Cannot install the latest PyPI package

Hi. I tried to install the audio-separator for the MDX23C-8KFFT-InstVoc_HQ_2 model but met package version issue.
I use the following command line:
conda create -n uvr python=3.10
conda activate uvr
conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia
pip install onnxruntime-gpu
pip install "audio-separator[gpu]"

And the output of audio-separator --env_info is:
2024-03-20 02:14:17,873 - INFO - separator - Separator version 0.14.1 instantiating with output_dir: None, output_format: WAV
2024-03-20 02:14:17,874 - INFO - separator - Operating System: Linux #1 SMP Thu Dec 7 15:39:45 UTC 2023
2024-03-20 02:14:17,874 - INFO - separator - System: Linux Node: <my.server.ip> Release: 3.10.0-1160.105.1.el7.x86_64 Machine: x86_64 Proc: x86_64
2024-03-20 02:14:17,874 - INFO - separator - Python Version: 3.10.13
2024-03-20 02:14:17,946 - INFO - separator - FFmpeg installed: ffmpeg version 4.3 Copyright (c) 2000-2020 the FFmpeg developers
2024-03-20 02:14:17,947 - DEBUG - separator - Python package: onnxruntime-silicon not installed
2024-03-20 02:14:17,947 - DEBUG - separator - Python package: onnxruntime not installed
2024-03-20 02:14:17,949 - INFO - separator - ONNX Runtime GPU package installed with version: 1.16.3
2024-03-20 02:14:18,109 - INFO - separator - CUDA is available in Torch, setting Torch device to CUDA
2024-03-20 02:14:18,109 - INFO - separator - ONNXruntime has CUDAExecutionProvider available, enabling acceleration

Since I want to use the MDX23C-8KFFT-InstVoc_HQ_2 model, which is supported in version 0.16.2. How should I fix the installation? Thanks for any suggestions.

It seems to be fine 🤔

Hey @Faultiness , thanks for raising this issue!
Honestly this is pretty confusing to me, but I'm pretty sure it's pip's dependency resolution for the onnxruntime-gpu or pytorch packages causing an older version of audio-separator to get installed by pip.
Here are the logs from me trying to reproduce on my own machine (I didn't have exactly the same experience as you, and I don't have a CUDA machine to test on, I'm on an Apple Silicon Macbook, so it's not exactly the same)
(base) ➜  Data conda create -n uvr-test-clean python=3.10
Retrieving notices: ...working... done
Channels:
 - conda-forge
Platform: osx-arm64
Collecting package metadata (repodata.json): done
Solving environment: done

==> WARNING: A newer version of conda exists. <==
    current version: 23.11.0
    latest version: 24.1.2

Please update conda by running

    $ conda update -n base -c conda-forge conda

## Package Plan ##

  environment location: /Users/andrew/miniforge3/envs/uvr-test-clean

  added / updated specs:
    - python=3.10

The following packages will be downloaded:

    package                    |            build
    ---------------------------|-----------------
    ncurses-6.4.20240210       |       h078ce10_0         801 KB  conda-forge
    openssl-3.2.1              |       h0d3ecfb_1         2.7 MB  conda-forge
    ------------------------------------------------------------
                                           Total:         3.5 MB

The following NEW packages will be INSTALLED:

  bzip2              conda-forge/osx-arm64::bzip2-1.0.8-h93a5062_5
  ca-certificates    conda-forge/osx-arm64::ca-certificates-2024.2.2-hf0a4a13_0
  libffi             conda-forge/osx-arm64::libffi-3.4.2-h3422bc3_5
  libsqlite          conda-forge/osx-arm64::libsqlite-3.45.2-h091b4b1_0
  libzlib            conda-forge/osx-arm64::libzlib-1.2.13-h53f4e23_5
  ncurses            conda-forge/osx-arm64::ncurses-6.4.20240210-h078ce10_0
  openssl            conda-forge/osx-arm64::openssl-3.2.1-h0d3ecfb_1
  pip                conda-forge/noarch::pip-24.0-pyhd8ed1ab_0
  python             conda-forge/osx-arm64::python-3.10.13-h2469fbe_1_cpython
  readline           conda-forge/osx-arm64::readline-8.2-h92ec313_1
  setuptools         conda-forge/noarch::setuptools-69.2.0-pyhd8ed1ab_0
  tk                 conda-forge/osx-arm64::tk-8.6.13-h5083fa2_1
  tzdata             conda-forge/noarch::tzdata-2024a-h0c530f3_0
  wheel              conda-forge/noarch::wheel-0.42.0-pyhd8ed1ab_0
  xz                 conda-forge/osx-arm64::xz-5.2.6-h57fd34a_0

Proceed ([y]/n)? y

Downloading and Extracting Packages:

Preparing transaction: done
Verifying transaction: done
Executing transaction: done
#
# To activate this environment, use
#
#     $ conda activate uvr-test-clean
#
# To deactivate an active environment, use
#
#     $ conda deactivate

(base) ➜  Data conda activate uvr-test-clean
(uvr-test-clean) ➜  Data conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia
Channels:
 - pytorch
 - nvidia
 - conda-forge
Platform: osx-arm64
Collecting package metadata (repodata.json): done
Solving environment: failed

LibMambaUnsatisfiableError: Encountered problems while solving:
  - nothing provides cuda 11.8.* needed by pytorch-cuda-11.8-h8dd9ede_2

Could not solve for environment specs
The following package could not be installed
└─ pytorch-cuda 11.8**  is not installable because it requires
   └─ cuda 11.8.* , which does not exist (perhaps a missing channel).

(uvr-test-clean) ➜  Data conda install pytorch torchvision torchaudio -c pytorch
Channels:
 - pytorch
 - conda-forge
Platform: osx-arm64
Collecting package metadata (repodata.json): done
Solving environment: done

==> WARNING: A newer version of conda exists. <==
    current version: 23.11.0
    latest version: 24.1.2

Please update conda by running

    $ conda update -n base -c conda-forge conda

## Package Plan ##

  environment location: /Users/andrew/miniforge3/envs/uvr-test-clean

  added / updated specs:
    - pytorch
    - torchaudio
    - torchvision

The following packages will be downloaded:

    package                    |            build
    ---------------------------|-----------------
    gmp-6.3.0                  |       hebf3989_1         438 KB  conda-forge
    gmpy2-2.1.2                |  py310h2e6cad2_1         161 KB  conda-forge
    libgfortran-5.0.0          |13_2_0_hd922786_3         108 KB  conda-forge
    libgfortran5-13.2.0        |       hf226fd6_3         974 KB  conda-forge
    llvm-openmp-18.1.1         |       hcd81f8e_0         270 KB  conda-forge
    markupsafe-2.1.5           |  py310hd125d64_0          23 KB  conda-forge
    mpc-1.3.1                  |       h91ba8db_0         101 KB  conda-forge
    mpfr-4.2.1                 |       h9546428_0         345 KB  conda-forge
    mpmath-1.3.0               |     pyhd8ed1ab_0         428 KB  conda-forge
    networkx-3.2.1             |     pyhd8ed1ab_0         1.1 MB  conda-forge
    numpy-1.26.4               |  py310hd45542a_0         5.2 MB  conda-forge
    openjpeg-2.5.2             |       h9f1df11_0         309 KB  conda-forge
    pytorch-2.2.1              |         py3.10_0        52.4 MB  pytorch
    sympy-1.12                 | pypyh9d50eac_103         4.1 MB  conda-forge
    torchaudio-2.2.1           |        py310_cpu         4.8 MB  pytorch
    torchvision-0.17.1         |        py310_cpu         6.5 MB  pytorch
    typing_extensions-4.10.0   |     pyha770c72_0          36 KB  conda-forge
    urllib3-2.2.1              |     pyhd8ed1ab_0          92 KB  conda-forge
    ------------------------------------------------------------
                                           Total:        77.3 MB

The following NEW packages will be INSTALLED:

