RuntimeError: TOCO failed see console for info.

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System information

Have I written custom code (as opposed to using a stock example script provided in TensorFlow):
OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device:
TensorFlow installed from (source or binary):
TensorFlow version (use command below):
Python version:
Bazel version (if compiling from source):
GCC/Compiler version (if compiling from source):
CUDA/cuDNN version:
GPU model and memory:
Exact command to reproduce:

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python -c "import tensorflow as tf; print(tf.GIT_VERSION, tf.VERSION)"
Describe the problem
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Source code / logs
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i download the ssd_mobilenet_v1_quantized_coco  model from https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md
i want to quantize it ,then used on mobile,but i got  the error:
WARNING:tensorflow:From quant.py:9: TocoConverter.from_frozen_graph (from tensorflow.contrib.lite.python.lite) is deprecated and will be removed in a future version. Instructions for updating: Use lite.TFLiteConverter.from_frozen_graphinstead. 2018-10-12 13:26:50.610018: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA but ^H^H^HTraceback (most recent call last):   File "quant.py", line 17, in <module>     mobilenet_tflite_file.write_bytes(converter.convert())   File "/usr/local/lib/python3.5/dist-packages/tensorflow/contrib/lite/python/lite.py", line 464, in convert     **converter_kwargs)   File "/usr/local/lib/python3.5/dist-packages/tensorflow/contrib/lite/python/convert.py", line 317, in toco_convert_graph_def     input_data.SerializeToString())   File "/usr/local/lib/python3.5/dist-packages/tensorflow/contrib/lite/python/convert.py", line 135, in toco_convert_protos     (stdout, stderr)) RuntimeError: TOCO failed see console for info. b'2018-10-12 13:26:54.789335: I tensorflow/contrib/lite/toco/import_tensorflow.cc:1109] Converting unsupported operation: TFLite_Detection_PostProcess\n2018-10-12 13:26:54.795610: I tensorflow/contrib/lite/toco/import_tensorflow.cc:1182] Unable to determine output type for op: TFLite_Detection_PostProcess\n2018-10-12 13:26:54.838750: I tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.cc:39] Before Removing unused ops: 900 operators, 1352 arrays (0 quantized)\n2018-10-12 13:26:54.886856: I tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.cc:39] Before general graph transformations: 900 operators, 1352 arrays (0 quantized)\n2018-10-12 13:26:56.012297: I tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.cc:39] After general graph transformations pass 1: 111 operators, 220 arrays (0 quantized)\n2018-10-12 13:26:56.016248: I tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.cc:39] Before dequantization graph transformations: 111 operators, 220 arrays (0 quantized)\n2018-10-12 13:26:56.022161: I tensorflow/contrib/lite/toco/allocate_transient_arrays.cc:345] Total transient array allocated size: 11520000 bytes, theoretical optimal value: 11520000 bytes.\n2018-10-12 13:26:56.022669: I tensorflow/contrib/lite/toco/toco_tooling.cc:397] Estimated count of arithmetic ops: 2.49483 billion (note that a multiply-add is counted as 2 ops).\n2018-10-12 13:26:56.023102: W tensorflow/contrib/lite/toco/tflite/export.cc:423] FAKE_QUANT operation {FakeQuant operator with output FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/act_quant/FakeQuantWithMinMaxVars} was not converted. If running quantized make sure you are passing --inference_type=QUANTIZED_UINT8 and values for --std_values and --mean_values.\n2018-10-12 13:26:56.023121: W tensorflow/contrib/lite/toco/tflite/export.cc:423] FAKE_QUANT operation {FakeQuant operator with output FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_depthwise/act_quant/FakeQuantWithMinMaxVars} was not converted. If running quantized make sure you are passing --inference_type=QUANTIZED_UINT8 and values for --std_values and --mean_values.\n2018-10-12 13:26:56.023129: W tensorflow/contrib/lite/toco/tflite/export.cc:423] FAKE_QUANT operation {FakeQuant operator with output FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_pointwise/act_quant/FakeQuantWithMinMaxVars} was not converted. If running quantized make sure you are passing --inference_type=QUANTIZED_UINT8 and values for --std_values and --mean_values.\n2018-10-12 13:26:56.023137: W tensorflow/contrib/lite/toco/tflite/export.cc:423] FAKE_QUANT operation {FakeQuant operator with output FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_depthwise/act_quant/FakeQuantWithMinMaxVars} was not converted. If running quantized make sure you are passing --inference_type=QUANTIZED_UINT8 and values for --std_values and --mean_values.\n2018-10-12 13:26:56.023144: W tensorflow/contrib/lite/toco/tflite/export.cc:423] FAKE_QUANT operation {FakeQuant operator with output FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_pointwise/act_quant/FakeQuantWithMinMaxVars} was not converted. If running quantized make sure you are passing --inference_type=QUANTIZED_UINT8 and values for --std_values and --mean_values.\n2018-10-12 13:26:56.023152: W tensorflow/contrib/lite/toco/tflite/export.cc:423] FAKE_QUANT operation {FakeQuant operator with output FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_depthwise/act_quant/FakeQuantWithMinMaxVars} was not converted. If running quantized make sure you are passing --inference_type=QUANTIZED_UINT8 and values for --std_values and --mean_values.\n2018-10-12 13:26:56.023160: W tensorflow/contrib/lite/toco/tflite/export.cc:423] FAKE_QUANT operation {FakeQuant operator with output FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_pointwise/act_quant/FakeQuantWithMinMaxVars} was not converted. If running quantized make sure you are passing --inference_type=QUANTIZED_UINT8 and values for --std_values and --mean_values.\n2018-10-12 13:26:56.023170: W tensorflow/contrib/lite/toco/tflite/export.cc:423] FAKE_QUANT operation {FakeQuant operator with output FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_depthwise/act_quant/FakeQuantWithMinMaxVars} was not converted. If running quantized make sure you are passing --inference_type=QUANTIZED_UINT8 and values for --std_values and --mean_values.\n2018-10-12 13:26:56.023179: W tensorflow/contrib/lite/toco/tflite/export.cc:423] FAKE_QUANT operation {FakeQuant operator with output FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_pointwise/act_quant/FakeQuantWithMinMaxVars} was not converted. If running quantized make sure you are passing --inference_type=QUANTIZED_UINT8 and values for --std_values and --mean_values.\n2018-10-12 13:26:56.023187: W tensorflow/contrib/lite/toco/tflite/export.cc:423] FAKE_QUANT operation {FakeQuant operator with output FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_depthwise/act_quant/FakeQuantWithMinMaxVars} was not converted. If running quantized make sure you are passing --inference_type=QUANTIZED_UINT8 and values for --std_values and --mean_values.\n2018-10-12 13:26:56.023198: W tensorflow/contrib/lite/toco/tflite/export.cc:423] FAKE_QUANT operation {FakeQuant operator with output FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_pointwise/act_quant/FakeQuantWithMinMaxVars} was not converted. If running quantized make sure you are passing --inference_type=QUANTIZED_UINT8 and values for --std_values and --mean_values.\n2018-10-12 13:26:56.023207: W tensorflow/contrib/lite/toco/tflite/export.cc:423] FAKE_QUANT operation {FakeQuant operator with output FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_depthwise/act_quant/FakeQuantWithMinMaxVars} was not converted. If running quantized make sure you are passing --inference_type=QUANTIZED_UINT8 and values for --std_values and --mean_values.\n2018-10-12 13:26:56.023214: W tensorflow/contrib/lite/toco/tflite/export.cc:423] FAKE_QUANT operation {FakeQuant operator with output FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_pointwise/act_quant/FakeQuantWithMinMaxVars} was not converted. If running quantized make sure you are passing --inference_type=QUANTIZED_UINT8 and values for --std_values and --mean_values.\n2018-10-12 13:26:56.023223: W tensorflow/contrib/lite/toco/tflite/export.cc:423] FAKE_QUANT operation {FakeQuant operator with output FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_depthwise/act_quant/FakeQuantWithMinMaxVars} was not converted. If running quantized make sure you are passing --inference_type=QUANTIZED_UINT8 and values for --std_values and --mean_values.\n2018-10-12 13:26:56.023234: W tensorflow/contrib/lite/toco/tflite/export.cc:423] FAKE_QUANT operation {FakeQuant operator with output FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_pointwise/act_quant/FakeQuantWithMinMaxVars} was not converted. If running quantized make sure you are passing --inference_type=QUANTIZED_UINT8 and values for --std_values and --mean_values.\n2018-10-12 13:26:56.023243: W tensorflow/contrib/lite/toco/tflite/export.cc:423] FAKE_QUANT operation {FakeQuant operator with output FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_depthwise/act_quant/FakeQuantWithMinMaxVars} was not converted. If running quantized make sure you are passing --inference_type=QUANTIZED_UINT8 and values for --std_values and --mean_values.\n2018-10-12 13:26:56.023253: W tensorflow/contrib/lite/toco/tflite/export.cc:423] FAKE_QUANT operation {FakeQuant operator with output FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_pointwise/act_quant/FakeQuantWithMinMaxVars} was not converted. If running quantized make sure you are passing --inference_type=QUANTIZED_UINT8 and values for --std_values and --mean_values.\n2018-10-12 13:26:56.023261: W tensorflow/contrib/lite/toco/tflite/export.cc:423] FAKE_QUANT operation {FakeQuant operator with output FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_depthwise/act_quant/FakeQuantWithMinMaxVars} was not converted. If running quantized make sure you are passing --inference_type=QUANTIZED_UINT8 and values for --std_values and --mean_values.\n2018-10-12 13:26:56.023269: W tensorflow/contrib/lite/toco/tflite/export.cc:423] FAKE_QUANT operation {FakeQuant operator with output FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_pointwise/act_quant/FakeQuantWithMinMaxVars} was not converted. If running quantized make sure you are passing --inference_type=QUANTIZED_UINT8 and values for --std_values and --mean_values.\n2018-10-12 13:26:56.023277: W tensorflow/contrib/lite/toco/tflite/export.cc:423] FAKE_QUANT operation {FakeQuant operator with output FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_depthwise/act_quant/FakeQuantWithMinMaxVars} was not converted. If running quantized make sure you are passing --inference_type=QUANTIZED_UINT8 and values for --std_values and --mean_values.\n2018-10-12 13:26:56.023285: W tensorflow/contrib/lite/toco/tflite/export.cc:423] FAKE_QUANT operation {FakeQuant operator with output FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_pointwise/act_quant/FakeQuantWithMinMaxVars} was not converted. If running quantized make sure you are passing --inference_type=QUANTIZED_UINT8 and values for --std_values and --mean_values.\n2018-10-12 13:26:56.023292: W tensorflow/contrib/lite/toco/tflite/export.cc:423] FAKE_QUANT operation {FakeQuant operator with output FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_depthwise/act_quant/FakeQuantWithMinMaxVars} was not converted. If running quantized make sure you are passing --inference_type=QUANTIZED_UINT8 and values for --std_values and --mean_values.\n2018-10-12 13:26:56.023301: W tensorflow/contrib/lite/toco/tflite/export.cc:423] FAKE_QUANT operation {FakeQuant operator with output FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_pointwise/act_quant/FakeQuantWithMinMaxVars} was not converted. If running quantized make sure you are passing --inference_type=QUANTIZED_UINT8 and values for --std_values and --mean_values.\n2018-10-12 13:26:56.023309: W tensorflow/contrib/lite/toco/tflite/export.cc:423] FAKE_QUANT operation {FakeQuant operator with output FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_depthwise/act_quant/FakeQuantWithMinMaxVars} was not converted. If running quantized make sure you are passing --inference_type=QUANTIZED_UINT8 and values for --std_values and --mean_values.\n2018-10-12 13:26:56.023318: W tensorflow/contrib/lite/toco/tflite/export.cc:423] FAKE_QUANT operation {FakeQuant operator with output FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_pointwise/act_quant/FakeQuantWithMinMaxVars} was not converted. If running quantized make sure you are passing --inference_type=QUANTIZED_UINT8 and values for --std_values and --mean_values.\n2018-10-12 13:26:56.023327: W tensorflow/contrib/lite/toco/tflite/export.cc:423] FAKE_QUANT operation {FakeQuant operator with output FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_depthwise/act_quant/FakeQuantWithMinMaxVars} was not converted. If running quantized make sure you are passing --inference_type=QUANTIZED_UINT8 and values for --std_values and --mean_values.\n2018-10-12 13:26:56.023336: W tensorflow/contrib/lite/toco/tflite/export.cc:423] FAKE_QUANT operation {FakeQuant operator with output FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_pointwise/act_quant/FakeQuantWithMinMaxVars} was not converted. If running quantized make sure you are passing --inference_type=QUANTIZED_UINT8 and values for --std_values and --mean_values.\n2018-10-12 13:26:56.023345: W tensorflow/contrib/lite/toco/tflite/export.cc:423] FAKE_QUANT operation {FakeQuant operator with output FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_2_1x1_256/act_quant/FakeQuantWithMinMaxVars} was not converted. If running quantized make sure you are passing --inference_type=QUANTIZED_UINT8 and values for --std_values and --mean_values.\n2018-10-12 13:26:56.023354: W tensorflow/contrib/lite/toco/tflite/export.cc:423] FAKE_QUANT operation {FakeQuant operator with output FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_512/act_quant/FakeQuantWithMinMaxVars} was not converted. If running quantized make sure you are passing --inference_type=QUANTIZED_UINT8 and values for --std_values and --mean_values.\n2018-10-12 13:26:56.023363: W tensorflow/contrib/lite/toco/tflite/export.cc:423] FAKE_QUANT operation {FakeQuant operator with output FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_3_1x1_128/act_quant/FakeQuantWithMinMaxVars} was not converted. If running quantized make sure you are passing --inference_type=QUANTIZED_UINT8 and values for --std_values and --mean_values.\n2018-10-12 13:26:56.023372: W tensorflow/contrib/lite/toco/tflite/export.cc:423] FAKE_QUANT operation {FakeQuant operator with output FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_3_3x3_s2_256/act_quant/FakeQuantWithMinMaxVars} was not converted. If running quantized make sure you are passing --inference_type=QUANTIZED_UINT8 and values for --std_values and --mean_values.\n2018-10-12 13:26:56.023381: W tensorflow/contrib/lite/toco/tflite/export.cc:423] FAKE_QUANT operation {FakeQuant operator with output FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_4_1x1_128/act_quant/FakeQuantWithMinMaxVars} was not converted. If running quantized make sure you are passing --inference_type=QUANTIZED_UINT8 and values for --std_values and --mean_values.\n2018-10-12 13:26:56.023389: W tensorflow/contrib/lite/toco/tflite/export.cc:423] FAKE_QUANT operation {FakeQuant operator with output FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_4_3x3_s2_256/act_quant/FakeQuantWithMinMaxVars} was not converted. If running quantized make sure you are passing --inference_type=QUANTIZED_UINT8 and values for --std_values and --mean_values.\n2018-10-12 13:26:56.023398: W tensorflow/contrib/lite/toco/tflite/export.cc:423] FAKE_QUANT operation {FakeQuant operator with output FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_5_1x1_64/act_quant/FakeQuantWithMinMaxVars} was not converted. If running quantized make sure you are passing --inference_type=QUANTIZED_UINT8 and values for --std_values and --mean_values.\n2018-10-12 13:26:56.023407: W tensorflow/contrib/lite/toco/tflite/export.cc:423] FAKE_QUANT operation {FakeQuant operator with output FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_5_3x3_s2_128/act_quant/FakeQuantWithMinMaxVars} was not converted. If running quantized make sure you are passing --inference_type=QUANTIZED_UINT8 and values for --std_values and --mean_values.\n2018-10-12 13:26:56.023416: W tensorflow/contrib/lite/toco/tflite/export.cc:423] FAKE_QUANT operation {FakeQuant operator with output BoxPredictor_0/BoxEncodingPredictor/act_quant/FakeQuantWithMinMaxVars} was not converted. If running quantized make sure you are passing --inference_type=QUANTIZED_UINT8 and values for --std_values and --mean_values.\n2018-10-12 13:26:56.023425: W tensorflow/contrib/lite/toco/tflite/export.cc:423] FAKE_QUANT operation {FakeQuant operator with output BoxPredictor_0/ClassPredictor/act_quant/FakeQuantWithMinMaxVars} was not converted. If running quantized make sure you are passing --inference_type=QUANTIZED_UINT8 and values for --std_values and --mean_values.\n2018-10-12 13:26:56.023434: W tensorflow/contrib/lite/toco/tflite/export.cc:423] FAKE_QUANT operation {FakeQuant operator with output BoxPredictor_1/BoxEncodingPredictor/act_quant/FakeQuantWithMinMaxVars} was not converted. If running quantized make sure you are passing --inference_type=QUANTIZED_UINT8 and values for --std_values and --mean_values.\n2018-10-12 13:26:56.023443: W tensorflow/contrib/lite/toco/tflite/export.cc:423] FAKE_QUANT operation {FakeQuant operator with output BoxPredictor_1/ClassPredictor/act_quant/FakeQuantWithMinMaxVars} was not converted. If running quantized make sure you are passing --inference_type=QUANTIZED_UINT8 and values for --std_values and --mean_values.\n2018-10-12 13:26:56.023452: W tensorflow/contrib/lite/toco/tflite/export.cc:423] FAKE_QUANT operation {FakeQuant operator with output BoxPredictor_2/BoxEncodingPredictor/act_quant/FakeQuantWithMinMaxVars} was not converted. If running quantized make sure you are passing --inference_type=QUANTIZED_UINT8 and values for --std_values and --mean_values.\n2018-10-12 13:26:56.023461: W tensorflow/contrib/lite/toco/tflite/export.cc:423] FAKE_QUANT operation {FakeQuant operator with output BoxPredictor_2/ClassPredictor/act_quant/FakeQuantWithMinMaxVars} was not converted. If running quantized make sure you are passing --inference_type=QUANTIZED_UINT8 and values for --std_values and --mean_values.\n2018-10-12 13:26:56.023469: W tensorflow/contrib/lite/toco/tflite/export.cc:423] FAKE_QUANT operation {FakeQuant operator with output BoxPredictor_3/BoxEncodingPredictor/act_quant/FakeQuantWithMinMaxVars} was not converted. If running quantized make sure you are passing --inference_type=QUANTIZED_UINT8 and values for --std_values and --mean_values.\n2018-10-12 13:26:56.023477: W tensorflow/contrib/lite/toco/tflite/export.cc:423] FAKE_QUANT operation {FakeQuant operator with output BoxPredictor_3/ClassPredictor/act_quant/FakeQuantWithMinMaxVars} was not converted. If running quantized make sure you are passing --inference_type=QUANTIZED_UINT8 and values for --std_values and --mean_values.\n2018-10-12 13:26:56.023484: W tensorflow/contrib/lite/toco/tflite/export.cc:423] FAKE_QUANT operation {FakeQuant operator with output BoxPredictor_4/BoxEncodingPredictor/act_quant/FakeQuantWithMinMaxVars} was not converted. If running quantized make sure you are passing --inference_type=QUANTIZED_UINT8 and values for --std_values and --mean_values.\n2018-10-12 13:26:56.023491: W tensorflow/contrib/lite/toco/tflite/export.cc:423] FAKE_QUANT operation {FakeQuant operator with output BoxPredictor_4/ClassPredictor/act_quant/FakeQuantWithMinMaxVars} was not converted. If running quantized make sure you are passing --inference_type=QUANTIZED_UINT8 and values for --std_values and --mean_values.\n2018-10-12 13:26:56.023499: W tensorflow/contrib/lite/toco/tflite/export.cc:423] FAKE_QUANT operation {FakeQuant operator with output BoxPredictor_5/BoxEncodingPredictor/act_quant/FakeQuantWithMinMaxVars} was not converted. If running quantized make sure you are passing --inference_type=QUANTIZED_UINT8 and values for --std_values and --mean_values.\n2018-10-12 13:26:56.023508: W tensorflow/contrib/lite/toco/tflite/export.cc:423] FAKE_QUANT operation {FakeQuant operator with output BoxPredictor_5/ClassPredictor/act_quant/FakeQuantWithMinMaxVars} was not converted. If running quantized make sure you are passing --inference_type=QUANTIZED_UINT8 and values for --std_values and --mean_values.\n2018-10-12 13:26:56.023519: F tensorflow/contrib/lite/toco/tflite/export.cc:460] Some of the operators in the model are not supported by the standard TensorFlow Lite runtime. If you have a custom implementation for them you can disable this error with --allow_custom_ops, or by setting allow_custom_ops=True when calling tf.contrib.lite.TFLiteConverter(). Here is a list of operators for which  you will need custom implementations: TFLite_Detection_PostProcess.\nAborted (core dumped)\n' None

