WARNING:tensorflow:Entity <bound method Customized_DenseLayer.call of <main.Customized_DenseLayer object at 0x00000262A54DA488>> could not be transformed and will be executed as-is.

TensorFlow installed from binary
TensorFlow version 2.0.0
Python version 3.7.4
Cuda compilation tools, release 10.0, V10.0.130
cudnn version 7.6.4
GPU model and memory: GTX1060 6G
Code to reproduce the issue
class Customized_DenseLayer(tf.keras.layers.Layer):
def init(self, units, activation = None, **kwargs):
self.units = units
self.activation = tf.keras.layers.Activation(activation)
super(Customized_DenseLayer, self).init(**kwargs)
def build(self, input_shape):
    self.kernel = self.add_weight(name = 'kernel',
                                  shape = (input_shape[-1], self.units),
                                  dtype = tf.float32,
                                  initializer = 'uniform',
                                  trainable = True)
    self.bias = self.add_weight(name = 'bias',
                                shape = (self.units,),
                                dtype = tf.float32,
                                initializer = 'zeros',
                                trainable = True)
    super(Customized_DenseLayer, self).build(input_shape)

def call(self, inputs):
    return self.activation(inputs @ self.kernel + self.bias)

housing = fetch_california_housing()
x_train_all, x_test, y_train_all, y_test = train_test_split(
housing.data, housing.target, random_state = 7)
x_train, x_valid, y_train, y_valid = train_test_split(
x_train_all, y_train_all, random_state = 11)
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
x_train_scaled = scaler.fit_transform(x_train)
x_valid_scaled = scaler.transform(x_valid)
x_test_scaled = scaler.transform(x_test)
print(x_train_scaled.shape, y_train.shape)
print(x_valid_scaled.shape, y_valid.shape)
print(x_test_scaled.shape, y_test.shape)
print(x_train_scaled.shape[1:])
print(x_train.shape[1:])
model = tf.keras.models.Sequential()
model.add(Customized_DenseLayer(units = 30, input_shape = (8, ), activation = 'relu'))
model.add(Customized_DenseLayer(units = 1))
print(model.summary())
Other info / logs
WARNING:tensorflow:Entity <bound method Customized_DenseLayer.call of <main.Customized_DenseLayer object at 0x00000262FB272888>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, export AUTOGRAPH_VERBOSITY=10) and attach the full output. Cause: Unable to locate the source code of <bound method Customized_DenseLayer.call of <main.Customized_DenseLayer object at 0x00000262FB272888>>. Note that functions defined in certain environments, like the interactive Python shell do not expose their source code. If that is the case, you should to define them in a .py source file. If you are certain the code is graph-compatible, wrap the call using @tf.autograph.do_not_convert. Original error: could not get source code

@sakurakatana ,
When tried running the given code i got the error
NameError: name 'fetch_california_housing' is not defined can you please provide us the complete code to reproduce the error faced here?Thanks!

@sakurakatana ,
When tried running the given code i got the error
NameError: name 'fetch_california_housing' is not defined can you please provide us the complete code to reproduce the error faced here?Thanks!

Complete code：
'''
@Author:   MaoZi_Sakura
@Contact<EMAIL_ADDRESS>'''
'''
@Author:   MaoZi_Sakura
@Contact<EMAIL_ADDRESS>'''
%%
import tensorflow as tf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
import os, sys, time
#%%
class Customized_DenseLayer(tf.keras.layers.Layer):
def init(self, units, activation = None, **kwargs):
self.units = units
self.activation = tf.keras.layers.Activation(activation)
super(Customized_DenseLayer, self).init(**kwargs)
def build(self, input_shape):
    self.kernel = self.add_weight(name = 'kernel',
                                  shape = (input_shape[-1], self.units),
                                  dtype = tf.float32,
                                  initializer = 'uniform',
                                  trainable = True)
    self.bias = self.add_weight(name = 'bias',
                                shape = (self.units,),
                                dtype = tf.float32,
                                initializer = 'zeros',
                                trainable = True)
    super(Customized_DenseLayer, self).build(input_shape)

def call(self, inputs):
    return self.activation(inputs @ self.kernel + self.bias)

%%
housing = fetch_california_housing()
x_train_all, x_test, y_train_all, y_test = train_test_split(
housing.data, housing.target, random_state = 7)
x_train, x_valid, y_train, y_valid = train_test_split(
x_train_all, y_train_all, random_state = 11)
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
x_train_scaled = scaler.fit_transform(x_train)
x_valid_scaled = scaler.transform(x_valid)
x_test_scaled = scaler.transform(x_test)
print(x_train_scaled.shape, y_train.shape)
print(x_valid_scaled.shape, y_valid.shape)
print(x_test_scaled.shape, y_test.shape)
print(x_train_scaled.shape[1:])
print(x_train.shape[1:])
%%
model = tf.keras.models.Sequential()
model.add(Customized_DenseLayer(units = 30, input_shape = (8, ), activation = 'relu'))
model.add(Customized_DenseLayer(units = 1))
print(model.summary())
%%
model.compile(optimizer = tf.keras.optimizers.SGD(0.005), loss = 'mean_squared_error')
callbacks = [tf.keras.callbacks.EarlyStopping(min_delta = 0.01, patience = 5)]
history = model.fit(x_train_scaled, y_train, epochs = 100,
validation_data = (x_valid_scaled, y_valid),
callbacks = callbacks)
print(history.history)
%%
pd.DataFrame(history.history).plot(figsize = (8, 5))
plt.grid(True)
plt.gca().set_ylim(0, 1)
plt.show()
result = model.evaluate(x_test_scaled, y_test)

At the same time, I encountered a similar problem again.
Other info / logs
WARNING:tensorflow:Entity <function  at 0x00000207ACE9BBF8> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, export AUTOGRAPH_VERBOSITY=10) and attach the full output. Cause: Unable to locate the source code of <function  at 0x00000207ACE9BBF8>. Note that functions defined in certain environments, like the interactive Python shell do not expose their source code. If that is the case, you should to define them in a .py source file. If you are certain the code is graph-compatible, wrap the call using @tf.autograph.do_not_convert. Original error: could not get source code
The code：
import tensorflow as tf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
import os, sys, time
dataset = tf.data.Dataset.from_tensor_slices(np.arange(10))
dataset = dataset.repeat(3).batch(7)
data = dataset.interleave(map_func = lambda x: tf.data.Dataset.from_tensor_slices(x),
cycle_length = 5,
block_length = 1)
for item in data:
print(item)

@sakurakatana ,
Hi,when tried running the given code I got  TypeError: ('Keyword argument not understood:', 'units') kindly find the gist of colab for reference. Thanks!

@sakurakatana ,
Hi,when tried running the given code I got TypeError: ('Keyword argument not understood:', 'units') kindly find the gist of colab for reference. Thanks!

In the previous code, part of the content I copied incorrectly. I'm so sorry about that. I hope you can test it again. Thanks!
the code:
`# %%
import tensorflow as tf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import os, sys, time
%%
class Customized_DenseLayer(tf.keras.layers.Layer):
def init(self, units, activation = None, **kwargs):
self.units = units
self.activation = tf.keras.layers.Activation(activation)
super(Customized_DenseLayer, self).init(**kwargs)
def build(self, input_shape):
    self.kernel = self.add_weight(name = 'kernel',
                                  shape = (input_shape[-1], self.units),
                                  dtype = tf.float32,
                                  initializer = 'uniform',
                                  trainable = True)
    self.bias = self.add_weight(name = 'bias',
                                shape = (self.units,),
                                dtype = tf.float32,
                                initializer = 'zeros',
                                trainable = True)
    super(Customized_DenseLayer, self).build(input_shape)

def call(self, inputs):
    return self.activation(inputs @ self.kernel + self.bias)

%%
housing = fetch_california_housing()
x_train_all, x_test, y_train_all, y_test = train_test_split(
housing.data, housing.target, random_state = 7)
x_train, x_valid, y_train, y_valid = train_test_split(
x_train_all, y_train_all, random_state = 11)
scaler = StandardScaler()
x_train_scaled = scaler.fit_transform(x_train)
x_valid_scaled = scaler.transform(x_valid)
x_test_scaled = scaler.transform(x_test)
#%%
model = tf.keras.models.Sequential()
model.add(Customized_DenseLayer(units = 30, input_shape = (8,), activation = 'relu'))
model.add(Customized_DenseLayer(units = 1))
print(model.summary())
model.compile(optimizer = tf.keras.optimizers.SGD(0.005), loss = 'mean_squared_error')
callbacks = [tf.keras.callbacks.EarlyStopping(min_delta = 0.01, patience = 5, verbose = 1)]
history = model.fit(x_train_scaled, y_train, epochs = 100,
validation_data = (x_valid_scaled, y_valid),
callbacks = callbacks, verbose = 1)
print(history.history)
#%%
pd.DataFrame(history.history).plot(figsize = (8, 5))
plt.grid(True)
plt.gca().set_ylim(0, 1)
plt.show()
result = model.evaluate(x_test_scaled, y_test)`

