Tensorflow getting slower and slower

I am benchmarking tensorflow with the cifar10_train.py script from https://github.com/tensorflow/models (v.1.13.0)
Running for 100000 steps, in the first  190 steps
2020-06-01 03:26:25.877249: step 0, loss = 4.68 (387.6 examples/sec; 0.330 sec/batch)
2020-06-01 03:26:26.113249: step 10, loss = 4.60 (5423.4 examples/sec; 0.024 sec/batch)
2020-06-01 03:26:26.251981: step 20, loss = 4.53 (9227.0 examples/sec; 0.014 sec/batch)
2020-06-01 03:26:26.391191: step 30, loss = 4.36 (9194.3 examples/sec; 0.014 sec/batch)
2020-06-01 03:26:26.532910: step 40, loss = 4.52 (9032.0 examples/sec; 0.014 sec/batch)
2020-06-01 03:26:26.671122: step 50, loss = 4.30 (9261.1 examples/sec; 0.014 sec/batch)
2020-06-01 03:26:26.810607: step 60, loss = 4.22 (9176.7 examples/sec; 0.014 sec/batch)
2020-06-01 03:26:26.948641: step 70, loss = 4.18 (9273.1 examples/sec; 0.014 sec/batch)
2020-06-01 03:26:27.084231: step 80, loss = 4.16 (9440.1 examples/sec; 0.014 sec/batch)
2020-06-01 03:26:27.221754: step 90, loss = 4.20 (9307.6 examples/sec; 0.014 sec/batch)
INFO:tensorflow:global_step/sec: 59.5952
I0601 03:26:27.554355<PHONE_NUMBER>07392 basic_session_run_hooks.py:692] global_step/sec: 59.5952
2020-06-01 03:26:27.555495: step 100, loss = 4.20 (3835.3 examples/sec; 0.033 sec/batch)
2020-06-01 03:26:27.698553: step 110, loss = 4.02 (8947.4 examples/sec; 0.014 sec/batch)
2020-06-01 03:26:27.838008: step 120, loss = 3.89 (9178.6 examples/sec; 0.014 sec/batch)
2020-06-01 03:26:27.980759: step 130, loss = 4.19 (8966.7 examples/sec; 0.014 sec/batch)
2020-06-01 03:26:28.117689: step 140, loss = 3.95 (9347.8 examples/sec; 0.014 sec/batch)
2020-06-01 03:26:28.253787: step 150, loss = 3.97 (9405.1 examples/sec; 0.014 sec/batch)
2020-06-01 03:26:28.394747: step 160, loss = 3.93 (9080.5 examples/sec; 0.014 sec/batch)
2020-06-01 03:26:28.536241: step 170, loss = 3.99 (9046.3 examples/sec; 0.014 sec/batch)
2020-06-01 03:26:28.675036: step 180, loss = 3.81 (9222.2 examples/sec; 0.014 sec/batch)
2020-06-01 03:26:28.812280: step 190, loss = 3.95 (9326.5 examples/sec; 0.014 sec/batch)

then it progressively goes downhill.
in the last 200 steps;
2020-06-01 04:39:15.610507: step 99810, loss = 0.69 (2361.4 examples/sec; 0.054 sec/batch)
2020-06-01 04:39:16.214997: step 99820, loss = 0.60 (2117.5 examples/sec; 0.060 sec/batch)
2020-06-01 04:39:16.808370: step 99830, loss = 0.58 (2157.2 examples/sec; 0.059 sec/batch)
2020-06-01 04:39:17.416628: step 99840, loss = 0.74 (2104.4 examples/sec; 0.061 sec/batch)
2020-06-01 04:39:18.010663: step 99850, loss = 0.79 (2154.8 examples/sec; 0.059 sec/batch)
2020-06-01 04:39:18.617192: step 99860, loss = 0.67 (2110.4 examples/sec; 0.061 sec/batch)
2020-06-01 04:39:19.218318: step 99870, loss = 0.58 (2129.3 examples/sec; 0.060 sec/batch)
2020-06-01 04:39:19.815205: step 99880, loss = 0.57 (2144.5 examples/sec; 0.060 sec/batch)
2020-06-01 04:39:20.411179: step 99890, loss = 0.79 (2147.7 examples/sec; 0.060 sec/batch)
INFO:tensorflow:global_step/sec: 16.3652
I0601 04:39:21.177534<PHONE_NUMBER>67712 basic_session_run_hooks.py:692] global_step/sec: 16.3652
2020-06-01 04:39:21.178711: step 99900, loss = 0.71 (1667.6 examples/sec; 0.077 sec/batch)
2020-06-01 04:39:21.718429: step 99910, loss = 0.72 (2371.8 examples/sec; 0.054 sec/batch)
2020-06-01 04:39:22.323965: step 99920, loss = 0.77 (2113.8 examples/sec; 0.061 sec/batch)
2020-06-01 04:39:22.923835: step 99930, loss = 0.66 (2133.8 examples/sec; 0.060 sec/batch)
2020-06-01 04:39:23.516449: step 99940, loss = 0.59 (2159.9 examples/sec; 0.059 sec/batch)
2020-06-01 04:39:24.106146: step 99950, loss = 0.73 (2170.6 examples/sec; 0.059 sec/batch)
2020-06-01 04:39:24.705905: step 99960, loss = 0.54 (2134.2 examples/sec; 0.060 sec/batch)
2020-06-01 04:39:25.302246: step 99970, loss = 0.71 (2146.4 examples/sec; 0.060 sec/batch)
2020-06-01 04:39:25.897399: step 99980, loss = 0.63 (2150.7 examples/sec; 0.060 sec/batch)
2020-06-01 04:39:26.500119: step 99990, loss = 0.66 (2123.7 examples/sec; 0.060 sec/batch)

There is a steady drop in examples/sec
is this normal?
OS Ubuntu-16.04
Tensorflow 1.15.3 compiled from  source
python-3.7.6
tested for both cuda-10.0 and 10.1
Nvidia 418.56 driver
Not sure if  these warnings are relevant
WARNING:tensorflow:From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10_train.py:130: The name tf.app.run is deprecated. Please use tf.compat.v1.app.run instead.

WARNING:tensorflow:From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10_train.py:123: The name tf.gfile.Exists is deprecated. Please use tf.io.gfile.exists instead.

W0601 03:30:25.042990<PHONE_NUMBER>67712 module_wrapper.py:139] From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10_train.py:123: The name tf.gfile.Exists is deprecated. Please use tf.io.gfile.exists instead.

WARNING:tensorflow:From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10_train.py:124: The name tf.gfile.DeleteRecursively is deprecated. Please use tf.io.gfile.rmtree instead.

W0601 03:30:25.043155<PHONE_NUMBER>67712 module_wrapper.py:139] From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10_train.py:124: The name tf.gfile.DeleteRecursively is deprecated. Please use tf.io.gfile.rmtree instead.

WARNING:tensorflow:From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10_train.py:125: The name tf.gfile.MakeDirs is deprecated. Please use tf.io.gfile.makedirs instead.

W0601 03:30:25.045882<PHONE_NUMBER>67712 module_wrapper.py:139] From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10_train.py:125: The name tf.gfile.MakeDirs is deprecated. Please use tf.io.gfile.makedirs instead.

WARNING:tensorflow:From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10_train.py:65: The name tf.train.get_or_create_global_step is deprecated. Please use tf.compat.v1.train.get_or_create_global_step instead.

W0601 03:30:25.046512<PHONE_NUMBER>67712 module_wrapper.py:139] From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10_train.py:65: The name tf.train.get_or_create_global_step is deprecated. Please use tf.compat.v1.train.get_or_create_global_step instead.

WARNING:tensorflow:From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10_input.py:158: string_input_producer (from tensorflow.python.training.input) is deprecated and will be removed in a future version.
Instructions for updating:
Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.from_tensor_slices(string_tensor).shuffle(tf.shape(input_tensor, out_type=tf.int64)[0]).repeat(num_epochs)`. If `shuffle=False`, omit the `.shuffle(...)`.
W0601 03:30:25.050264<PHONE_NUMBER>67712 deprecation.py:323] From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10_input.py:158: string_input_producer (from tensorflow.python.training.input) is deprecated and will be removed in a future version.
Instructions for updating:
Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.from_tensor_slices(string_tensor).shuffle(tf.shape(input_tensor, out_type=tf.int64)[0]).repeat(num_epochs)`. If `shuffle=False`, omit the `.shuffle(...)`.
WARNING:tensorflow:From /home/bernard/opt/cuda-10.1/lib/python3.7/site-packages/tensorflow_core/python/training/input.py:277: input_producer (from tensorflow.python.training.input) is deprecated and will be removed in a future version.
Instructions for updating:
Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.from_tensor_slices(input_tensor).shuffle(tf.shape(input_tensor, out_type=tf.int64)[0]).repeat(num_epochs)`. If `shuffle=False`, omit the `.shuffle(...)`.
W0601 03:30:25.053800<PHONE_NUMBER>67712 deprecation.py:323] From /home/bernard/opt/cuda-10.1/lib/python3.7/site-packages/tensorflow_core/python/training/input.py:277: input_producer (from tensorflow.python.training.input) is deprecated and will be removed in a future version.
Instructions for updating:
Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.from_tensor_slices(input_tensor).shuffle(tf.shape(input_tensor, out_type=tf.int64)[0]).repeat(num_epochs)`. If `shuffle=False`, omit the `.shuffle(...)`.
WARNING:tensorflow:From /home/bernard/opt/cuda-10.1/lib/python3.7/site-packages/tensorflow_core/python/training/input.py:189: limit_epochs (from tensorflow.python.training.input) is deprecated and will be removed in a future version.
Instructions for updating:
Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.from_tensors(tensor).repeat(num_epochs)`.
W0601 03:30:25.054384<PHONE_NUMBER>67712 deprecation.py:323] From /home/bernard/opt/cuda-10.1/lib/python3.7/site-packages/tensorflow_core/python/training/input.py:189: limit_epochs (from tensorflow.python.training.input) is deprecated and will be removed in a future version.
Instructions for updating:
Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.from_tensors(tensor).repeat(num_epochs)`.
WARNING:tensorflow:From /home/bernard/opt/cuda-10.1/lib/python3.7/site-packages/tensorflow_core/python/training/input.py:198: QueueRunner.__init__ (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
Instructions for updating:
To construct input pipelines, use the `tf.data` module.
W0601 03:30:25.055722<PHONE_NUMBER>67712 deprecation.py:323] From /home/bernard/opt/cuda-10.1/lib/python3.7/site-packages/tensorflow_core/python/training/input.py:198: QueueRunner.__init__ (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
Instructions for updating:
To construct input pipelines, use the `tf.data` module.
WARNING:tensorflow:From /home/bernard/opt/cuda-10.1/lib/python3.7/site-packages/tensorflow_core/python/training/input.py:198: add_queue_runner (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
Instructions for updating:
To construct input pipelines, use the `tf.data` module.
W0601 03:30:25.056567<PHONE_NUMBER>67712 deprecation.py:323] From /home/bernard/opt/cuda-10.1/lib/python3.7/site-packages/tensorflow_core/python/training/input.py:198: add_queue_runner (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
Instructions for updating:
To construct input pipelines, use the `tf.data` module.
WARNING:tensorflow:From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10_input.py:79: FixedLengthRecordReader.__init__ (from tensorflow.python.ops.io_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.FixedLengthRecordDataset`.
W0601 03:30:25.060451<PHONE_NUMBER>67712 deprecation.py:323] From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10_input.py:79: FixedLengthRecordReader.__init__ (from tensorflow.python.ops.io_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.FixedLengthRecordDataset`.
WARNING:tensorflow:From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10_input.py:172: The name tf.random_crop is deprecated. Please use tf.image.random_crop instead.