Please provide details following details:
System information
Have I written custom code (as opposed to using a stock example script provided in TensorFlow):
OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device:
TensorFlow installed from (source or binary):
TensorFlow version (use command below):
Python version:
Bazel version (if compiling from source):
GCC/Compiler version (if compiling from source):
CUDA/cuDNN version:
GPU model and memory:
Exact command to reproduce:

INFO:
Have I written custom code (as opposed to using a stock example script provided in TensorFlow): No
OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Windows 10
Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device: No
TensorFlow installed from (source or binary): Anaconda
TensorFlow version (use command below): 1.11
Python version: 3.5
Bazel version (if compiling from source):
GCC/Compiler version (if compiling from source):
CUDA/cuDNN version:
GPU model and memory:
Exact command to reproduce:
Hi,
I get the same error as the above. And I've been using the example given in converter python api guide
import numpy as np
import tensorflow as tf
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Dense(2, input_shape=(3,)))
model.add(tf.keras.layers.RepeatVector(3))
model.add(tf.keras.layers.TimeDistributed(tf.keras.layers.Dense(3)))
model.compile(loss=tf.keras.losses.MSE,
optimizer=tf.keras.optimizers.RMSprop(lr=0.0001),
metrics=[tf.keras.metrics.categorical_accuracy],
sample_weight_mode='temporal')
x = np.random.random((1, 3))
y = np.random.random((1, 3, 3))
model.train_on_batch(x, y)
model.predict(x)
keras_file = "keras_model.h5"
tf.keras.models.save_model(model, keras_file)
converter = tf.contrib.lite.TFLiteConverter.from_keras_model_file(keras_file)
tflite_model = converter.convert()`
I ran this code and this is the error I get:

RuntimeError: TOCO failed see console for info.
b'Traceback (most recent call last):\r\n  File "C:\Users\sgavvala\AppData\Roaming\Python\Python35\site-packages\tensorflow\contrib\lite\toco\python\tensorflow_wrap_toco.py", line 18, in swig_import_helper\r\n    fp, pathname, description = imp.find_module('_tensorflow_wrap_toco', [dirname(file)])\r\n  File "c:\users\sgavvala\appdata\local\continuum\anaconda3\envs\tensorflow\lib\imp.py", line 297, in find_module\r\n    raise ImportError(_ERR_MSG.format(name), name=name)\r\nImportError: No module named '_tensorflow_wrap_toco'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File "c:\users\sgavvala\appdata\local\continuum\anaconda3\envs\tensorflow\lib\runpy.py", line 193, in _run_module_as_main\r\n    "main", mod_spec)\r\n  File "c:\users\sgavvala\appdata\local\continuum\anaconda3\envs\tensorflow\lib\runpy.py", line 85, in _run_code\r\n    exec(code, run_globals)\r\n  File "C:\Users\sgavvala\AppData\Local\Continuum\anaconda3\envs\tensorflow\Scripts\toco_from_protos.exe\main.py", line 5, in \r\n  File "C:\Users\sgavvala\AppData\Roaming\Python\Python35\site-packages\tensorflow\contrib\lite\toco\python\toco_from_protos.py", line 22, in \r\n    from tensorflow.contrib.lite.toco.python import tensorflow_wrap_toco\r\n  File "C:\Users\sgavvala\AppData\Roaming\Python\Python35\site-packages\tensorflow\contrib\lite\toco\python\tensorflow_wrap_toco.py", line 28, in \r\n    _tensorflow_wrap_toco = swig_import_helper()\r\n  File "C:\Users\sgavvala\AppData\Roaming\Python\Python35\site-packages\tensorflow\contrib\lite\toco\python\tensorflow_wrap_toco.py", line 20, in swig_import_helper\r\n    import _tensorflow_wrap_toco\r\nImportError: No module named '_tensorflow_wrap_toco'\r\n'
None

My plan is to convert a keras model with my custom layers into tflite quantized version. Figured I would start with a given example but that doesn't seem to run.

