Using conformer_transformer and rnnt_loss error

Environment info

Platform: Ubuntu 16.04
Python version: 3.7.10
PyTorch version (GPU?): PyTorch 1.7.0, CUDA 10.2
Using GPU in script?: GeForce RTX 2080 Ti

Information
Model I am using (ListenAttendSpell, Transformer, Conformer ...): conformer_transducer
The problem arises when using:
CUDA_VISIBLE_DEVICES=8 python ./openspeech_cli/hydra_train.py dataset=libri dataset.dataset_path=/data/dataset/Libri/LibriSpeech dataset.dataset_download=False dataset.manifest_file_path=/home/gpu/WorkSpace/Speech/OpenSpeech/dataset/libri_subword_manifest.txt vocab=libri_subword vocab.vocab_size=5000 vocab.vocab_path=/home/gpu/WorkSpace/Speech/OpenSpeech/dataset model=conformer_transducer audio=fbank lr_scheduler=warmup_reduce_lr_on_plateau trainer=gpu trainer.batch_size=4 criterion=transducer
Error executing job with overrides: ['dataset=libri', 'dataset.dataset_path=/data/dataset/Libri/LibriSpeech', 'dataset.dataset_download=False', 'dataset.manifest_file_path=/home/gpu/WorkSpace/Speech/OpenSpeech/dataset/libri_subword_manifest.txt', 'vocab=libri_subword', 'vocab.vocab_size=5000', 'vocab.vocab_path=/home/gpu/WorkSpace/Speech/OpenSpeech/dataset', 'model=conformer_transducer', 'audio=fbank', 'lr_scheduler=warmup_reduce_lr_on_plateau', 'trainer=gpu', 'trainer.batch_size=4', 'criterion=transducer']
Traceback (most recent call last):
  File "./openspeech_cli/hydra_train.py", line 51, in hydra_main
    trainer.fit(model, data_module)
  File "/home/gpu/anaconda3/envs/openspeech/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 458, in fit
    self._run(model)
  File "/home/gpu/anaconda3/envs/openspeech/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 756, in _run
    self.dispatch()
  File "/home/gpu/anaconda3/envs/openspeech/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 797, in dispatch
    self.accelerator.start_training(self)
  File "/home/gpu/anaconda3/envs/openspeech/lib/python3.7/site-packages/pytorch_lightning/accelerators/accelerator.py", line 96, in start_training
    self.training_type_plugin.start_training(trainer)
  File "/home/gpu/anaconda3/envs/openspeech/lib/python3.7/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py", line 144, in start_training
    self._results = trainer.run_stage()
  File "/home/gpu/anaconda3/envs/openspeech/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 807, in run_stage
    return self.run_train()
  File "/home/gpu/anaconda3/envs/openspeech/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 869, in run_train
    self.train_loop.run_training_epoch()
  File "/home/gpu/anaconda3/envs/openspeech/lib/python3.7/site-packages/pytorch_lightning/trainer/training_loop.py", line 499, in run_training_epoch
    batch_output = self.run_training_batch(batch, batch_idx, dataloader_idx)
  File "/home/gpu/anaconda3/envs/openspeech/lib/python3.7/site-packages/pytorch_lightning/trainer/training_loop.py", line 738, in run_training_batch
    self.optimizer_step(optimizer, opt_idx, batch_idx, train_step_and_backward_closure)
  File "/home/gpu/anaconda3/envs/openspeech/lib/python3.7/site-packages/pytorch_lightning/trainer/training_loop.py", line 442, in optimizer_step
    using_lbfgs=is_lbfgs,
  File "/home/gpu/anaconda3/envs/openspeech/lib/python3.7/site-packages/pytorch_lightning/core/lightning.py", line 1403, in optimizer_step
    optimizer.step(closure=optimizer_closure)
  File "/home/gpu/anaconda3/envs/openspeech/lib/python3.7/site-packages/pytorch_lightning/core/optimizer.py", line 214, in step
    self.__optimizer_step(*args, closure=closure, profiler_name=profiler_name, **kwargs)
  File "/home/gpu/anaconda3/envs/openspeech/lib/python3.7/site-packages/pytorch_lightning/core/optimizer.py", line 134, in __optimizer_step
    trainer.accelerator.optimizer_step(optimizer, self._optimizer_idx, lambda_closure=closure, **kwargs)
  File "/home/gpu/anaconda3/envs/openspeech/lib/python3.7/site-packages/pytorch_lightning/accelerators/accelerator.py", line 329, in optimizer_step
    self.run_optimizer_step(optimizer, opt_idx, lambda_closure, **kwargs)
  File "/home/gpu/anaconda3/envs/openspeech/lib/python3.7/site-packages/pytorch_lightning/accelerators/accelerator.py", line 336, in run_optimizer_step
    self.training_type_plugin.optimizer_step(optimizer, lambda_closure=lambda_closure, **kwargs)
  File "/home/gpu/anaconda3/envs/openspeech/lib/python3.7/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py", line 193, in optimizer_step
    optimizer.step(closure=lambda_closure, **kwargs)
  File "/home/gpu/anaconda3/envs/openspeech/lib/python3.7/site-packages/torch/autograd/grad_mode.py", line 26, in decorate_context
    return func(*args, **kwargs)
  File "/home/gpu/anaconda3/envs/openspeech/lib/python3.7/site-packages/torch/optim/adam.py", line 66, in step
    loss = closure()
  File "/home/gpu/anaconda3/envs/openspeech/lib/python3.7/site-packages/pytorch_lightning/trainer/training_loop.py", line 733, in train_step_and_backward_closure
    split_batch, batch_idx, opt_idx, optimizer, self.trainer.hiddens
  File "/home/gpu/anaconda3/envs/openspeech/lib/python3.7/site-packages/pytorch_lightning/trainer/training_loop.py", line 823, in training_step_and_backward
    result = self.training_step(split_batch, batch_idx, opt_idx, hiddens)
  File "/home/gpu/anaconda3/envs/openspeech/lib/python3.7/site-packages/pytorch_lightning/trainer/training_loop.py", line 290, in training_step
    training_step_output = self.trainer.accelerator.training_step(args)
  File "/home/gpu/anaconda3/envs/openspeech/lib/python3.7/site-packages/pytorch_lightning/accelerators/accelerator.py", line 204, in training_step
    return self.training_type_plugin.training_step(*args)
  File "/home/gpu/anaconda3/envs/openspeech/lib/python3.7/site-packages/pytorch_lightning/plugins/training_type/dp.py", line 98, in training_step
    return self.model(*args, **kwargs)
  File "/home/gpu/anaconda3/envs/openspeech/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/home/gpu/anaconda3/envs/openspeech/lib/python3.7/site-packages/torch/nn/parallel/data_parallel.py", line 159, in forward
    return self.module(*inputs[0], **kwargs[0])
  File "/home/gpu/anaconda3/envs/openspeech/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/home/gpu/anaconda3/envs/openspeech/lib/python3.7/site-packages/pytorch_lightning/overrides/data_parallel.py", line 77, in forward
    output = super().forward(*inputs, **kwargs)
  File "/home/gpu/anaconda3/envs/openspeech/lib/python3.7/site-packages/pytorch_lightning/overrides/base.py", line 46, in forward
    output = self.module.training_step(*inputs, **kwargs)
  File "/home/gpu/WorkSpace/Speech/OpenSpeech/openspeech/models/conformer_transducer/model.py", line 110, in training_step
    return super(ConformerTransducerModel, self).training_step(batch, batch_idx)
  File "/home/gpu/WorkSpace/Speech/OpenSpeech/openspeech/models/openspeech_transducer_model.py", line 268, in training_step
    target_lengths=target_lengths,
  File "/home/gpu/WorkSpace/Speech/OpenSpeech/openspeech/models/openspeech_transducer_model.py", line 90, in collect_outputs
    target_lengths=target_lengths.int(),
  File "/home/gpu/anaconda3/envs/openspeech/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/home/gpu/WorkSpace/Speech/OpenSpeech/openspeech/criterion/transducer/transducer.py", line 96, in forward
    gather=self.gather,
  File "/home/gpu/anaconda3/envs/openspeech/lib/python3.7/site-packages/warp_rnnt-0.4.0-py3.7-linux-x86_64.egg/warp_rnnt/__init__.py", line 74, in rnnt_loss
    index[:, :, :U-1, 1] = labels.unsqueeze(dim=1)
RuntimeError: The expanded size of the tensor (61) must match the existing size (62) at non-singleton dimension 2.  Target sizes: [4, 355, 61].  Tensor sizes: [4, 1, 62]

The Loss function:
def rnnt_loss(log_probs: torch.FloatTensor,
              labels: torch.IntTensor,
              frames_lengths: torch.IntTensor,
              labels_lengths: torch.IntTensor,
              average_frames: bool = False,
              reduction: Optional[AnyStr] = None,
              blank: int = 0,
              gather: bool = False) -> torch.Tensor:

    The CUDA-Warp RNN-Transducer loss.

    Args:
        log_probs (torch.FloatTensor): Input tensor with shape (N, T, U, V)
            where N is the minibatch size, T is the maximum number of
            input frames, U is the maximum number of output labels and V is
            the vocabulary of labels (including the blank).
        labels (torch.IntTensor): Tensor with shape (N, U-1) representing the
            reference labels for all samples in the minibatch.
        frames_lengths (torch.IntTensor): Tensor with shape (N,) representing the
            number of frames for each sample in the minibatch.
        labels_lengths (torch.IntTensor): Tensor with shape (N,) representing the
            length of the transcription for each sample in the minibatch.
        average_frames (bool, optional): Specifies whether the loss of each
            sample should be divided by its number of frames.
            Default: False.
        reduction (string, optional): Specifies the type of reduction.
            Default: None.
        blank (int, optional): label used to represent the blank symbol.
            Default: 0.
        gather (bool, optional): Reduce memory consumption.
            Default: False.