W0601 03:30:25.071967<PHONE_NUMBER>67712 module_wrapper.py:139] From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10_input.py:172: The name tf.random_crop is deprecated. Please use tf.image.random_crop instead.

WARNING:tensorflow:From /home/bernard/opt/cuda-10.1/lib/python3.7/site-packages/tensorflow_core/python/ops/image_ops_impl.py:1518: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Deprecated in favor of operator or tf.math.divide.
W0601 03:30:25.104054<PHONE_NUMBER>67712 deprecation.py:323] From /home/bernard/opt/cuda-10.1/lib/python3.7/site-packages/tensorflow_core/python/ops/image_ops_impl.py:1518: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Deprecated in favor of operator or tf.math.divide.
Filling queue with 20000 CIFAR images before starting to train. This will take a few minutes.
WARNING:tensorflow:From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10_input.py:126: shuffle_batch (from tensorflow.python.training.input) is deprecated and will be removed in a future version.
Instructions for updating:
Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.shuffle(min_after_dequeue).batch(batch_size)`.
W0601 03:30:25.105133<PHONE_NUMBER>67712 deprecation.py:323] From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10_input.py:126: shuffle_batch (from tensorflow.python.training.input) is deprecated and will be removed in a future version.
Instructions for updating:
Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.shuffle(min_after_dequeue).batch(batch_size)`.
WARNING:tensorflow:From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10_input.py:135: The name tf.summary.image is deprecated. Please use tf.compat.v1.summary.image instead.

W0601 03:30:25.113710<PHONE_NUMBER>67712 module_wrapper.py:139] From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10_input.py:135: The name tf.summary.image is deprecated. Please use tf.compat.v1.summary.image instead.

WARNING:tensorflow:From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10.py:203: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.

W0601 03:30:25.115518<PHONE_NUMBER>67712 module_wrapper.py:139] From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10.py:203: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.

WARNING:tensorflow:From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10.py:135: calling TruncatedNormal.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
W0601 03:30:25.115780<PHONE_NUMBER>67712 deprecation.py:506] From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10.py:135: calling TruncatedNormal.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
WARNING:tensorflow:From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10.py:111: The name tf.get_variable is deprecated. Please use tf.compat.v1.get_variable instead.

W0601 03:30:25.115960<PHONE_NUMBER>67712 module_wrapper.py:139] From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10.py:111: The name tf.get_variable is deprecated. Please use tf.compat.v1.get_variable instead.

WARNING:tensorflow:From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10.py:93: The name tf.summary.histogram is deprecated. Please use tf.compat.v1.summary.histogram instead.

W0601 03:30:25.125622<PHONE_NUMBER>67712 module_wrapper.py:139] From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10.py:93: The name tf.summary.histogram is deprecated. Please use tf.compat.v1.summary.histogram instead.

WARNING:tensorflow:From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10.py:94: The name tf.summary.scalar is deprecated. Please use tf.compat.v1.summary.scalar instead.

W0601 03:30:25.126545<PHONE_NUMBER>67712 module_wrapper.py:139] From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10.py:94: The name tf.summary.scalar is deprecated. Please use tf.compat.v1.summary.scalar instead.

WARNING:tensorflow:From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10.py:215: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.

W0601 03:30:25.138711<PHONE_NUMBER>67712 module_wrapper.py:139] From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10.py:215: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.

WARNING:tensorflow:From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10.py:138: The name tf.add_to_collection is deprecated. Please use tf.compat.v1.add_to_collection instead.

W0601 03:30:25.170348<PHONE_NUMBER>67712 module_wrapper.py:139] From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10.py:138: The name tf.add_to_collection is deprecated. Please use tf.compat.v1.add_to_collection instead.

WARNING:tensorflow:From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10.py:295: The name tf.get_collection is deprecated. Please use tf.compat.v1.get_collection instead.

W0601 03:30:25.234809<PHONE_NUMBER>67712 module_wrapper.py:139] From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10.py:295: The name tf.get_collection is deprecated. Please use tf.compat.v1.get_collection instead.

WARNING:tensorflow:From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10.py:343: The name tf.train.exponential_decay is deprecated. Please use tf.compat.v1.train.exponential_decay instead.

W0601 03:30:25.235462<PHONE_NUMBER>67712 module_wrapper.py:139] From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10.py:343: The name tf.train.exponential_decay is deprecated. Please use tf.compat.v1.train.exponential_decay instead.

INFO:tensorflow:Summary name local3/weight_loss (raw) is illegal; using local3/weight_loss__raw_ instead.
I0601 03:30:25.261295<PHONE_NUMBER>67712 summary_op_util.py:66] Summary name local3/weight_loss (raw) is illegal; using local3/weight_loss__raw_ instead.
INFO:tensorflow:Summary name local4/weight_loss (raw) is illegal; using local4/weight_loss__raw_ instead.
I0601 03:30:25.262904<PHONE_NUMBER>67712 summary_op_util.py:66] Summary name local4/weight_loss (raw) is illegal; using local4/weight_loss__raw_ instead.
INFO:tensorflow:Summary name cross_entropy (raw) is illegal; using cross_entropy__raw_ instead.
I0601 03:30:25.264466<PHONE_NUMBER>67712 summary_op_util.py:66] Summary name cross_entropy (raw) is illegal; using cross_entropy__raw_ instead.
INFO:tensorflow:Summary name total_loss (raw) is illegal; using total_loss__raw_ instead.
I0601 03:30:25.266004<PHONE_NUMBER>67712 summary_op_util.py:66] Summary name total_loss (raw) is illegal; using total_loss__raw_ instead.
WARNING:tensorflow:From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10.py:355: The name tf.train.GradientDescentOptimizer is deprecated. Please use tf.compat.v1.train.GradientDescentOptimizer instead.

W0601 03:30:25.267595<PHONE_NUMBER>67712 module_wrapper.py:139] From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10.py:355: The name tf.train.GradientDescentOptimizer is deprecated. Please use tf.compat.v1.train.GradientDescentOptimizer instead.

WARNING:tensorflow:From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10.py:362: The name tf.trainable_variables is deprecated. Please use tf.compat.v1.trainable_variables instead.

W0601 03:30:25.333956<PHONE_NUMBER>67712 module_wrapper.py:139] From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10.py:362: The name tf.trainable_variables is deprecated. Please use tf.compat.v1.trainable_variables instead.

WARNING:tensorflow:From /home/bernard/opt/cuda-10.1/lib/python3.7/site-packages/tensorflow_core/python/training/moving_averages.py:433: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.
W0601 03:30:25.350221<PHONE_NUMBER>67712 deprecation.py:323] From /home/bernard/opt/cuda-10.1/lib/python3.7/site-packages/tensorflow_core/python/training/moving_averages.py:433: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.
WARNING:tensorflow:From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10_train.py:84: The name tf.train.SessionRunHook is deprecated. Please use tf.estimator.SessionRunHook instead.

W0601 03:30:25.504043<PHONE_NUMBER>67712 module_wrapper.py:139] From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10_train.py:84: The name tf.train.SessionRunHook is deprecated. Please use tf.estimator.SessionRunHook instead.

WARNING:tensorflow:From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10_train.py:110: The name tf.train.MonitoredTrainingSession is deprecated. Please use tf.compat.v1.train.MonitoredTrainingSession instead.

W0601 03:30:25.504228<PHONE_NUMBER>67712 module_wrapper.py:139] From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10_train.py:110: The name tf.train.MonitoredTrainingSession is deprecated. Please use tf.compat.v1.train.MonitoredTrainingSession instead.

WARNING:tensorflow:From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10_train.py:112: The name tf.train.StopAtStepHook is deprecated. Please use tf.estimator.StopAtStepHook instead.

W0601 03:30:25.504341<PHONE_NUMBER>67712 module_wrapper.py:139] From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10_train.py:112: The name tf.train.StopAtStepHook is deprecated. Please use tf.estimator.StopAtStepHook instead.

WARNING:tensorflow:From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10_train.py:113: The name tf.train.NanTensorHook is deprecated. Please use tf.estimator.NanTensorHook instead.

W0601 03:30:25.504446<PHONE_NUMBER>67712 module_wrapper.py:139] From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10_train.py:113: The name tf.train.NanTensorHook is deprecated. Please use tf.estimator.NanTensorHook instead.

WARNING:tensorflow:From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10_train.py:115: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.

W0601 03:30:25.504545<PHONE_NUMBER>67712 module_wrapper.py:139] From /home/bernard/python-dev/test/models-master/tutorials/image/cifar10/cifar10_train.py:115: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.