  brotli-python      conda-forge/osx-arm64::brotli-python-1.1.0-py310h1253130_1
  certifi            conda-forge/noarch::certifi-2024.2.2-pyhd8ed1ab_0
  charset-normalizer conda-forge/noarch::charset-normalizer-3.3.2-pyhd8ed1ab_0
  filelock           conda-forge/noarch::filelock-3.13.1-pyhd8ed1ab_0
  freetype           conda-forge/osx-arm64::freetype-2.12.1-hadb7bae_2
  gmp                conda-forge/osx-arm64::gmp-6.3.0-hebf3989_1
  gmpy2              conda-forge/osx-arm64::gmpy2-2.1.2-py310h2e6cad2_1
  idna               conda-forge/noarch::idna-3.6-pyhd8ed1ab_0
  jinja2             conda-forge/noarch::jinja2-3.1.3-pyhd8ed1ab_0
  lcms2              conda-forge/osx-arm64::lcms2-2.16-ha0e7c42_0
  lerc               conda-forge/osx-arm64::lerc-4.0.0-h9a09cb3_0
  libblas            conda-forge/osx-arm64::libblas-3.9.0-21_osxarm64_openblas
  libcblas           conda-forge/osx-arm64::libcblas-3.9.0-21_osxarm64_openblas
  libcxx             conda-forge/osx-arm64::libcxx-16.0.6-h4653b0c_0
  libdeflate         conda-forge/osx-arm64::libdeflate-1.19-hb547adb_0
  libgfortran        conda-forge/osx-arm64::libgfortran-5.0.0-13_2_0_hd922786_3
  libgfortran5       conda-forge/osx-arm64::libgfortran5-13.2.0-hf226fd6_3
  libjpeg-turbo      conda-forge/osx-arm64::libjpeg-turbo-3.0.0-hb547adb_1
  liblapack          conda-forge/osx-arm64::liblapack-3.9.0-21_osxarm64_openblas
  libopenblas        conda-forge/osx-arm64::libopenblas-0.3.26-openmp_h6c19121_0
  libpng             conda-forge/osx-arm64::libpng-1.6.43-h091b4b1_0
  libtiff            conda-forge/osx-arm64::libtiff-4.6.0-ha8a6c65_2
  libwebp-base       conda-forge/osx-arm64::libwebp-base-1.3.2-hb547adb_0
  libxcb             conda-forge/osx-arm64::libxcb-1.15-hf346824_0
  llvm-openmp        conda-forge/osx-arm64::llvm-openmp-18.1.1-hcd81f8e_0
  markupsafe         conda-forge/osx-arm64::markupsafe-2.1.5-py310hd125d64_0
  mpc                conda-forge/osx-arm64::mpc-1.3.1-h91ba8db_0
  mpfr               conda-forge/osx-arm64::mpfr-4.2.1-h9546428_0
  mpmath             conda-forge/noarch::mpmath-1.3.0-pyhd8ed1ab_0
  networkx           conda-forge/noarch::networkx-3.2.1-pyhd8ed1ab_0
  numpy              conda-forge/osx-arm64::numpy-1.26.4-py310hd45542a_0
  openjpeg           conda-forge/osx-arm64::openjpeg-2.5.2-h9f1df11_0
  pillow             conda-forge/osx-arm64::pillow-10.2.0-py310hfae7ebd_0
  pthread-stubs      conda-forge/osx-arm64::pthread-stubs-0.4-h27ca646_1001
  pysocks            conda-forge/noarch::pysocks-1.7.1-pyha2e5f31_6
  python_abi         conda-forge/osx-arm64::python_abi-3.10-4_cp310
  pytorch            pytorch/osx-arm64::pytorch-2.2.1-py3.10_0
  pyyaml             conda-forge/osx-arm64::pyyaml-6.0.1-py310h2aa6e3c_1
  requests           conda-forge/noarch::requests-2.31.0-pyhd8ed1ab_0
  sympy              conda-forge/noarch::sympy-1.12-pypyh9d50eac_103
  torchaudio         pytorch/osx-arm64::torchaudio-2.2.1-py310_cpu
  torchvision        pytorch/osx-arm64::torchvision-0.17.1-py310_cpu
  typing_extensions  conda-forge/noarch::typing_extensions-4.10.0-pyha770c72_0
  urllib3            conda-forge/noarch::urllib3-2.2.1-pyhd8ed1ab_0
  xorg-libxau        conda-forge/osx-arm64::xorg-libxau-1.0.11-hb547adb_0
  xorg-libxdmcp      conda-forge/osx-arm64::xorg-libxdmcp-1.1.3-h27ca646_0
  yaml               conda-forge/osx-arm64::yaml-0.2.5-h3422bc3_2
  zstd               conda-forge/osx-arm64::zstd-1.5.5-h4f39d0f_0

Proceed ([y]/n)?

Downloading and Extracting Packages:

Preparing transaction: done
Verifying transaction: done
Executing transaction: done
(uvr-test-clean) ➜  Data pip install onnxruntime-gpu
ERROR: Could not find a version that satisfies the requirement onnxruntime-gpu (from versions: none)
ERROR: No matching distribution found for onnxruntime-gpu
(uvr-test-clean) ➜  Data pip install "audio-separator[gpu]"
Collecting audio-separator[gpu]
  Downloading audio_separator-0.16.2-py3-none-any.whl.metadata (22 kB)
Collecting diffq>=0.2 (from audio-separator[gpu])
  Downloading diffq-0.2.4-cp310-cp310-macosx_11_0_arm64.whl.metadata (8.5 kB)
Collecting einops>=0.7 (from audio-separator[gpu])
  Using cached einops-0.7.0-py3-none-any.whl.metadata (13 kB)
Collecting julius>=0.2 (from audio-separator[gpu])
  Using cached julius-0.2.7.tar.gz (59 kB)
  Preparing metadata (setup.py) ... done
Collecting librosa>=0.10 (from audio-separator[gpu])
  Using cached librosa-0.10.1-py3-none-any.whl.metadata (8.3 kB)
Collecting ml_collections (from audio-separator[gpu])
  Downloading ml_collections-0.1.1.tar.gz (77 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 77.9/77.9 kB 3.0 MB/s eta 0:00:00
  Preparing metadata (setup.py) ... done
Requirement already satisfied: numpy>=1.23 in /Users/andrew/miniforge3/envs/uvr-test-clean/lib/python3.10/site-packages (from audio-separator[gpu]) (1.26.4)
Collecting onnx>=1.14 (from audio-separator[gpu])
  Downloading onnx-1.15.0-cp310-cp310-macosx_10_12_universal2.whl.metadata (15 kB)
Collecting onnx2torch>=1.5 (from audio-separator[gpu])
  Using cached onnx2torch-1.5.13-py3-none-any.whl.metadata (22 kB)
Collecting pydub>=0.25 (from audio-separator[gpu])
  Using cached pydub-0.25.1-py2.py3-none-any.whl.metadata (1.4 kB)
Requirement already satisfied: pyyaml in /Users/andrew/miniforge3/envs/uvr-test-clean/lib/python3.10/site-packages (from audio-separator[gpu]) (6.0.1)
Requirement already satisfied: requests>=2 in /Users/andrew/miniforge3/envs/uvr-test-clean/lib/python3.10/site-packages (from audio-separator[gpu]) (2.31.0)
Collecting resampy>=0.4 (from audio-separator[gpu])
  Downloading resampy-0.4.3-py3-none-any.whl.metadata (3.0 kB)
Collecting samplerate==0.1.0 (from audio-separator[gpu])
  Downloading samplerate-0.1.0-py2.py3-none-any.whl.metadata (3.2 kB)
Collecting six>=1.16 (from audio-separator[gpu])
  Using cached six-1.16.0-py2.py3-none-any.whl.metadata (1.8 kB)
Requirement already satisfied: torch in /Users/andrew/miniforge3/envs/uvr-test-clean/lib/python3.10/site-packages (from audio-separator[gpu]) (2.2.1)
Collecting tqdm (from audio-separator[gpu])
  Using cached tqdm-4.66.2-py3-none-any.whl.metadata (57 kB)
INFO: pip is looking at multiple versions of audio-separator[gpu] to determine which version is compatible with other requirements. This could take a while.
Collecting audio-separator[gpu]
  Downloading audio_separator-0.16.1-py3-none-any.whl.metadata (22 kB)
  Downloading audio_separator-0.16.0-py3-none-any.whl.metadata (23 kB)
  Downloading audio_separator-0.15.3-py3-none-any.whl.metadata (21 kB)
  Using cached audio_separator-0.15.2-py3-none-any.whl.metadata (21 kB)
  Downloading audio_separator-0.15.1-py3-none-any.whl.metadata (21 kB)
  Downloading audio_separator-0.15.0-py3-none-any.whl.metadata (20 kB)
Collecting omegaconf<3.0.0,>=2.3.0 (from audio-separator[gpu])
  Downloading omegaconf-2.3.0-py3-none-any.whl.metadata (3.9 kB)
Collecting audio-separator[gpu]
  Downloading audio_separator-0.14.5-py3-none-any.whl.metadata (19 kB)
INFO: pip is still looking at multiple versions of audio-separator[gpu] to determine which version is compatible with other requirements. This could take a while.
  Downloading audio_separator-0.14.4-py3-none-any.whl.metadata (19 kB)
Collecting samplerate<0.3.0,>=0.2.1 (from audio-separator[gpu])
  Downloading samplerate-0.2.1.tar.gz (21 kB)
  Installing build dependencies ... done
  Getting requirements to build wheel ... done
  Installing backend dependencies ... done
  Preparing metadata (pyproject.toml) ... done
Collecting audio-separator[gpu]
  Downloading audio_separator-0.14.3-py3-none-any.whl.metadata (19 kB)
  Downloading audio_separator-0.14.2-py3-none-any.whl.metadata (19 kB)
  Downloading audio_separator-0.14.1-py3-none-any.whl.metadata (19 kB)
  Using cached audio_separator-0.14.0-py3-none-any.whl.metadata (16 kB)
INFO: This is taking longer than usual. You might need to provide the dependency resolver with stricter constraints to reduce runtime. See https://pip.pypa.io/warnings/backtracking for guidance. If you want to abort this run, press Ctrl + C.
  Downloading audio_separator-0.13.1-py3-none-any.whl.metadata (16 kB)
  Downloading audio_separator-0.13.0-py3-none-any.whl.metadata (16 kB)
  Downloading audio_separator-0.12.3-py3-none-any.whl.metadata (16 kB)
  Downloading audio_separator-0.12.2-py3-none-any.whl.metadata (16 kB)
  Downloading audio_separator-0.12.1-py3-none-any.whl.metadata (16 kB)
  Downloading audio_separator-0.12.0-py3-none-any.whl.metadata (16 kB)
  Downloading audio_separator-0.11.7-py3-none-any.whl.metadata (15 kB)
  Downloading audio_separator-0.11.6-py3-none-any.whl.metadata (15 kB)
  Downloading audio_separator-0.11.5-py3-none-any.whl.metadata (15 kB)
  Downloading audio_separator-0.11.4-py3-none-any.whl.metadata (15 kB)
Requirement already satisfied: torchaudio in /Users/andrew/miniforge3/envs/uvr-test-clean/lib/python3.10/site-packages (from audio-separator[gpu]) (2.2.1)
Requirement already satisfied: torchvision in /Users/andrew/miniforge3/envs/uvr-test-clean/lib/python3.10/site-packages (from audio-separator[gpu]) (0.17.1)
  Downloading audio_separator-0.11.3-py3-none-any.whl.metadata (15 kB)
  Downloading audio_separator-0.11.2-py3-none-any.whl.metadata (15 kB)
  Downloading audio_separator-0.11.1-py3-none-any.whl.metadata (14 kB)
  Downloading audio_separator-0.11.0-py3-none-any.whl.metadata (14 kB)
Collecting onnx2pytorch>=0.4 (from audio-separator[gpu])
  Downloading onnx2pytorch-0.4.1-py3-none-any.whl.metadata (3.8 kB)
Collecting audio-separator[gpu]
  Downloading audio_separator-0.10.5-py3-none-any.whl.metadata (12 kB)
  Downloading audio_separator-0.10.4-py3-none-any.whl.metadata (12 kB)
  Downloading audio_separator-0.10.3-py3-none-any.whl.metadata (12 kB)
  Downloading audio_separator-0.10.2-py3-none-any.whl.metadata (12 kB)
Collecting wget>=3 (from audio-separator[gpu])
  Downloading wget-3.2.zip (10 kB)
  Preparing metadata (setup.py) ... done
Collecting audio-separator[gpu]
  Downloading audio_separator-0.10.1-py3-none-any.whl.metadata (12 kB)
  Downloading audio_separator-0.10.0-py3-none-any.whl.metadata (12 kB)
  Downloading audio_separator-0.9.6-py3-none-any.whl.metadata (12 kB)
  Downloading audio_separator-0.9.5-py3-none-any.whl.metadata (11 kB)
  Downloading audio_separator-0.9.4-py3-none-any.whl.metadata (11 kB)
  Downloading audio_separator-0.9.3-py3-none-any.whl.metadata (11 kB)
  Downloading audio_separator-0.9.2-py3-none-any.whl.metadata (11 kB)
Collecting soundfile>=0.11 (from audio-separator[gpu])
  Downloading soundfile-0.12.1-py2.py3-none-macosx_11_0_arm64.whl.metadata (14 kB)
Collecting audio-separator[gpu]
  Downloading audio_separator-0.9.1-py3-none-any.whl.metadata (10 kB)
  Downloading audio_separator-0.9.0-py3-none-any.whl.metadata (10 kB)
Collecting onnxruntime>=1.15 (from audio-separator[gpu])
  Downloading onnxruntime-1.17.1-cp310-cp310-macosx_11_0_universal2.whl.metadata (4.2 kB)
Collecting audio-separator[gpu]
  Downloading audio_separator-0.8.1-py3-none-any.whl.metadata (10 kB)
  Downloading audio_separator-0.8.0-py3-none-any.whl.metadata (10 kB)
WARNING: audio-separator 0.8.0 does not provide the extra 'gpu'
Collecting librosa<0.10,>=0.9 (from audio-separator[gpu])
  Downloading librosa-0.9.2-py3-none-any.whl.metadata (8.2 kB)
Collecting onnxruntime-silicon<2.0,>=1.15 (from audio-separator[gpu])
  Downloading onnxruntime_silicon-1.16.3-cp310-cp310-macosx_14_0_arm64.whl.metadata (4.3 kB)
Collecting soundfile<0.12,>=0.11 (from audio-separator[gpu])
  Downloading soundfile-0.11.0-py2.py3-none-macosx_10_9_arm64.macosx_11_0_arm64.whl.metadata (13 kB)
Collecting audioread>=2.1.9 (from librosa<0.10,>=0.9->audio-separator[gpu])
  Using cached audioread-3.0.1-py3-none-any.whl.metadata (8.4 kB)
Collecting scipy>=1.2.0 (from librosa<0.10,>=0.9->audio-separator[gpu])
  Downloading scipy-1.12.0-cp310-cp310-macosx_12_0_arm64.whl.metadata (112 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 112.9/112.9 kB 5.8 MB/s eta 0:00:00
Collecting scikit-learn>=0.19.1 (from librosa<0.10,>=0.9->audio-separator[gpu])
  Downloading scikit_learn-1.4.1.post1-cp310-cp310-macosx_12_0_arm64.whl.metadata (11 kB)
Collecting joblib>=0.14 (from librosa<0.10,>=0.9->audio-separator[gpu])
  Using cached joblib-1.3.2-py3-none-any.whl.metadata (5.4 kB)
Collecting decorator>=4.0.10 (from librosa<0.10,>=0.9->audio-separator[gpu])
  Downloading decorator-5.1.1-py3-none-any.whl.metadata (4.0 kB)
Collecting numba>=0.45.1 (from librosa<0.10,>=0.9->audio-separator[gpu])
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Collecting fsspec (from torch->audio-separator[gpu])
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Collecting llvmlite<0.43,>=0.42.0dev0 (from numba>=0.45.1->librosa<0.10,>=0.9->audio-separator[gpu])
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Building wheels for collected packages: wget
  Building wheel for wget (setup.py) ... done
  Created wheel for wget: filename=wget-3.2-py3-none-any.whl size=9655 sha256=b2eaf0b17bdc64f10cebffdf2cfa367db7a0b516014a2e5e5e508b6c504d6893
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Successfully built wget
Installing collected packages: wget, flatbuffers, threadpoolctl, six, scipy, pycparser, protobuf, platformdirs, packaging, llvmlite, joblib, humanfriendly, fsspec, decorator, audioread, scikit-learn, pooch, onnx, numba, coloredlogs, cffi, soundfile, resampy, onnxruntime-silicon, librosa, audio-separator
Successfully installed audio-separator-0.8.0 audioread-3.0.1 cffi-1.16.0 coloredlogs-15.0.1 decorator-5.1.1 flatbuffers-24.3.7 fsspec-2024.3.1 humanfriendly-10.0 joblib-1.3.2 librosa-0.9.2 llvmlite-0.42.0 numba-0.59.1 onnx-1.15.0 onnxruntime-silicon-1.16.3 packaging-24.0 platformdirs-4.2.0 pooch-1.8.1 protobuf-5.26.0 pycparser-2.21 resampy-0.4.3 scikit-learn-1.4.1.post1 scipy-1.12.0 six-1.16.0 soundfile-0.11.0 threadpoolctl-3.3.0 wget-3.2
(uvr-test-clean) ➜  Data audio-separator --env_info
usage: audio-separator [-h] [-v] [--log_level LOG_LEVEL] [--model_name MODEL_NAME] [--model_file_dir MODEL_FILE_DIR] [--output_dir OUTPUT_DIR] [--use_cuda] [--use_coreml]
                       [--output_format OUTPUT_FORMAT] [--denoise DENOISE] [--normalize NORMALIZE] [--single_stem SINGLE_STEM]
                       [audio_file]
audio-separator: error: unrecognized arguments: --env_info
(uvr-test-clean) ➜  Data audio-separator -v
audio-separator 0.8.0