The log_probs and labels of model output and function requirements are inconsistent

@hasangchun

@hasangchun How's it going?

I'm really sorry for being late. I will solve it in a short time.

@zhaoyang9425  I fixed that part. Thanks for recording the issue.


Bump Iceberg to v1.6.1

We can't bump to v1.7.0 as java 8 support was dropped.

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assign set of reviewers

Hey @regadas, thanks for making this change!
Can you please resolve the conflicts and trigger the Iceberg integration tests by modifying this file

Hi @ahmedabu98 thanks for taking a look! let me know if smth else is missing

@ahmedabu98 I was not hitting any particular issue in specific. Trying to keep things updated since between 1.4.3 and 1.6.1, parquet support, table scan's have been improved and there's some spec additions.

Sounds good 👍🏽 merging now.
Thanks for tagging me on your other PRs, I'll take a look at them later today. Can you resolve their conflicts and similarly trigger the integration tests too?


Standard method checks should allow out-of-package messages

For example
rpc CreateBook(CreateBookRequest) returns (other.package.Book);

This should be allowed (I think), but am not sure if it's covered.

This is not coming up in user reports, and we do not agree on whether it should be allowed (it currently is), so going to close this.


Add databases and initial migrations files

Add databases for labels users labeldata projects images team and projectmembers and create its migrations file.
Fixes #424
Type of change

[ ] Bug fix (non-breaking change which fixes an issue)
[x] 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

Test Configuration:

Firmware version:
Hardware:
Toolchain:
SDK:

Also, include screenshots of app for the verification and reviewing purpose.
Checklist:

[ ] My code follows the style guidelines of this project
[ ] I have performed a self-review of my own code
[ ] I have commented my code, particularly in hard-to-understand areas
[ ] I have made corresponding changes to the documentation
[ ] My changes generate no new warnings
[ ] I have added tests that prove my fix is effective or that my feature works
[ ] New and existing unit tests pass locally with my changes
[ ] Any dependent changes have been merged and published in downstream modules

Ref #429 on the basis on changes suggested in the following discussion

@ivantha @udesh @Tushar19varshney Please review this

@carpecodeum Label can be a independent entity, so we don't have to attach project_id with it. We can attach a many to many keys in project model, and if needed you can create a through model. Any suggestions ?

@carpecodeum Label can be a independent entity, so we don't have to attach project_id with it. We can attach a many to many key in project model, and if needed you can create a through model. Any suggestions ?

I thought labels were subject to the kind of project we were working on like a certain project would be used to label only a particular type of object in that sense any of the labels in any other projects would hardly be similar.

@carpecodeum Label can be a independent entity, so we don't have to attach project_id with it. We can attach a many to many key in project model, and if needed you can create a through model. Any suggestions ?

I thought labels were subject to the kind of project we were working on like a certain project would be used to label only a particular type of object in that sense any of the labels in any other projects would hardly be similar.

Like many of the labels can be common, if it is, then we don't want user to recreate that label once again for only that project. I think we need to discuss this upon with other members @ivantha @UdeshUK

@ivantha @udesh please give your thoughts on this i think we should finalize this so that we can move on to creating apis.

Yes, labels can be similar. But let's keep the labels of projects separated.

Yes, labels can be similar. But let's keep the labels of projects separated.

Yes they are separate only! :)


Get list of 20 most recent points from Points

Task(s)

[ ] Get list of 20 most recent points object transactions from Points

Acceptance Criteria

Dependencies

Resources

Notes

you got it.

you got it.

yup just sent in a PR for it


Add trace logging on Provide method calls

That would allow to make sure that Parsec is indeed useful :)
Signed-off-by: Hugues de Valon<EMAIL_ADDRESS>

Could you please also add two trace logs for every method along the way to/from the providers?
For example for BackEndHandler, when entering the execute_request method you log execute_request ingress and after you get the result from provider you log execute_request egress

✨ It's all done ✨


support removing an addess from a pool by the ip address

Is your feature request related to a problem? Please describe.

The problem is if you are doing a rolling deploy of more than one VM at a time then there's no gaurantee that "--remove" (as it works by index) actually removed the correct address from the backend pool.
Describe the solution you'd like

Have a version of "az network application-gateway address-pool update" that lets a VM idempotently remove its own address from a backend pool.
Describe alternatives you've considered

Allow for a "synchronize" keyword in azure devops yaml pipelines
write a service that provides the synchronization around calling "az network application-gateway address-pool update".

Additional context

Using this call in a "rolling deploy" was not thought about clearly ! Otherwise "--remove" operate using an IP address !
 stage:
    displayName: "Deployment : ${{ parameters.ENVIRONMENT }} Hosted Applications And Tasks"
    jobs:
      - deployment:
        displayName: IIS
        environment:
          name: ${{ parameters.ENVIRONMENT }}
          resourceType: VirtualMachine
        strategy:
          rolling:
            # this effectively starts N number of deployments based on how many machines are currently in the  environment passed into ${{ parameters.ENVIRONMENT }} corresponding to a matching Azure Devops "Environments" entry ... For sake of argument assume the environment has 10 VMs in it.
            maxParallel: 50%
            preDeploy:
              #
              steps:
                - task: AzureCLI@2
                  displayName: Manage Pooled VM
                  inputs:
                    azureSubscription: "mysub"
                    scriptType: ps
                    scriptLocation: inlineScript
                    inlineScript: |
                      # remove myself from the pool ...
                      $IPAddress = Get-WmiObject Win32_NetworkAdapterConfiguration | Where-Object {$_.Ipaddress.length -gt 1}
                      $myIPAddr = $IPAddress.ipaddress[0]
                      echo "myIPAddr = $myIPAddr"
                      # this is not how remove works BUT IT SHOULD BE !!!! so that ANY VM can take itself out of the backend pool without affecting anyone else !!!
                      az network application-gateway address-pool update -g $(ResourceGroupName) --gateway-name $(AppGateway) -n $(AddressPoolName) --remove backendAddresses "{\"ip_address\": \"{$myIPAddr}\"}"

network

'az network application-gateway address-pool update -g yyc --gateway-name ag-yyc -n appGatewayBackendPool --remove backendAddresses 0' could remove the the first item of the list for 'backendAddresses'.


Asynchronous retry of KVM time sync. after downtime

Description
#3862 introduced KVM time resync on resume or migration via QEMU GA. It randomly happen that guest is not yet ready enough to process the command. It might be nice if in case of failure another domtime sync is triggered asynchronously on background with some delay, e.g., 5 seconds.
To Reproduce
Suspend and resume various OS randomly, check if time is synchronized.
Expected behavior
Try once again time resync in case of failure.
Details

Affected Component: KVM driver
Hypervisor: KVM
Version: 5.10.x

Progress Status

[ ] Branch created
[ ] Code committed to development branch
[ ] Testing - QA
[ ] Documentation
[ ] Release notes - resolved issues, compatibility, known issues
[ ] Code committed to upstream release/hotfix branches
[ ] Documentation committed to upstream release/hotfix branches

PRs to merge in master:

code: https://github.com/OpenNebula/one/pull/4567
docs: https://github.com/OpenNebula/docs/pull/1070
tests: https://github.com/OpenNebula/development/pull/928


prevent menu from closing when MenuItem is clicked

on @headlessui/react, i could use ev.preventDefault() to prevent menu from closing, but i don't find any way to do so with this library. Is there any way?

This is a major feature. Please help

I was noticing that my menu item was NOT closing when clicked, which was a bug in my case.
I found that returning an arrow function from the on:click will cause the menu to not close.  This is a little frustrating because I cannot pass parameters to my function if I want the menu to close.  I have to use a workaround and pass data via event.target.
Ex.
<MenuItem as="div" let:active>
     <button on:click={updateBrushLegendItem}>Click Me And Will Close</button>
     <button on:click={() => updateBrushLegendItem()}>But I Will Not Close</button>
</MenuItem>


Leave group button

For some reason i can't use it, cause when i press it it writes that i'm not a leader of group. Of course i will not be a leader when i'm requesting trade. It seems like mercurytrade is trying to "kick" me, not leave.

Sorry, it writes not about leader now:

Having the same issue

seems to only happen when you are the one inviting people

Is there any solution to this problem? It has been like this at least 6 months


MMDetWandbHook not logging train/loss or validation losses

Prerequisite

[X] I have searched Issues and Discussions but cannot get the expected help.
[X] I have read the FAQ documentation but cannot get the expected help.
[X] The bug has not been fixed in the latest version (master) or latest version (3.x).

Task
I have modified the scripts/configs, or I'm working on my own tasks/models/datasets.
Branch
master branch https://github.com/open-mmlab/mmdetection
Environment
sys.platform: linux
Python: 3.10.6 (main, Nov 14 2022, 16:10:14) [GCC 11.3.0]
CUDA available: True
GPU 0: NVIDIA GeForce RTX 2070
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 11.7, V11.7.99
GCC: x86_64-linux-gnu-gcc (Ubuntu 11.3.0-1ubuntu1~22.04) 11.3.0
PyTorch: 1.13.1+cu117
PyTorch compiling details: PyTorch built with:
  - GCC 9.3
  - C++ Version: 201402
  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - LAPACK is enabled (usually provided by MKL)
  - NNPACK is enabled
  - CPU capability usage: AVX2
  - CUDA Runtime 11.7
  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86
  - CuDNN 8.5
  - Magma 2.6.1
  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.7, CUDNN_VERSION=8.5.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.13.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF,

TorchVision: 0.14.1+cu117
OpenCV: 4.7.0
MMCV: 1.7.0
MMCV Compiler: GCC 9.3
MMCV CUDA Compiler: 11.7
MMDetection: 2.28.1+c14dd6c

Reproduces the problem - code sample
log_config = dict(
    interval=1,
    hooks=[
        dict(type='TextLoggerHook'),
        dict(
            type='MMDetWandbHook',
            init_kwargs={'entity': ENTITY,
                         'project': PROJECT,
                         'name': NAME,
                         'dir': work_dir,
                         'resume': 'allow',
                         'id': ID},
            interval=1,
            num_eval_images=15,
            bbox_score_thr=0.5,
            log_checkpoint=False,
            log_checkpoint_metadata=False)])

Reproduces the problem - command or script
python /home/leon/pheno-wheat/tools/detector/train.py /home/leon/pheno-wheat/configs_dev/models/cascade_mask_rcnn_mod_dconv_c3-c5_250e_coco.py --cfg-options resume_from=None

Reproduces the problem - error message
There's no error but the TextLoggerHook is logging the following metrics for train epoch and val epoch respectively.

