Edits for Tracing section

While https://github.com/microsoft/code-with-engineering-playbook/tree/master/observability#logging had a good first run of content, it could be formatted/edited in a more structured way.
Proposal for the structure of the 3 sections of the Observability Pillars:

Summarize what the particular is and how it fits into a dev/ops workflow
Point out primary use case examples
Best Practices & Guidance for the particular pillar (may expand into its own doc over time)
Links To Useful Tools & Further Guidance

Already fixed, but issue wasn't linked to PR, closing the issue


[Widget proposal]: toggle button ('boolean' facet)

I would like to have a widget able to toggle a single facet value.

what do you think?

It could make sense for boolean filtering, but I'm not very familiar with any search interface with such UX -> where is that screenshot coming from?

From a POC (the same hierarchical POC we did). The client had an index with a clear separation between selling items and sold items (a bit like searching for sold properties versus to buy properties)

properties = appartments

OK, I would put it as low priority then; I think it's not very common on the web but could make a lot of sense on mobile.
Here is what Amazon do:

Yes, on mobile makes sense. This widget is low priority of course because not present in the instant search demo

now focusing on #41, let's revisit this use case later on


Mobile view

Hi, when using ShinyProxy instead of deploying the apps directly through Docker, the responsiveness of the apps in mobile devices is lost.
I´ve seen there is a PR that tries to solve this. Any ETA on when this could be implemented?

Hi @algo-se,
the linked PR only makes changes to the default theme. You can achieve the same result with a custom theme. More info and examples here
there is also a workaround where you can use /app_direct/<app_name> endpoints which will serve the app content directly without the iframe that can cause issues on mobile.

Oh I see, thank you for the clarification @dseynaev! So do you have to use a custom theme in order to enable app_direct endpoints in the landing page? Is there any other way, can you do it from the application.yml?

app_direct endpoints are be available by default (provided you are using a recent enough version of shinyproxy)
the suggestion to use a custom theme is a completely separate workaround I meant that you could try out the fix implemented in https://github.com/openanalytics/shinyproxy/pull/179 by copying the app.html from that PR and use it as a custom theme

to have the landing page use app_direct links you would need a custom theme indeed. There is afaik no way to do it from the application.yaml (unless you only have one app, in which case you could use the landing-page setting to skip the landing page)

I´ve followed these steps but I can´t see any changes.
To set up Shiny Proxy I used this tutorial , so I´m using Docker Swarm.
My folder structure is:
//home/myuser/shinyproxy-docker-swarm-demo/
├── application
│   ├── application.yml
│   └── templates
├── shinyproxy.yml
└── usagestats.yml
templates folder is the one from the shinyproxy-config-examples.
In the application.yml I added: template-path: ./templates/1col
In order to make the changes effective I restarted Shiny Proxy: docker service update shinyproxy_shinyproxy --force but everything stayed the same. Any ideas?

Looking at the tutorial you linked my guess would be that you need to mount the templates folder into the shinyproxy container in docker-compose configuration shinyproxy.yml

Awesome guess! 😉   That worked perfectly, thank you.
In case it´s useful for other people, here is what I've done:

Add ./application/templates:/opt/shinyproxy/templates to volumes in shinyproxy.yml

Add all the templates to the 1col folder.

Edit the head tag of all the templates:
3.1. Add <meta name="viewport" content="width=device-width, initial-scale=1"> so that thymeleaf works well in mobile devices.
3.2. Add bootstrap calls manually
3.3. Add title manually
3.4. Change the reference to css files according to the new folder structure.
3.5 Add favicon reference (also see this for favicons when launching an app).

4 Edit the html templates to your liking, for example here is a nice trick to serve some apps in direct mode and some in iframe mode, if thats something you would like to do.
5. Add css files (to 1col/assets/css), 1, 2, 3
6. Add images for the apps and the favicon (to 1col/assets/img)
7. Add template-path: ./templates/1col to application.yml (right under proxy:)
8. Redeploy Shiny Proxy stack: cd shinyproxy-docker-swarm-demo/, export APP_DOMAIN=app.example.com and docker stack deploy -c shinyproxy.yml shinyproxy
9. Enjoy! 🚀


Index compilation broken with Laravel 10

We just upgraded to Laravel 10, but encountered an issue with our index compilation.
We're actually using CockroachDB, not PostgreSQL but the use-case should be identical and behave the same for both.
In our migrations we have some columns as jsonb and we're adding a GIN index on an attribute of that JSON column.
The migration looks like this:
Schema::create('examples', function (Blueprint $table) {
    $table->jsonb('meta');
    $table->rawIndex('("meta"->\'attribute\')', 'examples_meta_attribute_index')->algorithm('gin');
});

However starting with Laravel 10, expressions can't just be cast to string. This means that this line is now not applicable for all possible cases: https://github.com/tpetry/laravel-postgresql-enhanced/blob/aea2a6a6c727a2ffeb92c5473bcee8ad88c115ff/src/Schema/Grammars/GrammarIndex.php#L132
I believe fixing it would be as simple as changing from string to string|Expression and adding the same functionality that already exists in laravel-query-expressions  as stringize
https://github.com/tpetry/laravel-query-expressions/blob/4ca06ed9abf0ac5e52f83a613124389d4c03392b/src/Concerns/StringizeExpression.php#L12-L18
There are possibly more cases where there is currently an expectation for string but we might receive an Expression instead.
I'm happy to send a pull-request, but I'm not quite sure how one gets those fancy numbers for columns and tables that are used in other tests :)

Will be fixed in about an hour ;)

Its fixed with release 0.27.1. The problem was not changed in Laravel 10. I never handled rawIndex definitions correctly. They are not mentioned in the documentation so I completely missed them.
There's no real documentation for tests, its just PHPUnit with some additions. The numbers at the end of everything are just random nums to prevent name clashes.
As I rewrite every PR the best approach would be just asking whether I am ok with a certain feature, work on a draft and I polish it up to fit my style. Or the way I would do it.


Fetch block concurrently with import

Summary
This improves TPS ~5% for large blocks and ~10% for empty blocks.
Test Plan
I ran this code successfully. Any block reorderings are caught in the postgres implementation.

Codecov Report

Merging #668 (5361b3d) into develop (20a9d69) will not change coverage.
The diff coverage is n/a.

@@           Coverage Diff            @@
##           develop     #668   +/-   ##
========================================
  Coverage    52.35%   52.35%
========================================
  Files           23       23
  Lines         3637     3637
========================================
  Hits          1904     1904
  Misses        1477     1477
  Partials       256      256

Continue to review full report at Codecov.

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Failed to load the native tensorflow runtime

Please make sure that this is a build/installation issue. As per our GitHub Policy, we only address code/doc bugs, performance issues, feature requests and build/installation issues on GitHub. tag:build_template
System information

OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Windows 10
Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device:
TensorFlow installed from (source or binary): pip
TensorFlow version: 2.2
Python version: 3.5
Installed using virtualenv? pip? conda?: pip
Bazel version (if compiling from source):
GCC/Compiler version (if compiling from source):
CUDA/cuDNN version: 10.1
GPU model and memory: GTX 1650

issue while importing
Provide the exact sequence of commands / steps that you executed before running into the problem
installed cuda 10.1
cudnn
following command:
pip install --ignore-installed --upgrade tensorflow-gpu
Any other info / logs

import tensorflow
Traceback (most recent call last):
File "C:\Users\Lenovo\anaconda3\envs\tf\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 58, in
from tensorflow.python.pywrap_tensorflow_internal import *
File "C:\Users\Lenovo\anaconda3\envs\tf\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 28, in
_pywrap_tensorflow_internal = swig_import_helper()
File "C:\Users\Lenovo\anaconda3\envs\tf\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 24, in swig_import_helper
_mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description)
File "C:\Users\Lenovo\anaconda3\envs\tf\lib\imp.py", line 243, in load_module
return load_dynamic(name, filename, file)
File "C:\Users\Lenovo\anaconda3\envs\tf\lib\imp.py", line 343, in load_dynamic
return _load(spec)
ImportError: DLL load failed: The specified module could not be found.

During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "", line 1, in
File "C:\Users\Lenovo\anaconda3\envs\tf\lib\site-packages\tensorflow_init_.py", line 41, in
from tensorflow.python.tools import module_util as module_util
File "C:\Users\Lenovo\anaconda3\envs\tf\lib\site-packages\tensorflow\python_init.py", line 50, in
from tensorflow.python import pywrap_tensorflow
File "C:\Users\Lenovo\anaconda3\envs\tf\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 69, in
raise ImportError(msg)
ImportError: Traceback (most recent call last):
File "C:\Users\Lenovo\anaconda3\envs\tf\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 58, in
from tensorflow.python.pywrap_tensorflow_internal import *
File "C:\Users\Lenovo\anaconda3\envs\tf\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 28, in
_pywrap_tensorflow_internal = swig_import_helper()
File "C:\Users\Lenovo\anaconda3\envs\tf\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 24, in swig_import_helper
_mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description)
File "C:\Users\Lenovo\anaconda3\envs\tf\lib\imp.py", line 243, in load_module
return load_dynamic(name, filename, file)
File "C:\Users\Lenovo\anaconda3\envs\tf\lib\imp.py", line 343, in load_dynamic
return _load(spec)
ImportError: DLL load failed: The specified module could not be found.
Failed to load the native TensorFlow runtime.
See https://www.tensorflow.org/install/errors

From the template it looks like you are installing TensorFlow (TF) prebuilt binaries:

For TF-GPU - See point 1
For TF-CPU - See point 2

1. Installing TensorFlow-GPU (TF) prebuilt binaries
Make sure you are using compatible TF and CUDA versions. Please refer following TF version and CUDA version compatibility table.

TF
CUDA

2.1.0 - 2.2.0
10.1

1.13.1 - 2.0
10.0

1.5.0 - 1.12.0
9.0

If you have above configuration and using Windows platform -

Try adding the CUDA, CUPTI, and cuDNN installation directories to the %PATH% environment variable.
Refer windows setup guide.

If you have above configuration and using Ubuntu/Linux platform -

Try adding the CUDA, CUPTI, and cuDNN installation directories to the $LD_LIBRARY_PATH environment variable.
Refer linux setup guide.

If error still persists then, apparently your CPU model does not support AVX instruction sets.

Refer hardware requirements.

2. Installing TensorFlow (TF) CPU prebuilt binaries
TensorFlow release binaries version 1.6 and higher are prebuilt with AVX instruction sets.
Therefore on any CPU that does not have these instruction sets, either CPU or GPU version of TF will fail to load.
Apparently, your CPU model does not support AVX instruction sets. You can still use TensorFlow with the alternatives given below:

Try Google Colab to use TensorFlow.

