IndexError: Out of range:

Describe the bug
Traceback (most recent call last):
  File "train_with_wav2vec2.py", line 374, in <module>
    valid_loader_kwargs=hparams["dataloader_opts"],
  File "/home/zhengbeida/anaconda3/envs/slurp/lib/python3.7/site-packages/speechbrain/core.py", line 1225, in fit
    self._fit_train(train_set=train_set, epoch=epoch, enable=enable)
  File "/home/zhengbeida/anaconda3/envs/slurp/lib/python3.7/site-packages/speechbrain/core.py", line 1078, in _fit_train
    loss = self.fit_batch(batch)
  File "train_with_wav2vec2.py", line 143, in fit_batch
    loss = self.compute_objectives(predictions, batch, sb.Stage.TRAIN)
  File "train_with_wav2vec2.py", line 89, in compute_objectives
    for utt_seq in predicted_tokens
  File "train_with_wav2vec2.py", line 89, in <listcomp>
    for utt_seq in predicted_tokens
  File "/home/zhengbeida/anaconda3/envs/slurp/lib/python3.7/site-packages/sentencepiece/__init__.py", line 837, in DecodeIds
    return self.Decode(input=input, out_type=out_type, **kwargs)
  File "/home/zhengbeida/anaconda3/envs/slurp/lib/python3.7/site-packages/sentencepiece/__init__.py", line 780, in Decode
    return self._DecodeIds(input)
  File "/home/zhengbeida/anaconda3/envs/slurp/lib/python3.7/site-packages/sentencepiece/__init__.py", line 337, in _DecodeIds
    return _sentencepiece.SentencePieceProcessor__DecodeIds(self, ids)
IndexError: Out of range: piece id is out of range.

After running a few epochs, the problem suddenly appears and then disappears when running again. 35 epochs sometimes have this problem several times, will it have any effect on the final result?
Expected behaviour
Have you encountered this problem? The experiment I am running is SLURP. how do I fix this?
To Reproduce
I only modified the path to the dataset and the path to the saved file.
Versions
SpeechBrain system description
==============================
Python version:
3.7.16 (default, Jan 17 2023, 22:20:44)
[GCC 11.2.0]
==============================
Installed Python packages:
appdirs==1.4.4
attrs==23.1.0
black==19.10b0
certifi @ file:///croot/certifi_1671487769961/work/certifi
cfgv==3.3.1
charset-normalizer==3.1.0
click==8.0.4
distlib==0.3.6
entrypoints==0.3
filelock==3.12.0
flake8==3.7.9
fsspec==2023.1.0
huggingface-hub==0.15.1
HyperPyYAML==1.2.1
identify==2.5.24
idna==3.4
importlib-metadata==6.6.0
joblib==1.2.0
jsonlines==3.1.0
mccabe==0.6.1
more-itertools==9.1.0
nodeenv==1.7.0
numpy==1.21.6
packaging==23.1
pandas==1.3.5
pathspec==0.11.1
Pillow==9.5.0
platformdirs==3.5.1
pluggy==0.13.1
pre-commit==2.21.0
py==1.11.0
pycodestyle==2.5.0
pyflakes==2.1.1
pytest==5.4.1
python-dateutil==2.8.2
pytz==2023.3
PyYAML==6.0
regex==2023.5.5
requests==2.31.0
ruamel.yaml==0.17.28
ruamel.yaml.clib==0.2.7
scipy==1.7.3
sentencepiece==0.1.99
six==1.16.0
speechbrain==0.5.14
tokenizers==0.13.3
toml==0.10.2
torch==1.9.0
torchaudio==0.9.0
torchvision==0.10.0
tqdm==4.65.0
transformers==4.29.2
typed-ast==1.5.4
typing_extensions==4.5.0
urllib3==2.0.2
virtualenv==20.23.0
wcwidth==0.2.6
yamllint==1.23.0
zipp==3.15.0
==============================
Could not get git revision==============================
CUDA version:
10.2

Relevant log output
No response
Additional context
No response

Hello,
Could you please share the size of your vocab / embedding of your decoder etc? I suspect that there is a mismatch between both.
Best.

URL for the wav2vec2 model, you can change to benchmark diffrenet models
wav2vec2_hub: "facebook/hubert-base-ls960"
Training parameters
number_of_epochs: 35
batch_size: 6
lr: 0.0003
lr_wav2vec2: 0.00001
token_type: unigram # ["unigram", "bpe", "char"]
sorting: random
ckpt_interval_minutes: 15 # save checkpoint every N min
#freeze all wav2vec2
freeze_wav2vec2: False
#set to true to freeze the CONV part of the wav2vec2 model
freeze_wav2vec2_conv: True
Model parameters
sample_rate: 16000
emb_size: 128
dec_neurons: 512
output_neurons: 58 # index(eos/bos) = 0
encoder_dim: 768
Decoding parameters
bos_index: 0
eos_index: 0
min_decode_ratio: 0.0
max_decode_ratio: 10.0
slu_beam_size: 80
eos_threshold: 1.5
temperature: 1.25
dataloader_opts:
batch_size: !ref <batch_size>
shuffle: True
epoch_counter: !new:speechbrain.utils.epoch_loop.EpochCounter
limit: !ref <number_of_epochs>
Models
wav2vec2: !new:speechbrain.lobes.models.huggingface_wav2vec.HuggingFaceWav2Vec2
source: !ref <wav2vec2_hub>
output_norm: True
freeze: !ref <freeze_wav2vec2>
save_path: !ref <save_folder>/wav2vec2_checkpoint
output_emb: !new:speechbrain.nnet.embedding.Embedding
num_embeddings: !ref <output_neurons>
embedding_dim: !ref <emb_size>
dec: !new:speechbrain.nnet.RNN.AttentionalRNNDecoder
enc_dim: !ref <encoder_dim>
input_size: !ref <emb_size>
rnn_type: lstm
attn_type: content
hidden_size: !ref <dec_neurons>
attn_dim: 512
num_layers: 3
scaling: 1.0
dropout: 0.0
seq_lin: !new:speechbrain.nnet.linear.Linear
input_size: !ref <dec_neurons>
n_neurons: !ref <output_neurons>
augmentation: !new:speechbrain.lobes.augment.TimeDomainSpecAugment
sample_rate: !ref <sample_rate>
speeds: [95, 100, 105]
modules:
wav2vec2: !ref
output_emb: !ref <output_emb>
dec: !ref
seq_lin: !ref <seq_lin>
model: !new:torch.nn.ModuleList
- [!ref <output_emb>,
!ref , !ref <seq_lin>]
tokenizer: !new:sentencepiece.SentencePieceProcessor
pretrainer: !new:speechbrain.utils.parameter_transfer.Pretrainer
collect_in: !ref <save_folder>/SLURP_tokenizer
loadables:
tokenizer: !ref
paths:
tokenizer: !ref <tokenizer_file>/tokenizer_58_unigram.model
beam_searcher: !new:speechbrain.decoders.S2SRNNBeamSearcher
embedding: !ref <output_emb>
decoder: !ref
linear: !ref <seq_lin>
bos_index: !ref <bos_index>
eos_index: !ref <eos_index>
min_decode_ratio: !ref <min_decode_ratio>
max_decode_ratio: !ref <max_decode_ratio>
beam_size: !ref <slu_beam_size>
eos_threshold: !ref <eos_threshold>
temperature: !ref
using_max_attn_shift: False
max_attn_shift: 30
coverage_penalty: 0.
opt_class: !name:torch.optim.Adam
lr: !ref
wav2vec2_opt_class: !name:torch.optim.Adam
lr: !ref <lr_wav2vec2>
lr_annealing: !new:speechbrain.nnet.schedulers.NewBobScheduler
initial_value: !ref
improvement_threshold: 0.0025
annealing_factor: 0.8
patient: 0
lr_annealing_wav2vec2: !new:speechbrain.nnet.schedulers.NewBobScheduler
initial_value: !ref <lr_wav2vec2>
improvement_threshold: 0.0025
annealing_factor: 0.9
checkpointer: !new:speechbrain.utils.checkpoints.Checkpointer
checkpoints_dir: !ref <save_folder>
recoverables:
model: !ref
wav2vec2: !ref
scheduler: !ref <lr_annealing>
lr_annealing_wav2vec2: !ref <lr_annealing_wav2vec2>
counter: !ref <epoch_counter>
log_softmax: !new:speechbrain.nnet.activations.Softmax
apply_log: True
seq_cost: !name:speechbrain.nnet.losses.nll_loss
label_smoothing: 0.1
train_logger: !new:speechbrain.utils.train_logger.FileTrainLogger
save_file: !ref <train_log>
error_rate_computer: !name:speechbrain.utils.metric_stats.ErrorRateStats
cer_computer: !name:speechbrain.utils.metric_stats.ErrorRateStats
split_tokens: True
The content of yaml file I only modified the path of dataset and the path to save the model, the others were not modified, the problem sometimes appears suddenly after running several epochs, sometimes it is IndexError: Out of range: piece id is out of range.
To debug, wrap fit() with autograd's detect_anomaly(), e.g., the word list size is 128, and the decoding is done by the same method.
, the word list size is 128, and the input dimension of the decoder is 768 dimensions.

Hello
Could the reason for this problem be due to the version?

Hello
Sorry, my answer is wrong, vocab_size is 58, not 128.
The project I reproduced is SLURP, which is a sequence-to-sequence project. The model structure of this project consists of the following parts. (1) Extracting a 768-dimensional speech feature using HuBert. (2) Decoding using 3 layers of lstm+attention. Where the output of lstm is 512 dimensions and attention is used Q,K,V. Its output is 512 dimensions. (3) The output of (2) is passed through a linear layer, and the output of the linear layer is 58, which is vocab_size.(4) The loss function used is NLL loss.@Adel-Moumen

hello
https://github.com/speechbrain/speechbrain/blob/develop/recipes/SLURP/direct/train_with_wav2vec2.py,https://github.com/speechbrain/speechbrain/blob/develop/recipes/SLURP/direct/hparams/train_with_wav2vec2.yaml,
This is the SLURP link I am reproducing, I have changed the path to load the dataset, the path to save it and the batch size is set to 2. The reproduced device is 3090. thanks very much.

Hello:
I checked the solution for https://github.com/speechbrain/speechbrain/pull/1186, https://github.com/speechbrain/speechbrain/pull/1183, but still the Loss is not finite and patience is exhausted, I tried to retrain a tokenizer_bpe51 but got RuntimeError: Internal: src/trainer_ interface.cc(581) [(static_cast(required_chars_.size() + meta_pieces_.size())) <= (trainer_spec_.vocab_size())] Vocabulary size is Increase vocab size or decrease character_coverage with --character_coverage option. It indicates that vocab_size needs to be greater than or equal to 58. So, how do I solve this problem now?@ Adel-Moumen

Hi @Adel-Moumen, I also have this problem. Can someone take a look at this? Thanks.

Hi @https://github.com/WangHelin1997,You can try to set output_neurons = 51, for the time being, I have successfully reproduced the lstm+content and lstm+keyvalue experiments without the above problem in between. But the reason for this mismatch in the original code is still unclear to me. You can consult the person in charge.@https://github.com/Adel-Moumen

Hi ,@WangHelin1997,You can try to set output_neurons = 51, for the time being, I have successfully reproduced the lstm+content and lstm+keyvalue experiments without the above problem in between. But the reason for this mismatch in the original code is still unclear to me. You can consult the person in charge.@Adel-Moumen

Hi @zhengqing187 , should we consider this done also?

