Support For IOS Mobile

Describe the bug
Unhandled Promise Rejection: RangeError: Out of Memory. And then the line of code that the error points to is "await ffmpeg.load();".
To Reproduce
Steps to reproduce the behavior:

Try to use FFmpeg on Safari 15.2 or a Chrome browser on an IOS mobile device.

Expected behavior
There will not be memory crashes.
Screenshots
N/A
Smartphone (please complete the following information):

Device: Iphone 12
OS: IOS 15.2
Browser: Safari and Chrome App
Version: 15.2 for Safari and latest Chrome version.

Additional context
Add any other context about the problem here.

As has been already noted here - a couple of more findings from my side:

It is actually  await this.ffmpeg.load(); that is causing the exception. So it's not about the audio file size or exceeding the memory with await this.ffmpeg.run(); call.
Likely, this happens because of the ffmpeg wasm size (currently 23.7 MB), which is apparently too big for a mobile browser. This is only my speculation, though. Please, correct me if I am wrong.
Other WASM libs (coincidentally much smaller, under 1 MB) cause me no troubles in mobile browsers.
Again, only my speculation, but perhaps streaming the WASM would make things more efficient? https://developers.google.com/web/updates/2018/04/loading-wasm

This is the error log that I get on my iPhone. Usually after "ffmpeg-core.js script loaded" I will get "ffmpeg-core loaded" which I think is referring to the ffmpeg-core.wasm file. So I think it is the ffmpeg wasm size that may be causing the issue.

I'm seeing this same issue on iPad on both iPad OS 15.0 and 15.3 (which is the most recent). I'd love to see this resolved.

same here with iPhone13 iOS 15.2.1

same here with iPhone11 iOS 15.3.1

Same here

I'm facing the same issue. Is there any workaround for this?

Same here any workarounds so far?

+1

Same here.
xcode ios Simulator 15.4 iPhone 13
new WebAssembly.Memory() makes Out of Memory error.
INITIAL_MEMORY is 2GB, right?
in case of ammo.js,
INITIAL_MEMORY seems to be 64MB.

I built custom core files with Docker CE on wsl2 Ubuntu 20.04.
And, It works fine with ios Safari.
see also https://github.com/ffmpegwasm/ffmpeg.wasm/issues/267
setup
$ git clone https://github.com/ffmpegwasm/ffmpeg.wasm-core
$ cd ffmpeg.wasm-core
$ git checkout b59fb55619
$ git submodule update --init --recursive

edit wasm/build-scripts/build-ffmpeg.sh
-  -s INITIAL_MEMORY=2146435072                  # 64 KB * 1024 * 16 * 2047 =<PHONE_NUMBER> bytes ~= 2 GB
+  -s INITIAL_MEMORY=1073741824                  # 1GB

or
-  -s INITIAL_MEMORY=2146435072                  # 64 KB * 1024 * 16 * 2047 =<PHONE_NUMBER> bytes ~= 2 GB
+  -s INITIAL_MEMORY=33554432                    # 32MB
+  -s MAXIMUM_MEMORY=1073741824                  # 1GB
+  -s ALLOW_MEMORY_GROWTH=1

edit wasm/build-scripts/install-deps.sh
-for cmd in autoconf automake libtool pkg-config
+for cmd in autoconf automake libtool pkg-config ragel

and run
$ bash build-with-docker.sh

I wrote simple WebAssembly.Memory test for test memory allocation.
https://gist.github.com/novogrammer/cf4752f447c54e4b76fffe5e737a3064
ios Simulator screenshot below.

I built custom core files with Docker CE on wsl2 Ubuntu 20.04.
And, It works fine with ios Safari.
see also https://github.com/ffmpegwasm/ffmpeg.wasm/issues/267
setup

$ git clone https://github.com/ffmpegwasm/ffmpeg.wasm-core

$ cd ffmpeg.wasm-core

$ git checkout b59fb55619

$ git submodule update --init --recursive

edit wasm/build-scripts/build-ffmpeg.sh

-  -s INITIAL_MEMORY=2146435072                  # 64 KB * 1024 * 16 * 2047 =<PHONE_NUMBER> bytes ~= 2 GB

+  -s INITIAL_MEMORY=1073741824                  # 1GB

or

-  -s INITIAL_MEMORY=2146435072                  # 64 KB * 1024 * 16 * 2047 =<PHONE_NUMBER> bytes ~= 2 GB

+  -s INITIAL_MEMORY=33554432                    # 32MB

+  -s MAXIMUM_MEMORY=1073741824                  # 1GB

+  -s ALLOW_MEMORY_GROWTH=1

edit wasm/build-scripts/install-deps.sh

-for cmd in autoconf automake libtool pkg-config

+for cmd in autoconf automake libtool pkg-config ragel

and run

$ bash build-with-docker.sh

I wrote simple WebAssembly.Memory test for test memory allocation.
https://gist.github.com/novogrammer/cf4752f447c54e4b76fffe5e737a3064
ios Simulator screenshot below.

You're a godsend

I built custom core files with Docker CE on wsl2 Ubuntu 20.04.
And, It works fine with ios Safari.
see also https://github.com/ffmpegwasm/ffmpeg.wasm/issues/267
setup

$ git clone https://github.com/ffmpegwasm/ffmpeg.wasm-core

$ cd ffmpeg.wasm-core

$ git checkout b59fb55619

$ git submodule update --init --recursive

edit wasm/build-scripts/build-ffmpeg.sh

-  -s INITIAL_MEMORY=2146435072                  # 64 KB * 1024 * 16 * 2047 =<PHONE_NUMBER> bytes ~= 2 GB

+  -s INITIAL_MEMORY=1073741824                  # 1GB

or

-  -s INITIAL_MEMORY=2146435072                  # 64 KB * 1024 * 16 * 2047 =<PHONE_NUMBER> bytes ~= 2 GB

+  -s INITIAL_MEMORY=33554432                    # 32MB

+  -s MAXIMUM_MEMORY=1073741824                  # 1GB

+  -s ALLOW_MEMORY_GROWTH=1

edit wasm/build-scripts/install-deps.sh

-for cmd in autoconf automake libtool pkg-config

+for cmd in autoconf automake libtool pkg-config ragel

and run

$ bash build-with-docker.sh

I wrote simple WebAssembly.Memory test for test memory allocation.
https://gist.github.com/novogrammer/cf4752f447c54e4b76fffe5e737a3064
ios Simulator screenshot below.

Is there any option where we can split the resulting binary by its component libraries? So only the libraries that will be used for an operation get lazy loaded.

@nxtexe
Add a script tag like this, if you want.
https://gist.github.com/james2doyle/28a59f8692cec6f334773007b31a1523

@nxtexe
Add a script tag like this, if you want.
https://gist.github.com/james2doyle/28a59f8692cec6f334773007b31a1523

This wouldn't work much for me since I use ffmpeg in a web worker. But what I mean more specifically is lazy loading the libraries ffmpeg relies on to do certain conversions for example libx264. This way only when this library is specified its binary would be loaded. Since as we know the full binary is around 25MB this would be very beneficial for lower end devices.

@nxtexe

This wouldn't work much for me since I use ffmpeg in a web worker. But what I mean more specifically is lazy loading the libraries ffmpeg relies on to do certain conversions for example libx264. This way only when this library is specified only its binary would be loaded instead of the entire wasm binary containing all the libraries. Since as we know the full binary is around 25MB this would be very beneficial for lower end devices.

I feel you.
I want to reduce the binary size.
I am not familiar to Emscripten and FFmpeg.
But, I think dynamic linking is generally a difficult technique.
Based on this static linked program,
I think you need to create some custom binaries and switch between them as needed.
I think it's around here.
wasm/build-scripts/configure-ffmpeg.sh
wasm/build-scripts/build-ffmpeg.sh

Looks like it's working on iOS! Marvellous work, thank you so much for taking the time!

I was able to get my converter to work on my iPad using the new ffmpeg-core.js  Thanks for doing that! I would be great to get the changes into a new stable release but I'm happy nonetheless.


integrate remaining submission tests

tests adapted:

test_competition_field_set (from test_integration.test_competition_field)
test_competition_field_not_set (from test_integration.test_competition_field_none)
test_one_model_multiple_benchmarks (from test_integration.test_rerun_evaluation)
add assertion for score comment (from test_integration.test_evaluation)

tests deleted because already present:

test_integration.test_failure_evaluation
all tests in test_submission.py (these are already in brain-score/core)

tests not incorporated:

test_integration.test_model_failure_evaluation -- this could be interesting to add in the future, when the scoring fails in the middle, leaving an in-progress database state

Note that https://github.com/brain-score/core/pull/63 is assumed to be merged for the tests in this PR to be consistent

re-trigger jenkins tests. I think there's some race condition with the test database...

jenkins internal error, merging


feat(master): allow variadic on generic type

Add support for variadic generic type.
Before, the following examples would not highlight properly:
type A<T...> = B<T...>

Hey, thanks for the PR. I've gone ahead and incorporated it in #16 since there were a lot of outdated files to be updated (and to regenerate the parser).


composer gives memcached >= 1.0 README => 1.0.2

The documentation states that we need at least memcache 1.0.2.
Where the composer.json requires memcached >= 1.0
Is there a reason that 1.0.1 and 1.0.0 are not supported?

