tz::gl Documentation

Once Basic tz::gl functionality is implemented, make sure that documentation for it is upto-scratch.
Consider writing a design document about them and how it relates to OpenGL too.

Update: This is in progress.
Exhaustive list of current features:

tz::gl::Buffer, which can be used to read-and-write from VRAM.
tz::gl::Object, which manages tz::gl::Buffers
Good test coverage of existing features

List of additional upcoming functionality (only OTOH, plenty more incoming too):

More features of tz::gl::Buffer
Specialisations for SSBOs, UBOs etc...
tz::gl::Shader
tz::gl::ShaderPreprocessor (TZGLP)
tz::gl::DrawCommand to allow tz::gl::Objects to actually be rendered using an existing tz::gl::Shader

Design document for tz::gl can be found here.

Basic tz::gl functionality is now complete and a vast majority of the contents of tz::gl now have decent doxygen comment documentation (although the documentation has not yet been generated).


Hipercard not work

Validation for the Hipercard card does not work

Hi @lucasborges01, thanks for the issue:
Is this the card you're referring to: https://www.hipercard.com.br/pf/seu_hipercard/fatura.html
If it's a standard credit card format it should work as they use a Luhn formula to calculate. We can investigate a little more if it is a credit/debit card that should work.

I dont kwnow if its help but here have some info about hipercard bin https://github.com/erikhenrique/bin-cc


code-server: 4.16.1 -> 4.18.0

Description of changes

Things done

Built on platform(s)

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

For non-Linux: Is sandboxing enabled in nix.conf? (See Nix manual)

[ ] sandbox = relaxed
[ ] sandbox = true

[ ] 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/)
23.11 Release Notes (or backporting 23.05 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

[ ] Fits CONTRIBUTING.md.

@code-asher hi, for some reason no matter how hard I try the yarn release command at the end wants to download a package from the outside world breaking the build.
do you know how I could suppress that behaviour?

Sorry for the delayed response, I have been out for a few weeks and am just now catching up.
Is that log from yarn release?  It seems to be running the post-install script which I think is only supposed to happen from a yarn install.  Unless...yarn release does run npm shrinkwrap, maybe that triggers the post-install script?
The post-install script runs yarn install so maybe we need to pass through the --offline flag to those invocations.  VS Code itself also runs a bunch of yarn install commands so those might need to be patched as well?
Or, if it is from the npm shrinkwrap, that whole thing can probably be omitted.  We only need it for publishing the NPM package.

@microsoft/1ds-core-js is supposedly already specified in the package.json by the way, or at least I do see it in lib/vscode/remote/package.json which is the one we use.

This is indeed caused by running yarn release
passing --offline to that doesn't seem to change much so I will check if in any post-install scripts there are any yarn install's that need patching

Closed by #275429.


update readme.md

Fix links (hpp repo instead of anna-seppala repo. Fix installation path

I close this pull-request since it is very old. Please, reopen if needed.


Refactor nn.optim

Just a nice one-liner using list comprehension. Also fixes all tests that previously imported get_parameters from tinygrad.nn.optim or similar - removing that useless import at the end of optim.py.

Changes made in tinygrad/:
------------------------------------------------------------
files                             insertions       deletions
------------------------------------------------------------
tinygrad/nn/optim.py                       2               6
------------------------------------------------------------
lines added in the tinygrad folder: -4

Very incomplete. get_parameters is used in optim in many more places.
Also, I don't generally like the list comprehensions that are only for side effects.

Very incomplete. get_parameters is used in optim in many more places.
Also, I don't generally like the list comprehensions that are only for side effects.

Alright I'll find and replace the rest of get_parameters usage in optim. I'll also drop the list comprehension I guess

I think I've replaced all instances of optim.get_parameters and optim.get_state_dict now.

Missing the change in docs/quickstart.md

Missing the change in docs/quickstart.md

Oh yeah - missed the docs :P


Fix luabitop on OSX

#6260

I can do that, but dont know how to best handle the macosx target and CC=gcc. See PR #6269

This is fixed by #8261, and is merged into master


tf.recompute_grad() throws dimension mismatched error when concatenating tensors with different number of channels

System information

Have I written custom code (as opposed to using a stock example script provided in TensorFlow): yes
OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Linux Ubuntu 16.04
Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device:
TensorFlow installed from (source or binary): binary
TensorFlow version (use command below): 2.0
Python version: 3.6
Bazel version (if compiling from source):
GCC/Compiler version (if compiling from source):
CUDA/cuDNN version: 10.0 / 7.6
GPU model and memory: GTX 1080 / 8GB

Describe the current behavior
My model is a segmentation model with DenseNet like structure. There are many concatenate operations between the encoder and decoder tensors. I want to recompute the gradients on these concatenate layers during backpropagation to limit the amount of GPU memory usage. A similar approach can be found here : https://github.com/joeyearsley/efficient_densenet_tensorflow.  I tried to use tf.recompute_grad() on a wrapper function which has a concatenate layer inside but it would raise an error when the channel dimensions of input tensors are not matched.
Describe the expected behavior
The concatenate layer should not raise an error when concatenating inputs with different number of channels.
Code to reproduce the issue
Provide a reproducible test case that is the bare minimum necessary to generate the problem.
https://colab.research.google.com/drive/1d7zSGbYmocnupUpDIJLnlXt-3vH5bi83#scrollTo=xcPT-MWZAuvK&uniqifier=1
Other info / logs
InvalidArgumentError                      Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/ops.py in _create_c_op(graph, node_def, inputs, control_inputs)
1609   try:
-> 1610     c_op = c_api.TF_FinishOperation(op_desc)
1611   except errors.InvalidArgumentError as e:
InvalidArgumentError: Dimension 3 in both shapes must be equal, but are 16 and 32. Shapes are [?,30,30,16] and [?,30,30,32].
From merging shape 0 with other shapes. for 'packed_7' (op: 'Pack') with input shapes: [?,30,30,16], [?,30,30,32].
During handling of the above exception, another exception occurred:
ValueError                                Traceback (most recent call last)
16 frames
 in ()
----> 1 model = test_model()
2 history = model.fit(train_images, train_labels, epochs=10,
3                     validation_data=(test_images, test_labels))
 in test_model()
4     x_list.append(layers.Conv2D(16, (3,3), activation='relu')(x_in))
5     x_list.append(layers.Conv2D(32, (3,3), activation='relu')(x_in))
----> 6     x = efficient_concat(x_list)
7     x = layers.Flatten()(x)
8     x = layers.Dense(10, activation='softmax')(x)
 in efficient_concat(input_list)
4         return x
5     wraper = tf.recompute_grad(wraper)
----> 6     return wraper(input_list)
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/custom_gradient.py in decorated(*args, **kwargs)
164     """Decorated function with custom gradient."""
165     if context.executing_eagerly():
--> 166       return _eager_mode_decorator(f, *args, **kwargs)
167     else:
168       return _graph_mode_decorator(f, *args, **kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/custom_gradient.py in _eager_mode_decorator(f, *args, **kwargs)
333
334   input_tensors = [ops.convert_to_tensor(x) for x
--> 335                    in list(args) + list(variables)]
336   arg_count = len(args)
337   def actual_grad_fn(*result_grads):
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/custom_gradient.py in (.0)
332   flat_result = [gen_array_ops.identity(x) for x in flat_result]
333
--> 334   input_tensors = [ops.convert_to_tensor(x) for x
335                    in list(args) + list(variables)]
336   arg_count = len(args)
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/ops.py in convert_to_tensor(value, dtype, name, preferred_dtype, dtype_hint)
1182   preferred_dtype = deprecation.deprecated_argument_lookup(
1183       "dtype_hint", dtype_hint, "preferred_dtype", preferred_dtype)
-> 1184   return convert_to_tensor_v2(value, dtype, preferred_dtype, name)
1185
1186
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/ops.py in convert_to_tensor_v2(value, dtype, dtype_hint, name)
1240       name=name,
1241       preferred_dtype=dtype_hint,
-> 1242       as_ref=False)
1243
1244
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/ops.py in internal_convert_to_tensor(value, dtype, name, as_ref, preferred_dtype, ctx, accept_composite_tensors)
1294
1295     if ret is None:
-> 1296       ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
1297
1298     if ret is NotImplemented:
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/array_ops.py in _autopacking_conversion_function(v, dtype, name, as_ref)
1276   elif dtype != inferred_dtype:
1277     v = nest.map_structure(_cast_nested_seqs_to_dtype(dtype), v)
-> 1278   return _autopacking_helper(v, dtype, name or "packed")
1279
1280
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/array_ops.py in _autopacking_helper(list_or_tuple, dtype, name)
1182     # checking.
1183     if all(ops.is_dense_tensor_like(elem) for elem in list_or_tuple):
-> 1184       return gen_array_ops.pack(list_or_tuple, name=name)
1185   must_pack = False
1186   converted_elems = []
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/gen_array_ops.py in pack(values, axis, name)
6302   axis = _execute.make_int(axis, "axis")
6303   _, _, _op = _op_def_lib._apply_op_helper(
-> 6304         "Pack", values=values, axis=axis, name=name)
6305   _result = _op.outputs[:]
6306   _inputs_flat = _op.inputs
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/op_def_library.py in _apply_op_helper(self, op_type_name, name, **keywords)
791         op = g.create_op(op_type_name, inputs, dtypes=None, name=scope,
792                          input_types=input_types, attrs=attr_protos,
--> 793                          op_def=op_def)
794       return output_structure, op_def.is_stateful, op
795
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/func_graph.py in create_op(failed resolving arguments)
546     return super(FuncGraph, self)._create_op_internal(  # pylint: disable=protected-access
547         op_type, inputs, dtypes, input_types, name, attrs, op_def,
--> 548         compute_device)
549
550   def capture(self, tensor, name=None):
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/ops.py in _create_op_internal(self, op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_device)
3427           input_types=input_types,
3428           original_op=self._default_original_op,
-> 3429           op_def=op_def)
3430       self._create_op_helper(ret, compute_device=compute_device)
3431     return ret
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/ops.py in init(self, node_def, g, inputs, output_types, control_inputs, input_types, original_op, op_def)
1771           op_def, inputs, node_def.attr)
1772       self._c_op = _create_c_op(self._graph, node_def, grouped_inputs,
-> 1773                                 control_input_ops)
1774     # pylint: enable=protected-access
1775
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/ops.py in _create_c_op(graph, node_def, inputs, control_inputs)
1611   except errors.InvalidArgumentError as e:
1612     # Convert to ValueError for backwards compatibility.
-> 1613     raise ValueError(str(e))
1614
1615   return c_op
ValueError: Dimension 3 in both shapes must be equal, but are 16 and 32. Shapes are [?,30,30,16] and [?,30,30,32].
From merging shape 0 with other shapes. for 'packed_7' (op: 'Pack') with input shapes: [?,30,30,16], [?,30,30,32].

Issue replicating when tried replicating for code given in the gist of colab for TF-2.0 and tf-nightly. Thanks!

@ChunHsinWang The weights of the model depend on the number of channels. Changing channels is changing weights. Changing weights is having a completely new model.