INFO:tensorflow:Create CheckpointSaverHook.
I0601 03:30:25.504671<PHONE_NUMBER>67712 basic_session_run_hooks.py:541] Create CheckpointSaverHook.
WARNING:tensorflow:From /home/bernard/opt/cuda-10.1/lib/python3.7/site-packages/tensorflow_core/python/ops/array_ops.py:1475: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where
W0601 03:30:25.617982<PHONE_NUMBER>67712 deprecation.py:323] From /home/bernard/opt/cuda-10.1/lib/python3.7/site-packages/tensorflow_core/python/ops/array_ops.py:1475: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where

I encounter exactly same problem, but with a different test case. The case is exactly same as the tensorflow's first tutorial:
https://www.tensorflow.org/tutorials/quickstart/beginner
I copied its code as the following:
import tensorflow as tf
mnist = tf.keras.datasets.mnist

(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)),
  tf.keras.layers.Dense(128, activation='relu'),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.Dense(10)
])

predictions = model(x_train[:1]).numpy()
predictions
tf.nn.softmax(predictions).numpy()
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
loss_fn(y_train[:1], predictions).numpy()
model.compile(optimizer='adam',
              loss=loss_fn,
              metrics=['accuracy'])
model.fit(x_train, y_train, epochs=5)

My becoming slower process is much faser than @beew test, the following is my test output when executing model.fit(), the last line in above codes:
>>> model.fit(x_train, y_train, epochs=5)
Epoch 1/5
1875/1875 [==============================] - 10s 5ms/step - loss: 0.2963 - accuracy: 0.9147
Epoch 2/5
1875/1875 [==============================] - 14s 7ms/step - loss: 0.1440 - accuracy: 0.9572
Epoch 3/5
1875/1875 [==============================] - 32s 17ms/step - loss: 0.1072 - accuracy: 0.9677
Epoch 4/5
1875/1875 [==============================] - 28s 15ms/step - loss: 0.0884 - accuracy: 0.9725
Epoch 5/5
1875/1875 [==============================] - 25s 13ms/step - loss: 0.0745 - accuracy: 0.9768
<tensorflow.python.keras.callbacks.History object at 0x7fd6590f5a90>
>>>
>>>
>>>
>>> model.fit(x_train, y_train, epochs=20)
Epoch 1/20
1875/1875 [==============================] - 87s 46ms/step - loss: 0.0649 - accuracy: 0.9794
Epoch 2/20
1875/1875 [==============================] - 173s 92ms/step - loss: 0.0581 - accuracy: 0.9818
Epoch 3/20
1875/1875 [==============================] - 177s 94ms/step - loss: 0.0522 - accuracy: 0.9836
Epoch 4/20
1875/1875 [==============================] - 195s 104ms/step - loss: 0.0486 - accuracy: 0.9841
Epoch 5/20
1875/1875 [==============================] - 209s 112ms/step - loss: 0.0436 - accuracy: 0.9857
Epoch 6/20
1875/1875 [==============================] - 208s 111ms/step - loss: 0.0407 - accuracy: 0.9862

I found this bug after upgrading to Tensorflow 2.2. At first, I suspect it's caused by my tensorflow program's mistake, but when I only testing Tensorflow's official tuturial codes, it shows the same wrong results(only wrong on performance data, the result is not wrong). So I suspect it's caused by TF2.2 or maybe other factors in my TF environment factors(e.g. my Nvidia driver)
I'll try tf session(non-eager-mode) to see the performance, because I suspect the root cause is some change on the eager-mode implementation. I'll update it when it's done.

I've recorded my test training time increasing results after a freshly bootup, it shows a clear gradual time increasing for these training sessions:
>>> model.compile(optimizer='adam',
...               loss=loss_fn,
...               metrics=['accuracy'])
>>> model.fit(x_train, y_train, epochs=5)
Epoch 1/5
1875/1875 [==============================] - 3s 1ms/step - loss: 0.2945 - accuracy: 0.9139
Epoch 2/5
1875/1875 [==============================] - 3s 2ms/step - loss: 0.1452 - accuracy: 0.9568
Epoch 3/5
1875/1875 [==============================] - 3s 2ms/step - loss: 0.1078 - accuracy: 0.9665
Epoch 4/5
1875/1875 [==============================] - 3s 2ms/step - loss: 0.0890 - accuracy: 0.9726
Epoch 5/5
1875/1875 [==============================] - 3s 2ms/step - loss: 0.0772 - accuracy: 0.9753
<tensorflow.python.keras.callbacks.History object at 0x7f65ae876470>
>>> model.fit(x_train, y_train, epochs=50)
Epoch 1/50
1875/1875 [==============================] - 3s 2ms/step - loss: 0.0668 - accuracy: 0.9785
Epoch 2/50
1875/1875 [==============================] - 3s 2ms/step - loss: 0.0612 - accuracy: 0.9802
Epoch 3/50
1875/1875 [==============================] - 4s 2ms/step - loss: 0.0530 - accuracy: 0.9828
Epoch 4/50
1875/1875 [==============================] - 4s 2ms/step - loss: 0.0491 - accuracy: 0.9836
Epoch 5/50
1875/1875 [==============================] - 4s 2ms/step - loss: 0.0466 - accuracy: 0.9842
Epoch 6/50
1875/1875 [==============================] - 4s 2ms/step - loss: 0.0383 - accuracy: 0.9873
Epoch 7/50
1875/1875 [==============================] - 4s 2ms/step - loss: 0.0399 - accuracy: 0.9868
Epoch 8/50
1875/1875 [==============================] - 4s 2ms/step - loss: 0.0400 - accuracy: 0.9865
Epoch 9/50
1875/1875 [==============================] - 4s 2ms/step - loss: 0.0346 - accuracy: 0.9884
Epoch 10/50
1875/1875 [==============================] - 4s 2ms/step - loss: 0.0327 - accuracy: 0.9886
Epoch 11/50
1875/1875 [==============================] - 5s 2ms/step - loss: 0.0329 - accuracy: 0.9886
Epoch 12/50
1875/1875 [==============================] - 5s 3ms/step - loss: 0.0300 - accuracy: 0.9892
Epoch 13/50
1875/1875 [==============================] - 5s 3ms/step - loss: 0.0292 - accuracy: 0.9901
Epoch 14/50
1875/1875 [==============================] - 6s 3ms/step - loss: 0.0276 - accuracy: 0.9908
Epoch 15/50
1875/1875 [==============================] - 6s 3ms/step - loss: 0.0252 - accuracy: 0.9915
Epoch 16/50
1875/1875 [==============================] - 7s 4ms/step - loss: 0.0258 - accuracy: 0.9915
Epoch 17/50
1875/1875 [==============================] - 7s 4ms/step - loss: 0.0247 - accuracy: 0.9916
Epoch 18/50
1875/1875 [==============================] - 6s 3ms/step - loss: 0.0237 - accuracy: 0.9918
Epoch 19/50
1875/1875 [==============================] - 8s 4ms/step - loss: 0.0240 - accuracy: 0.9918
Epoch 20/50
1875/1875 [==============================] - 8s 4ms/step - loss: 0.0218 - accuracy: 0.9925
Epoch 21/50
1875/1875 [==============================] - 8s 4ms/step - loss: 0.0207 - accuracy: 0.9927
Epoch 22/50
1875/1875 [==============================] - 10s 5ms/step - loss: 0.0233 - accuracy: 0.9926
Epoch 23/50
1875/1875 [==============================] - 10s 5ms/step - loss: 0.0222 - accuracy: 0.9923
Epoch 24/50
1875/1875 [==============================] - 9s 5ms/step - loss: 0.0224 - accuracy: 0.9928
Epoch 25/50
1875/1875 [==============================] - 11s 6ms/step - loss: 0.0204 - accuracy: 0.9928
Epoch 26/50
1875/1875 [==============================] - 12s 6ms/step - loss: 0.0208 - accuracy: 0.9934
Epoch 27/50
1875/1875 [==============================] - 12s 6ms/step - loss: 0.0201 - accuracy: 0.9935
Epoch 28/50
1875/1875 [==============================] - 13s 7ms/step - loss: 0.0189 - accuracy: 0.9935
Epoch 29/50
1875/1875 [==============================] - 16s 8ms/step - loss: 0.0189 - accuracy: 0.9935
Epoch 30/50
1875/1875 [==============================] - 13s 7ms/step - loss: 0.0193 - accuracy: 0.9936
Epoch 31/50
1875/1875 [==============================] - 19s 10ms/step - loss: 0.0177 - accuracy: 0.9936
Epoch 32/50
1875/1875 [==============================] - 14s 8ms/step - loss: 0.0177 - accuracy: 0.9942
Epoch 33/50
1875/1875 [==============================] - 13s 7ms/step - loss: 0.0176 - accuracy: 0.9939
Epoch 34/50
1875/1875 [==============================] - 15s 8ms/step - loss: 0.0199 - accuracy: 0.9932
Epoch 35/50
1875/1875 [==============================] - 15s 8ms/step - loss: 0.0174 - accuracy: 0.9940
Epoch 36/50
1875/1875 [==============================] - 14s 8ms/step - loss: 0.0171 - accuracy: 0.9940
Epoch 37/50
1875/1875 [==============================] - 17s 9ms/step - loss: 0.0177 - accuracy: 0.9940
Epoch 38/50
1875/1875 [==============================] - 21s 11ms/step - loss: 0.0182 - accuracy: 0.9941
Epoch 39/50
1875/1875 [==============================] - 16s 8ms/step - loss: 0.0148 - accuracy: 0.9952
Epoch 40/50
1875/1875 [==============================] - 22s 12ms/step - loss: 0.0167 - accuracy: 0.9940
Epoch 41/50
1875/1875 [==============================] - 28s 15ms/step - loss: 0.0171 - accuracy: 0.9944
Epoch 42/50
1875/1875 [==============================] - 24s 13ms/step - loss: 0.0167 - accuracy: 0.9943
Epoch 43/50
1875/1875 [==============================] - 20s 11ms/step - loss: 0.0170 - accuracy: 0.9948
Epoch 44/50
1875/1875 [==============================] - 26s 14ms/step - loss: 0.0156 - accuracy: 0.9948
Epoch 45/50
1875/1875 [==============================] - 52s 28ms/step - loss: 0.0161 - accuracy: 0.9946
Epoch 46/50
1875/1875 [==============================] - 42s 22ms/step - loss: 0.0149 - accuracy: 0.9948
Epoch 47/50
1875/1875 [==============================] - 22s 12ms/step - loss: 0.0135 - accuracy: 0.9952
Epoch 48/50
1875/1875 [==============================] - 63s 33ms/step - loss: 0.0156 - accuracy: 0.9950
Epoch 49/50
1875/1875 [==============================] - 64s 34ms/step - loss: 0.0143 - accuracy: 0.9952
Epoch 50/50
1875/1875 [==============================] - 239s 127ms/step - loss: 0.0160 - accuracy: 0.9948

@beew,
Could you please the provide the complete code or link the cifar10_train.py file you are using.
I was unable to find that file in the link you have given. Thanks!