Hi @mhn226
Yes, I have no more problems for now, thank you very much.


refactor(edge-lab): expand storage attach logic to use array of inputs

This expands on the approach from @galthaus but removes the hard coded paths in favor of pulling from an array defined as a param.  It also allow for more error detection so that a bad mount will not break the logic (but k3s install tests for the mounts later).
This approach will allow operators to override the default order and type.
For now, the k3s install still relies on a specific path, but that could be split out and parameterized in the future.  If the storage approach is generally useful, this could be refactored as a stand alone task and pulled into common libraries.

updated the docs too.


discovery: flatten future msg cache and increase its size

Fixes #7396. This PR flattens the slice used in future message cache and increases the cache size.
Depends on https://github.com/lightninglabs/neutrino/pull/268

Can be rebased now with the neutrino PR merged!


🌐(translations) fix translations issues in fr-FR.json

Purpose
Fix translations issue
Body
Improve a few french translations issues in the fr-Fr.json

@RaoufCherif : Thank you replace réglages by paramètres and salle by espace virtuelle


💡[Feature]: Add Computer Vision Based Snake Game under OpenCV projects folder

Is there an existing issue for this?

[X] I have searched the existing issues

Feature Description
The Computer Vision-based snake game is a novel approach to the traditional game of snake, where the player controls the movement of a snake to earn rewards and grow in length while avoiding collision with obstacles or the boundaries of the game screen.
Use Case
The Computer Vision-based Snake Game allows players to control the snake using hand gestures detected by a webcam, making gameplay more interactive and engaging.
CV libraries such as Mediapipe, OpenCV and PyGame will bring used.
Benefits

Enhances user interaction through gesture-based control.
Provides a fun way to explore computer vision and image processing techniques.
Can be integrated with open-source projects to promote learning in computer vision and game development.

Add ScreenShots
No response
Priority
High
Record

[X] I have read the Contributing Guidelines
[X] I'm a GSSOC'24 contributor
[X] I want to work on this issue

@sanjay-kv I would request you to please assign me with this issue and assign all the necessary labels.

Is there an existing issue for this?

[x]  I have searched the existing issues

Feature Description
The Computer Vision-based snake game is a novel approach to the traditional game of snake, where the player controls the movement of a snake to earn rewards and grow in length while avoiding collision with obstacles or the boundaries of the game screen.
Use Case
The Computer Vision-based Snake Game allows players to control the snake using hand gestures detected by a webcam, making gameplay more interactive and engaging.
CV libraries such as Mediapipe, OpenCV and PyGame will be used.
Benefits

Enhances user interaction through gesture-based control.
Provides a fun way to explore computer vision and image processing techniques.
Can be integrated with open-source projects to promote learning in computer vision and game development.

Add ScreenShots
No response
Priority
High
Record

[x]  I have read the Contributing Guidelines
[x]  I'm a GSSOC'24 contributor
[x]  I want to work on this issue

https://github.com/recodehive/machine-learning-repos/issues/1367
not accetpting new issue,. refere above

#1367
not accetpting new issue,. refere above

Okay @sanjay-kv


Digital/Simulated PCR

Input

Genome
Primers

Genome
Four anticipated genome formats of common use, in order of priority:

[ ] FASTQ
[ ] FASTA
[ ] VCF+Reference
[ ] BAM/SAM

Primers
A CSV of initial format:

Forward
Reverse

seq 1
seq 2

seq 3
seq 4

seq 1
seq 4

Output
A multi-FASTA of target sequences where header lines follow the general trend of:

> T-<n> G-<ID> F-<seq> R-<seq>

T is the target (increments on multicapture), G is the genome (uniquely IDs the genome targets are captured from), F is the forward primer, R is the reverse primer
Rejection/Termination Conditions

Mismatched primer pair statistics (e.g., forward and reverse primers have deviated melting temperatures)
No pair of primers matching the genomic sequence
Too high of GC content (approx. >40%)

Similar tools
Primer-BLAST, except in reverse. Primer-BLAST is a primer design tool fro when you already know your target sequence. What is needed is a tool to find the target sequence in a genome when you have primers.
Description
Take a genome and primer pairs, finding the target sequence one would get with that combination.
Research Purpose
Could test in silico whether anything would be captured in a genome by a primer set designed for a related species.

@ThatLionLady, if you have any other tool requests, please open an issue on this repository. I have made an issue template that copies the above structure. (I figured I should formally create a place for such requests moving forward.)


[NDTensors] Start SparseArrayDOKs module

This introduces the module SparseArrayInterface, which defines sparse versions of various Julia Base functions like sparse_getindex, sparse_setindex!, sparse_map[!], etc. which make use of the sparsity structure of an array given some minimal overloads accessing the sparse storage and how multidimensional indices map back and forth to locations in the sparse storage.
Then, it defines a concrete sparse array type called SparseArrayDOK (for dictionary-of-keys) in a SparseArrayDOKs module that makes use of SparseArrayInterface to provide a lot of AbstractArray Julia functionality. The design is similar to that in SparseArrayKit.jl, with some key differences such as being based on SparseArrayInterface, using Dictionaries.jl for the dictionary implementation instead of Base.Dict, and allowing for a more generic zero element type which will be used by the BlockSparseArray type in BlockSparseArrays.
Some things to do:

[ ] TensorAlgebra overloads for SparseArrayInterface/SparseArrayDOK, such as contract.
[ ] Use SparseArrayDOK as a backend for BlockSparseArray (maybe call it BlockSparseArrayDOK?).
[ ] Consider making a BlockSparseArrayInterface package to define an interface and generic functionality for block sparse arrays, analogous to SparseArrayInterface.
[ ] Use SparseArrayInterface in DiagonalArrays to simplify the implementation there (i.e. make it simpler to define indexing, map/broadcast, etc.).

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

Comparison is base (fbf4a1d) 84.07% compared to head (f5274c5) 53.83%.

:exclamation: Current head f5274c5 differs from pull request most recent head a20e819. Consider uploading reports for the commit a20e819 to get more accurate results

:exclamation: Your organization needs to install the Codecov GitHub app to enable full functionality.
Additional details and impacted files
@@             Coverage Diff             @@
##             main    #1270       +/-   ##
===========================================
- Coverage   84.07%   53.83%   -30.25%
===========================================
  Files          99       98        -1
  Lines        8542     8489       -53
===========================================
- Hits         7182     4570     -2612
- Misses       1360     3919     +2559

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


No response from Bot, after changeing URL_BASE

Hi @all, im interested in a https://www.tutti.ch BOT and thought i can just change the URL_BASE from config.rpy.
I could run everything without an error message but my bot doesn't respond. What am i doing wrong here?

The thing is the scrapper is looking for tags in the HTML page that are custom made for the Ebay-kleinenzagen page.
If you want a tutti.ch, you will need to change the code in the scrapper to find the right tags and not have any errors.
But as such, it's not plug and play.


Heading (between locations) vs calc.direction

Is Movebank heading equal to UvA-BiTS calc.direction?
https://github.com/inbo/bird-tracking/blob/25495a0461f72dc69c7cf063779c1f5030aa2ba6/sql/movebank_gps_data.sql#L64
Movebank heading is defined as (https://www.movebank.org/node/2381#heading):

The direction in which the tag moved from this location to the subsequent location, calculated as the bearing between consecutive locations, in decimal degrees clockwise from north; for example, 0 = north, 90 = east, 180 = south.
example: 315.88
units: decimal degrees

UvA-BiTS calc.direction is defined as (https://wiki.pubserv.e-ecology.nl/wiki/index.php/Useful_Queries#get_uvagps_track_speed):

Not documented in the UvA-BiTS wiki, but returned by get_uvagps_track_speed_incl_shared() which calculates between current location and previous one.

@sarahcd: Movebank definition is heading between pos and pos +1, ours is heading between pos and pos -1. Can we map our value for heading or should we either not provide it or try to calculate it as pos and pos +1 to fit the definition?
@Wouter-Vansteelant @jshamoun: We already discussed this at https://github.com/inbo/bird-tracking-etl/issues/15#issuecomment-362687283, but can you confirm that UvA-BiTS calc.heading is the heading from a current position to a previous one (expressed as degrees from north)?

@peterdesmet see my resopnse to other issue on compass heading in movebank. Brief answer: the column "heading" in movebank is equivalent to "direction" in uva-bits, but indeed in oppossing directions (i.e. from i to i+1 in movebank, and from position i-1 to i in uvabits)

Thanks for bringing this up. I am changing the definition for the Movebank field to make it more broad because that original definition really doesn't describe what most users are storing there. In most cases heading is provided by a GPS sensor and not calculated using consecutive fixes. I'm sure there are values calculated using consecutive fixes in both ways (i to i+1, i-1 to i) stored by users in Movebank (and could be recalculated from the locations to check or ensure consistency across studies). The important thing is that the values are in degrees clockwise from north, however they are calculated. So long as that is the same I'd recommend keeping your values as you have them in UvA-BiTS.
New definition:
"The direction in which the tag is moving, in decimal degrees clockwise from north; for example, 0 = north, 90 = east, 180 = south. Values may be provided by the sensor or calculated between consecutive locations."

Nice! In that case I’ll provide values measured by the tag, as that is information that cannot be calculated. Note: 0 north to 180 south leaves it open if west should be 270 or -90 (and I’ve seen both). Would make it explicit:

The direction in which the tag is moving, in decimal degrees clockwise from north, ranging from 0 to 360, with 0 as north; for example, 90 = east, 180 = south. Values may be provided by the sensor or calculated between consecutive locations.

Good catch. I think both "decimal degrees clockwise from north" and the sample value 315 indicate positive values to 360, but it's a good idea to make the wording more explicit:
"The direction in which the tag is moving, in decimal degrees clockwise from north, as provided by the sensor or calculated between consecutive locations. Values range from 0–360: 0 = north, 90 = east, 180 = south, 270 = west."

Data is now uploaded with gps.direction (measured by tag) as heading:
https://github.com/inbo/bird-tracking/blob/daae6e9ac0934add7d828720bdc6eb5682903b40/sql/movebank_gps.sql#L100-L103
The values seem to make sense (indivual L143467):


Show real error message on LDAP authentification

Don't force error message to just be :invalid_username !

Can you fix the broken test please?

this fixes #540

LGTM. Could you squash these commits into a single one ? Thanks!

@mssola this PR should be clean now.

@rds13 sorry for being a bit of a tease, but could you please squash your commits into one ?

And you also have to sign off your commit, as specified in the contributing.md file.

The commit should be signed by now.

Thanks :)


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/vote reopen

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/vote close me down

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/vote reopen me :+1:

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/vote close

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/vote close me down

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/vote reopen me :+1:

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/vote close

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/vote reopen

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/vote close me down

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/vote reopen me :+1:

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/vote close

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/vote close me down

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/vote reopen me :+1:

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/vote close

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/vote reopen

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/vote reopen

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/vote close me down

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/vote reopen me :+1:

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/vote close

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/vote reopen

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/vote close me down

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/vote reopen me :+1:

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/vote close

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/vote reopen

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/vote reopen

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/vote close me down

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/vote reopen me :+1:

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/vote close

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/vote reopen

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/vote reopen

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/vote close me down

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/vote reopen me :+1:

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/vote close

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/vote reopen

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/vote close me down

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/vote reopen me :+1:

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/vote close

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The introduced swift templates caching using precompiled binary helper framework will not work because Swift lacks ABI stability.

#229 #240
The user can have different Swift compiler version than that was used for compilation of SourceryRuntime.framework.
Currently Swift does not have ABI stability. All frameworks and the swift source code files should be compiled using the same compiler.
Also ABI stability is deferred from Swift 4 https://lists.swift.org/pipermail/swift-evolution/Week-of-Mon-20170213/032116.html

Hey @eugeneego
Users who are using different version of swift compiler than the one used to build Sourcery binary (since the version with improved caching will be released) can build Sourcery from source code in this case. This will build SourceryRuntime framework with the same swift compiler version.