@gowthamkpr thanks for the reply, but concatenate layer does not change the number of channels, it merges channels from the list of input tensors, so the total number of weights in the model does not change.  To my understanding, TensorFlow's concatenate layer will always allocate new memory and copy from the input tensors, and the memory consumption grows quadratically I think. What I'm trying to achieve is to use tf.recompute_grad for having concatenate layers to use a shared memory space, so the output of every concatenate layer does not allocate new memory. During the backward pass, these outputs will have to be recalculated for calculating the gradients. The following figure from this paper https://arxiv.org/abs/1707.06990 demonstrates the process.

Issue replicating when tried replicating for code given in the gist of colab for TF 2.2.0-rc3.Please, find the gist here. Thanks!

The issue seems to be fixed, so I will close it. Thanks.


lacework-agent uses default service account by default

By default, Lacework uses the default service account, which is something that CIS Benchmark recommends against:

Create explicit service accounts wherever a Kubernetes workload requires specific access to the Kubernetes API server.

I noticed that a serviceAccountName has been added to laceworkConfig. However, this appears to be specific to OpenShift, and assigning a value to this will not create a ServiceAccount resource with this name.

Hi @joebowbeer, thanks for raising this. By default, the Lacework agent does not access the Kubernetes API server.
However, we do have a feature in preview where a Lacework workload will access the Kubernetes API server. If that feature is enabled in the Helm Charts, then the charts will create a service account for the workload that is accessing the API server.
Does this make sense and does this satisfy your concern?

Thanks for clarification.
Because lacework is not naming its own service account, it is using the default one.
I think it would be better to be explicit and create a dedicated one.


Updated list() to use HashMap for collisions

Developer's Certificate of Origin 1.1
   By making a contribution to this project, I certify that:

   (a) The contribution was created in whole or in part by me and I
       have the right to submit it under the Apache License 2.0; or

   (b) The contribution is based upon previous work that, to the best
       of my knowledge, is covered under an appropriate open source
       license and I have the right under that license to submit that
       work with modifications, whether created in whole or in part
       by me, under the same open source license (unless I am
       permitted to submit under a different license), as indicated
       in the file; or

   (c) The contribution was provided directly to me by some other
       person who certified (a), (b) or (c) and I have not modified
       it.

   (d) I understand and agree that this project and the contribution
       are public and that a record of the contribution (including all
       personal information I submit with it, including my sign-off) is
       maintained indefinitely and may be redistributed consistent with
       this project or the open source license(s) involved.

@gilv I tried simplifying the logic in list() by using a HashMap to check for collisions. It currently passes all existing tests. Can you take a look at this?

@djalova The list will always return objects in alphabettical order, otherwise it will not know how to page them. This is fundamental in the object stores - to return list in this order


78000FTHR "Insert SD card to continue" Not identifying SD card

I am trying to run both SDHC_weights and facial_recognition on my MAX78000FTHR. I have checked that my Micro SD card is correctly formatted with FAT32 and empty before inserting it into the board with the metal contacts facing the board. When I build the projects, they receive no errors or warnings. When they are loaded, I receive the expected outputs waiting for the SD cards to be inserted. For SDHC, I have tested with two different empty micro SD cards without partitions. I have also tried facial_recognition with empty SD cards as well as with the weights_2.bin file in the root directory and received the same waiting prompt. I have tested this by having the SD card already inserted upon running and inserting the SD card after the program has begun. I have tested this two boards with the same results.
I see here that the MAX780000FTHR is not included in the SDHC usage although I know these programs are both ( 1, 2) specifically tested for this board. I am developing from the repo and not from the maintenance tool so I am up fully updated as of this morning, 6/27/2023. This leads me to believe there is a setting I have not changed or I am building somehow incorrectly. To build, I am simply running ‘ make -r -j ’ Are there any additional changes to make?
Thank you!

Thanks @ryalberti
As part of our functional testing before release I have been tracking any issues in the fix/functional-tests branch.
Good catch in the UG, I added the MAX78000 to the list of SDHC supported parts (https://github.com/Analog-Devices-MSDK/msdk/pull/641/commits/056b0e51cd29f2f6711a95680df0f1f378a432c4)
On the example - you are building it correctly.  The default build settings will work.
I was able to run the SDHC_Weights project, which formatted and wrote to the card.
***** MAX78000 Writes CNN weights to SD card *****
Card inserted
SD card mounted.
SD Card Opened
Opened file 'weights_2.bin'
176844 total bytes to write
3000 bytes written to file
3000 bytes written to file
...
3000 bytes written to file
2844 bytes written to file
176844 total bytes written to file
File Closed
SD Unmonted

Afterwards, I ran the facial_recognition project successfully.
D[main      : 103]

MAX78000 Feather Facial Recognition Demo

D[main      : 124] Camera I2C slave address is 3c

D[main      : 134] Camera Product ID is 7692

D[main      : 144] Camera Manufacture ID is 7fa2

Card inserted
SD card mounted.
SD Card Opened
D[facedetection: 158] CNN load data time : 25ms
D[facedetection: 170] CNN wait time : 19ms
D[facedetection: 158] CNN load data time : 24ms
D[facedetection: 170] CNN wait time : 19ms
D[facedetection: 158] CNN load data time : 25ms
D[facedetection: 170] CNN wait time : 19ms
D[facedetection: 158] CNN load data time : 25ms

So the example seems to be functional.  Perhaps there is a difference in SD cards that we haven't caught.  Below is the card I'm using.  What is yours?

Thanks @ryalberti, looks like the 32GB capacity is the only major difference.  I'll order some higher capacity cards to test

Hi Jake, I found an 8GB which yielded the same results on my board.

I do have a second FTHR board I can test with. The second board used the above Micro Center sd and yielded the same issue, though it used the same build files as the first FTHR that tested the Samsung, SanDisk, and Netac.

Can you zip up your .elf file and attach it?  I'll test your binary on my working board

I just flashed a Sandisk Ultra Plus 32 GB micros SDHC card and was unable to reproduce the issue. This is similar to the SDHC card @ryalberti used.

Try looking at the Updating the MAX32625PICO ("PICO") Debug Adapter Firmware section in this link
Also, these are the parameters I set before formatting the micro SDHC.

I put the listed firmware onto the board and received the correct Git BASH, and copied your parameters when formatting my SD card. I still received the same results. Attached is my .elf from my computer.
max78000.zip
On the second board, we build the project from a previous build of the elf (though I have changed no values, I have updated the repo) and one build on another computer with the same update. I will be unable to test the above directions on the other board until tomorrow.
Thank you for your patience!

I was able to test the second board this morning, flashing with the above settings yielded the same results. Attached is the .elf used for this board. The computer that built this .elf is not developing from the repo, it is using the official release.
max78000.zip

I ran the above .elf file and I wasn't able to reproduce that issue.
I got the below output.

I think this might still be the way the SDHC is being formatted try running the SDHC_FTHR example (msdk/Examples/max78000/SDHC_FTHR) and select the Format option. Hope this helps.

We tried on both boards and neither of them are detecting an SD card being inserted at all. The first board was testing using the SanDisk (32gb) and the Netac (8gb) and the second was using the same MicroCenter card. I was initially under the impression that if it was two boards, then it must have been a software issue. Does it appear that this is a hardware issue and we should be replacing the board?

Given that flashing your binary on my board also works...
D[main      : 103]

MAX78000 Feather Facial Recognition Demo

D[main      : 124] Camera I2C slave address is 3c

D[main      : 134] Camera Product ID is 7692

D[main      : 144] Camera Manufacture ID is 7fa2

Card inserted
SD card mounted.

You've formatted your card with the same options as above
You've flashed and run the SDHC_weights project on the card and that fails
You've flashed and run the SDHC_FTHR example and that also fails
I can run your same binary on my board and it works
Multiple SD card sizes work on our side

The symptoms seem to indicate hardware-related failure on the SD card interface.  It's shocking to me that both your boards have it. I've never seen this happen before, but I'm not sure what else to try.
Shoot me an email to<EMAIL_ADDRESS>with your shipping info and I'll see about getting you some tested FTHR boards asap.

Hardware issue verified


Type inference in generic function-parameter types

Bug Report
🔎 Search Terms
inference generic function-parameter extends return type arrow function
🕗 Version & Regression Information
4.1.3
⏯ Playground Link
Playground Link
💻 Code

 // - here A extends (s: S, p?: any) => S - we want to return our "model" type  as result
export function createModel<S, A extends (s: S, p?: any) => S>({
    model,
    actions,
}: {
    model: S,
    actions: A
}) {
    return {
       model,
       actions
    }
}

type TModel = {
    name: string
    isActive: boolean
}

const model: TModel = {
    name: '',
    isActive: false
}

const R = createModel({
    model: model,
    actions: (model, p) => ({ // - model is TModel model here and its what i expect.
        ...model,
        meta: p,  // - here i return invalid model field, and haven't error, i expect TypeError
    }),
})

// if i delete spread,  it's works  - i have TypeError
const R = createModel({
    model: model,
    actions: (model, p) => ({ // - Type '{ meta: any; }' is missing the following properties from type 'TModel': name, isActive
        meta: p,
    }),
})

// if try to return primitive (for example - number), it's works  - i have TypeError
export function createModel<S, A extends (s: S, p?: any) => number>({  ......

const R = createModel({
    model: model,
    actions: (model, p) => ({ // - Type '{ meta: any; name: string; isActive: boolean; }' is not assignable to type 'number'.
        ...model,
        meta: p,
    }),
})

🙁 Actual behavior
In "action" function  i have types this function return, and this does not depend on the types specified in createModel.
🙂 Expected behavior
In "action" function  i have return type what i define in generic of createModel function.
Comment: maybe it's not a bug, and i just don't know something about typescript inference

I don't see any bug demonstrated here; this is normal inference results. A shorter example with a clearer explanation of why you expected / didn't expect an error would be useful. Excess properties are not always disallowed so the allowing of meta there is expected.

As i said before i just don't knew something about typescript
Here is the the solution - https://stackoverflow.com/questions/67889090/type-inference-in-generic-function-parameter-types
I close issue


Add super-naive compilation

Add test of super naive compilation (pure code without partial eval, rewrite and so on)

ref #128


fix off-by-one error when looking up boundary element attributes

thanks to @kswartz92 for noticing an error in a surface area calculation and providing a minimal reproducer

Codecov Report
All modified and coverable lines are covered by tests :white_check_mark:

Project coverage is 90.59%. Comparing base (0084a2e) to head (9cf9099).

Additional details and impacted files
@@           Coverage Diff            @@
##           develop    #1114   +/-   ##
========================================
  Coverage    90.59%   90.59%
========================================
  Files          161      161
  Lines        14412    14412
========================================
  Hits         13057    13057
  Misses        1355     1355

:umbrella: View full report in Codecov by Sentry.
:loudspeaker: Have feedback on the report? Share it here.

Would it make sense to add the example I provided as a test?

I don't want to pile on, but I agree with Kenny and Mike, this should be covered with a unit test. Sam, if you're busy with the Functional refactor, I can take that up.


Unable to make it working

Hi Kamil,
Really awesome angular module :+1: :)
But I'm having hard time to make it work. Can u look into this.
http://jsbin.com/fidukawozo/edit?html,js,output
What I'm trying to do like trello like list move/drag-drop, also in between single list and between lists as well but I think I'm not getting it right.
Really appreciate your kind insight into this, if is it possible and if I'm in right direction somehow.
Thanks

I have resolved the issue.