@ymodak  the system information is as fllowed:
Have I written custom code (as opposed to using a stock example script provided in TensorFlow):yes
import tensorflow as tf import pathlib archive_dir = pathlib.Path("/home/caojinrong/ssd_mobilenet_v1_quantized_coco/ssd_mobilenet_v1_quantized_300x300_coco14_sync_2018_07_18") print(str(archive_dir)) graph_def_file = pathlib.Path(archive_dir)/"tflite_graph.pb" input_arrays = ["normalized_input_image_tensor"] output_arrays = ["TFLite_Detection_PostProcess"] converter = tf.contrib.lite.TocoConverter.from_frozen_graph(  str(graph_def_file), input_arrays, output_arrays, input_shapes={"normalized_input_image_tensor":[1,300,300,3]}) converter.post_training_quantize = True mobilenet_tflite_file = graph_def_file.parent/"ssd_mobilenet_v1_coco_quantized.tflite" mobilenet_tflite_file.write_bytes(converter.convert()) ~
OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Linux Ubuntu 16.04
Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device: no
TensorFlow installed from (source or binary):
TensorFlow version (use command below):1.12.0-dev20181012
Python version: 3.5
Bazel version (if compiling from source):bazel-0.17.2-installer-linux-x86_64
GCC/Compiler version (if compiling from source):no
CUDA/cuDNN version :
GPU model and memory : 1080t
Exact command to reproduce:

@soumy94  Can you please open a new issue explaining your problem? You have a different system config and therefore its better to focus on it separately. Thanks!

@ymodak I have raised a new issue #22993

This is an issue with the Windows build. It's being tracked by https://github.com/tensorflow/tensorflow/issues/22897.

Let's keep this issue focused on the TFLite_Detection_PostProcess op. @achowdhery can advise on its use.

Nagging Assignee @achowdhery: It has been 14 days with no activity and this issue has an assignee. Please update the label and/or status accordingly.

Nagging Assignee @achowdhery: It has been 29 days with no activity and this issue has an assignee. Please update the label and/or status accordingly.

Closing this issue since its resolved. Feel free to reopen if still have problems. Thanks!


dataActions at management group scope limitation

It should be noted and called out in this document's limitation section that the use of role assignments containing dataActions  will not work when  assigned at management group scope, only subscription scoped assignments will work today. See ICM 215894589

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

ID: 1527b80e-5270-b5f0-9034-747c6f92ae4e
Version Independent ID: 50b9882c-dcb0-ecc1-241b-1361ad2fff1e
Content: Organize your resources with management groups - Azure Governance - Azure governance
Content Source: articles/governance/management-groups/overview.md
Service: governance
GitHub Login: @rthorn17
Microsoft Alias: rithorn

Thanks for the feedback @jasonfritts .
Assigning to content author @rthorn17 for review and updating document accordingly.

#reassign:DCtheGeek

#reassign:DCtheGeek

#label:"in-progress"

#label:"in-progress"

@jasonfritts Thanks for the feedback.  I've updated the article and as soon as the PM reviews, I'll merge in the change.  Doing so will automatically close this issue.  Thank you for pointing this out!

@jasonfritts Thanks for the feedback.  I've updated the article and as soon as the PM reviews, I'll merge in the change.  Doing so will automatically close this issue.  Thank you for pointing this out!

I see there used to be a mention of dataActions in Limitations section: fig install --input-method , but then it was removed in 02e8424. Should this issue be reopened?
@jasonfritts @DCtheGeek @timwarner-msft -- what do you think?

I'm not the content owner for Management Groups. @tfitzmac please review. Thanks,


Conditional syntax in index.html

Please provide us with the following information:

OS?

Ubuntu 16.04

Versions.

angular-cli: 1.0.0-beta.24
node: 7.2.1

Repro steps.

Was this an app that wasn't created using the CLI? What change did you do on your code? etc.
My app was created using the CLI

The log given by the failure.

Normally this include a stack trace and some more information.

Mention any other details that might be useful.

Thanks! We'll be in touch soon.

When you start the ng serve I use a dev environment. In my index.html the title tag written "Test". How to change title in index.html depending on the environment?
I tried to use the syntax as in this example: https://github.com/angular/mobile-toolkit/blob/master/hello-mobile/src/index.html
it's not work.
Thank's

unless i am mistaken this is something to ask the mobile toolkit folks. normally with an angular app the index html file is static.

@figuerres
not only mobile. Yes, In mobile app and desktop app index.html is static.
syntax works when build project. Example:
https://github.com/AngularClass/angular2-webpack-starter/blob/master/src/index.html
I want to know what syntax is used in build "ng build" What plugin is used in "ng build ". If it is not there then how to add it

@a50  the new link you give is totally different and has markup that looks like it goes for asp.net not for angular.
< %  is used by asp.net code on the server before the browser ever gets it.
if you just want to modify the title of the html i think you can do that in your app component code. I think this is a general angular / typescript / javascript issue not one that is for the cli

@A50    see if this is what you want:
https://angular.io/docs/ts/latest/cookbook/set-document-title.html

@figuerres sorry,  maybe I not clear ask the question. For webpack there is plugin html-webpack-plugin
https://github.com/jantimon/html-webpack-plugin
https://github.com/jantimon/html-webpack-plugin#writing-your-own-templates
this syntax is used by this plugin when building app on angular (asp.net nothing to do with)

This isn't a feature we are looking at adding.


API Failure

8/20/2020, 1:02:08 PM] [BLINKREX] Finished Launching
[8/20/2020, 1:02:08 PM] [BLINKREX] Discovering Cameras
[8/20/2020, 1:02:08 PM] [BLINKREX] Authenticating with Blink API as<EMAIL_ADDRESS>Making new blink client
[8/20/2020, 1:02:09 PM] [BLINKREX] Error authenticating with blink API BlinkAuthenticationException [Error]: Authentication problem: An app update is required
at Request._callback (/usr/local/lib/node_modules/homebridge-blinkcameras/node_modules/node-blink-security/lib/blink.js:396:25)
at Request.self.callback (/usr/local/lib/node_modules/homebridge-blinkcameras/node_modules/request/request.js:185:22)
at Request.emit (events.js:315:20)
at Request. (/usr/local/lib/node_modules/homebridge-blinkcameras/node_modules/request/request.js:1154:10)
at Request.emit (events.js:315:20)
at IncomingMessage. (/usr/local/lib/node_modules/homebridge-blinkcameras/node_modules/request/request.js:1076:12)
at Object.onceWrapper (events.js:421:28)
at IncomingMessage.emit (events.js:327:22)
at endReadableNT (_stream_readable.js:1220:12)
at processTicksAndRejections (internal/process/task_queues.js:84:21)

t8/20/2020, 1:02:08 PM] [BLINKREX] Finished Launching
[8/20/2020, 1:02:08 PM] [BLINKREX] Discovering Cameras
[8/20/2020, 1:02:08 PM] [BLINKREX] Authenticating with Blink API as<EMAIL_ADDRESS>Making new blink client
[8/20/2020, 1:02:09 PM] [BLINKREX] Error authenticating with blink API BlinkAuthenticationException [Error]: Authentication problem: An app update is required
at Request._callback (/usr/local/lib/node_modules/homebridge-blinkcameras/node_modules/node-blink-security/lib/blink.js:396:25)
at Request.self.callback (/usr/local/lib/node_modules/homebridge-blinkcameras/node_modules/request/request.js:185:22)
at Request.emit (events.js:315:20)
at Request. (/usr/local/lib/node_modules/homebridge-blinkcameras/node_modules/request/request.js:1154:10)
at Request.emit (events.js:315:20)
at IncomingMessage. (/usr/local/lib/node_modules/homebridge-blinkcameras/node_modules/request/request.js:1076:12)
at Object.onceWrapper (events.js:421:28)
at IncomingMessage.emit (events.js:327:22)
at endReadableNT (_stream_readable.js:1220:12)
at processTicksAndRejections (internal/process/task_queues.js:84:21)

@Sungamm  Would you be able to share how you created the Webhooks in IFTTT? I looked today and wasn’t clear on how to create a unique action

@Sungamm  Would you be able to share how you created the Webhooks in IFTTT? I looked today and wasn’t clear on how to create a unique action

Good Morning derfx4,
I assume you cannot understood german? ;-) I will try my best but it will be hard for us both :-D
Create an account at IFTTT.com if you dont have already. Afterwards click on "create" , -> Trigger "this" and search for "webhook". Enter a name for the Webhook (example blink-on). Click on That and search for blink. I use blink (Europe) because I am from Germany. I assume "blink" only is for Asia and the States. Choose "arm blink" and save.
Create a second one and name it (blink-off) and choose disarm blink.
Install the homebrige IFTTT plugin create the config.json
`"platform": "IFTTT",
		"name": "IFTTT",

		"makerkey": "PUT KEY OF YOUR MAKER CHANNEL HERE",

		"accessories": [

			{

				"name": "Accessory 1",

				"buttons": [

					{

						"caption": "Blink Security",

						"triggerOn": "blink-on",

						"triggerOff": "blink-off`

To catch the "maker key" it was a little bit tricky for me. I used this link for it. But it works only if you created the webhooks before.
Good luck. Great thing, if you spend few minutes to create the webhooks, it works perfect and smoothly. But be aware, if you sent the webhook request via Homebridge its needs about 15 seconds to arm / disarm. You can controll this in the blink app itself.

@Sungamm This is perfect and very clear!!! Got it up and running. Solved a months old problem!

@Sungamm This is perfect and very clear!!! Got it up and running. Solved a months old problem!

Hi derfx4,
good to read. Thank you for your feedback. I am really impressed about the effort from bartdorsey, but with IFTTT its a easy and reliable solution.
Have a good time.

Great. Thanks for the solution. I am not a pro with Homebridge etc. But I got it running. Solved my problem and Blink Cam is in Hinebridge again.

@Sungamm  Would you be able to share how you created the Webhooks in IFTTT? I looked today and wasn’t clear on how to create a unique action

Good Morning derfx4,
I assume you cannot understood german? ;-) I will try my best but it will be hard for us both :-D
Create an account at IFTTT.com if you dont have already. Afterwards click on "create" , -> Trigger "this" and search for "webhook". Enter a name for the Webhook (example blink-on). Click on "That" and search for blink. I use blink (Europe) because I am from Germany. I assume "blink" only is for Asia and the States. Choose "arm blink" and save.
Create a second one and name it (blink-off) and choose disarm blink.
Install the homebrige IFTTT plugin create the config.json
`"platform": "IFTTT",
		"name": "IFTTT",

		"makerkey": "PUT KEY OF YOUR MAKER CHANNEL HERE",

		"accessories": [

			{

				"name": "Accessory 1",

				"buttons": [

					{

						"caption": "Blink Security",

						"triggerOn": "blink-on",

						"triggerOff": "blink-off`

To catch the "maker key" it was a little bit tricky for me. I used this link for it. But it works only if you created the webhooks before.
Good luck. Great thing, if you spend few minutes to create the webhooks, it works perfect and smoothly. But be aware, if you sent the webhook request via Homebridge its needs about 15 seconds to arm / disarm. You can controll this in the blink app itself.