@ilyapuchka Sourcery uses CocoaPods for distributing and probably can compile SourceryRuntime framework during project build.

Currently with CocoaPods we distribute only release archive, not a source code. So to build runtime framework with CocoaPods will make us probably to include its source code into release archive, so that CocoaPods can use it to build the framework as a post step somehow. Or we will need to switch to distributing the whole source code instead of just release artefacts. But I'm not sure how it will behave in this case. Most likely it will link user project with SourcercyRuntime framework which is not needed at all and will require other hacks to disable that. And I'm not even sure if CocoaPods supports building binaries.
SourceryRuntime framework is used not only by the swift templates but also by Sourcery itself. Sourcery when it compiles swift templates (as a step of rendering) simply copy this framework from its  bundle to the build path of templates. To use different version of SourceryFramework in this case we will have to provide additional CLI argument for the path to this binary.
Sounds for me like a lot of work and much more complicated setup comparing with building from source code.

I will close this for now, if you will have any further questions or suggestions feel free to reopen it.


Implement Twilio transport

Closes #29

We still need add messages for all the different types. I saw that the mail transport also needs that implemented. Might make sense to share some of that code between the transports


Loopback

Hi
how we can do this in loopback server?

hi,
what exactly you mean by loopback server?

I used Loopback for my server
and this is link to the website https://loopback.io/

Hi,
How can we post the image to a backend spring boot service ? Can we also pass a message along with the image to the server?

@gnyani You can post the data as base64, and format the JSON however you want to post to your server. If you require I can submit an example of how I am doing this.


[Bug] OSS-GPT only shows on Code tab

Version
v1.9.0
Current Behavior
I am not sure it is a bug or not. The OSS-GPT only shows when I am on the code tab. Did I miss the discussion about where to show the OSS-GPT?
Expected Behavior
OSS-GPT is always there in a repository.
Environment
- OS: MacOS
- Browser: Chrome

Any additional comments?
No response

@wxharry It should appear in all tabs. Are you using the brand new GitHub UI design?

@wxharry It should appear in all tabs. Are you using the brand new GitHub UI design?

Yep. So you don't have this bug on the old design, right?

Yes, the new UI seems changing a lot. We shoud do something in advance 🤔

Yes, the new UI seems changing a lot. We shoud do something in advance 🤔

Agree. we should add the ui design to the Environment in the bug report template to help understand the issue.


Set Goal (Niveytha Waran):[2 Leetcode Qns/day]

What is your goal?
I want to complete 2 Leetcode Questions per day for the next 30 days.
Steps to achieve the goal (not required)

[ ] 1 Question in the morning on the way to work (weekday), in the morning after showering (weekend)
[ ] 1 Question in the evening on the way back home from home /at home (weekday), in the evening before gym (weekend)

Do you have a target date for this? If yes, write below
19 November 2022
What will you achieve by completing this goal?
Keep my skills and knowledge fresh so I can land a good internship/job. Prep for tehcnical interviews.

Additional Context:
Google Spreadsheet to track progress: https://docs.google.com/spreadsheets/d/1CBrMSh8dE8Hu38TL7_p-LRiK1QR7r79wKAL_sQ1IfjE/edit?usp=sharing
Leetcode List: https://leetcode.com/list/?selectedList=ebpg71ej

Great idea. Implemented it, thanku!

Sure, but fir now do update your folder in the report folder


Provide steps to setup OpenEBS with PodSecurityPolicies

The following steps were provided by @chtardif in the OpenEBS community channel - on how he configured PodSecurityPolicies(PSP) for OpenEBS.

Create a Privileged PSP

 apiVersion: extensions/v1beta1
 kind: PodSecurityPolicy
  metadata:
    name: privileged
    annotations:
      seccomp.security.alpha.kubernetes.io/allowedProfileNames: '*'
  spec:
    privileged: true
    allowPrivilegeEscalation: true
    allowedCapabilities:
    - '*'
    volumes:
    - '*'
    hostNetwork: true
    hostPorts:
    - min: 0
      max: 65535
    hostIPC: true
    hostPID: true
    runAsUser:
      rule: 'RunAsAny'
    seLinux:
      rule: 'RunAsAny'
    supplementalGroups:
      rule: 'RunAsAny'
    fsGroup:
      rule: 'RunAsAny'

Associate the above PSP to a ClusterRole

kind: ClusterRole
apiVersion: rbac.authorization.k8s.io/v1
metadata:
  name: privilegedpsp
rules:
- apiGroups: ['extensions']
  resources: ['podsecuritypolicies']
  verbs:     ['use']
  resourceNames:
  - privileged

Associate the above Privileged ClusterRole to OpenEBS Service Account

apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
  annotations:
    rbac.authorization.kubernetes.io/autoupdate: "true"
  name: openebspsp
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: ClusterRole
  name: privilegedpsp
subjects:
- kind: ServiceAccount
  name: openebs-maya-operator
  namespace: openebs

Proceed to install the OpenEBS. Note that the namespace and service account name used by the OpenEBS should match what is provided in the above ClusterRoleBinding.

@cjyar The latest OpenEBS release 0.9 has the feature for creating Jiva pods in OpenEBS namespace. This can be set by adding one parameter in the corresponding StorageClass.
Example snippet of SC:
apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
  name: jiva-pods-in-openebs-ns
  annotations:
    openebs.io/cas-type: jiva
    cas.openebs.io/config: |
      - name: DeployInOpenEBSNamespace
        enabled: "true"
provisioner: openebs.io/provisioner-iscsi

More information can be get from here.


Assistance with Rolling Fitting Using Specified Training and Testing Windows

Hi,
Thank you for creating this helpful tool.
I would like to perform rolling fitting with a specified training window and testing window.
For example, with train_win=3 and test_win=1:
Using [0, 1, 2] to fit and then predict [3].
Is there an easy way to achieve this?
Thanks!

What is the next fit and test going to be? Fit on [1,2,3] and test on [4]?

lets say the time series: [ 1 2 3 4 5 6 7], with train size 3 and test size 2:
Train -> Test
1 2 3 -> 4 5
   2 3 4 -> 5 6
      3 4 5 -> 6 7

I assume you are talking about the OnlineLR (online linear regression class) in the package. It is a very custom process and I do not intend to build this kind of iterators into the package.... First we generate some random data. This kind of stuff, it is better to do with NumPy matrices imo. So I will cast dataframe to NumPy matrices.
import polars_ds as pds
import polars as pl

size = 5000
df = (
    pds.frame(size=size)
    .select(
        pds.random(0.0, 1.0).alias("x1"),
        pds.random(0.0, 1.0).alias("x2"),
        pds.random(0.0, 1.0).alias("x3"),
    )
    .with_columns(
        y1 = pl.col("x1") + pl.col("x2") * 0.2 - 0.3 * pl.col("x3"),
        y2 = pl.col("x1") * 0.1 + pl.col("x2") * 0.15 - 0.1 * pl.col("x3"),
        y3 = pl.col("x1") * 0.5 + pl.col("x2") * 0.3 - 0.2 * pl.col("x3"),
    )
)

X = df.select("x1", "x2", "x3").to_numpy()
y = df.select("y1").to_numpy()

Now, the key is to understand what update does on this class. It removes/adds impact from a record with a scale c measuring the impact. For the most common use case, c = 1 means add the impact from the new x and new y and c = -1 means remove the impact from the given x and y.

from polars_ds.linear_models import OnlineLR

lr = OnlineLR(fit_bias = False)

period = 10
fit_len = 7

for i in range(0, X.shape[0] - period + 1):
    if i == 0:
        lr.fit(X[:fit_len], y[:fit_len]) # Initial fit
        test_pred = lr.predict(X[fit_len: period]) # Predict on the first period
    else:
        # To roll, we need to know what to forget and what to add
        j = i - 1
        forget_x, forget_y = X[j], y[j]
        latest_x, latest_y = X[j + period], y[j + period]
        # first remove the impact from forget_x, forget_y
        lr.update(forget_x, forget_y, -1)
        # add the impact of the new data
        lr.update(latest_x, latest_y, 1)
        # predict on test
        test_pred = lr.predict(X[i+fit_len: i+period])

    # Do whatever you want with predictions on test
    print(test_pred)

By the way, I found some not-so-obvious bugs when writing this answer. A PR is in the process and the code may not run if you have version <= v0.6. I will try and patch it as soon as possible and publish v0.6.1

Thank you very much!