The easiest way to use TF will be to switch to google colab. You get pre-installed latest stable TF version. Also you can use pip install  to install any other preferred TF version.
It has an added advantage since you can you easily switch to different hardware accelerators (cpu, gpu, tpu) as per the task.
All you need is a good internet connection and you are all set.

Try to build TF from sources by changing CPU optimization flags.

Please let us know if this helps.

@surabhijain123
What is make/model of your cpu?
I suspect your cpu model does not support AVX instructions sets.See hardware requirements
Make sure to download the latest microsoft visual c++ redistributable from here.
.Also, please follow the instructions from to install from Tensorflow website.
Please, check Your CPU/Python is on 32 bits?Please, refer #36167 and see if it helps you.Please, refer similar issue #36167 #36151 #36138 #36054 #36045 #36020 #36003 #35988 #35903 #35880 #35865 #35805 #35789 #35773 #35772 #35767 #35766 #35749 #35721 #35618 #35204
Thanks!

Tensorflow-gpu is installed using conda but running a program gives following output as error:
Figures now render in the Plots pane by default. To make them also appear inline in the Console, uncheck "Mute Inline Plotting" under the Plots pane options menu.
2020-07-23 00:20:27.296592: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
2020-07-23 00:25:43.601086: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library nvcuda.dll
2020-07-23 00:25:43.698117: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce GTX 1650 computeCapability: 7.5
coreClock: 1.56GHz coreCount: 16 deviceMemorySize: 4.00GiB deviceMemoryBandwidth: 119.24GiB/s
2020-07-23 00:25:43.700705: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
2020-07-23 00:25:43.706002: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll
2020-07-23 00:25:43.709851: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll
2020-07-23 00:25:43.711804: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll
2020-07-23 00:25:43.716109: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll
2020-07-23 00:25:43.719046: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll
2020-07-23 00:25:43.725904: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll
2020-07-23 00:25:43.727008: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0
2020-07-23 00:25:43.728448: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2
2020-07-23 00:25:43.731493: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce GTX 1650 computeCapability: 7.5
coreClock: 1.56GHz coreCount: 16 deviceMemorySize: 4.00GiB deviceMemoryBandwidth: 119.24GiB/s
2020-07-23 00:25:43.732498: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
2020-07-23 00:25:43.733014: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll
2020-07-23 00:25:43.733525: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll
2020-07-23 00:25:43.734032: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll
2020-07-23 00:25:43.734545: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll
2020-07-23 00:25:43.735064: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll
2020-07-23 00:25:43.735583: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll
2020-07-23 00:25:43.736528: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0
2020-07-23 00:25:44.352026: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1096] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-07-23 00:25:44.352581: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102]      0
2020-07-23 00:25:44.352914: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] 0:   N
2020-07-23 00:25:44.353923: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1241] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 2917 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1650, pci bus id: 0000:01:00.0, compute capability: 7.5)
Model: "model"

Layer (type)                    Output Shape         Param #     Connected to
input_1 (InputLayer)            [(None, 128, 128, 3) 0

lambda (Lambda)                 (None, 128, 128, 3)  0           input_1[0][0]

conv2d (Conv2D)                 (None, 128, 128, 16) 448         lambda[0][0]

dropout (Dropout)               (None, 128, 128, 16) 0           conv2d[0][0]

conv2d_1 (Conv2D)               (None, 128, 128, 16) 2320        dropout[0][0]

max_pooling2d (MaxPooling2D)    (None, 64, 64, 16)   0           conv2d_1[0][0]

conv2d_2 (Conv2D)               (None, 64, 64, 32)   4640        max_pooling2d[0][0]

dropout_1 (Dropout)             (None, 64, 64, 32)   0           conv2d_2[0][0]

conv2d_3 (Conv2D)               (None, 64, 64, 32)   9248        dropout_1[0][0]

max_pooling2d_1 (MaxPooling2D)  (None, 32, 32, 32)   0           conv2d_3[0][0]

conv2d_4 (Conv2D)               (None, 32, 32, 64)   18496       max_pooling2d_1[0][0]

dropout_2 (Dropout)             (None, 32, 32, 64)   0           conv2d_4[0][0]

conv2d_5 (Conv2D)               (None, 32, 32, 64)   36928       dropout_2[0][0]

max_pooling2d_2 (MaxPooling2D)  (None, 16, 16, 64)   0           conv2d_5[0][0]

conv2d_6 (Conv2D)               (None, 16, 16, 128)  73856       max_pooling2d_2[0][0]

dropout_3 (Dropout)             (None, 16, 16, 128)  0           conv2d_6[0][0]

conv2d_7 (Conv2D)               (None, 16, 16, 128)  147584      dropout_3[0][0]

max_pooling2d_3 (MaxPooling2D)  (None, 8, 8, 128)    0           conv2d_7[0][0]

conv2d_8 (Conv2D)               (None, 8, 8, 256)    295168      max_pooling2d_3[0][0]

dropout_4 (Dropout)             (None, 8, 8, 256)    0           conv2d_8[0][0]

conv2d_9 (Conv2D)               (None, 8, 8, 256)    590080      dropout_4[0][0]

conv2d_transpose (Conv2DTranspo (None, 16, 16, 128)  131200      conv2d_9[0][0]

concatenate (Concatenate)       (None, 16, 16, 256)  0           conv2d_7[0][0]
conv2d_transpose[0][0]

conv2d_10 (Conv2D)              (None, 16, 16, 128)  295040      concatenate[0][0]

dropout_5 (Dropout)             (None, 16, 16, 128)  0           conv2d_10[0][0]

conv2d_11 (Conv2D)              (None, 16, 16, 128)  147584      dropout_5[0][0]

conv2d_transpose_1 (Conv2DTrans (None, 32, 32, 64)   32832       conv2d_11[0][0]

concatenate_1 (Concatenate)     (None, 32, 32, 128)  0           conv2d_5[0][0]
conv2d_transpose_1[0][0]

conv2d_12 (Conv2D)              (None, 32, 32, 64)   73792       concatenate_1[0][0]

dropout_6 (Dropout)             (None, 32, 32, 64)   0           conv2d_12[0][0]

conv2d_13 (Conv2D)              (None, 32, 32, 64)   36928       dropout_6[0][0]

conv2d_transpose_2 (Conv2DTrans (None, 64, 64, 32)   8224        conv2d_13[0][0]

concatenate_2 (Concatenate)     (None, 64, 64, 64)   0           conv2d_3[0][0]
conv2d_transpose_2[0][0]

conv2d_14 (Conv2D)              (None, 64, 64, 32)   18464       concatenate_2[0][0]

dropout_7 (Dropout)             (None, 64, 64, 32)   0           conv2d_14[0][0]

conv2d_15 (Conv2D)              (None, 64, 64, 32)   9248        dropout_7[0][0]

conv2d_transpose_3 (Conv2DTrans (None, 128, 128, 16) 2064        conv2d_15[0][0]

concatenate_3 (Concatenate)     (None, 128, 128, 32) 0           conv2d_1[0][0]
conv2d_transpose_3[0][0]

conv2d_16 (Conv2D)              (None, 128, 128, 16) 4624        concatenate_3[0][0]

dropout_8 (Dropout)             (None, 128, 128, 16) 0           conv2d_16[0][0]

conv2d_17 (Conv2D)              (None, 128, 128, 16) 2320        dropout_8[0][0]

conv2d_18 (Conv2D)              (None, 128, 128, 1)  17          conv2d_17[0][0]
Total params: 1,941,105
Trainable params: 1,941,105
Non-trainable params: 0