Train epoch:

2023-02-19 17:23:40,182 - mmdet - INFO - Epoch [7][6/145]	lr: 1.000e-02, eta: 16:24:55, time: 1.495, data_time: 0.698, memory: 5184, loss_rpn_cls: 0.2895, loss_rpn_bbox: 0.1680, s0.loss_cls: 0.4240, s0.acc: 80.1758, s0.loss_bbox: 0.3301, s0.loss_mask: 0.3559, s1.loss_cls: 0.2292, s1.acc: 76.7578, s1.loss_bbox: 0.2336, s1.loss_mask: 0.1758, s2.loss_cls: 0.1115, s2.acc: 79.1016, s2.loss_bbox: 0.0752, s2.loss_mask: 0.0934, loss: 2.4861

Val epoch:

2023-02-19 17:19:04,940 - mmdet - INFO - Epoch(val) [5][8]	loss_rpn_cls_val: 0.2945, loss_rpn_bbox_val: 0.1613, s0.loss_cls_val: 0.4241, s0.acc_val: 80.9204, s0.loss_bbox_val: 0.3339, s0.loss_mask_val: 0.3588, s1.loss_cls_val: 0.2179, s1.acc_val: 79.5044, s1.loss_bbox_val: 0.2060, s1.loss_mask_val: 0.1909, s2.loss_cls_val: 0.1056, s2.acc_val: 80.0903, s2.loss_bbox_val: 0.0608, s2.loss_mask_val: 0.0964, loss_val: 2.4503

Additional information

I expected MMDetWandbHook to log all the metrics the texloggerhook would log.

MMDetWandbHook is not logging the train/loss metric neither it is logging the validation loss metrics.

I'm not sure what might be the reason for this.

Hello !
Can you try first to create a project in https://wandb.ai/new-project , and define in your config a simple pipeline such as:
log_config = dict(
    interval=1,
    hooks=[
        dict(type='TextLoggerHook'),
        dict(
            type='MMDetWandbHook',
            init_kwargs={'entity': "ENTITY", # The entity used to log on Wandb
                          'project': "PROJECT", # Project name in WandB
                          },
            interval=1,
            num_eval_images=15,
            bbox_score_thr=0.5,
            log_checkpoint=False,
            log_checkpoint_metadata=False)])

With ENTITY and PROJECT variable corresponding to the one created on the wandb.ai website ?
It should work, but maybe there is a cleaner way to do it in MMDetection implementation of MMDetWandbHook...


Shadow volume artifacts in geojson clamped to ground

var viewer = new Cesium.Viewer('cesiumContainer');

viewer.dataSources.add(Cesium.GeoJsonDataSource.load('../../../../Apps/SampleData/ne_10m_us_states.topojson', {
    clampToGround: true
}));

Came up on the forum:

I think this is the same issue again on the forum:

Are we sure these polygons causing this issue are not self-intersecting or potentially have any other oddness to them?  In my experience a lot of polygon data has precision issues that cause slight intersections that are not visible in 2D but create problems when you have to tessellate in 3D.  Otherwise, I would expect a simplified rectangle at the same location to have the same issue.

Looks like this issue is gone in Cesium 1.79.1: Sandcastle.

I couldn't reproduce this either.

Congratulations on closing the issue! I found these Cesium forum links in the comments above:
https://groups.google.com/d/msg/cesium-dev/MDXTSunXSPk/HNcW-6u-AgAJ
https://groups.google.com/d/msg/cesium-dev/5A2mfG1dq60/NLj1AOOlBQAJ
If this issue affects any of these threads, please post a comment like the following:

The issue at https://github.com/CesiumGS/cesium/issues/8049 has just been closed and may resolve your issue. Look for the change in the next stable release of Cesium or get it now in the master branch on GitHub https://github.com/AnalyticalGraphicsInc/cesium.


Encode MARCXML type literal as record attribute.

Treats type literals specially by encoding them as record attributes.
Fixes #402. (Supersedes #404 if accepted.)

+1 , merged and closed.


cuda memory occupied

speech_prob = model(chunk, sampling_rate).item()
The video memory occupied by this line of code will not be released, which will inevitably overflow when the voice is very long. Since the jit cannot be modified

Do you disable the gradients for PyTorch?
Running VAD is not advised on a GPU


Vertical section line breaks when file/folder has korean characters.

Describe the bug
Vertical section line breaks when file/folder has korean characters.
To Reproduce

Add file/folder with korean characters (ex. 테스트.txt)
Listed file breaks vertical seciton lines.

Expected behavior
Align vertical lines like english charter files.
Screenshots

System information (please complete the following information):

OS: macOS
Version 14.5(23F79)
Superfile v1.1.2

Is this issues related character byte sizes? Korean characters treated as 3 bytes per character.
func main() {
    english := "hello"
    korean := "안녕하세요"

    fmt.Printf("English: %s, Length: %d, Byte size: %d\n", english, len(english), len([]byte(english)))
    fmt.Printf("Korean: %s, Length: %d, Byte size: %d\n", korean, len(korean), len([]byte(korean)))
}

English: hello, Length: 5, Byte size: 5
Korean: 안녕하세요, Length: 5, Byte size: 15

Could it be because of your font? It doesn't seem to be a problem when I test locally.

What's your font name? I test some nerd fonts but, all fonts failed.
I use terminal wezterm and use Hack Nerd Font.

Then I don't think it's a font problem. Would you like to try another terminal?

left - iterm, right - mac natvie terminal
It's weired... Hmm.. I don't know why.

I think I know why. It's the font decoding.
Can you try to check out the wiki below?
It should be work.
https://superfile.netlify.app/troubleshooting/

@UltraBlackLinux Does troubleshooting work?

@blackironj Does troubleshooting work?

Below terminals are, from left to right, wezterm/iterm/mac terminal.
export RUNEWIDTH_EASTASIAN=0 fixed lines in iterm. (but breaks korean character display rule)
I think korean characters need to be some configuration, but I don't know korean character unicode details.
In now the vertical line breaks are not really annoying. So It could be close this issue.
@yorukot Thanks for help! 🙇

@yorukot
Sorry for replying late
It has been fixed after setting export RUNEWIDTH_EASTASIAN=0
Thank you


Refactor Morpheus unit tests and plugin to the conda recipe for per-lib testing

[x] Unit tests refactor - core, dfp, llm
[x] Conda recipe update to include package testing
[x] Github workflow changes to require GPUs for the conda packaging stage
[ ] Move away from the morpheus-build-container to a slimmer base image (rapidsai/ci-conda)

/ok to test

/ok to test

/ok to test

/ok to test

/ok to test

/ok to test

/ok to test

/ok to test

/ok to test

/ok to test

/ok to test

/ok to test

/merge


function matrix_normal_prec_log: unit test, add doc, add to function signatures

[ ] add unit tests
[ ] add documentation
[ ] add to src/stan/gm/function_signatures.h

#1344 is a duplicate of this one.

This issue was moved to stan-dev/math#24


Voice answer pagination links appear broken

Appear not to be showing at bottom of the table. See http://ancient-thicket-9575.herokuapp.com/voice_answers for a recent example

@jmadans FYI, you can access all of the voice files with "?all" at the end, ie http://ancient-thicket-9575.herokuapp.com/voice_answers?all

Correction: ?all=true , ie http://ancient-thicket-9575.herokuapp.com/voice_answers?all=true

The audio files are really small, so let's just load them all by default.


deprecating the thaumcraft jars

Hold off on merging this until Thaumic Insurgence's minor module wave 1 hits, should be soon, setting to draft until then.

Closing this due to the mergening, it should be really easy to port over to gt5u afterwards as it's just deleted code and some lang text changes


This note on the page is cryptic "Time of ingestion"

The consideration next to ingesting using ADF is cryptic "Time of ingestion"

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

ID: 7c758ab8-a66a-0d9d-af7a-a0fbe43ec6f4
Version Independent ID: 62e4d091-8261-eb79-0f50-92a302487dd0
Content: Azure Data Explorer data ingestion overview
Content Source: data-explorer/ingest-data-overview.md
Service: data-explorer
GitHub Login: @orspod
Microsoft Alias: orspodek

@ciufudean Thanks for the feedback !  I have assigned this issue to content author to investigate and update the document as appropriate.

Thanks for the feedback, @ciufudean. You're definitely not wrong here.  @TzviaGT can you help clarify this consideration?

@ciufudean The article in question has now been updated to clarify the following regarding ingestion with Azure Data Factory (ADF):
This method takes relatively more time until data is ingested. ADF uploads all data to memory and then begins ingestion.

#please-close


Does not appear to work on Ubuntu

It doesn't appear to be working on Ubuntu. I use this same extension on my Windows laptop and it works just fine. On Ubuntu, I get no warnings at all (tested on both with the same text).
Write Good Linter: 0.0.7
OS: Ubuntu 17.10
From "Visual Studio Code" about screen:
Version 1.17.2
Commit b813d12980308015bcd2b3a2f6efa5c810c33ba5
Date 2017-10-16T13:57:00.652Z
Shell 1.7.7
Renderer 58.0.3029.110
Node 7.9.0
Architecture x64
Anything else I can provide?