actually there is a typo in my previous


Error when provisioning OpenStack Vm

It is definitely happening after I change miq automate code, e.g. exposing another method,  it goes away when I restart evm.
The exception message is kind of confusing
[----] E, [2015-10-14T09:49:14.461011 #26042:1237994] ERROR -- :  MiqAeServiceModelBase.ar_method raised: : <MiqProvisionRequest Object expected, but received MiqProvisionRequest>
[----] E, [2015-10-14T09:49:14.461314 #26042:1237994] ERROR -- :  /home/Ladas/Projects/cfme/manageiq/lib/miq_automation_engine/engine/miq_ae_service_model_base.rb:188:in initialize' /home/Ladas/Projects/cfme/manageiq/lib/miq_automation_engine/engine/miq_ae_service_model_base.rb:117:in new'
/home/Ladas/Projects/cfme/manageiq/lib/miq_automation_engine/engine/miq_ae_service_model_base.rb:117:in block in wrap_results' /home/Ladas/Projects/cfme/manageiq/lib/miq_automation_engine/engine/miq_ae_service_model_base.rb:261:in ar_method'
/home/Ladas/Projects/cfme/manageiq/lib/miq_automation_engine/engine/miq_ae_service_model_base.rb:108:in wrap_results' /home/Ladas/Projects/cfme/manageiq/lib/miq_automation_engine/engine/miq_ae_service_model_base.rb:19:in method_missing'
/home/Ladas/Projects/cfme/manageiq/lib/miq_automation_engine/engine/miq_ae_object.rb:486:in convert_value_based_on_datatype' /home/Ladas/Projects/cfme/manageiq/lib/miq_automation_engine/engine/miq_ae_object.rb:266:in process_args_attribute'
/home/Ladas/Projects/cfme/manageiq/lib/miq_automation_engine/engine/miq_ae_object.rb:249:in block in process_args_as_attributes' /home/Ladas/Projects/cfme/manageiq/lib/miq_automation_engine/engine/miq_ae_object.rb:249:in each'
/home/Ladas/Projects/cfme/manageiq/lib/miq_automation_engine/engine/miq_ae_object.rb:249:in process_args_as_attributes' /home/Ladas/Projects/cfme/manageiq/lib/miq_automation_engine/engine/miq_ae_workspace.rb:138:in instantiate'
/home/Ladas/Projects/cfme/manageiq/lib/miq_automation_engine/engine/miq_ae_workspace.rb:63:in instantiate' /home/Ladas/Projects/cfme/manageiq/lib/miq_automation_engine/engine/miq_ae_engine.rb:323:in resolve_automation_object'
/home/Ladas/Projects/cfme/manageiq/app/models/miq_provision/naming.rb:16:in get_next_vm_name' /home/Ladas/Projects/cfme/manageiq/app/models/miq_provision_request.rb:51:in set_description'
/home/Ladas/Projects/cfme/manageiq/app/models/miq_request_workflow.rb:88:in create_request' /home/Ladas/Projects/cfme/manageiq/app/models/miq_provision_virt_workflow.rb:58:in create_request'
/home/Ladas/Projects/cfme/manageiq/app/models/miq_request_workflow.rb:67:in make_request' /home/Ladas/Projects/cfme/manageiq/app/controllers/application_controller/miq_request_methods.rb:573:in prov_req_submit'
/home/Ladas/Projects/cfme/manageiq/app/controllers/application_controller/miq_request_methods.rb:185:in prov_edit' /home/Ladas/Projects/cfme/manageiq/app/controllers/application_controller/miq_request_methods.rb:57:in pre_prov_continue'
/home/Ladas/.rvm/gems/ruby-2.2.3/gems/actionpack-4.2.4/lib/action_controller/metal/implicit_render.rb:4:in send_action' /home/Ladas/.rvm/gems/ruby-2.2.3/gems/actionpack-4.2.4/lib/abstract_controller/base.rb:198:in process_action'
/home/Ladas/.rvm/gems/ruby-2.2.3/gems/actionpack-4.2.4/lib/action_controller/metal/rendering.rb:10:in process_action' /home/Ladas/.rvm/gems/ruby-2.2.3/gems/actionpack-4.2.4/lib/abstract_controller/callbacks.rb:20:in block in process_action'
/home/Ladas/.rvm/gems/ruby-2.2.3/gems/activesupport-4.2.4/lib/active_support/callbacks.rb:117:in call' /home/Ladas/.rvm/gems/ruby-2.2.3/gems/activesupport-4.2.4/lib/active_support/callbacks.rb:117:in call'
/home/Ladas/.rvm/gems/ruby-2.2.3/gems/activesupport-4.2.4/lib/active_support/callbacks.rb:555:in block (2 levels) in compile' /home/Ladas/.rvm/gems/ruby-2.2.3/gems/activesupport-4.2.4/lib/active_support/callbacks.rb:505:in call'
/home/Ladas/.rvm/gems/ruby-2.2.3/gems/activesupport-4.2.4/lib/active_support/callbacks.rb:505:in call' /home/Ladas/.rvm/gems/ruby-2.2.3/gems/activesupport-4.2.4/lib/active_support/callbacks.rb:92:in run_callbacks'
/home/Ladas/.rvm/gems/ruby-2.2.3/gems/activesupport-4.2.4/lib/active_support/callbacks.rb:778:in _run_process_action_callbacks' /home/Ladas/.rvm/gems/ruby-2.2.3/gems/activesupport-4.2.4/lib/active_support/callbacks.rb:81:in run_callbacks'
/home/Ladas/.rvm/gems/ruby-2.2.3/gems/actionpack-4.2.4/lib/abstract_controller/callbacks.rb:19:in process_action' /home/Ladas/.rvm/gems/ruby-2.2.3/gems/actionpack-4.2.4/lib/action_controller/metal/rescue.rb:29:in process_action'
/home/Ladas/.rvm/gems/ruby-2.2.3/gems/actionpack-4.2.4/lib/action_controller/metal/instrumentation.rb:32:in block in process_action' /home/Ladas/.rvm/gems/ruby-2.2.3/gems/activesupport-4.2.4/lib/active_support/notifications.rb:164:in block in instrument'
/home/Ladas/.rvm/gems/ruby-2.2.3/gems/activesupport-4.2.4/lib/active_support/notifications/instrumenter.rb:20:in instrument' /home/Ladas/.rvm/gems/ruby-2.2.3/gems/activesupport-4.2.4/lib/active_support/notifications.rb:164:in instrument'
/home/Ladas/.rvm/gems/ruby-2.2.3/gems/actionpack-4.2.4/lib/action_controller/metal/instrumentation.rb:30:in process_action' /home/Ladas/.rvm/gems/ruby-2.2.3/gems/actionpack-4.2.4/lib/action_controller/metal/params_wrapper.rb:250:in process_action'
/home/Ladas/.rvm/gems/ruby-2.2.3/gems/activerecord-4.2.4/lib/active_record/railties/controller_runtime.rb:18:in process_action' /home/Ladas/.rvm/gems/ruby-2.2.3/gems/actionpack-4.2.4/lib/abstract_controller/base.rb:137:in process'
/home/Ladas/.rvm/gems/ruby-2.2.3/gems/actionview-4.2.4/lib/action_view/rendering.rb:30:in process' /home/Ladas/.rvm/gems/ruby-2.2.3/gems/actionpack-4.2.4/lib/action_controller/metal.rb:196:in dispatch'
/home/Ladas/.rvm/gems/ruby-2.2.3/gems/actionpack-4.2.4/lib/action_controller/metal/rack_delegation.rb:13:in dispatch' /home/Ladas/.rvm/gems/ruby-2.2.3/gems/actionpack-4.2.4/lib/action_controller/metal.rb:237:in block in action'
/home/Ladas/.rvm/gems/ruby-2.2.3/gems/actionpack-4.2.4/lib/action_dispatch/routing/route_set.rb:76:in call' /home/Ladas/.rvm/gems/ruby-2.2.3/gems/actionpack-4.2.4/lib/action_dispatch/routing/route_set.rb:76:in dispatch'
/home/Ladas/.rvm/gems/ruby-2.2.3/gems/actionpack-4.2.4/lib/action_dispatch/routing/route_set.rb:45:in serve' /home/Ladas/.rvm/gems/ruby-2.2.3/gems/actionpack-4.2.4/lib/action_dispatch/journey/router.rb:43:in block in serve'
/home/Ladas/.rvm/gems/ruby-2.2.3/gems/actionpack-4.2.4/lib/action_dispatch/journey/router.rb:30:in each' /home/Ladas/.rvm/gems/ruby-2.2.3/gems/actionpack-4.2.4/lib/action_dispatch/journey/router.rb:30:in serve'
/home/Ladas/.rvm/gems/ruby-2.2.3/gems/actionpack-4.2.4/lib/action_dispatch/routing/route_set.rb:821:in call' /home/Ladas/.rvm/gems/ruby-2.2.3/gems/rack-1.6.4/lib/rack/etag.rb:24:in call'
/home/Ladas/.rvm/gems/ruby-2.2.3/gems/rack-1.6.4/lib/rack/conditionalget.rb:38:in call' /home/Ladas/.rvm/gems/ruby-2.2.3/gems/rack-1.6.4/lib/rack/head.rb:13:in call'
/home/Ladas/.rvm/gems/ruby-2.2.3/gems/actionpack-4.2.4/lib/action_dispatch/middleware/params_parser.rb:27:in call' /home/Ladas/.rvm/gems/ruby-2.2.3/gems/actionpack-4.2.4/lib/action_dispatch/middleware/flash.rb:260:in call'
/home/Ladas/.rvm/gems/ruby-2.2.3/gems/rack-1.6.4/lib/rack/session/abstract/id.rb:225:in context' /home/Ladas/.rvm/gems/ruby-2.2.3/gems/rack-1.6.4/lib/rack/session/abstract/id.rb:220:in call'
/home/Ladas/.rvm/gems/ruby-2.2.3/gems/actionpack-4.2.4/lib/action_dispatch/middleware/cookies.rb:560:in call' /home/Ladas/.rvm/gems/ruby-2.2.3/gems/activerecord-4.2.4/lib/active_record/query_cache.rb:36:in call'
/home/Ladas/.rvm/gems/ruby-2.2.3/gems/activerecord-4.2.4/lib/active_record/connection_adapters/abstract/connection_pool.rb:653:in call' /home/Ladas/.rvm/gems/ruby-2.2.3/gems/actionpack-4.2.4/lib/action_dispatch/middleware/callbacks.rb:29:in block in call'
/home/Ladas/.rvm/gems/ruby-2.2.3/gems/activesupport-4.2.4/lib/active_support/callbacks.rb:88:in __run_callbacks__' /home/Ladas/.rvm/gems/ruby-2.2.3/gems/activesupport-4.2.4/lib/active_support/callbacks.rb:778:in _run_call_callbacks'
/home/Ladas/.rvm/gems/ruby-2.2.3/gems/activesupport-4.2.4/lib/active_support/callbacks.rb:81:in run_callbacks' /home/Ladas/.rvm/gems/ruby-2.2.3/gems/actionpack-4.2.4/lib/action_dispatch/middleware/callbacks.rb:27:in call'
/home/Ladas/.rvm/gems/ruby-2.2.3/gems/actionpack-4.2.4/lib/action_dispatch/middleware/reloader.rb:73:in call' /home/Ladas/.rvm/gems/ruby-2.2.3/gems/actionpack-4.2.4/lib/action_dispatch/middleware/remote_ip.rb:78:in call'
/home/Ladas/.rvm/gems/ruby-2.2.3/gems/actionpack-4.2.4/lib/action_dispatch/middleware/debug_exceptions.rb:17:in call' /home/Ladas/.rvm/gems/ruby-2.2.3/gems/actionpack-4.2.4/lib/action_dispatch/middleware/show_exceptions.rb:30:in call'
/home/Ladas/.rvm/gems/ruby-2.2.3/gems/railties-4.2.4/lib/rails/rack/logger.rb:38:in call_app' /home/Ladas/.rvm/gems/ruby-2.2.3/gems/railties-4.2.4/lib/rails/rack/logger.rb:22:in call'
/home/Ladas/.rvm/gems/ruby-2.2.3/gems/actionpack-4.2.4/lib/action_dispatch/middleware/request_id.rb:21:in call' /home/Ladas/.rvm/gems/ruby-2.2.3/gems/rack-1.6.4/lib/rack/methodoverride.rb:22:in call'
/home/Ladas/.rvm/gems/ruby-2.2.3/gems/rack-1.6.4/lib/rack/runtime.rb:18:in call' /home/Ladas/.rvm/gems/ruby-2.2.3/gems/activesupport-4.2.4/lib/active_support/cache/strategy/local_cache_middleware.rb:28:in call'
/home/Ladas/.rvm/gems/ruby-2.2.3/gems/rack-1.6.4/lib/rack/lock.rb:17:in call' /home/Ladas/.rvm/gems/ruby-2.2.3/gems/actionpack-4.2.4/lib/action_dispatch/middleware/static.rb:116:in call'
/home/Ladas/.rvm/gems/ruby-2.2.3/gems/rack-1.6.4/lib/rack/sendfile.rb:113:in call' /home/Ladas/.rvm/gems/ruby-2.2.3/gems/railties-4.2.4/lib/rails/engine.rb:518:in call'
/home/Ladas/.rvm/gems/ruby-2.2.3/gems/railties-4.2.4/lib/rails/application.rb:165:in call' /home/Ladas/.rvm/gems/ruby-2.2.3/gems/rack-1.6.4/lib/rack/content_length.rb:15:in call'
/home/Ladas/.rvm/gems/ruby-2.2.3/gems/thin-1.6.4/lib/thin/connection.rb:86:in block in pre_process' /home/Ladas/.rvm/gems/ruby-2.2.3/gems/thin-1.6.4/lib/thin/connection.rb:84:in catch'
/home/Ladas/.rvm/gems/ruby-2.2.3/gems/thin-1.6.4/lib/thin/connection.rb:84:in pre_process' /home/Ladas/.rvm/gems/ruby-2.2.3/gems/thin-1.6.4/lib/thin/connection.rb:53:in process'
/home/Ladas/.rvm/gems/ruby-2.2.3/gems/thin-1.6.4/lib/thin/connection.rb:39:in receive_data' /home/Ladas/.rvm/gems/ruby-2.2.3/gems/eventmachine-1.0.8/lib/eventmachine.rb:193:in run_machine'
/home/Ladas/.rvm/gems/ruby-2.2.3/gems/eventmachine-1.0.8/lib/eventmachine.rb:193:in run' /home/Ladas/.rvm/gems/ruby-2.2.3/gems/thin-1.6.4/lib/thin/backends/base.rb:73:in start'
/home/Ladas/.rvm/gems/ruby-2.2.3/gems/thin-1.6.4/lib/thin/server.rb:162:in start' /home/Ladas/.rvm/gems/ruby-2.2.3/gems/rack-1.6.4/lib/rack/handler/thin.rb:19:in run'
/home/Ladas/.rvm/gems/ruby-2.2.3/gems/rack-1.6.4/lib/rack/server.rb:286:in start' /home/Ladas/.rvm/gems/ruby-2.2.3/gems/railties-4.2.4/lib/rails/commands/server.rb:80:in start'
/home/Ladas/.rvm/gems/ruby-2.2.3/gems/railties-4.2.4/lib/rails/commands/commands_tasks.rb:80:in block in server' /home/Ladas/.rvm/gems/ruby-2.2.3/gems/railties-4.2.4/lib/rails/commands/commands_tasks.rb:75:in tap'
/home/Ladas/.rvm/gems/ruby-2.2.3/gems/railties-4.2.4/lib/rails/commands/commands_tasks.rb:75:in server' /home/Ladas/.rvm/gems/ruby-2.2.3/gems/railties-4.2.4/lib/rails/commands/commands_tasks.rb:39:in run_command!'
/home/Ladas/.rvm/gems/ruby-2.2.3/gems/railties-4.2.4/lib/rails/commands.rb:17:in <top (required)>' /home/Ladas/Projects/cfme/manageiq/bin/rails:4:in require'
/home/Ladas/Projects/cfme/manageiq/bin/rails:4:in `'

This is actually not an issue, changing Automate like this requires a restart.