Train on 603 samples, validate on 67 samples
Epoch 1/100
2020-07-23 00:20:27.296592: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
2020-07-23 00:25:43.601086: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library nvcuda.dll
2020-07-23 00:25:43.698117: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce GTX 1650 computeCapability: 7.5
coreClock: 1.56GHz coreCount: 16 deviceMemorySize: 4.00GiB deviceMemoryBandwidth: 119.24GiB/s
2020-07-23 00:25:43.700705: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
2020-07-23 00:25:43.706002: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll
2020-07-23 00:25:43.709851: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll
2020-07-23 00:25:43.711804: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll
2020-07-23 00:25:43.716109: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll
2020-07-23 00:25:43.719046: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll
2020-07-23 00:25:43.725904: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll
2020-07-23 00:25:43.727008: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0
2020-07-23 00:25:43.728448: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2
2020-07-23 00:25:43.731493: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce GTX 1650 computeCapability: 7.5
coreClock: 1.56GHz coreCount: 16 deviceMemorySize: 4.00GiB deviceMemoryBandwidth: 119.24GiB/s
2020-07-23 00:25:43.732498: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
2020-07-23 00:25:43.733014: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll
2020-07-23 00:25:43.733525: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll
2020-07-23 00:25:43.734032: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll
2020-07-23 00:25:43.734545: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll
2020-07-23 00:25:43.735064: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll
2020-07-23 00:25:43.735583: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll
2020-07-23 00:25:43.736528: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0
2020-07-23 00:25:44.352026: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1096] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-07-23 00:25:44.352581: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102]      0
2020-07-23 00:25:44.352914: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] 0:   N
2020-07-23 00:25:44.353923: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1241] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 2917 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1650, pci bus id: 0000:01:00.0, compute capability: 7.5)
2020-07-23 00:25:46.339563: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll
16/603 [..............................] - ETA: 3:23WARNING:tensorflow:Early stopping conditioned on metric val_loss which is not available. Available metrics are:
Traceback (most recent call last):
File "F:\Projects\Semantic_seg\Segmentation\untitled0.py", line 136, in
result=model.fit(X_train, Y_train,validation_split=0.1, batch_size=16, epochs=100, callbacks=callbacks)
File "C:\Users\Lenovo\anaconda3\envs\tf-gpu-cuda10\lib\site-packages\tensorflow_core\python\keras\engine\training.py", line 819, in fit
use_multiprocessing=use_multiprocessing)
File "C:\Users\Lenovo\anaconda3\envs\tf-gpu-cuda10\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py", line 342, in fit
total_epochs=epochs)
File "C:\Users\Lenovo\anaconda3\envs\tf-gpu-cuda10\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py", line 128, in run_one_epoch
batch_outs = execution_function(iterator)
File "C:\Users\Lenovo\anaconda3\envs\tf-gpu-cuda10\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py", line 98, in execution_function
distributed_function(input_fn))
File "C:\Users\Lenovo\anaconda3\envs\tf-gpu-cuda10\lib\site-packages\tensorflow_core\python\eager\def_function.py", line 568, in call
result = self._call(*args, **kwds)
File "C:\Users\Lenovo\anaconda3\envs\tf-gpu-cuda10\lib\site-packages\tensorflow_core\python\eager\def_function.py", line 632, in _call
return self._stateless_fn(*args, **kwds)
File "C:\Users\Lenovo\anaconda3\envs\tf-gpu-cuda10\lib\site-packages\tensorflow_core\python\eager\function.py", line 2363, in call
return graph_function._filtered_call(args, kwargs)  # pylint: disable=protected-access
File "C:\Users\Lenovo\anaconda3\envs\tf-gpu-cuda10\lib\site-packages\tensorflow_core\python\eager\function.py", line 1611, in _filtered_call
self.captured_inputs)
File "C:\Users\Lenovo\anaconda3\envs\tf-gpu-cuda10\lib\site-packages\tensorflow_core\python\eager\function.py", line 1692, in _call_flat
ctx, args, cancellation_manager=cancellation_manager))
File "C:\Users\Lenovo\anaconda3\envs\tf-gpu-cuda10\lib\site-packages\tensorflow_core\python\eager\function.py", line 545, in call
ctx=ctx)
File "C:\Users\Lenovo\anaconda3\envs\tf-gpu-cuda10\lib\site-packages\tensorflow_core\python\eager\execute.py", line 67, in quick_execute
six.raise_from(core._status_to_exception(e.code, message), None)
File "", line 3, in raise_from
UnknownError:  Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
[[node model/conv2d/Conv2D (defined at F:\Projects\Semantic_seg\Segmentation\untitled0.py:136) ]] [Op:__inference_distributed_function_3807]
Errors may have originated from an input operation.
Input Source operations connected to node model/conv2d/Conv2D:
model/lambda/truediv (defined at F:\Projects\Semantic_seg\Segmentation\untitled0.py:61)
Function call stack:
distributed_function
2020-07-23 00:20:27.296592: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
2020-07-23 00:25:43.601086: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library nvcuda.dll
2020-07-23 00:25:43.698117: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce GTX 1650 computeCapability: 7.5
coreClock: 1.56GHz coreCount: 16 deviceMemorySize: 4.00GiB deviceMemoryBandwidth: 119.24GiB/s
2020-07-23 00:25:43.700705: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
2020-07-23 00:25:43.706002: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll
2020-07-23 00:25:43.709851: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll
2020-07-23 00:25:43.711804: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll
2020-07-23 00:25:43.716109: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll
2020-07-23 00:25:43.719046: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll
2020-07-23 00:25:43.725904: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll
2020-07-23 00:25:43.727008: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0
2020-07-23 00:25:43.728448: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2
2020-07-23 00:25:43.731493: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce GTX 1650 computeCapability: 7.5
coreClock: 1.56GHz coreCount: 16 deviceMemorySize: 4.00GiB deviceMemoryBandwidth: 119.24GiB/s
2020-07-23 00:25:43.732498: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
2020-07-23 00:25:43.733014: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll
2020-07-23 00:25:43.733525: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll
2020-07-23 00:25:43.734032: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll
2020-07-23 00:25:43.734545: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll
2020-07-23 00:25:43.735064: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll
2020-07-23 00:25:43.735583: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll
2020-07-23 00:25:43.736528: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0
2020-07-23 00:25:44.352026: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1096] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-07-23 00:25:44.352581: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102]      0
2020-07-23 00:25:44.352914: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] 0:   N
2020-07-23 00:25:44.353923: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1241] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 2917 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1650, pci bus id: 0000:01:00.0, compute capability: 7.5)
2020-07-23 00:25:46.339563: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll
2020-07-23 00:25:50.484064: E tensorflow/stream_executor/cuda/cuda_dnn.cc:329] Could not create cudnn handle: CUDNN_STATUS_ALLOC_FAILED
2020-07-23 00:25:50.491286: E tensorflow/stream_executor/cuda/cuda_dnn.cc:329] Could not create cudnn handle: CUDNN_STATUS_ALLOC_FAILED
2020-07-23 00:25:50.498779: W tensorflow/core/common_runtime/base_collective_executor.cc:217] BaseCollectiveExecutor::StartAbort Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
[[{{node model/conv2d/Conv2D}}]]
2020-07-23 00:25:50.639169: I tensorflow/core/profiler/lib/profiler_session.cc:225] Profiler session started.
2020-07-23 00:25:50.640311: I tensorflow/core/profiler/internal/gpu/cupti_tracer.cc:1259] Profiler found 1 GPUs
2020-07-23 00:25:50.642950: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cupti64_101.dll'; dlerror: cupti64_101.dll not found
2020-07-23 00:25:50.643768: E tensorflow/core/profiler/internal/gpu/cupti_tracer.cc:1307] function cupti_interface_->Subscribe( &subscriber_, (CUpti_CallbackFunc)ApiCallback, this)failed with error CUPTI could not be loaded or symbol could not be found.
2020-07-23 00:25:50.644897: E tensorflow/core/profiler/internal/gpu/cupti_tracer.cc:1346] function cupti_interface_->ActivityRegisterCallbacks( AllocCuptiActivityBuffer, FreeCuptiActivityBuffer)failed with error CUPTI could not be loaded or symbol could not be found.
2020-07-23 00:25:50.687514: E tensorflow/core/profiler/internal/gpu/cupti_tracer.cc:1329] function cupti_interface_->EnableCallback( 0 , subscriber_, CUPTI_CB_DOMAIN_DRIVER_API, cbid)failed with error CUPTI could not be loaded or symbol could not be found.
2020-07-23 00:25:50.688439: I tensorflow/core/profiler/internal/gpu/device_tracer.cc:88]  GpuTracer has collected 0 callback api events and 0 activity events.

@surabhijain123
Did you try with tested build configuration from here and see if you are facing the issue.
Did you install the latest MSVC redistributable from https://support.microsoft.com/en-us/help/2977003/the-latest-supported-visual-c-downloads .
Tensorflow 2.2 is compiled using MSVC 2019, which appears to require an additional DLL.
Your CPU/Python is on 32 bits?
This issue is more suitable on Continuum Anaconda repo since its related to TF installation with Anaconda.
Please post it on Continuum Anaconda.
Thanks!


Slurs with contradictory startid/endid and staff attributes

It seems that the musicxml2mei stylesheet generates sometimes contradictory attributes regarding the positioning of slurs. Slurs have always startid/endid attributes and timestamps and staff attributes. In some cases, the staff value do not correspond to the staff of the startid. See measure 17 (startid="#d648110e5686" and staff="4")
https://raw.githubusercontent.com/music-encoding/sample-encodings/master/MEI 3.0/Music/Complete examples/Brahms_StringQuartet_Op51_No1.mei
I don't see the reason to duplicate the information using startid/endid together with timestamp. I would rather recommend to use either one. This would avoid this type of situation and/or the need to define an order of precedence to apply when the information do not match.

Don't it hate it when that happens?  :-)
Duplication is not the problem. The actual problem is the erroneous calculation of the value of @staff.  I wonder if we should just throw away this stylesheet and start again.

A couple more thoughts --
It's a shame we didn't preserve the MusicXML files for these examples because the problem might not lie in the stylesheet, but in the original MusicXML instead.  I'm just sayin'.  :-)
Also, could we turn this into a validation issue by adding a schematron rule that makes sure that @staff contains a value that matches that of the target of @startid and @endid?  It wouldn't fix the error of course, but it would alert one to the fact that an error exists.

This is where I am not sure about the utility to always duplicate the information. Per se it is not a problem, but it will inevitably yields contradictory situations since validating and maintaining duplicated information is always an issue. This is often the case with MusicXML data. Information is duplicated, but will still don't know what to expect.
I would rather have guidelines clearly specifying when duplicate and contradictory information can be useful. I can see cases for it. For example, using startid for precisely encoding the start position, and then staff="1 2 3 4 for encoding to what staves it applies. If we are clear about this in the guidelines, we do not have to discuss which one has the precedence or have a schematron rule for verifying that the information is contradictory (since we might want this).
I still cannot see the use of having both startid and tstamp (and endid and tstamp2 respectively). Any example?

I can't think of a reason right now to record both @startid and @tstamp (or @endid and @tstamp2) as they are closely related (though not quite the same thing since tstamp's value can be deduced from @startid, but not the other way around).  However, that doesn't mean such a reason might not exist.
I agree that it would be a good idea to put something in the Guidelines about when to choose each combination -- start/end ID or tstamp/tstamp2.  Where would be the most appropriate place?
We could also add a switch to the MusicXML -> MEI stylesheet which allows the user to choose one approach or the other (in fact, it would throw away the one not selected).  In addition, I wouldn't object to a schematron rule that issues a warning (but not an error) if both sets of attributes are present.

For the guidelines, I think the best place would be in section 4.1.5 Timestamps and Durations.
A switch would be ideal. You would probably also want to be able to choose between <tie> vs @tie since these also get duplicated.

OK, would you create an issue please?

Moved to music-encoding


(ForumsTeam) Broken Hyperlink

In this documentation under the section "Monitoring your storage service" "Receive Alert Notifications" hyperlink has broken. I am requesting you to review and update the hyperlink.

@v-viksur : Thanks for your contribution to the Azure documentation! The author, @fhryo-msft, has been notified to review your proposed change.

It looks good. thanks.

#signoff

#sign-off

I have reviewed and signed off. Thanks for the fix.
From: PRMerger14<EMAIL_ADDRESS>Sent: Sunday, February 11, 2018 4:20 AM
To: MicrosoftDocs/azure-docs<EMAIL_ADDRESS>Cc: Francis Yu<EMAIL_ADDRESS>Mention<EMAIL_ADDRESS>Subject: Re: [MicrosoftDocs/azure-docs] (ForumsTeam) Broken Hyperlink (#4836)
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is your code supports multi camera tracking ?

@ssbilakeri Hey! You can refer to my repo:
https://github.com/CaptainEven/MCMOT-ByteTrack

Hi,
As I have refer to your repository, it supports multiclass tracking, but I
would like to know is your work supports Multi camera multi object tracking
??
On Fri, Jan 28, 2022 at 7:51 AM Even @.***> wrote:

@ssbilakeri https://github.com/ssbilakeri Hey! You can refer to my repo:
https://github.com/CaptainEven/MCMOT-ByteTrack
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Updated Mirador dependencies.