[Feature request]: Add details to tab click event

Request type
Request for enhancement of a component
Functionality
When clicking a tab-title, the tab-group is emitting an event.
However, the event is lacking details; e.g. we do not know which item has been clicked.
We would have to go through all the children and find the active one.
We propose to instead have the new activeIndex/tab-title element directly in the Event (event.details).
Is there a design proposal?
No
Link to design proposal file (optional)
No response
Preflight Checklist

[X] I have read the Contributing Guidelines for this project.
[X] I agree to follow the Code of Conduct that this project adheres to.
[X] I have searched the issue tracker for an issue that matches the one I want to file, without success.

Closed by #2744


.net framework 4.5 , for support Tls12

support tls1.2

Any word if this PR will get merged?

I didn't get any word

Agree, it will be great if this PR can be merged.

Merged, thanks @juster111


[flink][logsystem] Introduce log store register interfaces

Purpose
Linked issue: close #1532
Introduce log system register interfaces to create and delete topic for paimon table ddl
Tests

Added test FlinkCatalogTest.testCreateTableWithLogSystemRegister to register and unRegister to logsystem

API and Format
no
Documentation
no

@JingsongLi Thanks for your advice, I have updated this PR and please help to review it again

Can this option be a catalog option?

Good point, then we can fix the heavy operation for unregister !

@JingsongLi I have rebased master and update this PR, please help to review it again, thanks.


.py in .rst versus .rst in .py

In GitLab by @Huite on Jul 1, 2019, 12:02
In taking a look at another package: https://github.com/fatiando/verde
It looks like you can set .rst in .py files as well, rather than just the other way around:
Input:
https://github.com/fatiando/verde/blob/master/tutorials/trends.py
Output:
https://www.fatiando.org/verde/latest/tutorials/trends.html
This seems to me like a much easier way to work, since the scripts are automatically fully runnable.
(This also looks like an easier to handle alternative to jupyter notebooks to me. Although this is around now as well:
https://github.com/mwouts/jupytext)

In GitLab by @Huite on Dec 20, 2021, 21:25
Both are in use now.

In GitLab by @visr on Jul 23, 2019, 14:50
removed milestone

In GitLab by @Huite on Jul 5, 2019, 15:44
Additionally, while we could run our examples using an actual compiled seawat binary, this doesn't seem all that straightforward. Instead, we could store the result online somewhere, and include a download in the script using some half-public functionality from xarray:
https://github.com/pydata/xarray/blob/master/xarray/tutorial.py


Sometimes I am experiencing some micro stutters while the skin changer is working. Is there a fix for it?

A notable moment was hitting Illaoi's E onto someone it makes my game stutter for a split second. Is there a fix to overall micro-stutters here and there?

I also get huge FPS drops while using the skin changer is on.

Same. When it happens, I press alt tab and it helps.

Enabling DirectX 9 should fix your problem

This depends on your overall system. Enabling DX9 (it's an old DirectX version that usually works better on very old hardware) does not necessarily mean better performance. DX 11 is the standard for league for a reason, it work's better and it's faster. It's normal that you have a performance drop.

Basing on the code I've seen and worked with this skin changer is active only in 2 cases:

1st when game started and you injected it, it will change your skin on injection and that's it.
2nd is when you change the skin via imgui menu.

In any other case this dll just idles doing nothing, which shouldn't cause any lags.
However if you experience such thing I've nothing to recommend, just letting you know how it works in general.

this probably won't help much because its been over a month. BUT the fix for me was just holding down pgup and holding pgdown so youd load in ALL the skins, after i did that it stopped stuttering for me.

Strange, i just have stuttering when the skin window is opening, nothing else.

ur CPU might be heavily utilized, close useless programs or just upgrade the cpu.


Workflows: UI problem with Firefox browser

Description: If a workflow setting is activated the check box position changes. If the menu is closed and opened again, the check box is correctly set and the settings works. Just a small UI bug.
Check box (disabled):

Check-box activated:

Reproduction:
0. Use Firefox

open a meeting
open motions > three-dot-menu > workflows
change a setting

What should happen:
The check box should stay on it's position during edit.

Can't replicate
@MSoeb
Are there some other factors that may take part in this?
If not please close this

Also cannot replicate. Used FF 115.6.

@MSoeb ping

I am closing this now as we did not receive a response and also the checkbox components got replaced.


Context in SLACK_SUCCESS_REDIRECT_URL

Hi @izdi,
Thanks for the awesome package! I've been using it in a personal project, but I'm unsure of how to access the current users Slack information in the SLACK_SUCCESS_REDIRECT_URL. While it is being stored in the database, getting the "most recently created access token" after redirection seems to introduce all sorts of race conditions. When checking request.user in the redirected view, all it displays is AnonymousUser. I assumed any information set on the request object would be available, but it looks like creating a new HttpResponseRedirect (views.py#L117) also creates a new request object, wiping any information stored on it from your pipelines. What's your recommended solution to this problem?
Thanks!

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Fix crash with TextEdit selection mode

Fixes https://github.com/godotengine/godot/issues/54195
Moved the code for column to be inside the check for positive lines, otherwise selection.selecting_line can be an unitialized or invalid value.

I've clamped the value for when the situation you described happens :+1:

My bad, should have specified earlier but we should still allow users to specify selection.selecting_column separately rather then nesting it, as it is here.

I've tried another way of fixing this problem, hopefully much simpler than what I had before :slightly_smiling_face:

Thanks!


Implement Zone File Processing

@bvandersloot going to assign this to you as it sounds like you're already working on implementing this.


Two buttons blank

Nice plugin, but the two left buttons are blank with my installation.

Running OctoPi 0.17.0, OctoPrint 1.4.0, Change Filament 0.3.1

What other plugins are you using? Last time that came up, it was from the Themeify plugin, but it could also be something else in your browser.

Thanks for that hint, it's actually Themeify that troubles.

OK -- So it's the same as #8
Themeify needs to fix that glitch, nothing I can change in the code here can help that.


Add SignalR function extension

The first commit of SignalR extension
Add ci.yml

Description

This checklist is used to make sure that common guidelines for a pull request are followed.

[x] Please add REST spec PR link to the SDK PR
[x] I have read the contribution guidelines.
[x] The pull request does not introduce breaking changes.

General Guidelines

[x] Title of the pull request is clear and informative.
[x] There are a small number of commits, each of which have an informative message. This means that previously merged commits do not appear in the history of the PR. For more information on cleaning up the commits in your PR, see this page.

Testing Guidelines

[x] Pull request includes test coverage for the included changes.

SDK Generation Guidelines

[ ] If an SDK is being regenerated based on a new swagger spec, a link to the pull request containing these swagger spec changes has been included above.
[ ] The generate.cmd file for the SDK has been updated with the version of AutoRest, as well as the commitid of your swagger spec or link to the swagger spec, used to generate the code.
[ ] The *.csproj and AssemblyInfo.cs files have been updated with the new version of the SDK.

/azp run prepare-pipelines

/azp run prepare-pipelines

/azp run net - signalr - ci

API changes have been detected in Microsoft.Azure.WebJobs.Extensions.SignalRService. You can review API changes here

API changes have been detected in Microsoft.Azure.WebJobs.Extensions.SignalRService. You can review API changes here

The class SignalROptions is removed in 1.3.0, but the base API version is 1.2.2. I would like to know how the base version is computed.


add cors header on get response

Allow simple requests (those made without a preflight request) to access the files from a cross-origin when the cors configuration is enabled.
Improve CORS test coverage for POST and PUT methods.

Rationale: while using this project to serve local files in a development environment, I found the need to have this to simplify the developer experience.

@felipelube sorry for late reply.
Now your changes are available in latest image.
Thank you for your contribution!
P.S. The "publish" check in this PR was failed but it is normal behavior (but unexpected). I'll disable it in PR build...


Add 'stale' bot for improved maintenance

Description
As the project grows, some issues will be opened that become 'stale.' We could implement this bot to close these issues and PR's after a certain period of inactivity. Might not be necessary now, something to keep in mind for the future.
https://probot.github.io/apps/stale/

closed with https://github.com/tinacms/tinacms/pull/2619


BadLinqExpressionException when calling an Any/Contains chain with an object array (v7)

Marten.Exceptions.BadLinqExpressionException
Marten does not (yet) support contains queries through collections of element type LinqTests.Bugs.linq_conditionals.NestedObject

public record NestedObject(Guid[] Guids, Guid[] MoreGuids, List<NestedObject> Obj);
public record ObjectWithGuids(Guid Id, NestedObject NestedObject, string SomeText);

[Fact]
public async Task broken_linq_condition_2()
{
    var id = Guid.NewGuid();

    await theSession.Query<ObjectWithGuids>().Where(x =>
            x.NestedObject.Obj.Any(obj => obj.Guids.Contains(id)))
        .ToListAsync();
}

@xvrlsc Open a new issue with a reproduction. This specific failure scenario was fixed at the time.


MAINT, DOC: make dist pipefail issue

gh-21361 generally simplified the release process, but I did want to confirm that https://github.com/scipy/scipy/pull/21361#discussion_r1731956848 isn't a problem that will affect the release process in any way:

When I run make dist locally on this branch (I still use it to build docs for releases) I see /bin/sh: 1: set: Illegal option -o pipefail on this branch.

That said, make dist still succeeds in the end, and the sdist build is also fine on this branch locally, so I'll go ahead and merge and just open a ticket for the pipefail thing just in case so I don't discover an issue during a rel process (if there is one).

If this is irrelevant just close. If we just need some extra shell prefix thing, should be quick to deal with I'd think.

Makefile uses POSIX shell on Linux by default and /bin/sh does not have a set -o pipefail. You can see the same error running sh -c "set -o pipefail".
Note that /bin/sh is bash on macOS, so it does not have an issue there.


Merge fails with TypeError

Hey there, I want to report the following bug.
In case I am not correct and this is wrong use on my end, I wonder if documentation needs to be updated.
All the best!

Python 3.8.5, traces 0.6.0
import traces
ts = [traces.TimeSeries(), traces.TimeSeries()]
ts[1][2] = 3
traces.TimeSeries.merge(ts, operation=sum)

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/local/lib/python3.8/site-packages/traces/timeseries.py", line 778, in merge
    default = operation(default)
TypeError: unsupported operand type(s) for +: 'int' and 'NoneType'

I found a work around for now. It would seem that NoneType is produced probably when data is not present for the time interval being merged and aggregated. Passing in a custom aggregate function that forces NoneType to 0 worked for me:
import traces

def custom_func(x):
    x_fillna = [i if i else 0 for i in x]
    return sum(x_fillna)

ts = [traces.TimeSeries(), traces.TimeSeries()]
ts[1][2] = 3
traces.TimeSeries.merge(ts, operation=custom_func)

@ThomDietrich thanks for opening this issue! It's a great idea to improve the documentation about this.
(The Exception is the desired behavior in this case. The first time series has the default value None for all times, and the second is None for all times before 2, and then 3 afterwards. At, for example, t=4, merge with operation=sum is summing None + 3, and does not automatically assume that Nones should be ignored).
Thanks @tasq-inc for posting a solution. This solution or something similar is exactly what I'd suggest using if you want to ignore Nones.


Created a requirements text file with useable libaries.

Drafted a requirements.txt file from previous assignments for main repository that contains the useable packets including cython and setuptools. This can be edited to include further libraries as needed.

@HeshamElsaman It seems the Flake8 linter is showing the linting errors here for the entire repository instead of just for this request. Could this be an issue with the workflow or is this running as intended?