So my test actually installed an ancient version 0.8.0 😬
Crucially, the message INFO: pip is looking at multiple versions of audio-separator[gpu] to determine which version is compatible with other requirements. This could take a while. explains the issue somewhat.
I suspect either onnxruntime-gpu or the version of pytorch from the pytorch channel is probably the cause.
If I create a clean conda env and install only audio-separator and onnxruntime, the latest version is installed as expected and works:
(base) ➜  Data conda create -n uvr-test-two python=3.10
Channels:
 - conda-forge
Platform: osx-arm64
Collecting package metadata (repodata.json): done
Solving environment: done

==> WARNING: A newer version of conda exists. <==
    current version: 23.11.0
    latest version: 24.1.2

Please update conda by running

    $ conda update -n base -c conda-forge conda

## Package Plan ##

  environment location: /Users/andrew/miniforge3/envs/uvr-test-two

  added / updated specs:
    - python=3.10

The following NEW packages will be INSTALLED:

  bzip2              conda-forge/osx-arm64::bzip2-1.0.8-h93a5062_5
  ca-certificates    conda-forge/osx-arm64::ca-certificates-2024.2.2-hf0a4a13_0
  libffi             conda-forge/osx-arm64::libffi-3.4.2-h3422bc3_5
  libsqlite          conda-forge/osx-arm64::libsqlite-3.45.2-h091b4b1_0
  libzlib            conda-forge/osx-arm64::libzlib-1.2.13-h53f4e23_5
  ncurses            conda-forge/osx-arm64::ncurses-6.4.20240210-h078ce10_0
  openssl            conda-forge/osx-arm64::openssl-3.2.1-h0d3ecfb_1
  pip                conda-forge/noarch::pip-24.0-pyhd8ed1ab_0
  python             conda-forge/osx-arm64::python-3.10.13-h2469fbe_1_cpython
  readline           conda-forge/osx-arm64::readline-8.2-h92ec313_1
  setuptools         conda-forge/noarch::setuptools-69.2.0-pyhd8ed1ab_0
  tk                 conda-forge/osx-arm64::tk-8.6.13-h5083fa2_1
  tzdata             conda-forge/noarch::tzdata-2024a-h0c530f3_0
  wheel              conda-forge/noarch::wheel-0.42.0-pyhd8ed1ab_0
  xz                 conda-forge/osx-arm64::xz-5.2.6-h57fd34a_0

Proceed ([y]/n)?