Reduce runtime context dependencies

@brancz
This uses the first object of the new nflog package (separate PR which is the base of this one). As we built the pipeline per receiver on configuration load, there's no need to make these runtime dependencies.
After all, context.Context should not be used as a disguise for interface{} (or Python kwargs).
As a consequence, the fanout stage is now truly just executing stages in parallel, without knowledge about integrations.
Thought about where to draw the boundary. There are other values in the context too. We could also build a static pipeline as a new aggregation group gets created in the dispatcher. I figured a reasonably boundary would be that we build the pipeline on configuration load time and build static pipelines only based on the information we have at this point in time.
I also removed the logging stages, as they we didn't want to keep these around anyway.

LGTM 👍

Thanks, holding off merge, as the base is still in review.


Add back the OwO/UwU-ification of monster names

There is someone I play with who refuses to upgrade from HunterPie-legacy for the sole reason that the OwO/UwU-ification of monster names is not supported. Could this be added back? If necessary, I can do it myself and submit a PR.

Well... PRs are welcome :)


Running of Planner Plugins Limited to 15 Seconds, and Provides No Feedback

Currently, if the underlying plan solver takes longer than 15 seconds to return with a plan, the planner client silently returns without a plan:
https://github.com/IntelligentRoboticsLabs/ros2_planning_system/blob/master/plansys2_planner/src/plansys2_planner/PlannerClient.cpp#L54
Also, no feedback is provided about how long the underlying planner is taking.
These are fine for relatively simple plans.  But can be problematic for plans with large search spaces.
To better accommodate more complex planning tasks, I propose:

Making the timeout configurable (ROS parameter).  Default would remain 15 seconds.
Converting the PlannerNode interface from a service to an action.  The feedback would - at least initially - be the percentage of the timeout period.

This would accommodate plans that take an arbitrary length of time to compute.
If this sounds reasonable, I will undertake this work.

Hi @kogut
I think it is perfectly reasonable. My concern is increasing the complexity in codes like https://github.com/IntelligentRoboticsLabs/ros2_planning_system_examples/blob/3b03903ad3bf8089a88ff767dd99f8c9cebbc165/plansys2_patrol_navigation_example/src/patrolling_controller_node.cpp#L78.
This complexity should be managed inside the PlannerClient. Maybe we could maintain the current blocking call, and add another non-blocking call (using std::futures?) so the users could have both alternatives, and maintain backward compatibility.
Regarding timeout, maybe it could be interesting to have a default timeout, set in parameters (with a default value in case of not being set). We could also add a last parameter timeout to the getPlan method, using this default value in case of not set.
What do you think? Any other contributions to this discussion, @teyssieuman @xydesa @jjzapf @jginesclavero ?

Given that solving a plan can take a varying amount of time, I think using an action makes sense for the underlying connection between the plansys2_planner/PlannerClient and the plansys2_planner/PlannerNode. Actions were built for longer running tasks like this: https://docs.ros.org/en/foxy/Tutorials/Understanding-ROS2-Actions.html#background
The best way to do this may involve changing the plansys2::PlanSolverBase interface to add feedback from the planner (if available).
I also like the idea of having both a blocking and non-blocking function call on the plansys2_planner/PlannerClient so developers could use whichever one they prefer. The non-blocking call could also provide feedback on progress. I'd be ok with just keeping the blocking function call for now and leaving a non-blocking call as a future enhancement.
I think a default timeout should definitely be set and a parameter provided to override that. I also like the idea of adding a timeout parameter to the plansys2_planner/PlannerClient::getPlan function, this would let other developers choose how long they'd like to wait.

Closing... reopen if you want to continue with this


Deploy for new languages

There’s a lot in this PR

itwewina et al. will run from the morphodict user out of /opt/morphodict/home/morphodict
docker-compose yaml files are now generated from templates
there’s a helper script to generate yaml and ensure needed files/directories exist with correct permissions
there’s an ansible script to automate initial setup of users, base directories, and the bind mount
the docker image will now have static assets for all language pairs
the docker image will run the app under a user with very limited permissions
lots of docs about how production is set up

This is currently running at itwewina.altlab.dev / itwiwina.altlab.dev / gunaha.altlab.dev / arpeng.itwewina.dev.
Before merging we will need to:

sync over the prod db
update the nginx config for itwewina.altlab.app to point at the new port number
remove any local changes in itw.altab.dev:~morphodict/morphodict
update deploy.altlab.dev to use the new username and path, as well as to register the new port numbers and UIDs

Oh, I almost forgot to mention the biggest feature and the reason for doing all of this: instead of one container, now there are four.

Codecov Report

Merging #955 (c715558) into main (18deff2) will decrease coverage by 0.01%.
The diff coverage is 20.00%.

:exclamation: Current head c715558 differs from pull request most recent head 9b1fc39. Consider uploading reports for the commit 9b1fc39 to get more accurate results

@@            Coverage Diff             @@
##             main     #955      +/-   ##
==========================================
- Coverage   79.58%   79.56%   -0.02%
==========================================
  Files         136      136
  Lines        4639     4640       +1
  Branches      609      610       +1
==========================================
  Hits         3692     3692
  Misses        820      820
- Partials      127      128       +1

Impacted Files
Coverage Δ

src/CreeDictionary/API/search/presentation.py
80.61% <ø> (+0.40%)
:arrow_up:

src/cwdeng/site/settings.py
0.00% <0.00%> (ø)

src/morphodict/site/wsgi.py
66.66% <0.00%> (ø)

src/srseng/site/settings.py
0.00% <0.00%> (ø)

src/morphodict/site/settings.py
81.42% <100.00%> (+0.26%)
:arrow_up:

Continue to review full report at Codecov.

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Version compatibility problem: Win10 earlier version failed to install the driver, code 39

High version of WDK written driver will be incompatible with low version of Win10? I followed the steps to install the driver development environment, configure the SimpleAudioSample project and generate it. I then install the generated file in device Manager, prompting :Windows cannot load the driver for this hardware device, the driver may be corrupted or missing,(code 39). Magic is that my colleagues can correctly install, after a day of investigation I suspected to be Win10 version problems. But see Microsoft document inside write support win7 above version, here I produced doubt, hope to get a solution.
Project compilation environment :VS2019 Win10SDk10.0.19041.0 WDK10.0.19041.0
My system: Win10 1909(OS Internal version 18363.592)
Win10 2004(OS Internal 19041.388)
<---------------------------------------------------------------------------------------->
高版本WDK编写的的驱动程序是否会不兼容低版本的win10？我按照步骤安装驱动开发环境，配置SimpleAudioSample项目并生成。然后我在设备管理器中安装生成的文件，提示：Windows 无法加载这个硬件设备的驱动程序 驱动程序可能已损坏或不见了(代码39)。神奇的是我的同事可以正确安装，经过一天的排查后怀疑是win10版本问题，但看见微软文档里面写的支持win7以上的版本。这里我产生了疑惑，希望得到解答。
项目编译环境:VS2019 win10SDK10.0.19041.0 WDK10.0.19041.0
本人系统:win10 1909(OS内部版本 18363.592)
同事系统:win10 2004(OS内部版本 19041.388)

Same question

Did you solve this problem? @MorphKyan

Same question

Hi,This may give you some help。
我的问题是2004及以上的版本使用了ExAllocatePool2，低版本不执行，要换成之前的函数。我直接使用了ExAllocatePoolWithTag。除了替换函数，还要进行的操作是把POOL_FLAGS类型的参数换成POOL_TYPE。
https://docs.microsoft.com/zh-cn/windows-hardware/drivers/kernel/updating-deprecated-exallocatepool-calls

Did you solve this problem? @MorphKyan
https://docs.microsoft.com/zh-cn/windows-hardware/drivers/kernel/updating-deprecated-exallocatepool-calls

@IM-Boting
Thanks, and I have found another repository that might be useful. @Guawazii
https://github.com/JannesP/AudioMirror

@IM-Boting
Thanks, and I have found another repository that might be useful. @Guawazii
https://github.com/JannesP/AudioMirror

I have a question about this project. It does not appear to use IRP MJ DEVICE CONTROL, So how to write PCM data to the virtual microphone.🤔

@IM-Boting
Thanks, and I have found another repository that might be useful. @Guawazii
https://github.com/JannesP/AudioMirror

I have a question about this project. It does not appear to use IRP MJ DEVICE CONTROL, So how to write PCM data to the virtual microphone.🤔

Sorry, i just use it as a virtual device. I don't know it well. :(

Same question

Hi,This may give you some help。
我的问题是2004及以上的版本使用了ExAllocatePool2，低版本不执行，要换成之前的函数。我直接使用了ExAllocatePoolWithTag。除了替换函数，还要进行的操作是把POOL_FLAGS类型的参数换成POOL_TYPE。 https://docs.microsoft.com/zh-cn/windows-hardware/drivers/kernel/updating-deprecated-exallocatepool-calls

May I ask if this problem is sovled by the solution you post above?


Bazel query failing for Mac M3 chip for bazel query  @rules_cc//...

ATTENTION! Please read and follow:

if this is a question about how to build / test / query / deploy using Bazel, ask it on StackOverflow instead: https://stackoverflow.com/questions/tagged/bazel
if this is a discussion starter, send it to<EMAIL_ADDRESS>or<EMAIL_ADDRESS>if this is a bug or feature request, fill the form below as best as you can.

Description of the problem / feature request:

Bazel query command fails on Mac M3 chipset with git init error.

bazel query  @rules_cc//...