I will try and setup a VM to verify (and hopefully correct) any issues. There was a problem with v0.0.6, but v0.0.7 should work fine.

Just verifying you were in a file with a .md extension when you tested it on linux?

Fresh 17.10, Fresh VS Code install (1.17.2, same sha), and fresh install of Write Good Linter (v0.0.7). I think everything is working as expected. I think next check to make sure you hit "reload" after installing the extension or that it was in an .md file (or you changed the write-good.languages setting in preferences).

Thank you! Yes, I verified I was running against a markdown file (.md). Will verify the settings this evening. If it's still not behaving, I'll uninstall everything and reinstall with just this extension to eliminate any possible conflicts and let you know.

If it still fails I can add some logging and make a new release that you
can turn the logging on so I can get some idea of what’s going on.
On Wed, Nov 8, 2017 at 7:33 AM David L. Day<EMAIL_ADDRESS>wrote:

Thank you! Yes, I verified I was running against a markdown file (.md).
Will verify the settings this evening. If it's still not behaving, I'll
uninstall everything and reinstall with just this extension to eliminate
any possible conflicts and let you know.
—
You are receiving this because you were assigned.
Reply to this email directly, view it on GitHub
https://github.com/TravisTheTechie/vscode-write-good/issues/8#issuecomment-342854046,
or mute the thread
https://github.com/notifications/unsubscribe-auth/AALXcY_g3V64gHoKUz3-v8VVJQ679wSIks5s0cm0gaJpZM4QUsTE
.

--
-Travis

So sorry for the false alert. It's working. I disabled all of my other extensions. I think another extension I was using was taking a long time and producing so much output so I was never seeing the results from yours. I'm closing this issue. I really appreciate your quick response and testing! And of course, I appreciate your work here :)


[FEAT] allow namespacing image with localhost/ to target local podman images

Description

~Podman stores locally built images as localhost/${IMAGE}, so we should prepend localhost/ to the image name to ensure we target the correct image.~
This PR makes sure to sanitize the destinationImage if it is namespaced with localhost/ so that it is pushed to the right location.
Currently, if images are namespaced as localhost/{IMAGE}, they are pushed either to registry/localhost/image, or registry/namespace/localhost/{IMAGE} if the registry is provided with a namespace.
Namespacing images as localhost/ also allows always targeting the podman image even if a docker image was built more recently as docker does not use localhost/ as a namespace. However, the PR currently sanitizes the image name so that it can be pulled from the docker daemon, but if this functionality is wanted, I can revert the changes on line 304 and line 307
Related Issue(s)

This may fix #66 by allowing a user to specifically target a local image
Checklist

[ ] This PR includes a documentation change
[x] This PR does not need a documentation change

[ ] This PR includes test changes
[ ] This PR's changes are already tested

[x] This change is not user-facing
[ ] This change is a patch change
[x] This change is a minor change
[ ] This change is a major (breaking) change

Changes made

allow namespacing images with localhost/ and sanitize the destination image

was experiencing this issue, but changed my workflows and now I'm no longer impacted by it.


Feature dump of minimal APIs

https://gist.github.com/davidfowl/ff1addd02d239d2d26f4648a06158727

@LadyNaggaga I made a few more changes and added a TOC:
https://gist.github.com/davidfowl/ff1addd02d239d2d26f4648a06158727#minimal-apis-at-a-glance

I will add the content update 👍🏿 . We may not need a TOC because it's auto populated in the side menu.

This listens to port http://localhost:5000 and https://localhost:5001 by default.

Should this section be updated as well since, when some creates an application today, they will get a random port ?

Yes I'll remove that part

I will add the content update 👍🏿 . We may not need a TOC because it's auto populated in the side menu.

I would put WebApplication and WebApplicationBuilder under Application.

https://gist.github.com/davidfowl/ff1addd02d239d2d26f4648a06158727#differences-with-aspnet-core-mvc
I like this section it should be a detailed blog post.  How do you feel about title being Minimal APIs Vs MVC?  The thing about this section is we may need to create section the highlights when you should use minimal or MVC.  Which is something we are trying to avoid; I thought.  Thoughts?

Differences with ASP.NET Core MVC

No support for filters. i.e. IAsyncAuthorizationFilter, IAsyncActionFilter, IAsyncExceptionFilter, IAsyncResultFilter, IAsyncResourceFilter
No support for model binding. i.e. IModelBinderProvider, IModelBinder. Support can be added with a custom binding shim.

No support for binding from forms. This includes binding IFormFile (this will be added in the future).

No built-in support for validation. i.e. IModelValidator
No support for application parts or the application model. There's no way to apply or build your own conventions.
No built-in view rendering support. We recommend using Razor Pages for rendering views.
No support for JsonPatch
No support for OData
No support for ApiVersioning, see this issue for more details.

I don't think this should be on this site but it should go into docs.asp.net. I also don't think it should be a blog post.

Fair enough.  My hesitation on this section is it's a lot of information that may take someone down a Google rabbit hole.

Fair enough. My hesitation on this section is it's a lot of information that may take someone down a Google rabbit hole.

Right, that's why it should be left out for this site and should only appear on docs.asp.net. That's where the existing .NET devs will go to find that information.

Cool it looks like we are on the same page


Adding E2e multicluster test

Fixes #91
Description of the changes and steps added to this PR:

Updated the common script to be able to run multicluster setup using the upstream setup kind script
Updated both setup scripts to add the capability to create multicluster setup on kind
Add multicluster test cases
Modify the k8s client init to be able to set from different kubeconfig files (This will be improved in a consecutive PR)
Moved GetVersionFromIstiod() to common utils
Added istioctl util to be able to run istioctl commands in the e2e framework
Modify kubectl to add the possibility to use different kubeconfig files (Pending the refactor of the kubectl util to be more flexible)
Add download istioctl binary and set variable to the make file
Created the util certs to avoid having to create the certificates by using the Make file from upstream

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@nrfox I opened a new PR with all the changes

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Hi, I updated the PR with all the fix from the review @nrfox @luksa


Numpy.fft.fft2() gives different result than tf.fft2d()

as mentioned in the issue #6401, the tf.fft2d() gives different result compared to np.fft.fft2(). Is there a reason for this ?
Note : numpy gives proper fourier transform after np.fft.fftshift(), and I have taken care of that in my code.
The differences are not visible here, but the mean squared error is significant.

Second image is the fft using tensorflow, and third one is using numpy. You can see the difference in the corners. The fourth image is the difference between the two images times 10.
(tf has some features while numpy does not)
I am working on an application which uses fft in backpropagation and thus it is of absolute importance that the fft in numpy are same as fft by tf.
My question is - Why is there a difference and how can I get the same fft as numpy ?

I am not sure if this is related to this issue #10735

Can you give any indication about the relative error between the two versions? How much did you magnify the difference image?

@aselle Hey, sorry for late response. I magnified the difference by a factor of 10.
I realized that numpy uses float64 whereas tf uses float32. I could not find a way to use float64 in tf (creates a complex128, which is not supported by tf.fft2d). Could this be a possible problem ?
The difference kind of seems huge to consider floating point accuracy as the reason.

We use cuFFT to compute fft's, so it is a property of the cufft. You could look at its documentation to see if it has any insight (or google for cufft vs fftw). Also, you could see whether numpy.fft when run with floats matches better.

This is still an issue. I am not sure how much it will matter in the long run if we are training a network around it anyway..., but it is quite shocking that the error is so high. This also makes debugging hard, because TF's FFT is undoubtedly not doing what we think it should be. Perhaps cuFFT's algorithm is not numerically stable? The error is there even for 1D, but it is much much smaller. This makes me think that the 2D implementation is not optimal, with a lot of fill in or non-optimal number of operations. If someone can point me to the code, maybe we can debug / suggest a fix without changing too much.
Some python code to verify error:
  ## TEST 2D FFT
   np.random.seed(13)
   M = np.random.random([1,2,2]).astype(np.complex64)
   M_tf = tf.placeholder(dtype=M.dtype, shape=[None,*M.shape[1:]], name='M_tf')
   eval_dict = {M_tf: M}
   sess, gsaver = nn_utils.init_network()
   Mhat = fft2D_np(M)
   Mhat_tf = fft2D_tf(M_tf)
   Mhat_tfeval = Mhat_tf.eval(feed_dict=eval_dict)
   e = Mhat - Mhat_tfeval
   err = np.linalg.norm(e.reshape([-1,1]))
   assert( np.isclose(err, 0.0) )

Doing some prints in my session:
>>> M
array([[[ 0.77770239+0.j,  0.23754121+0.j],
        [ 0.82427853+0.j,  0.96574920+0.j]]], dtype=complex64)
>>> Mhat
array([[[ 2.80527139+0.j,  0.39869052+0.j],
        [-0.77478409+0.j,  0.68163186+0.j]]], dtype=complex64)
>>> Mhat_tfeval
array([[[ 2.80527139+0.j,  0.398690**46**+0.j],
        [-0.774784**15**+0.j,  0.68163186+0.j]]], dtype=complex64)
>>> e
array([[[  0.00000000e+00+0.j,   5.96046448e-08+0.j],
        [  5.96046448e-08+0.j,   0.00000000e+00+0.j]]], dtype=complex64)
>>> err
8.4293696e-08
>>> np.isclose(err, 0.0)
False

@sirgogo, can you please post the source to fft2D_np and fft2D_tf in your example?
Are your ops running on GPU or CPU?
(have you verified that with tf.ConfigProto(log_device_placement=True)?)
FWIW, NumPy is the oracle used for all of the FFT unit tests.  The tolerance on those checks is 1e-4 at the moment, which is pretty high, but it's because we're using a one-size-fits all bound against Eigen's TensorFFT (CPU), and cuFFT (GPU). Please also note that the FFT unit tests include gradient tests, that verify the numerical gradient matches the symbolic gradient.
Due to differences in the floating point hardware across your CPU and GPU, the results between NumPy and cuFFT will differ by some amount for the exact same set of floating point operations. Additionally, NumPy uses fftpack for its FFTs -- so whether your ops are running on CPU or GPU, it's a different FFT implementation from TensorFlow's. That's not to say there isn't a bug in TensorFlow's invocation of these FFT implementations or the implementations themselves!
Here are code pointers:

Eigen TensorFFT
TensorFlow FFT kernels. Used to invoke either Eigen TensorFFT or cuFFT.
cuFFT documentation
StreamExecutor invocation of cuFFT
TensorFlow FFT gradient definitions

What is your expectation here?  e.g. would you be happy if np.isclose returned True in your example? That would imply you need something like rtol=1e-5, atol=1e-8 ?