Downloading and Extracting Packages:

Preparing transaction: done
Verifying transaction: done
Executing transaction: done
#
# To activate this environment, use
#
#     $ conda activate uvr-test-two
#
# To deactivate an active environment, use
#
#     $ conda deactivate

(base) ➜  Data conda activate uvr-test-two
(uvr-test-two) ➜  Data pip install audio-separator
Collecting audio-separator
  Using cached audio_separator-0.16.2-py3-none-any.whl.metadata (22 kB)
Collecting diffq>=0.2 (from audio-separator)
  Using cached diffq-0.2.4-cp310-cp310-macosx_11_0_arm64.whl.metadata (8.5 kB)
Collecting einops>=0.7 (from audio-separator)
  Using cached einops-0.7.0-py3-none-any.whl.metadata (13 kB)
Collecting julius>=0.2 (from audio-separator)
  Using cached julius-0.2.7.tar.gz (59 kB)
  Preparing metadata (setup.py) ... done
Collecting librosa>=0.10 (from audio-separator)
  Using cached librosa-0.10.1-py3-none-any.whl.metadata (8.3 kB)
Collecting ml_collections (from audio-separator)
  Using cached ml_collections-0.1.1.tar.gz (77 kB)
  Preparing metadata (setup.py) ... done
Collecting numpy>=1.23 (from audio-separator)
  Downloading numpy-1.26.4-cp310-cp310-macosx_11_0_arm64.whl.metadata (61 kB)
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Collecting onnx>=1.14 (from audio-separator)
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Collecting onnx2torch>=1.5 (from audio-separator)
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Collecting pydub>=0.25 (from audio-separator)
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Collecting pyyaml (from audio-separator)
  Downloading PyYAML-6.0.1-cp310-cp310-macosx_11_0_arm64.whl.metadata (2.1 kB)
Collecting requests>=2 (from audio-separator)
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Collecting resampy>=0.4 (from audio-separator)
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Collecting samplerate==0.1.0 (from audio-separator)
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Collecting six>=1.16 (from audio-separator)
  Using cached six-1.16.0-py2.py3-none-any.whl.metadata (1.8 kB)
Collecting torch (from audio-separator)
  Downloading torch-2.2.1-cp310-none-macosx_11_0_arm64.whl.metadata (25 kB)
Collecting tqdm (from audio-separator)
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Collecting cffi>=1.0.0 (from samplerate==0.1.0->audio-separator)
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Collecting Cython (from diffq>=0.2->audio-separator)
  Downloading Cython-3.0.9-py2.py3-none-any.whl.metadata (3.2 kB)
Collecting audioread>=2.1.9 (from librosa>=0.10->audio-separator)
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Collecting scipy>=1.2.0 (from librosa>=0.10->audio-separator)
  Using cached scipy-1.12.0-cp310-cp310-macosx_12_0_arm64.whl.metadata (112 kB)
Collecting scikit-learn>=0.20.0 (from librosa>=0.10->audio-separator)
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Collecting joblib>=0.14 (from librosa>=0.10->audio-separator)
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Collecting decorator>=4.3.0 (from librosa>=0.10->audio-separator)
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Collecting numba>=0.51.0 (from librosa>=0.10->audio-separator)
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Collecting soundfile>=0.12.1 (from librosa>=0.10->audio-separator)
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Collecting pooch>=1.0 (from librosa>=0.10->audio-separator)
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Collecting soxr>=0.3.2 (from librosa>=0.10->audio-separator)
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Collecting msgpack>=1.0 (from librosa>=0.10->audio-separator)
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Collecting torchvision>=0.9.0 (from onnx2torch>=1.5->audio-separator)
  Downloading torchvision-0.17.1-cp310-cp310-macosx_11_0_arm64.whl.metadata (6.6 kB)
Collecting charset-normalizer<4,>=2 (from requests>=2->audio-separator)
  Downloading charset_normalizer-3.3.2-cp310-cp310-macosx_11_0_arm64.whl.metadata (33 kB)
Collecting idna<4,>=2.5 (from requests>=2->audio-separator)
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Collecting certifi>=2017.4.17 (from requests>=2->audio-separator)
  Using cached certifi-2024.2.2-py3-none-any.whl.metadata (2.2 kB)
Collecting filelock (from torch->audio-separator)
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  Using cached networkx-3.2.1-py3-none-any.whl.metadata (5.2 kB)
Collecting jinja2 (from torch->audio-separator)
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Collecting fsspec (from torch->audio-separator)
  Using cached fsspec-2024.3.1-py3-none-any.whl.metadata (6.8 kB)
Collecting absl-py (from ml_collections->audio-separator)
  Downloading absl_py-2.1.0-py3-none-any.whl.metadata (2.3 kB)
Collecting contextlib2 (from ml_collections->audio-separator)
  Downloading contextlib2-21.6.0-py2.py3-none-any.whl.metadata (4.1 kB)
Collecting pycparser (from cffi>=1.0.0->samplerate==0.1.0->audio-separator)
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Collecting llvmlite<0.43,>=0.42.0dev0 (from numba>=0.51.0->librosa>=0.10->audio-separator)
  Using cached llvmlite-0.42.0-cp310-cp310-macosx_11_0_arm64.whl.metadata (4.8 kB)
Collecting platformdirs>=2.5.0 (from pooch>=1.0->librosa>=0.10->audio-separator)
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Collecting packaging>=20.0 (from pooch>=1.0->librosa>=0.10->audio-separator)
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Collecting threadpoolctl>=2.0.0 (from scikit-learn>=0.20.0->librosa>=0.10->audio-separator)
  Using cached threadpoolctl-3.3.0-py3-none-any.whl.metadata (13 kB)
Collecting pillow!=8.3.*,>=5.3.0 (from torchvision>=0.9.0->onnx2torch>=1.5->audio-separator)
  Downloading pillow-10.2.0-cp310-cp310-macosx_11_0_arm64.whl.metadata (9.7 kB)
Collecting MarkupSafe>=2.0 (from jinja2->torch->audio-separator)
  Downloading MarkupSafe-2.1.5-cp310-cp310-macosx_10_9_universal2.whl.metadata (3.0 kB)
Collecting mpmath>=0.19 (from sympy->torch->audio-separator)
  Using cached mpmath-1.3.0-py3-none-any.whl.metadata (8.6 kB)
Downloading audio_separator-0.16.2-py3-none-any.whl (160 kB)
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Using cached samplerate-0.1.0-py2.py3-none-any.whl (4.0 MB)
Downloading diffq-0.2.4-cp310-cp310-macosx_11_0_arm64.whl (97 kB)
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Using cached einops-0.7.0-py3-none-any.whl (44 kB)
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Downloading numpy-1.26.4-cp310-cp310-macosx_11_0_arm64.whl (14.0 MB)
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Downloading torch-2.2.1-cp310-none-macosx_11_0_arm64.whl (59.7 MB)
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Downloading PyYAML-6.0.1-cp310-cp310-macosx_11_0_arm64.whl (169 kB)
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Using cached tqdm-4.66.2-py3-none-any.whl (78 kB)
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Using cached cffi-1.16.0-cp310-cp310-macosx_11_0_arm64.whl (176 kB)
Downloading charset_normalizer-3.3.2-cp310-cp310-macosx_11_0_arm64.whl (120 kB)
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Using cached decorator-5.1.1-py3-none-any.whl (9.1 kB)
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Downloading msgpack-1.0.8-cp310-cp310-macosx_11_0_arm64.whl (84 kB)
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Using cached numba-0.59.1-cp310-cp310-macosx_11_0_arm64.whl (2.6 MB)
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Using cached protobuf-5.26.0-cp37-abi3-macosx_10_9_universal2.whl (404 kB)
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Using cached soundfile-0.12.1-py2.py3-none-macosx_11_0_arm64.whl (1.1 MB)
Downloading soxr-0.3.7-cp310-cp310-macosx_11_0_arm64.whl (390 kB)
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Downloading torchvision-0.17.1-cp310-cp310-macosx_11_0_arm64.whl (1.6 MB)
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Using cached typing_extensions-4.10.0-py3-none-any.whl (33 kB)
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Downloading absl_py-2.1.0-py3-none-any.whl (133 kB)
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Downloading contextlib2-21.6.0-py2.py3-none-any.whl (13 kB)
Downloading Cython-3.0.9-py2.py3-none-any.whl (1.2 MB)
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Using cached filelock-3.13.1-py3-none-any.whl (11 kB)
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Using cached llvmlite-0.42.0-cp310-cp310-macosx_11_0_arm64.whl (28.8 MB)
Downloading MarkupSafe-2.1.5-cp310-cp310-macosx_10_9_universal2.whl (18 kB)
Using cached mpmath-1.3.0-py3-none-any.whl (536 kB)
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Downloading pillow-10.2.0-cp310-cp310-macosx_11_0_arm64.whl (3.3 MB)
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Using cached pycparser-2.21-py2.py3-none-any.whl (118 kB)
Building wheels for collected packages: julius, ml_collections
  Building wheel for julius (setup.py) ... done
  Created wheel for julius: filename=julius-0.2.7-py3-none-any.whl size=21870 sha256=12d02c581ecdaeb70e70375ab7f6a193113f4724a4bec132e59b0fe8564a6be6
  Stored in directory: /Users/andrew/Library/Caches/pip/wheels/b9/b2/05/f883527ffcb7f2ead5438a2c23439aa0c881eaa9a4c80256f4
  Building wheel for ml_collections (setup.py) ... done
  Created wheel for ml_collections: filename=ml_collections-0.1.1-py3-none-any.whl size=94507 sha256=1215cbefb91f67a15755feb161eefb121f5f1810dd790cd6b7911299cf67544b
  Stored in directory: /Users/andrew/Library/Caches/pip/wheels/7b/89/c9/a9b87790789e94aadcfc393c283e3ecd5ab916aed0a31be8fe
Successfully built julius ml_collections
Installing collected packages: pydub, mpmath, urllib3, typing-extensions, tqdm, threadpoolctl, sympy, six, pyyaml, pycparser, protobuf, platformdirs, pillow, packaging, numpy, networkx, msgpack, MarkupSafe, llvmlite, lazy-loader, joblib, idna, fsspec, filelock, einops, decorator, Cython, contextlib2, charset-normalizer, certifi, audioread, absl-py, soxr, scipy, requests, onnx, numba, ml_collections, jinja2, cffi, torch, soundfile, scikit-learn, samplerate, resampy, pooch, torchvision, librosa, julius, diffq, onnx2torch, audio-separator
Successfully installed Cython-3.0.9 MarkupSafe-2.1.5 absl-py-2.1.0 audio-separator-0.16.2 audioread-3.0.1 certifi-2024.2.2 cffi-1.16.0 charset-normalizer-3.3.2 contextlib2-21.6.0 decorator-5.1.1 diffq-0.2.4 einops-0.7.0 filelock-3.13.1 fsspec-2024.3.1 idna-3.6 jinja2-3.1.3 joblib-1.3.2 julius-0.2.7 lazy-loader-0.3 librosa-0.10.1 llvmlite-0.42.0 ml_collections-0.1.1 mpmath-1.3.0 msgpack-1.0.8 networkx-3.2.1 numba-0.59.1 numpy-1.26.4 onnx-1.15.0 onnx2torch-1.5.13 packaging-24.0 pillow-10.2.0 platformdirs-4.2.0 pooch-1.8.1 protobuf-5.26.0 pycparser-2.21 pydub-0.25.1 pyyaml-6.0.1 requests-2.31.0 resampy-0.4.3 samplerate-0.1.0 scikit-learn-1.4.1.post1 scipy-1.12.0 six-1.16.0 soundfile-0.12.1 soxr-0.3.7 sympy-1.12 threadpoolctl-3.3.0 torch-2.2.1 torchvision-0.17.1 tqdm-4.66.2 typing-extensions-4.10.0 urllib3-2.2.1
(uvr-test-two) ➜  Data audio-separator -v
audio-separator 0.16.2

