React Native project tagged as Java instead of JavaScript

Hi, our React Native project https://github.com/salathegroup/react-native-scandit is tagged as Java. It's basically a JavaScript project, even if it includes native Java and Objective-C wrappers.
Any way I can induce JavaScript detection instead of Java? Can you fix it on your side?
Thanks
Boris

You can mark those wrapper files as vendored using Linguist overrides.


Train stateful DTLN model

Hi @breizhn ,
I'm trying train stateful DTLN model. And I see time_dat = Input(batch_shape=(1, self.blockLen)) .
Why is the self.batchsize = 1 ? Is this performance better?

The stateful model inside the class was only build for exporting the model as a SavedModel.
If you would like to train a stateful model use the standard model and change line 334 to a defined Input length and batch size.
In the next step add in the call to self.seperation_kernel in line 344 and 354 the argument stateful=True.

@breizhn Nils I am running python convert_weights_to_tf_light.py -m /name/of/the/model.h5
-t name_target to get the 2 tflite models
Then running real_time_processing_tf_lite.py
ValueError: Cannot set tensor: Dimension mismatch. Got 257 but expected 512 for dimension 2 of input 0

If you are using tflite do you have to retrain as a stateful with the above or I am just doing something wrong with convert weights to tf_lite?

I will have a look at the shapes and see if I can work it out as just trying a singlekw of "hey marvin" than a full language just out of interest to see how well it could would work.
Seems to work pretty good with the realtime tests but really need to get its as fast and as low load as possible.
I had 200k 1 sec heymarvins that I concatenated into 31 sec wavs and did the DNS Challenge aug & splits for 80 hours but was just wondering and will do a really long 400 hour run.
Google have targetted speech and it just made me think could you do targetted KW also if the functions are added then I guess it could also have 'on device training' where a small model shifts the weights on captured KW.

Got round to having another go so just posting as I go.
python convert_weights_to_tf_lite.py -m ./models_DTLN_model/DTLN_model.h5                                               -t DTLN_model
2022-05-16 11:24:40.691917: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-05-16 11:24:40.722002: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-05-16 11:24:40.722205: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-05-16 11:24:40.734035: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-05-16 11:24:40.734894: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-05-16 11:24:40.735114: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-05-16 11:24:40.735261: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-05-16 11:24:41.132514: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-05-16 11:24:41.132737: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-05-16 11:24:41.132914: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-05-16 11:24:41.133065: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5879 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3050, pci bus id: 0000:01:00.0, compute capability: 8.6
Model: "model"
__________________________________________________________________________________________________
 Layer (type)                   Output Shape         Param #     Connected to
==================================================================================================
 input_1 (InputLayer)           [(1, 512)]           0           []

 lambda (Lambda)                [(1, 1, 257),        0           ['input_1[0][0]']
                                 (1, 1, 257)]

 lstm (LSTM)                    (1, 1, 128)          197632      ['lambda[0][0]']

 dropout (Dropout)              (1, 1, 128)          0           ['lstm[0][0]']

 lstm_1 (LSTM)                  (1, 1, 128)          131584      ['dropout[0][0]']

 dense (Dense)                  (1, 1, 257)          33153       ['lstm_1[0][0]']

 activation (Activation)        (1, 1, 257)          0           ['dense[0][0]']

 multiply (Multiply)            (1, 1, 257)          0           ['lambda[0][0]',
                                                                  'activation[0][0]']

 lambda_1 (Lambda)              (1, 1, 512)          0           ['multiply[0][0]',
                                                                  'lambda[0][1]']

 conv1d (Conv1D)                (1, 1, 256)          131072      ['lambda_1[0][0]']

 instant_layer_normalization (I  (1, 1, 256)         512         ['conv1d[0][0]']
 nstantLayerNormalization)

 lstm_2 (LSTM)                  (1, 1, 128)          197120      ['instant_layer_normalization[0][
                                                                 0]']

 dropout_1 (Dropout)            (1, 1, 128)          0           ['lstm_2[0][0]']

 lstm_3 (LSTM)                  (1, 1, 128)          131584      ['dropout_1[0][0]']

 dense_1 (Dense)                (1, 1, 256)          33024       ['lstm_3[0][0]']

 activation_1 (Activation)      (1, 1, 256)          0           ['dense_1[0][0]']

 multiply_1 (Multiply)          (1, 1, 256)          0           ['conv1d[0][0]',
                                                                  'activation_1[0][0]']

 conv1d_1 (Conv1D)              (1, 1, 512)          131072      ['multiply_1[0][0]']

==================================================================================================
Total params: 986,753
Trainable params: 986,753
Non-trainable params: 0
__________________________________________________________________________________________________
None
WARNING:tensorflow:Layer lstm_4 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.
WARNING:tensorflow:Layer lstm_5 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.
WARNING:tensorflow:Layer lstm_6 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.
WARNING:tensorflow:Layer lstm_7 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.
2022-05-16 11:24:42.280438: W tensorflow/python/util/util.cc:368] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.
WARNING:absl:Found untraced functions such as lstm_cell_4_layer_call_fn, lstm_cell_4_layer_call_and_return_conditional_losses, lstm_cell_5_layer_call_fn, lstm_cell_5_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.
2022-05-16 11:24:43.167113: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:357] Ignored output_format.
2022-05-16 11:24:43.167139: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:360] Ignored drop_control_dependency.
2022-05-16 11:24:43.167801: I tensorflow/cc/saved_model/reader.cc:43] Reading SavedModel from: /tmp/tmp7t02n0xs
2022-05-16 11:24:43.169884: I tensorflow/cc/saved_model/reader.cc:78] Reading meta graph with tags { serve }
2022-05-16 11:24:43.169899: I tensorflow/cc/saved_model/reader.cc:119] Reading SavedModel debug info (if present) from: /tmp/tmp7t02n0xs
2022-05-16 11:24:43.178090: I tensorflow/cc/saved_model/loader.cc:228] Restoring SavedModel bundle.
2022-05-16 11:24:43.224443: I tensorflow/cc/saved_model/loader.cc:212] Running initialization op on SavedModel bundle at path: /tmp/tmp7t02n0xs
2022-05-16 11:24:43.239752: I tensorflow/cc/saved_model/loader.cc:301] SavedModel load for tags { serve }; Status: success: OK. Took 71952 microseconds.
2022-05-16 11:24:43.270659: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:237] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
2022-05-16 11:24:43.320343: I tensorflow/compiler/mlir/lite/flatbuffer_export.cc:1963] Estimated count of arithmetic ops: 0.824 M  ops, equivalently 0.412 M  MACs

Estimated count of arithmetic ops: 0.824 M  ops, equivalently 0.412 M  MACs
WARNING:absl:Buffer deduplication procedure will be skipped when flatbuffer library is not properly loaded
WARNING:absl:Found untraced functions such as lstm_cell_6_layer_call_fn, lstm_cell_6_layer_call_and_return_conditional_losses, lstm_cell_7_layer_call_fn, lstm_cell_7_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.
2022-05-16 11:24:44.867283: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:357] Ignored output_format.
2022-05-16 11:24:44.867310: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:360] Ignored drop_control_dependency.
2022-05-16 11:24:44.867543: I tensorflow/cc/saved_model/reader.cc:43] Reading SavedModel from: /tmp/tmpfr4beuqa
2022-05-16 11:24:44.870124: I tensorflow/cc/saved_model/reader.cc:78] Reading meta graph with tags { serve }
2022-05-16 11:24:44.870141: I tensorflow/cc/saved_model/reader.cc:119] Reading SavedModel debug info (if present) from: /tmp/tmpfr4beuqa
2022-05-16 11:24:44.878121: I tensorflow/cc/saved_model/loader.cc:228] Restoring SavedModel bundle.
2022-05-16 11:24:44.909595: I tensorflow/cc/saved_model/loader.cc:212] Running initialization op on SavedModel bundle at path: /tmp/tmpfr4beuqa
2022-05-16 11:24:44.927967: I tensorflow/cc/saved_model/loader.cc:301] SavedModel load for tags { serve }; Status: success: OK. Took 60424 microseconds.
2022-05-16 11:24:45.021548: I tensorflow/compiler/mlir/lite/flatbuffer_export.cc:1963] Estimated count of arithmetic ops: 1.349 M  ops, equivalently 0.674 M  MACs

Estimated count of arithmetic ops: 1.349 M  ops, equivalently 0.674 M  MACs
WARNING:absl:Buffer deduplication procedure will be skipped when flatbuffer library is not properly loaded
TF lite conversion complete!

https://github.com/breizhn/DTLN/blob/1de1f15a8b5b7e1c44905618ff2ef70ca8277fbc/real_time_processing_tf_lite.py#L34-L38
L39 insert
...
print(input_details_1, "\n")
print(output_details_1, "\n")
print(input_details_2, "\n")
print(output_details_2, "\n")
...
They are returned as lists not tensors? Been too long and my memory as guess tflite but sure they are tensors with tensorflow
python real_time_processing_tf_lite.py
INFO: Created TensorFlow Lite XNNPACK delegate for CPU.
[{'name': 'serving_default_input_4:0', 'index': 0, 'shape': array([  1,   1, 512], dtype=int32), 'shape_signature': array([  1,   1, 512], dtype=int32), 'dtype': <class 'numpy.float32'>, 'quantization': (0.0, 0), 'quantization_parameters': {'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}, {'name': 'serving_default_input_5:0', 'index': 1, 'shape': array([  1,   2, 128,   2], dtype=int32), 'shape_signature': array([  1,   2, 128,   2], dtype=int32), 'dtype': <class 'numpy.float32'>, 'quantization': (0.0, 0), 'quantization_parameters': {'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}]

[{'name': 'StatefulPartitionedCall:1', 'index': 97, 'shape': array([  1,   2, 128,   2], dtype=int32), 'shape_signature': array([  1,   2, 128,   2], dtype=int32), 'dtype': <class 'numpy.float32'>, 'quantization': (0.0, 0), 'quantization_parameters': {'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}, {'name': 'StatefulPartitionedCall:0', 'index': 92, 'shape': array([  1,   1, 512], dtype=int32), 'shape_signature': array([  1,   1, 512], dtype=int32), 'dtype': <class 'numpy.float32'>, 'quantization': (0.0, 0), 'quantization_parameters': {'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}]

[{'name': 'serving_default_input_3:0', 'index': 0, 'shape': array([  1,   2, 128,   2], dtype=int32), 'shape_signature': array([  1,   2, 128,   2], dtype=int32), 'dtype': <class 'numpy.float32'>, 'quantization': (0.0, 0), 'quantization_parameters': {'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}, {'name': 'serving_default_input_2:0', 'index': 1, 'shape': array([  1,   1, 257], dtype=int32), 'shape_signature': array([  1,   1, 257], dtype=int32), 'dtype': <class 'numpy.float32'>, 'quantization': (0.0, 0), 'quantization_parameters': {'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}]

[{'name': 'StatefulPartitionedCall:0', 'index': 64, 'shape': array([  1,   1, 257], dtype=int32), 'shape_signature': array([  1,   1, 257], dtype=int32), 'dtype': <class 'numpy.float32'>, 'quantization': (0.0, 0), 'quantization_parameters': {'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}, {'name': 'StatefulPartitionedCall:1', 'index': 69, 'shape': array([  1,   2, 128,   2], dtype=int32), 'shape_signature': array([  1,   2, 128,   2], dtype=int32), 'dtype': <class 'numpy.float32'>, 'quantization': (0.0, 0), 'quantization_parameters': {'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}]

Traceback (most recent call last):
  File "/home/stuart/DTLN/real_time_processing_tf_lite.py", line 74, in <module>
    interpreter_1.set_tensor(input_details_1[0]['index'], in_mag)
  File "/home/stuart/DTLN/venv/lib/python3.10/site-packages/tensorflow/lite/python/interpreter.py", line 698, in set_tensor
    self._interpreter.SetTensor(tensor_index, value)
ValueError: Cannot set tensor: Dimension mismatch. Got 257 but expected 512 for dimension 2 of input 0.