This command works fine on non M3 chipsets and on M3 chipsets, we are getting this error
ERROR: An error occurred during the fetch of repository 'rules_cc':
   Traceback (most recent call last):
	File "/private/var/tmp/_bazel_ktvs/58b3024c9bf635de3b5e2a175cb13598/external/bazel_tools/tools/build_defs/repo/git.bzl", line 181, column 30, in _git_repository_implementation
		update = _clone_or_update(ctx)
	File "/private/var/tmp/_bazel_ktvs/58b3024c9bf635de3b5e2a175cb13598/external/bazel_tools/tools/build_defs/repo/git.bzl", line 36, column 20, in _clone_or_update
		git_ = git_repo(ctx, directory)
	File "/private/var/tmp/_bazel_ktvs/58b3024c9bf635de3b5e2a175cb13598/external/bazel_tools/tools/build_defs/repo/git_worker.bzl", line 91, column 12, in git_repo
		_update(ctx, git_repo)
	File "/private/var/tmp/_bazel_ktvs/58b3024c9bf635de3b5e2a175cb13598/external/bazel_tools/tools/build_defs/repo/git_worker.bzl", line 101, column 9, in _update
		init(ctx, git_repo)
	File "/private/var/tmp/_bazel_ktvs/58b3024c9bf635de3b5e2a175cb13598/external/bazel_tools/tools/build_defs/repo/git_worker.bzl", line 118, column 15, in init
		_error(ctx.name, cl, st.stderr)
	File "/private/var/tmp/_bazel_ktvs/58b3024c9bf635de3b5e2a175cb13598/external/bazel_tools/tools/build_defs/repo/git_worker.bzl", line 190, column 9, in _error
		fail("error running '%s' while working with @%s:\n%s" % (command_text, name, stderr))
Error in fail: error running 'git init /private/var/tmp/_bazel_ktvs/58b3024c9bf635de3b5e2a175cb13598/external/rules_cc' while working with @rules_cc:
ERROR: no such package '@rules_cc//cc': error running 'git init /private/var/tmp/_bazel_ktvs/58b3024c9bf635de3b5e2a175cb13598/external/rules_cc' while working with @rules_cc:
INFO: Elapsed time: 108.304s

Feature requests: what underlying problem are you trying to solve with this feature?

This issue is happening only on Mac M3 chipsets. For other chips it is working as expected.

Bugs: what's the simplest, easiest way to reproduce this bug? Please provide a minimal example if possible.

Run the command on Mac M3 chipset M3 Max,

bazel query  @rules_cc//...

What operating system are you running Bazel on?

Apple M3 Max , Sonoma 14.1

What's the output of bazel info release?

3.7.1

If bazel info release returns "development version" or "(@non-git)", tell us how you built Bazel.

Replace this line with your answer.

What version of rules_cc do you use? Can you paste the workspace rule used to fetch rules_cc? What other relevant dependencies does your project have?

Latest

What Bazel options do you use to trigger the issue? What C++ toolchain do you use?

No options

Have you found anything relevant by searching the web?
NO

Replace these lines with your answer.
Places to look:

StackOverflow: http://stackoverflow.com/questions/tagged/bazel
GitHub issues:

https://github.com/bazelbuild/rules_cc/issues
https://github.com/bazelbuild/bazel/issues

email threads:

https://groups.google.com/forum/#!forum/bazel-discuss
https://groups.google.com/forum/#!forum/cc-bazel-discuss

Any other information, logs, or outputs that you want to share?

Replace these lines with your answer.
If the files are large, upload as attachment or provide link.

this seems to be not related to Mac M3. There could be some other reasons for the failure. Able to run this successfully on another mac M3


fix uint16_t bbrk on wasm.simd branch

@MikeHolman @Cellule fyi: @arunetm
Mike brought to our attention that our wasm.simd changes were breaking some of ChakraFull tests:

This is the error I see

2017-04-06T00:02:37.9142103Z        "E:\A_work\2\s\core\lib\WasmReader\Chakra.WasmReader.vcxproj" (default target) (77:3) ->
2017-04-06T00:02:37.9142103Z        (ClCompile target) ->
2017-04-06T00:02:37.9142103Z          > E:\A_work\2\s\core\lib\WasmReader\WasmBinaryReader.cpp(438): error C2065: 'uint16_t': undeclared identifier [E:\A_work\2\s\core\lib\WasmReader\Chakra.WasmReader.vcxproj]

I did a quick grep and looks like this was the only instance of using std types in WasmBinaryReader
$grep  -rP  'int\d+_t[ \t;]' * | grep -v wabt
Common/Codex/Utf8Codex.h:        typedef int8_t type;
Common/Codex/Utf8Codex.h:        typedef int16_t type;
Common/Codex/Utf8Codex.h:        typedef int32_t type;
Jsrt/ChakraCommon.h:typedef uint32_t UINT32;
Jsrt/ChakraCommon.h:typedef int64_t INT64;
Jsrt/ChakraCommon.h:typedef unsigned short uint16_t;
Jsrt/ChakraCore.h:        _In_ const uint16_t *content,
Jsrt/ChakraDebug.h:typedef __int64 int64_t;
Jsrt/ChakraDebug.h:typedef unsigned __int32 uint32_t;
Jsrt/ChakraDebug.h:        _In_opt_ uint32_t kthEvent,
Jsrt/ChakraDebug.h:        _In_ int64_t targetEventTime,
Jsrt/ChakraDebug.h:        _In_ int64_t currentSnapStartTime,
Jsrt/ChakraDebug.h:        _In_ int64_t startSnapTime,
Jsrt/ChakraDebug.h:        _In_ int64_t endSnapTime,
Jsrt/ChakraDebug.h:            _In_ int64_t snapshotTime,
Jsrt/ChakraDebug.h:            _In_ int64_t eventTime);
Jsrt/Jsrt.cpp:   _In_ JsTTDMoveMode moveMode, _In_opt_ uint32_t kthEvent,
Jsrt/Jsrt.cpp:    static_assert(sizeof(int64_t) == sizeof(int64), "int64_t and int64 size mis-match");
Jsrt/Jsrt.cpp:CHAKRA_API JsTTDGetSnapShotBoundInterval(_In_ JsRuntimeHandle runtimeHandle, _In_ int64_t targetEventTime, _Out_ int64_t* startSnapTime, _Out_ int64_t* endSnapTime)
Jsrt/Jsrt.cpp:    static_assert(sizeof(int64_t) == sizeof(int64), "int64_t and int64 size mis-match");
Jsrt/Jsrt.cpp:CHAKRA_API JsTTDGetPreviousSnapshotInterval(_In_ JsRuntimeHandle runtimeHandle, _In_ int64_t currentSnapStartTime, _Out_ int64_t* previousSnapTime)
Jsrt/Jsrt.cpp:JsErrorCode TTDHandleBreakpointInfoAndInflate(TTD::EventLog* elog, int64_t snapTime, JsrtRuntime* runtime, ThreadContext* threadContext)
Jsrt/Jsrt.cpp:CHAKRA_API JsTTDPreExecuteSnapShotInterval(_In_ JsRuntimeHandle runtimeHandle, _In_ int64_t startSnapTime, _In_ int64_t endSnapTime, _In_ JsTTDMoveMode moveMode, _Out_ int64_t* newTargetEventTime)
Jsrt/Jsrt.cpp:CHAKRA_API JsTTDMoveToTopLevelEvent(_In_ JsRuntimeHandle runtimeHandle, _In_ JsTTDMoveMode moveMode, _In_ int64_t snapshotTime, _In_ int64_t eventTime)
Jsrt/Jsrt.cpp:    _In_ const uint16_t *content,
Runtime/Language/AsmJsTypes.h:    typedef uint32 uint32_t;
Runtime/PlatformAgnostic/Platform/Common/Trace.cpp:    uint32_t path_size = SELF_PATH_SIZE;
Runtime/PlatformAgnostic/Platform/Linux/UnicodeText.ICU.cpp:            int32_t normalizedStringLength = destUniStr.length();
Runtime/PlatformAgnostic/Platform/Linux/UnicodeText.ICU.cpp:            int32_t resultStringLength = 0;
Runtime/PlatformAgnostic/Platform/Linux/UnicodeText.ICU.cpp:            int8_t charType = u_charType(ch);
Runtime/PlatformAgnostic/Platform/Unix/DateTime.cpp:        int64_t absoluteTime = tv / 1000;
Runtime/PlatformAgnostic/Platform/Unix/DateTime.cpp:        int64_t absoluteTime = tv / 1000;
WasmReader/WasmBinaryReader.cpp.bak:        uint16_t offset = (op - wbExtended + 1) * 256;

@Cellule Once this fix is signed off, i can validate it against the full repo and merge to the feature branch if that helps

@dotnet-bot test Ubuntu static_ubuntu_linux_debug please . Pretty please?

@dotnet-bot test OSX static_osx_osx_test please. Seems to be a tough day :-)

@arunetm LGTM after you confirm the build is fixed in full
Thanks

Thanks, Sorting out some access issues while validating Full. Will get back on this soon.

Full builds pass now. We can merge this.
@Cellule can you help merge this change. Seems like I dont have the permissions in place to merge.


Android Auto fail to run after recompiling

Decompiling+compiling succeeds, but later application cannot be run (closes right away).
Information

Apktool Version (apktool -version) - 2.4.1-6d2878-SNAPSHOT
Operating System (Mac, Linux, Windows) - Windows 10
APK From? (Playstore, ROM, Other) - apkmirror.com

Steps to Reproduce

E:\tmp>java -Xmx1024m -jar "C:\Program Files\APK Easy Tool\Apktool\apktool-cli-all.jar" d -f -k -m -o "E:\tmp\1-Decompiled APKs\com.google.android.projection.gearhead_4.7.593823-release-47593823_minAPI21(armeabi-v7a)(nodpi)_apkmirror.com" "E:\tmp\com.google.android.projection.gearhead_4.7.593823-release-47593823_minAPI21(armeabi-v7a)(nodpi)_apkmirror.com.apk"
I: Using Apktool 2.4.1-6d2878-SNAPSHOT on com.google.android.projection.gearhead_4.7.593823-release-47593823_minAPI21(armeabi-v7a)(nodpi)_apkmirror.com.apk
I: Loading resource table...
I: Decoding AndroidManifest.xml with resources...
I: Loading resource table from file: C:\Users\Tomasz\AppData\Local\apktool\framework\1.apk
I: Decoding file-resources...
I: Decoding values */* XMLs...
I: Baksmaling classes.dex...
I: Baksmaling classes2.dex...
I: Baksmaling classes3.dex...
I: Copying assets and libs...
I: Copying unknown files...
I: Copying original files...
I: Copying META-INF/services directory

E:\tmp>java -Xmx1024m -jar "C:\Program Files\APK Easy Tool\Apktool\apktool-cli-all.jar" b -o "E:\tmp\2-Recompiled APKs\com.google.android.projection.gearhead_4.7.593823-release-47593823_minAPI21(armeabi-v7a)(nodpi)_apkmirror.com.apk" "E:\tmp\1-Decompiled APKs\com.google.android.projection.gearhead_4.7.593823-release-47593823_minAPI21(armeabi-v7a)(nodpi)_apkmirror.com"
I: Using Apktool 2.4.1-6d2878-SNAPSHOT
I: Checking whether sources has changed...
I: Smaling smali folder into classes.dex...
I: Checking whether sources has changed...
I: Smaling smali_classes2 folder into classes2.dex...
I: Checking whether sources has changed...
I: Smaling smali_classes3 folder into classes3.dex...
I: Checking whether resources has changed...
I: Building resources...
I: Copying libs... (/lib)
I: Copying libs... (/META-INF/services)
I: Building apk file...
I: Copying unknown files/dir...
I: Built apk...

E:\tmp>java -Xmx1024m -jar "C:\Program Files\APK Easy Tool\Resources\ApkSigner.jar" sign  --key "C:\Program Files\APK Easy Tool\Resources\apkeasytool.pk8" --cert "C:\Program Files\APK Easy Tool\Resources\apkeasytool.pem" --out "E:\tmp\2-Recompiled APKs\com.google.android.projection.gearhead_4.7.593823-release-47593823_minAPI21(armeabi-v7a)(nodpi)_apkmirror.com.apk" "E:\tmp\2-Recompiled APKs\com.google.android.projection.gearhead_4.7.593823-release-47593823_minAPI21(armeabi-v7a)(nodpi)_apkmirror.com.apk"

E:\tmp>java -Xmx1024m -jar "C:\Program Files\APK Easy Tool\Apktool\apktool-cli-all.jar" -version
2.4.1-6d2878-SNAPSHOT

Frameworks
Nothing specific.
APK
Need to get manually from apkmirror.com by searching android auto, version 4.7.
Questions to ask before submission

Have you tried apktool d, apktool b without changing anything? Yes
If you are trying to install a modified apk, did you resign it? Yes
Are you using the latest apktool version? Yes

Do you have a logcat of the crash on the device? It may be anti-tamper or something bugged with resources. adb logcat will show you this during crash.