@oanush,
Because I am not familiar with the way github text is edited, causing some code to change after uploading, resulting in code error. I'm so sorry about that. I hope you can test it again, Thanks!
the new code：
import tensorflow as tf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import os, sys, time

class Customized_DenseLayer(tf.keras.layers.Layer):
    def __init__(self, units, activation = None, **kwargs):
        self.units = units
        self.activation = tf.keras.layers.Activation(activation)
        super(Customized_DenseLayer, self).__init__(**kwargs)
    def build(self, input_shape):
        self.kernel = self.add_weight(name = 'kernel',
                                      shape = (input_shape[-1], self.units),
                                      dtype = tf.float32,
                                      initializer = 'uniform',
                                      trainable = True)
        self.bias = self.add_weight(name = 'bias',
                                    shape = (self.units,),
                                    dtype = tf.float32,
                                    initializer = 'zeros',
                                    trainable = True)
        super(Customized_DenseLayer, self).build(input_shape)
    def call(self, inputs):
        return self.activation(inputs @ self.kernel + self.bias)

housing = fetch_california_housing()
x_train_all, x_test, y_train_all, y_test = train_test_split(
    housing.data, housing.target, random_state = 7)
x_train, x_valid, y_train, y_valid = train_test_split(
    x_train_all, y_train_all, random_state = 11)

scaler = StandardScaler()
x_train_scaled = scaler.fit_transform(x_train)
x_valid_scaled = scaler.transform(x_valid)
x_test_scaled = scaler.transform(x_test)

model = tf.keras.models.Sequential()
model.add(Customized_DenseLayer(units = 30, input_shape = (8,), activation = 'relu'))
model.add(Customized_DenseLayer(units = 1))

print(model.summary())

model.compile(optimizer = tf.keras.optimizers.SGD(0.005), loss = 'mean_squared_error')

callbacks = [tf.keras.callbacks.EarlyStopping(min_delta = 0.01, patience = 5, verbose = 1)]

history = model.fit(x_train_scaled, y_train, epochs = 100,
                    validation_data = (x_valid_scaled, y_valid),
                    callbacks = callbacks, verbose = 1)

print(history.history)

pd.DataFrame(history.history).plot(figsize = (8, 5))
plt.grid(True)
plt.gca().set_ylim(0, 1)
plt.show()

result = model.evaluate(x_test_scaled, y_test)

@sakurakatana ,
When tried executing the given code I did not face any error, kindly find the gist of the colab.Thanks!

@sakurakatana ,
Any update on the issue ?Thanks!

Automatically closing due to lack of recent activity. Please update the issue when new information becomes available, and we will reopen the issue. Thanks!


[SOA_bot] send English then DAIDE messages

For the messages the bot is currently sending (DAIDE), we can translate them into English with GPT3 then send these messages in addition to the DAIDE versions.


ClangSharpPInvokeGenerator fails on ARM64 host with dotnet arm64 sdk+runtime

Originally from https://github.com/microsoft/win32metadata/issues/1305.
Package Id                      Version      Commands
-----------------------------------------------------------------------
clangsharppinvokegenerator      15.0.1       ClangSharpPInvokeGenerator

dotnet --version
7.0.100-rc.2.22477.23

(Tried with other dotnet SDKs to no avail.)

Install dotnet arm64 sdk/runtime
dotnet tool update -g ClangSharpPInvokeGenerator
"" > test.input
ClangSharpPInvokeGenerator -f test.input -n test -o test.output
Observe exception

Unhandled exception: System.BadImageFormatException: An attempt was made to load a program with an incorrect format. (0x8007000B)
   at ClangSharp.Interop.clang.getClangVersion()
   at ClangSharp.PInvokeGenerator..ctor(PInvokeGeneratorConfiguration config, Func`2 outputStreamFactory) in /_/sources/ClangSharp.PInvokeGenerator/PInvokeGenerator.cs:line 75
   at ClangSharp.Program.Run(InvocationContext context) in /_/sources/ClangSharpPInvokeGenerator/Program.cs:line 686
   at System.CommandLine.Handler.<>c__DisplayClass1_0`1.<SetHandler>b__0(InvocationContext context)
   at System.CommandLine.Invocation.AnonymousCommandHandler.<>c__DisplayClass2_0.<.ctor>g__Handle|0(InvocationContext context)
   at System.CommandLine.Invocation.AnonymousCommandHandler.InvokeAsync(InvocationContext context)
   at System.CommandLine.Invocation.InvocationPipeline.<>c__DisplayClass4_0.<<BuildInvocationChain>b__0>d.MoveNext()
--- End of stack trace from previous location ---
   at System.CommandLine.Builder.CommandLineBuilderExtensions.<>c__DisplayClass18_0.<<UseParseErrorReporting>b__0>d.MoveNext()
--- End of stack trace from previous location ---
   at System.CommandLine.Builder.CommandLineBuilderExtensions.<>c__DisplayClass13_0.<<UseHelp>b__0>d.MoveNext()
--- End of stack trace from previous location ---
   at System.CommandLine.Builder.CommandLineBuilderExtensions.<>c__DisplayClass20_0.<<UseTypoCorrections>b__0>d.MoveNext()
--- End of stack trace from previous location ---
   at System.CommandLine.Builder.CommandLineBuilderExtensions.<>c.<<UseSuggestDirective>b__19_0>d.MoveNext()
--- End of stack trace from previous location ---
   at System.CommandLine.Builder.CommandLineBuilderExtensions.<>c__DisplayClass17_0.<<UseParseDirective>b__0>d.MoveNext()
--- End of stack trace from previous location ---
   at System.CommandLine.Builder.CommandLineBuilderExtensions.<>c.<<RegisterWithDotnetSuggest>b__6_0>d.MoveNext()
--- End of stack trace from previous location ---
   at System.CommandLine.Builder.CommandLineBuilderExtensions.<>c__DisplayClass9_0.<<UseExceptionHandler>b__0>d.MoveNext()

Appears the root cause here is the amd64 libclang in the package getting chosen to load into the AnyCPU (arm64) process.

https://github.com/dotnet/ClangSharp/pull/408 should minimally help by logging an error message directing users to acquire the relevant binaries from NuGet. In particular https://www.nuget.org/packages/libclang and https://www.nuget.org/packages/libClangSharp and the .runtime.<rid> specific versions such as https://www.nuget.org/packages/libClangSharp.runtime.win-arm64
I've logged https://github.com/dotnet/sdk/issues/28923 on the dotnet/SDK side to ensure this issue is more broadly tracked and an applicable fix can be made to the dotnet tool support.

See also https://github.com/microsoft/win32metadata/issues/1305#issuecomment-1295303952

Agreed, will close this as I don't need it and we're blocked on dotnet. https://github.com/microsoft/win32metadata/issues/1305#issuecomment-1570974007


Android Gradle Plugin version

Getting the following error on the latest gradle version
The Android Gradle plugin supports only Kotlin Gradle plugin version 1.5.20 and higher.
The following dependencies do not satisfy the required version:
project ':bitmap' -> org.jetbrains.kotlin:kotlin-gradle-plugin:1.3.50

@atnegrete Got Any Solution??

The package needs to be updated, we're open to PRs if anyone wants to fix that. :)

Hi @spydon, i have added a PR for it #49


Android touch delay

If I use vue-touch in a mobile project everything works fine in iOS. The problem is, if we use the same code on android the touches (especially a :active css class) will show up about 200ms after the actual touch.
Do you know why?
Thanks!

Hi @robin7331, i reduced de delay of press event overriding the options. (i don't know what event you talking about)
 VueTouch.registerCustomEvent('press', {
   type: 'press',
   time: 100
});

Check hammer documentation for more info.

also have a look at hammer-time here: https://hammerjs.github.io/
which aims to remove the typical click delay on touch devices.


Feature/and or tags switch

In this PR i've added a switch to make the user able to switch between the selected tags using the OR / AND operator to view the cards with selected tags.
Features made in the pull-request:

New switch button
New functions to handle the switch action & filtering

Changes made:

Modified the function executeSearch to swap between operators
Modified filter.scss for styling purpose

Testing:

Tested "old" filter functions.
Tested tags section with switch flipped both ways.
Tested filtering with sorting

Images

 Etikett inställning "AND"/"OR"

You did a fantastic job in such a short time! 😊
I only have a couple of observations:
1- The label name doesn't match the behavior of the switch. A recommendation of simple fix would be renaming it to "Match all tags," since "any" corresponds to "or," and "all" corresponds to "and."
I tested the styling in both Firefox and Chrome, and unfortunately, there’s an overlap in the styling.