Thanks to Nils but if your as confused as my befuddled brain just use this
"""
This is an example how to implement real time processing of the DTLN tf light
model in python.
Please change the name of the .wav file at line 43 before running the sript.
For .whl files of the tf light runtime go to:
    https://www.tensorflow.org/lite/guide/python

Author: Nils L. Westhausen<EMAIL_ADDRESS>Version: 30.06.2020
This code is licensed under the terms of the MIT-license.
"""

import soundfile as sf
import numpy as np
import tensorflow.lite as tflite
import time

##########################
# the values are fixed, if you need other values, you have to retrain.
# The sampling rate of 16k is also fix.
block_len = 512
block_shift = 128
# load models
interpreter_1 = tflite.Interpreter(model_path='DTLN_model_1.tflite')
interpreter_1.allocate_tensors()
interpreter_2 = tflite.Interpreter(model_path='DTLN_model_2.tflite')
interpreter_2.allocate_tensors()

# Get input and output tensors.
input_details_1 = interpreter_1.get_input_details()
output_details_1 = interpreter_1.get_output_details()

input_details_2 = interpreter_2.get_input_details()
output_details_2 = interpreter_2.get_output_details()
print(input_details_1, "\n")
print(output_details_1, "\n")
print(input_details_2, "\n")
print(output_details_2, "\n")
# create states for the lstms
states_1 = np.zeros(input_details_1[0]['shape']).astype('float32')
states_2 = np.zeros(input_details_2[1]['shape']).astype('float32')
# load audio file at 16k fs (please change)
audio,fs = sf.read('fileid_10.wav')
# check for sampling rate
if fs != 16000:
    raise ValueError('This model only supports 16k sampling rate.')
# preallocate output audio
out_file = np.zeros((len(audio)))
# create buffer
in_buffer = np.zeros((block_len)).astype('float32')
out_buffer = np.zeros((block_len)).astype('float32')
# calculate number of blocks
num_blocks = (audio.shape[0] - (block_len-block_shift)) // block_shift
time_array = []
# iterate over the number of blcoks
for idx in range(num_blocks):
    start_time = time.time()
    # shift values and write to buffer
    in_buffer[:-block_shift] = in_buffer[block_shift:]
    in_buffer[-block_shift:] = audio[idx*block_shift:(idx*block_shift)+block_shift]
    # calculate fft of input block
    in_block_fft = np.fft.rfft(in_buffer)
    in_mag = np.abs(in_block_fft)
    in_phase = np.angle(in_block_fft)
    # reshape magnitude to input dimensions
    in_mag = np.reshape(in_mag, (1,1,-1)).astype('float32')
    # set tensors to the first model
    print(np.shape(states_1), np.shape(in_mag))
    interpreter_1.set_tensor(input_details_1[0]['index'], states_1)
    interpreter_1.set_tensor(input_details_1[1]['index'], in_mag)
    # run calculation
    interpreter_1.invoke()
    # get the output of the first block
    out_mask = interpreter_1.get_tensor(output_details_1[0]['index'])
    states_1 = interpreter_1.get_tensor(output_details_1[1]['index'])
    print(np.shape(out_mask), np.shape(states_1))
    # calculate the ifft
    estimated_complex = in_mag * out_mask * np.exp(1j * in_phase)
    estimated_block = np.fft.irfft(estimated_complex)
    # reshape the time domain block
    estimated_block = np.reshape(estimated_block, (1,1,-1)).astype('float32')
    # set tensors to the second block
    print(np.shape(states_2), np.shape(estimated_block))
    interpreter_2.set_tensor(input_details_2[1]['index'], states_2)
    interpreter_2.set_tensor(input_details_2[0]['index'], estimated_block)
    # run calculation
    interpreter_2.invoke()
    # get output tensors
    out_block = interpreter_2.get_tensor(output_details_2[1]['index'])
    states_2 = interpreter_2.get_tensor(output_details_2[0]['index'])
    print(np.shape(out_block), np.shape(states_2))

    # shift values and write to buffer
    out_buffer[:-block_shift] = out_buffer[block_shift:]
    out_buffer[-block_shift:] = np.zeros((block_shift))
    out_buffer  += np.squeeze(out_block)
    # write block to output file
    out_file[idx*block_shift:(idx*block_shift)+block_shift] = out_buffer[:block_shift]
    time_array.append(time.time()-start_time)

# write to .wav file
sf.write('out.wav', out_file, fs)
print('Processing Time [ms]:')
print(np.mean(np.stack(time_array))*1000)
print('Processing finished.')


[DOC] K-NN Vector and Query Support in SQL Plugin

What do you want to do?

[ ] Request a change to existing documentation
[X] Add new documentation
[ ] Report a technical problem with the documentation
[ ] Other

Tell us about your request. Provide a summary of the request and all versions that are affected.
Upon launch of https://github.com/opensearch-project/sql/issues/1615, please update documentation within the k-NN section, SQL, and PPL sections of the documentation.
What other resources are available? Provide links to related issues, POCs, steps for testing, etc.

@brijos @acarbonetto Is this on track to be included in the 2.11 release?

Hi there, @kolchfa-aws this is going to slip due to changes in resourcing.


Update nodejs.md

added a new section for sdk log collection/self diagnostics
Please refer to lines 455 to 472
I am an Engineer from the Application Insights team who built this lab and also contributed to TSGs
I appreciate if the changes can be reviewed and published and, if my name is listed as a contributor to help me in my connect discussion.

@AaronMaxwell
Can you review the proposed changes?
When the changes are ready for publication, add a #sign-off comment to signal that the PR is ready for the review team to merge.
#label:"aq-pr-triaged"
@MicrosoftDocs/public-repo-pr-review-team


No panel's format defined makes the Settings tab crash.

What did you do?
Migrate a grafana dashboard that contains a panel with no format defined, or edit an existing panel (via JSON, our forms don't let you do that while they should) to remove its format.
What did you expect to see?
The panel display should work (= display value without a unit) & the Settings tab should remain accessible.
Also, the Unit dropdown should have a "None" option.
What did you see instead? Under which circumstances?
The panel display works but clicking on the Settings tab throws an error.
https://github.com/perses/perses/assets/7058693/c592724d-5c6a-4505-8e0d-3bea938ec344
If you add back a format via the JSON editing, it starts back to work.
System information
No response
Perses version
0.44.0
Perses configuration file
No response
Logs
No response

Actually if you choose Unit = Decimal you have no unit text being displayed. So maybe the real fix would be to make format a mandatory field.

ERRATUM this seems actually specific to the StatChart panel only


wandb local - endless "change password" loop

wandb, version 0.9.4 (installed via conda)
Python 3.7.7
Linux
Description

Setup wandb according to quickstart
wandb local
Opened http://localhost:8080/home
The screen pops up with "Change Password: Enter a new password for the account associated with<EMAIL_ADDRESS>I enter a password
The same "Change Password" screen appears again...

What I Did

I can actually just ignore it and click the wandb logo in the top left to see my projects etc...
However, when I want to access the linked tensorboard, the same "change password" screen pops up and I cannot ignore it any longer

More information

Top right button > Users > Teams reveals the following setup
row1: {Team name: xyz, Members: local}
row2: {Team name: wandb, Members: }

Probably something stupid, but don't know how to fix it and haven't found an existing thread

Thanks for reporting this, adding to our eng backlog to examine further

Hey @cgebbe when the instance boots we prompt you to create an admin account.  I think the issue is you've created an admin account with the email<EMAIL_ADDRESS>which is a reserved name.  The easiest fix would be to create a new admin account and remove the<EMAIL_ADDRESS>from the user management UI.  It would be easiest for me to hop on a screenshare to fix with you.  Can you email at<EMAIL_ADDRESS>to setup a quick screenshare?

Hi @vanpelt , sorry for the late reply. I believe I simply left the email empty at the beginning, so your guessed cause seems very likely. I'll check this evening and report back if I can't fix it myself.

I got the same issue and I found this workaround, you can delete the docker volume, and when the docker image is restarted you can create another user.
the list of commands:
docker stop wandb-local
docker volume ls # to check the volume name
docker volume rm wandb # delete the wandb volume
wandb local # now you can create another user


CMComponent must not rely on dependency injection

CMComponent must not rely on dependency injection as it should work out of the box.
README.md is updated in order to reflect new component name (
camel-cm-sms instead of camel-cm)

https://issues.apache.org/jira/browse/CAMEL-9727


fix(dev-watching): mixins not removed after function changed or deleted

fix #2282

Thanks. Any chance we can flush entire require cache to prevent such problems?

This seems a good option (By @sindresorhus)
https://www.npmjs.com/package/clear-require

Yes, I thought about it , we can just:
Object.keys(require.cache).each(key => delete require.cache[key])

But I'm not sure if it will affect performance, so I just delete vue and vue-router


error al procesar el make:crud

al ejecutar este comando me sale el siguiente mensaje de error
php artisan make:crud Zonas
Symfony\Component\Debug\Exception\FatalThrowableError  : Call to undefined function CrudGenerator\Console\Commands\str_plural()
gracias por tu colaboracion

Same here, using Laravel 7.
This is due to using Laravel > 5.6, which replaced str_plural() with Str::plural(), same for Str::singular() and Str::contains().
Workaround:
Add these functions as global functions manually:
Add app/Helpers/helpers.php:
<?php
use Illuminate\Support\Str;

function str_plural(...$args) {
    return Str::plural(...$args);
}

function str_singular(...$args) {
    return Str::singular(...$args);
}

function str_contains(...$args) {
    return Str::contains(...$args);
}

Register with composer autoload:
composer.json:
    "autoload": {
       ...
        "files": [
            "app/Helpers/helpers.php"
        ]
    }

Re-read autoloaded files:
$ composer dump-autoload
In the long term, I think it would be best to deprecate Laravel <= 5.6 in the package or add a new Version for Laravel >= 5.6.
@kEpEx If you want, I can implement these changes and thanks for the package!

thanks @messersm I will try to update in few next days or if you send a PR I can publish it as well if you like to contribute!
Thanks

This is the fix, I just couldn't figured out how to pull request
https://www.dropbox.com/sh/p67m94xu7rtu82j/AAAQPYtXPiP3xcMH05uFv6wBa?dl=1


Label API is much slower than equivalent series API

What did you do?
We have a metric called colo_metadata with colo_name as one of the labels. I'm interested in getting all the values of the label for Grafana reasons.
Querying this via labels API is ridiculously slow:
$ for i in $(seq 1 3); do time curl -s "http://localhost:3000/api/v1/label/colo_name/values?match%5B%5D=colo_metadata&start=$(date +%s | awk '{ print $1 - 3600 * 24 * 7 }')&end=$(date +%s)" | jq .data[] | sort | uniq | wc -l; done
640

real	0m29.146s
user	0m0.047s
sys	0m0.014s
640

real	0m28.510s
user	0m0.046s
sys	0m0.018s
640

real	0m30.322s
user	0m0.043s
sys	0m0.015s

Doing the exact same thing by querying the series API is fast:
$ for i in $(seq 1 3); do time curl -s "http://localhost:3000/api/v1/series?match%5B%5D=colo_metadata&start=$(date +%s | awk '{ print $1 - 3600 * 24 }')&end=$(date +%s)" | jq .data[].colo_name | sort | uniq -c | wc -l; done
638

real	0m0.057s
user	0m0.059s
sys	0m0.011s
638

real	0m0.057s
user	0m0.057s
sys	0m0.013s
638

real	0m0.055s
user	0m0.054s
sys	0m0.014s

What did you expect to see?
Labels API is at least as fast as series API.
What did you see instead? Under which circumstances?
Labels API is excruciatingly slow.
System information
Linux 6.6.41-cloudflare-2024.7.6
Prometheus version
prometheus, version 2.53.0 (branch: master, revision: 774cb1fad4cc043824538fc8a2354c2bcf59f4b8)
  build user:       builder@k8s-pdx-c-managed-h7bgt
  build date:       20240620-12:36:39
  go version:       go1.22.4
  platform:         linux/amd64
  tags:             netgo,builtinassets,stringlabels

Prometheus configuration file
No response
Alertmanager version
No response
Alertmanager configuration file
No response
Logs
No response

Unfortunately, tracing isn't very helpful here:

Here's a flamegraph of what it's doing during the 30s for /api/v1/label/colo_name/values:

Your two URLs are different; one is requesting 7x more data than the other.