No, there is no reason other than that I have no environment to test them. It might work.


Deprecate AnimatedListItemBuilder and AnimatedListRemovedItemBuilder

Description
Deprecate AnimatedListItemBuilder and AnimatedListRemovedItemBuilder in favor of AnimatedItemBuilder and AnimatedRemovedItemBuilder, since they have the same signature, and just need a new, more appropriate, name.
Tests

No tests needed: just a refactor without a semantic difference.

test-exempt: code refactor with no semantic change


fix: use keyup for escape key handler

Keyup is a more browser-compatible event for handling the escape key. Keydown is also viable, if you prefer to handle it that way.
The keypress event is not firing in Chrome (OSX v43).

Thanks for the contribution !


FLEX-3071 Improves transactions to prevent OptimisticLockException

SonarQube analysis reported 14 issues

 14 major

Top 10 extra issues
Note: The following issues were found on lines that were not modified in the pull request. Because these issues can't be reported as line comments, they are summarized here:

 OslpChannelHandler.java#L281: Refactor this code to not nest more than 3 if/for/while/switch/try statements.
 OslpChannelHandler.java#L363: Refactor this method to throw at most one checked exception instead of: java.io.IOException, com.alliander.osgp.webdevicesimulator.exceptions.DeviceSimulatorException
 OslpChannelHandler.java#L396: This block of commented-out lines of code should be removed.
 OslpChannelHandler.java#L429: The Cyclomatic Complexity of this method "handleRequest" is 31 which is greater than 10 authorized.
 OslpChannelHandler.java#L859: Make this IP "<IP_ADDRESS>" address configurable.
 OslpChannelHandler.java#L861: Make this IP "<IP_ADDRESS>" address configurable.
 OslpChannelHandler.java#L862: Make this IP "<IP_ADDRESS>" address configurable.
 OslpChannelHandler.java#L864: This block of commented-out lines of code should be removed.
 OslpChannelHandler.java#L870: Make this IP "<IP_ADDRESS>" address configurable.
 OslpChannelHandler.java#L1002: Make this IP "<IP_ADDRESS>" address configurable.

All is well.


Added cache disable prop

Still store the results in the cache for use in case the prop is changed without remounting.

Need to look through and update to follow your convention of docs


Faster-whisper installation replaces conda-installed CUDA PyTorch with CPU one

I cannot install faster-whisper and CUDA PyTorch side-by-side after PyTorch is added to requirements.txt.
How to reproduce
Environment: Windows 11 23H2 (22631.3880)
conda create -n whisper python=3.11
conda activate whisper
# Install PyTorch CUDA version as indicated on https://pytorch.org/get-started/locally/
conda install pytorch torchvision torchaudio pytorch-cuda=12.4 -c pytorch -c nvidia
# Install faster-whisper with current master (83a368e98a7b49621029f3067711fa895a97083f)
pip install --force-reinstall "faster-whisper @ https://github.com/SYSTRAN/faster-whisper/archive/83a368e98a7b49621029f3067711fa895a97083f.tar.gz"

