SIZE_PER_REPLICA * strategy.num_replicas_in_sync

  with strategy.scope():
    ds_train = make_datasets_unbatched().batch(BATCH_SIZE).repeat()
    options = tf.data.Options()
    options.experimental_distribute.auto_shard_policy = \
                                        tf.data.experimental.AutoShardPolicy.DATA
    ds_train = ds_train.with_options(options)
    # Model building/compiling need to be within `strategy.scope()`.
    multi_worker_model = build_and_compile_cnn_model()

  # Define the checkpoint directory to store the checkpoints
  checkpoint_dir = args.checkpoint_dir

  # Name of the checkpoint files
  checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt_{epoch}")

  # Function for decaying the learning rate.
  # You can define any decay function you need.
  # Callback for printing the LR at the end of each epoch.
  class PrintLR(tf.keras.callbacks.Callback):

    def on_epoch_end(self, epoch, logs=None):
      print('\nLearning rate for epoch {} is {}'.format(
        epoch + 1, multi_worker_model.optimizer.lr.numpy()))

  callbacks = [
    tf.keras.callbacks.TensorBoard(log_dir='./logs'),
    tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_prefix,
                                       save_weights_only=True),
    tf.keras.callbacks.LearningRateScheduler(decay),
    PrintLR()
  ]

  # Keras' `model.fit()` trains the model with specified number of epochs and
  # number of steps per epoch. Note that the numbers here are for demonstration
  # purposes only and may not sufficiently produce a model with good quality.
  multi_worker_model.fit(ds_train,
                         epochs=10,
                         steps_per_epoch=70,
                         callbacks=callbacks)

  # Saving a model
  # Let `is_chief` be a utility function that inspects the cluster spec and
  # current task type and returns True if the worker is the chief and False
  # otherwise.
  def is_chief():
    return (TASK_INDEX == 0)

  if is_chief():
    model_path = args.saved_model_dir

  else:
    # Save to a path that is unique across workers.
    model_path = args.saved_model_dir + '/worker_tmp_' + str(TASK_INDEX)

  multi_worker_model.save(model_path)

if __name__ == '__main__':
  os.environ['NCCL_DEBUG'] = 'INFO'

  tfds.disable_progress_bar()

  # to decide if a worker is chief, get TASK_INDEX in Cluster info
  tf_config = json.loads(os.environ.get('TF_CONFIG') or '{}')
  TASK_INDEX = tf_config['task']['index']

  parser = argparse.ArgumentParser()
  parser.add_argument('--saved_model_dir',
                      type=str,
                      required=True,
                      help='Tensorflow export directory.')

  parser.add_argument('--checkpoint_dir',
                      type=str,
                      required=True,
                      help='Tensorflow checkpoint directory.')

  args = parser.parse_args()
  main(args)

I think it may be caused by
# if your GPUs don't support NCCL, replace "communication" with another
  strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy(
    communication=tf.distribute.experimental.CollectiveCommunication.NCCL)

My nvidia GPU should be able to use NCCL, I don't know why this is not working
I clean that
strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy()

and it same situation

Try to use RING and show the new log, thanks.

code
import argparse
import json
import os

import tensorflow as tf
import tensorflow_datasets as tfds
from tensorflow.keras import datasets, layers, models

def make_datasets_unbatched():
  BUFFER_SIZE = 10000

  # Scaling MNIST data from (0, 255] to (0., 1.]
  def scale(image, label):
    image = tf.cast(image, tf.float32)
    image /= 255
    return image, label

  datasets, info = tfds.load(name='mnist', with_info=True, as_supervised=True)

  return datasets['train'].map(scale).cache().shuffle(BUFFER_SIZE)

def build_and_compile_cnn_model():
  model = models.Sequential()
  model.add(
    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
  model.add(layers.MaxPooling2D((2, 2)))
  model.add(layers.Conv2D(64, (3, 3), activation='relu'))
  model.add(layers.MaxPooling2D((2, 2)))
  model.add(layers.Conv2D(64, (3, 3), activation='relu'))
  model.add(layers.Flatten())
  model.add(layers.Dense(64, activation='relu'))
  model.add(layers.Dense(10, activation='softmax'))

  model.summary()

  model.compile(optimizer='adam',
                loss='sparse_categorical_crossentropy',
                metrics=['accuracy'])

  return model

def decay(epoch):
  if epoch < 3:
    return 1e-3
  elif epoch >= 3 and epoch < 7:
    return 1e-4
  else:
    return 1e-5

def main(args):

  # MultiWorkerMirroredStrategy creates copies of all variables in the model's
  # layers on each device across all workers
  # if your GPUs don't support NCCL, replace "communication" with another
  strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy(
    communication=tf.distribute.experimental.CollectiveCommunication.RING)

  BATCH_SIZE_PER_REPLICA = 64
  BATCH_SIZE = BATCH_SIZE_PER_REPLICA * strategy.num_replicas_in_sync

  with strategy.scope():
    ds_train = make_datasets_unbatched().batch(BATCH_SIZE).repeat()
    options = tf.data.Options()
    options.experimental_distribute.auto_shard_policy = \
                                        tf.data.experimental.AutoShardPolicy.DATA
    ds_train = ds_train.with_options(options)
    # Model building/compiling need to be within `strategy.scope()`.
    multi_worker_model = build_and_compile_cnn_model()

  # Define the checkpoint directory to store the checkpoints
  checkpoint_dir = args.checkpoint_dir

  # Name of the checkpoint files
  checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt_{epoch}")

  # Function for decaying the learning rate.
  # You can define any decay function you need.
  # Callback for printing the LR at the end of each epoch.
  class PrintLR(tf.keras.callbacks.Callback):

    def on_epoch_end(self, epoch, logs=None):
      print('\nLearning rate for epoch {} is {}'.format(
        epoch + 1, multi_worker_model.optimizer.lr.numpy()))

  callbacks = [
    tf.keras.callbacks.TensorBoard(log_dir='./logs'),
    tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_prefix,
                                       save_weights_only=True),
    tf.keras.callbacks.LearningRateScheduler(decay),
    PrintLR()
  ]

  # Keras' `model.fit()` trains the model with specified number of epochs and
  # number of steps per epoch. Note that the numbers here are for demonstration
  # purposes only and may not sufficiently produce a model with good quality.
  multi_worker_model.fit(ds_train,
                         epochs=10,
                         steps_per_epoch=70,
                         callbacks=callbacks)

  # Saving a model
  # Let `is_chief` be a utility function that inspects the cluster spec and
  # current task type and returns True if the worker is the chief and False
  # otherwise.
  def is_chief():
    return (TASK_INDEX == 0)

  if is_chief():
    model_path = args.saved_model_dir

  else:
    # Save to a path that is unique across workers.
    model_path = args.saved_model_dir + '/worker_tmp_' + str(TASK_INDEX)

  multi_worker_model.save(model_path)

if __name__ == '__main__':
  os.environ['NCCL_DEBUG'] = 'INFO'

  tfds.disable_progress_bar()

  # to decide if a worker is chief, get TASK_INDEX in Cluster info
  tf_config = json.loads(os.environ.get('TF_CONFIG') or '{}')
  TASK_INDEX = tf_config['task']['index']

  parser = argparse.ArgumentParser()
  parser.add_argument('--saved_model_dir',
                      type=str,
                      required=True,
                      help='Tensorflow export directory.')

  parser.add_argument('--checkpoint_dir',
                      type=str,
                      required=True,
                      help='Tensorflow checkpoint directory.')

  args = parser.parse_args()
  main(args)