My bad, here are both commands with a 7d lookback:
$ for i in $(seq 1 3); do time curl -s "http://localhost:3000/api/v1/series?match%5B%5D=colo_metadata&start=$(date +%s | awk '{ print $1 - 3600 * 24 * 7 }')&end=$(date +%s)" | jq .data[].colo_name | sort | uniq -c | wc -l; done
640

real	0m0.495s
user	0m0.054s
sys	0m0.016s
640

real	0m0.100s
user	0m0.055s
sys	0m0.014s
640

real	0m0.107s
user	0m0.057s
sys	0m0.014s
$ for i in $(seq 1 3); do time curl -s "http://localhost:3000/api/v1/label/colo_name/values?match%5B%5D=colo_metadata&start=$(date +%s | awk '{ print $1 - 3600 * 24 * 7 }')&end=$(date +%s)" | jq .data[] | sort | uniq | wc -l; done
640

real	0m42.850s
user	0m0.047s
sys	0m0.016s
640

real	0m32.592s
user	0m0.048s
sys	0m0.015s
640

real	0m32.157s
user	0m0.049s
sys	0m0.016s

The big picture is still the same: 100ms for /api/v1/series vs 32000ms for /api/v1/label/colo_name/values.

I believe this is the root cause of https://github.com/grafana/grafana/issues/88136, which is impacting Grafana users. I suspect many folks are just suffering with slow Grafana dashboards right now and don't know why 😦
I don't have full context here to get involved properly in a discussion, but I can 💯% agree that there are very clearly major use cases where it's more efficient to fetch the data directly from the TSDB and extract the labels yourself vs the labels endpoint. Perhaps the labels endpoint is optimised for some kind of different use case but if you can reimplement it as an external API that outperforms the build-in one by several orders of magnitude for standard use cases, I wonder if the drawbacks should be clearly documented (and perhaps we should discourage Grafana from encouraging the use of it?)


chore: update mongo version in docker compose

please merge after chart update

mongodb v6 has been deployed to staging


EI-1122 - Introduce delegation proof type

First commit:

create_reusable_delegation_proof new helper
allow for reusable proof usage in delegate_authentication and delegate_control helpers
validate proof: add fallback on reusable proof validation

Second commit after catch-up for new strategy: introduce delegation proof type:

New Delegation Proof Type {DID, GENERIC}
New API Proof objects: APIProof, APIDidDelegationProof, APIGenericDelegationProof used in advanced api
(decouple api from crypto proof, deal with deleagtion proof type and content, backward compatible)
New Advanced API helpers to create generic proof and allow delegation with generic proof
New validation mode according to the proof type
New Regisered document delegation proof type serialization

Thank you @conbon for the review :)
I pushed a fixup.
Like for the golang PR I will push force to squash the commit in a single one (since the first one correspond to the first attempt)


How we can use vipsdisp with pyvips to see live stitching process.

I am working on stitching part. but now i am able to see the result after the stitching part complete. so i want to see live process of stitching tile by tile.

The image never really exists, so it's not possible to view it.
You can use vipsdisp to open the output file before it has finished writing -- you'll see just the top part of the output. Would that help?
nip2 has a graphical interface for image mosaicing, if you've not seen that:
https://github.com/libvips/nip2
Click File / Open examples, then load 1_point_mosaic:


Rework thoughtbot section of README

Move contributors to contributing section
Use new "About thoughtbot" section

@jferris one comment; merge at your discretion

Duplicate of https://github.com/thoughtbot/factory_girl/pull/756 ?


Roll engine 7ed9c9fc5885..6a1f093398c6 (5 commits)

git@github.com:flutter/engine.git/compare/7ed9c9fc5885...6a1f093398c6
git log 7ed9c9fc5885..6a1f093398c6 --no-merges --oneline
2019-11-08<EMAIL_ADDRESS>Roll src/third_party/skia 345a2735e2c8..76eaab6b8cef (18 commits) (flutter/engine#13745)
2019-11-08<EMAIL_ADDRESS>Only specify --no-link-platform when not specifying --aot, roll dart-lang sdk (flutter/engine#13742)
2019-11-08<EMAIL_ADDRESS>Don't run engine tests under vm (causing warnings) (flutter/engine#13737)
2019-11-07<EMAIL_ADDRESS>Switch to Cirrus Dockerfile as CI (flutter/engine#13440)
2019-11-07<EMAIL_ADDRESS>Removed scary experimental warnings for new embedding. (#44314) (flutter/engine#13738)
If this roll has caused a breakage, revert this CL and stop the roller
using the controls here:
https://autoroll.skia.org/r/flutter-engine-flutter-autoroll
Please CC<EMAIL_ADDRESS>on the revert to ensure that a human
is aware of the problem.
To report a problem with the AutoRoller itself, please file a bug:
https://bugs.chromium.org/p/skia/issues/entry?template=Autoroller+Bug
Documentation for the AutoRoller is here:
https://skia.googlesource.com/buildbot/+/master/autoroll/README.md

Auto-roller completed checks. About to merge.


reported shift

I think that in the output log the reported "Planimetry 2D shift between DEM and REF" should be multiplied by the pixel size (or remove the meter unit)
-> row : xx m
-> col : yy m

Hello @sgascoin , thanks for the comment ! We are currently developing a refactor version of demcompare and I am reporting this remark in the list for next release.
Don't hesitate if you have other remarks, following the internal refactoring, we will try to change/clean the global output of demcompare to be more adequate for users (command line output logs and report)

ok great! If I may.. A very useful feature would be the possibility to apply a mask to exclude some areas of the DEM in the coregistration process. Ideally this mask could be a shapefile or a raster in any reference system (demcompare would handle the reprojection to the DEM grid..).

Hello, the last demcompare version 0.5.0 (with new API) is published just now. The bug should be fixed in this version.
I report the proposal in future possible features, thanks for the comment.
Can you tell me if I can close this issue ?

Cordially.


stereo_publisher has died when I run stereo_nodelet.launch with OAK-D Lite

Hi,
I tried to run roslaunch depthai_examples stereo_nodelet.launch, but process has died.

Error message
$ roslaunch depthai_examples stereo_nodelet.launch
... logging to /home/xxx/.ros/log/a35c3a4a-8ee5-11ec-a5b1-670792c734cc/roslaunch-ubuntu-66757.log
Checking log directory for disk usage. This may take a while.
Press Ctrl-C to interrupt
Done checking log file disk usage. Usage is <1GB.

started roslaunch server http://ubuntu:45569/

SUMMARY
========

PARAMETERS
 * /robot_description: <?xml version="1....
 * /rosdistro: noetic
 * /rosversion: 1.15.14
 * /stereo_publisher/LRchecktresh: 5
 * /stereo_publisher/camera_param_uri: package://depthai...
 * /stereo_publisher/confidence: 200
 * /stereo_publisher/extended: False
 * /stereo_publisher/lrcheck: True
 * /stereo_publisher/mode: depth
 * /stereo_publisher/subpixel: True
 * /stereo_publisher/tf_prefix: oak

NODES
  /
    depth_image_convertion_nodelet (nodelet/nodelet)
    depth_image_to_pointcloud_nodelet (nodelet/nodelet)
    nodelet_manager (nodelet/nodelet)
    oak_state_publisher (robot_state_publisher/robot_state_publisher)
    rviz (rviz/rviz)
    stereo_publisher (nodelet/nodelet)

auto-starting new master
process[master]: started with pid [66771]
ROS_MASTER_URI=http://localhost:11311

setting /run_id to a35c3a4a-8ee5-11ec-a5b1-670792c734cc
process[rosout-1]: started with pid [66783]
started core service [/rosout]
process[oak_state_publisher-2]: started with pid [66789]
process[nodelet_manager-3]: started with pid [66791]
process[stereo_publisher-4]: started with pid [66792]
process[depth_image_convertion_nodelet-5]: started with pid [66797]
process[depth_image_to_pointcloud_nodelet-6]: started with pid [66800]
[ INFO] [1644987749.408312651]: Loading nodelet /stereo_publisher of type depthai_examples/StereoNodelet to manager nodelet_manager with the following remappings:
process[rviz-7]: started with pid [66811]
[ INFO] [1644987749.413470156]: Initializing nodelet with 3 worker threads.
  So I don't think the cause is my environment.
I found this issue when checking for this fix.

Hello,
Sorry for the issue. Looks like @daxoft missed a parameter setting for that example. Just fixed it and updated it.

It works well. Thank you very much!


Failed to build engine with the 70B sq_int model

System Info
4*A800 80G
Who can help?
@Tracin
Information

[X] The official example scripts
[ ] My own modified scripts

Tasks

[X] An officially supported task in the examples folder (such as GLUE/SQuAD, ...)
 Full output logs are in below:

Logs
[TensorRT-LLM] TensorRT-LLM version: 0.10.0.dev2024041600
[04/19/2024-14:10:57] [TRT-LLM] [I] Set bert_attention_plugin to float16.
[04/19/2024-14:10:57] [TRT-LLM] [I] Set gpt_attention_plugin to float16.
[04/19/2024-14:10:57] [TRT-LLM] [I] Set gemm_plugin to float16.
[04/19/2024-14:10:57] [TRT-LLM] [I] Set nccl_plugin to float16.
[04/19/2024-14:10:57] [TRT-LLM] [I] Set lookup_plugin to None.
[04/19/2024-14:10:57] [TRT-LLM] [I] Set lora_plugin to None.
[04/19/2024-14:10:57] [TRT-LLM] [I] Set moe_plugin to float16.
[04/19/2024-14:10:57] [TRT-LLM] [I] Set mamba_conv1d_plugin to float16.
[04/19/2024-14:10:57] [TRT-LLM] [I] Set context_fmha to True.
[04/19/2024-14:10:57] [TRT-LLM] [I] Set context_fmha_fp32_acc to True.
[04/19/2024-14:10:57] [TRT-LLM] [I] Set paged_kv_cache to True.
[04/19/2024-14:10:57] [TRT-LLM] [I] Set remove_input_padding to True.
[04/19/2024-14:10:57] [TRT-LLM] [I] Set use_custom_all_reduce to True.
[04/19/2024-14:10:57] [TRT-LLM] [I] Set multi_block_mode to True.
[04/19/2024-14:10:57] [TRT-LLM] [I] Set enable_xqa to True.
[04/19/2024-14:10:57] [TRT-LLM] [I] Set attention_qk_half_accumulation to False.
[04/19/2024-14:10:57] [TRT-LLM] [I] Set tokens_per_block to 128.
[04/19/2024-14:10:57] [TRT-LLM] [I] Set use_paged_context_fmha to True.
[04/19/2024-14:10:57] [TRT-LLM] [I] Set use_fp8_context_fmha to False.
[04/19/2024-14:10:57] [TRT-LLM] [I] Set use_context_fmha_for_generation to False.
[04/19/2024-14:10:57] [TRT-LLM] [I] Set multiple_profiles to False.
[04/19/2024-14:10:57] [TRT-LLM] [I] Set paged_state to True.
[04/19/2024-14:10:57] [TRT-LLM] [I] Set streamingllm to False.
[04/19/2024-14:10:57] [TRT-LLM] [W] remove_input_padding is enabled, while opt_num_tokens is not set, setting to max_batch_size*max_beam_width.