Expected
faster-whisper just installs and works
Actual result
When trying to install faster-whisper, it uninstalls a LOT of stuffs including PyTorch and installs CPU version of pytorch.
Full log
Collecting faster-whisper@ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz
  Using cached https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz
  Preparing metadata (setup.py) ... done
Collecting ctranslate2<5,>=4.0 (from faster-whisper@ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz)
  Using cached ctranslate2-4.3.1-cp311-cp311-win_amd64.whl.metadata (10 kB)
Collecting huggingface_hub>=0.13 (from faster-whisper@ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz)
  Using cached huggingface_hub-0.24.2-py3-none-any.whl.metadata (13 kB)
Collecting tokenizers<1,>=0.13 (from faster-whisper@ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz)
  Using cached tokenizers-0.19.1-cp311-none-win_amd64.whl.metadata (6.9 kB)
Collecting onnxruntime<2,>=1.14 (from faster-whisper@ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz)
  Using cached onnxruntime-1.18.1-cp311-cp311-win_amd64.whl.metadata (4.5 kB)
Collecting transformers (from faster-whisper@ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz)
  Using cached transformers-4.43.2-py3-none-any.whl.metadata (43 kB)
Collecting pyannote-audio>=3.1.1 (from faster-whisper@ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz)
  Using cached pyannote.audio-3.3.1-py2.py3-none-any.whl.metadata (11 kB)
Collecting torch>=2.1.1 (from faster-whisper@ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz)
  Using cached torch-2.4.0-cp311-cp311-win_amd64.whl.metadata (27 kB)
Collecting torchaudio>=2.1.2 (from faster-whisper@ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz)
  Using cached torchaudio-2.4.0-cp311-cp311-win_amd64.whl.metadata (6.4 kB)
Collecting setuptools (from ctranslate2<5,>=4.0->faster-whisper@ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz)
  Using cached setuptools-71.1.0-py3-none-any.whl.metadata (6.6 kB)
Collecting numpy (from ctranslate2<5,>=4.0->faster-whisper@ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz)
  Using cached numpy-2.0.1-cp311-cp311-win_amd64.whl.metadata (60 kB)
Collecting pyyaml<7,>=5.3 (from ctranslate2<5,>=4.0->faster-whisper@ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz)
  Using cached PyYAML-6.0.1-cp311-cp311-win_amd64.whl.metadata (2.1 kB)
Collecting filelock (from huggingface_hub>=0.13->faster-whisper@ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz)
  Using cached filelock-3.15.4-py3-none-any.whl.metadata (2.9 kB)
Collecting fsspec>=2023.5.0 (from huggingface_hub>=0.13->faster-whisper@ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz)
  Using cached fsspec-2024.6.1-py3-none-any.whl.metadata (11 kB)
Collecting packaging>=20.9 (from huggingface_hub>=0.13->faster-whisper@ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz)
  Using cached packaging-24.1-py3-none-any.whl.metadata (3.2 kB)
Collecting requests (from huggingface_hub>=0.13->faster-whisper@ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz)
  Using cached requests-2.32.3-py3-none-any.whl.metadata (4.6 kB)
Collecting tqdm>=4.42.1 (from huggingface_hub>=0.13->faster-whisper@ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz)
  Using cached tqdm-4.66.4-py3-none-any.whl.metadata (57 kB)
Collecting typing-extensions>=<IP_ADDRESS> (from huggingface_hub>=0.13->faster-whisper@ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz)
  Using cached typing_extensions-4.12.2-py3-none-any.whl.metadata (3.0 kB)
Collecting coloredlogs (from onnxruntime<2,>=1.14->faster-whisper@ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz)
  Using cached coloredlogs-15.0.1-py2.py3-none-any.whl.metadata (12 kB)
Collecting flatbuffers (from onnxruntime<2,>=1.14->faster-whisper@ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz)
  Using cached flatbuffers-24.3.25-py2.py3-none-any.whl.metadata (850 bytes)
Collecting numpy (from ctranslate2<5,>=4.0->faster-whisper@ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz)
  Using cached numpy-1.26.4-cp311-cp311-win_amd64.whl.metadata (61 kB)
Collecting protobuf (from onnxruntime<2,>=1.14->faster-whisper@ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz)
  Using cached protobuf-5.27.2-cp310-abi3-win_amd64.whl.metadata (592 bytes)
Collecting sympy (from onnxruntime<2,>=1.14->faster-whisper@ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz)
  Using cached sympy-1.13.1-py3-none-any.whl.metadata (12 kB)
Collecting asteroid-filterbanks>=0.4 (from pyannote-audio>=3.1.1->faster-whisper@ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz)
  Using cached asteroid_filterbanks-0.4.0-py3-none-any.whl.metadata (3.3 kB)
Collecting einops>=0.6.0 (from pyannote-audio>=3.1.1->faster-whisper@ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz)
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Collecting lightning>=2.0.1 (from pyannote-audio>=3.1.1->faster-whisper@ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz)
  Using cached lightning-2.3.3-py3-none-any.whl.metadata (35 kB)
Collecting omegaconf<3.0,>=2.1 (from pyannote-audio>=3.1.1->faster-whisper@ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz)
  Using cached omegaconf-2.3.0-py3-none-any.whl.metadata (3.9 kB)
Collecting pyannote.core>=5.0.0 (from pyannote-audio>=3.1.1->faster-whisper@ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz)
  Using cached pyannote.core-5.0.0-py3-none-any.whl.metadata (1.4 kB)
Collecting pyannote.database>=5.0.1 (from pyannote-audio>=3.1.1->faster-whisper@ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz)
  Using cached pyannote.database-5.1.0-py3-none-any.whl.metadata (1.2 kB)
Collecting pyannote.metrics>=3.2 (from pyannote-audio>=3.1.1->faster-whisper@ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz)
  Using cached pyannote.metrics-3.2.1-py3-none-any.whl.metadata (1.3 kB)
Collecting pyannote.pipeline>=3.0.1 (from pyannote-audio>=3.1.1->faster-whisper@ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz)
  Using cached pyannote.pipeline-3.0.1-py3-none-any.whl.metadata (897 bytes)
Collecting pytorch-metric-learning>=2.1.0 (from pyannote-audio>=3.1.1->faster-whisper@ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz)
  Using cached pytorch_metric_learning-2.6.0-py3-none-any.whl.metadata (17 kB)
Collecting rich>=12.0.0 (from pyannote-audio>=3.1.1->faster-whisper@ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz)
  Using cached rich-13.7.1-py3-none-any.whl.metadata (18 kB)
Collecting semver>=3.0.0 (from pyannote-audio>=3.1.1->faster-whisper@ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz)
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Collecting soundfile>=0.12.1 (from pyannote-audio>=3.1.1->faster-whisper@ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz)
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Collecting speechbrain>=1.0.0 (from pyannote-audio>=3.1.1->faster-whisper@ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz)
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Collecting tensorboardX>=2.6 (from pyannote-audio>=3.1.1->faster-whisper@ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz)
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Collecting torch-audiomentations>=0.11.0 (from pyannote-audio>=3.1.1->faster-whisper@ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz)
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Collecting torchmetrics>=0.11.0 (from pyannote-audio>=3.1.1->faster-whisper@ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz)
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Collecting networkx (from torch>=2.1.1->faster-whisper@ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz)
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Collecting jinja2 (from torch>=2.1.1->faster-whisper@ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz)
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Collecting regex!=2019.12.17 (from transformers->faster-whisper@ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz)
  Using cached regex-2024.7.24-cp311-cp311-win_amd64.whl.metadata (41 kB)
Collecting safetensors>=0.4.1 (from transformers->faster-whisper@ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz)
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Collecting lightning-utilities<2.0,>=0.10.0 (from lightning>=2.0.1->pyannote-audio>=3.1.1->faster-whisper@ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz)
  Using cached lightning_utilities-0.11.6-py3-none-any.whl.metadata (5.2 kB)
Collecting pytorch-lightning (from lightning>=2.0.1->pyannote-audio>=3.1.1->faster-whisper@ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz)
  Using cached pytorch_lightning-2.3.3-py3-none-any.whl.metadata (21 kB)
Collecting antlr4-python3-runtime==4.9.* (from omegaconf<3.0,>=2.1->pyannote-audio>=3.1.1->faster-whisper@ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz)
  Using cached antlr4_python3_runtime-4.9.3-py3-none-any.whl
Collecting sortedcontainers>=2.0.4 (from pyannote.core>=5.0.0->pyannote-audio>=3.1.1->faster-whisper@ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz)
  Using cached sortedcontainers-2.4.0-py2.py3-none-any.whl.metadata (10 kB)
Collecting scipy>=1.1 (from pyannote.core>=5.0.0->pyannote-audio>=3.1.1->faster-whisper@ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz)
  Using cached scipy-1.14.0-cp311-cp311-win_amd64.whl.metadata (60 kB)
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Building wheels for collected packages: faster-whisper
  Building wheel for faster-whisper (setup.py) ... done
  Created wheel for faster-whisper: filename=faster_whisper-1.0.3-py3-none-any.whl size=18419679 sha256=2f2c79acc7198bd2fb8922b232e44f5721f3ff5d760cb3831cea237f76dd18ff
  Stored in directory: C:\Users\whyask37\AppData\Local\Temp\pip-ephem-wheel-cache-2jhbcqq5\wheels\c3\d8\cf\cb255bf478e6ffcc70ee3806d28748de68bc10d4a88ef8b5f6
Successfully built faster-whisper
Installing collected packages: sortedcontainers, sentencepiece, pytz, pyreadline3, primePy, mpmath, flatbuffers, docopt, antlr4-python3-runtime, urllib3, tzdata, typing-extensions, threadpoolctl, tabulate, sympy, six, shellingham, setuptools, semver, safetensors, ruamel.yaml.clib, regex, pyyaml, pyparsing, pygments, pycparser, protobuf, platformdirs, pillow, packaging, numpy, networkx, multidict, msgpack, mdurl, MarkupSafe, llvmlite, kiwisolver, joblib, idna, humanfriendly, greenlet, fsspec, frozenlist, fonttools, filelock, einops, decorator, cycler, colorama, charset-normalizer, certifi, audioread, attrs, yarl, tqdm, tensorboardX, sqlalchemy, soxr, scipy, ruamel.yaml, requests, python-dateutil, omegaconf, numba, markdown-it-py, Mako, lightning-utilities, lazy-loader, jinja2, ctranslate2, contourpy, colorlog, coloredlogs, click, cffi, aiosignal, torch, soundfile, scikit-learn, rich, pyannote.core, pooch, pandas, onnxruntime, matplotlib, hyperpyyaml, huggingface_hub, alembic, aiohttp, typer, torchmetrics, torchaudio, tokenizers, pytorch-metric-learning, optuna, librosa, julius, asteroid-filterbanks, transformers, torch-pitch-shift, speechbrain, pytorch-lightning, pyannote.database, torch-audiomentations, pyannote.pipeline, pyannote.metrics, lightning, pyannote-audio, faster-whisper
  Attempting uninstall: mpmath
    Found existing installation: mpmath 1.3.0
    Uninstalling mpmath-1.3.0:
      Successfully uninstalled mpmath-1.3.0
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    Found existing installation: urllib3 2.2.2
    Uninstalling urllib3-2.2.2:
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    Found existing installation: typing_extensions 4.12.2
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    Found existing installation: sympy 1.13.0
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    Found existing installation: PyYAML 6.0.1
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    Found existing installation: numpy 2.0.0
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    Found existing installation: networkx 3.3
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    Found existing installation: MarkupSafe 2.1.5
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    Uninstalling filelock-3.15.4:
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    Found existing installation: charset-normalizer 3.3.2
    Uninstalling charset-normalizer-3.3.2:
      Successfully uninstalled charset-normalizer-3.3.2
  Attempting uninstall: certifi
    Found existing installation: certifi 2024.7.4
    Uninstalling certifi-2024.7.4:
      Successfully uninstalled certifi-2024.7.4
  Attempting uninstall: requests
    Found existing installation: requests 2.32.3
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  Attempting uninstall: jinja2
    Found existing installation: Jinja2 3.1.4
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  Attempting uninstall: cffi
    Found existing installation: cffi 1.16.0
    Uninstalling cffi-1.16.0:
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  Attempting uninstall: torch
    Found existing installation: torch 2.4.0
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  Attempting uninstall: torchaudio
    Found existing installation: torchaudio 2.4.0
    Uninstalling torchaudio-2.4.0:
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Successfully installed Mako-1.3.5 MarkupSafe-2.1.5 aiohttp-3.9.5 aiosignal-1.3.1 alembic-1.13.2 antlr4-python3-runtime-4.9.3 asteroid-filterbanks-0.4.0 attrs-23.2.0 audioread-3.0.1 certifi-2024.7.4 cffi-1.16.0 charset-normalizer-3.3.2 click-8.1.7 colorama-0.4.6 coloredlogs-15.0.1 colorlog-6.8.2 contourpy-1.2.1 ctranslate2-4.3.1 cycler-0.12.1 decorator-5.1.1 docopt-0.6.2 einops-0.8.0 faster-whisper-1.0.3 filelock-3.15.4 flatbuffers-24.3.25 fonttools-4.53.1 frozenlist-1.4.1 fsspec-2024.6.1 greenlet-3.0.3 huggingface_hub-0.24.2 humanfriendly-10.0 hyperpyyaml-1.2.2 idna-3.7 jinja2-3.1.4 joblib-1.4.2 julius-0.2.7 kiwisolver-1.4.5 lazy-loader-0.4 librosa-0.10.2.post1 lightning-2.3.3 lightning-utilities-0.11.6 llvmlite-0.43.0 markdown-it-py-3.0.0 matplotlib-3.9.1 mdurl-0.1.2 mpmath-1.3.0 msgpack-1.0.8 multidict-6.0.5 networkx-3.3 numba-0.60.0 numpy-1.26.4 omegaconf-2.3.0 onnxruntime-1.18.1 optuna-3.6.1 packaging-24.1 pandas-2.2.2 pillow-10.4.0 platformdirs-4.2.2 pooch-1.8.2 primePy-1.3 protobuf-5.27.2 pyannote-audio-3.3.1 pyannote.core-5.0.0 pyannote.database-5.1.0 pyannote.metrics-3.2.1 pyannote.pipeline-3.0.1 pycparser-2.22 pygments-2.18.0 pyparsing-3.1.2 pyreadline3-3.4.1 python-dateutil-2.9.0.post0 pytorch-lightning-2.3.3 pytorch-metric-learning-2.6.0 pytz-2024.1 pyyaml-6.0.1 regex-2024.7.24 requests-2.32.3 rich-13.7.1 ruamel.yaml-0.18.6 ruamel.yaml.clib-0.2.8 safetensors-0.4.3 scikit-learn-1.5.1 scipy-1.14.0 semver-3.0.2 sentencepiece-0.2.0 setuptools-71.1.0 shellingham-1.5.4 six-1.16.0 sortedcontainers-2.4.0 soundfile-0.12.1 soxr-0.3.7 speechbrain-1.0.0 sqlalchemy-2.0.31 sympy-1.13.1 tabulate-0.9.0 tensorboardX-<IP_ADDRESS> threadpoolctl-3.5.0 tokenizers-0.19.1 torch-2.4.0 torch-audiomentations-0.11.1 torch-pitch-shift-1.2.4 torchaudio-2.4.0 torchmetrics-1.4.0.post0 tqdm-4.66.4 transformers-4.43.2 typer-0.12.3 typing-extensions-4.12.2 tzdata-2024.1 urllib3-2.2.2 yarl-1.9.4