(uvr-test-two) ➜  Data pip install onnxruntime-gpu
ERROR: Could not find a version that satisfies the requirement onnxruntime-gpu (from versions: none)
ERROR: No matching distribution found for onnxruntime-gpu
(uvr-test-two) ➜  Data pip install onnxruntime
Collecting onnxruntime
  Using cached onnxruntime-1.17.1-cp310-cp310-macosx_11_0_universal2.whl.metadata (4.2 kB)
Collecting coloredlogs (from onnxruntime)
  Using cached coloredlogs-15.0.1-py2.py3-none-any.whl.metadata (12 kB)
Collecting flatbuffers (from onnxruntime)
  Using cached flatbuffers-24.3.7-py2.py3-none-any.whl.metadata (849 bytes)
Requirement already satisfied: numpy>=1.21.6 in /Users/andrew/miniforge3/envs/uvr-test-two/lib/python3.10/site-packages (from onnxruntime) (1.26.4)
Requirement already satisfied: packaging in /Users/andrew/miniforge3/envs/uvr-test-two/lib/python3.10/site-packages (from onnxruntime) (24.0)
Requirement already satisfied: protobuf in /Users/andrew/miniforge3/envs/uvr-test-two/lib/python3.10/site-packages (from onnxruntime) (5.26.0)
Requirement already satisfied: sympy in /Users/andrew/miniforge3/envs/uvr-test-two/lib/python3.10/site-packages (from onnxruntime) (1.12)
Collecting humanfriendly>=9.1 (from coloredlogs->onnxruntime)
  Using cached humanfriendly-10.0-py2.py3-none-any.whl.metadata (9.2 kB)
Requirement already satisfied: mpmath>=0.19 in /Users/andrew/miniforge3/envs/uvr-test-two/lib/python3.10/site-packages (from sympy->onnxruntime) (1.3.0)
Downloading onnxruntime-1.17.1-cp310-cp310-macosx_11_0_universal2.whl (14.8 MB)
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Using cached coloredlogs-15.0.1-py2.py3-none-any.whl (46 kB)
Using cached flatbuffers-24.3.7-py2.py3-none-any.whl (26 kB)
Using cached humanfriendly-10.0-py2.py3-none-any.whl (86 kB)
Installing collected packages: flatbuffers, humanfriendly, coloredlogs, onnxruntime
Successfully installed coloredlogs-15.0.1 flatbuffers-24.3.7 humanfriendly-10.0 onnxruntime-1.17.1

(uvr-test-two) ➜  Data audio-separator --env_info
2024-03-19 18:53:40,783 - INFO - separator - Separator version 0.16.2 instantiating with output_dir: None, output_format: WAV
2024-03-19 18:53:40,783 - INFO - separator - Operating System: Darwin Darwin Kernel Version 23.3.0: Wed Dec 20 21:31:10 PST 2023; root:xnu-10002.81.5~7/RELEASE_ARM64_T6031
2024-03-19 18:53:40,796 - INFO - separator - System: Darwin Node: MacBook-Pro-2.local Release: 23.3.0 Machine: arm64 Proc: arm
2024-03-19 18:53:40,797 - INFO - separator - Python Version: 3.10.13
2024-03-19 18:53:40,797 - INFO - separator - PyTorch Version: 2.2.1
2024-03-19 18:53:41,135 - INFO - separator - FFmpeg installed: ffmpeg version 6.1.1 Copyright (c) 2000-2023 the FFmpeg developers
2024-03-19 18:53:41,136 - INFO - separator - ONNX Runtime CPU package installed with version: 1.17.1
2024-03-19 18:53:41,152 - INFO - separator - Apple Silicon MPS/CoreML is available in Torch, setting Torch device to MPS
2024-03-19 18:53:41,152 - INFO - separator - ONNXruntime has CoreMLExecutionProvider available, enabling acceleration

So, I fired up a Runpod with a GPU to test and installed the dependencies using conda, then audio-separator using pip:
base) root@ceb75bc742f2:/# conda create -n uvr-test-three python=3.10
Channels:
 - conda-forge
Platform: linux-64
Collecting package metadata (repodata.json): done
Solving environment: done

==> WARNING: A newer version of conda exists. <==
    current version: 23.11.0
    latest version: 24.1.2

Please update conda by running

    $ conda update -n base -c conda-forge conda

## Package Plan ##
  environment location: /root/miniforge3/envs/uvr-test-three

  added / updated specs:
    - python=3.10

The following packages will be downloaded:

    package                    |            build
    ---------------------------|-----------------
    ca-certificates-2024.2.2   |       hbcca054_0         152 KB  conda-forge
    libgcc-ng-13.2.0           |       h807b86a_5         752 KB  conda-forge
    libgomp-13.2.0             |       h807b86a_5         410 KB  conda-forge
    libsqlite-3.45.2           |       h2797004_0         837 KB  conda-forge
    libxcrypt-4.4.36           |       hd590300_1          98 KB  conda-forge
    ncurses-6.4.20240210       |       h59595ed_0         875 KB  conda-forge
    openssl-3.2.1              |       hd590300_1         2.7 MB  conda-forge
    pip-24.0                   |     pyhd8ed1ab_0         1.3 MB  conda-forge
    python-3.10.13             |hd12c33a_1_cpython        24.5 MB  conda-forge
    setuptools-69.2.0          |     pyhd8ed1ab_0         460 KB  conda-forge
    tzdata-2024a               |       h0c530f3_0         117 KB  conda-forge
    ------------------------------------------------------------
                                           Total:        32.2 MB

The following NEW packages will be INSTALLED:

  _libgcc_mutex      conda-forge/linux-64::_libgcc_mutex-0.1-conda_forge
  _openmp_mutex      conda-forge/linux-64::_openmp_mutex-4.5-2_gnu
  bzip2              conda-forge/linux-64::bzip2-1.0.8-hd590300_5
  ca-certificates    conda-forge/linux-64::ca-certificates-2024.2.2-hbcca054_0
  ld_impl_linux-64   conda-forge/linux-64::ld_impl_linux-64-2.40-h41732ed_0
  libffi             conda-forge/linux-64::libffi-3.4.2-h7f98852_5
  libgcc-ng          conda-forge/linux-64::libgcc-ng-13.2.0-h807b86a_5
  libgomp            conda-forge/linux-64::libgomp-13.2.0-h807b86a_5
  libnsl             conda-forge/linux-64::libnsl-2.0.1-hd590300_0
  libsqlite          conda-forge/linux-64::libsqlite-3.45.2-h2797004_0
  libuuid            conda-forge/linux-64::libuuid-2.38.1-h0b41bf4_0
  libxcrypt          conda-forge/linux-64::libxcrypt-4.4.36-hd590300_1
  libzlib            conda-forge/linux-64::libzlib-1.2.13-hd590300_5
  ncurses            conda-forge/linux-64::ncurses-6.4.20240210-h59595ed_0
  openssl            conda-forge/linux-64::openssl-3.2.1-hd590300_1
  pip                conda-forge/noarch::pip-24.0-pyhd8ed1ab_0
  python             conda-forge/linux-64::python-3.10.13-hd12c33a_1_cpython
  readline           conda-forge/linux-64::readline-8.2-h8228510_1
  setuptools         conda-forge/noarch::setuptools-69.2.0-pyhd8ed1ab_0
  tk                 conda-forge/linux-64::tk-8.6.13-noxft_h4845f30_101
  tzdata             conda-forge/noarch::tzdata-2024a-h0c530f3_0
  wheel              conda-forge/noarch::wheel-0.42.0-pyhd8ed1ab_0
  xz                 conda-forge/linux-64::xz-5.2.6-h166bdaf_0

Proceed ([y]/n)? y

Downloading and Extracting Packages:

Preparing transaction: done
Verifying transaction: done
Executing transaction: done
#
# To activate this environment, use
#
#     $ conda activate uvr-test-three
#
# To deactivate an active environment, use
#
#     $ conda deactivate

(base) root@ceb75bc742f2:/# conda activate uvr-test-three
(uvr-test-three) root@ceb75bc742f2:/# conda install onnxruntime cudatoolkit
Channels:
 - conda-forge
Platform: linux-64
Collecting package metadata (repodata.json): done
Solving environment: done