[04/19/2024-14:10:57] [TRT-LLM] [I] Compute capability: (8, 0)
[04/19/2024-14:10:57] [TRT-LLM] [I] SM count: 108
[04/19/2024-14:10:57] [TRT-LLM] [I] SM clock: 1410 MHz
[04/19/2024-14:10:57] [TRT-LLM] [I] int4 TFLOPS: 1247
[04/19/2024-14:10:57] [TRT-LLM] [I] int8 TFLOPS: 623
[04/19/2024-14:10:57] [TRT-LLM] [I] fp8 TFLOPS: 0
[04/19/2024-14:10:57] [TRT-LLM] [I] float16 TFLOPS: 311
[04/19/2024-14:10:57] [TRT-LLM] [I] bfloat16 TFLOPS: 311
[04/19/2024-14:10:57] [TRT-LLM] [I] float32 TFLOPS: 155
[04/19/2024-14:10:57] [TRT-LLM] [I] Total Memory: 80 GiB
[04/19/2024-14:10:57] [TRT-LLM] [I] Memory clock: 1593 MHz
[04/19/2024-14:10:57] [TRT-LLM] [I] Memory bus width: 5120
[04/19/2024-14:10:57] [TRT-LLM] [I] Memory bandwidth: 2039 GB/s
[04/19/2024-14:10:57] [TRT-LLM] [I] NVLink is active: True
[04/19/2024-14:10:57] [TRT-LLM] [I] NVLink version: 5
[04/19/2024-14:10:57] [TRT-LLM] [I] NVLink bandwidth: 300 GB/s
[TensorRT-LLM] TensorRT-LLM version: 0.10.0.dev2024041600
[TensorRT-LLM] TensorRT-LLM version: 0.10.0.dev2024041600
[TensorRT-LLM] TensorRT-LLM version: 0.10.0.dev2024041600
[TensorRT-LLM] TensorRT-LLM version: 0.10.0.dev2024041600
[04/19/2024-14:11:06] [TRT] [I] [MemUsageChange] Init CUDA: CPU +14, GPU +0, now: CPU 452, GPU 427 (MiB)
[04/19/2024-14:11:08] [TRT] [I] [MemUsageChange] Init CUDA: CPU +1, GPU +0, now: CPU 456, GPU 427 (MiB)
[04/19/2024-14:11:08] [TRT] [I] [MemUsageChange] Init CUDA: CPU +1, GPU +0, now: CPU 456, GPU 427 (MiB)
[04/19/2024-14:11:08] [TRT] [I] [MemUsageChange] Init CUDA: CPU +1, GPU +0, now: CPU 456, GPU 427 (MiB)
[04/19/2024-14:11:28] [TRT] [I] [MemUsageChange] Init builder kernel library: CPU +1973, GPU +350, now: CPU 2561, GPU 777 (MiB)
[04/19/2024-14:11:29] [TRT-LLM] [I] Set nccl_plugin to float16.
[04/19/2024-14:11:29] [TRT-LLM] [I] Set use_custom_all_reduce to True.
[04/19/2024-14:11:29] [TRT] [W] IElementWiseLayer with inputs LLaMAForCausalLM/transformer/vocab_embedding/PLUGIN_V2_AllReduce_0_output_0 and LLaMAForCausalLM/transformer/layers/0/input_layernorm/SHUFFLE_0_output_0: first input has type Half but second input has type Float.
[04/19/2024-14:11:29] [TRT] [W] IElementWiseLayer with inputs LLaMAForCausalLM/transformer/layers/0/input_layernorm/REDUCE_AVG_0_output_0 and LLaMAForCausalLM/transformer/layers/0/input_layernorm/SHUFFLE_1_output_0: first input has type Half but second input has type Float.
[04/19/2024-14:11:29] [TRT] [I] [MemUsageChange] Init builder kernel library: CPU +1973, GPU +350, now: CPU 2565, GPU 777 (MiB)
[04/19/2024-14:11:29] [TRT] [I] [MemUsageChange] Init builder kernel library: CPU +1973, GPU +350, now: CPU 2565, GPU 777 (MiB)
[04/19/2024-14:11:29] [TRT] [W] IElementWiseLayer with inputs LLaMAForCausalLM/transformer/layers/0/ELEMENTWISE_SUM_0_output_0 and LLaMAForCausalLM/transformer/layers/0/post_layernorm/SHUFFLE_0_output_0: first input has type Half but second input has type Float.
[04/19/2024-14:11:29] [TRT] [W] IElementWiseLayer with inputs LLaMAForCausalLM/transformer/layers/0/post_layernorm/REDUCE_AVG_0_output_0 and LLaMAForCausalLM/transformer/layers/0/post_layernorm/SHUFFLE_1_output_0: first input has type Half but second input has type Float.[04/19/2024-14:11:29] [TRT] [W] IElementWiseLayer with inputs LLaMAForCausalLM/transformer/layers/0/ELEMENTWISE_SUM_1_output_0 and LLaMAForCausalLM/transformer/layers/1/input_layernorm/SHUFFLE_0_output_0: first input has type Half but second input has type Float.
[04/19/2024-14:11:29] [TRT] [W] IElementWiseLayer with inputs LLaMAForCausalLM/transformer/layers/1/input_layernorm/REDUCE_AVG_0_output_0 and LLaMAForCausalLM/transformer/layers/1/input_layernorm/SHUFFLE_1_output_0: first input has type Half but second input has type Float.
[04/19/2024-14:11:29] [TRT] [W] IElementWiseLayer with inputs LLaMAForCausalLM/transformer/layers/1/ELEMENTWISE_SUM_0_output_0 and LLaMAForCausalLM/transformer/layers/1/post_layernorm/SHUFFLE_0_output_0: first input has type Half but second input has type Float.
[04/19/2024-14:11:29] [TRT] [W] IElementWiseLayer with inputs LLaMAForCausalLM/transformer/layers/1/post_layernorm/REDUCE_AVG_0_output_0 and LLaMAForCausalLM/transformer/layers/1/post_layernorm/SHUFFLE_1_output_0: first input has type Half but second input has type Float.[04/19/2024-14:11:29] [TRT] [W] IElementWiseLayer with inputs LLaMAForCausalLM/transformer/layers/1/ELEMENTWISE_SUM_1_output_0 and LLaMAForCausalLM/transformer/layers/2/input_layernorm/SHUFFLE_0_output_0: first input has type Half but second input has type Float.
[04/19/2024-14:11:29] [TRT] [W] IElementWiseLayer with inputs LLaMAForCausalLM/transformer/layers/2/input_layernorm/REDUCE_AVG_0_output_0 and LLaMAForCausalLM/transformer/layers/2/input_layernorm/SHUFFLE_1_output_0: first input has type Half but second input has type Float.
[04/19/2024-14:11:29] [TRT] [W] IElementWiseLayer with inputs LLaMAForCausalLM/transformer/layers/2/ELEMENTWISE_SUM_0_output_0 and LLaMAForCausalLM/transformer/layers/2/post_layernorm/SHUFFLE_0_output_0: first input has type Half but second input has type Float.
[04/19/2024-14:11:29] [TRT-LLM] [I] Set nccl_plugin to float16.
[04/19/2024-14:11:29] [TRT] [W] IElementWiseLayer with inputs LLaMAForCausalLM/transformer/layers/2/post_layernorm/REDUCE_AVG_0_output_0 and LLaMAForCausalLM/transformer/layers/2/post_layernorm/SHUFFLE_1_output_0: first input has type Half but second input has type Float.[04/19/2024-14:11:29] [TRT-LLM] [I] Set use_custom_all_reduce to True.
[04/19/2024-14:11:29] [TRT-LLM] [I] Set nccl_plugin to float16.
[04/19/2024-14:11:29] [TRT-LLM] [I] Set use_custom_all_reduce to True.
[04/19/2024-14:11:29] [TRT] [W] IElementWiseLayer with inputs LLaMAForCausalLM/transformer/layers/2/ELEMENTWISE_SUM_1_output_0 and LLaMAForCausalLM/transformer/layers/3/input_layernorm/SHUFFLE_0_output_0: first input has type Half but second input has type Float.
[04/19/2024-14:11:29] [TRT] [W] IElementWiseLayer with inputs LLaMAForCausalLM/transformer/layers/3/input_layernorm/REDUCE_AVG_0_output_0 and LLaMAForCausalLM/transformer/layers/3/input_layernorm/SHUFFLE_1_output_0: first input has type Half but second input has type Float.
[04/19/2024-14:11:29] [TRT] [W] IElementWiseLayer with inputs LLaMAForCausalLM/transformer/vocab_embedding/PLUGIN_V2_AllReduce_0_output_0 and LLaMAForCausalLM/transformer/layers/0/input_layernorm/SHUFFLE_0_output_0: first input has type Half but second input has type Float.
[04/19/2024-14:11:29] [TRT] [W] IElementWiseLayer with inputs LLaMAForCausalLM/transformer/layers/0/input_layernorm/REDUCE_AVG_0_output_0 and LLaMAForCausalLM/transformer/layers/0/input_layernorm/SHUFFLE_1_output_0: first input has type Half but second input has type Float.
[04/19/2024-14:11:29] [TRT] [W] IElementWiseLayer with inputs LLaMAForCausalLM/transformer/vocab_embedding/PLUGIN_V2_AllReduce_0_output_0 and LLaMAForCausalLM/transformer/layers/0/input_layernorm/SHUFFLE_0_output_0: first input has type Half but second input has type Float.
[04/19/2024-14:11:29] [TRT] [W] IElementWiseLayer with inputs LLaMAForCausalLM/transformer/layers/0/input_layernorm/REDUCE_AVG_0_output_0 and LLaMAForCausalLM/transformer/layers/0/input_layernorm/SHUFFLE_1_output_0: first input has type Half but second input has type Float.
[04/19/2024-14:11:29] [TRT] [W] IElementWiseLayer with inputs LLaMAForCausalLM/transformer/layers/3/ELEMENTWISE_SUM_0_output_0 and LLaMAForCausalLM/transformer/layers/3/post_layernorm/SHUFFLE_0_output_0: first input has type Half but second input has type Float.
[04/19/2024-14:11:29] [TRT] [W] IElementWiseLayer with inputs LLaMAForCausalLM/transformer/layers/3/post_layernorm/REDUCE_AVG_0_output_0 and LLaMAForCausalLM/transformer/layers/3/post_layernorm/SHUFFLE_1_output_0: first input has type Half but second input has type Float.[04/19/2024-14:11:29] [TRT] [W] IElementWiseLayer with inputs LLaMAForCausalLM/transformer/layers/3/ELEMENTWISE_SUM_1_output_0 and LLaMAForCausalLM/transformer/layers/4/input_layernorm/SHUFFLE_0_output_0: first input has type Half but second input has type Float.
[04/19/2024-14:11:29] [TRT] [W] IElementWiseLayer with inputs LLaMAForCausalLM/transformer/layers/4/input_layernorm/REDUCE_AVG_0_output_0 and LLaMAForCausalLM/transformer/layers/4/input_layernorm/SHUFFLE_1_output_0: first input has type Half but second input has type Float.
[04/19/2024-14:11:29] [TRT] [W] IElementWiseLayer with inputs LLaMAForCausalLM/transformer/layers/4/ELEMENTWISE_SUM_0_output_0 and LLaMAForCausalLM/transformer/layers/4/post_layernorm/SHUFFLE_0_output_0: first input has type Half but second input has type Float.
[04/19/2024-14:11:29] [TRT] [W] IElementWiseLayer with inputs LLaMAForCausalLM/transformer/layers/4/post_layernorm/REDUCE_AVG_0_output_0 and LLaMAForCausalLM/transformer/layers/4/post_layernorm/SHUFFLE_1_output_0: first input has type Half but second input has type Float.[04/19/2024-14:11:29] [TRT] [W] IElementWiseLayer with inputs LLaMAForCausalLM/transformer/layers/4/ELEMENTWISE_SUM_1_output_0 and LLaMAForCausalLM/transformer/layers/5/input_layernorm/SHUFFLE_0_output_0: first input has type Half but second input has type Float.
[04/19/2024-14:11:36] [TRT] [W] IElementWiseLayer with inputs LLaMAForCausalLM/transformer/ln_f/REDUCE_AVG_0_output_0 and LLaMAForCausalLM/transformer/ln_f/SHUFFLE_1_output_0: first input has type Half but second input has type Float.
[04/19/2024-14:11:36] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +2, GPU +10, now: CPU 2655, GPU 813 (MiB)
[04/19/2024-14:11:36] [TRT] [I] Global timing cache in use. Profiling results in this builder pass will be stored.
[04/19/2024-14:11:36] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +1, GPU +10, now: CPU 2659, GPU 813 (MiB)
[04/19/2024-14:11:36] [TRT] [I] Global timing cache in use. Profiling results in this builder pass will be stored.
[04/19/2024-14:11:36] [TRT-LLM] [I] Build TensorRT engine Unnamed Network 0
[04/19/2024-14:11:36] [TRT] [W] Unused Input: position_ids
[04/19/2024-14:11:36] [TRT] [W] Detected layernorm nodes in FP16.
[04/19/2024-14:11:36] [TRT] [W] Running layernorm after self-attention in FP16 may cause overflow. Exporting the model to the latest available ONNX opset (later than opset 17) to use the INormalizationLayer, or forcing layernorm layers to run in FP32 precision can help with preserving accuracy.
[04/19/2024-14:11:37] [TRT] [W] [RemoveDeadLayers] Input Tensor position_ids is unused or used only at compile-time, but is not being removed.
[04/19/2024-14:11:37] [TRT] [W] Detected layernorm nodes in FP16.
[04/19/2024-14:11:37] [TRT] [W] Running layernorm after self-attention in FP16 may cause overflow. Exporting the model to the latest available ONNX opset (later than opset 17) to use the INormalizationLayer, or forcing layernorm layers to run in FP32 precision can help with preserving accuracy.
[04/19/2024-14:11:37] [TRT] [W] [RemoveDeadLayers] Input Tensor position_ids is unused or used only at compile-time, but is not being removed.
[04/19/2024-14:11:37] [TRT] [I] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +8, now: CPU 2658, GPU 803 (MiB)
[04/19/2024-14:11:38] [TRT] [I] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +8, now: CPU 2658, GPU 803 (MiB)
[04/19/2024-14:11:38] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +1, GPU +10, now: CPU 2659, GPU 813 (MiB)
[04/19/2024-14:11:38] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +1, GPU +10, now: CPU 2659, GPU 813 (MiB)
[04/19/2024-14:11:38] [TRT] [I] Global timing cache in use. Profiling results in this builder pass will be stored.
[04/19/2024-14:11:38] [TRT] [I] Global timing cache in use. Profiling results in this builder pass will be stored.
[04/19/2024-14:11:40] [TRT] [E] 9: Skipping tactic 0x0000000000000000 due to exception [autotuner.cpp:get_best_tactics:1520] Autotuner: no tactics to implement operation:
  360: fc: cast__mye406-(f32[__mye172_sp,2560][]so[], mem_prop=0) | LLaMAForCausalLM/transformer/layers/0/attention/qkv/QUANTIZE_0_output_0'.1-(i8[__mye172_sp,8192][]so[], mem_prop=0), __mye367_dconst-{0, 0, 0, 0, 0, 0, 0, 0, ...}(i8[8192,2560][2560,1]so[1,0], mem_prop=0)<entry>, __mye370_dconst-{5.54005e-06, 1.75586e-05, 4.26856e-06, 9.56643e-06, 2.4461e-05,
[04/19/2024-14:11:40] [TRT] [E] 9: Skipping tactic 0x0000000000000000 due to exception [autotuner.cpp:get_best_tactics:1520] Autotuner: no tactics to implement operation:
  360: fc: cast__mye406-(f32[__mye172_sp,2560][]so[], mem_prop=0) | LLaMAForCausalLM/transformer/layers/0/attention/qkv/QUANTIZE_0_output_0'.1-(i8[__mye172_sp,8192][]so[], mem_prop=0), __mye367_dconst-{0, 0, 0, 0, 0, 0, 0, 0, ...}(i8[8192,2560][2560,1]so[1,0], mem_prop=0)<entry>, __mye370_dconst-{4.8922e-05, 2.0586e-05, 1.40469e-05, 1.46524e-05, 5.06173e-05, 4
[04/19/2024-14:11:40] [TRT] [E] 10: Could not find any implementation for node {ForeignNode[LLaMAForCausalLM/transformer/layers/0/input_layernorm/CONSTANT_1 + LLaMAForCausalLM/transformer/layers/0/input_layernorm/SHUFFLE_0...LLaMAForCausalLM/transformer/layers/0/attention/qkv/CAST_5]}.
[04/19/2024-14:11:41] [TRT] [E] 10: Could not find any implementation for node {ForeignNode[LLaMAForCausalLM/transformer/layers/0/input_layernorm/CONSTANT_1 + LLaMAForCausalLM/transformer/layers/0/input_layernorm/SHUFFLE_0...LLaMAForCausalLM/transformer/layers/0/attention/qkv/CAST_5]}.
[04/19/2024-14:11:41] [TRT] [E] 10: [optimizer.cpp::computeCosts::4048] Error Code 10: Internal Error (Could not find any implementation for node {ForeignNode[LLaMAForCausalLM/transformer/layers/0/input_layernorm/CONSTANT_1 + LLaMAForCausalLM/transformer/layers/0/input_layernorm/SHUFFLE_0...LLaMAForCausalLM/transformer/layers/0/attention/qkv/CAST_5]}.)
[04/19/2024-14:11:41] [TRT-LLM] [E] Engine building failed, please check the error log.
[04/19/2024-14:11:41] [TRT] [E] 10: [optimizer.cpp::computeCosts::4048] Error Code 10: Internal Error (Could not find any implementation for node {ForeignNode[LLaMAForCausalLM/transformer/layers/0/input_layernorm/CONSTANT_1 + LLaMAForCausalLM/transformer/layers/0/input_layernorm/SHUFFLE_0...LLaMAForCausalLM/transformer/layers/0/attention/qkv/CAST_5]}.)
[04/19/2024-14:11:41] [TRT-LLM] [E] Engine building failed, please check the error log.
[04/19/2024-14:11:41] [TRT] [I] Serialized 59 bytes of code generator cache.
[04/19/2024-14:11:41] [TRT] [I] Serialized 24982 bytes of compilation cache.
[04/19/2024-14:11:41] [TRT] [I] Serialized 0 timing cache entries
[04/19/2024-14:11:41] [TRT-LLM] [I] Timing cache serialized to model.cache
[04/19/2024-14:11:41] [TRT-LLM] [I] Serializing engine to trt_llm/hoyollama2-70b_int8-sq/rank3.engine...
concurrent.futures.process._RemoteTraceback:
"""
Traceback (most recent call last):
  File "/usr/lib/python3.10/concurrent/futures/process.py", line 246, in _process_worker
    r = call_item.fn(*call_item.args, **call_item.kwargs)
  File "/usr/local/lib/python3.10/dist-packages/tensorrt_llm/commands/build.py", line 308, in build_and_save
    engine.save(output_dir)
  File "/usr/local/lib/python3.10/dist-packages/tensorrt_llm/builder.py", line 569, in save
    serialize_engine(
  File "/usr/local/lib/python3.10/dist-packages/tensorrt_llm/_common.py", line 105, in serialize_engine
    f.write(engine)
TypeError: a bytes-like object is required, not 'NoneType'
"""