Pod logs
2020-06-08 05:28:41.691754: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libnvinfer.so.6
2020-06-08 05:28:41.699445: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libnvinfer_plugin.so.6
2020-06-08 05:28:43.429191: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2020-06-08 05:28:43.443933: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties:
pciBusID: 0000:05:00.0 name: GeForce GTX 1070 computeCapability: 6.1
coreClock: 1.7715GHz coreCount: 15 deviceMemorySize: 7.93GiB deviceMemoryBandwidth: 238.66GiB/s
2020-06-08 05:28:43.443975: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-06-08 05:28:43.444009: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2020-06-08 05:28:43.453214: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2020-06-08 05:28:43.454666: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2020-06-08 05:28:43.459652: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2020-06-08 05:28:43.462783: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2020-06-08 05:28:43.462898: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-06-08 05:28:43.465029: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0
2020-06-08 05:28:43.465817: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2020-06-08 05:28:43.499084: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency:<PHONE_NUMBER> Hz
2020-06-08 05:28:43.500562: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x437d190 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-06-08 05:28:43.500596: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2020-06-08 05:28:43.719362: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x4d32810 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2020-06-08 05:28:43.719397: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): GeForce GTX 1070, Compute Capability 6.1
2020-06-08 05:28:43.720061: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties:
pciBusID: 0000:05:00.0 name: GeForce GTX 1070 computeCapability: 6.1
coreClock: 1.7715GHz coreCount: 15 deviceMemorySize: 7.93GiB deviceMemoryBandwidth: 238.66GiB/s
2020-06-08 05:28:43.720101: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-06-08 05:28:43.720114: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2020-06-08 05:28:43.720137: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2020-06-08 05:28:43.720151: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2020-06-08 05:28:43.720163: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2020-06-08 05:28:43.720176: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2020-06-08 05:28:43.720186: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-06-08 05:28:43.721015: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0
2020-06-08 05:28:43.721050: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-06-08 05:28:44.856742: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1096] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-06-08 05:28:44.856780: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102]      0
2020-06-08 05:28:44.856788: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] 0:   N
2020-06-08 05:28:44.858089: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1241] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 2114 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1070, pci bus id: 0000:05:00.0, compute capability: 6.1)
2020-06-08 05:28:44.862250: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties:
pciBusID: 0000:05:00.0 name: GeForce GTX 1070 computeCapability: 6.1
coreClock: 1.7715GHz coreCount: 15 deviceMemorySize: 7.93GiB deviceMemoryBandwidth: 238.66GiB/s
2020-06-08 05:28:44.862304: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-06-08 05:28:44.862319: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2020-06-08 05:28:44.862338: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2020-06-08 05:28:44.862358: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2020-06-08 05:28:44.862373: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2020-06-08 05:28:44.862390: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2020-06-08 05:28:44.862404: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-06-08 05:28:44.863625: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0
2020-06-08 05:28:44.863670: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1096] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-06-08 05:28:44.863689: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102]      0
2020-06-08 05:28:44.863703: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] 0:   N
2020-06-08 05:28:44.864763: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1241] Created TensorFlow device (/job:worker/replica:0/task:0/device:GPU:0 with 2114 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1070, pci bus id: 0000:05:00.0, compute capability: 6.1)
2020-06-08 05:28:44.878685: I tensorflow/core/distributed_runtime/rpc/grpc_channel.cc:300] Initialize GrpcChannelCache for job worker -> {0 -> localhost:2222, 1 -> multi-worker-worker-1.kubeflow.svc:2222}
2020-06-08 05:28:44.879767: I tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc:390] Started server with target: grpc://localhost:2222
multi-worker-worker-0:1:195 [0] NCCL INFO NET/Socket : Using [0]eth0:<IP_ADDRESS><0>
multi-worker-worker-0:1:195 [0] NCCL INFO NET/Plugin : No plugin found (libnccl-net.so).

multi-worker-worker-0:1:195 [0] external/nccl_archive/src/misc/ibvwrap.cc:63 NCCL WARN Failed to open libibverbs.so[.1]
WARNING:absl:Dataset mnist is hosted on GCS. It will automatically be downloaded to your
local data directory. If you'd instead prefer to read directly from our public
GCS bucket (recommended if you're running on GCP), you can instead pass
`try_gcs=True` to `tfds.load` or set `data_dir=gs://tfds-data/datasets`.

WARNING:tensorflow:`eval_fn` is not passed in. The `worker_fn` will be used if an "evaluator" task exists in the cluster.
WARNING:tensorflow:`eval_fn` is not passed in. The `worker_fn` will be used if an "evaluator" task exists in the cluster.
WARNING:tensorflow:`eval_strategy` is not passed in. No distribution strategy will be used for evaluation.
WARNING:tensorflow:`eval_strategy` is not passed in. No distribution strategy will be used for evaluation.
2020-06-08 05:28:56.056109: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2020-06-08 05:28:57.943216: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-06-08 05:29:01.939470: I tensorflow/core/profiler/lib/profiler_session.cc:225] Profiler session started.
2020-06-08 05:29:01.939527: I tensorflow/core/profiler/internal/gpu/cupti_tracer.cc:1259] Profiler found 1 GPUs
2020-06-08 05:29:01.940754: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcupti.so.10.1
2020-06-08 05:29:02.041189: E tensorflow/core/profiler/internal/gpu/cupti_tracer.cc:1307] function cupti_interface_->Subscribe( &subscriber_, (CUpti_CallbackFunc)ApiCallback, this)failed with error CUPTI_ERROR_INSUFFICIENT_PRIVILEGES
2020-06-08 05:29:02.041943: E tensorflow/core/profiler/internal/gpu/cupti_tracer.cc:1346] function cupti_interface_->ActivityRegisterCallbacks( AllocCuptiActivityBuffer, FreeCuptiActivityBuffer)failed with error CUPTI_ERROR_INSUFFICIENT_PRIVILEGES
Downloading and preparing dataset mnist/3.0.1 (download: 11.06 MiB, generated: 21.00 MiB, total: 32.06 MiB) to /root/tensorflow_datasets/mnist/3.0.1...
Dataset mnist downloaded and prepared to /root/tensorflow_datasets/mnist/3.0.1. Subsequent calls will reuse this data.
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
conv2d (Conv2D)              (None, 26, 26, 32)        320
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 13, 13, 32)        0
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 11, 11, 64)        18496
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 5, 5, 64)          0
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 3, 3, 64)          36928
_________________________________________________________________
flatten (Flatten)            (None, 576)               0
_________________________________________________________________
dense (Dense)                (None, 64)                36928
_________________________________________________________________
dense_1 (Dense)              (None, 10)                650
=================================================================
Total params: 93,322
Trainable params: 93,322
Non-trainable params: 0
_________________________________________________________________
Train for 70 steps
Epoch 1/10
2020-06-08 05:29:02.141818: E tensorflow/core/profiler/internal/gpu/cupti_tracer.cc:1329] function cupti_interface_->EnableCallback( 0 , subscriber_, CUPTI_CB_DOMAIN_DRIVER_API, cbid)failed with error CUPTI_ERROR_INVALID_PARAMETER
2020-06-08 05:29:02.141878: I tensorflow/core/profiler/internal/gpu/device_tracer.cc:88]  GpuTracer has collected 0 callback api events and 0 activity events.
69/70 [============================>.] - ETA: 0s - loss: 0.9294 - accuracy: 0.7225
Learning rate for epoch 1 is 0.0010000000474974513
70/70 [==============================] - 17s 239ms/step - loss: 0.9222 - accuracy: 0.7246
Epoch 2/10
69/70 [============================>.] - ETA: 0s - loss: 0.2402 - accuracy: 0.9264
Learning rate for epoch 2 is 0.0010000000474974513
70/70 [==============================] - 7s 107ms/step - loss: 0.2405 - accuracy: 0.9260
Epoch 3/10
69/70 [============================>.] - ETA: 0s - loss: 0.1641 - accuracy: 0.9476
Learning rate for epoch 3 is 0.0010000000474974513
70/70 [==============================] - 7s 107ms/step - loss: 0.1628 - accuracy: 0.9481
Epoch 4/10
69/70 [============================>.] - ETA: 0s - loss: 0.1222 - accuracy: 0.9624
Learning rate for epoch 4 is 9.999999747378752e-05
70/70 [==============================] - 8s 109ms/step - loss: 0.1225 - accuracy: 0.9624
Epoch 5/10
69/70 [============================>.] - ETA: 0s - loss: 0.1166 - accuracy: 0.9649
Learning rate for epoch 5 is 9.999999747378752e-05
70/70 [==============================] - 7s 100ms/step - loss: 0.1170 - accuracy: 0.9645
Epoch 6/10
68/70 [============================>.] - ETA: 0s - loss: 0.1075 - accuracy: 0.9686
Learning rate for epoch 6 is 9.999999747378752e-05
70/70 [==============================] - 7s 102ms/step - loss: 0.1074 - accuracy: 0.9688
Epoch 7/10
69/70 [============================>.] - ETA: 0s - loss: 0.1058 - accuracy: 0.9698
Learning rate for epoch 7 is 9.999999747378752e-05
70/70 [==============================] - 8s 117ms/step - loss: 0.1064 - accuracy: 0.9695
Epoch 8/10
69/70 [============================>.] - ETA: 0s - loss: 0.0904 - accuracy: 0.9741
Learning rate for epoch 8 is 9.999999747378752e-06
70/70 [==============================] - 7s 103ms/step - loss: 0.0907 - accuracy: 0.9739
Epoch 9/10
69/70 [============================>.] - ETA: 0s - loss: 0.0933 - accuracy: 0.9701
Learning rate for epoch 9 is 9.999999747378752e-06
70/70 [==============================] - 7s 98ms/step - loss: 0.0934 - accuracy: 0.9700
Epoch 10/10
69/70 [============================>.] - ETA: 0s - loss: 0.0862 - accuracy: 0.9735
Learning rate for epoch 10 is 9.999999747378752e-06
70/70 [==============================] - 7s 99ms/step - loss: 0.0863 - accuracy: 0.9735
2020-06-08 05:30:15.889706: W tensorflow/core/kernels/data/generator_dataset_op.cc:103] Error occurred when finalizing GeneratorDataset iterator: Cancelled: Operation was cancelled
2020-06-08 05:30:16.353099: W tensorflow/python/util/util.cc:319] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1786: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version
`

OK, will dive into it. But seems that it does not affect the training process, right?