I successfully compiled the above apk if I do not decode resources.arsc.  Seems to be something in the resource folder that is breaking the compile.

@JimDunn1234, I've seen that one before (though not very often) and it's something to do with the Apk checking the Play Store for its authenticity upon launching.
I'm not sure how to bypass this myself since I've rarely ever seen this message and, when I looked into it, I ended up scrapping it as that Apk (personally) wasn't worth the time and effort.
~Ibuprophen

After some investigation I found that apktool has nothing to do with the failure here - the application fails just after re-signing (no decompilation phase). I found one place where the app checks for signatures and tried to modify it, but then it still does request to some content provider (content://com.google.android.gms.phenotype/...) which fails if the app signature is different. Seems I need to follow a different path with my reverse engineering task :-)


fix(data): load cached images with read-only mode (#1225)

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

Any reason of doing this ?

Any reason of doing this ?

To enable multiple processes to load from the same cache file, otherwise the writer lock blocks other processes from concurrent loading.

Any reason of doing this ?

To enable multiple processes to load from the same cache file, otherwise the writer lock blocks other processes from concurrent loading.

I see the reason in  #1225 and replied. Did you benchmark it ?


Evaluate https://pypi.org/project/saxonpy/ for use with compliance-trestle-fedramp

Issue description / feature objectives
It looks like someone has created an installable wheel of saxon. This will make life much easier on at least linux and mac (but not windows). Evaluate using this for compliance-trestle-fedramp.
Caveats / Assumptions
Completion Criteria

and update the setup.cfg. It looks like windows will still need a manual install.

@butler54 @vikas-agarwal76 Recommending we close this issue. It was accomplished through this PR. Using https://pypi.org/project/saxonche/.


Added College of Education Darazo

Added College of Education Darazo www.coedarazo.edu.ng
Institution/School Name
College of Education Darazo
Instiution/School Website
coedarazo.edu.ng
Email Users
Please place an x between the square brackets if yes

[x] Students
[x] Academic/Teaching Staff
[x] Other/Support Staff
[ ] Alumni

Education Levels
Please place an x between the square brackets if yes

[ ] Post-graduate research
[x] Graduate School
[x] College (University) or Undergraduate School or equivalent
[x] Community College or equivalent
[ ] High School or equivalent
[ ] Elementary School or equivalent

@yahmis Pull request merged. Thank you!


ISSUE-163 adding default tags and resource tags

Description
Thanks for contributing this Pull Request. Make sure that you submit this Pull Request against the master branch of this repository, add a brief description, and tag the relevant issue(s) and PR(s) below.

Relevant Issues : (compulsory)
https://github.com/intuit/Trapheus/issues/163
Relevant PRs : (optional)
Type of change :

[ ] New feature
[ ] Bug fix for existing feature
[ ] Code quality improvement
[ ] Addition or Improvement of tests
[ ] Addition or Improvement of documentation

This will add tags at cfn stack level as well as the resource level.

#165

@stationeros Please review

Sandip,
I will be able to get to this by Tuesday as I am out for a visa
appointments. Let me know if that works
On Sunday, October 8, 2023, Sandip Nahak @.***> wrote:

@stationeros https://github.com/stationeros Please review
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Reply to this email directly, view it on GitHub
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Event tracking for stageInfo: Getting metrics in stage's lifecycle and store them in mem

Why are the changes needed?
When the SQL is running, we want to get all metrics in its ruuning time. Those metrics can help us to analyse the conf is reasonable or not and how to improve the perference.
When the SQL is failed, we need to know what causes it.
Above these reason, we need to add some event tracking to collect those metrics.
For kyuubiStageInfo, there have some labels.
You can use statementId to get all stages' info that belong to this SQL.
How was this patch tested?

[ ] Add some test cases that check the changes thoroughly including negative and positive cases if possible

[ ] Add screenshots for manual tests if appropriate

[ ] Run test locally before make a pull request

Codecov Report

Merging #861 (55f1351) into master (7ee92ce) will decrease coverage by 0.15%.
The diff coverage is 33.33%.

@@             Coverage Diff              @@
##             master     #861      +/-   ##
============================================
- Coverage     78.60%   78.45%   -0.16%
  Complexity       10       10
============================================
  Files           135      136       +1
  Lines          5122     5137      +15
  Branches        641      642       +1
============================================
+ Hits           4026     4030       +4
- Misses          745      756      +11
  Partials        351      351

Impacted Files
Coverage Δ

.../engine/spark/monitor/KyuubiStatementMonitor.scala
0.00% <0.00%> (ø)

...bi/engine/spark/monitor/entity/KyuubiJobInfo.scala
0.00% <ø> (ø)

.../engine/spark/monitor/entity/KyuubiStageInfo.scala
0.00% <0.00%> (ø)

...ine/spark/monitor/entity/KyuubiStatementInfo.scala
0.00% <ø> (ø)

...uubi/engine/spark/operation/ExecuteStatement.scala
88.46% <100.00%> (ø)

...g/apache/spark/kyuubi/SparkSQLEngineListener.scala
87.50% <100.00%> (+0.96%)
:arrow_up:

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update 7ee92ce...55f1351. Read the comment docs.


Update K8s pins to 0.27.9 and controller-runtime to 0.15.3 / fix incompatibilities

This commit updates the pins to 0.27.9 (and controller-runtime to 0.15.3, where most of the incompabitilities lie) and fixes the incompatibilities with the new versions, specifically:

updating the manager creation, replacing the removed cache builder and mapper provider inputs with the replacements
Implementing a new Controller layer to cache the manager cache to deal with the new source.Kind requirement where you must provide a cache to the source.Kind function (previously a struct) when creatin a watch
Replace bits of deprecated functionality from the apiserver wait package.

For the second bullet, I've introduced our own Controller interface which extends the controller.Controller interface so the individual controller / controller utilities don't need to deal with cache management. As outlined in some of the comments for that package, I intendd to use this layer to house future adaptations for cache management (and possibly even multi tenancy) to the controller runtime package.
Description

For PR author

[ ] Tests for change.
[ ] If changing pkg/apis/, run make gen-files
[ ] If changing versions, run make gen-versions

For PR reviewers
A note for code reviewers - all pull requests must have the following:

[ ] Milestone set according to targeted release.
[ ] Appropriate labels:

kind/bug if this is a bugfix.
kind/enhancement if this is a a new feature.
enterprise if this PR applies to Calico Enterprise only.

K8s v1.27.10 is also released. Might be a good time to update to the latest v1.27 release.


WebSocket connection Error inside Bolt Project

Describe the bug
While building my app, I asked the AI to create a test data set, and since then, I've been getting connection errors with the websocket that it can’t resolve. I’ve spent over a million tokens trying to fix this. I’d appreciate your help, as the AI also isn’t able to revert to the stable version I had before making that request.:
"WebSocket connection to 'ws://zp1v56uxy8rdx5ypatb0ockcb9tr6a-oci3--3001--34c588ed.local-credentialless.webcontainer-api.io/socket.io/?EIO=4&transport=websocket' failed: Error during WebSocket handshake: Empty response"
Link to the Bolt URL that caused the error
https://bolt.new/~/sb1-qyzawp
Steps to reproduce
At first, I clicked the "fix" button automatically and then copied all the text with the error and added it to a prompt, but neither method was able to resolve the error.
Expected behavior
I expected it to solve the connection issue, and if not, to revert to the version of my code that didn’t have errors.
Screen Recording / Screenshot
No response
Platform
Browser name = Chrome
Full version = <IP_ADDRESS>
Major version = 130
navigator.appName = Netscape
navigator.userAgent = Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/<IP_ADDRESS> Safari/537.36
performance.memory = {
  "totalJSHeapSize": 120149300,
  "usedJSHeapSize": 77857876,
  "jsHeapSizeLimit":<PHONE_NUMBER>
}
Username = allthings-es
Chat ID = 0c1ed8f20d9a

Additional context
No response

@kc0tlh Please, when do you estimate that your team will be able to resolve the error? My project is on hold, and I’d like to keep moving forward.

The problem was resolved by migrating to a Firebase database.


feat(python): Add conversion helper functions to Python extension.

New helper functions for converting from Pywr v1.x to v2.x JSON strings added to the Python extension. Includes a new wrapper around Metric.

I should just have to run maturin develop to install it into a venv right?

I think so? I guess I should add some Python tests for these functions.


MM-20765: Client4.ts no longer throwing Error

Summary
The call to Client4.ts  updateConfig is no longer throwing an error, but rather it is returning an object containing an error.
Ticket Link
https://mattermost.atlassian.net/browse/MM-20765
Screenshots

I actually thing this issue should be fixed in the typescript if possible.  However, I was unable to determine a fix on that side. Everything seems to be working correctly.

Closing in favor of  - https://github.com/mattermost/mattermost-webapp/pull/4414


Checkbox::new_with_checked

Nice project! I would love to use it (it looks stunning!). However for my use case I want to run the checkbox prompt with some items pre-selected. I think this is common enough to fit the checkbox preset.
existing work:
dialoguer has MultiSelect::items_checked.

@dvdsk Thank you for your proposal! I'm considering incorporating it into the changes for v0.3.1, as it's not a difficult modification.

https://github.com/ynqa/promkit/pull/14

@dvdsk Thank you for your proposal! I'm considering incorporating it into the changes for v0.3.1, as it's not a difficult modification.
* [v0.3.1 #14](https://github.com/ynqa/promkit/pull/14)

Thats great! I'll work off that branch (and master) no need to rush 0.3.1 :)

@dvdsk I added Checkbox::new_with_checked at https://github.com/ynqa/promkit/tree/dev-0.3.1/main. It can be executed as follows:
use promkit::{preset::checkbox::Checkbox, Result};

fn main() -> Result {
    let mut p = Checkbox::new_with_checked(vec![
        ("Apple", true),
        ("Banana", false),
        ("Orange", true),
        ("Mango", false),
        ("Strawberry", false),
        ("Pineapple", false),
        ("Grape", false),
        ("Watermelon", false),
        ("Kiwi", false),
        ("Pear", true),
    ])
    .title("What are your favorite fruits?")
    .checkbox_lines(5)
    .prompt()?;
    println!("result: {:?}", p.run()?);
    Ok(())
}

Great! thanks a lot!


you-get: 0.4.1488 -> 0.4.1500

Automatic update generated by nixpkgs-update tools. This update was made based on information from passthru.updateScript.
meta.description for you-get is: "A tiny command line utility to download media contents from the web"
meta.homepage for you-get is: "https://you-get.org"
meta.changelog for you-get is: ""
Updates performed

Ran passthru.UpdateScript

To inspect upstream changes
Impact

Checks done (click to expand)

built on NixOS

The tests defined in passthru.tests, if any, passed

0 of 0 passed binary check by having a zero exit code.