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/tensorrt_llm/commands/build.py", line 357, in parallel_build
    future.result()
  File "/usr/lib/python3.10/concurrent/futures/_base.py", line 451, in result
    return self.__get_result()
  File "/usr/lib/python3.10/concurrent/futures/_base.py", line 403, in __get_result
    raise self._exception
TypeError: a bytes-like object is required, not 'NoneType'
[04/19/2024-14:11:41] [TRT-LLM] [I] Serializing engine to trt_llm/hoyollama2-70b_int8-sq/rank0.engine...
concurrent.futures.process._RemoteTraceback:
"""
Traceback (most recent call last):
  File "/usr/lib/python3.10/concurrent/futures/process.py", line 246, in _process_worker
    r = call_item.fn(*call_item.args, **call_item.kwargs)
  File "/usr/local/lib/python3.10/dist-packages/tensorrt_llm/commands/build.py", line 308, in build_and_save
    engine.save(output_dir)
  File "/usr/local/lib/python3.10/dist-packages/tensorrt_llm/builder.py", line 569, in save
    serialize_engine(
  File "/usr/local/lib/python3.10/dist-packages/tensorrt_llm/_common.py", line 105, in serialize_engine
    f.write(engine)
TypeError: a bytes-like object is required, not 'NoneType'
"""

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/tensorrt_llm/commands/build.py", line 357, in parallel_build
    future.result()
  File "/usr/lib/python3.10/concurrent/futures/_base.py", line 451, in result
    return self.__get_result()
  File "/usr/lib/python3.10/concurrent/futures/_base.py", line 403, in __get_result
    raise self._exception
TypeError: a bytes-like object is required, not 'NoneType'
[04/19/2024-14:11:43] [TRT] [E] 9: Skipping tactic 0x0000000000000000 due to exception [autotuner.cpp:get_best_tactics:1520] Autotuner: no tactics to implement operation:
  360: fc: cast__mye406-(f32[__mye172_sp,2560][]so[], mem_prop=0) | LLaMAForCausalLM/transformer/layers/0/attention/qkv/QUANTIZE_0_output_0'.1-(i8[__mye172_sp,8192][]so[], mem_prop=0), __mye367_dconst-{0, 0, 0, 0, 0, 0, 0, 0, ...}(i8[8192,2560][2560,1]so[1,0], mem_prop=0)<entry>, __mye370_dconst-{5.11017e-05, 3.01524e-05, 8.83986e-06, 1.58633e-05, 1.13223e-05,
[04/19/2024-14:11:43] [TRT] [E] 10: Could not find any implementation for node {ForeignNode[LLaMAForCausalLM/transformer/layers/0/input_layernorm/CONSTANT_1 + LLaMAForCausalLM/transformer/layers/0/input_layernorm/SHUFFLE_0...LLaMAForCausalLM/transformer/layers/0/attention/qkv/CAST_5]}.
[04/19/2024-14:11:43] [TRT] [E] 10: [optimizer.cpp::computeCosts::4048] Error Code 10: Internal Error (Could not find any implementation for node {ForeignNode[LLaMAForCausalLM/transformer/layers/0/input_layernorm/CONSTANT_1 + LLaMAForCausalLM/transformer/layers/0/input_layernorm/SHUFFLE_0...LLaMAForCausalLM/transformer/layers/0/attention/qkv/CAST_5]}.)
[04/19/2024-14:11:43] [TRT-LLM] [E] Engine building failed, please check the error log.
[04/19/2024-14:11:43] [TRT-LLM] [I] Serializing engine to trt_llm/hoyollama2-70b_int8-sq/rank2.engine...
concurrent.futures.process._RemoteTraceback:
"""
Traceback (most recent call last):
  File "/usr/lib/python3.10/concurrent/futures/process.py", line 246, in _process_worker
    r = call_item.fn(*call_item.args, **call_item.kwargs)
  File "/usr/local/lib/python3.10/dist-packages/tensorrt_llm/commands/build.py", line 308, in build_and_save
    engine.save(output_dir)
  File "/usr/local/lib/python3.10/dist-packages/tensorrt_llm/builder.py", line 569, in save
    serialize_engine(
  File "/usr/local/lib/python3.10/dist-packages/tensorrt_llm/_common.py", line 105, in serialize_engine
    f.write(engine)
TypeError: a bytes-like object is required, not 'NoneType'
"""