It still S NCCL WARN Failed to open libibverbs.so[.1]
But seems that it does not affect the training process, right?

yes

This is offical docker file
FROM tensorflow/tensorflow:2.1.0-gpu-py3

RUN pip install tensorflow_datasets

COPY multi_worker_strategy-with-keras.py /
ENTRYPOINT ["python", "/multi_worker_strategy-with-keras.py", "--saved_model_dir", "/train/saved_model/", "--checkpoint_dir", "/train/checkpoint"]

May I ack  did tensorflow/tensorflow:2.1.0-gpu-py3 have NCCL ?
Why I can not use NCCL ?

not sure if you can use NCCL or not. Just to avoid problems about it.

It traing but it still one gpu using
Why...

Hi, did you solve the problem?


update poc interval

please don't merge yet, may need to update the poc interval to 360. standing by for chain gang

updated, ready to merge

Can this be pulled from the api directly rather than hardcoded in the app in the future?
% http -b GET https://api.helium.io/v1/vars/poc_challenge_interval
{
    "data": 360
}


Ability to move cursor to a certain window

Is there a function I can call to move my cursor to the Scopes window? Or any of the debug windows?

There is nothing like that builit-in but there are many plugins to achieve this for windows in general (e.g. https://gitlab.com/yorickpeterse/nvim-window). Otherwise you could write a small helper function to jump between the dapui windows. Each one has a constant filetype and buffer name  so they are easy to interact with in scripts

Good point, I'll do that. Thanks and feel free to close this issue!


Improved map control zoom styling

Also fixed zoom in/out button styling which was broken in previous mapbox-gl-js update.
Before

After

Nice spot @orangemug - maybe use something different than the magnifying glass? I would assume that a lot of users would click on it because they would expect a place search functionality...
What about just Z or z?
Is the change in the styling of the compass (colors) intentional? I prefer how it was before.

maybe use something different than the magnifying glass? I would assume that a lot of users would click on it because they would expect a place search functionality...
What about just Z or z?

Good point @pathmapper, I guess we should probably see what iconography/conventions other editors use for this. Or maybe just zoom: rather than zoom level:?

Is the change in the styling of the compass (colors) intentional? I prefer how it was before.

Hmm it was intentional, I was just changing back to how it originally was pre mapbox-gl-js breaking the CSS overrides. However I think you're right the compass in the before image looks a little clearer. I might make the top arrow of the compass a little lighter somewhere between the before and after screenshots. It that ok?

Or maybe just zoom: rather than zoom level:?

Would be fine with me.
I assume your point is to shorten the label to make more of the map visible, which is a good idea.
In fact the zoom level doesn't need to be shown on the top right corner of the map, because it's also shown in the URL hash:

So I think it's also OK to remove it entirely from the map.
Instead we could also add the debugToolbox from the OL  to the GL JS renderer, which would be also good for consistency between the two renderers.

So the default would be to grasp the zoom level from the URL hash and if a user prefers the hash information on the map, the debugToolbox would fulfill this use case.
If we remove the zoom level from the top right corner, we should add the zoom level to the OL debugToolbox because of https://github.com/maputnik/editor/issues/255.

I might make the top arrow of the compass a little lighter somewhere between the before and after screenshots. It that ok?

Sure, my point is that the north part of the arrow is currently hard to spot.

I think it's worth having a UI element because not everybody will know to look in the URL bar, or which of those numbers represents the zoom. I think it's probably better to have it always displayed because it's one of the key bits of information for map styling, whereas center/rotation don't really come into play when styling. My preference is zoom: 2.05, are you happy with that @pathmapper ?
Also worth noting that the URL might not always be present. I have a local branch which makes Maputnik a desktop PWA https://developers.google.com/web/progressive-web-apps/desktop to it appears somewhat like a normal desktop app when 'installed'

Sure, my point is that the north part of the arrow is currently hard to spot.

Cool, yeah that's what I thought, I'll make some changes and push a new branch for review.

Are you happy with that @pathmapper ?

Absolutely

I have a local branch which makes Maputnik a desktop PWA

Sounds interesting, and yeah, no address bar :smile:

Updated with discussed changes.

Demo https://2255-84182601-gh.circle-artifacts.com/0/artifacts/build/index.html#0.25/0/0

With the demo the logo in the survey modal is missing:

Otherwise it looks good.

@pathmapper shall I remove that modal instead? At the moment it opens only for new users and it isn't very relevant for them. The idea when we first introduced it I believe was that old users would see it when they opened the app (once it was released).
Shall we just leave the button in the toolbar instead and remove the modal?

Shall we just leave the button in the toolbar instead and remove the modal?

Good idea @orangemug  :+1:

I've temporarily disabled the survey UI, after the next release it might be worth reenabling to popup after the 4-5th time using the app. As a result of possibly re-enabling it I've fixed the image issue highlighted also.

Merging!


Website does not have SASS version of 3.4 for download.  It's returning a 404

http://getbootstrap.com/docs/3.4/getting-started/#download has valid links for "Bootstrap" and "Source Code", but "Sass" is returning a 404 at https://codeload.github.com/twbs/bootstrap-sass/tar.gz/v3.4.0
Thanks!

There's no git tag v3.4.0 for bootstrap-sass. @glebm, I guess you already have that tag locally?
Ref: https://github.com/twbs/bootstrap-sass/releases

Also, twbs/bootstrap-sass#1180

I just added the tag, should be ok now


Glare can't compile with DirectX 12 SM6

Crash log:
[REDACTED]/Engine/Plugins/Runtime/PrettyPostProcess/Shaders/Glare.usf(): Shader FGlareGS, Permutation 0, VF None:
	Internal Compiler error: llvm::cast<X>() argument of incompatible type!

[REDACTED]/Engine/Plugins/Runtime/PrettyPostProcess/Shaders/Glare.usf(): Shader FGlareGS, Permutation 0, VF None:
	D3DCompileToDxil failed. Error code: Unknown error (0x80AA001D).

On a weird turn of event, the Glare shaders can compile in SM5 even with RT on, but can't compile in SM6 even with RT off.

I have met the same problem recently. Is there any solution?

I have met the same problem recently. Is there any solution?

Not yet. The error was too cryptic, and I haven't found way to debug the DXIL compiler.

Fixed for 5.3 per e954008
Finally the error become decipherable


No compatible EventSource for merged dl1 data products

meta description is "Merged DL1 Data Product" instead of "DL1 Data Product".

The DESCRIPTION field is meant to be human readable, and shouldn't be relied on to identify the file type.  That should be done checking the other fields like:

CTA PRODUCT DATA LEVEL == "DL1"
CTA PRODUCT DATA MODEL NAME == "ASWG DL1"
CTA PRODUCT DATA MODEL VERSION == a compatible version

The DESCRIPTION field is meant to be human readable, and shouldn't be relied on to identify the file type.  That should be done checking the other fields like:

CTA PRODUCT DATA LEVEL == "DL1"
CTA PRODUCT DATA MODEL NAME == "ASWG DL1"
CTA PRODUCT DATA MODEL VERSION == a compatible version

So checking for
if "CTA PRODUCT DATA LEVEL" not in metadata._v_attrnames:
                return False
if metadata["CTA PRODUCT DATA LEVEL"] != "DL1":
                return False

in DL1EventSource.is_compatible() would be better than
if "CTA PRODUCT DESCRIPTION" not in metadata._v_attrnames:
                return False
if metadata["CTA PRODUCT DESCRIPTION"] != "DL1 Data Product":
                return False

?

So checking for
if "CTA PRODUCT DATA LEVEL" not in metadata._v_attrnames:
                return False
if metadata["CTA PRODUCT DATA LEVEL"] != "DL1":
                return False

in DL1EventSource.is_compatible() would be better than
if "CTA PRODUCT DESCRIPTION" not in metadata._v_attrnames:
                return False
if metadata["CTA PRODUCT DESCRIPTION"] != "DL1 Data Product":
                return False

?