References

Fixes #1447

Description
Not sure when this started, but Mirador builds are currently failing. The issue appears to be in one of the plugins. Updating the plugin version fixes the problem.
Instructions for Reviewers
Includes package.json updates for mirador and the mirador-share-plugin. Updates the plugin from 0.11.0 to 0.13.0. Updates mirador from 3.0.0 to 3.3.0. Our Mirador dependencies are now:
    "mirador": "^3.3.0",
    "mirador-dl-plugin": "^0.13.0",
    "mirador-share-plugin": "^0.11.0",

I noticed that react and react-dom are actually devDependencies and made that update as well. (This doesn't affect the size of the compiled bundle.)
Checklist
This checklist provides a reminder of what we are going to look for when reviewing your PR. You need not complete this checklist prior to creating your PR (draft PRs are always welcome). If you are unsure about an item in the checklist, don't hesitate to ask. We're here to help!

[x] My PR is small in size (e.g. less than 1,000 lines of code, not including comments & specs/tests), or I have provided reasons as to why that's not possible.
[x] My PR passes TSLint validation using yarn run lint
[x] My PR doesn't introduce circular dependencies
[ ] My PR includes TypeDoc comments for all new (or modified) public methods and classes. It also includes TypeDoc for large or complex private methods.
[ ] My PR passes all specs/tests and includes new/updated specs or tests based on the Code Testing Guide.
[ ] If my PR includes new, third-party dependencies (in package.json), I've made sure their licenses align with the DSpace BSD License based on the Licensing of Contributions documentation.

There's an npm (yarn) subtly here that I don't fully understand.  Wouldn't ^0.10.0 require that yarn clean install pickup the minor version 0.11.0? In testing, the mirador build only succeeded when I explicitly updated the minor version in package.json. (I didn't actually check the lockfile after the fresh install.)
I guess related to this, is there a general recommendation for when we should avoid minor versions (between DSpace releases) and instead accept only patch updates (~) or require an exact version match?

Ran into this bug when trying to test #1436... so, I'll test this fix first.

@tdonohue thanks, that's what I needed. Good to get the brief on "update." Makes sense now!


2024 ESDS Annual Event Recap

Thanks for the input Teagan!  Should all be integrated now and tidied up.


Make a webapp

Alternatively, make it send email as per Zawinski's law. More seriously, I think cargo install is a barrier to getting users and I think a simple webpage with a backend that runs eugene lint could be excellent to use from documentation, and to share to people who are working on migrations but can't install.
Also I'd like to learn a rust web framework just to check the state of affairs. warp and axum both look pretty exciting to me.

This one sparks the most joy right now, going to take a look at what we need to do this and get eugene lint deployed as a service somewhere!
I think it makes sense to introduce usage of cargo workspaces at this point, since we shouldn't have to build a command line program that contains a webserver.

curl -s --header 'Content-Type: application/json' -XPOST https://kaveland.no/eugene/app/lint.json -d '{"script": "-- file: foo.sql\ncreate index books_idx on books(title);"}' | python -m json.tool
[
    {
        "name": "foo.sql",
        "statements": [
            {
                "statement_number": 1,
                "line_number": 1,
                "sql": "-- file: foo.sql\ncreate index books_idx on books(title)",
                "triggered_rules": [
                    {
                        "id": "E6",
                        "name": "Creating a new index on an existing table",
                        "condition": "A new index was created on an existing table without the `CONCURRENTLY` keyword",
                        "effect": "This blocks all writes to the table while the index is being created",
                        "workaround": "Run `CREATE INDEX CONCURRENTLY` instead of `CREATE INDEX`",
                        "help": "Statement takes `ShareLock` on `public.books`, blocking writes while creating index `public.books_idx`",
                        "url": "https://kaveland.no/eugene/hints/E6/"
                    },
                    {
                        "id": "E9",
                        "name": "Taking dangerous lock without timeout",
                        "condition": "A lock that would block many common operations was taken without a timeout",
                        "effect": "This can block all other operations on the table indefinitely if any other transaction holds a conflicting lock while `idle in transaction` or `active`",
                        "workaround": "Run `SET LOCAL lock_timeout = '2s';` before the statement and retry the migration if necessary",
                        "help": "Statement takes lock on `public.books`, but does not set a lock timeout",
                        "url": "https://kaveland.no/eugene/hints/E9/"
                    }
                ]
            }
        ],
        "passed_all_checks": false,
        "skip_summary": true
    }
]

Or this simple one:
curl -s -XPOST https://kaveland.no/eugene/app/lint.raw -d 'create index books_idx on books(title);'
unnamed:1 E6 Creating a new index on an existing table https://kaveland.no/eugene/hints/E6/
unnamed:1 E9 Taking dangerous lock without timeout https://kaveland.no/eugene/hints/E9/

I feel like we should have a textarea that we can expose on the webpage before closing this one, going to take a look at that next.


VehicleStep service times get multiplied by 10000

After assigning a VehicleStep to a vehicle, all its service values service_at , service_before and service_after get multiplied   by 10000.
To reproduce:
import vroom

step =  vroom.VehicleStepSingle(id = 1, service_at = 3, service_before= 5, service_after = 2)

print(step)
""">>> vroom.VehicleStepSingle(1, service_at=3, service_after=2, service_before=5)"""

vehicle = vroom.Vehicle(id = 100, start = 0, end = 0, steps = [step])
print(vehicle.steps)
"""
>>> [vroom.VehicleStepStart(),
>>>  vroom.VehicleStepSingle(1, service_at=30000, service_after=20000, service_before=50000),
>>>  vroom.VehicleStepEnd()]
"""

I think this makes the vehicles execute any preset steps much later than intended (often at the end of the route).
This can be seen in this example:
import numpy as np
import vroom

N_JOBS = 6

# distance between points is always 1
dur_matrix = (-1 * np.identity(N_JOBS) + 1).astype(int)
print(dur_matrix)
"""
[[0 1 1 1 1 1]
 [1 0 1 1 1 1]
 [1 1 0 1 1 1]
 [1 1 1 0 1 1]
 [1 1 1 1 0 1]
 [1 1 1 1 1 0]]
 """

jobs = [vroom.Job(i, location = i) for i in range(N_JOBS)]

# we force these jobs to be accomplished at the start of the route
steps = [
    vroom.VehicleStepSingle(id = 1, service_at = 1),
    vroom.VehicleStepSingle(id = 2, service_at = 2),
    vroom.VehicleStepSingle(id = 3, service_at = 3),
]

vehicle = vroom.Vehicle(id = 100, start = 0, end = 0, steps = steps)

problem = vroom.Input()

problem.set_durations_matrix("car", dur_matrix)

problem.add_job(jobs)
problem.add_vehicle(vehicle)
solution = problem.solve(exploration_level=5, nb_threads=4)

solution.routes

In this case, the three steps get completed at the end of the route.

For now I have realized that this line can be replaced by steps = steps since VehicleStep is converting the already created steps into new ones,  applying _vroom.scale_from_user_duration and multiplying times by 100.
When accessing the vehicle.steps property this happens again thus getting the time * 100 * 100 upscale.
I guess the second * 100 should be left there. However, the problem of the steps not matching the desired service time constraints is still unresolved. I am exploring the codebase, specially the ForcedServiceclass trying to understand more.

I opened #83 as a fix for the  *10000 issue
Still, service times don't work for me as I understand they should

Yeah, so upstream vroom made a change to scale up some costs and durations, and I've tried to patch all cases so that the user is unaffected by it. You found something I missed. Thanks for fixing that.
That being said, the underlying attributes accessed from cpp are 100, I think the idea is that all user inputs are scaled up like that and the system works internally in the space where all values are 100.
As for why you are seeing the results that you are, I think @jcoupey is the better person to answer that.

From what I can tell (problem.solve), you're using vehicle steps in solving mode, not plan mode. In that case, you have to be aware that you're only feeding an initial solution to VROOM (there is no guarantee the ordering won't be changed if the optimization phase finds a more suitable arrangement).
Plus the service_* keys are not used outside of plan mode, see https://github.com/VROOM-Project/vroom/blob/master/docs/API.md#vehicle-steps.

After digging a bit I realized that plan mode is called via the check method of the problem class (if I understood correctly).
However, this is not available in pyvroom since VROOM is compiled without libglpk.
I am trying to compile pyvroom passing the  -DUSE_LIBGLPK=true flag but then I get some other errors while importing the library. I'll open now a new issue.


MODE-2565 - Added test to demonstrate lucene re-indexing issue.

The test will not fail, but if you look at the log output you will see that after restarting the repository the entire repository is re-indexed again even though there have be no changes since the repository was stopped.

the reported issue is valid, but this test case is not.
The repository configuration used in this test case does not store data anywhere (which includes index definitions). As such, each restart is the equivalent to a full system area re-initialization, creating and storing index definitions etc. So it's perfectly normal for the indexes to grow as new system area data is created.


Feature request: add anonymising numeric transformation

Original author<EMAIL_ADDRESS>(November 01, 2010 21:26:54)
I would like to be able to anonymise data sets by adding fuzziness. This would involve a numeric transformation that (largely) retains aggregate function results, but alters each datum slightly.
Rough example of desired result:
5.0 => 5.1
4.0 => 3.9
5.0 => 5.0
5.0 => 4.9
Original issue: http://code.google.com/p/google-refine/issues/detail?id=175

Want to back this issue? Post a bounty on it! We accept bounties via Bountysource.

This is too specific and clearly outside the scope of this software, as argued above.


chore(artifact): update task name

Because

the name is confusing users

This commit

change the name

note
I do not do migration because we have not released to prod.


PowerRename crashed when reopening the interface after closing it once

Microsoft PowerToys version
0.70.0
Installation method
PowerToys auto-update
Running as admin
Yes
Area(s) with issue?
PowerRename
Steps to reproduce

Select files to rename in Windows Explorer
Right click on selected files to open the context menu
Click on PowerRename to open the UI
Apply to rename
Close PowerRename UI
Redo step 1 (PowerRename UI opened for a second and then crashed)

Bug report log:
PowerToysReport_2023-05-25-11-11-05.zip
✔ Expected Behavior
PowerRename UI should be able to open again after closing it once.
❌ Actual Behavior
PowerRename UI crashed when reopening the interface after closing it once
Other Software
No response

Not able to repro this, going to close. Let us know if you see this again!


chore: migrate ESLint config to flat config

PR Checklist

[x] Addresses an existing open issue: fixes #5058
[x] That issue was marked as status: accepting prs
[x] Steps in CONTRIBUTING.md were taken

Overview
Migrates from the old .eslintrc.yml to a new eslint.config.js per the new ESLint flat config. Applies a few touchups in the process:

Removes package.json references to ESLint plugins that weren't being used anymore

eslint-plugin-node: now eslint-plugin-n

Adds a standalone npm run lint script in the package.json
Adds a GitHub Actions ci.yml that runs that lint script, per #4117
Removes @babel/eslint-parser as it's not necessary
Removes the prettier and semistandard plugins/configs

coverage: 94.337%. remained the same
when pulling 9f1037512f875c563edc29838b8c71aa6b33d461 on JoshuaKGoldberg:eslint-config-flat
into 645469e1920e2e86458ff068e74eef2e2915083e on mochajs:master.