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/tensorrt_llm/commands/build.py", line 357, in parallel_build
    future.result()
  File "/usr/lib/python3.10/concurrent/futures/_base.py", line 451, in result
    return self.__get_result()
  File "/usr/lib/python3.10/concurrent/futures/_base.py", line 403, in __get_result
    raise self._exception
TypeError: a bytes-like object is required, not 'NoneType'
[04/19/2024-14:11:44] [TRT] [E] 9: Skipping tactic 0x0000000000000000 due to exception [autotuner.cpp:get_best_tactics:1520] Autotuner: no tactics to implement operation:
  360: fc: cast__mye406-(f32[__mye172_sp,2560][]so[], mem_prop=0) | LLaMAForCausalLM/transformer/layers/0/attention/qkv/QUANTIZE_0_output_0'.1-(i8[__mye172_sp,8192][]so[], mem_prop=0), __mye367_dconst-{0, 0, 0, 0, 0, 0, 0, 0, ...}(i8[8192,2560][2560,1]so[1,0], mem_prop=0)<entry>, __mye370_dconst-{3.10001e-05, 4.31095e-05, 5.25548e-05, 1.49551e-05, 3.68126e-05,
[04/19/2024-14:11:45] [TRT] [E] 10: Could not find any implementation for node {ForeignNode[LLaMAForCausalLM/transformer/layers/0/input_layernorm/CONSTANT_1 + LLaMAForCausalLM/transformer/layers/0/input_layernorm/SHUFFLE_0...LLaMAForCausalLM/transformer/layers/0/attention/qkv/CAST_5]}.
[04/19/2024-14:11:45] [TRT] [E] 10: [optimizer.cpp::computeCosts::4048] Error Code 10: Internal Error (Could not find any implementation for node {ForeignNode[LLaMAForCausalLM/transformer/layers/0/input_layernorm/CONSTANT_1 + LLaMAForCausalLM/transformer/layers/0/input_layernorm/SHUFFLE_0...LLaMAForCausalLM/transformer/layers/0/attention/qkv/CAST_5]}.)
[04/19/2024-14:11:45] [TRT-LLM] [E] Engine building failed, please check the error log.
[04/19/2024-14:11:45] [TRT-LLM] [I] Serializing engine to trt_llm/hoyollama2-70b_int8-sq/rank1.engine...
concurrent.futures.process._RemoteTraceback:
"""
Traceback (most recent call last):
  File "/usr/lib/python3.10/concurrent/futures/process.py", line 246, in _process_worker
    r = call_item.fn(*call_item.args, **call_item.kwargs)
  File "/usr/local/lib/python3.10/dist-packages/tensorrt_llm/commands/build.py", line 308, in build_and_save
    engine.save(output_dir)
  File "/usr/local/lib/python3.10/dist-packages/tensorrt_llm/builder.py", line 569, in save
    serialize_engine(
  File "/usr/local/lib/python3.10/dist-packages/tensorrt_llm/_common.py", line 105, in serialize_engine
    f.write(engine)
TypeError: a bytes-like object is required, not 'NoneType'
"""

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/tensorrt_llm/commands/build.py", line 357, in parallel_build
    future.result()
  File "/usr/lib/python3.10/concurrent/futures/_base.py", line 451, in result
    return self.__get_result()
  File "/usr/lib/python3.10/concurrent/futures/_base.py", line 403, in __get_result
    raise self._exception
TypeError: a bytes-like object is required, not 'NoneType'
Traceback (most recent call last):
  File "/usr/local/bin/trtllm-build", line 8, in <module>
    sys.exit(main())
  File "/usr/local/lib/python3.10/dist-packages/tensorrt_llm/commands/build.py", line 454, in main
    parallel_build(source, build_config, args.output_dir, workers,
  File "/usr/local/lib/python3.10/dist-packages/tensorrt_llm/commands/build.py", line 361, in parallel_build
    assert len(exceptions
AssertionError: Engine building failed, please check error log

additional notes
n/a

Could you try adding --strongly_typed --builder_opt=4?

@Opdoop Do you still have the question? If not , we will close this issue soon.


Fix custom mariadb settings

Summary

This PR will make sure that custom user MariaDB settings are being applied along with the default settings for Galera.

Checklist

[x] Categorize the PR by setting a good title and adding one of the labels:
bug, enhancement, documentation, change, breaking, dependency
as they show up in the changelog
[x] Update tests.

Did you test that we can actually set custom settings now?

Of course, with and without :)


Post merge request fixes

Fixes #41, #26

tests aren't passing because of a change to how things are registered via modeladmin but seems to work ok locally


Add minimal resolution support

Didn't realize this at first, but this is basically a duplicate of #10 :) This one is older, but the other one provides better info, so I'll close this one. Reopen if needed :)


feat: add page and server request events

onPageChange
onRequestStart
onRequestEnd
onRequestComplete
onRequestError

Please merge this branch.
The event emitter on page change is really interesting

Thank you

If you like, you could npm i @mykeels/ng2-smart-table till this is done, @arthurhamon

Yes, it's normal.

@nnixaa Can you pls consider merging this PR ASAP ?
Thank you and happy new year :)

Guys whats is the main reason for not merging this one so much time. We could give it a hand if something more is needed to be done

Can you upgrade to Angular9 ,I'm currently in version 1.5.3, but not compatible with Angular9, I need your help. Think you! @mykeels


Basic warning support implementation for the Godot Shading Language.

Basic warning support implementation for the Godot Shading Language.

Currently implemented warnings:

UNUSED_CONSTANT
UNUSED_VARYING
UNUSED_UNIFORM
UNUSED_FUNCTION
UNUSED_STRUCT

They are configured inside project settings:

Is it possible to add a warning for floating-point comparisons (if (i == 0.3))? This is probably one of the most useful warnings for shader beginners :slightly_smiling_face:

Is it possible to add a warning for floating-point comparisons (if (i == 0.3))? This is probably one of the most useful warnings for shader beginners :slightly_smiling_face:

Is it possible to add a warning for floating-point comparisons (if (i == 0.3))? This is probably one of the most useful warnings for shader beginners

I guess ), this is just a beginning I will make them gradually.

Is it possible to add a warning for floating-point comparisons (if (i == 0.3))? This is probably one of the most useful warnings for shader beginners

I guess ), this is just a beginning I will make them gradually.

@Calinou Thx for the pointing. Added FLOAT_COMPARSION warning:

@Calinou Thx for the pointing. Added FLOAT_COMPARSION warning:

@Chaosus Looks good, but I'd reword the message for better grammar:
Direct flaoting-point comparison (this may not evaluate to trueas you expect). Instead, useabs(a -b) < 0.0001 for an approximate but predictable comparison.
I'd also change the suggested epsilon to 0.0001 as it's more likely to work in all cases.

@Chaosus Looks good, but I'd reword the message for better grammar:
Direct flaoting-point comparison (this may not evaluate to trueas you expect). Instead, useabs(a -b) < 0.0001 for an approximate but predictable comparison.
I'd also change the suggested epsilon to 0.0001 as it's more likely to work in all cases.

@Calinou Thanks, fixed.

@Calinou Thanks, fixed.

I rebased it - It's working correctly. If you can please check and approve it @Calinou. This is basic implementation - in the future, I want to add a UNUSED_LOCAL_VARIABLE warning to this system, but for now, this just works and I want it to be merged. Thanks.

Thanks!

@Chaosus Instead of suggesting the expression abs(a - b) < 0.0001, would it be feasible to port is_equal_approx and maybe is_zero_approx to the shader language? We should ensure that the epsilon is appropriate for whatever float precision is being used by the shader.


Jenkins: SITL temporarily disable failing offboard tests

FYI @Jaeyoung-Lim @Pedro-Roque

Thanks for the note @dagar , I'll let you know once I have news on this.

@mrivi @jkflying FYI (avoidance and safe landing tests included).


Comments containing <, > or |

The Remarks section of the REM command documentation says:

You can't use a redirection character (< or >) or pipe (|) in a batch file comment.

It would be nice to allow those characters, though. Consider e.g. something like this (from one of my libraries):
@echo off
rem ==========================================================================
rem                        Copyright 2013 Gennaro Prota
rem
rem                  Licensed under the 3-Clause BSD License.
rem             (See accompanying file 3_CLAUSE_BSD_LICENSE.txt or
rem              <https://opensource.org/licenses/BSD-3-Clause>.)
rem __________________________________________________________________________

Can the angle brackets which enclose the URL really cause a problem, here?
FWIW, running
REM test > a_file.txt

from a command prompt under Windows 8.1, I don't get the text file. So perhaps the command is already treating the listed characters like any others and we just need to reflect that in the documentation?

I'm going to close this issue, as it lacks the section for the docs.microsoft.com ➟ GitHub link. Sorry.
I opened #5856 in its place.


[PT Run] Move launcher using win arrow keys

Summary of the Pull Request
What is this about:
Move PT Run search box using arrow keys. Search box is only moved if query text is empty.
What is include in the PR:
How does someone test / validate:
Quality Checklist

[x] Linked issue: #12104
[ ] Communication: I've discussed this with core contributors in the issue.
[ ] Tests: Added/updated and all pass
[ ] Installer: Added/updated and all pass
[ ] Localization: All end user facing strings can be localized
[ ] Docs: Added/ updated
[ ] Binaries: Any new files are added to WXS / YML

[ ] No new binaries
[ ] YML for signing for new binaries
[ ] WXS for installer for new binaries

Contributor License Agreement (CLA)
A CLA must be signed. If not, go over here and sign the CLA.

DPI awareness support is missing

@stefansjfw Is this enabling moving the Run window with arrow keys only? Shouldn't this be Windows key + arrow keys so it conforms with default OS interactions?

@stefansjfw Is this enabling moving the Run window with arrow keys only? Shouldn't this be Windows key + arrow keys so it conforms with default OS interactions?

Yes. Arrow keys only atm. Makes sense that it should be Win + arrow. I'll fix it

Hmm.. no idea. I can imagine that might be difficult due to OS behavior.
Since a WPF window has this by default, we seem to have disabled it by customizing the window (e.g. https://stackoverflow.com/questions/13422666/wpf-borderless-window-issues-aero-snap-maximizing)
Maybe there's a way to turn it back on without showing the window chrome?
If not, we might want to go ahead with this as is or wait for moving towards WinUI 3 and we might redesign the entire thing all together?

Forgive me for not understanding you immediately, but why do you need this feature?

Closing due to https://github.com/microsoft/PowerToys/issues/12104#issuecomment-927889719

Closing due to https://github.com/microsoft/PowerToys/issues/12104#issuecomment-927889719


Problem with ros dependencies

Brief Issue Summary
Hello,
I am a developer working on ros, every time I  compile my project, I need to source /opt/ros/kinetic/setup.bash to setup environment variables and paths. I also write it on my .bashrc, but when I use the cmake extension to build, seems like source /opt/ros/kinetic/setup.bash is not executed and will give out [cmake] -- Configuring incomplete, errors occurred! But when I use the integrated terminal to do cmake .., there is no error.
So is there a way to let the extension do the source /opt/ros/kinetic/setup.bash first and then build?
Thanks!
CMake Tools Diagnostics
No response
Debug Log
No response
Additional Information
No response

Have you tried using environmentSetupScript in the kit?
Here is the document on on how to configure a project using kits: https://github.com/microsoft/vscode-cmake-tools/blob/main/docs/configure.md#the-cmake-tools-configure-step
and here is some more info on kits options: https://github.com/microsoft/vscode-cmake-tools/blob/main/docs/kits.md#kit-options
Short example:
[
    {
        "name": "TestKit",
         ...
        "environmentSetupScript": "${workspaceFolder}/envsetup.sh"
    }
]

Hey Elaheh,
Much thanks for the info, it works well!
Best

Have you tried using environmentSetupScript in the kit? Here is the document on on how to configure a project using kits: https://github.com/microsoft/vscode-cmake-tools/blob/main/docs/configure.md#the-cmake-tools-configure-step and here is some more info on kits options: https://github.com/microsoft/vscode-cmake-tools/blob/main/docs/kits.md#kit-options
Short example:
[
    {
        "name": "TestKit",
         ...
        "environmentSetupScript": "${workspaceFolder}/envsetup.sh"
    }
]

Hey Elaheh,
Much thanks for the info, it works well!
Best


Fix example for Get-TpmSupportedFeature

Rename of Get-TpmSupportedFeatures to Get-TpmSupportedFeature
in example to support copying.
Resolves #435

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


Show better error message if gym can't detect provisioning profiles to use

          1 Warning

:warning:
Please provide a changelog summary in the Pull Request description @KrauseFx

  Generated by :no_entry_sign: Danger

Since you're here, could you check to see if the provisioningProfiles are formatted correctly? We have preprocessor macros for our PRODUCT_BUNDLE_IDENTIFIER and PROVISIONING_PROFILE_SPECIFIER and those were getting sent to this code: the exportOptionsPlist file had the variables rather than the proper values, i.e.
provisioningProfiles: {
  $PRODUCT_BUNDLE_IDENTIFIER_APP => $PROVISIONING_PROFILE_SPECIFIER_UUID,
  $PRODUCT_BUNDLE_IDENTIFIER_NOTIFICATION_SERVICE => $PROVISIONING_PROFILE_SPECIFIER_NOTIFICATION_SERVICE
}

The error we got from the export archive step was super confusing. If this code could check to see if the values on the left were of a bundle id (i.e. /\w+.\w+.*) at least, that would be great.