Oh great but it would be great if you help also with the config. I think it has errors in it.
Also for the makers challenge do you copy the complete hyperlink from IFTTT?

Thanks for the idea, can finally use blink via homekit again.

@Sungamm  Would you be able to share how you created the Webhooks in IFTTT? I looked today and wasn’t clear on how to create a unique action

Good Morning derfx4,
I assume you cannot understood german? ;-) I will try my best but it will be hard for us both :-D
Create an account at IFTTT.com if you dont have already. Afterwards click on "create" , -> Trigger "this" and search for "webhook". Enter a name for the Webhook (example blink-on). Click on "That" and search for blink. I use blink (Europe) because I am from Germany. I assume "blink" only is for Asia and the States. Choose "arm blink" and save.
Create a second one and name it (blink-off) and choose disarm blink.
Install the homebrige IFTTT plugin create the config.json
`"platform": "IFTTT",
		"name": "IFTTT",

		"makerkey": "PUT KEY OF YOUR MAKER CHANNEL HERE",

		"accessories": [

			{

				"name": "Accessory 1",

				"buttons": [

					{

						"caption": "Blink Security",

						"triggerOn": "blink-on",

						"triggerOff": "blink-off`

To catch the "maker key" it was a little bit tricky for me. I used this link for it. But it works only if you created the webhooks before.
Good luck. Great thing, if you spend few minutes to create the webhooks, it works perfect and smoothly. But be aware, if you sent the webhook request via Homebridge its needs about 15 seconds to arm / disarm. You can controll this in the blink app itself.

I am now having some problems with this. Blink is nit turning off with IFTTT.

@Sungamm  Would you be able to share how you created the Webhooks in IFTTT? I looked today and wasn’t clear on how to create a unique action

Hi ethempekin,
you copied my "instruction". What do you need excatly? What kind of trouble do you have?
Do you have a IFTTT account? Did not found the webhook function? Problem with the  maker key?
create the accout at IFTTT
create one webhook "turn on"
create one webhook "turn off"
check and copy your maker key
installe the IFTTT plug-in into homebridge
copy or create the entry for the config.json
reboot
have fun :-)
I am a user only but I can try to help. But please let me know first which of the above steps did not work.

@Sungamm  Would you be able to share how you created the Webhooks in IFTTT? I looked today and wasn’t clear on how to create a unique action

Hi ethempekin,
you copied my "instruction". What do you need excatly? What kind of trouble do you have?
Do you have a IFTTT account? Did not found the webhook function? Problem with the maker key?
create the accout at IFTTT
create one webhook "turn on"
create one webhook "turn off"
check and copy your maker key
installe the IFTTT plug-in into homebridge
copy or create the entry for the config.json
reboot
have fun :-)
I am a user only but I can try to help. But please let me know first which of the above steps did not work.
P.S.: Are you EU or NA user? I assume that is important for the BLINK connection.

Hi, I used your guide indeed to control Blinks (EU). Now I cannot toggle off. I check IFTTT there when I give a try (test) it doesn’t trigger it and no error pops up.

ok. that means
create the accout at IFTTT - done
create one webhook "turn on" - done
create one webhook "turn off" - done (check this one, please). There are a lot of THAT options for BLINK. choose the "turn off". If there more than one, try the next one please.
check and copy your maker key - done?
installe the IFTTT plug-in into homebridge -done?
copy or create the entry for the config.json - done? check the name and the "triggeroff" name. Both must be excact the same. Try simple wording, ala turnon, turnoff.

When I hit check now on IFTTT nothing happens for blink-off. It has been runned 338 times since september.... last run says February 23

Hmm.. ok. I just check it. For me its running perfect and quite fast. Therefore its not a general problem.
Do you have the blink app on your mobile?
Can you manual turn off the blink and activate via Apple Homekit. Does this work?
Can you manual turn on the blink and de-activate via Apple Homekit. Does this work?
If one action did not run, check at first the log at Homebridge.
You are sure that you have choose the correct THAT option for turnoff?

I created a new one in IFTTT for blink-off.
Still not working. I can control with no issues on Blink app.
I doubt if this is related to homebridge. My version there is v.1.0.3. There seems to be some changes when I hit the settings. My config is very simple. See below. Homebridge itself is  1.1.7. No errors in the log though.
"platform": "IFTTT",
"name": "IFTTT",
"makerkey": "abcd",
"accessories": [
{
"name": "Accessory 1",
"buttons": [
{
"caption": "Blink Security",
"triggerOn": "blink-on",
"triggerOff": "blink-off"
Sent from my iPhone

On 27 Feb 2021, at 19:27, Sungamm<EMAIL_ADDRESS>wrote:

Hmm.. ok. I just check it. For me its running perfect and quite fast. Therefore its not a general problem.
Do you have the blink app on your mobile?
Can you manual turn off the blink and activate via Apple Homekit. Does this work?
Can you manual turn on the blink and de-activate via Apple Homekit. Does this work?
If one action did not run, check at first the log at Homebridge.
You are sure that you have choose the correct THAT option for turnoff?
—
You are receiving this because you commented.
Reply to this email directly, view it on GitHub, or unsubscribe.

Puh... sorry I have no idea anymore. I have the latest Homebridge (stable) but with for former versions no issues in the past.
Only idea is to delete the blink-off webhook and create new (with same name). Try that out.
If this not running, its better to ask in the Homebridge Forum or IFTTT itself group. Or you give the bartdorsey plugin a chance again?

Re-creating IFTTT recipe solved my problem. Thanks


Reorder arguments to help avoid issues with --next

This moves the display arguments like --no-output and --cwd to before the hxml file so that they're applied to the first compilation rather than the last when --next is used
This change is expected to have no impact when --next isn't used
Additionally, the following flags are prepended when building the compiler cache
--no-output --each --no-output. This trick is necessary to ensure --no-output is applied to each compilation set, whether or not the user's hxml includes --each.
How it works: the first --no-output --each will apply --no-output to all the --next blocks, unless the user hxml itself include --each, in that case, the first --no-output --each will be ignored and replaced by the user's --each flags, so we need a second --no-output that gets added to the user's --each

Hmm... I'm not a huge fan of the complexity this adds, considering the general stance on --next/--each has been "it's not supported". Is the behavior with multiple --each even really specified, or would we be relying on undefined behavior there (@simn)?
Also, is it necessary to do this in both places? I thought it was just an issue with cache builds?

It's an issue with both autocomplete and cache builds.
With autocomplete, the existing approach in callDisplay arranges the arguments as:
build.hxml --no-output --display { ...
but if build.hxml contains --next then the --no-output is applied to the final compilation, but haxe always uses the first compilation when using --display. The re-order simply moves the --no-output to the start, so it's now:
--no-output build.hxml --display { ...
This completely fixes the autocomplete issue, with or without --each and doesn't really add any complexity.
The cache build is different because the cache build always executes all compilations, unlike --display which only executes the first. If there's a way to just execute the first then that would solve our problem too. So the change for the cache build is to make sure --no-output get applied to all compilations.


Fix a minor typo in Javadoc

While reading the FlywayCallback docs faced that typo and changed it from will to with

Good catch! Thanks!


Release Google.Apps.Chat.V1 version 1.0.0-beta08

Changes in this release:
New features

Add doc for permission settings & announcement space support (commit b8b7166)
Add doc for import mode external users support (commit b8b7166)

Documentation improvements

Messages API dev docs improvement (commit b8b7166)
Memberships API dev docs improvement (commit b8b7166)
Discoverable space docs improvement (commit b8b7166)

Created release for Google.Apps.Chat.V1-1.0.0-beta08


chore(main): release 2.102.0

:robot: I have created a release beep boop
2.102.0 (2024-02-27)
Features

add option to disable secure cookies and configure samesite (#3441) (7ec5491)

This PR was generated with Release Please. See documentation.

:robot: Release is at https://github.com/Flagsmith/flagsmith/releases/tag/v2.102.0 :sunflower:


helpers: expose to config.nixvim.helpers (take two)

I believe #51 is overwritten by #48. This is a rebased version.

Oh, sorry!


Security Issue on lambda of the CF template.

Lambda function does not have a Security Group -- This is the other security issue that came up when we deployed this CF template.  We would like to use the cross account nature of the Instance Scheduler (i.e. host the Scheduler in a "shared services account" and have it manage the scheduling of resources in other accounts).  However the lambda function needs a SG that keeps it private.  Can you also help with this ?

Fixed in v1.2.0


Add Everything Is Peechee Tee

Thanks Gigi! The store is looking great!

Very cool! Thanks for the PR! 🙏


Add auto re-cast after timeout for auto fishing script

Sometimes it seems like the auto fishing script gets stuck and doesn't detect fishes on the hook. This happens quite seldom, but could probably be quickly fixed by automatically re-casting the line after x seconds (30 maybe?) if nothing has happened.

I see, makes sense. I will add this in the next version

Added in 3.3.3


[ListItemButton]: Fix ts(4023) by adding missing export

[x] I have followed (at least) the PR section of the contributing guide.

I get the following error message when using ListItemButton, ListItemButtonProps in my custom component lib:
Exported variable 'ListItemButton' has or is using name 'ListItemButtonBaseProps' from external module ".../node_modules/@mui/material/ListItemButton/ListItemButton" but cannot be named. ts(4023)

Thanks for your work, but this has been fixed in #29571


Add Go types for specification

This adds Go types implementing the configuration specification.
Closes #42
I also took the liberty to clean up the resource types because some of the names did not make sense at all and was pretty bad.
Signed-off-by: Michael Crosby<EMAIL_ADDRESS>

LGTM

LGTM


Cherry pick #873 into release-1.11

Cherrypick #873
Signed-off-by: Yanqiang Miao<EMAIL_ADDRESS>

/lgtm


Update squad_v2.py

Change lines 100 and 102 to prevent overwriting predictions variable.

With this change all the checks are passed.

Good


Test master branch on Travis

@iqulus r+

:pushpin: Commit 9349ea9 has been approved by tarcieri

:hourglass: Testing commit 9349ea9 with merge a2a7396...

@iqulus retry

:hourglass: Testing commit 9349ea9 with merge 8cf97b6...

@iqulus retry

@iqulus r+

@iqulus r+

@iqulus r+

:bulb: This pull request was already approved, no need to approve it again.

This pull request is currently being tested. If there's no response from the continuous integration service, you may use retry to trigger a build again.

:pushpin: Commit 9349ea9 has been approved by tarcieri

@iqulus retry

:hourglass: Testing commit 9349ea9 with merge 3df638b...

@iqulus retry

:hourglass: Testing commit 9349ea9 with merge 65277c8...


Fabric version

Please? Mod looks fantastic.

yes please

Just going to link what you said in #8 about 2.0 being fabric

It's finally out now

actually never mind. It was someone else who ported the mod not the mod author himself

well it works now so gonna say there's a fabric version


Add support for tiered hardware to improve the performance of archive expired documents

AC

Users should be aware of a record is expired or not
Expired records should be moved to archive (the time can be different from the expiration date that users can aware. Expose properties for developers to decide)

Won't do. Expired doesn't mean that these collections should be archived.


PD Toolkit: Add some CSS class to the root when in PD Edit Mode

Arrow cursor should be disabled for Number node when in Edit mode (#268)


audio/vorbis: Context switching doesn't occur much less often on iOS than other platforms

I guess goroutine context is never switched during decoding process especially on iOS. This is just guessing. We should fix all of or a part of this to be written in Go.

https://github.com/jfreymuth/go-vorbis seems a very good candidate if we want to rewrite the package.