Copy-edited the log rotation page (docs)

I did my best not to change the meaning of anything in the page. I just rearranged the three subsections (MongoDB, BigchainDB Server, Tendermint) to have parallel structure.

Changed the titles to include both "Logging" and "Log Rotation"
Added a hyperlink to MongoDB's docs about logging (and kept the existing link to the MongoDB docs about log rotation).
Fixed the two hyperlinks to How to Set Up a BigchainDB Network.
Added link to the BigchainDB Server configuration settings related to logging.
In the Troubleshooting page, added a link to the page about Logging and Log Rotation.

Codecov Report

Merging #2546 into master will not change coverage.
The diff coverage is n/a.

@@           Coverage Diff           @@
##           master    #2546   +/-   ##
=======================================
  Coverage   93.67%   93.67%
=======================================
  Files          44       44
  Lines        2593     2593
=======================================
  Hits         2429     2429
  Misses        164      164

@muawiakh While setting up the new testnet, I noticed that bigchaindb.log and bigchaindb-errors.log were being written to $HOME, not $HOME/.bigchaindb-monit/logs.
@shahbazn said that's because those files get written to a locations configured in the BigchainDB config file, and the default location is $HOME.
The benchmark logs do get written to $HOME/.bigchaindb-monit/logs.
I will make a commit to update this PR.

This happened because when we run bigchaindb configure in the simple-network guide. It sets default to $HOME. Thats why they are not under $HOME/.bigchaindb-monit/logs

Don't BigchainDB and Tendermint have to be configured (i.e. don't their config files have to be created and/or edited) before using Monit to run them?
I looked in bigchaindb-monit-config and it seems to only redirect the logs that were being sent to stdout, i.e. the benchmark logs.

@muawiakh I made the commit mentioned above. I also removed the mention of bigchaindb-benchmark.log because it's going away anyhow: see issue #2560

I am thinking how we can make sure when we use Monit, the logs for BigchainDB and Tendermint are under ~/.bigchaindb-monit/logs/ OR we can specify the path to the log file. Because currently bigchaindb configure or bigchaindb start does not take the log file configuration and it is a bit inconsistent.

@muawiakh  If we change the BigchainDB configuration settings log.file or log.error_file to something other than the default values (~/bigchaindb.log and ~/bigchaindb-errors.log), will BigchainDB Server not care and continue writing the logs to those locations? If so, then that is a bug.
If those configuration settings do work, then I suppose the Monit script could just use them to change the log files to be under ~/.bigchaindb-monit/logs/

@muawiakh I just did a little test where I changed the values of log.file and log.error_file in my BigchainDB config file (in ~/.bigchaindb) to something other than the default values. It worked: when I ran BigchainDB, those log files got written to locations that I specified.

Needs to be updated, benchmark logs were removed. Also can we make sure we only run bigchaindb configure when inside ~/.bigchaindb-monit/logs?

@muawiakh I will fix the merge conflict. I don't understand what you meant when you wrote:

Also can we make sure we only run bigchaindb configure when inside ~/.bigchaindb-monit/logs?


Stress workers get stuck forever

When I run cachebench with my test config, stress workers get stuck forever.
test.json.txt
Total 3600.00M ops to be run
00:50:51       0.00M ops completed. Hit Ratio  -0.84%
00:51:51     703.34M ops completed. Hit Ratio  82.34%
00:52:51    1344.09M ops completed. Hit Ratio  85.85%
00:53:51    1923.65M ops completed. Hit Ratio  85.83%
00:54:51    2498.17M ops completed. Hit Ratio  85.83%
00:55:51    3033.92M ops completed. Hit Ratio  85.83%
00:56:51    3034.06M ops completed. Hit Ratio  85.75%
00:57:51    3034.06M ops completed. Hit Ratio   0.00%
00:58:51    3034.06M ops completed. Hit Ratio   0.00%

Backtrace of the stress worker
(gdb) bt
#0  syscall () at ../sysdeps/unix/sysv/linux/x86_64/syscall.S:38
#1  0x00007fd61bce4b8e in folly::detail::(anonymous namespace)::nativeFutexWaitImpl (waitMask=4294967295, absSteadyTime=0x0, absSystemTime=0x0, expected=4294967293, addr=0x7fd04801ffd8)
    at /home/mita/CacheLib-intel/cachelib/external/folly/folly/detail/Futex.cpp:126
#2  folly::detail::futexWaitImpl (futex=futex@entry=0x7fd04801ffd8, expected=expected@entry=4294967293, absSystemTime=absSystemTime@entry=0x0, absSteadyTime=absSteadyTime@entry=0x0, waitMask=waitMask@entry=4294967295)
    at /home/mita/CacheLib-intel/cachelib/external/folly/folly/detail/Futex.cpp:254
#3  0x00007fd61bde2896 in folly::detail::futexWait<std::atomic<unsigned int> > (waitMask=4294967295, expected=4294967293, futex=0x7fd04801ffd8) at /home/mita/CacheLib-intel/cachelib/external/folly/folly/detail/Futex-inl.h:94
#4  folly::detail::MemoryIdler::futexWait<std::atomic<unsigned int>, std::chrono::duration<long, std::ratio<1l,<PHONE_NUMBER>l> > > (expected=4294967293, waitMask=4294967295, stackToRetain=1024, timeoutVariationFrac=0.5,
    idleTimeout=..., fut=...) at /home/mita/CacheLib-intel/cachelib/external/folly/folly/detail/MemoryIdler.h:126
#5  folly::fibers::Baton::waitThread (this=0x7fd04801ffd8) at /home/mita/CacheLib-intel/cachelib/external/folly/folly/fibers/Baton.cpp:70
#6  0x00007fd61bde2b40 in folly::fibers::Baton::wait<folly::fibers::Baton::wait()::<lambda()> > (mainContextFunc=..., this=0x7fd04801ffd8) at /home/mita/CacheLib-intel/cachelib/external/folly/folly/fibers/Baton.h:46
#7  folly::fibers::Baton::wait (this=0x7fd04801ffd8) at /home/mita/CacheLib-intel/cachelib/external/folly/folly/fibers/Baton.cpp:46
#8  0x0000562ea64a1b95 in facebook::cachelib::detail::ReadHandleImpl<facebook::cachelib::CacheItem<facebook::cachelib::LruCacheTrait> >::ItemWaitContext::wait (this=<optimized out>) at /usr/include/c++/9/bits/atomic_base.h:734
#9  0x0000562ea6602938 in facebook::cachelib::detail::ReadHandleImpl<facebook::cachelib::CacheItem<facebook::cachelib::LruCacheTrait> >::ItemWaitContext::get (this=0x7fd04801ffd0)
    at /home/mita/CacheLib-intel/cachelib/../cachelib/allocator/Handle.h:260
#10 facebook::cachelib::detail::ReadHandleImpl<facebook::cachelib::CacheItem<facebook::cachelib::LruCacheTrait> >::getInternal (this=0x7fd06b7f4c50) at /home/mita/CacheLib-intel/cachelib/../cachelib/allocator/Handle.h:251
#11 facebook::cachelib::detail::ReadHandleImpl<facebook::cachelib::CacheItem<facebook::cachelib::LruCacheTrait> >::get (this=0x7fd06b7f4c50) at /home/mita/CacheLib-intel/cachelib/../cachelib/allocator/Handle.h:166
#12 facebook::cachelib::detail::ReadHandleImpl<facebook::cachelib::CacheItem<facebook::cachelib::LruCacheTrait> >::operator bool (this=0x7fd06b7f4c50) at /home/mita/CacheLib-intel/cachelib/../cachelib/allocator/Handle.h:156
#13 facebook::cachelib::CacheAllocator<facebook::cachelib::LruCacheTrait>::findFastInternal (mode=facebook::cachelib::AccessMode::kRead, key=..., this=<optimized out>)
    at /home/mita/CacheLib-intel/cachelib/../cachelib/allocator/CacheAllocator-inl.h:2132
#14 facebook::cachelib::CacheAllocator<facebook::cachelib::LruCacheTrait>::findImpl (mode=facebook::cachelib::AccessMode::kRead, key=..., this=<optimized out>)
    at /home/mita/CacheLib-intel/cachelib/../cachelib/allocator/CacheAllocator-inl.h:2184
#15 facebook::cachelib::CacheAllocator<facebook::cachelib::LruCacheTrait>::find (this=0x562ea7ce2350, key=...) at /home/mita/CacheLib-intel/cachelib/../cachelib/allocator/CacheAllocator-inl.h:2242
#16 0x0000562ea64aa425 in facebook::cachelib::cachebench::Cache<facebook::cachelib::CacheAllocator<facebook::cachelib::LruCacheTrait> >::find(facebook::cachelib::KAllocation::Key)::{lambda()#1}::operator()() const (
    this=0x562ea7cdb600) at /usr/include/c++/9/bits/unique_ptr.h:360
#17 0x0000562ea64c2e8d in facebook::cachelib::cachebench::Cache<facebook::cachelib::CacheAllocator<facebook::cachelib::LruCacheTrait> >::find (this=<optimized out>, key=...)
    at /home/mita/CacheLib-intel/cachelib/../cachelib/allocator/Handle.h:475
#18 0x0000562ea64c4d9a in facebook::cachelib::cachebench::CacheStressor<facebook::cachelib::CacheAllocator<facebook::cachelib::LruCacheTrait> >::stressByDiscreteDistribution (this=<optimized out>, stats=...)
    at /usr/include/c++/9/bits/basic_string.h:2316
#19 0x00007fd61b90cde4 in ?? () from /lib/x86_64-linux-gnu/libstdc++.so.6
#20 0x00007fd61ba20609 in start_thread (arg=<optimized out>) at pthread_create.c:477
#21 0x00007fd61b5f7133 in clone () at ../sysdeps/unix/sysv/linux/x86_64/clone.S:95

FWIW, I can't reproduce this problem without commit 68e66394 ("Create token before marking item as exclusive").