Yes, those fields have a list of allowed values, whereas the description does not

Yes, those fields have a list of allowed values, whereas the description does not


'Add to playlist' context menu scrolling broken

Describe the bug
Scrolling functionality in the Add to playlist context menu is missing or broken. Menu items can still be accessed with the arrow keys, but off-screen folders won't be visibly opened.
To Reproduce
Steps to reproduce the behavior:

Have enough playlists to require more space in the Add to playlist context menu than you have screen height
Right-click a song, Add to playlist, attempt to scroll down to the playlists at the bottom of the menu

I tried this without the Dribbblish theme/extension and it still works as expected.
Screenshots

You can see in the left sidebar that I have more playlists and folders below those in the context menu, but they're inaccessible (by mouse).

The menu items are still accessible with the arrow keys. Because this folder is still technically on the screen, the menu for it is opened in this screenshot, but the ones at the bottom of the menu would only be opened invisibly.
Adding overflow: auto to the containing data-tippy-root div's style re-enabled scrolling, but the menu's visible background and borders then scrolled too. I wanted to try changing the position, but it resets whenever the context menu comes into focus, and I don't know enough to know where to go from here.
Logs
vendor~xpui.js:29 MaxListenersExceededWarning: Possible EventEmitter memory leak detected. 11 change listeners added. Use emitter.setMaxListeners() to increase limit
    at c (https://xpui.app.spotify.com/vendor~xpui.js:29:2621881)
    at t.o.addListener (https://xpui.app.spotify.com/vendor~xpui.js:29:2624465)
    at https://xpui.app.spotify.com/vendor~xpui.js:29:2254849
    at Pa (https://xpui.app.spotify.com/vendor~xpui.js:29:2859683)
    at Is (https://xpui.app.spotify.com/vendor~xpui.js:29:2883415)
    at t.unstable_runWithPriority (https://xpui.app.spotify.com/vendor~xpui.js:29:3096998)
    at gr (https://xpui.app.spotify.com/vendor~xpui.js:29:2829219)
    at ks (https://xpui.app.spotify.com/vendor~xpui.js:29:2883194)
    at Os (https://xpui.app.spotify.com/vendor~xpui.js:29:2868073)
    at vs (https://xpui.app.spotify.com/vendor~xpui.js:29:2866995)
c @ vendor~xpui.js:29
2TypeError: Cannot read properties of null (reading 'text')
    at Info.set (Info.js:57)
    at main.js:843
tracklist-row-song-fallback.svg:1 Failed to load resource: net::ERR_NAME_NOT_RESOLVED
tracklist-row-song-fallback.svg:1 Failed to load resource: net::ERR_NAME_NOT_RESOLVED
tracklist-row-song-fallback.svg:1 Failed to load resource: net::ERR_NAME_NOT_RESOLVED
tracklist-row-song-fallback.svg:1 Failed to load resource: net::ERR_NAME_NOT_RESOLVED
tracklist-row-song-fallback.svg:1 Failed to load resource: net::ERR_NAME_NOT_RESOLVED
tracklist-row-song-fallback.svg:1 Failed to load resource: net::ERR_NAME_NOT_RESOLVED
tracklist-row-song-fallback.svg:1 Failed to load resource: net::ERR_NAME_NOT_RESOLVED
tracklist-row-song-fallback.svg:1 Failed to load resource: net::ERR_NAME_NOT_RESOLVED
tracklist-row-song-fallback.svg:1 Failed to load resource: net::ERR_NAME_NOT_RESOLVED
tracklist-row-song-fallback.svg:1 Failed to load resource: net::ERR_NAME_NOT_RESOLVED
tracklist-row-song-fallback.svg:1 Failed to load resource: net::ERR_NAME_NOT_RESOLVED
tracklist-row-song-fallback.svg:1 Failed to load resource: net::ERR_NAME_NOT_RESOLVED
tracklist-row-song-fallback.svg:1 Failed to load resource: net::ERR_NAME_NOT_RESOLVED
DevTools failed to load source map: Could not load content for https://xpui.app.spotify.com/vendor~xpui.js.map: Connection error: net::ERR_NAME_NOT_RESOLVED
DevTools failed to load source map: Could not load content for https://xpui.app.spotify.com/xpui.js.map: Connection error: net::ERR_NAME_NOT_RESOLVED
DevTools failed to load source map: Could not load content for https://xpui.app.spotify.com/696.js.map: Connection error: net::ERR_NAME_NOT_RESOLVED
DevTools failed to load source map: Could not load content for https://xpui.app.spotify.com/367.js.map: Connection error: net::ERR_NAME_NOT_RESOLVED
DevTools failed to load source map: Could not load content for https://xpui.app.spotify.com/xpui-desktop-modals.js.map: Connection error: net::ERR_NAME_NOT_RESOLVED
DevTools failed to load source map: Could not load content for https://xpui.app.spotify.com/xpui-routes-playlist.js.map: Connection error: net::ERR_NAME_NOT_RESOLVED

Info for Contributors:
Versions
OS: Windows v10.0.19042
Spotify: v<IP_ADDRESS>9
Spicetify: v2.8.1
Dribbblish: v4.0.0-8ba60b4
Extensions

autoSkipVideo.js
shuffle+.js
dribbblish-dynamic.js

Settings
{
    "EXPORTED_WITH": "4.0.0",
    "openSettingsInfo": true,
    "rightBigCover": true,
    "roundSidebarIcons": false,
    "sidebarHoverAnimation": true,
    "sidebarGapLeft": 0,
    "sidebarGapRight": 23,
    "fadeDuration": 0.5,
    "colorSelectionAlgorithm": "vibrant",
    "colorOverride": "#1ed760",
    "colorSelectionMode": "default",
    "lightModeLuminance": 0.6,
    "darkModeLuminance": 0.2,
    "theme": "time",
    "darkModeOnTime": "18:15",
    "darkModeOffTime": "07:00",
    "playbarShadow": false,
    "winTopBar": "none",
    "playerControlsStyle": "dribbblish",
    "showAlbumInfoInPlaybar": false,
    "hideAds": false
}

Do you have the same issue with original dribbblish theme?

Nope, context menu scrolling works with current_theme = Dribbblish, even if I leave dribbblish-dynamic.js in the extensions list.
With Dribbblish as the theme and without dribbblish-dynamic.js in the extensions list:

With Dribbblish as the theme and dribbblish-dynamic.js in the extensions list (weird screenshot because the Dribbblish Settings menu is stuck open and I can only get to a track context menu from the expanded cover image):

This isn't a huge issue since I can work around the problem by dragging and dropping tracks into playlists via the sidebar, but I figured it was worth mentioning since it seems to be a regression.

Ok so the problem is clearly created from these 2 lines

https://github.com/JulienMaille/dribbblish-dynamic-theme/blame/c2d7f851b1fff62377394ae2b6e9586964be9d68/src/styles/ContextMenu.scss#L3-L4


fix: leaderboard exports

This PR fixes an issue where leaderboard types weren't exported properly.

Successfully published:
 -<EMAIL_ADDRESS>


thunderbird-{,bin-}unwrapped: 102.3.2 -> 102.3.3

https://www.thunderbird.net/en-US/thunderbird/102.3.3/releasenotes/
Description of changes

Things done

Built on platform(s)

[ ] x86_64-linux
[ ] aarch64-linux
[ ] x86_64-darwin
[ ] aarch64-darwin

[ ] For non-Linux: Is sandbox = true set in nix.conf? (See Nix manual)
[ ] Tested, as applicable:

NixOS test(s) (look inside nixos/tests)
and/or package tests
or, for functions and "core" functionality, tests in lib/tests or pkgs/test
made sure NixOS tests are linked to the relevant packages

[ ] Tested compilation of all packages that depend on this change using nix-shell -p nixpkgs-review --run "nixpkgs-review rev HEAD". Note: all changes have to be committed, also see nixpkgs-review usage
[ ] Tested basic functionality of all binary files (usually in ./result/bin/)
22.11 Release Notes (or backporting 22.05 Release notes)

[ ] (Package updates) Added a release notes entry if the change is major or breaking
[ ] (Module updates) Added a release notes entry if the change is significant
[ ] (Module addition) Added a release notes entry if adding a new NixOS module
[ ] (Release notes changes) Ran nixos/doc/manual/md-to-db.sh to update generated release notes

[x] Fits CONTRIBUTING.md.

Result of nixpkgs-review pr 196408 run on x86_64-linux 1

1 package built:

thunderbird-unwrapped (thunderbirdPackages.thunderbird ,thunderbirdPackages.thunderbird-102)

When I run nixpkgs-review  rev HEAD from the thunderbird branch, I get more packages built (not sure why this happens):
Result of nixpkgs-review run on x86_64-linux 1

5 packages built:

thunderbird
thunderbird-bin
thunderbird-bin-unwrapped
thunderbird-unwrapped
thunderbird-wayland

${thunderbird}/bin/thunderbird ran fine for me.