Thank you!
I've made changes and now the label corresponds to the state, "Match any tag" and "Combine tags" is now the labels 2 states that swap when you flip the switch.
I find it weird that the styling is overlapping, i don't encounter this problem on whatever resolution i have enabled in devtools. I've done some changes that hopefully fixes this issue.


vtm.be/vtmgo not working

Checklist

[x] I'm reporting a broken site support
[x] I've verified that I'm running youtube-dl version 2021.12.17
[x] I've checked that all provided URLs are alive and playable in a browser
[x] I've checked that all URLs and arguments with special characters are properly quoted or escaped
[x] I've searched the bugtracker for similar issues including closed ones

Verbose log

[debug] System config: []
[debug] User config: []
[debug] Custom config: []
[debug] Command-line args: ['-u', 'PRIVATE', '-p', 'PRIVATE', '-g', 'https://vtm.be/vtmgo/afspelen/e1a1be143-14e8-4e7a-9300-65d1cda365d4', '--verbose']
[debug] Encodings: locale cp1252, fs mbcs, out cp850, pref cp1252
[debug] youtube-dl version 2021.12.17
[debug] Python version 3.4.4 (CPython) - Windows-10-10.0.22000
[debug] exe versions: ffmpeg 4.2.3, ffprobe 4.2.3
[debug] Proxy map: {}
WARNING: Could not send HEAD request to https://myprivacy.dpgmedia.be/consent?siteKey=fzflolcraql92kc0&callbackUrl=https%3A%2F%2Fvtm.be%2Fprivacygate-confirm%3FredirectUri%3D%252Fvtmgo%252Fafspelen%252Fe1a1be143-14e8-4e7a-9300-65d1cda365d4: HTTP Error 403: Forbidden
ERROR: Unable to download webpage: HTTP Error 406: Not Acceptable (caused by HTTPError()); please report this issue on https://yt-dl.org/bug . Make sure you are using the latest version; type  youtube-dl -U  to update. Be sure to call youtube-dl with the --verbose flag and include its complete output.
  File "C:\Users\dst\AppData\Roaming\Build archive\youtube-dl\ytdl-org\tmpupik7c6w\build\youtube_dl\extractor\common.py", line 634, in _request_webpage
  File "C:\Users\dst\AppData\Roaming\Build archive\youtube-dl\ytdl-org\tmpupik7c6w\build\youtube_dl\YoutubeDL.py", line 2288, in urlopen
  File "C:\Python\Python34\lib\urllib\request.py", line 470, in open
  File "C:\Python\Python34\lib\urllib\request.py", line 580, in http_response
  File "C:\Python\Python34\lib\urllib\request.py", line 502, in error
  File "C:\Python\Python34\lib\urllib\request.py", line 442, in _call_chain
  File "C:\Python\Python34\lib\urllib\request.py", line 685, in http_error_302
  File "C:\Python\Python34\lib\urllib\request.py", line 470, in open
  File "C:\Python\Python34\lib\urllib\request.py", line 580, in http_response
  File "C:\Python\Python34\lib\urllib\request.py", line 502, in error
  File "C:\Python\Python34\lib\urllib\request.py", line 442, in _call_chain
  File "C:\Python\Python34\lib\urllib\request.py", line 685, in http_error_302
  File "C:\Python\Python34\lib\urllib\request.py", line 470, in open
  File "C:\Python\Python34\lib\urllib\request.py", line 580, in http_response
  File "C:\Python\Python34\lib\urllib\request.py", line 502, in error
  File "C:\Python\Python34\lib\urllib\request.py", line 442, in _call_chain
  File "C:\Python\Python34\lib\urllib\request.py", line 685, in http_error_302
  File "C:\Python\Python34\lib\urllib\request.py", line 470, in open
  File "C:\Python\Python34\lib\urllib\request.py", line 580, in http_response
  File "C:\Python\Python34\lib\urllib\request.py", line 502, in error
  File "C:\Python\Python34\lib\urllib\request.py", line 442, in _call_chain
  File "C:\Python\Python34\lib\urllib\request.py", line 685, in http_error_302
  File "C:\Python\Python34\lib\urllib\request.py", line 470, in open
  File "C:\Python\Python34\lib\urllib\request.py", line 580, in http_response
  File "C:\Python\Python34\lib\urllib\request.py", line 508, in error
  File "C:\Python\Python34\lib\urllib\request.py", line 442, in _call_chain
  File "C:\Python\Python34\lib\urllib\request.py", line 588, in http_error_default

Description

My goal is to download a small time range from this video. I searched for possible ways of doing so and found this stackexchange. If I try to do youtube-dl -g "<VIDEO-URL>" I get an error. I tried using inputting my username (-u "<USERNAME>") and password (-p "<PASSWORD>") but even then, I get above error.
How can I download a small specific time range from https://vtm.be/vtmgo/ ?

The format https://vtm.be/vtmgo/afspelen/e1a1be143-14e8-4e7a-9300-65d1cda365d4 is not known to the VTM extractor Possibly the site has been redesigned. Certainly the login process for the site via myprivacy.dpgmedia.be is not handled. Also, e1a1be143-14e8-4e7a-9300-65d1cda365d4 looks like a GUID but has 4 bits (1 hex digit) too many: is it actually correct?

The format https://vtm.be/vtmgo/afspelen/e1a1be143-14e8-4e7a-9300-65d1cda365d4 is not known to the VTM extractor Possibly the site has been redesigned. Certainly the login process for the site via myprivacy.dpgmedia.be is not handled. Also, e1a1be143-14e8-4e7a-9300-65d1cda365d4 looks like a GUID but has 4 bits (1 hex digit) too many: is it actually correct?

It was a news recording, but now it's not online anymore.


Error on manual entry of date (values out of range)

When we type a date out of the range (months higher than 12 or days higher than 32) the datetime picker crashes
You can see the sreenshot, I'm using 50/08/2016

Current behavior

The app throws an Exception and it invalidates the datetime picker

Expected/desired behavior

The component should manage out of the range values

@eromero90 thanks for reporting this. I will take a look into it.

After this fix got 'Cannot read property 'getTime' of null' error.

@Kurity I don't see that error. Please provide me a plunker example.


Optional Upgrade Feature

Eventually, it would be awesome if this installer could handle upgrading Flarum to the latest build.
Requirements:

The script searches for any currently installed Flarum versions based on ./vendor/composer/installed,json (or any other easier means of identification).
Provide an upgrade button upon which upgrades Flarum to the current version.
Display any errors to why the current installation cannot be upgraded.

If not, I would recommend an upgrade.php option that handles upgrades.
That is, until, Flagrow introduces that magical "upgrade" option down the line <3 @luceos

I think Bazaar could also do that.. We haven't tried yet though :grin:

As I mentioned in the forum thread, I am somewhat unsure if I should.
How to handle outdated extensions for instance?
I could for instance have a disclaimer agreement checkbox thingy to make users aware of the risks and so on, but I am afraid people trust things to much and barges away without backup up before hand.
ref: https://discuss.flarum.org/d/5731-pockethold-a-web-based-installer/55
What do you guys think?

It seems Bazaar might get an upgrade function in the near feature TM see https://github.com/flagrow/bazaar/issues/80
If this happens, I might not spend time making an update version of pockethold.


select hier path

Codecov Report

Merging #3010 (583fdf4) into master (44fcbad) will decrease coverage by 0.00%.
The diff coverage is 0.00%.

@@            Coverage Diff             @@
##           master    #3010      +/-   ##
==========================================
- Coverage   27.40%   27.39%   -0.01%
==========================================
  Files          99       99
  Lines       32812    32815       +3
==========================================
  Hits         8991     8991
- Misses      23821    23824       +3

Impacted Files
Coverage Δ

src/DesignCompile/CompileExpression.cpp
29.69% <0.00%> (-0.03%)
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Request for Documentation Clarity: Is the proxy version requirements the requirement of the project to configure the proxy, or the version of the backends?

Hey, quick Q on the new proxy that is quite unclear from the proxy documentation:
In order to use the proxy, do all the backends need to be version 1.6.23 or newer, or is that only the requirement to be able to configure and run the proxy?
Ex: AWS's managed memcached doesn't support 1.6.23 yet as a server. Can I still use the proxy with backends that aren't at that version yet? Is there a minimum backend server version I should be aware of?