NVM. I wanted to use batched faster-whisper and installed the master version. Apparently I should have removed --force-reinstall flag.
It should be noted on the README.md that the flag reinstalls all dependencies as well, so one shouldn't use that flag to install master or specific commit of faster-whisper.

Yes, be careful when using option --force-reinstall. It will forcefully reinstall a package even if it is already installed.


Add the ability to filter pr by author

Duplicate with https://github.com/nvim-telescope/telescope-github.nvim/pull/48


[Question] Is this mod built into 1.21 mekanism?

Just a question.

I posted the mod on both CurseForge and Modrinth and announced it on r/feedthebeast. One of the Mekanism authors saw the post and integrated it into Mekanism version 1.20.4. As a result, the mod is now permanently included in Mekanism 1.20.4 and later versions


Automate the creation of release tags

Signed-off-by: Zelin Hao<EMAIL_ADDRESS>Description
Create the release tag for plugins from the distribution-build manifest based on the version and build_id.
This workflow will fetch the commit_id from the manifest for each plugin and create the GitHub tag based on that, which could avoid discrepancies of using different commit id from release.
Issues Resolved
#378
Check List

[x] Commits are signed per the DCO using --signoff

By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license.
For more information on following Developer Certificate of Origin and signing off your commits, please check here.

Codecov Report

Merging #1666 (b2cbf91) into main (e19608b) will increase coverage by 5.32%.
The diff coverage is n/a.

@@              Coverage Diff              @@
##               main     #1666      +/-   ##
=============================================
+ Coverage     94.67%   100.00%   +5.32%
=============================================
  Files           163         4     -159
  Lines          3438        65    -3373
  Branches         21        14       -7
=============================================
- Hits           3255        65    -3190
+ Misses          180         0     -180
+ Partials          3         0       -3

Impacted Files
Coverage Δ

src/jenkins/BuildManifest.groovy

src/jenkins/InputManifest.groovy

...workflow/ci_check_gradle_publish_to_maven_local.py

src/run_assemble.py

src/ci_workflow/ci_check_gradle_properties.py

src/system/thread_safe_counter.py

.../assemble_workflow/bundle_opensearch_dashboards.py

src/assemble_workflow/bundle_opensearch.py

src/assemble_workflow/bundle_file_location.py

tests/jenkins/jobs/InputManifest_Jenkinsfile

... and 149 more

Continue to review full report at Codecov.

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Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update e19608b...b2cbf91. Read the comment docs.

I dislike the implementation here, but with the rule of thumb that this is better than what we had (manual process) I vote to merge this as is.

@zelinh - Can you create an issue to look into this in the future?

Issue is created to use public release manifest here #1710. Will be pushing this from another PR.


Update do version 3 of signature-pad

Hi, do you plan to update to signature pad version 3? https://github.com/szimek/signature_pad/releases

Currently is v3 beta, I think to wait for the stable version.

signature pad has been in beta 3 for almost a year and also is default version when downloading it from npm

Looks good! I will upgrade and test. Thanks for your suggestion.

thanks!


Some problem about training model.

Hi,
I have few questions want to learn about.
1. How long does this code get convergence and what hardware did you use?
2. What does the mAP can be effected by using MobileNetV2 as a backbone?
3. I found some detiles in this code may be different with what it should be in paper, such as the batch size set to 2 images per GPU, but i found in this code it is a fixed number 1.
4. During training, i found the loss can be low, but the validation mAP is extramly low. I wonder if this is due to the training process does not comlete?
5. Do you have pretrained model that can offer some help?

Hey did you solve your issues..I am about to try training with this detector with shufflenet v2. But as per your comments it seems the training is not going properly. Have you found any other implementations?


Regular js react app asking for typescript declarations

Bear with me as I just got started with React.
Installed module through yarn
yarn add react-use-auth
Immediately when I slap
import { useAuth } from "react-use-auth";
in my index.js file I get a little warning on that line stating:
Could not find a declaration file for module 'react-use-auth'. '/Users/<omitted>/SERVER/with-apollo/with-apollo-npm/node_modules/react-use-auth/dist/index.js' implicitly has an 'any' type.
  Try `npm install @types/react-use-auth` if it exists or add a new declaration (.d.ts) file containing `declare module 'react-use-auth';`ts(7016)

Unsure why it is asking for typescript declarations - I thought I was doing everything in plain JS.
I thought that this was just a warning and this would not affect the functioning of the module but, while running the actual app I got a warning stating:
TypeError: Cannot read property 'state' of null
At this line:
  const { isAuthenticated, login, logout, isAuthenticating, user } = useAuth()

What I did to make sure that it becomes more plausible that the typescript warning above is causing this is the following 'reverse engineering' procedure I followed:
I copied all the files from this module into my 'local' filesystem (lib folder) and called them this way - in stead of via the node_modules dir.
Result: The typescript error went away and I no longer have the Cannot read property 'state' of null error anymore.
What's better : the login procedure works. I can log in and log out.

Hey @b2times4 thannks for reporting this, looks like it's been resolved at some point since September 18th. Current version 0.5.1 doesn't show any type errors in a basic CRA app.
You can look at how it's done in the examples directory. I didn't change anything except the version number :)

@all-contributors add @b2times4 for issues


Add Dockerfile, remove package-lock to avoid issues with stale modules

Added example Dockerfile. Also removed package-lock.json as this should be generated when npm install is run.

 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.:x: tomhosking sign nowYou have signed the CLA already but the status is still pending? Let us recheck it.

Please resolve the conflict and comments before we can merge the PR.


[firebase_admob] mediation

I'd like to request mediation in admob, or is already possible and simple to implement in flutter?

Is it confirmed that AdMob mediation does not work with firebase_admob, even if the required native SDKs  (e.g. Facebook Audience Network) are integrated and initialized with basic flutter plugins?
After all, the parent ad view is always created by AdMob so no specific code should be needed to support showing as views coming from other SDKs, right?

The firebase_admob plugin will be deprecated in April 2021 in favor of Google Mobile Ads SDK for Flutter.
Google Mobile Ads SDK for Flutter is a new Flutter plugin that supports more Ads formats than firebase_admob does. Google Mobile Ads SDK for Flutter currently supports loading and displaying banner, interstitial (full-screen), native ads, and rewarded video ads across AdMob and AdManager. It also supports displaying banner and native ads as Widgets as opposed to being overlayed over all app content.
We're now recommending projects currently using firebase_admob migrate to Google Mobile Ads SDK for Flutter following the instructions outlined here.
Issues with migrating or with using the new package should now be raised over on the Google Mobile Ads SDK for Flutter repository.


Update docs with persistent storage

Adds necessary config but commented out for now until there are more providers. Allows users to switch between the two configurations as needed.