GM Single Wire 33k - cant send packets

Hi,
i have started using your code for GMLAN sniffing on the low speed 29bit bus, i have some codes i would like to send back however when i do it never seems to work or my whole car electronics will reset - is there something i'm missing to get this to work to send 29bit packets back to the car?
Thank you

Hello,
The first think I always recommend, is to try out the arduino CAN examples, top make sure that you can send and receive packets. This will also test that your connection and hardware is correctly set up. Just run the Supported PID example and see if it's able to retrieve information.


Wanted, more information when I try to upload a CSV containing an error

Currently when a CSV containing one or more errors is saved the following error message pops up:

This makes debugging the CSV pretty difficult. It would be very nice if some more feedback about the cause of the error was provided.

I'm thinking this is part of a larger problem. In a way I understand why we support CSV editing in another way I would find it way better if we could get rid of it. 😈  Because you use the CSV editor it means that there's something wrong with the profile matrix editor or some essential feature(s) missing. I would like to know why it's preferable to use the CSV as input over the profile matrix editor?
I know that the CSV; for instance; mallforms Floats and Integers to Strings which causes problems like the one's were fixing in #1517. Typo's are more easily made and unfortunately this issue might take up more time than it should. It could be that the CSV is parsed just fine but the underlying calculation doesn't know what to do with the input due to missing numbers, missing attributes, accidentally deleted rows, but most likely - and this is purely judging by the Airbrake errors of late - Floats and Integers (as in numbers) that are interpreted as text.
Also in line with current ideas to merge the Topology and the technology matrix together into a single set of data, the CSV might be of little use or might become to much of a burden to keep supporting.
These are my 2 cents on the issue.
Ping @ChaelKruip @antw

@DorinevanderVlies could you maybe send me a CSV that doesn't currently work on your machine?


Always Loading Open Sans Font

How can I remove the Open Sans from loading ?

I already search the whole package for : 'Open Sans', 'OpenSans', 'Sans' and i didn't find anything relevant to this issue.

It's not included in the template view-source:https://laravel-vue-spa.cretueusebiu.com

Sorry it was some extension in my browser.


Provide a solution to migrate from MySQL or MSSQL to PostgreSQL

Hyades will exclusively support PostgreSQL. Support for MySQL and MSSQL will be dropped.
The reasoning behind this decision is as follows:

Supporting multiple RDBMSes multiplies maintenance efforts, for both development and testing
Syntax, supported features, ANSI SQL coverage varies across RDBMSes
Each RDBMS behaves slightly different, in various annoying ways:

MySQL has a maximum key length for indexes, covering at most 191 characters when using unicode
MySQL and PostgreSQL have a JSON / JSONB column type, MSSQL merely has functions to process text as JSON
PostgreSQL is capable of returning data from INSERT and UPDATE statements, MySQL and MSSQL merely return the number of modified rows, or the IDs of modified rows at most

PostgreSQL is FOSS, widely supported, well understood
PostgreSQL is widely supported

Officially listed hosting services: https://www.postgresql.org/support/professional_hosting/
PostgreSQL-compatible:

Neon: https://neon.tech/
Supabase: https://supabase.com/database
YugaByte: https://www.yugabyte.com/
CockroachDB: https://www.cockroachlabs.com/

TODO (This list is not complete yet :))

Users of DT 4.x that use any of those RDBMSes will need to migrate, and we need to provide guidance on how to do it.
A while back I documented how this can be achieved using pgloader, moving from MySQL to PostgreSQL:
https://gist.github.com/nscuro/1d32c6099a8a82f98f737fffa936b416#file-instructions-md
Because we have introduced changes to the database schema since branching off from DT v4.x, using off-the-shelf solutions like pgloader is not an option anymore. We will need to write a custom tool that performs the migration.

Tagging as good first issue since it's a purely technical task that doesn't require too much knowledge of the code base.
This task involves the creation of a CLI tool that:

connects to a DT v4.x database (can be MSSQL or PostgreSQL, potentially H2)
connects to a new DT v5.x database
copies data from DT v4.x to DT v5.x, performing additional mappings as needed

The current database schema for PostgreSQL can be generated using the following script: https://github.com/DependencyTrack/hyades-apiserver/blob/main/dev/scripts/dbschema-generate.sh
The same can be done for DT v4.x with this script: https://github.com/DependencyTrack/dependency-track/blob/master/dev/scripts/dbschema-generate.sh

Could we update the schema in the readme ? It is missleading that we could still use mssql server for hyades


New users broken with pipeline 2 and implicit grant flow

This is due to the missing identities in the id_token

Fixed with release 3.2.15: https://github.com/auth0/wp-auth0/pull/275


Issues with dismissing a ModalControl immediately after dismissing a LoadingControl

Short description of the problem:
A ModalController does not dismiss its view when called immediately after dismissing a LoadingController. I.e., my Modal view is displaying; something calls a function which invokes a LoadingController to display progress, once that LoadingController is dismissed, the ModalController is dismissed programatically, but it does not actually dismiss.
I did have a similar issue a few weeks back which may well be related, but the opposite is happening this time: #7839.
What behavior are you expecting?
When the LoadingController is dismissed, I expect the ModalController to be dismissed as controlled programmatically.
Steps to reproduce:

Clone the following repo: https://github.com/kelvindart/ionic-loading/
git clone https://github.com/kelvindart/ionic-loading/ && cd ionic-loading
Run Ionic emulate using the iOS simulator: ionic emulate ios
Observe the Modal display, press one of the buttons and observe.

I have tried two mechanisms to get it to display:
let loader = this.loadCtrl.create({
  content: 'Loading...'
});

loader.onDidDismiss = () => {
  this.close();
};

loader.present();

setTimeout(() => {
  loader.dismiss();
}, 3000);

and:
loadAndDismissTwo() {
  let loader = this.loadCtrl.create({
    content: 'Loading...'
  });

  loader.present();

  setTimeout(() => {
    loader.dismiss();

    this.close();
  }, 3000);
}

Neither seem to work!
Other information: (e.g. stacktraces, related issues, suggestions how to fix, stackoverflow links, forum links, etc)
There's no Plunker template for 2.0.0-beta.11, so I created a project for you to clone and try out for convenience sake!
Which Ionic Version? 1.x or 2.x
2.0.0-beta.11
Plunker that shows an example of your issue
No Plunker - but please refer to the Github URL above
Run ionic info from terminal/cmd prompt:
Cordova CLI: 6.3.1
Ionic Framework Version: 2.0.0-beta.11
Ionic CLI Version: 2.0.0-beta.37
Ionic App Lib Version: 2.0.0-beta.20
ios-deploy version: Not installed
ios-sim version: 5.0.8
OS: Mac OS X El Capitan
Node Version: v4.5.0
Xcode version: Xcode 7.3.1 Build version 7D1014

+1

I've just noticed there's a syntax error in my code - durrr. I'm ever so sorry!
The following should NOT be assigned:
loader.onDidDismiss = () => {
  this.close();
};

Instead:
loader.onDidDismiss(() => {
  this.close();
});

And it's working perfectly!


Docment vision.augment Affine Tfms to the end

Docment for this issue: https://github.com/muellerzr/fastai-docment-sprint/issues/29 . From Affine Tfms to the end

I accidentally make this PR based on this PR https://github.com/muellerzr/fastai-docment-sprint/pull/89  which hasn't been finished yet. Then it creates a big big PR. Hope we can step by step merging it

Thanks for your detailed suggestion @warner-benjamin . Update a new commit with your suggestions.

@dienhoa Did you do anything that might cause no_random to make this notebook non-deterministic for the no-random wrapped pieces? When I run the code I get the same results as the notebook so just wondering.

@dienhoa Did you do anything that might cause no_random to make this notebook non-deterministic for the no-random wrapped pieces? When I run the code I get the same results as the notebook so just wondering. I think that could be a bug in no_random.

I'm not sure but do we need to, after experimenting, restart and run all the notebooks? I usually play around with the notebook and once finish, I run nbdev_build_lib -> 'nbdev_build_doc' -> 'nbdev_clean_nbs'. Is play a cell multiple times create a different version of output?

@dienhoa no_random should always return the same result, even if you run the cell multiple times, which is why I'm curious.