@SpencerMalone The version requirement is for the proxy itself (since the code didn't exist before). The newer the better for that (but development should be stabilizing/slowing down).
The proxy can talk to almost any version of memcached as a backend. It might work all the way back to the 1.0 series.

Perfect, thank you!

fwiw we're in the middle of working on the proxy documentation. Please let us know immediately if something's not working right or not obvious. I'd love the feedback to help figure areas of focus + understand more real use cases. We also have the discord (linked from memcached.org) for realtime assistance if necessary.
good luck!


Return cost in minutes of inference

Users have a finite number of minutes of inference per-month.
It would be useful to see how many compute minutes Replicate has charged for a given inference once it has completed.
This could be useful for allocating time to a given user, or simply profiling.
I've been looking at the code, and am not seeing anything that does this.  However it seems like something that could be a member of the Prediction class.

I think is a great idea

I strongly agree it a good idea. I really need it.

any updates on this?

As of #136, you can now get the predict time in seconds through the metrics field of a prediction.
prediction.metrics['predict_time']


Dependency warnings in pixi-filters

When installing the pixi-filters package with Yarn I get a lot of warnings regarding unmet dependencies. This causes noise that makes it harder to see actual breaking dependency problems. Is this easily fixable, or do I have to install each Pixi package separately instead of using the pixi-filters collection?
> yarn
warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>warning "pixi-filters ><EMAIL_ADDRESS>has unmet peer dependency "<EMAIL_ADDRESS>

I think I see what's going on here. PeerDependencies are not met in the bundle. I can work on fixing this, but these are harmless warnings, especially if you're including pixi.js or pixi.js-legacy. Thanks for reporting.

Not relevant anymore. Close.


FIX: [TF] Data source "host" crashing Terraform provider

https://github.com/OpenVPN/terraform-provider-cloudconnexa/issues/18


fix - typo & prompt

Function typo
Original name - set_node_relataionships()
Fixed name - set_node_relationships()

In the seed_question_prompt, a keyphrase is given.
But in the instruction, it is mentioned that The question should be formed using topic.

The question should be formed using the given keyphrase. would be better

hey @omkar-334, we have shifted to a new approach of storing documents with v0.2, do check it out
closing this for now but I'm really sorry we couldn't merge it 🙁 but at the same time thanks a million for taking the time to raise this, really grateful too and do checkout this form https://docs.google.com/forms/d/e/1FAIpQLSdM9FrrZrnpByG4XxuTbcAB-zn-Z7i_a7CsMkgBVOWQjRJckg/viewform - our way of saying thank you 🙂


Suggestion: add a CHANGELOG.md at the root of this repo

https://keepachangelog.com/en/1.0.0/

Oops, duplicated your issue in https://github.com/NetanelBasal/spectator/issues/141. 😉


Change API_LOGIN_APPLICATION_VERSION

Since a week I have a new piazzetta stove. And after some experimentation I found that I needed to change API_LOGIN_APPLICATION_VERSION to 1.9.0 to be able to use py-agua-iot with this stove. Locally I have this working. But I do not know how to get that working in my Home Asisstant instance. Can anyone give me some pointers?

Hi, i changed the version in init.py_ but it don't work for me. Can you describe with more precision what did you do to connect the API?
Normaly you when you add the repository in "custom_component", restart HA. After you can go to "integration", "add integration" and choose "micronova" in the list.
You select it and you'll have a windows with the different information (API URL, login url, email, password, etc...) and after enter these information, your stove will appear as a climate.
You can add directly this repository from hacs by adding the adress and download it in hacs.

Hi @eternalpolo, I did indeed follow all the steps to use it in HA. But the connection to my Piazzetta P943 could not be established. I got different kinds of errors. After fiddling around and trying different things I figured out that the application version had changed from 1.6.0 to 1.9.0. And that the login url had changed to https://piazzetta-iot.app2cloud.it/api/bridge/endpoint/.
To test I tried it outside of HA just witht the "bare" py_agua_iot module. Changing the API_LOGIN_APPLICATION_VERSION variable (hardcoded) to 1.9.0 and using the new login url I was able to make a connection
My struggle is with getting that in HA correctly. I assume that a new version of py_agua_iot is needed. For now I changed the local py_agua_iot module in my HA instance to use the new application version and with that I am able to use it in HA. But I would like to do it more nicely. Maybe have the application version variable as well?

Hi @gjvanhalem
I think this issue is similar to the issues in https://github.com/fredericvl/py-agua-iot/issues/44
Where do you adjust the version? I can't seem to find a variable API_LOGIN_APPLICATION_VERSION...
It's already a long time quiet in this repo, the more recent changes are done in a fork https://github.com/vincentwolsink/py-agua-iot
There is no possibility to open issues but you can open a PR...

I made the change in line 31 in this file py_agua_iot/init.py

Thx! but... where is this file in Home Assistant :-) Sorry!!!

ah, sorry for the confusion. In your HA instance find it with this command pip show py_agua_iot that will give you the file location among other info. Do not forget to remove the pycache directory and restart HA after the change

Hi @Sylle99 , it was more luck than anything else :-). While investigating I stumbled on this website https://lefilliatre.github.io/lefilliatre-documentation/PoeleAgua/fr_FR/. Never heard of it before but they use the same py-agua-iot base to build a plugin for the jeedom platform. And that gave me a hint to use the different URL. It also gave me the hint to look at the application version.
I will see if I can make the application version a parameter for the HA plugin. Never made a plugin for HA before so it might take me a while to figure it out

Sounds great!
I must say all this is waaaay above my head as I have no IT or programming knowledge.
But I'm quickly picking up as we go and learn by looking at code made by others... :)
I installed this custom micronova integration by manually creating the folders and uploading files to Home assistant as described here https://github.com/fredericvl/py-agua-iot/blob/master/examples/home-assistant/README.md.
I notice however that most of you guys are adding a custom repository instead and find all the files that way?!
Does the command  "pip show py_agua_iot" also work on a X86 version of Home assistant OS?
Is there a benefit doing it that way compared to the manual upload I did??
Sorry for the questions - I appreciate all the help!
Sylvain

OK, got it working locally and opened a pull request for https://github.com/vincentwolsink/py-agua-iot since that repo is more up-to-date as mentioned by @Donder24
My change will give you the option in HA to also enter the application version when configuring the integration

@Sylle99 you did it the right way. Finding and fiddling with the py_agua_module in the way I described should only be done on a local instance to do some test. If you have no experience with programming do not try to do this. It will break stuff. You will have to wait for my pull request to be merged for it to work

@gjvanhalem I merged your PR in v0.0.10. Thanks!

Hi all,
I've tried the upgrade of @vincentwolsink  and it's work for me by changing the Login API version.
I've changed the separate login URL by  like @gjvanhalem said.
Is it possible to change this information in the description of the repository ?
Thanks a lot @gjvanhalem and @vincentwolsink for your help.

I installed this custom micronova integration by manually creating the folders and uploading files to Home assistant as described here https://github.com/fredericvl/py-agua-iot/blob/master/examples/home-assistant/README.md. I notice however that most of you guys are adding a custom repository instead and find all the files that way?! Does the command "pip show py_agua_iot" also work on a X86 version of Home assistant OS? Is there a benefit doing it that way compared to the manual upload I did??

Just to give some background on this. The code that you add manually to HA is the integration that takes care of thh setup and running your integration. I made some changes to this to be able to add an extra parameter (application version).
The module that you will find with  is imported by HA the first time you trigger the integration. You do not have to upload that manually.
I made some changes to it to do some test. But normally you shouldn't touch it
Hope that helps

@eternalpolo I created a pull request to change the description in the repository

all merged


backend loader linking problem?