Hi, thanks for reporting this. And thanks for posting the config!
We spent the last few weeks chasing this race condition down. It should now be fixed by f4e30a7f3c878bc119577a11d2ca69c72d41a925 and 08bf0b4e8510eb1646ccad057e6209b937b6fe89 which have been merged to our develop branch.
Let us know if you are still hitting this deadlock - and if you don't mind sharing - are you experimenting with tiered memory? If so, what does your environment look like?

Thank you. The problem doesn't happen anymore.
We are planning to evaluate it with a tiered memory system,
but we have not prepared an evaluation machine yet.


Empty import bugfix

Closes https://github.com/rwjblue/ember-qunit-codemod/issues/36
Let me know if this is what you were looking for... First time touching jscodeshift/recast/etc and the documentation is lacking...

Awesome, thank you!


"Missing Postcondition" with presence of specs

I got an "Missing Postcondition" error even with the last statement (line 9) is an assertion. It seems that the error is triggered by the presence of the spec (line 5 - 7):

The error persists if I add a new line:

But if I indent line 8 by one more space (compare that with line 8 in the previous picture), the error is gone.

I guess that could be a parsing issue, so I reported the issue to gcl.

Following up: in this situation, adding the space on line 10 does not resolve the error.

I modified the indentation parsing rule for Spec and Proof.
If there is nothing in between the start and end token of Spec or Proof, it should parsed successfully.
However since the Spec and Proof constructor both only take start and end token as arguments,
e.g.
| Spec (Token "{!") (Token "!}")
| Proof (Token "{-") (Token "-}")

Following up: in this situation, adding the space on line 10 does not resolve the error.

the above issue might not be able to solve from parsing.

However since the Spec and Proof constructor both only take start and end token as arguments,
e.g.
| Spec (Token "{!") (Token "!}")
| Proof (Token "{-") (Token "-}")

....

the above issue might not be able to solve from parsing.

Does that imply the spec and comments must always be empty? That should be an issue in itself.
I am opening another issue for it.

I think the issue should be fixed and pushed on branch lexer2.


Compilation fails under node 4.2

The cause is a dependency on keytar 2.1.0, which fails to build with node 4.2.

Thanks for the report, on it, unfortunately not that easy, because compiler woes on Travis (works fine locally).

Fixed in ba0a5130f6c6ab0a3c57ec3c0eb50811da0f2674, but for some reason semantic-release didn't run. Will update this tomorrow :)

Version 1.1.4 now supports Node 4.x & Node 5.x

:sparkles:  Thanks @rmehner! :sparkles:


Harbor is unhealthy due to unready postgresql, which is ready instead

Expected behavior and actual behavior:
There is a flaky issue spotted during harbor deployment. HarborCluster became unhealthy (at this moment postgresql and redis pods are ready and harbor components' pods are not initiated), and the only abnormal status when we check the harborCluster is
Last Transition Time:  2022-04-26T21:29:13Z
message: psql is Creating
reason: Database is not ready

however, the postgresql's status is RUNNING, and the harbor operator pod also logged as below:
2022-04-26T21:29:13.217Z    INFO    harbor-operator.controller.database    Creating Database.
{"controller": "harborcluster", "version": "v1.1.1-***", "git.commit": "none",
"namespace": "***", "name": "<postgresql cluster name>"}
2022-04-26T21:29:13.246Z    INFO    harbor-operator.controller.database    Database create complete.
{"controller": "harborcluster", "version": "v1.1.1-***", "git.commit" : "none",
"namespace": "***", "name": "<postgresql cluster name>"}

So it shows that there is nothing wrong on postgresql DB.
Restarting harbor operator pod fixed the issue. After restarting the harbor operator pod, the harbor components are deployed immediately and harborCluster becomes healthy. Therefore, it is possible that harbor operator doesn't properly show the status and report an unhealthy status, which might be a false positive. Hi team can you please give more insights on this issue? Also our team found a recent change on pg status check such as https://github.com/goharbor/harbor-operator/pull/476/files. Can you please help take a look and see if this is a regression? Thanks!
Steps to reproduce the problem:

changed storage class to support ReadWriteMany
changed harbor pvc access mode from ReadWriteOnce to ReadWriteMany

We have not found the connection between the storage class/pvc change and this harbor health issue. However, this flaky issue happened after the change was merged, and didn't occur after it is reverted.
Versions:
Please specify the versions of following systems.

harbor operator version: [v1.1.1]
harbor version: [v2.4.1]
kubernetes version: [v1.22.8]
Any additional relevant versions such as CertManager
postgresql operator version: 1.5.0
redis operator version: 1.0.0

Additional context:

Harbor dependent services:

Context info of postgreSQL - version 14.2
Context info of Redis
Context info of storage

Log files: Collect logs and attach them here if have.
Kubernetes: How Kubernetes access was provided (what cloud provider, service-account configuration, ...).

A follow up (status update):
Even without the additional changes mentioned at Steps to reproduce the problem, this issue occurred again. This means that this happens at a low possibility, but makes the system unstable.
My guess is that the postgresql database readiness check at https://github.com/goharbor/harbor-operator/blob/544d6737c197a5fefb9c729ae8785614f77ab005/pkg/cluster/controllers/database/readiness.go#L54 thinks that database is not ready becuase the status is creating. This might be able to be fixed after adding a retry or wait for the status turning from creating to running.
Can someone help confirm?


Update index.html

please commit the changes.

No required to make any changes for now. We will see in future


Updated Bootstrap version comment to v3.3.5

"Updating that is a simple search and replace through the IDE." - #625
:stuck_out_tongue_winking_eye:

hahahahahahaha. That is funny. I forgot to update all of the versions until after I created the release. I really need to create a list of things to do before I create a release.

A release list would be good @almasaeed2010 but I'm afraid it could get lengthy and cumbersome with the progress of AdminLTE. My opinion is that DRY needs to be applied heavily to AdminLTE to save a lot of time and effort that could be redirected to evolving the project and enhancing the release quality. That's why I suggest to start adopting templating at this point of the project. It may delay the next release a little but once applied, we can see a good progress rate afterwards. It does not need be a sudden change, could be done as a side activity by whoever wants to contribute and performed to a couple of pages at a time until the whole template is converted.


belongsToMany with asynchronous option querying: 404 Not Found

Hello,
I added Multiselect with "belongsToMany" for asynchronous option querying like this:
Multiselect::make('Agreements', 'operator_agreements')
    ->belongsToMany(Agreement::class),

When I edit the resource and perform a search I have "404 NOT FOUND" for this get route: /nova-api/administrators/associatable/operator_agreements?search=fc
If I set the second argument to 'false' I have no problems.
I don't know if this is an issue or I'm forgetting something.
Thanks

Are you sure that relationship exists on the Administrators model?


Why is there no agent service in SVC?

My profile:
apiVersion: jaegertracing.io/v1
kind: Jaeger
metadata:
  name: simple-prod
  namespace: observability
spec:
  strategy: production
  storage:
    type: elasticsearch
    options:
      es:
        server-urls: http://xxxxx:9200
  ingress:
    enabled: false
  agent:
    strategy: DaemonSet

I have noticed that the author has replied to similar questions before, but how does the port configured by the agent open up?

I see now what you mean. The agent ports are fixed, so, your application would have to discover only the IP:
https://github.com/jaegertracing/jaeger-operator/blob/11a063f97c97560f1a811d95478828b68c7aeba1/pkg/deployment/agent.go#L61-L65


not working

Error - Message: Process unexpectedly closed with status 1

ERROR
A proxy connection has been detected. To connect to California Community Colleges you must disable anonymizing services. This may include:
Tor web browser
VPN connections
Anonymizing browser plugins

check now

not working


singleton cache capacity is a prime number

Use prime number lookup to initialize SingletonCache capacity.
The lookup table now replicates the internal .NET framework HashHelpers, which caps out at 7,199,369. Above this size, the ConcurrentDictionary will resize to accommodate.

coverage: 97.66% (-0.02%) from 97.68% when pulling 26a6abc7f0deb50533b8e1c21723b95778b6355b on users/alexpeck/singcap into f4344e1892810926a295eed9ed8feb5770663e8b on main.


Local to remote issues

Hi folks,
I am using PJAX with the open-source CMS Processwire and I have been building this locally and all has been going well, no problems so thank you for such a great plugin.
However, upon moving my build from local to remote (mediatemple gs server) the site has lost its ajax and is falling back to standard page loading. I had a look at the Network Source upon clicking on a page and see the following image for the screenshot. Any thoughts?

I see one non-pjax request to /projects that was canceled (I don't know how or why) and one pjax request that looks alright. I can't possibly know where the first request originated or what canceled it, especially without seeing any setup code. I suggest you enable XHR logging in Chrome’s console and debug this there.

@mislav Thank you for the reply. It seems to cancel itself. And the other request seems to 301. It works 100% on my local though... which is why I believe it's a remote server issue. The site is http://www.juleslister.co.uk if that helps too. I will enable XHR logging as you suggest and have a look.

@mislav Having checked this, it looks like sometimes it works and sometimes it doesn't. Sometimes when clicking on a project, the request is logged in the console:
XHR finished loading: GET "http://juleslister.co.uk/projects/photography/s1-introduces/?_pjax=%23pjax". but then sometimes it just doesn't at all and loads the page as normal.

function ajaxInit() {

	isotopeInit();
	projectSlider();
	videoInit();
	infoTrigger();

	$(document).on('click', 'a[data-pjax]', function() {
		$('.menu-container ul li').removeClass('active');
		$(this).parent().addClass('active');
	});

	//
	$(document).ready(function() {
	    $('.menu-container ul li').each(function() {
	        var classes = this.classList;
	        for (var i=0,len=classes.length; i<len; i++) {
	            if ($('body').hasClass(classes[i])) {
	                $(this).addClass('active');
	            }
	        }
	    });
	});

	//
	$.fn.loadImage = function() {
		var toLoad;
	    return this.each(function() {
	        if ($(window).width() > 768) {
				toLoad = $(this).data('original');
	        } else {
		        toLoad = $(this).data('medium');
	        }
	        var $img = $(this);
			$('<img />').attr('src', toLoad).imagesLoaded(function() {
				$img.attr('src', toLoad).addClass('loaded').removeClass('loading');
			});
		});
	};

	//
	var loadImages = function() {
		$('.lazy').each(function() {
			var $img = $(this);
			$img.waypoint(function() {
		    	$img.loadImage();
		    	isotopeInit();
			}, { offset: '150%' });
	    });
	};

	loadImages();
	$(window).on('debouncedresize', function() {
    	loadImages();
	});

}
ajaxInit();

//
$(document).pjax('a[data-pjax]', '#pjax');
$(document).on('pjax:start', function() {

});
$(document).on('pjax:end', function() {
	isotopeInit();
	projectSlider();
	var bodyClass = $('meta[name=body-class]').attr('content');
	if (bodyClass !== undefined) { document.body.className = bodyClass; }
});
$(document).on('pjax:success', function() {
	ajaxInit();
});

Referencing https://github.com/defunkt/jquery-pjax/issues/66 I changed the timeout default using
$.pjax.defaults.timeout = 3000;
And this seemed to have solved the issue. Is it worth noting this? Should the default be changed in the repo? Just a thought.