@HeshamElsaman It seems the Flake8 linter is showing the linting errors here for the entire repository instead of just for this request. Could this be an issue with the workflow or is this running as intended?

I'll work on that.

@HeshamElsaman It seems the Flake8 linter is showing the linting errors here for the entire repository instead of just for this request. Could this be an issue with the workflow or is this running as intended?

Intended since it wasn’t listed before and everything that fits your github actions filter will be linted.


implement OpenGL abstraction layer

If I have questions about your implementation, where can I ask them? Only during the stream, here in the issues or do you plan to open the discussion?

If I have questions about your implementation, where can I ask them? Only during the stream, here in the issues or do you plan to open the discussion?

I activated the "discussions" section on this repo. Feel free to open a thread there 🙂


Get back to 100% coverage

Coverage has dipped below 100% due to the https://github.com/60frames/webpack-hot-server-middleware/commit/958d8975cc9646ad1fff4c598fb4f5b0e9ec60e5 because the source file always exists.
I think the only way to get into the catch would be to trigger an error somewhere other than the webpack bundle although that's not particularly useful as a test so could just put some instanbul ignore comments in the catch.
It'd also be nice to look into testing source maps more seriously buy trigger an error and checking the stack trace is mapped to the source files.

Turns out coverage hasn't reduced.
https://coveralls.io/github/60frames/webpack-hot-server-middleware?branch=master


Requirement already up to date when running pip install --upgrade tensorflow. Stuck at version 1.14.0

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: Rasbian GNU/Linux 10
TensorFlow installed from: python3 -m pip
TensorFlow version: 1.14.0 trying to get to 2.+
Python version: 3.7.3
Installed using virtualenv: pip
CUDA/cuDNN version: Not sure, this is an off-the-shelf Raspberry Pi 4
GPU model and memory: ^

Describe the problem
Provide the exact sequence of commands / steps that you executed before running into the problem
I tend to write scripts on my MacBook Pro and transfer them to my Raspberry Pi to run indefinitely. I set up my first LTSM network (so excited) and got it predicting and training. Brought it to the Pi, it threw an error while training complaining about the shape. After much googling I found a github issue where someone said this was fixed in tensorflow 2. Realized the pi was running tensorflow 1.14.0.
Long story short I've gone through all of the system requirements (upgrade pip, python is correct version, raspbian is correct version) and when I run pip install --upgrade tensorflow the console prints Requirement already up to date: tensorflow.... and tensorflow remains at 1.14.0.
Any other info / logs
I figure this is a system requirement issue but it seems like the Raspberry Pi satisfies everything.

I've been troubleshooting this for a few hours now. I tried to manually download the pip wheels and for the linux wheel and both rpi wheels that I tried pip install throws "xxxx.whl is not a supported wheel on this platform"
Is it because I am using a Raspberry Pi 4 and not a Raspberry Pi 3?

Hi There,
We are checking to see if you still need help on this, as you are using an older version of tensorflow which is officially considered end of life . We recommend that you upgrade to the latest 2.x version and let us know if the issue still persists in newer versions. Please open a new issue for any help you need against 2.x, and we will get you the right help.
This issue will be closed automatically 7 days from now. If you still need help with this issue, please provide us with more information.

The issue is still relevant since there are no working Tensorflow 2.x builds for Python 3.7 (or newer) on the Raspberry Pi.

Again: Please reopen the issue, it's still not resolved.
I mean I would upgrade to TF2.x, but it's either not available or not working.

Please go through this link for reference . Can you also try with these follow steps
1.upgrade your pip to latest version
2.sudo pip install --upgrade tensorflow
if that does not work , uninstall then install latest version from above link.


⚠ Bofrost.de has degraded performance

In 2fcddd7, Bofrost.de (https://www.bofrost.de) experienced degraded performance:

HTTP code: 200
Response time: 1898 ms

Resolved: Bofrost.de performance has improved in 6dbe2c8 after 6 minutes.


Cant run any example after 1_4_1

thread '<unnamed>' panicked at 'attempted to leave type `nodrop::NoDrop<(epoch::Epoch, garbage::Bag)>` uninitialized, which is invalid', /home/nick/.rustup/toolchains/stable-x86_64-unknown-linux-gnu/lib/rustlib/src/rust/library/core/src/mem/mod.rs:671:9
note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace
Segmentation fault

os : linux debian 10
version: rustc 1.51.0

how'd you fix it?  i ran into this same error. also on 1.51.0 on arch linux

I compiled the lesson source code  as a different project and somehow it worked

I'm having the same issue, on 1_4_1. Running Manjaro/arch linux.


Putting  inside  is illegal.

Putting block elements inside inline elements is against CSS standard. It's a pretty common mistake that often causes cross-browser problems.
Found while debugging https://bugs.chromium.org/p/chromium/issues/detail?id=598266

Good point.

Resolved.


Undesirable $access$ methods in some case classes in metac and metacp

case class C(private val x: Int)

--- expected
+++ actual
  symbols {
    symbol: "_empty_.C#"
    kind: CLASS
    properties: 128
    name: "C"
    tpe {
      tag: CLASS_INFO_TYPE
      classInfoType {
        parents {
          tag: TYPE_REF
          typeRef {
            symbol: "scala.AnyRef#"
          }
        }
        parents {
          tag: TYPE_REF
          typeRef {
            symbol: "scala.Product#"
          }
        }
        parents {
          tag: TYPE_REF
          typeRef {
            symbol: "scala.Serializable#"
          }
        }
        declarations: "_empty_.C#x()."
        declarations: "_empty_.C#`<init>`()."
        declarations: "_empty_.C#copy()."
        declarations: "_empty_.C#copy$default$1()."
+        declarations: "_empty_.C#x$access$0()."
        declarations: "_empty_.C#productPrefix()."
        declarations: "_empty_.C#productArity()."
        declarations: "_empty_.C#productElement()."
        declarations: "_empty_.C#productIterator()."
        declarations: "_empty_.C#canEqual()."
        declarations: "_empty_.C#hashCode()."
        declarations: "_empty_.C#toString()."
        declarations: "_empty_.C#equals()."
      }
    }
    accessibility {
      tag: PUBLIC
      symbol: ""
    }
    language: SCALA
  }

https://github.com/scala/scala/blob/v2.12.6/src/compiler/scala/tools/nsc/typechecker/SyntheticMethods.scala#L378-L410

In Scala 2.11, name synthesis for this case works differently. The corresponding symbol is called x$1 instead of x$access$0.

These methods are emitted by typer, but they don't seem to be used until patmat (https://github.com/scala/scala/commit/ff2cd805111b4fcd39ce7fb1f4bd0268a50faeee). Therefore, I conclude that they are indeed undesirable in SemanticDB. (Unlike e.g. default getters who are used in typer).


Full names for jobs in folders

Fixes #35
Resulting payloads to datadog, for a job named CD inside a folder named QA, are:
{"alert_type":"info","title":"QA » CD build #5100 started on jenkins","text":"%%% \n Follow build #5100 progress  \n %%%","date_happened":1477376660,"host":"jenkins","tags":["job:QA » CD"],"aggregation_key":"QA » CD","source_type_name":"jenkins"}
{"event_type":"build result","alert_type":"success","title":"QA » CD build #5100 succeeded on jenkins","text":"%%% \n See results for build #5100 (2,91 mins) \n %%%","date_happened":1477376834,"host":"jenkins","result":"SUCCESS","tags":["job:QA » CD","result:SUCCESS"],"aggregation_key":"QA » CD","source_type_name":"jenkins"}
{"series":[{"metric":"jenkins.job.duration","points":[[1477376835,174.67600011825562]],"type":"gauge","host":"jenkins","tags":["job:QA » CD","result:SUCCESS"]}]}
{"check":"jenkins.job.status","host_name":"jenkins","timestamp":1477376835,"status":0,"tags":["job:QA » CD"]}

This is excellent, thanks!

@JohnLZeller  @jniesen, any feedback on this?

@witokondoria  I'm following up with the team today and hope to have feedback for you with in the next week.

Thanks @witokondoria! I've rebased this to fix a merge conflict, and add a string normalizer so that we can digest this tag. The rebased merge is here: https://github.com/DataDog/jenkins-datadog-plugin/pull/77
Thanks again!

Awesome, thanks @JohnLZeller


Headless AI - Bunker - Strange friendly fire

Video reference: https://www.youtube.com/watch?v=W0LsoxKAGyM&ab_channel=ThePubHuB

Where: Single player
What: Bunker AI after around 5 seconds, upon my first wave of enemy spawning in, all turn and engage me for a few moments before returning to normal.

Please upload your mission so I can replicate this

Tested mission a few times, cannot replicate the strange friendly fire at the start as in the video


More caching of immutable data required

After some testing, that included modifying the goamz library to reuse connections, it seems s3 is just too slow. Please see the following table for requests and their response times:

Response Time
URL

390.063364ms
https://s3.amazonaws.com/docker-images-prod/?delimiter=&marker=&max-keys=1&prefix=registry-v2%2Fdocker%2Fregistry%2Fv2%2Frepositories%2Flibrary%2Fhello-world%2F_manifests%2Frevisions%2Fsha256%2F840559710d27cecc786ae6edc9ceb39e5d8697bb27b4c7917a404d7faaac83f0

154.370831ms
https://s3.amazonaws.com/docker-images-prod/registry-v2/docker/registry/v2/blobs/sha256/84/840559710d27cecc786ae6edc9ceb39e5d8697bb27b4c7917a404d7faaac83f0/data

140.791923ms
https://s3.amazonaws.com/docker-images-prod/?delimiter=%2F&marker=&max-keys=1000&prefix=registry-v2%2Fdocker%2Fregistry%2Fv2%2Frepositories%2Flibrary%2Fhello-world%2F_manifests%2Frevisions%2Fsha256%2F840559710d27cecc786ae6edc9ceb39e5d8697bb27b4c7917a404d7faaac83f0%2Fsignatures%2Fsha256%2F

142.28401ms
https://s3.amazonaws.com/docker-images-prod/registry-v2/docker/registry/v2/repositories/library/hello-world/_manifests/revisions/sha256/840559710d27cecc786ae6edc9ceb39e5d8697bb27b4c7917a404d7faaac83f0/signatures/sha256/e5f827e96b4e9716677da2a3500df4bb78e33b38b0e38dc33061504742d8ec92/link

244.259339ms
https://s3.amazonaws.com/docker-images-prod/?delimiter=&marker=&max-keys=1&prefix=registry-v2%2Fdocker%2Fregistry%2Fv2%2Fblobs%2Fsha256%2Fe5%2Fe5f827e96b4e9716677da2a3500df4bb78e33b38b0e38dc33061504742d8ec92%2Fdata

130.739956ms
https://s3.amazonaws.com/docker-images-prod/registry-v2/docker/registry/v2/blobs/sha256/e5/e5f827e96b4e9716677da2a3500df4bb78e33b38b0e38dc33061504742d8ec92/data

130.729732ms
https://s3.amazonaws.com/docker-images-prod/?delimiter=&marker=&max-keys=1&prefix=registry-v2%2Fdocker%2Fregistry%2Fv2%2Frepositories%2Flibrary%2Fhello-world%2F_manifests%2Frevisions%2Fsha256%2F840559710d27cecc786ae6edc9ceb39e5d8697bb27b4c7917a404d7faaac83f0