@rryan, thank you for the info and links! I will take a look shortly. Sorry for the  delay, I was out of town, server went down, etc etc.
You can run the example with the following simple definitions:
fft1D_np = np.fft.fft
fft1D_tf = tf.fft
ifft1D_np = np.fft.ifft
ifft1D_tf = tf.ifft
fft2D_np = np.fft.fft2
fft2D_tf = tf.fft2d
ifft2D_np = np.fft.ifft2
ifft2D_tf = tf.ifft2d

I am running with tensorflow-GPU. This is the only way I was able to get any of the tf.fft-like functions to work. It complains if you are using the CPU-only version (at least for 2D). Is this sufficient to assume the ops are running on the GPU? I can try the log_device_placement setting.
Yes, I would expect that the maximum relative error needs to be lower. Thanks for your insight into this. Just to clarify, you are saying this is an issue with Eigen's TensorFFT / cuFFT, and not something that would be fixed by compiling from source?

Hi,
using tensorflow to do some signal processing and the fft problem has appeared. I did some tests and hope they are helpful. I also created a repository with the example signal where the fft really goes wrong using the cpu, error after fft is >1e+14. With a gpu it looks good.
Code:
(https://github.com/rassibassi/fftTensorflow)
import tensorflow as tf
import scipy.io
import numpy as np

print('tf:',tf.__version__)
print('scipy:',scipy.__version__)
print('np:',np.__version__)

# numpy fft
signal = scipy.io.loadmat('signal.mat')['signal']
signal_f = np.fft.fft(signal)

# tf fft
tf_signal = tf.placeholder(shape=signal.shape,dtype=tf.complex64)
tf_signal_f = tf.fft(tf_signal)
session = tf.InteractiveSession()
out_signal_f, out_signal = session.run([tf_signal_f,tf_signal], feed_dict={tf_signal:signal})

errorAfter = np.mean(np.power(np.abs(out_signal-signal),2))
errorBefore = np.mean(np.power(np.abs(out_signal_f-signal_f),2))
print('Error before fft:',errorAfter)

unix ubuntu cpu:
tf: 1.4.0
scipy: 0.19.1
np: 1.13.1
Error before fft: 6.43755807539e-22
Error after fft: 4.21685308781e+14

scientific linux hpc-cluster cpu:
tf: 1.3.0
scipy: 0.17.1
np: 1.11.0
Error before fft: 6.43755807539e-22
Error after fft: 4.21685308781e+14

scientific linux hpc-cluster cpu (Tesla K80) with cudnn/v5.1:
('tf:', '1.0.1')
('scipy:', '0.18.1')
('np:', '1.11.2')
('Error before fft:', 6.4375580753914162e-22)
('Error after fft:', 2.8628744462657876e-15)

Hi,
I've gotten high errors also running one dimensional tf.rfft() on CPU over large signals (>280000 samples). On the other hand, after trimming the tensor length to the next power of two the results are quite better (in terms of error), as pointed out by @rryan .

Update: We think we tracked the issue (or part of the issue) down to this code in Eigen that computes twiddle factors for non-power-of-2 FFTs via Bluestein's algorithm.
https://bitbucket.org/eigen/eigen/src/034b6c3e101792a3cc3ccabd9bfaddcabe85bb58/unsupported/Eigen/CXX11/src/Tensor/TensorFFT.h?at=default&fileviewer=file-view-default#TensorFFT.h-230:249
We'll submit a fix to Eigen, then update the version of Eigen that TensorFlow uses.

https://bitbucket.org/eigen/eigen/pull-requests/356/disable-use-of-recurrence-for-computing

I'm not sure if this is the same issue @Sushobhan04 is seeing though (the subject of this issue).
@Sushobhan04, are you using power of 2 transforms?

Nagging Awaiting Response: It has been 14 days with no activityand the awaiting response label was assigned. Is this still an issue?

Nagging Awaiting Response: It has been 14 days with no activityand the awaiting response label was assigned. Is this still an issue?

It has been 14 days with no activity and the awaiting response label was assigned. Is this still an issue?

@Rassibassi, I'm working on adding complex128 support to the FFT ops, and I ran your code snippet on CPU with the new complex128 support. It looks like a good bit of the difference comes down to floating point precision, but there is still a difference between our Eigen CPU kernels and NumPy's FFTPACK-based implementation:
('tf', '1.7.0-rc1')
('scipy', '1.0.0')
('numpy', '1.13.3')

TF complex64 error:
relError():
5.69994795505e-08
2.28822314134e-05
0.00236386710254
error():
2.67673230982e-08
1.07452364853e-05
0.00111161453476

TF complex128 error:
relError():
2.03202621417e-25
8.12885273597e-23
8.1293612227e-21
error():
8.45195242854e-26
3.3806405275e-23
3.38062769196e-21

NP error:
relError():
1.46110214039e-30
2.56749381389e-28
2.15249955484e-26
error():
1.46110214039e-30
2.56749381389e-28
2.15249955484e-26

TF complex64 error:
relError():
2.80140031472e-06
0.00114172830632
0.137244893593
error():
6.57350034283e-07
0.000267167590172
0.0312076844667

TF complex128 error:
relError():
5.34248829151e-24
2.136542245e-21
2.1367747769e-19
error():
7.49076129451e-25
2.9962948344e-22
2.99628924207e-20

NP error:
relError():
1.32333991018e-29
4.18679573526e-27
3.71090920591e-25
error():
1.32333991018e-29
4.18679573526e-27
3.71090920591e-25

@Sushobhan04, running your code snippet using complex128 FFTs the error is 1.57009245868e-16. Under complex64 the error is 5.1619136559e-08.

@rryan Thanks, this sounds promising.
One thing I was wondering (perhaps unrelated to this issue), is some of us are seeing different results on TF-CPU vs TF-GPU computation, even for networks with only simple operations like Add, Multiply, MatMul, reduce_sum, reduce_max, etc. I need to come up with a good set of tests to expose this, but do you have any insight on why this might be the case?

@Sushobhan04 Hi!
According to the second that you have made, you used fftshift in tensorflow. Am I right?

I added complex128 FFT support for CPU and GPU in https://github.com/tensorflow/tensorflow/commit/8f0a90b711480c12716d1a3b1094cc8b34939f2d. It'll be part of TensorFlow 1.9.
RFFT/IRFFT support is on the way but a little more complicated, so I split it into a separate commit.

It has been 21 days with no activity and the awaiting response label was assigned. Is this still an issue?

It has been 36 days with no activity and the awaiting response label was assigned. Is this still an issue?

We are closing this issue for now due to lack of activity. Please comment if this is still an issue for you. Thanks!

I am finding the 2dfft produces nonsensical results on complex data.  #28192

@isaacgerg, commented on your issue. It looks like you were feeding a [batch, height, width, channels], tensor to tf.signal.fft2d, so your FFT was over [width, channels] instead of [height, width] as you intended.

@rryan Is the equivalent fix (https://github.com/tensorflow/tensorflow/commit/8f0a90b711480c12716d1a3b1094cc8b34939f2d)  for RFFT merged yet?

@rryan Is the equivalent fix (https://github.com/tensorflow/tensorflow/commit/8f0a90b711480c12716d1a3b1094cc8b34939f2d)  for RFFT merged yet?

@PCerles RFFT complex128 support has been merged now. Sorry for the long delay!
https://github.com/tensorflow/tensorflow/commit/4a3cbea5f3d1f79eb2ff6bb2c64875e951ca3ce2


MRG: 0.13 deprecations

We'll see if I broke anything.

Coverage decreased (-35.5%) to 55.331% when pulling 787e8ffac28a9e04fb6c1e19669ded1b5838c4ee on Eric89GXL:deps into 9ed79531c33cb5dcaf82dba59b1b8b7ec1a6273a on mne-tools:master.

Coverage decreased (-0.005%) to 90.853% when pulling 454465df636a4e076df5203bca72dc78480ea23d on Eric89GXL:deps into 9ed79531c33cb5dcaf82dba59b1b8b7ec1a6273a on mne-tools:master.

CIs are happy, ready for review/merge from my end.

thx @Eric89GXL


Screen goes black when clicking "Enter VR" twice - Quest 2

After exiting the experience, trying to click on "Enter VR" again is resulting in black screen with loading dots

restart the browser. Happened to me


How to solve 2D and 3D magnetostatics problems

Hi all,
I've developed a 3D nonlinear magnetostatics solver based on MFEM, but I'd now like to be able to solve 2D problems with it as well. I realize that in 2D the magnetostatic equations can be simplified and implemented as a scalar Poisson problem with H1 elements. I would rather not use this approach though, and instead want to use Nedelec elements with the curl-curl form of the problem. I want to do this so that I can use all the same integrators I've written for the 3D analysis (I don't believe I can use the Poisson form since elements from an H1 collection don't implement CalcCurlShape). I'm wondering if you all could offer some guidance on how I might be able to do this, and how CalcCurlShape actually works for 2D elements. Some specific questions:

Why does the curlshape matrix only have one column in 2D? What do it's entries mean?
How can I get the magnetic flux density with this 2D curlshape? In the 3D NonlinearFormIntegrator I'd do curlshape_dFt.AddMultTranspose(elfun, b_vec); to get the magnetic flux density as a vector with length 3, but in 2D since curlshape_dFt is ndofx1 b_vec would be a vector with length 1, and I don't know how to interpret that.
How can I visualize the 2D magnetic flux density field? For my 3D problem, I'm able to use a DiscreteLinearOperator with a CurlInterpolator added to it, but this doesn't work in 2D since CurlInterpolator::AssembleElementMatrix2 calls ProjectCurl from an RT element, which forwards to ProjectGrad_RT for the 2D RT elements (since it assumes the domain field would be in H1).