Hmm, I've reimplement stb_vorbis_decode_memory in Go, but context switching was not encouraged.

https://github.com/hajimehoshi/ebiten/commit/e3ef0540a33b438121ca719fc7163bc05d46f7c6 Fixed this problem!

https://github.com/jfreymuth/oggvorbis is the newer version. Let's try this after #270 is done.

Created #294


Use only one binary

Currently there seem to be two binaries ("entry points"), namely speedtest and speedtest-cli. This is not only confusing, but also makes autocompletion in various shells cumbersome. If there is not a technical reason for those two binaries, we should get rid of one.

The reason is for historical purposes.  speedtest-cli was the original name and some expect it to be there.  Some complained that cli was redundant, so an alternative was added.
  Just going to have to deal with it.


Suppress some warnings

Is it possible to add an option to suppress some warnings? For example:
ignoreRules: C26812

I will encourage users to use standard compiler options /wd6011 to suppress individual warnings. I will update the readme accordingly.

This is not a compiler warning though. The one particular I mentioned is this:
The enum type 'SOME_ENUM' is unscoped. Prefer 'enum class' over 'enum' (Enum.3).

This compiler option also works for analysis warnings.


Simplify the lightbox image caption

To remove extra queries on the obs index.
To reproduce the issue, on <main> go to http://localhost:3000/505457, and then hit "Index" to be sure we're on the same page of the index. Extra queries will fire after the line
  Rendered application/content/_sorter.html.erb (Duration: 0.2ms | Allocations: 172)

If you examine the lightbox caption for observations like 505457 (it's encoded as a data-attribute in the image) you'll find the ID's of some of the associations that are firing the extra queries.
This eliminates the complex show_obs_title and uses the matrix-box obs title instead, linking to the obs. It also provisionally eliminates the obs notes from the lightbox caption, because it's the only place i could think that might involve projects, which were one of the extra associations getting loaded. Please let me know if that's not the case. It could be the image copyright instead, for example.
The issue is compounded on the matrix-box carousel PR #1598.

Changes unknown when pulling 129c72cfca3c4e1583d68dac7bec2b1e7b034d39 on nimmo-simplify-lightbox-caption into ** on main**.


Write t-route simulated flow values to CHRTOUT files

A helper function to write t-route simulated flows to WRF-Hydro CHRTOUT files. Results are passed from compute_nhd_routing_SingleSeg.py to a newly created function in nhd_io.py called  write_q_to_wrf_hydro. The following workflow steps happen in that function

The stack of CHRTOUT files from which q-lateral data were extracted are opened using xarray.open_mfdataset.
The flowveldepth data frame is reindexed to match the segment ordering in CHRTOUT files
flow data are extracted from the flowveldepth data frame into a numpy array with the same dimensions as those within the CHRTOUT files.
A new xarray data array object is created that contains t-route flow data with coordinates, dimensions, and attributes consistent with the CHTROUT data.
t-route flow data are added to the CHTROUT data object imported by xarray.open_mfdataset
Break apart the CHTROUT stack data object into individual datasets, one for each export timestep
Save edited CHRTOUT files (containing t-route data) to disk.

I tried to numpy-ify the process handing the results array list to the function that writes flow data to CHRTOUT and unpacking/repacking data via numpy workflows. I ran into a speedbump whereby the number of segments simulated by t-route is fewer than the number of segments in listed in the CHRTOUT file.

[ ] Why do the number of segments differ between t-route and CHRTOUT? Note - even with no masking, the number still differ.
[ ] Do we expect these numbers to differ in operational contexts?
[ ] If they do differ, would we write NaN values to segments that have no t-route simulation results?

For discussion, reservoir outflow is coded to the replaced segment:
https://github.com/NOAA-OWP/t-route/blob/master/src/python_routing_v02/fast_reach/mc_reach.pyx#L842-L845
How do we handle that? (issues, need to address missing reach indexes; need to make output happen for reservoirs, ... )

Loose End: Writing individual netCDF files for each WRF Hydro timestep is pretty time intensive.  Have you profiled the code to see where the time is being spent? My guess would be that mf_dataset is a suboptimal approach to this particular problem.

@groutr The most time is spent reading and writing netCDF files with xr.open_mfdataset and xr.save_mfdataset, respectively. Indeed, xr.save_mfdataset takes 7.5x longer than xr.open_mfdataset. My hope was that mfdataset would allow me work much faster by using the Dask library. However, I don't think I am actually leveraging Dask to make mfdataset commands as fast as they could be. Do you have experience using Dask?

https://ncar.github.io/xdev/posts/writing-multiple-netcdf-files-in-parallel-with-xarray-and-dask/
This article describes basically the same process as we are trying to do here. However, they use the dask.distributed library. We can do the same, just need to understand what is happening.

@awlostowski-noaa Try passing in coords='minimal' and data_vars='minimal' to open_mfdataset.

xr.save_mfdataset(datasets, paths = chrtout_files_new)

vs.
for i, dat in enumerate(datasets):
    dat.to_netcdf(chrtout_files_new[i])

These two methods are equivalent, if dask.distributed is not used. My intitial attempts at using Dask to expedite save_mfdataset (clinet = Clinet()) led to the following warnings and a much longer compute time than without.
distributed.nanny - WARNING - Restarting worker ???

@awlostowski-noaa that is incorrect. You don't need dask-distributed here. Dask-distributed is for working on a cluster of machines.
What the documentation says for save_mfdataset is that if you don't use datasets with dask arrays, it is equivalent to calling to_netcdf on each dataset. If the datasets have dask arrays, it will use threading to attempt to parallelize the write.

@awlostowski-noaa Do the dask arrays in the datasets survive the groupby operation?

@groutr Here is one of the datasets after the groupby command. Dask arrays appear to  survive group by. Perhaps there is a scale issue? Writing 24 files with save_mfdataset may not show an advantage over sequential save_dataset, but maybe there is an advantage at 2400?
Dimensions:         (feature_id: 374, reference_time: 1)
Coordinates:
    latitude        (feature_id) float32 dask.array<chunksize=(374,), meta=np.ndarray>
    longitude       (feature_id) float32 dask.array<chunksize=(374,), meta=np.ndarray>
  * feature_id      (feature_id) int32 4186117 4186199 ... 4185689 4185589
  * reference_time  (reference_time) datetime64[ns] 2018-01-01T01:00:00
    time            datetime64[ns] 2018-01-01T02:00:00
Data variables:
    crs             |S1 b''
    order           (feature_id) int32 dask.array<chunksize=(374,), meta=np.ndarray>
    elevation       (feature_id) float32 dask.array<chunksize=(374,), meta=np.ndarray>
    streamflow      (feature_id) float32 dask.array<chunksize=(374,), meta=np.ndarray>
    q_lateral       (feature_id) float32 dask.array<chunksize=(374,), meta=np.ndarray>
    velocity        (feature_id) float32 dask.array<chunksize=(374,), meta=np.ndarray>
    qSfcLatRunoff   (feature_id) float32 dask.array<chunksize=(374,), meta=np.ndarray>
    qBucket         (feature_id) float32 dask.array<chunksize=(374,), meta=np.ndarray>
    qBtmVertRunoff  (feature_id) float32 dask.array<chunksize=(374,), meta=np.ndarray>
    qTroute         (feature_id) float32 dask.array<chunksize=(374,), meta=np.ndarray>
Attributes:
    featureType:                timeSeries
    proj4:                      +proj=lcc +units=m +a=6370000.0 +b=6370000.0 ...
    model_initialization_time:  2018-01-01_01:00:00
    station_dimension:          feature_id
    model_output_valid_time:    2018-01-01_02:00:00
    model_total_valid_times:    24
    stream_order_output:        1
    cdm_datatype:               Station
    Conventions:                CF-1.6
    code_version:
    model_output_type:          channel_rt
    model_configuration:        retrospective
    dev_OVRTSWCRT:              1
    dev_NOAH_TIMESTEP:          3600
    dev_channel_only:           0
    dev_channelBucket_only:     0
    dev:                        dev_ prefix indicates development/

@awlostowski-noaa is the underlying disk a networked filesystem? I'm presuming you are writing to local storage.


Compatibility with pytorch 1.3

setup.py explicitly restrict pytorch to  < 1.3, is it a conservative check?

It is conservative in the sense that MetaModule implementations are based on the implementations of nn.Module in PyTorch 1.2. I haven't checked how this changes in 1.3, but chances are it would still work in PyTorch 1.3. I will update that as soon as possible.
PyTorch 1.3 also introduces new features for dataloaders which might be of interest for Torchmeta, so a more general update is to be expected as well to make full use of the latest features.

I have upgraded the requirement for PyTorch in the latest version of Torchmeta. It now requires PyTorch>=1.3.0


Allow cross namespace waypoint capture

What would you like to be added:
Currently we have supported namespace/service waypoint capture since  kmesh v0.4, plus istio 1.22+
As istio upstream has supported, cross namespace waypoint in the upcoming 1.23 release, we need to figure out what do we miss in the kmesh to support it.
Why is this needed:

As to how to use that feature, it requires user to set
	// AmbientUseWaypointLabelLabel is the label used to specify which waypoint should be used for a given pod, service, etc...
	AmbientUseWaypointLabel = "istio.io/use-waypoint"

alongside with
/ AmbientUseWaypointNamespaceLabel is a label used to indicate the namespace of the waypoint (referred to by AmbientUseWaypointLabel).
	// This allows cross-namespace waypoint references. If unset, the same namespace is assumed.
	AmbientUseWaypointNamespaceLabel = "istio.io/use-waypoint-namespace"

Besides that, no other api is changed.  So in kmesh, we may not need to to do much work on this.
But some test coverage and document is needed


python311Packages.nbclient: 0.9.0 -> 0.10.0

Automatic update generated by nixpkgs-update tools. This update was made based on information from passthru.updateScript.
meta.description for python311Packages.nbclient is: A client library for executing notebooks
meta.homepage for python311Packages.nbclient is: https://github.com/jupyter/nbclient
Updates performed

Ran passthru.UpdateScript

To inspect upstream changes
Impact
Checks done

built on NixOS
Warning: a test defined in passthru.tests did not pass
found 0.10.0 with grep in /nix/store/viv84ifpdwrc8maj86v3zs93629kf75i-python3.11-nbclient-0.10.0
found 0.10.0 in filename of file in /nix/store/viv84ifpdwrc8maj86v3zs93629kf75i-python3.11-nbclient-0.10.0

Rebuild report (if merged into master) (click to expand)

189 total rebuild path(s)

189 package rebuild(s)

First fifty rebuilds by attrpath
cantor
ihaskell
jupyter
jupyter-all
labplot
libsForQt5.cantor
manim
nbqa
nitrokey-app2
opsdroid
plasma5Packages.cantor
pynitrokey
python311Packages.ansible-kernel
python311Packages.arviz
python311Packages.augmax
python311Packages.bambi
python311Packages.batchspawner
python311Packages.bqplot
python311Packages.bqscales
python311Packages.cirq
python311Packages.cirq-ft
python311Packages.dalle-mini
python311Packages.datashader
python311Packages.distrax
python311Packages.dm-haiku
python311Packages.dockerspawner
python311Packages.einops
python311Packages.elegy
python311Packages.encodec
python311Packages.experiment-utilities
python311Packages.fastai
python311Packages.flax
python311Packages.folium
python311Packages.ipytablewidgets
python311Packages.ipyvuetify
python311Packages.jupyter
python311Packages.jupyter-book
python311Packages.jupyter-cache
python311Packages.jupyter-collaboration
python311Packages.jupyter-contrib-core
python311Packages.jupyter-contrib-nbextensions
python311Packages.jupyter-lsp
python311Packages.jupyter-nbextensions-configurator
python311Packages.jupyter-server
python311Packages.jupyter-server-fileid
python311Packages.jupyter-server-mathjax
python311Packages.jupyter-sphinx
python311Packages.jupyterhub
python311Packages.jupyterhub-ldapauthenticator
python311Packages.jupyterhub-systemdspawner