241.451755ms
https://s3.amazonaws.com/docker-images-prod/registry-v2/docker/registry/v2/blobs/sha256/84/840559710d27cecc786ae6edc9ceb39e5d8697bb27b4c7917a404d7faaac83f0/data

145.856769ms
https://s3.amazonaws.com/docker-images-prod/?delimiter=%2F&marker=&max-keys=1000&prefix=registry-v2%2Fdocker%2Fregistry%2Fv2%2Frepositories%2Flibrary%2Fhello-world%2F_manifests%2Frevisions%2Fsha256%2F840559710d27cecc786ae6edc9ceb39e5d8697bb27b4c7917a404d7faaac83f0%2Fsignatures%2Fsha256%2F

141.434246ms
https://s3.amazonaws.com/docker-images-prod/registry-v2/docker/registry/v2/repositories/library/hello-world/_manifests/revisions/sha256/840559710d27cecc786ae6edc9ceb39e5d8697bb27b4c7917a404d7faaac83f0/signatures/sha256/e5f827e96b4e9716677da2a3500df4bb78e33b38b0e38dc33061504742d8ec92/link

245.136354ms
https://s3.amazonaws.com/docker-images-prod/?delimiter=&marker=&max-keys=1&prefix=registry-v2%2Fdocker%2Fregistry%2Fv2%2Fblobs%2Fsha256%2Fe5%2Fe5f827e96b4e9716677da2a3500df4bb78e33b38b0e38dc33061504742d8ec92%2Fdata

125.175728ms
https://s3.amazonaws.com/docker-images-prod/registry-v2/docker/registry/v2/blobs/sha256/e5/e5f827e96b4e9716677da2a3500df4bb78e33b38b0e38dc33061504742d8ec92/data

We'll need to do the following:

[ ] Cache small blob objects, such as signatures, to avoid round trips
[ ] Cache signatures for particular manifests
[ ] Cache most recent manifest revisions

Awesome job enumerating this slowness!
Was this primarily looking at the connections related to pulling the hello-world image?
Also these numbers were retrieved by a registry outside of EC2?

@dmcgowan This was just a registry running on my laptop, at the office, on a hardwired network. I just simply ran the following command pointed at that configuration:
$ curl http://localhost:5000/v2/library/hello-world/manifests/sha256:840559710d27cecc786ae6edc9ceb39e5d8697bb27b4c7917a404d7faaac83f0

My original goal with this exploration was to see if connect time was the limiting factor. While connect time (without tls handshake) is around 30ms, the entire request time seems daunting. Please note that these numbers are after hacking the s3 client (goamz) to use http.DefaultClient. Basically, we can make the conclusion that there are other factors that need to be addressed before fixing the s3 library.

S3 is unfortunately slow for small objects for various reasons, though I
think if you reran this test with a registry on EC2 machine you would see
some improvements.
There are a few pointers here http://aws.amazon.com/articles/1904 for S3
optimization (see 'Improving GET and PUT throughput', 'Optimizing Network
Performance' for some possible wins).
Apart from caching, an S3/DynamoDB hybrid solution might improve the
performance, but at the cost of complexity.
I really wish S3 had a batch API.  sigh
On Tue, Apr 7, 2015 at 8:31 PM, Stephen Day<EMAIL_ADDRESS>wrote:

@dmcgowan https://github.com/dmcgowan This was just a registry running
on my laptop, at the office, on a hardwired network. I just simply ran the
following command pointed at that configuration:
$ curl http://localhost:5000/v2/library/hello-world/manifests/sha256:840559710d27cecc786ae6edc9ceb39e5d8697bb27b4c7917a404d7faaac83f0
My original goal with this exploration was to see if connect time was the
limiting factor. While connect time (without tls handshake) is around 30ms,
the entire request time seems daunting. Please note that these numbers are
after hacking the s3 client (goamz) to use http.DefaultClient.
Basically, we can make the conclusion that there are other factors that
need to be addressed before fixing the s3 library.
—
Reply to this email directly or view it on GitHub
https://github.com/docker/distribution/issues/335#issuecomment-90793664.

@RichardScothern The main issue here is that we are an order or magnitude off for a reasonable request time. Running the same test this morning yielded numbers closer to 400ms.

@stevvooe you mentioned your connect time is 30ms, whats your RTT from your laptop to S3 for these tests? is RTT a factor at all here, or are you spending large amounts of time after the request is sent waiting for the start of a response?

@stevvooe to elaborate further on my comment, I think you are right re S3 performance. Time to First Byte on something like a ..../link file (71 bytes, inside 13 subdirs) is something around 166ms from my packet capture (http).
Did another test, this time on a 68 byte file in the root of the bucket, TTFB was closer to 24ms (jumped around a little, but always under 40~ms). To verify my assumption, did some further tests:

Same 68 byte file in 5 subdirs, TTFB - 18ms.
Same 68 byte file in 8 subdirs, TTFB - 21ms.
Same 68 byte file in 10 subdirs, TTFB - 26ms.

Note each time I only did a single test, these may fluctuate a little and could be tested further to get a standard deviation range. I then went to test putting the same 68 byte text file in the same dir as my .../link file (13 subdirs down, equivalent of your 4th example in the original comment, uploaded via docker/distribution on an EC2 instance).
So, this tells me S3 itself can be quick, but something being done with the particular files for this package is causing a longer TTFB. Digging continues, will update shortly once I isolate what differs between my file and the distribution uploaded file.

Full curl output below (some request information redacted, keys/bucket names etc), to confirm I am using s3-bash to generate the curl commands then executing slightly modified (adding -i -v, and running with time):
[root@ip-10-X-X-X s3-bash]# time curl  --get --dump-header "/dev/null"  --header "Date: Sat, 29 Aug 2015 00:14:36 +0000" --header "Authorization: AWS ......." -i -v "http://s3.amazonaws.com/bucketnamehere/registry/docker/registry/v2/repositories/imagenameoneword/_manifests/revisions/sha256/7ea3d84364d2a6b5d23f8eb6d22decd068fe8bfb0091818500b06838c61a1e9f/signatures/sha256/d8113d53ba965a806f6b4db1bfb22f4051e41dff015e1483748c397fa3d145f8/nathan.test"
*   Trying <IP_ADDRESS>...
* Connected to s3.amazonaws.com (<IP_ADDRESS>) port 80 (#0)
> GET /bucketnamehere/registry/docker/registry/v2/repositories/imagenameoneword/_manifests/revisions/sha256/7ea3d84364d2a6b5d23f8eb6d22decd068fe8bfb0091818500b06838c61a1e9f/signatures/sha256/d8113d53ba965a806f6b4db1bfb22f4051e41dff015e1483748c397fa3d145f8/nathan.test HTTP/1.1
> User-Agent: curl/7.40.0
> Host: s3.amazonaws.com
> Accept: */*
> Date: Sat, 29 Aug 2015 00:14:36 +0000
> Authorization: AWS ........:........
>
< HTTP/1.1 200 OK
HTTP/1.1 200 OK
< x-amz-id-2: ........
x-amz-id-2: ........
< x-amz-request-id: ........
x-amz-request-id: ........
< Date: Sat, 29 Aug 2015 00:17:53 GMT
Date: Sat, 29 Aug 2015 00:17:53 GMT
< Last-Modified: Sat, 29 Aug 2015 00:13:58 GMT
Last-Modified: Sat, 29 Aug 2015 00:13:58 GMT
< ETag: "........"
ETag: "........"

* Connection #0 to host s3.amazonaws.com left intact

real	0m0.061s
user	0m0.000s
sys	0m0.000s
[root@ip-10-X-X-X s3-bash]# time curl  --get --dump-header "/dev/null"  --header "Date: Sat, 29 Aug 2015 00:14:56 +0000" --header "Authorization: AWS ......." -i -v "http://s3.amazonaws.com/bucketnamehere/registry/docker/registry/v2/repositories/imagenameoneword/_manifests/revisions/sha256/7ea3d84364d2a6b5d23f8eb6d22decd068fe8bfb0091818500b06838c61a1e9f/signatures/sha256/d8113d53ba965a806f6b4db1bfb22f4051e41dff015e1483748c397fa3d145f8/link"
*   Trying <IP_ADDRESS>...
* Connected to s3.amazonaws.com (<IP_ADDRESS>) port 80 (#0)
> GET /bucketnamehere/registry/docker/registry/v2/repositories/imagenameoneword/_manifests/revisions/sha256/7ea3d84364d2a6b5d23f8eb6d22decd068fe8bfb0091818500b06838c61a1e9f/signatures/sha256/d8113d53ba965a806f6b4db1bfb22f4051e41dff015e1483748c397fa3d145f8/link HTTP/1.1
> User-Agent: curl/7.40.0
> Host: s3.amazonaws.com
> Accept: */*
> Date: Sat, 29 Aug 2015 00:14:56 +0000
> Authorization: AWS ........:........
>
< HTTP/1.1 200 OK
HTTP/1.1 200 OK
< x-amz-id-2: ........
x-amz-id-2: ........
< x-amz-request-id: ........
x-amz-request-id: ........
< Date: Sat, 29 Aug 2015 00:18:18 GMT
Date: Sat, 29 Aug 2015 00:18:18 GMT
< Last-Modified: Wed, 24 Jun 2015 05:05:02 GMT
Last-Modified: Wed, 24 Jun 2015 05:05:02 GMT
< ETag: "........"
ETag: "........"
< x-amz-server-side-encryption: AES256
x-amz-server-side-encryption: AES256
< Accept-Ranges: bytes
Accept-Ranges: bytes
< Content-Type: application/octet-stream
Content-Type: application/octet-stream
< Content-Length: 71
Content-Length: 71
< Server: AmazonS3
Server: AmazonS3

<
* Connection #0 to host s3.amazonaws.com left intact
sha256:d8113d53ba965a806f6b4db1bfb22f4051e41dff015e1483748c397fa3d145f8
real	0m0.191s
user	0m0.000s
sys	0m0.000s

@stevvooe found the difference, the file I uploaded manually had no server side encryption enabled, whereas the files uploaded by docker/distribution had SHA256 server side encryption. The decryption process adding 100-120ms on the S3 side definitely explains the added latency.
I assume you have SSE enabled also on your test configuration?
I wonder if the solution is to not encrypt metadata...? Only encrypt layer blobs? Regardless of if a user specifies encrypt: true or not. You could then add a mention in the docs for the SSE option, to specify it will impact performance using the S3 backend...

@stevvooe more testing, just tried the same request/s as above with HTTPS:

SSE encrypted (link), HTTPS, 3 runs:

real	0m0.272s
user	0m0.024s
sys	0m0.036s

real	0m0.245s
user	0m0.020s
sys	0m0.024s

real	0m0.272s
user	0m0.016s
sys	0m0.028s

no SSE (nathan.test), HTTPS: 3 runs:

real	0m0.096s
user	0m0.024s
sys	0m0.032s

real	0m0.099s
user	0m0.020s
sys	0m0.036s

real	0m0.085s
user	0m0.020s
sys	0m0.028s

The above tells me there is a penalty of somewhere between 30-60ms by using secure: true on the s3 backend config (depending how you look at those stats, and factor in local user CPU for crypto etc). I assume you have secure: true also? :)

@CpuID Thank you for the analysis. To tell you truth, I don't think the secure option is doing anything. This supposedly disables TLS, but it seems to have no effect on the actual URLs used by the s3 package.
The described performance problems have nothing to do with "secure" or "encrypt", although these options may impact performance. The issue is that a simple call to list a very small directory is taking >100ms. While I did quote a 30ms connect time, this isn't even worth looking at when performance is on the order of 100s of milliseconds for each operation.
Our solution here is not to optimize for a broken service, but rather seek performance even when a service is degraded. If we mitigate bad backend performance, we can make the registry perform well under a variety of conditions that may not be related to these specific bottlenecks.