@amahendrakar
Here is the link, https://github.com/tensorflow/models/releases, download V1.13.0 (between official models 1.11.0 and 2.0) untar it, go to /models-1.13.0/tutorials/image/cifar10/ the file is called cifar10_train.py

@beew,
I did not observe any difference, even after running for 11000 steps. Please find the gist of it here. Thanks!

@amahendrakar
Hi, doesn't seem like you were using GPU.

@amahendrakar
The first 11000 steps doesn't show appreciable deterioration for a fresh reboot, but it deteriorates when the steps increase. So I think you need to let it run for longer to see it and it may also be GPU related.
I0611 04:30:41.818451<PHONE_NUMBER>50592 basic_session_run_hooks.py:606] Saving checkpoints for 11000 into /tmp/cifar10_train/model.ckpt.

real	3m14.660s
user	11m16.581s
sys	1m22.507s
bernard@Netra2:~$ time python /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10_train.py -max_steps=11000
WARNING:tensorflow:From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10_train.py:127: The name tf.app.run is deprecated. Please use tf.compat.v1.app.run instead.

WARNING:tensorflow:From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10_train.py:120: The name tf.gfile.Exists is deprecated. Please use tf.io.gfile.exists instead.

W0611 12:30:29.483493<PHONE_NUMBER>83488 module_wrapper.py:139] From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10_train.py:120: The name tf.gfile.Exists is deprecated. Please use tf.io.gfile.exists instead.

WARNING:tensorflow:From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10_train.py:121: The name tf.gfile.DeleteRecursively is deprecated. Please use tf.io.gfile.rmtree instead.

W0611 12:30:29.483694<PHONE_NUMBER>83488 module_wrapper.py:139] From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10_train.py:121: The name tf.gfile.DeleteRecursively is deprecated. Please use tf.io.gfile.rmtree instead.

WARNING:tensorflow:From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10_train.py:122: The name tf.gfile.MakeDirs is deprecated. Please use tf.io.gfile.makedirs instead.

W0611 12:30:29.486235<PHONE_NUMBER>83488 module_wrapper.py:139] From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10_train.py:122: The name tf.gfile.MakeDirs is deprecated. Please use tf.io.gfile.makedirs instead.

WARNING:tensorflow:From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10_train.py:62: The name tf.train.get_or_create_global_step is deprecated. Please use tf.compat.v1.train.get_or_create_global_step instead.

W0611 12:30:29.486796<PHONE_NUMBER>83488 module_wrapper.py:139] From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10_train.py:62: The name tf.train.get_or_create_global_step is deprecated. Please use tf.compat.v1.train.get_or_create_global_step instead.

WARNING:tensorflow:From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10_input.py:158: string_input_producer (from tensorflow.python.training.input) is deprecated and will be removed in a future version.
Instructions for updating:
Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.from_tensor_slices(string_tensor).shuffle(tf.shape(input_tensor, out_type=tf.int64)[0]).repeat(num_epochs)`. If `shuffle=False`, omit the `.shuffle(...)`.
W0611 12:30:29.489988<PHONE_NUMBER>83488 deprecation.py:323] From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10_input.py:158: string_input_producer (from tensorflow.python.training.input) is deprecated and will be removed in a future version.
Instructions for updating:
Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.from_tensor_slices(string_tensor).shuffle(tf.shape(input_tensor, out_type=tf.int64)[0]).repeat(num_epochs)`. If `shuffle=False`, omit the `.shuffle(...)`.
WARNING:tensorflow:From /home/bernard/opt/python37/lib/python3.7/site-packages/tensorflow_core/python/training/input.py:277: input_producer (from tensorflow.python.training.input) is deprecated and will be removed in a future version.
Instructions for updating:
Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.from_tensor_slices(input_tensor).shuffle(tf.shape(input_tensor, out_type=tf.int64)[0]).repeat(num_epochs)`. If `shuffle=False`, omit the `.shuffle(...)`.
W0611 12:30:29.493128<PHONE_NUMBER>83488 deprecation.py:323] From /home/bernard/opt/python37/lib/python3.7/site-packages/tensorflow_core/python/training/input.py:277: input_producer (from tensorflow.python.training.input) is deprecated and will be removed in a future version.
Instructions for updating:
Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.from_tensor_slices(input_tensor).shuffle(tf.shape(input_tensor, out_type=tf.int64)[0]).repeat(num_epochs)`. If `shuffle=False`, omit the `.shuffle(...)`.
WARNING:tensorflow:From /home/bernard/opt/python37/lib/python3.7/site-packages/tensorflow_core/python/training/input.py:189: limit_epochs (from tensorflow.python.training.input) is deprecated and will be removed in a future version.
Instructions for updating:
Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.from_tensors(tensor).repeat(num_epochs)`.
W0611 12:30:29.493753<PHONE_NUMBER>83488 deprecation.py:323] From /home/bernard/opt/python37/lib/python3.7/site-packages/tensorflow_core/python/training/input.py:189: limit_epochs (from tensorflow.python.training.input) is deprecated and will be removed in a future version.
Instructions for updating:
Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.from_tensors(tensor).repeat(num_epochs)`.
WARNING:tensorflow:From /home/bernard/opt/python37/lib/python3.7/site-packages/tensorflow_core/python/training/input.py:198: QueueRunner.__init__ (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
Instructions for updating:
To construct input pipelines, use the `tf.data` module.
W0611 12:30:29.495112<PHONE_NUMBER>83488 deprecation.py:323] From /home/bernard/opt/python37/lib/python3.7/site-packages/tensorflow_core/python/training/input.py:198: QueueRunner.__init__ (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
Instructions for updating:
To construct input pipelines, use the `tf.data` module.
WARNING:tensorflow:From /home/bernard/opt/python37/lib/python3.7/site-packages/tensorflow_core/python/training/input.py:198: add_queue_runner (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
Instructions for updating:
To construct input pipelines, use the `tf.data` module.
W0611 12:30:29.495923<PHONE_NUMBER>83488 deprecation.py:323] From /home/bernard/opt/python37/lib/python3.7/site-packages/tensorflow_core/python/training/input.py:198: add_queue_runner (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
Instructions for updating:
To construct input pipelines, use the `tf.data` module.
WARNING:tensorflow:From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10_input.py:79: FixedLengthRecordReader.__init__ (from tensorflow.python.ops.io_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.FixedLengthRecordDataset`.
W0611 12:30:29.498490<PHONE_NUMBER>83488 deprecation.py:323] From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10_input.py:79: FixedLengthRecordReader.__init__ (from tensorflow.python.ops.io_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.FixedLengthRecordDataset`.
WARNING:tensorflow:From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10_input.py:172: The name tf.random_crop is deprecated. Please use tf.image.random_crop instead.

W0611 12:30:29.507227<PHONE_NUMBER>83488 module_wrapper.py:139] From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10_input.py:172: The name tf.random_crop is deprecated. Please use tf.image.random_crop instead.

WARNING:tensorflow:From /home/bernard/opt/python37/lib/python3.7/site-packages/tensorflow_core/python/ops/image_ops_impl.py:1518: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Deprecated in favor of operator or tf.math.divide.
W0611 12:30:29.535539<PHONE_NUMBER>83488 deprecation.py:323] From /home/bernard/opt/python37/lib/python3.7/site-packages/tensorflow_core/python/ops/image_ops_impl.py:1518: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Deprecated in favor of operator or tf.math.divide.
Filling queue with 20000 CIFAR images before starting to train. This will take a few minutes.
WARNING:tensorflow:From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10_input.py:126: shuffle_batch (from tensorflow.python.training.input) is deprecated and will be removed in a future version.
Instructions for updating:
Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.shuffle(min_after_dequeue).batch(batch_size)`.
W0611 12:30:29.536478<PHONE_NUMBER>83488 deprecation.py:323] From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10_input.py:126: shuffle_batch (from tensorflow.python.training.input) is deprecated and will be removed in a future version.
Instructions for updating:
Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.shuffle(min_after_dequeue).batch(batch_size)`.
WARNING:tensorflow:From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10_input.py:135: The name tf.summary.image is deprecated. Please use tf.compat.v1.summary.image instead.

W0611 12:30:29.543221<PHONE_NUMBER>83488 module_wrapper.py:139] From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10_input.py:135: The name tf.summary.image is deprecated. Please use tf.compat.v1.summary.image instead.

WARNING:tensorflow:From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10.py:203: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.

W0611 12:30:29.544828<PHONE_NUMBER>83488 module_wrapper.py:139] From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10.py:203: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.

WARNING:tensorflow:From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10.py:135: calling TruncatedNormal.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
W0611 12:30:29.545093<PHONE_NUMBER>83488 deprecation.py:506] From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10.py:135: calling TruncatedNormal.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
WARNING:tensorflow:From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10.py:111: The name tf.get_variable is deprecated. Please use tf.compat.v1.get_variable instead.

W0611 12:30:29.545320<PHONE_NUMBER>83488 module_wrapper.py:139] From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10.py:111: The name tf.get_variable is deprecated. Please use tf.compat.v1.get_variable instead.

WARNING:tensorflow:From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10.py:93: The name tf.summary.histogram is deprecated. Please use tf.compat.v1.summary.histogram instead.

W0611 12:30:29.553547<PHONE_NUMBER>83488 module_wrapper.py:139] From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10.py:93: The name tf.summary.histogram is deprecated. Please use tf.compat.v1.summary.histogram instead.

WARNING:tensorflow:From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10.py:94: The name tf.summary.scalar is deprecated. Please use tf.compat.v1.summary.scalar instead.

W0611 12:30:29.554385<PHONE_NUMBER>83488 module_wrapper.py:139] From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10.py:94: The name tf.summary.scalar is deprecated. Please use tf.compat.v1.summary.scalar instead.

WARNING:tensorflow:From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10.py:215: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.

W0611 12:30:29.564506<PHONE_NUMBER>83488 module_wrapper.py:139] From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10.py:215: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.

WARNING:tensorflow:From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10.py:138: The name tf.add_to_collection is deprecated. Please use tf.compat.v1.add_to_collection instead.

W0611 12:30:29.592494<PHONE_NUMBER>83488 module_wrapper.py:139] From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10.py:138: The name tf.add_to_collection is deprecated. Please use tf.compat.v1.add_to_collection instead.

WARNING:tensorflow:From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10.py:295: The name tf.get_collection is deprecated. Please use tf.compat.v1.get_collection instead.

W0611 12:30:29.645589<PHONE_NUMBER>83488 module_wrapper.py:139] From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10.py:295: The name tf.get_collection is deprecated. Please use tf.compat.v1.get_collection instead.