Godot 4

Is this somehow supposed to become ported to Godot 4?
😃

I am not currently using Godot 4 and don't have time to port it, so no plans for the near future. However, if I start using Godot 4 and have the time, I would certainly like to port it. Forks and contributions to do this are also welcome :slightly_smiling_face:

I started a branch https://github.com/Dan2552/godot-xterm/pull/1 (admittedly, mostly just changes made by Godot itself)
There's lots to do on it still (it's certainly nowhere near working) and there's a lot of TODOs, but just incase somebody else picks up, hopefully it saves them a bit of time even if I don't manage to make any more progress.
Notably the GDNative stuff needs changing:

[from https://godotengine.org/article/introducing-gd-extensions]
This is a breaking change to the system. GDNative plugins written for Godot 3 will not run on Godot 4 and vice versa. Plugins written for Godot 3 will need to be altered and compiled for Godot 4.

I have a bit of time over the new years break, so have started working on the port to Godot 4.
Using https://github.com/Dan2552/godot-xterm/pull/1 as a base, I have created the godot4 branch along with draft PR https://github.com/lihop/godot-xterm/pull/60.
The GDExtension currently compiles on Linux X86_64 and displays some output, but there are still a lot of things broken, so the work continues.
Thanks @Dan2552 for the work you've done so far, it's wonderful starting point. And thank you @ShalokShalom for motivating the port to Godot 4!


Error scraping URL

Great presentation at MSSN CTRL! This is my first attempt at running DIANA, so something might be misconfigured or I might be holding it wrong..
When trying to scrap any URL, I get an error that just dumps the entire content of the URL. For example:
https://www.threatshub.org/blog/aws-cloudquarry-digging-for-secrets-in-public-amis/
Error scraping URL: Error scraping URL: {'markdown': '[Packet Storm](https://www.threatshub.org/blog/category/packet-storm/)\n\n[May 9, 2024](https://www.threatshub.org/blog/aws-cloudquarry-digging-for-secrets-in-public-amis/)[TH Author](https://www.threatshub.org/blog/author/thauthor/)[headline,amazon,data loss,flaw,password](https://www.threatshub.org/blog/tag/headlineamazondata-lossflawpassword/)\n\n[Facebook](http://localhost:8501/#facebook) [Twitter](http://localhost:8501/#twitter) [LinkedIn](http://localhost:8501/#linkedin) [Reddit](http://localhost:8501/#reddit) [WhatsApp](http://localhost:8501/#whatsapp) [Email](http://localhost:8501/#email) [Share](https://www.addtoany.com/share#url=https%3A%2F%2Fwww.threatshub.org%2Fblog%2Faws-cloudquarry-digging-for-secrets-in-public-amis%2F&title=AWS%20CloudQuarry%3A%20Digging%20For%20Secrets%20In%20Public%20AMIs)\n\nMoney, secrets and mass exploitation: This research unveils a quarry of sensitive data stored in public AMIs. Digging through each AMI we managed to collect 500 GB of credentials, private repositories, access keys and more. The present article is the detailed analysis of how we did it and what the data represents.\n\nWe did a coordinated disclosure with AWS’s security team before publishing....

https://docs.datadoghq.com/security/account_takeover_protection
Error scraping URL: Error scraping URL: {'markdown': '[Datadog Webinar: Security Strategy for Cloud Applications\\\nWebinar: Cloud Security](https://www.datadoghq.com/event/security-strategy-24/?utm_source=inbound&utm_medium=corpsite-display&utm_campaign=dg-security-ww-docs-announcement-livewebinar-securitystrategyQ424).....

I figured this out. My response from Firecrawl was coming back as:
response['markdown'] = [the scraped markdown]
response['metadata']['statusCode'] == 200
So I threw this condition into the firecrawl integration:
elif isinstance(response, dict) and response.get('metadata', {}).get('statusCode') == 200 and 'markdown' in response:
        return response.get('markdown')

I'm sure there's a more elegant way, but this got things working.

Okay nice find, I know that there definitely can be some more elegant error handling for a lot of these parts (my fault lol) , but let me see if something changed with Firecrawl API. I will mark this and fix it, also I plan to package this all up into a container so this has more consistent deployment.

Yes same issue here, example below:


Improve the jazz-run create command and make it possible to subscribe to the peer sync state

In this PR I'm encapsulating all the peers optimisticKnownStates updates in a class called PeerKnownStates.
The idea is to make possible to subscribe to the optimisticKnownState to check the sync status of a coValue.
I've used this new "feature" to improve the create account command on jazz-run by replacing the sleep with a waitForSync function.

✨

changeset pls (even though it doesn't functionally change jazz-run account create - but it is more than a temporary fix now)

Changeset added

@aeplay changeset splitted!


Add script to generate list of supported coins

Create node script to generate table of supported coins.
The script searches the svg directory in search for coin names and compare them with CoinMarketCap names. If a coin symbol is different, the name defaults to ?. Then, it creates a markdown table and updates the README.md file.
The script also works independently for black, color, icon, white icons (although it seems that they are always present, so this feature might be removed if it is not necessary).
npm run table

Fix #68

Thanks for the contribution!
Little bit out of my depth here with this. OK, so I need to run that command, and the readme updates? I think a separate file might be best, and I'm not sure it needs to be a table, because all variants are always included.
Thinking how this could be paired with the manifest, so it can play nicely with #73. The manifest is something that I update by hand, but that's because I'm a designer, not a developer haha.

Hey,
I wrote the script using Node.js since you were already using npm, so I thought it might be easier to maintain it.
However, now looking at that site, I think that it doesn't make sense to create a script to generate a table, since

it is always necessary to manually create a new icon for new coins, so it is not a big deal to manually update a manifest file too
people can see the icons and search for specific coins on the website.

I will update this PR so that it works with #73

By the way, do you mind going through this process to create a github page under your username?
That's important because the page created by the contributor who did #73 is not automatically updated when you add new coins.
Also, I believe the project README could mention project website.

Hey again,
I just noticed that the PR #73 was very recently opened, so that's why the project page is not on the README :laughing:
Anyway, I will update this PR so that the manifest file is automatically populated with the coin names when the user opens the project website, so it will not be necessary to run any script at all.

Done @cjdowner

@aviggiano Great, thanks for helping out. I've got GH pages set up and the project has a URL - http://cryptoicons.co
Will merge this in and add the new URL to the readme when I commit with the newest icons tonight. 👍


feat: Suggestion to use the same import alias as Nextjs

Is your feature request related to a problem? Please describe.
Hello, I'm new to T3, and was giving it a try alongside the typical create-next-app@latest setup and I noticed that T3 uses ~ by default while nextjs uses @ by default. Since T3 is based on Nextjs, I was wondering why the difference? It would be an easier transition/switch from one to the other if it was kept the same, avoids confusion, makes it one less step to change, etc.
Thanks, keep up the great work and excited to try t3 with the newly added app router support.
Describe the solution you'd like to see
I'd like the default import alias to be the same as Nextjs, @
Describe alternate solutions
In case Nextjs used ~ in a previous version (or such when using page instead of app router) be able to auto detect this?
Additional information
No response

The import alias has nothing to do with Next.js or any other framework. It's just a setting in the tsconfig. We are defaulting to ~ because a lot of npm packages start with @ nowadays. If you want to use @, you can change it, that's why we made it an option in the CLI.

I think you misunderstood me, but I get the feeling that the answer is "it's an opinionated decision", which is absolutely fine. Thank you.


Upgrade JJWT library

Is your feature request related to a problem? Please describe.
JJWT has new Maven coordinates, so upgrades are not being detected. Soffit tokens are encrypted post-build, which breaks the validation check requiring 3 components separated by periods (.).
Describe the solution you'd like
Upgrade JJWT library to the latest version.
https://github.com/jwtk/jjwt#install-jdk-gradle
https://search.maven.org/search?q=a:jjwt

Upgrade requires a minimum length for signature key. CHANGEME is no longer accetable

Need to update code that checks for CHANGEME

Notifications will also need to be updated for the default key (though folks should be changing the default)


rm: cannot remove ‘/opt/oracle/install’: Directory not empty

Hi All,
I am creating an Oracle <IP_ADDRESS> docker image on RHEL 7.4.
However, during the installation, I get the below error message at Step 11:
The directory /opt is empty and there is no oracle folder, do let me know if I am missing something?
Also, the user with which I am running this installation has sudo access and is added to docker group.
Thanks a lot for all your help on this issue.
Below is the execution snippet:
Step 11/17 : RUN $ORACLE_BASE/oraInventory/orainstRoot.sh && $ORACLE_HOME/root.sh && rm -rf $INSTALL_DIR
—> Running in 461871045fa5
Changing permissions of /opt/oracle/oraInventory.
Adding read,write permissions for group.
Removing read,write,execute permissions for world.