Instructions to test this update (click to expand)

Either download from Cachix:
nix-store -r /nix/store/viv84ifpdwrc8maj86v3zs93629kf75i-python3.11-nbclient-0.10.0 \
  --option binary-caches 'https://cache.nixos.org/ https://nix-community.cachix.org/' \
  --option trusted-public-keys '
  nix-community.cachix.org-1:mB9FSh9qf2dCimDSUo8Zy7bkq5CX+/rkCWyvRCYg3Fs=
  cache.nixos.org-1:6NCHdD59X431o0gWypbMrAURkbJ16ZPMQFGspcDShjY=
  '

(The Cachix cache is only trusted for this store-path realization.)
For the Cachix download to work, your user must be in the trusted-users list or you can use sudo since root is effectively trusted.
Or, build yourself:
nix-build -A python311Packages.nbclient https://github.com/r-ryantm/nixpkgs/archive/f3e6a0893593a29eb8d6390ceb6ec4eecc691242.tar.gz

Or:
nix build github:r-ryantm/nixpkgs/f3e6a0893593a29eb8d6390ceb6ec4eecc691242#python311Packages.nbclient

After you've downloaded or built it, look at the files and if there are any, run the binaries:
ls -la /nix/store/viv84ifpdwrc8maj86v3zs93629kf75i-python3.11-nbclient-0.10.0
ls -la /nix/store/viv84ifpdwrc8maj86v3zs93629kf75i-python3.11-nbclient-0.10.0/bin

Pre-merge build results
We have automatically built all packages that will get rebuilt due to
this change.
This gives evidence on whether the upgrade will break dependent packages.
Note sometimes packages show up as failed to build independent of the
change, simply because they are already broken on the target branch.


[web] Add tests for ducks/utils/store.js

Try to add some tests for store.js. Since this is a new test for a file that we haven't covered before, if there is anything not good enough, please let me know.

Awesome, thanks!


Always store thumbnails as JPEG 99662722

When an image is uploaded on radio and thumbnails are generated, they should always be converted to JPEG. The original should be kept in PNG or whatever format it is uploaded in.
https://www.pivotaltracker.com/story/show/99662722

I don't want to be a party pooper - what you've done looks okay. But I kinda sorta think that maybe, just maybe, there should be some specs.

Cool, I'll have a look a unit test for attachment fu. If it turns out to be a huge hassle, I might just cover it with a feature spec in radio.

Sounds fair. Doesn't look like it's rspecced

Nah they're using test unit. I played around with the test suite and couldn't get it to run. I found this bit actually committed in one of the tests:
<<<<<<< HEAD
  def after_resize(img)
    self.aspect_ratio = img.columns.to_f / img.rows.to_f
  end
=======
  # after_resize do |record, img|
  #   record.aspect_ratio = img.columns.to_f / img.rows.to_f
  # end
>>>>>>> 33222f02ee9657d0ea9e4654b06cea7ed49cb85b

I think it makes sense to just do a feature spec and not rely on trying to get that test suite to run. Does not look like it is maintained, which is a bit worrying. We should always evaluate the quality of the specs before we start using third party code.

Yep. https://bellroywiki.trikeapps.com/display/TRIKE/Holy+Commandments+of+3rd+Party+Project+Use


[wip] Check attribute existence for datamodule teardown

What does this PR do?

Fixes #<issue_number>
Before submitting

[ ] Was this discussed/approved via a GitHub issue? (not for typos and docs)
[ ] Did you read the contributor guideline, Pull Request section?
[ ] Did you make sure your PR does only one thing, instead of bundling different changes together?
[ ] Did you make sure to update the documentation with your changes? (if necessary)
[ ] Did you write any new necessary tests? (not for typos and docs)
[ ] Did you verify new and existing tests pass locally with your changes?
[ ] Did you update the CHANGELOG? (not for typos, docs, test updates, or internal minor changes/refactorings)

PR review
Anyone in the community is free to review the PR once the tests have passed.
Before you start reviewing make sure you have read Review guidelines. In short, see the following bullet-list:

[ ] Is this pull request ready for review? (if not, please submit in draft mode)
[ ] Check that all items from Before submitting are resolved
[ ] Make sure the title is self-explanatory and the description concisely explains the PR
[ ] Add labels and milestones (and optionally projects) to the PR so it can be classified

Did you have fun?
Make sure you had fun coding 🙃

Hello @ananthsub! Thanks for opening this PR.

In the file pytorch_lightning/trainer/trainer.py:

Line 1144:121: E501 line too long (124 > 120 characters)

Do see the Hitchhiker's guide to code style

I have an idea for how to properly solve this.
I'll have something finished by tomorrow :)
Closing this in the meantime. Thanks!


dll_dlopen.hpp: error: RTLD_DI_ORIGIN was not declared in this scope with musl libc

Expected Behavior
Successfully build with musl libc
Actual Behavior
Fails to build with musl libc. With error sys-cluster/hpx-1.7.1-r1 - /.../dll_dlopen.hpp: error: RTLD_DI_ORIGIN was not declared in this scope. The reason is that RTLD_DI_ORIGIN is not present in musl.
Steps to Reproduce the Problem

Build hpx with from source with musl libc

Specifications

HPX Version: 1.8.0
Compiler: g++
OS: Gentoo
libc: musl

Complete build log: https://829242.bugs.gentoo.org/attachment.cgi?id=759092.
With help from my mentors, I came up with this patch (I'm a Google Summer of Code 22 student). I'm happy to send in a PR if devs think it's appropriate.
--- a/libs/core/plugin/include/hpx/plugin/detail/dll_dlopen.hpp
+++ b/libs/core/plugin/include/hpx/plugin/detail/dll_dlopen.hpp
@@ -319,20 +319,8 @@ namespace hpx { namespace util { namespace plugin {
             std::string result;

 #if !defined(__ANDROID__) && !defined(ANDROID) && !defined(__APPLE__)
-            char directory[PATH_MAX] = {'\0'};
             const_cast<dll&>(*this).LoadLibrary(ec);
-            if (!ec && ::dlinfo(dll_handle, RTLD_DI_ORIGIN, directory) < 0)
-            {
-                std::string str = hpx::util::format(
-                    "Hpx.Plugin: Could not extract path the shared library "
-                    "'{}' has been loaded from (dlerror: {})",
-                    dll_name, dlerror());
-
-                HPX_THROWS_IF(
-                    ec, filesystem_error, "plugin::get_directory", str);
-            }
-            result = directory;
-            ::dlerror();    // Clear the error state.
+            result = path(dll_name).parent_path().string();
 #elif defined(__APPLE__)
             // SO staticfloat's solution
             const_cast<dll&>(*this).LoadLibrary(ec);

With this patch, it builds on both musl and glibc systems.

Thanks for reporting this! I agree that with your patch the library would build. However, are you sure it is still fully functional? On some systems, dll_name will not have the full path of the loaded library, in which case path(dll_name).parent_path().string(); would return an empty string.
If HPX is fully functional with your patch on your system (please run the tests), then I'd suggest changing the patch such that both versions of the code are available by wrapping the versions into an:
#if defined(RTLD_DI_ORIGIN)
... old code
#else
... your new version of the code
#endif

Please go ahead and create a PR for this, I'd be happy to accept it.

@hkaiser Thanks. I'll run the test (only have glibc and musl, unfortunately) myself and also ask the testing team to test further with my patch.
And of course I'll send in the PR :)

@hkaiser To fix the building on musl it needs two other patches. See if they are appropriate then I'll add them in the PR
# Check for execinfo only on glibc and ulibc systems.
--- a/libs/core/debugging/src/backtrace.cpp
+++ b/libs/core/debugging/src/backtrace.cpp
@@ -19,7 +19,9 @@

 #if (defined(__linux) || defined(__APPLE__) || defined(__sun)) &&              \
     (!defined(__ANDROID__) || !defined(ANDROID))
+#if (defined(__UCLIBC__) || defined(__GLIBC__))
 #define HPX_HAVE_EXECINFO
+#endif
 #define HPX_HAVE_DLFCN
 #if defined(__GNUC__) && !defined(__clang__)
 #define HPX_HAVE_UNWIND

# EXEC_PAGESIZE is a preprocessor macro from the Linux Kernel headers. Include
# the appropriate Linux header file <linux/param.h>
--- a/components/performance_counters/memory/src/mem_counter_linux.cpp
+++ b/components/performance_counters/memory/src/mem_counter_linux.cpp
@@ -14,6 +14,11 @@
 #include <sys/types.h>
 #include <unistd.h>

+// Fix for musl. Use linux/param.h for EXEC_PAGESIZE
+#ifdef __linux__
+#include <linux/param.h>
+#endif
+
 #include <hpx/modules/errors.hpp>
 #include <hpx/modules/format.hpp>

--- a/libs/core/coroutines/include/hpx/coroutines/detail/context_linux_x86.hpp
+++ b/libs/core/coroutines/include/hpx/coroutines/detail/context_linux_x86.hpp
@@ -37,6 +37,11 @@
 #include <stdexcept>
 #include <sys/param.h>

+// Fix for musl. Use linux/param.h for EXEC_PAGESIZE
+#ifdef __linux__
+#include <linux/param.h>
+#endif
+
 #if defined(HPX_HAVE_STACKOVERFLOW_DETECTION)

 #include <cstring>
--- a/libs/core/coroutines/include/hpx/coroutines/detail/posix_utility.hpp
+++ b/libs/core/coroutines/include/hpx/coroutines/detail/posix_utility.hpp
@@ -67,6 +67,11 @@
 #define EXEC_PAGESIZE static_cast<std::size_t>(sysconf(_SC_PAGESIZE))
 #endif

+// Fix for musl. Use linux/param.h for EXEC_PAGESIZE
+#ifdef __linux__
+#include <linux/param.h>
+#endif
+
 /**
  * Stack allocation routines and trampolines for setcontext
  */


operator project-quay (3.9.0-nightly.20230613)

Add 3.9.0-nightly.20230613 to community operators

/merge possible

/merge possible


sf::Packet: Floats are not in network byte order

They are serialized in native endianness. It looks problematic for interoperability between machines with different endianness.

Do you have some evidence for this, or a minimal reproducibility case so that it's possible for someone to check this? More information would be helpful otherwise this issue looks a little barebones for someone who would like to resolve it.

Unfortunately, I was just reading the source code. It would be hard to reproduce.

Unfortunately, I was just reading the source code. It would be hard to reproduce.

Any additional information (even a link to the line in a SFML source file) would be more useful than the just a couple of sentences.

Here are the functions which read float/double:
https://github.com/SFML/SFML/blob/7faa5507a4ccc60dd669dbc0156db2f03238d1c6/src/SFML/Network/Packet.cpp#L257-L280
For comparison, reading uint64:
https://github.com/SFML/SFML/blob/7faa5507a4ccc60dd669dbc0156db2f03238d1c6/src/SFML/Network/Packet.cpp#L235-L254
Integral reads convert from network byte order to native. Floating points don't do that.