WARNING:tensorflow:From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10.py:343: The name tf.train.exponential_decay is deprecated. Please use tf.compat.v1.train.exponential_decay instead.

W0611 12:30:29.646215<PHONE_NUMBER>83488 module_wrapper.py:139] From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10.py:343: The name tf.train.exponential_decay is deprecated. Please use tf.compat.v1.train.exponential_decay instead.

INFO:tensorflow:Summary name local3/weight_loss (raw) is illegal; using local3/weight_loss__raw_ instead.
I0611 12:30:29.667191<PHONE_NUMBER>83488 summary_op_util.py:66] Summary name local3/weight_loss (raw) is illegal; using local3/weight_loss__raw_ instead.
INFO:tensorflow:Summary name local4/weight_loss (raw) is illegal; using local4/weight_loss__raw_ instead.
I0611 12:30:29.668576<PHONE_NUMBER>83488 summary_op_util.py:66] Summary name local4/weight_loss (raw) is illegal; using local4/weight_loss__raw_ instead.
INFO:tensorflow:Summary name cross_entropy (raw) is illegal; using cross_entropy__raw_ instead.
I0611 12:30:29.669985<PHONE_NUMBER>83488 summary_op_util.py:66] Summary name cross_entropy (raw) is illegal; using cross_entropy__raw_ instead.
INFO:tensorflow:Summary name total_loss (raw) is illegal; using total_loss__raw_ instead.
I0611 12:30:29.671325<PHONE_NUMBER>83488 summary_op_util.py:66] Summary name total_loss (raw) is illegal; using total_loss__raw_ instead.
WARNING:tensorflow:From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10.py:355: The name tf.train.GradientDescentOptimizer is deprecated. Please use tf.compat.v1.train.GradientDescentOptimizer instead.

W0611 12:30:29.673135<PHONE_NUMBER>83488 module_wrapper.py:139] From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10.py:355: The name tf.train.GradientDescentOptimizer is deprecated. Please use tf.compat.v1.train.GradientDescentOptimizer instead.

WARNING:tensorflow:From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10.py:362: The name tf.trainable_variables is deprecated. Please use tf.compat.v1.trainable_variables instead.

W0611 12:30:29.729470<PHONE_NUMBER>83488 module_wrapper.py:139] From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10.py:362: The name tf.trainable_variables is deprecated. Please use tf.compat.v1.trainable_variables instead.

WARNING:tensorflow:From /home/bernard/opt/python37/lib/python3.7/site-packages/tensorflow_core/python/training/moving_averages.py:433: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.
W0611 12:30:29.742350<PHONE_NUMBER>83488 deprecation.py:323] From /home/bernard/opt/python37/lib/python3.7/site-packages/tensorflow_core/python/training/moving_averages.py:433: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.
WARNING:tensorflow:From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10_train.py:81: The name tf.train.SessionRunHook is deprecated. Please use tf.estimator.SessionRunHook instead.

W0611 12:30:29.872007<PHONE_NUMBER>83488 module_wrapper.py:139] From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10_train.py:81: The name tf.train.SessionRunHook is deprecated. Please use tf.estimator.SessionRunHook instead.

WARNING:tensorflow:From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10_train.py:107: The name tf.train.MonitoredTrainingSession is deprecated. Please use tf.compat.v1.train.MonitoredTrainingSession instead.

W0611 12:30:29.872260<PHONE_NUMBER>83488 module_wrapper.py:139] From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10_train.py:107: The name tf.train.MonitoredTrainingSession is deprecated. Please use tf.compat.v1.train.MonitoredTrainingSession instead.

WARNING:tensorflow:From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10_train.py:109: The name tf.train.StopAtStepHook is deprecated. Please use tf.estimator.StopAtStepHook instead.

W0611 12:30:29.872472<PHONE_NUMBER>83488 module_wrapper.py:139] From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10_train.py:109: The name tf.train.StopAtStepHook is deprecated. Please use tf.estimator.StopAtStepHook instead.

WARNING:tensorflow:From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10_train.py:110: The name tf.train.NanTensorHook is deprecated. Please use tf.estimator.NanTensorHook instead.

W0611 12:30:29.872640<PHONE_NUMBER>83488 module_wrapper.py:139] From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10_train.py:110: The name tf.train.NanTensorHook is deprecated. Please use tf.estimator.NanTensorHook instead.

WARNING:tensorflow:From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10_train.py:112: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.

W0611 12:30:29.872808<PHONE_NUMBER>83488 module_wrapper.py:139] From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10_train.py:112: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.

INFO:tensorflow:Create CheckpointSaverHook.
I0611 12:30:29.872956<PHONE_NUMBER>83488 basic_session_run_hooks.py:541] Create CheckpointSaverHook.
WARNING:tensorflow:From /home/bernard/opt/python37/lib/python3.7/site-packages/tensorflow_core/python/ops/array_ops.py:1475: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where
W0611 12:30:30.001923<PHONE_NUMBER>83488 deprecation.py:323] From /home/bernard/opt/python37/lib/python3.7/site-packages/tensorflow_core/python/ops/array_ops.py:1475: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where
INFO:tensorflow:Graph was finalized.
I0611 12:30:30.053723<PHONE_NUMBER>83488 monitored_session.py:240] Graph was finalized.
2020-06-11 12:30:30.058675: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency:<PHONE_NUMBER> Hz
2020-06-11 12:30:30.058971: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x4ebb650 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-06-11 12:30:30.059002: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2020-06-11 12:30:30.060724: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2020-06-11 12:30:30.136275: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-11 12:30:30.136851: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x4f4a680 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2020-06-11 12:30:30.136871: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): GeForce GTX 1070, Compute Capability 6.1
2020-06-11 12:30:30.137061: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-11 12:30:30.137559: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Found device 0 with properties:
name: GeForce GTX 1070 major: 6 minor: 1 memoryClockRate(GHz): 1.695
pciBusID: 0000:01:00.0
2020-06-11 12:30:30.137813: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0
2020-06-11 12:30:30.138813: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0
2020-06-11 12:30:30.139621: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10.0
2020-06-11 12:30:30.139874: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10.0
2020-06-11 12:30:30.140925: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10.0
2020-06-11 12:30:30.141827: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10.0
2020-06-11 12:30:30.144418: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-06-11 12:30:30.144571: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-11 12:30:30.145116: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-11 12:30:30.145596: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1767] Adding visible gpu devices: 0
2020-06-11 12:30:30.145660: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0
2020-06-11 12:30:30.146507: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1180] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-06-11 12:30:30.146518: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1186]      0
2020-06-11 12:30:30.146541: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1199] 0:   N
2020-06-11 12:30:30.146810: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-11 12:30:30.147493: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-11 12:30:30.148067: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1325] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 7450 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1070, pci bus id: 0000:01:00.0, compute capability: 6.1)
INFO:tensorflow:Running local_init_op.
I0611 12:30:30.792880<PHONE_NUMBER>83488 session_manager.py:500] Running local_init_op.
INFO:tensorflow:Done running local_init_op.
I0611 12:30:30.802895<PHONE_NUMBER>83488 session_manager.py:502] Done running local_init_op.
WARNING:tensorflow:From /home/bernard/opt/python37/lib/python3.7/site-packages/tensorflow_core/python/training/monitored_session.py:882: start_queue_runners (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
Instructions for updating:
To construct input pipelines, use the `tf.data` module.
W0611 12:30:30.833667<PHONE_NUMBER>83488 deprecation.py:323] From /home/bernard/opt/python37/lib/python3.7/site-packages/tensorflow_core/python/training/monitored_session.py:882: start_queue_runners (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
Instructions for updating:
To construct input pipelines, use the `tf.data` module.
INFO:tensorflow:Saving checkpoints for 0 into /tmp/cifar10_train/model.ckpt.
I0611 12:30:31.232494<PHONE_NUMBER>83488 basic_session_run_hooks.py:606] Saving checkpoints for 0 into /tmp/cifar10_train/model.ckpt.
WARNING:tensorflow:From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10_train.py:90: The name tf.train.SessionRunArgs is deprecated. Please use tf.estimator.SessionRunArgs instead.

W0611 12:30:31.334620<PHONE_NUMBER>83488 module_wrapper.py:139] From /home/bernard/python-dev/test/models-1.13.0/tutorials/image/cifar10/cifar10_train.py:90: The name tf.train.SessionRunArgs is deprecated.
I0611 12:33:18.374850<PHONE_NUMBER>83488 basic_session_run_hooks.py:606] Saving checkpoints for 11000 into /tmp/cifar10_train/model.ckpt.

real	2m50.690s
user	9m45.761s
sys	1m10.234s

Could it be something environmental, like perhaps the GPU is getting boosted during the initial steps?  Can you try disabling GPU boost to see if that reduces the variance?

I figured it out. Turned out the GPU got overheated and slows down over time. Got a cooling pad and now no degradation over 100,000 steps. Thanks for everyone who tried to help and apologies for the false alarm.

@beew Thanks for your information.
After checking on my testing environment, I believe the same root cause triggers my platform's performance issue also. I've used the following cmd to monitor my GPU's temperature:
nvidia-smi --query-gpu=temperature.gpu --format=csv
During my training process, when the GPU's temperature is above 90 degree, the training performance start to decrease largely.
And I've used many other nvidia's utilties to fix the performance in my laptop ubuntu environment, but all failed, e.g. the following commands:
Set one frequencies setting for GPU&Memory:
sudo nvidia-smi -ac 2505,1202 -i 0
FAILED! Performance still going down when temperature is above 90 degree
Enable persistency mode:
nvidia-smi -pm 1
Still FAILED, the performance still not persistant
Anyway, it's a good HW design to prevent the GPU burning, and what I need do is just switching to another better HW platform when scaling-up my project data process. This issue took me above one month time to locate the root cause, it's finally clear, Thanks again!!!!

You also could use the following commands to monitor GPU's working frequencies:
for GPU working frequency
watch -n 0.5 nvidia-smi --query-gpu=clocks.gr  --format=csv
For GPU's memory frequncy
watch -n 0.5 nvidia-smi --query-gpu=clocks.mem  --format=csv
The following command could force your GPU working at 2.505Ghz for GPU and 1.006Ghz for GPU memory:
nvidia-smi --applications-clocks=2505,1006 -i 0
The frequencies options could be listed by command:
nvidia-smi  -q -i 0 -d SUPPORTED_CLOCKS
If your GPU is not toooooo hot, it could work at the frequencies as you request with command:"--applications-clocks", but if too hot, it'll decrease the two working frequencies. So cooling down your GPU is also an important part on your working platform.