@lyndsey-ferguson Sorry I don't have this setup currently, so it doesn't work for you? What are the steps required to fix it?

😢 We worked around the issue by passing the :export_options with the correct :provisioningProfiles but it was a monster to track down the problem. A warning about bad :provisioningProfiles would have saved us a lot of time.

I was hoping to be efficient (lazy) and have someone else update the error handling


Use title from front matter instead of file name

If we make files case insensitive (#17) we'll want a way to actually preserve the user's desired casing.

I think I've changed my mind on this -- maybe we should just de-emphasis the page name (not have it in a giant ), then you can just stick your own title at the top of the markdown?


Unrelated to this repo but I THINK MY GD ACCOUNT GOT DISABLED

(THIS IS FOR CALLOC AND CALLOC ONLY, IF YOU ARE NOT CALLOC DO NOT READ BELOW THIS LINE)
I can't DM you in GD so I have to post an issue on one of your GitHub repos...
My username is BombCraft2010. I am a 14 year old autistic Geometry Dash player and creator, and I am currently trying to beat Acu as my first extreme and am also working on a level called Gather The Gloinks, a TADC-inspired platformer that would be my 7th rateworthy level (none of the 6 I've uploaded are rated), and I have a problem.
1 hour and 45 minutes ago, I received an email from a GD friend with a video where he opened my account and found no profile posts, levels, or comments (although my stats remain: 3990 stars, 230 moons, 120 secret coins, 342 user coins, 89 demons, and 0 CP)
When I saw the email, I logged onto GD and he was right: my levels are gone, my comments are gone, I can't upload levels or comment, daily chests don't work, and I can't view others' profiles (or my own...)
Strangely, my lists are still on the servers, but I can't upload any more lists.

It might also be because this morning I uploaded three levels hating on Hatsune Miku:
The first was called "kys sasaki wataru" and its contents were a button that you had to press to kill Hatsune Miku (there's no blood)
The second was called "OddMod" and it literally just told GD creator OddMod to kill herself because she made a Hatsune Miku-themed insane demon called "MikuMikuMikuMiku"
The third was called "Why 2006 was best year" or smth like that and it just said that 2006 was the best year because Hatsune Miku was invented in 2007. (ignore the fact that I was born in 2010)
Can you help me get unbanned?

@ImmediateMurderProfessionals Unfortunately this question is a little off-topic. All I can really say is my famous quote, "Ask Robtop" otherwise I would ask an eldermod to release you from your silence since it doesn't seem like you did anything wrong. This is not the place to ask for these kind of questions and like I said before. Ask an Eldermod for help.

Ok

I’ll try to convince my parents to let me get discord


REQUEST: Complex object internal variables

Great gem so far btw :smile:, I like the variable output printed below the code! One request I have if you ever have the time to try it would be to have the objects output their internal values like pry-inline.
For example instead of:
#<Aws::S3::Bucket:0x007faec4a11043>

You would get:
#<Aws::S3::Bucket name="development-bucket">

@ootoovak Thanks. pry-inline also looks promising. Will start the work on getting that kind of output. ✌

Awesome great to hear. Thanks. :smile:

Hay I just noticed this today https://github.com/SudhagarS/pry-state/commit/e48b2631cf744737fe876f827b5ad99443c0c242 thanks for updating it so quickly. :+1:


Should save_discussions.py be deleted?

https://github.com/hasadna/anyway/blob/celery/anyway/save_discussions.py#L2
This is also a script that we need to decide whether to implement it in main.py or delete it. What does it do?

The counterpart of load discussions: prints all Disqus ids of discussion
markers currently in the DB.
On Thu, Sep 14, 2017, 19:44 Roey Darwish Dror<EMAIL_ADDRESS>wrote:

https://github.com/hasadna/anyway/blob/celery/anyway/save_discussions.py#L2
This is also a script that we need to decide whether to implement it in
main.py or delete it. What does it do?
—
You are receiving this because you are subscribed to this thread.
Reply to this email directly, view it on GitHub
https://github.com/hasadna/anyway/issues/736, or mute the thread
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Adding third-party Digi-Key provider to docs

In order to add your provider to our list of third-party providers, we require that the provider code include tests. Please include tests, and we'll accept this PR. Thanks!

Test added.

That was quick! Thanks!
Thank you for contributing! 🎉


can create a better demo configration in website

Have you read the FAQ and checked for duplicate open issues?
Is your feature request related to a problem? Please describe.

Describe the solution you'd like

Describe alternatives you've considered

Additional context

Can you describe more what you are asking?  I'm not sure what you mean.  If you want to change the configuration in the hosted demo, you can click the gear icon the the top-left of the site.  In your own site, you can change configuration with player.configure(...).  See the docs for the function and the object.

hello,
actually I am talking about demo-website. Its demo configration does'nt look decent. do I work upon it?

Hello @anshgo01yal, thank you for letting us know that the configs don't look good to you. If you are talking about the attributes of the configurations, we need to keep them as they are.
If not, please give us an example of your solution to make them look better. Thank you!

hello @michellezhuogg,

I can make it better may I try.

@anshgo01yal Please describe what do you find confusing about the current configuration and your proposed solution. Include screenshots for visual changes.

hello @ismena, I want to include some hover effects and add some padding in configuration to make it little more spacy.

I'm fine with that, you can proceed with a PR.
Make sure to verify your work on all major browsers and OSs.

@ismena can you please assign me to do this task

Done


Cannot run Ansible operator locally without roles/

Bug Report
Watches files can now specify a role from an ansible collection, so the roles directory should be optional, but without it operator-sdk run --local does not know the type of the operator.
---
- version: v1alpha1
  group: demo.example.com
  kind: Memcached
  role: asmacdo.memcached_collection.memcached

 operator-sdk run --local
INFO[0000] Running the operator locally in namespace default.
FATA[0000] Failed to run operator locally: unknown operator type

mkdir roles
operator-sdk run --local
INFO[0000] Running the operator locally in namespace default.
{"level":"info","ts":1585234928.2288132,"logger":"cmd","msg":"Go Version: go1.13.4"}
{"level":"info","ts":1585234928.2288342,"logger":"cmd","msg":"Go OS/Arch: linux/amd64"}
{"level":"info","ts":1585234928.2288425,"logger":"cmd","msg":"Version of operator-sdk: v0.16.0+git"}
{"level":"info","ts":1585234928.229738,"logger":"cmd","msg":"Watching single namespace.","Namespace":"default"}
{"level":"info","ts":1585234928.4853928,"logger":"controller-runtime.metrics","msg":"metrics server is starting to listen","addr":"<IP_ADDRESS>:8383"}
... (it works)

@asmacdo When we finish the KB integration and transition the ansible-based operator and scaffolding to a KB plugin, we'll have the project type stored in the PROJECT file.
In the meantime, do you think there's another unique indicator in an ansible-based operator project we can use?

/kind bug

Adding the --type override is actually a little trickier than expected, since there are no guards in place in our code specifying the wrong type seems to lead to a segfault most of the time. This means adding it is a much bigger undertaking than initially anticipated. I vote we punt adding the --type override until it's explicitly requested, and close this one out with better Ansible detection.


Import Error when import super_gradients.training.model

🐛 Describe the bug
when I try import  models module I got Import error!
ImportError: cannot import name 'DEV_PKGS' from 'pip._internal.commands.freeze'

Versions

Changing pip to pip==23.1 solved it to me. We'll wait for the team to come up with a better solution.

@okanegemen can you please share what python & pip version are you using?

pip==23.1

worked for me too


implot internal support?

I want to draw candlestick.  In the implot example, some internal functions must be used.
I tried to modify a little code. It seems to work normally, but I'm not sure whether there is a bug.

I too have a similar requirement.
The implot example mentioned by @SuperGod makes references to internal functions defined in implot_internal.h for example ImPlot::RoundTime. These functions are defined in cimplot.h in cimgui-go but for some reason are not in the generated cimplot_funcs.go

I ran into this issue a couple months back as well when I wanted to do some custom candlestick graphs following the same implot example.  I found the code below in the generator is what prevents the C functions from being wrapped for Go.  In my local copy I commented out this section and re-generated the code for cimplot (I did not re-run for cimgui) and was able to successfully get the candlestick demo running.  I did not submit a pull request as I do not know if this would expose problems with cimgui or if there is another reason for keeping the internal funcs hidden.
https://github.com/AllenDang/cimgui-go/blob/main/cmd/codegen/gencpp.go#L69
if len(f.FuncName) == 0 || strings.Contains(f.Location, "internal") {  			shouldSkip = true }

I ran into this issue a couple months back as well when I wanted to do some custom candlestick graphs following the same implot example. I found the code below in the generator is what prevents the C functions from being wrapped for Go. In my local copy I commented out this section and re-generated the code for cimplot (I did not re-run for cimgui) and was able to successfully get the candlestick demo running. I did not submit a pull request as I do not know if this would expose problems with cimgui or if there is another reason for keeping the internal funcs hidden.
https://github.com/AllenDang/cimgui-go/blob/main/cmd/codegen/gencpp.go#L69
if len(f.FuncName) == 0 || strings.Contains(f.Location, "internal") { shouldSkip = true }

I have modified some code locally. It is not enough to only modify this line. I can submit a pr after I have tested it.

I made fork with enabled implot internal features.
https://github.com/WinPooh32/cimgui-go
There are not much changes to make it work.
Full list of skipped ImPlot functions:
https://gist.github.com/WinPooh32/e33e155a3df135fc96aac35fb45f03a7
Also successfully ported candlestick plot demo.  Some helper functions can be easily implemented using standard go library (binary search and time conversions).
Demo: https://github.com/WinPooh32/cimgui-candlestick-demo
@gucio321, but there is issue with code generation, these functions were generated with same names:
https://github.com/WinPooh32/cimgui-go/blob/3477c3023e4a3a394efbec64389d6ef2595fe822/cimplot_funcs.go#L413-L431
func (self PlotItemGroup) ItemID(label_id string) ID {
	label_idArg, label_idFin := wrapString(label_id)
	defer func() {
		label_idFin()
	}()
	return ID(C.ImPlotItemGroup_GetItemID(self.handle(), label_idArg))
}

func (self PlotItemGroup) ItemID(id ID) PlotItem {
	return (PlotItem)(unsafe.Pointer(C.ImPlotItemGroup_GetItem_ID(self.handle(), C.ImGuiID(id))))
}

@WinPooh32 looks great! Please open a pr.
In case of these methods, in gengo_funcs there is a nap of functions that needs to be renamed somehow and another one for skipping then.
You need to handle duplicated functions there


fix showperformanceoverlay state not being changed and heard properly

somehow this got broken
i think it's probably in relation to listen: false on some providers being used incorrectly

reminder to self: just remove this entirely and use the showFPS monitor

removed.


Add WASM tests to CI

As noted in #3359, we want to add tests in CI to verify wasm, both on stable and minimum supported rustc versions.