Changing groupname of /opt/oracle/oraInventory to dba.
The execution of the script is complete.
Check /opt/oracle/product/<IP_ADDRESS>/dbhome_1/install/root_461871045fa5_2018-01-05_10-48-10.log for the output of root script
rm: cannot remove ‘/opt/oracle/install’: Directory not empty
Removing intermediate container 461871045fa5
The command ‘/bin/sh -c $ORACLE_BASE/oraInventory/orainstRoot.sh && $ORACLE_HOME/root.sh && rm -rf $INSTALL_DIR’ returned a non-zero code: 1
There was an error building the image.

Closing this for now, please reopen if the issue remains.


[Bug] Code Coverage

Component
Forge
Have you ensured that all of these are up to date?

[ ] Foundry
[X] Foundryup

What version of Foundry are you on?
forge 0.2.0 (3ca8723 2023-01-28T00:08:54.548717256Z)
What command(s) is the bug in?
No response
Operating System
Linux
Describe the bug
Following my previous issue:
https://github.com/foundry-rs/foundry/issues/4142 (Still open :upside_down_face: )
Code coverage is still broken in the case you call your function in an assertTrue:
For example if I have a function: yolo(uint256 a);
if I wrote in my test file:
assertTrue(yolo(1));

When running  forge coverage it will show me in the code coverage report that my function have not being tested.
:frowning:

Can you share the full target code that you're covering?
I have recently discovered quite a few bugs in the coverage command. Your issue might be a special case of other lower-level bugs.

Sure I will sample some test, hope my issue won't be forget like my last one :cry:

@0xAurelou Were you able to create a minimal test case?

@KholdStare make sure to see my list of reported bugs in forge coverage.


Press Enter after ":", the cursor should not be positioned at the beginning of the next line

VS Version: 28711.152.d16.0stg
Python Tool Version: 16.0.19066.1
Repro step:
type code:
def add(a, b):
except result:

actual result:

It might be that the smart indent option wasn't set correctly when PTVS was installed. That has happened before. See tools/options/text editor/python/tabs/Indenting and change it to smart if it isn't.

@huguesv thanks, I really need to change to smart from tools/option.../indenting manually, and I verified that smart was set as the default options in the latest build 28712.114 on lab/d16.0stg branch.

Let's reopen if the default is wrong again in the future.


Fine-tune most popular LLMs for movie sentiment analysis

Fine tune 3 (or more) popular models and compare performance to DistilBERT for the movie sentiment analysis task.
Some choices:
GPT-3
LaMDA
Turing-NLG
XGen
Llama 2 (7 billion)
Gemini
Pick based on suitability for sentiment analysis task, popularity and affordability of tuning.

BERT and its  DistilBERT (lean), and ROBERTA (more advanced) - flavors of the same model - have been implemented in the following branch https://github.com/bdzyubak/torch-control/issues/16. Llama is WIP.
Need to timebox the implementation of the other models - not all are available through transformers, and they may not have an easy interface.


[demo] Remove duplicate envvar on frontendproxy

OTEL_SERVICE_NAME envvar is set on all components as one of envvars listed in default.env, to the value of app.kubernetes.io/component label. Recently 51993de76504 ("Bump dependencies and fix grafana dashboards") duplicated OTEL_SERVICE_NAME on the frontendproxy deployment by setting it explicitly to frontend-proxy. A duplicated envvar causes an error when upgrading the opentelemetry-demo chart:
 Deployment.apps "opentelemetry-demo-frontendproxy" is invalid:
 spec.template.spec.containers[0].env[0].valueFrom: Invalid value: "": may
 not be specified when `value` is not empty

My understanding is that the duplicated envvar was introduced in #1422 as a follow-up to open-telemetry/opentelemetry-demo#1768 that introduced it in docker-compose setup of opentelemetry-demo? But I don't have context on why it was needed there.
cc @julianocosta89 @puckpuck as authors of the PRs.

Hello @lambdanis 👋🏽
Thanks for sending the PR, but the location is wrong.
The k8s manifests in the Demo repo are generated after the Helm chart is updated.
So, in this case, we need to send a PR to the Helm repo and remove the wrongly added OTEL_SERVICE_NAME env var.
https://github.com/open-telemetry/opentelemetry-helm-charts/blob/main/charts/opentelemetry-demo/values.yaml#L404-L405
Thanks again for spotting it.

I've sent the PR to fix the Helm chart here: https://github.com/open-telemetry/opentelemetry-helm-charts/pull/1439

Thanks for spotting it @lambdanis!
To update the Helm chart you also need to bump the minor version of the chart (https://github.com/open-telemetry/opentelemetry-helm-charts/blob/main/charts/opentelemetry-demo/Chart.yaml#L4), and then run:
make generate-examples CHARTS=opentelemetry-demo

Done, thanks for review @julianocosta89


Getting requirements to build wheel ... error

大佬帮帮看看这个问题
本地安装报错
运行环境 windows python 3.7
Getting requirements to build wheel ... error
error: subprocess-exited-with-error
× Getting requirements to build wheel did not run successfully.
│ exit code: 1
╰─> [25 lines of output]
setup.py:26: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
_CYTHON_INSTALLED = ver >= LooseVersion(min_cython_ver)
Traceback (most recent call last):
File "C:\Users\Seele.conda\envs\py39\lib\site-packages\pip_vendor\pyproject_hooks_in_process_in_process.py", line 353, in
main()
File "C:\Users\Seele.conda\envs\py39\lib\site-packages\pip_vendor\pyproject_hooks_in_process_in_process.py", line 335, in main
json_out['return_val'] = hook(**hook_input['kwargs'])
File "C:\Users\Seele.conda\envs\py39\lib\site-packages\pip_vendor\pyproject_hooks_in_process_in_process.py", line 118, in get_requires_for_build_wheel
return hook(config_settings)
File "C:\Users\Seele\AppData\Local\Temp\pip-build-env-qwqbot8u\overlay\Lib\site-packages\setuptools\build_meta.py", line 162, in get_requires_for_build_wheel
return self._get_build_requires(
File "C:\Users\Seele\AppData\Local\Temp\pip-build-env-qwqbot8u\overlay\Lib\site-packages\setuptools\build_meta.py", line 143, in _get_build_requires
self.run_setup()
File "C:\Users\Seele\AppData\Local\Temp\pip-build-env-qwqbot8u\overlay\Lib\site-packages\setuptools\build_meta.py", line 267, in run_setup
super(_BuildMetaLegacyBackend,
File "C:\Users\Seele\AppData\Local\Temp\pip-build-env-qwqbot8u\overlay\Lib\site-packages\setuptools\build_meta.py", line 158, in run_setup
exec(compile(code, file, 'exec'), locals())
File "setup.py", line 153, in
File "C:\Users\Seele.conda\envs\py39\lib\subprocess.py", line 505, in run
with Popen(*popenargs, **kwargs) as process:
File "C:\Users\Seele.conda\envs\py39\lib\subprocess.py", line 951, in init
self._execute_child(args, executable, preexec_fn, close_fds,
File "C:\Users\Seele.conda\envs\py39\lib\subprocess.py", line 1420, in _execute_child
hp, ht, pid, tid = _winapi.CreateProcess(executable, args,
FileNotFoundError: [WinError 2] 系统找不到指定的文件。
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
error: subprocess-exited-with-error
× Getting requirements to build wheel did not run successfully.
│ exit code: 1
╰─> See above for output.
note: This error originates from a subprocess, and is likely not a problem with pip.