Transient Error when reading extension's secretStorage from Remote window

VS Code Version:  1.78.2
OS Version: Windows_NT x64 10.0.22621
Steps to Reproduce:

Install Azure Machine Learning and Azure Machine Learning - Remote extension
Go to ml.azure.com and create a Compute instance
Click out to VSCode Desktop
Repeat this 5-10 times
One of those times, you can get an error _[Error - 2023-06-07 10:22:05.184] Error: Internal extension error: Token acquisition parameters cannot be read from extension context. Please try again
_

So, the backend flow typically works like this.

User clicks out to VSCode Desktop from ml.azure.com
A new blank window opens up and the Azure ML extension handles the Uri
The Azure ML Extension executes a Azure ML - Remote command azureml-remote.internal.acceptTokenAcquisitionParams with the secrets as arguments
The workspace instance of Azure ML - Remote extension runs the command and store those secrets in its own Secret Storage
After that, a remote Uri is opened by the Azure ML extension and the window is reloaded
In the remote window, the UI instance of the Azure ML - Remote extension tries to read its own secret storage and get those secrets
~1 in 5 times, one of four secrets is returned as undefined
As you can see from the logs, only refreshToken is Nil

@chrmarti Any updates on this ? We recently had a customer report this issue again.
https://github.com/microsoft/vscode-tools-for-ai/issues/2196

I don't think I understand the architecture here. Do you have 2 extensions at play here? I'm not sure I understand why you invoke this command azureml-remote.internal.acceptTokenAcquisitionParams
Additionally, why do you own the auth state instead of using the authentication APIs that vscode provides?

@TylerLeonhardt

Yes, we have two extensions at play. The first extension (workspace extension) is used to acquire auth token through Azure Account extension and the second extension (ui extension) is used for the remote connection
We are in the process of migrating to native VSCode auth but that might take a couple of iterations

Can you show me that these secrets are being stored? As in, you know these secrets are being passed to the Secret Storage API? Can you also attach the Main Output channel logs when you see this issue?

@TylerLeonhardt I haven't seen this issue in a while. I will try to capture the logs once I hit it again

Hey @TylerLeonhardt, this issue might need further attention.
@shsuman, you can help us out by closing this issue if the problem no longer exists, or adding more information.

We have made infrastructure changes and we don't have this flow anymore, so this is not an issue for us now. I will close the issue


Update clickjack.py

Suggestion: online scanner to check clickjacking vulnerability

Hey thanks for adding but the purpose was not to give online scanner link, instead to use command line to find out if XFO header is present.


KAFKA-9595: switch usage of alterConfigs to incrementalAlterConfigs for kafka-configs tool

Also, some minor refactoring for common code
Test changes to ConfigCommandTest
Verifies builds, tests pass

Committer Checklist (excluded from commit message)

[ ] Verify design and implementation
[ ] Verify test coverage and CI build status
[ ] Verify documentation (including upgrade notes)

Do we fallback if incremental alter configs is not supported by the kafka broker version?

As discussed, will pause this until resolving the right way to proceed with backward compatibility (will write a KIP), the options for handling older brokers are

explicitly don't support
automatically fallback
manually fallback (require passing an additional flag).

Will ping back here if/when that decision is reached.

@agam Going to close this for now.


Feature: roll debug tools verify_invoke.py and container_debug.py into a subcommand debug

Describe the Feature
I have been using tern/tools/verify_invoke.py and tern/tools/container_debug.py to test the scripts that get added to base.yml and snippets.yml or even just for looking around a container filesystem at a certain layer. However, they have been a pain to maintain as the project keeps growing and moving forward. Hence I propose to roll these tools into Tern as a new subcommand called debug.
Use Cases
"Debugging" container images could work well as a feature for developers to tinker with container image filesystems, although I see this more as a tool for folks working on Tern itself.
Implementation Changes
This would just be using functionality in verify_invoke.py and container_debug.py under a subcommand called debug and removing the scripts from the tools folder.

@rnjudge, I would like to take this feature.

@rnjudge, I would like to take this feature.

@nishakm have you started working on this?

@mukultaneja This one is a little out there - technical debt collected back when the project was new. It'll be better if I work on it. Thanks for volunteering!

@rnjudge This would mean adding a new command line argument and that means our Release number would be 3.0.0. Would you prefer I work on this two releases after?

@rnjudge This would mean adding a new command line argument and that means our Release number would be 3.0.0. Would you prefer I work on this two releases after?

As discussed, I agree that this is something that can be pushed back. Maybe we can time the feature with the Beta release.


Discussion: Support in NPM for JavaScript support

In an effort to use ocaml-protoc in JavaScript, I would like to publish ppx_deriving_protobuf to NPM.
This essentially consist in 2 files:

bsconfig.json: Build instruction for BuckleScript
package.json: Package specification for the NPM package

This issue is just for awareness, I am happy maintaining a fork which contains the NPM setup.

Sure, send a PR.

@whitequark will do. Thanks a lot


System.MissingMethodException: Method not found

I am following the "Getting Started" guide, but I get the following exception when I run the application:
System.MissingMethodException
  HResult=0x80131513
  Message=Method not found: 'System.Collections.Generic.List`1<System.Action`2<Swashbuckle.AspNetCore.Swagger.SwaggerDocument,Microsoft.AspNetCore.Http.HttpRequest>> Swashbuckle.AspNetCore.Swagger.SwaggerOptions.get_PreSerializeFilters()'.

It was thrown from the application builder UseSwagger().
I did disabled OpenApi document generate as it was causing the build to fail:
<PropertyGroup>
	<OpenApiGenerateDocuments>false</OpenApiGenerateDocuments>
</PropertyGroup>

I have no clue if it is related or not.
Thank you,

I can't explain why this is happening. Please create a minimal app (i.e. start from empty project) taht repro's the issue and post to github where I can pull down and troubleshoot.. Thx

We are using an in house framework and it turns out they reference version 4.0.1. I was not aware of it so I just added the latest version at our end. Once I matched the version it was working fine.


add pagination 10 and hotfix getVotesForDelegate

paginate every 10 past votes
hotfix getVotesForDelegate

Hi @kent I made this loom video where I added a 2s delay to the infinite scroll so you could see it better, this is what you meant right? https://www.loom.com/share/0a0fd54fa0c847a0905cee3371e505aa?sid=11e1a376-03c3-48dc-a1cc-94c606b92044


Added a CI workflow to use GitHub actions to build.

@nipafx This CI workflow (GitHub Action) will use GitHub's infrastructure to build and test on a push event. The Maven project builds on JDK 16ea for Windows, MacOS and Linux.

Thanks, @carldea. Merged.


html2canvas is not able to render full html on canvas in firefox and ie11. ( even though size of canvas is full  )

My html element is
const arr = [ 1,2,3, ........................... ,400 ];

	{
		arr.map(item =>
{item}
)
	}

javascript code :
html2canvas( document.getElementById( 'divToPrint' ) )
		.then( ( canvas ) => {
		      // canvas with cutt off image on firefox and ie11.
		});

html2canvas version tested with: "html2canvas": "^1.0.0-rc.7",
Browser & version: firefox and IE11
Operating system: windows

Hello,  do how  you solve the problem?


Proposal: Deprecate CloudSlang

SUMMARY
Begin deprecating CloudSlang runner
ISSUE TYPE

Other

The CloudSlang runner has always been marked "Experimental". It has seen no recent updates, and very little traction from users. Now that Orchestra has been released, we need to begin consolidating workflow engine choices.
We should begin the CloudSlang deprecation in 2.9, with formal removal in 3.1.
Tasks:

[x] Mark CloudSlang as DEPRECATED in Changelog
[x] Update documentation to show CloudSlang as deprecated
[ ] Create WARNING logs on pack registration if any packs are using CloudSlang

In 3.1, change WARNING logs to FATAL errors if packs use CloudSlang runner, and remove remaining documentation references.

:100:
I don't remember a single question during the last 12 months about it and TBH I even forgot that we have such runner at all.

Thinking a little more about it, I think what we'll do is stop shipping this runner as part of core, but leave the code available somewhere for people who wish to use it. Makes it clear that there's no ongoing testing/development of this by the ST2 team.

@LindsayHill +1, when we make runner pluggable w/ stevedore. This means when we are ready to deprecate Mistral, we do the same for related codes.

Has been removed


[lexical] Bug Fix: Make reconcileParagraphFormat respect editor readonly state

Description

As described in #6290, reconcileParagraphFormat attempts to update an element's format regardless of whether the current editor is editable or not
This PR implements a check similar to what's in reconcileBlockDirection, (as suggested by @etrepum)

Closes #6290
Test plan
Before

Go to https://playground.lexical.dev/ (or local version)
Copy content from https://lexical.dev/docs/intro. Specifically, start selecting from the header and to the right, so the whole page gets selected.
Paste content into clean playground editor
Add a bit of text anywhere
Now, use the Undo command
An error is printed to the console

https://github.com/facebook/lexical/assets/12970405/642ab539-0291-488f-9259-781188e0b00e
After

Go to https://playground.lexical.dev/ (or local version)
Copy content from https://lexical.dev/docs/intro. Specifically, start selecting from the header and to the right, so the whole page gets selected.
Paste content into clean playground editor
Add a bit of text anywhere
Now, use the Undo command
No error appears

some flaky test failures in e2e run for this PR https://github.com/facebook/lexical/actions/runs/9581395545/job/26418096107 , need to update https://github.com/facebook/lexical/discussions/6289


Problems with rkmpv UHD h264 video playback

I have installed bionic-lxde-rock64-0.6.44-239-arm64 on my rock64 4GB but when I try to play a UHD h264 mp4 file using rkmpv I get very jerky video playback (maybe 2/3 fps) with lots of green horizontal lines obscuring the video. These same videos play fine under LibreELEC on my rock64 and with mpv under bionic (Ubuntu 18.04) amd64.
Here is the mediainfo output from one such video:
$ mediainfo 20180223_223347.mp4
General
Complete name                            : 20180223_223347.mp4
Format                                   : MPEG-4
Format profile                           : Base Media / Version 2
Codec ID                                 : mp42 (isom/mp42)
File size                                : 598 MiB
Duration                                 : 1 min 44 s
Overall bit rate                         : 48.0 Mb/s
Performer                                : LGE
Encoded date                             : UTC 2018-02-23 22:35:33
Tagged date                              : UTC 2018-02-23 22:35:33
com.android.version                      : 6.0