@stevvooe No problem :) Yea admittedly I didn't test the directory listing requests, only the object GET requests (obviously those were a part of your issues). I wonder if it's worth at least documenting the performance penalties of Server Side Encryption on S3 for object retrievals? Being any latency savings are likely to be an improvement.
I wonder if for any weird reason LIST requests are slower on prefixes/directories with SSE encrypted objects in them...? I couldn't see how, but you never know I guess...
I still would agree any caching improvements that can be made is going to be a big gain overall.

@CpuID There is definitely value in understanding the performance trade offs of the various options.
Would you mind dropping a small PR with some of your findings added to the s3 docs? Even a little "common sense" might help people make some decisions around configuration. You're numbers definitely have me thinking about how some of these options really affect driver behavior.

@stevvooe no probs, will try drop some basic notes in a PR in the next day or two

Closing as wildly outdated. distribution now provides support for caching blobdescriptors and middleware for CDNs.


Missing "Use cached results" options

Hi,
I am missing "Use cached results" options on Import data module. Is there any restriction or is it an only bug in the current Azure Machine Learning designer version?
Thanks,
Pavel.

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

ID: 5a04c021-b7f1-42b8-f8be-2de5266e9336
Version Independent ID: bb968ce3-0be3-effe-7c9a-5a5fe56ded60
Content: Import Data: Module Reference - Azure Machine Learning
Content Source: articles/machine-learning/algorithm-module-reference/import-data.md
Service: machine-learning
Sub-service: core
GitHub Login: @xiaoharper
Microsoft Alias: zhanxia

@pbartos  Thanks for the question! We are investigating and will update you shortly.

@xiaoharper Hi Harper, Could you please provide more details for this part. I can not figure out where to find the setting for "Use Cached Results" as the tutorials as well. Thanks.

@likebupt can you take a look?
@YutongTie-MSFT  please assign bug issues to @likebupt  going forward. She will be driving the doc work.

@pbartos @YutongTie-MSFT
Hi, Thanks for you feedback.
Sorry that currently designer does not support cached result of import data module. We are refreshing the docs, and will delete that part.
The "Using cached result" is in our backlog list. It would be better if you could share your scenario of using such feature, to help us set priority.

We hope this solves the issue. We will now proceed to #please-close the issue.

@pbartos
Hi, we have released the feature of "Regenerate output" of each module, which allows to reuse cached result in import data module. You can set it by clicking the checkbox in the right panel of import data module like following screenshot:

@likebupt
Hi,
I am not able to see this option. Is this released also for WE region?
Thanks,
Pavel

@pbartos
Hi,
Sorry for the inconvenience. This feature will be released soon. Once it released, we will let you know.

Hi, @likebupt . I can see the feature, but caching is not working. Even this option is unchecked, the module is executed each run.

@pbartos  Do you mean the regenerate output checkbox? It should by default use the previous generated results. Could you please clarify more details about your scenario?

Looks ok now. Thank you


smt2: emit smtlib2_comb_expr outputs after all inputs

This is a small followup fix to #3319. Before merging, I had only tested it in unroll mode, in which the unrolling code automatically orders the unrolled definitions. When unroll mode isn't used, the define-fun statements need to go after any declaration/definition referred to in the new definition. This always moves smtlib2_comb_expr outputs to the end, which is sufficient for this feature.

you ran into the same issue I did earlier, macos's sed is kinda lame, so doesn't support all the same options as gnu sed.


Ability to set a custom React Native root directory via an environment variable

Title says it all.
If need be, I could un-refactor the get_root back into the version files.

Perfect, thanks!

@orta for some reason the version on RubyGems doesn't contain this merge.
Maybe something wrong with travis deploy?
I can see it's the right version number (2<IP_ADDRESS> as the merge date), but the contents of the gem are old.
Thanks


[Hold on#38778] [$500]  LHN - Report is not marked as unread in LHN when the IOU is received

If you haven’t already, check out our contributing guidelines for onboarding and email<EMAIL_ADDRESS>to request to join our Slack channel!

Version Number: 1.4.501
Reproducible in staging?: Y
Reproducible in production?: Y
If this was caught during regression testing, add the test name, ID and link from TestRail: N/A
Email or phone of affected tester (no customers<EMAIL_ADDRESS>Issue reported by: Applause - Internal Team
Issue found when executing PR https://github.com/Expensify/App/pull/35928
Action Performed:
Preconditions:
Set up an OldDot admin account, invite the employee and approver to the policy https://sites.google.com/applausemail.com/applause-expensifyproject/wiki-guides/newdot-categories?authuser=0

Open https://staging.new.expensify.com/
Log in with the account of the employee added to the policy
Open https://staging.new.expensify.com/
in incognito mode
Log in to the policy approver account
Under the approver account, go to the WS room.
Under the employee account, create a manual request and send it to the WS room
Click on the "Submit" button in the created IOU

Expected Result:
When an IOU is received, the Report in the LHN should be marked as unread
Actual Result:
Report is not marked as unread in LHN when the IOU is received
Workaround:
Unknown
Platforms:

Which of our officially supported platforms is this issue occurring on?

[x] Android: Native
[x] Android: mWeb Chrome
[ ] iOS: Native
[ ] iOS: mWeb Safari
[ ] MacOS: Chrome / Safari
[x] MacOS: Desktop

Screenshots/Videos
Add any screenshot/video evidence

https://github.com/Expensify/App/assets/78819774/df2c20c9-fb56-4b0a-a074-6c1185ac1654
View all open jobs on GitHub
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Upwork Job URL: https://www.upwork.com/jobs/~013810a6929404cade
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Last Price Increase: 2024-04-02

@MitchExpensify  FYI I haven't added the External label as I wasn't 100% sure about this issue. Please take a look and add the label if you agree it's a bug and can be handled by external contributors

@dylanexpensify you've been super close to message unread/read behavior - Should a new IOU show as unread? I think "yes", right?

Lets get this fixed

Waiting for proposals

Advertising for proposals here https://expensify.slack.com/archives/C01GTK53T8Q/p1711408083227239

I think this is a regression from https://github.com/Expensify/App/pull/36950,  because of adding the !!report.lastActorAccountID condition here:
https://github.com/Expensify/App/blob/e542ba7c2d9e216c2cd401de0de8ed225fff4a7b/src/libs/SidebarUtils.ts#L270-L272

I can take over this issue as a C+ reviewer because @situchan is OOO in April

@situchan is out OOO, reassigning a C+ via the auto-assigner for fairness.

Looks like the same issue as https://github.com/Expensify/App/issues/38778 based on @kmbcook finding.

Agree with @bernhardoj's comment and @kmbcook's analysis. This issue is older, but it probably makes sense to HOLD this in favour of https://github.com/Expensify/App/issues/38778 as the C+ there has already selected a proposal/plan of action. Once the BE fix is applied there we can retest this issue. cc @MitchExpensify

Holding on https://github.com/Expensify/App/issues/38778, thanks @jjcoffee

Holding on https://github.com/Expensify/App/issues/38778,

Holding on https://github.com/Expensify/App/issues/38778

Holding on https://github.com/Expensify/App/issues/38778

Holding on https://github.com/Expensify/App/issues/38778

Holding on https://github.com/Expensify/App/issues/38778

Holding on https://github.com/Expensify/App/issues/38778

Holding on https://github.com/Expensify/App/issues/38778

Reassigning while I'm on leave 🙇

@MitchExpensify https://github.com/Expensify/App/issues/38778 is closed. Is there anything else this should be held on?

Ah closed yesterday, nice. No that's all that was holding this up

Okay, I see this is still a thing:
https://github.com/user-attachments/assets/0d8871d9-bbbd-421b-87bf-bc4bade93ce0

Proposal
Please re-state the problem that we are trying to solve in this issue.
The chat item in LHN doesn't show as bold for the workspace admin/approver when there is an expense preview
What is the root cause of that problem?
https://github.com/Expensify/App/pull/36950 changes the unread logic a bit by checking whether the report has a lastActorAccountID or not.
https://github.com/Expensify/App/blob/c2ded1dc91252803795948fa97888e420b0e648f/src/libs/SidebarUtils.ts#L339-L341
But in case the last action is a report preview, the last actor account ID is 0.

What changes do you think we should make in order to solve the problem?

First of all, we should revert https://github.com/Expensify/App/pull/36950 as it doesn't fix the root cause of the issue. The issue that the PR is trying to fix is that when we delete the last message in a room from device A, the lastVisibleActionCreated property of the report isn't updated in device B, so the unread status stays true.
https://github.com/Expensify/App/blob/4e6c52cad9f893a00e72d989caecdaf457a0ea02/src/libs/ReportUtils.ts#L4182
It will be updated once the device B opens the room. So, the problem is in the pusher that doesn't send the update to device B. Instead of patching up the issue, we should fix the pusher.
fyi, when we delete the last message, we set the lastVisibleActionCreated to empty optimistically, that's why the device A doesn't experience the issue. I think it should be set to the CREATED action created value, but I guess that's another topic to discuss

The issue is actually the same, but because the other issue is closed, I reposted my proposal here

@twisterdotcom I can take this back as C+ while @jjcoffee is OOO

@trjExpensify I'm correct in thinking that a new green dot request should be an unread comment right?

Go from 1.30 in the video. You were in the chat when the IOU came through. So I wouldn't expect it to be unread in that case, would you?