My main goal is to be able to solve 2D magnetostatics problems without having to write new integrators just for the 2D case, so if there is a simpler approach to doing that without needing a 2D ND space, please let me know as well.
Thanks as always!

Hi, @tuckerbabcock,
I understand your desire to reuse your 3D code in 2D. You may be interested in PR #1989 which came from a similar motivation.
Regarding your specific questions:

The curl of a two dimensional vector field is always directed out of the plane i.e. it is always scalar valued. This is sometimes called the rot operator.
The scalar is just interpreted as the z-component of the magnetic flux (assuming your 2D domain is the xy-plane).
I'n not certain the CurlInterpolator supports this but the natural way to compute the curl of a Nedelec field in 2D produces its result as an L2 field (rather than H1).

I believe the PR I mentioned above goes into this in some detail but basically 2D EM often involves problems which are decoupled from each other. In 2D magnetostatics the usual thing to think about is the direction of the current. If the current is in the plane then B and H will only have z-components. If the current is in the z-direction then B and H will have only components in the x and y directions. In the first case you solve for A using 2D Nedelec basis functions, in the second you solve for A_z using H1 basis functions. If the magnetic permeability is anisotropic and blends these two polarizations then you may want to try PR #1989. If the magnetic permeability is isotropic but J has both in-plane and out-of-plane components then you'll need to couple the Nedelec and H1 polarizations using BlockOperators (assuming your nonlinearity involves both polarizations) or use something like PR #1989.
Best wishes,
Mark

Thanks for the reply @mlstowell.
Of course you're right, the curl of any 2D vector field would be the scalar out of the page value. I didn't jump to that at first since I was wanting to represent A_z with the 2D Nedelec field, and somehow just have the other vector component be zero/ignored, but I see now that that doesn't make much sense.
I took a look at PR #1989 and I'm not sure how I'd be able to use it. My current density is strictly out of the page, so I'm only dealing with A_z (in this 2D case). At first I thought I would want to use the ND_R1D collection (since the documentation suggests it's for when the vector field varies along one dimension), but it requiring dim == 1 rules that out for me.
Since representing A_z with H1 elements is the straightforward choice, maybe the right approach for me is to create a new FE collection that uses finite elements that inherit from the H1 2D elements but have been extended to have the same interface as the ND elements so that I may use them in the same integrators? Do you have any thoughts on that approach @mlstowell, or perhaps could tell me if I've misunderstood PR #1989? Also, what's the status with that PR? It looks like it's been two months since the last commit.
Thanks for your help as always,
Tucker

Hi, @tuckerbabcock,
The ND_R1D basis is for 3D vectors on 1D domains. Meaning that the vector field has x, y, and z components but each of these only varies as you move in the x direction. For a 2D domain you'd want to use ND_R2D. These are a direct sum of the 2D Nedelec basis and the H1 basis. If your current is always in the z direction and your permeability doesn't mix the z component with the xy components then you don't really need PR #1989 although it would work fine. By the way this PR is ready to go except that I was waiting until another change to the fe.[ch]pp files made its way into master. That happened last week so I'll start pushing on this PR again.
You could create a set of specialized basis functions and an accompanying FECollection. To be honest it might be easier to write a new integrator for the 2D case.
Best wishes,
Mark

Thanks @mlstowell!
I'll close this issue since you've been able to help me address my concerns. I'll look forward to the PR being merged!


Removed if statement that ignored last argument from getCustomFingerp…

Fix from https://github.com/jackspirou/clientjs/issues/19

Sorry! This should be on develop branch


Update QuickStart.md to address issue 720

To address https://github.com/ZOSOpenTools/meta/issues/720

Is it worth adding to the Note that by sourcing (depending on which packages are installed and sequence in .profile of automatic) some zopen-provided tools might run instead of natively-provided tools and so might have different behaviour...?

I'll handle this comment under https://github.com/ZOSOpenTools/meta/pull/766 with a suggestion of how to source zopen-config


Allow different activation functions for neurons

Logistic function is good for hidden layer, but inputs may benefit from negative activation.
Alternatively, input neurons types can be made different from the others.

The current model supports enough diversity through parameters.


object mutation creates a vault client even if there is nothing to mutate

Preflight Checklist

[X] I have searched the issue tracker for an issue that matches the one I want to file, without success.
[X] I am not looking for support or already pursued the available support channels without success.
[X] I agree to follow the Code of Conduct.

Vault Secrets Webhook Version
1.21.1
Installation Type
Official Helm chart
Bank-Vaults Version
No response
Kubernetes Version
1.25
Kubernetes Distribution/Provisioner
EKS
Expected Behavior
When enabling object mutation for a custom CRD, vault-secrets-webhook should replace secrets in the resources. But it should not require a ServiceAccount and VAULT_ROLE for a resource that does not contain anything that needs replacement.
Actual Behavior
Vault-secrets-webhook authenticates with vault before it checks if there is anything to replace in a custom CRD. This makes it necessary to have a working default role for vault-secrets-webhook in vault that does not have any capabilities.
If this role does not exists, the webhook ends with the following message:
could not mutate object: failed to create vault client: timeout [10s] during waiting for Vault token

Depending of the webhook configuration this might block a resource from getting onto the cluster for no good reason.
It would be better if there were some checks done if secrets from vault are needed before creating a vault client. This is implemented for ConfigMap mutation and Secrets, but not for any custom CRD.
Steps To Reproduce
No response
Configuration
No response
Logs
No response
Additional Information
No response

Hi @czenker, thanks for reporting this issue!
I think this behavior is for the code to traverse the object searching for secrets to be replaced only once. Could you please try if this issue is fixed by using the vault.security.banzaicloud.io/mutate annotation with the value skip on CRDs that should not be mutated?
Also if you have a solution in mind, we appreciate any contributions to the project!

vault.security.banzaicloud.io/mutate: skip prevents this behavior. But adding this to every resource is impractible and unintuitive if you need the replacement only in a few of the resources.
In my opinion, it should behave similar than secret and configmap mutation. The code just does not look to me as if it could easily be copied.

Hey @czenker,
In case of secret and configmap mutation It's very easy to confirm whether the resource needs to be mutated, but that is not the case with object mutation, because we need to search everywhere inside the object and see if we find a vault secret reference.
So basically the verification step would be the same as mutating. So instead of going through the object twice, the Vault client is always created.
If the object should be mutated, then creating the Vault client will obviously not raise an error.
If it shouldn't be mutated, then as @akijakya pointed out, the vault.security.banzaicloud.io/mutate annotation should be set on the resource with skip value.


Set up GitHub Sponsor account for Doom Emacs

Describe the feature
Doom Emacs is quite lovely and save a lot of time to me, and I am pretty sure I am not alone. So, it would be nice to support the project as I do for all OSS that save me time. I see that it is already possible to support it via PayPal and LiberaPay, but GitHub Sponsor feature is the most reliable and convenient tool from my perspective. And I know a lot of people who moved to GitHub to keep track of all the OSS projects they support. Please, set up the GitHub Sponsor feature if there is no reason not to.
If you need examples, there are some Emacs related projects with enabled GitHub Sponsor feature:

@abo-abo - Ivy/Swiper/Counsel, Hydra, etc.;
@bzg - org-mode;
@tarsius - Magit.

System information

emacs   version    27.0.90
        features   RSVG IMAGEMAGICK GLIB NOTIFY KQUEUE ACL GNUTLS LIBXML2 ZLIB TOOLKIT_SCROLL_BARS NS MODULES THREADS JSON PDUMPER LCMS2 GMP
        build      Mar 09, 2020
        buildopts  (--disable-dependency-tracking --disable-silent-rules --enable-locallisppath=/usr/local/share/emacs/site-lisp --infodir=/usr/local/Cellar/emacs-plus/HEAD-4537976/share/info/emacs --prefix=/usr/local/Cellar/emacs-plus/HEAD-4537976 --with-xml2 --without-dbus --with-gnutls --with-imagemagick --with-json --with-modules --with-rsvg --with-ns --disable-ns-self-contained)
        windowsys  ns
        daemonp    server-running
doom    version    2.0.9
        build      HEAD -> develop 0fafba7ee 2020-03-09 14:04:25 -0400
        dir        /Volumes/Extended/Projects/dotfiles/home/.config/doom/
system  type       darwin
        config     x86_64-apple-darwin19.3.0
        shell      /bin/zsh
        uname      Darwin 19.3.0 Darwin Kernel Version 19.3.0: Thu Jan  9 20:58:23 PST 2020; root:xnu-6153.81.5~1/RELEASE_X86_64 x86_64
        path       (/usr/local/bin /usr/bin /bin /usr/sbin /sbin /usr/local/sbin /usr/local/Cellar/emacs-plus/HEAD-4537976/libexec/emacs/27.0.90/x86_64-apple-darwin19.3.0/)
config  envfile    envvar-file
        elc-files  1
        modules    (:completion company ivy :ui doom doom-quit hl-todo modeline ophints (popup +all +defaults) pretty-code vc-gutter vi-tilde-fringe window-select workspaces zen :editor file-templates fold rotate-text snippets :emacs dired electric ibuffer vc :term eshell vterm :checkers syntax :tools (eval +overlay) (lookup +docsets) magit pdf :lang data emacs-lisp kotlin markdown (org +dragndrop +hugo +pandoc +pomodoro +present) php plantuml python ruby rust sh swift :app irc (rss +org) :config (default +bindings +smartparens))
        packages   ((org-super-agenda) (deadgrep) (pocket-reader))
        unpin      (n/a)
        elpa       (s request rainbow-identifiers pocket-reader pocket-lib ov org-web-tools kv ht esxml dash)

I can understand if maintainers don't want to use github for this, but I also tend to use the github sponsor feature than patreon etc., although I use both...