We spent some time on improving the CI jobs in rust-av recently, so I can take a look at this. It should be good enough to run cargo check for MSRV purposes, right? I don't think we'd expect changes that don't break the build and change runtime behaviour

it should be fine, -Zmsrv-policy in nightly might be an alternative otherwise.


notification options don't work

You can entirely turn notifications off or on, but individual options do not work.
For example:

Literally nothing in the blue circle does anything. The top turn off/on all is the only thing that works.

duplicate


Is this project still alive?

Is anyone working on releasing any updates? @niklasvh

I don't think so

Why bug the maintainer about whether it's alive? If the owner thought they were never going to update it again there would be a message in the README... We're talking about a very responsible and highly regarded developer here. The html2canvas website was updated 9-months ago, that seems pretty alive to me. The owner obviously has other things going on in their life.
Perhaps a more useful issue would be, "How can I help to sort through all these pull requests?" Perhaps the owner would be open to inviting others on the project as maintainers so the project could be updated more regularly? That would be very cool and beneficial too all.
Asking "Is this project still alive" to me serves no purpose other than to troll/guilt the owner into pushing an update which is not very inspiring. Please correct me if I'm missing something here...

@mudcube Thanks for the information. I was looking for some of the interesting projects to contribute to and being new to open source contribution I am not sure how to approach. I will try looking at the pull requests and see how I can be helpful. Thanks again!!

I also would like to help maintain this project. I think it would be cool if @eKoopmans also jumped on board too. There are a lot of great PRs ready to be merged in.

I'd be happy to help maintain, it just depends what @niklasvh wants. I definitely appreciate the project and think it should continue to be developed!


AppleGraphicsPowerManagement.kext not found

Hello. I have this issue AppleGraphicsPowerManagement.kext not found when update sierra os 10.12.5
on May 30,2017
Pls help me. I running Macbook pro mid 2010
Thank you very much
Log:
| Checking compatibility
MBPModelVersion :  MacBookPro6,2 ➔ Compatibility : OK ✓
| Checking SIP Status
SIP Disabled
| Searching for AppleGraphicsPowerManagement.kext
AppleGraphicsPowerManagement.kext not found

Hi, this is a weird issue, I'm also on 10.12.5 and I don't have this problem. Did you made any modification to the file before applying patch ?
Please can you check manually if AppleGraphicsPowerManagement.kext is in this folder :
/System/Library/Extensions. If it's here :

Check if there is only one file name like this (and no file with .bak extension)
Right click on the file, click to Show Package Content navigate to Contents folder and check if a Info.plist file is inside.

Thanks

hello. i was do your patch before update 10.12.5 and it is working perfect
but after update 10.12.5 my computer auto restart, so i apply this patch again but AppleGraphicsPowerManagement.kext not found
I try to find AppleGraphicsPowerManagement.kext on /System/Library/Extensions but not found
:(
should i reinstall mac os?

We have two choices here :

Manually replace AppleGraphicsPowerManagement.kext by a file I give you :

Since we are on same OS and same Mac, should not cause any problem. You can download the file here : AppleGraphicsPowerManagement.kext.zip
You can use KextDrop app to reload the .kext file correctly

You can re-install Mac OS using Recovery Mode it will just do a clean re-install of Mac OS, no data will be deleted from your computer.

This might restore AppleGraphicsPowerManagement.kext file and you might be able to patch again.

Did you tried the procedures ? Tell me if it worked so I can close the issue.

hello
sorry because late reply. I have try Recovery Mode and install your patch again. now AppleGraphicsPowerManagement.kext is found automatic a
I hope my Macbook will never restart again. Thank you very much.

Can anyone lend me a copy of AppleGraphicsPowerManagement.kext for 10.13.3?
Thanks in advance.

You can edit it manually following the instructions of this post :
https://forums.macrumors.com/threads/gpu-kernel-panic-in-mid-2010-whats-the-best-fix.1890097/#post-23312990
2018-02-02 16:27 GMT+01:00 Qytanius<EMAIL_ADDRESS>
Can anyone lend me a copy of AppleGraphicsPowerManagement.kext for
10.13.3?
Thanks in advance.
—
You are receiving this because you modified the open/close state.
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Gracias Julian.. after last security update the file was gone, and the same "not found" flag was kicking up.. so I did all your steps and it works --- after 30 reboots or more--- I finally install kextdrop reinstall the kext file and then patch the system... then restart my mac (now by myself)  is ok again. I create a folder on the desktop with all this files, for if something happends later .
thanks for all..
Saludos!!


AUD V2.3 - Patch ENONT Files

Context;

Some folders are missing, and eventually a creation portion will be needed in later versions of AUD for Rasberry PI's edition.
For now, patch the remaining ENONT Files so databases on Firebase, Github, and Roblox are in sync.

Patched https://github.com/TRENATTI/alapha-universe-docs/commit/03ed0e84ffdc6c7776f5239f017f60cae6cacdf3


Feature request: qmllint integration

QML is a language for front-end design using the QtQuick framework. With the Qt installation comes the command line utility qmllint that warns you about errors, like in the following:
// example.qml
Rectangle {
    width: 100

    Button {
    }

it reports example.qml:6 : Expected token \}'`.
It would be nice to have such warnings displayed by ale. I'm happy to implement a solution myself. However some people might have useful pointers. What do you think?

This issue can be closed now, I suppose. :-)


Multiple datasources not managed

Hello,
It looks like multiple datasource are not managed by this lib.
Since you rely on the default "spring.datasource" configuration, it appears only the main datasource is successfully monitored.
In our case we have muultiple datasources and we do not use "spring.datasource" configuration keys but "XX.datasource", "YY.datasource" keys.
Multiple datasources are programatically managed by configuration classes and beans.
Is there a way to configure this plugin to deal with these datasources or it is not possible by design?
Thanks

The DataSourceObservationBeanPostProcessor wraps DataSource beans with observation-aware datasources.
As long as each DataSource is defined as a bean — whether it's a single or multiple datasource — all datasource beans are automatically converted, regardless of whether they're created by spring.datasource.*, XX.datasource, or any other ways.
Please check that your datasources are defined as beans, and consider setting a breakpoint in DataSourceObservationBeanPostProcessor to confirm they’re being processed.


fix(rpc): Fix bugs and performance of getnetworksolps & getnetworkhashps RPCs

Motivation
When getnetworksolps & getnetworkhashps are run over a large block range, they are extremely slow or produce incorrect results.
There are also some bugs, zcashd incompatibilities, and unsupported argument values in the RPC implementation.
This is a partial performance and bug fix for #6688, but it doesn't limit the height range.
Specifications
zcashd implements these RPCs using this function:
https://github.com/zcash/zcash/blob/482f120f635e342061005d5218d7cc18d1f342b5/src/rpc/mining.cpp#L48-L83
I re-wrote the implementation to match what the zcashd function does.
Complex Code or Requirements
We want to match zcashd's behaviour and performance. Some performance improvements require database changes (#7109), but they are out of scope for this ticket and PR.
To improve performance, I iterated over block headers, rather than full blocks. To do this, I re-wrote the chain iterator code so it was generic over the chain item. (Block, block header, etc.)
The zcashd code is fairly complicated, so I tried to follow it closely.
Solution
Compatibility fixes:

Re-write the RPC and state implementations to match zcashd's code
Accept the argument values that zcashd accepts, and handle them like zcashd

Bug fixes:

Handle existing arguments exactly like zcashd
Use min and max times, rather than first and last times
Avoid dividing by a zero work duration (if the times are the same, which is valid)

Related changes:

Simplify the RPC and state code, removing manual iteration
Re-write the chain iterator so it is generic over the chain item
Simplify the chain iterator code

Unrelated changes:

Removed implemented TODO comments

Testing
Add extra test cases for RPC arguments.
I manually tested this change using zcash-rpc-diff, see the comments for details.
Review
This PR is ready for review.
Reviewer Checklist

[ ] Will the PR name make sense to users?

[ ] Does it need extra CHANGELOG info? (new features, breaking changes, large changes)

[ ] Are the PR labels correct?
[ ] Does the code do what the ticket and PR says?

[ ] Does it change concurrent code, unsafe code, or consensus rules?

[ ] How do you know it works? Does it have tests?

Follow Up Work
Test the performance of the new code to work out if we need to limit the height range.
Test for invalid answers when large heights are supplied.

Manual Performance and Compatibility Testing
This code produces identical results to zcashd. It is up to 20x faster than zcashd for small block ranges, but sometimes it is a few tens of milliseconds slower.
It becomes noticeably slower around 10,000 blocks. It takes 1 second for 100,000 blocks, 14 seconds for 1000,000 blocks, and 1 minute for the entire chain.
Since it's fast for the default range and typical ranges, and it finishes in a reasonable time for the whole chain, I don't think we need to do anything further here. If someone tells us the performance on large ranges is an issue, we can open a ticket then.
I used the following commands:
# since this is larger than the chain length, it just uses the whole chain
zcash-rpc-diff 28232 getnetworksolps 10000000 2256077
zcash-rpc-diff 28232 getnetworksolps 1000000 2256077
zcash-rpc-diff 28232 getnetworksolps<PHONE_NUMBER>077
zcash-rpc-diff 28232 getnetworksolps 10000 2256077
zcash-rpc-diff 28232 getnetworksolps 1000 2256077
zcash-rpc-diff 28232 getnetworksolps<PHONE_NUMBER>
zcash-rpc-diff 28232 getnetworksolps -1 2256077
zcash-rpc-diff 28232 getnetworksolps -99 2256077
zcash-rpc-diff 28232 getnetworksolps 2 2256077
zcash-rpc-diff 28232 getnetworksolps 1 2256077
zcash-rpc-diff 28232 getnetworksolps 0 2256077

Here is a sample output:
$ zcash-rpc-diff 28232 getnetworksolps<PHONE_NUMBER>
Using 'Zcash RPC client version v5.7.0-ge08571476d8' via command 'zcash-cli'.
Using bash shell '5.1.16(1)-release' launched from '/nix/store/afc0n4afniiipmwz8asn8z16xlwkwjb4-bash-interactive-5.1-p16/bin/bash'.

Checking first node release info...
Checking second node release info...
Connected to zebrad v1.2.0+113.gbcce69b (port 28232) and zcashd v5.7.0-ge08571476d8 (zcash.conf port).

Checking zebrad v1.2.0+113.gbcce69b network and tip height...
Checking zcashd v5.7.0-ge08571476d8 network and tip height...

Request:
getnetworksolps<PHONE_NUMBER>

Querying zebrad v1.2.0+113.gbcce69b main chain at height >=2256080...

real    0m0.007s
user    0m0.004s
sys     0m0.003s

Querying zcashd v5.7.0-ge08571476d8 main chain at height >=2256080...

real    0m0.007s
user    0m0.003s
sys     0m0.004s

Response diff between zebrad v1.2.0+113.gbcce69b and zcashd v5.7.0-ge08571476d8 (limited to 40 lines):
RPC responses were identical

/run/user/1000/tmp.7Rsnl3dX4P.rpc-diff/zebrad-main-2256080-getnetworksolps.json, limited to 40 lines:
8626536292

Initial Height: Compatibility Testing
I tried these heights and they produced the same results:
zcash-rpc-diff 28232 getnetworksolps 120 10
zcash-rpc-diff 28232 getnetworksolps -1 0
zcash-rpc-diff 28232 getnetworksolps 1 2
zcash-rpc-diff 28232 getnetworksolps 1 300000
zcash-rpc-diff 28232 getnetworksolps 120 300000
zcash-rpc-diff 28232 getnetworksolps -1 300000
zcash-rpc-diff 28232 getnetworksolps 120 5
zcash-rpc-diff 28232 getnetworksolps 120 -5
 zcash-rpc-diff 28232 getnetworksolps<PHONE_NUMBER>0
zcash-rpc-diff 28232 getnetworksolps 0 0
zcash-rpc-diff 28232 getnetworksolps 0 -1
zcash-rpc-diff 28232 getnetworksolps -1 0
zcash-rpc-diff 28232 getnetworksolps -1 -1

@mergifyio refresh