Ahhh good point. Okay, tested again and this is fixed:


Develop upstream sync 241001

@mmakevic-amd these XLA unit tests are expectable? if so, could we disable it in our develop-upstream, please?
[2024-10-08T23:16:41.880Z] @local_xla//xla/service/gpu/autotuning:autotuner_util_test               FAILED in 0.5s
[2024-10-08T23:16:41.880Z]   /root/.cache/bazel/_bazel_root/fbac33eb30dbfb6b11b15a7ff5ac830d/execroot/org_tensorflow/bazel-out/k8-opt/testlogs/external/local_xla/xla/service/gpu/autotuning/autotuner_util_test/test.log
[2024-10-08T23:16:41.880Z] @local_xla//xla/service/gpu/fusions/triton:triton_fusion_emitter_device_legacy_test_gpu_amd_any FAILED in 27.1s
[2024-10-08T23:16:41.880Z]   /root/.cache/bazel/_bazel_root/fbac33eb30dbfb6b11b15a7ff5ac830d/execroot/org_tensorflow/bazel-out/k8-opt/testlogs/external/local_xla/xla/service/gpu/fusions/triton/triton_fusion_emitter_device_legacy_test_gpu_amd_any/test.log
[2024-10-08T23:16:41.880Z] @local_xla//xla/service/gpu/transforms:gemm_rewriter_test_gpu_amd_any    FAILED in 80.6s
[2024-10-08T23:16:41.880Z]   /root/.cache/bazel/_bazel_root/fbac33eb30dbfb6b11b15a7ff5ac830d/execroot/org_tensorflow/bazel-out/k8-opt/testlogs/external/local_xla/xla/service/gpu/transforms/gemm_rewriter_test_gpu_amd_any/test.log
[2024-10-08T23:16:41.880Z] @local_xla//xla/tests:convert_test_gpu_amd_any                           FAILED in 27.7s
[2024-10-08T23:16:41.880Z]   /root/.cache/bazel/_bazel_root/fbac33eb30dbfb6b11b15a7ff5ac830d/execroot/org_tensorflow/bazel-out/k8-opt/testlogs/external/local_xla/xla/tests/convert_test_gpu_amd_any/test.log
[2024-10-08T23:16:41.880Z] @local_xla//xla/tests:dot_operation_single_threaded_runtime_test_gpu_amd_any FAILED in 221.5s
[2024-10-08T23:16:41.880Z]   /root/.cache/bazel/_bazel_root/fbac33eb30dbfb6b11b15a7ff5ac830d/execroot/org_tensorflow/bazel-out/k8-opt/testlogs/external/local_xla/xla/tests/dot_operation_single_threaded_runtime_test_gpu_amd_any/test.log
[2024-10-08T23:16:41.880Z] @local_xla//xla/tests:dot_operation_test_autotune_disabled_gpu_amd_any   FAILED in 136.8s
[2024-10-08T23:16:41.880Z]   /root/.cache/bazel/_bazel_root/fbac33eb30dbfb6b11b15a7ff5ac830d/execroot/org_tensorflow/bazel-out/k8-opt/testlogs/external/local_xla/xla/tests/dot_operation_test_autotune_disabled_gpu_amd_any/test.log
[2024-10-08T23:16:41.880Z] @local_xla//xla/tests:dot_operation_test_gpu_amd_any                     FAILED in 219.1s
[2024-10-08T23:16:41.880Z]   /root/.cache/bazel/_bazel_root/fbac33eb30dbfb6b11b15a7ff5ac830d/execroot/org_tensorflow/bazel-out/k8-opt/testlogs/external/local_xla/xla/tests/dot_operation_test_gpu_amd_any/test.log

retest gpu-pycpp please
retest gpu-non-pip-multi please

retest cpu-pycpp please

retest gpu-pycpp please

"""
[2024-10-09T17:02:40.676Z] @local_xla//xla/stream_executor/rocm:rocm_executor_test_gpu_amd_any FAILED TO BUILD
[2024-10-09T17:02:40.676Z] @local_xla//xla/stream_executor/rocm:rocm_kernel_test_gpu_amd_any FAILED TO BUILD
"""
something wrong?

[2024-10-09T17:02:40.676Z] @local_xla//xla/stream_executor/rocm:rocm_executor_test_gpu_amd_any FAILED TO BUILD
[2024-10-09T17:02:40.676Z] @local_xla//xla/stream_executor/rocm:rocm_kernel_test_gpu_amd_any FAILED TO BUILD

something wrong?

rocm_executor_test  should be okay now, but the rocm_kernel_test is failing in the upstream too. I'll try to have it fixed today. Should we leave this PR opened until that is fixed?

we could use this one for gemm_rewriter unit test as well I think? https://github.com/openxla/xla/pull/18062

retest cpu-pycpp please
retest gpu-non-pip-multi please
retest gpu-pycpp please

retest cpu-pycpp please
retest gpu-non-pip-multi please
retest gpu-pycpp please

retest cpu-pycpp please


Issue with culture != InvariantCulture

Hi,
Just noticed a bug when running the ClassCache C# test in a culture that uses ',' as decimal separator:
Errors during code-gen compilation:
C:\Users####\AppData\Local\Temp\2\FASTER\gzwgtyln\PersistentMemoryMalloc.cs(91,49): error CS1073: Unexpected token ','
Adding
System.Threading.Thread.CurrentThread.CurrentCulture = System.Globalization.CultureInfo.InvariantCulture;
fixed the issue, so seems to be a culture issue.

Sorry about that! Can you provide me with a repro? What could I set the current thread's culture do which would cause the bug to show up?

Thanks @nic-hartley, but I mean I don't even know how to set the culture such that I can see the problem. What should I do to get the test to fail?

This is now fixed, let us know if you run into any more issues.


Change base point slice thickness to half a unit

Description
Picking up here what emerged in this comment: when slicing Points, we currently consider a point to be "in the slice" if its distance from the slice is less than 1e-5. An unfortunate consequence of this is that any points that are more than 1e-5 away from a round number in world space are simply lost during slicing. Compare these two videos; the first is on main, the second is with this PR.
https://user-images.githubusercontent.com/23482191/150434291-e6ee318d-8e41-4729-9979-bbec839ee9a6.mp4
https://user-images.githubusercontent.com/23482191/150434301-38835d13-f790-410f-ae33-e5bbeaab066a.mp4
I found myself having this issue many times, not realizing that it was a very simple fix.
Are there any downsides to this?
PS: If this gets merged, the same should be done with Vectors; currently, those simply round down by using np.astype(int), which is admittedly similar to what I'm proposing, if offset by 0.5.
cc @alisterburt @jni @sofroniewn

Type of change

[x] Bug-fix (non-breaking change which fixes an issue)
[ ] New feature (non-breaking change which adds functionality)
[ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
[ ] This change requires a documentation update

References

How has this been tested?

[ ] example: the test suite for my feature covers cases x, y, and z
[ ] example: all tests pass with my change

Final checklist:

[ ] My PR is the minimum possible work for the desired functionality
[ ] I have commented my code, particularly in hard-to-understand areas
[ ] I have made corresponding changes to the documentation
[ ] I have added tests that prove my fix is effective or that my feature works
[ ] If I included new strings, I have used trans. to make them localizable.
For more information see our translations guide.

Trying to figure this out; with you change, this code does not behave the way I'd expect:
import napari
import numpy as np

pts = np.arange(0, 26) * 0.04
pts = np.stack([pts, pts, pts], axis=1)
v = napari.Viewer(ndisplay=3)
corners = v.add_points(
    [
        [0, 0, 0],
        [10, 0, 0],
        [0, 2, 0],
        [0, 0, 5],
        [10, 0, 5],
        [10, 2, 0],
        [0, 2, 5],
        [10, 2, 5],
    ],
    face_color="red",
    size=0.5,
    edge_width=0,
)
pl = v.add_points(pts, size=0.05, edge_width=0.01, scale=[3, 2, 3], face_color="green")
pl = v.add_points(pts, size=0.05, edge_width=0.01, scale=[10, 1, 5], face_color="blue")

I cna't really tell what's going on...

Yeah, with the PR without yout addition this code looks exactly like I would expect. What am I missing?

Yeah, with the PR without yout addition this code looks exactly like I would expect. What am I missing?

you were right! I was wrong!! Sorry to send you on a wild goose chase - 0.5 is the right choice because the slice thickness we can about here are in "data" coordinates which are "integer" based (in a sense) and not "world" coordinates. We havn't applied the scale multiplication yet.
Do you think we should add a more focused test, maybe with different scales too? Or do you think we have good test coverage. I'm approving regardless. Thanks!!

Maybe we could add a general test with a viewer (or just dims?) that makes sure slicing with weird scales returns the expected thing. But I'd say this is not the right PR for that :P

Maybe we could add a general test with a viewer (or just dims?) that makes sure slicing with weird scales returns the expected thing. But I'd say this is not the right PR for that :P

ok yeah, there might already be some stuff

An unfortunate consequence of this is that any points that are more than 1e-5 away from a round number in world space are simply lost during slicing

This above should read "in data space" i believe, as it is before the scale transform gets applied (that was my confusion). i will edit first comment
i will merge now.


gnu(25488)=2919

Unfortunately I have no record of computing time, but it should be just over an hour. This is another case of Mike's conjecture of gnu(16 * 27 * p)=2919 for p prime and p = 11 mod 12.

Thank you for submitting this gnu(n). It has been recalculated with the same result:
gnu(25488)=2919
GAP      4.8.3
GrpConst 2.5
Runtime: 5008593 ms
Isomorphic groups eliminated
Validated on Linux server (AMD Opteron 6376 1400 MHz processor, 512 GB RAM)
This entry is already contained in data/gnu50000.g deriving its content from http://www.icm.tu-bs.de/ag_algebra/software/small/number.html and now it has been successfully replicated.
Checklist for the reviewer:

[ ] reviewed checked and assigned labels "new (n<50000)" or "recomputation", if needed
[ ] reproducible value of gnu(n)
[ ] no essential runtime differences
[ ] correct arguments of command to store data
[ ] correct location of new line(s) in the data file
[ ] sufficient level of technical details
[ ] reviewed has assigned label "validated" after successful review


Remove ClientManageProcessor#heartBeat repeat judgment

Before Creating the Enhancement Request

[x] I have confirmed that this should be classified as an enhancement rather than a bug/feature.

Summary

Repeat judgment
Motivation

Remove repeat judgment
Describe the Solution You'd Like

Remove repeat judgment
Describe Alternatives You've Considered

Remove repeat judgment
Additional Context
no more

Great. It's not only about repetitive judgement, this can reduce useless cpu & mem usage.


Add proxy support to Slack integration

This is what my slack looks like:

// Slack
set('slack', [
    'channel'  => getenv('SLACK_CHANNEL'),
    'token' => getenv('SLACK_TOKEN'),
    'username' => 'GitLab',
    'team'  => getenv('SLACK_TEAM_NAME'),
    'app'   => getenv('PROJECT_NAME'),
    'icon' => ':whale:',
    'proxy' => [
      'user' => getenv('PROXY_USER'),
      'pass' => getenv('PROXY_PASS'),
      'url' => getenv('PROXY_URL'),
    ],
]);

after('deploy','deploy:slack');

What is proxy used for?

I'm closing this PR as it seems to me outdated.
If you still need this functionality, please open a new PR or an issues.


Update google-cloud-pubsub to 1.111.0

Updates com.google.cloud:google-cloud-pubsub from 1.107.0 to 1.111.0.
I'll automatically update this PR to resolve conflicts as long as you don't change it yourself.
If you'd like to skip this version, you can just close this PR. If you have any feedback, just mention me in the comments below.
Configure Scala Steward for your repository with a .scala-steward.conf file.
Have a fantastic day writing Scala!

Ignore future updates
Add this to your .scala-steward.conf file to ignore future updates of this dependency:
updates.ignore = [ { groupId = "com.google.cloud", artifactId = "google-cloud-pubsub" } ]

labels: library-update, semver-minor

Superseded by #186.


Choosing a separator

Hi! This issue is most like a recommendation.
When I tried to upload a .csv file in huggingface, I don't have the option to choose a separator.
I understand that the .csv file is a "comma separated values" but in some cases the .csv has another separator, like in this case.

I think the problem it will resolve if I change the "|" to "," but hopefully, in the future, you can implement something like what I said above.
Best regards!

Closing issue as obsolete to new autotrain-advanced that is soon releasing.


Update clock when host wakes up from sleep

Please browse the existing issues and filter by 'enhancement' to make sure this has not already been suggested!

I have updated to 4.5 for this but I do not think it works.
My Debian VM de-synchronizes just like before.
Are there some special steps that are needed to get the correct time into the VMs after the Macbook resuming from sleep ?

This is also not working for me on 4.5.0. aarch64 Fedora guest.