It's been requested a few times on our Discord. I'm on board with it, but haven't gotten around to it yet. I'll let you know once I have.

Just want to say that Liberapay supports recurring payments and is very pleasant to use. Maybe that's worth mentioning in the readme, I'd imagine many people have never seen it before :)

Another bump for this. I would also like to have my recurring sponsorships for OSS be in one place.

I'm not sure how payment via liberapay vs github sponsor for credit cards works, but on liberapay it warned me of high fees if I setup a monthly renewal. I assume that means you'll get less of the overall donation @hlissner? If the fee structure is different/more in your favor for github sponsors that might be another reason to add it.

Sorry for taking so long to do this!  With that, I'll consider this resolved. Thanks for the suggestion. From what I've heard and read, it seems Github Sponsors is much a better system than what liberapay offers (no fees for now and its integration with github will make it easy to add tier rewards for sponsors in the future).


add stage selection

Contents

you can select stage with exclude or include keyword

if keyword(exclude or include) is empty, you send slack message to all stage
if stage is on both sides, include takes precedence

example

 custom:
   serverlessDeployHistory:
     exclude:
       - dev
       - qa
     include:
       - production
     slack:
       webhook: 'https://hooks.slack.com/services/~'
       title: 'Sample Deploy History'

fix yaml

remove exclude, include fields

 custom:
   serverlessDeployHistory:
     stage:
       - dev
       - qa
     slack:
       webhook: 'https://hooks.slack.com/services/~'
       title: 'Sample Deploy History'


add left arrow support for slides

right now it only changes with right

You mean we want to be able to cycle around the slides, right? Pressing the left key in the first slide should go to the last one.

the problem is you can't go back, if you are in slide 1 you can't go to slide 0 pressing left key

Done.


Added feature to set an icon tint color

In order to match the appearance of the PickerNavigationController to an existing App-Design this pull request adds the capability to set a iconTintColor to set a specific color for the displayed icons (Camera, Facebook, Instagram, ...)
// Optional. Set the an icon tint color.
picker.iconTintColor = .orange

It was done on feature/stylizer branch.


A little fix

Use grunt.file.mkdir instead of fs.mkdirSync

Because fs.mkdirSync will throw error when the directory exists, and
that's really a trouble that i must remove '/test/results/' directory
when i run grunt test every second time.

Use your const variable UNICODE_PRIVATE_USE_AREA instead of magic
number 0xE001

And i wanna push a pull request to let this plugin support all options that your svgicons2svgfont project have. Could i ?

Of course you di. Would you like to be this project maintainer? I could give you github ownership and add you to the npm maintainers in order for your to continue improve this plugin without requiring me.

@nfroidure
That would be nice. Thank you.

I added you as a contributor but can't transfer ownership since you already have a repo with that name.

 Let me know when it's allright.

@nfroidure
OK, Thx. I've delete that fork.

@nfroidure
hi. Could you please do me a favor to publish a release on npm?

@poppinlp would be better you to create a npm account and i'll add you to maintainers list

@nfroidure
THX. My npm account named poppinl.
https://www.npmjs.com/~poppinl

Done. Have a nice day.
PS: Would you be interested in also maintaining grunt-(svg2ttf|ttf2eot|ttf2woff) ? If so i'll add you to the repos and the npm modules.

@nfroidure
Thanks. That will be great.

Done.
Thank you for taking back the leadership on those projects.
Have a nice day.


Update cherry-pick.sh

Fixes #TODO
Motivation

Modifications

Verification

Looks like this was replaced by #12486


Request is always empty

Hello. Why is request block always empty?

could you create a example from fork? to us simulate the problem?

Sorry, I have no time now to create example. Problem is that I see response json. Have no request json. It always empty. I will try to create example later)

Hello @YaroslavReznichenko!
Do you use any external library or it's just URLSession?

@heitara Alamofire

is the iOS 12?

https://github.com/DebugSwift/DebugSwift/tree/feature/post-with-alamofire
I tried recreate the issue, but here works:

@MaatheusGois he is referring to a form request. It might be upload of a file or similar.
@YaroslavReznichenko could you please be more specific?

is application/x-www-form-urlencoded, I tested only with json

working too:

I think problem was in version of Alamofire - 5.0.0
Was updated to the latest and fixed it.

@heitara thanks for the support!


MAINT: interpolate: move an internal utility from cython to python

A follow-up to https://github.com/scipy/scipy/pull/21223, chipping small bits and pieces from a cython extension: move the _handle_lhs_derivatives helper from Cython to python. The code is basically pure numpy, and the computational complexity is O(1), so there is no reason for this helper to stay in Cython or move to C++.

Thanks Evgeni, Lucas


Improve protobuf decoding performance

Our current protobuf decoding is very slow and there are a number of improvements we can do:

Unified object layout of typed data, external typed data and views (maybe also the new read-only classes)
-> Make VM aware that e.g. all implementations of Uint8List have same base class and use it for the CompileType.

Add pure/invariant calls which we know always return the same result.

Improve TFA to support limited control-flow sensitive analysis for null-ability propagation.

Automatic specialization of List<int> related code on kernel level (maybe)

Fast utf8 decoding also tracked at https://github.com/dart-lang/sdk/issues/31954

We have done a lot of optimizations to improve protobuf decoding. In the string-heavy protobuf benchmark we have reduce decoding time by 60%.
Closing this issue, since we have achieved major improvement and have no more immediate work planned on this atm.


[WIP] Bump aws_request version to use newest intl

Releated to: https://github.com/Zsmerritt/Flutter_AWS_CloudWatch/issues/12
Still work in progress. I will update it when aws_request will be available at 1.0.1 version.

Updated aws_request version in 1.0.1 and published a new version of this package with that change


Can't enable the GPU plugin

Hi!
I'm new here and I don't exactly know how this works but at least I can tell you what my issue is. So I've had an issue that I can't turn on my GPU plugin, and as soon as I do it it turns off by itself. I've taken a break from OSRS for the past couple of months and back then I could play the game with the GPU plugin with settings on the higher end of the spectrum without any issues.
I've been trying to search around for people who have the same issue and usually what pops up is either that the drivers are not up to date or that there's some problem with what graphics card the game is running on, the internal or external one. I've double-checked both of these things and they haven't helped me at all, and the problem still stands as is. I've been trying to check if the problem could be java related without any luck. I've also checked my OpenGL rendering GPU option in the NVIDIA control panel, and it is set to my NVIDIA GPU as recommended in your FAQ.
I've got an Acer Nitro 5 and (some of the) specs on my pc are:
Processor: IntelCore i5-7300HQ
RAM: 8GB
Graphics Card: NVIDIA GeForce GTX 1050 Ti
Thank you in advance for your help, it is much appreciated! Also if there's any other info you need just ask, as I'm not very experienced with these types of issues.

The issue tracker is not a support forum. Please join the Discord for questions of support.


Add tests for named capture group and named back reference

I forgot to add tests in my previous PR https://github.com/facebookexperimental/rome/pull/247
This PR adds tests for named backreference as well as named capture group
I was also thinking of adding more tests to the project in general. Let me know if this is something you'd like to see and what should be the priority. I was thinking start with the formatter maybe since it was refactored recently and followed along a little bit?

I was also thinking of adding more tests to the project in general. Let me know if this is something you'd like to see and what should be the priority.

Yes, definitely! js-generator used to contain all the test fixtures from babel-generator, but they were removed a long time ago. It would be good to bring them back, especially since it'll be a decent smoketest for the autoformatter.

Thank you!


[feat]: Make fast-theme be silent by default and do nothing if theme already set

If the feature request is for a plugin or theme, specify it here.
No response
If the feature solves a problem you have, specify it here.
No response
Describe the proposed feature.
I would like to use fast-theme to set the theme as part of a CLI script (that can be run several times) however, it is very noisy the output and there is no option to silence it.
Also if theme is already set nothing should be done on this script as well.
Describe alternatives you've considered
No response
Additional context
No response
Related Issues
No response

This would also be useful for declarative theme configuration, as it would be practical to call fast-theme from zshrc.


Fix ETW package to work on 386

The ETW registration handle is defined to be 64-bits on all platforms. The Go
type previously used a uintptr which created problems on 32-bit systems. This
change adds a new wrapper file which receives a 64-bit handle for the functions
defined in it, and correctly passes it to the native ETW functions either as-is
(on 64-bit) or as two 32-bit values (on 32-bit).
This required a minor change in mksyscall_windows.go, to allow multiple syscalls
that map to the same underlying function without causing a duplicate definition
error in the generated file.

Could you split the import of Go's mksyscall_windows.go file into a separate commit so that it's clear what changes you have made to it?

Could you split the import of Go's mksyscall_windows.go file into a separate commit so that it's clear what changes you have made to it?

@jstarks Added the stock mksyscall_windows.go as a new commit, so the diff shows up if you just view the latest commit.

LGTM. It would be nice to improve mksyscall_windows later to generate all this automatically based on the size of the input arguments.


Force on the CXX11_ABI flag

Description

Provide details of the change, and generalize the change in the PR title above.