Hi all,
can you think of any reason why I am getting these warnings? I compiled the library using syclcc --hipsycl-targets="cuda:sm_75". Do I need to link these libraries manually?
Output with HIPSYCL_DEBUG_LEVEL=4
Hello world
[hipSYCL Info] backend_loader: Searching path for backend libs: '"/opt/hipSYCL/bin/../lib/hipSYCL"'
[hipSYCL Warning] backend_loader: Could not load backend plugin: /opt/hipSYCL/bin/../lib/hipSYCL/librt-backend-omp.so
[hipSYCL Warning] /opt/hipSYCL/bin/../lib/hipSYCL/librt-backend-omp.so: undefined symbol: _ZTVN10__cxxabiv120__si_class_type_infoE
[hipSYCL Info] backend_loader: Successfully opened plugin: "/opt/hipSYCL/bin/../lib/hipSYCL/librt-backend-hip.so" for backend 'hip'
[hipSYCL Warning] backend_loader: Could not load backend plugin: /opt/hipSYCL/bin/../lib/hipSYCL/librt-backend-cuda.so
[hipSYCL Warning] /opt/hipSYCL/bin/../lib/hipSYCL/librt-backend-cuda.so: undefined symbol: _ZTVN10__cxxabiv120__si_class_type_infoE
[hipSYCL Info] Registering backend: 'hip'...
[hipSYCL Info] hip_device_manager: Switchting to device 1
[hipSYCL Info] multi_queue_executor: Spawned for backend HIP with configuration:
[hipSYCL Info]   device 0:
[hipSYCL Info]     memcpy lane: 0
[hipSYCL Info]     memcpy lane: 1
[hipSYCL Info]     kernel lane: 2
[hipSYCL Info]     kernel lane: 3
[hipSYCL Info]   device 1:
[hipSYCL Info]     memcpy lane: 0
[hipSYCL Info]     memcpy lane: 1
[hipSYCL Info]     kernel lane: 2
[hipSYCL Info]     kernel lane: 3
[hipSYCL Info] Discovered devices from backend 'HIP':
[hipSYCL Info]   device 0:
[hipSYCL Info]     vendor: AMD
[hipSYCL Info]     name: AMD Radeon VII
[hipSYCL Info]   device 1:
[hipSYCL Info]     vendor: AMD
[hipSYCL Info]     name:
[hipSYCL Error] No CPU backend has been loaded. Terminating.
terminate called without an active exception
Aborted (core dumped)

The location of the files is correct.
/opt/hipSYCL/lib/hipSYCL$ ll
total 1236
drwxr-xr-x 2 root root   4096 Apr  8 10:06 ./
drwxr-xr-x 4 root root   4096 Apr  8 10:06 ../
-rw-r--r-- 1 root root 980432 Apr  8 10:06 librt-backend-cuda.so
-rw-r--r-- 1 root root 146192 Apr  8 10:06 librt-backend-hip.so
-rw-r--r-- 1 root root 124744 Apr  8 10:06 librt-backend-omp.so
Thanks

I've never seen that before. The symbols that it cannot find are apparently some vtables. My guess is some kind of ABI compatibility issues between different compilers.. Can you describe more of your setup? How have you installed hipSYCL, which compilers are involved, any custom flags when building etc?

@nilsfriess @normallytangent If you have time, can one of you maybe try reproducing this?

I tried to reproduce your error @alexschroeter  but when trying to run the python script, I am getting the error ModuleNotFoundError: No module named 'pyexamples'. I tried this in a fresh conda environment based on python 3.8 with only Cython installed. I have never used Cython before, can you maybe give more details about your specific setup (in particular the Python version you are using).

Thank you @nilsfriess for looking into this and sorry for the late response but I was very busy.
I was using also conda with Python 3.10.4
Just to make sure we are doing the same thing.
Setting up:
conda create -n cython-sycl python=3.8
conda activate cython-sycl
conda install cython
git clone https://github.com/alexschroeter/cython-c-wrapper
cd cython-c-wrapper
git checkout cython-sycl

Compiling and Running:
cd lib
make
cd ..
make
[HIPSYCL_DEBUG_LEVEL=4] python main.py

I have now also tried with Python 3.8.13 and I basically get the same error
Hello world
[hipSYCL Info] backend_loader: Searching path for backend libs: '"/opt/hipSYCL/bin/../lib/hipSYCL"'
[hipSYCL Warning] backend_loader: Could not load backend plugin: /opt/hipSYCL/bin/../lib/hipSYCL/librt-backend-omp.so
[hipSYCL Warning] /opt/hipSYCL/bin/../lib/hipSYCL/librt-backend-omp.so: undefined symbol: _ZTVN10__cxxabiv120__si_class_type_infoE
[hipSYCL Info] backend_loader: Successfully opened plugin: "/opt/hipSYCL/bin/../lib/hipSYCL/librt-backend-hip.so" for backend 'hip'
[hipSYCL Warning] backend_loader: Could not load backend plugin: /opt/hipSYCL/bin/../lib/hipSYCL/librt-backend-cuda.so
[hipSYCL Warning] /opt/hipSYCL/bin/../lib/hipSYCL/librt-backend-cuda.so: undefined symbol: _ZTVN10__cxxabiv120__si_class_type_infoE
[hipSYCL Info] Registering backend: 'hip'...
HSA Error:  Incompatible kernel and userspace, AMD Radeon VII disabled. Upgrade amdgpu.
terminate called after throwing an instance of 'std::bad_function_call'
  what():  bad_function_call
Aborted (core dumped)

Thank you for the help.

I could not reproduce the exact error you got, however I am getting a very similar one:
[hipSYCL Warning] backend_loader: Could not load backend plugin: /opt/hipSYCL/lib/hipSYCL/librt-backend-hip.so
[hipSYCL Warning] /opt/hipSYCL/lib/hipSYCL/librt-backend-hip.so: undefined symbol: _ZTVN7hipsycl2rt20multi_queue_executorE

I have no idea why this happens but my guess is that this is not really hipSYCL-related but requires some additional Cython configuration (since the code runs fine when compiled normally). Unfortunately, I have no experience in Cython so I'm afraid I can't really help you here..


Find_cell updated with ordered cell parameters; update_xds updated with a few more options; conflicts resolved

Hi Bin, thanks for that! Looks mostly fine to me, I'm just wondering, why is the reference line problematic? "Delete the reference line since it is sometimes problematic"

Hi, I don't know if you've seen this before, but after a few rounds of update_xds, the reference line just becomes weirdly encoded and super long, which makes the xds.inp file a few megabytes in size. I don't know the exact reason, but the easiest thing to do is just to remove it. This happened to both me (who ran xds on a machine through virtualbox) and many people in our group (running under WSL). I agree that this is not the perfect solution though

Interesting, I haven't noticed this. If it's such a problem, maybe remove it by default!

I agree, I did not try to find where went wrong but probably removing it from Instamatic XDS template directly is the best :)


Vickel: Watch northite.com

Vickel requests the watch of the watch_keyword northite\.com. See the MS search here and the Stack Exchange search in text, in URLs, and in code.

northite\.com has been seen in 1 true positive, 0 false positives, and 0 NAAs.

Approved by double-beep in Charcoal HQ


Fix icons

[x] icons alignment
[x] captions
[ ]focus issue
[x] separated icons for normal and full-screen

icons alignment is now full of magic numbers. I thing we should discuss it with designer. Good news is, it looks much better.
Then:

Now:

Looks like ready to merge.

@maxvektor @BrooklynKing let's squash commits and merge it finally :)


Header event not fired

Using version 1.1.7, this does not give any output:

  const csv = require('csvtojson');
  csv()
    .on('header', () => {
      console.log('header');
    });

This feature is to be released with 1.1.8
Sent from my iPhone

On 13 Sep 2017, at 11:50, Per Sandstrom<EMAIL_ADDRESS>wrote:
Using version 1.1.7, this does not give any output:
const csv = require('csvtojson');
csv()
.on('header', () => {
console.log('header');
});
—
You are receiving this because you are subscribed to this thread.
Reply to this email directly, view it on GitHub, or mute the thread.


New release?

We're working towards our 1.0 release of this gem right now. Last night's announcement caught us somewhat off guard! 😆

Version 0.8.0 released now with many, many additions and bug fixes. Next release should be 1.0 and move towards stability.


Exporting to KDB (1.x) doesn't work after database has been unlocked

That's right. But it's not really a bug, just the way how KeePass works. You can't export to KDB files if you use something different from a password and/or a keyfile. You can't use a custom key provider (OTP, key cards, ...) with a KDB file.
See KdbFile.SetDatabaseKey for more informations.


Enum doesn't work/validate, when min_length in query parameters is defined

First Check

[X] I added a very descriptive title to this issue.
[X] I used the GitHub search to find a similar issue and didn't find it.
[X] I searched the FastAPI documentation, with the integrated search.
[X] I already searched in Google "How to X in FastAPI" and didn't find any information.
[X] I already read and followed all the tutorial in the docs and didn't find an answer.
[X] I already checked if it is not related to FastAPI but to Pydantic.
[X] I already checked if it is not related to FastAPI but to Swagger UI.
[X] I already checked if it is not related to FastAPI but to ReDoc.

Commit to Help

[X] I commit to help with one of those options 👆

Example Code
from fastapi import FastAPI
from fastapi.params import Query
import enum
from typing import List

app = FastAPI()

class RoomNumberEnum(str, enum.Enum):
    value_1 = "1"
    value_2 = "2"
    value_3 = "3"
    value_4 = "4"

@app.get("/")
def root(
        room_no_category: List[RoomNumberEnum] = Query(default=..., min_length=1)
):
    return {"message": f"Room number category: {room_no_category[0]}"}

Description
If min_length is declared in parameters, RoomNumberEnum doesn't validate. If I delete min_length, enum validation works.
Operating System
Windows
Operating System Details
No response
FastAPI Version
0.78.0
Python Version
3.7.7
Additional Context
No response

@aboubacs There's no error message, the input just passes through without any errors, even though it's incorrect and not defined in enum class. It should be caught by enum, in case a value is not defined.
The min_length parameter is messing up the validation.
When looking at the api docks, the values are not visible.