Yeah, that workaround sounds about right. Your server isn't playing well with pjax yet, though. It's 301-redirecting every request that has _pjax parameter in it to the version of the current URL with no _pjax parameter. You need to stop it from doing that, because now every pjax request needs to perform 2 requests behind the scenes (because xhr requests automatically follow redirects). This is probably a reason for the slowness and why you had to bump up the timeout value.

@mislav Thank you for this. I use MediaTemple so is there anything I should tell them that you would recommend? Is it not dealing with ?_pjax=%23pjax URLs? Or...?

The proper way for a server to respond to pjax requests is with a HTTP 200 and with HTML fragment that is to be injected into the pjax container (#pjax element in your case), and no website layout.
Your server consistently responds with 301 redirects and thus forces every pjax request to make another request. This seems unnecessary, but I don't know anything about your platform or how you can configure it. You need to figure this out for yourself.
I also don't know why you wanted to use pjax on this site in the first place. It's not a big site. It's not slow when it refreshes pages. I'm wondering why you felt that you need to speed it up by adding pjax.

@mislav Thanks. I'll do some digging around. Appreciate the time.
I wanted to use pjax on this site for several reasons, but mainly to get used to pjax/ajax. It's something I want to learn more of and use it more and more on larger sites... so that is why I wanted to start small to iron out/find any issues before taking it to the big leagues. It's a nice small site to experiment and play with.


Update Mockito/Byte Buddy

Problem
When trying to install dependencies using JDK 21, the tests fail because of an old Mockito/Byte Buddy version.
See logs below:
If you're not sure why you're getting this error, please open an issue on GitHub.

Java               : 21
JVM vendor name    : Red Hat, Inc.
JVM vendor version : 21+35
JVM name           : OpenJDK 64-Bit Server VM
JVM version        : 21+35
JVM info           : mixed mode, sharing
OS name            : Linux
OS version         : 6.4.15-200.fc38.x86_64

You are seeing this disclaimer because Mockito is configured to create inlined mocks.
You can learn about inline mocks and their limitations under item #39 of the Mockito class javadoc.

Underlying exception : org.mockito.exceptions.base.MockitoException: Could not modify all classes [interface java.io.Closeable, interface java.lang.AutoCloseable]
        at dev.knative.eventing.kafka.broker.core.utils.ShutdownTest.run(ShutdownTest.java:34)
Caused by: org.mockito.exceptions.base.MockitoException: Could not modify all classes [interface java.io.Closeable, interface java.lang.AutoCloseable]
        at dev.knative.eventing.kafka.broker.core.utils.ShutdownTest.run(ShutdownTest.java:34)
Caused by: java.lang.IllegalStateException:

Byte Buddy could not instrument all classes within the mock's type hierarchy

This problem should never occur for javac-compiled classes. This problem has been observed for classes that are:
 - Compiled by older versions of scalac
 - Classes that are part of the Android distribution
        at dev.knative.eventing.kafka.broker.core.utils.ShutdownTest.run(ShutdownTest.java:34)
Caused by: java.lang.IllegalArgumentException: Java 21 (65) is not supported by the current version of Byte Buddy which officially supports Java 20 (64) - update Byte Buddy or set net.bytebuddy.experimental as a VM property
        at dev.knative.eventing.kafka.broker.core.utils.ShutdownTest.run(ShutdownTest.java:34)

(END)

Discussion of this issue can be seen here: https://cloud-native.slack.com/archives/C04LMU33V1S/p1695143322622249
Persona:
Which persona is this feature for?
Exit Criteria
A clean install of dependencies works when using JDK 21.
Time Estimate (optional):
How many developer-days do you think this may take to resolve?
Additional context (optional)
Add any other context about the feature request here.

@Cali0707 to which version should we upgrade it to get the fix?

@pierDipi I think we should upgrade Mockito to at least v5.3.1, as we need Mockito to use Byte Buddy v1.14.3 or newer

I think this was resolved in a dependably update


refactor: Removed supervisord log & pid file from /tmp directory

:tada: This PR is included in version 2.0.1 :tada:
The release is available on GitHub release
Your semantic-release bot :package::rocket:


FingerprintPackage is broken after React Native 0.47

Hi Vasiliev90,
In React Native 0.47, facebook removed createJSModules method from com.facebook.react.ReactPackage interface. As a result, you have to remove this method from your FingerprintPackage class, too. Could you please update your code? Thanks.

hi @bit-sun, yeah, I will do this in a few days. If you want and can create a PR, you are welcome.


Fix mount config input serialisation

There is no need to convert the config keys to string. This will lead to decoding errors.
time="2019-05-16T08:01:17Z" level=error msg="error configuring vault: error configuring secret engines for vault: error parsing config for secret engine: 2 error(s) decoding:\n\n* 'force_no_cache' expected type 'bool', got unconvertible type 'string'\n* 'options' expected a map, got 'string'"

@bonifaido I fixed this a while back but it seems that was changed recently. This prevents us to mount the secrets engine when provisioning vault.

We use the default MountConfigInput https://github.com/hashicorp/vault/blob/b9f054d8abe0f5a6993bbfd13fb8e7e582df42e3/api/sys_mounts.go#L144. I think the type of the fields should be respected.

The conversion tries to convert every field to a string but they are not flatten. Also they are already serialised.

We agree that we this needs to be fixed but making it mandatory breaks user configurations, so let's fix it that way.

I think, instead of returning an error we can return the default if config is not found.

@bonifaido I pushed a fix which returns the default value instead of an error if the config is not found for a secret engine.


Not enough permission

I have this message :
rocket.cat
14:01
Vous seul pouvez voir ce message
Not enough permission

this is strange, since it seems to work for other gitlab users. The error just appears if my gitlab user which registered the webhook posts something to the gitlab issues.
even more strange, the webhook suceeds with 200

corresponding rocket.chat error message in log:
{"level":50,"time":"2022-04-06T12:50:28.340Z","pid":78,"hostname":"rocketchat.kilobaser.info","name":"System","msg":"Error sending message: {}"}

I'm running into the same issue. The Rocket Chat logs show this
=== UnHandledPromiseRejection ===
Error: Not enough permission
    at validateMessage (app/lib/server/functions/sendMessage.js:162:10)
    at sendMessage (app/lib/server/functions/sendMessage.js:210:2)
    at executeSendMessage (app/lib/server/methods/sendMessage.js:80:10)
    at app/apps/server/bridges/messages.ts:25:23
    at /app/bundle/programs/server/npm/node_modules/meteor/promise/node_modules/meteor-promise/fiber_pool.js:43:40


mhsave_vint

To run "make_packet.jl", we need "mhsave_vint***.h5".
Originally, there is "mhsave_vint181115.h5".
I am trying to process the latest file, such as 2022Q4.
How can I process "mhsave_vint***.h5"?

You need to generate the file "mhsave_vint***.h5" with a data vintage of your choosing by running "run_default.jl" and define your vintage and ":date_forecast_start" there. If I'm not mistaken, the data is estimated up to the date ":date_forecast_start", after which the forecast begins (out of sample).

Thank you for your reply.
I could make "mhsave_vint***.h5".


NoMethodError - private method `public'

When trying to include 'padrino-core/application/routing' in my existing Sinatra App...I get the
NoMethodError - private method `public' error.
/vendor/bundle/gems/padrino-core-0.9.21/lib/padrino-core/application/routing.rb:553:in `static_file?'

It appears that this is happening because :public setting has now been deprecated and is now using either :public_dir or :public_folder

Are you sure you are trying the correct version of Padrino padrino-core-0.9.21? The stable version is 0.12.4, you probably should upgrade. To fix your exact issue you could monkey patch this method: https://github.com/padrino/padrino-framework/blob/0.9.21/padrino-core/lib/padrino-core/application/routing.rb#L552-L560
Closing as wont fix.


custom vault URL

Hi,
can this be used on a self-hosted vault with a custom URL?

Yes, it works the same regarding of the login server. However I didn't tough of that so currently there's no option within the application.
I'll update to add this as is just a few lines of code, in the meantime you can issue the following commands:
cd "
"
"
';'"
"
%AppData%

Yes, it works the same regarding of the login server. However I didn't tough of that so currently there's no option within the application.
I'll update to add this as is just a few lines of code, in the meantime (once Auto-Type is installed), you can issue the following commands in a terminal:
cd /d "%AppData%\Auto-Type"
set "BITWARDENCLI_APPDATA_DIR=%CD%"
bw logout
bw config server
bw config server https://your.self.hosted.server:1234
bw config server

The output should be something like this:

Indicating the default server and then your server. After that you should be able to login with both the CLI and Auto-Type.

It works for me via the cli when I check it
but auto-type always comes up with the wrong username or password

+1
Same here. I made the changes with the CLI. The data.json file get changed correctly.
However, when I start Auto-Type, it create a new data.json file even if a file was already there...

Oh! Just find a fix!
After doing the "bw config server" command, you have to login using CLI and "SYNC" in order to populate the JSON correctly.
cd /d "%AppData%\Auto-Type" set "BITWARDENCLI_APPDATA_DIR=%CD%" bw logout bw config server bw config server https://your.self.hosted.server:1234 bw config server bw login <email_address> <password> bw sync --session <session_id provided using the last command>
After that, Auto-Type was able to login the my personal server.

The next update will include this, I uploaded the code almost a month ago https://github.com/anonymous1184/bitwarden-autotype/commit/a22cbdddd94d24c350f8291e5499aa9cc8eda435.
But I had a issue I haven't been able to reproduce, so I'm holding it... however if I cannot reproduce it again I'll will do the release.
Thanks a lot for the info on the manual fix, and as always since I started with the internet almost 30 years ago... Canadians are just awesome.

Just had a strange issue. It seems like after a reboot or update, I had to redo the procedure again... The "server" configuration was not kept... :(

to add one note to this issue, if you have a custom vault and click the "Open vault" right click menu option it will take you to the standard bitwarden vault page.

@KnightTim indeed, that an omission on my end. The next batch of updates is already on queue and will include this. Thanks.

@KnightTim indeed, that an omission on my end. The next batch of updates is already on queue and will include this. Thanks.

hows it coming along for the update?