I can not make no_random reproduce the same results as in master. I revert the output in my last commit to the output of master by VSCode.
There are some images that, visually I found no differences but notebook_review_app still doesn't like it.


inconsistencies in flags between --cluster and --cluster-name

Concern:
Flags between subcommands of the rosa CLI are inconsistent.  For example, the rosa create cluster command takes in --cluster-name as the flag input to specify the cluster name  However, most other commands take in --cluster as the flag input to specify the cluster name.
rosa create cluster -h:
-c, --cluster-name string                   Name of the cluster. This will be used when generating a sub-domain for your cluster on openshiftapps.com.

rosa create admin -h:
-c, --cluster string    Name or ID of the cluster.

Proposal:
Info

Most subcommands use --cluster as the input flag
The create cluster subcommand has special information notating that the cluster-name is used as part of the subdomain

Proposed Solution

Mark the --cluster-name flag as deprecated (https://github.com/openshift/rosa/blob/master/cmd/create/cluster/cmd.go#L180) much like the --name flag has been marked as deprecated (https://github.com/openshift/rosa/blob/master/cmd/create/cluster/cmd.go#L179).
Use --cluster as the flag for input for all subcommands that use --cluster-name.  This will make all subcommands use consistent flags.
Optionally change the AddClusterFlag method (https://github.com/openshift/rosa/blob/master/pkg/ocm/flag.go#L41) to something similar to this and use where necessary (e.g. create/delete/etc subcommands) to reduce duplication of code:

func AddClusterFlag(cmd *cobra.Command) {
        AddClusterFlagWithDescription(
                cmd,
                "Name or ID of the cluster.",
                &clusterKey,
        )
}

func AddClusterFlagWithDescription(cmd *cobra.Command, description string, key *string) {
	cmd.Flags().StringVarP(
		key,
		"cluster",
		"c",
		"",
		description,
	)
	cmd.MarkFlagRequired("cluster")
	cmd.RegisterFlagCompletionFunc("cluster", clusterCompletion)
}

https://github.com/openshift/rosa/issues/327


Fixes for device uplink counters

Fixing devices_test: the most important test had stopped working due to short-circuiting if err is not nil.
Getting uplink stats from the new place where they exist, rather than the now-nonexistent uplink prefix.

LGTM


Add shortcut for writing http backend mocks

Something like
http: {
  get: {
    '/foo/bar': 'baz'
  }
}

Will we be able to supply functions to enable using json spec dependencies? Or an alternative syntax?

I agree, but I am not sure what is the best syntax. I am thinking maybe even functions with injections or parameters routing
http: {
  get: {
    '/foo/:id': function (id) {
       // return data or promise
       return { hi: id };
    }
}

Added 'GET' support, need to add 'POST'


Fix for audit_log ForeignKey on custom AUTH_USER_MODEL.

We were getting an error when using "audit_log = AuditLog()" on a custom user model (inheriting from django.contrib.auth.mdoels.AbstractBaseUser):
$ manage.py validate
CommandError: System check identified some issues:

ERRORS:
karaage.PersonAuditLogEntry.action_user: (fields.E300) Field defines a relation with model 'karaage.Person', which is either not installed, or is abstract.

System check identified 1 issue (2 silenced).

The quick and dirty fix was to compare the kwargs['related_name'] with the relation target (to) and use "self" instead.  We're open to alternate fixes...

This request I believe was closed by 529327db618d63dccbce6f682cb27ec99c6997af.


WIKI free edit

I can make any changes to wiki without being in libgdx group. Is it correct behavior? Someone bad guy can delete all wiki of the awesome libGDX or change links for advertisement. Please be aware!
I discovered this after I followed link in JSON page today. The link was bad. So I edit and write !BAD LINK!
Please keep wiki safe!

Would have been more helpful to just remove the link and surrounding text (which I've just done).
The wiki is editable by anyone with a github account so that people can contribute. Yes, there is a certain level of trust that goes along with that, but in general the benefits outweigh the risks.
Thanks for letting us know of the bad link.

Thank you for your quick detailed explanation. I hope people are not evil enough to spoil it. I will use your advice in future.


Added Vagrantfile for isolating PostgreSQL

We have resumed working on slack-to-trello. In the next iteration, we are exploring allowing support for supporting multiple Slack accounts and Trello accounts. This PR sets up Vagrant since we will need some persistence for this information.
In this PR:

Added Vagrant file
Added a bunch of setup scripts

/cc @brettlangdon

We are missing documentation on how to use Vagrant but that is currently due to this being in a proof of concept stage.

lgtm

Closing this PR for now as the follow-up PR is inactive.


Fabrishot 1.10.0 for MC 1.20.1 wrong version tag

Ah, fixed

Versions of dependencies are also incorrect, xd


This Domain Name Has Expired - Renewal Instructions.

Preview site (http://simplelineicons.com/) not available any more, since a few weeks now ...

+1

:+1:

Duplicate of #85

@neeltjechanterie I just checked and the domain name appears to be renewed. I'm going to close my initial request (#85).

#113


Adds and uses disableGen Type directive

This is literally my very first attempt at contributing to open source so please be gentle. :-)
Sorry for the changes reflected in removing trailing spaces. Must be an editor configuration. I didn't do that intentionally.
I did my best to follow the contribution guidelines and everything seemed to run just fine.
I spent an hour or more reasoning through the ticket and walking through code. I think I did the right thing.
I tried to break tests by setting "disableGen": true in empty-test.js but couldn't get the tests to fail so I probably need a nudge in the right direction to update tests.
If @disableGen is looking right then I'll try and replicate it for @rest.

Love it! Extra whitespaces are not problem. Those were actually off our standard formatter. From my side this looks like perfect contribution. I really cannot point anything to correct.
Thank you so much for contribution.

@ankitjena - WDYT?


Changes in spec: 3 of four

We want delete or deprecate "add_curator". After discussion, we agreed that sticking with Dublin Core would be best. They use the more generic "dc:contributor" for everyone who might edit (augment or change) a document.
Thus, we also want to add a method "add_contributor" which roughly does the same thing as add_curator did. However, this will be a regular model-level annotation, and thus the subject should be the metaid of the  tag in the source code (the old add_curator referenced the rdf file; we have agreed this is wrong or misleading).
add_contributor("0000-0002-2390-6572") should produce rdf like:
http://omex-library.org/MyOmexArchive.omex/MyModel.sbml#model0034
dc:contributor  https://orcid.org/0000-0002-2390-6572 .

Implemented in ef7513c5f3b48762fdec9bc3c61e6d8d022a7789


create variable processing for Pictoralist

Pictoralist needs to receive the following variables from Esteemer for email text population via message templates:

Measure name
Recipient performance level
= performance level as percentage with numerator and denominator, such as "90% (9/10)"
Comparator performance level
= performance level as percentage such as "90%"
Consecutive positive gaps duration - post pilot?
Consecutive negative gaps duration - post pilot?
[here (link to measure spec)] - insert as url
[clinical quality dashboard (link)] - insert as url

The message library spreadsheet is the source of truth for these variables: https://docs.google.com/spreadsheets/d/1h81ZmTqMWvWDcpLIr5IFW-AAmEiqQAPB4ZtyQtY87HQ/edit#gid=439820594

From spreadsheet, for quick reference:
[Not top performer message template]
"You are not a top performer this month for the measure [measure name]. Your performance was [recipient performance level], below the top 10% peer benchmark, which was [comparator performance level] this month.
A case-by-case breakdown of your results are available at your [clinical quality dashboard (link)]. More information about the rationale for the measure [measure name] and how it is calculated is available [here (link to measure spec)]."

Resolved with commit https://github.com/Display-Lab/precision-feedback-pipeline/pull/113/commits/5fa3efed76b2eb1c3f3d6b127856e443d48f51b9 to pull request #113

Have left post-pilot work for another day, and documented rationale on leaving the link insertion to the MPOG team in the changelog for V 0.2.0
Closing issue as resolved for now, can re-open if needed at team review tomorrow


Bump version to 3.4.0

multivariate-test.js: Google Content Experiments support

Hi @benilovj,
Now this change has been merged with master:
https://github.com/alphagov/govuk_frontend_toolkit/pull/176
The changelog needs to be rebased to include the changes here as part of 3.4.0
https://github.com/alphagov/govuk_frontend_toolkit/pull/178/files


xwayland: characters replaced by "1"s

I am just testing 0.4's xwayland support. Intermittently, some characters are replaced by "1"s.
swaymsg bindsym "Mod4+Shift+v" "exec wtype hooh"
Pasting into an xwayland electron window, every third time or so, i get h11h. Similar although less frequent in xterm. hi -> h1 as well. Vowels? IDK.

Spamming wtype by holding the binding results in a long chain of "1"s in xterm.


Polish the document and provide with newer number from latest testing

Provided newer test number, that qps is 315k/s instead of 300k/s
Added 2 more slides, showing the expectation against tier 1 task engine among the open source world

@xieus      could you please review?