If I remove min_length from Query parameters, then it works as expected.

The min_length parameter is because is a list and is validating the list instead of its content, you should make a custom validator instead of using Query

@odiseo0 I need a list of values that are validated by the RoomNumberEnum. It works without min_length, so it's not a big issue, I thought it's a bug and wanted to report.

Do you want to limit the length of the list or the length of the contents of each item in the list.
If the former, you may need min_items parameter. And if the latter, why not delete the items with inconsistent length in the enum.


[Bug] Can't get it to start

Outputting a warning so far.
(spleeter-cpu) Roberts-MBP-2:Desktop USER$ spleeter separate -i someoneinthecrowd.mp3 -p spleeter:2stems -o output
INFO:spleeter:Downloading model archive https://github.com/deezer/spleeter/releases/download/v1.4.0/2stems.tar.gz
INFO:spleeter:Extracting downloaded 2stems archive
INFO:spleeter:2stems model file(s) extracted
INFO:spleeter:Loading audio b'someoneinthecrowd.mp3' from 0.0 to 600.0
WARNING:spleeter:module 'ffmpeg' has no attribute 'probe'
ffmpeg was installed using pip, then decided to try brew. Using os x

Similar issue here:
(spleeter-cpu) tobhar:~ t.hartmann$ spleeter separate -i audio_example.mp3 -o audio_output
INFO:spleeter:Loading audio b'audio_example.mp3' from 0.0 to 600.0
WARNING:spleeter:ffprobe error (see stderr output for detail)
See verbose logs here:
(spleeter-cpu) tobhar:~ t.hartmann$ spleeter separate -i audio_example.mp3 -c wav -o audio_output --verbose
INFO:tensorflow:Using config: {'_model_dir': 'pretrained_models/2stems', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': gpu_options {
per_process_gpu_memory_fraction: 0.7
}
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x1356ab0b8>, '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
INFO:tensorflow:Calling model_fn.
WARNING:tensorflow:From /Users/t.hartmann/opt/miniconda3/envs/spleeter-cpu/lib/python3.7/site-packages/spleeter/model/functions/unet.py:28: The name tf.keras.initializers.he_uniform is deprecated. Please use tf.compat.v1.keras.initializers.he_uniform instead.
INFO:tensorflow:Apply unet for vocals_spectrogram
INFO:tensorflow:Apply unet for accompaniment_spectrogram
INFO:tensorflow:Done calling model_fn.
WARNING:tensorflow:From /Users/t.hartmann/opt/miniconda3/envs/spleeter-cpu/lib/python3.7/site-packages/tensorflow/python/ops/array_ops.py:1354: add_dispatch_support..wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where
INFO:tensorflow:Graph was finalized.
WARNING:tensorflow:From /Users/t.hartmann/opt/miniconda3/envs/spleeter-cpu/lib/python3.7/site-packages/tensorflow/python/training/saver.py:1276: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.
Instructions for updating:
Use standard file APIs to check for files with this prefix.
INFO:tensorflow:Restoring parameters from pretrained_models/2stems/model
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
WARNING:tensorflow:The dtype of the watched tensor must be floating (e.g. tf.float32), got tf.string
WARNING:tensorflow:The dtype of the watched tensor must be floating (e.g. tf.float32), got tf.int32
WARNING:tensorflow:The dtype of the watched tensor must be floating (e.g. tf.float32), got tf.string
INFO:spleeter:Loading audio b'audio_example.mp3' from 0.0 to 600.0
WARNING:spleeter:ffprobe error (see stderr output for detail)
... any help? Idea?

You probaly have an invalid ffmpeg installation. pip only installs python wrappers,not the actual ffmpeg executable.

I am having a similar problem, getting the same error message
WARNING:spleeter:module 'ffmpeg' has no attribute 'probe'
I installed ffmpeg via a zip from https://ffmpeg.zeranoe.com/builds/
Also made a PATH to the bin folder of ffmpeg but still nothing

It is all about not getting the correct path to input and/or output file/folder.
Just use the complete path to your input audiofile for the terminal commands (like: user/audiofiles/audiofile.wav)
Same for the output folder.
And avoid spaces. Use underscore, minus or no space instead.
Then it should work.


select2 on delete of all the option opens the dropdown list at right hand side

I am using select 2 for multiple tags selection and when i use the backspace key for deleting all the tags the option drop down is shown at the left hand side of browser and when i click on it ,it throws error "Uncaught TypeError: Cannot read property 'opts' of undefined"

Closing this off because of the lack of activity. If this is not fixed in 4.0, and you are interested in seeing it fixed, add a :+1: to the ticket to bring it to our attention.
If you can add additional details, that would also be useful.


All 7 patches work great!

New custom compilation of linux-xanmod-edge with all 7 patches work on 16IAP7
I updated my github for that, later i will update mainline kernel too
I hope all them will become modules very soon in coming kernel release
Thank you!

Patch seven adds a quirk for your device's sound subsystem id. Closing this as it's not really an issue.


Embeddings correction

This supports all types of models. Until we do not need more than one sequence or pooling we should use this code.

Looks good, I'll merge it once the tests pass. Thanks for helping me investigate the embeddings issue and working on this PR.

Oops, I pushed my followup changes to straight to master into of a PR branch 🤦♂
@zsogitbe here's my followup - hopefully this looks ok to you? https://github.com/SciSharp/LLamaSharp/commit/3c764409579a417a85048984fb492b9530ef97d3

Oops, I pushed my followup changes to straight to master into of a PR branch 🤦♂
@zsogitbe here's my followup - hopefully this looks ok to you? 3c76440

Maybe double check that if (embeddings == null) is enough. C++ may return an empty list and then it will not be NULL...

The C++ doesn't return a size, just a pointer to the first element. So it's either null or a valid embedding vector.


Add Slack connectors

Adding the Slack sync.
What it does:

Check all the channels the bot is a member of
Fetch all messages and upload them:

One Dust document per Slack thread
One dust document / channel / week for unthreaded messages
It removes all emojis text (eg: :todo_done:)
replaces all mentions by real name (eg: Gabriel Hubert).

Concurrency model:

One child workflow per Slack channel is triggered
For one channel:

We fetch messages by batch of 100.
We trigger 2 sync activities per batch of 100:

One for all threaded messages

Inside, we have one promise per thread

One for all non threaded messages

Inside, we have one promise per week

Promises are sent and awaited by batch of activities.MAX_CONCURRENCY_LEVEL

Webhooks are not supported yet,  I am working on it.

Thanks @lasryaric for the fixes 🔥 Directionally looks good to me. Deferring to @fontanierh for final review and iteration tomorrow on the webhook flows 👍

@lasryaric

build fails
micro nit: please update main comment of the PR with latest state (eg concerning emojis)

@lasryaric

build fails
micro nit: please update main comment of the PR with latest state (eg concerning emojis)

Build failure is unrelated to you. protoc install rate limit. Ignore my comment

@spolu  here's a fix for the CI: https://github.com/dust-tt/dust/pull/324


Bookstore: styling

In this branch following functionalities are added:

Added any missing JSX elements to the project.
Used the CSS rules detailed in Zeplin to style the website.
Match the final design to Zeplin's design.

Status: Invalid 🔴
Hi @ngeelani48 🙋♀,
While you made a great effort in this project, I can't review your code.
Invalid Code Review Request
You have submitted a project that has a README that is not professional. Here are key points to consider when documenting your README.

Kindly delete the live demo section if the application is not deployed. If project requirements say to deploy the project, the section should be restored and the link updated. ✖

Kindly remove the example text on your Future Features section

Your Code Review Request will be marked as invalid in your Dashboard, so please submit a new one once you are ready 🙏
Cheers 🥂  and Happy coding!!! 👯
Feel free to leave any questions or comments in the PR thread if something is not 100% clear.
Remember to tag me @KDlamini  in your question so I can receive the notification.
Please, do not open a new Pull Request for re-reviews. You should use the same Pull Request submitted for the first review, either valid or invalid unless it is requested otherwise.

Invalid Code Review Request does not count into the code reviews limit.


iPhone X - Exception thrown while executing UI block

QRCodeScanner throws an error on iPhone X device. Works fine in other devices.
What's happening?
Unable to render QRCodeScanner. It throws following exception.