==> WARNING: A newer version of conda exists. <==
    current version: 23.11.0
    latest version: 24.1.2

Please update conda by running

    $ conda update -n base -c conda-forge conda

## Package Plan ##

  environment location: /root/miniforge3/envs/uvr-test-three

  added / updated specs:
    - cudatoolkit
    - onnxruntime

The following packages will be downloaded:

    package                    |            build
    ---------------------------|-----------------
    coloredlogs-15.0.1         |     pyhd8ed1ab_3          40 KB  conda-forge
    cuda-version-11.8          |       h70ddcb2_3          21 KB  conda-forge
    cudatoolkit-11.8.0         |      h4ba93d1_13       682.5 MB  conda-forge
    cudnn-<IP_ADDRESS>             |       hbc23b4c_3       443.9 MB  conda-forge
    gmp-6.3.0                  |       h59595ed_1         556 KB  conda-forge
    gmpy2-2.1.2                |  py310h3ec546c_1         215 KB  conda-forge
    humanfriendly-10.0         |     pyhd8ed1ab_6          72 KB  conda-forge
    libabseil-20240116.1       | cxx17_h59595ed_2         1.2 MB  conda-forge
    libblas-3.9.0              |21_linux64_openblas          14 KB  conda-forge
    libcblas-3.9.0             |21_linux64_openblas          14 KB  conda-forge
    libgfortran-ng-13.2.0      |       h69a702a_5          23 KB  conda-forge
    libgfortran5-13.2.0        |       ha4646dd_5         1.4 MB  conda-forge
    liblapack-3.9.0            |21_linux64_openblas          14 KB  conda-forge
    libopenblas-0.3.26         |pthreads_h413a1c8_0         5.3 MB  conda-forge
    libprotobuf-4.25.3         |       h08a7969_0         2.7 MB  conda-forge
    libstdcxx-ng-13.2.0        |       h7e041cc_5         3.7 MB  conda-forge
    mpc-1.3.1                  |       hfe3b2da_0         114 KB  conda-forge
    mpfr-4.2.1                 |       h9458935_0         626 KB  conda-forge
    mpmath-1.3.0               |     pyhd8ed1ab_0         428 KB  conda-forge
    numpy-1.26.4               |  py310hb13e2d6_0         6.7 MB  conda-forge
    onnxruntime-1.17.1         |py310h92cda0c_202_cuda        68.0 MB  conda-forge
    packaging-24.0             |     pyhd8ed1ab_0          49 KB  conda-forge
    protobuf-4.25.3            |  py310ha8c1f0e_0         325 KB  conda-forge
    python-flatbuffers-24.3.7  |     pyh59ac667_0          33 KB  conda-forge
    sympy-1.12                 | pypyh9d50eac_103         4.1 MB  conda-forge
    ------------------------------------------------------------
                                           Total:        1.19 GB

The following NEW packages will be INSTALLED:

  coloredlogs        conda-forge/noarch::coloredlogs-15.0.1-pyhd8ed1ab_3
  cuda-version       conda-forge/noarch::cuda-version-11.8-h70ddcb2_3
  cudatoolkit        conda-forge/linux-64::cudatoolkit-11.8.0-h4ba93d1_13
  cudnn              conda-forge/linux-64::cudnn-<IP_ADDRESS>-hbc23b4c_3
  gmp                conda-forge/linux-64::gmp-6.3.0-h59595ed_1
  gmpy2              conda-forge/linux-64::gmpy2-2.1.2-py310h3ec546c_1
  humanfriendly      conda-forge/noarch::humanfriendly-10.0-pyhd8ed1ab_6
  libabseil          conda-forge/linux-64::libabseil-20240116.1-cxx17_h59595ed_2
  libblas            conda-forge/linux-64::libblas-3.9.0-21_linux64_openblas
  libcblas           conda-forge/linux-64::libcblas-3.9.0-21_linux64_openblas
  libgfortran-ng     conda-forge/linux-64::libgfortran-ng-13.2.0-h69a702a_5
  libgfortran5       conda-forge/linux-64::libgfortran5-13.2.0-ha4646dd_5
  liblapack          conda-forge/linux-64::liblapack-3.9.0-21_linux64_openblas
  libopenblas        conda-forge/linux-64::libopenblas-0.3.26-pthreads_h413a1c8_0
  libprotobuf        conda-forge/linux-64::libprotobuf-4.25.3-h08a7969_0
  libstdcxx-ng       conda-forge/linux-64::libstdcxx-ng-13.2.0-h7e041cc_5
  mpc                conda-forge/linux-64::mpc-1.3.1-hfe3b2da_0
  mpfr               conda-forge/linux-64::mpfr-4.2.1-h9458935_0
  mpmath             conda-forge/noarch::mpmath-1.3.0-pyhd8ed1ab_0
  numpy              conda-forge/linux-64::numpy-1.26.4-py310hb13e2d6_0
  onnxruntime        conda-forge/linux-64::onnxruntime-1.17.1-py310h92cda0c_202_cuda
  packaging          conda-forge/noarch::packaging-24.0-pyhd8ed1ab_0
  protobuf           conda-forge/linux-64::protobuf-4.25.3-py310ha8c1f0e_0
  python-flatbuffers conda-forge/noarch::python-flatbuffers-24.3.7-pyh59ac667_0
  python_abi         conda-forge/linux-64::python_abi-3.10-4_cp310
  sympy              conda-forge/noarch::sympy-1.12-pypyh9d50eac_103

Proceed ([y]/n)? y

Downloading and Extracting Packages:

Preparing transaction: done
Verifying transaction: done
Executing transaction: \ By downloading and using the CUDA Toolkit conda packages, you accept the terms and conditions of the CUDA End User License Agreeme
nt (EULA): https://docs.nvidia.com/cuda/eula/index.html

\ By downloading and using the cuDNN conda packages, you accept the terms and conditions of the NVIDIA cuDNN EULA -
  https://docs.nvidia.com/deeplearning/cudnn/sla/index.html

done
(uvr-test-three) root@ceb75bc742f2:/# pip install audio-separator
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Installing collected packages: pydub, urllib3, typing-extensions, tqdm, threadpoolctl, soxr, six, scipy, pyyaml, pycparser, platformdirs, pillow, onnx, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, networkx, msgpack, MarkupSafe, llvmlite, lazy-loader, joblib, idna, fsspec, filelock, einops, decorator, Cython, contextlib2, charset-normalizer, certifi, audioread, absl-py, triton, scikit-learn, requests, nvidia-cusparse-cu12, nvidia-cudnn-cu12, numba, ml_collections, jinja2, cffi, soundfile, samplerate, resampy, pooch, nvidia-cusolver-cu12, torch, librosa, torchvision, julius, diffq, onnx2torch, audio-separator
Successfully installed Cython-3.0.9 MarkupSafe-2.1.5 absl-py-2.1.0 audio-separator-0.16.2 audioread-3.0.1 certifi-2024.2.2 cffi-1.16.0 charset-normalizer-3.3.2 contextlib2-21.6.0 decorator-5.1.1 diffq-0.2.4 einops-0.7.0 filelock-3.13.1 fsspec-2024.3.1 idna-3.6 jinja2-3.1.3 joblib-1.3.2 julius-0.2.7 lazy-loader-0.3 librosa-0.10.1 llvmlite-0.42.0 ml_collections-0.1.1 msgpack-1.0.8 networkx-3.2.1 numba-0.59.1 nvidia-cublas-cu12-<IP_ADDRESS> nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-<IP_ADDRESS> nvidia-cufft-cu12-<IP_ADDRESS> nvidia-curand-cu12-<IP_ADDRESS> nvidia-cusolver-cu12-<IP_ADDRESS> nvidia-cusparse-cu12-<IP_ADDRESS> nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.99 nvidia-nvtx-cu12-12.1.105 onnx-1.15.0 onnx2torch-1.5.13 pillow-10.2.0 platformdirs-4.2.0 pooch-1.8.1 pycparser-2.21 pydub-0.25.1 pyyaml-6.0.1 requests-2.31.0 resampy-0.4.3 samplerate-0.1.0 scikit-learn-1.4.1.post1 scipy-1.12.0 six-1.16.0 soundfile-0.12.1 soxr-0.3.7 threadpoolctl-3.3.0 torch-2.2.1 torchvision-0.17.1 tqdm-4.66.2 triton-2.2.0 typing-extensions-4.10.0 urllib3-2.2.1
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv

(uvr-test-three) root@ceb75bc742f2:/# audio-separator --env_info
2024-03-20 00:09:49,023 - INFO - separator - Separator version 0.16.2 instantiating with output_dir: None, output_format: WAV
2024-03-20 00:09:49,023 - INFO - separator - Operating System: Linux #98-Ubuntu SMP Mon Oct 2 15:18:56 UTC 2023
2024-03-20 00:09:49,025 - INFO - separator - System: Linux Node: ceb75bc742f2 Release: 5.15.0-88-generic Machine: x86_64 Proc: x86_64
2024-03-20 00:09:49,025 - INFO - separator - Python Version: 3.10.13
2024-03-20 00:09:49,025 - INFO - separator - PyTorch Version: 2.2.1+cu121
2024-03-20 00:09:49,045 - INFO - separator - FFmpeg installed: ffmpeg version 6.1.1 Copyright (c) 2000-2023 the FFmpeg developers
2024-03-20 00:09:49,046 - INFO - separator - ONNX Runtime GPU package installed with version: 1.17.1
2024-03-20 00:09:49,063 - INFO - separator - CUDA is available in Torch, setting Torch device to CUDA
2024-03-20 00:09:49,063 - INFO - separator - ONNXruntime has CUDAExecutionProvider available, enabling acceleration
(uvr-test-three) root@ceb75bc742f2:/#

In summary, =since you're already using conda, the easiest way to install a CUDA-compatible version of onnxruntime is probably using conda, then you can install audio-separator using pip:

conda create -n uvr python=3.10
conda activate uvr
conda install cudatoolkit ffmpeg onnxruntime
pip install audio-separator

Then you should find audio-separator --env_info shows you the latest version with CUDA working as expected!