HLS (m3u8) out of sync

Have you read the FAQ and checked for duplicate open issues?
Yes
If the problem is related to FairPlay, have you read the tutorial?
Not related to FairPlay.
What version of Shaka Player are you using?
4.10.9 (the demo website shows the issue)
Can you reproduce the issue with our latest release version?
Yes
Can you reproduce the issue with the latest code from main?
Yes
Are you using the demo app or your own custom app?
Tried both.
If custom app, can you reproduce the issue using our demo app?
Yes
What browser and OS are you using?
Tested on Chrome/Chromium/Firefox on Windows and Linux.
For embedded devices (smart TVs, etc.), what model and firmware version are you using?
What are the manifest and license server URIs?
An example (note that this is hosted by bitmovin and sometimes the URL doesn't work or the download speed is very low):
https://cdn.bitmovin.com/content/assets/sintel/hls/playlist.m3u8
What configuration are you using?  What is the output of player.getConfiguration()?
The issue is present in the demo:
https://shaka-player-demo.appspot.com/demo/#audiolang=en-US;textlang=en-US;uilang=en-US;asset=https://cdn.bitmovin.com/content/assets/sintel/hls/playlist.m3u8;panel=CUSTOM CONTENT;build=uncompiled
With subtitles enabled, it is possible to see that they don't match the audio (subtitles seem correct).
What did you do?
I just opened the demo link.
What did you expect to happen?
Video and audio should be synced.
What actually happened?
Video and audio are not in sync.
Are you planning send a PR to fix it?
I don't have a fix :)
Note: with other players, the issue is not present. I tested the JWPlayer demo and audio and video are in sync.

It seems that the manifest you share is using EXTINF with rounding instead of using a precise time, which causes this error that you report. You would have to fix that for it to work correctly.
Attached the mediastreamvalidator report:
validation_data.html.zip

I'll let @joeyparrish decide what should be done.

While I understand that it may be a 'not properly generated manifest', it works with basically any other player I tested (it works with Bitmovin, it works with JWPlayer, to make names, or it works with 'offline players' like Kodi) so I guess there are ways to overcome this without contacting all the manifest providers and ask them to fix it :) Which, as you can imagine, may be even impossible in some cases.

While this should surely be fixable, we don't have the time to fix it right now.  As a policy, to help us allocate our limited time, we don't go out of our way to support non-standard content.
If you or somebody else wants to fix this, we welcome your PRs, especially if the fix doesn't introduce risk or complexity to our support of standards-compliant content.
Thanks!


Dashboard filter default value is cleared when 2 dashboards opened in succession having filters on same column name of same type

Happens in both master and 1.5.1
How to reproduce the bug

open 1 dashboard add 1 native filter of type value on any column and set any default value and save it
save that dashboard by overwriting itself.
save it again creating a new copy
change the default value for that filter to something else
save this dashboard by overwriting itself.
now go to dashboard list
open original dashboard
it won't apply default value of its filter

Expected results
Both dashboard should correctly apply default values of filters
Actual results
The dashboard which is opened second time doesn't apply default value for native filters.
Screenshots
https://user-images.githubusercontent.com/12967587/188796575-98552db6-bf37-4e21-9a0d-0f296e79a8d0.mp4
Environment
(please complete the following information):

browser type and version:  100.0.4896.127 (Official Build) (64-bit)
superset version: 1.5.1 and master
python version: python 3.8
node.js version: 16.13.1
any feature flags active: bunch of them, but related here are "DASHBOARD_RBAC"

Checklist
Make sure to follow these steps before submitting your issue - thank you!

[ ] I have checked the superset logs for python stacktraces and included it here as text if there are any.
[ ] I have reproduced the issue with at least the latest released version of superset.
[ ] I have checked the issue tracker for the same issue and I haven't found one similar.

Additional context
From quick debugging this is happening in this useEffect hook https://github.com/apache/superset/blob/master/superset-frontend/src/dashboard/components/nativeFilters/FilterBar/index.tsx#L285
also the id for filters it receives for current and previous filters is same, so the real cause might be present somewhere else.

Gentle ping @michael-s-molina .
I am also happy to help fix it.
thanks.

don't know how to add test for this.


Breaks N-Central 11.1.1

N-Central server version 11.1.1 breaks with the current chrome extension. Nothing has changed on the server, it started in the last week or so. If I disable the extension the page loads up just fine. Is there any way to whitelist the domain, or somehow bypass it for a specific website?

nm, closing this issue, I can't consistently reproduce it. nm


Stop shuffling coupling map node indices in VF2 passes

Summary
This commit updates the preset pass manager construction usage of the VF2Layout and VF2PostLayout to stop shuffling the coupling map nodes by default. The theory behind the node shuffling is that since we limit the search space in the interest of runtime shuffling the node indices would change the search order to hopefully find a match that would be otherwise missed because we hit the internal state visit limit. However, this is showing in practice not to having a huge impact, especially since we're using the ordering heuristic from vf2++ that orders nodes by degree for the vf2 search. However, in the case of a circuit that was hardware efficient this can have the negative effect of making it harder for vf2 to find potential matches, especially on regular lattices. For example, in cases of a square grid lattice coupling map, and a path interaction graph (e.g. 0->1->2->3) the shuffling makes it much harder to find the mapping. This is because the lattice graphs the node degree is the same (or fall into the same few types of nodes) so the influence of the vf2++ heuristic isn't as significant and the index order has a larger impact because it is the search order for vf2. For smaller graphs this wasn't as noticeable but as devices scale up this effect has more of an impact.
Since we rely solely on VF2 to find a perfect layout at higher optimization levels this shuffling is not desirable because we always want to find the perfect layout if it exists, especially for hardware efficient circuits that are constructed to not require swaps. So prioritizing the results for hardware efficient circuits is desirable by default. Especially since most connectivity graphs are lattices and will exhibit the negative impacts for hardware efficient circuits.
From an API impact perspective this doesn't change any of the interfaces or defaults for the VF2 passes in the interest of backwards compatibility. The only change is that this updates how we instantiate the VF2 passes to always use a deterministic node ordering independent of any user specified seed. This will be fully deterministic even in cases the user specifies a seed value for the transpilation, the output just might not be the same as before with the fixed seed; which is not guaranteed between releases.
Details and comments

Thanks for looking into the regressions! I am weary of including this PR in the 1.3.0 release. I know we have included similar PRs in 1.3, but I also think that we already have a lot of (sometimes unintended) behavioral changes in the transpiler as a side effect of the migration to Rust, and it would be better to let the dust settle before introducing a last-minute one.

I'm fine deferring it until 2.0, that would be my normal inclination for something like this. The only reason I proposed it as something to potentially include is that I haven't come across a situation this makes quality worse and it also makes vf2 more deterministic and predictable in the preset pass managers. For 2.0 I might refactor this to just remove the randomization feature from the passes altogether.

The breaking unit test serves as a bit of confirmation for my suspicions, although I admit that the design of those backend estimator tests is improvable, they kind of rely on non-guaranteed behaviors.

At least in this case I didn't catch the failure because I didn't run the tests locally with aer installed. The issue is that vf2 is finding better qubits now and the fidelity is slightly better than before so the expected expectation value has changed (the ideal result is -1 and after this change it's closer now). Personally I view this as kind of a fix/improvement this PR is supposed to cause, although in this case it's mostly anecdotal.


[Feature Request]

Add options to keep screen awake while reading.

That is the default behaviour. Screen should always be awake in reader.

i've noticed it when i switch between chapters in the reader

i've noticed it when i switch between chapters in the reader

You mean when the next chapter is loading?

No, whenever i move to chapters using these.

No, when only the chapter is already loaded up, then around 15 seconds while reading then the screen would fall asleep.

I can't reproduce this. The screens stays awake for me in reader.

it's ok, it is what it is.


Revert "chore(deps-dev): bump msw from 0.44.2 to 0.47.1"

Reverts odanado/cloud-cryptographic-wallet#679

Pull Request Test Coverage Report for Build<PHONE_NUMBER>

0 of 0   changed or added relevant lines in 0 files are covered.
No unchanged relevant lines lost coverage.
Overall coverage remained the same at 88.0%

Totals

Change from base Build<PHONE_NUMBER>:
0.0%

Covered Lines:
118

Relevant Lines:
131

💛  - Coveralls


Added info message with form and telegram link to dashboard

Description
This PR adds a little section to the dashboard with the infos about the telegram channel.
A preview

/deploy-staging

/deploy-stating

/deploy-staging

/deploy-staging

/deploy-staging

/merge

/merge


minimal implementation of cookies

@mohe2015 Thanks for your contribution. Would you mind to refactor the code so that it does not depend on serde?
The -sys crate is a minimal wrapper that should have minimal overhead. More comfortable API can be implemented in crates with higher abstraction.
Thanks!

@mohe2015 Thanks for your contribution. Would you mind to refactor the code so that it does not depend on serde?
The -sys crate is a minimal wrapper that should have minimal overhead. More comfortable API can be implemented in crates with higher abstraction.
Thanks!
Should I use JsValue for the inputs then? So this basically just becomes these two extern function declarations?


Factor out hard coded pin handling to color look up tabe

I'd factor out all the repeated code for handlin pin colors like this:
match pair[0].board_pin_number {
            1 | 17 => {
                let pin_color = CustomButton {
                    bg_color: Color::new(1.0, 0.92, 0.016, 1.0),
                    text_color: Color::BLACK,
                };
                let mut left_pin_row = Row::new().align_items(Alignment::Center);
                left_pin_row = left_pin_row.push(
                    button(Text::new(pair[0].board_pin_number.to_string()).size(20))
                        .padding(10)
                        .width(Length::Fixed(40f32))
                        .style(pin_color.get_button_style())
                        .on_press(Message::Activate),
                );
                left_pin = left_pin.push(left_pin_row);
            }

By creating a lookup table:

either an an array of colors, corresponding to pin numbers or
an array of colors, one for each pin function, so that GPIO-Input, GPIO-Output, etc etc all have unique colors, and they change when the PinFunction is changed via the dropdown list

Then this long function should shrink to 1/20 of it's size or so....

Done will do


Repo name change to micro_ros_arduino_kaiaai

Repo name change from kaiaai/micro_ros_arduino_kaia to kaiaai/micro_ros_arduino_kaiaai

Closed by @kaiaai for unknown reasons. I'm waiting for an explanation at https://github.com/arduino/library-registry/pull/3378.


Adds marcs ssh key to KEYS

For release. I looked at apache KUDU and IMPALA and they both do not keep the KEYS file in the root of their git project. I wonder if we only need to keep this in the svn repo holding the releases.

LGTM, merging