我也是同样的问题，尝试用3.7.0、3.7、3.8的版本，包括修改跟colab一样的版本，也是这个问题
Cython==0.29.33
librosa==0.8.1
matplotlib==3.5.3
numpy==1.22.4
scipy==1.7.3
tensorboard==2.11.2
torch==1.13.1
torchvision==0.14.1
unidecode
pyopenjtalk
protobuf
tqdm

你好，需要提供更为详细的错误日志，从目前提供的日志里无法判断出是在安装哪个包时发生的错误

下面是我现在执行的语句顺序（ps：python没接触过，不太熟悉）
(base) PS C:\Users\Seele> cd D:\WorkPlace\Project\AI\VITS\vits-finetuning
(base) PS D:\WorkPlace\Project\AI\VITS\vits-finetuning> conda activate py37
(py37) PS D:\WorkPlace\Project\AI\VITS\vits-finetuning> pip install -r requirements.txt
Collecting Cython
Using cached Cython-0.29.33-py2.py3-none-any.whl (987 kB)
Collecting librosa==0.8.0
Using cached librosa-0.8.0.tar.gz (183 kB)
Preparing metadata (setup.py) ... done
Collecting matplotlib
Using cached matplotlib-3.5.3-cp37-cp37m-win_amd64.whl (7.2 MB)
Collecting numpy
Using cached numpy-1.21.6-cp37-cp37m-win_amd64.whl (14.0 MB)
Collecting scipy
Using cached scipy-1.7.3-cp37-cp37m-win_amd64.whl (34.1 MB)
Collecting tensorboard
Using cached tensorboard-2.11.2-py3-none-any.whl (6.0 MB)
Collecting torch
Using cached torch-1.13.1-cp37-cp37m-win_amd64.whl (162.6 MB)
Collecting torchvision
Using cached torchvision-0.14.1-cp37-cp37m-win_amd64.whl (1.1 MB)
Collecting unidecode
Using cached Unidecode-1.3.6-py3-none-any.whl (235 kB)
Collecting pyopenjtalk
Using cached pyopenjtalk-0.3.0.tar.gz (1.5 MB)
Installing build dependencies ... done
Getting requirements to build wheel ... error
error: subprocess-exited-with-error
× Getting requirements to build wheel did not run successfully.
│ exit code: 1
╰─> [25 lines of output]
setup.py:26: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
_CYTHON_INSTALLED = ver >= LooseVersion(min_cython_ver)
Traceback (most recent call last):
File "C:\Users\Seele.conda\envs\py37\lib\site-packages\pip_vendor\pyproject_hooks_in_process_in_process.py", line 353, in
main()
File "C:\Users\Seele.conda\envs\py37\lib\site-packages\pip_vendor\pyproject_hooks_in_process_in_process.py", line 335, in main
json_out['return_val'] = hook(**hook_input['kwargs'])
File "C:\Users\Seele.conda\envs\py37\lib\site-packages\pip_vendor\pyproject_hooks_in_process_in_process.py", line 118, in get_requires_for_build_wheel
return hook(config_settings)
File "C:\Users\Seele\AppData\Local\Temp\pip-build-env-ylce1_qo\overlay\Lib\site-packages\setuptools\build_meta.py", line 163, in get_requires_for_build_wheel
config_settings, requirements=['wheel'])
File "C:\Users\Seele\AppData\Local\Temp\pip-build-env-ylce1_qo\overlay\Lib\site-packages\setuptools\build_meta.py", line 143, in _get_build_requires
self.run_setup()
File "C:\Users\Seele\AppData\Local\Temp\pip-build-env-ylce1_qo\overlay\Lib\site-packages\setuptools\build_meta.py", line 268, in run_setup
self).run_setup(setup_script=setup_script)
File "C:\Users\Seele\AppData\Local\Temp\pip-build-env-ylce1_qo\overlay\Lib\site-packages\setuptools\build_meta.py", line 158, in run_setup
exec(compile(code, file, 'exec'), locals())
File "setup.py", line 153, in
File "C:\Users\Seele.conda\envs\py37\lib\subprocess.py", line 488, in run
with Popen(*popenargs, **kwargs) as process:
File "C:\Users\Seele.conda\envs\py37\lib\subprocess.py", line 800, in init
restore_signals, start_new_session)
File "C:\Users\Seele.conda\envs\py37\lib\subprocess.py", line 1207, in _execute_child
startupinfo)
FileNotFoundError: [WinError 2] 系统找不到指定的文件。
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
error: subprocess-exited-with-error
× Getting requirements to build wheel did not run successfully.
│ exit code: 1
╰─> See above for output.
note: This error originates from a subprocess, and is likely not a problem with pip.
(py37) PS D:\WorkPlace\Project\AI\VITS\vits-finetuning>

Windows下安装pyopenjtalk出错。
这边看到你有使用Anaconda，可以先尝试先conda install cmake，再pip install -r requirements.txt
或者你可以参考这个视频 https://b23.tv/ecuoe19

应该可以了  我只装了cmake还不够  需要同时再把msvc装上 ，感谢大佬

解决方案

pip install cmake
根据视频安装MSVC并配置cmake环境

不过 我是装了Anaconda这个  流程不一定通用  但如果也遇到这个问题  可以借鉴一下


TypeError --init() unexpected keyword argument 'capture-output'

I get this error while trying to transfer 1 save file. Couldnt find any resource on this kind of error. Input seems fine to me. Blacked out areas are just my personal Name.

Your version of Python is too old. My mistake, I thought Python 3 would be enough, but it looks like Python 3.10 or greater is necessary. I've updated the README and I'll be committing the changes soon. Try installing 3.10 and it should work for you.

Thanks and yep this worked


enhancement(helm): Allow deploying as a DaemonSet

Description
This adds support to the chart for deploying as a DaemonSet, and adds additional options
that are common in that scenario. The new settings are

type: defaults to deployment, but can be set to daemonset
priorityClassName: allows setting the pod priorityClassName
service.internalTrafficPolicy: allows forcing traffic to this service to the local pod instance.

TODO:

Not tested at all
should add schema validation of the fields

Fixes cerbos/cerbos#1640

[ ] The PR title has the correct prefix
[ ] PR is linked to the corresponding issue
[ ] All commits are signed-off (git commit -s ...) to provide the DCO

Please update the documentation at https://github.com/cerbos/cerbos/tree/main/docs/modules/deployment/pages and https://github.com/cerbos/cerbos/blob/main/docs/modules/ROOT/pages/installation/helm.adoc as well.


help me + new updated offsets by me

(sorry for bad english) hi, riz-ve I'm one of the few people who actually learns from your code. I'm new to C++ and just when fixing various bugs I encountered an error, since UnlockModule has numbers of 1 byte in length, it is much more reasonable to use uint8_t to avoid bugs with after re-entering the roblox playground with the disappearance of controls, but now I encountered an error that When I leave the roblox place my roblox crashes. I suspect this is the Xeno injection method, but I'm not sure. That's why I'm asking for help. also for our little friends who cannot update the offsets themselves, here they are:
constexpr std::uint64_t This = 0x8;
constexpr std::uint64_t Name = 0x68;
constexpr std::uint64_t Children = 0x70;
constexpr std::uint64_t Parent = 0x50;
constexpr std::uint64_t ClassDescriptor = 0x18;
constexpr std::uint64_t ClassName = 0x8;
constexpr std::uint64_t ModuleScriptEmbedded = 0x168;
constexpr std::uint64_t IsCoreScript = 0x1b0;
constexpr std::uint64_t ModuleFlags = IsCoreScript - 0x4;
constexpr std::uint64_t LocalScriptEmbedded = 0x1c0;
constexpr std::uint64_t Bytecode = 0x10;
constexpr std::uint64_t BytecodeSize = 0x20;
constexpr std::uint64_t LocalPlayer = 0x118;
constexpr std::uint64_t ObjectValue = 0xc8;
(changed) forgot to indicate that fakeDataModel = 0x1a0;

because it writes to unsigned bytecode (which can cause it to crash i believe), other executors like wave ballistic etc push bytecode directly without writing into unsigned bytecode.

because it writes to unsigned bytecode (which can cause it to crash i believe), other executors like wave ballistic etc push bytecode directly without writing into unsigned bytecode.

hmm, how to fix this? don't change the method completely

I can try to make it so that it is a regular bytecode, and not unsigned. although I don't think this is the problem since there is an error with CoreGui

The reason Roblox crashes once you leave after writing bytecode to a CoreGui module is because Roblox uses those modules but we are modifying the source of it by writing to its bytecode and since it's not the source that Roblox expects it will eventually crash.
This is the best explanation I can give you as to why Roblox is crashing. It could also be something else

This is why I constantly revert back the bytecode of a module to its original source after few seconds it was modified

The reason Roblox crashes once you leave after writing bytecode to a CoreGui module is because Roblox uses those modules but we are modifying the source of it by writing to its bytecode and since it's not the source that Roblox expects it will eventually crash.
This is the best explanation I can give you as to why Roblox is crashing. It could also be something else

it turns out I just need to return CoreGui back? can you help me, I will be grateful:)

I explained incorrectly when I made the injection and then left the roblox game (place), again when I log in, the roblox control menus disappear

Я неправильно объяснил, когда сделал инъекцию, а затем вышел из игры (места) roblox, снова, когда я вхожу в систему, меню управления roblox исчезает

this is if you DO NOT change the UnlockModule function


Delete room and messages

Hello everybody! I want to ask, is there any functionality for deleting messages and rooms? Do you need to develop it?

Released in v1.5.2. Sorry for the wait.