Thanks for your detailed test. I created a new conda env and tried the command as you suggested, and the audio-separator --env_info reports:
2024-03-20 13:05:20,331 - INFO - separator - Separator version 0.16.2 instantiating with output_dir: None, output_format: WAV
2024-03-20 13:05:20,331 - INFO - separator - Operating System: Linux #1 SMP Thu Dec 7 15:39:45 UTC 2023
2024-03-20 13:05:20,335 - INFO - separator - System: Linux Node: <my.server.ip> Release: 3.10.0-1160.105.1.el7.x86_64 Machine: x86_64 Proc: x86_64
2024-03-20 13:05:20,335 - INFO - separator - Python Version: 3.10.13
2024-03-20 13:05:20,335 - INFO - separator - PyTorch Version: 2.2.1+cu121
2024-03-20 13:05:20,920 - INFO - separator - FFmpeg installed: ffmpeg version 4.2.2 Copyright (c) 2000-2019 the FFmpeg developers
2024-03-20 13:05:20,923 - INFO - separator - ONNX Runtime CPU package installed with version: 1.15.1
2024-03-20 13:05:20,926 - INFO - separator - No hardware acceleration could be configured, running in CPU mode

The version 0.16.2 is installed correctly, but it seems that only CPU mode can be used. Then I tried pip install "audio-seperator[gpu]", then it uninstalled 0.16.2 version then reverse to 0.14.1:
Installing collected packages: onnxruntime-gpu, audio-separator
  Attempting uninstall: audio-separator
    Found existing installation: audio-separator 0.16.2
    Uninstalling audio-separator-0.16.2:
      Successfully uninstalled audio-separator-0.16.2
Successfully installed audio-separator-0.14.1 onnxruntime-gpu-1.16.3

and the audio-separator --env_info reports:
2024-03-20 13:14:53,121 - INFO - separator - Separator version 0.14.1 instantiating with output_dir: None, output_format: WAV
2024-03-20 13:14:53,122 - INFO - separator - Operating System: Linux #1 SMP Thu Dec 7 15:39:45 UTC 2023
2024-03-20 13:14:53,122 - INFO - separator - System: Linux Node: <my.server.ip> Release: 3.10.0-1160.105.1.el7.x86_64 Machine: x86_64 Proc: x86_64
2024-03-20 13:14:53,122 - INFO - separator - Python Version: 3.10.13
2024-03-20 13:14:53,160 - INFO - separator - FFmpeg installed: ffmpeg version 4.2.2 Copyright (c) 2000-2019 the FFmpeg developers
2024-03-20 13:14:53,161 - DEBUG - separator - Python package: onnxruntime-silicon not installed
2024-03-20 13:14:53,163 - INFO - separator - ONNX Runtime GPU package installed with version: 1.16.3
2024-03-20 13:14:53,165 - INFO - separator - ONNX Runtime CPU package installed with version: 1.15.1
/export/data/username/anaconda/envs/uvr2/lib/python3.10/site-packages/torch/cuda/__init__.py:141: UserWarning: CUDA initialization: The NVIDIA driver on your system is too old (found version 11020). Please update your GPU driver by downloading and installing a new version from the URL: http://www.nvidia.com/Download/index.aspx Alternatively, go to: https://pytorch.org to install a PyTorch version that has been compiled with your version of the CUDA driver. (Triggered internally at ../c10/cuda/CUDAFunctions.cpp:108.)
  return torch._C._cuda_getDeviceCount() > 0
2024-03-20 13:14:53,169 - INFO - separator - No hardware acceleration could be configured, running in CPU mode

Ahhh, I think the issue might be your conda channel config - I just noticed you're getting old versions of ONNX Runtime (1.16 or 1.15).
The latest version of ONNX Runtime, which is required for various things including CUDA 12 support, is 1.17.1: https://github.com/microsoft/onnxruntime/releases
What is your conda environment config? I highly recommend using only conda-forge, e.g. using miniforge: https://github.com/conda-forge/miniforge
However, you should be able to install everything needed using only pip; can you try creating a clean conda env but only installing audio-separator[gpu] using pip?

conda create -n uvr python=3.10
conda activate uvr
pip install audio-separator
audio-separator --env_info

By the way, just in case this was confusing at all; adding the [gpu] extra when installing audio-separator just tells pip to install the optional dependency onnxruntime-gpu - there isn't any other difference between audio-separator and audio-separator[gpu].
I deliberately leave onnxruntime as an optional dependency because it's been really annoying for a bunch of people to get a working version of onnxruntime for various different environments and CUDA versions in the past so this way leaves it the most flexible.
So, you can experiment with other methods of installing the latest onnxruntime (e.g. their docs suggest a different index URL for CUDA 12: https://onnxruntime.ai/docs/install/ ), then once you're confident you've got the latest version of onnxruntime installed, you can just do pip install audio-separator.
Hope that makes sense!

Oh, also that CUDA error in your output is actually pretty relevant: The NVIDIA driver on your system is too old (found version 11020)
You probably do need to fix that, as that might also be what's preventing you getting a working version of pytorch + onnxruntime installed!

Thanks for your suggestions. I noticed that directly using conda install onnxruntime may be interpreted as an old version, leading to audio-separator issues. So I created a new clean conda env and used conda install cudatoolkit ffmpeg conda-forge:onnxruntime instead.
Now audio-separator --env_info reports:
2024-03-20 13:47:14,501 - INFO - separator - Separator version 0.16.2 instantiating with output_dir: None, output_format: WAV
2024-03-20 13:47:14,501 - INFO - separator - Operating System: Linux #1 SMP Thu Dec 7 15:39:45 UTC 2023
2024-03-20 13:47:14,504 - INFO - separator - System: Linux Node: <my.server.ip> Release: 3.10.0-1160.105.1.el7.x86_64 Machine: x86_64 Proc: x86_64
2024-03-20 13:47:14,504 - INFO - separator - Python Version: 3.10.13
2024-03-20 13:47:14,504 - INFO - separator - PyTorch Version: 2.2.1+cu121
2024-03-20 13:47:14,543 - INFO - separator - FFmpeg installed: ffmpeg version 4.2.2 Copyright (c) 2000-2019 the FFmpeg developers
2024-03-20 13:47:14,546 - INFO - separator - ONNX Runtime GPU package installed with version: 1.17.1
2024-03-20 13:47:14,549 - INFO - separator - No hardware acceleration could be configured, running in CPU mode

You may notice that now I have successfully installed ONNX Runtime GPU package with the latest version 1.17.1. But audio-separator still works in CPU mode.
I tried to create a second clean conda and only installed audio-separator using pip install audio-separator as you suggested, and audio-separator --env_info reports error ModuleNotFoundError: No module named 'onnxruntime'. But pip list reports audio-separator version is 0.16.2.
Then I used pip install "audio-separator[gpu]" to specify the version to install. And pip, again, uninstalled 0.16.2 version then reversed to 0.14.1. Pretty confused...

Yeah, getting CUDA to work is often confusing and hard, you're not the first person to come here for help trying to get it working - unfortunately I don't have a machine with an Nvidia GPU myself and things "just work" on the runpod instances with GPUs and Google Colab etc. which I've tested on.
However, that most recent output does look like progress to me! At least PyTorch and ONNXRuntime are both installed with the latest versions!
The code is simply calling torch.cuda.is_available() to determine if CUDA is usable in your PyTorch installation - the fact that you're not seeing the log message "CUDA is available in Torch, setting Torch device to CUDA" tells me that PyTorch isn't happy with your CUDA installation.
Have you ever successfully used CUDA on this machine for anything else?
I don't know exactly what the steps are, but I believe you probably need to install the latest CUDA drivers - depending on your distro, it's probably something like sudo apt install nvidia-driver-535
Some additional info which would be helpful would be:

Distro: cat /etc/*release
GPU details: sudo lshw -C display
Nvidia CUDA version: nvcc --version
Other Nvidia info: nvidia-smi

Thanks for your suggestions. Sadly, due to the ToS of the server I am using, I can only use conda env for my code. But I'm quite sure that the CUDA env on this machine is working fine. To help debug, here I will provide additional information you suggested:
my.server.ip> cat /etc/*release
CentOS Linux release 7.9.2009 (Core)
NAME="CentOS Linux"
VERSION="7 (Core)"
ID="centos"
ID_LIKE="rhel fedora"
VERSION_ID="7"
PRETTY_NAME="CentOS Linux 7 (Core)"
ANSI_COLOR="0;31"
CPE_NAME="cpe:/o:centos:centos:7"
HOME_URL="https://www.centos.org/"
BUG_REPORT_URL="https://bugs.centos.org/"

CENTOS_MANTISBT_PROJECT="CentOS-7"
CENTOS_MANTISBT_PROJECT_VERSION="7"
REDHAT_SUPPORT_PRODUCT="centos"
REDHAT_SUPPORT_PRODUCT_VERSION="7"