[BUG] Unable to install latest pip python packages due to Prompt Flow SDK Docker base image.

Describe the bug
I want to access the latest version of Langchain in Prompt Flow. When I run langchain.__version__ it always defaults to the one specified in the Prompt Flow base image (langchain==0.0.236). Even when my docker file specifically selects a different version (RUN pip install langchain==0.0.300), it always go to the older version.
How To Reproduce the bug
Steps to reproduce the behavior, how frequent can you experience the bug:

DOCKER IMAGE

FROM mcr.microsoft.com/azureml/promptflow/promptflow-runtime-stable:latest

ENV APP_DIR=/code
ENV PYTHONUNBUFFERED 1
WORKDIR $APP_DIR
RUN apt-get update
COPY ./requirements.txt $APP_DIR/requirements.txt
RUN pip install --no-cache-dir --upgrade -r $APP_DIR/requirements.txt

RUN IN PROMPT FLOW UI

import langchain
print(langchain.__version__)

Expected behavior
output: 0.0.236
Running Information(please complete the following information):
mcr.microsoft.com/azureml/promptflow/promptflow-runtime-stable:latest

after killing the compute instance and creating a brand new one (because restarting it was not enough) it now works properly.

Cannot reproduce the bug with the dockerfile provided and the dockerfile
FROM mcr.microsoft.com/azureml/promptflow/promptflow-runtime-stable:latest RUN pip install langchain==0.0.300
They both return langchain version = 0.0.300 when run in promptflow UI


First character trimmed from output filename in screenshot command

Version information

fluent-ffmpeg version: 2.1.2
ffmpeg version: 4.2.2
OS: macos 10.14.6

Code to reproduce
const frame = `${filename}.jpg`;
console.log(filename); // /var/folders/_h/rr55lzjs4m15sk7b8n2rf1hr0000gn/T/d8489825dc85ff37ea9f25025431c31b71e94ec1c8551da0cebb699626179bf9
console.log(frame); // /var/folders/_h/rr55lzjs4m15sk7b8n2rf1hr0000gn/T/d8489825dc85ff37ea9f25025431c31b71e94ec1c8551da0cebb699626179bf9.jpg
return new Promise((resolve, reject) => {
    ffmpeg(filename)
        .on('start', info => console.log(info)) // ffmpeg -ss 1 -i /var/folders/_h/rr55lzjs4m15sk7b8n2rf1hr0000gn/T/d8489825dc85ff37ea9f25025431c31b71e94ec1c8551da0cebb699626179bf9 -y -filter_complex split=1[screen0] -vframes 1 -map [screen0] var/folders/_h/rr55lzjs4m15sk7b8n2rf1hr0000gn/T/d8489825dc85ff37ea9f25025431c31b71e94ec1c8551da0cebb699626179bf9.jpg
        .on('end', () => frame)
        .on('error', reject)
        .screenshots({
            timestamps: [1],
            filename: frame,
        });
});

Guessing this is the same issue as https://github.com/fluent-ffmpeg/node-fluent-ffmpeg/issues/564. Guessing this is because I did not specify folder but only filename and screenshots uses both, and trims leading slashes from filename.
(note: if the problem only happens with some inputs, include a link to such an input file)
Expected results
Filename should be as specified in filename option passed to screenshots
Observed results
Leading / is removed.
Checklist

[x] I have read the FAQ
[x] I tried the same with command line ffmpeg and it works correctly (hint: if the problem also happens this way, this is an ffmpeg problem and you're not reporting it to the right place)
[ ] I have included full stderr/stdout output from ffmpeg

Indeed, folder is required.


Crash: Outposts Build Menu

Details
When opening the build menu or the scanner at an outpost that has become bugged, the game will immediately hang every time. I've not managed to identify what causes an outpost to enter this state or any workarounds. Fast traveling to another location makes the outpost landing pad marker disappear.
Information that may or may not help:

This happened after building my third outpost and returning to my first outpost.
I've recently received an achievement stating that I've harvested 500 resources from outposts.
My first outpost has 4 extractors and a large landing pad.
Two crew members are assigned to the outpost, but unassigning them does not affect the crash.
Several people on Reddit and Steam are reporting the same issue.
PC Platform, Steam. Also reported on Xbox

Let me know if there are any other details I can provide.
Game Version
<IP_ADDRESS>
New Game
Unknown
Reproduction Steps
Unfortunately, I do not have reproduction steps in order to get the outpost bugged.

I can corroborate this one, and unfortunately don't know the steps to reproduce it. I've posted about it on Reddit, and indeed other people have encountered the same problem.
My post:
1.https://www.reddit.com/r/Starfield/comments/175a079/crash_when_opening_scanner_in_outposts/
Their posts:

https://www.reddit.com/r/Starfield/comments/16g0ljb/crashing_in_outpost_build_mode/
https://www.reddit.com/r/Starfield/comments/167nahr/starfield_crashing_when_accessing_outpost/
https://www.reddit.com/r/Starfield/comments/16urj8l/game_breaking_bug_repeat_crash_to_desktop_when/
https://www.reddit.com/r/Starfield/comments/16j07mt/scanner_crashing_game_at_outpost/

Closing this one as dated. I have not had this issue since launch. Will need more specific reproduction steps.


Inquiry for code to train baseline

Hi,
Is there code for training the baseline?
Best,
Jiahui.Li

Hi, @jimmylihui. Thanks for using MogaNet. This repository doesn't provide specific implementations of baselines. We recommend you refer to OpenMixup for our benchmarks of vision backbones on ImageNet, MMDetection and MMSegmentation for the object detection and segmentation tasks, MMPose for the 2D and 3D pose estimation tasks. Another way to benchmark backbone baselines is to train the model name in our training scripts, e.g., training DeiT-S with 8 GPUs as python -m torch.distributed.launch --nproc_per_node=8 --master_port=29501 train.py --model deit_small_patch16_224 --img_size 224 --drop_path 0.1 --batch_size 128 --lr 1e-3 --weight_decay 0.05 --update_freq 1. This repository mainly provides the pre-training and fine-tuning implementations of MogaNet, while you can simply modify the training script to train the relevant baselines.

Hi there, I will close this issue if there are no more questions. Feel free to contact us if you have new concerns.


Mobile app invalid webhook payload for vertical accuracy

So I had successfully installed 1.1.1 on a pixel 3 and pixel 4 with location working fine and no errors in my HA logs.  I then decided to install it on my Amazon Fire HD8 that has google services and the play store.  App installed and created a device tracker that did not update.  No errors were seen in the logs.  I then installed google maps on the tablet to see if a missing service was found and the following error shows up now.
2019-12-04 16:50:13 ERROR (MainThread) [homeassistant.components.mobile_app.webhook] Received invalid webhook payload: expected int for dictionary value @ data['vertical_accuracy']. Got None
This error shows up every few minutes so I suspect the tablet is attempting to send back data but it does not have vertical_accuracy to send back.
I know this is probably a stretch but I think this error should probably be fixed?

Not sure if we can fix that well in app.  Vertical accuracy depends on a certain version of Android and above.  We might need to enhance the mobile app integration to make it optional rather than mandatory.  Or we could hard code an accuracy for devices that don't support it.

@JBassett maybe just send back 0 if there is none?  I also wasn't sure where to file this bug.

@balloob just fixed this in mobile_app late last night at https://github.com/home-assistant/home-assistant/pull/29381. Will be in next version of HA, not the app.

I fixed allowing altitude to be negative. I did not allow vertical_accuracy to be None.
For vertical accuracy, in the Android app can we either send in 0 or not add the key if it's null ?

When will this be released?
I am noticing this same issue with an Samsung S6, while my Nokia 6 works just fine.
Received invalid webhook payload: expected int for dictionary value @ data['vertical_accuracy']. Got None

Has been released as 1.1.2

Received invalid webhook payload: required key not provided @ data['device_name']. Got None
I have this same problem with Samsung S7.  Mobile App version 1.7.2-128


composer asset plugin in require section ignores asset-installer-paths

I may have read it somewhere already, but i just want to be sure this wont work:
{
    "require": {
        "fxp/composer-asset-plugin" : "~1.1",
        "some_other_packges": " ...... "
    },
    "extra": {
        "asset-installer-paths": {
            "bower-asset-library": "vendor/bower"
        }
    }
}

Adding fxp/composer-asset-plugin in require section and define asset-installer-paths will ignore the extra options and copy the content to the default path "vendor/bower-asset"?

Extra section is only used by the root Composer package.


⚠ ClamAV has degraded performance

In 93d8e6b, ClamAV (https://spamassassin.apache.org/) experienced degraded performance:

HTTP code: 200
Response time: 101 ms

Resolved: ClamAV performance has improved in af5e858.