Source code in commit https://github.com/utmapp/UTM/commit/6f07ed07829121b3983c56984c34e814dc3674d5 is only referring to Qemu, and not Apple Virtualization, I think that's why it does not work

This is also not working for me on QEMU on a Mac with Version 4.4.5 (94) and a Ubuntu 22.04 image.
The UTM date is out of sync when the Macbook is waking up from sleep.

@osy, can we re-open this issue? Please and thank you!

@ahrex if you are experiencing this issue, open a new one and provide all the requested information from the issue template


Memory leak report

The standard valgrind --leak-check=full command produces quite a lot of possible future problems concerning memory leaks in alloyc.
Here is the report Gist: https://gist.github.com/bacondropped/f46fd75b9c2349c8a8d1

Cheers! We'll look into this :+1:

The commit I pushed fixes the memory leak error when we pass no arguments (i.e. files). With files, there are still a few errors to get to. Running alloyc on the test files results in an infinite loop of error: reached end of input, pos(1) ... len(0) errors.

@vnev That's only a small section of the error, lol.

Nope, fixed our valgrind error, and theres a few tiny memory leaks.

No I fixed the valgrind errors that occur when you don't pass a file. The errors with the files passed are (were?) still there. Also, I think the extra bits of leaks are from the malloc function, as that's what valgrind is complaining about among other functions.

I'm confused

Ok let me retrace my steps:
I ran valgrind --leak-check=full ./bin/alloyc which gave me the error in the alloyc.c file, which I fixed with the commit. After fixing, I ran valgrind --leak-check=full ./bin/alloyc and it gave me the output that you saw in the screenshot above. I didn't do valgrind --leak-check=full ./bin/alloyc tests/do.ay. Running this gives that infinite loop thingy I showed you

Oh weird, since I fixed that error, so have you tried pulling the repo?

Works now but new error. Lets fix


make the drawing_area a self-contained nvim widget

On problem I found with the existing UI is that upon resize it resizes the whole window, and it assumes the size of the "nvim widget" (or drawing area) is the same than the window size. If one wants to embed the drawing area in another application with menu bars, status bars, etc, this approach would not work.
This patch does all the resize work relative to the drawing area and not the whole window.

Not passing travis, and realized that mouse click positions need to be taken care of...


Can't find...

I've done my installation and later, I want to open that, how can I?

I just want usage guide...for now


impl DatabasePool for Box

I am writing a web application that uses sqlite in developer builds and postgres in production builds. The decision between the databases is made at runtime based on an environment variable. This currently does not work with axum-session-auth, because the generic parameter on AuthSession requires a choice between postgres and sqlite. I think it would make sense to impl DatabasePool for Box<dyn DatabasePool>, then I could just use Box<dyn DatabasePool> as the generic parameter for an AuthSession and have it  decide at runtime which database to use.

Actually, it may have to be impl DatabasePool for Arc<dyn DatabasePool> because AuthSessionLayer requires it to be Clone and Box<dyn DatabasePool + Clone> isn't supported by rust

this would be a issue on axum_session and not axum_session_auth as that is where the actual database's are. the only thing axum_sesison_auth does is just relay the database based on the Sessions settings. So you either need to add support for what you need via a PR or you can make your Own Database type and impl database to it. and there is no need for a PR then. you can see the databases here as an example. https://github.com/AscendingCreations/AxumSession/tree/main/src/databases
There is no need to Arc the type itself if you use a wrapper that can arc the correct types inside it and redirect it to the correct database.

I've implemented it in my own codebase. Here's the snippet in case others find this useful as well:
use async_trait::async_trait;
use axum_session::{databases::DatabasePool, SessionError};
use std::fmt::Debug;
use std::sync::Arc;

/// [axum_session_auth] requires a database pool as a generic parameter, and they offer choices for pg and sqlite.
/// But we want to support both pg and sqlite, so we implement one that dispatches at runtime
#[derive(Clone)]
pub struct DbPoolForSession {
    pool: Arc<dyn DatabasePool + Send + Sync>,
}

impl DbPoolForSession {
    pub fn new(pool: Arc<dyn DatabasePool + Send + Sync>) -> Self {
        Self { pool }
    }
}

impl Debug for DbPoolForSession {
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        f.debug_struct("DbPoolForSession").finish()
    }
}

#[async_trait]
impl DatabasePool for DbPoolForSession {
    async fn initiate(&self, table_name: &str) -> Result<(), SessionError> {
        self.pool.initiate(table_name).await
    }

    async fn count(&self, table_name: &str) -> Result<i64, SessionError> {
        self.pool.count(table_name).await
    }

    async fn store(
        &self,
        id: &str,
        session: &str,
        expires: i64,
        table_name: &str,
    ) -> Result<(), SessionError> {
        self.pool.store(id, session, expires, table_name).await
    }

    async fn load(&self, id: &str, table_name: &str) -> Result<Option<String>, SessionError> {
        self.pool.load(id, table_name).await
    }

    async fn delete_one_by_id(&self, id: &str, table_name: &str) -> Result<(), SessionError> {
        self.pool.delete_one_by_id(id, table_name).await
    }

    async fn exists(&self, id: &str, table_name: &str) -> Result<bool, SessionError> {
        self.pool.exists(id, table_name).await
    }

    async fn delete_by_expiry(&self, table_name: &str) -> Result<Vec<String>, SessionError> {
        self.pool.delete_by_expiry(table_name).await
    }

    async fn delete_all(&self, table_name: &str) -> Result<(), SessionError> {
        self.pool.delete_all(table_name).await
    }

    async fn get_ids(&self, table_name: &str) -> Result<Vec<String>, SessionError> {
        self.pool.get_ids(table_name).await
    }

    fn auto_handles_expiry(&self) -> bool {
        self.pool.auto_handles_expiry()
    }
}

Would you prefer this newtype implementation, maybe with a better name (DynDatabasePool? ArcDatabasePool?) or would you prefer a direct impl DatabasePool for Arc<dyn DatabasePool>?. If for Arc, then also for Box? Rc? Or just for T: Deref<DatabasePool>? The axum-session-auth use case only needs Arc and for that, Box doesn't work because of the trait bounds on AuthSessionLayer which require it to be Clone (and Box isn't Clone). But maybe other use cases want other smart pointers?

Call it SessionAnyPool as this will match the current naming schema and Any basically stands for anything.
I would also like you to add an Example using this pool to the example folder too.


No origins seem to exist

basically, orb of origin wont wont, the /origin gui command wont work, and /origin set only have origin:empty, here is both my server and client latest.log, it's 1.20.1 btw
latest(server).log
latest(client).log

okay, seems to be the same as #423, rejoining without restarting client fixes it but still, wtf, i'd like a proper fix


Skill - Frenzy

This PR may directly merged and I may fixed the issue then as this is the first PR you pull up

https://github.com/MokhaLeee/fe8u-cskillsys-kernel/commit/3db47edb5e0c3cc0ed931e94b695b7d3d221f7b7


FilterAggregation is missing in the 8.x library

Is your feature request related to a problem? Please describe.
FilterAggregation was supported in NEST, but isn't generated for the 8.x version of the client.
https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations-bucket-filter-aggregation.html
Describe the solution you'd like
The multi-bucket version of this aggregation (FiltersAggregation) is already implemented and can be used to achieve the same result, however it would be nice to re-introduce the FilterAggregation functionality to avoid the extra steps and potential performance impact as well.

Thanks for reporting! This will be back as soon as I finish my work on the code generator. I'm closing this issue for now.

Bump

@KPGDL There is no point in bumping this. As explained, it will be available after I finish the related work on the code generator. To be honest, this issue has rather low priority as there is a perfectly valid workaround that does not require a lot of extra effort to use (FiltersAggregation).

Hi @gpetrou,
the Filter aggregation is fully supported in 8.13.*. You probably missed it, because of the way it's modelled in the specification. Unless most of the other aggregations, it's not wrapped in a separate class, but uses the Query instead:
https://github.com/elastic/elasticsearch-net/blob/8db7bd4402db1a5e8e06354a94f4458b846ea5a6/src/Elastic.Clients.Elasticsearch/_Generated/Types/Aggregations/Aggregation.g.cs#L71-L72

Oh, I see. OK thanks.


Code Exchange Upgrade

Resolves #16

Coverage increased (+15.6%) to 100.0% when pulling 59b773f4ca3fc31762f5cbbcf7a9cb429a0f496c on code-exchange-upgrade into a8f9399527c19ab3badade8f8571d914174397d6 on master.


More than one hook script

Currently I use the hook to create dns-01 entries in powerdns. But it would be fine, to start a further script for internal server purposes, after updating the ssl certificates. Could this be possible?

Write a wrapper hook. Pass whatever hook functions you like to the existing hook (which can then remain unaltered), and overload the rest with whatever you want to customize.

Yes! Good idea! Thank you!


Iron out Rails/UniqBeforePluck

Follow-up to https://github.com/rubocop/rubocop-rails/pull/580#discussion_r744146536
Removed detection of distinct after pluck:
> User.pluck(:email).distinct
(1.3ms)  SELECT "users"."email" FROM "users"
NoMethodError: undefined method `distinct' for ["user@example.com"]:Array

Improved examples and reasoning text.
Unfold specs.

Before submitting the PR make sure the following are checked:

[x] Wrote good commit messages.
[-] Commit message starts with [Fix #issue-number] (if the related issue exists).
[x] Feature branch is up-to-date with master (if not - rebase it).
[x] Squashed related commits together.
[x] Added tests.
[-] Added an entry (file) to the changelog folder named {change_type}_{change_description}.md if the new code introduces user-observable changes. See changelog entry format for details.
[x] The PR relates to only one subject with a clear title and description in grammatically correct, complete sentences.
[x] Run bundle exec rake default. It executes all tests and RuboCop for itself, and generates the documentation.
[-] If this is a new cop, consider making a corresponding update to the Rails Style Guide.

Thanks! 👍


Update to mantid 6.10

Description of work:
The dead-time correction uses the Mantid algorithm LoadErrorEventsNexus which requires Mantid 6.10.
Check all that apply:

[x] added release notes
(if not, provide an explanation in the work description)

References:

Links to IBM EWM items:
Links to related issues or pull requests:

Manual test for the reviewer
(instructions to set up the environment)
Check list for the reviewer

[ ] release notes updated,
or an explanation is provided as to why release notes are unnecessary
[ ] best software practices

[ ] clearly named variables (better to be verbose in variable names)
[ ] code comments explaining the intent of code blocks

[ ] All the tests are passing
[ ] The documentation is up to date
[ ] code comments added when explaining intent

Codecov Report
All modified and coverable lines are covered by tests :white_check_mark:

Project coverage is 44.98%. Comparing base (a5adb70) to head (084f3da).

Additional details and impacted files
@@           Coverage Diff           @@
##             next     #104   +/-   ##
=======================================
  Coverage   44.98%   44.98%
=======================================
  Files          33       33
  Lines        6307     6307
=======================================
  Hits         2837     2837
  Misses       3470     3470

:umbrella: View full report in Codecov by Sentry.
:loudspeaker: Have feedback on the report? Share it here.


Stop Bots Triggering Ghost Alert

Currently, bots also trigger the alert, this is unwanted behaviour.

Don't worry, I'm coming for the rescue.


⚠ Secret Website 3 has degraded performance

In 28bb115, Secret Website 3 ($SECRET_WEBSITE_3) experienced degraded performance:

HTTP code: 200
Response time: 1989 ms

Resolved: Secret Website 3 performance has improved in 1a0ee90.