Addressed in my branch: feature/use-generic-endian-swap; PR to come

For clarity - the int functions don't do anything to help interoperability between different endian platforms. They assume little endian on both sides, and just convert to big endian while in transit (which is wasted work in most cases)
Interop between big and little endian machines is not something SFML currently supports and I don't think it should (but it also doesn't stop users from doing it if needed, which would be an edge case)


[BUG] services are exec twice and will fail

example: https://ci.woodpecker-ci.org/repos/3780/pipeline/13581/29
this only ocures in the unreleased woodpecker ... so it should be caused by a resent change

-> on workflow start you see the services successfully started and then run ... but when the steps are exec you get this.
cc @anbraten you reasently refactored something in that space? (I had not time to look into your pull jet)

Yea

cc @anbraten you reasently refactored something in that space? (I had not time to look into your pull jet)

Can't remember that I refactored sth related to services apart from the DAG complier which however shouldn't cause an issue here.


Increase package level for development docker images

Current Forge-FE docker image has a limited number of tools available, that developers often require to install additionally (ghcr.io/tenstorrent/tt-forge-fe/tt-forge-fe-ird-ubuntu-22-04).
It'll be good to additionally install:

ZSH (apt install zsh)
screen (apt install screen)
TBD

@pilkicTT @dgolubovicTT if you spot any additional packaged that are required, please feel free to update main description of this issue :))


Room name should always be parsed lowercased

Issue still happens on android chrome, joining Ffconly does not put me in the ffconly room

@ken-de-moor Have you cleared your cache? Tried it in incognito and it worked...


Support testing for bulkApply: false fixes

Opening for reference in https://github.com/flutter/flutter/pull/73604
We need a way to test dart fixes that that are only applied in the IDE. These fixes are not done using the dart fix command, and so the current dart fix --compare-to-golden testing does not cover these fixes.
These cases are most easily identified by the bulkApply: false setting in fix_data.yaml
In the case of https://github.com/flutter/flutter/pull/73604, there is more than one option for fixing, so the tool cannot decide automatically which path to take.
I think @bwilkerson already has some ideas/designs in mind for supporting this. :)

Is this item complete now?

Is this item complete now?

Yes, support has now been added.

Yes, support has now been added.


New site for "Built With" Page: Focal theme

Hi Gavin ;),
We have recently rewritten our Focal theme completely. It now uses CartJS under the hood.
The theme store link: https://themes.shopify.com/themes/focal/styles/standard
If you need to use an image, you can re-use the one we've made for our Standard preset: https://s3.amazonaws.com/shopify-theme-store/screenshots/895/main/original.jpg
Thanks! ;)

Awesome theme, will add it to the list shortly :). :+1:

Thanks a lot! :)


Azure Web App migrations

Hello,
I've checked the docs but found nothing regarding the best practice for handling EF7 migrations on Azure Web App projects and I'm not sure on how to proceed. I'd like migrations to be checked and applied on publishing. Have I missed anything or are they scheduled for later release?
Thanks for any suggestion.

It may be more straightforward to have the application check and apply the migrations, rather than the publish process. This can be done, for example, by adding the following to your app startup sequence, after configuration of EF has been done.
using(var dbContext = new YourDbContext()) {
    dbContext.Database.Migrate();
}

This will check the db level using __EFMigrationHistory table and run missing increments. The migration seems to run in a transaction, so starting multiple server instances at the same time should be fine.
To do this right after build, this command in the build script can be used to wake the application after publishing.
Invoke-RestMethod http://WEBSITENAME.azurewebsites.net -TimeOutSec 120

Thanks, I'll follow suggestion #1. I'm trying to call this from my ASP.NET vNext Startup.Configure() method:
if (!context.Database.EnsureCreated())
{
    context.Database.Migrate();
}

It seems that the "EnsureCreated" does create the DB and it's schema, but it does not create the "EFMigrationsHistory" table.
On next start of the app, the "Migrate" method is called, which instead tries to ally all migrations (since there is no history table and fails because Tables already exist.
I'm wondering if there is a method to create the DB and let migrations build the schema.

That pattern is not completely correct. EnsureCreated() is just a quick way to create the database, mostly useful in testing, and should not be mixed with migrations. You can just use context.Database.Migrate() and if you are going to do it from Startup.Configure() you should also wrap it into a DI scope to make sure the right services will be created with the right lifetime, e.g.:
using (var scope = serviceProvider.GetRequiredService<IServiceScopeFactory>().CreateScope())
{
    using(var db = scope.ServiceProvider.GetService<MyContext>())
    {
        context.Database.Migrate();
    }
}

Note that applying migrations at runtime is not in generally a good idea. E.g. if you have more than one Web server, this could cause several servers trying to update the database at the same time which will most likely cause errors and leave the database in a bad state.
To the original question, we are still working in the publish story. @sayedihashimi may be able to offer more details and the timeframe.
cc @rowanmiller

Here is a slightly outdated doc that walks thru what we are building in terms of publish integration https://github.com/aspnet/EntityFramework/wiki/Visual-Studio-Publish-Integration. There are some minor technical details that are incorrect but the general flow is correct.

Yes we are working on adding support for EF Migrations into the publish dialog now. I do not think this will make it for RC1 but should for the release after that.
I just posted the spec on what we are working on at https://github.com/aspnet/Tooling/issues/251. We should have the script working in a few weeks. When that happens if anyone wants to try it (without UI support) then I can describe the elements that need to be updated to get the migrations to be invoked.

Thanks to everybody for the useful insight.
@divega  regarding applying migrations at runtime, what about a command line tool that executes "Database.Migrate()"?
Regarding the DI scope, is it wrong to get a context in this way?
public void ConfigureServices(IServiceCollection services)
{
...
	// Add Entity Framework services to the services container.
	services.AddEntityFramework()
		.AddSqlServer()
		.AddDbContext<ApplicationDbContext>(options =>
			options.UseSqlServer(Configuration["Data:DefaultConnection:ConnectionString"]));
...
}

public void Configure(IApplicationBuilder app, IHostingEnvironment env, ILoggerFactory loggerFactory, ApplicationDbContext context)
{
...
    context.Database.Migrate();
...
}

The main issue with running code directly in Startup.Configure() is that any services (including the DbContext itself) that are registered as scoped will not be instantiated with the right lifetime. There are some more details at https://github.com/aspnet/EntityFramework/issues/3070#issuecomment-139486191 and a discussion on how we can potentially make that easier at https://github.com/aspnet/Hosting/issues/373.

@divega Thanks for the details.


Write crawler using scrapy and playwright

Ideally we would use python's scrapy library to do URL crawling as it is much more mature than most of the JS alternatives out there. Scrapy works great with static HTML sites, but has problems with single page javascript apps. One solution is to use a plugin between scrapy and playwright. Relevant links
https://github.com/scrapy/scrapy
https://github.com/scrapy-plugins/scrapy-playwright
https://github.com/microsoft/playwright-python
Other solution approaches are welcome

Closed in #48


[64] Pipeline script for topic-level novelty

Closes #64
The main addition is the pipeline for calculating a topic-level novelty score, characterising the "uncommonness" of the research related to a given topic (in a given year).
The usage is as follows:
python dap_aria_mapping/pipeline/novelty/openalex_topic_novelty.py

This will output five tables (on per each taxonomy level), with two alternative novelty scores per topic, per year.
At the moment, this is based only on the OpenAlex data.
In a forthcoming issue/PR, I will apply the same analysis on patent data, to generate novelty scores using patents as well.
Checklist:

[x] I have refactored my code out from notebooks/
[x] I have checked the code runs
[x] I have tested the code
[x] I have run pre-commit and addressed any issues not automatically fixed
[x] I have merged any new changes from dev
[x] I have documented the code

[x] Major functions have docstrings
[ ] Appropriate information has been added to READMEs

[x] I have explained this PR above
[x] I have requested a code review

Thanks a lot @ampudia19, will implement your suggestions!

Thanks again @ampudia19, I fixed the issues highlighted above, namely:

[x] Indicated “optional” for all optional variables in getters.novelty.py and adding defaults in the doctstring
[x] Improved docstrings in throughout novelty_utils.py, calculate_openalex_novelty.py and openalex_topic_novelty.py
[x] Fixing more upstream the issue with duplicated “work_id” on OpenAlex data, so that no duplicated papers are saved when calculating novelty.
[x] Adding an upload_to_s3 variable to the pipelines (default will be True).

Hope this is OK to merge @ampudia19 ?
Re topic names: I'm happy to add an adjustment to use chatgpt topic names via another issue  (once you've merged the corresponding PR). Hope that's alright?

All is looking good, Karlis :)


yml file inclusion

does emulator currently understand following syntax in serverless.yml?
environment: ${file(config/${self:provider.stage}.yml)}
I use it to sepparate configuration for different stages and it work on AWS but does not in emulator

Good question! It should given we use serverless directly to parse the config but if it's not working for you then clearly we need to fix something. Does using an absolute path work? What is the error?

I use file inclusion to include environment variables and then use in from my lambdas. In lambda they available under proccess.env but when I use it with emulator all they are undefined so I suppose that file inclusion format may be not supported by emulator or I'm missing something...

Does using an absolute path work?
the same in this case:

environment: ${file(env.yml):${self:provider.stage}}

as far as I see now, the problem is not in yml file inclusion, but it is in not passing values from provider.environment to lambda's process.env.

@cbaron Let's re-evaluate passing env to lambda. Seems like it's useful.

an updates on this? do you plan to implement this?

I will try to carve out some time this week.  As always, you're welcome to put up a PR.


Update name of "XENSIV PAS CO2"

Resolves https://github.com/arduino/library-registry/issues/2021

Thanks guys!


Typemill Roadmap

For full changelog please visit the Typemill Release Infos
1.0.X Series: The Birth

[x] Version 1.0.0: Introducing TYPEMILL
[x] Version 1.0.1: Added a google sitemap
[x] Version 1.0.5: Added a table of contents tag (TOC)

1.1.X Series: Plugin System and Author Panel

[x] Version 1.1.0: Introducing a plugin system
[x] Version 1.1.3: Introducing an author panel for configurations
[x] Version 1.1.5: Added math support (mathjax/katex)

1.2.X Series: Visual Markdown Editor

[x] Version 1.2.0: Basic content editor
[x] Version 1.2.2: Draft management for pages
[x] Version 1.2.3: Re-order content pages with drag&drop
[x] Version 1.2.5: Create new pages
[x] Version 1.2.6: First visual editor version
[x] Version 1.2.7: Reorder content blocks
[x] Version 1.2.8: Image component
[x] Version 1.2.9: Videos and public forms
[x] Version 1.2.10: New editor components
[x] Version 1.2.11: Table component
[x] Version 1.2.12: Content blocks everywhere
[x] Version 1.2.13: Definition lists and horizontal lines
[x] Version 1.2.14: Interactive table of contents
[x] Version 1.2.15: Math refactored
[x] Version 1.2.16: Footnotes and search plugin
[x] Version 1.2.17: Inline Formats
[x] Version 1.2.18: Discard changes and more inline formats
[x] Version 1.2.19: Modularize the visual editor

1.3.X Series: Meta-Information

[x] Version 1.3.0: Meta-YAML for files and folders (see Milestones)
[x] Version 1.3.1: Additional Standard Metas and Internationalization (see Milestones)
[x] Version 1.3.2: Rename and hide navigation (see Milestones)
[x] Version 1.3.3: Pages and Posts (see Milestones)
[x] Version 1.3.4: Improved Media Management (see Milestones)
[x] Version 1.3.5: Consolidation (bugfixing)
[x] Version 1.3.6: Image Field
[x] Version 1.3.7: Image Manipulation
[x] Version 1.3.8: Roles and rights concept (ACL)

1.4.X Series: ebook publishing

[x] Version 1.4.0: Generate eBooks (PDF) with the eBook-Plugin
[x] Version 1.4.1: New fieldtype Customfields (similar to WordPress)
[x] Version 1.4.2: Small improvements and eBook plugin 1.1.0 (generate eBooks in tabs of pages).
[x] Version 1.4.3: Improved user rights and user roles and a register plugin
[x] Version 1.4.4.: Access Control for individual pages (create membership content).
[ ] Version 1.4.5.: Optimized user management for more than 10 users.
[ ] Version 1.4.6.: Subscription Plugin.