Video
ID                                       : 1
Format                                   : AVC
Format/Info                              : Advanced Video Codec
Format profile<EMAIL_ADDRESS>Format settings                          : CABAC / 1 Ref Frames
Format settings, CABAC                   : Yes
Format settings, ReFrames                : 1 frame
Format settings, GOP                     : M=1, N=31
Codec ID                                 : avc1
Codec ID/Info                            : Advanced Video Coding
Duration                                 : 1 min 44 s
Source duration                          : 1 min 44 s
Bit rate                                 : 48.0 Mb/s
Width                                    : 3 840 pixels
Height                                   : 2 160 pixels
Display aspect ratio                     : 16:9
Frame rate mode                          : Variable
Frame rate                               : 29.937 FPS
Minimum frame rate                       : 29.183 FPS
Maximum frame rate                       : 30.706 FPS
Color space                              : YUV
Chroma subsampling                       : 4:2:0
Bit depth                                : 8 bits
Scan type                                : Progressive
Bits/(Pixel*Frame)                       : 0.193
Stream size                              : 596 MiB (100%)
Source stream size                       : 596 MiB (100%)
Title                                    : VideoHandle
Language                                 : English
Encoded date                             : UTC 2018-02-23 22:35:33
Tagged date                              : UTC 2018-02-23 22:35:33
mdhd_Duration                            : 104152

Audio
ID                                       : 2
Format                                   : AAC
Format/Info                              : Advanced Audio Codec
Format profile                           : LC
Codec ID                                 : mp4a-40-2
Duration                                 : 1 min 44 s
Source duration                          : 1 min 44 s
Source_Duration_FirstFrame               : 71 ms
Bit rate mode                            : Constant
Bit rate                                 : 156 kb/s
Nominal bit rate                         : 96.0 kb/s
Channel(s)                               : 2 channels
Channel positions                        : Front: L R
Sampling rate                            : 48.0 kHz
Frame rate                               : 46.875 FPS (1024 SPF)
Compression mode                         : Lossy
Stream size                              : 1.94 MiB (0%)
Source stream size                       : 1.94 MiB (0%)
Title                                    : SoundHandle
Language                                 : English
Encoded date                             : UTC 2018-02-23 22:35:33
Tagged date                              : UTC 2018-02-23 22:35:33
mdhd_Duration                            : 104446

I have now tried playing the same video with rkmpv under bionic-lxde-rock64-0.7.8-1061-arm64.img with exactly the same results.

I can upload a sample video if required?

I don't have any 4K TV or monitor, so I cannot test, but according to @LongChair the drmprime video plane needs to be the primary plane (note that the video plane is only used during hwdec), and the OSD plane (note: this actually displays the video when not  doing hwdec) should be an overlay plane. In addition the OSD plane should be downsized using --drm-osd-size to something like 1920x1080, because otherwise it will be too slow to render.
Try something like: --drm-osd-plane-id=0 --drm-video-plane-id=1 --drm-osd-size=1920x1080

It is possible, it just isn't neatly packaged yet. (If you wanted a painless experience, you bought the wrong device, I'm afraid. Also HDR support is still a ways away with Linux).
It seems you misinterpreted the meaning of --drm-osd-size. When playing the video hardware decoded (which you will. The rock64 can't even do 1080p software decoded) it will render at 4K, only the OSD is scaled down.
You'll have to build mpv 0.29 (check out https://github.com/avih/mpv-build) and then try using the command line options above.

I'm not so much bothered about HDR support as I don't have a HDR display but I would be interested in building the latest mpv if that option will fix 4K playback so please do post your full mpv configure / compile time options please.

@xantoz
Which distro have you used to build mpv from git? I have tried under both ayufan's bionic and his stretch distros but in both cases I am unable to install the required packages due to clashes over libEGL.so.1 / libmali or, in the case of Ubuntu, I am unable to install the xorg dev packages due to a clash with libglvnd.
It would also be good to know exactly which ffmpeg options you use.
Thanks

hi everyone, i'm also trying to playback a 4k video using latest bionic.
I installed the mali driver and using rkmpp, i seem to be able to get hardware decoding on 720p and i get the green lines mentioned by OP on the 4k videos.
i've managed to compile mpv 0.29 using mpv-build with ffmpeg_options
--enable-rkmpp
--enable-gpl
--enable-libx265
--enable-omx
--enable-libx264
--enable-libvpx
--enable-openal
--enable-version3
--enable-libdrm
--enable-ffplay
the thing i realized is that whether i use my custom built mpv 0.29 or the built in 0.28... i get software decoding for both 720p and 4k.
i'm guessing that i need to build rkmvp with my 0.29 mpv?

also running rkmpv --drm-mode=21 your_4k_video.mp4 seems to work for 4k 30fps videos but is rather jerky on 60fps.
at least you don't get the green lines

@danboid  for the EGL clash, this is what i ended up understanding.
you get software rendering using mesa by default but you can install the mali driver.
bother implementation (mali and mesa) provide an implementation of EGL and to get acceleration, you want to use the mali version.
to fix apt-get, i uninstalled mali (via dpkg purge). then i installed all the packages that i needed using mesa.
once i had everything, i force installed mali so that it overwrites the libEGL and then hardware acceleration seems to "work"

@fcoulombe
Thanks for sharing your build process / ffmpeg options.
I've not tried or seen anyone suggest using --drm-mode=21 yet - does this fix the green lines with the regular bionic 0.28 mpv build I wonder?
rkmpv is just a short shell script that calls mpv with a few specific (video output) options, its not a separate app.
I've not yet tried Armbian but I'm led to believe their kernel has some rockchip VPU support or at least it can be added on later.

`cat /usr/local/bin/rkmpv
#!/bin/bash
export LD_LIBRARY_PATH=/usr/lib/aarch64-linux-gnu/gbm:/usr/lib/arm-linux-gnueabihf/gbm
exec mpv --vo=gpu --gpu-context=drm --hwdec=rkmpp "$@"
`
so running this on my built 0.29, i get:
`./mpv --vo=gpu --gpu-context=drm --hwdec=rkmpp ~/Downloads/burn4k.mp4
Error parsing option gpu-context (option parameter could not be parsed)
Setting commandline option --gpu-context=drm failed.
Exiting... (Fatal error)
`

As per the script, you need to export that library path before you run mpv.

i did, i just didn't put it in the snipet

A lot of my videos require that I also use the switch --video-aspect=1.3333 (and --video-rotate=no) but using that rids of the letterboxing.
Excellent!

Great to hear that.
Maybe someone would extend and send PR to
https://github.com/ayufan-rock64/linux-build/blob/master/recipes/video-playback.md?
:)
Kamil
On Sat, Oct 27, 2018 at 8:39 PM Dan MacDonald<EMAIL_ADDRESS>wrote:

A lot of my videos require that I also use the switch
--video-aspect=1.3333 (and --video-rotate=no) but using that rids of the
letterboxing.
Excellent!
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@ayufan
I'd say rkmpv needs updating to include --drm-mode=21 as a standard option, unless this option is specific to certain models of rock64 or display type.

I don't know that.
On Sat, Oct 27, 2018 at 8:54 PM Dan MacDonald<EMAIL_ADDRESS>wrote:

@ayufan https://github.com/ayufan
I'd say rkmpv needs updating to include --drm-mode=21 as a standard
option, unless this option is specific to certain models of rock64 or
display type.
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i'd personally like to figure out what drm-mode actually does and whether it's actually desirable.
the forum post refered also to drm-mode=17 which i tried and did work but i think my kernel crashed when exiting play mode. i had to force reboot

So far its drm-mode=21 2, other options nil so until someone says otherwise, it would seem this is the recommended/working option
https://github.com/ayufan-rock64/linux-build/pull/295

I'm actually not sure about that.
It seems that --drm-mode defines used display modeset: https://github.com/mpv-player/mpv/blob/351c083487050c88adb0e3d60f2174850f869018/video/out/drm_common.c#L365. It likely depends on the video and target display.

for reference, i get the green lines on bunny video http://distribution.bbb3d.renderfarming.net/video/mp4/bbb_sunflower_2160p_60fps_normal.mp4
as far as target display, Asus MG28UQ (4k 60 fps hdmi input)

https://github.com/ayufan-rock64/linux-build/pull/296

I think me means he wants 60fps playback to work under mpv. mpv and kodi apparently use different versions of ffmpeg these days but I don't know much more that that. I read that causes a prob for rkmpp but YMMV.

i'd like it to play though ffplay but i'll manage with mpv.
i'm having issues right now where i can't make the video loop smoothly on mpv

mpv is a much more fully featured player than ffplay, or anything, actually.

@ayufan
Does mpv work for you (on rock64) without setting a drm mode?

As far as i remember, 4k can be achived on rock64, but only at 24fps, as 60fps will cause issues due to ddr bandwidth limitations. Now there might have been some kernel changes since last time i worked on this.
Regarding drmmode fixed setting, it doesn’t mean anything as the number is the mode you are using and it depends mostly on your display, so will only work for you :)

Most of my home videos, which are of a similar format to the one I posted details of in the OP, cause mpv to fall back to software rendering after it receives a few "errinfo frames" . At that point I have to quit out of mpv quickly after this happens or mpv/X freezes.
Where should I report such bugs @LongChair ? I get the impression @ayufan hasn't had much involvement with the graphics driver dev stuff?

@fcoulombe
Could you please give the exact, full commands you used to temp remove libmali, so as to be able to install the likes of xorg/sdl2-dev etc? I tried using dpkg --purge to remove mpv and the libmali... gles driver but apt wasn't letting it.
Maybe you had to use update-alternatives to switch to mesa first or did you end up hacking the dpkg status file to do this?

Having re-read this thread, I noticed that the forum post that recommended --drm-mode 21 says this is actually for a 1080 resolution, so maybe I did cry 'smooth 4K' too soon?
It would seem that getting the values to use with --drm-mode could be provide by this mpv PR by @xantoz , although I've not tried it yet:
https://github.com/mpv-player/mpv/pull/5875/commits/529ba575ee8e58a9c4925d192f719bf23bbab4a0

I've tried
mpv --drm-mode=-1
and
mpv --drm-mode=99999999999999
but both just print the standard mpv help text which lists some ffmpeg lib versions and some other basic options but nothing about drm mode options.