CentOS Linux release 7.9.2009 (Core)
CentOS Linux release 7.9.2009 (Core)

my.server.ip> lshw -C display
WARNING: you should run this program as super-user.
  *-display
       description: VGA compatible controller
       product: ASPEED Graphics Family
       vendor: ASPEED Technology, Inc.
       physical id: 0
       bus info: pci@0000:05:00.0
       version: 41
       width: 32 bits
       clock: 33MHz
       capabilities: vga_controller cap_list rom
       configuration: driver=ast latency=0
       resources: irq:16 memory:9c000000-9cffffff memory:9d000000-9d01ffff ioport:3000(size=128)
  *-display
       description: VGA compatible controller
       product: NVIDIA Corporation
       vendor: NVIDIA Corporation
       physical id: 0
       bus info: pci@0000:18:00.0
       version: a1
       width: 64 bits
       clock: 33MHz
       capabilities: vga_controller bus_master cap_list rom
       configuration: driver=nvidia latency=0
       resources: iomemory:387f0-387ef iomemory:387f0-387ef irq:201 memory:a9000000-a9ffffff memory:387fe0000000-387fefffffff memory:387ff0000000-387ff1ffffff ioport:5000(size=128) memory:aa000000-aa07ffff
  *-display
       description: VGA compatible controller
       product: NVIDIA Corporation
       vendor: NVIDIA Corporation
       physical id: 0
       bus info: pci@0000:3b:00.0
       version: a1
       width: 64 bits
       clock: 33MHz
       capabilities: vga_controller bus_master cap_list rom
       configuration: driver=nvidia latency=0
       resources: iomemory:38bf0-38bef iomemory:38bf0-38bef irq:202 memory:b7000000-b7ffffff memory:38bfe0000000-38bfefffffff memory:38bff0000000-38bff1ffffff ioport:7000(size=128) memory:b8000000-b807ffff
  *-display
       description: VGA compatible controller
       product: NVIDIA Corporation
       vendor: NVIDIA Corporation
       physical id: 0
       bus info: pci@0000:86:00.0
       version: a1
       width: 64 bits
       clock: 33MHz
       capabilities: vga_controller bus_master cap_list rom
       configuration: driver=nvidia latency=0
       resources: iomemory:397f0-397ef iomemory:397f0-397ef irq:203 memory:df000000-dfffffff memory:397fe0000000-397fefffffff memory:397ff0000000-397ff1ffffff ioport:d000(size=128) memory:e0000000-e007ffff
  *-display
       description: VGA compatible controller
       product: NVIDIA Corporation
       vendor: NVIDIA Corporation
       physical id: 0
       bus info: pci@0000:af:00.0
       version: a1
       width: 64 bits
       clock: 33MHz
       capabilities: vga_controller bus_master cap_list rom
       configuration: driver=nvidia latency=0
       resources: iomemory:39bf0-39bef iomemory:39bf0-39bef irq:204 memory:ed000000-edffffff memory:39bfe0000000-39bfefffffff memory:39bff0000000-39bff1ffffff ioport:e000(size=128) memory:ee000000-ee07ffff
WARNING: output may be incomplete or inaccurate, you should run this program as super-user.

my.server.ip> nvcc --version
nvcc: Command not found.

my.server.ip> nvidia-smi
Wed Mar 20 20:56:05 2024
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  GeForce RTX 3090    On   | 00000000:18:00.0 Off |                  N/A |
| 30%   31C    P8    22W / 350W |      1MiB / 24268MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
|   1  GeForce RTX 3090    On   | 00000000:3B:00.0 Off |                  N/A |
| 30%   31C    P8    19W / 350W |      1MiB / 24268MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
|   2  GeForce RTX 3090    On   | 00000000:86:00.0 Off |                  N/A |
| 30%   36C    P8    34W / 350W |      1MiB / 24268MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
|   3  GeForce RTX 3090    On   | 00000000:AF:00.0 Off |                  N/A |
| 30%   30C    P8    23W / 350W |      1MiB / 24268MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+

I hope this information can help. Thanks for any suggestions :-)

Oof, that's a 4 year old CUDA version 😬 which I believe is the root cause of your problems here.
ONNXRuntime doesn't support that version: https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html
and I've only ever tested audio-separator with CUDA 11.8 and 12.2 - I didn't see any reason to test with multiple-years old versions!

But I'm quite sure that the CUDA env on this machine is working fine

Are you sure? How have you tested that?
I think many modern things won't work with such an old CUDA version - for example, Stable Diffusion requires at least CUDA 11.3.

due to the ToS of the server I am using, I can only use conda env for my code

I don't really understand this - are you saying there is no way for you to upgrade CUDA?
What hosting provider is this? If you let me know the name or share their website, maybe I can help figure something else out from them!

I've updated the CUDA section in the README to make that super clear:
Supported CUDA Versions: 11.8 and 12.2
I think you'll need to either find a way to upgrade your CUDA driver / version, or find another server!

Closing this as I don't believe it's an issue with audio-separator, it's your CUDA setup.
Good luck! <3


Include hardware information in README.md

The runtime section in the readme should also include the hardware used for the measurements, otherwise the values are pretty meaningless.

Did it. I guess I'll be the person to test all solutions from now on.


False positive

Could you please fix it?
https://www.virustotal.com/gui/file/65aa9266c675e9e9ed55d4eb315a7a27804c24329c6ed7c908c504403317b12d/detection

I see. Wrong screenshot indeed. I'm sorry.
YARA rule to detect if a sqllang.dll version is targeted by skip-2.0. Each byte pattern corresponds to a function hooked by skip-2.0. If $1_0 or $1_1 match, it is probably targeted as it corresponds to the hook responsible for bypassing the authentication.

So, if I got it right, we will need to make changes in our code to make sure we aren't bypassing the authentication, right?

The matched YARA rules on VT is completely unrelated to the detection of the file by ESET products as Win64/CoinMiner.GG PUA.
If you think the latter is a false positive, please submit the file to our detection team. See https://support.eset.com/en/kb141-submit-a-virus-website-or-potential-false-positive-sample-to-the-eset-lab about how to reach out to them.


WIP: Foyer webpage

Provdided the basic RTD format that mBuild uses on its
github.io webpage.
Converted current markdown files to RST.
Added travis directives to generate these pages when a successful test
passes, like current mBuild functionality.
Modifications still need to be completed to make the site a bit more full-fledged.
Add more documentation for the actual data structures, etc. Adding that next.
Also document features of foyer like the residue templating, etc.

Not ready for actual merging, just putting this here so others can pull and generate the HTML pages themselves as more changes are made.

Nice thanks for starting on this @justinGilmer.
Not sure if it's worth the time right now since this works just fine but I would like to try moving back to readthedocs at some point so we can remove the doc deploy process that we currently use.

Maybe I'm misunderstanding, but what is the difference between that and what we use now? I assumed RTD is just a theme for generating a website? Or does it contain more than that?

They do have a sphinx them but they're also a service: https://readthedocs.org/
Every commit to master/tags/whatever-you-configure will create a new version of the docs and do all the sphinx build behind the scenes for you. We used to use it but ran into a lot of issues related to package installation. I think most of those have been resolved at this point but haven't fully re-evaluated

We can obviously continue to improve this, but I think the website is at a point now where it's good to go.

Am I able to preview the site (at a webpage) before merging?

@mattwthompson Yes, you’ll have to add justinGilmer as a remote and fetch this branch.. Then navigate to the docs directory, run make html to generate the html files, and view the website with open _build/html/index.html.


textlint-tester: remove babel dependencies

https://github.com/textlint/textlint/blob/986f28d6435ad74f7b2745a5842926c06a849319/packages/textlint-tester/package.json#L47-L51
textlint-tester was rewritten by TypeScript.
babel dependencies is not necessary.

Hi. I would like to work on this one.

@heanzyzabala Thanks! Welcome to PR!
If you can, power-assert also removable.
https://github.com/textlint/textlint/blob/986f28d6435ad74f7b2745a5842926c06a849319/packages/textlint-tester/package.json#L55
power-assert also is not necessary and it can be replaced with assert
- import assert from "power-assert"
+ import assert from "assert"

@azu Got it! Thanks

closed by #692
@heanzyzabala Thanks!


[MINOR] Fix Python Lint

What changes were proposed in this pull request?
Fix Python Lint
Why are the changes needed?
Python Linter is now broken
starting black test...
black checks failed:
would reformat python/pyspark/testing/connectutils.py

Oh no! 💥 💔 💥
1 file would be reformatted, 391 files would be left unchanged.
Please run 'dev/reformat-python' script.
1

How was this patch tested?
manually test

@HyukjinKwon @dongjoon-hyun

wait for a moment

there is another error:
python/pyspark/sql/tests/connect/test_connect_function.py:29: error: Incompatible import of "PandasOnSparkTestCase" (imported name has type "Type[ReusedSQLTestCase]", local name has type "Type[PandasOnSparkTestCase]")  [misc]

For the first one, I fixed here.

https://github.com/apache/spark/pull/38931

For the second one, let me check.

thanks, let me close this one

For the second one, does it come from GitHub Action log?

For the second one, does it come from GitHub Action log?

no, it came from local dev/lint-python

Thank you. Got it.


Task Delay and waiting over 1 hour

Delays the execution of the workflow by specified time in minutes. A 0 value means that workflow execution will start without delay. The maximum value is 86400 (60 days).

The page fails to mention a job's default timeout duration of 1 hour. Even though the maximum is 60 days, waiting for over 1 hour fails by default:
- task: Delay@1
  inputs:
    delayForMinutes: 120

It would be helpful to mention a job's timeoutInMinutes here.

Document Details
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ID: 00d9533b-ac17-be91-f1fe-e90e4185d960
Version Independent ID: 00d9533b-ac17-be91-f1fe-e90e4185d960
Content: Delay@1 - Delay v1 task
Content Source: task-reference/delay-v1.md
Service: azure-devops-pipelines
Sub-service: azure-devops-pipelines-tasks
GitHub Login: @steved0x
Microsoft Alias: sdanie

Thank you for your feedback. We are migrating this issue to our new documentation feedback system, and closing this issue.
As we phase out GitHub issues for this repository, we encourage you to propose changes using public PRs or submit feedback through our updated control at the top of the page. For more information on our feedback systems, see https://aka.ms/contentuserfeedback.