OperationalError: (_mysql_exceptions.OperationalError) (2006, 'MySQL server has gone away')

Hello I've been getting sporadic 500 errors and after some digging the apache error log has told me this:
root@server [~]# tail -f /usr/local/apache/logs/error_log
App 4986 stderr:   File "/home/admin/venv/lib/python2.6/site-packages/sqlalchemy/engine/base.py", line 1139, in _execute_context
App 4986 stderr:     context)
App 4986 stderr:   File "/home/admin/venv/lib/python2.6/site-packages/sqlalchemy/engine/default.py", line 442, in do_execute
App 4986 stderr:     cursor.execute(statement, parameters)
App 4986 stderr:   File "/home/admin/venv/lib/python2.6/site-packages/MySQLdb/cursors.py", line 205, in execute
App 4986 stderr:     self.errorhandler(self, exc, value)
App 4986 stderr:   File "/home/admin/venv/lib/python2.6/site-packages/MySQLdb/connections.py", line 36, in defaulterrorhandler
App 4986 stderr:     raise errorclass, errorvalue
App 4986 stderr: OperationalError: (_mysql_exceptions.OperationalError) (2006, 'MySQL server has gone away') [SQL: u'SELECT taxonomies.taxonomy_id AS taxonomies_taxonomy_id, taxonomies.taxonomy AS taxonomies_taxonomy, taxonomies.slug AS taxonomies_slug, taxonomies.parent AS taxonomies_parent, taxonomies.description AS taxonomies_description, taxonomies.taxonomy_picture AS taxonomies_taxonomy_picture \nFROM taxonomies']
App 24080 stdout:
Any suggestions on fixes? I've added these lines of code:
app.config['SQLALCHEMY_DATABASE_URI'] = 'myserverstuff'
app.config['SQLALCHEMY_POOL_SIZE'] = 100
app.config['SQLALCHEMY_POOL_RECYCLE'] = 3600
and still no luck.
Regards,
Itstoosteamy

I was having this problem on godaddy
godaddy's mysql interactive_timeout is set to 60 seconds.
I set SQLALCHEMY_POOL_RECYCLE to 50 seconds, per description on
http://pythonhosted.org/Flask-SQLAlchemy/config.html
On Fri, May 29, 2015 at 5:04 PM, itstoosteamy<EMAIL_ADDRESS>wrote:

Hello I've been getting sporadic 500 errors and after some digging the
apache error log has told me this:
root@server [~]# tail -f /usr/local/apache/logs/error_log
App 4986 stderr: File
"/home/admin/venv/lib/python2.6/site-packages/sqlalchemy/engine/base.py",
line 1139, in _execute_context
App 4986 stderr: context)
App 4986 stderr: File
"/home/admin/venv/lib/python2.6/site-packages/sqlalchemy/engine/default.py",
line 442, in do_execute
App 4986 stderr: cursor.execute(statement, parameters)
App 4986 stderr: File
"/home/admin/venv/lib/python2.6/site-packages/MySQLdb/cursors.py", line
205, in execute
App 4986 stderr: self.errorhandler(self, exc, value)
App 4986 stderr: File
"/home/admin/venv/lib/python2.6/site-packages/MySQLdb/connections.py", line
36, in defaulterrorhandler
App 4986 stderr: raise errorclass, errorvalue
App 4986 stderr: OperationalError: (_mysql_exceptions.OperationalError)
(2006, 'MySQL server has gone away') [SQL: u'SELECT taxonomies.taxonomy_id
AS taxonomies_taxonomy_id, taxonomies.taxonomy AS taxonomies_taxonomy,
taxonomies.slug AS taxonomies_slug, taxonomies.parent AS taxonomies_parent,
taxonomies.description AS taxonomies_description,
taxonomies.taxonomy_picture AS taxonomies_taxonomy_picture \nFROM
taxonomies']
App 24080 stdout:
Any suggestions on fixes? I've added these lines of code:
app.config['SQLALCHEMY_DATABASE_URI'] = 'myserverstuff'
app.config['SQLALCHEMY_POOL_SIZE'] = 100
app.config['SQLALCHEMY_POOL_RECYCLE'] = 3600
and still no luck.
Regards,
Itstoosteamy
—
Reply to this email directly or view it on GitHub
https://github.com/mitsuhiko/flask-sqlalchemy/issues/316.

This isn't an issue with the library, please make use of Stack Overflow (et al) for this kind of query.

My apologies. I noticed that after I posted.
Sent from my HTC
----- Reply message -----
From: "Phil Howell"<EMAIL_ADDRESS>To: "mitsuhiko/flask-sqlalchemy"<EMAIL_ADDRESS>Cc: "itstoosteamy"<EMAIL_ADDRESS>Subject: [flask-sqlalchemy] OperationalError: (_mysql_exceptions.OperationalError) (2006, 'MySQL server has gone away') (#316)
Date: Fri, May 29, 2015 4:31 PM
This isn't an issue with the library, please make use of Stack Overflow (et al) for this kind of query.
—
Reply to this email directly or view it on GitHub.


New --clean-reset and --request-state options to run worker in a single command

Allows us to run the worker in one command, no need to call it multiple
times to initialize and 'manually' do the file purging in the python script.

--clean-reset purges all files and initializes the shard and key files
--request-state, a new sub-command of run, will request the state from a peer worker (instead of having a separate call ./integritee-service request-state)
Do NOT check for the existence of the shielding key and signing key files at startup anymore. In case they're absent, they'll be initialized anyway.

Because we request state and keys now in a single run, we need a way to change the shielding key at runtime. This is done using the same mechanism as for the state key (aka key repository). So any components using a shielding key, access the key through a repository.
Closes #526

Good point about the documentation, I'll update the book and confluence 👍


On Android, baseline information is busted if line-height:normal

 Issue by Hixie
Thursday Jul 16, 2015 at 22:33 GMT
Originally opened as https://github.com/flutter/engine/issues/45

From @Hixie on June 23, 2015 22:54
See: https://code.google.com/p/chromium/issues/detail?id=503780
Copied from original issue: domokit/mojo#266

 Comment by Hixie
Thursday Jul 16, 2015 at 22:33 GMT

Maybe it's specific to the default sans-serif font.

 Comment by Hixie
Thursday Jul 16, 2015 at 22:33 GMT

(baseline.dart will demonstrate the problem once I've checked that in)

I can no longer reproduce this. Tried several devices, with Android versions 6.x and 5.x. Maybe it was just a problem with the fonts on older devices.


Refactor: 'confidence' to 'score'

This PR changes the naming from 'confidence' to 'score'. Closes #322
@dvsrepo Could you quickly approve the changes in the tutorials, i tried to replace all references to 'confidence' with either 'score' or 'prediction score'.
@frascuchon I think we still have to change the static json files in the server folder, right?
@leiyre To be honest i am not sure what has to be done for the web app to remove all 'confidence' references ... :)

I've made changes to frontend part with backward compatibility (allow to see current datasets).
@leiyre please verify i'm not missing something.
Thanks!


RSS Feed not working

如果反馈BUG，请提供以下信息，如果可能，同时提供英文描述，谢谢。

Transmssion 版本:
Tr Web Control 版本:
浏览器名称及版本：
问题描述:

For English User
Please provide the following information.

Transmssion version: 2.94
Tr Web Control version: 1.6.0 beta
Browser name & version：Chrome Mac Version 74.0.3729.169 (Official Build) (64-bit)
Problem description: I have added well over 8 different links from RSS feed provider (ShowRSS.info). They are combinations of different types of settings. None of them seem to work, it's not downloading in transmission.

I am using TerraMaster F2-220 Nas.
Attached are images:

Hey did you manage to get this working?


Google Sheet for collecting panel scores

This task follows on from #491 and focuses on exporting data to a google sheet.
'Starter for 10' code is available here:
https://github.com/jac-uk/digital-platform/blob/master/nodeScripts/google-sheets.js
Currently this reads content from the following google sheet and outputs it on the console:
https://docs.google.com/spreadsheets/d/1kOLipXEe657tKsreRbxc9uIC5x5TmOzLmpdAQONNsLA/edit#gid=0
To run the starter code run the following command from the root of the digital-platform project:
npm run nodeScript google-sheets.js

Your mission, should you choose to accept, is to update the starter script to write a few columns of data to the sheet.
Current empty sheet:

Expected updated sheet:

The following resource will be useful (as well as google search):
https://googleapis.dev/nodejs/googleapis/latest/docs/index.html

This investigative task is complete. We have a node script which populates a google sheet in digital-platform/nodeScripts/google-sheet.js
We will look at integrating this in Panel Packs Pt II epic