Could you please give the exact, full commands you used to temp remove libmali,

i think it was those:
force install sudo apt-get -o Dpkg::Options::="--force-overwrite" install libmali-rk-utgard-450-r7p0
force purge sudo dpkg --purge --force-all libmali-rk-utgard-450-r7p0
just install whatever packages you need and then "patch" your libs with the force install

@fcoulombe
That does indeed appear to be the correct Debian voodoo required to do-do d'GPU dance!
Thanks!

Maybe we should fix conflicts in mali package:
https://github.com/ayufan-rock64/libmali? :)
On Tue, Oct 30, 2018 at 10:35 PM Dan MacDonald<EMAIL_ADDRESS>wrote:

@fcoulombe https://github.com/fcoulombe
That does indeed appear to be the correct Debian voodoo required to do-do
d'GPU dance!
Thanks!
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Even better, yes!

@ayufan  @LongChair
I think this would be the place to report rockchip hardware video decoding bugs, right?
https://github.com/rockchip-linux/mpp

I would like to more actively test and report bugs for the rock64 video decoder but I'm not sure of the best way to do this. If I use ayufan's ubuntu, what versions of the libs etc is it using? Is it using the official rockchip libs or is it using the forked versions that eg Libreelec uses?
Maybe I'd be better off using armbian and building the various libs myself or using one of the rockchip developers armbian repos to do the testing?
Your thoughts and advice please!

It may help to someone struggling with slow video playback.
I too have problems playing videos on Rock64, and tried --drm-mode=21 option for mpv as it's suggested here. It didn't work but I've got the output:
[vo/gpu/opengl/kms] Bad mode index (max = 9).
[vo/gpu/opengl/kms] Available modes:
[vo/gpu/opengl/kms]   Mode 0: 1920x1080<EMAIL_ADDRESS>[vo/gpu/opengl/kms]   Mode 1: 1600x1200<EMAIL_ADDRESS>[vo/gpu/opengl/kms]   Mode 2: 1680x1050<EMAIL_ADDRESS>[vo/gpu/opengl/kms]   Mode 3: 1280x1024<EMAIL_ADDRESS>[vo/gpu/opengl/kms]   Mode 4: 1440x900<EMAIL_ADDRESS>[vo/gpu/opengl/kms]   Mode 5: 1280x960<EMAIL_ADDRESS>[vo/gpu/opengl/kms]   Mode 6: 1280x720<EMAIL_ADDRESS>[vo/gpu/opengl/kms]   Mode 7: 1024x768<EMAIL_ADDRESS>[vo/gpu/opengl/kms]   Mode 8: 800x600<EMAIL_ADDRESS>[vo/gpu/opengl/kms]   Mode 9: 640x480<EMAIL_ADDRESS>
The smooth playback can be achieved with Mode 7 at most in my case.


[Tooling] fix and add makefile targets

Summary

Adds the following make targets:

query_tx
query_tx_json
query_tx_log
params_get_tokenomics
params_get_proof
params_get_shared
params_get_service

Fixes the following make targets:

supplier1_unstake
supplier2_unstake
supplier3_unstake

Issue

Observations while working on #799

Type of change
Select one or more from the following:

[ ] New feature, functionality or library
[ ] Consensus breaking; add the consensus-breaking label if so. See #791 for details
[ ] Bug fix
[x] Code health or cleanup
[ ] Documentation
[ ] Other (specify)

Testing

[ ] Documentation: make docusaurus_start; only needed if you make doc changes
[ ] Unit Tests: make go_develop_and_test
[ ] LocalNet E2E Tests: make test_e2e
[ ] DevNet E2E Tests: Add the devnet-test-e2e label to the PR.

Sanity Checklist

[x] I have tested my changes using the available tooling
[x] I have commented my code
[x] I have performed a self-review of my own code; both comments & source code
[ ] I create and reference any new tickets, if applicable
[ ] I have left TODOs throughout the codebase, if applicable

Merging despite (unrelated) CI failures.
CI passed as of 747cb12 and the only subsequent commit (0a58920) adds/removes comments.


VSTGUI not cleanly exiting on Windows on plugin deletion

Recently, I've noticed that on removing the plugin (or closing Bitwig) on Windows an exception is often thrown:

I tried to debug into the stack, and the exception is being thrown by the assert here:
https://github.com/steinbergmedia/vstgui/blob/addf12b9486fd9460a3abf2cf2a2dd96c44ed807/vstgui/lib/platform/win32/win32directcomposition.cpp#L647
I presume this means that something has not been cleared that is expected, but I'm not knowledgeable enough on VSTGUI to have much more insight. I went back to the revision before the couple of PRs I recently made, and it still has the same problem, so it's not new - I just didn't notice it previously (it sometimes seems to popup in the background and briefly show on the taskbar).

on well darnit cleanup is hard
there's a call to VSTGUI::Exit in a finally inside clap-saw-demo-editor - for now just comment that out on windows i bet

Seems fixed with my other changes in #19


Unmounting and re-mounting existing fields gives weird issues

Describe the bug
I'm making a wizard type input flow and thus I'm mounting and unmounting input fields whenever I need them.
In this flow I'm using a mix of manual inputs and actual registered components. De manual input is stable and works as expected. The registered one however does not.

When re-mounting it, it forgets it's previously set value
In my actual implementation, my watch often doesn't get registered properly or something and I do not get component updates when I type in the input (it seems to be working stable in my sample...)

To Reproduce
I've made a small copy of my setup here: https://codesandbox.io/s/react-hook-form-basic-tqfe9?fontsize=14
The first button input is completely manual and if you go back to that step, you will see that your choice is correctly remembered.
The second input is a number input, if you have entered something I want to show a next button (therefore the watch) and I also want to move to the next panel if you press enter (therefore the strange register code).
If you enter an age and come back to that panel, you will see that it is empty.
Expected behavior
As the field is registered, and the value is entered into the form, I would expect it to remain there and when I re-mount the input field with the same name, that I see my previous value again.
Desktop (please complete the following information):

OS: Windows
Browser: Chrome
Version: 76.0.3809.87 (64-bits)

This is actually the expected behavior:

for registered input with ref, when input unmount. The input will get automatically unregistered hence value is lost
for custom registered input without ref, when input unmount. you will have to manually unregister yourself to during useEffect ( i will update that in the documentation)

Solution:

To achieve your Wizzard form. I would recommend using validationFields, so restrict fields to be validated per step. So you basically show and hide inputs.
Build the form to remember input state with state management lib: I have built one maybe can be useful to you https://github.com/bluebill1049/little-state-machine

That seems a bit extreme for a simple usecase not? I mean, a simple boolean inside react-hook-form that switched between remembering and forgetting would be enough to make this work I think?

your example unmounts a component, it shouldn't remember the state.

as long as you do not unmount the input, then the state will remain. Just like a normal input component.

https://codesandbox.io/s/react-hook-form-unmounting-fields-0tvoe

So you would suggest keeping the components mounted and just toggle their visibility.
That way hook-form can still manage the state and do the validations.
I'll think about a bit and see what would work best for me.
Thanks you for your time!


Update lua.rb

Correct way to install liblua5.2-dev

Labelling as wontfix and closing, as the package being installed is incorrect and unsupported by nginx.


[Update Request]: New Version for qBittorrent

What type of update are you requesting?
A new version of an existing package
Current Package Identifier
qBittorrent.qBittorrent
Package Version
5.0.1
Please describe the changes you would like to see
What type of update are you requesting?
A new version of an existing package
Current Package Identifier
qBittorrent.qBittorrent
Package Version
5.0.1
Please describe the changes you would like to see
There is a new release available.

#186410

[!NOTE]
Close with reason: Resolved.

Resolved by #186410

CC @stephengillie @H-Jonathan

Note
Close with reason: Resolved.

Resolved by New version: qBittorrent.qBittorrent version 5.0.1 #186410

CC @stephengillie @H-Jonathan


Added Makefile flexibility to use in CI and dev builds

Made user (for dockerhub) and tag configurable to be used in CI
changed env var to set debug in the plugin from DVV_... to VDVS_...
improved makefile info reporting
made default plugin tag to be constructed from git current tag

Contributes to #886
tested: manual runs and demo to @shuklanirdesh82  :-)
NOTE: Managed plugin are out of "experimental" in  Docker 1.13, so it works on 1.13 or new versioning scheme (Docker '17.xx...')
//CC @shuklanirdesh82

CI failing on the known issue VolumeDriver.Create: Internal issue: ret != 0 but errno is not set. Cancelling operation.
Merging


ReadMe is too sparse

Please flesh out the ReadMe - I know I'm a noob, but it would be really helpful to people if you had more information about what the package does and how it helps written in the ReadMe.

I 100% agree. What's a "run provider?" How do I set them?


drivers: clock_control_nrf: Fix releasing/stopping of HFCLK

After switching to the new ZTEST API, the test_bt_interrupted test in the nrf_onoff_and_bt suite for the clock_control driver started failing. This revealed a bug in this test (see the "tests: clock_control: nrf_onoff_and_bt: Add clearing of test_end" commit for details) and after it was fixed, it turned out that the HFCLK stopping routines in the clock_control_nrf driver do not entirely work as they are supposed to (and as that broken test falsely confirmed).
This PR fixes both the test (with some additional clean-up) and the driver.

@cvinayak Could you please check if these changes do not significantly degrade the BLE controller performance as it was in #24334.

@anangl Does this fix #49996?

@anangl Is this fix for #49996?

Yes, for the nrf_onoff_and_bt part of the issue. And the nrf_lf_clock_start* tests should be fixed by #52333.


Allow users to drag and drop boards

Graph / Workspace Outline

[x] Allow users to select and delete chiclets on the board
[x] Show drag status when a board is dragged onto a board port
[x] Allow users to drag boards from the Workspace Outline to board ports
[x] Support users double-clicking on chiclets to move between boards
[x] Allow users to drag boards from the Workspace Outline to the Workspace Outline
[x] Allow users to drag from sub boards to board ports in graph
[x] Allow users to drag from sub boards to the Workspace Outline
[ ] Show drop areas for drag and drop boards
[ ] Add icon to drop connectors
[ ] Allow users to jump to modules
[ ] Prevent drag and drop of non-runnable modules

Configurator

[ ] Update the node configurator from a dropdown to a better selector

Breadboard

[ ] #3965
[x] #3977
[ ] Update all kits to be URL-based

Later

[ ] Support icons for sub boards
[ ] Handle adjacent ports in the graph which have an array of boards

You should now be able to drop boards and modules directly into VE from workspace outline to create new components from them.


Request: Add --playlist-length option

Before downloading a playlist, youtube-dl prints out the total number of items. It would be great to have an option that only prints this number.

Duplicate of #4238.