0 of 0 passed binary check by having the new version present in output.

found 0.4.1500 with grep in /nix/store/crc605lh0ycgld4wsaiwc585n8wjy0aq-you-get-0.4.1500

directory tree listing: https://gist.github.com/86af4b7ef67e01c7ed21d4b1cb4d37e1

du listing: https://gist.github.com/9b9e960b414830770e6633727ee1ce24

Rebuild report (if merged into master) (click to expand)

4 total rebuild path(s)

1 package rebuild(s)

1 x86_64-linux rebuild(s)
1 i686-linux rebuild(s)
1 x86_64-darwin rebuild(s)
1 aarch64-linux rebuild(s)

First fifty rebuilds by attrpath
you-get

Instructions to test this update (click to expand)

Either download from Cachix:
nix-store -r /nix/store/crc605lh0ycgld4wsaiwc585n8wjy0aq-you-get-0.4.1500 \
  --option binary-caches 'https://cache.nixos.org/ https://nix-community.cachix.org/' \
  --option trusted-public-keys '
  nix-community.cachix.org-1:mB9FSh9qf2dCimDSUo8Zy7bkq5CX+/rkCWyvRCYg3Fs=
  cache.nixos.org-1:6NCHdD59X431o0gWypbMrAURkbJ16ZPMQFGspcDShjY=
  '

(The Cachix cache is only trusted for this store-path realization.)
For the Cachix download to work, your user must be in the trusted-users list or you can use sudo since root is effectively trusted.
Or, build yourself:
nix-build -A you-get https://github.com/r-ryantm/nixpkgs/archive/0e0b9c7af501aceebd1c3806e0ba1270f26847ae.tar.gz

After you've downloaded or built it, look at the files and if there are any, run the binaries:
ls -la /nix/store/crc605lh0ycgld4wsaiwc585n8wjy0aq-you-get-0.4.1500
ls -la /nix/store/crc605lh0ycgld4wsaiwc585n8wjy0aq-you-get-0.4.1500/bin

Pre-merge build results
We have automatically built all packages that will get rebuilt due to
this change.
This gives evidence on whether the upgrade will break dependent packages.
Note sometimes packages show up as failed to build independent of the
change, simply because they are already broken on the target branch.
Result of nixpkgs-review run on x86_64-linux 1

1 package built:

you-get

Maintainer pings
cc @ryneeverett for testing.

Result of nixpkgs-review pr 107694 run on x86_64-linux 1

1 package built:

you-get


About the VoVnet-99 checkpoint and config

Hi, thanks for the great work!
I wonder if you could release the VoVnet-99 version checkpoint and config, so that the test set results can be reproduced?
Also, I wonder what pre-trained checkpoint for VoVnet-99 did you use? Was it first pre-trained on the COCO dataset for 2D Detection, and then pre-trained on the DDAM-15M dataset for depth prediction? Did you freeze any layer/stage in VoVnet-99 when training for 3D Detection?
Thanks a lot!

Hi, @zeyuwang615 . We adopt DD3D pretrained VoVNet (V2-99) as backbone network for images. No freeze when training.

Does 'DD3D pretrained VoVNet (V2-99)' means the backbone is pre-trained sequentially on COCO and DDAM-15M as described in the paper?

More importantly, is there any checkpoint for such DD3D pretrained VoVNet (V2-99) available? I have managed to find two, but not sure which is the right one (or neither is): here in dd3d codebase

or dded_det_final.pth here in detr3d codebase.
Thanks a lot!

We use vovnet.py file like this


Filter logs that are coming from certain namespaces on fluent-bit

Is your feature request related to a problem? Please describe.
In some cases I need to have a separate logging system that is configured to process logs from a single namespace. At this moment it can be achieved with the use of a CRD Flow, which is namespace-specific. However in this solution all collected log events are filtered on a fluentd service, which will require a lot of additional CPU and Memory resources, depending on amount of logs stored on cluster nodes.
Here is how many resources a logging system require in my case
➜ kubectl -n logging-operator top pods
NAME                                 CPU(cores)   MEMORY(bytes)
logging-cloudwatch-fluentbit-444f2   44m          11Mi
logging-cloudwatch-fluentbit-5pmn7   1m           10Mi
logging-cloudwatch-fluentbit-bns4b   82m          15Mi
logging-cloudwatch-fluentbit-c2vc6   1m           12Mi
logging-cloudwatch-fluentbit-f8k5p   74m          14Mi
logging-cloudwatch-fluentbit-fqwm6   2m           11Mi
logging-cloudwatch-fluentbit-g9vwm   65m          10Mi
logging-cloudwatch-fluentbit-ms7r8   31m          12Mi
logging-cloudwatch-fluentbit-r8xx4   8m           9Mi
logging-cloudwatch-fluentd-0         374m         433Mi
logging-operator-8457774cc4-gsqld    5m           75Mi

The alternative solution is to filter logs on fluent-bit. A managed to do by adding the following section to the fluentbit config file between kubernetes and output blocks.
    [FILTER]
      Name         nest
      Match        *
      Operation    lift
      Nested_under kubernetes
      Prefix_with  k8s:

    [FILTER]
      Name         grep
      Match        *
      Regex        k8s:namespace_name ^({{ .Values.match_namespaces | join "|" }})$

    [FILTER]
      Name           nest
      Match          *
      Operation      nest
      Nest_under     kubernetes
      Remove_prefix  k8s:
      Wildcard       k8s:*

I am using logging.customConfigSecret to pass a custom config file to the fluent-bit daemonset.
This allows to consume significantly less resources in the logging system.
➜ kubectl -n logging-operator top pods
NAME                                 CPU(cores)   MEMORY(bytes)
logging-cloudwatch-fluentbit-2ln52   1m           5Mi
logging-cloudwatch-fluentbit-57bq6   1m           4Mi
logging-cloudwatch-fluentbit-97w8j   1m           5Mi
logging-cloudwatch-fluentbit-cdjzv   2m           5Mi
logging-cloudwatch-fluentbit-dx96l   8m           5Mi
logging-cloudwatch-fluentbit-jvlhv   2m           5Mi
logging-cloudwatch-fluentbit-ppj9k   13m          5Mi
logging-cloudwatch-fluentbit-vc2t5   2m           6Mi
logging-cloudwatch-fluentbit-wh9nc   2m           4Mi
logging-cloudwatch-fluentd-0         5m           92Mi
logging-operator-8457774cc4-gsqld    2m           75Mi

Describe the solution you'd like
I would like to have this configuration as a common case in the logging operator. I would like to be able to configure in the Logging CRD the list of namespaces from which fluent-bit has to collect logs and forward them to fluentd.
You can add, say, Logging.fluentbit.accept_namespaces (keep logs from a given list of namespaces) and Logging.fluentbit.ignore_namespaces (filter logs from a given list of namespaces) to the config part of a CRD Logging.
Describe alternatives you've considered
Alternatively you can add Logging.fluentbit.filter, to be able to pass a custom filter config blocks to the config file.
Additional context
Add any other context or screenshots about the feature request here.

Thank you for submitting your request!
Your use-case is really interesting however it sounds more like an edge case. I have some questions and notes regarding that.

Your measurement seems a bit strange to me. You moved a parsing logic to the edges and yet the fluent-bit resource consumption is lower than before. I would love to see some more specific instruction what kind of workload you have to transfer and how you are doing it.

If we move this kind of filtering to the edges it makes the behaviour of the operator inconsistent. If you create rules in a namespaces that filtered in fluent-bit you won't get any logs out of them.

Now fluent-bit is a thin layer of the Logging Operator ecosystem mainly for log collecting. It's not preferred to move a lot of logic there before live config reload is possible. Moreover you can move your fluentd workload to preferred nodes (as a statefulset it is obvious to use selectors, tolerations) but you can't move around / scale fluent-bit as it is required on all nodes.

The configuration you described is a workaround with 3 steps
lift -> grep -> nest
Implementing this as Stream processor rule or Lua filter seems much more efficient than moving around the metadata of the record.

tl&dr:
Now we cant support these kind of configurations but in the future we can come up with something generic / useful solution for these performance sensitive cases.

@tarokkk Thank you for your answer!
For a better understanding let me tell you a bit more about the use case I am workin with.
A kubernetes cluster contains some important resources (centralized authenticator, ingress controller, a number of jobs and daemons that mimic production installations) and the numerous amount of pods, deployments, statefulsets and jobs that developers create in their personal namespaces. I would like to collect logs from important resources and ignore everything else.
Requirements:

Collect logs to AWS CloudWatch to the /aws/eks/devel/applicatoin log group and the ${namespace_name}/${labels.release}/${labels.app}/${container_name} log stream.
Configure the list of namespaces from where the logs need to be collected
Be able to add/remove namespaces from log collection with 0 downtime
Do not allows developers to change a log tag which I use as a name for the log stream, especially to change the ${namespace_name}/ prefix.
Easy to support, ideally I would like to have a single helm release
Receive the alert if some container is producing too many log events, say, more than 1k/minute.
number 6) has to be generated based on prometheus metrics

btw, I created a separate issue for 6) and 7) https://github.com/banzaicloud/logging-operator/issues/297
Solution
using your building blocks
I can use the building blocks that logging-operator provides to achieve my goal. To do this I need the following objects: 1 Logging system + 1 ClusterOutput + N Flow, 1 for each namespace that has to be configured for log collection;
However, it will be very problematic to complete the requirements 4) 5) 6) 7), I will need to manage N+1 releases and will need to configure RBAC + admission control hooks, so that nobody can change the  value.
Same issues if I will add 1 logging system for each namespace, except that I can partially complete requirement 6) and 7), because I know for which namespace the prometheus metrics are generated.
a solution that I used
Generate a custom fluent-bit config, so that fluentbit will filter the namespaces for me. With this solution only 1 helm release is needed. But if fluent-bit must be reconfigured, the daemonset will need to perform rolling update, which means that there will be log duplicates in the target output (cloudwatch). I can avoid this if in a single helm release I will generate N x (custom fluentbit config, Logger) + ClusterFlow + ClusterOutput. But it does not look like the best solution...
a solution that I see
Add the list of namespaces to the ClusterFlow CRD. In this case only 1 release is needed, also the number of objects in the release is minimal: Logging, ClusterFlow and ClusterOutput CRDs. In case when the list of namespaces must be changed, the fluentd will perform a live config reload.
Do you think it is possible to add the list of namespaces to the spec of a ClusterFlow CRD ? Would you consider this as a general use-case ?

I beleive what you need will be possible as soon as we get to make the label router smarter. We are back and forth a little bit on how to implement it, but one of its planned new features is matching on multiple namespaces. (The others are to support excludes and combining multiple selectors with OR statements)

In the latest release, we provide namespaces filtering for ClusterFlow. I hope this solves your problem ;) https://github.com/banzaicloud/logging-operator/releases/tag/3.0.0-rc.1


error

Traceback (most recent call last):
File "X:\ComfyUI_windows_portable\ComfyUI\nodes.py", line 1998, in load_custom_node
module_spec.loader.exec_module(module)
File "", line 940, in exec_module
File "", line 241, in call_with_frames_removed
File "X:\ComfyUI_windows_portable\ComfyUI\custom_nodes\z-tipo-extension_init.py", line 1, in
from .nodes.tipo import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
File "X:\ComfyUI_windows_portable\ComfyUI\custom_nodes\z-tipo-extension\nodes\tipo.py", line 35, in
import kgen.models as models
ModuleNotFoundError: No module named 'kgen.models'
Cannot import X:\ComfyUI_windows_portable\ComfyUI\custom_nodes\z-tipo-extension module for custom nodes: No module named 'kgen.models'

same here

Install tipo-kgen by yourself
The node script should have installation but sometimes it break
pip install -U tipo-kgen

I added requirements.txt
Should work in some managers or wrapper


couchapp push does not push all attachments but leaves out some directories

if there are directories starting with a dot, couchapp push does not push all attachments but leaves out some directories.