How can it be reproduced?
This can be reproduced with the example given in the library documentation.
 <QRCodeScanner         onRead={this.onSuccess}         topContent={           <Text style={styles.centerText}>             Go to{' '}             <Text style={styles.textBold}>wikipedia.org/wiki/QR_code</Text> on             your computer and scan the QR code.           </Text>         }         bottomContent={           <TouchableOpacity style={styles.buttonTouchable}>             <Text style={styles.buttonText}>OK. Got it!</Text>           </TouchableOpacity>         }       />
Build details?
react-native : 0.60.0
react-native-camera: 3.28.0
react-native-permissions: 2.1.5

Upgrading react-native-camera to 3.30.0 and pod install solved the issue.

thank you for hsaring @Nirodha26


Steve bug: "Update" is not working for dynamic RBAC

This is a steve bug. The server side filtering of the links seems to work for remove, but not update, so everything still thinks it's editable.

I think this has been fixed. @maggieliu logged as a read-only user and this is what it gives me

If this gets re-opened, let's move it to 2.5.5.

Rancher version v2.5.4-rc6 (12/23/2020)
Dynamic RBAC is a Dashboard only feature.
I created a Standard User and Assigned the user as Project Read-Only.
As Project Owner I did the following:

Created one of each workload type.
Created one of each service type.
Secret
Ingress
Config Map

Then Logged in as the Standard User with Project Read-Only permissions.
None of the created objects from the list above nor the Pods created by the Workloads had the update or remove links returned by the API and the Dashboard UI elements weren't displaying these options in the UI (Edit/Delete respectively).


fix: data_tests.

sources.tables.data_tesets was not defined, so it was added.
https://github.com/dbt-labs/dbt-jsonschema/pull/162#issuecomment-2359942378

Good catch! Thank you


How bad is it, if close is never called?

Because watchOS doesn't have a proper applicationWillTerminate, there can never be a good place to call TDLib's close. Will there likely be any problems if close is never called?
Personal experience seems to suggest that it is fine, so just to check.

There could be some data loss if the app is killed by OS, but it should be pretty rare.


Unable to use YubiKey 5Ci

In Fastmate I can't register my YubiKey 5Ci as a second factor for Fastmail.
Additionally, if I first add it in a browser, I can't use the key to log in into Fastmail in Fastmate.
Am I doing it wrong or does Fastmate not support (WebAuthn) security keys?

You’re not doing anything wrong. WebKit/WKWebView does not support OAuth of of the box. Fastmate might be able to work around this by making a separate OAuth session. This would require opening Safari on the login screen and logging in via that before returning to Fastmate. It’s a non-trivial amount of work, and I’ll consider this a feature request that I’ll look into!

This issue is very old, @pro-sumer do you still have doubts or has the problem been solved?

This issue is very old, @pro-sumer do you still have doubts or has the problem been solved?

I'm "waiting" for @joelekstrom to implement this, but he might be waiting for Apple to make this even possible?
WebAuthn support would be so nice, in particular now that Apple implemented Passkeys!

Also see #44

I'll close this as a dupe of #44. It's really up to Fastmail. They don't have Webauthn which would be the easiest way for Fastmate to integrate. They do have an OAuth API but it's only for select partners. See the other issue


Fix problematic tiff conversions

eLife article elife-40092-vor-r2 contains tiff images that Python pillow is unable to convert to jpeg.

Currently blocking https://github.com/libero/libero/issues/170


Fix IPv6 address display in session info dialog

IPToUINT(IP*) is supposed to work on IPv4 addresses only but currently does not check the type of the input address. This leads to random outcome on IPv6 input and corrupts session info display like the one below.
Item                                      |Value
------------------------------------------+-------------------------------------------------------------
Client IP Address                         |<IP_ADDRESS>
Client Host Name                          |2600:1f13:559:8f00:6604:c537:3d54:cebd

This PR fixes both vpncmd and the server manager.

3d 54  ce  bd
 ↓  ↓   ↓   ↓
61 84 206 189


feat: createdBy and updatedBy in resource & revision

Add createdBy and updatedBy to resource
Add createdBy to revision

[WIP] Updating the latest proto changes

@ishanarya0 can you please link the proton changes here?

@ishanarya0 can you please link the proton changes here?

Proton changes - https://github.com/goto/proton/pull/40


travis - allow tickers to tick for coverage

added a sleep of 16 seconds to one of the tests to make sure that the logTicker and the fwdEventicker have time to tick. Without this, these tickers may not tick during coverage test which may lead to a reduction of coverage.
See discussion in #1590

Once #1668 is in, then we can ensure that all the tickers always tick, which will ensure that we aren't penalized coverage wise for this.

@offerm Did you try to force a tick?

Closing in favour of https://github.com/lightningnetwork/lnd/pull/1975.


Fix 2FA

Solves #10
Sorry, original PR was off master and I had some other changes in it that I had added. Here's just the fix.

Hi @bryandesigning
are you sure this fixes the issue?
As I tested already:
let body =  {
  email: email,
  hwCode: hwid,
  activateCode: code,
};

and it still gave 211 - Wrong activation code

This is what is working for me, as well as a few other repo's I looked at
that have similar API wrappers for tarkov.
On Tue, Feb 18, 2020 at 6:19 PM Matias Kovero<EMAIL_ADDRESS>wrote:

Hi @bryandesigning https://github.com/bryandesigning
are you sure this fixes the issue?
As I tested already:
let body =  {
email: email,
hwCode: hwid,
activateCode: code,
};
and it still gave 211 - Wrong activation code
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
https://github.com/matias-kovero/tarkov/pull/13?email_source=notifications&email_token=AA53OPT34PWPXRYQVDAFVUDRDRUIVA5CNFSM4KXNT4JKYY3PNVWWK3TUL52HS4DFVREXG43VMVBW63LNMVXHJKTDN5WW2ZLOORPWSZGOEMFXL4A#issuecomment-587953648,
or unsubscribe
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.

This is what works for me, as well as a few other repo's of Tarkov API wrappers in various languages. They all use hwCode

This is what works for me, as well as a few other repo's of Tarkov API wrappers in various languages. They all use hwCode

Yeah I know, this api uses also hwCode. Just changed it for 1 push to device_id as people were telling it's the right one. Got this fixed now on my version also, as I saw a major typo.
You have the typo also fixed on this version.


python virtual environment activation

I'm working in a mac(apple m1).
I used M-x pythonic-activate RET /path/to/virtualenv RET to change the virtual environment in anaconda-mode. But Python Shell, pdb and flycheck use three different environments.
It works fine when I used C-c C-p, in a Python Shell.
>>> import sys
>>> print(sys.path)
['', '/Users/felix/miniforge3/envs/gluon/lib/python38.zip', '/Users/felix/miniforge3/envs/gluon/lib/python3.8', '/Users/felix/miniforge3/envs/gluon/lib/python3.8/lib-dynload', '/Users/felix/miniforge3/envs/gluon/lib/python3.8/site-packages']

But the pdb( python -m pdb test.py) use the python2.7 from the system.
 ['', '/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python27.zip', '/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7', '/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/plat-darwin', '/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/plat-mac', '/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/plat-mac/lib-scriptpackages', '/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/lib-tk', '/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/lib-old', '/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/lib-dynload', '/Library/Python/2.7/site-packages', '/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python', '/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/PyObjC']

While the flycheck use python from homebrew.
Syntax checkers for buffer test.py in python-mode:

First checker to run:

  python-pycompile
    - may enable:    yes
    - executable:    Found at /opt/homebrew/bin/python3
    - next checkers: python-mypy

Checkers that are compatible with this mode, but will not run until properly configured:

  python-flake8 (automatically disabled) reset
    - may enable:         no
    - executable:         Found at /opt/homebrew/bin/python3
    - configuration file: Not found
    - `flake8' module:    Missing; sys.path is<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>'/opt/homebrew/lib/python3.9/site-packages']
    - next checkers:      python-pylint, python-mypy

  python-pylint (automatically disabled) reset
    - may enable:         no
    - executable:         Found at /opt/homebrew/bin/python3
    - configuration file: Not found
    - `pylint' module:    Missing; sys.path is<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>'/opt/homebrew/lib/python3.9/site-packages']
    - next checkers:      python-mypy

  python-pyright (automatically disabled) reset
    - may enable: no
    - executable: Not found

  python-mypy (automatically disabled) reset
    - may enable:         no
    - may run:            t
    - executable:         Not found
    - configuration file: Not found

Flycheck Mode is enabled.  Use C-u C-c ! x to enable disabled
checkers.

--------------------

Flycheck version: 32snapshot (package: 20201228.2104)
Emacs version:    27.1
System:           aarch64-apple-darwin20.2.0
Window system:    ns

You're right, this is more like a special use case. Just record it in case others encounter the same problem.