I think this issue needs an update
Also this page is 404: https://typemill.net/info/release-notes

Yes, thank you, roughly updated

sorry, i don't have the time to update this every time, so I close it. Follow the news on typemill for regular updates: https://typemill.net/news


As a player i want to see a team table in fifa channel

Player   | Win | Pens  | Game Wins | Games | W/L  | Ranking |  History
------------+-----+------+-------+-----------+-------+------+---------+-----------
@sjob & @jhodges  |  30 |  2   |     32    |   52  | 0.92 |  1585.0 | W W W W W
@jhodges & @elliot  |  35 |  3   |      38    |   72  | 0.53 |  1534.0 | L W W L L

One day I'm gonna merge that team-scores branch


Programmatic structure of "Bdv Actions"

@NicoKiaru @haesleinhuepf
I think these lines of demo code could be one good example to start discussing how we would the API to look like:
https://github.com/haesleinhuepf/bigdataviewer-playground/blob/6f56ea5d5113888e9e6337fca747a6cc5f725435/src/test/src/sc/fiji/bdv/navigate/ViewerTransformAdjusterDemo.java#L28
The same Action is called two times. Once just with constructor and run method. The second time also setting some parameters. I guess, here one could try e.g. a builder pattern.
I think especially for functions that do not return anything a builder pattern could be quite nice.

The builder pattern could be nice indeed but it's not ideal to get the outputs. We could think of a function to retrieve all the outputs as a List or a Map. Something like:
Action a = new Action(p0,p1).run();
a.getOutputs();

But it's not great : how to know the name of the outputs ?
My vote here would be to keep things simple and as they are right now:

no returned objects for input setters
no input object for output getters

Would that work ?

Works for me! Simple is good!

We are all set. Closing this. Please reopen if necessary.


[BUG] Not Working Properly on One UI 5.1 Devices

Describe the bug
There appears to be an issue that prevents One UI 5.1 devices from running or installing.
To Reproduce
Steps to reproduce the behavior:

On One UI 5.1 Devices

Expected behavior
Not running or installing
Screenshots
If applicable, add screenshots to help explain your problem.
Smartphone (please complete the following information):

Device: Galaxy
OS: One UI
Version 5.1

Additional context
Reference: https://www.clien.net/service/board/lecture/18258065

I'm running a D20 with OneUi 5.1. I can install the app, but I'm unable to discover any devices.

Same here with Samsung Galaxy F23 with One UI 5.1.
Installed the app, gave permissions and connected the device and iPad on the same network and turned on Airdrop for everyone but no devices are shown, just a blank screen

@rootd @Zarvis12 Have you tried this with a macbook or with an iPhone/iPad. iPhones and iPads seem to be generally unsupported, only macbooks on the same network.


req.Body not reset in doWithRetry

Scenario:
When using a net/http HTTP server as remote/receiver (instead of the receiver from this package), ceClient has issues when retrying a non-successful HTTP response.
Error: "msg":"Invalid result type, not HTTP Result: Post \"http://always-fail.default.<IP_ADDRESS>.sslip.io\": http: ContentLength=34 with Body length 0 (type: *protocol.Receipt)"}
This happens when the remote server returns a message in the response, e.g. „server down“ (not CE) but not when just returning a status code and empty response body.
The issue is caused by func (p *Protocol) doWithRetry() because it does not reset the req.Body during subsequent attempts.
Expected behavior:
Return encoding error, but correctly reset req.Body, e.g. return "failed to convert response into event: unknown Message encoding\n500:  (3x)"
Client code:
/*
 Copyright 2021 The CloudEvents Authors
 SPDX-License-Identifier: Apache-2.0
*/

package main

import (
	"context"
	"fmt"
	"net/http"
	"time"

	"github.com/cloudevents/sdk-go/observability/opencensus/v2/client"
	"go.uber.org/zap"
	"knative.dev/eventing/pkg/kncloudevents"
	"knative.dev/pkg/logging"

	cloudevents "github.com/cloudevents/sdk-go/v2"
	"github.com/cloudevents/sdk-go/v2/protocol"
	cehttp "github.com/cloudevents/sdk-go/v2/protocol/http"
)

func main() {
	ctx := cloudevents.ContextWithTarget(context.Background(), "http://always-fail.default.<IP_ADDRESS>.sslip.io")

	topts := []cehttp.Option{cehttp.WithIsRetriableFunc(func(statusCode int) bool {
		retry, _ := kncloudevents.SelectiveRetry(ctx, &http.Response{StatusCode: statusCode}, nil)
		return retry
	})}

	ceClient, err := client.NewClientHTTP(topts, nil)
	if err != nil {
		panic(err)
	}

	e := cloudevents.NewEvent()
	e.SetType("com.cloudevents.sample.sent")
	e.SetSource("https://github.com/cloudevents/sdk-go/v2/samples/httpb/sender")
	_ = e.SetData(cloudevents.ApplicationJSON, map[string]interface{}{
		"id":      1,
		"message": "Hello, World!",
	})

	ctx = cloudevents.ContextWithRetriesLinearBackoff(ctx, 10*time.Millisecond, 3)
	response, result := ceClient.Request(ctx, e)

	if !isSuccess(ctx, result) {
		logging.FromContext(ctx).Errorw("Failed to deliver", zap.Error(result))
		return
	}

	fmt.Printf("got: %v", response)
}

func isSuccess(ctx context.Context, result protocol.Result) bool {
	var retriesResult *cehttp.RetriesResult
	if cloudevents.ResultAs(result, &retriesResult) {
		var httpResult *cehttp.Result
		if cloudevents.ResultAs(retriesResult.Result, &httpResult) {
			retry, _ := kncloudevents.SelectiveRetry(ctx, &http.Response{StatusCode: httpResult.StatusCode}, nil)
			return !retry
		}
		logging.FromContext(ctx).Warnf("Invalid result type, not HTTP Result: %v (type: %T)", retriesResult.Result, retriesResult.Result)
		return false
	}

	logging.FromContext(ctx).Warnf("Invalid result type, not RetriesResult")
	return false
}

HTTP server code (note: just using net/http)
package main

import (
	"context"
	"errors"
	"net/http"
	"time"

	"github.com/go-chi/chi/v5"
	"github.com/go-chi/chi/v5/middleware"
	"knative.dev/pkg/logging"
	"knative.dev/pkg/signals"
)

const (
	addr    = ":8080"
	timeout = 5 * time.Second
)

func main() {
	ctx := signals.NewContext()

	r := chi.NewRouter()
	r.Use(middleware.Logger)
	r.Post("/", func(w http.ResponseWriter, r *http.Request) {
		// w.WriteHeader(http.StatusInternalServerError) <- just doing this works!
		http.Error(w, http.StatusText(http.StatusInternalServerError), http.StatusInternalServerError)
	})

	srv := http.Server{
		Addr:         addr,
		Handler:      r,
		ReadTimeout:  timeout,
		WriteTimeout: timeout,
	}

	go func() {
		<-ctx.Done()
		logging.FromContext(ctx).Info("shutting down")
		if err := srv.Shutdown(context.Background()); err != nil {
			logging.FromContext(ctx).Warnf("could not shutdown: %v", err)
		}
	}()

	logging.FromContext(ctx).Infow("listening", "address", addr)
	if err := srv.ListenAndServe(); !errors.Is(err, http.ErrServerClosed) {
		logging.FromContext(ctx).Fatal(err)
	}
}

Patch
diff --git a/v2/protocol/http/protocol_retry.go b/v2/protocol/http/protocol_retry.go
index fb7bcd2..9050469 100644
--- a/v2/protocol/http/protocol_retry.go
+++ b/v2/protocol/http/protocol_retry.go
@@ -6,8 +6,10 @@
 package http

 import (
+	"bytes"
 	"context"
 	"errors"
+	"io"
 	"net/http"
 	"net/url"
 	"time"
@@ -53,6 +55,17 @@ func (p *Protocol) doWithRetry(ctx context.Context, params *cecontext.RetryParam
 	retry := 0
 	results := make([]protocol.Result, 0)

+	var (
+		body []byte
+		err  error
+	)
+	if req.Body != nil {
+		body, err = io.ReadAll(req.Body)
+		if err != nil {
+			panic(err)
+		}
+	}
+
 	for {
 		msg, result := p.doOnce(req)

@@ -90,6 +103,13 @@ func (p *Protocol) doWithRetry(ctx context.Context, params *cecontext.RetryParam
 		}

 	DoBackoff:
+		if req.Body != nil {
+			req.Body = io.NopCloser(bytes.NewReader(body))
+			req.GetBody = func() (io.ReadCloser, error) {
+				return io.NopCloser(bytes.NewReader(body)), nil
+			}
+		}
+
 		// Wait for the correct amount of backoff time.

 		// total tries = retry + 1

nice! Want to make a PR?

Sure, I just need to dig a little deeper as there might be another anomaly in the way requests are handled. Not sure I get the full picture yet.

Update:
I can reproduce the issue with net/http the following handlers:
func(w http.ResponseWriter, r *http.Request) {
		http.Error(w, http.StatusText(http.StatusInternalServerError), http.StatusInternalServerError)
}

func(w http.ResponseWriter, r *http.Request) {
		w.WriteHeader(http.StatusInternalServerError)
}

The second handler does not always show the http: ContentLength=34 with Body length 0 *url.Error, but I managed to catch it several times with a debugger.
I was also able to catch it once using the cloudevents receiver and a simple event handler:
func receive(ctx context.Context, event cloudevents.Event) error {
	fmt.Printf("got event: %v\n", event)
	return http.NewResult(nethttp.StatusInternalServerError, nethttp.StatusText(nethttp.StatusInternalServerError))
}

Still not sure where and how it gets triggered in all these (rare) scenarios, but perhaps there's a race which only shows during debugging - typical Heisenbug :D
Also, I was wondering why the unit tests were not exhibiting the problem: this is caused by only using a RoundTripper mock instead of a "full" HTTP server which reads/modifies the body (also the tests didn't use a body which was another reason we did not see this).


Update metrics notebook locations

[ ] rename Metrics_NWM_StdSuite_v1.ipynb to Metrics_StdSuite_v1.ipynb
[ ] update any notebooks that use that helper to point to correct file name
[ ] move Usage_DScore_Suite_v1.ipynb and Usage_NWM_StdSuite_v1.ipynb into model_evaluation/tutorials/stats

A side note coming out of this discussion was that @alaws-USGS will be adding some additional stats in another helper notebook called Metrics_Supplementary.ipynb. This will contain any stats that need to be used that aren't a part of the Standard Suite of the DScore Suite, and will not be versioned like the metric suites are.

New branch created to address changes related to this issue: gt-217-relo-metrics

OK... I think I've found all references to these notebooks, and have updated to the new locations.
On to the PR.

Closed with #220


Does it can running on MacOS High Sierra?

After install the qlImageSize, My macbook pro(MacOS High Sierra,10.13.3 ) still can not open the webp file, how can I fix it?

Yes, it does and it can =)


1.10 Manaptation

Not recognizing Mana in a bottle.  I crafted and NEI'ed one into inventory also.

Will look into and forward on to quest author

Switched to fuzzy. should be fixed.


Minor proofreading edits

A separate comment: I don't know in which order the parts will get read, but Beniget and Gast don't seem to get a proper introduction. It would be good to add a short snippet about what they are the first time that the reader is expected to encounter them.

Big big thanks for your super efficient help!


FEATURE REQ: import another character model to dm ui

Hello Ghost this is a very niche feature but I am bringing it up still.
What if one could right-click another character/npc portrait and there will be "Show model on Datamine UI" option or something like this. and when clicked it will show it with the data in the DM UI  window?
Thanks!

I meant   here when i wrote ight-click another character/npc portrait . Just to be more clear .