Include Letsencrypt Root certificate in Azure Application Gateway Asked today

I'm trying to folllow Azure Tutorial on how to get Api Management under a vnet and accessible through an application gateway (WAF). I'm stuck trying to upload the root cert into application gateway. It says that the "Data for certificate is invalid", apparently Azure Application gateway doesn’t like Letsencrypt certs.
My certs are:
mydomain.com.br
api.mydomain.com.br
developer.mydomain.com.br
managemnet.mydomain.com.br
I have used acmesh to generate all certs:
./acme.sh --issue  -d mydomain.com.br --dns dns_gd    --server  letsencrypt
./acme.sh --issue  -d api.mydomain.com.br  --dns dns_gd    --server  letsencrypt
./acme.sh --issue  -d developer.mydomain.com.br ---dns dns_gd    --server  letsencrypt
./acme.sh --issue  -d management.mydomain.com.br --dns dns_gd    --server  letsencrypt

Vnet, Subnets, Security Groups and Api Management are all created successfully, all good except for the part I need to create the application gateway:
$appgwName = "apim-app-gw"
$appgw = New-AzApplicationGateway -Name $appgwName -ResourceGroupName $resGroupName -Location $location `
-BackendAddressPools $apimGatewayBackendPool,$apimPortalBackendPool,$apimManagementBackendPool `
-BackendHttpSettingsCollection $apimPoolGatewaySetting, $apimPoolPortalSetting, $apimPoolManagementSetting `
-FrontendIpConfigurations $fipconfig01 -GatewayIpConfigurations $gipconfig -FrontendPorts $fp01 `
-HttpListeners $gatewayListener,$portalListener,$managementListener `
-RequestRoutingRules $gatewayRule,$portalRule,$managementRule `
-Sku $sku -WebApplicationFirewallConfig $config -SslCertificates $certGateway,$certPortal,$certManagement `
-TrustedRootCertificate $trustedRootCert -Probes $apimGatewayProbe,$apimPortalProbe,$apimManagementProbe

The last line is where I need to inform the path to my .cer file. I have tried to add the mydomain.com.br.cer and fullchain.cer, no luck. Tried to use openssl to create a Base64 file, also no luck:
sudo openssl x509 -inform PEM -in /etc/letsencrypt/live/mydomain.com/fullchain.pem -outform DER -out trustedrootDER.cer
openssl x509 -inform der -in trustedrootDER.cer -out trustedroot.cer

I even created a VM running Windows to try this approach, no joy.
The solution from the Architecture point of view is pretty simple, but the certs problem makes it troublesome.
Any direction here is much appreciated! Thanks

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

ID: 37c1f253-44c2-bd07-4c74-9d4c77c45e6e
Version Independent ID: 281ce98c-115b-3c39-52b7-3df95dfd8b52
Content: How to use API Management in a virtual network with Azure Application Gateway - Azure API Management
Content Source: articles/api-management/api-management-howto-integrate-internal-vnet-appgateway.md
Service: api-management
GitHub Login: @solankisamir
Microsoft Alias: sasolank

Thanks for bringing this to our attention. We are actively investigating and will get back to you soon.

@bmoreira-cignifi I believe the -SslCertificates parameter expects a list of PSApplicationGatewaySslCertificate objects which can be created using the New-AzApplicationGatewaySslCertificate cmdlet and requires a PFX File for the Certs.
You can create one from the LE certs using the following
openssl pkcs12 -export -out api.mydomain.com.pfx -inkey api.mydomain.com.key -in fullchain.cer

Please do note that this repository is primarily to address doc related feedback. For other queries, Microsoft Q&A is a better forum to engage the wider community. Since there is no doc updated required here, we will now proceed to close this thread. If there are further questions regarding this matter, please tag me in your reply. We will gladly continue the discussion and we will reopen the issue.


Number of samples per epoch?

I just wanted to confirm my understanding because I didn't see this in the paper: for each generated dataset (e.g. 1m, 10m, 50m, etc), you don't actually iterate over the full augmented datasets in each epoch, right? You're simply sampling from the true and generated datasets such that each epoch corresponds to the same number of training steps for the original dataset (e.g. for CIFAR10, you'd only see 50k training examples), albeit different samples each epoch due to the random sampling from the real and generated datasets.
i.e. here:
https://github.com/wzekai99/DM-Improves-AT/blob/main/core/data/semisup.py#L15

Yes, you are right! Actually this practice inherits from the PyTorch implementation of Rebuffi et al., 2021, where the training time (i.e., number of iterations) is independent of the number of generated data. This is used for a fair comparison with W/O-generated-data baseline, and the only extra computation is to generate the 1m or 50m data.
Although we did not try, but we believe that fully running over the entire generated data (using certain original-to-generated ratios) could further improve the model performance beyond our results.


Support for cargo wrapper

Describe the problem you are trying to solve
For enterprise integration, it is desirable to control which version of cargo is being run and with what environment. This can be fixed by the user setting up an environment and then running the correct cargo binary. However, there are many touch points to this, including IDE integration and so on.
Here are some things we commonly need to do:

set CARGO_HOME in the environment
set PATH & LD_LIBRARY_PATH so that cargo can even be executed
run a specific version of cargo, which may not be the latest stable

Describe the solution you'd like
Gradle has the concept of the wrapper. Cargo already supports build.rustc-wrapper. I think it would be logical to support wrappers for cargo itself. I don't know that we need to support everything Gradle does (such as downloading the right version). To me, it would just need to be something that re-execs cargo. Here's how I imagine it going.
In some .cargo/config, a setting says "here is the cargo-wrapper". When cargo boots, it loads the config as normal and sees that setting. It then execs that wrapper (replaces the current process) with the exact same arguments and environment, modulo something that prevents recursion (either an env var or a CLI flag or something that looks at ARGV[0]). This means that a user that runs cargo foo will actually run $cargo_wrapper foo.
For the case where the user doesn't even have a working copy of cargo in their $PATH, they would be able to run the wrapper directly. It'd be nice, but not necessary, if that didn't immediately re-exec itself.
Notes
I did try to search to see if this was previously discussed but couldn't find anything.

Rustup seems to be what you're looking for: https://github.com/rust-lang/rustup#the-toolchain-file

Thanks for the link, I wasn't aware of the toolchain file. However, the docs say:

They may not name custom toolchains, nor host-specific toolchains.

I think that's my issue. Conceptually, each package has its own toolchain. A toolchain that has the exact environment required to build that package.
In our build system, we model additional dependencies (than just cargo ones). So for example, C libraries would be made available by the build system and the right env vars would be set. Building a different package might also require that same library, but perhaps a different version of it.

A toolchain that has the exact environment required to build that package.

That's not what "toolchain" means to rustup. That sentence is just saying that the toolchain file specifies the version of Rust only (e.g. "stable" or "1.43.0"), not a specific target architecture (e.g. "1.43.0-x86_64-unknown-linux-musl").
At least in my experience of using the gradle wrapper, the same thing goes there. The wrapper makes sure everyone is using specifically Gradle 6.3 or Gradle 5.6.2 or whatever. The logic of how the project should be built is in the build.gradle/settings.gradle/etc files.

Building a different package might also require that same library, but perhaps a different version of it.

That's typically a decision made by the build script for the Rust crate that provides an interface to the C library. They can typically be configured via environment variables, and also have their logic overridden entirely if necessary.

That's not what "toolchain" means to rustup. That sentence is just saying that the toolchain file specifies the version of Rust only (e.g. "stable" or "1.43.0"), not a specific target architecture (e.g. "1.43.0-x86_64-unknown-linux-musl").

Oh yeah, I understand that. I was just trying to apply your suggestion.

That's typically a decision made by the build script for the Rust crate that provides an interface to the C library. They can typically be configured via environment variables, and also have their logic overridden entirely if necessary.

That's not true. The build script is only part of the solution. The other part of the solution is the action users take to setup their environment so that the build script can find what it needs. An example might be yum install openssl-devel. The build script doesn't provide openssl, but has logic to find it and tell the compiler about it.
The build system we have is able to model these dependencies too. What it's not able to do is change the existing build script such that it asks our system where it placed the libs. So the pattern we have is we have a shim that will get our build system to provide openssl and then we'll export the right environment variables such that the build.rs can find it!
Meanwhile, users have IntelliJ or vim or something running and that setup typically just runs vanilla cargo. It doesn't know to run the setup first.
Furthermore, our build system models cargo itself. We don't use rustup for that. Our developers aren't doing rustup install stable. They're saying "do the build", and that is able to provide them with everything they need.
This is all kind of whacky, I get that. We're trying to fit a new build system in with a really old one. So if something I'm saying doesn't make sense please let me know.

It seems like rustup will take care of running the right toolchain already so the only thing that's missing here is the bootstrap scripts and the tasks to create those.