I've updated the note in init-python.el at least. :-)


gsl,tbb: split output

found with
nix path-info -r /run/current-system | xargs -i fd --type d --full-path 'include/' {}  | rg -v "\-dev" | xargs du -shc --apparent-size | sort -hk1

Things done

Built on platform(s)

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

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

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

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

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

[ ] Fits CONTRIBUTING.md.

other large include dirs
nix path-info -r /run/current-system | xargs -i fd --type d --full-path 'include/' {}  | rg -v "\-dev" | xargs du -shc --apparent-size | sort -hk1
389K    /nix/store/3bvlq0akai4x89jsn2dlvlikk27r93zi-libplacebo-4.208.0/include/libplacebo/
393K    /nix/store/rv6badsn64d17accb2iwkxfqngx7k2as-libbpf-1.1.0/include/linux/
398K    /nix/store/6d9cdiv9dlg635wfazr1bh4x5x4sq5jx-rocm-llvm-llvm-5.4.2/include/llvm-c/
411K    /nix/store/znwrpnp8dlh3b9zpgpm1i7c4nb51z93f-libvirt-8.10.0/include/libvirt/
432K    /nix/store/awylv4dyczcwa4qy9fx06nc87pbb72kd-aws-c-common-0.8.9/include/aws/
446K    /nix/store/lpir61yxxjhymlxy74ycgxqfdr86yjys-qca-qt5-2.3.4/include/Qca-qt5/
469K    /nix/store/3bfvwjayikvaz17lba8794bjvxm58nz8-modemmanager-1.18.12/include/libmm-glib/
480K    /nix/store/k45cjwh35qx1bdl4clg6mq6i0kxl3p8a-rocm-runtime-5.4.1/include/hsa/
509K    /nix/store/31idnad0cg2jhv5qs6y7fq5aw8q9g7g0-libyuv-1787/include/libyuv/
547K    /nix/store/zzwqcifb2hgm1qii6ixh86cjryzlf5zh-linux-headers-6.1/include/drm/
631K    /nix/store/yyahnn0xnwcshm8a21zqgg5b4pca7z7f-rocm-llvm-llvm-5.4.2/include/sanitizer/
638K    /nix/store/g9qkr3ncgh27kwsynq03r75amd9i7fwj-sqlcipher-4.5.3/include/sqlcipher/
645K    /nix/store/hyfid0yaiqgdp0hjy0z5xf85v0dqrp7h-taglib-1.13/include/taglib/
725K    /nix/store/abax98471z8fshv4b9p46bkh3lxmpy0z-python3-3.10.9/include/python3.10/
773K    /nix/store/fvgl8js6f2ig5f2w3m746ya8jzza1ihz-directfb-1.7.7/include/directfb-internal/
841K    /nix/store/kg6qk2qrjvfhqyhkmgvi1h193fj48qc3-rocm-opencl-runtime-5.4.2/include/CL/
852K    /nix/store/fvgl8js6f2ig5f2w3m746ya8jzza1ihz-directfb-1.7.7/include/directfb/
887K    /nix/store/ykm2v353pwr9yyj4vpgqxbncn3r6d56a-nlohmann_json-3.11.2/include/nlohmann/
912K    /nix/store/b19kysh00skk7n8fn676q69sxn5v2qhq-python3.10-mypy-0.991/lib/python3.10/site-packages/mypyc/external/googletest/include/gtest/
1.1M    /nix/store/w90327cv9z2q6czjhzsfh7hs3fa8hs71-libsigc++-2.10.8/include/sigc++-2.0/
1.2M    /nix/store/fpyyvrmgpv9ggawnfa8i8zckmpqp79jq-xorgproto-2021.5/include/X11/
1.2M    /nix/store/nlc8fv3a5r2vr1vq07y5qw6riz7d6v8h-gsl-2.7.1/include/gsl/
1.8M    /nix/store/hcbalan0xq30hb53myvw93i309ifh4yg-tbb-2020.3/include/tbb/
2.5M    /nix/store/hr6kxafywg7ar3gpfjzplgzxcr31g23w-zfs-kernel-2.1.7-6.0.19/src/zfs-2.1.7/include/sys/
2.8M    /nix/store/7y1qagav6xv4k3w0df5d7z91y2d3w4z7-nodejs-18.13.0/include/node/
2.8M    /nix/store/g57a0hbcvx6ph0a144s0fq0n0j2pqww6-protobuf-3.21.12/include/google/
3.8M    /nix/store/zadsncz62r5nd6wfsw77754lva7hzqvq-opencv-3.4.18/include/opencv2/
4.5M    /nix/store/zzwqcifb2hgm1qii6ixh86cjryzlf5zh-linux-headers-6.1/include/linux/
5.2M    /nix/store/5wb6fwc2g1f1pjq5hasnlz7y64b8g7by-msgpack-3.3.0/include/msgpack/
6.1M    /nix/store/yyahnn0xnwcshm8a21zqgg5b4pca7z7f-rocm-llvm-llvm-5.4.2/include/c++/
6.8M    /nix/store/j38c32v53zmn3ipy9cc5g806fyxc5fvv-opencv-4.7.0/include/opencv4/
11M     /nix/store/z1yw9cw97bcdfmxz5wk7j1bycw42r1mw-gcc-11.3.0/include/c++/
18M     /nix/store/9c4r6fcn5h15l1hzywf7w8aaqyvd9fhi-rocm-llvm-clang-5.4.2/include/clang/
21M     /nix/store/6d9cdiv9dlg635wfazr1bh4x5x4sq5jx-rocm-llvm-llvm-5.4.2/include/llvm/

@GrahamcOfBorg build cp2k d-seams

@markuskowa both build for me locally, are those just 2 packages you picked randomly or have they had issues when gsl/tbb have been updated

Yep, works for me too locally.
Those are two packages I care about, that's why checked them.
Good to merge from my side.


no 'session' and TypeError

Two questions come up when I run run_bert_coqa.py.
Traceback (most recent call last):     File "/data2/wangfuyu/NQ/ycl/SMRCToolkit-master/sogou_mrc/model/base_model.py", line 21, in __del__       self.session.close()   AttributeError: 'BertCoQA' object has no attribute 'session'
 File "/data2/wangfuyu/NQ/ycl/SMRCToolkit-master/examples/run_coqa/run_bert_coqa.py", line 37, in <module>                                   model = BertCoQA(bert_dir=bert_dir,answer_verificatioin=True)                        TypeError: __init__() got an unexpected keyword argument 'answer_verificatioin'
The version of my python is 3.6.8
Could anyone give me some advices?

By the way, what's the difference between run_bert_coqa.py and run_bert_coqa_base.py?

run_bert_coqa_base train model based on bert without answer verification. Sorry for misspelling the parameter, it should be answer_verification,

Thanks a lot.


Standardize Engine image on a single base OS

This change is meant to inform us of the following:

How does the CI behave after a few days and at least 5 runs using this new image?
What changes are required to make this work?
What are the unknowns?

Some of the obstacles that we encountered so far:

If we pin the Wolfi image using a SHA256, what guarantees do we have that this will remain available?
How do we consume a specific version of Go which is based on the Wolfi base image? The only option that I could find is "Contact us" meaning reach out to the Chainguard team.

Other note-worthy changes:

The GPU image variant is based on Wolfi (standardizing is nice)
We are still publishing the Ubuntu GPU variant as *-ubuntu-gpu
The Alpine variant is now suffixed with *-alpine. We can get the behaviour before this change by using that image variant.
We have minimized the glibc difference between Ubuntu & Debian by bumping to Debian 12 (Bookworm). More details in the comment.
We are now pinning the Alpine & Golang images using SHA256 digests (tags can change, we don't want to leave this to chance)
Bumped Dagger to v0.11.9 in ci

This adds on top of:

https://github.com/dagger/dagger/pull/7781

It closes:

https://github.com/dagger/dagger/issues/5668

After https://github.com/dagger/dagger/pull/7781 is merged, the second part of this change should start working, namely the CI change. My current though is to have this commit removed after we gather enough data about how this behaves. We don't want to merge this unless we are happy to always test against an unreleased version of the Engine. While I really like that idea, I'm not sure if this PR is the right one to introduce this change in. After all, this is still an experiment.

If we pin the Wolfi image using a SHA256, what guarantees do we have that this will remain available?

That should be fine -- we've never deleted old images.


⚠ ClamAV has degraded performance

In 9c871af, ClamAV (https://spamassassin.apache.org/) experienced degraded performance:

HTTP code: 200
Response time: 229 ms

Resolved: ClamAV performance has improved in efdabc2.