The Linux Unity editor was crashing in the Firestore testapp. From what I can tell, the Unity editor was likely build with this option enabled, and mixing the library without it was the cause of the crash. So we will force the option to always be on for Linux builds. Note that standalone builds were not crashing, so it was likely just a problem with the Unity editor being built with that option.

Testing

Describe how you've tested these changes.

Type of Change
Place an x the applicable box:

[ ] Bug fix. Add the issue # below if applicable.
[ ] New feature. A non-breaking change which adds functionality.
[x] Other, such as a build process or documentation change.

Desktop build: https://github.com/firebase/firebase-unity-sdk/actions/runs/2242859483

Details about the _GLIBCXX_USE_CXX11_ABI preprocessor define are here: https://gcc.gnu.org/onlinedocs/libstdc++/manual/using_dual_abi.html


the role does not support ntpd_role: server

the role name implies that you could configure ntpd server, but, it does not.

Personally, I do not like ntpd from ntpd.org, especially when running as a NTP server, because of its security track records and the lack of privilege separation.
openntpd is a way better alternative. However, as openntpd depends on LibreSSL and LibreSSL is not a fully drop-in replacement (OpenBSD project itself needs more fixes in the ports, FreeBSD has not completed the migration, other distributions simply do not support it yet) it would take some more time to replace OpenSSL with LibreSSL,
Mark this as pending until when it is clear for openntpd to be a viable replacement. Will close if openntpd is portable enough.


Dynamically downgrade index scan plan to table scan plan

HandleRDDExec is used for scanning handles as a LeafExecNode in index plan. Providing handle scan via a TiHandleRDD.

RegionTaskExec is used for issuing requests which are generated based on handles retrieved from HandleRDDExec aggregated by a SortAggregateExec with CollectSet as aggregate function. Refer to code in TiDBRelation.scala and CoprocessorRDD.scala for further details.

RegionTaskExec downgrade to table scan if handles retrieved from one region exceed spark.tispark.plan.downgrade.index_threshold

Refer to this issue https://github.com/pingcap/tikv-client-lib-java/issues/179
KV-Client side PR: https://github.com/pingcap/tikv-client-lib-java/pull/200

Example
SQL:
spark.sql("SELECT tp_int FROM full_data_type_table where tp_int > 22")

spark.tispark.plan.downgrade.index_threshold: 100
Plan:
== Physical Plan ==
TiSpark RegionTaskExec{downgradeThreshold=100}
+- SortAggregate(key=[RegionId#147L], functions=[collect_set(distinct Handle#148L, 0, 0)])
   +- *Sort [RegionId#147L ASC NULLS FIRST], false, 0
      +- Exchange hashpartitioning(RegionId#147L, 200)
         +- TiDB HandleRDD{[table: full_data_type_table] [Index: index2] , Ranges: Start:[1], End: [1], Columns: [tp_int], Filter: UnaryNot(IntIsNull([tp_int])), Downgrade ranges: Start:[-9223372036854775808], End: [9223372036854775807], Downgrade filters: UnaryNot(IntIsNull([tp_int])), GTInt([tp_int], 22)}

Important log:
17/12/17 14:10:54 WARN RegionTaskExec: Index scan handle size:988 exceed downgrade threshold:100, downgrade to table scan with 1 region tasks
17/12/17 14:10:54 INFO RegionTaskExec: Unary task downgraded, task info:Host={<IP_ADDRESS>}, Region={{Region[2] ConfVer[1] Version[58] Store[1] KeyRange[116,128,0,0,0,0,0,0,82,95,114,128,0,0,0,0,10,173,7]:[]}}, Store={id=1,addr=<IP_ADDRESS>:20160}

Result:
+----------+
|    tp_int|
+----------+
|1238733782|
|      2333|
|      2333|
| 274040870|
|1431435712|
|1301313514|
|1144303507|
| 601508558|
|1680320009|
| 334392758|
| 139617906|
| 261704108|
| 661116818|
| 627121337|
|2133386169|
|2006469139|
|1717638394|
|2045445934|
| 897582990|
| 353587789|
+----------+
only showing top 20 rows

Schema and data can be found in master branch.

Local:
spark-shell --driver-memory 2G --executor-memory 6G
SQL:
select count(tp_int) from tb_downgrade where tp_int > 0;
min time
avg time
max time
Downgraded branch:
Pure index scan(batch size=200000):
58s
1.0m
1.3m

Downgraded table scan(double scan):
Merged millions of key ranges(like 242021) to 1 range, so faster than pure index scan
44s
45s
47s

Master branch:
Pure index scan:
1.0m
1.1m
1.2m

Pure table scan:
20s
23s
24s

Cluster:
Spark with 3 Worker Nodes, up to 72 cores, 108GB memory
TiKV on 4 nodes, each has 4 tikv instances.
SQL:
select count(L_ORDERKEY) from lineitem where L_ORDERKEY < 300000000
Downgraded branch:
Pure index scan(batch size=2000000):
1.8m
1.9m
2.1m

Downgraded table scan(double scan):
Merge 1 keyRange to 1 range, so it is nearly same with pure index scan(actually it's slower)
1.8m
1.9m
2.1m

Master branch:
Pure index scan:
(some tasks failed due to 'peer is not leader'(leader change))
3.4min
3.6min
3.8min

Pure table scan:
48s
48s
50s

TPCH:
Result: Tests run: 21  Tests succeeded: 21  Tests failed: 0  Tests skipped: 0
DAG:
Result: Tests run: 6744  Tests succeeded: 3397  Tests failed: 16  Tests skipped: 3331

TPCH
Result: Tests run: 21  Tests succeeded: 21  Tests failed: 0  Tests skipped: 0
DAG
Tests run: 7335  Tests succeeded: 3767  Tests failed: 8  Tests skipped: 3560

@zhexuany Updated according to comment.

TPCH
Tests run: 21  Tests succeeded: 21  Tests failed: 0  Tests skipped: 0
DAG
Result: Tests run: 7335  Tests succeeded: 3767  Tests failed: 8  Tests skipped: 3560


Leaflet 0.7.7 version compatible

Hi good day ... Can you tell me what specific version of Leaflet.Draw compatible to Leaflet version 0.7.7

Current version (0.4.10) should be compatible with 0.7.7.


explorer: gas price stats card

current reference price
7 day average
historical data for last 7 and 30 epochs

https://user-images.githubusercontent.com/10210143/234885561-1a73d6c1-3a54-42ee-9016-514c60eede14.mov
https://user-images.githubusercontent.com/10210143/234885595-bdd2ebe3-76c2-4811-8a5c-1c3328422e70.mov
NOTE: currently we have to fetch one epoch extra because each epoch's info contain the gas price of the next one. This PR already added the gas price of the epoch itself as well but need to wait until it's deployed (and probably updating sdk as well).
closes APPS-829

Also, please shift the dropdown so it aligns exactly on top right with the dropdown


[WIP] adds entry point to new /order2 route

This PR:

Adds a reference to the new "New Buy Now Flow" lab feature (which I can rename and TODO have to add to production)
Updates the acquire flow for the non-auction e-commerce flow to create an order in exchange. The auction buy now flow follows a similar code path but in a different place. We'll have to update there as well but I wanted to focus on one spot for now.
Updates the /order2 route to instead be parameterized by an orderId and render a very basic react page.

WIP because we probably don't need to merge this before I can integrate it with @zephraph 's work in reaction. But interested in comments!

This has to be WIP still I believe, since it depends on a new version of reaction which hasn't been released yet.

Updated this to reuse the exiting minimal header (which we should be able to tweak for our needs)


Add ColorManager to Vectors

Description
This pull request integrates ColorManager into the Vectors layer. This is a non-breaking change. We will update the public API in a future PR (see https://github.com/napari/napari/issues/1727#issuecomment-788917315).
Type of change

[ ] 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
This is a follow up to #2204 and discussed in #1727
How has this been tested?

[x] ll tests pass with my change

Final checklist:

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

Thanks for the review, @goanpeca ! I've deleted the line that was commented out.


PR checker has links onto the old qaprosoft nexus snapshot repo

PR checker produce:
https://ci.qaprosoft.com/nexus/content/repositories/snapshots/com/qaprosoft/carina-core/<IP_ADDRESS>3-SNAPSHOT
but valid link is:
https://nexus.zebrunner.dev/#browse/browse:ce-snapshots:com%2Fqaprosoft%2Fcarina-core%2F<IP_ADDRESS>3-SNAPSHOT
let's update CI/CD pipeline to show valid links

fixed

verified both on pull request and on push jobs - links are valid now.


uppercase on network indicators

we should make this unified

Lets use Ahp since its used everywhere apart from explore cards

so on explore part, it should be Ahp instead of AHP

https://github.com/kodadot/nft-gallery/pull/8750#issuecomment-1868883447 did you change your mind?

@roiLeo yeah, saw that on the rest of cards and places its Ahp


riverpod_generator: Provide a way to create non-AutoDispose providers.

Currently
@riverpod
class SomeNotifier extends _$SomeNotifier

creates
final someNotifierProvider = AutoDisposeNotifierProvider<SomeNotifier>
I need a non-AutoDipose provider, therefore I would appreciate an option to trigger the creation of such.
Current workaround:
Use ref.keepAlive(); in build().

@Riverpod(keepAlive: true)
...

Thanks!
I didn't find it in the documentation. Maybe I'm blind or it may be missing.

That's normal. Docs are pending :)


SAK-43566 SameSite support for incompatible clients

 Here is an overview of what got changed by this pull request:

Issues
======
+ Solved 1
- Added 2

Complexity increasing per file
==============================
- kernel/api/src/test/java/org/sakaiproject/util/ServerCookieTest.java  1
- kernel/api/src/main/java/org/sakaiproject/util/ServerCookie.java  6

See the complete overview on Codacy

Looks good to me @ern , good job